# mindspore.nn.probability.distribution.Bernoulli¶

class mindspore.nn.probability.distribution.Bernoulli(probs=None, seed=None, dtype=mstype.int32, name="Bernoulli")[source]

Bernoulli Distribution.

Parameters
• probs (float, list, numpy.ndarray, Tensor) – 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’.

Supported Platforms:

Ascend GPU

Note

probs must be a proper probability (0 < p < 1). dist_spec_args is probs.

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)

property probs

Return the probability of that the outcome is 1 after casting to dtype.