SmoothedHingeLoss Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v4.0.0-preview.24522.7

Source:

LossFunctions.cs

A smooth version of the HingeLoss function, commonly used in classification tasks.

C#

public sealed class SmoothedHingeLoss : Microsoft.ML.Trainers.ILossFunction<float,float>, Microsoft.ML.Trainers.ISupportSdcaClassificationLoss

Inheritance

Object

SmoothedHingeLoss

Implements

IClassificationLoss ILossFunction<Single,Single> IScalarLoss ISupportSdcaClassificationLoss ISupportSdcaLoss

Remarks

Let $f(\hat{y},y)=1-y\hat{y}$, where $\hat{y}$ is the predicted score and $y\in \{-1,1\}$ is the true label. $f(\hat{y},y)$ here is the non-zero portion of the Hinge Loss.

Note that the labels used in this calculation are -1 and 1, unlike Log Loss, where the labels used are 0 and 1. Also unlike Log Loss, $\hat{y}$ is the raw predicted score, not the predicted probability (which is calculated by applying a sigmoid function to the predicted score).

The Smoothed Hinge Loss function is then defined as:

$L(f(\hat{y},y))=\{\begin{array}{ll}0& \text{if }f(\hat{y},y)<0\\ \frac{(f(\hat{y},y){)}^2}{2\alpha }& \text{if }f(\hat{y},y)<\alpha \\ f(\hat{y},y)-\frac{\alpha }{2}& \text{otherwise}\end{array}$

where $\alpha$ is a smoothing parameter set to 1 by default.

Constructors

SmoothedHingeLoss(Single)

Constructor for smoothed hinge losee.

Methods

ComputeDualUpdateInvariant(Single)

Derivative(Single, Single)

DualLoss(Single, Single)

DualUpdate(Single, Single, Single, Single, Int32)

Loss(Single, Single)

Applies to