minor 2-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31ff6ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "960ad452",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba1b171",
   "metadata": {},
   "source": [
    "# Loading - \"MNIST Data Set\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c158fdf",
   "metadata": {},
   "source": [
    "Containing Training samples=60,000,Testing Samples=10,000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ab8601",
   "metadata": {},
   "source": [
    "Containing Training samples=60,000,Testing Samples=10,000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04cbd318",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist=tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228f0dec",
   "metadata": {},
   "source": [
    "# After loading the MNIST data,Divide into Train and Test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63490f8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## unpacking the dataset into train and test datasets\n",
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a7698007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "998b18bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d6985c47c0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## just check the graph , how data looks like\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(x_train[0])\n",
    "plt.show()\n",
    "## inorder to execute the graph however we dont know whether its color image or binary images\n",
    "## so inorder to plot it change the configuration\n",
    "plt.imshow(x_train[0],cmap=plt.cm.binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3d5a7b",
   "metadata": {},
   "source": [
    "# Checking the values of each pixel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c903dde",
   "metadata": {},
   "source": [
    "# Before Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef7d3220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136\n",
      "  175  26 166 255 247 127   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253\n",
      "  225 172 253 242 195  64   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251\n",
      "   93  82  82  56  39   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  14   1 154 253  90   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  11 190 253  70   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  35 241 225 160 108   1\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119\n",
      "   25   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253\n",
      "  150  27   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252\n",
      "  253 187   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 249\n",
      "  253 249  64   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
      "  253 207   2   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253\n",
      "  250 182   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201\n",
      "   78   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(x_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358d3421",
   "metadata": {},
   "source": [
    "# As images are in Gray level(1 channel==>0 to 255),not Coloured(RGB)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d2456b",
   "metadata": {},
   "source": [
    "# Normalizing the data|Pre-Processing Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef78fa01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d69ca85120>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### you might have noticed that, its gray image and all values varies from 0 to 255\n",
    "### in order to normalize it\n",
    "x_train=tf.keras.utils.normalize(x_train,axis=1)\n",
    "x_test=tf.keras.utils.normalize(x_test,axis=1)\n",
    "plt.imshow(x_train[0],cmap=plt.cm.binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb2ad1e",
   "metadata": {},
   "source": [
    "# AFTER NORMALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6aa2da4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.00393124 0.02332955 0.02620568 0.02625207 0.17420356 0.17566281\n",
      "  0.28629534 0.05664824 0.51877786 0.71632322 0.77892406 0.89301644\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.05780486 0.06524513 0.16128198 0.22713296\n",
      "  0.22277047 0.32790981 0.36833534 0.3689874  0.34978968 0.32678448\n",
      "  0.368094   0.3747499  0.79066747 0.67980478 0.61494005 0.45002403\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.12250613 0.45858525 0.45852825 0.43408872 0.37314701\n",
      "  0.33153488 0.32790981 0.36833534 0.3689874  0.34978968 0.32420121\n",
      "  0.15214552 0.17865984 0.25626376 0.1573102  0.12298801 0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.04500225 0.4219755  0.45852825 0.43408872 0.37314701\n",
      "  0.33153488 0.32790981 0.28826244 0.26543758 0.34149427 0.31128482\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.1541463  0.28272888 0.18358693 0.37314701\n",
      "  0.33153488 0.26569767 0.01601458 0.         0.05945042 0.19891229\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.0253731  0.00171577 0.22713296\n",
      "  0.33153488 0.11664776 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.20500962\n",
      "  0.33153488 0.24625638 0.00291174 0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.01622378\n",
      "  0.24897876 0.32790981 0.10191096 0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.04586451 0.31235677 0.32757096 0.23335172 0.14931733 0.00129164\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.10498298 0.34940902 0.3689874  0.34978968 0.15370495\n",
