public static void MainFunc()//string[] args)
{
Console.WriteLine("\nBegin deep net training demo \n");
int numInput = 4;
int[] numHidden = new int[] { 10, 10, 10 };
int numOutput = 3;
int numDataItems = 2000;
Console.WriteLine("Generating " + numDataItems + " artificial training data items ");
double[][] trainData = MakeData(numDataItems, numInput, numHidden, numOutput, 5);
Console.WriteLine("\nDone. Training data is: ");
ShowMatrix(trainData, 3, 2, true);
Console.WriteLine("\nCreating a 4-(10,10,10)-3 deep neural network (tanh & softmax) \n");
DeepNet dn = new DeepNet(numInput, numHidden, numOutput);
//dn.Dump();
int maxEpochs = 2000;
double learnRate = 0.001;
double momentum = 0.01;
Console.WriteLine("Setting maxEpochs = " + maxEpochs);
Console.WriteLine("Setting learnRate = " + learnRate.ToString("F3"));
Console.WriteLine("Setting momentumm = " + momentum.ToString("F3"));
Console.WriteLine("\nStart training using back-prop with mean squared error \n");
double[] wts = dn.Train(trainData, maxEpochs, learnRate, momentum, 10); // show error every maxEpochs / 10
Console.WriteLine("Training complete \n");
double trainError = dn.Error(trainData, false);
double trainAcc = dn.Accuracy(trainData, false);
Console.WriteLine("Final model MS error = " + trainError.ToString("F4"));
Console.WriteLine("Final model accuracy = " + trainAcc.ToString("F4"));
Console.WriteLine("\nEnd demo ");
Console.ReadLine();
} // Main
