CCNKERAS.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
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    "colab_type": "code",
    "id": "m8J0a3CaATjU"
   },
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   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation , Dropout, Flatten\n",
    "import tensorflow as tf\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import collections\n",
    "from keras.layers import  MaxPooling2D\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.optimizers import Adam, SGD\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "APa_3AwR4fDc"
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Matrix de Confusion',\n",
    "                          cmap=plt.cm.Blues):\n",
    "\n",
    "    plt.figure(figsize = (10,10))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Original')\n",
    "    plt.xlabel('Predicciones')\n",
    "    \n",
    "    \n",
    "def plot_acc(history, title=\"Model Accuracy\"):\n",
    "    \"\"\"Imprime una gráfica mostrando la accuracy por epoch obtenida en un entrenamiento\"\"\"\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title(title)\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['Train', 'Val'], loc='upper left')\n",
    "    plt.show()\n",
    "    \n",
    "def plot_loss(history, title=\"Model Loss\"):\n",
    "    \"\"\"Imprime una gráfica mostrando la pérdida por epoch obtenida en un entrenamiento\"\"\"\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title(title)\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['Train', 'Val'], loc='upper right')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  \n",
    "  plt.imshow(img, cmap=\"gray\")\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'green'\n",
    "  else:\n",
    "    color = 'red'\n",
    "  \n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XW5I4CFGA0qe"
   },
   "outputs": [],
   "source": [
    "dataset_facial_expression = pd.read_csv(\"../fer2013.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
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    "id": "-_nxTmOWA-Dw",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emotion</th>\n",
       "      <th>pixels</th>\n",
       "      <th>Usage</th>\n",
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       "      <th>0</th>\n",
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       "      <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Training</td>\n",
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       "      <th>4</th>\n",
       "      <td>6</td>\n",
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       "      <td>Training</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   emotion                                             pixels     Usage\n",
       "0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n",
       "1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n",
       "2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n",
       "3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n",
       "4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training"
      ]
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the top five\n",
    "dataset_facial_expression.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
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    "colab_type": "code",
    "id": "1MOZOXENA-Gg",
    "outputId": "2ffda13a-c72b-4fef-ce8f-3b4d6a885fd1"
   },
   "outputs": [
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       "      <th>35882</th>\n",
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       "      <td>50 36 17 22 23 29 33 39 34 37 37 37 39 43 48 5...</td>\n",
       "      <td>PrivateTest</td>\n",
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       "      <td>PrivateTest</td>\n",
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      "text/plain": [
       "       emotion                                             pixels        Usage\n",
       "35882        6  50 36 17 22 23 29 33 39 34 37 37 37 39 43 48 5...  PrivateTest\n",
       "35883        3  178 174 172 173 181 188 191 194 196 199 200 20...  PrivateTest\n",
       "35884        0  17 17 16 23 28 22 19 17 25 26 20 24 31 19 27 9...  PrivateTest\n",
       "35885        3  30 28 28 29 31 30 42 68 79 81 77 67 67 71 63 6...  PrivateTest\n",
       "35886        2  19 13 14 12 13 16 21 33 50 57 71 84 97 108 122...  PrivateTest"
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "# display the last five\n",
    "dataset_facial_expression.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "sI-_4d9qA-KB",
    "outputId": "cc0d44ee-590c-4303-d78a-ea760f865f1c"
   },
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35887.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.323265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.873819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
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       "      <th>max</th>\n",
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      "text/plain": [
       "            emotion\n",
       "count  35887.000000\n",
       "mean       3.323265\n",
       "std        1.873819\n",
       "min        0.000000\n",
       "25%        2.000000\n",
       "50%        3.000000\n",
       "75%        5.000000\n",
       "max        6.000000"
      ]
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   ],
   "source": [
    "# Summary statistics: Numeric data\n",
    "dataset_facial_expression.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "n0U2McBuA-NH",
    "outputId": "b468b849-e3d0-4c0d-d854-5a87958dd9a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset_facial_expression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "DjPfjviZA-PR",
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    {
     "data": {
      "text/plain": [
       "35887"
      ]
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset_facial_expression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
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    "colab_type": "code",
    "id": "ussTIaDsA-R7",
    "outputId": "6d0e9bd9-8671-4375-fba8-55efa5029d0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35887 entries, 0 to 35886\n",
      "Data columns (total 3 columns):\n",
      "emotion    35887 non-null int64\n",
      "pixels     35887 non-null object\n",
      "Usage      35887 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 841.2+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset_facial_expression.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1969
    },
    "colab_type": "code",
    "id": "MknslybKA-Uw",
    "outputId": "69919f07-92c8-4a50-fd5f-b5d890722ddc"
   },
   "outputs": [
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>55 55 55 55 55 54 60 68 54 85 151 163 170 179 ...</td>\n",
       "      <td>Training</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>20 17 19 21 25 38 42 42 46 54 56 62 63 66 82 1...</td>\n",
       "      <td>Training</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>77 78 79 79 78 75 60 55 47 48 58 73 77 79 57 5...</td>\n",
       "      <td>Training</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>85 84 90 121 101 102 133 153 153 169 177 189 1...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>255 254 255 254 254 179 122 107 95 124 149 150...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>30 24 21 23 25 25 49 67 84 103 120 125 130 139...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>39 75 78 58 58 45 49 48 103 156 81 45 41 38 49...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>219 213 206 202 209 217 216 215 219 218 223 23...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>148 144 130 129 119 122 129 131 139 153 140 12...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>4 2 13 41 56 62 67 87 95 62 65 70 80 107 127 1...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>107 107 109 109 109 109 110 101 123 140 144 14...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>14 14 18 28 27 22 21 30 42 61 77 86 88 95 100 ...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>255 255 255 255 255 255 255 255 255 255 255 25...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6</td>\n",
       "      <td>134 124 167 180 197 194 203 210 204 203 209 20...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>219 192 179 148 208 254 192 98 121 103 145 185...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 7 12 23 45 38 ...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>174 51 37 37 38 41 22 25 22 24 35 51 70 83 98 ...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>123 125 124 142 209 226 234 236 231 232 235 22...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>8 9 14 21 26 32 37 46 52 62 72 70 71 73 76 83 ...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>252 250 246 229 182 140 98 72 53 44 67 95 95 8...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>224 227 219 217 215 210 187 177 189 200 206 21...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>162 200 187 180 197 198 196 192 176 152 136 11...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>236 230 225 226 228 209 199 193 196 211 199 19...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>210 210 210 210 211 207 147 103 68 60 47 70 12...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>50 44 74 141 187 187 169 113 80 128 181 172 76...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35857</th>\n",
