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()