diff --git a/.gitignore b/.gitignore index 70dcaea..7ab43ef 100644 --- a/.gitignore +++ b/.gitignore @@ -68,3 +68,4 @@ Samples/* # Exclude temporary Python version file in the GUI interface /Interface/GUI/Data/Python Ver.tmp +/venv_2 \ No newline at end of file diff --git a/.idea/Pneumonia AI Dev.iml b/.idea/Pneumonia AI Dev.iml index 269e1a8..58a6887 100644 --- a/.idea/Pneumonia AI Dev.iml +++ b/.idea/Pneumonia AI Dev.iml @@ -3,8 +3,9 @@ + - + diff --git a/BETA_E_Model_T&T.ipynb b/BETA_E_Model_T&T.ipynb index 73b7d4c..6a14532 100644 --- a/BETA_E_Model_T&T.ipynb +++ b/BETA_E_Model_T&T.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:44.939427800Z", @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.128539500Z", @@ -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)" + " tf.config.experimental.set_memory_growth(gpu_instance, True)\n" ] }, { @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.139048Z", @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:48.287855100Z", @@ -209,7 +209,15 @@ "groupValue": "12" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "SAVE_TYPE = 'H5'\n", "Use_mixed_float16 = False\n", @@ -231,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.059139500Z", @@ -241,7 +249,29 @@ "groupValue": "12" } }, - "outputs": [], + "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_d19-h00_m10_s16\u001b[0m\n", + "\u001b[0;32mDone.\u001b[0m\n" + ] + } + ], "source": [ "#Z_SCORE_normalize\n", "def Z_SCORE_normalize(arr):\n", @@ -648,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.380088800Z", @@ -848,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2023-12-27T17:34:12.077394600Z", @@ -858,7 +888,2164 @@ "groupValue": "" } }, - "outputs": [], + "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-L1-1024 (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-L1-1024[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" + ] + } + ], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -884,20 +3071,20 @@ " #GlobalAveragePooling2D\n", " base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n", " #Dense\n", - " Dense_L1 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.02),\n", - " name='FC_C_Dense-L1-512'\n", + " Dense_L1 = Dense(512, activation='swish',\n", + " kernel_regularizer=l2(0.008),\n", + " name='FC_C_Dense-L1-1024'\n", " )(base_model_FT)\n", " #Dropout\n", - " Dropout_L1 = Dropout(0.1,\n", + " Dropout_L1 = Dropout(0.125,\n", " name='FC_C_Dropout-L1-0.1'\n", " )(Dense_L1)\n", " #BatchNormalization\n", " BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1'\n", " )(Dropout_L1)\n", " #Dense\n", - " Dense_L2 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.01),\n", + " Dense_L2 = Dense(512, activation='swish',\n", + " kernel_regularizer=l2(0.004),\n", " name='FC_C_Dense-L2-512'\n", " )(BatchNorm_L2)\n", " #BatchNormalization\n", @@ -952,9 +3139,2471 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "from keras.applications.xception import Xception\n", @@ -1043,9 +5692,4150 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "from keras_efficientnet_v2 import EfficientNetV2XL\n", "\n", @@ -1082,9 +9872,1276 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "from efficientnet.keras import EfficientNetB4 as KENB4\n", "# FUNC\n", @@ -1229,14 +11286,2169 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:32.994176700Z", "start_time": "2023-12-28T02:31:27.381088600Z" } }, - "outputs": [], + "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" + ] + } + ], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -1322,9 +13534,1145 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "from keras.applications import ConvNeXtXLarge\n", "from keras.layers import Lambda\n", @@ -1504,7 +14852,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1584,9 +14932,2162 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "import efficientnet.tfkeras\n", "# Configuration\n", @@ -1665,9 +17166,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "for layer in model.layers[-7:]:\n", " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", @@ -1710,14 +17220,272 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:23.573633300Z", "start_time": "2023-12-28T02:31:32.468641900Z" } }, - "outputs": [], + "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_d20-h14_m28_s32]\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[|6484|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_d20-h15_m16_s13\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", + "406/406 [==============================] - 155s 319ms/step - loss: 8.5439 - accuracy: 0.6982 - val_loss: 6.6307 - val_accuracy: 0.8542\n", + "Epoch 2/6\n", + "406/406 [==============================] - 125s 306ms/step - loss: 4.7330 - accuracy: 0.8637 - val_loss: 3.2099 - val_accuracy: 0.8830\n", + "Epoch 3/6\n", + "406/406 [==============================] - 122s 300ms/step - loss: 2.3208 - accuracy: 0.8934 - val_loss: 1.5924 - val_accuracy: 0.9359\n", + "Epoch 4/6\n", + "406/406 [==============================] - 121s 297ms/step - loss: 1.2655 - accuracy: 0.9172 - val_loss: 1.0271 - val_accuracy: 0.8974\n", + "Epoch 5/6\n", + "406/406 [==============================] - 121s 298ms/step - loss: 0.8062 - accuracy: 0.9397 - val_loss: 0.7362 - val_accuracy: 0.9247\n", + "Epoch 6/6\n", + "406/406 [==============================] - 121s 296ms/step - loss: 0.6470 - accuracy: 0.9542 - val_loss: 0.7094 - 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-003-0.9359.h5...\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;32m1.5924\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 0.935897\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;32m1.5924291611\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;32m3664.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m766.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m2898.04 \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[|6484|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", + "406/406 [==============================] - 132s 306ms/step - loss: 1.5461 - accuracy: 0.8913 - val_loss: 1.2090 - val_accuracy: 0.9407\n", + "Epoch 8/12\n", + "406/406 [==============================] - 121s 297ms/step - loss: 1.0327 - accuracy: 0.8999 - val_loss: 0.7438 - val_accuracy: 0.9343\n", + "Epoch 9/12\n", + "406/406 [==============================] - 123s 301ms/step - loss: 0.6250 - accuracy: 0.9246 - val_loss: 0.4678 - val_accuracy: 0.9423\n", + "Epoch 10/12\n", + "406/406 [==============================] - 121s 297ms/step - loss: 0.4208 - accuracy: 0.9331 - val_loss: 0.4349 - val_accuracy: 0.9071\n", + "Epoch 11/12\n", + "406/406 [==============================] - 122s 299ms/step - loss: 0.3013 - accuracy: 0.9516 - val_loss: 0.3990 - val_accuracy: 0.8974\n", + "Epoch 12/12\n", + "406/406 [==============================] - 122s 299ms/step - loss: 0.2379 - accuracy: 0.9647 - val_loss: 0.3533 - 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-009-0.9423.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;32m0.4678\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.935897 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.942308\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;32m1.5924291611 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.4677759409\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;32m1287.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m741.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m546.01 \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[|6484|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", + "406/406 [==============================] - 134s 310ms/step - loss: 0.5146 - accuracy: 0.9133 - val_loss: 0.4275 - val_accuracy: 0.9359\n", + "Epoch 14/18\n", + "406/406 [==============================] - 122s 300ms/step - loss: 0.4136 - accuracy: 0.9201 - val_loss: 0.3370 - val_accuracy: 0.9327\n", + "Epoch 15/18\n", + "406/406 [==============================] - 124s 304ms/step - loss: 0.2975 - accuracy: 0.9334 - val_loss: 0.2745 - val_accuracy: 0.9439\n", + "Epoch 16/18\n", + "406/406 [==============================] - 123s 301ms/step - loss: 0.2354 - accuracy: 0.9468 - val_loss: 0.2115 - val_accuracy: 0.9423\n", + "Epoch 17/18\n", + "406/406 [==============================] - 122s 300ms/step - loss: 0.1641 - accuracy: 0.9625 - val_loss: 0.3363 - val_accuracy: 0.8462\n", + "Epoch 18/18\n", + "406/406 [==============================] - 128s 314ms/step - loss: 0.1336 - accuracy: 0.9729 - val_loss: 0.1910 - 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;32m0.1910\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.942308 \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;32m0.4677759409 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1910184920\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;32m1352.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m753.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m598.24 \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[|6484|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", + "406/406 [==============================] - 140s 326ms/step - loss: 0.2554 - accuracy: 0.9261 - val_loss: 0.3781 - val_accuracy: 0.9022\n", + "Epoch 20/24\n", + "406/406 [==============================] - 128s 313ms/step - loss: 0.2342 - accuracy: 0.9252 - val_loss: 0.1840 - val_accuracy: 0.9487\n", + "Epoch 21/24\n", + "406/406 [==============================] - 128s 315ms/step - loss: 0.1767 - accuracy: 0.9514 - val_loss: 0.1902 - val_accuracy: 0.9263\n", + "Epoch 22/24\n", + "406/406 [==============================] - 127s 312ms/step - loss: 0.1438 - accuracy: 0.9577 - val_loss: 0.1821 - val_accuracy: 0.9343\n", + "Epoch 23/24\n", + "406/406 [==============================] - 127s 312ms/step - loss: 0.1107 - accuracy: 0.9739 - val_loss: 0.1587 - val_accuracy: 0.9471\n", + "Epoch 24/24\n", + "406/406 [==============================] - 127s 313ms/step - loss: 0.0767 - accuracy: 0.9804 - val_loss: 0.1717 - 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-020-0.9487.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;32m0.1840\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;32m0.1910184920 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1839751452\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;32m1458.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m778.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m679.76 \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[|6484|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", + "406/406 [==============================] - 138s 320ms/step - loss: 0.2431 - accuracy: 0.9254 - val_loss: 0.1714 - val_accuracy: 0.9535\n", + "Epoch 26/30\n", + "406/406 [==============================] - 125s 308ms/step - loss: 0.2219 - accuracy: 0.9294 - val_loss: 0.1770 - val_accuracy: 0.9471\n", + "Epoch 27/30\n", + "406/406 [==============================] - 128s 313ms/step - loss: 0.1867 - accuracy: 0.9380 - val_loss: 0.1929 - val_accuracy: 0.9359\n", + "Epoch 28/30\n", + "406/406 [==============================] - 127s 312ms/step - loss: 0.1503 - accuracy: 0.9579 - val_loss: 0.2001 - val_accuracy: 0.9231\n", + "Epoch 29/30\n", + "406/406 [==============================] - 126s 310ms/step - loss: 0.1034 - accuracy: 0.9692 - val_loss: 0.2080 - val_accuracy: 0.9295\n", + "Epoch 30/30\n", + "406/406 [==============================] - 126s 309ms/step - loss: 0.0675 - accuracy: 0.9816 - val_loss: 0.2281 - val_accuracy: 0.9343\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-025-0.9535.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;32m0.1714\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;33mImproved model loss from \u001b[0m\u001b[0;32m0.1839751452 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1714088619\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;32m1441.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m770.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m670.08 \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[|6484|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", + "406/406 [==============================] - 139s 322ms/step - loss: 0.2094 - accuracy: 0.9308 - val_loss: 0.1686 - val_accuracy: 0.9455\n", + "Epoch 32/36\n", + "406/406 [==============================] - 128s 316ms/step - loss: 0.2059 - accuracy: 0.9358 - val_loss: 0.1710 - val_accuracy: 0.9535\n", + "Epoch 33/36\n", + "406/406 [==============================] - 126s 311ms/step - loss: 0.1653 - accuracy: 0.9517 - val_loss: 0.1398 - val_accuracy: 0.9503\n", + "Epoch 34/36\n", + "406/406 [==============================] - 127s 312ms/step - loss: 0.1331 - accuracy: 0.9605 - val_loss: 0.2802 - val_accuracy: 0.8862\n", + "Epoch 35/36\n", + "406/406 [==============================] - 126s 309ms/step - loss: 0.0965 - accuracy: 0.9710 - val_loss: 0.1547 - val_accuracy: 0.9503\n", + "Epoch 36/36\n", + "406/406 [==============================] - 127s 311ms/step - loss: 0.0665 - accuracy: 0.9812 - val_loss: 0.1512 - 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-036-0.9567.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;32m0.1512\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 0.956731\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;33mImproved model loss from \u001b[0m\u001b[0;32m0.1714088619 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1512367874\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;32m1460.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m774.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m685.67 \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[|6484|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", + "406/406 [==============================] - 135s 314ms/step - loss: 0.1921 - accuracy: 0.9408 - val_loss: 0.1780 - val_accuracy: 0.9519\n", + "Epoch 38/42\n", + "406/406 [==============================] - 123s 303ms/step - loss: 0.1800 - accuracy: 0.9423 - val_loss: 0.1972 - val_accuracy: 0.9455\n", + "Epoch 39/42\n", + "406/406 [==============================] - 124s 305ms/step - loss: 0.1627 - accuracy: 0.9528 - val_loss: 0.1435 - val_accuracy: 0.9503\n", + "Epoch 40/42\n", + "406/406 [==============================] - 124s 305ms/step - loss: 0.1165 - accuracy: 0.9650 - val_loss: 0.2610 - val_accuracy: 0.9054\n", + "Epoch 41/42\n", + "406/406 [==============================] - 124s 305ms/step - loss: 0.0855 - accuracy: 0.9744 - val_loss: 0.2104 - val_accuracy: 0.9375\n", + "Epoch 42/42\n", + "406/406 [==============================] - 131s 322ms/step - loss: 0.0587 - accuracy: 0.9823 - val_loss: 0.1848 - 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-037-0.9519.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;32m0.1780\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 0.1512367874. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m1414.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m763.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m651.08 \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[|6484|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" + ] + } + ], "source": [ "import gc\n", "# Garbage Collection (memory)\n", @@ -1729,10 +17497,10 @@ "subset_epoch = 6 # subset_epoch: Number of epochs to train each subset.\n", "subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs.\n", "PL_epoch = 26 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only. Common values: 8, 16, 26, 32, 64, 128.\n", - "subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192.\n", + "subset_size = 6484 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192.\n", "Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n", "RES_Train = False # RES_Train: Resume training if True.\n", - "MAX_LR = 0.011 # MAX_LR: Maximum learning rate.\n", + "MAX_LR = 0.011 # MAX_LR: Maximum learning rate. Common values: 0.011, 0.01, 0.001.\n", "DEC_LR = 0.00003 # DEC_LR: Learning rate decay.\n", "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n", "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n", @@ -1741,7 +17509,6 @@ "Debug_OUTPUT_DPS_freq = 42 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch).\n", "TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage.\n", "SAVE_FULLM = True # SAVE_FULLM: Save full model if True.\n", - "USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True.\n", "AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True.\n", "AdvSubsetC_SHR = 42 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs).\n", "load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy.\n", @@ -1755,11 +17522,12 @@ "EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️).\n", "Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler.\n", "Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work).\n", - "Use_tensorBoard_img = True # Use_tensorBoard_img: Enable tensorboard image logging.\n", + "Use_tensorBoard_img = False # Use_tensorBoard_img: Enable tensorboard image logging.\n", + "Use_noise_func_TRLRev2 = True # Use_noise_func_TRLRev2: Use noise function for IDG if True.\n", "Show_confusion_matrix_tensorBoard = False # Show_confusion_matrix_tensorBoard: Show confusion matrix on tensorboard.\n", "BEST_RSN = 'PAI_model_T' # Best model save name prefix. (Uses a lot of memory and storage).\n", "ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow).\n", - "IMAGE_GEN_PATH = 'Data\\\\image_SUB_generator.pkl'\n", + "IDG_FitP_PATH = 'Data\\\\image_SUB_generator.pkl'\n", "# CONF END <---------------------------------------------------------------------->\n", "#Prep\n", "if RES_Train:\n", @@ -1799,81 +17567,39 @@ "\n", " return noisy_image\n", "# noise_func_TRLRev2 ([REV1 OLD])\n", - "if not USE_REV2_DP:\n", - " def noise_func_TRLRev2(image): \n", - " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", - " new_image = np.copy(image)\n", - " \n", - " if noise_type == 'L3':\n", - " intensityL2 = random.uniform(-0.08, 0.08)\n", - " intensityL1 = random.uniform(-0.05, 0.05)\n", - " else:\n", - " intensityL2 = random.uniform(-0.09, 0.09)\n", - " intensityL1 = random.uniform(-0.06, 0.06)\n", - " \n", - " block_size_L1 = random.randint(16, 32)\n", - " block_size_L2 = random.randint(32, 112)\n", - " \n", - " if noise_type == 'L2' or noise_type == 'L3':\n", - " for i in range(0, image.shape[0], block_size_L2):\n", - " for j in range(0, image.shape[1], block_size_L2):\n", - " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", - " block = (np.random.rand() * intensityL2 + 1) * block\n", - " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", - " image = new_image \n", - " \n", - " if noise_type == 'L1' or noise_type == 'L3': \n", - " for i in range(0, image.shape[0], block_size_L1):\n", - " for j in range(0, image.shape[1], block_size_L1):\n", - " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", - " block = (np.random.rand() * intensityL1 + 1) * block\n", - " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", - " \n", - " if add_img_grain:\n", - " intensity = random.uniform(0, 0.07) # Random intensity \n", - " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", - " return new_image\n", - "# noise_func_TRLRev2 ([REV2 NEW])\n", - "else:\n", - " def noise_func_TRLRev2(image):\n", - " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", - " new_image = np.copy(image)\n", - " \n", - " if noise_type == 'L3':\n", - " intensityL2 = random.uniform(-0.07, 0.07)\n", - " intensityL1 = random.uniform(-0.06, 0.06)\n", - " else:\n", - " intensityL2 = random.uniform(-0.09, 0.09)\n", - " intensityL1 = random.uniform(-0.07, 0.07)\n", - " \n", - " block_size_L1 = random.randint(16, 32)\n", - " block_size_L2 = random.randint(32, 112)\n", + "def noise_func_TRLRev2(image): \n", + " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", + " new_image = np.copy(image)\n", + " \n", + " if noise_type == 'L3':\n", + " intensityL2 = random.uniform(-0.08, 0.08)\n", + " intensityL1 = random.uniform(-0.05, 0.05)\n", + " else:\n", + " intensityL2 = random.uniform(-0.09, 0.09)\n", + " intensityL1 = random.uniform(-0.06, 0.06)\n", " \n", - " for channel in range(3): # Iterate over each RGB channel\n", - " image_channel = image[:, :, channel]\n", - " new_image_channel = new_image[:, :, channel]\n", - " \n", - " if noise_type == 'L2' or noise_type == 'L3':\n", - " for i in range(0, image_channel.shape[0], block_size_L2):\n", - " for j in range(0, image_channel.shape[1], block_size_L2):\n", - " block = image_channel[i:i+block_size_L2, j:j+block_size_L2]\n", - " block = (np.random.rand() * intensityL2 + 1) * block\n", - " new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block\n", - " image_channel = new_image_channel \n", - " \n", - " if noise_type == 'L1' or noise_type == 'L3': \n", - " for i in range(0, image_channel.shape[0], block_size_L1):\n", - " for j in range(0, image_channel.shape[1], block_size_L1):\n", - " block = image_channel[i:i+block_size_L1, j:j+block_size_L1]\n", - " block = (np.random.rand() * intensityL1 + 1) * block\n", - " new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block\n", - " \n", - " new_image[:, :, channel] = new_image_channel\n", + " block_size_L1 = random.randint(16, 32)\n", + " block_size_L2 = random.randint(32, 112)\n", + " \n", + " if noise_type == 'L2' or noise_type == 'L3':\n", + " for i in range(0, image.shape[0], block_size_L2):\n", + " for j in range(0, image.shape[1], block_size_L2):\n", + " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", + " block = (np.random.rand() * intensityL2 + 1) * block\n", + " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", + " image = new_image \n", " \n", - " if add_img_grain:\n", - " intensity = random.uniform(0, 0.05) # Random intensity \n", - " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", - " return new_image\n", + " if noise_type == 'L1' or noise_type == 'L3': \n", + " for i in range(0, image.shape[0], block_size_L1):\n", + " for j in range(0, image.shape[1], block_size_L1):\n", + " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", + " block = (np.random.rand() * intensityL1 + 1) * block\n", + " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", + " \n", + " if add_img_grain:\n", + " intensity = random.uniform(0, 0.07) # Random intensity \n", + " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", + " return new_image\n", "#CONST\n", "train_SUB_datagen = ImageDataGenerator(\n", " horizontal_flip=True,\n", @@ -1884,13 +17610,12 @@ " width_shift_range=0.18,\n", " brightness_range=(0.82, 1.18),\n", " height_shift_range=0.18,\n", - " channel_shift_range=100,\n", " featurewise_center=True,\n", " featurewise_std_normalization=True,\n", " zca_whitening=False,\n", " interpolation_order=2,\n", " fill_mode='nearest',\n", - " preprocessing_function=noise_func_TRLRev2\n", + " preprocessing_function=noise_func_TRLRev2 if Use_noise_func_TRLRev2 else None\n", " )\n", "class TerminateOnHighTemp(tf.keras.callbacks.Callback):\n", " def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60):\n", @@ -1973,7 +17698,8 @@ "confusion_matrix_callback = LambdaCallback(on_epoch_end=plot_confusion_matrix_TensorBoard) if Show_confusion_matrix_tensorBoard else DummyCallback()\n", "# TensorBoard\n", "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - "file_writer = tf.summary.create_file_writer(log_dir)\n", + "if Show_confusion_matrix_tensorBoard:\n", + " file_writer = tf.summary.create_file_writer(log_dir)\n", "if Use_extended_tensorboard:\n", " tensorboard_callback = ExtendedTensorBoard(\n", " log_dir=log_dir,\n", @@ -2064,14 +17790,14 @@ " # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", " # print_Color('- ImageDataGenerator fit done.', ['yellow'])\n", " if epoch == 1 or ALWAYS_REFIT_IDG == 2:\n", - " if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG:\n", + " if os.path.exists(IDG_FitP_PATH) and not ALWAYS_REFIT_IDG:\n", " print_Color('- Loading fitted ImageDataGenerator...', ['yellow'])\n", - " train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) \n", + " train_SUB_datagen = pickle.load(open(IDG_FitP_PATH, 'rb')) \n", " else:\n", " print_Color('- Fitting ImageDataGenerator...', ['yellow'])\n", " IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12\n", " train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", - " pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb'))\n", + " pickle.dump(train_SUB_datagen, open(IDG_FitP_PATH, 'wb'))\n", " print_Color('- ImageDataGenerator fit done.', ['yellow']) \n", "\n", " print_Color('- Augmenting Image Data...', ['yellow']) \n", @@ -2166,7 +17892,7 @@ " print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True)\n", " # If the accuracy is higher than the best_acc\n", " if acc > best_acc:\n", - " print_Color_V2(f'Improved model accuracy from{best_acc:10f} to {acc:10f}. Saving model.')\n", + " print_Color_V2(f'Improved model accuracy from {best_acc:10f} to {acc:10f}. Saving model.')\n", " # Update the best_acc\n", " best_acc = acc\n", " if SAVE_FULLM:\n", @@ -2432,7 +18158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -2451,7 +18177,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -2471,14 +18197,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:52.565658900Z", "start_time": "2023-12-28T07:04:51.032425100Z" } }, - "outputs": [], + "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" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", @@ -2692,13 +18479,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "notebookRunGroups": { "groupValue": "" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 1s 1s/step\n", + "20/20 [==============================] - 1s 41ms/step\n", + "The accuracy of the model on validation data is 100.00%(100.00000%)\n", + "The accuracy of the model on test data is 96.31%(96.31410%)\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 33ms/step\n", + "1/1 [==============================] - 0s 21ms/step\n", + "1/1 [==============================] - 0s 20ms/step\n", + "1/1 [==============================] - 0s 17ms/step\n", + "1/1 [==============================] - 0s 24ms/step\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_24044\\1259512504.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6\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 69\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\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 70\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m 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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 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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 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_internal_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\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\\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 300\u001b[0m outputs = conv_utils.squeeze_batch_dims(\n\u001b[0;32m 301\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_apply_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minner_rank\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrank\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 302\u001b[0m )\n\u001b[0;32m 303\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--> 304\u001b[1;33m outputs = tf.nn.bias_add(\n\u001b[0m\u001b[0;32m 305\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tf_data_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 306\u001b[0m )\n\u001b[0;32m 307\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(value, bias, data_format, name)\u001b[0m\n\u001b[0;32m 3550\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m(\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 3551\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"input\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3552\u001b[0m \u001b[0mbias\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"bias\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3554\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mgen_nn_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\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[0m", + "\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(value, bias, data_format, name)\u001b[0m\n\u001b[0;32m 850\u001b[0m _ctx, \"BiasAdd\", name, value, bias, \"data_format\", data_format)\n\u001b[0;32m 851\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 852\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 853\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--> 854\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 855\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 856\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 857\u001b[0m return bias_add_eager_fallback(\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import seaborn as sns\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n", @@ -2710,7 +18567,7 @@ "# Garbage Collection (memory)\n", "gc.collect()\n", "\n", - "Extra_EXT = '_T' # _T or _T_BL\n", + "Extra_EXT = '_T_BL' # _T or _T_BL\n", "Train_data_test = False\n", "if SAVE_TYPE == 'TF':\n", " # Load the pre-trained model\n", @@ -2771,8 +18628,8 @@ "for i in range(10):\n", " plt.subplot(2, 5, i+1)\n", " img = x_val[i]\n", - " heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 1) \n", - " heatmap = cv2.resize(np.clip(heatmap, 0, 1), (img.shape[1], img.shape[0]))\n", + " heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 2) \n", + " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", " heatmap = np.uint8(255 * heatmap)\n", " # Apply Adaptive Histogram Equalization\n", " clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(8,8)) # Create CLAHE object\n", @@ -2854,7 +18711,51 @@ "plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3))\n", "\n", "plt.title('Number of Incorrect Predictions vs. Number of Data Points')\n", - "plt.show()" + "plt.show()\n", + "# Deprecated⚠️------------------------------>>>\n", + "# prob_L = 0.9995\n", + "# # Define the range of test data sizes to use\n", + "# data_sizes = range(1, len(x_test), 1) \n", + "\n", + "# # Calculate the probability of a wrong prediction based on test accuracy\n", + "# prob_wrong = 1 - test_accuracy\n", + "\n", + "# # Create a list to store the probability of getting at least one wrong answer for each test data size\n", + "# probabilities = []\n", + "\n", + "# # Calculate the probability of getting at least one wrong answer for each data size\n", + "# for size in data_sizes:\n", + "# # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0\n", + "# cdf = binom.cdf(0, size, prob_wrong)\n", + "# # Subtract the CDF from 1 to get the probability of getting at least one wrong answer\n", + "# prob = 1 - cdf\n", + "# probabilities.append(prob)\n", + "\n", + "# # Find the index of the first data point that has a probability greater than prob_L%\n", + "# index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities))\n", + "\n", + "# # Limit the x-axis to the first data point that has a probability greater than prob_L%\n", + "# data_sizes = data_sizes[:index+1]\n", + "# probabilities = probabilities[:index+1]\n", + "\n", + "# # Plot the probability vs. the number of data points\n", + "# plt.figure(figsize=(10, 6))\n", + "# plt.plot(data_sizes, probabilities)\n", + "# plt.xlabel('Number of Data Points')\n", + "# plt.ylabel('Probability')\n", + "\n", + "# # Add gridlines for the x and y axes\n", + "# plt.grid(True)\n", + "\n", + "# # Change the tick spacing for the x and y axes\n", + "# plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 5 + 10))\n", + "# plt.yticks(np.arange(0, max(probabilities)+0.1, 5 / 100))\n", + "\n", + "# plt.ylim(top=1.01)\n", + "\n", + "# plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points')\n", + "# plt.show()\n", + "# Deprecated⚠️------------------------------<<<" ] } ], diff --git a/Data/image_SUB_generator.pkl b/Data/image_SUB_generator.pkl index 1002b70..9fdb163 100644 Binary files a/Data/image_SUB_generator.pkl and b/Data/image_SUB_generator.pkl differ diff --git a/Interface/CLI/CLI.cmd b/Interface/CLI/CLI.cmd index 70aa4e7..53c6948 100644 --- a/Interface/CLI/CLI.cmd +++ b/Interface/CLI/CLI.cmd @@ -1,5 +1,7 @@ @echo off -TITLE Pneumonia AI CLI +REM Conf: +TITLE Pneumonia-Detection-Ai-CLI +set python_min_VER=10 set DEBUG=0 set arg=%1 set PV_filepath="Data\\Python Ver.tmp" @@ -31,10 +33,10 @@ if "%file_python_version%"=="%current_python_version% " ( :PASS_PVF_CHECK REM Write the current Python version to the file echo Checking Python version... -REM Ensure Python version is 3.9 or higher +REM Ensure Python version is %python_min_VER% or higher FOR /F "tokens=2 delims=." %%i IN ('python --version 2^>^&1') DO set python_version_major=%%i -if %python_version_major% LSS 9 ( - echo Warning: Please update your Python version to 3.9.x or higher! +if %python_version_major% LSS %python_min_VER% ( + echo Warning: Please update your Python version to 3.%python_min_VER%.x or higher! pause exit /B ) diff --git a/Model_T&T.ipynb b/Model_T&T.ipynb index 73b7d4c..80e3cdd 100644 --- a/Model_T&T.ipynb +++ b/Model_T&T.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:44.939427800Z", @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.128539500Z", @@ -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)" + " tf.config.experimental.set_memory_growth(gpu_instance, True)\n" ] }, { @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.139048Z", @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:48.287855100Z", @@ -209,7 +209,15 @@ "groupValue": "12" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "SAVE_TYPE = 'H5'\n", "Use_mixed_float16 = False\n", @@ -231,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.059139500Z", @@ -241,7 +249,29 @@ "groupValue": "12" } }, - "outputs": [], + "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_d15-h21_m53_s32\u001b[0m\n", + "\u001b[0;32mDone.\u001b[0m\n" + ] + } + ], "source": [ "#Z_SCORE_normalize\n", "def Z_SCORE_normalize(arr):\n", @@ -648,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.380088800Z", @@ -848,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2023-12-27T17:34:12.077394600Z", @@ -858,7 +888,2164 @@ "groupValue": "" } }, - "outputs": [], + "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" + ] + } + ], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -885,7 +3072,7 @@ " base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n", " #Dense\n", " Dense_L1 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.02),\n", + " kernel_regularizer=l2(0.008),\n", " name='FC_C_Dense-L1-512'\n", " )(base_model_FT)\n", " #Dropout\n", @@ -952,9 +3139,2471 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "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 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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 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(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" + ] + } + ], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "from keras.applications.xception import Xception\n", @@ -1043,9 +5692,4150 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "from keras_efficientnet_v2 import EfficientNetV2XL\n", "\n", @@ -1082,9 +9872,1276 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "from efficientnet.keras import EfficientNetB4 as KENB4\n", "# FUNC\n", @@ -1229,14 +11286,2169 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:32.994176700Z", "start_time": "2023-12-28T02:31:27.381088600Z" } }, - "outputs": [], + "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" + ] + } + ], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -1322,9 +13534,1145 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "from keras.applications import ConvNeXtXLarge\n", "from keras.layers import Lambda\n", @@ -1504,7 +14852,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1584,9 +14932,2162 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "import efficientnet.tfkeras\n", "# Configuration\n", @@ -1665,9 +17166,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "for layer in model.layers[-7:]:\n", " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", @@ -1710,14 +17220,3465 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:23.573633300Z", "start_time": "2023-12-28T02:31:32.468641900Z" } }, - "outputs": [], + "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_d15-h21_m53_s42]\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[|4596|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_d15-h22_m00_s18\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", + "288/288 [==============================] - 120s 349ms/step - loss: 11.5636 - accuracy: 0.6114 - val_loss: 9.2523 - val_accuracy: 0.8365\n", + "Epoch 2/6\n", + "288/288 [==============================] - 93s 322ms/step - loss: 6.8375 - accuracy: 0.7918 - val_loss: 4.6129 - val_accuracy: 0.8942\n", + "Epoch 3/6\n", + "288/288 [==============================] - 98s 339ms/step - loss: 3.4710 - accuracy: 0.8512 - val_loss: 2.3309 - val_accuracy: 0.9135\n", + "Epoch 4/6\n", + "288/288 [==============================] - 97s 337ms/step - loss: 1.8711 - accuracy: 0.8866 - val_loss: 1.3722 - val_accuracy: 0.9167\n", + "Epoch 5/6\n", + "288/288 [==============================] - 97s 337ms/step - loss: 1.1842 - accuracy: 0.9058 - val_loss: 0.9771 - val_accuracy: 0.9343\n", + "Epoch 6/6\n", + "288/288 [==============================] - 92s 320ms/step - loss: 0.9313 - accuracy: 0.9269 - val_loss: 0.9143 - 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-005-0.9343.h5...\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;32m0.9771\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 0.934295\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;32m0.9771378636\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;32m1013.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m598.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m414.55 \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[|4596|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", + "288/288 [==============================] - 98s 325ms/step - loss: 1.0342 - accuracy: 0.8801 - val_loss: 0.7943 - val_accuracy: 0.9263\n", + "Epoch 8/12\n", + "288/288 [==============================] - 92s 320ms/step - loss: 0.7540 - accuracy: 0.8803 - val_loss: 0.5783 - val_accuracy: 0.9167\n", + "Epoch 9/12\n", + "288/288 [==============================] - 93s 322ms/step - loss: 0.5021 - accuracy: 0.8938 - val_loss: 0.3369 - val_accuracy: 0.9407\n", + "Epoch 10/12\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.3636 - accuracy: 0.9121 - val_loss: 0.3181 - val_accuracy: 0.9391\n", + "Epoch 11/12\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.2734 - accuracy: 0.9321 - val_loss: 0.2641 - val_accuracy: 0.9359\n", + "Epoch 12/12\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.2000 - accuracy: 0.9517 - val_loss: 0.2481 - val_accuracy: 0.9391\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-009-0.9407.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;32m0.3369\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from\u001b[0m\u001b[0;32m 0.934295 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.940705\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;32m0.9771378636 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.3368934095\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;32m647.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m576.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.95 \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[|4596|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", + "288/288 [==============================] - 104s 343ms/step - loss: 0.4614 - accuracy: 0.8760 - val_loss: 0.3371 - val_accuracy: 0.9311\n", + "Epoch 14/18\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.4014 - accuracy: 0.8899 - val_loss: 0.3036 - val_accuracy: 0.9327\n", + "Epoch 15/18\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.3298 - accuracy: 0.8984 - val_loss: 0.2573 - val_accuracy: 0.9423\n", + "Epoch 16/18\n", + "288/288 [==============================] - 94s 325ms/step - loss: 0.2729 - accuracy: 0.9143 - val_loss: 0.2386 - val_accuracy: 0.9247\n", + "Epoch 17/18\n", + "288/288 [==============================] - 94s 324ms/step - loss: 0.2323 - accuracy: 0.9286 - val_loss: 0.3200 - val_accuracy: 0.9071\n", + "Epoch 18/18\n", + "288/288 [==============================] - 94s 325ms/step - loss: 0.1842 - accuracy: 0.9432 - val_loss: 0.1969 - val_accuracy: 0.9391\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-015-0.9423.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;32m0.2573\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from\u001b[0m\u001b[0;32m 0.940705 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.942308\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;32m0.3368934095 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2572792768\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;32m662.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m575.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.54 \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[|4596|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", + "288/288 [==============================] - 101s 332ms/step - loss: 0.3172 - accuracy: 0.8943 - val_loss: 0.2270 - val_accuracy: 0.9295\n", + "Epoch 20/24\n", + "288/288 [==============================] - 95s 330ms/step - loss: 0.2842 - accuracy: 0.9064 - val_loss: 0.2267 - val_accuracy: 0.9359\n", + "Epoch 21/24\n", + "288/288 [==============================] - 95s 328ms/step - loss: 0.2669 - accuracy: 0.9073 - val_loss: 0.2254 - val_accuracy: 0.9151\n", + "Epoch 22/24\n", + "288/288 [==============================] - 94s 325ms/step - loss: 0.2253 - accuracy: 0.9249 - val_loss: 0.2218 - val_accuracy: 0.9247\n", + "Epoch 23/24\n", + "288/288 [==============================] - 94s 326ms/step - loss: 0.1740 - accuracy: 0.9460 - val_loss: 0.2097 - val_accuracy: 0.9263\n", + "Epoch 24/24\n", + "288/288 [==============================] - 95s 328ms/step - loss: 0.1350 - accuracy: 0.9576 - val_loss: 0.2473 - 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-020-0.9359.h5...\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;32m0.2267\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9423077106. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2572792768 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2267218679\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;32m657.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m574.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.90 \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[|4596|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", + "288/288 [==============================] - 101s 334ms/step - loss: 0.2960 - accuracy: 0.9003 - val_loss: 0.2211 - val_accuracy: 0.9215\n", + "Epoch 26/30\n", + "288/288 [==============================] - 94s 326ms/step - loss: 0.2815 - accuracy: 0.9030 - val_loss: 0.3456 - val_accuracy: 0.8574\n", + "Epoch 27/30\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.2459 - accuracy: 0.9178 - val_loss: 0.2066 - val_accuracy: 0.9375\n", + "Epoch 28/30\n", + "288/288 [==============================] - 94s 326ms/step - loss: 0.2141 - accuracy: 0.9315 - val_loss: 0.2541 - val_accuracy: 0.9343\n", + "Epoch 29/30\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.1563 - accuracy: 0.9526 - val_loss: 0.1832 - val_accuracy: 0.9455\n", + "Epoch 30/30\n", + "288/288 [==============================] - 94s 325ms/step - loss: 0.1270 - accuracy: 0.9608 - val_loss: 0.1752 - val_accuracy: 0.9391\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-029-0.9455.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;32m0.1831\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from\u001b[0m\u001b[0;32m 0.942308 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.945513\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;32m0.2267218679 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1831418574\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;32m661.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m573.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.13 \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[|4596|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", + "288/288 [==============================] - 101s 333ms/step - loss: 0.2635 - accuracy: 0.9175 - val_loss: 0.1762 - val_accuracy: 0.9503\n", + "Epoch 32/36\n", + "288/288 [==============================] - 94s 325ms/step - loss: 0.2584 - accuracy: 0.9199 - val_loss: 0.2277 - val_accuracy: 0.9327\n", + "Epoch 33/36\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.2243 - accuracy: 0.9269 - val_loss: 0.1834 - val_accuracy: 0.9359\n", + "Epoch 34/36\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.1887 - accuracy: 0.9389 - val_loss: 0.2056 - val_accuracy: 0.9359\n", + "Epoch 35/36\n", + "288/288 [==============================] - 94s 326ms/step - loss: 0.1490 - accuracy: 0.9558 - val_loss: 0.2038 - val_accuracy: 0.9471\n", + "Epoch 36/36\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.1193 - accuracy: 0.9652 - val_loss: 0.1702 - 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-036-0.9519.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;32m0.1702\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from\u001b[0m\u001b[0;32m 0.945513 \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;32m0.1831418574 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1702173203\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;32m658.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m573.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.35 \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[|4596|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", + "288/288 [==============================] - 101s 334ms/step - loss: 0.2550 - accuracy: 0.9195 - val_loss: 0.2197 - val_accuracy: 0.8974\n", + "Epoch 38/42\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.2435 - accuracy: 0.9171 - val_loss: 0.2703 - val_accuracy: 0.8894\n", + "Epoch 39/42\n", + "288/288 [==============================] - 94s 328ms/step - loss: 0.2224 - accuracy: 0.9275 - val_loss: 0.3241 - val_accuracy: 0.8734\n", + "Epoch 40/42\n", + "288/288 [==============================] - 95s 328ms/step - loss: 0.1861 - accuracy: 0.9441 - val_loss: 0.1509 - val_accuracy: 0.9519\n", + "Epoch 41/42\n", + "288/288 [==============================] - 95s 328ms/step - loss: 0.1238 - accuracy: 0.9634 - val_loss: 0.1683 - val_accuracy: 0.9471\n", + "Epoch 42/42\n", + "288/288 [==============================] - 94s 325ms/step - loss: 0.0972 - accuracy: 0.9728 - val_loss: 0.1674 - 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-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;32m0.1510\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;32m0.1702173203 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1509560496\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;32m658.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m574.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.36 \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[|4596|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", + "288/288 [==============================] - 101s 334ms/step - loss: 0.2446 - accuracy: 0.9210 - val_loss: 0.2773 - val_accuracy: 0.9407\n", + "Epoch 44/48\n", + "288/288 [==============================] - 95s 328ms/step - loss: 0.2545 - accuracy: 0.9210 - val_loss: 0.1795 - val_accuracy: 0.9551\n", + "Epoch 45/48\n", + "288/288 [==============================] - 94s 324ms/step - loss: 0.2069 - accuracy: 0.9376 - val_loss: 0.4327 - val_accuracy: 0.9231\n", + "Epoch 46/48\n", + "288/288 [==============================] - 94s 324ms/step - loss: 0.1748 - accuracy: 0.9465 - val_loss: 0.3130 - val_accuracy: 0.9151\n", + "Epoch 47/48\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.1141 - accuracy: 0.9663 - val_loss: 0.1925 - val_accuracy: 0.9407\n", + "Epoch 48/48\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0994 - accuracy: 0.9730 - val_loss: 0.2087 - 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-044-0.9551.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;32m0.1795\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 0.955128\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 0.1509560496. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m663.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m577.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.54 \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[|4596|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", + "288/288 [==============================] - 104s 344ms/step - loss: 0.2451 - accuracy: 0.9212 - val_loss: 0.1571 - val_accuracy: 0.9487\n", + "Epoch 50/54\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.2397 - accuracy: 0.9217 - val_loss: 0.1886 - val_accuracy: 0.9391\n", + "Epoch 51/54\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.2060 - accuracy: 0.9397 - val_loss: 0.2339 - val_accuracy: 0.9423\n", + "Epoch 52/54\n", + "288/288 [==============================] - 97s 338ms/step - loss: 0.1575 - accuracy: 0.9569 - val_loss: 0.2164 - val_accuracy: 0.9295\n", + "Epoch 53/54\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.1335 - accuracy: 0.9615 - val_loss: 0.2269 - val_accuracy: 0.9423\n", + "Epoch 54/54\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0918 - accuracy: 0.9769 - val_loss: 0.2031 - 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-054-0.9535.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;32m0.2031\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 0.1509560496. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m680.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m591.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.98 \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[|4596|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", + "288/288 [==============================] - 105s 346ms/step - loss: 0.2198 - accuracy: 0.9278 - val_loss: 0.2083 - val_accuracy: 0.9519\n", + "Epoch 56/60\n", + "288/288 [==============================] - 97s 338ms/step - loss: 0.2095 - accuracy: 0.9315 - val_loss: 0.1658 - val_accuracy: 0.9519\n", + "Epoch 57/60\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.1898 - accuracy: 0.9349 - val_loss: 0.1539 - val_accuracy: 0.9583\n", + "Epoch 58/60\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.1493 - accuracy: 0.9554 - val_loss: 0.1619 - val_accuracy: 0.9519\n", + "Epoch 59/60\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1152 - accuracy: 0.9676 - val_loss: 0.1760 - val_accuracy: 0.9567\n", + "Epoch 60/60\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0703 - accuracy: 0.9822 - val_loss: 0.1830 - 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-057-0.9583.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;32m0.1539\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 0.958333\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 0.1509560496. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m687.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m592.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.31 \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[|4596|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", + "288/288 [==============================] - 104s 345ms/step - loss: 0.2175 - accuracy: 0.9297 - val_loss: 0.1571 - val_accuracy: 0.9455\n", + "Epoch 62/66\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.2126 - accuracy: 0.9293 - val_loss: 0.2065 - val_accuracy: 0.9119\n", + "Epoch 63/66\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.1824 - accuracy: 0.9454 - val_loss: 0.1446 - val_accuracy: 0.9599\n", + "Epoch 64/66\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1595 - accuracy: 0.9508 - val_loss: 0.1584 - val_accuracy: 0.9567\n", + "Epoch 65/66\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1198 - accuracy: 0.9678 - val_loss: 0.1865 - val_accuracy: 0.9487\n", + "Epoch 66/66\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.0905 - accuracy: 0.9778 - val_loss: 0.2153 - 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-063-0.9599.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;32m0.1446\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;33mImproved model loss from \u001b[0m\u001b[0;32m0.1509560496 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1446101964\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;32m696.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m594.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.39 \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[|4596|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", + "288/288 [==============================] - 104s 342ms/step - loss: 0.2365 - accuracy: 0.9273 - val_loss: 0.1459 - val_accuracy: 0.9487\n", + "Epoch 68/72\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.2126 - accuracy: 0.9332 - val_loss: 0.1455 - val_accuracy: 0.9551\n", + "Epoch 69/72\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1938 - accuracy: 0.9469 - val_loss: 0.1642 - val_accuracy: 0.9359\n", + "Epoch 70/72\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.3996 - accuracy: 0.8786 - val_loss: 0.2384 - val_accuracy: 0.9327\n", + "Epoch 71/72\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.2543 - accuracy: 0.9278 - val_loss: 0.2216 - val_accuracy: 0.9263\n", + "Epoch 72/72\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1870 - accuracy: 0.9500 - val_loss: 0.2036 - val_accuracy: 0.9391\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-068-0.9551.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;32m0.1455\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m689.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m592.