static void Main(string[] args)
{
NeuralNetwork.NeuralNetwork nn = new NeuralNetwork.NeuralNetwork();
List <double[]> input = new List <double[]>()
{
new double[] { 0, 0 },
new double[] { 0, 1 },
new double[] { 1, 0 },
new double[] { 1, 1 }
};
List <double[]> output = new List <double[]>()
{
new double[] { 0 },
new double[] { 1 },
new double[] { 0 },
new double[] { 1 }
};
for (int i = 0; i < 100000; i++)
{
nn.Train(input, output);
}
}
static void Main(string[] args)
{
handler = new ConsoleEventDelegate(ConsoleEventCallback);
SetConsoleCtrlHandler(handler, true);
var form = new NeuralForm(NeuralNetwork);
form.Show();
var r = new Random(1);
if (!File.Exists("neural.net"))
{
var ii = 0;
while (ii++ <= 100000)
{
var input = new double[8];
for (int i = 0; i < input.Length; i++)
{
input[i] = r.Next(100) > 50 ? 1 : 0;
}
var output = new double[input.Length];
for (int i = 0; i < input.Length; i++)
{
output[i] = input[i] == 1 ? 0 : 1;
}
NeuralNetwork.Train(input, output);
var networkOutput = new double[input.Length];
for (int j = 0; j < NeuralNetwork.Layers.Last().Neurons.Count; j++)
{
var neuron = NeuralNetwork.Layers.Last().Neurons[j];
networkOutput[j] = neuron.Value;
}
for (int j = 0; j < output.Length; j++)
{
if (experienceList.Count > 999)
{
experienceList.Remove(experienceList.First());
}
experienceList.Add(output[j] == Math.Round(networkOutput[j]) ? 1 : 0);
}
double temp = 0;
foreach (var exp in experienceList)
{
temp += exp;
}
double loss = 100 - ((temp / experienceList.Count) * 100);
if (ii % 100 == 0)
{
Console.CursorTop = 0;
Console.CursorLeft = 0;
Console.WriteLine($"Iteration: {ii} ");
form.UpdateNeural(NeuralNetwork, $"Loss: {loss:##.###}%");
}
}
}
else
{
NeuralNetwork.Load("neural.net");
form.Hide();
form = new NeuralForm(NeuralNetwork);
form.Show();
}
while (true)
{
Console.Clear();
var input = new double[8];
var networkOutput = new double[8];
var output = new double[8];
for (int i = 0; i < input.Length; i++)
{
input[i] = r.Next(100) > 50 ? 1 : 0;
}
for (int i = 0; i < input.Length; i++)
{
output[i] = input[i] == 1 ? 0 : 1;
}
var response = NeuralNetwork.Run(input);
for (int i = 0; i < response.Length; i++)
{
networkOutput[i] = Math.Round(response[i]);
}
DrawMap(input, networkOutput);
for (int i = 0; i < 10; i++)
{
NeuralNetwork.Train(input, output);
}
for (int j = 0; j < output.Length; j++)
{
if (experienceList.Count > 999)
{
experienceList.Remove(experienceList.First());
}
experienceList.Add(output[j] == Math.Round(networkOutput[j]) ? 1 : 0);
}
double temp = 0;
foreach (var exp in experienceList)
{
temp += exp;
}
double loss = 100 - ((temp / experienceList.Count) * 100);
form.UpdateNeural(NeuralNetwork, $"Loss: {loss:##.###}%");
//Thread.Sleep(300);
}
}
private void Form1_Load(object sender, EventArgs e)
{
Matrix.Matrix output = new Matrix.Matrix(1, 1);
double sum = 0;
double sumTest = 0;
int epochs = 0;
int epoch = 5000;
LoadFromFile(data, target);
LoadFromFileTest(dataTest, targetTest);
SortX(data, target);
this.Controls.Add(pv);
this.Controls.Add(pv2);
PlotModel pm = new PlotModel();
pv.Model = pm;
PlotModel pm2 = new PlotModel();
pv2.Model = pm2;
LineSeries punktySerii = new LineSeries
{
LineStyle = LineStyle.None,
MarkerType = MarkerType.Diamond,
MarkerSize = 2,
MarkerStroke = OxyColors.Red,
Title = "Punkty treningowe"
};
LineSeries punktySeriiTestu = new LineSeries
{
LineStyle = LineStyle.None,
MarkerType = MarkerType.Circle,
MarkerSize = 2,
MarkerStroke = OxyColors.Red,
Title = "Punkty wejściowe"
};
LineSeries punktySieci = new LineSeries()
{
LineStyle = LineStyle.None,
MarkerType = MarkerType.Plus,
