{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":14774,"databundleVersionId":875431,"sourceType":"competition"}],"dockerImageVersionId":31193,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# Step 1: Load the labels from the CSV file\nimport pandas as pd\nimport os\nimport numpy as np\nfrom PIL import Image\nfrom tensorflow.keras.utils import to_categorical\nimage_width = 128\nimage_height = 128\ndataset_path = '/kaggle/input/aptos2019-blindness-detection/'\ntry:\n labels_df = pd.read_csv(dataset_path + 'train.csv')\n print(labels_df)\nexcept FileNotFoundError:\n print(\"Error: Your labels CSV file not found. Please make sure the path is correct.\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:38:35.510803Z","iopub.execute_input":"2025-12-06T07:38:35.511361Z","iopub.status.idle":"2025-12-06T07:38:35.52383Z","shell.execute_reply.started":"2025-12-06T07:38:35.511336Z","shell.execute_reply":"2025-12-06T07:38:35.522992Z"}},"outputs":[{"name":"stdout","text":" id_code diagnosis\n0 000c1434d8d7 2\n1 001639a390f0 4\n2 0024cdab0c1e 1\n3 002c21358ce6 0\n4 005b95c28852 0\n... ... ...\n3657 ffa47f6a7bf4 2\n3658 ffc04fed30e6 0\n3659 ffcf7b45f213 2\n3660 ffd97f8cd5aa 0\n3661 ffec9a18a3ce 2\n\n[3662 rows x 2 columns]\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"image_width = 128\nimage_height = 128\nsplit_ratio = 0.2\nseed = 42\n\nnum_classes = 5\n\nimgnet_rgb_mean = [123.68, 116.779, 103.939]\nimgnet_rgb_mean_resnet = [0.485, 0.456, 0.406]\nimgnet_rgb_stddev_resnet = [0.229, 0.224, 0.225]\n\n\nbatch_size = 32\nnum_epochs = 20\nlr = 1e-4","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:38:35.524997Z","iopub.execute_input":"2025-12-06T07:38:35.525187Z","iopub.status.idle":"2025-12-06T07:38:35.539255Z","shell.execute_reply.started":"2025-12-06T07:38:35.525172Z","shell.execute_reply":"2025-12-06T07:38:35.538629Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"import tensorflow as tf\n#Process training images\ndef preproc_dataset(dataset_path, image_height, image_width):\n image_list = []\n y_labels = [] # List to store corresponding labels\n image_dir = dataset_path + 'train_images/'\n label_dict = labels_df.set_index('id_code')['diagnosis'].to_dict()\n for filename in os.listdir(image_dir):\n # Extract image ID from filename (assuming filename format is 'image_id.jpg')\n image_id = os.path.splitext(filename)[0]\n if filename.endswith(('.jpg', '.png', '.jpeg')) and image_id in label_dict: # Add more image extensions if needed and check if label exists\n img_path = os.path.join(image_dir, filename)\n try:\n img = Image.open(img_path)\n img = img.resize((image_width, image_height))\n image_list.append(img)\n y_labels.append(label_dict[image_id]) # Get the label from the dictionary\n except Exception as e:\n print(f\"Error loading image {filename}: {e}\")\n\n all_images = np.array(image_list)\n all_labels = np.array(y_labels)\n return all_images, all_labels","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:38:35.539902Z","iopub.execute_input":"2025-12-06T07:38:35.540088Z","iopub.status.idle":"2025-12-06T07:38:35.558077Z","shell.execute_reply.started":"2025-12-06T07:38:35.540073Z","shell.execute_reply":"2025-12-06T07:38:35.557256Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras import layers, models\nfrom sklearn.metrics import classification_report # Import classification_report\nimport numpy as np # Import numpy\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom tensorflow.keras.applications.resnet50 import preprocess_input","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:38:35.559433Z","iopub.execute_input":"2025-12-06T07:38:35.559614Z","iopub.status.idle":"2025-12-06T07:38:35.584044Z","shell.execute_reply.started":"2025-12-06T07:38:35.5596Z","shell.execute_reply":"2025-12-06T07:38:35.583444Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# Step 1: Split the dataset into training and testing sets\nfrom sklearn.model_selection import train_test_split\n\n# Read and preprocess images.\nall_images, all_labels = preproc_dataset(dataset_path, image_height, image_width)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:38:35.584614Z","iopub.execute_input":"2025-12-06T07:38:35.584869Z","iopub.status.idle":"2025-12-06T07:46:22.870528Z","shell.execute_reply.started":"2025-12-06T07:38:35.584845Z","shell.execute_reply":"2025-12-06T07:46:22.869876Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"class_weights = compute_class_weight(\n class_weight='balanced',\n classes=np.unique(all_labels), \n y=all_labels\n)\nprint(class_weights)\nclass_weights = dict(enumerate(class_weights))\n\n\nall_labels = to_categorical(all_labels)\n\nall_images = preprocess_input(all_images)\n \n\n# Split the data (e.g., 80% train, 20% test split)\n# random_state is used for reproducibility\nx_train, x_test, y_train, y_test = train_test_split(\n all_images, all_labels, test_size=0.2, random_state=42\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:46:22.871557Z","iopub.execute_input":"2025-12-06T07:46:22.871875Z","iopub.status.idle":"2025-12-06T07:46:23.839804Z","shell.execute_reply.started":"2025-12-06T07:46:22.871854Z","shell.execute_reply":"2025-12-06T07:46:23.838993Z"}},"outputs":[{"name":"stdout","text":"[0.40576177 1.97945946 0.73313313 3.79481865 2.48271186]\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"# How to do data augmentation using ImageDataGenerator\n\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\n# Create an ImageDataGenerator with desired augmentation parameters\ndatagen = ImageDataGenerator(\n rotation_range=20, # Rotate images by up to 20 degrees\n width_shift_range=0.1, # Shift images horizontally by up to 10% of the width\n height_shift_range=0.1, # Shift images vertically by up to 10% of the height\n shear_range=0.1, # Apply shear transformation\n zoom_range=0.1, # Zoom in or out by up to 10%\n horizontal_flip=True, # Randomly flip images horizontall\n)\n\n# Fit the data generator on your training data\ndatagen.fit(x_train)\n\n# Now you can use the datagen.flow() method or pass the generator directly to model.fit()\n# Example of using it with model.fit():\n# history = model.fit(datagen.flow(x_train, y_train, batch_size=32),\n# steps_per_epoch=len(x_train) // 32, # Or calculate based on your batch size\n# epochs=20,\n# validation_data=(x_test, y_test))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:46:23.840799Z","iopub.execute_input":"2025-12-06T07:46:23.841136Z","iopub.status.idle":"2025-12-06T07:46:24.042253Z","shell.execute_reply.started":"2025-12-06T07:46:23.841111Z","shell.execute_reply":"2025-12-06T07:46:24.04166Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"# Step 4: Implement ResNet baseline for fine-tuning\n\n\n# Load the pre-trained ResNet50 model, excluding the top classification layer\nbase_model_resnet = ResNet50(weights='imagenet', include_top=False, input_shape=(image_height, image_width, 3)) # Assuming your image size is 128x128x3\n# Freeze all layers initially\n# base_model_resnet.trainable = False\nset_trainable = False\n\nfor layer in base_model_resnet.layers:\n if 'conv5' in layer.name or 'conv4' in layer.name:\n layer.trainable = True\n else:\n layer.trainable = False\n\nbase_model_resnet.summary()\n# Add a new classification head for 5 classes\n#batch * 7 * 7 * 2048\n#batch * 1 * 1 * 2048\n#batch * 1 * 1 * 1024\nx = GlobalAveragePooling2D(name=\"avg_pool\")(base_model_resnet.output)\nx = Dense(1024)(x)\nx = layers.BatchNormalization()(x)\nx = layers.Activation('relu')(x)\n\n##batch * 1 * 1 * 576\nx = Dense(576)(x)\nx = layers.BatchNormalization()(x)\nx = layers.Activation('relu')(x)\n\nx = Dense(5, activation='softmax', use_bias=False)(x) # Final Dense layer for 5 classes with softmax activation\n\n# Create the new model\nmodel_resnet = Model(inputs=base_model_resnet.input, outputs=x)\n\nmodel_resnet.summary()\n# Unfreeze the last few convolutional blocks for fine-tuning\n# # You can experiment with how many layers to unfreeze\n# for layer in base_model_resnet.layers[-30:]: # Example: unfreeze the last 30 layers\n# if not isinstance(layer, tf.keras.layers.BatchNormalization): # It's common practice to keep BatchNormalization layers frozen\n# layer.trainable = True\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:46:24.043636Z","iopub.execute_input":"2025-12-06T07:46:24.043843Z","iopub.status.idle":"2025-12-06T07:46:25.538199Z","shell.execute_reply.started":"2025-12-06T07:46:24.043829Z","shell.execute_reply":"2025-12-06T07:46:25.537687Z"}},"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m94765736/94765736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"resnet50\"\u001b[0m\n","text/html":"Model: \"resnet50\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m134\u001b[0m, \u001b[38;5;34m134\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, │ \u001b[38;5;34m9,472\u001b[0m │ conv1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv1_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m4,160\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m32,896\u001b[0m │ conv2_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m131,584\u001b[0m │ conv2_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block4_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block4_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m131,328\u001b[0m │ conv3_block4_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m525,312\u001b[0m │ conv3_block4_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block4_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block4_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block4_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block5_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block5_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block5_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block5_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block5_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block6_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block6_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block6_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block6_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m524,800\u001b[0m │ conv4_block6_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m2,099,200\u001b[0m │ conv4_block6_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer │ (None, 128, 128, │ 0 │ - │\n│ (InputLayer) │ 3) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_pad │ (None, 134, 134, │ 0 │ input_layer[0][0] │\n│ (ZeroPadding2D) │ 3) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_conv (Conv2D) │ (None, 64, 64, │ 9,472 │ conv1_pad[0][0] │\n│ │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_bn │ (None, 64, 64, │ 256 │ conv1_conv[0][0] │\n│ (BatchNormalizatio… │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_relu │ (None, 64, 64, │ 0 │ conv1_bn[0][0] │\n│ (Activation) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pad │ (None, 66, 66, │ 0 │ conv1_relu[0][0] │\n│ (ZeroPadding2D) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pool │ (None, 32, 32, │ 0 │ pool1_pad[0][0] │\n│ (MaxPooling2D) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_conv │ (None, 32, 32, │ 4,160 │ pool1_pool[0][0] │\n│ (Conv2D) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_bn │ (None, 32, 32, │ 256 │ conv2_block1_1_c… │\n│ (BatchNormalizatio… │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_relu │ (None, 32, 32, │ 0 │ conv2_block1_1_b… │\n│ (Activation) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_conv │ (None, 32, 32, │ 36,928 │ conv2_block1_1_r… │\n│ (Conv2D) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_bn │ (None, 32, 32, │ 256 │ conv2_block1_2_c… │\n│ (BatchNormalizatio… │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_relu │ (None, 32, 32, │ 0 │ conv2_block1_2_b… │\n│ (Activation) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_conv │ (None, 32, 32, │ 16,640 │ pool1_pool[0][0] │\n│ (Conv2D) │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_conv │ (None, 32, 32, │ 16,640 │ conv2_block1_2_r… │\n│ (Conv2D) │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_bn │ (None, 32, 32, │ 1,024 │ conv2_block1_0_c… │\n│ (BatchNormalizatio… │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_bn │ (None, 32, 32, │ 1,024 │ conv2_block1_3_c… │\n│ (BatchNormalizatio… │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_add │ (None, 32, 32, │ 0 │ conv2_block1_0_b… │\n│ (Add) │ 256) │ │ conv2_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_out │ (None, 32, 32, │ 0 │ conv2_block1_add… │\n│ (Activation) │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_conv │ (None, 32, 32, │ 16,448 │ conv2_block1_out… │\n│ (Conv2D) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_bn │ (None, 32, 32, │ 256 │ conv2_block2_1_c… │\n│ (BatchNormalizatio… │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_relu │ (None, 32, 32, │ 0 │ conv2_block2_1_b… │\n│ (Activation) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_conv │ (None, 32, 32, │ 36,928 │ conv2_block2_1_r… │\n│ (Conv2D) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_bn │ (None, 32, 32, │ 256 │ conv2_block2_2_c… │\n│ (BatchNormalizatio… │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_relu │ (None, 32, 32, │ 0 │ conv2_block2_2_b… │\n│ (Activation) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_conv │ (None, 32, 32, │ 16,640 │ conv2_block2_2_r… │\n│ (Conv2D) │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_bn │ (None, 32, 32, │ 1,024 │ conv2_block2_3_c… │\n│ (BatchNormalizatio… │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_add │ (None, 32, 32, │ 0 │ conv2_block1_out… │\n│ (Add) │ 256) │ │ conv2_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_out │ (None, 32, 32, │ 0 │ conv2_block2_add… │\n│ (Activation) │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_conv │ (None, 32, 32, │ 16,448 │ conv2_block2_out… │\n│ (Conv2D) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_bn │ (None, 32, 32, │ 256 │ conv2_block3_1_c… │\n│ (BatchNormalizatio… │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_relu │ (None, 32, 32, │ 0 │ conv2_block3_1_b… │\n│ (Activation) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_conv │ (None, 32, 32, │ 36,928 │ conv2_block3_1_r… │\n│ (Conv2D) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_bn │ (None, 32, 32, │ 256 │ conv2_block3_2_c… │\n│ (BatchNormalizatio… │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_relu │ (None, 32, 32, │ 0 │ conv2_block3_2_b… │\n│ (Activation) │ 64) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_conv │ (None, 32, 32, │ 16,640 │ conv2_block3_2_r… │\n│ (Conv2D) │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_bn │ (None, 32, 32, │ 1,024 │ conv2_block3_3_c… │\n│ (BatchNormalizatio… │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_add │ (None, 32, 32, │ 0 │ conv2_block2_out… │\n│ (Add) │ 256) │ │ conv2_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_out │ (None, 32, 32, │ 0 │ conv2_block3_add… │\n│ (Activation) │ 256) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_conv │ (None, 16, 16, │ 32,896 │ conv2_block3_out… │\n│ (Conv2D) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_bn │ (None, 16, 16, │ 512 │ conv3_block1_1_c… │\n│ (BatchNormalizatio… │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_relu │ (None, 16, 16, │ 0 │ conv3_block1_1_b… │\n│ (Activation) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_conv │ (None, 16, 16, │ 147,584 │ conv3_block1_1_r… │\n│ (Conv2D) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_bn │ (None, 16, 16, │ 512 │ conv3_block1_2_c… │\n│ (BatchNormalizatio… │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_relu │ (None, 16, 16, │ 0 │ conv3_block1_2_b… │\n│ (Activation) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_conv │ (None, 16, 16, │ 131,584 │ conv2_block3_out… │\n│ (Conv2D) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_conv │ (None, 16, 16, │ 66,048 │ conv3_block1_2_r… │\n│ (Conv2D) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_bn │ (None, 16, 16, │ 2,048 │ conv3_block1_0_c… │\n│ (BatchNormalizatio… │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_bn │ (None, 16, 16, │ 2,048 │ conv3_block1_3_c… │\n│ (BatchNormalizatio… │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_add │ (None, 16, 16, │ 0 │ conv3_block1_0_b… │\n│ (Add) │ 512) │ │ conv3_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_out │ (None, 16, 16, │ 0 │ conv3_block1_add… │\n│ (Activation) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_conv │ (None, 16, 16, │ 65,664 │ conv3_block1_out… │\n│ (Conv2D) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_bn │ (None, 16, 16, │ 512 │ conv3_block2_1_c… │\n│ (BatchNormalizatio… │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_relu │ (None, 16, 16, │ 0 │ conv3_block2_1_b… │\n│ (Activation) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_conv │ (None, 16, 16, │ 147,584 │ conv3_block2_1_r… │\n│ (Conv2D) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_bn │ (None, 16, 16, │ 512 │ conv3_block2_2_c… │\n│ (BatchNormalizatio… │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_relu │ (None, 16, 16, │ 0 │ conv3_block2_2_b… │\n│ (Activation) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_conv │ (None, 16, 16, │ 66,048 │ conv3_block2_2_r… │\n│ (Conv2D) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_bn │ (None, 16, 16, │ 2,048 │ conv3_block2_3_c… │\n│ (BatchNormalizatio… │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_add │ (None, 16, 16, │ 0 │ conv3_block1_out… │\n│ (Add) │ 512) │ │ conv3_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_out │ (None, 16, 16, │ 0 │ conv3_block2_add… │\n│ (Activation) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_conv │ (None, 16, 16, │ 65,664 │ conv3_block2_out… │\n│ (Conv2D) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_bn │ (None, 16, 16, │ 512 │ conv3_block3_1_c… │\n│ (BatchNormalizatio… │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_relu │ (None, 16, 16, │ 0 │ conv3_block3_1_b… │\n│ (Activation) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_conv │ (None, 16, 16, │ 147,584 │ conv3_block3_1_r… │\n│ (Conv2D) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_bn │ (None, 16, 16, │ 512 │ conv3_block3_2_c… │\n│ (BatchNormalizatio… │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_relu │ (None, 16, 16, │ 0 │ conv3_block3_2_b… │\n│ (Activation) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_conv │ (None, 16, 16, │ 66,048 │ conv3_block3_2_r… │\n│ (Conv2D) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_bn │ (None, 16, 16, │ 2,048 │ conv3_block3_3_c… │\n│ (BatchNormalizatio… │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_add │ (None, 16, 16, │ 0 │ conv3_block2_out… │\n│ (Add) │ 512) │ │ conv3_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_out │ (None, 16, 16, │ 0 │ conv3_block3_add… │\n│ (Activation) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_conv │ (None, 16, 16, │ 65,664 │ conv3_block3_out… │\n│ (Conv2D) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_bn │ (None, 16, 16, │ 512 │ conv3_block4_1_c… │\n│ (BatchNormalizatio… │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_relu │ (None, 16, 16, │ 0 │ conv3_block4_1_b… │\n│ (Activation) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_conv │ (None, 16, 16, │ 147,584 │ conv3_block4_1_r… │\n│ (Conv2D) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_bn │ (None, 16, 16, │ 512 │ conv3_block4_2_c… │\n│ (BatchNormalizatio… │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_relu │ (None, 16, 16, │ 0 │ conv3_block4_2_b… │\n│ (Activation) │ 128) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_conv │ (None, 16, 16, │ 66,048 │ conv3_block4_2_r… │\n│ (Conv2D) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_bn │ (None, 16, 16, │ 2,048 │ conv3_block4_3_c… │\n│ (BatchNormalizatio… │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_add │ (None, 16, 16, │ 0 │ conv3_block3_out… │\n│ (Add) │ 512) │ │ conv3_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_out │ (None, 16, 16, │ 0 │ conv3_block4_add… │\n│ (Activation) │ 512) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_conv │ (None, 8, 8, 256) │ 131,328 │ conv3_block4_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block1_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block1_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block1_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block1_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block1_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_conv │ (None, 8, 8, │ 525,312 │ conv3_block4_out… │\n│ (Conv2D) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_conv │ (None, 8, 8, │ 263,168 │ conv4_block1_2_r… │\n│ (Conv2D) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_bn │ (None, 8, 8, │ 4,096 │ conv4_block1_0_c… │\n│ (BatchNormalizatio… │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_bn │ (None, 8, 8, │ 4,096 │ conv4_block1_3_c… │\n│ (BatchNormalizatio… │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_add │ (None, 8, 8, │ 0 │ conv4_block1_0_b… │\n│ (Add) │ 1024) │ │ conv4_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_out │ (None, 8, 8, │ 0 │ conv4_block1_add… │\n│ (Activation) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block1_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block2_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block2_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block2_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block2_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block2_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_conv │ (None, 8, 8, │ 263,168 │ conv4_block2_2_r… │\n│ (Conv2D) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_bn │ (None, 8, 8, │ 4,096 │ conv4_block2_3_c… │\n│ (BatchNormalizatio… │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_add │ (None, 8, 8, │ 0 │ conv4_block1_out… │\n│ (Add) │ 1024) │ │ conv4_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_out │ (None, 8, 8, │ 0 │ conv4_block2_add… │\n│ (Activation) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block2_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block3_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block3_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block3_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block3_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block3_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_conv │ (None, 8, 8, │ 263,168 │ conv4_block3_2_r… │\n│ (Conv2D) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_bn │ (None, 8, 8, │ 4,096 │ conv4_block3_3_c… │\n│ (BatchNormalizatio… │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_add │ (None, 8, 8, │ 0 │ conv4_block2_out… │\n│ (Add) │ 1024) │ │ conv4_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_out │ (None, 8, 8, │ 0 │ conv4_block3_add… │\n│ (Activation) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block3_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block4_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block4_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block4_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block4_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block4_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_3_conv │ (None, 8, 8, │ 263,168 │ conv4_block4_2_r… │\n│ (Conv2D) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_3_bn │ (None, 8, 8, │ 4,096 │ conv4_block4_3_c… │\n│ (BatchNormalizatio… │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_add │ (None, 8, 8, │ 0 │ conv4_block3_out… │\n│ (Add) │ 1024) │ │ conv4_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_out │ (None, 8, 8, │ 0 │ conv4_block4_add… │\n│ (Activation) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block4_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block5_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block5_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block5_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block5_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block5_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_3_conv │ (None, 8, 8, │ 263,168 │ conv4_block5_2_r… │\n│ (Conv2D) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_3_bn │ (None, 8, 8, │ 4,096 │ conv4_block5_3_c… │\n│ (BatchNormalizatio… │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_add │ (None, 8, 8, │ 0 │ conv4_block4_out… │\n│ (Add) │ 1024) │ │ conv4_block5_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_out │ (None, 8, 8, │ 0 │ conv4_block5_add… │\n│ (Activation) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_conv │ (None, 8, 8, 256) │ 262,400 │ conv4_block5_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block6_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_relu │ (None, 8, 8, 256) │ 0 │ conv4_block6_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_conv │ (None, 8, 8, 256) │ 590,080 │ conv4_block6_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_bn │ (None, 8, 8, 256) │ 1,024 │ conv4_block6_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_relu │ (None, 8, 8, 256) │ 0 │ conv4_block6_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_3_conv │ (None, 8, 8, │ 263,168 │ conv4_block6_2_r… │\n│ (Conv2D) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_3_bn │ (None, 8, 8, │ 4,096 │ conv4_block6_3_c… │\n│ (BatchNormalizatio… │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_add │ (None, 8, 8, │ 0 │ conv4_block5_out… │\n│ (Add) │ 1024) │ │ conv4_block6_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_out │ (None, 8, 8, │ 0 │ conv4_block6_add… │\n│ (Activation) │ 1024) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_conv │ (None, 4, 4, 512) │ 524,800 │ conv4_block6_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block1_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_relu │ (None, 4, 4, 512) │ 0 │ conv5_block1_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_conv │ (None, 4, 4, 512) │ 2,359,808 │ conv5_block1_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block1_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_relu │ (None, 4, 4, 512) │ 0 │ conv5_block1_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_conv │ (None, 4, 4, │ 2,099,200 │ conv4_block6_out… │\n│ (Conv2D) │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_3_conv │ (None, 4, 4, │ 1,050,624 │ conv5_block1_2_r… │\n│ (Conv2D) │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_bn │ (None, 4, 4, │ 8,192 │ conv5_block1_0_c… │\n│ (BatchNormalizatio… │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_3_bn │ (None, 4, 4, │ 8,192 │ conv5_block1_3_c… │\n│ (BatchNormalizatio… │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_add │ (None, 4, 4, │ 0 │ conv5_block1_0_b… │\n│ (Add) │ 2048) │ │ conv5_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_out │ (None, 4, 4, │ 0 │ conv5_block1_add… │\n│ (Activation) │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_conv │ (None, 4, 4, 512) │ 1,049,088 │ conv5_block1_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block2_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_relu │ (None, 4, 4, 512) │ 0 │ conv5_block2_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_conv │ (None, 4, 4, 512) │ 2,359,808 │ conv5_block2_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block2_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_relu │ (None, 4, 4, 512) │ 0 │ conv5_block2_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_3_conv │ (None, 4, 4, │ 1,050,624 │ conv5_block2_2_r… │\n│ (Conv2D) │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_3_bn │ (None, 4, 4, │ 8,192 │ conv5_block2_3_c… │\n│ (BatchNormalizatio… │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_add │ (None, 4, 4, │ 0 │ conv5_block1_out… │\n│ (Add) │ 2048) │ │ conv5_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_out │ (None, 4, 4, │ 0 │ conv5_block2_add… │\n│ (Activation) │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_conv │ (None, 4, 4, 512) │ 1,049,088 │ conv5_block2_out… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block3_1_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_relu │ (None, 4, 4, 512) │ 0 │ conv5_block3_1_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_conv │ (None, 4, 4, 512) │ 2,359,808 │ conv5_block3_1_r… │\n│ (Conv2D) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_bn │ (None, 4, 4, 512) │ 2,048 │ conv5_block3_2_c… │\n│ (BatchNormalizatio… │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_relu │ (None, 4, 4, 512) │ 0 │ conv5_block3_2_b… │\n│ (Activation) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_3_conv │ (None, 4, 4, │ 1,050,624 │ conv5_block3_2_r… │\n│ (Conv2D) │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_3_bn │ (None, 4, 4, │ 8,192 │ conv5_block3_3_c… │\n│ (BatchNormalizatio… │ 2048) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_add │ (None, 4, 4, │ 0 │ conv5_block2_out… │\n│ (Add) │ 2048) │ │ conv5_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_out │ (None, 4, 4, │ 0 │ conv5_block3_add… │\n│ (Activation) │ 2048) │ │ │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m23,587,712\u001b[0m (89.98 MB)\n","text/html":" Total params: 23,587,712 (89.98 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m22,084,608\u001b[0m (84.25 MB)\n","text/html":" Trainable params: 22,084,608 (84.25 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,503,104\u001b[0m (5.73 MB)\n","text/html":" Non-trainable params: 1,503,104 (5.73 MB)\n
\n"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":" from tensorflow.keras.callbacks import EarlyStopping\n from tensorflow.keras.optimizers import AdamW\n early_stop = EarlyStopping(\n monitor='val_loss', # what metric to watch\n patience=10, # stop after 5 epochs with no improvement\n restore_best_weights=True # revert to best model\n )\n\n optimizer = AdamW(\n learning_rate=1e-4,\n weight_decay=1e-3\n )\n# Compile the model (only the new head's weights will be updated)\n model_resnet.compile(optimizer=optimizer, # Use the defined optimizer\n loss='categorical_crossentropy',\n metrics=['accuracy', 'recall'])\n\n \n \n # Train the Resnet model (only the newly added classification head)\n # Use the train_dataset\n history_resnet = model_resnet.fit(datagen.flow(x_train, y_train, batch_size=64), epochs=30, # Increased batch size to 64\n validation_data=(x_test, y_test), class_weight = class_weights) # Use test_dataset for evaluation\n\n print(\"Training completed. Evaluating on test data...\")\n\n loss, acc = model_resnet.evaluate(x_test, y_test, verbose=2)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-06T07:46:25.538825Z","iopub.execute_input":"2025-12-06T07:46:25.539055Z","iopub.status.idle":"2025-12-06T07:52:52.671552Z","shell.execute_reply.started":"2025-12-06T07:46:25.539031Z","shell.execute_reply":"2025-12-06T07:52:52.670687Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n self._warn_if_super_not_called()\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/30\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1765007207.189271 85 service.cc:148] XLA service 0x7d6dc8002ce0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1765007207.191941 85 service.cc:156] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\nI0000 00:00:1765007207.191960 85 service.cc:156] StreamExecutor device (1): Tesla T4, Compute Capability 7.5\nI0000 00:00:1765007210.536683 85 cuda_dnn.cc:529] Loaded cuDNN version 90300\nI0000 00:00:1765007223.133342 85 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 715ms/step - accuracy: 0.4450 - loss: 1.4908 - recall: 0.3094 - val_accuracy: 0.5593 - val_loss: 1.3644 - val_recall: 0.4993\nEpoch 2/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 241ms/step - accuracy: 0.7039 - loss: 0.8551 - recall: 0.6038 - val_accuracy: 0.6671 - val_loss: 0.9021 - val_recall: 0.5798\nEpoch 3/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 230ms/step - accuracy: 0.7551 - loss: 0.7823 - recall: 0.6724 - val_accuracy: 0.7012 - val_loss: 0.7871 - val_recall: 0.6412\nEpoch 4/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 235ms/step - accuracy: 0.8053 - loss: 0.6349 - recall: 0.7376 - val_accuracy: 0.7353 - val_loss: 0.7535 - val_recall: 0.6780\nEpoch 5/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 228ms/step - accuracy: 0.8592 - loss: 0.4822 - recall: 0.8137 - val_accuracy: 0.7367 - val_loss: 0.7538 - val_recall: 0.6917\nEpoch 6/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 230ms/step - accuracy: 0.8418 - loss: 0.5063 - recall: 0.7928 - val_accuracy: 0.7422 - val_loss: 0.7413 - val_recall: 0.6985\nEpoch 7/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 227ms/step - accuracy: 0.8749 - loss: 0.4068 - recall: 0.8463 - val_accuracy: 0.7503 - val_loss: 0.7804 - val_recall: 0.7258\nEpoch 8/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 240ms/step - accuracy: 0.8902 - loss: 0.3488 - recall: 0.8660 - val_accuracy: 0.7135 - val_loss: 