nn_mse_loss | R Documentation |
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:
nn_mse_loss(reduction = "mean")
reduction |
(string, optional): Specifies the reduction to apply to the output:
|
\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
l_n = \left( x_n - y_n \right)^2,
where N
is the batch size. If reduction
is not 'none'
(default 'mean'
), then:
\ell(x, y) =
\begin{array}{ll}
\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
\mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.}
\end{array}
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'
.
Input: (N, *)
where *
means, any number of additional
dimensions
Target: (N, *)
, same shape as the input
if (torch_is_installed()) {
loss <- nn_mse_loss()
input <- torch_randn(3, 5, requires_grad = TRUE)
target <- torch_randn(3, 5)
output <- loss(input, target)
output$backward()
}
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