org.apache.ignite.ml.nn

Class MLPTrainer<P extends Serializable>

java.lang.Object

org.apache.ignite.ml.nn.MLPTrainer<P>

Type Parameters:

P - Type of model update used in this trainer.

All Implemented Interfaces:

DatasetTrainer<MultilayerPerceptron,double[]>, MultiLabelDatasetTrainer<MultilayerPerceptron>

public class MLPTrainer<P extends Serializable>
extends Object
implements MultiLabelDatasetTrainer<MultilayerPerceptron>

Multilayer perceptron trainer based on partition based Dataset.

Constructor Summary

Constructors

Constructor and Description
MLPTrainer(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy, int maxIterations, int batchSize, int locIterations, long seed) Constructs a new instance of multilayer perceptron trainer.
MLPTrainer(MLPArchitecture arch, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy, int maxIterations, int batchSize, int locIterations, long seed) Constructs a new instance of multilayer perceptron trainer.

Method Summary

Modifier and Type	Method and Description
<K,V> MultilayerPerceptron	fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,double[]> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Trains model based on the specified data.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.ignite.ml.trainers.DatasetTrainer

fit, fit

Constructor Detail

MLPTrainer

public MLPTrainer(MLPArchitecture arch,
                  IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss,
                  UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy,
                  int maxIterations,
                  int batchSize,
                  int locIterations,
                  long seed)

Constructs a new instance of multilayer perceptron trainer.

Parameters:

arch - Multilayer perceptron architecture that defines layers and activators.

loss - Loss function to be minimized during the training.

updatesStgy - Update strategy that defines how to update model parameters during the training.

maxIterations - Maximal number of iterations before the training will be stopped.

batchSize - Batch size (per every partition).

locIterations - Maximal number of local iterations before synchronization.

seed - Random initializer seed.

MLPTrainer

public MLPTrainer(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier,
                  IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss,
                  UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy,
                  int maxIterations,
                  int batchSize,
                  int locIterations,
                  long seed)

Constructs a new instance of multilayer perceptron trainer.

Parameters:

archSupplier - Multilayer perceptron architecture supplier that defines layers and activators.

loss - Loss function to be minimized during the training.

updatesStgy - Update strategy that defines how to update model parameters during the training.

maxIterations - Maximal number of iterations before the training will be stopped.

batchSize - Batch size (per every partition).

locIterations - Maximal number of local iterations before synchronization.

seed - Random initializer seed.

Method Detail

fit

public <K,V> MultilayerPerceptron fit(DatasetBuilder<K,V> datasetBuilder,
                                      IgniteBiFunction<K,V,double[]> featureExtractor,
                                      IgniteBiFunction<K,V,double[]> lbExtractor)

Trains model based on the specified data.

Specified by:

fit in interface DatasetTrainer<MultilayerPerceptron,double[]>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

datasetBuilder - Dataset builder.

featureExtractor - Feature extractor.

lbExtractor - Label extractor.

Returns:

Model.