Sign language classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "actyP_4V4Da1"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Importing basic libraries required for the project"
      ],
      "metadata": {
        "id": "o3mPIihYMk-c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ceW_inIOMdYT"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "tf.random.set_seed(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mounting google drive to download a dataset from there"
      ],
      "metadata": {
        "id": "J2Zy4dlH2N5F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-Bq1l_4N_vt",
        "outputId": "c5944859-e232-4b40-b29c-aefd88287733"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set = pd.read_csv('gdrive/My Drive/MNIST Sign Language Dataset/sign_mnist_train/sign_mnist_train.csv')\n",
        "test_set = pd.read_csv('gdrive/My Drive/MNIST Sign Language Dataset/sign_mnist_test/sign_mnist_test.csv')"
      ],
      "metadata": {
        "id": "i5X8H_VWj_GW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset depiction"
      ],
      "metadata": {
        "id": "9aX4hOxJ2Uwr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "1rIXyKy9pHRB",
        "outputId": "31afdec9-a1ed-4bc6-aa55-48b213e1cfa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
              "0      3     107     118     127     134     139     143     146     150   \n",
              "1      6     155     157     156     156     156     157     156     158   \n",
              "2      2     187     188     188     187     187     186     187     188   \n",
              "3      2     211     211     212     212     211     210     211     210   \n",
              "4     13     164     167     170     172     176     179     180     184   \n",
              "\n",
              "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
              "0     153  ...       207       207       207       207       206       206   \n",
              "1     158  ...        69       149       128        87        94       163   \n",
              "2     187  ...       202       201       200       199       198       199   \n",
              "3     210  ...       235       234       233       231       230       226   \n",
              "4     185  ...        92       105       105       108       133       163   \n",
              "\n",
              "   pixel781  pixel782  pixel783  pixel784  \n",
              "0       206       204       203       202  \n",
              "1       175       103       135       149  \n",
              "2       198       195       194       195  \n",
              "3       225       222       229       163  \n",
              "4       157       163       164       179  \n",
              "\n",
              "[5 rows x 785 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b94a5b77-8f40-43b5-b616-ad39ca4075d8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>label</th>\n",
              "      <th>pixel1</th>\n",
              "      <th>pixel2</th>\n",
              "      <th>pixel3</th>\n",
              "      <th>pixel4</th>\n",
              "      <th>pixel5</th>\n",
              "      <th>pixel6</th>\n",
              "      <th>pixel7</th>\n",
              "      <th>pixel8</th>\n",
              "      <th>pixel9</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
              "      <th>pixel778</th>\n",
              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
              "      <th>pixel781</th>\n",
              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>107</td>\n",
              "      <td>118</td>\n",
              "      <td>127</td>\n",
              "      <td>134</td>\n",
              "      <td>139</td>\n",
              "      <td>143</td>\n",
              "      <td>146</td>\n",
              "      <td>150</td>\n",
              "      <td>153</td>\n",
              "      <td>...</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>155</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
              "      <td>158</td>\n",
              "      <td>158</td>\n",
              "      <td>...</td>\n",
              "      <td>69</td>\n",
              "      <td>149</td>\n",
              "      <td>128</td>\n",
              "      <td>87</td>\n",
              "      <td>94</td>\n",
              "      <td>163</td>\n",
              "      <td>175</td>\n",
              "      <td>103</td>\n",
              "      <td>135</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>187</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>187</td>\n",
              "      <td>187</td>\n",
              "      <td>186</td>\n",
              "      <td>187</td>\n",
              "      <td>188</td>\n",
              "      <td>187</td>\n",
              "      <td>...</td>\n",
              "      <td>202</td>\n",
              "      <td>201</td>\n",
              "      <td>200</td>\n",
              "      <td>199</td>\n",
              "      <td>198</td>\n",
              "      <td>199</td>\n",
              "      <td>198</td>\n",
              "      <td>195</td>\n",
              "      <td>194</td>\n",
              "      <td>195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>211</td>\n",
              "      <td>211</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>211</td>\n",
              "      <td>210</td>\n",
              "      <td>211</td>\n",
              "      <td>210</td>\n",
              "      <td>210</td>\n",
              "      <td>...</td>\n",
              "      <td>235</td>\n",
              "      <td>234</td>\n",
              "      <td>233</td>\n",
              "      <td>231</td>\n",
              "      <td>230</td>\n",
              "      <td>226</td>\n",
              "      <td>225</td>\n",
              "      <td>222</td>\n",
              "      <td>229</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13</td>\n",
              "      <td>164</td>\n",
              "      <td>167</td>\n",
              "      <td>170</td>\n",
              "      <td>172</td>\n",
              "      <td>176</td>\n",
              "      <td>179</td>\n",
              "      <td>180</td>\n",
              "      <td>184</td>\n",
              "      <td>185</td>\n",
              "      <td>...</td>\n",
              "      <td>92</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>108</td>\n",
              "      <td>133</td>\n",
              "      <td>163</td>\n",
              "      <td>157</td>\n",
              "      <td>163</td>\n",
              "      <td>164</td>\n",
              "      <td>179</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b94a5b77-8f40-43b5-b616-ad39ca4075d8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b94a5b77-8f40-43b5-b616-ad39ca4075d8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b94a5b77-8f40-43b5-b616-ad39ca4075d8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "4LYac-uppcUv",
        "outputId": "c0202d08-ce12-40e5-d557-b72b9baf6250"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
              "0      6     149     149     150     150     150     151     151     150   \n",
              "1      5     126     128     131     132     133     134     135     135   \n",
              "2     10      85      88      92      96     105     123     135     143   \n",
              "3      0     203     205     207     206     207     209     210     209   \n",
              "4      3     188     191     193     195     199     201     202     203   \n",
              "\n",
