Keras_VGG16_cifar10.ipynb

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    "colab": {
      "name": "Keras-VGG16-cifar10.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1YtzyO3-FPM_uYGxfo7FszFoANSSGbpS_",
      "authorship_tag": "ABX9TyP8cqX/Ky8dZX0TrielRetX",
      "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/tsekine2/Keras-Tutorial/blob/master/Keras_VGG16_cifar10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVt7WquhBgRx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "outputId": "5f6d56c2-509e-4aa7-a36e-28e805b6196b"
      },
      "source": [
        "'''インポート'''\n",
        "import numpy as np\n",
        "import sys\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "from keras.datasets import cifar10\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras import backend as K"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iduxc5PGpCl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "outputId": "5744067a-e8c5-406a-cae0-69ae04f58973"
      },
      "source": [
        "'''データセットの読み込み'''\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "'''バッチサイズ、クラス数、エポック数の設定'''\n",
        "batch_size=64\n",
        "num_classes=10\n",
        "epochs=20\n",
        "'''one-hotベクトル化'''\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
        "'''shape表示'''\n",
        "print(\"x_train : \", x_train.shape)\n",
        "print(\"y_train : \", y_train.shape)\n",
        "print(\"x_test : \", x_test.shape)\n",
        "print(\"y_test : \", y_test.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 2s 0us/step\n",
            "x_train :  (50000, 32, 32, 3)\n",
            "y_train :  (50000, 10)\n",
            "x_test :  (10000, 32, 32, 3)\n",
            "y_test :  (10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_DOO1Y5talYE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "53641fbf-e326-4060-db9d-4225acf8c4fc"
      },
      "source": [
        "'''VGG16'''\n",
        "input_shape=x_train.shape[1:]\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='same', input_shape=input_shape, name='block1_conv1'))\n",
        "model.add(BatchNormalization(name='bn1'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='same', name='block1_conv2'))\n",
        "model.add(BatchNormalization(name='bn2'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same', name='block1_pool'))\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same', name='block2_conv1'))\n",
        "model.add(BatchNormalization(name='bn3'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same', name='block2_conv2'))\n",
        "model.add(BatchNormalization(name='bn4'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same', name='block2_pool'))\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same', name='block3_conv1'))\n",
        "model.add(BatchNormalization(name='bn5'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same', name='block3_conv2'))\n",
        "model.add(BatchNormalization(name='bn6'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same', name='block3_conv3'))\n",
        "model.add(BatchNormalization(name='bn7'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same', name='block3_pool'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same', name='block4_conv1'))\n",
        "model.add(BatchNormalization(name='bn8'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same', name='block4_conv2'))\n",
        "model.add(BatchNormalization(name='bn9'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same', name='block4_conv3'))\n",
