MNIST Image Classification on Sign Language.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST Image Classification on Sign Language\n",
    "* In this project I am using MNIST dataset of hand made sign language images to classify each image into a class of 26 alphabets from A-Z.\n",
    "* KAGGLE LINK: https://www.kaggle.com/datamunge/sign-language-mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train = pd.read_csv(\"sign_datasets/sign_mnist_train.csv\")\n",
    "mnist_test = pd.read_csv(\"sign_datasets/sign_mnist_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27455, 785) (7172, 785)\n"
     ]
    }
   ],
   "source": [
    "print(mnist_train.shape, mnist_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>pixel1</th>\n",
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       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
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       "      <th>pixel784</th>\n",
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       "      <td>...</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>211</td>\n",
       "      <td>210</td>\n",
       "      <td>211</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
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       "      <td>164</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
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      ],
      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      3     107     118     127     134     139     143     146     150   \n",
       "1      6     155     157     156     156     156     157     156     158   \n",
       "2      2     187     188     188     187     187     186     187     188   \n",
       "3      2     211     211     212     212     211     210     211     210   \n",
       "4     13     164     167     170     172     176     179     180     184   \n",
       "\n",
       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0     153  ...       207       207       207       207       206       206   \n",
       "1     158  ...        69       149       128        87        94       163   \n",
       "2     187  ...       202       201       200       199       198       199   \n",
       "3     210  ...       235       234       233       231       230       226   \n",
       "4     185  ...        92       105       105       108       133       163   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0       206       204       203       202  \n",
       "1       175       103       135       149  \n",
       "2       198       195       194       195  \n",
       "3       225       222       229       163  \n",
       "4       157       163       164       179  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>pixel5</th>\n",
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       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <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",
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       "      <td>210</td>\n",
