Skip to contents

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: '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.

Details

(x,y)=L={l1,,lN},ln=(xnyn)2,

where N is the batch size. If reduction is not 'none' (default 'mean'), then:

(x,y)=mean(L),if reduction='mean';sum(L),if reduction='sum'.

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 dimensions

  • Target: (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()
}