mindspore.Tensor.log_normal

Tensor.log_normal(mean=1.0, std=2.0)[source]

Fills the elements of the input tensor with log normal values initialized by given mean and std:

\[\text{f}(x;1.0,2.0)=\frac{1}{x\delta \sqrt[]{2\pi} }e^{-\frac{(\ln x-\mu )^2}{2\delta ^2} }\]

where \(\mu\), \(\delta\) is mean and standard deviation of lognormal distribution respectively.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

mean (float, optional) – the mean of normal distribution. With float data type. Default: 1.0.
std (float, optional) – the std of normal distribution. With float data type. Default: 2.0.

Returns

Tensor. A Tensor with the same type and shape of input.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> x = mindspore.Tensor(np.array([[1, 2], [3, 4]]), dtype=mindspore.float32)
>>> output = x.log_normal()
>>> print(output)
[[1.2788825 2.3305743]
[14.944194 0.16303174]]