FirstProject(TransferLearning_Monkey).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FirstProject(TransferLearning-Monkey).ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zaneho/TransferLearning-Monkey/blob/master/FirstProject(TransferLearning_Monkey).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "eZGZjUs3gkCK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Monkey Project\n",
        "We want to train a model that can tell the difference between 10 different \n",
        "Monkey species\n",
        "\n",
        "To do this we are going to use a famous Imagenet model called InceptionV3 to do transfer Learning.\n",
        "\n",
        "Along the way will also use the new tf.data along with Pandas to handle the input pipelines for training and testing.\n"
      ]
    },
    {
      "metadata": {
        "id": "r-EJYnxzevVO",
        "colab_type": "code",
        "outputId": "32d277e0-8267-4be8-b6c7-d3d29b18b2bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9tmaAHfrfoN7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Test for GPU"
      ]
    },
    {
      "metadata": {
        "id": "6IvtfV_0fCzU",
        "colab_type": "code",
        "outputId": "2ceffdf8-40c3-47e2-ab45-81a9bc0af5c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2_iI61cdfkfT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Download the data\n",
        "https://www.dropbox.com/s/je25ylpp8r1r8uc/10-monkey-species.zip"
      ]
    },
    {
      "metadata": {
        "id": "WF8noIU7fdC7",
        "colab_type": "code",
        "outputId": "39b2af3a-ff11-4056-fa88-a5e8a7f76dd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "!wget -qq https://www.dropbox.com/s/vj61417lofjebju/utils.zip\n",
        "!wget -qq https://www.dropbox.com/s/je25ylpp8r1r8uc/10-monkey-species.zip\n",
        "\n",
        "\n",
        "!unzip -qq 10-monkey-species.zip\n",
        "!rm 10-monkey-species.zip\n",
        "\n",
        "!unzip -qq utils.zip\n",
        "!rm utils.zip\n",
        "\n",
        "\n",
        "!ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gdrive.py  monkey_labels.txt  sutils.py     validation.zip\n",
            "__MACOSX   sample_data\t      training.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "V14E44gAh0O7",
        "colab_type": "code",
        "outputId": "0e34aedc-567c-4554-890a-f4b53d216837",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "!unzip -qq training.zip\n",
        "!rm training.zip\n",
        "\n",
        "!unzip -qq validation.zip\n",
        "!rm validation.zip\n",
        "\n",
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gdrive.py  monkey_labels.txt  sutils.py  validation\n",
            "__MACOSX   sample_data\t      training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_i6pbwqPjHGL",
        "colab_type": "code",
        "outputId": "33f16bbc-93a5-491d-ef38-a1b9485869d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from sutils import *\n",
        "import os, json\n",
        "from glob import glob\n",
        "import numpy as np\n",
        "\n",
        "import keras\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "\n",
        "from tensorflow.keras.applications import InceptionV3\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "\n",
        "\n",
        "from tensorflow.python.keras.preprocessing import image\n",
        "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "from tensorflow.python.keras.models import Model,load_model,Sequential\n",
        "\n",
        "from tensorflow.python.keras.layers import Dense, GlobalAveragePooling2D, Dropout,Flatten, Input, Conv2D, MaxPooling2D, Dropout, Flatten\n",
        "from tensorflow.python.keras import backend as K\n",
        "\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, Callback\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "pezLfXOtjMvi",
        "colab_type": "code",
        "outputId": "cbf9344c-c9c9-4c9a-dd81-3f01478199c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "print(tf.__version__)\n",
        "print(tf.keras.__version__)\n",
        "print(keras.__version__)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.12.0\n",
            "2.1.6-tf\n",
            "2.2.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_puC9monjSVq",
        "colab_type": "code",
        "outputId": "61c4489f-4538-4d6b-c735-728a190f1922",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls training"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "n0  n1\tn2  n3\tn4  n5\tn6  n7\tn8  n9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_80eRNWQjVzC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Set up some Hyperparameters\n",
        "\n",
        "batch_size = 32\n",
        "epochs = 10\n",
        "\n",
        "lr = 0.001"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MT3VnGsbjY1c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Converting raw files in folders into something we can feed into tf.data\n",
        "\n",
        "We use glob to get lists of the files in the directories and then convert them into dataframes and add in class numbers.\n",
        "\n",
        "\n",
        "Finally we randomly shuffle them up\n"
      ]
    },
    {
      "metadata": {
        "id": "tyxvIReujZ6a",
        "colab_type": "code",
        "outputId": "babb750c-a7ef-42c5-f1aa-811f0cef18c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import glob\n",
        "import pandas as pd\n",
        "\n",
        "#the groups\n",
        "filenames_n0 = glob.glob('./training/n0/*.jpg')\n",
        "filenames_n1 = glob.glob('./training/n1/*.jpg')\n",
        "filenames_n2 = glob.glob('./training/n2/*.jpg')\n",
        "filenames_n3 = glob.glob('./training/n3/*.jpg')\n",
        "filenames_n4 = glob.glob('./training/n4/*.jpg')\n",
        "filenames_n5 = glob.glob('./training/n5/*.jpg')\n",
        "filenames_n6 = glob.glob('./training/n6/*.jpg')\n",
        "filenames_n7 = glob.glob('./training/n7/*.jpg')\n",
        "filenames_n8 = glob.glob('./training/n8/*.jpg')\n",
        "filenames_n9 = glob.glob('./training/n9/*.jpg')\n",
        "\n",
        "names = ['alouatta_palliata', 'erythrocebus_patas','cacajao_calvus','macaca_fuscata','cebuella_pygmea'\n",
        "         ,'cebus_capucinus','mico_argentatus','saimiri_sciureus','aotus_nigriceps','trachypithecus_johnii']\n",
        "\n",
        "\n",
        "len(filenames_n0)\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "105"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "91WuVKltl1K6",
        "colab_type": "code",
        "outputId": "3dd32667-6d90-4320-f222-644ffe11ccfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# Make a dataframe based on the filenames\n",
        "df = pd.DataFrame(filenames_n0, columns = [\"filename\"])\n",
        "df2 = pd.DataFrame(filenames_n1, columns = [\"filename\"])\n",
        "df3 = pd.DataFrame(filenames_n2, columns = [\"filename\"])\n",
        "df4 = pd.DataFrame(filenames_n3, columns = [\"filename\"])\n",
        "df5 = pd.DataFrame(filenames_n4, columns = [\"filename\"])\n",
        "df6 = pd.DataFrame(filenames_n5, columns = [\"filename\"])\n",
        "df7 = pd.DataFrame(filenames_n6, columns = [\"filename\"])\n",
        "df8 = pd.DataFrame(filenames_n7, columns = [\"filename\"])\n",
        "df9 = pd.DataFrame(filenames_n8, columns = [\"filename\"])\n",
        "df10 = pd.DataFrame(filenames_n9, columns = [\"filename\"])\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>./training/n0/n0174.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>./training/n0/n0142.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>./training/n0/n0139.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>./training/n0/n0032.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>./training/n0/n0021.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  filename\n",
              "0  ./training/n0/n0174.jpg\n",
              "1  ./training/n0/n0142.jpg\n",
              "2  ./training/n0/n0139.jpg\n",
              "3  ./training/n0/n0032.jpg\n",
              "4  ./training/n0/n0021.jpg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "CIKDbXjImOzZ",
        "colab_type": "code",
        "outputId": "671c12ce-e64d-401b-f6e9-ca107077cdac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Add Class columns \n",
        "df['class'] = pd.Series([0 for x in range(len(df.index))], index=df.index)\n",
        "df2['class'] = pd.Series([1 for x in range(len(df2.index))], index=df2.index)\n",
        "df3['class'] = pd.Series([2 for x in range(len(df3.index))], index=df3.index)\n",
        "df4['class'] = pd.Series([3 for x in range(len(df4.index))], index=df4.index)\n",
        "df5['class'] = pd.Series([4 for x in range(len(df5.index))], index=df5.index)\n",
        "df6['class'] = pd.Series([5 for x in range(len(df6.index))], index=df6.index)\n",
        "df7['class'] = pd.Series([6 for x in range(len(df7.index))], index=df7.index)\n",
        "df8['class'] = pd.Series([7 for x in range(len(df8.index))], index=df8.index)\n",
        "df9['class'] = pd.Series([8 for x in range(len(df9.index))], index=df9.index)\n",
        "df10['class'] = pd.Series([9 for x in range(len(df10.index))], index=df10.index)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>./training/n0/n0174.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>./training/n0/n0142.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>./training/n0/n0139.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>./training/n0/n0032.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>./training/n0/n0021.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  filename  class\n",
              "0  ./training/n0/n0174.jpg      0\n",
              "1  ./training/n0/n0142.jpg      0\n",
              "2  ./training/n0/n0139.jpg      0\n",
              "3  ./training/n0/n0032.jpg      0\n",
