🐛 Bug When torch.distributions.Categorical is initialized with probs, the implementation normalizes it even if it is already normalized. However, if we give normalized values to probs, this normalization leads to incorrect gradients. Thi...
There is some unfortunate confusion in that many libraries implement random categorical samplers but name them multinomial samplers: PyTorch, Numpy, TensorFlow. On the one hand it would be nice to have an actual multinomial; on the other hand it would be nice to follow the naming convention of ...
manifold = geoopt.manifolds.SphereProjection().to(dtype=torch.float64) x = geoopt.ManifoldTensor(x, manifold=manifold) case = UnaryCase(shape, x, ex, v, ev, manifold)yieldcase manifold = geoopt.manifolds.SphereProjectionExact().to(dtype=torch.float64) x = geoopt.ManifoldTensor(x, manifold=...
norm((adj.to_dense() - torch.mm(result, result.t())) / np.power((len(batch)), 2) self.loss_dict["adj_loss"] = adj_loss entropy_loss = (torch.distributions.Categorical(probs=pooled).entropy()).mean() assert not torch.isnan(entropy_loss) self.loss_dict["entropy_loss"] = entr...
to(device,torch.float16 if fp16 else torch.float32) if fp16: z_ = z_.half() y_ = Distribution(torch.zeros(G_batch_size, requires_grad=False)) y_.init_distribution('categorical',num_categories=nclasses) y_ = y_.to(device, torch.int64) return z_, y_ ...
See https://pytorch.org/docs/master/distributions.html Instead of: probs = policy_network(state) action = probs.multinomial() next_state, reward = env.step(action) action.reinforce(reward) action.backward() Use: probs = policy_network(state) # NOTE: categorical is equivalent to what used...
m = torch.distributions.Categorical(probs=p) raw_action = m.sample() action = F.one_hot(raw_action, num_actions)assertaction.shape == (batch_size, num_actions) log_prob = m.log_prob(raw_action)assertlog_prob.shape == (batch_size,)returnrlt.ActorOutput(action=action, log_prob=log_...
Categorical variables were compared using the Fisher exact test. A p-value < 0.05 was considered to be statistically significant. Results The epidemiological characteristics and application of TORCH test During the study period, 18,104 reproductive age women (20–44 years) were included. The ...
We are happy to announce that the version 0.2.0 of torch just landed on CRAN. This release includes many bug fixes and some nice new features that we will present in this blog post. You can see the full changelog in the NEWS.md file. The features th...
loss <- nnf_cross_entropy(output, b[[2]]$to(device = "cuda")) Unlike categorical cross entropy inkeras, which would expectpredictionto contain probabilities, as obtained by applying asoftmaxactivation,torch’snnf_cross_entropy()works with the raw outputs (thelogits). This is why the networ...