Thanks for sharing your code, my question are:

1. I'm confused of the function of ComplementCrossEntropyLoss? what's the difference between ComplementCrossEntropyLoss and nn.CrossEntropyLoss()
2. In ComplementCrossEntropyLoss.py, when call for torch.nn.functional.nll_loss, why input is torch.log(1. - torch.nn.functional.softmax(input) + 1e-4)? what does 1- means?

class ComplementCrossEntropyLoss(torch.nn.Module):

Note: This is the cross entropy of the sum of all probabilities of other indices, except for the

This is used in Bayesian GAN semi-supervised learning

def init(self, except_index=None, weight=None, ignore_index=-100, size_average=True, reduce=True):
super(ComplementCrossEntropyLoss, self).init()
self.except_index = except_index
self.weight = weight
self.ignore_index = ignore_index
self.size_average = size_average
self.reduce = reduce

def forward(self, input, target=None):
# Use target if not None, else use self.except_index
if target is not None:
assert_no_grad(target)
else:
assert self.except_index is not None
target = torch.autograd.Variable(torch.LongTensor(input.data.shape[0]).fill(self.except_index).cuda())
result = torch.nn.functional.nll_loss(
torch.log(1. - torch.nn.functional.softmax(input) + 1e-4),
target, weight=self.weight,
size_average=self.size_average,
ignore_index=self.ignore_index)
return result

Hi, how do you get observed=1000 for generator and observed=50000 for discriminator in PriorLoss and NoiseLoss in bayes.py? Thanks！