{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"

APTOS 2019 Blindness Detection

\n

Detect diabetic retinopathy to stop blindness before it's too late

\n
\n\nIn this synchronous Kernels-only competition, you'll build a machine learning model to speed up disease detection. You’ll work with thousands of images collected in rural areas to help identify diabetic retinopathy automatically. If successful, you will not only help to prevent lifelong blindness, but these models may be used to detect other sorts of diseases in the future, like glaucoma and macular degeneration.\n\nIn this notebook, I will be using basic deep learning and transfer learning (ResNet50) to create a baseline.\n##### Image source: http://cceyemd.com/diabetes-and-eye-exams/\n\n### Dependencies","metadata":{}},{"cell_type":"code","source":"import os\nimport cv2\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, cohen_kappa_score,classification_report\nfrom keras.models import Model,load_model\nfrom keras import optimizers, applications\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom keras.layers import Dense, Dropout, GlobalAveragePooling2D, Input\n\n# Set seeds to make the experiment more reproducible.\nimport tensorflow as tf\ndef seed_everything(seed=0):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n tf.random.set_seed(0)\nseed_everything()\n\n%matplotlib inline\nsns.set(style=\"whitegrid\")\nwarnings.filterwarnings(\"ignore\")","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2022-09-25T15:46:33.83296Z","iopub.execute_input":"2022-09-25T15:46:33.833294Z","iopub.status.idle":"2022-09-25T15:46:33.844296Z","shell.execute_reply.started":"2022-09-25T15:46:33.833262Z","shell.execute_reply":"2022-09-25T15:46:33.843058Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"## Load data","metadata":{"_kg_hide-output":true}},{"cell_type":"code","source":"train = pd.read_csv('../input/aptos2019-blindness-detection/train.csv')\ntest = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')","metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_kg_hide-input":false,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","execution":{"iopub.status.busy":"2022-09-25T15:46:34.832132Z","iopub.execute_input":"2022-09-25T15:46:34.832465Z","iopub.status.idle":"2022-09-25T15:46:34.867259Z","shell.execute_reply.started":"2022-09-25T15:46:34.832432Z","shell.execute_reply":"2022-09-25T15:46:34.866388Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"# EDA\n\n## Data overview","metadata":{}},{"cell_type":"code","source":"print('Number of train samples: ', train.shape[0])\nprint('Number of test samples: ', test.shape[0])\ndisplay(train.head())","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-09-25T15:46:35.822864Z","iopub.execute_input":"2022-09-25T15:46:35.823185Z","iopub.status.idle":"2022-09-25T15:46:35.844521Z","shell.execute_reply.started":"2022-09-25T15:46:35.823153Z","shell.execute_reply":"2022-09-25T15:46:35.843604Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Number of train samples: 3662\nNumber of test samples: 1928\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" id_code diagnosis\n0 000c1434d8d7 2\n1 001639a390f0 4\n2 0024cdab0c1e 1\n3 002c21358ce6 0\n4 005b95c28852 0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosis
0000c1434d8d72
1001639a390f04
20024cdab0c1e1
3002c21358ce60
4005b95c288520
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Label class distribution\n\nAs we can see we have an unbalanced database, we have two times more class 0 than 2, and classes 1, 2 and 4 each have less than half of the class 2 data.","metadata":{}},{"cell_type":"code","source":"f, ax = plt.subplots(figsize=(14, 8.7))\nax = sns.countplot(x=\"diagnosis\", data=train, palette=\"GnBu_d\")\nsns.despine()\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-09-25T15:46:36.652621Z","iopub.execute_input":"2022-09-25T15:46:36.652934Z","iopub.status.idle":"2022-09-25T15:46:36.828653Z","shell.execute_reply.started":"2022-09-25T15:46:36.652905Z","shell.execute_reply":"2022-09-25T15:46:36.827827Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"##### Legend\n- 0 - No DR\n- 1 - Mild\n- 2 - Moderate\n- 3 - Severe\n- 4 - Proliferative DR ","metadata":{}},{"cell_type":"markdown","source":"### Now let's see some of the images\n\nThe images have different sizes, they may need resizing or some padding.","metadata":{}},{"cell_type":"code","source":"sns.set_style(\"white\")\ncount = 1\nplt.figure(figsize=[20, 20])\nfor img_name in train['id_code'][:15]:\n img = cv2.imread(\"../input/aptos2019-blindness-detection/train_images/%s.png\" % img_name)[...,[2, 1, 0]]\n plt.subplot(5, 5, count)\n plt.imshow(img)\n plt.title(\"Image %s\" % count)\n count += 1\n \nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-09-25T15:46:39.176306Z","iopub.execute_input":"2022-09-25T15:46:39.176671Z","iopub.status.idle":"2022-09-25T15:46:48.703027Z","shell.execute_reply.started":"2022-09-25T15:46:39.17664Z","shell.execute_reply":"2022-09-25T15:46:48.701896Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# Model parameters","metadata":{}},{"cell_type":"code","source":"# Model parameters\nBATCH_SIZE = 8\nEPOCHS = 20\nWARMUP_EPOCHS = 2\nLEARNING_RATE = 1e-4\nWARMUP_LEARNING_RATE = 1e-3\nHEIGHT = 512\nWIDTH = 512\nCANAL = 3\nN_CLASSES = train['diagnosis'].nunique()\nES_PATIENCE = 5\nRLROP_PATIENCE = 3\nDECAY_DROP = 0.5","metadata":{"execution":{"iopub.status.busy":"2022-09-25T15:46:48.712359Z","iopub.execute_input":"2022-09-25T15:46:48.712896Z","iopub.status.idle":"2022-09-25T15:46:48.720173Z","shell.execute_reply.started":"2022-09-25T15:46:48.712852Z","shell.execute_reply":"2022-09-25T15:46:48.7195Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# Preprocecss data\ntrain[\"id_code\"] = train[\"id_code\"].apply(lambda x: x + \".png\")\ntest[\"id_code\"] = test[\"id_code\"].apply(lambda x: x + \".png\")\ntrain['diagnosis'] = train['diagnosis'].astype('str')\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-25T15:46:48.721451Z","iopub.execute_input":"2022-09-25T15:46:48.722Z","iopub.status.idle":"2022-09-25T15:46:48.744535Z","shell.execute_reply.started":"2022-09-25T15:46:48.721958Z","shell.execute_reply":"2022-09-25T15:46:48.743679Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":" id_code diagnosis\n0 000c1434d8d7.png 2\n1 001639a390f0.png 4\n2 0024cdab0c1e.png 1\n3 002c21358ce6.png 0\n4 005b95c28852.png 0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosis
0000c1434d8d7.png2
1001639a390f0.png4
20024cdab0c1e.png1
3002c21358ce6.png0
4005b95c28852.png0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Data generator","metadata":{}},{"cell_type":"code","source":"train_datagen=ImageDataGenerator(rescale=1./255, \n validation_split=0.2,\n horizontal_flip=True)\n\ntrain_generator=train_datagen.flow_from_dataframe(\n dataframe=train,\n directory=\"../input/aptos2019-blindness-detection/train_images/\",\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n batch_size=BATCH_SIZE,\n class_mode=\"categorical\",\n target_size=(HEIGHT, WIDTH),\n subset='training')\n\nvalid_generator=train_datagen.flow_from_dataframe(\n dataframe=train,\n directory=\"../input/aptos2019-blindness-detection/train_images/\",\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n batch_size=BATCH_SIZE,\n class_mode=\"categorical\", \n target_size=(HEIGHT, WIDTH),\n subset='validation')\n\ntest_datagen = ImageDataGenerator(rescale=1./255)\n\ntest_generator = test_datagen.flow_from_dataframe( \n dataframe=test,\n directory = \"../input/aptos2019-blindness-detection/test_images/\",\n x_col=\"id_code\",\n target_size=(HEIGHT, WIDTH),\n batch_size=1,\n shuffle=False,\n class_mode=None)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-09-25T15:46:48.747039Z","iopub.execute_input":"2022-09-25T15:46:48.747287Z","iopub.status.idle":"2022-09-25T15:47:03.30367Z","shell.execute_reply.started":"2022-09-25T15:46:48.747263Z","shell.execute_reply":"2022-09-25T15:47:03.302718Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Found 2930 validated image filenames belonging to 5 classes.\nFound 732 validated image filenames belonging to 5 classes.\nFound 1928 validated image filenames.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Model","metadata":{}},{"cell_type":"code","source":"def create_model(input_shape, n_out):\n input_tensor = Input(shape=input_shape)\n base_model = tf.keras.applications.densenet.DenseNet201(weights='imagenet', \n include_top=False,\n input_tensor=input_tensor)\n #base_model.load_weights('../input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')\n\n x = GlobalAveragePooling2D()(base_model.output)\n x = Dropout(0.5)(x)\n x = Dense(2048, activation='relu')(x)\n x = Dropout(0.5)(x)\n final_output = Dense(n_out, activation='softmax', name='final_output')(x)\n model = Model(input_tensor, final_output)\n \n return model","metadata":{"execution":{"iopub.status.busy":"2022-09-25T15:54:42.81575Z","iopub.execute_input":"2022-09-25T15:54:42.816109Z","iopub.status.idle":"2022-09-25T15:54:42.821868Z","shell.execute_reply.started":"2022-09-25T15:54:42.816078Z","shell.execute_reply":"2022-09-25T15:54:42.820933Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"model = create_model(input_shape=(HEIGHT, WIDTH, CANAL), n_out=N_CLASSES)\n\nfor layer in model.layers:\n layer.trainable = False\n\nfor i in range(-5, 0):\n model.layers[i].trainable = True\n\nmetric_list = [\"accuracy\"]\noptimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=metric_list)\nmodel.summary()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-09-25T15:54:42.885508Z","iopub.execute_input":"2022-09-25T15:54:42.885757Z","iopub.status.idle":"2022-09-25T15:54:50.222597Z","shell.execute_reply.started":"2022-09-25T15:54:42.885732Z","shell.execute_reply":"2022-09-25T15:54:50.2218Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n74842112/74836368 [==============================] - 0s 0us/step\nModel: \"model\"\n__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_3 (InputLayer) [(None, 512, 512, 3) 0 \n__________________________________________________________________________________________________\nzero_padding2d (ZeroPadding2D) (None, 518, 518, 3) 0 input_3[0][0] \n__________________________________________________________________________________________________\nconv1/conv (Conv2D) (None, 256, 256, 64) 9408 zero_padding2d[0][0] \n__________________________________________________________________________________________________\nconv1/bn (BatchNormalization) (None, 256, 256, 64) 256 conv1/conv[0][0] \n__________________________________________________________________________________________________\nconv1/relu (Activation) (None, 256, 256, 64) 0 conv1/bn[0][0] \n__________________________________________________________________________________________________\nzero_padding2d_1 (ZeroPadding2D (None, 258, 258, 64) 0 conv1/relu[0][0] \n__________________________________________________________________________________________________\npool1 (MaxPooling2D) (None, 128, 128, 64) 0 zero_padding2d_1[0][0] \n__________________________________________________________________________________________________\nconv2_block1_0_bn (BatchNormali (None, 128, 128, 64) 256 pool1[0][0] \n__________________________________________________________________________________________________\nconv2_block1_0_relu (Activation (None, 128, 128, 64) 0 conv2_block1_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block1_1_conv (Conv2D) (None, 128, 128, 128 8192 conv2_block1_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block1_1_bn (BatchNormali (None, 128, 128, 128 512 conv2_block1_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block1_1_relu (Activation (None, 128, 128, 128 0 conv2_block1_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block1_2_conv (Conv2D) (None, 128, 128, 32) 36864 conv2_block1_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block1_concat (Concatenat (None, 128, 128, 96) 0 pool1[0][0] \n conv2_block1_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block2_0_bn (BatchNormali (None, 128, 128, 96) 384 conv2_block1_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block2_0_relu (Activation (None, 128, 128, 96) 0 conv2_block2_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block2_1_conv (Conv2D) (None, 128, 128, 128 12288 conv2_block2_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block2_1_bn (BatchNormali (None, 128, 128, 128 512 conv2_block2_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block2_1_relu (Activation (None, 128, 128, 128 0 conv2_block2_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block2_2_conv (Conv2D) (None, 128, 128, 32) 36864 conv2_block2_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block2_concat (Concatenat (None, 128, 128, 128 0 conv2_block1_concat[0][0] \n conv2_block2_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block3_0_bn (BatchNormali (None, 128, 128, 128 512 conv2_block2_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block3_0_relu (Activation (None, 128, 128, 128 0 conv2_block3_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block3_1_conv (Conv2D) (None, 128, 128, 128 16384 conv2_block3_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block3_1_bn (BatchNormali (None, 128, 128, 128 512 conv2_block3_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block3_1_relu (Activation (None, 128, 128, 128 0 conv2_block3_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block3_2_conv (Conv2D) (None, 128, 128, 32) 36864 conv2_block3_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block3_concat (Concatenat (None, 128, 128, 160 0 conv2_block2_concat[0][0] \n conv2_block3_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block4_0_bn (BatchNormali (None, 128, 128, 160 640 conv2_block3_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block4_0_relu (Activation (None, 128, 128, 160 0 conv2_block4_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block4_1_conv (Conv2D) (None, 128, 128, 128 20480 conv2_block4_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block4_1_bn (BatchNormali (None, 128, 128, 128 512 conv2_block4_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block4_1_relu (Activation (None, 128, 128, 128 0 conv2_block4_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block4_2_conv (Conv2D) (None, 128, 128, 32) 36864 conv2_block4_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block4_concat (Concatenat (None, 128, 128, 192 0 conv2_block3_concat[0][0] \n conv2_block4_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block5_0_bn (BatchNormali (None, 128, 128, 192 768 conv2_block4_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block5_0_relu (Activation (None, 128, 128, 192 0 conv2_block5_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block5_1_conv (Conv2D) (None, 128, 128, 128 24576 conv2_block5_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block5_1_bn (BatchNormali (None, 128, 128, 128 512 conv2_block5_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block5_1_relu (Activation (None, 128, 128, 128 0 conv2_block5_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block5_2_conv (Conv2D) (None, 128, 128, 32) 36864 conv2_block5_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block5_concat (Concatenat (None, 128, 128, 224 0 conv2_block4_concat[0][0] \n conv2_block5_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block6_0_bn (BatchNormali (None, 128, 128, 224 896 conv2_block5_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block6_0_relu (Activation (None, 128, 128, 224 0 conv2_block6_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block6_1_conv (Conv2D) (None, 128, 128, 128 28672 conv2_block6_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block6_1_bn (BatchNormali (None, 128, 128, 128 512 conv2_block6_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block6_1_relu (Activation (None, 128, 128, 128 0 conv2_block6_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block6_2_conv (Conv2D) (None, 128, 128, 32) 36864 conv2_block6_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block6_concat (Concatenat (None, 128, 128, 256 0 conv2_block5_concat[0][0] \n conv2_block6_2_conv[0][0] \n__________________________________________________________________________________________________\npool2_bn (BatchNormalization) (None, 128, 128, 256 1024 conv2_block6_concat[0][0] \n__________________________________________________________________________________________________\npool2_relu (Activation) (None, 128, 128, 256 0 pool2_bn[0][0] \n__________________________________________________________________________________________________\npool2_conv (Conv2D) (None, 128, 128, 128 32768 pool2_relu[0][0] \n__________________________________________________________________________________________________\npool2_pool (AveragePooling2D) (None, 64, 64, 128) 0 pool2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block1_0_bn (BatchNormali (None, 64, 64, 128) 512 pool2_pool[0][0] \n__________________________________________________________________________________________________\nconv3_block1_0_relu (Activation (None, 64, 64, 128) 0 conv3_block1_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block1_1_conv (Conv2D) (None, 64, 64, 128) 16384 conv3_block1_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block1_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block1_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block1_1_relu (Activation (None, 64, 64, 128) 0 conv3_block1_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block1_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block1_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block1_concat (Concatenat (None, 64, 64, 160) 0 pool2_pool[0][0] \n conv3_block1_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block2_0_bn (BatchNormali (None, 64, 64, 160) 640 conv3_block1_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block2_0_relu (Activation (None, 64, 64, 160) 0 conv3_block2_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block2_1_conv (Conv2D) (None, 64, 64, 128) 20480 conv3_block2_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block2_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block2_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block2_1_relu (Activation (None, 64, 64, 128) 0 conv3_block2_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block2_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block2_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block2_concat (Concatenat (None, 64, 64, 192) 0 conv3_block1_concat[0][0] \n conv3_block2_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block3_0_bn (BatchNormali (None, 64, 64, 192) 768 conv3_block2_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block3_0_relu (Activation (None, 64, 64, 192) 0 conv3_block3_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block3_1_conv (Conv2D) (None, 64, 64, 128) 24576 conv3_block3_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block3_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block3_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block3_1_relu (Activation (None, 64, 64, 128) 0 conv3_block3_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block3_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block3_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block3_concat (Concatenat (None, 64, 64, 224) 0 conv3_block2_concat[0][0] \n conv3_block3_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block4_0_bn (BatchNormali (None, 64, 64, 224) 896 conv3_block3_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block4_0_relu (Activation (None, 64, 64, 224) 0 conv3_block4_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block4_1_conv (Conv2D) (None, 64, 64, 128) 28672 conv3_block4_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block4_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block4_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block4_1_relu (Activation (None, 64, 64, 128) 0 conv3_block4_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block4_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block4_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block4_concat (Concatenat (None, 64, 64, 256) 0 conv3_block3_concat[0][0] \n conv3_block4_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block5_0_bn (BatchNormali (None, 64, 64, 256) 1024 conv3_block4_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block5_0_relu (Activation (None, 64, 64, 256) 0 conv3_block5_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block5_1_conv (Conv2D) (None, 64, 64, 128) 32768 conv3_block5_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block5_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block5_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block5_1_relu (Activation (None, 64, 64, 128) 0 conv3_block5_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block5_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block5_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block5_concat (Concatenat (None, 64, 64, 288) 0 conv3_block4_concat[0][0] \n conv3_block5_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block6_0_bn (BatchNormali (None, 64, 64, 288) 1152 conv3_block5_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block6_0_relu (Activation (None, 64, 64, 288) 0 conv3_block6_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block6_1_conv (Conv2D) (None, 64, 64, 128) 36864 conv3_block6_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block6_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block6_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block6_1_relu (Activation (None, 64, 64, 128) 0 conv3_block6_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block6_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block6_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block6_concat (Concatenat (None, 64, 64, 320) 0 conv3_block5_concat[0][0] \n conv3_block6_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block7_0_bn (BatchNormali (None, 64, 64, 320) 1280 conv3_block6_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block7_0_relu (Activation (None, 64, 64, 320) 0 conv3_block7_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block7_1_conv (Conv2D) (None, 64, 64, 128) 40960 conv3_block7_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block7_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block7_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block7_1_relu (Activation (None, 64, 64, 128) 0 conv3_block7_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block7_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block7_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block7_concat (Concatenat (None, 64, 64, 352) 0 conv3_block6_concat[0][0] \n conv3_block7_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block8_0_bn (BatchNormali (None, 64, 64, 352) 1408 conv3_block7_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block8_0_relu (Activation (None, 64, 64, 352) 0 conv3_block8_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block8_1_conv (Conv2D) (None, 64, 64, 128) 45056 conv3_block8_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block8_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block8_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block8_1_relu (Activation (None, 64, 64, 128) 0 conv3_block8_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block8_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block8_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block8_concat (Concatenat (None, 64, 64, 384) 0 conv3_block7_concat[0][0] \n conv3_block8_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block9_0_bn (BatchNormali (None, 64, 64, 384) 1536 conv3_block8_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block9_0_relu (Activation (None, 64, 64, 384) 0 conv3_block9_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block9_1_conv (Conv2D) (None, 64, 64, 128) 49152 conv3_block9_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block9_1_bn (BatchNormali (None, 64, 64, 128) 512 conv3_block9_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block9_1_relu (Activation (None, 64, 64, 128) 0 conv3_block9_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block9_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block9_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block9_concat (Concatenat (None, 64, 64, 416) 0 conv3_block8_concat[0][0] \n conv3_block9_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block10_0_bn (BatchNormal (None, 64, 64, 416) 1664 conv3_block9_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block10_0_relu (Activatio (None, 64, 64, 416) 0 conv3_block10_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block10_1_conv (Conv2D) (None, 64, 64, 128) 53248 conv3_block10_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block10_1_bn (BatchNormal (None, 64, 64, 128) 512 conv3_block10_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block10_1_relu (Activatio (None, 64, 64, 128) 0 conv3_block10_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block10_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block10_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block10_concat (Concatena (None, 64, 64, 448) 0 conv3_block9_concat[0][0] \n conv3_block10_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block11_0_bn (BatchNormal (None, 64, 64, 448) 1792 conv3_block10_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block11_0_relu (Activatio (None, 64, 64, 448) 0 conv3_block11_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block11_1_conv (Conv2D) (None, 64, 64, 128) 57344 conv3_block11_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block11_1_bn (BatchNormal (None, 64, 64, 128) 512 conv3_block11_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block11_1_relu (Activatio (None, 64, 64, 128) 0 conv3_block11_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block11_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block11_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block11_concat (Concatena (None, 64, 64, 480) 0 conv3_block10_concat[0][0] \n conv3_block11_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block12_0_bn (BatchNormal (None, 64, 64, 480) 1920 conv3_block11_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block12_0_relu (Activatio (None, 64, 64, 480) 0 conv3_block12_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block12_1_conv (Conv2D) (None, 64, 64, 128) 61440 conv3_block12_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block12_1_bn (BatchNormal (None, 64, 64, 128) 512 conv3_block12_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block12_1_relu (Activatio (None, 64, 64, 128) 0 conv3_block12_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block12_2_conv (Conv2D) (None, 64, 64, 32) 36864 conv3_block12_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block12_concat (Concatena (None, 64, 64, 512) 0 conv3_block11_concat[0][0] \n conv3_block12_2_conv[0][0] \n__________________________________________________________________________________________________\npool3_bn (BatchNormalization) (None, 64, 64, 512) 2048 conv3_block12_concat[0][0] \n__________________________________________________________________________________________________\npool3_relu (Activation) (None, 64, 64, 512) 0 pool3_bn[0][0] \n__________________________________________________________________________________________________\npool3_conv (Conv2D) (None, 64, 64, 256) 131072 pool3_relu[0][0] \n__________________________________________________________________________________________________\npool3_pool (AveragePooling2D) (None, 32, 32, 256) 0 pool3_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block1_0_bn (BatchNormali (None, 32, 32, 256) 1024 pool3_pool[0][0] \n__________________________________________________________________________________________________\nconv4_block1_0_relu (Activation (None, 32, 32, 256) 0 conv4_block1_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block1_1_conv (Conv2D) (None, 32, 32, 128) 32768 conv4_block1_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block1_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block1_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block1_1_relu (Activation (None, 32, 32, 128) 0 conv4_block1_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block1_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block1_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block1_concat (Concatenat (None, 32, 32, 288) 0 pool3_pool[0][0] \n conv4_block1_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block2_0_bn (BatchNormali (None, 32, 32, 288) 1152 conv4_block1_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block2_0_relu (Activation (None, 32, 32, 288) 0 conv4_block2_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block2_1_conv (Conv2D) (None, 32, 32, 128) 36864 conv4_block2_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block2_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block2_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block2_1_relu (Activation (None, 32, 32, 128) 0 conv4_block2_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block2_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block2_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block2_concat (Concatenat (None, 32, 32, 320) 0 conv4_block1_concat[0][0] \n conv4_block2_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block3_0_bn (BatchNormali (None, 32, 32, 320) 1280 conv4_block2_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block3_0_relu (Activation (None, 32, 32, 320) 0 conv4_block3_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block3_1_conv (Conv2D) (None, 32, 32, 128) 40960 conv4_block3_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block3_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block3_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block3_1_relu (Activation (None, 32, 32, 128) 0 conv4_block3_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block3_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block3_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block3_concat (Concatenat (None, 32, 32, 352) 0 conv4_block2_concat[0][0] \n conv4_block3_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block4_0_bn (BatchNormali (None, 32, 32, 352) 1408 conv4_block3_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block4_0_relu (Activation (None, 32, 32, 352) 0 conv4_block4_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block4_1_conv (Conv2D) (None, 32, 32, 128) 45056 conv4_block4_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block4_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block4_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block4_1_relu (Activation (None, 32, 32, 128) 0 conv4_block4_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block4_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block4_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block4_concat (Concatenat (None, 32, 32, 384) 0 conv4_block3_concat[0][0] \n conv4_block4_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block5_0_bn (BatchNormali (None, 32, 32, 384) 1536 conv4_block4_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block5_0_relu (Activation (None, 32, 32, 384) 0 conv4_block5_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block5_1_conv (Conv2D) (None, 32, 32, 128) 49152 conv4_block5_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block5_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block5_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block5_1_relu (Activation (None, 32, 32, 128) 0 conv4_block5_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block5_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block5_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block5_concat (Concatenat (None, 32, 32, 416) 0 conv4_block4_concat[0][0] \n conv4_block5_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block6_0_bn (BatchNormali (None, 32, 32, 416) 1664 conv4_block5_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block6_0_relu (Activation (None, 32, 32, 416) 0 conv4_block6_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block6_1_conv (Conv2D) (None, 32, 32, 128) 53248 conv4_block6_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block6_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block6_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block6_1_relu (Activation (None, 32, 32, 128) 0 conv4_block6_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block6_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block6_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block6_concat (Concatenat (None, 32, 32, 448) 0 conv4_block5_concat[0][0] \n conv4_block6_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block7_0_bn (BatchNormali (None, 32, 32, 448) 1792 conv4_block6_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block7_0_relu (Activation (None, 32, 32, 448) 0 conv4_block7_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block7_1_conv (Conv2D) (None, 32, 32, 128) 57344 conv4_block7_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block7_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block7_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block7_1_relu (Activation (None, 32, 32, 128) 0 conv4_block7_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block7_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block7_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block7_concat (Concatenat (None, 32, 32, 480) 0 conv4_block6_concat[0][0] \n conv4_block7_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block8_0_bn (BatchNormali (None, 32, 32, 480) 1920 conv4_block7_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block8_0_relu (Activation (None, 32, 32, 480) 0 conv4_block8_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block8_1_conv (Conv2D) (None, 32, 32, 128) 61440 conv4_block8_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block8_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block8_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block8_1_relu (Activation (None, 32, 32, 128) 0 conv4_block8_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block8_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block8_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block8_concat (Concatenat (None, 32, 32, 512) 0 conv4_block7_concat[0][0] \n conv4_block8_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block9_0_bn (BatchNormali (None, 32, 32, 512) 2048 conv4_block8_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block9_0_relu (Activation (None, 32, 32, 512) 0 conv4_block9_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block9_1_conv (Conv2D) (None, 32, 32, 128) 65536 conv4_block9_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block9_1_bn (BatchNormali (None, 32, 32, 128) 512 conv4_block9_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block9_1_relu (Activation (None, 32, 32, 128) 0 conv4_block9_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block9_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block9_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block9_concat (Concatenat (None, 32, 32, 544) 0 conv4_block8_concat[0][0] \n conv4_block9_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block10_0_bn (BatchNormal (None, 32, 32, 544) 2176 conv4_block9_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block10_0_relu (Activatio (None, 32, 32, 544) 0 conv4_block10_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block10_1_conv (Conv2D) (None, 32, 32, 128) 69632 conv4_block10_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block10_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block10_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block10_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block10_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block10_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block10_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block10_concat (Concatena (None, 32, 32, 576) 0 conv4_block9_concat[0][0] \n conv4_block10_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block11_0_bn (BatchNormal (None, 32, 32, 576) 2304 conv4_block10_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block11_0_relu (Activatio (None, 32, 32, 576) 0 conv4_block11_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block11_1_conv (Conv2D) (None, 32, 32, 128) 73728 conv4_block11_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block11_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block11_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block11_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block11_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block11_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block11_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block11_concat (Concatena (None, 32, 32, 608) 0 conv4_block10_concat[0][0] \n conv4_block11_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block12_0_bn (BatchNormal (None, 32, 32, 608) 2432 conv4_block11_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block12_0_relu (Activatio (None, 32, 32, 608) 0 conv4_block12_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block12_1_conv (Conv2D) (None, 32, 32, 128) 77824 conv4_block12_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block12_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block12_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block12_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block12_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block12_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block12_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block12_concat (Concatena (None, 32, 32, 640) 0 conv4_block11_concat[0][0] \n conv4_block12_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block13_0_bn (BatchNormal (None, 32, 32, 640) 2560 conv4_block12_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block13_0_relu (Activatio (None, 32, 32, 640) 0 conv4_block13_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block13_1_conv (Conv2D) (None, 32, 32, 128) 81920 conv4_block13_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block13_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block13_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block13_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block13_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block13_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block13_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block13_concat (Concatena (None, 32, 32, 672) 0 conv4_block12_concat[0][0] \n conv4_block13_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block14_0_bn (BatchNormal (None, 32, 32, 672) 2688 conv4_block13_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block14_0_relu (Activatio (None, 32, 32, 672) 0 conv4_block14_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block14_1_conv (Conv2D) (None, 32, 32, 128) 86016 conv4_block14_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block14_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block14_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block14_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block14_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block14_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block14_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block14_concat (Concatena (None, 32, 32, 704) 0 conv4_block13_concat[0][0] \n conv4_block14_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block15_0_bn (BatchNormal (None, 32, 32, 704) 2816 conv4_block14_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block15_0_relu (Activatio (None, 32, 32, 704) 0 conv4_block15_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block15_1_conv (Conv2D) (None, 32, 32, 128) 90112 conv4_block15_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block15_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block15_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block15_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block15_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block15_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block15_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block15_concat (Concatena (None, 32, 32, 736) 0 conv4_block14_concat[0][0] \n conv4_block15_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block16_0_bn (BatchNormal (None, 32, 32, 736) 2944 conv4_block15_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block16_0_relu (Activatio (None, 32, 32, 736) 0 conv4_block16_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block16_1_conv (Conv2D) (None, 32, 32, 128) 94208 conv4_block16_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block16_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block16_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block16_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block16_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block16_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block16_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block16_concat (Concatena (None, 32, 32, 768) 0 conv4_block15_concat[0][0] \n conv4_block16_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block17_0_bn (BatchNormal (None, 32, 32, 768) 3072 conv4_block16_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block17_0_relu (Activatio (None, 32, 32, 768) 0 conv4_block17_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block17_1_conv (Conv2D) (None, 32, 32, 128) 98304 conv4_block17_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block17_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block17_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block17_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block17_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block17_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block17_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block17_concat (Concatena (None, 32, 32, 800) 0 conv4_block16_concat[0][0] \n conv4_block17_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block18_0_bn (BatchNormal (None, 32, 32, 800) 3200 conv4_block17_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block18_0_relu (Activatio (None, 32, 32, 800) 0 conv4_block18_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block18_1_conv (Conv2D) (None, 32, 32, 128) 102400 conv4_block18_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block18_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block18_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block18_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block18_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block18_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block18_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block18_concat (Concatena (None, 32, 32, 832) 0 conv4_block17_concat[0][0] \n conv4_block18_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block19_0_bn (BatchNormal (None, 32, 32, 832) 3328 conv4_block18_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block19_0_relu (Activatio (None, 32, 32, 832) 0 conv4_block19_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block19_1_conv (Conv2D) (None, 32, 32, 128) 106496 conv4_block19_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block19_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block19_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block19_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block19_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block19_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block19_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block19_concat (Concatena (None, 32, 32, 864) 0 conv4_block18_concat[0][0] \n conv4_block19_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block20_0_bn (BatchNormal (None, 32, 32, 864) 3456 conv4_block19_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block20_0_relu (Activatio (None, 32, 32, 864) 0 conv4_block20_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block20_1_conv (Conv2D) (None, 32, 32, 128) 110592 conv4_block20_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block20_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block20_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block20_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block20_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block20_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block20_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block20_concat (Concatena (None, 32, 32, 896) 0 conv4_block19_concat[0][0] \n conv4_block20_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block21_0_bn (BatchNormal (None, 32, 32, 896) 3584 conv4_block20_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block21_0_relu (Activatio (None, 32, 32, 896) 0 conv4_block21_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block21_1_conv (Conv2D) (None, 32, 32, 128) 114688 conv4_block21_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block21_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block21_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block21_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block21_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block21_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block21_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block21_concat (Concatena (None, 32, 32, 928) 0 conv4_block20_concat[0][0] \n conv4_block21_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block22_0_bn (BatchNormal (None, 32, 32, 928) 3712 conv4_block21_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block22_0_relu (Activatio (None, 32, 32, 928) 0 conv4_block22_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block22_1_conv (Conv2D) (None, 32, 32, 128) 118784 conv4_block22_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block22_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block22_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block22_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block22_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block22_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block22_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block22_concat (Concatena (None, 32, 32, 960) 0 conv4_block21_concat[0][0] \n conv4_block22_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block23_0_bn (BatchNormal (None, 32, 32, 960) 3840 conv4_block22_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block23_0_relu (Activatio (None, 32, 32, 960) 0 conv4_block23_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block23_1_conv (Conv2D) (None, 32, 32, 128) 122880 conv4_block23_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block23_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block23_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block23_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block23_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block23_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block23_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block23_concat (Concatena (None, 32, 32, 992) 0 conv4_block22_concat[0][0] \n conv4_block23_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block24_0_bn (BatchNormal (None, 32, 32, 992) 3968 conv4_block23_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block24_0_relu (Activatio (None, 32, 32, 992) 0 conv4_block24_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block24_1_conv (Conv2D) (None, 32, 32, 128) 126976 conv4_block24_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block24_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block24_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block24_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block24_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block24_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block24_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block24_concat (Concatena (None, 32, 32, 1024) 0 conv4_block23_concat[0][0] \n conv4_block24_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block25_0_bn (BatchNormal (None, 32, 32, 1024) 4096 conv4_block24_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block25_0_relu (Activatio (None, 32, 32, 1024) 0 conv4_block25_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block25_1_conv (Conv2D) (None, 32, 32, 128) 131072 conv4_block25_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block25_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block25_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block25_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block25_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block25_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block25_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block25_concat (Concatena (None, 32, 32, 1056) 0 conv4_block24_concat[0][0] \n conv4_block25_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block26_0_bn (BatchNormal (None, 32, 32, 1056) 4224 conv4_block25_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block26_0_relu (Activatio (None, 32, 32, 1056) 0 conv4_block26_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block26_1_conv (Conv2D) (None, 32, 32, 128) 135168 conv4_block26_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block26_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block26_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block26_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block26_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block26_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block26_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block26_concat (Concatena (None, 32, 32, 1088) 0 conv4_block25_concat[0][0] \n conv4_block26_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block27_0_bn (BatchNormal (None, 32, 32, 1088) 4352 conv4_block26_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block27_0_relu (Activatio (None, 32, 32, 1088) 0 conv4_block27_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block27_1_conv (Conv2D) (None, 32, 32, 128) 139264 conv4_block27_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block27_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block27_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block27_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block27_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block27_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block27_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block27_concat (Concatena (None, 32, 32, 1120) 0 conv4_block26_concat[0][0] \n conv4_block27_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block28_0_bn (BatchNormal (None, 32, 32, 1120) 4480 conv4_block27_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block28_0_relu (Activatio (None, 32, 32, 1120) 0 conv4_block28_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block28_1_conv (Conv2D) (None, 32, 32, 128) 143360 conv4_block28_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block28_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block28_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block28_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block28_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block28_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block28_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block28_concat (Concatena (None, 32, 32, 1152) 0 conv4_block27_concat[0][0] \n conv4_block28_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block29_0_bn (BatchNormal (None, 32, 32, 1152) 4608 conv4_block28_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block29_0_relu (Activatio (None, 32, 32, 1152) 0 conv4_block29_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block29_1_conv (Conv2D) (None, 32, 32, 128) 147456 conv4_block29_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block29_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block29_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block29_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block29_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block29_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block29_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block29_concat (Concatena (None, 32, 32, 1184) 0 conv4_block28_concat[0][0] \n conv4_block29_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block30_0_bn (BatchNormal (None, 32, 32, 1184) 4736 conv4_block29_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block30_0_relu (Activatio (None, 32, 32, 1184) 0 conv4_block30_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block30_1_conv (Conv2D) (None, 32, 32, 128) 151552 conv4_block30_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block30_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block30_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block30_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block30_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block30_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block30_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block30_concat (Concatena (None, 32, 32, 1216) 0 conv4_block29_concat[0][0] \n conv4_block30_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block31_0_bn (BatchNormal (None, 32, 32, 1216) 4864 conv4_block30_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block31_0_relu (Activatio (None, 32, 32, 1216) 0 conv4_block31_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block31_1_conv (Conv2D) (None, 32, 32, 128) 155648 conv4_block31_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block31_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block31_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block31_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block31_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block31_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block31_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block31_concat (Concatena (None, 32, 32, 1248) 0 conv4_block30_concat[0][0] \n conv4_block31_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block32_0_bn (BatchNormal (None, 32, 32, 1248) 4992 conv4_block31_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block32_0_relu (Activatio (None, 32, 32, 1248) 0 conv4_block32_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block32_1_conv (Conv2D) (None, 32, 32, 128) 159744 conv4_block32_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block32_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block32_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block32_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block32_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block32_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block32_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block32_concat (Concatena (None, 32, 32, 1280) 0 conv4_block31_concat[0][0] \n conv4_block32_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block33_0_bn (BatchNormal (None, 32, 32, 1280) 5120 conv4_block32_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block33_0_relu (Activatio (None, 32, 32, 1280) 0 conv4_block33_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block33_1_conv (Conv2D) (None, 32, 32, 128) 163840 conv4_block33_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block33_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block33_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block33_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block33_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block33_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block33_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block33_concat (Concatena (None, 32, 32, 1312) 0 conv4_block32_concat[0][0] \n conv4_block33_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block34_0_bn (BatchNormal (None, 32, 32, 1312) 5248 conv4_block33_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block34_0_relu (Activatio (None, 32, 32, 1312) 0 conv4_block34_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block34_1_conv (Conv2D) (None, 32, 32, 128) 167936 conv4_block34_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block34_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block34_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block34_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block34_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block34_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block34_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block34_concat (Concatena (None, 32, 32, 1344) 0 conv4_block33_concat[0][0] \n conv4_block34_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block35_0_bn (BatchNormal (None, 32, 32, 1344) 5376 conv4_block34_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block35_0_relu (Activatio (None, 