Source code for torch.distributions.bernoulli

from numbers import Number

import torch
from torch._six import nan
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all, probs_to_logits, logits_to_probs, lazy_property
from torch.nn.functional import binary_cross_entropy_with_logits

__all__ = ['Bernoulli']

[docs]class Bernoulli(ExponentialFamily): r""" Creates a Bernoulli distribution parameterized by :attr:`probs` or :attr:`logits` (but not both). Samples are binary (0 or 1). They take the value `1` with probability `p` and `0` with probability `1 - p`. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterinistic") >>> m = Bernoulli(torch.tensor([0.3])) >>> m.sample() # 30% chance 1; 70% chance 0 tensor([ 0.]) Args: 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.boolean has_enumerate_support = True _mean_carrier_measure = 0 def __init__(self, probs=None, logits=None, validate_args=None): 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(Bernoulli, self).__init__(batch_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Bernoulli, _instance) batch_shape = torch.Size(batch_shape) 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(Bernoulli, new).__init__(batch_shape, validate_args=False) new._validate_args = self._validate_args return new
def _new(self, *args, **kwargs): return*args, **kwargs) @property def mean(self): return self.probs @property def mode(self): mode = (self.probs >= 0.5).to(self.probs) mode[self.probs == 0.5] = nan return mode @property def variance(self): return self.probs * (1 - self.probs) @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 sample(self, sample_shape=torch.Size()): shape = self._extended_shape(sample_shape) with torch.no_grad(): return torch.bernoulli(self.probs.expand(shape))
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) logits, value = broadcast_all(self.logits, value) return -binary_cross_entropy_with_logits(logits, value, reduction='none')
[docs] def entropy(self): return binary_cross_entropy_with_logits(self.logits, self.probs, reduction='none')
[docs] def enumerate_support(self, expand=True): values = torch.arange(2, dtype=self._param.dtype, device=self._param.device) values = values.view((-1,) + (1,) * len(self._batch_shape)) if expand: values = values.expand((-1,) + self._batch_shape) return values
@property def _natural_params(self): return (torch.log(self.probs / (1 - self.probs)), ) def _log_normalizer(self, x): return torch.log(1 + torch.exp(x))


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources