Class WrappedANN

java.lang.Object

org.ojalgo.ann.WrappedANN

All Implemented Interfaces:

Supplier<ArtificialNeuralNetwork>

Direct Known Subclasses:

NetworkInvoker, NetworkTrainer

abstract class WrappedANN
extends Object
implements Supplier<ArtificialNeuralNetwork>

Field Summary

Fields

Modifier and Type	Field	Description
private final int	myBatchSize
private PhysicalStore<Double>	myInput
private final ArtificialNeuralNetwork	myNetwork
private final PhysicalStore<Double>[]	myOutputs

Constructor Summary

Constructors

Constructor	Description
WrappedANN(ArtificialNeuralNetwork network, int batchSize)

Method Summary

Modifier and Type	Method	Description
(package private) void	adjust(int layer, PhysicalStore<Double> input, PhysicalStore<Double> output, PhysicalStore<Double> upstreamGradient, PhysicalStore<Double> downstreamGradient)
(package private) int	depth()
boolean	equals(Object obj)
ArtificialNeuralNetwork	get()
(package private) ArtificialNeuralNetwork.Activator	getActivator(int layer)
(package private) int	getBatchSize()
(package private) double	getBias(int layer, int output)
(package private) PhysicalStore<Double>	getInput()
(package private) PhysicalStore<Double>	getInput(int layer)
(package private) PhysicalStore<Double>	getOutput()
(package private) PhysicalStore<Double>	getOutput(int layer)
(package private) ArtificialNeuralNetwork.Activator	getOutputActivator()
(package private) double	getWeight(int layer, int input, int output)
(package private) List<MatrixStore<Double>>	getWeights()
int	hashCode()
(package private) MatrixStore<Double>	invoke(Access1D<Double> input, TrainingConfiguration configuration)
DataBatch	newInputBatch()	When using NetworkTrainer or NetworkInvoker with a batch size larger than 1 this utility may help with creating the batches.
(package private) DataBatch	newOutputBatch()
(package private) void	randomise()
(package private) void	setActivator(int layer, ArtificialNeuralNetwork.Activator activator)
(package private) void	setBias(int layer, int output, double bias)
private void	setInput(Access1D<Double> input)
(package private) void	setWeight(int layer, int input, int output, double weight)
(package private) Structure2D[]	structure()

Methods inherited from class Object

clone, finalize, getClass, notify, notifyAll, toString, wait, wait, wait

Field Details

myBatchSize

private final int myBatchSize

myInput

private PhysicalStore<Double> myInput

myNetwork

private final ArtificialNeuralNetwork myNetwork

myOutputs

private final PhysicalStore<Double>[] myOutputs

Constructor Details

WrappedANN

WrappedANN(ArtificialNeuralNetwork network,
 int batchSize)

Method Details

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

get

public ArtificialNeuralNetwork get()

Specified by:

get in interface Supplier<ArtificialNeuralNetwork>

hashCode

public int hashCode()

Overrides:

hashCode in class Object

newInputBatch

public DataBatch newInputBatch()

When using NetworkTrainer or NetworkInvoker with a batch size larger than 1 this utility may help with creating the batches.

setInput

private void setInput(Access1D<Double> input)

adjust

void adjust(int layer,
 PhysicalStore<Double> input,
 PhysicalStore<Double> output,
 PhysicalStore<Double> upstreamGradient,
 PhysicalStore<Double> downstreamGradient)

depth

int depth()

getActivator

ArtificialNeuralNetwork.Activator getActivator(int layer)

getBatchSize

int getBatchSize()

getBias

double getBias(int layer,
 int output)

getInput

PhysicalStore<Double> getInput()

getInput

PhysicalStore<Double> getInput(int layer)

getOutput

PhysicalStore<Double> getOutput()

getOutput

PhysicalStore<Double> getOutput(int layer)

getOutputActivator

ArtificialNeuralNetwork.Activator getOutputActivator()

getWeight

double getWeight(int layer,
 int input,
 int output)

getWeights

List<MatrixStore<Double>> getWeights()

invoke

MatrixStore<Double> invoke(Access1D<Double> input,
 TrainingConfiguration configuration)

newOutputBatch

DataBatch newOutputBatch()

randomise

void randomise()

setActivator

void setActivator(int layer,
 ArtificialNeuralNetwork.Activator activator)

setBias

void setBias(int layer,
 int output,
 double bias)

setWeight

void setWeight(int layer,
 int input,
 int output,
 double weight)

structure

Structure2D[] structure()