MarkerSize = 3,
MarkerStroke = OxyColors.Blue,
Title = "Punkty wyjściowe"
};
LineSeries punktyBledu = new LineSeries()
{
LineStyle = LineStyle.Solid,
MarkerType = MarkerType.Diamond,
Color = OxyColors.Red,
MarkerSize = 1,
MarkerStroke = OxyColors.Red,
Title = "Zbior treningowy"
};
LineSeries punktyBleduTestowego = new LineSeries()
{
LineStyle = LineStyle.Solid,
MarkerType = MarkerType.Diamond,
Color = OxyColors.Blue,
MarkerSize = 1,
MarkerStroke = OxyColors.Blue,
Title = "Zbior testowy"
};
LineSeries punktyKresek = new LineSeries
{
Color = OxyColors.Blue,
StrokeThickness = 1
};
/*
* for (int i = 0; i < data.Length; i++)
* {
* if (data[i] >= 1)
* target[i] = data[i] * -2;
* }
*/
for (int i = 0; i < data.Length; i++)
{
punktySerii.Points.Add(new DataPoint(data[i], target[i]));
}
for (int i = 0; i < dataTest.Length; i++)
{
punktySeriiTestu.Points.Add(new DataPoint(dataTest[i], targetTest[i]));
}
Matrix.Matrix outputFF = new Matrix.Matrix(1, 1);
double[] d = new double[1];
NeuralNetwork.NeuralNetwork nn = new NeuralNetwork.NeuralNetwork(1, 10, 1, true);
//uczenie
for (int i = 0; i < epoch; ++i)
{
sum = 0;
sumTest = 0;
foreach (int j in Enumerable.Range(0, 81).OrderBy(x => rnd.Next()))
{
d[0] = data[j];
double[] y = new double[1] {
target[j]
};
nn.Train(d, y);
// Console.Write("x: " + data[j]+ "y= ");
// nn.FeedForward(d).DisplayMatrix();
// Console.WriteLine();
}
Console.WriteLine("epoka: " + epochs);
for (int j = 0; j < data.Length; j++)
{
d[0] = data[j];
outputFF = nn.FeedForward(d);
sum += (outputFF.tab[0, 0] - target[j]) * (outputFF.tab[0, 0] - target[j]) / 2;
}
for (int j = 0; j < dataTest.Length; j++)
{
d[0] = dataTest[j];
outputFF = nn.FeedForward(d);
sumTest += (outputFF.tab[0, 0] - targetTest[j]) * (outputFF.tab[0, 0] - targetTest[j]) / 2;
}
EpochErrorTest.Add(new DataPoint(epochs, sumTest / dataTest.Length));
EpochError.Add(new DataPoint(epochs, sum / data.Length));
++epochs;
}
/*
* for (int i = 0; i < data.Length; ++i)
* {
* d[0] = data[i];
* outputFF = nn.FeedForward(d);
* punktySieci.Points.Add(new DataPoint(d[0], outputFF.tab[0, 0]));
* outerSpace[i] = outputFF.tab[0, 0];
* //Console.WriteLine("x: " + d[0] + " y: " + outputFF.tab[0, 0]);
* //outputFF.DisplayMatrix();
* }
*/
for (int i = 0; i < dataTest.Length; ++i)
{
d[0] = dataTest[i];
outputFF = nn.FeedForward(d);
punktySieci.Points.Add(new DataPoint(d[0], outputFF.tab[0, 0]));
outerSpaceTest[i] = outputFF.tab[0, 0];
}
SortX(data, outerSpace);
SortX(dataTest, outerSpaceTest);
/*
* for(int i = 0; i < data.Length; ++i)
* {
* punktyKresek.Points.Add(new DataPoint(data[i], outerSpace[i]));
* }
*
* for (int i = 0; i < dataTest.Length; ++i)
* {
* punktyKresek.Points.Add(new DataPoint(dataTest[i], outerSpaceTest[i]));
* }
*/
//for error:
for (int i = 0; i < epoch; ++i)
{
punktyBleduTestowego.Points.Add(EpochErrorTest[i]);
punktyBledu.Points.Add(EpochError[i]);
}
pm2.Series.Add(punktyBleduTestowego);
pm2.Series.Add(punktyBledu);
pm2.Axes.Add(new OxyPlot.Axes.LinearAxis {
Position = OxyPlot.Axes.AxisPosition.Bottom, MinimumPadding = 0.1, MaximumPadding = 0.1, MajorGridlineStyle = LineStyle.Solid, MinorGridlineStyle = LineStyle.Dot, Title = "Epoka"
});
pm2.Axes.Add(new OxyPlot.Axes.LinearAxis {
Position = OxyPlot.Axes.AxisPosition.Left, MinimumPadding = 0.1, MaximumPadding = 0.1, MajorGridlineStyle = LineStyle.Solid, MinorGridlineStyle = LineStyle.Dot, Title = "Błąd"
});
//for points
pm.Series.Add(punktySerii);
pm.Series.Add(punktySieci);
pm.Series.Add(punktySeriiTestu);
//pm.Series.Add(punktyKresekTestu);
//pm.Series.Add(punktyKresek);
}