11-CNNCifar.ipynb

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    "colab": {
      "name": "UCT2019-CNNCifar_R.ipynb",
      "version": "0.3.2",
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS9FclBXnTZL",
        "colab_type": "text"
      },
      "source": [
        "***Dr. Emmanuel Dufourq*** www.emmanueldufourq.com\n",
        "\n",
        "***African Institute for Mathematical Sciences***\n",
        "\n",
        "***2019***\n",
        "\n",
        "\n",
        "Credits: The data imports were extracted from https://keras.rstudio.com/articles/examples/mnist_cnn.html\n",
        "\n",
        "***NOTE***\n",
        "\n",
        "Be sure to use hardware acceleration to use the GPU. Click on `Runtime`, change `runtime type`, and select `GPU` for the *hardware accelerator* option."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdUuuxhRiXzg",
        "colab_type": "code",
        "outputId": "4cc82284-ec01-467a-8fe9-a1f1ba360573",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "devtools::install_github(\"rstudio/keras\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skipping install of 'keras' from a github remote, the SHA1 (34dc89ed) has not changed since last install.\n",
            "  Use `force = TRUE` to force installation\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQteA-lKkVo-",
        "colab_type": "text"
      },
      "source": [
        "## Import the Keras library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iMaChtciX8e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "library(keras)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPr7k2algV95",
        "colab_type": "text"
      },
      "source": [
        "## Load the CIFAR10 dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsHmgsjvgSEy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cifar10 <- dataset_cifar10()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcyJLtW2ZXql",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "outputId": "05a97feb-5c2b-435b-d721-c86a6de7bb25"
      },
      "source": [
        "dim(cifar10$train$x)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 50000    32    32     3"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 50000\n\\item 32\n\\item 32\n\\item 3\n\\end{enumerate*}\n",
            "text/markdown": "1. 50000\n2. 32\n3. 32\n4. 3\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>50000</li>\n",
              "\t<li>32</li>\n",
              "\t<li>32</li>\n",
              "\t<li>3</li>\n",
              "</ol>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fk7meqtia-v3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "outputId": "27ca5794-15d2-44d5-cd00-dd6c0d328dfb"
      },
      "source": [
        "dim(cifar10$test$x)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 10000    32    32     3"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 10000\n\\item 32\n\\item 32\n\\item 3\n\\end{enumerate*}\n",
            "text/markdown": "1. 10000\n2. 32\n3. 32\n4. 3\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>10000</li>\n",
              "\t<li>32</li>\n",
              "\t<li>32</li>\n",
              "\t<li>3</li>\n",
              "</ol>\n"
            ]
          },
          "metadata": {
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nB2p8bUSjTez",
        "colab_type": "text"
      },
      "source": [
        "## Load features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Th0mYc28jTjS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train <- cifar10$train$x\n",
        "x_test <- cifar10$test$x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ibdk4invjRlb",
        "colab_type": "text"
      },
      "source": [
        "## Load targets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBbgDsqAjRts",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train <- cifar10$train$y\n",
        "y_test <- cifar10$test$y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11gKaHAVbLLZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rm(cifar10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YUwKP2fI1hy",
        "colab_type": "text"
      },
      "source": [
        "## Check dimension\n",
        "\n",
        "Notice here that there are three channels in each image, one for the red, green and blue channels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XfY_xQrI1mH",
        "colab_type": "code",
        "outputId": "0d84b578-d315-4a8a-f58d-78e95b4cdb31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        }
