ch02/02_karaage_ai_book_image_classification_tf2_x.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "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/karaage0703/karaage-ai-book/blob/master/ch02/02_karaage_ai_book_image_classification_tf2_x.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtU8eIsOsNcv"
      },
      "source": [
        "# 画像分類\n",
        "\n",
        "画像分類を実践するノートブックです。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNgUsGFZBLEh"
      },
      "source": [
        "## 教師データのダウンロード\n",
        "\n",
        "ジャンケンの手の形の教師データをGitHubからダウンロード（Clone）します。\n",
        "\n",
        "2,3行目はダウンロードしたデータから、使用するデータ以外の不要なファイルを削除しています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qtkx-d0RWnZN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47aad40f-5359-4747-958e-f798c081095e"
      },
      "source": [
        "!git clone https://github.com/karaage0703/janken_dataset datasets\n",
        "!rm -rf datasets/.git\n",
        "!rm datasets/LICENSE"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'datasets'...\n",
            "remote: Enumerating objects: 210, done.\u001b[K\n",
            "remote: Total 210 (delta 0), reused 0 (delta 0), pack-reused 210\u001b[K\n",
            "Receiving objects: 100% (210/210), 4.60 MiB | 18.60 MiB/s, done.\n",
            "Resolving deltas: 100% (3/3), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hKLAhGSCufr"
      },
      "source": [
        "データの中身の確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUU8okD-Cwuo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dd34cc7-ac09-4b4e-dfaf-251898901dd4"
      },
      "source": [
        "!ls datasets"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "choki  gu  pa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdO2sCkkGXU6",
        "outputId": "4b29077c-7034-4493-d782-397f96f36a9f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls datasets/choki"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "choki_01.jpg  IMG_0783.JPG  IMG_0797.JPG  IMG_0811.JPG\tIMG_0876.JPG\n",
            "IMG_0770.JPG  IMG_0784.JPG  IMG_0798.JPG  IMG_0812.JPG\tIMG_0877.JPG\n",
            "IMG_0771.JPG  IMG_0785.JPG  IMG_0799.JPG  IMG_0813.JPG\tIMG_0878.JPG\n",
            "IMG_0772.JPG  IMG_0786.JPG  IMG_0800.JPG  IMG_0814.JPG\tIMG_0879.JPG\n",
            "IMG_0773.JPG  IMG_0787.JPG  IMG_0801.JPG  IMG_0815.JPG\tIMG_0880.JPG\n",
            "IMG_0774.JPG  IMG_0788.JPG  IMG_0802.JPG  IMG_0816.JPG\tIMG_0881.JPG\n",
            "IMG_0775.JPG  IMG_0789.JPG  IMG_0803.JPG  IMG_0817.JPG\tIMG_0882.JPG\n",
            "IMG_0776.JPG  IMG_0790.JPG  IMG_0804.JPG  IMG_0818.JPG\tIMG_0883.JPG\n",
            "IMG_0777.JPG  IMG_0791.JPG  IMG_0805.JPG  IMG_0870.JPG\tIMG_0884.JPG\n",
            "IMG_0778.JPG  IMG_0792.JPG  IMG_0806.JPG  IMG_0871.JPG\tIMG_0885.JPG\n",
            "IMG_0779.JPG  IMG_0793.JPG  IMG_0807.JPG  IMG_0872.JPG\n",
            "IMG_0780.JPG  IMG_0794.JPG  IMG_0808.JPG  IMG_0873.JPG\n",
            "IMG_0781.JPG  IMG_0795.JPG  IMG_0809.JPG  IMG_0874.JPG\n",
            "IMG_0782.JPG  IMG_0796.JPG  IMG_0810.JPG  IMG_0875.JPG\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63s1idu3Ct_Q",
        "outputId": "403b5ce1-a26e-4b95-f55b-c3fbdd736e89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        }
      },
      "source": [
        "from IPython.display import Image as IPImage\n",
        "from IPython.display import display_jpeg\n",
        "display_jpeg(IPImage('datasets/choki/choki_01.jpg'))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wWdaU-dW4kvI"
      },
      "source": [
        "## 教師データを訓練データ（Train Data）とテストデータ（Validation Data）に分割"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPVitbmqrXgi"
      },
      "source": [
        "ディレクトリの構造を可視化するための`tree`というソフトをインストールします。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dA0Me7ARH3gJ",
        "outputId": "fac094e9-8817-4371-dabc-fc736ae28d4b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!sudo apt install tree"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following NEW packages will be installed:\n",
            "  tree\n",
            "0 upgraded, 1 newly installed, 0 to remove and 20 not upgraded.\n",
            "Need to get 40.7 kB of archives.\n",
            "After this operation, 105 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tree amd64 1.7.0-5 [40.7 kB]\n",
            "Fetched 40.7 kB in 1s (50.8 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package tree.\n",
            "(Reading database ... 124013 files and directories currently installed.)\n",
            "Preparing to unpack .../tree_1.7.0-5_amd64.deb ...\n",
            "Unpacking tree (1.7.0-5) ...\n",
            "Setting up tree (1.7.0-5) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0h2uglmIzQv",
        "outputId": "075c42c7-855b-41e4-b73d-606d0fc6e105",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!tree -d datasets"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "datasets\n",
            "├── choki\n",
            "├── gu\n",
            "└── pa\n",
            "\n",
            "3 directories\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zmdetFKFvWp"
      },
      "source": [
        "教師データのディレクトリと、ターゲットとなるディレクトリ（この下に訓練データのディレクトリと検証データのディレクトリが生成される）を指定します。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZA4SGY54GY6b"
      },
      "source": [
        "dataset_original_dir = 'datasets'\n",
        "dataset_root_dir = 'target_datasets'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kevX13wW47Ub"
      },
      "source": [
