ASL DETECTION - USING CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AMERICAN SIGN LANGUAGE - CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The American Sign Language letter database of hand gestures represent a multi-class problem with 24 classes of letters (excluding J and Z which require motion).\n",
    "\n",
    "The dataset format is patterned to match closely with the classic MNIST. \n",
    "\n",
    "Each training and test case represents a label (0-25) as a one-to-one map for each alphabetic letter A-Z (and no cases for 9=J or 25=Z because of gesture motions). \n",
    "\n",
    "The training data (27,455 cases) and test data (7172 cases) are approximately half the size of the standard MNIST but otherwise similar with a header row of label, pixel1,pixel2….pixel784 which represent a single 28x28 pixel image with grayscale values between 0-255. \n",
    "\n",
    "The original hand gesture image data represented multiple users repeating the gesture against different backgrounds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://d.newsweek.com/en/full/1394686/asl-getty-images.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMPORTING LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
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   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import tensorflow as tf  # Importing the TensorFlow library for deep learning\n",
    "import pydot  # Importing the pydot library for generating model graphs\n",
    "import matplotlib.pyplot as plt  # Importing the matplotlib library for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing specific modules from TensorFlow and Keras\n",
    "from tensorflow.keras.datasets import cifar10  # Importing the CIFAR-10 dataset\n",
    "from tensorflow.keras.models import Sequential  # Importing the Sequential class for creating models\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization  # Importing specific layer types\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator  # Importing the ImageDataGenerator for image data augmentation\n",
    "from sklearn.preprocessing import LabelBinarizer  # Importing the LabelBinarizer class for binarizing class labels\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau  # Importing the ReduceLROnPlateau callback for reducing learning rate\n",
    "from IPython.display import SVG  # Importing the SVG class for displaying SVG images\n",
    "from tensorflow.keras.utils import model_to_dot  # Importing the model_to_dot function for converting models to graphs\n",
    "from tensorflow.keras.utils import plot_model  # Importing the plot_model function for plotting models\n",
    "from tensorflow.keras.preprocessing import image  # Importing the image module for loading and preprocessing images\n",
    "from matplotlib.pyplot import imshow  # Importing the imshow function for displaying images\n",
    "from tensorflow.keras.applications.imagenet_utils import preprocess_input  # Importing the preprocess_input function for image preprocessing\n",
    "from sklearn.model_selection import train_test_split  # Importing the train_test_split function for data splitting\n",
    "\n",
    "# The imports above provide necessary tools for deep learning model building and training using TensorFlow and Keras.\n",
    "# Each import statement brings in specific functionalities that will be used in the code later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## READING DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"C:/Users/saisr/Downloads/sign_mnist_train.csv/sign_mnist_train.csv\")\n",
    "test = pd.read_csv(\"C:/Users/saisr/Downloads/sign_mnist_test.csv/sign_mnist_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
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       "      <td>236</td>\n",
       "      <td>230</td>\n",
       "      <td>240</td>\n",
       "      <td>253</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "      <td>191</td>\n",
       "      <td>193</td>\n",
       "      <td>195</td>\n",
       "      <td>199</td>\n",
       "      <td>201</td>\n",
       "      <td>202</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "      <td>64</td>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>46</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21</td>\n",
       "      <td>72</td>\n",
       "      <td>79</td>\n",
       "      <td>87</td>\n",
       "      <td>101</td>\n",
       "      <td>115</td>\n",
       "      <td>124</td>\n",
       "      <td>131</td>\n",
       "      <td>135</td>\n",
       "      <td>139</td>\n",
       "      <td>...</td>\n",
       "      <td>187</td>\n",
       "      <td>189</td>\n",
       "      <td>192</td>\n",
       "      <td>193</td>\n",
       "      <td>194</td>\n",
       "      <td>194</td>\n",
       "      <td>194</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "      <td>112</td>\n",
       "      <td>118</td>\n",
       "      <td>123</td>\n",
       "      <td>127</td>\n",
       "      <td>131</td>\n",
       "      <td>133</td>\n",
       "      <td>136</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>175</td>\n",
       "      <td>177</td>\n",
       "      <td>178</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "      <td>178</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>178</td>\n",
       "      <td>...</td>\n",
       "      <td>232</td>\n",
       "      <td>223</td>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>223</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>220</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>191</td>\n",
       "      <td>194</td>\n",
       "      <td>196</td>\n",
