static void Main(string[] args)
{
FullyConnectedNetwork net = new FullyConnectedNetwork(2,
2, 2, 1);
BackPropagationGradiant teacher = new BackPropagationGradiant(net);
Sample sample1 = new Sample()
{
Inputs = new double[] { 0, 0 },
Outputs = new double[] { 1 },
};
Sample sample2 = new Sample()
{
Inputs = new double[] { 1, 0 },
Outputs = new double[] { -1 },
};
Sample sample3 = new Sample()
{
Inputs = new double[] { 0, 1 },
Outputs = new double[] { -1 },
};
Sample sample4 = new Sample()
{
Inputs = new double[] { 1, 1 },
Outputs = new double[] { 1 },
};
teacher.AddSample(sample1);
teacher.AddSample(sample2);
teacher.AddSample(sample3);
teacher.AddSample(sample4);
Console.WriteLine("*************");
Console.WriteLine("Test XOR gate");
Console.WriteLine("*************");
while (true)
{
for (int x = 0; x < 2; x++)
{
for (int i = 0; i < 20; i++)
{
teacher.LearnOneLoopBase();
}
Console.WriteLine("3 learnings, mean error: " + teacher.MeanQuadraticError);
Console.WriteLine("Ouputs, with {0,1}");
DisplayVector(net.ComputeOutputs(sample2.Inputs));
Console.WriteLine("Ouputs, with {1,1}");
DisplayVector(net.ComputeOutputs(sample4.Inputs));
Console.WriteLine(Environment.NewLine);
}
DisplayNeuronalNetwork(net);
Console.WriteLine("done.");
Console.ReadLine();
Console.Clear();
}
}
public void LearnSample()
{
try
{
FullyConnectedNetwork net = new FullyConnectedNetwork(2,
2, 2, 1);
BackPropagationGradiant teacher = new BackPropagationGradiant(net);
double[] ZeroInput = new double[] { 0, 0 };
double[] OutputsZeroInput = new double[] { 0 };
net.ComputeOutputs(ZeroInput);
Assert.AreEqual(0, teacher.ComputeQuadraticError(OutputsZeroInput)); //should equals because 0
Sample sample1 = new Sample()
{
Inputs = ZeroInput,
Outputs = OutputsZeroInput
};
teacher.LearnOneSample(sample1);
double[] OnesInput = new double[] { 1, 1 };
double[] OutputsOnesInput = new double[] { 1 };
net.ComputeOutputs(OnesInput);
double errorNoLearn = teacher.ComputeQuadraticError(OutputsOnesInput);
Assert.AreNotEqual(0, errorNoLearn);
Sample sample2 = new Sample()
{
Inputs = OnesInput,
Outputs = OutputsOnesInput
};
teacher.LearnOneSample(sample2);
net.ComputeOutputs(OnesInput);
double errorLearn = teacher.ComputeQuadraticError(OutputsOnesInput);
Assert.IsTrue(errorLearn < errorNoLearn);
}
catch (Exception ex)
{
Assert.Fail(ex.ToString());
}
}