       "      <td>5</td>\n",
       "      <td>253 255 229 150 89 61 54 60 55 49 61 50 56 45 ...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35858</th>\n",
       "      <td>4</td>\n",
       "      <td>11 11 11 13 20 27 38 41 38 34 20 13 10 39 85 1...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35859</th>\n",
       "      <td>4</td>\n",
       "      <td>11 13 16 27 24 26 89 161 190 197 201 206 210 2...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35860</th>\n",
       "      <td>3</td>\n",
       "      <td>27 42 62 91 112 118 122 123 119 124 129 131 13...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35861</th>\n",
       "      <td>6</td>\n",
       "      <td>233 232 208 188 194 179 177 167 157 180 185 19...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35862</th>\n",
       "      <td>2</td>\n",
       "      <td>73 54 63 76 82 71 67 69 73 72 92 98 117 119 14...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35863</th>\n",
       "      <td>5</td>\n",
       "      <td>196 196 197 197 198 198 198 196 176 148 122 10...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35864</th>\n",
       "      <td>4</td>\n",
       "      <td>68 59 65 78 118 131 137 141 142 135 135 137 13...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35865</th>\n",
       "      <td>3</td>\n",
       "      <td>102 109 109 106 104 107 112 109 116 119 117 12...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35866</th>\n",
       "      <td>6</td>\n",
       "      <td>87 82 59 61 72 102 143 130 90 95 143 173 146 1...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35867</th>\n",
       "      <td>3</td>\n",
       "      <td>198 198 197 196 196 197 196 196 196 195 196 18...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35868</th>\n",
       "      <td>2</td>\n",
       "      <td>204 209 215 218 214 214 214 217 205 175 170 16...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35869</th>\n",
       "      <td>3</td>\n",
       "      <td>217 220 222 223 223 224 225 223 223 225 223 22...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35870</th>\n",
       "      <td>2</td>\n",
       "      <td>6 8 4 5 30 48 61 70 76 79 98 117 130 137 143 1...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35871</th>\n",
       "      <td>6</td>\n",
       "      <td>112 102 98 89 98 133 164 185 180 179 185 169 1...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35872</th>\n",
       "      <td>5</td>\n",
       "      <td>131 159 90 59 10 0 1 1 1 0 1 1 0 0 2 2 5 7 9 1...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35873</th>\n",
       "      <td>4</td>\n",
       "      <td>54 57 77 122 121 76 73 80 58 22 26 27 35 41 66...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35874</th>\n",
       "      <td>5</td>\n",
       "      <td>43 43 51 73 94 97 102 95 99 107 126 144 154 17...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35875</th>\n",
       "      <td>5</td>\n",
       "      <td>248 251 239 144 102 95 82 77 91 138 153 145 14...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35876</th>\n",
       "      <td>6</td>\n",
       "      <td>29 29 27 31 49 56 29 19 22 20 34 43 55 71 85 9...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35877</th>\n",
       "      <td>6</td>\n",
       "      <td>139 143 145 154 159 168 176 181 190 191 195 19...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35878</th>\n",
       "      <td>3</td>\n",
       "      <td>0 39 81 80 104 97 51 64 68 46 41 67 53 68 70 5...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35879</th>\n",
       "      <td>2</td>\n",
       "      <td>0 0 6 16 19 31 47 18 26 19 17 8 15 3 4 2 14 20...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35880</th>\n",
       "      <td>2</td>\n",
       "      <td>164 172 175 171 172 173 178 181 188 192 197 20...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35881</th>\n",
       "      <td>0</td>\n",
       "      <td>181 177 176 156 178 144 136 132 122 107 131 16...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35882</th>\n",
       "      <td>6</td>\n",
       "      <td>50 36 17 22 23 29 33 39 34 37 37 37 39 43 48 5...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35883</th>\n",