              "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
              "0     151  ...       138       148       127        89        82        96   \n",
              "1     136  ...        47       104       194       183       186       184   \n",
              "2     147  ...        68       166       242       227       230       227   \n",
              "3     210  ...       154       248       247       248       253       236   \n",
              "4     203  ...        26        40        64        48        29        46   \n",
              "\n",
              "   pixel781  pixel782  pixel783  pixel784  \n",
              "0       106       112       120       107  \n",
              "1       184       184       182       180  \n",
              "2       226       225       224       222  \n",
              "3       230       240       253       255  \n",
              "4        49        46        46        53  \n",
              "\n",
              "[5 rows x 785 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d5452ac-ea44-4e2b-802f-45f28ec9d0fc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>label</th>\n",
              "      <th>pixel1</th>\n",
              "      <th>pixel2</th>\n",
              "      <th>pixel3</th>\n",
              "      <th>pixel4</th>\n",
              "      <th>pixel5</th>\n",
              "      <th>pixel6</th>\n",
              "      <th>pixel7</th>\n",
              "      <th>pixel8</th>\n",
              "      <th>pixel9</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
              "      <th>pixel778</th>\n",
              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
              "      <th>pixel781</th>\n",
              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>149</td>\n",
              "      <td>149</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>151</td>\n",
              "      <td>151</td>\n",
              "      <td>150</td>\n",
              "      <td>151</td>\n",
              "      <td>...</td>\n",
              "      <td>138</td>\n",
              "      <td>148</td>\n",
              "      <td>127</td>\n",
              "      <td>89</td>\n",
              "      <td>82</td>\n",
              "      <td>96</td>\n",
              "      <td>106</td>\n",
              "      <td>112</td>\n",
              "      <td>120</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>126</td>\n",
              "      <td>128</td>\n",
              "      <td>131</td>\n",
              "      <td>132</td>\n",
              "      <td>133</td>\n",
              "      <td>134</td>\n",
              "      <td>135</td>\n",
              "      <td>135</td>\n",
              "      <td>136</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>104</td>\n",
              "      <td>194</td>\n",
              "      <td>183</td>\n",
              "      <td>186</td>\n",
              "      <td>184</td>\n",
              "      <td>184</td>\n",
              "      <td>184</td>\n",
              "      <td>182</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>85</td>\n",
              "      <td>88</td>\n",
              "      <td>92</td>\n",
              "      <td>96</td>\n",
              "      <td>105</td>\n",
              "      <td>123</td>\n",
              "      <td>135</td>\n",
              "      <td>143</td>\n",
              "      <td>147</td>\n",
              "      <td>...</td>\n",
              "      <td>68</td>\n",
              "      <td>166</td>\n",
              "      <td>242</td>\n",
              "      <td>227</td>\n",
              "      <td>230</td>\n",
              "      <td>227</td>\n",
              "      <td>226</td>\n",
              "      <td>225</td>\n",
              "      <td>224</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>203</td>\n",
              "      <td>205</td>\n",
              "      <td>207</td>\n",
              "      <td>206</td>\n",
              "      <td>207</td>\n",
              "      <td>209</td>\n",
              "      <td>210</td>\n",
              "      <td>209</td>\n",
              "      <td>210</td>\n",
              "      <td>...</td>\n",
              "      <td>154</td>\n",
              "      <td>248</td>\n",
              "      <td>247</td>\n",
              "      <td>248</td>\n",
              "      <td>253</td>\n",
              "      <td>236</td>\n",
              "      <td>230</td>\n",
              "      <td>240</td>\n",
              "      <td>253</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>188</td>\n",
              "      <td>191</td>\n",
              "      <td>193</td>\n",
              "      <td>195</td>\n",
              "      <td>199</td>\n",
              "      <td>201</td>\n",
              "      <td>202</td>\n",
              "      <td>203</td>\n",
              "      <td>203</td>\n",
              "      <td>...</td>\n",
              "      <td>26</td>\n",
              "      <td>40</td>\n",
              "      <td>64</td>\n",
              "      <td>48</td>\n",
              "      <td>29</td>\n",
              "      <td>46</td>\n",
              "      <td>49</td>\n",
              "      <td>46</td>\n",
              "      <td>46</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d5452ac-ea44-4e2b-802f-45f28ec9d0fc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d5452ac-ea44-4e2b-802f-45f28ec9d0fc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d5452ac-ea44-4e2b-802f-45f28ec9d0fc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('The shape of the training set:' + str(train_set.shape))\n",
        "print('The shape of the test (hold_out) set:' + str(test_set.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AshC0-gOpgvu",
        "outputId": "d39cc8c0-88f1-4e0c-d215-8ed4d9927b5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of the training set:(27455, 785)\n",
            "The shape of the test (hold_out) set:(7172, 785)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Checking if the datasets have missing values in any of the columns"
      ],
      "metadata": {
        "id": "HkLGV5pwsIAX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(sum(train_set.isna().sum()))\n",
        "print(sum(test_set.isna().sum()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9KHYmfipy6W",
        "outputId": "4b537c05-dfd5-4301-8267-08524fd48d01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Separating input images from labels for both sets"
      ],
      "metadata": {
        "id": "obYGe33Zsbnl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train_set.drop(labels = ['label'], axis=1)\n",
        "y_train = train_set['label']\n",
        "X_test = test_set.drop(labels = ['label'], axis = 1)\n",
        "y_test = test_set['label']\n",
        "print('X_train shape:' + str(X_train.shape) + '\\ny_train shape:' + str(y_train.shape) + '\\nX_test shape:' + str(X_test.shape) + '\\ny_test shape:'\n",
        "+ str(y_test.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aeA86-SIsSK7",
        "outputId": "91923550-cd82-48da-ead9-75ca3977f8ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape:(27455, 784)\n",
            "y_train shape:(27455,)\n",
            "X_test shape:(7172, 784)\n",
            "y_test shape:(7172,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_labels = y_train.unique()\n",
        "unique_labels = np.sort(unique_labels)\n",
        "unique_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aAhcSRzut-gQ",
        "outputId": "4b917582-b81c-4bb5-fd02-76de0827a898"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8, 10, 11, 12, 13, 14, 15, 16, 17,\n",
