From 3a972a9ac542597256123c413d60bdf6673e94fc Mon Sep 17 00:00:00 2001 From: Aydin <108932477+Aydinhamedi@users.noreply.github.com> Date: Wed, 10 Jan 2024 15:21:35 +0330 Subject: [PATCH 1/2] modified: BETA_E_Model_T&T.ipynb modified: requirements.txt --- BETA_E_Model_T&T.ipynb | 2 +- requirements.txt | 12 +++++++----- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/BETA_E_Model_T&T.ipynb b/BETA_E_Model_T&T.ipynb index b64f528..6ebe38b 100644 --- a/BETA_E_Model_T&T.ipynb +++ b/BETA_E_Model_T&T.ipynb @@ -134,7 +134,7 @@ "tf.get_logger().setLevel('ERROR')\n", "physical_devices = tf.config.list_physical_devices('GPU')\n", "for gpu_instance in physical_devices:\n", - " tf.config.experimental.set_memory_growth(gpu_instance, True)\n" + " tf.config.experimental.set_memory_growth(gpu_instance, True)" ] }, { diff --git a/requirements.txt b/requirements.txt index 2955d17..58e4453 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,10 @@ -tensorflow==2.10.1 -keras==2.10.0 absl-py==1.4.0 adabelief-tf==0.2.1 efficientnet==1.1.1 gpu-control==1.0.0 hyperas==0.4.1 imbalanced-learn==0.11.0 +keras==2.10.0 keras-adabound==0.6.0 keras-efficientnet-v2==1.2.2 keras-gradient-noise==0.11 @@ -15,12 +14,15 @@ model-profiler==1.1.8 numpy==1.25.1 opencv-python==4.8.0.74 pandas==2.0.3 -Pillow==10.1.0 +Pillow==9.4.0 +psutil==5.9.5 py-cpuinfo==9.0.0 +pydicom==2.4.3 +requests==2.31.0 scikit-learn==1.3.0 scipy==1.11.1 seaborn==0.12.2 +tensorflow==2.10.1 tensorflow-addons==0.22.0 +tensorflow-model-optimization==0.7.5 tqdm==4.66.1 -imblearn~=0.0 -future~=0.18.3 \ No newline at end of file From e672393d90e67e2eb6e826217d291c3830612cdc Mon Sep 17 00:00:00 2001 From: Aydin <108932477+Aydinhamedi@users.noreply.github.com> Date: Wed, 10 Jan 2024 15:23:17 +0330 Subject: [PATCH 2/2] modified: Model_T&T.ipynb --- Model_T&T.ipynb | 21224 +--------------------------------------------- 1 file changed, 33 insertions(+), 21191 deletions(-) diff --git a/Model_T&T.ipynb b/Model_T&T.ipynb index b64f528..09bb288 100644 --- a/Model_T&T.ipynb +++ b/Model_T&T.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:44.939427800Z", @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.128539500Z", @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.139048Z", @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:48.287855100Z", @@ -209,15 +209,7 @@ "groupValue": "12" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "SAVE_TYPE = 'H5'\n", "Use_mixed_float16 = False\n", @@ -239,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.059139500Z", @@ -249,29 +241,7 @@ "groupValue": "12" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;33mUsing Def IDG...\u001b[0m\n", - "Found 23681 images belonging to 2 classes.\n", - "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n", - "\u001b[0;33mMaking categorical data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m0\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mNormalizing image data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat32\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m49.35% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m50.65% NORMAL\u001b[0m\n", - "\u001b[0;33mSetting LNTS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n", - "\u001b[0;33mshuffling data...\u001b[0m\n", - "\u001b[0;33mSaving TS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m01_d10-h09_m33_s30\u001b[0m\n", - "\u001b[0;32mDone.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "#Z_SCORE_normalize\n", "def Z_SCORE_normalize(arr):\n", @@ -678,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.380088800Z", @@ -878,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-27T17:34:12.077394600Z", @@ -888,2164 +858,7 @@ "groupValue": "" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " FC_INPUT_Avg-Pooling (GlobalAv (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " eragePooling2D) \n", - " \n", - " FC_C_Dense-L1-512 (Dense) (None, 512) 1311232 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n", - " \n", - " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n", - " \n", - " FC_C_Avg-BatchNormalization-L1 (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n", - " (BatchNormalization) \n", - " \n", - " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-BatchNormalization-L Y \n", - " 1[0][0]'] \n", - " \n", - " FC_C_Avg-BatchNormalization-L2 (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n", - " (BatchNormalization) \n", - " \n", - " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-BatchNormalization-L Y \n", - " 2[0][0]'] \n", - " \n", - " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -3139,2471 +952,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total base_model1 layers: 806\n", - "Total base_model2 layers: 132\n", - "Total model layers: 15\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " efficientnet-b7 (Functional) (None, 7, 7, 2560) 64097680 ['input_1[0][0]'] Y \n", - "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n", - "| input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", - "| )] |\n", - "| |\n", - "| stem_conv (Conv2D) (None, 112, 112, 64 1728 [] Y |\n", - "| ) |\n", - "| |\n", - "| stem_bn (BatchNormalization) (None, 112, 112, 64 256 [] Y |\n", - "| ) |\n", - "| |\n", - "| stem_activation (Activation) (None, 112, 112, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1a_bn (BatchNormalization (None, 112, 112, 64 256 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1a_activation (Activation (None, 112, 112, 64 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1a_se_squeeze (GlobalAver (None, 64) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 [] Y |\n", - "| |\n", - "| block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 [] Y |\n", - "| |\n", - "| block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 [] Y |\n", - "| |\n", - "| block1a_se_excite (Multiply) (None, 112, 112, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1b_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1b_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1b_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1b_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1b_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1c_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1c_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1c_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1c_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1c_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1d_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1d_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1d_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1d_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1d_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 [] Y |\n", - "| 2) |\n", - "| |\n", - "| block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 [] Y |\n", - "| ization) 2) |\n", - "| |\n", - "| block2a_expand_activation (Act (None, 112, 112, 19 0 [] Y |\n", - "| ivation) 2) |\n", - "| |\n", - "| block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2a_bn (BatchNormalization (None, 56, 56, 192) 768 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_activation (Activation (None, 56, 56, 192) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_se_squeeze (GlobalAver (None, 192) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 [] Y |\n", - "| |\n", - "| block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 [] Y |\n", - "| |\n", - "| block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 [] Y |\n", - "| |\n", - "| block2a_se_excite (Multiply) (None, 56, 56, 192) 0 [] Y |\n", - "| |\n", - "| block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 [] Y |\n", - "| |\n", - "| block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2b_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2b_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2b_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2b_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2b_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2b_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2c_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2c_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2c_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2c_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2c_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2c_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2d_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2d_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2d_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2d_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2d_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2d_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2e_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2e_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2e_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2e_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2e_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2e_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2f_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2f_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2f_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2f_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2f_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2f_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2g_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2g_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2g_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2g_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2g_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2g_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3a_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3a_activation (Activation (None, 28, 28, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3a_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block3a_se_excite (Multiply) (None, 28, 28, 288) 0 [] Y |\n", - "| |\n", - "| block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 [] Y |\n", - "| |\n", - "| block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3b_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3b_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3b_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3b_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3b_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3b_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3c_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3c_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3c_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3c_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3c_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3c_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3d_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3d_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3d_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3d_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3d_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3d_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3e_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3e_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3e_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3e_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3e_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3e_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3f_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3f_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3f_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3f_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3f_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3f_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3g_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3g_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3g_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3g_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3g_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3g_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4a_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4a_activation (Activation (None, 14, 14, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4a_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block4a_se_excite (Multiply) (None, 14, 14, 480) 0 [] Y |\n", - "| |\n", - "| block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 [] Y |\n", - "| |\n", - "| block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4b_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4b_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4b_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4b_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4b_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4b_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4c_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4c_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4c_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4c_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4c_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4c_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4d_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4d_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4d_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4d_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4d_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4d_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4e_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4e_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4e_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4e_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4e_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4e_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4f_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4f_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4f_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4f_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4f_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4f_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4g_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4g_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4g_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4g_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4g_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4g_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4h_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4h_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4h_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4h_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4h_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4h_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4i_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4i_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4i_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4i_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4i_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4i_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4j_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4j_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4j_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4j_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4j_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4j_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block5a_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5a_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5a_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block5a_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 [] Y |\n", - "| |\n", - "| block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5b_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5b_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5b_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5b_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5b_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5b_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5c_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5c_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5c_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5c_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5c_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5c_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5d_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5d_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5d_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5d_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5d_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5d_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5e_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5e_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5e_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5e_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5e_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5e_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5f_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5f_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5f_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5f_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5f_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5f_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5g_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5g_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5g_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5g_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5g_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5g_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5h_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5h_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5h_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5h_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5h_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5h_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5i_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5i_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5i_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5i_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5i_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5i_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5j_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5j_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5j_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5j_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5j_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5j_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block6a_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_activation (Activation (None, 7, 7, 1344) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 [] Y |\n", - "| |\n", - "| block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 [] Y |\n", - "| |\n", - "| block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6b_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6b_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6b_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6b_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6b_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6c_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6c_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6c_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6c_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6c_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6d_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6d_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6d_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6d_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6d_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6e_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6e_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6e_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6e_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6e_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6f_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6f_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6f_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6f_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6f_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6g_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6g_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6g_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6g_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6g_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6h_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6h_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6h_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6h_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6h_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6i_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6i_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6i_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6i_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6i_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6j_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6j_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6j_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6j_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6j_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6k_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6k_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6k_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6k_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6k_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6l_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6l_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6l_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6l_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6l_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6m_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6m_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6m_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6m_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6m_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7a_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7a_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7a_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 [] Y |\n", - "| |\n", - "| block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7b_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7b_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7b_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7b_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7b_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7c_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7c_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7c_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7c_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7c_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7d_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7d_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7d_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7d_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7d_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| top_conv (Conv2D) (None, 7, 7, 2560) 1638400 [] Y |\n", - "| |\n", - "| top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 [] Y |\n", - "| |\n", - "| top_activation (Activation) (None, 7, 7, 2560) 0 [] Y |\n", - "¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n", - " xception (Functional) (None, 7, 7, 2048) 20861480 ['input_1[0][0]'] Y \n", - "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n", - "| input_3 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", - "| )] |\n", - "| |\n", - "| block1_conv1 (Conv2D) (None, 111, 111, 32 864 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv1_bn (BatchNormaliz (None, 111, 111, 32 128 [] Y |\n", - "| ation) ) |\n", - "| |\n", - "| block1_conv1_act (Activation) (None, 111, 111, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv2 (Conv2D) (None, 109, 109, 64 18432 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv2_bn (BatchNormaliz (None, 109, 109, 64 256 [] Y |\n", - "| ation) ) |\n", - "| |\n", - "| block1_conv2_act (Activation) (None, 109, 109, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2_sepconv1 (SeparableConv (None, 109, 109, 12 8768 [] Y |\n", - "| 2D) 8) |\n", - "| |\n", - "| block2_sepconv1_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", - "| lization) 8) |\n", - "| |\n", - "| block2_sepconv2_act (Activatio (None, 109, 109, 12 0 [] Y |\n", - "| n) 8) |\n", - "| |\n", - "| block2_sepconv2 (SeparableConv (None, 109, 109, 12 17536 [] Y |\n", - "| 2D) 8) |\n", - "| |\n", - "| block2_sepconv2_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", - "| lization) 8) |\n", - "| |\n", - "| conv2d (Conv2D) (None, 55, 55, 128) 8192 [] Y |\n", - "| |\n", - "| block2_pool (MaxPooling2D) (None, 55, 55, 128) 0 [] Y |\n", - "| |\n", - "| batch_normalization (BatchNorm (None, 55, 55, 128) 512 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add (Add) (None, 55, 55, 128) 0 [] Y |\n", - "| |\n", - "| block3_sepconv1_act (Activatio (None, 55, 55, 128) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block3_sepconv1 (SeparableConv (None, 55, 55, 256) 33920 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block3_sepconv1_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3_sepconv2_act (Activatio (None, 55, 55, 256) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block3_sepconv2 (SeparableConv (None, 55, 55, 256) 67840 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block3_sepconv2_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", - "| lization) |\n", - "| |\n", - "| conv2d_1 (Conv2D) (None, 28, 28, 256) 32768 [] Y |\n", - "| |\n", - "| block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 [] Y |\n", - "| |\n", - "| batch_normalization_1 (BatchNo (None, 28, 28, 256) 1024 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_1 (Add) (None, 28, 28, 256) 0 [] Y |\n", - "| |\n", - "| block4_sepconv1_act (Activatio (None, 28, 28, 256) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block4_sepconv1 (SeparableConv (None, 28, 28, 728) 188672 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block4_sepconv1_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4_sepconv2_act (Activatio (None, 28, 28, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block4_sepconv2 (SeparableConv (None, 28, 28, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block4_sepconv2_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| conv2d_2 (Conv2D) (None, 14, 14, 728) 186368 [] Y |\n", - "| |\n", - "| block4_pool (MaxPooling2D) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| batch_normalization_2 (BatchNo (None, 14, 14, 728) 2912 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_2 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block5_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_3 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block6_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_4 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block7_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_5 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block8_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block8_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block8_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_6 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block9_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block9_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block9_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_7 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block10_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block10_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block10_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_8 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block11_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block11_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block11_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_9 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block12_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block12_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block12_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_10 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block13_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block13_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block13_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block13_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block13_sepconv2 (SeparableCon (None, 14, 14, 1024 752024 [] Y |\n", - "| v2D) ) |\n", - "| |\n", - "| block13_sepconv2_bn (BatchNorm (None, 14, 14, 1024 4096 [] Y |\n", - "| alization) ) |\n", - "| |\n", - "| conv2d_3 (Conv2D) (None, 7, 7, 1024) 745472 [] Y |\n", - "| |\n", - "| block13_pool (MaxPooling2D) (None, 7, 7, 1024) 0 [] Y |\n", - "| |\n", - "| batch_normalization_3 (BatchNo (None, 7, 7, 1024) 4096 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_11 (Add) (None, 7, 7, 1024) 0 [] Y |\n", - "| |\n", - "| block14_sepconv1 (SeparableCon (None, 7, 7, 1536) 1582080 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block14_sepconv1_bn (BatchNorm (None, 7, 7, 1536) 6144 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block14_sepconv1_act (Activati (None, 7, 7, 1536) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block14_sepconv2 (SeparableCon (None, 7, 7, 2048) 3159552 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block14_sepconv2_bn (BatchNorm (None, 7, 7, 2048) 8192 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block14_sepconv2_act (Activati (None, 7, 7, 2048) 0 [] Y |\n", - "| on) |\n", - "¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n", - " global_average_pooling2d (Glob (None, 2560) 0 ['efficientnet-b7[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " global_average_pooling2d_1 (Gl (None, 2048) 0 ['xception[0][0]'] Y \n", - " obalAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dense_1 (Dense) (None, 512) 1049088 ['global_average_pooling2d_1[0] Y \n", - " [0]'] \n", - " \n", - " concatenate (Concatenate) (None, 1024) 0 ['dense[0][0]', Y \n", - " 'dense_1[0][0]'] \n", - " \n", - " dense_2 (Dense) (None, 1024) 1049600 ['concatenate[0][0]'] Y \n", - " \n", - " dropout (Dropout) (None, 1024) 0 ['dense_2[0][0]'] Y \n", - " \n", - " batch_normalization_4 (BatchNo (None, 1024) 4096 ['dropout[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_3 (Dense) (None, 512) 524800 ['batch_normalization_4[0][0]'] Y \n", - " \n", - " batch_normalization_5 (BatchNo (None, 512) 2048 ['dense_3[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_4 (Dense) (None, 128) 65664 ['batch_normalization_5[0][0]'] Y \n", - " \n", - " dense_5 (Dense) (None, 2) 258 ['dense_4[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 88,965,946\n", - "Trainable params: 88,597,626\n", - "Non-trainable params: 368,320\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "from keras.applications.xception import Xception\n", @@ -5692,4150 +1043,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-xl-21k-ft1k.h5\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 32 864 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 32 128 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_swish (Activation) (None, 112, 112, 32 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " stack_0_block0_fu_conv (Conv2D (None, 112, 112, 32 9216 ['stem_swish[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block0_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block0_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block0_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block0_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add (Add) (None, 112, 112, 32 0 ['stem_swish[0][0]', Y \n", - " ) 'stack_0_block0_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block1_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block1_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block1_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block1_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block1_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_1 (Add) (None, 112, 112, 32 0 ['add[0][0]', Y \n", - " ) 'stack_0_block1_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block2_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_1[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block2_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block2_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block2_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block2_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_2 (Add) (None, 112, 112, 32 0 ['add_1[0][0]', Y \n", - " ) 'stack_0_block2_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block3_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_2[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block3_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block3_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block3_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block3_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_3 (Add) (None, 112, 112, 32 0 ['add_2[0][0]', Y \n", - " ) 'stack_0_block3_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_1_block0_sortcut_conv (C (None, 56, 56, 128) 36864 ['add_3[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block0_sortcut_bn (Bat (None, 56, 56, 128) 512 ['stack_1_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block0_sortcut_swish ( (None, 56, 56, 128) 0 ['stack_1_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block0_MB_pw_conv (Con (None, 56, 56, 64) 8192 ['stack_1_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block0_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_1_block1_sortcut_conv (C (None, 56, 56, 256) 147456 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_1_block1_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block1_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block1_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block1_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_4 (Add) (None, 56, 56, 64) 0 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_1_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block2_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_4[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block2_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block2_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block2_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block2_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_5 (Add) (None, 56, 56, 64) 0 ['add_4[0][0]', Y \n", - " 'stack_1_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block3_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_5[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block3_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block3_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block3_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block3_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_6 (Add) (None, 56, 56, 64) 0 ['add_5[0][0]', Y \n", - " 'stack_1_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block4_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_6[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block4_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block4_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block4_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block4_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block4_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_7 (Add) (None, 56, 56, 64) 0 ['add_6[0][0]', Y \n", - " 'stack_1_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block5_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_7[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block5_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block5_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block5_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block5_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block5_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_8 (Add) (None, 56, 56, 64) 0 ['add_7[0][0]', Y \n", - " 'stack_1_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block6_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_8[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block6_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block6_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block6_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block6_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block6_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_9 (Add) (None, 56, 56, 64) 0 ['add_8[0][0]', Y \n", - " 'stack_1_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block7_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_9[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block7_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block7_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block7_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block7_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block7_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_10 (Add) (None, 56, 56, 64) 0 ['add_9[0][0]', Y \n", - " 'stack_1_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block0_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_10[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block0_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block0_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block0_MB_pw_conv (Con (None, 28, 28, 96) 24576 ['stack_2_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block0_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_2_block1_sortcut_conv (C (None, 28, 28, 384) 331776 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_2_block1_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block1_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block1_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block1_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_11 (Add) (None, 28, 28, 96) 0 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_2_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block2_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_11[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block2_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block2_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block2_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block2_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_12 (Add) (None, 28, 28, 96) 0 ['add_11[0][0]', Y \n", - " 'stack_2_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block3_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_12[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block3_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block3_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block3_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block3_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_13 (Add) (None, 28, 28, 96) 0 ['add_12[0][0]', Y \n", - " 'stack_2_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block4_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_13[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block4_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block4_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block4_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block4_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block4_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_14 (Add) (None, 28, 28, 96) 0 ['add_13[0][0]', Y \n", - " 'stack_2_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block5_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_14[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block5_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block5_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block5_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block5_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block5_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_15 (Add) (None, 28, 28, 96) 0 ['add_14[0][0]', Y \n", - " 'stack_2_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block6_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_15[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block6_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block6_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block6_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block6_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block6_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_16 (Add) (None, 28, 28, 96) 0 ['add_15[0][0]', Y \n", - " 'stack_2_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block7_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_16[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block7_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block7_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block7_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block7_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block7_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_17 (Add) (None, 28, 28, 96) 0 ['add_16[0][0]', Y \n", - " 'stack_2_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block0_sortcut_conv (C (None, 28, 28, 384) 36864 ['add_17[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block0_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_3_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block0_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_3_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block0_MB_dw_ (Depthwi (None, 14, 14, 384) 3456 ['stack_3_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block0_MB_dw_bn (Batch (None, 14, 14, 384) 1536 ['stack_3_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block0_MB_dw_swish (Ac (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean (TFOpLambd (None, 1, 1, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " a) [0]'] \n", - " \n", - " stack_3_block0_se_1_conv (Conv (None, 1, 1, 24) 9240 ['tf.math.reduce_mean[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation (Activation) (None, 1, 1, 24) 0 ['stack_3_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block0_se_2_conv (Conv (None, 1, 1, 384) 9600 ['activation[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_1 (Activation) (None, 1, 1, 384) 0 ['stack_3_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply (Multiply) (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_1[0][0]'] \n", - " \n", - " stack_3_block0_MB_pw_conv (Con (None, 14, 14, 192) 73728 ['multiply[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block0_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_3_block1_sortcut_conv (C (None, 14, 14, 768) 147456 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_3_block1_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block1_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block1_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block1_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block1_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_1 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block1_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_1[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_2 (Activation) (None, 1, 1, 48) 0 ['stack_3_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block1_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_3 (Activation) (None, 1, 1, 768) 0 ['stack_3_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_1 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_3[0][0]'] \n", - " \n", - " stack_3_block1_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_1[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block1_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_18 (Add) (None, 14, 14, 192) 0 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_3_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block2_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_18[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block2_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block2_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block2_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block2_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block2_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_2 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block2_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_4 (Activation) (None, 1, 1, 48) 0 ['stack_3_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block2_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_5 (Activation) (None, 1, 1, 768) 0 ['stack_3_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_2 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_5[0][0]'] \n", - " \n", - " stack_3_block2_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_2[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block2_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_19 (Add) (None, 14, 14, 192) 0 ['add_18[0][0]', Y \n", - " 'stack_3_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block3_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_19[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block3_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block3_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block3_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block3_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block3_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_3 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block3_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_3[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_6 (Activation) (None, 1, 1, 48) 0 ['stack_3_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block3_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_7 (Activation) (None, 1, 1, 768) 0 ['stack_3_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_3 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_7[0][0]'] \n", - " \n", - " stack_3_block3_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_3[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block3_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_20 (Add) (None, 14, 14, 192) 0 ['add_19[0][0]', Y \n", - " 'stack_3_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block4_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_20[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block4_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block4_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block4_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block4_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block4_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_4 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block4_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_8 (Activation) (None, 1, 1, 48) 0 ['stack_3_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block4_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_9 (Activation) (None, 1, 1, 768) 0 ['stack_3_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_4 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_9[0][0]'] \n", - " \n", - " stack_3_block4_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_4[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block4_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_21 (Add) (None, 14, 14, 192) 0 ['add_20[0][0]', Y \n", - " 'stack_3_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block5_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_21[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block5_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block5_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block5_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block5_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block5_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_5 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block5_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_5[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_10 (Activation) (None, 1, 1, 48) 0 ['stack_3_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block5_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_10[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_11 (Activation) (None, 1, 1, 768) 0 ['stack_3_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_5 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_11[0][0]'] \n", - " \n", - " stack_3_block5_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_5[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block5_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_22 (Add) (None, 14, 14, 192) 0 ['add_21[0][0]', Y \n", - " 'stack_3_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block6_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_22[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block6_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block6_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block6_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block6_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block6_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_6 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block6_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_12 (Activation) (None, 1, 1, 48) 0 ['stack_3_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block6_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_12[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_13 (Activation) (None, 1, 1, 768) 0 ['stack_3_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_6 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_13[0][0]'] \n", - " \n", - " stack_3_block6_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_6[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block6_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_23 (Add) (None, 14, 14, 192) 0 ['add_22[0][0]', Y \n", - " 'stack_3_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block7_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_23[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block7_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block7_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block7_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block7_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block7_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_7 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block7_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_7[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_14 (Activation) (None, 1, 1, 48) 0 ['stack_3_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block7_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_14[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_15 (Activation) (None, 1, 1, 768) 0 ['stack_3_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_7 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_15[0][0]'] \n", - " \n", - " stack_3_block7_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_7[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block7_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_24 (Add) (None, 14, 14, 192) 0 ['add_23[0][0]', Y \n", - " 'stack_3_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block8_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_24[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block8_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block8_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block8_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block8_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block8_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_8 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block8_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_16 (Activation) (None, 1, 1, 48) 0 ['stack_3_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block8_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_16[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_17 (Activation) (None, 1, 1, 768) 0 ['stack_3_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_8 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_17[0][0]'] \n", - " \n", - " stack_3_block8_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_8[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block8_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_25 (Add) (None, 14, 14, 192) 0 ['add_24[0][0]', Y \n", - " 'stack_3_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block9_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_25[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block9_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block9_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block9_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block9_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block9_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_9 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block9_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_9[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_18 (Activation) (None, 1, 1, 48) 0 ['stack_3_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block9_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_18[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_19 (Activation) (None, 1, 1, 768) 0 ['stack_3_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_9 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_19[0][0]'] \n", - " \n", - " stack_3_block9_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_9[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block9_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_26 (Add) (None, 14, 14, 192) 0 ['add_25[0][0]', Y \n", - " 'stack_3_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block10_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_26[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block10_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block10_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block10_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block10_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block10_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_10 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block10_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_10[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_20 (Activation) (None, 1, 1, 48) 0 ['stack_3_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block10_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_21 (Activation) (None, 1, 1, 768) 0 ['stack_3_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_10 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_21[0][0]'] \n", - " \n", - " stack_3_block10_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_10[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block10_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_27 (Add) (None, 14, 14, 192) 0 ['add_26[0][0]', Y \n", - " 'stack_3_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block11_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_27[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block11_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block11_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block11_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block11_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block11_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_11 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block11_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_11[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_22 (Activation) (None, 1, 1, 48) 0 ['stack_3_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block11_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_23 (Activation) (None, 1, 1, 768) 0 ['stack_3_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_11 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_23[0][0]'] \n", - " \n", - " stack_3_block11_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_11[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block11_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_28 (Add) (None, 14, 14, 192) 0 ['add_27[0][0]', Y \n", - " 'stack_3_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block12_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_28[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block12_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block12_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block12_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block12_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block12_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_12 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block12_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_12[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_24 (Activation) (None, 1, 1, 48) 0 ['stack_3_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block12_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_25 (Activation) (None, 1, 1, 768) 0 ['stack_3_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_12 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_25[0][0]'] \n", - " \n", - " stack_3_block12_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_12[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block12_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_29 (Add) (None, 14, 14, 192) 0 ['add_28[0][0]', Y \n", - " 'stack_3_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block13_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_29[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block13_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block13_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block13_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block13_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block13_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_13 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block13_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_13[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_26 (Activation) (None, 1, 1, 48) 0 ['stack_3_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block13_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_26[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_27 (Activation) (None, 1, 1, 768) 0 ['stack_3_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_13 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_27[0][0]'] \n", - " \n", - " stack_3_block13_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_13[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block13_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_30 (Add) (None, 14, 14, 192) 0 ['add_29[0][0]', Y \n", - " 'stack_3_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block14_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_30[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block14_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block14_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block14_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block14_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block14_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_14 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block14_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_14[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_28 (Activation) (None, 1, 1, 48) 0 ['stack_3_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block14_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_28[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_29 (Activation) (None, 1, 1, 768) 0 ['stack_3_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_14 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_29[0][0]'] \n", - " \n", - " stack_3_block14_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_14[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block14_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_31 (Add) (None, 14, 14, 192) 0 ['add_30[0][0]', Y \n", - " 'stack_3_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block15_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_31[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block15_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block15_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block15_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block15_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block15_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block15_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block15_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block15_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_15 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block15_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_15[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_30 (Activation) (None, 1, 1, 48) 0 ['stack_3_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block15_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_30[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_31 (Activation) (None, 1, 1, 768) 0 ['stack_3_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_15 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_31[0][0]'] \n", - " \n", - " stack_3_block15_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_15[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block15_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_32 (Add) (None, 14, 14, 192) 0 ['add_31[0][0]', Y \n", - " 'stack_3_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block0_sortcut_conv (C (None, 14, 14, 1152 221184 ['add_32[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block0_sortcut_bn (Bat (None, 14, 14, 1152 4608 ['stack_4_block0_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block0_sortcut_swish ( (None, 14, 14, 1152 0 ['stack_4_block0_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block0_MB_dw_ (Depthwi (None, 14, 14, 1152 10368 ['stack_4_block0_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block0_MB_dw_bn (Batch (None, 14, 14, 1152 4608 ['stack_4_block0_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block0_MB_dw_swish (Ac (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_16 (TFOpLa (None, 1, 1, 1152) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block0_se_1_conv (Conv (None, 1, 1, 48) 55344 ['tf.math.reduce_mean_16[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_32 (Activation) (None, 1, 1, 48) 0 ['stack_4_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block0_se_2_conv (Conv (None, 1, 1, 1152) 56448 ['activation_32[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_33 (Activation) (None, 1, 1, 1152) 0 ['stack_4_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_16 (Multiply) (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_33[0][0]'] \n", - " \n", - " stack_4_block0_MB_pw_conv (Con (None, 14, 14, 256) 294912 ['multiply_16[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block0_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_4_block1_sortcut_conv (C (None, 14, 14, 1536 393216 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " onv2D) ) '] \n", - " \n", - " stack_4_block1_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block1_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block1_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block1_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block1_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block1_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block1_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block1_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block1_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_17 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block1_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_17[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_34 (Activation) (None, 1, 1, 64) 0 ['stack_4_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block1_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_34[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_35 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_17 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_35[0][0]'] \n", - " \n", - " stack_4_block1_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_17[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block1_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_33 (Add) (None, 14, 14, 256) 0 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_4_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block2_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_33[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block2_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block2_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block2_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block2_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block2_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block2_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block2_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block2_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block2_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_18 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block2_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_18[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_36 (Activation) (None, 1, 1, 64) 0 ['stack_4_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block2_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_36[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_37 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_18 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_37[0][0]'] \n", - " \n", - " stack_4_block2_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_18[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block2_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_34 (Add) (None, 14, 14, 256) 0 ['add_33[0][0]', Y \n", - " 'stack_4_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block3_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_34[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block3_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block3_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block3_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block3_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block3_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block3_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block3_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block3_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block3_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_19 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block3_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_19[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_38 (Activation) (None, 1, 1, 64) 0 ['stack_4_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block3_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_38[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_39 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_19 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_39[0][0]'] \n", - " \n", - " stack_4_block3_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_19[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block3_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_35 (Add) (None, 14, 14, 256) 0 ['add_34[0][0]', Y \n", - " 'stack_4_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block4_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_35[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block4_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block4_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block4_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block4_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block4_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block4_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block4_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block4_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block4_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_20 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block4_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_20[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_40 (Activation) (None, 1, 1, 64) 0 ['stack_4_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block4_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_40[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_41 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_20 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_41[0][0]'] \n", - " \n", - " stack_4_block4_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block4_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_36 (Add) (None, 14, 14, 256) 0 ['add_35[0][0]', Y \n", - " 'stack_4_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block5_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_36[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block5_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block5_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block5_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block5_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block5_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block5_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block5_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block5_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block5_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_21 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block5_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_21[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_42 (Activation) (None, 1, 1, 64) 0 ['stack_4_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block5_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_42[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_43 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_21 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_43[0][0]'] \n", - " \n", - " stack_4_block5_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_21[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block5_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_37 (Add) (None, 14, 14, 256) 0 ['add_36[0][0]', Y \n", - " 'stack_4_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block6_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_37[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block6_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block6_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block6_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block6_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block6_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block6_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block6_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block6_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block6_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_22 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block6_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_22[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_44 (Activation) (None, 1, 1, 64) 0 ['stack_4_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block6_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_44[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_45 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_22 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_45[0][0]'] \n", - " \n", - " stack_4_block6_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block6_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_38 (Add) (None, 14, 14, 256) 0 ['add_37[0][0]', Y \n", - " 'stack_4_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block7_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_38[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block7_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block7_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block7_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block7_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block7_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block7_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block7_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block7_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block7_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_23 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block7_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_23[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_46 (Activation) (None, 1, 1, 64) 0 ['stack_4_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block7_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_46[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_47 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_23 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_47[0][0]'] \n", - " \n", - " stack_4_block7_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_23[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block7_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_39 (Add) (None, 14, 14, 256) 0 ['add_38[0][0]', Y \n", - " 'stack_4_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block8_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_39[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block8_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block8_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block8_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block8_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block8_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block8_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block8_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block8_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block8_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_24 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block8_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_24[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_48 (Activation) (None, 1, 1, 64) 0 ['stack_4_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block8_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_48[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_49 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_24 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_49[0][0]'] \n", - " \n", - " stack_4_block8_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block8_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_40 (Add) (None, 14, 14, 256) 0 ['add_39[0][0]', Y \n", - " 'stack_4_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block9_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_40[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block9_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block9_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block9_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block9_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block9_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block9_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block9_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block9_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block9_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_25 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block9_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_25[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_50 (Activation) (None, 1, 1, 64) 0 ['stack_4_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block9_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_50[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_51 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_25 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_51[0][0]'] \n", - " \n", - " stack_4_block9_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_25[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block9_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_41 (Add) (None, 14, 14, 256) 0 ['add_40[0][0]', Y \n", - " 'stack_4_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block10_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_41[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block10_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block10_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block10_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block10_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block10_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block10_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block10_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block10_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_26 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block10_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_26[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_52 (Activation) (None, 1, 1, 64) 0 ['stack_4_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block10_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_52[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_53 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_26 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_53[0][0]'] \n", - " \n", - " stack_4_block10_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_26[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block10_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_42 (Add) (None, 14, 14, 256) 0 ['add_41[0][0]', Y \n", - " 'stack_4_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block11_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_42[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block11_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block11_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block11_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block11_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block11_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block11_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block11_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block11_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_27 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block11_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_27[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_54 (Activation) (None, 1, 1, 64) 0 ['stack_4_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block11_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_54[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_55 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_27 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_55[0][0]'] \n", - " \n", - " stack_4_block11_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_27[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block11_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_43 (Add) (None, 14, 14, 256) 0 ['add_42[0][0]', Y \n", - " 'stack_4_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block12_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_43[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block12_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block12_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block12_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block12_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block12_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block12_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block12_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block12_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_28 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block12_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_28[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_56 (Activation) (None, 1, 1, 64) 0 ['stack_4_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block12_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_56[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_57 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_28 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_57[0][0]'] \n", - " \n", - " stack_4_block12_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_28[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block12_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_44 (Add) (None, 14, 14, 256) 0 ['add_43[0][0]', Y \n", - " 'stack_4_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block13_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_44[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block13_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block13_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block13_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block13_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block13_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block13_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block13_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block13_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_29 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block13_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_29[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_58 (Activation) (None, 1, 1, 64) 0 ['stack_4_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block13_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_58[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_59 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_29 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_59[0][0]'] \n", - " \n", - " stack_4_block13_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_29[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block13_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_45 (Add) (None, 14, 14, 256) 0 ['add_44[0][0]', Y \n", - " 'stack_4_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block14_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_45[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block14_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block14_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block14_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block14_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block14_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block14_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block14_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block14_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_30 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block14_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_30[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_60 (Activation) (None, 1, 1, 64) 0 ['stack_4_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block14_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_60[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_61 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_30 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_61[0][0]'] \n", - " \n", - " stack_4_block14_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_30[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block14_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_46 (Add) (None, 14, 14, 256) 0 ['add_45[0][0]', Y \n", - " 'stack_4_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block15_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_46[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block15_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block15_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block15_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block15_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block15_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block15_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block15_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block15_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_31 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block15_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_31[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_62 (Activation) (None, 1, 1, 64) 0 ['stack_4_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block15_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_62[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_63 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_31 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_63[0][0]'] \n", - " \n", - " stack_4_block15_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_31[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block15_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_47 (Add) (None, 14, 14, 256) 0 ['add_46[0][0]', Y \n", - " 'stack_4_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block16_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_47[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block16_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block16_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block16_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block16_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block16_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block16_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block16_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block16_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block16_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_32 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block16_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block16_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_32[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_64 (Activation) (None, 1, 1, 64) 0 ['stack_4_block16_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block16_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_64[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_65 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block16_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_32 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_65[0][0]'] \n", - " \n", - " stack_4_block16_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_32[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block16_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block16_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_48 (Add) (None, 14, 14, 256) 0 ['add_47[0][0]', Y \n", - " 'stack_4_block16_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block17_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_48[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block17_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block17_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block17_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block17_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block17_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block17_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block17_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block17_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block17_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_33 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block17_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block17_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_33[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_66 (Activation) (None, 1, 1, 64) 0 ['stack_4_block17_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block17_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_66[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_67 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block17_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_33 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_67[0][0]'] \n", - " \n", - " stack_4_block17_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_33[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block17_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block17_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_49 (Add) (None, 14, 14, 256) 0 ['add_48[0][0]', Y \n", - " 'stack_4_block17_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block18_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_49[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block18_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block18_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block18_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block18_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block18_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block18_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block18_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block18_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block18_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_34 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block18_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block18_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_34[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_68 (Activation) (None, 1, 1, 64) 0 ['stack_4_block18_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block18_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_68[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_69 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block18_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_34 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_69[0][0]'] \n", - " \n", - " stack_4_block18_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_34[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block18_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block18_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_50 (Add) (None, 14, 14, 256) 0 ['add_49[0][0]', Y \n", - " 'stack_4_block18_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block19_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_50[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block19_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block19_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block19_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block19_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block19_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block19_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block19_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block19_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block19_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_35 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block19_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block19_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_35[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_70 (Activation) (None, 1, 1, 64) 0 ['stack_4_block19_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block19_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_70[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_71 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block19_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_35 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_71[0][0]'] \n", - " \n", - " stack_4_block19_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_35[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block19_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block19_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_51 (Add) (None, 14, 14, 256) 0 ['add_50[0][0]', Y \n", - " 'stack_4_block19_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block20_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_51[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block20_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block20_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block20_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block20_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block20_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block20_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block20_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block20_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block20_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_36 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block20_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block20_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_36[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_72 (Activation) (None, 1, 1, 64) 0 ['stack_4_block20_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block20_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_72[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_73 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block20_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_36 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_73[0][0]'] \n", - " \n", - " stack_4_block20_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_36[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block20_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block20_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_52 (Add) (None, 14, 14, 256) 0 ['add_51[0][0]', Y \n", - " 'stack_4_block20_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block21_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_52[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block21_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block21_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block21_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block21_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block21_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block21_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block21_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block21_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block21_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_37 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block21_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block21_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_37[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_74 (Activation) (None, 1, 1, 64) 0 ['stack_4_block21_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block21_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_74[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_75 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block21_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_37 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_75[0][0]'] \n", - " \n", - " stack_4_block21_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_37[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block21_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block21_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_53 (Add) (None, 14, 14, 256) 0 ['add_52[0][0]', Y \n", - " 'stack_4_block21_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block22_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_53[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block22_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block22_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block22_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block22_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block22_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block22_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block22_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block22_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block22_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_38 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block22_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block22_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_38[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_76 (Activation) (None, 1, 1, 64) 0 ['stack_4_block22_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block22_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_76[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_77 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block22_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_38 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_77[0][0]'] \n", - " \n", - " stack_4_block22_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_38[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block22_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block22_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_54 (Add) (None, 14, 14, 256) 0 ['add_53[0][0]', Y \n", - " 'stack_4_block22_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block23_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_54[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block23_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block23_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block23_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block23_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block23_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block23_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block23_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block23_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block23_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_39 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block23_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block23_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_39[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_78 (Activation) (None, 1, 1, 64) 0 ['stack_4_block23_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block23_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_78[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_79 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block23_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_39 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_79[0][0]'] \n", - " \n", - " stack_4_block23_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_39[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block23_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block23_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_55 (Add) (None, 14, 14, 256) 0 ['add_54[0][0]', Y \n", - " 'stack_4_block23_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block0_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_55[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_5_block0_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_5_block0_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_5_block0_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_5_block0_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_5_block0_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block0_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block0_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_40 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block0_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_40[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_80 (Activation) (None, 1, 1, 64) 0 ['stack_5_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block0_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_80[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_81 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_40 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_81[0][0]'] \n", - " \n", - " stack_5_block0_MB_pw_conv (Con (None, 7, 7, 512) 786432 ['multiply_40[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block0_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_5_block1_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_5_block1_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block1_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block1_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block1_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block1_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_41 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block1_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_41[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_82 (Activation) (None, 1, 1, 128) 0 ['stack_5_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block1_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_82[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_83 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_41 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_83[0][0]'] \n", - " \n", - " stack_5_block1_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_41[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block1_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_56 (Add) (None, 7, 7, 512) 0 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_5_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block2_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_56[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block2_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block2_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block2_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block2_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block2_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_42 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block2_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_42[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_84 (Activation) (None, 1, 1, 128) 0 ['stack_5_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block2_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_84[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_85 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_42 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_85[0][0]'] \n", - " \n", - " stack_5_block2_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_42[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block2_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_57 (Add) (None, 7, 7, 512) 0 ['add_56[0][0]', Y \n", - " 'stack_5_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block3_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_57[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block3_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block3_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block3_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block3_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block3_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_43 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block3_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_43[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_86 (Activation) (None, 1, 1, 128) 0 ['stack_5_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block3_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_86[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_87 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_43 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_87[0][0]'] \n", - " \n", - " stack_5_block3_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_43[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block3_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_58 (Add) (None, 7, 7, 512) 0 ['add_57[0][0]', Y \n", - " 'stack_5_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block4_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_58[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block4_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block4_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block4_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block4_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block4_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_44 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block4_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_44[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_88 (Activation) (None, 1, 1, 128) 0 ['stack_5_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block4_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_88[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_89 