transfer_learning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xm_s6t-aJAeR"
      },
      "source": [
        "## Transfer Learning\n",
        "\n",
        "*Prepared by:*  \n",
        "**Jude Michael Teves**  \n",
        "Faculty, Software Technology Department  \n",
        "College of Computer Studies - De La Salle University"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we’ll explore transfer learning. First, we’ll train a neural network model from scratch, and then we’ll see how using a pre-trained model can significantly boost performance."
      ],
      "metadata": {
        "id": "E_CubdZYDk3C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preliminaries"
      ],
      "metadata": {
        "id": "2WgIXce5DZ7G"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "id": "Yg8gIm97-bav",
        "outputId": "51059ae2-9ca7-4338-fb20-d6d617cd9fc8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.4, 0.7607843137254902, 0.6470588235294118),\n",
              " (0.9882352941176471, 0.5529411764705883, 0.3843137254901961),\n",
              " (0.5529411764705883, 0.6274509803921569, 0.796078431372549),\n",
              " (0.9058823529411765, 0.5411764705882353, 0.7647058823529411),\n",
              " (0.6509803921568628, 0.8470588235294118, 0.32941176470588235),\n",
              " (1.0, 0.8509803921568627, 0.1843137254901961),\n",
              " (0.8980392156862745, 0.7686274509803922, 0.5803921568627451),\n",
              " (0.7019607843137254, 0.7019607843137254, 0.7019607843137254)]"
            ],
            "text/html": [
              "<svg  width=\"440\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#66c2a5;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fc8d62;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8da0cb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e78ac3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a6d854;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffd92f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e5c494;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b3b3b3;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "\n",
        "sns.set_palette('Set2')\n",
        "sns.color_palette('Set2')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Simple CNN Model\n",
        "\n",
        "We'll start by building a simple CNN model for comparison. I'll skip the detailed explanation since I've already covered this in a previous lecture and notebook. To keep training and inference efficient, we'll use 5,000 samples for training and 1,000 for testing—still solid sample sizes for our purposes."
      ],
      "metadata": {
        "id": "gcVyGM6z7Qd9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preparation"
      ],
      "metadata": {
        "id": "-lCUDAZeDftm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "\n",
        "from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "\n",
        "seed = 42\n",
        "np.random.seed(seed)\n",
        "tf.random.set_seed(seed)\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
        "\n",
        "# Extract the random 1k samples\n",
        "random_indices_train = np.random.choice(train_images.shape[0], 5000, replace=False)\n",
        "random_indices_test = np.random.choice(test_images.shape[0], 1000, replace=False)\n",
        "train_images_sampled = train_images[random_indices_train]\n",
        "test_images_sampled = test_images[random_indices_test]\n",
        "train_labels_sampled = train_labels[random_indices_train]\n",
        "test_labels_sampled = test_labels[random_indices_test]\n",
        "\n",
        "# Normalize pixel values to be between 0 and 1\n",
        "train_images = train_images_sampled / 255.0\n",
        "test_images = test_images_sampled / 255.0\n",
        "\n",
        "# Convert class vectors to binary class matrices\n",
        "train_labels = to_categorical(train_labels_sampled)\n",
        "test_labels = to_categorical(test_labels_sampled)\n",
        "\n",
        "train_images.min(), train_images.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zz3PsnkH7Nlv",
        "outputId": "a17c1259-59e1-4fa8-fa12-66eba1ffdfd1"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels.shape, test_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVWKL6TU7sNF",
        "outputId": "3e63cc33-f224-4249-f0b5-d802d80c1856"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5000, 10), (1000, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Building the model"
      ],
      "metadata": {
        "id": "pSO37Wk27yU7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(32, activation='relu'))\n",
        "model.add(layers.Dense(10, activation='softmax'))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "k6TYMUJG7s1z",
