a3_q2_ASLMNIST_ternary.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "a3-q2-ASLMNIST_ternary.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "175FrH0dybh57q-BaLLjhL64GLooYQqkD",
      "authorship_tag": "ABX9TyNrozI1swRv4u43bHijit8k",
      "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-terneryneuralnets-ASLMNIST/a3_q2_ASLMNIST_ternary.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": "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 tempfile\n",
        "import zipfile\n",
        "import os"
      ],
      "execution_count": 1,
      "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": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYto8j-T4gBz",
        "outputId": "f2379412-e9d4-4b33-c3d8-b03749934225",
        "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": 3,
      "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": "547dd0e1-b040-418e-eeb4-bf374cb71c94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "traindf = pd.read_csv(all_train[0])\n",
        "traindf"
      ],
      "execution_count": 4,
      "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>3</td>\n",
              "      <td>107</td>\n",
              "      <td>118</td>\n",
              "      <td>127</td>\n",
              "      <td>134</td>\n",
              "      <td>139</td>\n",
              "      <td>143</td>\n",
              "      <td>146</td>\n",
              "      <td>150</td>\n",
              "      <td>153</td>\n",
              "      <td>156</td>\n",
              "      <td>158</td>\n",
              "      <td>160</td>\n",
              "      <td>163</td>\n",
              "      <td>165</td>\n",
              "      <td>159</td>\n",
              "      <td>166</td>\n",
              "      <td>168</td>\n",
              "      <td>170</td>\n",
              "      <td>170</td>\n",
              "      <td>171</td>\n",
              "      <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",
              "      <td>169</td>\n",
              "      <td>111</td>\n",
              "      <td>121</td>\n",
              "      <td>129</td>\n",
              "      <td>135</td>\n",
              "      <td>141</td>\n",
              "      <td>144</td>\n",
              "      <td>148</td>\n",
              "      <td>151</td>\n",
              "      <td>154</td>\n",
              "      <td>157</td>\n",
              "      <td>160</td>\n",
              "      <td>...</td>\n",
              "      <td>205</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>204</td>\n",
              "      <td>205</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>202</td>\n",
              "      <td>142</td>\n",
              "      <td>151</td>\n",
              "      <td>160</td>\n",
              "      <td>172</td>\n",
              "      <td>196</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>190</td>\n",
              "      <td>135</td>\n",
              "      <td>96</td>\n",
              "      <td>86</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>79</td>\n",
              "      <td>176</td>\n",
              "      <td>205</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>155</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
              "      <td>158</td>\n",
              "      <td>158</td>\n",
              "      <td>157</td>\n",
              "      <td>158</td>\n",
              "      <td>156</td>\n",
              "      <td>154</td>\n",
              "      <td>154</td>\n",
              "      <td>153</td>\n",
              "      <td>152</td>\n",
              "      <td>151</td>\n",
              "      <td>149</td>\n",
              "      <td>149</td>\n",
              "      <td>148</td>\n",
              "      <td>147</td>\n",
              "      <td>146</td>\n",
              "      <td>144</td>\n",
              "      <td>142</td>\n",
              "      <td>143</td>\n",
              "      <td>138</td>\n",
              "      <td>92</td>\n",
              "      <td>108</td>\n",
              "      <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",
              "      <td>...</td>\n",
              "      <td>100</td>\n",
              "      <td>78</td>\n",
              "      <td>120</td>\n",
              "      <td>157</td>\n",
              "      <td>168</td>\n",
              "      <td>107</td>\n",
              "      <td>99</td>\n",
              "      <td>121</td>\n",
              "      <td>133</td>\n",
              "      <td>97</td>\n",
              "      <td>95</td>\n",
              "      <td>120</td>\n",
              "      <td>135</td>\n",
              "      <td>116</td>\n",
              "      <td>95</td>\n",
              "      <td>79</td>\n",
              "      <td>69</td>\n",
              "      <td>86</td>\n",
              "      <td>139</td>\n",
              "      <td>173</td>\n",
              "      <td>200</td>\n",
              "      <td>185</td>\n",
              "      <td>175</td>\n",
              "      <td>198</td>\n",
              "      <td>124</td>\n",
              "      <td>118</td>\n",
              "      <td>94</td>\n",
              "      <td>140</td>\n",
              "      <td>133</td>\n",
              "      <td>84</td>\n",
              "      <td>69</td>\n",
              "      <td>149</td>\n",
              "      <td>128</td>\n",
              "      <td>87</td>\n",
              "      <td>94</td>\n",
              "      <td>163</td>\n",
              "      <td>175</td>\n",
              "      <td>103</td>\n",
              "      <td>135</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>187</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>187</td>\n",
              "      <td>187</td>\n",
              "      <td>186</td>\n",
              "      <td>187</td>\n",
              "      <td>188</td>\n",
              "      <td>187</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>211</td>\n",