              "       18, 19, 20, 21, 22, 23, 24])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dE4FN-yJvNO",
        "outputId": "7045b9b9-c271-43bc-b938-453814e128b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reshaping the images"
      ],
      "metadata": {
        "id": "LU9Eq7amuSYo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = int((X_train.shape[1])**0.5)\n",
        "print(f'Shape of the images: {d} x {d}')\n",
        "X_train = X_train.values.reshape(-1,d,d,1)\n",
        "X_test = X_test.values.reshape(-1,d,d,1)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print('Number of unique classes:', y_train.nunique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfy9ZQFNt5sx",
        "outputId": "dde7bb55-6fc1-44a8-b9e6-5931f32a7e7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the images: 28 x 28\n",
            "(27455, 28, 28, 1)\n",
            "(7172, 28, 28, 1)\n",
            "Number of unique classes: 24\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plor the distribution of classes"
      ],
      "metadata": {
        "id": "-jWzWyT91dWC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "sns.countplot(y_train, palette = sns.color_palette())\n",
        "plt.xlabel(\"Classes\", fontsize = 20)\n",
        "plt.ylabel(\"Count\", fontsize = 20)\n",
        "plt.title(\"Images per Class for the Training Set\", fontsize=25)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "D4RrROZjz9NZ",
        "outputId": "ef84f094-b89b-42c6-e523-1931e380fd18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "sns.countplot(y_test, palette = sns.color_palette())\n",
        "plt.xlabel(\"Classes\", fontsize = 20)\n",
        "plt.ylabel(\"Count\", fontsize = 20)\n",
        "plt.title(\"Images per Class for the Test Set\", fontsize=25)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "zR6uAeiQ33ek",
        "outputId": "10996a64-71b7-45cf-ea57-ee871e1a7599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Viewing the images"
      ],
      "metadata": {
        "id": "U_TZdS693g_m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training images"
      ],
      "metadata": {
        "id": "actyP_4V4Da1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 7),\n",
        "                        subplot_kw={'xticks': [], 'yticks': []})\n",
        "\n",
        "for i in range(6):\n",
        "    plt.subplot(2,3,i+1)\n",
        "    plt.imshow(X_train[i].squeeze(), cmap='gray')\n",
        "    plt.title('Train label:'+ str(y_train[i]))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "ch2oLXnO28mn",
        "outputId": "c2df8d78-d477-4bf9-956f-3e26c41ec505"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test images"
      ],
      "metadata": {
        "id": "YSY_td0o7wSm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 7),\n",
        "                        subplot_kw={'xticks': [], 'yticks': []})\n",
        "\n",
        "for i in range(6):\n",
        "    plt.subplot(2,3,i+1)\n",
        "    plt.imshow(X_test[i].squeeze(), cmap='gray')\n",
        "    plt.title('Test label:'+ str(y_train[i]))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "KiNxrPBF71Dp",
        "outputId": "4bd8c3fb-8007-42ae-c203-c882aeca61a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Label Binarizer (One-hot encoding)"
      ],
      "metadata": {
        "id": "oFgt_yyyb0pU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelBinarizer\n",
        "label_binarizer = LabelBinarizer()\n",
        "y_train = label_binarizer.fit_transform(y_train)\n",
        "y_test = label_binarizer.fit_transform(y_test)"
      ],
      "metadata": {
        "id": "OSBFbIgDb1KP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[5]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GIwJUCBjGl2u",
        "outputId": "4ac2cd34-f509-4438-9e87-19753b42f5e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaling the images"
      ],
      "metadata": {
        "id": "Dy-3wyUmtuXj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train / 255.0\n",
        "X_test = X_test / 255.0"
      ],
      "metadata": {
        "id": "zonLSujrtrBg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data augmentation"
      ],
      "metadata": {
        "id": "1k99zqL2KIdf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train_1,  X_val, y_train_1, y_val = train_test_split(X_train, y_train, test_size = 0.25, random_state = 42, shuffle = True, stratify = y_train)"
      ],
      "metadata": {
        "id": "44wNptgMLr9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy import expand_dims\n",
        "from tensorflow.keras.utils import img_to_array\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "        rotation_range=10,  # randomly rotate images \n",
        "        zoom_range = 0.1, # randomly zoom image \n",
        "        width_shift_range=0.1,  # randomly shift images horizontally \n",
        "        height_shift_range=0.1,  # randomly shift images vertically \n",
        "        ) \n",
        "\n",
        "data = img_to_array(X_train_1[2]*255)\n",
        "samples = expand_dims(data, 0)\n",
        "it = datagen.flow(samples, batch_size=1)\n",
        "\n",
        "# plotting the augmented images\n",
        "fig, ax = plt.subplots(figsize=(10, 10))\n",
        "for i in range(9):\n",
        "\tplt.subplot(330 + 1 + i)\n",
        "\tbatch = it.next() \n",
        "\timage = batch[0].astype('uint8')\n",
        "\tplt.imshow(image.squeeze(), cmap='gray')\n",
        "plt.show()\n",
        "\n",
        "datagen.fit(X_train_1)"
      ],
      "metadata": {
        "id": "WGNcEI1pKJIt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "87dbeb57-93ae-40e9-cde2-4e35bb7d1500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building a CNN model"
      ],
      "metadata": {
        "id": "yWaoQ8sPPHvO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(hp):  \n",
        "  model = keras.Sequential([\n",
        "    keras.layers.Conv2D(\n",
        "        filters=hp.Int('conv_1_filter', min_value=75, max_value=200, step=25),\n",
        "        kernel_size=(3,3),\n",
        "        activation='relu',\n",
        "        \n",
        "        input_shape=(28,28,1)\n",
        "    ),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPool2D(pool_size=(2,2),strides=2,padding='same'),\n",
        "    keras.layers.Conv2D(\n",
        "        filters=hp.Int('conv_2_filter', min_value=50, max_value=125, step=25),\n",
        "        kernel_size=(3,3),\n",
        "        activation='relu',\n",
        "    ),\n",
        "    \n",
        "    keras.layers.Dropout(\n",
        "        rate = hp.Choice('drop_1_rate', values = [0.1,0.5])\n",
        "    ),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPool2D(pool_size=(2,2),strides=2,padding='same'),\n",
        "    \n",
        "    \n",
        "    keras.layers.Conv2D(\n",
        "        filters=hp.Int('conv_3_filter', min_value=25, max_value=75, step=25),\n",
        "        kernel_size=(3,3),\n",
        "        activation='relu',\n",
        "    ),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPool2D(pool_size=(2,2),strides=2,padding='same'),  \n",
        "    keras.layers.Flatten(),\n",
        "    keras.layers.Dense(\n",
        "        units=hp.Int('dense_1_units', min_value=128, max_value=1024, step=32),\n",
        "        activation='relu'\n",
        "    ),\n",
        "    keras.layers.Dropout(\n",
        "        rate = hp.Choice('drop_2_rate', values = [0.1,0.3])\n",
        "    ),\n",