        "outputId": "2b1d1f25-dc30-4854-e67a-65ff94b1c94f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "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: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m896\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │          \u001b[38;5;34m32,800\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │             \u001b[38;5;34m330\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "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>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<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\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<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\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<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\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<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\">32</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">32,800</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<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\">330</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m89,450\u001b[0m (349.41 KB)\n"
            ],
            "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\">89,450</span> (349.41 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m89,450\u001b[0m (349.41 KB)\n"
            ],
            "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\">89,450</span> (349.41 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "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\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the model"
      ],
      "metadata": {
        "id": "tIw-ZA_b7_0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_images, train_labels, epochs=10,\n",
        "                    validation_data=(test_images, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wW_8e1d_7-9w",
        "outputId": "e261decc-93f7-4db5-fe26-c491c6c236bb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 43ms/step - accuracy: 0.1462 - loss: 2.2419 - val_accuracy: 0.2670 - val_loss: 1.9387\n",
            "Epoch 2/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 49ms/step - accuracy: 0.3099 - loss: 1.8591 - val_accuracy: 0.3240 - val_loss: 1.7574\n",
            "Epoch 3/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 44ms/step - accuracy: 0.3812 - loss: 1.6532 - val_accuracy: 0.3740 - val_loss: 1.6387\n",
            "Epoch 4/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 38ms/step - accuracy: 0.4445 - loss: 1.5163 - val_accuracy: 0.4200 - val_loss: 1.5336\n",
            "Epoch 5/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 48ms/step - accuracy: 0.4914 - loss: 1.4033 - val_accuracy: 0.4490 - val_loss: 1.4979\n",
            "Epoch 6/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 41ms/step - accuracy: 0.5317 - loss: 1.3177 - val_accuracy: 0.4720 - val_loss: 1.4350\n",
            "Epoch 7/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 51ms/step - accuracy: 0.5527 - loss: 1.2530 - val_accuracy: 0.4940 - val_loss: 1.4075\n",
            "Epoch 8/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 39ms/step - accuracy: 0.5758 - loss: 1.1874 - val_accuracy: 0.4910 - val_loss: 1.4127\n",
            "Epoch 9/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 39ms/step - accuracy: 0.5949 - loss: 1.1247 - val_accuracy: 0.5120 - val_loss: 1.4073\n",
            "Epoch 10/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 48ms/step - accuracy: 0.6239 - loss: 1.0658 - val_accuracy: 0.5180 - val_loss: 1.3812\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performance Evaluation\n",
        "\n",
        "We can see that we have achieved around 52% test accuracy with a simple CNN model."
      ],
      "metadata": {
        "id": "eThHi9LbEdbt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'], label='train accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'test accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Cifar10 CNN Performance')\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "Srt33J1I8Ccz",
        "outputId": "cd3d960a-cdfc-474c-c6ab-a52d6c3a1e8e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 - 0s - 12ms/step - accuracy: 0.5180 - loss: 1.3812\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4oGQJvDJVj1"
      },
      "source": [
        "## Introduction to Transfer Learning\n",
        "\n",
        "Transfer learning leverages features from a model trained on one problem to tackle a related task—like using cat breed detection model to help identify other feline creatures such as lion and tiger. This approach is especially useful when data is limited, making training a full model from scratch impractical."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Transfer learning workflow\n",
        "\n",
        "The typical workflow in deep learning transfer learning involves:\n",
        "\n",
        "- `Reusing Pre-trained Model`: Import layers and weights from an already trained model.\n",
        "- `Freezing`: Lock these layers to preserve their learned information. We set `trainable = False`.\n",
        "- `Adding New Layers`: Stack new, trainable layers on top of the base model that adapt the existing features to predictions on your specific dataset.\n",
        "- `Fine-tuning (Optional)`: Unfreeze part (or all) of the model and training it on new data with a low learning rate. This can further refine features, enhancing performance for the new task.\n",
        "- `Training on New Data`: Train the new model on your dataset."
      ],
      "metadata": {
        "id": "_AH9BGeVGW9V"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFIIYQJBbdO_"
      },
      "source": [
        "### Building our base model: MobileNetV2\n",
        "\n",
        "MobileNetV2 is a deep neural network architecture designed by Google for efficient mobile and edge device applications. It builds on its predecessor, MobileNetV1, by improving both speed and accuracy while minimizing computational costs, making it ideal for devices with limited resources like smartphones and IoT devices.\n",
        "\n",
        "I'll be using <a href=\"https://keras.io/api/applications/mobilenet/\">MobileNetV2 base model</a> in this notebook as it is very lightweight and Google Colaboratory's memory limit can handle it. This is also one of the available pre-trained models in Keras. Normally, I'd make use of more powerful ones."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NHLOWV0aZiRz",
        "outputId": "88976403-5de9-4ce7-c429-51ae3b3ceca1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_96_no_top.h5\n",
            "\u001b[1m9406464/9406464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"mobilenetv2_1.00_96\"\u001b[0m\n"
            ],
            "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: \"mobilenetv2_1.00_96\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTraina…\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩\n",
              "│ input_layer_1          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ -                   │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)           │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ Conv1 (\u001b[38;5;33mConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m864\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ bn_Conv1               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m128\u001b[0m │ Conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ Conv1_relu (\u001b[38;5;33mReLU\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ bn_Conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │    \u001b[1m-\u001b[0m    │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_depthwi… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m288\u001b[0m │ Conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_depthwi… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m128\u001b[0m │ expanded_conv_dept… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_depthwi… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ expanded_conv_dept… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_project  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m16\u001b[0m)   │          \u001b[38;5;34m512\u001b[0m │ expanded_conv_dept… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_project… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m16\u001b[0m)   │           \u001b[38;5;34m64\u001b[0m │ expanded_conv_proj… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m96\u001b[0m)   │        \u001b[38;5;34m1,536\u001b[0m │ expanded_conv_proj… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m96\u001b[0m)   │          \u001b[38;5;34m384\u001b[0m │ block_1_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m96\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ block_1_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_pad            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m96\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ block_1_expand_rel… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)        │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m96\u001b[0m)   │          \u001b[38;5;34m864\u001b[0m │ block_1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m96\u001b[0m)   │          \u001b[38;5;34m384\u001b[0m │ block_1_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m96\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ block_1_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)   │        \u001b[38;5;34m2,304\u001b[0m │ block_1_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)   │           \u001b[38;5;34m96\u001b[0m │ block_1_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │        \u001b[38;5;34m3,456\u001b[0m │ block_1_project_BN… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │          \u001b[38;5;34m576\u001b[0m │ block_2_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_2_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │        \u001b[38;5;34m1,296\u001b[0m │ block_2_expand_rel… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │          \u001b[38;5;34m576\u001b[0m │ block_2_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_2_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)   │        \u001b[38;5;34m3,456\u001b[0m │ block_2_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)   │           \u001b[38;5;34m96\u001b[0m │ block_2_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_add (\u001b[38;5;33mAdd\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ block_1_project_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_2_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │        \u001b[38;5;34m3,456\u001b[0m │ block_2_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │          \u001b[38;5;34m576\u001b[0m │ block_3_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_3_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_pad            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_3_expand_rel… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)        │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │        \u001b[38;5;34m1,296\u001b[0m │ block_3_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │          \u001b[38;5;34m576\u001b[0m │ block_3_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m144\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_3_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │        \u001b[38;5;34m4,608\u001b[0m │ block_3_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m128\u001b[0m │ block_3_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │        \u001b[38;5;34m6,144\u001b[0m │ block_3_project_BN… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │          \u001b[38;5;34m768\u001b[0m │ block_4_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_4_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │        \u001b[38;5;34m1,728\u001b[0m │ block_4_expand_rel… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │          \u001b[38;5;34m768\u001b[0m │ block_4_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_4_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │        \u001b[38;5;34m6,144\u001b[0m │ block_4_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m128\u001b[0m │ block_4_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_add (\u001b[38;5;33mAdd\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ block_3_project_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_4_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │        \u001b[38;5;34m6,144\u001b[0m │ block_4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │          \u001b[38;5;34m768\u001b[0m │ block_5_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_5_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │        \u001b[38;5;34m1,728\u001b[0m │ block_5_expand_rel… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │          \u001b[38;5;34m768\u001b[0m │ block_5_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_5_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │        \u001b[38;5;34m6,144\u001b[0m │ block_5_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │          \u001b[38;5;34m128\u001b[0m │ block_5_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_add (\u001b[38;5;33mAdd\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ block_4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],  │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_5_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │        \u001b[38;5;34m6,144\u001b[0m │ block_5_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │          \u001b[38;5;34m768\u001b[0m │ block_6_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_6_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_pad            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m192\u001b[0m)  │            \u001b[38;5;34m0\u001b[0m │ block_6_expand_rel… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)        │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │        \u001b[38;5;34m1,728\u001b[0m │ block_6_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │          \u001b[38;5;34m768\u001b[0m │ block_6_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m192\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_6_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │       \u001b[38;5;34m12,288\u001b[0m │ block_6_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │          \u001b[38;5;34m256\u001b[0m │ block_6_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │       \u001b[38;5;34m24,576\u001b[0m │ block_6_project_BN… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m1,536\u001b[0m │ block_7_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_7_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m3,456\u001b[0m │ block_7_expand_rel… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m1,536\u001b[0m │ block_7_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_7_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │       \u001b[38;5;34m24,576\u001b[0m │ block_7_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │          \u001b[38;5;34m256\u001b[0m │ block_7_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_add (\u001b[38;5;33mAdd\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │            \u001b[38;5;34m0\u001b[0m │ block_6_project_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_7_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │       \u001b[38;5;34m24,576\u001b[0m │ block_7_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m1,536\u001b[0m │ block_8_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_8_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m3,456\u001b[0m │ block_8_expand_rel… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m1,536\u001b[0m │ block_8_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_8_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │       \u001b[38;5;34m24,576\u001b[0m │ block_8_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │          \u001b[38;5;34m256\u001b[0m │ block_8_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_add (\u001b[38;5;33mAdd\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │            \u001b[38;5;34m0\u001b[0m │ block_7_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],  │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_8_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_expand         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │       \u001b[38;5;34m24,576\u001b[0m │ block_8_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_expand_BN      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m1,536\u001b[0m │ block_9_expand[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_expand_relu    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_9_expand_BN[\u001b[38;5;34m…\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_depthwise      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m3,456\u001b[0m │ block_9_expand_rel… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_depthwise_BN   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m1,536\u001b[0m │ block_9_depthwise[\u001b[38;5;34m…\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_depthwise_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_9_depthwise_… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_project        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │       \u001b[38;5;34m24,576\u001b[0m │ block_9_depthwise_… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_project_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │          \u001b[38;5;34m256\u001b[0m │ block_9_project[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_add (\u001b[38;5;33mAdd\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │            \u001b[38;5;34m0\u001b[0m │ block_8_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],  │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_9_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_expand        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │       \u001b[38;5;34m24,576\u001b[0m │ block_9_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_expand_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m1,536\u001b[0m │ block_10_expand[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_expand_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_10_expand_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_depthwise     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m3,456\u001b[0m │ block_10_expand_re… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_depthwise_BN  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │        \u001b[38;5;34m1,536\u001b[0m │ block_10_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_depthwise_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_10_depthwise… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_project       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │       \u001b[38;5;34m36,864\u001b[0m │ block_10_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_project_BN    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │          \u001b[38;5;34m384\u001b[0m │ block_10_project[\u001b[38;5;34m0\u001b[0m… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_expand        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │       \u001b[38;5;34m55,296\u001b[0m │ block_10_project_B… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_expand_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m2,304\u001b[0m │ block_11_expand[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_expand_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_11_expand_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_depthwise     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m5,184\u001b[0m │ block_11_expand_re… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_depthwise_BN  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m2,304\u001b[0m │ block_11_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_depthwise_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_11_depthwise… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_project       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │       \u001b[38;5;34m55,296\u001b[0m │ block_11_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_project_BN    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │          \u001b[38;5;34m384\u001b[0m │ block_11_project[\u001b[38;5;34m0\u001b[0m… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_add (\u001b[38;5;33mAdd\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │            \u001b[38;5;34m0\u001b[0m │ block_10_project_B… │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_11_project_B… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_expand        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │       \u001b[38;5;34m55,296\u001b[0m │ block_11_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_expand_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m2,304\u001b[0m │ block_12_expand[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_expand_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_12_expand_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_depthwise     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m5,184\u001b[0m │ block_12_expand_re… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_depthwise_BN  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m2,304\u001b[0m │ block_12_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_depthwise_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_12_depthwise… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_project       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │       \u001b[38;5;34m55,296\u001b[0m │ block_12_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_project_BN    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │          \u001b[38;5;34m384\u001b[0m │ block_12_project[\u001b[38;5;34m0\u001b[0m… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_add (\u001b[38;5;33mAdd\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │            \u001b[38;5;34m0\u001b[0m │ block_11_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_12_project_B… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_expand        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │       \u001b[38;5;34m55,296\u001b[0m │ block_12_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_expand_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m2,304\u001b[0m │ block_13_expand[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_expand_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_13_expand_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_pad           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_13_expand_re… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)        │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_depthwise     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m5,184\u001b[0m │ block_13_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_depthwise_BN  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │        \u001b[38;5;34m2,304\u001b[0m │ block_13_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_depthwise_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m576\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_13_depthwise… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_project       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)    │       \u001b[38;5;34m92,160\u001b[0m │ block_13_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_project_BN    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)    │          \u001b[38;5;34m640\u001b[0m │ block_13_project[\u001b[38;5;34m0\u001b[0m… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_expand        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │      \u001b[38;5;34m153,600\u001b[0m │ block_13_project_B… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_expand_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m3,840\u001b[0m │ block_14_expand[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_expand_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_14_expand_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_depthwise     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m8,640\u001b[0m │ block_14_expand_re… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_depthwise_BN  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m3,840\u001b[0m │ block_14_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_depthwise_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_14_depthwise… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_project       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)    │      \u001b[38;5;34m153,600\u001b[0m │ block_14_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_project_BN    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)    │          \u001b[38;5;34m640\u001b[0m │ block_14_project[\u001b[38;5;34m0\u001b[0m… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_add (\u001b[38;5;33mAdd\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_13_project_B… │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_14_project_B… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_expand        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │      \u001b[38;5;34m153,600\u001b[0m │ block_14_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_expand_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m3,840\u001b[0m │ block_15_expand[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_expand_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_15_expand_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_depthwise     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m8,640\u001b[0m │ block_15_expand_re… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_depthwise_BN  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m3,840\u001b[0m │ block_15_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_depthwise_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_15_depthwise… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_project       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)    │      \u001b[38;5;34m153,600\u001b[0m │ block_15_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_project_BN    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)    │          \u001b[38;5;34m640\u001b[0m │ block_15_project[\u001b[38;5;34m0\u001b[0m… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_add (\u001b[38;5;33mAdd\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m160\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_14_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │    \u001b[1m-\u001b[0m    │\n",
              "│                        │                      │              │ block_15_project_B… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_expand        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │      \u001b[38;5;34m153,600\u001b[0m │ block_15_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_expand_BN     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m3,840\u001b[0m │ block_16_expand[\u001b[38;5;34m0\u001b[0m]… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_expand_relu   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_16_expand_BN… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_depthwise     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m8,640\u001b[0m │ block_16_expand_re… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_depthwise_BN  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │        \u001b[38;5;34m3,840\u001b[0m │ block_16_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_depthwise_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m960\u001b[0m)    │            \u001b[38;5;34m0\u001b[0m │ block_16_depthwise… │    \u001b[1m-\u001b[0m    │\n",
              "│ (\u001b[38;5;33mReLU\u001b[0m)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_project       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │      \u001b[38;5;34m307,200\u001b[0m │ block_16_depthwise… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mConv2D\u001b[0m)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_project_BN    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │        \u001b[38;5;34m1,280\u001b[0m │ block_16_project[\u001b[38;5;34m0\u001b[0m… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ Conv_1 (\u001b[38;5;33mConv2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m1280\u001b[0m)   │      \u001b[38;5;34m409,600\u001b[0m │ block_16_project_B… │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ Conv_1_bn              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m1280\u001b[0m)   │        \u001b[38;5;34m5,120\u001b[0m │ Conv_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ out_relu (\u001b[38;5;33mReLU\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m1280\u001b[0m)   │            \u001b[38;5;34m0\u001b[0m │ Conv_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │    \u001b[1m-\u001b[0m    │\n",
              "└────────────────────────┴──────────────────────┴──────────────┴─────────────────────┴─────────┘\n"
            ],
            "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>┃<span style=\"font-weight: bold\"> Traina… </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩\n",
              "│ input_layer_1          │ (<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\">96</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                   │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)           │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ Conv1 (<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\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ bn_Conv1               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ Conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ Conv1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ bn_Conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │    <span style=\"font-weight: bold\">-</span>    │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_depthwi… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">288</span> │ Conv1_relu[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_depthwi… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ expanded_conv_dept… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_depthwi… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ expanded_conv_dept… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_project  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ expanded_conv_dept… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ expanded_conv_project… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ expanded_conv_proj… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ expanded_conv_proj… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_1_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_1_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_pad            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_1_expand_rel… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)        │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │ block_1_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_1_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_1_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_1_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_1_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span> │ block_1_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_1_project_BN… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block_2_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_2_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,296</span> │ block_2_expand_rel… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block_2_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_2_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_2_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span> │ block_2_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_2_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\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_1_project_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_2_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_2_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block_3_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_3_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_pad            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_3_expand_rel… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)        │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,296</span> │ block_3_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ block_3_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_3_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │ block_3_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_3_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block_3_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_3_project_BN… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_4_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_4_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,728</span> │ block_4_expand_rel… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_4_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_4_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_4_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block_4_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_4_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\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_3_project_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_4_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_4_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_5_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_5_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,728</span> │ block_5_expand_rel… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_5_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_5_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_5_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block_5_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_5_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\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_4_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],  │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_5_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block_5_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_6_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_6_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_pad            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_6_expand_rel… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)        │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,728</span> │ block_6_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span> │ block_6_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_6_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">12,288</span> │ block_6_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_6_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block_6_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_6_project_BN… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_7_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_7_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_7_expand_rel… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_7_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_7_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_7_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block_7_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_7_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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_6_project_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_7_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_7_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_8_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_8_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_8_expand_rel… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_8_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_8_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_8_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block_8_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_8_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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_7_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],  │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_8_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_expand         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_8_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_expand_BN      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_9_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_expand_relu    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_9_expand_BN[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_depthwise      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_9_expand_rel… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_depthwise_BN   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_9_depthwise[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_depthwise_relu │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_9_depthwise_… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_project        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_9_depthwise_… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_project_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block_9_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_9_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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_8_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],  │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_9_project_BN… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_expand        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">24,576</span> │ block_9_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_expand_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_10_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_expand_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_10_expand_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_depthwise     