Package ai.djl.training.loss

Class TabNetRegressionLoss

java.lang.Object

ai.djl.training.evaluator.Evaluator

ai.djl.training.loss.Loss

ai.djl.training.loss.TabNetRegressionLoss

public class TabNetRegressionLoss
extends Loss

Calculates the loss of tabNet for regression tasks.

Actually, tabNet is not only used for Supervised Learning, it's also widely used in unsupervised learning. For unsupervised learning, it should come from the decoder(aka attentionTransformer of tabNet)

Field Summary

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

totalInstances

Constructor Summary

Constructors

Constructor	Description
TabNetRegressionLoss()	Calculates the loss of a TabNet instance for regression tasks.
TabNetRegressionLoss(java.lang.String name)	Calculates the loss of a TabNet instance for regression tasks.

Method Summary

Modifier and Type	Method	Description
NDArray	evaluate(NDList labels, NDList predictions)	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

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 Detail

TabNetRegressionLoss

public TabNetRegressionLoss()

Calculates the loss of a TabNet instance for regression tasks.

TabNetRegressionLoss

public TabNetRegressionLoss(java.lang.String name)

Calculates the loss of a TabNet instance for regression tasks.

Parameters:

name - the name of the loss function

Method Detail

evaluate

public NDArray evaluate(NDList labels,
                        NDList predictions)

Calculates the evaluation between the labels and the predictions.

Specified by:

evaluate in class Evaluator

Parameters:

labels - the correct values

predictions - the predicted values

Returns:

the evaluation result