        "    keras.layers.Dense(24, activation='softmax')\n",
        "  ])\n",
        "  \n",
        "  model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3])),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "  \n",
        "  return model"
      ],
      "metadata": {
        "id": "Dg-XCP5YPK-D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Installing keras-tuner"
      ],
      "metadata": {
        "id": "NeJQADAD2jNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install keras-tuner"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_ekrZPEYceE",
        "outputId": "7b96fe63-cdbf-4f99-c4cf-dee629bfd742"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting keras-tuner\n",
            "  Downloading keras_tuner-1.1.3-py3-none-any.whl (135 kB)\n",
            "\u001b[K     |████████████████████████████████| 135 kB 4.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (21.3)\n",
            "Collecting kt-legacy\n",
            "  Downloading kt_legacy-1.0.4-py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (2.23.0)\n",
            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (2.9.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.21.6)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (7.9.0)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (4.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (2.0.10)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (2.6.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (0.2.0)\n",
            "Collecting jedi>=0.10\n",
            "  Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 32.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (57.4.0)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (5.1.1)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from jedi>=0.10->ipython->keras-tuner) (0.8.3)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->keras-tuner) (0.2.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->keras-tuner) (1.15.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->keras-tuner) (3.0.9)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->ipython->keras-tuner) (0.7.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (2022.9.24)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (2.10)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.50.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (0.6.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.8.1)\n",
            "Requirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (3.19.6)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (3.4.1)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (0.4.6)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (0.38.4)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (2.14.1)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.3.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner) (5.2.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner) (4.9)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras-tuner) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->keras-tuner) (4.13.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->keras-tuner) (4.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->keras-tuner) (3.10.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->keras-tuner) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras-tuner) (3.2.2)\n",
            "Installing collected packages: jedi, kt-legacy, keras-tuner\n",
            "Successfully installed jedi-0.18.2 keras-tuner-1.1.3 kt-legacy-1.0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Applying Random Search optimization"
      ],
      "metadata": {
        "id": "Dqo6I9CS2pnG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from kerastuner import RandomSearch\n",
        "tuner_search=RandomSearch(build_model,\n",
        "                          objective='val_accuracy',\n",
        "                          max_trials=5,directory='output',project_name=\"SignLanguageDetection\")"
      ],
      "metadata": {
        "id": "m5JIzzEle1PM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "np.random.seed(123)\n",
        "random.seed(123)\n",
        "tf.random.set_seed(20)\n",
        "\n",
        "tuner_search.search(X_train_1,y_train_1,epochs=5,validation_data = (X_val, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vE9MuUcrjU-D",
        "outputId": "890baef0-6889-4ed4-bc64-9cbf446f6d04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 5 Complete [00h 00m 25s]\n",
            "val_accuracy: 0.9803321957588196\n",
            "\n",
            "Best val_accuracy So Far: 1.0\n",
            "Total elapsed time: 00h 02m 51s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuner_search.search_space_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPKOXjDMkmvU",
        "outputId": "fb97ab0b-4e17-4ce3-d7d5-eb8123cdefc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Search space summary\n",
            "Default search space size: 7\n",
            "conv_1_filter (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 75, 'max_value': 200, 'step': 25, 'sampling': None}\n",
            "conv_2_filter (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 50, 'max_value': 125, 'step': 25, 'sampling': None}\n",
            "drop_1_rate (Choice)\n",
            "{'default': 0.1, 'conditions': [], 'values': [0.1, 0.5], 'ordered': True}\n",
            "conv_3_filter (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 25, 'max_value': 75, 'step': 25, 'sampling': None}\n",
            "dense_1_units (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 128, 'max_value': 1024, 'step': 32, 'sampling': None}\n",
            "drop_2_rate (Choice)\n",
            "{'default': 0.1, 'conditions': [], 'values': [0.1, 0.3], 'ordered': True}\n",
            "learning_rate (Choice)\n",
            "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001], 'ordered': True}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tuner_search.get_best_models(num_models = 1)[0]\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WoUDz6Iyl0TO",
        "outputId": "9eba40bf-42f0-4b9e-d53f-f2c1e4b01434"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.sequential.Sequential at 0x7f70e2525210>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuner_search.results_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVIvS2vSmp5r",
        "outputId": "c42c22c3-655d-476a-e587-935ea1cfa3c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results summary\n",
            "Results in output/SignLanguageDetection\n",
            "Showing 10 best trials\n",
            "<keras_tuner.engine.objective.Objective object at 0x7f70e255f510>\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "conv_1_filter: 100\n",
            "conv_2_filter: 75\n",
            "drop_1_rate: 0.1\n",
            "conv_3_filter: 50\n",
            "dense_1_units: 768\n",
            "drop_2_rate: 0.3\n",
            "learning_rate: 0.001\n",
            "Score: 1.0\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "conv_1_filter: 125\n",
            "conv_2_filter: 100\n",
            "drop_1_rate: 0.5\n",
            "conv_3_filter: 50\n",
            "dense_1_units: 320\n",
            "drop_2_rate: 0.3\n",
            "learning_rate: 0.01\n",
            "Score: 0.9855769276618958\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "conv_1_filter: 100\n",
            "conv_2_filter: 50\n",
            "drop_1_rate: 0.5\n",
            "conv_3_filter: 50\n",
            "dense_1_units: 864\n",
            "drop_2_rate: 0.3\n",
            "learning_rate: 0.01\n",
            "Score: 0.9803321957588196\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "conv_1_filter: 175\n",
            "conv_2_filter: 75\n",
            "drop_1_rate: 0.1\n",
            "conv_3_filter: 25\n",
            "dense_1_units: 480\n",
            "drop_2_rate: 0.3\n",
            "learning_rate: 0.01\n",
            "Score: 0.9733391404151917\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "conv_1_filter: 150\n",
            "conv_2_filter: 100\n",
            "drop_1_rate: 0.5\n",
            "conv_3_filter: 25\n",
            "dense_1_units: 608\n",
            "drop_2_rate: 0.3\n",
            "learning_rate: 0.001\n",
            "Score: 0.9526515007019043\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introducing the callback, which reduces the learning rate after 3 epochs if it is not changing"
      ],
      "metadata": {
        "id": "u-GR6Cb12vZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ReduceLROnPlateau\n",
        "lr_reduction = ReduceLROnPlateau(monitor='val_accuracy', patience = 3, verbose=1,factor=0.5, min_lr=0.00001)"
      ],
      "metadata": {
        "id": "f4DR5m4Nnibh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the model with the best parameters"
      ],
      "metadata": {
        "id": "XnccbdnS27DS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "np.random.seed(123)\n",
        "random.seed(123)\n",
        "tf.random.set_seed(20)\n",
        "\n",
        "history = model.fit(datagen.flow(X_train,y_train, batch_size = 128) \n",
        "                    ,epochs = 20\n",
        "                    , validation_data = (X_test, y_test)\n",
        "                    , callbacks = [lr_reduction])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olF5nyjynigf",
        "outputId": "bda62e17-bd22-4196-c24d-8064f1dd1804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "215/215 [==============================] - 11s 49ms/step - loss: 0.1994 - accuracy: 0.9412 - val_loss: 0.1208 - val_accuracy: 0.9573 - lr: 0.0010\n",
            "Epoch 2/20\n",
            "215/215 [==============================] - 10s 48ms/step - loss: 0.0538 - accuracy: 0.9826 - val_loss: 0.1506 - val_accuracy: 0.9552 - lr: 0.0010\n",
            "Epoch 3/20\n",
            "215/215 [==============================] - 10s 48ms/step - loss: 0.0286 - accuracy: 0.9912 - val_loss: 0.0722 - val_accuracy: 0.9808 - lr: 0.0010\n",
            "Epoch 4/20\n",
            "215/215 [==============================] - 11s 50ms/step - loss: 0.0242 - accuracy: 0.9920 - val_loss: 0.0118 - val_accuracy: 0.9954 - lr: 0.0010\n",
            "Epoch 5/20\n",
            "215/215 [==============================] - 11s 50ms/step - loss: 0.0165 - accuracy: 0.9949 - val_loss: 0.0090 - val_accuracy: 0.9978 - lr: 0.0010\n",
            "Epoch 6/20\n",
            "215/215 [==============================] - 10s 47ms/step - loss: 0.0156 - accuracy: 0.9949 - val_loss: 0.0837 - val_accuracy: 0.9735 - lr: 0.0010\n",
            "Epoch 7/20\n",
            "215/215 [==============================] - 11s 49ms/step - loss: 0.0154 - accuracy: 0.9952 - val_loss: 1.5064 - val_accuracy: 0.7634 - lr: 0.0010\n",
            "Epoch 8/20\n",
            "214/215 [============================>.] - ETA: 0s - loss: 0.0184 - accuracy: 0.9937\n",
            "Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "215/215 [==============================] - 11s 49ms/step - loss: 0.0184 - accuracy: 0.9937 - val_loss: 0.1313 - val_accuracy: 0.9511 - lr: 0.0010\n",
            "Epoch 9/20\n",
            "215/215 [==============================] - 10s 48ms/step - loss: 0.0074 - accuracy: 0.9980 - val_loss: 0.0012 - val_accuracy: 0.9999 - lr: 5.0000e-04\n",
            "Epoch 10/20\n",
            "215/215 [==============================] - 10s 44ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 0.0040 - val_accuracy: 0.9987 - lr: 5.0000e-04\n",
            "Epoch 11/20\n",
            "215/215 [==============================] - 10s 47ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 8.6178e-04 - val_accuracy: 1.0000 - lr: 5.0000e-04\n",
            "Epoch 12/20\n",
            "215/215 [==============================] - 10s 47ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 9.3039e-04 - val_accuracy: 1.0000 - lr: 5.0000e-04\n",
            "Epoch 13/20\n",
            "215/215 [==============================] - 10s 45ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 0.0157 - val_accuracy: 0.9941 - lr: 5.0000e-04\n",
            "Epoch 14/20\n",
            "215/215 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9982\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "215/215 [==============================] - 10s 47ms/step - loss: 0.0050 - accuracy: 0.9982 - val_loss: 0.0854 - val_accuracy: 0.9781 - lr: 5.0000e-04\n",
            "Epoch 15/20\n",
            "215/215 [==============================] - 10s 48ms/step - loss: 0.0025 - accuracy: 0.9994 - val_loss: 0.0086 - val_accuracy: 0.9962 - lr: 2.5000e-04\n",
            "Epoch 16/20\n",
            "215/215 [==============================] - 10s 48ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.0027 - val_accuracy: 0.9986 - lr: 2.5000e-04\n",
            "Epoch 17/20\n",
            "214/215 [============================>.] - ETA: 0s - loss: 0.0027 - accuracy: 0.9994\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "215/215 [==============================] - 10s 48ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 4.3001e-04 - val_accuracy: 1.0000 - lr: 2.5000e-04\n",
            "Epoch 18/20\n",
            "215/215 [==============================] - 10s 45ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 7.2388e-05 - val_accuracy: 1.0000 - lr: 1.2500e-04\n",
            "Epoch 19/20\n",
            "215/215 [==============================] - 10s 45ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 6.0846e-05 - val_accuracy: 1.0000 - lr: 1.2500e-04\n",
            "Epoch 20/20\n",
            "215/215 [==============================] - ETA: 0s - loss: 0.0015 - accuracy: 0.9997\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "215/215 [==============================] - 10s 48ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 2.0191e-04 - val_accuracy: 1.0000 - lr: 1.2500e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0c15e3a9pVys",
        "outputId": "0f521bb4-4f81-41aa-f047-810f0ecb8179"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 100)       1000      \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 26, 26, 100)      400       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 100)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 75)        67575     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 11, 11, 75)        0         \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 11, 11, 75)       300       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 6, 6, 75)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 4, 4, 50)          33800     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 4, 4, 50)         200       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 50)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 200)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 768)               154368    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 768)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 24)                18456     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 276,099\n",
            "Trainable params: 275,649\n",
            "Non-trainable params: 450\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(\n",
        "    model,\n",
