Creates a criterion that measures the loss given
inputs \(x1\), \(x2\), two 1D mini-batch Tensors,
and a label 1D mini-batch tensor \(y\) (containing 1 or -1).
If \(y = 1\) then it assumed the first input should be ranked higher
(have a larger value) than the second input, and vice-versa for \(y = -1\).
Arguments
- margin
(float, optional): Has a default value of \(0\).
- 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
The loss function for each pair of samples in the mini-batch is:
$$ \mbox{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \mbox{margin}) $$
Shape
Input1: \((N)\) where
Nis the batch size.Input2: \((N)\), same shape as the Input1.
Target: \((N)\), same shape as the inputs.
Output: scalar. If
reductionis'none', then \((N)\).
Examples
if (torch_is_installed()) {
loss <- nn_margin_ranking_loss()
input1 <- torch_randn(3, requires_grad = TRUE)
input2 <- torch_randn(3, requires_grad = TRUE)
target <- torch_randn(3)$sign()
output <- loss(input1, input2, target)
output$backward()
}