cat_vs_dog_classification_[transfer_learning].ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HasibAlMuzdadid/Cat-vs-Dog-Classification/blob/main/cat_vs_dog_classification_%5Btransfer_learning%5D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bT0to3TL2q7H"
      },
      "source": [
        "### **Cats vs Dogs Classification [Transfer Learning]**\n",
        "\n",
        "We will need to prepare pretrained model and configure the layers that we need. We will use the convolution layers of the [InceptionV3](https://arxiv.org/abs/1512.00567) architecture as base model. \n",
        "\n",
        "\n",
        "So for preparing the input to the model, we want to fetch the pretrained weights of the `InceptionV3` model and remove the fully connected layer at the end because we will be replacing it later. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import random\n",
        "import zipfile\n",
        "import shutil\n",
        "from shutil import copyfile\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"
      ],
      "metadata": {
        "id": "4R-JlemSPmuq"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "O4s8HckqGlnb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f736e1d-4595-46e1-88e4-111c69110e11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-06-30 10:58:40--  https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip\n",
            "Resolving download.microsoft.com (download.microsoft.com)... 23.219.8.107, 2600:1408:9000:793::e59, 2600:1408:9000:780::e59\n",
            "Connecting to download.microsoft.com (download.microsoft.com)|23.219.8.107|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 824887076 (787M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/cats-and-dogs.zip’\n",
            "\n",
            "/tmp/cats-and-dogs. 100%[===================>] 786.67M   141MB/s    in 5.6s    \n",
            "\n",
            "2022-06-30 10:58:46 (140 MB/s) - ‘/tmp/cats-and-dogs.zip’ saved [824887076/824887076]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download the dataset\n",
        "!wget --no-check-certificate \\\n",
        "    \"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip\" \\\n",
        "    -O \"/tmp/cats-and-dogs.zip\"\n",
        "\n",
        "local_zip = '/tmp/cats-and-dogs.zip'\n",
        "zip_ref   = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "source_path = '/tmp/PetImages'\n",
        "\n",
        "source_path_dogs = os.path.join(source_path, 'Dog')\n",
        "source_path_cats = os.path.join(source_path, 'Cat')\n",
        "\n",
        "\n",
        "# os.listdir returns a list containing all files under the given path\n",
        "print(f\"There are {len(os.listdir(source_path_dogs))} images of dogs.\")\n",
        "print(f\"There are {len(os.listdir(source_path_cats))} images of cats.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDBA3mdrRBD8",
        "outputId": "93f2376e-2a3d-4611-d421-d75d07a8c86b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 12501 images of dogs.\n",
            "There are 12501 images of cats.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define root directory\n",
        "root_dir = '/tmp/cats-v-dogs'\n",
        "\n",
        "# Empty directory to prevent FileExistsError is the function is run several times\n",
        "if os.path.exists(root_dir):\n",
        "  shutil.rmtree(root_dir)\n",
        "\n",
        "\n",
        "def create_train_val_dirs(root_path):\n",
        "  \n",
        "  os.makedirs(os.path.join(root_dir,\"training\"))\n",
        "  os.makedirs(os.path.join(f\"{root_dir}/training\", \"dogs\"))\n",
        "  os.makedirs(os.path.join(f\"{root_dir}/training\", \"cats\"))\n",
        "  os.makedirs(os.path.join(root_dir,\"validation\"))\n",
        "  os.makedirs(os.path.join(f\"{root_dir}/validation\", \"dogs\"))\n",
        "  os.makedirs(os.path.join(f\"{root_dir}/validation\", \"cats\"))\n",
        "\n",
        "  \n",
        "try:\n",
        "  create_train_val_dirs(root_path=root_dir)\n",
        "except FileExistsError:\n",
        "  print(\"You should not be seeing this since the upper directory is removed beforehand\")"
      ],
      "metadata": {
        "id": "wCzLar4aPQV_"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing create_train_val_dirs() function\n",
        "\n",
        "for rootdir, dirs, files in os.walk(root_dir):\n",
        "    for subdir in dirs:\n",
        "        print(os.path.join(rootdir, subdir))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01d1mQrpRcMO",
        "outputId": "ae419663-0383-4dfa-84ed-05aa384e9881"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/tmp/cats-v-dogs/validation\n",
            "/tmp/cats-v-dogs/training\n",
            "/tmp/cats-v-dogs/validation/cats\n",
            "/tmp/cats-v-dogs/validation/dogs\n",
            "/tmp/cats-v-dogs/training/cats\n",
            "/tmp/cats-v-dogs/training/dogs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  Spliting the whole data into train and test set\n",
        "def split_data(SOURCE_DIR, TRAINING_DIR, VALIDATION_DIR, SPLIT_SIZE):\n",
        "\n",
        "  processed_data = []\n",
        "  \n",
        "  for filename in os.listdir(SOURCE_DIR) :\n",
        "    file = SOURCE_DIR + filename\n",
        "    if os.path.getsize(file) > 0 :\n",
        "      processed_data.append(filename)\n",
        "    else :\n",
        "      print(f\"{filename} is zero length, so ignoring.\")\n",
        "\n",
        "  randomized_data = random.sample(processed_data, len(processed_data))\n",
        "\n",