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,456</span> │ block_10_expand_re… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_depthwise_BN  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> │ block_10_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_depthwise_re… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_10_depthwise… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_project       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">36,864</span> │ block_10_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_10_project_BN    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_10_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_expand        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_10_project_B… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_expand_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_11_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_expand_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_11_expand_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_depthwise     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">5,184</span> │ block_11_expand_re… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_depthwise_BN  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_11_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_depthwise_re… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_11_depthwise… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_project       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_11_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_project_BN    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_11_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_11_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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_10_project_B… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_11_project_B… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_expand        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_11_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_expand_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_12_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_expand_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_12_expand_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_depthwise     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">5,184</span> │ block_12_expand_re… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_depthwise_BN  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_12_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_depthwise_re… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_12_depthwise… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_project       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_12_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_project_BN    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> │ block_12_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_12_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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_11_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_12_project_B… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_expand        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">55,296</span> │ block_12_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_expand_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_13_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_expand_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_13_expand_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_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\">576</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_13_expand_re… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)        │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_depthwise     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">5,184</span> │ block_13_pad[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_depthwise_BN  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │ block_13_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_depthwise_re… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_13_depthwise… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_project       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">92,160</span> │ block_13_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_13_project_BN    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ block_13_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_expand        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_13_project_B… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_expand_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_14_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_expand_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_14_expand_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_depthwise     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">8,640</span> │ block_14_expand_re… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_depthwise_BN  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_14_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_depthwise_re… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_14_depthwise… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_project       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_14_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_project_BN    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ block_14_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_14_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\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_13_project_B… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_14_project_B… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_expand        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_14_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_expand_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_15_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_expand_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_15_expand_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_depthwise     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">8,640</span> │ block_15_expand_re… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_depthwise_BN  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_15_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_depthwise_re… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_15_depthwise… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_project       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_15_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_project_BN    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ block_15_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_15_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\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_14_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│                        │                      │              │ block_15_project_B… │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_expand        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">153,600</span> │ block_15_add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_expand_BN     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_16_expand[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_expand_relu   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_16_expand_BN… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_depthwise     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">8,640</span> │ block_16_expand_re… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)      │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_depthwise_BN  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> │ block_16_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_depthwise_re… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">960</span>)    │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block_16_depthwise… │    <span style=\"font-weight: bold\">-</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                 │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_project       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">307,200</span> │ block_16_depthwise… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ block_16_project_BN    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │ block_16_project[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ Conv_1 (<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\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)   │      <span style=\"color: #00af00; text-decoration-color: #00af00\">409,600</span> │ block_16_project_B… │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ Conv_1_bn              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">5,120</span> │ Conv_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │                      │              │                     │         │\n",
              "├────────────────────────┼──────────────────────┼──────────────┼─────────────────────┼─────────┤\n",
              "│ out_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)   │            <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ Conv_1_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │    <span style=\"font-weight: bold\">-</span>    │\n",
              "└────────────────────────┴──────────────────────┴──────────────┴─────────────────────┴─────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,257,984\u001b[0m (8.61 MB)\n"
            ],
            "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\">2,257,984</span> (8.61 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,223,872\u001b[0m (8.48 MB)\n"
            ],
            "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\">2,223,872</span> (8.48 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m34,112\u001b[0m (133.25 KB)\n"
            ],
            "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\">34,112</span> (133.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input\n",
        "\n",
        "HEIGHT = 96\n",
        "WIDTH = 96\n",
        "\n",
        "base_model = MobileNetV2(weights='imagenet',\n",
        "                      include_top=False,\n",
        "                      input_shape=(HEIGHT, WIDTH, 3),\n",
        "                      classes=train_labels.shape[1])\n",
        "\n",
        "base_model.summary(show_trainable=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Input Data Preprocessing\n",
        "\n",
        "1. We will be resizing out input images because `MobileNetV2` works better with higher resolution images.\n",
        "\n",
        "2. We will preprocess the input to match the base model."
