Training Imagenet Improved-Gan on Different Dataset

[CLT29]

Hello, I am trying to use your code to train a generator on a different dataset besides imagenet. I have prepared my data in the same format by rewriting the convert to records tool. Because I want to generate 256 sized images, I added two new layers to generator and discriminator so that they can take correct sized inputs.

Unfortunately, my results have been very poor. Can you explain why the num_classes = 1001 in the discriminator as opposed to 1000? Imagenet has 1000 classes, not 1001 and the class indices are from 0 to 999. So what is the last one for?

Besides simply adding those new layers, is there anything else I need to do to handle this sized data and work on a different data set? I should point out my code is training, I just can't seem to get any meaninful results. I have manually verified data is loading correctly in and out of the models. I am also using a batch size of 8 due to memory issues with 256 sized images

Thanks Chris

[dbparedes]

Hi, I have started reading this work as well but meanwhile you can check out this link about the number of classes.