              "4  ./training/n0/n0021.jpg      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "bxoBvYCByxRf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Split into train and validation sets\n",
        "train_set_percentage = .9\n",
        "\n",
        "\n",
        "train_df = df[:int(len(df)*train_set_percentage)]\n",
        "val_df = df[int(len(df)*train_set_percentage):]\n",
        "\n",
        "train_df2 = df2[:int(len(df2)*train_set_percentage)]\n",
        "val_df2 = df2[int(len(df2)*train_set_percentage):]\n",
        "\n",
        "train_df3 = df3[:int(len(df3)*train_set_percentage)]\n",
        "val_df3 = df3[int(len(df3)*train_set_percentage):]\n",
        "\n",
        "train_df4 = df4[:int(len(df4)*train_set_percentage)]\n",
        "val_df4 = df4[int(len(df4)*train_set_percentage):]\n",
        "\n",
        "train_df5 = df5[:int(len(df5)*train_set_percentage)]\n",
        "val_df5 = df5[int(len(df5)*train_set_percentage):]\n",
        "\n",
        "train_df6 = df6[:int(len(df6)*train_set_percentage)]\n",
        "val_df6 = df6[int(len(df6)*train_set_percentage):]\n",
        "\n",
        "train_df7 = df7[:int(len(df7)*train_set_percentage)]\n",
        "val_df7 = df7[int(len(df7)*train_set_percentage):]\n",
        "\n",
        "train_df8 = df8[:int(len(df8)*train_set_percentage)]\n",
        "val_df8 = df8[int(len(df8)*train_set_percentage):]\n",
        "\n",
        "train_df9 = df9[:int(len(df9)*train_set_percentage)]\n",
        "val_df9 = df9[int(len(df9)*train_set_percentage):]\n",
        "\n",
        "train_df10 = df10[:int(len(df10)*train_set_percentage)]\n",
        "val_df10 = df10[int(len(df10)*train_set_percentage):]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "df_new_train = pd.concat([train_df, train_df2, train_df3, train_df4, train_df5, train_df6, train_df7, train_df8, train_df9, train_df10])\n",
        "df_new_val = pd.concat([val_df, val_df2, val_df3, val_df4, val_df5, val_df6, val_df7, val_df8, val_df9, val_df10])\n",
        "\n",
        "df_new_train.shape\n",
        "\n",
        "#shuffle\n",
        "df = df_new_train.sample(frac=1).reset_index(drop=True)\n",
        "df_val = df_new_val.sample(frac=1).reset_index(drop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bel1rePkreA6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Examine the Dataset\n",
        "\n",
        "lets look at what we have now. "
      ]
    },
    {
      "metadata": {
        "id": "8a4WmnNmrdi1",
        "colab_type": "code",
        "outputId": "6ef77a38-f399-4269-d7cc-d7a7e629360e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>./training/n6/n6037.jpg</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>./training/n4/n4138.jpg</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>./training/n0/n0169.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>./training/n1/n1026.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>./training/n1/n1165.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  filename  class\n",
              "0  ./training/n6/n6037.jpg      6\n",
              "1  ./training/n4/n4138.jpg      4\n",
              "2  ./training/n0/n0169.jpg      0\n",
              "3  ./training/n1/n1026.jpg      1\n",
              "4  ./training/n1/n1165.jpg      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "04nZezeDsqDZ",
        "colab_type": "code",
        "outputId": "a239e9ba-49a4-4d04-d676-4bec2814a6d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "#training - 1096 row, 2 col (filename & class)\n",
        "#validation - 272 row, 2 col (filename & class)\n",
        "print(df.shape)\n",
        "print(df_val.shape)\n",
        "\n",
        "df['class'].unique()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(981, 2)\n",
            "(115, 2)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 4, 0, 1, 2, 9, 7, 3, 5, 8])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "HTjCRJgZs7kx",
        "colab_type": "code",
        "outputId": "d61a5da2-fef9-48e2-d493-21ecc9e46686",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        }
      },
      "cell_type": "code",
      "source": [
        "print(df['class'].value_counts())\n",
        "\n",
        "\n",
        "ax = df['class'].value_counts().plot.barh()\n",
        "ax.set_xlabel(\"Number of Examples\", fontsize=12)\n",
        "ax.set_ylabel(\"Monkey - training set\", fontsize=12)\n",
        "\n",
        "\n",
        "names = ['alouatta_palliata', 'erythrocebus_patas','cacajao_calvus','macaca_fuscata','cebuella_pygmea'\n",
        "         ,'cebus_capucinus','mico_argentatus','saimiri_sciureus','aotus_nigriceps','trachypithecus_johnii']\n",
        "\n",
        "ax.set_yticklabels(names, rotation=0, fontsize=12)\n",
        "\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3    109\n",
            "7    102\n",
            "5    101\n",
            "2     99\n",
            "1     99\n",
            "8     95\n",
            "6     95\n",
            "4     94\n",
            "0     94\n",
            "9     93\n",
            "Name: class, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAEICAYAAACOKIcAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f463c22ee80>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ITs6MjVhtxS5",
        "colab_type": "code",
        "outputId": "1d539de8-3f1d-4594-f1d9-62555e2f5afe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# convert the dataframe into 2 lists to use for filename and labels\n",
        "train_filenames_list = df[\"filename\"].tolist()\n",
        "train_labels_list = df[\"class\"].astype('int32').tolist()\n",
        "\n",
        "# convert the dataframe into 2 lists to use for filename and labels\n",
        "val_filenames_list = df_val[\"filename\"].tolist()\n",
        "val_labels_list = df_val[\"class\"].astype('int32').tolist()\n",
        "\n",
        "#number of classes\n",
        "num_classes = 10\n",
        "\n",
        "df.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(981, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "dwJ765_st-Dx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Make the pipeline for loading and resizing the images"
      ]
    },
    {
      "metadata": {
        "id": "Xb742qcxt_81",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Reads an image from a file, decodes it into a tensor, and resizes it\n",
        "# to a fixed shape.\n",
        "img_rows, img_cols = 299,299\n",
        "\n",
        "def _parse_function(filename, label):\n",
        "  image_string = tf.read_file(filename)\n",
        "  image_decoded = tf.image.decode_jpeg(image_string)\n",
        "  image_resized = tf.image.resize_images(image_decoded, [img_rows, img_cols])\n",
        "  label = tf.one_hot(label, num_classes)\n",
        "  return image_resized, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wjZVB5BlvXmS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# A vector of filenames - from a list\n",
        "filenames = tf.constant(train_filenames_list)\n",
        "\n",
        "# Labels that match the training files - from a list\n",
        "labels = tf.constant(train_labels_list)\n",
        "\n",
        "# Same as above but for validation set\n",
        "val_filenames = tf.constant(val_filenames_list)\n",
        "val_labels = tf.constant(val_labels_list)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eWQhp0EFvZ0Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Assembling the Data pipeline using tf.data"
      ]
    },
    {
      "metadata": {
        "id": "u6DcyADhvaiQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))\n",
        "train_dataset = train_dataset.map(_parse_function)\n",
        "train_dataset = train_dataset.repeat(100)\n",
        "train_dataset = train_dataset.batch(23)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gTFey-tcvdIB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "valid_dataset = tf.data.Dataset.from_tensor_slices((val_filenames, val_labels))\n",
        "valid_dataset = valid_dataset.map(_parse_function)\n",
        "valid_dataset = valid_dataset.repeat(100)\n",
        "valid_dataset = valid_dataset.batch(23)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qeWic5kEHJmV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_dataset = tf.data.Dataset.from_tensor_slices((val_filenames, val_labels))\n",
        "test_dataset = test_dataset.map(_parse_function)\n",
        "test_dataset = test_dataset.repeat(1)\n",
        "test_dataset = test_dataset.batch(115)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BU50P0JMvf_D",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create the network"
      ]
    },
    {
      "metadata": {
        "id": "XGZ0OO1LvjBJ",
        "colab_type": "code",
        "outputId": "b6ff6725-60f5-40ce-e6b2-57951608441b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# create the base pre-trained model\n",
        "base_model = InceptionV3(weights='imagenet', include_top=False)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 8s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UtUYvqbNvkI5",
        "colab_type": "code",
        "outputId": "29f49619-22f7-430b-b036-9a8b3b571555",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 11305
        }
      },
      "cell_type": "code",
      "source": [
