Full_Connected_Layer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.utils import np_utils\n",
    "from keras import objectives\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.layers import Input, Dense, Lambda\n",
    "from keras.layers.core import Dropout\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "from keras.utils.visualize_util import plot\n",
    "from IPython.display import Image\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "nb_classes = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load MNIST data\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "X_train = X_train.reshape(60000, 784).astype('float32')\n",
    "X_test = X_test.reshape(10000, 784).astype('float32')\n",
    "X_train /= 255.0\n",
    "X_test /= 255.0\n",
    "\n",
    "# convert class vectors to 1-of-K format\n",
    "y_train = np_utils.to_categorical(y_train, nb_classes)\n",
    "y_test = np_utils.to_categorical(y_test, nb_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train samples:  (60000, 784)\n",
      "test samples:  (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "print('train samples: ', X_train.shape)\n",
    "print('test samples: ', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Connected Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "in_layer = Input((784,))\n",
    "hidden = Dense(512, activation = 'relu')(in_layer)\n",
    "hidden = Dense(512, activation = 'relu')(hidden)\n",
    "output = Dense(10, activation = 'softmax')(hidden)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Model(input=in_layer, output=output)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=RMSprop(),\n",
    "              metrics=['accuracy'])\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 54000 samples, validate on 6000 samples\n",
      "Epoch 1/100\n",
      "54000/54000 [==============================] - 21s - loss: 0.2390 - acc: 0.9254 - val_loss: 0.0918 - val_acc: 0.9728\n",
      "Epoch 2/100\n",
      "54000/54000 [==============================] - 26s - loss: 0.0855 - acc: 0.9739 - val_loss: 0.0720 - val_acc: 0.9788\n",
      "Epoch 3/100\n",
      "54000/54000 [==============================] - 26s - loss: 0.0568 - acc: 0.9821 - val_loss: 0.0787 - val_acc: 0.9783\n",
      "Epoch 4/100\n",
      "54000/54000 [==============================] - 24s - loss: 0.0407 - acc: 0.9874 - val_loss: 0.0852 - val_acc: 0.9772\n",
      "Epoch 5/100\n",
      "54000/54000 [==============================] - 18s - loss: 0.0287 - acc: 0.9910 - val_loss: 0.0829 - val_acc: 0.9790\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(X_train, y_train,\n",
    "                 batch_size=batch_size,\n",
    "                 verbose=1,\n",
    "                 nb_epoch=100,\n",
    "                 validation_split=0.1,\n",
    "                 callbacks=[early_stopping]\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 2s     \n",
      "Test loss: 0.0830930528447\n",
      "Test accuracy: 0.9796\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "score = model.evaluate(X_test, y_test, verbose=1)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 2 1 0 4 1 4 9 6 9]\n",
      "[7 2 1 0 4 1 4 9 5 9]\n"
     ]
    }
   ],
   "source": [
    "print (np.argmax(model.predict(X_test[:10]),axis=1))\n",
    "print (np.argmax(y_test[:10],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEPCAYAAABRHfM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ0REEqKCISIiCfn//AjbRfaHO8EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d624750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot loss\n",
    "loss = hist.history['loss']\n",
    "val_loss = hist.history['val_loss']\n",
    "\n",
    "nb_epoch = len(loss)\n",
    "plt.plot(range(nb_epoch), loss, marker='.', label='loss')\n",
    "plt.plot(range(nb_epoch), val_loss, marker='.', label='val_loss')\n",
    "plt.legend(loc='best', fontsize=10)\n",
    "plt.grid()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL0AAAFgCAYAAADuAHh1AAAAAXNSR0IArs4c6QAAL+pJREFUeAHt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\njcD/A2XG4DCkMRnkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(model, to_file='model.png')\n",
    "Image(filename='model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Full Connected Layer with DropOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "in_layer2 = Input((784,))\n",
    "hidden2 = Dropout(0.2)(Dense(512, activation = 'relu')(in_layer2))\n",
    "hidden2 = Dropout(0.2)(Dense(512, activation = 'relu')(hidden2))\n",
    "output2 = Dense(10, activation = 'softmax')(hidden2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model2 = Model(input=in_layer2, output=output2)\n",
    "model2.compile(loss='categorical_crossentropy',\n",
    "              optimizer=RMSprop(),\n",
    "              metrics=['accuracy'])\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 54000 samples, validate on 6000 samples\n",
      "Epoch 1/100\n",
      "54000/54000 [==============================] - 20s - loss: 0.2665 - acc: 0.9178 - val_loss: 0.1063 - val_acc: 0.9677\n",
      "Epoch 2/100\n",
      "54000/54000 [==============================] - 20s - loss: 0.1053 - acc: 0.9675 - val_loss: 0.0867 - val_acc: 0.9747\n",
      "Epoch 3/100\n",
      "54000/54000 [==============================] - 19s - loss: 0.0789 - acc: 0.9760 - val_loss: 0.0782 - val_acc: 0.9775\n",
      "Epoch 4/100\n",
      "54000/54000 [==============================] - 19s - loss: 0.0611 - acc: 0.9812 - val_loss: 0.0665 - val_acc: 0.9810\n",
      "Epoch 5/100\n",
      "54000/54000 [==============================] - 20s - loss: 0.0519 - acc: 0.9850 - val_loss: 0.0765 - val_acc: 0.9808\n",
      "Epoch 6/100\n",
      "54000/54000 [==============================] - 20s - loss: 0.0437 - acc: 0.9869 - val_loss: 0.0678 - val_acc: 0.9830\n",
      "Epoch 7/100\n",
      "54000/54000 [==============================] - 20s - loss: 0.0387 - acc: 0.9884 - val_loss: 0.0711 - val_acc: 0.9818\n"
     ]
    }
   ],
   "source": [
    "hist2 = model2.fit(X_train, y_train,\n",
    "                 batch_size=batch_size,\n",
    "                 verbose=1,\n",
    "                 nb_epoch=100,\n",
    "                 validation_split=0.1,\n",
    "                 callbacks=[early_stopping]\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9984/10000 [============================>.] - ETA: 0sTest loss: 0.0823358456761\n",
      "Test accuracy: 0.9809\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "score2 = model2.evaluate(X_test, y_test, verbose=1)\n",
    "print('Test loss:', score2[0])\n",
    "print('Test accuracy:', score2[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w/DMz+NaKs1zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124c97910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot loss\n",
    "loss2 = hist2.history['loss']\n",
    "val_loss2 = hist2.history['val_loss']\n",
    "\n",
    "nb_epoch2 = len(loss2)\n",
    "plt.plot(range(nb_epoch2), loss2, marker='.', label='loss')\n",
    "plt.plot(range(nb_epoch2), val_loss2, marker='.', label='val_loss')\n",
    "plt.legend(loc='best', fontsize=10)\n",
    "plt.grid()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}