Source code for torch.distributions.one_hot_categorical

import torch
from torch.distributions import constraints
from torch.distributions.categorical import Categorical
from torch.distributions.distribution import Distribution

[docs]class OneHotCategorical(Distribution): r""" Creates a one-hot categorical distribution parameterized by :attr:`probs` or :attr:`logits`. Samples are one-hot coded vectors of size ``probs.size(-1)``. .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum, and it will be normalized to sum to 1 along the last dimension. attr:`probs` will return this normalized value. The `logits` argument will be interpreted as unnormalized log probabilities and can therefore be any real number. It will likewise be normalized so that the resulting probabilities sum to 1 along the last dimension. attr:`logits` will return this normalized value. See also: :func:`torch.distributions.Categorical` for specifications of :attr:`probs` and :attr:`logits`. Example:: >>> m = OneHotCategorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ])) >>> m.sample() # equal probability of 0, 1, 2, 3 tensor([ 0., 0., 0., 1.]) Args: probs (Tensor): event probabilities logits (Tensor): event log probabilities (unnormalized) """ arg_constraints = {'probs': constraints.simplex, 'logits': constraints.real_vector} support = constraints.one_hot has_enumerate_support = True def __init__(self, probs=None, logits=None, validate_args=None): self._categorical = Categorical(probs, logits) batch_shape = self._categorical.batch_shape event_shape = self._categorical.param_shape[-1:] super(OneHotCategorical, self).__init__(batch_shape, event_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(OneHotCategorical, _instance) batch_shape = torch.Size(batch_shape) new._categorical = self._categorical.expand(batch_shape) super(OneHotCategorical, new).__init__(batch_shape, self.event_shape, validate_args=False) new._validate_args = self._validate_args return new
def _new(self, *args, **kwargs): return self._categorical._new(*args, **kwargs) @property def _param(self): return self._categorical._param @property def probs(self): return self._categorical.probs @property def logits(self): return self._categorical.logits @property def mean(self): return self._categorical.probs @property def variance(self): return self._categorical.probs * (1 - self._categorical.probs) @property def param_shape(self): return self._categorical.param_shape
[docs] def sample(self, sample_shape=torch.Size()): sample_shape = torch.Size(sample_shape) probs = self._categorical.probs num_events = self._categorical._num_events indices = self._categorical.sample(sample_shape) return torch.nn.functional.one_hot(indices, num_events).to(probs)
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) indices = value.max(-1)[1] return self._categorical.log_prob(indices)
[docs] def entropy(self): return self._categorical.entropy()
[docs] def enumerate_support(self, expand=True): n = self.event_shape[0] values = torch.eye(n, dtype=self._param.dtype, device=self._param.device) values = values.view((n,) + (1,) * len(self.batch_shape) + (n,)) if expand: values = values.expand((n,) + self.batch_shape + (n,)) return values
class OneHotCategoricalStraightThrough(OneHotCategorical): r""" Creates a reparameterizable :class:`OneHotCategorical` distribution based on the straight- through gradient estimator from [1]. [1] Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation (Bengio et al, 2013) """ has_rsample = True def rsample(self, sample_shape=torch.Size()): samples = self.sample(sample_shape) probs = self._categorical.probs # cached via @lazy_property return samples + (probs - probs.detach())


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