transferLearning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prakr97/observations/blob/master/transferLearning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHU60-Ob6v2n",
        "outputId": "7d2363ef-7929-47a8-b8b4-6e37f00e67ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "AWvkgG6N6hKG"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "VDMN2qJY6hKN"
      },
      "outputs": [],
      "source": [
        "Dataset = pd.read_csv(\"/content/drive/MyDrive/frames/4K - Sheet1.csv\")    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "uIhzjgnK6hKO"
      },
      "outputs": [],
      "source": [
        "Dataset[\"label\"] = Dataset[\"label\"].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "zcUtVQL9gyk0"
      },
      "outputs": [],
      "source": [
        "Dataset[\"name\"] = Dataset[\"name\"].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OnrnFmQz6hKP",
        "outputId": "da14e7dd-3ad8-4f5e-f003-217b0d333e43"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    name label\n",
              "0  1.jpg     3\n",
              "1  2.jpg     3\n",
              "2  3.jpg     1\n",
              "3  4.jpg     3\n",
              "4  5.jpg     2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c377549-02ca-4e94-a219-80e78ea4e538\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.jpg</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.jpg</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.jpg</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.jpg</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c377549-02ca-4e94-a219-80e78ea4e538')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8c377549-02ca-4e94-a219-80e78ea4e538 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8c377549-02ca-4e94-a219-80e78ea4e538');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "Dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "uEbN84rg6hKQ"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "u9f0akfS6hKR",
        "outputId": "1f34e725-9c62-48b2-9093-084031ccb55a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "fig, ax = plt.subplots(figsize = (10, 4))                                \n",
        "sns.countplot(x ='label', data=Dataset)                                  \n",
        "plt.xlabel(\"Class Label\")                                                \n",
        "plt.ylabel(\"Number of Samples\")                                          \n",
        "plt.show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JeqwGnJL6hKT"
      },
      "source": [
        "## Splitting Data into Train/Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Qn6OCGo6hKT"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jV1teFOt6hKU"
      },
      "outputs": [],
      "source": [
        "Data_train, Data_test = train_test_split(Dataset, test_size=0.2)                   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zDWzXZxt6hKV"
      },
      "outputs": [],
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wGCSAU8q6hKV"
      },
      "outputs": [],
      "source": [
        "datagen = ImageDataGenerator(rescale=1./255)                            "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0e_-dIvgXPam",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c98082e-23c3-4fa9-f0ad-9e8a81a89bec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: frames/998.jpg          \n",
            "  inflating: frames/999.jpg          \n"
          ]
        }
      ],
      "source": [
        "! unzip drive/MyDrive/frames/frames.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y8eRs8yN6hKW"
      },
      "outputs": [],
      "source": [
        "\n",
        "dir1='/content/frames'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Create an ImageDataGenerator for data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest'\n",
        ")\n",
        "\n",
        "# Train data generator with augmentation\n",
        "train_gen = datagen.flow_from_dataframe(\n",
        "    dataframe=Data_train,           \n",
        "    directory=dir1,                   \n",
        "    batch_size=30,                    \n",
        "    class_mode=\"categorical\",         \n",
        "    x_col=\"name\",                     \n",
        "    color_mode=\"rgb\",                 \n",
        "    y_col=\"label\",                    \n",
        "    target_size=(224,224)\n",
        ")\n",
        "\n",
        "# Validation data generator without augmentation\n",
        "valid_gen = datagen.flow_from_dataframe(\n",
        "    dataframe=Data_test,            \n",
        "    directory=dir1,                  \n",
        "    batch_size=30,                    \n",
        "    class_mode=\"categorical\",        \n",
        "    x_col=\"name\",                     \n",
        "    color_mode=\"rgb\",                \n",
        "    y_col=\"label\",                    \n",
        "    target_size=(224,224)\n",
        ")\n"
      ],
      "metadata": {
        "id": "eLyWFCBqWjGT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "823b2db2-d4fb-4da4-b515-13e20c414532"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4800 validated image filenames belonging to 5 classes.\n",
            "Found 1200 validated image filenames belonging to 5 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fQ4EeEcq6hKY"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "from tensorflow.keras import applications"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rpvCOVK76hKY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1886b680-6b47-43aa-9fbe-89b7b635c70f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet152v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "234545216/234545216 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "ResNet_model = keras.applications.ResNet152V2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMA-wra86hKZ"
      },
      "source": [
        "## Building Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RBunEUpF6hKZ"
      },
      "outputs": [],
      "source": [
        "from keras import Model \n",
        "from keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten,GlobalAveragePooling2D\n",
        "from keras.models import Sequential"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8vPWzWBP6hKZ"
      },
      "outputs": [],
      "source": [
        "for layer in ResNet_model.layers[:-15]:       \n",
        "    layer.trainable = False\n",
        "accuracy=92\n",
        "x = ResNet_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Flatten()(x)\n",
        "x = Dense(units=512, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = Dense(units=512, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)\n",
        "output  = Dense(units=5, activation='softmax')(x)\n",
        "model = Model(ResNet_model.input, output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rT3a2js66hKZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcbc6de8-703b-447d-cfa1-b1bcf31ad537"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_conv[0][0]']             \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv2_block1_preact_bn (BatchN  (None, 56, 56, 64)  256         ['pool1_pool[0][0]']             \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2_block1_preact_relu (Acti  (None, 56, 56, 64)  0           ['conv2_block1_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4096        ['conv2_block1_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block1_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36864       ['conv2_block1_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_out (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_conv[0][0]',    \n",
            "                                                                  'conv2_block1_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_preact_bn (BatchN  (None, 56, 56, 256)  1024       ['conv2_block1_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2_block2_preact_relu (Acti  (None, 56, 56, 256)  0          ['conv2_block2_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16384       ['conv2_block2_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block2_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36864       ['conv2_block2_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_out (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
            "                                                                  'conv2_block2_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_preact_bn (BatchN  (None, 56, 56, 256)  1024       ['conv2_block2_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2_block3_preact_relu (Acti  (None, 56, 56, 256)  0          ['conv2_block3_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16384       ['conv2_block3_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block3_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2D)   (None, 28, 28, 64)   36864       ['conv2_block3_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNormal  (None, 28, 28, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activatio  (None, 28, 28, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2D)   (None, 28, 28, 256)  0           ['conv2_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2D)   (None, 28, 28, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_out (Add)         (None, 28, 28, 256)  0           ['max_pooling2d[0][0]',          \n",
            "                                                                  'conv2_block3_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_preact_bn (BatchN  (None, 28, 28, 256)  1024       ['conv2_block3_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv3_block1_preact_relu (Acti  (None, 28, 28, 256)  0          ['conv3_block1_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32768       ['conv3_block1_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block1_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block1_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv3_block1_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_out (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_conv[0][0]',    \n",
            "                                                                  'conv3_block1_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block1_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv3_block2_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block2_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block2_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block2_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block2_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_out (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