      },
      "source": [
        "dim(x_train)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 50000    32    32     3"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 50000\n\\item 32\n\\item 32\n\\item 3\n\\end{enumerate*}\n",
            "text/markdown": "1. 50000\n2. 32\n3. 32\n4. 3\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>50000</li>\n",
              "\t<li>32</li>\n",
              "\t<li>32</li>\n",
              "\t<li>3</li>\n",
              "</ol>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25KoZ4FwIDt3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train <- array_reshape(x_train, c(nrow(x_train), 32, 32, 3))\n",
        "x_test <- array_reshape(x_test, c(nrow(x_test), 32, 32, 3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bZHFjjSJd0o",
        "colab_type": "text"
      },
      "source": [
        "## Take a look at a  training example\n",
        "\n",
        "The dimension is correct but take a look at the values in the data point. The values are large, between 0 and 255. Thus we will need to normalise the values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMJgcAncJd98",
        "colab_type": "code",
        "outputId": "1059ba69-6829-4da4-fcc8-a7c253231b4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 96
        }
      },
      "source": [
        "dim(x_train[1,,,])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 32 32  3"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 32\n\\item 32\n\\item 3\n\\end{enumerate*}\n",
            "text/markdown": "1. 32\n2. 32\n3. 3\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>32</li>\n",
              "\t<li>32</li>\n",
              "\t<li>3</li>\n",
              "</ol>\n"
            ]
          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OuQ7eyFJKqB",
        "colab_type": "code",
        "outputId": "e4a00c59-6b77-4da2-f025-2980d2595524",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(x_train[1,,,1])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]\n",
            " [1,]   59   43   50   68   98  119  139  145  149   149   131   125   142\n",
            " [2,]   16    0   18   51   88  120  128  127  126   116   106   101   105\n",
            " [3,]   25   16   49   83  110  129  130  121  113   112   112   106   105\n",
            " [4,]   33   38   87  106  115  117  114  105  107   121   125   109   113\n",
            " [5,]   50   59  102  127  124  121  120  114  107   125   129   106   108\n",
            " [6,]   71   84  110  129  136  131  129  119  108   122   123   105   107\n",
            " [7,]   97  111  123  130  136  132  122  121  127   138   124   120   107\n",
            " [8,]  115  119  130  140  133  127  138  137  131   133   134   108    72\n",
            " [9,]  137  128  132  128  119  123  128  130  121   137   131    74    54\n",
            "[10,]  154  154  156  140  123  125  126  127  133   132    90    63    62\n",
            "[11,]  154  155  156  147  133  137  139  134  141   121    80    97    90\n",
            "[12,]  145  146  146  135  127  129  117  103  130   120   111   146   136\n",
            "[13,]  142  141  140  144  147  121   84   88  109   101   138   213   178\n",
            "[14,]  158  154  142  143  132   90   72   81   84   107   165   229   183\n",
            "[15,]  145  149  147  147  136   80   89  105   96   129   192   185   145\n",
            "[16,]  148  146  145  147  133   63   66   88  113   182   220   138   162\n",
            "[17,]  149  143  144  151  132   64   84  112  163   223   206   145   196\n",
            "[18,]  147  134  140  148  135  100  108  144  210   248   175   175   220\n",
            "[19,]  152  117  114  123  126  122   93  179  238   248   170   185   241\n",
            "[20,]  145  127  128  133  132  135  171  237  252   229   173   169   220\n",
            "[21,]  143  127  129  129  130  140  219  244  210   193   166   153   191\n",
            "[22,]  143  125  131  128  123  153  148  166  188   182   171   165   195\n",
            "[23,]  141  131  139  139  138  151  128  136  175   173   189   205   201\n",
            "[24,]  143  139  138  149  160  150  147  151  169   167   179   212   203\n",
            "[25,]  149  133  136  147  150  153  157  162  175   190   166   202   224\n",
            "[26,]  172  144  135  136  135  139  153  163  166   184   166   150   184\n",
