public static T CreateBrain <T>(BrainFactoryInput input) where T : Brain.Brain
{
var nNetwork = new Brain.NeuralNetwork(input.ActivationFunctionInputOutput, input.ActivationFunctionHiddenLayers,
input.Inputs, input.Outputs, input.HiddenLayers, input.NeuronsPerHiddenLayer, input.Alpha);
return((T)Activator.CreateInstance(typeof(T), nNetwork));
}
private static void TrainNeuralNetwork()
{
var input = new BrainFactoryInput()
{
ActivationFunctionInputOutput = new Sigmoid(),
ActivationFunctionHiddenLayers = new Sigmoid(), //do not use TanH for the xorbrain it is counter productive. The xor needs 0 or 1, TanH brings in negative values as well
Inputs = 2,
Outputs = 1,
HiddenLayers = 1,
NeuronsPerHiddenLayer = 2,
Alpha = 0.8 //how much impact the training has, sometimes you'll see NaN come back and this dials back the calculations a bit
};
var brain = BrainFactory.CreateBrain <XorBrain>(input);
brain.Think(trainingIterations: 1000);
}