        "model.add(BatchNormalization(name='bn10'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same', name='block4_pool'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same', name='block5_conv1'))\n",
        "model.add(BatchNormalization(name='bn11'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same', name='block5_conv2'))\n",
        "model.add(BatchNormalization(name='bn12'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same', name='block5_conv3'))\n",
        "model.add(BatchNormalization(name='bn13'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same', name='block5_pool'))\n",
        "model.add(Flatten(name='flatten'))\n",
        "model.add(Dense(units=4096, activation='relu', name='fc1'))\n",
        "model.add(Dense(units=4096, activation='relu', name='fc2'))\n",
        "model.add(Dense(units=num_classes, activation='softmax', name='predictions'))\n",
        "model.summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "block1_conv1 (Conv2D)        (None, 32, 32, 64)        1792      \n",
            "_________________________________________________________________\n",
            "bn1 (BatchNormalization)     (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 32, 32, 64)        36928     \n",
            "_________________________________________________________________\n",
            "bn2 (BatchNormalization)     (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 16, 16, 128)       73856     \n",
            "_________________________________________________________________\n",
            "bn3 (BatchNormalization)     (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 16, 16, 128)       147584    \n",
            "_________________________________________________________________\n",
            "bn4 (BatchNormalization)     (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "bn5 (BatchNormalization)     (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "bn6 (BatchNormalization)     (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "bn7 (BatchNormalization)     (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 4, 4, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "bn8 (BatchNormalization)     (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "bn9 (BatchNormalization)     (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "activation_9 (Activation)    (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "bn10 (BatchNormalization)    (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "activation_10 (Activation)   (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "bn11 (BatchNormalization)    (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "activation_11 (Activation)   (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "bn12 (BatchNormalization)    (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "activation_12 (Activation)   (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "bn13 (BatchNormalization)    (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "activation_13 (Activation)   (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              2101248   \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "predictions (Dense)          (None, 10)                40970     \n",
            "=================================================================\n",
            "Total params: 33,655,114\n",
            "Trainable params: 33,646,666\n",