       "      <td>3</td>\n",
       "      <td>178 174 172 173 181 188 191 194 196 199 200 20...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35884</th>\n",
       "      <td>0</td>\n",
       "      <td>17 17 16 23 28 22 19 17 25 26 20 24 31 19 27 9...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35885</th>\n",
       "      <td>3</td>\n",
       "      <td>30 28 28 29 31 30 42 68 79 81 77 67 67 71 63 6...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35886</th>\n",
       "      <td>2</td>\n",
       "      <td>19 13 14 12 13 16 21 33 50 57 71 84 97 108 122...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35887 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       emotion                                             pixels        Usage\n",
       "0            0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...     Training\n",
       "1            0  151 150 147 155 148 133 111 140 170 174 182 15...     Training\n",
       "2            2  231 212 156 164 174 138 161 173 182 200 106 38...     Training\n",
       "3            4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...     Training\n",
       "4            6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...     Training\n",
       "5            2  55 55 55 55 55 54 60 68 54 85 151 163 170 179 ...     Training\n",
       "6            4  20 17 19 21 25 38 42 42 46 54 56 62 63 66 82 1...     Training\n",
       "7            3  77 78 79 79 78 75 60 55 47 48 58 73 77 79 57 5...     Training\n",
       "8            3  85 84 90 121 101 102 133 153 153 169 177 189 1...     Training\n",
       "9            2  255 254 255 254 254 179 122 107 95 124 149 150...     Training\n",
       "10           0  30 24 21 23 25 25 49 67 84 103 120 125 130 139...     Training\n",
       "11           6  39 75 78 58 58 45 49 48 103 156 81 45 41 38 49...     Training\n",
       "12           6  219 213 206 202 209 217 216 215 219 218 223 23...     Training\n",
       "13           6  148 144 130 129 119 122 129 131 139 153 140 12...     Training\n",
       "14           3  4 2 13 41 56 62 67 87 95 62 65 70 80 107 127 1...     Training\n",
       "15           5  107 107 109 109 109 109 110 101 123 140 144 14...     Training\n",
       "16           3  14 14 18 28 27 22 21 30 42 61 77 86 88 95 100 ...     Training\n",
       "17           2  255 255 255 255 255 255 255 255 255 255 255 25...     Training\n",
       "18           6  134 124 167 180 197 194 203 210 204 203 209 20...     Training\n",
       "19           4  219 192 179 148 208 254 192 98 121 103 145 185...     Training\n",
       "20           4  1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 7 12 23 45 38 ...     Training\n",
       "21           2  174 51 37 37 38 41 22 25 22 24 35 51 70 83 98 ...     Training\n",
       "22           0  123 125 124 142 209 226 234 236 231 232 235 22...     Training\n",
       "23           0  8 9 14 21 26 32 37 46 52 62 72 70 71 73 76 83 ...     Training\n",
       "24           3  252 250 246 229 182 140 98 72 53 44 67 95 95 8...     Training\n",
       "25           3  224 227 219 217 215 210 187 177 189 200 206 21...     Training\n",
       "26           5  162 200 187 180 197 198 196 192 176 152 136 11...     Training\n",
       "27           0  236 230 225 226 228 209 199 193 196 211 199 19...     Training\n",
       "28           3  210 210 210 210 211 207 147 103 68 60 47 70 12...     Training\n",
       "29           5  50 44 74 141 187 187 169 113 80 128 181 172 76...     Training\n",
       "...        ...                                                ...          ...\n",
       "35857        5  253 255 229 150 89 61 54 60 55 49 61 50 56 45 ...  PrivateTest\n",
       "35858        4  11 11 11 13 20 27 38 41 38 34 20 13 10 39 85 1...  PrivateTest\n",
       "35859        4  11 13 16 27 24 26 89 161 190 197 201 206 210 2...  PrivateTest\n",
       "35860        3  27 42 62 91 112 118 122 123 119 124 129 131 13...  PrivateTest\n",
       "35861        6  233 232 208 188 194 179 177 167 157 180 185 19...  PrivateTest\n",
       "35862        2  73 54 63 76 82 71 67 69 73 72 92 98 117 119 14...  PrivateTest\n",
       "35863        5  196 196 197 197 198 198 198 196 176 148 122 10...  PrivateTest\n",
       "35864        4  68 59 65 78 118 131 137 141 142 135 135 137 13...  PrivateTest\n",
       "35865        3  102 109 109 106 104 107 112 109 116 119 117 12...  PrivateTest\n",
       "35866        6  87 82 59 61 72 102 143 130 90 95 143 173 146 1...  PrivateTest\n",
       "35867        3  198 198 197 196 196 197 196 196 196 195 196 18...  PrivateTest\n",
