C2W3_Assignment.ipynb

c2w3_assignment.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/johnleung8888/a3ed65df7566ff8c918d5e63ae01d97f/c2w3_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8cj-HBNoEZy"
      },
      "source": [
        "# Week 3: Transfer Learning\n",
        "\n",
        "Welcome to this assignment! This week, you are going to use a technique called `Transfer Learning` in which you utilize an already trained network to help you solve a similar problem to the one it was originally trained to solve.\n",
        "\n",
        "Let's get started!"
      ],
      "id": "f8cj-HBNoEZy"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lbFmQdsZs5eW"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import zipfile\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.preprocessing.image import img_to_array, load_img"
      ],
      "id": "lbFmQdsZs5eW"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPvtLK1GyUWr"
      },
      "source": [
        "## Dataset\n",
        "\n",
        "For this assignment, you will use the `Horse or Human dataset`, which contains images of horses and humans. \n",
        "\n",
        "Download the `training` and `validation` sets by running the cell below:"
      ],
      "id": "RPvtLK1GyUWr"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dIeTNcPEo79J",
        "outputId": "ce39fcb2-deae-42b3-a82e-0728be164ba3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1LYeusSEIiZQpwN-mthh5nKdA75VsKG1U\n",
            "To: /content/validation-horse-or-human.zip\n",
            "100% 11.5M/11.5M [00:00<00:00, 22.6MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Get the Horse or Human validation dataset\n",
        "!gdown --id 1LYeusSEIiZQpwN-mthh5nKdA75VsKG1U\n",
        "\n",
        "test_local_zip = './horse-or-human.zip'\n",
        "zip_ref = zipfile.ZipFile(test_local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/training')\n",
        "\n",
        "val_local_zip = './validation-horse-or-human.zip'\n",
        "zip_ref = zipfile.ZipFile(val_local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/validation')\n",
        "\n",
        "zip_ref.close()"
      ],
      "id": "dIeTNcPEo79J"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4OMDxYS6tmv"
      },
      "source": [
        "This dataset already has an structure that is compatible with Keras' `flow_from_directory` so you don't need to move the images into subdirectories as you did in the previous assignments. However, it is still a good idea to save the paths of the images so you can use them later on:"
      ],
      "id": "x4OMDxYS6tmv"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lHRrmo5CpEw_",
        "lines_to_next_cell": 2,
        "outputId": "d87d89d4-fffd-4691-8adf-41f114ef8aba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 500 images of horses for training.\n",
            "\n",
            "There are 527 images of humans for training.\n",
            "\n",
            "There are 128 images of horses for validation.\n",
            "\n",
            "There are 128 images of humans for validation.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Define the training and validation base directories\n",
        "train_dir = '/tmp/training'\n",
        "validation_dir = '/tmp/validation'\n",
        "\n",
        "# Directory with training horse pictures\n",
        "train_horses_dir = os.path.join(train_dir, 'horses')\n",
        "# Directory with training humans pictures\n",
        "train_humans_dir = os.path.join(train_dir, 'humans')\n",
        "# Directory with validation horse pictures\n",
        "validation_horses_dir = os.path.join(validation_dir, 'horses')\n",
        "# Directory with validation human pictures\n",
        "validation_humans_dir = os.path.join(validation_dir, 'humans')\n",
        "\n",
        "# Check the number of images for each class and set\n",
        "print(f\"There are {len(os.listdir(train_horses_dir))} images of horses for training.\\n\")\n",
        "print(f\"There are {len(os.listdir(train_humans_dir))} images of humans for training.\\n\")\n",
        "print(f\"There are {len(os.listdir(validation_horses_dir))} images of horses for validation.\\n\")\n",
        "print(f\"There are {len(os.listdir(validation_humans_dir))} images of humans for validation.\\n\")"
      ],
      "id": "lHRrmo5CpEw_"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1G5hXBB57c78"
      },
      "source": [
        "Now take a look at a sample image of each one of the classes:"
      ],
      "id": "1G5hXBB57c78"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HgbMs7p0qSKr",
        "outputId": "100f089e-8c0e-4d24-97c4-772df2539b06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample horse image:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Sample human image:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(\"Sample horse image:\")\n",
        "plt.imshow(load_img(f\"{os.path.join(train_horses_dir, os.listdir(train_horses_dir)[0])}\"))\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nSample human image:\")\n",
        "plt.imshow(load_img(f\"{os.path.join(train_humans_dir, os.listdir(train_humans_dir)[0])}\"))\n",
        "plt.show()"
      ],
      "id": "HgbMs7p0qSKr"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LBnbnY0c8Zd0"
      },
      "source": [
        "`matplotlib` makes it easy to see that these images have a resolution of 300x300 and are colored, but you can double check this by using the code below:"
      ],
      "id": "LBnbnY0c8Zd0"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4lIGjHC5pxua",
        "outputId": "f2e82165-ee3c-4653-aeb4-45a80033167c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Each image has shape: (300, 300, 3)\n"
          ]
        }
      ],
      "source": [
        "# Load the first example of a horse\n",
        "sample_image  = load_img(f\"{os.path.join(train_horses_dir, os.listdir(train_horses_dir)[0])}\")\n",
        "\n",
        "# Convert the image into its numpy array representation\n",
        "sample_array = img_to_array(sample_image)\n",
        "\n",
        "print(f\"Each image has shape: {sample_array.shape}\")"
      ],
      "id": "4lIGjHC5pxua"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4fYwAYyd8zEm"
      },
      "source": [
        "As expected, the sample image has a resolution of 300x300 and the last dimension is used for each one of the RGB channels to represent color."
      ],
      "id": "4fYwAYyd8zEm"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HcE1TSqNRY2"
      },
      "source": [
        "## Training and Validation Generators\n",
        "\n",
        "Now that you know the images you are dealing with, it is time for you to code the generators that will fed these images to your Network. For this, complete the `train_val_generators` function below:\n",
        "\n",
        "**Important Note:** The images have a resolution of 300x300 but the `flow_from_directory` method you will use allows you to set a target resolution. In this case, **set a `target_size` of (150, 150)**. This will heavily lower the number of trainable parameters in your final network, yielding much quicker training times without compromising the accuracy!"