       "      <td>209</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>248</td>\n",
       "      <td>247</td>\n",
       "      <td>248</td>\n",
       "      <td>253</td>\n",
       "      <td>236</td>\n",
       "      <td>230</td>\n",
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       "      <td>253</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>199</td>\n",
       "      <td>201</td>\n",
       "      <td>202</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "      <td>64</td>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>46</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      6     149     149     150     150     150     151     151     150   \n",
       "1      5     126     128     131     132     133     134     135     135   \n",
       "2     10      85      88      92      96     105     123     135     143   \n",
       "3      0     203     205     207     206     207     209     210     209   \n",
       "4      3     188     191     193     195     199     201     202     203   \n",
       "\n",
       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0     151  ...       138       148       127        89        82        96   \n",
       "1     136  ...        47       104       194       183       186       184   \n",
       "2     147  ...        68       166       242       227       230       227   \n",
       "3     210  ...       154       248       247       248       253       236   \n",
       "4     203  ...        26        40        64        48        29        46   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0       106       112       120       107  \n",
       "1       184       184       182       180  \n",
       "2       226       225       224       222  \n",
       "3       230       240       253       255  \n",
       "4        49        46        46        53  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n"
     ]
    }
   ],
   "source": [
    "print(mnist_train.isna().sum().sum(), mnist_test.isna().sum().sum())\n",
    "# Data is completely clean without any missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train_data = mnist_train.loc[:, \"pixel1\":]\n",
    "mnist_train_label = mnist_train.loc[:, \"label\"]\n",
    "\n",
    "mnist_test_data = mnist_test.loc[:, \"pixel1\":]\n",
    "mnist_test_label = mnist_test.loc[:, \"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Normalization\n",
    "mnist_train_data = mnist_train_data/255.0\n",
    "mnist_test_data = mnist_test_data/255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.41960784, 0.4627451 , 0.49803922, ..., 0.8       , 0.79607843,\n",
       "        0.79215686],\n",
       "       [0.60784314, 0.61568627, 0.61176471, ..., 0.40392157, 0.52941176,\n",
       "        0.58431373],\n",
       "       [0.73333333, 0.7372549 , 0.7372549 , ..., 0.76470588, 0.76078431,\n",
       "        0.76470588],\n",
       "       ...,\n",
       "       [0.68235294, 0.68235294, 0.68235294, ..., 0.79215686, 0.78431373,\n",
