mindspore.nn.probability.distribution.Bernoulli

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

Bernoulli Distribution. A Bernoulli Distribution is a discrete distribution with the range {0, 1} and the probability mass function as \(P(X = 0) = p, P(X = 1) = 1-p\).

Parameters

  • 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 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)
property probs

Return the probability of success, namely the output is 1.

Returns

Tensor, the probability of success.

cdf(value, probs1)

Compute the cumulatuve distribution function(CDF) of the given value.

Parameters

  • value (Tensor) - the value to compute.

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the value of the cumulatuve distribution function for the given input.

cross_entropy(dist, probs1_b, probs1_a)

Compute the cross entropy of two distribution

Parameters

  • dist (str) - the type of the other distribution.

  • probs1_b (Tensor) - the probability of success of the other distribution.

  • probs1_a (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the value of the cross entropy.

entropy(probs1)

Compute the value of the entropy.

Parameters

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the value of the entropy.

kl_loss(dist, probs1_b, probs1_a)

Compute the value of the K-L loss between two distribution, namely KL(a||b).

Parameters

  • dist (str) - the type of the other distribution.

  • probs1_b (Tensor) - the probability of success of the other distribution.

  • probs1_a (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the value of the K-L loss.

log_cdf(value, probs1)

Compute the log value of the cumulatuve distribution function.

Parameters

  • value (Tensor) - the value to compute.

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the log value of the cumulatuve distribution function.

log_prob(value, probs1)

the log value of the probability.

Parameters

  • value (Tensor) - the value to compute.

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the log value of the probability.

log_survival(value, probs1)

Compute the log value of the survival function.

Parameters

  • value (Tensor) - the value to compute.

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the value of the K-L loss.

mean(probs1)

Compute the mean value of the distribution.

Parameters

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the mean of the distribution.

mode(probs1)

Compute the mode value of the distribution.

Parameters

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the mode of the distribution.

prob(value, probs1)

The probability of the given value. For the discrete distribution, it is the probability mass function(pmf).

Parameters

  • value (Tensor) - the value to compute.

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the value of the probability.

sample(shape, probs1)

Generate samples.

Parameters

  • shape (tuple) - the shape of the sample.

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the sample following the distribution.

sd(probs1)

The standard deviation.

Parameters

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the standard deviation of the distribution.

survival_function(value, probs1)

Compute the value of the survival function.

Parameters

  • value (Tensor) - the value to compute.

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the value of the survival function.

var(probs1)

Compute the variance of the distribution.

Parameters

  • probs1 (Tensor) - the probability of success. Default value: None.

Returns

Tensor, the variance of the distribution.