Margin ranking loss — nn_margin_ranking

Creates a criterion that measures the loss given inputs $x 1$ , $x 2$ , 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 $y = - 1$ .

Usage

nn_margin_ranking_loss(margin = 0, reduction = "mean")

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:

$loss (x 1, x 2, y) = max (0, - y * (x 1 - x 2) + margin)$

Shape

Input1: $(N)$ where N is the batch size.
Input2: $(N)$ , same shape as the Input1.
Target: $(N)$ , same shape as the inputs.
Output: scalar. If reduction is '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()
}