public static void ShowOutputs(Network network)
{
Console.WriteLine(":::::::::::OUTPUTS START::::::::::::::::::::::");
for (int i = 0; i < network.Layers.Count; i++)
{
if (network.Layers[i].Neurons.Count > 0)
{
foreach (Neuron neuron in network.Layers[i].Neurons)
{
Console.Write(neuron.Output + " ");
}
Console.WriteLine();
}
}
Console.WriteLine(":::::::::::OUTPUTS END::::::::::::::::::::::");
}
public static void ShowError(Network network)
{
Console.WriteLine(":::::::::::ERROR::::::::::::::::::::::");
Console.WriteLine(network.Layers.Last().Neurons.First().Error);
Console.WriteLine(":::::::::::ERROR end::::::::::::::::::::::");
}
public static void Calculate(Network network, double[] inputs)
{
for (int i = 0; i < network.Layers.Count; i++)
{
if (i == 0)
{
for (int k = 0; k < network.Layers[i].Neurons.Count; k++)
{
network.Layers[i].Neurons[k].Accept(inputs[k]);
}
}
else
{
foreach (Neuron neuron in network.Layers[i].Neurons)
{
neuron.Reflex();
}
}
}
//for (int i = 0; i < network.Layers.Count; i++)
//{
// if (network.Layers[i].Neurons.Count > 0)
// {
// foreach (Neuron neuron in network.Layers[i].Neurons)
// {
// Console.Write(neuron.Output + " ");
// }
// Console.WriteLine();
// }
//}
//Console.WriteLine(":::::::::::::::::::::::::::::::::");
}
public static void TrainNetwork(Network network, double rightAnswer)
{
double h = 0.1;
double tempError = rightAnswer - network.Layers.Last().Neurons.FirstOrDefault().Output;
double error = tempError * tempError * 0.5;
//Console.WriteLine(" Error : " + tempError);
network.Layers.Last().Neurons.FirstOrDefault().Error = tempError;
for (int i = network.Layers.Count - 2; i >= 0; i--)
{
foreach (Neuron neuron in network.Layers[i].Neurons)
{
double err = 0;
foreach (Link link in neuron.OutgoingLinks)
{
err += link.Neuron.Error * link.Weight;
}
neuron.Error = err * (1 - Math.Tanh(neuron.Output) * Math.Tanh(neuron.Output));
//Console.Write(neuron.Error);
}
//Console.WriteLine("EndLayer : ");
}
//Console.WriteLine("EndERROR COUNTING ||||||||||||||||||||||||||| : ");
for (int i = 0; i < network.Layers.Count; i++)
{
foreach (Neuron neuron in network.Layers[i].Neurons)
{
foreach (Link link in neuron.OutgoingLinks)
{
link.Weight = link.Weight + h * link.Neuron.Error * neuron.Output;
if (link.Neuron.IncomingLinks.Count > 0)
{
link.Neuron.IncomingLinks.First(f => f.Neuron.Id == neuron.Id).Weight = link.Weight;
}
}
}
}
//for (int i = 0; i < network.Layers.Count - 1; i++)
//{
// foreach (Neuron neuron in network.Layers[i].Neurons)
// {
// foreach (Link link in neuron.OutgoingLinks)
// {
// Console.Write(link.Weight + " ");
// }
// Console.WriteLine();
// }
// Console.WriteLine();
//}
//Console.WriteLine("|||||||||||||||||||||||||||||||||||||||||||||||||||");
}
public static void RelaxNetwork(Network network)
{
for (int i = 0; i < network.Layers.Count; i++)
{
foreach (Neuron neuron in network.Layers[i].Neurons)
{
neuron.Relax();
}
}
}
static void Main(string[] args)
{
Random random = new Random();
Network network = new Network();
network.Layers.Add(new Layer());
network.Layers.Add(new Layer());
network.Layers.Add(new Layer());
network.Layers.Add(new Layer());
network.Layers.Add(new Layer());
for (int i = 0; i < network.Layers.Count; i++)
{
if (i == 0)
{
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
}
if (i == 1)
{
network.Layers[i].Neurons.Add(new Neuron());
}
if (i == 2)
{
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
}
if (i == 3)
{
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
}
if (i == 4)
{
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
network.Layers[i].Neurons.Add(new Neuron());
}
if (i == 5)
{
network.Layers[i].Neurons.Add(new Neuron());
}
}
#region Initialization
for (int i = 0; i < network.Layers.Count - 1; i++)
{
foreach (Neuron neuron in network.Layers[i].Neurons)
{
foreach (Neuron attachingNeuron in network.Layers[i + 1].Neurons)
{
neuron.Attach(attachingNeuron, random.NextDouble());
}
}
}
for (int i = 0; i < network.Layers.Count - 1; i++)
{
foreach (Neuron neuron in network.Layers[i].Neurons)
{
foreach (Link link in neuron.OutgoingLinks)
{
Console.Write(link.Weight + " ");
}
Console.WriteLine();
}
Console.WriteLine();
}
Console.WriteLine("|||||||||||||||||||||||||||||||||||||||||||||||||||");
#endregion
double[] inputs1 = new double[2];
inputs1[0] = 1;
inputs1[1] = 0;
double answer1 = 0;
double[] inputs2 = new double[2];
inputs2[0] = 0;
inputs2[1] = 0;
double answer2 = 0;
double[] inputs3 = new double[2];
inputs3[0] = 1;
inputs3[1] = 1;
double answer3 = 1;
double[] inputs4 = new double[2];
inputs4[0] = 0;
inputs4[1] = 1;
double answer4 = 0;
Calculate(network, inputs1);
TrainNetwork(network, answer1);
ShowError(network);
//ShowOutputs(network);
RelaxNetwork(network);
//Calculate(network, inputs2);
//TrainNetwork(network, answer2);
//ShowError(network);
//ShowOutputs(network);
//RelaxNetwork(network);
Calculate(network, inputs3);
TrainNetwork(network, answer3);
ShowError(network);
//ShowOutputs(network);
RelaxNetwork(network);
Calculate(network, inputs4);
TrainNetwork(network, answer4);
ShowError(network);
//ShowOutputs(network);
RelaxNetwork(network);
for (int i = 0; i < 300000; i++)
{
Calculate(network, inputs1);
TrainNetwork(network, answer1);
RelaxNetwork(network);
//Calculate(network, inputs2);
//TrainNetwork(network, answer2);
//RelaxNetwork(network);
Calculate(network, inputs3);
TrainNetwork(network, answer3);
RelaxNetwork(network);
Calculate(network, inputs4);
TrainNetwork(network, answer4);
RelaxNetwork(network);
}
Console.WriteLine();
Console.WriteLine();
Console.WriteLine("AFTER : ");
Console.WriteLine();
Calculate(network, inputs1);
TrainNetwork(network, answer1);
ShowError(network);
//ShowOutputs(network);
RelaxNetwork(network);
//Calculate(network, inputs2);
//TrainNetwork(network, answer2);
//ShowError(network);
//ShowOutputs(network);
//RelaxNetwork(network);
Calculate(network, inputs3);
TrainNetwork(network, answer3);
ShowError(network);
//ShowOutputs(network);
RelaxNetwork(network);
Calculate(network, inputs4);
TrainNetwork(network, answer4);
ShowError(network);
//ShowOutputs(network);
RelaxNetwork(network);
Console.ReadLine();
}