Soft margin loss — nn_soft_margin_loss • torch

Creates a criterion that optimizes a two-class classification logistic loss between input tensor $x$ and target tensor $y$ (containing 1 or -1).

Usage

nn_soft_margin_loss(reduction = "mean")

Arguments

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

$loss (x, y) = \sum_{i} \frac{\log (1 + \exp (- y [i] * x [i]))}{x.nelement ()}$

Shape

Input: $(*)$ where $*$ means, any number of additional dimensions
Target: $(*)$ , same shape as the input
Output: scalar. If reduction is 'none', then same shape as the input