Keras mlp 手写数字识别示例

#基于mnist数据集的手写数字识别

#构造了三层全连接层组成的多层感知机，最后一层为输出层

#基于Keras 2.1.1 Tensorflow 1.4.0

代码：

 import keras

 from keras.datasets import mnist

 from keras.models import Sequential

 from keras.layers import Dense,Dropout

 from keras.optimizers import RMSprop

 (x_train,y_train),(x_test,y_test) = mnist.load_data()

 #载入数据，第一次运行时会从外部网络下载数据集到对应目录下

 print(x_train.shape,y_train.shape)

 print(x_test.shape,y_test.shape)

 # import matplotlib.pyplot as plt

 # im = plt.imshow(x_train[0],cmap='gray')

 # plt.show()

 # im2 = plt.imshow(x_train[1],cmap='gray')

 # plt.show()

 x_train = x_train.reshape(60000,784)

 x_test = x_test.reshape(10000,784)

 x_train = x_train.astype('float32')

 x_train = x_train.astype('float32')

 print(x_train.shape)

 x_train = x_train/255

 x_test = x_test/255

 y_train = keras.utils.to_categorical(y_train,10)

 y_test = keras.utils.to_categorical(y_test,10)

 model = Sequential()

 model.add(Dense(512,activation='relu',input_shape=(784,)))

 model.add(Dropout(0.2))

 model.add(Dense(512,activation='relu'))

 model.add(Dropout(0.2))

 model.add(Dense(10,activation='softmax'))

 model.summary()

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

 model.fit(x_train,y_train,batch_size=64,epochs=2,verbose=1,validation_data=(x_test,y_test))

 score = model.evaluate(x_test,y_test,verbose=1)

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

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

结果：

Test loss: 0.123420921481

Test accuracy 0.9682