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_44 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_89[0][0]'] \n", - " \n", - " stack_5_block4_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_44[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block4_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_59 (Add) (None, 7, 7, 512) 0 ['add_58[0][0]', Y \n", - " 'stack_5_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block5_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_59[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block5_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block5_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block5_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block5_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block5_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_45 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block5_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_45[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_90 (Activation) (None, 1, 1, 128) 0 ['stack_5_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block5_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_90[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_91 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_45 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_91[0][0]'] \n", - " \n", - " stack_5_block5_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_45[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block5_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_60 (Add) (None, 7, 7, 512) 0 ['add_59[0][0]', Y \n", - " 'stack_5_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block6_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_60[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block6_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block6_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block6_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block6_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block6_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_46 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block6_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_46[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_92 (Activation) (None, 1, 1, 128) 0 ['stack_5_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block6_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_92[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_93 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_46 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_93[0][0]'] \n", - " \n", - " stack_5_block6_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_46[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block6_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_61 (Add) (None, 7, 7, 512) 0 ['add_60[0][0]', Y \n", - " 'stack_5_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block7_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_61[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block7_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block7_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block7_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block7_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block7_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_47 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block7_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_47[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_94 (Activation) (None, 1, 1, 128) 0 ['stack_5_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block7_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_94[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_95 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_47 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_95[0][0]'] \n", - " \n", - " stack_5_block7_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_47[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block7_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_62 (Add) (None, 7, 7, 512) 0 ['add_61[0][0]', Y \n", - " 'stack_5_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block8_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_62[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block8_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block8_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block8_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block8_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block8_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_48 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block8_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_48[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_96 (Activation) (None, 1, 1, 128) 0 ['stack_5_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block8_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_96[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_97 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_48 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_97[0][0]'] \n", - " \n", - " stack_5_block8_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_48[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block8_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_63 (Add) (None, 7, 7, 512) 0 ['add_62[0][0]', Y \n", - " 'stack_5_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block9_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_63[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block9_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block9_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block9_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block9_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block9_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_49 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block9_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_49[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_98 (Activation) (None, 1, 1, 128) 0 ['stack_5_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block9_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_98[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_99 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_49 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_99[0][0]'] \n", - " \n", - " stack_5_block9_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_49[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block9_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_64 (Add) (None, 7, 7, 512) 0 ['add_63[0][0]', Y \n", - " 'stack_5_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block10_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_64[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block10_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block10_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block10_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block10_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block10_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_50 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block10_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_50[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_100 (Activation) (None, 1, 1, 128) 0 ['stack_5_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block10_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_100[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_101 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_50 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_101[0][0]'] \n", - " \n", - " stack_5_block10_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_50[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block10_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_65 (Add) (None, 7, 7, 512) 0 ['add_64[0][0]', Y \n", - " 'stack_5_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block11_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_65[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block11_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block11_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block11_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block11_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block11_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_51 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block11_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_51[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_102 (Activation) (None, 1, 1, 128) 0 ['stack_5_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block11_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_102[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_103 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_51 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_103[0][0]'] \n", - " \n", - " stack_5_block11_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_51[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block11_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_66 (Add) (None, 7, 7, 512) 0 ['add_65[0][0]', Y \n", - " 'stack_5_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block12_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_66[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block12_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block12_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block12_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block12_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block12_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_52 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block12_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_52[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_104 (Activation) (None, 1, 1, 128) 0 ['stack_5_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block12_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_104[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_105 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_52 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_105[0][0]'] \n", - " \n", - " stack_5_block12_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_52[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block12_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_67 (Add) (None, 7, 7, 512) 0 ['add_66[0][0]', Y \n", - " 'stack_5_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block13_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_67[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block13_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block13_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block13_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block13_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block13_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_53 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block13_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_53[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_106 (Activation) (None, 1, 1, 128) 0 ['stack_5_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block13_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_106[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_107 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_53 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_107[0][0]'] \n", - " \n", - " stack_5_block13_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_53[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block13_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_68 (Add) (None, 7, 7, 512) 0 ['add_67[0][0]', Y \n", - " 'stack_5_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block14_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_68[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block14_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block14_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block14_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block14_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block14_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_54 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block14_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_54[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_108 (Activation) (None, 1, 1, 128) 0 ['stack_5_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block14_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_108[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_109 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_54 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_109[0][0]'] \n", - " \n", - " stack_5_block14_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_54[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block14_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_69 (Add) (None, 7, 7, 512) 0 ['add_68[0][0]', Y \n", - " 'stack_5_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block15_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_69[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block15_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block15_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block15_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block15_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block15_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block15_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block15_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block15_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_55 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block15_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_55[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_110 (Activation) (None, 1, 1, 128) 0 ['stack_5_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block15_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_110[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_111 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_55 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_111[0][0]'] \n", - " \n", - " stack_5_block15_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_55[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block15_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_70 (Add) (None, 7, 7, 512) 0 ['add_69[0][0]', Y \n", - " 'stack_5_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block16_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_70[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block16_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block16_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block16_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block16_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block16_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block16_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block16_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block16_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block16_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_56 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block16_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_56[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_112 (Activation) (None, 1, 1, 128) 0 ['stack_5_block16_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block16_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_112[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_113 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block16_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_56 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_113[0][0]'] \n", - " \n", - " stack_5_block16_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_56[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block16_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block16_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_71 (Add) (None, 7, 7, 512) 0 ['add_70[0][0]', Y \n", - " 'stack_5_block16_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block17_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_71[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block17_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block17_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block17_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block17_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block17_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block17_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block17_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block17_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block17_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_57 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block17_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_57[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_114 (Activation) (None, 1, 1, 128) 0 ['stack_5_block17_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block17_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_114[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_115 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block17_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_57 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_115[0][0]'] \n", - " \n", - " stack_5_block17_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_57[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block17_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block17_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_72 (Add) (None, 7, 7, 512) 0 ['add_71[0][0]', Y \n", - " 'stack_5_block17_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block18_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_72[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block18_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block18_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block18_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block18_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block18_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block18_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block18_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block18_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block18_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_58 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block18_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_58[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_116 (Activation) (None, 1, 1, 128) 0 ['stack_5_block18_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block18_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_116[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_117 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block18_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_58 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_117[0][0]'] \n", - " \n", - " stack_5_block18_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_58[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block18_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block18_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_73 (Add) (None, 7, 7, 512) 0 ['add_72[0][0]', Y \n", - " 'stack_5_block18_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block19_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_73[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block19_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block19_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block19_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block19_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block19_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block19_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block19_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block19_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block19_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_59 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block19_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_59[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_118 (Activation) (None, 1, 1, 128) 0 ['stack_5_block19_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block19_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_118[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_119 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block19_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_59 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_119[0][0]'] \n", - " \n", - " stack_5_block19_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_59[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block19_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block19_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_74 (Add) (None, 7, 7, 512) 0 ['add_73[0][0]', Y \n", - " 'stack_5_block19_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block20_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_74[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block20_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block20_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block20_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block20_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block20_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block20_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block20_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block20_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block20_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_60 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block20_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_60[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_120 (Activation) (None, 1, 1, 128) 0 ['stack_5_block20_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block20_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_120[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_121 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block20_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_60 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_121[0][0]'] \n", - " \n", - " stack_5_block20_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_60[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block20_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block20_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_75 (Add) (None, 7, 7, 512) 0 ['add_74[0][0]', Y \n", - " 'stack_5_block20_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block21_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_75[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block21_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block21_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block21_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block21_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block21_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block21_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block21_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block21_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block21_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_61 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block21_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_61[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_122 (Activation) (None, 1, 1, 128) 0 ['stack_5_block21_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block21_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_122[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_123 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block21_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_61 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_123[0][0]'] \n", - " \n", - " stack_5_block21_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_61[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block21_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block21_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_76 (Add) (None, 7, 7, 512) 0 ['add_75[0][0]', Y \n", - " 'stack_5_block21_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block22_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_76[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block22_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block22_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block22_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block22_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block22_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block22_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block22_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block22_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block22_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_62 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block22_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_62[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_124 (Activation) (None, 1, 1, 128) 0 ['stack_5_block22_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block22_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_124[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_125 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block22_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_62 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_125[0][0]'] \n", - " \n", - " stack_5_block22_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_62[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block22_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block22_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_77 (Add) (None, 7, 7, 512) 0 ['add_76[0][0]', Y \n", - " 'stack_5_block22_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block23_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_77[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block23_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block23_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block23_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block23_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block23_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block23_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block23_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block23_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block23_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_63 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block23_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_63[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_126 (Activation) (None, 1, 1, 128) 0 ['stack_5_block23_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block23_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_126[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_127 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block23_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_63 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_127[0][0]'] \n", - " \n", - " stack_5_block23_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_63[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block23_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block23_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_78 (Add) (None, 7, 7, 512) 0 ['add_77[0][0]', Y \n", - " 'stack_5_block23_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block24_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_78[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block24_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block24_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block24_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block24_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block24_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block24_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block24_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block24_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block24_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_64 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block24_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_64[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_128 (Activation) (None, 1, 1, 128) 0 ['stack_5_block24_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block24_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_128[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_129 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block24_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_64 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_129[0][0]'] \n", - " \n", - " stack_5_block24_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_64[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block24_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block24_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_79 (Add) (None, 7, 7, 512) 0 ['add_78[0][0]', Y \n", - " 'stack_5_block24_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block25_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_79[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block25_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block25_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block25_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block25_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block25_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block25_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block25_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block25_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block25_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_65 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block25_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_65[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_130 (Activation) (None, 1, 1, 128) 0 ['stack_5_block25_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block25_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_130[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_131 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block25_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_65 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_131[0][0]'] \n", - " \n", - " stack_5_block25_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_65[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block25_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block25_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_80 (Add) (None, 7, 7, 512) 0 ['add_79[0][0]', Y \n", - " 'stack_5_block25_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block26_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_80[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block26_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block26_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block26_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block26_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block26_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block26_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block26_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block26_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block26_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_66 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block26_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_66[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_132 (Activation) (None, 1, 1, 128) 0 ['stack_5_block26_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block26_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_132[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_133 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block26_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_66 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_133[0][0]'] \n", - " \n", - " stack_5_block26_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_66[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block26_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block26_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_81 (Add) (None, 7, 7, 512) 0 ['add_80[0][0]', Y \n", - " 'stack_5_block26_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block27_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_81[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block27_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block27_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block27_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block27_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block27_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block27_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block27_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block27_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block27_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_67 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block27_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_67[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_134 (Activation) (None, 1, 1, 128) 0 ['stack_5_block27_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block27_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_134[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_135 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block27_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_67 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_135[0][0]'] \n", - " \n", - " stack_5_block27_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_67[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block27_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block27_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_82 (Add) (None, 7, 7, 512) 0 ['add_81[0][0]', Y \n", - " 'stack_5_block27_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block28_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_82[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block28_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block28_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block28_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block28_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block28_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block28_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block28_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block28_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block28_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_68 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block28_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_68[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_136 (Activation) (None, 1, 1, 128) 0 ['stack_5_block28_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block28_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_136[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_137 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block28_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_68 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_137[0][0]'] \n", - " \n", - " stack_5_block28_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_68[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block28_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block28_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_83 (Add) (None, 7, 7, 512) 0 ['add_82[0][0]', Y \n", - " 'stack_5_block28_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block29_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_83[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block29_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block29_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block29_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block29_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block29_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block29_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block29_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block29_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block29_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_69 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block29_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_69[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_138 (Activation) (None, 1, 1, 128) 0 ['stack_5_block29_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block29_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_138[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_139 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block29_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_69 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_139[0][0]'] \n", - " \n", - " stack_5_block29_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_69[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block29_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block29_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_84 (Add) (None, 7, 7, 512) 0 ['add_83[0][0]', Y \n", - " 'stack_5_block29_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block30_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_84[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block30_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block30_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block30_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block30_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block30_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block30_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block30_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block30_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block30_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_70 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block30_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_70[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_140 (Activation) (None, 1, 1, 128) 0 ['stack_5_block30_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block30_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_140[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_141 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block30_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_70 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_141[0][0]'] \n", - " \n", - " stack_5_block30_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_70[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block30_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block30_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_85 (Add) (None, 7, 7, 512) 0 ['add_84[0][0]', Y \n", - " 'stack_5_block30_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block31_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_85[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block31_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block31_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block31_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block31_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block31_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block31_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block31_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block31_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block31_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_71 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block31_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_71[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_142 (Activation) (None, 1, 1, 128) 0 ['stack_5_block31_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block31_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_142[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_143 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block31_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_71 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_143[0][0]'] \n", - " \n", - " stack_5_block31_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_71[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block31_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block31_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_86 (Add) (None, 7, 7, 512) 0 ['add_85[0][0]', Y \n", - " 'stack_5_block31_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_6_block0_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_86[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block0_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_6_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block0_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_6_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block0_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_6_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block0_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_6_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block0_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_72 (TFOpLa (None, 1, 1, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block0_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_72[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_144 (Activation) (None, 1, 1, 128) 0 ['stack_6_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block0_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_144[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_145 (Activation) (None, 1, 1, 3072) 0 ['stack_6_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_72 (Multiply) (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_145[0][0]'] \n", - " \n", - " stack_6_block0_MB_pw_conv (Con (None, 7, 7, 640) 1966080 ['multiply_72[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block0_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_6_block1_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['stack_6_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_6_block1_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block1_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block1_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block1_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block1_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_73 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block1_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_73[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_146 (Activation) (None, 1, 1, 160) 0 ['stack_6_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block1_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_146[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_147 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_73 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_147[0][0]'] \n", - " \n", - " stack_6_block1_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_73[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block1_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_87 (Add) (None, 7, 7, 640) 0 ['stack_6_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_6_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block2_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_87[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block2_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block2_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block2_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block2_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block2_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_74 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block2_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_74[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_148 (Activation) (None, 1, 1, 160) 0 ['stack_6_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block2_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_148[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_149 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_74 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_149[0][0]'] \n", - " \n", - " stack_6_block2_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_74[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block2_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_88 (Add) (None, 7, 7, 640) 0 ['add_87[0][0]', Y \n", - " 'stack_6_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block3_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_88[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block3_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block3_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block3_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block3_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block3_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_75 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block3_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_75[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_150 (Activation) (None, 1, 1, 160) 0 ['stack_6_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block3_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_150[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_151 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_75 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_151[0][0]'] \n", - " \n", - " stack_6_block3_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_75[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block3_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_89 (Add) (None, 7, 7, 640) 0 ['add_88[0][0]', Y \n", - " 'stack_6_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block4_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_89[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block4_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block4_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block4_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block4_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block4_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_76 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block4_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_76[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_152 (Activation) (None, 1, 1, 160) 0 ['stack_6_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block4_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_152[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_153 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_76 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_153[0][0]'] \n", - " \n", - " stack_6_block4_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_76[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block4_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_90 (Add) (None, 7, 7, 640) 0 ['add_89[0][0]', Y \n", - " 'stack_6_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block5_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_90[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block5_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block5_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block5_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block5_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block5_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_77 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block5_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_77[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_154 (Activation) (None, 1, 1, 160) 0 ['stack_6_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block5_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_154[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_155 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_77 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_155[0][0]'] \n", - " \n", - " stack_6_block5_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_77[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block5_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_91 (Add) (None, 7, 7, 640) 0 ['add_90[0][0]', Y \n", - " 'stack_6_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block6_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_91[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block6_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block6_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block6_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block6_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block6_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_78 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block6_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_78[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_156 (Activation) (None, 1, 1, 160) 0 ['stack_6_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block6_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_156[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_157 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_78 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_157[0][0]'] \n", - " \n", - " stack_6_block6_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_78[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block6_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_92 (Add) (None, 7, 7, 640) 0 ['add_91[0][0]', Y \n", - " 'stack_6_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block7_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_92[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block7_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block7_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block7_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block7_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block7_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_79 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block7_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_79[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_158 (Activation) (None, 1, 1, 160) 0 ['stack_6_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block7_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_158[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_159 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_79 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_159[0][0]'] \n", - " \n", - " stack_6_block7_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_79[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block7_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_93 (Add) (None, 7, 7, 640) 0 ['add_92[0][0]', Y \n", - " 'stack_6_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " post_conv (Conv2D) (None, 7, 7, 1280) 819200 ['add_93[0][0]'] Y \n", - " \n", - " post_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['post_conv[0][0]'] Y \n", - " \n", - " post_swish (Activation) (None, 7, 7, 1280) 0 ['post_bn[0][0]'] Y \n", - " \n", - " avg_pool (GlobalAveragePooling (None, 1280) 0 ['post_swish[0][0]'] Y \n", - " 2D) \n", - " \n", - " dropout (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] Y \n", - " \n", - " predictions (Dense) (None, 2) 2562 ['dropout[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 207,618,394\n", - "Trainable params: 206,841,370\n", - "Non-trainable params: 777,024\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from keras_efficientnet_v2 import EfficientNetV2XL\n", "\n", @@ -9872,1276 +1082,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total layers in the base model: 467\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 475\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 48 1296 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 48 192 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 48 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 48 432 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 48 192 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 48 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 48) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 48) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 12) 588 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 48) 624 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 48 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 