How to apply deeplift on autoencoder made by keras?

[xiaofangyuan]

Hello! How to apply deeplift on autoencoder made by keras?
Since the autoencoder on keras now is like this:

input_img = Input(shape=(784,))

"encoded" is the encoded representation of the input

encoded = Dense(encoding_dim, activation='relu')(input_img)

"decoded" is the lossy reconstruction of the input

decoded = Dense(784, activation='sigmoid')(encoded)

this model maps an input to its reconstruction

autoencoder = Model(input_img, decoded)

so how to convert the autoencoder layer to deeplift format?
Thank you so much!

[AvantiShri]

Hi Fangyuan,

(moving from the private email thread to hear) currently, we are focused on studying how to define the reference in an automated way for DeepLIFT - thus, the most I can do in the near term is provide a very alpha implementation of autoencoders with DeepLIFT. If you are comfortable with using an alpha implementation, I can provide it but I can't make any promises about how well it will work. If you want results in the next couple of weeks, my short-term recommendation would be to use the gradients of the input w.r.t. the autoencoder loss as a quick way to get a sense of which inputs are most relevant. I can give you pointers on how to compute the gradients if you are interested in that route.