/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.train.strategy;
import org.encog.ml.train.MLTrain;
/**
* 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
*
*/
public interface Strategy {
/**
* Initialize this strategy.
* @param train The training algorithm.
*/
void init(MLTrain train);
/**
* Called just before a training iteration.
*/
void preIteration();
/**
* Called just after a training iteration.
*/
void postIteration();
}