nn_mse_loss function

MSE loss

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")

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

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'.

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()
}