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- 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
- 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::
- >>> 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)
- 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):
- 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()
- def sample(self, sample_shape=torch.Size()):
- shape = self._extended_shape(sample_shape)
- with torch.no_grad():
- return torch.bernoulli(self.probs.expand(shape))
- 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')
- def entropy(self):
- return binary_cross_entropy_with_logits(self.logits, self.probs, reduction='none')
- 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))
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