        "  training_length = int(len(randomized_data)*SPLIT_SIZE)\n",
        "  validation_length = int(len(randomized_data) - training_length)\n",
        "\n",
        "  training_data = randomized_data[:training_length]\n",
        "  validation_data = randomized_data[-validation_length:]\n",
        "\n",
        "\n",
        "  for filename in training_data :\n",
        "    present_file = SOURCE_DIR + filename\n",
        "    destination = TRAINING_DIR + filename\n",
        "    copyfile(present_file, destination)\n",
        "\n",
        "\n",
        "  for filename in validation_data :\n",
        "    present_file = SOURCE_DIR + filename\n",
        "    destination = VALIDATION_DIR + filename\n",
        "    copyfile(present_file, destination)\n"
      ],
      "metadata": {
        "id": "Hf6tlwXzRgfH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining paths\n",
        "CAT_SOURCE_DIR = \"/tmp/PetImages/Cat/\"\n",
        "DOG_SOURCE_DIR = \"/tmp/PetImages/Dog/\"\n",
        "\n",
        "TRAINING_DIR = \"/tmp/cats-v-dogs/training/\"\n",
        "VALIDATION_DIR = \"/tmp/cats-v-dogs/validation/\"\n",
        "\n",
        "TRAINING_CATS_DIR = os.path.join(TRAINING_DIR, \"cats/\")\n",
        "VALIDATION_CATS_DIR = os.path.join(VALIDATION_DIR, \"cats/\")\n",
        "\n",
        "TRAINING_DOGS_DIR = os.path.join(TRAINING_DIR, \"dogs/\")\n",
        "VALIDATION_DOGS_DIR = os.path.join(VALIDATION_DIR, \"dogs/\")\n",
        "\n",
        "# Empty directories in case running this cell multiple times\n",
        "if len(os.listdir(TRAINING_CATS_DIR)) > 0:\n",
        "  for file in os.scandir(TRAINING_CATS_DIR):\n",
        "    os.remove(file.path)\n",
        "if len(os.listdir(TRAINING_DOGS_DIR)) > 0:\n",
        "  for file in os.scandir(TRAINING_DOGS_DIR):\n",
        "    os.remove(file.path)\n",
        "if len(os.listdir(VALIDATION_CATS_DIR)) > 0:\n",
        "  for file in os.scandir(VALIDATION_CATS_DIR):\n",
        "    os.remove(file.path)\n",
        "if len(os.listdir(VALIDATION_DOGS_DIR)) > 0:\n",
        "  for file in os.scandir(VALIDATION_DOGS_DIR):\n",
        "    os.remove(file.path)\n",
        "\n",
        "# proportion of images used for training\n",
        "split_size = .9\n",
        "\n",
        "# Messages about zero length images will be printed out\n",
        "split_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, VALIDATION_CATS_DIR, split_size)\n",
        "split_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, VALIDATION_DOGS_DIR, split_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PiRPmT9RwfA",
        "outputId": "8d4835fc-e351-4c96-eb90-80de2d0a0bef"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "666.jpg is zero length, so ignoring.\n",
            "11702.jpg is zero length, so ignoring.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of images for each class and set\n",
        "print(f\"There are {len(os.listdir(TRAINING_CATS_DIR))} images of cats for training\")\n",
        "print(f\"There are {len(os.listdir(TRAINING_DOGS_DIR))} images of dogs for training\")\n",
        "print(f\"There are {len(os.listdir(VALIDATION_CATS_DIR))} images of cats for validation\")\n",
        "print(f\"There are {len(os.listdir(VALIDATION_DOGS_DIR))} images of dogs for validation\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6MKkfjrPyRv",
        "outputId": "22287728-c216-4501-9c5d-b0a9febba992"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 11250 images of cats for training\n",
            "There are 11250 images of dogs for training\n",
            "There are 1250 images of cats for validation\n",
            "There are 1250 images of dogs for validation\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Sample Cat Image:\")\n",
        "plt.imshow(load_img(f\"{os.path.join(TRAINING_CATS_DIR, os.listdir(TRAINING_CATS_DIR)[0])}\"))\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nSample Dog Image:\")\n",
        "plt.imshow(load_img(f\"{os.path.join(TRAINING_DOGS_DIR, os.listdir(TRAINING_DOGS_DIR)[0])}\"))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "CZ34tLV0tHFB",
        "outputId": "b8aab840-25aa-436c-f881-293325306521"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample Cat Image:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Sample Dog Image:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the first example of a cat\n",
        "sample_image  = load_img(f\"{os.path.join(TRAINING_CATS_DIR, os.listdir(TRAINING_CATS_DIR)[0])}\")\n",
        "\n",
        "# Convert the image into its numpy array representation\n",
        "sample_array = img_to_array(sample_image)\n",
        "\n",
        "print(f\"This image has shape: {sample_array.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_W14lVAitlXt",
        "outputId": "9549fdac-065d-4fe9-a084-1ac8cce6fa4a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This image has shape: (375, 500, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the first example of a dog\n",
        "sample_image  = load_img(f\"{os.path.join(TRAINING_DOGS_DIR, os.listdir(TRAINING_DOGS_DIR)[0])}\")\n",
        "\n",
        "# Convert the image into its numpy array representation\n",
        "sample_array = img_to_array(sample_image)\n",
        "\n",
        "print(f\"This image has shape: {sample_array.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4Bileowt7hw",
        "outputId": "318f2988-ff2a-4470-e67d-a5d7e1d576a6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This image has shape: (360, 480, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All the images are not in same shape."
