class torch.nn.MSELoss(size_average=None, reduce=None, reduction: str = 'mean')
[source]
Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input $x$ and target $y$ .
The unreduced (i.e. with reduction
set to 'none'
) loss can be described as:
where $N$ is the batch size. If reduction
is not 'none'
(default 'mean'
), then:
$x$ and $y$ are tensors of arbitrary shapes with a total of $n$ elements each.
The mean operation still operates over all the elements, and divides by $n$ .
The division by $n$ can be avoided if one sets reduction = 'sum'
.
reduction
). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field size_average
is set to False
, the losses are instead summed for each minibatch. Ignored when reduce is False
. Default: True
reduction
). By default, the losses are averaged or summed over observations for each minibatch depending on size_average
. When reduce
is False
, returns a loss per batch element instead and ignores size_average
. Default: True
'none'
| 'mean'
| 'sum'
. 'none'
: no reduction will be applied, 'mean'
: the sum of the output will be divided by the number of elements in the output, 'sum'
: the output will be summed. Note: size_average
and reduce
are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction
. Default: 'mean'
Examples:
>>> loss = nn.MSELoss() >>> input = torch.randn(3, 5, requires_grad=True) >>> target = torch.randn(3, 5) >>> output = loss(input, target) >>> output.backward()
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.MSELoss.html