      ],
      "id": "6HcE1TSqNRY2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "code",
        "id": "AX5Q3NL_FXMT"
      },
      "outputs": [],
      "source": [
        "# GRADED FUNCTION: train_val_generators\n",
        "def train_val_generators(TRAINING_DIR, VALIDATION_DIR):\n",
        "  ### START CODE HERE\n",
        "\n",
        "  # Instantiate the ImageDataGenerator class \n",
        "  # Don't forget to normalize pixel values and set arguments to augment the images \n",
        "  train_datagen = ImageDataGenerator(rescale=1.0/255.,\n",
        "                                     rotation_range=40,\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",
        "  # Pass in the appropriate arguments to the flow_from_directory method\n",
        "  train_generator = train_datagen.flow_from_directory(directory=TRAINING_DIR,\n",
        "                                                      batch_size=32, \n",
        "                                                      class_mode='binary',\n",
        "                                                      target_size=(150, 150))\n",
        "\n",
        "  # Instantiate the ImageDataGenerator class (don't forget to set the rescale argument)\n",
        "  # Remember that validation data should not be augmented\n",
        "  validation_datagen = ImageDataGenerator(rescale=1.0/255.)\n",
        "\n",
        "  # Pass in the appropriate arguments to the flow_from_directory method\n",
        "  validation_generator = validation_datagen.flow_from_directory(directory=VALIDATION_DIR,\n",
        "                                                                batch_size=32, \n",
        "                                                                class_mode='binary',\n",
        "                                                                target_size=(150, 150))\n",
        "  ### END CODE HERE\n",
        "  return train_generator, validation_generator\n"
      ],
      "id": "AX5Q3NL_FXMT"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8FLUUqMKFwVR",
        "outputId": "64865d92-e5b4-4ef0-da8d-2f6dc08bade7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1027 images belonging to 2 classes.\n",
            "Found 256 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "# Test your generators\n",
        "train_generator, validation_generator = train_val_generators(train_dir, validation_dir)"
      ],
      "id": "8FLUUqMKFwVR"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TszKWhunQaj4"
      },
      "source": [
        "**Expected Output:**\n",
        "```\n",
        "Found 1027 images belonging to 2 classes.\n",
        "Found 256 images belonging to 2 classes.\n",
        "```"
      ],
      "id": "TszKWhunQaj4"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Izx51Ju1rXwd"
      },
      "source": [
        "## Transfer learning - Create the pre-trained model\n",
        "\n",
        "Download the `inception V3` weights into the `/tmp/` directory:"
      ],
      "id": "Izx51Ju1rXwd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-lEzPAqxrPcU",
        "outputId": "d9ea123e-7b23-4975-80f5-73de79e7f57a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-02-18 08:43:18--  https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.253.123.128, 142.250.98.128, 142.250.97.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.253.123.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 87910968 (84M) [application/x-hdf]\n",
            "Saving to: ‘/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5’\n",
            "\n",
            "/tmp/inception_v3_w 100%[===================>]  83.84M   141MB/s    in 0.6s    \n",
            "\n",
            "2022-02-18 08:43:19 (141 MB/s) - ‘/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5’ saved [87910968/87910968]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download the inception v3 weights\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \\\n",
        "    -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5"
      ],
      "id": "-lEzPAqxrPcU"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zlXNulm9USZ"
      },
      "source": [
        "Now load the `InceptionV3` model and save the path to the weights you just downloaded:"
      ],
      "id": "_zlXNulm9USZ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zfmRpsMf7E3-"
      },
      "outputs": [],
      "source": [
        "# Import the inception model  \n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "\n",
        "# Create an instance of the inception model from the local pre-trained weights\n",
        "local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'"
      ],
      "id": "zfmRpsMf7E3-"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPQb0PkT9_3w"
      },
      "source": [
        "Complete the `create_pre_trained_model` function below. You should specify the correct `input_shape` for the model (remember that you set a new resolution for the images instead of the native 300x300) and make all of the layers non-trainable:"
      ],
      "id": "ZPQb0PkT9_3w"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "code",
        "id": "x2JnQ6m8r5oe"
      },
      "outputs": [],
      "source": [
        "# GRADED FUNCTION: create_pre_trained_model\n",
        "def create_pre_trained_model(local_weights_file):\n",
        "  ### START CODE HERE\n",
        "  pre_trained_model = InceptionV3(input_shape = (150, 150, 3),\n",
        "                                  include_top = False, \n",
        "                                  weights = None) \n",
        "\n",
        "  pre_trained_model.load_weights(local_weights_file)\n",
        "\n",
        "  # Make all the layers in the pre-trained model non-trainable\n",
        "  for layer in pre_trained_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "  ### END CODE HERE\n",
        "\n",
        "  return pre_trained_model\n",
        "  "
      ],
      "id": "x2JnQ6m8r5oe"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phE00SCr-RCT"
      },
      "source": [
        "Check that everything went well by comparing the last few rows of the model summary to the expected output:"
      ],
      "id": "phE00SCr-RCT"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ve7eh9iztT4q",
        "outputId": "af63c7b5-c37c-4c1d-fb0b-c4e424cf18a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"inception_v3\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 150, 150, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)                (None, 74, 74, 32)   864         ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 74, 74, 32)  96          ['conv2d[0][0]']                 \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " activation (Activation)        (None, 74, 74, 32)   0           ['batch_normalization[0][0]']    \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)              (None, 72, 72, 32)   9216        ['activation[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 72, 72, 32)  96          ['conv2d_1[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_1 (Activation)      (None, 72, 72, 32)   0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)              (None, 72, 72, 64)   18432       ['activation_1[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 72, 72, 64)  192         ['conv2d_2[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_2 (Activation)      (None, 72, 72, 64)   0           ['batch_normalization_2[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2D)   (None, 35, 35, 64)   0           ['activation_2[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)              (None, 35, 35, 80)   5120        ['max_pooling2d[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_3 (BatchNo  (None, 35, 35, 80)  240         ['conv2d_3[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_3 (Activation)      (None, 35, 35, 80)   0           ['batch_normalization_3[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)              (None, 33, 33, 192)  138240      ['activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_4 (BatchNo  (None, 33, 33, 192)  576        ['conv2d_4[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_4 (Activation)      (None, 33, 33, 192)  0           ['batch_normalization_4[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 192)  0          ['activation_4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)              (None, 16, 16, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_8 (BatchNo  (None, 16, 16, 64)  192         ['conv2d_8[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_8 (Activation)      (None, 16, 16, 64)   0           ['batch_normalization_8[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)              (None, 16, 16, 48)   9216        ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)              (None, 16, 16, 96)   55296       ['activation_8[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_6 (BatchNo  (None, 16, 16, 48)  144         ['conv2d_6[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_9 (BatchNo  (None, 16, 16, 96)  288         ['conv2d_9[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_6 (Activation)      (None, 16, 16, 48)   0           ['batch_normalization_6[0][0]']  \n",
            "                                                                                                  \n",
            " activation_9 (Activation)      (None, 16, 16, 96)   0           ['batch_normalization_9[0][0]']  \n",
            "                                                                                                  \n",
            " average_pooling2d (AveragePool  (None, 16, 16, 192)  0          ['max_pooling2d_1[0][0]']        \n",
