"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Set the size of the drawing\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "\n",
+ "# Loop through each subfolder\n",
+ "for i, folder in enumerate(class_folders):\n",
+ " path = os.path.join(train_path, folder)\n",
+ " \n",
+ " # Set subplot index\n",
+ " plt.subplot(10, 5, i+1)\n",
+ " \n",
+ " # Loop through each file in the directory\n",
+ " for j, img in enumerate(os.listdir(path)):\n",
+ " # Make sure to display only a single image per folder\n",
+ " if j >= 1:\n",
+ " break\n",
+ " \n",
+ " img_array = cv2.imread(os.path.join(path,img))\n",
+ " \n",
+ " # Show image\n",
+ " plt.imshow(cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB))\n",
+ " plt.title(folder)\n",
+ " plt.axis('off')\n",
+ "\n",
+ "# Set space between rows and columns\n",
+ "plt.subplots_adjust(hspace=5, wspace=0.5)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81f9fbda",
+ "metadata": {
+ "papermill": {
+ "duration": 0.018024,
+ "end_time": "2024-05-09T15:42:55.911415",
+ "exception": false,
+ "start_time": "2024-05-09T15:42:55.893391",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# **2. Create training and validation datasets**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1820c9eb",
+ "metadata": {
+ "papermill": {
+ "duration": 0.017835,
+ "end_time": "2024-05-09T15:42:55.947077",
+ "exception": false,
+ "start_time": "2024-05-09T15:42:55.929242",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
2. Create training and validation datasets "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "9dc2c5bb",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:42:55.984338Z",
+ "iopub.status.busy": "2024-05-09T15:42:55.984054Z",
+ "iopub.status.idle": "2024-05-09T15:43:14.959062Z",
+ "shell.execute_reply": "2024-05-09T15:43:14.958273Z"
+ },
+ "papermill": {
+ "duration": 18.996339,
+ "end_time": "2024-05-09T15:43:14.961370",
+ "exception": false,
+ "start_time": "2024-05-09T15:42:55.965031",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-05-09 15:42:59.535726: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "2024-05-09 15:42:59.535839: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2024-05-09 15:42:59.807956: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "f9d68453",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:14.999759Z",
+ "iopub.status.busy": "2024-05-09T15:43:14.999207Z",
+ "iopub.status.idle": "2024-05-09T15:43:15.005050Z",
+ "shell.execute_reply": "2024-05-09T15:43:15.004225Z"
+ },
+ "papermill": {
+ "duration": 0.026807,
+ "end_time": "2024-05-09T15:43:15.006955",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:14.980148",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "train_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255,\n",
+ " rotation_range=10,\n",
+ " width_shift_range=0.1,\n",
+ " height_shift_range=0.1,\n",
+ " shear_range=0.15,\n",
+ " zoom_range=0.15,\n",
+ " horizontal_flip=False,\n",
+ " vertical_flip=False,\n",
+ " fill_mode='nearest',\n",
+ " validation_split=0.2,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "51632943",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:15.044118Z",
+ "iopub.status.busy": "2024-05-09T15:43:15.043871Z",
+ "iopub.status.idle": "2024-05-09T15:43:15.177452Z",
+ "shell.execute_reply": "2024-05-09T15:43:15.176653Z"
+ },
+ "papermill": {
+ "duration": 0.154401,
+ "end_time": "2024-05-09T15:43:15.179417",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:15.025016",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 2012 images belonging to 36 classes.\n",
+ "Found 503 images belonging to 36 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "train_images = train_generator.flow_from_directory(train_path,\n",
+ " target_size=(150, 150),\n",
+ " class_mode='categorical',\n",
+ " batch_size=32,\n",
+ " subset='training',\n",
+ " )\n",
+ "\n",
+ "val_images = train_generator.flow_from_directory(train_path,\n",
+ " target_size=(150,150),\n",
+ " class_mode='categorical',\n",
+ " batch_size=32,\n",
+ " subset='validation',\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0557cbe",
+ "metadata": {
+ "papermill": {
+ "duration": 0.018122,
+ "end_time": "2024-05-09T15:43:15.216089",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:15.197967",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# **3. Build InceptionV3 Model**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "311e6018",
+ "metadata": {
+ "papermill": {
+ "duration": 0.018452,
+ "end_time": "2024-05-09T15:43:15.253156",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:15.234704",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
3. Build InceptionV3 Model "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f9212931",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:15.291299Z",
+ "iopub.status.busy": "2024-05-09T15:43:15.290512Z",
+ "iopub.status.idle": "2024-05-09T15:43:15.299855Z",
+ "shell.execute_reply": "2024-05-09T15:43:15.299001Z"
+ },
+ "papermill": {
+ "duration": 0.030614,
+ "end_time": "2024-05-09T15:43:15.301962",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:15.271348",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras.models import Model, Sequential"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9ed6145",
+ "metadata": {
+ "papermill": {
+ "duration": 0.018032,
+ "end_time": "2024-05-09T15:43:15.338279",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:15.320247",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
3.1. Use transfer learning "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "12c9781b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:15.376098Z",
+ "iopub.status.busy": "2024-05-09T15:43:15.375356Z",
+ "iopub.status.idle": "2024-05-09T15:43:19.100174Z",
+ "shell.execute_reply": "2024-05-09T15:43:19.099329Z"
+ },
+ "papermill": {
+ "duration": 3.746223,
+ "end_time": "2024-05-09T15:43:19.102610",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:15.356387",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+ "\u001b[1m87910968/87910968\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load the pre-trained model\n",
+ "pre_trained_model_InceptionV3 = tf.keras.applications.InceptionV3(\n",
+ " include_top=False,\n",
+ " pooling='avg',\n",
+ " weights=\"imagenet\",\n",
+ " input_shape=(150,150,3),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae2bd490",
+ "metadata": {
+ "papermill": {
+ "duration": 0.019105,
+ "end_time": "2024-05-09T15:43:19.141280",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.122175",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "### **Note : You can run this line of code to get the corresponding final layer to build the model**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "053e1ff5",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:19.220807Z",
+ "iopub.status.busy": "2024-05-09T15:43:19.220442Z",
+ "iopub.status.idle": "2024-05-09T15:43:19.224606Z",
+ "shell.execute_reply": "2024-05-09T15:43:19.223722Z"
+ },
+ "papermill": {
+ "duration": 0.064619,
+ "end_time": "2024-05-09T15:43:19.226568",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.161949",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# pre_trained_model_InceptionV3.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "41a3b4bb",
+ "metadata": {
+ "papermill": {
+ "duration": 0.019675,
+ "end_time": "2024-05-09T15:43:19.265566",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.245891",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
3.2. Use Warm up and Fine tuning technique "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd35101b",
+ "metadata": {
+ "papermill": {
+ "duration": 0.020238,
+ "end_time": "2024-05-09T15:43:19.306159",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.285921",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "* **Note :** \n",
+ "* If you want to achieve fast convergence efficiency, you will use Warm up\n",
+ "* To increase accuracy we use Fine turning technique"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2fee6fd",
+ "metadata": {
+ "papermill": {
+ "duration": 0.020221,
+ "end_time": "2024-05-09T15:43:19.346807",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.326586",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
Warm up technique "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4185198d",
+ "metadata": {
+ "papermill": {
+ "duration": 0.019666,
+ "end_time": "2024-05-09T15:43:19.386692",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.367026",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "* Warm up is a necessary process for the model to converge faster. The Warm up process freezes the CNN layers so that their coefficients do not change and retrains only on the last Fully Connected Layers. The purpose of Warm up is to retain the high-level features learned from the pre-trained model, which is good because they are trained on a larger and more accurate data set. Higher accuracy than random coefficient initialization.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "9aa19f19",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:19.426557Z",
+ "iopub.status.busy": "2024-05-09T15:43:19.426234Z",
+ "iopub.status.idle": "2024-05-09T15:43:19.430076Z",
+ "shell.execute_reply": "2024-05-09T15:43:19.429251Z"
+ },
+ "papermill": {
+ "duration": 0.026075,
+ "end_time": "2024-05-09T15:43:19.431966",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.405891",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# for layer in pre_trained_model_InceptionV3.layers:\n",
+ "# layer.trainable = False"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "82cf9dd7",
+ "metadata": {
+ "papermill": {
+ "duration": 0.019036,
+ "end_time": "2024-05-09T15:43:19.469859",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.450823",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
Fine tuning technique "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f14cfa4",
+ "metadata": {
+ "papermill": {
+ "duration": 0.018973,
+ "end_time": "2024-05-09T15:43:19.508006",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.489033",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "* The main purpose of warming up the model is for the model to converge faster to the global optimal value.\n",
+ "\n",
+ "* After the model reaches the optimal threshold on Fully Connected Layers, it will be difficult for us to increase the accuracy further.\n",
+ "\n",
+ "* Now we will need to unfreeze the layers of the base network and train the model on all the layers from the pretrained-model. This process is called fine tuning."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "0a115f94",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:19.548207Z",
+ "iopub.status.busy": "2024-05-09T15:43:19.547490Z",
+ "iopub.status.idle": "2024-05-09T15:43:19.554312Z",
+ "shell.execute_reply": "2024-05-09T15:43:19.553365Z"
+ },
+ "papermill": {
+ "duration": 0.028867,
+ "end_time": "2024-05-09T15:43:19.556214",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.527347",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "for layer in pre_trained_model_InceptionV3.layers:\n",
+ " layer.trainable = True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d92f0820",
+ "metadata": {
+ "papermill": {
+ "duration": 0.018958,
+ "end_time": "2024-05-09T15:43:19.594445",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.575487",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "* You can choose arbitrarily as long as it has enough characteristics and can improve performance\n",
+ "* Here I am using mixed7 class"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "8c4e3cce",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:19.633797Z",
+ "iopub.status.busy": "2024-05-09T15:43:19.633268Z",
+ "iopub.status.idle": "2024-05-09T15:43:19.638503Z",
+ "shell.execute_reply": "2024-05-09T15:43:19.637842Z"
+ },
+ "papermill": {
+ "duration": 0.026958,
+ "end_time": "2024-05-09T15:43:19.640305",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.613347",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "last_layer_InceptionV3 = pre_trained_model_InceptionV3.get_layer('mixed7')\n",
+ "last_output_InceptionV3 = last_layer_InceptionV3.output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e4ed34d",
+ "metadata": {
+ "papermill": {
+ "duration": 0.018961,
+ "end_time": "2024-05-09T15:43:19.678367",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.659406",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
3.3. InceptionV3 training "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "7a380778",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:19.718804Z",
+ "iopub.status.busy": "2024-05-09T15:43:19.718123Z",
+ "iopub.status.idle": "2024-05-09T15:43:20.135473Z",
+ "shell.execute_reply": "2024-05-09T15:43:20.134754Z"
+ },
+ "papermill": {
+ "duration": 0.439824,
+ "end_time": "2024-05-09T15:43:20.137692",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:19.697868",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras import layers\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense,BatchNormalization,Flatten,Conv2D,MaxPool2D,Dropout,Activation\n",
+ "from keras.optimizers import Adam"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "78fc6de4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:20.180796Z",
+ "iopub.status.busy": "2024-05-09T15:43:20.180065Z",
+ "iopub.status.idle": "2024-05-09T15:43:20.191311Z",
+ "shell.execute_reply": "2024-05-09T15:43:20.190473Z"
+ },
+ "papermill": {
+ "duration": 0.033964,
+ "end_time": "2024-05-09T15:43:20.193309",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:20.159345",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "optimizer = Adam(learning_rate=0.0001, # Learning speed\n",
+ " beta_1=0.9, # Beta coefficient1\n",
+ " beta_2=0.999, # Beta coefficient2\n",
+ " ) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "6c463159",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:20.232791Z",
+ "iopub.status.busy": "2024-05-09T15:43:20.232462Z",
+ "iopub.status.idle": "2024-05-09T15:43:20.324189Z",
+ "shell.execute_reply": "2024-05-09T15:43:20.323515Z"
+ },
+ "papermill": {
+ "duration": 0.113811,
+ "end_time": "2024-05-09T15:43:20.326121",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:20.212310",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "x = layers.Flatten()(last_output_InceptionV3)\n",
+ "x = layers.BatchNormalization()(x)\n",
+ "x = layers.Dense(128,activation='relu')(x)\n",
+ "x = layers.BatchNormalization()(x)\n",
+ "x = layers.Dense(64,activation='relu')(x)\n",
+ "x = layers.BatchNormalization()(x)\n",
+ "output = layers.Dense(36,activation='softmax')(x)\n",
+ "\n",
+ "model_trans_InceptionV3 = Model(pre_trained_model_InceptionV3.input,output)\n",
+ "\n",
+ "model_trans_InceptionV3.compile(optimizer=optimizer,\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "859fd109",
+ "metadata": {
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+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
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+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 288 │ conv2d_10[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 96 ) │ │ │\n",
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+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 96 │ conv2d_11[0 ][0 ] │\n",
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+ "│ mixed0 │ (None , 16 , 16 , │ 0 │ activation_5[0 ][… │\n",
+ "│ (Concatenate ) │ 256 ) │ │ activation_7[0 ][… │\n",
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+ "│ │ │ │ activation_11[0 ]… │\n",
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+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
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+ "│ │ 48 ) │ │ │\n",
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+ "│ │ 96 ) │ │ │\n",
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+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 288 │ conv2d_16[0 ][0 ] │\n",
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+ "│ activation_13 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
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+ "│ │ 64 ) │ │ │\n",
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+ "│ conv2d_14 (Conv2D ) │ (None , 16 , 16 , │ 76,800 │ activation_13[0 ]… │\n",
+ "│ │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_17 (Conv2D ) │ (None , 16 , 16 , │ 82,944 │ activation_16[0 ]… │\n",
+ "│ │ 96 ) │ │ │\n",
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+ "│ conv2d_18 (Conv2D ) │ (None , 16 , 16 , │ 16,384 │ average_pooling2… │\n",
+ "│ │ 64 ) │ │ │\n",
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+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 192 │ conv2d_12[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 192 │ conv2d_14[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 288 │ conv2d_17[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 96 ) │ │ │\n",
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+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 192 │ conv2d_18[0 ][0 ] │\n",
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+ "│ (Activation ) │ 64 ) │ │ │\n",
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+ "│ activation_17 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
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+ "│ (Activation ) │ 64 ) │ │ │\n",
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+ "│ mixed1 │ (None , 16 , 16 , │ 0 │ activation_12[0 ]… │\n",
+ "│ (Concatenate ) │ 288 ) │ │ activation_14[0 ]… │\n",
+ "│ │ │ │ activation_17[0 ]… │\n",
+ "│ │ │ │ activation_18[0 ]… │\n",
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+ "│ conv2d_22 (Conv2D ) │ (None , 16 , 16 , │ 18,432 │ mixed1[0 ][0 ] │\n",
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+ "│ conv2d_23 (Conv2D ) │ (None , 16 , 16 , │ 55,296 │ activation_22[0 ]… │\n",
+ "│ │ 96 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 144 │ conv2d_20[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 48 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 288 │ conv2d_23[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 96 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_20 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ 48 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_23 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ 96 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_2 │ (None , 16 , 16 , │ 0 │ mixed1[0 ][0 ] │\n",
+ "│ (AveragePooling2D ) │ 288 ) │ │ │\n",
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+ "│ conv2d_19 (Conv2D ) │ (None , 16 , 16 , │ 18,432 │ mixed1[0 ][0 ] │\n",
+ "│ │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_21 (Conv2D ) │ (None , 16 , 16 , │ 76,800 │ activation_20[0 ]… │\n",
+ "│ │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_24 (Conv2D ) │ (None , 16 , 16 , │ 82,944 │ activation_23[0 ]… │\n",
+ "│ │ 96 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_25 (Conv2D ) │ (None , 16 , 16 , │ 18,432 │ average_pooling2… │\n",
+ "│ │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 192 │ conv2d_19[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 192 │ conv2d_21[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 288 │ conv2d_24[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 96 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 192 │ conv2d_25[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_19 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
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+ "│ activation_21 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
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+ "│ activation_24 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
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+ "│ activation_25 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed2 │ (None , 16 , 16 , │ 0 │ activation_19[0 ]… │\n",
+ "│ (Concatenate ) │ 288 ) │ │ activation_21[0 ]… │\n",
+ "│ │ │ │ activation_24[0 ]… │\n",
+ "│ │ │ │ activation_25[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_27 (Conv2D ) │ (None , 16 , 16 , │ 18,432 │ mixed2[0 ][0 ] │\n",
+ "│ │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 192 │ conv2d_27[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_27 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_28 (Conv2D ) │ (None , 16 , 16 , │ 55,296 │ activation_27[0 ]… │\n",
+ "│ │ 96 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 16 , 16 , │ 288 │ conv2d_28[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ 96 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_28 │ (None , 16 , 16 , │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ 96 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_26 (Conv2D ) │ (None , 7 , 7 , 384 ) │ 995,328 │ mixed2[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_29 (Conv2D ) │ (None , 7 , 7 , 96 ) │ 82,944 │ activation_28[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 384 ) │ 1,152 │ conv2d_26[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 96 ) │ 288 │ conv2d_29[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_26 │ (None , 7 , 7 , 384 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_29 │ (None , 7 , 7 , 96 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling2d_2 │ (None , 7 , 7 , 288 ) │ 0 │ mixed2[0 ][0 ] │\n",
+ "│ (MaxPooling2D ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed3 │ (None , 7 , 7 , 768 ) │ 0 │ activation_26[0 ]… │\n",
+ "│ (Concatenate ) │ │ │ activation_29[0 ]… │\n",
+ "│ │ │ │ max_pooling2d_2[… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_34 (Conv2D ) │ (None , 7 , 7 , 128 ) │ 98,304 │ mixed3[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 128 ) │ 384 │ conv2d_34[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_34 │ (None , 7 , 7 , 128 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_35 (Conv2D ) │ (None , 7 , 7 , 128 ) │ 114,688 │ activation_34[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 128 ) │ 384 │ conv2d_35[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_35 │ (None , 7 , 7 , 128 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_31 (Conv2D ) │ (None , 7 , 7 , 128 ) │ 98,304 │ mixed3[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_36 (Conv2D ) │ (None , 7 , 7 , 128 ) │ 114,688 │ activation_35[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 128 ) │ 384 │ conv2d_31[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 128 ) │ 384 │ conv2d_36[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_31 │ (None , 7 , 7 , 128 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_36 │ (None , 7 , 7 , 128 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_32 (Conv2D ) │ (None , 7 , 7 , 128 ) │ 114,688 │ activation_31[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_37 (Conv2D ) │ (None , 7 , 7 , 128 ) │ 114,688 │ activation_36[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 128 ) │ 384 │ conv2d_32[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 128 ) │ 384 │ conv2d_37[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_32 │ (None , 7 , 7 , 128 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_37 │ (None , 7 , 7 , 128 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_3 │ (None , 7 , 7 , 768 ) │ 0 │ mixed3[0 ][0 ] │\n",
+ "│ (AveragePooling2D ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_30 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ mixed3[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_33 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 172,032 │ activation_32[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_38 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 172,032 │ activation_37[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_39 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ average_pooling2… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_30[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_33[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_38[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_39[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_30 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_33 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_38 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_39 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed4 │ (None , 7 , 7 , 768 ) │ 0 │ activation_30[0 ]… │\n",
+ "│ (Concatenate ) │ │ │ activation_33[0 ]… │\n",
+ "│ │ │ │ activation_38[0 ]… │\n",
+ "│ │ │ │ activation_39[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_44 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 122,880 │ mixed4[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_44[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_44 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_45 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 179,200 │ activation_44[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_45[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_45 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_41 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 122,880 │ mixed4[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_46 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 179,200 │ activation_45[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_41[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_46[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_41 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_46 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_42 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 179,200 │ activation_41[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_47 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 179,200 │ activation_46[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_42[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_47[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_42 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_47 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_4 │ (None , 7 , 7 , 768 ) │ 0 │ mixed4[0 ][0 ] │\n",
+ "│ (AveragePooling2D ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_40 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ mixed4[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_43 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 215,040 │ activation_42[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_48 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 215,040 │ activation_47[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_49 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ average_pooling2… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_40[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_43[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_48[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_49[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_40 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_43 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_48 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_49 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed5 │ (None , 7 , 7 , 768 ) │ 0 │ activation_40[0 ]… │\n",