              "      <td>211</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>211</td>\n",
              "      <td>210</td>\n",
              "      <td>211</td>\n",
              "      <td>210</td>\n",
              "      <td>210</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13</td>\n",
              "      <td>164</td>\n",
              "      <td>167</td>\n",
              "      <td>170</td>\n",
              "      <td>172</td>\n",
              "      <td>176</td>\n",
              "      <td>179</td>\n",
              "      <td>180</td>\n",
              "      <td>184</td>\n",
              "      <td>185</td>\n",
              "      <td>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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cz96wFHV42O0",
        "outputId": "07afc731-48c9-4250-b558-3da9ac8d4c02",
        "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": 5,
      "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 0x7f862236b0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "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": "60a14da8-d70c-4e65-cb6f-e792efe6bc87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "testdf = pd.read_csv(all_test[0])\n",
        "testdf"
      ],
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsOeJJ7dm0nw"
      },
      "source": [
        ""
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdnYyxVx5joy",
        "outputId": "25651a0c-a564-4eec-fa64-6a0c931908a5",
        "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": 7,
      "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 0x7f865ca4af60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "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": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZhrN6MA5s2N"
      },
      "source": [
        "x_train = traindf.values\n",
        "x_test = testdf.values"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5d8PQq66U-r",
        "outputId": "ee63398f-6847-455e-d712-8efb9e53b9f2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_train.shape, y_test.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27455,), (7172,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmiEo_ZD6L3a",
        "outputId": "1a13e8bb-4e23-4100-ea5f-40769f399331",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_train.shape, x_test.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27455, 784), (7172, 784))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e53lugi1551m"
      },
      "source": [
        "# Normalize the data\n",
        "x_train, x_test = x_train / 255, x_test / 255"
      ],
      "execution_count": 12,
      "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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGV1g-ig6uL8",
        "outputId": "84f896dc-959d-492e-ea13-61b8a01f0122",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "class_names = [chr(a) for a in range(ord('a'),ord('z'))]\n",
        "class_names"
      ],
      "execution_count": 14,
      "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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bbL74th6Yl8",
        "outputId": "c0c53436-505b-41e7-a59c-5423de03f62d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "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": 15,
      "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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2s8NG0d59NV",
        "outputId": "66d88648-6048-4d75-954f-3e8dacf6bcec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_train.shape, x_test.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27455, 28, 28, 1), (7172, 28, 28, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3y5M98pEGiFe"
      },
      "source": [
        "# Ternary Layer Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7h54T2cCL0DI"
      },
      "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": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcJDZIKnGFNi"
      },
      "source": [
        "import ternary_layers as tl\n",
        "import ternary_ops as to"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWCB6EVHGoXW"
      },
      "source": [
        "model = models.Sequential([\n",
        "tl.TernaryConv2D(64, kernel_size=(3,3), padding='same', activation='relu',use_bias=False),\n",
        "layers.MaxPool2D(pool_size=(2,2)),\n",
        "layers.BatchNormalization(center=True, scale=False),\n",
        "tl.TernaryConv2D(64, kernel_size=(3,3), padding='same', activation='relu',use_bias=False),\n",
        "layers.MaxPool2D(pool_size=(2,2)),\n",
        "layers.BatchNormalization(center=True, scale=False),\n",
        "layers.Flatten(),\n",
        "tl.TernaryDense(128, activation='relu',use_bias=False),\n",
        "layers.Dropout(0.60),\n",
        "layers.Dense(25,use_bias=False)\n",
        "])"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfQvN9dkIqQh",
        "outputId": "8c12a886-8da2-4fbf-dc8d-a617ed62069d",
        "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=150,\n",
        "                    batch_size=128,\n",
        "                    validation_data=(x_test, y_test))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/150\n",
            "215/215 [==============================] - 1s 7ms/step - loss: 20.0134 - accuracy: 0.1225 - val_loss: 4.8831 - val_accuracy: 0.1390\n",
            "Epoch 2/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 3.4127 - accuracy: 0.1345 - val_loss: 3.0404 - val_accuracy: 0.1859\n",
            "Epoch 3/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 2.9682 - accuracy: 0.1646 - val_loss: 2.9110 - val_accuracy: 0.2285\n",
            "Epoch 4/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 2.7513 - accuracy: 0.2018 - val_loss: 2.6799 - val_accuracy: 0.2447\n",
            "Epoch 5/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 2.5011 - accuracy: 0.2528 - val_loss: 3.0038 - val_accuracy: 0.3338\n",
