Module ojalgo

Package org.ojalgo.ann

Class ArtificialNeuralNetwork

java.lang.Object

org.ojalgo.ann.ArtificialNeuralNetwork

public final class ArtificialNeuralNetwork
extends java.lang.Object

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	ArtificialNeuralNetwork.Activator	https://en.wikipedia.org/wiki/Activation_function
static class	ArtificialNeuralNetwork.Error

Field Summary

Fields

Modifier and Type	Field	Description
private TrainingConfiguration	myConfiguration
private PhysicalStore.Factory<java.lang.Double,?>	myFactory
private CalculationLayer[]	myLayers

Constructor Summary

Constructors

Constructor	Description
ArtificialNeuralNetwork(NetworkBuilder builder)
ArtificialNeuralNetwork(PhysicalStore.Factory<java.lang.Double,?> factory, int inputs, int[] layers)

Method Summary

Modifier and Type	Method	Description
(package private) void	adjust(int layer, PhysicalStore<java.lang.Double> input, PhysicalStore<java.lang.Double> output, PhysicalStore<java.lang.Double> upstreamGradient, PhysicalStore<java.lang.Double> downstreamGradient)
static NetworkBuilder	builder(int numberOfNetworkInputNodes)
static NetworkTrainer	builder(int numberOfInputNodes, int... nodesPerCalculationLayer)	Deprecated. Use builder(int) instead
static NetworkBuilder	builder(PhysicalStore.Factory<java.lang.Double,?> factory, int numberOfNetworkInputNodes)
static NetworkTrainer	builder(PhysicalStore.Factory<java.lang.Double,?> factory, int numberOfInputNodes, int... nodesPerCalculationLayer)	Deprecated. Use builder(org.ojalgo.matrix.store.PhysicalStore.Factory, int) instead
(package private) int	countInputNodes()
(package private) int	countInputNodes(int layer)
(package private) int	countOutputNodes()
(package private) int	countOutputNodes(int layer)
int	depth()
(package private) static void	doIdentity(PhysicalStore<java.lang.Double> output)
(package private) static void	doReLU(PhysicalStore<java.lang.Double> output)
(package private) static void	doSigmoid(PhysicalStore<java.lang.Double> output)
(package private) static void	doSoftMax(PhysicalStore<java.lang.Double> output)
(package private) static void	doTanh(PhysicalStore<java.lang.Double> output)
boolean	equals(java.lang.Object obj)
static ArtificialNeuralNetwork	from(java.io.DataInput input)	Read (reconstruct) an ANN from the specified input previously written by writeTo(DataOutput).
static ArtificialNeuralNetwork	from(java.io.File file)
static ArtificialNeuralNetwork	from(java.nio.file.Path path, java.nio.file.OpenOption... options)
static ArtificialNeuralNetwork	from(PhysicalStore.Factory<java.lang.Double,?> factory, java.io.DataInput input)	Read (reconstruct) an ANN from the specified input previously written by writeTo(DataOutput).
static ArtificialNeuralNetwork	from(PhysicalStore.Factory<java.lang.Double,?> factory, java.io.File file)
static ArtificialNeuralNetwork	from(PhysicalStore.Factory<java.lang.Double,?> factory, java.nio.file.Path path, java.nio.file.OpenOption... options)
ArtificialNeuralNetwork.Activator	getActivator(int layer)
double	getBias(int layer, int output)
(package private) ArtificialNeuralNetwork.Activator	getOutputActivator()
double	getWeight(int layer, int input, int output)
(package private) java.util.List<MatrixStore<java.lang.Double>>	getWeights()
int	hashCode()
(package private) PhysicalStore<java.lang.Double>	invoke(int layer, PhysicalStore<java.lang.Double> input, PhysicalStore<java.lang.Double> output)
(package private) DataBatch	newBatch(int rows, int columns)
NetworkInvoker	newInvoker()	With batch size 1
NetworkInvoker	newInvoker(int batchSize)	If you create multiple invokers you can use them in different threads simutaneously - the invoker contains any/all invocation specific state.
(package private) PhysicalStore<java.lang.Double>	newStore(int rows, int columns)
NetworkTrainer	newTrainer()	With batch size 1
NetworkTrainer	newTrainer(int batchSize)	Only 1 trainer at the time.
(package private) void	randomise()
(package private) void	scale(int layer, double factor)
(package private) void	setActivator(int layer, ArtificialNeuralNetwork.Activator activator)
(package private) void	setBias(int layer, int output, double bias)
(package private) void	setConfiguration(TrainingConfiguration configuration)
(package private) void	setWeight(int layer, int input, int output, double weight)
Structure2D[]	structure()
java.lang.String	toString()
int	width()
void	writeTo(java.io.DataOutput output)	Will write (save) the ANN to the specified output.
void	writeTo(java.io.File file)
void	writeTo(java.nio.file.Path path, java.nio.file.OpenOption... options)

Methods inherited from class java.lang.Object

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

Field Detail

myConfiguration

private transient TrainingConfiguration myConfiguration

myFactory

private final PhysicalStore.Factory<java.lang.Double,?> myFactory

myLayers

private final CalculationLayer[] myLayers

Constructor Detail

ArtificialNeuralNetwork

ArtificialNeuralNetwork(NetworkBuilder builder)

ArtificialNeuralNetwork

ArtificialNeuralNetwork(PhysicalStore.Factory<java.lang.Double,?> factory,
                        int inputs,
                        int[] layers)

Method Detail

builder

public static NetworkBuilder builder(int numberOfNetworkInputNodes)

builder

@Deprecated
public static NetworkTrainer builder(int numberOfInputNodes,
                                     int... nodesPerCalculationLayer)

Deprecated.