       "35868        2  204 209 215 218 214 214 214 217 205 175 170 16...  PrivateTest\n",
       "35869        3  217 220 222 223 223 224 225 223 223 225 223 22...  PrivateTest\n",
       "35870        2  6 8 4 5 30 48 61 70 76 79 98 117 130 137 143 1...  PrivateTest\n",
       "35871        6  112 102 98 89 98 133 164 185 180 179 185 169 1...  PrivateTest\n",
       "35872        5  131 159 90 59 10 0 1 1 1 0 1 1 0 0 2 2 5 7 9 1...  PrivateTest\n",
       "35873        4  54 57 77 122 121 76 73 80 58 22 26 27 35 41 66...  PrivateTest\n",
       "35874        5  43 43 51 73 94 97 102 95 99 107 126 144 154 17...  PrivateTest\n",
       "35875        5  248 251 239 144 102 95 82 77 91 138 153 145 14...  PrivateTest\n",
       "35876        6  29 29 27 31 49 56 29 19 22 20 34 43 55 71 85 9...  PrivateTest\n",
       "35877        6  139 143 145 154 159 168 176 181 190 191 195 19...  PrivateTest\n",
       "35878        3  0 39 81 80 104 97 51 64 68 46 41 67 53 68 70 5...  PrivateTest\n",
       "35879        2  0 0 6 16 19 31 47 18 26 19 17 8 15 3 4 2 14 20...  PrivateTest\n",
       "35880        2  164 172 175 171 172 173 178 181 188 192 197 20...  PrivateTest\n",
       "35881        0  181 177 176 156 178 144 136 132 122 107 131 16...  PrivateTest\n",
       "35882        6  50 36 17 22 23 29 33 39 34 37 37 37 39 43 48 5...  PrivateTest\n",
       "35883        3  178 174 172 173 181 188 191 194 196 199 200 20...  PrivateTest\n",
       "35884        0  17 17 16 23 28 22 19 17 25 26 20 24 31 19 27 9...  PrivateTest\n",
       "35885        3  30 28 28 29 31 30 42 68 79 81 77 67 67 71 63 6...  PrivateTest\n",
       "35886        2  19 13 14 12 13 16 21 33 50 57 71 84 97 108 122...  PrivateTest\n",
       "\n",
       "[35887 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modify a sequence in-place by shuffling its contents. \n",
    "# dataset_facial_expression = dataset_facial_expression.sample(frac=1).reset_index(drop=True)\n",
    "dataset_facial_expression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "colab_type": "code",
    "id": "LURpU35vA-XQ",
    "outputId": "81a9a9ce-c64e-412a-c258-8c93b90c54ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lBkpQV0v6WM9CSYcACwZqXNKmwN7Az/NzAbvx0u3pJwH75ul98nPy/N1z/X2AORHxbETcASwCdhho3WZmNropIvqeKU0AfksaOaI7IbUDawLvi4j7+21cOgs4kjQCxReAacAV+SwJSZsBv4uIbSTdAOzZPYSSpL8DOwIz8zK/yuUn5GXO6rGu6cB0gAkTJmw/Z86cBl+C3i28Z+mwlq+asA48sLw5bU2eOK45DQ3CsmXLGDt27Iius9TXHxp7D5oZP7RmHyr1PXD8g9eKY6A/U6ZMWRAR7QPV6/cuvoh4AHirpCnANrl4XkRcMlDDkt4DPBgRCyR1dBf3tpoB5vW3TDXW2cBsgPb29ujo6OhZZVCmzZg3rOWrDpvcxdELG72jv3+LD+poSjuD0dnZyXBfz8Eq9fWHxt6DZsYPrdmHSn0PHP/gteIYaIZGfw/qUuDSQba9C/AvkvYiXctaj3Tn33hJYyKiC9gUuDfXXwJsBiyRNAYYBzxaKe9WXcbMzFZStY0GERFfiYhNI6KNdJPDJRFxECnRdY/jNxU4J0/Pzc/J8y+J1P84Fzgg3+W3Oel3qK6qK24zMytD8875GvdlYI6kI4BrgRNy+QnAKZIWkc6cDgCIiBslnQHcRPqpj0Mj4vmRD9vMzEbSiCSoiOgEOvP07fRyF15EPEMaLb235b8DfKe+CM3MrDQe8NXMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK5ARlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSLUlKEmbSbpU0s2SbpT02Vy+gaSLJN2W/66fyyXpeEmLJF0vabtKW1Nz/dskTa0rZjMzK0edZ1BdwGER8UZgJ+BQSVsBM4CLI2IScHF+DvBuYFJ+TAd+CimhAYcDOwI7AId3JzUzM1t51ZagIuK+iLgmTz8J3AxMBPYBTsrVTgL2zdP7ACdHcgUwXtImwLuAiyLi0Yh4DLgI2LOuuM3MrAyKiPpXIrUBlwHbAHdFxPjKvMciYn1J5wGzIuLyXH4x8GWgA1g7Io7I5f8FLI+Io3qsYzrpzIsJEyZsP2fOnGHFvPCepcNavmrCOvDA8ua0NXniuOY0NAjLli1j7NixI7rOUl9/aOw9aGb80Jp9qNT3wPEPXiuOgf5MmTJlQUS0D1RvzLDW0gBJY4HfAP8ZEU9I6rNqL2XRT/mKBRGzgdkA7e3t0dHRMaR4u02bMW9Yy1cdNrmLoxc256VefFBHU9oZjM7OTob7eg5Wqa8/NPYeNDN+aM0+VOp74PgHrxXHQDPUehefpDVIyenUiDg7Fz+Qu+7Ifx/M5UuAzSqLbwrc20+5mZmtxOq8i0/ACcDNEXFMZdZcoPtOvKnAOZXyg/PdfDsBSyPiPuBC4J2S1s83R7wzl5mZ2Uqszi6+XYAPAwslXZfLvgrMAs6QdAhwF/CBPO98YC9gEfA08BGAiHhU0reBq3O9b0XEozXGbWZmBagtQeWbHfq64LR7L/UDOLSPtk4ETmxedGZmVjqPJGFmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmViQnKDMzK5ITlJmZFckJyszMiuQEZWZmRXKCMjOzIjlBmZlZkZygzMysSE5QZmZWJCcoMzMrkhOUmZkVyQnKzMyK5ARlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhjWh2A1aNtxrymtXXY5C6mNbG9xbP2blpbZrby8hmUmZkVyQnKzMyK5ARlZmZFcoIyM7MiOUGZmVmRnKDMzKxITlBmZlYkJygzMyuSE5SZmRVp1CQoSXtKulXSIkkzWh2PmZnVa1QkKEmrAz8G3g1sBRwoaavWRmVmZnUaFQkK2AFYFBG3R8Q/gDnAPi2OyczMaqSIaHUMA5K0H7BnRHw0P/8wsGNEfKpSZzowPT99A3DriAfat42Ah1sdxDA4/tYb7dvg+FurtPhfExEbD1RptIxmrl7KVsisETEbmD0y4QyOpPkR0d7qOIbK8bfeaN8Gx99aozX+0dLFtwTYrPJ8U+DeFsViZmYjYLQkqKuBSZI2l7QmcAAwt8UxmZlZjUZFF19EdEn6FHAhsDpwYkTc2OKwBqPIrsdBcPytN9q3wfG31qiMf1TcJGFmZque0dLFZ2ZmqxgnKDMzK5IT1CBJep+kkLRlq2MZiKTnJV0n6UZJf5X0eUmr5Xntko4fgRjaJH2opra7t6/70VbHeppF0rIez6dJ+lGr4mlE3tePrjz/gqSZQ2xrvKRPDnHZxZI2GsqyDbb/tXycXJ/3pR0bXK5N0g0lxTSE9ZwvaXwdbQ/XqLhJojAHApeT7iScOdzGJI2JiK7httOH5RGxbV7Pq4BfA+OAwyNiPjC/pvVWtQEfyututhe3rxkkiXRd9oVmtbkSeBZ4v6QjI2K4X/QcD3wS+EnPGZJWj4jnh9n+kEjaGXgPsF1EPJsT4ZqtiKUZMTX6P6Wyv+81vGjr4zOoQZA0FtgFOISUoJDUIalT0lmSbpF0an7jkbRXLrtc0vGSzsvlMyXNlvR74GRJf5K0bWU9f5b0pmbGHhEPkkba+JSSjko876ichVwr6ZWSVpP0k/wJ7rz8KWu/XP/FT7P5TKyzr3aAWcDbctnnmrlNvZG0uqTvS7o6f/L8j1w+VtLFkq6RtFDSPrm8TdLNkn4CXMOK37cbMZLeK+nK/Lr9QdKEXD5T0imSLpF0m6SP5fIOSZdJ+q2kmyT9LL9nh0g6ttLuxyQdM4zQukh3gL3svZO0saTf5Nf6akm7VGL+QqXeDfnsdhbw2rwvfD9vw6WSfg0szHX/R9KCvN9N77nOmmwCPBwRzwJExMMRca+kb+TtuiEfr93H9fZKPRJ/AQ4d4Zj6OvZ6/k+ZJukcSRcoDbJ9eK73sv29u01J60qal7ftBkn7V7b3j/l9uVDSJjVt88tFhB8NPoB/A07I0/8LbAd0AEtJXx5eDfgLsCuwNnA3sHmufxpwXp6eCSwA1snPpwLH5enXA/ObFO+yXsoeAybkuLvjORfYJU+PJZ1Z7wecn7fpn/Jy++U6i4GN8nQ70NlPOy+up4b343nguvz4bS6bDnw9T69FOkvcPMeyXi7fCFhEGqGkDXgB2GkE9p9qvNcBdwE/yvPW56W7aj8KHF3ZV/4KrJPjvht4dX5dnwG2IH314qL8nq0L/B1Yo7KfTh7OPgSsl9/zccAXgJl53q+BXfP0PwM3V2L+QqWNG/Lr3AbcUCnvAJ4iHyO5bIP8d5283IY997ka3pex+f34G+ns7h3VWPL0KcB78/T1lTrfr27TCMT04uvAisfeTFb8nzINuA/YsPJatve2v3e3Cfwr8N+V8nHAGnkf2jiX7U/6mk+tx0r3w2dQg3MgaaBa8t8D8/RVEbEkUtfQdaSdYEvg9oi4I9c5rUdbcyNieZ4+E3iPpDWAfwd+WU/4QO/DRv0ZOEbSZ4DxkboHdgXOjIgXIuJ+4NIG2u6tnTotj4ht8+N9ueydwMGSrgOuJB2gk0jb/V1J1wN/ACaSEjXAnRFxRc2x9ox3W+AblXmbAhdKWgh8Edi6Mu+ciFgeqYvtUtLgyZD2u9sjdY2dRkoWTwGXkPanLUmJauFwgo6IJ4CTgc/0mLUH8KP8Ws8F1stnzYNxVeUYAfiMpL8CV5DOZicNMeyGRcQyYHvSh5uHgNMlTQOm5LPahcBuwNaSxpH27T/mxU8Z4Zj6U/2fAnBRRDySy84mHdPQ9/6+ENhD0vckvS0ilpLGNd0GuCi/z18n7asjwtegGiRpQ9JOuo2kIH1qDdJZxrOVqs+TXtfeEkHVU90TEfG0pItII7R/kPRJp+kkbZHjexB4Y2X9syTNA/YCrpC0B/3H38VL3cNrD9DOSBPw6Yi4cIXCdHBvDGwfEc9JWsxLsT9F6/0QOCYi5krqYMXrmz2/rBgDlP8c+CpwC/CLJsV3HKlLqNreasDOPf4pIqm6f0BlH+nFi6993u49cptP5+6r/pZtmpzkO4HOnJD+A3gT0B4RdyvdGLI2af8akS+P9hLTVPo49rKe+3Ff+0ev+3tE/E3S9qTj98jcXfhb4MaI2HlIGzFMPoNq3H7AyRHxmohoi4jNgDt46VNJT7cAW+ilO8v2H6D9nwPHA1dHxKNNiHcFkjYGfkbqUooe814bEQsj4nukLrEtSTeC/Gu+rtHdJdhtMenTHaRugf7aeRIY7Kfq4bgQ+EQ+G0XS6yWtS+queDAnpynAa0YwpkaMA+7J01N7zNtH0tr5Q1IHaegvgB2Uhv9ajbR/XQ4QEVeSzj4+xMvP3Ick75NnkK6/dvs9UP1Fge7rqItJ3d9I2o7UxQoD7wvjgMdyctoS2KkZsQ9E0hskVc/UtuWlX0N4WOna834AEfE4sFRS93F/0AjGdCd9HHt9+D+SNpC0DrAvqYejv3W+Gng6In4FHEV6D28FNla6aQNJa0jaup9mmspnUI07kHSRt+o3wCdIff4riIjlSrfUXiDpYeCq/hqPiAWSnqB5n3gB1smn5WuQPnmdAvR2wfw/8z/t54GbgN8BzwG7k/qu/0bqLlua638TOEHSV3N5f+28AHTlbptfRsSx1OvnpC7Wa/JF7YdIB+epwLmS5pO6YW+pOY7BmgmcKekeUvfW5pV5VwHzSNd5vh3pYvnrSdc7ZwGTgctIn3a7nQFsGxGPNTHGo6kkJFKX349zt+mYHMPHScdFdzfr1aT9h4h4ROkGoBtI+8a8Hu1fAHw8t3cr6XUYCWOBHyrdat1Fuj45HXic1O21mJc+FAB8BDhR0tOkD0QjGdMb6f3Y683lpGP+dcCvI2J+5QNzbyYD35f0Aun4/0RE/EPp5qjjc/fmGNLZ9IgMNeehjmokaWxELMv/KH8M3NbXP+j86aUT2DIKuc25Ev+GpH+Su+TrUTZCctfSsog4qkd5B+lGhPf0sdx5wLERcXHtQVpxcpd2e1R+M280chdfvT6WP0XeSOq++H+9VZJ0MOnT0NdKSU7ZeTn+P5E+uTs5FU7py7B/I92Q4eRko5rPoMzMrEg+gzIzsyI5QZmZWZGcoMzMrEhOUGZNpJePsD6jCW2uMCK8RmgkerNW800SZk0kaVlEjG1ymx30c0u52crKZ1BmI0BpxOjvSvqLpPmStssjQ/9d0sdzHSmN8n2D0ojr3aOPrDAivFYciX4DpRHAr5d0hfIo+EqjW5+oNNL+7UrjI6I+Rqw2K5FHkjBrru7RO7odGRGn5+m7I2JnpZ/D+CXpp1vWJn1P7mfA+0lD2ryZNLr01ZIuA2ZQOYPKZ1TdvglcGxH7StqNNKhr95BDWwJTSMML3Srpp8CewL0RsXdua1wzN96smZygzJqrvx9RnJv/LgTGRsSTwJOSnslD2uwKnJYHCX1A0h+BtwBP9LO+XcljskXEJZI2rCSdeZF+T+hZSQ+SRm9fCBwl6Xukn0H50zC21axW7uIzGzndo96/wIoj4L9AYyPg96a3ZbovLL9slP2I+BtpsNGFpBGrv9FzYbNSOEGZleMyYH+lXwXeGHg7aQzE/kYBv4w8onbu+ns4/35Tr/oYsdqsSO7iM2uuntegLoiIRm81/y2wM+kXdAP4UkTcL+kRKiPCA9dWlpkJ/CKPAP40L/+pjp5eNmJ1g7GZjTjfZm5mZkVyF5+ZmRXJCcrMzIrkBGVmZkVygjIzsyI5QZmZWZGcoMzMrEhOUGZmVqT/D1SAHyj9t5agAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfb4cab8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emotion</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Happy</td>\n",
       "      <td>8989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>6198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sad</td>\n",
       "      <td>6077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fear</td>\n",
       "      <td>5121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Angry</td>\n",
       "      <td>4953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Surprise</td>\n",
       "      <td>4002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disgust</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    emotion  count\n",
       "3     Happy   8989\n",
       "6   Neutral   6198\n",
       "4       Sad   6077\n",
       "2      Fear   5121\n",
       "0     Angry   4953\n",
       "5  Surprise   4002\n",
       "1   Disgust    547"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delais_emotion_label = (\"Happy\" , \"Neutral\", \"Sad\", \"Fear\" , \"Angry\", \"Surprise\" ,\"Disgust\")\n",
