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 .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name, set_param_type
from ._utils.custom_ops import exp_generic, log_generic


[docs]class Bernoulli(Distribution): """ Bernoulli Distribution. Args: probs (float, list, numpy.ndarray, Tensor, Parameter): The probability of that the outcome is 1. 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'. Note: `probs` must be a proper probability (0 < p < 1). `dist_spec_args` is `probs`. Examples: >>> # To initialize a Bernoulli distribution of the probability 0.5. >>> import mindspore.nn.probability.distribution as msd >>> b = msd.Bernoulli(0.5, dtype=mstype.int32) >>> >>> # The following creates two independent Bernoulli distributions. >>> b = msd.Bernoulli([0.5, 0.5], dtype=mstype.int32) >>> >>> # A Bernoulli distribution can be initilized without arguments. >>> # In this case, `probs` must be passed in through arguments during function calls. >>> b = msd.Bernoulli(dtype=mstype.int32) >>> >>> # To use the Bernoulli distribution in a network. >>> class net(Cell): >>> def __init__(self): >>> super(net, self).__init__(): >>> self.b1 = msd.Bernoulli(0.5, dtype=mstype.int32) >>> self.b2 = msd.Bernoulli(dtype=mstype.int32) >>> >>> # All the following calls in construct are valid. >>> def construct(self, value, probs_b, probs_a): >>> >>> # 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 = self.b1.prob(value) >>> # Evaluate `prob` with respect to distribution b. >>> ans = self.b1.prob(value, probs_b) >>> # `probs` must be passed in during function calls. >>> ans = self.b2.prob(value, probs_a) >>> >>> >>> # 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 = self.b1.mean() # return 0.5 >>> ans = self.b1.mean(probs_b) # return probs_b >>> # `probs` must be passed in during function calls. >>> ans = self.b2.mean(probs_a) >>> >>> >>> # 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 = self.b1.kl_loss('Bernoulli', probs_b) >>> ans = self.b1.kl_loss('Bernoulli', probs_b, probs_a) >>> # An additional `probs_a` must be passed in. >>> ans = self.b2.kl_loss('Bernoulli', probs_b, probs_a) >>> >>> >>> # Examples of `sample`. >>> # Args: >>> # shape (tuple): the shape of the sample. Default: (). >>> # probs1 (Tensor): the probability of success. Default: self.probs. >>> ans = self.b1.sample() >>> ans = self.b1.sample((2,3)) >>> ans = self.b1.sample((2,3), probs_b) >>> ans = self.b2.sample((2,3), probs_a) """ def __init__(self, probs=None, seed=None, dtype=mstype.int32, name="Bernoulli"): """ Constructor of Bernoulli. """ param = dict(locals()) valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type check_type(dtype, valid_dtype, type(self).__name__) super(Bernoulli, self).__init__(seed, dtype, name, param) self.parameter_type = set_param_type({'probs1': probs}, mstype.float32) if probs is not None: self._probs = cast_to_tensor(probs, self.parameter_type) check_prob(self.probs) else: self._probs = probs self.default_parameters = [self.probs] self.parameter_names = ['probs1'] # 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.dtypeop = P.DType() self.floor = P.Floor() self.fill = P.Fill() self.less = P.Less() self.shape = P.Shape() self.select = P.Select() self.sq = P.Square() self.sqrt = P.Sqrt() self.uniform = C.uniform def extend_repr(self): if self.is_scalar_batch: str_info = f'probs = {self.probs}' else: str_info = f'batch_shape = {self._broadcast_shape}' return str_info @property def probs(self): """ Return the probability of that the outcome is 1. """ return self._probs 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) prob_type = self.dtypeop(probs1) zeros = self.fill(prob_type, self.shape(probs1), 0.0) ones = self.fill(prob_type, 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) 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) prob_type = self.dtypeop(probs1) value = value * self.fill(prob_type, self.shape(probs1), 1.0) probs0 = 1.0 - probs1 * self.fill(prob_type, self.shape(value), 1.0) comp_zero = self.less(value, 0.0) comp_one = self.less(value, 1.0) zeros = self.fill(prob_type, self.shape(value), 0.0) ones = self.fill(prob_type, self.shape(value), 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 (Tensor, Number): `probs1` of distribution b. probs1_a (Tensor, 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, Number): `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