org.encog.ml.train.strategy

Interface Strategy

All Known Subinterfaces:

EndTrainingStrategy

All Known Implementing Classes:

EarlyStoppingStrategy, EndIterationsStrategy, EndMaxErrorStrategy, EndMinutesStrategy, Greedy, HybridStrategy, RegularizationStrategy, RequiredImprovementStrategy, ResetStrategy, SimpleEarlyStoppingStrategy, SmartLearningRate, SmartMomentum, StopTrainingStrategy

public interface Strategy

Training strategies can be added to training algorithms. Training strategies allow different additional logic to be added to an existing training algorithm. There are a number of different training strategies that can perform various tasks, such as adjusting the learning rate or momentum, or terminating training when improvement diminishes. Other strategies are provided as well.

Author:

jheaton

Method Summary

Methods

Modifier and Type	Method and Description
void	init(MLTrain train) Initialize this strategy.
void	postIteration() Called just after a training iteration.
void	preIteration() Called just before a training iteration.

Method Detail

init

void init(MLTrain train)

Initialize this strategy.

Parameters:

train - The training algorithm.

preIteration

void preIteration()

Called just before a training iteration.

postIteration

void postIteration()

Called just after a training iteration.