            " ing2D)                                                                                           \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)              (None, 16, 16, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)              (None, 16, 16, 64)   76800       ['activation_6[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)             (None, 16, 16, 96)   82944       ['activation_9[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)             (None, 16, 16, 32)   6144        ['average_pooling2d[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_5 (BatchNo  (None, 16, 16, 64)  192         ['conv2d_5[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_7 (BatchNo  (None, 16, 16, 64)  192         ['conv2d_7[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_10 (BatchN  (None, 16, 16, 96)  288         ['conv2d_10[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_11 (BatchN  (None, 16, 16, 32)  96          ['conv2d_11[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_5 (Activation)      (None, 16, 16, 64)   0           ['batch_normalization_5[0][0]']  \n",
            "                                                                                                  \n",
            " activation_7 (Activation)      (None, 16, 16, 64)   0           ['batch_normalization_7[0][0]']  \n",
            "                                                                                                  \n",
            " activation_10 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_10[0][0]'] \n",
            "                                                                                                  \n",
            " activation_11 (Activation)     (None, 16, 16, 32)   0           ['batch_normalization_11[0][0]'] \n",
            "                                                                                                  \n",
            " mixed0 (Concatenate)           (None, 16, 16, 256)  0           ['activation_5[0][0]',           \n",
            "                                                                  'activation_7[0][0]',           \n",
            "                                                                  'activation_10[0][0]',          \n",
            "                                                                  'activation_11[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)             (None, 16, 16, 64)   16384       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_15 (BatchN  (None, 16, 16, 64)  192         ['conv2d_15[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_15 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_15[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 16, 16, 48)   12288       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)             (None, 16, 16, 96)   55296       ['activation_15[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_13 (BatchN  (None, 16, 16, 48)  144         ['conv2d_13[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_16 (BatchN  (None, 16, 16, 96)  288         ['conv2d_16[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_13 (Activation)     (None, 16, 16, 48)   0           ['batch_normalization_13[0][0]'] \n",
            "                                                                                                  \n",
            " activation_16 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_16[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (AveragePo  (None, 16, 16, 256)  0          ['mixed0[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 16, 16, 64)   16384       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)             (None, 16, 16, 64)   76800       ['activation_13[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)             (None, 16, 16, 96)   82944       ['activation_16[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)             (None, 16, 16, 64)   16384       ['average_pooling2d_1[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_12 (BatchN  (None, 16, 16, 64)  192         ['conv2d_12[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_14 (BatchN  (None, 16, 16, 64)  192         ['conv2d_14[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_17 (BatchN  (None, 16, 16, 96)  288         ['conv2d_17[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_18 (BatchN  (None, 16, 16, 64)  192         ['conv2d_18[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_12 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_12[0][0]'] \n",
            "                                                                                                  \n",
            " activation_14 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_14[0][0]'] \n",
            "                                                                                                  \n",
            " activation_17 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_17[0][0]'] \n",
            "                                                                                                  \n",
            " activation_18 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_18[0][0]'] \n",
            "                                                                                                  \n",
            " mixed1 (Concatenate)           (None, 16, 16, 288)  0           ['activation_12[0][0]',          \n",
            "                                                                  'activation_14[0][0]',          \n",
            "                                                                  'activation_17[0][0]',          \n",
            "                                                                  'activation_18[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)             (None, 16, 16, 64)   18432       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_22 (BatchN  (None, 16, 16, 64)  192         ['conv2d_22[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_22 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_22[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)             (None, 16, 16, 48)   13824       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)             (None, 16, 16, 96)   55296       ['activation_22[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_20 (BatchN  (None, 16, 16, 48)  144         ['conv2d_20[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_23 (BatchN  (None, 16, 16, 96)  288         ['conv2d_23[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_20 (Activation)     (None, 16, 16, 48)   0           ['batch_normalization_20[0][0]'] \n",
            "                                                                                                  \n",
            " activation_23 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_23[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_2 (AveragePo  (None, 16, 16, 288)  0          ['mixed1[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)             (None, 16, 16, 64)   18432       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)             (None, 16, 16, 64)   76800       ['activation_20[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)             (None, 16, 16, 96)   82944       ['activation_23[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)             (None, 16, 16, 64)   18432       ['average_pooling2d_2[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_19 (BatchN  (None, 16, 16, 64)  192         ['conv2d_19[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_21 (BatchN  (None, 16, 16, 64)  192         ['conv2d_21[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_24 (BatchN  (None, 16, 16, 96)  288         ['conv2d_24[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_25 (BatchN  (None, 16, 16, 64)  192         ['conv2d_25[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_19 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_19[0][0]'] \n",
            "                                                                                                  \n",
            " activation_21 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_21[0][0]'] \n",
            "                                                                                                  \n",
            " activation_24 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_24[0][0]'] \n",
            "                                                                                                  \n",
            " activation_25 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_25[0][0]'] \n",
            "                                                                                                  \n",
            " mixed2 (Concatenate)           (None, 16, 16, 288)  0           ['activation_19[0][0]',          \n",
            "                                                                  'activation_21[0][0]',          \n",
            "                                                                  'activation_24[0][0]',          \n",
            "                                                                  'activation_25[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)             (None, 16, 16, 64)   18432       ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_27 (BatchN  (None, 16, 16, 64)  192         ['conv2d_27[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_27 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_27[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)             (None, 16, 16, 96)   55296       ['activation_27[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_28 (BatchN  (None, 16, 16, 96)  288         ['conv2d_28[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_28 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_28[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)             (None, 7, 7, 384)    995328      ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)             (None, 7, 7, 96)     82944       ['activation_28[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_26 (BatchN  (None, 7, 7, 384)   1152        ['conv2d_26[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_29 (BatchN  (None, 7, 7, 96)    288         ['conv2d_29[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_26 (Activation)     (None, 7, 7, 384)    0           ['batch_normalization_26[0][0]'] \n",
