mindspore.Tensor.std

Tensor.std(dim, *, correction=1, keepdim=False) → Tensor

Calculates the standard deviation over the dimensions specified by dim. dim can be a single dimension, list of dimensions, or None to reduce over all dimensions.

The standard deviation (\(\sigma\)) is calculated as:

\[\sigma =\sqrt{\frac{1}{N-\delta N}\sum_{j-1}^{N-1}\left(s e l f_{i j}-\overline{x_{i}}\right)^{2}}\]

where \(x\) is the sample set of elements, \(\bar{x}\) is the sample mean, \(N\) is the number of samples and \(\delta N\) is the correction.

Warning

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

Parameters

dim (None, int, tuple(int), optional) – The dimension or dimensions to reduce. Defaults to None. If None, all dimensions are reduced.

Keyword Arguments

  • correction (int, optional) – The difference between the sample size and sample degrees of freedom. Defaults to Bessel's correction. Defaults to 1.

  • keepdim (bool, optional) – Whether the output tensor has dim retained or not. If True , keep these reduced dimensions and the length is 1. If False, don't keep these dimensions. Defaults to False.

Returns

Tensor, the standard deviation. Suppose the shape of self is \((x_0, x_1, ..., x_R)\):

  • If dim is () and keepdim is set to False , returns a 0-D Tensor, indicating the standard deviation of all elements in self.

  • If dim is int, e.g. 1 and keepdim is set to False , then the returned Tensor has shape \((x_0, x_2, ..., x_R)\).

  • If dim is tuple(int) or list(int), e.g. (1, 2) and keepdim is set to False , then the returned Tensor has shape \((x_0, x_3, ..., x_R)\).

Raises

  • TypeError – If self is not a Tensor.

  • TypeError – If self is not in bfloat16, float16, float32.

  • TypeError – If dim is not one of the followings: None, int, tuple.

  • TypeError – If correction is not an int.

  • TypeError – If keepdim is not a bool.

  • ValueError – If dim is out of range \([-self.ndim, self.ndim)\).

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import mint, Tensor
>>> input = Tensor(np.array([[1, 2, 3], [-1, 1, 4]]).astype(np.float32))
>>> output = input.std(dim=1, correction=1, keepdim=False)
>>> print(output)
[1.      2.5166113]