            "Epoch 6/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 2.2631 - accuracy: 0.3017 - val_loss: 2.3382 - val_accuracy: 0.3905\n",
            "Epoch 7/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 2.0496 - accuracy: 0.3511 - val_loss: 2.2367 - val_accuracy: 0.4340\n",
            "Epoch 8/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 1.8739 - accuracy: 0.3922 - val_loss: 1.7094 - val_accuracy: 0.5453\n",
            "Epoch 9/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 1.6834 - accuracy: 0.4362 - val_loss: 1.5826 - val_accuracy: 0.5289\n",
            "Epoch 10/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 1.5511 - accuracy: 0.4664 - val_loss: 2.5915 - val_accuracy: 0.4657\n",
            "Epoch 11/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 1.4196 - accuracy: 0.5020 - val_loss: 1.3423 - val_accuracy: 0.5929\n",
            "Epoch 12/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 1.3570 - accuracy: 0.5301 - val_loss: 1.4525 - val_accuracy: 0.6025\n",
            "Epoch 13/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 1.2756 - accuracy: 0.5526 - val_loss: 1.2365 - val_accuracy: 0.5993\n",
            "Epoch 14/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 1.1603 - accuracy: 0.5857 - val_loss: 2.7050 - val_accuracy: 0.5141\n",
            "Epoch 15/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 1.1145 - accuracy: 0.6061 - val_loss: 1.3148 - val_accuracy: 0.6362\n",
            "Epoch 16/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 1.0133 - accuracy: 0.6406 - val_loss: 2.1176 - val_accuracy: 0.6171\n",
            "Epoch 17/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.9512 - accuracy: 0.6581 - val_loss: 2.5643 - val_accuracy: 0.5798\n",
            "Epoch 18/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.8502 - accuracy: 0.6895 - val_loss: 1.3492 - val_accuracy: 0.6714\n",
            "Epoch 19/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.7833 - accuracy: 0.7159 - val_loss: 1.4625 - val_accuracy: 0.6839\n",
            "Epoch 20/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.7494 - accuracy: 0.7315 - val_loss: 1.4973 - val_accuracy: 0.6255\n",
            "Epoch 21/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.6874 - accuracy: 0.7474 - val_loss: 1.6182 - val_accuracy: 0.7312\n",
            "Epoch 22/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.6142 - accuracy: 0.7742 - val_loss: 1.2552 - val_accuracy: 0.7149\n",
            "Epoch 23/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.5837 - accuracy: 0.7848 - val_loss: 0.9768 - val_accuracy: 0.7899\n",
            "Epoch 24/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.5578 - accuracy: 0.7977 - val_loss: 1.1819 - val_accuracy: 0.7313\n",
            "Epoch 25/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.5272 - accuracy: 0.8064 - val_loss: 1.7057 - val_accuracy: 0.7131\n",
            "Epoch 26/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.5579 - accuracy: 0.8005 - val_loss: 0.9578 - val_accuracy: 0.7928\n",
            "Epoch 27/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.5107 - accuracy: 0.8123 - val_loss: 1.3838 - val_accuracy: 0.7540\n",
            "Epoch 28/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.5001 - accuracy: 0.8186 - val_loss: 1.4863 - val_accuracy: 0.7542\n",
            "Epoch 29/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.4604 - accuracy: 0.8341 - val_loss: 1.4652 - val_accuracy: 0.7446\n",
            "Epoch 30/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.4428 - accuracy: 0.8400 - val_loss: 1.4939 - val_accuracy: 0.7080\n",
            "Epoch 31/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.4504 - accuracy: 0.8373 - val_loss: 1.3185 - val_accuracy: 0.7542\n",
            "Epoch 32/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.4086 - accuracy: 0.8541 - val_loss: 1.2711 - val_accuracy: 0.7136\n",
            "Epoch 33/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.4131 - accuracy: 0.8550 - val_loss: 0.9664 - val_accuracy: 0.7943\n",
            "Epoch 34/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.3776 - accuracy: 0.8654 - val_loss: 0.9355 - val_accuracy: 0.8027\n",
            "Epoch 35/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.3530 - accuracy: 0.8735 - val_loss: 1.2129 - val_accuracy: 0.7888\n",
            "Epoch 36/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.3381 - accuracy: 0.8783 - val_loss: 1.5606 - val_accuracy: 0.7511\n",
            "Epoch 37/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.3313 - accuracy: 0.8824 - val_loss: 1.9053 - val_accuracy: 0.7015\n",
            "Epoch 38/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2989 - accuracy: 0.8948 - val_loss: 1.3252 - val_accuracy: 0.7851\n",
            "Epoch 39/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2880 - accuracy: 0.8983 - val_loss: 1.4348 - val_accuracy: 0.7546\n",
            "Epoch 40/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.2709 - accuracy: 0.9037 - val_loss: 1.1978 - val_accuracy: 0.7978\n",
            "Epoch 41/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2692 - accuracy: 0.9078 - val_loss: 1.4539 - val_accuracy: 0.7963\n",
            "Epoch 42/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2452 - accuracy: 0.9140 - val_loss: 1.7346 - val_accuracy: 0.8002\n",
            "Epoch 43/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.2502 - accuracy: 0.9116 - val_loss: 1.6211 - val_accuracy: 0.7798\n",
            "Epoch 44/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2436 - accuracy: 0.9157 - val_loss: 1.4427 - val_accuracy: 0.7991\n",
            "Epoch 45/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2355 - accuracy: 0.9179 - val_loss: 0.9435 - val_accuracy: 0.8574\n",
            "Epoch 46/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2266 - accuracy: 0.9225 - val_loss: 2.0130 - val_accuracy: 0.7458\n",