+ "│ (Concatenate ) │ │ │ activation_43[0 ]… │\n",
+ "│ │ │ │ activation_48[0 ]… │\n",
+ "│ │ │ │ activation_49[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_54 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 122,880 │ mixed5[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_54[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_54 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_55 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 179,200 │ activation_54[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_55[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_55 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_51 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 122,880 │ mixed5[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_56 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 179,200 │ activation_55[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_51[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_56[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_51 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_56 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_52 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 179,200 │ activation_51[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_57 (Conv2D ) │ (None , 7 , 7 , 160 ) │ 179,200 │ activation_56[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_52[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 160 ) │ 480 │ conv2d_57[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_52 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_57 │ (None , 7 , 7 , 160 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_5 │ (None , 7 , 7 , 768 ) │ 0 │ mixed5[0 ][0 ] │\n",
+ "│ (AveragePooling2D ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_50 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ mixed5[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_53 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 215,040 │ activation_52[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_58 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 215,040 │ activation_57[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_59 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ average_pooling2… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_50[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_53[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_58[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_59[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_50 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_53 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_58 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_59 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed6 │ (None , 7 , 7 , 768 ) │ 0 │ activation_50[0 ]… │\n",
+ "│ (Concatenate ) │ │ │ activation_53[0 ]… │\n",
+ "│ │ │ │ activation_58[0 ]… │\n",
+ "│ │ │ │ activation_59[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_64 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ mixed6[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_64[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_64 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_65 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 258,048 │ activation_64[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_65[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_65 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_61 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ mixed6[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_66 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 258,048 │ activation_65[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_61[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_66[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_61 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_66 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_62 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 258,048 │ activation_61[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_67 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 258,048 │ activation_66[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_62[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_67[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_62 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_67 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_6 │ (None , 7 , 7 , 768 ) │ 0 │ mixed6[0 ][0 ] │\n",
+ "│ (AveragePooling2D ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_60 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ mixed6[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_63 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 258,048 │ activation_62[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_68 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 258,048 │ activation_67[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_69 (Conv2D ) │ (None , 7 , 7 , 192 ) │ 147,456 │ average_pooling2… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_60[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_63[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_68[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 7 , 7 , 192 ) │ 576 │ conv2d_69[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_60 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_63 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_68 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_69 │ (None , 7 , 7 , 192 ) │ 0 │ batch_normalizat… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed7 │ (None , 7 , 7 , 768 ) │ 0 │ activation_60[0 ]… │\n",
+ "│ (Concatenate ) │ │ │ activation_63[0 ]… │\n",
+ "│ │ │ │ activation_68[0 ]… │\n",
+ "│ │ │ │ activation_69[0 ]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ flatten (Flatten ) │ (None , 37632 ) │ 0 │ mixed7[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 37632 ) │ 150,528 │ flatten[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense (Dense ) │ (None , 128 ) │ 4,817,024 │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 128 ) │ 512 │ dense[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_1 (Dense ) │ (None , 64 ) │ 8,256 │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 64 ) │ 256 │ dense_1[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_2 (Dense ) │ (None , 36 ) │ 2,340 │ batch_normalizat… │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+ " \n"
+ ],
+ "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;34m150\u001b[0m, \u001b[38;5;34m150\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m74\u001b[0m, \u001b[38;5;34m74\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m74\u001b[0m, \u001b[38;5;34m74\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m74\u001b[0m, \u001b[38;5;34m74\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m72\u001b[0m, \u001b[38;5;34m72\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m72\u001b[0m, \u001b[38;5;34m72\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m72\u001b[0m, \u001b[38;5;34m72\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m72\u001b[0m, \u001b[38;5;34m72\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m72\u001b[0m, \u001b[38;5;34m72\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_2[\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",
+ "│ activation_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m72\u001b[0m, \u001b[38;5;34m72\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m35\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m35\u001b[0m, │ \u001b[38;5;34m5,120\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ \u001b[38;5;34m80\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m35\u001b[0m, │ \u001b[38;5;34m240\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m35\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m, │ \u001b[38;5;34m138,240\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_8[\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",
+ "│ activation_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
+ "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ average_pooling2… │\n",
+ "│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_5[\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",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_7[\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",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed0 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ │ │ activation_10[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_11[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_15[\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",
+ "│ activation_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ average_pooling2… │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_12[\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",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_14[\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",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_18[\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",
+ "│ activation_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_18 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ activation_14[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_17[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_18[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_22[\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",
+ "│ activation_22 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_20 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_23 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ average_pooling2… │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_19[\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",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_21[\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",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_25[\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",
+ "│ activation_19 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_21 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_24 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_25 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ activation_21[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_24[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_25[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_27[\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",
+ "│ activation_27 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_27[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_28 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m995,328\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m82,944\u001b[0m │ activation_28[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m288\u001b[0m │ conv2d_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_26 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_29 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ max_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_26[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_29[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ max_pooling2d_2[\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_34 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m98,304\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m384\u001b[0m │ conv2d_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_34 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_35 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m114,688\u001b[0m │ activation_34[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m384\u001b[0m │ conv2d_35[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_35 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m98,304\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m114,688\u001b[0m │ activation_35[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m384\u001b[0m │ conv2d_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m384\u001b[0m │ conv2d_36[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_31 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_36 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_32 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m114,688\u001b[0m │ activation_31[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_37 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m114,688\u001b[0m │ activation_36[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m384\u001b[0m │ conv2d_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m384\u001b[0m │ conv2d_37[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_32 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_37 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_33 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m172,032\u001b[0m │ activation_32[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_38 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m172,032\u001b[0m │ activation_37[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_39 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_39[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_30 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_33 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_38 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_39 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_30[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_33[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_38[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_39[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_44 