    "delais_emotion = pd.DataFrame(data=dataset_facial_expression[\"emotion\"].value_counts())\n",
    "\n",
    "delais_emotion[\"count\"] = delais_emotion[\"emotion\"]\n",
    "delais_emotion[\"emotion\"] = delais_emotion_label\n",
    "delais_emotion\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.bar(delais_emotion[\"emotion\"], delais_emotion[\"count\"] )\n",
    "ax.set_title(\"Data Exploration\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_xlabel(\"Emotions\")\n",
    "ax.grid()\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "delais_emotion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vbfip0RfBM7M"
   },
   "outputs": [],
   "source": [
    "y = np.zeros((35887, 48, 48))\n",
    "#35887\n",
    "for i in range(35887):\n",
    "    imagen = dataset_facial_expression[\"pixels\"][0+i]\n",
    "    imagen_list_pixel = imagen.split(\" \")\n",
    "    imagen_array_pixel = np.asarray(imagen_list_pixel, dtype=float)\n",
    "    imagen_rezise_pixel = imagen_array_pixel.reshape(-1,48,48)    \n",
    "    y[i] = y[i]+imagen_rezise_pixel[0]\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "vpxQe7YDBPX9",
    "outputId": "abbda9ea-34cc-44c5-b157-4e70a1ab6076"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Labels = Counter({3: 7215, 6: 4965, 4: 4830, 2: 4097, 0: 3996, 5: 3171, 1: 436})\n",
      "Test Labels = Counter({3: 895, 4: 653, 6: 607, 2: 496, 0: 467, 5: 415, 1: 55})\n",
      "Validation Labels = Counter({3: 879, 6: 626, 4: 594, 2: 528, 0: 490, 5: 415, 1: 55})\n"
     ]
    }
   ],
   "source": [
    "train_labels = np.array(dataset_facial_expression[\"emotion\"][:28710])\n",
    "print(\"Train Labels = \"+ str(collections.Counter(train_labels)))\n",
    "  \n",
    "test_labels = np.array( dataset_facial_expression[\"emotion\"][28711:32299])  \n",
    "print(\"Test Labels = \"+ str(collections.Counter(test_labels)))  \n",
    "\n",
    "validation_labels = np.array( dataset_facial_expression[\"emotion\"][32300:])  \n",
    "print(\"Validation Labels = \"+ str(collections.Counter(validation_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sRdyd8IxBUpu"
   },
   "outputs": [],
   "source": [
    "train_images = y[:28710]\n",
    "train_labels = np.array(dataset_facial_expression[\"emotion\"][:28710])\n",
    "test_images = y[28711:32299]\n",
    "test_images2= y[28711:32299]\n",
    "test_labels = np.array( dataset_facial_expression[\"emotion\"][28711:32299])\n",
    "test_labels2 = np.array( dataset_facial_expression[\"emotion\"][28711:32299])\n",
    "validation_images = y[32300:]\n",
    "validation_labels = np.array( dataset_facial_expression[\"emotion\"][32300:])\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R6OpseAKBnI9"
   },
   "outputs": [],
   "source": [
    "train_images = train_images.astype(\"float32\")\n",
    "test_images = test_images.astype(\"float32\")\n",
    "train_images /= 255\n",
    "test_images /= 255\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 589
    },
    "colab_type": "code",
    "id": "yZV5AKva2n0H",
    "outputId": "5cfb78fd-3bac-4e47-e776-3282669eb2a3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfb132102b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = [\"Angry\", \"Disgust\", \"Fear\", \"Happy\", \"Sad\", \"Surprise\", \"Neutral\"]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=\"gray\")\n",
    "    plt.xlabel(class_names[train_labels[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 918
    },
    "colab_type": "code",
    "id": "wTUVeVCB2uhh",
    "outputId": "91169c60-62c3-4566-e661-7c128ffd421a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_7 (Conv2D)            (None, 48, 48, 32)        320       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 48, 48, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 46, 46, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 46, 46, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 23, 23, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 23, 23, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 23, 23, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 23, 23, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 21, 21, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 21, 21, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 10, 10, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 10, 10, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 10, 10, 256)       147712    \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 10, 10, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1024)              4195328   \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 7)                 7175      \n",