        "    to_file=\"model.png\",\n",
        "    show_shapes=False,\n",
        "    show_dtype=False,\n",
        "    show_layer_names=True,\n",
        "    rankdir=\"TB\",\n",
        "    expand_nested=False,\n",
        "    dpi=96,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IJc7u4KR7HBH",
        "outputId": "b223cd53-28cd-46ca-e37c-d3e5b1e3ef99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZR0NkiNY7pl9",
        "outputId": "12ff0b57-8152-4024-e65e-fe6f6102e144"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "225/225 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train and Test accuracy/loss graphs\n"
      ],
      "metadata": {
        "id": "6_TD_hQgdZH1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = [i for i in range(20)]\n",
        "fig , ax = plt.subplots(1,2)\n",
        "train_acc = history.history['accuracy']\n",
        "train_loss = history.history['loss']\n",
        "val_acc = history.history['val_accuracy']\n",
        "val_loss = history.history['val_loss']\n",
        "fig.set_size_inches(16,9)\n",
        "\n",
        "ax[0].plot(epochs , train_acc , 'go-' , label = 'Training Accuracy')\n",
        "ax[0].plot(epochs , val_acc , 'ro-' , label = 'Test Accuracy')\n",
        "ax[0].legend()\n",
        "ax[0].set_xlabel(\"Epochs\")\n",
        "ax[0].set_ylabel(\"Accuracy\")\n",
        "\n",
        "ax[1].plot(epochs , train_loss , 'g-o' , label = 'Training Loss')\n",
        "ax[1].plot(epochs , val_loss , 'r-o' , label = 'Test Loss')\n",
        "ax[1].legend()\n",
        "ax[1].set_xlabel(\"Epochs\")\n",
        "ax[1].set_ylabel(\"Loss\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "b3ZM2-1OdYQF",
        "outputId": "bf80559b-58d1-4dcc-cd01-67afa76680aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def predictions_to_labels(pred):\n",
        "    labels =[]\n",
        "    for p in pred:\n",
        "        labels.append(unique_labels[np.argmax(p)])\n",
        "    return labels"
      ],
      "metadata": {
        "id": "Z5STc5PB70Ad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_labels = predictions_to_labels(y_predicted)\n",
        "y_test_labels = predictions_to_labels(y_test)"
      ],
      "metadata": {
        "id": "HbaKsn6V71oV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "accuracy_score(y_test_labels,y_pred_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzoBTjni8qKN",
        "outputId": "b088a80f-354a-4d1f-d610-58ba79da0c1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion Matrix\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "vLR7Cm5wkdZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm= confusion_matrix(y_test_labels, y_pred_labels)\n",
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(cm,annot=True,cmap='twilight_shifted')"
      ],
      "metadata": {
        "id": "JhSBVfZwi7E9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3828a410-6cb9-468f-f3fd-3eda2e356f54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f70e16a58d0>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification report"
      ],
      "metadata": {
        "id": "0VnZk6OTknwt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score, classification_report\n",
        "print(classification_report(y_test_labels, y_pred_labels))\n",
        "print(\"Macro Average Recall = {:.3f}\".format(recall_score(y_test_labels, y_pred_labels, average='macro')))\n",
        "print(\"Macro Average Precision = {:.3f}\".format(precision_score(y_test_labels, y_pred_labels, average='macro')))\n",
        "print(\"Macro Average F1 score = {:.3f}\".format(f1_score(y_test_labels, y_pred_labels, average='macro')))\n",
        "print(\"Macro Average ROC AUC = {:.3f}\".format(roc_auc_score(y_test, y_predicted, multi_class='ovr', average='macro')))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-hbDZAbjz8M",
        "outputId": "a7a99d0e-db72-4fca-fd3d-8f7b1c49bc33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       331\n",
            "           1       1.00      1.00      1.00       432\n",
            "           2       1.00      1.00      1.00       310\n",
            "           3       1.00      1.00      1.00       245\n",
            "           4       1.00      1.00      1.00       498\n",
            "           5       1.00      1.00      1.00       247\n",
            "           6       1.00      1.00      1.00       348\n",
            "           7       1.00      1.00      1.00       436\n",
            "           8       1.00      1.00      1.00       288\n",
            "          10       1.00      1.00      1.00       331\n",
            "          11       1.00      1.00      1.00       209\n",
            "          12       1.00      1.00      1.00       394\n",
            "          13       1.00      1.00      1.00       291\n",
            "          14       1.00      1.00      1.00       246\n",
            "          15       1.00      1.00      1.00       347\n",
            "          16       1.00      1.00      1.00       164\n",
            "          17       1.00      1.00      1.00       144\n",
            "          18       1.00      1.00      1.00       246\n",
            "          19       1.00      1.00      1.00       248\n",
            "          20       1.00      1.00      1.00       266\n",
            "          21       1.00      1.00      1.00       346\n",
            "          22       1.00      1.00      1.00       206\n",
            "          23       1.00      1.00      1.00       267\n",
            "          24       1.00      1.00      1.00       332\n",
            "\n",
            "    accuracy                           1.00      7172\n",
            "   macro avg       1.00      1.00      1.00      7172\n",
            "weighted avg       1.00      1.00      1.00      7172\n",
            "\n",
            "Macro Average Recall = 1.000\n",
            "Macro Average Precision = 1.000\n",
            "Macro Average F1 score = 1.000\n",
            "Macro Average ROC AUC = 1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Machine Learning (ML) Methods"
      ],
      "metadata": {
        "id": "WIClZHqSieGX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier as KNN \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression as LRR\n",
        "from sklearn.ensemble import RandomForestClassifier "
      ],
      "metadata": {
        "id": "2v_Lb8_XlQN1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train_set.drop(labels = ['label'], axis=1)\n",
        "y_train = train_set['label']\n",
        "X_test = test_set.drop(labels = ['label'], axis = 1)\n",
        "y_test = test_set['label']\n",
        "X_train = X_train/255.0\n",
        "X_test = X_test/255.0\n",
        "X_train_1,  X_val, y_train_1, y_val = train_test_split(X_train, y_train, test_size = 0.25, random_state = 42, shuffle = True, stratify = y_train)"
      ],
      "metadata": {
        "id": "FKilLJgIl_Kj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### k-Nearest Neighbors (kNN) Classifier"
      ],
      "metadata": {
        "id": "lAzhJWOryMW4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "np.random.seed(123)\n",
        "random.seed(123)\n",
        "\n",
        "no_of_neighbors = [3,5,7,9,11]\n",
        "for i in no_of_neighbors:\n",
        "  kNN= KNN(n_neighbors=i).fit(X_train_1,y_train_1)\n",
        "  y_predictions_val = kNN.predict(X_val)\n",
        "  print('The accuracy for NN={} on the training data is {:.3f}'.format(i, kNN.score(X_train_1, y_train_1)))\n",
