/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* 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.
*/
package org.neuroph.nnet.flat;
/**
* These are the training methods provided by the flat network. All methods can be trained
* either single threaded, or multithreaded. GPU support is provided as well.
* @author Jeff Heaton (http://www.jeffheaton.com)
*/
public enum FlatLearningType {
/**
* Classic momentum based propagation. The learning rate and momentum are specified by using the
* setLearningRate and setMomentum on the FlatNetworkLearning class.
*/
BackPropagation,
/**
* Resilient Propagation. No parameters are needed for this learning method. RPROP is the best
* general purpose training method.
*/
ResilientPropagation,
/**
* Manhattan update rule. Not at all a good general purpose learning method, only useful in
* some situations. A learning rate must be specified by using the setLearningRate method on
* the FlatNetworkLearning class.
*/
ManhattanUpdateRule
}