chap5_MNIST.ipynb

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        "<a href=\"https://colab.research.google.com/github/imji0319/PDSH/blob/master/chap5_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# MNIST 숫자 이미지 분류 \n",
        "\n",
        "from keras import layers\n",
        "from keras import models\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(32, (3,3), activation = 'relu', input_shape= (28,28,1)))\n",
        "#input_shape : MNIST 이미지 포맷의 크기 (28, 28, 1)\n",
        "\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3,3), activation = 'relu' ))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3,3), activation = 'relu' ))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
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        "model.summary()"
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      "outputs": [
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          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "=================================================================\n",
            "Total params: 55,744\n",
            "Trainable params: 55,744\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
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      "source": [
        "model.add(layers.Flatten())    #3D -> 1D \n",
        "model.add(layers.Dense(64,activation ='relu'))\n",
        "model.add(layers.Dense(10,activation = 'softmax' ))"
      ],
      "execution_count": 0,
      "outputs": []
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        "model.summary()"
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      "outputs": [
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          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                36928     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 93,322\n",
            "Trainable params: 93,322\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "cell_type": "code",
      "source": [
        "from keras.datasets import mnist\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) =mnist.load_data()\n",
        "\n",
        "train_images =train_images.reshape((60000,28,28,1))\n",
        "train_images = train_images.astype('float32')/255\n",
        "\n",
        "test_images =test_images.reshape((10000,28,28,1))\n",
        "test_images = test_images.astype('float32')/255\n",
        "\n",
        "train_labels = to_categorical(train_labels)\n",
        "test_labels = to_categorical(test_labels)\n",
        "\n",
        "\n",
        "model.compile(optimizer ='rmsprop',\n",
        "             loss = 'categorical_crossentropy',\n",
        "             metrics = ['accuracy'])\n",
        "\n",
        "model.fit(train_images, train_labels, epochs=5, batch_size=64)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 61s 1ms/step - loss: 0.1798 - acc: 0.9450\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 61s 1ms/step - loss: 0.0493 - acc: 0.9845\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 61s 1ms/step - loss: 0.0335 - acc: 0.9896\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 61s 1ms/step - loss: 0.0251 - acc: 0.9921\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 61s 1ms/step - loss: 0.0207 - acc: 0.9934\n"
          ],
          "name": "stdout"
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        {
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          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f96c7bf4150>"
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      },
      "cell_type": "code",
      "source": [
        "test_loss, test_acc =model.evaluate(test_images, test_labels)\n",
        "test_acc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 3s 327us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9929"
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