{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentinel Satellite Image Classification Project\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Overview\n",
    "\n",
    "This project focuses on the development and deployment of a machine learning application for satellite image classification. The goal is to automate the classification of satellite images into predefined categories that represent different types of land cover.\n",
    "\n",
    "## Motivation\n",
    "\n",
    "### End Users\n",
    "The end users of this project are environmental scientists and urban planners.\n",
    "\n",
    "### Goal of End Users\n",
    "Their goal is to utilize automated tools to classify large volumes of satellite imagery quickly and accurately for environmental monitoring and urban planning purposes.\n",
    "\n",
    "### Obstacle to be Solved\n",
    "The main obstacles include the high variability and similarity between different land cover types in satellite images and the volume of data that requires processing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.16.1'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Collection and Augmentation\n",
    "\n",
    "### Images Collected\n",
    "The dataset used in this project is the EuroSAT collection, which consists of 30,988 satellite images derived from Sentinel satellites. These images are categorized into ten classes representing different types of land cover: AnnualCrop, Forest, HerbaceousVegetation, Highway, Industrial, Pasture, PermanentCrop, Residential, River, SeaLake.\n",
    "\n",
    "### Description of Splitting Images into Classes/Labeling Images\n",
    "The EuroSAT images come pre-labeled, which facilitates the classification task. The dataset was split into a training set comprising 80% of the images and a validation set comprising 20%, ensuring a comprehensive evaluation of the model across varied image data.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras import layers\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 30988 files belonging to 10 classes.\n",
      "Using 24791 files for training.\n",
      "Found 30988 files belonging to 10 classes.\n",
      "Using 6197 files for validation.\n",
      "['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']\n"
     ]
    }
   ],
   "source": [
    "def load_data():\n",
    "    train_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "        'data',\n",
    "        validation_split=0.2,\n",
    "        subset=\"training\",\n",
    "        seed=123,\n",
    "        image_size=(64, 64),\n",
    "        batch_size=32,\n",
    "        label_mode='categorical'\n",
    "    )\n",
    "    \n",
    "    val_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "        'data',\n",
    "        validation_split=0.2,\n",
    "        subset=\"validation\",\n",
    "        seed=123,\n",
    "        image_size=(64, 64),\n",
    "        batch_size=32,\n",
    "        label_mode='categorical'\n",
    "    )\n",
    "    \n",
    "    return train_ds, val_ds, train_ds.class_names\n",
    "\n",
    "train_ds, val_ds, class_names = load_data()\n",
    "\n",
    "class_names = train_ds.class_names\n",
    "\n",
    "print(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample pixel values (0 to 1 range): [180. 183. 156. 177. 186.]\n",
      "Min and max pixel values: 74.0 248.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-05 01:03:10.842952: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for images, labels in train_ds.take(1):\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.imshow(images[0].numpy().astype('uint8'))\n",
    "    plt.title(class_names[tf.argmax(labels[0])])\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"Sample pixel values (0 to 1 range):\", images[0].numpy().flatten()[0:5])\n",
    "    print(\"Min and max pixel values:\", images[0].numpy().min(), images[0].numpy().max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training batches: 775\n",
      "Number of validation batches: 156\n",
      "Number of test batches: 38\n"
     ]
    }
   ],
   "source": [
    "val_batches = tf.data.experimental.cardinality(val_ds)\n",
    "test_ds = val_ds.take(val_batches // 5)\n",
    "validation_ds = val_ds.skip(val_batches // 5)\n",
    "\n",
    "\n",
    "print('Number of training batches:', tf.data.experimental.cardinality(train_ds).numpy())\n",
    "print('Number of validation batches:', tf.data.experimental.cardinality(validation_ds).numpy())\n",
    "print('Number of test batches:', tf.data.experimental.cardinality(test_ds).numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-05 01:03:10.923071: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for images, labels in train_ds.take(1):\n",
    "    for i in range(9):\n",
    "        ax = plt.subplot(3, 3, i + 1)\n",
    "        plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
    "        class_index = np.argmax(labels[i])\n",
    "        plt.title(class_names[class_index])\n",
    "        plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_classes = len(train_ds.class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Augmentation Description\n",
    "To enhance the robustness of the model against variations in real-world satellite images, several data augmentation techniques were applied. These included random flips (both horizontal and vertical), random rotations (up to 20 degrees), random zoom (up to 20%), and random contrast adjustments. These techniques help simulate different capture conditions and photographic variations, aiding the model in learning more generalized features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "data_augmentation = keras.Sequential([\n",
    "    layers.RandomFlip(\"horizontal_and_vertical\"),\n",
    "    layers.RandomRotation(0.2),\n",
    "    layers.RandomZoom(0.2),\n",
    "    layers.RandomContrast(0.1)\n",
    "])\n",
    "\n",
    "def augment_data(dataset):\n",
    "    return dataset.map(lambda x, y: (data_augmentation(x, training=True), y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-05 01:03:11.358116: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for images, labels in train_ds.take(1):\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    first_image = images[0]\n",
    "    class_index = np.argmax(labels[0])\n",
    "    class_name = class_names[class_index]\n",
    "    \n",
    "    for i in range(9):\n",
    "        ax = plt.subplot(3, 3, i + 1)\n",
    "        augmented_image = data_augmentation(np.expand_dims(first_image, 0))\n",
    "        plt.imshow(augmented_image[0].numpy().astype(\"uint8\"))\n",
    "        plt.title(class_name)\n",
    "        plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the TensorFlow dataset: 775\n"
     ]
    }
   ],
   "source": [
    "dataset_length = tf.data.experimental.cardinality(train_ds).numpy()\n",
    "\n",
    "print(\"Length of the TensorFlow dataset:\", dataset_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "\n",
    "### Initial Training and Fine Tuning\n",
    "The model's initial training utilized a pre-trained EfficientNetB0 architecture with the top layers tailored for our classification needs. The base model's layers were initially frozen. Fine-tuning was later applied by unfreezing all layers and continuing training, which refined the model's ability to classify complex images more accurately.\n",
    "\n",
    "### Comparison of Performance\n",
    "Initially, the model achieved a validation accuracy of around 92%. Post fine-tuning, this accuracy improved to approximately 94%. This indicates the effectiveness of fine-tuning in enhancing the model's capability to distinguish subtle features in satellite images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from tensorflow.keras.applications import EfficientNetB0\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications import EfficientNetB0\n",
    "\n",
    "def create_efficientnet_model(input_shape, num_classes):\n",