            "                                                                                                  \n",
            " activation_29 (Activation)     (None, 7, 7, 96)     0           ['batch_normalization_29[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 288)   0           ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " mixed3 (Concatenate)           (None, 7, 7, 768)    0           ['activation_26[0][0]',          \n",
            "                                                                  'activation_29[0][0]',          \n",
            "                                                                  'max_pooling2d_2[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)             (None, 7, 7, 128)    98304       ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_34 (BatchN  (None, 7, 7, 128)   384         ['conv2d_34[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_34 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_34[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)             (None, 7, 7, 128)    114688      ['activation_34[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_35 (BatchN  (None, 7, 7, 128)   384         ['conv2d_35[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_35 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_35[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)             (None, 7, 7, 128)    98304       ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)             (None, 7, 7, 128)    114688      ['activation_35[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_31 (BatchN  (None, 7, 7, 128)   384         ['conv2d_31[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_36 (BatchN  (None, 7, 7, 128)   384         ['conv2d_36[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_31 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_31[0][0]'] \n",
            "                                                                                                  \n",
            " activation_36 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_36[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)             (None, 7, 7, 128)    114688      ['activation_31[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)             (None, 7, 7, 128)    114688      ['activation_36[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_32 (BatchN  (None, 7, 7, 128)   384         ['conv2d_32[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_37 (BatchN  (None, 7, 7, 128)   384         ['conv2d_37[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_32 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_32[0][0]'] \n",
            "                                                                                                  \n",
            " activation_37 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_37[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_3 (AveragePo  (None, 7, 7, 768)   0           ['mixed3[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)             (None, 7, 7, 192)    172032      ['activation_32[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)             (None, 7, 7, 192)    172032      ['activation_37[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)             (None, 7, 7, 192)    147456      ['average_pooling2d_3[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_30 (BatchN  (None, 7, 7, 192)   576         ['conv2d_30[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_33 (BatchN  (None, 7, 7, 192)   576         ['conv2d_33[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_38 (BatchN  (None, 7, 7, 192)   576         ['conv2d_38[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_39 (BatchN  (None, 7, 7, 192)   576         ['conv2d_39[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_30 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_30[0][0]'] \n",
            "                                                                                                  \n",
            " activation_33 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_33[0][0]'] \n",
            "                                                                                                  \n",
            " activation_38 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_38[0][0]'] \n",
            "                                                                                                  \n",
            " activation_39 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_39[0][0]'] \n",
            "                                                                                                  \n",
            " mixed4 (Concatenate)           (None, 7, 7, 768)    0           ['activation_30[0][0]',          \n",
            "                                                                  'activation_33[0][0]',          \n",
            "                                                                  'activation_38[0][0]',          \n",
            "                                                                  'activation_39[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_44 (Conv2D)             (None, 7, 7, 160)    122880      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_44 (BatchN  (None, 7, 7, 160)   480         ['conv2d_44[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_44 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_44[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_45 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_44[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_45 (BatchN  (None, 7, 7, 160)   480         ['conv2d_45[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_45 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_45[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)             (None, 7, 7, 160)    122880      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_46 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_45[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_41 (BatchN  (None, 7, 7, 160)   480         ['conv2d_41[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_46 (BatchN  (None, 7, 7, 160)   480         ['conv2d_46[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_41 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_41[0][0]'] \n",
            "                                                                                                  \n",
            " activation_46 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_46[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_41[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_47 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_46[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_42 (BatchN  (None, 7, 7, 160)   480         ['conv2d_42[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_47 (BatchN  (None, 7, 7, 160)   480         ['conv2d_47[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_42 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_42[0][0]'] \n",
            "                                                                                                  \n",
            " activation_47 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_47[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_4 (AveragePo  (None, 7, 7, 768)   0           ['mixed4[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)             (None, 7, 7, 192)    215040      ['activation_42[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_48 (Conv2D)             (None, 7, 7, 192)    215040      ['activation_47[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_49 (Conv2D)             (None, 7, 7, 192)    147456      ['average_pooling2d_4[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_40 (BatchN  (None, 7, 7, 192)   576         ['conv2d_40[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_43 (BatchN  (None, 7, 7, 192)   576         ['conv2d_43[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_48 (BatchN  (None, 7, 7, 192)   576         ['conv2d_48[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_49 (BatchN  (None, 7, 7, 192)   576         ['conv2d_49[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_40 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_40[0][0]'] \n",
            "                                                                                                  \n",
            " activation_43 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_43[0][0]'] \n",
            "                                                                                                  \n",
            " activation_48 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_48[0][0]'] \n",
            "                                                                                                  \n",
            " activation_49 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_49[0][0]'] \n",
            "                                                                                                  \n",
            " mixed5 (Concatenate)           (None, 7, 7, 768)    0           ['activation_40[0][0]',          \n",
            "                                                                  'activation_43[0][0]',          \n",
            "                                                                  'activation_48[0][0]',          \n",
