Examples of org.encog.neural.networks.training.cross.CrossValidationKFold

Package org.encog.neural.networks.training.cross

Examples of org.encog.neural.networks.training.cross.CrossValidationKFold

org.encog.neural.networks.training.cross.CrossValidationKFold
Train using K-Fold cross validation. Each iteration will train a number of times equal to the number of folds - 1. Each of these sub iterations will train all of the data minus the fold. The fold is used to validate. Therefore, you are seeing an error that reflects data that was not always used as part of training. This should give you a better error result based on how the network will perform on non-trained data.(validation). The cross validation trainer must be provided with some other sort of trainer, perhaps RPROP, to actually perform the training. The training data must be the FoldedDataSet. The folded dataset can wrap most other training sets.

  }


  public void train(BasicNetwork network, MLDataSet training) {
    final FoldedDataSet folded = new FoldedDataSet(training); 
    final MLTrain train = new ResilientPropagation(network, folded);
    final CrossValidationKFold trainFolded = new CrossValidationKFold(train,4);


    int epoch = 1;


    do {
      trainFolded.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + trainFolded.getError());
      epoch++;
    } while (trainFolded.getError() > MAX_ERROR);
  }

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    final FoldedDataSet folded = new FoldedDataSet(trainingData);
    return folded;
  }
  
  private MLTrain wrapTrainer(MLDataSet folded, MLTrain train, int foldCount) { 
    final CrossValidationKFold trainFolded = new CrossValidationKFold(train,foldCount);
    return trainFolded;
  }

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    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    
    final FoldedDataSet folded = new FoldedDataSet(trainingData); 
    final MLTrain train = new ResilientPropagation(network, folded);
    final CrossValidationKFold trainFolded = new CrossValidationKFold(train,4);
    
    EncogUtility.trainToError(trainFolded, 0.2);
    
    XOR.verifyXOR((MLRegression)trainFolded.getMethod(), 0.2);
    
  }

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    MLTrain train = factory.create(method, trainingSet, type, args);


    if (this.kfold > 0) {
      train = new CrossValidationKFold(train, this.kfold);
    }


    return train;
  }

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    MLTrain train = factory.create(method, trainingSet, type, args);


    if ( getKfold() > 0) {
      train = new CrossValidationKFold(train, getKfold() );
    }


    return train;
  }

View Full Code Here

    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    
    final FoldedDataSet folded = new FoldedDataSet(trainingData); 
    final MLTrain train = new ResilientPropagation(network, folded);
    final CrossValidationKFold trainFolded = new CrossValidationKFold(train,4);
    
    EncogUtility.trainToError(trainFolded, 0.2);
    
    XOR.verifyXOR((MLRegression)trainFolded.getMethod(), 0.2);
    
  }

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Related Classes of org.encog.neural.networks.training.cross.CrossValidationKFold

org.encog.app.analyst.commands.CmdTrain

org.encog.examples.neural.cross.CrossValidateSunspot

org.encog.neural.networks.training.TestFolded

org.encog.workbench.process.TrainBasicNetwork

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