Use builder(int) instead

builder

public static NetworkBuilder builder(PhysicalStore.Factory<java.lang.Double,?> factory,
                                     int numberOfNetworkInputNodes)

builder

@Deprecated
public static NetworkTrainer builder(PhysicalStore.Factory<java.lang.Double,?> factory,
                                     int numberOfInputNodes,
                                     int... nodesPerCalculationLayer)

Deprecated.

Use builder(org.ojalgo.matrix.store.PhysicalStore.Factory, int) instead

from

public static ArtificialNeuralNetwork from(java.io.DataInput input)
                                    throws java.io.IOException

Read (reconstruct) an ANN from the specified input previously written by writeTo(DataOutput).

Throws:

java.io.IOException

from

public static ArtificialNeuralNetwork from(java.io.File file)

See Also:

from(DataInput)

from

public static ArtificialNeuralNetwork from(java.nio.file.Path path,
                                           java.nio.file.OpenOption... options)

See Also:

from(DataInput)

from

public static ArtificialNeuralNetwork from(PhysicalStore.Factory<java.lang.Double,?> factory,
                                           java.io.DataInput input)
                                    throws java.io.IOException

Read (reconstruct) an ANN from the specified input previously written by writeTo(DataOutput).

Throws:

java.io.IOException

from

public static ArtificialNeuralNetwork from(PhysicalStore.Factory<java.lang.Double,?> factory,
                                           java.io.File file)

See Also:

from(DataInput)

from

public static ArtificialNeuralNetwork from(PhysicalStore.Factory<java.lang.Double,?> factory,
                                           java.nio.file.Path path,
                                           java.nio.file.OpenOption... options)

See Also:

from(DataInput)

doIdentity

static void doIdentity(PhysicalStore<java.lang.Double> output)

doReLU

static void doReLU(PhysicalStore<java.lang.Double> output)

doSigmoid

static void doSigmoid(PhysicalStore<java.lang.Double> output)

doSoftMax

static void doSoftMax(PhysicalStore<java.lang.Double> output)

doTanh

static void doTanh(PhysicalStore<java.lang.Double> output)

depth

public int depth()

Returns:

The number of calculation layers

equals

public boolean equals(java.lang.Object obj)

Overrides:

equals in class java.lang.Object

getActivator

public ArtificialNeuralNetwork.Activator getActivator(int layer)

getBias

public double getBias(int layer,
                      int output)

getWeight

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

hashCode

public int hashCode()

Overrides:

hashCode in class java.lang.Object

newInvoker

public NetworkInvoker newInvoker()

With batch size 1

See Also:

newInvoker(int)

newInvoker

public NetworkInvoker newInvoker(int batchSize)

If you create multiple invokers you can use them in different threads simutaneously - the invoker contains any/all invocation specific state.

Parameters:

batchSize - The batch size - the number of batched invocations

Returns:

The invoker

newTrainer

public NetworkTrainer newTrainer()

With batch size 1

See Also:

newTrainer(int)

newTrainer

public NetworkTrainer newTrainer(int batchSize)

Only 1 trainer at the time.

Parameters:

batchSize - The batch size - the number of batched training examples

Returns:

The trainer

structure

public Structure2D[] structure()

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

width

public int width()

Returns:

The max number of nodes in any layer

writeTo

public void writeTo(java.io.DataOutput output)
             throws java.io.IOException

Will write (save) the ANN to the specified output. Can then later be read back by using from(DataInput).

Throws:

java.io.IOException

writeTo

public void writeTo(java.io.File file)

See Also:

writeTo(DataOutput)

writeTo

public void writeTo(java.nio.file.Path path,
                    java.nio.file.OpenOption... options)

See Also:

writeTo(DataOutput)

adjust

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

countInputNodes

int countInputNodes()

countInputNodes

int countInputNodes(int layer)

countOutputNodes

int countOutputNodes()

countOutputNodes

int countOutputNodes(int layer)

getOutputActivator

ArtificialNeuralNetwork.Activator getOutputActivator()

getWeights

java.util.List<MatrixStore<java.lang.Double>> getWeights()

invoke

PhysicalStore<java.lang.Double> invoke(int layer,
                                       PhysicalStore<java.lang.Double> input,
                                       PhysicalStore<java.lang.Double> output)

newBatch

DataBatch newBatch(int rows,
                   int columns)

newStore

PhysicalStore<java.lang.Double> newStore(int rows,
                                         int columns)

randomise

void randomise()

scale

void scale(int layer,
           double factor)

setActivator

void setActivator(int layer,
                  ArtificialNeuralNetwork.Activator activator)

setBias

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

setConfiguration

void setConfiguration(TrainingConfiguration configuration)

setWeight

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