Handwritten Digit Recognition System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c3a2502f-ce4c-41d3-be2f-6a79913b9d3f",
   "metadata": {},
   "source": [
    "# Handwritten Digit Recognition System"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98753ecc-597b-4b3d-838e-2b60315170b7",
   "metadata": {},
   "source": [
    "### 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df0bc042-b15a-47a7-874e-6530b06fcbdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ba95bc-5dc9-434e-928c-3ad44fe40531",
   "metadata": {},
   "source": [
    "### 2. Load and Preprocess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "47395f82-7a1a-4d56-92da-96257c92fa02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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qldasWaOKigodPHhQoVBIU6ZMUUtLS9IXDwDIbJ6+MOHzzz+P+7iyslI5OTk6fPiwJkyYIOec3nvvPS1btiz2kyM3bNig3Nxcbdq0Sa+88kryVg4AyHj39Z5QJBKRJGVnZ0uS6urq1NjYqOLi4tg+fr9fEydOVG1t7W1/j7a2NkWj0bgNANA9JBwh55yWLFmi8ePHa9iwYZKkxsZGSVJubm7cvrm5ubHnblVeXq5gMBjb8vPzE10SACDDJByhhQsX6osvvtDHH3/c6Tmfzxf3sXOu02M3LV26VJFIJLbV19cnuiQAQIZJ6JtVFy1apG3btmnv3r0aOHBg7PGb31TY2NiocDgce7ypqanT1dFNfr9ffr8/kWUAADKcpysh55wWLlyoLVu2aPfu3SosLIx7vrCwUKFQSFVVVbHHrl27ppqaGhUVFSVnxQCALsPTldCCBQu0adMmffbZZwoEArH3eYLBoPr06SOfz6fFixdrxYoVGjx4sAYPHqwVK1aob9++evnll1PyBwAAZC5PEVq3bp0kadKkSXGPV1ZWau7cuZKkN998U1euXNH8+fN14cIFjRkzRrt27VIgEEjKggEAXYfPOeesF/FN0WhUwWDQehm4B9/2Pt+d/OAHP/A8U1FR4XnmiSee8DyT7g4cOOB55t13303otT777DPPMx0dHQm9FrquSCSirKysO+7DveMAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABgJqGfrIr0lZ2d7Xlm/fr1Cb3Wk08+6Xnme9/7XkKvlc5qa2s9z6xevdrzzM6dOz3PXLlyxfMM8CBxJQQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmOEGpg/ImDFjPM+88cYbnmeefvppzzOPPvqo55l0d/ny5YTm1q5d63lmxYoVnmdaW1s9zwBdEVdCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZbmD6gMyYMeOBzDxIJ06c8Dzzt7/9zfNMe3u755nVq1d7npGkixcvJjQHIDFcCQEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZnzOOWe9iG+KRqMKBoPWywAA3KdIJKKsrKw77sOVEADADBECAJjxFKHy8nKNHj1agUBAOTk5mj59uk6ePBm3z9y5c+Xz+eK2sWPHJnXRAICuwVOEampqtGDBAu3fv19VVVVqb29XcXGxWltb4/abOnWqGhoaYtuOHTuSumgAQNfg6Serfv7553EfV1ZWKicnR4cPH9aECRNij/v9foVCoeSsEADQZd3Xe0KRSESSlJ2dHfd4dXW1cnJyNGTIEM2bN09NTU3f+nu0tbUpGo3GbQCA7iHhL9F2zumFF17QhQsXtG/fvtjjmzdv1ne+8x0VFBSorq5Ov/71r9Xe3q7Dhw/L7/d3+n3Kysr09ttvJ/4nAACkpXv5Em25BM2fP98VFBS4+vr6O+537tw516tXL/eXv/zlts9fvXrVRSKR2FZfX+8ksbGxsbFl+BaJRO7aEk/vCd20aNEibdu2TXv37tXAgQPvuG84HFZBQYFOnTp12+f9fv9tr5AAAF2fpwg557Ro0SJ9+umnqq6uVmFh4V1nmpubVV9fr3A4nPAiAQBdk6cvTFiwYIH+/Oc/a9OmTQoEAmpsbFRjY6OuXLkiSbp06ZJef/11/fOf/9SZM2dUXV2tadOmqX///poxY0ZK/gAAgAzm5X0gfcvn/SorK51zzl2+fNkVFxe7AQMGuF69erlBgwa50tJSd/bs2Xt+jUgkYv55TDY2Nja2+9/u5T0hbmAKAEgJbmAKAEhrRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzaRch55z1EgAASXAvf5+nXYRaWlqslwAASIJ7+fvc59Ls0qOjo0Pnzp1TIBCQz+eLey4ajSo/P1/19fXKysoyWqE9jsMNHIcbOA43cBxuSIfj4JxTS0uL8vLy9NBDd77W6fmA1nTPHnroIQ0cOPCO+2RlZXXrk+wmjsMNHIcbOA43cBxusD4OwWDwnvZLu0/HAQC6DyIEADCTURHy+/1avny5/H6/9VJMcRxu4DjcwHG4geNwQ6Ydh7T7wgQAQPeRUVdCAICuhQgBAMwQIQCAGSIEADCTURH64IMPVFhYqIcfflgjR47Uvn37rJf0QJWVlcnn88VtoVDIelkpt3fvXk2bNk15eXny+XzaunVr3PPOOZWVlSkvL099+vTRpEmTdPz4cZvFptDdjsPcuXM7nR9jx461WWyKlJeXa/To0QoEAsrJydH06dN18uTJuH26w/lwL8chU86HjInQ5s2btXjxYi1btkxHjhzRM888o5KSEp09e9Z6aQ/U0KFD1dDQENuOHTtmvaSUa21t1YgRI1RRUXHb51etWqU1a9aooqJCBw8eVCgU0pQpU7rcfQjvdhwkaerUqXHnx44dOx7gClOvpqZGCxYs0P79+1VVVaX29nYVFxertbU1tk93OB/u5ThIGXI+uAzx9NNPu1dffTXusSeeeML96le/MlrRg7d8+XI3YsQI62WYkuQ+/fTT2McdHR0uFAq5lStXxh67evWqCwaD7ne/+53BCh+MW4+Dc86Vlpa6F154wWQ9VpqampwkV1NT45zrvufDrcfBucw5HzLiSujatWs6fPiwiouL4x4vLi5WbW2t0apsnDp1Snl5eSosLNRLL72k06dPWy/JVF1dnRobG+PODb/fr4kTJ3a7c0OSqqurlZOToyFDhmjevHlqamqyXlJKRSIRSVJ2drak7ns+3HocbsqE8yEjInT+/Hldv35dubm5cY/n5uaqsbHRaFUP3pgxY7Rx40bt3LlTH374oRobG1VUVKTm5mbrpZm5+d+/u58bklRSUqKPPvpIu3fv1urVq3Xw4EE9++yzamtrs15aSjjntGTJEo0fP17Dhg2T1D3Ph9sdBylzzoe0u4v2ndz6ox2cc50e68pKSkpivx4+fLjGjRunxx57TBs2bNCSJUsMV2avu58bkjR79uzYr4cNG6ZRo0apoKBA27dv18yZMw1XlhoLFy7UF198oX/84x+dnutO58O3HYdMOR8y4kqof//+6tGjR6d/yTQ1NXX6F0930q9fPw0fPlynTp2yXoqZm18dyLnRWTgcVkFBQZc8PxYtWqRt27Zpz549cT/6pbudD992HG4nXc+HjIhQ7969NXLkSFVVVcU9XlVVpaKiIqNV2Wtra9OXX36pcDhsvRQzhYWFCoVCcefGtWvXVFNT063PDUlqbm5WfX19lzo/nHNauHChtmzZot27d6uwsDDu+e5yPtztONxO2p4Phl8U4cknn3zievXq5f7whz+4EydOuMWLF7t+/fq5M2fOWC/tgXnttddcdXW1O336tNu/f797/vnnXSAQ6PLHoKWlxR05csQdOXLESXJr1qxxR44ccf/5z3+cc86tXLnSBYNBt2XLFnfs2DE3Z84cFw6HXTQaNV55ct3pOLS0tLjXXnvN1dbWurq6Ordnzx43btw49+ijj3ap4/CLX/zCBYNBV11d7RoaGmLb5cuXY/t0h/Phbschk86HjImQc869//77rqCgwPXu3ds99dRTcV+O2B3Mnj3bhcNh16tXL5eXl+dmzpzpjh8/br2slNuzZ4+T1GkrLS11zt34stzly5e7UCjk/H6/mzBhgjt27JjtolPgTsfh8uXLrri42A0YMMD16tXLDRo0yJWWlrqzZ89aLzupbvfnl+QqKytj+3SH8+FuxyGTzgd+lAMAwExGvCcEAOiaiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAz/wdVbyhNmNF0pQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the MNIST dataset from Keras\n",
    "(X_train, y_train), (X_test, y_test) = datasets.mnist.load_data()\n",
    "\n",
    "# Normalize the data (Convert pixel values to float and normalize between 0 and 1)\n",
    "X_train, X_test = X_train / 255.0, X_test / 255.0\n",
    "\n",
    "# Reshape data to add an extra dimension for channels (grayscale image has 1 channel)\n",
    "X_train = X_train.reshape(-1, 28, 28, 1)\n",
    "X_test = X_test.reshape(-1, 28, 28, 1)\n",
    "\n",
    "# Display a sample digit\n",
    "plt.imshow(X_train[0], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e1cd26-20b0-4575-a9c8-441233668325",
   "metadata": {},
   "source": [
    "### 3. Build CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7b43979-88f5-4bab-b10e-2c4be210cf0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │          \u001b[38;5;34m36,928\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m576\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │          \u001b[38;5;34m36,928\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │             \u001b[38;5;34m650\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">93,322</span> (364.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m93,322\u001b[0m (364.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">93,322</span> (364.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m93,322\u001b[0m (364.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the CNN model for handwritten digit recognition\n",
