static void Main(string[] args)
{
int[] nodesPerLayer = new int[] { 2, 10, 10, 1 };
NetworkStructure networkStructure = new NetworkStructure(nodesPerLayer);
Random r = new Random();
int numNetworks = 100;
int generations = 100;
Network[] networks = new Network[numNetworks];
for (int i = 0; i < networks.Length; i++)
{
Weights weights = new Weights(nodesPerLayer);
networks[i] = new Network(networkStructure, weights);
}
MutateNetworks mutateNetworks = new MutateNetworks(0.05f, 10, 2, nodesPerLayer);
for (int i = 0; i < generations; i++)
{
for (int j = 0; j < networks.Length; j++)
{
networks[j].ResetFitness();
float[] output = networks[j].EvaluateNetwork(new float[] { 1, 0 });
networks[j].AddFitness(1 - Math.Abs(output[0]));
Console.WriteLine(output[0]);
}
mutateNetworks.Mutate(networks);
}
for (int i = 0; i < networks.Length; i++)
{
Console.WriteLine("Fitness: " + networks[i].Fitness);
}
Console.WriteLine();
Network bestNetwork = SortNetworks.GetBestNetwork(networks);
Console.WriteLine(bestNetwork.EvaluateNetwork(new float[] { 1, 0 })[0]);
Console.ReadKey();
}
static void Main(string[] args)
{
int[] nodesPerLayer = new int[] { 2, 3, 1 };
float[][] inputs = new float[][] { new float[] { 0, 0 },
new float[] { 1, 0 },
new float[] { 0, 1 },
new float[] { 1, 1 } };
float[] outputs = new float[] { 0, 1, 1, 0 };
NetworkStructure networkStructure = new NetworkStructure(nodesPerLayer, new SigmoidActivation());
Random r = new Random();
int numNetworks = 100;
Network[] networks = new Network[numNetworks];
bool fromFile = false;
Console.Write("Load from file ? (y/n) ");
if (Console.ReadKey().Key == ConsoleKey.Y)
{
fromFile = true;
}
for (int i = 0; i < networks.Length; i++)
{
if (fromFile)
{
networks[i] = new Network(networkStructure, WEIGHTS_FILE);
}
else
{
networks[i] = new Network(networkStructure, nodesPerLayer);
}
}
MutateNetworks mutateNetworks = new MutateNetworks(0.05f, 10, 2, nodesPerLayer);
for (int i = 0; ; i++)
{
Console.WriteLine("Generation " + i + ": ");
for (int j = 0; j < networks.Length; j++)
{
networks[j].ResetFitness();
for (int k = 0; k < inputs.Length; k++)
{
float[] output = networks[j].EvaluateNetwork(inputs[k]);
networks[j].AddFitness(1 - Math.Abs(output[0] - outputs[k]));
}
}
Network bestNetwork = SortNetworks.GetBestNetwork(networks);
Console.WriteLine("Fitness: " + bestNetwork.Fitness);
for (int a = 0; a < inputs.Length; a++)
{
Console.WriteLine(bestNetwork.EvaluateNetwork(inputs[a])[0]);
if (bestNetwork.Fitness > 3.8f)
{
goto generationLoop;
}
}
mutateNetworks.Mutate(networks);
}
generationLoop:
for (int i = 0; i < networks.Length; i++)
{
Console.WriteLine("Fitness: " + networks[i].Fitness);
}
Console.WriteLine();
//Network bestNetwork = SortNetworks.GetBestNetwork(networks);
//Console.WriteLine(bestNetwork.EvaluateNetwork(new float[] { 1, 0 })[0]);
Console.Write("Save Weights? (y/n)");
if (Console.ReadKey().Key == ConsoleKey.Y)
{
Network bestNetwork = SortNetworks.GetBestNetwork(networks);
bestNetwork.SaveNetwork(WEIGHTS_FILE);
}
Console.WriteLine();
Console.WriteLine("Press any key to exit");
Console.ReadKey();
}
static void Main(string[] args)
{
int[] nodesPerLayer = new int[] { 400, 200, 100, 50, 30, 13 };
NetworkStructure networkStructure = new NetworkStructure(nodesPerLayer, new TanHActivation());
Network[] networks = new Network[100];
bool fromFile = false;
fromFile = true;
string fileName = WEIGHTS_FILE;
//Console.Write("Load from file (empty if using no file): ");
//string fileName = Console.ReadLine();
//if(fileName != "")
//{
// fromFile = true;
//}
//else
//{
// fileName = WEIGHTS_FILE;
//}
for (int i = 0; i < networks.Length; i++)
{
if (fromFile)
{
networks[i] = new Network(networkStructure, fileName);
}
else
{
networks[i] = new Network(networkStructure, nodesPerLayer);
}
}
MutateNetworks mutateNetworks = new MutateNetworks(0.05f, 10, 2, nodesPerLayer);
int CurrentGeneration = 1;
//infinite loop for training
for (; ;)
{
Console.WriteLine("generation " + CurrentGeneration++ + ": ");
//testing each networks
for (int i = 0; i < networks.Length; i++)
{
networks[i].ResetFitness();
Console.Write(".");
//testing 130 images
for (int j = 0; j < 130; j++)
{
PositionData evaluatedImage = ImageLoading.LoadImage(j / 10, TRAINIG_DATA_PATH);
float[] output = networks[i].EvaluateNetwork(evaluatedImage.imageInfo);
for (int k = 0; k < output.Length; k++)
{
if (evaluatedImage.expectedResult[k] == 1)
{
networks[i].AddFitness(output[k] * 10);
}
else
{
networks[i].AddFitness(-Math.Abs(output[k]));
}
}
}
}
Network bestNetwork = SortNetworks.GetBestNetwork(networks);
Console.WriteLine();
Console.WriteLine("Fitness: " + bestNetwork.Fitness);
for (int j = 0; j < 13; j++)
{
PositionData evaluatedImage = ImageLoading.LoadImage(j, TRAINIG_DATA_PATH);
float[] output = bestNetwork.EvaluateNetwork(evaluatedImage.imageInfo);
float maxValue = output.Max();
Console.WriteLine(output.ToList().IndexOf(maxValue));
}
bestNetwork.SaveNetwork(fileName);
mutateNetworks.Mutate(networks);
}
}