            "                                                                  'conv3_block2_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block2_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv3_block3_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block3_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block3_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block3_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block3_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_out (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
            "                                                                  'conv3_block3_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block3_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv3_block4_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block4_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block4_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block4_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block4_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_out (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
            "                                                                  'conv3_block4_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block5_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block4_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv3_block5_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block5_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv3_block5_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block5_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block5_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block5_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block5_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block5_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block5_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block5_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3_block5_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block5_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv3_block5_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block5_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block5_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block5_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block5_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block5_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block5_out (Add)         (None, 28, 28, 512)  0           ['conv3_block4_out[0][0]',       \n",
            "                                                                  'conv3_block5_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block6_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block5_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv3_block6_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block6_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv3_block6_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block6_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block6_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block6_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block6_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block6_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block6_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block6_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3_block6_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block6_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv3_block6_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block6_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block6_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block6_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block6_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block6_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block6_out (Add)         (None, 28, 28, 512)  0           ['conv3_block5_out[0][0]',       \n",
            "                                                                  'conv3_block6_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block7_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block6_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv3_block7_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block7_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv3_block7_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block7_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block7_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block7_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block7_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block7_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block7_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block7_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3_block7_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block7_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv3_block7_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block7_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block7_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block7_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block7_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block7_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block7_out (Add)         (None, 28, 28, 512)  0           ['conv3_block6_out[0][0]',       \n",
            "                                                                  'conv3_block7_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block8_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block7_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv3_block8_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block8_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv3_block8_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block8_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv3_block8_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block8_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block8_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block8_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block8_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block8_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3_block8_2_conv (Conv2D)   (None, 14, 14, 128)  147456      ['conv3_block8_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv3_block8_2_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv3_block8_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block8_2_relu (Activatio  (None, 14, 14, 128)  0          ['conv3_block8_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPooling2D)  (None, 14, 14, 512)  0          ['conv3_block7_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block8_3_conv (Conv2D)   (None, 14, 14, 512)  66048       ['conv3_block8_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block8_out (Add)         (None, 14, 14, 512)  0           ['max_pooling2d_1[0][0]',        \n",
            "                                                                  'conv3_block8_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block1_preact_bn (BatchN  (None, 14, 14, 512)  2048       ['conv3_block8_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv4_block1_preact_relu (Acti  (None, 14, 14, 512)  0          ['conv4_block1_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131072      ['conv4_block1_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block1_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block1_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv4_block1_preact_relu[0][0]'\n",
            "                                )                                ]                                \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_out (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_conv[0][0]',    \n",
            "                                )                                 'conv4_block1_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block2_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block1_out[0][0]']       \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block2_preact_bn[0][0]'] \n",
            " vation)                        )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block2_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block2_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block2_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_out (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
            "                                )                                 'conv4_block2_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block3_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block2_out[0][0]']       \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block3_preact_bn[0][0]'] \n",
            " vation)                        )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block3_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block3_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block3_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_out (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
            "                                )                                 'conv4_block3_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block4_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block3_out[0][0]']       \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block4_preact_bn[0][0]'] \n",
            " vation)                        )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block4_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block4_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block4_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_out (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
            "                                )                                 'conv4_block4_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block5_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block4_out[0][0]']       \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block5_preact_bn[0][0]'] \n",
