improved-gan
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Inference mode vs training mode
I don't know if this question belongs here, but I am currently making a custom tf keras gan with feature matching loss and I am struggling to understand when to use inference mode on a model, that is, making use of training layers like dropout and updating batch norm parameters. This goes both for discriminator and generator as I understand that they should be trained separately.