            "[27,]  202  187  151  128  122  134  142  150  153   148   135   127   153\n",
            "[28,]  216  193  168  151  131  126  138  144  142   137   120   131   145\n",
            "[29,]  220  201  186  172  156  142  142  153  150   139   126   136   148\n",
            "[30,]  208  201  198  191  183  171  159  147  135   130   139   147   144\n",
            "[31,]  180  173  186  194  198  201  189  173  156   139   142   145   141\n",
            "[32,]  177  168  179  188  202  218  218  207  191   175   166   163   163\n",
            "      [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]\n",
            " [1,]   144   137   129   137   134   124   139   139   133   136   139   152\n",
            " [2,]   113   109   112   119   109   105   125   127   122   131   124   121\n",
            " [3,]   128   124   130   127   122   115   120   130   131   139   127   126\n",
            " [4,]   146   133   127   118   117   127   122   132   137   136   131   124\n",
            " [5,]   124   121   108    98   110   117   120   134   140   131   141   135\n",
            " [6,]   111   108    98    94    97    83    88   102    97    88   118   140\n",
            " [7,]    80    68    74   101   105    65    58    63    78   136   122   139\n",
            " [8,]    51    41    72   181   209   125    68    64    82   123   112   135\n",
            " [9,]    50    44    86   203   217   162   100    77    75    74    76   107\n",
            "[10,]    70    79   103   152   148   141   121   101    96    86    75   101\n",
            "[11,]    98   137   139   148   134   138   134   140   175   142   102   108\n",
            "[12,]   163   169   152   161   148   177   161   195   209   189   125   108\n",
            "[13,]   191   211   189   205   207   213   191   199   188   161   130   124\n",
            "[14,]   191   239   219   228   225   214   216   210   200   189   174   161\n",
            "[15,]   203   223   242   244   238   241   227   225   235   219   224   215\n",
            "[16,]   206   196   247   255   255   245   236   230   215   231   250   241\n",
            "[17,]   204   220   243   245   239   234   231   195   150   208   250   227\n",
            "[18,]   226   230   233   224   201   184   181   190   170   179   231   223\n",
            "[19,]   230   187   180   166   146   149   157   184   216   212   236   236\n",
            "[20,]   194   123   135   127   151   165   132   151   202   240   240   222\n",
            "[21,]   179   128   147   149   172   147   128   141   173   202   190   198\n",
            "[22,]   190   152   143   152   153   142   141   135   136   148   141   141\n",
            "[23,]   168   151   145   146   149   153   149   144   144   145   143   129\n",
            "[24,]   207   149   139   144   137   151   155   152   140   107    91    84\n",
            "[25,]   197   192   180   146   126   141   156   153   115    77    79    93\n",
            "[26,]   156   158   168   149   135   130   132   128   127   135   143   139\n",
            "[27,]   166   143   130   128   151   152   135   139   155   161   154   154\n",
            "[28,]   144   137   127   126   139   153   149   140   135   147   148   149\n",
            "[29,]   141   131   126   127   138   150   154   149   124   126   141   145\n",
            "[30,]   145   137   136   137   148   152   150   155   138   120   128   142\n",
            "[31,]   141   139   140   143   139   138   143   146   135   117   112   122\n",
            "[32,]   161   153   159   162   149   140   148   161   144   112   119   130\n",
            "      [,26] [,27] [,28] [,29] [,30] [,31] [,32]\n",
            " [1,]   163   168   159   158   158   152   148\n",
            " [2,]   131   132   133   133   123   119   122\n",
            " [3,]   127   130   142   130   118   120   109\n",
            " [4,]   130   132   135   130   125   121    94\n",
            " [5,]   127   121   119   103    87    75    67\n",
            " [6,]   136   120   107    88    67    35    32\n",
            " [7,]   151   129   108    95    96    89    66\n",
            " [8,]   151   137   114   105   101   126   102\n",
            " [9,]   135   135   129   127   119   125   134\n",
            "[10,]   136   136   134   133   132   128   133\n",
            "[11,]   135   131   133   138   136   130   134\n",
            "[12,]   140   137   132   136   133   132   133\n",
