public void CreateResilientPropagationAlgorithm(AnnBuild annComp, ResilientPropagation rprop) { errorTarget = annComp.ErrorTarget; network.InstantiateResilientPropagationAlgorithm( errorTarget, annComp.MaxEpochs, rprop.MaxUpdateValue, rprop.MinUpdateValue, rprop.GrowthFactor, rprop.DecreaseFactor); network.BuildStatsListener(); }
public void CreateBackPropagationTrainingAlgorithm(AnnBuild annComp, BackPropagation prop) { errorTarget = annComp.ErrorTarget; network.InstantiateBackPropagationAlgorithm( errorTarget, annComp.MaxEpochs, prop.LearningRate, prop.Beta); network.BuildStatsListener(); }
public void SetData(AnnBuild obj) { this.Bias = obj.Bias; this.ErrorTarget = obj.ErrorTarget; this.HiddenLayers = obj.HiddenLayers; this.HiddenUnits = obj.HiddenUnits; this.InputUnits = obj.InputUnits; this.LearningMode = obj.LearningMode; this.MaxEpochs = obj.MaxEpochs; this.ActivationNeuroMode = obj.ActivationNeuroMode; this.OutputUnits = obj.OutputUnits; this.SynInitMode = obj.SynInitMode; }
public void Build(AnnBuild AnnBuilding) { if (network != null) { network.Destroy(); } network = new Mlp( AnnBuilding.InputUnits, AnnBuilding.HiddenUnits, AnnBuilding.HiddenLayers, AnnBuilding.OutputUnits, AnnBuilding.Bias, AnnBuilding.ActivationNeuroMode ); synInitMode = AnnBuilding.SynInitMode; network.Instantiate(); status = NetworkStatus.UNTRAINED; logger.InfoFormat("Building neural network classifier: {0}", this.ToString()); }