    "cnn = models.Sequential([\n",
    "\n",
    "    # First convolutional layer (32 filters of size 3x3)\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "\n",
    "    # Second convolutional layer (64 filters of size 3x3)\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "\n",
    "    # Third convolutional layer (64 filters)\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "\n",
    "    # Flatten the output from the conv layers\n",
    "    layers.Flatten(),\n",
    "\n",
    "    # Fully connected layer (Dense layer)\n",
    "    layers.Dense(64, activation='relu'),\n",
    "\n",
    "    # Output layer (10 output units for 10 digit classes)\n",
    "    layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "# Print the model summary to understand the architecture\n",
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0aa39a-0a9f-456f-9840-b8da426129cb",
   "metadata": {},
   "source": [
    "### 4. Compile and Train the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "399c4a3d-6de9-4809-b777-45dbe00f7350",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 23ms/step - accuracy: 0.9009 - loss: 0.3232 - val_accuracy: 0.9860 - val_loss: 0.0418\n",
      "Epoch 2/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 21ms/step - accuracy: 0.9844 - loss: 0.0491 - val_accuracy: 0.9886 - val_loss: 0.0345\n",
      "Epoch 3/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 21ms/step - accuracy: 0.9897 - loss: 0.0318 - val_accuracy: 0.9912 - val_loss: 0.0276\n",
      "Epoch 4/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 22ms/step - accuracy: 0.9926 - loss: 0.0232 - val_accuracy: 0.9902 - val_loss: 0.0319\n",
      "Epoch 5/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 20ms/step - accuracy: 0.9944 - loss: 0.0187 - val_accuracy: 0.9888 - val_loss: 0.0377\n",
      "Epoch 6/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 21ms/step - accuracy: 0.9951 - loss: 0.0149 - val_accuracy: 0.9896 - val_loss: 0.0381\n",
      "Epoch 7/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 21ms/step - accuracy: 0.9962 - loss: 0.0124 - val_accuracy: 0.9879 - val_loss: 0.0437\n",
      "Epoch 8/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 18ms/step - accuracy: 0.9965 - loss: 0.0106 - val_accuracy: 0.9903 - val_loss: 0.0379\n",
      "Epoch 9/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 13ms/step - accuracy: 0.9969 - loss: 0.0095 - val_accuracy: 0.9897 - val_loss: 0.0428\n",
      "Epoch 10/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 19ms/step - accuracy: 0.9977 - loss: 0.0071 - val_accuracy: 0.9911 - val_loss: 0.0409\n"
     ]
    }
   ],
   "source": [
    "# Compile the model with Adam optimizer and sparse categorical crossentropy loss\n",
    "cnn.compile(optimizer='adam',\n",
    "            loss='sparse_categorical_crossentropy',\n",
    "            metrics=['accuracy'])\n",
    "\n",
    "# Train the model on the training set\n",
    "history = cnn.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb4adc1-a8e0-4a82-af55-d250c30b775f",
   "metadata": {},
   "source": [
    "### 5. Evaluate Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3ef42d4-8b4a-459a-baa7-0aca0f1fe000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9897 - loss: 0.0514\n",
      "Test Accuracy: 0.991100013256073\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = cnn.evaluate(X_test, y_test)\n",
    "print(f\"Test Accuracy: {test_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ed140f-00ec-4d2a-8873-f7008e99acaf",
   "metadata": {},
   "source": [
    "### 6. Plot Accuracy and Loss Over Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d3dddbd-8dbc-4e9f-b948-bb0abb1a606c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and validation accuracy\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5896f395-2d2a-42c6-89fc-f828b0aa5b2f",
   "metadata": {},
   "source": [
    "### 7. Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "08f0feef-d84e-4af1-92bf-22cc97a2d968",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step\n",
      "Predicted classes: [7, 2, 1, 0, 4]\n",
      "True classes: [7 2 1 0 4]\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test set\n",
    "y_pred = cnn.predict(X_test)\n",
    "\n",
    "# Convert predictions to class labels\n",
    "y_pred_classes = [np.argmax(element) for element in y_pred]\n",
    "\n",
    "# Display the first 5 predictions and their true labels\n",
    "print(\"Predicted classes:\", y_pred_classes[:5])\n",
    "print(\"True classes:\", y_test[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0b1a17-563b-44a9-9fa3-1e0a9566cddb",
   "metadata": {},
   "source": [
    "### 8. Test the Model on Custom Handwritten Digit Image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22c02407-a79b-46cc-aa4b-a2e435341e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step\n",
      "Predicted digit: 4\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import tensorflow as tf\n",
    "\n",
    "# Load the image from your local directory\n",
    "img_path = ''  # Replace with the path to your image\n",
    "img = Image.open(r'C:\\Users\\ASUS\\Downloads\\images.jpeg').convert('L')  # Convert to grayscale\n",
    "\n",
    "# Resize the image to 28x28 pixels (size expected by the model)\n",
    "img = img.resize((28, 28))\n",
    "\n",
    "# Invert the image (depends on the background color)\n",
    "img = np.invert(img)\n",
    "\n",
    "# Normalize the image (convert pixel values to 0-1)\n",
    "img = img / 255.0\n",
    "\n",
    "# Reshape the image to match the input shape of the model (1, 28, 28, 1)\n",
    "img = img.reshape(1, 28, 28, 1)\n",
    "\n",
    "# Show the image that is being predicted\n",
    "plt.imshow(img.reshape(28, 28), cmap='gray')\n",
    "plt.title(\"Handwritten Digit Input\")\n",
    "plt.show()\n",
    "\n",
    "# Use the trained model to make a prediction\n",
    "prediction = cnn.predict(img)\n",
    "\n",
    "# Print the predicted class\n",
    "predicted_digit = np.argmax(prediction)\n",
    "print(f\"Predicted digit: {predicted_digit}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "334683d6-e48a-464f-9daf-6d7cf33d72d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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