Package org.encog.examples.neural.benchmark

Source Code of org.encog.examples.neural.benchmark.FahlmanEncoder

package org.encog.examples.neural.benchmark;

import org.encog.ml.MLMethod;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.networks.ContainsFlat;
import org.encog.neural.networks.training.propagation.Propagation;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.EngineArray;
import org.encog.util.simple.EncogUtility;

/**
* This example implements a Fahlman Encoder.  Though probably not invented by Scott
* Fahlman, such encoders were used in many of his papers, particularly:
*
* "An Empirical Study of Learning Speed in Backpropagation Networks"
* (Fahlman,1988)
*
* It provides a very simple way of evaluating classification neural networks.
*   Basically, the input and output neurons are the same in count.  However,
*   there is a smaller number of hidden neurons.  This forces the neural
*   network to learn to encode the patterns from the input neurons to a
*   smaller vector size, only to be expanded again to the outputs.
*
* The training data is exactly the size of the input/output neuron count. 
* Each training element will have a single column set to 1 and all other
* columns set to zero.  You can also perform in "complement mode", where
* the opposite is true.  In "complement mode" all columns are set to 1,
* except for one column that is 0.  The data produced in "complement mode"
* is more difficult to train.
*
* Fahlman used this simple training data to benchmark neural networks when
* he introduced the Quickprop algorithm in the above paper.
*
*/
public class FahlmanEncoder {
  public static final int INPUT_OUTPUT_COUNT = 10;
  public static final int HIDDEN_COUNT = 5;
  public static final int TRIES = 2500;
  public static final boolean COMPL = false;

  public static MLDataSet generateTraining(int inputCount, boolean compl) {
    double[][] input = new double[INPUT_OUTPUT_COUNT][INPUT_OUTPUT_COUNT];
    double[][] ideal = new double[INPUT_OUTPUT_COUNT][INPUT_OUTPUT_COUNT];

    for (int i = 0; i < inputCount; i++) {
      for (int j = 0; j < inputCount; j++) {
        if (compl) {
          input[i][j] = (j == i) ? 0.0 : 1.0;
        } else {
          input[i][j] = (j == i) ? 1.0 : 0.0;
        }

        ideal[i][j] = input[i][j];
      }
    }

    return new BasicMLDataSet(input, ideal);
  }
 
  public static void evaluate() {
    int[] count = new int[TRIES];
   
    MLDataSet trainingData = generateTraining(INPUT_OUTPUT_COUNT, COMPL);
   
    for(int i=0;i<TRIES;i++) {
   
      MLMethod method = EncogUtility.simpleFeedForward(INPUT_OUTPUT_COUNT,
          HIDDEN_COUNT, 0, INPUT_OUTPUT_COUNT, false);
     
      Propagation train = new Backpropagation((ContainsFlat)method, trainingData,1.7,0);
      //Propagation train = new ResilientPropagation((ContainsFlat)method, trainingData);
      ((Propagation)train).fixFlatSpot(true);
     
      int iteration = 0;
      do {
        train.iteration();
       
        iteration++;
      } while( train.getError()>0.01 );
      count[i] = iteration;
      System.out.println("Begin Try #" + (i+1) + ", took " + iteration + " iterations.");     
    }
   
    System.out.println("Tries: " + TRIES);
    System.out.println("Max Iterations: " +EngineArray.max(count));
    System.out.println("Min Iterations: " +EngineArray.min(count));
    System.out.println("Mean Iterations: " +EngineArray.mean(count));
    System.out.println("SDev Iterations: " +EngineArray.sdev(count));
  }

  public static void main(String[] args) {   
    evaluate();
  }
}
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