nn_soft_margin_loss: Soft margin loss

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

Soft margin loss

Description

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

\mbox{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\mbox{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

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