Uses of Interface
org.nd4j.linalg.activations.IActivation

Packages that use IActivation

Package	Description
org.nd4j.linalg.activations
org.nd4j.linalg.activations.impl
org.nd4j.linalg.lossfunctions
org.nd4j.linalg.lossfunctions.impl
org.nd4j.serde.json

Uses of IActivation in org.nd4j.linalg.activations

Classes in org.nd4j.linalg.activations that implement IActivation

Modifier and Type	Class	Description
class	BaseActivationFunction

Methods in org.nd4j.linalg.activations that return IActivation

Modifier and Type	Method	Description
IActivation	Activation.getActivationFunction()	Creates an instance of the activation function

Uses of IActivation in org.nd4j.linalg.activations.impl

Classes in org.nd4j.linalg.activations.impl that implement IActivation

Modifier and Type	Class	Description
class	ActivationCube	f(x) = x^3
class	ActivationELU
class	ActivationGELU
class	ActivationHardSigmoid	f(x) = min(1, max(0, 0.2*x + 0.5))
class	ActivationHardTanH	⎧ 1, if x > 1 f(x) = ⎨ -1, if x < -1 ⎩ x, otherwise
class	ActivationIdentity	f(x) = x
class	ActivationLReLU	Leaky RELU f(x) = max(0, x) + alpha * min(0, x) alpha defaults to 0.01
class	ActivationMish	https://arxiv.org/ftp/arxiv/papers/1908/1908.08681.pdf
class	ActivationPReLU
class	ActivationRationalTanh
class	ActivationRectifiedTanh
class	ActivationReLU	f(x) = max(0, x)
class	ActivationReLU6
class	ActivationRReLU
class	ActivationSELU	https://arxiv.org/pdf/1706.02515.pdf
class	ActivationSigmoid	f(x) = 1 / (1 + exp(-x))
class	ActivationSoftmax	f_i(x) = exp(x_i - shift) / sum_j exp(x_j - shift) where shift = max_i(x_i)
class	ActivationSoftPlus	f(x) = log(1+e^x)
class	ActivationSoftSign	f_i(x) = x_i / (1+|x_i|)
class	ActivationSwish	f(x) = x * sigmoid(x)
class	ActivationTanH	f(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
class	ActivationThresholdedReLU	Thresholded RELU f(x) = x for x > theta, f(x) = 0 otherwise.

Uses of IActivation in org.nd4j.linalg.lossfunctions

Methods in org.nd4j.linalg.lossfunctions with parameters of type IActivation

Modifier and Type	Method	Description
INDArray	ILossFunction.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)	Compute the gradient of the loss function with respect to the inputs: dL/dOutput
INDArray	SameDiffLoss.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)	Compute the gradient of the loss function with respect to the inputs: dL/dOutput
Pair<Double,INDArray>	ILossFunction.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)	Compute both the score (loss function value) and gradient.
Pair<Double,INDArray>	SameDiffLoss.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)	Compute both the score (loss function value) and gradient.
double	ILossFunction.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)	Compute the score (loss function value) for the given inputs.
double	SameDiffLoss.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)	Compute the score (loss function value) for the given inputs.
INDArray	ILossFunction.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)	Compute the score (loss function value) for each example individually.
INDArray	SameDiffLoss.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)	Compute the score (loss function value) for each example individually.

Uses of IActivation in org.nd4j.linalg.lossfunctions.impl

Methods in org.nd4j.linalg.lossfunctions.impl with parameters of type IActivation

Modifier and Type	Method	Description
INDArray	LossBinaryXENT.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossCosineProximity.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossFMeasure.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossHinge.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossKLD.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossL1.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossL2.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMAE.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMAPE.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMCXENT.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMixtureDensity.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)	This method returns the gradient of the cost function with respect to the output from the previous layer.
INDArray	LossMSE.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMSLE.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMultiLabel.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossPoisson.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossSparseMCXENT.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossSquaredHinge.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossWasserstein.computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
Pair<Double,INDArray>	LossBinaryXENT.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossCosineProximity.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossFMeasure.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossHinge.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossKLD.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossL1.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossL2.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossMAPE.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossMCXENT.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossMixtureDensity.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossMSLE.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossMultiLabel.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossPoisson.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossSparseMCXENT.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossSquaredHinge.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
Pair<Double,INDArray>	LossWasserstein.computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossBinaryXENT.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossCosineProximity.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossFMeasure.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossHinge.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossKLD.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossL1.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossL2.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossMAE.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossMAPE.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossMCXENT.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossMixtureDensity.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)	Computes the aggregate score as a sum of all of the individual scores of each of the labels against each of the outputs of the network.
double	LossMSE.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossMSLE.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossMultiLabel.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossPoisson.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossSparseMCXENT.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossSquaredHinge.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
double	LossWasserstein.computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
INDArray	LossBinaryXENT.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossCosineProximity.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossFMeasure.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossHinge.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossKLD.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossL1.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossL2.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMAE.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMAPE.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMCXENT.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMixtureDensity.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)	This method returns the score for each of the given outputs against the given set of labels.
INDArray	LossMSE.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMSLE.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMultiLabel.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossPoisson.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossSparseMCXENT.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossSquaredHinge.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossWasserstein.computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossCosineProximity.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossHinge.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossL1.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
protected INDArray	LossL2.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMAPE.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
protected INDArray	LossMCXENT.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMSLE.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossMultiLabel.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossPoisson.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
INDArray	LossSquaredHinge.scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
protected INDArray	LossSparseMCXENT.sparseScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)

Uses of IActivation in org.nd4j.serde.json

Method parameters in org.nd4j.serde.json with type arguments of type IActivation

Modifier and Type	Method	Description
static void	LegacyIActivationDeserializer.registerLegacyClassDefaultName(@NonNull Class<? extends IActivation> clazz)
static void	LegacyIActivationDeserializer.registerLegacyClassSpecifiedName(@NonNull String name, @NonNull Class<? extends IActivation> clazz)