24 1152 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 24 216 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 24 96 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 24 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 24) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 24 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 24 576 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 24 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 24 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 14 3456 ['block1b_add[0][0]'] Y \n", - " 4) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 14 576 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 4) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 14 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 4) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 144) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 144) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 32) 4608 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 192) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 192) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 192) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 32) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 192) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 192) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 192) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 32) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 192) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 192) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 192) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 32) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2d_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 192) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 192) 4800 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 192) 768 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 192) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 192) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 192) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 56) 10752 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 336) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 336) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 336) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 56) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 336) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 336) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 336) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 56) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 336) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 336) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 336) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 56) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3d_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 336) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 336) 3024 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 336) 1344 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 336) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 336) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 336) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 112) 37632 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 672) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 672) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 672) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 112) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 672) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 672) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 672) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 112) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 672) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 672) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 672) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 112) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 672) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 672) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 672) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 112) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 672) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 672) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 672) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 112) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4f_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 672) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 672) 16800 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 672) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 672) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 672) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 160) 107520 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5a_project_bn[0][0]'] Y \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 960) 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 960) 0 ['block5b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 960) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5b_activation[0][0]', Y \n", - " 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 160) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5b_add[0][0]'] Y \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 960) 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 960) 0 ['block5c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 960) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5c_activation[0][0]', Y \n", - " 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 160) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5c_add[0][0]'] Y \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 960) 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 960) 0 ['block5d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 960) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5d_activation[0][0]', Y \n", - " 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 160) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5d_add[0][0]'] Y \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 960) 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 960) 0 ['block5e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 960) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5e_activation[0][0]', Y \n", - " 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 160) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5e_add[0][0]'] Y \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 960) 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 960) 0 ['block5f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 960) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5f_activation[0][0]', Y \n", - " 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 160) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5f_add[0][0]'] Y \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block6a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 960) 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 960) 24000 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 960) 3840 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 960) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 960) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 960) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 272) 261120 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 1632) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 1632) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 1632) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 272) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 1632) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 1632) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 1632) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 272) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 1632) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 1632) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 1632) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 272) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 1632) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 1632) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 1632) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 272) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 1632) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 1632) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 1632) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 272) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 1632) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 1632) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 1632) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 272) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 1632) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 1632) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 1632) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 272) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6h_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 1632) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 14688 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 1632) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 1632) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 448) 731136 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 448) 1792 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 2688) 1204224 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 2688) 10752 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 2688) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 2688) 24192 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 2688) 10752 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 2688) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 2688) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 2688) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 112) 301168 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 2688) 303744 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 2688) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 448) 1204224 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 448) 1792 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 448) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 448) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 1792) 802816 ['block7b_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 1792) 7168 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 1792) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 1792) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 918016 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 18,924,506\n", - "Trainable params: 18,797,258\n", - "Non-trainable params: 127,248\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB4 as KENB4\n", "# FUNC\n", @@ -11286,2169 +1229,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:32.994176700Z", "start_time": "2023-12-28T02:31:27.381088600Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " FC_INPUT_Avg-Pooling (GlobalAv (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " eragePooling2D) \n", - " \n", - " FC_C_Dense-L1-512 (Dense) (None, 512) 1311232 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n", - " \n", - " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n", - " \n", - " FC_C_Avg-Pooling-L1 (BatchNorm (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n", - " alization) \n", - " \n", - " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-Pooling-L1[0][0]'] Y \n", - " \n", - " FC_C_Avg-Pooling-L2 (BatchNorm (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n", - " alization) \n", - " \n", - " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-Pooling-L2[0][0]'] Y \n", - " \n", - " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -13534,1145 +1322,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total model layers: 11\n", - "Model: \"model\"\n", - "____________________________________________________________________________\n", - " Layer (type) Output Shape Param # Trainable \n", - "============================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3)] 0 Y \n", - " \n", - " lambda (Lambda) (None, 224, 224, 3) 0 Y \n", - " \n", - " convnext_xlarge (Functional (None, None, None, 2048) 34814796 Y \n", - " ) 8 \n", - "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n", - "| input_2 (InputLayer) [(None, None, None, 3)] 0 Y |\n", - "| |\n", - "| convnext_xlarge_prestem_nor (None, None, None, 3) 0 Y |\n", - "| malization (Normalization) |\n", - "| |\n", - "| convnext_xlarge_stem (Seque (None, None, None, 256) 13056 Y |\n", - "| ntial) |\n", - "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n", - "|| convnext_xlarge_stem_conv ( (None, None, None, 256) 12544 Y ||\n", - "|| Conv2D) ||\n", - "|| ||\n", - "|| convnext_xlarge_stem_layern (None, None, None, 256) 512 Y ||\n", - "|| orm (LayerNormalization) ||\n", - "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add (TFOpL (None, None, None, 256) 0 Y |\n", - "| ambda) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_1 (TFO (None, None, None, 256) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_2 (TFO (None, None, None, 256) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 512) 525312 Y |\n", - "| g_block_0 (Sequential) |\n", - "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 256) 512 Y ||\n", - "|| g_layernorm_0 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 512) 524800 Y ||\n", - "|| g_conv_0 (Conv2D) ||\n", - "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_3 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_4 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_5 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 1024) 2099200 Y |\n", - "| g_block_1 (Sequential) |\n", - "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 512) 1024 Y ||\n", - "|| g_layernorm_1 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2098176 Y ||\n", - "|| g_conv_1 (Conv2D) ||\n", - "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_6 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_7 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_8 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_3_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_3_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_3_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_3_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_3_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_3_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_3_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_9 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_4_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_4_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_4_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_4_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_4_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_4_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_4_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_10 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_5_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_5_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_5_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_5_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_5_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_5_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_5_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_11 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_6_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_6_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_6_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_6_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_6_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_6_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_6_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_12 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_7_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_7_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_7_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_7_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_7_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_7_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_7_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_13 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_8_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_8_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_8_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_8_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_8_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_8_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_8_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_14 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_9_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_9_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_9_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_9_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_9_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_9_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_9_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_15 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_10_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_10_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_10_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_10_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_10_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_10_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_10_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_16 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_11_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_11_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_11_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_11_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_11_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_11_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_11_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_17 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_12_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_12_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_12_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_12_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_12_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_12_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_12_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_18 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_13_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_13_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_13_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_13_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_13_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_13_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_13_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_19 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_14_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_14_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_14_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_14_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_14_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_14_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_14_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_20 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_15_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_15_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_15_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_15_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_15_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_15_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_15_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_21 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_16_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_16_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_16_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_16_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_16_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_16_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_16_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_22 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_17_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_17_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_17_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_17_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_17_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_17_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_17_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_23 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_18_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_18_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_18_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_18_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_18_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_18_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_18_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_24 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_19_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_19_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_19_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_19_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_19_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_19_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_19_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_25 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_20_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_20_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_20_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_20_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_20_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_20_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_20_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_26 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_21_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_21_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_21_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_21_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_21_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_21_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_21_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_27 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_22_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_22_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_22_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_22_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_22_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_22_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_22_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_28 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_23_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_23_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_23_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_23_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_23_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_23_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_23_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_29 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_24_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_24_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_24_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_24_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_24_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_24_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_24_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_30 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_25_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_25_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_25_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_25_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_25_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_25_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_25_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_31 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_26_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_26_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_26_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_26_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_26_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_26_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_26_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_32 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 2048) 8392704 Y |\n", - "| g_block_2 (Sequential) |\n", - "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2048 Y ||\n", - "|| g_layernorm_2 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 2048) 8390656 Y ||\n", - "|| g_conv_2 (Conv2D) ||\n", - "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_33 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_34 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_35 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| layer_normalization (LayerN (None, None, None, 2048) 4096 Y |\n", - "| ormalization) |\n", - "¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n", - " global_average_pooling2d (G (None, 2048) 0 Y \n", - " lobalAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1049088 Y \n", - " \n", - " dropout (Dropout) (None, 512) 0 Y \n", - " \n", - " batch_normalization (BatchN (None, 512) 2048 Y \n", - " ormalization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 Y \n", - " \n", - " batch_normalization_1 (Batc (None, 512) 2048 Y \n", - " hNormalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 Y \n", - " \n", - "============================================================================\n", - "Total params: 349,529,730\n", - "Trainable params: 349,527,682\n", - "Non-trainable params: 2,048\n", - "____________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from keras.applications import ConvNeXtXLarge\n", "from keras.layers import Lambda\n", @@ -14852,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -14932,2162 +1584,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[92mLoading model done.\n", - "Compiling the AI model...\u001b[0m\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "import efficientnet.tfkeras\n", "# Configuration\n", @@ -17166,18 +1665,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "for layer in model.layers[-7:]:\n", " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", @@ -17220,5532 +1710,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:23.573633300Z", "start_time": "2023-12-28T02:31:32.468641900Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training the model...\n", - "\u001b[0;33m\n", - "Setup Verbose:\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m01_d09-h15_m34_s38]\u001b[0m\u001b[0;36m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0;33mSetup Verbose END.\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", - "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d09-h15_m39_s53\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1/6\n", - "256/256 [==============================] - 52s 153ms/step - loss: 18.4951 - accuracy: 0.7188 - val_loss: 12.9223 - val_accuracy: 0.8766\n", - "Epoch 2/6\n", - "256/256 [==============================] - 38s 149ms/step - loss: 7.8523 - accuracy: 0.8381 - val_loss: 4.3373 - val_accuracy: 0.8141\n", - "Epoch 3/6\n", - "256/256 [==============================] - 38s 148ms/step - loss: 2.7484 - accuracy: 0.8806 - val_loss: 1.7122 - val_accuracy: 0.8766\n", - "Epoch 4/6\n", - "256/256 [==============================] - 38s 149ms/step - loss: 1.1812 - accuracy: 0.9133 - val_loss: 0.8132 - val_accuracy: 0.9279\n", - "Epoch 5/6\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.6760 - accuracy: 0.9285 - val_loss: 0.5877 - val_accuracy: 0.9199\n", - "Epoch 6/6\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.5018 - accuracy: 0.9519 - val_loss: 0.5245 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-006-0.9311.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5246\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.000000\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.931090\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.5245769024\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m577.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m246.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m330.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 7/12\n", - "256/256 [==============================] - 41s 149ms/step - loss: 0.6206 - accuracy: 0.8831 - val_loss: 0.5092 - val_accuracy: 0.9391\n", - "Epoch 8/12\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.4720 - accuracy: 0.8972 - val_loss: 0.3166 - val_accuracy: 0.9407\n", - "Epoch 9/12\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.3603 - accuracy: 0.8984 - val_loss: 0.2787 - val_accuracy: 0.9407\n", - "Epoch 10/12\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.2707 - accuracy: 0.9185 - val_loss: 0.2552 - val_accuracy: 0.9054\n", - "Epoch 11/12\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1933 - accuracy: 0.9453 - val_loss: 0.2240 - val_accuracy: 0.9407\n", - "Epoch 12/12\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1362 - accuracy: 0.9622 - val_loss: 0.1874 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-012-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1874\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.931090\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.951923\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.5245769024\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1873790175\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m301.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 13/18\n", - "256/256 [==============================] - 40s 149ms/step - loss: 0.2789 - accuracy: 0.9097 - val_loss: 0.2713 - val_accuracy: 0.9423\n", - "Epoch 14/18\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.2658 - accuracy: 0.9131 - val_loss: 0.1819 - val_accuracy: 0.9471\n", - "Epoch 15/18\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.2128 - accuracy: 0.9358 - val_loss: 0.2889 - val_accuracy: 0.9135\n", - "Epoch 16/18\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1630 - accuracy: 0.9551 - val_loss: 0.2035 - val_accuracy: 0.9471\n", - "Epoch 17/18\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.1318 - accuracy: 0.9663 - val_loss: 0.1878 - val_accuracy: 0.9503\n", - "Epoch 18/18\n", - "256/256 [==============================] - 37s 146ms/step - loss: 0.0988 - accuracy: 0.9749 - val_loss: 0.1613 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-018-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1613\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1873790175\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1613359898\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m296.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m228.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 19/24\n", - "256/256 [==============================] - 41s 150ms/step - loss: 0.2874 - accuracy: 0.9082 - val_loss: 0.1646 - val_accuracy: 0.9471\n", - "Epoch 20/24\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.2620 - accuracy: 0.9136 - val_loss: 0.2170 - val_accuracy: 0.9471\n", - "Epoch 21/24\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.2355 - accuracy: 0.9260 - val_loss: 0.2217 - val_accuracy: 0.9439\n", - "Epoch 22/24\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.1663 - accuracy: 0.9507 - val_loss: 0.2019 - val_accuracy: 0.9391\n", - "Epoch 23/24\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1422 - accuracy: 0.9558 - val_loss: 0.4787 - val_accuracy: 0.8878\n", - "Epoch 24/24\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.0926 - accuracy: 0.9741 - val_loss: 0.2400 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-019-0.9471.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1646\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1613359898. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m291.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m230.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m61.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 25/30\n", - "256/256 [==============================] - 41s 150ms/step - loss: 0.2645 - accuracy: 0.9077 - val_loss: 0.2325 - val_accuracy: 0.9439\n", - "Epoch 26/30\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.2508 - accuracy: 0.9065 - val_loss: 0.1603 - val_accuracy: 0.9471\n", - "Epoch 27/30\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.2211 - accuracy: 0.9294 - val_loss: 0.1559 - val_accuracy: 0.9503\n", - "Epoch 28/30\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1713 - accuracy: 0.9424 - val_loss: 0.2317 - val_accuracy: 0.9391\n", - "Epoch 29/30\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1236 - accuracy: 0.9626 - val_loss: 0.1729 - val_accuracy: 0.9471\n", - "Epoch 30/30\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.0771 - accuracy: 0.9807 - val_loss: 0.2440 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-027-0.9503.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1559\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1613359898\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1559069157\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m295.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m63.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 31/36\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.2558 - accuracy: 0.9165 - val_loss: 0.1511 - val_accuracy: 0.9455\n", - "Epoch 32/36\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.2471 - accuracy: 0.9177 - val_loss: 0.1910 - val_accuracy: 0.9407\n", - "Epoch 33/36\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.2144 - accuracy: 0.9314 - val_loss: 0.2215 - val_accuracy: 0.9439\n", - "Epoch 34/36\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1696 - accuracy: 0.9470 - val_loss: 0.1857 - val_accuracy: 0.9375\n", - "Epoch 35/36\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1236 - accuracy: 0.9612 - val_loss: 0.2154 - val_accuracy: 0.9327\n", - "Epoch 36/36\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0747 - accuracy: 0.9822 - val_loss: 0.2786 - val_accuracy: 0.9263\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-031-0.9455.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1512\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1559069157\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1511523277\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m298.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 37/42\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.2247 - accuracy: 0.9260 - val_loss: 0.2073 - val_accuracy: 0.9423\n", - "Epoch 38/42\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.2261 - accuracy: 0.9226 - val_loss: 0.2622 - val_accuracy: 0.9455\n", - "Epoch 39/42\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1798 - accuracy: 0.9417 - val_loss: 0.1671 - val_accuracy: 0.9407\n", - "Epoch 40/42\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1507 - accuracy: 0.9558 - val_loss: 0.1425 - val_accuracy: 0.9519\n", - "Epoch 41/42\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1146 - accuracy: 0.9651 - val_loss: 0.2348 - val_accuracy: 0.9343\n", - "Epoch 42/42\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0776 - accuracy: 0.9807 - val_loss: 0.3067 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-040-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1425\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1511523277\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1424551010\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m299.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m233.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 43/48\n", - "256/256 [==============================] - 41s 151ms/step - loss: 0.2495 - accuracy: 0.9121 - val_loss: 0.2087 - val_accuracy: 0.9215\n", - "Epoch 44/48\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.2325 - accuracy: 0.9192 - val_loss: 0.1930 - val_accuracy: 0.9183\n", - "Epoch 45/48\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1859 - accuracy: 0.9438 - val_loss: 0.2480 - val_accuracy: 0.9279\n", - "Epoch 46/48\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1634 - accuracy: 0.9519 - val_loss: 0.1840 - val_accuracy: 0.9391\n", - "Epoch 47/48\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1267 - accuracy: 0.9626 - val_loss: 0.1583 - val_accuracy: 0.9519\n", - "Epoch 48/48\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.0798 - accuracy: 0.9802 - val_loss: 0.2041 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-047-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m296.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m63.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 49/54\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.2206 - accuracy: 0.9280 - val_loss: 0.1844 - val_accuracy: 0.9535\n", - "Epoch 50/54\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.2264 - accuracy: 0.9290 - val_loss: 0.2793 - val_accuracy: 0.9391\n", - "Epoch 51/54\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1927 - accuracy: 0.9368 - val_loss: 0.1743 - val_accuracy: 0.9439\n", - "Epoch 52/54\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1474 - accuracy: 0.9521 - val_loss: 0.2213 - val_accuracy: 0.9471\n", - "Epoch 53/54\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1092 - accuracy: 0.9697 - val_loss: 0.1963 - val_accuracy: 0.9471\n", - "Epoch 54/54\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0614 - accuracy: 0.9836 - val_loss: 0.2508 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-049-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1844\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.951923\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.953526\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m298.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 55/60\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.2358 - accuracy: 0.9175 - val_loss: 0.2603 - val_accuracy: 0.9423\n", - "Epoch 56/60\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.2284 - accuracy: 0.9246 - val_loss: 0.1728 - val_accuracy: 0.9551\n", - "Epoch 57/60\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.2043 - accuracy: 0.9387 - val_loss: 0.4146 - val_accuracy: 0.9022\n", - "Epoch 58/60\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1502 - accuracy: 0.9585 - val_loss: 0.1864 - val_accuracy: 0.9407\n", - "Epoch 59/60\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1184 - accuracy: 0.9685 - val_loss: 0.2023 - val_accuracy: 0.9423\n", - "Epoch 60/60\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0749 - accuracy: 0.9812 - val_loss: 0.2414 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-056-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1727\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.953526\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.955128\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m297.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 61/66\n", - "256/256 [==============================] - 41s 153ms/step - loss: 0.2261 - accuracy: 0.9216 - val_loss: 0.1606 - val_accuracy: 0.9503\n", - "Epoch 62/66\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.2113 - accuracy: 0.9280 - val_loss: 0.1811 - val_accuracy: 0.9471\n", - "Epoch 63/66\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1757 - accuracy: 0.9475 - val_loss: 0.1544 - val_accuracy: 0.9487\n", - "Epoch 64/66\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1440 - accuracy: 0.9546 - val_loss: 0.1624 - val_accuracy: 0.9487\n", - "Epoch 65/66\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1015 - accuracy: 0.9736 - val_loss: 0.2215 - val_accuracy: 0.9455\n", - "Epoch 66/66\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0693 - accuracy: 0.9829 - val_loss: 0.1988 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-061-0.9503.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1606\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m299.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m233.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 67/72\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.2247 - accuracy: 0.9209 - val_loss: 0.1566 - val_accuracy: 0.9487\n", - "Epoch 68/72\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.2037 - accuracy: 0.9297 - val_loss: 0.1695 - val_accuracy: 0.9327\n", - "Epoch 69/72\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1744 - accuracy: 0.9451 - val_loss: 0.1753 - val_accuracy: 0.9455\n", - "Epoch 70/72\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1370 - accuracy: 0.9521 - val_loss: 0.1410 - val_accuracy: 0.9503\n", - "Epoch 71/72\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1060 - accuracy: 0.9717 - val_loss: 0.2152 - val_accuracy: 0.9503\n", - "Epoch 72/72\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0614 - accuracy: 0.9844 - val_loss: 0.2432 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-072-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2433\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m297.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m64.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 73/78\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.2328 - accuracy: 0.9270 - val_loss: 0.1630 - val_accuracy: 0.9519\n", - "Epoch 74/78\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.2236 - accuracy: 0.9319 - val_loss: 0.1650 - val_accuracy: 0.9423\n", - "Epoch 75/78\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1780 - accuracy: 0.9465 - val_loss: 0.2459 - val_accuracy: 0.9343\n", - "Epoch 76/78\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1394 - accuracy: 0.9548 - val_loss: 0.2597 - val_accuracy: 0.9423\n", - "Epoch 77/78\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0975 - accuracy: 0.9744 - val_loss: 0.2506 - val_accuracy: 0.9455\n", - "Epoch 78/78\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0619 - accuracy: 0.9856 - val_loss: 0.1965 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-073-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1629\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m298.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m233.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m64.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 79/84\n", - "256/256 [==============================] - 41s 153ms/step - loss: 0.2163 - accuracy: 0.9290 - val_loss: 0.1827 - val_accuracy: 0.9503\n", - "Epoch 80/84\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.2090 - accuracy: 0.9324 - val_loss: 0.2182 - val_accuracy: 0.9455\n", - "Epoch 81/84\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.2019 - accuracy: 0.9436 - val_loss: 0.2320 - val_accuracy: 0.9439\n", - "Epoch 82/84\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1406 - accuracy: 0.9570 - val_loss: 0.3348 - val_accuracy: 0.9263\n", - "Epoch 83/84\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.0850 - accuracy: 0.9773 - val_loss: 0.2899 - val_accuracy: 0.9311\n", - "Epoch 84/84\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0620 - accuracy: 0.9846 - val_loss: 0.2283 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-084-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2283\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m298.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 85/90\n", - "256/256 [==============================] - 41s 151ms/step - loss: 0.2243 - accuracy: 0.9299 - val_loss: 0.2002 - val_accuracy: 0.9439\n", - "Epoch 86/90\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1945 - accuracy: 0.9375 - val_loss: 0.2288 - val_accuracy: 0.9359\n", - "Epoch 87/90\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1548 - accuracy: 0.9509 - val_loss: 0.3075 - val_accuracy: 0.9359\n", - "Epoch 88/90\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.1140 - accuracy: 0.9653 - val_loss: 0.1632 - val_accuracy: 0.9503\n", - "Epoch 89/90\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0928 - accuracy: 0.9739 - val_loss: 0.2463 - val_accuracy: 0.9391\n", - "Epoch 90/90\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0520 - accuracy: 0.9883 - val_loss: 0.3022 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-088-0.9503.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1632\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m298.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m233.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 91/96\n", - "256/256 [==============================] - 41s 153ms/step - loss: 0.2295 - accuracy: 0.9243 - val_loss: 0.1890 - val_accuracy: 0.9487\n", - "Epoch 92/96\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.2104 - accuracy: 0.9333 - val_loss: 0.3140 - val_accuracy: 0.9183\n", - "Epoch 93/96\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1662 - accuracy: 0.9485 - val_loss: 0.3296 - val_accuracy: 0.9199\n", - "Epoch 94/96\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1241 - accuracy: 0.9626 - val_loss: 0.2256 - val_accuracy: 0.9487\n", - "Epoch 95/96\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.0859 - accuracy: 0.9756 - val_loss: 0.4548 - val_accuracy: 0.9087\n", - "Epoch 96/96\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.0567 - accuracy: 0.9858 - val_loss: 0.3490 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1890\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m295.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m229.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 97/102\n", - "256/256 [==============================] - 41s 151ms/step - loss: 0.1970 - accuracy: 0.9307 - val_loss: 0.1901 - val_accuracy: 0.9471\n", - "Epoch 98/102\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.2049 - accuracy: 0.9377 - val_loss: 0.3009 - val_accuracy: 0.9119\n", - "Epoch 99/102\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1711 - accuracy: 0.9448 - val_loss: 0.4473 - val_accuracy: 0.8910\n", - "Epoch 100/102\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1293 - accuracy: 0.9609 - val_loss: 0.4102 - val_accuracy: 0.9199\n", - "Epoch 101/102\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0909 - accuracy: 0.9766 - val_loss: 0.2734 - val_accuracy: 0.9471\n", - "Epoch 102/102\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2757 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-097-0.9471.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1901\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m294.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m227.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 103/108\n", - "256/256 [==============================] - 40s 148ms/step - loss: 0.2053 - accuracy: 0.9329 - val_loss: 0.2365 - val_accuracy: 0.8990\n", - "Epoch 104/108\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.1868 - accuracy: 0.9431 - val_loss: 0.3855 - val_accuracy: 0.9279\n", - "Epoch 105/108\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1459 - accuracy: 0.9541 - val_loss: 0.4715 - val_accuracy: 0.8990\n", - "Epoch 106/108\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.1216 - accuracy: 0.9673 - val_loss: 0.1835 - val_accuracy: 0.9487\n", - "Epoch 107/108\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.0764 - accuracy: 0.9807 - val_loss: 0.3512 - val_accuracy: 0.9279\n", - "Epoch 108/108\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.3090 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-106-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1835\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m292.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m227.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m64.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 109/114\n", - "256/256 [==============================] - 40s 147ms/step - loss: 0.2124 - accuracy: 0.9336 - val_loss: 0.2637 - val_accuracy: 0.9503\n", - "Epoch 110/114\n", - "256/256 [==============================] - 37s 143ms/step - loss: 0.2062 - accuracy: 0.9348 - val_loss: 0.3938 - val_accuracy: 0.8702\n", - "Epoch 111/114\n", - "256/256 [==============================] - 37s 143ms/step - loss: 0.1657 - accuracy: 0.9512 - val_loss: 0.3525 - val_accuracy: 0.9359\n", - "Epoch 112/114\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1356 - accuracy: 0.9651 - val_loss: 0.2216 - val_accuracy: 0.9551\n", - "Epoch 113/114\n", - "256/256 [==============================] - 37s 143ms/step - loss: 0.0933 - accuracy: 0.9753 - val_loss: 0.3289 - val_accuracy: 0.9311\n", - "Epoch 114/114\n", - "256/256 [==============================] - 37s 143ms/step - loss: 0.0654 - accuracy: 0.9858 - val_loss: 0.4380 - val_accuracy: 0.9199\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-112-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2217\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m290.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m225.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 115/120\n", - "256/256 [==============================] - 40s 148ms/step - loss: 0.2120 - accuracy: 0.9263 - val_loss: 0.2124 - val_accuracy: 0.9455\n", - "Epoch 116/120\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1835 - accuracy: 0.9409 - val_loss: 0.1879 - val_accuracy: 0.9439\n", - "Epoch 117/120\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1406 - accuracy: 0.9595 - val_loss: 0.4328 - val_accuracy: 0.9103\n", - "Epoch 118/120\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1028 - accuracy: 0.9688 - val_loss: 0.2159 - val_accuracy: 0.9423\n", - "Epoch 119/120\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.0752 - accuracy: 0.9810 - val_loss: 0.4316 - val_accuracy: 0.9343\n", - "Epoch 120/120\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.0457 - accuracy: 0.9907 - val_loss: 0.3136 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-115-0.9455.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2124\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m292.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m227.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 121/126\n", - "256/256 [==============================] - 40s 148ms/step - loss: 0.1920 - accuracy: 0.9341 - val_loss: 0.3173 - val_accuracy: 0.9199\n", - "Epoch 122/126\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.2049 - accuracy: 0.9399 - val_loss: 0.2842 - val_accuracy: 0.9407\n", - "Epoch 123/126\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1531 - accuracy: 0.9507 - val_loss: 0.4877 - val_accuracy: 0.8974\n", - "Epoch 124/126\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1303 - accuracy: 0.9653 - val_loss: 0.2943 - val_accuracy: 0.9183\n", - "Epoch 125/126\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0821 - accuracy: 0.9797 - val_loss: 0.3613 - val_accuracy: 0.9407\n", - "Epoch 126/126\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.0548 - accuracy: 0.9866 - val_loss: 0.4427 - val_accuracy: 0.9199\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-122-0.9407.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2842\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m293.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m227.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 127/132\n", - "256/256 [==============================] - 40s 146ms/step - loss: 0.2108 - accuracy: 0.9329 - val_loss: 0.2948 - val_accuracy: 0.9407\n", - "Epoch 128/132\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1797 - accuracy: 0.9370 - val_loss: 0.2020 - val_accuracy: 0.9439\n", - "Epoch 129/132\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1565 - accuracy: 0.9529 - val_loss: 0.2528 - val_accuracy: 0.9487\n", - "Epoch 130/132\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1152 - accuracy: 0.9683 - val_loss: 0.2923 - val_accuracy: 0.9343\n", - "Epoch 131/132\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0777 - accuracy: 0.9792 - val_loss: 0.4047 - val_accuracy: 0.9135\n", - "Epoch 132/132\n", - "256/256 [==============================] - 37s 143ms/step - loss: 0.0486 - accuracy: 0.9880 - val_loss: 0.4693 - val_accuracy: 0.9119\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-129-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2529\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m292.