Hinge Loss¶

Module Interface¶

class torchmetrics.HingeLoss(task: Literal['binary', 'multiclass'], num_classes: Optional[int] = None, squared: bool = False, multiclass_mode: Optional[Literal['crammer-singer', 'one-vs-all']] = 'crammer-singer', ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any)[source]

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

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the task argument to either 'binary' or 'multiclass'. See the documentation of BinaryHingeLoss and MulticlassHingeLoss for the specific details of each argument influence and examples.

Legacy Example:

>>> import torch
>>> target = torch.tensor([0, 1, 1])
>>> preds = torch.tensor([0.5, 0.7, 0.1])
>>> hinge = HingeLoss(task="binary")
>>> hinge(preds, target)
tensor(0.9000)

>>> 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(task="multiclass", num_classes=3)
>>> hinge(preds, target)
tensor(1.5551)

>>> 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(task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
>>> hinge(preds, target)
tensor([1.3743, 1.1945, 1.2359])

BinaryHingeLoss¶

class torchmetrics.classification.BinaryHingeLoss(squared=False, ignore_index=None, validate_args=True, **kwargs)[source]

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

Accepts the following input tensors:

preds (float tensor): (N, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.
target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

Parameters

squared¶ (bool) – If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
ignore_index¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics.classification import BinaryHingeLoss
>>> preds = torch.tensor([0.25, 0.25, 0.55, 0.75, 0.75])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> metric = BinaryHingeLoss()
>>> metric(preds, target)
tensor(0.6900)
>>> metric = BinaryHingeLoss(squared=True)
>>> metric(preds, target)
tensor(0.6905)

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

MulticlassHingeLoss¶

class torchmetrics.classification.MulticlassHingeLoss(num_classes, squared=False, multiclass_mode='crammer-singer', ignore_index=None, validate_args=True, **kwargs)[source]

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

The metric can be computed in two ways. Either, the definition by Crammer and Singer is used:

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

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. Alternatively, the metric can also be computed in one-vs-all approach, where each class is valued against all other classes in a binary fashion.

Accepts the following input tensors:

preds (float tensor): (N, C, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply softmax per sample.
target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

Parameters

num_classes¶ (int) – Integer specifing the number of classes
squared¶ (bool) – If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
multiclass_mode¶ (Literal[‘crammer-singer’, ‘one-vs-all’]) – Determines how to compute the metric
ignore_index¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics.classification import MulticlassHingeLoss
>>> preds = torch.tensor([[0.25, 0.20, 0.55],
...                       [0.55, 0.05, 0.40],
...                       [0.10, 0.30, 0.60],
...                       [0.90, 0.05, 0.05]])
>>> target = torch.tensor([0, 1, 2, 0])
>>> metric = MulticlassHingeLoss(num_classes=3)
>>> metric(preds, target)
tensor(0.9125)
>>> metric = MulticlassHingeLoss(num_classes=3, squared=True)
>>> metric(preds, target)
tensor(1.1131)
>>> metric = MulticlassHingeLoss(num_classes=3, multiclass_mode='one-vs-all')
>>> metric(preds, target)
tensor([0.8750, 1.1250, 1.1000])

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, task, num_classes=None, squared=False, multiclass_mode='crammer-singer', ignore_index=None, validate_args=True)[source]

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

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the task argument to either 'binary' or 'multiclass'. See the documentation of binary_hinge_loss() and multiclass_hinge_loss() for the specific details of each argument influence and examples.

Legacy Example:

>>> import torch
>>> target = torch.tensor([0, 1, 1])
>>> preds = torch.tensor([0.5, 0.7, 0.1])
>>> hinge_loss(preds, target, task="binary")
tensor(0.9000)

>>> 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, task="multiclass", num_classes=3)
tensor(1.5551)

>>> 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, task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
tensor([1.3743, 1.1945, 1.2359])

Return type: Tensor

binary_hinge_loss¶

torchmetrics.functional.classification.binary_hinge_loss(preds, target, squared=False, ignore_index=None, validate_args=False)[source]

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

Accepts the following input tensors:

preds (float tensor): (N, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.
target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
squared¶ (bool) – If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
ignore_index¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Example

>>> from torchmetrics.functional.classification import binary_hinge_loss
>>> preds = torch.tensor([0.25, 0.25, 0.55, 0.75, 0.75])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> binary_hinge_loss(preds, target)
tensor(0.6900)
>>> binary_hinge_loss(preds, target, squared=True)
tensor(0.6905)

Return type: Tensor

multiclass_hinge_loss¶

torchmetrics.functional.classification.multiclass_hinge_loss(preds, target, num_classes, squared=False, multiclass_mode='crammer-singer', ignore_index=None, validate_args=False)[source]

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

The metric can be computed in two ways. Either, the definition by Crammer and Singer is used:

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

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. Alternatively, the metric can also be computed in one-vs-all approach, where each class is valued against all other classes in a binary fashion.

Accepts the following input tensors:

preds (float tensor): (N, C, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply softmax per sample.
target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
num_classes¶ (int) – Integer specifing the number of classes
squared¶ (bool) – If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
multiclass_mode¶ (Literal[‘crammer-singer’, ‘one-vs-all’]) – Determines how to compute the metric
ignore_index¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Example

>>> from torchmetrics.functional.classification import multiclass_hinge_loss
>>> preds = torch.tensor([[0.25, 0.20, 0.55],
...                       [0.55, 0.05, 0.40],
...                       [0.10, 0.30, 0.60],
...                       [0.90, 0.05, 0.05]])
>>> target = torch.tensor([0, 1, 2, 0])
>>> multiclass_hinge_loss(preds, target, num_classes=3)
tensor(0.9125)
>>> multiclass_hinge_loss(preds, target, num_classes=3, squared=True)
tensor(1.1131)
>>> multiclass_hinge_loss(preds, target, num_classes=3, multiclass_mode='one-vs-all')
tensor([0.8750, 1.1250, 1.1000])

Return type: Tensor