       "      <td>198</td>\n",
       "      <td>201</td>\n",
       "      <td>203</td>\n",
       "      <td>204</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>57</td>\n",
       "      <td>78</td>\n",
       "      <td>64</td>\n",
       "      <td>47</td>\n",
       "      <td>62</td>\n",
       "      <td>65</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>171</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>173</td>\n",
       "      <td>173</td>\n",
       "      <td>173</td>\n",
       "      <td>173</td>\n",
       "      <td>173</td>\n",
       "      <td>172</td>\n",
       "      <td>...</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>198</td>\n",
       "      <td>196</td>\n",
       "      <td>195</td>\n",
       "      <td>194</td>\n",
       "      <td>183</td>\n",
       "      <td>85</td>\n",
       "      <td>65</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      6     149     149     150     150     150     151     151     150   \n",
       "1      5     126     128     131     132     133     134     135     135   \n",
       "2     10      85      88      92      96     105     123     135     143   \n",
       "3      0     203     205     207     206     207     209     210     209   \n",
       "4      3     188     191     193     195     199     201     202     203   \n",
       "5     21      72      79      87     101     115     124     131     135   \n",
       "6     10      93     100     112     118     123     127     131     133   \n",
       "7     14     177     177     177     177     177     178     179     179   \n",
       "8      3     191     194     196     198     201     203     204     205   \n",
       "9      7     171     172     172     173     173     173     173     173   \n",
       "\n",
       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0     151  ...       138       148       127        89        82        96   \n",
       "1     136  ...        47       104       194       183       186       184   \n",
       "2     147  ...        68       166       242       227       230       227   \n",
       "3     210  ...       154       248       247       248       253       236   \n",
       "4     203  ...        26        40        64        48        29        46   \n",
       "5     139  ...       187       189       192       193       194       194   \n",
       "6     136  ...       173       175       177       178       180       180   \n",
       "7     178  ...       232       223       224       224       223       221   \n",
       "8     205  ...        43        57        78        64        47        62   \n",
       "9     172  ...       199       199       198       196       195       194   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0       106       112       120       107  \n",
       "1       184       184       182       180  \n",
       "2       226       225       224       222  \n",
       "3       230       240       253       255  \n",
       "4        49        46        46        53  \n",
       "5       194       195       195       194  \n",
       "6       181       181       181       183  \n",
       "7       221       221       220       219  \n",
       "8        65        62        62        68  \n",
       "9       183        85        65       124  \n",
       "\n",
       "[10 rows x 785 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separating dependent and independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train.drop(labels = [\"label\"],axis = 1) \n",
    "y_train = train[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>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>...</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>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>...</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>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>...</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>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>...</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>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>...</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>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>...</td>\n",
       "      <td>92</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>108</td>\n",
       "      <td>133</td>\n",
       "      <td>163</td>\n",
       "      <td>157</td>\n",
       "      <td>163</td>\n",
       "      <td>164</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  pixel9  \\\n",
       "0     107     118     127     134     139     143     146     150     153   \n",
       "1     155     157     156     156     156     157     156     158     158   \n",
       "2     187     188     188     187     187     186     187     188     187   \n",
       "3     211     211     212     212     211     210     211     210     210   \n",
       "4     164     167     170     172     176     179     180     184     185   \n",
       "\n",
       "   pixel10  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0      156  ...       207       207       207       207       206       206   \n",
       "1      157  ...        69       149       128        87        94       163   \n",
       "2      186  ...       202       201       200       199       198       199   \n",
       "3      211  ...       235       234       233       231       230       226   \n",
       "4      186  ...        92       105       105       108       133       163   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0       206       204       203       202  \n",
       "1       175       103       135       149  \n",
