public void TrainXor()
{
NeuralNetworkBuilder builder = new NeuralNetworkBuilder();
NeuralNetwork network = builder.CreateNew()
.AddInputLayer(2, ActivationFunctions.Sigmoid, false)
.AddHiddenLayer(2, ActivationFunctions.Sigmoid, false)
.AddOutputLayer(1, ActivationFunctions.Sigmoid)
.GetNetwork();
BackPropagationTrainer trainer = new BackPropagationTrainer(network);
TrainingData[] data = new TrainingData[]
{
new TrainingData(new double[] { 1, 1 }, new double[] { 0 }),
new TrainingData(new double[] { 1, 0 }, new double[] { 1 }),
new TrainingData(new double[] { 0, 1 }, new double[] { 1 }),
new TrainingData(new double[] { 0, 0 }, new double[] { 0 }),
};
var result = trainer.Train(data, 100000, 0.7, 0.0, 0.005);
string csv = result.Errors.ToCsvString();
Assert.IsTrue(result.IsSuccessful, "could not traing against expected error");
double[] result1 = network.Run(new double[] { 1.0, 1.0 });
double[] result2 = network.Run(new double[] { 1.0, 0.0 });
double[] result3 = network.Run(new double[] { 0.0, 1.0 });
double[] result4 = network.Run(new double[] { 0.0, 0.0 });
Assert.IsTrue(result1[0].IsEqual(0, 0.0005));
Assert.IsTrue(result2[0].IsEqual(1, 0.0005));
Assert.IsTrue(result3[0].IsEqual(1, 0.0005));
Assert.IsTrue(result4[0].IsEqual(0, 0.0005));
}