        "base_model.summary()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, None, None, 3 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, None, None, 3 96          conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, None, None, 3 0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, None, None, 3 9216        activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, None, None, 6 18432       activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, None, None, 6 192         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, None, None, 6 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, None, None, 6 0           activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, None, None, 8 5120        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, None, None, 8 240         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, None, None, 8 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, None, None, 1 138240      activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, None, None, 1 576         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, None, None, 1 0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 1 0           activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, None, None, 6 192         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, None, None, 9 55296       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, None, None, 4 144         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, None, None, 9 288         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, None, None, 4 0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, None, None, 9 0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, None, None, 1 0           max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, None, None, 6 76800       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, None, None, 9 82944       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, None, None, 3 6144        average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, None, None, 6 192         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, None, None, 6 192         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, None, None, 3 96          conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, None, None, 6 0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, None, None, 6 0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, None, None, 3 0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, None, None, 2 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, None, None, 6 16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, None, None, 6 192         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, None, None, 6 0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, None, None, 4 12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, None, None, 9 55296       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, None, None, 4 144         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, None, None, 9 288         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, None, None, 4 0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, None, None, 9 0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, None, None, 2 0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, None, None, 6 76800       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, None, None, 9 82944       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, None, None, 6 16384       average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, None, None, 6 192         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, None, None, 6 192         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, None, None, 9 288         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, None, None, 6 192         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, None, None, 6 0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, None, None, 6 0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, None, None, 9 0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, None, None, 6 0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, None, None, 2 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, None, None, 6 18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, None, None, 6 192         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, None, None, 6 0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, None, None, 4 13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, None, None, 9 55296       activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, None, None, 4 144         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, None, None, 9 288         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, None, None, 4 0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, None, None, 9 0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, None, None, 2 0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, None, None, 6 76800       activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, None, None, 9 82944       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, None, None, 6 18432       average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, None, None, 6 192         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, None, None, 6 192         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, None, None, 9 288         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, None, None, 6 192         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, None, None, 6 0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, None, None, 6 0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, None, None, 9 0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, None, None, 6 0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, None, None, 2 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, None, None, 6 18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, None, None, 6 192         conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, None, None, 6 0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, None, None, 9 55296       activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, None, None, 9 288         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, None, None, 9 0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, None, None, 3 995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, None, None, 9 82944       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, None, None, 3 1152        conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, None, None, 9 288         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, None, None, 3 0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, None, None, 9 0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, None, None, 7 0           activation_26[0][0]              \n",
            "                                                                 activation_29[0][0]              \n",
            "                                                                 max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, None, None, 1 384         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, None, None, 1 0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, None, None, 1 114688      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, None, None, 1 384         conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, None, None, 1 0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, None, None, 1 114688      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, None, None, 1 384         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, None, None, 1 384         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, None, None, 1 0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, None, None, 1 0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, None, None, 1 114688      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, None, None, 1 114688      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, None, None, 1 384         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, None, None, 1 384         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, None, None, 1 0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, None, None, 1 0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, None, None, 7 0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, None, None, 1 147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, None, None, 1 172032      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, None, None, 1 172032      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, None, None, 1 576         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, None, None, 1 576         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, None, None, 1 576         conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, None, None, 1 576         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, None, None, 1 0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, None, None, 1 0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, None, None, 1 0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, None, None, 1 0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, None, None, 7 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, None, None, 1 122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, None, None, 1 480         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, None, None, 1 179200      activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, None, None, 1 480         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, None, None, 1 0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, None, None, 1 179200      activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, None, None, 1 480         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, None, None, 1 480         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, None, None, 1 0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, None, None, 1 179200      activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, None, None, 1 179200      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, None, None, 1 480         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, None, None, 1 480         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, None, None, 1 0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, None, None, 1 0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, None, None, 7 0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, None, None, 1 147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, None, None, 1 215040      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, None, None, 1 215040      activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, None, None, 1 576         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, None, None, 1 576         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, None, None, 1 576         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, None, None, 1 576         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, None, None, 1 0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, None, None, 1 0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, None, None, 1 0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, None, None, 7 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, None, None, 1 122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, None, None, 1 480         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, None, None, 1 179200      activation_54[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, None, None, 1 480         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, None, None, 1 0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, None, None, 1 179200      activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, None, None, 1 480         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, None, None, 1 480         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, None, None, 1 179200      activation_51[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, None, None, 1 179200      activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, None, None, 1 480         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, None, None, 1 480         conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, None, None, 1 0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, None, None, 7 0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, None, None, 1 147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, None, None, 1 215040      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, None, None, 1 215040      activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, None, None, 1 576         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, None, None, 1 576         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, None, None, 1 576         conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, None, None, 1 576         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, None, None, 1 0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, None, None, 7 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, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, None, None, 1 576         conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, None, None, 1 0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, None, None, 1 258048      activation_64[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, None, None, 1 576         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, None, None, 1 0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, None, None, 1 258048      activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, None, None, 1 576         conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, None, None, 1 576         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, None, None, 1 0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, None, None, 1 258048      activation_61[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, None, None, 1 258048      activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, None, None, 1 576         conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, None, None, 1 576         conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, None, None, 1 0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_6 (AveragePoo (None, None, None, 7 0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, None, None, 1 258048      activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, None, None, 1 258048      activation_67[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, None, None, 1 576         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, None, None, 1 576         conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, None, None, 1 576         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, None, None, 1 576         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, None, None, 1 0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, None, None, 1 0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, None, None, 1 0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, None, None, 7 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, None, None, 1 147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, None, None, 1 576         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, None, None, 1 0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, None, None, 1 258048      activation_72[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, None, None, 1 576         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, None, None, 1 0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, None, None, 1 258048      activation_73[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, None, None, 1 576         conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, None, None, 1 576         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, None, None, 1 0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, None, None, 1 0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, None, None, 3 552960      activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, None, None, 1 331776      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, None, None, 3 960         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, None, None, 1 576         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, None, None, 3 0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, None, None, 1 0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, None, None, 1 0           activation_71[0][0]              \n",
            "                                                                 activation_75[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, None, None, 4 573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, None, None, 4 1344        conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, None, None, 4 0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, None, None, 3 491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, None, None, 3 1548288     activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, None, None, 3 1152        conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, None, None, 3 1152        conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, None, None, 3 0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, None, None, 3 0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, None, None, 3 442368      activation_77[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, None, None, 3 442368      activation_77[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, None, None, 3 442368      activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, None, None, 3 442368      activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_7 (AveragePoo (None, None, None, 1 0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, None, None, 3 409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, None, None, 3 1152        conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, None, None, 3 1152        conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, None, None, 3 1152        conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, None, None, 3 1152        conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, None, None, 1 245760      average_pooling2d_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, None, None, 3 960         conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, None, None, 3 0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, None, None, 3 0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, None, None, 3 0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, None, None, 3 0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, None, None, 1 576         conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, None, None, 3 0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_78[0][0]              \n",
            "                                                                 activation_79[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, None, None, 7 0           activation_82[0][0]              \n",
            "                                                                 activation_83[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, None, None, 1 0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, None, None, 2 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, None, None, 4 917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, None, None, 4 1344        conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, None, None, 4 0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, None, None, 3 786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, None, None, 3 1548288     activation_89[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, None, None, 3 1152        conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, None, None, 3 1152        conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, None, None, 3 0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, None, None, 3 0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, None, None, 3 442368      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, None, None, 3 442368      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, None, None, 3 442368      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, None, None, 3 442368      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_8 (AveragePoo (None, None, None, 2 0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, None, None, 3 655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, None, None, 3 1152        conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, None, None, 3 1152        conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, None, None, 3 1152        conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, None, None, 3 1152        conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, None, None, 1 393216      average_pooling2d_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, None, None, 3 960         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, None, None, 3 0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, None, None, 3 0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, None, None, 3 0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, None, None, 3 0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, None, None, 1 576         conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, None, None, 3 0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_87[0][0]              \n",
