def entropy_loss(arch_params):
loss = []
for arch_param in arch_params:
probs = Bernoulli(logits=arch_param)
loss.append(probs.entropy().mean())
loss = torch.mean(torch.stack(loss))
return loss
def entropy(self, x):
p = self.forward(x)
m = Bernoulli(p)
return m.entropy().mean()