       "        0.78431373],\n",
       "       [0.69411765, 0.70980392, 0.72156863, ..., 0.25098039, 0.34117647,\n",
       "        0.36470588],\n",
       "       [0.70196078, 0.70588235, 0.70588235, ..., 0.80392157, 0.81960784,\n",
       "        0.84313725]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(mnist_train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMAGE LABEL: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS4AAAD8CAYAAADJwUnTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHoNJREFUeJzt3X+wXGWd5/H3JxdCID8ISUgISYCAGQuKGmE3BbtiOSCi0aVAx5VKrLGgSo1bZWbG1dkaZKeQYmtq0VVcqybrbhhS4g8EJuoYxwi4rBRoDVYiMEIS8oNsgEsCISQkJAjJhe/+0R230337ebrvOfd2n/B5Vd3K7f72ec7Tfe/95pznfM/zKCIwM6uScb3ugJlZt5y4zKxynLjMrHKcuMyscpy4zKxynLjMrHKcuMyscpy4zKxynLjMrHKOG8udjR8/PiZMmNA2XqSKX9KIty0q1+9e9q3Xirz3fv7cpk2bloxPmTIlGR8aGmobe+utt5LbpuIvvPACr7zySqEPTlI3f4j3RcSiIvsbiUKJS9Ii4JvAAPD3EXFL6vUTJkxg4cKFbeO9TFy57VN9K5q4cvFx40bvwHi0k8Nxx438V2xgYKDEnhyt6Pu+5pprkvEPfvCDyfjevXvbxvbv35/c9vXXX28b+/SnP53cdhTMGOsdQoFTRUkDwHLgQ8B5wBJJ55XVMTPrHUkdffVKkSOui4CtEbENQNJdwNXAhjI6Zma90+lR/ptvvjnKPRlekcQ1B3iu4fEgcHHziyQtBZYCnHDCCQV2Z2ZjpZ/HF6FY4hrunbUM9kTECmAFwJQpUzyHjlmf6/VpYCeKJK5BYF7D47nAjmLdMbN+0O+Jq8jlqrXAAknzJY0HFgOry+mWmfXSMTs4HxFDkpYB91Erh1gZEesz2xQqKxhNudqZIj+k3PsqGi9SNpCqJ4L8uOT48eOT8UOHDnXdpyNyfSvyM8l9ZrlB5+3btyfje/bsScZTJQ3HH398cttULWRZJSRlJqVc2ZSkM4GVwKnAHuDPImIw1WahOq6IWAOsKdKGmfUXSaXVDjaUTV1BbXhpraTVEdFYffA14DsRcYek9wH/Ffhkql3f8mNmLUo8VfxD2VREHAKOlE01Og94oP79L4eJt3DiMrMWXSSuGZLWNXwtbWpquLKpOU2v+RfgY/XvPwpMljQ91b8xvVfRzKqhizGu3RHR/j6+zsqm/gr4O0nXAQ8BzwPJAU4nLjNrUeLgfLZsKiJ2AH9a3+8k4GMRsS/VqBOXmR2lzMF5GsqmqB1JLQY+0bS/GcCeiHgL+BK1K4xJHuMysxZlDc5HxBBwpGxqI3BPRKyXdLOkq+ovuxTYJGkzMAv421y7Y37ElauXGq12+7kSONe33HtL1XkdPnw4ue1JJ52UjL/rXe9KxlPTFAGsW7eubSz3v3quRuzhhx8e8fa5Oq1c7dzTTz+djOfquE488cS2sVztXGrbfqzjGq5sKiJubPh+FbCqmzZ9qmhmLfr5P3pw4jKzJr2+nacTTlxm1sKJy8wqZzSnCy+DE5eZtfARl5lVise4hpG6zJybxmQ0D19zbY/mlDu5koXUFCgAJ598ctvY2Wefndz2ggsuSMbf8Y53JOO59mfOnNk2dvDgweS2uVKNbdu2JeMvvPBC21jRsoF9+5KF3bzxxhvJ+OzZs9vGJk+ePOK2y0o4TlxmVjlOXGZWOR6cN7NK8RiXmVWSE5eZVY4Tl5lVjhOXmVWOE1eDiCg0rU1qKpKiH3SRqWNyy0nt378/Gc/VaU2bNi0Z//CHP9w2dv755ye3PfXUU5PxiRMnJuOpKVYgfXVq6tSpyW2POy7967l0afP05kf7yle+0jaWq7PKXVU7cOBAMr5ly5Zk/OKLL24by03n8+KLLybjRZU8keCo8BGXmbXwEZeZVY4Tl5lVTr8nrv4+kTWzMdfpfPOdJjdJiyRtkrRV0vXDxM+Q9EtJj0n6naT2g7Z1Tlxm1qKsxCVpAFgOfIjaitVLJJ3X9LK/obaIxoXUVgH6H7l2fapoZi1KvKp4EbA1IrYBSLoLuBrY0PCaAKbUvz+ZpnUXh+PEZWYtShzjmgM81/B4EGiuBbkJuF/SnwMTgffnGi2UuCRtB14F3gSGMktxA6O3PNlozpeVk6sJ+v3vf5+MT58+PRl/73vfm4y/+93vbhs766yzktvm5kDLfa6TJk1KxlP/cx86dKjQvnN1YKk5t3LvO1dDlutbalk2gCVLlrSN5eYKmzJlSttYGcuTdXmT9QxJjW92RUSsaGxumG2aP7wlwLcj4uuS/i3wXUnn1xeIHVYZR1yXRcTuEtoxsz7RReLanTlgGQTmNTyeS+up4KeARQAR8c+SJgAzgF3tGvXgvJm1KPGq4lpggaT5ksZTG3xf3fSaZ4HL6/s9F5gAvJRqtGjiCmrnpr+VlL7/wswqY9y4cR195UTEELAMuA/YSO3q4XpJN0u6qv6yLwKfkfQvwA+A6yJzLl70VPGSiNghaSbwC0lPRcRDjS+oJ7SlkL8Hy8x6r+yJBCNiDbCm6bkbG77fAFzSTZuFjrgiYkf9313Aj6ld+mx+zYqIWBgRC3M3I5tZfyizAHU0jDhxSZooafKR74EPAE+W1TEz651+T1xFThVnAT+ud/444M6IuLeUXplZT/X7vYojTlz1Sth3jWC7ke4yqWh9WK5fqfqY3Bp7uXUTr7