Source code for mindspore.nn.probability.distribution.bernoulli

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"""Bernoulli Distribution"""
from mindspore.common import dtype as mstype
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore._checkparam import Validator
from .distribution import Distribution
from ._utils.utils import check_prob, check_distribution_name, clamp_probs
from ._utils.custom_ops import exp_generic, log_generic


[文档]class Bernoulli(Distribution): """ Bernoulli Distribution. A Bernoulli Distribution is a discrete distribution with the range {0, 1} and the probability mass function as :math:`P(X = 0) = p, P(X = 1) = 1-p`. Args: probs (float, list, numpy.ndarray, Tensor): The probability of that the outcome is 1. Default: None. seed (int): The seed used in sampling. The global seed is used if it is None. Default: None. dtype (mindspore.dtype): The type of the event samples. Default: mstype.int32. name (str): The name of the distribution. Default: 'Bernoulli'. Inputs and Outputs of APIs: The accessible APIs of Bernoulli distribution are defined in the base class, including: - `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival` - `mean`, `sd`, `var`, and `entropy` - `kl_loss` and `cross_entropy` - `sample` For more details of all APIs, including the inputs and outputs of the APIs of the Bernoulli distribution, please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below. Supported Platforms: ``Ascend`` ``GPU`` Note: `probs` must be a proper probability (0 < p < 1). `dist_spec_args` is `probs`. Raises: ValueError: When p <= 0 or p >=1. Examples: >>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.distribution as msd >>> from mindspore import Tensor >>> # To initialize a Bernoulli distribution of the probability 0.5. >>> b1 = msd.Bernoulli(0.5, dtype=mindspore.int32) >>> # A Bernoulli distribution can be initialized without arguments. >>> # In this case, `probs` must be passed in through arguments during function calls. >>> b2 = msd.Bernoulli(dtype=mindspore.int32) >>> # Here are some tensors used below for testing >>> value = Tensor([1, 0, 1], dtype=mindspore.int32) >>> probs_a = Tensor([0.6], dtype=mindspore.float32) >>> probs_b = Tensor([0.2, 0.3, 0.4], dtype=mindspore.float32) >>> # Private interfaces of probability functions corresponding to public interfaces, including >>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, are the same as follows. >>> # Args: >>> # value (Tensor): the value to be evaluated. >>> # probs1 (Tensor): the probability of success. Default: self.probs. >>> # Examples of `prob`. >>> # Similar calls can be made to other probability functions >>> # by replacing `prob` by the name of the function. >>> ans = b1.prob(value) >>> print(ans.shape) (3,) >>> # Evaluate `prob` with respect to distribution b. >>> ans = b1.prob(value, probs_b) >>> print(ans.shape) (3,) >>> # `probs` must be passed in during function calls. >>> ans = b2.prob(value, probs_a) >>> print(ans.shape) (3,) >>> # Functions `mean`, `sd`, `var`, and `entropy` have the same arguments. >>> # Args: >>> # probs1 (Tensor): the probability of success. Default: self.probs. >>> # Examples of `mean`. `sd`, `var`, and `entropy` are similar. >>> ans = b1.mean() # return 0.5 >>> print(ans.shape) () >>> ans = b1.mean(probs_b) # return probs_b >>> print(ans.shape) (3,) >>> # `probs` must be passed in during function calls. >>> ans = b2.mean(probs_a) >>> print(ans.shape) (1,) >>> # Interfaces of `kl_loss` and `cross_entropy` are the same as follows: >>> # Args: >>> # dist (str): the name of the distribution. Only 'Bernoulli' is supported. >>> # probs1_b (Tensor): the probability of success of distribution b. >>> # probs1_a (Tensor): the probability of success of distribution a. Default: self.probs. >>> # Examples of `kl_loss`. `cross_entropy` is similar. >>> ans = b1.kl_loss('Bernoulli', probs_b) >>> print(ans.shape) (3,) >>> ans = b1.kl_loss('Bernoulli', probs_b, probs_a) >>> print(ans.shape) (3,) >>> # An additional `probs_a` must be passed in. >>> ans = b2.kl_loss('Bernoulli', probs_b, probs_a) >>> print(ans.shape) (3,) >>> # Examples of `sample`. >>> # Args: >>> # shape (tuple): the shape of the sample. Default: (). >>> # probs1 (Tensor): the probability of success. Default: self.probs. >>> ans = b1.sample() >>> print(ans.shape) () >>> ans = b1.sample((2,3)) >>> print(ans.shape) (2, 3) >>> ans = b1.sample((2,3), probs_b) >>> print(ans.shape) (2, 3, 3) >>> ans = b2.sample((2,3), probs_a) >>> print(ans.shape) (2, 3, 1) """ def __init__(self, probs=None, seed=None, dtype=mstype.int32, name="Bernoulli"): """ Constructor of Bernoulli. """ param = dict(locals()) param['param_dict'] = {'probs': probs} valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type Validator.check_type_name( "dtype", dtype, valid_dtype, type(self).__name__) super(Bernoulli, self).