Simple neural network on MNIST data of handwritten digits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Simple Neural Networks on MNIST data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.preprocessing.image import load_img, array_to_img\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Understanding the image data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbec52f39b0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbec53232e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0],cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the image data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_height,image_width = 28,28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "X_train = X_train.reshape(60000, image_height*image_width)\n",
    "X_test = X_test.reshape(10000, image_height*image_width)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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   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   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   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   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   3  18  18  18 126 136 175  26 166 255\n",
      " 247 127   0   0   0   0   0   0   0   0   0   0   0   0  30  36  94 154\n",
      " 170 253 253 253 253 253 225 172 253 242 195  64   0   0   0   0   0   0\n",
      "   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251  93  82\n",
      "  82  56  39   0   0   0   0   0   0   0   0   0   0   0   0  18 219 253\n",
      " 253 253 253 253 198 182 247 241   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   0   0   0   0   0   0   0   0\n",
      "   0  14   1 154 253  90   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   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  11 190 253  70   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  35 241\n",
      " 225 160 108   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0  81 240 253 253 119  25   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  45 186 253 253 150  27   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252 253 187\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   0 249 253 249  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   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0  39 148 229 253 253 253 250 182   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253\n",
      " 253 201  78   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0  23  66 213 253 253 253 253 198  81   2   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0  18 171 219 253 253 253 253 195\n",
      "  80   9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "  55 172 226 253 253 253 253 244 133  11   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0 136 253 253 253 212 135 132  16\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   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   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])\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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.         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.         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.         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.         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.         0.         0.\n",
      " 0.         0.         0.01176471 0.07058824 0.07058824 0.07058824\n",
      " 0.49411765 0.53333336 0.6862745  0.10196079 0.6509804  1.\n",
      " 0.96862745 0.49803922 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.11764706 0.14117648 0.36862746 0.6039216\n",
      " 0.6666667  0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n",
      " 0.88235295 0.6745098  0.99215686 0.9490196  0.7647059  0.2509804\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.19215687\n",
      " 0.93333334 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.9843137  0.3647059  0.32156864\n",
      " 0.32156864 0.21960784 0.15294118 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.07058824 0.85882354 0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.99215686 0.7764706  0.7137255\n",
      " 0.96862745 0.94509804 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.3137255  0.6117647  0.41960785 0.99215686\n",
      " 0.99215686 0.8039216  0.04313726 0.         0.16862746 0.6039216\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.05490196 0.00392157 0.6039216  0.99215686 0.3529412\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.         0.54509807 0.99215686 0.74509805 0.00784314 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.         0.04313726\n",
      " 0.74509805 0.99215686 0.27450982 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.         0.13725491 0.94509804\n",
      " 0.88235295 0.627451   0.42352942 0.00392157 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.         0.31764707 0.9411765  0.99215686\n",
      " 0.99215686 0.46666667 0.09803922 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.         0.1764706  0.7294118  0.99215686 0.99215686\n",
      " 0.5882353  0.10588235 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.         0.0627451  0.3647059  0.9882353  0.99215686 0.73333335\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.         0.9764706  0.99215686 0.9764706  0.2509804  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.         0.18039216 0.50980395 0.7176471  0.99215686\n",
      " 0.99215686 0.8117647  0.00784314 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.15294118 0.5803922\n",
      " 0.8980392  0.99215686 0.99215686 0.99215686 0.98039216 0.7137255\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.09411765 0.44705883 0.8666667  0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.7882353  0.30588236 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.09019608 0.25882354 0.8352941  0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.7764706  0.31764707 0.00784314\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.07058824 0.67058825\n",
      " 0.85882354 0.99215686 0.99215686 0.99215686 0.99215686 0.7647059\n",
      " 0.3137255  0.03529412 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.21568628 0.6745098  0.8862745  0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.95686275 0.52156866 0.04313726 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.53333336 0.99215686\n",
      " 0.99215686 0.99215686 0.83137256 0.5294118  0.5176471  0.0627451\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.         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.         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.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "X_train /= 255.0\n",
    "X_test /= 255.0\n",
    "print(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "y_train = to_categorical(y_train,10)\n",
    "y_test = to_categorical(y_test,10)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Build a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(10,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 669,706\n",
      "Trainable params: 669,706\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 360s - loss: 0.1841 - acc: 0.9450 - val_loss: 0.1021 - val_acc: 0.9679\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 349s - loss: 0.0806 - acc: 0.9755 - val_loss: 0.0755 - val_acc: 0.9751\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 306s - loss: 0.0580 - acc: 0.9819 - val_loss: 0.0784 - val_acc: 0.9750\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 288s - loss: 0.0422 - acc: 0.9865 - val_loss: 0.0779 - val_acc: 0.9781\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 275s - loss: 0.0339 - acc: 0.9894 - val_loss: 0.1044 - val_acc: 0.9739\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 279s - loss: 0.0272 - acc: 0.9920 - val_loss: 0.0745 - val_acc: 0.9803\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 289s - loss: 0.0277 - acc: 0.9917 - val_loss: 0.0965 - val_acc: 0.9801\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 284s - loss: 0.0244 - acc: 0.9926 - val_loss: 0.0786 - val_acc: 0.9813\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 279s - loss: 0.0201 - acc: 0.9943 - val_loss: 0.1027 - val_acc: 0.9788\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 224s - loss: 0.0211 - acc: 0.9940 - val_loss: 0.0917 - val_acc: 0.9810loss: 0.0212 - acc:\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 199s - loss: 0.0184 - acc: 0.9946 - val_loss: 0.0940 - val_acc: 0.9810\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 175s - loss: 0.0159 - acc: 0.9954 - val_loss: 0.1179 - val_acc: 0.9779\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 168s - loss: 0.0187 - acc: 0.9947 - val_loss: 0.1035 - val_acc: 0.9787\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 177s - loss: 0.0145 - acc: 0.9960 - val_loss: 0.1096 - val_acc: 0.9805\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 216s - loss: 0.0168 - acc: 0.9953 - val_loss: 0.1126 - val_acc: 0.9806\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 203s - loss: 0.0131 - acc: 0.9963 - val_loss: 0.1245 - val_acc: 0.9790\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 197s - loss: 0.0142 - acc: 0.9963 - val_loss: 0.1063 - val_acc: 0.9820\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 187s - loss: 0.0116 - acc: 0.9971 - val_loss: 0.1236 - val_acc: 0.9822\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 186s - loss: 0.0145 - acc: 0.9964 - val_loss: 0.1082 - val_acc: 0.9830\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 187s - loss: 0.0124 - acc: 0.9970 - val_loss: 0.1100 - val_acc: 0.9833\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=20, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the accuracy of the model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the accuracy of the training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbec4bb9c88>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbec532ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Plot the accuracy of training and validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbec4b15f28>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbec4b77278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Accuracy of training and validation with loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbec4af85c0>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbec4b16470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.plot(history.history['loss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Run a prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 8s     \n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.10995577097022702, 0.9833]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}