            "[13,]   131   130   131   134   135   136   133\n",
            "[14,]   139   134   126   131   142   136   138\n",
            "[15,]   156   128   129   131   133   128   130\n",
            "[16,]   158   125   126   124   125   126   124\n",
            "[17,]   163   145   143   140   136   121   114\n",
            "[18,]   162   146   140   139   145   142   128\n",
            "[19,]   166   136   134   130   127   137   151\n",
            "[20,]   156   119   120   112   100    99   140\n",
            "[21,]   152   100   109   119   121   108   136\n",
            "[22,]   138   111   111   121   129   138   179\n",
            "[23,]   123   124   113   108   113   148   199\n",
            "[24,]   105   132   118    96   102   159   190\n",
            "[25,]   126   133   119   113   140   187   154\n",
            "[26,]   136   127   121   135   189   211   136\n",
            "[27,]   143   130   132   171   215   186   117\n",
            "[28,]   149   137   143   203   206   124    71\n",
            "[29,]   147   127   114   186   173    56    33\n",
            "[30,]   135    90    50   137   160    56    53\n",
            "[31,]   104    58    34   131   184    97    83\n",
            "[32,]   120    92   103   170   216   151   123\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25rl1ReBLJVY",
        "colab_type": "text"
      },
      "source": [
        "## Normalise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-b-wMlDSKyYK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train <- x_train / 255\n",
        "x_test <- x_test / 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNa9k3I6LO5S",
        "colab_type": "text"
      },
      "source": [
        "Now we can check the values. This looks better and is suitable for a CNN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8T1veQYrLI5G",
        "colab_type": "code",
        "outputId": "017fe9dc-7ef1-4edf-f0a8-76d6db2ea7a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(x_train[1,,,1])"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            [,1]      [,2]       [,3]      [,4]      [,5]      [,6]      [,7]\n",
            " [1,] 0.23137255 0.1686275 0.19607843 0.2666667 0.3843137 0.4666667 0.5450980\n",
            " [2,] 0.06274510 0.0000000 0.07058824 0.2000000 0.3450980 0.4705882 0.5019608\n",
            " [3,] 0.09803922 0.0627451 0.19215686 0.3254902 0.4313725 0.5058824 0.5098039\n",
            " [4,] 0.12941176 0.1490196 0.34117647 0.4156863 0.4509804 0.4588235 0.4470588\n",
            " [5,] 0.19607843 0.2313725 0.40000000 0.4980392 0.4862745 0.4745098 0.4705882\n",
            " [6,] 0.27843137 0.3294118 0.43137255 0.5058824 0.5333333 0.5137255 0.5058824\n",
            " [7,] 0.38039216 0.4352941 0.48235294 0.5098039 0.5333333 0.5176471 0.4784314\n",
            " [8,] 0.45098039 0.4666667 0.50980392 0.5490196 0.5215686 0.4980392 0.5411765\n",
            " [9,] 0.53725490 0.5019608 0.51764706 0.5019608 0.4666667 0.4823529 0.5019608\n",
            "[10,] 0.60392157 0.6039216 0.61176471 0.5490196 0.4823529 0.4901961 0.4941176\n",
            "[11,] 0.60392157 0.6078431 0.61176471 0.5764706 0.5215686 0.5372549 0.5450980\n",
            "[12,] 0.56862745 0.5725490 0.57254902 0.5294118 0.4980392 0.5058824 0.4588235\n",
            "[13,] 0.55686275 0.5529412 0.54901961 0.5647059 0.5764706 0.4745098 0.3294118\n",
            "[14,] 0.61960784 0.6039216 0.55686275 0.5607843 0.5176471 0.3529412 0.2823529\n",
            "[15,] 0.56862745 0.5843137 0.57647059 0.5764706 0.5333333 0.3137255 0.3490196\n",
            "[16,] 0.58039216 0.5725490 0.56862745 0.5764706 0.5215686 0.2470588 0.2588235\n",
            "[17,] 0.58431373 0.5607843 0.56470588 0.5921569 0.5176471 0.2509804 0.3294118\n",
            "[18,] 0.57647059 0.5254902 0.54901961 0.5803922 0.5294118 0.3921569 0.4235294\n",
            "[19,] 0.59607843 0.4588235 0.44705882 0.4823529 0.4941176 0.4784314 0.3647059\n",
            "[20,] 0.56862745 0.4980392 0.50196078 0.5215686 0.5176471 0.5294118 0.6705882\n",
            "[21,] 0.56078431 0.4980392 0.50588235 0.5058824 0.5098039 0.5490196 0.8588235\n",
            "[22,] 0.56078431 0.4901961 0.51372549 0.5019608 0.4823529 0.6000000 0.5803922\n",
            "[23,] 0.55294118 0.5137255 0.54509804 0.5450980 0.5411765 0.5921569 0.5019608\n",
            "[24,] 0.56078431 0.5450980 0.54117647 0.5843137 0.6274510 0.5882353 0.5764706\n",
