/// <summary> /// Construct a cross trainer. /// </summary> /// <param name="network">The network.</param> /// <param name="training">The training data.</param> public CrossTraining(BasicNetwork network, FoldedDataSet training) { this.network = network; Training = training; this.folded = training; }
public void TestRPROPFolded() { IMLDataSet trainingData = XOR.CreateNoisyXORDataSet(10); BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained(); var folded = new FoldedDataSet(trainingData); IMLTrain train = new ResilientPropagation(network, folded); var trainFolded = new CrossValidationKFold(train, 4); EncogUtility.TrainToError(trainFolded, 0.2); XOR.VerifyXOR((IMLRegression)trainFolded.Method, 0.2); }
/// <summary> /// Obtain the training set. /// </summary> /// <returns>The training set.</returns> private IMLDataSet ObtainTrainingSet() { String trainingID = Prop.GetPropertyString( ScriptProperties.MlConfigTrainingFile); FileInfo trainingFile = Script.ResolveFilename(trainingID); IMLDataSet trainingSet = EncogUtility.LoadEGB2Memory(trainingFile); if (_kfold > 0) { trainingSet = new FoldedDataSet(trainingSet); } return(trainingSet); }
public NeuralNetworkModel(VersatileMLDataSet dataset) { dataset.NormHelper.NormStrategy = new BasicNormalizationStrategy(0, 1, 0, 1); dataset.Normalize(); var inputs = dataset.NormHelper.InputColumns.Count; var outputs = dataset.NormHelper.OutputColumns.Count; var hiddens = (inputs + outputs) * 1.5; var method = (BasicNetwork) new MLMethodFactory().Create( MLMethodFactory.TypeFeedforward, $"?:B->SIGMOID->{hiddens}:B->SIGMOID->?", inputs, outputs); var folds = new FoldedDataSet(dataset); folds.Fold(5); var propTrainer = new ResilientPropagation(method, folds); _kfoldTrainer = new CrossValidationKFold(propTrainer, 5); }
/// <summary> /// Construct a cross trainer. /// </summary> /// /// <param name="network">The network.</param> /// <param name="training">The training data.</param> protected CrossTraining(IMLMethod network, FoldedDataSet training) : base(TrainingImplementationType.Iterative) { _network = network; Training = training; _folded = training; }