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m122,880\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_44[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_44 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_45 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m179,200\u001b[0m │ activation_44[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_45[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_45 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_41 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m122,880\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_46 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m179,200\u001b[0m │ activation_45[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_41[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_46[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_41 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_46 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_42 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m179,200\u001b[0m │ activation_41[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_47 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m179,200\u001b[0m │ activation_46[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_42[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_47[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_42 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_47 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_40 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_43 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m215,040\u001b[0m │ activation_42[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_48 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m215,040\u001b[0m │ activation_47[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_49 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_40[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_43[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_48[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_49[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_40 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_43 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_48 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_49 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_40[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_43[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_48[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_49[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_54 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m122,880\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_54[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_54 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_55 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m179,200\u001b[0m │ activation_54[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_55[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_55 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_51 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m122,880\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_56 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m179,200\u001b[0m │ activation_55[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_51[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_56[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_51 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_56 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_52 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m179,200\u001b[0m │ activation_51[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_57 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m179,200\u001b[0m │ activation_56[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_52[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │ conv2d_57[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_52 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_57 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_50 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_53 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m215,040\u001b[0m │ activation_52[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_58 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m215,040\u001b[0m │ activation_57[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_59 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_50[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_53[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_58[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_59[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_50 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_53 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_58 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_59 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_50[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_53[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_58[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_59[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_64 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_64[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_64 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_65 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m258,048\u001b[0m │ activation_64[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_65[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_65 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_61 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_66 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m258,048\u001b[0m │ activation_65[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_61[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_66[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_61 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_66 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_62 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m258,048\u001b[0m │ activation_61[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_67 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m258,048\u001b[0m │ activation_66[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_62[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_67[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_62 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_67 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ average_pooling2d_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_60 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_63 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m258,048\u001b[0m │ activation_62[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_68 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m258,048\u001b[0m │ activation_67[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2d_69 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_60[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_63[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_68[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_69[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_60 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_63 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_68 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ activation_69 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ mixed7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_60[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_63[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_68[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ activation_69[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m37632\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m37632\u001b[0m) │ \u001b[38;5;34m150,528\u001b[0m │ flatten[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m4,817,024\u001b[0m │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m36\u001b[0m) │ \u001b[38;5;34m2,340\u001b[0m │ batch_normalizat… │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 13,954,180 (53.23 MB)\n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m13,954,180\u001b[0m (53.23 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 13,859,716 (52.87 MB)\n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m13,859,716\u001b[0m (52.87 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 94,464 (369.00 KB)\n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m94,464\u001b[0m (369.00 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model_trans_InceptionV3.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "7d5554a5",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:20.744250Z",
+ "iopub.status.busy": "2024-05-09T15:43:20.743960Z",
+ "iopub.status.idle": "2024-05-09T15:43:20.748859Z",
+ "shell.execute_reply": "2024-05-09T15:43:20.748042Z"
+ },
+ "papermill": {
+ "duration": 0.033264,
+ "end_time": "2024-05-09T15:43:20.750779",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:20.717515",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Define a Callback class that stops training once accuracy reaches 99.9%\n",
+ "class myCallback(tf.keras.callbacks.Callback):\n",
+ " def on_epoch_end(self, epoch, logs={}):\n",
+ " if(logs.get('accuracy')>0.99):\n",
+ " print(\"\\nReached 99.9% accuracy so cancelling training!\")\n",
+ " self.model.stop_training = True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "04118f60",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:43:20.804471Z",
+ "iopub.status.busy": "2024-05-09T15:43:20.803941Z",
+ "iopub.status.idle": "2024-05-09T15:48:47.822198Z",
+ "shell.execute_reply": "2024-05-09T15:48:47.821033Z"
+ },
+ "papermill": {
+ "duration": 327.04745,
+ "end_time": "2024-05-09T15:48:47.824243",
+ "exception": false,
+ "start_time": "2024-05-09T15:43:20.776793",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "----------------------------Model is being built......................\n",
+ "\n",
+ "Epoch 1/100\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: 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"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:10:35\u001b[0m 68s/step - accuracy: 0.0000e+00 - loss: 4.0365"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "I0000 00:00:1715269469.700691 90 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n",
+ "W0000 00:00:1715269469.792709 90 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 870ms/step - accuracy: 0.2792 - loss: 2.8087"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "W0000 00:00:1715269529.342289 90 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 1s/step - accuracy: 0.2824 - loss: 2.7977 - val_accuracy: 0.6083 - val_loss: 2.6084\n",
+ "Epoch 2/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 273ms/step - accuracy: 0.8335 - loss: 1.0122 - val_accuracy: 0.8131 - val_loss: 1.7646\n",
+ "Epoch 3/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 268ms/step - accuracy: 0.8968 - loss: 0.6413 - val_accuracy: 0.8648 - val_loss: 1.2211\n",
+ "Epoch 4/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 271ms/step - accuracy: 0.9537 - loss: 0.4122 - val_accuracy: 0.9066 - val_loss: 0.7450\n",
+ "Epoch 5/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 266ms/step - accuracy: 0.9600 - loss: 0.3475 - val_accuracy: 0.9463 - val_loss: 0.4972\n",
+ "Epoch 6/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 269ms/step - accuracy: 0.9779 - loss: 0.2419 - val_accuracy: 0.9264 - val_loss: 0.4213\n",
+ "Epoch 7/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 270ms/step - accuracy: 0.9805 - loss: 0.2059 - val_accuracy: 0.9245 - val_loss: 0.3678\n",
+ "Epoch 8/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 268ms/step - accuracy: 0.9827 - loss: 0.1687 - val_accuracy: 0.9364 - val_loss: 0.3031\n",
+ "Epoch 9/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9838 - loss: 0.1598 - val_accuracy: 0.9304 - val_loss: 0.2861\n",
+ "Epoch 10/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 268ms/step - accuracy: 0.9903 - loss: 0.1213 - val_accuracy: 0.9344 - val_loss: 0.2823\n",
+ "Epoch 11/100\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step - accuracy: 0.9949 - loss: 0.1028\n",
+ "Reached 99.9% accuracy so cancelling training!\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 265ms/step - accuracy: 0.9949 - loss: 0.1029 - val_accuracy: 0.9423 - val_loss: 0.2866\n",
+ "\n",
+ "----------------------------Model completed......................\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "callbacks =myCallback()\n",
+ "\n",
+ "print('----------------------------Model is being built......................\\n')\n",
+ "history_Incep = model_trans_InceptionV3.fit(train_images,\n",
+ " epochs=100,\n",
+ " validation_data=val_images,\n",
+ " verbose=1,\n",
+ " callbacks = [callbacks]\n",
+ " )\n",
+ "print('\\n----------------------------Model completed......................\\n')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "330360b8",
+ "metadata": {
+ "papermill": {
+ "duration": 0.083368,
+ "end_time": "2024-05-09T15:48:47.994291",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:47.910923",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# **4. Build ResNet50 Model**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "772b711e",
+ "metadata": {
+ "papermill": {
+ "duration": 0.083559,