    "    inputs = keras.Input(shape=input_shape)\n",
    "\n",
    "    scale_layer = keras.layers.Rescaling(scale=1 / 127.5, offset=-1)\n",
    "    x = scale_layer(inputs)\n",
    "    \n",
    "    base_model = EfficientNetB0(include_top=False, weights=\"imagenet\", input_tensor=inputs)\n",
    "    base_model.trainable = False \n",
    "\n",
    "\n",
    "    x = layers.GlobalAveragePooling2D()(base_model.output)\n",
    "    x = layers.Dense(512, activation='relu')(x)\n",
    "    x = layers.Dense(256, activation='relu')(x)\n",
    "    x = layers.Dropout(0.3)(x) \n",
    "    outputs = layers.Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "    return model\n",
    "\n",
    "def fine_tune_model(model, train_ds, val_ds, epochs):\n",
    "    base_model = model.layers[1]\n",
    "    base_model.trainable = True\n",
    "\n",
    "    model.compile(optimizer=keras.optimizers.Adam(1e-5),\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    history_fine = model.fit(train_ds, epochs=epochs, validation_data=val_ds, callbacks=callbacks)\n",
    "\n",
    "    return history_fine\n",
    "\n",
    "train_ds = augment_data(train_ds)\n",
    "\n",
    "model = create_efficientnet_model((64, 64, 3), len(class_names))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint('best_model.keras', save_best_only=True, monitor='val_loss', mode='min'),\n",
    "    keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, min_lr=0.00001),\n",
    "    keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n",
    "]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 62ms/step - accuracy: 0.8013 - loss: 0.6025 - val_accuracy: 0.9022 - val_loss: 0.3121 - learning_rate: 0.0010\n",
      "Epoch 2/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 49ms/step - accuracy: 0.8930 - loss: 0.3150 - val_accuracy: 0.9083 - val_loss: 0.2748 - learning_rate: 0.0010\n",
      "Epoch 3/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 51ms/step - accuracy: 0.9116 - loss: 0.2614 - val_accuracy: 0.9187 - val_loss: 0.2372 - learning_rate: 0.0010\n",
      "Epoch 4/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 55ms/step - accuracy: 0.9210 - loss: 0.2349 - val_accuracy: 0.9179 - val_loss: 0.2646 - learning_rate: 0.0010\n",
      "Epoch 5/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 50ms/step - accuracy: 0.9255 - loss: 0.2102 - val_accuracy: 0.9239 - val_loss: 0.2526 - learning_rate: 0.0010\n",
      "Epoch 6/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 46ms/step - accuracy: 0.9359 - loss: 0.1938 - val_accuracy: 0.9261 - val_loss: 0.2490 - learning_rate: 0.0010\n",
      "Epoch 7/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 48ms/step - accuracy: 0.9340 - loss: 0.1869 - val_accuracy: 0.9209 - val_loss: 0.2702 - learning_rate: 0.0010\n",
      "Epoch 8/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 47ms/step - accuracy: 0.9416 - loss: 0.1678 - val_accuracy: 0.9231 - val_loss: 0.2535 - learning_rate: 0.0010\n",
      "Epoch 9/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 48ms/step - accuracy: 0.9444 - loss: 0.1588 - val_accuracy: 0.9219 - val_loss: 0.2727 - learning_rate: 0.0010\n",
      "Epoch 10/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 50ms/step - accuracy: 0.9485 - loss: 0.1468 - val_accuracy: 0.9171 - val_loss: 0.2794 - learning_rate: 0.0010\n"
     ]
    }
   ],
   "source": [
    "initial_epochs = 10\n",
    "history = model.fit(train_ds, validation_data=validation_ds, epochs=initial_epochs, callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 62ms/step - accuracy: 0.9292 - loss: 0.2041 - val_accuracy: 0.9219 - val_loss: 0.2278 - learning_rate: 1.0000e-05\n",
      "Epoch 2/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 51ms/step - accuracy: 0.9348 - loss: 0.1898 - val_accuracy: 0.9249 - val_loss: 0.2211 - learning_rate: 1.0000e-05\n",
      "Epoch 3/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 55ms/step - accuracy: 0.9367 - loss: 0.1840 - val_accuracy: 0.9287 - val_loss: 0.2178 - learning_rate: 1.0000e-05\n",
      "Epoch 4/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 53ms/step - accuracy: 0.9371 - loss: 0.1831 - val_accuracy: 0.9283 - val_loss: 0.2168 - learning_rate: 1.0000e-05\n",
      "Epoch 5/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 51ms/step - accuracy: 0.9410 - loss: 0.1729 - val_accuracy: 0.9291 - val_loss: 0.2138 - learning_rate: 1.0000e-05\n",
      "Epoch 6/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 51ms/step - accuracy: 0.9419 - loss: 0.1698 - val_accuracy: 0.9299 - val_loss: 0.2147 - learning_rate: 1.0000e-05\n",
      "Epoch 7/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 50ms/step - accuracy: 0.9427 - loss: 0.1690 - val_accuracy: 0.9299 - val_loss: 0.2139 - learning_rate: 1.0000e-05\n",
      "Epoch 8/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 50ms/step - accuracy: 0.9438 - loss: 0.1659 - val_accuracy: 0.9307 - val_loss: 0.2151 - learning_rate: 1.0000e-05\n",
      "Epoch 9/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 50ms/step - accuracy: 0.9399 - loss: 0.1711 - val_accuracy: 0.9315 - val_loss: 0.2115 - learning_rate: 1.0000e-05\n",
      "Epoch 10/10\n",
      "\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 49ms/step - accuracy: 0.9461 - loss: 0.1608 - val_accuracy: 0.9327 - val_loss: 0.2127 - learning_rate: 1.0000e-05\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "history_fine = fine_tune_model(model, train_ds, validation_ds, epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(acc, label='Training Accuracy')\n",
    "plt.plot(val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([min(plt.ylim()),1])\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel('Cross Entropy')\n",
    "#plt.ylim([0,1.0])\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc += history_fine.history['accuracy']\n",
    "val_acc += history_fine.history['val_accuracy']\n",
    "\n",
    "loss += history_fine.history['loss']\n",
    "val_loss += history_fine.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(acc, label='Training Accuracy')\n",
    "plt.plot(val_acc, label='Validation Accuracy')\n",
    "plt.ylim([0.4, 1]) # set the y-axis limits\n",
    "plt.plot([initial_epochs-1,initial_epochs-1],\n",
    "plt.ylim(), label='Start Fine Tuning')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, label='Validation Loss')\n",
    "plt.plot([initial_epochs-1,initial_epochs-1],\n",
    "plt.ylim(), label='Start Fine Tuning')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test dataset evaluation\n",
      "\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - accuracy: 0.9317 - loss: 0.2277\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.19601286947727203, 0.9358552694320679]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Test dataset evaluation\")\n",
    "model.evaluate(test_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_15\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_15\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_11      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ rescaling_16        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)         │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ normalization_5     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span> │ rescaling_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Normalization</span>)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ rescaling_17        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ normalization_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)         │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem_conv_pad       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ rescaling_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem_conv (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │ stem_conv_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem_bn             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ stem_conv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem_activation     