01.mnist_mpl.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 程序说明\n",
    "时间：2016年11月16日\n",
    "\n",
    "说明：该程序是一个包含两个隐藏层的神经网络。\n",
    "\n",
    "数据集：MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.加载keras模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "np.random.seed(1337)  # for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.optimizers import SGD, Adam, RMSprop\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如需绘制模型请加载plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.utils.visualize_util import plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.变量初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128 \n",
    "nb_classes = 10\n",
    "nb_epoch = 20 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "# the data, shuffled and split between train and test sets\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "X_train = X_train.reshape(60000, 784)\n",
    "X_test = X_test.reshape(10000, 784)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 转换类标号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert class vectors to binary class matrices\n",
    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
    "Y_test = np_utils.to_categorical(y_test, nb_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.建立模型\n",
    "### 使用Sequential（）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(512, input_shape=(784,)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 打印模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "dense_1 (Dense)                  (None, 512)           401920      dense_input_1[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "activation_1 (Activation)        (None, 512)           0           dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 512)           0           activation_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "dense_2 (Dense)                  (None, 512)           262656      dropout_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_2 (Activation)        (None, 512)           0           dense_2[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)              (None, 512)           0           activation_2[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "dense_3 (Dense)                  (None, 10)            5130        dropout_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_3 (Activation)        (None, 10)            0           dense_3[0][0]                    \n",
      "====================================================================================================\n",
      "Total params: 669706\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制模型结构图，并保存成图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot(model, to_file='model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 显示绘制的图片\n",
    "![image](http://p1.bpimg.com/4851/4025c3b85df0e4f2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.训练与评估\n",
    "### 编译模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 迭代训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.2448 - acc: 0.9239 - val_loss: 0.1220 - val_acc: 0.9623\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1026 - acc: 0.9689 - val_loss: 0.0788 - val_acc: 0.9749\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0752 - acc: 0.9770 - val_loss: 0.0734 - val_acc: 0.9779\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0609 - acc: 0.9817 - val_loss: 0.0777 - val_acc: 0.9780\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0515 - acc: 0.9847 - val_loss: 0.0888 - val_acc: 0.9782\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0451 - acc: 0.9864 - val_loss: 0.0799 - val_acc: 0.9803\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0398 - acc: 0.9878 - val_loss: 0.0814 - val_acc: 0.9809\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0362 - acc: 0.9896 - val_loss: 0.0765 - val_acc: 0.9830\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0325 - acc: 0.9905 - val_loss: 0.0917 - val_acc: 0.9802\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0279 - acc: 0.9921 - val_loss: 0.0808 - val_acc: 0.9844\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0272 - acc: 0.9925 - val_loss: 0.0991 - val_acc: 0.9811\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0248 - acc: 0.9930 - val_loss: 0.0864 - val_acc: 0.9839\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0240 - acc: 0.9935 - val_loss: 0.1061 - val_acc: 0.9809\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0240 - acc: 0.9931 - val_loss: 0.1010 - val_acc: 0.9843\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0200 - acc: 0.9946 - val_loss: 0.1102 - val_acc: 0.9803\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0207 - acc: 0.9942 - val_loss: 0.1020 - val_acc: 0.9833\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0196 - acc: 0.9946 - val_loss: 0.1205 - val_acc: 0.9812\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0208 - acc: 0.9950 - val_loss: 0.1081 - val_acc: 0.9829\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 3s - loss: 0.0199 - acc: 0.9951 - val_loss: 0.1113 - val_acc: 0.9835\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0186 - acc: 0.9953 - val_loss: 0.1168 - val_acc: 0.9849\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train,\n",
    "                    batch_size=batch_size, nb_epoch=nb_epoch,\n",
    "                    verbose=1, validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.11684127673\n",
      "Test accuracy: 0.9849\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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