notebook/15_keras_mnist_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n",
      "Populating the interactive namespace from numpy and matplotlib\n",
      "CPython 2.7.12\n",
      "IPython 5.1.0\n",
      "\n",
      "scipy 0.18.1\n",
      "sklearn 0.18.1\n",
      "numpy 1.12.0\n",
      "\n",
      "compiler   : GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00)\n",
      "system     : Darwin\n",
      "release    : 16.4.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%pylab\n",
    "%matplotlib inline\n",
    "%load_ext watermark\n",
    "%watermark -v -m -p scipy,sklearn,numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
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dL3MXo8TuEuQDBTpUsNvtoioiFAohGo0iEokgEonA6/VqTqWokoJSTmSoEoNK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11157ed10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ad hoc mnist instances\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load (downloaded if needed) the MNIST dataset\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "# plot 4 images as gray scale\n",
    "plt.subplot(221)\n",
    "plt.imshow(X_train[0], cmap=plt.get_cmap('gray'))\n",
    "plt.subplot(222)\n",
    "plt.imshow(X_train[1], cmap=plt.get_cmap('gray'))\n",
    "plt.subplot(223)\n",
    "plt.imshow(X_train[2], cmap=plt.get_cmap('gray'))\n",
    "plt.subplot(224)\n",
    "plt.imshow(X_train[3], cmap=plt.get_cmap('gray'))\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.utils import np_utils\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (60000,)\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "print X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# flatten 28*28 images to a 784 vector for each image\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# normalize inputs from 0-255 to 0-1\n",
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "# one hot encode outputs\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)\n",
    "num_classes = y_test.shape[1]\n",
    "\n",
    "print y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define baseline model\n",
    "def baseline_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(num_pixels, input_dim=num_pixels, init='normal', activation='relu'))\n",
    "    model.add(Dense(num_classes, init='normal', activation='softmax'))\n",
    "    \n",
    "    # compile model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "3s - loss: 0.2848 - acc: 0.9186 - val_loss: 0.1400 - val_acc: 0.9593\n",
      "Epoch 2/10\n",
      "3s - loss: 0.1125 - acc: 0.9674 - val_loss: 0.0943 - val_acc: 0.9717\n",
      "Epoch 3/10\n",
      "3s - loss: 0.0728 - acc: 0.9789 - val_loss: 0.0796 - val_acc: 0.9758\n",
      "Epoch 4/10\n",
      "3s - loss: 0.0513 - acc: 0.9853 - val_loss: 0.0740 - val_acc: 0.9779\n",
      "Epoch 5/10\n",
      "3s - loss: 0.0379 - acc: 0.9893 - val_loss: 0.0687 - val_acc: 0.9784\n",
      "Epoch 6/10\n",
      "3s - loss: 0.0271 - acc: 0.9929 - val_loss: 0.0630 - val_acc: 0.9807\n",
      "Epoch 7/10\n",
      "3s - loss: 0.0208 - acc: 0.9948 - val_loss: 0.0592 - val_acc: 0.9810\n",
      "Epoch 8/10\n",
      "3s - loss: 0.0138 - acc: 0.9972 - val_loss: 0.0596 - val_acc: 0.9812\n",
      "Epoch 9/10\n",
      "3s - loss: 0.0111 - acc: 0.9975 - val_loss: 0.0547 - val_acc: 0.9810\n",
      "Epoch 10/10\n",
      "3s - loss: 0.0084 - acc: 0.9986 - val_loss: 0.0605 - val_acc: 0.9817\n",
      "Baseline Error: 1.83%\n"
     ]
    }
   ],
   "source": [
    "# build the model\n",
    "model = baseline_model()\n",
    "\n",
    "# fit the model\n",
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=10, batch_size=200, verbose=2)\n",
    "\n",
    "# final evaluation of the model\n",
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Baseline Error: %.2f%%\" % (100-scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
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 },
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