            "                                                                  'activation_49[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_54 (Conv2D)             (None, 7, 7, 160)    122880      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_54 (BatchN  (None, 7, 7, 160)   480         ['conv2d_54[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_54 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_54[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_55 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_54[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_55 (BatchN  (None, 7, 7, 160)   480         ['conv2d_55[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_55 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_55[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_51 (Conv2D)             (None, 7, 7, 160)    122880      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_56 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_55[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_51 (BatchN  (None, 7, 7, 160)   480         ['conv2d_51[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_56 (BatchN  (None, 7, 7, 160)   480         ['conv2d_56[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_51 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_51[0][0]'] \n",
            "                                                                                                  \n",
            " activation_56 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_56[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_52 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_51[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_57 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_56[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_52 (BatchN  (None, 7, 7, 160)   480         ['conv2d_52[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_57 (BatchN  (None, 7, 7, 160)   480         ['conv2d_57[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_52 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_52[0][0]'] \n",
            "                                                                                                  \n",
            " activation_57 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_57[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_5 (AveragePo  (None, 7, 7, 768)   0           ['mixed5[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_50 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_53 (Conv2D)             (None, 7, 7, 192)    215040      ['activation_52[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_58 (Conv2D)             (None, 7, 7, 192)    215040      ['activation_57[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_59 (Conv2D)             (None, 7, 7, 192)    147456      ['average_pooling2d_5[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_50 (BatchN  (None, 7, 7, 192)   576         ['conv2d_50[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_53 (BatchN  (None, 7, 7, 192)   576         ['conv2d_53[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_58 (BatchN  (None, 7, 7, 192)   576         ['conv2d_58[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_59 (BatchN  (None, 7, 7, 192)   576         ['conv2d_59[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_50 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_50[0][0]'] \n",
            "                                                                                                  \n",
            " activation_53 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_53[0][0]'] \n",
            "                                                                                                  \n",
            " activation_58 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_58[0][0]'] \n",
            "                                                                                                  \n",
            " activation_59 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_59[0][0]'] \n",
            "                                                                                                  \n",
            " mixed6 (Concatenate)           (None, 7, 7, 768)    0           ['activation_50[0][0]',          \n",
            "                                                                  'activation_53[0][0]',          \n",
            "                                                                  'activation_58[0][0]',          \n",
            "                                                                  'activation_59[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_64 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_64 (BatchN  (None, 7, 7, 192)   576         ['conv2d_64[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_64 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_64[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_65 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_64[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_65 (BatchN  (None, 7, 7, 192)   576         ['conv2d_65[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_65 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_65[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_61 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_66 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_65[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_61 (BatchN  (None, 7, 7, 192)   576         ['conv2d_61[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_66 (BatchN  (None, 7, 7, 192)   576         ['conv2d_66[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_61 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_61[0][0]'] \n",
            "                                                                                                  \n",
            " activation_66 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_66[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_62 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_61[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_67 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_66[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_62 (BatchN  (None, 7, 7, 192)   576         ['conv2d_62[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_67 (BatchN  (None, 7, 7, 192)   576         ['conv2d_67[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_62 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_62[0][0]'] \n",
            "                                                                                                  \n",
            " activation_67 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_67[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_6 (AveragePo  (None, 7, 7, 768)   0           ['mixed6[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_60 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_63 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_62[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_68 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_67[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_69 (Conv2D)             (None, 7, 7, 192)    147456      ['average_pooling2d_6[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_60 (BatchN  (None, 7, 7, 192)   576         ['conv2d_60[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_63 (BatchN  (None, 7, 7, 192)   576         ['conv2d_63[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_68 (BatchN  (None, 7, 7, 192)   576         ['conv2d_68[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_69 (BatchN  (None, 7, 7, 192)   576         ['conv2d_69[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_60 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_60[0][0]'] \n",
            "                                                                                                  \n",
            " activation_63 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_63[0][0]'] \n",
            "                                                                                                  \n",
            " activation_68 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_68[0][0]'] \n",
            "                                                                                                  \n",
            " activation_69 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_69[0][0]'] \n",
            "                                                                                                  \n",
            " mixed7 (Concatenate)           (None, 7, 7, 768)    0           ['activation_60[0][0]',          \n",
            "                                                                  'activation_63[0][0]',          \n",
            "                                                                  'activation_68[0][0]',          \n",
            "                                                                  'activation_69[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_72 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_72 (BatchN  (None, 7, 7, 192)   576         ['conv2d_72[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_72 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_72[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_73 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_72[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_73 (BatchN  (None, 7, 7, 192)   576         ['conv2d_73[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_73 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_73[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_70 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_74 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_73[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_70 (BatchN  (None, 7, 7, 192)   576         ['conv2d_70[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_74 (BatchN  (None, 7, 7, 192)   576         ['conv2d_74[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_70 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_70[0][0]'] \n",
            "                                                                                                  \n",
