cat_vs_dog_classification_[transfer_learning_with_augmentation].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_with_augmentation%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 with Augmentation]**\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. Also we will augment training data here."
      ]
    },
    {
      "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": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "O4s8HckqGlnb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a010727-b722-42b6-e7e8-92b97cdab3ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-06-30 19:56:19--  https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip\n",
            "Resolving download.microsoft.com (download.microsoft.com)... 23.54.48.114, 2600:1409:3000:4a4::e59, 2600:1409:3000:48e::e59\n",
            "Connecting to download.microsoft.com (download.microsoft.com)|23.54.48.114|: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   133MB/s    in 6.3s    \n",
            "\n",
            "2022-06-30 19:56:26 (125 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": "1c92ecb2-69e2-4500-8054-62c035f59494"
      },
      "execution_count": 6,
      "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": 7,
      "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": "332496d0-42d7-4e3f-f943-a40a8592faa5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/tmp/cats-v-dogs/training\n",
            "/tmp/cats-v-dogs/validation\n",
            "/tmp/cats-v-dogs/training/dogs\n",
            "/tmp/cats-v-dogs/training/cats\n",
            "/tmp/cats-v-dogs/validation/dogs\n",
            "/tmp/cats-v-dogs/validation/cats\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": 9,
      "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": "6bf409f8-4753-442d-bad5-216bbf367e36"
      },
      "execution_count": 10,
      "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": "a6cff3bc-0229-49df-adee-1428547a958f"
      },
      "execution_count": 11,
      "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": "85682b05-eac1-4333-eaaf-e9cc53326113"
      },
      "execution_count": 12,
      "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": "d8f54725-88f2-42d8-b441-0aa8def2eee0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This image has shape: (249, 218, 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": "1f33825c-56fd-4291-c481-8a8546c03930"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This image has shape: (500, 497, 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 and augmentation\n",
        "  train_datagen = ImageDataGenerator(rescale=1.0/255.0,\n",
        "                                     rotation_range = 40,\n",
        "                                     width_shift_range = 0.2,\n",
        "                                     height_shift_range = 0.2,\n",
        "                                     shear_range = 0.2,\n",
        "                                     zoom_range = 0.2,\n",
        "                                     horizontal_flip = True)\n",
        "\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": 15,
      "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": "4c3a6234-2792-4e53-d9b7-ca8fcc80c6ae"
      },
      "execution_count": 16,
      "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": 17,
      "metadata": {
        "id": "1xJZ5glPPCRz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15ad4f38-9384-4e95-9751-37549261fae0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-06-30 19:57:35--  https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.107.128, 173.194.203.128, 74.125.199.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.107.128|: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   246MB/s    in 0.3s    \n",
            "\n",
            "2022-06-30 19:57:35 (246 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": 18,
      "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": 19,
      "metadata": {
        "id": "qeGP0Ust5kCR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a71a04fb-5a3c-4ab0-d08f-11416452d8bb"
      },
      "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": "17f46b74-c192-4f9d-f244-8376fa67523c"
      },
      "execution_count": 20,
      "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": 21,
      "metadata": {
        "id": "jDmGO9tg5iPc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13deae67-6567-4b48-e991-cc3bacac67ab"
      },
      "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": "f5fc4b38-fd2d-45ca-c173-1b4808e5d131"
      },
      "execution_count": 22,
      "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": 23,
      "metadata": {
        "id": "BMXb913pbvFg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e93c45ad-1619-4753-e09c-92f7a86d83d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\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 (Flatten)              (None, 37632)        0           ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 1024)         38536192    ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 1024)         0           ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_1 (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": "c4cd4424-ff89-470e-dd32-b70e7e992ecd"