       "2       198       195       194       195  \n",
       "3       225       222       229       163  \n",
       "4       157       163       164       179  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3\n",
       "1     6\n",
       "2     2\n",
       "3     2\n",
       "4    13\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see the frequency distribution for each label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d7f48ef520>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = test.drop(labels = [\"label\"],axis = 1) \n",
    "y_test = test[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train.drop(labels = [\"label\"],axis = 1) \n",
    "y_train = train[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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>...</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>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>...</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>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>...</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>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>...</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>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>...</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>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>...</td>\n",
       "      <td>92</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>108</td>\n",
       "      <td>133</td>\n",
       "      <td>163</td>\n",
       "      <td>157</td>\n",
       "      <td>163</td>\n",
       "      <td>164</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  pixel9  \\\n",
       "0     107     118     127     134     139     143     146     150     153   \n",
       "1     155     157     156     156     156     157     156     158     158   \n",
       "2     187     188     188     187     187     186     187     188     187   \n",
       "3     211     211     212     212     211     210     211     210     210   \n",
       "4     164     167     170     172     176     179     180     184     185   \n",
       "\n",
       "   pixel10  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0      156  ...       207       207       207       207       206       206   \n",
       "1      157  ...        69       149       128        87        94       163   \n",
       "2      186  ...       202       201       200       199       198       199   \n",
       "3      211  ...       235       234       233       231       230       226   \n",
       "4      186  ...        92       105       105       108       133       163   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0       206       204       203       202  \n",
       "1       175       103       135       149  \n",
       "2       198       195       194       195  \n",
       "3       225       222       229       163  \n",
       "4       157       163       164       179  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalize data set to 0 to 1 range. This helps the optimizer to converge faster as compared to 0-255 range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.788235</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.780392</td>\n",
       "      <td>0.776471</td>\n",
       "      <td>0.780392</td>\n",
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       "      <td>0.760784</td>\n",
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       "      <td>0.831373</td>\n",
       "      <td>0.827451</td>\n",
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       "      <td>0.827451</td>\n",
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       "      <td>0.921569</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>0.913725</td>\n",
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       "      <td>0.901961</td>\n",
       "      <td>0.886275</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.870588</td>\n",
       "      <td>0.898039</td>\n",
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       "      <td>0.674510</td>\n",
       "      <td>0.690196</td>\n",
       "      <td>0.701961</td>\n",
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       "      <td>0.721569</td>\n",
       "      <td>0.725490</td>\n",
       "      <td>0.729412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360784</td>\n",
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       "      <td>0.411765</td>\n",
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       "      <td>0.521569</td>\n",
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       "      <td>0.917647</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.870588</td>\n",
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       "      <th>27452</th>\n",
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       "      <td>0.682353</td>\n",
       "      <td>0.678431</td>\n",
       "      <td>0.678431</td>\n",
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       "    <tr>\n",
       "      <th>27453</th>\n",
       "      <td>0.694118</td>\n",
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       "      <td>0.721569</td>\n",
       "      <td>0.725490</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.741176</td>\n",
       "      <td>0.745098</td>\n",
       "      <td>0.749020</td>\n",
       "      <td>0.749020</td>\n",
       "      <td>0.745098</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.219608</td>\n",
       "      <td>0.105882</td>\n",
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       "      <td>0.400000</td>\n",
       "      <td>0.309804</td>\n",