      "  0.04089933 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.06551419 0.27127137 0.34978968 0.32678448\n",
      "  0.245396   0.05882702 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.02333517 0.12857881 0.32549285\n",
      "  0.41390126 0.40743158 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.32161793\n",
      "  0.41390126 0.54251585 0.20001074 0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.06697006 0.18959827 0.25300993 0.32678448\n",
      "  0.41390126 0.45100715 0.00625034 0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.05110617 0.19182076 0.33339444 0.3689874  0.34978968 0.32678448\n",
      "  0.40899334 0.39653769 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.04117838 0.16813739\n",
      "  0.28960162 0.32790981 0.36833534 0.3689874  0.34978968 0.25961929\n",
      "  0.12760592 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.04431706 0.11961607 0.36545809 0.37314701\n",
      "  0.33153488 0.32790981 0.36833534 0.28877275 0.111988   0.00258328\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.05298497 0.42752138 0.4219755  0.45852825 0.43408872 0.37314701\n",
      "  0.33153488 0.25273681 0.11646967 0.01312603 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.37491383 0.56222061\n",
      "  0.66525569 0.63253163 0.48748768 0.45852825 0.43408872 0.359873\n",
      "  0.17428513 0.01425695 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.92705966 0.82698729\n",
      "  0.74473314 0.63253163 0.4084877  0.24466922 0.22648107 0.02359823\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b078d96e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(y_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "818b3ee2",
   "metadata": {},
   "source": [
    "# Resizing image to make it suitable for apply Convolution operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "16db38e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Samples dimension (60000, 28, 28, 1)\n",
      "Testing Samples dimension (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "IMG_SIZE=28\n",
    "x_trainr=np.array(x_train).reshape(-1,IMG_SIZE,IMG_SIZE,1) ## increasing one dimension for kernel=filter operation\n",
    "x_testr=np.array(x_test).reshape(-1,IMG_SIZE,IMG_SIZE,1) ## increasing one dimension for kernel operation\n",
    "print(\"Training Samples dimension\",x_trainr.shape)\n",
    "print(\"Testing Samples dimension\",x_testr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "73dfa3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Dropout,Activation,Flatten,Conv2D,MaxPooling2D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c2efbf",
   "metadata": {},
   "source": [
    "# Creating a Deep Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93837da1",
   "metadata": {},
   "source": [
    "Training on 60,000 samples of MNIST handwritten dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "08c4d4b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Creating a neural network now\n",
    "\n",
    "model=Sequential()\n",
    "\n",
    "\n",
    "###### First Convolution layer  0 1 2 3   (60000,28,28,1)  28-3+1 = 26*26\n",
    "model.add(Conv2D(64,(3,3),input_shape=x_trainr.shape[1:])) ### only for first convolution layer to mention input layer size\n",
    "model.add(Activation(\"relu\")) ## activation function to make it non-linear, <0,remove, >0\n",
    "model.add(MaxPooling2D(pool_size=(2,2))) ## Maxpooling single maximum value of 2*2,\n",
    "\n",
    "###### Second Convolution layer  26-3+1 = 24*24\n",
    "model.add(Conv2D(64,(3,3))) \n",
    "model.add(Activation(\"relu\")) \n",
    "model.add(MaxPooling2D(pool_size=(2,2))) ## Maxpooling \n",
    "\n",
    "###### 3rd Convolution layer\n",
    "model.add(Conv2D(64,(3,3))) \n",
    "model.add(Activation(\"relu\")) \n",
    "model.add(MaxPooling2D(pool_size=(2,2))) ## Maxpooling \n",
    "\n",
    "\n",
    "### Fully Connected Layer # 1 20*20= 400\n",
    "model.add(Flatten()) ### before using fully connected layer, need to be flatten so that 20 to 10\n",
    "model.add(Dense(64))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "### Fully Connected Layer # 2\n",
    "model.add(Dense(32))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "## Last Fully Connected Layer output must be equal to number of classes,10 (0-9)\n",
    "model.add(Dense(10)) ## this last dense layer must be equal to 10\n",
    "model.add(Activation('softmax')) ### activation function is changed to Softmax (Class probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2babb31d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 64)        640       \n",
      "                                                                 \n",
      " activation (Activation)     (None, 26, 26, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 64)        36928     \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 11, 11, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 3, 3, 64)          36928     \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 3, 3, 64)          0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 1, 1, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                4160      \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " activation_4 (Activation)   (None, 32)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                330       \n",
      "                                                                 \n",
      " activation_5 (Activation)   (None, 10)                0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 81,066\n",
