05_Karas_김우섭.ipynb

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  "metadata": {
    "colab": {
      "name": "05.Karas_김우섭.ipynb",
      "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/Woosub-Kim/test_git/blob/master/05_Karas_%EA%B9%80%EC%9A%B0%EC%84%AD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AcUYe822YxGp",
        "colab_type": "text"
      },
      "source": [
        "## load MNIST data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNdAlRXi2ky2",
        "colab_type": "code",
        "outputId": "faca6cec-fd48-4646-ab68-5dfde15c1770",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "import keras\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential"
      ],
      "execution_count": 0,
      "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>"
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          "metadata": {
            "tags": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "w24J5wa53dJy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Dropout\n",
        "from keras.optimizers import RMSprop, Adam"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rpef0RND37pQ",
        "colab_type": "code",
        "outputId": "06c88202-c335-4b93-ab2c-65a9254cf369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(x_train[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f72ff1cf208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
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J82zPtX2YpC9KWtuBPt7H9rTaiRPZniZpkbpvKuq1ki6u3b9Y0n0d7OVdumUa73rTjKvD\n713Hpz+PiLb/STpXQ2fkX5T0V53ooU5fn5T0RO3v6U73JukODR3W7dPQuY1LJX1U0jpJmyT9l6Se\nLurtPyQ9JelJDQVrdod6W6ihQ/QnJW2o/Z3b6feu0Fdb3jculwWS4AQdkARhB5Ig7EAShB1IgrAD\nSRB2IAnCDiTx/65XcTNOWsh5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lfwQbFoM0lU",
        "colab_type": "text"
      },
      "source": [
        "## 원래 x_train데이터는 0~255까지의 색 코드를 담고있는 \n",
        "## 28 x 28 행렬 60000개를 담고있는 \n",
        "## np.array이다(60000x28x28)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3tXk7Bm4RZb",
        "colab_type": "code",
        "outputId": "c8c44001-329d-4e9e-f0f1-bd6715c36578",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mnVMfrvmGt-g",
        "colab_type": "code",
        "outputId": "55bbf82f-31a1-4c60-b2d2-07e4963f1b2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in range(3):\n",
        "  print(x_train[i])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136\n",
            "  175  26 166 255 247 127   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253\n",
            "  225 172 253 242 195  64   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251\n",
            "   93  82  82  56  39   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  14   1 154 253  90   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0  11 190 253  70   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  35 241 225 160 108   1\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119\n",
            "   25   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253\n",
            "  150  27   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252\n",
            "  253 187   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 249\n",
            "  253 249  64   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
            "  253 207   2   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253\n",
            "  250 182   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201\n",
            "   78   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  51 159 253\n",
            "  159  50   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  48 238 252 252\n",
            "  252 237   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  54 227 253 252 239\n",
            "  233 252  57   6   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0  10  60 224 252 253 252 202\n",
            "   84 252 253 122   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0 163 252 252 252 253 252 252\n",
            "   96 189 253 167   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  51 238 253 253 190 114 253 228\n",
            "   47  79 255 168   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  48 238 252 252 179  12  75 121  21\n",
            "    0   0 253 243  50   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  38 165 253 233 208  84   0   0   0   0\n",
            "    0   0 253 252 165   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   7 178 252 240  71  19  28   0   0   0   0\n",
            "    0   0 253 252 195   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  57 252 252  63   0   0   0   0   0   0   0\n",
            "    0   0 253 252 195   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0 198 253 190   0   0   0   0   0   0   0   0\n",
            "    0   0 255 253 196   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  76 246 252 112   0   0   0   0   0   0   0   0\n",
            "    0   0 253 252 148   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  85 252 230  25   0   0   0   0   0   0   0   0\n",
            "    7 135 253 186  12   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  85 252 223   0   0   0   0   0   0   0   0   7\n",
            "  131 252 225  71   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  85 252 145   0   0   0   0   0   0   0  48 165\n",
            "  252 173   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  86 253 225   0   0   0   0   0   0 114 238 253\n",
