mindspore.ops.normal

mindspore.ops.normal(shape, mean, stddev, seed=None)[source]

Generates random numbers according to the Normal (or Gaussian) random number distribution.

Parameters

shape (tuple) – The shape of random tensor to be generated. The format is \((N,*)\) where \(*\) means, any number of additional dimensions.
mean (Union[Tensor, int, float]) – The mean μ distribution parameter, which specifies the location of the peak.
stddev (Union[Tensor, int, float]) – The deviation σ distribution parameter. It should be greater than 0.
seed (int) – Seed is used as entropy source for the Random number engines to generate pseudo-random numbers. The value must be non-negative. Default: None , which will be treated as 0.

Returns

Tensor. The shape should be equal to the broadcasted shape between the input shape and shapes of mean and stddev. The dtype is [float32, float64].

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> shape = (3, 1, 2)
>>> mean = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32)
>>> stddev = Tensor(1.0, mindspore.float32)
>>> output = ops.normal(shape, mean, stddev, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 2)
>>> shape = (3, 1, 3)
>>> mean = Tensor(np.array([[3, 4, 3], [3, 5, 6]]), mindspore.float32)
>>> stddev = Tensor(1.0, mindspore.float32)
>>> output = ops.normal(shape, mean, stddev, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 3)
>>> shape = (3, 1, 3)
>>> mean = Tensor(np.array([[1, 2, 3], [3, 4, 3], [3, 5, 6]]), mindspore.float32)
>>> stddev = Tensor(1.0, mindspore.float32)
>>> output = ops.normal(shape, mean, stddev, seed=5)
>>> result = output.shape
>>> print(result)
(3, 3, 3)