I did I couple of examples for auto encoders for images and they worked fine. Now I want to do an auto encoder for text that takes as input a sentence and returns the same sentence. But when I try to use the same auto encoders as the ones I used for the images I get bad results.
I guess the reason for this is that my text is sparse and I have a big vocabulary size of 500K words.
Do you have a link of a working example of an auto encoder for text in Keras?
I saw that in most papers they use cross-entropy as a loss function. How does cross-entropy calculate the loss exactly ? Does it make sense to use cross-entropy even if I do a character by character auto encoder?