nnf_hinge_embedding_loss: Hinge_embedding_loss

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

Hinge_embedding_loss

Description

Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1). This is usually used for measuring whether two inputs are similar or dissimilar, e.g. using the L1 pairwise distance as xx , and is typically used for learning nonlinear embeddings or semi-supervised learning.

Usage

nnf_hinge_embedding_loss(input, target, margin = 1, reduction = "mean")

Arguments

input	tensor (N,*) where ** means, any number of additional dimensions
target	tensor (N,*) , same shape as the input
margin	Has a default value of 1.
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. Default: 'mean'

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