Package org.neuroph.nnet

Source Code of org.neuroph.nnet.MultiLayerPerceptron

/**
* 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;


import java.util.List;
import java.util.Vector;

import org.neuroph.core.Layer;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.input.WeightedSum;
import org.neuroph.nnet.comp.BiasNeuron;
import org.neuroph.nnet.comp.InputNeuron;
import org.neuroph.nnet.flat.FlatNetworkPlugin;
import org.neuroph.nnet.learning.MomentumBackpropagation;
import org.neuroph.util.ConnectionFactory;
import org.neuroph.util.LayerFactory;
import org.neuroph.util.NeuralNetworkFactory;
import org.neuroph.util.NeuralNetworkType;
import org.neuroph.util.NeuronProperties;
import org.neuroph.util.TransferFunctionType;

/**
*  Multi Layer Perceptron neural network with Back propagation learning algorithm.
*
@see org.neuroph.nnet.learning.BackPropagation
@see org.neuroph.nnet.learning.MomentumBackpropagation
@author Zoran Sevarac <sevarac@gmail.com>
*/
public class MultiLayerPerceptron extends NeuralNetwork {
 
  /**
   * The class fingerprint that is set to indicate serialization
   * compatibility with a previous version of the class.
   */ 
  private static final long serialVersionUID = 2L;

  /**
   * Creates new MultiLayerPerceptron with specified number of neurons in layers
   *
   * @param neuronsInLayers
   *            collection of neuron number in layers
   */
  public MultiLayerPerceptron(List<Integer> neuronsInLayers) {
    // init neuron settings
    NeuronProperties neuronProperties = new NeuronProperties();
                neuronProperties.setProperty("useBias", true);
    neuronProperties.setProperty("transferFunction", TransferFunctionType.SIGMOID);

    this.createNetwork(neuronsInLayers, neuronProperties);
  }
 
  public MultiLayerPerceptron(int ... neuronsInLayers) {
    // init neuron settings
    NeuronProperties neuronProperties = new NeuronProperties();
                neuronProperties.setProperty("useBias", true);
    neuronProperties.setProperty("transferFunction",
        TransferFunctionType.SIGMOID);
                neuronProperties.setProperty("inputFunction", WeightedSum.class);

    Vector<Integer> neuronsInLayersVector = new Vector<Integer>();
    for(int i=0; i<neuronsInLayers.length; i++)
      neuronsInLayersVector.add(new Integer(neuronsInLayers[i]));
   
    this.createNetwork(neuronsInLayersVector, neuronProperties);
  }

  public MultiLayerPerceptron(TransferFunctionType transferFunctionType, int ... neuronsInLayers) {
    // init neuron settings
    NeuronProperties neuronProperties = new NeuronProperties();
                neuronProperties.setProperty("useBias", true);
    neuronProperties.setProperty("transferFunction", transferFunctionType);
                neuronProperties.setProperty("inputFunction", WeightedSum.class);


    Vector<Integer> neuronsInLayersVector = new Vector<Integer>();
    for(int i=0; i<neuronsInLayers.length; i++)
      neuronsInLayersVector.add(new Integer(neuronsInLayers[i]));

    this.createNetwork(neuronsInLayersVector, neuronProperties);
  }

  public MultiLayerPerceptron(List<Integer> neuronsInLayers, TransferFunctionType transferFunctionType) {
    // init neuron settings
    NeuronProperties neuronProperties = new NeuronProperties();
                neuronProperties.setProperty("useBias", true);
    neuronProperties.setProperty("transferFunction", transferFunctionType);

    this.createNetwork(neuronsInLayers, neuronProperties);
  }

  /**
   * Creates new MultiLayerPerceptron net with specified number neurons in
   * getLayersIterator
   *
   * @param neuronsInLayers
   *            collection of neuron numbers in layers
   * @param neuronProperties
   *            neuron properties
   */
  public MultiLayerPerceptron(List<Integer> neuronsInLayers,NeuronProperties neuronProperties) {
    this.createNetwork(neuronsInLayers, neuronProperties);
  }

  /**
   * Creates MultiLayerPerceptron Network architecture - fully connected
   * feed forward with specified number of neurons in each layer
   *
   * @param neuronsInLayers
   *            collection of neuron numbers in getLayersIterator
   * @param neuronProperties
   *            neuron properties
   */
  private void createNetwork(List<Integer> neuronsInLayers, NeuronProperties neuronProperties) {

    // set network type
    this.setNetworkType(NeuralNetworkType.MULTI_LAYER_PERCEPTRON);

                // create input layer
                NeuronProperties inputNeuronProperties = new NeuronProperties(InputNeuron.class, TransferFunctionType.LINEAR);
                Layer layer = LayerFactory.createLayer(neuronsInLayers.get(0), inputNeuronProperties);

                boolean useBias = true; // use bias neurons by default
                if (neuronProperties.hasProperty("useBias")) {
                    useBias = (Boolean)neuronProperties.getProperty("useBias");
                }

                if (useBias) {
                    layer.addNeuron(new BiasNeuron());
                }

                this.addLayer(layer);

    // create layers
    Layer prevLayer = layer;

    //for(Integer neuronsNum : neuronsInLayers)
                for(int layerIdx = 1; layerIdx < neuronsInLayers.size(); layerIdx++){
                        Integer neuronsNum = neuronsInLayers.get(layerIdx);
      // createLayer layer
      layer = LayerFactory.createLayer(neuronsNum, neuronProperties);

                        if ( useBias && (layerIdx< (neuronsInLayers.size()-1)) ) {
                            layer.addNeuron(new BiasNeuron());
                        }

      // add created layer to network
      this.addLayer(layer);
      // createLayer full connectivity between previous and this layer
      if (prevLayer != null)
        ConnectionFactory.fullConnect(prevLayer, layer);

      prevLayer = layer;
    }

    // set input and output cells for network
                  NeuralNetworkFactory.setDefaultIO(this);

                  // set learnng rule
    //this.setLearningRule(new BackPropagation(this));
    this.setLearningRule(new MomentumBackpropagation());
               // this.setLearningRule(new DynamicBackPropagation());
   
    // flatten the network, if desired
    if( Neuroph.getInstance().shouldFlattenNetworks() ) {
      FlatNetworkPlugin.flattenNeuralNetworkNetwork(this);
    }
   
  }

        public void connectInputsToOutputs() {
            // connect first and last layer
            ConnectionFactory.fullConnect( getLayers().get(0), getLayers().get(getLayers().size()-1) , false);
        }

}
TOP

Related Classes of org.neuroph.nnet.MultiLayerPerceptron

TOP
Copyright © 2018 www.massapi.com. All rights reserved.
All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.