public static void Jeff()
{
Random rnd = new Random();
NeuronalNet Jeff = new NeuronalNet(0, 209, 200, 100, 5);
Jeff.StandartInitialisierung();
Tetris.MapAktualisieren(true);
bool nochmal = true;
while (nochmal)
{
Tetris.MapAktualisieren(true);
while (Tetris.leben)
{
Jeff.InputGeben(Tetris.GetV());
Jeff.AllesBerechnen();
double[] b = Tetris.UserBewegung(Console.ReadKey().KeyChar);
Jeff.DeepLearning(b);
}
Tetris.AllesReset();
Console.Clear();
Console.Write("Nochmal");
string text = Console.ReadLine();
if (text == "nein" || text == "n")
{
nochmal = false;
}
}
Console.Clear();
while (true)
{
Console.Clear();
Tetris.MapAktualisieren(true);
while (Tetris.leben && !Tetris.kiTutNichts && Tetris.sinnloseBewegung < 350)
{
Console.SetCursorPosition(0, 0);
ArrayAusgeben(Jeff.GetOutput());
Jeff.InputGeben(Tetris.GetV());
Jeff.AllesBerechnen();
Tetris.BewegungShow(Jeff.GetOutput(), 0.5);
}
Tetris.AllesReset();
}
}
private void Train()
{
nn = new NeuronalNet(3, 2, 2);
nn.NetworkInit();
nn.learningRate = 0.5f;
predictText.text = "TRAINING";
for (int j = 0; j < 10000; ++j)
{
for (int i = 0; i < input.Count; ++i)
{
nn.TrainNetwork(input[i], output[i]);
}
}
predictText.text = "DONE";
}
public static void SimplesSpielKI()
{
NeuronalNet Queckeline = new NeuronalNet(2, 100, 4);
Queckeline.StandartInitialisierung();
}
public static void Run(int durchlaufe, int ausgebenPro)
{
double Fehler = 0;
NeuronalNet Chantall = new NeuronalNet(0, 784, 20, 20, 10);
Chantall.StatischeInitialisierung(true);
Chantall.Etha = 4;
FileStream ifsLabels = new FileStream(@"C:\Users\Gregor\memBrain\train-labels.idx1-ubyte", FileMode.Open); // test labels
FileStream ifsImages = new FileStream(@"C:\Users\Gregor\memBrain\train-images.idx3-ubyte", FileMode.Open); // test images
BinaryReader brLabels = new BinaryReader(ifsLabels);
BinaryReader brImages = new BinaryReader(ifsImages);
int magic1 = brImages.ReadInt32(); // discard
int numImages = brImages.ReadInt32();
int numRows = brImages.ReadInt32();
int numCols = brImages.ReadInt32();
int magic2 = brLabels.ReadInt32();
int numLabels = brLabels.ReadInt32();
byte[][] pixels = new byte[28][];
for (int i = 0; i < pixels.Length; ++i)
{
pixels[i] = new byte[28];
}
for (int i = 1; i < durchlaufe; ++i)
{
if (i % 60000 == 0)
{
ifsImages.Close();
brImages.Close();
ifsLabels.Close();
brLabels.Close();
ifsLabels = new FileStream(@"C:\Users\Gregor\memBrain\train-labels.idx1-ubyte", FileMode.Open); // test labels
ifsImages = new FileStream(@"C:\Users\Gregor\memBrain\train-images.idx3-ubyte", FileMode.Open); // test images
brLabels = new BinaryReader(ifsLabels);
brImages = new BinaryReader(ifsImages);
magic1 = brImages.ReadInt32(); // discard
numImages = brImages.ReadInt32();
numRows = brImages.ReadInt32();
numCols = brImages.ReadInt32();
magic2 = brLabels.ReadInt32();
numLabels = brLabels.ReadInt32();
pixels = new byte[28][];
for (int j = 0; j < pixels.Length; ++j)
{
pixels[j] = new byte[28];
}
}
for (int i1 = 0; i1 < 28; ++i1)
{
for (int j = 0; j < 28; ++j)
{
byte b = brImages.ReadByte();
pixels[i1][j] = b;
}
}
byte lbl = brLabels.ReadByte();
double[] erwrteterOutput = new double[10];
erwrteterOutput[lbl] = 1;
Chantall.InputGeben(ConvertD(pixels));
Chantall.AllesBerechnen();
Chantall.DeepLearning(erwrteterOutput);
Fehler += Chantall.GetFehlerDif(erwrteterOutput);
if (i % ausgebenPro == 0)
{
Fehler /= ausgebenPro;
DigitImage dImage = new DigitImage(pixels, lbl);
Console.WriteLine(dImage.ToString());
ZweiArrayAusgeben(erwrteterOutput, Chantall.GetOutput());
Console.WriteLine("Treffer Quote: {0}", 100 - (Fehler * 10));
Console.WriteLine(Chantall.ToString());
Chantall.Etha = Fehler * 2;
Fehler = 0;
}
}
}