__init__(seed, dtype, name, param) self._probs = self._add_parameter(probs, 'probs') if self._probs is not None: check_prob(self.probs) # ops needed for the class self.exp = exp_generic self.log = log_generic self.squeeze = P.Squeeze(0) self.cast = P.Cast() self.const = P.ScalarToArray() self.floor = P.Floor() self.fill = P.Fill() self.less = P.Less() self.shape = P.Shape() self.select = P.Select() self.uniform = C.uniform def extend_repr(self): """Display instance object as string.""" if self.is_scalar_batch: s = 'probs = {}'.format(self.probs) else: s = 'batch_shape = {}'.format(self._broadcast_shape) return s @property def probs(self): """ Return the probability of that the outcome is 1 after casting to dtype. Output: Tensor, the probs of the distribution. """ return self._probs def _get_dist_type(self): return "Bernoulli" def _get_dist_args(self, probs1=None): if probs1 is not None: self.checktensor(probs1, 'probs') else: probs1 = self.probs return (probs1,) def _mean(self, probs1=None): r""" .. math:: MEAN(B) = probs1 """ probs1 = self._check_param_type(probs1) return probs1 def _mode(self, probs1=None): r""" .. math:: MODE(B) = 1 if probs1 > 0.5 else = 0 """ probs1 = self._check_param_type(probs1) zeros = self.fill(self.dtype, self.shape(probs1), 0.0) ones = self.fill(self.dtype, self.shape(probs1), 1.0) comp = self.less(0.5, probs1) return self.select(comp, ones, zeros) def _var(self, probs1=None): r""" .. math:: VAR(B) = probs1 * probs0 """ probs1 = self._check_param_type(probs1) probs0 = 1.0 - probs1 return self.exp(self.log(probs0) + self.log(probs1)) def _entropy(self, probs1=None): r""" .. math:: H(B) = -probs0 * \log(probs0) - probs1 * \log(probs1) """ probs1 = self._check_param_type(probs1) probs0 = 1.0 - probs1 return -(probs0 * self.log(probs0)) - (probs1 * self.log(probs1)) def _cross_entropy(self, dist, probs1_b, probs1=None): """ Evaluate cross entropy between Bernoulli distributions. Args: dist (str): The type of the distributions. Should be "Bernoulli" in this case. probs1_b (Tensor): `probs1` of distribution b. probs1_a (Tensor): `probs1` of distribution a. Default: self.probs. """ check_distribution_name(dist, 'Bernoulli') return self._entropy(probs1) + self._kl_loss(dist, probs1_b, probs1) def _log_prob(self, value, probs1=None): r""" Log probability mass function of Bernoulli distributions. Args: value (Tensor): A Tensor composed of only zeros and ones. probs (Tensor): The probability of outcome is 1. Default: self.probs. .. math:: pmf(k) = probs1 if k = 1; pmf(k) = probs0 if k = 0; """ value = self._check_value(value, 'value') value = self.cast(value, self.parameter_type) probs1 = self._check_param_type(probs1) # clamp value for numerical stability probs1 = clamp_probs(probs1) probs0 = 1.0 - probs1 return self.log(probs1) * value + self.log(probs0) * (1.0 - value) def _cdf(self, value, probs1=None): r""" Cumulative distribution function (cdf) of Bernoulli distributions. Args: value (Tensor): The value to be evaluated. probs (Tensor): The probability of that the outcome is 1. Default: self.probs. .. math:: cdf(k) = 0 if k < 0; cdf(k) = probs0 if 0 <= k <1; cdf(k) = 1 if k >=1; """ value = self._check_value(value, 'value') value = self.cast(value, self.parameter_type) value = self.floor(value) probs1 = self._check_param_type(probs1) broadcast_shape_tensor = value * probs1 value = self.broadcast(value, broadcast_shape_tensor) probs0 = self.broadcast((1.0 - probs1), broadcast_shape_tensor) comp_zero = self.less(value, 0.0) comp_one = self.less(value, 1.0) zeros = self.fill(self.parameter_type, self.shape( broadcast_shape_tensor), 0.0) ones = self.fill(self.parameter_type, self.shape( broadcast_shape_tensor), 1.0) less_than_zero = self.select(comp_zero, zeros, probs0) return self.select(comp_one, less_than_zero, ones) def _kl_loss(self, dist, probs1_b, probs1=None): r""" Evaluate bernoulli-bernoulli kl divergence, i.e. KL(a||b). Args: dist (str): The type of the distributions. Should be "Bernoulli" in this case. probs1_b (Union[Tensor, numbers.Number]): `probs1` of distribution b. probs1_a (Union[Tensor, numbers.Number]): `probs1` of distribution a. Default: self.probs. .. math:: KL(a||b) = probs1_a * \log(\frac{probs1_a}{probs1_b}) + probs0_a * \log(\frac{probs0_a}{probs0_b}) """ check_distribution_name(dist, 'Bernoulli') probs1_b = self._check_value(probs1_b, 'probs1_b') probs1_b = self.cast(probs1_b, self.parameter_type) probs1_a = self._check_param_type(probs1) probs0_a = 1.0 - probs1_a probs0_b = 1.0 - probs1_b return probs1_a * self.log(probs1_a / probs1_b) + probs0_a * self.log(probs0_a / probs0_b) def _sample(self, shape=(), probs1=None): """ Sampling. Args: shape (tuple): The shape of the sample. Default: (). probs1 (Tensor): `probs1` of the samples. Default: self.probs. Returns: Tensor, shape is shape + batch_shape. """ shape = self.checktuple(shape, 'shape') probs1 = self._check_param_type(probs1) origin_shape = shape + self.shape(probs1) if origin_shape == (): sample_shape = (1,) else: sample_shape = origin_shape l_zero = self.const(0.0) h_one = self.const(1.0) sample_uniform = self.uniform(sample_shape, l_zero, h_one, self.seed) sample = self.less(sample_uniform, probs1) value = self.cast(sample, self.dtype) if origin_shape == (): value = self.squeeze(value) return value