/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.model.config;
import org.encog.EncogError;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.ml.data.versatile.VersatileMLDataSet;
import org.encog.ml.data.versatile.normalizers.strategies.BasicNormalizationStrategy;
import org.encog.ml.data.versatile.normalizers.strategies.NormalizationStrategy;
import org.encog.ml.factory.MLMethodFactory;
import org.encog.neural.networks.BasicNetwork;
/**
* Config class for EncogModel to use a feedforward neural network.
*/
public class FeedforwardConfig implements MethodConfig {
/**
* {@inheritDoc}
*/
@Override
public String getMethodName() {
return MLMethodFactory.TYPE_FEEDFORWARD;
}
/**
* {@inheritDoc}
*/
@Override
public String suggestModelArchitecture(VersatileMLDataSet dataset) {
int inputColumns = dataset.getNormHelper().getInputColumns().size();
int outputColumns = dataset.getNormHelper().getOutputColumns().size();
int hiddenCount = (int) ((double)(inputColumns+outputColumns) * 1.5);
StringBuilder result = new StringBuilder();
result.append("?:B->TANH->");
result.append(hiddenCount);
result.append(":B->TANH->?");
return result.toString();
}
/**
* {@inheritDoc}
*/
@Override
public NormalizationStrategy suggestNormalizationStrategy(VersatileMLDataSet dataset, String architecture) {
double inputLow = -1;
double inputHigh = 1;
double outputLow = -1;
double outputHigh = 1;
// Create a basic neural network, just to examine activation functions.
MLMethodFactory methodFactory = new MLMethodFactory();
BasicNetwork network = (BasicNetwork)methodFactory.create(getMethodName(), architecture, 1, 1);
if( network.getLayerCount()<1 ) {
throw new EncogError("Neural network does not have an output layer.");
}
ActivationFunction outputFunction = network.getActivation(network.getLayerCount()-1);
double[] d = { -1000, -100, -50 };
outputFunction.activationFunction(d, 0, d.length);
if( d[0]>0 && d[1]>0 && d[2]>0 ) {
inputLow=0;
}
NormalizationStrategy result = new BasicNormalizationStrategy(
inputLow,
inputHigh,
outputLow,
outputHigh);
return result;
}
/**
* {@inheritDoc}
*/
@Override
public String suggestTrainingType() {
return "rprop";
}
/**
* {@inheritDoc}
*/
@Override
public String suggestTrainingArgs(String trainingType) {
return "";
}
/**
* {@inheritDoc}
*/
@Override
public int determineOutputCount(VersatileMLDataSet dataset) {
return dataset.getNormHelper().calculateNormalizedOutputCount();
}
}