(x_train,y_train),(x_test,y_test)=mnist.load_data() #加载数据

#数据预处理

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')

#归一化

x_train/=255

x_test/=255

#将类标签转换为one-hot向量

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

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

batch_size=128 #批处理大小

num_classes=10 #类标签数量

epochs=20

model=Sequential() #定义一个序贯模型

#input_shape:模型的第一层需要指明输入数据的大小,后面各层自动推断

model.add(Dense(512,activation='relu',input_shape=(784,)))#全连接层，然后激活层

model.add(Dropout(0.2))#Dropout

model.add(Dense(512,activation='relu')) #全连接层

model.add(Dropout(0.2))

model.add(Dense(num_classes,activation='softmax')) #全连接，然后使用Softmax激活求概率

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

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

score=model.evaluate(x_test,y_test,verbose=0)

print('Test loss:',score[0]) #损失

print('Test accuracy:',score[1]) #精度

model.summary()

Layer (type)                 Output Shape              Param #  

=================================================================

dense_4 (Dense)              (None, 512)               401920   

_________________________________________________________________

dropout_3 (Dropout)          (None, 512)               0        

_________________________________________________________________

dense_5 (Dense)              (None, 512)               262656   

_________________________________________________________________

dropout_4 (Dropout)          (None, 512)               0        

_________________________________________________________________

dense_6 (Dense)              (None, 10)                5130     

=================================================================

Total params: 669,706

Trainable params: 669,706

Non-trainable params: 0