+ "end_time": "2024-05-09T15:48:48.161438",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:48.077879",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
4. Build ResNet50 Model "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43aa8e3e",
+ "metadata": {
+ "papermill": {
+ "duration": 0.086111,
+ "end_time": "2024-05-09T15:48:48.331023",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:48.244912",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
4.1. Use transfer learning "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "b5332b93",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:48:48.502858Z",
+ "iopub.status.busy": "2024-05-09T15:48:48.502141Z",
+ "iopub.status.idle": "2024-05-09T15:48:50.580809Z",
+ "shell.execute_reply": "2024-05-09T15:48:50.579648Z"
+ },
+ "papermill": {
+ "duration": 2.166838,
+ "end_time": "2024-05-09T15:48:50.583390",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:48.416552",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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[1m1s\u001b[0m 0us/step\n"
+ ]
+ }
+ ],
+ "source": [
+ "pre_trained_model_ResNet50 = tf.keras.applications.ResNet50(\n",
+ " include_top=False,\n",
+ " weights=\"imagenet\",\n",
+ " input_shape=(150,150,3),\n",
+ " pooling='avg',\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "9ac1cd00",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:48:50.764542Z",
+ "iopub.status.busy": "2024-05-09T15:48:50.764193Z",
+ "iopub.status.idle": "2024-05-09T15:48:50.768670Z",
+ "shell.execute_reply": "2024-05-09T15:48:50.767712Z"
+ },
+ "papermill": {
+ "duration": 0.096631,
+ "end_time": "2024-05-09T15:48:50.770499",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:50.673868",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# pre_trained_model_ResNet50.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b967257f",
+ "metadata": {
+ "papermill": {
+ "duration": 0.088926,
+ "end_time": "2024-05-09T15:48:50.946539",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:50.857613",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
4.2. Use Warm up and Fine tuning technique "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76d57a40",
+ "metadata": {
+ "papermill": {
+ "duration": 0.086275,
+ "end_time": "2024-05-09T15:48:51.121869",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:51.035594",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
Warm up "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "fedd481c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:48:51.310024Z",
+ "iopub.status.busy": "2024-05-09T15:48:51.309286Z",
+ "iopub.status.idle": "2024-05-09T15:48:51.313879Z",
+ "shell.execute_reply": "2024-05-09T15:48:51.312855Z"
+ },
+ "papermill": {
+ "duration": 0.105593,
+ "end_time": "2024-05-09T15:48:51.316069",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:51.210476",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# for layer in pre_trained_model_ResNet50.layers:\n",
+ "# layer.trainable = False"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cbd10dad",
+ "metadata": {
+ "papermill": {
+ "duration": 0.087018,
+ "end_time": "2024-05-09T15:48:51.498544",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:51.411526",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
Fine tuning "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "38d8bcf7",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:48:51.678699Z",
+ "iopub.status.busy": "2024-05-09T15:48:51.678300Z",
+ "iopub.status.idle": "2024-05-09T15:48:51.684522Z",
+ "shell.execute_reply": "2024-05-09T15:48:51.683507Z"
+ },
+ "papermill": {
+ "duration": 0.100586,
+ "end_time": "2024-05-09T15:48:51.686774",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:51.586188",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "for layer in pre_trained_model_ResNet50.layers:\n",
+ " layer.trainable = True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "8bf07106",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:48:51.864033Z",
+ "iopub.status.busy": "2024-05-09T15:48:51.863724Z",
+ "iopub.status.idle": "2024-05-09T15:48:51.868359Z",
+ "shell.execute_reply": "2024-05-09T15:48:51.867459Z"
+ },
+ "papermill": {
+ "duration": 0.091847,
+ "end_time": "2024-05-09T15:48:51.870199",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:51.778352",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "last_layer_ResNet50 = pre_trained_model_ResNet50.get_layer('conv5_block1_out')\n",
+ "last_output_ResNet50 = last_layer_ResNet50.output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bff90d52",
+ "metadata": {
+ "papermill": {
+ "duration": 0.086455,
+ "end_time": "2024-05-09T15:48:52.042580",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:51.956125",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
4.3. ResNet50 training "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "7f383fd3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:48:52.222734Z",
+ "iopub.status.busy": "2024-05-09T15:48:52.222353Z",
+ "iopub.status.idle": "2024-05-09T15:48:52.314242Z",
+ "shell.execute_reply": "2024-05-09T15:48:52.313438Z"
+ },
+ "papermill": {
+ "duration": 0.185787,
+ "end_time": "2024-05-09T15:48:52.316592",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:52.130805",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "x = layers.Flatten()(last_output_ResNet50)\n",
+ "x = layers.BatchNormalization()(x)\n",
+ "x = layers.Dense(128,activation='relu')(x)\n",
+ "x = layers.BatchNormalization()(x)\n",
+ "x = layers.Dropout(0.4)(x)\n",
+ "x = layers.Dense(64,activation='relu')(x)\n",
+ "x = layers.BatchNormalization()(x)\n",
+ "x = layers.Dropout(0.4)(x)\n",
+ "output = layers.Dense(36,activation='softmax')(x)\n",
+ "\n",
+ "model_trans_ResNet50 = Model(pre_trained_model_ResNet50.input,output)\n",
+ "\n",
+ "model_trans_ResNet50.compile(optimizer=Adam(learning_rate=0.00001,beta_1=0.9,beta_2=0.999),\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "1923e7d4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:48:52.492429Z",
+ "iopub.status.busy": "2024-05-09T15:48:52.492099Z",
+ "iopub.status.idle": "2024-05-09T15:48:52.715859Z",
+ "shell.execute_reply": "2024-05-09T15:48:52.714897Z"
+ },
+ "papermill": {
+ "duration": 0.317206,
+ "end_time": "2024-05-09T15:48:52.722944",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:52.405738",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional_3\" \n",
+ " \n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
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+ "│ conv1_bn │ (None , 75 , 75 , │ 256 │ conv1_conv[0 ][0 ] │\n",
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+ "│ pool1_pad │ (None , 77 , 77 , │ 0 │ conv1_relu[0 ][0 ] │\n",
+ "│ (ZeroPadding2D ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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+ "│ (Conv2D ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_1_bn │ (None , 38 , 38 , │ 256 │ conv2_block1_1_c… │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_1_relu │ (None , 38 , 38 , │ 0 │ conv2_block1_1_b… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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+ "│ conv2_block1_2_bn │ (None , 38 , 38 , │ 256 │ conv2_block1_2_c… │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_2_relu │ (None , 38 , 38 , │ 0 │ conv2_block1_2_b… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_0_conv │ (None , 38 , 38 , │ 16,640 │ pool1_pool[0 ][0 ] │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_3_conv │ (None , 38 , 38 , │ 16,640 │ conv2_block1_2_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_0_bn │ (None , 38 , 38 , │ 1,024 │ conv2_block1_0_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_3_bn │ (None , 38 , 38 , │ 1,024 │ conv2_block1_3_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_add │ (None , 38 , 38 , │ 0 │ conv2_block1_0_b… │\n",
+ "│ (Add ) │ 256 ) │ │ conv2_block1_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block1_out │ (None , 38 , 38 , │ 0 │ conv2_block1_add… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_1_conv │ (None , 38 , 38 , │ 16,448 │ conv2_block1_out… │\n",
+ "│ (Conv2D ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_1_bn │ (None , 38 , 38 , │ 256 │ conv2_block2_1_c… │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_1_relu │ (None , 38 , 38 , │ 0 │ conv2_block2_1_b… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_2_conv │ (None , 38 , 38 , │ 36,928 │ conv2_block2_1_r… │\n",
+ "│ (Conv2D ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_2_bn │ (None , 38 , 38 , │ 256 │ conv2_block2_2_c… │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_2_relu │ (None , 38 , 38 , │ 0 │ conv2_block2_2_b… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_3_conv │ (None , 38 , 38 , │ 16,640 │ conv2_block2_2_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_3_bn │ (None , 38 , 38 , │ 1,024 │ conv2_block2_3_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_add │ (None , 38 , 38 , │ 0 │ conv2_block1_out… │\n",
+ "│ (Add ) │ 256 ) │ │ conv2_block2_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block2_out │ (None , 38 , 38 , │ 0 │ conv2_block2_add… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_1_conv │ (None , 38 , 38 , │ 16,448 │ conv2_block2_out… │\n",
+ "│ (Conv2D ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_1_bn │ (None , 38 , 38 , │ 256 │ conv2_block3_1_c… │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_1_relu │ (None , 38 , 38 , │ 0 │ conv2_block3_1_b… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_2_conv │ (None , 38 , 38 , │ 36,928 │ conv2_block3_1_r… │\n",
+ "│ (Conv2D ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_2_bn │ (None , 38 , 38 , │ 256 │ conv2_block3_2_c… │\n",
+ "│ (BatchNormalizatio… │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_2_relu │ (None , 38 , 38 , │ 0 │ conv2_block3_2_b… │\n",
+ "│ (Activation ) │ 64 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_3_conv │ (None , 38 , 38 , │ 16,640 │ conv2_block3_2_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_3_bn │ (None , 38 , 38 , │ 1,024 │ conv2_block3_3_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_add │ (None , 38 , 38 , │ 0 │ conv2_block2_out… │\n",
+ "│ (Add ) │ 256 ) │ │ conv2_block3_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv2_block3_out │ (None , 38 , 38 , │ 0 │ conv2_block3_add… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_1_conv │ (None , 19 , 19 , │ 32,896 │ conv2_block3_out… │\n",
+ "│ (Conv2D ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_1_bn │ (None , 19 , 19 , │ 512 │ conv3_block1_1_c… │\n",
+ "│ (BatchNormalizatio… │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_1_relu │ (None , 19 , 19 , │ 0 │ conv3_block1_1_b… │\n",
+ "│ (Activation ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_2_conv │ (None , 19 , 19 , │ 147,584 │ conv3_block1_1_r… │\n",
+ "│ (Conv2D ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_2_bn │ (None , 19 , 19 , │ 512 │ conv3_block1_2_c… │\n",
+ "│ (BatchNormalizatio… │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_2_relu │ (None , 19 , 19 , │ 0 │ conv3_block1_2_b… │\n",
+ "│ (Activation ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_0_conv │ (None , 19 , 19 , │ 131,584 │ conv2_block3_out… │\n",
+ "│ (Conv2D ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_3_conv │ (None , 19 , 19 , │ 66,048 │ conv3_block1_2_r… │\n",
+ "│ (Conv2D ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_0_bn │ (None , 19 , 19 , │ 2,048 │ conv3_block1_0_c… │\n",
+ "│ (BatchNormalizatio… │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_3_bn │ (None , 19 , 19 , │ 2,048 │ conv3_block1_3_c… │\n",
+ "│ (BatchNormalizatio… │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_add │ (None , 19 , 19 , │ 0 │ conv3_block1_0_b… │\n",
+ "│ (Add ) │ 512 ) │ │ conv3_block1_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block1_out │ (None , 19 , 19 , │ 0 │ conv3_block1_add… │\n",
+ "│ (Activation ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_1_conv │ (None , 19 , 19 , │ 65,664 │ conv3_block1_out… │\n",
+ "│ (Conv2D ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_1_bn │ (None , 19 , 19 , │ 512 │ conv3_block2_1_c… │\n",
+ "│ (BatchNormalizatio… │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_1_relu │ (None , 19 , 19 , │ 0 │ conv3_block2_1_b… │\n",
+ "│ (Activation ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_2_conv │ (None , 19 , 19 , │ 147,584 │ conv3_block2_1_r… │\n",
+ "│ (Conv2D ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_2_bn │ (None , 19 , 19 , │ 512 │ conv3_block2_2_c… │\n",
+ "│ (BatchNormalizatio… │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_2_relu │ (None , 19 , 19 , │ 0 │ conv3_block2_2_b… │\n",
+ "│ (Activation ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_3_conv │ (None , 19 , 19 , │ 66,048 │ conv3_block2_2_r… │\n",
+ "│ (Conv2D ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_3_bn │ (None , 19 , 19 , │ 2,048 │ conv3_block2_3_c… │\n",
+ "│ (BatchNormalizatio… │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_add │ (None , 19 , 19 , │ 0 │ conv3_block1_out… │\n",
+ "│ (Add ) │ 512 ) │ │ conv3_block2_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block2_out │ (None , 19 , 19 , │ 0 │ conv3_block2_add… │\n",
+ "│ (Activation ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_1_conv │ (None , 19 , 19 , │ 65,664 │ conv3_block2_out… │\n",
+ "│ (Conv2D ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_1_bn │ (None , 19 , 19 , │ 512 │ conv3_block3_1_c… │\n",
+ "│ (BatchNormalizatio… │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_1_relu │ (None , 19 , 19 , │ 0 │ conv3_block3_1_b… │\n",