            "                                                                 activation_88[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, None, None, 7 0           activation_91[0][0]              \n",
            "                                                                 activation_92[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, None, None, 1 0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, None, None, 2 0           activation_85[0][0]              \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_1[0][0]              \n",
            "                                                                 activation_93[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 21,802,784\n",
            "Trainable params: 21,768,352\n",
            "Non-trainable params: 34,432\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NSdicPFIvmzG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create a simple head for the model that we can train"
      ]
    },
    {
      "metadata": {
        "id": "Ir9ukuA9vogi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# add a global spatial average pooling layer\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "\n",
        "# let's add a fully-connected layer\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "\n",
        "\n",
        "# 5 Classes for flowers\n",
        "predictions = Dense(num_classes, activation='softmax')(x)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "l7k2OZyHvqAa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Creating the model that we will train\n",
        "model = Model(inputs=base_model.input, outputs=predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6nhWA6vfvrDp",
        "colab_type": "code",
        "outputId": "56a66353-530b-4ae8-b161-4fa48c3a61bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1615
        }
      },
      "cell_type": "code",
      "source": [
        "# first: train only the top layers (which were randomly initialized)\n",
        "# i.e. freeze all convolutional VGG16 layers\n",
        "for layer in base_model.layers:\n",
        "    if 'batch' in layer.name:\n",
        "      print(layer.name)\n",
        "      layer.trainable = True\n",
        "    else:\n",
        "      layer.trainable = False \n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "batch_normalization\n",
            "batch_normalization_1\n",
            "batch_normalization_2\n",
            "batch_normalization_3\n",
            "batch_normalization_4\n",
            "batch_normalization_8\n",
            "batch_normalization_6\n",
            "batch_normalization_9\n",
            "batch_normalization_5\n",
            "batch_normalization_7\n",
            "batch_normalization_10\n",
            "batch_normalization_11\n",
            "batch_normalization_15\n",
            "batch_normalization_13\n",
            "batch_normalization_16\n",
            "batch_normalization_12\n",
            "batch_normalization_14\n",
            "batch_normalization_17\n",
            "batch_normalization_18\n",
            "batch_normalization_22\n",
            "batch_normalization_20\n",
            "batch_normalization_23\n",
            "batch_normalization_19\n",
            "batch_normalization_21\n",
            "batch_normalization_24\n",
            "batch_normalization_25\n",
            "batch_normalization_27\n",
            "batch_normalization_28\n",
            "batch_normalization_26\n",
            "batch_normalization_29\n",
            "batch_normalization_34\n",
            "batch_normalization_35\n",
            "batch_normalization_31\n",
            "batch_normalization_36\n",
            "batch_normalization_32\n",
            "batch_normalization_37\n",
            "batch_normalization_30\n",
            "batch_normalization_33\n",
            "batch_normalization_38\n",
            "batch_normalization_39\n",
            "batch_normalization_44\n",
            "batch_normalization_45\n",
            "batch_normalization_41\n",
            "batch_normalization_46\n",
            "batch_normalization_42\n",
            "batch_normalization_47\n",
            "batch_normalization_40\n",
            "batch_normalization_43\n",
            "batch_normalization_48\n",
            "batch_normalization_49\n",
            "batch_normalization_54\n",
            "batch_normalization_55\n",
            "batch_normalization_51\n",
            "batch_normalization_56\n",
            "batch_normalization_52\n",
            "batch_normalization_57\n",
            "batch_normalization_50\n",
            "batch_normalization_53\n",
            "batch_normalization_58\n",
            "batch_normalization_59\n",
            "batch_normalization_64\n",
            "batch_normalization_65\n",
            "batch_normalization_61\n",
            "batch_normalization_66\n",
            "batch_normalization_62\n",
            "batch_normalization_67\n",
            "batch_normalization_60\n",
            "batch_normalization_63\n",
            "batch_normalization_68\n",
            "batch_normalization_69\n",
            "batch_normalization_72\n",
            "batch_normalization_73\n",
            "batch_normalization_70\n",
            "batch_normalization_74\n",
            "batch_normalization_71\n",
            "batch_normalization_75\n",
            "batch_normalization_80\n",
            "batch_normalization_77\n",
            "batch_normalization_81\n",
            "batch_normalization_78\n",
            "batch_normalization_79\n",
            "batch_normalization_82\n",
            "batch_normalization_83\n",
            "batch_normalization_76\n",
            "batch_normalization_84\n",
            "batch_normalization_89\n",
            "batch_normalization_86\n",
            "batch_normalization_90\n",
            "batch_normalization_87\n",
            "batch_normalization_88\n",
            "batch_normalization_91\n",
            "batch_normalization_92\n",
            "batch_normalization_85\n",
            "batch_normalization_93\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qA16SJ_1vsVW",
        "colab_type": "code",
        "outputId": "de6d7176-750c-49b4-b638-3d7a630cdb81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 10693
        }
      },
      "cell_type": "code",
      "source": [
        "for layer in model.layers:\n",
        "    print(layer.name)\n",
        "    print(layer.trainable)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_1\n",
            "False\n",
            "conv2d\n",
            "False\n",
            "batch_normalization\n",
            "True\n",
            "activation\n",
            "False\n",
            "conv2d_1\n",
            "False\n",
            "batch_normalization_1\n",
            "True\n",
            "activation_1\n",
            "False\n",
            "conv2d_2\n",
            "False\n",
            "batch_normalization_2\n",
            "True\n",
            "activation_2\n",
            "False\n",
            "max_pooling2d\n",
            "False\n",
            "conv2d_3\n",
            "False\n",
            "batch_normalization_3\n",
            "True\n",
            "activation_3\n",
            "False\n",
            "conv2d_4\n",
            "False\n",
            "batch_normalization_4\n",
            "True\n",
            "activation_4\n",
            "False\n",
            "max_pooling2d_1\n",
            "False\n",
            "conv2d_8\n",
            "False\n",
            "batch_normalization_8\n",
            "True\n",
            "activation_8\n",
            "False\n",
            "conv2d_6\n",
            "False\n",
            "conv2d_9\n",
            "False\n",
            "batch_normalization_6\n",
            "True\n",
            "batch_normalization_9\n",
            "True\n",
            "activation_6\n",
            "False\n",
            "activation_9\n",
            "False\n",
            "average_pooling2d\n",
            "False\n",
            "conv2d_5\n",
            "False\n",
            "conv2d_7\n",
            "False\n",
            "conv2d_10\n",
            "False\n",
            "conv2d_11\n",
            "False\n",
            "batch_normalization_5\n",
            "True\n",
            "batch_normalization_7\n",
            "True\n",
            "batch_normalization_10\n",
            "True\n",
            "batch_normalization_11\n",
            "True\n",
            "activation_5\n",
            "False\n",
            "activation_7\n",
            "False\n",
            "activation_10\n",
            "False\n",
            "activation_11\n",
            "False\n",
            "mixed0\n",
            "False\n",
            "conv2d_15\n",
            "False\n",
            "batch_normalization_15\n",
            "True\n",
            "activation_15\n",
