machine-learning - 如何在 Keras 中拆分和合并模型？

Question

我正在尝试构建一个由 2 个自动编码器组成的堆叠式自动编码器模型。我有 2 个 AE，但我无法加入他们。

这就是我目前所拥有的

### 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

此时我想做的是通过堆叠创建另一个模型

• ae1 层从 0 到编码
• 与ae2相同的层
• 更多密集层

所以最后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 >密集> softmax

我尝试过做类似的事情

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)

但我认为这是不正确的。你能建议我一个正确的方法吗？

谢谢。

Answer 1

首先，您应该更改 enc_layer_ae2 层的输入。由于层在 keras 中是可调用的，因此您可以轻松地在另一层上调用一个层。

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)