mindspore.nn.probability.distribution.LogNormal
- class mindspore.nn.probability.distribution.LogNormal(loc=None, scale=None, seed=0, dtype=mstype.float32, name='LogNormal')[source]
LogNormal distribution. A log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. The log-normal distribution has the range \((0, \inf)\) with the pdf as
\[f(x, \mu, \sigma) = 1 / x\sigma\sqrt{2\pi} \exp(-(\ln(x) - \mu)^2 / 2\sigma^2).\]where \(\mu, \sigma\) are the mean and the standard deviation of the underlying normal distribution respectively. It is constructed as the exponential transformation of a Normal distribution.
- Parameters
loc (int, float, list, numpy.ndarray, Tensor) – The mean of the underlying Normal distribution. Default: None.
scale (int, float, list, numpy.ndarray, Tensor) – The standard deviation of the underlying Normal distribution. Default: None.
seed (int) – the seed used in sampling. The global seed is used if it is None. Default: 0.
dtype (mindspore.dtype) – type of the distribution. Default: mstype.float32.
name (str) – the name of the distribution. Default: ‘LogNormal’.
- Inputs and Outputs of APIs:
The accessible APIs of the Log-Normal distribution are defined in the base class, including:
prob, log_prob, cdf, log_cdf, survival_function, and log_survival
mean, sd, mode, var, and entropy
kl_loss and cross_entropy
sample
For more details of all APIs, including the inputs and outputs of APIs of the Log-Normal distribution, please refer to
mindspore.nn.probability.distribution.Distribution
, and examples below.- Supported Platforms:
Ascend
GPU
Note
scale must be greater than zero. dist_spec_args are loc and scale. dtype must be a float type because LogNormal distributions are continuous.
- Raises
ValueError – When scale <= 0.
TypeError – When the input dtype is not a subclass of float.
Examples
>>> import numpy as np >>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.distribution as msd >>> from mindspore import Tensor >>> class Prob(nn.Cell): ... def __init__(self): ... super(Prob, self).__init__() ... self.ln = msd.LogNormal(np.array([0.3]), np.array([[0.2], [0.4]]), dtype=mindspore.float32) ... def construct(self, x_): ... return self.ln.prob(x_) >>> pdf = Prob() >>> output = pdf(Tensor([1.0, 2.0], dtype=mindspore.float32)) >>> print(output.shape) (2, 2)
- property loc
Distribution parameter for the pre-transformed mean after casting to dtype.
- Output:
Tensor, the loc parameter of the distribution.
- property scale
Distribution parameter for the pre-transformed standard deviation after casting to dtype.
- Output:
Tensor, the scale parameter of the distribution.