            "False\n",
            "conv2d_13\n",
            "False\n",
            "conv2d_16\n",
            "False\n",
            "batch_normalization_13\n",
            "True\n",
            "batch_normalization_16\n",
            "True\n",
            "activation_13\n",
            "False\n",
            "activation_16\n",
            "False\n",
            "average_pooling2d_1\n",
            "False\n",
            "conv2d_12\n",
            "False\n",
            "conv2d_14\n",
            "False\n",
            "conv2d_17\n",
            "False\n",
            "conv2d_18\n",
            "False\n",
            "batch_normalization_12\n",
            "True\n",
            "batch_normalization_14\n",
            "True\n",
            "batch_normalization_17\n",
            "True\n",
            "batch_normalization_18\n",
            "True\n",
            "activation_12\n",
            "False\n",
            "activation_14\n",
            "False\n",
            "activation_17\n",
            "False\n",
            "activation_18\n",
            "False\n",
            "mixed1\n",
            "False\n",
            "conv2d_22\n",
            "False\n",
            "batch_normalization_22\n",
            "True\n",
            "activation_22\n",
            "False\n",
            "conv2d_20\n",
            "False\n",
            "conv2d_23\n",
            "False\n",
            "batch_normalization_20\n",
            "True\n",
            "batch_normalization_23\n",
            "True\n",
            "activation_20\n",
            "False\n",
            "activation_23\n",
            "False\n",
            "average_pooling2d_2\n",
            "False\n",
            "conv2d_19\n",
            "False\n",
            "conv2d_21\n",
            "False\n",
            "conv2d_24\n",
            "False\n",
            "conv2d_25\n",
            "False\n",
            "batch_normalization_19\n",
            "True\n",
            "batch_normalization_21\n",
            "True\n",
            "batch_normalization_24\n",
            "True\n",
            "batch_normalization_25\n",
            "True\n",
            "activation_19\n",
            "False\n",
            "activation_21\n",
            "False\n",
            "activation_24\n",
            "False\n",
            "activation_25\n",
            "False\n",
            "mixed2\n",
            "False\n",
            "conv2d_27\n",
            "False\n",
            "batch_normalization_27\n",
            "True\n",
            "activation_27\n",
            "False\n",
            "conv2d_28\n",
            "False\n",
            "batch_normalization_28\n",
            "True\n",
            "activation_28\n",
            "False\n",
            "conv2d_26\n",
            "False\n",
            "conv2d_29\n",
            "False\n",
            "batch_normalization_26\n",
            "True\n",
            "batch_normalization_29\n",
            "True\n",
            "activation_26\n",
            "False\n",
            "activation_29\n",
            "False\n",
            "max_pooling2d_2\n",
            "False\n",
            "mixed3\n",
            "False\n",
            "conv2d_34\n",
            "False\n",
            "batch_normalization_34\n",
            "True\n",
            "activation_34\n",
            "False\n",
            "conv2d_35\n",
            "False\n",
            "batch_normalization_35\n",
            "True\n",
            "activation_35\n",
            "False\n",
            "conv2d_31\n",
            "False\n",
            "conv2d_36\n",
            "False\n",
            "batch_normalization_31\n",
            "True\n",
            "batch_normalization_36\n",
            "True\n",
            "activation_31\n",
            "False\n",
            "activation_36\n",
            "False\n",
            "conv2d_32\n",
            "False\n",
            "conv2d_37\n",
            "False\n",
            "batch_normalization_32\n",
            "True\n",
            "batch_normalization_37\n",
            "True\n",
            "activation_32\n",
            "False\n",
            "activation_37\n",
            "False\n",
            "average_pooling2d_3\n",
            "False\n",
            "conv2d_30\n",
            "False\n",
            "conv2d_33\n",
            "False\n",
            "conv2d_38\n",
            "False\n",
            "conv2d_39\n",
            "False\n",
            "batch_normalization_30\n",
            "True\n",
            "batch_normalization_33\n",
            "True\n",
            "batch_normalization_38\n",
            "True\n",
            "batch_normalization_39\n",
            "True\n",
            "activation_30\n",
            "False\n",
            "activation_33\n",
            "False\n",
            "activation_38\n",
            "False\n",
            "activation_39\n",
            "False\n",
            "mixed4\n",
            "False\n",
            "conv2d_44\n",
            "False\n",
            "batch_normalization_44\n",
            "True\n",
            "activation_44\n",
            "False\n",
            "conv2d_45\n",
            "False\n",
            "batch_normalization_45\n",
            "True\n",
            "activation_45\n",
            "False\n",
            "conv2d_41\n",
            "False\n",
            "conv2d_46\n",
            "False\n",
            "batch_normalization_41\n",
            "True\n",
            "batch_normalization_46\n",
            "True\n",
            "activation_41\n",
            "False\n",
            "activation_46\n",
            "False\n",
            "conv2d_42\n",
            "False\n",
            "conv2d_47\n",
            "False\n",
            "batch_normalization_42\n",
            "True\n",
            "batch_normalization_47\n",
            "True\n",
            "activation_42\n",
            "False\n",
            "activation_47\n",
            "False\n",
            "average_pooling2d_4\n",
            "False\n",
            "conv2d_40\n",
            "False\n",
            "conv2d_43\n",
            "False\n",
            "conv2d_48\n",
            "False\n",
            "conv2d_49\n",
            "False\n",
            "batch_normalization_40\n",
            "True\n",
            "batch_normalization_43\n",
            "True\n",
            "batch_normalization_48\n",
            "True\n",
            "batch_normalization_49\n",
            "True\n",
            "activation_40\n",
            "False\n",
            "activation_43\n",
            "False\n",
            "activation_48\n",
            "False\n",
            "activation_49\n",
            "False\n",
            "mixed5\n",
            "False\n",
            "conv2d_54\n",
            "False\n",
            "batch_normalization_54\n",
            "True\n",
            "activation_54\n",
            "False\n",
            "conv2d_55\n",
            "False\n",
            "batch_normalization_55\n",
            "True\n",
            "activation_55\n",
            "False\n",
            "conv2d_51\n",
            "False\n",
            "conv2d_56\n",
            "False\n",
            "batch_normalization_51\n",
            "True\n",
            "batch_normalization_56\n",
            "True\n",
            "activation_51\n",
            "False\n",
            "activation_56\n",
            "False\n",
            "conv2d_52\n",
            "False\n",
            "conv2d_57\n",
            "False\n",
            "batch_normalization_52\n",
            "True\n",
            "batch_normalization_57\n",
            "True\n",
            "activation_52\n",
            "False\n",
            "activation_57\n",
            "False\n",
            "average_pooling2d_5\n",
            "False\n",
            "conv2d_50\n",
            "False\n",
            "conv2d_53\n",
            "False\n",
            "conv2d_58\n",
            "False\n",
            "conv2d_59\n",
            "False\n",
            "batch_normalization_50\n",
            "True\n",
            "batch_normalization_53\n",
            "True\n",
            "batch_normalization_58\n",
            "True\n",
            "batch_normalization_59\n",
            "True\n",
            "activation_50\n",
            "False\n",
            "activation_53\n",
            "False\n",
            "activation_58\n",
            "False\n",
            "activation_59\n",
            "False\n",
            "mixed6\n",
            "False\n",
            "conv2d_64\n",
            "False\n",
            "batch_normalization_64\n",
            "True\n",
            "activation_64\n",
            "False\n",
            "conv2d_65\n",
            "False\n",
            "batch_normalization_65\n",
            "True\n",
            "activation_65\n",
            "False\n",
            "conv2d_61\n",
            "False\n",
            "conv2d_66\n",
            "False\n",
            "batch_normalization_61\n",
            "True\n",
            "batch_normalization_66\n",
            "True\n",
            "activation_61\n",
            "False\n",
            "activation_66\n",
            "False\n",
            "conv2d_62\n",
            "False\n",
            "conv2d_67\n",
            "False\n",
            "batch_normalization_62\n",
            "True\n",
            "batch_normalization_67\n",
            "True\n",
            "activation_62\n",
            "False\n",
            "activation_67\n",
            "False\n",
            "average_pooling2d_6\n",
            "False\n",
            "conv2d_60\n",
            "False\n",
            "conv2d_63\n",
            "False\n",
            "conv2d_68\n",
            "False\n",
            "conv2d_69\n",
            "False\n",
            "batch_normalization_60\n",
            "True\n",
            "batch_normalization_63\n",
            "True\n",
            "batch_normalization_68\n",
            "True\n",
            "batch_normalization_69\n",
            "True\n",
            "activation_60\n",
