public void TeachWithConjugateGradients(DataCollection teacherList, int batchSize, int batchAmmount, double accuracy)
{
int epochCount = 0;
simpleNet.eps = 0;
double error = double.MaxValue;
while (error > accuracy)
{
simpleNet.eps = 0;
error = 0;
DataCollection batch = new DataCollection();
Random rd = new Random();
batch.BatchLength = batchSize;
for (int i = 0; i < batchSize; i++)
{
batch.List.Add(teacherList.List[rd.Next(0, teacherList.BatchLength)]);
}
Random rd1 = new Random();
for (int btch = 0; btch < batchAmmount; btch++)
{
for (int m = 0; m < batch.BatchLength; m++)
{
simpleNet.SetDropout(rd1, 0);
List <double> result = Result(batch.List[m].image);
string[] standartResult = batch.List[m].label.Split();
List <List <double> > simpleNetMistake = simpleNet.CalculateMistakeBackProp(standartResult);
int p = 0;
for (int l = 0; l < convNet.LayersList.Last().TablesCount; l++)
{
for (int i = 0; i < convNet.LayersList.Last().TablesList[l].NeuronsList.Count; i++)
{
for (int j = 0; j < convNet.LayersList.Last().TablesList[l].NeuronsList[i].Count; j++)
{
double sum = 0;
for (int k = 1; k < simpleNet.LayersList[1].NeuronsCount; k++)
{
sum += simpleNet.LayersList[1].NeuronsList[k].IncomingLinksList[p + 1].Weight * simpleNetMistake[0][k];
}
p++;
convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Mistake = sum;
convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Delta = sum
* convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Derivate(convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Sum);
}
}
}
for (int l = convNet.layersCount - 2; l > -1; l--)
{
convNet.CountMistakeForLayer(l);
}
}
}
epochCount++;
if (epochCount % 3 == 0)
{
SaveToFile("C://1/svertka");
}
}
}
public void TeachWithBFGS(DataCollection teacherList, int batchLength, int batchAmmount, double accuracy)
{
int epochCount = 0;
double error = double.MaxValue;
while (error > accuracy)
{
error = 0;
simpleNet.eps = 0;
DataCollection batchCollection = new DataCollection();
Random rd = new Random();
batchCollection.BatchLength = batchLength;
for (int i = 0; i < batchLength; i++)
{
batchCollection.List.Add(teacherList.List[rd.Next(0, teacherList.BatchLength)]);
}
for (int btch = 0; btch < batchAmmount; btch++)
{
for (int m = 0; m < batchLength; m++)
{
simpleNet.SetDropout(rd, 0);
List <double> result = Result(batchCollection.List[m].image);
string[] standartResult = batchCollection.List[m].label.Split();
List <List <double> > simpleNetMistake = simpleNet.CalculateMistakeBackProp(standartResult);
//Связь двух сетей . Считаем ошибку на последнем слое сверточной сети
int p = 0;
for (int l = 0; l < convNet.LayersList.Last().TablesCount; l++)
{
for (int i = 0; i < convNet.LayersList.Last().TablesList[l].NeuronsList.Count; i++)
{
for (int j = 0; j < convNet.LayersList.Last().TablesList[l].NeuronsList[i].Count; j++)
{
double sum = 0;
for (int k = 1; k < simpleNet.LayersList[1].NeuronsCount; k++)
{
sum += simpleNet.LayersList[1].NeuronsList[k].IncomingLinksList[p + 1].Weight * simpleNetMistake[0][k];
}
p++;
convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Mistake = sum;
convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Delta = sum
* convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Derivate(convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Sum);
}
}
}
for (int l = convNet.layersCount - 2; l > -1; l--)
{
convNet.CountMistakeForLayer(l);
}
for (int l = simpleNet.layersCount - 1; l > -1; l--)
{
var r = convNet.LayersList[1] as Convolutional_Layer;
r.CountHessian();
}
}
}
}
}
public void TeachWithBackPropagation(DataCollection teacherList, int batchSize, int batchAmmount, double accuracy)
{
int epochCount = 0;
simpleNet.eps = 0;
double error = double.MaxValue;
while (error > accuracy)
{
simpleNet.eps = 0;
error = 0;
DataCollection batch = new DataCollection();
Random rd = new Random();
batch.BatchLength = batchSize;
for (int i = 0; i < batchSize; i++)
{
batch.List.Add(teacherList.List[rd.Next(0, teacherList.BatchLength)]);
}
Random rd1 = new Random();
for (int btch = 0; btch < batchAmmount; btch++)
{
for (int m = 0; m < batch.BatchLength; m++)
{
simpleNet.SetDropout(rd1, 0);
List <double> result = Result(batch.List[m].image);
string[] standartResult = batch.List[m].label.Split();
List <List <double> > simpleNetMistake = simpleNet.CalculateMistakeBackProp(standartResult);
//Связь двух сетей . Считаем ошибку на последнем слое сверточной сети
int p = 0;
for (int l = 0; l < convNet.LayersList.Last().TablesCount; l++)
{
for (int i = 0; i < convNet.LayersList.Last().TablesList[l].NeuronsList.Count; i++)
{
for (int j = 0; j < convNet.LayersList.Last().TablesList[l].NeuronsList[i].Count; j++)
{
double sum = 0;
for (int k = 1; k < simpleNet.LayersList[1].NeuronsCount; k++)
{
sum += simpleNet.LayersList[1].NeuronsList[k].IncomingLinksList[p + 1].Weight * simpleNetMistake[0][k];
}
p++;
convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Mistake = sum;
convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Delta = sum
* convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Derivate(convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Sum);
//convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Delta = sum
// * convNet.LayersList.Last().TablesList[l].NeuronsList[i][j].Derivate(sum);
}
}
}
for (int l = convNet.layersCount - 2; l > -1; l--)
{
convNet.CountMistakeForLayer(l);
}
foreach (ILayersList ll in convNet.LayersList)
{
if (ll.Type == 2)
{
foreach (ITables l in ll.TablesList)
{
for (int i = 0; i < l.Core.height; i++)
{
for (int j = 0; j < l.Core.width; j++)
{
l.Core.weigth[i][j] += Math.Round(al * l.Core.mistake[i][j], 5);
l.Core.mistake[i][j] = 0;
}
}
}
}
}
simpleNet.RecalculateWeights(simpleNetMistake);
NullifyNeuronsMistake();
}
}
error = simpleNet.eps / (batchSize * batchAmmount);
System.IO.StreamWriter sw = new System.IO.StreamWriter("C://1/svertka/eps.txt", true);
sw.WriteLine(epochCount + "\t" + error);
sw.Close();
epochCount++;
if (epochCount % 3 == 0)
{
SaveToFile("C://1/svertka");
}
}
}