        "教師データを訓練データのディレクトリ(train)と検証データのディレクトリ（val）に分割するスクリプトをダウンロードします。\n",
        "\n",
        "スクリプトのプログラムに関しては、本ノートブックの主題では無いので割愛します。興味ある方は以下のアドレスで、ソフトの中身を確認して下さい。\n",
        "\n",
        "https://raw.githubusercontent.com/karaage0703/karaage-ai-book/master/util/split_train_val.py"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGSRbbuIreaQ",
        "outputId": "f94671ad-4463-41c1-f96a-e04ce2aca9f7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget https://raw.githubusercontent.com/karaage0703/karaage-ai-book/master/util/split_train_val.py"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-12-13 12:52:17--  https://raw.githubusercontent.com/karaage0703/karaage-ai-book/master/util/split_train_val.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2683 (2.6K) [text/plain]\n",
            "Saving to: ‘split_train_val.py’\n",
            "\n",
            "split_train_val.py  100%[===================>]   2.62K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-12-13 12:52:17 (40.9 MB/s) - ‘split_train_val.py’ saved [2683/2683]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U17652B6BMbY",
        "outputId": "bbe55618-13d8-4a7a-cfd4-9d0524806604",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import split_train_val\n",
        "split_train_val.image_dir_train_val_split(\n",
        "    dataset_original_dir, dataset_root_dir, train_size=0.67)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "datasets/choki copy is done\n",
            "datasets/gu copy is done\n",
            "datasets/pa copy is done\n",
            "processing is done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHugFL3VIJfb",
        "outputId": "7f9474e0-12eb-4fc6-9415-9378a41e9393",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!tree -d target_datasets"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "target_datasets\n",
            "├── train\n",
            "│   ├── choki\n",
            "│   ├── gu\n",
            "│   └── pa\n",
            "└── val\n",
            "    ├── choki\n",
            "    ├── gu\n",
            "    └── pa\n",
            "\n",
            "8 directories\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVNXMfrLD97Y",
        "outputId": "bdcb3d4f-34f1-4c39-fbe8-6fee662b461d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls target_datasets/train/choki"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "choki_01.jpg  IMG_0781.JPG  IMG_0793.JPG  IMG_0808.JPG\tIMG_0872.JPG\n",
            "IMG_0770.JPG  IMG_0782.JPG  IMG_0794.JPG  IMG_0809.JPG\tIMG_0874.JPG\n",
            "IMG_0771.JPG  IMG_0784.JPG  IMG_0799.JPG  IMG_0811.JPG\tIMG_0875.JPG\n",
            "IMG_0772.JPG  IMG_0785.JPG  IMG_0800.JPG  IMG_0812.JPG\tIMG_0876.JPG\n",
            "IMG_0773.JPG  IMG_0787.JPG  IMG_0801.JPG  IMG_0814.JPG\tIMG_0880.JPG\n",
            "IMG_0774.JPG  IMG_0788.JPG  IMG_0802.JPG  IMG_0816.JPG\tIMG_0881.JPG\n",
            "IMG_0777.JPG  IMG_0789.JPG  IMG_0803.JPG  IMG_0817.JPG\tIMG_0883.JPG\n",
            "IMG_0778.JPG  IMG_0790.JPG  IMG_0804.JPG  IMG_0870.JPG\tIMG_0884.JPG\n",
            "IMG_0780.JPG  IMG_0791.JPG  IMG_0805.JPG  IMG_0871.JPG\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3-W-iIAEDXc",
        "outputId": "853b137a-5635-417b-f42d-670042791ae5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls target_datasets/val/choki"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IMG_0775.JPG  IMG_0792.JPG  IMG_0806.JPG  IMG_0818.JPG\tIMG_0882.JPG\n",
            "IMG_0776.JPG  IMG_0795.JPG  IMG_0807.JPG  IMG_0873.JPG\tIMG_0885.JPG\n",
            "IMG_0779.JPG  IMG_0796.JPG  IMG_0810.JPG  IMG_0877.JPG\n",
            "IMG_0783.JPG  IMG_0797.JPG  IMG_0813.JPG  IMG_0878.JPG\n",
            "IMG_0786.JPG  IMG_0798.JPG  IMG_0815.JPG  IMG_0879.JPG\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3PHCUYZJd-W"
      },
      "source": [
        "train_dir = 'target_datasets/train'\n",
        "val_dir = 'target_datasets/val'"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roHDM7MB1w9S"
      },
      "source": [
        "## ラベルファイルの作成"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSq7-H5b2DVx"
      },
      "source": [
        "学習するファイルのラベルを作成します"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UN5gSnfyz7Tp"
      },
      "source": [
        "必要なライブラリをインポートします"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRQkx3zp1Pwt"
      },
      "source": [
        "import sys\n",
        "import os\n",
        "import shutil"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZjsFU39e3ncO"
      },
      "source": [
        "データを保存する場所を指定します"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UhMCgO53m4M"
      },
      "source": [
        "backup_dir = './model'"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKkS5ed_Jx6c"
      },
      "source": [
        "ラベルデータを作成します（最後に表示される class numberが画像の種類の数です）"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRk9ZAWZlj9C",
        "outputId": "923bdecc-cd9f-4d3d-b58b-c1eafc4ba180",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "labels = [d for d in os.listdir(dataset_original_dir) \\\n",
        "    if os.path.isdir(os.path.join(dataset_original_dir, d))]\n",
        "labels.sort()\n",
        "\n",
        "if os.path.exists(backup_dir):\n",
        "  shutil.rmtree(backup_dir)\n",
        "\n",
        "os.makedirs(backup_dir)\n",
        "\n",
        "with open(backup_dir + '/labels.txt','w') as f:\n",
        "  for label in labels:\n",
        "    f.write(label+\"\\n\")\n",
        "\n",
        "NUM_CLASSES = len(labels)\n",
