def sample(self, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)
with torch.no_grad():
return torch.binomial(self.total_count.expand(shape),
self.probs.expand(shape))
def gibbs_step(v):
mean_h = nn.Sigmoid(torch.dot(v, W) + bh)
h = torch.binomial(mean_h)
mean_v = nn.Sigmoid(torch.dot(h, W.T) + bv)
v = torch.binomial(mean_v)
return mean_v, v