zyymT8kkvSp/+pObVOOOGE5La5+GuvvZaM59ZGTP1ccm3v2tX2ajgAa9asScZz86ClFPl9AHjppeRFMVatWtU2llvLMhVPzVnXjWM2cZnZsckTCZpZJfmIy8wqx4nLzCrHicvMKqXXpQ6dcOIysxZOXF3IlTSkrnSM1nQ5nchdds9NLXP22Wcn47lpb1LvPVeqkbubIXd5PVfScPvtt7eNPfPMM8ltX3nllWS8yNQ0Rf8wc59LrgQmtbzZT3/60+S2qVvncmUYnfJVRTOrHB9xmVmleIzLzCrJicvMKseJy8wqx4PzZlYpHuMys0py4upCbiqRVN1Oro4rF88tR5VaYiy3PNmcOXMK7bvIYXvR+rZNmzYl4/fcc08y/tRTT7WN5f44cjVmue2LvPfc72Ku7Vzt3bnnnts29utf/3rE+3799deT23aqzMQlaRHwTWAA+PuIuKUp/g3gsvrDk4CZEZFce66vEpeZ9YeyEpekAWA5cAW1pcrWSlpdn2cegIj4jw2v/3Pgwly7/T0CZ2Y9UeLUzRcBWyNiW0QcAu4Crk68fgm1lX6SfMRlZkcpeSLBOcBzDY8HgYvb7PdMYD7wf3KNOnGZWYsuThVnSFrX8HhFRKxobGqYbdoNIC4GVkVEdv5pJy4za9FF4todEQsT8UFgXsPjucCONq9dDHyuk516jMvMWpQ4xrUWWCBpvqTx1JLT6mH2907gFOCfO2nUicvMjtJp0uokcUXEELAMuA/YCNwTEesl3SzpqoaXLgHuig6XARvTU8WISNZi5eZXGk25upzU3FC5mp358+cn47NmzUrGJ06cmIyn5H4PcjVo3/3ud5PxjRs3JuMnnnhi21huADjX99Eskiy6jF6ubzt2tDtbys9DduGF7asFHnrooXTHOlTmZxsRa4A1Tc/d2PT4pm7a9BiXmbXwvYpmVim+V9HMKsmJy8wqx4nLzCrHicvMKqXkW35GhROXmbWo/BGXpJXAlcCuiDi//tw04G7gLGA7cE1E7O1kh71c/zAlV0P26quvto2dc845yW1z820dPHgwGd+7N/3RnnDCCW1jkyZNSm576NChZDzXtyK1WLm1CYuss5mLF60hy8nVYq1du7Zt7LLLLmsbg/Tv2913353uWIf6PXF1cjz4bWBR03PXAw9ExALggfpjMztGlHjLz6jIJq6IeAjY0/T01cAd9e/vAD5Scr/MrIf6PXGNdIxrVkTsBIiInZJmltgnM+uhXielToz64LykpcBSgPHjx4/27sysBP1+VXGkvXtR0myA+r+72r0wIlZExMKIWJgbpDaz/tDvp4ojTVyrgWvr318L/KSc7phZP+j3xNVJOcQPgEupTdE6CHwZuAW4R9KngGeBj49mJ81s7PQ6KXUim7giYkmb0OXd7iwiRnWtu5TcD2Lfvn3J+OHDh9vGJk+enNz25JNPTsZzXnvttWS8yFp6qfo0gF272o4CAPkatSKKrm1YZNsiNWKQXocTYPPmzW1juXU4n3766bax3Pxqnap84jKzt59+H5x34jKzFv1+xNXfadXMxlyZc87X21skaZOkrZKGvctG0jWSNkhaL+nOXJs+4jKzFmUdcUkaAJYDV1BbqmytpNURsaHhNQuALwGXRMTeTgrafcRlZi1KPOK6CNgaEdsi4hBwF7VbBht9Blh+ZKKGiEhfEcKJy8yGUWLimgM81/B4sP5coz8C/kjSryU9Iql5UocWfXWqWKTcoeg0JC+//HIyftJJJ7WN5ZYXmzp1ajI+YcKEZDx3hWdgYKBtLFcqsXXr1mQ8d1k/dxtXauqa3C9+0alniuy7qFz7qb49/vjjyW1TS74VKY05osuJBGdIWtfweEVErGhsbphtmn9wxwELqNWLzgUelnR+RLSdG6ivEpeZ9YcuEvvuiFiYiA8C8xoezwWaF5UcBB6JiMPA/5W0iVoiaztpmU8VzaxFiaeKa4EFkuZLGg8spnbLYKN/BC6r73cGtVPHbalGfcRlZi3KOpWOiCFJy4D7gAFgZUSsl3QzsC4iVtdjH5C0AXgT+E8RkRy7ceIysxZljgFGxBpgTdNzNzZ8H8AX6l8dceIys6McEzdZm9nbj+9VNLPK8RFXk9Gq1cp90LnpPnJTx8yePbttLDetzcSJE5Px1PJiAMcff3wyXkSu7ie3bFuujiv1Mytae1fkqKDolDm5fed+H1Pt535XU0ufpaZf6oYTl5lVise4zKySnLjMrHI8OG9mleMjLjOrFI9xmVklOXGZWeU4cXWhyPxKuVqn3DJcuX2n5uNKzY8E+b4Ved+QrgnK1YilaoJybUOxeqii820VWZ6s6OBzbt9F5uM6cOBActvU/G9lLRfnxGVmldLlRII94cRlZi18xGVmlePEZWaV48RlZpXjxGVmlVKFAtT+vnRgZj0xbty4jr46IWmRpE2Stkq6fpj4dZJekvR4/evTuTazR1ySVgJXArsi4vz6czdRW332pfrLbqjPK50UEdmapJTUtrn6ld27dyfjuTm1pk+f3jY2adKk5LapGjDI1xQVmRsqt2Zjbt3EIj+vnKJzXuUUmQus6JqPuc8ttX1uPq7U71NZZQxlHXFJGgCWA1dQW4ZsraTVEbGh6aV3R8SyTtvt5F1+GxhuZdlvRMQF9a9s0jKz6ihxebKLgK0RsS0iDgF3AVcX7V82cUXEQ8Ceojsys2roNGl1mLjmAM81PB6sP9fsY5J+J2mVpHnDxI9S5LhyWX1HKyWdUqAdM+szXSSuGZLWNXwtbW5qmOabz9N/CpwVEX8M/G/gjlz/Rpq4vgWcA1wA7AS+3u6FkpYeeVOjOV5iZuXpYnB+d0QsbPha0dTUINB4BDUX2NH4goh4OSKODOzdBvzrbP9G8qYi4sWIeDMi3qrv6KLEa1cceVMDAwMj2Z2ZjbESTxXXAgskzZc0HlgMrG7aV+NKNFcBG3ONjqiOS9LsiNhZf/hR4MmRtGNm/afMOq6IGJK0DLgPGABWRsR6STcD6yJiNfAXkq4ChqiNp1+Xa7eTcogfAJdSO5cdBL4MXCrpAmrnqtuBz47kTZlZfyqzALVedbCm6bkbG77/EvClbtrMJq6IWDLM07d3s5NGRca5UnNL5eaVytXGnHrqqcn4ySef3DY2ZcqU5La5WqrcKXRuPq9UDVvu837f+96XjN9///3JeG6esyJ1RUXm24J0bd4pp6SvJ02dOjUZz22f+5nv3LmzbWz79u3Jbbds2dI2lvs971S/V877lh8za+HEZWaV4okEzaySfMRlZpXjxGVmlePEZWaV48TVhdxUI6mygT170veB55bpKnL5O1cOkRvozE3Jk5sWZ2hoqG3s8OHDyW2nTZuWjC9evDgZv/XWW5PxiRMnto3lPpdcycGZZ55ZaPuU8ePHJ+O5n8lpp52WjF9++eVtY7nP9ODBg21jub+hTlRhIsG+Slxm1h98VdHMKsdHXGZWOU5cZlYpHuMys0py4jKzyvHgvJlVik8Vm0REcqqSXD1TaimtAwcOJLfN1Wnl6plS09rkasRyctPW5OJFpgrKTYPynve8Jxlfv359Mj44ONg2lptKKLUkXCdS9U65Oq3c78vpp5+ejB86dCgZf+qpp9rGiixtVhYnLjOrHCcuM6ucfk9c/T0CZ2Y9UeJiGUhaJGmTpK2Srk+87t9LCkkLc236iMvMjlLmRIKSBoDlwBXUlipbK2l1RGxoet1k4C+A33TSro+4zKxFiUdcFwFbI2JbRBwC7gKuHuZ1/wX4KvB6J406cZlZixIT1xzguYbHg/XnGvd1ITAvIv6p0/75VNHMWnQxOD9D0rqGxyuaVrMerqE/zL0jaRzwDTpYS7HRmCauXJY+8cQTk9tv27Yt2XZKbv6k1LxRkF7qKrcUVa4OK9e3nNdfb390navTyi0Blotfe+21yfjmzZvbxnLLcO3YsSMZz809NXPmzLaxd77zncltcz/TBx98MBl/4oknkvFUrVavV3zvsgB1d0SkBtMHgXkNj+cCjT/YycD5wIP1fZ4GrJZ0VUQ0JsSj+IjLzFqUWOS6FlggaT7wPLAY+MSRYETsA2YceSzpQeCvUkkLPMZlZsMoa4wrIoaAZcB9wEbgnohYL+lmSVeNtH8+4jKzFmUWoEbEGmBN03M3tnntpZ206cRlZkfxTdZmVklOXGZWOU5cZlY5lZ9IUNI84DvU6iveolZg9k1J04C7gbOA7cA1EbE31da4ceOSNUv79+9P9mXv3vbN5+bEytVp5WrIUj/IXM1Pbu6nXPzll18ecTw1hxnk543KzZGWq/NK1Zjl5p1KzYEGMG/evGT87LPPbhvLHVHcdtttyfgzzzyTjOd+pqn95z7TXF1gUVUY4+okrQ4BX4yIc4F/A3xO0nnA9cADEbEAeKD+2MyOAWXODjEasokrInZGxKP171+lVosxh9qNknfUX3YH8JHR6qSZja1+T1xdjXFJOgu4kNrUE7MiYifUkpuk9vdXmFml9PupYseJS9Ik4IfA5yNifxeTiC0FlkL+vN/M+sMxkbgkHU8taX0/In5Uf/pFSbPrR1uzgV3DbVu/U3wFwMSJE9N3xZpZz5U5keBoyfZOtdR7O7AxIm5tCK0GjkwNcC3wk/K7Z2a9cCyMcV0CfBJ4QtLj9eduAG4B7pH0KeBZ4OO5hnLLkz377LPJ7VMfVG5qmFw8Vw6R2r7o5emhoaFk/JVXXknGX3vttRHFIF2uAMWXXkuVU5xxxhnJbXPlEPv27UvGf/7zn7eN5ZZVy7VddNgjdUTT62lt4Bg4VYyIXzH8ZGAAl5fbHTPrB5VPXGb29tLr08BOOHGZWYt+H5x34jKzFj7iMrPKceIys0qpwhhXf5/ImllPlFnHJWmRpE2StkpqmYxB0n+Q9ISkxyX9qj6JQ9KYHnG98cYbySXGcvVMU6ZMGVEM8lPPFJkWJ1fHlavLOXDgQDJ+8ODBZDxVi5Vbwis3CJt7b7npglJTz7z00kvJbe+9995k/LHHHkvGU1P25P7oRnvqmCLG4miorH1IGgCWA1dQW6psraTVEbGh4WV3RsT/rL/+KuBWYFGqXZ8qmlmLEq8qXgRsjYhtAJLuojazzB8SV0Q0TsQ3kYYFY9tx4jKzo5Q8xjUHeK7h8SBw8TD7/BzwBWA88L5cox7jMrMWXYxxzZC0ruFraXNTwzTfckQVEcsj4hzgr4G/yfXPR1xm1qKLI67dEbEwER8EGgc65wI7Eq+/C/hWbqc+4jKzFiVeVVwLLJA0X9J4YDG1mWUa97Wg4eG/A7bkGvURl5m1KGuMKyKGJC0D7gMGgJURsV7SzcC6iFgNLJP0fuAwsJf/P11WW05cZnaUsicSjIg1wJqm525s+P4vu21zTBNX7gPJzZmVqsXKzaeVm9spF582bVrbWNH5k4rMtwW1+rh2cvVIM2bMSMYnTZqUjOd+Zo888kjb2KpVq5Lb7t69OxnPLZ1W5KghV/+WW0JsNG9SzvWtDP1eOe8jLjNr4cRlZpXjxGVmlVKFm6yduMyshScSNLPK8RGXmVWOE5eZVYrHuJqMGzcuWW+Vq8tJ1VrNnTs3ue3pp5+ejOe2T9UzFa0nStVhQb5OLDWX2OTJk5PbzpkzJxnfvHlzMn7nnXcm41u2tL97I1ePlHvfo/nHlavTKrrvXPspYzH+5MRlZpXjxGVmleOrimZWKR7jMrNKcuIys8px4jKzynHiMrPKqXzikjQP+A5wGvAWsCIivinpJuAzwJHF8W6oTxiWais5p9b06dOTfUmt0Xfqqacmt83VK51xxhnJeGrdxtzagqn1/TqRm/MqNefWaaedltz20UcfTcaXL1+ejO/duzcZT/Wt6DxmvVT0DztVw1akxquMubrKnkhwNHRyxDUEfDEiHpU0GfitpF/UY9+IiK+NXvfMrBf6/Ygrm1YjYmdEPFr//lVgI7W10szsGFXiYhlIWiRpk6Stkq4fJv4FSRsk/U7SA5LOzLXZ1fGgpLOAC4Hf1J9aVt/ZSkmntNlm6ZE11958881udmdmPVJW4pI0ACwHPgScByyRdF7Tyx4DFkbEHwOrgK/m2u04cUmaBPwQ+Hx9yexvAecAFwA7ga