            "Non-trainable params: 8,448\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbklZ3JeC4UY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 903
        },
        "outputId": "c67950d5-e2cd-47b8-8e01-667cfaca9123"
      },
      "source": [
        "'''optimizer定義'''\n",
        "optimizer=keras.optimizers.adam()\n",
        "model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "'''データ正規化'''\n",
        "x_train=x_train.astype('float32')\n",
        "x_train/=255\n",
        "x_test=x_test.astype('float32')\n",
        "x_test/=255\n",
        "'''fit'''\n",
        "history=model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "50000/50000 [==============================] - 42s 849us/step - loss: 2.1699 - acc: 0.1591 - val_loss: 2.7100 - val_acc: 0.1319\n",
            "Epoch 2/20\n",
            "50000/50000 [==============================] - 33s 662us/step - loss: 1.6754 - acc: 0.3359 - val_loss: 1.9222 - val_acc: 0.3466\n",
            "Epoch 3/20\n",
            "50000/50000 [==============================] - 33s 666us/step - loss: 1.2925 - acc: 0.5181 - val_loss: 1.6019 - val_acc: 0.4707\n",
            "Epoch 4/20\n",
            "50000/50000 [==============================] - 33s 669us/step - loss: 1.0070 - acc: 0.6419 - val_loss: 1.5776 - val_acc: 0.5295\n",
            "Epoch 5/20\n",
            "50000/50000 [==============================] - 33s 661us/step - loss: 0.8200 - acc: 0.7197 - val_loss: 1.0380 - val_acc: 0.6598\n",
            "Epoch 6/20\n",
            "50000/50000 [==============================] - 33s 667us/step - loss: 0.6815 - acc: 0.7725 - val_loss: 1.1489 - val_acc: 0.6556\n",
            "Epoch 7/20\n",
            "50000/50000 [==============================] - 33s 661us/step - loss: 0.5833 - acc: 0.8079 - val_loss: 0.6422 - val_acc: 0.7836\n",
            "Epoch 8/20\n",
            "50000/50000 [==============================] - 33s 667us/step - loss: 0.4990 - acc: 0.8353 - val_loss: 0.8517 - val_acc: 0.7421\n",
            "Epoch 9/20\n",
            "50000/50000 [==============================] - 33s 662us/step - loss: 0.4320 - acc: 0.8584 - val_loss: 0.6216 - val_acc: 0.7978\n",
            "Epoch 10/20\n",
            "50000/50000 [==============================] - 33s 664us/step - loss: 0.3757 - acc: 0.8768 - val_loss: 0.6047 - val_acc: 0.8095\n",
            "Epoch 11/20\n",
            "50000/50000 [==============================] - 33s 663us/step - loss: 0.3237 - acc: 0.8939 - val_loss: 0.7685 - val_acc: 0.7744\n",
            "Epoch 12/20\n",
            "50000/50000 [==============================] - 33s 664us/step - loss: 0.2701 - acc: 0.9120 - val_loss: 0.6324 - val_acc: 0.8091\n",
            "Epoch 13/20\n",
            "50000/50000 [==============================] - 33s 668us/step - loss: 0.2418 - acc: 0.9212 - val_loss: 0.7416 - val_acc: 0.7908\n",
            "Epoch 14/20\n",
            "50000/50000 [==============================] - 33s 662us/step - loss: 0.2072 - acc: 0.9321 - val_loss: 0.6675 - val_acc: 0.8123\n",
            "Epoch 15/20\n",
            "50000/50000 [==============================] - 33s 665us/step - loss: 0.1740 - acc: 0.9421 - val_loss: 0.6594 - val_acc: 0.8158\n",
            "Epoch 16/20\n",
            "50000/50000 [==============================] - 33s 661us/step - loss: 0.1514 - acc: 0.9509 - val_loss: 0.6563 - val_acc: 0.8243\n",
            "Epoch 17/20\n",
            "50000/50000 [==============================] - 33s 665us/step - loss: 0.1361 - acc: 0.9569 - val_loss: 0.7174 - val_acc: 0.8200\n",
