public void Learn(List <SygmoidNeural> learn)
{
CSVFileGetData cSVFileGetData = new CSVFileGetData();
for (int g = 0; g < Epoch; g++)
{
// считать данные
cSVFileGetData.GetData(g);
// передать данные на вход
for (int i = 0; i < 784; i++)
{
if (cSVFileGetData.Data[i] == 0.0)
{
}
learn[i].X = cSVFileGetData.Data[i];
}
int count = 0;
foreach (ILearn n in learn)
{
//Console.WriteLine(count);
n.Function();
n.OutWeight();
count++;
}
// передать ошибку выходным нейронам
for (int i = 884; i < 894; i++)
{
learn[i].ErrorNumber = Math.Abs(learn[i].XResult - cSVFileGetData.TrueAnswer[i - 884]);
//Console.WriteLine(OutputNeurals[i].ErrorNumber+"Error output");
//Console.WriteLine();
//Console.WriteLine();
//Console.WriteLine();
//Console.WriteLine();
//Console.WriteLine(OutputNeurals[i].XResult+"--------------");
//Console.WriteLine(OutputNeurals[i].X + "Input-");
}
for (int i = learn.Count - 1; i >= 0; i--)
{
//Console.WriteLine(i + " learn elemnt");
learn[i].Learn();
}
Console.WriteLine(g + " epoch complete");
double SumLayerInput = 0;
double SumLayerHidden = 0;
double SumLayerOutput = 0;
foreach (SygmoidNeural n in InputNeurals)
{
SumLayerInput += n.ErrorNumberLayer;
}
foreach (SygmoidNeural n in HiddenNeurals)
{
SumLayerHidden += n.ErrorNumberLayer;
}
foreach (SygmoidNeural n in OutputNeurals)
{
SumLayerOutput += n.ErrorNumberLayer;
}
Console.WriteLine(SumLayerInput + " SumLayerInput");
Console.WriteLine(SumLayerHidden + " SumLayerHidden");
Console.WriteLine(SumLayerOutput + " SumLayerOutput");
SumLayerInput = 0;
SumLayerHidden = 0;
SumLayerOutput = 0;
//for (int i = 0; i < 10; i++)
//{
// Console.WriteLine(AllNeurals[i].Weight[0].weight + "------------neural epoch");
//}
//Console.WriteLine();
//Console.WriteLine();
//Console.WriteLine();
//foreach (SygmoidNeural n in HiddenNeurals)
//{
// if (n.Weight != null)
// {
// foreach (Weight nn in n.Weight)
// Console.WriteLine(nn.weight + "-------------------");
// }
//}
}
}
public void ToDetermine()
{
// считать данные
CSVFileGetData cSVFileGetData = new CSVFileGetData();
List <double> outAnswer = new List <double>();
for (int g = 500; g < 530; g++)
{
// передать данные на входные нейроны
cSVFileGetData.GetData(g);
for (int i = 0; i < InputNeurals.Count; i++)
{
//if (i == 295)
//{
// Console.WriteLine(cSVFileGetData.Data[i] + "-----------------");
//}
//Console.WriteLine(cSVFileGetData.Data[i] + "-----------------"+i);
InputNeurals[i].X = cSVFileGetData.Data[i];
}
//for(int i=0;i<784;i++)
//{
// Console.WriteLine(AllNeurals[i].X + " AllNeurals input");
// Console.WriteLine(InputNeurals[i].X + " InputNeurals input");
//}
// посчитать результат
foreach (SygmoidNeural n in AllNeurals)
{
n.Function();
n.OutWeight();
}
foreach (SygmoidNeural n in OutputNeurals)
{
Console.WriteLine(n.XResult);
//Console.WriteLine(n.X + "-----------------------");
outAnswer.Add(n.XResult);
}
//for (int i = 884; i < 894; i++)
//{
// Console.WriteLine(AllNeurals[i].XResult + " AllNeurals input");
// //Console.WriteLine(OutputNeurals[i - 884].XResult + " OutputNeurals input");
//}
// считать результат выходных нейронов
for (int i = 0; i < outAnswer.Count; i++)
{
if (CompareBoolean(outAnswer[i], outAnswer))
{
//foreach (double nn in outAnswer)
//{
// Console.WriteLine(nn);
//}
Console.WriteLine("Answer:" + i + ". Real answer:" + cSVFileGetData.TrueAnswerNumber);
break;
}
}
outAnswer.Clear();
// другой метод подсчёта
// передать данные на входные нейроны
for (int i = 0; i < InputNeurals.Count; i++)
{
//if (i == 295)
//{
// Console.WriteLine(cSVFileGetData.Data[i] + "-----------------");
//}
//Console.WriteLine(cSVFileGetData.Data[i] + "-----------------"+i);
InputNeurals[i].X = cSVFileGetData.Data[i];
}
// пропустить через сигмоид
for (int i = 0; i < InputNeurals.Count; i++)
{
InputNeurals[i].XResult = SygmoidFunction(InputNeurals[i].X);
InputNeurals[i].X = 0;
}
double X;
// посчитать результат
for (int i = 0; i < HiddenNeurals.Count; i++)
{
X = 0;
for (int ii = 0; ii < HiddenNeurals[i].PreviousLayer.Count; ii++)
{
X += HiddenNeurals[i].PreviousLayer[ii].XResult * HiddenNeurals[i].PreviousWeight[ii].weight;
}
HiddenNeurals[i].X = X;
}
// пропустить через сигмоид
for (int i = 0; i < HiddenNeurals.Count; i++)
{
HiddenNeurals[i].XResult = SygmoidFunction(HiddenNeurals[i].X);
HiddenNeurals[i].X = 0;
}
for (int i = 0; i < OutputNeurals.Count; i++)
{
X = 0;
for (int ii = 0; ii < OutputNeurals[i].PreviousLayer.Count; ii++)
{
X += OutputNeurals[i].PreviousLayer[ii].XResult * OutputNeurals[i].PreviousWeight[ii].weight;
}
OutputNeurals[i].X = X;
}
// пропустить через сигмоид
for (int i = 0; i < OutputNeurals.Count; i++)
{
OutputNeurals[i].XResult = SygmoidFunction(OutputNeurals[i].X);
OutputNeurals[i].X = 0;
}
foreach (SygmoidNeural n in OutputNeurals)
{
Console.WriteLine(n.XResult);
//Console.WriteLine(n.X + "-----------------------");
outAnswer.Add(n.XResult);
}
// считать результат выходных нейронов
for (int i = 0; i < outAnswer.Count; i++)
{
if (CompareBoolean(outAnswer[i], outAnswer))
{
//foreach (double nn in outAnswer)
//{
// Console.WriteLine(nn);
//}
Console.WriteLine("Answer:" + i + ". Real answer:" + cSVFileGetData.TrueAnswerNumber);
break;
}
}
outAnswer.Clear();
Console.WriteLine();
Console.WriteLine(g + " Iteration complete");
Console.WriteLine();
}
}