32, 32, 1344) 0 conv4_block35_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block35_1_conv (Conv2D) (None, 32, 32, 128) 172032 conv4_block35_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block35_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block35_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block35_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block35_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block35_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block35_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block35_concat (Concatena (None, 32, 32, 1376) 0 conv4_block34_concat[0][0] \n conv4_block35_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block36_0_bn (BatchNormal (None, 32, 32, 1376) 5504 conv4_block35_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block36_0_relu (Activatio (None, 32, 32, 1376) 0 conv4_block36_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block36_1_conv (Conv2D) (None, 32, 32, 128) 176128 conv4_block36_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block36_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block36_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block36_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block36_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block36_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block36_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block36_concat (Concatena (None, 32, 32, 1408) 0 conv4_block35_concat[0][0] \n conv4_block36_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block37_0_bn (BatchNormal (None, 32, 32, 1408) 5632 conv4_block36_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block37_0_relu (Activatio (None, 32, 32, 1408) 0 conv4_block37_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block37_1_conv (Conv2D) (None, 32, 32, 128) 180224 conv4_block37_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block37_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block37_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block37_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block37_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block37_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block37_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block37_concat (Concatena (None, 32, 32, 1440) 0 conv4_block36_concat[0][0] \n conv4_block37_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block38_0_bn (BatchNormal (None, 32, 32, 1440) 5760 conv4_block37_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block38_0_relu (Activatio (None, 32, 32, 1440) 0 conv4_block38_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block38_1_conv (Conv2D) (None, 32, 32, 128) 184320 conv4_block38_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block38_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block38_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block38_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block38_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block38_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block38_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block38_concat (Concatena (None, 32, 32, 1472) 0 conv4_block37_concat[0][0] \n conv4_block38_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block39_0_bn (BatchNormal (None, 32, 32, 1472) 5888 conv4_block38_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block39_0_relu (Activatio (None, 32, 32, 1472) 0 conv4_block39_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block39_1_conv (Conv2D) (None, 32, 32, 128) 188416 conv4_block39_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block39_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block39_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block39_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block39_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block39_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block39_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block39_concat (Concatena (None, 32, 32, 1504) 0 conv4_block38_concat[0][0] \n conv4_block39_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block40_0_bn (BatchNormal (None, 32, 32, 1504) 6016 conv4_block39_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block40_0_relu (Activatio (None, 32, 32, 1504) 0 conv4_block40_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block40_1_conv (Conv2D) (None, 32, 32, 128) 192512 conv4_block40_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block40_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block40_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block40_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block40_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block40_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block40_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block40_concat (Concatena (None, 32, 32, 1536) 0 conv4_block39_concat[0][0] \n conv4_block40_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block41_0_bn (BatchNormal (None, 32, 32, 1536) 6144 conv4_block40_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block41_0_relu (Activatio (None, 32, 32, 1536) 0 conv4_block41_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block41_1_conv (Conv2D) (None, 32, 32, 128) 196608 conv4_block41_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block41_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block41_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block41_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block41_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block41_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block41_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block41_concat (Concatena (None, 32, 32, 1568) 0 conv4_block40_concat[0][0] \n conv4_block41_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block42_0_bn (BatchNormal (None, 32, 32, 1568) 6272 conv4_block41_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block42_0_relu (Activatio (None, 32, 32, 1568) 0 conv4_block42_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block42_1_conv (Conv2D) (None, 32, 32, 128) 200704 conv4_block42_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block42_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block42_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block42_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block42_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block42_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block42_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block42_concat (Concatena (None, 32, 32, 1600) 0 conv4_block41_concat[0][0] \n conv4_block42_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block43_0_bn (BatchNormal (None, 32, 32, 1600) 6400 conv4_block42_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block43_0_relu (Activatio (None, 32, 32, 1600) 0 conv4_block43_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block43_1_conv (Conv2D) (None, 32, 32, 128) 204800 conv4_block43_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block43_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block43_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block43_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block43_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block43_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block43_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block43_concat (Concatena (None, 32, 32, 1632) 0 conv4_block42_concat[0][0] \n conv4_block43_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block44_0_bn (BatchNormal (None, 32, 32, 1632) 6528 conv4_block43_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block44_0_relu (Activatio (None, 32, 32, 1632) 0 conv4_block44_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block44_1_conv (Conv2D) (None, 32, 32, 128) 208896 conv4_block44_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block44_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block44_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block44_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block44_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block44_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block44_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block44_concat (Concatena (None, 32, 32, 1664) 0 conv4_block43_concat[0][0] \n conv4_block44_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block45_0_bn (BatchNormal (None, 32, 32, 1664) 6656 conv4_block44_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block45_0_relu (Activatio (None, 32, 32, 1664) 0 conv4_block45_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block45_1_conv (Conv2D) (None, 32, 32, 128) 212992 conv4_block45_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block45_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block45_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block45_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block45_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block45_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block45_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block45_concat (Concatena (None, 32, 32, 1696) 0 conv4_block44_concat[0][0] \n conv4_block45_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block46_0_bn (BatchNormal (None, 32, 32, 1696) 6784 conv4_block45_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block46_0_relu (Activatio (None, 32, 32, 1696) 0 conv4_block46_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block46_1_conv (Conv2D) (None, 32, 32, 128) 217088 conv4_block46_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block46_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block46_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block46_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block46_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block46_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block46_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block46_concat (Concatena (None, 32, 32, 1728) 0 conv4_block45_concat[0][0] \n conv4_block46_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block47_0_bn (BatchNormal (None, 32, 32, 1728) 6912 conv4_block46_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block47_0_relu (Activatio (None, 32, 32, 1728) 0 conv4_block47_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block47_1_conv (Conv2D) (None, 32, 32, 128) 221184 conv4_block47_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block47_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block47_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block47_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block47_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block47_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block47_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block47_concat (Concatena (None, 32, 32, 1760) 0 conv4_block46_concat[0][0] \n conv4_block47_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block48_0_bn (BatchNormal (None, 32, 32, 1760) 7040 conv4_block47_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block48_0_relu (Activatio (None, 32, 32, 1760) 0 conv4_block48_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block48_1_conv (Conv2D) (None, 32, 32, 128) 225280 conv4_block48_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block48_1_bn (BatchNormal (None, 32, 32, 128) 512 conv4_block48_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block48_1_relu (Activatio (None, 32, 32, 128) 0 conv4_block48_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block48_2_conv (Conv2D) (None, 32, 32, 32) 36864 conv4_block48_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block48_concat (Concatena (None, 32, 32, 1792) 0 conv4_block47_concat[0][0] \n conv4_block48_2_conv[0][0] \n__________________________________________________________________________________________________\npool4_bn (BatchNormalization) (None, 32, 32, 1792) 7168 conv4_block48_concat[0][0] \n__________________________________________________________________________________________________\npool4_relu (Activation) (None, 32, 32, 1792) 0 pool4_bn[0][0] \n__________________________________________________________________________________________________\npool4_conv (Conv2D) (None, 32, 32, 896) 1605632 pool4_relu[0][0] \n__________________________________________________________________________________________________\npool4_pool (AveragePooling2D) (None, 16, 16, 896) 0 pool4_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block1_0_bn (BatchNormali (None, 16, 16, 896) 3584 pool4_pool[0][0] \n__________________________________________________________________________________________________\nconv5_block1_0_relu (Activation (None, 16, 16, 896) 0 conv5_block1_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block1_1_conv (Conv2D) (None, 16, 16, 128) 114688 conv5_block1_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block1_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block1_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block1_1_relu (Activation (None, 16, 16, 128) 0 conv5_block1_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block1_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block1_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block1_concat (Concatenat (None, 16, 16, 928) 0 pool4_pool[0][0] \n conv5_block1_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block2_0_bn (BatchNormali (None, 16, 16, 928) 3712 conv5_block1_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block2_0_relu (Activation (None, 16, 16, 928) 0 conv5_block2_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block2_1_conv (Conv2D) (None, 16, 16, 128) 118784 conv5_block2_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block2_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block2_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block2_1_relu (Activation (None, 16, 16, 128) 0 conv5_block2_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block2_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block2_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block2_concat (Concatenat (None, 16, 16, 960) 0 conv5_block1_concat[0][0] \n conv5_block2_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block3_0_bn (BatchNormali (None, 16, 16, 960) 3840 conv5_block2_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block3_0_relu (Activation (None, 16, 16, 960) 0 conv5_block3_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block3_1_conv (Conv2D) (None, 16, 16, 128) 122880 conv5_block3_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block3_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block3_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block3_1_relu (Activation (None, 16, 16, 128) 0 conv5_block3_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block3_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block3_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block3_concat (Concatenat (None, 16, 16, 992) 0 conv5_block2_concat[0][0] \n conv5_block3_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block4_0_bn (BatchNormali (None, 16, 16, 992) 3968 conv5_block3_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block4_0_relu (Activation (None, 16, 16, 992) 0 conv5_block4_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block4_1_conv (Conv2D) (None, 16, 16, 128) 126976 conv5_block4_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block4_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block4_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block4_1_relu (Activation (None, 16, 16, 128) 0 conv5_block4_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block4_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block4_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block4_concat (Concatenat (None, 16, 16, 1024) 0 conv5_block3_concat[0][0] \n conv5_block4_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block5_0_bn (BatchNormali (None, 16, 16, 1024) 4096 conv5_block4_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block5_0_relu (Activation (None, 16, 16, 1024) 0 conv5_block5_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block5_1_conv (Conv2D) (None, 16, 16, 128) 131072 conv5_block5_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block5_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block5_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block5_1_relu (Activation (None, 16, 16, 128) 0 conv5_block5_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block5_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block5_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block5_concat (Concatenat (None, 16, 16, 1056) 0 conv5_block4_concat[0][0] \n conv5_block5_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block6_0_bn (BatchNormali (None, 16, 16, 1056) 4224 conv5_block5_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block6_0_relu (Activation (None, 16, 16, 1056) 0 conv5_block6_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block6_1_conv (Conv2D) (None, 16, 16, 128) 135168 conv5_block6_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block6_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block6_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block6_1_relu (Activation (None, 16, 16, 128) 0 conv5_block6_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block6_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block6_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block6_concat (Concatenat (None, 16, 16, 1088) 0 conv5_block5_concat[0][0] \n conv5_block6_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block7_0_bn (BatchNormali (None, 16, 16, 1088) 4352 conv5_block6_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block7_0_relu (Activation (None, 16, 16, 1088) 0 conv5_block7_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block7_1_conv (Conv2D) (None, 16, 16, 128) 139264 conv5_block7_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block7_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block7_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block7_1_relu (Activation (None, 16, 16, 128) 0 conv5_block7_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block7_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block7_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block7_concat (Concatenat (None, 16, 16, 1120) 0 conv5_block6_concat[0][0] \n conv5_block7_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block8_0_bn (BatchNormali (None, 16, 16, 1120) 4480 conv5_block7_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block8_0_relu (Activation (None, 16, 16, 1120) 0 conv5_block8_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block8_1_conv (Conv2D) (None, 16, 16, 128) 143360 conv5_block8_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block8_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block8_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block8_1_relu (Activation (None, 16, 16, 128) 0 conv5_block8_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block8_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block8_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block8_concat (Concatenat (None, 16, 16, 1152) 0 conv5_block7_concat[0][0] \n conv5_block8_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block9_0_bn (BatchNormali (None, 16, 16, 1152) 4608 conv5_block8_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block9_0_relu (Activation (None, 16, 16, 1152) 0 conv5_block9_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block9_1_conv (Conv2D) (None, 16, 16, 128) 147456 conv5_block9_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block9_1_bn (BatchNormali (None, 16, 16, 128) 512 conv5_block9_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block9_1_relu (Activation (None, 16, 16, 128) 0 conv5_block9_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block9_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block9_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block9_concat (Concatenat (None, 16, 16, 1184) 0 conv5_block8_concat[0][0] \n conv5_block9_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block10_0_bn (BatchNormal (None, 16, 16, 1184) 4736 conv5_block9_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block10_0_relu (Activatio (None, 16, 16, 1184) 0 conv5_block10_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block10_1_conv (Conv2D) (None, 16, 16, 128) 151552 conv5_block10_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block10_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block10_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block10_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block10_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block10_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block10_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block10_concat (Concatena (None, 16, 16, 1216) 0 conv5_block9_concat[0][0] \n conv5_block10_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block11_0_bn (BatchNormal (None, 16, 16, 1216) 4864 conv5_block10_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block11_0_relu (Activatio (None, 16, 16, 1216) 0 conv5_block11_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block11_1_conv (Conv2D) (None, 16, 16, 128) 155648 conv5_block11_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block11_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block11_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block11_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block11_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block11_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block11_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block11_concat (Concatena (None, 16, 16, 1248) 0 conv5_block10_concat[0][0] \n conv5_block11_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block12_0_bn (BatchNormal (None, 16, 16, 1248) 4992 conv5_block11_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block12_0_relu (Activatio (None, 16, 16, 1248) 0 conv5_block12_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block12_1_conv (Conv2D) (None, 16, 16, 128) 159744 conv5_block12_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block12_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block12_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block12_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block12_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block12_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block12_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block12_concat (Concatena (None, 16, 16, 1280) 0 conv5_block11_concat[0][0] \n conv5_block12_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block13_0_bn (BatchNormal (None, 16, 16, 1280) 5120 conv5_block12_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block13_0_relu (Activatio (None, 16, 16, 1280) 0 conv5_block13_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block13_1_conv (Conv2D) (None, 16, 16, 128) 163840 conv5_block13_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block13_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block13_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block13_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block13_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block13_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block13_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block13_concat (Concatena (None, 16, 16, 1312) 0 conv5_block12_concat[0][0] \n conv5_block13_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block14_0_bn (BatchNormal (None, 16, 16, 1312) 5248 conv5_block13_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block14_0_relu (Activatio (None, 16, 16, 1312) 0 conv5_block14_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block14_1_conv (Conv2D) (None, 16, 16, 128) 167936 conv5_block14_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block14_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block14_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block14_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block14_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block14_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block14_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block14_concat (Concatena (None, 16, 16, 1344) 0 conv5_block13_concat[0][0] \n conv5_block14_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block15_0_bn (BatchNormal (None, 16, 16, 1344) 5376 conv5_block14_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block15_0_relu (Activatio (None, 16, 16, 1344) 0 conv5_block15_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block15_1_conv (Conv2D) (None, 16, 16, 128) 172032 conv5_block15_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block15_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block15_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block15_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block15_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block15_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block15_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block15_concat (Concatena (None, 16, 16, 1376) 0 conv5_block14_concat[0][0] \n conv5_block15_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block16_0_bn (BatchNormal (None, 16, 16, 1376) 5504 conv5_block15_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block16_0_relu (Activatio (None, 16, 16, 1376) 0 conv5_block16_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block16_1_conv (Conv2D) (None, 16, 16, 128) 176128 conv5_block16_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block16_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block16_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block16_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block16_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block16_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block16_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block16_concat (Concatena (None, 16, 16, 1408) 0 conv5_block15_concat[0][0] \n conv5_block16_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block17_0_bn (BatchNormal (None, 16, 16, 1408) 5632 conv5_block16_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block17_0_relu (Activatio (None, 16, 16, 1408) 0 conv5_block17_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block17_1_conv (Conv2D) (None, 16, 16, 128) 180224 conv5_block17_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block17_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block17_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block17_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block17_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block17_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block17_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block17_concat (Concatena (None, 16, 16, 1440) 0 conv5_block16_concat[0][0] \n conv5_block17_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block18_0_bn (BatchNormal (None, 16, 16, 1440) 5760 conv5_block17_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block18_0_relu (Activatio (None, 16, 16, 1440) 0 conv5_block18_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block18_1_conv (Conv2D) (None, 16, 16, 128) 184320 conv5_block18_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block18_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block18_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block18_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block18_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block18_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block18_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block18_concat (Concatena (None, 16, 16, 1472) 0 conv5_block17_concat[0][0] \n conv5_block18_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block19_0_bn (BatchNormal (None, 16, 16, 1472) 5888 conv5_block18_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block19_0_relu (Activatio (None, 16, 16, 1472) 0 conv5_block19_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block19_1_conv (Conv2D) (None, 16, 16, 128) 188416 conv5_block19_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block19_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block19_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block19_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block19_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block19_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block19_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block19_concat (Concatena (None, 16, 16, 1504) 0 conv5_block18_concat[0][0] \n conv5_block19_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block20_0_bn (BatchNormal (None, 16, 16, 1504) 6016 conv5_block19_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block20_0_relu (Activatio (None, 16, 16, 1504) 0 conv5_block20_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block20_1_conv (Conv2D) (None, 16, 16, 128) 192512 conv5_block20_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block20_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block20_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block20_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block20_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block20_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block20_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block20_concat (Concatena (None, 16, 16, 1536) 0 conv5_block19_concat[0][0] \n conv5_block20_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block21_0_bn (BatchNormal (None, 16, 16, 1536) 6144 conv5_block20_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block21_0_relu (Activatio (None, 16, 16, 1536) 0 conv5_block21_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block21_1_conv (Conv2D) (None, 16, 16, 128) 196608 conv5_block21_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block21_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block21_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block21_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block21_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block21_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block21_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block21_concat (Concatena (None, 16, 16, 1568) 0 conv5_block20_concat[0][0] \n conv5_block21_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block22_0_bn (BatchNormal (None, 16, 16, 1568) 6272 conv5_block21_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block22_0_relu (Activatio (None, 16, 16, 1568) 0 conv5_block22_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block22_1_conv (Conv2D) (None, 16, 16, 128) 200704 conv5_block22_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block22_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block22_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block22_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block22_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block22_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block22_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block22_concat (Concatena (None, 16, 16, 1600) 0 conv5_block21_concat[0][0] \n conv5_block22_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block23_0_bn (BatchNormal (None, 16, 16, 1600) 6400 conv5_block22_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block23_0_relu (Activatio (None, 16, 16, 1600) 0 conv5_block23_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block23_1_conv (Conv2D) (None, 16, 16, 128) 204800 conv5_block23_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block23_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block23_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block23_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block23_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block23_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block23_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block23_concat (Concatena (None, 16, 16, 1632) 0 conv5_block22_concat[0][0] \n conv5_block23_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block24_0_bn (BatchNormal (None, 16, 16, 1632) 6528 conv5_block23_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block24_0_relu (Activatio (None, 16, 16, 1632) 0 conv5_block24_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block24_1_conv (Conv2D) (None, 16, 16, 128) 208896 conv5_block24_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block24_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block24_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block24_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block24_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block24_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block24_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block24_concat (Concatena (None, 16, 16, 1664) 0 conv5_block23_concat[0][0] \n conv5_block24_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block25_0_bn (BatchNormal (None, 16, 16, 1664) 6656 conv5_block24_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block25_0_relu (Activatio (None, 16, 16, 1664) 0 conv5_block25_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block25_1_conv (Conv2D) (None, 16, 16, 128) 212992 conv5_block25_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block25_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block25_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block25_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block25_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block25_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block25_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block25_concat (Concatena (None, 16, 16, 1696) 0 conv5_block24_concat[0][0] \n conv5_block25_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block26_0_bn (BatchNormal (None, 16, 16, 1696) 6784 conv5_block25_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block26_0_relu (Activatio (None, 16, 16, 1696) 0 conv5_block26_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block26_1_conv (Conv2D) (None, 16, 16, 128) 217088 conv5_block26_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block26_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block26_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block26_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block26_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block26_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block26_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block26_concat (Concatena (None, 16, 16, 1728) 0 conv5_block25_concat[0][0] \n conv5_block26_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block27_0_bn (BatchNormal (None, 16, 16, 1728) 6912 conv5_block26_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block27_0_relu (Activatio (None, 16, 16, 1728) 0 conv5_block27_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block27_1_conv (Conv2D) (None, 16, 16, 128) 221184 conv5_block27_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block27_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block27_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block27_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block27_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block27_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block27_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block27_concat (Concatena (None, 16, 16, 1760) 0 conv5_block26_concat[0][0] \n conv5_block27_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block28_0_bn (BatchNormal (None, 16, 16, 1760) 7040 conv5_block27_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block28_0_relu (Activatio (None, 16, 16, 1760) 0 conv5_block28_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block28_1_conv (Conv2D) (None, 16, 16, 128) 225280 conv5_block28_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block28_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block28_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block28_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block28_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block28_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block28_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block28_concat (Concatena (None, 16, 16, 1792) 0 conv5_block27_concat[0][0] \n conv5_block28_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block29_0_bn (BatchNormal (None, 16, 16, 1792) 7168 conv5_block28_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block29_0_relu (Activatio (None, 16, 16, 1792) 0 conv5_block29_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block29_1_conv (Conv2D) (None, 16, 16, 128) 229376 conv5_block29_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block29_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block29_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block29_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block29_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block29_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block29_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block29_concat (Concatena (None, 16, 16, 1824) 0 conv5_block28_concat[0][0] \n conv5_block29_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block30_0_bn (BatchNormal (None, 16, 16, 1824) 7296 conv5_block29_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block30_0_relu (Activatio (None, 16, 16, 1824) 0 conv5_block30_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block30_1_conv (Conv2D) (None, 16, 16, 128) 233472 