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I realize now that implementing it like this would have been a good idea. However, I have an already trained and fine-tuned autoencoder that looks like this:

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
_________________________________________________________________

Now I want to split it into encoder and decoder. I believe I already found the right way for the encoder, which would be:

encoder_in = model.input
encoder_out = model.get_layer(name='user_out').output
encoder = Model(encoder_in, encoder_out, name='encoder')

For the decoder I would like to do something like:

decoder_in = model.get_layer("user_d00").input
decoder_out = model.output
decoder = Model(decoder_in, decoder_out, name='decoder')

but that throws:

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

I believe I have to create an Input layer with the shape of the output of emb_dropout and probably add it to user_d00 (since the Dropout layer is not needed anymore since training has ended). Anyone knows how to do it correctly?

Daniel
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    Please see: https://stackoverflow.com/questions/52800025/keras-give-input-to-intermediate-layer-and-get-final-output Same would apply to `tensorflow.keras`. As a side comment: this is just a pain in tf/keras. Strongly recommend to rewrite your solution if possible – Proko Feb 06 '21 at 09:33
  • Thanks, do you by any chance know if I can take the `user_d00` directly? the dropout layer should not contain any weights.. or does it? – Daniel Feb 06 '21 at 10:34
  • Note: since the network is very fast to train, I ended up reimplementing it correctly and doing that instead. – Daniel Feb 06 '21 at 12:18

0 Answers0