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Hi, thanks for your great implementation.
I run the model on CelebA dataset. However, when I test the model, I find if the categorical codes are fixed, different noise vectors introduce the same results.
How do you get the CelebA results shown in the GitHub page?
The text was updated successfully, but these errors were encountered:
I think the optimization may have some problems. optimD = optim.Adam([{'params': discriminator.parameters()}, {'params': netD.parameters()}], lr=params['learning_rate'], betas=(params['beta1'], params['beta2'])) optimG = optim.Adam([{'params': netG.parameters()}, {'params': netQ.parameters()}], lr=params['learning_rate'], betas=(params['beta1'], params['beta2']))
when optimizing Q, the shared layers are not optimized
Hi, thanks for your great implementation.
I run the model on CelebA dataset. However, when I test the model, I find if the categorical codes are fixed, different noise vectors introduce the same results.
How do you get the CelebA results shown in the GitHub page?
The text was updated successfully, but these errors were encountered: