我有一个带有两个输出的自动编码器，经过一些更改后，第一个输出应用作自动编码器下一部分的输入。我将代码放在这里，我对此代码在逻辑上是否正确感到有些困惑。在以下代码中，我在decoder部分中有一个输出，名为act11，是S形激活的输出，第二个输出在w extraction部分中，名为pred_W。我将bncv11而不是GaussianNoise馈入act11。我想知道这是正确的还是错误的？根据反向传播规则和网络结构可以做到这一点吗？我可以仅使用此代码act11=Activation('sigmoid',name='imageprim')(bncv11)中的激活输出作为模型的输出吗？
我所有的问题都与代码的这一部分有关：
decoded = Conv2D(1, (5, 5), padding='same', name='decoder_output',dilation_rate=(2,2))(BNd)
bncv11=BatchNormalization()(decoded)
act11=Activation('sigmoid',name='imageprim')(bncv11)
decoded_noise = GaussianNoise(0.5)(bncv11)
#----------------------w extraction------------------------------------
convw1 = Conv2D(64, (3,3), activation='relu', padding='same', name='conl1w',dilation_rate=(2,2))(decoded_noise)
watermark_extraction=Model(inputs=[image,wtm],outputs=[act11,pred_w])
我想知道上述基于深度学习的代码是否正确？
我只是在激活过程中使用act11进行输出，而在学习过程中使用bncv11进行进餐。
wtm=Input((28,28,1))
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)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e',dilation_rate=(2,2))(conv2)
BN=BatchNormalization()(conv3)
encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I',dilation_rate=(2,2))(BN)
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([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)
deconv3 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl3d',dilation_rate=(2,2))(deconv2)
deconv4 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl4d',dilation_rate=(2,2))(deconv3)
BNd=BatchNormalization()(deconv3)
decoded = Conv2D(1, (5, 5), padding='same', name='decoder_output',dilation_rate=(2,2))(BNd)
bncv11=BatchNormalization()(decoded)
act11=Activation('sigmoid',name='imageprim')(bncv11)
decoded_noise = GaussianNoise(0.5)(bncv11)
#----------------------w extraction------------------------------------
convw1 = Conv2D(64, (3,3), activation='relu', padding='same', name='conl1w',dilation_rate=(2,2))(decoded_noise)
convw2 = Conv2D(64, (3, 3), activation='relu', padding='same', name='convl2w',dilation_rate=(2,2))(convw1)
convw3 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl3w',dilation_rate=(2,2))(convw2)
convw4 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl4w',dilation_rate=(2,2))(convw3)
convw5 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl5w',dilation_rate=(2,2))(convw4)
convw6 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl6w',dilation_rate=(2,2))(convw5)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W',dilation_rate=(2,2))(convw6)
watermark_extraction=Model(inputs=[image,wtm],outputs=[act11,pred_w])