smile.regression

Class NeuralNetwork

java.lang.Object

smile.regression.NeuralNetwork

All Implemented Interfaces:

java.io.Serializable, OnlineRegression<double[]>, Regression<double[]>

public class NeuralNetwork
extends java.lang.Object
implements OnlineRegression<double[]>, java.io.Serializable

Multilayer perceptron neural network for regression. An MLP consists of several layers of nodes, interconnected through weighted acyclic arcs from each preceding layer to the following, without lateral or feedback connections. Each node calculates a transformed weighted linear combination of its inputs (output activations from the preceding layer), with one of the weights acting as a trainable bias connected to a constant input. The transformation, called activation function, is a bounded non-decreasing (non-linear) function, such as the sigmoid functions (ranges from 0 to 1). Another popular activation function is hyperbolic tangent which is actually equivalent to the sigmoid function in shape but ranges from -1 to 1.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	NeuralNetwork.ActivationFunction
static class	NeuralNetwork.Trainer Trainer for neural networks.

Constructor Summary

Constructors

Constructor and Description
NeuralNetwork(int... numUnits) Constructor.
NeuralNetwork(NeuralNetwork.ActivationFunction activation, double alpha, double lambda, int... numUnits) Constructor.
NeuralNetwork(NeuralNetwork.ActivationFunction activation, int... numUnits) Constructor.

Method Summary

Modifier and Type	Method and Description
NeuralNetwork	clone()
double	getLearningRate() Returns the learning rate.
double	getMomentum() Returns the momentum factor.
double[][]	getWeight(int layer) Returns the weights of a layer.
double	getWeightDecay() Returns the weight decay factor.
void	learn(double[][] x, double[] y) Trains the neural network with the given dataset for one epoch by stochastic gradient descent.
void	learn(double[] x, double y) Online update the regression model with a new training instance.
double	learn(double[] x, double y, double weight) Update the neural network with given instance and associated target value.
double	predict(double[] x) Predicts the dependent variable of an instance.
void	setLearningRate(double eta) Sets the learning rate.
void	setMomentum(double alpha) Sets the momentum factor.
void	setWeightDecay(double lambda) Sets the weight decay factor.

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.regression.Regression

predict

Constructor Detail

NeuralNetwork

public NeuralNetwork(int... numUnits)

Constructor. The default activation function is the logistic sigmoid function.

Parameters:

numUnits - the number of units in each layer.

NeuralNetwork

public NeuralNetwork(NeuralNetwork.ActivationFunction activation,
                     int... numUnits)

Constructor.

Parameters:

activation - the activation function of output layer.

numUnits - the number of units in each layer.

NeuralNetwork

public NeuralNetwork(NeuralNetwork.ActivationFunction activation,
                     double alpha,
                     double lambda,
                     int... numUnits)

Constructor.

Parameters:

activation - the activation function of output layer.

numUnits - the number of units in each layer.

Method Detail

clone

public NeuralNetwork clone()

Overrides:

clone in class java.lang.Object

setLearningRate

public void setLearningRate(double eta)

Sets the learning rate.

Parameters:

eta - the learning rate.

getLearningRate

public double getLearningRate()

Returns the learning rate.

setMomentum

public void setMomentum(double alpha)

Sets the momentum factor.

Parameters:

alpha - the momentum factor.

getMomentum

public double getMomentum()

Returns 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.

getWeightDecay

public double getWeightDecay()

Returns the weight decay factor.

getWeight

public double[][] getWeight(int layer)

Returns the weights of a layer.

Parameters:

layer - the layer of netural network, 0 for input layer.

predict

public double predict(double[] x)

Description copied from interface: Regression

Predicts the dependent variable of an instance.

Specified by:

predict in interface Regression<double[]>

Parameters:

x - the instance.

Returns:

the predicted value of dependent variable.

learn

public double learn(double[] x,
                    double y,
                    double weight)

Update the neural network with given instance and associated target value. Note that this method is NOT multi-thread safe.

Parameters:

x - the training instance.

y - the target value.

weight - a positive weight value associated with the training instance.

Returns:

the weighted training error before back-propagation.

learn

public void learn(double[] x,
                  double y)

Description copied from interface: OnlineRegression

Online update the regression model with a new training instance. In general, this method may be NOT multi-thread safe.

Specified by:

learn in interface OnlineRegression<double[]>

Parameters:

x - training instance.

y - response variable.

learn

public void learn(double[][] x,
                  double[] y)

Trains the neural network with the given dataset for one epoch by stochastic gradient descent.

Parameters:

x - training instances.

y - training labels in [0, k), where k is the number of classes.