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m225.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 133/138\n", - "256/256 [==============================] - 40s 148ms/step - loss: 0.2190 - accuracy: 0.9309 - val_loss: 0.2403 - val_accuracy: 0.9455\n", - "Epoch 134/138\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1992 - accuracy: 0.9387 - val_loss: 0.2301 - val_accuracy: 0.9359\n", - "Epoch 135/138\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1678 - accuracy: 0.9495 - val_loss: 0.1970 - val_accuracy: 0.9535\n", - "Epoch 136/138\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1170 - accuracy: 0.9666 - val_loss: 0.2603 - val_accuracy: 0.9407\n", - "Epoch 137/138\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0856 - accuracy: 0.9780 - val_loss: 0.2859 - val_accuracy: 0.9343\n", - "Epoch 138/138\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0582 - accuracy: 0.9858 - val_loss: 0.3280 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-135-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1970\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m292.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m226.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 139/144\n", - "256/256 [==============================] - 40s 147ms/step - loss: 0.1960 - accuracy: 0.9414 - val_loss: 0.2166 - val_accuracy: 0.9423\n", - "Epoch 140/144\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1826 - accuracy: 0.9463 - val_loss: 0.2812 - val_accuracy: 0.9391\n", - "Epoch 141/144\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1361 - accuracy: 0.9626 - val_loss: 0.5396 - val_accuracy: 0.9022\n", - "Epoch 142/144\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1039 - accuracy: 0.9724 - val_loss: 0.4560 - val_accuracy: 0.8654\n", - "Epoch 143/144\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0792 - accuracy: 0.9790 - val_loss: 0.6379 - val_accuracy: 0.8702\n", - "Epoch 144/144\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0545 - accuracy: 0.9841 - val_loss: 0.4643 - val_accuracy: 0.9135\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-139-0.9423.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2167\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m293.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m226.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 145/150\n", - "256/256 [==============================] - 40s 148ms/step - loss: 0.2086 - accuracy: 0.9294 - val_loss: 0.2573 - val_accuracy: 0.9263\n", - "Epoch 146/150\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1959 - accuracy: 0.9346 - val_loss: 0.1740 - val_accuracy: 0.9487\n", - "Epoch 147/150\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.2253 - val_accuracy: 0.9439\n", - "Epoch 148/150\n", - "256/256 [==============================] - 37s 143ms/step - loss: 0.1379 - accuracy: 0.9568 - val_loss: 0.3328 - val_accuracy: 0.9407\n", - "Epoch 149/150\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0926 - accuracy: 0.9753 - val_loss: 0.3504 - val_accuracy: 0.9311\n", - "Epoch 150/150\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0558 - accuracy: 0.9866 - val_loss: 0.3565 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-146-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1740\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m294.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m226.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01097\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 151/156\n", - "256/256 [==============================] - 40s 147ms/step - loss: 0.1930 - accuracy: 0.9402 - val_loss: 0.1864 - val_accuracy: 0.9471\n", - "Epoch 152/156\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1591 - accuracy: 0.9495 - val_loss: 0.2040 - val_accuracy: 0.9439\n", - "Epoch 153/156\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1391 - accuracy: 0.9590 - val_loss: 0.1526 - val_accuracy: 0.9519\n", - "Epoch 154/156\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1003 - accuracy: 0.9734 - val_loss: 0.1631 - val_accuracy: 0.9535\n", - "Epoch 155/156\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0660 - accuracy: 0.9839 - val_loss: 0.2393 - val_accuracy: 0.9471\n", - "Epoch 156/156\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0430 - accuracy: 0.9910 - val_loss: 0.2375 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-154-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m294.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m226.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 157/162\n", - "256/256 [==============================] - 40s 149ms/step - loss: 0.2079 - accuracy: 0.9324 - val_loss: 0.2412 - val_accuracy: 0.9247\n", - "Epoch 158/162\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1816 - accuracy: 0.9448 - val_loss: 0.1585 - val_accuracy: 0.9487\n", - "Epoch 159/162\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1471 - accuracy: 0.9519 - val_loss: 0.2119 - val_accuracy: 0.9519\n", - "Epoch 160/162\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1202 - accuracy: 0.9675 - val_loss: 0.2652 - val_accuracy: 0.9295\n", - "Epoch 161/162\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0798 - accuracy: 0.9797 - val_loss: 0.2192 - val_accuracy: 0.9407\n", - "Epoch 162/162\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.0513 - accuracy: 0.9875 - val_loss: 0.2231 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1585}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9551}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2232\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m296.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m227.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01091\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 163/168\n", - "256/256 [==============================] - 40s 148ms/step - loss: 0.2046 - accuracy: 0.9348 - val_loss: 0.1895 - val_accuracy: 0.9439\n", - "Epoch 164/168\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1899 - accuracy: 0.9373 - val_loss: 0.3501 - val_accuracy: 0.9231\n", - "Epoch 165/168\n", - "256/256 [==============================] - 37s 146ms/step - loss: 0.1484 - accuracy: 0.9558 - val_loss: 0.1855 - val_accuracy: 0.9471\n", - "Epoch 166/168\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1148 - accuracy: 0.9661 - val_loss: 0.1736 - val_accuracy: 0.9423\n", - "Epoch 167/168\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0750 - accuracy: 0.9829 - val_loss: 0.2400 - val_accuracy: 0.9359\n", - "Epoch 168/168\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0461 - accuracy: 0.9890 - val_loss: 0.2330 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1736}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9551}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2329\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m296.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m227.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 169/174\n", - "256/256 [==============================] - 40s 148ms/step - loss: 0.2073 - accuracy: 0.9353 - val_loss: 0.1430 - val_accuracy: 0.9455\n", - "Epoch 170/174\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.1835 - accuracy: 0.9385 - val_loss: 0.1962 - val_accuracy: 0.9407\n", - "Epoch 171/174\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1316 - accuracy: 0.9600 - val_loss: 0.2272 - val_accuracy: 0.9407\n", - "Epoch 172/174\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.1098 - accuracy: 0.9697 - val_loss: 0.2172 - val_accuracy: 0.9471\n", - "Epoch 173/174\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0677 - accuracy: 0.9802 - val_loss: 0.2132 - val_accuracy: 0.9407\n", - "Epoch 174/174\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.0447 - accuracy: 0.9907 - val_loss: 0.2410 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1430}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9551}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2411\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m297.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m227.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01085\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 175/180\n", - "256/256 [==============================] - 40s 148ms/step - loss: 0.1905 - accuracy: 0.9373 - val_loss: 0.3589 - val_accuracy: 0.9038\n", - "Epoch 176/180\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.1704 - accuracy: 0.9463 - val_loss: 0.2379 - val_accuracy: 0.9391\n", - "Epoch 177/180\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1303 - accuracy: 0.9624 - val_loss: 0.2592 - val_accuracy: 0.9375\n", - "Epoch 178/180\n", - "256/256 [==============================] - 37s 146ms/step - loss: 0.1033 - accuracy: 0.9709 - val_loss: 0.2161 - val_accuracy: 0.9407\n", - "Epoch 179/180\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0671 - accuracy: 0.9849 - val_loss: 0.2937 - val_accuracy: 0.9359\n", - "Epoch 180/180\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.0392 - accuracy: 0.9915 - val_loss: 0.2632 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2161}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9551}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2632\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m298.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m227.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 181/186\n", - "256/256 [==============================] - 40s 149ms/step - loss: 0.2013 - accuracy: 0.9409 - val_loss: 0.1792 - val_accuracy: 0.9327\n", - "Epoch 182/186\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1829 - accuracy: 0.9436 - val_loss: 0.1814 - val_accuracy: 0.9455\n", - "Epoch 183/186\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1789 - val_accuracy: 0.9551\n", - "Epoch 184/186\n", - "256/256 [==============================] - 37s 145ms/step - loss: 0.1167 - accuracy: 0.9714 - val_loss: 0.3051 - val_accuracy: 0.9231\n", - "Epoch 185/186\n", - "256/256 [==============================] - 38s 146ms/step - loss: 0.0724 - accuracy: 0.9824 - val_loss: 0.2020 - val_accuracy: 0.9567\n", - "Epoch 186/186\n", - "256/256 [==============================] - 37s 144ms/step - loss: 0.0451 - accuracy: 0.9895 - val_loss: 0.1934 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-185-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2019\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.955128\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.956731\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m300.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m228.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01079\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 187/192\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1812 - accuracy: 0.9448 - val_loss: 0.1522 - val_accuracy: 0.9503\n", - "Epoch 188/192\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1484 - accuracy: 0.9565 - val_loss: 0.1625 - val_accuracy: 0.9439\n", - "Epoch 189/192\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.1245 - accuracy: 0.9624 - val_loss: 0.1709 - val_accuracy: 0.9551\n", - "Epoch 190/192\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0872 - accuracy: 0.9758 - val_loss: 0.2185 - val_accuracy: 0.9439\n", - "Epoch 191/192\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0581 - accuracy: 0.9873 - val_loss: 0.2299 - val_accuracy: 0.9487\n", - "Epoch 192/192\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0389 - accuracy: 0.9919 - val_loss: 0.2411 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1522}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9567}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2411\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307830. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m308.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m237.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 193/198\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1938 - accuracy: 0.9380 - val_loss: 0.3726 - val_accuracy: 0.9423\n", - "Epoch 194/198\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1584 - accuracy: 0.9519 - val_loss: 0.2806 - val_accuracy: 0.9215\n", - "Epoch 195/198\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9675 - val_loss: 0.2781 - val_accuracy: 0.9359\n", - "Epoch 196/198\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0985 - accuracy: 0.9756 - val_loss: 0.1518 - val_accuracy: 0.9583\n", - "Epoch 197/198\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0507 - accuracy: 0.9893 - val_loss: 0.2088 - val_accuracy: 0.9487\n", - "Epoch 198/198\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0401 - accuracy: 0.9900 - val_loss: 0.2536 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-196-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1518\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.956731\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.958333\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m316.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m237.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01073\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 199/204\n", - "256/256 [==============================] - 43s 156ms/step - loss: 0.1902 - accuracy: 0.9341 - val_loss: 0.1454 - val_accuracy: 0.9487\n", - "Epoch 200/204\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9456 - val_loss: 0.2456 - val_accuracy: 0.9006\n", - "Epoch 201/204\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1379 - accuracy: 0.9614 - val_loss: 0.1637 - val_accuracy: 0.9487\n", - "Epoch 202/204\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1076 - accuracy: 0.9705 - val_loss: 0.1591 - val_accuracy: 0.9535\n", - "Epoch 203/204\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0620 - accuracy: 0.9849 - val_loss: 0.1447 - val_accuracy: 0.9583\n", - "Epoch 204/204\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0402 - accuracy: 0.9912 - val_loss: 0.1564 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-204-0.9599.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1564\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.958333\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.959936\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 205/210\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1866 - accuracy: 0.9390 - val_loss: 0.1432 - val_accuracy: 0.9583\n", - "Epoch 206/210\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1585 - accuracy: 0.9541 - val_loss: 0.1619 - val_accuracy: 0.9455\n", - "Epoch 207/210\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1245 - accuracy: 0.9646 - val_loss: 0.2026 - val_accuracy: 0.9375\n", - "Epoch 208/210\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0925 - accuracy: 0.9775 - val_loss: 0.2121 - val_accuracy: 0.9439\n", - "Epoch 209/210\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0674 - accuracy: 0.9841 - val_loss: 0.1664 - val_accuracy: 0.9503\n", - "Epoch 210/210\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0448 - accuracy: 0.9924 - val_loss: 0.1779 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1432}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1778\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m318.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01067\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 211/216\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1866 - accuracy: 0.9402 - val_loss: 0.1426 - val_accuracy: 0.9519\n", - "Epoch 212/216\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1667 - accuracy: 0.9448 - val_loss: 0.2078 - val_accuracy: 0.9423\n", - "Epoch 213/216\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1176 - accuracy: 0.9636 - val_loss: 0.1825 - val_accuracy: 0.9391\n", - "Epoch 214/216\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0952 - accuracy: 0.9736 - val_loss: 0.1985 - val_accuracy: 0.9455\n", - "Epoch 215/216\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0662 - accuracy: 0.9846 - val_loss: 0.2120 - val_accuracy: 0.9455\n", - "Epoch 216/216\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0336 - accuracy: 0.9939 - val_loss: 0.2300 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1426}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2298\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m318.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 217/222\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1923 - accuracy: 0.9426 - val_loss: 0.1441 - val_accuracy: 0.9519\n", - "Epoch 218/222\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1672 - accuracy: 0.9485 - val_loss: 0.3356 - val_accuracy: 0.8926\n", - "Epoch 219/222\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1281 - accuracy: 0.9614 - val_loss: 0.1707 - val_accuracy: 0.9439\n", - "Epoch 220/222\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0903 - accuracy: 0.9746 - val_loss: 0.2300 - val_accuracy: 0.9487\n", - "Epoch 221/222\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0575 - accuracy: 0.9868 - val_loss: 0.2154 - val_accuracy: 0.9535\n", - "Epoch 222/222\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0442 - accuracy: 0.9893 - val_loss: 0.2091 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1441}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2090\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01061\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 223/228\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1730 - accuracy: 0.9438 - val_loss: 0.2107 - val_accuracy: 0.9519\n", - "Epoch 224/228\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1480 - accuracy: 0.9507 - val_loss: 0.2149 - val_accuracy: 0.9535\n", - "Epoch 225/228\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1192 - accuracy: 0.9685 - val_loss: 0.3342 - val_accuracy: 0.9391\n", - "Epoch 226/228\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0756 - accuracy: 0.9805 - val_loss: 0.2476 - val_accuracy: 0.9471\n", - "Epoch 227/228\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0583 - accuracy: 0.9844 - val_loss: 0.2303 - val_accuracy: 0.9535\n", - "Epoch 228/228\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0330 - accuracy: 0.9939 - val_loss: 0.2802 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2107}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2801\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 229/234\n", - "256/256 [==============================] - 41s 151ms/step - loss: 0.1948 - accuracy: 0.9358 - val_loss: 0.3844 - val_accuracy: 0.9391\n", - "Epoch 230/234\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1581 - accuracy: 0.9495 - val_loss: 0.1972 - val_accuracy: 0.9535\n", - "Epoch 231/234\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1226 - accuracy: 0.9639 - val_loss: 0.2950 - val_accuracy: 0.9279\n", - "Epoch 232/234\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.0918 - accuracy: 0.9734 - val_loss: 0.4289 - val_accuracy: 0.9343\n", - "Epoch 233/234\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.0615 - accuracy: 0.9851 - val_loss: 0.4367 - val_accuracy: 0.9247\n", - "Epoch 234/234\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0410 - accuracy: 0.9902 - val_loss: 0.4152 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1972}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4154\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m312.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01055\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 235/240\n", - "256/256 [==============================] - 41s 151ms/step - loss: 0.1861 - accuracy: 0.9407 - val_loss: 0.2462 - val_accuracy: 0.9535\n", - "Epoch 236/240\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1706 - accuracy: 0.9443 - val_loss: 0.1819 - val_accuracy: 0.9407\n", - "Epoch 237/240\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.1215 - accuracy: 0.9658 - val_loss: 0.1537 - val_accuracy: 0.9471\n", - "Epoch 238/240\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.0766 - accuracy: 0.9788 - val_loss: 0.2049 - val_accuracy: 0.9471\n", - "Epoch 239/240\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0456 - accuracy: 0.9880 - val_loss: 0.2153 - val_accuracy: 0.9487\n", - "Epoch 240/240\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0330 - accuracy: 0.9922 - val_loss: 0.1996 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1537}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1996\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m307.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m232.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 241/246\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.1861 - accuracy: 0.9424 - val_loss: 0.1451 - val_accuracy: 0.9503\n", - "Epoch 242/246\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1454 - accuracy: 0.9529 - val_loss: 0.4667 - val_accuracy: 0.8990\n", - "Epoch 243/246\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1187 - accuracy: 0.9666 - val_loss: 0.2655 - val_accuracy: 0.9279\n", - "Epoch 244/246\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0841 - accuracy: 0.9766 - val_loss: 0.2098 - val_accuracy: 0.9487\n", - "Epoch 245/246\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0455 - accuracy: 0.9888 - val_loss: 0.3056 - val_accuracy: 0.9359\n", - "Epoch 246/246\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0310 - accuracy: 0.9937 - val_loss: 0.3570 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1451}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3569\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m311.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m233.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d09-h19_m06_s20\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01049\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 247/252\n", - "256/256 [==============================] - 42s 153ms/step - loss: 0.1870 - accuracy: 0.9451 - val_loss: 0.1887 - val_accuracy: 0.9535\n", - "Epoch 248/252\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1557 - accuracy: 0.9529 - val_loss: 0.1667 - val_accuracy: 0.9551\n", - "Epoch 249/252\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1138 - accuracy: 0.9680 - val_loss: 0.2490 - val_accuracy: 0.9407\n", - "Epoch 250/252\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0817 - accuracy: 0.9780 - val_loss: 0.1334 - val_accuracy: 0.9535\n", - "Epoch 251/252\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0634 - accuracy: 0.9861 - val_loss: 0.1828 - val_accuracy: 0.9583\n", - "Epoch 252/252\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0447 - accuracy: 0.9927 - val_loss: 0.1474 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-251-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1827\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m330.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m234.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01046\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 253/258\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1808 - accuracy: 0.9443 - val_loss: 0.1644 - val_accuracy: 0.9583\n", - "Epoch 254/258\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1520 - accuracy: 0.9514 - val_loss: 0.4366 - val_accuracy: 0.8670\n", - "Epoch 255/258\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1198 - accuracy: 0.9670 - val_loss: 0.1464 - val_accuracy: 0.9583\n", - "Epoch 256/258\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0817 - accuracy: 0.9753 - val_loss: 0.1702 - val_accuracy: 0.9583\n", - "Epoch 257/258\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0529 - accuracy: 0.9856 - val_loss: 0.1923 - val_accuracy: 0.9567\n", - "Epoch 258/258\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0396 - accuracy: 0.9897 - val_loss: 0.1960 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1464}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1959\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01043\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 259/264\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1742 - accuracy: 0.9495 - val_loss: 0.1507 - val_accuracy: 0.9599\n", - "Epoch 260/264\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1385 - accuracy: 0.9568 - val_loss: 0.1779 - val_accuracy: 0.9439\n", - "Epoch 261/264\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1090 - accuracy: 0.9692 - val_loss: 0.2340 - val_accuracy: 0.9423\n", - "Epoch 262/264\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0765 - accuracy: 0.9800 - val_loss: 0.2335 - val_accuracy: 0.9471\n", - "Epoch 263/264\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0459 - accuracy: 0.9897 - val_loss: 0.2634 - val_accuracy: 0.9375\n", - "Epoch 264/264\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0317 - accuracy: 0.9922 - val_loss: 0.2839 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1507}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2839\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0104\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 265/270\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1896 - accuracy: 0.9414 - val_loss: 0.2125 - val_accuracy: 0.9423\n", - "Epoch 266/270\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1479 - accuracy: 0.9500 - val_loss: 0.1624 - val_accuracy: 0.9503\n", - "Epoch 267/270\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1144 - accuracy: 0.9648 - val_loss: 0.1842 - val_accuracy: 0.9519\n", - "Epoch 268/270\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0788 - accuracy: 0.9788 - val_loss: 0.1713 - val_accuracy: 0.9535\n", - "Epoch 269/270\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0507 - accuracy: 0.9858 - val_loss: 0.4003 - val_accuracy: 0.9295\n", - "Epoch 270/270\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0294 - accuracy: 0.9941 - val_loss: 0.3835 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1624}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3835\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01037\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 271/276\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1934 - accuracy: 0.9387 - val_loss: 0.2447 - val_accuracy: 0.9215\n", - "Epoch 272/276\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1641 - accuracy: 0.9458 - val_loss: 0.1811 - val_accuracy: 0.9583\n", - "Epoch 273/276\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1161 - accuracy: 0.9656 - val_loss: 0.2235 - val_accuracy: 0.9423\n", - "Epoch 274/276\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0795 - accuracy: 0.9768 - val_loss: 0.2107 - val_accuracy: 0.9311\n", - "Epoch 275/276\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0516 - accuracy: 0.9841 - val_loss: 0.1931 - val_accuracy: 0.9535\n", - "Epoch 276/276\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0320 - accuracy: 0.9934 - val_loss: 0.1943 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1811}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1425}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1942\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1424551010. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01034\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 277/282\n", - "256/256 [==============================] - 43s 156ms/step - loss: 0.1867 - accuracy: 0.9421 - val_loss: 0.2352 - val_accuracy: 0.9375\n", - "Epoch 278/282\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1354 - accuracy: 0.9570 - val_loss: 0.1312 - val_accuracy: 0.9631\n", - "Epoch 279/282\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1016 - accuracy: 0.9736 - val_loss: 0.1376 - val_accuracy: 0.9631\n", - "Epoch 280/282\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0683 - accuracy: 0.9819 - val_loss: 0.1659 - val_accuracy: 0.9631\n", - "Epoch 281/282\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0445 - accuracy: 0.9915 - val_loss: 0.1624 - val_accuracy: 0.9615\n", - "Epoch 282/282\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0339 - accuracy: 0.9937 - val_loss: 0.1724 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-278-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1312\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.959936\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.963141\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1424551010\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1312424541\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m331.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01031\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 283/288\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1770 - accuracy: 0.9458 - val_loss: 0.1281 - val_accuracy: 0.9615\n", - "Epoch 284/288\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1475 - accuracy: 0.9541 - val_loss: 0.1583 - val_accuracy: 0.9407\n", - "Epoch 285/288\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1020 - accuracy: 0.9702 - val_loss: 0.2989 - val_accuracy: 0.9343\n", - "Epoch 286/288\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0799 - accuracy: 0.9790 - val_loss: 0.1610 - val_accuracy: 0.9615\n", - "Epoch 287/288\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0425 - accuracy: 0.9878 - val_loss: 0.1682 - val_accuracy: 0.9631\n", - "Epoch 288/288\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0311 - accuracy: 0.9927 - val_loss: 0.2243 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-287-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1682\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1312424541. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m331.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01028\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 289/294\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1742 - accuracy: 0.9495 - val_loss: 0.1302 - val_accuracy: 0.9647\n", - "Epoch 290/294\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1339 - accuracy: 0.9583 - val_loss: 0.1543 - val_accuracy: 0.9583\n", - "Epoch 291/294\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1058 - accuracy: 0.9744 - val_loss: 0.1463 - val_accuracy: 0.9535\n", - "Epoch 292/294\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0686 - accuracy: 0.9817 - val_loss: 0.1600 - val_accuracy: 0.9535\n", - "Epoch 293/294\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0542 - accuracy: 0.9868 - val_loss: 0.2527 - val_accuracy: 0.9327\n", - "Epoch 294/294\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0393 - accuracy: 0.9922 - val_loss: 0.1866 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-289-0.9647.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1302\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.963141\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.964744\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1312424541\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1301687509\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m331.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01025\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 295/300\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1643 - accuracy: 0.9478 - val_loss: 0.1308 - val_accuracy: 0.9551\n", - "Epoch 296/300\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1502 - accuracy: 0.9524 - val_loss: 0.1627 - val_accuracy: 0.9647\n", - "Epoch 297/300\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1104 - accuracy: 0.9690 - val_loss: 0.2258 - val_accuracy: 0.9455\n", - "Epoch 298/300\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0760 - accuracy: 0.9773 - val_loss: 0.1795 - val_accuracy: 0.9503\n", - "Epoch 299/300\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0540 - accuracy: 0.9851 - val_loss: 0.1417 - val_accuracy: 0.9631\n", - "Epoch 300/300\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0286 - accuracy: 0.9946 - val_loss: 0.1597 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1308}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1597\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m333.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01022\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 301/306\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1613 - accuracy: 0.9492 - val_loss: 0.1384 - val_accuracy: 0.9551\n", - "Epoch 302/306\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1318 - accuracy: 0.9587 - val_loss: 0.2021 - val_accuracy: 0.9599\n", - "Epoch 303/306\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0895 - accuracy: 0.9763 - val_loss: 0.1874 - val_accuracy: 0.9503\n", - "Epoch 304/306\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0661 - accuracy: 0.9832 - val_loss: 0.1431 - val_accuracy: 0.9583\n", - "Epoch 305/306\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0408 - accuracy: 0.9905 - val_loss: 0.1453 - val_accuracy: 0.9567\n", - "Epoch 306/306\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.1441 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1384}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1441\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m333.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01019\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 307/312\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1930 - accuracy: 0.9358 - val_loss: 0.1491 - val_accuracy: 0.9615\n", - "Epoch 308/312\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1630 - accuracy: 0.9490 - val_loss: 0.2030 - val_accuracy: 0.9519\n", - "Epoch 309/312\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1076 - accuracy: 0.9675 - val_loss: 0.1842 - val_accuracy: 0.9503\n", - "Epoch 310/312\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0998 - accuracy: 0.9734 - val_loss: 0.1446 - val_accuracy: 0.9535\n", - "Epoch 311/312\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0671 - accuracy: 0.9836 - val_loss: 0.1996 - val_accuracy: 0.9567\n", - "Epoch 312/312\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0388 - accuracy: 0.9924 - val_loss: 0.2040 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1446}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2039\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m335.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01016\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 313/318\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1694 - accuracy: 0.9468 - val_loss: 0.1702 - val_accuracy: 0.9359\n", - "Epoch 314/318\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1240 - accuracy: 0.9590 - val_loss: 0.1441 - val_accuracy: 0.9567\n", - "Epoch 315/318\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0726 - accuracy: 0.9778 - val_loss: 0.1964 - val_accuracy: 0.9375\n", - "Epoch 316/318\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0549 - accuracy: 0.9832 - val_loss: 0.1331 - val_accuracy: 0.9599\n", - "Epoch 317/318\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0387 - accuracy: 0.9910 - val_loss: 0.1472 - val_accuracy: 0.9535\n", - "Epoch 318/318\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0214 - accuracy: 0.9937 - val_loss: 0.1514 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1331}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1515\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01013\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 319/324\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.2054 - accuracy: 0.9397 - val_loss: 0.2244 - val_accuracy: 0.9215\n", - "Epoch 320/324\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1552 - accuracy: 0.9539 - val_loss: 0.1453 - val_accuracy: 0.9487\n", - "Epoch 321/324\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1128 - accuracy: 0.9644 - val_loss: 0.1667 - val_accuracy: 0.9455\n", - "Epoch 322/324\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0620 - accuracy: 0.9832 - val_loss: 0.3559 - val_accuracy: 0.9199\n", - "Epoch 323/324\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0443 - accuracy: 0.9902 - val_loss: 0.3549 - val_accuracy: 0.9215\n", - "Epoch 324/324\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0297 - accuracy: 0.9924 - val_loss: 0.4312 - val_accuracy: 0.9038\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1453}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9038\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4313\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0101\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 325/330\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1714 - accuracy: 0.9434 - val_loss: 0.2541 - val_accuracy: 0.9407\n", - "Epoch 326/330\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1535 - accuracy: 0.9529 - val_loss: 0.1831 - val_accuracy: 0.9551\n", - "Epoch 327/330\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1077 - accuracy: 0.9697 - val_loss: 0.4742 - val_accuracy: 0.8750\n", - "Epoch 328/330\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0784 - accuracy: 0.9788 - val_loss: 0.1694 - val_accuracy: 0.9647\n", - "Epoch 329/330\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0460 - accuracy: 0.9861 - val_loss: 0.2006 - val_accuracy: 0.9551\n", - "Epoch 330/330\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0285 - accuracy: 0.9934 - val_loss: 0.1967 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1694}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1966\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01007\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 331/336\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1551 - accuracy: 0.9512 - val_loss: 0.1414 - val_accuracy: 0.9487\n", - "Epoch 332/336\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1319 - accuracy: 0.9634 - val_loss: 0.1835 - val_accuracy: 0.9583\n", - "Epoch 333/336\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0927 - accuracy: 0.9727 - val_loss: 0.2515 - val_accuracy: 0.9391\n", - "Epoch 334/336\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0605 - accuracy: 0.9851 - val_loss: 0.1802 - val_accuracy: 0.9551\n", - "Epoch 335/336\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0398 - accuracy: 0.9897 - val_loss: 0.1586 - val_accuracy: 0.9599\n", - "Epoch 336/336\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0215 - accuracy: 0.9963 - val_loss: 0.1603 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1414}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1603\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m331.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [56] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m57\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 336)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01004\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 337/342\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1827 - accuracy: 0.9373 - val_loss: 0.1448 - val_accuracy: 0.9567\n", - "Epoch 338/342\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1476 - accuracy: 0.9512 - val_loss: 0.1387 - val_accuracy: 0.9535\n", - "Epoch 339/342\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9690 - val_loss: 0.1384 - val_accuracy: 0.9599\n", - "Epoch 340/342\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0691 - accuracy: 0.9797 - val_loss: 0.1577 - val_accuracy: 0.9551\n", - "Epoch 341/342\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0440 - accuracy: 0.9878 - val_loss: 0.2005 - val_accuracy: 0.9487\n", - "Epoch 342/342\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0272 - accuracy: 0.9944 - val_loss: 0.2301 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1384}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2301\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m332.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m241.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [57] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m58\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 342)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01001\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 343/348\n", - "256/256 [==============================] - 44s 160ms/step - loss: 0.1875 - accuracy: 0.9407 - val_loss: 0.1582 - val_accuracy: 0.9567\n", - "Epoch 344/348\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1359 - accuracy: 0.9551 - val_loss: 0.1314 - val_accuracy: 0.9519\n", - "Epoch 345/348\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0971 - accuracy: 0.9727 - val_loss: 0.2146 - val_accuracy: 0.9487\n", - "Epoch 346/348\n", - "256/256 [==============================] - 40s 153ms/step - loss: 0.0689 - accuracy: 0.9785 - val_loss: 0.3341 - val_accuracy: 0.9423\n", - "Epoch 347/348\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0417 - accuracy: 0.9885 - val_loss: 0.1464 - val_accuracy: 0.9631\n", - "Epoch 348/348\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0255 - accuracy: 0.9944 - val_loss: 0.1878 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9631}, \u001b[0m\u001b[0;33mloss{0.1314}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1878\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m336.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [58] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m59\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 348)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00998\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 349/354\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1603 - accuracy: 0.9512 - val_loss: 0.1231 - val_accuracy: 0.9615\n", - "Epoch 350/354\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1276 - accuracy: 0.9609 - val_loss: 0.1272 - val_accuracy: 0.9487\n", - "Epoch 351/354\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0784 - accuracy: 0.9783 - val_loss: 0.1614 - val_accuracy: 0.9615\n", - "Epoch 352/354\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0494 - accuracy: 0.9883 - val_loss: 0.1601 - val_accuracy: 0.9535\n", - "Epoch 353/354\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0323 - accuracy: 0.9915 - val_loss: 0.1586 - val_accuracy: 0.9663\n", - "Epoch 354/354\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0266 - accuracy: 0.9946 - val_loss: 0.2120 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-353-0.9663.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9663\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1586\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.964744\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.966346\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m339.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m96.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [59] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m60\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 354)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00995\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 355/360\n", - "256/256 [==============================] - 44s 160ms/step - loss: 0.1893 - accuracy: 0.9419 - val_loss: 0.1307 - val_accuracy: 0.9487\n", - "Epoch 356/360\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.1424 - accuracy: 0.9551 - val_loss: 0.1543 - val_accuracy: 0.9503\n", - "Epoch 357/360\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.1052 - accuracy: 0.9692 - val_loss: 0.1641 - val_accuracy: 0.9567\n", - "Epoch 358/360\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0637 - accuracy: 0.9819 - val_loss: 0.2168 - val_accuracy: 0.9647\n", - "Epoch 359/360\n", - "256/256 [==============================] - 39s 154ms/step - loss: 0.0473 - accuracy: 0.9858 - val_loss: 0.2556 - val_accuracy: 0.9519\n", - "Epoch 360/360\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0241 - accuracy: 0.9946 - val_loss: 0.2786 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1307}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9663}, loss{0.1302}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2785\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461447. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1301687509. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m342.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m244.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [60] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m61\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 360)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00992\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 361/366\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1694 - accuracy: 0.9487 - val_loss: 0.1526 - val_accuracy: 0.9583\n", - "Epoch 362/366\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.1303 - accuracy: 0.9573 - val_loss: 0.1236 - val_accuracy: 0.9615\n", - "Epoch 363/366\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0869 - accuracy: 0.9773 - val_loss: 0.1995 - val_accuracy: 0.9535\n", - "Epoch 364/366\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0567 - accuracy: 0.9866 - val_loss: 0.1833 - val_accuracy: 0.9567\n", - "Epoch 365/366\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0450 - accuracy: 0.9883 - val_loss: 0.1672 - val_accuracy: 0.9583\n", - "Epoch 366/366\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0278 - accuracy: 0.9929 - val_loss: 0.1701 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-362-0.9615.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1236\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461447. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1301687509\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1235836595\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m342.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m244.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [61] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m62\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 366)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00989\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 367/372\n", - "256/256 [==============================] - 43s 159ms/step - loss: 0.1530 - accuracy: 0.9524 - val_loss: 0.1182 - val_accuracy: 0.9631\n", - "Epoch 368/372\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1248 - accuracy: 0.9607 - val_loss: 0.1993 - val_accuracy: 0.9583\n", - "Epoch 369/372\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0837 - accuracy: 0.9763 - val_loss: 0.1673 - val_accuracy: 0.9551\n", - "Epoch 370/372\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0661 - accuracy: 0.9834 - val_loss: 0.1923 - val_accuracy: 0.9535\n", - "Epoch 371/372\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0349 - accuracy: 0.9927 - val_loss: 0.1806 - val_accuracy: 0.9599\n", - "Epoch 372/372\n", - "256/256 [==============================] - 38s 147ms/step - loss: 0.0210 - accuracy: 0.9961 - val_loss: 0.2325 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-367-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1182\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461447. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1235836595\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1182044595\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m241.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [62] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m63\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 372)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00986\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 373/378\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1644 - accuracy: 0.9446 - val_loss: 0.1301 - val_accuracy: 0.9631\n", - "Epoch 374/378\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1417 - accuracy: 0.9541 - val_loss: 0.2066 - val_accuracy: 0.9391\n", - "Epoch 375/378\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0961 - accuracy: 0.9707 - val_loss: 0.1939 - val_accuracy: 0.9599\n", - "Epoch 376/378\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0663 - accuracy: 0.9814 - val_loss: 0.1417 - val_accuracy: 0.9631\n", - "Epoch 377/378\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0330 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679\n", - "Epoch 378/378\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0223 - accuracy: 0.9949 - val_loss: 0.1841 - val_accuracy: 0.9679\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-377-0.9679.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1729\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.966346\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.967949\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [63] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m64\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 378)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00983\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 379/384\n", - "256/256 [==============================] - 44s 160ms/step - loss: 0.1561 - accuracy: 0.9517 - val_loss: 0.1277 - val_accuracy: 0.9663\n", - "Epoch 380/384\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1222 - accuracy: 0.9622 - val_loss: 0.1734 - val_accuracy: 0.9583\n", - "Epoch 381/384\n", - "256/256 [==============================] - 39s 154ms/step - loss: 0.0891 - accuracy: 0.9741 - val_loss: 0.1304 - val_accuracy: 0.9615\n", - "Epoch 382/384\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0620 - accuracy: 0.9817 - val_loss: 0.1671 - val_accuracy: 0.9551\n", - "Epoch 383/384\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0381 - accuracy: 0.9912 - val_loss: 0.1707 - val_accuracy: 0.9599\n", - "Epoch 384/384\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0244 - accuracy: 0.9944 - val_loss: 0.1800 - val_accuracy: 0.9647\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9663}, \u001b[0m\u001b[0;33mloss{0.1277}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1799\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m342.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [64] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m65\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 384)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0098\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 385/390\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1681 - accuracy: 0.9514 - val_loss: 0.1239 - val_accuracy: 0.9647\n", - "Epoch 386/390\n", - "256/256 [==============================] - 39s 154ms/step - loss: 0.1151 - accuracy: 0.9651 - val_loss: 0.1510 - val_accuracy: 0.9567\n", - "Epoch 387/390\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0776 - accuracy: 0.9795 - val_loss: 0.2598 - val_accuracy: 0.9295\n", - "Epoch 388/390\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0569 - accuracy: 0.9851 - val_loss: 0.1815 - val_accuracy: 0.9599\n", - "Epoch 389/390\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0483 - accuracy: 0.9897 - val_loss: 0.2450 - val_accuracy: 0.9487\n", - "Epoch 390/390\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0333 - accuracy: 0.9915 - val_loss: 0.2274 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1239}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2274\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [65] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m66\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 390)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00977\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 391/396\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1693 - accuracy: 0.9460 - val_loss: 0.1734 - val_accuracy: 0.9535\n", - "Epoch 392/396\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1355 - accuracy: 0.9553 - val_loss: 0.2058 - val_accuracy: 0.9615\n", - "Epoch 393/396\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0936 - accuracy: 0.9768 - val_loss: 0.1606 - val_accuracy: 0.9551\n", - "Epoch 394/396\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0632 - accuracy: 0.9814 - val_loss: 0.2177 - val_accuracy: 0.9519\n", - "Epoch 395/396\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0387 - accuracy: 0.9917 - val_loss: 0.1880 - val_accuracy: 0.9647\n", - "Epoch 396/396\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0262 - accuracy: 0.9946 - val_loss: 0.1981 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1606}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1981\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [66] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m67\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 396)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00974\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 397/402\n", - "256/256 [==============================] - 43s 159ms/step - loss: 0.1757 - accuracy: 0.9475 - val_loss: 0.2396 - val_accuracy: 0.9471\n", - "Epoch 398/402\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1379 - accuracy: 0.9578 - val_loss: 0.2541 - val_accuracy: 0.9503\n", - "Epoch 399/402\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0923 - accuracy: 0.9727 - val_loss: 0.1901 - val_accuracy: 0.9535\n", - "Epoch 400/402\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0691 - accuracy: 0.9814 - val_loss: 0.1761 - val_accuracy: 0.9535\n", - "Epoch 401/402\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0440 - accuracy: 0.9900 - val_loss: 0.1729 - val_accuracy: 0.9583\n", - "Epoch 402/402\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0247 - accuracy: 0.9939 - val_loss: 0.2025 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1729}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2025\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [67] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m68\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 402)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00971\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 403/408\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1688 - accuracy: 0.9497 - val_loss: 0.1966 - val_accuracy: 0.9455\n", - "Epoch 404/408\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1128 - accuracy: 0.9631 - val_loss: 0.1718 - val_accuracy: 0.9535\n", - "Epoch 405/408\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0863 - accuracy: 0.9744 - val_loss: 0.1658 - val_accuracy: 0.9503\n", - "Epoch 406/408\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0598 - accuracy: 0.9822 - val_loss: 0.1798 - val_accuracy: 0.9487\n", - "Epoch 407/408\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0326 - accuracy: 0.9922 - val_loss: 0.2373 - val_accuracy: 0.9567\n", - "Epoch 408/408\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0248 - accuracy: 0.9937 - val_loss: 0.1997 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1658}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1996\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m241.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [68] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m69\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 408)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00968\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 409/414\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1784 - accuracy: 0.9465 - val_loss: 0.1492 - val_accuracy: 0.9535\n", - "Epoch 410/414\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1288 - accuracy: 0.9570 - val_loss: 0.1798 - val_accuracy: 0.9551\n", - "Epoch 411/414\n", - "256/256 [==============================] - 39s 154ms/step - loss: 0.0865 - accuracy: 0.9766 - val_loss: 0.2647 - val_accuracy: 0.9279\n", - "Epoch 412/414\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0633 - accuracy: 0.9832 - val_loss: 0.2380 - val_accuracy: 0.9535\n", - "Epoch 413/414\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0395 - accuracy: 0.9895 - val_loss: 0.4269 - val_accuracy: 0.9247\n", - "Epoch 414/414\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0227 - accuracy: 0.9951 - val_loss: 0.3673 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1492}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3675\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [69] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m70\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 414)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00965\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 415/420\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1588 - accuracy: 0.9534 - val_loss: 0.1991 - val_accuracy: 0.9327\n", - "Epoch 416/420\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1057 - accuracy: 0.9661 - val_loss: 0.1404 - val_accuracy: 0.9583\n", - "Epoch 417/420\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0709 - accuracy: 0.9807 - val_loss: 0.2250 - val_accuracy: 0.9407\n", - "Epoch 418/420\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0458 - accuracy: 0.9856 - val_loss: 0.2620 - val_accuracy: 0.9439\n", - "Epoch 419/420\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0363 - accuracy: 0.9915 - val_loss: 0.4401 - val_accuracy: 0.9231\n", - "Epoch 420/420\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0246 - accuracy: 0.9929 - val_loss: 0.3387 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1404}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3390\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [70] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m71\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 420)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00962\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 421/426\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1653 - accuracy: 0.9529 - val_loss: 0.2217 - val_accuracy: 0.9263\n", - "Epoch 422/426\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1176 - accuracy: 0.9651 - val_loss: 0.2058 - val_accuracy: 0.9535\n", - "Epoch 423/426\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0749 - accuracy: 0.9812 - val_loss: 0.4122 - val_accuracy: 0.9279\n", - "Epoch 424/426\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0723 - accuracy: 0.9814 - val_loss: 0.3298 - val_accuracy: 0.9263\n", - "Epoch 425/426\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0423 - accuracy: 0.9905 - val_loss: 0.2900 - val_accuracy: 0.9375\n", - "Epoch 426/426\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.2642 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2058}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2642\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [71] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m72\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 426)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00959\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 427/432\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1578 - accuracy: 0.9521 - val_loss: 0.2611 - val_accuracy: 0.9391\n", - "Epoch 428/432\n", - "256/256 [==============================] - 39s 154ms/step - loss: 0.1223 - accuracy: 0.9619 - val_loss: 0.5082 - val_accuracy: 0.8702\n", - "Epoch 429/432\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0934 - accuracy: 0.9724 - val_loss: 0.3640 - val_accuracy: 0.9006\n", - "Epoch 430/432\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0609 - accuracy: 0.9819 - val_loss: 0.3574 - val_accuracy: 0.9167\n", - "Epoch 431/432\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0356 - accuracy: 0.9934 - val_loss: 0.2416 - val_accuracy: 0.9391\n", - "Epoch 432/432\n", - "256/256 [==============================] - 40s 153ms/step - loss: 0.0254 - accuracy: 0.9941 - val_loss: 0.2910 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.2416}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2909\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m241.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [72] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m73\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 432)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00956\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 433/438\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1637 - accuracy: 0.9509 - val_loss: 0.2104 - val_accuracy: 0.9391\n", - "Epoch 434/438\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.1287 - accuracy: 0.9561 - val_loss: 0.3213 - val_accuracy: 0.9279\n", - "Epoch 435/438\n", - "256/256 [==============================] - 38s 150ms/step - loss: 0.0861 - accuracy: 0.9753 - val_loss: 0.3391 - val_accuracy: 0.8974\n", - "Epoch 436/438\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0504 - accuracy: 0.9871 - val_loss: 0.2372 - val_accuracy: 0.9439\n", - "Epoch 437/438\n", - "256/256 [==============================] - 39s 149ms/step - loss: 0.0292 - accuracy: 0.9929 - val_loss: 0.3254 - val_accuracy: 0.9295\n", - "Epoch 438/438\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0217 - accuracy: 0.9951 - val_loss: 0.3205 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.2104}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3205\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m236.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [73] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m74\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 438)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00953\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 439/444\n", - "256/256 [==============================] - 42s 154ms/step - loss: 0.1550 - accuracy: 0.9492 - val_loss: 0.2690 - val_accuracy: 0.9311\n", - "Epoch 440/444\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1100 - accuracy: 0.9612 - val_loss: 0.4604 - val_accuracy: 0.9022\n", - "Epoch 441/444\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0795 - accuracy: 0.9766 - val_loss: 0.2737 - val_accuracy: 0.9199\n", - "Epoch 442/444\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0463 - accuracy: 0.9858 - val_loss: 0.6477 - val_accuracy: 0.8894\n", - "Epoch 443/444\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0310 - accuracy: 0.9922 - val_loss: 0.4088 - val_accuracy: 0.9215\n", - "Epoch 444/444\n", - "256/256 [==============================] - 38s 150ms/step - loss: 0.0225 - accuracy: 0.9944 - val_loss: 0.5178 - val_accuracy: 0.9135\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9311}, \u001b[0m\u001b[0;33mloss{0.2690}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9135\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5179\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m332.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m234.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [74] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m75\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 444)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0095\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 445/450\n", - "256/256 [==============================] - 42s 154ms/step - loss: 0.1691 - accuracy: 0.9514 - val_loss: 0.3258 - val_accuracy: 0.9135\n", - "Epoch 446/450\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1263 - accuracy: 0.9570 - val_loss: 0.5084 - val_accuracy: 0.8878\n", - "Epoch 447/450\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0896 - accuracy: 0.9729 - val_loss: 0.2486 - val_accuracy: 0.9327\n", - "Epoch 448/450\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0514 - accuracy: 0.9875 - val_loss: 0.3568 - val_accuracy: 0.9199\n", - "Epoch 449/450\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0324 - accuracy: 0.9915 - val_loss: 0.2177 - val_accuracy: 0.9551\n", - "Epoch 450/450\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0183 - accuracy: 0.9963 - val_loss: 0.2980 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.2177}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2980\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m237.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [75] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m76\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 450)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00947\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 451/456\n", - "256/256 [==============================] - 42s 154ms/step - loss: 0.1663 - accuracy: 0.9475 - val_loss: 0.3637 - val_accuracy: 0.9135\n", - "Epoch 452/456\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1226 - accuracy: 0.9646 - val_loss: 0.1565 - val_accuracy: 0.9567\n", - "Epoch 453/456\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0760 - accuracy: 0.9773 - val_loss: 0.2494 - val_accuracy: 0.9343\n", - "Epoch 454/456\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0410 - accuracy: 0.9895 - val_loss: 0.1871 - val_accuracy: 0.9503\n", - "Epoch 455/456\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0332 - accuracy: 0.9932 - val_loss: 0.3146 - val_accuracy: 0.9279\n", - "Epoch 456/456\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0212 - accuracy: 0.9966 - val_loss: 0.3845 - val_accuracy: 0.9199\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1565}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3845\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m236.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [76] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m77\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 456)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00944\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 457/462\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1568 - accuracy: 0.9512 - val_loss: 0.3572 - val_accuracy: 0.9071\n", - "Epoch 458/462\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1199 - accuracy: 0.9595 - val_loss: 0.2567 - val_accuracy: 0.9359\n", - "Epoch 459/462\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0786 - accuracy: 0.9736 - val_loss: 0.2041 - val_accuracy: 0.9599\n", - "Epoch 460/462\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0394 - accuracy: 0.9895 - val_loss: 0.2514 - val_accuracy: 0.9551\n", - "Epoch 461/462\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0328 - accuracy: 0.9919 - val_loss: 0.4373 - val_accuracy: 0.9279\n", - "Epoch 462/462\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0160 - accuracy: 0.9968 - val_loss: 0.3829 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.2041}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3831\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m342.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [77] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m78\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 462)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00941\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 463/468\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1715 - accuracy: 0.9521 - val_loss: 0.2007 - val_accuracy: 0.9407\n", - "Epoch 464/468\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1245 - accuracy: 0.9644 - val_loss: 0.1713 - val_accuracy: 0.9551\n", - "Epoch 465/468\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0818 - accuracy: 0.9778 - val_loss: 0.2987 - val_accuracy: 0.9343\n", - "Epoch 466/468\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0655 - accuracy: 0.9832 - val_loss: 0.2901 - val_accuracy: 0.9263\n", - "Epoch 467/468\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0423 - accuracy: 0.9893 - val_loss: 0.2012 - val_accuracy: 0.9599\n", - "Epoch 468/468\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0272 - accuracy: 0.9946 - val_loss: 0.2142 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1713}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2143\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [78] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m79\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 468)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00938\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 469/474\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1376 - accuracy: 0.9553 - val_loss: 0.3622 - val_accuracy: 0.9359\n", - "Epoch 470/474\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1079 - accuracy: 0.9673 - val_loss: 0.1790 - val_accuracy: 0.9503\n", - "Epoch 471/474\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0760 - accuracy: 0.9788 - val_loss: 0.2087 - val_accuracy: 0.9487\n", - "Epoch 472/474\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0456 - accuracy: 0.9883 - val_loss: 0.2008 - val_accuracy: 0.9535\n", - "Epoch 473/474\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0260 - accuracy: 0.9932 - val_loss: 0.2495 - val_accuracy: 0.9551\n", - "Epoch 474/474\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0178 - accuracy: 0.9961 - val_loss: 0.2813 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1790}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2814\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [79] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m80\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 474)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00935\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 475/480\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1595 - accuracy: 0.9509 - val_loss: 0.4507 - val_accuracy: 0.9167\n", - "Epoch 476/480\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1255 - accuracy: 0.9585 - val_loss: 0.1970 - val_accuracy: 0.9583\n", - "Epoch 477/480\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0808 - accuracy: 0.9731 - val_loss: 0.2976 - val_accuracy: 0.9423\n", - "Epoch 478/480\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0570 - accuracy: 0.9829 - val_loss: 0.2987 - val_accuracy: 0.9519\n", - "Epoch 479/480\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0306 - accuracy: 0.9919 - val_loss: 0.2197 - val_accuracy: 0.9551\n", - "Epoch 480/480\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0190 - accuracy: 0.9963 - val_loss: 0.2628 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1970}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2628\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [80] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m81\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 480)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00932\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 481/486\n", - "256/256 [==============================] - 43s 156ms/step - loss: 0.1802 - accuracy: 0.9458 - val_loss: 0.2472 - val_accuracy: 0.9343\n", - "Epoch 482/486\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1290 - accuracy: 0.9568 - val_loss: 0.3062 - val_accuracy: 0.9423\n", - "Epoch 483/486\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0947 - accuracy: 0.9712 - val_loss: 0.1801 - val_accuracy: 0.9519\n", - "Epoch 484/486\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0525 - accuracy: 0.9844 - val_loss: 0.3521 - val_accuracy: 0.9151\n", - "Epoch 485/486\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0405 - accuracy: 0.9907 - val_loss: 0.2485 - val_accuracy: 0.9471\n", - "Epoch 486/486\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0237 - accuracy: 0.9939 - val_loss: 0.2983 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1801}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2982\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [81] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m82\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 486)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00929\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 487/492\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1630 - accuracy: 0.9519 - val_loss: 0.2194 - val_accuracy: 0.9263\n", - "Epoch 488/492\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1177 - accuracy: 0.9585 - val_loss: 0.1526 - val_accuracy: 0.9503\n", - "Epoch 489/492\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0764 - accuracy: 0.9788 - val_loss: 0.1910 - val_accuracy: 0.9423\n", - "Epoch 490/492\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0416 - accuracy: 0.9900 - val_loss: 0.2859 - val_accuracy: 0.9263\n", - "Epoch 491/492\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0291 - accuracy: 0.9924 - val_loss: 0.3821 - val_accuracy: 0.9231\n", - "Epoch 492/492\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0216 - accuracy: 0.9951 - val_loss: 0.3704 - val_accuracy: 0.9231\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1526}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3706\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [82] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m83\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 492)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00926\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 493/498\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1771 - accuracy: 0.9480 - val_loss: 0.2042 - val_accuracy: 0.9231\n", - "Epoch 494/498\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1203 - accuracy: 0.9641 - val_loss: 0.5006 - val_accuracy: 0.8974\n", - "Epoch 495/498\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0907 - accuracy: 0.9734 - val_loss: 0.2175 - val_accuracy: 0.9407\n", - "Epoch 496/498\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0569 - accuracy: 0.9883 - val_loss: 0.2766 - val_accuracy: 0.9295\n", - "Epoch 497/498\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0451 - accuracy: 0.9878 - val_loss: 0.1830 - val_accuracy: 0.9583\n", - "Epoch 498/498\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0240 - accuracy: 0.9954 - val_loss: 0.2463 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1830}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2463\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [83] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m84\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 498)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d09-h23_m04_s24\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00923\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 499/504\n", - "256/256 [==============================] - 42s 154ms/step - loss: 0.1730 - accuracy: 0.9448 - val_loss: 0.1524 - val_accuracy: 0.9631\n", - "Epoch 500/504\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1308 - accuracy: 0.9583 - val_loss: 0.1928 - val_accuracy: 0.9503\n", - "Epoch 501/504\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0853 - accuracy: 0.9741 - val_loss: 0.2692 - val_accuracy: 0.9391\n", - "Epoch 502/504\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0566 - accuracy: 0.9839 - val_loss: 0.3383 - val_accuracy: 0.9215\n", - "Epoch 503/504\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0299 - accuracy: 0.9922 - val_loss: 0.2657 - val_accuracy: 0.9471\n", - "Epoch 504/504\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0253 - accuracy: 0.9944 - val_loss: 0.2846 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9631}, \u001b[0m\u001b[0;33mloss{0.1524}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2846\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m361.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m234.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m126.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [84] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m85\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 504)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0092\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 505/510\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.1465 - accuracy: 0.9543 - val_loss: 0.2371 - val_accuracy: 0.9295\n", - "Epoch 506/510\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.1198 - accuracy: 0.9609 - val_loss: 0.2146 - val_accuracy: 0.9535\n", - "Epoch 507/510\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0680 - accuracy: 0.9832 - val_loss: 0.2223 - val_accuracy: 0.9471\n", - "Epoch 508/510\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0517 - accuracy: 0.9836 - val_loss: 0.2635 - val_accuracy: 0.9295\n", - "Epoch 509/510\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0293 - accuracy: 0.9927 - val_loss: 0.2960 - val_accuracy: 0.9455\n", - "Epoch 510/510\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0189 - accuracy: 0.9951 - val_loss: 0.3007 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2146}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3008\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m336.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m234.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [85] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m86\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 510)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00917\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 511/516\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.1501 - accuracy: 0.9558 - val_loss: 0.1352 - val_accuracy: 0.9503\n", - "Epoch 512/516\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1067 - accuracy: 0.9634 - val_loss: 0.2148 - val_accuracy: 0.9455\n", - "Epoch 513/516\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0671 - accuracy: 0.9795 - val_loss: 0.1551 - val_accuracy: 0.9551\n", - "Epoch 514/516\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0433 - accuracy: 0.9885 - val_loss: 0.1964 - val_accuracy: 0.9535\n", - "Epoch 515/516\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0318 - accuracy: 0.9915 - val_loss: 0.2511 - val_accuracy: 0.9535\n", - "Epoch 516/516\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0167 - accuracy: 0.9971 - val_loss: 0.2522 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1352}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2522\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m335.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m233.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [86] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m87\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 516)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00914\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 517/522\n", - "256/256 [==============================] - 42s 154ms/step - loss: 0.1513 - accuracy: 0.9509 - val_loss: 0.2767 - val_accuracy: 0.9359\n", - "Epoch 518/522\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1073 - accuracy: 0.9673 - val_loss: 0.2305 - val_accuracy: 0.9487\n", - "Epoch 519/522\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0725 - accuracy: 0.9797 - val_loss: 0.1932 - val_accuracy: 0.9519\n", - "Epoch 520/522\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0560 - accuracy: 0.9836 - val_loss: 0.2946 - val_accuracy: 0.9439\n", - "Epoch 521/522\n", - "256/256 [==============================] - 39s 149ms/step - loss: 0.0283 - accuracy: 0.9946 - val_loss: 0.2860 - val_accuracy: 0.9503\n", - "Epoch 522/522\n", - "256/256 [==============================] - 38s 150ms/step - loss: 0.0219 - accuracy: 0.9958 - val_loss: 0.3581 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1932}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3582\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m235.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [87] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m88\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 522)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00911\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 523/528\n", - "256/256 [==============================] - 41s 152ms/step - loss: 0.1557 - accuracy: 0.9531 - val_loss: 0.2541 - val_accuracy: 0.9375\n", - "Epoch 524/528\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.1029 - accuracy: 0.9648 - val_loss: 0.3809 - val_accuracy: 0.9279\n", - "Epoch 525/528\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0695 - accuracy: 0.9802 - val_loss: 0.2316 - val_accuracy: 0.9295\n", - "Epoch 526/528\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0468 - accuracy: 0.9868 - val_loss: 0.1787 - val_accuracy: 0.9615\n", - "Epoch 527/528\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0279 - accuracy: 0.9922 - val_loss: 0.2162 - val_accuracy: 0.9535\n", - "Epoch 528/528\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0175 - accuracy: 0.9958 - val_loss: 0.2415 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1787}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2415\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m338.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m233.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [88] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m89\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 528)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00908\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 529/534\n", - "256/256 [==============================] - 41s 153ms/step - loss: 0.1550 - accuracy: 0.9543 - val_loss: 0.1508 - val_accuracy: 0.9599\n", - "Epoch 530/534\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.1106 - accuracy: 0.9644 - val_loss: 0.1444 - val_accuracy: 0.9567\n", - "Epoch 531/534\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0772 - accuracy: 0.9775 - val_loss: 0.2089 - val_accuracy: 0.9471\n", - "Epoch 532/534\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0461 - accuracy: 0.9890 - val_loss: 0.2093 - val_accuracy: 0.9455\n", - "Epoch 533/534\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.2218 - val_accuracy: 0.9519\n", - "Epoch 534/534\n", - "256/256 [==============================] - 38s 148ms/step - loss: 0.0165 - accuracy: 0.9966 - val_loss: 0.2487 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1444}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2486\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m234.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [89] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m90\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 534)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00905\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 535/540\n", - "256/256 [==============================] - 41s 153ms/step - loss: 0.1707 - accuracy: 0.9507 - val_loss: 0.1537 - val_accuracy: 0.9503\n", - "Epoch 536/540\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1255 - accuracy: 0.9587 - val_loss: 0.2825 - val_accuracy: 0.9391\n", - "Epoch 537/540\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0761 - accuracy: 0.9763 - val_loss: 0.2941 - val_accuracy: 0.9327\n", - "Epoch 538/540\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0549 - accuracy: 0.9832 - val_loss: 0.2780 - val_accuracy: 0.9455\n", - "Epoch 539/540\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0371 - accuracy: 0.9900 - val_loss: 0.2523 - val_accuracy: 0.9423\n", - "Epoch 540/540\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0238 - accuracy: 0.9949 - val_loss: 0.3310 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1537}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3309\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [90] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m91\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 540)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00902\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 541/546\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1643 - accuracy: 0.9490 - val_loss: 0.1564 - val_accuracy: 0.9439\n", - "Epoch 542/546\n", - "256/256 [==============================] - 40s 153ms/step - loss: 0.1201 - accuracy: 0.9614 - val_loss: 0.1450 - val_accuracy: 0.9583\n", - "Epoch 543/546\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0769 - accuracy: 0.9756 - val_loss: 0.3203 - val_accuracy: 0.9375\n", - "Epoch 544/546\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0507 - accuracy: 0.9849 - val_loss: 0.2396 - val_accuracy: 0.9455\n", - "Epoch 545/546\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0303 - accuracy: 0.9924 - val_loss: 0.2311 - val_accuracy: 0.9439\n", - "Epoch 546/546\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0254 - accuracy: 0.9937 - val_loss: 0.2374 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1450}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2374\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m348.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [91] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m92\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 546)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00899\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 547/552\n", - "256/256 [==============================] - 43s 159ms/step - loss: 0.1533 - accuracy: 0.9514 - val_loss: 0.2819 - val_accuracy: 0.9391\n", - "Epoch 548/552\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1154 - accuracy: 0.9631 - val_loss: 0.1504 - val_accuracy: 0.9519\n", - "Epoch 549/552\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0679 - accuracy: 0.9812 - val_loss: 0.2743 - val_accuracy: 0.9391\n", - "Epoch 550/552\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0571 - accuracy: 0.9854 - val_loss: 0.1746 - val_accuracy: 0.9503\n", - "Epoch 551/552\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0312 - accuracy: 0.9929 - val_loss: 0.2282 - val_accuracy: 0.9551\n", - "Epoch 552/552\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0254 - accuracy: 0.9934 - val_loss: 0.2345 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1504}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2346\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m355.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [92] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m93\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 552)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00896\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 553/558\n", - "256/256 [==============================] - 42s 157ms/step - loss: 0.1508 - accuracy: 0.9526 - val_loss: 0.1696 - val_accuracy: 0.9391\n", - "Epoch 554/558\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1013 - accuracy: 0.9658 - val_loss: 0.1661 - val_accuracy: 0.9551\n", - "Epoch 555/558\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0614 - accuracy: 0.9802 - val_loss: 0.2462 - val_accuracy: 0.9375\n", - "Epoch 556/558\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0378 - accuracy: 0.9915 - val_loss: 0.2274 - val_accuracy: 0.9503\n", - "Epoch 557/558\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0260 - accuracy: 0.9927 - val_loss: 0.2477 - val_accuracy: 0.9519\n", - "Epoch 558/558\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0133 - accuracy: 0.9976 - val_loss: 0.2638 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1661}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2638\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [93] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m94\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 558)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00893\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 559/564\n", - "256/256 [==============================] - 43s 159ms/step - loss: 0.1586 - accuracy: 0.9526 - val_loss: 0.2560 - val_accuracy: 0.9263\n", - "Epoch 560/564\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.1121 - accuracy: 0.9673 - val_loss: 0.2344 - val_accuracy: 0.9295\n", - "Epoch 561/564\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0659 - accuracy: 0.9817 - val_loss: 0.2532 - val_accuracy: 0.9471\n", - "Epoch 562/564\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0424 - accuracy: 0.9885 - val_loss: 0.2157 - val_accuracy: 0.9535\n", - "Epoch 563/564\n", - "256/256 [==============================] - 39s 154ms/step - loss: 0.0297 - accuracy: 0.9946 - val_loss: 0.2417 - val_accuracy: 0.9535\n", - "Epoch 564/564\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0187 - accuracy: 0.9958 - val_loss: 0.2439 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2157}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m357.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [94] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m95\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 564)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0089\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 565/570\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1534 - accuracy: 0.9546 - val_loss: 0.1983 - val_accuracy: 0.9487\n", - "Epoch 566/570\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1136 - accuracy: 0.9634 - val_loss: 0.2006 - val_accuracy: 0.9487\n", - "Epoch 567/570\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0617 - accuracy: 0.9814 - val_loss: 0.3126 - val_accuracy: 0.9439\n", - "Epoch 568/570\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0427 - accuracy: 0.9890 - val_loss: 0.2854 - val_accuracy: 0.9519\n", - "Epoch 569/570\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0272 - accuracy: 0.9927 - val_loss: 0.2407 - val_accuracy: 0.9519\n", - "Epoch 570/570\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0214 - accuracy: 0.9944 - val_loss: 0.2640 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1983}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2640\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m359.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [95] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m96\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 570)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00887\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 571/576\n", - "256/256 [==============================] - 43s 159ms/step - loss: 0.1514 - accuracy: 0.9524 - val_loss: 0.2522 - val_accuracy: 0.9487\n", - "Epoch 572/576\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1110 - accuracy: 0.9678 - val_loss: 0.2966 - val_accuracy: 0.9423\n", - "Epoch 573/576\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0706 - accuracy: 0.9817 - val_loss: 0.3170 - val_accuracy: 0.9487\n", - "Epoch 574/576\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0533 - accuracy: 0.9866 - val_loss: 0.2584 - val_accuracy: 0.9551\n", - "Epoch 575/576\n", - "256/256 [==============================] - 40s 153ms/step - loss: 0.0292 - accuracy: 0.9932 - val_loss: 0.2763 - val_accuracy: 0.9439\n", - "Epoch 576/576\n", - "256/256 [==============================] - 40s 153ms/step - loss: 0.0195 - accuracy: 0.9958 - val_loss: 0.2902 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.2522}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2901\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m358.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [96] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m97\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 576)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00884\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 577/582\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1429 - accuracy: 0.9568 - val_loss: 0.2084 - val_accuracy: 0.9359\n", - "Epoch 578/582\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.1038 - accuracy: 0.9666 - val_loss: 0.1538 - val_accuracy: 0.9519\n", - "Epoch 579/582\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0649 - accuracy: 0.9807 - val_loss: 0.1837 - val_accuracy: 0.9503\n", - "Epoch 580/582\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0350 - accuracy: 0.9922 - val_loss: 0.2968 - val_accuracy: 0.9455\n", - "Epoch 581/582\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0258 - accuracy: 0.9937 - val_loss: 0.1886 - val_accuracy: 0.9615\n", - "Epoch 582/582\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0199 - accuracy: 0.9951 - val_loss: 0.2060 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1538}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2060\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m361.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [97] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m98\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 582)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00881\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 583/588\n", - "256/256 [==============================] - 43s 159ms/step - loss: 0.1544 - accuracy: 0.9521 - val_loss: 0.2222 - val_accuracy: 0.9359\n", - "Epoch 584/588\n", - "256/256 [==============================] - 39s 154ms/step - loss: 0.1078 - accuracy: 0.9614 - val_loss: 0.4065 - val_accuracy: 0.8974\n", - "Epoch 585/588\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0602 - accuracy: 0.9844 - val_loss: 0.2544 - val_accuracy: 0.9503\n", - "Epoch 586/588\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0408 - accuracy: 0.9880 - val_loss: 0.1851 - val_accuracy: 0.9599\n", - "Epoch 587/588\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0311 - accuracy: 0.9919 - val_loss: 0.1654 - val_accuracy: 0.9583\n", - "Epoch 588/588\n", - "256/256 [==============================] - 40s 153ms/step - loss: 0.0187 - accuracy: 0.9968 - val_loss: 0.1914 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1654}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1913\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m359.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m116.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [98] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m99\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 588)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00878\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 589/594\n", - "256/256 [==============================] - 43s 160ms/step - loss: 0.1395 - accuracy: 0.9553 - val_loss: 0.1258 - val_accuracy: 0.9631\n", - "Epoch 590/594\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1010 - accuracy: 0.9702 - val_loss: 0.2199 - val_accuracy: 0.9295\n", - "Epoch 591/594\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0748 - accuracy: 0.9800 - val_loss: 0.2295 - val_accuracy: 0.9295\n", - "Epoch 592/594\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0418 - accuracy: 0.9883 - val_loss: 0.1766 - val_accuracy: 0.9599\n", - "Epoch 593/594\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0285 - accuracy: 0.9924 - val_loss: 0.1622 - val_accuracy: 0.9567\n", - "Epoch 594/594\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0197 - accuracy: 0.9954 - val_loss: 0.1794 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9631}, \u001b[0m\u001b[0;33mloss{0.1258}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1182}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1794\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1182044595. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m361.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m242.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [99] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m100\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 594)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00875\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 595/600\n", - "256/256 [==============================] - 44s 160ms/step - loss: 0.1629 - accuracy: 0.9509 - val_loss: 0.1116 - val_accuracy: 0.9631\n", - "Epoch 596/600\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1158 - accuracy: 0.9634 - val_loss: 0.1746 - val_accuracy: 0.9423\n", - "Epoch 597/600\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0780 - accuracy: 0.9756 - val_loss: 0.1808 - val_accuracy: 0.9407\n", - "Epoch 598/600\n", - "256/256 [==============================] - 39s 154ms/step - loss: 0.0428 - accuracy: 0.9895 - val_loss: 0.2088 - val_accuracy: 0.9471\n", - "Epoch 599/600\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0275 - accuracy: 0.9927 - val_loss: 0.1591 - val_accuracy: 0.9583\n", - "Epoch 600/600\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0220 - accuracy: 0.9949 - val_loss: 0.1594 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-595-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1116\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1182044595\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1115868539\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m365.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [100] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m101\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 600)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00872\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 601/606\n", - "256/256 [==============================] - 44s 161ms/step - loss: 0.1502 - accuracy: 0.9480 - val_loss: 0.1278 - val_accuracy: 0.9599\n", - "Epoch 602/606\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1194 - accuracy: 0.9617 - val_loss: 0.1330 - val_accuracy: 0.9567\n", - "Epoch 603/606\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0792 - accuracy: 0.9766 - val_loss: 0.1405 - val_accuracy: 0.9599\n", - "Epoch 604/606\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0468 - accuracy: 0.9880 - val_loss: 0.1912 - val_accuracy: 0.9535\n", - "Epoch 605/606\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0236 - accuracy: 0.9949 - val_loss: 0.2338 - val_accuracy: 0.9551\n", - "Epoch 606/606\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0196 - accuracy: 0.9963 - val_loss: 0.2171 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1278}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2171\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m365.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [101] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m102\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 606)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00869\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 607/612\n", - "256/256 [==============================] - 43s 159ms/step - loss: 0.1704 - accuracy: 0.9502 - val_loss: 0.1842 - val_accuracy: 0.9439\n", - "Epoch 608/612\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1092 - accuracy: 0.9685 - val_loss: 0.1507 - val_accuracy: 0.9551\n", - "Epoch 609/612\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0643 - accuracy: 0.9836 - val_loss: 0.4382 - val_accuracy: 0.9006\n", - "Epoch 610/612\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0415 - accuracy: 0.9905 - val_loss: 0.4373 - val_accuracy: 0.9231\n", - "Epoch 611/612\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0225 - accuracy: 0.9954 - val_loss: 0.2273 - val_accuracy: 0.9551\n", - "Epoch 612/612\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0234 - accuracy: 0.9932 - val_loss: 0.2815 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1507}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2816\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m365.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m243.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [102] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m103\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 612)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00866\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 613/618\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1465 - accuracy: 0.9526 - val_loss: 0.1927 - val_accuracy: 0.9199\n", - "Epoch 614/618\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0992 - accuracy: 0.9685 - val_loss: 0.1163 - val_accuracy: 0.9599\n", - "Epoch 615/618\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0615 - accuracy: 0.9839 - val_loss: 0.1536 - val_accuracy: 0.9615\n", - "Epoch 616/618\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0404 - accuracy: 0.9890 - val_loss: 0.1652 - val_accuracy: 0.9535\n", - "Epoch 617/618\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0240 - accuracy: 0.9941 - val_loss: 0.2414 - val_accuracy: 0.9423\n", - "Epoch 618/618\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0146 - accuracy: 0.9971 - val_loss: 0.2323 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1163}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2324\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m362.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m237.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [103] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m104\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 618)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00863\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 619/624\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1425 - accuracy: 0.9590 - val_loss: 0.2897 - val_accuracy: 0.9327\n", - "Epoch 620/624\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0990 - accuracy: 0.9685 - val_loss: 0.2290 - val_accuracy: 0.9375\n", - "Epoch 621/624\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0597 - accuracy: 0.9824 - val_loss: 0.1626 - val_accuracy: 0.9487\n", - "Epoch 622/624\n", - "256/256 [==============================] - 38s 150ms/step - loss: 0.0317 - accuracy: 0.9905 - val_loss: 0.3142 - val_accuracy: 0.9407\n", - "Epoch 623/624\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0272 - accuracy: 0.9937 - val_loss: 0.2963 - val_accuracy: 0.9423\n", - "Epoch 624/624\n", - "256/256 [==============================] - 39s 149ms/step - loss: 0.0114 - accuracy: 0.9976 - val_loss: 0.3267 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1626}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3266\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m357.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m236.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [104] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m105\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 624)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0086\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 625/630\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1429 - accuracy: 0.9563 - val_loss: 0.1590 - val_accuracy: 0.9375\n", - "Epoch 626/630\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0952 - accuracy: 0.9705 - val_loss: 0.2185 - val_accuracy: 0.9423\n", - "Epoch 627/630\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0649 - accuracy: 0.9819 - val_loss: 0.1880 - val_accuracy: 0.9423\n", - "Epoch 628/630\n", - "256/256 [==============================] - 39s 149ms/step - loss: 0.0428 - accuracy: 0.9912 - val_loss: 0.2347 - val_accuracy: 0.9375\n", - "Epoch 629/630\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0324 - accuracy: 0.9941 - val_loss: 0.2428 - val_accuracy: 0.9391\n", - "Epoch 630/630\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0277 - accuracy: 0.9922 - val_loss: 0.2895 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1590}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2895\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m358.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m236.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [105] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m106\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 630)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00857\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 631/636\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1455 - accuracy: 0.9543 - val_loss: 0.1608 - val_accuracy: 0.9519\n", - "Epoch 632/636\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.1131 - accuracy: 0.9653 - val_loss: 0.4005 - val_accuracy: 0.9183\n", - "Epoch 633/636\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0821 - accuracy: 0.9778 - val_loss: 0.2674 - val_accuracy: 0.9311\n", - "Epoch 634/636\n", - "256/256 [==============================] - 38s 149ms/step - loss: 0.0514 - accuracy: 0.9861 - val_loss: 0.4156 - val_accuracy: 0.9183\n", - "Epoch 635/636\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0321 - accuracy: 0.9922 - val_loss: 0.3776 - val_accuracy: 0.9199\n", - "Epoch 636/636\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0240 - accuracy: 0.9939 - val_loss: 0.4011 - val_accuracy: 0.9231\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1608}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4011\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m358.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m236.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [106] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m107\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 636)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00854\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 637/642\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1539 - accuracy: 0.9504 - val_loss: 0.4090 - val_accuracy: 0.9006\n", - "Epoch 638/642\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1235 - accuracy: 0.9641 - val_loss: 0.3717 - val_accuracy: 0.9199\n", - "Epoch 639/642\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0774 - accuracy: 0.9790 - val_loss: 0.2327 - val_accuracy: 0.9327\n", - "Epoch 640/642\n", - "256/256 [==============================] - 39s 150ms/step - loss: 0.0492 - accuracy: 0.9873 - val_loss: 0.2661 - val_accuracy: 0.9263\n", - "Epoch 641/642\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0292 - accuracy: 0.9939 - val_loss: 0.4519 - val_accuracy: 0.9087\n", - "Epoch 642/642\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0191 - accuracy: 0.9958 - val_loss: 0.4002 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9327}, \u001b[0m\u001b[0;33mloss{0.2327}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4000\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m360.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [107] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m108\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 642)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00851\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 643/648\n", - "256/256 [==============================] - 42s 155ms/step - loss: 0.1638 - accuracy: 0.9524 - val_loss: 0.2009 - val_accuracy: 0.9439\n", - "Epoch 644/648\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1080 - accuracy: 0.9673 - val_loss: 0.1429 - val_accuracy: 0.9519\n", - "Epoch 645/648\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0764 - accuracy: 0.9785 - val_loss: 0.1549 - val_accuracy: 0.9503\n", - "Epoch 646/648\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0476 - accuracy: 0.9875 - val_loss: 0.2257 - val_accuracy: 0.9375\n", - "Epoch 647/648\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0339 - accuracy: 0.9900 - val_loss: 0.2569 - val_accuracy: 0.9343\n", - "Epoch 648/648\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0205 - accuracy: 0.9949 - val_loss: 0.2795 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1429}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2795\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m362.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [108] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m109\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 648)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00848\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 649/654\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1412 - accuracy: 0.9563 - val_loss: 0.1806 - val_accuracy: 0.9327\n", - "Epoch 650/654\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1059 - accuracy: 0.9639 - val_loss: 0.2058 - val_accuracy: 0.9279\n", - "Epoch 651/654\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0587 - accuracy: 0.9829 - val_loss: 0.2061 - val_accuracy: 0.9423\n", - "Epoch 652/654\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0390 - accuracy: 0.9888 - val_loss: 0.2299 - val_accuracy: 0.9503\n", - "Epoch 653/654\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0251 - accuracy: 0.9939 - val_loss: 0.3330 - val_accuracy: 0.9327\n", - "Epoch 654/654\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0168 - accuracy: 0.9951 - val_loss: 0.2911 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1806}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2912\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m363.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m238.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [109] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m110\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 654)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00845\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 655/660\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1377 - accuracy: 0.9570 - val_loss: 0.1542 - val_accuracy: 0.9503\n", - "Epoch 656/660\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.1012 - accuracy: 0.9661 - val_loss: 0.3146 - val_accuracy: 0.9311\n", - "Epoch 657/660\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0632 - accuracy: 0.9849 - val_loss: 0.2442 - val_accuracy: 0.9423\n", - "Epoch 658/660\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0348 - accuracy: 0.9915 - val_loss: 0.2267 - val_accuracy: 0.9487\n", - "Epoch 659/660\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0269 - accuracy: 0.9937 - val_loss: 0.2400 - val_accuracy: 0.9519\n", - "Epoch 660/660\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0148 - accuracy: 0.9968 - val_loss: 0.2382 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1542}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2382\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m364.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [110] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m111\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 660)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00842\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 661/666\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1435 - accuracy: 0.9558 - val_loss: 0.1453 - val_accuracy: 0.9519\n", - "Epoch 662/666\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0983 - accuracy: 0.9673 - val_loss: 0.1993 - val_accuracy: 0.9551\n", - "Epoch 663/666\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0610 - accuracy: 0.9834 - val_loss: 0.2806 - val_accuracy: 0.9439\n", - "Epoch 664/666\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0347 - accuracy: 0.9905 - val_loss: 0.3510 - val_accuracy: 0.9407\n", - "Epoch 665/666\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0318 - accuracy: 0.9912 - val_loss: 0.2234 - val_accuracy: 0.9551\n", - "Epoch 666/666\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0174 - accuracy: 0.9966 - val_loss: 0.2281 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1453}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2282\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m367.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [111] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m112\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 666)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00839\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 667/672\n", - "256/256 [==============================] - 42s 156ms/step - loss: 0.1468 - accuracy: 0.9565 - val_loss: 0.2649 - val_accuracy: 0.9247\n", - "Epoch 668/672\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1025 - accuracy: 0.9688 - val_loss: 0.1961 - val_accuracy: 0.9439\n", - "Epoch 669/672\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0747 - accuracy: 0.9807 - val_loss: 0.1758 - val_accuracy: 0.9599\n", - "Epoch 670/672\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0472 - accuracy: 0.9885 - val_loss: 0.4345 - val_accuracy: 0.9279\n", - "Epoch 671/672\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0345 - accuracy: 0.9924 - val_loss: 0.2553 - val_accuracy: 0.9487\n", - "Epoch 672/672\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0217 - accuracy: 0.9954 - val_loss: 0.2679 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1758}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2678\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m367.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m126.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [112] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m113\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 672)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00836\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 673/678\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1660 - accuracy: 0.9492 - val_loss: 0.2700 - val_accuracy: 0.9311\n", - "Epoch 674/678\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.1190 - accuracy: 0.9607 - val_loss: 0.1585 - val_accuracy: 0.9487\n", - "Epoch 675/678\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0747 - accuracy: 0.9768 - val_loss: 0.2216 - val_accuracy: 0.9455\n", - "Epoch 676/678\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0430 - accuracy: 0.9871 - val_loss: 0.2401 - val_accuracy: 0.9503\n", - "Epoch 677/678\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0248 - accuracy: 0.9956 - val_loss: 0.2127 - val_accuracy: 0.9503\n", - "Epoch 678/678\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0224 - accuracy: 0.9954 - val_loss: 0.2591 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1585}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2591\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m368.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [113] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m114\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 678)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00833\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 679/684\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1596 - accuracy: 0.9536 - val_loss: 0.2003 - val_accuracy: 0.9503\n", - "Epoch 680/684\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.1081 - accuracy: 0.9656 - val_loss: 0.2207 - val_accuracy: 0.9487\n", - "Epoch 681/684\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0697 - accuracy: 0.9807 - val_loss: 0.2134 - val_accuracy: 0.9359\n", - "Epoch 682/684\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0532 - accuracy: 0.9841 - val_loss: 0.1884 - val_accuracy: 0.9551\n", - "Epoch 683/684\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0242 - accuracy: 0.9927 - val_loss: 0.2151 - val_accuracy: 0.9503\n", - "Epoch 684/684\n", - "256/256 [==============================] - 39s 151ms/step - loss: 0.0191 - accuracy: 0.9956 - val_loss: 0.2353 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1884}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2353\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m367.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m239.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [114] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m115\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 684)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0083\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 685/690\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1278 - accuracy: 0.9631 - val_loss: 0.2046 - val_accuracy: 0.9519\n", - "Epoch 686/690\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0971 - accuracy: 0.9688 - val_loss: 0.2208 - val_accuracy: 0.9519\n", - "Epoch 687/690\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0573 - accuracy: 0.9839 - val_loss: 0.1718 - val_accuracy: 0.9599\n", - "Epoch 688/690\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0365 - accuracy: 0.9883 - val_loss: 0.1805 - val_accuracy: 0.9503\n", - "Epoch 689/690\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0253 - accuracy: 0.9937 - val_loss: 0.2198 - val_accuracy: 0.9535\n", - "Epoch 690/690\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0148 - accuracy: 0.9968 - val_loss: 0.2119 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1718}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2120\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m370.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m241.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m128.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [115] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m116\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 690)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00827\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 691/696\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1392 - accuracy: 0.9587 - val_loss: 0.2104 - val_accuracy: 0.9535\n", - "Epoch 692/696\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.1011 - accuracy: 0.9719 - val_loss: 0.3227 - val_accuracy: 0.9295\n", - "Epoch 693/696\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0778 - accuracy: 0.9783 - val_loss: 0.2605 - val_accuracy: 0.9295\n", - "Epoch 694/696\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.3277 - val_accuracy: 0.9327\n", - "Epoch 695/696\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0328 - accuracy: 0.9927 - val_loss: 0.2852 - val_accuracy: 0.9295\n", - "Epoch 696/696\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0195 - accuracy: 0.9951 - val_loss: 0.2895 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2104}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2896\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m370.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m240.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m130.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [116] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m117\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 696)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00824\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 697/702\n", - "256/256 [==============================] - 43s 157ms/step - loss: 0.1595 - accuracy: 0.9521 - val_loss: 0.1917 - val_accuracy: 0.9391\n", - "Epoch 698/702\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1105 - accuracy: 0.9661 - val_loss: 0.1516 - val_accuracy: 0.9583\n", - "Epoch 699/702\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0696 - accuracy: 0.9788 - val_loss: 0.2022 - val_accuracy: 0.9503\n", - "Epoch 700/702\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0462 - accuracy: 0.9875 - val_loss: 0.2063 - val_accuracy: 0.9503\n", - "Epoch 701/702\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0271 - accuracy: 0.9949 - val_loss: 0.1945 - val_accuracy: 0.9503\n", - "Epoch 702/702\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0148 - accuracy: 0.9973 - val_loss: 0.1883 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1516}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1882\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m372.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m241.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m130.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [117] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m118\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 702)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00821\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 703/708\n", - "256/256 [==============================] - 43s 158ms/step - loss: 0.1540 - accuracy: 0.9553 - val_loss: 0.1369 - val_accuracy: 0.9551\n", - "Epoch 704/708\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1023 - accuracy: 0.9697 - val_loss: 0.1292 - val_accuracy: 0.9567\n", - "Epoch 705/708\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0735 - accuracy: 0.9778 - val_loss: 0.2065 - val_accuracy: 0.9519\n", - "Epoch 706/708\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0449 - accuracy: 0.9868 - val_loss: 0.2213 - val_accuracy: 0.9519\n", - "Epoch 707/708\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0256 - accuracy: 0.9941 - val_loss: 0.2537 - val_accuracy: 0.9567\n", - "Epoch 708/708\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0209 - accuracy: 0.9954 - val_loss: 0.1997 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1292}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1998\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m376.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m241.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [118] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m119\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 708)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00818\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 709/714\n", - "256/256 [==============================] - 43s 159ms/step - loss: 0.1598 - accuracy: 0.9541 - val_loss: 0.3163 - val_accuracy: 0.9167\n", - "Epoch 710/714\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.1186 - accuracy: 0.9626 - val_loss: 0.1671 - val_accuracy: 0.9567\n", - "Epoch 711/714\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0695 - accuracy: 0.9802 - val_loss: 0.2920 - val_accuracy: 0.9391\n", - "Epoch 712/714\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0469 - accuracy: 0.9866 - val_loss: 0.4274 - val_accuracy: 0.9103\n", - "Epoch 713/714\n", - "256/256 [==============================] - 39s 153ms/step - loss: 0.0266 - accuracy: 0.9946 - val_loss: 0.3768 - val_accuracy: 0.9247\n", - "Epoch 714/714\n", - "256/256 [==============================] - 39s 152ms/step - loss: 0.0216 - accuracy: 0.9954 - val_loss: 0.4768 - val_accuracy: 0.9135\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1671}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9135\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4768\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m375.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m241.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m133.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [119] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m120\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 714)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00815\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 715/720\n", - "256/256 [==============================] - 44s 160ms/step - loss: 0.1508 - accuracy: 0.9534 - val_loss: 0.3127 - val_accuracy: 0.9071\n", - "Epoch 716/720\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1036 - accuracy: 0.9702 - val_loss: 0.2547 - val_accuracy: 0.9455\n", - "Epoch 717/720\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0719 - accuracy: 0.9812 - val_loss: 0.1895 - val_accuracy: 0.9535\n", - "Epoch 718/720\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0390 - accuracy: 0.9902 - val_loss: 0.3887 - val_accuracy: 0.9343\n", - "Epoch 719/720\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0283 - accuracy: 0.9939 - val_loss: 0.3734 - val_accuracy: 0.9407\n", - "Epoch 720/720\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0190 - accuracy: 0.9949 - val_loss: 0.2868 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1895}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2868\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m244.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m136.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [120] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m121\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 720)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00812\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 721/726\n", - "256/256 [==============================] - 44s 161ms/step - loss: 0.1395 - accuracy: 0.9580 - val_loss: 0.2089 - val_accuracy: 0.9439\n", - "Epoch 722/726\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1048 - accuracy: 0.9688 - val_loss: 0.1852 - val_accuracy: 0.9551\n", - "Epoch 723/726\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0591 - accuracy: 0.9834 - val_loss: 0.5150 - val_accuracy: 0.9263\n", - "Epoch 724/726\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0411 - accuracy: 0.9888 - val_loss: 0.3247 - val_accuracy: 0.9135\n", - "Epoch 725/726\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0265 - accuracy: 0.9917 - val_loss: 0.3085 - val_accuracy: 0.9311\n", - "Epoch 726/726\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0125 - accuracy: 0.9971 - val_loss: 0.3504 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1852}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3505\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m382.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m244.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m138.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [121] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m122\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 726)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00809\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 727/732\n", - "256/256 [==============================] - 44s 162ms/step - loss: 0.1362 - accuracy: 0.9600 - val_loss: 0.2427 - val_accuracy: 0.9247\n", - "Epoch 728/732\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0915 - accuracy: 0.9736 - val_loss: 0.2904 - val_accuracy: 0.9359\n", - "Epoch 729/732\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0507 - accuracy: 0.9868 - val_loss: 0.3330 - val_accuracy: 0.9391\n", - "Epoch 730/732\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0409 - accuracy: 0.9873 - val_loss: 0.2567 - val_accuracy: 0.9407\n", - "Epoch 731/732\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0179 - accuracy: 0.9963 - val_loss: 0.3642 - val_accuracy: 0.9423\n", - "Epoch 732/732\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0156 - accuracy: 0.9958 - val_loss: 0.3649 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2427}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3650\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m386.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m248.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m137.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [122] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m123\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 732)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00806\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 733/738\n", - "256/256 [==============================] - 44s 160ms/step - loss: 0.1530 - accuracy: 0.9580 - val_loss: 0.2046 - val_accuracy: 0.9247\n", - "Epoch 734/738\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.1113 - accuracy: 0.9656 - val_loss: 0.2615 - val_accuracy: 0.9343\n", - "Epoch 735/738\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0744 - accuracy: 0.9812 - val_loss: 0.2300 - val_accuracy: 0.9247\n", - "Epoch 736/738\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0488 - accuracy: 0.9875 - val_loss: 0.4281 - val_accuracy: 0.9199\n", - "Epoch 737/738\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0300 - accuracy: 0.9910 - val_loss: 0.3654 - val_accuracy: 0.9247\n", - "Epoch 738/738\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0208 - accuracy: 0.9951 - val_loss: 0.3338 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.2046}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3338\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m245.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m142.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [123] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m124\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 738)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00803\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 739/744\n", - "256/256 [==============================] - 44s 162ms/step - loss: 0.1666 - accuracy: 0.9519 - val_loss: 0.3117 - val_accuracy: 0.9199\n", - "Epoch 740/744\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.1090 - accuracy: 0.9663 - val_loss: 0.1538 - val_accuracy: 0.9455\n", - "Epoch 741/744\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0701 - accuracy: 0.9810 - val_loss: 0.2645 - val_accuracy: 0.9327\n", - "Epoch 742/744\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0407 - accuracy: 0.9893 - val_loss: 0.1927 - val_accuracy: 0.9567\n", - "Epoch 743/744\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0313 - accuracy: 0.9919 - val_loss: 0.1683 - val_accuracy: 0.9615\n", - "Epoch 744/744\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0245 - accuracy: 0.9941 - val_loss: 0.1777 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1538}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1778\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m391.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m247.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m143.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [124] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m125\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 744)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.008\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 745/750\n", - "256/256 [==============================] - 44s 160ms/step - loss: 0.1468 - accuracy: 0.9553 - val_loss: 0.1825 - val_accuracy: 0.9535\n", - "Epoch 746/750\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1150 - accuracy: 0.9634 - val_loss: 0.2238 - val_accuracy: 0.9295\n", - "Epoch 747/750\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0735 - accuracy: 0.9810 - val_loss: 0.4200 - val_accuracy: 0.9183\n", - "Epoch 748/750\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0542 - accuracy: 0.9849 - val_loss: 0.3020 - val_accuracy: 0.9343\n", - "Epoch 749/750\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0353 - accuracy: 0.9905 - val_loss: 0.3529 - val_accuracy: 0.9311\n", - "Epoch 750/750\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0177 - accuracy: 0.9968 - val_loss: 0.3707 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1825}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3704\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m244.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m142.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [125] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m126\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 750)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d10-h03_m19_s01\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00797\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 751/756\n", - "256/256 [==============================] - 44s 161ms/step - loss: 0.1555 - accuracy: 0.9526 - val_loss: 0.1911 - val_accuracy: 0.9439\n", - "Epoch 752/756\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1062 - accuracy: 0.9666 - val_loss: 0.2257 - val_accuracy: 0.9263\n", - "Epoch 753/756\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0648 - accuracy: 0.9807 - val_loss: 0.3387 - val_accuracy: 0.9247\n", - "Epoch 754/756\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0451 - accuracy: 0.9878 - val_loss: 0.2495 - val_accuracy: 0.9487\n", - "Epoch 755/756\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0300 - accuracy: 0.9924 - val_loss: 0.2763 - val_accuracy: 0.9343\n", - "Epoch 756/756\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0160 - accuracy: 0.9963 - val_loss: 0.3294 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1911}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3293\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m406.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m245.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m161.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [126] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m127\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 756)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00794\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 757/762\n", - "256/256 [==============================] - 44s 161ms/step - loss: 0.1537 - accuracy: 0.9529 - val_loss: 0.1893 - val_accuracy: 0.9391\n", - "Epoch 758/762\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1058 - accuracy: 0.9661 - val_loss: 0.1951 - val_accuracy: 0.9359\n", - "Epoch 759/762\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0736 - accuracy: 0.9778 - val_loss: 0.2543 - val_accuracy: 0.9295\n", - "Epoch 760/762\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0401 - accuracy: 0.9880 - val_loss: 0.2083 - val_accuracy: 0.9567\n", - "Epoch 761/762\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0301 - accuracy: 0.9924 - val_loss: 0.2424 - val_accuracy: 0.9487\n", - "Epoch 762/762\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0228 - accuracy: 0.9946 - val_loss: 0.2606 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1893}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2605\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m385.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m245.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m140.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [127] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m128\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 762)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00791\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 763/768\n", - "256/256 [==============================] - 43s 160ms/step - loss: 0.1365 - accuracy: 0.9551 - val_loss: 0.2341 - val_accuracy: 0.9343\n", - "Epoch 764/768\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0992 - accuracy: 0.9678 - val_loss: 0.1997 - val_accuracy: 0.9343\n", - "Epoch 765/768\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0748 - accuracy: 0.9827 - val_loss: 0.1890 - val_accuracy: 0.9503\n", - "Epoch 766/768\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0370 - accuracy: 0.9912 - val_loss: 0.3924 - val_accuracy: 0.9167\n", - "Epoch 767/768\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0244 - accuracy: 0.9946 - val_loss: 0.2212 - val_accuracy: 0.9487\n", - "Epoch 768/768\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0171 - accuracy: 0.9963 - val_loss: 0.2145 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1890}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2146\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m389.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m244.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m144.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [128] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m129\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 768)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00788\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 769/774\n", - "256/256 [==============================] - 44s 162ms/step - loss: 0.1286 - accuracy: 0.9604 - val_loss: 0.1608 - val_accuracy: 0.9535\n", - "Epoch 770/774\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0872 - accuracy: 0.9746 - val_loss: 0.1639 - val_accuracy: 0.9631\n", - "Epoch 771/774\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0597 - accuracy: 0.9827 - val_loss: 0.1443 - val_accuracy: 0.9567\n", - "Epoch 772/774\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0347 - accuracy: 0.9897 - val_loss: 0.1922 - val_accuracy: 0.9535\n", - "Epoch 773/774\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0182 - accuracy: 0.9956 - val_loss: 0.1516 - val_accuracy: 0.9599\n", - "Epoch 774/774\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0132 - accuracy: 0.9968 - val_loss: 0.1540 - val_accuracy: 0.9679\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9679}, \u001b[0m\u001b[0;33mloss{0.1443}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1539\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m245.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m142.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [129] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m130\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 774)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00785\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 775/780\n", - "256/256 [==============================] - 44s 161ms/step - loss: 0.1490 - accuracy: 0.9565 - val_loss: 0.1132 - val_accuracy: 0.9647\n", - "Epoch 776/780\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0957 - accuracy: 0.9690 - val_loss: 0.1511 - val_accuracy: 0.9583\n", - "Epoch 777/780\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0669 - accuracy: 0.9836 - val_loss: 0.2137 - val_accuracy: 0.9407\n", - "Epoch 778/780\n", - "256/256 [==============================] - 40s 154ms/step - loss: 0.0400 - accuracy: 0.9902 - val_loss: 0.1926 - val_accuracy: 0.9487\n", - "Epoch 779/780\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0308 - accuracy: 0.9922 - val_loss: 0.2119 - val_accuracy: 0.9519\n", - "Epoch 780/780\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0175 - accuracy: 0.9963 - val_loss: 0.2033 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1132}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2033\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m244.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m143.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [130] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m131\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 780)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00782\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 781/786\n", - "256/256 [==============================] - 44s 164ms/step - loss: 0.1481 - accuracy: 0.9553 - val_loss: 0.1318 - val_accuracy: 0.9583\n", - "Epoch 782/786\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0999 - accuracy: 0.9673 - val_loss: 0.1943 - val_accuracy: 0.9503\n", - "Epoch 783/786\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0649 - accuracy: 0.9807 - val_loss: 0.1241 - val_accuracy: 0.9615\n", - "Epoch 784/786\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0372 - accuracy: 0.9902 - val_loss: 0.1713 - val_accuracy: 0.9551\n", - "Epoch 785/786\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0252 - accuracy: 0.9932 - val_loss: 0.1606 - val_accuracy: 0.9631\n", - "Epoch 786/786\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0187 - accuracy: 0.9958 - val_loss: 0.1437 - val_accuracy: 0.9647\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1241}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1438\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m247.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m140.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [131] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m132\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 786)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00779\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 787/792\n", - "256/256 [==============================] - 44s 163ms/step - loss: 0.1377 - accuracy: 0.9609 - val_loss: 0.1491 - val_accuracy: 0.9535\n", - "Epoch 788/792\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0926 - accuracy: 0.9719 - val_loss: 0.1219 - val_accuracy: 0.9599\n", - "Epoch 789/792\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0636 - accuracy: 0.9841 - val_loss: 0.5144 - val_accuracy: 0.8958\n", - "Epoch 790/792\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0470 - accuracy: 0.9885 - val_loss: 0.3339 - val_accuracy: 0.9279\n", - "Epoch 791/792\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0236 - accuracy: 0.9946 - val_loss: 0.2802 - val_accuracy: 0.9503\n", - "Epoch 792/792\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0185 - accuracy: 0.9963 - val_loss: 0.2901 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1219}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2900\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m386.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m246.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m140.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [132] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m133\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 792)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00776\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 793/798\n", - "256/256 [==============================] - 44s 162ms/step - loss: 0.1393 - accuracy: 0.9612 - val_loss: 0.1650 - val_accuracy: 0.9455\n", - "Epoch 794/798\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.1057 - accuracy: 0.9685 - val_loss: 0.3658 - val_accuracy: 0.9071\n", - "Epoch 795/798\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0635 - accuracy: 0.9812 - val_loss: 0.1767 - val_accuracy: 0.9567\n", - "Epoch 796/798\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0374 - accuracy: 0.9905 - val_loss: 0.1738 - val_accuracy: 0.9599\n", - "Epoch 797/798\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0226 - accuracy: 0.9954 - val_loss: 0.1989 - val_accuracy: 0.9615\n", - "Epoch 798/798\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0198 - accuracy: 0.9951 - val_loss: 0.1974 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1650}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1973\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m386.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m246.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m139.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [133] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m134\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 798)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00773\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 799/804\n", - "256/256 [==============================] - 43s 160ms/step - loss: 0.1348 - accuracy: 0.9590 - val_loss: 0.1370 - val_accuracy: 0.9647\n", - "Epoch 800/804\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0882 - accuracy: 0.9751 - val_loss: 0.1774 - val_accuracy: 0.9567\n", - "Epoch 801/804\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0577 - accuracy: 0.9846 - val_loss: 0.1652 - val_accuracy: 0.9631\n", - "Epoch 802/804\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0335 - accuracy: 0.9922 - val_loss: 0.2367 - val_accuracy: 0.9487\n", - "Epoch 803/804\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0302 - accuracy: 0.9927 - val_loss: 0.2749 - val_accuracy: 0.9551\n", - "Epoch 804/804\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0198 - accuracy: 0.9958 - val_loss: 0.2424 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1370}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2424\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m389.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m245.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m144.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [134] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m135\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 804)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0077\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 805/810\n", - "256/256 [==============================] - 44s 161ms/step - loss: 0.1282 - accuracy: 0.9568 - val_loss: 0.1318 - val_accuracy: 0.9631\n", - "Epoch 806/810\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0857 - accuracy: 0.9744 - val_loss: 0.3410 - val_accuracy: 0.8494\n", - "Epoch 807/810\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0547 - accuracy: 0.9851 - val_loss: 0.1843 - val_accuracy: 0.9599\n", - "Epoch 808/810\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0345 - accuracy: 0.9917 - val_loss: 0.2540 - val_accuracy: 0.9519\n", - "Epoch 809/810\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0236 - accuracy: 0.9961 - val_loss: 0.2146 - val_accuracy: 0.9567\n", - "Epoch 810/810\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0177 - accuracy: 0.9958 - val_loss: 0.2083 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9631}, \u001b[0m\u001b[0;33mloss{0.1318}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2083\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m386.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m245.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m141.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [135] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m136\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 810)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00767\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 811/816\n", - "256/256 [==============================] - 44s 161ms/step - loss: 0.1453 - accuracy: 0.9570 - val_loss: 0.1412 - val_accuracy: 0.9487\n", - "Epoch 812/816\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.1046 - accuracy: 0.9673 - val_loss: 0.2235 - val_accuracy: 0.9423\n", - "Epoch 813/816\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0584 - accuracy: 0.9824 - val_loss: 0.1933 - val_accuracy: 0.9631\n", - "Epoch 814/816\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0317 - accuracy: 0.9924 - val_loss: 0.2571 - val_accuracy: 0.9535\n", - "Epoch 815/816\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0277 - accuracy: 0.9932 - val_loss: 0.1998 - val_accuracy: 0.9535\n", - "Epoch 816/816\n", - "256/256 [==============================] - 40s 155ms/step - loss: 0.0212 - accuracy: 0.9941 - val_loss: 0.2416 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9631}, \u001b[0m\u001b[0;33mloss{0.1412}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2415\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487348. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m389.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m246.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m143.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [136] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m137\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 816)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00764\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 817/822\n", - "256/256 [==============================] - 44s 162ms/step - loss: 0.1451 - accuracy: 0.9600 - val_loss: 0.1265 - val_accuracy: 0.9631\n", - "Epoch 818/822\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0956 - accuracy: 0.9700 - val_loss: 0.1227 - val_accuracy: 0.9663\n", - "Epoch 819/822\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0517 - accuracy: 0.9878 - val_loss: 0.1201 - val_accuracy: 0.9712\n", - "Epoch 820/822\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0359 - accuracy: 0.9905 - val_loss: 0.1761 - val_accuracy: 0.9583\n", - "Epoch 821/822\n", - "256/256 [==============================] - 40s 156ms/step - loss: 0.0228 - accuracy: 0.9958 - val_loss: 0.1756 - val_accuracy: 0.9679\n", - "Epoch 822/822\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.1672 - val_accuracy: 0.9696\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-819-0.9712.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9712\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1201\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.967949\u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.971154\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m397.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m247.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m149.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [137] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m138\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 822)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00761\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 823/828\n", - "256/256 [==============================] - 44s 162ms/step - loss: 0.1326 - accuracy: 0.9558 - val_loss: 0.1478 - val_accuracy: 0.9599\n", - "Epoch 824/828\n", - "256/256 [==============================] - 41s 157ms/step - loss: 0.1018 - accuracy: 0.9678 - val_loss: 0.2267 - val_accuracy: 0.9487\n", - "Epoch 825/828\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0598 - accuracy: 0.9834 - val_loss: 0.3210 - val_accuracy: 0.9375\n", - "Epoch 826/828\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0372 - accuracy: 0.9905 - val_loss: 0.2718 - val_accuracy: 0.9535\n", - "Epoch 827/828\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0233 - accuracy: 0.9934 - val_loss: 0.2519 - val_accuracy: 0.9567\n", - "Epoch 828/828\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0191 - accuracy: 0.9951 - val_loss: 0.3001 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1478}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3001\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m400.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m248.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m152.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [138] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m139\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 828)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00758\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 829/834\n", - "256/256 [==============================] - 44s 163ms/step - loss: 0.1510 - accuracy: 0.9553 - val_loss: 0.2288 - val_accuracy: 0.9407\n", - "Epoch 830/834\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.1075 - accuracy: 0.9690 - val_loss: 0.2159 - val_accuracy: 0.9519\n", - "Epoch 831/834\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0680 - accuracy: 0.9834 - val_loss: 0.4250 - val_accuracy: 0.8894\n", - "Epoch 832/834\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0386 - accuracy: 0.9905 - val_loss: 0.3416 - val_accuracy: 0.9311\n", - "Epoch 833/834\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.3111 - val_accuracy: 0.9199\n", - "Epoch 834/834\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0190 - accuracy: 0.9954 - val_loss: 0.4102 - val_accuracy: 0.9135\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.2159}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9135\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4102\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m400.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m248.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m152.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [139] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m140\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 834)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00755\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 835/840\n", - "256/256 [==============================] - 44s 164ms/step - loss: 0.1266 - accuracy: 0.9595 - val_loss: 0.1958 - val_accuracy: 0.9503\n", - "Epoch 836/840\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0905 - accuracy: 0.9717 - val_loss: 0.1855 - val_accuracy: 0.9455\n", - "Epoch 837/840\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0522 - accuracy: 0.9854 - val_loss: 0.4793 - val_accuracy: 0.9199\n", - "Epoch 838/840\n", - "256/256 [==============================] - 41s 157ms/step - loss: 0.0347 - accuracy: 0.9895 - val_loss: 0.2565 - val_accuracy: 0.9455\n", - "Epoch 839/840\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0242 - accuracy: 0.9946 - val_loss: 0.2508 - val_accuracy: 0.9455\n", - "Epoch 840/840\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0195 - accuracy: 0.9961 - val_loss: 0.2276 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1855}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2275\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m404.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m249.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m154.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [140] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m141\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 840)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00752\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 841/846\n", - "256/256 [==============================] - 44s 163ms/step - loss: 0.1258 - accuracy: 0.9587 - val_loss: 0.1290 - val_accuracy: 0.9615\n", - "Epoch 842/846\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0890 - accuracy: 0.9695 - val_loss: 0.3904 - val_accuracy: 0.9022\n", - "Epoch 843/846\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0511 - accuracy: 0.9861 - val_loss: 0.3047 - val_accuracy: 0.9135\n", - "Epoch 844/846\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0245 - accuracy: 0.9951 - val_loss: 0.3773 - val_accuracy: 0.9263\n", - "Epoch 845/846\n", - "256/256 [==============================] - 41s 157ms/step - loss: 0.0196 - accuracy: 0.9946 - val_loss: 0.3663 - val_accuracy: 0.9311\n", - "Epoch 846/846\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0132 - accuracy: 0.9971 - val_loss: 0.4153 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1290}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4153\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m403.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m248.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m154.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [141] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m142\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 846)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00749\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 847/852\n", - "256/256 [==============================] - 45s 165ms/step - loss: 0.1322 - accuracy: 0.9629 - val_loss: 0.3370 - val_accuracy: 0.9295\n", - "Epoch 848/852\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0997 - accuracy: 0.9705 - val_loss: 0.1452 - val_accuracy: 0.9615\n", - "Epoch 849/852\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0630 - accuracy: 0.9839 - val_loss: 0.3465 - val_accuracy: 0.9423\n", - "Epoch 850/852\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0420 - accuracy: 0.9888 - val_loss: 0.2604 - val_accuracy: 0.9567\n", - "Epoch 851/852\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0266 - accuracy: 0.9941 - val_loss: 0.2065 - val_accuracy: 0.9551\n", - "Epoch 852/852\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0205 - accuracy: 0.9954 - val_loss: 0.2384 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1452}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2385\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m404.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m154.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [142] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m143\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 852)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00746\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 853/858\n", - "256/256 [==============================] - 45s 165ms/step - loss: 0.1333 - accuracy: 0.9587 - val_loss: 0.1851 - val_accuracy: 0.9535\n", - "Epoch 854/858\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0941 - accuracy: 0.9692 - val_loss: 0.2120 - val_accuracy: 0.9231\n", - "Epoch 855/858\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0550 - accuracy: 0.9851 - val_loss: 0.2228 - val_accuracy: 0.9439\n", - "Epoch 856/858\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0306 - accuracy: 0.9929 - val_loss: 0.2338 - val_accuracy: 0.9471\n", - "Epoch 857/858\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0183 - accuracy: 0.9958 - val_loss: 0.2860 - val_accuracy: 0.9423\n", - "Epoch 858/858\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0139 - accuracy: 0.9968 - val_loss: 0.3258 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1851}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3259\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m407.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m157.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [143] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m144\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 858)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00743\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 859/864\n", - "256/256 [==============================] - 45s 165ms/step - loss: 0.1402 - accuracy: 0.9573 - val_loss: 0.1675 - val_accuracy: 0.9615\n", - "Epoch 860/864\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0920 - accuracy: 0.9712 - val_loss: 0.1658 - val_accuracy: 0.9615\n", - "Epoch 861/864\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0537 - accuracy: 0.9814 - val_loss: 0.1802 - val_accuracy: 0.9567\n", - "Epoch 862/864\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0288 - accuracy: 0.9932 - val_loss: 0.3665 - val_accuracy: 0.9375\n", - "Epoch 863/864\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0235 - accuracy: 0.9941 - val_loss: 0.3595 - val_accuracy: 0.9311\n", - "Epoch 864/864\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0191 - accuracy: 0.9956 - val_loss: 0.3331 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9615}, \u001b[0m\u001b[0;33mloss{0.1658}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3331\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m407.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m249.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m158.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [144] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m145\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 864)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0074\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 865/870\n", - "256/256 [==============================] - 45s 166ms/step - loss: 0.1426 - accuracy: 0.9607 - val_loss: 0.1832 - val_accuracy: 0.9471\n", - "Epoch 866/870\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0987 - accuracy: 0.9729 - val_loss: 0.3228 - val_accuracy: 0.9135\n", - "Epoch 867/870\n", - "256/256 [==============================] - 41s 161ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.1585 - val_accuracy: 0.9503\n", - "Epoch 868/870\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0363 - accuracy: 0.9900 - val_loss: 0.2348 - val_accuracy: 0.9471\n", - "Epoch 869/870\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0292 - accuracy: 0.9932 - val_loss: 0.3199 - val_accuracy: 0.9295\n", - "Epoch 870/870\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0163 - accuracy: 0.9971 - val_loss: 0.3123 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1585}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3124\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m409.