torch (version 0.1.0)

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'

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 $(N)$.

Details

The loss function for each pair of samples in the mini-batch is:

$$\text{loss}(x1,x2,y)=max(0,-y\ast (x1-x2)+\text{margin})$$

Examples

# NOT RUN {
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()

}
# }