        "print(\"class number=\" + str(NUM_CLASSES))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class number=3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CzE-lgUNJ3e8"
      },
      "source": [
        "ラベルを確認します。ラベル名（choki, gu, pa）が並んでいればOKです"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRBOzF1i6HnT",
        "outputId": "af802059-fb8e-4b21-c214-8c63ff1ccc02",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!cat ./model/labels.txt"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "choki\n",
            "gu\n",
            "pa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLcN1JHv2Q6g"
      },
      "source": [
        "## 学習の事前準備"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gDNFkOpyQ0C"
      },
      "source": [
        "### ライブラリのインポート"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ln1wU_8DFe22"
      },
      "source": [
        "必要なライブラリをインポートします\n",
        "\n",
        "このNotebookはTensorFlow、2.x系で動作するので、TensorFlow 2.x系を選択してインポートします。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ltMN9XGBqSa",
        "outputId": "761685e6-c908-4e72-f41f-50a10a561e49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "2.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDmuYdo-BnJK"
      },
      "source": [
        "続いて、他に必要なライブラリをインポートします。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvUS5Z5CEjQp"
      },
      "source": [
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from tensorflow.keras.preprocessing.image import array_to_img, img_to_array, load_img, ImageDataGenerator\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qwz5rPa333ch"
      },
      "source": [
        "先ほど作成したラベルファイルから、ラベル情報を読み込みます"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkbhFC1Dxrb-",
        "outputId": "b39a9564-c86e-4f92-843d-fc2d0c11cc88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "labels = []\n",
        "with open(backup_dir + '/labels.txt','r') as f:\n",
        "  for line in f:\n",
        "    labels.append(line.rstrip())\n",
        "print(labels)\n",
        "\n",
        "NUM_CLASSES = len(labels)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['choki', 'gu', 'pa']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyuTS7gwyVP0"
      },
      "source": [
        "### 学習のハイパーパラメータの設定"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8E3MR7dzTddI"
      },
      "source": [
        "学習のハイパーパラメータの設定をします"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBSQYa3QE9jQ"
      },
      "source": [
        "# 学習率\n",
        "LEARNING_RATE = 0.001\n",
        "# エポック（世代数）\n",
        "EPOCHS = 20\n",
        "# バッチサイズ\n",
        "BATCH_SIZE = 8"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zRKlr-AcTVEn"
      },
      "source": [
        "### データセットの前処理"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kaT6RgL5yeYQ"
      },
      "source": [
        "データセットの前処理（変換）をします"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BP3ogIEXGQzf",
        "outputId": "a5fad846-59f1-4e9f-c763-3997b5ea658d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "IMAGE_SIZE = 64\n",
        "\n",
        "train_data_gen = ImageDataGenerator(rescale=1./255)\n",
        "val_data_gen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_data = train_data_gen.flow_from_directory(\n",
        "    train_dir, target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    color_mode='rgb', batch_size=BATCH_SIZE,\n",
        "    class_mode='categorical', shuffle=True)\n",
        "\n",
        "validation_data = val_data_gen.flow_from_directory(\n",
        "    val_dir, target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    color_mode='rgb', batch_size=BATCH_SIZE,\n",
        "    class_mode='categorical', shuffle=True)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 111 images belonging to 3 classes.\n",
            "Found 56 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XkrwuMQOykJO"
      },
      "source": [
        "### 前処理の確認"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVicgfQp8z6r"
      },
      "source": [
        "　イテレータを使うと、まとまったデータを順々に処理するのに便利なので、大量のデータを処理するディープラーニングではよく使われます。イテレータに関してより詳しく知りたい方は、Pythonの入門書やWebでの情報を調べてみて下さい。\n",
        "\n",
        "ここでは、イテレータの中身を確認しておきます。イテレータの中身を確認するには next関数を使うのが簡単です。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6OJHTv_8BB6",
        "outputId": "6fd61353-51dd-457d-aef8-7d100ee74177",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "(image_data,label_data) = train_data.next()\n",
        "print(image_data)\n",
        "print(label_data)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[[0.882353   0.8078432  0.7411765 ]\n",
            "   [0.8745099  0.8000001  0.73333335]\n",
            "   [0.83921576 0.76470596 0.69803923]\n",
            "   ...\n",
            "   [0.68235296 0.6        0.53333336]\n",
            "   [0.69803923 0.60784316 0.5372549 ]\n",
            "   [0.7725491  0.6745098  0.59607846]]\n",
            "\n",
            "  [[0.8313726  0.7490196  0.6745098 ]\n",
            "   [0.7725491  0.6901961  0.6156863 ]\n",
            "   [0.7607844  0.6784314  0.6039216 ]\n",
            "   ...\n",
            "   [0.7843138  0.70980394 0.6431373 ]\n",
            "   [0.7176471  0.63529414 0.56078434]\n",
            "   [0.73333335 0.6509804  0.5764706 ]]\n",
            "\n",
            "  [[0.8862746  0.8000001  0.7176471 ]\n",
            "   [0.85098046 0.76470596 0.68235296]\n",
            "   [0.8745099  0.78823537 0.7058824 ]\n",
            "   ...\n",
            "   [0.7372549  0.654902   0.5803922 ]\n",
