torch / torch
torch.var_mean¶
-
torch.
var_mean
(input, unbiased=True) -> (Tensor, Tensor)¶ Returns the variance and mean of all elements in the
input
tensor.If
unbiased
isFalse
, then the variance will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.- Parameters
Example:
>>> a = torch.randn(1, 3) >>> a tensor([[0.0146, 0.4258, 0.2211]]) >>> torch.var_mean(a) (tensor(0.0423), tensor(0.2205))
-
torch.
var_mean
(input, dim, keepdim=False, unbiased=True) -> (Tensor, Tensor)
Returns the variance and mean of each row of the
input
tensor in the given dimensiondim
.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 variance will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.- Parameters
Example:
>>> a = torch.randn(4, 4) >>> a tensor([[-1.5650, 2.0415, -0.1024, -0.5790], [ 0.2325, -2.6145, -1.6428, -0.3537], [-0.2159, -1.1069, 1.2882, -1.3265], [-0.6706, -1.5893, 0.6827, 1.6727]]) >>> torch.var_mean(a, 1) (tensor([2.3174, 1.6403, 1.4092, 2.0791]), tensor([-0.0512, -1.0946, -0.3403, 0.0239]))
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