            "Epoch 47/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2324 - accuracy: 0.9207 - val_loss: 2.3414 - val_accuracy: 0.6592\n",
            "Epoch 48/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.2079 - accuracy: 0.9279 - val_loss: 0.5911 - val_accuracy: 0.8734\n",
            "Epoch 49/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1886 - accuracy: 0.9336 - val_loss: 0.5520 - val_accuracy: 0.8908\n",
            "Epoch 50/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.2022 - accuracy: 0.9316 - val_loss: 1.4120 - val_accuracy: 0.7950\n",
            "Epoch 51/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1830 - accuracy: 0.9355 - val_loss: 1.0391 - val_accuracy: 0.8696\n",
            "Epoch 52/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1766 - accuracy: 0.9397 - val_loss: 0.8881 - val_accuracy: 0.8769\n",
            "Epoch 53/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1784 - accuracy: 0.9384 - val_loss: 0.7489 - val_accuracy: 0.8621\n",
            "Epoch 54/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.1835 - accuracy: 0.9383 - val_loss: 1.4183 - val_accuracy: 0.8222\n",
            "Epoch 55/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1829 - accuracy: 0.9392 - val_loss: 1.2729 - val_accuracy: 0.8519\n",
            "Epoch 56/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1629 - accuracy: 0.9439 - val_loss: 0.8026 - val_accuracy: 0.8749\n",
            "Epoch 57/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1686 - accuracy: 0.9451 - val_loss: 0.5590 - val_accuracy: 0.9140\n",
            "Epoch 58/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1695 - accuracy: 0.9447 - val_loss: 2.1358 - val_accuracy: 0.7308\n",
            "Epoch 59/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1740 - accuracy: 0.9437 - val_loss: 0.8931 - val_accuracy: 0.8873\n",
            "Epoch 60/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1455 - accuracy: 0.9513 - val_loss: 0.9243 - val_accuracy: 0.8834\n",
            "Epoch 61/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1416 - accuracy: 0.9510 - val_loss: 0.8723 - val_accuracy: 0.8246\n",
            "Epoch 62/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1443 - accuracy: 0.9512 - val_loss: 0.6053 - val_accuracy: 0.8915\n",
            "Epoch 63/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.1590 - accuracy: 0.9479 - val_loss: 0.8983 - val_accuracy: 0.8695\n",
            "Epoch 64/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1483 - accuracy: 0.9524 - val_loss: 0.7974 - val_accuracy: 0.8943\n",
            "Epoch 65/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1402 - accuracy: 0.9546 - val_loss: 0.8284 - val_accuracy: 0.8843\n",
            "Epoch 66/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1461 - accuracy: 0.9517 - val_loss: 0.9502 - val_accuracy: 0.8749\n",
            "Epoch 67/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1361 - accuracy: 0.9543 - val_loss: 0.8107 - val_accuracy: 0.8758\n",
            "Epoch 68/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1352 - accuracy: 0.9564 - val_loss: 3.1696 - val_accuracy: 0.6842\n",
            "Epoch 69/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1331 - accuracy: 0.9576 - val_loss: 1.7374 - val_accuracy: 0.7750\n",
            "Epoch 70/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1395 - accuracy: 0.9556 - val_loss: 2.0777 - val_accuracy: 0.8160\n",
            "Epoch 71/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.1414 - accuracy: 0.9563 - val_loss: 1.2424 - val_accuracy: 0.8818\n",
            "Epoch 72/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1351 - accuracy: 0.9574 - val_loss: 0.7826 - val_accuracy: 0.9023\n",
            "Epoch 73/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1303 - accuracy: 0.9582 - val_loss: 0.9762 - val_accuracy: 0.8947\n",
            "Epoch 74/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1253 - accuracy: 0.9606 - val_loss: 1.1221 - val_accuracy: 0.8496\n",
            "Epoch 75/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1304 - accuracy: 0.9590 - val_loss: 1.4766 - val_accuracy: 0.8240\n",
            "Epoch 76/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.1183 - accuracy: 0.9619 - val_loss: 0.7461 - val_accuracy: 0.9154\n",
            "Epoch 77/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1246 - accuracy: 0.9596 - val_loss: 1.0381 - val_accuracy: 0.8932\n",
            "Epoch 78/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.1205 - accuracy: 0.9622 - val_loss: 0.8802 - val_accuracy: 0.8822\n",
            "Epoch 79/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1233 - accuracy: 0.9604 - val_loss: 1.1557 - val_accuracy: 0.8702\n",
            "Epoch 80/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1156 - accuracy: 0.9640 - val_loss: 2.4862 - val_accuracy: 0.8165\n",
            "Epoch 81/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1118 - accuracy: 0.9653 - val_loss: 0.9895 - val_accuracy: 0.8868\n",
            "Epoch 82/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1174 - accuracy: 0.9640 - val_loss: 1.1353 - val_accuracy: 0.8971\n",
            "Epoch 83/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.1124 - accuracy: 0.9656 - val_loss: 2.5157 - val_accuracy: 0.8126\n",
            "Epoch 84/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1152 - accuracy: 0.9651 - val_loss: 2.2598 - val_accuracy: 0.8263\n",
            "Epoch 85/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1132 - accuracy: 0.9655 - val_loss: 0.9307 - val_accuracy: 0.8940\n",
            "Epoch 86/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0987 - accuracy: 0.9686 - val_loss: 0.7993 - val_accuracy: 0.9046\n",
            "Epoch 87/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1051 - accuracy: 0.9670 - val_loss: 0.8650 - val_accuracy: 0.9016\n",