            "   [0.75294125 0.67058825 0.59607846]\n",
            "   [0.654902   0.5803922  0.5137255 ]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.8862746  0.8117648  0.7843138 ]\n",
            "   [0.9058824  0.83921576 0.8078432 ]\n",
            "   [0.8705883  0.80392164 0.7725491 ]\n",
            "   ...\n",
            "   [0.37254903 0.32156864 0.25882354]\n",
            "   [0.43921572 0.38823533 0.3254902 ]\n",
            "   [0.40784317 0.3529412  0.3019608 ]]\n",
            "\n",
            "  [[0.91372555 0.8470589  0.81568635]\n",
            "   [0.89019614 0.8235295  0.79215693]\n",
            "   [0.92549026 0.8588236  0.82745105]\n",
            "   ...\n",
            "   [0.11764707 0.10588236 0.08627451]\n",
            "   [0.01568628 0.00392157 0.        ]\n",
            "   [0.02352941 0.00784314 0.        ]]\n",
            "\n",
            "  [[0.96470594 0.909804   0.8745099 ]\n",
            "   [0.8745099  0.8196079  0.7843138 ]\n",
            "   [0.90196085 0.8470589  0.8117648 ]\n",
            "   ...\n",
            "   [0.00784314 0.         0.00392157]\n",
            "   [0.01176471 0.00392157 0.00784314]\n",
            "   [0.00392157 0.         0.00784314]]]\n",
            "\n",
            "\n",
            " [[[0.7843138  0.69803923 0.6156863 ]\n",
            "   [0.76470596 0.6784314  0.59607846]\n",
            "   [0.7960785  0.70980394 0.627451  ]\n",
            "   ...\n",
            "   [0.6745098  0.5921569  0.5176471 ]\n",
            "   [0.7058824  0.62352943 0.54901963]\n",
            "   [0.72156864 0.6392157  0.5647059 ]]\n",
            "\n",
            "  [[0.7725491  0.6862745  0.6039216 ]\n",
            "   [0.7607844  0.6745098  0.5921569 ]\n",
            "   [0.76470596 0.6784314  0.59607846]\n",
            "   ...\n",
            "   [0.63529414 0.54901963 0.49411768]\n",
            "   [0.6745098  0.5882353  0.53333336]\n",
            "   [0.7019608  0.6156863  0.56078434]]\n",
            "\n",
            "  [[0.7568628  0.67058825 0.5882353 ]\n",
            "   [0.7725491  0.6862745  0.6039216 ]\n",
            "   [0.7843138  0.7019608  0.61960787]\n",
            "   ...\n",
            "   [0.7254902  0.654902   0.5764706 ]\n",
            "   [0.70980394 0.627451   0.5529412 ]\n",
            "   [0.68235296 0.6        0.5176471 ]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.9607844  0.9921569  1.        ]\n",
            "   [0.9607844  0.9921569  1.        ]\n",
            "   [0.96470594 0.9960785  1.        ]\n",
            "   ...\n",
            "   [0.04705883 0.03137255 0.02745098]\n",
            "   [0.04313726 0.02745098 0.02352941]\n",
            "   [0.04705883 0.03137255 0.02745098]]\n",
            "\n",
            "  [[0.96470594 0.9960785  1.        ]\n",
            "   [0.9607844  0.9921569  1.        ]\n",
            "   [0.9568628  0.9960785  1.        ]\n",
            "   ...\n",
            "   [0.03921569 0.02352941 0.01960784]\n",
            "   [0.03921569 0.02352941 0.01960784]\n",
            "   [0.03921569 0.02352941 0.01960784]]\n",
            "\n",
            "  [[0.9568628  0.9960785  1.        ]\n",
            "   [0.9568628  0.9960785  1.        ]\n",
            "   [0.95294124 0.9960785  1.        ]\n",
            "   ...\n",
            "   [0.03921569 0.02352941 0.01960784]\n",
            "   [0.04313726 0.02745098 0.02352941]\n",
            "   [0.04705883 0.03137255 0.02745098]]]\n",
            "\n",
            "\n",
            " [[[0.81568635 0.73333335 0.6666667 ]\n",
            "   [0.7843138  0.7019608  0.63529414]\n",
            "   [0.7490196  0.6666667  0.6       ]\n",
            "   ...\n",
            "   [0.5921569  0.5176471  0.45882356]\n",
            "   [0.60784316 0.5254902  0.45882356]\n",
            "   [0.61960787 0.5372549  0.46274513]]\n",
            "\n",
            "  [[0.7568628  0.6745098  0.6       ]\n",
            "   [0.7411765  0.65882355 0.58431375]\n",
            "   [0.7568628  0.6745098  0.6       ]\n",
            "   ...\n",
            "   [0.6784314  0.59607846 0.5294118 ]\n",
            "   [0.627451   0.5529412  0.48627454]\n",
            "   [0.64705884 0.57254905 0.5058824 ]]\n",
            "\n",
            "  [[0.8235295  0.7411765  0.65882355]\n",
            "   [0.82745105 0.74509805 0.6627451 ]\n",
            "   [0.8000001  0.7176471  0.63529414]\n",
            "   ...\n",
            "   [0.7019608  0.61960787 0.5529412 ]\n",
            "   [0.68235296 0.60784316 0.5411765 ]\n",
            "   [0.72156864 0.64705884 0.5882353 ]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.8745099  0.8078432  0.7686275 ]\n",
            "   [0.8745099  0.8196079  0.77647066]\n",
            "   [0.8705883  0.8235295  0.77647066]\n",
            "   ...\n",
            "   [0.02352941 0.00784314 0.01176471]\n",
            "   [0.01960784 0.00392157 0.00784314]\n",
            "   [0.01568628 0.         0.00392157]]\n",
            "\n",
            "  [[0.9568628  0.9568628  0.96470594]\n",
            "   [0.95294124 0.9568628  0.96470594]\n",
            "   [0.95294124 0.9686275  0.9803922 ]\n",
            "   ...\n",
            "   [0.01960784 0.00392157 0.00784314]\n",
            "   [0.01960784 0.00392157 0.00784314]\n",
            "   [0.02352941 0.00784314 0.01176471]]\n",
            "\n",
            "  [[0.95294124 1.         1.        ]\n",
            "   [0.94117653 0.9843138  0.9921569 ]\n",
            "   [0.95294124 0.9960785  1.        ]\n",
