nnf_cosine_embedding_loss: Cosine_embedding_loss

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

Cosine_embedding_loss

Description

Creates a criterion that measures the loss given input tensors x_1, x_2 and a Tensor label y with values 1 or -1. This is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning.

Usage

nnf_cosine_embedding_loss(
  input1,
  input2,
  target,
  margin = 0,
  reduction = c("mean", "sum", "none")
)

Arguments

input1	the input x_1 tensor
input2	the input x_2 tensor
target	the target tensor
margin	Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin is missing, the default value is 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. Default: 'mean'

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