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# torch.mean¶

torch.mean(input) → Tensor

Returns the mean value of all elements in the input tensor.

Parameters

input (Tensor) – the input tensor.

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 0.2294, -0.5481,  1.3288]])
>>> torch.mean(a)
tensor(0.3367)

torch.mean(input, dim, keepdim=False, *, out=None) → Tensor

Returns the mean value of each row of the input tensor in the given dimension dim. If dim is a list of dimensions, reduce over all of them.

If keepdim is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensor having 1 (or len(dim)) fewer dimension(s).

Parameters
• input (Tensor) – the input tensor.

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

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

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[-0.3841,  0.6320,  0.4254, -0.7384],
[-0.9644,  1.0131, -0.6549, -1.4279],
[-0.2951, -1.3350, -0.7694,  0.5600],
[ 1.0842, -0.9580,  0.3623,  0.2343]])
>>> torch.mean(a, 1)
tensor([-0.0163, -0.5085, -0.4599,  0.1807])
>>> torch.mean(a, 1, True)
tensor([[-0.0163],
[-0.5085],
[-0.4599],
[ 0.1807]]) ## Docs

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