torch / torch
torch.std_mean¶
-
torch.
std_mean
(input, unbiased=True) -> (Tensor, Tensor)¶ Returns the standard-deviation and mean of all elements in the
input
tensor.If
unbiased
isFalse
, then the standard-deviation will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.- Parameters
Example:
>>> a = torch.randn(1, 3) >>> a tensor([[0.3364, 0.3591, 0.9462]]) >>> torch.std_mean(a) (tensor(0.3457), tensor(0.5472))
-
torch.
std_mean
(input, dim, unbiased=True, keepdim=False) -> (Tensor, Tensor)
Returns the standard-deviation and mean of each row of the
input
tensor in the dimensiondim
. Ifdim
is a list of dimensions, reduce over all of them.If
keepdim
isTrue
, the output tensor is of the same size asinput
except in the dimension(s)dim
where it is of size 1. Otherwise,dim
is squeezed (seetorch.squeeze()
), resulting in the output tensor having 1 (orlen(dim)
) fewer dimension(s).If
unbiased
isFalse
, then the standard-deviation will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.- Parameters
Example:
>>> a = torch.randn(4, 4) >>> a tensor([[ 0.5648, -0.5984, -1.2676, -1.4471], [ 0.9267, 1.0612, 1.1050, -0.6014], [ 0.0154, 1.9301, 0.0125, -1.0904], [-1.9711, -0.7748, -1.3840, 0.5067]]) >>> torch.std_mean(a, 1) (tensor([0.9110, 0.8197, 1.2552, 1.0608]), tensor([-0.6871, 0.6229, 0.2169, -0.9058]))
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