smile.classification

Class NeuralNetwork.Trainer

java.lang.Object

smile.classification.ClassifierTrainer<double[]>

smile.classification.NeuralNetwork.Trainer

Enclosing class:

NeuralNetwork

public static class NeuralNetwork.Trainer
extends ClassifierTrainer<double[]>

Trainer for neural networks.

Constructor Summary

Constructors

Constructor and Description
NeuralNetwork.Trainer(NeuralNetwork.ErrorFunction error, int... numUnits) Constructor.
NeuralNetwork.Trainer(NeuralNetwork.ErrorFunction error, NeuralNetwork.ActivationFunction activation, int... numUnits) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	setLearningRate(double eta) Sets the learning rate.
void	setMomentum(double alpha) Sets the momentum factor.
void	setNumEpochs(int epochs) Sets the number of epochs of stochastic learning.
void	setWeightDecay(double lambda) Sets the weight decay factor.
NeuralNetwork	train(double[][] x, int[] y) Learns a classifier with given training data.

Methods inherited from class smile.classification.ClassifierTrainer

setAttributes

Methods inherited from class java.lang.Object

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

Constructor Detail

NeuralNetwork.Trainer

public NeuralNetwork.Trainer(NeuralNetwork.ErrorFunction error,
                     int... numUnits)

Constructor. The activation function of output layer will be chosen by natural pairing based on the error function and the number of classes.

Parameters:

error - the error function.

numUnits - the number of units in each layer.

NeuralNetwork.Trainer

public NeuralNetwork.Trainer(NeuralNetwork.ErrorFunction error,
                     NeuralNetwork.ActivationFunction activation,
                     int... numUnits)

Constructor.

Parameters:

error - the error function.

activation - the activation function of output layer.

numUnits - the number of units in each layer.

Method Detail

setLearningRate

public void setLearningRate(double eta)

Sets the learning rate.

Parameters:

eta - the learning rate.

setMomentum

public void setMomentum(double alpha)

Sets the momentum factor.

Parameters:

alpha - the momentum factor.

setWeightDecay

public void setWeightDecay(double lambda)

Sets the weight decay factor. After each weight update, every weight is simply ''decayed'' or shrunk according w = w * (1 - eta * lambda).

Parameters:

lambda - the weight decay for regularization.

setNumEpochs

public void setNumEpochs(int epochs)

Sets the number of epochs of stochastic learning.

Parameters:

epochs - the number of epochs of stochastic learning.

train

public NeuralNetwork train(double[][] x,
                  int[] y)

Description copied from class: ClassifierTrainer

Learns a classifier with given training data.

Specified by:

train in class ClassifierTrainer<double[]>

Parameters:

x - the training instances.

y - the training labels.

Returns:

a trained classifier.