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.16 \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[|4596|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", + "288/288 [==============================] - 105s 346ms/step - loss: 0.2211 - accuracy: 0.9312 - val_loss: 0.1757 - val_accuracy: 0.9439\n", + "Epoch 74/78\n", + "288/288 [==============================] - 99s 345ms/step - loss: 0.2233 - accuracy: 0.9289 - val_loss: 0.1800 - val_accuracy: 0.9487\n", + "Epoch 75/78\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1785 - accuracy: 0.9404 - val_loss: 0.2395 - val_accuracy: 0.9503\n", + "Epoch 76/78\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.1481 - accuracy: 0.9571 - val_loss: 0.2408 - val_accuracy: 0.9391\n", + "Epoch 77/78\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.1185 - accuracy: 0.9650 - val_loss: 0.2028 - val_accuracy: 0.9503\n", + "Epoch 78/78\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0770 - accuracy: 0.9780 - val_loss: 0.2357 - val_accuracy: 0.9455\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-075-0.9503.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;32m0.2395\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m697.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m596.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.28 \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[|4596|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", + "288/288 [==============================] - 105s 347ms/step - loss: 0.2077 - accuracy: 0.9352 - val_loss: 0.1866 - val_accuracy: 0.9327\n", + "Epoch 80/84\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.1976 - accuracy: 0.9354 - val_loss: 0.1795 - val_accuracy: 0.9375\n", + "Epoch 81/84\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1725 - accuracy: 0.9513 - val_loss: 0.3393 - val_accuracy: 0.8478\n", + "Epoch 82/84\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1319 - accuracy: 0.9628 - val_loss: 0.1916 - val_accuracy: 0.9423\n", + "Epoch 83/84\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.1021 - accuracy: 0.9721 - val_loss: 0.1797 - val_accuracy: 0.9487\n", + "Epoch 84/84\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0731 - accuracy: 0.9822 - val_loss: 0.1781 - 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-084-0.9503.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;32m0.1781\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m700.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m598.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.16 \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[|4596|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", + "288/288 [==============================] - 105s 346ms/step - loss: 0.2063 - accuracy: 0.9360 - val_loss: 0.1959 - val_accuracy: 0.9359\n", + "Epoch 86/90\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1935 - accuracy: 0.9410 - val_loss: 0.1570 - val_accuracy: 0.9487\n", + "Epoch 87/90\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1638 - accuracy: 0.9502 - val_loss: 0.1688 - val_accuracy: 0.9455\n", + "Epoch 88/90\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.1189 - accuracy: 0.9669 - val_loss: 0.1613 - val_accuracy: 0.9535\n", + "Epoch 89/90\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0945 - accuracy: 0.9730 - val_loss: 0.1582 - val_accuracy: 0.9535\n", + "Epoch 90/90\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0730 - accuracy: 0.9822 - val_loss: 0.1687 - 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-088-0.9535.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;32m0.1613\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m698.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m596.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.83 \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[|4596|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", + "288/288 [==============================] - 104s 344ms/step - loss: 0.2121 - accuracy: 0.9302 - val_loss: 0.1881 - val_accuracy: 0.9359\n", + "Epoch 92/96\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.1831 - accuracy: 0.9428 - val_loss: 0.2082 - val_accuracy: 0.9215\n", + "Epoch 93/96\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.1628 - accuracy: 0.9519 - val_loss: 0.1685 - val_accuracy: 0.9375\n", + "Epoch 94/96\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1243 - accuracy: 0.9595 - val_loss: 0.1638 - val_accuracy: 0.9439\n", + "Epoch 95/96\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0882 - accuracy: 0.9739 - val_loss: 0.1516 - val_accuracy: 0.9535\n", + "Epoch 96/96\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0677 - accuracy: 0.9832 - val_loss: 0.1554 - 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-095-0.9535.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;32m0.1516\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m701.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m595.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.14 \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[|4596|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", + "288/288 [==============================] - 102s 336ms/step - loss: 0.2153 - accuracy: 0.9291 - val_loss: 0.1542 - val_accuracy: 0.9375\n", + "Epoch 98/102\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.2044 - accuracy: 0.9373 - val_loss: 0.1700 - val_accuracy: 0.9375\n", + "Epoch 99/102\n", + "288/288 [==============================] - 95s 330ms/step - loss: 0.1662 - accuracy: 0.9491 - val_loss: 0.1823 - val_accuracy: 0.9391\n", + "Epoch 100/102\n", + "288/288 [==============================] - 95s 331ms/step - loss: 0.1328 - accuracy: 0.9593 - val_loss: 0.1750 - val_accuracy: 0.9455\n", + "Epoch 101/102\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.0893 - accuracy: 0.9761 - val_loss: 0.2107 - val_accuracy: 0.9279\n", + "Epoch 102/102\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.0757 - accuracy: 0.9813 - val_loss: 0.1918 - val_accuracy: 0.9391\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-100-0.9455.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;32m0.1750\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m683.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m577.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.50 \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[|4596|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", + "288/288 [==============================] - 102s 338ms/step - loss: 0.2090 - accuracy: 0.9323 - val_loss: 0.1679 - val_accuracy: 0.9439\n", + "Epoch 104/108\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.1911 - accuracy: 0.9406 - val_loss: 0.1756 - val_accuracy: 0.9423\n", + "Epoch 105/108\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.1557 - accuracy: 0.9500 - val_loss: 0.2690 - val_accuracy: 0.9439\n", + "Epoch 106/108\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.1204 - accuracy: 0.9634 - val_loss: 0.1712 - val_accuracy: 0.9423\n", + "Epoch 107/108\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0802 - accuracy: 0.9765 - val_loss: 0.2136 - val_accuracy: 0.9407\n", + "Epoch 108/108\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0703 - accuracy: 0.9822 - val_loss: 0.1706 - 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-108-0.9487.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;32m0.1706\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m683.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m579.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.06 \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[|4596|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", + "288/288 [==============================] - 101s 334ms/step - loss: 0.1965 - accuracy: 0.9432 - val_loss: 0.1714 - val_accuracy: 0.9407\n", + "Epoch 110/114\n", + "288/288 [==============================] - 94s 327ms/step - loss: 0.1776 - accuracy: 0.9495 - val_loss: 0.2489 - val_accuracy: 0.9263\n", + "Epoch 111/114\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.1546 - accuracy: 0.9584 - val_loss: 0.1706 - val_accuracy: 0.9407\n", + "Epoch 112/114\n", + "288/288 [==============================] - 95s 328ms/step - loss: 0.1197 - accuracy: 0.9678 - val_loss: 0.1792 - val_accuracy: 0.9343\n", + "Epoch 113/114\n", + "288/288 [==============================] - 96s 331ms/step - loss: 0.0834 - accuracy: 0.9774 - val_loss: 0.1549 - val_accuracy: 0.9439\n", + "Epoch 114/114\n", + "288/288 [==============================] - 95s 327ms/step - loss: 0.0632 - accuracy: 0.9859 - val_loss: 0.1806 - val_accuracy: 0.9439\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-113-0.9439.h5...\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;32m0.1549\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m684.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m576.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.51 \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[|4596|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", + "288/288 [==============================] - 102s 337ms/step - loss: 0.1957 - accuracy: 0.9349 - val_loss: 0.2359 - val_accuracy: 0.8990\n", + "Epoch 116/120\n", + "288/288 [==============================] - 95s 331ms/step - loss: 0.1848 - accuracy: 0.9436 - val_loss: 0.1649 - val_accuracy: 0.9327\n", + "Epoch 117/120\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.1550 - accuracy: 0.9547 - val_loss: 0.1497 - val_accuracy: 0.9471\n", + "Epoch 118/120\n", + "288/288 [==============================] - 95s 331ms/step - loss: 0.1104 - accuracy: 0.9730 - val_loss: 0.1626 - val_accuracy: 0.9503\n", + "Epoch 119/120\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.0937 - accuracy: 0.9756 - val_loss: 0.1732 - val_accuracy: 0.9455\n", + "Epoch 120/120\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.0584 - accuracy: 0.9876 - val_loss: 0.2074 - 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-118-0.9503.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;32m0.1626\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m689.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m580.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.20 \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[|4596|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", + "288/288 [==============================] - 102s 337ms/step - loss: 0.2000 - accuracy: 0.9362 - val_loss: 0.1995 - val_accuracy: 0.9247\n", + "Epoch 122/126\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1798 - accuracy: 0.9484 - val_loss: 0.1677 - val_accuracy: 0.9423\n", + "Epoch 123/126\n", + "288/288 [==============================] - 96s 331ms/step - loss: 0.1550 - accuracy: 0.9574 - val_loss: 0.1896 - val_accuracy: 0.9487\n", + "Epoch 124/126\n", + "288/288 [==============================] - 95s 330ms/step - loss: 0.1211 - accuracy: 0.9680 - val_loss: 0.2190 - val_accuracy: 0.9199\n", + "Epoch 125/126\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.0747 - accuracy: 0.9832 - val_loss: 0.1991 - val_accuracy: 0.9471\n", + "Epoch 126/126\n", + "288/288 [==============================] - 95s 329ms/step - loss: 0.0635 - accuracy: 0.9856 - val_loss: 0.2082 - val_accuracy: 0.9439\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-123-0.9487.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;32m0.1896\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m691.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m579.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.94 \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[|4596|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", + "288/288 [==============================] - 102s 337ms/step - loss: 0.2160 - accuracy: 0.9332 - val_loss: 0.2025 - val_accuracy: 0.9535\n", + "Epoch 128/132\n", + "288/288 [==============================] - 95s 330ms/step - loss: 0.1841 - accuracy: 0.9441 - val_loss: 0.2332 - val_accuracy: 0.9087\n", + "Epoch 129/132\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1726 - accuracy: 0.9513 - val_loss: 0.1685 - val_accuracy: 0.9439\n", + "Epoch 130/132\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1336 - accuracy: 0.9652 - val_loss: 0.1603 - val_accuracy: 0.9439\n", + "Epoch 131/132\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0838 - accuracy: 0.9787 - val_loss: 0.1779 - val_accuracy: 0.9439\n", + "Epoch 132/132\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0628 - accuracy: 0.9876 - val_loss: 0.2066 - 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-127-0.9535.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;32m0.2025\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m704.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m592.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.76 \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[|4596|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", + "288/288 [==============================] - 106s 351ms/step - loss: 0.1934 - accuracy: 0.9330 - val_loss: 0.1736 - val_accuracy: 0.9391\n", + "Epoch 134/138\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1890 - accuracy: 0.9439 - val_loss: 0.2156 - val_accuracy: 0.9391\n", + "Epoch 135/138\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1486 - accuracy: 0.9558 - val_loss: 0.1649 - val_accuracy: 0.9391\n", + "Epoch 136/138\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1059 - accuracy: 0.9693 - val_loss: 0.1875 - val_accuracy: 0.9487\n", + "Epoch 137/138\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0877 - accuracy: 0.9778 - val_loss: 0.2154 - val_accuracy: 0.9455\n", + "Epoch 138/138\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0564 - accuracy: 0.9874 - val_loss: 0.2141 - 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-136-0.9487.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;32m0.1875\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m727.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m603.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.74 \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[|4596|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", + "288/288 [==============================] - 106s 350ms/step - loss: 0.1947 - accuracy: 0.9434 - val_loss: 0.1870 - val_accuracy: 0.9343\n", + "Epoch 140/144\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.1808 - accuracy: 0.9450 - val_loss: 0.1864 - val_accuracy: 0.9407\n", + "Epoch 141/144\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1555 - accuracy: 0.9552 - val_loss: 0.2194 - val_accuracy: 0.9439\n", + "Epoch 142/144\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.1289 - accuracy: 0.9645 - val_loss: 0.1612 - val_accuracy: 0.9391\n", + "Epoch 143/144\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0762 - accuracy: 0.9811 - val_loss: 0.2153 - val_accuracy: 0.9327\n", + "Epoch 144/144\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0552 - accuracy: 0.9876 - val_loss: 0.2392 - val_accuracy: 0.9343\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-141-0.9439.h5...\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;32m0.2194\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m729.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m604.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.67 \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[|4596|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", + "288/288 [==============================] - 107s 352ms/step - loss: 0.2043 - accuracy: 0.9382 - val_loss: 0.1680 - val_accuracy: 0.9455\n", + "Epoch 146/150\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.1771 - accuracy: 0.9443 - val_loss: 0.1692 - val_accuracy: 0.9471\n", + "Epoch 147/150\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1332 - accuracy: 0.9606 - val_loss: 0.1773 - val_accuracy: 0.9407\n", + "Epoch 148/150\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1105 - accuracy: 0.9691 - val_loss: 0.1836 - val_accuracy: 0.9471\n", + "Epoch 149/150\n", + "288/288 [==============================] - 100s 345ms/step - loss: 0.0718 - accuracy: 0.9830 - val_loss: 0.1634 - val_accuracy: 0.9471\n", + "Epoch 150/150\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0443 - accuracy: 0.9876 - val_loss: 0.1745 - 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-150-0.9487.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;32m0.1745\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m742.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m606.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m135.60 \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[|4596|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", + "288/288 [==============================] - 107s 352ms/step - loss: 0.1839 - accuracy: 0.9456 - val_loss: 0.1453 - val_accuracy: 0.9567\n", + "Epoch 152/156\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1713 - accuracy: 0.9469 - val_loss: 0.1729 - val_accuracy: 0.9439\n", + "Epoch 153/156\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.1198 - accuracy: 0.9658 - val_loss: 0.1647 - val_accuracy: 0.9439\n", + "Epoch 154/156\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0999 - accuracy: 0.9719 - val_loss: 0.2015 - val_accuracy: 0.9487\n", + "Epoch 155/156\n", + "288/288 [==============================] - 99s 345ms/step - loss: 0.0714 - accuracy: 0.9809 - val_loss: 0.1847 - val_accuracy: 0.9455\n", + "Epoch 156/156\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0512 - accuracy: 0.9874 - val_loss: 0.1959 - val_accuracy: 0.9455\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-151-0.9567.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;32m0.1453\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m736.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m603.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m133.26 \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[|4596|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", + "288/288 [==============================] - 107s 354ms/step - loss: 0.1945 - accuracy: 0.9347 - val_loss: 0.1653 - val_accuracy: 0.9471\n", + "Epoch 158/162\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.1883 - accuracy: 0.9386 - val_loss: 0.2118 - val_accuracy: 0.9455\n", + "Epoch 159/162\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1513 - accuracy: 0.9571 - val_loss: 0.1822 - val_accuracy: 0.9423\n", + "Epoch 160/162\n", + "288/288 [==============================] - 96s 331ms/step - loss: 0.1133 - accuracy: 0.9656 - val_loss: 0.1577 - val_accuracy: 0.9471\n", + "Epoch 161/162\n", + "288/288 [==============================] - 97s 334ms/step - loss: 0.0863 - accuracy: 0.9785 - val_loss: 0.1519 - val_accuracy: 0.9503\n", + "Epoch 162/162\n", + "288/288 [==============================] - 95s 330ms/step - loss: 0.0620 - accuracy: 0.9865 - val_loss: 0.1708 - 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{0.9503}, \u001b[0m\u001b[0;33mloss{0.1519}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1446}]\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;32m0.1708\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m726.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m587.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m138.30 \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[|4596|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", + "288/288 [==============================] - 103s 341ms/step - loss: 0.1850 - accuracy: 0.9415 - val_loss: 0.1459 - val_accuracy: 0.9535\n", + "Epoch 164/168\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1726 - accuracy: 0.9478 - val_loss: 0.1514 - val_accuracy: 0.9519\n", + "Epoch 165/168\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1496 - accuracy: 0.9576 - val_loss: 0.2390 - val_accuracy: 0.9471\n", + "Epoch 166/168\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1027 - accuracy: 0.9708 - val_loss: 0.1639 - val_accuracy: 0.9471\n", + "Epoch 167/168\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.0793 - accuracy: 0.9795 - val_loss: 0.1720 - val_accuracy: 0.9455\n", + "Epoch 168/168\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.0539 - accuracy: 0.9874 - val_loss: 0.1643 - 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{0.1459}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1446}]\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;32m0.1643\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m712.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m584.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m128.02 \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[|4596|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", + "288/288 [==============================] - 104s 341ms/step - loss: 0.1818 - accuracy: 0.9430 - val_loss: 0.2232 - val_accuracy: 0.9311\n", + "Epoch 170/174\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.1627 - accuracy: 0.9510 - val_loss: 0.1492 - val_accuracy: 0.9503\n", + "Epoch 171/174\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1294 - accuracy: 0.9639 - val_loss: 0.1664 - val_accuracy: 0.9503\n", + "Epoch 172/174\n", + "288/288 [==============================] - 95s 331ms/step - loss: 0.0957 - accuracy: 0.9756 - val_loss: 0.2122 - val_accuracy: 0.9311\n", + "Epoch 173/174\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0854 - accuracy: 0.9806 - val_loss: 0.2193 - val_accuracy: 0.9487\n", + "Epoch 174/174\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0530 - accuracy: 0.9902 - val_loss: 0.2383 - 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{0.1492}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1446}]\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;32m0.2383\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m720.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m585.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.98 \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[|4596|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", + "288/288 [==============================] - 103s 341ms/step - loss: 0.2015 - accuracy: 0.9406 - val_loss: 0.1936 - val_accuracy: 0.9503\n", + "Epoch 176/180\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1652 - accuracy: 0.9506 - val_loss: 0.1743 - val_accuracy: 0.9551\n", + "Epoch 177/180\n", + "288/288 [==============================] - 97s 334ms/step - loss: 0.1453 - accuracy: 0.9597 - val_loss: 0.1739 - val_accuracy: 0.9439\n", + "Epoch 178/180\n", + "288/288 [==============================] - 97s 334ms/step - loss: 0.1169 - accuracy: 0.9680 - val_loss: 0.2032 - val_accuracy: 0.9423\n", + "Epoch 179/180\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0858 - accuracy: 0.9802 - val_loss: 0.2255 - val_accuracy: 0.9423\n", + "Epoch 180/180\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0529 - accuracy: 0.9878 - val_loss: 0.2475 - 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{0.1739}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9599}, loss{0.1446}]\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;32m0.2475\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 0.1446101964. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m723.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m588.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m135.01 \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[|4596|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", + "288/288 [==============================] - 104s 342ms/step - loss: 0.1870 - accuracy: 0.9450 - val_loss: 0.1341 - val_accuracy: 0.9599\n", + "Epoch 182/186\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.1605 - accuracy: 0.9523 - val_loss: 0.1376 - val_accuracy: 0.9583\n", + "Epoch 183/186\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.1291 - accuracy: 0.9591 - val_loss: 0.1362 - val_accuracy: 0.9615\n", + "Epoch 184/186\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0925 - accuracy: 0.9767 - val_loss: 0.1379 - val_accuracy: 0.9583\n", + "Epoch 185/186\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0628 - accuracy: 0.9832 - val_loss: 0.1821 - val_accuracy: 0.9551\n", + "Epoch 186/186\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0393 - accuracy: 0.9911 - val_loss: 0.1547 - 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-183-0.9615.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;32m0.1362\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 0.961538\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;32m0.1446101964 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1361950189\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;32m732.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m586.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m145.10 \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[|4596|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", + "288/288 [==============================] - 104s 342ms/step - loss: 0.1910 - accuracy: 0.9367 - val_loss: 0.1396 - val_accuracy: 0.9647\n", + "Epoch 188/192\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1635 - accuracy: 0.9458 - val_loss: 0.1298 - val_accuracy: 0.9599\n", + "Epoch 189/192\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1276 - accuracy: 0.9611 - val_loss: 0.2675 - val_accuracy: 0.9311\n", + "Epoch 190/192\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.1248 - accuracy: 0.9665 - val_loss: 0.2903 - val_accuracy: 0.9423\n", + "Epoch 191/192\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.0817 - accuracy: 0.9824 - val_loss: 0.2038 - val_accuracy: 0.9519\n", + "Epoch 192/192\n", + "288/288 [==============================] - 100s 345ms/step - loss: 0.0649 - accuracy: 0.9839 - val_loss: 0.1894 - 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-187-0.9647.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;32m0.1396\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from\u001b[0m\u001b[0;32m 0.961538 \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;91mModel loss did not improve from 0.1361950189. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m747.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m603.82 \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 [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[|4596|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", + "288/288 [==============================] - 107s 353ms/step - loss: 0.1717 - accuracy: 0.9441 - val_loss: 0.1453 - val_accuracy: 0.9471\n", + "Epoch 194/198\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.1676 - accuracy: 0.9465 - val_loss: 0.1411 - val_accuracy: 0.9647\n", + "Epoch 195/198\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1430 - accuracy: 0.9563 - val_loss: 0.1607 - val_accuracy: 0.9551\n", + "Epoch 196/198\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.1099 - accuracy: 0.9682 - val_loss: 0.1460 - val_accuracy: 0.9551\n", + "Epoch 197/198\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0763 - accuracy: 0.9800 - val_loss: 0.2118 - val_accuracy: 0.9423\n", + "Epoch 198/198\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0588 - accuracy: 0.9863 - val_loss: 0.1737 - 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{0.1411}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1362}]\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;32m0.1737\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 0.1361950189. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m760.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m609.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m150.54 \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[|4596|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", + "288/288 [==============================] - 108s 356ms/step - loss: 0.1845 - accuracy: 0.9408 - val_loss: 0.1426 - val_accuracy: 0.9503\n", + "Epoch 200/204\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.1700 - accuracy: 0.9469 - val_loss: 0.1711 - val_accuracy: 0.9519\n", + "Epoch 201/204\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1335 - accuracy: 0.9604 - val_loss: 0.1786 - val_accuracy: 0.9503\n", + "Epoch 202/204\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1072 - accuracy: 0.9691 - val_loss: 0.1860 - val_accuracy: 0.9519\n", + "Epoch 203/204\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0743 - accuracy: 0.9813 - val_loss: 0.1679 - val_accuracy: 0.9487\n", + "Epoch 204/204\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0531 - accuracy: 0.9859 - val_loss: 0.2112 - 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{0.9519}, \u001b[0m\u001b[0;33mloss{0.1426}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1362}]\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;32m0.2112\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 0.1361950189. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m766.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m609.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m157.69 \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[|4596|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", + "288/288 [==============================] - 108s 354ms/step - loss: 0.1761 - accuracy: 0.9452 - val_loss: 0.1507 - val_accuracy: 0.9551\n", + "Epoch 206/210\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.1525 - accuracy: 0.9513 - val_loss: 0.2917 - val_accuracy: 0.8910\n", + "Epoch 207/210\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.1208 - accuracy: 0.9637 - val_loss: 0.1389 - val_accuracy: 0.9647\n", + "Epoch 208/210\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.0886 - accuracy: 0.9752 - val_loss: 0.1407 - val_accuracy: 0.9551\n", + "Epoch 209/210\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0858 - accuracy: 0.9798 - val_loss: 0.1564 - val_accuracy: 0.9551\n", + "Epoch 210/210\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0573 - accuracy: 0.9869 - val_loss: 0.1522 - 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{0.1389}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1362}]\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;32m0.1521\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 0.1361950189. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m769.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m610.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m159.26 \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[|4596|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", + "288/288 [==============================] - 107s 354ms/step - loss: 0.1618 - accuracy: 0.9471 - val_loss: 0.2487 - val_accuracy: 0.9279\n", + "Epoch 212/216\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.1722 - accuracy: 0.9515 - val_loss: 0.1590 - val_accuracy: 0.9519\n", + "Epoch 213/216\n", + "288/288 [==============================] - 102s 352ms/step - loss: 0.1228 - accuracy: 0.9685 - val_loss: 0.1300 - val_accuracy: 0.9631\n", + "Epoch 214/216\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0769 - accuracy: 0.9795 - val_loss: 0.1660 - val_accuracy: 0.9535\n", + "Epoch 215/216\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.0683 - accuracy: 0.9843 - val_loss: 0.1402 - val_accuracy: 0.9599\n", + "Epoch 216/216\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.0485 - accuracy: 0.9874 - val_loss: 0.1402 - 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-213-0.9631.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;32m0.1300\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;33mImproved model loss from \u001b[0m\u001b[0;32m0.1361950189 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1299719810\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;32m776.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m613.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m163.32 \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[|4596|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", + "288/288 [==============================] - 105s 344ms/step - loss: 0.1983 - accuracy: 0.9347 - val_loss: 0.1431 - val_accuracy: 0.9615\n", + "Epoch 218/222\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1690 - accuracy: 0.9469 - val_loss: 0.1479 - val_accuracy: 0.9567\n", + "Epoch 219/222\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.1358 - accuracy: 0.9584 - val_loss: 0.1302 - val_accuracy: 0.9679\n", + "Epoch 220/222\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.1088 - accuracy: 0.9721 - val_loss: 0.1738 - val_accuracy: 0.9439\n", + "Epoch 221/222\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0683 - accuracy: 0.9826 - val_loss: 0.1385 - val_accuracy: 0.9615\n", + "Epoch 222/222\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0456 - accuracy: 0.9900 - val_loss: 0.1442 - val_accuracy: 0.9647\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-219-0.9679.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;32m0.1302\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 0.967949\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m755.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m592.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m163.36 \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[|4596|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", + "288/288 [==============================] - 104s 344ms/step - loss: 0.1813 - accuracy: 0.9430 - val_loss: 0.1279 - val_accuracy: 0.9615\n", + "Epoch 224/228\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.1636 - accuracy: 0.9526 - val_loss: 0.1458 - val_accuracy: 0.9535\n", + "Epoch 225/228\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1262 - accuracy: 0.9663 - val_loss: 0.1701 - val_accuracy: 0.9471\n", + "Epoch 226/228\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0894 - accuracy: 0.9772 - val_loss: 0.1331 - val_accuracy: 0.9647\n", + "Epoch 227/228\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.0813 - accuracy: 0.9785 - val_loss: 0.1331 - val_accuracy: 0.9615\n", + "Epoch 228/228\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0493 - accuracy: 0.9872 - val_loss: 0.1418 - val_accuracy: 0.9631\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-226-0.9647.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;32m0.1331\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m742.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m592.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m150.05 \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[|4596|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", + "288/288 [==============================] - 105s 344ms/step - loss: 0.1835 - accuracy: 0.9443 - val_loss: 0.1303 - val_accuracy: 0.9647\n", + "Epoch 230/234\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1631 - accuracy: 0.9497 - val_loss: 0.1348 - val_accuracy: 0.9583\n", + "Epoch 231/234\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.1213 - accuracy: 0.9682 - val_loss: 0.1418 - val_accuracy: 0.9519\n", + "Epoch 232/234\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.1023 - accuracy: 0.9708 - val_loss: 0.2232 - val_accuracy: 0.9215\n", + "Epoch 233/234\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0670 - accuracy: 0.9826 - val_loss: 0.1543 - val_accuracy: 0.9599\n", + "Epoch 234/234\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.0576 - accuracy: 0.9856 - val_loss: 0.1710 - 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{0.9647}, \u001b[0m\u001b[0;33mloss{0.1303}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1710\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m748.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m590.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m157.79 \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[|4596|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", + "288/288 [==============================] - 104s 343ms/step - loss: 0.1673 - accuracy: 0.9467 - val_loss: 0.1652 - val_accuracy: 0.9503\n", + "Epoch 236/240\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1562 - accuracy: 0.9550 - val_loss: 0.1616 - val_accuracy: 0.9471\n", + "Epoch 237/240\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1260 - accuracy: 0.9687 - val_loss: 0.1551 - val_accuracy: 0.9519\n", + "Epoch 238/240\n", + "288/288 [==============================] - 97s 338ms/step - loss: 0.1049 - accuracy: 0.9726 - val_loss: 0.1735 - val_accuracy: 0.9503\n", + "Epoch 239/240\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0613 - accuracy: 0.9869 - val_loss: 0.1676 - val_accuracy: 0.9423\n", + "Epoch 240/240\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0467 - accuracy: 0.9889 - val_loss: 0.1718 - 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{0.9519}, \u001b[0m\u001b[0;33mloss{0.1551}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1718\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m747.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m592.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m154.79 \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[|4596|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", + "288/288 [==============================] - 105s 346ms/step - loss: 0.1668 - accuracy: 0.9482 - val_loss: 0.1519 - val_accuracy: 0.9487\n", + "Epoch 242/246\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.1453 - accuracy: 0.9547 - val_loss: 0.1740 - val_accuracy: 0.9423\n", + "Epoch 243/246\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1224 - accuracy: 0.9678 - val_loss: 0.1610 - val_accuracy: 0.9455\n", + "Epoch 244/246\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0864 - accuracy: 0.9785 - val_loss: 0.1596 - val_accuracy: 0.9535\n", + "Epoch 245/246\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0642 - accuracy: 0.9819 - val_loss: 0.1437 - val_accuracy: 0.9583\n", + "Epoch 246/246\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.0345 - accuracy: 0.9933 - val_loss: 0.1671 - 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{0.1437}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1671\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m755.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m598.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m157.31 \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[|4596|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_d16-h06_m07_s10\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", + "288/288 [==============================] - 109s 359ms/step - loss: 0.1561 - accuracy: 0.9528 - val_loss: 0.1308 - val_accuracy: 0.9519\n", + "Epoch 248/252\n", + "288/288 [==============================] - 102s 352ms/step - loss: 0.1284 - accuracy: 0.9630 - val_loss: 0.1273 - val_accuracy: 0.9551\n", + "Epoch 249/252\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.1179 - accuracy: 0.9661 - val_loss: 0.1948 - val_accuracy: 0.9439\n", + "Epoch 250/252\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.0716 - accuracy: 0.9828 - val_loss: 0.1970 - val_accuracy: 0.9631\n", + "Epoch 251/252\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0418 - accuracy: 0.9919 - val_loss: 0.2130 - val_accuracy: 0.9567\n", + "Epoch 252/252\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.0325 - accuracy: 0.9928 - val_loss: 0.1755 - 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-250-0.9631.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;32m0.1970\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m809.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m616.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m193.74 \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[|4596|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", + "288/288 [==============================] - 109s 359ms/step - loss: 0.1705 - accuracy: 0.9443 - val_loss: 0.1392 - val_accuracy: 0.9551\n", + "Epoch 254/258\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.1712 - accuracy: 0.9463 - val_loss: 0.2115 - val_accuracy: 0.9439\n", + "Epoch 255/258\n", + "288/288 [==============================] - 102s 355ms/step - loss: 0.1214 - accuracy: 0.9624 - val_loss: 0.1501 - val_accuracy: 0.9567\n", + "Epoch 256/258\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.0873 - accuracy: 0.9774 - val_loss: 0.3050 - val_accuracy: 0.9359\n", + "Epoch 257/258\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.0585 - accuracy: 0.9843 - val_loss: 0.2479 - val_accuracy: 0.9487\n", + "Epoch 258/258\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.0384 - accuracy: 0.9909 - val_loss: 0.3113 - 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{0.9567}, \u001b[0m\u001b[0;33mloss{0.1392}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.3113\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m786.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m618.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m168.01 \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[|4596|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", + "288/288 [==============================] - 109s 358ms/step - loss: 0.1892 - accuracy: 0.9432 - val_loss: 0.1468 - val_accuracy: 0.9535\n", + "Epoch 260/264\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.1669 - accuracy: 0.9508 - val_loss: 0.1495 - val_accuracy: 0.9519\n", + "Epoch 261/264\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.1249 - accuracy: 0.9652 - val_loss: 0.1674 - val_accuracy: 0.9439\n", + "Epoch 262/264\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.1070 - accuracy: 0.9711 - val_loss: 0.2571 - val_accuracy: 0.9247\n", + "Epoch 263/264\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0982 - accuracy: 0.9728 - val_loss: 0.1905 - val_accuracy: 0.9311\n", + "Epoch 264/264\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.0609 - accuracy: 0.9887 - val_loss: 0.1996 - 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{0.9535}, \u001b[0m\u001b[0;33mloss{0.1468}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1996\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m793.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m613.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m180.46 \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[|4596|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", + "288/288 [==============================] - 109s 358ms/step - loss: 0.1977 - accuracy: 0.9408 - val_loss: 0.1645 - val_accuracy: 0.9471\n", + "Epoch 266/270\n", + "288/288 [==============================] - 103s 356ms/step - loss: 0.1584 - accuracy: 0.9530 - val_loss: 0.1422 - val_accuracy: 0.9487\n", + "Epoch 267/270\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.1221 - accuracy: 0.9689 - val_loss: 0.1686 - val_accuracy: 0.9407\n", + "Epoch 268/270\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.1118 - accuracy: 0.9724 - val_loss: 0.1935 - val_accuracy: 0.9407\n", + "Epoch 269/270\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.0733 - accuracy: 0.9835 - val_loss: 0.2112 - val_accuracy: 0.9391\n", + "Epoch 270/270\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.0485 - accuracy: 0.9891 - val_loss: 0.2151 - 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{0.1422}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.2151\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m794.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m615.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m179.71 \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[|4596|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", + "288/288 [==============================] - 109s 359ms/step - loss: 0.1923 - accuracy: 0.9408 - val_loss: 0.1819 - val_accuracy: 0.9423\n", + "Epoch 272/276\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.1579 - accuracy: 0.9508 - val_loss: 0.1983 - val_accuracy: 0.9407\n", + "Epoch 273/276\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.1242 - accuracy: 0.9663 - val_loss: 0.2944 - val_accuracy: 0.9487\n", + "Epoch 274/276\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0946 - accuracy: 0.9767 - val_loss: 0.2544 - val_accuracy: 0.9407\n", + "Epoch 275/276\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0736 - accuracy: 0.9806 - val_loss: 0.2562 - val_accuracy: 0.9471\n", + "Epoch 276/276\n", + "288/288 [==============================] - 97s 338ms/step - loss: 0.0508 - accuracy: 0.9893 - val_loss: 0.2495 - 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{0.9487}, \u001b[0m\u001b[0;33mloss{0.1819}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.2495\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m778.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m600.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m178.10 \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[|4596|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", + "288/288 [==============================] - 105s 346ms/step - loss: 0.1662 - accuracy: 0.9480 - val_loss: 0.1510 - val_accuracy: 0.9503\n", + "Epoch 278/282\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1406 - accuracy: 0.9584 - val_loss: 0.1420 - val_accuracy: 0.9551\n", + "Epoch 279/282\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1024 - accuracy: 0.9711 - val_loss: 0.1615 - val_accuracy: 0.9423\n", + "Epoch 280/282\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0775 - accuracy: 0.9804 - val_loss: 0.2376 - val_accuracy: 0.9327\n", + "Epoch 281/282\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.2302 - accuracy: 0.9397 - val_loss: 0.1717 - val_accuracy: 0.9487\n", + "Epoch 282/282\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1360 - accuracy: 0.9685 - val_loss: 0.1669 - 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{0.9551}, \u001b[0m\u001b[0;33mloss{0.1420}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1669\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m763.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m596.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m167.12 \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[|4596|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", + "288/288 [==============================] - 105s 346ms/step - loss: 0.2123 - accuracy: 0.9389 - val_loss: 0.1537 - val_accuracy: 0.9503\n", + "Epoch 284/288\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.1848 - accuracy: 0.9478 - val_loss: 0.1594 - val_accuracy: 0.9455\n", + "Epoch 285/288\n", + "288/288 [==============================] - 98s 342ms/step - loss: 0.1366 - accuracy: 0.9628 - val_loss: 0.1574 - val_accuracy: 0.9535\n", + "Epoch 286/288\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0965 - accuracy: 0.9761 - val_loss: 0.1594 - val_accuracy: 0.9503\n", + "Epoch 287/288\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0684 - accuracy: 0.9865 - val_loss: 0.1912 - val_accuracy: 0.9503\n", + "Epoch 288/288\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0440 - accuracy: 0.9906 - val_loss: 0.1948 - 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{0.9535}, \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{0.1300}]\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;32m0.1948\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m767.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m596.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m171.53 \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[|4596|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", + "288/288 [==============================] - 106s 347ms/step - loss: 0.1896 - accuracy: 0.9430 - val_loss: 0.2086 - val_accuracy: 0.9407\n", + "Epoch 290/294\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.1606 - accuracy: 0.9500 - val_loss: 0.1940 - val_accuracy: 0.9487\n", + "Epoch 291/294\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.1608 - val_accuracy: 0.9439\n", + "Epoch 292/294\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0911 - accuracy: 0.9743 - val_loss: 0.1863 - val_accuracy: 0.9471\n", + "Epoch 293/294\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0541 - accuracy: 0.9867 - val_loss: 0.3903 - val_accuracy: 0.9247\n", + "Epoch 294/294\n", + "288/288 [==============================] - 98s 338ms/step - loss: 0.0383 - accuracy: 0.9906 - val_loss: 0.3326 - 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{0.1608}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.3327\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m768.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m596.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m171.92 \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[|4596|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", + "288/288 [==============================] - 105s 345ms/step - loss: 0.1705 - accuracy: 0.9478 - val_loss: 0.1772 - val_accuracy: 0.9391\n", + "Epoch 296/300\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.1460 - accuracy: 0.9543 - val_loss: 0.2833 - val_accuracy: 0.9439\n", + "Epoch 297/300\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1119 - accuracy: 0.9706 - val_loss: 0.3768 - val_accuracy: 0.9295\n", + "Epoch 298/300\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0772 - accuracy: 0.9780 - val_loss: 0.2689 - val_accuracy: 0.9327\n", + "Epoch 299/300\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0637 - accuracy: 0.9852 - val_loss: 0.2265 - val_accuracy: 0.9471\n", + "Epoch 300/300\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0445 - accuracy: 0.9911 - val_loss: 0.2597 - 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{0.9471}, \u001b[0m\u001b[0;33mloss{0.1772}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.2598\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m762.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m594.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m168.06 \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[|4596|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", + "288/288 [==============================] - 109s 358ms/step - loss: 0.1792 - accuracy: 0.9447 - val_loss: 0.1732 - val_accuracy: 0.9423\n", + "Epoch 302/306\n", + "288/288 [==============================] - 103s 355ms/step - loss: 0.1613 - accuracy: 0.9493 - val_loss: 0.1663 - val_accuracy: 0.9439\n", + "Epoch 303/306\n", + "288/288 [==============================] - 103s 355ms/step - loss: 0.1186 - accuracy: 0.9654 - val_loss: 0.1610 - val_accuracy: 0.9535\n", + "Epoch 304/306\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.0911 - accuracy: 0.9776 - val_loss: 0.2643 - val_accuracy: 0.9327\n", + "Epoch 305/306\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0586 - accuracy: 0.9863 - val_loss: 0.2897 - val_accuracy: 0.9215\n", + "Epoch 306/306\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.0412 - accuracy: 0.9913 - val_loss: 0.2949 - 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{0.9535}, \u001b[0m\u001b[0;33mloss{0.1610}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.2949\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m796.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m619.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m177.49 \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[|4596|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", + "288/288 [==============================] - 110s 362ms/step - loss: 0.1540 - accuracy: 0.9493 - val_loss: 0.2221 - val_accuracy: 0.9263\n", + "Epoch 308/312\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.1353 - accuracy: 0.9617 - val_loss: 0.2441 - val_accuracy: 0.9439\n", + "Epoch 309/312\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.1067 - accuracy: 0.9695 - val_loss: 0.2983 - val_accuracy: 0.9103\n", + "Epoch 310/312\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0665 - accuracy: 0.9824 - val_loss: 0.2716 - val_accuracy: 0.9391\n", + "Epoch 311/312\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.0498 - accuracy: 0.9880 - val_loss: 0.4547 - val_accuracy: 0.9167\n", + "Epoch 312/312\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.0404 - accuracy: 0.9904 - val_loss: 0.3900 - 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{0.9439}, \u001b[0m\u001b[0;33mloss{0.2221}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.3899\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m798.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m618.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m180.57 \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[|4596|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", + "288/288 [==============================] - 109s 358ms/step - loss: 0.1605 - accuracy: 0.9478 - val_loss: 0.2270 - val_accuracy: 0.9407\n", + "Epoch 314/318\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.1471 - accuracy: 0.9528 - val_loss: 0.2527 - val_accuracy: 0.9487\n", + "Epoch 315/318\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.1178 - accuracy: 0.9658 - val_loss: 0.2520 - val_accuracy: 0.9359\n", + "Epoch 316/318\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.1061 - accuracy: 0.9691 - val_loss: 0.1725 - val_accuracy: 0.9359\n", + "Epoch 317/318\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.0959 - accuracy: 0.9785 - val_loss: 0.2385 - val_accuracy: 0.9343\n", + "Epoch 318/318\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.0703 - accuracy: 0.9876 - val_loss: 0.2490 - 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{0.9487}, \u001b[0m\u001b[0;33mloss{0.1725}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.2490\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m793.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m615.