      "Trainable params: 81,066\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c3d613c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Training Samples= 60000\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Training Samples=\", len(x_trainr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "16798204",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\",optimizer=\"adam\",metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "755efaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1313/1313 [==============================] - 50s 37ms/step - loss: 0.3366 - accuracy: 0.8970 - val_loss: 0.1416 - val_accuracy: 0.9567\n",
      "Epoch 2/5\n",
      "1313/1313 [==============================] - 54s 41ms/step - loss: 0.1053 - accuracy: 0.9678 - val_loss: 0.0830 - val_accuracy: 0.9753\n",
      "Epoch 3/5\n",
      "1313/1313 [==============================] - 56s 42ms/step - loss: 0.0764 - accuracy: 0.9760 - val_loss: 0.0847 - val_accuracy: 0.9748\n",
      "Epoch 4/5\n",
      "1313/1313 [==============================] - 56s 43ms/step - loss: 0.0560 - accuracy: 0.9828 - val_loss: 0.0777 - val_accuracy: 0.9763\n",
      "Epoch 5/5\n",
      "1313/1313 [==============================] - 55s 42ms/step - loss: 0.0477 - accuracy: 0.9850 - val_loss: 0.0695 - val_accuracy: 0.9793\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1d69971ad70>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_trainr,y_train,epochs=5,validation_split=0.3) ## Training my model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "40f9a732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 4s 12ms/step - loss: 0.0644 - accuracy: 0.9808\n",
      "test loss on 10,000 test samples 0.06441796571016312\n",
      "validation Accuracy on 10,000 test samples 0.9807999730110168\n"
     ]
    }
   ],
   "source": [
    "### Evaluating on testing data set MNIST\n",
    "test_loss,test_acc=model.evaluate(x_testr,y_test)\n",
    "print(\"test loss on 10,000 test samples\", test_loss)\n",
    "print(\"validation Accuracy on 10,000 test samples\",test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cb18130d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 4s 11ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions=model.predict([x_testr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9db3ab08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.04187882e-06 1.64780417e-04 3.89806883e-05 ... 9.99776542e-01\n",
      "  5.38160521e-06 7.64166543e-06]\n",
      " [4.44412726e-04 2.88778347e-06 9.99515414e-01 ... 5.92287506e-07\n",
      "  3.38036371e-05 1.11441375e-06]\n",
      " [9.37361278e-07 9.99929905e-01 7.63433945e-07 ... 1.44037067e-05\n",
      "  6.13892098e-06 9.49770583e-06]\n",
      " ...\n",
      " [7.71184071e-12 5.36165778e-09 1.36815193e-09 ... 3.25861947e-07\n",
      "  2.31703918e-08 3.06435754e-06]\n",
      " [1.22037374e-04 5.58798456e-06 5.53472397e-08 ... 2.64522299e-07\n",
      "  8.53097299e-04 8.60054297e-06]\n",
      " [3.03507259e-05 3.94603816e-09 1.63858509e-08 ... 1.28841174e-11\n",
      "  6.19880575e-06 3.07618507e-06]]\n"
     ]
    }
   ],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9b40653c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "## in order to understand,convert the predictions from one hot encoding , we need to use numpy for that\n",
    "print(np.argmax(predictions[0])) ## so actually argmax will return the maximum value index and fing the value of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6b11c36e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d69848a410>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### now to check that is our answer is true or not\n",
    "plt.imshow(x_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3537e01d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(predictions[130]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6cf4997d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d68f0f89d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test[130])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "381f2337",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "print(y_test[130])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8173d9bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6454d59f",
   "metadata": {},
   "outputs": [],
   "source": [
    "img=cv2.imread('zero.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2606a613",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "811a85a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3987e2b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(460, 819, 3)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0bf6ffc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(460, 819)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "gray.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1e1ddc72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resized=cv2.resize(gray,(28,28),interpolation=cv2.INTER_AREA)\n",
    "resized.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "07c400e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28, 28, 1)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newimg=tf.keras.utils.normalize(resized,axis=1) ## 0 to 1 scaling\n",
    "newimg=np.array(newimg).reshape(-1,IMG_SIZE,IMG_SIZE,1) ## kernal operation of convolution layer\n",
    "newimg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f630bf3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 108ms/step\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "predictions=model.predict(newimg)\n",
    "print(np.argmax(predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ae9a897",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}