如何冻结TensorFlow中的自动编码器层

Question

这是一个自动编码器网络，第一部分是编码器，第二部分是解码器。我想冻结前三个卷积层并保存编码器零件。你能帮我如何做吗？谢谢

def encoder(input_img):
    #encoder
    #input = 28 x 28 x 1 (wide and thin)
    conv1 = Conv2D(64, (2,2), activation='relu', padding='same')(input_img) #28 x 28 x 32
    conv2 = BatchNormalization()(conv1)
    conv3 = Conv2D(32, (2,2), activation='relu', padding='same')(conv2)
    conv4 = BatchNormalization()(conv3)
    pool5 = MaxPooling2D(pool_size=(2,2))(conv4) #14 x 14 x 32
    conv6 = Conv2D(16, (2,2), activation='relu', padding='same')(pool5) #14 x 14 x 64
    conv7 = BatchNormalization()(conv6)
    conv8 = Conv2D(8, (2,2), activation='relu', padding='same')(conv7)
    conv9 = BatchNormalization()(conv8)
    conv10 = Conv2D(4, (2,2), activation='relu', padding='same')(conv9)
    return conv10



def decoder(conv11):    
    #decoder
    conv12 = Conv2D(4, (2,2), activation='relu', padding='same')(conv11)
    conv13 = Conv2D(8, (2,2), activation='relu', padding='same')(conv12) #7 x 7 x 128
    conv14 = BatchNormalization()(conv13)
    conv15 = Conv2D(16, (2,2), activation='relu', padding='same')(conv14)
    conv16 = BatchNormalization()(conv15)
    conv17 = Conv2D(32, (2,2), activation='relu', padding='same')(conv16) #7 x 7 x 64
    conv18 = BatchNormalization()(conv17)
    conv19 = Conv2D(64, (2,2), activation='relu', padding='same')(conv18)
    conv20 = BatchNormalization()(conv19)
    up21 = UpSampling2D((2,2))(conv20) #14 x 14 x 64
    decoded = Conv2D(3, (2,2), activation='sigmoid', padding='same')(up21) # 28 x 28 x
    return decoded

autoencoder = Model(input_img, decoder(encoder(input_img)))
autoencoder.compile(loss='mae', optimizer = 'SGD')
autoencoder.summary()


train = np.concatenate((normal[0:1900,:,:,:],un_informative[0:1900,:,:,:]),axis=0) 
valid = np.concatenate((normal[1900:,:,:,:],un_informative[1900:,:,:,:]),axis=0)
history = autoencoder.fit(train ,train , batch_size=batch_size,epochs=200,verbose=1, validation_data=(valid, valid))

Answer 1

编译模型后，您可以选择通过指定冻结的层：

layer_x.trainable = False

我建议您进行以下操作：

layers_to_freeze = [name of the layers]

for layer in model.layers:
    if layer in layers_to_freeze:
       layer.trainable=False