            "   ...\n",
            "   [0.01960784 0.00392157 0.00784314]\n",
            "   [0.01960784 0.00392157 0.00784314]\n",
            "   [0.02352941 0.00784314 0.01176471]]]\n",
            "\n",
            "\n",
            " ...\n",
            "\n",
            "\n",
            " [[[0.8000001  0.7176471  0.63529414]\n",
            "   [0.7607844  0.6784314  0.6039216 ]\n",
            "   [0.7803922  0.69803923 0.6313726 ]\n",
            "   ...\n",
            "   [0.77647066 0.7019608  0.6431373 ]\n",
            "   [0.7843138  0.7019608  0.63529414]\n",
            "   [0.78823537 0.7019608  0.61960787]]\n",
            "\n",
            "  [[0.7843138  0.7137255  0.63529414]\n",
            "   [0.8235295  0.7490196  0.68235296]\n",
            "   [0.882353   0.8078432  0.7411765 ]\n",
            "   ...\n",
            "   [0.73333335 0.6666667  0.6039216 ]\n",
            "   [0.74509805 0.67058825 0.6039216 ]\n",
            "   [0.6745098  0.5921569  0.5254902 ]]\n",
            "\n",
            "  [[0.86274517 0.78823537 0.72156864]\n",
            "   [0.85098046 0.77647066 0.70980394]\n",
            "   [0.8000001  0.7294118  0.6509804 ]\n",
            "   ...\n",
            "   [0.7019608  0.627451   0.5686275 ]\n",
            "   [0.7137255  0.6392157  0.57254905]\n",
            "   [0.70980394 0.63529414 0.5686275 ]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.91372555 0.8470589  0.81568635]\n",
            "   [0.8352942  0.7686275  0.7372549 ]\n",
            "   [0.91372555 0.8588236  0.8235295 ]\n",
            "   ...\n",
            "   [0.40000004 0.38431376 0.34117648]\n",
            "   [0.38431376 0.36862746 0.3254902 ]\n",
            "   [0.32941177 0.3137255  0.2784314 ]]\n",
            "\n",
            "  [[0.8941177  0.83921576 0.80392164]\n",
            "   [0.882353   0.82745105 0.79215693]\n",
            "   [0.8588236  0.80392164 0.7686275 ]\n",
            "   ...\n",
            "   [0.00392157 0.00392157 0.01176471]\n",
            "   [0.00392157 0.00392157 0.01176471]\n",
            "   [0.         0.         0.        ]]\n",
            "\n",
            "  [[0.9058824  0.85098046 0.81568635]\n",
            "   [0.8980393  0.8431373  0.8078432 ]\n",
            "   [0.91372555 0.8588236  0.8235295 ]\n",
            "   ...\n",
            "   [0.00784314 0.         0.00392157]\n",
            "   [0.00784314 0.00392157 0.        ]\n",
            "   [0.00392157 0.         0.        ]]]\n",
            "\n",
            "\n",
            " [[[0.8117648  0.7294118  0.6627451 ]\n",
            "   [0.8980393  0.81568635 0.7411765 ]\n",
            "   [0.89019614 0.8078432  0.73333335]\n",
            "   ...\n",
            "   [0.6901961  0.61960787 0.5411765 ]\n",
            "   [0.7254902  0.6431373  0.5686275 ]\n",
            "   [0.7490196  0.6666667  0.5921569 ]]\n",
            "\n",
            "  [[0.75294125 0.6784314  0.6117647 ]\n",
            "   [0.6862745  0.6117647  0.54509807]\n",
            "   [0.7137255  0.6431373  0.5647059 ]\n",
            "   ...\n",
            "   [0.68235296 0.60784316 0.5411765 ]\n",
            "   [0.6901961  0.60784316 0.5411765 ]\n",
            "   [0.69803923 0.6156863  0.54901963]]\n",
            "\n",
            "  [[0.85098046 0.7686275  0.69411767]\n",
            "   [0.86274517 0.7803922  0.7058824 ]\n",
            "   [0.8000001  0.7176471  0.63529414]\n",
            "   ...\n",
            "   [0.7568628  0.68235296 0.62352943]\n",
            "   [0.78823537 0.7058824  0.6392157 ]\n",
            "   [0.8078432  0.7254902  0.65882355]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.90196085 0.8313726  0.79215693]\n",
            "   [0.8470589  0.7803922  0.7411765 ]\n",
            "   [0.83921576 0.7725491  0.73333335]\n",
            "   ...\n",
            "   [0.         0.00392157 0.        ]\n",
            "   [0.         0.00392157 0.        ]\n",
            "   [0.00784314 0.01568628 0.        ]]\n",
            "\n",
            "  [[0.91372555 0.83921576 0.8117648 ]\n",
            "   [0.882353   0.81568635 0.7843138 ]\n",
            "   [0.909804   0.8431373  0.8117648 ]\n",
            "   ...\n",
            "   [0.00392157 0.         0.00784314]\n",
            "   [0.         0.         0.00784314]\n",
            "   [0.00392157 0.00392157 0.01176471]]\n",
            "\n",
            "  [[0.9333334  0.94117653 0.9294118 ]\n",
            "   [0.9215687  0.94117653 0.92549026]\n",
            "   [0.9215687  0.94117653 0.92549026]\n",
            "   ...\n",
            "   [0.00392157 0.00392157 0.        ]\n",
            "   [0.00784314 0.00392157 0.        ]\n",
            "   [0.01568628 0.         0.        ]]]\n",
            "\n",
            "\n",
            " [[[0.8235295  0.7294118  0.63529414]\n",
            "   [0.77647066 0.6901961  0.60784316]\n",
            "   [0.7803922  0.7019608  0.60784316]\n",
            "   ...\n",
            "   [0.6862745  0.6039216  0.5372549 ]\n",
            "   [0.7254902  0.63529414 0.5647059 ]\n",
            "   [0.69411767 0.59607846 0.5176471 ]]\n",
            "\n",
            "  [[0.77647066 0.6784314  0.5921569 ]\n",
            "   [0.7725491  0.6862745  0.6039216 ]\n",
            "   [0.7803922  0.69411767 0.6117647 ]\n",
            "   ...\n",
            "   [0.6784314  0.59607846 0.5294118 ]\n",
            "   [0.6862745  0.59607846 0.5254902 ]\n",
            "   [0.654902   0.5686275  0.48627454]]\n",
            "\n",