      "=================================================================\n",
      "Total params: 5,005,287\n",
      "Trainable params: 5,005,287\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "input_shape = (48, 48, 1)\n",
    "batch_size = 128\n",
    "num_classes = 7\n",
    "epochs = 70\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "train_labels = keras.utils.to_categorical(train_labels, num_classes)\n",
    "test_labels = keras.utils.to_categorical(test_labels, num_classes)\n",
    "validation_labels = keras.utils.to_categorical(validation_labels, num_classes)\n",
    "\n",
    "\n",
    "train_images = train_images.reshape(28710 , 2304)\n",
    "test_images = test_images.reshape(3588 , 2304)\n",
    "\n",
    "\n",
    "train_images = train_images.reshape(train_images.shape[0], 48, 48, 1)\n",
    "test_images = test_images.reshape(test_images.shape[0], 48, 48, 1)\n",
    "input_shape = (48, 48, 1)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(64, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv2D(256, (3, 3), padding='same')) \n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(256, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1024))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "    \n",
    "model.summary()    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1358
    },
    "colab_type": "code",
    "id": "EBMmAqAo40C_",
    "outputId": "4001148c-f3b9-42d6-cd8f-7abc709d3422"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/70\n",
      " 89/897 [=>............................] - ETA: 59:28 - loss: 1.8432 - acc: 0.2445  "
     ]
    }
   ],
   "source": [
    "MAP_CHARACTERS = {0:\"Angry\", 1:\"Disgust\", 2:\"Fear\", 3:\"Happy\", 4:\"Sad\", 5:\"Surprise\", 6:\"Neutral\"}\n",
    "opt = SGD(lr=0.009, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "    \n",
    "model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])\n",
    "    \n",
    "datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "        featurewise_center=False,  \n",
    "        samplewise_center=False,  \n",
    "        featurewise_std_normalization=False,  \n",
    "        samplewise_std_normalization=False,  \n",
    "        zca_whitening=False,  \n",
    "        rotation_range=10,  \n",
    "        width_shift_range=0.1,  \n",
    "        height_shift_range=0.1, \n",
    "        horizontal_flip=True,  \n",
    "        vertical_flip=False)  \n",
    "    \n",
    "datagen.fit(train_images)\n",
    "    \n",
    "history = model.fit_generator(datagen.flow(train_images,train_labels, batch_size=32),\n",
    "                        steps_per_epoch=len(train_images) / 32, epochs=epochs,validation_data=(test_images, test_labels))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "score = model.evaluate(test_images, test_labels, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "score = model.evaluate(test_images,test_labels, verbose=0)\n",
    "print('\\nKeras CNN #3B - accuracy:', score[1])\n",
    "print('')\n",
    "y_pred = model.predict(test_images)\n",
    "print('\\n', metrics.classification_report(np.where(test_labels > 0)[1], np.argmax(y_pred, axis=1), target_names=list(MAP_CHARACTERS.values())), sep='') \n",
    "Y_pred_classes = np.argmax(y_pred,axis = 1) \n",
    "Y_true = np.argmax(test_labels,axis = 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 571
    },
    "colab_type": "code",
    "id": "P1WMJmm4gKzQ",
    "outputId": "6d14f6b1-8d57-4b3c-80c8-495e9686f0f0"
   },
   "outputs": [],
   "source": [
    "plt.subplot(1,1,1)\n",
    "plot_acc(history)\n",
    "plt.subplot(1,1,1)\n",
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 729
    },
    "colab_type": "code",
    "id": "dp1OU4Zs8V4S",
    "outputId": "bfe94a7c-e410-4643-9c85-045ec7014ba3"
   },
   "outputs": [],
   "source": [
    "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
    "plot_confusion_matrix(confusion_mtx, classes = list(MAP_CHARACTERS.values()))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ppkHR46x9dXM"
   },
   "outputs": [],
   "source": [
    "model.save(\"/content/gdrive/My Drive/Colab Notebooks/model.h5\")\n",
    "model.save_weights('/content/gdrive/My Drive/Colab Notebooks/my_model_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oVJ7KA1N9eRa"
   },
   "outputs": [],
   "source": [
    "num_rows = 5\n",
    "num_cols = 5\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, y_pred, test_labels2, test_images2)"
   ]
  }
 ],
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