Module ojalgo

Package org.ojalgo.ann

Class NetworkTrainer

java.lang.Object

org.ojalgo.ann.NetworkTrainer

All Implemented Interfaces:

Supplier<ArtificialNeuralNetwork>

public final class NetworkTrainer
extends Object

An Artificial Neural Network (ANN) builder/trainer.

Author:

apete

Method Summary

Modifier and Type	Method	Description
NetworkTrainer	activator(int layer, ArtificialNeuralNetwork.Activator activator)	Deprecated. Use NetworkBuilder and NetworkBuilder.layer(int, Activator) instead.
NetworkTrainer	activators(ArtificialNeuralNetwork.Activator activator)	Deprecated. Use NetworkBuilder and NetworkBuilder.layer(int, Activator) instead.
NetworkTrainer	activators(ArtificialNeuralNetwork.Activator... activators)	Deprecated. Use NetworkBuilder and NetworkBuilder.layer(int, Activator) instead.
NetworkTrainer	bias(int layer, int output, double bias)
NetworkTrainer	dropouts()
boolean	equals(Object obj)
NetworkTrainer	error(ArtificialNeuralNetwork.Error error)
ArtificialNeuralNetwork	get()
int	hashCode()
NetworkTrainer	lasso(double factor)	L1 lasso regularisation
DataBatch	newInputBatch()	When using NetworkTrainer or NetworkInvoker with a batch size larger than 1 this utility may help with creating the batches.
DataBatch	newOutputBatch()
NetworkTrainer	rate(double rate)
NetworkTrainer	ridge(double factor)	L2 ridge regularisation
Structure2D[]	structure()
String	toString()
void	train(Iterable<? extends Access1D<Double>> givenInputs, Iterable<? extends Access1D<Double>> targetOutputs)	Deprecated. Just use train(Access1D, Access1D) instead
void	train(Access1D<Double> givenInput, Access1D<Double> targetOutput)	The arguments are typed as Access1D but it's probably best to think of (create) them as something 2D where the number of rows should match the batch size and the number of columns the number of inputs and outputs respectively.
NetworkTrainer	weight(int layer, int input, int output, double weight)

Methods inherited from class java.lang.Object

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

Method Details

activator

@Deprecated
public NetworkTrainer activator(int layer,
 ArtificialNeuralNetwork.Activator activator)

Deprecated.

Use NetworkBuilder and NetworkBuilder.layer(int, Activator) instead.

Parameters:

layer - 0-based index among the calculation layers (excluding the input layer)

activator - The activator function to use

activators

@Deprecated
public NetworkTrainer activators(ArtificialNeuralNetwork.Activator activator)

Deprecated.

Use NetworkBuilder and NetworkBuilder.layer(int, Activator) instead.

activators

@Deprecated
public NetworkTrainer activators(ArtificialNeuralNetwork.Activator... activators)

Deprecated.

Use NetworkBuilder and NetworkBuilder.layer(int, Activator) instead.

bias

public NetworkTrainer bias(int layer,
 int output,
 double bias)

dropouts

public NetworkTrainer dropouts()

equals

public boolean equals(Object obj)

error

public NetworkTrainer error(ArtificialNeuralNetwork.Error error)

hashCode

public int hashCode()

lasso

public NetworkTrainer lasso(double factor)

L1 lasso regularisation

newOutputBatch

public DataBatch newOutputBatch()

See Also:

• WrappedANN.newInputBatch()

rate

public NetworkTrainer rate(double rate)

ridge

public NetworkTrainer ridge(double factor)

L2 ridge regularisation

structure

public Structure2D[] structure()

toString

public String toString()

Overrides:

toString in class Object

train

public void train(Access1D<Double> givenInput,
 Access1D<Double> targetOutput)

The arguments are typed as Access1D but it's probably best to think of (create) them as something 2D where the number of rows should match the batch size and the number of columns the number of inputs and outputs respectively. When the batch size is 1 then the arguments can actually be 1D.

Parameters:

givenInput - One or more input examples, depending on the batch size

targetOutput - One or more, matching, output targets

train

@Deprecated
public void train(Iterable<? extends Access1D<Double>> givenInputs,
 Iterable<? extends Access1D<Double>> targetOutputs)

Deprecated.

Just use train(Access1D, Access1D) instead

Note that the required Iterable:s can be obtained from calling Access2D.rows() or Access2D.columns() on anything "2D".

weight

public NetworkTrainer weight(int layer,
 int input,
 int output,
 double weight)

get

public ArtificialNeuralNetwork get()

Specified by:

get in interface Supplier<ArtificialNeuralNetwork>

newInputBatch

public DataBatch newInputBatch()

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