Hinge Loss¶

Module Interface¶

class torchmetrics.HingeLoss(squared=False, multiclass_mode=None, compute_on_step=None, **kwargs)[source]

Computes the mean Hinge loss, typically used for Support Vector Machines (SVMs).

In the binary case it is defined as:

$\text{Hinge loss} = \max(0, 1 - y \times \hat{y})$

Where $y \in {-1, 1}$ is the target, and $\hat{y} \in \mathbb{R}$ is the prediction.

In the multi-class case, when multiclass_mode=None (default), multiclass_mode=MulticlassMode.CRAMMER_SINGER or multiclass_mode="crammer-singer", this metric will compute the multi-class hinge loss defined by Crammer and Singer as:

$\text{Hinge loss} = \max\left(0, 1 - \hat{y}_y + \max_{i \ne y} (\hat{y}_i)\right)$

Where $y \in {0, ..., \mathrm{C}}$ is the target class (where $\mathrm{C}$ is the number of classes), and $\hat{y} \in \mathbb{R}^\mathrm{C}$ is the predicted output per class.

In the multi-class case when multiclass_mode=MulticlassMode.ONE_VS_ALL or multiclass_mode='one-vs-all', this metric will use a one-vs-all approach to compute the hinge loss, giving a vector of C outputs where each entry pits that class against all remaining classes.

This metric can optionally output the mean of the squared hinge loss by setting squared=True

Only accepts inputs with preds shape of (N) (binary) or (N, C) (multi-class) and target shape of (N).

Parameters

squared¶ (bool) – If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss (default).
multiclass_mode¶ (Union[str, MulticlassMode, None]) – Which approach to use for multi-class inputs (has no effect in the binary case). None (default), MulticlassMode.CRAMMER_SINGER or "crammer-singer", uses the Crammer Singer multi-class hinge loss. MulticlassMode.ONE_VS_ALL or "one-vs-all" computes the hinge loss in a one-vs-all fashion.
compute_on_step¶ (Optional[bool]) –
Forward only calls update() and returns None if this is set to False.

Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Raises

ValueError – If multiclass_mode is not: None, MulticlassMode.CRAMMER_SINGER, "crammer-singer", MulticlassMode.ONE_VS_ALL or "one-vs-all".

Example (binary case):

>>> import torch
>>> from torchmetrics import HingeLoss
>>> target = torch.tensor([0, 1, 1])
>>> preds = torch.tensor([-2.2, 2.4, 0.1])
>>> hinge = HingeLoss()
>>> hinge(preds, target)
tensor(0.3000)

Example (default / multiclass case):

>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge = HingeLoss()
>>> hinge(preds, target)
tensor(2.9000)

Example (multiclass example, one vs all mode):

>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge = HingeLoss(multiclass_mode="one-vs-all")
>>> hinge(preds, target)
tensor([2.2333, 1.5000, 1.2333])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type: Tensor

update(preds, target)[source]

Override this method to update the state variables of your metric class.

Return type: None

Functional Interface¶

torchmetrics.functional.hinge_loss(preds, target, squared=False, multiclass_mode=None)[source]

Computes the mean Hinge loss typically used for Support Vector Machines (SVMs).