Source code for torch.distributions.relaxed_bernoulli

from numbers import Number

import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import SigmoidTransform
from torch.distributions.utils import (

__all__ = ["LogitRelaxedBernoulli", "RelaxedBernoulli"]

[docs]class LogitRelaxedBernoulli(Distribution): r""" Creates a LogitRelaxedBernoulli distribution parameterized by :attr:`probs` or :attr:`logits` (but not both), which is the logit of a RelaxedBernoulli distribution. Samples are logits of values in (0, 1). See [1] for more details. Args: temperature (Tensor): relaxation temperature probs (Number, Tensor): the probability of sampling `1` logits (Number, Tensor): the log-odds of sampling `1` [1] The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables (Maddison et al, 2017) [2] Categorical Reparametrization with Gumbel-Softmax (Jang et al, 2017) """ arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real} support = constraints.real def __init__(self, temperature, probs=None, logits=None, validate_args=None): self.temperature = temperature if (probs is None) == (logits is None): raise ValueError( "Either `probs` or `logits` must be specified, but not both." ) if probs is not None: is_scalar = isinstance(probs, Number) (self.probs,) = broadcast_all(probs) else: is_scalar = isinstance(logits, Number) (self.logits,) = broadcast_all(logits) self._param = self.probs if probs is not None else self.logits if is_scalar: batch_shape = torch.Size() else: batch_shape = self._param.size() super().__init__(batch_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(LogitRelaxedBernoulli, _instance) batch_shape = torch.Size(batch_shape) new.temperature = self.temperature if "probs" in self.__dict__: new.probs = self.probs.expand(batch_shape) new._param = new.probs if "logits" in self.__dict__: new.logits = self.logits.expand(batch_shape) new._param = new.logits super(LogitRelaxedBernoulli, new).__init__(batch_shape, validate_args=False) new._validate_args = self._validate_args return new
def _new(self, *args, **kwargs): return*args, **kwargs) @lazy_property def logits(self): return probs_to_logits(self.probs, is_binary=True) @lazy_property def probs(self): return logits_to_probs(self.logits, is_binary=True) @property def param_shape(self): return self._param.size()
[docs] def rsample(self, sample_shape=torch.Size()): shape = self._extended_shape(sample_shape) probs = clamp_probs(self.probs.expand(shape)) uniforms = clamp_probs( torch.rand(shape, dtype=probs.dtype, device=probs.device) ) return ( uniforms.log() - (-uniforms).log1p() + probs.log() - (-probs).log1p() ) / self.temperature
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) logits, value = broadcast_all(self.logits, value) diff = logits - value.mul(self.temperature) return self.temperature.log() + diff - 2 * diff.exp().log1p()
[docs]class RelaxedBernoulli(TransformedDistribution): r""" Creates a RelaxedBernoulli distribution, parametrized by :attr:`temperature`, and either :attr:`probs` or :attr:`logits` (but not both). This is a relaxed version of the `Bernoulli` distribution, so the values are in (0, 1), and has reparametrizable samples. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterinistic") >>> m = RelaxedBernoulli(torch.tensor([2.2]), ... torch.tensor([0.1, 0.2, 0.3, 0.99])) >>> m.sample() tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) Args: temperature (Tensor): relaxation temperature probs (Number, Tensor): the probability of sampling `1` logits (Number, Tensor): the log-odds of sampling `1` """ arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real} support = constraints.unit_interval has_rsample = True def __init__(self, temperature, probs=None, logits=None, validate_args=None): base_dist = LogitRelaxedBernoulli(temperature, probs, logits) super().__init__(base_dist, SigmoidTransform(), validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(RelaxedBernoulli, _instance) return super().expand(batch_shape, _instance=new)
@property def temperature(self): return self.base_dist.temperature @property def logits(self): return self.base_dist.logits @property def probs(self): return self.base_dist.probs


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