            " vation)                        )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block5_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block5_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block5_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_out (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
            "                                )                                 'conv4_block5_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block6_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block5_out[0][0]']       \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block6_preact_bn[0][0]'] \n",
            " vation)                        )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block6_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block6_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block6_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_out (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
            "                                )                                 'conv4_block6_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block7_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block6_out[0][0]']       \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block7_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block7_preact_bn[0][0]'] \n",
            " vation)                        )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block7_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block7_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block7_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block7_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block7_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block7_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block7_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block7_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block7_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block7_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block7_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block7_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block7_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block7_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block7_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block7_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block7_out (Add)         (None, 14, 14, 1024  0           ['conv4_block6_out[0][0]',       \n",
            "                                )                                 'conv4_block7_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block8_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block7_out[0][0]']       \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block8_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block8_preact_bn[0][0]'] \n",
            " vation)                        )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block8_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block8_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block8_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block8_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block8_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block8_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block8_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block8_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block8_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block8_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block8_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block8_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block8_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block8_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block8_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block8_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block8_out (Add)         (None, 14, 14, 1024  0           ['conv4_block7_out[0][0]',       \n",
            "                                )                                 'conv4_block8_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block9_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block8_out[0][0]']       \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block9_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block9_preact_bn[0][0]'] \n",
            " vation)                        )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block9_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block9_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv4_block9_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block9_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block9_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block9_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block9_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block9_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv4_block9_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block9_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv4_block9_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block9_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block9_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block9_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block9_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block9_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block9_out (Add)         (None, 14, 14, 1024  0           ['conv4_block8_out[0][0]',       \n",
            "                                )                                 'conv4_block9_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block10_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block9_out[0][0]']       \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block10_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block10_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block10_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block10_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block10_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block10_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block10_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block10_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block10_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block10_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block10_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block10_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block10_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block10_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block10_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block10_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block10_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block10_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block10_out (Add)        (None, 14, 14, 1024  0           ['conv4_block9_out[0][0]',       \n",
            "                                )                                 'conv4_block10_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block11_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block10_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block11_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block11_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block11_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block11_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block11_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block11_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block11_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block11_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block11_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block11_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block11_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block11_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block11_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block11_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block11_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block11_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block11_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block11_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block11_out (Add)        (None, 14, 14, 1024  0           ['conv4_block10_out[0][0]',      \n",
            "                                )                                 'conv4_block11_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block12_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block11_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block12_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block12_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block12_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block12_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block12_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block12_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block12_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block12_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block12_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block12_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block12_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block12_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block12_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block12_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block12_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block12_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block12_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block12_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block12_out (Add)        (None, 14, 14, 1024  0           ['conv4_block11_out[0][0]',      \n",
            "                                )                                 'conv4_block12_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block13_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block12_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block13_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block13_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block13_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block13_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block13_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block13_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block13_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block13_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block13_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block13_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block13_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block13_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block13_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block13_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block13_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block13_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block13_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block13_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block13_out (Add)        (None, 14, 14, 1024  0           ['conv4_block12_out[0][0]',      \n",
            "                                )                                 'conv4_block13_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block14_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block13_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block14_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block14_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block14_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block14_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block14_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block14_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block14_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block14_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block14_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block14_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block14_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block14_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block14_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block14_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block14_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block14_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block14_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block14_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block14_out (Add)        (None, 14, 14, 1024  0           ['conv4_block13_out[0][0]',      \n",
            "                                )                                 'conv4_block14_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block15_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block14_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block15_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block15_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block15_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block15_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block15_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block15_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block15_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block15_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block15_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block15_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block15_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block15_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block15_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block15_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block15_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block15_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block15_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block15_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block15_out (Add)        (None, 14, 14, 1024  0           ['conv4_block14_out[0][0]',      \n",
            "                                )                                 'conv4_block15_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block16_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block15_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block16_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block16_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block16_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block16_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block16_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block16_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block16_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block16_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block16_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block16_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block16_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block16_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block16_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block16_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block16_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block16_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block16_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block16_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block16_out (Add)        (None, 14, 14, 1024  0           ['conv4_block15_out[0][0]',      \n",
            "                                )                                 'conv4_block16_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block17_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block16_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block17_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block17_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block17_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block17_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block17_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block17_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block17_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block17_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block17_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block17_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block17_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block17_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block17_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block17_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block17_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block17_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block17_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block17_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block17_out (Add)        (None, 14, 14, 1024  0           ['conv4_block16_out[0][0]',      \n",
            "                                )                                 'conv4_block17_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block18_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block17_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block18_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block18_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block18_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block18_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block18_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block18_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block18_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block18_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block18_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block18_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block18_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block18_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block18_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block18_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block18_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block18_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block18_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block18_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block18_out (Add)        (None, 14, 14, 1024  0           ['conv4_block17_out[0][0]',      \n",
            "                                )                                 'conv4_block18_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block19_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block18_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block19_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block19_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block19_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block19_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block19_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block19_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block19_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block19_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block19_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block19_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block19_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block19_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block19_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block19_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block19_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block19_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block19_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block19_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block19_out (Add)        (None, 14, 14, 1024  0           ['conv4_block18_out[0][0]',      \n",
            "                                )                                 'conv4_block19_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block20_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block19_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block20_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block20_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block20_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block20_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block20_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block20_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block20_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block20_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block20_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block20_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block20_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block20_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block20_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block20_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block20_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block20_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block20_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block20_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block20_out (Add)        (None, 14, 14, 1024  0           ['conv4_block19_out[0][0]',      \n",