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ stem_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">288</span> │ stem_activation[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block1a_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1a_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1a_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">264</span> │ block1a_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">288</span> │ block1a_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │ block1a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ block1a_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ block1a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block1a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block2a_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2a_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_dwconv_pad  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2a_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │ block2a_dwconv_p… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block2a_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2a_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2a_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">388</span> │ block2a_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span> │ block2a_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │ block2a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block2a_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span> │ block2a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block2a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block2b_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2b_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,296</span> │ block2b_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block2b_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2b_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2b_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">870</span> │ block2b_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,008</span> │ block2b_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │ block2b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block2b_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span> │ block2b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2b_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │ block2a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block2b_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block3a_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3a_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_dwconv_pad  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">19</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">19</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3a_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,600</span> │ block3a_dwconv_p… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block3a_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3a_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3a_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">870</span> │ block3a_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,008</span> │ block3a_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block3a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">5,760</span> │ block3a_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │ block3a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,600</span> │ block3a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span> │ block3b_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3b_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">6,000</span> │ block3b_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span> │ block3b_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3b_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3b_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,410</span> │ block3b_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,640</span> │ block3b_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block3b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,600</span> │ block3b_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │ block3b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3b_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block3a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,600</span> │ block3b_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span> │ block4a_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4a_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_dwconv_pad  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4a_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,160</span> │ block4a_dwconv_p… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span> │ block4a_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4a_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4a_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,410</span> │ block4a_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,640</span> │ block4a_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block4a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">19,200</span> │ block4a_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │ block4a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">38,400</span> │ block4a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,920</span> │ block4b_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4b_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,320</span> │ block4b_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,920</span> │ block4b_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4b_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4b_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,620</span> │ block4b_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">10,080</span> │ block4b_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block4b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">38,400</span> │ block4b_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │ block4b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4b_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block4a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">38,400</span> │ block4b_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,920</span> │ block4c_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4c_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,320</span> │ block4c_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,920</span> │ block4c_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4c_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4c_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4c_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,620</span> │ block4c_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">10,080</span> │ block4c_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4c_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block4c_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">38,400</span> │ block4c_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │ block4c_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4c_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4c_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block4b_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">38,400</span> │ block4c_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,920</span> │ block5a_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5a_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">12,000</span> │ block5a_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,920</span> │ block5a_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5a_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5a_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,620</span> │ block5a_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">10,080</span> │ block5a_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block5a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">53,760</span> │ block5a_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │ block5a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">75,264</span> │ block5a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,688</span> │ block5b_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5b_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">16,800</span> │ block5b_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,688</span> │ block5b_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5b_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5b_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,844</span> │ block5b_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">19,488</span> │ block5b_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block5b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">75,264</span> │ block5b_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │ block5b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5b_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block5a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">75,264</span> │ block5b_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,688</span> │ block5c_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5c_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">16,800</span> │ block5c_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,688</span> │ block5c_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5c_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5c_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5c_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,844</span> │ block5c_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">19,488</span> │ block5c_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5c_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block5c_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">75,264</span> │ block5c_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │ block5c_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5c_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5c_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block5b_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">75,264</span> │ block5c_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,688</span> │ block6a_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6a_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_dwconv_pad  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6a_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">16,800</span> │ block6a_dwconv_p… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,688</span> │ block6a_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6a_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6a_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,844</span> │ block6a_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │     <span style=\"color: #00af00; text-decoration-color: #00af00\">19,488</span> │ block6a_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">672</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ block6a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │    <span style=\"color: #00af00; text-decoration-color: #00af00\">129,024</span> │ block6a_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block6a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,184</span> │ block6a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block6b_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6b_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">28,800</span> │ block6b_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block6b_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6b_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6b_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,344</span> │ block6b_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>,      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">56,448</span> │ block6b_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6b_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │ block6b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,184</span> │ block6b_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block6b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6b_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6b_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block6a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,184</span> │ block6b_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block6c_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6c_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">28,800</span> │ block6c_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block6c_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6c_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6c_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6c_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,344</span> │ block6c_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>,      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">56,448</span> │ block6c_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6c_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │ block6c_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,184</span> │ block6c_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block6c_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6c_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6c_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block6b_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,184</span> │ block6c_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block6d_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6d_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">28,800</span> │ block6d_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block6d_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6d_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6d_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6d_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,344</span> │ block6d_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>,      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">56,448</span> │ block6d_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6d_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │ block6d_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,184</span> │ block6d_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block6d_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_drop        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6d_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6d_drop[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block6c_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_expand_conv │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,184</span> │ block6d_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_expand_bn   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block7a_expand_c… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_expand_act… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7a_expand_b… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_dwconv      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">10,368</span> │ block7a_expand_a… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_bn          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block7a_dwconv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_activation  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7a_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_squeeze  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_reshape  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7a_se_squee… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_reduce   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,344</span> │ block7a_se_resha… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_expand   