a3_q3_WeightPruning.ipynb

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  "metadata": {
    "colab": {
      "name": "a3-q3-WeightPruning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1DzjWpCnJFCS3OV0PlQYvSkoodw7f3MVG",
      "authorship_tag": "ABX9TyOVOUdWazFj8quQfF78GvyO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mehrotrasan16/Keras-Deep-Learning/blob/03-weightpruning-ASLMNIST/a3_q3_WeightPruning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kYaSVKb85Xyl"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cs1XYS7ZAkMU"
      },
      "source": [
        " pip install -q tensorflow-model-optimization\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRTmqtBX3uFY"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import models,layers\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzurVo1b5af6"
      },
      "source": [
        "# Reading and Visualizing Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Xc1a2DV3wNG"
      },
      "source": [
        "train_path = '/content/drive/My Drive/Colab Notebooks/CS581/SignLanguageMNIST/sign_mnist_train'\n",
        "test_path = '/content/drive/My Drive/Colab Notebooks/CS581/SignLanguageMNIST/sign_mnist_test'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYto8j-T4gBz",
        "outputId": "25dc8da2-3525-4da0-8b5b-bec61c857f25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import glob\n",
        "all_train = glob.glob(train_path + \"/*.csv\") \n",
        "all_test = glob.glob(test_path + \"/*.csv\")\n",
        "print(all_train,all_test)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['/content/drive/My Drive/Colab Notebooks/CS581/SignLanguageMNIST/sign_mnist_train/sign_mnist_train.csv'] ['/content/drive/My Drive/Colab Notebooks/CS581/SignLanguageMNIST/sign_mnist_test/sign_mnist_test.csv']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwStBWBr4KNX",
        "outputId": "111297f2-562e-4ee3-d7c7-884e606c8e55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "traindf = pd.read_csv(all_train[0])\n",
        "traindf"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>172</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>170</td>\n",
              "      <td>170</td>\n",
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              "      <td>205</td>\n",
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              "      <td>207</td>\n",
              "      <td>207</td>\n",
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              "      <td>206</td>\n",
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              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
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              "      <td>149</td>\n",
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              "      <td>147</td>\n",
              "      <td>146</td>\n",
              "      <td>144</td>\n",
              "      <td>142</td>\n",
              "      <td>143</td>\n",
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              "      <td>158</td>\n",
              "      <td>159</td>\n",
              "      <td>159</td>\n",
              "      <td>159</td>\n",
              "      <td>160</td>\n",
              "      <td>160</td>\n",
              "      <td>160</td>\n",
              "      <td>160</td>\n",
              "      <td>160</td>\n",
              "      <td>160</td>\n",
              "      <td>160</td>\n",
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              "      <td>100</td>\n",
              "      <td>78</td>\n",
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              "      <td>157</td>\n",
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              "      <td>97</td>\n",
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              "      <td>116</td>\n",
              "      <td>95</td>\n",
              "      <td>79</td>\n",
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              "      <td>86</td>\n",
              "      <td>139</td>\n",
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              "      <td>185</td>\n",
              "      <td>175</td>\n",
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              "      <td>94</td>\n",
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              "      <td>103</td>\n",
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              "      <td>149</td>\n",
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              "      <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>186</td>\n",
              "      <td>185</td>\n",
              "      <td>185</td>\n",
              "      <td>185</td>\n",
              "      <td>184</td>\n",
              "      <td>184</td>\n",
              "      <td>184</td>\n",
              "      <td>181</td>\n",
              "      <td>181</td>\n",
              "      <td>179</td>\n",
              "      <td>179</td>\n",
              "      <td>179</td>\n",
              "      <td>178</td>\n",
              "      <td>178</td>\n",
              "      <td>109</td>\n",
              "      <td>52</td>\n",
              "      <td>66</td>\n",
              "      <td>77</td>\n",
              "      <td>83</td>\n",
              "      <td>188</td>\n",
              "      <td>189</td>\n",
              "      <td>189</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>189</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>187</td>\n",
              "      <td>...</td>\n",
              "      <td>203</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>201</td>\n",
              "      <td>200</td>\n",
              "      <td>200</td>\n",
              "      <td>199</td>\n",
              "      <td>198</td>\n",
              "      <td>196</td>\n",
              "      <td>195</td>\n",
              "      <td>194</td>\n",
              "      <td>193</td>\n",
              "      <td>198</td>\n",
              "      <td>166</td>\n",
              "      <td>132</td>\n",
              "      <td>114</td>\n",
              "      <td>89</td>\n",
              "      <td>74</td>\n",
              "      <td>79</td>\n",
              "      <td>77</td>\n",
              "      <td>74</td>\n",
              "      <td>78</td>\n",
              "      <td>132</td>\n",
              "      <td>188</td>\n",
              "      <td>210</td>\n",
              "      <td>209</td>\n",
              "      <td>206</td>\n",
              "      <td>205</td>\n",
              "      <td>204</td>\n",
              "      <td>203</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",
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              "      <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>211</td>\n",
              "      <td>209</td>\n",
              "      <td>207</td>\n",
              "      <td>208</td>\n",
              "      <td>207</td>\n",
              "      <td>206</td>\n",
              "      <td>203</td>\n",
              "      <td>202</td>\n",
              "      <td>201</td>\n",
              "      <td>200</td>\n",
              "      <td>198</td>\n",
              "      <td>197</td>\n",
              "      <td>195</td>\n",
              "      <td>192</td>\n",
              "      <td>197</td>\n",
              "      <td>171</td>\n",
              "      <td>51</td>\n",
              "      <td>52</td>\n",
              "      <td>54</td>\n",
              "      <td>212</td>\n",
              "      <td>213</td>\n",
              "      <td>215</td>\n",
              "      <td>215</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>213</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>211</td>\n",
              "      <td>211</td>\n",
              "      <td>...</td>\n",
              "      <td>247</td>\n",
              "      <td>242</td>\n",
              "      <td>233</td>\n",
              "      <td>231</td>\n",
              "      <td>230</td>\n",
              "      <td>229</td>\n",
              "      <td>227</td>\n",
              "      <td>225</td>\n",
              "      <td>223</td>\n",
              "      <td>221</td>\n",
              "      <td>220</td>\n",
              "      <td>216</td>\n",
              "      <td>58</td>\n",
              "      <td>51</td>\n",
              "      <td>49</td>\n",
              "      <td>50</td>\n",
              "      <td>57</td>\n",
              "      <td>60</td>\n",
              "      <td>17</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>159</td>\n",
              "      <td>255</td>\n",
              "      <td>237</td>\n",
              "      <td>239</td>\n",
              "      <td>237</td>\n",
              "      <td>236</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",
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              "    <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>186</td>\n",
              "      <td>188</td>\n",
              "      <td>189</td>\n",
              "      <td>189</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "      <td>189</td>\n",
              "      <td>190</td>\n",
              "      <td>190</td>\n",
              "      <td>187</td>\n",
              "      <td>190</td>\n",
              "      <td>192</td>\n",
              "      <td>193</td>\n",
              "      <td>191</td>\n",
              "      <td>191</td>\n",
              "      <td>192</td>\n",
