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());
}