nn_margin_ranking_loss: Margin ranking loss

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Margin ranking loss

Description

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.

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:

\mbox{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \mbox{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()
}

torch documentation built on May 29, 2024, 9:54 a.m.