org.deeplearning4j.nn

Interface NeuralNetwork

All Superinterfaces:

Cloneable, Serializable

All Known Implementing Classes:

BaseNeuralNetwork, CRBM, DenoisingAutoEncoder, RBM

public interface NeuralNetwork
extends Serializable, Cloneable

Method Summary

Methods

Modifier and Type	Method and Description
NeuralNetwork	clone()
double	fanIn()
org.apache.commons.math3.distribution.RealDistribution	getDist()
NeuralNetworkGradient	getGradient(Object[] params)
org.jblas.DoubleMatrix	gethBias()
org.jblas.DoubleMatrix	getInput()
double	getL2()
double	getMomentum()
int	getnHidden()
int	getnVisible()
double	getReConstructionCrossEntropy()
int	getRenderEpochs()
org.apache.commons.math3.random.RandomGenerator	getRng()
double	getSparsity()
org.jblas.DoubleMatrix	getvBias()
org.jblas.DoubleMatrix	getW()
double	l2RegularizedCoefficient()
void	merge(NeuralNetwork network, int batchSize) Performs a network merge in the form of a += b - a / n where a is a matrix here b is a matrix on the incoming network and n is the batch size
void	setDist(org.apache.commons.math3.distribution.RealDistribution dist)
void	setFanIn(double fanIn)
void	sethBias(org.jblas.DoubleMatrix hBias)
void	setInput(org.jblas.DoubleMatrix input)
void	setL2(double l2)
void	setMomentum(double momentum)
void	setnHidden(int nHidden)
void	setnVisible(int nVisible)
void	setRenderEpochs(int renderEpochs)
void	setRng(org.apache.commons.math3.random.RandomGenerator rng)
void	setSparsity(double sparsity)
void	setvBias(org.jblas.DoubleMatrix vBias)
void	setW(org.jblas.DoubleMatrix w)
double	squaredLoss()
void	train(org.jblas.DoubleMatrix input, double lr, Object[] params)
void	trainTillConvergence(org.jblas.DoubleMatrix input, double lr, Object[] params)
NeuralNetwork	transpose()

Method Detail

getnVisible

int getnVisible()

setnVisible

void setnVisible(int nVisible)

getnHidden

int getnHidden()

setnHidden

void setnHidden(int nHidden)

getW

org.jblas.DoubleMatrix getW()

setW

void setW(org.jblas.DoubleMatrix w)

gethBias

org.jblas.DoubleMatrix gethBias()

sethBias

void sethBias(org.jblas.DoubleMatrix hBias)

getvBias

org.jblas.DoubleMatrix getvBias()

setvBias

void setvBias(org.jblas.DoubleMatrix vBias)

getRng

org.apache.commons.math3.random.RandomGenerator getRng()

setRng

void setRng(org.apache.commons.math3.random.RandomGenerator rng)

getInput

org.jblas.DoubleMatrix getInput()

setInput

void setInput(org.jblas.DoubleMatrix input)

squaredLoss

double squaredLoss()

getSparsity

double getSparsity()

setSparsity

void setSparsity(double sparsity)

setDist

void setDist(org.apache.commons.math3.distribution.RealDistribution dist)

getDist

org.apache.commons.math3.distribution.RealDistribution getDist()

getGradient

NeuralNetworkGradient getGradient(Object[] params)

getL2

double getL2()

setL2

void setL2(double l2)

getMomentum

double getMomentum()

setMomentum

void setMomentum(double momentum)

setRenderEpochs

void setRenderEpochs(int renderEpochs)

getRenderEpochs

int getRenderEpochs()

transpose

NeuralNetwork transpose()

clone

NeuralNetwork clone()

fanIn

double fanIn()

setFanIn

void setFanIn(double fanIn)

l2RegularizedCoefficient

double l2RegularizedCoefficient()

getReConstructionCrossEntropy

double getReConstructionCrossEntropy()

train

void train(org.jblas.DoubleMatrix input,
         double lr,
         Object[] params)

trainTillConvergence

void trainTillConvergence(org.jblas.DoubleMatrix input,
                        double lr,
                        Object[] params)

merge

void merge(NeuralNetwork network,
         int batchSize)

Performs a network merge in the form of a += b - a / n where a is a matrix here b is a matrix on the incoming network and n is the batch size

Parameters:

network - the network to merge with

batchSize - the batch size (number of training examples) to average by