      ],
      "metadata": {
        "id": "YrN94LkfLxH0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Resize images to 96x96 for better performance\n",
        "train_images = np.array(tf.image.resize(train_images_sampled, (96, 96)))\n",
        "test_images = np.array(tf.image.resize(test_images_sampled, (96, 96)))\n",
        "\n",
        "# Match the preprocessing in the base model\n",
        "train_images = preprocess_input(train_images)\n",
        "test_images = preprocess_input(test_images)\n",
        "\n",
        "train_images.min(), train_images.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFuXw45ULjeJ",
        "outputId": "2fb49503-9d90-4717-f660-b966e6ebaae3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RrHM71N2LmXu",
        "outputId": "fc6f6484-5a63-4696-8d40-757064fdb4a0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 96, 96, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels.shape, test_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEYL83UsLppr",
        "outputId": "ced1402d-d03e-4d9d-94bf-3b6b07086b4c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5000, 10), (1000, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCo2GfI1KCgi"
      },
      "source": [
        "### Freezing layers and fine-tuning\n",
        "\n",
        "The layers in a neural network model have 2 types of weights:\n",
        "\n",
        "- `trainable_weights`: weights that are updated during training to minimize loss (e.g., via gradient descent).\n",
        "- `non_trainable_weights`: weights that won’t be trained. During the forward pass, these weights are used as is.\n",
        "\n",
        "You can call these attributes in Keras.\n",
        "\n",
        "Also including here the code snippet that you can use if you want to retrain specific layers of the base model (fine-tuning) for future reference.\n",
        "\n",
        "```python\n",
        "base_model.trainable = False\n",
        "\n",
        "# Set a specific layer by name to be trainable\n",
        "layer_names = ['Conv_1']  # Replace with your target layer's name\n",
        "for layer in base_model.layers:\n",
        "    if layer.name in layer_names:\n",
        "        layer.trainable = True\n",
        "        print(f\"Layer {layer.name} set to trainable\")\n",
        "\n",
        "base_model.summary(show_trainable=True)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have added a few layers after the base model so that it can adapt to our new dataset."
      ],
      "metadata": {
        "id": "g5LblKz7F6fG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "LEdSQwRzsIev",
        "outputId": "f45a2135-3135-45b8-fab2-3009cdd70dac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_8\"\u001b[0m\n"
            ],
            "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_8\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape                \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTraina…\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━┩\n",
              "│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m3\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ mobilenetv2_1.00_96 (\u001b[38;5;33mFunctional\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m1280\u001b[0m)           │     \u001b[38;5;34m2,257,984\u001b[0m │    \u001b[1;91mN\u001b[0m    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11520\u001b[0m)                │             \u001b[38;5;34m0\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11520\u001b[0m)                │             \u001b[38;5;34m0\u001b[0m │    \u001b[1m-\u001b[0m    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                  │     \u001b[38;5;34m1,474,688\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                   │         \u001b[38;5;34m8,256\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                   │           \u001b[38;5;34m650\u001b[0m │    \u001b[1;38;5;34mY\u001b[0m    │\n",
              "└─────────────────────────────────────┴──────────────────────────────┴───────────────┴─────────┘\n"
            ],
            "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\"> Traina… </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━┩\n",
              "│ input_layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)          │ (<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\">96</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ mobilenetv2_1.00_96 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,257,984</span> │    <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">N</span>    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11520</span>)                │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ dropout (<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\">11520</span>)                │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │    <span style=\"font-weight: bold\">-</span>    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ dense_2 (<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\">128</span>)                  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,474,688</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ dense_3 (<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\">64</span>)                   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "├─────────────────────────────────────┼──────────────────────────────┼───────────────┼─────────┤\n",
