Class HingeLoss<T>

Namespace

AiDotNet.LossFunctions

Assembly

AiDotNet.dll

Implements the Hinge loss function commonly used in support vector machines.

public class HingeLoss<T> : LossFunctionBase<T>, ILossFunction<T>

Type Parameters

T

The numeric type used for calculations (e.g., float, double).

Inheritance

object

LossFunctionBase<T>

HingeLoss<T>

Implements

ILossFunction<T>

Inherited Members

LossFunctionBase<T>.NumOps

LossFunctionBase<T>.Engine

LossFunctionBase<T>.ValidateVectorLengths(Vector<T>, Vector<T>)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Extension Methods

LossFunctionExtensions.ComputeGradient<T>(ILossFunction<T>, Tensor<T>, Tensor<T>)

LossFunctionExtensions.ComputeLoss<T>(ILossFunction<T>, Tensor<T>, Tensor<T>)

Remarks

For Beginners: Hinge loss is used for binary classification problems, particularly in support vector machines (SVMs). It measures how well your model separates different classes.

The formula is: max(0, 1 - y * f(x)), where:

• y is the true label (usually -1 or 1)
• f(x) is the model's prediction

Key properties of hinge loss:

• It penalizes predictions that are incorrect or not confident enough
• It's zero when the prediction is correct and confident (y*f(x) = 1)
• It increases linearly when the prediction is incorrect or not confident enough
• It encourages the model to find a decision boundary with a large margin between classes

This loss function is ideal for binary classification tasks where you want to maximize the margin between different classes, which often improves generalization to new data.

Methods

CalculateDerivative(Vector<T>, Vector<T>)

Calculates the derivative of the Hinge loss function.

public override Vector<T> CalculateDerivative(Vector<T> predicted, Vector<T> actual)

Parameters

predicted Vector<T>

The predicted values from the model.

actual Vector<T>

The actual (target) values, typically -1 or 1.

Returns

Vector<T>

A vector containing the derivatives of Hinge loss for each prediction.

CalculateLoss(Vector<T>, Vector<T>)

Calculates the Hinge loss between predicted and actual values.

public override T CalculateLoss(Vector<T> predicted, Vector<T> actual)

Parameters

predicted Vector<T>

The predicted values from the model.

actual Vector<T>

The actual (target) values, typically -1 or 1.

Returns

T

The average Hinge loss across all samples.

CalculateLossAndGradientGpu(IGpuTensor<T>, IGpuTensor<T>)

Calculates both Hinge loss and gradient on GPU in a single efficient pass.

public override (T Loss, IGpuTensor<T> Gradient) CalculateLossAndGradientGpu(IGpuTensor<T> predicted, IGpuTensor<T> actual)

Parameters

predicted IGpuTensor<T>

The predicted GPU tensor from the model.

actual IGpuTensor<T>

The actual (target) GPU tensor.

Returns

(T Loss, IGpuTensor<T> Gradient)

A tuple containing the loss value and gradient tensor.