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m177.59 \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[|4596|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", + "288/288 [==============================] - 110s 359ms/step - loss: 0.2042 - accuracy: 0.9413 - val_loss: 0.2325 - val_accuracy: 0.9375\n", + "Epoch 320/324\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.1650 - accuracy: 0.9539 - val_loss: 0.1632 - val_accuracy: 0.9487\n", + "Epoch 321/324\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.1247 - accuracy: 0.9671 - val_loss: 0.1749 - val_accuracy: 0.9423\n", + "Epoch 322/324\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.0933 - accuracy: 0.9767 - val_loss: 0.1697 - val_accuracy: 0.9391\n", + "Epoch 323/324\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0667 - accuracy: 0.9832 - val_loss: 0.1830 - val_accuracy: 0.9471\n", + "Epoch 324/324\n", + "288/288 [==============================] - 103s 356ms/step - loss: 0.0502 - accuracy: 0.9885 - val_loss: 0.1805 - 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{0.9503}, \u001b[0m\u001b[0;33mloss{0.1632}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1805\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m805.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m620.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m185.07 \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[|4596|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", + "288/288 [==============================] - 109s 360ms/step - loss: 0.1689 - accuracy: 0.9489 - val_loss: 0.1539 - val_accuracy: 0.9471\n", + "Epoch 326/330\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.1461 - accuracy: 0.9563 - val_loss: 0.2300 - val_accuracy: 0.9407\n", + "Epoch 327/330\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1161 - accuracy: 0.9669 - val_loss: 0.1394 - val_accuracy: 0.9535\n", + "Epoch 328/330\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0789 - accuracy: 0.9772 - val_loss: 0.1718 - val_accuracy: 0.9391\n", + "Epoch 329/330\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0501 - accuracy: 0.9887 - val_loss: 0.1595 - val_accuracy: 0.9503\n", + "Epoch 330/330\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0404 - accuracy: 0.9904 - val_loss: 0.1820 - 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{0.1394}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1820\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m794.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m605.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m189.38 \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[|4596|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", + "288/288 [==============================] - 106s 349ms/step - loss: 0.1657 - accuracy: 0.9495 - val_loss: 0.1488 - val_accuracy: 0.9439\n", + "Epoch 332/336\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1392 - accuracy: 0.9567 - val_loss: 0.1396 - val_accuracy: 0.9519\n", + "Epoch 333/336\n", + "288/288 [==============================] - 99s 341ms/step - loss: 0.1105 - accuracy: 0.9682 - val_loss: 0.1535 - val_accuracy: 0.9503\n", + "Epoch 334/336\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0751 - accuracy: 0.9769 - val_loss: 0.1379 - val_accuracy: 0.9631\n", + "Epoch 335/336\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0508 - accuracy: 0.9876 - val_loss: 0.1759 - val_accuracy: 0.9439\n", + "Epoch 336/336\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0416 - accuracy: 0.9926 - val_loss: 0.2090 - 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{0.9631}, \u001b[0m\u001b[0;33mloss{0.1379}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.2090\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m786.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m602.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m184.05 \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[|4596|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", + "288/288 [==============================] - 107s 351ms/step - loss: 0.1570 - accuracy: 0.9482 - val_loss: 0.2104 - val_accuracy: 0.9407\n", + "Epoch 338/342\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.1312 - accuracy: 0.9600 - val_loss: 0.2403 - val_accuracy: 0.9343\n", + "Epoch 339/342\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0964 - accuracy: 0.9719 - val_loss: 0.1523 - val_accuracy: 0.9551\n", + "Epoch 340/342\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0740 - accuracy: 0.9813 - val_loss: 0.2091 - val_accuracy: 0.9455\n", + "Epoch 341/342\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0472 - accuracy: 0.9874 - val_loss: 0.1904 - val_accuracy: 0.9519\n", + "Epoch 342/342\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0409 - accuracy: 0.9896 - val_loss: 0.1986 - 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{0.1523}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1985\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m784.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m601.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m182.93 \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[|4596|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", + "288/288 [==============================] - 106s 348ms/step - loss: 0.1710 - accuracy: 0.9460 - val_loss: 0.1330 - val_accuracy: 0.9583\n", + "Epoch 344/348\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1414 - accuracy: 0.9584 - val_loss: 0.1351 - val_accuracy: 0.9599\n", + "Epoch 345/348\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.1209 - accuracy: 0.9650 - val_loss: 0.1442 - val_accuracy: 0.9551\n", + "Epoch 346/348\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0779 - accuracy: 0.9787 - val_loss: 0.1421 - val_accuracy: 0.9535\n", + "Epoch 347/348\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0588 - accuracy: 0.9867 - val_loss: 0.1487 - val_accuracy: 0.9487\n", + "Epoch 348/348\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0425 - accuracy: 0.9911 - val_loss: 0.1808 - 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{0.1330}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1808\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m781.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m600.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m181.62 \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[|4596|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", + "288/288 [==============================] - 106s 350ms/step - loss: 0.1663 - accuracy: 0.9489 - val_loss: 0.1617 - val_accuracy: 0.9503\n", + "Epoch 350/354\n", + "288/288 [==============================] - 99s 345ms/step - loss: 0.1461 - accuracy: 0.9539 - val_loss: 0.1339 - val_accuracy: 0.9535\n", + "Epoch 351/354\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1047 - accuracy: 0.9706 - val_loss: 0.1995 - val_accuracy: 0.9407\n", + "Epoch 352/354\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0762 - accuracy: 0.9795 - val_loss: 0.1838 - val_accuracy: 0.9487\n", + "Epoch 353/354\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0513 - accuracy: 0.9885 - val_loss: 0.1936 - val_accuracy: 0.9439\n", + "Epoch 354/354\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0363 - accuracy: 0.9926 - val_loss: 0.2099 - 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{0.1339}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.2099\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m784.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m600.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m183.86 \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[|4596|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", + "288/288 [==============================] - 110s 360ms/step - loss: 0.1729 - accuracy: 0.9465 - val_loss: 0.1443 - val_accuracy: 0.9535\n", + "Epoch 356/360\n", + "288/288 [==============================] - 102s 352ms/step - loss: 0.1482 - accuracy: 0.9528 - val_loss: 0.1544 - val_accuracy: 0.9503\n", + "Epoch 357/360\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.1178 - accuracy: 0.9656 - val_loss: 0.1693 - val_accuracy: 0.9487\n", + "Epoch 358/360\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.0896 - accuracy: 0.9776 - val_loss: 0.1947 - val_accuracy: 0.9487\n", + "Epoch 359/360\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0609 - accuracy: 0.9839 - val_loss: 0.1884 - val_accuracy: 0.9535\n", + "Epoch 360/360\n", + "288/288 [==============================] - 103s 355ms/step - loss: 0.0444 - accuracy: 0.9904 - val_loss: 0.1692 - 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{0.1443}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1692\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m817.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m619.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m197.73 \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[|4596|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", + "288/288 [==============================] - 110s 362ms/step - loss: 0.1635 - accuracy: 0.9506 - val_loss: 0.1540 - val_accuracy: 0.9439\n", + "Epoch 362/366\n", + "288/288 [==============================] - 103s 355ms/step - loss: 0.1369 - accuracy: 0.9589 - val_loss: 0.1404 - val_accuracy: 0.9535\n", + "Epoch 363/366\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.1055 - accuracy: 0.9693 - val_loss: 0.1638 - val_accuracy: 0.9551\n", + "Epoch 364/366\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.0752 - accuracy: 0.9802 - val_loss: 0.1653 - val_accuracy: 0.9551\n", + "Epoch 365/366\n", + "288/288 [==============================] - 103s 357ms/step - loss: 0.0585 - accuracy: 0.9867 - val_loss: 0.1554 - val_accuracy: 0.9567\n", + "Epoch 366/366\n", + "288/288 [==============================] - 103s 356ms/step - loss: 0.0410 - accuracy: 0.9904 - val_loss: 0.1603 - 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{0.1404}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1300}]\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;32m0.1603\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 0.1299719810. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m817.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m621.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m195.74 \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[|4596|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", + "288/288 [==============================] - 111s 363ms/step - loss: 0.1412 - accuracy: 0.9543 - val_loss: 0.1235 - val_accuracy: 0.9583\n", + "Epoch 368/372\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.1415 - accuracy: 0.9597 - val_loss: 0.2197 - val_accuracy: 0.9151\n", + "Epoch 369/372\n", + "288/288 [==============================] - 102s 352ms/step - loss: 0.1027 - accuracy: 0.9711 - val_loss: 0.1658 - val_accuracy: 0.9487\n", + "Epoch 370/372\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.0687 - accuracy: 0.9817 - val_loss: 0.1746 - val_accuracy: 0.9535\n", + "Epoch 371/372\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.0550 - accuracy: 0.9869 - val_loss: 0.1832 - val_accuracy: 0.9535\n", + "Epoch 372/372\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0292 - accuracy: 0.9948 - val_loss: 0.2033 - 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-367-0.9583.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;32m0.1235\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;32m0.1299719810 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1235409379\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;32m821.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m617.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m204.09 \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[|4596|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", + "288/288 [==============================] - 110s 361ms/step - loss: 0.1460 - accuracy: 0.9513 - val_loss: 0.1427 - val_accuracy: 0.9391\n", + "Epoch 374/378\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.1294 - accuracy: 0.9613 - val_loss: 0.2381 - val_accuracy: 0.9103\n", + "Epoch 375/378\n", + "288/288 [==============================] - 103s 356ms/step - loss: 0.0990 - accuracy: 0.9732 - val_loss: 0.1389 - val_accuracy: 0.9583\n", + "Epoch 376/378\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.0790 - accuracy: 0.9817 - val_loss: 0.1446 - val_accuracy: 0.9551\n", + "Epoch 377/378\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.0483 - accuracy: 0.9880 - val_loss: 0.1641 - val_accuracy: 0.9551\n", + "Epoch 378/378\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.0330 - accuracy: 0.9935 - val_loss: 0.1825 - 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{0.1389}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1826\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m825.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m621.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m203.81 \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[|4596|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", + "288/288 [==============================] - 110s 361ms/step - loss: 0.1645 - accuracy: 0.9467 - val_loss: 0.1366 - val_accuracy: 0.9599\n", + "Epoch 380/384\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.1434 - accuracy: 0.9565 - val_loss: 0.1588 - val_accuracy: 0.9519\n", + "Epoch 381/384\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1211 - accuracy: 0.9643 - val_loss: 0.1985 - val_accuracy: 0.9503\n", + "Epoch 382/384\n", + "288/288 [==============================] - 99s 341ms/step - loss: 0.0903 - accuracy: 0.9774 - val_loss: 0.1618 - val_accuracy: 0.9599\n", + "Epoch 383/384\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0559 - accuracy: 0.9861 - val_loss: 0.1734 - val_accuracy: 0.9583\n", + "Epoch 384/384\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0473 - accuracy: 0.9893 - val_loss: 0.1604 - 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{0.1366}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1604\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m810.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m606.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m203.17 \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[|4596|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", + "288/288 [==============================] - 107s 350ms/step - loss: 0.1642 - accuracy: 0.9497 - val_loss: 0.1449 - val_accuracy: 0.9519\n", + "Epoch 386/390\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1361 - accuracy: 0.9558 - val_loss: 0.1508 - val_accuracy: 0.9535\n", + "Epoch 387/390\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0999 - accuracy: 0.9708 - val_loss: 0.1614 - val_accuracy: 0.9519\n", + "Epoch 388/390\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0715 - accuracy: 0.9819 - val_loss: 0.1559 - val_accuracy: 0.9551\n", + "Epoch 389/390\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0475 - accuracy: 0.9867 - val_loss: 0.1671 - val_accuracy: 0.9535\n", + "Epoch 390/390\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0344 - accuracy: 0.9902 - val_loss: 0.1852 - 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{0.1449}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1852\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m783.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m603.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m179.90 \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[|4596|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", + "288/288 [==============================] - 107s 352ms/step - loss: 0.1622 - accuracy: 0.9515 - val_loss: 0.1473 - val_accuracy: 0.9535\n", + "Epoch 392/396\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.1410 - accuracy: 0.9576 - val_loss: 0.1586 - val_accuracy: 0.9471\n", + "Epoch 393/396\n", + "288/288 [==============================] - 100s 345ms/step - loss: 0.1042 - accuracy: 0.9695 - val_loss: 0.1404 - val_accuracy: 0.9567\n", + "Epoch 394/396\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0855 - accuracy: 0.9789 - val_loss: 0.1457 - val_accuracy: 0.9583\n", + "Epoch 395/396\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0630 - accuracy: 0.9848 - val_loss: 0.1722 - val_accuracy: 0.9487\n", + "Epoch 396/396\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0437 - accuracy: 0.9896 - val_loss: 0.1787 - 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{0.1404}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1787\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m786.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m604.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m181.57 \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[|4596|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", + "288/288 [==============================] - 107s 353ms/step - loss: 0.1559 - accuracy: 0.9521 - val_loss: 0.1701 - val_accuracy: 0.9487\n", + "Epoch 398/402\n", + "288/288 [==============================] - 100s 345ms/step - loss: 0.1185 - accuracy: 0.9643 - val_loss: 0.1612 - val_accuracy: 0.9583\n", + "Epoch 399/402\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0866 - accuracy: 0.9726 - val_loss: 0.1220 - val_accuracy: 0.9535\n", + "Epoch 400/402\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0705 - accuracy: 0.9813 - val_loss: 0.1287 - val_accuracy: 0.9551\n", + "Epoch 401/402\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0554 - accuracy: 0.9867 - val_loss: 0.1424 - val_accuracy: 0.9503\n", + "Epoch 402/402\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0377 - accuracy: 0.9926 - val_loss: 0.1770 - 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-398-0.9583.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;32m0.1612\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m791.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m603.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m187.69 \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[|4596|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", + "288/288 [==============================] - 107s 351ms/step - loss: 0.1496 - accuracy: 0.9550 - val_loss: 0.1496 - val_accuracy: 0.9487\n", + "Epoch 404/408\n", + "288/288 [==============================] - 100s 345ms/step - loss: 0.1374 - accuracy: 0.9600 - val_loss: 0.1473 - val_accuracy: 0.9551\n", + "Epoch 405/408\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.1061 - accuracy: 0.9680 - val_loss: 0.1491 - val_accuracy: 0.9615\n", + "Epoch 406/408\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0657 - accuracy: 0.9824 - val_loss: 0.1587 - val_accuracy: 0.9583\n", + "Epoch 407/408\n", + "288/288 [==============================] - 100s 345ms/step - loss: 0.0516 - accuracy: 0.9887 - val_loss: 0.1821 - val_accuracy: 0.9599\n", + "Epoch 408/408\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0402 - accuracy: 0.9915 - val_loss: 0.1616 - 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{0.1473}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1616\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m799.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m605.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m194.45 \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[|4596|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", + "288/288 [==============================] - 109s 357ms/step - loss: 0.1706 - accuracy: 0.9454 - val_loss: 0.1336 - val_accuracy: 0.9583\n", + "Epoch 410/414\n", + "288/288 [==============================] - 101s 352ms/step - loss: 0.1393 - accuracy: 0.9552 - val_loss: 0.1516 - val_accuracy: 0.9551\n", + "Epoch 411/414\n", + "288/288 [==============================] - 101s 349ms/step - loss: 0.1012 - accuracy: 0.9695 - val_loss: 0.1936 - val_accuracy: 0.9535\n", + "Epoch 412/414\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.0710 - accuracy: 0.9811 - val_loss: 0.1686 - val_accuracy: 0.9551\n", + "Epoch 413/414\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.0564 - accuracy: 0.9839 - val_loss: 0.1505 - val_accuracy: 0.9663\n", + "Epoch 414/414\n", + "288/288 [==============================] - 102s 352ms/step - loss: 0.0375 - accuracy: 0.9924 - val_loss: 0.1790 - 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{0.9663}, \u001b[0m\u001b[0;33mloss{0.1336}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1790\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m824.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m616.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m207.81 \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[|4596|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", + "288/288 [==============================] - 109s 360ms/step - loss: 0.1530 - accuracy: 0.9569 - val_loss: 0.1322 - val_accuracy: 0.9599\n", + "Epoch 416/420\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.1358 - accuracy: 0.9593 - val_loss: 0.1591 - val_accuracy: 0.9423\n", + "Epoch 417/420\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.1001 - accuracy: 0.9750 - val_loss: 0.1576 - val_accuracy: 0.9535\n", + "Epoch 418/420\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.0678 - accuracy: 0.9806 - val_loss: 0.2333 - val_accuracy: 0.9423\n", + "Epoch 419/420\n", + "288/288 [==============================] - 103s 355ms/step - loss: 0.0441 - accuracy: 0.9893 - val_loss: 0.1609 - val_accuracy: 0.9615\n", + "Epoch 420/420\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.0357 - accuracy: 0.9915 - val_loss: 0.1755 - 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{0.1322}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1755\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m818.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m619.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m198.91 \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[|4596|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", + "288/288 [==============================] - 110s 362ms/step - loss: 0.1581 - accuracy: 0.9480 - val_loss: 0.1503 - val_accuracy: 0.9583\n", + "Epoch 422/426\n", + "288/288 [==============================] - 102s 352ms/step - loss: 0.1391 - accuracy: 0.9580 - val_loss: 0.1536 - val_accuracy: 0.9487\n", + "Epoch 423/426\n", + "288/288 [==============================] - 103s 358ms/step - loss: 0.0973 - accuracy: 0.9741 - val_loss: 0.1573 - val_accuracy: 0.9647\n", + "Epoch 424/426\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0678 - accuracy: 0.9830 - val_loss: 0.1522 - val_accuracy: 0.9599\n", + "Epoch 425/426\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0517 - accuracy: 0.9885 - val_loss: 0.1740 - val_accuracy: 0.9551\n", + "Epoch 426/426\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.0332 - accuracy: 0.9939 - val_loss: 0.1764 - 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{0.1503}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1764\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m830.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m620.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m210.19 \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[|4596|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", + "288/288 [==============================] - 109s 360ms/step - loss: 0.1560 - accuracy: 0.9480 - val_loss: 0.1299 - val_accuracy: 0.9535\n", + "Epoch 428/432\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.1323 - accuracy: 0.9602 - val_loss: 0.1249 - val_accuracy: 0.9631\n", + "Epoch 429/432\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.0963 - accuracy: 0.9726 - val_loss: 0.1321 - val_accuracy: 0.9519\n", + "Epoch 430/432\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0665 - accuracy: 0.9804 - val_loss: 0.1560 - val_accuracy: 0.9583\n", + "Epoch 431/432\n", + "288/288 [==============================] - 102s 352ms/step - loss: 0.0399 - accuracy: 0.9915 - val_loss: 0.1520 - val_accuracy: 0.9631\n", + "Epoch 432/432\n", + "288/288 [==============================] - 103s 356ms/step - loss: 0.0344 - accuracy: 0.9930 - val_loss: 0.1471 - 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{0.1249}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1470\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m827.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m620.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m207.73 \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[|4596|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", + "288/288 [==============================] - 110s 361ms/step - loss: 0.1555 - accuracy: 0.9521 - val_loss: 0.1379 - val_accuracy: 0.9551\n", + "Epoch 434/438\n", + "288/288 [==============================] - 102s 352ms/step - loss: 0.1214 - accuracy: 0.9624 - val_loss: 0.1439 - val_accuracy: 0.9503\n", + "Epoch 435/438\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0845 - accuracy: 0.9761 - val_loss: 0.2891 - val_accuracy: 0.9375\n", + "Epoch 436/438\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0528 - accuracy: 0.9872 - val_loss: 0.1667 - val_accuracy: 0.9599\n", + "Epoch 437/438\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0397 - accuracy: 0.9900 - val_loss: 0.2075 - val_accuracy: 0.9519\n", + "Epoch 438/438\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0325 - accuracy: 0.9950 - val_loss: 0.2128 - 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{0.1379}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.2128\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m822.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m607.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m215.19 \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[|4596|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", + "288/288 [==============================] - 107s 352ms/step - loss: 0.1678 - accuracy: 0.9487 - val_loss: 0.1359 - val_accuracy: 0.9631\n", + "Epoch 440/444\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1378 - accuracy: 0.9560 - val_loss: 0.1420 - val_accuracy: 0.9503\n", + "Epoch 441/444\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.1006 - accuracy: 0.9700 - val_loss: 0.1400 - val_accuracy: 0.9567\n", + "Epoch 442/444\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0704 - accuracy: 0.9815 - val_loss: 0.1678 - val_accuracy: 0.9599\n", + "Epoch 443/444\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0454 - accuracy: 0.9898 - val_loss: 0.1736 - val_accuracy: 0.9567\n", + "Epoch 444/444\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0374 - accuracy: 0.9924 - val_loss: 0.1773 - 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{0.9631}, \u001b[0m\u001b[0;33mloss{0.1359}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1772\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m797.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m602.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m195.27 \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[|4596|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", + "288/288 [==============================] - 108s 353ms/step - loss: 0.1562 - accuracy: 0.9517 - val_loss: 0.1340 - val_accuracy: 0.9503\n", + "Epoch 446/450\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1239 - accuracy: 0.9613 - val_loss: 0.1413 - val_accuracy: 0.9519\n", + "Epoch 447/450\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0878 - accuracy: 0.9732 - val_loss: 0.1576 - val_accuracy: 0.9535\n", + "Epoch 448/450\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0608 - accuracy: 0.9826 - val_loss: 0.1741 - val_accuracy: 0.9503\n", + "Epoch 449/450\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0411 - accuracy: 0.9887 - val_loss: 0.1697 - val_accuracy: 0.9487\n", + "Epoch 450/450\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0283 - accuracy: 0.9937 - val_loss: 0.1960 - 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{0.1340}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1960\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m805.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m606.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m199.25 \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[|4596|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", + "288/288 [==============================] - 107s 352ms/step - loss: 0.1590 - accuracy: 0.9539 - val_loss: 0.1489 - val_accuracy: 0.9471\n", + "Epoch 452/456\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.1289 - accuracy: 0.9630 - val_loss: 0.1505 - val_accuracy: 0.9535\n", + "Epoch 453/456\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0904 - accuracy: 0.9758 - val_loss: 0.1655 - val_accuracy: 0.9247\n", + "Epoch 454/456\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0614 - accuracy: 0.9826 - val_loss: 0.1416 - val_accuracy: 0.9455\n", + "Epoch 455/456\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0482 - accuracy: 0.9880 - val_loss: 0.1601 - val_accuracy: 0.9471\n", + "Epoch 456/456\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0316 - accuracy: 0.9930 - val_loss: 0.1519 - 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{0.1416}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1519\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m808.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m605.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m203.16 \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[|4596|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", + "288/288 [==============================] - 107s 351ms/step - loss: 0.1545 - accuracy: 0.9515 - val_loss: 0.1370 - val_accuracy: 0.9519\n", + "Epoch 458/462\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1361 - accuracy: 0.9587 - val_loss: 0.1704 - val_accuracy: 0.9519\n", + "Epoch 459/462\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.1001 - accuracy: 0.9724 - val_loss: 0.1315 - val_accuracy: 0.9551\n", + "Epoch 460/462\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0779 - accuracy: 0.9795 - val_loss: 0.1282 - val_accuracy: 0.9551\n", + "Epoch 461/462\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0477 - accuracy: 0.9891 - val_loss: 0.1532 - val_accuracy: 0.9471\n", + "Epoch 462/462\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0347 - accuracy: 0.9928 - val_loss: 0.1634 - 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{0.1282}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1633\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m797.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m602.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m194.71 \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[|4596|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", + "288/288 [==============================] - 110s 363ms/step - loss: 0.1499 - accuracy: 0.9541 - val_loss: 0.1475 - val_accuracy: 0.9567\n", + "Epoch 464/468\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.1261 - accuracy: 0.9621 - val_loss: 0.1361 - val_accuracy: 0.9551\n", + "Epoch 465/468\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0878 - accuracy: 0.9745 - val_loss: 0.1346 - val_accuracy: 0.9503\n", + "Epoch 466/468\n", + "288/288 [==============================] - 102s 355ms/step - loss: 0.0606 - accuracy: 0.9841 - val_loss: 0.2047 - val_accuracy: 0.9583\n", + "Epoch 467/468\n", + "288/288 [==============================] - 102s 355ms/step - loss: 0.0432 - accuracy: 0.9887 - val_loss: 0.1369 - val_accuracy: 0.9663\n", + "Epoch 468/468\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.0380 - accuracy: 0.9915 - val_loss: 0.1519 - 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{0.9663}, \u001b[0m\u001b[0;33mloss{0.1346}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1235}]\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;32m0.1519\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 0.1235409379. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m828.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m618.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m210.06 \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[|4596|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", + "288/288 [==============================] - 109s 359ms/step - loss: 0.1469 - accuracy: 0.9547 - val_loss: 0.1825 - val_accuracy: 0.9503\n", + "Epoch 470/474\n", + "288/288 [==============================] - 102s 354ms/step - loss: 0.1177 - accuracy: 0.9630 - val_loss: 0.1198 - val_accuracy: 0.9663\n", + "Epoch 471/474\n", + "288/288 [==============================] - 101s 351ms/step - loss: 0.0872 - accuracy: 0.9765 - val_loss: 0.1564 - val_accuracy: 0.9567\n", + "Epoch 472/474\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.0597 - accuracy: 0.9811 - val_loss: 0.1515 - val_accuracy: 0.9551\n", + "Epoch 473/474\n", + "288/288 [==============================] - 101s 350ms/step - loss: 0.0402 - accuracy: 0.9896 - val_loss: 0.1597 - val_accuracy: 0.9631\n", + "Epoch 474/474\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.0264 - accuracy: 0.9933 - val_loss: 0.1660 - 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-470-0.9663.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;32m0.1198\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;32m0.1235409379 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1198362187\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;32m835.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m617.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m218.44 \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[|4596|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", + "288/288 [==============================] - 105s 345ms/step - loss: 0.1467 - accuracy: 0.9539 - val_loss: 0.1205 - val_accuracy: 0.9583\n", + "Epoch 476/480\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1251 - accuracy: 0.9617 - val_loss: 0.1933 - val_accuracy: 0.9519\n", + "Epoch 477/480\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0892 - accuracy: 0.9750 - val_loss: 0.2389 - val_accuracy: 0.9375\n", + "Epoch 478/480\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0748 - accuracy: 0.9817 - val_loss: 0.1450 - val_accuracy: 0.9599\n", + "Epoch 479/480\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0569 - accuracy: 0.9883 - val_loss: 0.1480 - val_accuracy: 0.9567\n", + "Epoch 480/480\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0379 - accuracy: 0.9924 - val_loss: 0.1521 - 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{0.1205}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1521\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m785.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m595.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m190.14 \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[|4596|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", + "288/288 [==============================] - 106s 348ms/step - loss: 0.1520 - accuracy: 0.9526 - val_loss: 0.1309 - val_accuracy: 0.9535\n", + "Epoch 482/486\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1393 - accuracy: 0.9604 - val_loss: 0.1419 - val_accuracy: 0.9551\n", + "Epoch 483/486\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0901 - accuracy: 0.9741 - val_loss: 0.1837 - val_accuracy: 0.9471\n", + "Epoch 484/486\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0580 - accuracy: 0.9846 - val_loss: 0.2463 - val_accuracy: 0.9471\n", + "Epoch 485/486\n", + "288/288 [==============================] - 103s 358ms/step - loss: 0.0489 - accuracy: 0.9878 - val_loss: 0.1897 - val_accuracy: 0.9471\n", + "Epoch 486/486\n", + "288/288 [==============================] - 97s 338ms/step - loss: 0.0324 - accuracy: 0.9928 - val_loss: 0.1908 - 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{0.1309}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1909\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m786.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m602.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m184.22 \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[|4596|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", + "288/288 [==============================] - 105s 347ms/step - loss: 0.1512 - accuracy: 0.9543 - val_loss: 0.1437 - val_accuracy: 0.9487\n", + "Epoch 488/492\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.1233 - accuracy: 0.9624 - val_loss: 0.2031 - val_accuracy: 0.9407\n", + "Epoch 489/492\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0911 - accuracy: 0.9743 - val_loss: 0.1860 - val_accuracy: 0.9487\n", + "Epoch 490/492\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0639 - accuracy: 0.9852 - val_loss: 0.1883 - val_accuracy: 0.9439\n", + "Epoch 491/492\n", + "288/288 [==============================] - 99s 345ms/step - loss: 0.0432 - accuracy: 0.9902 - val_loss: 0.1871 - val_accuracy: 0.9503\n", + "Epoch 492/492\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0345 - accuracy: 0.9939 - val_loss: 0.1876 - 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{0.1437}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1876\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m775.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m599.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m176.04 \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[|4596|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", + "288/288 [==============================] - 105s 348ms/step - loss: 0.1342 - accuracy: 0.9578 - val_loss: 0.1600 - val_accuracy: 0.9471\n", + "Epoch 494/498\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.1114 - accuracy: 0.9634 - val_loss: 0.1435 - val_accuracy: 0.9519\n", + "Epoch 495/498\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0785 - accuracy: 0.9761 - val_loss: 0.1542 - val_accuracy: 0.9455\n", + "Epoch 496/498\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0556 - accuracy: 0.9863 - val_loss: 0.1803 - val_accuracy: 0.9487\n", + "Epoch 497/498\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0337 - accuracy: 0.9904 - val_loss: 0.2065 - val_accuracy: 0.9519\n", + "Epoch 498/498\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0276 - accuracy: 0.9930 - val_loss: 0.2202 - 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{0.1435}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.2202\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m779.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m598.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m180.98 \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[|4596|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_d16-h15_m26_s14\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", + "288/288 [==============================] - 106s 348ms/step - loss: 0.1529 - accuracy: 0.9519 - val_loss: 0.1781 - val_accuracy: 0.9519\n", + "Epoch 500/504\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.1204 - accuracy: 0.9637 - val_loss: 0.2081 - val_accuracy: 0.9471\n", + "Epoch 501/504\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0903 - accuracy: 0.9774 - val_loss: 0.1785 - val_accuracy: 0.9455\n", + "Epoch 502/504\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0861 - accuracy: 0.9793 - val_loss: 0.1699 - val_accuracy: 0.9503\n", + "Epoch 503/504\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0558 - accuracy: 0.9889 - val_loss: 0.2281 - val_accuracy: 0.9439\n", + "Epoch 504/504\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.2068 - 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{0.1699}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.2068\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m803.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m598.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m205.17 \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[|4596|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", + "288/288 [==============================] - 106s 349ms/step - loss: 0.1595 - accuracy: 0.9526 - val_loss: 0.1337 - val_accuracy: 0.9535\n", + "Epoch 506/510\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.1299 - accuracy: 0.9595 - val_loss: 0.1963 - val_accuracy: 0.9471\n", + "Epoch 507/510\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0899 - accuracy: 0.9750 - val_loss: 0.1514 - val_accuracy: 0.9471\n", + "Epoch 508/510\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0613 - accuracy: 0.9856 - val_loss: 0.1598 - val_accuracy: 0.9519\n", + "Epoch 509/510\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0486 - accuracy: 0.9887 - val_loss: 0.1474 - val_accuracy: 0.9583\n", + "Epoch 510/510\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0338 - accuracy: 0.9922 - 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{0.9583}, \u001b[0m\u001b[0;33mloss{0.1337}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1777\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m788.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m598.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m190.16 \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[|4596|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", + "288/288 [==============================] - 106s 349ms/step - loss: 0.1429 - accuracy: 0.9550 - val_loss: 0.1308 - val_accuracy: 0.9599\n", + "Epoch 512/516\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.1189 - accuracy: 0.9624 - val_loss: 0.1477 - val_accuracy: 0.9439\n", + "Epoch 513/516\n", + "288/288 [==============================] - 103s 356ms/step - loss: 0.0851 - accuracy: 0.9743 - val_loss: 0.1600 - val_accuracy: 0.9599\n", + "Epoch 514/516\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0946 - accuracy: 0.9732 - val_loss: 0.1563 - val_accuracy: 0.9583\n", + "Epoch 515/516\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0620 - accuracy: 0.9843 - val_loss: 0.1932 - val_accuracy: 0.9567\n", + "Epoch 516/516\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0396 - accuracy: 0.9917 - val_loss: 0.2018 - 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{0.1308}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.2018\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m788.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m602.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m185.57 \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[|4596|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", + "288/288 [==============================] - 106s 349ms/step - loss: 0.1580 - accuracy: 0.9526 - val_loss: 0.1420 - val_accuracy: 0.9535\n", + "Epoch 518/522\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1201 - accuracy: 0.9606 - val_loss: 0.1359 - val_accuracy: 0.9599\n", + "Epoch 519/522\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0880 - accuracy: 0.9732 - val_loss: 0.1511 - val_accuracy: 0.9551\n", + "Epoch 520/522\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.0579 - accuracy: 0.9869 - val_loss: 0.1626 - val_accuracy: 0.9487\n", + "Epoch 521/522\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0429 - accuracy: 0.9885 - val_loss: 0.1717 - val_accuracy: 0.9519\n", + "Epoch 522/522\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0321 - accuracy: 0.9935 - val_loss: 0.1917 - 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{0.1359}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1917\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m790.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m601.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m188.97 \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[|4596|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", + "288/288 [==============================] - 106s 349ms/step - loss: 0.1642 - accuracy: 0.9523 - val_loss: 0.1420 - val_accuracy: 0.9519\n", + "Epoch 524/528\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.1266 - accuracy: 0.9624 - val_loss: 0.1771 - val_accuracy: 0.9423\n", + "Epoch 525/528\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0976 - accuracy: 0.9745 - val_loss: 0.1656 - val_accuracy: 0.9503\n", + "Epoch 526/528\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0712 - accuracy: 0.9832 - val_loss: 0.3041 - val_accuracy: 0.9295\n", + "Epoch 527/528\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0519 - accuracy: 0.9885 - val_loss: 0.1911 - val_accuracy: 0.9439\n", + "Epoch 528/528\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0398 - accuracy: 0.9919 - val_loss: 0.2027 - 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{0.1420}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.2027\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m793.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m599.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m194.66 \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[|4596|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", + "288/288 [==============================] - 106s 350ms/step - loss: 0.1462 - accuracy: 0.9537 - val_loss: 0.1649 - val_accuracy: 0.9487\n", + "Epoch 530/534\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.1269 - accuracy: 0.9608 - val_loss: 0.1626 - val_accuracy: 0.9503\n", + "Epoch 531/534\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0935 - accuracy: 0.9726 - val_loss: 0.2118 - val_accuracy: 0.9471\n", + "Epoch 532/534\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0630 - accuracy: 0.9828 - val_loss: 0.1800 - val_accuracy: 0.9487\n", + "Epoch 533/534\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0416 - accuracy: 0.9902 - val_loss: 0.1796 - val_accuracy: 0.9503\n", + "Epoch 534/534\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0309 - accuracy: 0.9924 - val_loss: 0.1865 - 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{0.1626}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1865\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m794.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m601.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m193.31 \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[|4596|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", + "288/288 [==============================] - 106s 349ms/step - loss: 0.1393 - accuracy: 0.9569 - val_loss: 0.1412 - val_accuracy: 0.9535\n", + "Epoch 536/540\n", + "288/288 [==============================] - 99s 345ms/step - loss: 0.1152 - accuracy: 0.9650 - val_loss: 0.1230 - val_accuracy: 0.9615\n", + "Epoch 537/540\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0842 - accuracy: 0.9776 - val_loss: 0.1290 - val_accuracy: 0.9535\n", + "Epoch 538/540\n", + "288/288 [==============================] - 98s 339ms/step - loss: 0.0552 - accuracy: 0.9852 - val_loss: 0.1595 - val_accuracy: 0.9599\n", + "Epoch 539/540\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0349 - accuracy: 0.9913 - val_loss: 0.1529 - val_accuracy: 0.9631\n", + "Epoch 540/540\n", + "288/288 [==============================] - 98s 341ms/step - loss: 0.0269 - accuracy: 0.9941 - val_loss: 0.1509 - 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{0.1230}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1509\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m794.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m600.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m194.14 \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[|4596|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", + "288/288 [==============================] - 103s 339ms/step - loss: 0.1523 - accuracy: 0.9545 - val_loss: 0.1391 - val_accuracy: 0.9599\n", + "Epoch 542/546\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1199 - accuracy: 0.9645 - val_loss: 0.1310 - val_accuracy: 0.9599\n", + "Epoch 543/546\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1091 - accuracy: 0.9700 - val_loss: 0.1356 - val_accuracy: 0.9567\n", + "Epoch 544/546\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0700 - accuracy: 0.9832 - val_loss: 0.1912 - val_accuracy: 0.9423\n", + "Epoch 545/546\n", + "288/288 [==============================] - 95s 330ms/step - loss: 0.0470 - accuracy: 0.9889 - val_loss: 0.1941 - val_accuracy: 0.9455\n", + "Epoch 546/546\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0378 - accuracy: 0.9924 - val_loss: 0.2505 - 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{0.9599}, \u001b[0m\u001b[0;33mloss{0.1310}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.2505\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m783.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m582.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m200.48 \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[|4596|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", + "288/288 [==============================] - 103s 341ms/step - loss: 0.1459 - accuracy: 0.9558 - val_loss: 0.1470 - val_accuracy: 0.9423\n", + "Epoch 548/552\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.1083 - accuracy: 0.9654 - val_loss: 0.1598 - val_accuracy: 0.9519\n", + "Epoch 549/552\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0897 - accuracy: 0.9728 - val_loss: 0.2083 - val_accuracy: 0.9295\n", + "Epoch 550/552\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0604 - accuracy: 0.9837 - val_loss: 0.1405 - val_accuracy: 0.9567\n", + "Epoch 551/552\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0372 - accuracy: 0.9930 - val_loss: 0.1642 - val_accuracy: 0.9615\n", + "Epoch 552/552\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0310 - accuracy: 0.9935 - val_loss: 0.1640 - 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{0.9631}, \u001b[0m\u001b[0;33mloss{0.1405}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1640\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m778.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m589.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m188.90 \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[|4596|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", + "288/288 [==============================] - 103s 341ms/step - loss: 0.1610 - accuracy: 0.9497 - val_loss: 0.1337 - val_accuracy: 0.9599\n", + "Epoch 554/558\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.1314 - accuracy: 0.9606 - val_loss: 0.1558 - val_accuracy: 0.9583\n", + "Epoch 555/558\n", + "288/288 [==============================] - 96s 331ms/step - loss: 0.1022 - accuracy: 0.9713 - val_loss: 0.1706 - val_accuracy: 0.9487\n", + "Epoch 556/558\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0733 - accuracy: 0.9809 - val_loss: 0.1606 - val_accuracy: 0.9551\n", + "Epoch 557/558\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0446 - accuracy: 0.9869 - val_loss: 0.1940 - val_accuracy: 0.9535\n", + "Epoch 558/558\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0362 - accuracy: 0.9935 - val_loss: 0.1896 - 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{0.1337}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1896\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m774.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m584.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m190.74 \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[|4596|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", + "288/288 [==============================] - 103s 341ms/step - loss: 0.1577 - accuracy: 0.9519 - val_loss: 0.1500 - val_accuracy: 0.9471\n", + "Epoch 560/564\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.1240 - accuracy: 0.9621 - val_loss: 0.1653 - val_accuracy: 0.9583\n", + "Epoch 561/564\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0861 - accuracy: 0.9785 - val_loss: 0.1530 - val_accuracy: 0.9519\n", + "Epoch 562/564\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.0746 - accuracy: 0.9839 - val_loss: 0.1829 - val_accuracy: 0.9487\n", + "Epoch 563/564\n", + "288/288 [==============================] - 96s 331ms/step - loss: 0.0517 - accuracy: 0.9880 - val_loss: 0.2554 - val_accuracy: 0.9343\n", + "Epoch 564/564\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0477 - accuracy: 0.9906 - val_loss: 0.2052 - 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{0.9583}, \u001b[0m\u001b[0;33mloss{0.1500}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.2052\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m774.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m585.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m189.19 \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[|4596|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", + "288/288 [==============================] - 103s 341ms/step - loss: 0.1427 - accuracy: 0.9558 - val_loss: 0.1746 - val_accuracy: 0.9423\n", + "Epoch 566/570\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1162 - accuracy: 0.9656 - val_loss: 0.2195 - val_accuracy: 0.9407\n", + "Epoch 567/570\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0783 - accuracy: 0.9778 - val_loss: 0.1312 - val_accuracy: 0.9599\n", + "Epoch 568/570\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0501 - accuracy: 0.9876 - val_loss: 0.1428 - val_accuracy: 0.9631\n", + "Epoch 569/570\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0392 - accuracy: 0.9913 - val_loss: 0.1463 - val_accuracy: 0.9647\n", + "Epoch 570/570\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0348 - accuracy: 0.9922 - val_loss: 0.1557 - 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{0.1312}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1557\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m781.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m587.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m194.10 \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[|4596|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", + "288/288 [==============================] - 103s 340ms/step - loss: 0.1458 - accuracy: 0.9560 - val_loss: 0.1656 - val_accuracy: 0.9471\n", + "Epoch 572/576\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1145 - accuracy: 0.9645 - val_loss: 0.1642 - val_accuracy: 0.9455\n", + "Epoch 573/576\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0828 - accuracy: 0.9793 - val_loss: 0.1567 - val_accuracy: 0.9519\n", + "Epoch 574/576\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0546 - accuracy: 0.9865 - val_loss: 0.2497 - val_accuracy: 0.9423\n", + "Epoch 575/576\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0376 - accuracy: 0.9913 - val_loss: 0.2912 - val_accuracy: 0.9247\n", + "Epoch 576/576\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0344 - accuracy: 0.9922 - val_loss: 0.2713 - 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{0.9519}, \u001b[0m\u001b[0;33mloss{0.1567}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.2713\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m778.