Comments (4)
I think you need to use dim of images as the power of 2 such as 32, 64, 128. The repo is tested with dim 64.
from improved-wgan-pytorch.
I think you need to use dim of images as the power of 2 such as 32, 64, 128. The repo is tested with dim 64.
ok,thank you
from improved-wgan-pytorch.
Otherwise, you need to change the code in the Discriminator and Generator network
from improved-wgan-pytorch.
Otherwise, you need to change the code in the Discriminator and Generator network
OK,it has worked just now.Thank you for your reply
from improved-wgan-pytorch.
Related Issues (20)
- Why comment aux_errD_fake loss (aux error of fake data during training D)? HOT 3
- RuntimeError: Mismatch in shape: grad_output[0] has a shape of torch.Size([1]) and output[0] has a shape of torch.Size([]) HOT 3
- why doesnt Wasserstein distance in Wgan converge? HOT 1
- Compare to the tensorflow version, this code cost too much time. HOT 2
- Generator loss function HOT 1
- Model Diverge! HOT 2
- Questions about noise HOT 1
- Backward HOT 4
- One and Mone HOT 4
- Regarding conditioning in the generator HOT 1
- lsun data issue HOT 1
- Increasing dimensionality HOT 3
- Is Wasserstein-distance calculated correctly? HOT 2
- about the size of images HOT 1
- Conditional GAN: NUM_CLASS param for image completion task and whats the deal with random noise? HOT 5
- Some question in model.py HOT 2
- batch norm in conditional output branch? HOT 3
- Missing components HOT 1
- A question about loss_G.backward(Mone) HOT 2
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