torch (version 0.13.0)

nn_cosine_embedding_loss: 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

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