我现在意识到像 this 一样实现它本来是个好主意。但是，我有一个经过训练和微调的自动编码器，如下所示：
Model: "autoencoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
user_input (InputLayer) [(None, 5999)] 0
_________________________________________________________________
user_e00 (Dense) (None, 64) 384000
_________________________________________________________________
user_e01 (Dense) (None, 64) 4160
_________________________________________________________________
user_e02 (Dense) (None, 64) 4160
_________________________________________________________________
user_e03 (Dense) (None, 64) 4160
_________________________________________________________________
user_out (Dense) (None, 32) 2080
_________________________________________________________________
emb_dropout (Dropout) (None, 32) 0
_________________________________________________________________
user_d00 (Dense) (None, 64) 2112
_________________________________________________________________
user_d01 (Dense) (None, 64) 4160
_________________________________________________________________
user_d02 (Dense) (None, 64) 4160
_________________________________________________________________
user_d03 (Dense) (None, 64) 4160
_________________________________________________________________
user_res (Dense) (None, 5999) 389935
=================================================================
Total params: 803,087
Trainable params: 0
Non-trainable params: 803,087
_________________________________________________________________
现在我想把它分成编码器和解码器。我相信我已经找到了编码器的正确方法，那就是：
encoder_in = model.input
encoder_out = model.get_layer(name='user_out').output
encoder = Model(encoder_in, encoder_out, name='encoder')
对于解码器，我想做类似的事情：
decoder_in = model.get_layer("user_d00").input
decoder_out = model.output
decoder = Model(decoder_in, decoder_out, name='decoder')
但这会抛出：
WARNING:tensorflow:Functional inputs must come from `tf.keras.Input` (thus holding past layer metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to "decoder" was not an Input tensor, it was generated by layer emb_dropout.
Note that input tensors are instantiated via `tensor = tf.keras.Input(shape)`.
The tensor that caused the issue was: emb_dropout/cond_3/Identity:0
我相信我必须创建一个具有 Input 输出形状的 emb_dropout 层，并可能将其添加到 user_d00 （因为不再需要 Dropout 层，因为训练已经结束）。任何人都知道如何正确地做到这一点？