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Specifications and Common Mistakes

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mindspore.Tensor.log_normal

Tensor.log_normal(mean=1.0, std=2.0)

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]]