Handwritten_Digit_Classification_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6gmtnJuOX-2T"
   },
   "outputs": [],
   "source": [
    "## Import all the dependencies\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import np_utils\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dhKis64YYNfj"
   },
   "outputs": [],
   "source": [
    "## Load the dataset\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "tNs8gKEpatYi",
    "outputId": "8ca875e4-c84c-40d5-9854-2358b6de00e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (60000, 28, 28)\n",
      "Shape of X_test: (10000, 28, 28)\n",
      "Size of X_train: 47040000\n",
      "Size of X_test: 7840000\n"
     ]
    }
   ],
   "source": [
    "## Print the shape and size of the data\n",
    "\n",
    "print(\"Shape of X_train: {}\".format(X_train.shape))\n",
    "print(\"Shape of X_test: {}\".format(X_test.shape))\n",
    "print(\"Size of X_train: {}\".format(X_train.size))\n",
    "print(\"Size of X_test: {}\".format(X_test.size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 498
    },
    "colab_type": "code",
    "id": "jtzfoBopbFbq",
    "outputId": "b05264cd-6c63-4f34-fa8f-ab6336d40c16"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualize a few digits\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "grid_param = 221\n",
    "for i in range(0,4):\n",
    "    plt.subplot(grid_param)\n",
    "    plt.imshow(X_train[i], cmap=plt.get_cmap('gray'))\n",
    "    plt.title('Label:{}'.format(y_train[i]))\n",
    "    grid_param += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "80ncH0jPhaJN"
   },
   "outputs": [],
   "source": [
    "## Data Normalization\n",
    "\n",
    "to_predict = X_test[0]\n",
    "num_pixels = X_train.shape[1] * X_train.shape[2]\n",
    "X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')\n",
    "X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')\n",
    "\n",
    "X_train = X_train / 255\n",
    "X_test = X_test / 255\n",
    "\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://www.lucenaresearch.com/wp-content/uploads/2018/04/8-Digit-Recognition.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "NPomPLplfpCC",
    "outputId": "a87dd1e2-f6e4-4704-95f0-5cf7b109072a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 10s 166us/step - loss: 0.3574 - acc: 0.8942\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 10s 159us/step - loss: 0.2180 - acc: 0.9360\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 9s 157us/step - loss: 0.1873 - acc: 0.9450\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 9s 157us/step - loss: 0.1692 - acc: 0.9502\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 9s 157us/step - loss: 0.1572 - acc: 0.9535\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 10s 160us/step - loss: 0.1482 - acc: 0.9558\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 9s 157us/step - loss: 0.1411 - acc: 0.9575\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 9s 157us/step - loss: 0.1345 - acc: 0.9596\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 9s 157us/step - loss: 0.1298 - acc: 0.9614\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 9s 157us/step - loss: 0.1257 - acc: 0.9629\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x273c3e00978>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building an Artificial Neural Network to classify Handwitten Digits\n",
