我正在尝试构建一个由两个自编码器组成的堆叠自编码器模型。我已经有了两个自编码器，但无法将它们连接起来。
这是我目前的进展
### AUTOENCODER 1 ###X_input = Input(input_shape)x = Conv2D(64, (4,1), activation='relu', padding='same')(X_input)x = Conv2D(32, (3,2), activation='relu', padding='same')(x)x = MaxPooling2D(name='encoded')(x)encoded_shape = x.shape.as_list()x = Conv2D(32, (3,2), activation='relu', padding='same')(x)x = UpSampling2D(name='up1')(x)x = Conv2D(64, (4,1), activation='relu', padding='same')(x)x = Conv2D(1, (3,3), name='decoded', padding='same')(x)ae1 = Model(X_input, x)enc_layer_ae1 = ae1.get_layer('encoded').output–
### AUTOENCODER 2 ###X_input1 = Input(encoded_shape[1:])x1 = Conv2D(24, (3,3), activation='relu', padding='same')(X_input1)x1 = Conv2D(16, (2,2), activation='relu', padding='same')(x1)x1 = MaxPooling2D((2,3), name='encoded')(x1)x1 = UpSampling2D((2,3), name='up')(x1)x1 = Conv2D(16, (2,2), activation='relu', padding='same')(x1)x1 = Conv2D(24, (3,3), activation='relu', padding='same')(x1)x1 = Conv2D(32, (1,1), padding='same')(x1)ae2 = Model(X_input1, x1)enc_layer_ae2 = ae2.get_layer('encoded').output此时，我想通过堆叠创建另一个模型
ae1从第0层到encoded层ae2的相同层- 一些额外的
Dense层
所以最终我的模型应该看起来像ae1_input > ae1_conv2d > ae1_conv2d > ae1_encoded > ae2_input > ae2_conv > ae2_conv > ae2_encoded > dense > softmax
我尝试过这样做
ae2_split = Model(X_input1, enc_layer_ae2)full_output = ae2_split(enc_layer_ae1)full_output = Dense(150, activation='relu')(full_output)full_output = Dense(7, activation='softmax')(full_output)full_model = Model(enc_layer_ae1.input, full_output)但我认为这不是正确的做法。你能建议我一个正确的方法吗？
谢谢。
回答：
首先，你应该更改enc_layer_ae2层的输入。由于Keras中的层是可调用的，你可以轻松地将一个层应用到另一个层上。
enc_layer_ae1 = ae1.get_layer('encoded')enc_layer_ae2 = ae2.get_layer('encoded')enc_layer_ae2 = enc_layer_ae2(enc_layer_ae1.output)full_output = Dense(150, activation='relu')(enc_layer_ae2)full_output = Dense(7, activation='softmax')(full_output)model = Model(enc_layer_ae1.input, full_output)