Thanks a lot for your answer !
I’ve done what you said but now I have this error when I start the training loop :

Starting Training Loop…
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
 in ()
**     31         noise = torch.randn(b_size, nz, 1, 1, device=device)**
**     32         # Generate fake image batch with G**
—> 33         fake = netG(noise)
**     34         label.fill_(fake_label)**
**     35         # Classify all fake batch with D**
6 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
**   1695     return torch.batch_norm(**
**   1696         input, weight, bias, running_mean, running_var,**
-> 1697         training, momentum, eps, torch.backends.cudnn.enabled
**   1698     )**
**   1699 **
RuntimeError: running_mean should contain 128 elements not 1024

Do you advise me to change some number here ?

# Number of workers for dataloader
workers = 2
# Batch size during training
batch_size = 128
# Spatial size of training images. All images will be resized to this
#   size using a transformer.
image_size = 64
# Number of channels in the training images. For color images this is 3
nc = 3
# Size of z latent vector (i.e. size of generator input)
nz = 100
# Size of feature maps in generator
ngf = 64
# Size of feature maps in discriminator
ndf = 64
# Number of training epochs
num_epochs = 5
# Learning rate for optimizers
lr = 0.0002
# Beta1 hyperparam for Adam optimizers
beta1 = 0.5
# Number of GPUs available. Use 0 for CPU mode.
ngpu = 1

I tried to change “image_size” from 64 to 554 and 1024 but this didn’t worked. I also tried to change batch_size from 128 to 1024.
Thanks again for your help