            "Epoch 88/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0978 - accuracy: 0.9710 - val_loss: 1.1879 - val_accuracy: 0.8861\n",
            "Epoch 89/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1022 - accuracy: 0.9677 - val_loss: 0.5899 - val_accuracy: 0.9147\n",
            "Epoch 90/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1056 - accuracy: 0.9689 - val_loss: 1.3736 - val_accuracy: 0.9013\n",
            "Epoch 91/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0937 - accuracy: 0.9710 - val_loss: 1.1169 - val_accuracy: 0.8544\n",
            "Epoch 92/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0990 - accuracy: 0.9704 - val_loss: 0.8974 - val_accuracy: 0.8806\n",
            "Epoch 93/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0961 - accuracy: 0.9703 - val_loss: 1.9030 - val_accuracy: 0.8503\n",
            "Epoch 94/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.1021 - accuracy: 0.9708 - val_loss: 1.0751 - val_accuracy: 0.8790\n",
            "Epoch 95/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.1019 - accuracy: 0.9709 - val_loss: 0.8291 - val_accuracy: 0.9162\n",
            "Epoch 96/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0908 - accuracy: 0.9721 - val_loss: 1.0391 - val_accuracy: 0.9123\n",
            "Epoch 97/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0952 - accuracy: 0.9735 - val_loss: 0.9204 - val_accuracy: 0.8878\n",
            "Epoch 98/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0938 - accuracy: 0.9733 - val_loss: 0.9777 - val_accuracy: 0.8851\n",
            "Epoch 99/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0953 - accuracy: 0.9715 - val_loss: 2.0544 - val_accuracy: 0.8698\n",
            "Epoch 100/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0904 - accuracy: 0.9737 - val_loss: 0.9012 - val_accuracy: 0.8960\n",
            "Epoch 101/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0872 - accuracy: 0.9747 - val_loss: 0.8564 - val_accuracy: 0.8855\n",
            "Epoch 102/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0825 - accuracy: 0.9753 - val_loss: 0.8399 - val_accuracy: 0.9158\n",
            "Epoch 103/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0890 - accuracy: 0.9737 - val_loss: 1.0187 - val_accuracy: 0.9084\n",
            "Epoch 104/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0929 - accuracy: 0.9738 - val_loss: 0.8618 - val_accuracy: 0.8967\n",
            "Epoch 105/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0897 - accuracy: 0.9745 - val_loss: 1.8383 - val_accuracy: 0.8572\n",
            "Epoch 106/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0896 - accuracy: 0.9735 - val_loss: 0.6694 - val_accuracy: 0.9347\n",
            "Epoch 107/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0979 - accuracy: 0.9739 - val_loss: 1.0734 - val_accuracy: 0.8942\n",
            "Epoch 108/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0947 - accuracy: 0.9737 - val_loss: 1.1513 - val_accuracy: 0.8852\n",
            "Epoch 109/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0866 - accuracy: 0.9747 - val_loss: 1.4032 - val_accuracy: 0.8769\n",
            "Epoch 110/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0929 - accuracy: 0.9739 - val_loss: 0.8807 - val_accuracy: 0.8957\n",
            "Epoch 111/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0841 - accuracy: 0.9758 - val_loss: 1.2551 - val_accuracy: 0.8595\n",
            "Epoch 112/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0779 - accuracy: 0.9767 - val_loss: 3.0459 - val_accuracy: 0.8028\n",
            "Epoch 113/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0830 - accuracy: 0.9776 - val_loss: 0.9735 - val_accuracy: 0.9055\n",
            "Epoch 114/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0819 - accuracy: 0.9760 - val_loss: 1.7302 - val_accuracy: 0.8723\n",
            "Epoch 115/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0858 - accuracy: 0.9773 - val_loss: 1.4437 - val_accuracy: 0.8940\n",
            "Epoch 116/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0848 - accuracy: 0.9746 - val_loss: 2.8130 - val_accuracy: 0.7322\n",
            "Epoch 117/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0784 - accuracy: 0.9778 - val_loss: 1.6974 - val_accuracy: 0.8784\n",
            "Epoch 118/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0708 - accuracy: 0.9794 - val_loss: 0.9975 - val_accuracy: 0.8861\n",
            "Epoch 119/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0701 - accuracy: 0.9804 - val_loss: 1.1582 - val_accuracy: 0.9140\n",
            "Epoch 120/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0809 - accuracy: 0.9777 - val_loss: 1.0038 - val_accuracy: 0.9126\n",
            "Epoch 121/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0784 - accuracy: 0.9787 - val_loss: 0.9236 - val_accuracy: 0.9124\n",
            "Epoch 122/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0864 - accuracy: 0.9771 - val_loss: 0.7088 - val_accuracy: 0.9329\n",
            "Epoch 123/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0773 - accuracy: 0.9789 - val_loss: 1.2499 - val_accuracy: 0.8886\n",
            "Epoch 124/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0787 - accuracy: 0.9784 - val_loss: 0.6449 - val_accuracy: 0.9354\n",
            "Epoch 125/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0728 - accuracy: 0.9794 - val_loss: 0.7217 - val_accuracy: 0.9197\n",
            "Epoch 126/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0865 - accuracy: 0.9765 - val_loss: 1.1370 - val_accuracy: 0.8760\n",
            "Epoch 127/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0758 - accuracy: 0.9781 - val_loss: 2.5592 - val_accuracy: 0.8267\n",
            "Epoch 128/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0711 - accuracy: 0.9803 - val_loss: 1.7326 - val_accuracy: 0.8836\n",