            "[25,] 0.58431373 0.5215686 0.53333333 0.5764706 0.5882353 0.6000000 0.6156863\n",
            "[26,] 0.67450980 0.5647059 0.52941176 0.5333333 0.5294118 0.5450980 0.6000000\n",
            "[27,] 0.79215686 0.7333333 0.59215686 0.5019608 0.4784314 0.5254902 0.5568627\n",
            "[28,] 0.84705882 0.7568627 0.65882353 0.5921569 0.5137255 0.4941176 0.5411765\n",
            "[29,] 0.86274510 0.7882353 0.72941176 0.6745098 0.6117647 0.5568627 0.5568627\n",
            "[30,] 0.81568627 0.7882353 0.77647059 0.7490196 0.7176471 0.6705882 0.6235294\n",
            "[31,] 0.70588235 0.6784314 0.72941176 0.7607843 0.7764706 0.7882353 0.7411765\n",
            "[32,] 0.69411765 0.6588235 0.70196078 0.7372549 0.7921569 0.8549020 0.8549020\n",
            "           [,8]      [,9]     [,10]     [,11]     [,12]     [,13]     [,14]\n",
            " [1,] 0.5686275 0.5843137 0.5843137 0.5137255 0.4901961 0.5568627 0.5647059\n",
            " [2,] 0.4980392 0.4941176 0.4549020 0.4156863 0.3960784 0.4117647 0.4431373\n",
            " [3,] 0.4745098 0.4431373 0.4392157 0.4392157 0.4156863 0.4117647 0.5019608\n",
            " [4,] 0.4117647 0.4196078 0.4745098 0.4901961 0.4274510 0.4431373 0.5725490\n",
            " [5,] 0.4470588 0.4196078 0.4901961 0.5058824 0.4156863 0.4235294 0.4862745\n",
            " [6,] 0.4666667 0.4235294 0.4784314 0.4823529 0.4117647 0.4196078 0.4352941\n",
            " [7,] 0.4745098 0.4980392 0.5411765 0.4862745 0.4705882 0.4196078 0.3137255\n",
            " [8,] 0.5372549 0.5137255 0.5215686 0.5254902 0.4235294 0.2823529 0.2000000\n",
            " [9,] 0.5098039 0.4745098 0.5372549 0.5137255 0.2901961 0.2117647 0.1960784\n",
            "[10,] 0.4980392 0.5215686 0.5176471 0.3529412 0.2470588 0.2431373 0.2745098\n",
            "[11,] 0.5254902 0.5529412 0.4745098 0.3137255 0.3803922 0.3529412 0.3843137\n",
            "[12,] 0.4039216 0.5098039 0.4705882 0.4352941 0.5725490 0.5333333 0.6392157\n",
            "[13,] 0.3450980 0.4274510 0.3960784 0.5411765 0.8352941 0.6980392 0.7490196\n",
            "[14,] 0.3176471 0.3294118 0.4196078 0.6470588 0.8980392 0.7176471 0.7490196\n",
            "[15,] 0.4117647 0.3764706 0.5058824 0.7529412 0.7254902 0.5686275 0.7960784\n",
            "[16,] 0.3450980 0.4431373 0.7137255 0.8627451 0.5411765 0.6352941 0.8078431\n",
            "[17,] 0.4392157 0.6392157 0.8745098 0.8078431 0.5686275 0.7686275 0.8000000\n",
            "[18,] 0.5647059 0.8235294 0.9725490 0.6862745 0.6862745 0.8627451 0.8862745\n",
            "[19,] 0.7019608 0.9333333 0.9725490 0.6666667 0.7254902 0.9450980 0.9019608\n",
            "[20,] 0.9294118 0.9882353 0.8980392 0.6784314 0.6627451 0.8627451 0.7607843\n",
            "[21,] 0.9568627 0.8235294 0.7568627 0.6509804 0.6000000 0.7490196 0.7019608\n",
            "[22,] 0.6509804 0.7372549 0.7137255 0.6705882 0.6470588 0.7647059 0.7450980\n",
            "[23,] 0.5333333 0.6862745 0.6784314 0.7411765 0.8039216 0.7882353 0.6588235\n",
            "[24,] 0.5921569 0.6627451 0.6549020 0.7019608 0.8313725 0.7960784 0.8117647\n",
            "[25,] 0.6352941 0.6862745 0.7450980 0.6509804 0.7921569 0.8784314 0.7725490\n",
            "[26,] 0.6392157 0.6509804 0.7215686 0.6509804 0.5882353 0.7215686 0.6117647\n",
            "[27,] 0.5882353 0.6000000 0.5803922 0.5294118 0.4980392 0.6000000 0.6509804\n",
            "[28,] 0.5647059 0.5568627 0.5372549 0.4705882 0.5137255 0.5686275 0.5647059\n",
            "[29,] 0.6000000 0.5882353 0.5450980 0.4941176 0.5333333 0.5803922 0.5529412\n",
            "[30,] 0.5764706 0.5294118 0.5098039 0.5450980 0.5764706 0.5647059 0.5686275\n",
            "[31,] 0.6784314 0.6117647 0.5450980 0.5568627 0.5686275 0.5529412 0.5529412\n",
            "[32,] 0.8117647 0.7490196 0.6862745 0.6509804 0.6392157 0.6392157 0.6313725\n",
            "          [,15]     [,16]     [,17]     [,18]     [,19]     [,20]     [,21]\n",
            " [1,] 0.5372549 0.5058824 0.5372549 0.5254902 0.4862745 0.5450980 0.5450980\n",
            " [2,] 0.4274510 0.4392157 0.4666667 0.4274510 0.4117647 0.4901961 0.4980392\n",
            " [3,] 0.4862745 0.5098039 0.4980392 0.4784314 0.4509804 0.4705882 0.5098039\n",
            " [4,] 0.5215686 0.4980392 0.4627451 0.4588235 0.4980392 0.4784314 0.5176471\n",
            " [5,] 0.4745098 0.4235294 0.3843137 0.4313725 0.4588235 0.4705882 0.5254902\n",
            " [6,] 0.4235294 0.3843137 0.3686275 0.3803922 0.3254902 0.3450980 0.4000000\n",
            " [7,] 0.2666667 0.2901961 0.3960784 0.4117647 0.2549020 0.2274510 0.2470588\n",
            " [8,] 0.1607843 0.2823529 0.7098039 0.8196078 0.4901961 0.2666667 0.2509804\n",
            " [9,] 0.1725490 0.3372549 0.7960784 0.8509804 0.6352941 0.3921569 0.3019608\n",
            "[10,] 0.3098039 0.4039216 0.5960784 0.5803922 0.5529412 0.4745098 0.3960784\n",
            "[11,] 0.5372549 0.5450980 0.5803922 0.5254902 0.5411765 0.5254902 0.5490196\n",
            "[12,] 0.6627451 0.5960784 0.6313725 0.5803922 0.6941176 0.6313725 0.7647059\n",
            "[13,] 0.8274510 0.7411765 0.8039216 0.8117647 0.8352941 0.7490196 0.7803922\n",
            "[14,] 0.9372549 0.8588235 0.8941176 0.8823529 0.8392157 0.8470588 0.8235294\n",
            "[15,] 0.8745098 0.9490196 0.9568627 0.9333333 0.9450980 0.8901961 0.8823529\n",
            "[16,] 0.7686275 0.9686275 1.0000000 1.0000000 0.9607843 0.9254902 0.9019608\n",
            "[17,] 0.8627451 0.9529412 0.9607843 0.9372549 0.9176471 0.9058824 0.7647059\n",
            "[18,] 0.9019608 0.9137255 0.8784314 0.7882353 0.7215686 0.7098039 0.7450980\n",
            "[19,] 0.7333333 0.7058824 0.6509804 0.5725490 0.5843137 0.6156863 0.7215686\n",
            "[20,] 0.4823529 0.5294118 0.4980392 0.5921569 0.6470588 0.5176471 0.5921569\n",
            "[21,] 0.5019608 0.5764706 0.5843137 0.6745098 0.5764706 0.5019608 0.5529412\n",
            "[22,] 0.5960784 0.5607843 0.5960784 0.6000000 0.5568627 0.5529412 0.5294118\n",
            "[23,] 0.5921569 0.5686275 0.5725490 0.5843137 0.6000000 0.5843137 0.5647059\n",
            "[24,] 0.5843137 0.5450980 0.5647059 0.5372549 0.5921569 0.6078431 0.5960784\n",
            "[25,] 0.7529412 0.7058824 0.5725490 0.4941176 0.5529412 0.6117647 0.6000000\n",
            "[26,] 0.6196078 0.6588235 0.5843137 0.5294118 0.5098039 0.5176471 0.5019608\n",
            "[27,] 0.5607843 0.5098039 0.5019608 0.5921569 0.5960784 0.5294118 0.5450980\n",
            "[28,] 0.5372549 0.4980392 0.4941176 0.5450980 0.6000000 0.5843137 0.5490196\n",
            "[29,] 0.5137255 0.4941176 0.4980392 0.5411765 0.5882353 0.6039216 0.5843137\n",
            "[30,] 0.5372549 0.5333333 0.5372549 0.5803922 0.5960784 0.5882353 0.6078431\n",
            "[31,] 0.5450980 0.5490196 0.5607843 0.5450980 0.5411765 0.5607843 0.5725490\n",
            "[32,] 0.6000000 0.6235294 0.6352941 0.5843137 0.5490196 0.5803922 0.6313725\n",
            "          [,22]     [,23]     [,24]     [,25]     [,26]     [,27]     [,28]\n",
            " [1,] 0.5215686 0.5333333 0.5450980 0.5960784 0.6392157 0.6588235 0.6235294\n",
            " [2,] 0.4784314 0.5137255 0.4862745 0.4745098 0.5137255 0.5176471 0.5215686\n",
            " [3,] 0.5137255 0.5450980 0.4980392 0.4941176 0.4980392 0.5098039 0.5568627\n",
            " [4,] 0.5372549 0.5333333 0.5137255 0.4862745 0.5098039 0.5176471 0.5294118\n",
            " [5,] 0.5490196 0.5137255 0.5529412 0.5294118 0.4980392 0.4745098 0.4666667\n",
            " [6,] 0.3803922 0.3450980 0.4627451 0.5490196 0.5333333 0.4705882 0.4196078\n",
            " [7,] 0.3058824 0.5333333 0.4784314 0.5450980 0.5921569 0.5058824 0.4235294\n",
            " [8,] 0.3215686 0.4823529 0.4392157 0.5294118 0.5921569 0.5372549 0.4470588\n",
            " [9,] 0.2941176 0.2901961 0.2980392 0.4196078 0.5294118 0.5294118 0.5058824\n",
            "[10,] 0.3764706 0.3372549 0.2941176 0.3960784 0.5333333 0.5333333 0.5254902\n",
            "[11,] 0.6862745 0.5568627 0.4000000 0.4235294 0.5294118 0.5137255 0.5215686\n",
            "[12,] 0.8196078 0.7411765 0.4901961 0.4235294 0.5490196 0.5372549 0.5176471\n",
            "[13,] 0.7372549 0.6313725 0.5098039 0.4862745 0.5137255 0.5098039 0.5137255\n",
            "[14,] 0.7843137 0.7411765 0.6823529 0.6313725 0.5450980 0.5254902 0.4941176\n",
            "[15,] 0.9215686 0.8588235 0.8784314 0.8431373 0.6117647 0.5019608 0.5058824\n",
            "[16,] 0.8431373 0.9058824 0.9803922 0.9450980 0.6196078 0.4901961 0.4941176\n",
            "[17,] 0.5882353 0.8156863 0.9803922 0.8901961 0.6392157 0.5686275 0.5607843\n",
            "[18,] 0.6666667 0.7019608 0.9058824 0.8745098 0.6352941 0.5725490 0.5490196\n",
            "[19,] 0.8470588 0.8313725 0.9254902 0.9254902 0.6509804 0.5333333 0.5254902\n",
            "[20,] 0.7921569 0.9411765 0.9411765 0.8705882 0.6117647 0.4666667 0.4705882\n",
            "[21,] 0.6784314 0.7921569 0.7450980 0.7764706 0.5960784 0.3921569 0.4274510\n",
            "[22,] 0.5333333 0.5803922 0.5529412 0.5529412 0.5411765 0.4352941 0.4352941\n",