      },
      "execution_count": 24,
      "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": 25,
      "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 99.9%\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>0.999):\n",
        "      print(\"\\nReached 99.9% accuracy so canceling training!\")\n",
        "      self.model.stop_training = True"
      ],
      "metadata": {
        "id": "OwUMToRbXJm0"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Blhq2MAUeyGA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "831a95cc-fcad-4d85-e577-860230ccd8cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "225/225 [==============================] - ETA: 0s - loss: 0.5613 - accuracy: 0.8942"
          ]
        },
        {
          "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": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r225/225 [==============================] - 189s 771ms/step - loss: 0.5613 - accuracy: 0.8942 - val_loss: 0.1079 - val_accuracy: 0.9620\n",
            "Epoch 2/20\n",
            "225/225 [==============================] - 172s 763ms/step - loss: 0.1776 - accuracy: 0.9276 - val_loss: 0.1311 - val_accuracy: 0.9512\n",
            "Epoch 3/20\n",
            "225/225 [==============================] - 171s 760ms/step - loss: 0.1570 - accuracy: 0.9370 - val_loss: 0.1137 - val_accuracy: 0.9648\n",
            "Epoch 4/20\n",
            "225/225 [==============================] - 171s 758ms/step - loss: 0.1553 - accuracy: 0.9390 - val_loss: 0.1006 - val_accuracy: 0.9640\n",
            "Epoch 5/20\n",
            "225/225 [==============================] - 171s 758ms/step - loss: 0.1449 - accuracy: 0.9422 - val_loss: 0.0937 - val_accuracy: 0.9676\n",
            "Epoch 6/20\n",
            "225/225 [==============================] - 170s 757ms/step - loss: 0.1417 - accuracy: 0.9437 - val_loss: 0.0861 - val_accuracy: 0.9700\n",
            "Epoch 7/20\n",
            "225/225 [==============================] - 171s 759ms/step - loss: 0.1321 - accuracy: 0.9486 - val_loss: 0.1492 - val_accuracy: 0.9516\n",
            "Epoch 8/20\n",
            "225/225 [==============================] - 168s 747ms/step - loss: 0.1340 - accuracy: 0.9473 - val_loss: 0.0902 - val_accuracy: 0.9724\n",
            "Epoch 9/20\n",
            "225/225 [==============================] - 168s 747ms/step - loss: 0.1252 - accuracy: 0.9508 - val_loss: 0.0968 - val_accuracy: 0.9676\n",
            "Epoch 10/20\n",
            "225/225 [==============================] - 169s 751ms/step - loss: 0.1242 - accuracy: 0.9527 - val_loss: 0.0935 - val_accuracy: 0.9700\n",
            "Epoch 11/20\n",
            "225/225 [==============================] - 168s 747ms/step - loss: 0.1259 - accuracy: 0.9503 - val_loss: 0.0940 - val_accuracy: 0.9700\n",
            "Epoch 12/20\n",
            "225/225 [==============================] - 169s 749ms/step - loss: 0.1250 - accuracy: 0.9516 - val_loss: 0.0846 - val_accuracy: 0.9728\n",
            "Epoch 13/20\n",
            "225/225 [==============================] - 168s 748ms/step - loss: 0.1266 - accuracy: 0.9528 - val_loss: 0.0982 - val_accuracy: 0.9716\n",
            "Epoch 14/20\n",
            "225/225 [==============================] - 168s 748ms/step - loss: 0.1169 - accuracy: 0.9559 - val_loss: 0.1015 - val_accuracy: 0.9728\n",
            "Epoch 15/20\n",
            "225/225 [==============================] - 168s 748ms/step - loss: 0.1209 - accuracy: 0.9538 - val_loss: 0.1077 - val_accuracy: 0.9736\n",
            "Epoch 16/20\n",
            "225/225 [==============================] - 168s 747ms/step - loss: 0.1187 - accuracy: 0.9552 - val_loss: 0.1106 - val_accuracy: 0.9684\n",
            "Epoch 17/20\n",
            "225/225 [==============================] - 168s 746ms/step - loss: 0.1172 - accuracy: 0.9570 - val_loss: 0.0949 - val_accuracy: 0.9700\n",
            "Epoch 18/20\n",
            "225/225 [==============================] - 168s 745ms/step - loss: 0.1187 - accuracy: 0.9563 - val_loss: 0.1013 - val_accuracy: 0.9712\n",
            "Epoch 19/20\n",
            "225/225 [==============================] - 168s 747ms/step - loss: 0.1115 - accuracy: 0.9563 - val_loss: 0.0997 - val_accuracy: 0.9724\n",
            "Epoch 20/20\n",
            "225/225 [==============================] - 168s 748ms/step - loss: 0.1163 - accuracy: 0.9580 - val_loss: 0.1077 - val_accuracy: 0.9720\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": 28,
      "metadata": {
        "id": "C2Fp6Se9rKuL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "b75c8ad7-7bc1-49d8-843b-22029a317d6f"
      },
      "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()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "cat vs dog classification [transfer learning with augmentation].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
}