        "  print('The accuracy for NN={} on the validation data is {:.3f}'.format(i, accuracy_score(y_val, y_predictions_val)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYBCUAJ5oh7J",
        "outputId": "db4cbc34-db13-452e-bb4e-09ec002a7e15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy for NN=3 on the training data is 1.000\n",
            "The accuracy for NN=3 on the validation data is 0.998\n",
            "The accuracy for NN=5 on the training data is 0.998\n",
            "The accuracy for NN=5 on the validation data is 0.997\n",
            "The accuracy for NN=7 on the training data is 0.997\n",
            "The accuracy for NN=7 on the validation data is 0.993\n",
            "The accuracy for NN=9 on the training data is 0.994\n",
            "The accuracy for NN=9 on the validation data is 0.990\n",
            "The accuracy for NN=11 on the training data is 0.992\n",
            "The accuracy for NN=11 on the validation data is 0.987\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "np.random.seed(123)\n",
        "random.seed(123)\n",
        "\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score, classification_report\n",
        "kNN_best = KNN(n_neighbors = 3).fit(X_train, y_train)\n",
        "y_test_predictions_kNN = kNN_best.predict(X_test)\n",
        "\n",
        "print(\"Training accuracy = {:.3f}\".format(kNN_best.score(X_train,y_train)))\n",
        "print(\"Test accuracy = {:.3f}\".format(accuracy_score(y_test, y_test_predictions_kNN)))\n",
        "\n",
        "print(\"Macro Average Recall = {:.3f}\".format(recall_score(y_test, y_test_predictions_kNN, average='macro')))\n",
        "print(\"Macro Average Precision = {:.3f}\".format(precision_score(y_test, y_test_predictions_kNN, average='macro')))\n",
        "print(\"Macro Average F1 score = {:.3f}\".format(f1_score(y_test, y_test_predictions_kNN, average='macro')))\n",
        "print(\"Macro Average ROC AUC = {:.3f}\".format(roc_auc_score(y_test, kNN_best.predict_proba(X_test), multi_class='ovr', average='macro')))\n",
        "\n",
        "# Here we set average=None to print these metrics for each class individually (when the class is considered positive)\n",
        "np.set_printoptions(precision=24)\n",
        "print(classification_report(y_test, y_test_predictions_kNN)) # classification_report is used to print them out nicely"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-PC0OKu3_Hh",
        "outputId": "1eb1dc06-2d07-4559-fa0c-ee8bcb4a0fb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy = 1.000\n",
            "Test accuracy = 0.804\n",
            "Macro Average Recall = 0.798\n",
            "Macro Average Precision = 0.810\n",
            "Macro Average F1 score = 0.793\n",
            "Macro Average ROC AUC = 0.923\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      1.00      0.89       331\n",
            "           1       0.95      0.94      0.94       432\n",
            "           2       0.95      1.00      0.97       310\n",
            "           3       0.76      0.95      0.85       245\n",
            "           4       0.78      0.97      0.86       498\n",
            "           5       0.87      0.91      0.89       247\n",
            "           6       0.91      0.94      0.92       348\n",
            "           7       0.95      0.94      0.94       436\n",
            "           8       0.87      0.65      0.75       288\n",
            "          10       0.85      0.59      0.70       331\n",
            "          11       0.93      0.89      0.91       209\n",
            "          12       0.81      0.49      0.61       394\n",
            "          13       0.78      0.59      0.67       291\n",
            "          14       1.00      0.88      0.94       246\n",
            "          15       1.00      1.00      1.00       347\n",
            "          16       0.95      1.00      0.97       164\n",
            "          17       0.32      0.60      0.41       144\n",
            "          18       0.67      0.87      0.76       246\n",
            "          19       0.70      0.70      0.70       248\n",
            "          20       0.42      0.68      0.52       266\n",
            "          21       0.68      0.53      0.59       346\n",
            "          22       0.72      0.68      0.70       206\n",
            "          23       0.80      0.69      0.74       267\n",
            "          24       0.97      0.68      0.80       332\n",
            "\n",
            "    accuracy                           0.80      7172\n",
            "   macro avg       0.81      0.80      0.79      7172\n",
            "weighted avg       0.83      0.80      0.80      7172\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Logistic Regression (LR) Classifier"
      ],
      "metadata": {
        "id": "zzEwuVsTyRmA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "np.random.seed(123)\n",
        "random.seed(123)\n",
        "\n",
        "C = [0.01, 0.1, 1, 10]\n",
        "for i in C:\n",
        "  lrr = LRR(C=i, penalty = 'l2', solver = 'liblinear').fit(X_train_1, y_train_1)\n",
        "  y_predictions_val = lrr.predict(X_val)\n",
        "  print('The accuracy for C={} on the training data is {:.3f}'.format(i, lrr.score(X_train_1, y_train_1)))\n",
        "  print('The accuracy for C={} on the validation data is {:.3f}'.format(i, accuracy_score(y_val, y_predictions_val)))"
      ],
      "metadata": {
        "id": "4N0n1zFaxmPC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af94e06d-6984-4d70-c627-a8b9068fbb0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy for C=0.01 on the training data is 0.816\n",
            "The accuracy for C=0.01 on the validation data is 0.807\n",
            "The accuracy for C=0.1 on the training data is 0.961\n",
            "The accuracy for C=0.1 on the validation data is 0.952\n",
            "The accuracy for C=1 on the training data is 0.999\n",
            "The accuracy for C=1 on the validation data is 0.998\n",
            "The accuracy for C=10 on the training data is 1.000\n",
            "The accuracy for C=10 on the validation data is 0.999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score, classification_report\n",
        "import random\n",
        "import numpy as np\n",
        "np.random.seed(123)\n",
        "random.seed(123)\n",
        "\n",
        "lrr_best = LRR(C= 10, penalty = 'l2', solver = 'liblinear').fit(X_train, y_train)\n",
        "y_test_predictions_lrr = lrr_best.predict(X_test)\n",
        "\n",
        "print(\"Training accuracy = {:.3f}\".format(lrr_best.score(X_train,y_train)))\n",
        "print(\"Test accuracy = {:.3f}\".format(accuracy_score(y_test, y_test_predictions_lrr)))\n",
        "\n",
        "print(\"Macro Average Recall = {:.3f}\".format(recall_score(y_test, y_test_predictions_lrr, average='macro')))\n",
        "print(\"Macro Average Precision = {:.3f}\".format(precision_score(y_test, y_test_predictions_lrr, average='macro')))\n",
        "print(\"Macro Average F1 score = {:.3f}\".format(f1_score(y_test, y_test_predictions_lrr, average='macro')))\n",
        "print(\"Macro Average ROC AUC = {:.3f}\".format(roc_auc_score(y_test, lrr_best.predict_proba(X_test), multi_class='ovr', average='macro')))\n",
        "\n",
        "# Here we set average=None to print these metrics for each class individually (when the class is considered positive)\n",
        "np.set_printoptions(precision=24)\n",
        "print(classification_report(y_test, y_test_predictions_lrr)) # classification_report is used to print them out nicely"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjmsQUfCHaux",
