nn_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. The loss function for each sample is:

Usage

nn_cosine_embedding_loss(margin = 0, reduction = "mean")

Arguments

margin	(float, optional): 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.

Details

\mbox{loss}(x, y) = \begin{array}{ll} 1 - \cos(x_1, x_2), & \mbox{if } y = 1 \\ \max(0, \cos(x_1, x_2) - \mbox{margin}), & \mbox{if } y = -1 \end{array}

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