            "Epoch 18/20\n",
            "50000/50000 [==============================] - 33s 661us/step - loss: 0.1178 - acc: 0.9629 - val_loss: 0.6840 - val_acc: 0.8273\n",
            "Epoch 19/20\n",
            "50000/50000 [==============================] - 33s 664us/step - loss: 0.1088 - acc: 0.9650 - val_loss: 0.8861 - val_acc: 0.7892\n",
            "Epoch 20/20\n",
            "50000/50000 [==============================] - 33s 661us/step - loss: 0.0994 - acc: 0.9678 - val_loss: 0.9703 - val_acc: 0.7818\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVUTeNRtPTlp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "outputId": "9be9592a-df27-4733-b2aa-b25f05bf9738"
      },
      "source": [
        "'''結果の可視化'''\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(history.history['acc'], color='b', linewidth=3)\n",
        "plt.plot(history.history['val_acc'], color='r', linewidth=3)\n",
        "plt.tick_params(labelsize=18)\n",
        "plt.ylabel('acuuracy', fontsize=20)\n",
        "plt.xlabel('epoch', fontsize=20)\n",
        "plt.legend(['training', 'test'], loc='best', fontsize=20)\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(history.history['loss'], color='b', linewidth=3)\n",
        "plt.plot(history.history['val_loss'], color='r', linewidth=3)\n",
        "plt.tick_params(labelsize=18)\n",
        "plt.ylabel('loss', fontsize=20)\n",
        "plt.xlabel('epoch', fontsize=20)\n",
        "plt.legend(['training', 'test'], loc='best', fontsize=20)\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VBY1FiZ2UZQsXmp1+es7PvwYNfC6yZ0+8IywGyclmt95q9ve/m9WpU/AEDsxq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\ngg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UiV69evHkk09yySWXcMUVV9C9e3c6dOhAgwYNPIg+fim5iyUdO0JSktsOZelS2LbNbZMi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\nsEJERAohJXdSYMaPh1NOceW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Y5ULJncgxJCW5fe+++QaaNvVf//RTl9O8+aZ3sQWpWdOd\no+ZTUAsrPv8cLr8cMjJcvXFjd82XtB1Po0Zu9ZrvLMcVK9wpG9oDT0RC9fjjcNtt/nqnTsceNSji\nlNyJnECLFrBkiftj0Rh3bccO14F15ZWwfbu38QHBCysmTXLnqkXSV1+5DYl97davD19+6TYLDFW7\ndjBliv8fceFC6NMnhlereMBaN0/x5pvhhx+8jkYkdjzxhBK7PFByJxKCEiXcivtZs6BuXf/1t95y\nvXgff+xZaM755/sPzN2yxc1zi5RvvnEbAfp62WrWdImd7/3y4rLL3F/fPu++qz3wfA4cgL593WPi\nRLdUe8wYt6pHpCh78kl3rKFPx45K7E5AyZ1IHnTu7LZICVzDsGmTGxW9/nrYu9ejwJKSYOBAfz1S\nCyu+/97tXef7xk46yc3rC8xw8+qWW9y2KD6PP+5+eBdlf/zheiKmTPFfy8iAe+91PZ4//+xdbCJe\nmjAh+OdFhw5K7EKg5E4kj8qWdVu8ffBB8Kjk88+7FbXz5nkU2IAB/iHPzz+HNWvy196yZe4M2507\nXb1yZddj16hR/toF1w162WX++pAhbifpomjBAtdLt2SJ/5pvextw11u0cKd+xPMQ9r59OoZO8mbC\nhOC9NDt0cMMkZct6F1OcUHInEqYLL4SffnJrDHxWr3ar8ocO9eD4sjp1XC+bT34WVqxYAWef7V/R\nWqECfPZZ8MqS/MhtD7zevYveHngvveQ2Xt282dUTE+E//3GJ+bhxULy4u56e7j5UHTvCb795Fm5Y\nVq6EXr3cL+Ratdz3oZ5IOZGJE4MTu/btldjlhbVWD2tp2bKlFQlHVpa1r71mbfny1rosxT1OP93a\n776LcjDTp/sDOOkka9PT897GmjXW1qrlb6dMGWu//jrioVprrd2yxdqGDf3vVbmytStWFMx7xZL0\ndGtvuin4A1OpkrWzZgU/7+efrU1NDX5eiRLWPv64tZmZnoQess2brR082NqkpOD4fY8WLax94gn3\nPJFAEycGf1bat7d2926vo4o5wGJ7jJzG86QqVh5K7iS/fv/d2u7dg38mFStm7YMPWnv4cJSCSE+3\ntnp1fwDvvpu31//xh7X16/tfX7KktXPmFEysPitWuKTO954NG7qkr7DautXaLl2CPyh/+YtLqnOT\nnm7tmDHuwxT4mrPOsnbVqqiGHpI9e6wdNcr9UZBbUnf0IzHR2gsvtPbtt609cMDr6MVr//lP8Oej\nXTtrd+3yOqqYpOROyZ1ESWam+9lUsmTOn0+//RalIIYP97/xeeeF/ro//7S2cWP/a5OTrf3ss4KL\nM9DCha5Hyvfebdtau39/dN47mr7/3tq6dYM/HFdcYe3evaG9tlmz4NeWLm3t00+77mOvpae7WKpV\ny5nAdepk7bx51s6YYe3f/+4+W7klehUqWHv99dbOnx8b31NRkpZm7ZQp1vbq5X4OdOvmepefecb9\ngbd1a8HH8NRTSuzyQMmdkjuJsl9/tbZNm+CfU6VKuZ9dBf47a/Vq/5saY+3atSd+zfbt1p5xhv91