GAN loss curve Weird

Hi,
Like the picture attached, I’m undergoing the abnormal loss curve situation.
Do you know how to solve this?

Without knowing anything about your use case, I would suggest to play around with some hyperparameters and if that doesn’t help, dig deeper into the model architecture.

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I appreciate with your help, ptrblck However i have one more quick questions,

What is an ideal loss curve of G and D when i use the criterion as “torch.nn.BCELoss”?

criterion = torch.nn.BCELoss()
outputs= = D(real_images, y)
real_loss = criterion(outputs, real_labels)
outputs = D(fake_images, y)
fake_loss = criterion(outputs, fake_labels)     
d_loss = real_loss + fake_loss

g_loss = criterion(discriminator_outputs, real_labels)

Should both G_loss and D_loss be decreasing?
or D_loss should be decreasing while the G_loss should be increasing??
And Can i just sum real and fake loss for D_loss or should they be averaged for D_loss?

And my case, i want to generate numeric features with 12 dimensions like attached.