              "      <td>192</td>\n",
              "      <td>194</td>\n",
              "      <td>194</td>\n",
              "      <td>166</td>\n",
              "      <td>169</td>\n",
              "      <td>172</td>\n",
              "      <td>174</td>\n",
              "      <td>177</td>\n",
              "      <td>180</td>\n",
              "      <td>182</td>\n",
              "      <td>185</td>\n",
              "      <td>186</td>\n",
              "      <td>187</td>\n",
              "      <td>190</td>\n",
              "      <td>...</td>\n",
              "      <td>90</td>\n",
              "      <td>77</td>\n",
              "      <td>88</td>\n",
              "      <td>117</td>\n",
              "      <td>123</td>\n",
              "      <td>127</td>\n",
              "      <td>129</td>\n",
              "      <td>134</td>\n",
              "      <td>145</td>\n",
              "      <td>152</td>\n",
              "      <td>156</td>\n",
              "      <td>179</td>\n",
              "      <td>105</td>\n",
              "      <td>106</td>\n",
              "      <td>105</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>175</td>\n",
              "      <td>199</td>\n",
              "      <td>178</td>\n",
              "      <td>152</td>\n",
              "      <td>136</td>\n",
              "      <td>130</td>\n",
              "      <td>136</td>\n",
              "      <td>150</td>\n",
              "      <td>118</td>\n",
              "      <td>92</td>\n",
              "      <td>85</td>\n",
              "      <td>76</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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27450</th>\n",
              "      <td>13</td>\n",
              "      <td>189</td>\n",
              "      <td>189</td>\n",
              "      <td>190</td>\n",
              "      <td>190</td>\n",
              "      <td>192</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>194</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>194</td>\n",
              "      <td>194</td>\n",
              "      <td>195</td>\n",
              "      <td>195</td>\n",
              "      <td>196</td>\n",
              "      <td>194</td>\n",
              "      <td>195</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>197</td>\n",
              "      <td>196</td>\n",
              "      <td>197</td>\n",
              "      <td>197</td>\n",
              "      <td>196</td>\n",
              "      <td>195</td>\n",
              "      <td>195</td>\n",
              "      <td>189</td>\n",
              "      <td>190</td>\n",
              "      <td>192</td>\n",
              "      <td>192</td>\n",
              "      <td>193</td>\n",
              "      <td>194</td>\n",
              "      <td>195</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>193</td>\n",
              "      <td>...</td>\n",
              "      <td>115</td>\n",
              "      <td>92</td>\n",
              "      <td>132</td>\n",
              "      <td>173</td>\n",
              "      <td>118</td>\n",
              "      <td>84</td>\n",
              "      <td>53</td>\n",
              "      <td>135</td>\n",
              "      <td>253</td>\n",
              "      <td>213</td>\n",
              "      <td>219</td>\n",
              "      <td>208</td>\n",
              "      <td>82</td>\n",
              "      <td>82</td>\n",
              "      <td>84</td>\n",
              "      <td>85</td>\n",
              "      <td>85</td>\n",
              "      <td>87</td>\n",
              "      <td>84</td>\n",
              "      <td>87</td>\n",
              "      <td>78</td>\n",
              "      <td>113</td>\n",
              "      <td>216</td>\n",
              "      <td>189</td>\n",
              "      <td>172</td>\n",
              "      <td>175</td>\n",
              "      <td>155</td>\n",
              "      <td>131</td>\n",
              "      <td>116</td>\n",
              "      <td>93</td>\n",
              "      <td>132</td>\n",
              "      <td>165</td>\n",
              "      <td>99</td>\n",
              "      <td>77</td>\n",
              "      <td>52</td>\n",
              "      <td>200</td>\n",
              "      <td>234</td>\n",
              "      <td>200</td>\n",
              "      <td>222</td>\n",
              "      <td>225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27451</th>\n",
              "      <td>23</td>\n",
              "      <td>151</td>\n",
              "      <td>154</td>\n",
              "      <td>157</td>\n",
              "      <td>158</td>\n",
              "      <td>160</td>\n",
              "      <td>161</td>\n",
              "      <td>163</td>\n",
              "      <td>164</td>\n",
              "      <td>166</td>\n",
              "      <td>167</td>\n",
              "      <td>169</td>\n",
              "      <td>169</td>\n",
              "      <td>169</td>\n",
              "      <td>169</td>\n",
              "      <td>169</td>\n",
              "      <td>169</td>\n",
              "      <td>169</td>\n",
              "      <td>169</td>\n",
              "      <td>169</td>\n",
              "      <td>167</td>\n",
              "      <td>167</td>\n",
              "      <td>167</td>\n",
              "      <td>166</td>\n",
              "      <td>165</td>\n",
              "      <td>164</td>\n",
              "      <td>164</td>\n",
              "      <td>163</td>\n",
              "      <td>162</td>\n",
              "      <td>153</td>\n",
              "      <td>156</td>\n",
              "      <td>159</td>\n",
              "      <td>160</td>\n",
              "      <td>162</td>\n",
              "      <td>163</td>\n",
              "      <td>164</td>\n",
              "      <td>165</td>\n",
              "      <td>167</td>\n",
              "      <td>169</td>\n",
              "      <td>172</td>\n",
              "      <td>...</td>\n",
              "      <td>199</td>\n",
              "      <td>200</td>\n",
              "      <td>199</td>\n",
              "      <td>199</td>\n",
              "      <td>198</td>\n",
              "      <td>199</td>\n",
              "      <td>198</td>\n",
              "      <td>197</td>\n",
              "      <td>196</td>\n",
              "      <td>195</td>\n",
              "      <td>195</td>\n",
              "      <td>194</td>\n",
              "      <td>66</td>\n",
              "      <td>67</td>\n",
              "      <td>65</td>\n",
              "      <td>62</td>\n",
              "      <td>61</td>\n",
              "      <td>41</td>\n",
              "      <td>32</td>\n",
              "      <td>37</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>38</td>\n",
              "      <td>187</td>\n",
              "      <td>202</td>\n",
              "      <td>199</td>\n",
              "      <td>199</td>\n",
              "      <td>199</td>\n",
              "      <td>199</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>196</td>\n",
              "      <td>195</td>\n",
              "      <td>195</td>\n",
              "      <td>195</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27452</th>\n",
              "      <td>18</td>\n",
              "      <td>174</td>\n",
              "      <td>174</td>\n",
              "      <td>174</td>\n",
              "      <td>174</td>\n",
              "      <td>174</td>\n",
              "      <td>175</td>\n",
              "      <td>175</td>\n",
              "      <td>174</td>\n",
              "      <td>173</td>\n",
              "      <td>173</td>\n",
              "      <td>173</td>\n",
              "      <td>172</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>170</td>\n",
              "      <td>169</td>\n",
              "      <td>168</td>\n",
              "      <td>166</td>\n",
              "      <td>166</td>\n",
              "      <td>165</td>\n",
              "      <td>165</td>\n",
              "      <td>163</td>\n",
              "      <td>162</td>\n",
              "      <td>161</td>\n",
              "      <td>160</td>\n",
              "      <td>158</td>\n",
              "      <td>156</td>\n",
              "      <td>178</td>\n",
              "      <td>178</td>\n",
              "      <td>177</td>\n",
              "      <td>176</td>\n",
              "      <td>177</td>\n",
              "      <td>177</td>\n",
              "      <td>177</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>175</td>\n",
              "      <td>...</td>\n",
              "      <td>110</td>\n",
              "      <td>85</td>\n",
              "      <td>90</td>\n",
              "      <td>166</td>\n",
              "      <td>205</td>\n",
              "      <td>207</td>\n",
              "      <td>205</td>\n",
              "      <td>203</td>\n",
              "      <td>202</td>\n",
              "      <td>201</td>\n",
              "      <td>200</td>\n",
              "      <td>198</td>\n",
              "      <td>217</td>\n",
              "      <td>217</td>\n",
              "      <td>217</td>\n",
              "      <td>218</td>\n",
              "      <td>218</td>\n",
              "      <td>211</td>\n",
              "      <td>203</td>\n",
              "      <td>200</td>\n",
              "      <td>189</td>\n",
