torch: nn_margin_ranking_loss – R documentation

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nn_margin_ranking_loss

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. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'

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

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0

MIT + file LICENSE

Authors

Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]

Initial release