            " activation_74 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_74[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_71 (Conv2D)             (None, 3, 3, 320)    552960      ['activation_70[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_75 (Conv2D)             (None, 3, 3, 192)    331776      ['activation_74[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_71 (BatchN  (None, 3, 3, 320)   960         ['conv2d_71[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_75 (BatchN  (None, 3, 3, 192)   576         ['conv2d_75[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_71 (Activation)     (None, 3, 3, 320)    0           ['batch_normalization_71[0][0]'] \n",
            "                                                                                                  \n",
            " activation_75 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_75[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPooling2D)  (None, 3, 3, 768)   0           ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " mixed8 (Concatenate)           (None, 3, 3, 1280)   0           ['activation_71[0][0]',          \n",
            "                                                                  'activation_75[0][0]',          \n",
            "                                                                  'max_pooling2d_3[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_80 (Conv2D)             (None, 3, 3, 448)    573440      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_80 (BatchN  (None, 3, 3, 448)   1344        ['conv2d_80[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_80 (Activation)     (None, 3, 3, 448)    0           ['batch_normalization_80[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_77 (Conv2D)             (None, 3, 3, 384)    491520      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_81 (Conv2D)             (None, 3, 3, 384)    1548288     ['activation_80[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_77 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_77[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_81 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_81[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_77 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_77[0][0]'] \n",
            "                                                                                                  \n",
            " activation_81 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_81[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_78 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_77[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_79 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_77[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_82 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_81[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_83 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_81[0][0]']          \n",
            "                                                                                                  \n",
            " average_pooling2d_7 (AveragePo  (None, 3, 3, 1280)  0           ['mixed8[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_76 (Conv2D)             (None, 3, 3, 320)    409600      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_78 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_78[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_79 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_79[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_82 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_82[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_83 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_83[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_84 (Conv2D)             (None, 3, 3, 192)    245760      ['average_pooling2d_7[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_76 (BatchN  (None, 3, 3, 320)   960         ['conv2d_76[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_78 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_78[0][0]'] \n",
            "                                                                                                  \n",
            " activation_79 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_79[0][0]'] \n",
            "                                                                                                  \n",
            " activation_82 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_82[0][0]'] \n",
            "                                                                                                  \n",
            " activation_83 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_83[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_84 (BatchN  (None, 3, 3, 192)   576         ['conv2d_84[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_76 (Activation)     (None, 3, 3, 320)    0           ['batch_normalization_76[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9_0 (Concatenate)         (None, 3, 3, 768)    0           ['activation_78[0][0]',          \n",
            "                                                                  'activation_79[0][0]']          \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 3, 3, 768)    0           ['activation_82[0][0]',          \n",
            "                                                                  'activation_83[0][0]']          \n",
            "                                                                                                  \n",
            " activation_84 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_84[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9 (Concatenate)           (None, 3, 3, 2048)   0           ['activation_76[0][0]',          \n",
            "                                                                  'mixed9_0[0][0]',               \n",
            "                                                                  'concatenate[0][0]',            \n",
            "                                                                  'activation_84[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_89 (Conv2D)             (None, 3, 3, 448)    917504      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_89 (BatchN  (None, 3, 3, 448)   1344        ['conv2d_89[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_89 (Activation)     (None, 3, 3, 448)    0           ['batch_normalization_89[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_86 (Conv2D)             (None, 3, 3, 384)    786432      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_90 (Conv2D)             (None, 3, 3, 384)    1548288     ['activation_89[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_86 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_86[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_90 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_90[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_86 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_86[0][0]'] \n",
            "                                                                                                  \n",
            " activation_90 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_90[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_87 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_86[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_88 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_86[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_91 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_90[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_92 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_90[0][0]']          \n",
            "                                                                                                  \n",
            " average_pooling2d_8 (AveragePo  (None, 3, 3, 2048)  0           ['mixed9[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_85 (Conv2D)             (None, 3, 3, 320)    655360      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_87 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_87[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_88 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_88[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_91 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_91[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_92 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_92[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_93 (Conv2D)             (None, 3, 3, 192)    393216      ['average_pooling2d_8[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_85 (BatchN  (None, 3, 3, 320)   960         ['conv2d_85[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_87 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_87[0][0]'] \n",
            "                                                                                                  \n",
            " activation_88 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_88[0][0]'] \n",
            "                                                                                                  \n",
            " activation_91 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_91[0][0]'] \n",
            "                                                                                                  \n",
            " activation_92 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_92[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_93 (BatchN  (None, 3, 3, 192)   576         ['conv2d_93[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_85 (Activation)     (None, 3, 3, 320)    0           ['batch_normalization_85[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9_1 (Concatenate)         (None, 3, 3, 768)    0           ['activation_87[0][0]',          \n",