       "      <td>0.184314</td>\n",
       "      <td>0.250980</td>\n",
       "      <td>0.341176</td>\n",
       "      <td>0.364706</td>\n",
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       "    <tr>\n",
       "      <th>27454</th>\n",
       "      <td>0.701961</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.713726</td>\n",
       "      <td>0.709804</td>\n",
       "      <td>0.713726</td>\n",
       "      <td>0.717647</td>\n",
       "      <td>0.713726</td>\n",
       "      <td>0.713726</td>\n",
       "      <td>...</td>\n",
       "      <td>0.423529</td>\n",
       "      <td>0.517647</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.760784</td>\n",
       "      <td>0.839216</td>\n",
       "      <td>0.796078</td>\n",
       "      <td>0.772549</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>0.819608</td>\n",
       "      <td>0.843137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27455 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         pixel1    pixel2    pixel3    pixel4    pixel5    pixel6    pixel7  \\\n",
       "0      0.419608  0.462745  0.498039  0.525490  0.545098  0.560784  0.572549   \n",
       "1      0.607843  0.615686  0.611765  0.611765  0.611765  0.615686  0.611765   \n",
       "2      0.733333  0.737255  0.737255  0.733333  0.733333  0.729412  0.733333   \n",
       "3      0.827451  0.827451  0.831373  0.831373  0.827451  0.823529  0.827451   \n",
       "4      0.643137  0.654902  0.666667  0.674510  0.690196  0.701961  0.705882   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
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       "27451  0.592157  0.603922  0.615686  0.619608  0.627451  0.631373  0.639216   \n",
       "27452  0.682353  0.682353  0.682353  0.682353  0.682353  0.686275  0.686275   \n",
       "27453  0.694118  0.709804  0.721569  0.725490  0.733333  0.741176  0.745098   \n",
       "27454  0.701961  0.705882  0.705882  0.705882  0.713726  0.709804  0.713726   \n",
       "\n",
       "         pixel8    pixel9   pixel10  ...  pixel775  pixel776  pixel777  \\\n",
       "0      0.588235  0.600000  0.611765  ...  0.811765  0.811765  0.811765   \n",
       "1      0.619608  0.619608  0.615686  ...  0.270588  0.584314  0.501961   \n",
       "2      0.737255  0.733333  0.729412  ...  0.792157  0.788235  0.784314   \n",
       "3      0.823529  0.823529  0.827451  ...  0.921569  0.917647  0.913725   \n",
       "4      0.721569  0.725490  0.729412  ...  0.360784  0.411765  0.411765   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "27450  0.756863  0.756863  0.760784  ...  0.517647  0.647059  0.388235   \n",
       "27451  0.643137  0.650980  0.654902  ...  0.776471  0.776471  0.776471   \n",
       "27452  0.682353  0.678431  0.678431  ...  0.474510  0.768627  0.819608   \n",
       "27453  0.749020  0.749020  0.745098  ...  0.466667  0.219608  0.105882   \n",
       "27454  0.717647  0.713726  0.713726  ...  0.423529  0.517647  0.666667   \n",
       "\n",
       "       pixel778  pixel779  pixel780  pixel781  pixel782  pixel783  pixel784  \n",
       "0      0.811765  0.807843  0.807843  0.807843  0.800000  0.796078  0.792157  \n",
       "1      0.341176  0.368627  0.639216  0.686275  0.403922  0.529412  0.584314  \n",
       "2      0.780392  0.776471  0.780392  0.776471  0.764706  0.760784  0.764706  \n",
       "3      0.905882  0.901961  0.886275  0.882353  0.870588  0.898039  0.639216  \n",
       "4      0.423529  0.521569  0.639216  0.615686  0.639216  0.643137  0.701961  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "27450  0.301961  0.203922  0.784314  0.917647  0.784314  0.870588  0.882353  \n",
       "27451  0.776471  0.776471  0.768627  0.764706  0.764706  0.764706  0.760784  \n",
       "27452  0.815686  0.807843  0.800000  0.796078  0.792157  0.784314  0.784314  \n",
       "27453  0.227451  0.400000  0.309804  0.184314  0.250980  0.341176  0.364706  \n",
       "27454  0.760784  0.839216  0.796078  0.772549  0.803922  0.819608  0.843137  \n",
       "\n",
       "[27455 rows x 784 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping to the format(3D)that is accepted by CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.values.reshape(-1,28,28,1)\n",
    "x_test = x_test.values.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets see the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d7f4aff190>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[1].reshape(28,28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data augmentation is a technique which allows us to create more samples artifically. New images are created by distorting original images slightly. For example, taking random crop of the image or rotating it or flipping it sideways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataAug = ImageDataGenerator(\n",
    "        rotation_range=15,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        zoom_range = 0.2, # Randomly zoom image \n",
    "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=False)  # randomly flip images\n",
    "\n",
    "dataAug.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27455, 28, 28, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ONE-HOT ENCODING - DEPENDENT VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_binarizer = LabelBinarizer()\n",
    "y_train = label_binarizer.fit_transform(y_train)\n",