            "False\n",
            "activation_63\n",
            "False\n",
            "activation_68\n",
            "False\n",
            "activation_69\n",
            "False\n",
            "mixed7\n",
            "False\n",
            "conv2d_72\n",
            "False\n",
            "batch_normalization_72\n",
            "True\n",
            "activation_72\n",
            "False\n",
            "conv2d_73\n",
            "False\n",
            "batch_normalization_73\n",
            "True\n",
            "activation_73\n",
            "False\n",
            "conv2d_70\n",
            "False\n",
            "conv2d_74\n",
            "False\n",
            "batch_normalization_70\n",
            "True\n",
            "batch_normalization_74\n",
            "True\n",
            "activation_70\n",
            "False\n",
            "activation_74\n",
            "False\n",
            "conv2d_71\n",
            "False\n",
            "conv2d_75\n",
            "False\n",
            "batch_normalization_71\n",
            "True\n",
            "batch_normalization_75\n",
            "True\n",
            "activation_71\n",
            "False\n",
            "activation_75\n",
            "False\n",
            "max_pooling2d_3\n",
            "False\n",
            "mixed8\n",
            "False\n",
            "conv2d_80\n",
            "False\n",
            "batch_normalization_80\n",
            "True\n",
            "activation_80\n",
            "False\n",
            "conv2d_77\n",
            "False\n",
            "conv2d_81\n",
            "False\n",
            "batch_normalization_77\n",
            "True\n",
            "batch_normalization_81\n",
            "True\n",
            "activation_77\n",
            "False\n",
            "activation_81\n",
            "False\n",
            "conv2d_78\n",
            "False\n",
            "conv2d_79\n",
            "False\n",
            "conv2d_82\n",
            "False\n",
            "conv2d_83\n",
            "False\n",
            "average_pooling2d_7\n",
            "False\n",
            "conv2d_76\n",
            "False\n",
            "batch_normalization_78\n",
            "True\n",
            "batch_normalization_79\n",
            "True\n",
            "batch_normalization_82\n",
            "True\n",
            "batch_normalization_83\n",
            "True\n",
            "conv2d_84\n",
            "False\n",
            "batch_normalization_76\n",
            "True\n",
            "activation_78\n",
            "False\n",
            "activation_79\n",
            "False\n",
            "activation_82\n",
            "False\n",
            "activation_83\n",
            "False\n",
            "batch_normalization_84\n",
            "True\n",
            "activation_76\n",
            "False\n",
            "mixed9_0\n",
            "False\n",
            "concatenate\n",
            "False\n",
            "activation_84\n",
            "False\n",
            "mixed9\n",
            "False\n",
            "conv2d_89\n",
            "False\n",
            "batch_normalization_89\n",
            "True\n",
            "activation_89\n",
            "False\n",
            "conv2d_86\n",
            "False\n",
            "conv2d_90\n",
            "False\n",
            "batch_normalization_86\n",
            "True\n",
            "batch_normalization_90\n",
            "True\n",
            "activation_86\n",
            "False\n",
            "activation_90\n",
            "False\n",
            "conv2d_87\n",
            "False\n",
            "conv2d_88\n",
            "False\n",
            "conv2d_91\n",
            "False\n",
            "conv2d_92\n",
            "False\n",
            "average_pooling2d_8\n",
            "False\n",
            "conv2d_85\n",
            "False\n",
            "batch_normalization_87\n",
            "True\n",
            "batch_normalization_88\n",
            "True\n",
            "batch_normalization_91\n",
            "True\n",
            "batch_normalization_92\n",
            "True\n",
            "conv2d_93\n",
            "False\n",
            "batch_normalization_85\n",
            "True\n",
            "activation_87\n",
            "False\n",
            "activation_88\n",
            "False\n",
            "activation_91\n",
            "False\n",
            "activation_92\n",
            "False\n",
            "batch_normalization_93\n",
            "True\n",
            "activation_85\n",
            "False\n",
            "mixed9_1\n",
            "False\n",
            "concatenate_1\n",
            "False\n",
            "activation_93\n",
            "False\n",
            "mixed10\n",
            "False\n",
            "global_average_pooling2d\n",
            "True\n",
            "dense\n",
            "True\n",
            "dense_1\n",
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zzrjHOjVv3xT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Select an Optimizer "
      ]
    },
    {
      "metadata": {
        "id": "te7M0Y0Ov5B_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# creates the optimizer using a tensorFlow optimizer with lr setting the Learning Rate\n",
        "\n",
        "# tf.keras optimizer\n",
        "opt = optimizers.Adam(lr)\n",
        "\n",
        "# We could use a TensorFlow Optimizer but they can't save the weight as a whole\n",
        "# opt = tf.train.AdamOptimizer(lr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HsMyZ5_Hv7F5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Compile the model"
      ]
    },
    {
      "metadata": {
        "id": "lVStHInBv8sF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# compile the model \n",
        "\n",
        "model.compile(optimizer= opt, loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5imyMnQQv-RK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Call backs"
      ]
    },
    {
      "metadata": {
        "id": "Jfxwux4SwBir",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir checkpoints\n",
        "\n",
        "\n",
        "checkpoint = ModelCheckpoint('./checkpoints/weights_{epoch:02d}_{val_acc:.2f}.hdf5', verbose=1, save_best_only=True, mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O7xuHy5HwDm0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_steps = 50\n",
        "val_steps = 50\n",
        "epochs = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sR-g2c0PwFfS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Fit the model\n",
        "\n",
        "we pass in:\n",
        "- The training set\n",
        "- the steps for how many times we want to sample a batch to be one epoch\n",
        "- number of epochs\n",
        "- validation set\n",
        "- number of steps for validation"
      ]
    },
    {
      "metadata": {
        "id": "nBssvi1IwHhZ",
        "colab_type": "code",
        "outputId": "897dec40-39d0-4cf0-b007-71728f0781d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "cell_type": "code",
      "source": [
        "# # Train the model with validation \n",
        "history = model.fit( train_dataset, steps_per_epoch = train_steps,\n",
        "                   epochs = epochs,\n",
        "                   validation_data = valid_dataset,\n",
        "                   validation_steps = val_steps,\n",
        "                   callbacks=[checkpoint])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "49/50 [============================>.] - ETA: 1s - loss: 0.8486 - acc: 0.7445\n",
            "Epoch 00001: val_loss improved from inf to 0.80504, saving model to ./checkpoints/weights_01_0.75.hdf5\n",
            "50/50 [==============================] - 124s 2s/step - loss: 0.8388 - acc: 0.7461 - val_loss: 0.8050 - val_acc: 0.7478\n",
            "Epoch 2/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.1335 - acc: 0.9574\n",
            "Epoch 00002: val_loss improved from 0.80504 to 0.10141, saving model to ./checkpoints/weights_02_0.97.hdf5\n",
            "50/50 [==============================] - 72s 1s/step - loss: 0.1346 - acc: 0.9565 - val_loss: 0.1014 - val_acc: 0.9652\n",
            "Epoch 3/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.0848 - acc: 0.9725\n",
            "Epoch 00003: val_loss improved from 0.10141 to 0.07759, saving model to ./checkpoints/weights_03_0.97.hdf5\n",
            "50/50 [==============================] - 72s 1s/step - loss: 0.0831 - acc: 0.9730 - val_loss: 0.0776 - val_acc: 0.9739\n",
            "Epoch 4/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.0298 - acc: 0.9911\n",
            "Epoch 00004: val_loss did not improve from 0.07759\n",
            "50/50 [==============================] - 71s 1s/step - loss: 0.0298 - acc: 0.9913 - val_loss: 0.1307 - val_acc: 0.9739\n",
            "Epoch 5/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.0239 - acc: 0.9938\n",
            "Epoch 00005: val_loss did not improve from 0.07759\n",
            "50/50 [==============================] - 71s 1s/step - loss: 0.0236 - acc: 0.9939 - val_loss: 0.1539 - val_acc: 0.9652\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iUuTu4psTxy9",
        "colab_type": "code",
        "outputId": "bc8fe479-00fa-44bc-b434-124b863651c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        }
      },
      "cell_type": "code",
      "source": [
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEVCAYAAAAPRfkLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMUMsKRhjjBnyfwEmTVXLmS1gwgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f461ba7c0b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEVCAYAAADpbDJPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f463c14e780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "LScy90EM_D5P",
        "colab_type": "code",
        "outputId": "d2163c93-eed4-453e-ca6f-ded1f50b62c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "cell_type": "code",
      "source": [
