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I am new to GANs, but I was able to train a DCGAN decently. I decided to try a WGAN (not the improved one). I seem to get outputs, but my loss doesn't seem to converge for the generator.

I am using the loss function

from keras import backend as K
def wasserstein_loss(y_true, y_pred):
    return K.mean(y_true * y_pred)

for both the critic and generator. I am labelling real images as -1 and fake ones as 1.

Here's my loss over time:
critic loss converging, generator diverging
Is the generator loss supposed to behave like that? Even though the discriminator loss seems to behave as expected, is a swing that large normal?

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1 Answer 1

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If anyone's wondering, yes it is right. The weight-clipping WGAN is very sensitive to changes in the weight-clipping hyperparameter, which is probably causing this. Upgrading to the gradient descent version fixed it. Loss should look like this btw

enter image description here

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    $\begingroup$ How did you fix it? I have the same problem. $\endgroup$ Nov 20, 2020 at 18:29

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