            "  [[0.7568628  0.6666667  0.59607846]\n",
            "   [0.7725491  0.6862745  0.6039216 ]\n",
            "   [0.75294125 0.6666667  0.58431375]\n",
            "   ...\n",
            "   [0.6666667  0.58431375 0.50980395]\n",
            "   [0.6784314  0.59607846 0.52156866]\n",
            "   [0.7058824  0.62352943 0.54901963]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.9490197  0.9843138  1.        ]\n",
            "   [0.94117653 0.97647065 1.        ]\n",
            "   [0.9450981  0.9803922  1.        ]\n",
            "   ...\n",
            "   [0.01176471 0.00392157 0.00784314]\n",
            "   [0.01960784 0.01176471 0.01568628]\n",
            "   [0.02745098 0.01176471 0.00784314]]\n",
            "\n",
            "  [[0.9568628  0.9921569  0.98823535]\n",
            "   [0.9568628  0.9921569  0.98823535]\n",
            "   [0.96470594 1.         0.9960785 ]\n",
            "   ...\n",
            "   [0.03529412 0.01960784 0.01568628]\n",
            "   [0.03137255 0.01568628 0.01176471]\n",
            "   [0.02745098 0.01176471 0.00784314]]\n",
            "\n",
            "  [[0.9725491  0.9921569  1.        ]\n",
            "   [0.9725491  0.9921569  1.        ]\n",
            "   [0.96470594 0.9960785  1.        ]\n",
            "   ...\n",
            "   [0.03921569 0.01568628 0.01568628]\n",
            "   [0.02352941 0.00784314 0.00392157]\n",
            "   [0.02745098 0.01176471 0.01568628]]]]\n",
            "[[0. 0. 1.]\n",
            " [0. 1. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 1. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-QJyJme-YgW",
        "outputId": "32aff192-203c-4db8-b566-92b32253286e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(image_data.shape)\n",
        "print(label_data.shape)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8, 64, 64, 3)\n",
            "(8, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yNNJhWyCrHW",
        "outputId": "025f53ff-f16f-4f40-ba28-89f65130d55d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "image_numb = 6 # 3の倍数を指定してください\n",
        "for i in range(0, image_numb):\n",
        "  ax = plt.subplot(image_numb // 3, 3, i + 1)\n",
        "  plt.tight_layout()\n",
        "  ax.set_title(str(i))\n",
        "  plt.imshow(image_data[i])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KeYkR1giyql0"
      },
      "source": [
        "## AIモデル作成"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHZhFZp22I1K"
      },
      "source": [
        "ニューラルネットワーク（CNN）モデルを作成します\n",
        "これは、KerasのMNISTと呼ばれる文字認識に使われるニューラルネットワークモデルをベースにしています。\n",
        "\n",
        "MNISTは、0,1の2値ですが、RGB画像に対応できるように改造しています。具体的には、最初の層の入力サイズを `input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)`とすることで対応しています。\n",
        "\n",
        "https://keras.io/examples/mnist_cnn/\n",
        "\n",
        "注：以下のコード、書籍では`lr`となっているところを最新のバージョンの推奨に合わせて`learning_rate`に変更しています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2W57_jd8K8Ox"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), padding='same',\n",
        "    input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(NUM_CLASSES))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "opt = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n",
        "#opt = tf.keras.optimizers.SGD(learning_rate=LEARNING_RATE)\n",
        "\n",
        "model.compile(opt, loss='categorical_crossentropy', \n",
        "    metrics=['accuracy'])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01E8ayYiGL9T"
      },
      "source": [
        "モデルの概要を確認します"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pAr9iyrGNWI",
        "outputId": "4c053300-46ea-48e5-9944-59234a1aecf1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_2 (Conv2D)           (None, 64, 64, 32)        896       \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 62, 62, 64)        18496     \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 62, 62, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 31, 31, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 31, 31, 64)        0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 61504)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               7872640   \n",
            "                                                                 \n",
            " activation_6 (Activation)   (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 387       \n",
            "                                                                 \n",
            " activation_7 (Activation)   (None, 3)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,892,419\n",
            "Trainable params: 7,892,419\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqLJDFl3rznt"
      },
      "source": [
        "AIモデルの学習を行います"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxzfODkyl5hN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "731bb7b0-427e-43dc-950b-f615e6883351"
      },
      "source": [
        "%%time\n",
        "history = model.fit(train_data, epochs=EPOCHS, validation_data=validation_data, verbose=1)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "14/14 [==============================] - 9s 49ms/step - loss: 1.9891 - accuracy: 0.4054 - val_loss: 1.0485 - val_accuracy: 0.5714\n",
            "Epoch 2/20\n",
            "14/14 [==============================] - 0s 27ms/step - loss: 1.0438 - accuracy: 0.5676 - val_loss: 0.9180 - val_accuracy: 0.5536\n",
            "Epoch 3/20\n",