            "                                                                  'activation_88[0][0]']          \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate)    (None, 3, 3, 768)    0           ['activation_91[0][0]',          \n",
            "                                                                  'activation_92[0][0]']          \n",
            "                                                                                                  \n",
            " activation_93 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_93[0][0]'] \n",
            "                                                                                                  \n",
            " mixed10 (Concatenate)          (None, 3, 3, 2048)   0           ['activation_85[0][0]',          \n",
            "                                                                  'mixed9_1[0][0]',               \n",
            "                                                                  'concatenate_1[0][0]',          \n",
            "                                                                  'activation_93[0][0]']          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 21,802,784\n",
            "Trainable params: 0\n",
            "Non-trainable params: 21,802,784\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "pre_trained_model = create_pre_trained_model(local_weights_file)\n",
        "\n",
        "# Print the model summary\n",
        "pre_trained_model.summary()"
      ],
      "id": "ve7eh9iztT4q"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cAY2gQytr0-"
      },
      "source": [
        "**Expected Output:**\n",
        "```\n",
        "batch_normalization_v1_281 (Bat (None, 3, 3, 192)    576         conv2d_281[0][0]                 \n",
        "__________________________________________________________________________________________________\n",
        "activation_273 (Activation)     (None, 3, 3, 320)    0           batch_normalization_v1_273[0][0] \n",
        "__________________________________________________________________________________________________\n",
        "mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_275[0][0]             \n",
        "                                                                activation_276[0][0]             \n",
        "__________________________________________________________________________________________________\n",
        "concatenate_5 (Concatenate)     (None, 3, 3, 768)    0           activation_279[0][0]             \n",
        "                                                                activation_280[0][0]             \n",
        "__________________________________________________________________________________________________\n",
        "activation_281 (Activation)     (None, 3, 3, 192)    0           batch_normalization_v1_281[0][0] \n",
        "__________________________________________________________________________________________________\n",
        "mixed10 (Concatenate)           (None, 3, 3, 2048)   0           activation_273[0][0]             \n",
        "                                                                mixed9_1[0][0]                   \n",
        "                                                                concatenate_5[0][0]              \n",
        "                                                                activation_281[0][0]             \n",
        "==================================================================================================\n",
        "Total params: 21,802,784\n",
        "Trainable params: 0\n",
        "Non-trainable params: 21,802,784\n",
        "\n",
        "\n",
        "```"
      ],
      "id": "4cAY2gQytr0-"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRHkV9jo-hkh"
      },
      "source": [
        "To check that all the layers in the model were set to be non-trainable, you can also run the cell below:"
      ],
      "id": "MRHkV9jo-hkh"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VASOaB8xDbhU",
        "outputId": "05a330f4-8918-4f70-a872-f1b40151586a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 21,802,784 total parameters in this model.\n",
            "There are 0 trainable parameters in this model.\n"
          ]
        }
      ],
      "source": [
        "total_params = pre_trained_model.count_params()\n",
        "num_trainable_params = sum([w.shape.num_elements() for w in pre_trained_model.trainable_weights])\n",
        "\n",
        "print(f\"There are {total_params:,} total parameters in this model.\")\n",
        "print(f\"There are {num_trainable_params:,} trainable parameters in this model.\")"
      ],
      "id": "VASOaB8xDbhU"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRioO7FH5a8I"
      },
      "source": [
        "**Expected Output:**\n",
        "```\n",
        "There are 21,802,784 total parameters in this model.\n",
        "There are 0 trainable parameters in this model.\n",
        "```"
      ],
      "id": "mRioO7FH5a8I"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFtwDyKj-4GR"
      },
      "source": [
        "## Creating callbacks for later\n",
        "\n",
        "You have already worked with callbacks in the first course of this specialization so the callback to stop training once an accuracy of 99.9% is reached, is provided for you:"
      ],
      "id": "dFtwDyKj-4GR"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SeVjZD2o7gWS"
      },
      "outputs": [],
      "source": [
        "# Define a Callback class that stops training once accuracy reaches 99.9%\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>0.999):\n",
        "      print(\"\\nReached 99.9% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True"
      ],
      "id": "SeVjZD2o7gWS"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHZnFl-5_p3a"
      },
      "source": [
        "## Pipelining the pre-trained model with your own\n",
        "\n",
        "Now that the pre-trained model is ready, you need to \"glue\" it to your own model to solve the task at hand.\n",
        "\n",
        "For this you will need the last output of the pre-trained model, since this will be the input for your own. Complete the `output_of_last_layer` function below.\n",
        "\n",
        "**Note:** For grading purposes use the `mixed7` layer as the last layer of the pre-trained model. However, after submitting feel free to come back here and play around with this."
      ],
      "id": "lHZnFl-5_p3a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFsUlwdfs_wg"
      },
      "outputs": [],
      "source": [
        "# GRADED FUNCTION: output_of_last_layer\n",
        "def output_of_last_layer(pre_trained_model):\n",
        "  ### START CODE HERE\n",
        "  last_desired_layer = pre_trained_model.get_layer('mixed7')\n",
        "  print('last layer output shape: ', last_desired_layer.output_shape)\n",
        "  last_output = last_desired_layer.output\n",
        "  print('last layer output: ', last_output)\n",
        "  ### END CODE HERE\n",
        "\n",
        "  return last_output\n"
      ],
      "id": "CFsUlwdfs_wg"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13AEzKG2A6_J"
      },
      "source": [
        "Check that everything works as expected:"
      ],
      "id": "13AEzKG2A6_J"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zOJPUtMN6PHo",
        "outputId": "f596c14f-ced2-493b-8009-dc130c2ffc2a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "last layer output shape:  (None, 7, 7, 768)\n",
            "last layer output:  KerasTensor(type_spec=TensorSpec(shape=(None, 7, 7, 768), dtype=tf.float32, name=None), name='mixed7/concat:0', description=\"created by layer 'mixed7'\")\n"
          ]
        }
      ],
      "source": [
        "last_output = output_of_last_layer(pre_trained_model)"
      ],
      "id": "zOJPUtMN6PHo"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqIWKZ_h7CuY"
      },
      "source": [
        "**Expected Output (if `mixed7` layer was used):**\n",
        "```\n",
        "last layer output shape:  (None, 7, 7, 768)\n",
        "last layer output:  KerasTensor(type_spec=TensorSpec(shape=(None, 7, 7, 768), dtype=tf.float32, name=None), name='mixed7/concat:0', description=\"created by layer 'mixed7'\")\n",
        "```"
      ],
      "id": "XqIWKZ_h7CuY"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Rp-J6JuwJTq"
      },
      "source": [
        "Now you will create the final model by adding some additional layers on top of the pre-trained model.\n",
        "\n",
        "Complete the `create_final_model` function below. You will need to use Tensorflow's [Functional API](https://www.tensorflow.org/guide/keras/functional) for this since the pretrained model has been created using it. \n",
        "\n",
        "Let's double check this first:"
      ],
      "id": "0Rp-J6JuwJTq"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cKQknB4j7K9y",
        "outputId": "f3ae0ee0-aa34-4e30-88d4-43fa70689e78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The pretrained model has type: <class 'keras.engine.functional.Functional'>\n"
          ]
        }
      ],
      "source": [
        "# Print the type of the pre-trained model\n",
        "print(f\"The pretrained model has type: {type(pre_trained_model)}\")"
      ],
      "id": "cKQknB4j7K9y"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kt7AU7jP7LW9"
      },
      "source": [
        "To create the final model, you will use Keras' Model class by defining the appropriate inputs and outputs as described in the first way to instantiate a Model in the [docs](https://www.tensorflow.org/api_docs/python/tf/keras/Model).\n",
        "\n",
        "Note that you can get the input from any existing model by using its `input` attribute and by using the Funcional API you can use the last layer directly as output when creating the final model."
