public void Test_NeuralNetworkLogic()
{
INeuralNetworkBuilder <double> builder = new SigmoidNeuralNetworkBuilder <GeneticTrainingNeuralNetwork>();
builder.BuildNetwork(2, new int[] { 3 }, 1);
INeuralNetwork <double> network = builder.InitializeNeuralNetworkWithData(
new double[] { 1, 1 },
null,
null);
// First Hidden Layer
network.HiddenLayers.ElementAt(0)[0].Inputs.ElementAt(0).DendriteWeight.Weight = 0.8;
network.HiddenLayers.ElementAt(0)[0].Inputs.ElementAt(1).DendriteWeight.Weight = 0.2;
network.HiddenLayers.ElementAt(0)[1].Inputs.ElementAt(0).DendriteWeight.Weight = 0.4;
network.HiddenLayers.ElementAt(0)[1].Inputs.ElementAt(1).DendriteWeight.Weight = 0.9;
network.HiddenLayers.ElementAt(0)[2].Inputs.ElementAt(0).DendriteWeight.Weight = 0.3;
network.HiddenLayers.ElementAt(0)[2].Inputs.ElementAt(1).DendriteWeight.Weight = 0.5;
// Output Nueron
network.Output.Neurons.ElementAt(0)[0].DendriteWeight.Weight = 0.3;
network.Output.Neurons.ElementAt(0)[0].DendriteWeight.Weight = 0.5;
network.Output.Neurons.ElementAt(0)[0].DendriteWeight.Weight = 0.9;
// Run sigmoid function
network.Pulse(null);
;
}
static void Main(string[] args)
{
INeuralNetworkBuilder <double> builder = new SigmoidNeuralNetworkBuilder <GeneticTrainingNeuralNetwork>();
builder.BuildNetwork(9, null, 1); // no hidden layers
INeuralNetwork <double> network = builder.InitializeNeuralNetworkWithData(new double[]
{
2, 0, 1,
0, 1, 0,
2, 0, 0
}, null, null);
network.LearningRate = 0.1;
for (int i = 0; i < 10000; ++i)
{
// Calculates Sigmoid Neurons value.
network.Pulse(null);
// Function prepared for Genetic Training of Feed Forward Network
network.ApplyLearning(null);
}
}
