{
 "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，程序会保存每轮训练的acc和loss，并且绘制它们。\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": [
    "import keras\n",
    "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\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 写一个LossHistory类，保存loss和acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class LossHistory(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = {'batch':[], 'epoch':[]}\n",
    "        self.accuracy = {'batch':[], 'epoch':[]}\n",
    "        self.val_loss = {'batch':[], 'epoch':[]}\n",
    "        self.val_acc = {'batch':[], 'epoch':[]}\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        self.losses['batch'].append(logs.get('loss'))\n",
    "        self.accuracy['batch'].append(logs.get('acc'))\n",
    "        self.val_loss['batch'].append(logs.get('val_loss'))\n",
    "        self.val_acc['batch'].append(logs.get('val_acc'))\n",
    "        \n",
    "    def on_epoch_end(self, batch, logs={}):\n",
    "        self.losses['epoch'].append(logs.get('loss'))\n",
    "        self.accuracy['epoch'].append(logs.get('acc'))\n",
    "        self.val_loss['epoch'].append(logs.get('val_loss'))\n",
    "        self.val_acc['epoch'].append(logs.get('val_acc'))\n",
    "        \n",
    "    def loss_plot(self, loss_type):\n",
    "        iters = range(len(self.losses[loss_type]))\n",
    "        plt.figure()\n",
    "        # acc\n",
    "        plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')\n",
    "        # loss\n",
    "        plt.plot(iters, self.losses[loss_type], 'g', label='train loss')\n",
    "        if loss_type == 'epoch':\n",
    "            # val_acc\n",
    "            plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')\n",
    "            # val_loss\n",
    "            plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')\n",
    "        plt.grid(True)\n",
    "        plt.xlabel(loss_type)\n",
    "        plt.ylabel('acc-loss')\n",
    "        plt.legend(loc=\"upper right\")\n",
    "        plt.show()"
   ]
  },
  {
   "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": [
    "## 5.训练与评估\n",
    "### 编译模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建一个实例history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "history = LossHistory()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 迭代训练（注意这个地方要加入callbacks）"
   ]
  },
  {
   "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 [==============================] - 5s - loss: 0.2448 - acc: 0.9239 - val_loss: 0.1220 - val_acc: 0.9623\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1025 - acc: 0.9689 - val_loss: 0.0775 - val_acc: 0.9760\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0752 - acc: 0.9773 - val_loss: 0.0792 - val_acc: 0.9775\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0608 - acc: 0.9819 - val_loss: 0.0758 - val_acc: 0.9790\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0500 - acc: 0.9852 - val_loss: 0.0903 - val_acc: 0.9766\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0449 - acc: 0.9863 - val_loss: 0.0780 - val_acc: 0.9798\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0398 - acc: 0.9875 - val_loss: 0.0798 - val_acc: 0.9823\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0349 - acc: 0.9899 - val_loss: 0.0794 - val_acc: 0.9834\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0301 - acc: 0.9912 - val_loss: 0.0932 - val_acc: 0.9805\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0281 - acc: 0.9919 - val_loss: 0.0819 - val_acc: 0.9842\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0276 - acc: 0.9921 - val_loss: 0.1007 - val_acc: 0.9814\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0250 - acc: 0.9931 - val_loss: 0.0978 - val_acc: 0.9824\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0228 - acc: 0.9935 - val_loss: 0.1063 - val_acc: 0.9827\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0234 - acc: 0.9939 - val_loss: 0.1014 - val_acc: 0.9815\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0188 - acc: 0.9946 - val_loss: 0.1052 - val_acc: 0.9839\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0180 - acc: 0.9946 - val_loss: 0.1027 - val_acc: 0.9833\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0213 - acc: 0.9946 - val_loss: 0.1103 - val_acc: 0.9840\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0204 - acc: 0.9949 - val_loss: 0.1168 - val_acc: 0.9818\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0185 - acc: 0.9954 - val_loss: 0.1318 - val_acc: 0.9809\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0194 - acc: 0.9954 - val_loss: 0.1173 - val_acc: 0.9831\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f1ef8066590>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train,\n",
    "            batch_size=batch_size, nb_epoch=nb_epoch,\n",
    "            verbose=1, \n",
    "            validation_data=(X_test, Y_test),\n",
    "            callbacks=[history])"
   ]
  },
  {
   "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: 0.117298825225\n",
      "Test accuracy: 0.9831\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": "markdown",
   "metadata": {},
   "source": [
    "### 绘制acc-loss曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xfAPdT6nqgyJyHaCquqDZsk8Db1hiMMYYZyRkYnCKJQZjjIleLE9wM91MaD+u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\nM/MZzvCoXutVL5X1lWHJovn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f200c10c690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history.loss_plot('epoch')"
   ]
  }
 ],
 "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"
  },
  "ssap_exp_config": {
   "error_alert": "Error Occurs!",
   "initial": [],
   "max_iteration": 1000,
   "recv_id": "",
   "running": [],
   "summary": [],
   "version": "1.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}