时域卷积网络（TCN）案例模型

from tensorflow.examples.tutorials.mnist import input_data from keras.models import Model from keras.layers import add,Input,Conv1D,Activation,Lambda,Dense #载入数据 def read_data(path): mnist=input_data.read_data_sets(path,one_hot=True) train_x,train_y=mnist.train.images.reshape(-1,28,28),mnist.train.labels, valid_x,valid_y=mnist.validation.images.reshape(-1,28,28),mnist.validation.labels, test_x,test_y=mnist.test.images.reshape(-1,28,28),mnist.test.labels return train_x,train_y,valid_x,valid_y,test_x,test_y #残差块 def ResBlock(x,filters,kernel_size,dilation_rate): r=Conv1D(filters,kernel_size,padding='same',dilation_rate=dilation_rate,activation='relu')(x) #第一卷积 r=Conv1D(filters,kernel_size,padding='same',dilation_rate=dilation_rate)(r) #第二卷积 if x.shape[-1]==filters: shortcut=x else: shortcut=Conv1D(filters,kernel_size,padding='same')(x) #shortcut（捷径） o=add([r,shortcut]) o=Activation('relu')(o) #激活函数 return o #序列模型 def TCN(train_x,train_y,valid_x,valid_y,test_x,test_y): inputs=Input(shape=(28,28)) x=ResBlock(inputs,filters=32,kernel_size=3,dilation_rate=1) x=ResBlock(x,filters=32,kernel_size=3,dilation_rate=2) x=ResBlock(x,filters=16,kernel_size=3,dilation_rate=4) x=Lambda(lambda x: x[:,0,:])(x) x=Dense(10,activation='softmax')(x) model=Model(input=inputs,output=x) #查看网络结构 model.summary() #编译模型 model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy']) #训练模型 model.fit(train_x,train_y,batch_size=500,nb_epoch=30,verbose=2,validation_data=(valid_x,valid_y)) #评估模型 pre=model.evaluate(test_x,test_y,batch_size=500,verbose=2) print('test_loss:',pre[0],'- test_acc:',pre[1]) train_x,train_y,valid_x,valid_y,test_x,test_y=read_data('MNIST_data') TCN(train_x,train_y,valid_x,valid_y,test_x,test_y)