Hello,
sorry for the newbie question but: I am trying to learn pytorch by example with: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
Having successfully executed dcgan_faces_tutorial.ipynb with the provided 64x64 celeba dataset, i now try to adapt it to the 250x250 lfw dataset for more interesting results, adapting image_size, ngf and ndf to 250.
Dataset loading, Generator and Discriminator construction work out fine. However the training loop exits on:
ValueError                                Traceback (most recent call last)
 in 


27         output = netD(real_cpu).view(-1)*



28         # Calculate loss on all-real batch*



—> 29         errD_real = criterion(output, label)


30         # Calculate gradients for D in backward pass*



31         errD_real.backward()*



…
ValueError: Target and input must have the same number of elements. target nelement (128) != input nelement (18432)
Aparently, some other variables need adapting (batch size? latent vector?). Is there a formula for variable adaptation?
Any help would be greatly appreciated!!
Walter