static void Main(string[] args)
{
var mlp = new VectorizedMatrixMLP();
mlp.InputValue = new double[, ] {
{ 1, 0 },
{ 0, 0 },
{ 0, 1 },
{ 1, 1 }
};
mlp.TargetValue = new double[, ] {
{ 1 },
{ 0 },
{ 1 },
{ 0 }
};
mlp.activFct = new ActivationFunction.SigmoidFunction();
mlp.nbIterations = 10000; // Sigmoid: works
//mlp.activFct = new ActivationFunction.HyperbolicTangentFunction();
//mlp.nbIterations = 5000; // Hyperbolic tangent: works
//mlp.activFct = new ActivationFunction.ELUFunction();
//mlp.nbIterations = 1000; // ELU: works fine
//mlp.activFct = new ActivationFunction.GaussianFunction();
//mlp.nbIterations = 1000; // Gaussian: works fine
//mlp.activFct = new ActivationFunction.SinusFunction();
//mlp.nbIterations = 500; // Sinus: works fine
//mlp.activFct = new ActivationFunction.ArcTangentFunction();
//mlp.nbIterations = 1000; // ArcTangent: works fine
//mlp.activFct = new ActivationFunction.ReluFunction();
//mlp.nbIterations = 100000; // ReLU: Does not work yet, but this next one yes:
//mlp.activFct = new ActivationFunction.ReLUSigmoidFunction();
//mlp.nbIterations = 5000; // ReLUSigmoid: works fine
mlp.lambdaFct = (x) => mlp.activFct.Activation(x, gain: 1, center: 0);
mlp.lambdaFctD = (x) => mlp.activFct.Derivative(x, gain: 1, center: 0);
mlp.activFctIsNonLinear = mlp.activFct.IsNonLinear();
//int[] NeuronCount = new int[] { 2, 3, 3, 1 };
int[] NeuronCount = new int[] { 2, 2, 1 };
mlp.LearningRate = 0.1;
mlp.InitStruct(NeuronCount, addBiasColumn: true);
mlp.Randomize();
mlp.Train();
Console.WriteLine("Press a key to quit.");
Console.Read();
}
public void VectorizedMatrixMLPXORSigmoid()
{
m_mlp.nbIterations = 30000; // Sigmoid: works
m_mlp.activFct = new ActivationFunction.SigmoidFunction();
m_mlp.lambdaFct = x => m_mlp.activFct.Activation(x, gain: 1, center: 0);
m_mlp.lambdaFctD = x => m_mlp.activFct.Derivative(x, gain: 1, center: 0);
m_mlp.activFctIsNonLinear = m_mlp.activFct.IsNonLinear();
m_mlp.InitStruct(NeuronCount, addBiasColumn: true);
m_mlp.W[0] = new double[, ] {
{ -0.5, -0.78, -0.07 },
{ 0.54, 0.32, -0.13 },
{ -0.29, 0.89, -0.8 }
};
m_mlp.W[1] = new double[, ] {
{ 0.28 },
{ -0.94 },
{ -0.5 },
{ -0.36 }
};
m_mlp.Train();
var expectedOutput = new double[, ] {
{ 0.97 },
{ 0.02 },
{ 0.97 },
{ 0.02 }
};
var output = m_mlp.output.ToString();
Matrix expectedMatrix = expectedOutput; // double(,) -> Matrix
var expectedOutputString = expectedMatrix.ToString();
Assert.AreEqual(output, expectedOutputString);
var expectedLoss = 0.1;
float[] targetArray = m_mlp.TargetValue.ToArray();
var loss = m_mlp.ComputeAverageError(targetArray);
var lossRounded = Math.Round(loss, 2);
Assert.AreEqual(expectedLoss, lossRounded);
}
