Package ai.djl.training.loss

Class L2Loss

java.lang.Object

ai.djl.training.evaluator.Evaluator

ai.djl.training.loss.Loss

ai.djl.training.loss.L2Loss

public class L2Loss
extends Loss

Calculates L2Loss between label and prediction, a.k.a. MSE(Mean Square Error).

L2 loss is defined by \(L = \frac{1}{2} \sum_i \vert {label}_i - {prediction}_i \vert^2\)

Field Summary

Fields inherited from class ai.djl.training.evaluator.Evaluator

totalInstances

Constructor Summary

Constructors

Constructor	Description
L2Loss()	Calculate L2Loss between the label and prediction, a.k.a.
L2Loss(String name)	Calculate L2Loss between the label and prediction, a.k.a.
L2Loss(String name, float weight)	Calculates L2Loss between the label and prediction, a.k.a.

Method Summary

Modifier and Type	Method	Description
NDArray	evaluate(NDList label, NDList prediction)	Calculates the evaluation between the labels and the predictions.

Methods inherited from class ai.djl.training.loss.Loss

addAccumulator, elasticNetWeightedDecay, elasticNetWeightedDecay, elasticNetWeightedDecay, elasticNetWeightedDecay, getAccumulator, hingeLoss, hingeLoss, hingeLoss, l1Loss, l1Loss, l1Loss, l1WeightedDecay, l1WeightedDecay, l1WeightedDecay, l2Loss, l2Loss, l2Loss, l2WeightedDecay, l2WeightedDecay, l2WeightedDecay, maskedSoftmaxCrossEntropyLoss, maskedSoftmaxCrossEntropyLoss, maskedSoftmaxCrossEntropyLoss, quantileL1Loss, quantileL1Loss, resetAccumulator, sigmoidBinaryCrossEntropyLoss, sigmoidBinaryCrossEntropyLoss, sigmoidBinaryCrossEntropyLoss, softmaxCrossEntropyLoss, softmaxCrossEntropyLoss, softmaxCrossEntropyLoss, updateAccumulator, updateAccumulators

Methods inherited from class ai.djl.training.evaluator.Evaluator

checkLabelShapes, checkLabelShapes, getName

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

L2Loss

public L2Loss()

Calculate L2Loss between the label and prediction, a.k.a. MSE(Mean Square Error).

L2Loss

public L2Loss(String name)

Calculate L2Loss between the label and prediction, a.k.a. MSE(Mean Square Error).

Parameters:

name - the name of the loss

L2Loss

public L2Loss(String name,
 float weight)

Calculates L2Loss between the label and prediction, a.k.a. MSE(Mean Square Error).

Parameters:

name - the name of the loss

weight - the weight to apply on loss value, default 1/2

Method Details

evaluate

public NDArray evaluate(NDList label,
 NDList prediction)

Calculates the evaluation between the labels and the predictions.

Specified by:

evaluate in class Evaluator

Parameters:

label - the correct values

prediction - the predicted values

Returns:

the evaluation result