Uses of Interface
de.jungblut.math.activation.ActivationFunction

Packages that use ActivationFunction

Package	Description
de.jungblut.classification.nn
de.jungblut.math.activation

Uses of ActivationFunction in de.jungblut.classification.nn

Fields in de.jungblut.classification.nn declared as ActivationFunction

Modifier and Type	Field	Description
ActivationFunction[]	MultilayerPerceptronCostFunction.NetworkConfiguration.activations

Methods in de.jungblut.classification.nn that return ActivationFunction

Modifier and Type	Method	Description
ActivationFunction[]	MultilayerPerceptron.getActivations()

Methods in de.jungblut.classification.nn with parameters of type ActivationFunction

Modifier and Type	Method	Description
static MultilayerPerceptron.MultilayerPerceptronBuilder	MultilayerPerceptron.MultilayerPerceptronBuilder.create(int[] layer, ActivationFunction[] activations, LossFunction errorFunction, Minimizer minimizer, int maxIteration)	Creates a new TrainingConfiguration with the mandatory configurations of the activation functions, the to be used minimizer and the maximum iterations.
static RBM.RBMBuilder	RBM.RBMBuilder.create(ActivationFunction activation, int... layer)	Creates a new RBM.RBMBuilder from an activation function and layersizes.
static RBM	RBM.single(int numHiddenNodes, ActivationFunction func)
static RBM	RBM.singleGPU(int numHiddenNodes, ActivationFunction func)
static RBM	RBM.stacked(ActivationFunction func, int... numHiddenNodes)	Creates a new stacked RBM with sigmoid activation and with the given number of hidden nodes in each stacked layer.
static RBM	RBM.stackedGPU(ActivationFunction func, int... numHiddenNodes)	Creates a new stacked RBM with sigmoid activation and with the given number of hidden nodes in each stacked layer.

Constructors in de.jungblut.classification.nn with parameters of type ActivationFunction

Constructor	Description
RBMCostFunction(de.jungblut.math.DoubleVector[] currentTrainingSet, int batchSize, int numThreads, int numHiddenUnits, ActivationFunction activationFunction, TrainingType type, double lambda, long seed, boolean stochastic)

Uses of ActivationFunction in de.jungblut.math.activation

Classes in de.jungblut.math.activation that implement ActivationFunction

Modifier and Type	Class	Description
class	AbstractActivationFunction	Implements the boiler plate code for applying functions on container classes like vectors and matrices by applying the function on every element.
class	ElliotActivationFunction	Implementation of the elliot activation function.
class	LinearActivationFunction	Linear activation function.
class	LogActivationFunction	Log activation function, guarded against NaN and infinity edge cases.
class	ReluActivationFunction	Rectified linear units implementation.
class	SigmoidActivationFunction	Implementation of the sigmoid function.
class	SoftMaxActivationFunction	Softmax activation that only works on vectors, because it needs to sum and divide the probabilities.
class	SoftplusReluActivationFunction	Smoothed approximation to a ReluActivationFunction.
class	StepActivationFunction	Classic perceptron-like step function.
class	TanhActivationFunction	Implementation of the Tanh activation based on FastMath.

Methods in de.jungblut.math.activation that return ActivationFunction

Modifier and Type	Method	Description
ActivationFunction	ActivationFunctionSelector.get()