# torch.std_mean¶

torch.std_mean(input, dim, unbiased, keepdim=False, *, out=None)

If unbiased is True, Bessel’s correction will be used to calculate the standard deviation. Otherwise, the sample deviation is calculated, without any correction.

Parameters
• input (Tensor) – the input tensor.

• dim (int or tuple of python:ints) – the dimension or dimensions to reduce.

Keyword Arguments
• unbiased (bool) – whether to use Bessel’s correction ($\delta N = 1$).

• keepdim (bool) – whether the output tensor has dim retained or not.

• out (Tensor, optional) – the output tensor.

Returns

A tuple (std, mean) containing the standard deviation and mean.

torch.std_mean(input, unbiased)

Calculates the standard deviation and mean of all elements in the input tensor.

If unbiased is True, Bessel’s correction will be used. Otherwise, the sample deviation is calculated, without any correction.

Parameters
• input (Tensor) – the input tensor.

• unbiased (bool) – whether to use Bessel’s correction ($\delta N = 1$).

Returns

A tuple (std, mean) containing the standard deviation and mean.

Example:

>>> a = torch.tensor([[-0.8166, -1.3802, -0.3560]])
>>> torch.std_mean(a, unbiased=False)
(tensor(0.4188), tensor(-0.8509))