mindspore.nn.probability.distribution.Bernoulli
- class mindspore.nn.probability.distribution.Bernoulli(probs=None, seed=None, dtype=mindspore.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) (1,) >>> print(ans.shape) >>> # 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.