public Backpropagation(Perceptron perceptron, Data.LearningParam param)
{
this.perceptron = perceptron;
this.param = param;
layers = perceptron.Size;
int n = perceptron.MaxLayerSize;
w = new double[layers, n, n];
w1 = new double[layers, n, n];
endthread = true;
}
private void _learnButton_Click(object sender, EventArgs e)
{
Data.LearningParam param = new Data.LearningParam();
String inputName = this._comboInputLearnData.Text,
outputName;
outputName = this._comboOutputLearnData.Text;
this._tboxError.Text = "";
try
{
if (((List<double[]>)data[inputName])[0].Length != perceptron.getLayer(0).Size)
throw new Exception("Wrong input data size");
if (((List<double[]>)data[outputName])[0].Length != perceptron.getLayer(perceptron.Size-1).Size)
throw new Exception("Wrong output data size");
}
catch(Exception ex)
{
MessageBox.Show(ex.Message, "Warning");
return;
}
try
{
//check if size of input and output data is proper
param.Input = (List<double[]>)data[inputName];
param.Output = (List<double[]>)data[outputName];
param.Alpha = Double.Parse(this._tboxAlpha.Text);
param.Epsilon = Double.Parse(this._tboxEpsilon.Text);
if (this._radioDiffTeta.Checked == true)
param.OneTeta = false;
else
param.OneTeta = true;
param.Teta = Double.Parse(this._tboxTeta.Text);
if (this._radioEarlyStop.Checked == true)
{
param.EarlyStop = true;
param.LearnSet = double.Parse(this._tboxLearnSet.Text);
param.ValidateSet = double.Parse(this._tboxValidateSet.Text);
param.TestSet = double.Parse(this._tboxTestSet.Text);
}
else
{
param.EarlyStop = true;
param.KFoldSamples = int.Parse(this._tboxKFold.Text);
}
backprop.Param = param;
backprop.TBoxError = this._tboxError;
backprop.StartButton= this._buttonLearn;
backprop.ContinueButton = this._buttonContinueLearn;
backprop.StopButton = this._buttonStopLearn;
backprop.StartLearn();
}
catch(Exception ex)
{
Console.Out.WriteLine(ex.Message);
MessageBox.Show("Some learning problem has occured", "Warning");
}
}