            "Epoch 129/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0846 - accuracy: 0.9792 - val_loss: 0.9310 - val_accuracy: 0.9161\n",
            "Epoch 130/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0761 - accuracy: 0.9799 - val_loss: 1.3878 - val_accuracy: 0.8766\n",
            "Epoch 131/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0706 - accuracy: 0.9803 - val_loss: 1.4148 - val_accuracy: 0.9016\n",
            "Epoch 132/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0853 - accuracy: 0.9787 - val_loss: 1.3531 - val_accuracy: 0.8977\n",
            "Epoch 133/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0749 - accuracy: 0.9788 - val_loss: 1.8538 - val_accuracy: 0.8825\n",
            "Epoch 134/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0673 - accuracy: 0.9804 - val_loss: 1.0738 - val_accuracy: 0.8986\n",
            "Epoch 135/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0826 - accuracy: 0.9788 - val_loss: 1.9412 - val_accuracy: 0.8374\n",
            "Epoch 136/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0760 - accuracy: 0.9792 - val_loss: 1.3605 - val_accuracy: 0.8928\n",
            "Epoch 137/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0732 - accuracy: 0.9818 - val_loss: 1.5643 - val_accuracy: 0.8805\n",
            "Epoch 138/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0736 - accuracy: 0.9810 - val_loss: 1.9050 - val_accuracy: 0.8482\n",
            "Epoch 139/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0645 - accuracy: 0.9816 - val_loss: 1.0431 - val_accuracy: 0.9127\n",
            "Epoch 140/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0695 - accuracy: 0.9812 - val_loss: 1.0447 - val_accuracy: 0.9200\n",
            "Epoch 141/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0697 - accuracy: 0.9803 - val_loss: 1.1240 - val_accuracy: 0.9020\n",
            "Epoch 142/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0727 - accuracy: 0.9808 - val_loss: 1.6741 - val_accuracy: 0.8714\n",
            "Epoch 143/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0612 - accuracy: 0.9827 - val_loss: 0.8200 - val_accuracy: 0.9226\n",
            "Epoch 144/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0735 - accuracy: 0.9813 - val_loss: 1.7115 - val_accuracy: 0.8450\n",
            "Epoch 145/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0634 - accuracy: 0.9835 - val_loss: 1.2428 - val_accuracy: 0.8903\n",
            "Epoch 146/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0793 - accuracy: 0.9796 - val_loss: 1.2736 - val_accuracy: 0.9039\n",
            "Epoch 147/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0660 - accuracy: 0.9827 - val_loss: 2.2262 - val_accuracy: 0.8344\n",
            "Epoch 148/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0713 - accuracy: 0.9815 - val_loss: 1.0914 - val_accuracy: 0.9221\n",
            "Epoch 149/150\n",
            "215/215 [==============================] - 1s 5ms/step - loss: 0.0714 - accuracy: 0.9818 - val_loss: 0.9896 - val_accuracy: 0.9193\n",
            "Epoch 150/150\n",
            "215/215 [==============================] - 1s 6ms/step - loss: 0.0688 - accuracy: 0.9823 - val_loss: 1.3708 - val_accuracy: 0.9070\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXzFlse1KbTI",
        "outputId": "4b3425ef-b4e4-4dc9-a72e-48476b3db4df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "test_loss, test_acc = model.evaluate(x_test,  y_test, verbose=2)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "225/225 - 0s - loss: 1.3708 - accuracy: 0.9070\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4walLUcSG5j"
      },
      "source": [
        "# Saving the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AP3F8PAERsOb",
        "outputId": "051e932e-0462-4ba8-e569-5f74ee301e8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "saved_model_dir = '/content/drive/My Drive/Colab Notebooks/CS581/assignment3/sign_mnist_ternary.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": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving Model to: /content/drive/My Drive/Colab Notebooks/CS581/assignment3/sign_mnist_ternary.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6laqYd5UK6S"
      },
      "source": [
        "saved_model_zip = '/content/drive/My Drive/Colab Notebooks/CS581/assignment3/final_ternary_model.zip'\n",
        "with zipfile.ZipFile(saved_model_zip,'w',compression=zipfile.ZIP_DEFLATED) as f:\n",
        "    f.write(saved_model_dir)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWBZt8cwVsL9",
        "outputId": "287720f8-2b60-4747-de73-1f4af6076a93",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(f'Size of the binary model before compression: {os.path.getsize(saved_model_dir)/float(2**20)} Mb')\n",
        "print(f'Size of the binary model after compression: {os.path.getsize(saved_model_zip)/float(2**20)} Mb')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size of the binary model before compression: 1.71795654296875 Mb\n",
            "Size of the binary model after compression: 1.5622930526733398 Mb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yfbj8FiqXiZn",
        "outputId": "090b08dd-acec-4241-bed2-1b85f1e7e7d5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "open('sign_mnist_model_ternary.yaml', 'w').write(model.to_yaml())"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5668"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKRVtzEHegiB",
        "outputId": "0b7bf110-0ff3-431c-f33a-b1d6a01bb09c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for line in open('sign_mnist_model_ternary.yaml','r'):\n",
        "    print(line)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "backend: tensorflow\n",
            "\n",
            "class_name: Sequential\n",
            "\n",
            "config:\n",
            "\n",
            "  layers:\n",
            "\n",
            "  - class_name: InputLayer\n",
            "\n",
            "    config:\n",
            "\n",
