#!/usr/bin/env python
# coding: utf-8

# # 程序说明
# 时间：2016年11月16日
# 
# 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。
# 
# 数据集：MNIST

# ## 1.加载keras模块

# In[1]:


from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility


# In[2]:


import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# ### 写一个LossHistory类，保存loss和acc

# In[3]:


class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = {'batch':[], 'epoch':[]}
        self.accuracy = {'batch':[], 'epoch':[]}
        self.val_loss = {'batch':[], 'epoch':[]}
        self.val_acc = {'batch':[], 'epoch':[]}

    def on_batch_end(self, batch, logs={}):
        self.losses['batch'].append(logs.get('loss'))
        self.accuracy['batch'].append(logs.get('acc'))
        self.val_loss['batch'].append(logs.get('val_loss'))
        self.val_acc['batch'].append(logs.get('val_acc'))
        
    def on_epoch_end(self, batch, logs={}):
        self.losses['epoch'].append(logs.get('loss'))
        self.accuracy['epoch'].append(logs.get('acc'))
        self.val_loss['epoch'].append(logs.get('val_loss'))
        self.val_acc['epoch'].append(logs.get('val_acc'))
        
    def loss_plot(self, loss_type):
        iters = range(len(self.losses[loss_type]))
        plt.figure()
        # acc
        plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')
        # loss
        plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
        if loss_type == 'epoch':
            # val_acc
            plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
            # val_loss
            plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
        plt.grid(True)
        plt.xlabel(loss_type)
        plt.ylabel('acc-loss')
        plt.legend(loc="upper right")
        plt.show()


# ## 2.变量初始化

# In[4]:


batch_size = 128 
nb_classes = 10
nb_epoch = 20


# ## 3.准备数据

# In[5]:


# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')


# ### 转换类标号

# In[6]:


# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)


# ## 4.建立模型
# ### 使用Sequential（）

# In[7]:


model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))


# ### 打印模型

# In[8]:


model.summary()


# ## 5.训练与评估
# ### 编译模型

# In[9]:


model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])


# ### 创建一个实例history

# In[10]:


history = LossHistory()


# ### 迭代训练（注意这个地方要加入callbacks）

# In[11]:


model.fit(X_train, Y_train,
            batch_size=batch_size, nb_epoch=nb_epoch,
            verbose=1, 
            validation_data=(X_test, Y_test),
            callbacks=[history])


# ### 模型评估

# In[12]:


score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])


# ### 绘制acc-loss曲线

# In[13]:


history.loss_plot('epoch')