nn_hinge_embedding_loss {torch} | R Documentation |
Hinge embedding loss
Description
Measures the loss given an input tensor x
and a labels tensor y
(containing 1 or -1).
Usage
nn_hinge_embedding_loss(margin = 1, reduction = "mean")
Arguments
margin |
(float, optional): Has a default value of |
reduction |
(string, optional): Specifies the reduction to apply to the output:
|
Details
This is usually used for measuring whether two inputs are similar or
dissimilar, e.g. using the L1 pairwise distance as x
, and is typically
used for learning nonlinear embeddings or semi-supervised learning.
The loss function for n
-th sample in the mini-batch is
l_n = \begin{array}{ll}
x_n, & \mbox{if}\; y_n = 1,\\
\max \{0, \Delta - x_n\}, & \mbox{if}\; y_n = -1,
\end{array}
and the total loss functions is
\ell(x, y) = \begin{array}{ll}
\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
\mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.}
\end{array}
where L = \{l_1,\dots,l_N\}^\top
.
Shape
Input:
(*)
where*
means, any number of dimensions. The sum operation operates over all the elements.Target:
(*)
, same shape as the inputOutput: scalar. If
reduction
is'none'
, then same shape as the input