            "      batch_input_shape: !!python/tuple [null, 28, 28, 1]\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      name: ternary_conv2d_input\n",
            "\n",
            "      ragged: false\n",
            "\n",
            "      sparse: false\n",
            "\n",
            "  - class_name: TernaryConv2D\n",
            "\n",
            "    config:\n",
            "\n",
            "      H: 1.0\n",
            "\n",
            "      activation: relu\n",
            "\n",
            "      activity_regularizer: null\n",
            "\n",
            "      bias_constraint: null\n",
            "\n",
            "      bias_initializer:\n",
            "\n",
            "        class_name: Zeros\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      bias_lr_multiplier: null\n",
            "\n",
            "      bias_regularizer: null\n",
            "\n",
            "      data_format: channels_last\n",
            "\n",
            "      dilation_rate: &id002 !!python/tuple [1, 1]\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      filters: 64\n",
            "\n",
            "      groups: 1\n",
            "\n",
            "      kernel_constraint:\n",
            "\n",
            "        class_name: Clip\n",
            "\n",
            "        config: {max_value: 1.0, min_value: -1.0}\n",
            "\n",
            "      kernel_initializer:\n",
            "\n",
            "        class_name: RandomUniform\n",
            "\n",
            "        config: {maxval: 1.0, minval: -1.0, seed: null}\n",
            "\n",
            "      kernel_lr_multiplier: !!python/object/apply:numpy.core.multiarray.scalar\n",
            "\n",
            "      - &id003 !!python/object/apply:numpy.dtype\n",
            "\n",
            "        args: [f4, 0, 1]\n",
            "\n",
            "        state: !!python/tuple [3, <, null, null, null, -1, -1, 0]\n",
            "\n",
            "      - !!binary |\n",
            "\n",
            "        wvydQQ==\n",
            "\n",
            "      kernel_regularizer: null\n",
            "\n",
            "      kernel_size: !!python/tuple [3, 3]\n",
            "\n",
            "      name: ternary_conv2d\n",
            "\n",
            "      padding: same\n",
            "\n",
            "      strides: &id004 !!python/tuple [1, 1]\n",
            "\n",
            "      trainable: true\n",
            "\n",
            "      use_bias: false\n",
            "\n",
            "  - class_name: MaxPooling2D\n",
            "\n",
            "    config:\n",
            "\n",
            "      data_format: channels_last\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      name: max_pooling2d\n",
            "\n",
            "      padding: valid\n",
            "\n",
            "      pool_size: &id001 !!python/tuple [2, 2]\n",
            "\n",
            "      strides: *id001\n",
            "\n",
            "      trainable: true\n",
            "\n",
            "  - class_name: BatchNormalization\n",
            "\n",
            "    config:\n",
            "\n",
            "      axis: !!python/object/apply:tensorflow.python.training.tracking.data_structures.ListWrapper\n",
            "\n",
            "      - [3]\n",
            "\n",
            "      beta_constraint: null\n",
            "\n",
            "      beta_initializer:\n",
            "\n",
            "        class_name: Zeros\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      beta_regularizer: null\n",
            "\n",
            "      center: true\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      epsilon: 0.001\n",
            "\n",
            "      gamma_constraint: null\n",
            "\n",
            "      gamma_initializer:\n",
            "\n",
            "        class_name: Ones\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      gamma_regularizer: null\n",
            "\n",
            "      momentum: 0.99\n",
            "\n",
            "      moving_mean_initializer:\n",
            "\n",
            "        class_name: Zeros\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      moving_variance_initializer:\n",
            "\n",
            "        class_name: Ones\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      name: batch_normalization\n",
            "\n",
            "      scale: false\n",
            "\n",
            "      trainable: true\n",
            "\n",
            "  - class_name: TernaryConv2D\n",
            "\n",
            "    config:\n",
            "\n",
            "      H: 1.0\n",
            "\n",
            "      activation: relu\n",
            "\n",
            "      activity_regularizer: null\n",
            "\n",
            "      bias_constraint: null\n",
            "\n",
            "      bias_initializer:\n",
            "\n",
            "        class_name: Zeros\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      bias_lr_multiplier: null\n",
            "\n",
            "      bias_regularizer: null\n",
            "\n",
            "      data_format: channels_last\n",
            "\n",
            "      dilation_rate: *id002\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      filters: 64\n",
            "\n",
            "      groups: 1\n",
            "\n",
            "      kernel_constraint:\n",
            "\n",
            "        class_name: Clip\n",
            "\n",
            "        config: {max_value: 1.0, min_value: -1.0}\n",
            "\n",
            "      kernel_initializer:\n",
            "\n",
            "        class_name: RandomUniform\n",
            "\n",
            "        config: {maxval: 1.0, minval: -1.0, seed: null}\n",
            "\n",
            "      kernel_lr_multiplier: !!python/object/apply:numpy.core.multiarray.scalar\n",
            "\n",
            "      - *id003\n",
            "\n",
            "      - !!binary |\n",
