torch.distributions.bernoulli — PyTorch 2.7 documentation (original) (raw)

Source code for torch.distributions.bernoulli

mypy: allow-untyped-defs

import torch from torch import nan, Tensor from torch.distributions import constraints from torch.distributions.exp_family import ExponentialFamily from torch.distributions.utils import ( broadcast_all, lazy_property, logits_to_probs, probs_to_logits, ) from torch.nn.functional import binary_cross_entropy_with_logits from torch.types import _Number

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-deterministic")
    >>> 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().__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 self._param.new(*args, **kwargs)

@property
def mean(self) -> Tensor:
    return self.probs

@property
def mode(self) -> Tensor:
    mode = (self.probs >= 0.5).to(self.probs)
    mode[self.probs == 0.5] = nan
    return mode

@property
def variance(self) -> Tensor:
    return self.probs * (1 - self.probs)

@lazy_property
def logits(self) -> Tensor:
    return probs_to_logits(self.probs, is_binary=True)

@lazy_property
def probs(self) -> Tensor:
    return logits_to_probs(self.logits, is_binary=True)

@property
def param_shape(self) -> torch.Size:
    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) -> tuple[Tensor]:
    return (torch.logit(self.probs),)

def _log_normalizer(self, x):
    return torch.log1p(torch.exp(x))