public static bool Test3()
{
var mlp = new MLP(2,2,1);
mlp[0, 0, 0][0] = 1;
mlp[0, 0, 1][0] = 2;
mlp[0, 1, 0][0] = 3;
mlp[0, 1, 1][0] = 4;
mlp[0, 2, 0][0] = 5; //weight of bias to neuron #1 connection in next layer
mlp[0, 2, 1][0] = 6; //bias to neuron #2 in next layer
mlp[1, 0, 0][0] = 7;
mlp[1, 1, 0][0] = 8;
mlp[1, 2, 0][0] = 9; //bias to output
return true;
}
public static bool Test4()
{
var xorinput = new[] {new[]{0.0, 0.0}, new[]{1.0, 0.0}, new[]{0.0, 1.0}, new[]{1.0, 1.0}};
var xoroutput = new[] { new[] { 0.0 }, new[] { 1.0 }, new[] { 1.0 }, new[] { 0.0 } };
var mlp = new MLP(2, 4, 1);
var stsp = new StandardTrainingSetProvider(xorinput, xoroutput);
stsp.Split();
var gdbp = new BackPropagation(mlp, stsp);
((GD)gdbp.Solver).Criterion = LearningCriterion.CrossEntropy;
//((GD)gdbp.Solver).LearningRate = .01;
((GD)gdbp.Solver).AdaGrad = true;
var minerr = double.MaxValue;
gdbp.ReportReady += optimizer =>
{
Console.WriteLine("Epoch = {0}, Error = {1}", optimizer.CurrentEpoch,
optimizer.TrainingSetProvider.TrainError);
minerr = Math.Min(minerr, optimizer.Solver.Error);
if (optimizer.Done)
{
Console.ReadLine();
}
};
gdbp.RunAsync().Wait();
return true;
}