            "14/14 [==============================] - 0s 27ms/step - loss: 0.8038 - accuracy: 0.6757 - val_loss: 0.8035 - val_accuracy: 0.7321\n",
            "Epoch 4/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.5846 - accuracy: 0.7838 - val_loss: 0.5056 - val_accuracy: 0.8750\n",
            "Epoch 5/20\n",
            "14/14 [==============================] - 0s 27ms/step - loss: 0.3247 - accuracy: 0.8829 - val_loss: 0.3335 - val_accuracy: 0.8929\n",
            "Epoch 6/20\n",
            "14/14 [==============================] - 0s 27ms/step - loss: 0.1735 - accuracy: 0.9550 - val_loss: 0.2509 - val_accuracy: 0.9286\n",
            "Epoch 7/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0603 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9464\n",
            "Epoch 8/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0494 - accuracy: 0.9910 - val_loss: 0.1971 - val_accuracy: 0.9464\n",
            "Epoch 9/20\n",
            "14/14 [==============================] - 0s 29ms/step - loss: 0.0286 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9464\n",
            "Epoch 10/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0337 - accuracy: 1.0000 - val_loss: 0.1694 - val_accuracy: 0.9643\n",
            "Epoch 11/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.2404 - val_accuracy: 0.9286\n",
            "Epoch 12/20\n",
            "14/14 [==============================] - 0s 27ms/step - loss: 0.0209 - accuracy: 0.9910 - val_loss: 0.2727 - val_accuracy: 0.9286\n",
            "Epoch 13/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.2449 - val_accuracy: 0.9286\n",
            "Epoch 14/20\n",
            "14/14 [==============================] - 0s 29ms/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9464\n",
            "Epoch 15/20\n",
            "14/14 [==============================] - 0s 27ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.3922 - val_accuracy: 0.9286\n",
            "Epoch 16/20\n",
            "14/14 [==============================] - 0s 30ms/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9286\n",
            "Epoch 17/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.2388 - val_accuracy: 0.9643\n",
            "Epoch 18/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0245 - accuracy: 1.0000 - val_loss: 0.3930 - val_accuracy: 0.8750\n",
            "Epoch 19/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.3461 - val_accuracy: 0.9286\n",
            "Epoch 20/20\n",
            "14/14 [==============================] - 0s 28ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.3205 - val_accuracy: 0.9286\n",
            "CPU times: user 12.2 s, sys: 1.14 s, total: 13.4 s\n",
            "Wall time: 16.5 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELuHBiwnGIk7"
      },
      "source": [
        "## 学習結果の可視化"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZbIQD0KHgFt"
      },
      "source": [
        "lossを確認します。低いほど良い性能を示します。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Oz6q5XnQGEW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "86aeb0bc-3401-4701-d472-10c19d2a8cc6"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Training and validation loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlim([0.0, EPOCHS])\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['loss', 'val_loss'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SFJtbPg5Hd7"
      },
      "source": [
        "acc（精度）を確認します。accが訓練データでの精度で、この値が高いほど良い性能を意味します。\n",
        "例えば0.5だと50%の正解率ということになります。\n",
        "\n",
        "val_accというのが訓練に使っていないテストデータを使っての精度です。  \n",
        "いわゆる、本当の精度と言われるものは、val_accの方となります。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nKjkoxfG7Ox",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "260b1499-da63-4c75-b1f3-972e1241608b"
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlim([0.0, EPOCHS])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.legend(['acc', 'val_acc'], loc='lower right')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vWiax4zg70l"
      },
      "source": [
        "## 学習させたモデルを使った推定\n",
        "\n",
        "学習させたモデルを使って、画像の推定を行います"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fy5DOL0JUQar",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "aa62a055-5d35-4674-de35-f94bc9f5fa12"
      },
      "source": [
        "# Get the ordered list of class names:\n",
        "import PIL.Image as Image\n",
        "class_names = validation_data.class_indices.items()\n",
        "class_names = np.array([key.title() for key, value in class_names])\n",
        "\n",
        "validation_data.reset()\n",
        "validation_data.shuffle = True\n",
        "validation_data.batch_size = BATCH_SIZE\n",
        "\n",
        "# Retrieve the first batch from the validation data\n",
        "for validation_image_batch, validation_label_batch in validation_data:\n",
        "  break\n",
        "\n",
        "validation_id = np.argmax(validation_label_batch, axis=-1)\n",
        "validation_label = class_names[validation_id]\n",
        "predicted_batch = model.predict(validation_image_batch)\n",
        "\n",
        "# Returns the indices of the maximum values along a given axis\n",
        "predicted_id = np.argmax(predicted_batch, axis=-1)\n",
        "\n",
        "# Return the maximum values along a given axis\n",
        "predicted_score = np.max(predicted_batch, axis=-1)\n",
        "\n",
        "predicted_label_batch = class_names[predicted_id]\n",
        "\n",
        "plt.figure(figsize=(16, 9))\n",
        "plt.subplots_adjust(hspace=0.5)\n",
        "\n",