8Pt11ErIiIhRGxsMpjGmZvF50mrQ6PuC4CtkbEtog4RG3dxKsbXxARv4yIIwsrPEJt7cWkjq4qSjqeWtL6fkT8qL6zFxvitwH/1ElbZtb/ShycnwM81/B4ELg48fpPAT/PNdrJVUUBtwMbI+LWhudnR8TO+sOPAk/m2jKzauhicH6GpHUNj1dExIrGpobZZthLn5L+DFgI/Elup50ccV0CfBJ4QtLj9eduoHauekG9E9uBz+YaGhgYSJYV5C7dp6aeyS0vNmvWrGR85syZyXhqepihoaHktrlL1Lm+506x9+3b1zb2ve99L7ntz372s2Q8N+VObkqf1P/cvVziq6giJQuQfm9Fl0YrQxeJa3dELEzEB4HGOqa5wI5h9vd+4D8DfxIR6V86OkhcEfErhs+ayZotM6umkm+yXgsskDQfeB5YDHyiaX8XAv8LWBQRuzpp1JXzZtairMQVEUOSlgH3AQPAyohYL+lmYF1ErAb+GzAJ+If6fp+NiKtS7TpxmVmLMgtQ63fUrGl67saG79/fbZtOXGZ2lGPllh8ze5vp91t+nLjMrIUTl5lVjhNXA0nJaU6mTp2a3H727NltY7k6rTPPTN+3maovg/TUNLnxgNy0NIcPH07G77333mT8/vvvbxt7/vnnk9vmfkGL3qaVqjnKfW6j+ceT23cuPpp1XDllTF2T48RlZpXixTLMrJJ8VdHMKsdHXGZWOU5cZlYpHuMys0py4jKzyun3wXmNRU3IH3YmvQQ80/DUDGD3mHWgO/3at37tF7hvI1Vm386MiPRafRmS7qXWp07sjohFRfY3EmOauFp2Lq3LTELWM/3at37tF7hvI9XPfetX/X08aGY2DCcuM6ucXieuFfmX9Ey/9q1f+wXu20j1c9/6Uk/HuMzMRqLXR1xmZl3rSeKStEjSJklbJV3fiz60I2m7pCckPd60Xlwv+rJS0i5JTzY8N03SLyRtqf97Sh/17SZJz9c/u8clfbhHfZsn6ZeSNkpaL+kv68/39LNL9KsvPrcqGfNTRUkDwGbgCmprrq0FlkTEhjHtSBuStgMLI6LnNT+S3gscAL4TEefXn/sqsCcibqkn/VMi4q/7pG83AQci4mtj3Z+mvs0GZkfEo5ImA78FPgJcRw8/u0S/rqEPPrcq6cUR10XA1ojYFhGHgLuAq3vQj74XEQ8Be5qevhq4o/79HdR+8cdcm771hYjYGRGP1r9/FdhIbSn4nn52iX5Zl3qRuOYAzzU8HqS/fngB3C/pt5KW9rozw5gVETuh9ocApJfgHnvLJP2ufirZk9PYRpLOAi4EfkMffXZN/YI++9z6XS8S13B3b/bTpc1LIuJfAR8CPlc/JbLOfAs4B7gA2Al8vZedkTQJ+CHw+YjY38u+NBqmX331uVVBLxLXIDCv4fFcYEcP+jGsiNhR/3cX8GNqp7b95MX6WMmRMZOOliwfCxHxYkS8GRFvAbfRw89O0vHUksP3I+JH9ad7/tkN169++tyqoheJay2wQNJ8SeOBxcDqHvSjhaSJ9UFTJE0EPgA8md5qzK0Grq1/fy3wkx725ShHkkLdR+nRZ6fanCy3Axsj4taGUE8/u3b96pfPrUp6UoBav9z734EBYGVE/O2Yd2IYks6mdpQFtSl/7uxl3yT9ALiU2p36LwJfBv4RuAc4A3gW+HhEjPkgeZu+XUrtdCeA7cBnj4wpjXHf3gM8DDwBHFmO5wZq40k9++wS/VpCH3xuVeLKeTOrHFfOm1nlOHGZWeU4cZlZ5ThxmVnlOHGZWeU4cZlZ5ThxmVnlOHGZWeX8Pz+824Osq2tAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_array = np.array(mnist_train_data.loc[2, :])\n",
    "shaped_data = np.reshape(data_array, (28, 28))\n",
    "sign_img = plt.imshow(shaped_data, cmap=plt.cm.binary)\n",
    "plt.colorbar(sign_img)\n",
    "print(\"IMAGE LABEL: {}\".format(mnist_train.loc[2, \"label\"]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1126, 1010, 1144, 1196, 957, 1204, 1090, 1013, 1162, 1114, 1241, 1055, 1151, 1196, 1088, 1279, 1294, 1199, 1186, 1161, 1082, 1225, 1164, 1118]\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": [
    "sns.countplot(mnist_train.label)\n",
    "print(list(mnist_train.label.value_counts().sort_index()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\paras\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(784,)),\n",
    "    keras.layers.Dense(256, \"relu\"),\n",
    "    keras.layers.Dense(26, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7172 samples\n",
      "Epoch 1/50\n",
      "7172/7172 [==============================] - 0s 40us/sample - loss: 3.0286 - acc: 0.1686\n",
      "Epoch 2/50\n",
      "7172/7172 [==============================] - 0s 29us/sample - loss: 2.5877 - acc: 0.3277\n",