            "                                )                                 'conv4_block20_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block21_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block20_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block21_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block21_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block21_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block21_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block21_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block21_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block21_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block21_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block21_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block21_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block21_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block21_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block21_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block21_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block21_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block21_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block21_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block21_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block21_out (Add)        (None, 14, 14, 1024  0           ['conv4_block20_out[0][0]',      \n",
            "                                )                                 'conv4_block21_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block22_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block21_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block22_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block22_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block22_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block22_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block22_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block22_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block22_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block22_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block22_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block22_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block22_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block22_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block22_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block22_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block22_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block22_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block22_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block22_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block22_out (Add)        (None, 14, 14, 1024  0           ['conv4_block21_out[0][0]',      \n",
            "                                )                                 'conv4_block22_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block23_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block22_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block23_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block23_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block23_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block23_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block23_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block23_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block23_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block23_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block23_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block23_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block23_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block23_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block23_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block23_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block23_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block23_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block23_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block23_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block23_out (Add)        (None, 14, 14, 1024  0           ['conv4_block22_out[0][0]',      \n",
            "                                )                                 'conv4_block23_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block24_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block23_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block24_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block24_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block24_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block24_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block24_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block24_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block24_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block24_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block24_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block24_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block24_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block24_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block24_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block24_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block24_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block24_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block24_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block24_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block24_out (Add)        (None, 14, 14, 1024  0           ['conv4_block23_out[0][0]',      \n",
            "                                )                                 'conv4_block24_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block25_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block24_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block25_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block25_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block25_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block25_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block25_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block25_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block25_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block25_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block25_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block25_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block25_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block25_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block25_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block25_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block25_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block25_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block25_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block25_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block25_out (Add)        (None, 14, 14, 1024  0           ['conv4_block24_out[0][0]',      \n",
            "                                )                                 'conv4_block25_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block26_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block25_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block26_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block26_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block26_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block26_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block26_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block26_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block26_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block26_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block26_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block26_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block26_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block26_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block26_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block26_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block26_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block26_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block26_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block26_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block26_out (Add)        (None, 14, 14, 1024  0           ['conv4_block25_out[0][0]',      \n",
            "                                )                                 'conv4_block26_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block27_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block26_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block27_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block27_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block27_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block27_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block27_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block27_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block27_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block27_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block27_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block27_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block27_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block27_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block27_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block27_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block27_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block27_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block27_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block27_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block27_out (Add)        (None, 14, 14, 1024  0           ['conv4_block26_out[0][0]',      \n",
