private TrainStats ExecuteSingleIteration(ArchitectureTestConfig config)
{
var neuralNetworkBuilder = new NeuralNetworkBuilder()
.SetActivationFunction(new SigmoidActivationFunction())
.SetErrorFunction(new MeanSquaredErrorFunction(1))
.SetNumberOfInputNeurons(2)
.SetNumberOfHiddenNeurons(config.NumberOfHiddenNeurons)
.SetNumberOfOutputNeurons(1);
if (config.EnableBias)
{
neuralNetworkBuilder.AddBiasConnections();
}
var network = neuralNetworkBuilder.Build();
var trainer = new Trainer(neuralNetwork: network, learningRate: 0.01, logger: new DummyLogger());
var trainDataCollection = new[]
{
new TrainData(new double [] { 0, 0 }, new double [] { 1 }),
new TrainData(new double [] { 1, 0 }, new double [] { 0 }),
new TrainData(new double [] { 0, 1 }, new double [] { 0 }),
new TrainData(new double [] { 1, 1 }, new double [] { 1 }),
};
var trainStats = trainer.Train(trainDataCollection, numberOfEpochs: _maxNumberOfEpochs, terminalEpochError: _terminalEpochError);
return(trainStats);
}
public static void BipolarNXORExample(ILogger logger)
{
var neuralNetworkBuilder = new NeuralNetworkBuilder();
var network = neuralNetworkBuilder
.AddBiasConnections()
.SetActivationFunction(new TANHActivationFunction())
.SetErrorFunction(new MeanSquaredErrorFunction(1))
.SetNumberOfInputNeurons(2)
.SetNumberOfHiddenNeurons(3)
.SetNumberOfOutputNeurons(1)
.Build();
var trainer = new Trainer(neuralNetwork: network, learningRate: 0.01, logger: logger);
var trainDataCollection = new[]
{
new TrainData(new double [] { -1, -1 }, new double [] { 1 }),
new TrainData(new double [] { 1, -1 }, new double [] { -1 }),
new TrainData(new double [] { -1, 1 }, new double [] { -1 }),
new TrainData(new double [] { 1, 1 }, new double [] { 1 }),
};
trainer.Train(trainDataCollection, numberOfEpochs: 100000, terminalEpochError: 0.01);
TestNetwork(network, trainDataCollection, logger);
}