            "  162   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  85 252 249 146  48  29  85 178 225 253 223 167\n",
            "   56   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  85 252 252 252 229 215 252 252 252 196 130   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  28 199 252 252 253 252 252 233 145   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  25 128 252 253 252 141  37   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0  67 232  39   0   0   0   0   0]\n",
            " [  0   0   0   0  62  81   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0 120 180  39   0   0   0   0   0]\n",
            " [  0   0   0   0 126 163   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   2 153 210  40   0   0   0   0   0]\n",
            " [  0   0   0   0 220 163   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0  27 254 162   0   0   0   0   0   0]\n",
            " [  0   0   0   0 222 163   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0 183 254 125   0   0   0   0   0   0]\n",
            " [  0   0   0  46 245 163   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0 198 254  56   0   0   0   0   0   0]\n",
            " [  0   0   0 120 254 163   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   23 231 254  29   0   0   0   0   0   0]\n",
            " [  0   0   0 159 254 120   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "  163 254 216  16   0   0   0   0   0   0]\n",
            " [  0   0   0 159 254  67   0   0   0   0   0   0   0   0   0  14  86 178\n",
            "  248 254  91   0   0   0   0   0   0   0]\n",
            " [  0   0   0 159 254  85   0   0   0  47  49 116 144 150 241 243 234 179\n",
            "  241 252  40   0   0   0   0   0   0   0]\n",
            " [  0   0   0 150 253 237 207 207 207 253 254 250 240 198 143  91  28   5\n",
            "  233 250   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0 119 177 177 177 177 177  98  56   0   0   0   0   0 102\n",
            "  254 220   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169\n",
            "  254 137   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169\n",
            "  254  57   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169\n",
            "  254  57   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169\n",
            "  255  94   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169\n",
            "  254  96   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169\n",
            "  254 153   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169\n",
            "  255 153   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  96\n",
            "  254 153   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3IO6t_VNLoe",
        "colab_type": "text"
      },
      "source": [
        "## 0~255까지 색깔보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5Szjpm8HZhw",
        "colab_type": "code",
        "outputId": "aba3359d-d187-4bd1-fbd2-b2de1543d2ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "import numpy as np\n",
        "image = np.array(list(x for x in range(256))).reshape(16,16)\n",
        "plt.imshow(image)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f72815a6cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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78IvAu4GfAQ4Bf9LEL80Z/oPA8p6fl1XXTbqNpFFgIfDfTRciaRbd4G+JiPsn3h4RxyLi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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UcI__fiwXQYu",
        "colab_type": "text"
      },
      "source": [
        "## Create Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lp3M1Yhh5z2o",
        "colab_type": "code",
        "outputId": "5e3c0d1f-de53-47ee-db92-f57c05fc0afd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "model = Sequential()"
      ],
      "execution_count": 0,
      "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TkAhHhv6U4F",
        "colab_type": "code",
        "outputId": "3d4f59ca-8bd9-43f3-df83-004217c5dc84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "source": [
        "num_classes = 10\n",
        "model.add(Dense(512, activation='relu', input_shape=(784,))) # input layer\n",
        "model.add(Dense(512, activation='relu')) # hidden layer1\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 512)               401920    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 669,706\n",
            "Trainable params: 669,706\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CKSUlF36Y8m",
        "colab_type": "code",
        "outputId": "40d31eb6-59c4-461b-90ae-2f18c4741fb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "source": [
        "# model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.001), metrics=['accuracy'])\n",
        "model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.00001), metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "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",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 512)               401920    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 669,706\n",
            "Trainable params: 669,706\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQn4qKtSDUK1",
        "colab_type": "text"
      },
      "source": [
        "## Model fit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfeLUR5L6_xV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "x_train = x_train.reshape(60000, 784)\n",
        "x_test = x_test.reshape(10000, 784)\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tD-9JSSMFViy",
        "colab_type": "code",
        "outputId": "f424dff6-7324-4e19-ba27-fbf7fe4a4dd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "x_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MV76sGjocvI",
        "colab_type": "code",
        "outputId": "e2923bfb-c135-4f7d-e221-e890df4b7d73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        }
      },
      "source": [
        "\n",
        "batch_size = 128\n",
        "epochs = 10\n",
        "# with tf.device('/device:GPU:0'):\n",
        "# history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test), callbacks=[tbCallBack])\n",
        "history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\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: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",
            "60000/60000 [==============================] - 3s 55us/step - loss: 1.7282 - acc: 0.6305 - val_loss: 1.1504 - val_acc: 0.8090\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 2s 34us/step - loss: 0.8197 - acc: 0.8405 - val_loss: 0.5669 - val_acc: 0.8712\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 2s 33us/step - loss: 0.4948 - acc: 0.8790 - val_loss: 0.4096 - val_acc: 0.8977\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 2s 34us/step - loss: 0.3900 - acc: 0.8972 - val_loss: 0.3456 - val_acc: 0.9080\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 2s 33us/step - loss: 0.3390 - acc: 0.9080 - val_loss: 0.3083 - val_acc: 0.9164\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 2s 32us/step - loss: 0.3072 - acc: 0.9156 - val_loss: 0.2844 - val_acc: 0.9222\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 2s 32us/step - loss: 0.2840 - acc: 0.9220 - val_loss: 0.2669 - val_acc: 0.9248\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 2s 33us/step - loss: 0.2661 - acc: 0.9260 - val_loss: 0.2513 - val_acc: 0.9282\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 2s 31us/step - loss: 0.2511 - acc: 0.9307 - val_loss: 0.2387 - val_acc: 0.9313\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 2s 32us/step - loss: 0.2384 - acc: 0.9342 - val_loss: 0.2286 - val_acc: 0.9339\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDA6u1yCLyjy",
        "colab_type": "code",
        "outputId": "2f769128-efe3-4c81-9bf4-6f8bc848f86f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "  score = model.evaluate(x_test, y_test, verbose=0)\n",
        "  print(model.metrics_names)\n",
        "  print('test loss: ', score[0])\n",
        "  print('test acc: ', score[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "test loss:  0.2286375914245844\n",
            "test acc:  0.9339\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQcLtDUApc5t",
        "colab_type": "code",
        "outputId": "331c6dc7-14f1-4610-8c7b-11d00cf6a990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "y_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqOVKKS_B08-",
        "colab_type": "text"
      },
      "source": [
        "## left keras logs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Bn4nQhbnIQA",
        "colab_type": "code",
        "outputId": "c1d47837-4323-4719-d0be-db70a028657e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        }
      },
      "source": [
        "from keras import backend as K\n",
        "from keras.callbacks import TensorBoard\n",
        "\n",
        "LOG_DIR = 'drive/data/tb_logs'\n",
        "\t\n",
        "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "!unzip ngrok-stable-linux-amd64.zip\n",
        "\t\n",
        "import os\n",
        "if not os.path.exists(LOG_DIR):\n",
        "  os.makedirs(LOG_DIR)\n",
        "\t\n",
        "get_ipython().system_raw('tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'.format(LOG_DIR))\n",
        "\t\n",
        "get_ipython().system_raw('./ngrok http 6006 &')\n",
        "\t\n",
        "!curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "\"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n",
        "\n",
        "tbCallBack = TensorBoard(\n",
        "    log_dir=LOG_DIR, histogram_freq=1,\n",
        "    write_graph=True,\n",
        "    write_grads=True,\n",
        "    batch_size=batch_size,\n",
        "    write_images=True\n",
        ")\n",
        "\n",
        "with tf.device('/device:GPU:0'):\n",
        "  history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test), callbacks=[tbCallBack])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-04 05:54:19--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 34.227.168.133, 52.201.174.2, 50.17.165.171, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|34.227.168.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13773305 (13M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip.3’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  13.13M  14.0MB/s    in 0.9s    \n",
            "\n",
            "2020-03-04 05:54:20 (14.0 MB/s) - ‘ngrok-stable-linux-amd64.zip.3’ saved [13773305/13773305]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "replace ngrok? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: ngrok                   \n",