      ],
      "metadata": {
        "id": "3OmWhmLCuGss"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Creating the training and validation data generators\n",
        "def train_val_generators(TRAINING_DIR, VALIDATION_DIR):\n",
        "\n",
        "  # Instantiating the ImageDataGenerator class also normalization \n",
        "  train_datagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "\n",
        "  train_generator = train_datagen.flow_from_directory(directory=TRAINING_DIR,\n",
        "                                                      batch_size=100,\n",
        "                                                      class_mode=\"binary\",\n",
        "                                                      target_size=(150, 150))\n",
        "\n",
        "  \n",
        "  validation_datagen = ImageDataGenerator(rescale=1.0/255)\n",
        "  \n",
        "  validation_generator = validation_datagen.flow_from_directory(directory=VALIDATION_DIR,\n",
        "                                                                batch_size=100,\n",
        "                                                                class_mode=\"binary\",\n",
        "                                                                target_size=(150, 150))\n",
        "\n",
        "\n",
        "  return train_generator, validation_generator"
      ],
      "metadata": {
        "id": "uuOQfDeESjxk"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing the generators\n",
        "train_generator, validation_generator = train_val_generators(TRAINING_DIR, VALIDATION_DIR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bihfZ03S52N",
        "outputId": "665bda35-f11d-4327-a0a9-d83cc37d4788"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 22498 images belonging to 2 classes.\n",
            "Found 2500 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "1xJZ5glPPCRz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80f7c756-1bfe-4133-f4c8-f58c1b6d1640"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-06-30 10:43:18--  https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.204.128, 142.250.98.128, 142.250.97.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.204.128|: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   225MB/s    in 0.4s    \n",
            "\n",
            "2022-06-30 10:43:19 (225 MB/s) - ‘/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5’ saved [87910968/87910968]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download the pre-trained weights. No top means it excludes the fully connected layer it uses for classification.\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "KsiBCpQ1VvPp"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "\n",
        "local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
        "\n",
        "# Initialize the base model.\n",
        "# Set the input shape and remove the dense layers.\n",
        "pre_trained_model = InceptionV3(input_shape = (150, 150, 3), \n",
        "                                include_top = False, \n",
        "                                weights = None)\n",
        "\n",
        "# Load the pre-trained weights.\n",
        "pre_trained_model.load_weights(local_weights_file)\n",
        "\n",
        "# Freeze the weights of the layers.\n",
        "for layer in pre_trained_model.layers:\n",
        "  layer.trainable = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1y2rEnqFaa9k"
      },
      "source": [
        "We will use up to `mixed_7` as our base model and add to that. This is because the original last layer might be too specialized in what it has learned so it might not translate well into our application. `mixed_7` on the other hand will be more generalized and we can start with that for our application. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "qeGP0Ust5kCR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a08c1dc0-0779-45fa-a8e8-ffdf2e303e5a"
      },
      "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.summary()"
      ]
    },
    {
      "cell_type": "code",
      "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.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3VZonw2Wn_8",
        "outputId": "f5a51af6-ea7b-4810-f46f-ea89ada7a551"
      },
      "execution_count": 25,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "jDmGO9tg5iPc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "531440c9-8385-4a44-c226-17cb39a89432"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "last layer output shape:  (None, 7, 7, 768)\n"
          ]
        }
      ],
      "source": [
        "# Choosing `mixed_7` as the last layer of your base model\n",
        "last_layer = pre_trained_model.get_layer('mixed7')\n",
        "print('last layer output shape: ', last_layer.output_shape)\n",
        "last_output = last_layer.output"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the type of the pre-trained model\n",
        "print(f\"The pretrained model has type: {type(pre_trained_model)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HIImb3X3XczH",
        "outputId": "8013b404-4809-4fa1-d1b1-fa9f4f03aab5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The pretrained model has type: <class 'keras.engine.functional.Functional'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXT9SDMK7Ioa"
      },
      "source": [
