Tensorflow Keras use encoder and decoder separately in autoencoder

Question

I'm messing around with the Keras api in tensorflow, attempting to implement an autoencoder. The sequential model works, but I want to be able to use the encoder (first two layers) and the decoder (last two layers) separately, but using the weights of my already trained model. Is there a way to do this? Do I have to make a custom model?

model = keras.Sequential()
model.add(encoder_1)
model.add(leaky_relu)
model.add(encoder_2)
model.add(leaky_relu2)
model.add(decoder_1)
model.add(leaky_relu3)
model.add(decoder_2)

encoder_model = keras.Sequential()
encoder_model.add(encoder_1)
encoder_model.add(leaky_relu)
encoder_model.add(encoder_2)
encoder_model.add(leaky_relu2)

decoder_model = keras.Sequential()
decoder_model.add(decoder_1)
model.add(leaky_relu3)
decoder_model.add(decoder_2)

I define my models like this but trying to run predict on either the encoder or decoder outputs

'Sequential' object has no attribute '_feed_input_names'

sdcbr · Accepted Answer · 2018-08-24 17:13:14Z

2

Yes, you should wrap the encoding and decoding layers in separate Model instances that you call separately. The Keras blogporst on autoencoders should contain everything you need to know: https://blog.keras.io/building-autoencoders-in-keras.html

edited Aug 24, 2018 at 17:13

answered Jul 29, 2018 at 7:37

sdcbr

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3 Comments

TheMagicalCake Over a year ago

This does not seem to work in tensorflow, I've edited my original post with a code sample and error

sdcbr Over a year ago

You want to use the functional api, as in the example, and not the sequential api.

Rodrigo Laguna Over a year ago

checkout this answer on how to define and run encoder and decoder independently

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