from __future__ import print_function

 import numpy as np

 np.random.seed(1337)

 from keras.datasets import mnist

 from keras.models import Sequential

 from keras.layers import Dense, Dropout, Activation, Flatten

 from keras.layers import Convolution2D, MaxPooling2D

 from keras.utils import np_utils

 from keras import backend as K

 batch_size = 128

 nb_classes = 10

 nb_epoch = 12

 img_rows, img_cols = 28, 28

 nb_filters = 32

 pool_size = (2,2)

 kernel_size = (3,3)

 (X_train, y_train), (X_test, y_test) = mnist.load_data()

 X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)

 X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)

 input_shape = (img_rows, img_cols, 1)

 X_train = X_train.astype('float32')

 X_test = X_test.astype('float32')

 X_train /= 255

 X_test /= 255

 Y_train = np_utils.to_categorical(y_train, nb_classes)

 Y_test = np_utils.to_categorical(y_test, nb_classes)

 # 建立序贯模型

 model = Sequential()

 model.add(Convolution2D(nb_filters, kernel_size[0] ,kernel_size[1],border_mode='valid',input_shape=input_shape))

 model.add(Activation('relu'))

 model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))

 model.add(Activation('relu'))

 model.add(MaxPooling2D(pool_size=pool_size))

 model.add(Dropout(0.25))

 model.add(Flatten())

 model.add(Dense(128))

 model.add(Activation('relu'))

 model.add(Dropout(0.5))

 model.add(Dense(nb_classes))

 model.add(Activation('softmax'))

 model.summary()

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

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

 score = model.evaluate(X_test, Y_test, verbose=0)

 print('Test score:', score[0])

 print('Test accuracy:', score[1])