            "                                )                                 'conv4_block27_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block28_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block27_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block28_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block28_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block28_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block28_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block28_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block28_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block28_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block28_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block28_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block28_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block28_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block28_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block28_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block28_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block28_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block28_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block28_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block28_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block28_out (Add)        (None, 14, 14, 1024  0           ['conv4_block27_out[0][0]',      \n",
            "                                )                                 'conv4_block28_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block29_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block28_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block29_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block29_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block29_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block29_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block29_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block29_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block29_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block29_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block29_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block29_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block29_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block29_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block29_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block29_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block29_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block29_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block29_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block29_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block29_out (Add)        (None, 14, 14, 1024  0           ['conv4_block28_out[0][0]',      \n",
            "                                )                                 'conv4_block29_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block30_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block29_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block30_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block30_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block30_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block30_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block30_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block30_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block30_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block30_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block30_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block30_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block30_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block30_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block30_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block30_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block30_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block30_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block30_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block30_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block30_out (Add)        (None, 14, 14, 1024  0           ['conv4_block29_out[0][0]',      \n",
            "                                )                                 'conv4_block30_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block31_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block30_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block31_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block31_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block31_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block31_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block31_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block31_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block31_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block31_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block31_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block31_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block31_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block31_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block31_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block31_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block31_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block31_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block31_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block31_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block31_out (Add)        (None, 14, 14, 1024  0           ['conv4_block30_out[0][0]',      \n",
            "                                )                                 'conv4_block31_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block32_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block31_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block32_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block32_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block32_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block32_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block32_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block32_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block32_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block32_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block32_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block32_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block32_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block32_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block32_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block32_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block32_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block32_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block32_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block32_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block32_out (Add)        (None, 14, 14, 1024  0           ['conv4_block31_out[0][0]',      \n",
            "                                )                                 'conv4_block32_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block33_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block32_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block33_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block33_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block33_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block33_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block33_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block33_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block33_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block33_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block33_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block33_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block33_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block33_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block33_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block33_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block33_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block33_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block33_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block33_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block33_out (Add)        (None, 14, 14, 1024  0           ['conv4_block32_out[0][0]',      \n",
            "                                )                                 'conv4_block33_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block34_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block33_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block34_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block34_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block34_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block34_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block34_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block34_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block34_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block34_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block34_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block34_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block34_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block34_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block34_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block34_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block34_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block34_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block34_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block34_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block34_out (Add)        (None, 14, 14, 1024  0           ['conv4_block33_out[0][0]',      \n",