              "      <td>171</td>\n",
              "      <td>157</td>\n",
              "      <td>145</td>\n",
              "      <td>136</td>\n",
              "      <td>129</td>\n",
              "      <td>128</td>\n",
              "      <td>125</td>\n",
              "      <td>101</td>\n",
              "      <td>77</td>\n",
              "      <td>121</td>\n",
              "      <td>196</td>\n",
              "      <td>209</td>\n",
              "      <td>208</td>\n",
              "      <td>206</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>202</td>\n",
              "      <td>200</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27453</th>\n",
              "      <td>17</td>\n",
              "      <td>177</td>\n",
              "      <td>181</td>\n",
              "      <td>184</td>\n",
              "      <td>185</td>\n",
              "      <td>187</td>\n",
              "      <td>189</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "      <td>191</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "      <td>193</td>\n",
              "      <td>192</td>\n",
              "      <td>191</td>\n",
              "      <td>191</td>\n",
              "      <td>191</td>\n",
              "      <td>191</td>\n",
              "      <td>190</td>\n",
              "      <td>190</td>\n",
              "      <td>189</td>\n",
              "      <td>187</td>\n",
              "      <td>186</td>\n",
              "      <td>184</td>\n",
              "      <td>183</td>\n",
              "      <td>182</td>\n",
              "      <td>181</td>\n",
              "      <td>179</td>\n",
              "      <td>177</td>\n",
              "      <td>179</td>\n",
              "      <td>182</td>\n",
              "      <td>185</td>\n",
              "      <td>187</td>\n",
              "      <td>189</td>\n",
              "      <td>190</td>\n",
              "      <td>192</td>\n",
              "      <td>192</td>\n",
              "      <td>192</td>\n",
              "      <td>192</td>\n",
              "      <td>194</td>\n",
              "      <td>...</td>\n",
              "      <td>190</td>\n",
              "      <td>173</td>\n",
              "      <td>140</td>\n",
              "      <td>80</td>\n",
              "      <td>28</td>\n",
              "      <td>60</td>\n",
              "      <td>70</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>78</td>\n",
              "      <td>100</td>\n",
              "      <td>86</td>\n",
              "      <td>136</td>\n",
              "      <td>135</td>\n",
              "      <td>135</td>\n",
              "      <td>135</td>\n",
              "      <td>132</td>\n",
              "      <td>132</td>\n",
              "      <td>136</td>\n",
              "      <td>136</td>\n",
              "      <td>137</td>\n",
              "      <td>144</td>\n",
              "      <td>103</td>\n",
              "      <td>120</td>\n",
              "      <td>197</td>\n",
              "      <td>200</td>\n",
              "      <td>195</td>\n",
              "      <td>196</td>\n",
              "      <td>170</td>\n",
              "      <td>149</td>\n",
              "      <td>119</td>\n",
              "      <td>56</td>\n",
              "      <td>27</td>\n",
              "      <td>58</td>\n",
              "      <td>102</td>\n",
              "      <td>79</td>\n",
              "      <td>47</td>\n",
              "      <td>64</td>\n",
              "      <td>87</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27454</th>\n",
              "      <td>23</td>\n",
              "      <td>179</td>\n",
              "      <td>180</td>\n",
              "      <td>180</td>\n",
              "      <td>180</td>\n",
              "      <td>182</td>\n",
              "      <td>181</td>\n",
              "      <td>182</td>\n",
              "      <td>183</td>\n",
              "      <td>182</td>\n",
              "      <td>182</td>\n",
              "      <td>182</td>\n",
              "      <td>181</td>\n",
              "      <td>181</td>\n",
              "      <td>182</td>\n",
              "      <td>181</td>\n",
              "      <td>181</td>\n",
              "      <td>181</td>\n",
              "      <td>181</td>\n",
              "      <td>179</td>\n",
              "      <td>178</td>\n",
              "      <td>178</td>\n",
              "      <td>176</td>\n",
              "      <td>177</td>\n",
              "      <td>178</td>\n",
              "      <td>159</td>\n",
              "      <td>47</td>\n",
              "      <td>30</td>\n",
              "      <td>39</td>\n",
              "      <td>180</td>\n",
              "      <td>182</td>\n",
              "      <td>183</td>\n",
              "      <td>183</td>\n",
              "      <td>183</td>\n",
              "      <td>183</td>\n",
              "      <td>185</td>\n",
              "      <td>184</td>\n",
              "      <td>185</td>\n",
              "      <td>183</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>97</td>\n",
              "      <td>97</td>\n",
              "      <td>95</td>\n",
              "      <td>90</td>\n",
              "      <td>92</td>\n",
              "      <td>101</td>\n",
              "      <td>125</td>\n",
              "      <td>160</td>\n",
              "      <td>191</td>\n",
              "      <td>202</td>\n",
              "      <td>213</td>\n",
              "      <td>215</td>\n",
              "      <td>96</td>\n",
              "      <td>88</td>\n",
              "      <td>109</td>\n",
              "      <td>216</td>\n",
              "      <td>208</td>\n",
              "      <td>207</td>\n",
              "      <td>203</td>\n",
              "      <td>178</td>\n",
              "      <td>151</td>\n",
              "      <td>112</td>\n",
              "      <td>87</td>\n",
              "      <td>80</td>\n",
              "      <td>76</td>\n",
              "      <td>80</td>\n",
              "      <td>86</td>\n",
              "      <td>97</td>\n",
              "      <td>102</td>\n",
              "      <td>103</td>\n",
              "      <td>108</td>\n",
              "      <td>132</td>\n",
              "      <td>170</td>\n",
              "      <td>194</td>\n",
              "      <td>214</td>\n",
              "      <td>203</td>\n",
              "      <td>197</td>\n",
              "      <td>205</td>\n",
              "      <td>209</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27455 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       label  pixel1  pixel2  pixel3  ...  pixel781  pixel782  pixel783  pixel784\n",
              "0          3     107     118     127  ...       206       204       203       202\n",
              "1          6     155     157     156  ...       175       103       135       149\n",
              "2          2     187     188     188  ...       198       195       194       195\n",
              "3          2     211     211     212  ...       225       222       229       163\n",
              "4         13     164     167     170  ...       157       163       164       179\n",
              "...      ...     ...     ...     ...  ...       ...       ...       ...       ...\n",
              "27450     13     189     189     190  ...       234       200       222       225\n",
              "27451     23     151     154     157  ...       195       195       195       194\n",
              "27452     18     174     174     174  ...       203       202       200       200\n",
              "27453     17     177     181     184  ...        47        64        87        93\n",
              "27454     23     179     180     180  ...       197       205       209       215\n",
              "\n",
              "[27455 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cz96wFHV42O0",
        "outputId": "251be615-2fc3-4c5a-b08c-8120b2fe8fc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        }
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize = (10,10)) # Label Count\n",
        "sns.set_style(\"darkgrid\")\n",
        "sns.countplot(traindf['label'])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde59b560b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sbp8uvlv5efP",
        "outputId": "b98259ab-1e94-4385-cad0-6c833dee82de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "testdf = pd.read_csv(all_test[0])\n",
        "testdf"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>pixel10</th>\n",
              "      <th>pixel11</th>\n",
              "      <th>pixel12</th>\n",
              "      <th>pixel13</th>\n",
              "      <th>pixel14</th>\n",
              "      <th>pixel15</th>\n",
              "      <th>pixel16</th>\n",
              "      <th>pixel17</th>\n",
              "      <th>pixel18</th>\n",
              "      <th>pixel19</th>\n",
              "      <th>pixel20</th>\n",
              "      <th>pixel21</th>\n",
              "      <th>pixel22</th>\n",
              "      <th>pixel23</th>\n",
              "      <th>pixel24</th>\n",
              "      <th>pixel25</th>\n",
              "      <th>pixel26</th>\n",
              "      <th>pixel27</th>\n",
              "      <th>pixel28</th>\n",
              "      <th>pixel29</th>\n",
              "      <th>pixel30</th>\n",
              "      <th>pixel31</th>\n",
