Comments (3)
Just don't understand this loss term:
wass = lambda y_true, y_pred: torch.mean(y_true * y_pred)
Could you give me some tips, thanks a lot
from denn.
Hi there,
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The results are very sensitive to initialization (with random seed). But moreover, different computing architectures can differ in how they handle this randomness (e.g. I have observed differences between MacOS and Linux environments). This could be the case for you. I would try various random seeds to see if one of them converges. We have no guarantees for convergence with GANs, unfortunately.
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The loss term is the Wasserstein loss function (where the labels are -1 and 1 instead of 0 and 1 as with vanilla GAN), see here: https://arxiv.org/abs/1701.07875.
from denn.
Going to close this is this is not a new/unknown issue, but one that has been observed and is detailed in the documentation (i.e. the importance of the platform).
from denn.
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