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m585.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m192.64 \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[|4596|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", + "288/288 [==============================] - 104s 344ms/step - loss: 0.1430 - accuracy: 0.9556 - val_loss: 0.1709 - val_accuracy: 0.9439\n", + "Epoch 578/582\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.1185 - accuracy: 0.9645 - val_loss: 0.2330 - val_accuracy: 0.9391\n", + "Epoch 579/582\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0818 - accuracy: 0.9774 - val_loss: 0.1661 - val_accuracy: 0.9439\n", + "Epoch 580/582\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0554 - accuracy: 0.9865 - val_loss: 0.1825 - val_accuracy: 0.9519\n", + "Epoch 581/582\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.0348 - accuracy: 0.9933 - val_loss: 0.3574 - val_accuracy: 0.9231\n", + "Epoch 582/582\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0322 - accuracy: 0.9930 - val_loss: 0.3012 - 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{0.9519}, \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{0.1198}]\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;32m0.3013\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m783.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m587.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m196.23 \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[|4596|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", + "288/288 [==============================] - 104s 342ms/step - loss: 0.1409 - accuracy: 0.9582 - val_loss: 0.1433 - val_accuracy: 0.9455\n", + "Epoch 584/588\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.1160 - accuracy: 0.9645 - val_loss: 0.1213 - val_accuracy: 0.9551\n", + "Epoch 585/588\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0857 - accuracy: 0.9743 - val_loss: 0.1276 - val_accuracy: 0.9631\n", + "Epoch 586/588\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.0541 - accuracy: 0.9863 - val_loss: 0.1489 - val_accuracy: 0.9535\n", + "Epoch 587/588\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0343 - accuracy: 0.9928 - val_loss: 0.1314 - val_accuracy: 0.9631\n", + "Epoch 588/588\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0245 - accuracy: 0.9941 - val_loss: 0.1643 - 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{0.1213}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1643\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m778.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m587.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m190.23 \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[|4596|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", + "288/288 [==============================] - 104s 343ms/step - loss: 0.1491 - accuracy: 0.9565 - val_loss: 0.1288 - val_accuracy: 0.9631\n", + "Epoch 590/594\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.1144 - accuracy: 0.9626 - val_loss: 0.1257 - val_accuracy: 0.9551\n", + "Epoch 591/594\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0794 - accuracy: 0.9752 - val_loss: 0.1270 - val_accuracy: 0.9631\n", + "Epoch 592/594\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0501 - accuracy: 0.9880 - val_loss: 0.1505 - val_accuracy: 0.9615\n", + "Epoch 593/594\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0303 - accuracy: 0.9917 - val_loss: 0.1535 - val_accuracy: 0.9583\n", + "Epoch 594/594\n", + "288/288 [==============================] - 96s 332ms/step - loss: 0.0274 - accuracy: 0.9922 - val_loss: 0.1783 - 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{0.1257}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.1783\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m778.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m585.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m193.74 \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[|4596|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", + "288/288 [==============================] - 104s 342ms/step - loss: 0.1458 - accuracy: 0.9563 - val_loss: 0.1291 - val_accuracy: 0.9567\n", + "Epoch 596/600\n", + "288/288 [==============================] - 97s 337ms/step - loss: 0.0977 - accuracy: 0.9682 - val_loss: 0.1397 - val_accuracy: 0.9631\n", + "Epoch 597/600\n", + "288/288 [==============================] - 96s 333ms/step - loss: 0.0684 - accuracy: 0.9806 - val_loss: 0.1294 - val_accuracy: 0.9615\n", + "Epoch 598/600\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0491 - accuracy: 0.9850 - val_loss: 0.1605 - val_accuracy: 0.9519\n", + "Epoch 599/600\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0332 - accuracy: 0.9915 - val_loss: 0.2136 - val_accuracy: 0.9471\n", + "Epoch 600/600\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.0203 - accuracy: 0.9956 - val_loss: 0.2115 - 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{0.9631}, \u001b[0m\u001b[0;33mloss{0.1291}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1198}]\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;32m0.2115\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 0.1198362187. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m779.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m588.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m191.54 \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[|4596|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", + "288/288 [==============================] - 105s 345ms/step - loss: 0.1553 - accuracy: 0.9519 - val_loss: 0.1177 - val_accuracy: 0.9631\n", + "Epoch 602/606\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.1158 - accuracy: 0.9645 - val_loss: 0.1312 - val_accuracy: 0.9631\n", + "Epoch 603/606\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0859 - accuracy: 0.9774 - val_loss: 0.1675 - val_accuracy: 0.9471\n", + "Epoch 604/606\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0528 - accuracy: 0.9843 - val_loss: 0.2553 - val_accuracy: 0.9311\n", + "Epoch 605/606\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0318 - accuracy: 0.9922 - val_loss: 0.1886 - val_accuracy: 0.9599\n", + "Epoch 606/606\n", + "288/288 [==============================] - 97s 338ms/step - loss: 0.0256 - accuracy: 0.9946 - val_loss: 0.1781 - 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-601-0.9631.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;32m0.1177\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;32m0.1198362187 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1177379042\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;32m794.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m590.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m204.28 \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[|4596|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", + "288/288 [==============================] - 105s 346ms/step - loss: 0.1298 - accuracy: 0.9556 - val_loss: 0.1258 - val_accuracy: 0.9583\n", + "Epoch 608/612\n", + "288/288 [==============================] - 99s 345ms/step - loss: 0.1145 - accuracy: 0.9619 - val_loss: 0.1319 - val_accuracy: 0.9647\n", + "Epoch 609/612\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.0828 - accuracy: 0.9761 - val_loss: 0.1215 - val_accuracy: 0.9647\n", + "Epoch 610/612\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0568 - accuracy: 0.9863 - val_loss: 0.1591 - val_accuracy: 0.9599\n", + "Epoch 611/612\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.0372 - accuracy: 0.9902 - val_loss: 0.1479 - val_accuracy: 0.9663\n", + "Epoch 612/612\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0260 - accuracy: 0.9952 - val_loss: 0.2028 - 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{0.9663}, \u001b[0m\u001b[0;33mloss{0.1215}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1177}]\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;32m0.2028\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 0.1177379042. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m810.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m601.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m209.09 \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[|4596|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", + "288/288 [==============================] - 107s 352ms/step - loss: 0.1586 - accuracy: 0.9543 - val_loss: 0.1508 - val_accuracy: 0.9551\n", + "Epoch 614/618\n", + "288/288 [==============================] - 100s 347ms/step - loss: 0.1161 - accuracy: 0.9641 - val_loss: 0.1316 - val_accuracy: 0.9631\n", + "Epoch 615/618\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0873 - accuracy: 0.9748 - val_loss: 0.1605 - val_accuracy: 0.9503\n", + "Epoch 616/618\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0836 - accuracy: 0.9830 - val_loss: 0.1465 - val_accuracy: 0.9599\n", + "Epoch 617/618\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0512 - accuracy: 0.9878 - val_loss: 0.1940 - val_accuracy: 0.9423\n", + "Epoch 618/618\n", + "288/288 [==============================] - 100s 345ms/step - loss: 0.0374 - accuracy: 0.9917 - val_loss: 0.1663 - 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{0.9631}, \u001b[0m\u001b[0;33mloss{0.1316}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1177}]\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;32m0.1664\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 0.1177379042. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m807.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m604.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m203.25 \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[|4596|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", + "288/288 [==============================] - 107s 353ms/step - loss: 0.1540 - accuracy: 0.9497 - val_loss: 0.1838 - val_accuracy: 0.9487\n", + "Epoch 620/624\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.1293 - accuracy: 0.9604 - val_loss: 0.1644 - val_accuracy: 0.9487\n", + "Epoch 621/624\n", + "288/288 [==============================] - 101s 348ms/step - loss: 0.0905 - accuracy: 0.9715 - val_loss: 0.1282 - val_accuracy: 0.9551\n", + "Epoch 622/624\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0574 - accuracy: 0.9839 - val_loss: 0.1471 - val_accuracy: 0.9503\n", + "Epoch 623/624\n", + "288/288 [==============================] - 99s 344ms/step - loss: 0.0384 - accuracy: 0.9893 - val_loss: 0.1711 - val_accuracy: 0.9551\n", + "Epoch 624/624\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0252 - accuracy: 0.9935 - val_loss: 0.1798 - 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{0.1282}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1177}]\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;32m0.1798\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 0.1177379042. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m805.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m604.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m200.67 \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[|4596|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", + "288/288 [==============================] - 107s 351ms/step - loss: 0.1342 - accuracy: 0.9571 - val_loss: 0.1812 - val_accuracy: 0.9407\n", + "Epoch 626/630\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.0976 - accuracy: 0.9695 - val_loss: 0.1704 - val_accuracy: 0.9503\n", + "Epoch 627/630\n", + "288/288 [==============================] - 100s 348ms/step - loss: 0.0675 - accuracy: 0.9813 - val_loss: 0.1300 - val_accuracy: 0.9551\n", + "Epoch 628/630\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0411 - accuracy: 0.9913 - val_loss: 0.2162 - val_accuracy: 0.9487\n", + "Epoch 629/630\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.0319 - accuracy: 0.9928 - val_loss: 0.1620 - val_accuracy: 0.9583\n", + "Epoch 630/630\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0325 - accuracy: 0.9928 - val_loss: 0.2097 - 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{0.9583}, \u001b[0m\u001b[0;33mloss{0.1300}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9679}, loss{0.1177}]\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;32m0.2097\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 0.1177379042. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m808.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m606.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m201.47 \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[|4596|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", + "288/288 [==============================] - 108s 354ms/step - loss: 0.1502 - accuracy: 0.9513 - val_loss: 0.1185 - val_accuracy: 0.9599\n", + "Epoch 632/636\n", + "288/288 [==============================] - 100s 345ms/step - loss: 0.1239 - accuracy: 0.9624 - val_loss: 0.1506 - val_accuracy: 0.9535\n", + "Epoch 633/636\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.0859 - accuracy: 0.9767 - val_loss: 0.1487 - val_accuracy: 0.9535\n", + "Epoch 634/636\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0649 - accuracy: 0.9826 - val_loss: 0.1316 - val_accuracy: 0.9599\n", + "Epoch 635/636\n", + "288/288 [==============================] - 100s 346ms/step - loss: 0.0416 - accuracy: 0.9891 - val_loss: 0.1249 - val_accuracy: 0.9696\n", + "Epoch 636/636\n", + "288/288 [==============================] - 97s 336ms/step - loss: 0.0289 - accuracy: 0.9930 - val_loss: 0.1437 - val_accuracy: 0.9631\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-635-0.9696.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9696\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1249\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 0.969551\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 0.1177379042. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m819.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m603.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m215.40 \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[|4596|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", + "288/288 [==============================] - 104s 344ms/step - loss: 0.1485 - accuracy: 0.9521 - val_loss: 0.1166 - val_accuracy: 0.9647\n", + "Epoch 638/642\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.1223 - accuracy: 0.9624 - val_loss: 0.1186 - val_accuracy: 0.9631\n", + "Epoch 639/642\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0826 - accuracy: 0.9776 - val_loss: 0.1379 - val_accuracy: 0.9631\n", + "Epoch 640/642\n", + "288/288 [==============================] - 96s 334ms/step - loss: 0.0576 - accuracy: 0.9835 - val_loss: 0.1655 - val_accuracy: 0.9551\n", + "Epoch 641/642\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0394 - accuracy: 0.9891 - val_loss: 0.1735 - val_accuracy: 0.9599\n", + "Epoch 642/642\n", + "288/288 [==============================] - 97s 335ms/step - loss: 0.0250 - accuracy: 0.9954 - val_loss: 0.1763 - 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-637-0.9647.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;32m0.1166\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1177379042 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1166177914\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;32m800.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m588.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m211.87 \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[|4596|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", + "288/288 [==============================] - 107s 352ms/step - loss: 0.1277 - accuracy: 0.9587 - val_loss: 0.1339 - val_accuracy: 0.9615\n", + "Epoch 644/648\n", + "288/288 [==============================] - 98s 340ms/step - loss: 0.1085 - accuracy: 0.9678 - val_loss: 0.1385 - val_accuracy: 0.9583\n", + "Epoch 645/648\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0801 - accuracy: 0.9802 - val_loss: 0.1251 - val_accuracy: 0.9599\n", + "Epoch 646/648\n", + "288/288 [==============================] - 99s 343ms/step - loss: 0.0526 - accuracy: 0.9841 - val_loss: 0.1472 - val_accuracy: 0.9599\n", + "Epoch 647/648\n", + "288/288 [==============================] - 98s 342ms/step - loss: 0.0361 - accuracy: 0.9906 - val_loss: 0.1552 - val_accuracy: 0.9583\n", + "Epoch 648/648\n", + "288/288 [==============================] - 99s 342ms/step - loss: 0.0242 - accuracy: 0.9946 - val_loss: 0.1559 - 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{0.9615}, \u001b[0m\u001b[0;33mloss{0.1251}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9696}, loss{0.1166}]\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;32m0.1559\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1166177914. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m811.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m600.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m210.87 \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[|4596|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", + "288/288 [==============================] - 106s 348ms/step - loss: 0.1404 - accuracy: 0.9584 - val_loss: 0.1469 - val_accuracy: 0.9567\n", + "Epoch 650/654\n", + "288/288 [==============================] - 99s 341ms/step - loss: 0.1144 - accuracy: 0.9632 - val_loss: 0.1407 - val_accuracy: 0.9535\n", + "Epoch 651/654\n", + "288/288 [==============================] - 102s 353ms/step - loss: 0.1263 - accuracy: 0.9691 - val_loss: 0.1833 - val_accuracy: 0.9503\n", + "Epoch 652/654\n", + " 90/288 [========>.....................] - ETA: 51s - loss: 0.0787 - accuracy: 0.9826" + ] + } + ], "source": [ "import gc\n", "# Garbage Collection (memory)\n", @@ -1729,7 +20690,7 @@ "subset_epoch = 6 # subset_epoch: Number of epochs to train each subset.\n", "subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs.\n", "PL_epoch = 26 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only. Common values: 8, 16, 26, 32, 64, 128.\n", - "subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192.\n", + "subset_size = 4596 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192.\n", "Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n", "RES_Train = False # RES_Train: Resume training if True.\n", "MAX_LR = 0.011 # MAX_LR: Maximum learning rate.\n", @@ -2166,7 +21127,7 @@ " print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True)\n", " # If the accuracy is higher than the best_acc\n", " if acc > best_acc:\n", - " print_Color_V2(f'Improved model accuracy from{best_acc:10f} to {acc:10f}. Saving model.')\n", + " print_Color_V2(f'Improved model accuracy from {best_acc:10f} to {acc:10f}. Saving model.')\n", " # Update the best_acc\n", " best_acc = acc\n", " if SAVE_FULLM:\n", @@ -2432,7 +21393,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -2451,7 +21412,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -2471,14 +21432,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:52.565658900Z", "start_time": "2023-12-28T07:04:51.032425100Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Ldm7MpqamwOWXX345du7ciW9961toa2vD17/+9ZzblgvZfg8GzLana3cux8vm+2opyPZcd+zYkbKt/73t6vs0IaR8MPycENJjSRf+LXPWWWfhpJNOwo9+9COsXbsW48aNw+zZs/HCCy/gxhtvdHJoHnroobjkkkvw8MMPo7m5Gccccwzmzp2LVatWpezzZz/7GebPn4/x48fj2muvxYEHHogdO3Zg8eLFeO211wK/LGXDs88+6/ya7D/PH/7wh/jrX/+K008/HTfccAPq6+vxxBNPYM2aNXj22WehquZvWKeeeiqGDRuGY489FkOHDsWyZcvw4IMP4owzzkDfvn3R1NSEPffcExdeeCHGjRuHPn364LXXXsP777+PX/3qV3m1mxBCCCGk2Lz88sv4/PPPkUwm0djYiHnz5mHOnDkYOXIkXnzxRdTW1jrrPvTQQzjuuONwyCGH4Nprr8Xee++NxsZGLFy4EBs2bMBHH32U07GfeOIJPPzwwzjvvPOwzz77oLW1Fb///e/Rr18/TJ06Ne12hx9+OADgRz/6ES6++GKEw2GcddZZjtj51a9+FQcffDD+8Y9/YMyYMTjssMPy6JnsyfZ7sN321157Dffeey+GDx+O0aNHY/z48TkdL9vvq/nywQcf4K677kqZP3HixKzP9Y477sCCBQtwxhlnYOTIkdiyZQsefvhh7Lnnnk6Bo66+TxNCykiFqq4TQkhReeyxxwQA8f7772dcb+TIkeKMM87wzGttbRU33XSTGD58uAiHw2K//fYT99xzjzAMw7NeZ2enuOGGG8SgQYNE7969xVlnnSW+/PJLAUDceuutnnUbGxvF9ddfL0aMGCHC4bAYNmyYOOWUU8Tvfvc7Z501a9YIAOKxxx7L2Ob58+cLAGn/vfnmm0IIIb744gtx4YUXigEDBoja2lpx1FFHiZdeesmzr9/+9rfihBNOEIMGDRKRSETss88+4gc/+IFobm4WQggRi8XED37wAzFu3DjRt29f0bt3bzFu3Djx8MMPZ2wjIYQQQkg5sL/z2f9qamrEsGHDxOTJk8X//d//iZaWlsDtvvjiC3HFFVeIYcOGiXA4LPbYYw9x5plnimeeeSZl3/7vk/Z3sfnz5wshhFi8eLG45JJLxF577SUikYgYMmSIOPPMM8UHH3zg2S7oO+Kdd94p9thjD6GqqgAg1qxZ41n+i1/8QgAQP/3pT7PukxNPPFEcdNBBXa535ZVXipEjR3rmZfs9+PPPPxcnnHCC6NWrlwAgrrzyyozHAiCuv/76lPnZfF+1+/sf//hHl+ckHy/dvzvvvDPrc507d64455xzxPDhw0VNTY0YPny4uOSSS8SKFSucdbr6Pk0IKR+KED7fPCGEEEIIIYQQQsrO//3f/+Gmm27C2rVrU6qwE0II8UJRkxBCCCGEEEIIqTBCCIwbNw6DBg3yFKUhhBASDHNqEkIIIYQQQgghFaK9vR0vvvgi5s+fj08++QQvvPBCpZtECCHdAjo1CSGEEEIIIYSQCrF27VqMHj0aAwYMwHe+8x3cfffdlW4SIYR0CyhqEkIIIYQQQgghhBBCuhVqpRtACCGEEEJItTBz5kwceeSR6Nu3L4YMGYJzzz0Xy5cv96wTjUZx/fXXY9CgQejTpw8uuOACNDY2etZZv349zjjjDNTV1WHIkCH4wQ9+gGQyWc5TIYQQQgjp0VDUJIQQQgghxOKNN97A9ddfj3fffRdz5sxBIpHAqaeeivb2dmedm266Cf/617/wj3/8A2+88QY2bdqE888/31mu6zrOOOMMxONxvPPOO3jiiSfw+OOP45ZbbqnEKRFCCCGE9EgYfh6AYRjYtGkT+vbtC0VRKt0cQgghhJCcEUKgtbUVw4cPh6ryd+x82bp1K4YMGYI33ngDJ5xwApqbmzF48GA89dRTuPDCCwEAn3/+OcaMGYOFCxfi6KOPxssvv4wzzzwTmzZtwtChQwEAjzzyCG6++WZs3boVNTU1XR6X30cJIYQQ0t0p9fdRVj8PYNOmTRgxYkSlm0EIIYQQUjBffvkl9txzz0o3o9vS3NwMAKivrwcAfPjhh0gkEpg0aZKzzgEHHIC99trLETUXLlyIQw45xBE0AWDKlCn49re/jaVLl+KrX/1qynFisRhisZgzvXHjRhx44IGlOi1CCCGEkLJRqu+jFDUD6Nu3LwCz0/v161ey4yQSCcyePRunxm9AOLEN39h4Hx6/6ZvO8ltf+BTPLt4IAIiEVXz448kla0t3wum3U09FOByudHO6Beyz/GC/5Qf7LT/Yb/lR8X777B7g07uAkV8Dxv++/MfPQEtLC0aMGOF8ryG5YxgGbrzxRhx77LE4+OCDAQANDQ2oqanBgAEDPOsOHToUDQ0NzjqyoGkvt5cFMXPmTNx+++0p8//whz+grq6u0FMhhBBCCCk7HR0duOaaa0r2fZSiZgB2iE+/fv1KLmrW1dWhX1hDOA6EIxHP8Wp69YEaMb/EKiG1pG3pTjj91q8fB/5Zwj7LD/ZbfrDf8oP9lh8V77feYaAOQJ0GVOnnNEOX8+f666/Hp59+irfeeqvkx5oxYwamT5/uTNui9LnnntsjvgMmEgnMmTMHkydP5jMuT9iHhcH+Kwz2X2Gw/wqD/VcYley/lpYWXHPNNSX7PkpRsyow31x/dlMBgZP7vgcDCt7pGF+BdhFCCCEkI0K3/hqVbQcpOtOmTcNLL72EBQsWeMKlhg0bhng8jqamJo9bs7GxEcOGDXPWee+99zz7s6uj2+v4iUQiiEQiKfPD4XCPGsD1tPOpBOzDwmD/FQb7rzDYf4XB/iuMSvRfqY/HrPFVgGKLmvAOiDQjht+M/CkeGTkTGuKVaBohhBBCMuGImnpl20GKhhAC06ZNwz//+U/MmzcPo0eP9iw//PDDEQ6HMXfuXGfe8uXLsX79ekyYMAEAMGHCBHzyySfYsmWLs86cOXPQr18/5skkhBBCCCkSdGpWA5YNVxheq6aGGCJq0nrNwRIhhBBSdThiJp2aPYXrr78eTz31FF544QX07dvXyYHZv39/9OrVC/3798fVV1+N6dOno76+Hv369cN3v/tdTJgwAUcffTQA4NRTT8WBBx6Iyy+/HL/4xS/Q0NCAH//4x7j++usD3ZiEEEIIISR3KGpWBbZT0x9/LqQ1KGoSQgghVQfDz3scv/nNbwAAEydO9Mx/7LHH8I1vfAMAcN9990FVVVxwwQWIxWKYMmUKHn74YWddTdPw0ksv4dvf/jYmTJiA3r1748orr8Qdd9xRrtMghBBCCOnxUNSsBuyEqb4BkSxkKhwsEUIIIdUHRc0eh/AnOQ+gtrYWDz30EB566KG064wcORKzZs0qZtMIIYQQQogEc2pWBbao6ZsrfalWFSOrL9mEEEIIKSPMqUkIIYQQQkhFoKhZFQQXCpLzc6kQMKhpEkIIIdUFnZqEEEIIIYRUBIqa1YBdKMjnxFQk14dWLKemoQOx7YXvhxBCCCEsFEQIIYQQQkiFoKhZFVjh5xA+4VJ2aurFcWq++w3guWFA66oi7IwQQgjZxaFTkxBCCCGEkIpAUbMasJyaCgQ8mqY0QFIVkVodPR+aPwVEEmhZUfi+CCGEkF0dipqEEEIIIYRUBIqaVYDiODUBQ1I1FUnE1GCgKHWCnEEXB1+EEEJIwbBQECGEEEIIIRWBomZV4Do15RBzRRIeFUUUR9S0hVIOvgghhJDCMZhTkxBCCCGEkEpAUbMaUGRRMzinpgbdtyxPbKcmRU1CCCGkcBh+TgghhBBCSEUIVboBBPA6NeXwc1d4NHNqFgFH1OTgixBCCCkYipqEEEJIybjttkq3oDioKjBuHDBzJmDk8JWhp5w/IaWCTs1qwHZqKvCGn0sCpwqjOE5N0KlJCCGEFA3m1CSEEEIIIaQiUNSsApQ0Tk3ITk0YxTGBCObUJIQQQooGnZqEEEIIIYRUBIqa1YCUU1MeE8lOTU0xUJQAdObUJIQQQoqHYKEgQgghhBBCKgFFzapAcV7pslNTUjhVX2X0/GFOTUIIIaRo0KlJCCGEEEJIRaCoWQ0o7p901c9VxYBg9XNCCCGkuuDnKiGEEEIIIRWBomYVoDhOTV/1c49T0yiSU5M5NQkhhJCiQacmIYQQQgghFYGiZlVgVz8XkM2YiuTULH5OTQ6+CCGEkIJhTk1CCCGEEEIqAkXNasApFJQ+/FyBV/DMG4bJEUIIIcWDTk1CCCGEEEIqAkXNqsCtfq5LMeaq36lZ1EJBFDUJIYSQgqGoSQghhBBCSEWgqFkVuKKmR7iUBkgaDJ+LM0/o1CSEEEKKhyNq8nOVEEIIIYSQckJRsxpQXFHTK1xKRYOUIomaTqEgOkoIIYSQgqFTkxBCCCGEkIpAUbMqkHNqSnMl14eGIoWf06lJCCGEFA8WCiKEEEIIIaQiUNSsCtzq57IbU86pqbJQECGEEFJ90KlJCCGEEEJIRaCoWQUIK/wcEDCM4OrnqmJAoIg5NekoIYQQQgqHOTUJIYQQQgipCBQ1q4I04efwujaNolQ/t3ZicPBFCCGEFAydmoQQQgghhFQEippVQbpCQVL1c8WAYPVzQgghpLpgTk1CCCGEEEIqAkXNaiBN9XNFcn2Yy4pxMIqahBBCSNGgU5MQQgghhJCKQFGzKrALBcFTDEjxOTXBnJqEEEJIdUFRkxBCCCGEkIrQLUTNhx56CKNGjUJtbS3Gjx+P9957L+P6TU1NuP7667H77rsjEongK1/5CmbNmlWm1uaD69TUjXTVz4ucU5NOTUIIIaRwWCiIEEIIIYSQihCqdAO64umnn8b06dPxyCOPYPz48bj//vsxZcoULF++HEOGDElZPx6PY/LkyRgyZAieeeYZ7LHHHli3bh0GDBhQ/sZnS5rwc3/1c4M5NQkhhJDqgk5NQgghhBBCKkLVi5r33nsvrr32Wlx11VUAgEceeQT//ve/8eijj+KHP/xhyvqPPvooduzYgXfeeQfhcBgAMGrUqHI2OQ9kUVOaK2SnpkAxNE1X1OTgixBCCCkYFgoihBBCCCGkIlS1qBmPx/Hhhx9ixowZzjxVVTFp0iQsXLgwcJsXX3wREyZMwPXXX48XXngBgwcPxqWXXoqbb74ZmqYFbhOLxRCLxZzplpYWAEAikUAikSjiGXmx9y2LlZ5jSm5KTTEQL0J7QsKAAkDXEzBKeG6lxO6DUr43PQ32WX6w3/KD/ZYflew3ZdO/oOxcAuPAHzvRA92FSl9vIUOHAkAIA8kqu+Z5DxJCCCGEkJ5MVYua27Ztg67rGDp0qGf+0KFD8fnnnwdus3r1asybNw+XXXYZZs2ahVWrVuE73/kOEokEbr311sBtZs6cidtvvz1l/uzZs1FXV1f4iXRBU1MTBsF0ar6zcCEal5rz29pagF7maxU63nrrLaztXdixzrGcJOvWfIFPNlVzntGumTNnTqWb0O1gn+UH+y0/2G/5UYl+m9TxHfQWjXhjzWC0qnuV/fjFoFLX22mxTkQAGHqi6vJ3d3R0VLoJhBBCCCGElIyqFjXzwTAMDBkyBL/73e+gaRoOP/xwbNy4Effcc09aUXPGjBmYPn26M93S0oIRI0bg1FNPRb9+/UrW1kQigTlz5mDAwHpgO6AoAkeNPxrjR9cDANY9sdhZV1UEjjnmOBy8RwHtEQJ4xnw5cuQIjDhsaiHNrxh2v02ePNlJMUAywz7LD/ZbfrDf8qOS/Rb6lwJEgROOOQKi/oiyHrtQKn29hZ7XgASgKsDUqdX1uWpHnpDcWbBgAe655x58+OGH2Lx5M/75z3/i3HPPdZYraRzNv/jFL/CDH/wAgJn+aN26dZ7lM2fODEyfRAghhBBCcqeqRc3ddtsNmqahsbHRM7+xsRHDhg0L3Gb33XdHOBz2hJqPGTMGDQ0NiMfjqKmpSdkmEokgEomkzA+Hw2UZICmKW4ReVTXnmKrixqVrMKCFtMLaI+XR1BRA6+ZiQ7nen54E+yw/2G/5wX7Lj4r0m5XuJKQpQDd9zyp2vVl9p8Couuu92trTnWhvb8e4cePwzW9+E+eff37K8s2bN3umX375ZVx99dW44IILPPPvuOMOXHvttc503759S9NgQgghhJBdkKoWNWtqanD44Ydj7ty5zq/jhmFg7ty5mDZtWuA2xx57LJ566ikYhgFVNcXCFStWYPfddw8UNKsJBfAWCoK3UJBRaKEguTgQq58TQghxsIvI8bMhZ1j9vEdy+umn4/TTT0+73P/j+gsvvICTTjoJe++9t2d+37590/4QTwghhBBCCqOqRU0AmD59Oq688kocccQROOqoo3D//fejvb3dqYZ+xRVXYI899sDMmTMBAN/+9rfx4IMP4nvf+x6++93vYuXKlfjpT3+KG264oZKnkRlFrn7uKpceUVPRPcvygqImIYSQIAxbmONnQ+7In60GIEVfkF2DxsZG/Pvf/8YTTzyRsuxnP/sZ7rzzTuy111649NJLcdNNNyEUCv76XanCleWi0kW9egLsw8Jg/xVGpfpP7SEfq6qa8PzNFl6uJrx/C6OS/VfqY1a9qHnRRRdh69atuOWWW9DQ0IBDDz0Ur7zyilM8aP369Y4jEwBGjBiBV199FTfddBPGjh2LPfbYA9/73vdw8803V+oUsiALURMChWqagLQDOkoIIYTYCIqaeSP3GUXNXZInnngCffv2TQlTv+GGG3DYYYehvr4e77zzDmbMmIHNmzfj3nvvDdxPpQtXlgsWkSsc9mFhsP8Ko9z9N25cWQ9Xcg45JLf+q7IahBWH929hVKL/Sl24supFTQCYNm1a2nDz119/PWXehAkT8O6775a4VcUkjagpvdYUA4JOTUIIISWB4ed54xc1yS7Ho48+issuuwy1tbWe+XIRyrFjx6Kmpgbf+ta3MHPmzMBc7pUqXFkuKl3UqyfAPiwM9l9hVKr/rIDMbo+qJnDIIXPwySeTYRjZ99+MGSVsVDeC929hVLL/Sl24sluImj0eO/xcAQxDnu1OKDBQsFETFDUJIYQEYH8mGPxsyAkhfEImRc1djTfffBPLly/H008/3eW648ePRzKZxNq1a7H//vunLK904cpy0dPOpxKwDwuD/VcY5e4/o4d9tBpGOCdRk5eqF96/hVGJ/iv18RgjVRUo1l+/U1OuVm7AKLRSEJ2ahBBCgmD4eX74nZl0au5y/PGPf8Thhx+OcVnERy5ZsgSqqmLIkCFlaBkhhBBCSM+HTs2qQA4/l+f6cmoWfBzm1CSEEBIARc388PcX+6/H0NbWhlWrVjnTa9aswZIlS1BfX4+99toLgBlO9Y9//AO/+tWvUrZfuHAhFi1ahJNOOgl9+/bFwoULcdNNN+HrX/86Bg4cWLbzIIQQQgjpyVDUrAac6ufw5M30Vj83WP2cEEJIaXA+H/iDV06kiJoB/RfdCjTOB/Y8F9BqytIsUjgffPABTjrpJGfaznV55ZVX4vHHHwcA/O1vf4MQApdccknK9pFIBH/7299w2223IRaLYfTo0bjppps8OTMJIYQQQkhhUNSsClynpu4RNd3XKgwUbNWkqEkIIcQPPxvyJ6W/AkTNj38MrPodcMxfgVEXl6VZpHAmTpzYZYHG6667Dtddd13gssMOO6ybFa0khBBCCOl+MKdmVdB1+LmmGCg0paZ34Eo3DiGEEPiqd1PUzIlsnZoAEN1c+vYQQgghhBCyC0FRsxqQqp+nDT9HEcLPWf2cEEKIH1mIY/Xz3Mgmp6Y9T4+Vvj2EEEIIIYTsQlDUrApkp6Zc/VwOPy9CoSBZFKWoSQghBKBTsxCycWra6xgUNQkhhBBCCCkmFDWrAknUlMZDiuIOlrRiFAqiU5MQQogfipr5k4uoSacmIYQQQgghRYWiZjVghZ/DXyhIGhwpxS4UxAq3hBBCAIqahZBNoSD7s5dOTUIIIYQQQooKRc0qQoE/p6b7uihOTVa4JYQQ4oefDfnDnJqEEEIIIYRUDIqaVYFdKCh99XMVAgVHn4M5NQkhhPigUzN/csqpGS19ewghhBBCCNmFoKhZDShpCgXJombRnZoMPyeEEAKKmoXAnJqEEEIIIYRUDIqaVYHi/C87NVXI1c+Nwqufs1AQIYQQPww/z59sRE0wpyYhhBBCCCGlgKJmVSBXP/cKmTaaYnjybeYFB66EEEL8eJyadPHnRDaFggw6NQkhhBBCCCkFFDWrgWzCz+HNt5kXgjk1CSGE+GD4ef7kUiiITk1CCCGEEEKKCkXNqkBxXqUtFKQYRSgUxJyahBBCfFDUzJ9cws91FgoihBBCCCGkmFDUrCIUwBNirvhyaha3UBAHroQQQsDPhkJgoSBCCCGEEEIqBkXNqsByairpw8+1YhQK4sCVEEKIHzo18ydFxMwgajL8nBBCCCGEkKJCUbMakHJq6tJ4SBY1FUUUXiiI4eeEEEL8UNTMH+bUJIQQQgghpGJQ1KwKggsFqX6nJgsFEUIIKTp08edNVuHndk5NipqEEEIIIYQUE4qaVYHi/C/ShZ8rRcipyYErIYQQP3Rq5k8uOTXp1CSEEEIIIaSoUNSsBhTZqSnNhlfgLNypSVGTEEKID4qa+ZNToSBWPyeEEEIIIaSYUNSsCoLDz4vu1BTMqUkIIcSHQVEzb1L6i9XPCSGEEEIIKRcUNasCS9RUAMOQRU05v6Yo3KkJ5tQkhBDihz945Y2RTaEgq08Zfk4IIYQQQkhRoahZDaQNP3cHl6qiQ6CYTk2KmoQQQsDw80LIKadmHEX4dZIQQgghhBBiQVGzKlCsv+mrn6s+wTMvPIMtunEIIYSAomYh5CJqAqawSQghhBBCCCkKFDWrgmCnpurLqVm4wUMabPlD5gghhOya0MWfP7nk1ARYLIgQQgghhJAiQlGzGnDCzwEhgnNq+osI5YVgTk1CCCE+6NTMH39/Bf1gKIvGzKtJCCGEEEJI0aCoWRXYhYIEdCNN9XPohWbUhNdBwvBzQgghoKhZCDk7NSlqEkIIIYQQUiwoalYF6cLPpfyaivC4OPOCIYaEEEL8UNTMn5xzalLUJIQQQgghpFhQ1KwGpPBzQwQ7NVUUIacmRU1CCCF++NmQP7mKmnRqEkIIIYQQUjQoalYFrlNTpBM1lSLk1AQHroQQQnzQqZk/KaImc2oSQgghhBBSLihqVgVpws8Vb05No2CnplwoiDk1CSGEgKJmIbD6OSGEEEIIIRWDomY1YIWfA4Ce1qlpFDenZtA0IYSQXQ+Gn+cPc2oSQgghhBBSMShqVgVu9XNZuPQUCoIoPKem30HCwSshhBCPU5M/duVEV6KmEID0Wc6cmoQQQgghhBQPippVgRR+bshzpfBzxYAAnZqEEEKKDMPP86dLUdM3TacmIYQQQgghRYOiZhUgpPBzb/Vz2alpFJ5T0y+KcvBKCCGEomb+dFUoyD9NpyYhhBBCCCFFg6JmFZFSKCglp2aBB0hxkHDwSoiH6BYUIc8D6Yns/AhomFfpVpQG5tTMn64KBaWImiwU1F1YsGABzjrrLAwfPhyKouD555/3LP/GN74BRVE8/0477TTPOjt27MBll12Gfv36YcCAAbj66qvR1tZWxrMghBBCCOnZUNSsChTnf0N43Znua+FZlhcUNQlJz8ZZwHNDgc9+XumWkGrkjbOA+acCse2VbknxoVMzf7osFMTw8+5Ke3s7xo0bh4ceeijtOqeddho2b97s/PvrX//qWX7ZZZdh6dKlmDNnDl566SUsWLAA1113XambTgghhBCyyxCqdAMI4MmpKYefK5LAWYzq5ykOEubUJMShean199PKtoNUJ9EtpoAV3wlEBlW6NcWFomb+dJVDk+Hn3ZbTTz8dp59+esZ1IpEIhg0bFrhs2bJleOWVV/D+++/jiCOOAAD8+te/xtSpU/HLX/4Sw4cPL3qbCSGEEEJ2NbqFqPnQQw/hnnvuQUNDA8aNG4df//rXOOqoowLXffzxx3HVVVd55kUiEUSjVRzypbjVz9OGn6MY4efMqUlIWuz7gfcFCUIkrb898fpg+Hne5JpTk07NHsXrr7+OIUOGYODAgTj55JNx1113YdAg80ePhQsXYsCAAY6gCQCTJk2CqqpYtGgRzjvvvJT9xWIxxGLuNdLS0gIASCQSSCQSJT6b0mOfQ084l0rBPiwM9l9hVKr/1B4SW6qqCc/fbOHlasL7tzAq2X+lPmbVi5pPP/00pk+fjkceeQTjx4/H/fffjylTpmD58uUYMmRI4Db9+vXD8uXLnWlFKsRTnQSHn3uqnxelUBDDzwlJj3V/0MFM/AjRs0VvOjXzp6ucmgZFzZ7KaaedhvPPPx+jR4/GF198gf/93//F6aefjoULF0LTNDQ0NKR8Tw2FQqivr0dDQ0PgPmfOnInbb789Zf7s2bNRV1dXkvOoBHPmzKl0E7o97MPCYP8VRrn7b9y4sh6u5BxySG79N2tWiRrSTeH9WxiV6L+Ojo6S7r/qRc17770X1157reO+fOSRR/Dvf/8bjz76KH74wx8GbqMoStpwoOrEFl2FJ8RclaufKwLCX708V7oKkyNkV8bowaIVKQz5mjCSlWtHqTAoauZNrjk1GX7eY7j44oud14cccgjGjh2LffbZB6+//jpOOeWUvPY5Y8YMTJ8+3ZluaWnBiBEjcOqpp6Jfv34Ft7nSJBIJzJkzB5MnT0Y4HK50c7ol7MPCYP8VRqX6b+bMsh2qpKhqAoccMgeffDIZhpF9/82YUcJGdSN4/xZGJfvPjjwpFVUtasbjcXz44YeYId3Jqqpi0qRJWLhwYdrt2traMHLkSBiGgcMOOww//elPcdBBB6Vdv1LhPva+DUNAg5lTM5E0nPmKL/w8mdQLao+SjHve8EQi2i397LSe5w77rGtUPQENgKEnoPv6i/2WGz2u3/Qo7I/+Uj43KxbWpcehWa8NPelc/92FSl5vatLtOwDQkwkYcjvi7rUDAHqi3bu8xPSYe7AbsPfee2O33XbDqlWrcMopp2DYsGHYsmWLZ51kMokdO3ak/eE9EokgEomkzA+Hwz1qANfTzqcSsA8Lg/1XGOXuP6OH+XAMI5yTqMlL1Qvv38KoRP+V+nhVLWpu27YNuq5j6NChnvlDhw7F559/HrjN/vvvj0cffRRjx45Fc3MzfvnLX+KYY47B0qVLseeeewZuU+lwny+//BJ7w/RrNjQ0YJblMR8tOUBUxcDKlaswK7Yi7+PskfwPjpCm5897DZ3q0LTrVzu0nucO+yw9B8SXY38AjY2b8Z4vzoP9lh89pd800YkzrdfvvPUmmrTNJT1euftt78RSHGK9bm7agQXdNM6pEtfbmPgKfEWaXrr0E6xZ4fZfrbENU6Tla79Yjk83lK9/Sx3uQ1w2bNiA7du3Y/fddwcATJgwAU1NTfjwww9x+OGHAwDmzZsHwzAwfvz4SjaVEEIIIaTHUNWiZj5MmDABEyZMcKaPOeYYjBkzBr/97W9x5513Bm5TqXAf2wI8Yq+9gNWmU3PwkKGYOvWrAIAVT/7AWVeDgb333QdTJ+2X9/GUdTuB99zpkyaeAPTZJ+/9VQpaz3OHfdY16qfvAsuAoYN3w9TjpwJgv+VLj+u3eBPwgvny2GOOhhgUXKiuUCrVb+ryz4GPzdf9+/fF1ElTy3bsYlDJ6039+C3ATeGNgw48AGP2k/qvfR0gaZij9hqOvQ4vX/+WOtynJ9PW1oZVq1Y502vWrMGSJUtQX1+P+vp63H777bjgggswbNgwfPHFF/if//kf7LvvvpgyxZSxx4wZg9NOOw3XXnstHnnkESQSCUybNg0XX3wxK58TQgghhBSJqhY1d9ttN2iahsbGRs/8xsbGrHNmhsNhfPWrX/V8MfVT6XAfVTPfBkURgKI4x5TDzxVFQFXVwtrjKx0X1rRu7Wen9Tx32GcZsFLbqooB1ddH7Lf86DH9prvF5kIaSv7cLHu/qfLL1Ou/u1CR681XkVVTFWhyG0KadzkS3uUlpkfcfxXigw8+wEknneRM2z9+X3nllfjNb36Djz/+GE888QSampowfPhwnHrqqbjzzjs93yf/8pe/YNq0aTjllFOgqiouuOACPPDAA2U/F0IIIYSQnkpVi5o1NTU4/PDDMXfuXJx77rkAAMMwMHfuXEybNi2rfei6jk8++QRTp1az88Sufi481c/lQkGsfk5IiXGqW/ewxD09jWQnsGUBMPREQKstzzGFVByoJz435Wu+J55fKfFXN08pyOdbzkJB3YaJEyd6ijf6efXVV7vcR319PZ566qliNosQQgghhEioXa9SWaZPn47f//73eOKJJ7Bs2TJ8+9vfRnt7u1MN/YorrvAUErrjjjswe/ZsrF69GosXL8bXv/51rFu3Dtdcc02lTiELFOd/XVIuZaemphgewTMvuhpsEbIrY98fvC+qm/e+Bbx+GvD+9eU7Zo8XNVn9PG+6qn7uX25ES9seQgghhBBCdiGq2qkJABdddBG2bt2KW265BQ0NDTj00EPxyiuvOMWD1q9fD1UKq965cyeuvfZaNDQ0YODAgTj88MPxzjvv4MADD6zUKXSN4jo1Zd1SVXwCZ8FOTf8O6EgjxMFxalLUqWrWPmn+Xf0ocPQfy3NMg6ImSUOKqNnFNJ2ahBBCCCGEFI2qFzUBYNq0aWnDzV9//XXP9H333Yf77ruvDK0qJna+Nm/4OZ2ahJQROjVJOmSnpixw9hTkzwZ/OHU1Ed0CRAY7PwRWBSnPC//nrG/aoKhJCCGEEEJIsaj68PNdA8upqcAjXGrS4Ej1uTjzgqImIemhU5Okg07NyrPlTeC5ocBHM7pet5zkGn5OpyYhhBBCCCFFg6JmNaDYf4SnGJACuWhQCQoFVbMjh5CyYzs1mZahW6CWsaozc2pWnual5t+mpeU97qrfAXMnAYmW4OU559SkqEkIIYQQQkixoKhZFUjVzyXlUlUMz2tRaFLNFKsnxRtCHOjU7F6oNeU7FkXNymO7ZcvdvpWPAI1zga1vBy9nTk1CCCGEEEIqBkXNqkASNT05Nd3XIcVg+DkhpYQ5NbsXaqR8x/KEn/fwnJrVev2LComatrPSiAcv79Kp6Y+QYPVzQgghhBBCigVFzWrAqX4OT4i56nNSCqNQZyVFTULSQqdm96JSTs2emLajOzg1HVGzzKKykfD+9dNloSA6NQkhhBBCCCkVFDWrAtepKYQcfu6zZhY62OzKQULIrgydmt0LrVJOzR54fXjOqUo/FyoVfp6tqKlYX6eYU5MQQgghhJCyQVGzKlCcVxmdmgWLkEUWSQnpSdCp2b1gTs3iIX+2VKsTtVL3Z7aipn09svo5IYQQQgghZYOiZhWhKAK64a14LmMYBYbdMacmIekRBVQ/b/8yoBAXKSnlzKkpenpOze4Ufl7m9omE92/KctupGfZOO8ut54nWy/xLpyYhhBBCCCFFg6JmNaDITk2pUJAv/FzXC3RqUtQkJD35OsE2zgJe2Av45LaiN4lkoJxOzV0p/Lxaz69S4ee6VSCoS6emLWqm+Zy1RU06NQkhhBBCCCkaFDWrAjmnpjtX9YWL6+kGVVnTA3JqbnkToVcOxmD9o0q3hPQ48syp2fK5+bf5s+I2h6TieUAy/LxodAdR034PCo1YyPm4XYWfW88NW9RMVyjIzgErknR1E0IIIYQQUiQoalYFrqgpOzVTw8+LXSioSgevmXjtBCitK3BM9NZKt4T0NPJ1atpiix4tbntIKrKwVNZCQdJxu+Nzs0ukz4ZqPb9KhZ8XnFPTFj0j3nmtq4C2teUXaQkhhBBCCOlBUNSsBhRb1PSGn/urn+t6oYMfFgoiJC35Vj+31zcoapYcvdN9XSmnZk8UoYxu4NSsdPXzrnJqqulyavqcmva+/rUf8OJoILateG0lhBBCCCFkF4OiZlUgOzXNOUKIMjg1u2H4OSGlIl+npkGnZtnQO9zXSqh8x92VcmpCVGd4dCWcmoYO58dAO7emn2xzaqq17jw5r6YTtk4IIYQQQgjJFYqaVYElaipu+LkQgKr4RU0WCrIxeOmSYpNv9XP7PpJdhKQ0yH1czudXT8+pmS4PZDVRCaem7M7Mtvp5Vzk1Ae91XE5xnhBCCCGEkB4Gv01XA0pqTk2B1EJBRqGFgnqQqKmjjPn0yK4Bc2pWP0nJqVlWx14PFzUDQ6ar7OuBc3+WMfxf/szNN6emLXLK6RI8aRTo1CSEEEJIF3x8W3mPN7bMxyOkAGh3qwqknJq2WUwIKP7wc73QcPGek1NTVyhqkiJTaE5Nipqlpyqcmj0wp2a6PJDVREXCz6WQ8y5FzTQ5NQ1puWJ95aJTkxBCCCGEkKJAUbMqUKy/AkJ2avoLBRU6mO5BOTXp1CRFh07N6qdSTs2eHn6eImpW4WdDJcLPc3JqdpFTU9FcAZNOTUIIIYQQQooCRc1qQAo/1+Wcmv7w84Kdmgw/JyQ9eTo1bbGF1c9LT6Wcmj0+/LwbfDZUxKmZQ07NXETNZKc0TwEhhBBCCCEkPyhqVgV2oSBI1c8NaCmFgort1KzCgWsmpOrvDD8nRcdxauZbKIiiZsmpivDzbvbczIbuFH5e6OdgTsfMwamZrlCQM626wqd9HTP0nBBCCCGEkIKgqFkVuE5NJ/xciJS1DCPDQFMI4IPvASseznAc/z6rMMQwE8kW9yWdmqTY5J1Tk+HnZaMaCgWVU1QrF91B1OwJ4eeqHX4e9W5DCCGEEEIIyQvaBKoBT/Vza540MBJQzaJBmQZzjfOBFQ+Yr7/yneB1urtTM9EiTTBkjxSZvHNqStsZSVe4IMWHTs3S0B0+Gyodfp5voSBnuea6OenUJIQQQkiW3HYbMHFQeY/5+nPlPV4mbrut0i0g1Q6dmlWBVP3ccWhKoqY18NEzOTU7NnR9GP/ANdP+qpF4s/NSQTdrO6l+nPtDmM7nbJGde3Rrlha9Cpya1Sj4FUp3cGrm+6NDIRQ7p6bj1OzwbkMIIYQQQgjJC4qaVYFUKMiyagpDFjXNgY/QMwzmkq1dH6Y7uHEykXBFTZWiJik28v2Qy70hr0tRs7TQqVkauoOoWZHw87j0Oo2oaf84qNbYM7zLhZRTU2H4OSGEEEIIIcWEomY1oMg5Nc1ZQhq4CUWz5mXI5ZbIQtRMW8CgmyCFn9OpSYqOLPrnJGrK+RYpamZNxwbg49uAzobst6lUTk2PqMmcmhXBCT8vY/8XPacmw88JIYQQQggpJhQ1qwIrP6QinPBzIQ+MLAeIYWQQIeV8k+mqN/tDaqtx4JoJj1OzBwoLlUYYwLqngbY1lW5JZfA4NXMQ/OnUzI8VDwKf3g588Yfst6mUU7PHh593Axd/JXJqeqqfx9Os46t+nk4gVjTJqWldx3RqEkIIIYQQUhAUNauC1JyaslPTHviITFV35fDztIO+bjBwzYQkaiq5iE4kO7a+Bbx9MfD+tyvdkgqRp1OTOTXzw3aXZ+Uyt6gKp2Y3e25mQ3dwajrh50ZuOW8LOmYxnZqqm1MzaYuadGoSQgghhBBSCBQ1q4GA6ufCkMPPzYGPIQygdRXwr/2BL/7o3Yfs1EwnfqYMtrqZMMjw89IS3WL+7dxc2XZUCubULC/5hBPTqVkauoOo6RGWy/TZVZRCQda0ElT9nE5NQgghhBBCCoGiZlWgOK8MV9V0F9uDJSMJbHkDaF0BrH/GuwvZ7ZR28NXNnZpxhp+XFD1m/k22V7YdlaIYOTVl0Y1kxhYKMznQ/VSq+rknb2oPfPZ0hx+8KiEs5+TUTFcoKKj6OZ2ahBBCCCGEFAOKmlWE6dRMzampOMUEBAzdGlj5B3Vy+HnaQXd3z6kpOzWrcNDd3bFzxu2yomaeTk2Gn+dHd3JqMvy88lSiWJModaEgOjUJIYQQQggpBIqaVYGcU9OaZRUFMoQCqGb1cw0GDD2NEOApFJRt+HkVDlwzwUJBpYWiZvDrXLajqJk9Rh6iZqVyajL8vPLke38Wgi4VB+pK1OyyUJDKQkGEEEIIIYQUGYqa1YCdU1OxXZoCwsoZaUCBoqjWcgOGLTz5B07xne7rtE7NbhBimAkWCiot9rWlt5evEEc14Qk/z6X6uRyaTFEza+xnWE7h53RqloRuIWpW4D0QeeTUTLYDH98G7PjQWi7n1GT4eXdiwYIFOOusszB8+HAoioLnn3/eWZZIJHDzzTfjkEMOQe/evTF8+HBcccUV2LRpk2cfo0aNgqIonn8/+9nPynwmhBBCCCE9F4qaVYFbKAgw3Zp2oSBDqE74uQYDup4mD50savZYp6Ycfk6nZtFxBHPDfb0rQadmeckn/Fx2ahqVcmr2xGdPN/hsqNqcmlbf2aLm9kXAp7cDS2ZYywPCz5MMP+8OtLe3Y9y4cXjooYdSlnV0dGDx4sX4yU9+gsWLF+O5557D8uXLcfbZZ6ese8cdd2Dz5s3Ov+9+97vlaD4hhBBCyC4BbQJVgSL9D+imqgkAMKA6Tk0VAoYdDuevBBvfIU331Jyacvi57j8bUiiykJlsB7RI5dpSCYpSKIiiZtaIND/QZKIanJrlFFPLhf+cqvGzoRJOzSBR09CBN88H+u4HHPbLVKemTedm868satKp2a04/fTTcfrppwcu69+/P+bMmeOZ9+CDD+Koo47C+vXrsddeeznz+/bti2HDhpW0rYQQQgghuyr8Rl0NKLacaTs1BYRww8/tnJqqYkBYuTY9g7pEq1eQ6cpRAsU8VncL4ZaqnyswKGoWG7v6OWCKmpH6yrWlEuRdKIhOzbzIJ6dmNVQ/L9dxEy3mM6/3iNIfqzuEn8vid7kq0AeJmq0rgY0vAlovr6jpd13Gt5t/nfBzlYWCejjNzc1QFAUDBgzwzP/Zz36GO++8E3vttRcuvfRS3HTTTQiFgr9+x2IxxGLuZ3FLixmhkkgkkEik+W7XjbDPoSecS6VgHxYG+68wKtV/ag+JLVXVhOdvLgilvJ2QTxtLhX258f4tjEr2X6mPSVGzKvCGnwsB16kpVHMwBECFAUMPyEMnh577l8k4YXIhc4BWjQPXTCQZfl5S/E7NXQ46NctKd6p+XonQ57mnAE2fAOdtKsMPDN0g/LxacmraYqUR87bF79SMbTM/zD3h5ywU1FOJRqO4+eabcckll6Bfv37O/BtuuAGHHXYY6uvr8c4772DGjBnYvHkz7r333sD9zJw5E7fffnvK/NmzZ6Ourq5k7S83fpcryR32YWGw/wqj3P03blxZD1dyDjkk9/5rQXk7YdyIWWU9XiZm+ZrC+7cwKtF/HR0dXa9UABQ1qwJ/Tk0BYRVqMQsFWdXPFQPCdovIAzw59Ny/zDPfdoyEAXQzUVMIj1NThQF9VyxmU0pkUVPfBUVNj1Mzl0JBdGrmhZFH+Hmlqp+LCuTUbF1pCmedG0svanYHp2a1hJ/HJAemkYCT1kWtSd022cbw812ARCKBr33taxBC4De/+Y1n2fTp053XY8eORU1NDb71rW9h5syZiERSU7zMmDHDs01LSwtGjBiBU0891SOWdlcSiQTmzJmDyZMnIxymqJ8P7MPCYP8VRqX6b+bMsh2qpKhqAoccMgeffDIZhpFb/x1fX95OeHPHjLIeLxMzrKbw/i2MSvafHXlSKviNuhpwqp+bk4ZwQ8OFUMzBEEzR07ALBcmDOr9TM+2gWypooKM6B67p0DtTz0skAdQErk7ywOPULO2vKVVJMXJqsvq5izCgNM4FBh8JRAYFLKdTM/Mx46nHLhX+c6rGvKFymyoialrvhy1qAt7nZJDrMrYtuFAQw897DLaguW7dOsybN69L4XH8+PFIJpNYu3Yt9t9//5TlkUgkUOwMh8M9agDX086nErAPC4P9Vxjl7j+jm2VM6wrDCOcsaiplThuXa/tKif9S4/1bGJXov1Ifr4dkqOju+JyaBiCsgb4BN/xcUwwYTrGCDE7NtOHntqPE0rK7U07NZFvqvB5ZhbiC7Orh56x+XlQGG58gtOB04IM0lX5zLRSUkjJDuM+0UlMRl6Ad3lxGUdMW3arxB6+KvAfSM9H+7JU/b+XPJa02dfvYdm9OTdupmaRTsydgC5orV67Ea6+9hkGDAn688bFkyRKoqoohQ4aUoYWEEEIIIT0ffqOuClLDz+2BkA7VcWpq0CGCimvk6tS0B1bVOHBNR1Dxo3QFkUh+7PKiZp5OTVmUo6jp0MvYar7o2BC8Qq6FgoLcw0J3n2elpNyCmtDd67EsTk3bxV9jPVer8AevSqQAkD9jhG6K6LJTM9HqvtZ6pW4f2x7s1LQd3cypWdW0tbVh1apVzvSaNWuwZMkS1NfXY/fdd8eFF16IxYsX46WXXoKu62hoaAAA1NfXo6amBgsXLsSiRYtw0kknoW/fvli4cCFuuukmfP3rX8fAgQMrdVqEEEIIIT0KippVgLDDz63plPBzy1CrKEISNaWBdczv1Oyi+nk1u3HSYZ+TGnZf06lZXPzVz3c18nZqUtQMQoN1PcliuUyu4edy6LmzDx1l+Rgrd+Vtuc9K9ZxLtAHhPtYxfBW8q/GzoSIpABKp03FZ1JTyAwU6NX3h505OTes5wfDzquaDDz7ASSed5EzbuS6vvPJK3HbbbXjxxRcBAIceeqhnu/nz52PixImIRCL429/+httuuw2xWAyjR4/GTTfd5MmZSQghhBBCCoOiZlXgd2q6eTMMj1PTKMyp6SkUZO6922APLrVe7utyiAu7Ert8oaB8c2oy/DwITdiiZix4BadQUJaO6yChvWxVsMssqJVa1Fz9BPDuVcCxfwVGXuSek1YDJFB9oqYQla9+bk97cmpaoqYacT6nPcT9Tk3fVy6Gn1c1EydOdIo2BpFpGQAcdthhePfdd4vdLEIIIYQQItEtcmo+9NBDGDVqFGprazF+/Hi89957WW33t7/9DYqi4Nxzzy1tAwvGLhRkfkEWQkBYAyEBxcmpqSrCrX6eT05N+HJqVmMxiHQI16kpnPB5hp8XlV2+UFCe1c89Lj6KmjaOU1NPI2rmmlPTzl8YlgpxlM2xV0FRsxQ/3mx/H4AAdnxgTsvh50Du52gkgeUPAE2fFK2JHvz3Y0WdmtLnre3U1Gqdz2kPsW1w076oqeHmdGoSQgghhBBSEFUvaj799NOYPn06br31VixevBjjxo3DlClTsGXLlozbrV27Ft///vdx/PHHl6mlBWCFn9tvhi6FnxvCdWqq0KU8dHlUPxcVzKm59W2g/cv8t7cHl0rYbT+dmsVlV8+piSI4NZMBIdK7KG74eReiZtY5NW1Rs7+0jx7q1JSF4FI4NW3x3b5e/eHnuf7g1TgP+PB7wOIShdX6+6BcP8gFiZqxgPBzLYLAr1Ox7W5b5fBzGzo1CSGEEEIIKYiqFzXvvfdeXHvttbjqqqtw4IEH4pFHHkFdXR0effTRtNvouo7LLrsMt99+O/bee+8ytjZfvE5NQ8BxahopTs0AIcAnQOnJdDnsfDk1yxV+3rYamHMc8NaF+e9Dzqmp0qlZEnZlUVP6IcGczjOnJp2aDiHRRU7NXAsFJapF1CxzTs1SFESz0yToPlFTy9OpaQt9sW2Fty0If59XolCQPR0kaqqRNE7NNIWCbFgoiBBCCCGEkIKoaptAPB7Hhx9+iBkzZjjzVFXFpEmTsHDhwrTb3XHHHRgyZAiuvvpqvPnmm10eJxaLIRZznTEtLeZAJZFIIJEonXBm71vXdYTgGDYRjyegWssMqDCEAhVmTk3DGmQJI4mktY6mJzzqdDQWRU1AuzVDhwpAQIMCwNAT0Et4fjZK65cIARDtG5w257yPRKe5DyXkuImS8ShQhvb3BOxrLdP1rOkx1y0cb4WxK/WtMCDLC8lkDEK6/zP1W8jQ3SJfyc6y3FPVTiKRcJyaQo8F3vchIwkFgNATWT0XlFgzQgCMUF/nOk3Eo4Ba+v4O6QnnPZafvcXGvs6S8Q7nekwmohBFPp6W7IQKwEi0Q08kEBLmNSyUMBS413+2KPF28/mc7ChN3ySi3vsz4W1fNvdpPmh61PPZmoh3IBTf4VwLeqwJGgChRqDrIuULlRHdCkQGQQWgGwCEAjnzpi6Ukj9nS/kdhhBCCCGEkEpT1aLmtm3boOs6hg4d6pk/dOhQfP7554HbvPXWW/jjH/+IJUuWZH2cmTNn4vbbb0+ZP3v2bNTV1eXU5nxYuvQzHAa3ONC8+fMxVHyOMwEYQkFD4xYMB6BCoGnHVgzXTEFy1qxZAIBjOhsxWNrff/7zIbYv651ynKOjjRgKYGdLO+oBNGzeiPetfZSSQfonOA5AItqKl/M83iB9KY4D0N4RQ1gYiABY+M6baFXXF7OpPZ45c+akXXZC5xYMtF5vXLcc/2ko/bVRLSgiibOl6fcWLcRWzQ0lz9RvUyXBpWXnFrxRhnuqO3CE5dRMxtqdZ5XMqZ1t6AWgva0Fc7PosxGJd3AYgK1NUQyBAgUCc1+bjZhaX+SWpzKpowX2E7WjvRWvlfg9fvedBbBrLv9n8fvY9HGky21UkcBQ/QNs1Q5BUumTcd3x0fUYBqBx01q8N2sWzrY+e5pbOzEAwCcfLcH6zwZl3d5RiQ8wDkBn2w7MKUHfhEUrpkrT7777NrZrLSnrZbpP8+HI6JcYLk2/Of/fOFl3nwurly/BfgBa2+P4zzsLcaJv+5Zta9CuRLEHgKWfLUNIdOBAafmKlWuwYl1pr6WOjl0wPzIhhBBCCNllqGpRM1daW1tx+eWX4/e//z122223rLebMWMGpk93c4G1tLRgxIgROPXUU9GvX78MWxZGIpHAnDlzcNDBBwOLAdWyap544kT0aokAHwACKobtvgewAVAVHQP69wXaAFUxMHWqOczT5t8DSFF/B4zZH/3HTE05nrbgYaARGDBwN2D7CgwbOhhTj01dr9gojTXAAiCsuW3OfR+1wAKgd98BEHEBRJsxYfwRCA0+ssit7ZnY19rkyZMRDgeHPIZm3wI0m6/3HFaP3SeU/tqoGvQY8Jw7edSRh0MMm5Jdvz2nAFaEaf/eNZh62i7Ub2lIJBJo+dddAICQqgfe96F/hYEo0LsuktVzQV21HvgPMHjYSGDTJ4BI4pSTJwJ1exa7+alt/XcEsLShul61eT/HusK+3iYcdTjwhjnvq4cegkP36vp4yuo/IvThz6F/5SYY436ecV3tjV8DW4Chu/XH1ONPh/KMmfqk34DdgB1f4JBDDsLBe2d/juqKlcBHQK8wStM30S3Av9zJo486EmLoyc50NvdpPmhv/R7Y7E6fcOT+wAJ3eu8Rg4HVQN/+g3DMEccDr3m37x9Jol/9EGAjcNDBY828sB+7y79ywEHY94DSPi/syBNCCCGEEEJ6IlUtau62227QNA2NjY2e+Y2NjRg2bFjK+l988QXWrl2Ls846y5lnGKYDJRQKYfny5dhnn31StotEIohEUp0w4XC4qAOkdGia+TbYOTUVTUPIys9lQIWqmW1QIaBY+bkUoSMcCpkx6/78Z8IIbre1f9XKm6YqgFqG84NiWH+i+fenavdNjZOHLKwpCJWj/T2IjNe0lKNUNTrLc21UC4o3RDOkqoB0/pn7zc3vpxixsjwzzOMK89hVmpdPs5yaihF3n1UyVr8pIpldnwnTIafW9DPzE4okwiHv+1Qy5Pc42/YWQEh1n+khBdmdY3QjAECLb4XW1frCzNmpihjUkBtgbX82hNQsj+lg7k/RO0rTNwnvtZOufcX/zPZ+toYS3pyhmm7meVVCtQiHalK2VuLbnM91LVQDwPs9QwtFun6vCqRszyNCCCGEEEIqQFUXCqqpqcHhhx+OuXPnOvMMw8DcuXMxYcKElPUPOOAAfPLJJ1iyZInz7+yzz8ZJJ52EJUuWYMSIEeVsfvZYg30nZ5sQENZgSkCB/TZpiuErWGEVNrHmJYSZrSuZTJNDyykUVObq53axBaHnX7XWU/087J1HisMuXSjIVzQr3+rn5SwUtOga4LlhpoutCtFgX08iuLBLroWC7Gsy1McUNYHiP8OESDO/zNXP5Xsx6/5ptbbN4rkoFwqSr301z0JBnv2l6cNCSCkUVKHq59EG77Td5+kKBemdboErRXM/u2yq9AcJQgghhBBCugtV7dQEgOnTp+PKK6/EEUccgaOOOgr3338/2tvbcdVVVwEArrjiCuyxxx6YOXMmamtrcfDBB3u2HzBgAACkzK9GVEvVNISbX9MQqjOAV+AXNZMANGdezKhBWOtEUk9Tbdiudm4PrMpWOVgaGBoxQM0jT6m9DzUMqLagUaYKuLsKu7So6Xc7Z3lvCOFdVy+CqKnHgPlTgMHHAePuSr/elgVAfAfQ/BlQO6Tw4xYZu1AQAPOc/AKOff8a2Yp2ljgki5r5/kgSxNKfAp/fB0x+B+i3n3eZ0Q1EzYQlsIksRE1DFiGl87HfI7/I3xXOdS/MZ7xWm9v2XVEpUdPfl50+UdOufq7Vutekn5j9o4Pq/qBoo1T9VzBCCCGEEEKqmqr/Rn3RRRdh69atuOWWW9DQ0IBDDz0Ur7zyilM8aP369VDVqjacZoHt1DQdLoYQELaoCdVxgGiKAcXwi5oRZ8AdE2H0QSd0PZ1T03LQOE7NHAeu+SK7XfQoEMpD1LTPWw27A0E6NYuLLKTou5ioiTydmv71iiFqNn8KbHnD/JtJ1LQFlyq9D+zwcwDea8tG5OrUtEXN3qVxam56BYhtA7YvShU1g5yaQgCbZwMDDgbq9iheOwBvf2Ut+tpOzSzW1633JukXNQt0agJAsqP4oqb/nKrFqemImmmcmgAQ3Wr+VbRUEZNOTUIIIYQQQgqi6kVNAJg2bRqmTZsWuOz111/PuO3jjz9e/AYVHUvUtJ2aBhzB0YDiDOA1GFBk54gzuDbnRQ0zX5eeLvzccWoWMfx8+/vAR/8LfPUeYOChaQ7rc2rmgyE7NW03EZ2aRcXj1NzFKub6HX+FiJpCpOaPzAVbIIo3Z95XruHbZcbj1Ay6741cnZpS+LlaAlHTfsZkaivg9veOD4HXTwOGnQqc/Grx2gH4RM0sRetEvuHnRRY1pergAIDWL8z99i4g/Uu+TupC8YvxUW9+b6fP1QhSsvnUDATiO91tFC1VxKSoSQghhBBCSEF0d4tjz0AJcmraOTVdp6ai+EJdfaJATJgDJD1d+LmTU7OI4edr/wo0vAasezr9On6nZj4IN6emcETZ6hRzui26JObsauHnKU7NLF3MKdegKNw5ab8PIgnoGcRlJ3y7Gzg1dZ9QKKQ8m7k6NcMlyqlpP2P8bQWCnZqdZmGeFKErH7a/Dyw4D2hdaU4XklMzl/BzIwrPtZ/vZ4PfqSm//te+wAt7ZS9eB5ESfl6mZ7//3vKHnyctp2ZQTk0nJYQVIRHk1GT4OSGEEEIIIQVBUbMqMEVN+80whMjg1Ex1DAlb1DRMl42hpxnwlULUtF1NQUKAc1xZ1CyiU7NKxZxuC3Nqpp/Odjug8GJBslMw3pRhPbsAV3WK+xmdmrJonHXOSEvU1EoUfp7JqRkkatpCXjH6f+XDwIbnoX5p/ThUSE7NrJyaUvi5UQSnppHGqRnb7r62Bdt86Bbh576cmuGB3mlFpVOTEEIIIYSQIkNRsyqww89tpyacnJpCuE5NVfGJmoZP1HScmukGtbZjpIg5NR0hIF1xIngHhvkKPrKoSadm8RG+IlR6R/lyrlYD+VY/D3KfFZpXU94+k6iZa6GdcmIkoUF+VvmeDwHPsS4ptVPTSOPUFIZPhC2BqNn+pfnXPsd8cmo6hYKyyanZVaGgHPs1KQmZsrtY/kFr55Lc9ilT8ernVgqI2Dbzry3+2v0Y5NSsGeCdVjQWCiKEEEIIIaTIUNSsBgLCz+3q50JyaqowoCDVMWRYA6+YlVPTSFsoyHZqFjGnpiOsZClq0qlZnQT1pT83Xk+mGE5N1bz/Chc1pXsk0ZR+PecHhSq8D/zXTopQKItUIr2ALgSw6vfAtkXenJolETWtZ1iKq9R3DCftR8w7XQh2KLslDiqFhJ93dT0IIZ2jcN8rRc2/X9M5NeX3vRBRs1JOTVuU9Re3c0LLLfyFgtSwWdBKhoWCCCGEEEIIKToUNasCb6EgIeXUNKDCfptUCCjyYM7n1LKdmka6QbYtHCjFDD/P1amZp6gp5dSsCqdmsgN4/Qxg1e/y2z66BVj/j+oRpILev12pWFC+Tk37GlRUQOtlvi5UDM42/DzXnJTlRPelL/Df9/5nVLpnVtMnwHvXAe9+Q6p+XmanZkrbhCkMFsupKQTQYTo1FdvlWIhTs6tniv9et8ViRcu/X9Pl1DSKJGpW2qmp+UTNiE/U9BcKUmvd54ENw88JIYQQQggpOhQ1qwI7p6bp1NQ91c9d94ymGF5R01f92K5+LtIVCoI/p2YFws/zdbEFhZ9XMux220Jg0yxg+a/z2/4//wO89TVg/bPFbVe+yO+fHVq5K+XVLNSpqWiAVmu+LtSpmY2oKaSCRNUijMv4BfGMTs2AaZvYFvNv+zpJ1CxRTk2R5lkW1Dahu+dU6HMo0eLea0GiZjaiqZGQnKNdXA/+69PuVxTg1ExX/Vx+35uW5LZPGX8flOvZb+Ti1JRyamq17vPAhoWCCCGEEEIIKToUNasBJ/zcxFsoSAVUzVpuQA0IP8/eqenPqVmm8POiFwrK4NRs/hz47J7SD3rtgXu+ztP2Nebf1hXFaU+h2O+LogHhvubrXUrU9Av8WQr+9nWmhIBwP/N1fGdhbckm/DyfQjvlxF+13f98SAknTnMOjtjXCcR3mK9L7dRMCT9PJ2oWyalph54DrhgsXwPZ7N92aWazfoqoWSanZnRLavXwbKlYoSDruvU7NVNEzVpv+LkW5NTU6NQkhBBCCCGkyFDUrAr8hYIEBMxBmxAK7LcpxalpDV7tPJt29XOR1qlTgpyaFSwUpKQMdAXw7zHAkv8B1j2d33Gybo+dfy9Pl5wtfHVuKk57CsU+HzViVpcGdjFR0583MQ+nZt2e5uuODYW1JZtCQZ5CO9Xn1FT8IfhdCYXpfoSwK54DrpBb8fBzmO03iiRqyteL82OJHH6exfublETNLsPPfefnETWtrwQFOTUlUdPfl/mGoOfrpC6UbJ2a/kJBaoROTUIIIYQQQsoARc2qwOvUFML+z18oSPicmpaoKbxOTdFVTs18K9wG7rNMhYLknJpO+32D9+2L3NfRxvyOky16lqGe6YhZrrOqEzVr3AIX/ryIPZpCc2qGgLoR5msrP2L+TZGdms1p1pGuu2p0ahYr/DzoGixV+HnaQkElDj/vkJyaTvi5/ENQjk7NfMPPPYWCckxNkq5QkL8v8xY1K1woqKucmpovp6YWSZNTk4WCCCGEEEIIKSYUNauBgOrnMOScmlahIMWACtmpqQPCgGoJMq5TsytRs8bdvlCycmpKy0qZU/OLP7ivSz1YdPLXZTjvTFSrU1OTRM1d2amZ7b1hX4OqVjxRU88mp6bs1KxCUTMl/DzLiuJ+ZKcmYBVkqq2QU1Nx5xUz/FxyairJPHNq5hJ+nuLUtEXNQsLPJSEzmcGp2bk5t/3aVCL8XM5bK1cyV0JAzQDvumrESRNjTtcw/JwQQgghhJAyQFGzKvCGn+uGgLAcmUIq3qDCJ2oaSc/gTqhWuFuX4ec1XayXA+Wqfu7JqRng1Ey0Auv+5k4XWoG6KwpxaupRV/TJd5BfbDxOTcuVtEtVPy+0UFAI6G2Jmu3FdGo2pVlHdmpWX/h57jk105yDX1gP9TF/BCq2qCmE5DpP49SUw4kNXfpho4g5NfMtFJRL+Hlap2aRcmp6nJr+okt5XqvZOnuLidwHcvh5TX+r2rlESvVzhp8TQgghhBBSDviNuiqwq5+bmOHnqdXPQ4oOVSSlOPWkZ0Ctha2BV7pBrV0oyHFqFkEMsQeXaSuu+45TaKEgJRTs1Nz6tlcAKbUgZw/W8+lDuZBMtMEUSGSXTyWQRU17wJ6vC7U74g+3zTn8nE5ND7mGn6c7h6TPqRnqY/4tuqgpHT+dU1OtcQU7kSyJUxN6p1mEXBZWSx5+XqZCQdm0LR3ldmo2f+4WcwO84efh/la4uYTmy6mp0alJCCGEEEJIOaCoWQ0oqYWCFEfUVBwRMqwkA8LPpcFeyBpEpR1k205NW7QqolMzk7hXjEJBwnVqikCnZot3fb9TrNgYBTg1ZVFTGEBsC9Br9+K0K19sIUetkZy8u5Ko6RdJsswpKDs1i5ZTM4tCQZ57qvqcml0WCsq1+rmNHQZcbFHTU5gnjQCrhszjCt0Xfq6bPxgpCvLCX/28xteerJyackGlfAsFqfn1qy9iwOPUTBGIi+TUzLaQV74sOAdoXeFOy07N8IBgp6Ysairh4JyadGoSQgghhBBSVEoWfv7ll19iwwbXgfLee+/hxhtvxO9+97tSHbIb48+pCQB2oSDVI2pqKeHn7mBPscPd0g1IbTea7TIpRghfruHnhTo10+XU9IsoZQs/j7sO2GyxiwTZVENeTY9Tc1cUNX0iZraiiSEJXraoGd9RWD5SPYvwc/nercZCQf4fFarJqbnq98Cnd/mOnyFFhlwMyn72CN27XiHiqsepGRB+nrNTs4v1ix1+7t+fnsmpmeczpdyFgmShGQA0KadmTX/3Geksj7h9B1g5NQPCz1koiBBCCCGEkKJSMlHz0ksvxfz58wEADQ0NmDx5Mt577z386Ec/wh133FGqw3ZTvNXP5UJBAqojQkaUOFRFEl984edKyHZgZlkoqBgOr2zCzz2iZhEKBdkDQ0/IqE9EKXn4eQGChuzUBKojr6YjakZ2UVGz0Jyamil2hPqa04Xk1TSyCD+X7/EqdGqm3H9d5tTM1qnpEzXzcey9dx3w8U+AHYul9mT44cWQRU1J9JOfZfkKy3oUiG1zJhUjZgmmBeTU7MqpWezw8xRRsxROzTzvz7yOJXzXr+INNw8KP/fn1EwXfq74w8/p1CSEEEIIIaQQSiZqfvrppzjqqKMAAH//+99x8MEH45133sFf/vIXPP7446U6bPfEClusVTpxRN1SGLoBYTkyhXDDz3upARWErQFvUqjQNHM9Je0g2M6pWYLw80wCmMjggsr1OEoap2bS79QsU05NuW3ZEq9ip6ZGp6Y5nWtOTeua7F2EEHS/UzPICewpFNQNnJrp3I/ppm1SnJoFhp/L/dayPHh+V+HngPnsyTXvZRD2DxpS6LKGeO7pBTzVz43U61nGLzQ6Ts18w8/9ImkGp6bnsyCZ+gNP2mOUsVCQHoXzWQlY7nVJjAwHFArSar3h59k6Nf0iJyGEEEIIISQnSiZqJhIJRCLmF//XXnsNZ599NgDggAMOwObNVeBMqypMUXOEthbP7HszBra96QwqDajOAKrOL2oarlNTFxpCml0AqAunplZEp2bO4eeF59QMdmpWKPwcyF388w/kO6pI1FRrpOtjVxI1i+DUBIqTV9MjliWCr2VR5U7NlPuxSsLP5XbJPyZkKmZWSqem7ZKsGejM0hDzOcFzdGoCma8JvwiZKDD8PNMPSnZf2oV25Ha99V/AP4dn9/wrZ/i5X5BXw17xsWZAmvDzbHJqslAQIYQQQgghxaRkouZBBx2ERx55BG+++SbmzJmD0047DQCwadMmDBo0qFSH7aZ4C0z06/zEcWcZUk7N2hSnZtIZjCeFhlC4C6emP/xcJHPPB5myT+tY2YqaeTs1bbeUNMD0iKXWwNoWB8oZft6TnJpyTs1MKQV6HH5nW5aFguScmkBxRE2/8J9oDjhudTs1Fev+E/azrVjh5+ECRU35udAh5U3UsywUpEo5NfUiODWd+67WEcE0ESssp6bc5iBSwsULDD9PcWpKIqfdl/b7Jl+3Oz8y29K2KotjlFHU9H92qOFUp2Zg+Ln0Oa7VuIX7bAJzajL8nBBCCCGEkEIomaj585//HL/97W8xceJEXHLJJRg3bhwA4MUXX3TC0omFb4Cm6W3OPDmnZp0aTd3OdmpCQyhkDrxUZFn9PODYOVOJQkHWQNAj3toD0RpLMC9n+HlXOez82IWCaoeYf6shpyarn2ee7mq7Ujk1geC8mh6nZvWJms79F+5v/u0q/Dxfp2a24rO/XQDQ9oXUngzPKJHGqWkUI6em7WSMOBW2Q4gXllMTyPxDS0r4uVz93PpKUKxCQfaxQgGipn2O2XwmVNqp2WX4ecRJI2NuU2MK1TKKFlD9nE5NQgghhBBCCqFkNoGJEydi27ZtaGlpwcCBbmjdddddh7q6ulIdtlsiBowzc8VZg8twcieA3cxlcHNqpoiaUvXzpNAQtpyaatrq55b7U6lx1WwjUZhbRGQhaooihp8rXTg1I/VAG1JDIouNXohT0wo/738QEN1SvU7NXUrUzLP6uT+npi1qFlIoyC/yBImaueZcLDdyWHWiKX1It03WhYKsnJpqEZyasqiZyU0eGH6eLE74uSftQx2A7dBEDEquoqbfqZlL+Hlg9fMcxOJMhYLs87BFTfmzIJvPDmfdcjo1fdec4hM1a/q7KTps/CKnms6pyUJBhBBCCCGEFJOSOTU7OzsRi8UcQXPdunW4//77sXz5cgwZMqRUh+2e1O0JXLANf05MBwCEk9udQaXs1EwtFCTn1FRRE7LCz7twaq7YVoTBuLNLKfw8XSh7UcLPs8ypGdnNmi5n+HmW4t/GWcD71wNRy5nZ/2Dzb1WJmqx+Hjiddjtb8LLEIDv9QaIl/7b475FEU/rj+l9XC/b9GB5g/i3UqWnvp9Ccmh5Rc7X7zMrkJg8qFFS08HPJIW05Nc2cmgWGn2d0ahY5p2ZKNfWAQkFBTk075D+b54y/D0rpTk4JPw/5nJoDAkTMAFEzMKem36lJUZMQQgghhJBCKJmoec455+BPf/oTAKCpqQnjx4/Hr371K5x77rn4zW9+U6rDdl+0WnQo9QCAGn2HJGpmcGpK1c8TIoRw2A4/T+fUNPe5XR6zFeryyia3XzEKBcmiphIkavrDzyvk1Ey0pa5r89H/AisfBhrnm9N99zP/ZlsB2KbQPKhBsPq5bzrH8HNbaLfFj1xTEsjY94i9r0CnZrUXCrJyatYMMKf911I21c+FcF1zffY2//pFzWwdtb52ma873dQPWTs15Zya0rMs3/dA/jHBKqajiZg3x2c+4ecZc2pa52f3YbLAnJp2P4T7WdPSs9c+Vriv+dfoBk5N/w9iiRZfoaA04ecyaaufa3Bybyohb8g6qToWLFiAs846C8OHD4eiKHj++ec9y4UQuOWWW7D77rujV69emDRpElauXOlZZ8eOHbjsssvQr18/DBgwAFdffTXa2jJ8TyCEEEIIITlRMlFz8eLFOP744wEAzzzzDIYOHYp169bhT3/6Ex544IFSHbZbE9UGAABqkq6oKVc/r1UDhAEpp2ZN2Fwvffi5uc/mmPS2FyqIyOJNusFp0Z2aAeHnSSn8HChDoSDZSWW1Y9XvgWf6A18+H7xN5wbvdO1Q828uAtjK3wLPDQZ2LM5+m2zY5cPP83RqGj6nZtC1mSu2EBSxHO1BTs1uUijIcVjmE35uxNz3Yf8bgEFHAcOnWgcoglMTcIvUeMK9da9Yms6pmWuF8iCcnJoVcGragrOTt1QtrFBQjfXs1bN0aho5ODXzvT/zwR9+Ht/prXaetlCQPB3k1LSfESHvX1K1tLe3Y9y4cXjooYcCl//iF7/AAw88gEceeQSLFi1C7969MWXKFESj7g8el112GZYuXYo5c+bgpZdewoIFC3DdddeV6xQIIYQQQno8JRM1Ozo60Lev6c6YPXs2zj//fKiqiqOPPhrr1q0r1WG7NVHVdBlG9B0A7EJBindAJePJqemGn2uKjqQelBPNnNcS1ZEQ5gDLKLTCtccxlk7UlOYXmlNTDUMEOjUrGH5ut237+6ZwvDNAcNRjQGy7d16vYdb2RvaOs82vmPvZ+lbubc7ELi9q5ln93CkU5HNqFiJq2teWXUgqHlD9XHQPp6YTjp9P+LksLo26DJiyCOj3FXM6X1HT7+ButfJq+n9YCBIslZCby9Modk5NyanpFzWLXSjIFiHDA737z9Wp2bwMmHUosObP5rT9XutR937yFwqy+1kIt435hJ+Xs/o5EFAoyJ9T058rsyZV6LT71n5WsEhQ1XP66afjrrvuwnnnnZeyTAiB+++/Hz/+8Y9xzjnnYOzYsfjTn/6ETZs2OY7OZcuW4ZVXXsEf/vAHjB8/Hscddxx+/etf429/+xs2baqCtDOEEEIIIT2AklkF9t13Xzz//PM477zz8Oqrr+Kmm24CAGzZsgX9+vUr1WG7NTGtHkgCEWMnYNhOTS3VFWIjhZ/rQkNNxBxohZQkYkkDIc2nWVthy80xHUkRQljR0RmPoXe+DRbCJyxm4dQstPq5XLRBHujaIkpECj8XonThffJ5+HPDBZ1jtCF1nu3UtLdVe6Wuk+64+fZjV/vdZUXNQnNq+oSKYjg1g5xvNlXu1LTFQyf8PB+npu0gVCOprra8RU2/U9MSNf3vlxEDUOdtq1woSI8CkNJA5J1TUy4UZD7nNREDRIATPB1CpDo1M7m/netrgHe+mqOo+eWzQNNH5j/AvV4Bs39CdZJT0/qUsc9FZPFjmIyzvgJAlPaaD7rf7H4BzH6TRU2tNvVzRq0x52m1kvhtfR6rYfP+oFOzW7NmzRo0NDRg0qRJzrz+/ftj/PjxWLhwIS6++GIsXLgQAwYMwBFHHOGsM2nSJKiqikWLFgWKpbFYDLGY+7xsaTHzMycSCSQSVfgDVo7Y59ATzqVSsA8Lg/1XGJXqP7VkNqzyoqoJz99cEEp5OyGfNpYK+3Lj/VsYley/Uh+zZN+qb7nlFlx66aW46aabcPLJJ2PChAkATNfmV7/61VIdtlsT1wYCSUBDElqyCUAXTk0p/DwpNESs8PMQdMSSBnr7tVDLPdPaqSMpzAdjZzRagKjpF4LSXKyeEPX8xDhhJKAA+PN7m3DxGKkCsY3twLIH1sIwB8vpBOFC8TipfI6joEG6nbtPxi9qIgtR0+6/fB2vafcriyu7oKjpd2bmmlPTH35eSE5N+z22BXp/OCzgE4WqUNT0h5+n5NT09W8mp2a4T+qyooWfr7GO73u/9ACnpirl1NR970neTk07/LwAp6aRcNcJ9Tb7LdM14Q8/d8gx/LxttXfadmoCZj97RE07/Nx+RgYUDMqEfT5arfWDVZmdmvLzNtzfFCzVGuvHqIDPGPs5oEbcbf1OTb+7k3QrGhrMHyqHDh3qmT906FBnWUNDQ0phzFAohPr6emcdPzNnzsTtt9+eMn/27Nmoq6srRtOrgjlz5lS6Cd0e9mFhsP8Ko9z9N25cWQ9Xcg45JPf+a0F5O2HciFllPV4mZvmawvu3MCrRfx0dpY2iLZmoeeGFF+K4447D5s2bMU56Ep1yyimBv04TQA31QntHLXprUYQTWwFY1c+DBk2AOdCzBoc6NIQ0c5CkKQZiyaBBn5VTM2ogKcy3viNWgDiW4m7KxqmZ3/EMPQ4NwN8XN+DCAwabF65nv3b4+SBpXkcJRc2AQkEZRc2AULOw5Fg24njxo02IJXT81xEjuj6uUaCo6XexBoWfF5qaoDvhD//PNh2AIQleQOHh50JIopOdHzZA1AwquFJN5Bp+HiTc2UW3tICfXYrl1ExYof2BTk37dYBT0/+e5Css69J9Z+fUFDnm1JSfBaE+lqiZRfi5LEICViGbUHbHBFxB2Dl2b1fos5/H/vDzoJDzXJyaaqT0oqZfsAa81439mWKfa9BnjP0MlX+QVH0/fDD8nAQwY8YMTJ8+3ZluaWnBiBEjcOqpp/aIKKdEIoE5c+Zg8uTJTnFLkhvsw8Jg/xVGpfpv5syyHaqkqGoChxwyB598MhmGkVv/HV9f3k54c8eMsh4vEzOspvD+LYxK9p8deVIqShr/NGzYMAwbNgwbNpgFUvbcc08cddRRpTxktyakqdih90dvLYpQchsAwMjo1HTDz5NCQy9rQBpSdHQmAnICCjunZhLJGnOA1RktoEq4X0zJJvw8T6emoSegAUiKENrjQC3gFUIcZ1h/c3AudLN4kH/gXiw81c/jwX9lgpyaimoKJSKJZCKK7//9UyQMA6ceOAz969I8aPQiODU3vQwsvAIY/yiw51mWCGJXP4/smuHnRXdq5ilyiSScsGb72g1yjlWzU1MIKHb183SFgrIRNfUSOjVDfc08lLY46b/WPU5NO59vBlGzUKemlFMzhCgU+dna1b7lttph3tmEn9vvjY2iuT+gZXPvt/tETa3WPAcj7oqA9vmF/aJmFgXmZOw+0CJAAuV3agbN0yJmioRAp2aAqJni1GT4eXdm2DAzJ3ZjYyN23313Z35jYyMOPfRQZ50tW7Z4tksmk9ixY4ezvZ9IJIJIJPWaCofDPWoA19POpxKwDwuD/VcY5e4/I8tU990FwwjnLGoqKfn/S0uu7Ssl/kuN929hVKL/Sn28kiVnMAwDd9xxB/r374+RI0di5MiRGDBgAO68804YPe3JVCTCmoodSfOX+HDCFDUF1Aw5Nd1CQbpQoTlOTTP8PGADAEBzVEfCcmpG4wUIV34xpYROTXsfCaGhLaGkHt92Bmm9HHGgpMWC8nVqDjra/LubmY7BHvRG452I6waEABpbM/RRMcLP3zgLiG0DFpwNNL4O/KM/8MUf3PbsiqJmikiSbaGgIufUlAWqiJ1TM8ipWcWFguRrU3Zqigw5KIOE2VI6Ne2CYo6omatT0/dsKbhQkOvUDIsc9223VQlJomQ24ed+p6ZqCpPyOmmPmQA6vvTO03oBISuFRrGdmo4j2no2lUPU3PsbwLBTgeOfc68XGbuvg0TNOstt7/lB0s6pyUJBPYHRo0dj2LBhmDt3rjOvpaUFixYtctItTZgwAU1NTfjwww+ddebNmwfDMDB+/Piyt5kQQgghpCdSMqvAj370I/zxj3/Ez372Mxx77LEAgLfeegu33XYbotEo7r777lIduttSoynY6Yia5q/7Zvh5hurndk5NaFAsUdPMqRkw6LOdmp069L7m4DxajvBz2TVUYKGgpAih1T6MCAg/D9WZA+tka7C7plh4BuW+gXrQOdpOzT3PAo5+FKjbw5xWawC9A4mEu822thi+MrRv8HGL4dS0nayAWUVdFgh2WVGzyE7NfIVGWUzLFH6ei5Ov1DR/Biz/P+CgHwG99/L+mGALZ52bgRf2AvY4BzjyweycmqXMqVk72HQa2m1NcZ2nq34e8rbNJu/wc6lAl+3UFDnu296HFskup6t9jQWFn9uiZlfpLTq+TL1nbKcm4Paz/QzxVz/P2alpvc/ZiLaFYv+I0Hs0cPRj7vF2fAAMPdldz35Oyj86Hv8ssPMjYPdTrWVBTk073yadmtVOW1sbVq1a5UyvWbMGS5YsQX19Pfbaay/ceOONuOuuu7Dffvth9OjR+MlPfoLhw4fj3HPPBQCMGTMGp512Gq699lo88sgjSCQSmDZtGi6++GIMHz68QmdFCCGEENKzKNm36ieeeAJ/+MMfcPbZZzvzxo4diz322APf+c53KGoGENZU7NBNUbMmm5yanvDzEFRrQBtK59S0BqFNUR0JYYma8QLEMf/AORunpu3YyrEquR2OmYSGVtuYZA9shXAH0R6nZpah9fEmM7+lr6rcjnbzfOp7B4jKep5OzV7Dgf5j3PlaDZAAYnG3rdvbMgzynWMUUP28pt6txu7P9bnLipp5Vj8vdk5N+7pSNDfnamBOzSpxahpJ4N8Hma+13sDh9zr3oo4whOP86wQ6NgAbnjNFzWycmslSOjUHW8fIwakph58XrVCQlPbBdmoix33LIey2YzjTNZGuUJAcft7Vjyb+fJqAJWr6nJophYIKzKlpX0/lcGqGpOtODQFHPOBdTwtwao443/xnI7sxnR8+WCiou/DBBx/gpJNOcqbtXJdXXnklHn/8cfzP//wP2tvbcd1116GpqQnHHXccXnnlFdTW1jrb/OUvf8G0adNwyimnQFVVXHDBBXjggQdSjkUIIYQQQvKjZKLmjh07cMABB6TMP+CAA7Bjx45SHbZbEw654ec2pqipQUCFkpL3z1v9XNUkUTMop6a1fXvcLRQUjxcgjmUTfi5EQKXj3KuSK1Lu0Ba/U9OIw8lDqNU54kBW4edtq4GXxgB7fQ045klndkI3MOX+BVAAvPPDkxHSfJkaPKKH7dDMIqdm7e7e+ZaAmIh7nZppKUb4eWSQJGr6cn3usqJmvk5N28VXpOrntkNOjbiiSpDj2KgSp+YXv3dfJ1vNv7otakag+H+QiW4BDD21zWVzalpiW61f1MyUU7OEhYKMVKdmuBhOzaxEzQxOzXxFzZDPqenk77Sdmrr5mZCrU9Opfh5x91Mq7M8N+1zSYV/bmT7LgnJqslBQt2HixIkQctoMH4qi4I477sAdd9yRdp36+no89dRTpWgeIYQQQghBCXNqjhs3Dg8++GDK/AcffBBjx44t1WG7NWZOzf6eecJ+i7QAt6Avp6aqmUKlmVMzKPxcWPtUkHScmoXk1MzCqRk0uDZipgC45c3sKmwLAQ1u+HmLPd62B7