    "\n",
    "#Intialize the model\n",
    "ann = Sequential()\n",
    "\n",
    "#Input Layer\n",
    "ann.add(Dense(16, input_dim=num_pixels, activation='relu'))\n",
    "\n",
    "#Hidden Layer\n",
    "ann.add(Dense(16,  activation='relu'))\n",
    "\n",
    "#Output Layer\n",
    "ann.add(Dense(10, activation='softmax'))\n",
    "\n",
    "#Compile and Fit\n",
    "ann.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "ann.fit(X_train, y_train, epochs=10, batch_size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://chsasank.github.io/assets/images/crash_course/mnist_net.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "rWUWi-h7gcbb",
    "outputId": "0a3ec241-6403-46cd-8f41-bcd2afa28e5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number to Predict\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x273c7a36438>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADQNJREFUeJzt3W+MVfWdx/HPZylNjPQBWLHEgnQb3bgaAzoaE3AzamxYbYKN1NQHGzbZMH2AZps0ZA1PypMmjemfrU9IpikpJtSWhFbRGBeDGylRGwejBYpQICzMgkAzJgUT0yDfPphDO8W5v3u5/84dv+9XQube8z1/vrnhM+ecOefcnyNCAPL5h7obAFAPwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+IKnP9HNjtrmdEOixiHAr83W057e9wvZB24dtP9nJugD0l9u9t9/2LEmHJD0gaVzSW5Iei4jfF5Zhzw/0WD/2/HdJOhwRRyPiz5J+IWllB+sD0EedhP96SSemvB+vpv0d2yO2x2yPdbAtAF3WyR/8pju0+MRhfUSMShqVOOwHBkkne/5xSQunvP+ipJOdtQOgXzoJ/1uSbrT9JduflfQNSdu70xaAXmv7sD8iLth+XNL/SJolaVNE7O9aZwB6qu1LfW1tjHN+oOf6cpMPgJmL8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaTaHqJbkmwfk3RO0seSLkTEUDeaAtB7HYW/cm9E/LEL6wHQRxz2A0l1Gv6QtMP2Htsj3WgIQH90eti/LCJO2p4v6RXb70XErqkzVL8U+MUADBhHRHdWZG+QdD4ivl+YpzsbA9BQRLiV+do+7Ld9te3PXXot6SuS9rW7PgD91clh/3WSfm370np+HhEvd6UrAD3XtcP+ljbGYT/Qcz0/7AcwsxF+ICnCDyRF+IGkCD+QFOEHkurGU30prFq1qmFtzZo1xWVPnjxZrH/00UfF+pYtW4r1999/v2Ht8OHDxWWRF3t+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iKR3pbdPTo0Ya1xYsX96+RaZw7d65hbf/+/X3sZLCMj483rD311FPFZcfGxrrdTt/wSC+AIsIPJEX4gaQIP5AU4QeSIvxAUoQfSIrn+VtUemb/tttuKy574MCBYv3mm28u1m+//fZifXh4uGHt7rvvLi574sSJYn3hwoXFeicuXLhQrJ89e7ZYX7BgQdvbPn78eLE+k6/zt4o9P5AU4QeSIvxAUoQfSIrwA0kRfiApwg8k1fR5ftubJH1V0pmIuLWaNk/SLyUtlnRM0qMR8UHTjc3g5/kH2dy5cxvWlixZUlx2z549xfqdd97ZVk+taDZewaFDh4r1ZvdPzJs3r2Ft7dq1xWU3btxYrA+ybj7P/zNJKy6b9qSknRFxo6Sd1XsAM0jT8EfELkkTl01eKWlz9XqzpIe73BeAHmv3nP+6iDglSdXP+d1rCUA/9PzeftsjkkZ6vR0AV6bdPf9p2wskqfp5ptGMETEaEUMRMdTmtgD0QLvh3y5pdfV6taTnu9MOgH5pGn7bz0p6Q9I/2R63/R+SvifpAdt/kPRA9R7ADML39mNgPfLII8X61q1bi/V9+/Y1rN17773FZScmLr/ANXPwvf0Aigg/kBThB5Ii/EBShB9IivADSXGpD7WZP7/8SMjevXs7Wn7VqlUNa9u2bSsuO5NxqQ9AEeEHkiL8QFKEH0iK8ANJEX4gKcIPJMUQ3ahNs6/Pvvbaa4v1Dz4of1v8wYMHr7inTNjzA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBSPM+Pnlq2bFnD2quvvlpcdvbs2cX68PBwsb5r165i/dOK5/kBFBF+ICnCDyRF+IGkCD+QFOEHkiL8QFJNn+e3vUnSVyWdiYhbq2kbJK2RdLaabX1EvNSrJjFzPfjggw1rza7j79y5s1h/44032uoJk1rZ8/9M0opppv8oIpZU/wg+MMM0DX9E7JI00YdeAPRRJ+f8j9v+ne1Ntud2rSMAfdFu+DdK+rKkJZJOSfpBoxltj9gesz3W5rYA9EBb4Y+I0xHxcURclPQTSXcV5h2NiKGIGGq3SQDd11b4bS+Y8vZrkvZ1px0A/dLKpb5nJQ1L+rztcUnfkTRse4mkkHRM0jd72COAHuB5fnTkqquuKtZ3797dsHbLLbcUl73vvvuK9ddff71Yz4rn+QEUEX4gKcIPJEX4gaQIP5AU4QeSYohudGTdunXF+tKlSxvWXn755eKyXMrrLfb8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AUj/Si