efficientNet_grad-CAM.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"1CpP3m06ne08"},"source":["「使用データセット」\n","Chest X-Ray Images (Pneumonia)  \n","5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).  \n","link: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia/data\n","\n","「参照notebook」\n","このnotebookのアーキテクチャを参考にしています。\n","https://www.kaggle.com/code/momomooo/efficientnet-chest-x-ray-classification/notebook\n","\n","「アーキテクチャ」\n","input (None, 600, 600, 3)  \n","efficientnet-b7 (weights='imagenet')  \n","GlobalAveragePooling2D()  \n","BatchNormalization()  \n","Dropout(0.4)  \n","Dense(1)  \n","BatchNormalization  \n","Activation('sigmoid')  \n","\n","「確認済み動作環境」  \n","python==3.10.12  \n","pandas==1.5.3  \n","matplotlib==3.7.1  \n","scikit-learn==1.2.2  \n","tensorflow==2.13.0  \n","efficientnet==1.1.1  \n","numpy==1.23.5  "]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14611,"status":"ok","timestamp":1700026875476,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"pGWmbhxlSZek","outputId":"a45b0e2a-1bd9-4888-e2c2-eaabd04dcfc5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting efficientnet\n","  Downloading efficientnet-1.1.1-py3-none-any.whl (18 kB)\n","Collecting keras-applications<=1.0.8,>=1.0.7 (from efficientnet)\n","  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.7/50.7 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from efficientnet) (0.19.3)\n","Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.23.5)\n","Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (3.9.0)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (1.11.3)\n","Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (3.2.1)\n","Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (9.4.0)\n","Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (2.31.6)\n","Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (2023.9.26)\n","Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (1.4.1)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (23.2)\n","Installing collected packages: keras-applications, efficientnet\n","Successfully installed efficientnet-1.1.1 keras-applications-1.0.8\n"]}],"source":["!pip install efficientnet"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16529,"status":"ok","timestamp":1700026912097,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"Kph3aS5rSRVF","outputId":"6b6b0840-b832-4ced-adb7-c996f5b1abdf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive/\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive/')"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"sAB-MnwoVZOv","executionInfo":{"status":"ok","timestamp":1700027160944,"user_tz":-540,"elapsed":232,"user":{"displayName":"ll skip","userId":"07017385437905851028"}}},"outputs":[],"source":["IMG_SIZE = 600\n","BATCH = 32\n","SEED = 42"]},{"cell_type":"markdown","metadata":{"id":"z2Skm0RBVknN"},"source":["# データフレーム作成\n","---"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"bsxpqe7JVbgD","executionInfo":{"status":"ok","timestamp":1700027162165,"user_tz":-540,"elapsed":384,"user":{"displayName":"ll skip","userId":"07017385437905851028"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import glob\n","import os"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"GSpS7EwLV2kY","executionInfo":{"status":"ok","timestamp":1700027189443,"user_tz":-540,"elapsed":27058,"user":{"displayName":"ll skip","userId":"07017385437905851028"}}},"outputs":[],"source":["main_path = '/content/drive/MyDrive/bigData/chest_xray/'\n","\n","train_path = os.path.join(main_path,\"train\")\n","test_path=os.path.join(main_path,\"test\")\n","\n","train_normal = glob.glob(train_path+\"/NORMAL/*.jpeg\")\n","train_pneumonia = glob.glob(train_path+\"/PNEUMONIA/*.jpeg\")\n","\n","test_normal = glob.glob(test_path+\"/NORMAL/*.jpeg\")\n","test_pneumonia = glob.glob(test_path+\"/PNEUMONIA/*.jpeg\")\n","\n","\n","train_list = [x for x in train_normal]\n","train_list.extend([x for x in train_pneumonia])\n","\n","df_train = pd.DataFrame(np.concatenate([['Normal']*len(train_normal) , ['Pneumonia']*len(train_pneumonia)]), columns = ['class'])\n","df_train['image'] = [x for x in train_list]\n","\n","test_list = [x for x in test_normal]\n","test_list.extend([x for x in test_pneumonia])\n","\n","df_test = pd.DataFrame(np.concatenate([['Normal']*len(test_normal) , ['Pneumonia']*len(test_pneumonia)]), columns = ['class'])\n","df_test['image'] = [x for x in test_list]"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1700027189444,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"8hcxVfysWJKM","outputId":"c2a0203a-32da-40a1-a939-0a7940cf871a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    class                                              image\n","0  Normal  /content/drive/MyDrive/bigData/chest_xray/trai...\n","1  Normal  /content/drive/MyDrive/bigData/chest_xray/trai...\n","2  Normal  /content/drive/MyDrive/bigData/chest_xray/trai...\n","3  Normal  /content/drive/MyDrive/bigData/chest_xray/trai...\n","4  Normal  /content/drive/MyDrive/bigData/chest_xray/trai..."],"text/html":["\n","  <div id=\"df-7cc4030c-841e-47ef-aab0-cc2c7e03a9dc\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    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データ前処理、データセット生成\n","---"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1059,"status":"ok","timestamp":1700027190880,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"j50OzLlVWqwj","outputId":"2c3e5050-642d-4d7e-8b8d-c2a3550e5615"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 4172 validated image filenames belonging to 2 classes.\n","Found 1044 validated image filenames belonging to 2 classes.\n","Found 624 validated image filenames belonging to 2 classes.\n"]}],"source":["from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","\n","train_datagen = ImageDataGenerator(rescale=1/255.,\n","                                  zoom_range = 0.1,\n","                                  #rotation_range = 0.1,\n","                                  width_shift_range = 0.1,\n","                                  height_shift_range = 0.1)\n","\n","\n","val_datagen = ImageDataGenerator(rescale=1/255.)\n","\n","ds_train = train_datagen.flow_from_dataframe(train_df,\n","                                             #directory=train_path, #dataframe contains the full paths\n","                                             x_col = 'image',\n","                                             y_col = 'class',\n","                                             target_size = (IMG_SIZE, IMG_SIZE),\n","                                             class_mode = 'binary',\n","                                             batch_size = BATCH,\n","                                             seed = SEED)\n","\n","ds_val = val_datagen.flow_from_dataframe(val_df,\n","                                            #directory=train_path,\n","                                            x_col = 'image',\n","                                            y_col = 'class',\n","                                            target_size = (IMG_SIZE, IMG_SIZE),\n","                                            class_mode = 'binary',\n","                                            batch_size = BATCH,\n","                                            seed = SEED)\n","\n","ds_test = val_datagen.flow_from_dataframe(df_test,\n","                                            #directory=test_path,\n","                                            x_col = 'image',\n","                                            y_col = 'class',\n","                                            target_size = (IMG_SIZE, IMG_SIZE),\n","                                            class_mode = 'binary',\n","                                            batch_size = 1,\n","                                            shuffle = False)"]},{"cell_type":"markdown","metadata":{"id":"kFeShgKiXqLU"},"source":["1枚表示してみる"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"elapsed":919,"status":"ok","timestamp":1700027191792,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"R1qrPlIOWz9b","outputId":"d400c944-9cfb-44be-b01e-554f56bfcc74"},"outputs":[{"output_type":"stream","name":"stdout","text":["画像枚数: 624\n","入力index番号: 21\n"]},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x78ddc4752ce0>"]},"metadata":{},"execution_count":14},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","print(f'画像枚数: {ds_test.n}')\n","\n","# 見たい画像のインデックス番号を指定する。\n","index = 21\n","medical_image = ds_test[index][0]\n","print(f'入力index番号: {index}')\n","plt.imshow(medical_image[0])"]},{"cell_type":"markdown","metadata":{"id":"NuAaTxBgVb0s"},"source":["# EfficientNet(出力をカスタム)を実装\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14669,"status":"ok","timestamp":1697694697525,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"PDQ-m1_yUKED","outputId":"a3ddf0de-0b26-4a50-8401-3fbe27f173dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b7_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5\n","258434480/258434480 [==============================] - 3s 0us/step\n"]}],"source":["'''\n","既存の学習済みモデルを使用する際には、\n","レイヤーがネストしないようにする事が望ましい。\n","\n","ネストしている畳み込みレイヤーに対してCAMを出力しようとした時、\n","計算グラフが繋がらないケースがあるため。\n","'''\n","\n","from tensorflow.keras import layers, Model, metrics\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.losses import binary_crossentropy\n","import efficientnet.tfkeras as efn\n","from tensorflow.keras.layers import Input, GlobalAveragePooling2D, Dropout, BatchNormalization, Dense, Activation, Conv2D\n","\n","\n","input_shape = (600, 600, 3)\n","\n","# EfficientNetB7 モデルをロード\n","pretrained_model_B7 = efn.EfficientNetB7(weights='imagenet', input_shape=input_shape, include_top=False)\n","\n","input_tensor = pretrained_model_B7.input\n","output_tensor = pretrained_model_B7.output\n","\n","# 新たに追加。\n","x = Conv2D(filters=128, kernel_size=(3,3), activation='relu')(output_tensor)\n","\n","x = GlobalAveragePooling2D()(x)\n","x = BatchNormalization()(x)\n","x = Dropout(0.4)(x)\n","x = Dense(1)(x)\n","x = BatchNormalization()(x)\n","x = Activation('sigmoid')(x)\n","\n","\n","# トレーニング可能なレイヤーを設定\n","set_trainable = False\n","for layer in pretrained_model_B7.layers:\n","    if layer.name == 'block7c_project_conv':\n","        set_trainable = True\n","    # if layer.name == 'top_conv':\n","        # set_trainable = True\n","    layer.trainable = set_trainable\n","\n","\n","# モデルをコンパイル\n","model = Model(inputs=input_tensor, outputs=x)\n","model.compile(loss=binary_crossentropy,\n","                  optimizer=Adam(learning_rate=1e-3),\n","                  metrics=[metrics.AUC(name='auc'), 'accuracy'])\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9414,"status":"ok","timestamp":1697694728683,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"YCbgHnXrKe3v","outputId":"83428366-5ad9-4043-eb2e-31fc17f2bfb9"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                Output Shape                 Param #   Connected to                  \n","==================================================================================================\n"," input_2 (InputLayer)        [(None, 600, 600, 3)]        0         []                            \n","                                                                                                  \n"," stem_conv (Conv2D)          (None, 300, 300, 64)         1728      ['input_2[0][0]']             \n","                                                                                                  \n"," stem_bn (BatchNormalizatio  (None, 300, 300, 64)         256       ['stem_conv[0][0]']           \n"," n)                                                                                               \n","                                                                                                  \n"," stem_activation (Activatio  (None, 300, 300, 64)         0         ['stem_bn[0][0]']             \n"," n)                                                                                               \n","                                                                                                  \n"," block1a_dwconv (DepthwiseC  (None, 300, 300, 64)         576       ['stem_activation[0][0]']     \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block1a_bn (BatchNormaliza  (None, 300, 300, 64)         256       ['block1a_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block1a_activation (Activa  (None, 300, 300, 64)         0         ['block1a_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block1a_se_squeeze (Global  (None, 64)                   0         ['block1a_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block1a_se_reshape (Reshap  (None, 1, 1, 64)             0         ['block1a_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block1a_se_reduce (Conv2D)  (None, 1, 1, 16)             1040      ['block1a_se_reshape[0][0]']  \n","                                                                                                  \n"," block1a_se_expand (Conv2D)  (None, 1, 1, 64)             1088      ['block1a_se_reduce[0][0]']   \n","                                                                                                  \n"," block1a_se_excite (Multipl  (None, 300, 300, 64)         0         ['block1a_activation[0][0]',  \n"," y)                                                                  'block1a_se_expand[0][0]']   \n","                                                                                                  \n"," block1a_project_conv (Conv  (None, 300, 300, 32)         2048      ['block1a_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block1a_project_bn (BatchN  (None, 300, 300, 32)         128       ['block1a_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block1b_dwconv (DepthwiseC  (None, 300, 300, 32)         288       ['block1a_project_bn[0][0]']  \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block1b_bn (BatchNormaliza  (None, 300, 300, 32)         128       ['block1b_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block1b_activation (Activa  (None, 300, 300, 32)         0         ['block1b_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block1b_se_squeeze (Global  (None, 32)                   0         ['block1b_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block1b_se_reshape (Reshap  (None, 1, 1, 32)             0         ['block1b_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block1b_se_reduce (Conv2D)  (None, 1, 1, 8)              264       ['block1b_se_reshape[0][0]']  \n","                                                                                                  \n"," block1b_se_expand (Conv2D)  (None, 1, 1, 32)             288       ['block1b_se_reduce[0][0]']   \n","                                                                                                  \n"," block1b_se_excite (Multipl  (None, 300, 300, 32)         0         ['block1b_activation[0][0]',  \n"," y)                                                                  'block1b_se_expand[0][0]']   \n","                                                                                                  \n"," block1b_project_conv (Conv  (None, 300, 300, 32)         1024      ['block1b_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block1b_project_bn (BatchN  (None, 300, 300, 32)         128       ['block1b_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block1b_drop (FixedDropout  (None, 300, 300, 32)         0         ['block1b_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block1b_add (Add)           (None, 300, 300, 32)         0         ['block1b_drop[0][0]',        \n","                                                                     'block1a_project_bn[0][0]']  \n","                                                                                                  \n"," block1c_dwconv (DepthwiseC  (None, 300, 300, 32)         288       ['block1b_add[0][0]']         \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block1c_bn (BatchNormaliza  (None, 300, 300, 32)         128       ['block1c_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block1c_activation (Activa  (None, 300, 300, 32)         0         ['block1c_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block1c_se_squeeze (Global  (None, 32)                   0         ['block1c_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block1c_se_reshape (Reshap  (None, 1, 1, 32)             0         ['block1c_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block1c_se_reduce (Conv2D)  (None, 1, 1, 8)              264       ['block1c_se_reshape[0][0]']  \n","                                                                                                  \n"," block1c_se_expand (Conv2D)  (None, 1, 1, 32)             288       ['block1c_se_reduce[0][0]']   \n","                                                                                                  \n"," block1c_se_excite (Multipl  (None, 300, 300, 32)         0         ['block1c_activation[0][0]',  \n"," y)                                                                  'block1c_se_expand[0][0]']   \n","                                                                                                  \n"," block1c_project_conv (Conv  (None, 300, 300, 32)         1024      ['block1c_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block1c_project_bn (BatchN  (None, 300, 300, 32)         128       ['block1c_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block1c_drop (FixedDropout  (None, 300, 300, 32)         0         ['block1c_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block1c_add (Add)           (None, 300, 300, 32)         0         ['block1c_drop[0][0]',        \n","                                                                     'block1b_add[0][0]']         \n","                                                                                                  \n"," block1d_dwconv (DepthwiseC  (None, 300, 300, 32)         288       ['block1c_add[0][0]']         \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block1d_bn (BatchNormaliza  (None, 300, 300, 32)         128       ['block1d_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block1d_activation (Activa  (None, 300, 300, 32)         0         ['block1d_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block1d_se_squeeze (Global  (None, 32)                   0         ['block1d_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block1d_se_reshape (Reshap  (None, 1, 1, 32)             0         ['block1d_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block1d_se_reduce (Conv2D)  (None, 1, 1, 8)              264       ['block1d_se_reshape[0][0]']  \n","                                                                                                  \n"," block1d_se_expand (Conv2D)  (None, 1, 1, 32)             288       ['block1d_se_reduce[0][0]']   \n","                                                                                                  \n"," block1d_se_excite (Multipl  (None, 300, 300, 32)         0         ['block1d_activation[0][0]',  \n"," y)                                                                  'block1d_se_expand[0][0]']   \n","                                                                                                  \n"," block1d_project_conv (Conv  (None, 300, 300, 32)         1024      ['block1d_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block1d_project_bn (BatchN  (None, 300, 300, 32)         128       ['block1d_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block1d_drop (FixedDropout  (None, 300, 300, 32)         0         ['block1d_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block1d_add (Add)           (None, 300, 300, 32)         0         ['block1d_drop[0][0]',        \n","                                                                     'block1c_add[0][0]']         \n","                                                                                                  \n"," block2a_expand_conv (Conv2  (None, 300, 300, 192)        6144      ['block1d_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block2a_expand_bn (BatchNo  (None, 300, 300, 192)        768       ['block2a_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block2a_expand_activation   (None, 300, 300, 192)        0         ['block2a_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block2a_dwconv (DepthwiseC  (None, 150, 150, 192)        1728      ['block2a_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block2a_bn (BatchNormaliza  (None, 150, 150, 192)        768       ['block2a_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block2a_activation (Activa  (None, 150, 150, 192)        0         ['block2a_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block2a_se_squeeze (Global  (None, 192)                  0         ['block2a_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block2a_se_reshape (Reshap  (None, 1, 1, 192)            0         ['block2a_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block2a_se_reduce (Conv2D)  (None, 1, 1, 8)              1544      ['block2a_se_reshape[0][0]']  \n","                                                                                                  \n"," block2a_se_expand (Conv2D)  (None, 1, 1, 192)            1728      ['block2a_se_reduce[0][0]']   \n","                                                                                                  \n"," block2a_se_excite (Multipl  (None, 150, 150, 192)        0         ['block2a_activation[0][0]',  \n"," y)                                                                  'block2a_se_expand[0][0]']   \n","                                                                                                  \n"," block2a_project_conv (Conv  (None, 150, 150, 48)         9216      ['block2a_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block2a_project_bn (BatchN  (None, 150, 150, 48)         192       ['block2a_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block2b_expand_conv (Conv2  (None, 150, 150, 288)        13824     ['block2a_project_bn[0][0]']  \n"," D)                                                                                               \n","                                                                                                  \n"," block2b_expand_bn (BatchNo  (None, 150, 150, 288)        1152      ['block2b_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block2b_expand_activation   (None, 150, 150, 288)        0         ['block2b_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block2b_dwconv (DepthwiseC  (None, 150, 150, 288)        2592      ['block2b_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block2b_bn (BatchNormaliza  (None, 150, 150, 288)        1152      ['block2b_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block2b_activation (Activa  (None, 150, 150, 288)        0         ['block2b_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block2b_se_squeeze (Global  (None, 288)                  0         ['block2b_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block2b_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2b_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block2b_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2b_se_reshape[0][0]']  \n","                                                                                                  \n"," block2b_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2b_se_reduce[0][0]']   \n","                                                                                                  \n"," block2b_se_excite (Multipl  (None, 150, 150, 288)        0         ['block2b_activation[0][0]',  \n"," y)                                                                  'block2b_se_expand[0][0]']   \n","                                                                                                  \n"," block2b_project_conv (Conv  (None, 150, 150, 48)         13824     ['block2b_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block2b_project_bn (BatchN  (None, 150, 150, 48)         192       ['block2b_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block2b_drop (FixedDropout  (None, 150, 150, 48)         0         ['block2b_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block2b_add (Add)           (None, 150, 150, 48)         0         ['block2b_drop[0][0]',        \n","                                                                     'block2a_project_bn[0][0]']  \n","                                                                                                  \n"," block2c_expand_conv (Conv2  (None, 150, 150, 288)        13824     ['block2b_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block2c_expand_bn (BatchNo  (None, 150, 150, 288)        1152      ['block2c_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block2c_expand_activation   (None, 150, 150, 288)        0         ['block2c_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block2c_dwconv (DepthwiseC  (None, 150, 150, 288)        2592      ['block2c_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block2c_bn (BatchNormaliza  (None, 150, 150, 288)        1152      ['block2c_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block2c_activation (Activa  (None, 150, 150, 288)        0         ['block2c_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block2c_se_squeeze (Global  (None, 288)                  0         ['block2c_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block2c_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2c_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block2c_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2c_se_reshape[0][0]']  \n","                                                                                                  \n"," block2c_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2c_se_reduce[0][0]']   \n","                                                                                                  \n"," block2c_se_excite (Multipl  (None, 150, 150, 288)        0         ['block2c_activation[0][0]',  \n"," y)                                                                  'block2c_se_expand[0][0]']   \n","                                                                                                  \n"," block2c_project_conv (Conv  (None, 150, 150, 48)         13824     ['block2c_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block2c_project_bn (BatchN  (None, 150, 150, 48)         192       ['block2c_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block2c_drop (FixedDropout  (None, 150, 150, 48)         0         ['block2c_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block2c_add (Add)           (None, 150, 150, 48)         0         ['block2c_drop[0][0]',        \n","                                                                     'block2b_add[0][0]']         \n","                                                                                                  \n"," block2d_expand_conv (Conv2  (None, 150, 150, 288)        13824     ['block2c_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block2d_expand_bn (BatchNo  (None, 150, 150, 288)        1152      ['block2d_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block2d_expand_activation   (None, 150, 150, 288)        0         ['block2d_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block2d_dwconv (DepthwiseC  (None, 150, 150, 288)        2592      ['block2d_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block2d_bn (BatchNormaliza  (None, 150, 150, 288)        1152      ['block2d_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block2d_activation (Activa  (None, 150, 150, 288)        0         ['block2d_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block2d_se_squeeze (Global  (None, 288)                  0         ['block2d_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block2d_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2d_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block2d_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2d_se_reshape[0][0]']  \n","                                                                                                  \n"," block2d_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2d_se_reduce[0][0]']   \n","                                                                                                  \n"," block2d_se_excite (Multipl  (None, 150, 150, 288)        0         ['block2d_activation[0][0]',  \n"," y)                                                                  'block2d_se_expand[0][0]']   \n","                                                                                                  \n"," block2d_project_conv (Conv  (None, 150, 150, 48)         13824     ['block2d_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block2d_project_bn (BatchN  (None, 150, 150, 48)         192       ['block2d_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block2d_drop (FixedDropout  (None, 150, 150, 48)         0         ['block2d_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block2d_add (Add)           (None, 150, 150, 48)         0         ['block2d_drop[0][0]',        \n","                                                                     'block2c_add[0][0]']         \n","                                                                                                  \n"," block2e_expand_conv (Conv2  (None, 150, 150, 288)        13824     ['block2d_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block2e_expand_bn (BatchNo  (None, 150, 150, 288)        1152      ['block2e_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block2e_expand_activation   (None, 150, 150, 288)        0         ['block2e_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block2e_dwconv (DepthwiseC  (None, 150, 150, 288)        2592      ['block2e_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block2e_bn (BatchNormaliza  (None, 150, 150, 288)        1152      ['block2e_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block2e_activation (Activa  (None, 150, 150, 288)        0         ['block2e_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block2e_se_squeeze (Global  (None, 288)                  0         ['block2e_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block2e_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2e_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block2e_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2e_se_reshape[0][0]']  \n","                                                                                                  \n"," block2e_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2e_se_reduce[0][0]']   \n","                                                                                                  \n"," block2e_se_excite (Multipl  (None, 150, 150, 288)        0         ['block2e_activation[0][0]',  \n"," y)                                                                  'block2e_se_expand[0][0]']   \n","                                                                                                  \n"," block2e_project_conv (Conv  (None, 150, 150, 48)         13824     ['block2e_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block2e_project_bn (BatchN  (None, 150, 150, 48)         192       ['block2e_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block2e_drop (FixedDropout  (None, 150, 150, 48)         0         ['block2e_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block2e_add (Add)           (None, 150, 150, 48)         0         ['block2e_drop[0][0]',        \n","                                                                     'block2d_add[0][0]']         \n","                                                                                                  \n"," block2f_expand_conv (Conv2  (None, 150, 150, 288)        13824     ['block2e_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block2f_expand_bn (BatchNo  (None, 150, 150, 288)        1152      ['block2f_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block2f_expand_activation   (None, 150, 150, 288)        0         ['block2f_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block2f_dwconv (DepthwiseC  (None, 150, 150, 288)        2592      ['block2f_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block2f_bn (BatchNormaliza  (None, 150, 150, 288)        1152      ['block2f_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block2f_activation (Activa  (None, 150, 150, 288)        0         ['block2f_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block2f_se_squeeze (Global  (None, 288)                  0         ['block2f_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block2f_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2f_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block2f_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2f_se_reshape[0][0]']  \n","                                                                                                  \n"," block2f_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2f_se_reduce[0][0]']   \n","                                                                                                  \n"," block2f_se_excite (Multipl  (None, 150, 150, 288)        0         ['block2f_activation[0][0]',  \n"," y)                                                                  'block2f_se_expand[0][0]']   \n","                                                                                                  \n"," block2f_project_conv (Conv  (None, 150, 150, 48)         13824     ['block2f_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block2f_project_bn (BatchN  (None, 150, 150, 48)         192       ['block2f_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block2f_drop (FixedDropout  (None, 150, 150, 48)         0         ['block2f_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block2f_add (Add)           (None, 150, 150, 48)         0         ['block2f_drop[0][0]',        \n","                                                                     'block2e_add[0][0]']         \n","                                                                                                  \n"," block2g_expand_conv (Conv2  (None, 150, 150, 288)        13824     ['block2f_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block2g_expand_bn (BatchNo  (None, 150, 150, 288)        1152      ['block2g_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block2g_expand_activation   (None, 150, 150, 288)        0         ['block2g_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block2g_dwconv (DepthwiseC  (None, 150, 150, 288)        2592      ['block2g_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block2g_bn (BatchNormaliza  (None, 150, 150, 288)        1152      ['block2g_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block2g_activation (Activa  (None, 150, 150, 288)        0         ['block2g_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block2g_se_squeeze (Global  (None, 288)                  0         ['block2g_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block2g_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2g_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block2g_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2g_se_reshape[0][0]']  \n","                                                                                                  \n"," block2g_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2g_se_reduce[0][0]']   \n","                                                                                                  \n"," block2g_se_excite (Multipl  (None, 150, 150, 288)        0         ['block2g_activation[0][0]',  \n"," y)                                                                  'block2g_se_expand[0][0]']   \n","                                                                                                  \n"," block2g_project_conv (Conv  (None, 150, 150, 48)         13824     ['block2g_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block2g_project_bn (BatchN  (None, 150, 150, 48)         192       ['block2g_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block2g_drop (FixedDropout  (None, 150, 150, 48)         0         ['block2g_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block2g_add (Add)           (None, 150, 150, 48)         0         ['block2g_drop[0][0]',        \n","                                                                     'block2f_add[0][0]']         \n","                                                                                                  \n"," block3a_expand_conv (Conv2  (None, 150, 150, 288)        13824     ['block2g_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block3a_expand_bn (BatchNo  (None, 150, 150, 288)        1152      ['block3a_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block3a_expand_activation   (None, 150, 150, 288)        0         ['block3a_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block3a_dwconv (DepthwiseC  (None, 75, 75, 288)          7200      ['block3a_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block3a_bn (BatchNormaliza  (None, 75, 75, 288)          1152      ['block3a_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block3a_activation (Activa  (None, 75, 75, 288)          0         ['block3a_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block3a_se_squeeze (Global  (None, 288)                  0         ['block3a_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block3a_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block3a_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block3a_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block3a_se_reshape[0][0]']  \n","                                                                                                  \n"," block3a_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block3a_se_reduce[0][0]']   \n","                                                                                                  \n"," block3a_se_excite (Multipl  (None, 75, 75, 288)          0         ['block3a_activation[0][0]',  \n"," y)                                                                  'block3a_se_expand[0][0]']   \n","                                                                                                  \n"," block3a_project_conv (Conv  (None, 75, 75, 80)           23040     ['block3a_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block3a_project_bn (BatchN  (None, 75, 75, 80)           320       ['block3a_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block3b_expand_conv (Conv2  (None, 75, 75, 480)          38400     ['block3a_project_bn[0][0]']  \n"," D)                                                                                               \n","                                                                                                  \n"," block3b_expand_bn (BatchNo  (None, 75, 75, 480)          1920      ['block3b_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block3b_expand_activation   (None, 75, 75, 480)          0         ['block3b_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block3b_dwconv (DepthwiseC  (None, 75, 75, 480)          12000     ['block3b_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block3b_bn (BatchNormaliza  (None, 75, 75, 480)          1920      ['block3b_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block3b_activation (Activa  (None, 75, 75, 480)          0         ['block3b_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block3b_se_squeeze (Global  (None, 480)                  0         ['block3b_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block3b_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3b_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block3b_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3b_se_reshape[0][0]']  \n","                                                                                                  \n"," block3b_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3b_se_reduce[0][0]']   \n","                                                                                                  \n"," block3b_se_excite (Multipl  (None, 75, 75, 480)          0         ['block3b_activation[0][0]',  \n"," y)                                                                  'block3b_se_expand[0][0]']   \n","                                                                                                  \n"," block3b_project_conv (Conv  (None, 75, 75, 80)           38400     ['block3b_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block3b_project_bn (BatchN  (None, 75, 75, 80)           320       ['block3b_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block3b_drop (FixedDropout  (None, 75, 75, 80)           0         ['block3b_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block3b_add (Add)           (None, 75, 75, 80)           0         ['block3b_drop[0][0]',        \n","                                                                     'block3a_project_bn[0][0]']  \n","                                                                                                  \n"," block3c_expand_conv (Conv2  (None, 75, 75, 480)          38400     ['block3b_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block3c_expand_bn (BatchNo  (None, 75, 75, 480)          1920      ['block3c_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block3c_expand_activation   (None, 75, 75, 480)          0         ['block3c_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block3c_dwconv (DepthwiseC  (None, 75, 75, 480)          12000     ['block3c_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block3c_bn (BatchNormaliza  (None, 75, 75, 480)          1920      ['block3c_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block3c_activation (Activa  (None, 75, 75, 480)          0         ['block3c_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block3c_se_squeeze (Global  (None, 480)                  0         ['block3c_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block3c_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3c_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block3c_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3c_se_reshape[0][0]']  \n","                                                                                                  \n"," block3c_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3c_se_reduce[0][0]']   \n","                                                                                                  \n"," block3c_se_excite (Multipl  (None, 75, 75, 480)          0         ['block3c_activation[0][0]',  \n"," y)                                                                  'block3c_se_expand[0][0]']   \n","                                                                                                  \n"," block3c_project_conv (Conv  (None, 75, 75, 80)           38400     ['block3c_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block3c_project_bn (BatchN  (None, 75, 75, 80)           320       ['block3c_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block3c_drop (FixedDropout  (None, 75, 75, 80)           0         ['block3c_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block3c_add (Add)           (None, 75, 75, 80)           0         ['block3c_drop[0][0]',        \n","                                                                     'block3b_add[0][0]']         \n","                                                                                                  \n"," block3d_expand_conv (Conv2  (None, 75, 75, 480)          38400     ['block3c_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block3d_expand_bn (BatchNo  (None, 75, 75, 480)          1920      ['block3d_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block3d_expand_activation   (None, 75, 75, 480)          0         ['block3d_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block3d_dwconv (DepthwiseC  (None, 75, 75, 480)          12000     ['block3d_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block3d_bn (BatchNormaliza  (None, 75, 75, 480)          1920      ['block3d_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block3d_activation (Activa  (None, 75, 75, 480)          0         ['block3d_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block3d_se_squeeze (Global  (None, 480)                  0         ['block3d_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block3d_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3d_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block3d_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3d_se_reshape[0][0]']  \n","                                                                                                  \n"," block3d_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3d_se_reduce[0][0]']   \n","                                                                                                  \n"," block3d_se_excite (Multipl  (None, 75, 75, 480)          0         ['block3d_activation[0][0]',  \n"," y)                                                                  'block3d_se_expand[0][0]']   \n","                                                                                                  \n"," block3d_project_conv (Conv  (None, 75, 75, 80)           38400     ['block3d_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block3d_project_bn (BatchN  (None, 75, 75, 80)           320       ['block3d_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block3d_drop (FixedDropout  (None, 75, 75, 80)           0         ['block3d_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block3d_add (Add)           (None, 75, 75, 80)           0         ['block3d_drop[0][0]',        \n","                                                                     'block3c_add[0][0]']         \n","                                                                                                  \n"," block3e_expand_conv (Conv2  (None, 75, 75, 480)          38400     ['block3d_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block3e_expand_bn (BatchNo  (None, 75, 75, 480)          1920      ['block3e_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block3e_expand_activation   (None, 75, 75, 480)          0         ['block3e_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block3e_dwconv (DepthwiseC  (None, 75, 75, 480)          12000     ['block3e_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block3e_bn (BatchNormaliza  (None, 75, 75, 480)          1920      ['block3e_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block3e_activation (Activa  (None, 75, 75, 480)          0         ['block3e_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block3e_se_squeeze (Global  (None, 480)                  0         ['block3e_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block3e_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3e_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block3e_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3e_se_reshape[0][0]']  \n","                                                                                                  \n"," block3e_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3e_se_reduce[0][0]']   \n","                                                                                                  \n"," block3e_se_excite (Multipl  (None, 75, 75, 480)          0         ['block3e_activation[0][0]',  \n"," y)                                                                  'block3e_se_expand[0][0]']   \n","                                                                                                  \n"," block3e_project_conv (Conv  (None, 75, 75, 80)           38400     ['block3e_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block3e_project_bn (BatchN  (None, 75, 75, 80)           320       ['block3e_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block3e_drop (FixedDropout  (None, 75, 75, 80)           0         ['block3e_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block3e_add (Add)           (None, 75, 75, 80)           0         ['block3e_drop[0][0]',        \n","                                                                     'block3d_add[0][0]']         \n","                                                                                                  \n"," block3f_expand_conv (Conv2  (None, 75, 75, 480)          38400     ['block3e_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block3f_expand_bn (BatchNo  (None, 75, 75, 480)          1920      ['block3f_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block3f_expand_activation   (None, 75, 75, 480)          0         ['block3f_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block3f_dwconv (DepthwiseC  (None, 75, 75, 480)          12000     ['block3f_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block3f_bn (BatchNormaliza  (None, 75, 75, 480)          1920      ['block3f_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block3f_activation (Activa  (None, 75, 75, 480)          0         ['block3f_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block3f_se_squeeze (Global  (None, 480)                  0         ['block3f_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block3f_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3f_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block3f_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3f_se_reshape[0][0]']  \n","                                                                                                  \n"," block3f_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3f_se_reduce[0][0]']   \n","                                                                                                  \n"," block3f_se_excite (Multipl  (None, 75, 75, 480)          0         ['block3f_activation[0][0]',  \n"," y)                                                                  'block3f_se_expand[0][0]']   \n","                                                                                                  \n"," block3f_project_conv (Conv  (None, 75, 75, 80)           38400     ['block3f_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block3f_project_bn (BatchN  (None, 75, 75, 80)           320       ['block3f_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block3f_drop (FixedDropout  (None, 75, 75, 80)           0         ['block3f_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block3f_add (Add)           (None, 75, 75, 80)           0         ['block3f_drop[0][0]',        \n","                                                                     'block3e_add[0][0]']         \n","                                                                                                  \n"," block3g_expand_conv (Conv2  (None, 75, 75, 480)          38400     ['block3f_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block3g_expand_bn (BatchNo  (None, 75, 75, 480)          1920      ['block3g_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block3g_expand_activation   (None, 75, 75, 480)          0         ['block3g_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block3g_dwconv (DepthwiseC  (None, 75, 75, 480)          12000     ['block3g_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block3g_bn (BatchNormaliza  (None, 75, 75, 480)          1920      ['block3g_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block3g_activation (Activa  (None, 75, 75, 480)          0         ['block3g_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block3g_se_squeeze (Global  (None, 480)                  0         ['block3g_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block3g_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3g_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block3g_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3g_se_reshape[0][0]']  \n","                                                                                                  \n"," block3g_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3g_se_reduce[0][0]']   \n","                                                                                                  \n"," block3g_se_excite (Multipl  (None, 75, 75, 480)          0         ['block3g_activation[0][0]',  \n"," y)                                                                  'block3g_se_expand[0][0]']   \n","                                                                                                  \n"," block3g_project_conv (Conv  (None, 75, 75, 80)           38400     ['block3g_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block3g_project_bn (BatchN  (None, 75, 75, 80)           320       ['block3g_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block3g_drop (FixedDropout  (None, 75, 75, 80)           0         ['block3g_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block3g_add (Add)           (None, 75, 75, 80)           0         ['block3g_drop[0][0]',        \n","                                                                     'block3f_add[0][0]']         \n","                                                                                                  \n"," block4a_expand_conv (Conv2  (None, 75, 75, 480)          38400     ['block3g_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4a_expand_bn (BatchNo  (None, 75, 75, 480)          1920      ['block4a_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4a_expand_activation   (None, 75, 75, 480)          0         ['block4a_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4a_dwconv (DepthwiseC  (None, 38, 38, 480)          4320      ['block4a_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4a_bn (BatchNormaliza  (None, 38, 38, 480)          1920      ['block4a_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4a_activation (Activa  (None, 38, 38, 480)          0         ['block4a_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4a_se_squeeze (Global  (None, 480)                  0         ['block4a_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4a_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block4a_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4a_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block4a_se_reshape[0][0]']  \n","                                                                                                  \n"," block4a_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block4a_se_reduce[0][0]']   \n","                                                                                                  \n"," block4a_se_excite (Multipl  (None, 38, 38, 480)          0         ['block4a_activation[0][0]',  \n"," y)                                                                  'block4a_se_expand[0][0]']   \n","                                                                                                  \n"," block4a_project_conv (Conv  (None, 38, 38, 160)          76800     ['block4a_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4a_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4a_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4b_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4a_project_bn[0][0]']  \n"," D)                                                                                               \n","                                                                                                  \n"," block4b_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4b_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4b_expand_activation   (None, 38, 38, 960)          0         ['block4b_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4b_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4b_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4b_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4b_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4b_activation (Activa  (None, 38, 38, 960)          0         ['block4b_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4b_se_squeeze (Global  (None, 960)                  0         ['block4b_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4b_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4b_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4b_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4b_se_reshape[0][0]']  \n","                                                                                                  \n"," block4b_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4b_se_reduce[0][0]']   \n","                                                                                                  \n"," block4b_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4b_activation[0][0]',  \n"," y)                                                                  'block4b_se_expand[0][0]']   \n","                                                                                                  \n"," block4b_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4b_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4b_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4b_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4b_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4b_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4b_add (Add)           (None, 38, 38, 160)          0         ['block4b_drop[0][0]',        \n","                                                                     'block4a_project_bn[0][0]']  \n","                                                                                                  \n"," block4c_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4b_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4c_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4c_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4c_expand_activation   (None, 38, 38, 960)          0         ['block4c_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4c_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4c_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4c_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4c_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4c_activation (Activa  (None, 38, 38, 960)          0         ['block4c_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4c_se_squeeze (Global  (None, 960)                  0         ['block4c_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4c_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4c_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4c_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4c_se_reshape[0][0]']  \n","                                                                                                  \n"," block4c_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4c_se_reduce[0][0]']   \n","                                                                                                  \n"," block4c_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4c_activation[0][0]',  \n"," y)                                                                  'block4c_se_expand[0][0]']   \n","                                                                                                  \n"," block4c_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4c_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4c_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4c_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4c_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4c_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4c_add (Add)           (None, 38, 38, 160)          0         ['block4c_drop[0][0]',        \n","                                                                     'block4b_add[0][0]']         \n","                                                                                                  \n"," block4d_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4c_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4d_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4d_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4d_expand_activation   (None, 38, 38, 960)          0         ['block4d_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4d_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4d_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4d_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4d_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4d_activation (Activa  (None, 38, 38, 960)          0         ['block4d_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4d_se_squeeze (Global  (None, 960)                  0         ['block4d_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4d_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4d_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4d_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4d_se_reshape[0][0]']  \n","                                                                                                  \n"," block4d_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4d_se_reduce[0][0]']   \n","                                                                                                  \n"," block4d_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4d_activation[0][0]',  \n"," y)                                                                  'block4d_se_expand[0][0]']   \n","                                                                                                  \n"," block4d_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4d_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4d_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4d_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4d_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4d_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4d_add (Add)           (None, 38, 38, 160)          0         ['block4d_drop[0][0]',        \n","                                                                     'block4c_add[0][0]']         \n","                                                                                                  \n"," block4e_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4d_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4e_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4e_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4e_expand_activation   (None, 38, 38, 960)          0         ['block4e_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4e_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4e_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4e_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4e_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4e_activation (Activa  (None, 38, 38, 960)          0         ['block4e_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4e_se_squeeze (Global  (None, 960)                  0         ['block4e_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4e_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4e_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4e_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4e_se_reshape[0][0]']  \n","                                                                                                  \n"," block4e_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4e_se_reduce[0][0]']   \n","                                                                                                  \n"," block4e_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4e_activation[0][0]',  \n"," y)                                                                  'block4e_se_expand[0][0]']   \n","                                                                                                  \n"," block4e_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4e_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4e_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4e_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4e_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4e_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4e_add (Add)           (None, 38, 38, 160)          0         ['block4e_drop[0][0]',        \n","                                                                     'block4d_add[0][0]']         \n","                                                                                                  \n"," block4f_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4e_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4f_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4f_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4f_expand_activation   (None, 38, 38, 960)          0         ['block4f_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4f_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4f_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4f_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4f_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4f_activation (Activa  (None, 38, 38, 960)          0         ['block4f_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4f_se_squeeze (Global  (None, 960)                  0         ['block4f_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4f_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4f_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4f_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4f_se_reshape[0][0]']  \n","                                                                                                  \n"," block4f_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4f_se_reduce[0][0]']   \n","                                                                                                  \n"," block4f_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4f_activation[0][0]',  \n"," y)                                                                  'block4f_se_expand[0][0]']   \n","                                                                                                  \n"," block4f_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4f_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4f_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4f_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4f_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4f_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4f_add (Add)           (None, 38, 38, 160)          0         ['block4f_drop[0][0]',        \n","                                                                     'block4e_add[0][0]']         \n","                                                                                                  \n"," block4g_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4f_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4g_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4g_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4g_expand_activation   (None, 38, 38, 960)          0         ['block4g_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4g_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4g_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4g_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4g_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4g_activation (Activa  (None, 38, 38, 960)          0         ['block4g_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4g_se_squeeze (Global  (None, 960)                  0         ['block4g_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4g_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4g_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4g_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4g_se_reshape[0][0]']  \n","                                                                                                  \n"," block4g_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4g_se_reduce[0][0]']   \n","                                                                                                  \n"," block4g_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4g_activation[0][0]',  \n"," y)                                                                  'block4g_se_expand[0][0]']   \n","                                                                                                  \n"," block4g_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4g_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4g_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4g_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4g_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4g_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4g_add (Add)           (None, 38, 38, 160)          0         ['block4g_drop[0][0]',        \n","                                                                     'block4f_add[0][0]']         \n","                                                                                                  \n"," block4h_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4g_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4h_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4h_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4h_expand_activation   (None, 38, 38, 960)          0         ['block4h_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4h_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4h_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4h_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4h_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4h_activation (Activa  (None, 38, 38, 960)          0         ['block4h_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4h_se_squeeze (Global  (None, 960)                  0         ['block4h_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4h_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4h_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4h_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4h_se_reshape[0][0]']  \n","                                                                                                  \n"," block4h_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4h_se_reduce[0][0]']   \n","                                                                                                  \n"," block4h_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4h_activation[0][0]',  \n"," y)                                                                  'block4h_se_expand[0][0]']   \n","                                                                                                  \n"," block4h_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4h_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4h_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4h_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4h_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4h_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4h_add (Add)           (None, 38, 38, 160)          0         ['block4h_drop[0][0]',        \n","                                                                     'block4g_add[0][0]']         \n","                                                                                                  \n"," block4i_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4h_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4i_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4i_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4i_expand_activation   (None, 38, 38, 960)          0         ['block4i_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4i_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4i_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4i_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4i_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4i_activation (Activa  (None, 38, 38, 960)          0         ['block4i_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4i_se_squeeze (Global  (None, 960)                  0         ['block4i_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4i_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4i_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4i_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4i_se_reshape[0][0]']  \n","                                                                                                  \n"," block4i_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4i_se_reduce[0][0]']   \n","                                                                                                  \n"," block4i_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4i_activation[0][0]',  \n"," y)                                                                  'block4i_se_expand[0][0]']   \n","                                                                                                  \n"," block4i_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4i_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4i_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4i_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4i_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4i_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4i_add (Add)           (None, 38, 38, 160)          0         ['block4i_drop[0][0]',        \n","                                                                     'block4h_add[0][0]']         \n","                                                                                                  \n"," block4j_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4i_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block4j_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block4j_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block4j_expand_activation   (None, 38, 38, 960)          0         ['block4j_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block4j_dwconv (DepthwiseC  (None, 38, 38, 960)          8640      ['block4j_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block4j_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block4j_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block4j_activation (Activa  (None, 38, 38, 960)          0         ['block4j_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block4j_se_squeeze (Global  (None, 960)                  0         ['block4j_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block4j_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4j_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block4j_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4j_se_reshape[0][0]']  \n","                                                                                                  \n"," block4j_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4j_se_reduce[0][0]']   \n","                                                                                                  \n"," block4j_se_excite (Multipl  (None, 38, 38, 960)          0         ['block4j_activation[0][0]',  \n"," y)                                                                  'block4j_se_expand[0][0]']   \n","                                                                                                  \n"," block4j_project_conv (Conv  (None, 38, 38, 160)          153600    ['block4j_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block4j_project_bn (BatchN  (None, 38, 38, 160)          640       ['block4j_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block4j_drop (FixedDropout  (None, 38, 38, 160)          0         ['block4j_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block4j_add (Add)           (None, 38, 38, 160)          0         ['block4j_drop[0][0]',        \n","                                                                     'block4i_add[0][0]']         \n","                                                                                                  \n"," block5a_expand_conv (Conv2  (None, 38, 38, 960)          153600    ['block4j_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5a_expand_bn (BatchNo  (None, 38, 38, 960)          3840      ['block5a_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5a_expand_activation   (None, 38, 38, 960)          0         ['block5a_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5a_dwconv (DepthwiseC  (None, 38, 38, 960)          24000     ['block5a_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5a_bn (BatchNormaliza  (None, 38, 38, 960)          3840      ['block5a_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5a_activation (Activa  (None, 38, 38, 960)          0         ['block5a_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5a_se_squeeze (Global  (None, 960)                  0         ['block5a_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5a_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5a_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5a_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5a_se_reshape[0][0]']  \n","                                                                                                  \n"," block5a_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5a_se_reduce[0][0]']   \n","                                                                                                  \n"," block5a_se_excite (Multipl  (None, 38, 38, 960)          0         ['block5a_activation[0][0]',  \n"," y)                                                                  'block5a_se_expand[0][0]']   \n","                                                                                                  \n"," block5a_project_conv (Conv  (None, 38, 38, 224)          215040    ['block5a_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5a_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5a_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5b_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5a_project_bn[0][0]']  \n"," D)                                                                                               \n","                                                                                                  \n"," block5b_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5b_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5b_expand_activation   (None, 38, 38, 1344)         0         ['block5b_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5b_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5b_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5b_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5b_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5b_activation (Activa  (None, 38, 38, 1344)         0         ['block5b_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5b_se_squeeze (Global  (None, 1344)                 0         ['block5b_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5b_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5b_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5b_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5b_se_reshape[0][0]']  \n","                                                                                                  \n"," block5b_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5b_se_reduce[0][0]']   \n","                                                                                                  \n"," block5b_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5b_activation[0][0]',  \n"," y)                                                                  'block5b_se_expand[0][0]']   \n","                                                                                                  \n"," block5b_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5b_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5b_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5b_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5b_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5b_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5b_add (Add)           (None, 38, 38, 224)          0         ['block5b_drop[0][0]',        \n","                                                                     'block5a_project_bn[0][0]']  \n","                                                                                                  \n"," block5c_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5b_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5c_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5c_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5c_expand_activation   (None, 38, 38, 1344)         0         ['block5c_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5c_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5c_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5c_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5c_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5c_activation (Activa  (None, 38, 38, 1344)         0         ['block5c_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5c_se_squeeze (Global  (None, 1344)                 0         ['block5c_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5c_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5c_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5c_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5c_se_reshape[0][0]']  \n","                                                                                                  \n"," block5c_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5c_se_reduce[0][0]']   \n","                                                                                                  \n"," block5c_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5c_activation[0][0]',  \n"," y)                                                                  'block5c_se_expand[0][0]']   \n","                                                                                                  \n"," block5c_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5c_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5c_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5c_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5c_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5c_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5c_add (Add)           (None, 38, 38, 224)          0         ['block5c_drop[0][0]',        \n","                                                                     'block5b_add[0][0]']         \n","                                                                                                  \n"," block5d_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5c_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5d_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5d_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5d_expand_activation   (None, 38, 38, 1344)         0         ['block5d_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5d_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5d_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5d_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5d_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5d_activation (Activa  (None, 38, 38, 1344)         0         ['block5d_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5d_se_squeeze (Global  (None, 1344)                 0         ['block5d_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5d_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5d_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5d_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5d_se_reshape[0][0]']  \n","                                                                                                  \n"," block5d_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5d_se_reduce[0][0]']   \n","                                                                                                  \n"," block5d_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5d_activation[0][0]',  \n"," y)                                                                  'block5d_se_expand[0][0]']   \n","                                                                                                  \n"," block5d_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5d_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5d_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5d_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5d_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5d_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5d_add (Add)           (None, 38, 38, 224)          0         ['block5d_drop[0][0]',        \n","                                                                     'block5c_add[0][0]']         \n","                                                                                                  \n"," block5e_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5d_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5e_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5e_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5e_expand_activation   (None, 38, 38, 1344)         0         ['block5e_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5e_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5e_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5e_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5e_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5e_activation (Activa  (None, 38, 38, 1344)         0         ['block5e_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5e_se_squeeze (Global  (None, 1344)                 0         ['block5e_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5e_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5e_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5e_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5e_se_reshape[0][0]']  \n","                                                                                                  \n"," block5e_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5e_se_reduce[0][0]']   \n","                                                                                                  \n"," block5e_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5e_activation[0][0]',  \n"," y)                                                                  'block5e_se_expand[0][0]']   \n","                                                                                                  \n"," block5e_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5e_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5e_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5e_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5e_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5e_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5e_add (Add)           (None, 38, 38, 224)          0         ['block5e_drop[0][0]',        \n","                                                                     'block5d_add[0][0]']         \n","                                                                                                  \n"," block5f_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5e_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5f_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5f_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5f_expand_activation   (None, 38, 38, 1344)         0         ['block5f_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5f_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5f_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5f_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5f_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5f_activation (Activa  (None, 38, 38, 1344)         0         ['block5f_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5f_se_squeeze (Global  (None, 1344)                 0         ['block5f_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5f_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5f_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5f_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5f_se_reshape[0][0]']  \n","                                                                                                  \n"," block5f_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5f_se_reduce[0][0]']   \n","                                                                                                  \n"," block5f_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5f_activation[0][0]',  \n"," y)                                                                  'block5f_se_expand[0][0]']   \n","                                                                                                  \n"," block5f_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5f_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5f_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5f_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5f_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5f_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5f_add (Add)           (None, 38, 38, 224)          0         ['block5f_drop[0][0]',        \n","                                                                     'block5e_add[0][0]']         \n","                                                                                                  \n"," block5g_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5f_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5g_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5g_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5g_expand_activation   (None, 38, 38, 1344)         0         ['block5g_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5g_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5g_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5g_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5g_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5g_activation (Activa  (None, 38, 38, 1344)         0         ['block5g_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5g_se_squeeze (Global  (None, 1344)                 0         ['block5g_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5g_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5g_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5g_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5g_se_reshape[0][0]']  \n","                                                                                                  \n"," block5g_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5g_se_reduce[0][0]']   \n","                                                                                                  \n"," block5g_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5g_activation[0][0]',  \n"," y)                                                                  'block5g_se_expand[0][0]']   \n","                                                                                                  \n"," block5g_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5g_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5g_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5g_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5g_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5g_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5g_add (Add)           (None, 38, 38, 224)          0         ['block5g_drop[0][0]',        \n","                                                                     'block5f_add[0][0]']         \n","                                                                                                  \n"," block5h_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5g_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5h_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5h_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5h_expand_activation   (None, 38, 38, 1344)         0         ['block5h_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5h_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5h_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5h_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5h_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5h_activation (Activa  (None, 38, 38, 1344)         0         ['block5h_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5h_se_squeeze (Global  (None, 1344)                 0         ['block5h_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5h_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5h_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5h_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5h_se_reshape[0][0]']  \n","                                                                                                  \n"," block5h_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5h_se_reduce[0][0]']   \n","                                                                                                  \n"," block5h_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5h_activation[0][0]',  \n"," y)                                                                  'block5h_se_expand[0][0]']   \n","                                                                                                  \n"," block5h_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5h_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5h_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5h_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5h_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5h_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5h_add (Add)           (None, 38, 38, 224)          0         ['block5h_drop[0][0]',        \n","                                                                     'block5g_add[0][0]']         \n","                                                                                                  \n"," block5i_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5h_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5i_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5i_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5i_expand_activation   (None, 38, 38, 1344)         0         ['block5i_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5i_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5i_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5i_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5i_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5i_activation (Activa  (None, 38, 38, 1344)         0         ['block5i_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5i_se_squeeze (Global  (None, 1344)                 0         ['block5i_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5i_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5i_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5i_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5i_se_reshape[0][0]']  \n","                                                                                                  \n"," block5i_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5i_se_reduce[0][0]']   \n","                                                                                                  \n"," block5i_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5i_activation[0][0]',  \n"," y)                                                                  'block5i_se_expand[0][0]']   \n","                                                                                                  \n"," block5i_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5i_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5i_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5i_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5i_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5i_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5i_add (Add)           (None, 38, 38, 224)          0         ['block5i_drop[0][0]',        \n","                                                                     'block5h_add[0][0]']         \n","                                                                                                  \n"," block5j_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5i_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block5j_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block5j_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block5j_expand_activation   (None, 38, 38, 1344)         0         ['block5j_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block5j_dwconv (DepthwiseC  (None, 38, 38, 1344)         33600     ['block5j_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block5j_bn (BatchNormaliza  (None, 38, 38, 1344)         5376      ['block5j_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block5j_activation (Activa  (None, 38, 38, 1344)         0         ['block5j_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block5j_se_squeeze (Global  (None, 1344)                 0         ['block5j_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block5j_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5j_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block5j_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5j_se_reshape[0][0]']  \n","                                                                                                  \n"," block5j_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5j_se_reduce[0][0]']   \n","                                                                                                  \n"," block5j_se_excite (Multipl  (None, 38, 38, 1344)         0         ['block5j_activation[0][0]',  \n"," y)                                                                  'block5j_se_expand[0][0]']   \n","                                                                                                  \n"," block5j_project_conv (Conv  (None, 38, 38, 224)          301056    ['block5j_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block5j_project_bn (BatchN  (None, 38, 38, 224)          896       ['block5j_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block5j_drop (FixedDropout  (None, 38, 38, 224)          0         ['block5j_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block5j_add (Add)           (None, 38, 38, 224)          0         ['block5j_drop[0][0]',        \n","                                                                     'block5i_add[0][0]']         \n","                                                                                                  \n"," block6a_expand_conv (Conv2  (None, 38, 38, 1344)         301056    ['block5j_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6a_expand_bn (BatchNo  (None, 38, 38, 1344)         5376      ['block6a_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6a_expand_activation   (None, 38, 38, 1344)         0         ['block6a_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6a_dwconv (DepthwiseC  (None, 19, 19, 1344)         33600     ['block6a_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6a_bn (BatchNormaliza  (None, 19, 19, 1344)         5376      ['block6a_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6a_activation (Activa  (None, 19, 19, 1344)         0         ['block6a_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6a_se_squeeze (Global  (None, 1344)                 0         ['block6a_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6a_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block6a_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6a_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block6a_se_reshape[0][0]']  \n","                                                                                                  \n"," block6a_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block6a_se_reduce[0][0]']   \n","                                                                                                  \n"," block6a_se_excite (Multipl  (None, 19, 19, 1344)         0         ['block6a_activation[0][0]',  \n"," y)                                                                  'block6a_se_expand[0][0]']   \n","                                                                                                  \n"," block6a_project_conv (Conv  (None, 19, 19, 384)          516096    ['block6a_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6a_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6a_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6b_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6a_project_bn[0][0]']  \n"," D)                                                                                               \n","                                                                                                  \n"," block6b_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6b_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6b_expand_activation   (None, 19, 19, 2304)         0         ['block6b_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6b_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6b_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6b_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6b_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6b_activation (Activa  (None, 19, 19, 2304)         0         ['block6b_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6b_se_squeeze (Global  (None, 2304)                 0         ['block6b_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6b_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6b_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6b_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6b_se_reshape[0][0]']  \n","                                                                                                  \n"," block6b_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6b_se_reduce[0][0]']   \n","                                                                                                  \n"," block6b_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6b_activation[0][0]',  \n"," y)                                                                  'block6b_se_expand[0][0]']   \n","                                                                                                  \n"," block6b_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6b_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6b_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6b_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6b_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6b_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6b_add (Add)           (None, 19, 19, 384)          0         ['block6b_drop[0][0]',        \n","                                                                     'block6a_project_bn[0][0]']  \n","                                                                                                  \n"," block6c_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6b_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6c_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6c_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6c_expand_activation   (None, 19, 19, 2304)         0         ['block6c_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6c_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6c_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6c_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6c_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6c_activation (Activa  (None, 19, 19, 2304)         0         ['block6c_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6c_se_squeeze (Global  (None, 2304)                 0         ['block6c_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6c_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6c_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6c_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6c_se_reshape[0][0]']  \n","                                                                                                  \n"," block6c_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6c_se_reduce[0][0]']   \n","                                                                                                  \n"," block6c_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6c_activation[0][0]',  \n"," y)                                                                  'block6c_se_expand[0][0]']   \n","                                                                                                  \n"," block6c_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6c_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6c_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6c_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6c_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6c_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6c_add (Add)           (None, 19, 19, 384)          0         ['block6c_drop[0][0]',        \n","                                                                     'block6b_add[0][0]']         \n","                                                                                                  \n"," block6d_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6c_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6d_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6d_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6d_expand_activation   (None, 19, 19, 2304)         0         ['block6d_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6d_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6d_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6d_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6d_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6d_activation (Activa  (None, 19, 19, 2304)         0         ['block6d_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6d_se_squeeze (Global  (None, 2304)                 0         ['block6d_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6d_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6d_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6d_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6d_se_reshape[0][0]']  \n","                                                                                                  \n"," block6d_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6d_se_reduce[0][0]']   \n","                                                                                                  \n"," block6d_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6d_activation[0][0]',  \n"," y)                                                                  'block6d_se_expand[0][0]']   \n","                                                                                                  \n"," block6d_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6d_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6d_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6d_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6d_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6d_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6d_add (Add)           (None, 19, 19, 384)          0         ['block6d_drop[0][0]',        \n","                                                                     'block6c_add[0][0]']         \n","                                                                                                  \n"," block6e_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6d_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6e_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6e_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6e_expand_activation   (None, 19, 19, 2304)         0         ['block6e_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6e_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6e_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6e_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6e_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6e_activation (Activa  (None, 19, 19, 2304)         0         ['block6e_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6e_se_squeeze (Global  (None, 2304)                 0         ['block6e_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6e_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6e_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6e_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6e_se_reshape[0][0]']  \n","                                                                                                  \n"," block6e_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6e_se_reduce[0][0]']   \n","                                                                                                  \n"," block6e_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6e_activation[0][0]',  \n"," y)                                                                  'block6e_se_expand[0][0]']   \n","                                                                                                  \n"," block6e_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6e_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6e_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6e_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6e_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6e_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6e_add (Add)           (None, 19, 19, 384)          0         ['block6e_drop[0][0]',        \n","                                                                     'block6d_add[0][0]']         \n","                                                                                                  \n"," block6f_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6e_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6f_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6f_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6f_expand_activation   (None, 19, 19, 2304)         0         ['block6f_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6f_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6f_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6f_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6f_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6f_activation (Activa  (None, 19, 19, 2304)         0         ['block6f_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6f_se_squeeze (Global  (None, 2304)                 0         ['block6f_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6f_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6f_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6f_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6f_se_reshape[0][0]']  \n","                                                                                                  \n"," block6f_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6f_se_reduce[0][0]']   \n","                                                                                                  \n"," block6f_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6f_activation[0][0]',  \n"," y)                                                                  'block6f_se_expand[0][0]']   \n","                                                                                                  \n"," block6f_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6f_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6f_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6f_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6f_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6f_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6f_add (Add)           (None, 19, 19, 384)          0         ['block6f_drop[0][0]',        \n","                                                                     'block6e_add[0][0]']         \n","                                                                                                  \n"," block6g_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6f_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6g_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6g_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6g_expand_activation   (None, 19, 19, 2304)         0         ['block6g_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6g_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6g_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6g_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6g_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6g_activation (Activa  (None, 19, 19, 2304)         0         ['block6g_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6g_se_squeeze (Global  (None, 2304)                 0         ['block6g_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6g_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6g_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6g_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6g_se_reshape[0][0]']  \n","                                                                                                  \n"," block6g_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6g_se_reduce[0][0]']   \n","                                                                                                  \n"," block6g_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6g_activation[0][0]',  \n"," y)                                                                  'block6g_se_expand[0][0]']   \n","                                                                                                  \n"," block6g_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6g_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6g_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6g_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6g_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6g_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6g_add (Add)           (None, 19, 19, 384)          0         ['block6g_drop[0][0]',        \n","                                                                     'block6f_add[0][0]']         \n","                                                                                                  \n"," block6h_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6g_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6h_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6h_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6h_expand_activation   (None, 19, 19, 2304)         0         ['block6h_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6h_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6h_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6h_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6h_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6h_activation (Activa  (None, 19, 19, 2304)         0         ['block6h_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6h_se_squeeze (Global  (None, 2304)                 0         ['block6h_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6h_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6h_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6h_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6h_se_reshape[0][0]']  \n","                                                                                                  \n"," block6h_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6h_se_reduce[0][0]']   \n","                                                                                                  \n"," block6h_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6h_activation[0][0]',  \n"," y)                                                                  'block6h_se_expand[0][0]']   \n","                                                                                                  \n"," block6h_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6h_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6h_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6h_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6h_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6h_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6h_add (Add)           (None, 19, 19, 384)          0         ['block6h_drop[0][0]',        \n","                                                                     'block6g_add[0][0]']         \n","                                                                                                  \n"," block6i_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6h_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6i_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6i_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6i_expand_activation   (None, 19, 19, 2304)         0         ['block6i_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6i_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6i_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6i_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6i_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6i_activation (Activa  (None, 19, 19, 2304)         0         ['block6i_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6i_se_squeeze (Global  (None, 2304)                 0         ['block6i_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6i_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6i_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6i_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6i_se_reshape[0][0]']  \n","                                                                                                  \n"," block6i_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6i_se_reduce[0][0]']   \n","                                                                                                  \n"," block6i_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6i_activation[0][0]',  \n"," y)                                                                  'block6i_se_expand[0][0]']   \n","                                                                                                  \n"," block6i_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6i_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6i_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6i_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6i_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6i_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6i_add (Add)           (None, 19, 19, 384)          0         ['block6i_drop[0][0]',        \n","                                                                     'block6h_add[0][0]']         \n","                                                                                                  \n"," block6j_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6i_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6j_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6j_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6j_expand_activation   (None, 19, 19, 2304)         0         ['block6j_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6j_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6j_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6j_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6j_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6j_activation (Activa  (None, 19, 19, 2304)         0         ['block6j_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6j_se_squeeze (Global  (None, 2304)                 0         ['block6j_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6j_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6j_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6j_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6j_se_reshape[0][0]']  \n","                                                                                                  \n"," block6j_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6j_se_reduce[0][0]']   \n","                                                                                                  \n"," block6j_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6j_activation[0][0]',  \n"," y)                                                                  'block6j_se_expand[0][0]']   \n","                                                                                                  \n"," block6j_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6j_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6j_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6j_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6j_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6j_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6j_add (Add)           (None, 19, 19, 384)          0         ['block6j_drop[0][0]',        \n","                                                                     'block6i_add[0][0]']         \n","                                                                                                  \n"," block6k_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6j_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6k_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6k_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6k_expand_activation   (None, 19, 19, 2304)         0         ['block6k_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6k_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6k_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6k_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6k_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6k_activation (Activa  (None, 19, 19, 2304)         0         ['block6k_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6k_se_squeeze (Global  (None, 2304)                 0         ['block6k_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6k_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6k_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6k_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6k_se_reshape[0][0]']  \n","                                                                                                  \n"," block6k_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6k_se_reduce[0][0]']   \n","                                                                                                  \n"," block6k_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6k_activation[0][0]',  \n"," y)                                                                  'block6k_se_expand[0][0]']   \n","                                                                                                  \n"," block6k_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6k_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6k_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6k_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6k_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6k_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6k_add (Add)           (None, 19, 19, 384)          0         ['block6k_drop[0][0]',        \n","                                                                     'block6j_add[0][0]']         \n","                                                                                                  \n"," block6l_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6k_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6l_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6l_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6l_expand_activation   (None, 19, 19, 2304)         0         ['block6l_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6l_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6l_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6l_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6l_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6l_activation (Activa  (None, 19, 19, 2304)         0         ['block6l_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6l_se_squeeze (Global  (None, 2304)                 0         ['block6l_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6l_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6l_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6l_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6l_se_reshape[0][0]']  \n","                                                                                                  \n"," block6l_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6l_se_reduce[0][0]']   \n","                                                                                                  \n"," block6l_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6l_activation[0][0]',  \n"," y)                                                                  'block6l_se_expand[0][0]']   \n","                                                                                                  \n"," block6l_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6l_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6l_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6l_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6l_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6l_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6l_add (Add)           (None, 19, 19, 384)          0         ['block6l_drop[0][0]',        \n","                                                                     'block6k_add[0][0]']         \n","                                                                                                  \n"," block6m_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6l_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block6m_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block6m_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block6m_expand_activation   (None, 19, 19, 2304)         0         ['block6m_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block6m_dwconv (DepthwiseC  (None, 19, 19, 2304)         57600     ['block6m_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block6m_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block6m_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block6m_activation (Activa  (None, 19, 19, 2304)         0         ['block6m_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block6m_se_squeeze (Global  (None, 2304)                 0         ['block6m_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block6m_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6m_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block6m_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6m_se_reshape[0][0]']  \n","                                                                                                  \n"," block6m_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6m_se_reduce[0][0]']   \n","                                                                                                  \n"," block6m_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block6m_activation[0][0]',  \n"," y)                                                                  'block6m_se_expand[0][0]']   \n","                                                                                                  \n"," block6m_project_conv (Conv  (None, 19, 19, 384)          884736    ['block6m_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block6m_project_bn (BatchN  (None, 19, 19, 384)          1536      ['block6m_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block6m_drop (FixedDropout  (None, 19, 19, 384)          0         ['block6m_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block6m_add (Add)           (None, 19, 19, 384)          0         ['block6m_drop[0][0]',        \n","                                                                     'block6l_add[0][0]']         \n","                                                                                                  \n"," block7a_expand_conv (Conv2  (None, 19, 19, 2304)         884736    ['block6m_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block7a_expand_bn (BatchNo  (None, 19, 19, 2304)         9216      ['block7a_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block7a_expand_activation   (None, 19, 19, 2304)         0         ['block7a_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block7a_dwconv (DepthwiseC  (None, 19, 19, 2304)         20736     ['block7a_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block7a_bn (BatchNormaliza  (None, 19, 19, 2304)         9216      ['block7a_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block7a_activation (Activa  (None, 19, 19, 2304)         0         ['block7a_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block7a_se_squeeze (Global  (None, 2304)                 0         ['block7a_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block7a_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block7a_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block7a_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block7a_se_reshape[0][0]']  \n","                                                                                                  \n"," block7a_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block7a_se_reduce[0][0]']   \n","                                                                                                  \n"," block7a_se_excite (Multipl  (None, 19, 19, 2304)         0         ['block7a_activation[0][0]',  \n"," y)                                                                  'block7a_se_expand[0][0]']   \n","                                                                                                  \n"," block7a_project_conv (Conv  (None, 19, 19, 640)          1474560   ['block7a_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block7a_project_bn (BatchN  (None, 19, 19, 640)          2560      ['block7a_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block7b_expand_conv (Conv2  (None, 19, 19, 3840)         2457600   ['block7a_project_bn[0][0]']  \n"," D)                                                                                               \n","                                                                                                  \n"," block7b_expand_bn (BatchNo  (None, 19, 19, 3840)         15360     ['block7b_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block7b_expand_activation   (None, 19, 19, 3840)         0         ['block7b_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block7b_dwconv (DepthwiseC  (None, 19, 19, 3840)         34560     ['block7b_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block7b_bn (BatchNormaliza  (None, 19, 19, 3840)         15360     ['block7b_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block7b_activation (Activa  (None, 19, 19, 3840)         0         ['block7b_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block7b_se_squeeze (Global  (None, 3840)                 0         ['block7b_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block7b_se_reshape (Reshap  (None, 1, 1, 3840)           0         ['block7b_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block7b_se_reduce (Conv2D)  (None, 1, 1, 160)            614560    ['block7b_se_reshape[0][0]']  \n","                                                                                                  \n"," block7b_se_expand (Conv2D)  (None, 1, 1, 3840)           618240    ['block7b_se_reduce[0][0]']   \n","                                                                                                  \n"," block7b_se_excite (Multipl  (None, 19, 19, 3840)         0         ['block7b_activation[0][0]',  \n"," y)                                                                  'block7b_se_expand[0][0]']   \n","                                                                                                  \n"," block7b_project_conv (Conv  (None, 19, 19, 640)          2457600   ['block7b_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block7b_project_bn (BatchN  (None, 19, 19, 640)          2560      ['block7b_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block7b_drop (FixedDropout  (None, 19, 19, 640)          0         ['block7b_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block7b_add (Add)           (None, 19, 19, 640)          0         ['block7b_drop[0][0]',        \n","                                                                     'block7a_project_bn[0][0]']  \n","                                                                                                  \n"," block7c_expand_conv (Conv2  (None, 19, 19, 3840)         2457600   ['block7b_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block7c_expand_bn (BatchNo  (None, 19, 19, 3840)         15360     ['block7c_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block7c_expand_activation   (None, 19, 19, 3840)         0         ['block7c_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block7c_dwconv (DepthwiseC  (None, 19, 19, 3840)         34560     ['block7c_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block7c_bn (BatchNormaliza  (None, 19, 19, 3840)         15360     ['block7c_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block7c_activation (Activa  (None, 19, 19, 3840)         0         ['block7c_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block7c_se_squeeze (Global  (None, 3840)                 0         ['block7c_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block7c_se_reshape (Reshap  (None, 1, 1, 3840)           0         ['block7c_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block7c_se_reduce (Conv2D)  (None, 1, 1, 160)            614560    ['block7c_se_reshape[0][0]']  \n","                                                                                                  \n"," block7c_se_expand (Conv2D)  (None, 1, 1, 3840)           618240    ['block7c_se_reduce[0][0]']   \n","                                                                                                  \n"," block7c_se_excite (Multipl  (None, 19, 19, 3840)         0         ['block7c_activation[0][0]',  \n"," y)                                                                  'block7c_se_expand[0][0]']   \n","                                                                                                  \n"," block7c_project_conv (Conv  (None, 19, 19, 640)          2457600   ['block7c_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block7c_project_bn (BatchN  (None, 19, 19, 640)          2560      ['block7c_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block7c_drop (FixedDropout  (None, 19, 19, 640)          0         ['block7c_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block7c_add (Add)           (None, 19, 19, 640)          0         ['block7c_drop[0][0]',        \n","                                                                     'block7b_add[0][0]']         \n","                                                                                                  \n"," block7d_expand_conv (Conv2  (None, 19, 19, 3840)         2457600   ['block7c_add[0][0]']         \n"," D)                                                                                               \n","                                                                                                  \n"," block7d_expand_bn (BatchNo  (None, 19, 19, 3840)         15360     ['block7d_expand_conv[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block7d_expand_activation   (None, 19, 19, 3840)         0         ['block7d_expand_bn[0][0]']   \n"," (Activation)                                                                                     \n","                                                                                                  \n"," block7d_dwconv (DepthwiseC  (None, 19, 19, 3840)         34560     ['block7d_expand_activation[0]\n"," onv2D)                                                             [0]']                         \n","                                                                                                  \n"," block7d_bn (BatchNormaliza  (None, 19, 19, 3840)         15360     ['block7d_dwconv[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," block7d_activation (Activa  (None, 19, 19, 3840)         0         ['block7d_bn[0][0]']          \n"," tion)                                                                                            \n","                                                                                                  \n"," block7d_se_squeeze (Global  (None, 3840)                 0         ['block7d_activation[0][0]']  \n"," AveragePooling2D)                                                                                \n","                                                                                                  \n"," block7d_se_reshape (Reshap  (None, 1, 1, 3840)           0         ['block7d_se_squeeze[0][0]']  \n"," e)                                                                                               \n","                                                                                                  \n"," block7d_se_reduce (Conv2D)  (None, 1, 1, 160)            614560    ['block7d_se_reshape[0][0]']  \n","                                                                                                  \n"," block7d_se_expand (Conv2D)  (None, 1, 1, 3840)           618240    ['block7d_se_reduce[0][0]']   \n","                                                                                                  \n"," block7d_se_excite (Multipl  (None, 19, 19, 3840)         0         ['block7d_activation[0][0]',  \n"," y)                                                                  'block7d_se_expand[0][0]']   \n","                                                                                                  \n"," block7d_project_conv (Conv  (None, 19, 19, 640)          2457600   ['block7d_se_excite[0][0]']   \n"," 2D)                                                                                              \n","                                                                                                  \n"," block7d_project_bn (BatchN  (None, 19, 19, 640)          2560      ['block7d_project_conv[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," block7d_drop (FixedDropout  (None, 19, 19, 640)          0         ['block7d_project_bn[0][0]']  \n"," )                                                                                                \n","                                                                                                  \n"," block7d_add (Add)           (None, 19, 19, 640)          0         ['block7d_drop[0][0]',        \n","                                                                     'block7c_add[0][0]']         \n","                                                                                                  \n"," top_conv (Conv2D)           (None, 19, 19, 2560)         1638400   ['block7d_add[0][0]']         \n","                                                                                                  \n"," top_bn (BatchNormalization  (None, 19, 19, 2560)         10240     ['top_conv[0][0]']            \n"," )                                                                                                \n","                                                                                                  \n"," top_activation (Activation  (None, 19, 19, 2560)         0         ['top_bn[0][0]']              \n"," )                                                                                                \n","                                                                                                  \n"," conv2d (Conv2D)             (None, 17, 17, 128)          2949248   ['top_activation[0][0]']      \n","                                                                                                  \n"," global_average_pooling2d (  (None, 128)                  0         ['conv2d[0][0]']              \n"," GlobalAveragePooling2D)                                                                          \n","                                                                                                  \n"," batch_normalization (Batch  (None, 128)                  512       ['global_average_pooling2d[0][\n"," Normalization)                                                     0]']                          \n","                                                                                                  \n"," dropout (Dropout)           (None, 128)                  0         ['batch_normalization[0][0]'] \n","                                                                                                  \n"," dense (Dense)               (None, 1)                    129       ['dropout[0][0]']             \n","                                                                                                  \n"," batch_normalization_1 (Bat  (None, 1)                    4         ['dense[0][0]']               \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," activation (Activation)     (None, 1)                    0         ['batch_normalization_1[0][0]'\n","                                                                    ]                             \n","                                                                                                  \n","==================================================================================================\n","Total params: 67047573 (255.77 MB)\n","Trainable params: 2949635 (11.25 MB)\n","Non-trainable params: 64097938 (244.51 MB)\n","__________________________________________________________________________________________________\n"]}],"source":["model.summary()"]},{"cell_type":"markdown","metadata":{"id":"Xw5h1UrhXTFs"},"source":["学習開始"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4069328,"status":"ok","timestamp":1697701443104,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"MIws3Z86UUPY","outputId":"802c5e8e-3e23-440e-953b-2e4657a3c296"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","130/130 [==============================] - 941s 7s/step - loss: 0.5221 - auc: 0.8956 - accuracy: 0.7773 - val_loss: 0.2960 - val_auc: 0.9808 - val_accuracy: 0.9297\n","Epoch 2/10\n","130/130 [==============================] - 678s 5s/step - loss: 0.4380 - auc: 0.9484 - accuracy: 0.8623 - val_loss: 0.2969 - val_auc: 0.9899 - val_accuracy: 0.9365\n","Epoch 3/10\n","130/130 [==============================] - 664s 5s/step - loss: 0.3943 - auc: 0.9578 - accuracy: 0.8836 - val_loss: 0.3064 - val_auc: 0.9914 - val_accuracy: 0.9443\n","Epoch 4/10\n","130/130 [==============================] - 601s 5s/step - loss: 0.3601 - auc: 0.9652 - accuracy: 0.9012 - val_loss: 0.2871 - val_auc: 0.9927 - val_accuracy: 0.9414\n","Epoch 5/10\n","130/130 [==============================] - 655s 5s/step - loss: 0.3314 - auc: 0.9673 - accuracy: 0.9106 - val_loss: 0.3125 - val_auc: 0.9927 - val_accuracy: 0.9238\n","Epoch 6/10\n","130/130 [==============================] - 595s 5s/step - loss: 0.3049 - auc: 0.9737 - accuracy: 0.9263 - val_loss: 0.2457 - val_auc: 0.9936 - val_accuracy: 0.9570\n","Epoch 7/10\n","130/130 [==============================] - 656s 5s/step - loss: 0.2860 - auc: 0.9739 - accuracy: 0.9273 - val_loss: 0.2379 - val_auc: 0.9943 - val_accuracy: 0.9346\n","Epoch 8/10\n","130/130 [==============================] - 602s 5s/step - loss: 0.2707 - auc: 0.9765 - accuracy: 0.9242 - val_loss: 0.2357 - val_auc: 0.9943 - val_accuracy: 0.9502\n","Epoch 9/10\n","130/130 [==============================] - 600s 5s/step - loss: 0.2549 - auc: 0.9773 - accuracy: 0.9302 - val_loss: 0.2152 - val_auc: 0.9948 - val_accuracy: 0.9473\n","Epoch 10/10\n","130/130 [==============================] - 650s 5s/step - loss: 0.2426 - auc: 0.9788 - accuracy: 0.9360 - val_loss: 0.2364 - val_auc: 0.9951 - val_accuracy: 0.9424\n"]}],"source":["from tensorflow.keras import callbacks\n","from tensorflow.keras.callbacks import EarlyStopping\n","\n","es_callback = EarlyStopping(monitor='val_auc', mode='max', patience=8,\n","                                              verbose=1, min_delta=0.0001, restore_best_weights=True)\n","\n","history = model.fit(ds_train,\n","                    steps_per_epoch=ds_train.samples//BATCH,\n","                    epochs = 10,\n","                    validation_data=ds_val,\n","                    validation_steps=ds_val.samples//BATCH,\n","                    callbacks= [es_callback],\n","                    verbose=1)"]},{"cell_type":"markdown","metadata":{"id":"ZbSLvNYNQ5w1"},"source":["精度を検証"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12099,"status":"ok","timestamp":1697701644622,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"TMWdWchfbsrV","outputId":"7d66a704-df70-401e-f566-a4f18657e6da"},"outputs":[{"name":"stdout","output_type":"stream","text":["624/624 [==============================] - 188s 284ms/step\n"]}],"source":["predictions = model.predict(ds_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":378,"status":"ok","timestamp":1697701654011,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"W3tPAiWIbyVc","outputId":"208bdd3a-e2c9-44d5-ee5f-38d66c6cb0c8"},"outputs":[{"name":"stdout","output_type":"stream","text":["The accuracy is 0.9198717948717948\n"]}],"source":["from sklearn.metrics import accuracy_score\n","\n","accuracy = accuracy_score(ds_test.labels, np.round(predictions))\n","print(f'The accuracy is {accuracy}')"]},{"cell_type":"markdown","metadata":{"id":"VjYQh0qHYC3u"},"source":["モデルを保存"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3592,"status":"ok","timestamp":1697607361250,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"tSg9WFyCYd9w","outputId":"3271f9fc-bd4e-4d2e-c395-c88fee6f6fc8"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n","  saving_api.save_model(\n"]}],"source":["model.save('/content/drive/MyDrive/ColabNotebooks/models/efficientNet_xray4.h5')"]},{"cell_type":"markdown","metadata":{"id":"ShW6ZfZEXzXk"},"source":["# grad-CAMの出力\n","---"]},{"cell_type":"markdown","metadata":{"id":"XxE8hiNAdS-1"},"source":["学習済みモデルをロード"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"N0RNObSlX3zm","executionInfo":{"status":"ok","timestamp":1700026928193,"user_tz":-540,"elapsed":16099,"user":{"displayName":"ll skip","userId":"07017385437905851028"}}},"outputs":[],"source":["from tensorflow.keras.models import load_model\n","\n","# you need A pre-trained efficientNet model.\n","import efficientnet.tfkeras as efn\n","\n","\n","model = load_model(\"/content/drive/MyDrive/ColabNotebooks/models/efficientNet_xray4.h5\")"]},{"cell_type":"code","source":["predictions = model.predict(ds_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wwpgOVKri6PI","executionInfo":{"status":"ok","timestamp":1697712988801,"user_tz":-540,"elapsed":327751,"user":{"displayName":"ll skip","userId":"07017385437905851028"}},"outputId":"a47cb9be-9745-43e1-af8c-a892398f37f1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["624/624 [==============================] - 310s 477ms/step\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","\n","accuracy = accuracy_score(ds_test.labels, np.round(predictions))\n","print(f'The accuracy is {accuracy}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4JuNyFUAi-iu","executionInfo":{"status":"ok","timestamp":1697712988801,"user_tz":-540,"elapsed":7,"user":{"displayName":"ll skip","userId":"07017385437905851028"}},"outputId":"bee7316c-b825-48ce-e087-e308046bc009"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The accuracy is 0.8958333333333334\n"]}]},{"cell_type":"markdown","metadata":{"id":"rxVyfPWRdWEe"},"source":["入力画像を準備する。"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"1fw6xvEIdtf-","colab":{"base_uri":"https://localhost:8080/","height":470},"executionInfo":{"status":"ok","timestamp":1700027225948,"user_tz":-540,"elapsed":1500,"user":{"displayName":"ll skip","userId":"07017385437905851028"}},"outputId":"44bd6fb1-c68e-403c-9dcf-ec3894ba854f"},"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 600, 600, 3)\n"]},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x78ddc42bbd60>"]},"metadata":{},"execution_count":18},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import tensorflow as tf\n","import matplotlib.pyplot as plt\n","\n","\n","\n","# 入力する画像のインデックス指定\n","idx = 556\n","input_img = ds_test[idx][0]\n","# NumPy配列をテンソルに変換\n","input_img_tensor = tf.convert_to_tensor(input_img.reshape(1, 600, 600, 3))\n","# 中身を確認\n","print(input_img.shape)\n","plt.imshow(input_img[0])"]},{"cell_type":"markdown","metadata":{"id":"E9L1VO0fd9pm"},"source":["intermediate_modelを作成し、入力画像を予測。  \n","計算過程をtapeに記録する。"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":869,"status":"ok","timestamp":1700027229067,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"bWXdtSmOOuFd","outputId":"0f48eeb8-5626-4ffb-a63b-7a210c36890e"},"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor([0.92196935], shape=(1,), dtype=float32)\n"]}],"source":["from tensorflow.keras import Model\n","\n","# target_layer = model.get_layer(\"top_conv\")\n","# target_layer = model.get_layer(\"conv2d\")\n","target_layer = model.get_layer(\"block7c_project_conv\")\n","intermediate_model = Model(inputs=[model.inputs], outputs=[target_layer.output, model.output])\n","with tf.GradientTape() as tape:\n","    tape.watch(input_img_tensor)  # ここでテンソルをwatch\n","    conv_output, predictions = intermediate_model(input_img_tensor)\n","    class_idx = np.argmax(predictions[0])\n","    loss = predictions[:, class_idx]\n","\n","print(loss)"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":291},"executionInfo":{"elapsed":1551,"status":"ok","timestamp":1700027230852,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"BM5BjcrGPFRE","outputId":"58f546e9-e2a0-426d-90ef-e3a9d0c59f2c"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# 勾配を取得\n","# 通常の勾配の計算(誤差を重みで微分)とは異なり、grad_CAMでは、対象クラスの誤差を特徴マップで微分している。\n","grads = tape.gradient(loss, conv_output)[0]\n","\n","# グローバル平均プーリング\n","# ここでのweights(重み)は、通常の機械学習の文脈における重みではなく、\n","# Grad-CAMの文脈で特定の特徴マップがどれだけ重要であるかを示す量。\n","weights = np.mean(grads, axis=(0, 1))\n","\n","# Grad-CAMの計算\n","# conv_output[0]はバッチのindex0番目に対応している。今回は１枚のみなので[0]のみ存在している。\n","cam = np.dot(conv_output[0], weights)\n","cam = np.maximum(cam, 0)  # ReLU\n","cam = cam / cam.max()  # 正規化\n","\n","# 可視化\n","plt.subplot(121)\n","plt.imshow(cam, cmap=\"jet\")\n","\n","plt.subplot(122)\n","plt.imshow(input_img[0], cmap='gray')\n","\n","plt.show()"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":228},"executionInfo":{"elapsed":1289,"status":"ok","timestamp":1700027234276,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"6OuijeEiPQG_","outputId":"10c49ca3-f4a2-4ea8-cb0c-1fc689fff433"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 3 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from scipy.ndimage import zoom\n","\n","# 50x50の特徴マップを224x224にアップサンプリング\n","zoom_factor = 600 / 19  # 600: ターゲットのサイズ, 19: 元のサイズ\n","cam_resized = zoom(cam, zoom_factor)\n","\n","# grad_CAMの位置を調整する。\n","# shift_x, shift_y = 4, 0  # ずらすピクセル数\n","# cam_resized = np.roll(cam_resized, shift_x, axis=0)\n","# cam_resized = np.roll(cam_resized, shift_y, axis=1)\n","\n","# プロット\n","plt.subplot(131)\n","plt.title(\"Original Image\")\n","plt.imshow(input_img[0], cmap=\"gray\")\n","\n","plt.subplot(132)\n","plt.title(\"grad_CAM\")\n","plt.imshow(cam_resized, cmap=\"jet\")\n","\n","plt.subplot(133)\n","plt.title(\"Combined\")\n","plt.imshow(input_img[0], cmap=\"gray\")\n","plt.imshow(cam_resized, cmap='jet', alpha=0.5)\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"84IntTpQvefN"},"source":["# カラーマップ作成、画像を合成\n","---"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"4sVVao0uvc7U","executionInfo":{"status":"ok","timestamp":1700027241362,"user_tz":-540,"elapsed":222,"user":{"displayName":"ll skip","userId":"07017385437905851028"}}},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import matplotlib.colors as mcolors"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":122},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1700027242232,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"vNrEaGQ1xMVU","outputId":"0e5106ee-da15-47b8-e9fe-ba43dc7ab9a2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.colors.LinearSegmentedColormap at 0x78ddc413a740>"],"text/html":["<div style=\"vertical-align: middle;\"><strong>custom_cmap</strong> </div><div class=\"cmap\"><img alt=\"custom_cmap colormap\" title=\"custom_cmap\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#ffffff00\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ffffff00;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#ad0000ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ad0000ff;\"></div></div>"],"image/png":"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\n"},"metadata":{},"execution_count":23}],"source":["import matplotlib.pyplot as plt\n","import matplotlib.colors as mcolors\n","\n","cmap = plt.get_cmap('jet')\n","cmaplist = [cmap(i) for i in range(cmap.N)]\n","\n","# 範囲を変更\n","shift_amount = 10\n","new_cmaplist = [(1, 1, 1, 0.0) for _ in range(shift_amount)] + cmaplist[:-shift_amount]\n","\n","\n","for i in range(45):\n","    new_cmaplist[i] = (1, 1, 1, 0.0)  # (R, G, B, Alpha)\n","\n","\n","cmap_custom = mcolors.LinearSegmentedColormap.from_list('custom_cmap', new_cmaplist, cmap.N)\n","cmap_custom"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":435},"executionInfo":{"elapsed":884,"status":"ok","timestamp":1700027247350,"user":{"displayName":"ll skip","userId":"07017385437905851028"},"user_tz":-540},"id":"HAzP3st0vkyZ","outputId":"bc114968-9fdb-4f7e-b564-51d6cb87c9cc"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.colors as mcolors\n","\n","\n","# 元の画像\n","plt.imshow(input_img[0], cmap='gray')\n","# CAMを透過してオーバーレイ表示\n","plt.imshow(cam_resized, cmap=cmap_custom, alpha=1, vmin=0)\n","\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"65aLcI1-SIiA"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyP+HTaB9ZarkE+wIvaK1a2S"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}