qyI1Pr5bqFklk4NZs+Ndu98z+e2Q3NUWxtjWFLa8xxbHrPQZoncnFqBouacckxm9GpWYzwc7lCfMfG1PbkImqu/Suw6Jrqy+uYKynCe67h5748ecJIFUqzQRaoHMdxUPh5FTg1k53Ax7e407Ybz3ZqKhGkpM4QOhDfnirUBYqallMzVAqnZg45NYXs1EwTfl5wTk3XqVkj2nz77uL9lZ2a2RSqcor3DPDOzyWnpr9IEACocvi53a/29Sy9h0Yif6emWgZR03H9dyVq2sWAMoiasuBpRwKwUBAhhBBCCCFFo2Tfqn/xi1/gjDPOwGuvveYkTV+4cCG+/PJLzJo1q1SH7dbUaAp26l6npgErTFuNpA405fBzaE6hoLCiI5YIGvSZIouA6oia8USJw8+DBuR6FFj1B+A//w189VfAmOldHMc9l4TQ0By3xCLhEzUV1RzU51IoyBZOfHkwG1vcftnREceQfm44GYThcxp1IWrqcbMwD2CGn8tYA+Nk0t1me3uWTk1DB5qXAgMOTgmdz4idqxEAWpZ5l+Va/fzT24GW5cDobwBDjsu+DVVHnk7NdOHngHld5OhIdkWgWsmpGRR+XgVOzaZP3OsakK5N16kZChJ8OhtS2xwkzNrnHSqmU9Mnauodqfcz4EsvUaZCQbZT0x9+XjKn5gDv/HycmjUDgfhO6/i1QNjKBey4dn1OTcD8TCik+jlQYlHTvu4CxHQZuy1abfp15OeBnXJFYfg5IYQQQgghxaJkTs0TTzwRK1aswHnnnYempiY0NTXh/PPPx9KlS/Hkk092vYNdELn6uY3t1FSCigUZSQhrcKgLDYo0SIolAwa1lnPMEK5TM5EocfVzz+DaGtTpMaDlc/N1+9osjuPuw3RqCu/xZWeNokhOzRxETV+OygZZ1PQ7J/3n2VWhIDvUWw17XZL2PABJSVze2pqFU9OIAit+Dbw8Dlj5m/TrByLlM036XGFqjesKzkZssCtU+/eTD4m20hYAyUSKMzPb6udpCgUB+Tkodcl1l0nU9Dg1K9RnbV94p+3rxWqvrtQEi7rRIFGz3E7NwdK8aHZOzVKEn8uCZChNoSCR9FaO95NvTs1wP6D3KHe+p1BQLPMxbVFzt2PceVotELJEzUSrFWZu59SUCp/5nZrZuPUNv1PTmt65BPj41uIWhdOzdWrmGH7uzLPDz+nUJIQQQgghpFBKJmoCwPDhw3H33Xfj2WefxbPPPou77roLO3fuxB//+MdSHrbbkknUDBw4iSSEbodla9CkEPUUsVIaoBpQkLByasYCcmqu2tKGi367EG+t3JayzLtPv7upC1HTdlzpUSBu5VXNppiPdJyE0LDD1v/8Tk1bzHRyamaxb3+YpEVDs9ep6cEvWjoOzYAiGIA3n6a/QJLj1JTCz9M5NYUhnXMUaF1hvm5ZHrx+OjIJHrmGn9t9nG1RpnQkWs3q2PMmFbafvCk0p2aA+yqfvJqGLHJlyqkpiWj55u/Ml/b15vvVuso7374vkq5TMzA0t7Mhy/DzMjg17eNkqn5ui4BaRBI1/SHixXNqhmAXD5JEtUypDHTJ3ZuNU1N2Ax8uFSzZucTrOvQXI+vcDMSs57Ydfj5YcmdrvdziVokW37PfF34uCnRq2j9CfPRj4NM7oGx+qet9ZItTKKgI4eeBoiadmoQQQgghhBSLkoqaJDfCIRWbE4M98xxRM41T0zDsnJpuoSAgSNR0B8VyTs1EMnVA+erSBixaswNPvbc2swMpi5ya8YRUzVl2AeUiakptSIoQmhynZsK7D1vUzCX8PBEcfr6l1Z1OyanZlVMzRdRMk08TcEXNhHu8tDk1PUJLzB1826Ge2ZJJRFDD7rWWjYPKETULSGMAAO3rzDDW7e8Vtp98cRyXqnc66+0ssUt2X+Xl1AwoFGTEUp2kHlGojE7N5mXAv/YD3r7YdWrWDrPaYTuVZVEz4LkV5NQsd07NUB/J0d0ecE9L95osrtqClP/ZUoxCQX4RTZ7OJkdmtjk1HZG2FtjzLKDvV8zpISeaeTGd9eQ+6DALqr16pPkDWXSLOX/QEe46/vBzuQ+1Wvc9M+LeZ0suhYL8OTWtdiixIhYftN/vrpyaQdXP/QRd/wqdmoQQQgghhBQLippVRI2mYENiKBbET3XmhRTfYE5G6E74eRIqtJAkaqaIlbJTU4UOc4CZDBA1O+LmMS9N3Ay8MMp03QSRRfj5zJc+MhcpYW++tth263U2oqYr4OhQ0dTpy6npd9aEcgg/17NwaqaImn6nZheipt1//hx2gOTUzKL6ueFzj9nCSiJHUdPvSut3gPs6tiN7p6YQxXNqOoJYZ2XCqW3R3z73fHNqKkp2IcDpkMPPZVHFXyxI7qNyFgraNMt8rxrmmPlcAaD/QVY7vE7NpBIJFm46N6e+x02fAnOOAza94s7L5NRULYGsswF4/zvAjsXZtV9+VjiVutsz59SUcyymCz/P16lpi3taJFVE07IUNeUQdqULp6aRdK9t+3l82gfAYfcDB//YEkXtNCHSDxWdm4FEM9C22rrXrc+TPvt69+84NVu9fSgLrnk5NXV3P/J0osn8W4z0FzaO8N1FTs28w8/p1CSEEEIIIaRYUNSsIkKq+Xb8rv0GZ56m2IO54OrnQnJqatIgKZ7JqSkUJ1Q9GZBTsyNuHnNcaBHQuRFo/jy4wX5xLCAMdv32ZvM4IuQOAj1Oza4dfsIa9MaNEAAF2zuzdWpmIbTJTk0pRN/OqRlWEtij+Xkg3uRukzb8PI2oKVc49mO9r4a0z4647gjLgfsBzJyayTxFTX/7+uztvu5/kFfUzJjLT86LVyRRE8jdeVoMHMdl2Dud9XaaOy+bEOB0yKHBWi0cgSklh2OFCgVtfcs9/o4PzNf9DzT/2gKd7NT0p1sAgp2am/4NbH0bWP2YOy8bp+bGF82csp/fm1375XyJdkVuvSNzTk25HcUWNbN1amZ0zMtOTZ+grse9zy7DJzQCprvygO8BvfeychLbjnrp2SyLhnZhIMDrPo/vlHJqtrj3tKKZIrQsuOZb/dxfKMg+t2KKmkUNPw8QLlkoiBBCCCGEkKJR9Pin888/P+PypqamYh+yxxAOmaLmjmQ/fPiV57H9/Z9htnEW/gsIdoMYSRhOTs0QNE2DgAIFIlXUlHIGGlAQDpv7C3JqRhM6FBjordjhzemcml3n1IzFzIFxQmiocZyasZycmrF4DLUwK7wDQLMdft5VTs1cnJoQ5vlYYu8WS9T876FP4r9izwGfNACHW8KJfxAu/E7NNKJnoGvHnKcnvdtsb4ujrt53e/rz/NkCTc7h5773rdfuwLlfmrkSBxzk5s2DMMWDdGGS8ntXaPi53KeJVrOqcjlxnJq28JJnTk17H3pnYU5NzRIEQ71NwSZTte1yOTWFcEVNmf5jrHb4cmoqacSezgbX0ecnLoURO2JihpyaNunc5H7kZ4VciCmjqCk5RotdKEj+wSNfp6YR4NS0n0lvXwRsfAk4cxnQd1/vfZpOjNNqzX6S15XP1xE1Fe8zbcAhZl5OwBt+bh9HldqWd/XzWndaiOI7NYXIvlCQlq9Tk+HnhBBCCCGEFIuif6vu379/l8uvuOKKYh+2RxDWTFdTQjfQ1PtoXLfuxxi7p9WfgU5N3XFqJoUGTVWgI4QQEojFfMKaL6dmTdgS0wKEyI64jjo1ClWxxENZMBDCdV9lEX5uFyKKGxp624O/RJMrLiS7FjVbOjpMUdMqbmQXOXIGxn5njS1u5pJTEzAH4VoNhBBoaImilxLFJfWvmst2vO9dT8ZIWJV+u3BqBg1+HVHTu822thhG1PsG1f7wc1toKNSp2Ws4ULen+U9qk7Oumo2oWahTUxI5shWoiokT3mq7p8z7RQiBZKZC6EFOTVusKKRQkC0EOaJmF05N+b4sFa0rgJiveFhkEFA71GqT1XbZqRlEtAHou4/5Wqv1imdybkT7urbzNMr4Rc1srz9P+LkkamYqFOQJPw9559kUo1BQilOzF0ynrsgsXMspCxxRPgnEm4ENz5vTG/9tujHtvlZCbgi/HzlNiE2QU1PrZV5zZ39hVkOvP8wMTwfMe1gW6AGvgzlXp2ZQTk2par1SLFFTvo66DD+3+kkurpSyTsA9QKcmIYQQQgghRaPoouZjjz3W9Uo58tBDD+Gee+5BQ0MDxo0bh1//+tc46qijAtd97rnn8NOf/hSrVq1CIpHAfvvth//+7//G5ZdfXvR2FZsazXRqJnRXRXFkisCcmkknp6YOFaoCJJUQIBKIpjg1pZyaQkGkJgLEU8U0wBQ1+2qSIGiLTPEm4JXDgeFnAkf8X6oQ4BucCiGcgkUxXYVQa83z6djkrpSFGNHa0YkhAHSE0K82BD1hDsYVGKZYW0j4uSxO6DEg3BctnUlEEwYurZ+P/iFrefNSVzhKCT9PeEUNv1DsiA7pnZpGgFMzBb9T0wk/z1EEDHJqBrTJXDcOII1jqaiips+pWUza15vCbTpxFkibU/OuWcvx1/c0HDy+HfsPHxCwnS+nJlBY+LlcaRtwr+WuRDRhpAp9xcZ2aaoRV8Dss09qYSnHqRlwvQPe6ueqT9TM16mZjStbvk/9oqb/nu0q/LxYhYJk4S/UFyLUF4rtvFZrzOvK/3xJ2Yd0zcjX3uZX3XVq+vvOJaBPbdRsRU1rvT57uyksnPDzDE7NYoWf2y5Nf/sKQb6O7M+TdIz8mvlj14gM0SlBwiWdmoQQQgghhBSNqs+p+fTTT2P69Om49dZbsXjxYowbNw5TpkzBli1bAtevr6/Hj370IyxcuBAff/wxrrrqKlx11VV49dVXA9evJsKOqCncVIa2+yqLnJqKokBYOnU8kcmpqaI2Yg5IDT0O4cub2BnX0VeVRBRbNNvxgenEWf93c7qL6ucdcR0qzHXihoa4sAZznZKoaXQdttzaaTu/QqjvXeNUbjdPRi8s/Dzpc2oCaGyNAhC4YtBL7rL4TtNhJq3nbhfPPEjPIvzc8O1ze3tAsSB5HZF0w84LrX7ea7ivTeH068rILtssHLdZt6mYTs2tC4EXRgIfXJ95PeHLXWtN/+nd9UgIBb+cszJ4OyNN+DlQWE5N2akJdC2i5eMKzRVb1Bz9dXden329uXKB9E7NfvubfxNN7n3nd7nFA5yaoSI5NT2CVZ1XMLb7T06R4WwXEH7upyunZtsa4OXDgTV/8c6Xnw2qBrHbce4ytUYqOpVlTk3ZJbzxX+46dl/aOSiDipbZ2MKhLGom0jg1/cjVz/0/5nhyauYYfh7k1JRzhRbNqdnhHiedk9VmyAnAlEVA/eHp1wmsfk6nJiGEEEIIIcWi6kXNe++9F9deey2uuuoqHHjggXjkkUdQV1eHRx99NHD9iRMn4rzzzsOYMWOwzz774Hvf+x7Gjh2Lt94KyAVXZdiiZlw3HF+l49QMCl2Wws8Na6AkrEF3LJ6+UJABBXW15uBdhYFowhtf2xFPBjs17TyYsa3m/roIP2+NJhGyCh0lRBjtSWswF93srpSFGNbeGbXOLYQBdTVObk3zmAlnIPraylbc/9qK3MLPZQecJQ40NEfRV+3AAb3WmdOJQeZyu9pzimgZ4DySheJMoqbmc7lZbOvKqQm44kKiNXNBHz9+sa3W59RUlOyEOVlIykKcztwmuVBQEUXN5k/Nv3auv/QNMP84ef+8OTU37ExznQaGnxfi1JQKBQFeN6HnuP4fFMpQLGjnR+bfPc4G6kaYr/tKTk3D59T0i5qyANqx0fzrFzUTLea5COEKVcUKP7efB4pqhXsHFAqyHYyBOTV7pxc1u+r/za8COxd7CyHJx7H6RQw5wV2m1kjXY9Js4/vTgC//6TuvgPBzPWpWqnfOwerLrETNHJ2aMk718xZvrk/Al1NTLjKWg1NT/tEh0Sy1rz11m3xw3usu8mlmC6ufE0IIIYQQUlKqOv4pHo/jww8/xIwZM5x5qqpi0qRJWLhwYZfbCyEwb948LF++HD//+c/TrheLxTw5KFtaTFElkUggkSidA8ret/1XsQSSRNJAMmkPkgUSiQQ0hBwFOilUhBQDhp6ArpjtNqCZ+7HEzXg85m17IgZ7CCWEgl415mArrCSxs60Tob6uANERS2KY5g4S9dhOGIkE1M6tppwodCTaG6AkOz0XkJ6MwpCOuaOtEyHFPI+E0NASU1APwGjf6JyL0DuR7KKPW9vNthhKGP17haBLTs1EvBNqvA0agM2tCu5/bSWOmtSOYwAYiQ7oXew7lGh1hONErA2IJLBxZzt6qeaAPilUfNSxH4b13w59x8cwBp0IJd7uOW9Dj0GPtcMdogok4p3OoFVNdkKD6TQ1fO1REYIGN/xcUUw9Z0tLZ8q15z+uIzaLpNn2DLnd5GstpMedcxZQkIwMB3zHCik1UJBAIt4OhIP7UIm1Ou0xEu1d9nUmlIR7LSWjTRBFuu/U6E5oAER0W8brTE3GzfWUkJnF0Eh61t+wM/X9AABNj0MFoAvFeW9D1j6Sic6cz0NNdFjXShhGIgFNq4MKIBlr9uzLPq5NIt4BpMthWSRCiTbzvNS+UIZNgbb6D0gOHA8IDSEAQo8hmUhAS7SZfaJEkEgknPtC77Un1NphUDrWQXRsMPtZjcCfCTTRsRXQeiFs/bSTQG3K9akY3g8vkezo8jmCaDPCAIRWh2QyCU3tZbYz1gJFj0EFILQ+ULANRqLTuZ5DSfO8E6iBChVBsqaux1LubRk1ut28vmLe69C+F5NChUgkoA88ztm/IRQo1rWUiHdCaXkdoZUPQTTOR3LYme6+7eeL9ezXABhb3oQquV71WBOMRAJKdBtCAIxQ/7T3q6ZEzGsu3uZcc2qs2WmXHt1mnotaG9DntWYfJ1qhW88rodQgmUi490W8E0oi6uxPGPEu37uQkYQC877QYN6feuc25xoQlhO14M/rWIt1jfTu+nrKAhWac5522+x5ulAzXjPFopTfYQghhJBdkYmDbstrO6GoaME4HF8/E4rIlLSfEJILVS1qbtu2DbquY+jQoZ75Q4cOxeeff552u+bmZuyxxx6IxWLQNA0PP/wwJk+enHb9mTNn4vbbb0+ZP3v2bNTVFcmxkYE5c+YAALZFASCEzlgcH3z4IQANTTubMGvWLBwW2wbLG4WYUYOQFsXWLZux3RDoDyBpKJg1axZOThqIKMCOnTsxa5br1AmLVky1XhtQ0NjQiH4AQoqOf8+eiyFSJOG2Jg371bqi5vovluLjDbPwlfi7sOoc4805/8BA/Qt8VTqPdatX4JON7jHXtAJhmEJtEiGs35nEqF5A55aPYZdg0GOtnnYGsaZhBU7rCyR0oGPHFiQkUXPO7FnYL7EU+wGIClOoffy9RhwzCli9cROWdrHvUzt2wD71t9+Yi2ZtDd7aoKBWNd1DUSOCFdGRmNL/XXz56Sv4aOU+2D25EHJG100b1mPpllcwRZr36sv/gq6YIuPBsRXYB8AXa9Zj2UZvew6ObcQ+AFqbTRds35BAS0LBJyvWYhZWe9YdnPwPjklzHq+98k/ElcxFugDzWjst2o4IgE9qvomoUo9N8z5MWe90XUENgAXzX0ObukfgvgbrS5z2bPpyNT7cmrmvMzEi8QEOs14v+/hdrF42JO99yewfX4wDACTbGzJeZwfGV2I/AM2tHRgAoK21FfNmzYL9iGyNJQO3PyK6AXsAWPrZ51iz0lx+UkcM/QAsevctbNNySw0wJv4ZvgJg7fpN+LRxFsZHWzEMwCf/WYT1n7oVw4+KboDsr31tzisZ339NRKEhirgyIKf2yEzu2Ik6AG+/uxit6iT0rj0QLYuT6K+/h4kAoh0tmD1rFsZH12EYTKfmnDlzsG/4CuyVnIe3Nk/A+Oh81ANQOjcAAJrbE/C36I05/0QSdTgNpug+69XXU4og7ZVY6nn2xDua8Yr8/gQUTuprrMXJAGJJDa/OmoWxsW0YDWDl5x9hoLEJQwG0dJrP041frsFi63o+yxJz573xHvZNrMc+AX3zxcrlWLYu/fU1Jv4ffAVAZ/NGzJHaOaWzBbUA3nx7EVq0rYDQcY61rGPzBwgJ3Vy+YD76Gl/iSADR1kbMlvZxSGwF9gawavWXUCDwFQDJ7Z9A9giuW7UUn3w5CyMTb+NQAI07ongvzf1wTGc7BgP4z4fvYtNH5vV/QHwJ9rf3tWIJ9gbQ1BbDAt8+7M8ZxYjhw0VvYDyA5rYo3pg1Cyd2RjEAwPuL3kF/YzUOtLaJdbbg1S6e06d0tKAPgE+XrcQ4ALFoBz5973UcYfdV8xagzv0szZd6fSmOB9AeNTC3izZlw37xNc552s+PsbGNGA1gzdovsXRz4cfoio6OLCIWCCGEEEII6aZUtaiZL3379sWSJUvQ1taGuXPnYvr06dh7770xceLEwPVnzJiB6dOnO9MtLS0YMWIETj31VPTr1y9wm2KQSCQwZ84cTJ48GeFwGJubo7jzPwtgKCoOO+xQ/HH5R6ivH4ipU4+C9sELwJrXAQAxUYPeiGLwoHpEQkOATQDUMKZOnQrjhd5AfAcikRpMnTrVPVhsG/Ci+bK2Joy9994PWGk6NY+ccBwOGu6e512fvI4+mhvOOXL3Adjz6KlQP3odWGHOO+HI/YG2MLDYPcTIvYZjxOHuMRes3IZP1pmCWVJo2KjuA2A+eotGZx1NSXjbGcDTL20BOoFwTS8c/JXReH/bWmfZ5FNOgrrsXWAV0GmYTrUOwxQTddH1vkPPJ2Gl/cRxxxwJMWg83vvXMvTa+qXZbajFytheAIC9+rdjj5OnQlnfCixy9zF82CAMHXsc8LI7b8rkk4CagQAAdfHLwBfAPvuNweiDvO1RP14ALAf69DbbPnJIf3yysQV96gdj6lRvrjZlkwG8HXwek04c7xbrkDHigFrjudZq/g0gARxw8nSg71dwaFC/vNgbiLXhhOOPBvofEnhMZZPutGf40AEYemzmvjbbk4Sy8XmIwce7VbMBKKs3AZa2OmbfEfjDMlPC/+WFwcfOFnXJPPM6RwemnjYpOBwU7vvQb8BgYMdq9Oldi6mnT8X3Fs521jn99NOh+IQy7Z3HgI3AQQePw5h9zPMPzbkVaFqP8UceBjFsClIQAtpb5wCxLdBPXuBpk7pkPrASGLXP/thr7FRo7/4F+PIDjD1wHxy8n9u/2lu/A6QsDpNOPjE1N6pEaM6RQNsXSJ65Bgh3LX4H7uNFBYgBx5xwMtD/YHdB81JgNlAbVjB16lRor98PbDWdmuazzWz3JADa288Am1Y4m/YbOATY7hXvJ04YC1FTD7wCINQHU884I6UtytptwPvudE1Id+51dentUFc9guRJ84F+B7jbbH8PmAdE6gZg6tSp1nv+MvYbPRxK01ZgC9C3fndg+zrssfsgDJswFTDiUJ81f5g5+dSzoC77xHkGAoDQekHRO7HP3iMxemz661/9cBawGuildnieSaEXFCAOHHfiKUC/Maar7nlzWR+xCaJ2OBBtxvHHTYC6TQH+A9SGhWcf2gcvAmuAfb9ykBna/Zl5vcuM2nMQRhw5FernS4FPgKEj9sfUI4Pbq735G6DhU3x17AE4dJTVp9Z1CQCjhvcH1gP964di6km+fRgJ4FmzKN/hB48CPjTf46knT4U2925gx2ocecShUJoAWNk8IiEl/XNaCKBzI0Lza4AO4KCxhwMfAJGaEL560Gjn86d3renqtT9L80VpCAFvAr37D8bUyVk8z7pi5zDgtb9AKGH3+lwyF1gJjN5nP4zMcM0UCzvyhBBCCCGEkJ5IVYuau+22GzRNQ2Njo2d+Y2Mjhg0blnY7VVWx7777AgAOPfRQLFu2DDNnzkwrakYiEUQiqaGb4XC4oAFSttjH6RUxbegJXUDTTDeiqihmG0KulTIuzLdNhe5Y14USQjgcRtzK15XQE9626667cVCfCLSQeb6aoqMzCc+60YSBvr1cp6aqt0ENh4FkkzMvlNiO9kTCcwFpSEKT9tOREFJOzRCWNA3DJb63TRE6trZGcdM/PsW3J+6DifunOvRiVgV1Ra3B+L0H4bF31iFuhFCjJhE2YzrNdhsRHLPPIPxo/OHAp0BEiWV+/4Tw5GILKToQDiOuC/SywvoTqMWKqClqqi2fQQ2FAMWbb3FzUxsG+3IwhlUBOMc2Q/C1cC9P/5gHrbX6wVxnaL9e+GRjC1qiemrbfcf1HA9R6XgWH04Hvvg9cNpioNcoc71wGIqVPzBcU5e6jY2V69N7Hn7csEbViJvXSFesfxF491Jg9JXAhMelBe65xWLNeOEjU7G787xD0K+2gHtQd3MBho0WILJ78Hp2PS7rvBUYCIfDGNovgsYW81poihoY0s8f4m+KKVqoxn1vLZEypCK471qWAw2vmKtG1wP9x0gLzT7VwnXm/qx8kpqIeq8dX2GasKakf58SLUCTmQ8zHNsI1O0WvF5XWHkrw5F+3mNFzDyUioib16xhrpdEJPUZGqn37FINSJkQ0lsAmA55Jdw3+B4OeZ/Xit6BcChkujM/u9ts53tXAqdLv7ooVpqPUJ25T7tvjU7Y96hqzVNF0ryeY67TNtxrgJsD195lqA+gd0JTROq9LaO3uu1Uku7z3MotGa7p7fTp0vCVOCjxBHDIHVC++IO5XFWc56+SbPf2iXCvGTuPpeJkZTZR9Q7zfKx2qJH69Per1bYQku77bLgiqZo0c1mqobqAfYTNNBh6FKHkTmu9WnM9q+9CigH5fneumyDW/g145xK3aWHruhA6NMO9t5VkG1BTjM9sq/9CvYvz2T9kPHDah1DqRrj72+sCYOsb0PY6P/M1UyTK8R2GEEIIIYSQSlHVhYJqampw+OGHY+7cuc48wzAwd+5cTJgwIev9GIbhyZlZrdRo7tuR0M1BqWMMk9xcMcMulpB0xA3dkhcVzfybTFP9XBcqBvWOOMUKQoqO9pgrkAgh0JHQ0Vd1B7F63CrIYBcKAoBoI1o6vMU5hK/gQ2s0aQ7gYTo1l7QEC9G/nvMxFq3ZgW889n7gcj1piWdqGKceOAz7DemNmF1JXY86xYZiogY1IRXD6k2HZC8lCsPIUEDHiHvFIUsQ0A2BXqr1WqnF6tieMKCZVZs7N6dUP1+5eSfeXbnZMy+wEIaaKpzb76tirT+0n7lOc2dAHjR/oSCZRECY8/L7zAIfq34b3LY0rkXPskxFPOTiLNkUagGAdrP4EqLeHyrkwjed7U3u63h6ITcr5MrwsW3p1xPBhYJUyZm5ZltAMRL7+sml+vlm1/2JaIN3mVNcpatCQf7q5xkK1bSucl/LxVVyxS4c4xcinWvFV/1cCbje/S7RoDyw8R1uYRq7cI8ff8EeYaT2987/eAto2dXP7SIw2RQKsvvdLtrjP66zfheFguRK3XHpOeovpgNgVfhcJKYsAQ7+kVtUxki42xkxwNCD9+EvPmNXjrfvg7wLBUnXX6ZCQfIx7fvNfu7lU/185YO+tmWofp5LsbR0ONdI78zr5UL9YUDtYHd66InA1I+AwemSiRBCCCGEEEKypapFTQCYPn06fv/73+OJJ57AsmXL8O1vfxvt7e246qqrAABXXHGFp5DQzJkzMWfOHKxevRrLli3Dr371Kzz55JP4+te/XqlTyJpwyBVQ4klTZFFsC5k06I0JtwKsU/3cFjWtQW1ST3gFPdvRCWC3PjXOADOsJNEedwfkcd2Abgj0lQoFJaOWECIVnggSNRMJbwXstljCcWpCDWNtbHeIlLIgQBiuWLelNbWKtmILXkoIqqrg+on7oMMw3USxaIsjqHUaEYRUBZFeAwAAvdVOxJIZkjD7hSJLlEkaArW2qKn2QlyEEVVNoRTx7SniYlhJoLm9zTPPI3xmEhF9laOHWU7Apo6Agb6RQdRM+kRNuap8reR+FSK1knAQ/orWQeQjatqCSEoFeXc61tnkvO4oQNTc3NyJHU2SkJlR1HSvU3k6qbvXz7rtAbnppOrnm5o68eG6nbmJmp0+UVOuZA1kL2pmqrTeutJ9ncgzFNVwf0CB1su7zG6rEbPcz2mqnwNudWybdKKmLdIHVT4HgquQ6x2pwpZc9d6ufq75RM1ke6qoqftETXvdFFHTmp9JVAa8YrL945AQ7nHle1FRgH4HmlXabbFcJL0/KunStWj/GIOwV1wHgF7WD0m2SJxoMv+GB6Rvq/2eeCrAS883ex/+68DGfo/t+62r6udGIr0gOfCr3mm7bSLpETUVoUNFhnsgW3Sf8E0IIYQQQgipaqpe1Lzooovwy1/+ErfccgsOPfRQLFmyBK+88opTPGj9+vXYvNl1ybW3t+M73/kODjroIBx77LF49tln8ec//xnXXHNNpU4ha0Kq+3bEbTElwKkZt12KktAgrMG2qtoVcHVEk7IgZA4aDcepae4jpOhok5yatjNOFjUdISQmi5oNaOv0ClnxuFeQlJ2a4VANYiKCaM2eKedtC4gA8MbyrSnLbRefYQ3YTztoqJM3c2fzTmcgGjVqEFJVRKzw2j5aJzpjqSKpQzJYiJSdmsISQGKwBtPJdndAbg2wQ4qOuN8Z6xm020JVelFTtc5xaH9zn82diVSXaSZR0+/UbJUS/8muI8kRmeLqCmhXPNGJD9ftgAgSHSThdHtLCxav35l+f047m8y/flFTcoQmY64AJLuIHeLNwOtnAMt+mfFQVz32PtZIz4Zfvfg2mjvSCB+OU9P9wQAwBW6btdsDnJq2Q08N4bonP8AFv3kHnbp1HwcJjXoc2DLfnU7n1LSFQluA032Cqn/fmZyCslMznqdTU3bt+YVIOSRbJB0hMFjUzMKpGZNEzVAuomane33ZfPms+zqdU1O+p20R1XFqtnnXTefU7FLUlNpli5PyPRDk4gZcp6Zf1Ey614OwBNh7561znpEOtZaomcjBqakGOTWlZ2VXTs1wGqemmsapac8Lwv/cUSWnpu+9DkFqr5EEtrzpPQd7/kc/BhpeCz6eLWJrFDUJIYQQQgjpDlS9qAkA06ZNw7p16xCLxbBo0SKMHz/eWfb666/j8ccfd6bvuusurFy5Ep2dndixYwfeeecdXHTRRRVode6EtSCnpoU06I0ZtuMl6QwG/U5NTdHRHpNETcepqWBQnxpX1ITuHAtwnXH9NXfQrDihi16nZnvUHDBGrXD4ZDJ9+Hm4xmz/NnVUynm3d7hi0esBoqbiuJmsc1MVRO1iQHHXqRkVEWiaAi0ywNk21rEdaUnr1DQcUVOxBJAO3RJukh2u4GEJGmElmRru7xE1u3ZqKsLr1DSEWXE7qH3B5+ITNVs+l5bJri5Z1OzaqfnPD9figt8sxAtLNqWuI7kzW9tbcedLn3mXGzrwzuXA+9PceY5T0y9quP0l4u65dCYCnJqrHwc2zQL+8wNg06uBzTcMgZVb2tBHuo53Nm3C+2t3BK6f6tS00jVIomawU9MNP1+91byeOpPWY1UECDXb3vFedylOTTvEuwunpl/EDDqWjcepma+oKf2AkRJ+Lglyeixz+HlNicLP7TZGfc+Pjf+SlvudmtbfrJya1nzVJxqGsw0/l/o9HiRqprkXFekHLPn5q7vXg2455L9sNtCe8Dnhe1k5ZO3+zDv8PEjUTOPUdMLPrffCPjePqJneqe2d73vmecLPvdeyJqRrdPVjwGsnAJ/93Lv9hheApXcD8yYHH895vylqEkIIIYQQ0h3oFqLmroKiKI6w6YiaQTk1pfBzv1NTcRyYBjri8kDbsP5XMahPxAlTDCnJQFGzX8gdIIb0VtMx48up2WGJmnbV8aQv/LwlmkDIEjUjYXOgvCE5MuW8o1F3wLxg5VYkdG/IuB1+LhTXWdgpbFGz1XEMdhoRs6CGGkKbbg5K451pRCwgwKlptl833EJBfXqboZRNcTuHZ4creFiDd9Op6XME6bmJmrZTs1+vMGrD5m3Z4s+rmYtTUxY1PaGqspCSwalpue9WbDLf80VrAvpREj1q1Rg2N/n6YM0TwNo/AysfctonbFElg6ih6O65BDo11/zJfb3oKq+D2KK5MwHdEOijutdxvdaSIpKu396BGc99jPa47z2yRE5Z1AzOqWmuF9PdeydhWIJbkPtsyxveab9TM+vw81ycmsUQNa33Vq0xw6Jl5OvaiGcOPw/5ws/VdKJmHuHnyQ4gusU7r2ODdzngClZaak7NdsMXet1V+HmtGTGQl1NT/pEinaiZhVNTT5rvTcwII6r72+dzatrvfzbh5+lEzXS5VW3ShZ/LOTVTrt90oqbtoO0P7PdtoO9+5nRXTs22L8y/dg5f5zyk+6hjY+rxHIdwv9RlhBBCCCGEkKqDomaVEbaKBcV9wp63UJDs1DSFFGHXIZfCz4OdmlZOTSn8XBY1OwOcmioS5gBVHrhHG9EZcx2SAKAHFgoy99fLqi6/snO4u4IlrMai7Z5tPtvkzfunOG44SdS0nZqJNm/4udV/rcJ0UCUzipqZcmqaA+T+ffqjRlPRkpCdmuZ5CkvoCCtJ6Nk4NbX0oqZm5YOLhFQM6GXOa/KHSmcq2uMXNZuXua/TOTX9oaoB7drWYu53ZWNAISLJvVerxLG9PeaGqRtJYOlMd93YdlzzxPtYvGoNACDqTwsgtSssVTVOKRTU/Bmwc7HZ9t6jzMJNshvPYnu7+X70la7jgaEWRH2i5s9eWYa/vvclVjZYYo99jQWEnwfle7XFxBbp7Y9nEjXtAkm9R3qnnf35BKNsnZqZcmq2FaFQkCNkBbjz1JArdCbb3OJl+To1Y/nm1OwEYpao2XuU+TfR5IYwZ8qpaYls9863HMlO0aMMoma4n5vzMZOobCS9758t9tnPBUUD1IDzAdx71Eh6CwxJ+7NFzbgII6b7nZppcmpm49Q0pOs90RawXrqcmtZ7Zrtm/eHnIpH6LEsnatrvw9g7gCMflnKMGq5j1CIkAtrrd7dLhb+w9e3U43UlphNCCCGEEEKqCoqaVYYtasayKRRkuOHntlPTdWDqPqemm1Nztz7enJoxj1PT3EYWgwAA7Ws9k6JziyNMJRVTJBC+AWSr5NTsFTEHyh+1DHVXsEIjY3FzgN4nYrZ9c7NPPLLdqFLoZ1SYA2oRb3UG+FFhFgoCgA5b1Izm4tRMzamp1fTGwXv0c9yoslNTV11RM5n0i5rStN61U1OzRJXasIYBdeZ709TpH/jnGX4e5NRUw3hw/ir87z8/SXHGyu2yHaTLG1tT82pKomZETSChC7R0JIDPfgHMm+QR0xIdWzH38y3oq5p93tzuF+ncc40Id1lKoaA1T5p/h58ODLLSUMhVkC22tcUBCPRR3XOv11oQla71aEJ3cri2R91+ARDo1NzRHvdMy+u1xtz9xjKJmnZb+37F/OsPP7fFGFtUsV2F/pyafqdbOqdgosXrXsy3UJD9Xqdz59nClfRe5J1Ts6Dwc+tc7f4VhruvTiu/amBOzTROzYSvHfJxB58g5XjMIGr6hWR/Ts2MaSBsUTPmvc6l68GwnkdxEXZTH9jYTk290xJGrX1kzKlpP+vSODVtunJq2s+koJyauTo17T6S+98Jxzef+R6npi1GG77PEllcDhI17fvDX9CKEEIIIYQQUpVQ1KwyHKdmpvBzIyj83OvUDClJTwEg26lp+HNqKkmPK9QOz+2t+kQnW9SMmEV4FOio1S3HkSUOGAFOTbv6eV0vU0h4e9vupgAb7g9EBgMAknFzgH7AMFPI2epzxLnVz12nZtQKPxfJNkc0aNF7I2SF77cLc1BqxDIUr0nj1Ezobk5NaHU4clS9K2om2x3BI6ma5xRSdOgpomaW4eea16lZG1bRv5clavqdmtmGnwsDaF3uTstOTSeUvwa/nL0CTy1aj9++8UXq/qy22jlRW6NJNLb4ji+JmmZ/CTRv+RhYcnNKmPXOpgYIAfTTTHHEziHqnps73VsSIr3CPIDNVg7NkZe4opTuex8BbGuLoU6NQlVcEXJgqAUxyam58IvtaLdE045YqqgphPA4NQ0RUJXeuv+aY+56UT1DTk1b4Op3gLWyT9S0hSBbRNMKdGrKoedA4YWC0oqa1rVtuecEVCfPr4dcq5+nKxQU5GyUw897j3Tfy0SzKSTaaQuGnmztO7VQ0M5kP7cNQgSEn0vnNHSiNzw8HWlFTV+qgSDs48W2wv5hymmzjfXexI1Qqqhp59QEzHOy79lCws/96/nxv2f+6uf55NS0+0gWNe1+rB1iHlbOqZnOqSmfx7YgUZNOTUIIIYQQQroTFDWrjBpLlLPdc66o6Q58nernwq1+7ggIjlPT8LjcdN0KUxeKWf3cWi8cGH4u0FsxB3+6sC6RtrXm39phaLMEw93DpqgZrrEG/EaqqGmLYnWRWmiqgoZYP+w88nlg4izHMRVRzO2+YomaW1q9A1HVFgykHJAxy6mJRKvjrmnV65wK8p2KuS8jIN8iADQ0R2HEfe7GAKcmQnU4fORARB1R0y0UFFfM8w4pyYyiZsJa9uYXAS45SwwK2aJmyHVqNvtzamZbKKh9vVeQCHBqGpJA/ND8L7Bhp88JaLWrRnHFmhX+EHS5eAyAiJJAe9OX5kTdCGDSG8CQEwEALc1mmHV/zRRjVL8IJPVXrRpHCObyFKem7c7qs3f60GwA29viHpcmANT7ws9nf+aGfnfG7WJU7g8GflMmYLo1PVhOzZZOyampZ3BqOqLm/tbKW50UEua5+J2a6XJq+gsFpRHVWnyiZqGFgtKFHNvClR3eHKrzhvraZHJq2kJbvjk1Zadm7RBpf03A5/eafTvwUGCPs9w2Ah6n5pfxYe68REtq+LnM0JO84eHp8DuJ/YWCgtJS2Nj77/SlKZB/qJCcmo6gblNT7+7DySGpZBbt/KKmHg8WHbsKP7dxBMlM1c+7dmoKIfDuGulHKru4V92eZnNkUdO+j/w/BMn30c4lqWH1zo8KdGoSQgghhBDSHaCoWWWEQz6nphN+LhcKciviKn6npiLn1HQH2i2d5uBOQMHAurBU/TyJWNIVVTriOmqVmOOwbEzUmwvazVyIiNSjWQwCAAyvMQfnStgc8CspomYCIZj7UbUa7DHAHASv0o4BBh/jDIpr1TgG1IWxu1X5u7HF69S0Q6C9oqY5UNbi25zBbZtR54SfRxVzUKr4ikkApkPv6JlzMefj1d4Fck5NxXVqHj5yIDqt43VGW51w8jjM9tcoyRSXqjxI74yag+1XlwUIrD5HZG1Yc5yaKaJmtk5NOfQcCMypmZRcdJ0JHY/43ZpOuxKoCanoq7ZjzebN3nX8oqYaR7TNEl/6jAaGnOA4eztaG1GjJNywfvjPzTvdWzP33Z4iatqFTvphZ8w8h2Qs1UW2vS2WkkJhoFQoyDAEXlvmCkXCFhal6udJwxUqh/Y13/9tbXFTCLEFZuv+2yk5NTttYSlT+Hmffc08lMJwq0QDkkPRcmqmzalpu5dtATWNqGYXTLHdcwXm1NzSoaSG4AMpTk0nb6Uff05NuVBQ773cfdhhwLmGn8ckUdMOsY42AMt/bb4++FZXbLVdsEYMwrq3WvQ6tMMStDo2pIafN33sHm/AuDydmpbD3V8UKgh7//7cq9L1YLue4yKMjqRPSA73dUXGDusHh3C/1GJPMimiZkCBLHk9PyluXNupaf9gkINT0+4jLYLH3l6LKx9fnLpOrz0A+MLPbVFT94efS88KoQPb3/Mud8LP6dQkhBBCCCGkO0BRs8qww88zOTUzh5+7uTJll5sdNisU1SymI1VJ9+TUTOjoawlKAgq2JC1R03Zq1tSjWZjz9oiYIp1WYw74FSncVghhhZ+7Lsu96k2hY912a5DsiJox1NfVYKglavqdmkqAU9MRFBOm0GZAQ4dRC81yusZsUTMg3+LSTabIsLPFJzKmcWoO6hNxxKX29hZnPTuvZ0jRYfidmpLIKayB9db29GKQLWpGQioG1NmFgnwD/SCnpu1Gk52acsVnwOfUNN+jhPCGBm/c6RUoDcVql5rE6WMGYt7+38IZG890i64ATtV5m4gSR7zDEuhqBlkzzb/R9q3oK6U0CKWImt5z7Wu5LDvl8HMhgKQtOvTHgtXmOhu2bYOfbe2SU9MScEynpnmtb26JYmtrDGFNwdB+EaiKdQ9I4ed6+yaolig/2BI1d7Y2Af/aF5h9tLMeADRLTk0nBDhA1OxsN6+5OasTTvoFJwRdGFK4sy+nZtLnpLXvCdstFxTqLu+7/xjzb56i5ifrzfvsy2aBN1ZsSV3Bn1MznagZ6gM7B6K5niTo1VmipjBcV2HO1c+t6y8yxHWFNn1q3h+h3sCe50htcd2XinWPJhHCNt16Xzq+TA0/322C+bd2qBkCn5VT0y5CZV0XueTUVNKImtI9/f/Ze+8wS667zP+teGPn7slJo2xFW7KiLRwk2RrbsDiBWZJZYAF7d0GwgHfXgGEXBwzLruEHLMGASTaLAYdxkGRLtmTJVg6WRhqNJofO3Tffir8/TqhTp07dWzM9kkba8z6Pnpnurq46depUjepz3+/3NaMBUNOuJUCWPRcG9dMEEtDM+lEqnNAA8p2acvk5mztzgFMzLwRNmKP/sfsphLF03e0Rfj6poCAONQc4NQEF1NTl51paWlpaWlpaWlovJWmoeYZJ7qnJJbz4svLzOA6Sl2nm6GHp50aItgCEVtrshY9eckPoqZkqPw8wQkuEQ2sEjZC+kLKemu4kVqNxcoyYvDDatPzcEvok9tpL+P0tH8GuMdq3zHQ4tCQhLkigpuFhouZiZpQADrl3o6r83APZV9knL/t91AAYcGj5uWcRqGkGK5DFSogN2YHEwEYYo2yyMVZTf/pei0PNTsx6agaIBzmP6N9n21E2bMdM4KFrmTBNI7enZkBTjnlLAACo0OAl0anJQJZLgbQAxAwKE1hC9znrCPBo9NJQpuUTOFKxQrztHB8zzgqmcTjtFpVCOMqmh7DLoNI0HQOBmlF3AWN24pJyjCAdUCTNH3NqpsrPgzbvDQtnFLMdcg6Bl3VqLjT7qNN9MFhWMfsI6basDL3iWDh7pg4TdL9C+nn1i9vwPzb/IQBgHYWa4fKTBDAtP0KDuqhTU4CaHQY1FaDRoFD2K890kxAXFhYUdMD6Jv7V/Qv45t75/L6hDAoxsJQH1RgMGzmX/HkKQUEHF9v45DeJ+7cfu9hzopndSHZq2jnAyzDTwEh0+5WmkvPtHKT7OcX0c9GpyfqKltalS+KtMlKAFYAf2TjhM6h5JJl35urc+WPA9Z8GbnmEjqOIU3OF/MmgrdxT0xrk1KTrcYBT0wbZTz9y0fakf9LtusKpOZ5/PCDr1FQln4vbyUoBQQPY/FbyVw41FeXsBXpqhlGMUP5fFnecQ1tLdGqyMcvudvb9Mn1uLj0g/VwHBWlpaWlpaWlpaWm9lKSh5hkm1lOzz52aqvRzofycOsliuacmQnT6glOTlp8bzC3EA4XSPTU7XsgddZE9ilZI4QQPCppCO073l3NL5AXQQoCIlqZ6R3bjrePfxLYSfRk3HQ7rGj0GZMhLccn0MFF1OTiSg4JM5uoToKZPoWYlIECoZ9AXW1p+Hpjj5Osg60xb7shQk85xjlNT/DP02txV1I6S8nM5+V18mWbl8y3fQrMvwQ96Tq4RoOSQa5PXU7PfJ5COXxMgeTkXnZoMktV3kj8VPTVZ38dLNxM3W0M+VkTWx1gpxvljye/HvgDXJKdm2egnwIZBTerUhLeIUcGp6RgBmiJIlaDGljoZTwpqMuBgmIBdw2yXrnlVT03RqVnZhBBkTi2fOCUZUHVtE9unqkmgkOSau7hCyrfXUeDO2zAAZM6pUzMFNX22nmQ3aogyyFjvOxwiYteOQWjqLoth4Nd378fP/e1DgDsBwCD7YundQAIxOdTMc2qyNHAKNU8hKOjb+5dgUYDdi1zsnVWFxjCnJg0KynNqAum+mmL5uV1P3KtsHTmnmH4u9tRkULM8I+3DyPTK9GHjcJ9+GCCWn7NxmA6w/d1AhQJpsfy8N5+5JwAkc87uR3+FXL9wDU5N9kFFHPMPk/qxg7avgJon69SUoaYqJAgY0FNTAIJbvjc570FOzSE9NRe65P6M5f9lccb4+dmqnpp55efrXkf+lKEm76mpnZpaWlpaWlpaWlpaLwVpqHmGyZbTz9kPctLPeWk2e8k3ElgpOjW9gG1H90gdaY4RpMrPu16Y9CJ0x9CKKJxg/RPdSTSjNAgolUfovnwO4vptqSTYcDBaIWPjsE5wak7WEifnYttLufhMGhpjiE5NWvpdiQj86IG82DoUCgcUnDhhFuIwp6bNoKY7Qc+R9dSMhJ6a5DgmhR+hn6Sft0Ma8mMEA51HDGr6sY05OUFc6F1Zdsg1HK/Q8nMZNHrkGqTmn7n9BKdmROHXkkF6zanSzzsBOdbFFGo2Jacmg4CuGWC9u8K/32wLTj+pp2bZ9GD5DGpSmEndonawjDErgSOWEaHREYCDBDleMU2uYyr9nPdZHEUviLDYI2M0Qqk0G6SnJndqOqPoWWQcTkDG5wcEkriWiW2TNViQys+pJuwmDMSYqdNr3TmQHg+9/5ZSTs0cqBkkczfXK2M5onPEIDS9hq2QOAibvQC9uEzCbQBgVkiUp9fRA3sW5Dk1JagZNNItBArICyKUDHpfx242MAoQnJor5E+rioYHvOfPvoN/feRoelsReqWCgurAyDnpbU/Gqek304nYGafmdPZ37DR8DWILB7v0edAVnJqqoCBAgI5zwL/uAL72RvJ1FCauYubUrJ+V/J63XKz8PK+nZtgGlh4Cms9wIO/FDlLmbtMhvZhtyal52qBmjlNThJ3n/6fk76mgoJPrqfnkbPLcTDvVNyVQU9VTMy8oaN1rARhA+2DSsiAKk59rp6aWlpaWlpaWlpbWS0Iaap5hYlCOQ01FT82Q9juMxaAgqfzcNtJOzTCk27FLTuGNpXJqsoRqdxytUHJclSbRkqCmRYOCXCPAIgWGQU/qVyk4NTNQ0yTl55NVF7ZpII6BhZbodMxCzQDpF+ouaAk8LT+P7XEypkjh1GyT4zug0IsBOJVTkzrOLFpiHwcJ1GwGZAy2EcGM1S/pcRzz/pF+bGNOcqHy9HMjRJk6Nfk8yeXnPnXLGYl7LS6vI38RoObyInFkffopOl8KpyYLs7l0C3Vq9tLHYlCzZIZw/aSH4mJDSB/OpJ97cMMV+kXaqVmOVjBqpeFIs5sdF9N5kwTUpJ2a9Fq6Yzix2kOXJtKbkQpqCk5NZxQehZrlgAAMj0Jzhzo1Dd5TMw2Yxq0mTAOYrJHvV73DyQ+9Ve6YXOok42znOTWpY68XufBiBwc7FDYxYEVBDHMAA8Dx1R5PkMecADXpcZ9dDNXHYupLUDOO8iHV7NeBL18FLD+a+rYfRijR+6EXuXh2rpUNCzLTTk3YVTy5YuCBgyv4++8cSm8rOjVFAGbXgZHzpW1PAmp2DoOU7xuk7QGDdx16/NJM9nekIKIgtnHCp2u3cySBXFYO1GTP3eZecp+tPEbm+CtXAl+5mvydOTVL04l7tL+QKq3OFYOm7Joxp337EOnrevv38E29yEFT7KnJzs2RnJonW36et17MHKg5fgkBqTPXJ45IQOhXe/JOzcePJ8+aEMK1r5/Fz89iPTXF3rSZnpr0XCobgVG61pYeTP8M0D01tbS0tLS0tLS0tF4i0lDzDJPcU1Pl1IzoS7AR56ef20aQcmqGIYMQivJzwRXZ9UMe0mKVxtCOJajpTqIpg07qYnKMAIsURkb9LNQcLdPycwY17XRQkGkamK6TcxMdjRZ3aiZzEBgS1KQl8QwKR/TF3Y2yPQSXaPl51aQvwSzURkg/l8vPbRqGhKDDt1sNkjHwfTHRl/HltsdDgPzYwXxT7dR0jQAlmzo1afn5Sjf9oh/49MXeTlxEnkOhZtBKHHh94vw75BEXZ6xIP/cjG1XXws4Zcl4dL0QgrIOQriPX8JPyaAArDQESK5yalZhCLQaK6Z91o4FRK10m3uoIv0/nK4rJ9ds6GvJxcQn97k40EqhpS1Cz54do9gPUrQRqdkpnAwDWx8+SXTGoaZnYNlnNdWrWrS7Kpo8pCjVHQiGEyW/w8nMRavZpv9JMT00KZdn989QyvY8YbKKlryLUPLbSBda/jnwxdyfdb8ydmT3q2u70pfUHkGvNnIu17Qkg+86/B/71rMTFyXTPe4Cl+4EvXZ76tujUDOCiH0Q4siyBZFZ+zlyJVgVNevpdX+oPnOfUtOsJaKL6xnOKcm5ADTXbtA9naYqE+DB4xxyTcvk5AFS3pL70YwvHRagpl59nxsHKz+n1D9pkXpcfIaXN3RPJnDhjyX3RXzw5pyZTZRP5c/W75PoKDk4vdtDqK6AmLz8/Sacmg65yAjzff075eWkKePsJ4A13pHuYmgqnJruOQ3pqPnI0WQeB6NSsn8WdqDb7kCrsgvWmzZafM+dtHZi8kvydlaCz0nPDHgyatbS0tLS0tLS0tLTOGGmoeYbJZVBzQE9N7pBRlZ+7xAU1ZrVSQCiMGPyUys8RoB8k23W9EON2k24yhsAUHCumC0y9Gs1Qci1RqGkaMZZaNDm9v5zeRuypyaAmPY8SDQoCkt6FYgI6Lz+3sj01+bi5U5OeHy0pL8dZqLnc9uAaPmomc2rSHnoDnJpOiezfiLp8uxU/uSYZqEn75R1bafHy0H7sDCg/DzJOTTkoiCWsG27idOuaFJIg5i/s1ZCU/jOoGflZR6QPC9unahgpJ9BELEEP4sSpycujAay2suXnDQrpyqaHOgRXGsCB8YTVwKQjQc2u8DUdFwumYmC9LfYgZU5Nhzg1OxFZA06cBl/MLTxmJ+Xn7dolAICteJrsSoCa26eqMCkE6UfZR+Kk3eROzSnjmDCepPzco79nmQYCltAsudFCek80QgKH7jpB52j1Cbo/CjWFnqlHV7rADC2VbewBurMJQAPQi8kavO27Uok3QNyAAHH3uVP82YCDf0965M59M729sF9Eyd/9MEKZ3g+lMhn7M3JfTTkoyKrysKlUgj2S9QIAoQiP7Dowcl5q25/7zF7c+bQibX0Q1GTuZRneqZyarN8jCCyLYQpQ8/Dw8nMZOgLp3qedQ4LDeFzoGTqfuAgHBQUZ0v6rW8mf7FypwthECAtNkQ2yMbPycwYOhzk1zRynply+n+fUBMiHQfJ5qXpqMlCaW35Ovr9/KbmXIhFq1s4Semoqgo0y5ecCoGVQc/F++ntCSJAIY7W0tLS0tLS0tLS0zlhpqHmGqYhTkzlpDES8XyNoSToqpI/iBmcpBYRC7sJLl59ng4ICXFWjkGX8YjiOcNwLfxmobUczkp2aydfLLQIBLDl13HQwVmVBQXRc1OlTMj1MVinUHCHnNttIIKHKqRlKULMd0RdbOn8GhZqVuJk4tQBEUYzteByPX/QuXFOn58mdmjT9PIo5xGHnVirREseoy/sGzvUT4Jt1apLfn1tJIKAf5ZefO4aPsuTU7AcRT+kGgJiOzxRgTQvjCSzwlhB7TVQMAgS90nYy5riXzIHg1NwxVYVjmai65Lgi1PSR9FwVnZqNFNQk42GQrmR4GLfoz6Xy8zG7ja21tLuv3c06NRcDcm5VCke7vtqpeXy1hy6DmpCgJnULT5d6fPte/VIAwA4zDTVdy8BI2YFtkq9Zr1FRExxqxlhvJnMhOjXD2MRI2cZ4xUEQUxAlQc12i7gmm1EVIyUbDzXJ9UHjaQKkKXDpRGVsGiPndmylS6D7OBk/5u5K9c9kTs2ji4pUc+bELM0Q56It9QkUQ48AYOzi5O+L9/G/ekGEkknOpV4hoCzTV5PBSQpSY7suODXD1KaHmgmoW+hKzkLJqdmOyrh332L23HLLz0FSzoF0mTughpo1EWqScZ3w6fPAbySAMq/8XIaOANAVAHP7YNJn1BlLgGtvrphTMwM1aYK6VBLu0Q8hWimoKZWfM51qT015/vKcmnlSOTUZeB3i1OyFyfVO9dSs7eDnx3tqBhLUFHvIcqhZA6YkpyZr4aFLz7UKaseOHTAMI/Pf+973PgDA6173uszPfuZnfuZFHrWWlpaWlpaW1stLGmqeYXJsCWoqemoawsukGbO+bPSlr0qg5npnMe3UDOnf2Q5zoGbgtfGaOu2pt/ltWDK3J4O76L8AAFYD6WVWeOFf5VBT6mVpJOXnyqCger5T0wF1KAqlqqFUft6h5ec2dWpa1H1pGukegs1egIvKe1EyBfdYlZZ0svLzMELFSEPNcoWWOEYd7kY73ktgAXd2MtGX9HkRasZ26rzIAFlPzQjUoIl6yeaO01QCOt2nXU5gTSsocZCNzhEsLBCw04lKOG/72cnvslJxuo8ANjaOkflnbk2xryZzarpmkHJqtjv0fOKY73MlJHMz4XSTcB4eFDTBf3dHOe246/REqEmOfcQn0KcckLJaZU9NZwyzjR661KVYQhoUL1KyM+kmUNMfvQwAsMkiJcVszbMPEahJFr1Q5dRsYKrmYsZeRtkU4EvQ4L0tg9jCdL2E0YoDnzk1pfCedpPAuS7quHjzGBaCCXStGQAxsPJEEhQUVbBtiqy7Yyt0jlhfzfm7U7CUQU0/UPTUFJPAgcSpydQ6kP5a7L169Av8r14Y8+CskToBo8/OqZ2aMYWafWMELQY1vXT5+T7BxH10VQBOdhWobUttG8PEQ4ck1zeQhpq8VyOdb1aiLcM7Vfm54NRk160dVdE3KNhibtdh5eeiWDsBgPS+FNZtAjVni/XUlJ2go+cpN/Mieg+roKYctiScs1K8/LxH7vNTcWqqJAYFxQWdmvT7XuzApf82WoawnuqiU5PeKyLUjKP0fSiWn09cTlzM3WNA51jaqamlVUD3338/jh8/zv+77bbbAADvete7+DY/9VM/ldrmYx/72Is1XC0tLS0tLS2tl6U01DzDxIOCuLOSQcjEzSPCPR5Qw15+uVNzMd1Tk5aT8qAgISVdTD8/F99B2fTQdTYDYxfjOec1+MCR9+OLZ93NnTmNTPl5BRHdb6tNXuBtOXVcCgqK45hDzXGrie2LfwW0D2PdCOupmYCqikFeVg0nOa7cU7MtQU2nXEM/Yg7GBIosdTzUqKsyiE08tv43aXkvCGTY+8d4dfmBTPl5pUJedMto8ZfmE90Khwl8e3ad6Mv4XCNxtAWwsuXnVnJdf6b8YeBbPwwD6hJ0g+6zVJ3k32v6blKS2jmMQ8f2kd+LpjA9ngBFDqxYuXRso0RJHu91KkJNqHtqdrrNZK6oVgICFc6qknmOYSUuOdPmgGiLLZRuQ4aa5NwOe+sBAGWfHLOTKj8XnZpdXn5eNtJQk4VMjdkJ1LRqG3HCnyStAFYehUd7zCZQk3zdDRRQ02pivOJge0lKoBacmhFMTNVcjA5wanY7BGp6xigu3kzW0xFQSLX8iODUrGD7JFnPx1boOUxeQf5ceSwFaVzaFiEMpHUF8H6LgTND7jfZuSg7NX3hnhWhpuDUHK+Ta5lxalqszy+Zj7sP+rz8XHQbzzZ6OLiauDMPrQrQ2qokQTiCHjuymvrghRxIgJrMac1U2Uj+lMush5af2wngN9entzuZ8vNOjlPTHQfKdL+FnZrpHq+ZICUq5tRsDuqpCZB5Y4A8T6xsnAFBBgJlqHmqTs3Y52XlfF5DBdSMQn5/+bGN9aMl2KaRfHDCxkTPz0IPjx1ZRehJa5OV+cexADVr5L/RV5Cvlx5MemrKEFhLK0czMzPYsGED/+8LX/gCzj77bHzP9yT3WLVaTW0zOqqhuZaWlpaWlpbW6ZTijUzrxZQrl5+zd1ShP5llJ0DPpk5NHhREnZrr7MUUEAqivPLzIAUMXmndAwBYmbgZFcPAaKWMv196M84ONvJtVnyp/Nxw0DcnUIkW4XWJO8yVoWbkYbRCxhhGMTpeiKpZggHg+pHHgKd+Aeg9jPWjvwUg7dTk0EooC4wll1CbJrLbFApXHAuNsIYZc4VAzRpxnC61PVQo1Py7xVsQn/UjuNSmcKexB7j/Z/E7G+u8DyZzalar5EVk3FzhxzzeLSOABRdB0p/TrgPeEgcWSw0CqvzYAWDklp8DwPXmF4ADAC77MNZVfNj9RTQpaPTDCBZowFEtgZqNwE3CTjqHMTtH5qVvz2B6tILenEvchTQsiIHRILbhUADMQU5XKD+PRaiZOCx7DGoKIUGrtPx8W5lAO98ehyvAqa4xjlLcxDQEBxuAbj8LNY9QqOl4pOy344eI45j0lmUp0s4oTjT6HGraRkigCAXES7Sn5igLCrJHUHZMfLd7NjY4S8DSw/ADsh6Y+2sQ1By3mjBNAxeMSGXQQk/NILYwWXPRCyL4LXVPTa9LoG9gjeDizQQwPtY+C+eW7yFQkzoMW2EF26clp+YELT9feSy13xotBw+jAEEY8fYLAPh1+8IzIZYqB/ATMtRsyVBTKGFffQLoLQDladJTkzo1x0fIfXBosZNcFyAD5r5xIEiVn7Ntb39qlrcNAICDKwKsZB/WVDam+lL2gwhPHm/g8q3jybYi1CxNpcA7h5qZnpoSlANSUNNAjHPW1fHwoRUsReswg2eFsZ1i+XnnUPKhiujU7M8l4xzUU1OEpvZI8juSfJD5T31mwtylYjn11NVZx27mmMKzNeydPqemeZJOTeF7XuxgslbK9BmGYfDrbEcNvPNP7sVf3bKKG1L76QOopwOE2HGnriRrfemBZC3o8nOtU5Dnefibv/kb3HrrrclzEcDf/u3f4m/+5m+wYcMGvO1tb8MHP/hBVKvV3P30+330+8mN3GiQ57Lv+/B9hSP/JSZ2Di+Hc3mxpOdwbdLztza9WPNnnmE2rFjxIfzJ/N6p/v4LKdM8c+4Rttz0/bs2vZjz93wfU0PNM0yOFBTEJUADy8m+TBrs5bpMXnpLZgA3TCBMxMvPB/TUjGNc5ZJeep2ZWwAgG+4DRfm56cB3plHpL5IgEwBuJEHN9gFUHAuOZcAPY6x2fbhGGSkfUuMZTG0h58nCXgBwCGkKbinLsNGPHO4ga9HEaJv+q1dxLKyGdcw4K4lTCiQkiPW/7EQl9Dt+Uv5JQd2YJZQvUjdprSq5K5xRrPQi+JENmP2kp6YENRcbTaAOxHS+M+XnshMLALpH8T9nfhk7NzyNB1oPA5jEfLPPE6irtcSBuerZwBgLDzmMxnKFmHvL6zFdc9GNSgRqMqcm66kZ2xyAjdJr3BScmj51fo0biylnoNenTqeAzFUYm2jRHqubXLLeOsYERMTVMcYxjsNwpd6XPcGpGUceDCROTatHXJ1xDPT8CBXXIuXeAA0K6vL0c7KvBso1Al2Y87hidAjDcEZRcSx8t7sTbxy9H1h+GL75vWRXklOz5wvl0FQTNjnuOdX59A/6iyknWb1ko+TEuU7NoLdCvu2M4aJNBCzds7gZ79gMAjVp2WsnKnOn5tGVLgGCoxcSkOct85CYIDYxWqsCHdL7tNUPMF4VZr5PoOZiMI6/uHs/3nvDGAQfHwkLiuPkkxMONQ0AMbD6OFB+fdqpSaFmsx9gpePzgC+5hNopj6FFWV4YxfDDGK5t4L7nljAlXLfnlsLkXyF6r6GyKR22A+Chg8uDoaaokyk/Z5ARwLjdwtkzBGruCV6B861vCSeULj9faPVxYrWHi6tDnJqL95PngWGS/o/cqTl78j01a1tzHaMx7ancCUSnJgsKSsYerHvj8H/0RcgqQs2yBDWtk4WazMUu9tSkY+seA07cDqx/Q/JvlAA1/djBZNXBCVe47gzw1rYjNhy48LDJWcD8suzUVPTaZH2gJ68EnvtLAjUZtNXl51qnoH/5l3/BysoKfvzHf5x/74d+6Iewfft2bNq0CY899hh+5Vd+BU8//TQ++9nP5u7nwx/+MD70oQ9lvv/Vr351IAx9qYmV6muduvQcrk16/tamF3r+LrvsBT3cUDWwtgE1t1xymkby/Omyrbtf7CFw7ZaGou/ftenFmL9OpzN8ozVIQ80zTEWCgky7CoTSLzJHj+UidNfB8uYwGs9xh1TEko3lnpoI0WcANexhvU3ATTx1DQCkSsaZVv0s1IzcdUD/aVjePBBHcGOp59709TAMA2MVBwstD42ej0kZanYOo+yQl9Y+LVmN4xhV6tQUy89tE2hHFQFqpsvPK67JHYRy+TkDkN2ojGbXz3VKxYYDg87TSC39ohs7E+j5EXxI5efM5UNLHlfabaAODn2avQA9P+TnCdNCBAumeEHbB3GuvQe2EcJoPAXgYsw1+zibQk0xKGjFS5efe606MAKURzZjeqSEblTCBJqZnpp+bHFX6wgvPxfTz8l5TRnpkmu/3yJriu6vH7u8r+M6ax6IgWY8hnHhdzpQQ4K+l7hW49CHAeBESECF4S+jbPTQi8voeAGBmhS6hdYI5pt9RDHpX+kYIdrtVQ41WQhQGXQNOqMoOxa+2yU9RuPlh+GPk23G7Baw/1OomwTWdoIs1By3CCRh5eeBUYUdd4DWc2RKQdaaa5uomAYavKdmGmrGFK4b7jjOmq6h6lp4tLWD/HDlMWCC/A9SO6pi62QFhkEA7XLHx2StREJ0Vp8Elh4i44ht1CploANYiNDspaFm3J2FAWAxGMPRlS5OdBykfH5hl7g5K+tJqS8rzZ25nvTuXH4MWP/6lFPTcWtYP1rCbKOPQ0sdDjVj00kB0/1NF1GcfKfrh3BtE62ej4rg1Ny36AOs0ptBsg03k3JgAJvHKzi60sVDh5bxEzgrOUAKakqwTVV+brrqsmIp5fqcdeSZ8anGe/B9188AT32MQFIrDRNe/zt3otkPcNdPTWM7JIlOTeYgHbuYgNGTDQoSnZrVralQNlExfYZxoA4I5efJc/MHPz+CXx1fwpU7JpErwyRjijwCBFmauDNG/t2I6AdBJ5sQzpyaoRBcxsa253fJf2+4DdhwI/me0OLCi21M1kqouuJ8bEv2Wz8baO7BztIR+H1pXGw/bH1b1QScsgT0pQeA6evomLRTU+vk9ed//ue45ZZbsGnTJv69n/7pn+Z/v+SSS7Bx40a88Y1vxL59+3D22WerdoMPfOADuPXWW/nXjUYDW7duxc033/yyKF33fR+33XYbbrrpJjiO4kNdraHSc7g26flbm16s+fvwh1+wQxXSaydPbUCxYaK55RKMHHkcRhwN/4UXUd9c+sCLPQSuD9Ch6Pt3bXox549Vnjxf0lDzDJNjkxcyBmb4e6Nh8BdK1y1BMr2lHX+VTYA3hxlrEf0gQtmxEMjp59QB5JoBvICWhwpBISUaRsOSuEWo2Y0s9CI3CU0xbBiVdUATcPwFwF+FQcv8fr92G37+qpi/qI6WCdRc7fjw7VI6w7x3HGWb/B5z20Vx4tS0BLeUYxJH2yTIDdIMycs+cx+WHQtzrPen4NRc6XiYpgCyE5Wx0vVygzpiq8pBzWh9PPWzkAKTgAKsVPk5wIFFl7oRTctF2THR8yPMNfo8CAYAQjhpqLn0ACmpBnhIT8cLSCk4kOqNuNS3iIMLADqHMRITaGLVNmKq5vIwHSOQnZoOHJP11GTl58k19igcGTWWUudtxz00+wFGKdTsRS569BhjIM7ApWAEW4XfaUNd7trrC6X4LBDEniLQIexge2UZT3c2ouOFmAJ4+XkzqiKKCcDuRSU4VgedTgPMr0dczjGq8Qr5hjuOsm3h2T4dVWsfvDpZXzcbfwPc++fYQm+LrsKpyaDmtEPW2qq9A1P+k0BzL/kdYxIRLDgWSUBfzHFqsp6VZmkclmngok2jePDAJgRGGXbYAZYfJvMVlVF1LczUS5hr9nFspUvS18cvJVBzmUFNC7UyTYA3Aqx2/dS8d5vHUQWwQBPln5g3EqjpjJHxtPcDlfXodVeSe3HmNQRqrjwGAOiHiVMTZhnbJqscal5G3ZOh4ab+MWlKfXd7foixigMvjFIO272LPuKdO2D0F4GRc8k3L/k1/N239+P/zl6Bt1+zGZ/42rN44qgcPCb21JQAHYOado1sF4ekn2YOhIudcRj+CgDgnBly/x5vArj8t4ELfp7sQ/jdhVYfTdra4+m5bhZqik5NpqmryZ8smb03m/R6HFR+Ljo1q1sG9PYsoWSb8IWUcP4sEv6n+ZH2efjanrnBUBMggJlBTZ4YXidu2sg/eZcmkPwbJQZSyQFMYksE+kwIYQMwMFlzUHGE8xNCpeLR82E09+Ds0hEE3jggbMbnmZ2HeMzxS8kc9+bIvQVop6bWSevgwYO4/fbbBzowAeDqq8lz4Nlnn82FmqVSCaVS9pngOM7L6gXu5XY+L4b0HK5Nev7Wphd6/qIzjP+tFUgacXTGQ80oOnPuD3mp6ft3bXox5u/5Pt6Z39Dh/zEx0JQ4NYUXcerocRw3SVimMgRHj1kjPRY3OIs8/CWK1OXnAHF5eWGEyCNOFi+yUSmT/6lmpckrAvAKwphDRLYvu7YBAFCNFhD1CAjrRiX03C3Axps4FGD7a/QC3geOK44wEhE3HAsX8cOIOytNN3kZtQ3wnopAAlG4U5OWn5MTSsDcUttPOTUbXT8XaoquKNctI4yT28U3CagLZaemBDUNHuRUwroRMt5jq2kiHUK6yefv4X81aNiL54dJYrvQE2+pZwtOzSOYMOm5ltdjql5CjwIkr99KjStQODWbglOTBSDJqph9EnZESzp7kcsDmZg7d7afdjp1IUCE+jnolsnLnCc4NVmvT8N0eF/YbZUVclosAZ06NT/7OIGM60ZK6MXENdztJiWnfhCTpHK0yHqv70TJMXHUI+XHht+AGRD37oyRBlByyz4ggZp1k8CYFYNaC9sHyB8mAUSOZWKs4vCQJRlqWrR83imPAwAu2jSGCBbmjR1kAwoR22EFtmli0zg5t6Osr+Y4KVWJFgWoWSHbWEaYun4A0GuSEu6+TZyMj56gwLa8jsAcAGjtx2rXxzs/8SU6yHLiXqPj8YPEqQm7gq2T5L44tJTAqVC6l1PPByTX0Aui1H3bD2KcuOER4B1zHO71Igf/7cAP46HOhbj+HDJ2lmjPZYpQczz9M1Z+bhjJBwCq0nM29soW/vcdtJcp/xCnvC5T3n7vvqSth+soXJYUkKY0TaEmKz/3lhPIdjJOTUvt1IRZwkjZRggF1Nz0ZjwUvQ4fPPozCGDjmdmWeh+ixAR0GWoCyZ8nI/ZvDnNMAtlepWJYFYWR7N8J4tQUoWbi3I1pgNLZpSOIPOn8WPk5c5yKx7QrwPjF5O9zXyd/6p6aWiepT37yk1i3bh3e8pa3DNzukUceAQBs3KjujaulpaWlpaWlpXXy0lDzDBMrPw8iAiBS5qKZG4DqFgSlrQgHQE2jmiSgM/fdIKjJ+mr6PnnZ7MUlXh49UiL7bVNnUhzHCKIYjUh4MTQdlGrkf9In7VU0m6SEfTWs8SAWJrGc3Y+zMLEWEmcig5phFHMIaQkvm7aZhpqrAXNqsvJzC8shddz0F/h2Yk/NdlQmwRN5Tinxxd0wuCMRADxrnIyPAkneU9ORoCYDW5aLizaR8XzynnRASygbpmnpLQDYHoOagquROomi2MBSz0ygZm8WG2ziljSqGzFatvmYWx1q+Y5VPTWpU1PsqZlj4i6bfZJMz5yascvBKdOxbhq8nIgJfGiZ64Eb7+KwhZ9THMFgTlWrBFTI+t1SJoC241FYR4HHbXvJ77354o3wqL+w100s7X4Y4bzyIfJF/WzAKqNkm+ijjHl/nJxHn/x8xEg7ADt+9lPTMYvsu2IQiLcUUUBGWzq0qEfUsQ2afq4OCnIisp9ylWx/9gy5h2ZDCrpCti4rcGwTm8YpBOdQk4BIY5msjxAWKi4BPrYRpnqiAoDRJ/fhK887DxNVBwss4Gv0QqBOgVD7AB4/sgq/R+YhtEY5PMXqE0AUwlM4NQESFsQkg/lmlIZVXQo1+0GU3JdUB5aClPPv4GIHUUyePeetJ/d8sx9w9zo5OeH5J5aZ27U0lKLAczkcw3f2p13HTJ6blItO0PL9Vj8Q3O1pfUuAmm2/YAk2beeB0mTyDGZl6nkfqgCSU3NAT02zhFrJ5gFfAPh9tm8pwNuf+CV8avGtAIC9c03VLtJiIUAZpyb9/qk4NXn5+QCnpifcj0KoGQDi1HQt/PHc29FytgGv+GW+KYea5SNJijnfDwXyYVt9TAbxWR9X7dTUOglFUYRPfvKT+LEf+zHYdnL/7du3D7/1W7+FBx98EAcOHMDnPvc5/OiP/ihuuOEGXHrppS/iiLW0tLS0tLS0Xl7SUPMMEys/Z0pBzdd9EXjbPrileso1CCAFKRkUWu8scsdRyKAmc34aItQM0A8i7ubrRiWewl6hzhjmtAopbE05sQwbdpWAmSl7BS0ONesoSVBzVICaHrIvxlWfvOj3KFwKAg8udSiaqfLzGO1QdGpSx5rg1Jz3SaBO1E36Qi51PO6q7ERlMj95L+iSK0ocb8+gYJHCnCQtPYGacRzD5E5NF79w03mwTANf+e4svrUvAa2BDBCFgAzHJ5AyFKFm/Rwsly/H7tXrSR/M0jQ9hxhnl0iIjFHZCMMwEBpkXlrtZmrffir9PBsUlOfULBt9EnbEempGLvpx2ml2tFNLQCSAb8bfjx997kP4xw23A9VNMClE9n0KGwT4Z5gud2puchnUTDs1m1EVH3vnpfi1t70CHj2/fk9waoYRzi1RqDl2EdmvYaBsWzyIqOaTn9eR9FsFAMGQjHaJOErHqFOzYhAoMhemezg2QdZZiTo1OViSemqWYrofGvTEPjhYiNaltmtHFTimQUrOASwz+yiDmrS1Q2Q4ZL5AeuOKPVERx6jFZI2du3Unpusl3N64GgsTbwMu+i+Jy621H88ttLgLtYsqBcEVArRa+2hPTbomLQFqCk7NwEivgYZUft71E6fmQ50LMDf5Tvxr8F4AwMHFdmrbA/Trs2ZqGKs4/BmYSr42cpyaZckBRYHnXQeBd//Jvfz5JWp26l0ACPhkzycAGeerF0To+WHq3u0UgZr2CDB6AR23SUrhAaBzmPxZNChogFMzMl3UXFKmHTO3JgWgsw3y7Jii6+nQUodD5lxZAtTkPTVPt1PTyO5HdGpSGOnR0LKJqouKY+EjJ34C/7L1GykHbVRPnJpGmF5PvPxc5dQEEqjJpHtqap2Ebr/9dhw6dAg/8RM/kfq+67q4/fbbcfPNN+OCCy7AL/7iL+Id73gHPv/5z79II9XS0tLS0tLSenlKQ80zTAwmMqXKzw0DsFxUXCtdZghIL7/UqWknUJOlnxvcqZlsz5yaIXVqdqMSHOp4ZMEM7CU44FAz7dRkARjT9io6TfLS31BAzTHmCuz68JF1KJU9CjVpn8/IT0oJbVd2aiYvxA0KWR2hp+ZiSEpPRagpOjW7UYmU1RcoPwcAD8nx+gbdtyHBPwY1wz5JfDYIGDFMF+etH8G/vZr0gfvfd+zlvxLI5eeCKiEBxBwA0nE9ctFX8P5Dv0rclYYB0BJaxwixFIzCnHwlGR+FILw8OyLjSTk1eU/NAeXn9XPIeMw+5pqCUzMq8aAgppVwBEeXkxL7XuzgG60reNCTaZP5jsI+cd8JEBemS3oHgjiNARFq0p6aYZW7HAODnB8vrwfpqXkuc2qOvYJ/v+yYOOyRNgn1gAClWpyGmm2hp2bbJVBzlELNUkzuj+OeDDWT8vPxAU7NCsjv1+vTdDxkuxOBDDXLsCkgBYRep9WtKeBiGDa/jy3Jqdlpz/MerBeedS4mai4Wggncu+mPgY03A/Ud9GD78dx8GyMWAZSrfoWUdo/RktyVx2j6OYOaFSXUFJ2aEUy0ozSs6glQEzBw4Pw/wYNjJAzj8HI6DY89a0bLDizT4POw0hHWSZ5Ts5q4LgEITs3R7D6oDlTfhp888EH8p9U/h2OZvMRZ7CMMAD/0p/fh8t/8Kg4KDtVmdnfCsQm8xtSr0+XyrAS9TaHmoJ6a4odV1S1kP4rnVWy4qFNXfcQ+sKIfArH+xBvHy5iquYhj4Nm5ISXo1ulzaq52fLI22bhYf1/TzQJdX2giHpLJ7dNn0VTd5ddGhrItdycAYL2zhHHMp/cpBwVlnJpXpL/W5edaJ6Gbb74ZcRzjvPPOS31/69atuOuuu7C4uIher4e9e/fiYx/72Msi7EdLS0tLS0tL60yShppnmBwJakJhBKqVrAScUJli7zWlU5PCGgY1BRjnGEEKavbiEgyDQU1ynDZ13gUqp6bp8Bf1aXsFvQ6BUary89Fy4tTsp8rPyfEc7xgAII4JnAo98pLrxxZMJ9neMQn8AQBYZfQiMk7m1CzZJhZpQErcm+O/R9LPCXBLnJpqqGBIUDMwBKemSV5MMv0wBaemH0ZwqMvUsMjL+/ddTq4N75MIIIjzoWYtIoA4DAiIDWMLMEw+jxxEsrAgAF9evZaESQHcCdXrpZ2aQWxzcM321ewnEKcvQs2Z1wLn/gwAoadmkJSfe4JTM4xNfKd9EY4IUJOV8TKIatm0NyzrAylATdN2+fpdZzOoGdB0bgJXWmEVrkWud2iS8/P7olMzTqDmqAg1E6fmSHAUJEwoXZLc8RKo2XKJm3HUbAJxDCcixz/UT4esrMYUatomxqqCU1OAmkEYYcQkvz86OsXHAwDHfQlqhhXYlpFq1QCAwOuRJFwiNix+HztGmILS/sIjAIAj3jqMj4xhsspcn3SuazTepnMY++ZbGKHp7/N9usYnaHnk4c8iDH2URKcmDbk6vtrlvX994T4IzDrkB5dYfg4Arm1mnahs7Hy9kH1MVBXbpZyaE8nfZacmhZqLAfkQYrGdpZD9MMLtjavRscjvcpgslfM/cHCZO8iZ2opgKa4t/4Y8b7e9M/19loDOem8OcmqKpdqszQQrQa8lEUWk/JzMScSdmrTNA5tzy8S568n3npkdUoKeCzVPzqnZ80O84XfvxFs/cTdi/m8OnTPTUUDNrFOzR59FE1UXFfohW0eCmithFXPUmX9x5dn0PllPTX4eklNz/JI0PNbl51paWlpaWlpaWlovGWmoeYZJhpqq4saKY6fKz8PYhGUJL/lCT83VjlR+zqGmwYGIhRD9IELokRdoT4CNcvk5A1StKF1+zl7Up+xVeF0GNesZ56kIDPoizJu4DABg947wb/WDCBF3j6adQSQoiL5YO2MIQvKizIKWDMNAk8ImQ4CaxKnZ5/v0ggi9MA2Ik9NKQ00G0ACgC/LiG+c5NRnUpG45g4JTBolFOJLXvxIARkCgZuATSMjKfMfkPphC2MkXVl/LgaVBX+D7PfJCz3p8erENm6Wfc/dsAsWO4Rw81D4fT7jfC7z+K7xktmL2CWSjgKAXufCNZL3cF+/CQW9Tyn3Hrw0dk8kCr4yAuBBZynFswrGSoKBpkziuOl4CNAHSr5G1aYhMco2M/jzw1O8Cd7wRPxR+EOex8vPxi/jvlR0LRyhAHAuPoGZ2YSMNrtpezFO494+/BwBgGRHgLcKKCRw52JlI/c5yRL52LRPjVZd/4BALUHOl3UaZuh3HxtbR8ZD5PyY5P0n5uZmFmgApDaeKDIfDGNmpaaw8CgB4qrcTpmlgsk7mfIlBPeYW7M3hufk26hZZX3PdEnEzbtpFfn7w7/EbtZ9PesZaFczUSyg7JqI46feZcmra2bR7Vn7OoGbJJq5WAPwZxeTz9ULmZ7zqpMcOSFBTOF4lDTWDDbdgORjFPa3LAZDkclnseAyiquY9jhN4+ZpzpnHVWeTZ0vIGlJ+f9WPAu5rAuT+b/n4pDbGX0rlhabE+j0DiMGTPpcpmdGNyf8dmCXX64QR3j9tpp2bJtniP0qFQk4G/oJk4HE/BqTnX6GOx7eHgYgee9EGc2qmZ7anZD6lTUwgK6vjp1gCNboB9ffIMPKt0PPWzjFPTlpyaVikJzgJ0+bmWlpaWlpaWlpbWS0gaap5hYuCHyTCyL81V10IoXLogNmGawnYUCk3YTbQ65EUujqTyc4ADEYeWnzOAKDoo5XK/YeXnFbMPiwZgNMI6Sk4O1Oz66IsBM9PXkV11D/Meej0/5Em2HRlqCk7N2Bnl47KEeWCp1KY3R6yfIICsQgFNn5aTr3TDtFOHSepfF5vJ122QF9/IkH5PCArywggOLT9nL+8V6s7rCU6jIB4ENVcAv4V67xmyLU0CTpyaPgEu1Kk574/jvtYlHCZbLhmzT5PtB6efC07N2MHb9/0uvjr6MZIQTEFK2ezjrN4dwMO/CAA47k/DFkprHxx5PwCknJoec95RiAqLQU2fQNkoCS9ybIM7NSdMAnQ7Xshhhxc58GKHA6+YwpfXtD8OPPxLwOzXcK35RYzbLUQwARogAjCnJik/H4+PYtIWSl2p2n4M3PIo8P3HsOqcjS5bo51DfJt9rfHU76yEBGo6Nis/J9dThJpeJwmXsSiEY07NI146mbsdEaemeI256jv5X2OITs0g5Sy0Vh4BADzdJ20DuFNThpreMuYbDd5TsxlV8MCBZWDr24Hr/h6wKrjUfQQ1i0HNMgzD4CXoB2kJui+4dQ03H2p6AfnTtQkABoCVbto9yZyabA0zp2a6/Fx4rliVBLKx5HOqh52345VP/i0e7pCelpkUdeF4Dm/HILmgkYBBAPj/fvhVeN355JopdpfIGcm0sACQzD3V549uy99H90T2eww4lqbRxDgAIDZd1KlTkz0jWBm1J7hjzy0KNVlqfPcEuLPyFHpqite2E2T7QC90JKerGBQUJj01LdPASNnOLT9f7frY28uZR9ZTU3ScyhJL0As6NVv9AJ+5/3Cqf7CWlpaWlpaWlpaW1gsrDTXPMMnl2iofUNVNl5+HsGCJ8NMZQ0x/s09hShSRF9sU1KRAxDYCeGHIy8/7QiBO1SHbBFEML4i4664VS1DTrvGek3VvHwACNV0r7c4Rg4L8yMChPn3B3/6DZEidw7wPZ9+PEFN3TTdOQ01H6KkZ22PcQSpC4a5JynzNKCmh9MOQu85st87HouyrKQGJWHiRb8cEDgx2ago9NalTk4EsBnkAwBcdq8L+otggAURfvxm3LP002wBAMo9RDLS9EFj/BgDAXy++BbblcBju0HMMGNTk6edOpvy80Qu4Iy1xrzEQSeaiYvTxw+ZvAX4D3bGr8PuzP4Tv9F8NzFwPvPJ3UZ8ifcWOKJyaDKJCcGquCk5NL7YJWKJQfgwLMBCh0w9SIUGA0HuWhn5YoGBBcFx1nG0EyFKRnppkvU3ExzBtr0BWqx+R36lsRBDFWA7IdTbaJLG+F7k40bERC+tlKRqnYzJQdS3EZhZqRh3iHlsMxnh/xbJN/jzWH0td93ZUgW0qys+BfKcmaCn/4X8GFh+A3XgMALDXJ9tP0FJvXn7tTvBjTlqrmC6Re6IVVvH4UQqWdvwgMPHK9ATRe2DLBLkOzKnpC88jp5w4WSeoy7InOTVdi5TqA8By+xTKzwFS3j11DTB+WQLZJKfmN56Zh/gUXVQ4NQPJGTqqmHdPSEIv2SYN5QFa0pBSyoNj5cSp+UD7QsyZO5WbtfoBVibfRL6gPW0BJB+2lKbRiMfJ380SH9O3Sj8J7PhhYIyk2ItQ83wONYf01ORQ86hw3MpJOzXFa9bxZajp4pkF6XoonJp+bGOi6sI0DV45oIKaD3fOT32Pg+9h5edAOiyoYE/N//yPj+KX/+kxfPKeA4W219LS0tLS0tLS0tI6/dJQ8wxTpvxcQTUrroVIKD8PYivlUIRhwjPIi5vXIy+JYRzxn3FRIGIbpPw8pgEOYso3e4kEyIskAw59ETJSONKzSBntREgA0GpYz0DaxKlJQOqb9/4B3rO8m6dUo7+AMZcAqp4fIvZZmXPaGWQbiVMzskc5mEjNg11LEtJ7swijGDYC2AYFoGXy8rrc8fhLemSPYt4fJ78jOTUNIWCiFbPyc6l8kpUuhn14Qb5TM4hiPpciEMLYheR77nreBxAL9/Ifl0Hmo2SbHOytdn1gwxtx6A1H8Im5H0yBXbdExhyxcA4OCizunByhQUFhFPM2AzJYYoB3wm5g1FgBABy45P9iLphC36gDN90NXHgrtlIH32GhpjaI0s47NheuBDUDBjWpk81CgHGriY6fODVbUiCU6UhOuMs/iiPRDrJtOR3cULYtHPNmEMOEgz7OKx+ErLbQUzOMYqyEFGq2yJpuRRUEUYxYAFaL1Knp2iYMw0ClRNeqADXjDoFDC0JyOis/7/jg4UgA4KMCwzCUcC3TU9NkH0xEqPb3Ad98O/CVV8Nt7wEAPOuTOZisUYDI3I6GKYR7rWBz3aPnVyXp9kz1s9ITRO8TOcRI7Ktqlsaxc7oKAzHOoz0cOx4J/mJwsOSYHFbKgTxy+TkDo5mQnxv+GQ9f8EW88r/fgU5IIbMENfcvpJOwVT01PekDERVM9gSnpiuECTX6A3pq5kDNjpFA308v3Ux61Cr0w3/2bbzyr6ex75J/Bd707eQHDMqVZ7BKgTqsEmo0KOhOvAe47lMcnvepO7Zkm9hB+6EeW+0iUiTBZ8ZO1y3sGlkz3KlZDGqK16wtJ8WbDo6vSlQ4J/2crV/2/Oz4aajZ6AV4oPOK1PfgTqb2k1t+DgCTr0r+rvq5pMePrOJLTxAX7WNHVoZur6WlpaWlpaWlpaX1/EhDzTNMRXpq1lwbgZB+HspQE0BgEhgTUKiZST8HMlAz8mkpqQA1XduETffd8QMeONQXksDZfjybAJspEFfaqiL9XAwK8oIYnagCzxwjzjH6sr6tRMJbeoJTsxdny8+XKPQL3Rlefi7OX8W1EjDYm4MfRrz0HACqFfLivtz2uFMzcKYwG0zRg0hQU/i6GZH5bZgbUtuI5ediT00G8spuMj7mXjMhlC+Ok96iUWUb5oMEfvRRxbw/jkfdt5OxGIbQC5Mco2+MAjBSILlUpuPhUDMp9bZ5wn2yfpo9GgjFQA8vGSfXmyWSwyyhD7Jvcc43jZPrdHw1gZoZ12eOU9OPbTJ20+GJ1pN2Q+nU5P055STj0fPxh50P4MnuWTg2/cOpH5UdEwFsdGwCvi6r7IWsthdzt2oQxcn6aR8gf1CoGlmJm2vRH03NQ61MAZsANY0uCcBaDKeE8Qj9VWukdLYfJeEpaqem4OozLJ4obRsBpoPkfIw4wEpQx4pBAPFkjYxpSXRFClBzXZlcg2ZYw7wINWs70hNEYRYD4Wy9pNzGzhj+z4+8Cv/hohA7p8k93fVCAoMpRytZFu+pKcNK2XU9wQOFskDyr+87hOWOj691bgBGLyRJ44IYjFw3Qs5/QVEvLgdZjcr9apEO2zEMgwPE9ilAzYPN5Nn9xdXXYLaphpqPHF5BDBM/9PkqYjEMSXBqrkZ0fZolfk1a/XQ5tOjUZCX/cZxcO/XYJacmA30nWX4urt1s+bmLTlPqfymmnwvPBRYqNaj8/LC3PvlACgBK9F7LlJ8rnJpjl5D7rjST/N4A/d5tT/O/7x2WJK+lpaWlpaWlpaWl9bxJQ80zTIV7agruvoxTE0BEHYOhtwIAiGMpKAhIQU0viICQhtEgDRDFsCDmuvNEyEj34zvpl8FBTs3Vri/0sjOIJZX2UtxUWgYA9IIQMX0R7cbpl2jTAL7afC1++/h7sXruf+PjEueh4lg8AR29OXhhxEvPY9PFWI3AgcW2xxPQQ2caJ3zq8JGcmuLXzYi85C/Z56Q2WepTx1rkKZ2aBIqQb7ES9AoEN9ko6f2H2vYU1DxgvRqvfupT+ProbyabSj0XmeMsDTXJOjCjNNTkrkiQNcaACAM5fiSVjFOgy1yuKE3xbcQwKDamdl/oGRpJrk8zAXErHR8IBajJ9lWirl+rgVY/5L32msypabOeoQLUNCyguhXf9V6BXXs/gdWJN0EUW8dNm6yzy6rPQJYfGTzEKQwjLIcETLHy8y4IEPFNelxnDJ2Q9qalY6+WyL1hxALU7FGoGSf9M1m/2V4QIq4SqNmi/TSBpAy6H0QcgKOa9A2sxUuCUzPEeHQ4dS5P9nbyMbGemkttAaBRR+y0vYIpofx8vpmAfxFqxobNj5dAzQSSczlj2D5ZxdmjYjBWmHY72iYPAGp76Z/JPS6ToKBsmfrX9pAQsF898BOIdn03AxLZPbFxnDw/VOXnDLqztTfIqcnuLXZebS9MtQ7gMh11SwsAx+tvwv9deiP+06FfRCeqYK7RU27HNNvo43OPHku+MUrLrMcvxf39a7AcjKAx+hoBtKZhpRgU5NomanTsKkjMxfqiUhifQM1i5eerXR9BGJH7m6qlcGp22yvp70VeUi7Oe2omUDNJP88GBQEGHuxcKJwD/fcoYuXnA5yalgu8fRZ46x51f2VBc80evv70PP/64GKHu2G1tLS0tLS0tLS0tF5Yaah5hklOC1emn7sWelFS7hnEFkwJfsY2LY+mzpdBPTUdI4AXJK5I30i/sIruGAYAUtvQ/QROOvCkEdYyULNWSnpKJqCAAtoy+f1pl4RY9PwQBoWafWRfoiOrhv8z/w503LO4U9MWoHDZsbDAoeYs/CCBmrBrmBIToSmA8O1J7O1tp4Pdmj6g4NRs0KCkFefc1Cbv+wwFZdSpWZKcmoZhCGFB5PwrhgA1z/phYNu7YFzwnzDvJ1DzOVwOwEg5X0d4kjx5wWfzKTonbZeM06HJ3UilnydzNSqFBQUSWMqEJpWm4SuOx9eKH3JXrx+kk+kzTs04GROH+hRqTtoNMiaPOERXQwIk2H3ilIT+d7XtgGkr5wFIelgu0R6GF5b3Q1YEA80+nQPBqWm0ngMA9FlbB4OCkfK6DEyuVchaNWOfB1RZPeJIWxagJnNqxjEQ0vT6TlTh4x4p2RyAc9egmQC00ei44NQMMYMjqXP5bjeBmhOs/LztJ0neAtSsm+QDjWZUSTs1hfLz2Ew+WEjCpejaE52aQlCQ2EO2L0HNkbLDzy/dv1IuP1cEBQG4/8AS/71WP8BhoY8rEwOkG0fJNVGVn/sMupvp8nMxoKkvQU0GEDtemLomXPaIuncIyDr/pSO/gDu6NwFAer6pxLR1APiDrz2bfO9Vv0vg24abcFfvdXjlk3+HxsTr+ZjynJrs2ZEENA1oCMqcmrz8nK53Vt4vlfmLWmz1cdmHvorv/YN7UlCzLfXUDGIHf3biJhzxZvC/Zn+Q94HmYUGs127k8DFXnRynJr0/Hu0JJei5Ts2c8nJ3HChN5p4XE1vzI2UbI2UbYRRn2hxoaWlpaWlpaWlpab0w0lDzDJNtDb8kVdfGt9qX8a9DWClABQCGSx1mFGqGKqgphIz0BadmFmomL/AMVIl9N2FQOFNKgE0zrOHp3vYMpC05icOUvXy7EsiapqnUPT/iZdP9OFvu6NoGHxd73+fgDMSpuZAqP49RMckLrmHXuPtnSXBqBs40PjH3A/iFuY8AZ/146nimQ4BWK6qBVW6ultJ9G+d7iVPTD+OMU5ONCyAOPQCoGAKMqW0HXvMZuBteg6UoecHeG19Oz1l0RabLz2U3GZD01CwZDGpK/SvZYTkQYT01GVhKOzWZIncq6UVoJ2uP7QdInKh+xqkp9NTspHtq8rEzp6bdIBCBpkDPURctGzs7PwA8RMdXhEYBydo7bhHwYRrZ0uEwNtFiJfhRjOWA9RYk/Td9CjN7YFBzPYe2Li8/F+aKOqTtPnG8rUKAmnZyL/glAtBbYSUprTdzEtCpTCRAzUKIjRYFUNvfg0btCnx66ebEqUnXuhdGxF1Ixw4A0/YySnGTHr+K+VY/AWhi+bmVOA/FcClAgpq0dQCQrPWu4Ma0TQMW/S9pR5HAxiA3KCgNJG97cjb19ZPHsmn27JgbaVsEpVOTQXc7nX6udGqya+wKrkjm1DRLYB9D+eYInp1TJ4wzaLuZuUfbHl+zfExhem3unWvhoUMr9DgOcWsaBv09A45l8PRz0SVNjpd+LjDn60CnJoOarB8la/Nw3n8Arvs78meO7n52AQDw5PFGCkQ3pcN1QxNzwRRes+eT+J+zP4zIosdgfTXpsf3Y4euoKlQNiGL3x5x9cfJNBjUzPTUV5ecnITFY6tx1ZMx7hwUvaWlpaWlpaWlpaWk9L9JQ8wyTDGJUVk3LNPCV5g38a9fwYUpQ06JuKTMkL9as/FwFNR1Wfk4BYmikASIPZ/CCJNxG7KlJHUlBjbgWu1EZP3n0d7AajvAwFKayANyYK5DDNQpFJ23yUtsPQv4i2kMWapbsZFz8vIX5q7gWFlgJd5/01BSdmqzP4KLg1PTsKXSiCh7yryIliYJM+jLciEa4cyt01qXKTDs0vAhxCD/wlVCzLLmNakbWYQYATRCAFxku9oXnp84ZSMqTecm4VEYLAK6bQM0ggpB+bqdcrSMMarIeiQwsST01mXxrMhPoQsaXlNd3+qw/p7Qdd2r6WOl6qfJzvk2ZOjWtBnFO9gjAmg/GYRpJm4E01NyZmgdHcgmztXjYkAJFhJssgslhexjFvG+rEdNzoWXnrAwd5XWZcul6VZgr+nuuR6DsKpLka8cywG7bbo0kVR/0NiZzDnUptA9hXQpu660O7U947s/iO+d+Ec/2twnl0jY//2XmVqQ9NafsVdgRhZpRFX4Y446n5vCaj34NX34ugdRGkEA6uV1BPxLciimnJjlm2pmdnN84DwESzi9MA0TmMl2R0s9vf4qsifWj5P578rgCatK1sGmMlZ8rempGrH9svlPToz2JWcuAGgeIAtR06twF+NyKiXf80b0ZWAmAO5zXjZb4h1GyW1NMW3/LpcQV+en7DynGntxbKdAqqO8nQUFAMuercpq8oAeOSeXUzN3ojAA73jMwIZwBdAAp92zLS/8b1ZZ6bPom/QDBl5yascPvrUoO1GSu087olVgORjAbbQRK9F4Le0AUAp0j6XMZoH9++Ag++uU9GccsID4bDZxH0+R1X00tLS0tLS0tLS2tF0caap5hygYFqUsY90cX8L+vc5Yhs1C7RMBCOW6jH4SIB5Sf20YALwhhRLSnpgQ1xfJz9hK9J7iYQKT1b+TbtTf+IH5i/6/h+478HR7vkLJV17JS+7ItMwmloS/fCdQkIGvcIi+1PT9Kys8VTk32ki6WW4pOzZGyLQQFzaIfRKjSMltYNUxxp2afu9A8m7h75B6lAGDRUu5GWE/61Dk2MJKUoLeFlPYg6MExA/bL/Psi6AHUjkEAWDJI78f2yJVoBwREpJ2aSZI8kICXlFOTBgVVzD68CMr0cwCo85ARVn4ulfNL5ec9azID8wBSXs/hCgUPcvDLwKAguacmc2r2CBRcCCbSELWcwJWwStacDMWYGJw/Eu1M94AU3IgRBKdmGGMxTPdojCgQadPAIpTXZ0qTRyrCWqXl/iWfjL9hJlDTMAwOuFv1K/DdS7+OXzr8C6kPNpIwqGSNH4qTe599MFG3utjs0j5/9bOVc8D6avISbKH83IkolKEOvZ/86wdwZLmLn/n7J5LxRgl4k4OCPBFqOgnU5K5kP+R9B1NQs8JcgwXKz7s+T+yOohiHl8i9/O4rict1kFNzwxj5sKHZD5L+pPx46XU8KrV1AITycyuBxABJ4Y5Z+blV4z09V8MKVrs+FpQ9PJMelzM0wGhOhppCqf6PXkPaYXz+0eP8gyB5O8cy+T3clMvPpbUwXskPXiLn7eN/3HYs/c0CIJBJfHbuOZGAcNISNflZy0vfn33W0oFBzZA5NW3u5mfz3pXTz+n9sXlqAtc99Um8d+7PE3d52AcO/A0J+3LGgOmrhp7Dhz7/JP7ozn14VgErRZB8DnVq5rlyAXKNVHBUS0tLS0tLS0tLS2vt0lDzDJPcgzKnLRuqroOH2ufzr2UI55QJWKhbHax2fVgxfWkWQxCk9HOTlp9HUghEVegfx2BXZJaAtz4NvOGrfLtKuYSvNa/CbL+mdGUxMRjJgEhSckycmuMmg5ohjJA4fTylU9Pk41LNw3S9lEk/r9Lycwjl54stD3CJK7LrEFeUXM4PABYtP18J6hzQlBwzDTXDZJxxvwGX99RM3JwVITxlkB6PX4MPHv0ZPLn1Y8nxhPlkjrIVWrorl8gCgEOdmlWzDy8EDJ5+7qTOUS4/Z+41vi/T4W0GAKBrjCuPB4glotT1yfqdmsK+QBzCK510T025/HzSSpefzwcTaWhbSaBmv0Lgjwq2AolDth3YOBAIKeKjSQuBMBadmlFSfk7FetXuwyvJeax/fabcfbQqQc3QQykkJbkdc71yTL0gxGrlYrSiaqoFhcqp+Sf+b+L+9itw98b/A4xdBAA4r3wIlhEhMitAZaOyNQBPEVdATZs6ut3KOIpoROrB2o/VUFPVUzO1hhX9MuXyc+YsDKM4gaiCk/FV24gbW+XUZNdmqu7y67Mk9dVMAH7xoCDm1IxjJE5Nu8odjOw5oOqXKa4XlsouhwV5ghvwqrMmsXOmhq4fcncqH7vQD7Q+JChILj+Xna9Mf3PfQR7IxXUSUFMsnRcT1lv9gPStpGpQ5yZ7fnD3M0tApx929AWn5qD0cwDYMVVFNy7jeMtKnrneMvDofyV/v+i/AmKSvEJekAQcLSvmSFyfzKn5TE75+eGlDq767dvx/r9/eOAxtbS0tLS0tLS0tLROTYqEA60XUxUn7WzMYZqouBZ+6cgv4J/P+SV8YeU1sM6WYCgtAa2bXTS6PjbapDQ1FtKTxZARL4h4QrZcfs7CGTp+kn7uWGYmIKMilD8yN0spB2p2vJA74pKSYwI1x1JQk5Sfe0Y+1BRf4m0Jan6blZ/35PLzerqn5qX/HZi+BvPWmwE8BsvMjjtafyMee+Ac/OPSG9F3yDyUbQsoJwnoAWwsxzOYMOZh946oy8/tNNT8l5U34N+Mfw2trT8JER1USmV86thbcRXOgheQno6q0t1Vnn6eLQdnL/BjVhN9wakZxFaq/LwulZ97EuiBYRC3Ji1BbmFCcLilV2mtZAPNPofNmR6XslOTlZ9Hdsa1O0GDguLeCRgA5v1xqbw+gY5tZxuqEN1r6XExh2zPD/G0dy7OdWio08h5wPGvAEiXn4tBQUysV+190c343nf9B8By4YcE7LNxZaAmDQnqRzb6djqIhLVj6PnJBwbiGlYBtuPhBrxr38fwe1dcBtS3AOOXASuPkmOUt6NiGMrWAKn1DnCoud5ZgkXv/WptAoAEjIIaxux0EMqo5NTshWJQ0Dj/q9gDUe7tCAATVUV5veQsLNkWqq6FjhdiueNhrOqkoOZlW8nxjq/2sNz2OLwF0iE5U7USTjR6WGx52DSeXKPkeOny89UuCVUyDCMDNcu2BcMgUDOCBQugvRqpe5w6eVVQU7xPZ0bKAFZznZqubcIwDLz5og34/+7ch6/tmcf3v3KLMPaYbyeGF0VRzFuSyDBZfm6I6vkh/uLu/bDWADUDRck9QNtRXPzrwEM/DwCYiIkb9PwNI3j86CraMYWaPCgocWqyVhIiJBfPkbVB2D5Jxr3S9RGZJXI1Dn0GQAxUtwHn5/cCZRIdrKpetr5wn567nszLgYU2vCDKfIj31/cewErHx1eeOIGeH/Lxa2lpaWlpaWmdyXrd1G+84Me8c/GFP6bWy0PaqXmGSX7pyXNqVhwLz/W34KqnPoX/evT9yOQL0TJI5tTc5pAQkaieALjEqRnACxOnZiyVGifumECZMi6OCUjK84A8pybZjrm85KCgEWMZAKh7lEJNSC/Zwr4ZPDMNpHqLTtdLQlDQCQR+F5Wc9PN47BXAK34FPoi7R+XULI3txPc++/v4p+XXc/AlOzUBYDbcAABw+gfVQUFCQjgAfODI+/Bjz30InYs/ntoPc8O1+oEAJpL1MSE5rpTuWNo3cdTqIAh9IKLuSSkoiIeMeKz0Wgr3AVJhQc14LNcRydZLW+qpyQGp0FNTLD8PkIWak1aDHEdwaqaPJ7jCLALsVUAPEFyRfoin+sI1G0mcmlGc7qm5LJWfWxRqrnZ83lJABEsASZfm5dixD3QIvJkLpuDa6fs7GVOUuGMVbtxBrkFs+T7+s7a7I7WNOAeZwB0KNSftxOFYr6VdbGMVB/NB1tnGSrRb/QBxHKOXU35eVgQFiVCalZ+ne2pmr588drE8e6LqYMsEgZTPzqcdc8kxLX6/L7TTANGX1icr+Q+jmD9b5PJz0zT4hz0xd2rWuFOTOR0HOzVNrBsdXH7OrvEbLyT38V1Pz6WgodjfsS6EdLWFPsPJvsh484KXAGD348ex0PLQjKQwHScNNbteyEFi9vzUpdatfgic/x/5en2gezkAYCNrDRAypyYrP0/Sz2WnJpAErUVRzFsFbJ+qctjcCdh80PGsfx0gVSGoJPZdbfYVTk3hg70No2WUbBNBFGNWctv2/BD/+OAR+jsxnji6OvTYWlpaWlpaWlpaWlonJw01zzBVXNmpqaaazHXTjwgUMGX6SaHmiEmg5naXgJVYADjMacmCgsyYvJRFphQUJLitGKBSORlllymghprMMcfTz6Xy8zoI1Oz5IYeavsKpyX6P7UdOjp8ZcRMg4y3j0u9cgDePfot8LZSfB1HMe7Il56eAmkLoEYNMJdsEtr4DsGtYqV8DADgeEFhU7uc4NQWQFUYxulEJd7WugOOmz5H1LWz1AiWwHKukS3eVUNMdRxiTr+2okQrfEMFtvcTKiaVwH/E6C2FBq9EYDzyRrzHrqTnMqTlutfCJTb+GaO8f0zGp08/HrBYMWqK+EEykSqpR287/2orrqePJ4xIdsk/0CNyPDRuon8W3iWAkcyCmn1PZtK2DCjI6QvlyAHovPPSLwFO/AwCY9SdRktZoSQCtvsKpqUo/l3tAilBzlYPd7BzwdgvMqVmaRhQLc2mWMDUm9RCNY3xx9bXkCzdxmbK1ycBfP7enZuJEVYF5Xn4upJ/7CqAuhwWJblzDMLjz8vhqGiz5Qhn+VJ0Gg0lhQTKcrzgWX6vcBa1Y68wZGbJrbSVQc5BTM7lvDKwfIZAtr/ycQdTLt05gouqg0Qvw4MFl5dhLdtKvWOwzLDs1xxQgmWn348RV3InKCGJhrVKnZhzH+PT9h3D1b9+O1/3OncoWGqpwJICFKhnAaz8LvOEO/O+FHweQ9DtdDeiHJpn086SnpvhvDHu+NPsBWMvKyarDe8c2fOnfnnK69UOexPYEYi9bJrHfsGEYWD9Kxi9DzS89cTw1xw8dWoaWlpaWlpaWlpaW1umVhppnmDLl5zlOTdnRmXFOCk7NhWYfOyjUNEVXISs/B3ECWtEwp2aYhL6ooJ+iH6jS8cidmury81q8AiAmUJOWxfaNrFMz6alJoaZ0rOl6CSvhKH5v7scQlzfADhu4fuQx8kO7hpJtcXfTInVvhUKPOtXx2PVIoKYFlCaBt8/i2Yv/BQBw1CeuqrJ3WOipqU4/FwGADOHY2Jr9QNlTk/fG67L0c0WPS8PEajxOzilqIKY9NUn5uQhoaGBNn/XBVJRwC07NRX9EmbYOANWS5NSMJOcdnYvr6o/ijaP3w1y8lx7Tzrh2J+0GZmwCAwJrPJWETA62Ge9f/CN8z57/g1Y/QBTF2eNRld0EJn+3ezY+u/x6LG2/FShN8W1CyakZwkIrSvp2umUylwzCxXGcgYzjVQcB6zF56DPAkX8GAJzwp7KgVYB+GUcrEkeksjyb7WvilViKCbBZwJb0Noryc95T07SxEgkQc/R8zNST3q8AuUf/YO7d+Mj8zwFv+jb/fsWxOEBr9Hx0QweHvfVoGjP8Pibnl7iSlennSqfmcJep7Ppkbr8Tq93U+MXtpnkP3TRolIOsDMNIEtB7rLVDFsgyqBkxqGlXAdpzlfXUlB2Y8vkVdWpapoHXn0+eK1/bMyeMPVnrhqHuq+lJAU0Tij6mAHHOf+MZ0vv1xgvXoyWWoNvERfk39x3Er/zT42j0Aiy1PRwR0s35mKIcqMnco4YJbHgDln0yRwwKsq+T8vNs+rlpGknQGoWaDPg7RoySk4QvNTzpQ7aCUHNRcPKqy8/ZvxEmHT853mwjfQ3/+eFjqZ8/fGil0PG1tLS0tLS0tLS0tIrrJQE1//AP/xA7duxAuVzG1Vdfje985zu52/7pn/4pXvva12JiYgITExO48cYbB25/pskyjdRLfz7UTF+6PKdm3eyg2ziKmtVDGJuwxoSAFFp+btGgIDtmyeCyU1MIChpQfm6aRgrKlmg/OFnM8ZiBmhRk2fBQM7sEtFKnppzITvbP4Bl5uVVBTQD43yfehd7l/yv9y/QlXe4zyM5P5dQ0DINDxZRTk+6vWiZjPNQn8KHqH4bLnJpC+nlFSD/vC2W0MoTjacY9P6cfIdnn6qDycwBNCjWduJFKGheBJXPeMRiSuOVEp2YaamYcYtXZGgABAABJREFUg1SiUzOKYoSR5ECk68410y4oXwSWdC2MWm1sdAho8RwCy2SIeti8BAe9Tej0Qw5jybiknpq2OO8Gbj38i2ie92up4JBYTD+n+2rFCdQs0SAddv1TrRaEdGk/zrqWV8N6vns0iISy1sE9NTNl3IaB293/hG+3LsLDeCPZRtHvdELuqQlg3ExKz3H5RzhkE+XHDv65/f3ASNK6wjCMVAK6FwG3PPMJ/MnI51JhZBVV+fmAvrBAMqfidR6TUtJlsMvcfrJTU7xvePl5S11+Lq5j5pCV7y3xQwX2YU/IrrVdQ2/zu/Fk9yzc1iCu7flmHx/98h785F/dz+8DsacmDwpq5o+b6fUXkOfKPfvI/RDHIsAn17kuBX6JY88EBUnA7mt75uCFEXbO1PDKbRPpEnTq1HxuId1bVQ1t1eXncoARGxeDmosefcZL6eee1CqjKjnBGSxl1fcMaq701E7NKFKPj0l08qpK7OU5Z+M/ITk1mUv3XVdsBUCcmjoFXUtLS0tLS0tLS+v06oyHmp/+9Kdx66234td//dfx0EMP4bLLLsOb3vQmzM3NKbe/88478Z73vAdf//rXce+992Lr1q24+eabcfTo0Rd45KeutFszp/xccmpmIJxNQEzN6gLNvQCAI9462LbQU4w61La48/CCCDYtP48lqCmGfSRpu+qlI5bPy/CJH5ann9OemgIYZEB1wmqg54c8wERVfi4HBcnl57WSzce+ZGxP/7IENVlJbjgA2gLJtWEv1GJJOnM7PtchQK7mH4GjcGpWUiXH+RBODO/p+1moIsIJ0TEoz3sbBNq50SpPGvdjO3UNWfl5Sy4/z3Fqznv1VGmvKLYG2l6QgoxyT01ZQWwl8MIZJ44ukGRvAOjbM3RMsqNVOJ4AVAb11EzSwU0pDTnmgI2thXYslFPTnpNyGTSQrOORsp04NQUd9dZlro3o1GT7Eq+LCmrKPTwB4Mj4O/ADz30UhzsjqXGlQV063AdIXMVheROw6ZaMU5NJnkt2nmR/PvwgRiuqIrLHU9tUBHfsILex2N+Rz0MKuqfXp5zovZGBJQFqyvcEg11ySbiq3F12yCrLz12p/NyuYZ/zWuza+wk82SMfHh1Z6eCP7tyH25+aw+O0p2LKqUnLzw8vdSV3ZRbsbp8i998ShW7iWmf3Vk1ySYtzJQcFLUsp8Kz0fNfFG1FzLbRC4ZlLoaa43gFgQSrlF89PVkdILBevzQZ67RZ6dO1J6ede7PCgIEB8BgepMbHHEFvDyz3pGV5eh8NLHbzyt27Dr/7TY8oxAsPLz7NOTXULAbber9gxAcs0MNvo45gE3bW0tLS0tLS0tLS01qYzHmr+3u/9Hn7qp34K733ve/GKV7wCf/zHf4xqtYq/+Iu/UG7/t3/7t/i5n/s5XH755bjgggvwZ3/2Z4iiCHfccccLPPJTlwg1c52aUuCINaCnZqn7LADggLcpDammrwUAXFF9Ev0ghMOcmnZO+bmfQKNh0A9IgilkMYcl76kpAhPaV3PKXkXPjzjUDAaWn6udmkDi1pyNNqd/QKHmlOzUHNAzFMiW/avKUfd1yDmMhEcHlp+LUNO1sq5WBqFa/UAof80CrzCK0ewnfTdlyNgxCIgroQEjx6mZKT/n/R2zTs1uVMJi38kNCqoxCN5PSqrZOcpzISrVU9O0eA/H88ok+b1nr6PHS58fc261+yHvV6galxjQlOrz6YzzbfzYzqyFdpyUaNfrZEzNXoAwipVQ2jQNhBCg5gW34gH7XfiHpTelIDiQrIW+H2YcYEAC1xo9EXhl4fV0Pb2OVT01eWkyhUFRFOO/HHkfPrdyA1ZvuBsAlE5NeT9MI6VkbHlrgT0PvDDiwVjpNcxKoQeXnyf3QrocPHFqZntqhlHMey26tgA1M05NxfGkeVd9YMDaLIhOzQMLHbovcg2fPCY4YaXjuRZJz94wWsZq18dvfO67fBvV/c7ALoPSqdYVHGomzwy+r4xT0+XnFgquxUcPE+j6+gtmUC3ZaIRZp6YMNVU9QwPJqcnOITUmYewbxugzmkNNRfq58GwX26EAAmSkm0zT67ykcGp+4bHjWO362P348VzX5KIANZVBQdK/gRtynJrsg6iJqosLN5IPG76zf1F5TC0tLS0tLS0tLS2tU1PWTnQGyfM8PPjgg/jABz7Av2eaJm688Ubce++9hfbR6XTg+z4mJydzt+n3++j3hT5aDfIi6vs+fF+d8Ho6xPYtH0MsLY+jSDkGV+aFsbSdUYEDoG51UfP2Aw5w0NuEIBCcJxNXwQHwyurT+JTfh+2QOYiMcmpfLh1OuxfA88nvW0Z23ED6JbxkGzljJy+D7H3aNGK+ne1OwegcwqTdQNcLYLkEtPqoZOaLHapFHZ+WmT3eVM3BoSXgWBM4396EWkD6nIVGGZHvY7xKboH5Rhe+76PPzy8een4AYCKZd9cgJ3TMp47CuIN1Du0HGVuI2Xb02rX7Pto98gLtWNmxV+hbeqPro0+BkHg8C2St9PwIC40Oeqy3qHRtOiZZ+xWsALSnZmTYqbXAjtXskTXP+gwacZgczyzBBLAUjGLV9zBJ505eC6zMu9Xz0O0ngCCOAvh+BCM2lQ8eP3ZS52e70zD6CzifQs2OQZzFtnSdK/R4jW4fHXo8yzQQhQEiIcekTOd9tePztWdEEfzIgvGaf8FTR+aw8tgo5ps9+L7PexG2BKhZLiWl6IvNDgcchgFEYQA/Mvj88vPa9m/xf/dbWAqPZOaK3Qvtvs/nTdym5tDAmo7Hv8fGZQj3/BgFbGzsbC2I+6KboEWvcT+I8M3Wq/DN1qvwUGk9fN/HRFn9QYSjuLdG6LYrrR560n3Dr6GRXIBlChPF61en57fSSX7H52svOT/WsqHRJfPQYfeNacL3fczUyHwfX+3y3+kICeCIQkzQVPO5Ri91LgzUmcLxRuhkLbXItp1+8szhY6LXiwXqhEYJz82Rfzsu2TyGhw6tQKx0bnR6qWeMaQAWInz8nRfjRz75AP7xwSN4w3nkXmX3jfhcKFtkZy0vQL/voStAQnZvsQ8UVtt9/nsszMcCuTZV4eZbbHZ4Gwu2XdU2ULaAZpR8kBQYZcR+8hximhPmm6nnpb/eNFbG/sUO2v2Ab9sWIP1khYyZQdTIW0Ho+7CCPkyQ9HNDeC6UefsSthbS14Y9l+Y7aWjp25P4+p7D5Fi9AEeWWhxIiloQWgGsCPcdU589D+m9NUXX3glpLphT00KEG86ZxhNHG/jkPfvxlovWKduyPF96Pv8fRktLS0tLS0tLS+vF1hkNNRcWFhCGIdavTzf4X79+Pfbs2VNoH7/yK7+CTZs24cYbb8zd5sMf/jA+9KEPZb7/1a9+FdVq1iF4unXbbbelvva7FljZ+aFDh7B794HM7xw7YkI02j700IPw9gult3ETuwBUzD4mu88QqNnfiN27dyc7iSPcHNdQs9qozX8T5gbycv/s/qOp7fYsGgAsHDkxj0eCWQAW5udm0/tSjN3vdZXbLC2kx/70U9/F7qUnAADX9ID1IAEx3z12HPa2NmAAxxZamX0dP3IYgInDJ+YBGPAUxwta5Fh3fvshXFCZxjk2gZqPPPEsjjy9GysnyM8f/O4z2N3eg0dnybkuzM0px+4J5wcA9979Deyl78VxDBiw0I9ddDCBKpaxo0SO98BDj2H2MbLhwaPkGM/uP4Q7+gcA2IijIHO8PUt03mcXQJiKgbvvuhNjgtGxbFjowcDu2+7E03ReDx88gN27n0uuScsC6kDdWIEBco3DyEod71gbAGwsNTvYvXs3un1ynvd88xt4hlahvqq3gq0AlsNRHDwxh2A1BmDi0P7nsHv3s3xfR46QcT+97wC+7D8H9pj56pe/DMMAZoJHcV1mZokj69GHH0JwgKzj67smppE4NZ+dJS/nq8uLqbEvzpLzfuSJp1CaexKADRNRZj4Xe+QcV7se2DX8+h23cdh3pE2coMeWmti9ezcO03tMLD//2je+jZI5in5k4HNfuh3EIGfDRowvfelLfLubzCTp+Et3P4fnDpQAmNj/7DPY3X2a/2zuODnGY999io7DwvzsCT72uS7Z/2IrWdttugbv+9Y3sZ9em72rZM4PzS5j9+7d2LvfzFybwy2yr4XVNnbv3g3Clci1ueO22zhs37XVQBgZ+MrR5B7ttJqZ+eyskGPc++AjONAwAJjYt/dp7G4nz+a7vvY1GLAQw8BD330agIn52ePYvZu0A2n5ZAytfoDPf2E3LBNYXCbn9/CDD6C7j6yFQ8fJ+T2z/zB27z6IPSvk636HPBdWPbKfuUYPn//iblgG0PaT8/vabV/FHL3+R5fSz5LZeXK8Jx57FM6xR8i46L1036NPYf3Kd7HnIPn66OGD2L17PwBgaY58r9HxsM4G9uw9jHsPPwPAxLi/BLkQ4hvf+g6W98TYT/f13N6nsbtD5urV0ya+M2/in77xCN62DXjw4ccAWGisLPGxEuOfjTgG/uULX4IXJefH7q3mEtn3dx5+FKXj5FwabXJ+99/3LZwgj1mULQu90MC/ful2rKNrqEPv+Xvv/gbmugbOF5ya93z7UaxYHRyi98SYE2PVN/Dwnn3YHexNnecT9PnG5AYtACaaXY+fS0u4NnfdcRtKloUmDSZqLx/D13bvxnXdY5gBeS488tCD6D9H1kK3Scb5rW8/gN6+GE8tk+NZBvm39Og8+Xrf8VVgIxlDDAP//JX78cBBF+ze/9svfB0XjmfdmvuOJM/4wycWM+v+4RNk/4vz5N+I/Q1yLs8dX0pty+7Te+/5JjbZgGNYeOxIA7//91/G+YrjPl/qdLJhTlpaWlpaWlpaWlovF53RUHOt+shHPoJ/+Id/wJ133olyOevIYPrABz6AW2+9lX/daDR4L87R0dHc31urfN/HbbfdhptuugmOk4Rr/NXR7+AoTUrdsX07du26MPO7T922F3cd38+/vvrVV+J7zktShxH5wD/9CADgvPI+AMCxaBt+ddeu1H4Wd1+FSvvruG4iAS3nX3gpdr0mSUmvPjOPv3zmYZRHRnH+hZuA/U9j66ZN2LXr0sy4PnXsOzjcJmOfGBvBrl1ZfPX1zuN4ZPE4//pVl12KXa8i5eHWtz8DHHoYF5T3Y3NtAiZ1P05t2I5du65Pzdt5Z5+Frx8/iFJtFGg2MVqvYdeu16SOdW/wJB6//wg2bD8XgXcx0CK91C674jpcumUXjt19AHccewZjM5uxa9clWP72IeC5Pdi8aSN27bosM/ZPHvk2jnVW+ddvuvENvKcaAPy3h7+GVj+AMXoO0LgfDnWqXXnVdYg33AwAWLjvED53aA+m12/EtdfvBB69F/VKGbt2fU/qWFP7l/BnTz8Ao1RD1CIvprfcfBPviQcAf/Tct7Ay28IrXnUVTuyZB44fxgXnnYNdb0xCXW773P1AHxg1lvj3DLuEXcJaOLrSxUcf+yZ8WNi16034z/ffDkQRbnzj67F5nFAP64HPA/vvwlIwCqc6gs1bx4ETR3DB+edi1+vP5vuau/cgvnj4aUyu34QbXnce8MA3YJsG3vIWcjxjrgrclZla+LGN6665CtefTRyZ1rc+CRx9ElWTOPxGN18K7AE2rl+HXbtexX/vsS8/jXtmD2Lz9p14zau3AA/dg7LrYNeuN6X2v9zx8JsP34lYgNJvueXNvCx3rtnH7zx2F9qBgTe9+RZ8qfEYsDiLruDUvHnX2zH51D04vtrD5Vdfj7GKDTx0D0rS8Zx/TFK