| nn_mse_loss {torch} | R Documentation |
MSE loss
Description
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:
Usage
nn_mse_loss(reduction = "mean")
Arguments
reduction |
(string, optional): Specifies the reduction to apply to the output:
|
Details
\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'.
Shape
Input:
(N, *)where*means, any number of additional dimensionsTarget:
(N, *), same shape as the input
Examples
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()
}