      "Epoch 3/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 2.2694 - acc: 0.4200\n",
      "Epoch 4/50\n",
      "7172/7172 [==============================] - 0s 29us/sample - loss: 2.0058 - acc: 0.4974\n",
      "Epoch 5/50\n",
      "7172/7172 [==============================] - 0s 27us/sample - loss: 1.7776 - acc: 0.5764\n",
      "Epoch 6/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 1.6081 - acc: 0.6288\n",
      "Epoch 7/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 1.4641 - acc: 0.6594\n",
      "Epoch 8/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 1.3377 - acc: 0.7012\n",
      "Epoch 9/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 1.2362 - acc: 0.7271\n",
      "Epoch 10/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 1.1347 - acc: 0.7581\n",
      "Epoch 11/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 1.0631 - acc: 0.7616\n",
      "Epoch 12/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 0.9821 - acc: 0.7878\n",
      "Epoch 13/50\n",
      "7172/7172 [==============================] - 0s 25us/sample - loss: 0.9121 - acc: 0.8045\n",
      "Epoch 14/50\n",
      "7172/7172 [==============================] - 0s 24us/sample - loss: 0.8480 - acc: 0.8282\n",
      "Epoch 15/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 0.7982 - acc: 0.8359\n",
      "Epoch 16/50\n",
      "7172/7172 [==============================] - 0s 26us/sample - loss: 0.7473 - acc: 0.8491\n",
      "Epoch 17/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 0.6951 - acc: 0.86530s - loss: 0.7074 - acc: 0.86\n",
      "Epoch 18/50\n",
      "7172/7172 [==============================] - 0s 24us/sample - loss: 0.6518 - acc: 0.8779\n",
      "Epoch 19/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 0.6120 - acc: 0.8929\n",
      "Epoch 20/50\n",
      "7172/7172 [==============================] - 0s 25us/sample - loss: 0.5721 - acc: 0.9062\n",
      "Epoch 21/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 0.5401 - acc: 0.9163\n",
      "Epoch 22/50\n",
      "7172/7172 [==============================] - 0s 23us/sample - loss: 0.4976 - acc: 0.9278\n",
      "Epoch 23/50\n",
      "7172/7172 [==============================] - 0s 24us/sample - loss: 0.4710 - acc: 0.9334\n",
      "Epoch 24/50\n",
      "7172/7172 [==============================] - 0s 28us/sample - loss: 0.4423 - acc: 0.9375\n",
      "Epoch 25/50\n",
      "7172/7172 [==============================] - 0s 25us/sample - loss: 0.4276 - acc: 0.9370\n",
      "Epoch 26/50\n",
      "7172/7172 [==============================] - 0s 34us/sample - loss: 0.3914 - acc: 0.9487\n",
      "Epoch 27/50\n",
      "7172/7172 [==============================] - 0s 34us/sample - loss: 0.3651 - acc: 0.9604\n",
      "Epoch 28/50\n",
      "7172/7172 [==============================] - 0s 31us/sample - loss: 0.3407 - acc: 0.9654\n",
      "Epoch 29/50\n",
      "7172/7172 [==============================] - 0s 31us/sample - loss: 0.3195 - acc: 0.9721\n",
      "Epoch 30/50\n",