\nSUnWfvBBAQd6lHffdfH6Yrj0UmszMqIbQ0GaOtV9CAI/FPffn7cPxKFD1o4Y4Xq7Atvp3t3adesK\nLvbjycpy31ujRjmTtdNOc5+jo7/HHTusff55azt0yD3JA2sbNLB29Gj3eZaCsWKFtY895nqSj/5M\n5faoWtU9d/Bg98Ns1qzI9bIfndi1bavE7gSU3Cm5Ew8cPux+dx895eivf3VDuAXqr3/1v+GIEcd/\n7s6dwfO6EhLcL2svPPlk8D/Wrbd6E0ckZWa6/waB31fZsta+/374bf7vf9Y2bRrcZrly1r70UnR7\nvObMyflXDFhbs6a1//1vaMn5ypXWjhxpbb16x04qOnWy9oUX3GdVwpeR4XpQ77rL2lNPPXEyF+qj\ncmVrO3e29p//dEMXM2e6kYBQP4tPP63ELgxK7pTciYe+/dYtrjh69GnKlAL8PTxtmv/NTj752JP+\n9uxxk5UDe/pefbWAggrRkCHB/1iPP+5tPPmxa5e1PXsGfz8NG7qFEvl14ID7JZ2QENz+BRdYu2FD\n/ts/nh9/dPPkjv4lX768tQ89FN6QelaWtXPnWnvddS5RzS2JKFHCDRt+9FEUJ7LGud273c+Dfv2C\n57bm9mjd2v1FunChtf/3f9aOHWtt375u8cvRc01CeVSq5BLzG26wdsIEa7/80tpNm4J/8D3zTPBr\n2rRREh8iJXdK7sRjBw5Ye+edwaOOYO3f/lZAU1nS04PnPuXWS7R/v7VduwYH9NxzBRBMHmVmWnv5\n5cEJ57RpXkeVdytW5Oxd++tf3dymSFqwIOeQaEqKW8Id6b8efv/d2muvzZlQFi9u7e23u+H9SNi/\n39q33nKJ6rGGC6tVs/a229xcRAm2fr0b5jz3XPff5ljJV8mS7o+PF16wduPG47eZmekW8HzwgbUP\nP2ztNde4Hv+jpxqE8khJsbZjRzf/Uold2JTcKbmTGPHVVzlHnypVcn8sR/xn2t13+9+kR4/gewcP\nusUWgYE88USEA8iHeW4YDgAAIABJREFU/fvdZOrAHpv5872OKnSffuq6ZwP/fe+4o+B6m/bts/aW\nW3L+Er30Ujc8ll87drhewsBFL77Eu2/f0OZ1hmvTJmvHj7e2efNjJwt/+Yu1jz564gSlsMrMdEP1\n9957/H8ncKvpr7vOJWn79kXmvdessfbDD60dN87a/v2tbdXKLfbJS8LXurUSuzxScqfkTmLI7t3u\nZ+vRP9sqVHA/myPV+WFXrvQ3npDg/pq31vXqXXpp8JuPHRuhN42grVuDe6QqVYrikuMwZWW5RCSw\nZys5OXpD3bNn5/zroXJlt81IOA4ccElTSkrOD+x550W/1+yHH1ySfNJJuScICQkurldecfMB//c/\nNwS+dq2b+L9vX+zvDxiq/fvd6uNBg9zUi+MlTs2auXmfixZF7/vPzHT/7h995D5D117reubKls09\nsduxIzpxFSLHS+6Muy+pqal28eLFXochRciHH8JNN8HatcHXy5Rx14cMgapV8/km3bu7I8MARo2C\ne+5xB9O/8Yb/OffeC6NH5/ONCsiqVe5s1a1bXb1BA1i4EKpU8Tau3Bw86A4YfuUV/7WTT4b33oNW\nraIXx969cOed8Oyzwdd79YKnnoJKlU7cRmamO+d2xAhYvz74XsuW7vSMbt0iF3NeZWS4z/Urr7hj\n6w4cyNvrS5aE0qWhVCn3OFH5ePdLlHBnO/seiYnB9dweiYn+owLzYvNmmDHDnX34+eewf3/uzytW\nDLp2hZ493VE6derk/b0KirXuLOVly/wnlVx3nU6eCIMxZom1NjXXm8fK+oraQz134oX0dGsnTcp9\nF4mSJd3agnyNNL31lr/BmjXdX89HDxXG+n5iX38dPJm7TZvIDCdF0h9/uKGowH/bdu28HSb87LPg\nk0Z889SOt0o3K8vajz92w5xHfyDr17f2zTdjr+dr1y63Srhz5+P3XsXaIyHBzYcrVcotIKlY0W01\ncvLJ1tau7f69GzWytkkTt01R06Y5J+0GPipVcvPgpk7VStMiAg3LKrmT2JaRYe3rr+ecf+8b1bvx\nxjC3MTt0yNoqVXL/ZXDjjbGf2PlMnx78i+2SS2JnD7yFC3MOEw4Y4OY1em3nThfL0f/tr7km5zDY\n//7nNq49+rlVqrjjoA4d8uZ7yIs1a9xpHued5ybst2jhtvyoVcslP+Gs+Izlx6mnupVac+fGzv8P\nEjXHS+40LJtNw7ISC7Ky3CjT/ffD998H3ytWDPr1g2HDoH79PDR6113wyCPB1669Fl58ERIS8h1z\n1EycCDff7K//61/w5JPhDW9FyqRJcMMNkJ7u6omJ8MQTMHiwt3Ed7cMP3dDXpk3+azVquM9Ao0Yw\nfDi89Vbwa0qVgttvhzvugHLlohtvQcrKcsO4+/fDvn3ua37KBw+6YeyMjNwfx7oXjsRE6NjRDbde\ndJH7bydF1vGGZZXcZVNyJ7HEWvf7eMwYWLQo+F5iIlx9tUvyTj01hMZ++w0aN/bXr7oKXn3VNRRv\nbr8dxo/318ePd5MToy0jwyU9Tz7pv1apErz9trdz0Y4nLQ1uuQVeey34elJScLKRmOgSwXvvherV\noxtjUWGtSzJPlAAenSTWrQspKV5HLzFCyV0IlNxJLLLWzZseMwbmzQu+Zwz8/e+u0+WMM07Q0O23\nw4QJbjHFc8+5bsB4lJXlFgZMm+bqxsDLL7tJ4xUqRKcncvt29w8/c6b/2hlnwPvvQ716Bf/++TV9\nuutt3LIl573LLoMHHwz+Y0BEYpKSuxAouZNYN2eOS/J8i18DXXKJW9jYosVxGsjIcL008e7gQbcK\neP784OuJiW4VbZUqbplxbl8DyxUq5H3o9Mcf4eKLYc0a/7XLL3cJZpky+f7WombrVjd0PHWqq3fs\n6FbAtmvnbVwiEjIldyFQcifxYuFCl+R9/HHOexdc4JK8tm2jH1dUbd/uEpEVK8Jvo1gxqFw5tESw\nalWXVV9zjZtr5XPffa7rNJ7mLgb6+mvXPdy2bWzNERSRE1JyFwIldxJvFi+GBx5w26gdrXt3t6Vd\n587Rjytq1q+HoUPhf/9zQ4x79kTvvcuUcXPXLr44eu8pIhJAyV0IlNxJvFq61CV5U6e6TphAnTq5\nnrzu3YtAx8zBg2640ffYsiX3r77y3r3hvU+DBm5+3WmnRTZ+EZE8UHIXAiV3Eu9++cXNhX/9dbew\nLlCbNq4nr0ePIpDkherAgZyJ4PGSwfR011P33HNQsaLX0YtIEafkLgRK7qSwWLUKxo6FyZNzbqfV\ntKmbR9+3r077ybPMzPjcPkZECqXjJXdxOgtYRI6lQQO3N+3KlXDjjVC8uP/esmUuuatRw51fu2yZ\nd3HGHSV2IhInlNyJFFJ16rhz4tesgVtvDd6pY88ed++009yeu++8E/6m+SIiEluU3IkUciefDI8/\nDhs2uBO8jj7VYtYsuOIKt/n9mDHw55+ehCkiIhGi5E6kiChXzj8U++WX7jCCwJHGDRvciVO1a0Pv\n3m6PYE3JFRGJP0ruRIoYY/xDsWvXulW0Vav67x8+DG+84Q4tOPNMeOGF4H17ReT/27vzOKnKO9/j\nnx/IYtOC0CAKKCCKCibugpoouERHb2IyiTomOte4cMc7mtFX7iR3nCTjXJPM5GZc7miSV8wyqElm\nXBJn9CbR4ILrVQQjoyAuaAOiEBYF2bff/eN3KlVdVHWfqq6qrqr+vl+v86ruU8956qnTp6u/fc7z\nPEekvincifRiY8bEpdhly2IKlRNP7Pj8/PkwY0YMwLj22u7dEEJERGqjLsKdmfUxs2vNbJGZbTGz\nZWZ2o5kNSrm9F1nKnKVUpHfp3x8uvDAuxf7+93D55bDnntnn162DW26BiRPhzDPhgQd2n0tPRETq\nQ13Mc2dm/wf4EnA/8FvgMOBq4CngdHff1cX2npS9Pe+p7e5+d5o2aJ47kY7efx9mzoTvfz+mVck3\ndixceSVcdlncolVERGqnricxNrPJwMvA/e7+2Zz1VwP/DHzB3X/RRR0O3OHul5TbDoU7kcJ27YJZ\ns+C22+DXv959kMWAAXDBBTF/3vHH90wbRUR6m3qfxPhCwIBb8tb/CNgEXJS2IjPrb2atXZcUkbT6\n9IlLsQ8+GHe/+MpXoK0t+/zWrXDnnXGLs+OOiztjbNnSc+0VEent6iHcHQfsAubkrnT3LcBLyfNp\nfI4Igx+a2R/M7FYzG1LRlor0cuPHw3e+A++8E5dsj8v77Zw7Fy65JAZgzJgRZ/w0ObKISG3Vw2XZ\nl4F93H1kgefuAc4DBrj7tk7qeB64F3gTGAycDVxAXO490d0LDqwwsxnADIADDjjgmCVLlnTz3Yj0\nPi+8EHe7+Ld/i7N4+dra4DOfgfPPh+nTYY89at9GEZFmU+997hYD/dz9gALP3QlcDAx19w9KrPc6\n4FvA19z9W12VV587ke5ZvRp++lP4wQ9i/rxCMkHvvPMi6PXrV9Mmiog0jXrvc7cJGFDkuYE5ZUr1\nXWAbcE45jRKR0gwfHv3xFi+OKVWuuSYuz+ZaswZ+/OPow7fvvjHlysMPx8TJIiJSGfUQ7t4FhptZ\noYA3Gljd2SXZYtx9e6bubrZPRErQp09MhnzzzbB0aTbojRnTsdzatfCTn8BZZynoiYhUUj2EuxeI\ndnSYRMHMBgJHAmVdK022HwOs7G4DRaQ8uUFvyRJ49tl0Qe+yy+ChhxT0RETKUQ/h7m7AgWvy1l8B\ntAA/z6wwswlmdmhuITNro7AbgD2AByvXVBEpV58+cMIJHYPetdfC/vt3LLd2bfTd+5M/gZEjFfRE\nRErV4wMqAMzsVuAq4g4VvyHuUPEl4Bng1MwdKsysHRjr7paz7c3AVOBxYCnQSoyWnQ48D0x3981d\ntUEDKkR6xq5dMGcO3HMP3Hdf3Oe2kKFD4dOfjsEYp50Wt0wTEemt6nq0LICZ9SXO3M0AxgGriTN6\n38idxqRIuDsX+O/A4UAbsBN4A7gHuCmZL69LCnciPS8T9O69N5bOgt6558b0Kgp6ItIb1X24qwcK\ndyL1xb3jGb2lSwuX22uvCHhnnRWjcMeNq2kzRUR6hMJdCgp3IvUrE/QyZ/SKBT2AQw6JkHfWWXDK\nKdDSUrt2iojUisJdCgp3Io0hN+j98pfFJ0wGGDAATj45G/YmTQKz4uVFRBqFwl0KCncijccdXnst\n5sd76CGYPRu2dNLLdsyYCHpnngmnnx5990REGpHCXQoKdyKNb/NmeOqpCHsPPwwLFhQv26cPTJmS\n7at37LHQt2/t2ioi0h0Kdyko3Ik0n2XL4He/i7N6jzwCH3Ryh+phw+CMM7Jn9kaNql07RURKpXCX\ngsKdSHPbsSP66mUu4b7wQlzWLeajH8321TvppOi/JyJSLxTuUlC4E+ld1qyBWbOyYW/FiuJlW1pg\n+vQ4s3fqqTB5clzWFRHpKQp3KSjcifRe7vDyy9mg9/TTsG1b8fIjRkTYO+20CHsTJmgUrojUlsJd\nCgp3IpKxYUOMvM2EvTff7Lz8/vtHyMssY8bUpJki0osp3KWgcCcixbz1VgzMeOwxePxxWL268/IH\nH5wNetOmwT771KSZItKLKNyloHAnImns2gWvvBJB77HH4IknYP36zrf5yEeyYe/kk2HvvWvTVhFp\nXgp3KSjciUg5duyAF1+MoPfoo9Ffr7OJlPv0gWOOyYa9k06CQYNq114RaQ4Kdyko3IlIJWzdCs89\nlz2z99xzEQCL6dcPpk7NDs6YMgX6969de0WkMSncpaBwJyLVsGEDPPNMNuzNm9f5/Hp77gkf/3iE\nvTPOgCOO0LQrIrI7hbsUFO5EpBbefx+efDIu4T72WOe3SANoa4ugd/rpsYwfX5t2ikh9U7hLQeFO\nRHrCypUxAjdzZm/x4s7LH3hghLwzzoi59traatNOEakvCncpKNyJSD1YsiRC3qxZcT/cVauKlzWD\no4/OntU76aS4rCsizU/hLgWFOxGpN5lpVx55JJYnnoBNm4qXHzgQPvaxbNg78kjo27d27RWR2lG4\nS0HhTkTqXWYkbibszZkTAbCYYcNiBG7mMu6BB9aurSJSXQp3KSjciUij+eCDOJv3yCNxGfe11zov\nP3589qzeqafC8OG1aaeIVJ7CXQoKdyLS6JYti1G4mTN7K1d2Xv6oo+Iy7sSJcNBBsYwdG3PviUh9\nU7hLQeFORJqJ++799TZu7Hq7vn1h3DiYMCEb+DLL+PHRr09Eep7CXQoKdyLSzLZtg+efz17CnTMH\ndu4srQ4z2H//3UPfQQdFfz7dRk2kdhTuUlC4E5HeZN26mEx5wQJ4883ssnx5+XWOGpUNe/ln/gYP\nrlzbRUThLhWFOxGRmGrlrbeyYW/x4uzXS5Z0fuu0zowYAYcdBocfnl0mT44RvSJSus7C3R61boyI\niNSvlpZs+Mq3dSu0t3c805dZ2tthx47i9a5aFcuTT3Zcv99+HQPf4YfDpEnQ2lrJdyXSuyjciYhI\nKgMGwCGHxJJvxw5YurRw8Fu8OPr8FfLee7HMmtVx/fjxu4e+Qw6JNohI53RZNqHLsiIi1bFzZ0zT\nsnBhjODNLAsXxtnAtPr2jWlb8kPfhAm6E4f0Pupzl4LCnYhIbe3cGWf1cgPfK6/A66+XNpJ3wIDC\n/fkOOAD69Kle+0V6ksJdCgp3IiL1YevWCHj5oe+tt0qrp6UlQt+kSbFMnhyP48bpTJ80Pg2oEBGR\nhjFgAHzkI7Hk2rABXn1199D37ruF69m0CebNiyXXwIFw6KEdA9+kSTFX3x76qyhNQGfuEjpzJyLS\nmN5/P+bry+/Pt2pVafX07x+DNnID36RJMU+fbskm9UaXZVNQuBMRaS6rVsWZvgULIuxllhUrSqun\nX78YyJEb+CZPhoMPjkAo0hN0WVZERHqdESNiOfnkjuvXru0Y9hYujABY7PLu9u3x/IIFHdf37RuX\ncvffH8aMicfcr8eMgaFD47ZtIrWkM3cJnbkTEendPvggzvTlBr6FC2Mal3K1tBQPf5mvhwxRAJTS\n6bJsCgp3IiJSyPr1sGhRx8C3cGHclaMSWlu7DoC6N6/kU7hLQeFORERKsWFD3G932bJY3nln9683\nbqzMaw0eDCNHwvDh0NbW9eOwYRoE0uzU505ERKTCWltjYMXkyYWfd49LvYVCX+7Xmzd3/Vrr18fy\nxhvp2zdkSPowmHnUAJHmoHAnIiJSBWYxoGLo0N3n7MtwjwEeXQXAUm7TlrFuXSyLF6ffprU1gt7I\nkR2Xfffd/fu99lJfwXqlcCciItJDzOKMWVsbHHFE4TLusGZNTO2yenV8Xewx8/XatbFdqTZsiCVN\nf8KBAzsPf7nfDx6sIFhLCnciIiJ1zCzOpg0fnn6bnTvjknBnQbBQMCzlnr5btkSfwyVLui6bGwRz\nw9/w4XG2sLUVBg3a/THzdf/+CoelULgTERFpMn37Zs8ITpyYbhv3uIy7ejWsXBnLihXZr/O/37Qp\nfXtKCYLF3k+xAFjKY/4yYEBzhkaFOxEREcEM9t47loMO6rr8hg2dh7/c70sJgoXs3JntQ1hJmdCY\nuxQLgmmXQYOgT5/KtrNUCnciIiJSstbWCIFpg2Ch8LdmTUwXs3FjlCn2uH17dd5DtUJjS0vMiThu\nXGXrTUvhTkRERKoqc1ZrwoTytt+2LV0ILPaY/3Xm+3JGIaexaVMEvJ6icCciIiJ1rX//WIYOrWy9\n27fvHvq6s2TqggizPUXhTkRERHqlfv2y/QwrZdeumJh6zz0rV2epFO5EREREKqRPnxhU0aNt6NmX\nFxEREZFKUrgTERERaSIKdyIiIiJNROFOREREpIko3ImIiIg0kboId2bWx8yuNbNFZrbFzJaZ2Y1m\nlmq8SXe3FxEREWkWdRHugJuBm4CFwNXAvcCXgAfNLE0bu7u9iIiISFPo8XnuzGwyEch+5e6fzVn/\nNvDPwJ8Bv6jW9iIiIiLNpB7Oal0IGHBL3vofAZuAi6q8vYiIiEjTqIdwdxywC5iTu9LdtwAvJc9X\nc3sRERGRplEP4W4UsNrdtxZ4bjkw3Mz6V2N7M5thZnPNbO6qVatKbriIiIhIvamHcNcCFApmAFty\nylR8e3e/3d2PdfdjR4wY0WVDRUREROpdPYS7TcCAIs8NzClTre1FREREmkY9hLt3iUunhQLaaOKS\n67Yqbi8iIiLSNHp8KhTgBeATwPHAU5mVZjYQOBJ4ssrbAzBv3rzVZrakpJaXbjiwusqv0Si0L7K0\nL7K0L4L2Q5b2RZb2RZb2BYwt9kQ9hLu7geuAa8gJZ8AVRF+5n2dWmNkEoJ+7Lypn+864e9U73ZnZ\nXHc/ttqv0wi0L7K0L7K0L4L2Q5b2RZb2RZb2Red6PNy5+8tm9j3gKjP7FfAb4DDiDhNP0HEC4keJ\npGplbi8iIiLS1Ho83CWuAdqBGcA5xKnWW4FvuPuuGmwvIiIi0hTqIty5+07gxmTprNy47mxfB27v\n6QbUEe2LLO2LLO2LoP2QpX2RpX2RpX3RCXP3nm6DiIiIiFRIPUyFIiIiIiIVonAnIiIi0kQU7kRE\nRESaiMJdN5hZHzO71swWmdkWM1tmZjea2aBabF8vzGyimf0vM3vOzFaZ2Ydm9pKZ/W0J+2K2mXmR\npaHmMurkfWwooY6zzexZM9toZmvN7F4zG1/NdleamV3fyb5wM9ueoo6GOi7M7G+Sn9VbSRvbuyg/\nxcweSX5n1pvZQ2Z2ZImv2e06qiHtvjCzgWZ2hZn9h5m1m9nmZJt/NbPDSni9Szo5Vm6r2BsrQynH\nhZnN7OR9fK6E1xxlZncmn8mbzWyumZ1XkTdUphKOiXFdfHa4mX0hxevV7TFRbXUxWraB3UzMp3c/\nMVI3M7/eUWZ2eoppWLq7fb24FPhL4AFi0ujtwHTgm8D5ZjbV3TenqGc1cG2B9W9VqqE19BS7j+bq\nMswAmNmfAvcB84G/BoYQ0/08Y2bHuvu7lWxoFf0KeLPA+o8S7+vBlPU00nHxbWAt8CKwd2cFzWwq\nMBtYDnwjWX0V8JSZnejuL3f1YpWoo4rS7otxxO/K08BPiFtKHghcCfypmZ3l7o+X+Lqv5q17rYTt\nqyH1cZHj4gLr5qTZ0MyGEftzH+Am4B3g88A9Znapu/9LyjZUWtr9sIrC7x/gNmBP4OESX7fejonq\ncnctZSzAZGAX8Mu89VcDDny+mtvX0wIcCwwpsP6byXu5KkUds4H2nn4vFdofDswsc9t+xB/qJUBr\nzvojgZ3A7T39/iqwf36Y7KNzmu24AA7M+fqVztpO/KFeD4zOWTc6Wfe7lK/X7Tp6el8AbcCRBdZP\nArYCc1O+3iXJcTWtp4+Dbh4XM+NPc7de738n++KTOev6JsfLmtzPlnrdD0W2PyF5X/c2+jFR7UWX\nZct3IXGnjFvy1v8I2ARcVOXt64a7z3X3dQWeujt5PDxtXRaXqgebmXVdur6ZWX8zay1xs1OAUcCP\n3f2Pl3Hd/SUi6FxgZv0q18rasrhM/2fEmYSHStiuIY4Ld091NtHMDgKOI/5ILc/ZfjlwL3C6me1b\n7TqqKe2+cPc1yfGdv34hEQBSf35kmNleZta/1O2qJe2+yGVhsJmV83f688Bid//j2XGP+WBvBYYB\nZ5dRZ7eVsx/yXJ48/rjUDevtmKg2hbvyHUeceetwmtzdtwAvJc9Xc/tGMCZ5XJmy/GhgA7AO2GBm\nvzKzQ6vSsur7HBHSPzSzP5jZrWY2JMV2mZ/7/yvw3HPAYGBihdrYE84j3sPM5I9NGs10XGR09XM2\n4Jga1FG3klCzH+k/PzIeIM5cbjGz+WbWMP8o51mXLJvNbJaZTUmzkZntR/zOPFfg6cy6hvv7kvyj\nfD5xVWNWiZs3yzGRmvrclW8UsNrdtxZ4bjlwopn1d/dtVdq+rplZX+DrwA7S3d/3beAZ4D+Jy49T\niL5Dp5nZx7xn+w6Vag5x5uRNIsicTbyXU5J+UJ0NrBiVPC4v8Fxm3WhgQYXaWmuXEZdJfpqyfDMd\nF7nS/pyrXUc9+wsi3N2Qsvwm4rPmMeAPwHiiL/BdZjbB3f++Kq2svBVEf+x5wEbgCKLP7VNmdra7\nP9LF9s16XFwAtAL/5On7ozfLMVEyhbvytRD9QQrZklOmWDjr7vb17haif8R17t5lx1V3/2LeqvvM\n7AHiUuRNwBkVb2GVuHv+f9h3mtl/At8C/ip5LKYleSx0bGzJK9NQzOwQ4GPAo+7+dpptmum4yFOJ\nn3MzHysnEj/f+URn+C65+z3APXn1/BCYC3zNzO5w9/YKN7Xi3P1/5q36dzP7BXFF5wfAwV1U0azH\nxeXE1a7Ug0Ga5Zgohy7Llm8TMKDIcwNzylRr+7plZjcQZ1dud/d/KLced38KeBKYbmZ7Vqp9PeS7\nRFA/p4tymZ95oWOjoY8L4qwdlNFfJleTHBeV+Dk35bFiZscAvyZGzZ6TdFUpS3Jl5J+IExmfqEwL\na8/d3yBCykFm1lW3jKY7LsxsEjAVmOXuS7tTV7McE11RuCvfu8BwMyv0CzSauOTa2Vm37m5fl8zs\neuBrxH9Xf1GBKtuJUV5DK1BXj3H37SQ/8y6KZqY5KXTZJLOu0OWWumZmewB/TozUu78CVbbT2MdF\nJX7OTXesmNnRRH+qdcD03IEi3dCePHb1u1fv2pPH3vgZUpF/DHO0J4+NfkwUpXBXvheI/Xd87koz\nG0hMWzG3ytvXnSTY/R1wB3C5J2PRu+lgot/e2grU1WOSn+sYuu4c/kLyeEKB56YSnYJfr2DTauWT\nwEjgZ0X6mZaq0Y+Lrn7OTvS5qnYddSMJdo8AHxLBbkmFqs5cxix1YEa9SfU+3P09IrxNLfB0Zl3D\n/H1JRrheTMx99x8VqrZZjomiFO7Kdzfx4XlN3voriP4MP8+sMLMJBUb3pd6+EZjZN4hgdxdwabEO\nr2a2n5kdamYtOeuGJAMw8sueA5xEnIov+9JMLZlZW5GnbiAuAzyYU3a3fQE8AbwHXJ47jYqZHQFM\nI6a9SDUZcp3J/Of9k0JPNvtxkc/d3yT+wJ5nZpkO8CRfnwc85u4rctYPT/bPkHLrqGdmdhRxxm4D\nEeyK9sk0s5ZkX+yXt363371kf32V6BJRyqS3PcLMBiX/COavP4r4mb7q7otz1hfcF8C/AhPM7JM5\nZfsS86h+APymKm+gOj4FjADuKvbZ18zHRLmsMidXeiczu5XoW3Y/8cuSucPEM8CpmYBjcYuVse5u\n5Wxf78zsL4lZw5cSI2Tz273S3WclZWcC/5X4AJ+drPs00Xn6QeKuAzuIM5oXEWdmTnL3hjhbZWY3\nE/8dP07sj1ZitOx04HnifW9Oys4kb18k688jwv98Yt7DwcQdGhw4pkKXqmomCRtLgXkFBptkysyk\nCY4LM7sYGJt8ezXQn7j7DMASd78rp+yJxHHyDjH/WGabkcR7m59T9nrin6cvuvvMcuqotbT7wszG\nEmcYhwF/Dyxmd/e7+8ak/DTiPd/h7pfkvN67xD9HLxMjI8cRd8/ZD/iyu99UuXdXmhL2xZHAb4F/\nB94gO1r2UuJz9RPu/nROvdMovC/aiH3aRvwOLSfmVp1GXFUp+E9WtZXy+5GzzW+Bs4BJ7p5/l4lM\nmWk02DFRdT0xc3KzLESfny8TtzHZSvwC3UTe7N/E9X0vd/t6X0hmVO9kmV2g7LScdYcRnYUXE/+5\nb02+/h45M+83wgKcS/w3uJwYmbaRGOV2HTCwyH6bVqCe/0LMSbUJeJ+4HdmEnn5/Ze6T65L3eUWK\nY6ihjwtiFG+Xvwc55U8AHk3e34fJsXN0gXLXJ3VcUm4d9boviMDR2eeHA+MKlJ+Z93o3EoFmDXGr\nv9XEP81nNtC+2Je4+rGI6IKxnfjH6A7g0AL1FtwXyXOjk7pWJ59FLwIXNMJ+yCm/PzEF0jNd1Ntw\nx0S1F525ExEREWki6nMnIiIi0kQU7kRERESaiMKdiIiISBNRuBMRERFpIgp3IiIiIk1E4U5ERESk\niSjciYiIiDQiRI33AAAEHUlEQVQRhTsRkQZhZrPNTJOTikinFO5EREREmojCnYiIiEgTUbgTERER\naSIKdyLS65jZFDO7z8xWmNk2M1tmZj80s1F55WabmZvZADP7ppm9bWZbzWyxmf2dmfUvUv9pZvaQ\nma1Nyr9uZv9oZkOKlB9mZt8ys1fMbJOZrTOz+ck2gwqU38PMrjOzN5L6l5nZd4q1R0R6F3NX31wR\n6T3M7FLgdmAr8ACwDDgY+BSwEpjq7kuTsrOBU5JyxwH3AduBc4EJwP8FPuU5H6Rm9t+AHwAbgXuB\nPwDTgCnAQuAkd/8gp/x44HFgLDAPeIL4x3sicDpwiLu357XnXuDjwG+B9cDZyXuY6e5frMiOEpGG\npXAnIr2GmU0EXgGWAqe4+/Kc504Dfgc84O6fSdbNJsLUG8AUd38/WT+QCGRTgT9397uS9WOB14ng\neLy7L8qp//vAlcCP3H1GzvpngROA69z9H/LaOxzY4O5b8trzInCGu69N1g8C5gPjgdHuvqLbO0tE\nGpYuy4pIb3Il0A/4q9xgB+DujxJn6D5pZnvlbXdDJtglZbcAf5N8e2lOuYuA/sBtucEu8bfAh8DF\nZjYAwMyOIYLdS8B38hvr7qszwS7PVzPBLim3Efg58Zl+bKE3LiK9xx493QARkRo6IXk8xcyOK/D8\nPkBf4pLovJz1TxQo+zSwEzgqZ93RyeNj+YXd/X0z+z1wMnAocaZtavL0w+6+K+2bAOYWWLcseRxa\nQj0i0oQU7kSkN2lLHv+6i3Kted+vzC/g7jvMbDURCDMyAybeK1JvZv3eeY/LC5QtKrfPXo4dyWPf\nUuoSkeajcCcivcm65HGIu68vYbuRRD+9PzKzPYDhxICG/Pr3BRYUqGe/vHKZkDa6hLaIiHRKfe5E\npDd5Lnn8eInbnVJg3ceIs2S/z1mX+XpafmEz2xs4EtgCvJrXnjPNTJ/HIlIR+jARkd7kNmIqk5uT\nkbMdmFl/MysU/L5uZkNzyg0EMiNb/yWn3M+S+q82s4Py6rgBGAz8zN23Arj7POBZIvR9tUB72pLX\nEhFJTZdlRaTXcPdFyTx3PwUWmNlDxNQl/YADiDN6q4gBD7leTcrnz3P3a+CunPrbzewa4HvAi2Z2\nT1LfKcRgjkXsHuIuAmYD3zazzyZfGzFv3SeStrR3/92LSG+hcCcivYq7/8zM5gNfBqYTAWoj8C4x\nSfHdBTY7H/g68AVgFDEA4nrgHz1vslB3/76ZvQn8D+CzQAsxkvW7wLfzB0O4+9tmdjTwFeDTwFXE\npdt24EZiEmQRkdQ0ibGISBGZSYPd3Xq6LSIiaanPnYiIiEgTUbgTERERaSIKdyIiIiJNRH3uRERE\nRJqIztyJiIiINBGFOxEREZEmonAnIiIi0kQU7kRERESaiMKdiIiISBP5/3djeqoWKPsGAAAAAElF\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YGt7LFy8QoB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''データ保存'''\n",
        "model.save('cifar10-CNN.h5')\n",
        "model.save_weights('cifar10-CNN-weights.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2eOOjcMisqVY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}