private void button2_Click(object sender, System.EventArgs e)
{
try
{
if(of.ShowDialog() == DialogResult.OK && of.FileName != "")
{
network = BackPropNet.BinaryLoad(of.FileName);
rtb1.Text = "Network: "+network.GetStructure();
}
patterns = null;
targets = null;
}
catch(System.Exception ex)
{
MessageBox.Show(ex.Message, "Exception", MessageBoxButtons.OK, MessageBoxIcon.Error);
network = null;
}
//
}
private void bStart_Click(object sender, System.EventArgs e)
{
//
System.IO.StreamWriter sw = new System.IO.StreamWriter(results);
sw.AutoFlush = true;
keepTraining = true;
//
int numberOfTimesToTrain = Convert.ToInt32(cbTrainTimes.SelectedItem);
//
uint inputSize = Convert.ToUInt32(cbInputNeurons.SelectedItem);
uint outputSize = Convert.ToUInt32(cbOutputNeurons.SelectedItem);
//
uint minNeurons = Convert.ToUInt32(cbMinHiddenNeurons.SelectedItem);
uint maxNeurons = Convert.ToUInt32(cbMaxHiddenNeurons.SelectedItem);
//
tbResults.Text = "Hidden Neurons ---- Mean Train Error --- Mean Validation Error ------ Mean Test Error";
uint n = minNeurons-1;
while(keepTraining)
{
n++;
if(n > maxNeurons)
break;
//
double meanTrError = 0;
double meanVaError = 0;
double meanTeError = 0;
//
Application.DoEvents();
for(int i=0; i<numberOfTimesToTrain; i++)
{
nnet = new BackPropNet(inputSize, outputSize, n);
//
if(cbHiddenActFunction.SelectedItem.ToString() != "LogSig")
{
nnet.SetHiddenActivationFunction(ActivationFunction.TanSig);
}
//
if(cbOutputActFunction.SelectedItem.ToString() == "TanSig")
nnet.SetOutputActivationFunction(ActivationFunction.TanSig);
else if(cbOutputActFunction.SelectedItem.ToString() == "Linear")
nnet.SetOutputActivationFunction(ActivationFunction.Linear);
//
nnet.Randomize(-0.2, 0.2);
//
if(cbAlgorithm.Text == "RProp")
{
algorithm = new RProp(nnet, trainingInputs, trainingTargets);
}
else
{
algorithm = new SimpleBPM(nnet, trainingInputs, trainingTargets);
((SimpleBPM)algorithm).Momentum = Convert.ToDouble(this.tbMomentum.Text);
((SimpleBPM)algorithm).LearnRate = Convert.ToDouble(this.tbLearnRate.Text);
}
//
algorithm.Epochs = Convert.ToUInt32(this.tbEpochs.Text);
algorithm.MaxFailures = Convert.ToUInt32(this.tbMaxFails.Text);
//
if(valIsOk)
algorithm.SetValidationData(validationInputs, validationTargets);
//
if(cbAlgorithm.Text == "RProp")
{
((RProp)algorithm).Train();
}
else
{
((SimpleBPM)algorithm).Train();
}
//
double trError = algorithm.TrainingPerformance;
double vaError = Double.NaN;
double teError = Double.NaN;
//
if(valIsOk)
{
vaError = algorithm.ValidationPerformance;
meanVaError += vaError;
}
//
if(testIsOk)
{
teError = nnet.Simulate(new NeuralLib.DataSet(testInputs), new NeuralLib.DataSet(testTargets));
meanTeError += teError;
}
//
meanTrError += trError;
Application.DoEvents();
}
//
meanTrError = meanTrError/numberOfTimesToTrain;
//
if(valIsOk)
meanVaError = meanVaError/numberOfTimesToTrain;
//
if(testIsOk)
meanTeError = meanTeError/numberOfTimesToTrain;
//
tbResults.AppendText("\n"+"\t"+n.ToString()+"\t\t"+meanTrError.ToString("E5")+"\t\t"+
meanVaError.ToString("E6")+"\t\t"+meanTeError.ToString("E6"));
//
sw.WriteLine(nnet.GetStructure()+"\t\t"+meanTrError.ToString("E5")+"\t\t"+
meanVaError.ToString("E6")+"\t\t"+meanTeError.ToString("E6"));
Application.DoEvents();
}
//
if(cbUseSecondLayer.Checked == true)
{
uint k = 2;
while(keepTraining)
{
k++;
if(k > n)
break;
//
double meanTrError = 0;
double meanVaError = 0;
double meanTeError = 0;
//
Application.DoEvents();
for(int i=0; i<numberOfTimesToTrain; i++)
{
nnet = new BackPropNet(inputSize, outputSize, n-1, k-1);
//
if(cbHiddenActFunction.SelectedItem.ToString() != "LogSig")
{
nnet.SetHiddenActivationFunction(ActivationFunction.TanSig);
}
//
if(cbOutputActFunction.SelectedItem.ToString() == "TanSig")
nnet.SetOutputActivationFunction(ActivationFunction.TanSig);
else if(cbOutputActFunction.SelectedItem.ToString() == "Linear")
nnet.SetOutputActivationFunction(ActivationFunction.Linear);
//
nnet.Randomize(-0.2, 0.2);
//
if(cbAlgorithm.Text == "RProp")
{
algorithm = new RProp(nnet, trainingInputs, trainingTargets);
}
else
{
algorithm = new SimpleBPM(nnet, trainingInputs, trainingTargets);
((SimpleBPM)algorithm).Momentum = Convert.ToDouble(this.tbMomentum.Text);
((SimpleBPM)algorithm).LearnRate = Convert.ToDouble(this.tbLearnRate.Text);
}
//
algorithm.Epochs = Convert.ToUInt32(this.tbEpochs.Text);
algorithm.MaxFailures = Convert.ToUInt32(this.tbMaxFails.Text);
//
if(valIsOk)
algorithm.SetValidationData(validationInputs, validationTargets);
//
if(cbAlgorithm.Text == "RProp")
{
((RProp)algorithm).Train();
}
else
{
((SimpleBPM)algorithm).Train();
}
//
double trError = algorithm.TrainingPerformance;
double vaError = Double.NaN;
double teError = Double.NaN;
//
if(valIsOk)
{
vaError = algorithm.ValidationPerformance;
meanVaError += vaError;
}
//
if(testIsOk)
{
teError = nnet.Simulate(new NeuralLib.DataSet(testInputs), new NeuralLib.DataSet(testTargets));
meanTeError += teError;
}
//
meanTrError += trError;
Application.DoEvents();
}
//
meanTrError = meanTrError/numberOfTimesToTrain;
//
if(valIsOk)
meanVaError = meanVaError/numberOfTimesToTrain;
//
if(testIsOk)
meanTeError = meanTeError/numberOfTimesToTrain;
//
tbResults.AppendText("\n"+"\t"+nnet.GetStructure()+"\t\t"+meanTrError.ToString("E5")+"\t\t"+
meanVaError.ToString("E6")+"\t\t"+meanTeError.ToString("E6"));
//
sw.WriteLine(nnet.GetStructure()+"\t\t"+meanTrError.ToString("E5")+"\t\t"+
meanVaError.ToString("E6")+"\t\t"+meanTeError.ToString("E6"));
Application.DoEvents();
}
}
sw.Flush();
sw.Close();
}