        "outputId": "9cec951a-d0e5-446d-b565-06dffb8afc4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy = 1.000\n",
            "Test accuracy = 0.660\n",
            "Macro Average Recall = 0.648\n",
            "Macro Average Precision = 0.664\n",
            "Macro Average F1 score = 0.641\n",
            "Macro Average ROC AUC = 0.935\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.89      0.85       331\n",
            "           1       1.00      0.82      0.90       432\n",
            "           2       0.93      0.86      0.89       310\n",
            "           3       0.78      0.92      0.84       245\n",
            "           4       0.80      0.88      0.84       498\n",
            "           5       0.61      0.89      0.72       247\n",
            "           6       0.70      0.80      0.75       348\n",
            "           7       0.83      0.71      0.77       436\n",
            "           8       0.70      0.57      0.62       288\n",
            "          10       0.65      0.35      0.46       331\n",
            "          11       0.76      0.90      0.82       209\n",
            "          12       0.67      0.49      0.57       394\n",
            "          13       0.54      0.45      0.49       291\n",
            "          14       1.00      0.58      0.73       246\n",
            "          15       0.79      0.77      0.78       347\n",
            "          16       0.57      0.74      0.65       164\n",
            "          17       0.19      0.43      0.26       144\n",
            "          18       0.37      0.53      0.44       246\n",
            "          19       0.38      0.51      0.43       248\n",
            "          20       0.45      0.54      0.49       266\n",
            "          21       0.60      0.48      0.53       346\n",
            "          22       0.34      0.50      0.40       206\n",
            "          23       0.77      0.39      0.51       267\n",
            "          24       0.70      0.57      0.62       332\n",
            "\n",
            "    accuracy                           0.66      7172\n",
            "   macro avg       0.66      0.65      0.64      7172\n",
            "weighted avg       0.70      0.66      0.66      7172\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest classifier"
      ],
      "metadata": {
        "id": "Kh5ulpkL3OcK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "np.random.seed(123)\n",
        "random.seed(123)\n",
        "\n",
        "no_of_estimators = [50,100,150,200]\n",
        "for i in no_of_estimators:\n",
        "  RF= RandomForestClassifier(n_estimators = i, random_state = 42).fit(X_train_1,y_train_1)\n",
        "  y_predictions_val = RF.predict(X_val)\n",
        "  print('The accuracy for no. of trees={} on the training data is {:.3f}'.format(i, RF.score(X_train_1, y_train_1)))\n",
        "  print('The accuracy for no. of trees={} on the validation data is {:.3f}'.format(i, accuracy_score(y_val, y_predictions_val)))"
      ],
      "metadata": {
        "id": "cUFaIj7h7fp7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5bf9aba-b6ba-464d-ca3d-e473da01b166"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy for no. of trees=50 on the training data is 1.000\n",
            "The accuracy for no. of trees=50 on the validation data is 0.995\n",
            "The accuracy for no. of trees=100 on the training data is 1.000\n",
            "The accuracy for no. of trees=100 on the validation data is 0.997\n",
            "The accuracy for no. of trees=150 on the training data is 1.000\n",
            "The accuracy for no. of trees=150 on the validation data is 0.998\n",
            "The accuracy for no. of trees=200 on the training data is 1.000\n",
            "The accuracy for no. of trees=200 on the validation data is 0.998\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "np.random.seed(123)\n",
        "random.seed(123)\n",
        "\n",
        "rf_best = RandomForestClassifier(n_estimators=150).fit(X_train, y_train)\n",
        "y_test_predictions_rf = rf_best.predict(X_test)\n",
        "\n",
        "print(\"Training accuracy = {:.3f}\".format(rf_best.score(X_train,y_train)))\n",
        "print(\"Test accuracy = {:.3f}\".format(accuracy_score(y_test, y_test_predictions_rf)))\n",
        "\n",
        "print(\"Macro Average Recall = {:.3f}\".format(recall_score(y_test, y_test_predictions_rf, average='macro')))\n",
        "print(\"Macro Average Precision = {:.3f}\".format(precision_score(y_test, y_test_predictions_rf, average='macro')))\n",
        "print(\"Macro Average F1 score = {:.3f}\".format(f1_score(y_test, y_test_predictions_rf, average='macro')))\n",
        "print(\"Macro Average ROC AUC = {:.3f}\".format(roc_auc_score(y_test, rf_best.predict_proba(X_test), multi_class='ovr', average='macro')))\n",
        "\n",
        "# Here we set average=None to print these metrics for each class individually (when the class is considered positive)\n",
        "np.set_printoptions(precision=24)\n",
        "print(classification_report(y_test, y_test_predictions_rf)) # classification_report is used to print them out nicely"
      ],
      "metadata": {
        "id": "y6ystJ_C67VS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e46b247-83c4-4f1a-dba9-1f9794c5a5df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy = 1.000\n",
            "Test accuracy = 0.820\n",
            "Macro Average Recall = 0.814\n",
            "Macro Average Precision = 0.817\n",
            "Macro Average F1 score = 0.806\n",
            "Macro Average ROC AUC = 0.984\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      1.00      0.96       331\n",
            "           1       0.98      0.94      0.96       432\n",
            "           2       0.97      0.99      0.98       310\n",
            "           3       0.94      0.98      0.96       245\n",
            "           4       0.88      0.97      0.92       498\n",
            "           5       0.90      0.92      0.91       247\n",
            "           6       0.92      0.84      0.88       348\n",
            "           7       0.96      0.94      0.95       436\n",
            "           8       0.82      0.77      0.79       288\n",
            "          10       0.77      0.69      0.73       331\n",
            "          11       0.79      1.00      0.89       209\n",
            "          12       0.85      0.65      0.74       394\n",
            "          13       0.80      0.53      0.64       291\n",
            "          14       0.98      0.88      0.93       246\n",
            "          15       0.93      1.00      0.96       347\n",
            "          16       0.93      1.00      0.96       164\n",
            "          17       0.29      0.54      0.38       144\n",
            "          18       0.54      0.83      0.65       246\n",
            "          19       0.59      0.82      0.69       248\n",
            "          20       0.68      0.67      0.68       266\n",
            "          21       0.86      0.55      0.67       346\n",
            "          22       0.53      0.72      0.61       206\n",
            "          23       0.86      0.71      0.78       267\n",
            "          24       0.90      0.60      0.72       332\n",
            "\n",
            "    accuracy                           0.82      7172\n",
            "   macro avg       0.82      0.81      0.81      7172\n",
            "weighted avg       0.84      0.82      0.82      7172\n",
            "\n"
          ]
        }
      ]
    }
  ]
}