              "      <th>pixel32</th>\n",
              "      <th>pixel33</th>\n",
              "      <th>pixel34</th>\n",
              "      <th>pixel35</th>\n",
              "      <th>pixel36</th>\n",
              "      <th>pixel37</th>\n",
              "      <th>pixel38</th>\n",
              "      <th>pixel39</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel745</th>\n",
              "      <th>pixel746</th>\n",
              "      <th>pixel747</th>\n",
              "      <th>pixel748</th>\n",
              "      <th>pixel749</th>\n",
              "      <th>pixel750</th>\n",
              "      <th>pixel751</th>\n",
              "      <th>pixel752</th>\n",
              "      <th>pixel753</th>\n",
              "      <th>pixel754</th>\n",
              "      <th>pixel755</th>\n",
              "      <th>pixel756</th>\n",
              "      <th>pixel757</th>\n",
              "      <th>pixel758</th>\n",
              "      <th>pixel759</th>\n",
              "      <th>pixel760</th>\n",
              "      <th>pixel761</th>\n",
              "      <th>pixel762</th>\n",
              "      <th>pixel763</th>\n",
              "      <th>pixel764</th>\n",
              "      <th>pixel765</th>\n",
              "      <th>pixel766</th>\n",
              "      <th>pixel767</th>\n",
              "      <th>pixel768</th>\n",
              "      <th>pixel769</th>\n",
              "      <th>pixel770</th>\n",
              "      <th>pixel771</th>\n",
              "      <th>pixel772</th>\n",
              "      <th>pixel773</th>\n",
              "      <th>pixel774</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>152</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>151</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>153</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>151</td>\n",
              "      <td>151</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>149</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>151</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>152</td>\n",
              "      <td>...</td>\n",
              "      <td>131</td>\n",
              "      <td>134</td>\n",
              "      <td>144</td>\n",
              "      <td>147</td>\n",
              "      <td>125</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>103</td>\n",
              "      <td>107</td>\n",
              "      <td>110</td>\n",
              "      <td>116</td>\n",
              "      <td>113</td>\n",
              "      <td>75</td>\n",
              "      <td>74</td>\n",
              "      <td>74</td>\n",
              "      <td>74</td>\n",
              "      <td>76</td>\n",
              "      <td>74</td>\n",
              "      <td>82</td>\n",
              "      <td>134</td>\n",
              "      <td>168</td>\n",
              "      <td>155</td>\n",
              "      <td>146</td>\n",
              "      <td>137</td>\n",
              "      <td>145</td>\n",
              "      <td>146</td>\n",
              "      <td>149</td>\n",
              "      <td>135</td>\n",
              "      <td>124</td>\n",
              "      <td>125</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>138</td>\n",
              "      <td>137</td>\n",
              "      <td>137</td>\n",
              "      <td>138</td>\n",
              "      <td>138</td>\n",
              "      <td>139</td>\n",
              "      <td>137</td>\n",
              "      <td>142</td>\n",
              "      <td>140</td>\n",
              "      <td>138</td>\n",
              "      <td>139</td>\n",
              "      <td>137</td>\n",
              "      <td>137</td>\n",
              "      <td>136</td>\n",
              "      <td>135</td>\n",
              "      <td>134</td>\n",
              "      <td>133</td>\n",
              "      <td>134</td>\n",
              "      <td>132</td>\n",
              "      <td>129</td>\n",
              "      <td>132</td>\n",
              "      <td>134</td>\n",
              "      <td>135</td>\n",
              "      <td>135</td>\n",
              "      <td>137</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>140</td>\n",
              "      <td>141</td>\n",
              "      <td>...</td>\n",
              "      <td>114</td>\n",
              "      <td>112</td>\n",
              "      <td>89</td>\n",
              "      <td>48</td>\n",
              "      <td>133</td>\n",
              "      <td>194</td>\n",
              "      <td>182</td>\n",
              "      <td>185</td>\n",
              "      <td>184</td>\n",
              "      <td>184</td>\n",
              "      <td>182</td>\n",
              "      <td>181</td>\n",
              "      <td>172</td>\n",
              "      <td>174</td>\n",
              "      <td>177</td>\n",
              "      <td>178</td>\n",
              "      <td>178</td>\n",
              "      <td>179</td>\n",
              "      <td>181</td>\n",
              "      <td>183</td>\n",
              "      <td>187</td>\n",
              "      <td>175</td>\n",
              "      <td>165</td>\n",
              "      <td>154</td>\n",
              "      <td>118</td>\n",
              "      <td>107</td>\n",
              "      <td>100</td>\n",
              "      <td>75</td>\n",
              "      <td>96</td>\n",
              "      <td>83</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>152</td>\n",
              "      <td>157</td>\n",
              "      <td>163</td>\n",
              "      <td>168</td>\n",
              "      <td>171</td>\n",
              "      <td>182</td>\n",
              "      <td>172</td>\n",
              "      <td>175</td>\n",
              "      <td>185</td>\n",
              "      <td>183</td>\n",
              "      <td>184</td>\n",
              "      <td>185</td>\n",
              "      <td>185</td>\n",
              "      <td>185</td>\n",
              "      <td>183</td>\n",
              "      <td>183</td>\n",
              "      <td>182</td>\n",
              "      <td>181</td>\n",
              "      <td>178</td>\n",
              "      <td>86</td>\n",
              "      <td>88</td>\n",
              "      <td>93</td>\n",
              "      <td>96</td>\n",
              "      <td>108</td>\n",
              "      <td>125</td>\n",
              "      <td>137</td>\n",
              "      <td>145</td>\n",
              "      <td>149</td>\n",
              "      <td>154</td>\n",
              "      <td>160</td>\n",
              "      <td>...</td>\n",
              "      <td>145</td>\n",
              "      <td>123</td>\n",
              "      <td>78</td>\n",
              "      <td>162</td>\n",
              "      <td>239</td>\n",
              "      <td>227</td>\n",
              "      <td>229</td>\n",
              "      <td>226</td>\n",
              "      <td>226</td>\n",
              "      <td>225</td>\n",
              "      <td>224</td>\n",
              "      <td>222</td>\n",
              "      <td>89</td>\n",
              "      <td>91</td>\n",
              "      <td>94</td>\n",
              "      <td>111</td>\n",
              "      <td>136</td>\n",
              "      <td>154</td>\n",
              "      <td>167</td>\n",
              "      <td>184</td>\n",
              "      <td>125</td>\n",
              "      <td>3</td>\n",
              "      <td>166</td>\n",
              "      <td>225</td>\n",
              "      <td>195</td>\n",
              "      <td>188</td>\n",
              "      <td>172</td>\n",
              "      <td>185</td>\n",
              "      <td>161</td>\n",
              "      <td>122</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>209</td>\n",
              "      <td>208</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>209</td>\n",
              "      <td>208</td>\n",
              "      <td>210</td>\n",
              "      <td>210</td>\n",
              "      <td>207</td>\n",
              "      <td>209</td>\n",
              "      <td>209</td>\n",
              "      <td>208</td>\n",
              "      <td>209</td>\n",
              "      <td>210</td>\n",
              "      <td>209</td>\n",
              "      <td>207</td>\n",
              "      <td>208</td>\n",
              "      <td>209</td>\n",
              "      <td>207</td>\n",
              "      <td>206</td>\n",
              "      <td>208</td>\n",
              "      <td>209</td>\n",
              "      <td>208</td>\n",
              "      <td>208</td>\n",
              "      <td>210</td>\n",
              "      <td>211</td>\n",
              "      <td>210</td>\n",
              "      <td>211</td>\n",
              "      <td>209</td>\n",
              "      <td>209</td>\n",
              "      <td>...</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>84</td>\n",
              "      <td>151</td>\n",
              "      <td>238</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>250</td>\n",
              "      <td>237</td>\n",
              "      <td>245</td>\n",
              "      <td>250</td>\n",
              "      <td>232</td>\n",
              "      <td>103</td>\n",
              "      <td>101</td>\n",
              "      <td>102</td>\n",
              "      <td>103</td>\n",
              "      <td>95</td>\n",
              "      <td>208</td>\n",