            "                                )                                 'conv4_block34_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block35_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block34_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block35_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block35_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block35_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block35_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block35_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block35_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block35_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block35_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block35_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block35_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block35_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block35_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block35_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block35_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block35_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block35_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block35_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block35_2_relu[0][0]']   \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block35_out (Add)        (None, 14, 14, 1024  0           ['conv4_block34_out[0][0]',      \n",
            "                                )                                 'conv4_block35_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block36_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block35_out[0][0]']      \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block36_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block36_preact_bn[0][0]']\n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block36_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block36_preact_relu[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " conv4_block36_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block36_1_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block36_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block36_1_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block36_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block36_1_relu[0][0]']   \n",
            " ng2D)                                                                                            \n",
            "                                                                                                  \n",
            " conv4_block36_2_conv (Conv2D)  (None, 7, 7, 256)    589824      ['conv4_block36_2_pad[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block36_2_bn (BatchNorma  (None, 7, 7, 256)   1024        ['conv4_block36_2_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " conv4_block36_2_relu (Activati  (None, 7, 7, 256)   0           ['conv4_block36_2_bn[0][0]']     \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 1024)  0           ['conv4_block35_out[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block36_3_conv (Conv2D)  (None, 7, 7, 1024)   263168      ['conv4_block36_2_relu[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block36_out (Add)        (None, 7, 7, 1024)   0           ['max_pooling2d_2[0][0]',        \n",
            "                                                                  'conv4_block36_3_conv[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block1_preact_bn (BatchN  (None, 7, 7, 1024)  4096        ['conv4_block36_out[0][0]']      \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv5_block1_preact_relu (Acti  (None, 7, 7, 1024)  0           ['conv5_block1_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524288      ['conv5_block1_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block1_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block1_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv5_block1_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_conv[0][0]',    \n",
            "                                                                  'conv5_block1_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_preact_bn (BatchN  (None, 7, 7, 2048)  8192        ['conv5_block1_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv5_block2_preact_relu (Acti  (None, 7, 7, 2048)  0           ['conv5_block2_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1048576     ['conv5_block2_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block2_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block2_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
            "                                                                  'conv5_block2_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_preact_bn (BatchN  (None, 7, 7, 2048)  8192        ['conv5_block2_out[0][0]']       \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv5_block3_preact_relu (Acti  (None, 7, 7, 2048)  0           ['conv5_block3_preact_bn[0][0]'] \n",
            " vation)                                                                                          \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1048576     ['conv5_block3_preact_relu[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block3_1_relu[0][0]']    \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block3_2_pad[0][0]']     \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
            "                                                                  'conv5_block3_3_conv[0][0]']    \n",
            "                                                                                                  \n",
            " post_bn (BatchNormalization)   (None, 7, 7, 2048)   8192        ['conv5_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " post_relu (Activation)         (None, 7, 7, 2048)   0           ['post_bn[0][0]']                \n",
            "                                                                                                  \n",
            " global_average_pooling2d (Glob  (None, 2048)        0           ['post_relu[0][0]']              \n",
            " alAveragePooling2D)                                                                              \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 2048)         0           ['global_average_pooling2d[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 512)          1049088     ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 512)          0           ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 512)          262656      ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)            (None, 512)          0           ['dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 5)            2565        ['dropout_1[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 59,645,957\n",
            "Trainable params: 6,833,669\n",
            "Non-trainable params: 52,812,288\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(model.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZ5miZsj6hKa"
      },
      "source": [
        "### Setting Loss function, Optimizer and Compling the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wqhUMq046hKa"
      },
      "outputs": [],
      "source": [
        "loss = keras.losses.CategoricalCrossentropy()\n",
        "optimizer = keras.optimizers.Adam(learning_rate=0.001)\n",
        "model.compile(optimizer=optimizer, loss=loss, metrics= ['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mk7kR0Tn6hKa"
      },
      "source": [
        "### Compiling the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9c7CP-I6hKb",
        "outputId": "e897cffe-ec28-4e32-f3e3-13302bb78cc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "160\n",
            "40\n"
          ]
        }
      ],
      "source": [
        "STEP_SIZE_TRAIN=train_gen.n//train_gen.batch_size\n",
        "STEP_SIZE_VALID=valid_gen.n//valid_gen.batch_size\n",
        "\n",
        "print(STEP_SIZE_TRAIN)\n",
        "print(STEP_SIZE_VALID)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dwXycWc26hKb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07b0c461-bdd1-4b54-82dd-14d6865c456a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-683ada8cab79>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  transfer_learning_history = model.fit_generator(generator=train_gen,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "160/160 [==============================] - 172s 941ms/step - loss: 1.2279 - accuracy: 0.5729 - val_loss: 2.7376 - val_accuracy: 0.5683\n",
            "Epoch 2/5\n",
            "160/160 [==============================] - 146s 915ms/step - loss: 1.1001 - accuracy: 0.6252 - val_loss: 1.1916 - val_accuracy: 0.6000\n",
            "Epoch 3/5\n",
            "160/160 [==============================] - 158s 989ms/step - loss: 1.0403 - accuracy: 0.6342 - val_loss: 1.1304 - val_accuracy: 0.6242\n",
            "Epoch 4/5\n",
            "160/160 [==============================] - 145s 904ms/step - loss: 0.9883 - accuracy: 0.6544 - val_loss: 1.0830 - val_accuracy: 0.6250\n",
            "Epoch 5/5\n",
            "160/160 [==============================] - 145s 910ms/step - loss: 0.9702 - accuracy: 0.6608 - val_loss: 1.2965 - val_accuracy: 0.6300\n"
          ]
        }
      ],
      "source": [
        "transfer_learning_history = model.fit_generator(generator=train_gen,\n",
        "                            steps_per_epoch=STEP_SIZE_TRAIN,\n",
        "                            validation_data=valid_gen,\n",
        "                            validation_steps=STEP_SIZE_VALID,\n",
        "                            epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GFrh9b56hKb"
      },
      "source": [
        "## Visualizing accuracy and loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cavVmR2N6hKc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "outputId": "5a9d9976-6394-46ac-e460-08b09de80d3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = transfer_learning_history.history['accuracy']\n",
        "val_acc = transfer_learning_history.history['val_accuracy']\n",
        "\n",
        "loss = transfer_learning_history.history['loss']\n",
        "val_loss = transfer_learning_history.history['val_loss']\n",
        "\n",
        "epochs_range = range(5)\n",
        "\n",
        "plt.figure(figsize=(20, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the model to a file\n",
        "model.save('/content/drive/MyDrive/my_model.h5')"
      ],
      "metadata": {
        "id": "QAwF2yDkPXma"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}