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How to get encoder and decoder parts of autoencoder in order to stack them?

4 vues (au cours des 30 derniers jours)
I want to represent 128x128 images in a 1x64 vector, and for that I want to use autoencoders.
I have trained autoencoders in stages following the example in "Train Stacked Autoencoders for Image Classificatio".
However, I dont want to build a classifier, I want to build an autoencoder to embedd images.
(I tried build my own autoencoder with CNNs without using the built in autoencoder functions but I can't get good results for some reason)
Following the example I created two autoencoders, and added another one of my own instead of the softmax output and trained them:
encoder1 : 128x128 ->256-> 128x128
encoder2 : 256->128->256
encoder3 : 128->64->128
But how do I combine them?
If I use the "stack" function on these encoders like in the example:
stackednet = stack(encoder1,encoder2,encoder3)
I get this transformation :
128x128 -> 256 -> 128 -> 64
but what I want is this:
128x128 -> 256 -> 128 -> 64 ->128 -> 256 -> 128x128
So basicaly I need to stack this:
stackednet = stack(encoder1-encoder,encoder2-encoder,encoder3-encoder,encoder3-decoder,encoder2-decoder,encoder1-decoder)
Is there a way to extract the encoder and the decoder parts from an Autoencoder object and then stack them?

Réponse acceptée

David Willingham
David Willingham le 23 Juin 2021
That example is using the older Neural Networks functionalilty. I'd recommend looking at one of the examples using the newer framework like this one here: Train Variational Autoencoder (VAE) to Generate Images.
  1 commentaire
ytzhak goussha
ytzhak goussha le 23 Juin 2021
Thank you for your reply,
I did try that one as well,
I mannaged to get the loss as low as 5 but then it stoped.
That is why I wanted to train the network like an onion, train the outer encoder-decoder first, and the move inwards, and finally stack all the encoders, and the decoders.

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