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>,      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">56,448</span> │ block7a_se_reduc… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_excite   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7a_activati… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1152</span>)             │            │ block7a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_project_co… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>) │    <span style=\"color: #00af00; text-decoration-color: #00af00\">368,640</span> │ block7a_se_excit… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_project_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>) │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │ block7a_project_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ top_conv (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">409,600</span> │ block7a_project_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ top_bn              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">5,120</span> │ top_conv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ top_activation      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ top_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ top_activation[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)       │    <span style=\"color: #00af00; text-decoration-color: #00af00\">655,872</span> │ global_average_p… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)       │    <span style=\"color: #00af00; text-decoration-color: #00af00\">131,328</span> │ dense_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,570</span> │ dropout_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\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_11      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ rescaling_16        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ input_layer_11[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mRescaling\u001b[0m)         │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ normalization_5     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m) │          \u001b[38;5;34m7\u001b[0m │ rescaling_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mNormalization\u001b[0m)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ rescaling_17        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ normalization_5[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mRescaling\u001b[0m)         │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem_conv_pad       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m65\u001b[0m, \u001b[38;5;34m65\u001b[0m, \u001b[38;5;34m3\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ rescaling_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem_conv (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │        \u001b[38;5;34m864\u001b[0m │ stem_conv_pad[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem_bn             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ stem_conv[\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",
       "│ stem_activation     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ stem_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │        \u001b[38;5;34m288\u001b[0m │ stem_activation[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ block1a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block1a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ block1a_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ block1a_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m8\u001b[0m)   │        \u001b[38;5;34m264\u001b[0m │ block1a_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m)  │        \u001b[38;5;34m288\u001b[0m │ block1a_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block1a_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │ \u001b[38;5;34m32\u001b[0m)               │            │ block1a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │        \u001b[38;5;34m512\u001b[0m │ block1a_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1a_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │         \u001b[38;5;34m64\u001b[0m │ block1a_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │      \u001b[38;5;34m1,536\u001b[0m │ block1a_project_… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m96\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │        \u001b[38;5;34m384\u001b[0m │ block2a_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2a_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m96\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_dwconv_pad  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2a_expand_a… │\n",
       "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)     │ \u001b[38;5;34m96\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │        \u001b[38;5;34m864\u001b[0m │ block2a_dwconv_p… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │ \u001b[38;5;34m96\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │        \u001b[38;5;34m384\u001b[0m │ block2a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m96\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ block2a_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ block2a_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m4\u001b[0m)   │        \u001b[38;5;34m388\u001b[0m │ block2a_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m)  │        \u001b[38;5;34m480\u001b[0m │ block2a_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2a_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │ \u001b[38;5;34m96\u001b[0m)               │            │ block2a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │      \u001b[38;5;34m2,304\u001b[0m │ block2a_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2a_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │         \u001b[38;5;34m96\u001b[0m │ block2a_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │      \u001b[38;5;34m3,456\u001b[0m │ block2a_project_… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │        \u001b[38;5;34m576\u001b[0m │ block2b_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2b_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │      \u001b[38;5;34m1,296\u001b[0m │ block2b_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │        \u001b[38;5;34m576\u001b[0m │ block2b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m144\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block2b_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m144\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block2b_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m6\u001b[0m)   │        \u001b[38;5;34m870\u001b[0m │ block2b_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m144\u001b[0m) │      \u001b[38;5;34m1,008\u001b[0m │ block2b_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2b_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │ \u001b[38;5;34m144\u001b[0m)              │            │ block2b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │      \u001b[38;5;34m3,456\u001b[0m │ block2b_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │         \u001b[38;5;34m96\u001b[0m │ block2b_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2b_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2b_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m24\u001b[0m)               │            │ block2a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │      \u001b[38;5;34m3,456\u001b[0m │ block2b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │        \u001b[38;5;34m576\u001b[0m │ block3a_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3a_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_dwconv_pad  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m19\u001b[0m, \u001b[38;5;34m19\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3a_expand_a… │\n",