+ "│ (Activation ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_2_conv │ (None , 19 , 19 , │ 147,584 │ conv3_block3_1_r… │\n",
+ "│ (Conv2D ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_2_bn │ (None , 19 , 19 , │ 512 │ conv3_block3_2_c… │\n",
+ "│ (BatchNormalizatio… │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_2_relu │ (None , 19 , 19 , │ 0 │ conv3_block3_2_b… │\n",
+ "│ (Activation ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_3_conv │ (None , 19 , 19 , │ 66,048 │ conv3_block3_2_r… │\n",
+ "│ (Conv2D ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_3_bn │ (None , 19 , 19 , │ 2,048 │ conv3_block3_3_c… │\n",
+ "│ (BatchNormalizatio… │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_add │ (None , 19 , 19 , │ 0 │ conv3_block2_out… │\n",
+ "│ (Add ) │ 512 ) │ │ conv3_block3_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block3_out │ (None , 19 , 19 , │ 0 │ conv3_block3_add… │\n",
+ "│ (Activation ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_1_conv │ (None , 19 , 19 , │ 65,664 │ conv3_block3_out… │\n",
+ "│ (Conv2D ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_1_bn │ (None , 19 , 19 , │ 512 │ conv3_block4_1_c… │\n",
+ "│ (BatchNormalizatio… │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_1_relu │ (None , 19 , 19 , │ 0 │ conv3_block4_1_b… │\n",
+ "│ (Activation ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_2_conv │ (None , 19 , 19 , │ 147,584 │ conv3_block4_1_r… │\n",
+ "│ (Conv2D ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_2_bn │ (None , 19 , 19 , │ 512 │ conv3_block4_2_c… │\n",
+ "│ (BatchNormalizatio… │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_2_relu │ (None , 19 , 19 , │ 0 │ conv3_block4_2_b… │\n",
+ "│ (Activation ) │ 128 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_3_conv │ (None , 19 , 19 , │ 66,048 │ conv3_block4_2_r… │\n",
+ "│ (Conv2D ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_3_bn │ (None , 19 , 19 , │ 2,048 │ conv3_block4_3_c… │\n",
+ "│ (BatchNormalizatio… │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_add │ (None , 19 , 19 , │ 0 │ conv3_block3_out… │\n",
+ "│ (Add ) │ 512 ) │ │ conv3_block4_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv3_block4_out │ (None , 19 , 19 , │ 0 │ conv3_block4_add… │\n",
+ "│ (Activation ) │ 512 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_1_conv │ (None , 10 , 10 , │ 131,328 │ conv3_block4_out… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_1_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block1_1_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_1_relu │ (None , 10 , 10 , │ 0 │ conv4_block1_1_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_2_conv │ (None , 10 , 10 , │ 590,080 │ conv4_block1_1_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_2_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block1_2_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_2_relu │ (None , 10 , 10 , │ 0 │ conv4_block1_2_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_0_conv │ (None , 10 , 10 , │ 525,312 │ conv3_block4_out… │\n",
+ "│ (Conv2D ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_3_conv │ (None , 10 , 10 , │ 263,168 │ conv4_block1_2_r… │\n",
+ "│ (Conv2D ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_0_bn │ (None , 10 , 10 , │ 4,096 │ conv4_block1_0_c… │\n",
+ "│ (BatchNormalizatio… │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_3_bn │ (None , 10 , 10 , │ 4,096 │ conv4_block1_3_c… │\n",
+ "│ (BatchNormalizatio… │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_add │ (None , 10 , 10 , │ 0 │ conv4_block1_0_b… │\n",
+ "│ (Add ) │ 1024 ) │ │ conv4_block1_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_out │ (None , 10 , 10 , │ 0 │ conv4_block1_add… │\n",
+ "│ (Activation ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_1_conv │ (None , 10 , 10 , │ 262,400 │ conv4_block1_out… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_1_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block2_1_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_1_relu │ (None , 10 , 10 , │ 0 │ conv4_block2_1_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_2_conv │ (None , 10 , 10 , │ 590,080 │ conv4_block2_1_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_2_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block2_2_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_2_relu │ (None , 10 , 10 , │ 0 │ conv4_block2_2_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_3_conv │ (None , 10 , 10 , │ 263,168 │ conv4_block2_2_r… │\n",
+ "│ (Conv2D ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_3_bn │ (None , 10 , 10 , │ 4,096 │ conv4_block2_3_c… │\n",
+ "│ (BatchNormalizatio… │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_add │ (None , 10 , 10 , │ 0 │ conv4_block1_out… │\n",
+ "│ (Add ) │ 1024 ) │ │ conv4_block2_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_out │ (None , 10 , 10 , │ 0 │ conv4_block2_add… │\n",
+ "│ (Activation ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_1_conv │ (None , 10 , 10 , │ 262,400 │ conv4_block2_out… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_1_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block3_1_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_1_relu │ (None , 10 , 10 , │ 0 │ conv4_block3_1_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_2_conv │ (None , 10 , 10 , │ 590,080 │ conv4_block3_1_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_2_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block3_2_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_2_relu │ (None , 10 , 10 , │ 0 │ conv4_block3_2_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_3_conv │ (None , 10 , 10 , │ 263,168 │ conv4_block3_2_r… │\n",
+ "│ (Conv2D ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_3_bn │ (None , 10 , 10 , │ 4,096 │ conv4_block3_3_c… │\n",
+ "│ (BatchNormalizatio… │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_add │ (None , 10 , 10 , │ 0 │ conv4_block2_out… │\n",
+ "│ (Add ) │ 1024 ) │ │ conv4_block3_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_out │ (None , 10 , 10 , │ 0 │ conv4_block3_add… │\n",
+ "│ (Activation ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_1_conv │ (None , 10 , 10 , │ 262,400 │ conv4_block3_out… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_1_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block4_1_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_1_relu │ (None , 10 , 10 , │ 0 │ conv4_block4_1_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_2_conv │ (None , 10 , 10 , │ 590,080 │ conv4_block4_1_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_2_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block4_2_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_2_relu │ (None , 10 , 10 , │ 0 │ conv4_block4_2_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_3_conv │ (None , 10 , 10 , │ 263,168 │ conv4_block4_2_r… │\n",
+ "│ (Conv2D ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_3_bn │ (None , 10 , 10 , │ 4,096 │ conv4_block4_3_c… │\n",
+ "│ (BatchNormalizatio… │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_add │ (None , 10 , 10 , │ 0 │ conv4_block3_out… │\n",
+ "│ (Add ) │ 1024 ) │ │ conv4_block4_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_out │ (None , 10 , 10 , │ 0 │ conv4_block4_add… │\n",
+ "│ (Activation ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_1_conv │ (None , 10 , 10 , │ 262,400 │ conv4_block4_out… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_1_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block5_1_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_1_relu │ (None , 10 , 10 , │ 0 │ conv4_block5_1_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_2_conv │ (None , 10 , 10 , │ 590,080 │ conv4_block5_1_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_2_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block5_2_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_2_relu │ (None , 10 , 10 , │ 0 │ conv4_block5_2_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_3_conv │ (None , 10 , 10 , │ 263,168 │ conv4_block5_2_r… │\n",
+ "│ (Conv2D ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_3_bn │ (None , 10 , 10 , │ 4,096 │ conv4_block5_3_c… │\n",
+ "│ (BatchNormalizatio… │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_add │ (None , 10 , 10 , │ 0 │ conv4_block4_out… │\n",
+ "│ (Add ) │ 1024 ) │ │ conv4_block5_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_out │ (None , 10 , 10 , │ 0 │ conv4_block5_add… │\n",
+ "│ (Activation ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_1_conv │ (None , 10 , 10 , │ 262,400 │ conv4_block5_out… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_1_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block6_1_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_1_relu │ (None , 10 , 10 , │ 0 │ conv4_block6_1_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_2_conv │ (None , 10 , 10 , │ 590,080 │ conv4_block6_1_r… │\n",
+ "│ (Conv2D ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_2_bn │ (None , 10 , 10 , │ 1,024 │ conv4_block6_2_c… │\n",
+ "│ (BatchNormalizatio… │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_2_relu │ (None , 10 , 10 , │ 0 │ conv4_block6_2_b… │\n",
+ "│ (Activation ) │ 256 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_3_conv │ (None , 10 , 10 , │ 263,168 │ conv4_block6_2_r… │\n",
+ "│ (Conv2D ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_3_bn │ (None , 10 , 10 , │ 4,096 │ conv4_block6_3_c… │\n",
+ "│ (BatchNormalizatio… │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_add │ (None , 10 , 10 , │ 0 │ conv4_block5_out… │\n",
+ "│ (Add ) │ 1024 ) │ │ conv4_block6_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_out │ (None , 10 , 10 , │ 0 │ conv4_block6_add… │\n",
+ "│ (Activation ) │ 1024 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_1_conv │ (None , 5 , 5 , 512 ) │ 524,800 │ conv4_block6_out… │\n",
+ "│ (Conv2D ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_1_bn │ (None , 5 , 5 , 512 ) │ 2,048 │ conv5_block1_1_c… │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_1_relu │ (None , 5 , 5 , 512 ) │ 0 │ conv5_block1_1_b… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_2_conv │ (None , 5 , 5 , 512 ) │ 2,359,808 │ conv5_block1_1_r… │\n",
+ "│ (Conv2D ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_2_bn │ (None , 5 , 5 , 512 ) │ 2,048 │ conv5_block1_2_c… │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_2_relu │ (None , 5 , 5 , 512 ) │ 0 │ conv5_block1_2_b… │\n",
+ "│ (Activation ) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_0_conv │ (None , 5 , 5 , │ 2,099,200 │ conv4_block6_out… │\n",
+ "│ (Conv2D ) │ 2048 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_3_conv │ (None , 5 , 5 , │ 1,050,624 │ conv5_block1_2_r… │\n",
+ "│ (Conv2D ) │ 2048 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_0_bn │ (None , 5 , 5 , │ 8,192 │ conv5_block1_0_c… │\n",
+ "│ (BatchNormalizatio… │ 2048 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_3_bn │ (None , 5 , 5 , │ 8,192 │ conv5_block1_3_c… │\n",
+ "│ (BatchNormalizatio… │ 2048 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_add │ (None , 5 , 5 , │ 0 │ conv5_block1_0_b… │\n",
+ "│ (Add ) │ 2048 ) │ │ conv5_block1_3_b… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv5_block1_out │ (None , 5 , 5 , │ 0 │ conv5_block1_add… │\n",
+ "│ (Activation ) │ 2048 ) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ flatten_1 (Flatten ) │ (None , 51200 ) │ 0 │ conv5_block1_out… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 51200 ) │ 204,800 │ flatten_1[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_3 (Dense ) │ (None , 128 ) │ 6,553,728 │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 128 ) │ 512 │ dense_3[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout (Dropout ) │ (None , 128 ) │ 0 │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_4 (Dense ) │ (None , 64 ) │ 8,256 │ dropout[0 ][0 ] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (None , 64 ) │ 256 │ dense_4[0 ][0 ] │\n",
+ "│ (BatchNormalizatio… │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_1 (Dropout ) │ (None , 64 ) │ 0 │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_5 (Dense ) │ (None , 36 ) │ 2,340 │ dropout_1[0 ][0 ] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+ " \n"
+ ],