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m158.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [145] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m146\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 870)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00737\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 871/876\n", - "256/256 [==============================] - 45s 166ms/step - loss: 0.1316 - accuracy: 0.9561 - val_loss: 0.2023 - val_accuracy: 0.9423\n", - "Epoch 872/876\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0910 - accuracy: 0.9722 - val_loss: 0.1757 - val_accuracy: 0.9471\n", - "Epoch 873/876\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0542 - accuracy: 0.9817 - val_loss: 0.1813 - val_accuracy: 0.9471\n", - "Epoch 874/876\n", - "256/256 [==============================] - 42s 162ms/step - loss: 0.0335 - accuracy: 0.9910 - val_loss: 0.1739 - val_accuracy: 0.9583\n", - "Epoch 875/876\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0235 - accuracy: 0.9927 - val_loss: 0.2043 - val_accuracy: 0.9583\n", - "Epoch 876/876\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0163 - accuracy: 0.9958 - val_loss: 0.2381 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1739}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2381\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m414.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m162.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [146] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m147\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 876)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00734\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 877/882\n", - "256/256 [==============================] - 45s 165ms/step - loss: 0.1319 - accuracy: 0.9580 - val_loss: 0.1716 - val_accuracy: 0.9439\n", - "Epoch 878/882\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0874 - accuracy: 0.9719 - val_loss: 0.1500 - val_accuracy: 0.9599\n", - "Epoch 879/882\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0476 - accuracy: 0.9873 - val_loss: 0.1762 - val_accuracy: 0.9567\n", - "Epoch 880/882\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.2778 - val_accuracy: 0.9231\n", - "Epoch 881/882\n", - "256/256 [==============================] - 42s 161ms/step - loss: 0.0199 - accuracy: 0.9954 - val_loss: 0.2365 - val_accuracy: 0.9631\n", - "Epoch 882/882\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0180 - accuracy: 0.9946 - val_loss: 0.2195 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9631}, \u001b[0m\u001b[0;33mloss{0.1500}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2196\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m410.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m160.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [147] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m148\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 882)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00731\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 883/888\n", - "256/256 [==============================] - 45s 164ms/step - loss: 0.1327 - accuracy: 0.9607 - val_loss: 0.1906 - val_accuracy: 0.9599\n", - "Epoch 884/888\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0980 - accuracy: 0.9714 - val_loss: 0.1611 - val_accuracy: 0.9567\n", - "Epoch 885/888\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0597 - accuracy: 0.9829 - val_loss: 0.1945 - val_accuracy: 0.9583\n", - "Epoch 886/888\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0337 - accuracy: 0.9912 - val_loss: 0.1810 - val_accuracy: 0.9679\n", - "Epoch 887/888\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2032 - val_accuracy: 0.9551\n", - "Epoch 888/888\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0197 - accuracy: 0.9961 - val_loss: 0.2361 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9679}, \u001b[0m\u001b[0;33mloss{0.1611}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2361\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m409.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m249.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m160.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [148] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m149\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 888)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00728\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 889/894\n", - "256/256 [==============================] - 44s 164ms/step - loss: 0.1379 - accuracy: 0.9561 - val_loss: 0.2060 - val_accuracy: 0.9311\n", - "Epoch 890/894\n", - "256/256 [==============================] - 41s 161ms/step - loss: 0.0962 - accuracy: 0.9680 - val_loss: 0.1204 - val_accuracy: 0.9599\n", - "Epoch 891/894\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0656 - accuracy: 0.9802 - val_loss: 0.2364 - val_accuracy: 0.9551\n", - "Epoch 892/894\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0382 - accuracy: 0.9883 - val_loss: 0.4108 - val_accuracy: 0.9054\n", - "Epoch 893/894\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0254 - accuracy: 0.9944 - val_loss: 0.2448 - val_accuracy: 0.9487\n", - "Epoch 894/894\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0153 - accuracy: 0.9971 - val_loss: 0.2755 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1204}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2754\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m412.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m161.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [149] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m150\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 894)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00725\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 895/900\n", - "256/256 [==============================] - 45s 165ms/step - loss: 0.1440 - accuracy: 0.9568 - val_loss: 0.2377 - val_accuracy: 0.9311\n", - "Epoch 896/900\n", - "256/256 [==============================] - 42s 162ms/step - loss: 0.1026 - accuracy: 0.9700 - val_loss: 0.2438 - val_accuracy: 0.9375\n", - "Epoch 897/900\n", - "256/256 [==============================] - 41s 161ms/step - loss: 0.0610 - accuracy: 0.9829 - val_loss: 0.1895 - val_accuracy: 0.9551\n", - "Epoch 898/900\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0352 - accuracy: 0.9902 - val_loss: 0.2907 - val_accuracy: 0.9375\n", - "Epoch 899/900\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0239 - accuracy: 0.9939 - val_loss: 0.6258 - val_accuracy: 0.9022\n", - "Epoch 900/900\n", - "256/256 [==============================] - 41s 157ms/step - loss: 0.0169 - accuracy: 0.9951 - val_loss: 0.5949 - val_accuracy: 0.9006\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1895}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9006\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5949\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m410.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m159.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [150] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m151\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 900)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00722\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 901/906\n", - "256/256 [==============================] - 45s 165ms/step - loss: 0.1360 - accuracy: 0.9600 - val_loss: 0.1986 - val_accuracy: 0.9215\n", - "Epoch 902/906\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0912 - accuracy: 0.9729 - val_loss: 0.3233 - val_accuracy: 0.9135\n", - "Epoch 903/906\n", - "256/256 [==============================] - 41s 161ms/step - loss: 0.0571 - accuracy: 0.9829 - val_loss: 0.3079 - val_accuracy: 0.9343\n", - "Epoch 904/906\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0485 - accuracy: 0.9868 - val_loss: 0.4133 - val_accuracy: 0.9199\n", - "Epoch 905/906\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0272 - accuracy: 0.9934 - val_loss: 0.3950 - val_accuracy: 0.9135\n", - "Epoch 906/906\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0155 - accuracy: 0.9968 - val_loss: 0.4164 - val_accuracy: 0.9199\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.1986}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4165\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m160.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [151] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m152\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 906)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00719\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 907/912\n", - "256/256 [==============================] - 44s 164ms/step - loss: 0.1126 - accuracy: 0.9641 - val_loss: 0.3591 - val_accuracy: 0.9103\n", - "Epoch 908/912\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0686 - accuracy: 0.9783 - val_loss: 0.2877 - val_accuracy: 0.9279\n", - "Epoch 909/912\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0378 - accuracy: 0.9890 - val_loss: 0.3857 - val_accuracy: 0.9231\n", - "Epoch 910/912\n", - "256/256 [==============================] - 41s 161ms/step - loss: 0.0227 - accuracy: 0.9939 - val_loss: 0.2957 - val_accuracy: 0.9455\n", - "Epoch 911/912\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0173 - accuracy: 0.9954 - val_loss: 0.3349 - val_accuracy: 0.9407\n", - "Epoch 912/912\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0123 - accuracy: 0.9978 - val_loss: 0.2853 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2853}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2853\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m415.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m164.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [152] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m153\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 912)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00716\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 913/918\n", - "256/256 [==============================] - 45s 165ms/step - loss: 0.1397 - accuracy: 0.9578 - val_loss: 0.1995 - val_accuracy: 0.9519\n", - "Epoch 914/918\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0886 - accuracy: 0.9727 - val_loss: 0.6203 - val_accuracy: 0.8878\n", - "Epoch 915/918\n", - "256/256 [==============================] - 42s 162ms/step - loss: 0.0539 - accuracy: 0.9854 - val_loss: 0.2018 - val_accuracy: 0.9567\n", - "Epoch 916/918\n", - "256/256 [==============================] - 40s 157ms/step - loss: 0.0343 - accuracy: 0.9919 - val_loss: 0.2684 - val_accuracy: 0.9487\n", - "Epoch 917/918\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0231 - accuracy: 0.9941 - val_loss: 0.4024 - val_accuracy: 0.9359\n", - "Epoch 918/918\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0150 - accuracy: 0.9968 - val_loss: 0.3597 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1995}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3597\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m417.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m166.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [153] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m154\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 918)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00713\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 919/924\n", - "256/256 [==============================] - 45s 165ms/step - loss: 0.1227 - accuracy: 0.9656 - val_loss: 0.1812 - val_accuracy: 0.9551\n", - "Epoch 920/924\n", - "256/256 [==============================] - 41s 161ms/step - loss: 0.0910 - accuracy: 0.9712 - val_loss: 0.1398 - val_accuracy: 0.9647\n", - "Epoch 921/924\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0533 - accuracy: 0.9846 - val_loss: 0.2057 - val_accuracy: 0.9503\n", - "Epoch 922/924\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0349 - accuracy: 0.9890 - val_loss: 0.2351 - val_accuracy: 0.9503\n", - "Epoch 923/924\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0210 - accuracy: 0.9961 - val_loss: 0.1912 - val_accuracy: 0.9631\n", - "Epoch 924/924\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0174 - accuracy: 0.9954 - val_loss: 0.2010 - val_accuracy: 0.9631\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9647}, \u001b[0m\u001b[0;33mloss{0.1398}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2010\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m417.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m167.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [154] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m155\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 924)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0071\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 925/930\n", - "256/256 [==============================] - 45s 164ms/step - loss: 0.1351 - accuracy: 0.9595 - val_loss: 0.1894 - val_accuracy: 0.9583\n", - "Epoch 926/930\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0971 - accuracy: 0.9685 - val_loss: 0.2676 - val_accuracy: 0.9167\n", - "Epoch 927/930\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0612 - accuracy: 0.9822 - val_loss: 0.3774 - val_accuracy: 0.9343\n", - "Epoch 928/930\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0338 - accuracy: 0.9905 - val_loss: 0.3938 - val_accuracy: 0.9263\n", - "Epoch 929/930\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0275 - accuracy: 0.9944 - val_loss: 0.3886 - val_accuracy: 0.9279\n", - "Epoch 930/930\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0174 - accuracy: 0.9968 - val_loss: 0.4244 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1894}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4244\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m418.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m167.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [155] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m156\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 930)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00707\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 931/936\n", - "256/256 [==============================] - 45s 166ms/step - loss: 0.1245 - accuracy: 0.9624 - val_loss: 0.3571 - val_accuracy: 0.9167\n", - "Epoch 932/936\n", - "256/256 [==============================] - 42s 162ms/step - loss: 0.0912 - accuracy: 0.9719 - val_loss: 0.2748 - val_accuracy: 0.9423\n", - "Epoch 933/936\n", - "256/256 [==============================] - 42s 162ms/step - loss: 0.0507 - accuracy: 0.9863 - val_loss: 0.2641 - val_accuracy: 0.9455\n", - "Epoch 934/936\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0326 - accuracy: 0.9929 - val_loss: 0.3578 - val_accuracy: 0.9407\n", - "Epoch 935/936\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0226 - accuracy: 0.9949 - val_loss: 0.2784 - val_accuracy: 0.9439\n", - "Epoch 936/936\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0170 - accuracy: 0.9963 - val_loss: 0.3152 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2641}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3153\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m424.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m171.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [156] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m157\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 936)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00704\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 937/942\n", - "256/256 [==============================] - 45s 167ms/step - loss: 0.1299 - accuracy: 0.9587 - val_loss: 0.2590 - val_accuracy: 0.9487\n", - "Epoch 938/942\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.1004 - accuracy: 0.9683 - val_loss: 0.2953 - val_accuracy: 0.9487\n", - "Epoch 939/942\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0673 - accuracy: 0.9778 - val_loss: 0.1960 - val_accuracy: 0.9567\n", - "Epoch 940/942\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0413 - accuracy: 0.9880 - val_loss: 0.2249 - val_accuracy: 0.9567\n", - "Epoch 941/942\n", - "256/256 [==============================] - 42s 161ms/step - loss: 0.0290 - accuracy: 0.9934 - val_loss: 0.2104 - val_accuracy: 0.9551\n", - "Epoch 942/942\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0214 - accuracy: 0.9956 - val_loss: 0.2171 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1960}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2171\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m428.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m175.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [157] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m158\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 942)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00701\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 943/948\n", - "256/256 [==============================] - 46s 169ms/step - loss: 0.1133 - accuracy: 0.9634 - val_loss: 0.1615 - val_accuracy: 0.9567\n", - "Epoch 944/948\n", - "256/256 [==============================] - 41s 158ms/step - loss: 0.0898 - accuracy: 0.9731 - val_loss: 0.1627 - val_accuracy: 0.9487\n", - "Epoch 945/948\n", - "256/256 [==============================] - 42s 161ms/step - loss: 0.0463 - accuracy: 0.9861 - val_loss: 0.1868 - val_accuracy: 0.9583\n", - "Epoch 946/948\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0300 - accuracy: 0.9927 - val_loss: 0.2804 - val_accuracy: 0.9455\n", - "Epoch 947/948\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0175 - accuracy: 0.9954 - val_loss: 0.2375 - val_accuracy: 0.9567\n", - "Epoch 948/948\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0133 - accuracy: 0.9971 - val_loss: 0.2272 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1615}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2272\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m433.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m255.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m178.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [158] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m159\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 948)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00698\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 949/954\n", - "256/256 [==============================] - 47s 171ms/step - loss: 0.1411 - accuracy: 0.9614 - val_loss: 0.1635 - val_accuracy: 0.9471\n", - "Epoch 950/954\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0924 - accuracy: 0.9722 - val_loss: 0.1930 - val_accuracy: 0.9439\n", - "Epoch 951/954\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0614 - accuracy: 0.9827 - val_loss: 0.1820 - val_accuracy: 0.9535\n", - "Epoch 952/954\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0331 - accuracy: 0.9919 - val_loss: 0.2176 - val_accuracy: 0.9535\n", - "Epoch 953/954\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0211 - accuracy: 0.9946 - val_loss: 0.1849 - val_accuracy: 0.9583\n", - "Epoch 954/954\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0188 - accuracy: 0.9954 - val_loss: 0.2180 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1635}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2181\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m433.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m173.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [159] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m160\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 954)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00695\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 955/960\n", - "256/256 [==============================] - 47s 172ms/step - loss: 0.1347 - accuracy: 0.9575 - val_loss: 0.1630 - val_accuracy: 0.9551\n", - "Epoch 956/960\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0919 - accuracy: 0.9724 - val_loss: 0.1584 - val_accuracy: 0.9583\n", - "Epoch 957/960\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0586 - accuracy: 0.9832 - val_loss: 0.1895 - val_accuracy: 0.9551\n", - "Epoch 958/960\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0293 - accuracy: 0.9927 - val_loss: 0.2159 - val_accuracy: 0.9535\n", - "Epoch 959/960\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0207 - accuracy: 0.9951 - val_loss: 0.1914 - val_accuracy: 0.9583\n", - "Epoch 960/960\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0176 - accuracy: 0.9966 - val_loss: 0.2150 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1584}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2151\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m438.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m178.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [160] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m161\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 960)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00692\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 961/966\n", - "256/256 [==============================] - 47s 172ms/step - loss: 0.1375 - accuracy: 0.9575 - val_loss: 0.2083 - val_accuracy: 0.9471\n", - "Epoch 962/966\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0955 - accuracy: 0.9712 - val_loss: 0.2929 - val_accuracy: 0.9311\n", - "Epoch 963/966\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0588 - accuracy: 0.9839 - val_loss: 0.2239 - val_accuracy: 0.9455\n", - "Epoch 964/966\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0307 - accuracy: 0.9932 - val_loss: 0.2957 - val_accuracy: 0.9455\n", - "Epoch 965/966\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0270 - accuracy: 0.9934 - val_loss: 0.3422 - val_accuracy: 0.9295\n", - "Epoch 966/966\n", - "256/256 [==============================] - 42s 163ms/step - loss: 0.0205 - accuracy: 0.9956 - val_loss: 0.3498 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.2083}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3498\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m436.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m178.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [161] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m162\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 966)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00689\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 967/972\n", - "256/256 [==============================] - 46s 170ms/step - loss: 0.1298 - accuracy: 0.9614 - val_loss: 0.1907 - val_accuracy: 0.9263\n", - "Epoch 968/972\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0898 - accuracy: 0.9707 - val_loss: 0.3238 - val_accuracy: 0.9343\n", - "Epoch 969/972\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0624 - accuracy: 0.9814 - val_loss: 0.7272 - val_accuracy: 0.8638\n", - "Epoch 970/972\n", - "256/256 [==============================] - 43s 165ms/step - loss: 0.0347 - accuracy: 0.9905 - val_loss: 0.4252 - val_accuracy: 0.9038\n", - "Epoch 971/972\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0162 - accuracy: 0.9958 - val_loss: 0.4909 - val_accuracy: 0.9119\n", - "Epoch 972/972\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0123 - accuracy: 0.9973 - val_loss: 0.4672 - val_accuracy: 0.9263\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.1907}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4672\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m436.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m176.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [162] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m163\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 972)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00686\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 973/978\n", - "256/256 [==============================] - 46s 169ms/step - loss: 0.1243 - accuracy: 0.9648 - val_loss: 0.4568 - val_accuracy: 0.8846\n", - "Epoch 974/978\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0807 - accuracy: 0.9739 - val_loss: 0.3000 - val_accuracy: 0.9295\n", - "Epoch 975/978\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0506 - accuracy: 0.9836 - val_loss: 0.3319 - val_accuracy: 0.9359\n", - "Epoch 976/978\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0317 - accuracy: 0.9905 - val_loss: 0.4548 - val_accuracy: 0.9295\n", - "Epoch 977/978\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0186 - accuracy: 0.9946 - val_loss: 0.4510 - val_accuracy: 0.9087\n", - "Epoch 978/978\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0112 - accuracy: 0.9978 - val_loss: 0.5131 - val_accuracy: 0.9183\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.3000}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9183\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5132\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m434.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m174.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [163] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m164\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 978)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00683\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 979/984\n", - "256/256 [==============================] - 46s 169ms/step - loss: 0.1304 - accuracy: 0.9636 - val_loss: 0.4299 - val_accuracy: 0.8990\n", - "Epoch 980/984\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0840 - accuracy: 0.9761 - val_loss: 0.3845 - val_accuracy: 0.9054\n", - "Epoch 981/984\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0528 - accuracy: 0.9854 - val_loss: 0.5915 - val_accuracy: 0.9022\n", - "Epoch 982/984\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0361 - accuracy: 0.9905 - val_loss: 0.3931 - val_accuracy: 0.9263\n", - "Epoch 983/984\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0250 - accuracy: 0.9939 - val_loss: 0.3693 - val_accuracy: 0.9391\n", - "Epoch 984/984\n", - "256/256 [==============================] - 42s 162ms/step - loss: 0.0129 - accuracy: 0.9980 - val_loss: 0.3749 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.3693}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3750\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m439.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m180.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [164] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m165\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 984)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0068\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 985/990\n", - "256/256 [==============================] - 47s 172ms/step - loss: 0.1343 - accuracy: 0.9614 - val_loss: 0.2650 - val_accuracy: 0.9391\n", - "Epoch 986/990\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0899 - accuracy: 0.9749 - val_loss: 0.3404 - val_accuracy: 0.9327\n", - "Epoch 987/990\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0580 - accuracy: 0.9832 - val_loss: 0.3183 - val_accuracy: 0.9279\n", - "Epoch 988/990\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0301 - accuracy: 0.9927 - val_loss: 0.3087 - val_accuracy: 0.9391\n", - "Epoch 989/990\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0240 - accuracy: 0.9944 - val_loss: 0.3673 - val_accuracy: 0.9247\n", - "Epoch 990/990\n", - "256/256 [==============================] - 43s 165ms/step - loss: 0.0150 - accuracy: 0.9978 - val_loss: 0.3473 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.2650}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m439.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m179.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [165] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m166\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 990)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00677\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 991/996\n", - "256/256 [==============================] - 47s 173ms/step - loss: 0.1257 - accuracy: 0.9604 - val_loss: 0.1785 - val_accuracy: 0.9455\n", - "Epoch 992/996\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0822 - accuracy: 0.9709 - val_loss: 0.2909 - val_accuracy: 0.9279\n", - "Epoch 993/996\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0479 - accuracy: 0.9849 - val_loss: 0.1524 - val_accuracy: 0.9631\n", - "Epoch 994/996\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0334 - accuracy: 0.9902 - val_loss: 0.1816 - val_accuracy: 0.9535\n", - "Epoch 995/996\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0235 - accuracy: 0.9949 - val_loss: 0.2346 - val_accuracy: 0.9455\n", - "Epoch 996/996\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0139 - accuracy: 0.9963 - val_loss: 0.2372 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9631}, \u001b[0m\u001b[0;33mloss{0.1524}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2373\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m442.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m181.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [166] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m167\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 996)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00674\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 997/1002\n", - "256/256 [==============================] - 46s 169ms/step - loss: 0.1316 - accuracy: 0.9587 - val_loss: 0.1300 - val_accuracy: 0.9696\n", - "Epoch 998/1002\n", - "256/256 [==============================] - 42s 161ms/step - loss: 0.0953 - accuracy: 0.9680 - val_loss: 0.2118 - val_accuracy: 0.9583\n", - "Epoch 999/1002\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0566 - accuracy: 0.9814 - val_loss: 0.2258 - val_accuracy: 0.9567\n", - "Epoch 1000/1002\n", - "256/256 [==============================] - 41s 160ms/step - loss: 0.0334 - accuracy: 0.9905 - val_loss: 0.3355 - val_accuracy: 0.9487\n", - "Epoch 1001/1002\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0187 - accuracy: 0.9946 - val_loss: 0.2652 - val_accuracy: 0.9503\n", - "Epoch 1002/1002\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0154 - accuracy: 0.9956 - val_loss: 0.2672 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9696}, \u001b[0m\u001b[0;33mloss{0.1300}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2672\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m435.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m182.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [167] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m168\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1002)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d10-h08_m07_s34\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00671\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1003/1008\n", - "256/256 [==============================] - 46s 168ms/step - loss: 0.1109 - accuracy: 0.9622 - val_loss: 0.1559 - val_accuracy: 0.9551\n", - "Epoch 1004/1008\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0768 - accuracy: 0.9741 - val_loss: 0.2385 - val_accuracy: 0.9359\n", - "Epoch 1005/1008\n", - "256/256 [==============================] - 42s 162ms/step - loss: 0.0433 - accuracy: 0.9885 - val_loss: 0.1272 - val_accuracy: 0.9631\n", - "Epoch 1006/1008\n", - "256/256 [==============================] - 42s 162ms/step - loss: 0.0236 - accuracy: 0.9924 - val_loss: 0.3081 - val_accuracy: 0.9247\n", - "Epoch 1007/1008\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0135 - accuracy: 0.9966 - val_loss: 0.3317 - val_accuracy: 0.9343\n", - "Epoch 1008/1008\n", - "256/256 [==============================] - 41s 159ms/step - loss: 0.0135 - accuracy: 0.9958 - val_loss: 0.3712 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9631}, \u001b[0m\u001b[0;33mloss{0.1272}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3712\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m451.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m255.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m196.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [168] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m169\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1008)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00668\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1009/1014\n", - "256/256 [==============================] - 46s 170ms/step - loss: 0.1353 - accuracy: 0.9607 - val_loss: 0.2478 - val_accuracy: 0.9311\n", - "Epoch 1010/1014\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0872 - accuracy: 0.9734 - val_loss: 0.4125 - val_accuracy: 0.9135\n", - "Epoch 1011/1014\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0542 - accuracy: 0.9849 - val_loss: 0.4417 - val_accuracy: 0.9231\n", - "Epoch 1012/1014\n", - "256/256 [==============================] - 42s 164ms/step - loss: 0.0320 - accuracy: 0.9924 - val_loss: 0.8545 - val_accuracy: 0.8686\n", - "Epoch 1013/1014\n", - "256/256 [==============================] - 43s 165ms/step - loss: 0.0210 - accuracy: 0.9958 - val_loss: 0.6204 - val_accuracy: 0.8990\n", - "Epoch 1014/1014\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0156 - accuracy: 0.9971 - val_loss: 0.7137 - val_accuracy: 0.8958\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9311}, \u001b[0m\u001b[0;33mloss{0.2478}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8974\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7136\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m447.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m187.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [169] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m170\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1014)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00665\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1015/1020\n", - "256/256 [==============================] - 47s 173ms/step - loss: 0.1194 - accuracy: 0.9636 - val_loss: 0.2442 - val_accuracy: 0.9519\n", - "Epoch 1016/1020\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0896 - accuracy: 0.9727 - val_loss: 0.3656 - val_accuracy: 0.9006\n", - "Epoch 1017/1020\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0579 - accuracy: 0.9861 - val_loss: 0.3540 - val_accuracy: 0.9279\n", - "Epoch 1018/1020\n", - "256/256 [==============================] - 42s 165ms/step - loss: 0.0370 - accuracy: 0.9915 - val_loss: 0.3445 - val_accuracy: 0.9359\n", - "Epoch 1019/1020\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0292 - accuracy: 0.9929 - val_loss: 0.4268 - val_accuracy: 0.9263\n", - "Epoch 1020/1020\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0154 - accuracy: 0.9963 - val_loss: 0.4503 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.2442}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4505\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m448.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m188.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [170] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m171\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1020)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00662\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1021/1026\n", - "256/256 [==============================] - 47s 173ms/step - loss: 0.1330 - accuracy: 0.9587 - val_loss: 0.3474 - val_accuracy: 0.9151\n", - "Epoch 1022/1026\n", - "256/256 [==============================] - 43s 169ms/step - loss: 0.0929 - accuracy: 0.9727 - val_loss: 0.3196 - val_accuracy: 0.9215\n", - "Epoch 1023/1026\n", - "256/256 [==============================] - 43s 169ms/step - loss: 0.0541 - accuracy: 0.9824 - val_loss: 0.2571 - val_accuracy: 0.9471\n", - "Epoch 1024/1026\n", - "256/256 [==============================] - 43s 168ms/step - loss: 0.0367 - accuracy: 0.9890 - val_loss: 0.2576 - val_accuracy: 0.9487\n", - "Epoch 1025/1026\n", - "256/256 [==============================] - 43s 168ms/step - loss: 0.0212 - accuracy: 0.9956 - val_loss: 0.2730 - val_accuracy: 0.9503\n", - "Epoch 1026/1026\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0145 - accuracy: 0.9976 - val_loss: 0.3372 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2571}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3372\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m452.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m188.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [171] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m172\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1026)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00659\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1027/1032\n", - "256/256 [==============================] - 47s 173ms/step - loss: 0.1352 - accuracy: 0.9585 - val_loss: 0.1919 - val_accuracy: 0.9503\n", - "Epoch 1028/1032\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0898 - accuracy: 0.9700 - val_loss: 0.3318 - val_accuracy: 0.9279\n", - "Epoch 1029/1032\n", - "256/256 [==============================] - 43s 168ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2086 - val_accuracy: 0.9519\n", - "Epoch 1030/1032\n", - "256/256 [==============================] - 44s 170ms/step - loss: 0.0315 - accuracy: 0.9912 - val_loss: 0.2407 - val_accuracy: 0.9535\n", - "Epoch 1031/1032\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0191 - accuracy: 0.9958 - val_loss: 0.3219 - val_accuracy: 0.9519\n", - "Epoch 1032/1032\n", - "256/256 [==============================] - 43s 168ms/step - loss: 0.0123 - accuracy: 0.9976 - val_loss: 0.3429 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1919}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3430\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m453.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m263.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m190.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [172] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m173\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1032)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00656\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1033/1038\n", - "256/256 [==============================] - 47s 171ms/step - loss: 0.1242 - accuracy: 0.9658 - val_loss: 0.3473 - val_accuracy: 0.9263\n", - "Epoch 1034/1038\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0834 - accuracy: 0.9739 - val_loss: 0.5006 - val_accuracy: 0.8894\n", - "Epoch 1035/1038\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0483 - accuracy: 0.9849 - val_loss: 0.2745 - val_accuracy: 0.9327\n", - "Epoch 1036/1038\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.3518 - val_accuracy: 0.9295\n", - "Epoch 1037/1038\n", - "256/256 [==============================] - 44s 170ms/step - loss: 0.0204 - accuracy: 0.9944 - val_loss: 0.2868 - val_accuracy: 0.9455\n", - "Epoch 1038/1038\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0165 - accuracy: 0.9963 - val_loss: 0.3198 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2745}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3198\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m456.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m262.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m193.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [173] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m174\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1038)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00653\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1039/1044\n", - "256/256 [==============================] - 47s 172ms/step - loss: 0.1196 - accuracy: 0.9634 - val_loss: 0.1919 - val_accuracy: 0.9439\n", - "Epoch 1040/1044\n", - "256/256 [==============================] - 43s 165ms/step - loss: 0.0752 - accuracy: 0.9749 - val_loss: 0.3084 - val_accuracy: 0.8990\n", - "Epoch 1041/1044\n", - "256/256 [==============================] - 43s 169ms/step - loss: 0.0485 - accuracy: 0.9890 - val_loss: 0.1478 - val_accuracy: 0.9583\n", - "Epoch 1042/1044\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0304 - accuracy: 0.9919 - val_loss: 0.3258 - val_accuracy: 0.9311\n", - "Epoch 1043/1044\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.3111 - val_accuracy: 0.9375\n", - "Epoch 1044/1044\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0132 - accuracy: 0.9978 - val_loss: 0.3261 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1478}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3262\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m456.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m262.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m193.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [174] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m175\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1044)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0065\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1045/1050\n", - "256/256 [==============================] - 47s 174ms/step - loss: 0.1252 - accuracy: 0.9651 - val_loss: 0.2465 - val_accuracy: 0.9327\n", - "Epoch 1046/1050\n", - "256/256 [==============================] - 44s 169ms/step - loss: 0.0836 - accuracy: 0.9751 - val_loss: 0.2461 - val_accuracy: 0.9407\n", - "Epoch 1047/1050\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0576 - accuracy: 0.9834 - val_loss: 0.2755 - val_accuracy: 0.9295\n", - "Epoch 1048/1050\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0406 - accuracy: 0.9888 - val_loss: 0.3435 - val_accuracy: 0.9407\n", - "Epoch 1049/1050\n", - "256/256 [==============================] - 43s 166ms/step - loss: 0.0264 - accuracy: 0.9949 - val_loss: 0.3248 - val_accuracy: 0.9295\n", - "Epoch 1050/1050\n", - "256/256 [==============================] - 43s 167ms/step - loss: 0.0207 - accuracy: 0.9954 - val_loss: 0.3905 - val_accuracy: 0.9263\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.2461}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9712}, loss{0.1116}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3906\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9711538553. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1115868539. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m455.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m263.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m192.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [175] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m176\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m384 (TSEC: 1050)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "import gc\n", "# Garbage Collection (memory)\n", @@ -23460,7 +2432,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -23479,7 +2451,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -23499,75 +2471,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:52.565658900Z", "start_time": "2023-12-28T07:04:51.032425100Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", @@ -23781,82 +2692,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "notebookRunGroups": { "groupValue": "" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 2s 2s/step\n", - "20/20 [==============================] - 2s 92ms/step\n", - "The accuracy of the model on validation data is 93.75%(93.75000%)\n", - "The accuracy of the model on test data is 97.12%(97.11538%)\n" - ] - }, - { - "data": { - "image/png": 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", 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"\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\Utils\\Grad_cam.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(img_array, model, last_conv_layer_name, second_last_conv_layer_name, pred_index, threshold, sensitivity_map)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[0mpred_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;31m# Compute heatmap for the last convolutional layer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m \u001b[0mheatmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_compute_heatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_conv_layer_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[1;31m# Apply threshold and adjust sensitivity\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[0mheatmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheatmap\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheatmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\Utils\\Grad_cam.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(model, img_array, conv_layer_name, pred_index)\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_layer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconv_layer_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m )\n\u001b[0;32m 22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGradientTape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0mconv_layer_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrad_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 25\u001b[0m \u001b[0mclass_channel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[0mgrads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclass_channel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconv_layer_output\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - 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"\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - 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"\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[0mnew_e\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mnew_e\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mbound_signature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - 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"\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs, training, mask)\u001b[0m\n\u001b[0;32m 663\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_id\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtensor_dict\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mt_id\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat_input_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 664\u001b[0m \u001b[1;32mcontinue\u001b[0m \u001b[1;31m# Node is not computable, try skipping.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 665\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 666\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_arguments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 669\u001b[0m \u001b[1;31m# Update tensor_dict.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 670\u001b[0m for x_id, y in zip(\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1093\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1094\u001b[0m with autocast_variable.enable_auto_cast_variables(\n\u001b[0;32m 1095\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1096\u001b[0m ):\n\u001b[1;32m-> 1097\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1098\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1099\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1100\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[0mnew_e\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mnew_e\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mbound_signature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\layers\\convolutional\\base_conv.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m 279\u001b[0m outputs = self._jit_compiled_convolution_op(\n\u001b[0;32m 280\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 281\u001b[0m )\n\u001b[0;32m 282\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 283\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvolution_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_bias\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 286\u001b[0m \u001b[0moutput_rank\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrank\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\layers\\convolutional\\base_conv.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs, kernel)\u001b[0m\n\u001b[0;32m 251\u001b[0m \u001b[0mtf_padding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 252\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 253\u001b[0m \u001b[0mtf_padding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 255\u001b[1;33m return tf.nn.convolution(\n\u001b[0m\u001b[0;32m 256\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 257\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[0mstrides\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrides\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 151\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 1173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1174\u001b[0m \u001b[1;31m# Fallback dispatch system (dispatch v1):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1175\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1176\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdispatch_target\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1177\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1178\u001b[0m \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1179\u001b[0m \u001b[1;31m# TypeError, when given unexpected types. So we need to catch both.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1180\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_dispatch_handler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, filters, strides, padding, data_format, dilations, name)\u001b[0m\n\u001b[0;32m 1177\u001b[0m \u001b[0mpadding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"VALID\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1178\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1179\u001b[0m \u001b[0mdilations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1180\u001b[0m name=None):\n\u001b[1;32m-> 1181\u001b[1;33m return convolution_internal(\n\u001b[0m\u001b[0;32m 1182\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m# pylint: disable=redefined-builtin\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1183\u001b[0m \u001b[0mfilters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1184\u001b[0m \u001b[0mstrides\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstrides\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, filters, strides, padding, data_format, dilations, name, call_from_convolution, num_spatial_dims)\u001b[0m\n\u001b[0;32m 1309\u001b[0m \u001b[0mop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_conv3d_expanded_batch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1310\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1311\u001b[0m \u001b[0mop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconv1d\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1313\u001b[1;33m return op(\n\u001b[0m\u001b[0;32m 1314\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1315\u001b[0m \u001b[0mfilters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1316\u001b[0m \u001b[0mstrides\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, filters, strides, padding, data_format, dilations, name)\u001b[0m\n\u001b[0;32m 2783\u001b[0m \u001b[0minput_rank\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrank\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2784\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minput_rank\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0minput_rank\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2785\u001b[0m \u001b[1;31m# We avoid calling squeeze_batch_dims to reduce extra python function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2786\u001b[0m \u001b[1;31m# call slowdown in eager mode. This branch doesn't require reshapes.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2787\u001b[1;33m return gen_nn_ops.conv2d(\n\u001b[0m\u001b[0;32m 2788\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2789\u001b[0m \u001b[0mfilter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2790\u001b[0m \u001b[0mstrides\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstrides\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\gen_nn_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)\u001b[0m\n\u001b[0;32m 1104\u001b[0m \"dilations\", dilations)\n\u001b[0;32m 1105\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1106\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1107\u001b[0m \u001b[0m_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1108\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1109\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1110\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1111\u001b[0m return conv2d_eager_fallback(\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import seaborn as sns\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n",