       "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)     │ \u001b[38;5;34m144\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m144\u001b[0m) │      \u001b[38;5;34m3,600\u001b[0m │ block3a_dwconv_p… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m144\u001b[0m) │        \u001b[38;5;34m576\u001b[0m │ block3a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m144\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block3a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m144\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block3a_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m144\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block3a_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m6\u001b[0m)   │        \u001b[38;5;34m870\u001b[0m │ block3a_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m144\u001b[0m) │      \u001b[38;5;34m1,008\u001b[0m │ block3a_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m144\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block3a_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block3a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m40\u001b[0m)  │      \u001b[38;5;34m5,760\u001b[0m │ block3a_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3a_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m40\u001b[0m)  │        \u001b[38;5;34m160\u001b[0m │ block3a_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │      \u001b[38;5;34m9,600\u001b[0m │ block3a_project_… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │        \u001b[38;5;34m960\u001b[0m │ block3b_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block3b_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │      \u001b[38;5;34m6,000\u001b[0m │ block3b_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │        \u001b[38;5;34m960\u001b[0m │ block3b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block3b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m240\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block3b_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block3b_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m10\u001b[0m)  │      \u001b[38;5;34m2,410\u001b[0m │ block3b_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m240\u001b[0m) │      \u001b[38;5;34m2,640\u001b[0m │ block3b_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block3b_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block3b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m40\u001b[0m)  │      \u001b[38;5;34m9,600\u001b[0m │ block3b_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m40\u001b[0m)  │        \u001b[38;5;34m160\u001b[0m │ block3b_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m40\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ block3b_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3b_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m40\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ block3b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block3a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │      \u001b[38;5;34m9,600\u001b[0m │ block3b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │        \u001b[38;5;34m960\u001b[0m │ block4a_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4a_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_dwconv_pad  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4a_expand_a… │\n",
       "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m240\u001b[0m) │      \u001b[38;5;34m2,160\u001b[0m │ block4a_dwconv_p… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m240\u001b[0m) │        \u001b[38;5;34m960\u001b[0m │ block4a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m240\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block4a_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4a_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m10\u001b[0m)  │      \u001b[38;5;34m2,410\u001b[0m │ block4a_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m240\u001b[0m) │      \u001b[38;5;34m2,640\u001b[0m │ block4a_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m240\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4a_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block4a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │     \u001b[38;5;34m19,200\u001b[0m │ block4a_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4a_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │        \u001b[38;5;34m320\u001b[0m │ block4a_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │     \u001b[38;5;34m38,400\u001b[0m │ block4a_project_… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │      \u001b[38;5;34m1,920\u001b[0m │ block4b_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4b_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │      \u001b[38;5;34m4,320\u001b[0m │ block4b_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │      \u001b[38;5;34m1,920\u001b[0m │ block4b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block4b_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4b_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m)  │      \u001b[38;5;34m9,620\u001b[0m │ block4b_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │     \u001b[38;5;34m10,080\u001b[0m │ block4b_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4b_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block4b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │     \u001b[38;5;34m38,400\u001b[0m │ block4b_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │        \u001b[38;5;34m320\u001b[0m │ block4b_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ block4b_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4b_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ block4b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block4a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │     \u001b[38;5;34m38,400\u001b[0m │ block4b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │      \u001b[38;5;34m1,920\u001b[0m │ block4c_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4c_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │      \u001b[38;5;34m4,320\u001b[0m │ block4c_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │      \u001b[38;5;34m1,920\u001b[0m │ block4c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block4c_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4c_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m)  │      \u001b[38;5;34m9,620\u001b[0m │ block4c_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │     \u001b[38;5;34m10,080\u001b[0m │ block4c_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block4c_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block4c_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │     \u001b[38;5;34m38,400\u001b[0m │ block4c_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │        \u001b[38;5;34m320\u001b[0m │ block4c_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ block4c_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4c_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m80\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ block4c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block4b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │     \u001b[38;5;34m38,400\u001b[0m │ block4c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │      \u001b[38;5;34m1,920\u001b[0m │ block5a_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5a_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │     \u001b[38;5;34m12,000\u001b[0m │ block5a_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │      \u001b[38;5;34m1,920\u001b[0m │ block5a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block5a_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5a_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m)  │      \u001b[38;5;34m9,620\u001b[0m │ block5a_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │     \u001b[38;5;34m10,080\u001b[0m │ block5a_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m480\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5a_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block5a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │     \u001b[38;5;34m53,760\u001b[0m │ block5a_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5a_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │        \u001b[38;5;34m448\u001b[0m │ block5a_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m75,264\u001b[0m │ block5a_project_… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │      \u001b[38;5;34m2,688\u001b[0m │ block5b_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5b_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m16,800\u001b[0m │ block5b_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │      \u001b[38;5;34m2,688\u001b[0m │ block5b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m672\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block5b_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5b_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m28\u001b[0m)  │     \u001b[38;5;34m18,844\u001b[0m │ block5b_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m19,488\u001b[0m │ block5b_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5b_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block5b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │     \u001b[38;5;34m75,264\u001b[0m │ block5b_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │        \u001b[38;5;34m448\u001b[0m │ block5b_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5b_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5b_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block5a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m75,264\u001b[0m │ block5b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │      \u001b[38;5;34m2,688\u001b[0m │ block5c_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5c_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m16,800\u001b[0m │ block5c_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │      \u001b[38;5;34m2,688\u001b[0m │ block5c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m672\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block5c_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5c_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m28\u001b[0m)  │     \u001b[38;5;34m18,844\u001b[0m │ block5c_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m19,488\u001b[0m │ block5c_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5c_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block5c_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │     \u001b[38;5;34m75,264\u001b[0m │ block5c_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │        \u001b[38;5;34m448\u001b[0m │ block5c_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5c_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5c_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m112\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block5c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block5b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m75,264\u001b[0m │ block5c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │      \u001b[38;5;34m2,688\u001b[0m │ block6a_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6a_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_dwconv_pad  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6a_expand_a… │\n",
       "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m16,800\u001b[0m │ block6a_dwconv_p… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m672\u001b[0m) │      \u001b[38;5;34m2,688\u001b[0m │ block6a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m672\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ block6a_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6a_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m28\u001b[0m)  │     \u001b[38;5;34m18,844\u001b[0m │ block6a_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m672\u001b[0m) │     \u001b[38;5;34m19,488\u001b[0m │ block6a_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m672\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6a_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ block6a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │    \u001b[38;5;34m129,024\u001b[0m │ block6a_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6a_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │        \u001b[38;5;34m768\u001b[0m │ block6a_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │    \u001b[38;5;34m221,184\u001b[0m │ block6a_project_… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m4,608\u001b[0m │ block6b_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6b_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │     \u001b[38;5;34m28,800\u001b[0m │ block6b_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m4,608\u001b[0m │ block6b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1152\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ block6b_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6b_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m48\u001b[0m)  │     \u001b[38;5;34m55,344\u001b[0m │ block6b_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m,      │     \u001b[38;5;34m56,448\u001b[0m │ block6b_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6b_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │ \u001b[38;5;34m1152\u001b[0m)             │            │ block6b_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │    \u001b[38;5;34m221,184\u001b[0m │ block6b_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │        \u001b[38;5;34m768\u001b[0m │ block6b_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6b_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6b_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block6a_project_… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │    \u001b[38;5;34m221,184\u001b[0m │ block6b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m4,608\u001b[0m │ block6c_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6c_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │     \u001b[38;5;34m28,800\u001b[0m │ block6c_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m4,608\u001b[0m │ block6c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1152\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ block6c_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6c_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m48\u001b[0m)  │     \u001b[38;5;34m55,344\u001b[0m │ block6c_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m,      │     \u001b[38;5;34m56,448\u001b[0m │ block6c_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6c_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │ \u001b[38;5;34m1152\u001b[0m)             │            │ block6c_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │    \u001b[38;5;34m221,184\u001b[0m │ block6c_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │        \u001b[38;5;34m768\u001b[0m │ block6c_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6c_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6c_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block6b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │    \u001b[38;5;34m221,184\u001b[0m │ block6c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m4,608\u001b[0m │ block6d_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6d_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │     \u001b[38;5;34m28,800\u001b[0m │ block6d_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m4,608\u001b[0m │ block6d_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6d_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1152\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ block6d_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6d_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m48\u001b[0m)  │     \u001b[38;5;34m55,344\u001b[0m │ block6d_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m,      │     \u001b[38;5;34m56,448\u001b[0m │ block6d_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block6d_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │ \u001b[38;5;34m1152\u001b[0m)             │            │ block6d_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │    \u001b[38;5;34m221,184\u001b[0m │ block6d_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │        \u001b[38;5;34m768\u001b[0m │ block6d_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_drop        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6d_project_… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6d_add (\u001b[38;5;33mAdd\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m192\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ block6d_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block6c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │    \u001b[38;5;34m221,184\u001b[0m │ block6d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_expand_bn   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m4,608\u001b[0m │ block7a_expand_c… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block7a_expand_b… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_dwconv      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │     \u001b[38;5;34m10,368\u001b[0m │ block7a_expand_a… │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_bn          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m4,608\u001b[0m │ block7a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_activation  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block7a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_squeeze  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1152\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ block7a_activati… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_reshape  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block7a_se_squee… │\n",
       "│ (\u001b[38;5;33mReshape\u001b[0m)           │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_reduce   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m48\u001b[0m)  │     \u001b[38;5;34m55,344\u001b[0m │ block7a_se_resha… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_expand   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m,      │     \u001b[38;5;34m56,448\u001b[0m │ block7a_se_reduc… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m1152\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_se_excite   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block7a_activati… │\n",
       "│ (\u001b[38;5;33mMultiply\u001b[0m)          │ \u001b[38;5;34m1152\u001b[0m)             │            │ block7a_se_expan… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m320\u001b[0m) │    \u001b[38;5;34m368,640\u001b[0m │ block7a_se_excit… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7a_project_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m320\u001b[0m) │      \u001b[38;5;34m1,280\u001b[0m │ block7a_project_… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ top_conv (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │    \u001b[38;5;34m409,600\u001b[0m │ block7a_project_… │\n",
       "│                     │ \u001b[38;5;34m1280\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ top_bn              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │      \u001b[38;5;34m5,120\u001b[0m │ top_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1280\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ top_activation      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ top_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1280\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1280\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ top_activation[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)       │    \u001b[38;5;34m655,872\u001b[0m │ global_average_p… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_16 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)       │    \u001b[38;5;34m131,328\u001b[0m │ dense_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ dense_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_17 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)        │      \u001b[38;5;34m2,570\u001b[0m │ dropout_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,418,883</span> (24.49 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,418,883\u001b[0m (24.49 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">789,770</span> (3.01 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m789,770\u001b[0m (3.01 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,049,571</span> (15.45 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m4,049,571\u001b[0m (15.45 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,579,542</span> (6.03 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m1,579,542\u001b[0m (6.03 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test dataset evaluation\n",
      "\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - accuracy: 0.9380 - loss: 0.2024\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.20488472282886505, 0.9358552694320679]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Test dataset evaluation\")\n",
    "model.evaluate(test_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5s/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-05 01:16:54.328388: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "y_true = []\n",
    "y_pred = []\n",
    "\n",
    "for images, labels in test_ds:\n",
    "\n",
    "    predictions = model.predict(images)\n",
    "    predicted_labels = np.argmax(predictions, axis=1) \n",
    "\n",
    "\n",
    "    if labels.ndim > 1 and labels.shape[1] > 1:\n",
    "        labels = np.argmax(labels, axis=1)\n",
    "\n",
    "    y_true.extend(labels)\n",
    "    y_pred.extend(predicted_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "cm = confusion_matrix(y_true, y_pred)a\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('sentinel_classificatiion_model_generated.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-04 23:42:16.121259: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "class_names = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']\n",
    "\n",
    "def plot_images(images, labels, predictions):\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(9):\n",
    "        plt.subplot(3, 3, i + 1)\n",
    "        plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
    "        plt.title(f\"True: {class_names[np.argmax(labels[i])]}, Pred: {class_names[np.argmax(predictions[i])]}\")\n",
    "        plt.axis(\"off\")\n",
    "\n",
    "\n",
    "for images, labels in test_ds.take(1): \n",
    "    predictions = model.predict(images)\n",
    "    plot_images(images, labels, predictions)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Application\n",
    "\n",
    "### Deployment\n",
    "The model was deployed using a Gradio web interface, which provides a user-friendly GUI for uploading images and receiving instant classifications.\n",
    "\n",
    "### Demo\n",
    "A live demo of the application can be accessed at: [https://huggingface.co/spaces/Lars2000/sentinel](https://huggingface.co/spaces/Lars2000/sentinel)\n",
    "\n",
    "### Results of User Validation\n",
    "User feedback highlighted the application's ease of use and accuracy. Positive points included quick response times and informative confidence scores for different classifications. Suggestions for improvement were focused on enhancing performance with low-contrast images and those affected by cloud cover.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "The project successfully demonstrated the application of convolutional neural networks in classifying satellite imagery, utilizing both transfer learning and fine-tuning approaches to achieve high accuracy. Future improvements could address the challenges identified through user feedback, potentially involving the incorporation of additional data preprocessing steps or advanced neural network architectures.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}