    "y_test = label_binarizer.fit_transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoded classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_binarizer.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"K\",\"L\",'M','N','O','P','Q','R','S','T','U','V','W','X','Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27455, 24)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SPLITTING THE TRAIN DATA INTO TRAIN AND VALIDATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size = 0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reduce learning rate when a metric (val_accuracy) has stopped improving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_reduce = ReduceLROnPlateau(monitor='val_accuracy', patience = 2, verbose=1,factor=0.5, min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Designing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), padding = \"same\", input_shape=(28,28,1), activation=\"relu\"))\n",
    "model.add(Conv2D(32, (3, 3), padding = \"same\", input_shape=(28,28,1), activation=\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2),strides = (2,2)))\n",
    "\n",
    "\n",
    "model.add(Conv2D(64, (3, 3),padding = \"same\", activation=\"relu\"))\n",
    "model.add(Conv2D(64, (3, 3),padding = \"same\", activation=\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Conv2D(128, (3, 3),padding = \"same\", activation=\"relu\"))\n",
    "model.add(Conv2D(128, (3, 3),padding = \"same\", activation=\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(3, 3),strides = (2,2)))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(512, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(24, activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 28, 28, 32)        9248      \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 28, 28, 32)       128       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 14, 14, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 14, 14, 64)        36928     \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 14, 14, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 14, 14, 64)        0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 14, 14, 128)       73856     \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 14, 14, 128)       147584    \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 14, 14, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 6, 6, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 6, 6, 128)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 4608)              0         \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 4608)             18432     \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               2359808   \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 24)                12312     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,677,880\n",
      "Trainable params: 2,668,216\n",
      "Non-trainable params: 9,664\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer=\"adam\",\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "344/344 [==============================] - 126s 358ms/step - loss: 1.0060 - accuracy: 0.7178 - val_loss: 5.5156 - val_accuracy: 0.1268 - lr: 0.0010\n",
      "Epoch 2/30\n",
      "344/344 [==============================] - 162s 472ms/step - loss: 0.1159 - accuracy: 0.9626 - val_loss: 0.0081 - val_accuracy: 0.9985 - lr: 0.0010\n",
      "Epoch 3/30\n",
      "344/344 [==============================] - 124s 360ms/step - loss: 0.0403 - accuracy: 0.9867 - val_loss: 0.0138 - val_accuracy: 0.9960 - lr: 0.0010\n",
      "Epoch 4/30\n",
      "344/344 [==============================] - ETA: 0s - loss: 0.0283 - accuracy: 0.9903\n",
      "Epoch 4: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "344/344 [==============================] - 140s 407ms/step - loss: 0.0283 - accuracy: 0.9903 - val_loss: 0.0167 - val_accuracy: 0.9933 - lr: 0.0010\n",
      "Epoch 5/30\n",
      "344/344 [==============================] - 3501s 10s/step - loss: 0.0127 - accuracy: 0.9957 - val_loss: 0.0013 - val_accuracy: 0.9998 - lr: 5.0000e-04\n",
      "Epoch 6/30\n",
      "344/344 [==============================] - 119s 346ms/step - loss: 0.0081 - accuracy: 0.9975 - val_loss: 1.9392e-05 - val_accuracy: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 7/30\n",
      "344/344 [==============================] - 114s 331ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 2.4074e-05 - val_accuracy: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 8/30\n",
      "344/344 [==============================] - ETA: 0s - loss: 0.0065 - accuracy: 0.9980\n",
      "Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "344/344 [==============================] - 110s 321ms/step - loss: 0.0065 - accuracy: 0.9980 - val_loss: 5.9523e-05 - val_accuracy: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 9/30\n",
      "344/344 [==============================] - 119s 348ms/step - loss: 0.0044 - accuracy: 0.9985 - val_loss: 6.7615e-04 - val_accuracy: 0.9998 - lr: 2.5000e-04\n",
      "Epoch 10/30\n",
      "344/344 [==============================] - ETA: 0s - loss: 0.0035 - accuracy: 0.9990\n",
      "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "344/344 [==============================] - 151s 439ms/step - loss: 0.0035 - accuracy: 0.9990 - val_loss: 1.3297e-06 - val_accuracy: 1.0000 - lr: 2.5000e-04\n",
      "Epoch 11/30\n",
      "344/344 [==============================] - 134s 391ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 1.5789e-05 - val_accuracy: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 12/30\n",
      "344/344 [==============================] - ETA: 0s - loss: 0.0030 - accuracy: 0.9991\n",
      "Epoch 12: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "344/344 [==============================] - 137s 398ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.0150 - val_accuracy: 0.9991 - lr: 1.2500e-04\n",
      "Epoch 13/30\n",