        "# Display the classification results for the first 30 images\n",
        "for n in range(min(validation_image_batch.shape[0], 30)):\n",
        "  plt.subplot(6, 5, n + 1)\n",
        "\n",
        "  # Convert the range from -1 to 1 to the range from 0 to 1\n",
        "  plt.imshow(np.array(validation_image_batch[n]*255,np.int32))\n",
        "  color = 'green' if predicted_id[n] == validation_id[n] else 'red'\n",
        "  predicted_label = predicted_label_batch[n].title()\n",
        "  plt.title(predicted_label + ' ({:.2f}, {})'.format(\n",
        "      predicted_score[n], validation_label[n]), color=color)\n",
        "  plt.axis('off')\n",
        "\n",
        "_ = plt.suptitle('Model predictions (green: correct, red: incorrect)')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 80ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7h85uRoRODs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "outputId": "ce12f20a-4d96-402e-a4bb-89d3be9ee484"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "validation_data.reset()\n",
        "validation_data.shuffle =  False\n",
        "validation_data.batch_size = 1\n",
        "\n",
        "# Retrieve the first batch from the validation data\n",
        "for validation_image_batch, validation_label_batch in validation_data:\n",
        "  break\n",
        "\n",
        "predicted = model.predict(validation_data, steps=validation_data.n)\n",
        "predicted_classes = np.argmax(predicted, axis=-1)\n",
        "\n",
        "# Apply normalization\n",
        "# https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
        "cm = confusion_matrix(validation_data.classes, predicted_classes)\n",
        "cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "plt.figure(figsize=(12, 9))\n",
        "\n",
        "# https://seaborn.pydata.org/generated/seaborn.heatmap.html\n",
        "# https://matplotlib.org/users/colormaps.html\n",
        "sns.heatmap(cm, annot=True, square=True, cmap=plt.cm.Blues,\n",
        "            xticklabels=validation_data.class_indices,\n",
        "            yticklabels=validation_data.class_indices)\n",
        "\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.xlim([0.0, 3.0])\n",
        "plt.ylim([0.0, 3.0])\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "56/56 [==============================] - 0s 7ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yD25Q-GQOmdg"
      },
      "source": [
        "## 学習モデルの保存と読み込み\n",
        "\n",
        "\n",
        "Google Colaboratory上のファイルは、自動的に消えてしまうのでファイルを保存します"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RW0cyDqaoc5u"
      },
      "source": [
        "save_model_path = os.path.join(backup_dir, 'my_model.h5')\n",
        "model.save(save_model_path)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QmNcAlLpwwU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3a4caf2-81d6-4961-c414-b4930f407701"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqlA0xCoqC-y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f3864fb-6a67-4ffe-a633-0e2b4eb96820"
      },
      "source": [
        "!ls model"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "labels.txt  my_model.h5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_ezVnJDpy4a"
      },
      "source": [
        "!cp './model/my_model.h5' '/content/drive/MyDrive'\n",
        "!cp './model/labels.txt' '/content/drive/MyDrive'"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZPa6nEnoglz"
      },
      "source": [
        "あとは、Google Drive経由でファイルをダウンロードします。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3jKFzqu-00L"
      },
      "source": [
        "## まとめ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W-BVvtN2aqKX"
      },
      "source": [
        "ここまでで、学習から推論は完了です。\n",
        "\n",
        "発展として、以下を実施してどうなるか確認してみましょう。\n",
        "\n",
        "- ハイパーパラメータを変更して学習の変更度合いを確認\n",
        "- 教師データをジャンケン以外のものにしてみる"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3H26WGdKAze"
      },
      "source": [
        "## 参考リンク"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F7UIdrA1KFlO"
      },
      "source": [
        "以下は多くを参考にした情報です。\n",
        "\n",
        "CNN構造\n",
        "- http://aidiary.hatenablog.com/entry/20161127/1480240182\n",
        "\n",
        "\n",
        "データN増し\n",
        "- https://github.com/bohemian916/deeplearning_tool/blob/master/increase_picture.py\n",
        "\n",
        "GradCam\n",
        "- https://github.com/shinmura0/Python-study-group/blob/master/Text3.ipynb\n",
        "\n",
        "GradCam Confusion Matrix\n",
        "- https://colab.research.google.com/drive/1mirG8BSoB3k87mh-qyY3-8-ZXj0XB6h6\n",
        "\n",
        "TensorFlow 2.x対応\n",
        "- http://tensorflow.classcat.com/2019/11/04/tf20-tutorials-images-classification/\n",
        "\n",
        "乱数(Seed)固定\n",
        "- https://scrapbox.io/nwtgck/Tensorflow+Keras%E3%81%A7%E5%86%8D%E7%8F%BE%E6%80%A7%E3%81%AE%E3%81%82%E3%82%8B%E4%B9%B1%E6%95%B0%E3%82%92%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B_-_%E3%82%B7%E3%83%BC%E3%83%89%E5%9B%BA%E5%AE%9A\n",
        "- https://qiita.com/okotaku/items/8d682a11d8f2370684c9\n",
        "\n",
        "\n",
        "以下は、ヒントとなった情報\n",
        "- http://aidiary.hatenablog.com/entry/20161212/1481549365\n",
        "- https://qiita.com/yampy/items/706d44417c433e68db0d\n",
        "- https://qiita.com/haru1977/items/17833e508fe07c004119\n",
        "- http://hatakazu.hatenablog.com/entry/2017/06/08/045953\n",
        "- https://qiita.com/Mco7777/items/2b76aba1bae35f2623ea\n",
        "\n"
      ]
    }
  ]
}