Examples of org.encog.neural.networks.training.lma.LevenbergMarquardtTraining

Package org.encog.neural.networks.training.lma

Examples of org.encog.neural.networks.training.lma.LevenbergMarquardtTraining

org.encog.neural.networks.training.lma.LevenbergMarquardtTraining
kipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm The LMA training technique has some important limitations that you should be aware of, before using it. Only neural networks that have a single output neuron can be used with this training technique. The entire training set must be loaded into memory. Because of this an Indexable training set must be used. However, despite these limitations, the LMA training technique can be a very effective training method. References: - http://www-alg.ist.hokudai.ac.jp/~jan/alpha.pdf - http://www.inference.phy.cam.ac.uk/mackay/Bayes_FAQ.html ---------------------------------------------------------------- This implementation of the Levenberg Marquardt algorithm is based heavily on code published in an article by Cesar Roberto de Souza. The original article can be found here: http://crsouza.blogspot.com/2009/11/neural-network-learning-by-levenberg_18.html Portions of this class are under the following copyright/license. Copyright 2009 by Cesar Roberto de Souza, Released under the LGPL.

  public void testLMA() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    MLTrain rprop = new LevenbergMarquardtTraining(network, trainingData);
    NetworkUtil.testTraining(rprop,0.03);
  }

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  private void performLMA(ProjectEGFile file, MLDataSet trainingData) {
    InputLMA dialog = new InputLMA();


    if (dialog.process()) {
      LevenbergMarquardtTraining train = new LevenbergMarquardtTraining(
          (BasicNetwork) file.getObject(), trainingData);
      startup(file, train, dialog.getMaxError().getValue() / 100.0);
    }
  }

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    }


    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);


    final LevenbergMarquardtTraining result 
      = new LevenbergMarquardtTraining(
        (BasicNetwork) method, training);
    return result;
  }

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      throw new EncogError(
          "LMA training cannot be used on a method of type: "
              + method.getClass().getName());
    }


    final LevenbergMarquardtTraining result 
      = new LevenbergMarquardtTraining(
        (BasicNetwork) method, training);
    return result;
  }

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  public void testLMA() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    MLTrain rprop = new LevenbergMarquardtTraining(network, trainingData);
    NetworkUtil.testTraining(trainingData,rprop,0.03);
  }

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public class LevenbergMarquardtFactory implements EnsembleTrainFactory {


  @Override
  public MLTrain getTraining(MLMethod mlMethod, MLDataSet trainingData) {
    return (MLTrain) new LevenbergMarquardtTraining((BasicNetwork) mlMethod, trainingData);
  }

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org.encog.mathutil.matrices.hessian.HessianCR

org.encog.mathutil.matrices.Matrix

org.encog.ml.data.basic.BasicMLData

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org.encog.ml.factory.train.LMAFactory

org.encog.neural.networks.training.TestTraining

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