# 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 (Tensor) – The mean μ distribution parameter, which specifies the location of the peak, with data type in [int8, int16, int32, int64, float16, float32].

• stddev (Tensor) – The deviation σ distribution parameter. It should be greater than 0, with data type in [int8, int16, int32, int64, float16, float32].

• 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.

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import Tensor, ops
>>> import mindspore
>>> 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)