        "# # Train the model with validation \n",
        "history = model.fit( train_dataset, steps_per_epoch = train_steps,\n",
        "                   epochs = epochs,\n",
        "                   validation_data = valid_dataset,\n",
        "                   validation_steps = val_steps,\n",
        "                   callbacks=[checkpoint])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.0271 - acc: 0.9911\n",
            "Epoch 00001: val_loss did not improve from 0.07759\n",
            "50/50 [==============================] - 71s 1s/step - loss: 0.0267 - acc: 0.9913 - val_loss: 0.1108 - val_acc: 0.9652\n",
            "Epoch 2/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.0088 - acc: 1.0000\n",
            "Epoch 00002: val_loss did not improve from 0.07759\n",
            "50/50 [==============================] - 72s 1s/step - loss: 0.0088 - acc: 1.0000 - val_loss: 0.1097 - val_acc: 0.9826\n",
            "Epoch 3/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.0070 - acc: 1.0000\n",
            "Epoch 00003: val_loss did not improve from 0.07759\n",
            "50/50 [==============================] - 72s 1s/step - loss: 0.0069 - acc: 1.0000 - val_loss: 0.1309 - val_acc: 0.9739\n",
            "Epoch 4/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.0148 - acc: 0.9938\n",
            "Epoch 00004: val_loss did not improve from 0.07759\n",
            "50/50 [==============================] - 72s 1s/step - loss: 0.0149 - acc: 0.9939 - val_loss: 0.1353 - val_acc: 0.9739\n",
            "Epoch 5/5\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.0134 - acc: 0.9973\n",
            "Epoch 00005: val_loss did not improve from 0.07759\n",
            "50/50 [==============================] - 72s 1s/step - loss: 0.0132 - acc: 0.9974 - val_loss: 0.0914 - val_acc: 0.9739\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NIi6l1CuwJC0",
        "colab_type": "code",
        "outputId": "de4e6d13-0a20-432b-afc6-c944e7e4ab0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls checkpoints"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "weights_01_0.75.hdf5  weights_02_0.97.hdf5  weights_03_0.97.hdf5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "s8-NQU0iwKZh",
        "colab_type": "code",
        "outputId": "0ee99b68-f67b-4842-bf39-22fcf99303d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "metrics = model.evaluate(valid_dataset,steps=50)\n",
        "print(\"model accuracy:\",metrics[1])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 26s 521ms/step\n",
            "model accuracy: 0.973913049697876\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Jwcbsb-uwM7g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights('./checkpoints/weights_03_0.97.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g74ikoqXwOIW",
        "colab_type": "code",
        "outputId": "874390a4-dac1-4370-cfdd-1f1af5dcd808",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        }
      },
      "cell_type": "code",
      "source": [
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEVCAYAAAARjMm4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPlmyMMYY45MlC2OMMT5ZsjDGGOPT/we+sGtb3+1lugAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f461ca9da90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEVCAYAAAAPRfkLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEYlTKIiISJxCQURE4v4/aO6xNZfGKfwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f461b19a978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fflEgXIHwQHZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Make some predictions\n"
      ]
    },
    {
      "metadata": {
        "id": "5vEVFahfwRZi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5f5f19f8-8e6e-41d5-c380-3fb07af69eba"
      },
      "cell_type": "code",
      "source": [
        "preds = model.predict(valid_dataset,steps=5)\n",
        "preds = preds.argmax(axis=-1)\n",
        "\n",
        "preds.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(115,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "aqQraLiowS6T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "val_labels = val_labels_list[0:320]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gjddejiowUEH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a0fa3e8c-5169-4d20-fa90-eda2131287d3"
      },
      "cell_type": "code",
      "source": [
        "len(val_labels)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "115"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "Eh2LvDU3wV5_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Make a Confusion Matrix\n"
      ]
    },
    {
      "metadata": {
        "id": "FszDbDBkwXHC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "\n",
        "cm = confusion_matrix(val_labels,preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pAtLuzTUwXqY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gsEYlgkqwZ52",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "outputId": "c62790aa-bf1f-4e16-f125-bbaedd4c0fcb"
      },
      "cell_type": "code",
      "source": [
        "cm_plot_labels = names\n",
        "\n",
        "# # Plot normalized confusion matrix\n",
        "plt.figure(figsize=(16,8))\n",
        "plot_confusion_matrix(cm, cm_plot_labels, normalize=True,\n",
        "                      title='Monkey Normalized')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized confusion matrix\n",
            "[[0.90909091 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.09090909 0.        ]\n",
            " [0.         0.91666667 0.         0.         0.         0.\n",
            "  0.         0.         0.08333333 0.        ]\n",
            " [0.         0.         1.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         1.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         1.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         1.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  1.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.91666667 0.08333333 0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         1.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         1.        ]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArAAAAJGCAYAAACjj7ayAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f46187b7400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7FjVYLxhCWAM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Make some predictions\n"
      ]
    },
    {
      "metadata": {
        "id": "W5UAEG9XCZ2N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f26d18c9-72e7-40ed-ed7a-3ea0f0c8ca57"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "checkpoints  __MACOSX\t\t__pycache__  sutils.py\tvalidation\n",
            "gdrive.py    monkey_labels.txt\tsample_data  training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3Kzg7mzoCs32",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "image_path = './validation/'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HqIBueRZC6BW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "335dc1dd-d5cd-4cd0-cdb6-a8b054c7b7d6"
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "\n",
        "image_name = 'n5/n500.jpg'\n",
        "Image(image_path+image_name)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": "/9j/4AAQSkZJRgABAQEASABIAAD/4UTsRXhpZgAASUkqAAgAAAAKAA8BAgAGAAAAhgAAABABAgAX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\nevWjWw+2jHSpuMAa4A6Y7Vz8vHjfabAfgpk8Vxz4/HL1Bp//2Q==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "E_zzCBARDQOU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4a62336a-cef5-4fa8-999f-79937464e544"
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing import image\n",
        "from keras.applications.imagenet_utils import preprocess_input, decode_predictions\n",
        "import numpy as np\n",
        "\n",
        "img_path = os.path.join(image_path, image_name)\n",
        "img = image.load_img(img_path, target_size=(299, 299))\n",
        "x = image.img_to_array(img)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "# x = preprocess_input(x)\n",
        "print('Input image shape:', x.shape)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input image shape: (1, 299, 299, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mZW2BnqSDgU-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9d97718b-dded-449b-ffad-d3882467752b"
      },
      "cell_type": "code",
      "source": [
        "pred = model.predict(x)\n",
        "print('Predicted:', pred)\n",
        "\n",
        "print(np.argmax(pred))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted: [[3.5922412e-02 1.5111899e-05 5.2671214e-05 1.4853434e-04 2.9812058e-04\n",
            "  9.5019561e-01 6.1569287e-04 1.6476671e-03 9.6441731e-03 1.4599805e-03]]\n",
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DvPM-xLPDij9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "348ad165-97be-4fbb-8c67-ace4ef191729"
      },
      "cell_type": "code",
      "source": [
        "result= np.argmax(pred)\n",
        "if result==0:\n",
        "    print(\"Its a alouatta_palliata\")\n",
        "elif result==1:\n",
        "    print(\"Its a erythrocebus_patas\")\n",
        "elif result==2:\n",
        "    print(\"Its a cacajao_calvus\")\n",
        "elif result==3:\n",
        "    print(\"Its a macaca_fuscata\")\n",
        "elif result==4:\n",
        "    print(\"Its a cebuella_pygmea\")\n",
        "elif result==5:\n",
        "    print(\"Its a cebus_capucinus\")\n",
        "elif result==6:\n",
        "    print(\"Its a mico_argentatus\")\n",
        "elif result==7:\n",
        "    print(\"Its a saimiri_sciureus\")\n",
        "elif result==8:\n",
        "    print(\"Its a aotus_nigriceps\")\n",
        "elif result==9:\n",
        "    print(\"Its a trachypithecus_johnii\")\n",
        "    "
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Its a cebus_capucinus\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}