conv5_block30_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block30_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block30_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block30_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block30_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block30_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block30_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block30_concat (Concatena (None, 16, 16, 1856) 0 conv5_block29_concat[0][0] \n conv5_block30_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block31_0_bn (BatchNormal (None, 16, 16, 1856) 7424 conv5_block30_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block31_0_relu (Activatio (None, 16, 16, 1856) 0 conv5_block31_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block31_1_conv (Conv2D) (None, 16, 16, 128) 237568 conv5_block31_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block31_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block31_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block31_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block31_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block31_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block31_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block31_concat (Concatena (None, 16, 16, 1888) 0 conv5_block30_concat[0][0] \n conv5_block31_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block32_0_bn (BatchNormal (None, 16, 16, 1888) 7552 conv5_block31_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block32_0_relu (Activatio (None, 16, 16, 1888) 0 conv5_block32_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block32_1_conv (Conv2D) (None, 16, 16, 128) 241664 conv5_block32_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block32_1_bn (BatchNormal (None, 16, 16, 128) 512 conv5_block32_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block32_1_relu (Activatio (None, 16, 16, 128) 0 conv5_block32_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block32_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv5_block32_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block32_concat (Concatena (None, 16, 16, 1920) 0 conv5_block31_concat[0][0] \n conv5_block32_2_conv[0][0] \n__________________________________________________________________________________________________\nbn (BatchNormalization) (None, 16, 16, 1920) 7680 conv5_block32_concat[0][0] \n__________________________________________________________________________________________________\nrelu (Activation) (None, 16, 16, 1920) 0 bn[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d (Globa (None, 1920) 0 relu[0][0] \n__________________________________________________________________________________________________\ndropout (Dropout) (None, 1920) 0 global_average_pooling2d[0][0] \n__________________________________________________________________________________________________\ndense (Dense) (None, 2048) 3934208 dropout[0][0] \n__________________________________________________________________________________________________\ndropout_1 (Dropout) (None, 2048) 0 dense[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 5) 10245 dropout_1[0][0] \n==================================================================================================\nTotal params: 22,266,437\nTrainable params: 3,944,453\nNon-trainable params: 18,321,984\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Train top layers","metadata":{}},{"cell_type":"code","source":"STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\nSTEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n\nhistory_warmup = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=WARMUP_EPOCHS,\n verbose=1).history","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-09-25T15:55:29.896754Z","iopub.execute_input":"2022-09-25T15:55:29.897092Z","iopub.status.idle":"2022-09-25T16:11:00.297129Z","shell.execute_reply.started":"2022-09-25T15:55:29.897061Z","shell.execute_reply":"2022-09-25T16:11:00.296242Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Epoch 1/2\n366/366 [==============================] - 524s 1s/step - loss: 1.4007 - accuracy: 0.5894 - val_loss: 0.8882 - val_accuracy: 0.7143\nEpoch 2/2\n366/366 [==============================] - 404s 1s/step - loss: 0.8380 - accuracy: 0.6843 - val_loss: 0.7848 - val_accuracy: 0.6978\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Fine-tune the complete model","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"for layer in model.layers:\n layer.trainable = True\n\nes = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)\nrlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)\n\ncallback_list = [es, rlrop]\noptimizer = optimizers.Adam(lr=LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=metric_list)\nmodel.summary()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-08-23T16:32:38.318221Z","iopub.execute_input":"2022-08-23T16:32:38.318667Z","iopub.status.idle":"2022-08-23T16:32:38.503178Z","shell.execute_reply.started":"2022-08-23T16:32:38.318599Z","shell.execute_reply":"2022-08-23T16:32:38.502354Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Model: \"model\"\n__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) [(None, 512, 512, 3) 0 \n__________________________________________________________________________________________________\nconv2d (Conv2D) (None, 255, 255, 32) 864 input_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization (BatchNorma (None, 255, 255, 32) 96 conv2d[0][0] \n__________________________________________________________________________________________________\nactivation (Activation) (None, 255, 255, 32) 0 batch_normalization[0][0] \n__________________________________________________________________________________________________\nconv2d_1 (Conv2D) (None, 253, 253, 32) 9216 activation[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_1 (BatchNor (None, 253, 253, 32) 96 conv2d_1[0][0] \n__________________________________________________________________________________________________\nactivation_1 (Activation) (None, 253, 253, 32) 0 batch_normalization_1[0][0] \n__________________________________________________________________________________________________\nconv2d_2 (Conv2D) (None, 253, 253, 64) 18432 activation_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_2 (BatchNor (None, 253, 253, 64) 192 conv2d_2[0][0] \n__________________________________________________________________________________________________\nactivation_2 (Activation) (None, 253, 253, 64) 0 batch_normalization_2[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 126, 126, 64) 0 activation_2[0][0] \n__________________________________________________________________________________________________\nconv2d_3 (Conv2D) (None, 126, 126, 80) 5120 max_pooling2d[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_3 (BatchNor (None, 126, 126, 80) 240 conv2d_3[0][0] \n__________________________________________________________________________________________________\nactivation_3 (Activation) (None, 126, 126, 80) 0 batch_normalization_3[0][0] \n__________________________________________________________________________________________________\nconv2d_4 (Conv2D) (None, 124, 124, 192 138240 activation_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_4 (BatchNor (None, 124, 124, 192 576 conv2d_4[0][0] \n__________________________________________________________________________________________________\nactivation_4 (Activation) (None, 124, 124, 192 0 batch_normalization_4[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_1 (MaxPooling2D) (None, 61, 61, 192) 0 activation_4[0][0] \n__________________________________________________________________________________________________\nconv2d_8 (Conv2D) (None, 61, 61, 64) 12288 max_pooling2d_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_8 (BatchNor (None, 61, 61, 64) 192 conv2d_8[0][0] \n__________________________________________________________________________________________________\nactivation_8 (Activation) (None, 61, 61, 64) 0 batch_normalization_8[0][0] \n__________________________________________________________________________________________________\nconv2d_6 (Conv2D) (None, 61, 61, 48) 9216 max_pooling2d_1[0][0] \n__________________________________________________________________________________________________\nconv2d_9 (Conv2D) (None, 61, 61, 96) 55296 activation_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_6 (BatchNor (None, 61, 61, 48) 144 conv2d_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_9 (BatchNor (None, 61, 61, 96) 288 conv2d_9[0][0] \n__________________________________________________________________________________________________\nactivation_6 (Activation) (None, 61, 61, 48) 0 batch_normalization_6[0][0] \n__________________________________________________________________________________________________\nactivation_9 (Activation) (None, 61, 61, 96) 0 batch_normalization_9[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d (AveragePooli (None, 61, 61, 192) 0 max_pooling2d_1[0][0] \n__________________________________________________________________________________________________\nconv2d_5 (Conv2D) (None, 61, 61, 64) 12288 max_pooling2d_1[0][0] \n__________________________________________________________________________________________________\nconv2d_7 (Conv2D) (None, 61, 61, 64) 76800 activation_6[0][0] \n__________________________________________________________________________________________________\nconv2d_10 (Conv2D) (None, 61, 61, 96) 82944 activation_9[0][0] \n__________________________________________________________________________________________________\nconv2d_11 (Conv2D) (None, 61, 61, 32) 6144 average_pooling2d[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_5 (BatchNor (None, 61, 61, 64) 192 conv2d_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_7 (BatchNor (None, 61, 61, 64) 192 conv2d_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_10 (BatchNo (None, 61, 61, 96) 288 conv2d_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_11 (BatchNo (None, 61, 61, 32) 96 conv2d_11[0][0] \n__________________________________________________________________________________________________\nactivation_5 (Activation) (None, 61, 61, 64) 0 batch_normalization_5[0][0] \n__________________________________________________________________________________________________\nactivation_7 (Activation) (None, 61, 61, 64) 0 batch_normalization_7[0][0] \n__________________________________________________________________________________________________\nactivation_10 (Activation) (None, 61, 61, 96) 0 batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nactivation_11 (Activation) (None, 61, 61, 32) 0 batch_normalization_11[0][0] \n__________________________________________________________________________________________________\nmixed0 (Concatenate) (None, 61, 61, 256) 0 activation_5[0][0] \n activation_7[0][0] \n activation_10[0][0] \n activation_11[0][0] \n__________________________________________________________________________________________________\nconv2d_15 (Conv2D) (None, 61, 61, 64) 16384 mixed0[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_15 (BatchNo (None, 61, 61, 64) 192 conv2d_15[0][0] \n__________________________________________________________________________________________________\nactivation_15 (Activation) (None, 61, 61, 64) 0 batch_normalization_15[0][0] \n__________________________________________________________________________________________________\nconv2d_13 (Conv2D) (None, 61, 61, 48) 12288 mixed0[0][0] \n__________________________________________________________________________________________________\nconv2d_16 (Conv2D) (None, 61, 61, 96) 55296 activation_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_13 (BatchNo (None, 61, 61, 48) 144 conv2d_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_16 (BatchNo (None, 61, 61, 96) 288 conv2d_16[0][0] \n__________________________________________________________________________________________________\nactivation_13 (Activation) (None, 61, 61, 48) 0 batch_normalization_13[0][0] \n__________________________________________________________________________________________________\nactivation_16 (Activation) (None, 61, 61, 96) 0 batch_normalization_16[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d_1 (AveragePoo (None, 61, 61, 256) 0 mixed0[0][0] \n__________________________________________________________________________________________________\nconv2d_12 (Conv2D) (None, 61, 61, 64) 16384 mixed0[0][0] \n__________________________________________________________________________________________________\nconv2d_14 (Conv2D) (None, 61, 61, 64) 76800 activation_13[0][0] \n__________________________________________________________________________________________________\nconv2d_17 (Conv2D) (None, 61, 61, 96) 82944 activation_16[0][0] \n__________________________________________________________________________________________________\nconv2d_18 (Conv2D) (None, 61, 61, 64) 16384 average_pooling2d_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_12 (BatchNo (None, 61, 61, 64) 192 conv2d_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_14 (BatchNo (None, 61, 61, 64) 192 conv2d_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_17 (BatchNo (None, 61, 61, 96) 288 conv2d_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_18 (BatchNo (None, 61, 61, 64) 192 conv2d_18[0][0] \n__________________________________________________________________________________________________\nactivation_12 (Activation) (None, 61, 61, 64) 0 batch_normalization_12[0][0] \n__________________________________________________________________________________________________\nactivation_14 (Activation) (None, 61, 61, 64) 0 batch_normalization_14[0][0] \n__________________________________________________________________________________________________\nactivation_17 (Activation) (None, 61, 61, 96) 0 batch_normalization_17[0][0] \n__________________________________________________________________________________________________\nactivation_18 (Activation) (None, 61, 61, 64) 0 batch_normalization_18[0][0] \n__________________________________________________________________________________________________\nmixed1 (Concatenate) (None, 61, 61, 288) 0 activation_12[0][0] \n activation_14[0][0] \n activation_17[0][0] \n activation_18[0][0] \n__________________________________________________________________________________________________\nconv2d_22 (Conv2D) (None, 61, 61, 64) 18432 mixed1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_22 (BatchNo (None, 61, 61, 64) 192 conv2d_22[0][0] \n__________________________________________________________________________________________________\nactivation_22 (Activation) (None, 61, 61, 64) 0 batch_normalization_22[0][0] \n__________________________________________________________________________________________________\nconv2d_20 (Conv2D) (None, 61, 61, 48) 13824 mixed1[0][0] \n__________________________________________________________________________________________________\nconv2d_23 (Conv2D) (None, 61, 61, 96) 55296 activation_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_20 (BatchNo (None, 61, 61, 48) 144 conv2d_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_23 (BatchNo (None, 61, 61, 96) 288 conv2d_23[0][0] \n__________________________________________________________________________________________________\nactivation_20 (Activation) (None, 61, 61, 48) 0 batch_normalization_20[0][0] \n__________________________________________________________________________________________________\nactivation_23 (Activation) (None, 61, 61, 96) 0 batch_normalization_23[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d_2 (AveragePoo (None, 61, 61, 288) 0 mixed1[0][0] \n__________________________________________________________________________________________________\nconv2d_19 (Conv2D) (None, 61, 61, 64) 18432 mixed1[0][0] \n__________________________________________________________________________________________________\nconv2d_21 (Conv2D) (None, 61, 61, 64) 76800 activation_20[0][0] \n__________________________________________________________________________________________________\nconv2d_24 (Conv2D) (None, 61, 61, 96) 82944 activation_23[0][0] \n__________________________________________________________________________________________________\nconv2d_25 (Conv2D) (None, 61, 61, 64) 18432 average_pooling2d_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_19 (BatchNo (None, 61, 61, 64) 192 conv2d_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_21 (BatchNo (None, 61, 61, 64) 192 conv2d_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_24 (BatchNo (None, 61, 61, 96) 288 conv2d_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_25 (BatchNo (None, 61, 61, 64) 192 conv2d_25[0][0] \n__________________________________________________________________________________________________\nactivation_19 (Activation) (None, 61, 61, 64) 0 batch_normalization_19[0][0] \n__________________________________________________________________________________________________\nactivation_21 (Activation) (None, 61, 61, 64) 0 batch_normalization_21[0][0] \n__________________________________________________________________________________________________\nactivation_24 (Activation) (None, 61, 61, 96) 0 batch_normalization_24[0][0] \n__________________________________________________________________________________________________\nactivation_25 (Activation) (None, 61, 61, 64) 0 batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nmixed2 (Concatenate) (None, 61, 61, 288) 0 activation_19[0][0] \n activation_21[0][0] \n activation_24[0][0] \n activation_25[0][0] \n__________________________________________________________________________________________________\nconv2d_27 (Conv2D) (None, 61, 61, 64) 18432 mixed2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_27 (BatchNo (None, 61, 61, 64) 192 conv2d_27[0][0] \n__________________________________________________________________________________________________\nactivation_27 (Activation) (None, 61, 61, 64) 0 batch_normalization_27[0][0] \n__________________________________________________________________________________________________\nconv2d_28 (Conv2D) (None, 61, 61, 96) 55296 activation_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_28 (BatchNo (None, 61, 61, 96) 288 conv2d_28[0][0] \n__________________________________________________________________________________________________\nactivation_28 (Activation) (None, 61, 61, 96) 0 batch_normalization_28[0][0] \n__________________________________________________________________________________________________\nconv2d_26 (Conv2D) (None, 30, 30, 384) 995328 mixed2[0][0] \n__________________________________________________________________________________________________\nconv2d_29 (Conv2D) (None, 30, 30, 96) 82944 activation_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_26 (BatchNo (None, 30, 30, 384) 1152 conv2d_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_29 (BatchNo (None, 30, 30, 96) 288 conv2d_29[0][0] \n__________________________________________________________________________________________________\nactivation_26 (Activation) (None, 30, 30, 384) 0 batch_normalization_26[0][0] \n__________________________________________________________________________________________________\nactivation_29 (Activation) (None, 30, 30, 96) 0 batch_normalization_29[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_2 (MaxPooling2D) (None, 30, 30, 288) 0 mixed2[0][0] \n__________________________________________________________________________________________________\nmixed3 (Concatenate) (None, 30, 30, 768) 0 activation_26[0][0] \n activation_29[0][0] \n max_pooling2d_2[0][0] \n__________________________________________________________________________________________________\nconv2d_34 (Conv2D) (None, 30, 30, 128) 98304 mixed3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_34 (BatchNo (None, 30, 30, 128) 384 conv2d_34[0][0] \n__________________________________________________________________________________________________\nactivation_34 (Activation) (None, 30, 30, 128) 0 batch_normalization_34[0][0] \n__________________________________________________________________________________________________\nconv2d_35 (Conv2D) (None, 30, 30, 128) 114688 activation_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_35 (BatchNo (None, 30, 30, 128) 384 conv2d_35[0][0] \n__________________________________________________________________________________________________\nactivation_35 (Activation) (None, 30, 30, 128) 0 batch_normalization_35[0][0] \n__________________________________________________________________________________________________\nconv2d_31 (Conv2D) (None, 30, 30, 128) 98304 mixed3[0][0] \n__________________________________________________________________________________________________\nconv2d_36 (Conv2D) (None, 30, 30, 128) 114688 activation_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_31 (BatchNo (None, 30, 30, 128) 384 conv2d_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_36 (BatchNo (None, 30, 30, 128) 384 conv2d_36[0][0] \n__________________________________________________________________________________________________\nactivation_31 (Activation) (None, 30, 30, 128) 0 batch_normalization_31[0][0] \n__________________________________________________________________________________________________\nactivation_36 (Activation) (None, 30, 30, 128) 0 batch_normalization_36[0][0] \n__________________________________________________________________________________________________\nconv2d_32 (Conv2D) (None, 30, 30, 128) 114688 activation_31[0][0] \n__________________________________________________________________________________________________\nconv2d_37 (Conv2D) (None, 30, 30, 128) 114688 activation_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_32 (BatchNo (None, 30, 30, 128) 384 conv2d_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_37 (BatchNo (None, 30, 30, 128) 384 conv2d_37[0][0] \n__________________________________________________________________________________________________\nactivation_32 (Activation) (None, 30, 30, 128) 0 batch_normalization_32[0][0] \n__________________________________________________________________________________________________\nactivation_37 (Activation) (None, 30, 30, 128) 0 batch_normalization_37[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d_3 (AveragePoo (None, 30, 30, 768) 0 mixed3[0][0] \n__________________________________________________________________________________________________\nconv2d_30 (Conv2D) (None, 30, 30, 192) 147456 mixed3[0][0] \n__________________________________________________________________________________________________\nconv2d_33 (Conv2D) (None, 30, 30, 192) 172032 activation_32[0][0] \n__________________________________________________________________________________________________\nconv2d_38 (Conv2D) (None, 30, 30, 192) 172032 activation_37[0][0] \n__________________________________________________________________________________________________\nconv2d_39 (Conv2D) (None, 30, 30, 192) 147456 average_pooling2d_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_30 (BatchNo (None, 30, 30, 192) 576 conv2d_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_33 (BatchNo (None, 30, 30, 192) 576 conv2d_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_38 (BatchNo (None, 30, 30, 192) 576 conv2d_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_39 (BatchNo (None, 30, 30, 192) 576 conv2d_39[0][0] \n__________________________________________________________________________________________________\nactivation_30 (Activation) (None, 30, 30, 192) 0 batch_normalization_30[0][0] \n__________________________________________________________________________________________________\nactivation_33 (Activation) (None, 30, 30, 192) 0 batch_normalization_33[0][0] \n__________________________________________________________________________________________________\nactivation_38 (Activation) (None, 30, 30, 192) 0 batch_normalization_38[0][0] \n__________________________________________________________________________________________________\nactivation_39 (Activation) (None, 30, 30, 192) 0 batch_normalization_39[0][0] \n__________________________________________________________________________________________________\nmixed4 (Concatenate) (None, 30, 30, 768) 0 activation_30[0][0] \n activation_33[0][0] \n activation_38[0][0] \n activation_39[0][0] \n__________________________________________________________________________________________________\nconv2d_44 (Conv2D) (None, 30, 30, 160) 122880 mixed4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_44 (BatchNo (None, 30, 30, 160) 480 conv2d_44[0][0] \n__________________________________________________________________________________________________\nactivation_44 (Activation) (None, 30, 30, 160) 0 batch_normalization_44[0][0] \n__________________________________________________________________________________________________\nconv2d_45 (Conv2D) (None, 30, 30, 160) 179200 activation_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_45 (BatchNo (None, 30, 30, 160) 480 conv2d_45[0][0] \n__________________________________________________________________________________________________\nactivation_45 (Activation) (None, 30, 30, 160) 0 batch_normalization_45[0][0] \n__________________________________________________________________________________________________\nconv2d_41 (Conv2D) (None, 30, 30, 160) 122880 mixed4[0][0] \n__________________________________________________________________________________________________\nconv2d_46 (Conv2D) (None, 30, 30, 160) 179200 activation_45[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_41 (BatchNo (None, 30, 30, 160) 480 conv2d_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_46 (BatchNo (None, 30, 30, 160) 480 conv2d_46[0][0] \n__________________________________________________________________________________________________\nactivation_41 (Activation) (None, 30, 30, 160) 0 batch_normalization_41[0][0] \n__________________________________________________________________________________________________\nactivation_46 (Activation) (None, 30, 30, 160) 0 batch_normalization_46[0][0] \n__________________________________________________________________________________________________\nconv2d_42 (Conv2D) (None, 30, 30, 160) 179200 activation_41[0][0] \n__________________________________________________________________________________________________\nconv2d_47 (Conv2D) (None, 30, 30, 160) 179200 activation_46[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_42 (BatchNo (None, 30, 30, 160) 480 conv2d_42[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_47 (BatchNo (None, 30, 30, 160) 480 conv2d_47[0][0] \n__________________________________________________________________________________________________\nactivation_42 (Activation) (None, 30, 30, 160) 0 batch_normalization_42[0][0] \n__________________________________________________________________________________________________\nactivation_47 (Activation) (None, 30, 30, 160) 0 batch_normalization_47[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d_4 (AveragePoo (None, 30, 30, 768) 0 mixed4[0][0] \n__________________________________________________________________________________________________\nconv2d_40 (Conv2D) (None, 30, 30, 192) 147456 mixed4[0][0] \n__________________________________________________________________________________________________\nconv2d_43 (Conv2D) (None, 30, 30, 192) 215040 activation_42[0][0] \n__________________________________________________________________________________________________\nconv2d_48 (Conv2D) (None, 30, 30, 192) 215040 activation_47[0][0] \n__________________________________________________________________________________________________\nconv2d_49 (Conv2D) (None, 30, 30, 192) 147456 average_pooling2d_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_40 (BatchNo (None, 30, 30, 192) 576 conv2d_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_43 (BatchNo (None, 30, 30, 192) 576 conv2d_43[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_48 (BatchNo (None, 30, 30, 192) 576 conv2d_48[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_49 (BatchNo (None, 30, 30, 192) 576 conv2d_49[0][0] \n__________________________________________________________________________________________________\nactivation_40 (Activation) (None, 30, 30, 192) 0 batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nactivation_43 (Activation) (None, 30, 30, 192) 0 batch_normalization_43[0][0] \n__________________________________________________________________________________________________\nactivation_48 (Activation) (None, 30, 30, 192) 0 batch_normalization_48[0][0] \n__________________________________________________________________________________________________\nactivation_49 (Activation) (None, 30, 30, 192) 0 batch_normalization_49[0][0] \n__________________________________________________________________________________________________\nmixed5 (Concatenate) (None, 30, 30, 768) 0 activation_40[0][0] \n activation_43[0][0] \n activation_48[0][0] \n activation_49[0][0] \n__________________________________________________________________________________________________\nconv2d_54 (Conv2D) (None, 30, 30, 160) 122880 mixed5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_54 (BatchNo (None, 30, 30, 160) 480 conv2d_54[0][0] \n__________________________________________________________________________________________________\nactivation_54 (Activation) (None, 30, 30, 160) 0 batch_normalization_54[0][0] \n__________________________________________________________________________________________________\nconv2d_55 (Conv2D) (None, 30, 30, 160) 179200 activation_54[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_55 (BatchNo (None, 30, 30, 160) 480 conv2d_55[0][0] \n__________________________________________________________________________________________________\nactivation_55 (Activation) (None, 30, 30, 160) 0 batch_normalization_55[0][0] \n__________________________________________________________________________________________________\nconv2d_51 (Conv2D) (None, 30, 30, 160) 122880 mixed5[0][0] \n__________________________________________________________________________________________________\nconv2d_56 (Conv2D) (None, 30, 30, 160) 179200 activation_55[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_51 (BatchNo (None, 30, 30, 160) 480 conv2d_51[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_56 (BatchNo (None, 30, 30, 160) 480 conv2d_56[0][0] \n__________________________________________________________________________________________________\nactivation_51 (Activation) (None, 30, 30, 160) 0 batch_normalization_51[0][0] \n__________________________________________________________________________________________________\nactivation_56 (Activation) (None, 30, 30, 160) 0 batch_normalization_56[0][0] \n__________________________________________________________________________________________________\nconv2d_52 (Conv2D) (None, 30, 30, 160) 179200 activation_51[0][0] \n__________________________________________________________________________________________________\nconv2d_57 (Conv2D) (None, 30, 30, 160) 179200 activation_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_52 (BatchNo (None, 30, 30, 160) 480 conv2d_52[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_57 (BatchNo (None, 30, 30, 160) 480 conv2d_57[0][0] \n__________________________________________________________________________________________________\nactivation_52 (Activation) (None, 30, 30, 160) 0 batch_normalization_52[0][0] \n__________________________________________________________________________________________________\nactivation_57 (Activation) (None, 30, 30, 160) 0 batch_normalization_57[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d_5 (AveragePoo (None, 30, 30, 768) 0 mixed5[0][0] \n__________________________________________________________________________________________________\nconv2d_50 (Conv2D) (None, 30, 30, 192) 147456 mixed5[0][0] \n__________________________________________________________________________________________________\nconv2d_53 (Conv2D) (None, 30, 30, 192) 215040 activation_52[0][0] \n__________________________________________________________________________________________________\nconv2d_58 (Conv2D) (None, 30, 30, 192) 215040 activation_57[0][0] \n__________________________________________________________________________________________________\nconv2d_59 (Conv2D) (None, 30, 30, 192) 147456 average_pooling2d_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_50 (BatchNo (None, 30, 30, 192) 576 conv2d_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_53 (BatchNo (None, 30, 30, 192) 576 conv2d_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_58 (BatchNo (None, 30, 30, 192) 576 conv2d_58[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_59 (BatchNo (None, 30, 30, 192) 576 conv2d_59[0][0] \n__________________________________________________________________________________________________\nactivation_50 (Activation) (None, 30, 30, 192) 0 batch_normalization_50[0][0] \n__________________________________________________________________________________________________\nactivation_53 (Activation) (None, 30, 30, 192) 0 batch_normalization_53[0][0] \n__________________________________________________________________________________________________\nactivation_58 (Activation) (None, 30, 30, 192) 0 batch_normalization_58[0][0] \n__________________________________________________________________________________________________\nactivation_59 (Activation) (None, 30, 30, 192) 0 batch_normalization_59[0][0] \n__________________________________________________________________________________________________\nmixed6 (Concatenate) (None, 30, 30, 768) 0 activation_50[0][0] \n activation_53[0][0] \n activation_58[0][0] \n activation_59[0][0] \n__________________________________________________________________________________________________\nconv2d_64 (Conv2D) (None, 30, 30, 192) 147456 mixed6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_64 (BatchNo (None, 30, 30, 192) 576 conv2d_64[0][0] \n__________________________________________________________________________________________________\nactivation_64 (Activation) (None, 30, 30, 192) 0 batch_normalization_64[0][0] \n__________________________________________________________________________________________________\nconv2d_65 (Conv2D) (None, 30, 30, 192) 258048 activation_64[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_65 (BatchNo (None, 30, 30, 192) 576 conv2d_65[0][0] \n__________________________________________________________________________________________________\nactivation_65 (Activation) (None, 30, 30, 192) 0 batch_normalization_65[0][0] \n__________________________________________________________________________________________________\nconv2d_61 (Conv2D) (None, 30, 30, 192) 147456 mixed6[0][0] \n__________________________________________________________________________________________________\nconv2d_66 (Conv2D) (None, 30, 30, 192) 258048 activation_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_61 (BatchNo (None, 30, 30, 192) 576 conv2d_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_66 (BatchNo (None, 30, 30, 192) 576 conv2d_66[0][0] \n__________________________________________________________________________________________________\nactivation_61 (Activation) (None, 30, 30, 192) 0 batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nactivation_66 (Activation) (None, 30, 30, 192) 0 batch_normalization_66[0][0] \n__________________________________________________________________________________________________\nconv2d_62 (Conv2D) (None, 30, 30, 192) 258048 activation_61[0][0] \n__________________________________________________________________________________________________\nconv2d_67 (Conv2D) (None, 30, 30, 192) 258048 activation_66[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_62 (BatchNo (None, 30, 30, 192) 576 conv2d_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_67 (BatchNo (None, 30, 30, 192) 576 conv2d_67[0][0] \n__________________________________________________________________________________________________\nactivation_62 (Activation) (None, 30, 30, 192) 0 batch_normalization_62[0][0] \n__________________________________________________________________________________________________\nactivation_67 (Activation) (None, 30, 30, 192) 0 batch_normalization_67[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d_6 (AveragePoo (None, 30, 30, 768) 0 mixed6[0][0] \n__________________________________________________________________________________________________\nconv2d_60 (Conv2D) (None, 30, 30, 192) 147456 mixed6[0][0] \n__________________________________________________________________________________________________\nconv2d_63 (Conv2D) (None, 30, 30, 192) 258048 activation_62[0][0] \n__________________________________________________________________________________________________\nconv2d_68 (Conv2D) (None, 30, 30, 192) 258048 activation_67[0][0] \n__________________________________________________________________________________________________\nconv2d_69 (Conv2D) (None, 30, 30, 192) 147456 average_pooling2d_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_60 (BatchNo (None, 30, 30, 192) 576 conv2d_60[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_63 (BatchNo (None, 30, 30, 192) 576 conv2d_63[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_68 (BatchNo (None, 30, 30, 192) 576 conv2d_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_69 (BatchNo (None, 30, 30, 192) 576 conv2d_69[0][0] \n__________________________________________________________________________________________________\nactivation_60 (Activation) (None, 30, 30, 192) 0 batch_normalization_60[0][0] \n__________________________________________________________________________________________________\nactivation_63 (Activation) (None, 30, 30, 192) 0 batch_normalization_63[0][0] \n__________________________________________________________________________________________________\nactivation_68 (Activation) (None, 30, 30, 192) 0 batch_normalization_68[0][0] \n__________________________________________________________________________________________________\nactivation_69 (Activation) (None, 30, 30, 192) 0 batch_normalization_69[0][0] \n__________________________________________________________________________________________________\nmixed7 (Concatenate) (None, 30, 30, 768) 0 activation_60[0][0] \n activation_63[0][0] \n activation_68[0][0] \n activation_69[0][0] \n__________________________________________________________________________________________________\nconv2d_72 (Conv2D) (None, 30, 30, 192) 147456 mixed7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_72 (BatchNo (None, 30, 30, 192) 576 conv2d_72[0][0] \n__________________________________________________________________________________________________\nactivation_72 (Activation) (None, 30, 30, 192) 0 batch_normalization_72[0][0] \n__________________________________________________________________________________________________\nconv2d_73 (Conv2D) (None, 30, 30, 192) 258048 activation_72[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_73 (BatchNo (None, 30, 30, 192) 576 conv2d_73[0][0] \n__________________________________________________________________________________________________\nactivation_73 (Activation) (None, 30, 30, 192) 0 batch_normalization_73[0][0] \n__________________________________________________________________________________________________\nconv2d_70 (Conv2D) (None, 30, 30, 192) 147456 mixed7[0][0] \n__________________________________________________________________________________________________\nconv2d_74 (Conv2D) (None, 30, 30, 192) 258048 activation_73[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_70 (BatchNo (None, 30, 30, 192) 576 conv2d_70[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_74 (BatchNo (None, 30, 30, 192) 576 conv2d_74[0][0] \n__________________________________________________________________________________________________\nactivation_70 (Activation) (None, 30, 30, 192) 0 batch_normalization_70[0][0] \n__________________________________________________________________________________________________\nactivation_74 (Activation) (None, 30, 30, 192) 0 batch_normalization_74[0][0] \n__________________________________________________________________________________________________\nconv2d_71 (Conv2D) (None, 14, 14, 320) 552960 activation_70[0][0] \n__________________________________________________________________________________________________\nconv2d_75 (Conv2D) (None, 14, 14, 192) 331776 activation_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_71 (BatchNo (None, 14, 14, 320) 960 conv2d_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_75 (BatchNo (None, 14, 14, 192) 576 conv2d_75[0][0] \n__________________________________________________________________________________________________\nactivation_71 (Activation) (None, 14, 14, 320) 0 batch_normalization_71[0][0] \n__________________________________________________________________________________________________\nactivation_75 (Activation) (None, 14, 14, 192) 0 batch_normalization_75[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_3 (MaxPooling2D) (None, 14, 14, 768) 0 mixed7[0][0] \n__________________________________________________________________________________________________\nmixed8 (Concatenate) (None, 14, 14, 1280) 0 activation_71[0][0] \n activation_75[0][0] \n max_pooling2d_3[0][0] \n__________________________________________________________________________________________________\nconv2d_80 (Conv2D) (None, 14, 14, 448) 573440 mixed8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_80 (BatchNo (None, 14, 14, 448) 1344 conv2d_80[0][0] \n__________________________________________________________________________________________________\nactivation_80 (Activation) (None, 14, 14, 448) 0 batch_normalization_80[0][0] \n__________________________________________________________________________________________________\nconv2d_77 (Conv2D) (None, 14, 14, 384) 491520 mixed8[0][0] \n__________________________________________________________________________________________________\nconv2d_81 (Conv2D) (None, 14, 14, 384) 1548288 activation_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_77 (BatchNo (None, 14, 14, 384) 1152 conv2d_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_81 (BatchNo (None, 14, 14, 384) 1152 conv2d_81[0][0] \n__________________________________________________________________________________________________\nactivation_77 (Activation) (None, 14, 14, 384) 0 batch_normalization_77[0][0] \n__________________________________________________________________________________________________\nactivation_81 (Activation) (None, 14, 14, 384) 0 batch_normalization_81[0][0] \n__________________________________________________________________________________________________\nconv2d_78 (Conv2D) (None, 14, 14, 384) 442368 activation_77[0][0] \n__________________________________________________________________________________________________\nconv2d_79 (Conv2D) (None, 14, 14, 384) 442368 activation_77[0][0] \n__________________________________________________________________________________________________\nconv2d_82 (Conv2D) (None, 14, 14, 384) 442368 activation_81[0][0] \n__________________________________________________________________________________________________\nconv2d_83 (Conv2D) (None, 14, 14, 384) 442368 activation_81[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d_7 (AveragePoo (None, 14, 14, 1280) 0 mixed8[0][0] \n__________________________________________________________________________________________________\nconv2d_76 (Conv2D) (None, 14, 14, 320) 409600 mixed8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_78 (BatchNo (None, 14, 14, 384) 1152 conv2d_78[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_79 (BatchNo (None, 14, 14, 384) 1152 conv2d_79[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_82 (BatchNo (None, 14, 14, 384) 1152 conv2d_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_83 (BatchNo (None, 14, 14, 384) 1152 conv2d_83[0][0] \n__________________________________________________________________________________________________\nconv2d_84 (Conv2D) (None, 14, 14, 192) 245760 average_pooling2d_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_76 (BatchNo (None, 14, 14, 320) 960 conv2d_76[0][0] \n__________________________________________________________________________________________________\nactivation_78 (Activation) (None, 14, 14, 384) 0 batch_normalization_78[0][0] \n__________________________________________________________________________________________________\nactivation_79 (Activation) (None, 14, 14, 384) 0 batch_normalization_79[0][0] \n__________________________________________________________________________________________________\nactivation_82 (Activation) (None, 14, 14, 384) 0 batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nactivation_83 (Activation) (None, 14, 14, 384) 0 batch_normalization_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_84 (BatchNo (None, 14, 14, 192) 576 conv2d_84[0][0] \n__________________________________________________________________________________________________\nactivation_76 (Activation) (None, 14, 14, 320) 0 batch_normalization_76[0][0] \n__________________________________________________________________________________________________\nmixed9_0 (Concatenate) (None, 14, 14, 768) 0 activation_78[0][0] \n activation_79[0][0] \n__________________________________________________________________________________________________\nconcatenate (Concatenate) (None, 14, 14, 768) 0 activation_82[0][0] \n activation_83[0][0] \n__________________________________________________________________________________________________\nactivation_84 (Activation) (None, 14, 14, 192) 0 batch_normalization_84[0][0] \n__________________________________________________________________________________________________\nmixed9 (Concatenate) (None, 14, 14, 2048) 0 activation_76[0][0] \n mixed9_0[0][0] \n concatenate[0][0] \n activation_84[0][0] \n__________________________________________________________________________________________________\nconv2d_89 (Conv2D) (None, 14, 14, 448) 917504 mixed9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_89 (BatchNo (None, 14, 14, 448) 1344 conv2d_89[0][0] \n__________________________________________________________________________________________________\nactivation_89 (Activation) (None, 14, 14, 448) 0 batch_normalization_89[0][0] \n__________________________________________________________________________________________________\nconv2d_86 (Conv2D) (None, 14, 14, 384) 786432 mixed9[0][0] \n__________________________________________________________________________________________________\nconv2d_90 (Conv2D) (None, 14, 14, 384) 1548288 activation_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_86 (BatchNo (None, 14, 14, 384) 1152 conv2d_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_90 (BatchNo (None, 14, 14, 384) 1152 conv2d_90[0][0] \n__________________________________________________________________________________________________\nactivation_86 (Activation) (None, 14, 14, 384) 0 batch_normalization_86[0][0] \n__________________________________________________________________________________________________\nactivation_90 (Activation) (None, 14, 14, 384) 0 batch_normalization_90[0][0] \n__________________________________________________________________________________________________\nconv2d_87 (Conv2D) (None, 14, 14, 384) 442368 activation_86[0][0] \n__________________________________________________________________________________________________\nconv2d_88 (Conv2D) (None, 14, 14, 384) 442368 activation_86[0][0] \n__________________________________________________________________________________________________\nconv2d_91 (Conv2D) (None, 14, 14, 384) 442368 activation_90[0][0] \n__________________________________________________________________________________________________\nconv2d_92 (Conv2D) (None, 14, 14, 384) 442368 activation_90[0][0] \n__________________________________________________________________________________________________\naverage_pooling2d_8 (AveragePoo (None, 14, 14, 2048) 0 mixed9[0][0] \n__________________________________________________________________________________________________\nconv2d_85 (Conv2D) (None, 14, 14, 320) 655360 mixed9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_87 (BatchNo (None, 14, 14, 384) 1152 conv2d_87[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_88 (BatchNo (None, 14, 14, 384) 1152 conv2d_88[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_91 (BatchNo (None, 14, 14, 384) 1152 conv2d_91[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_92 (BatchNo (None, 14, 14, 384) 1152 conv2d_92[0][0] \n__________________________________________________________________________________________________\nconv2d_93 (Conv2D) (None, 14, 14, 192) 393216 average_pooling2d_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_85 (BatchNo (None, 14, 14, 320) 960 conv2d_85[0][0] \n__________________________________________________________________________________________________\nactivation_87 (Activation) (None, 14, 14, 384) 0 batch_normalization_87[0][0] \n__________________________________________________________________________________________________\nactivation_88 (Activation) (None, 14, 14, 384) 0 batch_normalization_88[0][0] \n__________________________________________________________________________________________________\nactivation_91 (Activation) (None, 14, 14, 384) 0 batch_normalization_91[0][0] \n__________________________________________________________________________________________________\nactivation_92 (Activation) (None, 14, 14, 384) 0 batch_normalization_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_93 (BatchNo (None, 14, 14, 192) 576 conv2d_93[0][0] \n__________________________________________________________________________________________________\nactivation_85 (Activation) (None, 14, 14, 320) 0 batch_normalization_85[0][0] \n__________________________________________________________________________________________________\nmixed9_1 (Concatenate) (None, 14, 14, 768) 0 activation_87[0][0] \n activation_88[0][0] \n__________________________________________________________________________________________________\nconcatenate_1 (Concatenate) (None, 14, 14, 768) 0 activation_91[0][0] \n activation_92[0][0] \n__________________________________________________________________________________________________\nactivation_93 (Activation) (None, 14, 14, 192) 0 batch_normalization_93[0][0] \n__________________________________________________________________________________________________\nmixed10 (Concatenate) (None, 14, 14, 2048) 0 activation_85[0][0] \n mixed9_1[0][0] \n concatenate_1[0][0] \n activation_93[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d (Globa (None, 2048) 0 mixed10[0][0] \n__________________________________________________________________________________________________\ndropout (Dropout) (None, 2048) 0 global_average_pooling2d[0][0] \n__________________________________________________________________________________________________\ndense (Dense) (None, 2048) 4196352 dropout[0][0] \n__________________________________________________________________________________________________\ndropout_1 (Dropout) (None, 2048) 0 dense[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 5) 10245 dropout_1[0][0] \n==================================================================================================\nTotal params: 26,009,381\nTrainable params: 25,974,949\nNon-trainable params: 34,432\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"history_finetunning = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=EPOCHS,\n callbacks=callback_list,\n verbose=1).history","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-08-23T16:32:38.504983Z","iopub.execute_input":"2022-08-23T16:32:38.505255Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"Epoch 1/20\n366/366 [==============================] - 452s 1s/step - loss: 0.9715 - accuracy: 0.6740 - val_loss: 0.6117 - val_accuracy: 0.7788\nEpoch 2/20\n366/366 [==============================] - 444s 1s/step - loss: 0.5647 - accuracy: 0.7976 - val_loss: 0.5311 - val_accuracy: 0.8077\nEpoch 3/20\n366/366 [==============================] - 444s 1s/step - loss: 0.5154 - accuracy: 0.8059 - val_loss: 0.7381 - val_accuracy: 0.7734\nEpoch 4/20\n366/366 [==============================] - 447s 1s/step - loss: 0.4154 - accuracy: 0.8455 - val_loss: 0.4878 - val_accuracy: 0.8338\nEpoch 5/20\n253/366 [===================>..........] - ETA: 1:48 - loss: 0.3839 - accuracy: 0.8597","output_type":"stream"}]},{"cell_type":"markdown","source":"# Model loss graph ","metadata":{}},{"cell_type":"code","source":"history = {'loss': history_warmup['loss'] + history_finetunning['loss'], \n 'val_loss': history_warmup['val_loss'] + history_finetunning['val_loss'], \n 'accuracy': history_warmup['accuracy'] + history_finetunning['accuracy'], \n 'val_accuracy': history_warmup['val_accuracy'] + history_finetunning['val_accuracy']}\n\nsns.set_style(\"whitegrid\")\nfig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 14))\n\nax1.plot(history['loss'], label='Train loss')\nax1.plot(history['val_loss'], label='Validation loss')\nax1.legend(loc='best')\nax1.set_title('Loss')\n\nax2.plot(history['accuracy'], label='Train Accuracy')\nax2.plot(history['val_accuracy'], label='Validation accuracy')\nax2.legend(loc='best')\nax2.set_title('Accuracy')\n\nplt.xlabel('Epochs')\nsns.despine()\nplt.show()","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.save(\"./my_model.h5\")","metadata":{"execution":{"iopub.status.busy":"2022-08-23T16:14:16.437055Z","iopub.status.idle":"2022-08-23T16:14:16.437906Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model Evaluation","metadata":{}},{"cell_type":"code","source":"complete_datagen = ImageDataGenerator(rescale=1./255)\ncomplete_generator = complete_datagen.flow_from_dataframe( \n dataframe=train,\n directory = \"../input/aptos2019-blindness-detection/train_images/\",\n x_col=\"id_code\",\n target_size=(HEIGHT, WIDTH),\n batch_size=1,\n shuffle=False,\n class_mode=None)\n\nSTEP_SIZE_COMPLETE = complete_generator.n//complete_generator.batch_size\ntrain_preds = model.predict_generator(complete_generator, steps=STEP_SIZE_COMPLETE)\ntrain_preds = [np.argmax(pred) for pred in train_preds]","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Confusion Matrix","metadata":{}},{"cell_type":"code","source":"labels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\ncnf_matrix = confusion_matrix(train['diagnosis'].astype('int'), train_preds)\ncnf_matrix_norm = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:, np.newaxis]\ndf_cm = pd.DataFrame(cnf_matrix_norm, index=labels, columns=labels)\nplt.figure(figsize=(16, 7))\nsns.heatmap(df_cm, annot=True, fmt='.2f', cmap=\"Blues\")\nplt.show()","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(train['diagnosis'].astype('int'), train_preds))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Quadratic Weighted Kappa","metadata":{}},{"cell_type":"code","source":"print(\"Train Cohen Kappa score: %.3f\" % cohen_kappa_score(train_preds, train['diagnosis'].astype('int'), weights='quadratic'))","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Apply model to test set and output predictions","metadata":{}},{"cell_type":"code","source":"test_generator.reset()\nSTEP_SIZE_TEST = test_generator.n//test_generator.batch_size\npreds = model.predict_generator(test_generator, steps=STEP_SIZE_TEST,verbose =1)\npredictions = [np.argmax(pred) for pred in preds]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"filenames = test_generator.filenames\nresults = pd.DataFrame({'id_code':filenames, 'diagnosis':predictions})\nresults['id_code'] = results['id_code'].map(lambda x: str(x)[:-4])\nresults.to_csv('submission.csv',index=False)\nresults.head(10)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Predictions class distribution","metadata":{}},{"cell_type":"code","source":"f, ax = plt.subplots(figsize=(14, 8.7))\nax = sns.countplot(x=\"diagnosis\", data=results, palette=\"GnBu_d\")\nsns.despine()\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-08-08T10:21:23.352476Z","iopub.execute_input":"2021-08-08T10:21:23.352797Z","iopub.status.idle":"2021-08-08T10:21:23.518607Z","shell.execute_reply.started":"2021-08-08T10:21:23.352766Z","shell.execute_reply":"2021-08-08T10:21:23.517509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"my_model = load_model(\"./my_model.h5\")\nmy_model.summary()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T10:22:29.161684Z","iopub.execute_input":"2021-08-08T10:22:29.162035Z","iopub.status.idle":"2021-08-08T10:22:31.427042Z","shell.execute_reply.started":"2021-08-08T10:22:29.161981Z","shell.execute_reply":"2021-08-08T10:22:31.426246Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}