      ],
      "id": "Kt7AU7jP7LW9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "code",
        "id": "BMXb913pbvFg"
      },
      "outputs": [],
      "source": [
        "# GRADED FUNCTION: create_final_model\n",
        "def create_final_model(pre_trained_model, last_output):\n",
        "  # Flatten the output layer to 1 dimension\n",
        "  x = layers.Flatten()(last_output)\n",
        "\n",
        "  ### START CODE HERE\n",
        "\n",
        "  # Add a fully connected layer with 1024 hidden units and ReLU activation\n",
        "  x = layers.Dense(1024, activation='relu')(x)\n",
        "  # Add a dropout rate of 0.2\n",
        "  x = layers.Dropout(0.2)(x)  \n",
        "  # Add a final sigmoid layer for classification\n",
        "  x = layers.Dense  (1, activation='sigmoid')(x)        \n",
        "\n",
        "  # Create the complete model by using the Model class\n",
        "  model = Model(pre_trained_model.input, x)\n",
        "\n",
        "  # Compile the model\n",
        "  model.compile(optimizer = RMSprop(learning_rate=0.0001), \n",
        "                loss = 'binary_crossentropy',\n",
        "                metrics = ['accuracy'])\n",
        "\n",
        "  ### END CODE HERE\n",
        "  \n",
        "  return model\n"
      ],
      "id": "BMXb913pbvFg"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cL6ga5Z1783H",
        "outputId": "4aad5dc1-f51e-420b-8ef2-90ed0777bd4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 47,512,481 total parameters in this model.\n",
            "There are 38,537,217 trainable parameters in this model.\n"
          ]
        }
      ],
      "source": [
        "# Save your model in a variable\n",
        "model = create_final_model(pre_trained_model, last_output)\n",
        "\n",
        "# Inspect parameters\n",
        "total_params = model.count_params()\n",
        "num_trainable_params = sum([w.shape.num_elements() for w in model.trainable_weights])\n",
        "\n",
        "print(f\"There are {total_params:,} total parameters in this model.\")\n",
        "print(f\"There are {num_trainable_params:,} trainable parameters in this model.\")"
      ],
      "id": "cL6ga5Z1783H"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4d3zlcQDrvm"
      },
      "source": [
        "**Expected Output:**\n",
        "```\n",
        "There are 47,512,481 total parameters in this model.\n",
        "There are 38,537,217 trainable parameters in this model.\n",
        "```"
      ],
      "id": "J4d3zlcQDrvm"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eqwHj5xEBZ7"
      },
      "source": [
        "Wow, that is a lot of parameters!\n",
        "\n",
        "After submitting your assignment later, try re-running this notebook but use the original resolution of 300x300, you will be surprised to see how many more parameters are for that case.\n",
        "\n",
        "Now train the model:"
      ],
      "id": "_eqwHj5xEBZ7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Blhq2MAUeyGA",
        "outputId": "00a9c082-9606-4e89-8702-f19818cc52c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "33/33 - 26s - loss: 0.2526 - accuracy: 0.9211 - val_loss: 0.0073 - val_accuracy: 1.0000 - 26s/epoch - 773ms/step\n",
            "Epoch 2/100\n",
            "33/33 - 11s - loss: 0.0268 - accuracy: 0.9922 - val_loss: 0.0299 - val_accuracy: 0.9883 - 11s/epoch - 330ms/step\n",
            "Epoch 3/100\n",
            "33/33 - 11s - loss: 0.0221 - accuracy: 0.9922 - val_loss: 0.6146 - val_accuracy: 0.8984 - 11s/epoch - 331ms/step\n",
            "Epoch 4/100\n",
            "33/33 - 11s - loss: 0.0341 - accuracy: 0.9903 - val_loss: 0.0093 - val_accuracy: 0.9961 - 11s/epoch - 332ms/step\n",
            "Epoch 5/100\n",
            "33/33 - 11s - loss: 0.0265 - accuracy: 0.9912 - val_loss: 0.0607 - val_accuracy: 0.9766 - 11s/epoch - 343ms/step\n",
            "Epoch 6/100\n",
            "33/33 - 11s - loss: 0.0253 - accuracy: 0.9932 - val_loss: 0.0188 - val_accuracy: 0.9961 - 11s/epoch - 334ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Reached 99.9% accuracy so cancelling training!\n",
            "33/33 - 11s - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0149 - val_accuracy: 0.9961 - 11s/epoch - 333ms/step\n"
          ]
        }
      ],
      "source": [
        "# Run this and see how many epochs it should take before the callback\n",
        "# fires, and stops training at 99.9% accuracy\n",
        "# (It should take a few epochs)\n",
        "callbacks = myCallback()\n",
        "history = model.fit(train_generator,\n",
        "                    validation_data = validation_generator,\n",
        "                    epochs = 100,\n",
        "                    verbose = 2,\n",
        "                    callbacks=callbacks)"
      ],
      "id": "Blhq2MAUeyGA"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y94djl4t0sK5"
      },
      "source": [
        "The training should have stopped after less than 10 epochs and it should have reached an accuracy over 99,9% (firing the callback). This happened so quickly because of the pre-trained model you used, which already contained information to classify humans from horses. Really cool!\n",
        "\n",
        "Now take a quick look at the training and validation accuracies for each epoch of training:"
      ],
      "id": "Y94djl4t0sK5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C2Fp6Se9rKuL",
        "outputId": "28f77cba-aeea-405f-8da5-744848c56f4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the training and validation accuracies for each epoch\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "plt.show()"
      ],
      "id": "C2Fp6Se9rKuL"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-4-4i9U1a0s"
      },
      "source": [
        "You will need to submit this notebook for grading. To download it, click on the `File` tab in the upper left corner of the screen then click on `Download` -> `Download .ipynb`. You can name it anything you want as long as it is a valid `.ipynb` (jupyter notebook) file."
      ],
      "id": "g-4-4i9U1a0s"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7w54-pbB1W9r"
      },
      "source": [
        "**Congratulations on finishing this week's assignment!**\n",
        "\n",
        "You have successfully implemented a convolutional neural network that leverages a pre-trained network to help you solve the problem of classifying humans from horses.\n",
        "\n",
        "**Keep it up!**"
      ],
      "id": "7w54-pbB1W9r"
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "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": "3.7.4"
    },
    "colab": {
      "name": "C2W3_Assignment.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}