org.tribuo.math.optimisers

Class AdaGrad

java.lang.Object

org.tribuo.math.optimisers.AdaGrad

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>, StochasticGradientOptimiser

public class AdaGrad
extends Object
implements StochasticGradientOptimiser

An implementation of the AdaGrad gradient optimiser.

Creates one copy of the parameters to store learning rates.

See:

 Duchi, J., Hazan, E., and Singer, Y.
 "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization"
 Journal of Machine Learning Research, 2012, 2121-2159.
 

Constructor Summary

Constructors

Constructor and Description
AdaGrad(double initialLearningRate) Sets epsilon to 1e-6.
AdaGrad(double initialLearningRate, double epsilon)

Method Summary

Modifier and Type	Method and Description
AdaGrad	copy() Copies a gradient optimiser with it's configuration.
com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance	getProvenance()
void	initialise(Parameters parameters) Initialises the gradient optimiser.
void	reset() Resets the optimiser so it's ready to optimise a new Parameters.
Tensor[]	step(Tensor[] updates, double weight) Take a Tensor array of gradients and transform them according to the current weight and learning rates.
String	toString()

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.tribuo.math.StochasticGradientOptimiser

finalise

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

Constructor Detail

AdaGrad

public AdaGrad(double initialLearningRate,
               double epsilon)

AdaGrad

public AdaGrad(double initialLearningRate)

Sets epsilon to 1e-6.

Parameters:

initialLearningRate - The learning rate.

Method Detail

initialise

public void initialise(Parameters parameters)

Description copied from interface: StochasticGradientOptimiser

Initialises the gradient optimiser.

Configures any learning rate parameters.

Specified by:

initialise in interface StochasticGradientOptimiser

Parameters:

parameters - The parameters to optimise.

step

public Tensor[] step(Tensor[] updates,
                     double weight)

Description copied from interface: StochasticGradientOptimiser

Take a Tensor array of gradients and transform them according to the current weight and learning rates.

Can return the same Tensor array or a new one.

Specified by:

step in interface StochasticGradientOptimiser

Parameters:

updates - An array of gradients.

weight - The weight for the current gradients.

Returns:

A Tensor array of gradients.

toString

public String toString()

Overrides:

toString in class Object

reset

public void reset()

Description copied from interface: StochasticGradientOptimiser

Resets the optimiser so it's ready to optimise a new Parameters.

Specified by:

reset in interface StochasticGradientOptimiser

copy

public AdaGrad copy()

Description copied from interface: StochasticGradientOptimiser

Copies a gradient optimiser with it's configuration. Usually calls the copy constructor.

Specified by:

copy in interface StochasticGradientOptimiser

Returns:

A gradient optimiser with the same configuration, but independent state.

getProvenance

public com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>