      "344/344 [==============================] - 123s 359ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 2.9206e-07 - val_accuracy: 1.0000 - lr: 6.2500e-05\n",
      "Epoch 14/30\n",
      "344/344 [==============================] - ETA: 0s - loss: 0.0015 - accuracy: 0.9996\n",
      "Epoch 14: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "344/344 [==============================] - 123s 359ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 2.4345e-07 - val_accuracy: 1.0000 - lr: 6.2500e-05\n",
      "Epoch 15/30\n",
      "344/344 [==============================] - 125s 362ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 2.4771e-07 - val_accuracy: 1.0000 - lr: 3.1250e-05\n",
      "Epoch 16/30\n",
      "344/344 [==============================] - ETA: 0s - loss: 0.0015 - accuracy: 0.9995\n",
      "Epoch 16: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "344/344 [==============================] - 138s 401ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 3.0244e-07 - val_accuracy: 1.0000 - lr: 3.1250e-05\n",
      "Epoch 17/30\n",
      "344/344 [==============================] - 134s 391ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 2.6134e-07 - val_accuracy: 1.0000 - lr: 1.5625e-05\n",
      "Epoch 18/30\n",
      "344/344 [==============================] - ETA: 0s - loss: 9.9753e-04 - accuracy: 0.9998\n",
      "Epoch 18: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "344/344 [==============================] - 131s 381ms/step - loss: 9.9753e-04 - accuracy: 0.9998 - val_loss: 2.0857e-07 - val_accuracy: 1.0000 - lr: 1.5625e-05\n",
      "Epoch 19/30\n",
      "344/344 [==============================] - 131s 379ms/step - loss: 8.3153e-04 - accuracy: 0.9998 - val_loss: 2.1653e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 20/30\n",
      "344/344 [==============================] - 130s 377ms/step - loss: 7.6739e-04 - accuracy: 0.9998 - val_loss: 1.8069e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 21/30\n",
      "344/344 [==============================] - 137s 397ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 1.7776e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 22/30\n",
      "344/344 [==============================] - 150s 436ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 1.8914e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 23/30\n",
      "344/344 [==============================] - 139s 406ms/step - loss: 8.9272e-04 - accuracy: 0.9998 - val_loss: 1.5010e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 24/30\n",
      "344/344 [==============================] - 134s 389ms/step - loss: 8.0947e-04 - accuracy: 0.9998 - val_loss: 8.2507e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 25/30\n",
      "344/344 [==============================] - 125s 364ms/step - loss: 9.5502e-04 - accuracy: 0.9998 - val_loss: 1.5138e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 26/30\n",
      "344/344 [==============================] - 130s 379ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 1.3784e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 27/30\n",
      "344/344 [==============================] - 132s 385ms/step - loss: 8.4535e-04 - accuracy: 0.9998 - val_loss: 2.5022e-04 - val_accuracy: 0.9998 - lr: 1.0000e-05\n",
      "Epoch 28/30\n",
      "344/344 [==============================] - 134s 390ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 1.2496e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 29/30\n",
      "344/344 [==============================] - 130s 378ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 1.1454e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n",
      "Epoch 30/30\n",
      "344/344 [==============================] - 139s 405ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 1.3280e-07 - val_accuracy: 1.0000 - lr: 1.0000e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1d78ed55c40>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    batch_size=64,\n",
    "    epochs=30,\n",
    "    validation_data=(x_val, y_val),\n",
    "    shuffle=True,\n",
    "    callbacks = [lr_reduce]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225/225 [==============================] - 11s 50ms/step - loss: 0.0450 - accuracy: 0.9898\n",
      "\n",
      "Loss = 0.045014481991529465\n",
      "Test Accuracy = 0.9898215532302856\n"
     ]
    }
   ],
   "source": [
    "preds = model.evaluate(x_test,y_test)\n",
    "\n",
    "print()\n",
    "print (\"Loss = \" + str(preds[0]))\n",
    "print (\"Test Accuracy = \" + str(preds[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try prediction on your own image!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 223ms/step\n",
      "Predicted letter is F\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = \"C:/Users/saisr/Desktop/WIN_20230525_23_50_53_Pro.jpg\"\n",
    "img = image.load_img(img_path, target_size=(28, 28),  color_mode=\"grayscale\")\n",
    "imshow(img,cmap = \"gray\")\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "#x = x/255\n",
    "x = np.vstack([x])\n",
    "print(\"Predicted letter is \"+ class_names[np.argmax(model.predict(x))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking on Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225/225 [==============================] - 10s 44ms/step\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted letter is V \n",
      "Actual Answer: V\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=5\n",
    "plt.imshow(x_test[n].reshape(28,28),cmap=\"gray\") \n",
    "plt.grid(False) \n",
    "print(\"Predicted letter is\",class_names[np.argmax(preds[n])],\"\\nActual Answer:\",class_names[np.argmax(y_test[n])]) # Prediction - True Answer"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}