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

Class CrossValidationKFold

  • All Implemented Interfaces:
    MLTrain


    public class CrossValidationKFoldextends CrossTraining
    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.
    • Constructor Detail

      • CrossValidationKFold

        public CrossValidationKFold(MLTrain train,                    int k)
        Construct a cross validation trainer.
        Parameters:
        train - The training
        k - The number of folds.
    • Method Detail

      • iteration

        public void iteration()
        Perform one iteration.
      • canContinue

        public final boolean canContinue()
        Returns:
        True if the training can be paused, and later continued.
      • pause

        public final TrainingContinuation pause()
        Pause the training to continue later.
        Returns:
        A training continuation object.
      • resume

        public final void resume(TrainingContinuation state)
        Resume training.
        Parameters:
        state - The training continuation object to use to continue.

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