torch.std_mean#

torch.std_mean(input,dim=None,*,correction=1,keepdim=False,out=None)#

Calculates the standard deviation and mean over the dimensions specified bydim.dim can be a single dimension, list of dimensions, orNone to reduce over all dimensions.

The standard deviation ($\sigma$σ) is calculated as

$$\sigma =\sqrt{\frac{1}{\max (0,N-\delta N)}\sum _{i=0}^{N-1}(x_i-\bar{x}{)}^2}$$σ=max(0, N−δN)1​i=0∑N−1​(xi​−xˉ)2​

where$x$x is the sample set of elements,$\bar{x}$xˉ is thesample mean,$N$N is the number of samples and$\delta N$δN isthecorrection.

Ifkeepdim isTrue, the output tensor is of the same sizeasinput except in the dimension(s)dim where it is of size 1.Otherwise,dim is squeezed (seetorch.squeeze()), resulting in theoutput tensor having 1 (orlen(dim)) fewer dimension(s).

Parameters:

• input (Tensor) – the input tensor.

• dim (int ortuple ofints,optional) – the dimension or dimensions to reduce.IfNone, all dimensions are reduced.

Keyword Arguments:

• correction (int) –

  difference between the sample size and sample degrees of freedom.Defaults toBessel’s correction,correction=1.

  Changed in version 2.0:Previously this argument was calledunbiased and was a booleanwithTrue corresponding tocorrection=1 andFalse beingcorrection=0.

• keepdim (bool,optional) – whether the output tensor hasdim retained or not. Default:False.

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

Returns:

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

Example

>>>a=torch.tensor(...[[0.2035,1.2959,1.8101,-0.4644],...[1.5027,-0.3270,0.5905,0.6538],...[-1.5745,1.3330,-0.5596,-0.6548],...[0.1264,-0.5080,1.6420,0.1992]]...)# fmt: skip>>>torch.std_mean(a,dim=0,keepdim=True)(tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), tensor([[ 0.0645,  0.4485,  0.8707, -0.0665]]))