static void Main(string[] args)
{
/*TemporalNetwork network = new TemporalNetwork(ActivationFunctions.Sine, 4, 4, 0, 1, 1);
* network.AddInterConnectedLayer(0, 5);
* network.AddInterConnectedLayer(0, 2);
* network.AddInterConnectedLayer(1, 1);
* network.AddInterConnectedLayer(0, 3);
* double lastCost = 5;
* for (int a = 0; a < 1000; a++)
* {
* network.Train(new double[] { 1, 2, 1 }, new double[] { 1 }, .27, lastCost, out lastCost);
* }*/
NeuronalNetwork n = new NeuronalNetwork(2, ActivationFunctions.Sine, new int[] { 4, 5, 3, 1 });
List <double[]> X = new List <double[]>();
List <double[]> Y = new List <double[]>();
X.Add(new double[] { 0, 0 });
Y.Add(new double[] { 1 });
X.Add(new double[] { 0, 1 });
Y.Add(new double[] { 0 });
X.Add(new double[] { 1, 1 });
Y.Add(new double[] { 1 });
X.Add(new double[] { 1, 0 });
Y.Add(new double[] { 0 });
List <double[]> inputs, expecteds = new List <double[]>();
inputs = new List <double[]>();
for (int g = 0; g < 200; g++)
{
int index = new Random(g).Next(X.Count);
inputs.Add(X[index]);
expecteds.Add(Y[index]);
}
n.Train(inputs, expecteds, 24);
/*while (true)
* {
* Console.WriteLine("Mutating...");
* for (int i = 0; i < 20; i++)
* {
* network.MutateNetwork();
* }
* Console.Clear();
* double[] exeRes = network.ExecuteNetwork(new double[] { 1, 2, 1, 5 });
* string exeResS = string.Empty;
* foreach (var item in exeRes)
* {
* exeResS += item + " ";
* }
* Console.WriteLine(exeResS);
* ConsoleKeyInfo key = Console.ReadKey();
* if (key == new ConsoleKeyInfo('r', ConsoleKey.R, false, false, false))
* {
* Console.WriteLine("\n" + network.GetRepresentation());
* Console.ReadKey();
* }
* }*/
}
static void Main(string[] args)
{
string location = Path.GetDirectoryName(Assembly.GetEntryAssembly().Location);
var images = GetImagesFromFile(location, "train", 60000);
Stopwatch watch = new Stopwatch();
watch.Start();
long iterations = 0;
long errors = 0;
double errorRate = 1;
double epsilon = 1;
Random rand = new Random();
var nn = new NeuronalNetwork(784, 89, 10);
double[] target = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
// Train Network until desired epsilon is reached
while (iterations <= 60000)
{
iterations++;
int randInd = rand.Next(images.Count);
int label = images[randInd].GetLabel();
target[label] = 1;
var res = nn.Train(images[randInd].GetData(), target);
target[label] = 0;
if (label != res)
{
errors++;
}
if (iterations % 1000 == 0)
{
errorRate = errors / 1000.0;
errors = 0;
if (label != res)
{
Console.ForegroundColor = ConsoleColor.DarkRed;
}
epsilon = nn.CalculateEpsilon(label);
Console.Write("Expected: " + label + " - was: " + res + " errorRate: " + errorRate.ToString("0.0000") + " Epsilon: " + epsilon.ToString("0.000000") + "\n");
Console.Write("\n");
Console.ForegroundColor = ConsoleColor.White;
}
}
watch.Stop();
var ts = watch.Elapsed;
string elapsedTime = $"{ts.Hours} Hours, {ts.Minutes} Minutes, {ts.Seconds} Seconds, {ts.Milliseconds} ms";
images.Clear();
images = GetImagesFromFile(location, "t10k", 10000);
errors = 0;
errorRate = 1;
int[,] confusionMatrix = new int[10, 10];
for (int i = 0; i < images.Count; i++)
{
int res = nn.FeedForward(images[i].GetData());
int label = images[i].GetLabel();
if (res != label)
{
errors++;
}
confusionMatrix[label, res]++;
}
string timePerPicture = (ts.TotalMilliseconds / iterations).ToString("N2");
PrintResult(confusionMatrix, timePerPicture, elapsedTime);
Console.ReadKey();
}