        "Adding dense layers to our model. These will be the layers that we will train and is tasked with recognizing cats and dogs. We will add a [Dropout](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout) layer as well to regularize the output and avoid overfitting."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "BMXb913pbvFg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "781c5942-0b37-48be-8ba3-6b15177f4fed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\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",
            " flatten_2 (Flatten)            (None, 37632)        0           ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 1024)         38536192    ['flatten_2[0][0]']              \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 1024)         0           ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 1)            1025        ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 47,512,481\n",
            "Trainable params: 38,537,217\n",
            "Non-trainable params: 8,975,264\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "x = layers.Flatten()(last_output)\n",
        "x = layers.Dense(1024, activation='relu')(x)\n",
        "x = layers.Dropout(0.2)(x)                  \n",
        "x = layers.Dense(1, activation='sigmoid')(x)           \n",
        "\n",
        "# Append the dense network to the base model\n",
        "model = Model(inputs = pre_trained_model.input, outputs = x) \n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 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.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dAMsfUcuYF8I",
        "outputId": "457322fc-4fb0-4011-c76c-70c7fcd8525a"
      },
      "execution_count": 35,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "SAwTTkWr56uC"
      },
      "outputs": [],
      "source": [
        "# Set the training parameters\n",
        "model.compile(optimizer = RMSprop(learning_rate=0.001), \n",
        "              loss = 'binary_crossentropy', \n",
        "              metrics = ['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a Callback class that stops training once accuracy reaches 98%\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>0.98):\n",
        "      print(\"\\nReached 98% accuracy so canceling training!\")\n",
        "      self.model.stop_training = True"
      ],
      "metadata": {
        "id": "OwUMToRbXJm0"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "Blhq2MAUeyGA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e966d4af-aa0d-43a8-d381-aece9b57302f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "105/225 [=============>................] - ETA: 8:48 - loss: 0.8505 - accuracy: 0.9150"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 32 bytes but only got 0. Skipping tag 270\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 5 bytes but only got 0. Skipping tag 271\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 8 bytes but only got 0. Skipping tag 272\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 8 bytes but only got 0. Skipping tag 282\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 8 bytes but only got 0. Skipping tag 283\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 20 bytes but only got 0. Skipping tag 306\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 48 bytes but only got 0. Skipping tag 532\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data.  Expecting to read 2 bytes but only got 0. \n",
            "  warnings.warn(str(msg))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "225/225 [==============================] - 1084s 5s/step - loss: 0.4661 - accuracy: 0.9346 - val_loss: 0.0779 - val_accuracy: 0.9680\n",
            "Epoch 2/20\n",
            "225/225 [==============================] - 1058s 5s/step - loss: 0.0971 - accuracy: 0.9675 - val_loss: 0.1834 - val_accuracy: 0.9484\n",
            "Epoch 3/20\n",
            "225/225 [==============================] - ETA: 0s - loss: 0.0568 - accuracy: 0.9824\n",
            "Reached 98% accuracy so cancelling training!\n",
            "225/225 [==============================] - 1061s 5s/step - loss: 0.0568 - accuracy: 0.9824 - val_loss: 0.0886 - val_accuracy: 0.9704\n"
          ]
        }
      ],
      "source": [
        "callbacks = myCallback()\n",
        "\n",
        "# Train the model.\n",
        "history = model.fit(\n",
        "            train_generator,\n",
        "            validation_data = validation_generator,\n",
        "            epochs = 20,\n",
        "            verbose = 1,\n",
        "            callbacks=[callbacks])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "C2Fp6Se9rKuL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "6042b364-3743-40bd-ced0-be69ef4358a3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "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)) # number of epochs\n",
        "\n",
        "# Plot training and validation accuracy per epoch\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()\n",
        "plt.show()\n",
        "print(\"\")\n",
        "\n",
        "\n",
        "# Plot training and validation loss per epoch\n",
        "plt.plot(epochs, loss, 'r', label= \"Training Loss\")\n",
        "plt.plot(epochs, val_loss, 'b', label= \"Validation Loss\")\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwcB2bPj7lIx"
      },
      "source": [
        "We can see, the validation accuracy is also trending upwards as our training accuracy improves. This is a good sign that our model is no longer overfitting!"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "cat vs dog classification [transfer learning].ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "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"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}