            "\n",
            "        17PdQQ==\n",
            "\n",
            "      kernel_regularizer: null\n",
            "\n",
            "      kernel_size: !!python/tuple [3, 3]\n",
            "\n",
            "      name: ternary_conv2d_1\n",
            "\n",
            "      padding: same\n",
            "\n",
            "      strides: *id004\n",
            "\n",
            "      trainable: true\n",
            "\n",
            "      use_bias: false\n",
            "\n",
            "  - class_name: MaxPooling2D\n",
            "\n",
            "    config:\n",
            "\n",
            "      data_format: channels_last\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      name: max_pooling2d_1\n",
            "\n",
            "      padding: valid\n",
            "\n",
            "      pool_size: &id005 !!python/tuple [2, 2]\n",
            "\n",
            "      strides: *id005\n",
            "\n",
            "      trainable: true\n",
            "\n",
            "  - class_name: BatchNormalization\n",
            "\n",
            "    config:\n",
            "\n",
            "      axis: !!python/object/apply:tensorflow.python.training.tracking.data_structures.ListWrapper\n",
            "\n",
            "      - [3]\n",
            "\n",
            "      beta_constraint: null\n",
            "\n",
            "      beta_initializer:\n",
            "\n",
            "        class_name: Zeros\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      beta_regularizer: null\n",
            "\n",
            "      center: true\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      epsilon: 0.001\n",
            "\n",
            "      gamma_constraint: null\n",
            "\n",
            "      gamma_initializer:\n",
            "\n",
            "        class_name: Ones\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      gamma_regularizer: null\n",
            "\n",
            "      momentum: 0.99\n",
            "\n",
            "      moving_mean_initializer:\n",
            "\n",
            "        class_name: Zeros\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      moving_variance_initializer:\n",
            "\n",
            "        class_name: Ones\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      name: batch_normalization_1\n",
            "\n",
            "      scale: false\n",
            "\n",
            "      trainable: true\n",
            "\n",
            "  - class_name: Flatten\n",
            "\n",
            "    config: {data_format: channels_last, dtype: float32, name: flatten, trainable: true}\n",
            "\n",
            "  - class_name: TernaryDense\n",
            "\n",
            "    config:\n",
            "\n",
            "      H: 1.0\n",
            "\n",
            "      activation: relu\n",
            "\n",
            "      activity_regularizer: null\n",
            "\n",
            "      bias_constraint: null\n",
            "\n",
            "      bias_initializer:\n",
            "\n",
            "        class_name: Zeros\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      bias_lr_multiplier: null\n",
            "\n",
            "      bias_regularizer: null\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      kernel_constraint:\n",
            "\n",
            "        class_name: Clip\n",
            "\n",
            "        config: {max_value: 1.0, min_value: -1.0}\n",
            "\n",
            "      kernel_initializer:\n",
            "\n",
            "        class_name: RandomUniform\n",
            "\n",
            "        config: {maxval: 1.0, minval: -1.0, seed: null}\n",
            "\n",
            "      kernel_lr_multiplier: !!python/object/apply:numpy.core.multiarray.scalar\n",
            "\n",
            "      - *id003\n",
            "\n",
            "      - !!binary |\n",
            "\n",
            "        KJc6Qg==\n",
            "\n",
            "      kernel_regularizer: null\n",
            "\n",
            "      name: ternary_dense\n",
            "\n",
            "      trainable: true\n",
            "\n",
            "      units: 128\n",
            "\n",
            "      use_bias: false\n",
            "\n",
            "  - class_name: Dropout\n",
            "\n",
            "    config: {dtype: float32, name: dropout, noise_shape: null, rate: 0.6, seed: null,\n",
            "\n",
            "      trainable: true}\n",
            "\n",
            "  - class_name: Dense\n",
            "\n",
            "    config:\n",
            "\n",
            "      activation: linear\n",
            "\n",
            "      activity_regularizer: null\n",
            "\n",
            "      bias_constraint: null\n",
            "\n",
            "      bias_initializer:\n",
            "\n",
            "        class_name: Zeros\n",
            "\n",
            "        config: {}\n",
            "\n",
            "      bias_regularizer: null\n",
            "\n",
            "      dtype: float32\n",
            "\n",
            "      kernel_constraint: null\n",
            "\n",
            "      kernel_initializer:\n",
            "\n",
            "        class_name: GlorotUniform\n",
            "\n",
            "        config: {seed: null}\n",
            "\n",
            "      kernel_regularizer: null\n",
            "\n",
            "      name: dense\n",
            "\n",
            "      trainable: true\n",
            "\n",
            "      units: 25\n",
            "\n",
            "      use_bias: false\n",
            "\n",
            "  name: sequential\n",
            "\n",
            "keras_version: 2.4.0\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RsPEEnXISESN"
      },
      "source": [
        "# END"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m10nubtmSFXG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}