              "│ dense_4 (<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\">650</span> │    <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>    │\n",
              "└─────────────────────────────────────┴──────────────────────────────┴───────────────┴─────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,741,578\u001b[0m (14.27 MB)\n"
            ],
            "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\">3,741,578</span> (14.27 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,483,594\u001b[0m (5.66 MB)\n"
            ],
            "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\">1,483,594</span> (5.66 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,257,984\u001b[0m (8.61 MB)\n"
            ],
            "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\">2,257,984</span> (8.61 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "base_model.trainable = False\n",
        "\n",
        "inputs = keras.Input(shape=(HEIGHT, WIDTH, 3))\n",
        "\n",
        "# Ensure that the base_model is running in inference mode by passing `training=False`.\n",
        "x = base_model(inputs, training=False)\n",
        "\n",
        "x = keras.layers.Flatten()(x)\n",
        "x = keras.layers.Dropout(0.2)(x)\n",
        "x = keras.layers.Dense(128, activation='relu')(x)\n",
        "x = keras.layers.Dense(64, activation='relu')(x)\n",
        "\n",
        "# A Dense classifier with 10 outputs (multi-class classification)\n",
        "outputs = keras.layers.Dense(10, activation=('softmax'))(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "model.summary(show_trainable=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the model"
      ],
      "metadata": {
        "id": "5eQqNSvzGxQn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=keras.optimizers.Adam(),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgI56Hl1Apeg",
        "outputId": "4662bb6f-1ed2-46c0-e851-62c0f13621f3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 261ms/step - accuracy: 0.6476 - loss: 1.1322 - val_accuracy: 0.8240 - val_loss: 0.5590\n",
            "Epoch 2/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 276ms/step - accuracy: 0.8812 - loss: 0.3652 - val_accuracy: 0.8180 - val_loss: 0.5974\n",
            "Epoch 3/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 297ms/step - accuracy: 0.9239 - loss: 0.2065 - val_accuracy: 0.8120 - val_loss: 0.6646\n",
            "Epoch 4/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 275ms/step - accuracy: 0.9559 - loss: 0.1261 - val_accuracy: 0.7880 - val_loss: 0.9277\n",
            "Epoch 5/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 258ms/step - accuracy: 0.9606 - loss: 0.1093 - val_accuracy: 0.8180 - val_loss: 0.7808\n",
            "Epoch 6/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 255ms/step - accuracy: 0.9662 - loss: 0.1080 - val_accuracy: 0.8160 - val_loss: 0.8970\n",
            "Epoch 7/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 258ms/step - accuracy: 0.9688 - loss: 0.0911 - val_accuracy: 0.8200 - val_loss: 0.9294\n",
            "Epoch 8/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 254ms/step - accuracy: 0.9795 - loss: 0.0607 - val_accuracy: 0.8260 - val_loss: 0.8874\n",
            "Epoch 9/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 249ms/step - accuracy: 0.9867 - loss: 0.0356 - val_accuracy: 0.8390 - val_loss: 0.9044\n",
            "Epoch 10/10\n",
            "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 268ms/step - accuracy: 0.9934 - loss: 0.0255 - val_accuracy: 0.8320 - val_loss: 0.8881\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performance Evaluation\n",
        "\n",
        "We can see that our train and test set accuracies (99% and 83%) are much higher than the manually trained CNN model."
      ],
      "metadata": {
        "id": "CDEWbC5GG5Mj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'], label='train accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'test accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Cifar10 MobileNetV2 Transfer Learning Performance')\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "0v42BMsa6xCL",
        "outputId": "a4d28c9d-8c23-4663-fd4e-26017d903de4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 - 6s - 202ms/step - accuracy: 0.8320 - loss: 0.8881\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBqMSg8vI9zI"
      },
      "source": [
        "## End\n",
        "<sup>made by **Jude Michael Teves**</sup> <br>\n",
        "<sup>for comments, corrections, suggestions, please email:</sup><sup> <href>judemichaelteves@gmail.com</href> or <href>jude.teves@dlsu.edu.ph</href></sup><br>\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}