+ "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_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m150\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;34m156\u001b[0m, \u001b[38;5;34m156\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ input_layer_1[\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;34m75\u001b[0m, \u001b[38;5;34m75\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;34m75\u001b[0m, \u001b[38;5;34m75\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;34m75\u001b[0m, \u001b[38;5;34m75\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;34m77\u001b[0m, \u001b[38;5;34m77\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m38\u001b[0m, \u001b[38;5;34m38\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m19\u001b[0m, \u001b[38;5;34m19\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;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m131,328\u001b[0m │ conv3_block4_out… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_1_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_1_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block1_1_r… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_2_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_2_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block1_out… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_1_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_1_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block2_1_r… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_2_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_2_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block2_out… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_1_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_1_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block3_1_r… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_2_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_2_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block3_out… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_1_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_1_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block4_1_r… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_2_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_2_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block4_out… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_1_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_1_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block5_1_r… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_2_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_2_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block5_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block5_out… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_1_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_1_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block6_1_r… │\n",
+ "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_2_c… │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_2_b… │\n",
+ "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ conv4_block6_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m10\u001b[0m, \u001b[38;5;34m10\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m5\u001b[0m, \u001b[38;5;34m5\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",
+ "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m51200\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_out… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m51200\u001b[0m) │ \u001b[38;5;34m204,800\u001b[0m │ flatten_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m6,553,728\u001b[0m │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m36\u001b[0m) │ \u001b[38;5;34m2,340\u001b[0m │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 21,413,988 (81.69 MB)\n",
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+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m21,413,988\u001b[0m (81.69 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 21,270,372 (81.14 MB)\n",
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m21,270,372\u001b[0m (81.14 MB)\n"
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model_trans_ResNet50.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "ac6dc648",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T15:48:52.907416Z",
+ "iopub.status.busy": "2024-05-09T15:48:52.906612Z",
+ "iopub.status.idle": "2024-05-09T16:26:38.648757Z",
+ "shell.execute_reply": "2024-05-09T16:26:38.647669Z"
+ },
+ "papermill": {
+ "duration": 2265.837967,
+ "end_time": "2024-05-09T16:26:38.650880",
+ "exception": false,
+ "start_time": "2024-05-09T15:48:52.812913",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "----------------------------Model is being built......................\n",
+ "\n",
+ "Epoch 1/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m131s\u001b[0m 996ms/step - accuracy: 0.0486 - loss: 4.2715 - val_accuracy: 0.0278 - val_loss: 3.7711\n",
+ "Epoch 2/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.1126 - loss: 3.5146 - val_accuracy: 0.0139 - val_loss: 3.9262\n",
+ "Epoch 3/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 281ms/step - accuracy: 0.2224 - loss: 3.0052 - val_accuracy: 0.0298 - val_loss: 4.0786\n",
+ "Epoch 4/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.2492 - loss: 2.7066 - val_accuracy: 0.0239 - val_loss: 4.1811\n",
+ "Epoch 5/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 287ms/step - accuracy: 0.3620 - loss: 2.3578 - val_accuracy: 0.0099 - val_loss: 4.1978\n",
+ "Epoch 6/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.3982 - loss: 2.2265 - val_accuracy: 0.0278 - val_loss: 4.0088\n",
+ "Epoch 7/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.4095 - loss: 2.1813 - val_accuracy: 0.0517 - val_loss: 3.9037\n",
+ "Epoch 8/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.4751 - loss: 1.9834 - val_accuracy: 0.0477 - val_loss: 3.8396\n",
+ "Epoch 9/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 275ms/step - accuracy: 0.4940 - loss: 1.8771 - val_accuracy: 0.0497 - val_loss: 3.7580\n",
+ "Epoch 10/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.5340 - loss: 1.7820 - val_accuracy: 0.0915 - val_loss: 3.6092\n",
+ "Epoch 11/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 288ms/step - accuracy: 0.5637 - loss: 1.7215 - val_accuracy: 0.1272 - val_loss: 3.4089\n",
+ "Epoch 12/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.5816 - loss: 1.6239 - val_accuracy: 0.2187 - val_loss: 3.0367\n",
+ "Epoch 13/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.6153 - loss: 1.5466 - val_accuracy: 0.3300 - val_loss: 2.7232\n",
+ "Epoch 14/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 276ms/step - accuracy: 0.6214 - loss: 1.5059 - val_accuracy: 0.4254 - val_loss: 2.3480\n",
+ "Epoch 15/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.6546 - loss: 1.4432 - val_accuracy: 0.5885 - val_loss: 1.9992\n",
+ "Epoch 16/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 289ms/step - accuracy: 0.6577 - loss: 1.4222 - val_accuracy: 0.6839 - val_loss: 1.6699\n",
+ "Epoch 17/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.6798 - loss: 1.3652 - val_accuracy: 0.7734 - val_loss: 1.3487\n",
+ "Epoch 18/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.7094 - loss: 1.2885 - val_accuracy: 0.8350 - val_loss: 1.0997\n",
+ "Epoch 19/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.6993 - loss: 1.2996 - val_accuracy: 0.8748 - val_loss: 0.9432\n",
+ "Epoch 20/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 281ms/step - accuracy: 0.7184 - loss: 1.2387 - val_accuracy: 0.8986 - val_loss: 0.8590\n",
+ "Epoch 21/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 281ms/step - accuracy: 0.7447 - loss: 1.1699 - val_accuracy: 0.8867 - val_loss: 0.8290\n",
+ "Epoch 22/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.7555 - loss: 1.1573 - val_accuracy: 0.9006 - val_loss: 0.7797\n",
+ "Epoch 23/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 289ms/step - accuracy: 0.7694 - loss: 1.1232 - val_accuracy: 0.9026 - val_loss: 0.7384\n",
+ "Epoch 24/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.7749 - loss: 1.1325 - val_accuracy: 0.8907 - val_loss: 0.7354\n",
+ "Epoch 25/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.7868 - loss: 1.0806 - val_accuracy: 0.8986 - val_loss: 0.6958\n",
+ "Epoch 26/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.7856 - loss: 1.0532 - val_accuracy: 0.9145 - val_loss: 0.6969\n",
+ "Epoch 27/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 285ms/step - accuracy: 0.8154 - loss: 1.0074 - val_accuracy: 0.9066 - val_loss: 0.6845\n",
+ "Epoch 28/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.8234 - loss: 0.9843 - val_accuracy: 0.9264 - val_loss: 0.6442\n",
+ "Epoch 29/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 289ms/step - accuracy: 0.8184 - loss: 0.9957 - val_accuracy: 0.9085 - val_loss: 0.6266\n",
+ "Epoch 30/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.8020 - loss: 0.9891 - val_accuracy: 0.9105 - val_loss: 0.6193\n",
+ "Epoch 31/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.8295 - loss: 0.9598 - val_accuracy: 0.9264 - val_loss: 0.5763\n",
+ "Epoch 32/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 287ms/step - accuracy: 0.8444 - loss: 0.9150 - val_accuracy: 0.9304 - val_loss: 0.5814\n",
+ "Epoch 33/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.8409 - loss: 0.9084 - val_accuracy: 0.9304 - val_loss: 0.5672\n",
+ "Epoch 34/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.8503 - loss: 0.8784 - val_accuracy: 0.9225 - val_loss: 0.5678\n",
+ "Epoch 35/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.8457 - loss: 0.8547 - val_accuracy: 0.9225 - val_loss: 0.5433\n",
+ "Epoch 36/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 274ms/step - accuracy: 0.8528 - loss: 0.8556 - val_accuracy: 0.9245 - val_loss: 0.5304\n",
+ "Epoch 37/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.8714 - loss: 0.8367 - val_accuracy: 0.9284 - val_loss: 0.5292\n",
+ "Epoch 38/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.8882 - loss: 0.8005 - val_accuracy: 0.9245 - val_loss: 0.5355\n",
+ "Epoch 39/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 281ms/step - accuracy: 0.8655 - loss: 0.8061 - val_accuracy: 0.9185 - val_loss: 0.4996\n",
+ "Epoch 40/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 279ms/step - accuracy: 0.8836 - loss: 0.7613 - val_accuracy: 0.9304 - val_loss: 0.4976\n",
+ "Epoch 41/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 285ms/step - accuracy: 0.8807 - loss: 0.7562 - val_accuracy: 0.9185 - val_loss: 0.5096\n",
+ "Epoch 42/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9010 - loss: 0.7045 - val_accuracy: 0.9443 - val_loss: 0.4708\n",
+ "Epoch 43/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.8957 - loss: 0.7637 - val_accuracy: 0.9324 - val_loss: 0.4784\n",
+ "Epoch 44/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 283ms/step - accuracy: 0.8902 - loss: 0.7327 - val_accuracy: 0.9443 - val_loss: 0.4579\n",
+ "Epoch 45/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 282ms/step - accuracy: 0.9020 - loss: 0.7167 - val_accuracy: 0.9503 - val_loss: 0.4306\n",
+ "Epoch 46/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9107 - loss: 0.6933 - val_accuracy: 0.9284 - val_loss: 0.4594\n",
+ "Epoch 47/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 286ms/step - accuracy: 0.9046 - loss: 0.7133 - val_accuracy: 0.9344 - val_loss: 0.4394\n",
+ "Epoch 48/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 282ms/step - accuracy: 0.8937 - loss: 0.6804 - val_accuracy: 0.9443 - val_loss: 0.4143\n",
+ "Epoch 49/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 272ms/step - accuracy: 0.9058 - loss: 0.6778 - val_accuracy: 0.9284 - val_loss: 0.4426\n",
+ "Epoch 50/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9295 - loss: 0.6246 - val_accuracy: 0.9523 - val_loss: 0.4121\n",
+ "Epoch 51/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.9118 - loss: 0.6631 - val_accuracy: 0.9344 - val_loss: 0.4133\n",
+ "Epoch 52/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9328 - loss: 0.6057 - val_accuracy: 0.9483 - val_loss: 0.3881\n",
+ "Epoch 53/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.9163 - loss: 0.6155 - val_accuracy: 0.9384 - val_loss: 0.4004\n",
+ "Epoch 54/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9352 - loss: 0.6024 - val_accuracy: 0.9523 - val_loss: 0.3655\n",
+ "Epoch 55/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.9192 - loss: 0.5948 - val_accuracy: 0.9324 - val_loss: 0.4072\n",
+ "Epoch 56/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 286ms/step - accuracy: 0.9202 - loss: 0.6235 - val_accuracy: 0.9404 - val_loss: 0.3749\n",
+ "Epoch 57/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 282ms/step - accuracy: 0.9256 - loss: 0.5937 - val_accuracy: 0.9404 - val_loss: 0.4024\n",
+ "Epoch 58/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 278ms/step - accuracy: 0.9251 - loss: 0.6221 - val_accuracy: 0.9503 - val_loss: 0.3734\n",
+ "Epoch 59/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9402 - loss: 0.5692 - val_accuracy: 0.9324 - val_loss: 0.3688\n",
+ "Epoch 60/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 283ms/step - accuracy: 0.9328 - loss: 0.5578 - val_accuracy: 0.9463 - val_loss: 0.3680\n",
+ "Epoch 61/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.9405 - loss: 0.5557 - val_accuracy: 0.9483 - val_loss: 0.3826\n",
+ "Epoch 62/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 274ms/step - accuracy: 0.9455 - loss: 0.5298 - val_accuracy: 0.9463 - val_loss: 0.3566\n",
+ "Epoch 63/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.9417 - loss: 0.5072 - val_accuracy: 0.9344 - val_loss: 0.3863\n",
+ "Epoch 64/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9421 - loss: 0.5209 - val_accuracy: 0.9463 - val_loss: 0.3314\n",
+ "Epoch 65/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9602 - loss: 0.5070 - val_accuracy: 0.9523 - val_loss: 0.3462\n",
+ "Epoch 66/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 283ms/step - accuracy: 0.9478 - loss: 0.5075 - val_accuracy: 0.9384 - val_loss: 0.3568\n",
+ "Epoch 67/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 290ms/step - accuracy: 0.9577 - loss: 0.4690 - val_accuracy: 0.9423 - val_loss: 0.3526\n",
+ "Epoch 68/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9589 - loss: 0.5034 - val_accuracy: 0.9304 - val_loss: 0.3480\n",
+ "Epoch 69/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.9553 - loss: 0.4802 - val_accuracy: 0.9443 - val_loss: 0.3505\n",
+ "Epoch 70/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9558 - loss: 0.4898 - val_accuracy: 0.9404 - val_loss: 0.3460\n",