      "7172/7172 [==============================] - 0s 35us/sample - loss: 0.2991 - acc: 0.9743\n",
      "Epoch 31/50\n",
      "7172/7172 [==============================] - 0s 37us/sample - loss: 0.2829 - acc: 0.9791\n",
      "Epoch 32/50\n",
      "7172/7172 [==============================] - 0s 36us/sample - loss: 0.2722 - acc: 0.9776\n",
      "Epoch 33/50\n",
      "7172/7172 [==============================] - 0s 35us/sample - loss: 0.2551 - acc: 0.9808\n",
      "Epoch 34/50\n",
      "7172/7172 [==============================] - 0s 33us/sample - loss: 0.2381 - acc: 0.9875\n",
      "Epoch 35/50\n",
      "7172/7172 [==============================] - 0s 32us/sample - loss: 0.2209 - acc: 0.9911\n",
      "Epoch 36/50\n",
      "7172/7172 [==============================] - 0s 32us/sample - loss: 0.2089 - acc: 0.9912\n",
      "Epoch 37/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.1951 - acc: 0.9941\n",
      "Epoch 38/50\n",
      "7172/7172 [==============================] - 0s 32us/sample - loss: 0.1824 - acc: 0.9954\n",
      "Epoch 39/50\n",
      "7172/7172 [==============================] - 0s 32us/sample - loss: 0.1752 - acc: 0.9948\n",
      "Epoch 40/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.1666 - acc: 0.9964\n",
      "Epoch 41/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.1587 - acc: 0.9967\n",
      "Epoch 42/50\n",
      "7172/7172 [==============================] - 0s 32us/sample - loss: 0.1489 - acc: 0.9969\n",
      "Epoch 43/50\n",
      "7172/7172 [==============================] - 0s 32us/sample - loss: 0.1394 - acc: 0.9976\n",
      "Epoch 44/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.1325 - acc: 0.9983\n",
      "Epoch 45/50\n",
      "7172/7172 [==============================] - 0s 31us/sample - loss: 0.1262 - acc: 0.9987\n",
      "Epoch 46/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.1205 - acc: 0.9993\n",
      "Epoch 47/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.1125 - acc: 0.9990\n",
      "Epoch 48/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.1081 - acc: 0.9993\n",
      "Epoch 49/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.1028 - acc: 0.9994\n",
      "Epoch 50/50\n",
      "7172/7172 [==============================] - 0s 30us/sample - loss: 0.0978 - acc: 0.9996\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d142fc8ef0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(mnist_test_data, mnist_test_label, shuffle=True, batch_size=500, epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7172/7172 [==============================] - 0s 39us/sample - loss: 0.0929 - acc: 0.9999\n",
      "\n",
      "LOSS: 0.0928997958854996\n",
      "ACCURACY: 0.9998605847358704\n"
     ]
    }
   ],
   "source": [
    "vc_loss, vc_accuracy = model.evaluate(mnist_test_data, mnist_test_label)\n",
    "print(\"\\nLOSS: {}\\nACCURACY: {}\".format(vc_loss, vc_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Result\n",
    "* LOSS: 9%\n",
    "* ACCURACY: 99.9%"
   ]
  }
 ],
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