              "      <td>231</td>\n",
              "      <td>227</td>\n",
              "      <td>209</td>\n",
              "      <td>190</td>\n",
              "      <td>179</td>\n",
              "      <td>182</td>\n",
              "      <td>152</td>\n",
              "      <td>150</td>\n",
              "      <td>159</td>\n",
              "      <td>119</td>\n",
              "      <td>83</td>\n",
              "      <td>63</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>203</td>\n",
              "      <td>204</td>\n",
              "      <td>204</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>202</td>\n",
              "      <td>198</td>\n",
              "      <td>216</td>\n",
              "      <td>217</td>\n",
              "      <td>135</td>\n",
              "      <td>181</td>\n",
              "      <td>200</td>\n",
              "      <td>195</td>\n",
              "      <td>194</td>\n",
              "      <td>193</td>\n",
              "      <td>190</td>\n",
              "      <td>189</td>\n",
              "      <td>187</td>\n",
              "      <td>185</td>\n",
              "      <td>190</td>\n",
              "      <td>194</td>\n",
              "      <td>196</td>\n",
              "      <td>197</td>\n",
              "      <td>200</td>\n",
              "      <td>202</td>\n",
              "      <td>204</td>\n",
              "      <td>206</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>206</td>\n",
              "      <td>...</td>\n",
              "      <td>93</td>\n",
              "      <td>52</td>\n",
              "      <td>24</td>\n",
              "      <td>53</td>\n",
              "      <td>63</td>\n",
              "      <td>33</td>\n",
              "      <td>41</td>\n",
              "      <td>51</td>\n",
              "      <td>48</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>55</td>\n",
              "      <td>149</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>148</td>\n",
              "      <td>147</td>\n",
              "      <td>151</td>\n",
              "      <td>124</td>\n",
              "      <td>82</td>\n",
              "      <td>84</td>\n",
              "      <td>81</td>\n",
              "      <td>69</td>\n",
              "      <td>81</td>\n",
              "      <td>111</td>\n",
              "      <td>103</td>\n",
              "      <td>84</td>\n",
              "      <td>75</td>\n",
              "      <td>53</td>\n",
              "      <td>28</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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7167</th>\n",
              "      <td>1</td>\n",
              "      <td>135</td>\n",
              "      <td>119</td>\n",
              "      <td>108</td>\n",
              "      <td>102</td>\n",
              "      <td>105</td>\n",
              "      <td>99</td>\n",
              "      <td>61</td>\n",
              "      <td>103</td>\n",
              "      <td>121</td>\n",
              "      <td>133</td>\n",
              "      <td>143</td>\n",
              "      <td>148</td>\n",
              "      <td>156</td>\n",
              "      <td>160</td>\n",
              "      <td>162</td>\n",
              "      <td>164</td>\n",
              "      <td>168</td>\n",
              "      <td>171</td>\n",
              "      <td>173</td>\n",
              "      <td>175</td>\n",
              "      <td>178</td>\n",
              "      <td>179</td>\n",
              "      <td>181</td>\n",
              "      <td>182</td>\n",
              "      <td>184</td>\n",
              "      <td>185</td>\n",
              "      <td>185</td>\n",
              "      <td>186</td>\n",
              "      <td>137</td>\n",
              "      <td>116</td>\n",
              "      <td>111</td>\n",
              "      <td>101</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>66</td>\n",
              "      <td>108</td>\n",
              "      <td>124</td>\n",
              "      <td>132</td>\n",
              "      <td>155</td>\n",
              "      <td>...</td>\n",
              "      <td>147</td>\n",
              "      <td>132</td>\n",
              "      <td>115</td>\n",
              "      <td>112</td>\n",
              "      <td>117</td>\n",
              "      <td>118</td>\n",
              "      <td>112</td>\n",
              "      <td>153</td>\n",
              "      <td>189</td>\n",
              "      <td>178</td>\n",
              "      <td>173</td>\n",
              "      <td>166</td>\n",
              "      <td>140</td>\n",
              "      <td>121</td>\n",
              "      <td>157</td>\n",
              "      <td>125</td>\n",
              "      <td>93</td>\n",
              "      <td>99</td>\n",
              "      <td>100</td>\n",
              "      <td>101</td>\n",
              "      <td>104</td>\n",
              "      <td>100</td>\n",
              "      <td>118</td>\n",
              "      <td>167</td>\n",
              "      <td>154</td>\n",
              "      <td>141</td>\n",
              "      <td>136</td>\n",
              "      <td>139</td>\n",
              "      <td>135</td>\n",
              "      <td>125</td>\n",
              "      <td>108</td>\n",
              "      <td>112</td>\n",
              "      <td>116</td>\n",
              "      <td>114</td>\n",
              "      <td>118</td>\n",
              "      <td>180</td>\n",
              "      <td>184</td>\n",
              "      <td>176</td>\n",
              "      <td>167</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7168</th>\n",
              "      <td>12</td>\n",
              "      <td>157</td>\n",
              "      <td>159</td>\n",
              "      <td>161</td>\n",
              "      <td>164</td>\n",
              "      <td>166</td>\n",
              "      <td>166</td>\n",
              "      <td>171</td>\n",
              "      <td>174</td>\n",
              "      <td>175</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>178</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>178</td>\n",
              "      <td>177</td>\n",
              "      <td>176</td>\n",
              "      <td>178</td>\n",
              "      <td>178</td>\n",
              "      <td>177</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>174</td>\n",
              "      <td>174</td>\n",
              "      <td>173</td>\n",
              "      <td>173</td>\n",
              "      <td>159</td>\n",
              "      <td>163</td>\n",
              "      <td>164</td>\n",
              "      <td>166</td>\n",
              "      <td>168</td>\n",
              "      <td>169</td>\n",
              "      <td>173</td>\n",
              "      <td>175</td>\n",
              "      <td>177</td>\n",
              "      <td>178</td>\n",
              "      <td>177</td>\n",
              "      <td>...</td>\n",
              "      <td>90</td>\n",
              "      <td>78</td>\n",
              "      <td>191</td>\n",
              "      <td>215</td>\n",
              "      <td>212</td>\n",
              "      <td>214</td>\n",
              "      <td>212</td>\n",
              "      <td>209</td>\n",
              "      <td>209</td>\n",
              "      <td>208</td>\n",
              "      <td>208</td>\n",
              "      <td>207</td>\n",
              "      <td>189</td>\n",
              "      <td>192</td>\n",
              "      <td>196</td>\n",
              "      <td>199</td>\n",
              "      <td>191</td>\n",
              "      <td>208</td>\n",
              "      <td>230</td>\n",
              "      <td>201</td>\n",
              "      <td>197</td>\n",
              "      <td>185</td>\n",
              "      <td>184</td>\n",
              "      <td>187</td>\n",
              "      <td>169</td>\n",
              "      <td>136</td>\n",
              "      <td>131</td>\n",
              "      <td>107</td>\n",
              "      <td>69</td>\n",
              "      <td>127</td>\n",
              "      <td>213</td>\n",
              "      <td>213</td>\n",
              "      <td>213</td>\n",
              "      <td>214</td>\n",
              "      <td>213</td>\n",
              "      <td>211</td>\n",
              "      <td>210</td>\n",
              "      <td>210</td>\n",
              "      <td>209</td>\n",
              "      <td>208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7169</th>\n",
              "      <td>2</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "      <td>190</td>\n",
              "      <td>190</td>\n",
              "      <td>192</td>\n",
              "      <td>192</td>\n",
              "      <td>191</td>\n",
              "      <td>192</td>\n",
              "      <td>193</td>\n",
              "      <td>192</td>\n",
              "      <td>191</td>\n",
              "      <td>192</td>\n",
              "      <td>191</td>\n",
              "      <td>191</td>\n",
              "      <td>192</td>\n",
              "      <td>189</td>\n",
              "      <td>189</td>\n",
              "      <td>189</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>187</td>\n",
              "      <td>186</td>\n",
              "      <td>185</td>\n",
              "      <td>184</td>\n",
              "      <td>183</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>194</td>\n",
