mindspore.ops.normal

mindspore.ops.normal(shape, mean, stddev, seed=None)

根据正态（高斯）随机数分布生成随机数。

参数：

• shape (tuple) - Tuple: \((N,*)\) ，其中 \(*\) 表示任何数量的附加维度。

• mean (Tensor) - 均值μ，指定分布的峰值，数据类型支持[int8, int16, int32, int64, float16, float32]。

• stddev (Tensor) - 标准差σ。大于0。数据类型支持[int8, int16, int32, int64, float16, float32]。

• seed (int) - 随机种子。取值须为非负数。默认值：None，等同于0。

返回：

Tensor，shape应与输入 shape 与 mean 和 stddev 进行广播之后的shape相同。数据类型支持float32。

支持平台：

Ascend GPU CPU

样例：

>>> 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)