4b3nL9+Ern3kMmD+BSy56BXZdl/R3ffCLe3Dv3CFsP+scUvJ84Bls20LWIkDKyf/HI3eiHxq4+U1vhm2Z+MCDdwBhiDe+/nXYNklA0N65Fv7gyW/BM1zs2vV63Pu5JzPX5uBiBx9//G6Eho1du95EStXvvxMA8La33ML78u4C6Xl44W/czsuTZ6YmsGvXVan5vLP7OB5fPo5tZ1+A9vEmsHACl158EXZds43fozfffBMqD30DHS/E1MatwLGj2Ll9K3btuggASR7/rw/cDgC47vU3Yqrm4n/tvQfotHH9tVfj6rOIc7H38FF89sB3MTI5g127rkBpzxzw1COYmhjDrl3XIIxi/ObDtyOIgCtf8wZsHCuT0ugH7oJhAG99yy1Y6fr4yKN3ohMYeOPNb+au6786+h2gsYJXX/kq3PwK8gHaiXsO4K7jz6AySZ5zD+3eAxw7hAvOPRu7bjqXX7tvzx+CU64BAXDBxVdg2t8MzB3HVZddgG8v7kNb6CN5wSWXY9dlG3EbXQsXC2th7x3P4jt3PofpjVsAHML5r7gIeHZPaq3HcYwPPHA7/DDGtd/zBrqAvgHHSu6tu3pP4NGlY9hxzgXYdcNZAIDfePTrgOfjDd9zA85dT8rIf+epb+DISg+XXXUdXrl1HHEc4xfuIx+uvemmN+LQUgd7dydQ87rvuRkYvRD/uvQwsDSPnRvG8fDhVVTG0/ciAOy/8zngUPIhx2XnbMXTDx5FEBu46U1vhmOZpE0AfS689S278Lt7voFmjxyv7gbYtWsXrK99FFgkPTWvv+ZqXLOTrIXPLj6EZxsLOP+iS7Hris0oPTUH7HkEtgncdNNNGD/UwKeefRB9oa0EStOonXMVogce5d8a334hdl2/A7L+1967gSZ53pql7L8pC/cdAvbvwRb6b8TBxQ4+8d270Y5s3HLLzdyF+cv33w4gws30Gfq0vQefuu8QHu7P4Bd2XZk57vMlVnmipaWlpaWlpaWl9XLUGQ01p6enYVkWZmdnU9+fnZ3Fhg0bBv7uxz/+cXzkIx/B7bffjksvzcI3UaVSCaVStpec4zgp2Ph8ST4O6xEIAJZlKsdQLaW/52bG6iAwyrDjHmYs0sfrWLg1s6/GyDXY0P46LrbuBwD4sYVSuZrabqRC5qbrR2DuI8cePq6yYym3qZRs6Wth7BXilvupmX8B8C98m8iqZ/ZVocfq0JJI1ZjWjRIgt9QNsFLawb9vl8YAx8EM/fly14fjOIgNdn45Y3fSY69XSqnt6iXiOuuXtqGC+5PjuVWAblenvfH6QYyIHU9xncdrNBVY6PFWq7hwhDGM056gLS8Ga3dXce3UvsLSOqAPjBlCPzfTTW0zQfmFF0SIDYuXn1dKwnYuASLLwSia/RCsdV5ZOt5ohZWzRjBM4tiyLSPZxsmGPgEEalZcYS2U16V+3rEItC9J62qUrc8gRmyQ47nK+cyWVlfLLocQ68bIz6MYaPsxLx3ugEJNuw7HLWOs4uD4ag9tP8ZolR4v537wjDpcx+H9MlNrHcn94kVARGGrK5zf5Egy5m5oYLLs8HLpWjlZexvGyQVc6fqAaYG1LBSvzRhdT20vhGXZiClwt0wD5VK2z2nVsdDss2Tp7P0wViXz3vEjBLQ/obwWHMdBxbHQ8UI0e+R4ZSfZxnGSFgp+RNZIGGfnaqzKxh7BcRxE9DnE1oIDEthydKWLhU6AbdMOItrP1rFMuK6LGcch5dxhjEY/wia6blTXZusUWesnmn04jsPvLfH8RmjfzYD21LRKo/yeqLgOZkZKaC8mLrl+GNPzY9sk+5qok/Nr9iPAAcLYoHOVnveRsoOltodemPRVFp8d7N7rBhH/Hl8vleRenqiVcGSlhxadTz+M+Hqvl0sYrYap8nOnPAE4Dny60eaJKh4+vIqFtpdZF3K4+JbJBI76kYlq2UFskGcau28m6yWcaBFHtOEtkP6mcRIUJF4bFmrmsfmka8E2yHpj98JcGwDJbINRXo+79y3xY3pBhGfnO8p7dkno79rsB5ltYuk+3TxJ1krHC9GLDIyWHcRxzAOa2H363uvPwqfuO4TvHFiGbdsvWAn6C/H/MFpaWlpaWlpaWlovls7ooCDXdXHFFVekQn5Y6M+1116b+3sf+9jH8Fu/9Vv48pe/jCuvfOEcEadLYhhN3mtPWQocUYXkhFbS/68blTAbb8xs0xq5GgBwgf04304O+xCDGViatZw0zlQRxqUKFwHS4TqAFC5SnoFKppXl70n6OYMz2ePN0ACVhWYfS+aO5Ac5QUE8/Vwxn0B23uXgIBYe0nG2SCeQDQoSA2uUQSyKtGo5PZv1w1vteEmit7RN5JI3+3ErcWrKYT0sKAgAGr2k52RqXzS4ZzaYJL03WQp3Zr0kgTTKAJmcoCA/tlMpxyhNp37eNMjXcgAVG3tHDEtSrE/XNlM9UeVwJscyMUFdsAstj6+FLoOaDrmfmFN2tesrQ3tELZrELce2Kw1IP09SuJNtHMvkfRIbXR9RFCsDhcYrDtiSXW57qQAqprrwYYJ4bfLGXhXWhDNkfbK1oJp3FtDEksTltV4T1gsAHvYk7ktMWgfUaeQbxtIJ6GxMbM4Nw+DJ2HNCwI0qFGsj3dexlW7u8dg6fwC7gMkrgPVvTKWys2AipjYP4cqeHwv94ufHnwvptZ5KnGfPYuFZxQO/xGApxTOGrWEW0NQXAoBKjol6yUZTDApy6qmxb54gH0wstLJBQQx8nr9+BNedPYXvvWwTX2MtKbGcjWmq5mIpGCPAMA4Bb5Gnn5NQs2TsbD2xD7PYmCyTuorpvC90hGd4eT0eoUFIb7t0EwDgmdlmZuxBGKVCq5o9PxMoJK+XimvxICuWgC4GIbFwMDauMIpT862lpaWlpaWlpaWldeo6o6EmANx666340z/9U/zVX/0VnnrqKfzsz/4s2u023vve9wIAfvRHfzQVJPTRj34UH/zgB/EXf/EX2LFjB06cOIETJ06g1Wq9WKdw0qpkUoCykmGaqYBwkQA1n+ltg60Ag95o2sXai0oKSEVfIr0QYZgFKultk2PkQk0JDKZAiD0ClVSQMUk/D3LHxNLPF1p9LBjbhB0SF9KUlBrNgJGVAzXlayPPFQM0x+tvTP9iXvr5AChWlxytlmlkYLIIJ1TgBQBPlB81yUt8L3JhW+nzsC2TAzbxpT6Vcn/OT6N97n/FXyx8H5r9gAdh5EHwjicmegvbWPlQMzUPm3aha05h1p/Eg9GNmDfPoftSp5+3+kKqua2+fgwKqfYDAFN0vSy2+nzsc/F2xIYDjJGScAagUiBZOt6ytQMAcK/1LgBqsASIayHioUPyWuDH6/opWCLuyzQNTFJAv9DyEuAsbFN2TL6u2/1QgOAF7mXF+kwBtgGAlCWgr1CoKYdtscRu9uGEpwCk7F5oDUgjZ1CTJaBzwC0cjyegp6BmFjKylguzjR6CMEpgpbANg83fCN4CvPkBoLY19cFCFmrmnx+DYqtdCfpJ88nmoSkkzqcT7slaadFjRVGs/PCBrallBjWFACDXMlF1bbRCoZ+0VUuNi83PQstDJFkz2TPtdRfM4O9+6hrsmK5xQM7Arjyf60fLCGCjZ4yTnfRmgZBcIy92UnMlp5+zMbFbcLziwDYN9GOx+fB6HoTEWho8M9vMjH1ZePYBBGD2/DSADBT3TQLU+6nzA5L1XhPuJzEJXktLS0tLS0tLS0vr1HXGQ80f+IEfwMc//nH82q/9Gi6//HI88sgj+PKXv8zDgw4dOoTjx5P+jH/0R38Ez/Pwzne+Exs3buT/ffzjH3+xTuGkJbod80rUypKDRwXhIjvpBfhMb7vSRWWVxjDvj/Ovu1Ep48qq0Jcx4tQcDP1E2JrnAJOdminQM5L08vz52d8GAHy7dZHShcmO1fGSMlpZ3LXT8rCITZmfMxC02PYQx/Fwp6adPj8ZJjOn1DHnCnwDP5D8QICaFe7UjNAfAOHq5TTUlGEQAIzRctPlAlCT6ZHOeWlYyY5HgclKJyl3d8R5r2yAdfmHcMKfRhwDy+2kvFcUd2r2E/CSms9cp6aVHvu61+DL5z+Cq5/6a/xe77fgReRnGZDMnJreYMcg2VaAmor5nKaQe77V52uhFU8jeMuzwPd8DgAwTud8tesrXYUA8OUN/4B/t/+D+Lr3FgCCU1OGmvTrXhAqnXcASK9NdjwBasrHnKqRtb7UVrt2DcPgIK4luFplNyBTRbyXFetztMzchYMdsuyDALau5PXJIBUDXkGUhUYcoPbVTj8A2DjKwBJxV6pAqwpqqiDVdL0ExzIQxcBss6+8ftVS2mFKxhXycTFXKBN3aipgK4OMjW6+EzU1D72AQ3DRYVqX4GHaMZhcT9HhLW7Hnmm1koUGLT+PTZd/EMG2Y6nhYRRzWM3EP8gQxlUTngmp49HzW0f3twrSTxfdEymnprj+Km76ue/xZwz5uWkamK6X0I+FsuvSFAe356yvw7VJy4PDy+kQHfbh1kTV4f+eNHoS6GT/RgjrhfVVnqVOzb4AQtn6M00js9a1tLS0tLS0tLS0tNamM7qnJtP73/9+vP/971f+7M4770x9feDAged/QM+zKo4aMoiS3Y6mCn46aaemypFVsk0c8DZhxlkBAPRiN+vUpOPxwoi/GNoKyCiPXYaX4jFTwxTHtfFmzJ33O/ihz9ewYJ2NP7vwDvyvJxbwb9Zlx16TXJOOYkzMqTnf7KMX2vj92ffgxi2ruHjicgAJCPKCCG0vFEoZ1VCzlDq/7PHEl/c74vdjc/8ebK2swq0l4TDspVx0aqpgUMm24FpmrssPEJyaXU9d6g3Arkwjig0eunRf+xLlXNVLNhZaXsqtJMPPkm3yvoSL7T49nhrsdrxQDRlzoOaI1cmMfaSUgLN+zlzVFBA1D6iL7lfVnCdOTY/DNcsAUNkIWKy/Y+KOzSvBL41sxh3Nq/HaIZCKgfm+HyaQStqXCDW9IAtLkrG7wCyw2O7nQsZ6yUajF6DVD3i5upvr1Bz8AcWIADVVIJKJATtWqpwpP6fXpDOg/JwB/lY/QBTFSiArOzX5ehGAGIOac80e/55qjZqmgQ1jZRxe6uL4Spffg+Izja2lTj/M7Mu1TJy3gTx/DQOIY/DQoGSNJuNi17ghOVHlZ0xduB9ULuGaBFr7OesleW4wp2Z6fVYcC62IuDNjq87boPhBzI8zUXWw3PEx3+zzD4fE81M5bblbVbof1o9SIB9OYoMJoHcCiKhTM0o7NStC+w5xX+LSmx5x8WxT+N8bZ4zPV9W1cM5MHU8eb2DPiSa2TyVl9uyZxs5nueOj0fVTYXABh6jJmNjPTzCoSeF2yU63uKiVbHS8UDs1tbS0tLS0tLS0tE6Tznin5v+LqghlanlZAjIwVDoLnTH+12d625Ug0rVNHOwnvTa7USnjyhJLrtlLd17JasUVehbm9tRMfz8FTAwT/Z0/h2f729DzQywbm9CMakpnodgHElCDyAn6ctr1QzT7AX5/9t9i98jvAkbSD429JC8JfRSL9NSUwTIZE4MKIVpBGbv2/m/861n3Au54sg9bgJo5UIxJdGuqICrrAbna8XNBSLXkYjlMAPd9rUvUTk16rGXBqSnPg2EYHFAw+Jl13jFIFSYAQDyeqQ6umLGXM+sq3bcxB2oKc57XV5SfowA1VXM+I7QrYGtBXgrpcvDEmSdK7LsJINVrUVS6/FwNBrmLT+hjaptGxiWcLj9XH49d47bYfzTnPq0OAcDs2jR6vgDYstsxYJffg5W51yj0U7QsYHA7jkkvRVX5+cYxUhKd6YOZcmoS+KQqP5fvCba/oyvdnJ6aZNzNvujUTO7BH3z1Nnz2567Df37T+QAEaDugp2aj5yOO8yH4KIe7PofgKUdkKYG/QALXDEPqwVpNoB3ZLv3sMAwDvkmc/pGVQD9Veb3cV1M1n7z8XOqpyf4dW0evy5xP/83qzXKnpif11JTLz33JqQmQ+9gTnZrueKrk/TyaAr9vPt2Whjk1p2olDu1lp6aqpy2DstypmePMluGulpaWlpaWlpaWltbapKHmGahKKiioWGCNCuhZdlL++HR/uxI4uJaJA14CNUlPzTQsLNkmBzssyEJVDg4U66kp9wOVtyvx8JRIKLFUQc200VgF6kS3GQvckY+XlKD3hZ6ap+ZErQnln34YoR+7MAS4DCTgt+uHuVCMSewBqdomKT/3cvvwVVwLSwEZQ2iU8HDnAiXgZo5HVibsWIay/QGHn2223UmWgwtOzSBOvj9pNxTltkmAiq9w3onHa/cDweV3ij012VpoeXwt5EHNla4PL1CXu7PrwvqT5l0bMSjIU5QTi8dLBRMpS+dZ+Xlf6QYE0tBLBQZFVVPl5/lQs9kLuINPta/xShpil6T7vy44NeM4VkKxsmPyZ0CrJ5bOJ9tsnyLl0gdp4ngCdpPjJU7NLNSUx75JcH6q5n1UCvcBkHLIWqaBV22b4KXeSel1ds2MCeDXi5Jt5DENg/wjcu9RYd2J9zK7JuxeF52FTPvCC/Gl1eswvyWpkhDnQXTBiwoUfZcTmKduH8Cg4NEefVZ2TyAOk/TzdFBQ2tkr99QEyHWOIKwzAWqWHAtnz1CoOddOjX2xRY45WXMxWmHQPu2qVAV6bR4na+/QEll7zPkqr/WaBHe1tLS0tLS0tLS0tNYmDTXPQIluxzynZiYoSLGh7c3xv5/wp+AowGDVtXGgn/SaJOnnWXceg5XDnJrlIeXZqu/LQEjcR9IvMx/CManAp2MlMIT1q8v0IqwnICtU9EvLG9vA8vNU8nd6Xzz93As5DCriLFRBVLGMVAVLAAJ2GdRcrb4K/dhVXj8GTBiIy2sxwM5RGQKEBGz7YYyuH9B9qXtqNsI6/mT+7fAiG5+Y+4FciNPo5ZeWp3t4Fu+pqQSDI8OdmqILM29MSYATg0ZqGMmgRy/IcbVCHRSkLJ0XgGzeWhDh0qA+mED6AwG1U1N0kObva0yGmjnXr9UPEUYxWNi0OKeGYXCY3uypU+cZ1Fxse8Q9GmTB7owCxOWV/W8aT5yffcV1Zj1FG0JPSVWriIwTVTFXVdfiH0x1g3ynZl0BNcX1UhsCD5kmaur081R7jVIFP3vwv+DIup/Mnt8Ap6Zqjcq9JLmrXAgKAoCDHdoHWig/lwPEqlIvZT4PYvm51M80dsZSa+bsdWqnJruW41VHeX0BAdoKD4adM8TN+tw8gaQqSEzmIX19tLS0tLS0tLS0tLTWJg01z0ClnZpqyVBTBeGs3mHhK0MJHGolCwe8NNRUwR5Wgs5e8Ir01MxPP5d6YcpQU4B3rIxSBSyrJXkOBo+JlRHKAIo5NZfaHn9hzQ9CGlxeXxX67OUBL95HMYj4y2+RHpCqbZLAj3wXX9W1MBdMAAAWqtcCUM9VTSorzwO7I1KAUTaNPLkuqyqQnIKaNXz4+E/gku9+Gt/tnpNZCwwseEHEE51zQZ033Pk6tKcmK+EW10Je+bkw57ILelzokRhGcX76OW9FEHFILF9nEa4McmpO1pPy80E9NQFyX6kS0kWJbSdUAH9EKGXvD3DIMsDLlO2pmTh7AyGNOm/sTdFlKuxrpOzwoKeDCx1lCf660SzUzEuB38ihZo/3Ek47NdN9PgE1RKxLfS6TcSXHMwyDr6tOqIaj7BwBBjWz4Dpbfp7tBQoITuIuhe7MWWiL+0pCpZjE81MBYkANicUWEfJ+AHLfmQaS0LruCRiR4NQU5kouP2fAWVwubM09YtwEjJwHb92b+c9Kjskh5L75FuI4WXNi2Ti/7ySnJusfK54f29+R5Q76QVig/FxDTS0tLS0tLS0tLa3TIQ01z0CJwDLfqZm+dCqnprHzJwAAdzVfBUANqWquneqpaRuREvbw/nG9wcDrVHpqqoKDGFNkL38qyFiXgoJy+2BKQFaGF2ICesgCTwqku8twFkinD+eBJRH8cufrKZafi07NfqAGehXXxh/NvQt/s3gL9o39KAD1XMnp50Xco6rjOZbJv7eiAqQS1ASAflxS7kvsKbrEg4kkaEvnPIziXPCp2p9qG+7UbPY5vMg4NcX085wyb9GdKMLIzNoXys/zejuOsTLY7uAgJBZ6tSiUn+f1H02lnxdJih8AwaM4eS6oWyRITs1c91rSKxPIzgMDeuny8/S+dtDQl/2LbaVjUARxDGblOY43jxP34DEhKCgFNel4oljoFam4PrI7Ly8cjPXLTDk188Buz1f2YE0B/iDKXXf8udGm7RHCrLOQ98YV+j+K45pWlPIDCfRzT6L83LZIOfs8/fAFneQDOS92Uh+iyennzO0uOjXZWvkD778Db90DD4lz07VM7JiqwTAIHJ5vZQF3ybGS8nPJqcn72koO4JGSjSgm7Q/yYHJy/+memlpaWlpaWlpaWlqnQxpqnoESHVKqnoZA9mVJ6Sx8xS/jfUc+hPcd/FUAajBhmgYie5R/vc5ZVDs1udsx3zlJthPKpXNgybDyc8MwODxksKBYT81hTk01QJziTs3hPTWHlZ9XhfJzXgKcEw5DxqQGrUzp8vN8YBRGcW6Py6pj4cneTvy3o+9DA2PKbcRjLQs9NVWS510FxRgE505NcT6FoKBWXE/9nnydLdPgKfdLbfW4xDYEq0PGXhd7vqqgpgAGmeOMpcYzJT018wN5bMvk/Q1XUlAzfd+mnJo5ZdBj1Wz5ubqnZuI4zg0KEuBS3jZMw1zXVQWcV80pcwXm7asu9mAV0rod6R4c4UBP7Aeans8d0wRqHlhoK8EnK5n2wgirXR9RFPM2A/I9wYKCjq92lXCw7Fj8fBtyD0s7C/QYhMtrDcDWVTcwcqHtiJACr4K2NQHwX/DBL+F/7H5KuR/m8G6y3r9+9njsvhP7P4rrb8sEmZ+Di+m+lKresDXh2gFQlvOvHy0LUPNQsr/ITt3P7BnL088pkBVPMWlbEQJGMp/smGXHwtYJ0q6AlYwD4I7ckm3mBwWxeRfOzzAMoQS9lezHUa917dTU0tLS0tLS0tLSOj3SUPMMlFi+m19+nr50lgp+WiXc71+HVkRe3nJBj+BeW28vKcEEeyllkCoXILqDnYxk7HL5eXZcbBvmaLEU25RsMwVz80Gr7NSUy88ZyFp7T00x3TbPVWeZRgJDukHuvoD0tVGBpbJj8bXAysblfanS65XOV+7UHNxiIFt+rlgvbnq9pMJ9DBMwaAmpADXlMBM+Lp7KrnYDWqaRmYNCTk07e6zpEQJ7en7EYUZe+XnPj3jytbKXpNBXM7f8nI6774fc4Sa7hMWemoOCkHiiddvj2+U5/US3Y95zYVhPTVMAzoO2k52a2fYICahLPlTIprvXheTvPOh3FoOaglNTnIOyY3E4utj24Ef5ztCNNChouePzEuxMGrnk5kucjMm8VKUy7jwX7WiB8vNRofxcWeYtQPsoBr6zfwlA9pkwKtwHq11f6SyU+3OGAgB2bRM7p8m9+9xCGmqqesNOSD1mVddv/WgJC6z8PKQp4pGD2HRTz4Wqm0BwIHFqWsKHD2KZPoBU8jlbV2cLJehM4naj0j6Y/Jx/I3ay8KH5dm75eU331HxZ6Td+4zdgGEbqvwsuuID/vNfr4X3vex+mpqZQr9fxjne8A7Ozsy/iiLW0tLS0tLS0Xn7SUPMMVAr65ZWfy07N3HLwZLs8ECm6AVfDurIUmr1I8qTZIQARyC9rzTg1Fcdj27QH9NQkAUbC+Q0pP+/ngJ4psaemAFWU+5KcWrKqgrtJ1Tsv+V3m8BoM4djLOaAOCgIS1xWTvK9Uen03P+ipLgUFFXGP5o29ykv+2fWTtqEl6F0jcQnnOQZrkoNUCd3d9NiL9ChVj9vma3iBJiHLS2GkbPO2EKyfoGpfrMRXDKDK66/aC4T085yemqtdP3G4DQLqXlLGnU2KT8pfB7k+ARlqFnPtqu7lTE/NTEl8Uk48CLSKyd95UJOFBYlOTXlMEzWWTO/xUmLVuEbLDr/OKzkfGMhhMir3a42ndYepdPfM8bhTMylRz/RkFMKSVK0PZBDMz03hJB4V7ndVsI04bvHcAHJ9GEBe6fjcRZ2aA+H82DNqqSPB31S/0zKWw5FUavk9rctgWen1IwatAQkAVjk1WVsE1fFUCejckeuYueXnCbRNz+nO6SQsaHj5uYaaLxdddNFFOH78OP/v7rvv5j/7hV/4BXz+85/HP/7jP+Kuu+7CsWPH8Pa3v/1FHK2WlpaWlpaW1stP9vBNtF5opYOChjsGgRynJopBxnrJxjuf/Sh+eeNf4UNH/z3+cgA0YsrtqVkkKEh60VONi5ef856a+WNnTpr8MQ0udxeDgtjLdy4gHZZ+Lpb3DkiXrrgWGr2AvzAXgXB5bs6xioPjqz3+tTzvhmGg4lpo90MOUVUuTBke5kHwTPm5AtpWJWdvBlKZLhB20TdG+LeGQVR2nfP6Oy62vaH9QEeG9NQEyHo4utLlX2eGbhoYLTtY7focfA7qJSn2HMw6Ncl68sOYl6zK8zBWScpgB60psfS4kTNXYvnroMRyIHFQAgOcxCU7dX6qa5jpqemoQc+wMYnrQJVsDiQ9NQ8sJkFBchuMiaqDQ0vAUjvpS6k6pmmS8B4GNMnx0mMfEQKhoihOwp4UgTthFKMfRMk52urr3AkAz1KDT7H8nDsGpXv54s2jeOJoI/U91fUbr7po9AKsdj0h/TzZriqVSvfFEm7bRMm2sHm8gqMrXTw338JkbRKA2HMy2ddUPWnxAeQ4NUfKiGChhUmMYh4AcHvj6mw7DRYU5EtQU5jO0XL6maFyTqoS0EUYWZeeYUyq9HMgcWo+t9DCq7aPZ44HJGtBOzVfPrJtGxs2bMh8f3V1FX/+53+Ov/u7v8Mb3vAGAMAnP/lJXHjhhbjvvvtwzTXXKPfX7/fR7yfP1EaD3Mu+78P3feXvvJTEzuHlcC4vlvQcrk16/tamF2v+cl4DXzTFxqkNiP3eqf7+y12mqV5XbLnp+3dtejHn7/k+poaaZ6DSPTXV28gvS3kP+0oBJ2O9bOOezkV4976PAchxwsn9K/OcmkMSk4FsnzEVwGC/Oyj9XB5XkUR2cjwpKIi+cC+2PGyfGuzUTJ9f1jlZE8rP83rnAQnMOpmgoLz5HJYuDRAoTaBmPgBmZbn9AeAMKObUrPGemhSQ5jg1fVOEmjkQVQLqg5zEK6pyd3FfYpp8gTkH1Hb28SqBmvNNApNV9wwLFJpr5gNnsY0E612Y12uxIZQJD3KrAgOCZlhiuTc8KGhY+TlQrL/qUKem4AjMC+0Rx97KST8Hkp6aS20Piy014GZOzeV20hbAMNT3/LgMNXPKuBtCv1NyzGwPSDZ2VWo52Rd1aoYGvHhwT9RGT/jQRNrmL37s1Ti01ME7//je3HEDCdxdbvtC+rlQfj7Aqcmu4c6ZGoWabVy5g0BN3kYhVX7O5jwdTJTuqUnagERxzCsUbm9cBaeshpp+SFyvSfVAsg0PleoHiON4sFNTVX5um7z9gPiBESCWn0tOzZnEqdlj85kH8D0dFPRy0d69e7Fp0yaUy2Vce+21+PCHP4xt27bhwQcfhO/7uPHGG/m2F1xwAbZt24Z77703F2p++MMfxoc+9KHM97/61a+iWq0+b+fxQuu22257sYfwkpeew7VJz9/a9ELP32WXvaCHG6oG1jag5pZLTtNIXl66bOtu5fd3S9/W9+/a9GLMX6fTeV73r6HmGai0U1Mt0zTg2iZ/WSvi1MwDZzI0UkKxkpw0ngMQ3eFOTdHtaJmGEiZknZrDIVUREKkal1h+ztLPc52awku/DGeBBOZ1vAD03Vft1KTn18zp88lULwDhxity+Xl27Px4A9yOo5KjLs85WaSnZlXqqZmBqAxqWmMD9wOk+2ACWXceoAo5KjCfBaGtaikw0JiUnyscitW0U9MwsutKXE+tnBAudl2iWCjBV6wF0yTtGDoCMJG3Y/d6s1cgKKjAvSw/F/LWumMZHOZle2omLRsGlZ8nPQ7ze2rWSzZmRkqYb/bxzGxTuc0kA2wdL3Hd5ayFsaoLLCb/CGd7aiYuWtnJyGSZBiqOha4fplx/+UFBgGfmBQWRbbwg4j0lZcfgutEyZkZK6TlX9nylZfjdnPJzqVRahOCsx+XO6Rq+uXcB+xYSMMh6XIrnJ7rhxX2Jx1s/SiDiuLHAvzcXTGGTtBbEddnxkt7F4mbsGRVGMTpeyM9PnM9NNN1edBqL87BpnAQhHV3uIo5jfs6q1Hkg6ee62vVxYrWbOT8gmwKv9dLW1Vdfjb/8y7/E+eefj+PHj+NDH/oQXvva1+KJJ57AiRMn4LouxsfHU7+zfv16nDhxInefH/jAB3DrrbfyrxuNBrZu3Yqbb74Zo6Ojub/3UpHv+7jttttw0003wXGc4b+glZGew7VJz9/a9GLN34c//IIdqpBeO3lqA4oNE80tl2DkyOMw4mj4L/w/pm8ufUD5/Q/Qb+v7d216MeePVZ48X9JQ8wyU7CzMU0mAmkVcivLLL5MMjVRAoXpay8+T7+eBJbYfBgZznZoF+v7JpfryMdkLd9cPOfTLTz9Pvj+4/DzkczSovJ6Vgw8q7x10PACYqEnlvVZ2/TAQkJSfDy8TLuKOBQaXQq/mldfTBPTQTl7SisxB3vF4ufuQnprp8vNi98MgqLmQ4wYEiMsPAOYaBJqU7GwQkklDo7wwygXOZcfiH2AsUACTd361kp2Cmtny8wSqeEMcueI9vxbXrmEYGKu4WGgl8yCPmY1poFNTgGyqQB6mbZNVzDf7PMBG3tc47++YpMTnPRvH5dL5AcE9Kidjco4EarL2CKptipSfi/M9KBTLMAxM1lzM8rWXnSd2bisdTwkZa0IqPQAlcOYl10KCuK/4YEh8xnaF3qmqZPqvtF6HN9XvxPz6HyXHk+bctUhAXBjFqX2Jm1Uci28jtisQz4/NtxdE6Pkhyo6VKj/fQJ2a5Lr53OEbKNLdAXKfrh8tYbbRx3669nRQ0Mtbt9xyC//7pZdeiquvvhrbt2/HZz7zGVQqlVPaZ6lUQqlUynzfcZyX1Qvcy+18XgzpOVyb9PytTS/0/EVnGP9bK5A04khDTYWiSL2m5KWm79+16cWYv+f7eLqhwxko0YkivijLEmFdkfLzImDCNNS9FOWU4yIQtUhQUH4AidQztEj5+RBAyo+pcM+wsTIAVaynpqL8nL60emHE4ZKqFJoHBQ0I7gHSQUF5kHhMcmqqtssE9xRIqS4SLAWor3NVCu7Jc2pG9jj/Vn6J83A3IFufw5ya4nopCupUl0ZMJAfUc87KrgeVqAOJ45clqas+MODO0AEQVTV2eV3x8vN+wNsjFAkKGhbilHe8ZPz5cJ7to9MPC/fUHBSYxGDdYk6/00n6IcByOwkKynOxD2vtIIbJiNBPhtfy/cC2U+2rGxi5TlRLSJxnrse8D5gmawkYySs/Z2PqK45XdZMPaAB1CFJSci04NXnP0GS7esnm57vU8ZTJ9Gyuf/3YzwDX/S2e3U7aochrwTAMVJ0EuKqcmoZhCBBcHbBVc23+gUVDEShUdixM18kcij12ObQd4CZmH3booKD/tzQ+Po7zzjsPzz77LDZs2ADP87CyspLaZnZ2VtmDU0tLS0tLS0tL69SkoeYZKBGcsTAE9XbpEkeVRKBXBFIV7Z2X9yKdcjLmOE7FF72isCTveOLYiySyA+qXZOYkYv0Pc9PPhwYFZc95UPm5NySsJd1TUz2fMngZXH6en2w+NqT3IVMGnCmgLRs3gyWZtTdyLgADrdK5A/cDFHWGkm0GlfzLYy8y54DaqZmdcxVcY2uKuitzrh9bU4Nc14kzlO2rGADOlJ8zMFjIqTn8A4o0JM7CPCbmjlSOSSg/9weUn4vp534O9BO3Y+BI3ob31OwkCeJ5H9KITk1V+wCefi6WxA9Yn8xdqerhyZ2aIQYCZ/ZBB4OaedePtdXI28+YUIavSuuu5wQFpaEmcWoeWurwsuwkSCfZzjAMoa+mp9wXO68TvTr6W34AfmTmnl+Ft/lQOzXJ/pL+o6qeoaZp8GOyD3vkQKHNtET9mAA1k5YFijYY9JiLNBBJblGSOKV1T82Xo1qtFvbt24eNGzfiiiuugOM4uOOOO/jPn376aRw6dAjXXnvtizhKLS0tLS0tLa2XlzTUPAMlvsSxwAGVxH58eRAuDSZOPTwl65ZT78swDA7Q8kAIgR/s78NLX4H8cnBxXEX6fLLjy5oUQAdQFNpmj2dbZsbxqDpH0YEJ5M97kZ6aEwJgy3Pacqcm79uocMIJziVgAEgu0FMzE+4jr8/X/CPwvfvQK5/Fv5ULUTM9X/OBFz9eHiB1h89nkZ6aogsOyOtZmO6pmVdeL0N31fnJPTxz79PMvA8oPw+zfQZFVU9yrvKunzh+IAvnqwKQZm7VokFBavgpw/n0NiJcC3jPyRyHqQhjrWz7AN5Ts5sfXgQk4HZFcBJn9sWCggJ1z0kmNg+JKznPqZmMXbUf7tTM6anJnZpeOmVcvDYbR8soOyb8MMbhZQL+EverFMhGx7PY9pRO1JGSzf9dSPV8VX1IIySgMwAs3zYjQmsAlTMUEJy29MMeufcm66spQs1BIJwdc5E7NdPbyInyWi9t/dIv/RLuuusuHDhwAN/61rfw/d///bAsC+95z3swNjaGf/fv/h1uvfVWfP3rX8eDDz6I9773vbj22mtzQ4K0tLS0tLS0tLROXhpqnuHqDXRqClAzxyFVdoc7NUUglJ+EXAwyAskLZx4IMQyDA9l8WCKXuw+HVLl9PuWgIMU5TtXTJdx5gHRY+TmQhox5xxOBA1AsJCcPionl58NKiRNolJ0r0zRSYUF5cyBDP9W1kcFnZu3ZFaB+VrFy8AIQlZWJMuWtY9NMylJze2qW0tdPtdkGGmrCjzegDDqMBpd5Z3uZZrdjKdvMqVnEZWqbBkzp2tRFgEgBdx7QK9K6Qrz/8kq4gbTjMRMUJNxTDPqpnlUj9Lq0cnok8u3kYCnZqSn01GSwK+/ZOGjcgJB+PiC8CEg+OMrtMQspKIhDuOwzhp3fMKfm5BCnJnMbr3aE9HPhg5qaBOBU52eaBrZMkETm4ysMaqqh36SYOp+zL57u3vUHtiJgHwSQnprk30jbiFPbJM5eH31fDfC507abLj8vyVBTSEAPePq5AqizVgoeg8Tp68c/VPBIKrvWS1tHjhzBe97zHpx//vl497vfjampKdx3332YmZkBAPzP//k/8da3vhXveMc7cMMNN2DDhg347Gc/+yKPWktLS0tLS0vr5SUdFHSGa6BT8yTLz4uAicJOzZzjASSoY6Xj8XRZlUqOia4fnoRTs0BPzVMsPweSl9thx3MsE7ZpIIjiAcE9Lg4MSEwGgGkJohYp9c5NPxcg6rCya/YenQeAxyoO7/uXD/3SEFxVcixD6fy1dyp9GxVQuiAkJvuz0OoHhSGqaimsH01DVOWYCoJWuZR9YE/NIa7PYZC46lowDLIOGBRbW0/N4c8XIHE0WqaRubfE1PYVHvQ0qPzc5444NfQb7JQW4VpekjWTeG1Ucy6mnw9qJcHmaZC7kl3jfmQAXr6Llp3fcjsfAAPpe0IZFCSUn7Nji9uxD0vYmP0ct+NUzcWzABboePLmNOXUzHNOlh00ewEavaTna946Blj6OdlO3mxUaFcQ0QdfXtATg81yGf5mIQGdKRhwnWWgntc/NorJv+3yB25aLy39wz/8w8Cfl8tl/OEf/iH+8A//8AUakZaWlpaWlpbW/3vSTs0zXKwcTiX24mUYyO1lVy2QDl4vD3fLZdPP85fOX/z4q/Hln78BG8fy0z/Zy17RwJMiSdxFwC6gdpSNVoodD0jcmnlgaVIoWTUVvfMARflyzr7KjsVf/Iv01CyaIF6kh2CxsJ1ibse87aoFypeLJGzLAHEQYGP7y5vzkQLl5+slp6bq/Fh6MlNRp6Zq7Bx4Deg5CQy/NoZh8A8xhoUqOZbJr0lRwJ0ntkaHOcEZXFKX9ibl0N0c5x2QvZezPTUTkNUf0lc0fW9l7z/u1OwGA52aclCQ8oOVigP5MT6oZygrC89z2or3xCAnMQkKypafMyjKSsHz3LHTNLV8gQJ3PwdGDnNqAmI5v8/7pqogP5vPrp/Me375eeJElY83xqG02o3KnJqpoCCefq76MGfw2hNdyTosSEtLS0tLS0tLS2vt0lDzDNfg8nNy+QYBuCJOzZECbsBMIvSAY07WXJy3fiT350ACCHJdYkXTzwuUyBbpqSm7u6wcUAAk854XhDQhOKRyQzzqxZ2FDDrn99RM9/1T7qNgT9RU+XnONo5l8jnIKzmWnb1FStmLhlSpwK3sfM07PwColwcDNtmpqdqVDDVV16ZesqWgp4JOTcVaF3s75h2PHTPZRr0+2TbL7XzAxsQAoOxkZioCuIEEHqn60JL90J6TFGqqP3hIxjAodX6YU5MFOEVxEuiSNwfDWjsUDQpicz4IalqmkW1dMeCYTHkfMA3rqcmcmitiUJBwfcYqDgf6IoiUxz7NHZgUauakg6fK/nMAqRgGFUT581lJOTVp+rm0mbgv5gyVwbSYXg8gA3c3K3pqsnEV6ZUsn58ppNfrvppaWlpaWlpaWlpaa5eGmme4BqWfM6hm5rg0gXQPyDxIVaSvYbUAGDwZsZe9PJdREXceUMypWZbgY0lVslrQGQokL8blAsE9eeAsWy49AMLRseX31BTclXmwa1iPS9W+BvRNTfpS5oDyzPEKOIkL9lct1FNzQH9Htr+iLmHVUpiquak1knedNwpuzSJQOm9c60aKOVFTrSRy73dy/sPKzwHgd991OX77+y/B1smq8udFoDRwEk7NAeXnjmVmnkODnIxM8n3j2ia/32cbBMTlrc/xIfcyA63NIX0+2bhXuoPnXH4uqI9Z7NkofnAyKCio7YVJUrzwbDRNQ9l/VB47c4QutjyEUcxbXMjPDzaepVb+vlKQuED5eVdMP884NQWomecMFY4Xx3EG7rIWKnPNPgeeLFxKtWbk56zqgy/2b5Z2amppaWlpaWlpaWmtXRpqnuEqkn4+yDUplo0X6SFYtK9hnvOuqBhoXGtPzXoBp1i2/Dy73ahUAjxoTocFIaWcmnnw4qQg3GCnZtmxuHMyDxrJqdh50FaEOHmgJzWmgu7KtZQvFyllPxnnK3NfzUigkB9P7qmp2MY0jRRozDveBqEFw1rKz2WoWajH5ZDj8RLuAXP1mnOn8UNXb8v9eZEPRMRjDgsGY9Av7/kiuxSV0E8OClJsw+7RucZgp+bwoKAkDIq7TJU9NWWnpvp+kOG8ajt5Doqkn+c5WtnnYWweZPjJ5mmplV8yzsa80Opz1ySQXX8qp6bcE1V0TrLyc9U6Fntq8rCnjFMzAZaq8npyvCS9PhCALPvga7Lm8t+ZXWXl9QPSzwu4ytla0E5NLS0tLS0tLS0trbVLQ80zXEXKz/OSzwGg4iaXuBhYGu6oAwYDryIa1lMzW75cpKdm0fLz4e6uQdD2rZduxM6ZGi7bMq78udhTswhwAAaDpXdfuQWXbB7Dq3dM5m7DgEFR92F+ue3wnppAAv6KrKlB+6qmQqoGOwv5vnJ6FhZptQAAv/LmC/DHP3wFbnrFeuXPRyT3b97ttb6AC3ODECiUHxSUXguqe2udXO5e4Drn94ksVspeREUgKgDsnK4DALZMqPvsZnpOFgTAqnkYVn4OJC7FuWaPbpMfnMWPpRhT2TH577JkemX6OXNqDig/B9JOTddWh3DJ0LbQvnLK3dn5sXmQ2wOw55To1JSPxz5QWGh5KagpP7ML9dQUQKQ/IMSp4iQp4xxqFnBqZoOCEojKXJpAMg+GYfAPQY6skPA3ln6ubmNSBGrS8nNPQ00tLS0tLS0tLS2ttUqnn5/hGpx+Tp2aAwAje/kDigGvwkFBA5yMRcReGnMdZwWdmkWCkGSnpmrsRdPPAeDnbzwPP3/jebk/L9JTc7Rsw7GM3FANUT9+/Vn48evPyv05QODL8dVevhOuXAxKp6Fm/hyw61O0B2teuXuR9OxM+MaActsjNKV40Nin6iW8+eINuT8XnZqDlvn6kQQ0rsWpOS47NRVAXU5bL+KozoWoBZyhRVXkAxEA2DFdw5d//rXYIMFZJrYOVruDnYwZqFmg/Fy1DXdqNgc7NW2LlKo3+4FyPg3DwGjZwWLb40E5SqgplRwXKhnPe3ZIc5D3LB4tO7BMA2EU5/YyHa84WOn4/Dkk95xkH9AstT1EFOZlnZpJT01Wmg3kBwUttT3+b1d+UFDAn9vqnprke62+z92V8pJha7PVCziwzD1ez0df+ABRPOZ0vYTnFtq8XUNSfp4dV6b8XNHXlj07W/38Dyy1tLS0tLS0tLS0tIpJOzXPcG0cU0MAIHGBDHZqCj01c9yHVdfibrQ8WGKZRgoOrrX8nAcFFSxfLhI0U6Snpmup3U9Zp+apQ9siwT2GYQwtDz0Z8Z6FBSFjXr/MsVRQUP6YRrhTs2D5eQGnbeF95Wwnlu6eLlAXxfnbbUg5NdXnV6SnZiYoSAH0puullGO00AcUeW7HAmE0RVW0/BwALtgwmnGJMmWcmnnl50OSzYFiTk0G6+YpiBz0PGPzlQcGGRhbZD1KB6TX83HnreECz4RMCX7OdmJPTLnMm0m+HrKzcLKegEgetiMHBbHy82bi1DSN7POYOzU7Hi8Hl+dhlLsrh/XUpD1Yu4nbUZ4GNk/Nvp/r1BwT0tY9wRlqCmNnYVnLdG2yICTVMy0TFKRYM+we7ejycy0tLS0tLS0tLa01S0PNM1Sf/bnrcOOF6/GJ97wydxsG68xB/R9FoJcDXgzDQN0dDKmAtKtureXnPD17SJgJU276uQBV8sBEpYCbM5N+vgaoOVkrVsI9VTs9EA5IEp2LlCUDg5yagst0wBywec8bt2OZKYCQ69RMlZ/n74v9TAVLmMQE9LXMpzimcADVXJcqLVdDIxF8Fk0/V43dscxUW4MiHwbkOSfHK2mQtZbQr3Ta+tpBMncy5jyrRJeiZRrKtVBzrZTDVjUuBvNmGz26Tf4cDAs5YiCOlZ+rYLIMD/POT3Rq5s3nybTKYPdE0bWXKT8XnJp5JeOsP3DXD9HoEfCnut/ZsaKYlKqr9pWUnwdC+bnCqemwcn6Pf29Q+flwp2aAvq9OSGdgeKWdDkJSOjVPoqemDgrS0tLS0tLS0tLSWrs01DxD9aptE/izH7sSO6ZrudsU6alZLeDUBJIXrUFgQixBX3tPzcFhOxmnZoHejXnuylSvxTz3U6XY8YpIdGoOuDRpgLFGqMncRGstBy/q1BwWFASk4UseOCs7JgdQg0DkyBCICsiQ+NSv36APCUSJpdR5kGpjCmqqwedYQci4LnW8Uy/nz4CsNcDIkm1ysLgWkCz37M1v2zA4jRwgH9KIH1Kozo998NDxiGNw0LORf2CQCxnJvpjrU1U2XgRcA+kAsVynZqZ9QP56fe/1O3D9OVN49VmTyp/LrQjkORVLxhnUlMdecy0+x8dXe8r9AGT9D+s5qQ4Kyp4fWy8N2q7AMLKtIth1SffUlIKJylmnpjzv40LAUapnqDLEqUD5OQ8K0uXnWlpaWlpaWlpaWmuVhpovYfGemgMgjFh6PTDNujwcUhUFpEU0LCio6hRzaopQLO/8igTIyE7NtZyfCAa7A4Ke5FCQtWjsJJ2aeS7Mkw4KGuBwK1KabBhG0p9zUA/PAhB16jQ5NYtq/ejwnpobR5OemnGsdn3Kpcmq9ghAOgF9bUFBp6+nJrl+g1tJFFHRFgPDgnuY0kA9u91ETQbJw8vPhzknWc9FpTO0YB/TYeE+gCr9PH/sP/Dqbfjbn7wms8b4uOTyc+m5m4KaobqnpmEYvAT9BIWaec/imfrg3rCqoCB1T03q1BQS5+XbZkQoZefl7jkQdbXr81C+TAI8vf4rHZ+HBAHqFgnZnpqq9gjJuLS0tLS0tLS0tLS01iYNNV/CKtvDoaZYej0IOhSBRilX5POcfm5bJneiAvkuTBGK5YFI8cUy7/xGSnbqpXgtPTVFh2Orl19imA4UWtt8MudZXt+/smPCRPJCnuvUrIpQcwAEL9CuoIiLFgCqpcGuXUAodx+wzXQBl9vplAg1cwGU4ABe6qghRtGxpqBmDkwuUg4uA661AmD2gcBa1nB2TKeWRi6PKW+7qQzUHFB+To+Z52gtAjVld2VuT80C5eeyq3wt12+Ya1fl1FSNi42bQc0ifW+B7DyMcOdkUKinJuvBqppPdl38MEaDPoezPTzJ8YIo5vvKQs2kF2gwxKlZcazUv8eq5zFbwwzIamlpaWlpaWlpaWmdujTUfAmLvTANhJopp2aB8t4Bzruq6AJbo1OTlVlO5ASHAOnehoPOkQGvPNBqmsbQHp6mmfQVHXa8k9GgvmmjQ8DLyeiWizfixgvX4T1XbVP+3DAMiAHoeZBRdJT5YX4/SeZIKtLSABjWq7UIILXoNoNSzU+fU3NQ2wAmMZE8ynFhiq7L5ban3Kao0hBVXcpeKP1cuufWuvZq/Nqc+n7WS6noueXnItRcQ+uDyVoarg0a+zqaci87JJNjJWAsb19lx0p9SJO3jkXYyvpAypLB2elqlQEMgZohcTKqzo89z4/THqV5TvDpkcFrj5efC05N1fEYvGc9PFXzWXOTD6pYv1MZMlbdZC7ZNtny8yQoSHwmqp6hhmGkPlhQlZ/zHp2dtT0PtLS0tLS0tLS0tLQAe/gmWmeqSgWcmpZpwLVNeEE02HnHnZpqWAKAl5kOO2YR/ei12zFZc/HWSzfmH69k80ThQfBs50wNs80eNo9XcrepOBZ6fjS0ZLVJIeRanahMeWCCHY9prRBu62QVf/Zjrx64TckCOrQaPu/8xBYD7QFA9rXnzuDsmRrecumG3G1GCjp7a4VCqob3fE2nn6/t+lUci/dbzNNI2cFVZ01iue0NXHtMiwOgJrtHB0kMJso7v5JtwjYNBFGcu82wPoonqyIu2mESATGw9vJzsbeh0qlZT8O1Qevzh67ehhgx3n3lVuXP5dYOuSn3FRcn/MFORrFdSF55smEYGC3bPI379Do11eXnyx0vN0EcyDo18z5AK1p+3vFC3rpDtY7ZuNlnCao5N00CGJu9AAu036m81g3DwFjFwVLbS3qiykFBtQRCBjT53DaN3DYRI2Ubq12161Mc+0qOc1tLS0tLS0tLS0tLq7g01HwJi5WWDwoKAsiL4kKrnwr6kVUr4tR0B7ufTkbjVRc/fM32gdtUC0LUP/6RKzDf7GPrZDV3m4pjYRn+QAAwWnFwjL6Uny6n5iAVCT05nUo7NfN7XDK1vXyoec66Ou74xdcNPF5xp+ZJlJ8X7Km51vksAjUB4NM/fQ2iePB6ee250/jm3gX8wJVbcrepl2wsBYOdW+ny8wE9SksEqhTtqblWp2aR0Khhyjo1C5SfD3RqDt5OLj8ftK+ZkRJ+/sbzBhxLgpoD5v0EczIWmPNGd4DLu+IIUPPUn1XjUqiZvC8GNf0wxlI7v9SbOzV5+XmOU1OAmoaRdTuKc7lEE9JV67hoX9iJqotmL+Dl53LPUIAA8DTUVPfUXG57CKhTc2B/6pRTM3/sy9qpqaWlpaWlpaWlpbVmaaj5EtblW8dx0aZRvGWA2xEAPvS9F+HZuRZ2TOVDP5bSLDtpRLESYGuAS+V0qpIKJso/3mjZyS0NZSq7w0tk0z0gn3/IKL7AF03cXotE9lIEhHQLQL1BqhdIPweAq3ZM4qFDK3jFxtHcbUYKgLPJFKBZ23yWFfBDJcMwMGwq/+RHrsATRxu4YvtE7ja1koWl9uD9rCsQTASQdbza9QemdRtG4nJbq0s4cdqe+pxP1VyYBsByWPLuP7Gf5FqcmqNlB5ZpIIyGQ6phkp89RVLLiwDgQSFj4jHX5NQUxlSyzcx9U3YsVF0C+E80uvR42blikHi2MaSnphR2JR/PtkzUXAttL8Riu5+7L5ZIL+5LpYmqg0NLg7dj12U+t/ycHKvRC3jg0KD2K+y57irmk4yJ7G9V99TU0tLS0tLS0tLSWrM01HwJa6zi4Iv/8bVDtxsGPQHgp27YiXPW1XHTK9bnbsN6ar4QLkYg7XKxTkM5MTAYJlROY3l9Ee2cqT/vxxBVtmIA5LwG9VdlOl0OPmAwJL715vPxc68/ZyBILOLUZK4yw8gGz5ysxLWwVlVdG1edNTlwm9oAFzVTEacmMLzHpWUaGC07HKqsvafm8GszTLZlYqpe4m65PCfjKQUFKcZlmgYmqi7vo7iWsRd2alZEELm254sId9fm1BwOWieqLjpeFydWGfTL3hszdG2ysKS8+ZwpEOY1UnYI1OROzez5lR0z1bLBzWu1UKB/LAPEeU5N8bot0DENguBs7eUFS4nl53EcvyAfEGppaWlpaWlpaWm9XKWDgrQAEFjwb165OVUyLIv11MwLgTjdEiHXWtLIgQRqDiqvF8HSWo/30zfsBADceGE+JD5/wwg+9s5L8cn3Du6FebpUJCgIAD72zkvxqm3jeP8bzlnT8eoFe2oCw52RCTjL349tmXjiQ2/CI79285pBXaWgU/N0Se7LqNKMADUHlcYX6T86XjDlvoi20bYPzO19qhL7auZBqqItGxhoNIz8DyimT1OwVF2GmkNg1lqPBwAjpdPk1BSgn6o0G0jaOuQF6QDZwKG8+110auZBPwZsFwekyRuGkYKNeXM+MSTdXTxe3vnZlsnX0xwFn4M+FGL3siokiIyJzFUQxQOD5LS0tLS0tLS0tLS0hks7NbUKi/XULOLyOx1KOTXXCjULlJ8X7eFZRL908/m49uwpXLVjsEMvL3zk+ZAINQfNw7uv3HpaxlW0p2YR1QsmbBeBg0X0QkPNQR8mMJVsC6/eMYFjKz1csGEkd7t6AQA8XnFwkP59rf1H//337MSrd0zgyiFrfZjWj5TxBBoA8p29VdfiQUhFnJqqEmemyZoINU/9fh8pWH4uAsRB63hDJcaJrjHwGSQ6NdfyPB4p2bzsPw8yysBSdX6ZHpc5168ISJbL+QeFRjHImNtTszY43Z3tB0iApQpGTtZIb07uJB5wbRgAzZvPsmOhZJvoBxFWOn5m/WhpaWlpaWlpaWlpFZeGmlqFxWDJWl2MRSW+XK61xyVzAg6CCUV7eBaRa5t4/fnr1rSP062S6NQ8TenugzRyGqFmEffh6VT5NJafF1GtVOx4n/7pa4cCvSJp8mMUVDnW2vvjlh0L150zvaZ9AFLP0AFBSGMVB4ttb+D58b6GBdoVAGt7vhQtPx8rWH7+784PcV9vI973hnNzt0n31Dz162eaBsarLpbaXi6EEx3CgNpFK/e4zHPEi0FBfVo6Lmu0UgxqpkrnCwJZFbBksJmlkavDfVwcXOxwqDnQqcmgppO/zUTVxYlGD8sdb2DAnZaWlpaWlpaWlpbWYOnyc63CqlLw8kIAMSD9crlWjlqkp+bpdGqeiSI9NYkGBV2cLtVKp6+c/4rtE6iXbFyzc2qtwyp2vG35oT7Ph15V8HimaQwFu8ydNsi1ykp31wqbT6eKlJ8DCfTKg3BAOqwlT2ICepE08txjSfNcpB/ooHlfVwH++N++cuCaEMHfWu/lcT6farC+abyS+lo1p2OSUzMPEottJvKCckYlSJwHbccqw522cvm5auyTBZyobD8J1BwcXAfkzyeQ7quppaWlpaWlpaWlpXXq0k5NrcJiDrAXIhkcSL8Ar9VNxntqDngZrQg9NV+O4Q3lF9ipmU4/X9uauWDDKB799ZtfMNj8M6/bCcMAbjhnEgcfuft5P96PX7cD/SDCa89du+Pxp2/YiYmqg+9/5ebcbRhge6Gcr0W0XnBqDnrGjBYYO3PfDerVOiW4BtfSJzhTfl7AWbjmnprivbXGa8jGlecs3Dye7pXqWtk5HS3bqTT5IufHtpW1YUyCqEWcmrnbDC8/z5aoZ8+POT5ZQvogkJz01Bze03a54+Vuo6WlpaWlpaWlpaU1XBpqahXWsF5hp1un8zistHxw2e4LW3L8QusFh5pCSfXpON4L6Z4t2Rb+4xvPhe/7OPjI83882zLxvtevLZiJ6azpGn75zRcM3IZBlTPVqTkI1HEgO2DsF28axTuv2IIrtue7HdM9NU99HsqOyft8AgNSryunxxkKpMvP1+qCZuAvb9ybx9Pl0aoPhlhbgCT9/NTHtGNKOl7ufA6HxJO14S7MyZoUJqSAu+Mn4dQs8u8kg6R5blUtLS0tLS0tLS0trWLSUFOrsC7fOo4fuHIrrj37hSkBHpaIfSr7KhoU9HJUKijoBXDb1kunr0RW6/SqCBh8obVuROipObC8d3hpuW2Z+Pi7Lht4PLH8fC3Q3TAMjJRtLNNS4rxxieXng8rri2i0YCl7EY1z52te+bnk1BwAGRnUXEt40fapWurrtfTUlAOMiiS3q+4J7tQs0FPztefO4Kodk3jPVdtyt+FOzbaGmlpaWlpaWlpaWlprkYaaWoVlWyY++s5LX7DjnU6n5uVbx2AawKVbxnK3qbov79vhRS0/zwkO0XpxxNx5Z1L5+TrBqWkOaP9wukrnRRffWuFuXYCaRSDcWkEkA7umsXYH8zCnZpGemkC6r+YgKD1SttHsBbk/3zE93BlKjjfcaSsCS9NQu1plN6fKqcl6ai60hqefz4yU8JmfuTb354AQTtTV5edaWlpaWlpaWv8/e/cd5kS1/gH8m2RTthd22UIHERQREAWBq4giRURQbNiw4VXBAl5/glcF9F5Rr125evUqeBUbqNhQ6SiyICC9l13q9mV7Szm/P5KZzCQz2Wxhswvfz/PsA5uczJw5mWQnb95zXqKGaD6faAOYM2cOOnbsCJvNhv79++OPP/7Qbbtz506MGzcOHTt2hMFgwOuvv950HaVG1ZiZmiPOS8X2mcNx00X62TM9AwQ8Twe2MEWhoCbI0IsNN8PkKWzTnKY5kyI7rxk9L60ivUHNQNNy+3gK6PRIi2nY/pRrajZ0jUtr7VmDsY26pmbjLR8gr6mp02+b2YTEKG/gT396fXCZ2TE+a5D6So62qfahu16mMvNV50sTZVDTEmbUXCs5mDU1fdfmbOiXQlLfWSiIiIiIiKhhms8nWh1ffPEFpk6dihkzZuDPP/9Er169MHz4cOTm5mq2r6ioQOfOnfHCCy8gJSWliXtLjUmvcEV9RQaoBg0AZydHY/69/bF0yqWNut/mwqrM1GyC9SmjrGF4/abeePPm3gxqNjMXdUrA+W1jMa6vfjGhpqbMOGzjkx2odH3fttg2cxiuu6Btg/bXyieY1RDKwj16Qbhoa5h8jA19PbSJC4c1zIi28frjFKwBXVoh0mIKuKyIMlszmKzIQJnZjw/vBgAYp/P8GY0GdFCsq9mQzNdwiwk2z98RvWrk0dYw1fuh9rqb6nOloc+fNFZFLBRERERERNQgzX6+7auvvoqJEyfirrvuAgC8++67+PHHH/Hhhx9i2rRpfu0vuugiXHTRRQCgeT+1HFeem4xnvt2Js1pHNdk+B53V8OrTzZVy+nlTFd0Z3SutSfZDdRMbbsZ3k/8S6m74+fHhv+BwQQXOaxM4a7q2bL9gKNe4LK1uWMacKqipk8loMBgQ45mmrpdZGKzYCDOWTLm01i9qgnFRxwRsmzk84HtCm7hwbDtWDCC4TNRA1evH9E5Dr3ZxaJ8QodumQ6tI7Mspc29LJytSWXjJYjICTu1txUdYkFVcFfB5iY+0yOtlamWitoryydRs8JR/qfo5MzWJiIiIiBqiWQc1a2pqsGnTJkyfPl2+zWg0YujQoUhPT2+0/VRXV6O6ulr+vaSkBABgt9tht5+6Dx3Stk/lPlqyxIgwrJ92GaKsYaox4rjVnd1uR5wF6N8xDonRNjgc+mvakRfPtfqp77idnRSBs5Mimny8i8urG7TPSEWRMYNw6m4rNtyMkxV2GITQbFOXcUuLsQTdNhgunaAgACRHe4N6BuHS3Ge0IhXcZNA+PknbWAtcTofuPtspihPp7U+ZPGkyuJfW0GoXG252BzVNBt0+xYeb5aCmCf59j7OpszxNhoaNe5TFHTg9WV5zys91vncRERER0emsWQc18/Pz4XQ6kZycrLo9OTkZe/bsabT9zJ49G7NmzfK7fcmSJYiI0M8maSxLly495fs4HXHc6sZoAG5JzQcALF58LMS9aVl4rtVPcx+33q2M2FtkgDl7BxYv3lHv7RRmGyGt5rJm9SrEWbXb9Y02IMxuQO7u9Vi8T397zW3cCo4bALgDe6uWL0O4xpXD8Sxvm8OZGVi8+GC991es2NbqFcsRqZGYW+kApEuYI5mHcE5b7XFzVrifG3tVJRYvXqy5P1eV9/nbuW0Lwo5vVt8vACNMcMGdoZmfm6O7rWBkV7j7nldS3qDtBKOiouKUbp+IiIiIKJSadVCzqUyfPh1Tp06Vfy8pKUG7du0wbNgwxMQ0rBhFIHa7HUuXLsWVV14Js7nh0ynPFBy3uuOY1Q/HrX5ayriNHClQ4xS6xW+CtWfpfvyWkwEAGDFsqN8ajJKratlOcx23qs3H8f2RnQCAq0cOh1WjiJtjaxa+ytwOAOje9SxcdcVZ9d6fZXcuvsrcAgAYOWIYojSm2Qsh8PdNy+B0CZzT7WygfK/muP1SuhX7d+QgITYaV101UHN/P5dsxYGdOQCAAf0vxGVnJ/m1eWHnauR4sjnbtEnDVVedX+/jyyutxuytq1HpNGD4iJGndDkQaeYJEREREdHpqFkHNRMTE2EymZCTk6O6PScnp1GLAFmtVlit/qk1ZrO5ST5YNtV+Tjcct7rjmNUPx61+WsK4NUa5oJgI79+PyHArzOaG/WltbuOWEhcp/z/CZoVRIwiXGOMtJmSzhDWo/x2TohX7s8CsU+RHWqPUZgkDyrXHLcFT5d4aoE+tor3PX4TVotkuKcYqBzWtYaYGHV9ijPt4hACqnECc9dQ9183pPCIiIiIiamzNuiSxxWJB3759sXz5cvk2l8uF5cuXY8CAASHsGRERkZuyUJBZp7BNS3a+onCTVkATAOIUhZfCGlgdvHtKNEadn4pxF7TVrVoOAHGeKuJ6FecBb6Vxa4A2CYrK7XpZu4lR3sCnXvGiYFnCjDi/bSwu6hiPGoerQdsiIiIiIjqTNeugJgBMnToV77//Pj766CPs3r0bDzzwAMrLy+Vq6HfccYeqkFBNTQ22bNmCLVu2oKamBsePH8eWLVtw4MCBUB0CERGdxlTVzxsY0GuO4iMt+O3/huCPJ6/QbROnqn7esKCfwWDAnFsuwCs39grYTqpgbw4w5nLgM8ASA/GRyqCmdhA1SRXUbPhz/N3kv2DB/QPROsZWe2NqlmbPno2LLroI0dHRaN26NcaOHYu9e/eq2lx22WUwGAyqn/vvvz9EPSYiIiI6/TTr6ecAcNNNNyEvLw/PPPMMsrOz0bt3b/z8889y8aAjR47AaPR+wDhx4gT69Okj//7yyy/j5ZdfxuDBg7Fq1aqm7j4REZ3mpKCmxWSEwXD6ZWoCQLuEwEXz4sIbYyJ/3ZzVOgpbjhahbbwNJ3O025ydHAUgcP+Va6DqBT+TFFPUzadwDUxqOVavXo1JkybhoosugsPhwJNPPolhw4Zh165diIz0LtkwceJEPPvss/LvTVGAkoiIiOhM0eyDmgAwefJkTJ48WfM+30Blx44dIYRogl4REREB0TZ3xmCgbMDTnTJbtbza2ST7fHZMD0wY0BHdWofjpz3abf5yViJ+euQSdEqM1G4A7xR1QH/6uTKo2RiZmtTy/fzzz6rf582bh9atW2PTpk249NJL5dsjIiIadR14IiIiIvJqEUFNIiKi5krK9Iu06q//eLpTrrVZXuNokn1GWMLQs20s7Ha7bhuDwYBzUmMCbkeZqWk1BxPUZKYm+SsuLgYAJCQkqG6fP38+PvnkE6SkpGD06NF4+umndbM1q6urUV1dLf8uVa+32+0Bz/OWQjqG0+FYQoVj2DAcv4YJ1fgZm9l3icJQvw5Jj6vv4093RqP2eSWdbnz9Nkwox+9U75NBTSIiogbonBiJaSO7o3OAbMAzSVl10wQ1G4tyTU29NVGVhYLMze3TFYWcy+XCo48+ikGDBuG8886Tb7/lllvQoUMHpKWlYdu2bXjiiSewd+9efP3115rbmT17NmbNmuV3+5IlS06raetLly4NdRdaPI5hw3D8Gqapx69X4CW2m1wJGtah0rY9G6knp5de7RZr3r7Y52a+fhsmFONXUVFxSrfPoCYREVEDGAwG3D+4S6i70WzoTeFurlpFWmA2GSCEO/tTCzM1KZBJkyZhx44dWLNmjer2++67T/5/z549kZqaiiuuuAIHDx5Ely7+7xnTp0/H1KlT5d9LSkrQrl07DBs2DDExgTOOWwK73Y6lS5fiyiuvhNlsrv0B5Idj2DAcv4YJ1fjNnt1kuwrKJQn165AwGFHatieij22HQbgauVenr7/8xf2v3WXE0qM9cWW77TAbG2n8ekyvvc1pIpTvf9LMk1OFQU0iIiJqsJdv6IUvNxzFpCFnhbordWIzm/DmzX3gcAmEW3SqnyuCmk4X1+0mr8mTJ+OHH37Ar7/+irZt2wZs279/fwDAgQMHNIOaVqsVVqvV73az2XxaBWBOt+MJBY5hw3D8Gqapx8/VzOJ/DQ1IGoSLQc068F0ZyGx0NV5Q8wx8HwjF+9+p3h+DmkRERNRg1/dti+v7Bg7qNFcje6YGvD/a6r1cKqrgWk4ECCHw0EMP4ZtvvsGqVavQqVOnWh+zZcsWAEBqauDzjYiIiIiCw6AmERERUQAGg3fK+cmKmhD2hJqLSZMm4dNPP8W3336L6OhoZGdnAwBiY2MRHh6OgwcP4tNPP8VVV12FVq1aYdu2bZgyZQouvfRSnH/++SHuPREREdHpgUFNIiIioiAxU5MA4J133gEAXHbZZarb586dizvvvBMWiwXLli3D66+/jvLycrRr1w7jxo3DU089FYLeEhEREZ2eGNQkIiIiqkVyjBU5JdW49OzEUHeFmgEhAq+t2q5dO6xevbqJekNERER0ZmJQk4iIiKgW30/+C9IPFWDkeVwPkYiIiIioOWBQk4iIiKgWrWNsGNO7Tai7QUREREREHsZQd4CIiIiIiIiIiIioLhjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqUcJC3QEiIiIiIiIiIqIms21m0+/z/BDs8zTHTE0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqURjUJCIiIiIiIiIiohaFQU0iIiIiIiIiIiJqUcJC3YFgzJkzB//617+QnZ2NXr164a233kK/fv102y9YsABPP/00MjMz0bVrV7z44ou46qqrmrDHRERERER1v44lIiIit1Wr3f8KA4B2wJo1gEGEtEsNsurruj9m5swG7nTbTMBlBNAL2DkbMLoauME6Kqs+pZtv9pmaX3zxBaZOnYoZM2bgzz//RK9evTB8+HDk5uZqtl+7di3Gjx+Pe+65B5s3b8bYsWMxduxY7Nixo4l7TkRERERnsrpexxIRERFR8Jp9puarr76KiRMn4q677gIAvPvuu/jxxx/x4YcfYtq0aX7t33jjDYwYMQKPP/44AOC5557D0qVL8fbbb+Pdd99t0r4TERER0ZmrrtexREREdPq6rNXMOj9m1VsN328oM13LK0/t9pt1ULOmpgabNm3C9OnT5duMRiOGDh2K9PR0zcekp6dj6tSpqtuGDx+ORYsW6e6nuroa1dXelNji4mIAQGFhIex2ewOOIDC73Y6KigoUFBTAbDafsv2cbjhudccxqx+OW/1w3OqH41Y/HDd9paWlAAAhWvA8rRasrtexoboebSp8rTYcx7BhOH4NE6rxq6lpsl0Fpayqfh0SBiMqKipgrKqBQTTx9N/TAMevYUI5fhXV7tfMqboebdZBzfz8fDidTiQnJ6tuT05Oxp49ezQfk52drdk+Oztbdz+zZ8/GrFmz/G7v1KlTPXpNRERE1HyUlpYiNjY21N0449T1OpbXo0RERHS6OlXXo806qNlUpk+frsrudLlcKCwsRKtWrWAwGE7ZfktKStCuXTscPXoUMTExp2w/pxuOW91xzOqH41Y/HLf64bjVD8dNnxACpaWlSEtLC3VXKAihuh5tKnytNhzHsGE4fg3D8WsYjl/DcPwaJpTjd6qvR5t1UDMxMREmkwk5OTmq23NycpCSkqL5mJSUlDq1BwCr1Qqr1aq6LS4urn6droeYmBi+MOuB41Z3HLP64bjVD8etfjhu9cNx08YMzdCp63VsqK9Hmwpfqw3HMWwYjl/DcPwahuPXMBy/hgnV+J3K69FmXf3cYrGgb9++WL58uXyby+XC8uXLMWDAAM3HDBgwQNUeAJYuXarbnoiIiIiosdXnOpaIiIiIgtesMzUBYOrUqZgwYQIuvPBC9OvXD6+//jrKy8vlKpJ33HEH2rRpg9mzZwMAHnnkEQwePBivvPIKRo0ahc8//xwbN27Ee++9F8rDICIiIqIzTG3XsURERERUf80+qHnTTTchLy8PzzzzDLKzs9G7d2/8/PPP8qLrR44cgdHoTTgdOHAgPv30Uzz11FN48skn0bVrVyxatAjnnXdeqA5Bl9VqxYwZM/ymGlFgHLe645jVD8etfjhu9cNxqx+OGzVntV3Hnkn4Wm04jmHDcPwahuPXMBy/huH4NczpPH4GcarqqhMRERERERERERGdAs16TU0iIiIiIiIiIiIiXwxqEhERERERERERUYvCoCYRERERERERERG1KAxqEhERERERERERUYvCoGaIzJkzBx07doTNZkP//v3xxx9/hLpLzcrMmTNhMBhUP927d5fvr6qqwqRJk9CqVStERUVh3LhxyMnJCWGPQ+PXX3/F6NGjkZaWBoPBgEWLFqnuF0LgmWeeQWpqKsLDwzF06FDs379f1aawsBC33norYmJiEBcXh3vuuQdlZWVNeBRNr7Zxu/POO/3OvxEjRqjanGnjNnv2bFx00UWIjo5G69atMXbsWOzdu1fVJpjX5ZEjRzBq1ChERESgdevWePzxx+FwOJryUJpUMON22WWX+Z1v999/v6rNmTZu77zzDs4//3zExMQgJiYGAwYMwE8//STfz3ONqGWo79/K9PR0XH755YiMjERMTAwuvfRSVFZWNkGPm5eGXGsIITBy5EjN65wzRV3Hr7CwEA899BC6deuG8PBwtG/fHg8//DCKi4ubsNehVdfPqAsWLED37t1hs9nQs2dPLF68uIl62jzVZfzef/99XHLJJYiPj0d8fDyGDh16xscE6hsj+fzzz2EwGDB27NhT28Fmrq7jV1RUhEmTJiE1NRVWqxVnn312i3wNM6gZAl988QWmTp2KGTNm4M8//0SvXr0wfPhw5ObmhrprzUqPHj2QlZUl/6xZs0a+b8qUKfj++++xYMECrF69GidOnMB1110Xwt6GRnl5OXr16oU5c+Zo3v/SSy/hzTffxLvvvov169cjMjISw4cPR1VVldzm1ltvxc6dO7F06VL88MMP+PXXX3Hfffc11SGERG3jBgAjRoxQnX+fffaZ6v4zbdxWr16NSZMmYd26dVi6dCnsdjuGDRuG8vJyuU1tr0un04lRo0ahpqYGa9euxUcffYR58+bhmWeeCcUhNYlgxg0AJk6cqDrfXnrpJfm+M3Hc2rZtixdeeAGbNm3Cxo0bcfnll2PMmDHYuXMnAJ5rRC1Fff5WpqenY8SIERg2bBj++OMPbNiwAZMnT4bReOZ9bGnItcbrr78Og8FwinvYvNV1/E6cOIETJ07g5Zdfxo4dOzBv3jz8/PPPuOeee5qw16FT18+oa9euxfjx43HPPfdg8+bNGDt2LMaOHYsdO3Y0cc+bh7qO36pVqzB+/HisXLkS6enpaNeuHYYNG4bjx483cc+bh/rGSDIzM/G3v/0Nl1xySRP1tHmq6/jV1NTgyiuvRGZmJhYuXIi9e/fi/fffR5s2bZq4541AUJPr16+fmDRpkvy70+kUaWlpYvbs2SHsVfMyY8YM0atXL837ioqKhNlsFgsWLJBv2717twAg0tPTm6iHzQ8A8c0338i/u1wukZKSIv71r3/JtxUVFQmr1So+++wzIYQQu3btEgDEhg0b5DY//fSTMBgM4vjx403W91DyHTchhJgwYYIYM2aM7mM4bkLk5uYKAGL16tVCiOBel4sXLxZGo1FkZ2fLbd555x0RExMjqqurm/YAQsR33IQQYvDgweKRRx7RfQzHzS0+Pl7897//5blG1ELU929l//79xVNPPdUUXWzWGnKtsXnzZtGmTRuRlZWleZ1zJmisa7Uvv/xSWCwWYbfbT0U3m5W6fka98cYbxahRo1S39e/fX/z1r389pf1srhr6Gd/hcIjo6Gjx0UcfnaouNmv1GT+HwyEGDhwo/vvf/9b6+e10V9fxe+edd0Tnzp1FTU1NU3XxlDnzvvIMsZqaGmzatAlDhw6VbzMajRg6dCjS09ND2LPmZ//+/UhLS0Pnzp1x66234siRIwCATZs2wW63q8awe/fuaN++PcdQISMjA9nZ2apxio2NRf/+/eVxSk9PR1xcHC688EK5zdChQ2E0GrF+/fom73NzsmrVKrRu3RrdunXDAw88gIKCAvk+jhvkqVgJCQkAgntdpqeno2fPnkhOTpbbDB8+HCUlJXIG3unOd9wk8+fPR2JiIs477zxMnz4dFRUV8n1n+rg5nU58/vnnKC8vx4ABA3iuEbUQ9flbmZubi/Xr16N169YYOHAgkpOTMXjwYNVsnTNFfa81KioqcMstt2DOnDlISUlpiq42S411rVZcXIyYmBiEhYWdim42G/X5jJqenq5qD7j/1p6Jn8ca4zN+RUUF7Ha73zXimaC+4/fss8+idevWZ0w2tZ76jN93332HAQMGYNKkSUhOTsZ5552H559/Hk6ns6m63WhO73fnZig/Px9Op1P1QQsAkpOTsWfPnhD1qvnp378/5s2bh27duiErKwuzZs3CJZdcgh07diA7OxsWiwVxcXGqxyQnJyM7Ozs0HW6GpLHQOtek+7Kzs9G6dWvV/WFhYUhISDijx3LEiBG47rrr0KlTJxw8eBBPPvkkRo4cifT0dJhMpjN+3FwuFx599FEMGjQI5513HgAE9brMzs7WPB+l+053WuMGALfccgs6dOiAtLQ0bNu2DU888QT27t2Lr7/+GsCZO27bt2/HgAEDUFVVhaioKHzzzTc499xzsWXLFp5rRC1Aff5WHjp0CIB7bfWXX34ZvXv3xv/+9z9cccUV2LFjB7p27XrK+91c1PdaY8qUKRg4cCDGjBlzqrvYrDXGtVp+fj6ee+6503p5IUl9PqPq/a09E//ONsZn/CeeeAJpaWl+geIzQX3Gb82aNfjggw+wZcuWJuhh81af8Tt06BBWrFiBW2+9FYsXL8aBAwfw4IMPwm63Y8aMGU3R7UbDoCY1SyNHjpT/f/7556N///7o0KEDvvzyS4SHh4ewZ3QmuPnmm+X/9+zZE+effz66dOmCVatW4Yorrghhz5qHSZMmYceOHWdk5kxD6I2b8sNSz549kZqaiiuuuAIHDx5Ely5dmrqbzUa3bt2wZcsWFBcXY+HChZgwYQJWr14d6m4RnfGmTZuGF198MWCb3bt312vbLpcLAPDXv/4Vd911FwCgT58+WL58OT788EPMnj27XtttTk7l+H333XdYsWIFNm/eXK/HtwSncvyUSkpKMGrUKJx77rmYOXNmg7dHFMgLL7yAzz//HKtWrYLNZgt1d5q90tJS3H777Xj//feRmJgY6u60SC6XC61bt8Z7770Hk8mEvn374vjx4/jXv/7FoCYFlpiYCJPJ5FelNScn54yeIlKbuLg4nH322Thw4ACuvPJK1NTUoKioSJWpwzFUk8YiJycHqamp8u05OTno3bu33MZ38WCHw4HCwkKOpULnzp2RmJiIAwcO4Iorrjijx23y5MnyYvtt27aVb09JSan1dZmSkuJXhU96LzxTx01L//79AQAHDhxAly5dzthxs1gsOOusswAAffv2xYYNG/DGG2/gpptu4rlGFEKPPfYY7rzzzoBtOnfuXK+/ldL1yrnnnqu6/ZxzzpGXIWrpTuX4rVixAgcPHvTLZB83bhwuueQSrFq1qgE9bx5O5fhJSktLMWLECERHR+Obb76B2WxuaLebvfp8Rk1JSeFnWo+GfMZ/+eWX8cILL2DZsmU4//zzT2U3m626jt/BgweRmZmJ0aNHy7dJX4qFhYVh7969Z1RiQH3Ov9TUVJjNZphMJvm2c845B9nZ2aipqYHFYjmlfW5MXFOziVksFvTt2xfLly+Xb3O5XFi+fDkGDBgQwp41b2VlZTh48CBSU1PRt29fmM1m1Rju3bsXR44c4RgqdOrUCSkpKapxKikpwfr16+VxGjBgAIqKirBp0ya5zYoVK+ByueTACgHHjh1DQUGB/GHrTBw3IQQmT56Mb775BitWrECnTp1U9wfzuhwwYAC2b9+u+pCxdOlSxMTE+H2APV3UNm5apGk0yvPtTBs3LS6XC9XV1TzXiEIsKSkJ3bt3D/hjsVjq9beyY8eOSEtLw969e1W379u3Dx06dDilx9VUTuX4TZs2Ddu2bcOWLVvkHwB47bXXMHfu3KY4vFPuVI4f4L5WHjZsGCwWC7777rszJmuuPp9RBwwYoGoPuP/Wnomfx+r7Gf+ll17Cc889h59//lm1/uuZpq7j1717d2zfvl31XnfNNddgyJAh2LJlC9q1a9eU3Q+5+px/gwYNwoEDB+RgMOD+W5uamtqiApoAWP08FD7//HNhtVrFvHnzxK5du8R9990n4uLiVFVaz3SPPfaYWLVqlcjIyBC///67GDp0qEhMTBS5ublCCCHuv/9+0b59e7FixQqxceNGMWDAADFgwIAQ97rplZaWis2bN4vNmzcLAOLVV18VmzdvFocPHxZCCPHCCy+IuLg48e2334pt27aJMWPGiE6dOonKykp5GyNGjBB9+vQR69evF2vWrBFdu3YV48ePD9UhNYlA41ZaWir+9re/ifT0dJGRkSGWLVsmLrjgAtG1a1dRVVUlb+NMG7cHHnhAxMbGilWrVomsrCz5p6KiQm5T2+vS4XCI8847TwwbNkxs2bJF/PzzzyIpKUlMnz49FIfUJGobtwMHDohnn31WbNy4UWRkZIhvv/1WdO7cWVx66aXyNs7EcZs2bZpYvXq1yMjIENu2bRPTpk0TBoNBLFmyRAjBc42opajtb+WxY8dEt27dxPr16+XbXnvtNRETEyMWLFgg9u/fL5566ilhs9nEgQMHQnEIIVWf8fOFM7T6uRB1H7/i4mLRv39/0bNnT3HgwAHV322HwxGqw2gytX1Gvf3228W0adPk9r///rsICwsTL7/8sti9e7eYMWOGMJvNYvv27aE6hJCq6/i98MILwmKxiIULF6rOtdLS0lAdQkjVdfx8nenVz+s6fkeOHBHR0dFi8uTJYu/eveKHH34QrVu3Fv/4xz9CdQj1xqBmiLz11luiffv2wmKxiH79+ol169aFukvNyk033SRSU1OFxWIRbdq0ETfddJPqYrayslI8+OCDIj4+XkRERIhrr71WZGVlhbDHobFy5UoBwO9nwoQJQgghXC6XePrpp0VycrKwWq3iiiuuEHv37lVto6CgQIwfP15ERUWJmJgYcdddd532f0wDjVtFRYUYNmyYSEpKEmazWXTo0EFMnDjR70uHM23ctMYLgJg7d67cJpjXZWZmphg5cqQIDw8XiYmJ4rHHHhN2u72Jj6bp1DZuR44cEZdeeqlISEgQVqtVnHXWWeLxxx8XxcXFqu2caeN29913iw4dOgiLxSKSkpLEFVdcIQc0heC5RtRS1Pa3MiMjQwAQK1euVD1u9uzZom3btiIiIkIMGDBA/Pbbb03c8+ahvuOndCYHNes6fnrXhwBERkZGaA6iiQX6jDp48GD5M4bkyy+/FGeffbawWCyiR48e4scff2ziHjcvdRm/Dh06aJ5rM2bMaPqONxN1Pf+UzvSgphB1H7+1a9eK/v37C6vVKjp37iz++c9/tsgvcAxCCHHq8kCJiIiIiIiIiIiIGhfX1CQiIiIiIiIiIqIWhUFNIiIiIiIiIiIialEY1CQiIiIiIiIiIqIWhUFNIiIiIiIiIiIialEY1CQiIiIiIiIiIqIWhUFNIiIiIiIiIiIialEY1CQiIiIiIiIiIqIWhUFNIiIiIiIiIiIialEY1CQiqieDwYBFixbV2u7pp5/Gfffd16j7XrVqFQwGA4qKihp1u7XZtWsX2rZti/Ly8ibdLxEREREREZESg5pE1CLdeeedMBgMfj8jRowIdddUsrOz8cYbb+Dvf/+7fFteXh4eeOABtG/fHlarFSkpKRg+fDh+//33EPbU32WXXYZHH31Uddu5556Liy++GK+++mpoOkVEREREfu68806MHTs21N0gImpSYaHuABFRfY0YMQJz585V3Wa1WkPUG23//e9/MXDgQHTo0EG+bdy4caipqcFHH32Ezp07IycnB8uXL0dBQUEIexq8u+66CxMnTsT06dMRFsY/I0RERERERNT0mKlJRC2WlOWo/ImPjwfgnhr+zjvvYOTIkQgPD0fnzp2xcOFC1eO3b9+Oyy+/HOHh4WjVqhXuu+8+lJWVqdp8+OGH6NGjB6xWK1JTUzF58mTV/fn5+bj22msRERGBrl274rvvvlPd//nnn2P06NHy70VFRfjtt9/w4osvYsiQIejQoQP69euH6dOn45prrgEAZGZmwmAwYMuWLarHGQwGrFq1SrX933//Heeffz5sNhsuvvhi7NixQ77v8OHDGD16NOLj4xEZGYkePXpg8eLF8v07duzAyJEjERUVheTkZNx+++3Iz88H4P62f/Xq1XjjjTfkLNjMzEwAwJVXXonCwkKsXr26tqeIiIiIiEJs9erV6Nevn3w9O23aNDgcDvn+hQsXomfPnvI18dChQ+WlhlatWoV+/fohMjIScXFxGDRoEA4fPhyqQyEiUmFQk4hOW08//TTGjRuHrVu34tZbb8XNN9+M3bt3AwDKy8sxfPhwxMfHY8OGDViwYAGWLVumClq+8847mDRpEu677z5s374d3333Hc466yzVPmbNmoUbb7wR27Ztw1VXXYVbb70VhYWFAIDCwkLs2rULF154odw+KioKUVFRWLRoEaqrqxt8jI8//jheeeUVbNiwAUlJSRg9ejTsdjsAYNKkSaiursavv/6K7du348UXX0RUVBQAd5D08ssvR58+fbBx40b8/PPPyMnJwY033ggAeOONNzBgwABMnDgRWVlZyMrKQrt27QAAFosFvXv3xm+//dbg/hMRERHRqXP8+HFcddVVuOiii7B161a88847+OCDD/CPf/wDAJCVlYXx48fj7rvvxu7du7Fq1Spcd911EELA4XBg7NixGDx4MLZt24b09HTcd999MBgMIT4qIiIPQUTUAk2YMEGYTCYRGRmp+vnnP/8phBACgLj//vtVj+nfv7944IEHhBBCvPfeeyI+Pl6UlZXJ9//444/CaDSK7OxsIYQQaWlp4u9//7tuHwCIp556Sv69rKxMABA//fSTEEKIzZs3CwDiyJEjqsctXLhQxMfHC5vNJgYOHCimT58utm7dKt+fkZEhAIjNmzfLt508eVIAECtXrhRCCLFy5UoBQHz++edym4KCAhEeHi6++OILIYQQPXv2FDNnztTs+3PPPSeGDRumuu3o0aMCgNi7d68QQojBgweLRx55RPPx1157rbjzzjt1x4aIiIiIms6ECRPEmDFj/G5/8sknRbdu3YTL5ZJvmzNnjoiKihJOp1Ns2rRJABCZmZl+jy0oKBAAxKpVq05l14mI6o2ZmkTUYg0ZMgRbtmxR/dx///3y/QMGDFC1HzBggJypuXv3bvTq1QuRkZHy/YMGDYLL5cLevXuRm5uLEydO4IorrgjYh/PPP1/+f2RkJGJiYpCbmwsAqKysBADYbDbVY8aNG4cTJ07gu+++w4gRI7Bq1SpccMEFmDdvXp3HQHmMCQkJ6Natm3yMDz/8MP7xj39g0KBBmDFjBrZt2ya33bp1K1auXClnjkZFRaF79+4AgIMHD9a63/DwcFRUVNS5v0RERETUdHbv3o0BAwaosisHDRqEsrIyHDt2DL169cIVV1yBnj174oYbbsD777+PkydPAnBfW955550YPnw4Ro8ejTfeeANZWVmhOhQiIj8MahJRixUZGYmzzjpL9ZOQkNAo2w4PDw+qndlsVv1uMBjgcrkAAImJiQAgXxgq2Ww2XHnllXj66aexdu1a3HnnnZgxYwYAwGh0vzULIeT20pTyurj33ntx6NAh3H777di+fTsuvPBCvPXWWwCAsrIyjB492i8ovH//flx66aW1bruwsBBJSUl17hMRERERNR8mkwlLly7FTz/9hHPPPRdvvfUWunXrhoyMDADA3LlzkZ6ejoEDB+KLL77A2WefjXXr1oW410REbgxqEtFpy/eCa926dTjnnHMAAOeccw62bt0qL4IOuIvuGI1GdOvWDdHR0ejYsSOWL19e7/136dIFMTEx2LVrV61tzz33XLkvUrBQ+U24smiQkvIYT548iX379snHCADt2rXD/fffj6+//hqPPfYY3n//fQDABRdcgJ07d6Jjx45+gWEpe9ViscDpdGrud8eOHejTp0+tx0VEREREoXPOOecgPT1d9WX577//jujoaLRt2xaA+0v5QYMGYdasWdi8eTMsFgu++eYbuX2fPn0wffp0rF27Fueddx4+/fTTJj8OIiItDGoSUYtVXV2N7Oxs1Y9UvRsAFixYgA8//BD79u3DjBkz8Mcff8iFgG699VbYbDZMmDABO3bswMqVK/HQQw/h9ttvR3JyMgBg5syZeOWVV/Dmm29i//79+PPPP+VMx2AYjUYMHToUa9askW8rKCjA5Zdfjk8++QTbtm1DRkYGFixYgJdeegljxowB4M4Svfjii/HCCy9g9+7dWL16NZ566inNfTz77LNYvnw5duzYgTvvvBOJiYkYO3YsAODRRx/FL7/8goyMDPz5559YuXKlHPCcNGkSCgsLMX78eGzYsAEHDx7EL7/8grvuuksOZHbs2BHr169HZmYm8vPz5QzUzMxMHD9+HEOHDg16LIiIiIjo1CouLvabhXPffffh6NGjeOihh7Bnzx58++23mDFjBqZOnQqj0Yj169fj+eefx8aNG3HkyBF8/fXXyMvLwznnnIOMjAxMnz4d6enpOHz4MJYsWYL9+/ervkAnIgqpUC/qSURUHxMmTBAA/H66desmhHAX8ZkzZ4648sorhdVqFR07dpQL6Ei2bdsmhgwZImw2m0hISBATJ04UpaWlqjbvvvuu6NatmzCbzSI1NVU89NBD8n0AxDfffKNqHxsbK+bOnSv/vnjxYtGmTRvhdDqFEEJUVVWJadOmiQsuuEDExsaKiIgI0a1bN/HUU0+JiooK+XG7du0SAwYMEOHh4aJ3795iyZIlmoWCvv/+e9GjRw9hsVhEv379VAWHJk+eLLp06SKsVqtISkoSt99+u8jPz5fv37dvn7j22mtFXFycCA8PF927dxePPvqovJD83r17xcUXXyzCw8MFAJGRkSGEEOL5558Xw4cPr8OzRURERESnkt618T333CNWrVolLrroImGxWERKSop44oknhN1uF0K4rzmHDx8ukpKShNVqFWeffbZ46623hBBCZGdni7Fjx4rU1FRhsVhEhw4dxDPPPCNf1xIRhZpBCEUeOhHRacJgMOCbb76RsxZDRQiB/v37Y8qUKRg/fnxI+9IYampq0LVrV3z66acYNGhQqLtDREREREREZyhOPyciOoUMBgPee+89OByOUHelURw5cgRPPvkkA5pEREREREQUUszUJKLTUnPJ1CQiIiIiIiKixhcW6g4QEZ0K/L6GiIiIiIiI6PTF6edERERERERERETUojCoSURERERERERERC0Kg5pERERERERERETUojCoSURnHIPBgJkzZ9b5cZmZmTAYDJg3b16j94mIiIiIQq++14mnyp133omOHTs2yb5ycnJw/fXXo1WrVjAYDHj99debZL9ERPXFoCYRhcS8efNgMBhgMBiwZs0av/uFEGjXrh0MBgOuvvrqEPSwcSxevBgGgwFpaWlwuVyh7g4RERFRk1Ne9xkMBthsNqSlpWH48OF48803UVpaGuou6lq7di1mzpyJoqKiRt3uZZddphqThIQEXHTRRfjwww8b7Zrx+eefx6JFi4JuP2XKFPzyyy+YPn06Pv74Y4wYMaJR+lGboqIi2Gw2GAwG7N69u0n2SUSnBwY1iSikbDYbPv30U7/bV69ejWPHjsFqtYagV41n/vz56NixI7KysrBixYpQd4eIiIgoZJ599ll8/PHHeOedd/DQQw8BAB599FH07NkT27ZtC3Hv3CorK/HUU0/Jv69duxazZs1q9KAmALRt2xYff/wxPv74Yzz99NNwOBy455578OSTTzbK9usa1FyxYgXGjBmDv/3tb7jtttvQvXv3RulHbRYsWACDwYCUlBTMnz+/SfZJRKcHBjWJKKSuuuoqLFiwAA6HQ3X7p59+ir59+yIlJSVEPWu48vJyfPvtt5g6dSr69OnTrC/SysvLQ90FIiIiOs2NHDkSt912G+666y5Mnz4dv/zyC5YtW4bc3Fxcc801qKysDHUXYbPZEBYW1iT7io2NxW233YbbbrsNU6ZMwe+//462bdvi7bffht1ub5I+KOXm5iIuLq7RtldVVRVU1uknn3yCq666CuPHj9dMdmgugj0eImo6DGoSUUiNHz8eBQUFWLp0qXxbTU0NFi5ciFtuuUXzMeXl5XjsscfQrl07WK1WdOvWDS+//DKEEKp21dXVmDJlCpKSkhAdHY1rrrkGx44d09zm8ePHcffddyM5ORlWqxU9evTAhx9+2KBj++abb1BZWYkbbrgBN998M77++mtUVVX5tauqqsLMmTNx9tlnw2azITU1Fddddx0OHjwot3G5XHjjjTfQs2dP2Gw2JCUlYcSIEdi4cSOAwOt9+q4NNXPmTBgMBuzatQu33HIL4uPj8Ze//AUAsG3bNtx5553o3LkzbDYbUlJScPfdd6OgoEBzzO655x6kpaXBarWiU6dOeOCBB1BTU4NDhw7BYDDgtdde83vc2rVrYTAY8Nlnn9V1SImIiOg0c/nll+Ppp5/G4cOH8cknn6ju27NnD66//nokJCTAZrPhwgsvxHfffadqI01t//333zF16lQkJSUhMjIS1157LfLy8lRtN27ciOHDhyMxMRHh4eHo1KkT7r77blUb5XXTzJkz8fjjjwMAOnXqJE8Vz8zMxODBg9GrVy/NY+rWrRuGDx9e57GIiIjAxRdfjPLycr++KwVzLWwwGFBeXo6PPvpI7vedd96puT1pDIUQmDNnjtxecujQIdxwww1ISEiQ+/jjjz+qtrFq1SoYDAZ8/vnneOqpp9CmTRtERESgpKQk4DEfOXIEv/32G26++WbcfPPNyMjIwNq1azXbfvLJJ+jXrx8iIiIQHx+PSy+9FEuWLFG1+emnnzB48GBER0cjJiYGF110kSpQ2rFjR81xuOyyy3DZZZcFdTyFhYX429/+hp49eyIqKgoxMTEYOXIktm7d6rfdQNf5Qgh07NgRY8aM0XxcbGws/vrXvwYcP6IzXdN8BUVEpKNjx44YMGAAPvvsM4wcORKA+2KkuLgYN998M958801VeyEErrnmGqxcuRL33HMPevfujV9++QWPP/44jh8/rgqi3Xvvvfjkk09wyy23YODAgVixYgVGjRrl14ecnBxcfPHFMBgMmDx5MpKSkvDTTz/hnnvuQUlJCR599NF6Hdv8+fMxZMgQpKSk4Oabb8a0adPw/fff44YbbpDbOJ1OXH311Vi+fDluvvlmPPLIIygtLcXSpUuxY8cOdOnSBQBwzz33YN68eRg5ciTuvfdeOBwO/Pbbb1i3bh0uvPDCevXvhhtuQNeuXfH888/LF8FLly7FoUOHcNdddyElJQU7d+7Ee++9h507d2LdunXyBe6JEyfQr18/FBUV4b777kP37t1x/PhxLFy4EBUVFejcuTMGDRqE+fPnY8qUKX7jEh0drXkBR0RERGee22+/HU8++SSWLFmCiRMnAgB27tyJQYMGoU2bNpg2bRoiIyPx5ZdfYuzYsfjqq69w7bXXqrbx0EMPIT4+HjNmzEBmZiZef/11TJ48GV988QUAdxbisGHDkJSUhGnTpiEuLg6ZmZn4+uuvdft13XXXYd++ffjss8/w2muvITExEQCQlJSE22+/HRMnTsSOHTtw3nnnyY/ZsGED9u3bp5rCXheHDh2CyWTSzZgM9lr4448/xr333ot+/frhvvvuAwD5utLXpZdeio8//hi33347rrzyStxxxx3yfTk5ORg4cCAqKirw8MMPo1WrVvjoo49wzTXXYOHChX7Pw3PPPQeLxYK//e1vqK6uhsViCXi8n332GSIjI3H11VcjPDwcXbp0wfz58zFw4EBVu1mzZmHmzJkYOHAgnn32WVgsFqxfvx4rVqzAsGHDALiDs3fffTd69OiB6dOnIy4uDps3b8bPP/+smyxRG63j2bVrFxYtWoQbbrgBnTp1Qk5ODv7zn/9g8ODB2LVrF9LS0gAEd51/22234aWXXkJhYSESEhLk/X7//fcoKSnBbbfdVq9+E50xBBFRCMydO1cAEBs2bBBvv/22iI6OFhUVFUIIIW644QYxZMgQIYQQHTp0EKNGjZIft2jRIgFA/OMf/1Bt7/rrrxcGg0EcOHBACCHEli1bBADx4IMPqtrdcsstAoCYMWOGfNs999wjUlNTRX5+vqrtzTffLGJjY+V+ZWRkCABi7ty5tR5fTk6OCAsLE++//75828CBA8WYMWNU7T788EMBQLz66qt+23C5XEIIIVasWCEAiIcffli3TaC++R7vjBkzBAAxfvx4v7bSsSp99tlnAoD49ddf5dvuuOMOYTQaxYYNG3T79J///EcAELt375bvq6mpEYmJiWLChAl+jyMiIqLTk/K6T09sbKzo06eP/PsVV1whevbsKaqqquTbXC6XGDhwoOjatavftocOHSpfgwghxJQpU4TJZBJFRUVCCCG++eabWvsghP9107/+9S8BQGRkZKjaFRUVCZvNJp544gnV7Q8//LCIjIwUZWVlAfczePBg0b17d5GXlyfy8vLE7t27xcMPPywAiNGjR8vtJkyYIDp06CD/Huy1sBBCREZG1umaC4CYNGmS6rZHH31UABC//fabfFtpaano1KmT6Nixo3A6nUIIIVauXCkAiM6dO2teT+rp2bOnuPXWW+Xfn3zySZGYmCjsdrt82/79+4XRaBTXXnutvD+J9JwXFRWJ6Oho0b9/f1FZWanZRgj3ZwutMRk8eLAYPHiw/Hug46mqqvLrR0ZGhrBareLZZ5+VbwvmOn/v3r0CgHjnnXdU919zzTWiY8eOqr4TkT9OPyeikLvxxhtRWVmJH374AaWlpfjhhx90v01dvHgxTCYTHn74YdXtjz32GIQQ+Omnn+R2APza+WZdCiHw1VdfYfTo0RBCID8/X/4ZPnw4iouL8eeff9b5mD7//HMYjUaMGzdOvm38+PH46aefcPLkSfm2r776ComJifJi+UpSVuRXX30Fg8GAGTNm6Lapj/vvv9/vtvDwcPn/VVVVyM/Px8UXXwwA8ji4XC4sWrQIo0eP1swSlfp04403wmazqdYS/eWXX5Cfn89vnYmIiEglKipKroJeWFiIFStW4MYbb0Rpaal8bVZQUIDhw4dj//79OH78uOrx9913n+q66JJLLoHT6cThw4cBQM58/OGHHxplvcrY2FiMGTMGn332mTzjxel04osvvsDYsWMRGRlZ6zb27NmDpKQkJCUl4ZxzzsFbb72FUaNGBVwCKdhr4cayePFi9OvXT16qCHA/V/fddx8yMzOxa9cuVfsJEyaoricD2bZtG7Zv347x48fLt40fPx75+fn45Zdf5NsWLVoEl8uFZ555BkajOoQhPedLly5FaWkppk2bBpvNptmmPrSOx2q1yv1wOp0oKChAVFQUunXrpvrcEMx1/tlnn43+/furrpcLCwvx008/4dZbb21Q34nOBAxqElHIJSUlYejQofj000/x9ddfw+l04vrrr9dse/jwYaSlpSE6Olp1+znnnCPfL/1rNBr9ptl069ZN9XteXh6Kiorw3nvvyReV0s9dd90FwD1dqa6kNX8KCgpw4MABHDhwAH369EFNTQ0WLFggtzt48CC6desWcEH6gwcPIi0tTTUlpTF06tTJ77bCwkI88sgjSE5ORnh4OJKSkuR2xcXFANxjVlJSoppqpSUuLg6jR49WrWM0f/58tGnTBpdffnkjHgkRERG1dGVlZfL13YEDByCEwNNPP+13fSZ9yet7fda+fXvV7/Hx8QAgf5k8ePBgjBs3DrNmzUJiYiLGjBmDuXPnorq6ut59vuOOO+Q1IQFg2bJlyMnJwe233x7U4zt27IilS5di2bJlWLNmDbKzs/HDDz/I09y1BHst3FgOHz7sd/0caH9a15d6PvnkE0RGRqJz587y9bLNZkPHjh1VQb6DBw/CaDTi3HPP1d2WtBZ9bdendaV1PC6XC6+99hq6du0Kq9WKxMREJCUlYdu2bfL1stSn2q7zAfd59Pvvv8tjuWDBAtjt9qDPI6IzGdfUJKJm4ZZbbsHEiRORnZ2NkSNHNmrlxUCkCoa33XYbJkyYoNnm/PPPr9M29+/fjw0bNgAAunbt6nf//Pnz5bWNGovet7hOp1P3MVrfot94441Yu3YtHn/8cfTu3RtRUVFwuVwYMWJEvao93nHHHViwYAHWrl2Lnj174rvvvsODDz7o9y07ERERnbmOHTuG4uJinHXWWQC812d/+9vfdAvuSG0lJpNJs52URWkwGLBw4UKsW7cO33//PX755RfcfffdeOWVV7Bu3TpERUXVud/Dhw9HcnIyPvnkE1x66aX45JNPkJKSgqFDhwb1+MjIyKDbthTBZmkKIfDZZ5+hvLxcM1iZm5uLsrKyej0vgQS6ZtY6h7SO5/nnn8fTTz+Nu+++G8899xwSEhJgNBrx6KOP1ut6+eabb8aUKVMwf/58PPnkk/jkk09w4YUXagaTiUiNQU0iahauvfZa/PWvf8W6devkBd21dOjQAcuWLUNpaanqG+o9e/bI90v/ulwu+RtSyd69e1XbkyqjO53ORruonD9/PsxmMz7++GO/i6M1a9bgzTffxJEjR9C+fXt06dIF69evh91uh9ls1txely5d8Msvv/gtIK4kZSMUFRWpbq/Lt/UnT57E8uXLMWvWLDzzzDPy7fv371e1S0pKQkxMDHbs2FHrNkeMGIGkpCTMnz8f/fv3R0VFBb91JiIiIpWPP/4YAOQAZufOnQEAZrO50YN+F198MS6++GL885//xKeffopbb70Vn3/+Oe69917N9oGm/5pMJtxyyy2YN28eXnzxRSxatAgTJ07UDbA2hmCvhWvre13253v9rLe/uli9ejWOHTuGZ599Vs76lJw8eRL33XcfFi1ahNtuuw1dunSBy+XCrl270Lt3b83tSbOzduzY4RfwVoqPj/e7Xgbc18zSeVebhQsXYsiQIfjggw9UtxcVFamybIO5zgeAhIQEjBo1CvPnz8ett96K33//Ha+//npQfSE60zFVhoiahaioKLzzzjuYOXMmRo8erdvuqquugtPpxNtvv626/bXXXoPBYJArqEv/+lZP971AMJlMGDduHL766ivNIF1eXl6dj2X+/Pm45JJLcNNNN+H6669X/Tz++OMA3JUeAWDcuHHIz8/3Ox7Am1kwbtw4CCEwa9Ys3TYxMTFITEzEr7/+qrr/3//+d9D9li7ApW1KfMfMaDRi7Nix+P7777Fx40bdPgFAWFgYxo8fjy+//BLz5s1Dz54965z5SkRERKevFStW4LnnnkOnTp1w6623AgBat26Nyy67DP/5z3+QlZXl95j6XJ+dPHnS7xpHCpAFmoIurY2pFQgD3JXbT548ib/+9a8oKys75euGB3stDLj7rtfvuuzvjz/+QHp6unxbeXk53nvvPXTs2DHglPBApKnnjz/+uN/18sSJE9G1a1d5CvrYsWNhNBrx7LPP+mVCSs/psGHDEB0djdmzZ6OqqkqzDeAONK5btw41NTXybT/88AOOHj0adN9NJpPfubRgwQK/dV6Duc6X3H777di1axcef/xxmEwm3HzzzUH3h+hMxkxNImo29KZ/K40ePRpDhgzB3//+d2RmZqJXr15YsmQJvv32Wzz66KPyt7S9e/fG+PHj8e9//xvFxcUYOHAgli9fjgMHDvht84UXXsDKlSvRv39/TJw4Eeeeey4KCwvx559/YtmyZSgsLAz6GNavX48DBw5g8uTJmve3adMGF1xwAebPn48nnngCd9xxB/73v/9h6tSp+OOPP3DJJZegvLwcy5Ytw4MPPogxY8ZgyJAhuP322/Hmm29i//798lTw3377DUOGDJH3de+99+KFF17AvffeiwsvvBC//vor9u3bF3TfY2JicOmll+Kll16C3W5HmzZtsGTJEmRkZPi1ff7557FkyRIMHjwY9913H8455xxkZWVhwYIFWLNmjWr5gDvuuANvvvkmVq5ciRdffDHo/hAREdHp5aeffsKePXvgcDiQk5ODFStWYOnSpejQoQO+++47VYGXOXPm4C9/+Qt69uyJiRMnonPnzsjJyUF6ejqOHTuGrVu31mnfH330Ef7973/j2muvRZcuXVBaWor3338fMTExuOqqq3Qf17dvXwDA3//+d9x8880wm80YPXq0HOzs06cPzjvvPCxYsADnnHMOLrjggnqMTPCCvRaW+r5s2TK8+uqrSEtLQ6dOndC/f/867W/atGn47LPPMHLkSDz88MNISEjARx99hIyMDHz11Vf1WlKouroaX331Fa688kq/oj6Sa665Bm+88QZyc3Nx1lln4e9//zuee+45XHLJJbjuuutgtVqxYcMGpKWlYfbs2YiJicFrr72Ge++9FxdddBFuueUWxMfHY+vWraioqMBHH30EwH29vHDhQowYMQI33ngjDh48iE8++cRvHf5Arr76ajz77LO46667MHDgQGzfvh3z58/3y/QM5jpfMmrUKLRq1QoLFizAyJEj0bp16zqPK9EZqanLrRMRCSHE3LlzBQCxYcOGgO06dOggRo0apbqttLRUTJkyRaSlpQmz2Sy6du0q/vWvfwmXy6VqV1lZKR5++GHRqlUrERkZKUaPHi2OHj0qAIgZM2ao2ubk5IhJkyaJdu3aCbPZLFJSUsQVV1wh3nvvPblNRkaGACDmzp2r29+HHnpIABAHDx7UbTNz5kwBQGzdulUIIURFRYX4+9//Ljp16iTv+/rrr1dtw+FwiH/961+ie/fuwmKxiKSkJDFy5EixadMmuU1FRYW45557RGxsrIiOjhY33nijyM3N9TveGTNmCAAiLy/Pr2/Hjh0T1157rYiLixOxsbHihhtuECdOnNAcs8OHD4s77rhDJCUlCavVKjp37iwmTZokqqur/bbbo0cPYTQaxbFjx3THhYiIiE5P0nWf9GOxWERKSoq48sorxRtvvCFKSko0H3fw4EFxxx13iJSUFGE2m0WbNm3E1VdfLRYuXOi3bd9rypUrVwoAYuXKlUIIIf78808xfvx40b59e2G1WkXr1q3F1VdfLTZu3Kh6nNY1z3PPPSfatGkjjEajACAyMjJU97/00ksCgHj++eeDHpPBgweLHj161NpuwoQJokOHDqrbgr0W3rNnj7j00ktFeHi4ACAmTJgQcF8AxKRJk/xuP3jwoLj++utFXFycsNlsol+/fuKHH35QtZHGe8GCBbUe01dffSUAiA8++EC3zapVqwQA8cYbb8i3ffjhh6JPnz7CarWK+Ph4MXjwYLF06VLV47777jsxcOBAER4eLmJiYkS/fv3EZ599pmrzyiuviDZt2gir1SoGDRokNm7cKAYPHiwGDx4c1PFUVVWJxx57TKSmporw8HAxaNAgkZ6e7rcNIYK7zpc8+OCDAoD49NNPAw0fESkYhPDJeyYiImpkffr0QUJCApYvXx7qrhARERE1qjfeeANTpkxBZmamXxV2omBNmTIFH3zwAbKzsxERERHq7hC1CFxTk4iITqmNGzdiy5YtuOOOO0LdFSIiIqJGJYTABx98gMGDBzOgSfVWVVWFTz75BOPGjWNAk6gOuKYmERGdEjt27MCmTZvwyiuvIDU1FTfddFOou0RERETUKMrLy/Hdd99h5cqV2L59O7799ttQd4laoNzcXCxbtgwLFy5EQUEBHnnkkVB3iahFYVCTiIhOiYULF+LZZ59Ft27d8Nlnn+kuBE9ERETU0uTl5eGWW25BXFwcnnzySVxzzTWh7hK1QLt27cKtt96K1q1b480330Tv3r1D3SWiFoVrahIREREREREREVGLEtI1NX/99VeMHj0aaWlpMBgMWLRoUa2PWbVqFS644AJYrVacddZZmDdvnl+bOXPmoGPHjrDZbOjfvz/++OOPxu88ERERERERERERhURIg5rl5eXo1asX5syZE1T7jIwMjBo1CkOGDMGWLVvw6KOP4t5778Uvv/wit/niiy8wdepUzJgxA3/++Sd69eqF4cOHIzc391QdBhERERERERERETWhZjP93GAw4JtvvsHYsWN12zzxxBP48ccfsWPHDvm2m2++GUVFRfj5558BAP3798dFF12Et99+GwDgcrnQrl07PPTQQ5g2bVpQfXG5XDhx4gSio6NhMBjqf1BEREREISKEQGlpKdLS0mA0hvR7bKoHXo8SERFRS3eqr0dbVKGg9PR0DB06VHXb8OHD8eijjwIAampqsGnTJkyfPl2+32g0YujQoUhPT9fdbnV1Naqrq+Xfjx8/jnPPPbdxO09EREQUAkePHkXbtm1D3Q2qoxMnTqBdu3ah7gYRERFRg52q69EWFdTMzs5GcnKy6rbk5GSUlJSgsrISJ0+ehNPp1GyzZ88e3e3Onj0bs2bN8rv9v//9LyIiIhqn80RERERNqKKiAvfeey+io6ND3RWqB+l5O3r0KGJiYkLcm5bJbrdjyZIlGDZsGMxmc6i7c0bicxBaHP/Q4viHFsc/tKTxHzBgADp16nTKrkdbVFDzVJk+fTqmTp0q/15SUoJ27dph7Nixp/Qi0m63Y+nSpbjyyiv5IqsDjlvdcczqh+NWPxy3+uG41Q/HTV9JSQnuvfdeTl1uoaTnLSYmhkHNerLb7YiIiEBMTAzfH0KEz0FocfxDi+MfWhz/0JLGXwpmnqrr0RYV1ExJSUFOTo7qtpycHMTExCA8PBwmkwkmk0mzTUpKiu52rVYrrFar3+1ms7lJTv6m2s/phuNWdxyz+uG41Q/HrX44bvXDcfPH8SAiIiKi01mLWjV+wIABWL58ueq2pUuXYsCAAQAAi8WCvn37qtq4XC4sX75cbkNEREREREREREQtW0iDmmVlZdiyZQu2bNkCAMjIyMCWLVtw5MgRAO5p4XfccYfc/v7778ehQ4fwf//3f9izZw/+/e9/48svv8SUKVPkNlOnTsX777+Pjz76CLt378YDDzyA8vJy3HXXXU16bERERERERERERHRqhHT6+caNGzFkyBD5d2ldywkTJmDevHnIysqSA5wA0KlTJ/z444+YMmUK3njjDbRt2xb//e9/MXz4cLnNTTfdhLy8PDzzzDPIzs5G79698fPPP/sVDyIiIiIiIiIiIqKWKaRBzcsuuwxCCN37582bp/mYzZs3B9zu5MmTMXny5IZ2j4iIiIiIiIiIiJqhFrWmJhERERFRqDmdTjz99NPo1KkTwsPD0aVLFzz33HOqL+uFEHjmmWeQmpqK8PBwDB06FPv37w9hr4mIiIhOLwxqEhERERHVwYsvvoh33nkHb7/9Nnbv3o0XX3wRL730Et566y25zUsvvYQ333wT7777LtavX4/IyEgMHz4cVVVVIew5ERER0ekjpNPPiYiIiIhamrVr12LMmDEYNWoUAKBjx4747LPP8McffwBwZ2m+/vrreOqppzBmzBgAwP/+9z8kJydj0aJFuPnmm0PWdyIiIqLTBYOaRERERER1MHDgQLz33nvYt28fzj77bGzduhVr1qzBq6++CgDIyMhAdnY2hg4dKj8mNjYW/fv3R3p6umZQs7q6GtXV1fLvJSUlAAC73Q673X6Kj+j0JI0bxy90+ByEFsc/tDj+ocXxD62mGn8GNYmIiIiI6mDatGkoKSlB9+7dYTKZ4HQ68c9//hO33norACA7OxsAkJycrHpccnKyfJ+v2bNnY9asWX63L1myBBEREY18BGeWpUuXhroLZzw+B6HF8Q8tjn9ocfxDa+XKlad0+wxqEhERERHVwZdffon58+fj008/RY8ePbBlyxY8+uijSEtLw4QJE+q1zenTp2Pq1Kny7yUlJWjXrh2GDRuGmJiYxur6GcVut2Pp0qW48sorYTabQ92dMxKfg9Di+IcWxz+0OP6hJY3/kCFDTul+GNQkIiKiBsstqcKOE8W47OzWMBoNoe4O0Sn1+OOPY9q0afI08p49e+Lw4cOYPXs2JkyYgJSUFABATk4OUlNT5cfl5OSgd+/emtu0Wq2wWq1+t5vNZn4YayCOYejxOQgtjn9ocfxDi+MfWqd67Fn9nIiIiBrsyW+24+55G7EuoyDUXSE65SoqKmA0qi+jTSYTXC4XAKBTp05ISUnB8uXL5ftLSkqwfv16DBgwoEn7SkRERHS6YqYmERERNdjurFIAQF5pdS0tiVq+0aNH45///Cfat2+PHj16YPPmzXj11Vdx9913AwAMBgMeffRR/OMf/0DXrl3RqVMnPP3000hLS8PYsWND23kiIqKWbtvM2tu4jAB6ATtnA72fOcUdolBhUJOIiIgaxOF0IbukCgBgd4oQ9+bUcLoE8kqrkRJrC3VXqBl466238PTTT+PBBx9Ebm4u0tLS8Ne//hXPPOP90PR///d/KC8vx3333YeioiL85S9/wc8//wybjecQERERUWNgUJOIiKiB9mSXID7CguSYMzNYkVVcBafLHcy0O10h7o2XEAKbDp/EOakxiLQ27JLn8YVb8fWfx/Hpvf0x8KzERuohtVTR0dF4/fXX8frrr+u2MRgMePbZZ/Hss882XceIiIiIziBcU5OIiKgBMvPLMeL139D/+eUB2zldAsUV9ibqVdM6drJS/n9zCmp+ufEorn83HZM+/TNgu7dX7Mfry/YFbPP1n8cBAJ+sP9xo/SMiIiIiovpjUJOIiKgB1h0KrjDOXfM24KJ/LsPRwopT3KOmd/Sk95hqHM0nqPnm8gMAgFV783TbFFXU4OUl+/D6sv0oq3Zotskv864TenZydON2koiIiIiI6oVBTSKFKrtTnkJ5Otp8pAi7TpSEuhtEp5XiytqzL+1OF37dl4capwur9ukH2FoqdaZm83kPPV5UWWubzAJvQLayxqnZ5s/DJ+X/W8NMDe8YERERERE1GIOaRB5FFTXo+9xS3Dn3j1B35ZQoqQFufP8PXPXmb6HuCtFpRRnUdOl8KbLHUxkcAFpHW095n5raMUWmZnOafh6MwwXl8v+rHTpBzSNF8v9b2vEREREREZ2uGNQk8li8PRvlNU78tj8/1F05JXIqDfL/myob1ekSp3XmKzW9KrsTK/fmosquHXyqCyFEowSolEHNGp3tbTlWJP+/MfY5Z+UBLNp8vMHbCYbD6cL499bhkc8367YJxZqaH6dnYsjLq3BYZzq/MusyNtysu53MfO/jq+zaff/ziDdTszlNryciIiIiOpMxqEnN2ndbT2D7seIm2Vd2ce3TFFuycsVScU0RdHA4XRjx+q+49t+/QwgGNqlxfLAmA3fN3YD/pWcGbJdXWl1r4HPKF1vQ75/LUFhe06A+lVR5X1zVOkGxLYpMv4YGxTYfOYl//bIXj36xJWC7GocLOSVVDdoXAGzIPIn0QwX4dssJ3dfy8Uaefl5cYUdJVeBp/U9/uxMZ+eV4d3WG5v0H88rk/0cFqHxeW6am3enCNkVQWi9wTURERERETYtBTaqT7ceKVQUTTqV1hwrw8GebMfrtNU2yv6zihn/4b84qFUFNRxNkTx4urMD+3DJsO1aMykbIqjvd6U1bJrUDue5AVaDX69HCClz0z2W4/t21Abe1aMsJnKywY8nO7Ab1qajCGxTVm768VREUq25gUPNIkIWGbv9gPfo/vxx7s0trbxyAqgiQRkDP7nQhq7jxMjUrahzo9ewSXPHK6qBeF5Ywg+bt+3O9xx0owJ2pCmr6931PVqkqg7MxMjW//vMYlu/OQUWNdmEiIiIiIiKqHYOazVRheQ2+2nSsWX3gycgvx+i31+DBT/5skv1tUhRmaKjyage+2nQMxRX6mT/ZjZDR1JxVKDM1m2D6ZJkie62hmVsny2vw0GebseY0XRrg2MkKXPCPpXjp5z0N3tbWo0WY9OmfAStsCyHw/dYTyMgv120DuIM3tWXLNTUp8zBQYPC7rScAADuO6xfFUk4ZT4i0NKhPeaXeL3q0+lVSZVdlDQYKihVUAY98sVWVGegrmDU8AWB9RiEA4NstgaepF5RVB8ymPqEotqN1fFlFVVB2o6FBzS1HiwC4x1XveVa+lydFaa9Rui/HO+aBg5re14pWpu2240Wq3xuaqelyCTzx1Tbc89FGFAX4m0RERERERIExqNlMzVl5AI8t2IqvNh0LdVdkhzwfyoOpJtsYTjZwSqjSp+uP4LEFW/H+b4d02ygzv07H6dLlDm82U1NMPy9VBDUbmtm0dHcOvt96Av9do//8tWQv/rwXRRV2/HvVwQZv66P0TPy4LQs/bMvSbbN4ezYe+mwzhry8KuC2bv3vOgyavSJgYPNEUSXeWLa/yTK45aCmzjRvAEH1RRn0DTNpZ/oFK1cV1PQPnm07WgzlW0qg18O6XCMW78jBZ38c0W2jDOjZXdrbKlU8Z6lx4brb+nFbFvr+YxnmrDyg20a5XqZW35VFgoDA7y+Z+eUYM+d3/LxDPzv2UJ432K43VocLvW0MBp1MzRxFpqbDpfm+XlxpVy0/UKXx/EnBcWk3gZ4/IQR2nigO+IVkflk17E4Bo+H0LBpFRERERNRUGNRspqTMmLyyxgvsNZT0wa+pikCcbMQMFilgGSjYka0IajbF9OymVqoYzqZYE65QMSW3oeeMFOBWFv44nTTGuocSKWsw0JT/Jbtqn24thMDmI0UorXao1kv09f5vh/Dasn34YsPRune2HnJL3McX6BwuCOJ9UxmIa0jQ3e50qYNiGsHWQ/llqt8D9T3fcypUBDjXlZmaelnQyuI3lgBB20e/cBf/eXnJPt02mfmBp2cf8zk/ahz675+PfL4ZW48W4f5PNum2UWa1Vju1x+GwKrtSu83+XO92nC6hOVZHCtQBWa1g+Y7j7nWdL2gfDyDw+9mGzJMY9eYa/P2bHbptTnj+1iTH2BBm4mUYEREREVF96a+cTyElfWhtjADiukMFOFJYgRsvbNeg7Zz0BKmaqkiCcp26hiqrdo+nXvCixuFCWbU3s8bhFDCb6r+//TmleGrRDjw69GwM6NKq/htqRMqgZmMU8qiNMtM2UNCoxuHC0ZMV6JIUpdtGej001blnd7rw0dpMDOwU3yT7yy8NLsvxlSV7cbigAm/c3Fs3O00K6Omt7QgEF0QtrXbIwf1A70NSlmJjTFPfkFmIA7llGN+vveb95dUOlHpep3qBLCC4TE1lIK4ha1z6BlC1tpXn8/wG2l9Btft5DZSJWqSstu5wARrJfspAql