            "[23,] 0.5647059 0.5686275 0.5607843 0.5058824 0.4823529 0.4862745 0.4431373\n",
            "[24,] 0.5490196 0.4196078 0.3568627 0.3294118 0.4117647 0.5176471 0.4627451\n",
            "[25,] 0.4509804 0.3019608 0.3098039 0.3647059 0.4941176 0.5215686 0.4666667\n",
            "[26,] 0.4980392 0.5294118 0.5607843 0.5450980 0.5333333 0.4980392 0.4745098\n",
            "[27,] 0.6078431 0.6313725 0.6039216 0.6039216 0.5607843 0.5098039 0.5176471\n",
            "[28,] 0.5294118 0.5764706 0.5803922 0.5843137 0.5843137 0.5372549 0.5607843\n",
            "[29,] 0.4862745 0.4941176 0.5529412 0.5686275 0.5764706 0.4980392 0.4470588\n",
            "[30,] 0.5411765 0.4705882 0.5019608 0.5568627 0.5294118 0.3529412 0.1960784\n",
            "[31,] 0.5294118 0.4588235 0.4392157 0.4784314 0.4078431 0.2274510 0.1333333\n",
            "[32,] 0.5647059 0.4392157 0.4666667 0.5098039 0.4705882 0.3607843 0.4039216\n",
            "          [,29]     [,30]     [,31]     [,32]\n",
            " [1,] 0.6196078 0.6196078 0.5960784 0.5803922\n",
            " [2,] 0.5215686 0.4823529 0.4666667 0.4784314\n",
            " [3,] 0.5098039 0.4627451 0.4705882 0.4274510\n",
            " [4,] 0.5098039 0.4901961 0.4745098 0.3686275\n",
            " [5,] 0.4039216 0.3411765 0.2941176 0.2627451\n",
            " [6,] 0.3450980 0.2627451 0.1372549 0.1254902\n",
            " [7,] 0.3725490 0.3764706 0.3490196 0.2588235\n",
            " [8,] 0.4117647 0.3960784 0.4941176 0.4000000\n",
            " [9,] 0.4980392 0.4666667 0.4901961 0.5254902\n",
            "[10,] 0.5215686 0.5176471 0.5019608 0.5215686\n",
            "[11,] 0.5411765 0.5333333 0.5098039 0.5254902\n",
            "[12,] 0.5333333 0.5215686 0.5176471 0.5215686\n",
            "[13,] 0.5254902 0.5294118 0.5333333 0.5215686\n",
            "[14,] 0.5137255 0.5568627 0.5333333 0.5411765\n",
            "[15,] 0.5137255 0.5215686 0.5019608 0.5098039\n",
            "[16,] 0.4862745 0.4901961 0.4941176 0.4862745\n",
            "[17,] 0.5490196 0.5333333 0.4745098 0.4470588\n",
            "[18,] 0.5450980 0.5686275 0.5568627 0.5019608\n",
            "[19,] 0.5098039 0.4980392 0.5372549 0.5921569\n",
            "[20,] 0.4392157 0.3921569 0.3882353 0.5490196\n",
            "[21,] 0.4666667 0.4745098 0.4235294 0.5333333\n",
            "[22,] 0.4745098 0.5058824 0.5411765 0.7019608\n",
            "[23,] 0.4235294 0.4431373 0.5803922 0.7803922\n",
            "[24,] 0.3764706 0.4000000 0.6235294 0.7450980\n",
            "[25,] 0.4431373 0.5490196 0.7333333 0.6039216\n",
            "[26,] 0.5294118 0.7411765 0.8274510 0.5333333\n",
            "[27,] 0.6705882 0.8431373 0.7294118 0.4588235\n",
            "[28,] 0.7960784 0.8078431 0.4862745 0.2784314\n",
            "[29,] 0.7294118 0.6784314 0.2196078 0.1294118\n",
            "[30,] 0.5372549 0.6274510 0.2196078 0.2078431\n",
            "[31,] 0.5137255 0.7215686 0.3803922 0.3254902\n",
            "[32,] 0.6666667 0.8470588 0.5921569 0.4823529\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2UJ0_RnvkbQ-",
        "colab_type": "text"
      },
      "source": [
        "## Convert targets/labels to their one-hot encoded equivalent"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzmu19wwkbVV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train <- to_categorical(y_train, 10)\n",
        "y_test <- to_categorical(y_test, 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qthOKREbl0wA",
        "colab_type": "text"
      },
      "source": [
        "## Create a model\n",
        "\n",
        "We specify an input shape of `c(28, 28, 3)`. Note here we specify three channels\n",
        "\n",
        "The architecture is as follows: two convolutional layers, followed by max pooling and dropout, then a fully connected layer with dropout and finally the output fully connected layer. The last layer has 10 units (one for each digit 0 to 9). Note that between the max pooling layer and the first fully connected layer is the flattening operation `layer_flatten()`. This is how we go from convolutional based layers to fully connected ones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lpe8mJN8UNnk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model <- keras_model_sequential() %>%\n",
        "  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu',input_shape = c(32,32,3)) %>% \n",
        "  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>% \n",
        "  layer_max_pooling_2d(pool_size = c(2, 2)) %>% \n",
