mindspore.Tensor.std

Tensor.std(axis=None, ddof=0, keepdims=False) → Tensor

For details, please refer to mindspore.ops.std().

Tensor.std(dim=None, *, 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:

$$\sigma =\sqrt{\frac{1}{N-\delta N}\sum _{j-1}^{N-1}{(sel{f}_{ij}-\overline{x_i})}^2}$$

where $x$ is the sample set of elements, $\overline{x}$ 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]