+ "Epoch 71/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 281ms/step - accuracy: 0.9576 - loss: 0.4661 - val_accuracy: 0.9443 - val_loss: 0.3199\n",
+ "Epoch 72/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.9673 - loss: 0.4419 - val_accuracy: 0.9423 - val_loss: 0.3263\n",
+ "Epoch 73/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9657 - loss: 0.4628 - val_accuracy: 0.9483 - val_loss: 0.3347\n",
+ "Epoch 74/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9603 - loss: 0.4381 - val_accuracy: 0.9583 - val_loss: 0.3119\n",
+ "Epoch 75/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 276ms/step - accuracy: 0.9572 - loss: 0.4630 - val_accuracy: 0.9583 - val_loss: 0.2931\n",
+ "Epoch 76/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.9560 - loss: 0.4642 - val_accuracy: 0.9463 - val_loss: 0.3239\n",
+ "Epoch 77/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 274ms/step - accuracy: 0.9711 - loss: 0.4128 - val_accuracy: 0.9463 - val_loss: 0.3121\n",
+ "Epoch 78/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 281ms/step - accuracy: 0.9697 - loss: 0.4237 - val_accuracy: 0.9483 - val_loss: 0.3197\n",
+ "Epoch 79/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 286ms/step - accuracy: 0.9679 - loss: 0.4414 - val_accuracy: 0.9543 - val_loss: 0.2967\n",
+ "Epoch 80/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.9710 - loss: 0.4128 - val_accuracy: 0.9523 - val_loss: 0.3226\n",
+ "Epoch 81/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 285ms/step - accuracy: 0.9696 - loss: 0.4050 - val_accuracy: 0.9463 - val_loss: 0.3184\n",
+ "Epoch 82/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9665 - loss: 0.4062 - val_accuracy: 0.9443 - val_loss: 0.3137\n",
+ "Epoch 83/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 276ms/step - accuracy: 0.9649 - loss: 0.3974 - val_accuracy: 0.9364 - val_loss: 0.3228\n",
+ "Epoch 84/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9708 - loss: 0.3925 - val_accuracy: 0.9563 - val_loss: 0.3088\n",
+ "Epoch 85/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.9787 - loss: 0.3717 - val_accuracy: 0.9404 - val_loss: 0.2957\n",
+ "Epoch 86/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9664 - loss: 0.4126 - val_accuracy: 0.9423 - val_loss: 0.2982\n",
+ "Epoch 87/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9779 - loss: 0.3564 - val_accuracy: 0.9443 - val_loss: 0.2921\n",
+ "Epoch 88/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 280ms/step - accuracy: 0.9811 - loss: 0.3739 - val_accuracy: 0.9463 - val_loss: 0.2887\n",
+ "Epoch 89/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9741 - loss: 0.3696 - val_accuracy: 0.9443 - val_loss: 0.2913\n",
+ "Epoch 90/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 281ms/step - accuracy: 0.9781 - loss: 0.3447 - val_accuracy: 0.9364 - val_loss: 0.2976\n",
+ "Epoch 91/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 284ms/step - accuracy: 0.9792 - loss: 0.3642 - val_accuracy: 0.9642 - val_loss: 0.2667\n",
+ "Epoch 92/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9814 - loss: 0.3490 - val_accuracy: 0.9602 - val_loss: 0.2620\n",
+ "Epoch 93/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 276ms/step - accuracy: 0.9691 - loss: 0.3802 - val_accuracy: 0.9443 - val_loss: 0.2900\n",
+ "Epoch 94/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.9765 - loss: 0.3591 - val_accuracy: 0.9483 - val_loss: 0.2603\n",
+ "Epoch 95/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 270ms/step - accuracy: 0.9803 - loss: 0.3566 - val_accuracy: 0.9463 - val_loss: 0.2730\n",
+ "Epoch 96/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9756 - loss: 0.3348 - val_accuracy: 0.9423 - val_loss: 0.3036\n",
+ "Epoch 97/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.9734 - loss: 0.3404 - val_accuracy: 0.9503 - val_loss: 0.2747\n",
+ "Epoch 98/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 278ms/step - accuracy: 0.9815 - loss: 0.3219 - val_accuracy: 0.9523 - val_loss: 0.2998\n",
+ "Epoch 99/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 284ms/step - accuracy: 0.9815 - loss: 0.3041 - val_accuracy: 0.9583 - val_loss: 0.2721\n",
+ "Epoch 100/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9736 - loss: 0.3356 - val_accuracy: 0.9463 - val_loss: 0.2949\n",
+ "Epoch 101/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9794 - loss: 0.3399 - val_accuracy: 0.9463 - val_loss: 0.2808\n",
+ "Epoch 102/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 274ms/step - accuracy: 0.9888 - loss: 0.2863 - val_accuracy: 0.9523 - val_loss: 0.2738\n",
+ "Epoch 103/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 282ms/step - accuracy: 0.9844 - loss: 0.3119 - val_accuracy: 0.9523 - val_loss: 0.2676\n",
+ "Epoch 104/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 281ms/step - accuracy: 0.9883 - loss: 0.2869 - val_accuracy: 0.9443 - val_loss: 0.2740\n",
+ "Epoch 105/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9825 - loss: 0.3024 - val_accuracy: 0.9483 - val_loss: 0.2617\n",
+ "Epoch 106/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9816 - loss: 0.2997 - val_accuracy: 0.9463 - val_loss: 0.2788\n",
+ "Epoch 107/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 276ms/step - accuracy: 0.9859 - loss: 0.3004 - val_accuracy: 0.9443 - val_loss: 0.2623\n",
+ "Epoch 108/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9872 - loss: 0.2835 - val_accuracy: 0.9404 - val_loss: 0.3040\n",
+ "Epoch 109/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 283ms/step - accuracy: 0.9799 - loss: 0.2904 - val_accuracy: 0.9583 - val_loss: 0.2281\n",
+ "Epoch 110/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 279ms/step - accuracy: 0.9875 - loss: 0.2993 - val_accuracy: 0.9423 - val_loss: 0.2880\n",
+ "Epoch 111/150\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 219ms/step - accuracy: 0.9904 - loss: 0.2702\n",
+ "Reached 99.9% accuracy so cancelling training!\n",
+ "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 277ms/step - accuracy: 0.9904 - loss: 0.2702 - val_accuracy: 0.9503 - val_loss: 0.2764\n",
+ "\n",
+ "----------------------------Model completed......................\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "callbacks =myCallback()\n",
+ "\n",
+ "print('----------------------------Model is being built......................\\n')\n",
+ "history_Res = model_trans_ResNet50.fit(train_images,\n",
+ " epochs=150,\n",
+ " validation_data=val_images,\n",
+ " verbose=1,\n",
+ " callbacks = [callbacks]\n",
+ " )\n",
+ "print('\\n----------------------------Model completed......................\\n')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1abd28d",
+ "metadata": {
+ "papermill": {
+ "duration": 0.697795,
+ "end_time": "2024-05-09T16:26:40.042510",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:39.344715",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# **5. Result**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b915e243",
+ "metadata": {
+ "papermill": {
+ "duration": 0.692189,
+ "end_time": "2024-05-09T16:26:41.476086",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:40.783897",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
5. Result "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b5f5dfd5",
+ "metadata": {
+ "papermill": {
+ "duration": 0.753692,
+ "end_time": "2024-05-09T16:26:42.925854",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:42.172162",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
5.1. InceptionV3 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "5f9e4400",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T16:26:44.328322Z",
+ "iopub.status.busy": "2024-05-09T16:26:44.327966Z",
+ "iopub.status.idle": "2024-05-09T16:26:44.888849Z",
+ "shell.execute_reply": "2024-05-09T16:26:44.887895Z"
+ },
+ "papermill": {
+ "duration": 1.253087,
+ "end_time": "2024-05-09T16:26:44.891167",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:43.638080",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot the loss of training and validation\n",
+ "plt.plot(history_Incep.history['loss'])\n",
+ "plt.plot(history_Incep.history['val_loss'])\n",
+ "plt.title('Model loss')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.legend(['Train', 'Validation'], loc='upper right')\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "# Plot the accuracy of training and validation\n",
+ "plt.plot(history_Incep.history['accuracy'])\n",
+ "plt.plot(history_Incep.history['val_accuracy'])\n",
+ "plt.title('Model Accuracy')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.legend(['Train', 'Validation'], loc='lower right')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eeba505b",
+ "metadata": {
+ "papermill": {
+ "duration": 0.692403,
+ "end_time": "2024-05-09T16:26:46.316704",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:45.624301",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
5.2. ResNet50 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "e3365f29",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T16:26:47.779606Z",
+ "iopub.status.busy": "2024-05-09T16:26:47.778737Z",
+ "iopub.status.idle": "2024-05-09T16:26:48.345517Z",
+ "shell.execute_reply": "2024-05-09T16:26:48.344637Z"
+ },
+ "papermill": {
+ "duration": 1.320008,
+ "end_time": "2024-05-09T16:26:48.347458",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:47.027450",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot the loss of training and validation\n",
+ "plt.plot(history_Res.history['loss'])\n",
+ "plt.plot(history_Res.history['val_loss'])\n",
+ "plt.title('Model loss')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.legend(['Train', 'Validation'], loc='upper right')\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "# Plot the accuracy of training and validation\n",
+ "plt.plot(history_Res.history['accuracy'])\n",
+ "plt.plot(history_Res.history['val_accuracy'])\n",
+ "plt.title('Model Accuracy')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.legend(['Train', 'Validation'], loc='lower right')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "754f8de8",
+ "metadata": {
+ "papermill": {
+ "duration": 0.684086,
+ "end_time": "2024-05-09T16:26:49.715786",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:49.031700",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# **6. Save model**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b5b1a51",
+ "metadata": {
+ "papermill": {
+ "duration": 0.733476,
+ "end_time": "2024-05-09T16:26:51.132306",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:50.398830",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
6. Save model "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "28a7eb46",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T16:26:52.557085Z",
+ "iopub.status.busy": "2024-05-09T16:26:52.556730Z",
+ "iopub.status.idle": "2024-05-09T16:26:52.561132Z",
+ "shell.execute_reply": "2024-05-09T16:26:52.560080Z"
+ },
+ "papermill": {
+ "duration": 0.745341,
+ "end_time": "2024-05-09T16:26:52.563025",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:51.817684",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "path_save_model = '/kaggle/working/'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "493faa7c",
+ "metadata": {
+ "papermill": {
+ "duration": 0.683977,
+ "end_time": "2024-05-09T16:26:53.933545",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:53.249568",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
6.1. InceptionV3 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "13f9bf4e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T16:26:55.380665Z",
+ "iopub.status.busy": "2024-05-09T16:26:55.380255Z",
+ "iopub.status.idle": "2024-05-09T16:26:56.096650Z",
+ "shell.execute_reply": "2024-05-09T16:26:56.095866Z"
+ },
+ "papermill": {
+ "duration": 1.475068,
+ "end_time": "2024-05-09T16:26:56.098848",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:54.623780",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "model_trans_InceptionV3.save('model_trans_InceptionV3.h5')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1de8046d",
+ "metadata": {
+ "papermill": {
+ "duration": 0.70238,
+ "end_time": "2024-05-09T16:26:57.489975",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:56.787595",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "
6.2. ResNet50 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "15eefe2b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-09T16:26:58.933574Z",
+ "iopub.status.busy": "2024-05-09T16:26:58.932728Z",
+ "iopub.status.idle": "2024-05-09T16:26:59.744956Z",
+ "shell.execute_reply": "2024-05-09T16:26:59.744106Z"
+ },
+ "papermill": {
+ "duration": 1.565611,
+ "end_time": "2024-05-09T16:26:59.747299",
+ "exception": false,
+ "start_time": "2024-05-09T16:26:58.181688",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "model_trans_ResNet50.save('model_trans_ResNet50.h5')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kaggle": {
+ "accelerator": "nvidiaTeslaT4",
+ "dataSources": [
+ {
+ "datasetId": 4830635,
+ "sourceId": 8164081,
+ "sourceType": "datasetVersion"
+ }
+ ],
+ "dockerImageVersionId": 30698,
+ "isGpuEnabled": true,
+ "isInternetEnabled": true,
+ "language": "python",
+ "sourceType": "notebook"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 2660.759448,
+ "end_time": "2024-05-09T16:27:04.173760",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "__notebook__.ipynb",
+ "output_path": "__notebook__.ipynb",
+ "parameters": {},
+ "start_time": "2024-05-09T15:42:43.414312",
+ "version": "2.5.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}