protected void SetupNetworkTraining()
{
NetworkSetuped = false;
var training = DataTrainingSet.Buffer.Text.Trim();
if (string.IsNullOrEmpty(training))
{
return;
}
NetworkSetuped = SetupInputData(training);
// Reset Network
Network.Free();
Options.Alpha = Convert.ToDouble(LearningRate.Value, ci) * 1.0e-2;
Options.Epochs = Convert.ToInt32(Epochs.Value, ci);
Options.Inputs = Convert.ToInt32(InputLayerNodes.Value, ci);
Options.Categories = Convert.ToInt32(Categories.Value, ci);
Options.Items = InputData.y;
Options.Nodes = Convert.ToInt32(HiddenLayerNodes.Value, ci);
Options.Tolerance = Convert.ToDouble(Tolerance.Value, ci) * 1.0e-5;
if (UseOptimizer.Active)
{
Network.SetupOptimizer(InputData, OutputData, Options);
}
else
{
Network.Setup(OutputData, Options);
}
DataTrainingSet.Buffer.Text = training;
}
protected void SetupNetworkTraining()
{
NetworkSetuped = false;
var training = DataTrainingSet.Buffer.Text.Trim();
if (string.IsNullOrEmpty(training))
{
return;
}
NetworkSetuped = SetupInputData(training);
// Reset Network
Network.Free();
Options.Alpha = Convert.ToDouble(LearningRate.Value, ci) / 100;
Options.Epochs = Convert.ToInt32(Epochs.Value, ci);
Options.Inputs = Convert.ToInt32(InputLayerNodes.Value, ci);
Options.Categories = Convert.ToInt32(Categories.Value, ci);
Options.Items = InputData.y;
Options.Nodes = Convert.ToInt32(HiddenLayerNodes.Value, ci);
Options.Tolerance = Convert.ToDouble(Tolerance.Value, ci) / 100000;
if (NormalizationData != null && NormalizationData.y > 1)
{
Network.Min = new double[NormalizationData.x];
Network.Max = new double[NormalizationData.x];
for (var i = 0; i < 2; i++)
{
Network.Min[i] = NormalizationData[i, 0];
Network.Max[i] = NormalizationData[i, 1];
}
}
if (UseOptimizer.Active)
{
Network.SetupOptimizer(InputData, OutputData, Options, true);
}
else
{
Network.Setup(OutputData, Options, true);
}
DataTrainingSet.Buffer.Text = training;
}
