Package ai.djl.training.loss

Class ElasticNetWeightDecay

java.lang.Object

ai.djl.training.evaluator.Evaluator

ai.djl.training.loss.Loss

ai.djl.training.loss.ElasticNetWeightDecay

public class ElasticNetWeightDecay
extends Loss

ElasticWeightDecay calculates L1+L2 penalty of a set of parameters. Used for regularization.

L loss is defined as \(L = \lambda_1 \sum_i \vert W_i\vert + \lambda_2 \sum_i {W_i}^2\).

Field Summary

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

totalInstances

Constructor Summary

Constructors

Constructor	Description
ElasticNetWeightDecay(NDList parameters)	Calculates Elastic Net weight decay for regularization.
ElasticNetWeightDecay(String name, NDList parameters)	Calculates Elastic Net weight decay for regularization.
ElasticNetWeightDecay(String name, NDList parameters, float lambda)	Calculates Elastic Net weight decay for regularization.
ElasticNetWeightDecay(String name, NDList parameters, float lambda1, float lambda2)	Calculates Elastic Net weight decay for regularization.

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

ElasticNetWeightDecay

public ElasticNetWeightDecay(NDList parameters)

Calculates Elastic Net weight decay for regularization.

Parameters:

parameters - holds the model weights that will be penalized

ElasticNetWeightDecay

public ElasticNetWeightDecay(String name,
 NDList parameters)

Calculates Elastic Net weight decay for regularization.

Parameters:

name - the name of the penalty

parameters - holds the model weights that will be penalized

ElasticNetWeightDecay

public ElasticNetWeightDecay(String name,
 NDList parameters,
 float lambda)

Calculates Elastic Net weight decay for regularization.

Parameters:

name - the name of the penalty

parameters - holds the model weights that will be penalized

lambda - the weight to apply to the penalty value, default 1 (both L1 and L2)

ElasticNetWeightDecay

public ElasticNetWeightDecay(String name,
 NDList parameters,
 float lambda1,
 float lambda2)

Calculates Elastic Net weight decay for regularization.

Parameters:

name - the name of the penalty

parameters - holds the model weights that will be penalized

lambda1 - the weight to apply to the L1 penalty value, default 1

lambda2 - the weight to apply to the L2 penalty value, default 1

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