6KGHHirWn3vuuWL9ww8/bFhbsWK6L4X+mzfffLNYx/R4pBdAEeEHkiL8QFKEH0iK8ANJEX4gKcIPJMXz/Mldc801xfrTTz9drM+aNatYf+mlxgM4cx2/Xuz5gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiCpps/z214o6RlJX5B0UdJoRPzY9jxJv5S0WNIxSY9GxAdN1sXz/H3W7Dp8s2vtd9xxR7F+5MiRYr30zH6zZdGebj7Pf0HStyPiZkl3S1pr+58lPSlpZ0TcKGln9R7ADNE0/BFxKiLerl6fk3RA0vWSVkraXM22WdLDvWoSQPdd0Tm/7cWSlkr6raTrIuKUNPkLQtL8bjcHoHdavrff9hxJ2yR9KyL+ZLd0WiHbI5JG2msPQK+0tOe3PVuTwd8SEb+qJp+2vaCqL5B0ZrplI2I0IoYiYqgbDQPojqbh9+Qu/qeSDkTED6eUtktaXb1eLen57rcHoFdaudS3XNJvJO3V5KU+SVqvyfP+rZIWSTou6esRMdFkXVzq67ObbrqpWH/vvfc6Wv/KlSuL9RdeeKGj9ePKtXqpr+k5f0TsltRoZfdfSVMABgd3+AFJEX4gKcIPJEX4gaQIP5AU4QeS4qu7PwVuuOGGhrUdO3Z0tO5169YV6y+++GJH60d92PMDSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFJc5/8UGBlp/C1pixYt6mjdr732WrHe7PsgMLjY8wNJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUlznnwGWL19erD/xxBN96gSfJuz5gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiCpptf5bS+U9IykL0i6KGk0In5se4OkNZLOVrOuj4iXetVoZvfcc0+xPmfOnLbXfeTIkWL9/Pnzba8bg62Vm3wuSPp2RLxt+3OS9th+par9KCK+37v2APRK0/BHxClJp6rX52wfkHR9rxsD0FtXdM5ve7GkpZJ+W0163PbvbG+yPbfBMiO2x2yPddQpgK5qOfy250jaJulbEfEnSRslfVnSEk0eGfxguuUiYjQihiJiqAv9AuiSlsJve7Ymg78lIn4lSRFxOiI+joiLkn4i6a7etQmg25qG37Yl/VTSgYj44ZTpC6bM9jVJ+7rfHoBeaeWv/csk/Zukvbbfqaatl/SY7SWSQtIxSd/sSYfoyLvvvlus33///cX6xMREN9vBAGnlr/27JXmaEtf0gRmMO/yApAg/kBThB5Ii/EBShB9IivADSbmfQyzbZjxnoMciYrpL85/Anh9IivADSRF+ICnCDyRF+IGkCD+QFOEHkur3EN1/lPR/U95/vpo2iAa1t0HtS6K3dnWztxtanbGvN/l8YuP22KB+t9+g9jaofUn01q66euOwH0iK8ANJ1R3+0Zq3XzKovQ1qXxK9tauW3mo95wdQn7r3/ABqUkv4ba+wfdD2YdtP1tFDI7aP2d5r+526hxirhkE7Y3vflGnzbL9i+w/Vz2mHSauptw22/7/67N6x/WBNvS20/b+2D9jeb/s/q+m1fnaFvmr53Pp+2G97lqRDkh6QNC7pLUmPRcTv+9pIA7aPSRqKiNqvCdv+F0nnJT0TEbdW056SNBER36t+cc6NiP8akN42SDpf98jN1YAyC6aOLC3pYUn/rho/u0Jfj6qGz62OPf9dkg5HxNGI+LOkX0haWUMfAy8idkm6fNSMlZI2V683a/I/T9816G0gRMSpiHi7en1O0qWRpWv97Ap91aKO8F8v6cSU9+MarCG/Q9IO23tsj9TdzDSuq4ZNvzR8+vya+7lc05Gb++mykaUH5rNrZ8Trbqsj/NN9xdAgXXJYFhG3S/pXSWurw1u0pqWRm/tlmpGlB0K7I153Wx3hH5e0cMr7L0o6WUMf04qIk9XPM5J+rcEbffj0pUFSq59nau7nrwZp5ObpRpbWAHx2gzTidR3hf0vSjba/ZPuzkr4haXsNfXyC7aurP8TI9tWSvqLBG314u6TV1evVkp6vsZe/MygjNzcaWVo1f3aDNuJ1LTf5VJcy/lvSLEmbIuK7fW9iGrb/UZN7e2nyicef19mb7WclDWvyqa/Tkr4j6TlJWyUtknRc0tcjou9/eGvQ27AmD13/OnLzpXPsPve2XNJvJO2VdLGavF6T59e1fXaFvh5TDZ8bd/gBSXGHH5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpP4CIJjqosJxHysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Prediction\n",
    "print(\"Number to Predict\")\n",
    "plt.imshow(to_predict, cmap=plt.get_cmap('gray'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "WSdD2dJimVue",
    "outputId": "f9c02767-fbae-4cac-9afd-2223f684eb65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANN Prediction:7\n"
     ]
    }
   ],
   "source": [
    "print(\"ANN Prediction:{}\".format(ann.predict_classes(X_test)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kx2S3F09kYQz"
   },
   "outputs": [],
   "source": [
    "## Image Credits: \n",
    "## 1. https://chsasank.github.io/assets/images/crash_course/mnist_net.png\n",
    "## 2. https://www.lucenaresearch.com/wp-content/uploads/2018/04/8-Digit-Recognition.png\n",
    "\n",
    "## Ends here"
   ]
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Handwritten Digit Classification .ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}