              "      <td>194</td>\n",
              "      <td>193</td>\n",
              "      <td>195</td>\n",
              "      <td>195</td>\n",
              "      <td>...</td>\n",
              "      <td>215</td>\n",
              "      <td>215</td>\n",
              "      <td>215</td>\n",
              "      <td>214</td>\n",
              "      <td>213</td>\n",
              "      <td>213</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>210</td>\n",
              "      <td>209</td>\n",
              "      <td>208</td>\n",
              "      <td>207</td>\n",
              "      <td>215</td>\n",
              "      <td>215</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>208</td>\n",
              "      <td>207</td>\n",
              "      <td>203</td>\n",
              "      <td>196</td>\n",
              "      <td>170</td>\n",
              "      <td>99</td>\n",
              "      <td>92</td>\n",
              "      <td>92</td>\n",
              "      <td>89</td>\n",
              "      <td>155</td>\n",
              "      <td>213</td>\n",
              "      <td>216</td>\n",
              "      <td>216</td>\n",
              "      <td>216</td>\n",
              "      <td>216</td>\n",
              "      <td>215</td>\n",
              "      <td>213</td>\n",
              "      <td>214</td>\n",
              "      <td>214</td>\n",
              "      <td>213</td>\n",
              "      <td>210</td>\n",
              "      <td>211</td>\n",
              "      <td>209</td>\n",
              "      <td>208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7170</th>\n",
              "      <td>4</td>\n",
              "      <td>201</td>\n",
              "      <td>205</td>\n",
              "      <td>208</td>\n",
              "      <td>209</td>\n",
              "      <td>214</td>\n",
              "      <td>216</td>\n",
              "      <td>218</td>\n",
              "      <td>223</td>\n",
              "      <td>226</td>\n",
              "      <td>229</td>\n",
              "      <td>234</td>\n",
              "      <td>233</td>\n",
              "      <td>233</td>\n",
              "      <td>237</td>\n",
              "      <td>237</td>\n",
              "      <td>237</td>\n",
              "      <td>240</td>\n",
              "      <td>241</td>\n",
              "      <td>238</td>\n",
              "      <td>240</td>\n",
              "      <td>240</td>\n",
              "      <td>242</td>\n",
              "      <td>243</td>\n",
              "      <td>241</td>\n",
              "      <td>239</td>\n",
              "      <td>239</td>\n",
              "      <td>239</td>\n",
              "      <td>239</td>\n",
              "      <td>206</td>\n",
              "      <td>209</td>\n",
              "      <td>212</td>\n",
              "      <td>215</td>\n",
              "      <td>218</td>\n",
              "      <td>220</td>\n",
              "      <td>223</td>\n",
              "      <td>226</td>\n",
              "      <td>227</td>\n",
              "      <td>229</td>\n",
              "      <td>236</td>\n",
              "      <td>...</td>\n",
              "      <td>168</td>\n",
              "      <td>173</td>\n",
              "      <td>128</td>\n",
              "      <td>127</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>159</td>\n",
              "      <td>99</td>\n",
              "      <td>80</td>\n",
              "      <td>77</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>231</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>249</td>\n",
              "      <td>231</td>\n",
              "      <td>188</td>\n",
              "      <td>164</td>\n",
              "      <td>169</td>\n",
              "      <td>176</td>\n",
              "      <td>112</td>\n",
              "      <td>169</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>237</td>\n",
              "      <td>113</td>\n",
              "      <td>91</td>\n",
              "      <td>67</td>\n",
              "      <td>70</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7171</th>\n",
              "      <td>2</td>\n",
              "      <td>173</td>\n",
              "      <td>174</td>\n",
              "      <td>173</td>\n",
              "      <td>174</td>\n",
              "      <td>173</td>\n",
              "      <td>173</td>\n",
              "      <td>175</td>\n",
              "      <td>175</td>\n",
              "      <td>174</td>\n",
              "      <td>175</td>\n",
              "      <td>176</td>\n",
              "      <td>175</td>\n",
              "      <td>174</td>\n",
              "      <td>174</td>\n",
              "      <td>173</td>\n",
              "      <td>173</td>\n",
              "      <td>174</td>\n",
              "      <td>170</td>\n",
              "      <td>172</td>\n",
              "      <td>172</td>\n",
              "      <td>170</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>170</td>\n",
              "      <td>168</td>\n",
              "      <td>168</td>\n",
              "      <td>167</td>\n",
              "      <td>166</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>177</td>\n",
              "      <td>177</td>\n",
              "      <td>176</td>\n",
              "      <td>178</td>\n",
              "      <td>178</td>\n",
              "      <td>...</td>\n",
              "      <td>198</td>\n",
              "      <td>200</td>\n",
              "      <td>199</td>\n",
              "      <td>199</td>\n",
              "      <td>197</td>\n",
              "      <td>197</td>\n",
              "      <td>197</td>\n",
              "      <td>196</td>\n",
              "      <td>194</td>\n",
              "      <td>193</td>\n",
              "      <td>192</td>\n",
              "      <td>191</td>\n",
              "      <td>199</td>\n",
              "      <td>201</td>\n",
              "      <td>190</td>\n",
              "      <td>190</td>\n",
              "      <td>194</td>\n",
              "      <td>192</td>\n",
              "      <td>187</td>\n",
              "      <td>181</td>\n",
              "      <td>154</td>\n",
              "      <td>72</td>\n",
              "      <td>66</td>\n",
              "      <td>74</td>\n",
              "      <td>54</td>\n",
              "      <td>139</td>\n",
              "      <td>210</td>\n",
              "      <td>198</td>\n",
              "      <td>201</td>\n",
              "      <td>201</td>\n",
              "      <td>201</td>\n",
              "      <td>200</td>\n",
              "      <td>197</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>197</td>\n",
              "      <td>195</td>\n",
              "      <td>195</td>\n",
              "      <td>193</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7172 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      label  pixel1  pixel2  pixel3  ...  pixel781  pixel782  pixel783  pixel784\n",
              "0         6     149     149     150  ...       106       112       120       107\n",
              "1         5     126     128     131  ...       184       184       182       180\n",
              "2        10      85      88      92  ...       226       225       224       222\n",
              "3         0     203     205     207  ...       230       240       253       255\n",
              "4         3     188     191     193  ...        49        46        46        53\n",
              "...     ...     ...     ...     ...  ...       ...       ...       ...       ...\n",
              "7167      1     135     119     108  ...       184       176       167       163\n",
              "7168     12     157     159     161  ...       210       210       209       208\n",
              "7169      2     190     191     190  ...       210       211       209       208\n",
              "7170      4     201     205     208  ...        91        67        70        63\n",
              "7171      2     173     174     173  ...       195       195       193       192\n",
              "\n",
              "[7172 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsOeJJ7dm0nw",
        "outputId": "90a80b58-7d6a-4a75-9882-8391aa838a86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.unique(testdf['label'])"
      ],
      "execution_count": 8,
      "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": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdnYyxVx5joy",
        "outputId": "18f3d639-d8e7-4bd4-fb23-0049c3c1b77b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        }
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize = (10,10)) # Label Count\n",
        "sns.set_style(\"darkgrid\")\n",
        "sns.countplot(testdf['label'])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde59a2eb38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zk-78MJl5Iqt"
      },
      "source": [
        "y_train = traindf['label']\n",
        "y_test = testdf['label']\n",
        "del(traindf['label'])\n",
        "del(testdf['label'])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZhrN6MA5s2N"
      },
      "source": [
        "x_train = traindf.values\n",
        "x_test = testdf.values"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5d8PQq66U-r",