5FbyC49wLlMgVa416XTM3tx2sv/KZcA1TvvUO5rmiVRhshBLKK1Od5lcMJS5g6iKhcTxPwf93UOFxyf/p2iMemwycDvp/t82SHBlr+IcvzpWVqrE23DREREdEZZ9vMUPeAWiCmCDRTUoCgMQoSTPliC/5v4baAH7KCUSBlajbBeoyAOtOvocqr3R9U9YIJWT6Vz/WmdAZrwod/YH1GIW7977oGbacxldqDm36+/lABZn2/s8HruSqz1wLtb+b3O3HFK6ux7lCBbpvGfD0E471fD+EfP+7GVW8HLjTTWHyDXloqa5x4a8UBfLf1hFwsR4s07oGCYjklte8v2KC0NBW6oc+NEAI3vJuO6V9vx/Zj2oEv5TTvYDM1HTrtlO+HDel7bqk6cKa1Lf+gpn5AtsCzOa1p0HIbRdBW77WlDETWVgUeAExG7SB5cYVdfu8HAmdqtvFMcw/0eq8tCV4Igd1Z3rVQdaefFwQ+vvIap985otXusG9Q0+d1sy+nFDVOF2LDzeicGBmwT4D3y7hAgWspUzPQsgBERERERFQ7BjWbKTkzrRECBY2VSXVSnn7eNFOzlQUUAhXD2HmiGAs3HQu4Dqac3aUTKPCdPuls4DFKH1pr+wBfUFatyhA9ldSZmvrn1U3vrcPc3zMxb22mbpsquxMfrzvsl6GldLIicCBEss+TBeUbXFAq9pRub6qg5oo9uU2yH0lpEOeAMpCpk6QJIQQKymufnp0doHK4pDDIoGZRZU2tbYKhXNO2tFr7vUqZYRooaCuNAaA/Dqp1IhuQARxMwFJqIxUk0hur8moHyhy1Z2rml9X+3KiDmtptlO8DaXHaWYMZfpmM/tuSqqN38gT9gplhYNaZEp9XWq1aekTvuVEW99HK1JT+XtnMRtjM7ksdrTHN9J1+7vP8SVPPz2sTI2d5BjpfCsvtmttRkjI105ipSURERETUIAxqNlPBZj/9eeSkKmvHV3mNE05PZC3Qto4WVmD24t0Bp6VKH9ZqnNoFFxqbKtMvQObkqDfX4G8LtmLNAf3K2OVyULP2rK3a9lcX0odpLQVl1ej7j2W45q01jbKvQIQQKKvj9PNA2YM/bsvC04t24NUA6/AFm6kpTaUNFPgMdvr5obwyHMgtDdgmGIFeU40t2NfSXmXRE50gVUmVQ35u9YJiQoiA621KVEHpQM9fI2VqKqclG6Ad8FIFNXWCRk6XUGUW6vXraCOtqZkbxNTyPM/5JGUy6u1PGWgNlKmZF0TGqiqoGcSXOXHh2hXgM3zXAw2QqSkFNWt03l+US4qk6AT09mSrX7965/GRWtbUlN5/EiIsCPesJRIoU1MKWPo+f9J5eV5arDeo2eBMTWn6OTM1iYiIiIgagkHNZsjlEnLmVqAgTvrBAlz377UY/vqvum1KgigoAQA3/icd//n1EKZ+uUW3zUlV4ZdTG9QUQqiKYTjquO6br7KqWoKaPhmHzgYUClI+Ni3A9MLlnmzAQwH63ViKKx1witqnnys/9LeO1s8iOu7JNDoZYIkA5X0Bpy8HkZUcTJvyagcuf2U1hr76a8AsqWAEU2imsSgDcL7r/SntUwQ19d4XlMFYvTFQvq4A/aCq9CUGEOT08waud7pTEdTU67syqKm3v/yyalWlca3XvBACRwu9Ab3aMgsDrdEZzHqZUpvagppHlet86gTzXC6hzkTVOb6MvNqnnysDlroZn3m1rzmZ7XleOid5MjV1tqV8rzPppBvvyS5R/a71PFfZnfI+3b/7t5GWL4mPtMAmBzX1MzXP8qzp6ztW0lT4Hm1iYfYU9Ql0vkjve4EybU941voM9PeBiIiIiIhqx6BmM1Ra7ZA/lAcKJvy4/QQA9VREX8op54G2JU393HT4pG4b1Rp7DQxg5JRU4bWl+3QzQ32n4+oFNZUfLqOs+nWvpCnewWRIBdpfMKTK9QCQFKVRwcNDWeE30PT6xuAblNELOijHIcKiXylJCp4FLHhSFlwQvDiITM2SIIKaGzIL5f+XVjVsSn8w08Ebi/J8QYDTIJjiKcFMGT/sN91Wu10wa2ranS7vFzCNmKmpty3lWqB6QaPjRb6VuDUCXuU1qmzVQH3/eN1hXPiPZfhfeqbm/b5ravpmDbpcQn79tYl3B7H0v1xRFi/SD0orX09aAbb8shrVOayX2Xsor/ZsTt8vXXzHPau4EkK4s9JTYmy6fQLgE2jVbrM/p/bMUN/Meq3xlM7fBGVQ0+cYy6sdcsC5W0q05rakpRraxYcHNf1cmjofcPq5J1NTb8o/EREREREFh0HNZkiZXRl4Sm7tgZfiCmWmZu1BhxibWfc+ZeGehhYL+jj9MN5Yvh+frDuseb9v9pPedHDlupsRltqDmvqFgtSBiYZUnVdmjAbK+DyY6213qqt6K7MBA+0v2Cm50vYCnZ+qTE2n9gf8KrtT3k9DMzV/Vyw/0FRrb879PQOvLtnboG0og5qBlnZQZWrqHJ/yCw695+ZIEAEhAEFN4S7xrcJdT0IIbD/uzdDT65N6+rnOa9mn4rX2GpC1Bz4lTy/aAQB45tudmvfXlqmpDEKm1ZapqazorRP08/uCQuO17Ju1rlcpXl3VPLilOXyPT/oipG18hBz0C6p4kU7QL7uk9sJLvl9CaQUQpeBiXIQFVk+/fLMwpQB/QqQFSdFWz7a8+xNCyK+pxCgrrKbap5+frGX6ud3pkpcs4PRzIiIiIqKGYVCzGVJODw0U7PKdRqqlRJGxFigAJYkJ1w5qOpwu1f4aEvQDvIU8SnSOIdenOrNe5qQycKYXQBRCyGtq1uh8kC70Cfo1ZPp5pqKwRqAxP6TI1AzmuQkkI78cN/4nHav35Wne75vNq5c5eUxZETqI6tJ6H+6FEDipmr6svb9gznXlUgSB1nZcc8BbPb0h46mslq1XzARwB8pnfb8Lb644gNwAa9EeKajAO6sOolSnUJdvgEbruSmusKsC73rjrpyWrJcp5h/U1G4XTGZ2UZDvVbXJLa1WBev0jk/5vqDX5kQQmZq+Ba4CnVcSaV1GX0c809ilTHHfc09aTzMuwoxoTxv9LxWUmZrabYKZ7r7fZ11ZvTVUVUFNnfNA6lNilFWznRT0bBsfLk/P1ltTM5iK7L7Z+1rH5xvY1QoAy5maEWY5U7Oyxjeo6e5Ph1YRcuBTGQAuqXLIz1VStBVmOWir//fhZC1f+OSUVEEIwGIyolWk9jqmREREREQUHAY1myHVOphBZK8Fva0gPrhH27SzHYsr7bWuU1cXZdXOgNvJCyIbCQiuGE21wwWHJ0iptz/foGZD1gxVTunUC7xU2Z04XNg4hUoAYNL8P/FHRiEmfPiH5v2+mZoOnbE6FkRQBfCOl14gpLzGqQrc6AVxlOewXqaYb7ErrUzGvNJqee27QP0K5n7luaf3egCAPVm1ZxYCwLDXV+PFn/fgvV8Pad5/wiezUGus9vkEqfTX1AyiQI5vUFNn3AuDWBNVmSndkPeE7ceKVb/rBoRKay8U5Dv9XKvd5iNFqt8DBeclWlOFXS4hfzlxbmqM5v6kIGRSlLXWQjPBTD/3f2/0fz1IrwUpaKa/pmbg6eBl1Q75td7Fs16mb9+9mZrhta45eShf/YWP1mtZmu6dGOWpFK+xLalP0nR3reNTr6npydT06bu0nmbHVpGa625KwdNoaxhsZhMstWRqOpwu+YtEp0tovs9KX06kxNpgNOp/aUJERERERLVjULMZUq2DGSAQqZflqLetYIrDROtMP/cP+jUsCFdbNXLfzDeHTuZkMNlkZYq15bQ+jDpdQs74lD60NiRTM5jspwO5Zap9BAqy7c8pxafrjwTs026f4hq+fDObgpl+Hug5rm36+Unf80WnnTpTM7jCNlpBnLUH81W/ByrSMWflAfScuQR/HtFeP1aZERlwzBVBTb2gkdMl5CDJ3mztquzHi9RBRq2x8n2s3vmifJ3qPTeNuaZmcWVwxaBq4xe01Tg+IURw08+La8/UXOkp0tUt2b2OYjBfmmhNFT5eVIlqhwsWkxFdWrsLzfiee3JQM9qqqLDtf3wul1B/qaBzDvtmamod3+4s93j2aR8HQDtgWe1wqs71aofTL8goBcDjI8yKTE3foKaUqRkBS5g7SKc1nkUVNap1hIXwfx+qrHHKQcG28RG6xye9/0iBZt9gJaCzpqbf9HONTE3FcyONdaJnarpehXT5GH3eq7TaSZnEqTrV34mIiIiIKHgMajZDxUGuUxdMpmYw21J+SJaCer58g5oNXQNSCjTqBYN8s5H0MgsLgsjULKtlCv7JihoIARgMkNdV01vDEwD+veoARr35m2q9UiVlUFNvzHdl+VT4DfA8X/nar3jym+34butx3TY6yzDKfKt5604/VwRV9PrkUgSBgylYAwSYvlxR+/npO85a2/rTp8BVoKzB9IMFqHG4/LIDJdmKQE+gjN1dWd5AnN7+lIFPKejlK5hMTd81EvXX1FROP9eZnl1ceyYj4JOpGcTzV1umptMldNcLzSmufcpxSZVDFZwLthCSb98P5ZXhUH45zCYDrjindcBtHfSp/O3rgCdI1ykxUi6s5Tf9XBHUtAbI1Mwrq1Y9tsbp0iwgVlsWu8sl5CziPu3jAWivX+kbHHUJ/y+PpKBmuwTtoB/gfc9oFx/hzdTUOL65v2eixuGSMz4B/2CrVHTJZjbKQVTNoGaZFNT0FF7SytT0vAfFR1hgCzNptpOWCunYKlJxfP6ZmlLWaG2ZqEUV6vc97aAmK58TERERETUWBjWboZLKwJmFkuCmn3u3pfdBTLWWnU6bkxW+mXf6wZ7P/jiC/1u4NWCWW22Zmn6FgvTW1Awim0yZqakVwJE+IMeFm+UPtoH6vnDTMew8UYLNR/0z/exOl2q9Pr3j23XCJ6gZRJB4j06mXzBOVvhmO9Y+NVk/O88uj4/udH7f8yWI6ee6xWh81qIMlLklCZT56i3kod0mSxXU1H9egsnUVGaQ6mWr+mXRarQLJvsQUAev9Y4vP4g1GQH1a0s3M00VlNYf89ySKvT75zJM+2q75v05JbWPgZS9Lc3YdbiE3+vU4XTJyz9IU699t7XCk6XZv1MrJOi0kSjXvdV6jg/muu/v0jpSN+gnBSFbqzI1/fcnrXUaGeY9Jq33hfxSny8MfLZ1pLAC5TVOWMKMOCc12tN3/+1IYy59kaN1jFKf2sVHwGqW1pz0bqva4ZSziDu0itBdU7O0yo65v2cAAKZe2Q0Gz3PoG2SUvlBIibHJ+9M6rwrL1dXktTI1pXMzIdKCcIv/1HLAGwDv0CoCVo1sTum1IgVYAwWlAf/3Wa3XoJRtnMJMTSIiIiKiBmNQsxkKtniK8gO9XgaUMiCkF5jIV63DpzettfZsOcnry/bhy43H/AJ3St6gpk6mpk/gxaGTOamqyB7k9HPfsZKKqyREWmAy6k+flLdXJWWZ+rcpr3ZAGWdp6LqGygzVuPDai0pEWrSLmfhP4dZeP+9kEJmTymI0upmaZYEDL1r9CmbdTb1tFfkGEwJMP5fa6lWXzlYEEO1OoZkF63QJ1ZRwvddW+kFv8SK9Yi3KTGJAexyk7C6pWI1+xfLAz015tQPlnmIp0nqEWmPldAnVVFrdzLQgCwW9unQfCspr8MXGo5r3S2tlyn3S6LsUhFNmuPke4+HCCtQ4XQg3m9ApMVJzW1JQ8/Lurb1rXAa5BqQvaTp1l6QoWMNqz9SU12TU2J/0npCiSODTCqRml/gEuH22JQXbz06OQqTFU7xIYztSkLhdvHeHepXN2yVEaPZ9zf58lFY7kBxjxbmpMbqZjAs3HUNJlQNntY7CyPNSvJmTPvvL8YxV6xibXGlca9ylLzHaeM6FgGtqRijW1FS0q7J7p9/rZ2q6t5HkM/1c98s/3y9XNF5bUgYpiwQRERERETUcg5rNUDDZa8pAHaC/5qS6Yrl2m4Igpqz6ZmoGyiCVgkZ6QRxAUShIJ7BU6hPoCSZTU6+NMmikNcVS/pAZZUWY54O0XrV1d9/1A7K+z4veePq20/uQrJxqGmnVDlgqK/omRGl/UJbWX42PcK+ZGkxF6GCK0ehWzvY9XxpQKCiooGZl7dM+ffsWTKYmAGidCocLylXnt1ZQxe504Y+MQkUb/z65XAJlNT7nQoBMzQ6tPOsMBrEGZKCq0eFmE+I9QRWtcfAtDKa/NEBwa2puOVqkex/gnX7ePkH/+HLkIFyEfJtv3/fnuAPNXZOjFJl+3m0JIbAx051hfenZSd7MwnpnarqDnme1jtLMZAS019TU2p+UFdk6XMhfrmg9h1J2oRQU8w0gSkHNc1NjdNeSBNQZg1JATzdTMyFczmRU9unH7VkAgJHnpcJoNMiBT98+7ctxj+NVPd3ttIKMgDfQmhxjCzhW8vRzzzqndqdQfZkkhJD/NsRHmuWAs3Ia/rpD7i8c4iPMiIswawalvdPP3UFNs2LNZa1sft/3Pa3nr1Cx1icRERERETUMg5rNUF2zKwH9TKpgqp+rpp8HXR1cu12V3Sn3OdAU4LJqu6eN/naC2V9hEGv6ldcSNJI+ILeKtCDME0zQm37udAlUeIKIWoGCihr1bTVO7Qq/tfVJogyw6bfxZm7prYla7AnsSoEQrSD48ZNBTnH2CSRrrfvnt6am3tTyIDL9fAtiaRUUOunJJJaqleude9UOp/wc6QVRs4MIavquiaoVsNydVSJnRbrbaJwvdqccPEzQCVLVOFzI9QTGOraK1O270yXUQU2NNnKQJtqbvab13AT7/BUFuf5voKUTXC4hH187T1BTK7NQyuZMiwvXDfpJwbOuraPlIJWyX6XVDvk8axsfrsg+1H69H1Ksqam1LqU6U1Nn+rlc/dwW1PTzVjYBW5h2gLTG4ZILzZzlWaPVd9yltV7PUQY1tTJfpazIaJtmliKgWFMzXrGmpue5qXY4sXRnDgBg1PmpAACzTqGgvFIpWClN49aeDu6dfm4NmBUpnZ9tFFmmyk2VVjvk9zh3pqb//j5Y454Of22ftjAYtAOteT7Tz6U+aR0jENz0cwY1iYiIiIgaD4OazZA6U1NnXTzfYhE6a1yWVNW+Pmd+WeDsLkCjmrVOAEqZYak3vdfhdMn36QWffANAepmTJ4MoFOSb9el7jAWKD5lhpsDTz5UZlnrTzwEgxhNcA7Q/lFdU+wQ/dcZdWUBFb91GVeCzlsCgVPBCa31H/w/ktQc19fbptwarb5st04HvzoK9Iq/W/flmamq1k9okB5i+DKinqWsFqQD/TE2tTe3Jqr0auW8gTytzudTzBYbZZJADsr7nQk5JFYRwB6yldfi0xryooka99IFGG2+AzaobyAKCz8wOptCTbwawr8KKGjhcAgaDN0il1fdcz/Tz5Bj9gjv7FJmaUsCyWrGtIk/w22Y2wmY2KbIBtTNtjyiWifB9vZ8sr5FfC50SIzUz/YQQyPZkHyZGWzQDrRIpgNjK6g2e+Z6jx05WwCXcmbbS1Gvf19beHHfA3R3U1M6IBJTjafNmYdrVfT/qee7aqwoFudv8fsA79byvpyCRd/q5uihUniKACsDbL5/jkwKtyTE2b8DZZ6wqahzya0m5FIHy6ZH+LkRYTLCZTfL+pMftzS7Fb/vzYTQAdw3qCAC1ZGq63zeVXxoF87phpiYRERER0anFoGYzFEz2WoFvcZEGZGrmBZOpGWSQQ51lqh00Umav6QWfpA+fUlEQvWrkyowyvUIs5b5Tvf0yNd3H3yrKCrMxcKEgdVDT//jKPcFK5QdWrWOUtuNdI1F7rKSAiHt/tWdzamXnOZwuecxbeTKOtM4F/4xInTH3yRLWzgh0t4myagfqcPgLoOwgWlVv8+4vyKCmb7sah0seT+86kbVPi9fqd7XD6VcdXCuevjdHHbDUem72eYKaUkBE63yRlkaIsobprrcoPb8psTY540yvejYAVbaxwzdjzvO8JEZZddeABOpQvT6I96r1hwpVv/u+tqTsvFaRVnlNWO01Nf2nJvtPP3dnTp6dHOVtoxh36flPiPAEqQJMcT52slKV0ez7/B3Kd+8rLdaGSGuYZpA4I78cxZV2WMKMqnUbtYOa7vOulU0osiLV7Q4XegvbaPVdCIGcYm/Wq3L6uW/GuFRpvHW0VTPImFdWjSq7CwaDO3jom2W6N9t9/AO7JMLoOefMJmUmo3d/uXJQ0/3+ozctXnqOW8foZ7VKmfWWMCNibGEwS19EKZopK59r7e9/6ZkAgBHnpcjZwfLyAYoxkN7HEqOl6ecGxfFpBDVrWVNTCKFa65OIiIiIiBqGQc1mKJh1MPPqMf1cdyp7ad2nnwcTRNWb3qsMMuq1kdaJjLa514DUzdSsY6EgwD8QoizcIBcK0gtq1pKJKk0rj1N8YNUr2AJ4g596z012MJmaRd4gnNZ2lNm6crVnjfGUzrvYcGndTe39KYvRuPepVVHe3SbVk1nodx7XuNc1dNV4p3HXN6gp3W8weAOI+oU8lJmaGlOACyogBBCtCDJqJUFLaze2CrAupRT47NU2DoBOpqbnPIiyhekG2KTlBdLiAq8zeMBTiVualuzul+80YO/6jnrTpYHgp58Xq15/2ksRKNcVBTTWUZSKBMUGzh71BjWtijUgve0cTpccaOzaOlpz+rIUUIrzDWpqnC+HC9xTz6Ot0pIG6ja5PoWLvGtqeo9POvbe7eLUmaE++6uyO+UvMFpZvUE43+fmSIE3c1Kr0nhJpXd6fatIi1yQxyX8X4PKILEc4FaMp7R2Z1qsO6Dpm2UqVSBXVk9XZjJKxd1cLuHN1JSmn2tkhir7lKJcU9NnrJTv1waDQT5G5aaUlc8B+E3nl5aPGNUzTX6MzWcMhBDyFwVJni+DDAaDbgYp4J/t7nuuV9Q45ce10ln/mIiIiIiIgsegZjNUXOkNQukVJMj3qQ6uFdBzuoQcNNFrA6inE+tlDEofJGN0pshKlAE03TUulUFN3enn7sdKmX6+GWfuNk7VGpZ6gSy/ojw6089bRXmnnzt1MkNVmZoafa/wBDWjbd6gmO84uFwCFZ4Pu/GR+oV7AJ9MTb3p0oo2ekVfAMBqEgg3axfyULaTA4NBTj8PVJ1Ymi6tauNyAvYiAICwe4OaeueCX1DTp+9FnkBVbLgZ4XK1Z73p0spMTf/9SdWuOyVFykEV36GqsjvljLnz2sR6btPI1PQENc/3BDW12ngzNc2qqbtKUuXztNjwgJl+UmZoT0+ftNop1wjUCyw5XQLLd7vXSgxUsAZQZ2oC2q/B3dm+64/6Zud5phxH22AJMD1batdaJ+CVWVABu1MgwuKemq01VtLzL73urAECVNLU87NTouU2yqCt75cAWpmvUlCzf6cEAN6gn28WrVRlPNJqQmQYNIO2gDfQqJepmVfmPleibWGwmU1yoBXQmOqtmM6vlakpnb9SkNw3CF5YLhUf8wbnVJmMnm8DTnqWFwDc2biAN8io3J8QQhW41gvgS1+qSEFB6TzWzNSM1M7UlJb1UK7JafWZql9a7ZD3rQrcBngNFtUy/VzqlzXMKGfpExERERFR/TGo2Qwpp3AD2h+e/NbU1AgmlAaxHd9t6bU56ROk0ssgVWZq6mUWllUHDnw6nC45WCGtM6iVOembFVOjs65oWW1ranqOP0FRKEi3knot08+lqu4RFpPuh99KRXEYKSAQXKamzvRzn0xN32mmUvAl3KRc804ro9PdTvoArzcGvksfaAY1/aoTK9rYi+X/Ghze6tLBVEgH/MdTCqzFhZsDZvoB6nNGq02GFNRMjJQDNL7DcDCvDEIAcRFmtPUERXwDskUVNXLQ6Py2UuBTa01NRRC8lkzN1Dib7hR1wJsZek5qjNx332OUXuvqTE11m+cX78ay3bmwmIy4+aJ2uvtzuUStAWfA/0sF3wxZ6RxvHaNfsEYIIWd06mUWbj9eBADo2jrKXYlbYzylTF0pU9McIEAlBRDPTo6Wb1P2yzeoqbW/9Z6gZj9PUFMZZFSOlbIgj8HgH0CUHCl0n5/tW0VqvpbzSt2vO+k1bA0zwuCJMyrPvyq7U+5/a53xlILk3TxBXe/0bHWmZoInQAy4g+DS/qTjk6aeJ0Ra5DGyahTuKalyyL8rn2O9wm4JUoBU+qJG0cy7zIC7b+EWb/Vzp0vIa3dKy1VIY6U8PukLgChrmBwUBbyB20CFgrxfaOnPCjAYDCAiIiIiooZhULOZqbI7/T7EBRPU1ArolVSqgwlaH8LsTpeq2IdWoKfK7pTXZJQKsQRTlEc/UzPwmprKoEeMPP3cv12wFdl9K43rZc+0irQiTJpyrBfUrGX6eYUngKNeY0/9wVbKVDUalFO9g8jUDKJQkNCYZioFLyLCoJsNCHgD0kmeYh7BLkXge3xVdqd3jctYjW15pp4DgMlZBrPBji7Wo0FVSNfqlxRwj4uwaK6Lp2qryKSqsjvdA1a8C1KUOSNPGdT0ZNX5DJW8bmPraM2qygCwPysfHS3H0SYuXA4wVdZoBcE9VdsV0919z2MpUzM1Nlx3LUnAXfwEcAeh9IIqtU0/L66w48Pf3VWhX7mxF/p3bgVAeymC0ioHfOLnmhmyvkWxdKefKzMwNYJB0jnbWtF3ZWBw6S53dulfuiYCgOaajEW+a2oGeD14g5re6fzKvkuvqxg5U1M9nsdOVuB4USXCjAb07RCv2p/7GL39OiIHNaWp7Npri8qZmgneTE3l+eItbOOdLq21Pqc0dd5mdq9LqXUuSEHybp6grsWkDnwWylO8vVmMBoPBL9jqu54moMjUVIynFNyO8WSZ6gX5pfefRJ8sTLvLGySUMj6lNYSV1dbzy6rhdAmYjAZVBqYy01YIIc+GSPSZJq631ue6QwU4lOd+b2jfKkKzjW8GKRERERERNQyDms2MFMAxKpI4qp3+AYX8INbU9M341Prg7hugcmhMd5eCniajQV5DsCGFgqRADqBdzET5QTfS6v6gqRVk9M3UDL76uXf7DqdL3k6rKIuiyIre9HPlmqEahYI8gatIi37mnbKNnI2k0XchRHCZmj7Vun3HXR3U1M8y8pt+rlcoqJb1FqWp52aTQXsqe02h/N9wQwVmpf0Hy7s9gL9Yf9Xcn9SveE/Wld/0cylTM8IcsPgN4DP93OECdvwD+LEHcOA/AHwzNbXX1FRW2NYLXLfaOQmruv8Vw1OP6lZ6BrznZkPX1Kys8U6JPzs5Wjcopgx6aY3VAU8WamqsDaN7pQVcP7Co0j2W4QHWigS8SzJIdKefx+hnj0ptEqMsMJuMfkWAquxOrNqbBwAY3iMFgPbUcm+RFnV2pdb5ImVFdk6Kkt8XlM+h//Rz9bakqefntYlFhGdZhDCTUX5vV/broBQMSwhXbUs5Vi6XkIOfHVpFwOJ5LSu3k++zBiSgXZTHWyTI5l6X0mcpAiGEKkiuOj6nfqYmAL/gfK4nwKgMIGr1SXpddU7yTHfXCcwX+FQP18rUlIouSUFiZRV46f2ydbRVXl5B2ca9T5e3SJBiLAHtdVjzy6rx0Geb4RLAdX3ayGPm+zeClc9PLx07doTBYPD7mTRpEgCgqqoKkyZNQqtWrRAVFYVx48YhJycnxL0mIiIiOr0wqBliFlEEQ8Ef8u/K7J9Aa3f5ByP1g1QSrQ/uUlAlMsyOiyO3IQwO3cyY+AhLwOAFEFxhojKfzC3fdlJGW7jZ5M360Ti+k0FWZPcvFKSYjlrhLTITH2GRMzX1pl4rA6RaQSopCzPCatJd/1DVRi4uov38KfuqFSQ+WliB4kq7KgiuV0gn3CQCTz/3ZPZKwQet8XS6hHw+SEsDaE3nNxvsuCzhADyzPtX7U2RqRhorcUurnwEATyW94be/ihqHHFzo0CpSc39FimrCelWjJcpAuNNeCex70/3L4S8AeNfU7JwYpbum5j65wrZ+pqatfA8AoH/sIbmNdqamt/q5PGVcp/p5qmJNTd8xOJDrDkYmRFqQGGXRPPeEUBRsibZqnnsHPcWGuiSp11HUer1LX3bERZh116YUQsjr3uqtEykF7pNj9IO2OYognHJb0jj8fiAfFTVOpMTY5DVFNaefy332rX6ufm6EUAQQFVXElWNVVMuamrs9xWh6t4tTbds3kFpR48C3W04AAC70ZHRqPc+5pdWodrhgMhpU1ch9g2uAOrvQFuZ/jioDyer9ucchr6waJyvsMBgUa2r6FEKSpvInhRUBhZvkbft+ceLN1PRO9baZ/Y9PCqKekxqtGiffc0+afi5lYWoVCjpW5Ml89VQ2l1+DdieyPV8SSFnkEun5k/p12BPUVq676T4+z3uoou9fbTqGvNJqdG0dhX9ce57u65RBzdPLhg0bkJWVJf8sXboUAHDDDTcAAKZMmYLvv/8eCxYswOrVq3HixAlcd911oewyERER0WmHQc0Qu6jqXwhb8RegaDsARVDTph8oALzrGsrrl2lOP689k1HKlHko5Rt83uVJ3NLqJ//pvdKUzUhvMZNgMjX1pkuXBwgyKh9nMxu92XIBqnVL9AKtvvtTZW5JU5fDzTAZDTDLmZrBrKmpn5UWpcjU9D2+MsUUde8aif5j5ZuBqRUU+/1APgCgT/t43f2V1DFTU8ry0i4AVA2XcGcSS+tl+n1wLy3HR51m4P3UR9A/bzoAoX5uqr2ZmpEm73qg8WEl8J3PfMgzHTw+wixPX/VbU7PCG1wKVNHb3dYbCO9vXg1Uu8cP+b+jpOykHBTqmBihWFNTvfbd/lxPpmZrRaamz7ke5nSvG9rRVqCoZO3yqw7uXVPTLBfJUQZLKmuc8jmapph+7jsGyqnCBoNBc5p6abVDfq7cmZr+baSMwS5J7gBywKIoiqCeXrsap5CLxEiBHN+x0lwr02c7uYoCMoA3ACXtb8lOd/bTsB7J8lqFWlnQ8vTzSPX0c9/3jtzSalTZ3QHENvHhmtm2Jb5BTZ+gn1T8p4NnKrLEt4DYV5uOobjSjg6tIjCkWxIAZSDSP5OxTVw4zCaj5nuxshCURKvv0vTs1jHaQeJ92e7zoGOrSPn8VY55tcO7xETatvHAzxcCZe5lC7z9Eqo+SZXP3X2SgsTePu3xFJTqnhLjHifd6eeeQkGRUqEg/UzNtvHqoGa13aX4kkAd1DSbvOuBVjuc8ntP58QoVTutcyazwN32qp6piFBk4Pu9Nyq+gKGWLykpCSkpKfLPDz/8gC5dumDw4MEoLi7GBx98gFdffRWXX345+vbti7lz52Lt2rVYt25dqLtOREREdNoIC3UHznRRwp2hg/z1QFxPOSgYG252B+Oq/T9w250uucp4crQN2SVVQU0/1wpMVHo+VPYM3w8A6Gw97pnu7p1SqJWpqTfVW7mOp36mpv50cGWfws0muRq51v6k9S3DzSZU2p26fZLaRVpMKK9xqvqlLBIEeKs9a2WGKrcFaAdtpfVCI6z604nlwKdi3U2t50a5niagnRm6xhPUHHRWIvbllHoCDjqZmmH+AQetdoly8NB/f3ly0Q+rXHzDt+9tMp5C16htAICOxZ/jjlax2OuY4G1g92ZqRhkrUeKMQozJHURxleyHMfZs+X45czIpSnc8pey7+AiL7rRr37YAcHX4t947XHYUHFwCIApJ0VZE28ya08+r7E45g69rcjT2ezIbffcXDncAKsGQrapyXO1wyeMGeM+naJs3U1P5epcCPa0iLYgJD9MNwkkBL7+pwopIT76i8Em4xaQZeJGCmlJ2Xoz9IC6M2IkCZz/4kgKEcRFm+dzRmhYviY+wIKu4Sn59S+2lTNzkGKt8n1+mppxZ6A5E+Qbw1x5yvw6GnpMsP0YrsOstFKRf3Afwrl2ZFmeD2WRUrckoKa5Ub8s3MHj0pLf4j5LVbAKqHHI19Q9/zwQA3D2ok/z+Y9HIZJQq0l/cOQGAdtEvaSxrm+rtzXy1arbxXU9TOVbVDpc8jiajAabyg+4G5UeAqE4aa2qq96UcK+X6ybuz1OdwUtWfOC/8AGocfVXj5zf93CdTs6TKLj83bTWmn0uZwSkx6gxMg8EAW5j7b0m13SWvj9nZE+D3HQfluEsBbGl/el+unFQUCqLTS01NDT755BNMnToVBoMBmzZtgt1ux9ChQ+U23bt3R/v27ZGeno6LL75YczvV1dWorvaumV5S4v4bYLfbYbfbNR9DgUnjxvELHT4HocXxDy2Ofx25Gjfnzu4yev/lc9Dkmur8Z1AzlISARbg/yKF4l/sfefp5GPLLpGlu2mtcGgzuD696QU15LUWLCRU12kE/6UNsW3MWACDeVOo3dfekIrtJr5iJRLWmps4UYL9MTbt2IMRmMSHMqD8dXFqbMj7CjMpiZ60Vy1tFWVFeWKEKYEhZO9JUxFoLBdVS/Vw6tkhV0EjdTlkhPVC1bmmapcVkRI3T5ZcZ6nIJrD1YAAC4pGsiPl1/GKUa+yuukDI1hRw4812uoMbhkgNKUqamVuZrbYVmUHYIXYvmAQA2G4ahj1iCv6d+gL+WjlLsTDn9vAJhBu/jnSeWqIOaUmAhMRJOTxanb7+KK73BNatJfzwB77mcFFaI3rbtgMEIpI4ETvwIV9ZSANeiU6I7iCEXClJs6nhRJYRwBwYToyyqYIlMuBBpcAdjIx0n5EAr4A7Yq4Ka1f4BbmWm5rZj7ozP89vGqjIwfYNw+xXrfAKKqdCqytjqqcla595BT3Zal6QowFGJnpsvxsKzgDHHv/AbSzmoF25BVpj7deSbcSwF8C1hRkRZwzxjpXz9uYNBNrMRCZEW+fXoew7rZxY63ZXRPUHPjq28ASjt6efqTDnp/cwlIBePAYDDnsy7Dgnu7Vk1nmf/NTXVhWbkQFeCOnimDEwfyi9HRn45bGYjru/bFoBQH59ijcslnkJI0pqhWsfnWyjI3Xf/gGy2T7aif6am+3w6O8Ub1FSOufeLLjMM9iJ3A0e5ql/S34g8zenn6iBqSZUdx4vc49U9JRo49h0u2j8Gn3aOwC0F36nGz3suqAOy0uEd82RpJkRaEOk555SZr3qZmu6xMrqDmg6n/IWKtBSDRGudWbl6vWe6u96yIgUsFHTaWrRoEYqKinDnnXcCALKzs2GxWBAXF6dql5ycjOzsbN3tzJ49G7NmzfK7fcmSJYiIiNB4BAVLWh6AQofPQWhx/EOL4x+sXqdkq0uP9gSOLj4l26barVy58pRun0HNUHKUwQhPkKxkt/sfT6ajakqnT6BAOWXaGiBzUvowmRJjQ2Z+CS4xfQ+UpQBRneU27gCiQIrJnTEaH1aiu4ZZQqQl4HRUd/9rLxTkG9T0zUBUZmrKQTifPiU6t6OD/SCAboiLsOBEcZVmn4QQcuAoIdKCI4UVqn7JRVg8U6mlgiAOnennpbVMP5cqrSunlvsG2MoVgaxA4ymNZXKsFUcLKzAwbDlwMg6Id7/Z78oqQWF5DSItJvRuFwdrmAnnh+9DxLGjQMr93u1UaVU/95mirghGt9Iq7uOhDGoKKciobFd6AACwp7IDfkp5DV3styGmYjMuC/sBwGh3G8X089iwckQYvVPQkb0EOGey/Ks8BTQpSi7c4l/93I7EsJPoV/5vmEQVhkQnoNoxwq/vgPcLgRSzOxgMWyrQ+U7gxI+IL14F4Fp08AQmLBqZmlLwrHWM1VNZ2j/b0VVdAqPBE5yqOQ6T0aAITKvP9VLVmpr+WZjeoGacu086QXBp3cK0OJ9MMWWmpk8Wn+906WqHNwu1S+so4Ig3kBkH/+IW0ljGR5p1z/UKuSiWSTMwePykVAQpXFWp2z9TU3/6eYUi+zpBsZakVp/8gpph3m+DaxRZtNI4SFWsbRrPs19QU1FoprC8Rh4fv0xNxTGedLr7kxJjQ6Q1TP4WU64O7nmv2n68GFnFVYiwmDDorETV8Sm/zJErdtdSaVy5TivgH2QsL9iF6+L+wNmte/v1u9rhgjNrOYbHbER2xF8A4dmu0z1mvhnHuYr3DLlPPmuUSkHU1Fgb4hwZwNrbAAAxpgrEIFd+XLXDKWftSlPLfaefezNkvcFk6Xmtcri8mZpaQU3PMWYVV8nPn/Qlh8T3Neh0CTkg683U1M4YZ6bm6euDDz7AyJEjkZaW1qDtTJ8+HVOnTpV/LykpQbt27TBs2DDExMQ0tJtnJLvdjqVLl+LKK6+E2Wyu/QHU6PgchBbHP7Q4/nW0c3ajbs7uMmLp0Z64st12mHs+0ajbptpJ5/+QIUNO6X4Y1AylmgLv/30yNWPDvdNf9YoNxEdaFIEQ/yCc9MG1fasI3G19CbdF/QSsWwsMXS23qbQ70TqsEFaDJ1BaS6ZmoP0BkKfFA/rVuv0KBfm0kx6nLhSk3t+gqqcxCMA829tIiLzQ3UYzE9UF6aFalduPF7nHSAoGhekEUeW+11IoSA7iKIsA6QQ1I2sLanoCja2jbRiEb/Bs4tvAin8D49xTbdcedP97cedWnimyRnzXfiqwD0Dni4AE97RNb6EgZZEL9XhKbaJtYXIAxyXc4yBlrwLuAiKAO5tTeozq+CrdwfEcRyu0irIix3IXYjI2Y4TtO0C8486MVGRqppjzVf0w5a9V/X4o3zsFVCrwobXm62PJH+OcvCUAgHc7hOHu0kv8xtPlEvKU6VZh7mAhrIlA8uUAgATnQUQby+VghzlMWlPTu408n+rSWpmaZWX5kD56GquOAy4nbGZ3ULPSN6hZ5R13ranl244VAXBnagLQDfpJXzwkRrr7pTX1OsenCrVv4OVwQQWcLoEoa5h7qnD6u/Jjza4K+PKuZar/ZYf0eoiwhHkDZ4o+SdmMbeICB4Pk16knCKcMLEnvh5YwIyIVWbDS60/qU5XdKb+3xEeqp59L7fyCmnKhGfXz7HIJ/zU1Fds64FmWQJktKFE+N1JWeqzPGovyOHj6+8tOd2bX4LOT5HH0LaQjhFBU7FYUCtKYfi59maOXqfmI9e/o2v4QskrCALzkbiNlRDrsOGf/7fh3h0r8vfIDb6c9mZrK7HplFq329HN3n/YoK61vnwU4SuW28YqA+gnPeRBhMckV7KXjq5GCmp7nrq0imCy9pzldAsc8QU+tTE1pW1KRpzZx4arMakD5xZD7jSG3tAp2p0CY0YAUjUxipUJmap6WDh8+jGXLluHrr7+Wb0tJSUFNTQ2KiopU2Zo5OTlISUnR3ZbVaoXVavW73Ww288NwA3EMQ4/PQWhx/EOL4x8ko/Zn8IYyG10c/xA61WPfuIsW1MOcOXPQsWNH2Gw29O/fH3/88YduW7vdjmeffRZdunSBzWZDr1698PPPP6vazJw5EwaDQfXTvXv3U30Y9VPjzVhDxRHAXip/UI6xmTWnuQHeD0atIi0wa0xZlUhBzQ7x4bit1U/uG3N/VbWpsjvRwZIl/x5nKvXL1FSuqRmoejZQv0xNvUJB4RZTrUHGQVFb5TXttKZLS4Ecm9mIWE875f5OFKmDKt4px7VPP9eaXl9W7cDo2NXoUPQFLCYjLo/+Ax0L5qrayOtuKgtKBKhG3jraisdT/ufZqTcQLgVeeqS5Q2gpFsX5VFMk/1e9pqb/uo3KNsoMYa12mtPPlYE6KahpT0CrKCtKWl+LYkckUkzZwJobgb1vq4KaiVJw0cNoLwAclUDmpxAH5yJDMR1arxpycWUNLov2Vl+2Gh0wu9TbBdxZkdLTmmBy3++yJgKWeLmN2eCQgy9ScEa5u7zSatwYvwRXRP/u3pdGEK601BuoNQgnkJ+OyUmfwmao8iv2JAXJo2xh8mtZer2XVTtwwDP9XsrU9C2QA7iDWQVS8RSfqeXqpRakQJZvYNDdJ7nyeesoGIq2AgXeYhYWlMNXkWLaf+1BTZO8tqjyy45jPhluetuR+x7nHzQqULwfSkWCAP9MTenLmTCjQZ4KH2ZUFIdRZMRneYJn0vuCb2CwrMZ7LsV4gpoWRfBfmsbf1qdytrLvNQ6XXGxJCtD5tpGem9X78gC4CyFJfAsFlVQ6UON0YWDUFiTnfCQX3fIWCnK3c7kEcord50uqFExWZE46XQJdLYfc9x/9l1+fYlGAMFc5TAYXzg4/7u20lKmp+JtUVu2QA/mtY6zAsW+Bff/2BjXtUlDTHUTsnhzlztYG4DK4z+VW8E7VlQKSbTyZvYByTU337wm5X2BM3ErVtH9lFu2JIDI1d51w98d3PU3A/xyVihKlxtnkL4B8g8S/7MzG9e+slae0s/r56WXu3Llo3bo1Ro3yLrPSt29fmM1mLF++XL5t7969OHLkCAYMGBCKbhIRERGdlkKaqfnFF19g6tSpePfdd9G/f3+8/vrrGD58OPbu3YvWrVv7tX/qqafwySef4P3330f37t3xyy+/4Nprr8XatWvRp08fuV2PHj2wbNky+fewsOaZkGpQBKgAACV7UFzp/rATE6CisLKCqsvzwVUryChNs+sXtQOQEq1a9Ve1qbK70MHq/dAYF1aKLJ9sMmWmphRA0Jt+XqrIZGxooSBrmDdTU286eA/bQTg0MjAlyowrOaNHI6gpBUvkQkF6a2rWUihI1JThrQ7/Ag4B94SNRv9O3wMFAMpuk6f9l8uFgkzewJJGgFTK1Dw7Mg+tKkv87/cEPaWgSg/bfu+dRu+HZnlt1TChP/1cGUz3yV5TJpEpg5pSRWpVkLFCCmq2wnlRFoRZLFh4cijuSfoWOPqV+yc81e9YcuyJiDWVwGasAUr3AmtvhQHAmKhJ+KLoKrRPiNB9PcQ7DiLVUgBhtMIFE0yuCoQ5S/32IWVpmowGJHiCqS5LEowGAwADAAGTwYUkz9p/WoWCwk6ux0vt3oQLJqDiFljN7sFRZWqW5ql3vOYG3BefjZxKC6od6tR76bUQbTX7rVe743gxhADSYm1ydqVWYLekyiGfr1KwRCvY6ruWoG/gRVX5PPNTVT+t8M/UlNZqjQs36xYwqrB7g5paWa3H5QIrEao+OVxCXuOyssYpZ4VKAVllYFCqhu0bKPI9X+RlOyK8wU+DwQCzyYgah0v9viAtS+EbRPW8TqVjt4YZ5fcVafp8tcMlZ2r6Tj337ZdyDJW8U/XdQcb9Oe7t9WkX77cdee1KTxbxp52fAv4EYIkEOt/hV2m8oLwGNU4XDAZv9qQyc7KgrBpmRzTiwzyvoYKNQKsL5f21sXjP7/aWLED6HktaU1NR3E1aPiIxyoKIMAPw2/WAcOCChEcBDJUD3Adz3e36x2UCRQVAWDTKE0ciOvtLJBq9mZrHfQryAN6grd0FwF6CsVVP4Zp2wILYu73jGWaEwSDHeT3HrhXUdI/VLk+mZufEQEFNqcq9f0Eoq8/0+n+vOoitR4vk+xnUPH24XC7MnTsXEyZMUF1rxsbG4p577sHUqVORkJCAmJgYPPTQQxgwYIBukSAiIiIiqruQZmq++uqrmDhxIu666y6ce+65ePfddxEREYEPP/xQs/3HH3+MJ598EldddRU6d+6MBx54AFdddRVeeeUVVbuwsDCkpKTIP4mJiU1xOHVX4xPULN6tKBRk1s1Mk9flirLoTs92uoScpdi3RlHkI0zxIa1gI/rkzcJ54Qfkm2JMFbDbvZU3AaCwXFo7z6L6wOp3OA719Nqgg5o6hYLCLSZ5jUvV/oT3/+eGH0KcJ+qm1SdvUDNSkbnl3r4QQg5qStPPzZ79OfWqn9dSKChGqmYPoD++995h9wbZVNPPdYJBgDfQeKnRW6Vb2LyZWlLQUwpqdjfv8T7Y5c2Y9QY1vZmavmOlzNRUZq8FzNTUKoYhTz9PQGKkFWaTEa/m3IpXCh4AIjt62mTBV6mIQo49wf1Lfrp8+8y0/2BEcgYsYUZYTCYMid6AoZUvyONZZXeiv82dpelIvAQOcxIAwOwq89uHVPk8KcqKhDB30MJhbuW+0+A+FgNc8rqNFo3p5+eUuYN9RjiBg//1VsVWBOaryn1e11XuLw262zIhSg4C6ycCJfsAqDM1fYNw2z3rafb0TD0HtIuUFHiCWdHWML+pyfJr8NA8XFr1NgDhFxiUnj/ptdKpVSSQtUR1CJGGSr9s6SJF9W9pf2EVh4ENDwLF2wEAFZ5zXTn9XJnZe7zIm3mn7LfyGKUAY5Q1DDG2MHXfHS7Vmr9Kvu+fUmA0IdIngOgzndjpEopCOj6Zmp7n2Xc9TXlbnn1KAWKtTM3wMCdmpP0H8UVLFNmuvtPPve9VRwsrUO1wwRpmlAvRwOVA56wXcV3ccnmc3EWCFCfr3tfdfQ9TZ5lKWa+to63y3w9lEC67pAoVLkXAL/12IP1OWB3uDOQ0szeomWpUZGo63M+lxQRMS/kQiYXfequop0QDFUcB4T4fehS+jsFRm+Q+pVWtxdOp76N79Q/ubaVcDldkF3c/Td41NY/5BMEBn0JBVVkwGlwIM7jQJcL7OlSu1wq4CykpzzWJ1GZfjrTsRZRfG9+1TKVMTVVQ0yfTVvpCReIbxKaWa9myZThy5Ajuvvtuv/tee+01XH311Rg3bhwuvfRSpKSkqKaoExEREVHDhSyFsaamBps2bcL06dPl24xGI4YOHYr09HTNx1RXV8NmU2dXhIeHY82aNarb9u/fj7S0NNhsNgwYMACzZ89G+/btdftSXV2N6mpvIK+kxB3wsNvtp7T8vKhUZ3Q5T25HceVZAIAos0EOsFVWq/uRV+r+wB1rC0Op58N1VY26TU5JFRwuAYvRhdYl3gCFy+WE09Mu7PfxOL/8AM73ifnaK/JgtyfJv0uZUDEWI0yeAihVdoff2EiZUJLqGv82AFBWpb6torpG1a68yr0dq8kAo+dDeo3dKbex11RC+kh4Tngm1lo9U6odLr/9ZeS5P1S3jbNCmoFYWe3uV0mlXa6gnhQhFelw76/a4dTse6mi71U++xNCIE5oVzW126sAT1vpObOFGRDmGc9qjfGUqnqfVePNOhYuBxyediVysNIAu92Os8K8QU2HvRLCbofTJeTs2XATYPAEhO0+fT9Z5j6nom0mOBwOWEzujLOKqhrYbd415aRzLyHcBE/MDxWKc89UcRxGALn2BMTajKiocaLcFYH/FY7BlAvKYCzP1ByfMlcUapwR6GDNhit3rfxti8XowORWc2G33wcTXJjd9i2kOAvh+r0YzoELkFdag79EbwEAGJIvh6s8G6gGzK5Sv/GUjjEuPAyJZvdrvMoYD5PdjjCDCQbhhMngQkK4CXa7HZ74Lxwu93sBagrR2/WLO6kTgDjwPsIu+Kvn+fOOZ1WZep1QSVfbEXTbcx1gPwbXye2wD/kVZTXScyO8r60a97m383gRAODclGh520a4nz/l+ZlT7A4mJURa5NukguvSe4d53V240QYsiuqCpKj+sNvt8rlX5XltSc9tmvUkkLMVAGBvfSXMuUsRaapAeVU1IizePxnSlytRFiPMRgOijeUYcehq93NRVQTgJpR5Xss2s0F+/ZUr3s+kIFVytBl2ux1GxRcWZZXVCDOYcbTAHWBKibHC4XCPlxSPqqpxIK/UvY34cLPqOQ+TXsue48svcbeL9WlnDjMA1UBFVTXsdov83mk0APE2I+x2u/xlToWn7wWefcbYwlTbkoJZB3KlwjdWv/Owb1g67kr8HkXZe7HI+CUAINpqVP29kV5bVTUO7D5RBMCdQetyOuByAsYtj6Hd8bfwj7ZWjDxxNex2O3KKKhCpLLp1cjPsxQchLQkpjfsxT2X35Bhv38yec6HS7sDxwnK0NSq+2CrZA5TsgSn6HADnIU2RqdkK3qCms6YULrsdXU27cX/rr1GRvRY/hf8FAHBWUiQcRXtVFxwPtl6A52suhyNnHf6ZMN2dpe35vsOZdDmcNe6xTA7Llft5xNP31FjFue45F+wuwFF6XP7bkGrKUY29LcwkZ4Z2TozQfH+XvsiQtI+3+bWTzr1Kz/vekcIyT5/8x1N6bUl/NyKtJozokQzhcsLu0l6epbGdymsYAoYNGyYXzfNls9kwZ84czJkzp4l7RURERHTmCFlQMz8/H06nE8nJyarbk5OTsWfPHs3HDB8+HK+++iouvfRSdOnSBcuXL8fXX38Np2IttP79+2PevHno1q0bsrKyMGvWLFxyySXYsWMHoqOjNbc7e/ZszJo1y+/2JUuWICLCf/pgY+lWswHdAbhgghFO5B5YjWM5gwEYsHfHFhQVGgAYsWnzVlhObJEft3O/EYAR2YcPILfC3Wb7jl1YfHKn3OZwKQCE4fzIwzC6vB90Cwvy8fvixQCAMeXeDE2lXVt+Q8mRTADu6XoFpSYABmz5Yw0OlhgAmJCVnYvFnu1IKipy8Z8OH+KD/LH4o/w8FBSX+rUBgPxi9/YiwgQqHAb8sWkzxBHvh4LtR9zHl3PiKCryBAATDmUexuLFGQAAk6jG1Yrtle1fCqADKqtr/Pa3Ya97W6VZGSizu8dq/8EMLF58EMfL3WMUGSawctkvAIBDx93Hl5F5BIsXZ6q2JQRQWuXuO+DO6Pr+h8WQltKrcQKpniymLGMf/K9gLO6Oeg1J5iKkr/kVJ03ugOeho+4+ZezbjXwzAJiQk1eo6nsn+2KMdx3BbtOdiKk5KAfSHDWV+MnT7kSeuy+7t/0JZ6YLl2GX/PiNf6QjJ8wBd5Kt+2UeEQZs3rgBQBiKy8pV+9twzH3cxXnZWLx4MYzCve2ly1eitSLZLOuk+/Zdf67D8Rz3cezddxCLa9xT34eWH0Ik3Gtq/vHbChTXuPdfWW3H7hMG9IC2Ekc4ShzuLyzKj6xENIAjjs5oY8rEOcZNWP7DeyjPE0iJcq8bajzxPfZ+ew9W28fi9kh3VuBv+8PRvcqFcAAmezF2fzsJsa4MbLE8CBiM2FrgPsbqilK0CndnQW4/WIDCrMUY5XKPkhECG9esRJgRyMlyH59TAEuXLkUX+yKcZ6jB7sqO6GArRETlMWSvfQPAIJRWVMnj6czfA/gn6OEs61GE292vRVfhZiz64ScI4X5u1qxajv057v4dOX4cixcfxc4M9/7zD+/F4sXu98RSz/Npdwr88ONiGA2Qj8to9z6n2cfdj92xew9+Kt2Oazx96BW+H7s3rkXWdiDT8x5RVFqGxYsX49Bx93NrOvg5YAaKjJ1QUGBGFwCRxkr8+NMSKJMccz2v420b16Ewz4h/tXvd+3we/xMIvwl/bt/lPq/yc3GsDO7z5cAhHP3mCVQYEnGi6AYABuz9Mx25O93n/QttMvHMiQfw05KliLUA63LdxxdW430/OXjCfduhw0eRn+XebnGee9wkR8vcx1dSVoHFixdjTbbn+S8pUJ37Lrv7OFau/g0HorzjEmMWWPKLe83mXM+5sG3nbiwu3oUtnjF3VpWptuX0bOs227twJJuQvf8WLM7fjghXNnrUfITDYUORVrUbsACWmhPYc+IIACNOZOzH4sX75O0c2Oset2NZOfjht2wAJoTbi7F48WK0ty9Hn5q3AAARxmqIihwsXrwYv2YZEGtSZygfWjIdx7NuB2DEbs/r9Ncsd98NFUVy33fnu287kZ2L5ek5GBzpDmputkxCinMDUp1/4OiulTAbeqgyNSOqvO9Nhw/uwvZjixFRtgewAVZHHtbuPOQe85wM7Fj3C3oDKDW0QbQ4jj4Re2DK34ea5f+HCKP6C7GVe8ww2HNwBYDUsDz5XN+e4R7f3Iw9WFyy230ueJ5XuwvYtH45/uLZRs7ulfjzkPeErag2IdxQjZfbvYZekXko+DoaW6wPIdyVh7PtC7DXfDOKC8+CcgLL0R3roXhaAABZx9znws49+7C4fA+2HnD/XqB4ne4odPcpO68A3/+wGIXl7tf5kz2rEWU+gsWLj6CpVFT4Lx1BRERERHS6aJ6LTep44403MHHiRHTv3h0GgwFdunTBXXfdpZqu/v/snXd4HNW5/7/Ttq96l2VZ7sa9gG1qMC4gIHQSkhsI4UJCICRxchN6SwJJfiGQQuolkEYghIQkFwIYAyE0A6ZjbNyriiVZbaVtM/P745xpW+SVtCtp5ffzPHp2d3bKmTNldb7zfd/3tNNOM9/PmzcPS5cuRX19Pf785z/jsssuS7ne6667DmvXrjU/d3d3o66uDqtXr0ZBQUHKZbLCxqeAHYBetADo3IgqXw/g8gJ9YZxywnLsfHEXPjjUihlHzUbjUstp+nDrG0B7B45bMh9v7D6E1w7ux5RpM9D4scnmPE9+0AK8/w5Oqtjv2GRJSQkaT24EAGjPnQCx7T/md5ouQBR0zJ5SheOWs3lCkTjirz4LADj39NV4bksb/rDtXRSWlKKx8WjHulteuwMT9FchChpeC82B7PaisfHEpN2+6a1nAcRRVeTHjrY+zJw9F41LJpjfv/vkFmD/bsycOhmFXgVP7N2K6toJaGycAwCI9XUAj1vrWzlDx91bAU2Q0Ni4xrGtX+x8BUAP1hy/BJube/DU/m2omlCHxsbZeHbLQeDdt1BfXoDGRpa4f/+LO/F/e7aiprYWjY1zHevqi8ah874w+NjK1QjykNj2UBRNu/8EACifdDRigfPQc/CXKEcnjl1+DPSyYwEAD7W8ARzqwNLFC1DoVfC/W96EL2i1Af0HID9+HuYVqjgQCUAQLMFXFoHGRnZs7vjg30B/BCtPPA5zig5BecLKu7lk8QLotY3Y3dEHvPEivIoIWQSOO3YZfvj+G1BcHjQ2nuTs8727MWd6AxpPnYHb330e/aEolh93AgsdBXMd9b/Cih6c17gKnS/txrNNO1A7sR6NjbMAXYP8aCegAz1iOc46oxHN3WF8660XoEHEjGPOBl76nblN43wDgKhUhJZ+9gAhqLNzdr/7GGzpKMeqwg342IStCHqCwCEgrPvgEfow0/MGuqZeAf+HYYQ0H44/40pEn3sSaN8EnxzBXDwKId6Bmo99ByheiPBb+4GPPsCEyjKUicxJVz9zKZbNaYTwqAJoURR5JXz8DNa/L/99E147uA+qDqxZUgr3fx4CAPy2/Qz8zzER+A78GssntAPvOM+9V//xFBABVMiQYKUrCEjWwwWpcDqWL10BvP4CFEnAx08/Df1v7MejuzahtLwSjY0L8aOtLwEIYfUJS7FsMgvN7wnHceMb7BxctXoN3IqEztf2Ah99iKkTKtHYuAAA8NYTm/FSyx7UN0zFaScVA/9g253sOYAzzzoNkihgU1M37n7/VUiKG42NH8P/2/wfAP04rvoA0AYEp52LIERgyxMIiP04acUpZg5GXdfxtQ3PANBxxuoV2PP0RpwKy2FfWFoN9AF1DVOBXTsxdVId6oq9eGr/NiyvacXk+BP8HLgQsijgk2edBinSDPnx8zGvNI43+o7C8SfdirpiH7Y/tx3Yvh3zprLrFgA6NuzB33dvRlllNXwuCThwAEtmT0fjSdY9cGtLL37w3ssQFRcaG0/Gjue2Azu3Y2aDtR4AuGvLf9DZ0Y+jlx2LRROL8K/3m4H330VDZREaG1kO4jce34xXWll/Nq6ahp439gEfbUJDTQUaG618zj/a+hIQP4ArK/4CAPjouDvQUF4E+dnjIfR/iOpgPzbzPvQJvQgWFgLtPVi+ZAEa51cjFoth3bp1WLRgHv6w7QMEi0shFriBvc342ILpOH1OK6Tnfwk71d4eNDaejy3PbENh827Hd9M97+Ko6VPx7IEdqKmbiMbGo/D+Ux8Bu3ZhwYxJaGxkRfSUTa347da3ESgsRtmEYvh4IaE5a66F0PwU8PprqC+Jw+NWUKNYTuQCwSpONmlCBeqObkT8YXZ+SoKKGFQAEs5buRxzD/0H2AL4ppyF8N7/gyeyB7dW3wMf2rE9Uotftv8Xvlfz/4CCWThp9WXoa98EPHcbalwHUbNqDbwuid3zEMEZJx+L+TwtQ2jjfvxl5weIacCUiSUAq3GEhdMKMX9Oo9m+L7/yNM4pehmnF7EiX1CBVVObIXS8AXHfa6hS9uGJmvvwwSH2kLSqwI2Lzl6NRN56YjNebNmDSQ1T0Lh6Gr7/4QsAwjj9Y8uwuJ7lPA1sbcN9/L6+5ISFwAZ2nZ9/5mkQRSFpnbnEiDwhCIIgCIIgiPHIqOXULCsrgyRJaGlpcUxvaWlBVVVVymXKy8vx2GOPIRQKYffu3di8eTMCgQAmT56ccn4AKCoqwvTp07FtW2pXIgC43W4UFBQ4/gBWej6Xf1K8EwAgFM9jr+Fms/hLadALt8LEMlUXHMt19rF5ygq8cPHE9Cqc8xzsZSFn83zbAQB7IswRKwi6OY8oOHPkNWMSAEBWD5nz9EStCrqFfi+8buZ8ial60v5ovKr1NPc+ACwcPHEeWZbNkO/SABvcxzXe19t+BOXl86HHmdvI71Hg4eGumm47HgkhgjMP3IA/NlzPCsTIsmNbRnjr5IogvIqEW2t+gU/HvwFFltDaw9xBtcU+cxm3oqTsc0VREFHZdu1jUhUiFL0Pysvnw7v5NtRyF5McbIDPJUPVWeynLFrtNypCF3jd8LmNfKC2/tz9O1Y1G8BnSpl6+354Ojt+etw6NjysvCTogdL9jqNPZJGtr5cfv9Ulb+O4/htQqDKHUExzHr/eCDsXiv1u1g88xlITRHOeUOtbeGTKN7As+CFKgl54+bGJGcdP64Kgx6HpAopKJkJRFHP/4poOscgpErfHrVyR/QiiOVbq+L5Vq8QfO9hDCmn379DQz9IobNSYGCvEOhHpY+dcL4qhuNwQ3WydPvRA4OejEmuDoigwajwFvYpZ/TyqlENRFOjcblYRtM4fI1efpEXgfu0iCFoY67uPxp87VsFdvgAA4IkwESkS18xzT4gxEeGQPMXcl269xLFvgrsEEW4wD7hluFwu89oyrgcjf2lNid9sk99r5V7URIndD/p5CoUCD5uv+Z/478glmObezY6NblUun+fbCY/bxdbl4ddfPAbl1U/iCv+P4RaiKO75N9vv2tMgeYoAAH6pHxqscyGqi2bxrvICH2pkZ55UgV/DRrF3v0eBz8PaXqI6hbeqQg9r057fQ+A5Fz9d8i9oYPvX0m1cp1Y/+NwKbqz+NS4VbjBze5YXeB3ntLE94z7Uye+tZUGPYz4jL6pxD23l9077fcHHq6VH+bHpjbLrpcjvcqzLo0ioUqxcjvWeFihvXQ2BuwqFzrcxWXjX6qcou1+U2trE+ou3XdWx7SBz2s2t0CC/fCEELQpMOAeRwFEAgGKxHYqioCscR5Hh1PRUAYIIoWcLKrgIGVVZ21v4fW9CcfJ5FYnr6Oix3J6KpwBy4TQAgNi3E25ZcoSf2xG1fiiKgnJbYR+Vp1iZVVsMqW8XAEAqmIZQyQoAwDw3c1n/tOUT2CicCuGMTRBWPgvF5YKvhF0/BVIf9HgvNEFEK78mJpUHrbZ7+G+SBsT7rW1L4f2OY7O4vhgnBN9iX8osV6bU9TbEQywnrxA+gM9L18NIQbJwYnHK322Pi1+nOgBRQnM3a1NDRYHtfDHOPR3t/Pe6IuiB2+1Kuc5c/xEEQRAEQRDEeGXURE2Xy4XFixdj/fr15jRN07B+/XosX758wGU9Hg9qa2sRj8fx6KOP4qyzzko7b29vL7Zv33kQvUsAAMumSURBVI7q6uSKy6NOlLlc9ELmQESsE7EoG1AOWCiIFx0o9bvSVrM2ikFMldhg+p1+JorBnvtJtUL+2pQ5aEcNAECKWe4bI09mCS9kkVgkwY4e6QQA1Lma4RYiKQsFReKsmi9gFfaIxDVAU4H3bgP2/wPzIiyRvkexFQqyF0LSnbnIpHgXjgu+i8W+D811A6wwSA8vVDKh2IelvXfjs2X/hwX6OqDrQxzgxUCMIiWAVUhHTVFt3VhXwC1b1YKjUeCli4D9/0DBjrswxcMEXfgnwiWLiHNR0yiQAVjFhvxuObmgixYHtv/KnDfI3X0fRI9yrCeuaqY4GvQorGK4HV4oaGcbO5++U3EnyrQPMOnt0wGwnJp2EgufKCkqjYs7H8DR/k24qeY3rPCGkjAPLxLUHi/E/IksUauj8It3IiCxEPOIWIBu1SrCERYK0BJ3Cn8HouV4oWchepQGIHoI5eHXAACvxFbwFXainxfliYrMTSq62AOJSvkgzKIpYSZ0GFXn/S4ZRVzUDAlsmxq/FZbZ4quNa2uCtA1C/z6orgp8Zc/XIYoyfCUzAABy3w5zfuMYivxhxUH/8Uw8qTwFu4QFjn1DtAvdXGUNclHGOPeicQ190bh5vhnuSMC6/oz5AKNADFDmdwFtrwL/OQ8T4m/hgpJnEFVVINppLjPFtRNQ2XlvnMMN8jZg719xccnf8cT0L0GONAPuUqD8OEBm/eoX+x33IaPwiUsW4VFEVIpOR7gQ7+Z9rpp9blSpDmpWMS0RKrv+NBXYZp33C/1bIBx6E4BVKKi6yMqn7JY0/Hf537FUfBpaHxNUD1coyBDEKguceZmVhOJLRpX4Gtt9wWNWkz9MoSBFRIVN1HSHtgJ7/sxXUsGmwXLsyhEm+iVVP+dt74vEzaJD8yOPsKJTBTOB5b+F5qkFABQLTLQ81BdFgSFqBhqAkmMAAA2xlwFYhYKMIkhVhbb+tBW2OdTTaTVE8gIBLs6HdsMna47wcwcqz+0qWMJiidyNCcVeBNwy0Ludt20KImWnmPNEpSI80XUcq0ZeMMPsJ9kVQEecXc/x3t1o6gxD19kDtlLbsbYKBQnQbKImQs4Q73sunI/Ty99nH6Z/ib22PAuEdprzTMFbKJbYuTu/rijlbtoLeu1uD0HVdPhdkuM6NYuoxTW0dFvF1QiCIAiCIAiCyC6jWv187dq1+PWvf43f/va3+PDDD3HllVciFArh0ksvBQBcfPHFjkJCGzZswF//+lfs2LED//nPf3DqqadC0zR84xvfMOf5+te/jn//+9/YtWsXXn75ZZxzzjmQJAkXXXTRiO/fYYmwwa/ubwAkFnpboTB3WaFd1LQJS7quo50LjcU+l+ladIhUuo7Grmvwu4abUKWxhGBv9zFRU7MXJ9DYeq7e/Q2sn/AI+oUiAAmiJhcvivkg0mhTPB4DnjkJePlia7PcGScJGo4PvI1/TLoMeOdGxy4b7kKj/XfU/hSrD/430PU+wN1dy9RHAOjwKhJkQ0R17B9bh6YL+NjmX0ItWQYAqFQ6HGKrUc25qsADz8FnMK/rZ9Y6Im22yufW4F5KVW2dY1aqtlWZ9m7/IdD0LwCscvY8L8stCV8d3LIEFVzU1Kz9NsRIQxz9WPB1/KnyU0DL80DTk0DfPmhKMTTdsoR+FJ9tbgO65ujHoEcG+p2OZ2N7RhXfgMDCreXYQZRKnUlCeWIldUM8C7Y8CvxjCtCxEToXCWa7PwQ63jIdbqYg22dUPi/FooksDFOxiXAxTQCCTAzsRwF6NUs0CgsFaI05Rc29kTJokPDupF8ycQXAwVgR3ovO4/sYQZwLWnGJOTRFF3ud4LIqJpuiJhcJgy4dQZH1SwhFbFU6a2d5gGfk6N2By0Pn4dMlT8AjsHnD7ono0fwoC7ghBpnQI/TtZpXQYVURl1VeWT0wAzh7D3DSP9AmTXXsG2KdprgdcDsresdUDa1cCPG5JPN7gFVxTqyA3h6K4JLSf+Lq9qOBdceb8xZJPaxNsU5zmiyowH/OB/4cRO26SlxT8SeUS9a5M8W9H7ogAsv/yARohYmaAbEfBdu/Bzw+G4i0m5XEi7wKBEFAucBEzVZpBt+/HkRUlrIBALwuCV5+zRRqVjEtRYijttgL7H0U6NsDuErwQh9L01Cw/wEANpGx0Dpf/IKVJ7Cvnx2f0kDqCuIxVYem6WjpZuupSBCXXLb52Pb6sargVXwx9HHgEHNAm9XP+TEeqPp5pWLdP9H0JHsIIweBhouRiCvOztN01c+3tvYiGtfgVYCiA/ezL4/6JjsuXvYQqkw6CLxwLq5Qr0KxzEONXSVANQudrosyUXNh/J/A/82EGGL3KPt9z15NvquXrUMTZEBUAG81Oxd0FdO8+1BibCMRXv28BNbxLZW7MKMyyB6mmaLmZKjlJ0Pl19xm77mI6G5UFjiPiyAIaI6xhyN6727s77QqnwuCdW+0Vz8XIrbrvm8P8NqVwPpTgHg/6sTtcMcPst/aGVzUDPP5A1NN96aRl3RBOlFTsn4jtvL769SKgKNNdpG4lRfgStw/giAIgiAIgiCGz6iKmp/4xCfwgx/8ADfffDMWLFiAt99+G08++aRZPGjPnj1oarLCGsPhMG688UYcddRROOecc1BbW4sXX3wRRUVF5jz79u3DRRddhBkzZuDCCy9EaWkpXn31VZSXlyduftQRuFMTrlJzgFopt8PnkqBIolO84FWB+6KqKWaUBlw256RNpIp1YiHW48TgW5AQhe4qxp4oc6o6qnRyUfNgvBguTwBhsQgAIMcPmbMYFY4NF5QhUlUJu4HWF4Bdvwd4uK0Q6zKX+5+q32Gyez/wwXccDixDDCjwyAjIcXyq9Ek0RJ8HtvzInKdK2IWl/vfhVSTTvRa3Oye5YBfXJbToEyEUzDD7zi4A7+ai5sQSH7DvMUffI3LQFDWrbWKJIrL9U1NUpjVFKI/lOvO2MkEzojHhycgRaTk1+SWmx5LW43NLcMkiTi18BbVKM7Dzd0Azq3Qeqr4A7/ZbQtg2bY7VEF21Kpor7FwxhDtrHra9rS1MzIyIVqj3+SXPOPsTtuNiiJp8UF7a9megdwew92+QwzY33rZf2lymXNTrZUVaWmIlWMRzyyU5CwuZ47RHCyJkEzUjYhAtCeHnO/qYyCmULGTONMGFRw+tQFfcAwhsva4wE1o1he2fxMPPa5VkUbOPC7flLtYnqi6iVy/g79l5VurjAuLzp6NS3YzvTPgZPGDh2/1gAl9FgRvw1QGiAkGLosbV7ugHl8auB7e3BHAVA7IPh5QEUTPaaYnkPCer6RhUNVOAqyzwOMQSAEkPO9p6o/iv0ifg0nscLuZy+RATnG1OTQDAgceBeC9EtRcXljyNGtly3kV1BcKiu4EanpvWcGpK/Shoegjo2gQcfMk8X4p87HwpBTv2TQK7FiP9nbh1o4RmnpvRrwim+FQE61x1CXHMDTYDr13OJkz7Ap6PrGT9F9oEXdfRlOLhgxdWSH2on31f4k8tVhp9ajg1KxKcmi5ZhADN7M/9nWGcW/wsimLbgI9YQR7jejfcjulFTQmVsk3UPMDuDyg8CqhKzs8YRAcEaCj2OtNbG85QgwtqP4QQ2gUoRcDEC3knsN+M2Z5twL6/YYG8ATM8PLTfVQxUrQIAVPe/CLcQxWXyrUD3FpzuegQAUGW779lF2x4uakLk3wsiEGApXhZ7nGkuHMTZMSnULVGzRO7C9Kogi0rgvxMITIbLV4onu5ajMx7A8zrbn0QHLQC0qMy1qYf2Yv8h4zxwVuFy8+MX0wAlZnORhnYD237B3JjNzwDNLH0FKk5iQm3AlramdAkTgmGJmnNrrXumHfv1t62VzTulIuCYx/7Ax34tEwRBEARBEASRXUZV1ASAq6++Grt370YkEsGGDRuwdOlS87vnn38eDzzwgPn5pJNOwqZNmxAOh9HW1obf/e53qKmpcazvoYcewoEDBxCJRLBv3z489NBDmDJlCsYkUe7UdJcAPrYfVUo7Cng4quH2mBD+D/BIIbDjt2Y4uFsWLUELCeHZEdugGgBKlpg5A3V76LbGBvkxXYZXkRCVuLtOtUTNDpsrFLBCZON2EbWLhbhLcUvUnOm15c17/Sqgm7mDuvrZ+op8LhTL1naw8/fsVWCDwU+X/gsel5Q6vJ7vgwoJfrcMgfddhdLhcCDu5aJmXYkP6LYqgwMAIgfRzAeb1YVOp+YXyv+CnyorgGZnUSC7s84QAVz9LHTx3z1LzPk0XQS8NXDLoplT0xBidV03HYOGU7OUh0Kj/XWg4w3WT75F+E/PIjY9MBlh0eZi1OI2ZyUXQxJFTR5+vrW1F36xD27NOjYXlTwFVdOTQvUBKwzWGLh7wrv4zm+HN2YTNXf90XTLGU7N1hbWFz1COWp4n4qiYJ4zMVU3Rc3OuB8h1RInomIBWuPF1voFCbtCTHAs8CjAxAvw8oIt+G7zpSw3JhcxfTEW7i+4itj20jk13/offC10EqqUNpTIrC8OqUEzr2Xc7tSM9QDdm83FPQITa0J6gM/jBkQJ8E8CAEz1sW0ZLj4vmGjq9VsibY97GhzEutHTz66/IHdi2sWSFkOASxGympiyoC90CFN4Hluc9hZwEsvDWql0MKEuUdQEgDk3AwBqlDZMcjOH7a8OnoML254AZlxjzWc6NfsgxXiBmFiX7XzheTI1Jmru46KmR+hHWNXw9t5OXFt1Py7adzSKNHb+2MOTFSGOc/r/hwle5ccDc25BTGT9LKhhdIfjZgi7/eGDT7TyPqpxdh2nCz83+qo1TZ/Ocb2P12ZdjKlN3wEANHX2o1LmIeRN6wBdN0Uq4xh3pxE1Q5E4Km3h56ZLtvAotn9G+gWNLVfnasazMz6Pwo2fcqzHbWu7AA2fKWRCJCZfAsjM1W/c944NWDk6p7l5yLWrBChbCshBuNUO3FTza3OeqCZBFJz9YGyvrTcCXWXiocC3AwDwMwFwoftttlu6U3QFwMLP4yH40WlOKpW6UF/iA3q4S9NbA8heeGQJV+/5Jo758Pf4oJtdJ4liMwC0qjwXdN9OM2qgLMGR6xPD+PeMy3FH1Z3wxK0iRo40Jc3rgKan2HvuYEWJdc9GyRImBANmXlK/2yk0GxgPaiKqhq2tllPTjunUjFmuaxI1CYIgCIIgCCL7jLqoecSixS1no6sU8LL8aBVKhzlQNgblHwvfzUKzX/2slePS74IgCJboZ3MoqjZRU5MLIUz6NCTuQEzl1IzqCtyKhIjIBnVum6hp5O8sSQg/FzUrLxx4EQxZtYQzcxOeCSxcvIMVYzDEkGKfgiLBJmoaOSdnfAUAcGrByyhAB9xCGAI0xO05PPm8qi4i4JYg+FjfsfDzVKKm1xRe3+3jjrnwQbT3GoNkNwuPDO1FQ8+juLb6AXiFPmD3Q4596TGddQo8sgSf2A8lxgSt/+s6weozlAGiwp2asqPNkbgGQ0s0cmoaIhu6NwEdLI/gQdd8/PXQCrSppcCUyyArtoG8bomaRj5GQ9Rs5znooMXQH1Wxp6MP1bZqxQAwyd0ElxAz+yquaqbAa7igXBJzP3mjTKxC5/vw6ey86pWqgXgvJoUeM/cJALo7mJAtB2od7kJHzsLaMwClCC+EjnaEn8elAoQ0H6ICFwd8E9AZZus1rgfZHQQgMOFaKQIAUyiT3FwQ5c7CYrnH2uFwM7D7zwjgEOZ5t6KYi8gd8UKz7cbpVeKTgV1/NBdtjxfAy0XNbtUPgDs1ATPX4GQPc6aF4yrLr8fD1f1BS9QMuafjjdAsbBWX8Sk6PtjN+nZisQfo3Ql/fD8AnbkK07m7tDi8sm71J4CK+IcQBR0x9wSgeAHgm8CmKx3MPcqFtU39DYiJBcCs/wHm3gpd9kMUdBzj/wAA0BQtQzAQdG6P92eBFILMc4Ui2olO/nCikDs1i1S2L3v16eaiAbEfoaiKL1Q8Crd2CDMO3AIRqqOQjFeMoCjM8xwu/z0guaCLbJ8Frd/MDVzsU+B1WUKa3ampCHEokoACj1OEsruED/aEzf5y5Dbs24cvu65FudKJ6u5/IRrXcLA3ggojhLxvD9DzkenUjMRVIB4y72OJoua7+7qsZe0UHgXIXmDqF9AtTcD/dbL7xYqCN9DgboJw4HFn2xWr7V+r/AOm6a8BohuYdpU5XfKz+16BZPWFw6kpKkA9c0H+V+m/zHmK5G4cVVPgSA1hPKQB2DEBAEG2OSJ5uoW5Ciu0szU8MXkf4yEgtNcxqVjuRnUAQPsGNoG7I92KCB0iorqC3e3sPp0qPHtrjJ1PwdZ/4lCIXRPFCaH6RdGPMNHdghXBVxDU2f1YQ4IguecRoJnn7649g72mETUL5V4sm8wfIsV6nXmoYeUbjtmcmtMqnNeN23a+NKdJe0AQBEEQBEEQxPAhUXO0MIoEQWCDKSP8XOkw3XfGoLxXt8LgunqYC8gKB0/OAdnXw0LwPuyfBPXcdmDyJZBENmhNJWoaTs24zEVNrdOcpSNkiJDOQkGSHrbW08VckG7dJiSBVVyPFi5gH7iAa4oBPhcKhRSD/6mXY3NsFlxiHEc13YrVW5bg15O+hbiWwqmpi8xNYwvdt/eDIQBP8PUCvCDHGyHmFIz1tZi5LcuCbuDtbwJ/n4hFB75ibSfB/djOC7KU+l3wKCImupiYFZWK8VLPfHO+TlSx/pAlK/ycOzUNtycA+BQJLskmauoaoPYDcgCtmIid0Vpc0fN3YPb1UOyiphZDN6/kbAo5YdYWIwcddFZgRNeBGUHWz93CRNOxWyj1mn3V1BWGqulwSSJzIYKJ15VKBySd7TO6mPAUUj1orr4SANDQ8XsAuikW6SEmqBQW1zv6zVGwpXgBtHPb8KN9ZzrCz2M8J2ZIYuGmmrfOdMUlhsRH4xrAnZmlAktP4fJxAVEpQBKhvUAfE1sCYh8KBUPULDDDiWMaD7f3S8D235iLFkgh+LhIeSjGnGtGHxmi5iQ36/tITEN3f8wUmYIFVsoLt9uD87f/P9yLn5luvdc/YkWGrnbfAPxjMha9tRDfqb0XMVv4uUMI0XVg/cn4a+1n4BXCiKoqYqqGySK7/vSSxWw+I9ei3IV4PGLmut0QmoP3l+8EFn4fEAQIvP2zvLsAAAdi5Y4CLKw/mVhjr+iNaKfT2atGEOB5Mvepk6AKrM0Bqc+xqkDf+6hS2uESrFQMPtH2cMTD+ssQNUU1bObTtIdKA857jUuIodjnSgrTFwTBPGf28tDlIp9V1R7xfuCFs837kC++Hy1d/YCuocLuIm9aZy5Tp70HPFKEz7tuA6CzByI2vK6E8HODAl7oa/HdeKj6eWwKNwAAjvLwQlNahLXH6Are7sW+Tbi6khcaWnofUGA5fiX+MMdOudLJO4WL/IvuQcg7yzmP3IlTZlY6ptmdoR6BF5CzOzX5uWLk5v2gP0X0g9rHRGAb1Uo7ln94HLDxGsd63LII43AZD58qgslOxhejH0O36oM7vAPlvS8CSC6qVKawsHZR0CEL7L7Y45nhXFG4GYAOVJ4CBPmDrVJD1BSAkoWAmwmZVx5bgl9fvARo2wA8EgTeuc6xKuM3MBzXzCJOyU5Ndr5oOsw0J+TUJAiCIAiCIIjsQ6LmaMGLBMXgZyHXpjCX7NTs060Bk9T6PIBk56S9QE44xAS8Hj0IhQ+uRMlwatrEQdUpasZkNqjz2ERNK6ems02yIXYBpqhphN0a/Kd3IeIiF5m4qGk4P4u8CgqQMPhXCoDgNDzWeyYAoKLjUShaF1YWvJ4Qfs6dmjz83Oi7CiPclmMIL7UCE9vC7onYH2OiWTTEHD1uWYTfJbHwRACq4EGzUbDGKGzBOdgTwWT3Psz3bsYEV6spaoaUiWhXi7AvykSZbrHa7CuzUBBvsxF67nNJEEUBbkVCaWLhjZLF6A4zsc0Q9NyKbSCvx52FfdQowIWrJiMvpRbD1lZ2POaVsNc+scJyI0k95jmzjws+tcVeiLxQklsWUe+y8tkaHIiVI3DUZYDkQTC8CYt8m5nbMdyKGXgdACBXLHUsk1hduq0vhqiqOURNlYuavQI/Pm4m2AiCLTxbShY1KyTmQvX4+DFLJWr2boNRCT0g9SEodAJgVdojcQ26rsM4bUp8iimAAoAiqCgR2bXaHmUiT5khNHL3Wp3Lcmp2hMIIiEykUTxWygBDFOuPqabLVIx3ozzoRknoVXO+o/2bELWFSjuEkO7NwMEXUSm3YoZnNyJxDYdCUczzbQMAyGVHs/ncpdAEdr741YNoOcjPUy2AyeU2R5k9pyCA/bGKpLyURn9KgjNnr5FTssbTA3S+BwE6QqoH7WoRIgJztBr9YOCO7DGvGYN6e9pCkQuqvCiUoIfRYoiaCS4+t26Fn7uEeFLouYFX0jHbsx3hAy+hUOqxRGJdZ3k8OzailxeLkvUIOg4dQLHUA5doPXxA89OmU7MB7wF6HGcEn8EXK/6C6ZVOh97PPr0IEzyHkEShJSy6ZQltcbZNMwcvYOadVPReUxQ7McickbHa84CGTztWKfqTRU1rI8b1EEDX0X/BB/2TsbmfPWwokztxyqwK5+w2p6aHOzWN4wAg6Vx5sms5VNHnmMacmk5Rc6n/Pbgi+wAIgKcSqP8kACY4G0KqkV4glVNTlwL466EVAIBF0YcBAEUJx9qnO/u7R/Ui5ptm7UPxIuvLaV+w3pcdy/KcTr+Knef83jirJM4c8Bu/zObb9D3H+o3fwJ1tvYjENbhkEXXFyXk+DfZ2sPtrBRUKIgiCIAiCIIisQ6LmaMFFzajAB8WmU7PdFLIMMcgDS/QKdj7H5uNihyzaXHDGqvvYuvthCTySYDg1beKEEX6usdBOlQ/qfHqnOYsV7u52tMnh1Oz+EFAj8AhsMKwLTIT6T89CxCS+f1zUtBcYCSaKmiWLAUHEE90noCvud3yl2EQMw/XIws9lh8s1GrfECCNEtkJn4mTENwPtcaaiqP1M+C0LuJl3ked8e33m0/jE9u+yFfTucIQelvc8g2dnfAGf7b0IP/FfgFUFLKSyS6oDALzXzwbSB6LMLcnCzxNFTTaAN/K1uRB3hI+yfliC7rDhxGTngkeRrTx2WtwMhQ96FCBihFxKaDPyUmoxs/L59AAb9PcLZYBiiJqWU9OqKmwNzF2yiIkpRM1WtQKVpdWmOHFB8ToWkrvjfihCHG/3TYdYstCxTGIxqwOd7NwRbAKkJrPj0iMyYbhfYYJNwC07hFYAjvBzQ2zzB7lDNZWoaSMg9iMA1h8d8UKEYypCUdWsxFzoEZ25+ABUyMyxe5CLmmb4Kxd6amXWT5GYhu7udkuoUizFzlE9mwuyBVIvGmdXQIhaLsgSuYvl1DScmnYhhAvvADDR3YRoXENbbxRzvSxfrWiImoKAiMLcwgG9FdsPsHybU2przXBx9qXTbXcgWp5UQdxwajqIdqKzL4oiqRtf7D0NeIptd0+0CtG4jj6N9VNAYueV0beALTyaM8XUfQWA3zcMJ6ukhc3Q3apCp8st0amZ1G7OVyp+h8enfxmnNZ2Lf037EmqD3B647zGWZkCQ8Hvp/6E1xq6LvkM7bDkx+bwtz8EtsWMatxUQ+3rl7+CN7HJs77jJRSiRuMjm4W5IyQv4LfeySxat69ROpA3yUwuxqu8KePvZMTWOrVK9Inl+TyU0XUieDlhOTQA1E47CfYFH8bW9awEAFUoX5tQ4i+B47E5NU9RMdmoCLPfq+p6l0N3Owl6I95kPBJqi7DvTOVpxAnBuM1Bzqjm7O6EYUiqnpksW8cf20wAA84UXUCj1oNjndGoifNDxsS1eBDHA+7viJCvc3FMFTDjLmlFyAyueApb8hG+Mn4z8AZHh7k9qE7+fGWLl5DI/ZMn5r5Rd1DR+mytT7B9BEARBEARBEMODRM3RggsZMTOHoOU2NIQswxHi1S1Rs6afheAZuQ9ThZ/H+plYGBZsomYqp6aZU1OGR5agcaemD12mmGcUZyj2O4VW2S5q9u6E3nfA/Ng1/VasD52E53qWICpyUSTqDD8v8rkQ0Nmg8RV1NVB5Msv1B6AzouCO5s+hv/h4c52Fum2AyUWnuG44Naug6QIUQYUetvJHHjK2FWNOtrh/Bjp4zkkhYoiaLiDSBsR7AAiIeidhf7SCCTFqvxnWDQDHxVkxIx0iREHD2cVMYG4XmKj5u7Yz8EZoFv6v+2QASFkoqKWH9VuAi5pK3BbWa1CyxCxEYqQi8MiStS49bn3vkc0w+Yhchhivwg4tho+amfBT52Z90i+UQ+eCmilq6hpira/CLUSdoqYkYqIrofgQmNgoCAJQw4SCmZ7diMXjwNZfAgD+0N5oivLmumSn8G5UMVbc1vmpczHyLfkcoPw4NBefw9ppW5c9/FyVnaKMy3BFHkbUDEp98Grs+uhQmVOzsy8Kjd8KPbKQJGpWSuwcOBhmooQlajKhp0ZieT3DMRXdPayvI7qbiSYcr82pqXNBtkAK4cxZHpZ2gFMs9SCuqvD2bUGJ1OV0ajY9bb6tdzFRs7WtCQ1uLj4b4ecAYi4mavYe2oNIH9vfpTOcbjvDaQoAYc2FDrUgOfxcThY1dzcfwJPvN2Ox70OHY7I5VoqoqqE7zs4jw6lpXHMA0Fj4kmNdkwq5ACy6YMYj81yOkhZGCy+yUh0QgPY3zPuSolmipiLGUwpiAHCsb6P5vsbVhlMC7P6JFl4EbOoXsNe1FAdiTEyP9exBpZETs2gOIAeAeC+KYty1rVqh86KgAz1bnRsMt7LjKUhA2XI2rWAWqyDOcUkiDsaKkhvbvQVC/14o6INvw3koEHsxz8fXb8//aDZARruaQhwFLIGO89VV09GpM6GxRO6ECLvzXYN86DUzLYBXSOHULJgOTPoMnoqche81fZZ97SlzblOPm+729/unOr8zwu9teGx5Qws8siNnqrkbsoitkXpEpWJIgooyuTMpp2ai+NgWL4Y49XNAxYnAnJuAqVcAFR8DFv+I5RlNhyEEG6JmOI2oKTv/bVpc0ZeUS9Se+gBgx7woUYwlCIIgCIIgCGLYkKg5WqRzasodKPQ4qyH7YBXgKdN3o0JuR22RxzGPXdTUjHVLRea0pJyauma6B2O6DI9LhO5ig14JcStc3FaYyL49083DVoZwy8sAWDEV99xr8Z2uWxHR3YgITqdmp+HU9CrwaaydH8WPAk55FqhhjpxwTMPDHWtwaPnT6POyQhGFsAaYgln9nBUKgqigU+P72n/A3M8uLmoGwkwYUAtmoV1lYpjMKzmXBtxWmLmvFrLsRRwyDmo8PNOo2tv9EeZIb0DVReyrZvnhFIG146DOjt178UX4rz134cLVZ5p9FU8IP//TBhaeuXwK62vDpRfWbAP10iVmeLkh6nkUp+vT4dTsZ+JjVC43txdXo9iwk4kzlTJzcvaJZabYUWSEn+97DBcdugDfqPotaoucTs16d7JTU/fxAiFcEJvobsJM6T0gtBNdqh//13l8UvEUV0L4uZFjzu0tYjPIQSgy2/+twjHAqhfRIrLjbgj8RpsAoC+q4vdvJhSl4mLt4Z2affBohlOzAJG4is6+GFTjVqirpsio82lB7qRt5qKmKU5wp2ZA7EWx1IVIXENLGw9FF5xioCHghGMqIjwlQ7EcwvwKLpLJzJksCRomy9vx6/LL8EDDLVa4tBoFeOoJAKh3NSMS1/DRFibSdQg1gM05p7qZqFmpdJhO4JLiKmdn+C2R80CsDIDArgc7ooyo7py2r+UAusNxnFzuDDXeHa1GJK6hI8r6yXBqukVLCDyaFyUyqAtyAVm0zn+Bi2kSomjpZus4I/It5gjd8QAAQFGdTs0VM53h1KwTwpji2gkAeCrMRPiTxL+y79rfYK/lx8ElidjPU0cgtAcVRk5MXx1QwkKXC/vfYW3To85txBJSR/D7DzxVQJAXTSp0CnppnZo9H5lvxdB2/HDiXSiTu6BCBornJc8PsEJiqXA5119X4sOnTlwETReYoGlzB+PD/wc8vRzfrHoAAFDi4cfEnlNTEIFjf4dbmr5kptQQjPPN5khG92YAwPuJOTcLk0XNoO3aTlX5HLDuHarAvveKkWRxMOIshNYeL0JBxVxg5b+B8mMBXy2w8jmzaFJaTFGTH/94T8rZ7AWWAmIfvoPTgCcXAmrYMZ/drVlZ6E7K+UoQBEEQBEEQxPAhUXO0cBVDKz0W3eIk9tnL8jD6pTBKPUwwdJuipnNwFZD6Tadmdd9/UKW0IRa3wqT1CBNt4g5R0xBtuPipWYPzmK7Aq0iQXQHLQdSzFZqm41BfBCcFNpp5H41Bpkd0Du5jzUzU7NH88LokU4CKGE5Us1AQz6npU+BV2WDUyC8HAKqmm44+ryIhzh1nxYJt4GoWCpLgdzEBuF1jriExzESFvqhqrkcJsYG2XnAUOnj4uUs9hCqlDYt9H7AwcwAITIHEQ533x5lQaQqe25gT8bmexYhOutSx780qC5W+9LhJ+OC2U3HyDCawJDo1d7WFsO5DJkB+7jhWKMRwGe2JVqF92u3AnFuAwBRbISBD1JSsMF7NnlPTcmrGXeWm8Hmgowce9SBOL/8AgTjrk36h3BT/TKcmd5pN8+zBhGJLxFAkW/h50VxzurdoEu8rJoiVyt2Y53obAPBq71yEdU+SqKnIrE+jCeHuPj9rC1xF1vliVFI3wu+9VhVje7hqRzQhn1+moqbUB0Vl18chNYhITENnXwy6Ecara+b5FZWdglFTHxP3ig03o+wz3ZqzvTsQjqloa2eippEj1MBrhp+r6FLZNTGpIAYlxgUUb40Zgr/I8y5kQcN0zx5L1Gx7heUs5NS5mtEfVbF1DxPtJL+zGrXqZveTSqXdSm9g9JGBzal5gIt6qXJTRuDs6wIphHkTCvGpaUwsx6T/Qkvh6fht25lo6Q6jM85FTSO3qGClhBAFHd2qH1GdnSM1fv6dlCxqAkBHVzdKpU5M6n2MTdhyN6DrkFRLTPRKKlbOshW+6W9hla4PvQtZUNEWL8RdTZ9CXBdRF98IdLwFdL7N5i1ZArcsmk5NObzXCj/31gAlLLQ+EGLzC1oKUTN8kLlodc0SNb01wJTPARPOBmZ82bGISxZxSA1aRcQMurc4Pq4sYDlqDykzzJD8RNo1do6269XOL1zJoulVp8yC4OHntL0I2tvXAgAuK/87AGBmhZEGwJknEoCZDoBtg6/LXc6cqYD5EGhzeJJzQVtOUYPrG2fihGllmFDsxSePrkv6HrAeZMQF1haPEEGp0AI0P2PNlOCo7EGpmbJiUCSGn6ebzSZWGm59RNqTCsvZ71fLJ6cRnwmCIAiCIAiCGBYkao4WdedAXfE8NrkuZp9lP0K8IJBRPdcliRChwi/0OhYVoTFRc//jOGbXRfjrlK8jZqsOLsbYoEzjYa4AzJxfekpRU4ZHkXioHxdHuj5EdziG5f538NvJt6BsXQMQ6bCcmoJzcC91vAIA6NOZQ80oPNGvJ4qaVk5NdwpR06hGDbBKwnEuzjhFTaNQkGjmpjykc1EzwoQ4wxFa5gqZQqdYNMsUNSXE8LuGm3FV/FJg14NsvYHJZj/tjxmiJhM89d0PAQAebD8NReUzzJBzANjL5w24ZVMUBZioaQoXehwPvLwLug6smFlhVcvlA/KOeAEO1n0ZmHcrIAjOQkC8P03XpxZDD/8+6FHMEPm4qwJxnfXHnrYufLv2Z7i3+psQQmwf+oUy6EZOTbmXOSe506xI6knKqVlvFHWpWm1OLynnQphSAFVhfX6C7zUAwNbwREiiAF9CGGmiU9MQNf1B7q5zlzkrmwNmeH2q8HOAOYIdGOe67HfmGRRkx2wBqQ9KvJOvI4BIXMWhvqjl1IQlasZkpzDUEWPbdOT042HB87xbEYlrKA/9m++TU8TwuKzw8/YI6+eJwagVOusuh+5mwtosLxMqPWIUAZlfZ0a4dJDlba13N+HfHx2EwN1kwaAz3Fi3Ob9NUdN2PwAA+OtN0d0Q9ZLCzwGz8I9BgRRCXbEX4iEe2j39Kmyedj92Rmuxu70PvSoTQYO8+rm92rmqi7h69zewK8Ku6wpvslNTUqzzsKu3CxeUPANR5+vofA9oexWyzak5r8bjDF1+5WLg2ZXAO0yse69vKj7qKcC67mXs+43XMFedUggEp0CRRDTx/ffGDljh594a8/j6eljBHk1NIWpu/Arw3Bpgw2WWqOmrAQpmACf+zVZlm+GWRegQzXuRCXdqtolHQTdcngAKapYhHe3cUb4xmhCenkLUBADByPNpCHCadWya4+x6XlrH+19OeHAA4DPLWK7Kj8+vsYoRuYpNpzFinQCAXdEaRDTbtZci/HzFzEr8/rKlePGbK/DfJ0xO+h6wrvkILKdm+buXAs+uAtp4gS1+DfWorN19UgrXbiYYfRbpAFRbJIKQcD8z70M6Li570voi4swRbaSGAYDT59UMrU0EQRAEQRAEQQwIiZpjiINxJoTUeZko6ZJFRxEZIxefCA01hV5g+68BsFxx9vBziYs29rxqRvi56dS0Dc51QYYiiXDLIraFuVjXvQkdoSga3CxfoKDHgZc+CQkaRCEx/BzwhlhYqRFubhSe6EdCTs1+w6npgjvGBqMt0SJzPf02UdMti6bjrERM7dQ0clN2clFT5gKfETa/tHAXW8bfAJenBBHdhV4++J3u4eGzBx5nr4Ep5kB0X4yH6vZuZ3n8uAiwOTIFxT4XdivHAQDiULA/ygbDhsBqtd+ZB3PrtjdwdtFz+MxSm6uOD8jb44WOyu1Wzkx7+LlsrstyclpOTc1VYRYT2tfRg1MLX3G0JyyU2KqfM6emFmHHpVjucTg1g2IvimUmHIXLTjan19TOMN9rfuY2nedlDrOtkToUepWkMEslqVAQEzXl6pOAGV8B5t9hCZ98nq6E/QcscRRIIWoaLkRBRNjuLOQioEFA7DdF/854EOGYhs7+GDRTfFYBnm8wnuDU7Fb9cMmi6boEYApWc33bUNH5OC7w/pHt6+TPO5b1yFahoBYexl7tDVsuM0854Gbn8CzPTmtB4/t25trDpM8AAKqUDjzz/i5TOBRdToFM8Fo5eovkNE5NUUGbzkQuQ9RLVXAnmsKpOdl3iOWPFCSgaL7j2PTyQkElSh9EqFbl9MmX4qv7vokXehebjmI/D1G3i5qy7DLP4/5wLz5V8i/2BXezY9svINqcmosm2M6FWI8lALcwF927vIDXH9ob2fSDPK9myRJAEOGSrfDzgNrkdGry46v0vAdFiEGxCbRse93A3kfZ+x0PAFt/Zi2bhjm1hagt8joe5gAAupmoGRUKoU2+wpzsqjwm7br+0X8e/th+Kn5x8JNWCgvJ58jn6sAQNXnKCrRtML8qrJyLh65YhjrD7JzCqXnD6bPwvxcvwffOm2c5NV1FzqJCAA7Gii3RVim0jt0gMQTEfr5vQSUKsZ8VvsL+/2Ov/B56Z9Pn8Mf2U7FROitpPZltzJZTs2+fNV32s9+AXX8COj8wz/VFvs2Y7rZdqwkOz6Yuy9V67BRyahIEQRAEQRBELiBRc4wQUzXsjxQBAGrdlqhZKHGXphyAKrCBXZFXYs6krg+t5W3h54rKhCrRbbl1jPBzM6cmd2rGdRFuxcqXuTXCRc2uTTjUF0WBaKvM3bwO2P9PKJJohZ/zHIsizzUXk9iI2HBq9ul8sGs4NUPcqenWIKudbLVRS5DpjzLB0qOIEAQBOh8Ml9pFTV50xywUBKALTJRQosypaYhiiwI8tLx0Cdw8r+EhNU2Isi38fE+UD8J7tgNqn5nHU/EUQxQF7PGcCABoEyahJ8JEm6DHKWrac2pGohH8seqzuGfiXThGeMKaKWI4NQvN0Gt7++2Fghw5NSM20Y+Lmrqn0hRR4/EINoct95Pum8iq0jtETR3h/k4AQLHUbYU6A6gQ2KC+FyVoEmdB0wXEdBlFpbZ18tBro9r3tvDEpNBzox+AZKdmTXEhsPhuoOZU89hEuKid6FQFnM6nbi1gvtcgsoIuRpfCeo+CmY62BKV+CFzU7OJOzc5QFLpR6doWfm44UQ16VD+KfQmiLQ9PXujbjGXtNwAA7m8/G2VznCkKDCdhOKpiXy/r5zJ3v82pWQbBw85hU2wH2Pe6DnRwUbN6Dfr5/k10NZsh3olh94KfiWoTXC3wCFxcSRQ1AbSAOe92R6rgkkTzIYGdqJjs1JzlZikdUDgHkL0OF63h1CxV+hwuTSz+EZ7rP4mtkwv0gsrbbxM13YqICBexFvo2Y6K7BbocBI5lgjH2PALBJiDNrrQJeC3Pm05ug/e4qPly7zzEfLZcj1ywdNnCz4vRbDrl4atl6QWUQghaBNPde+ASnOtGrBsITLI+d77HXm3VzhMpC7jx4jdPxqSJPCTbEEC5yzEiFEKb9F9WyHlpeqdmi9CAG/ZfjbcPFaIlZnNOpiPRqdlsFZ/ySiqWTS5lBdKAJKESYGkwVh5Vyc5nXtwOnkqHq1ODhENqEB08fzErlDS0fJJuLiCGVHaMSz0qBJX/JjWv4/vCrqH3InNxw/6rIfgnDGlbpvM0egjos12Dapilf3j5U8ATc8xz/bSEolfpwtarCz2OPJwEQRAEQRAEQWQP+k97jLC3o8/Ma1egMWFOkUQUSTzM0lVi5lSsDHIBwFZYwu7U9OidAADZa7lDJMlwlzlFzZiumAKkW5awLWyFn3eEYiiUnaHvaHoKLlm0ws+L5ji+NqpSG/lAQ7oRltiNmKqhJ8JEgRKJtTGqyWiLWqKJEX5uuOF0Dxs4l8u2whZctNAgIsCFxB6BhRy6Ysyp6d//Bxztex9HeXgflRxtOmza42lETR6KCthEzd7tpss0rosIBNj+HfCvxM37P48/izejl+d/TBSE7Dk1D3Zb4rC/9R/WTEb4uZraqWkU0/C6nDk1lwtP4GPB1x05NeGpRIyLRbKgosDNj/WEs6Ae/b+s62yiZlzVEO5jA/GA1G+F+AKo0JkDqVVowL7+IL657xp8r+sGhwNMtOVk1CFge6SWOUcTcNmcmqFI3ExBUFPkSZrHcGom5hQFWEVhQ9i0OzXDCDpEk4hgE2MScvkVyz1myH2XGmA5NftjVt/qqilqajansyp4ENFdyZWXSxZCg4AqpQMBHEJTtBSPatc40hAA1vncF1Oxo5v1UZEcspyY7nKIXp6L1VZYB+GDTGCJtLHKzcXz0SEw0Waiq9lyOiaImpKP5XltsBd7kpPPe23enXio/3N4IXwSzphfnbKYSUwIOD5LgoapOg89L2Wirr0oSg93ahbJfU4RUHTDw/shxnNqItZrfmfgkiSEddbPVfy6FwKTgPITWMEatd9ZVMdevMcm0hm828cqcesQgSmWA9IILXdJopVTVGzHBBfPFeqtYedVieHG3Wo6NeO8cA3iPUC0k72fegUw5XJg1v8AU/47qR12BEGA7+jvshy6077o+C4qFDCX/Yl/B475ddoiQYDlgtZ0oCXOz1d3Sdr54eEOdOOe0bTO+s4odBPnQnMKp6aD+k+x9s+50Qo/BxCTS6FDRLvh1ExRJChTjIcdPTF2vpS441Zu2fbXgf4ms6CPyH97Sv1pXKqHwxCD1T6gZ5s1XYsCfVZ1c3d4F2uLnFCsLOoMPz99LvsN+dZZzt9IgiAIgiAIgiCyR7ICQYwKu9pDpltI4AMoh1PTVYx4pBVuABVBl1k93SCmWuKBV+8BBMDlSxY1E3NqRjUZXpdobm+b4dQM7UB3T7e1/dJlQPurQPPTcEnnWuHnhbOBAzbnIQ+RN0VNzRA1u9DdZ4kPAZ21vz1eiIjNZdqfIGoaLqZyyS5qsnniusSqnwPoFZirzhU7CHRvwYL9X8UjU62wdJQugSgyUSwpl53ZqCmQupmoszvC87JFDpo5K3tVH8oCTMzwKDJ+134mzpxQgx4ewp0oarpk0Qyjbe3qhekfCiU48cByahqiZkzV0NLD+re60NierZJ673bcUPJdoAT4yL3WFChEb5U5j4I4vJLKIqlnfRN60RIAT5gD9yKpBz2qhnjENjCPdgBeJnqUa6zgRxMa0NQVxiOHVuHE8nLH/tlFzR5pAsK6x+GstPcDwJyaTV1MhAt6ZEf146Scmmb1d2efxlR2rthFTbfPKeJEhYCp3aN4AQAg4p4Ad2QfKmXL8dulBhDmOTU1s/q5ZqZo0FzW9RMTWRqFpMrLSgE6xAaUacwR/FDHGkyuLkrqg/KgG9WFHjR1hdEaZmJRQOh15NS0Gm0jctCq1F04F5DcOCTWoVbdjHp3M6q8UbMddgLFk5LaCSMFhY0F807Cgnkn4ZPJWzaJiX5AdU6rjvCwZV4d3O7UDPH0DoVSH1x2gVZUzCrwpus4boiaTqemEUpdwguUQfIDoszuB337nPc/e/GeJi5qTrsS2PpzHNSq0MrTehw/tQzKtM8Bm25hy5QuNdverhYiprugCFFrm4aDsmQh0LLe4dSUvOVM6Ip1W6Lm7BuAhIJNA1I0l/3xiu4GUYEfy+rVycskYA/7b40Z4eADODW9hlOzmZ3nhgMYsPJIGk7NFDk1nRsvZDmAAYerM6aw+4RZUZ5fg0PB2L/umAtQgBJXxFZlXAd2P8zeCRIKvD6gC6grOYwYmw6lEIDA1nvoHed3mvX7Whd5CafMXIzZPgB2426CU/O7583FV1dNw9SK4NDaQxAEQRAEQRDEYSGn5hhhZ1ufNQgM7QG6PkT9zmsxzcMdIq5iGNpfZVAGOjY6lhc1PhDVYvAKzGnj8Vnhs7Lh1EwIP4/yyucAEyIPxovQrQYBXYPavcUSNevOYUVXendgkrvZcmq6ywCPVZjBCHk3HFk9hqipq+gKMQGtwCNDjjIxpy1ehEjcUkzM8HMerityx1mF3G613XBq6qJZ/dwQnWSth+X64xQZBYaKmfhS5HM5RM14kDv5lELAVQyFh+kfivusAhG9O819Kech2h5bNWvTqZkYfi6ldmraXT+IsPZ1xAvMftjb0QdV0+FzSWZIuMeWn1O3FaQoVCJAPxNdJX+VOY8kqJbjz1ZZGooz/Fy1i5o2oag0zkTN/XoDmnluuOoCZwVmsWCq+b5FYGHpqcLPFdOFqWPfIXae1hY5hQd3mkJBqURSwClqSm6niBPlzsKwEGTVp4/+BT6adA8A5mAFgLgYgAoJkZiGrj57Tk1LpTAK97B1MaEpyakJoEWezdapi3ioYzWmVwaS5pFEAZ89dpKj7WK8yxI1PeVc2EwgchDo4KImD5fukphwNtHVhGpfalFT9hRBLbSq1icVCRoEqmjtj+FoDYR5pe4gy7FqF9fiEpu/QOqzKp+LLkAQzNyihqPYdN3ZRE2XJCKiG6ImPz8V3gZfCtHQEONCu5mDU5CA+XcAxz+C74buMGe768L5gKcMWPEMcOI/AX+dre0CDmrWfUwXJOt4KOx+UeFToYhsfwSe/xThVkDj208R3p8RCSJkREjjJE+1qE1MtsLPB3Jq2sLPtaj5gAgAoHGxUM3QqWnHJoDGXKwf72n5FLpn382qwA8RY/+6ouy8qXD1OGcwiry5ynDqBB13njMbn0hTSf2wCKJ1DDsTRM2odW8UW9bhvs8ejZkl/PfIcL8mODWDHoUETYIgCIIgCILIMSRqjhF2tYXQFOMD5b49wAd3onT/r/CF8r+wae4SxDXmIiwPKJbQwZF1Lmra3CK+oE3UNMJhE5yaRuVzwBhACtgRY8KBK7TFCn/3TQTKjwUALPe/CbeRU1Pysbxzxna8bIBuiFS9cbcpDvZ2MwGvyOcy3YVM1NTMXJ+JTk3Bx0L4XGIcuiG6GU5NWDk1DSFF1kKW+8sgOJ25igCsmFGBdtUSNYV5t7E3JYsAQYDEw5vjGixxILQLANCj+lAWMERNtn/hmIoQD6kPup0CnCyJ0Pi+t/f0WV8YQpbtfbtq5dTc1c6EnvpSvxkO7FGsnJpRmwhcILSbg2nZV2U6Q2VBdQpKHJ0P2o1CQULcKrhiH7gXxVn45V51klnwoqrQKWraj/veODtnDufU/OAA215diS/lPJFEUdOTWtT0B2z5LhPEpLjEhISwUMBCtqd9Hi2SMwTWqGxuOTX59WFz/Qm2CuZ9PI9lUQpRs9nNHH9Pdy1DS7wM0ypTCxmfPGYi/C7JEmSjnY7w89SiZpt1rfMw6JDMPL+1rlaUebigpiQLYZLd6TdUwQ2AKlmipnGPEngxJXC3rl1cKy1m++GX+q2cmvwc7OTHNWqKmvz+IjmdmkZOzWKJn59GeHMqJ6QWBTQVeO1K9rlsOdvfiedj1XGnYWZVEH/+/HJUGqJ8+XFAbaO5uNH2rRFr3UJwmuVs5aHxK6YXYcW0It5I270aYIKYnCxmZ0SCCBkV0jjJU2DP1bgjwh4AwT8p/QKJoqYdM/w8Q6emHcl6yNAnsr5piZfBN+caR2j6YHFzEbwnzs6HUqXbOYPhNHWXIaAA5y+qhc81jAAUQ2A+9LZzur2yefOzzLnJ80QjwAqmpcupSRAEQRAEQRBE7iBRc4xgDz9H316g630AQLnSyaa5ihHjOkJ5QLFCUjkujYtmfGDVrfpR6LNEKMOpqRtiBHc3JYuaMEPQ/eGPHOHvqFoFADjGs9EMP9/ZqTnELTfP42kMRiOqbgouoRAbGBb5FJuoWQxdt8KK23rZQNsouqMoXnTwHJjxEKvEHo2yeTTdKmyi8kG1ovWyCsh2uBgEAKfPqzbXdyBWCan+AubcWvZbtjwXf+OqZg1wDaemmuzU7I+qCHF3qd+dHN6r8wwPoXA44Qvu8jFyatqqn+9sY8eyocwSFdyKCJWHlvf1W/vn6d/O3wlw+crNCumKEIcMp6AEwBRQCqVexOIq6y+Dvv3AhsuBPY8iGNsNANgTq0czDxmvThQ1vdVmmLAhCA3o1Ixr+MtGVoBo9VGVjnmM88UKP2eCbGFiuDentKiECUlAsqjJnYV9sMShrrjTdWbkfjVzahq3Qs0KlzbdeABCvDBRcYr27Ci4AJ/fdT2+se8rKPW7cMyk1E65Qq+CTy2diG6Vi1+xTtOpyxzPKUTNcKt1rfPzOMqrshdLPSiSU+fUBOAMX1YyF8oSMUTNPt3vrNgtKoCXCax2UbOqlO1HodyHEyZzgZfnYj3I0yrETVEzOfzcJVnh56VGKLg8gFNTiwDv3w40/YsV11l8j/nVqXOq8ORXTsQxDendi0bb/2fX53Hjvi/ix11fAk74mzUDb7tPiiOg8PunKWpy17VSaJ2PgyXRqYlBODVtouZm33nA8Y8Ac29Ov4Bd1DQcrgZm+PnwnJqa23K8ysMskDOlgt3X+zV2DApFLiSKLufDmlQPBIaCITAnPhizuzBjnexBgyFqGiIyiZoEQRAEQRAEMeKQqDlG2NkWMotVINZtipomrhJEVSa4lQUUoHeb42tZZwNRIzS5Sw04XG5GTk0hIfw8psuO8HMA2B5mjp+C2C5L1HSXABWs4vc01zYz/PxXLx2AHrAqYnv9bFBoVbPWTEElHGKDvkKvAhx8EQCwL8oGwEbo9Tt7OwEAc2rYMrIkmGGVGhc1H3+PCWOiJKO2mA28dZmJJ4reZxaCMalaab5dPqUU27lo+1IPD8+tOsUMRZVthTf0BKdmt+aziZq86FDIlic0RZEciFbhnh7V5nzqb2Ku2aiVW9QokrOrjTk1J5VaDqdSv9t0au5rswbYQjcPA3aXwK0oplPTJaoQDCeWQ9Qs5t/H0dffAw9sDtIdvwG2/y/w8kUQoKFb9aM1XpLeqSkI+CjKKku/3sNeU4maxnn18vY27GwLweeS0MiLaJjNMtycZqGggZ2atcUBS6hLCK02nJq9uuWYDMWAPs0qIKLxMPxIXENnXwy6nixqil5LKOk2Rc1kp+byqZV4XzwFpy6cjsevOQHF/uR5DL556kx868Lj2IdYt63IUxqnZsdGJqKICstfCyAqsbYXSz3wG9XP5RTu0PITrPc8L+xQ0Lio2akWOcL+4Z9kuhntomZNORPO3OjDLY383iA6+2Sg8HOPIpnh58WmqMm360sRWqxFgZ2/Y++X/BQoWTyY3TMr07fGS/GHjka8gE8ChTOtGYwiRlrEcjcaoqZxvgwjvH9YTk1bv6+YPRGYeP7AOTWNdkfa0zs1B6h+nhabG3NC1SR88WNT8Iv/WpT58mlYPpm1N6yzY1AgdLIvlELmuDWwPYAYFun6LiGHNTreTBY17W5OgiAIgiAIgiBGBBI1R4kn3mvChb/agH/uFhGJazjQ2Y9+3QNN4SGvNnEFAJrDXsS4qFlb6HYULgAAN9hANBxizq/OeMAhMEk8V6Tp1EwhahrCxL4wG2T79VanU5MPHP1CCB4eft4RltCn1JvbCQSZMGPkzovEVdNFFulnouYUbxtw4F8AgL93nsTazW2ob3NRc8HEIgBM1GzlVX21vgPYuPsQXt7G3I2TKwqt6r9ceBGgmwLOv7qOxYYpfwMmX2K2T5FEvKMdj/O3fQ+3HbBVQjb7yar+rBtCRYg5NVmhICa2GCGORr5JlySabkM7s2tZ2yVozsrSvTvYIJinAzgUL2ACMKzw80llllAwrSIAgQuk7+22VbTu3sxe3eWQbTk8/Qos0cIuKEl+Uxw90LwHfsnmIG1/jb3yc29ruA5RVUdzN8+pWZjs3Lql81v4+NYfYkMHyyuXSoQ0KpZv2MkG/WfMqzbTBhgYjrO+aBxv7OpAl5lT0znfeYsmQBIFXL1iqiUkJTg1AwEmTDT1We3tjcTRaxOVdS5q9sdUdPZFUzo1RY8llHTGeUXvFE7NObWFeOnaFfjBBfOThd8EZEnE8bOsXKRmDk93eWqnZud77DU4wwzR3tXDtlEkd1tO21ROTdl2vHq2DtiugTAeGByMBS2XKeBwaHts535DNROsZb0fgtGfXBg0Lq/SoOFWTXZqHje1DB4P+77SyKFoODVThZ+rESuMvWz5oPYNABZNLLbScwAoSRSluVMTasTKn5koog0jvD85p2bmeRjtqSgS3c8pkfj5qUVtBXc4Rk7NTKufO9ZrXVuCtwrfOHUmTp1TPcACmVFV6MHkMr/p1PTr3A0p+83IASCbTk3bsSg/AfDx8m4J+TIRaQeiFH5OEARBEARBEKMNiZqjRF9UxVt7u7AnxArDaDrgd0kQAqmr5/59U5+Z9y/oFgFDnOS4hTBUTUc/FzV7tKDpJgQAReLiUIpCQcZ8hijXwqsFl6IZAYm7dlzFpjPOLmqGNTd2RKzBdLCADfbdZs5Jy6kZC7NB3wrlMQA6ULUSh6RJAICOUBThmIoPm5gza0FdEWu3KGI/d3OiewueeK8JEi/FXOy3Dbolv1nEBP0HAACH4kHo5SckhYX+/r+Xocm1BNeftRSJGAIcAGhcNNN7dwFg4edGXj4jJ6SRAzSlSxPMxQcAfkWHW7IV5ejdboau9qIYccimS3End2o22ERNURQQ8LBtRyK2okM2URMA4jzcPaBolmAmWg5FCIIZln3ooNPtm+hw3RqZiO7+GDr7mDCVSrALFNbg3f7p6DHCxQfIqWlw4ZJkt50xT0t3BOf/4hXENR2SKKDI6xSYfnDBPLx7y2rWN4YAkeCSq5nInHobu2pN0TkUiaNXs50vfNm23gg0HdB0I+esJWpKtgJYbRF2vFM5NQeN5LaKiwBMEJJ9Dqem5Yjk12vhLPO7Hp0dvyKpF4LhFkslagLAgu/z1+8Oublh/3QAwAd9k5xOTZtD2yWLuL5xJr5x6gxMrOCiJvosEZCLlo98YTnOXzwBixv4PUM1nJrWOVrid2FhA1tHoZlTcwBRU4taeSAHI8RxCr0Klk+x8qeWBtzOGURDCEzh1DQYjqgpe83915VC6EJqd3IqXt5uOQinVmSQ09N+L0gMsVYj7Pch0+rnduzzeqvSzzcElk0pNUVNj8bFRdnvTK+QLaem/Tqa9gXr2Cc6Nfv3WfdXM/ycnJoEQRAEQRAEMdKQqDlKGIJVa7+AXe3MGTOpzA8hVXglgHdbJauYia5ZBX84frEfMVVDhIuafQg6nIESd8IJSBF+7nKGnxvh3tWyLWRVKTLFSUlQUcTFhrDuwjuHrAGl4ilxrIs5NdlyaqQTElQsjvN8ddOuRA2vgn2gqx8fHOhGXNNRFnCb1bFFUcD7/czZJnW+ieauMERD0BUsd5giSwhpfADKRc2Q5k3prJtZVYCXrl2BTy1NFkjsTk1NYfsi8BxzXn8JJvPjVl3gcYh1AXdqUVMQ2fbPXVgJBTZ3be92oGsTAOCgyJw+0biGSFzF/k4mKtjDzwGgwMf6xCvacuH18PBz7vIzws+Dsm0eySnE9fPqytHunSnbbLAtPAF7Ovi+KxIKUgi3MxKK4qQSNafzeaoKPLjt47OxJEXOyboSn7lsdaEHK2ZW4PvnzTPPTQNBECyXpyEkJTjdfNM+jSs7/4iftl6IF7ex6yEUUR1OTZmfp4bGb55LNqem4vaZIeutYXZuFfszF5wGpOFi673h0JR9pnizS53qnL/AKnR06QqWW1MSNBaaDqQXNWd9DTj9A2DW/wy5qfUzT8NJm3+Fm/d/IUHUnOKY74oTp+CLH5tqtkWErX3c7bi4vgQ/uGA+3C5+raZwarL5uThpVOc2w89TOTXDtjyQgxDibKyebQlxhhvbaovh1AynFzWHE34OsPQeQOoUBANwxrwaAMCKmRVOJ3g6JJuoaeQeNoVgnZ3/Q8qpaTsvPBk4RgfB8smWqOlSbU7N4oWAUcwrW6KmkSMVAOrOs9zOhmBpiOu9O9irIFopERKdmh/dC6w7ITksfc+jwONzgNb/ZKfNBEEQBEEQBHEEM4wyocRwMMSxzqiAd/czt9W0ikBqJxJYjkxZMgQVzRzs65IfghqCTwwjpmqI9zNHSX9CXjazUFBKp6YlavpdkilqGuhKIQRRAgQ/E390FeUyG8CFNRdea1KwNFyHYiWMMi8bZBvrjMQ1s/J4rP8QqpR2+PV2JmLUnomawrfxYVM3DnT2Y0eMubYW1BU6BuiboswpJne+iebufswQuNBhEzVdsohe1YcCqQ963wEIYKLmYJ11imgJlapc5PjumOkNZrtEUcDEEh+2tTJRJp2oaeTUFLS45ewBgJ7tpjDdITFxKBSNY097H3SdrS9RXCnye4E+wCPYBMtwK3s1nJo8V6FfjsKoEwTR5TD29gsFgA5USmnyLJYcDb3jTfynd5FZjby60JNSNJle5RQ1E8PFAeCsBbVY2lCKiqAbophaeAm4Zbx63Snoj6nJ4b/pKD0GaH2eVa63IwiYPGURtD3b8eT7TTjYE8FbezuxWrEEL1+wHNWFHjNfqJFz1p5nUJEVtMUD8LkiaOrnId/ZcGoCwNQrgA+5i9LmkG2PF2CC6yDalZkA3rHmtzk1J5YXM1EnbnPsphM1BREoPCr1dxlSX+qHUjQd8dZep6gZnJJ6AdkPHQJ7gGIUQkoULY3PhltQShQSE1zBhpjkKmbCpSG8AVboOeAMuR8Eq2ZV4qbHWB5jVdOdX6bKqekqBiDAdNIOx6lprK+/CbqrFIgcfnaDa0+diYUTi3DGvAxDve3HwTjvlALLnamFh+bUtIvJWRY1l00uxdNagntW9rNze/KlwOYfQis7Fvho//A3Nv0aoOkpYMH3mABsODUNUdNXB3R/aImacoElSMe6AE0188xi0/eYSLr/n1YKlPY3gJc/zc6l7fcBFba8twRBEARBEARBDBpyao4SxX4Xirgzbf2HLEfktMqg04lkq9pdUFCOigLDvWRzanIxg4maOlTuColJqUVNIUVOTUOAFAQBUyoCCOse9MMSqwTDCScI5vbcIhPowrob7x/oxulbf4yb1L+Y4oTl1LTCzw91tiEgcTFCKQRExXJqdvZb+TR56LnBrlgDIpoMMd4JKbQTkiFqipaA5pIEhHh4sd7HnJq9qjelc3AgRFEw8/7FE9xX9VVO4cDupEwragp8uiEUGPRuM52afV4m2nb0Rs3Q80llviQRUVFY3zqcmgZc1DxtLssBV+Sy5WRNCGcNc8G71nUwdZtP+ifeW74TW8KTzEnpckVm4tQ0lk8naBp4XVLmgiYAzL8DOK8NKD826avjpjLn1jMftuJ7T27GO3s7HU5NwVWC5ZOtkGPJECJsuWplSUSnyvbPEPOyEn4OOAVBm8PLG2SOwUVzj3eI9knCpKvU9kFwOuVywCkzWSi+M6fm5NQzC4JZuEiI8HNMTAzpNs4TLgqmc2oaGPsnCMkPfqKd6ZfLEPv5Pas6QSB25NSMWtPsQvJwnZrGPXaQjsNCn4ILl9SZOX4PiyBafW+IwfYiU2p4aKH8hgAqiAnn5vApD7pxzLQJzokSPx/mfxe4oAcomp+djdU2Ahd0AUd9g2/HEDU72avhygztYa9KgdMpbjiT+1ss1ye/z0OLA/85z0rJ0PFGdtpMEARBEARBEEcwJGqOIpPK2EDwI+72m14ZdA7Y684z3/7sc6sQ9PCBv66aoqbgMkRNFn6ucrdjoshhiJqmiKCygVXUVigIsPKyNUVtbk17dV7FKZaGNRd2tIUQ1RUUF1qDO2OdPeGYuYyi96DCwwd0XBCwRM0w3t3XCQCYnyBqQnLhwzAL0a6Kvw/JDD+3BvKKJJqiphBhDsSo4DcF28Egc7dmXEpoR8K+Tyq1RLJ0OTXTipqH3gYOvcm2E2AuvI5Q1Az3ri9NIVJxMWJCqjoiPIT53CWsn0TDASdIlnOIE+Hh5xOUluT1yEHAU4H6yir4bKHf6UTNaZUB2LXXwYrIw0IQ0lYrXlxf7DivAaAnIafmMlseRcnIOWuEnwsSBEHAS6FF6FZ9eLtvOgQhy/u38gXW34t/Yk4qnfpxQA6gsH6lFVoriEBwunNZt004UoJJeWOzzQpD1NRS59RMQuEnqeFwS+fUTPc5SdS0ianGgx9DKDWEJEG2iaWD58VvnowfXDAfp89NcD3anZpq1JpmFzWH7dQcWvj5kDD7jTs1JY81baih/Mbvjbs86X6TDT5zfIKob2xPlAbnKM0E+3G1h+YDgJ+Lmobr3sUezpnnZ6SDuTXtgqUhavbvB/r2WNO7P7TSLxAEQRAEQRAEMSRI1BxFGkqdg7FpFQFrwC5IwISzrC9dxZZwoWu2XHOGqBlBNK4hFmeDLUV2Du6NKuGCnuDU1FKLmvsjNrHILhylEDUNKoOW8DWtkq3nw6ZuqLyNQbEPi2u5eGSKmmyZba292M1zi86uSXCZiiLe7WOizhzvR1DE5PBzRRLRq3JRk/eNz1eEoSDzYkHxhPDzxH2vL8vAqWmILPZwWU8F6//QbjYLDy1uD0XRwiuN16QSEblAOrsyxbYMh5fpwjIKsCQ7C3t1Lmq6eOi6fRAfnAIIAgp9Cu48d645OVVVc4BVgZ9YYp3HwTTzjTRuWcL3zp+HL5w0BXddwFxcdqcm3E6npnltGU48fm7d3XY5Fn7wJ+yLVaHAozhyrg6bihOYK2zG1da0+d8Gzj8EFM60BK7AFGcuRCBB1EwTep5FFteze4AZfu6pGtgdysUgwagQndj+RPExSdRMDD+3bct48GMUcjJcdMMUtyYU+3D+4gnJjmKjLfbq56Iry6Im6189W7khB8I4FkZOTdFlm9ZlzTeYUH5DALUXwMomiQJrjp3J1nYTzsPEnNfGb4LxG/nMicDf61jIuUE3FzWN32zJB3hr2e/4obey32aCIAiCIAiCOIIgUXMUsYcvu2WRVdQuXgAUzQMaLgEKZgBVK4HqNSy80S5qGm5FxenUjHNR0604hS8zvDapUJDiqJI+rYI5rFrteTXddtdmgqipW2JFZYH1fnJZAAUeGeGYhuY+JlgEpRAWVomOdhtOzfd4XtGKoDspBFmRBLzHiwXN827l1d/hcGq6ZMupabB6wTQMBUO4iorOfU3c94ycmkaIfNzm1KxptN7LQQSKJwEA2kMRHOxhokl5MEEEAtK7PgFLADPn4SJqClGzpKweADDRzZ2a/gbrS1vxl7MW1OK/j2+AKACnzKpAOoxCQEG3nF3Rb5h8fH4Nrj1tJs5dVIvT51UjDJsQ4io2K9gDQE+EXxdG9XN+rSmSCBXs2kkqIJMNUhV3Mc4Z45imyonpGllRU5ZEfP/8eSirPx5aYJqVIzAdhsPNcFFm06lZdy4ThSacwz4bouYQQ88PiyH4abZCQaLLGbY93PDzCWcB3lro1acNbz2ZkOTUdNvCrG3FbgbTn+XHsvtI/Sez08ZEEgXrMSdq8t/IcDPQ3wRs+5U1T+9Odv/XbA/jSnlqGQpBJwiCIAiCIIhhQYWCRpGGMmugNrUiwAQh0Qs02gqErFhnvTedick5Nf1iGHFNR0xloqZLcTqh3EbOtUSnpi5b1aRhOTVb4nYhM0Onps1dKIoCFk4sxr8/OojN7QJqAQSlPkwtA3AQSaKmwczEfHZgzsl3+5hAOce7HVuEYxP6A3BJInoTRM2Fk1NXkj8chqs1nFBsKVE8sovSwUxzagoiULUG2PEA+1x4FEoDTGTo6I2idSBR0xRI+5K/MypoH84BB2DWtAVAK6wq8oEGoJOfcwkhxTeecRTWrp4+YM6+GZVBrNvUgoKRDD0fBIIg4KcXLUT8/fnAe39mE/k5PbnMjx1tIRT5DOHKCj8HmKBusGhi6lD3nGEc04IUoqbdqSnnXtQEgAuX1OHCJXUAPjrsvLrkgwBYzr+knJqJombC9+lyagJAzWnAOfuApnXARz+23JNDrHx+WMzQ7AgcOUCz6dSsOxeoOxd6LAbgieGt63AY+xO3OTXFBFFzsKH8vgnAWTuy18ZERs2pmXAe+hJyeyY6NU105/ueLda6BInly973d1Y4iCAIgiAIgiCIIUNOzVHELopNr0yVKDGBVOHntkJB0bgGVWVClUt2ilCzqtjgS9U0lufSrH4uO/IE1hV74ZJFtMRsoolD1HQKKBE9dfg5YIlAf9/EQqFLXP0oT8ipWRl0w27um1mV3A+KKKKZtycg9aPYw/ZRtxUKUmQBIdU58BWG6GAz3IZ7QwkunQRBt7rQY4peGRcKEhTmvgXf6cJZKOEOwFBUxd5DTLCsCKYPPx/QqZkoRCSG/QIONyYAFjJqOJISvwMOW4RkBj9mxf6xKWoCTNhU3Lbjx51VD16+DF9ZOQ0za/g5niRqWrfIYxpsQv9I0HAxULwQmHRR8ncjHH4+aAxnnSGSJYmYiedphtXPHfMkVsTOtVPTXigoy6LmSGL0tRl+7rb2kReay5nrdaiMFaemHHT+DhjufXeKe4MgASWL2fuuTdZvtiBaRQDJqZn37N+/H//1X/+F0tJSeL1ezJ07F2+8YR1XXddx8803o7q6Gl6vFytXrsTWrVtHscUEQRAEQRDjCxI1R5H6UmvgaDgkB8YQNdXk6udSGDFVg8arNyuSU4iqKWaDQAE6Xt7e7nBq2h12siRicpkfLfbw8zSFgnTRA1OcgzP8HAAW1RcBAA6E2CC62huBYAykebtlSURVgTVwTCVqypKAuG65MovdvEiDPfxckpLCzx3hoYNA4aLm9q4E4SVB1JQlEXXFbLDtT5tTM0GIFBXAUwaULGKfC45C0C3DxcWzvR1svgGdmmoKp6aRi084vFPTUXkbYMfCEEUTv8uAlbMqce7CWlx98tDC/UcMU4QSzPdVhR58ZeV0eIx0DYaoya81VbMcV0sbslvV+bDUngGc9iZQNDf5O0f4+dDO85xi5NQ0nJpJOTUHGX6upLg/Ji0zAk5Ne/h5NqufjySJ4eeiKzn8PNvFd4bLQM7dnG43UdT0JTxQ4L8J9uNfupS9Fs5OI2raxM6ejwAj7yyRdxw6dAjHHXccFEXBv/71L2zatAl33XUXioutB8Hf//738eMf/xi/+MUvsGHDBvj9fqxZswbhcHgUW04QBEEQBDF+oPDzUcTnklHk0tEZFYbg1EzMqRlGTNWhaSogAbKceGiZUCcIOv790UGsabCJmgnFXaZWBHBgWwbh57IPLklEVNWgSEJSLswFdUUQBKBTZftWIPZYA2mbIFBd5MWBLvYP/oxUoqYoIqZbbSxQVCAOR/h5XNOSws+HKvbIXGDc3qGiX3PDK0bAhLDk9U2vDGJHWwgVBSlESCDZXWk41ObfCXz0U2DyZyEIrO+au61BTkXKnJpGEaAEUVMpsEQjMeG4pxI1XcXszxAwlAJg9nVA87NA+Qmp92MAvC4JP/zEgkEvN+IY55yrKLlauHEuGTk1eQ7aFp4OAADqSsaQe83uDBuLTk3pcE7NwebUTCFiHS5kPVuYgqwO6DkKPx9JEsPPJXdy+PlYc2qOlfBzyccfKPBQe+P3sH+/Nc8JfwE2fhmY9BkgtItN6/7Q+s0WJJZaomg+E0ij7Un5mon84Hvf+x7q6upw//33m9MaGqwc1bqu45577sGNN96Is85ihR9/97vfobKyEo899hg++ckc5aAlCIIgCII4giBRc5Q5rU5Dt7cWJ0zLoOqtKbykCT9XNYiGqJng1DREHAE6/r3lIPSJYQgAorqCAq9z3hmVQbz1oT38PHWhIEHyojzoxv7OflQEPRASip4EPQpmVAbR3sbEQCHWCcRsQhqnpsiLjbsPQRKFlI5VRRIQszk1g0oM6IfDqbm0oRSbxYRlhypqcqfm7vYQuhQ/EzWVYLIQBuDGM2bhYzPKseqoytQrS+XUBIDqVeyPUxqwRE2XJDpSAiSvK0HUNFyWQGZOTYCFmRuhj0oBMO1K9jeeMcT5VBWmjWOrOqufGxT7lKTze1QZ4UJBg8Zw+pmFgoZZ/VxKJWomLJMrd6GYIhVEPouaUj46NTM4H0Ziu4lOTeP30FttTfNNAE54lL0/8BR77d7iDD8HgMa3s95cYmT5xz/+gTVr1uCCCy7Av//9b9TW1uKLX/wiLr/8cgDAzp070dzcjJUrV5rLFBYWYunSpXjllVdSipqRSASRiPUwrbubXaexWAyxWCxpfuLwGP1G/Td60DEYXaj/Rxfq/0GiZTeQOMbXF9NEgI7BiDNS5z+JmqPMsgodjY3zoCjS4Wc2RbVU1c/D6FU1uPjASZET1scFGVHQsb+zH+3dvSgDq36eKKB98piJ6OxdAIT4hHROTcmLMi5qJoaeG9x+1hy8sLkIOAQAOhDazb6whYbXFLGB4+QyP9yJ7QZzTuoQoeoiJEGDX04WnqoKPfifM5YArxn7Kw45HFXmeTJ3tfWhqyKAKqUjKfTcYEKxD588ZmL6laXKqZkCu8u1POhOLaAZ6zIcPwZ2UTODQkEAWEEgU9Qcg+HLuaDsWGDaF4HKk5O/S3RqJmTm+NiMcowpxnhOTZ1fe4Ih5CTmzByMU1PymM5Z5zwj7dS0IbqtfhfE1Dk/xypm+LnNqWnsY3SM5tQUBHY/Nx7ojJXwc6NI19zb2X15+pec8xvniNrvDD8nxgU7duzAz3/+c6xduxbXX389Xn/9dVxzzTVwuVy45JJL0NzcDACorHQ+9KysrDS/S+TOO+/EbbfdljT96aefhs83xh425Bnr1q07/ExETqFjMLpQ/48u1P+ZMj8na123dy6wN8fFOIm0PPfcczldP4maecVA4ef9iMU1yBqbrsiJ4hkTyYxizu09TNSMQ4Y3QVAtD7px01kLgb9VA/1NgMf2D7kj/NyLcl65u6owhZsJrLjKMQ0lwJ+DLNyxd5ej3QAwvYKJagsnFqVch+GcjOkyJCEKn+QMETYQXTZxRw6YQu5gkUTWz/s7+9FZwgW/NKLmYUkUItOIjKU2UbMsVeg5kBxabmB3HmYsatpyZ45BUSwniDJw9L2pvzMeGCQUCvp/583Bb9a/ixtOmzECDRwEY1zUTHqgcNjq5wOImukEw5HKqSmI7DrW47ZtK1a/K4UpXdxjllROzaTw8zEonsijIWqmCz/nGE5Nfx2w/LfJy5sPS1Rn+DkxLtA0DUuWLMEdd9wBAFi4cCHef/99/OIXv8All1wypHVed911WLt2rfm5u7sbdXV1WL16NQoKxuC9Pg+IxWJYt24dVq1aBUUZuwUNxzN0DEYX6v/Rhfp/kHxwZ1ZXF9NErNs7F6vq3oMy95tZXTdxeIzz/+STU5iKsgiJmvmEmVNTtVwf3PHo5zk1vZrh1Ewdfi6C5YTrDzPnoCSncQUCwNG/BLo+AAqPsqYlODWNXJIpq3XbcZcyUbNvD1+P9c/5xxfUIOCRccyk1NWlXTK3jesSPAA8As89mTg4tBcGGobQY1Q0B4BulQsqQ815lpTjMvWPWWnAEn3KA2lETSHN5eopTz9POlEzeASKmgOSWtQ8e0ENXAfeTp0OYDQZ66JmYiXyw1U/Hyj8PJ2AlSiU5qr6OcCEwDgXNUWFPTAxRc2i3G03FyTm1BTdVn+HW9nrWDyn7ELraDg1BZGdp6kKBaVDtIuaCeHnRN5TXV2No446yjFt1qxZePRRln6gqqoKANDS0oLqaitFQUtLCxYsWJBynW63G2538v8AiqLQYHiYUB+OPnQMRhfq/9GF+j9DRO3w8wwBRdSo/0eRXPc9/XedTwxQKMgrhhHXNGh84ORKOnGsQkGAJWrKchoBDQAmnAnMvtbpeHQ5Rc1zF9Zi0cQinL2wduC2GwPBhFygAKBIItbMrkKxP7UAZzg14zoT7CTdCD9PEPDsYdRDrHwOAJJo7a9R5GjYTk2DNKKmPfz8sEWHEvHVpV9/Jk5NeQwKGCONIZCbouYYvzXa3YFj8fglOv0OV/18oFDydE7NxJD2XIZM2wVUo+2Gg92eTzEfSJlTk08Lt7DXod7vcok8CqKmPZ+q5GO/hYMRNYVUoiY5NccLxx13HLZs2eKY9tFHH6G+vh4AKxpUVVWF9evXm993d3djw4YNWL58+Yi2lSAIgiAIYrxCTs18whwM2XJqcpHRL4YRiVoDJ0VKzKnpdGpGomFAZk7NQZHg1FwyqQR//eJxh1/OHrIHDMoJZFQjj+mJ+SkHEDWHkSdSES1BK6RzQWWozqUkkTGNU9OeUzOdUzNx2WlXMnGy4eL086TKBwiwnJoGY9GVNdKYDwycTs0xiyCyAl6RtrF5/A4bfn4Y8V3OJPw8UQjNYci03bFntLXiRGDxT4CK43O33Vxg9JvGHw5JNqdmhDs1x2I17tFwajrOQ779VOHn6SBRc1zz1a9+FcceeyzuuOMOXHjhhXjttdfwq1/9Cr/61a8AAIIg4Ctf+Qq+/e1vY9q0aWhoaMBNN92EmpoanH322aPbeIIgCIIgiHECiZr5hCP8nIua3JEoCjr6wiFzuktJPLTcqclFTTXOQrgV5TBh44nYBZTBOKPcQxc1tx/sBWCJmoIpag4Qfj4Mp6ZsCz+PeXgRILsbcjAkCq9pCgXZw8/TOjUTQ9l9E4BZX0uYJ0Onpm8CKwAV6wY8FannOZJIcmrmgfDgn8RETd9hXNKjgJ5YPftwOTQHzKmZLvx8lJ2aggjMuDp328wVqVyzhiPROP/HolA+FpyawOCcmkjxm50P9xYiI44++mj87W9/w3XXXYfbb78dDQ0NuOeee/DpT3/anOcb3/gGQqEQrrjiCnR2duL444/Hk08+CY9nkP97EQRBEARBECkhUTOvsA2QDGyOxK6eQ6ZomRR+bobTsu8VgeWHU1yDFTWdTs2MGYao2RFijqK4zgeDhqiZKPJlyalpDz/fWXgRcPQSoOb0oa0sw5yaJZk4NTMRSDPNqSmIwMeeBGJdgDt1LtMjiqRCQWM8/BwAlv8e6N4EFOemSuCwSCqwMthCQfacmumcmjI7TuYDnlw6NVOImvlKkmvWjaQq32Mx/Nzu1BypQkaO8zCFqHm4h2emU1OjnJrjlDPOOANnnHFG2u8FQcDtt9+O22+/fQRbRRAEQRAEceRAomY+kSi8AICoIAYPFITR03MIEozw83ROTe7kFNg6FGWQ7iYlyNelD64wxzDCz79/3jz8cN1HqCouAPqaATWDQkHDcGoqkjXonFxdATQsG/K6Ms2pWerIqZlGaM5EsEzsk4EEmLJj0n93pJGPTs3CmexvLJIUfj7YQkEZODWN5Yz7wYg5NQeZsmOskUpgTpw2FkXN0XBq2s8p45z217Pz11trFQJKB4WfEwRBEARBEEROIVEzn0glakJETPBB0cPoDXVB5IWAxESHIF9WSHBqulyDHKALIhM2Y91Dd2oK4qCcNqtnV2H17CrgCTfQh/Q5NSUXG6Br0aw5NadXDn09AAZR/dzm1AxmGH6eal2CwKYb50i+u8pGCvPaMopQkfAwLJLCzwfp1LTPn86pacxniJoj5dRMLFCUbyTlInU7w6yBsSlqGr8Zoiv5XpizbaZxaq5+JbOq947q5xR+ThAEQRAEQRDZhkTNfMJ0fcRt00Sooh9QO9DX3wnRpZnTExa2vddNUdPjGoK7SSkcnqgpB50V1TPFCLdOJ2oCTMyMtA9L1NQ03Xw/o2qYomaGOTUDbhkrZ1WgNxJH9XCcmuZ8JGoOCvPayqPw87HMYZ2ah/ksCExQUsOHd2qa28yhUzNVoaB8JZXAnBh+PhYLBRmi4ki5NAFnv9jP6ZLFmS2fyqkJurcQBEEQBEEQRLYgUTOvSOHUFCSokg9QgUh/D0Q3F+QGEDUFu6jpHqKoib2DExHs4edDriTOxYR0hYIAJphG2ocVfr67o898X5HONZkpGTo1BUHA/15y9GHWlVkldYiKLe9ongswI0ZiTk1yUw0H/bA5NZWBvwfY/UUND+zUdDgoc+jUTFUoKF9JOhbu/Ag/l0ZD1ExR/XwwUPg5QRAEQRAEQeQUsgzkE6nCzwXRHPRrsV6ISBPiZhM5S3wyXIao6RmCqGm4eIbq1ByyqMmEECOEXk8Vgmg4NIfh1DSqrQNMbBwWGebUHNq60ogr9m3ke6jsSJGPOTXHMon3hsE6Ne3rGAtOzfEkambi1ByLouaoOzWHcH6lEjUPl4eTIAiCIAiCIIiMIVEznzCESXv4OURzkOcVw5AEzZruWNYS50r9ChReKMjnHmT1c8Aa8A7GuZJFUdMknVPT/joEdP3w82RMNkXNDF2fjhD3fC9qMlIkPTCgW+OwGG5OTcASlA6XUzPdNrPJeKp+nujKTJVTcyyGnxuiojQGws8zxvjN1sipSRAEQRAEQRA5gEbu+USim4xPE2Q28HILUdPFmDxwsg51acBlhp/7vEMYqFWcyAb2JUsyXyYromZi3r0Ug8OKE9lAtHQQbUvgBxfMh0cR8bvPZaE6eJIQOQxBJFOB1L7NfBdgRorEnJrkphoew61+DlgiljKQqElOzUGTSlAmp2ZqshV+DtgeRtK/XQRBEARBEASRLSinZj5husmchYIkLmJJgmY5NRNzatqcmmV+GS4ujPqHImoe9U1gxpeTB8IDIQeZKKfHs+jUTHH6LrgTmHvL4NqWwPmLJ+CchbWOKuhDJqdOzXSFgpTDz0M4SXRqkptqeCSKmkl5HLMUfi6NglMzVf7PfCJV9XOH+x/DcrrnjFHJqTlMp6b94QjdWwiCIAiCIAgi65BlIK9IEX4uCJBlLmpCs+XUTDy0NqemXzGdmn7PEAeIgxUNBcFya+bSqQkMS9A0V5ENQRNIIcRmM6fmAIWCzPckamYEhZ9nl8RrcCg5NWvPADxVQOmy9Nuh6ueDJ1WhIHv4uRwYm07lihMAdzlQc/rIbVMQreM9XKemFk2eRhAEQRAEQRDEsCCnZj6Rxk0mS+wwCoKePvw8IaemK8RFTV8OhYBE3KVAuAWQs+TUTFUoaKyRVadmBiG7ifNRoaDMMFM7kPCQFQQRcbggg/dnopBm719BTC2izb0FmHOz496VhEjVzwdNykJBmvV5LIaeA0DRXODcloHPh1wgedh9YSjnl5DKqUkPTAiCIAiCIAgiW9B/1/mEWSjIOTiSJMupmTb8HPbwc8up6XaNoKjpGqZTM9HlmA/CU6bFfTIhUSBN5/oUKKfm4ElwQefDuTXGUTGAECgI1rSBilkdTsCyr1fOpVNzHImaqQoF2aeNxSJBBiMtaAK2glXk1CQIgiAIgiCIsQaJmvmE6SZzFhwQ+HRJsIefJzo1rUNd4hMt8XMkB+juMvY6VFEz0XWYKqfmWCPxOGQzp2Y6FyaFnw8es1BQuocCxGBRBbsQmEK4NM7T4ZyjjlyXVCgoI1I6NW3h52PVqTlamFXXSdQkCIIgCIIgiLFGHqhChEWaYib8VYQK0RArk/Rqy+EysdA2qBrJAfrUK4DoIWDCWUNbPh+dmoLI/kyxbAScmiRqDp6kwlp5cG6NcQZ0aprTQsM7R41lBXl4DwwOx3h2aopuOH4vSNR0Mhynpr1fqVAQQRAEQRAEQWSdUbcj3XvvvZg0aRI8Hg+WLl2K1157Le28sVgMt99+O6ZMmQKPx4P58+fjySefHNY68wohMURWdLyKgg7RzKmZKNJYn2eWj5LoVXMqsPI5IDhlaMtnUv18LOIIB89moaB01c8p/HzQJDmbSXgYLg6nZqqK4Wb4+XBETb7eXLo0AWchnXy/plI6NW3TSNR0IvFienJg8Mvaf4dNp+ao/9tFEARBEARBEOOGUf3v+uGHH8batWtxyy234M0338T8+fOxZs0atLa2ppz/xhtvxC9/+Uv85Cc/waZNm/CFL3wB55xzDt56660hrzOvSCoUZIiaPPwcA4Sf25yagh61JufS3ZRtkiomH2GiZqb5OR1OzQHyFRIWAzwEIIbG4Z2a2Qg/H0Zl6kFtZxw7NSW3M/x8LOfUHA1mXw80XAxUnDT4ZQUhbYE/giAIgiAIgiCGz6iqQj/84Q9x+eWX49JLLwUA/OIXv8Djjz+O3/zmN7j22muT5v/973+PG264AY2NjQCAK6+8Es888wzuuusu/OEPfxjSOgEgEokgEomYn7u7uwEwZ2gsFsveDidgrDvTbYgaIAHQ1AhEADokxGMxSLoAEYAoaGb4eVzVoNvXq2swpK5YpAcKAF2QEY+rANQs7VFuESHCPhyMq7pzH8cosiCbkrKqi9CG2GZBdV6wMU0AUqxLgmw+rYjrInTbeZzL8zmfMa4tA00XoFK/DZlYLAYVTPxLd5+RBRcEALqoID7E/hWhQAKgi94hryMTBF02rz0V8pCv4cMxIuebJsL+OCSmCgAkc5oqBXK2f7kip/1W/XH2pyPl/fZwyJAgQIMaD7Pfb35vGSno3kUQBEEQBEGMZ0ZN1IxGo9i4cSOuu+46c5ooili5ciVeeeWVlMtEIhF4PB7HNK/XixdffHHI6wSAO++8E7fddlvS9Keffho+X44dQADWrVuX0XxHRXdhGoCDrc2oBBCNxfHkE09gXmQ/GmA4NVn4+YsvvYwuyeZO1XUYmSxf+c9zOBGAqst44oknsrgnuWVmdDdm2D6/9vqbOCSFRq09mXJaXIfh7dq0eSt2bB9anxeoO3Gy7fP6Z19ARCxOmm9puANV/P2rr72JdskS7DM914406mMfYIHtc1NzK96wXRvUb4PnGB5+rupSyvvMir4oggC6eyN4foj3oTmR/ZgCoKdfxXM5vJdNiH2Ixfz9lq07sXV3bu+buTzfPFob1tg+P7nuOQDAmfzzRzta8NG+/PldsDMWr9MzdAESgF07tmIKgP0HmvHmCP7u9vX1jdi2CIIgCIIgCGKkGTVRs62tDaqqorKy0jG9srISmzdvTrnMmjVr8MMf/hAnnngipkyZgvXr1+Ovf/0rVFUd8joB4LrrrsPatWvNz93d3airq8Pq1atRUDDESt0ZEIvFsG7dOqxatQqKcviwZPG9l4HNQHlpMdAKuNweNDY2QnzzX8B25tQ0qpofd8KJQNEC5woeYS/HLlsMPA9Iitd0veYD4qa3gQ+sz8csXQ6pYtmotSdT5H94gUgPAOCoOQswc8oQ+7zrA+Bp6+Mpq08DXCVJs0kv/QY4wN4vO/ZE6KVLB32uHWkIO1uAN6zP1TW1aFzWSP02RGKxGDr+eRcAQFJ8Ke8z8tNFQNc+BIvK0LhyaNeE+O6LwJZ/IlBUPuR1ZIKwNwS8yt7PmDUX02bkZlsjcr5FDgL/sD6e2vhxAALwF/Z5+pyjMXVq/vwuACPUb0NE/KsCqFFMmlgD7ABqJ9Sh6piR618j8oQgCIIgCIIgxiN5kpSQ8aMf/QiXX345Zs6cCUEQMGXKFFx66aX4zW9+M6z1ut1uuN3JuQcVRRmRAVLG25HYPCJYoSBBkNhy5nQrp6Yiu4GkdQoAdMgCX15yjbkB4IAoTpeupLjzo/22XJiS7IE01Da7nMVQFJcvxTEGIFvnspwwz0id03mH5OwTUVIgUr8NC6NQUNr7DM/tKEpuR18PCoVdE6LsH/o6MsFlFYmRFO/Qr+EMye35Zit4I0hQXPy+KiqAFoPkLs75/uWKMXmdihKgAhL/3U68t+SaMdcfBEEQBEEQBJFFRq0aRllZGSRJQktLi2N6S0sLqqqqUi5TXl6Oxx57DKFQCLt378bmzZsRCAQwefLkIa8zrzhcoSBBgyDojmnO5XlmR5WHI+dbwQshYXCWL4WCHIV7RqL6+ShVt89nkgoFUTGP4WIWCkpXrCqvqp+Po0JB6fbFqPBOhYKyi3EvoUJBBEEQBEEQBJF1Rk3UdLlcWLx4MdavX29O0zQN69evx/Llywdc1uPxoLa2FvF4HI8++ijOOuusYa8zLzAGQ3qcf06ufi6Z1c9THVpDFA3zj3k2OE9sb74MDke1+nmeHePRIvFcournw0YVDiNaZqP6uUjVzweNQ8i07ZexjwqJmlnFfBgZdX4mCIIgCIIgCGLYjKrVbe3atbjkkkuwZMkSHHPMMbjnnnsQCoXMyuUXX3wxamtrceeddwIANmzYgP3792PBggXYv38/br31Vmiahm984xsZrzO/SXRqciGGD5IEQTfDz1MOnASBVXA1nZp5FpaW2N5E5+JYRcySqGnfX0FKPzh2bC/PBZiRgpyaWcd0ako5dGp6a/hr7dDXkQnjyakpCGwftCgg2fbFWwtE2gB//ei1bTxiPowkpyZBEARBEARBZJtRVYU+8YlP4ODBg7j55pvR3NyMBQsW4MknnzQL/ezZsweiaIkN4XAYN954I3bs2IFAIIDGxkb8/ve/R1FRUcbrzGsM4cVwaiLRqalCFDTHNCcJy+fb4CpJ1MyT9jvEyOE4NTMMY7dvI52gRDhJcmrmybk1hjls+LlxnkrDEAnrLwSUIFBxwtDXkQlSCkdjPmOImvZjc8KjQN8eINAweu0ajxj3EjXq/EwQBEEQBEEQxLAZdavb1Vdfjauvvjrld88//7zj80knnYRNmzYNa515TVJOTcnxKgkaROjOeR3L85yaupp+nrFMUvj5qJ++mZGt8HMhQwcmhZ8PnqRrIc+ujTGIUSgo7TkoZSOnpgJM+PjQl894O+PIqQkwYTbe69yX4BT2R2SXRKcm3VsIgiAIgiAIImvQf9f5xGEKBQnQIQ5UKMh0aqrGCnPSzJyRKAjmTaGgHOTUHGg9JGoOHnJqZp3Mw8/zwPkoeaz34+GaEg9zbIjsYRYKIqcmQRAEQRAEQWQbEjXziXSFgvhhlKBBMsLPUx3avHdqjoPw85FwamY6H2GDcmpmmx5xAntTMCP1DEIWCgWNFOOpUBBgiZrjYV/GOomipkj3FoIgCIIgCILIFnlidSMYhlMzIScmf102uQhyj86KAaUULLmoqRlOzXwTNfM0/FzMUk7NTHNzZpp7k7Cg6udZ55A0A7HGrVAKJqWeIRvh5yPFeAw/B/LDJZvvmKImhZ8TBEEQBEEQRLah/67zCbNQUEL4OXd+TCv32qqfp3CDJBYayjfhJlHIyxc3XbacmplWNTe2Icj5d4xHC6p+nhv89enTRGSj+vlIQU5NYqiYaWMo/JwgCIIgCIIgsg0pHvlEYk5NOMPPoauArjnnda7Ami/tPGOYpPDzPHFqZiscXBBtQvZA1c/l4W/rSINyao482ah+PlLYHY350N7DIVFOzREj0alJ9xaCIAiCIAiCyBp5pmod6Qxc/dwMS7dPs5Po1My3w58Ufp4ng8NsFQoCMhMsxTzKVThWIKfmyFN5EiAHgPITRrslh8d+LY2HkG1yao4cJGoSBEEQBEEQRM7IE6sbAcAqMJAYPp6UswtHhlMzX6qfC1nMcSnIAKKZVT8nF1bmJImaeXZt5CMTLwAmnJsfhVMEgQmBWmR85KmlnJojR1L1c7q3EARBEARBEES2oP+u84rEQkGJomY0eV47Zvj6OBE18yX8PJtOTTEDp2Y+VZUeK1D4+eiQD4KmwaRPA+XHAf5Jo92S4UNOzZEjSdTMo3OeGJBbb70VgiA4/mbOnGl+Hw6HcdVVV6G0tBSBQADnnXceWlpaRrHFBEEQBEEQ4488UYUIACkK/Uipp9u/c64gYfl8EzUTw8/zpP2ZVi0fzLoGdGpSTs3BQ+HnxGFYdt9otyB7UE7NkYNEzXHN7Nmz8cwzz5ifZdn6vf/qV7+Kxx9/HI888ggKCwtx9dVX49xzz8VLL700Gk0lCIIgCIIYl5ComU+kC5HNNPxcSAg/zzejrk3I05BHA8ORdmpSTs3Bk+TUzLNrgyAGAzk1R46knJp0bxlPyLKMqqqqpOldXV2477778OCDD2LFihUAgPvvvx+zZs3Cq6++imXLlo10UwmCIAiCIMYlJGrmE0kOjwHCz1O6QRKdnnk2uLIJgno+CbJCNgsFGYLlQNXPSdQcNFQoiDiSoJyaI4eZ9oWcmuORrVu3oqamBh6PB8uXL8edd96JiRMnYuPGjYjFYli5cqU578yZMzFx4kS88soraUXNSCSCSCRifu7u7gYAxGIxxGKxlMsQA2P0G/Xf6EHHYHSh/h9dqP8HiZbdMX6Mry+miQAdgxFnpM5/EjXzijTCy5CdmkJWW5dzbCKdTk7NAeYhUXPQUE5N4kiCnJojB1U/H7csXboUDzzwAGbMmIGmpibcdtttOOGEE/D++++jubkZLpcLRUVFjmUqKyvR3Nycdp133nknbrvttqTpTz/9NHw+X7Z34Yhi3bp1o92EIx46BqML9f/oQv2fKfNzstZ1e+cCe5/IybqJw/Pcc8/ldP0kauYTacPPDSeIXQFP9ZQjzwsFCePAqTkSOTVlPvCR/cPb1pEEVT8njiTkAHtVAqPbjiMBQ8RMzIVN5D2nnXaa+X7evHlYunQp6uvr8ec//xler3dI67zuuuuwdu1a83N3dzfq6uqwevVqFBQUDLvNRyKxWAzr1q3DqlWroCjD/B+MGBJ0DEYX6v/Rhfp/kHxwZ1ZXF9NErNs7F6vq3oMy95tZXTdxeIzz/+STT87pdkjUzCcOl1NTH6RTM9+EG0f4uZQ/PtNshp9n4tSsWglMuxKoO2942zqSIKcmcSQx9XIg1gVM/uxot2T8Q/l6jxiKioowffp0bNu2DatWrUI0GkVnZ6fDrdnS0pIyB6eB2+2G252cFkJRFBoMDxPqw9GHjsHoQv0/ulD/Z4io5WS1iqhR/48iue57+u86r8g0/FywBMxUy5tV0vPs8NuEPC2f2m4PPx+uWJaRU9MPHP0zoOqU4W3rSIJyahJHEgUzgKW/BgKTR7sl4x96YHLE0Nvbi+3bt6O6uhqLFy+GoihYv369+f2WLVuwZ88eLF++fBRbSRAEQRAEMb4gp2Y+IWZYKCjdoGkcOTXzqu2CzV2ZUmwe4rqI7JGuCBdBEMRwIFFz3PL1r38dZ555Jurr63HgwAHccsstkCQJF110EQoLC3HZZZdh7dq1KCkpQUFBAb70pS9h+fLlVPmcIAiCIAgii5ComVdkmFMzreA3fkRNDVL+lAoSM3BXZrwuvo7h5uYkEki4FpIeIBAEQQwBCj8ft+zbtw8XXXQR2tvbUV5ejuOPPx6vvvoqysvLAQB33303RFHEeeedh0gkgjVr1uBnP/vZKLeaIAiCIAhifEGiZj6RLkQ2qbpqmkGTkBB+nm+DK0Fk+6qr+VX9XMiiEElOzdxAbiqCIHIBpbYYtzz00EMDfu/xeHDvvffi3nvvHaEWEQRBEARBHHkMWtWaNGkSbr/9duzZsycX7SEGItNCQWkHTdypaVQ/z8cQW+5UzKvq51l1amZxXYRFksCfR+cXQRBjF3pgQhAEQRAEQRA5Y9Aj96985Sv461//ismTJ2PVqlV46KGHEIlEctE2IpF0YWxJhYLGqVMTMB2K+eXUzKIQSU7N3EBuKoIgcgGJmgRBEARBEASRM4Ykar799tt47bXXMGvWLHzpS19CdXU1rr76arz55pu5aCNhki78/AjJqQmQU5OcmrmBhAeCIHIBFSEjCIIgCIIgiJwx5P+uFy1ahB//+Mdmxcf//d//xdFHH40FCxbgN7/5DXRdz2Y7CSB9iGxSTs101c8TnJr5OLjieSn1fBJkDXcl5dQcu6RL7UAQBDEcEn+PqQgZQRAEQRAEQWSNIRcKisVi+Nvf/ob7778f69atw7Jly3DZZZdh3759uP766/HMM8/gwQcfzGZbicPl1NSiqeezFmAvunaY+cYwZvh5HrU9m+HnxjrIqZldyKlJEEQuoHsLQRAEQRAEQeSMQYuab775Ju6//3786U9/giiKuPjii3H33Xdj5syZ5jznnHMOjj766Kw2lMDhq5+buTIP59Q0CgUJWW3eiGCGn+fRwDCbIePk1MwRlFOTIIgcQOHnBEEQBEEcqbx762i3gDgCGLSoefTRR2PVqlX4+c9/jrPPPhuKkizUNDQ04JOf/GRWGkjYSFsoKNPQWaP6eT4XCmLnm5ZPomY2nZpFc4F9fwMKZw9/XYRFumuLIAhiOFARMoIgCIIgCILIGYMWNXfs2IH6+voB5/H7/bj//vuH3CgiHYcJP083nzn/eCgUlIfh52IWc2rOvRWYfjXgKR/+uggLEh4IgsgFFH5OEARBEARBEDlj0MpQa2srNmzYkDR9w4YNeOONN7LSKCINhws/T5yeREKhoLwUNfMw/DybTk1BIEEzF5DwQBBELqB7C0EQBEEQBEHkjEGrWldddRX27t2bNH3//v246qqrstIoIg3pqp+nc3AmLZ/g1Mwnt6OBUf08n9qeTVGTyA1U/ZwgiFxAqS0IgiAIgiAIImcM+r/rTZs2YdGiRUnTFy5ciE2bNmWlUUQ6Mgw/H89OTYmHn+eT26X8WMBbC9SeOdotIdJBbiqCIHIB3VsIgiAIgiAIImcMOqem2+1GS0sLJk+e7Jje1NQEWR706ojBkG5wJGboBDGcmloe59TMR6dmcCpw9l6r/4mxB+XUJAgiF5CoSRAEQRAEQRA5Y9DK0OrVq3Hdddehq6vLnNbZ2Ynrr78eq1atymrjiATShchmHN6W4NTMJ2HQQMxDURMgQXPMQ+HnBEHkAAo/JwiCIAiCIIicMWhr5Q9+8AOceOKJqK+vx8KFCwEAb7/9NiorK/H73/8+6w0kbGScUzONE2RcVT8ntwuRRchNRRBELiAXOEEQBEEQBEHkjEGLmrW1tXj33Xfxxz/+Ee+88w68Xi8uvfRSXHTRRVAUKoSSUzKtfp7WxTgeRM08dWoSYxsSHgiCyAX0wIQgCIIgCIIgcsaQkmD6/X5cccUV2W4LcTjShbFlOmgy5s/n6udc1NTIqUlkk4wfDBAEQQwCCj8nCIIgCIIgiJwx5Mo+mzZtwp49exCNRh3TP/7xjw+7UUQ6hptTczw4NfOw+jkx9knMeUrnF0EQ2YCcmgRBEARBEASRMwYtau7YsQPnnHMO3nvvPQiCAF3XAQACFwVUVR1ocWI4pA0/z7DIiTFdMwoF5WHxGgo/J3KFINkEfxIeCILIAiRqEgRBEARBEETOGLQy9OUvfxkNDQ1obW2Fz+fDBx98gBdeeAFLlizB888/n4MmEiYZVz9PN2gaB05NgURNIkfYr4d8vDYIghh7kKhJEARBEARBEDlj0E7NV155Bc8++yzKysogiiJEUcTxxx+PO++8E9dccw3eeuutXLSTAJCsQacRNdMJfmZOzbjzcz5B1c+JXCFIAGK29wRBEMOEcmoSBEEQBEEQRM4Y9H/XqqoiGAwCAMrKynDgwAEAQH19PbZs2ZLd1hFOxDSOj8RBU+J81gLsJZ+dmhR+TuQMu1OTRE2CILJAurQxBEEQBEEQBEEMm0E7NefMmYN33nkHDQ0NWLp0Kb7//e/D5XLhV7/6FSZPnpyLNhIm6cLP0zg4EzGKoRhOzXwUBj3lAICoEBzlhhDjDrvYkI+CP0EQYw8KPycIgiAIgiCInDFoUfPGG29EKBQCANx+++0444wzcMIJJ6C0tBQPP/xw1htI2Mg4p2Y6QcYIP89jp+a0KxGXS7FjkxdTRrstxPhCIKcmQRBZhsLPCYIgCIIgCCJnDFrUXLNmjfl+6tSp2Lx5Mzo6OlBcXGxWQCdyRNrq5xk6QYzjo+WxqKkUQG+4BLEPnxjtlhDjDRI1CYLINuTUJAiCIAiCIIicMShVKxaLQZZlvP/++47pJSUlJGiOCNlyauZx+DlB5AoKPycIItuQqEkQBEEQBEEQOWNQI3dFUTBx4kSoqpqr9hADka7KeaaFCIRxUCiIIHIFOTUJgsg2JGoSBEEQBEEQRM4YtKp1ww034Prrr0dHR0cu2kMMRKbh52kP6zjIqUkQucLh1CThgSCILJDx7zNBEARBEARBEINl0Dk1f/rTn2Lbtm2oqalBfX09/H6/4/s333wza40jEhhuoSByahLEANidmnRtEASRBTKNpCAIgiAIgiAIYtAMWtQ8++yzc9AMIiMyFjXTDZoSnJqgPKgEYUJOTYIgsk3ivUSkewtBEARBEARBZItBi5q33HJLLtpBZEI68TKd2Jm0vJDZfARxJEI5NQmCyDYUfk4QBEEQBEEQOWPU/7u+9957MWnSJHg8HixduhSvvfbagPPfc889mDFjBrxeL+rq6vDVr34V4XDY/P7WW2+FIAiOv5kzZ+Z6N0aIdE7NDEXNJGfmqB9+ghg7OMQHujYIgsgCVCiIIAiCIAiCIHLGoEfuoihCkqS0f4Ph4Ycfxtq1a3HLLbfgzTffxPz587FmzRq0tramnP/BBx/Etddei1tuuQUffvgh7rvvPjz88MO4/vrrHfPNnj0bTU1N5t+LL7442N0cmySJlWmcZWmrn2cqfhLEEYj9eqAQUYIgsgGJmkcM3/3udyEIAr7yla+Y08LhMK666iqUlpYiEAjgvPPOQ0tLy+g1kiAIgiAIYpwx6PDzv/3tb47PsVgMb731Fn7729/itttuG9S6fvjDH+Lyyy/HpZdeCgD4xS9+gccffxy/+c1vcO211ybN//LLL+O4447Dpz71KQDApEmTcNFFF2HDhg2O+WRZRlVV1aDakhcMVHBAEG25MjN0apKoSRAWlFOTIIhsQ6LmEcHrr7+OX/7yl5g3b55j+le/+lU8/vjjeOSRR1BYWIirr74a5557Ll566aVRailBEARBEMT4YtCi5llnnZU07fzzz8fs2bPx8MMP47LLLstoPdFoFBs3bsR1111nThNFEStXrsQrr7yScpljjz0Wf/jDH/Daa6/hmGOOwY4dO/DEE0/gM5/5jGO+rVu3oqamBh6PB8uXL8edd96JiRMnpm1LJBJBJBIxP3d3dwNggm0sFstof4aCse6Mt6GpUGwf45oOnS8rQ4IA9l6DADXFOiXdKXeqmg4th/uXKwbdbwT1WQbIEEzZPxZXAcSo34YI9dvQoH4bGmO53wRNd/yjFYurgDZy7RyLfTLe6O3txac//Wn8+te/xre//W1zeldXF+677z48+OCDWLFiBQDg/vvvx6xZs/Dqq69i2bJlo9VkgiAIgiCIccOgRc10LFu2DFdccUXG87e1tUFVVVRWVjqmV1ZWYvPmzSmX+dSnPoW2tjYcf/zx0HUd8XgcX/jCFxzh50uXLsUDDzyAGTNmoKmpCbfddhtOOOEEvP/++wgGgynXe+edd6Z0mT799NPw+XwZ79NQWbduXWYz6irskvK7776PvR8+AQA4XbMO5v79TXjziSeSFl8SbkGt7fMHmz7Ezq3J8+ULGfcbYUJ9lp6T+0Io4O+ffGodNMFtfkf9NjSo34YG9dvQGIv9Vhl/C3bp6ol/PZVctC+H9PX1jdi2jlSuuuoqnH766Vi5cqVD1Ny4cSNisRhWrlxpTps5cyYmTpyIV155JaWoOVoP2cczY/mhx5ECHYPRhfp/dDni+18b3cjQGN9+TBOBI/UYjCIjdf5nRdTs7+/Hj3/8Y9TW1h5+5mHw/PPP44477sDPfvYzLF26FNu2bcOXv/xlfOtb38JNN90EADjttNPM+efNm4elS5eivr4ef/7zn9O6SK+77jqsXbvW/Nzd3Y26ujqsXr0aBQUFKZfJBrFYDOvWrcOqVaugKMrhF9B14C/Wx3kLFmBufSMAQPqbC4izgkm1E+pQdUxj0uLSK38A9lmfZ8+ei1lTk+cb6wy63wjqswyQn74Z6NoDADj1tNMB0UX9NkSo34YG9dvQGMv9JjSJAE/rrUNA4+mnj+j2DVGMyA0PPfQQ3nzzTbz++utJ3zU3N8PlcqGoqMgxvbKyEs3NzSnXN9oP2cczY/Ghx5EGHYPRhfp/dDly+3/+aDcAALBu71xgb/6aufKd5557LqfrH7SoWVxcDMHmMtB1HT09PfD5fPjDH/6Q8XrKysogSVJSwvSWlpa0+TBvuukmfOYzn8F///d/AwDmzp2LUCiEK664AjfccANEMflJQFFREaZPn45t27albYvb7Ybb7U6arijKiAyQhrodWXIBxnK2/JiipEBMtb6E4ieSrEAaYwPAwTBSx2c8QX02ALbrQ1HcgCjbPlO/DQXqt6FB/TY0xmS/Kdb/FoIgjnj7xlx/jCP27t2LL3/5y1i3bh08Hk9W1jlaD9nHM2P5oceRAh2D0YX6f3Q54vv/gztHdfMxTcS6vXOxqu49KHO/OaptORIxzv+TTz45p9sZtKh59913O0RNURRRXl6OpUuXori4OOP1uFwuLF68GOvXr8fZZ58NANA0DevXr8fVV1+dcpm+vr4k4dKouK7resplent7sX379qS8m3mLIAK6xt/bREoxoWhQymUTQ96oUBBBmFChIIIgsg3dV8YtGzduRGtrKxYtWmROU1UVL7zwAn7605/iqaeeQjQaRWdnp8OtOdDD+9F+yD6eoT4cfegYjC7U/6PLEdv/ojbaLQAAKKJ2ZPb/GCHXfT9oUfOzn/1s1ja+du1aXHLJJViyZAmOOeYY3HPPPQiFQmY19Isvvhi1tbW4806m8J955pn44Q9/iIULF5rh5zfddBPOPPNMU9z8+te/jjPPPBP19fU4cOAAbrnlFkiShIsuuihr7R5dRACGqGkTJTMaOCVWTydRkyBMzOtBGNGcdwRBjGNI1By3nHLKKXjvvfcc0y699FLMnDkT3/zmN1FXVwdFUbB+/Xqcd955AIAtW7Zgz549WL58+Wg0mSAIgiAIYtwxaFHz/vvvRyAQwAUXXOCY/sgjj6Cvrw+XXHJJxuv6xCc+gYMHD+Lmm29Gc3MzFixYgCeffNIsHrRnzx6HM/PGG2+EIAi48cYbsX//fpSXl+PMM8/Ed77zHXOeffv24aKLLkJ7ezvKy8tx/PHH49VXX0V5eflgd3VsIkiAHufv04ia6RyYiUINiZoEYWFcQ3RdEASRLUjUHLcEg0HMmTPHMc3v96O0tNScftlll2Ht2rUoKSlBQUEBvvSlL2H58uVU+ZwgCIIgCCJLDFrUvPPOO/HLX/4yaXpFRQWuuOKKQYmaAHD11VenDTd//vnnHZ9lWcYtt9yCW265Je36HnrooUFtP+/IRMhMK8okTic3GkGYGNcNCQ8EQWQLIYPUMMS45e6774YoijjvvPMQiUSwZs0a/OxnPxvtZhEEQRAEQYwbBi1q7tmzBw0NDUnT6+vrsWfPnqw0ihgAIY14mYkbhJyaBJEe06lJoiZBEFmCnJpHFIkP4z0eD+69917ce++9o9MggiAIgiCIcc6gVa2Kigq8++67SdPfeecdlJaWZqVRxECIqd9n4gZJmk6iJkFYGE5Nui4IgsgSaaMrCIIgCIIgCIIYLoMevV900UW45ppr8Nxzz0FVVaiqimeffRZf/vKX8clPfjIXbSTspHN9ZOQGIacmQaSFnJoEQWQbCj8nCIIgCIIgiJwx6PDzb33rW9i1axdOOeUUyDJbXNM0XHzxxbjjjjuy3kAigbTh5xnk1EycTgMsgrCgnJoEQWQbCj8nCIIgCIIgiJwxaFHT5XLh4Ycfxre//W28/fbb8Hq9mDt3Lurr63PRPiKRTHJqpjXgJhYGIlGTIEyo+jlBENmGRE2CIAiCIAiCyBmDFjUNpk2bhmnTpmWzLUQmZFIciMLPCWLwkFOTIIhsk9EDR4IgCIIgCIIghsKg/8M+77zz8L3vfS9p+ve//31ccMEFWWkUMRCZVD+n8HOCGDwkahIEkWXIqUkQBEEQBEEQOWPQTs0XXngBt956a9L00047DXfddVc22kQMRDrXR0YVVsmpSRBpEY3rhq4LgiCyBImaBEEQBEGMF969dbRbQBBJDHr03tvbC5fLlTRdURR0d3dnpVHEAGQUfp6hU5PEG4KwQU5NgiCyjP13V6R7C0EQBEEQBEFkk0GrWnPnzsXDDz+cNP2hhx7CUUcdlZVGEQOQzUJB5NQkCAuzUBAJDwRBZAnKqUkQBEEQBEEQOWPQ4ec33XQTzj33XGzfvh0rVqwAAKxfvx4PPvgg/vKXv2S9gUQiGYia6dwglFOTINJDhYIIgsg2FH5OEARBEARBEDlj0KLmmWeeicceewx33HEH/vKXv8Dr9WL+/Pl49tlnUVJSkos2EnYyCjnP0KmZ9JkgjmBMpyaJ/QRBZAkSNQmCIAiCIAgiZwxa1ASA008/HaeffjoAoLu7G3/605/w9a9/HRs3boSqqlltIJFAJuHn6QZOAoWfE0RayKlJEES2ySTfNUEQBEEQBEEQQ2LI/2G/8MILuOSSS1BTU4O77roLK1aswKuvvprNthGpSOfIzGjgRIWCCCItlFOTIIhsQ05NgiAIgiAIgsgZg3JqNjc344EHHsB9992H7u5uXHjhhYhEInjssceoSNBIkdadmcbB6ViWnJoEkRbTqUnXBUEQWYJETYIgCIIgCILIGRmP3s8880zMmDED7777Lu655x4cOHAAP/nJT3LZNiIlwwk/p0JBBJEWcmoSBJFt0v1OEwRBEARBEAQxbDJ2av7rX//CNddcgyuvvBLTpk3LZZuIgUjrzrQPljIsFESiJkHYoJyaBEFkGcqpSRAEQRAEQRA5I+P/sF988UX09PRg8eLFWLp0KX7605+ira0tl20jUpGJOzNt+Dnl1CSItFD4OUEQ2YbCzwmCIAiCIAgiZ2Q8el+2bBl+/etfo6mpCZ///Ofx0EMPoaamBpqmYd26dejp6cllOwmDtIWCMglxI6cmQaSFws8Jgsg2JGoSBEEQBEEQRM4YtKrl9/vxuc99Di+++CLee+89fO1rX8N3v/tdVFRU4OMf/3gu2kg4yCSnJoWfE8SgESj8nCCILEPh5wRBEARBEASRM4b1H/aMGTPw/e9/H/v27cOf/vSnbLWJGIiMws8zLBRE4ecEYWE6Nem6IAgiSwgCzAeK9MCEIAiCIAiCILJKVkbvkiTh7LPPxj/+8Y9srI4YiEwKBZFTkyCGADk1CYLIAZTagiAIgiAIgiByAqla+YaQJvwc6d6nWZZNyFKjCGIcIJLwQBBEDqDUFgRBEARBEASRE0jUzDeGE35OTk2CGAAx4ZUgCCILUGoLgiAIgiAIgsgJ9B923pHGkSlmEH6eOJ0GWARhQSGiBEHkArq3EARBEARBEEROIFUr38gopyY5NQli0FCIKEEQuYBETYIgCIIgCILICaRq5RvphEyky7WZZtnEZQjiSIdCRAmCyAXmbzXdWwiCIAiCIAgim9B/2PlGukJBmVQ/F8ipSRBpIacmQRC5gJyaBEEQBEEQBJETSNXKOzIRNSn8nCAGD4maBEHkABI1CYIgCIIgCCInkKqVb2QkZFL4OUEMGpHCzwmCyAHGPUUkUZMgCIIgCIIgsgmN3vONTPJoklOTIIYAOTUJgsgBlFOTIAiCIAiCIHIC/Yedbwwrp6Y48GeCOJKhEFGCIHIB3VsIgiAIgiAIIifIo90AYpBkEn6eVqwkpyZBpKVkMSAqQOkxo90SgiDGEyR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", 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", 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", 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4XFxc1Lp1a7Vu3VoTJ07U22+/rddee02bN282vT6yOh+HDh1SsWLFbAE6J661rERHRysuLk6ff/55toNTdpQqVUqSMs1Q+PdRxTvRpEkTPf/883rkkUfUpUsXLVu2zGlT7gNwLm7VA1CgXbt2TUuXLtUjjzyixx9/PNMycOBAXb582Tabl4uLix5//HGtXLlSn332mW7cuGF3m54kde3aVadOndInn3yS5fGuXLliWperq6ssFovdb7yPHz+eaUa+W8/6TJ061a79gw8+yLS/zp07a8mSJfrxxx8zHe/cuXOmNUk3RwY6deqkNWvWaNq0aSpevLgeffRRu+NIN5+puiUtLS1Tfdnh6uqqqKgoLV++XCdOnLC1HzhwQOvXr8/U9+/HTUpKyhQ0Jal48eKZfkDOSsOGDVWmTBlNnz7dbhr5tWvX6sCBA3k6K1pkZKQ8PDz0/vvv273HGTNmKCkpyVZLcnKybty4Ybdt7dq15eLiYnsPFy5cyLT/evXqSVKm6fKzsnPnTrtnlE6ePKkvv/xSbdq0kaura45da1mJiIhQ27Zt9emnn2b6uyDdvNb++rUA2VWxYkW5urpmmlb9Tq7brERGRmrBggVat26dnn766TwZAQWQ//ArEwAF2ooVK3T58mV16NAhy/VNmjSxfRnurYDUrVs3ffDBBxo5cqRq166te++9126bp59+WosWLdLzzz+vzZs3q1mzZkpPT9cvv/yiRYsWaf369WrYsOE/1tW+fXtNnDhRbdu2Vffu3ZWYmKgpU6aoatWq+uGHH2z9wsLC1LlzZ02ePFl//PGHbTryQ4cOSbIfXRk7dqw2b96s8PBw9e/fXzVr1tSFCxe0d+9ebdy4McsfqLPy1FNPac6cOVq/fr169Oih4sWL29Y1bdpUpUqVUq9evfTiiy/KYrHos88+s/th3xGjR4/WunXr1Lx5cw0YMEA3btzQBx98oPvuu8/uc2jTpo08PDwUHR2t5557TikpKfrkk09UpkwZnTlzxm6fYWFhmjZtmt566y1VrVpVZcqUyTSiJEnu7u5655131KdPH7Vo0UJPPvmkbTrykJAQDR069I7e0+2cO3fONiL0V5UqVVKPHj00fPhwjR49Wm3btlWHDh108OBBTZ06VY0aNbI9Q/b1119r4MCB6tKli6pXr64bN27os88+s4UZSRozZoy2bdum9u3bq2LFikpMTNTUqVNVvnx53X///aZ11qpVS1FRUXbTkUs3z9UtOXWtZWXOnDlq06aNHnvsMUVHR6t169YqXry4Dh8+rAULFujMmTNZfpfTP/H19VWXLl30wQcfyGKxqEqVKlq1apXpc3+O6Nixo2bNmqWePXvKx8fntt9HBqAQc96EfgBw96Kjow1PT0/jypUrt+3Tu3dvw93d3Ta1ckZGhhEcHGxIMt56660st0lLSzPeeecd47777jOsVqtRqlQpIywszBg9erSRlJRk6yfJiImJyXIfM2bMMKpVq2ZYrVYjNDTUmDVrljFy5MhM03BfuXLFiImJMfz8/Axvb2+jY8eOxsGDBw1JxtixY+36nj171oiJiTGCg4MNd3d3IzAw0GjdurXx8ccfZ+vzMgzDuHHjhlG2bFlDkrFmzZpM63fs2GE0adLE8PLyMoKCgox///vftqnc/zrdc3amIzcMw9i6dasRFhZmeHh4GJUrVzamT5+e5eewYsUKo06dOoanp6cREhJivPPOO8bMmTMNScaxY8ds/RISEoz27dsbJUqUMCTZpia/3ZTUCxcuNOrXr29YrVbDz8/P6NGjh/H777/b9enVq5dRvHjxTJ9FVnVmpUWLFrapvv++tG7d2tbvww8/NEJDQw13d3cjICDAeOGFF+ym5j569KjRt29fo0qVKoanp6fh5+dntGzZ0ti4caOtz6ZNm4xHH33UCAoKMjw8PIygoCDjySefNA4dOmRa563r9fPPP7ddm/Xr18/0mRlG9q61W5/5P02fnpWrV68a48ePNxo1amR4e3sbHh4eRrVq1YxBgwYZR44csfVz5LycO3fO6Ny5s1GsWDGjVKlSxnPPPWf8+OOPWU5Hnp19/nU68r+aOnWqIckYNmyYQ+8ZQMFnMYw7/DUiACDXxMfHq379+vr888/tnkEC7obFYlFMTIw+/PBDZ5cCAAUOzzgBgJNdu3YtU9vkyZPl4uJiN0EDAABwHp5xAgAnGzdunPbs2aOWLVvKzc1Na9eu1dq1a/Xss89mmvocAAA4B8EJAJysadOm2rBhg958802lpKSoQoUKGjVqVKZp0gEAgPPwjBMAAAAAmOAZJwAAAAAwQXACAAAAABNF7hmnjIwMnT59WiVKlLD7YkkAAAAARYthGLp8+bKCgoLk4vLPY0pFLjidPn2aWaoAAAAA2Jw8eVLly5f/xz5FLjiVKFFC0s0Px8fHx8nVAAAAAHCW5ORkBQcH2zLCPylywenW7Xk+Pj4EJwAAAADZeoSHySEAAAAAwATBCQAAAABMEJwAAAAAwESRe8YJAAAABV96erquX7/u7DJQALi7u8vV1fWu90NwAgAAQIGSkpKi33//XYZhOLsUFAAWi0Xly5eXt7f3Xe2H4AQAAIACIz09Xb///ruKFSsmf3//bM2GhqLLMAydO3dOv//+u6pVq3ZXI08EJwAAABQY169fl2EY8vf3l5eXl7PLQQHg7++v48eP6/r163cVnJgcAgAAAAUOI03Irpy6VghOAAAAAGCC4AQAAAAAJnjGCQAAAAWeZXTe3rpnjGRGv6KGEScAAAAgj+zcuVOurq5q3769s0vJExaLRcuXL3d2GTmC4AQAAADkkRkzZmjQoEHatm2bTp8+navHMgxDN27cyNVjFCUEJwAAACAPpKSkaOHChXrhhRfUvn17zZ4927aue/fu6tatm13/69evq3Tp0pozZ44kKSMjQ3FxcapUqZK8vLxUt25dLV682NZ/y5YtslgsWrt2rcLCwmS1WrV9+3b9+uuvevTRRxUQECBvb281atRIGzdutDvWmTNn1L59e3l5ealSpUqaN2+eQkJCNHnyZFufS5cu6ZlnnpG/v798fHzUqlUr/e9//7vjzyMjI0NjxoxR+fLlZbVaVa9ePa1bt862Pi0tTQMHDlTZsmXl6empihUrKi4uTtLNUDhq1ChVqFBBVqtVQUFBevHFF++4luwgOAEAAAB5YNGiRQoNDVWNGjX01FNPaebMmTKMm89K9ejRQytXrlRKSoqt//r163X16lV16tRJkhQXF6c5c+Zo+vTp+umnnzR06FA99dRT2rp1q91xXnnlFY0dO1YHDhxQnTp1lJKSoocfflibNm3Svn371LZtW0VHR+vEiRO2bXr27KnTp09ry5YtWrJkiT7++GMlJiba7bdLly5KTEzU2rVrtWfPHjVo0ECtW7fWhQsX7ujzeO+99zRhwgSNHz9eP/zwg6KiotShQwcdPnxYkvT+++9rxYoVWrRokQ4ePKi5c+cqJCREkrRkyRJNmjRJH330kQ4fPqzly5erdu3ad1RHdjE5BAAAAJAHZsyYoaeeekqS1LZtWyUlJWnr1q168MEHFRUVpeLFi2vZsmV6+umnJUnz5s1Thw4dVKJECaWmpurtt9/Wxo0bFRERIUmqXLmytm/fro8++kgtWrSwHWfMmDF66KGHbK/9/PxUt25d2+s333xTy5Yt04oVKzRw4ED98ssv2rhxo77//ns1bNhQkvTpp5+qWrVqtm22b9+uXbt2KTExUVarVZI0fvx4LV++XIsXL9azzz7r8Ocxfvx4vfzyy3riiSckSe+88442b96syZMna8qUKTpx4oSqVaum+++/XxaLRRUrVrRte+LECQUGBioyMlLu7u6qUKGCGjdu7HANjmDECQAAAMhlBw8e1K5du/Tkk09Kktzc3NStWzfNmDHD9rpr166aO3euJOnKlSv68ssv1aNHD0nSkSNHdPXqVT300EPy9va2LXPmzNGvv/5qd6xb4eeWlJQUDRs2TPfee69Kliwpb29vHThwwDbidPDgQbm5ualBgwa2bapWrapSpUrZXv/vf/9TSkqK7rnnHrvjHzt2LNPxsyM5OVmnT59Ws2bN7NqbNWumAwcOSJJ69+6t+Ph41ahRQy+++KK++uorW78uXbro2rVrqly5svr3769ly5bl+vNcjDgBAAAAuWzGjBm6ceOGgoKCbG2GYchqterDDz+Ur6+vevTooRYtWigxMVEbNmyQl5eX2rZtK0m2W/hWr16tcuXK2e371gjQLcWLF7d7PWzYMG3YsEHjx49X1apV5eXlpccff1xpaWnZrj8lJUVly5bVli1bMq0rWbJktvfjiAYNGujYsWNau3atNm7cqK5duyoyMlKLFy9WcHCwDh48qI0bN2rDhg0aMGCA3n33XW3dulXu7u65Ug/BCQAAAMhFN27c0Jw5czRhwgS1adPGbl3Hjh01f/58Pf/882ratKmCg4O1cOFCrV27Vl26dLGFgJo1a8pqterEiRN2t+Vlx44dO9S7d2/bs1IpKSk6fvy4bX2NGjV048YN7du3T2FhYZJujnBdvHjR1qdBgwZKSEiQm5ub7Tmju+Hj46OgoCDt2LHD7v3s2LHD7pY7Hx8fdevWTd26ddPjjz+utm3b6sKFC/Lz85OXl5eio6MVHR2tmJgYhYaGav/+/XYjZzmJ4JQPZPcL2/iiNQAAgIJn1apVunjxovr16ydfX1+7dZ07d9aMGTP0/PPPS7o5u9706dN16NAhbd682davRIkSGjZsmIYOHaqMjAzdf//9SkpK0o4dO+Tj46NevXrd9vjVqlXT0qVLFR0dLYvFojfeeEMZGRm29aGhoYqMjNSzzz6radOmyd3dXS+99JK8vLxksdz8OTUyMlIRERHq2LGjxo0bp+rVq+v06dNavXq1OnXqlOn2wL86duyY4uPjM9X0r3/9SyNHjlSVKlVUr149zZo1S/Hx8bbbFSdOnKiyZcuqfv36cnFx0RdffKHAwECVLFlSs2fPVnp6usLDw1WsWDF9/vnn8vLysnsOKqcRnAAAAFDg5edfMM+YMUORkZGZQpN0MziNGzdOP/zwg+rUqaMePXroP//5jypWrJjp+Z8333xT/v7+iouL09GjR1WyZEk1aNBAr7766j8ef+LEierbt6+aNm2q0qVL6+WXX1ZycrJdnzlz5qhfv3564IEHFBgYqLi4OP3000/y9PSUdPOLbNesWaPXXntNffr00blz5xQYGKgHHnhAAQEB/3j82NjYTG3//e9/9eKLLyopKUkvvfSSEhMTVbNmTa1YscI2KUWJEiU0btw4HT58WK6urmrUqJHWrFkjFxcXlSxZUmPHjlVsbKzS09NVu3ZtrVy5Uvfcc88/1nI3LMatORCLiOTkZPn6+iopKUk+Pj7OLkcSI04AAADZ9eeff+rYsWOqVKmS7Yd65Lzff/9dwcHB2rhxo1q3bu3scu7KP10zjmQDRpwAAACAIu7rr79WSkqKateurTNnzujf//63QkJC9MADDzi7tHyD4AQAAAAUcdevX9err76qo0ePqkSJEmratKnmzp2bazPUFUQEJwAAAKCIi4qKUlRUlLPLyNf4AlwAAAAAMEFwAgAAQIFTxOY3w13IqWvFqcFp27Ztio6OVlBQkCwWi5YvX266TWpqql577TVVrFhRVqtVISEhmjlzZu4XCwAAAKdzdXWVJKWlpTm5EhQUt66VW9fOnXLqM05XrlxR3bp11bdvXz322GPZ2qZr1646e/asZsyYoapVq+rMmTN2X+AFAACAwsvNzU3FihXTuXPn5O7uLhcXbqDC7WVkZOjcuXMqVqyY3NzuLvo4NTi1a9dO7dq1y3b/devWaevWrTp69Kj8/PwkSSEhIblUHQAAAPIbi8WismXL6tixY/rtt9+cXQ4KABcXF1WoUEEWS/a+O/V2CtSseitWrFDDhg01btw4ffbZZypevLg6dOigN998U15eXlluk5qaqtTUVNvrv39LMgAAAAoWDw8PVatWjdv1kC0eHh45MjJZoILT0aNHtX37dnl6emrZsmU6f/68BgwYoD/++EOzZs3Kcpu4uDiNHj06jysFAABAbnJxcZGnp6ezy0ARUqBuCs3IyJDFYtHcuXPVuHFjPfzww5o4caL+3//7f7p27VqW2wwfPlxJSUm25eTJk3lcNQAAAICCrkCNOJUtW1blypWTr6+vre3ee++VYRj6/fffVa1atUzbWK1WWa3WvCwTAAAAQCFToEacmjVrptOnTyslJcXWdujQIbm4uKh8+fJOrAwAAABAYebU4JSSkqL4+HjFx8dLko4dO6b4+HidOHFC0s3b7Hr27Gnr3717d91zzz3q06ePfv75Z23btk3/+te/1Ldv39tODgEAAAAAd8upwWn37t2qX7++6tevL0mKjY1V/fr1NWLECEnSmTNnbCFKkry9vbVhwwZdunRJDRs2VI8ePRQdHa3333/fKfUDAAAAKBoshmEYzi4iLyUnJ8vX11dJSUny8fFxdjmSJMvo7M0pb4wsUqcKAAAAyFWOZIMC9YwTAAAAADgDwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMCEU4PTtm3bFB0draCgIFksFi1fvjzb2+7YsUNubm6qV69ertUHAAAAAJKTg9OVK1dUt25dTZkyxaHtLl26pJ49e6p169a5VBkAAAAA/B83Zx68Xbt2ateuncPbPf/88+revbtcXV1NR6lSU1OVmppqe52cnOzw8QAAAAAUbQXuGadZs2bp6NGjGjlyZLb6x8XFydfX17YEBwfncoUAAAAACpsCFZwOHz6sV155RZ9//rnc3LI3WDZ8+HAlJSXZlpMnT+ZylQAAAAAKG6fequeI9PR0de/eXaNHj1b16tWzvZ3VapXVas3FygAAAAAUdgUmOF2+fFm7d+/Wvn37NHDgQElSRkaGDMOQm5ubvvrqK7Vq1crJVQIAAAAojApMcPLx8dH+/fvt2qZOnaqvv/5aixcvVqVKlZxUGQAAAIDCzqnBKSUlRUeOHLG9PnbsmOLj4+Xn56cKFSpo+PDhOnXqlObMmSMXFxfVqlXLbvsyZcrI09MzUzsAAAAA5CSnBqfdu3erZcuWttexsbGSpF69emn27Nk6c+aMTpw44azyAAAAAECSZDEMw3B2EXkpOTlZvr6+SkpKko+Pj7PLkSRZRluy1c8YWaROFQAAAJCrHMkGBWo6cgAAAABwBoITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJhwanDatm2boqOjFRQUJIvFouXLl/9j/6VLl+qhhx6Sv7+/fHx8FBERofXr1+dNsQAAAACKLKcGpytXrqhu3bqaMmVKtvpv27ZNDz30kNasWaM9e/aoZcuWio6O1r59+3K5UgAAAABFmZszD96uXTu1a9cu2/0nT55s9/rtt9/Wl19+qZUrV6p+/fo5XB0AAAAA3OTU4HS3MjIydPnyZfn5+d22T2pqqlJTU22vk5OT86I0AAAAAIVIgZ4cYvz48UpJSVHXrl1v2ycuLk6+vr62JTg4OA8rBAAAAFAYFNjgNG/ePI0ePVqLFi1SmTJlbttv+PDhSkpKsi0nT57MwyoBAAAAFAYF8la9BQsW6JlnntEXX3yhyMjIf+xrtVpltVrzqDIAAAAAhVGBG3GaP3+++vTpo/nz56t9+/bOLgcAAABAEeDUEaeUlBQdOXLE9vrYsWOKj4+Xn5+fKlSooOHDh+vUqVOaM2eOpJu35/Xq1UvvvfeewsPDlZCQIEny8vKSr6+vU94DAAAAgMLPqSNOu3fvVv369W1TicfGxqp+/foaMWKEJOnMmTM6ceKErf/HH3+sGzduKCYmRmXLlrUtgwcPdkr9AAAAAIoGi2EYhrOLyEvJycny9fVVUlKSfHx8nF2OJMky2pKtfsbIInWqAAAAgFzlSDYocM84AQAAAEBeIzgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgIm7Dk7p6emKj4/XxYsXc6IeAAAAAMh3HA5OQ4YM0YwZMyTdDE0tWrRQgwYNFBwcrC1btuR0fQAAAADgdA4Hp8WLF6tu3bqSpJUrV+rYsWP65ZdfNHToUL322ms5XiAAAAAAOJvDwen8+fMKDAyUJK1Zs0ZdunRR9erV1bdvX+3fvz/HCwQAAAAAZ3M4OAUEBOjnn39Wenq61q1bp4ceekiSdPXqVbm6ujq0r23btik6OlpBQUGyWCxavny56TZbtmxRgwYNZLVaVbVqVc2ePdvRtwAAAAAADnE4OPXp00ddu3ZVrVq1ZLFYFBkZKUn67rvvFBoa6tC+rly5orp162rKlCnZ6n/s2DG1b99eLVu2VHx8vIYMGaJnnnlG69evd/RtAAAAAEC2uTm6wahRo1SrVi2dPHlSXbp0kdVqlSS5urrqlVdecWhf7dq1U7t27bLdf/r06apUqZImTJggSbr33nu1fft2TZo0SVFRUQ4dGwAAAACyy+HgJEmPP/643etLly6pV69eOVLQP9m5c6dthOuWqKgoDRky5LbbpKamKjU11fY6OTk5t8oDAAAAUEg5fKveO++8o4ULF9ped+3aVffcc4/Kly+vH374IUeL+7uEhAQFBATYtQUEBCg5OVnXrl3Lcpu4uDj5+vraluDg4FytEQAAAEDh43Bwmj59ui18bNiwQRs2bNDatWvVtm1bDRs2LMcLvFvDhw9XUlKSbTl58qSzSwIAAABQwDh8q15CQoItOK1atUpdu3ZVmzZtFBISovDw8Bwv8K8CAwN19uxZu7azZ8/Kx8dHXl5eWW5jtVptz2EBAAAAwJ1weMSpVKlStlGbdevW2Z45MgxD6enpOVvd30RERGjTpk12bRs2bFBERESuHhcAAABA0eZwcHrsscfUvXt3PfTQQ/rjjz9ss+Lt27dPVatWdWhfKSkpio+PV3x8vKSb043Hx8frxIkTkm7eZtezZ09b/+eff15Hjx7Vv//9b/3yyy+aOnWqFi1apKFDhzr6NgAAAAAg2xy+VW/SpEkKCQnRyZMnNW7cOHl7e0uSzpw5owEDBji0r927d6tly5a217GxsZKkXr16afbs2Tpz5owtRElSpUqVtHr1ag0dOlTvvfeeypcvr08//ZSpyAEAAADkKothGIazi8hLycnJ8vX1VVJSknx8fJxdjiTJMtqSrX7GyCJ1qgAAAIBc5Ug2uKPvcfr11181efJkHThwQJJUs2ZNDRkyRJUrV76T3QEAAABAvubwM07r169XzZo1tWvXLtWpU0d16tTRd999p5o1a2rDhg25USMAAAAAOJXDI06vvPKKhg4dqrFjx2Zqf/nll/XQQw/lWHEAAAAAkB84POJ04MAB9evXL1N737599fPPP+dIUQAAAACQnzgcnPz9/W3Th/9VfHy8ypQpkxM1AQAAAEC+4vCtev3799ezzz6ro0ePqmnTppKkHTt26J133rFNJw4AAAAAhYnDwemNN95QiRIlNGHCBA0fPlySFBQUpFGjRmnw4ME5XiAAAAAAONtdfY/T5cuXJUklSpTQ1atXFR8fbxuFyq/4HicAAAAAUh58j9MtJUqUsP358OHDat68udLT0+9mlwAAAACQ7zg8OQQAAAAAFDUEJwAAAAAwQXACAAAAABPZfsZpxYoV/7j+2LFjd10MAAAAAORH2Q5OHTt2NO1jsWRvdjgAAAAAKEiyHZwyMjJysw4AAAAAyLd4xgkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATNxRcLp06ZI+/fRTDR8+XBcuXJAk7d27V6dOncrR4gAAAAAgP8j2dOS3/PDDD4qMjJSvr6+OHz+u/v37y8/PT0uXLtWJEyc0Z86c3KgTAAAAAJzG4RGn2NhY9e7dW4cPH5anp6et/eGHH9a2bdtytDgAAAAAyA8cDk7ff/+9nnvuuUzt5cqVU0JCQo4UBQAAAAD5icPByWq1Kjk5OVP7oUOH5O/vnyNFAQAAAEB+4nBw6tChg8aMGaPr169LkiwWi06cOKGXX35ZnTt3zvECAQAAAMDZHA5OEyZMUEpKisqUKaNr166pRYsWqlq1qkqUKKH//Oc/uVEjAAAAADiVw7Pq+fr6asOGDdq+fbt++OEHpaSkqEGDBoqMjMyN+gAAAADA6RwOTrfcf//9uv/++3OyFgAAAADIlxwOTu+//36W7RaLRZ6enqpataoeeOABubq63nVxAAAAAJAfOBycJk2apHPnzunq1asqVaqUJOnixYsqVqyYvL29lZiYqMqVK2vz5s0KDg7O8YIBAAAAIK85PDnE22+/rUaNGunw4cP6448/9Mcff+jQoUMKDw/Xe++9pxMnTigwMFBDhw7NjXoBAAAAIM9ZDMMwHNmgSpUqWrJkierVq2fXvm/fPnXu3FlHjx7VN998o86dO+vMmTM5WWuOSE5Olq+vr5KSkuTj4+PsciRJltGWbPUzRjp0qgAAAAD8A0eygcMjTmfOnNGNGzcytd+4cUMJCQmSpKCgIF2+fNnRXQMAAABAvuRwcGrZsqWee+457du3z9a2b98+vfDCC2rVqpUkaf/+/apUqVLOVQkAAAAATuRwcJoxY4b8/PwUFhYmq9Uqq9Wqhg0bys/PTzNmzJAkeXt7a8KECTleLAAAAAA4g8Oz6gUGBmrDhg365ZdfdOjQIUlSjRo1VKNGDVufli1b5lyFAAAAAOBkd/wFuKGhoQoNDc3JWgAAAAAgX7qj4PT7779rxYoVOnHihNLS0uzWTZw4MUcKAwAAAID8wuHgtGnTJnXo0EGVK1fWL7/8olq1aun48eMyDEMNGjTIjRoBAAAAwKkcnhxi+PDhGjZsmPbv3y9PT08tWbJEJ0+eVIsWLdSlS5fcqBEAAAAAnMrh4HTgwAH17NlTkuTm5qZr167J29tbY8aM0TvvvJPjBQIAAACAszkcnIoXL257rqls2bL69ddfbevOnz+fc5UBAAAAQD7hcHBq0qSJtm/fLkl6+OGH9dJLL+k///mP+vbtqyZNmtxREVOmTFFISIg8PT0VHh6uXbt2/WP/yZMnq0aNGvLy8lJwcLCGDh2qP//8846ODQAAAABmHJ4cYuLEiUpJSZEkjR49WikpKVq4cKGqVat2RzPqLVy4ULGxsZo+fbrCw8M1efJkRUVF6eDBgypTpkym/vPmzdMrr7yimTNnqmnTpjp06JB69+4ti8XCjH4AAAAAcoXFMAwju53T09O1Y8cO1alTRyVLlsyRAsLDw9WoUSN9+OGHkqSMjAwFBwdr0KBBeuWVVzL1HzhwoA4cOKBNmzbZ2l566SV99913tpGwf5KcnCxfX18lJSXJx8cnR97D3bKMtmSrnzEy26cKAAAAgAlHsoFDt+q5urqqTZs2unjx4l0VeEtaWpr27NmjyMjI/yvIxUWRkZHauXNnlts0bdpUe/bssd3Od/ToUa1Zs0YPP/xwlv1TU1OVnJxstwAAAACAIxx+xqlWrVo6evRojhz8/PnzSk9PV0BAgF17QECAEhISstyme/fuGjNmjO6//365u7urSpUqevDBB/Xqq69m2T8uLk6+vr62JTg4OEdqBwAAAFB0OByc3nrrLQ0bNkyrVq3SmTNn8nw0Z8uWLXr77bc1depU7d27V0uXLtXq1av15ptvZtl/+PDhSkpKsi0nT57M9RoBAAAAFC4OTw5x65a4Dh06yGL5v2dzDMOQxWJRenp6tvdVunRpubq66uzZs3btZ8+eVWBgYJbbvPHGG3r66af1zDPPSJJq166tK1eu6Nlnn9Vrr70mFxf7LGi1WmW1WrNdEwAAAAD8ncPBafPmzTl2cA8PD4WFhWnTpk3q2LGjpJuTQ2zatEkDBw7McpurV69mCkeurq6SboY3AAAAAMhpDgenFi1a5GgBsbGx6tWrlxo2bKjGjRtr8uTJunLlivr06SNJ6tmzp8qVK6e4uDhJUnR0tCZOnKj69esrPDxcR44c0RtvvKHo6GhbgAIAAACAnORwcJKk//73v/roo4909OhRffHFFypXrpw+++wzVapUSffff79D++rWrZvOnTunESNGKCEhQfXq1dO6detsE0acOHHCboTp9ddfl8Vi0euvv65Tp07J399f0dHR+s9//nMnbwUAAAAATDn0PU6StGTJEj399NPq0aOHPvvsM/3888+qXLmyPvzwQ61Zs0Zr1qzJrVpzBN/jBAAAAEDKxe9xkm7Oqjd9+nR98skncnd3t7U3a9ZMe/fudbxaAAAAAMjnHA5OBw8e1AMPPJCp3dfXV5cuXcqJmgAAAAAgX3E4OAUGBurIkSOZ2rdv367KlSvnSFEAAAAAkJ84HJz69++vwYMH67vvvpPFYtHp06c1d+5cDRs2TC+88EJu1AgAAAAATuXwrHqvvPKKMjIy1Lp1a129elUPPPCArFarhg0bpkGDBuVGjQAAAADgVA7PqndLWlqajhw5opSUFNWsWVPe3t45XVuuYFY9AAAAAFIuz6r3+eef6+rVq/Lw8FDNmjXVuHHjAhOaAAAAAOBOOBychg4dqjJlyqh79+5as2aN0tPTc6MuAAAAAMg3HA5OZ86c0YIFC2SxWNS1a1eVLVtWMTEx+uabb3KjPgAAAABwOoeDk5ubmx555BHNnTtXiYmJmjRpko4fP66WLVuqSpUquVEjAAAAADiVw7Pq/VWxYsUUFRWlixcv6rffftOBAwdyqi4AAAAAyDccHnGSpKtXr2ru3Ll6+OGHVa5cOU2ePFmdOnXSTz/9lNP1AQAAAIDTOTzi9MQTT2jVqlUqVqyYunbtqjfeeEMRERG5URsAAAAA5AsOBydXV1ctWrRIUVFRcnV1tVv3448/qlatWjlWHAAAAADkBw4Hp7lz59q9vnz5subPn69PP/1Ue/bsYXpyAAAAAIXOHT3jJEnbtm1Tr169VLZsWY0fP16tWrXSt99+m5O1AQAAAEC+4NCIU0JCgmbPnq0ZM2YoOTlZXbt2VWpqqpYvX66aNWvmVo0AAAAA4FTZHnGKjo5WjRo19MMPP2jy5Mk6ffq0Pvjgg9ysDQAAAADyhWyPOK1du1YvvviiXnjhBVWrVi03awIAAACAfCXbI07bt2/X5cuXFRYWpvDwcH344Yc6f/58btYGAAAAAPlCtoNTkyZN9Mknn+jMmTN67rnntGDBAgUFBSkjI0MbNmzQ5cuXc7NOAAAAAHAah2fVK168uPr27avt27dr//79eumllzR27FiVKVNGHTp0yI0aAQAAAMCp7ng6ckmqUaOGxo0bp99//13z58/PqZoAAAAAIF+5q+B0i6urqzp27KgVK1bkxO4AAAAAIF/JkeAEAAAAAIUZwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATOSL4DRlyhSFhITI09NT4eHh2rVr1z/2v3TpkmJiYlS2bFlZrVZVr15da9asyaNqAQAAABQ1bs4uYOHChYqNjdX06dMVHh6uyZMnKyoqSgcPHlSZMmUy9U9LS9NDDz2kMmXKaPHixSpXrpx+++03lSxZMu+LBwAAAFAkOD04TZw4Uf3791efPn0kSdOnT9fq1as1c+ZMvfLKK5n6z5w5UxcuXNA333wjd3d3SVJISEhelgwAAACgiHHqrXppaWnas2ePIiMjbW0uLi6KjIzUzp07s9xmxYoVioiIUExMjAICAlSrVi29/fbbSk9Pz7J/amqqkpOT7RYAAAAAcIRTg9P58+eVnp6ugIAAu/aAgAAlJCRkuc3Ro0e1ePFipaena82aNXrjjTc0YcIEvfXWW1n2j4uLk6+vr20JDg7O8fcBAAAAoHDLF5NDOCIjI0NlypTRxx9/rLCwMHXr1k2vvfaapk+fnmX/4cOHKykpybacPHkyjysGAAAAUNA59Rmn0qVLy9XVVWfPnrVrP3v2rAIDA7PcpmzZsnJ3d5erq6ut7d5771VCQoLS0tLk4eFh199qtcpqteZ88QAAAACKDKeOOHl4eCgsLEybNm2ytWVkZGjTpk2KiIjIcptmzZrpyJEjysjIsLUdOnRIZcuWzRSaAAAAACAnOP1WvdjYWH3yySf6f//v/+nAgQN64YUXdOXKFdssez179tTw4cNt/V944QVduHBBgwcP1qFDh7R69Wq9/fbbiomJcdZbAAAAAFDIOX068m7duuncuXMaMWKEEhISVK9ePa1bt842YcSJEyfk4vJ/+S44OFjr16/X0KFDVadOHZUrV06DBw/Wyy+/7Ky3AAAAAKCQsxiGYTi7iLyUnJwsX19fJSUlycfHx9nlSJIsoy3Z6meMLFKnCgAAAMhVjmQDp9+qBwAAAAD5HcEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADAhJuzCwAAAACQ/1hGW7LVzxhp5HIl+QMjTgAAAABgguAEAAAAACYITgAAAABgIl8EpylTpigkJESenp4KDw/Xrl27srXdggULZLFY1LFjx9wtEAAAAIApy2hLtpaCyOnBaeHChYqNjdXIkSO1d+9e1a1bV1FRUUpMTPzH7Y4fP65hw4apefPmeVQpAAAAgKLK6cFp4sSJ6t+/v/r06aOaNWtq+vTpKlasmGbOnHnbbdLT09WjRw+NHj1alStXzsNqAQAAABRFTg1OaWlp2rNnjyIjI21tLi4uioyM1M6dO2+73ZgxY1SmTBn169fP9BipqalKTk62WwAAAADAEU4NTufPn1d6eroCAgLs2gMCApSQkJDlNtu3b9eMGTP0ySefZOsYcXFx8vX1tS3BwcF3XTcAAACAosXpt+o54vLly3r66af1ySefqHTp0tnaZvjw4UpKSrItJ0+ezOUqAQAAABQ2bs48eOnSpeXq6qqzZ8/atZ89e1aBgYGZ+v/66686fvy4oqOjbW0ZGRmSJDc3Nx08eFBVqlSx28ZqtcpqteZC9QAAAACKCqeOOHl4eCgsLEybNm2ytWVkZGjTpk2KiIjI1D80NFT79+9XfHy8benQoYNatmyp+Ph4bsMDAAAAkCucOuIkSbGxserVq5caNmyoxo0ba/Lkybpy5Yr69OkjSerZs6fKlSunuLg4eXp6qlatWnbblyxZUpIytQMAAABATnF6cOrWrZvOnTunESNGKCEhQfXq1dO6detsE0acOHFCLi4F6lEsAAAAAIWMxTAMw9lF5KXk5GT5+voqKSlJPj4+zi5HkrL97cnGyCJ1qgAAAOBEd/IzakH7udaRbMBQDgAAAACYIDgBAAAAgAmnP+MEAACKtoJ2aw+AoongBAAAABRy/ILi7hGcCqDsXvgSFz8AAACQE3jGCQAAAABMEJwAAAAAwAS36gEoEri3GwAA3A1GnAAAAADABMEJAAAAAEwQnAAAAADABMEJAAAAAEwQnAAAAADABLPqAbfBFw0DAADgFoITAABADuGrDxx3J58ZnzOcgeAE4Lb4jwkAAMfwf2fhxTNOAAAAAGCCEScAAJAlfnMOR/F8MAozRpwAAAAAwAQjTrit/PybxvxcGwAUZfz7nDfy4nNm9AiwR3ACABQ4efXDeWEKAYXpvQCAM3CrHgAAAACYYMQJAAAUetx2BuBuEZyKCP7DAADAMdzeWLhwPnG3CE5ADiKgAgDyK4IDcHcITgCAHMMPZgCAworghBzFD00AAAAojAhOAJyqsN3eyC8PAAAonAhOQAHED+cAirLC9gsXAAUDwQlORwgAAABAfkdwAooIAiqQPzF6AgAFA8EJAFAk8MsDAMDdIDgBQBFwJ6MajIQAeYNQD0fx77NzEJwAJ+M/TKDw4IcZACi8XJxdAAAAAADkdwQnAAAAADDBrXoAUABxiycAAHmL4AQgR+XFD/R59RwJ4SRv8Dk7js8MAPIewQkAAABwEiaVKTgITgAAAEAWGN3FXxGcAMDJ+I8ZAID8j1n1AAAAAMBEvghOU6ZMUUhIiDw9PRUeHq5du3bdtu8nn3yi5s2bq1SpUipVqpQiIyP/sT9wi2W0JVsLAAAA8HdOD04LFy5UbGysRo4cqb1796pu3bqKiopSYmJilv23bNmiJ598Ups3b9bOnTsVHBysNm3a6NSpU3lcOQAAAICiwunBaeLEierfv7/69OmjmjVravr06SpWrJhmzpyZZf+5c+dqwIABqlevnkJDQ/Xpp58qIyNDmzZtyuPKAQAAABQVTg1OaWlp2rNnjyIjI21tLi4uioyM1M6dO7O1j6tXr+r69evy8/PLcn1qaqqSk5PtFgAAAABwhFOD0/nz55Wenq6AgAC79oCAACUkJGRrHy+//LKCgoLswtdfxcXFydfX17YEBwffdd0AAAAAihan36p3N8aOHasFCxZo2bJl8vT0zLLP8OHDlZSUZFtOnjyZx1UCAAAAKOic+j1OpUuXlqurq86ePWvXfvbsWQUGBv7jtuPHj9fYsWO1ceNG1alT57b9rFarrFZrjtQLAAAAoGhy6oiTh4eHwsLC7CZ2uDXRQ0RExG23GzdunN58802tW7dODRs2zItSAQAAABRhTh1xkqTY2Fj16tVLDRs2VOPGjTV58mRduXJFffr0kST17NlT5cqVU1xcnCTpnXfe0YgRIzRv3jyFhITYnoXy9vaWt7e3094HAAAAgMLL6cGpW7duOnfunEaMGKGEhATVq1dP69ats00YceLECbm4/N/A2LRp05SWlqbHH3/cbj8jR47UqFGj8rJ0AAAAAEWE04OTJA0cOFADBw7Mct2WLVvsXh8/fjz3CwIAAACAvyjQs+oBAAAAQF4gOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJjIF8FpypQpCgkJkaenp8LDw7Vr165/7P/FF18oNDRUnp6eql27ttasWZNHlQIAAAAoipwenBYuXKjY2FiNHDlSe/fuVd26dRUVFaXExMQs+3/zzTd68skn1a9fP+3bt08dO3ZUx44d9eOPP+Zx5QAAAACKCqcHp4kTJ6p///7q06ePatasqenTp6tYsWKaOXNmlv3fe+89tW3bVv/6179077336s0331SDBg304Ycf5nHlAAAAAIoKN2cePC0tTXv27NHw4cNtbS4uLoqMjNTOnTuz3Gbnzp2KjY21a4uKitLy5cuz7J+amqrU1FTb66SkJElScnLyXVafg/7MXjdbzdnsn1fb2H2WhWkbPmc+58K0DZ8zn3Nh2obPmc+5MG3D5+xUt+owDMO8s+FEp06dMiQZ33zzjV37v/71L6Nx48ZZbuPu7m7MmzfPrm3KlClGmTJlsuw/cuRIQxILCwsLCwsLCwsLC0uWy8mTJ02zi1NHnPLC8OHD7UaoMjIydOHCBd1zzz2yWCxOrOz2kpOTFRwcrJMnT8rHx8fZ5SCPcf7BNVC0cf7BNQCugbxjGIYuX76soKAg075ODU6lS5eWq6urzp49a9d+9uxZBQYGZrlNYGCgQ/2tVqusVqtdW8mSJe+86Dzk4+PDX5YijPMProGijfMPrgFwDeQNX1/fbPVz6uQQHh4eCgsL06ZNm2xtGRkZ2rRpkyIiIrLcJiIiwq6/JG3YsOG2/QEAAADgbjn9Vr3Y2Fj16tVLDRs2VOPGjTV58mRduXJFffr0kST17NlT5cqVU1xcnCRp8ODBatGihSZMmKD27dtrwYIF2r17tz7++GNnvg0AAAAAhZjTg1O3bt107tw5jRgxQgkJCapXr57WrVungIAASdKJEyfk4vJ/A2NNmzbVvHnz9Prrr+vVV19VtWrVtHz5ctWqVctZbyHHWa1WjRw5MtMthigaOP/gGijaOP/gGgDXQP5kMYzszL0HAAAAAEWX078AFwAAAADyO4ITAAAAAJggOAEAAACACYITAAAAAJggOOUzU6ZMUUhIiDw9PRUeHq5du3Y5uyTkkm3btik6OlpBQUGyWCxavny53XrDMDRixAiVLVtWXl5eioyM1OHDh51TLHJcXFycGjVqpBIlSqhMmTLq2LGjDh48aNfnzz//VExMjO655x55e3urc+fOmb4AHAXXtGnTVKdOHdsXXEZERGjt2rW29Zz/omXs2LGyWCwaMmSIrY1roHAbNWqULBaL3RIaGmpbz/nPfwhO+cjChQsVGxurkSNHau/evapbt66ioqKUmJjo7NKQC65cuaK6detqypQpWa4fN26c3n//fU2fPl3fffedihcvrqioKP355595XClyw9atWxUTE6Nvv/1WGzZs0PXr19WmTRtduXLF1mfo0KFauXKlvvjiC23dulWnT5/WY4895sSqkZPKly+vsWPHas+ePdq9e7datWqlRx99VD/99JMkzn9R8v333+ujjz5SnTp17Nq5Bgq/++67T2fOnLEt27dvt63j/OdDBvKNxo0bGzExMbbX6enpRlBQkBEXF+fEqpAXJBnLli2zvc7IyDACAwONd99919Z26dIlw2q1GvPnz3dChchtiYmJhiRj69athmHcPN/u7u7GF198Yetz4MABQ5Kxc+dOZ5WJXFaqVCnj008/5fwXIZcvXzaqVatmbNiwwWjRooUxePBgwzD4N6AoGDlypFG3bt0s13H+8ydGnPKJtLQ07dmzR5GRkbY2FxcXRUZGaufOnU6sDM5w7NgxJSQk2F0Pvr6+Cg8P53oopJKSkiRJfn5+kqQ9e/bo+vXrdtdAaGioKlSowDVQCKWnp2vBggW6cuWKIiIiOP9FSExMjNq3b293riX+DSgqDh8+rKCgIFWuXFk9evTQiRMnJHH+8ys3ZxeAm86fP6/09HQFBATYtQcEBOiXX35xUlVwloSEBEnK8nq4tQ6FR0ZGhoYMGaJmzZqpVq1akm5eAx4eHipZsqRdX66BwmX//v2KiIjQn3/+KW9vby1btkw1a9ZUfHw8578IWLBggfbu3avvv/8+0zr+DSj8wsPDNXv2bNWoUUNnzpzR6NGj1bx5c/3444+c/3yK4AQAThYTE6Mff/zR7t52FA01atRQfHy8kpKStHjxYvXq1Utbt251dlnIAydPntTgwYO1YcMGeXp6OrscOEG7du1sf65Tp47Cw8NVsWJFLVq0SF5eXk6sDLfDrXr5ROnSpeXq6ppptpSzZ88qMDDQSVXBWW6dc66Hwm/gwIFatWqVNm/erPLly9vaAwMDlZaWpkuXLtn15xooXDw8PFS1alWFhYUpLi5OdevW1Xvvvcf5LwL27NmjxMRENWjQQG5ubnJzc9PWrVv1/vvvy83NTQEBAVwDRUzJkiVVvXp1HTlyhH8D8imCUz7h4eGhsLAwbdq0ydaWkZGhTZs2KSIiwomVwRkqVaqkwMBAu+shOTlZ3333HddDIWEYhgYOHKhly5bp66+/VqVKlezWh4WFyd3d3e4aOHjwoE6cOME1UIhlZGQoNTWV818EtG7dWvv371d8fLxtadiwoXr06GH7M9dA0ZKSkqJff/1VZcuW5d+AfIpb9fKR2NhY9erVSw0bNlTjxo01efJkXblyRX369HF2acgFKSkpOnLkiO31sWPHFB8fLz8/P1WoUEFDhgzRW2+9pWrVqqlSpUp64403FBQUpI4dOzqvaOSYmJgYzZs3T19++aVKlChhu2fd19dXXl5e8vX1Vb9+/RQbGys/Pz/5+Pho0KBBioiIUJMmTZxcPXLC8OHD1a5dO1WoUEGXL1/WvHnztGXLFq1fv57zXwSUKFHC9kzjLcWLF9c999xja+caKNyGDRum6OhoVaxYUadPn9bIkSPl6uqqJ598kn8D8itnT+sHex988IFRoUIFw8PDw2jcuLHx7bffOrsk5JLNmzcbkjItvXr1Mgzj5pTkb7zxhhEQEGBYrVajdevWxsGDB51bNHJMVudekjFr1ixbn2vXrhkDBgwwSpUqZRQrVszo1KmTcebMGecVjRzVt29fo2LFioaHh4fh7+9vtG7d2vjqq69s6zn/Rc9fpyM3DK6Bwq5bt25G2bJlDQ8PD6NcuXJGt27djCNHjtjWc/7zH4thGIaTMhsAAAAAFAg84wQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAKFQsFouWL1+e68d58MEHNWTIkFw/DgAgfyA4AQAKlISEBA0aNEiVK1eW1WpVcHCwoqOjtWnTJmeXBgAoxNycXQAAANl1/PhxNWvWTCVLltS7776r2rVr6/r161q/fr1iYmL0yy+/OLtEAEAhxYgTAKDAGDBggCwWi3bt2qXOnTurevXquu+++xQbG6tvv/3W1u/8+fPq1KmTihUrpmrVqmnFihW2dbNnz1bJkiXt9rt8+XJZLBbb61GjRqlevXr67LPPFBISIl9fXz3xxBO6fPnybWtbvXq1fH19NXfu3Jx7wwCAfIPgBAAoEC5cuKB169YpJiZGxYsXz7T+r2Fo9OjR6tq1q3744Qc9/PDD6tGjhy5cuODQ8X799VctX75cq1at0qpVq7R161aNHTs2y77z5s3Tk08+qblz56pHjx4OHQcAUDAQnAAABcKRI0dkGIZCQ0NN+/bu3VtPPvmkqlatqrffflspKSnatWuXQ8fLyMjQ7NmzVatWLTVv3lxPP/10ls9RTZkyRQMGDNDKlSv1yCOPOHQMAEDBwTNOAIACwTCMbPetU6eO7c/FixeXj4+PEhMTHTpeSEiISpQoYXtdtmzZTPtYvHixEhMTtWPHDjVq1Mih/QMAChZGnAAABUK1atVksViyNQGEu7u73WuLxaKMjAxJkouLS6YQdv36dYf2cUv9+vXl7++vmTNnOhTsAAAFD8EJAFAg+Pn5KSoqSlOmTNGVK1cyrb906VK29uPv76/Lly/b7SM+Pv6OaqpSpYo2b96sL7/8UoMGDbqjfQAACgaCEwCgwJgyZYrS09PVuHFjLVmyRIcPH9aBAwf0/vvvKyIiIlv7CA8PV7FixfTqq6/q119/1bx58zR79uw7rql69eravHmzlixZwhfiAkAhRnACABQYlStX1t69e9WyZUu99NJLqlWrlh566CFt2rRJ06ZNy9Y+/Pz89Pnnn2vNmjWqXbu25s+fr1GjRt1VXTVq1NDXX3+t+fPn66WXXrqrfQEA8ieLwU3ZAAAAAPCPGHECAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABP/HwKZB4LZBogFAAAAAElFTkSuQmCC", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", @@ -2692,13 +21714,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "notebookRunGroups": { "groupValue": "" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 1s 1s/step\n", + "20/20 [==============================] - 1s 41ms/step\n", + "The accuracy of the model on validation data is 100.00%(100.00000%)\n", + "The accuracy of the model on test data is 96.31%(96.31410%)\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 33ms/step\n", + "1/1 [==============================] - 0s 21ms/step\n", + "1/1 [==============================] - 0s 20ms/step\n", + "1/1 [==============================] - 0s 17ms/step\n", + "1/1 [==============================] - 0s 24ms/step\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_24044\\1259512504.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6\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 69\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\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 70\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m 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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 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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 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_internal_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\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\\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 300\u001b[0m outputs = conv_utils.squeeze_batch_dims(\n\u001b[0;32m 301\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_apply_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minner_rank\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrank\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 302\u001b[0m )\n\u001b[0;32m 303\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--> 304\u001b[1;33m outputs = tf.nn.bias_add(\n\u001b[0m\u001b[0;32m 305\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tf_data_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 306\u001b[0m )\n\u001b[0;32m 307\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(value, bias, data_format, name)\u001b[0m\n\u001b[0;32m 3550\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m(\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 3551\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"input\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3552\u001b[0m \u001b[0mbias\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"bias\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3554\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mgen_nn_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\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[0m", + "\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(value, bias, data_format, name)\u001b[0m\n\u001b[0;32m 850\u001b[0m _ctx, \"BiasAdd\", name, value, bias, \"data_format\", data_format)\n\u001b[0;32m 851\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 852\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 853\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--> 854\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 855\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 856\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 857\u001b[0m return bias_add_eager_fallback(\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import seaborn as sns\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n", @@ -2710,7 +21802,7 @@ "# Garbage Collection (memory)\n", "gc.collect()\n", "\n", - "Extra_EXT = '_T' # _T or _T_BL\n", + "Extra_EXT = '_T_BL' # _T or _T_BL\n", "Train_data_test = False\n", "if SAVE_TYPE == 'TF':\n", " # Load the pre-trained model\n", @@ -2771,8 +21863,8 @@ "for i in range(10):\n", " plt.subplot(2, 5, i+1)\n", " img = x_val[i]\n", - " heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 1) \n", - " heatmap = cv2.resize(np.clip(heatmap, 0, 1), (img.shape[1], img.shape[0]))\n", + " heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 2) \n", + " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", " heatmap = np.uint8(255 * heatmap)\n", " # Apply Adaptive Histogram Equalization\n", " clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(8,8)) # Create CLAHE object\n", @@ -2854,7 +21946,51 @@ "plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3))\n", "\n", "plt.title('Number of Incorrect Predictions vs. Number of Data Points')\n", - "plt.show()" + "plt.show()\n", + "# Deprecated⚠️------------------------------>>>\n", + "# prob_L = 0.9995\n", + "# # Define the range of test data sizes to use\n", + "# data_sizes = range(1, len(x_test), 1) \n", + "\n", + "# # Calculate the probability of a wrong prediction based on test accuracy\n", + "# prob_wrong = 1 - test_accuracy\n", + "\n", + "# # Create a list to store the probability of getting at least one wrong answer for each test data size\n", + "# probabilities = []\n", + "\n", + "# # Calculate the probability of getting at least one wrong answer for each data size\n", + "# for size in data_sizes:\n", + "# # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0\n", + "# cdf = binom.cdf(0, size, prob_wrong)\n", + "# # Subtract the CDF from 1 to get the probability of getting at least one wrong answer\n", + "# prob = 1 - cdf\n", + "# probabilities.append(prob)\n", + "\n", + "# # Find the index of the first data point that has a probability greater than prob_L%\n", + "# index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities))\n", + "\n", + "# # Limit the x-axis to the first data point that has a probability greater than prob_L%\n", + "# data_sizes = data_sizes[:index+1]\n", + "# probabilities = probabilities[:index+1]\n", + "\n", + "# # Plot the probability vs. the number of data points\n", + "# plt.figure(figsize=(10, 6))\n", + "# plt.plot(data_sizes, probabilities)\n", + "# plt.xlabel('Number of Data Points')\n", + "# plt.ylabel('Probability')\n", + "\n", + "# # Add gridlines for the x and y axes\n", + "# plt.grid(True)\n", + "\n", + "# # Change the tick spacing for the x and y axes\n", + "# plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 5 + 10))\n", + "# plt.yticks(np.arange(0, max(probabilities)+0.1, 5 / 100))\n", + "\n", + "# plt.ylim(top=1.01)\n", + "\n", + "# plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points')\n", + "# plt.show()\n", + "# Deprecated⚠️------------------------------<<<" ] } ], diff --git a/Samples/TSR400_y2023_m12_d21-h15_m25_s44.tar_STR.gz b/Samples/TSR400_y2023_m12_d21-h15_m25_s44.tar_STR.gz deleted file mode 100644 index 96d429f..0000000 Binary files a/Samples/TSR400_y2023_m12_d21-h15_m25_s44.tar_STR.gz and /dev/null differ diff --git a/Samples/TSR_SUB_400_y2023_m12_d22-h17_m02_s57.tar_STR.gz b/Samples/TSR_SUB_400_y2023_m12_d22-h17_m02_s57.tar_STR.gz deleted file mode 100644 index be59d85..0000000 Binary files a/Samples/TSR_SUB_400_y2023_m12_d22-h17_m02_s57.tar_STR.gz and /dev/null differ diff --git a/env/Test_ENV3.ipynb b/env/Test_ENV3.ipynb index b95a1e5..4e08b45 100644 --- a/env/Test_ENV3.ipynb +++ b/env/Test_ENV3.ipynb @@ -2,9 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": 22, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow_addons\\utils\\tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n", + "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow_addons\\utils\\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). \n", + " The versions of TensorFlow you are currently using is 2.10.1 and is not supported. \n", + "Some things might work, some things might not.\n", + "If you were to encounter a bug, do not file an issue.\n", + "If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n", + "You can find the compatibility matrix in TensorFlow Addon's readme:\n", + "https://github.com/tensorflow/addons\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Libs\n", "import os\n", @@ -84,10 +108,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from\u001b[0m\u001b[0;32m 0.458953\u001b[0m\u001b[0;33m to \u001b[0m\u001b[0;32m 0.458953\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n" + ] + } + ], + "source": [ + "# Copyright (c) 2024 Aydin Hamedi\n", + "# \n", + "# This software is released under the MIT License.\n", + "# https://opensource.org/licenses/MIT\n", + "\n", + "print_Color_V2(f'Improved model accuracy from{0.4589532546:10f} to {0.4589532546:10f}. Saving model.')" + ] } ], "metadata": { diff --git a/history_vis.py b/history_vis.py index 3d08457..87df676 100644 --- a/history_vis.py +++ b/history_vis.py @@ -1,8 +1,3 @@ -# Copyright (c) 2023 Aydin Hamedi -# -# This software is released under the MIT License. -# https://opensource.org/licenses/MIT - from Utils.Other import * import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D