            "https://711a9db1.ngrok.io\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1068: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1112: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1122: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1125: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
            "\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 2s 32us/step - loss: 0.2274 - acc: 0.9371 - val_loss: 0.2184 - val_acc: 0.9374\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1265: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
            "\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 2s 37us/step - loss: 0.2174 - acc: 0.9396 - val_loss: 0.2098 - val_acc: 0.9397\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 2s 34us/step - loss: 0.2086 - acc: 0.9420 - val_loss: 0.2027 - val_acc: 0.9421\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 2s 36us/step - loss: 0.2006 - acc: 0.9441 - val_loss: 0.1958 - val_acc: 0.9430\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1931 - acc: 0.9463 - val_loss: 0.1896 - val_acc: 0.9448\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1864 - acc: 0.9476 - val_loss: 0.1835 - val_acc: 0.9465\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1802 - acc: 0.9493 - val_loss: 0.1787 - val_acc: 0.9473\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 2s 36us/step - loss: 0.1743 - acc: 0.9513 - val_loss: 0.1738 - val_acc: 0.9487\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 2s 36us/step - loss: 0.1688 - acc: 0.9523 - val_loss: 0.1695 - val_acc: 0.9498\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 2s 37us/step - loss: 0.1636 - acc: 0.9542 - val_loss: 0.1647 - val_acc: 0.9505\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXKUTOcQ8lo5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rFcvFPPD9oR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(history.history)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfQ5VuBb8qcN",
        "colab_type": "code",
        "outputId": "8dc094f0-b6a2-4e34-f7e6-6979fd715149",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "df = pd.DataFrame(history.history)\n",
        "df.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f72fe3b3c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FgWJpSHsu7F+bLgb9soUcA5UBsOGDRAFiBSxWwKwAsTz5WIFdYVssViCe2IvF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\nIhJBCncRkQj6/8e4uvQT9s41AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4oux0YEnqpfm",
        "colab_type": "code",
        "outputId": "cf147e8b-54fe-4729-a336-53d02095d860",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        }
      },
      "source": [
        "history.history"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'acc': [0.9370999999682108,\n",
              "  0.9395500000317891,\n",
              "  0.9420333333015442,\n",
              "  0.9440833333651225,\n",
              "  0.9462833333651225,\n",
              "  0.94765,\n",
              "  0.9492500000317892,\n",
              "  0.9512666666348776,\n",
              "  0.9522833333651225,\n",
              "  0.9541666666666667],\n",
              " 'loss': [0.22737287734349568,\n",
              "  0.21742865818341572,\n",
              "  0.2085715906023979,\n",
              "  0.20059612621863684,\n",
              "  0.19310847152868907,\n",
              "  0.18638081984917323,\n",
              "  0.18018879838784535,\n",
              "  0.17427873318990073,\n",
              "  0.16878442698717117,\n",
              "  0.16364366029898325],\n",
              " 'val_acc': [0.9374,\n",
              "  0.9397,\n",
              "  0.9421,\n",
              "  0.943,\n",
              "  0.9448,\n",
              "  0.9465,\n",
              "  0.9473,\n",
              "  0.9487,\n",
              "  0.9498,\n",
              "  0.9505],\n",
              " 'val_loss': [0.2183712172985077,\n",
              "  0.20983006801605225,\n",
              "  0.20272022205591203,\n",
              "  0.1957892426133156,\n",
              "  0.18961155593395232,\n",
              "  0.1835458195269108,\n",
              "  0.17869619890451433,\n",
              "  0.17375004712343217,\n",
              "  0.16946835255622864,\n",
              "  0.16469463438987733]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SwWKgCoNfNQ",
        "colab_type": "text"
      },
      "source": [
        "## 파라미터 조절하기 \n",
        "#### compile의 lr값(학습률)\n",
        "#### activation의 학습방법\n",
        "#### compile의 loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3JWa7CvEIis",
        "colab_type": "code",
        "outputId": "97d3c440-fe4b-430a-b4e0-21515c29a1eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "# compile과정의 lr값을 조정 학습률\n",
        "model1 = Sequential()\n",
        "model1.add(Dense (512, activation='relu', input_shape=(784,))) # input layer\n",
        "model1.add(Dense(512, activation='relu')) # hidden layer1\n",
        "model1.add(Dense(num_classes, activation='softmax'))\n",
        "model1.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.001), metrics=['accuracy'])\n",
        "history1 = model1.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0, validation_data=(x_test, y_test))\n",
        "\n",
        "#add에서 activation을 모두 softmax \n",
        "model2 = Sequential()\n",
        "model2.add(Dense (512, activation='softmax', input_shape=(784,))) # input layer\n",
        "model2.add(Dense(512, activation='softmax')) # hidden layer1\n",
        "model2.add(Dense(num_classes, activation='softmax'))\n",
        "model2.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.00001), metrics=['accuracy'])\n",
        "history2 = model2.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0, validation_data=(x_test, y_test))\n",
        "\n",
        "#add에서 activation을 모두 relu\n",
        "model3 = Sequential()\n",
        "model3.add(Dense (512, activation='relu', input_shape=(784,))) # input layer\n",
        "model3.add(Dense(512, activation='relu')) # hidden layer1\n",
        "model3.add(Dense(num_classes, activation='relu'))\n",
        "model3.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.00001), metrics=['accuracy'])\n",
        "history3 = model3.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0, validation_data=(x_test, y_test))\n",
        "\n",
        "#compile과정에서 loss 를 mean_squared_error\n",
        "model4 = Sequential()\n",
        "model4.add(Dense (512, activation='relu', input_shape=(784,))) # input layer\n",
        "model4.add(Dense(512, activation='relu')) # hidden layer1\n",
        "model4.add(Dense(num_classes, activation='softmax'))\n",
        "model4.compile(loss='mean_squared_error', optimizer=RMSprop(lr=0.00001), metrics=['accuracy'])\n",
        "history4 = model4.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0, validation_data=(x_test, y_test))\n",
        "\n",
        "\n",
        "score1 = model1.evaluate(x_test, y_test, verbose=0)\n",
        "score2 = model2.evaluate(x_test, y_test, verbose=0)\n",
        "score3 = model3.evaluate(x_test, y_test, verbose=0)\n",
        "score4 = model4.evaluate(x_test, y_test, verbose=0)\n",
        "\n",
        "print(model1.metrics_names)\n",
        "print('test loss: ', score1[0])\n",
        "print('test acc: ', score1[1])\n",
        "\n",
        "print(model2.metrics_names)\n",
        "print('test loss: ', score2[0])\n",
        "print('test acc: ', score2[1])\n",
        "\n",
        "print(model3.metrics_names)\n",
        "print('test loss: ', score3[0])\n",
        "print('test acc: ', score3[1])\n",
        "\n",
        "print(model4.metrics_names)\n",
        "print('test loss: ', score4[0])\n",
        "print('test acc: ', score4[1])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "test loss:  0.11290730886588217\n",
            "test acc:  0.9805\n",
            "['loss', 'acc']\n",
            "test loss:  2.301802775192261\n",
            "test acc:  0.1135\n",
            "['loss', 'acc']\n",
            "test loss:  1.0921153957953678\n",
            "test acc:  0.7785\n",
            "['loss', 'acc']\n",
            "test loss:  0.010363718561944553\n",
            "test acc:  0.9363\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WAbviFRrv2z",
        "colab_type": "text"
      },
      "source": [
        "## 결론\n",
        "###### 1. lr 0.00001=>0.001 성적좋아짐\n",
        "###### 2,3activation을 하나로 통일해서는 안됨\n",
        "###### 4. loss를 바꾸는것은 경우에 따라 가능함\n",
        "\n",
        "## 참고\n",
        "\n",
        "### activation : 활성화 함수 설정합니다.\n",
        "###### linear’ : 디폴트 값, 입력뉴런과 가중치로 계산된 결과값이 그대로 출력으로 나옵니다.\n",
        "###### relu : rectifier 함수, 은닉층에 주로 쓰입니다.\n",
        "###### sigmoid : 시그모이드 함수, 이진 분류 문제에서 출력층에 주로 쓰입니다.\n",
        "###### softmax : 소프트맥스 함수, 다중 클래스 분류 문제에서 출력층에 주로 쓰입니다.\n",
        "\n",
        "## loss손실 함수, 모델이 최적화에 사용되는 목적 함수입니다. \n",
        "###### categorical_crossentropy \n",
        "###### mse .....\n",
        "\n",
        "## lr 학습률\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1blny40nN71i",
        "colab_type": "text"
      },
      "source": [
        "## sklearn의 classification을 활용한다면??"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZeZqbvlsejD",
        "colab_type": "code",
        "outputId": "eb71bb6b-39e7-46cf-df72-b5df5ab200d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "tree = DecisionTreeClassifier()\n",
        "tree.fit(x_train, y_train)\n",
        "pred = tree.predict(x_test)\n",
        "from sklearn.metrics import classification_report\n",
        "report = classification_report(y_test, pred)\n",
        "print(report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.94      0.93       980\n",
            "           1       0.96      0.96      0.96      1135\n",
            "           2       0.87      0.86      0.86      1032\n",
            "           3       0.83      0.86      0.84      1010\n",
            "           4       0.87      0.88      0.88       982\n",
            "           5       0.83      0.84      0.83       892\n",
            "           6       0.89      0.89      0.89       958\n",
            "           7       0.91      0.90      0.90      1028\n",
            "           8       0.82      0.80      0.81       974\n",
            "           9       0.86      0.85      0.85      1009\n",
            "\n",
            "   micro avg       0.88      0.88      0.88     10000\n",
            "   macro avg       0.88      0.88      0.88     10000\n",
            "weighted avg       0.88      0.88      0.88     10000\n",
            " samples avg       0.88      0.88      0.88     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-OLgPDO_wtt",
        "colab_type": "code",
        "outputId": "afe2b42b-6575-4c86-80b7-432520be11f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqENPcSlN_7A",
        "colab_type": "text"
      },
      "source": [
        "# 이미지 처리도 비교적 간단한 케이스라면 \n",
        "# 일반 싸이킷런 머신러닝으로 할 수 있다?? "
      ]
    }
  ]
}