public void TestForward()
{
var parameterGenerator = new ActionParameterGenerator(() => 0.1, () => 1.0);
var neuronFactor = new NeuronFactory(parameterGenerator);
var synapseFactory = new SynapseFactory(parameterGenerator);
var n = NetworkFactory.CreateMultilayerPerceptron(new[] { 2, 2, 1 }, ActivationFunction.Sigmoid,
ActivationFunction.Identity, null, neuronFactor, synapseFactory);
var x = new Vector(3, 5);
Assert.IsTrue(Math.Abs(n.Compute(x)[0] - 2.0993931) < 0.001);
}
public void TestBack()
{
var parameterGenerator = new ActionParameterGenerator(() => 0.1, () => 1.0);
var neuronFactor = new NeuronFactory(parameterGenerator);
var synapseFactory = new SynapseFactory(parameterGenerator);
var network = NetworkFactory.CreateMultilayerPerceptron(new[] { 2, 2, 1, 1 }, ActivationFunction.Sigmoid,
ActivationFunction.Identity, null, neuronFactor, synapseFactory);
var networkTrainer = new NetworkTrainer(network);
var x = new Vector(3, 5);
network.Compute(x);
networkTrainer.Back(42.0);
var n = network.OutputLayer[0].Inputs[0].Source;
Console.WriteLine(n.InputDerivative);
Console.WriteLine(n.OutputDerivative);
Console.WriteLine(n.InputDerivativeCount);
Assert.IsTrue(Math.Abs(-3.987764 - n.InputDerivative) < 0.01);
Assert.IsTrue(Math.Abs(-41.009155 - n.OutputDerivative) < 0.01);
Assert.AreEqual(1, n.InputDerivativeCount);
}
public void TestTrainXor()
{
var pg = new PositiveUniformParameterGenerator();
var neuronFactor = new NeuronFactory(pg);
var synapseFactory = new SynapseFactory(pg);
var n = NetworkFactory.CreateMultilayerPerceptron(new[] { 2, 2, 1 }, ActivationFunction.Sigmoid,
ActivationFunction.Identity, null, neuronFactor, synapseFactory);
var trainer = new NetworkTrainer(n, 0.9, 0.1);
var examples = new Matrix(new double[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
});
var labels = new Vector(0, 1, 1, 0);
trainer.Train(examples, labels, 1000);
for (var i = 0; i < labels.Length; i++)
{
var x = examples.GetRow(i);
var y = labels[i];
Console.WriteLine("Actual: {0}, Result: {1}", y, n.Compute(x));
Assert.IsTrue(Math.Abs(y - n.Compute(x)[0]) < 0.01);
}
}