I have a question regarding DCGAN architecture.

I am trying to implement DCGAN using pytorch.

I am sample noise z using `torch.randn(batch_size, latent_dim)` where batch\_size=128 and latent\_dim is 100.

If I follow the original DCGAN paper, and if I understand correctly, I should pass z through a Linear layer to project it and then reshape it according to (channel, height, width) for next layers. This I am doing like this.

proj = nn.Linear(latent_dim, 4*4*1024)
x = proj(z)
x = x.view(batch_size, 1024, 4, 4)
#then pass through generator …

architecture dcgan deeplearning layer linear noise paper project pytorch question reshape sample through torch