        "outputId": "d4baf7cf-c7d3-4c36-c7db-2dd62ba846d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_train.shape, y_test.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27455,), (7172,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmiEo_ZD6L3a",
        "outputId": "a089f525-454e-4a57-e24d-2c138749ace8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_train.shape, x_test.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27455, 784), (7172, 784))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e53lugi1551m"
      },
      "source": [
        "#cast weights to float 32\n",
        "x_train,x_test = x_train.astype('float32'),x_test.astype('float32')\n",
        "# Normalize the data\n",
        "x_train, x_test = x_train / 255, x_test / 255"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGV1g-ig6uL8",
        "outputId": "e6dea0ec-7208-4243-be22-6e9d04026d57",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "class_names = [chr(a) for a in range(ord('a'),ord('z'))]\n",
        "class_names"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['a',\n",
              " 'b',\n",
              " 'c',\n",
              " 'd',\n",
              " 'e',\n",
              " 'f',\n",
              " 'g',\n",
              " 'h',\n",
              " 'i',\n",
              " 'j',\n",
              " 'k',\n",
              " 'l',\n",
              " 'm',\n",
              " 'n',\n",
              " 'o',\n",
              " 'p',\n",
              " 'q',\n",
              " 'r',\n",
              " 's',\n",
              " 't',\n",
              " 'u',\n",
              " 'v',\n",
              " 'w',\n",
              " 'x',\n",
              " 'y']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bbL74th6Yl8",
        "outputId": "3f058bd5-e4a8-43f4-a3e2-8b659f81d5d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "#plotting the images with their labels\n",
        "# Reshaping the data from 1-D to 2-D as required to plot by matplotlib\n",
        "x_train = x_train.reshape(-1,28,28)\n",
        "x_test = x_test.reshape(-1,28,28)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[y_train[i]])\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NS8628lZ564M"
      },
      "source": [
        "# Reshaping the data from 1-D to 3-D as required through input by CNN's\n",
        "x_train = x_train.reshape(-1,28,28,1)\n",
        "x_test = x_test.reshape(-1,28,28,1)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2s8NG0d59NV",
        "outputId": "d6414036-f3b8-4731-95a6-7bf00cf3c1c5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_train.shape, x_test.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27455, 28, 28, 1), (7172, 28, 28, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1kX0n1dCj_X"
      },
      "source": [
        "# # convert class vectorr to binary class matrix\n",
        "# print(y_train.head(),len(y_train),'\\n',y_test.head(),len(y_test))\n",
        "# y_train = tf.keras.utils.to_categorical(y_train)\n",
        "# y_test = tf.keras.utils.to_categorical(y_test)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFPgsfO0DvDE"
      },
      "source": [
        "# print('y_train',y_train[:5],f'One Row is of Length = {len(y_train[0])} \\n\\n y_test',y_test[:5])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YS2lYOgL7fHV"
      },
      "source": [
        "# Reshaping the data from 1-D to 3-D as required through input by CNN's\n",
        "x_train = x_train.reshape(-1,28,28)\n",
        "x_test = x_test.reshape(-1,28,28)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__7tIo_st2eG"
      },
      "source": [
        "# Baseline Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYShVVOQ7L9D"
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "model.add(layers.Conv2D(128, kernel_size=(3,3), padding='same', activation='relu', input_shape=(28,28)))\n",
        "model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Conv2D(128, kernel_size=(3,3), padding='same', activation='elu'))\n",
        "model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(512, activation='relu'))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Dropout(0.50))\n",
        "model.add(layers.Dense(25))\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YR1AV6Ce7Lgu",
        "outputId": "6032d43f-0264-4489-ed56-86c2c1fce207",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_1 (Reshape)          (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 28, 28, 128)       1280      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 14, 14, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 512)               3211776   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 25)                12825     \n",
            "=================================================================\n",
            "Total params: 3,373,465\n",
            "Trainable params: 3,373,465\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMeTUBTGfZsx",
        "outputId": "cf1c8a2e-535c-4a8f-dae5-0f39dcc1d04d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(x_train, y_train, epochs=10, \n",
        "                    validation_data=(x_test, y_test))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "858/858 [==============================] - ETA: 0s - loss: 0.0149 - accuracy: 0.9969WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0149 - accuracy: 0.9969 - val_loss: 0.6994 - val_accuracy: 0.9254\n",
            "Epoch 2/10\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0210 - accuracy: 0.9953 - val_loss: 0.6549 - val_accuracy: 0.9366\n",
            "Epoch 3/10\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0146 - accuracy: 0.9964 - val_loss: 0.7279 - val_accuracy: 0.9339\n",
            "Epoch 4/10\n",
            "858/858 [==============================] - 10s 11ms/step - loss: 0.0085 - accuracy: 0.9980 - val_loss: 0.7710 - val_accuracy: 0.9389\n",
            "Epoch 5/10\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0201 - accuracy: 0.9962 - val_loss: 0.9169 - val_accuracy: 0.9412\n",
            "Epoch 6/10\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0201 - accuracy: 0.9962 - val_loss: 0.9660 - val_accuracy: 0.9426\n",
            "Epoch 7/10\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0097 - accuracy: 0.9980 - val_loss: 0.9106 - val_accuracy: 0.9458\n",
            "Epoch 8/10\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0195 - accuracy: 0.9971 - val_loss: 0.8231 - val_accuracy: 0.9466\n",
            "Epoch 9/10\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0200 - accuracy: 0.9966 - val_loss: 0.9475 - val_accuracy: 0.9378\n",
            "Epoch 10/10\n",
            "858/858 [==============================] - 9s 11ms/step - loss: 0.0081 - accuracy: 0.9984 - val_loss: 0.9578 - val_accuracy: 0.9384\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhZd7_7h7Qb5",
        "outputId": "f74ecbf2-14e7-4c81-ee79-f4c995d6ec93",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "scores = model.evaluate(x_test,y_test,verbose=0)\n",
        "print(f'Test Loss:{scores[0]}')\n",
        "print(f'Test Accuracy:{scores[1]}')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Loss:0.9577723741531372\n",
            "Test Accuracy:0.9383714199066162\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuL16ezM6W_P",
        "outputId": "179bf5a4-fb07-4fff-b039-d9cfdc7911be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "saved_model_dir = '/content/drive/My Drive/Colab Notebooks/CS581/assignment3/baseline_asl_mnist_q3.h5'\n",
        "print(f\"Saving Model to: {saved_model_dir}\")\n",
        "tf.keras.models.save_model(model,saved_model_dir,include_optimizer=False)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving Model to: /content/drive/My Drive/Colab Notebooks/CS581/assignment3/baseline_asl_mnist_q3.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uy6hb-lUKWxG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Mm7rk1cJx1p"
      },
      "source": [
        "# Pruning - Layer wise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXb-k29OHY2K"
      },
      "source": [
        "from tensorflow_model_optimization.sparsity import keras as sparsity\n",
        "%load_ext tensorboard"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llUlYnZyKHXX",
        "outputId": "e3b12cb0-bed4-4161-bc4a-f87ab4acd110",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# we need 4 arguments for Polynomial Decay - begin step, end step, begin sparsity and end sparsity\n",
        "\n",
        "# here we are calculating end step based on batch size \n",
        "epochs = 12\n",
        "batch_size = 128\n",
        "num_train_samples = x_train.shape[0]\n",
        "end_step = np.ceil(1 * num_train_samples / batch_size).astype(np.int32) * epochs\n",
        "end_step"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2580"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNThRZ-rLNR7"
      },
      "source": [
        "pruning_params = {\n",
        "    'pruning_schedule': sparsity.PolynomialDecay(initial_sparsity=0.5,\n",
        "                                                 final_sparsity=0.9,\n",
        "                                                 begin_step = 2000,\n",
        "                                                 end_step=end_step,\n",
        "                                                 frequency=100)\n",
        "}"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWDDqSr9L88t",
        "outputId": "c042c3fd-e6c8-4698-a61c-b365ca2a9a54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pruned_model = tf.keras.Sequential([\n",
        "    keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1)),\n",
        "    sparsity.prune_low_magnitude(layers.Conv2D(128, kernel_size=(3,3), padding='same', activation='relu', input_shape=(28,28)),pruning_schedule=pruning_params['pruning_schedule']),\n",
        "    layers.MaxPool2D(pool_size=(2,2)),\n",
        "    sparsity.prune_low_magnitude(layers.Conv2D(128, kernel_size=(3,3), padding='same', activation='elu'),pruning_schedule=pruning_params['pruning_schedule']),\n",
        "    layers.MaxPool2D(pool_size=(2,2)),\n",
        "    layers.Flatten(),\n",
        "    sparsity.prune_low_magnitude(layers.Dense(512, activation='relu'),pruning_schedule=pruning_params['pruning_schedule']),\n",
        "    layers.Dropout(0.50),\n",
        "    sparsity.prune_low_magnitude(layers.Dense(25, activation='softmax'),pruning_schedule=pruning_params['pruning_schedule'])\n",
        "])\n",
        "\n",
        "pruned_model.summary()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_7 (Reshape)          (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "prune_low_magnitude_conv2d_1 (None, 28, 28, 128)       2434      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "prune_low_magnitude_conv2d_1 (None, 14, 14, 128)       295042    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_7 (Flatten)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "prune_low_magnitude_dense_14 (None, 512)               6423042   \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "prune_low_magnitude_dense_15 (None, 25)                25627     \n",
            "=================================================================\n",
            "Total params: 6,746,145\n",
            "Trainable params: 3,373,465\n",
            "Non-trainable params: 3,372,680\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ooqlif82PIlN",
        "outputId": "01450cdc-e803-485d-ca9c-9b5423b4c894",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        " pruned_model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "callbacks = [\n",
        "             sparsity.UpdatePruningStep(),\n",
        "             sparsity.PruningSummaries('./pruning_logs',profile_batch=0)\n",
        "]\n",
        "\n",
        "history = pruned_model.fit(x_train,\n",
        "                           y_train,\n",
        "                           epochs=10, \n",
        "                           batch_size=batch_size,\n",
        "                           callbacks=callbacks,\n",
        "                    validation_data=(x_test, y_test))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "215/215 [==============================] - ETA: 0s - loss: 2.9168 - accuracy: 0.3717WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "215/215 [==============================] - 7s 32ms/step - loss: 2.9168 - accuracy: 0.3717 - val_loss: 2.7982 - val_accuracy: 0.4866\n",
            "Epoch 2/10\n",
            "215/215 [==============================] - 6s 30ms/step - loss: 2.5892 - accuracy: 0.7007 - val_loss: 2.6022 - val_accuracy: 0.6871\n",
            "Epoch 3/10\n",
            "215/215 [==============================] - 6s 30ms/step - loss: 2.4553 - accuracy: 0.8337 - val_loss: 2.4415 - val_accuracy: 0.8530\n",
            "Epoch 4/10\n",
            "215/215 [==============================] - 6s 30ms/step - loss: 2.3481 - accuracy: 0.9398 - val_loss: 2.4005 - val_accuracy: 0.8907\n",
            "Epoch 5/10\n",
            "215/215 [==============================] - 7s 31ms/step - loss: 2.3359 - accuracy: 0.9506 - val_loss: 2.4005 - val_accuracy: 0.8871\n",
            "Epoch 6/10\n",
            "215/215 [==============================] - 7s 30ms/step - loss: 2.3069 - accuracy: 0.9800 - val_loss: 2.3714 - val_accuracy: 0.9184\n",
            "Epoch 7/10\n",
            "215/215 [==============================] - 6s 30ms/step - loss: 2.2902 - accuracy: 0.9961 - val_loss: 2.3603 - val_accuracy: 0.9289\n",
            "Epoch 8/10\n",
            "215/215 [==============================] - 7s 32ms/step - loss: 2.2884 - accuracy: 0.9975 - val_loss: 2.3561 - val_accuracy: 0.9317\n",
            "Epoch 9/10\n",
            "215/215 [==============================] - 7s 31ms/step - loss: 2.2886 - accuracy: 0.9974 - val_loss: 2.3463 - val_accuracy: 0.9405\n",
            "Epoch 10/10\n",
            "215/215 [==============================] - 7s 32ms/step - loss: 2.2954 - accuracy: 0.9930 - val_loss: 2.3588 - val_accuracy: 0.9283\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0QS2hbORsLQ",
        "outputId": "c80561d7-c89f-4a27-d3a4-6eb5f0b1ff8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#the history variable collects the accuracy and loss for both train and test validation at each step\n",
        "history.history"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': [0.37169915437698364,\n",
              "  0.7007466554641724,\n",
              "  0.833655059337616,\n",
              "  0.9398288130760193,\n",
              "  0.9506465196609497,\n",
              "  0.9800036549568176,\n",
              "  0.9960662722587585,\n",
              "  0.9975232481956482,\n",
              "  0.9973775148391724,\n",
              "  0.9930067658424377],\n",
              " 'loss': [2.916839599609375,\n",
              "  2.5892460346221924,\n",
              "  2.4553170204162598,\n",
              "  2.3481013774871826,\n",
              "  2.335944175720215,\n",
              "  2.3069474697113037,\n",
              "  2.2902305126190186,\n",
              "  2.28840970993042,\n",
              "  2.2886013984680176,\n",
              "  2.2954108715057373],\n",
              " 'val_accuracy': [0.4866146147251129,\n",
              "  0.6871165633201599,\n",
              "  0.8530396223068237,\n",
              "  0.8906859755516052,\n",
              "  0.887060821056366,\n",
              "  0.9184327721595764,\n",
              "  0.9288901090621948,\n",
              "  0.9316787719726562,\n",
              "  0.9404628872871399,\n",
              "  0.9283323884010315],\n",
              " 'val_loss': [2.7982404232025146,\n",
              "  2.6021759510040283,\n",
              "  2.4415361881256104,\n",
              "  2.4005391597747803,\n",
              "  2.400548219680786,\n",
              "  2.371382236480713,\n",
              "  2.360299587249756,\n",
              "  2.3560643196105957,\n",
              "  2.3463382720947266,\n",
              "  2.3587896823883057]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jevkn1UiSIqK",
        "outputId": "3c963723-75b4-42c8-a515-dd180b5dca80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pruned_scores = pruned_model.evaluate(x_test,y_test)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "225/225 [==============================] - 1s 6ms/step - loss: 2.3588 - accuracy: 0.9283\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ml4XLh1fSamm",
        "outputId": "11537301-d7f5-4b77-8c76-e721f5b53c04",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(f'Test Loss:{pruned_scores[0]}')\n",
        "print(f'Test Accuracy:{pruned_scores[1]}')"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Loss:2.358790159225464\n",
            "Test Accuracy:0.9283323884010315\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsAk5AakSkwn",
        "outputId": "564560c1-7c60-48be-a04e-828f6546f64e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pruned_saved_model_dir = '/content/drive/My Drive/Colab Notebooks/CS581/assignment3/pruned_asl_mnist_q3.h5'\n",
        "print(f\"Saving Model to: {pruned_saved_model_dir}\")\n",
        "tf.keras.models.save_model(pruned_model,pruned_saved_model_dir,include_optimizer=True)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving Model to: /content/drive/My Drive/Colab Notebooks/CS581/assignment3/pruned_asl_mnist_q3.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8micYDLPS-hC"
      },
      "source": [
        "## Post-optimization training for finesse and testing the pruned model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QlBvw8kaS9ze",
        "outputId": "a6179ff5-c99d-4b58-8772-16cdc53b0146",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "with sparsity.prune_scope():\n",
        "    restored_pruned = tf.keras.models.load_model(pruned_saved_model_dir)\n",
        "\n",
        "callbacks = [\n",
        "             sparsity.UpdatePruningStep(),\n",
        "             sparsity.PruningSummaries('./pruning_logs',profile_batch=0)\n",
        "]\n",
        "\n",
        "#restored_pruned.fit(x_train, y_train, batch_size=batch_size, epochs = 2, verbose=1, callbacks=callbacks, validation_data=(x_test,y_test))\n",
        "\n",
        "restored_pruned_scores = restored_pruned.evaluate(x_test,y_test)\n",
        "print(f'Test loss: {restored_pruned_scores[0]}')\n",
        "print(f'Test Accutacy: {restored_pruned_scores[1]}')"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input_3:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "225/225 [==============================] - 1s 6ms/step - loss: 2.3588 - accuracy: 0.0491\n",
            "Test loss: 2.358790159225464\n",
            "Test Accutacy: 0.04907975345849991\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqMkLE8xJ05i"
      },
      "source": [
        "# Pruning - Whole Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opclcZhNJ2g0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}