我想考虑CNN中各层之间的某些身份连接，并将输入发送到下一层。我为此使用了下面的代码，只是将输入与另一层的输出连接起来，然后发送到下一层，但是我不确定这是不是真的，因为该层的输出与我期望的不同。我是否使用一种真正的方式将输入发送到其他层和ResNet等身份连接？
wtm=Input((4,4,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e',dilation_rate=(2,2))(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e',dilation_rate=(2,2))(conv1)
convaux1=Concatenate(axis=3)([conv2,image])
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e',dilation_rate=(2,2))(convaux1)
BN=BatchNormalization()(conv3)
encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)
#-----------------------adding w---------------------------------------
wpad=Kr.layers.Lambda(lambda xy: xy[0] + Kr.backend.spatial_2d_padding(xy[1], padding=((0, 24), (0, 24))))
encoded_merged=wpad([encoded,wtm])
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
deconv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1d',dilation_rate=(2,2))(encoded_merged)
deconv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2d',dilation_rate=(2,2))(deconv1)
convaux2=Concatenate(axis=3)([deconv2,image])
BNda=BatchNormalization()(convaux2)
deconv3 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl3d',dilation_rate=(2,2))(BNda)
deconv4 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl4d',dilation_rate=(2,2))(deconv3)
BNd=BatchNormalization()(deconv4)
decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd)
model=Model(inputs=[image,wtm],outputs=decoded)