private static void Main(string[] args)
{
Console.Out.WriteLine(" BACKPROPAGATION neural network demo.");
Console.Out.WriteLine("");
Console.Out.WriteLine(" Copyright(C) XP Idea.com 2001-2004 ");
Console.Out.WriteLine("");
Console.Out.WriteLine("The purpose of this demo is to show learning abilities of BACKPROP network.");
Console.Out.WriteLine("The BACKPROP network is able to learn much more complex data patterns, than");
Console.Out.WriteLine("Adaline network (please see OCR demo application). ");
Console.Out.WriteLine("This example simple shows that the Backprop network is able to learn ");
Console.Out.WriteLine("an 'exclusive OR' (XOR) operation, but the Adaline network is not able to do so.");
Console.Out.WriteLine("");
Console.Out.WriteLine(" false XOR false = false");
Console.Out.WriteLine(" true XOR false = true");
Console.Out.WriteLine(" false XOR true = true");
Console.Out.WriteLine(" true XOR true = false");
Console.Out.WriteLine("");
Console.Out.WriteLine(" As result of the training, the network will produce output ‘0’");
Console.Out.WriteLine("corresponding to logical ‘false’ or ‘1’ corresponding to logical ‘true’ value.");
Console.Out.WriteLine("");
Console.Out.WriteLine("PLEASE HIT ENTER TO CONTINUE");
Console.In.ReadLine();
Console.Out.WriteLine("");
Console.Out.WriteLine("During this demo you will be prompted to enter input values");
Console.Out.WriteLine("for the network. Then network will perform “XOR” operation on ");
Console.Out.WriteLine("the entered values and result will be displayed to you. ");
Console.Out.WriteLine("Please enter any values in range from 0 to 1 and hit [ENTER] when prompted. ");
Console.Out.WriteLine("");
Console.Out.WriteLine("NOW THE NETWORK IS READY TO LEARN FOLLOWING PATTERNS");
Console.Out.WriteLine("");
Console.Out.WriteLine(" false XOR false = false;");
Console.Out.WriteLine(" true XOR false = true;");
Console.Out.WriteLine(" false XOR true = true;");
Console.Out.WriteLine(" true XOR true = false;");
Console.Out.WriteLine("PLEASE HIT ENTER TO BEGIN TRAINING");
Console.In.ReadLine();
Console.Out.Write("TRAINING....");
double d;
BackPropagationNetwork BackPropNet;
var patterns = new PatternsCollection(TrainingSets, 2, 1);
SetPattern(patterns[0], 0, 0, 0);
SetPattern(patterns[1], 0, 1, 1);
SetPattern(patterns[2], 1, 0, 1);
SetPattern(patterns[3], 1, 1, 0);
//Network(0.55,0.6,
BackPropNet = new BackPropagationNetwork(0.55, 0.6, new int[3] {
2, 3, 1
});
BackPropNet.Train(patterns);
Console.Out.WriteLine("DONE!");
Console.Out.WriteLine("");
//BackPropNet.SaveToFile("test.net");
while (true)
{
try
{
Console.Out.Write("Enter 1st value: ");
d = double.Parse(Console.In.ReadLine());
BackPropNet.InputNode(0).Value = d;
Console.Out.Write("Enter 2nd value: ");
d = double.Parse(Console.In.ReadLine());
BackPropNet.InputNode(1).Value = d;
BackPropNet.Run();
Console.Out.WriteLine("Result: " + Math.Round(BackPropNet.OutputNode(0).Value));
Console.Out.WriteLine("");
}
catch
{
return;
}
}
}
