def get_sample_image(G, n_noise):
"""
save sample 100 images
"""
z = torch.randn(10, n_noise).to(DEVICE)
y_hat = G(z).view(10, 3, 28, 28).permute(0, 2, 3, 1) # (100, 28, 28)
result = (y_hat.detach().cpu().numpy()+1)/2.
return result
What is the deal with the 10? I understand the 100 is random noise as an input, but what about the 10? Thank you so much for your repo btw, very helpful to me.
What is the deal with the 10? I understand the 100 is random noise as an input, but what about the 10? Thank you so much for your repo btw, very helpful to me.