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:
Where is the target, and is the prediction.
In the multi-class case, when
multiclass_mode=None
(default),multiclass_mode=MulticlassMode.CRAMMER_SINGER
ormulticlass_mode="crammer-singer"
, this metric will compute the multi-class hinge loss defined by Crammer and Singer as:Where is the target class (where is the number of classes), and is the predicted output per class.
In the multi-class case when
multiclass_mode=MulticlassMode.ONE_VS_ALL
ormulticlass_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
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).
In the binary case it is defined as:
Where is the target, and is the prediction.
In the multi-class case, when
multiclass_mode=None
(default),multiclass_mode=MulticlassMode.CRAMMER_SINGER
ormulticlass_mode="crammer-singer"
, this metric will compute the multi-class hinge loss defined by Crammer and Singer as:Where is the target class (where is the number of classes), and is the predicted output per class.
In the multi-class case when
multiclass_mode=MulticlassMode.ONE_VS_ALL
ormulticlass_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
preds¶ (
Tensor
) – Predictions from model (as float outputs from decision function).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.
- Raises
ValueError – If preds shape is not of size (N) or (N, C).
ValueError – If target shape is not of size (N).
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.functional import hinge_loss >>> target = torch.tensor([0, 1, 1]) >>> preds = torch.tensor([-2.2, 2.4, 0.1]) >>> hinge_loss(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_loss(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_loss(preds, target, multiclass_mode="one-vs-all") tensor([2.2333, 1.5000, 1.2333])
- Return type