        "  layer_dropout(rate = 0.25) %>% \n",
        "  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>% \n",
        "  layer_max_pooling_2d(pool_size = c(2, 2)) %>% \n",
        "  layer_flatten() %>%  # We use the flatten function to go from the CNN structure to the MLP\n",
        "  layer_dense(units = 128, activation = 'relu') %>% # the MLP starts here\n",
        "  layer_dropout(rate = 0.5) %>% \n",
        "  layer_dense(units = 10, activation = 'softmax')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ap-4xGUvbqCJ",
        "colab_type": "text"
      },
      "source": [
        "## Compile the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWwCI-yJUylq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model %>% compile(\n",
        "  loss = loss_categorical_crossentropy,\n",
        "  optimizer = optimizer_adadelta(),\n",
        "  metrics = c('accuracy')\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMAd3c8zbsNu",
        "colab_type": "text"
      },
      "source": [
        "## Allow printing to the screen"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKi3HWp7WrUI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print_dot_callback <- callback_lambda(\n",
        "  on_epoch_end = function(epoch, logs) {\n",
        "    if (epoch %% 1 == 0) cat(epoch, '\\n')\n",
        "  }\n",
        ")   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQGEEfa6bvlu",
        "colab_type": "text"
      },
      "source": [
        "## Early stopping to avoid over-fitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ke_QJs3FXIwO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The patience parameter is the amount of epochs to check for improvement.\n",
        "early_stop <- callback_early_stopping(monitor = \"val_loss\", patience = 6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8UxbwiKbyWP",
        "colab_type": "text"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "Training on this dataset can take 3-4 minutes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5gGCDUXBWzL9",
        "colab_type": "code",
        "outputId": "c71e2791-6add-448f-bda5-813c4a06a02b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "history <- model %>% fit(\n",
        "  x_train,\n",
        "  y_train,\n",
        "  epochs = 15,\n",
        "  validation_split = 0.2,\n",
        "  verbose = 0,\n",
        "  batch_size = 8,\n",
        "  callbacks = list(early_stop, print_dot_callback)\n",
        ")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 \n",
            "1 \n",
            "2 \n",
            "3 \n",
            "4 \n",
            "5 \n",
            "6 \n",
            "7 \n",
            "8 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmu7cOgRk2zR",
        "colab_type": "text"
      },
      "source": [
        "### Plot the performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuM3-FyUWtqq",
        "colab_type": "code",
        "outputId": "61a1d8d3-7a99-4710-cbfa-2d81c61d1d59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        }
      },
      "source": [
        "plot(history)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hiDJXAc53A4JElCXw1Jm99HRw2JYZis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\nB2DHUnzPWpDkAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxIn0FARk4wh",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate the performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gbs8pa6GWtyw",
        "colab_type": "code",
        "outputId": "4d4f7fce-8dcb-4b99-cf3f-036c8d1e7022",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        }
      },
      "source": [
        "model %>% evaluate(x_test, y_test)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "$loss\n",
              "[1] 1.389019\n",
              "\n",
              "$acc\n",
              "[1] 0.5672\n"
            ],
            "text/latex": "\\begin{description}\n\\item[\\$loss] 1.38901949729919\n\\item[\\$acc] 0.567200005054474\n\\end{description}\n",
            "text/markdown": "$loss\n:   1.38901949729919\n$acc\n:   0.567200005054474\n\n\n",
            "text/html": [
              "<dl>\n",
              "\t<dt>$loss</dt>\n",
              "\t\t<dd>1.38901949729919</dd>\n",
              "\t<dt>$acc</dt>\n",
              "\t\t<dd>0.567200005054474</dd>\n",
              "</dl>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4_96MBshxlD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}