#加载keras模块
from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibilityimport 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
%matplotlib inline#写一个LossHistory类，保存loss和acc
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()# accplt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')# lossplt.plot(iters, self.losses[loss_type], 'g', label='train loss')if loss_type == 'epoch':# val_accplt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')# val_lossplt.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()
#变量初始化
batch_size = 128
nb_classes = 10
nb_epoch = 20# 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')# 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)#建立模型 使用Sequential（）
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
model.summary()#训练与评估
#编译模型
model.compile(loss='categorical_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])
#创建一个实例history
history = LossHistory()#迭代训练（注意这个地方要加入callbacks）
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
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
history.loss_plot('epoch')

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