private TrainStats ExecuteSingleIteration(ArchitectureTestConfig config)
{
var neuralNetworkBuilder = new NeuralNetworkBuilder()
.SetActivationFunction(new SigmoidActivationFunction())
.SetErrorFunction(new MeanSquaredErrorFunction(1))
.SetNumberOfInputNeurons(2)
.SetNumberOfHiddenNeurons(config.NumberOfHiddenNeurons)
.SetNumberOfOutputNeurons(1);
if (config.EnableBias)
{
neuralNetworkBuilder.AddBiasConnections();
}
var network = neuralNetworkBuilder.Build();
var trainer = new Trainer(neuralNetwork: network, learningRate: 0.01, logger: new DummyLogger());
var trainDataCollection = new[]
{
new TrainData(new double [] { 0, 0 }, new double [] { 1 }),
new TrainData(new double [] { 1, 0 }, new double [] { 0 }),
new TrainData(new double [] { 0, 1 }, new double [] { 0 }),
new TrainData(new double [] { 1, 1 }, new double [] { 1 }),
};
var trainStats = trainer.Train(trainDataCollection, numberOfEpochs: _maxNumberOfEpochs, terminalEpochError: _terminalEpochError);
return(trainStats);
}
public static trainerParams BuildNet(FlowLayoutPanel layers, FileDialog LoadData_dlg, Result r, Telerik.WinControls.UI.RadDiagram radDiagram1)
{
Training.Trainer trainer = new Training.Trainer();
NeuralNetworkBuilder b = new NeuralNetworkBuilder();
L_ctrl_mat temp;
int neuronsnumber;
IActivatorFunction AF = null;
FunctionApplier functionApplier = new FunctionApplier();
foreach (var layer in layers.Controls)
{
temp = layer as L_ctrl_mat;
neuronsnumber = Convert.ToInt16(temp.NN_drpdn.Value);
if (ActivatorFunctions.FunctionName.SIGMOID.ToString() == temp.AF_drpdn.SelectedItem.Text)
{
AF = new SigmoidFunction();
imgs.Add(Resources.Layer__Sigmoid);
}
if (ActivatorFunctions.FunctionName.TANH.ToString() == temp.AF_drpdn.SelectedItem.Text)
{
AF = new TanhFunction();
imgs.Add(Resources.Layer_Tan_H);
}
functionApplier.ActivatorFunction = AF;
b.Layer(neuronsnumber, functionApplier, (double)temp.Lr_drpdn.Value);
}
NeuralNetwork.NeuralNetwork nn = b.Build();
string FileName = LoadData_dlg.FileName;
var tuples = DataReader.DataReader.Instance.ReadFromFile(FileName);
var inputs = tuples.Item1;
var outputs = tuples.Item2;
// StaticDivider Divider = new StaticDivider(.6,.3);
//var temp2 = Divider.Divide(inputs, outputs);
//ActivatorFunctions.FunctionName applier ;
//// test case (should belong to the second class = [0 1 0])
//var tt = nn.ForwardInput(Vector<double>.Build.DenseOfArray(new double[] { 5.5, 2.5, 4.0, 1.3 }));
Params = new trainerParams();
Params.nn = nn;
Params.Tuple = tuples;
NetGraph(nn, radDiagram1);
return(Params);
}