static void SimpleTest()
{
var neuralNetwork = new NeuronalNetwork();
var network = new SimpleNetwork(neuralNetwork, Sigmoid, Sigmoid);
var layers = new int[5];
layers[0] = 2;
layers[1] = 3;
layers[2] = 5;
layers[3] = 4;
layers[4] = 3;
network.Init(layers);
Console.WriteLine($"Created Network with {layers[0]} input neurons and {layers[layers.Length-1]} output neurons.");
var consoleInput = string.Empty;
var stopWatch = new Stopwatch();
do
{
Console.WriteLine("Please enter all input values:");
for (var i = 0; i < layers[0]; i++)
{
Console.Write($"{i}. > ");
consoleInput = Console.ReadLine();
if (double.TryParse(consoleInput, out var value))
{
network.SetInputValue(i, value);
}
}
Console.WriteLine("Process..");
stopWatch.Reset();
stopWatch.Start();
network.Process();
stopWatch.Stop();
Console.WriteLine($"Finished! Time: {stopWatch.ElapsedTicks} Ticks ({stopWatch.ElapsedMilliseconds} ms)");
Console.WriteLine("Output values:");
for (var i = 0; i < layers[layers.Length - 1]; i++)
{
Console.WriteLine($"{i}.: {network.GetOutputValue(i)}");
}
Console.Write("Enter anything to exit.");
consoleInput = Console.ReadLine();
} while (consoleInput == string.Empty);
}
static void Main(string[] args)
{
//We create the training template
TrainingTemplate xor_template = new TrainingTemplate();
xor_template.addTrainingRow(new TrainingRow(new List <double> {
0, 0
}, new List <double> {
0
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
0, 1
}, new List <double> {
1
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
1, 0
}, new List <double> {
1
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
1, 1
}, new List <double> {
0
}));
//We create the network
SimpleNetwork sn = new SimpleNetwork();
// We create the neurons
Neuron input1Neuron = new Neuron("input1", 0, 1);
Neuron input2Neuron = new Neuron("input2", 0, 1);
Neuron hidden1Neuron = new Neuron("hidden1", 0, 1);
Neuron hidden2Neuron = new Neuron("hidden2", 0, 1);
Neuron outputNeuron = new Neuron("output", 0, 1);
//We asign them Sigmoid fire functions
input1Neuron.fireRule = new SigmoidFireRule();
input2Neuron.fireRule = new SigmoidFireRule();
hidden1Neuron.fireRule = new SigmoidFireRule();
hidden1Neuron.fireRule = new SigmoidFireRule();
outputNeuron.fireRule = new SigmoidFireRule();
// We bind them together
input1Neuron.addNewInput("network_in_1", 1, 1);
input2Neuron.addNewInput("network_in_2", 1, 1);
hidden1Neuron.inputs.Add(input1Neuron.output);
hidden1Neuron.inputs.Add(input2Neuron.output);
hidden2Neuron.inputs.Add(input1Neuron.output);
hidden2Neuron.inputs.Add(input2Neuron.output);
outputNeuron.inputs.Add(hidden1Neuron.output);
// We put them into layers
sn.inputLayer.neurons.Add(input1Neuron);
sn.inputLayer.neurons.Add(input2Neuron);
sn.hiddenLayer.neurons.Add(hidden1Neuron);
sn.hiddenLayer.neurons.Add(hidden2Neuron);
sn.outputLayer.neurons.Add(outputNeuron);
// We train
double error = sn.train(xor_template, 5000, new ErrorHistory());
Console.WriteLine(error);
Console.ReadKey();
}