0.8926 - val_recall: 0.6780\nEpoch 9/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 236ms/step - accuracy: 0.9012 - loss: 0.3263 - recall: 0.8848 - val_accuracy: 0.7381 - val_loss: 0.8024 - val_recall: 0.7162\nEpoch 10/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 236ms/step - accuracy: 0.9135 - loss: 0.2834 - recall: 0.8897 - val_accuracy: 0.7681 - val_loss: 0.8163 - val_recall: 0.7408\nEpoch 11/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 233ms/step - accuracy: 0.9215 - loss: 0.2530 - recall: 0.9043 - val_accuracy: 0.7763 - val_loss: 0.8009 - val_recall: 0.7585\nEpoch 12/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 233ms/step - accuracy: 0.9300 - loss: 0.2370 - recall: 0.9157 - val_accuracy: 0.7653 - val_loss: 0.8785 - val_recall: 0.7476\nEpoch 13/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 234ms/step - accuracy: 0.9394 - loss: 0.2016 - recall: 0.9312 - val_accuracy: 0.7531 - val_loss: 0.9366 - val_recall: 0.7435\nEpoch 14/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 238ms/step - accuracy: 0.9464 - loss: 0.1837 - recall: 0.9392 - val_accuracy: 0.7804 - val_loss: 0.9449 - val_recall: 0.7613\nEpoch 15/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 232ms/step - accuracy: 0.9496 - loss: 0.1749 - recall: 0.9424 - val_accuracy: 0.7531 - val_loss: 1.0472 - val_recall: 0.7394\nEpoch 16/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 233ms/step - accuracy: 0.9323 - loss: 0.2022 - recall: 0.9248 - val_accuracy: 0.7449 - val_loss: 0.9318 - val_recall: 0.7340\nEpoch 17/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 245ms/step - accuracy: 0.9554 - loss: 0.1646 - recall: 0.9469 - val_accuracy: 0.7763 - val_loss: 0.9414 - val_recall: 0.7694\nEpoch 18/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 239ms/step - accuracy: 0.9468 - loss: 0.1778 - recall: 0.9449 - val_accuracy: 0.7435 - val_loss: 1.0158 - val_recall: 0.7408\nEpoch 19/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 232ms/step - accuracy: 0.9614 - loss: 0.1379 - recall: 0.9559 - val_accuracy: 0.7640 - val_loss: 1.0338 - val_recall: 0.7572\nEpoch 20/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 231ms/step - accuracy: 0.9673 - loss: 0.1168 - recall: 0.9664 - val_accuracy: 0.7735 - val_loss: 1.0211 - val_recall: 0.7694\nEpoch 21/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 234ms/step - accuracy: 0.9661 - loss: 0.1254 - recall: 0.9616 - val_accuracy: 0.7585 - val_loss: 1.0590 - val_recall: 0.7490\nEpoch 22/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 239ms/step - accuracy: 0.9712 - loss: 0.1012 - recall: 0.9673 - val_accuracy: 0.7681 - val_loss: 1.0342 - val_recall: 0.7517\nEpoch 23/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 230ms/step - accuracy: 0.9798 - loss: 0.0753 - recall: 0.9787 - val_accuracy: 0.7544 - val_loss: 1.2288 - val_recall: 0.7531\nEpoch 24/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 235ms/step - accuracy: 0.9669 - loss: 0.1296 - recall: 0.9639 - val_accuracy: 0.7763 - val_loss: 1.1039 - val_recall: 0.7735\nEpoch 25/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 238ms/step - accuracy: 0.9775 - loss: 0.0845 - recall: 0.9745 - val_accuracy: 0.7817 - val_loss: 1.1129 - val_recall: 0.7763\nEpoch 26/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 231ms/step - accuracy: 0.9746 - loss: 0.0884 - recall: 0.9739 - val_accuracy: 0.7804 - val_loss: 1.1136 - val_recall: 0.7749\nEpoch 27/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 227ms/step - accuracy: 0.9808 - loss: 0.0686 - recall: 0.9796 - val_accuracy: 0.7531 - val_loss: 1.1261 - val_recall: 0.7435\nEpoch 28/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 234ms/step - accuracy: 0.9819 - loss: 0.0758 - recall: 0.9810 - val_accuracy: 0.7517 - val_loss: 1.1497 - val_recall: 0.7422\nEpoch 29/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 235ms/step - accuracy: 0.9651 - loss: 0.1053 - recall: 0.9636 - val_accuracy: 0.7722 - val_loss: 1.0271 - val_recall: 0.7640\nEpoch 30/30\n\u001b[1m46/46\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 239ms/step - accuracy: 0.9777 - loss: 0.0887 - recall: 0.9753 - val_accuracy: 0.7749 - val_loss: 1.1193 - val_recall: 0.7722\nTraining completed. Evaluating on test data...\n23/23 - 1s - 31ms/step - accuracy: 0.7749 - loss: 1.1193 - recall: 0.7722\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_47/4012816985.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Training completed. Evaluating on test data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_resnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"],"ename":"ValueError","evalue":"too many values to unpack (expected 2)","output_type":"error"}],"execution_count":10}]} |