public NetworkTrainer(BackPropagationNetwork network, DataPointCollection dataSet) { Nudge = true; NudgeTolerance = 0.0001; NudgeScale = 0.4; NudgeWindow = 100; TargetError = 0.001; MaxIterations = 100000; TrainingRate = 0.15; Momentum = 0.10; Network = network; DataSet = dataSet; Initialize(); }
public BasicNeuralNetworkTrainerWrapper(DataPointCollection dataSet) { DataSet = dataSet; int[] layerSizes = new int[10] { DataSet.DataPointBound, 5, 5, 5, 5, 5, 5, 5, 3, 1 }; TransferFunction[] transferFunctions = new TransferFunction[10] { TransferFunction.None, TransferFunction.RationalSigmoid, TransferFunction.RationalSigmoid, TransferFunction.Sigmoid, TransferFunction.Sigmoid, TransferFunction.Sigmoid, TransferFunction.Sigmoid, TransferFunction.Gaussian, TransferFunction.Gaussian, TransferFunction.Linear }; Network.Initialize(layerSizes, transferFunctions); _network = new BackPropagationNetwork(layerSizes, transferFunctions); _networkTrainer = new SimpleNetworkTrainer(_network, DataSet); }
public void Initialize(int[] layerSizes, TransferFunction[] layerFunctions) { _network = new BackPropagationNetwork(layerSizes, layerFunctions); }
public SimpleNetworkTrainer(BackPropagationNetwork network, DataPointCollection dataSet) : base(network, dataSet) { }
public AutoRegressiveTrainer(BackPropagationNetwork network, DataPointCollection dataSet) : base(network, dataSet) { lastOutput = new double[dataSet.Count]; }