public NNValue Nonlin(INonlinearity neuron, NNValue m)
{
NNValue returnObj = new NNValue(m.H, m.W);
int n = m.DataInTensor.Length;
returnObj = neuron.Forward(m);
if (this.ApplyBackprop)
{
Runnable bp = new Runnable();
bp.Run = delegate()
{
var data = neuron.Backward(m);
for (int i = 0; i < n; i++)
{
m.DifData[i] += data.DataInTensor[i] * returnObj.DifData[i];
}
};
Backprop.Add(bp);
}
return(returnObj);
}
/// <summary>
/// Макс пул
/// </summary>
/// <param name="inp"></param>
/// <param name="h"></param>
/// <param name="w"></param>
/// <returns></returns>
public NNValue MaxPooling(NNValue input, int h, int w)
{
//Func<double[], double> funcMax = (dataArray) =>
//{
// return dataArray.Max();
//};
int outpH = input.H / h, outpW = input.W / w, outpD = input.D;
double[] data = new double[h * w];
bool[,,] map = new bool[input.H, input.W, input.D];
if ((outpW < 1) || (outpH < 1))
{
throw new Exception("Недостаточная размерность выхода");
}
int modW = input.W % w;
int modH = input.H % h;
int maxX = input.W - modW;
int maxY = input.H - modH;
NNValue returnObj = new NNValue(outpH, outpW, outpD);
for (int s = 0; s < outpD; s++)
{
for (int y = 0, y1 = 0; y < maxY; y += h, y1++)
{
for (int x = 0, x1 = 0; x < maxX; x += w, x1++)
{
int i = y, j = x;
for (int dy = 0; dy < h; dy++)
{
for (int dx = 0; dx < w; dx++)
{
if (input[i, j, s] < input[y + dy, x + dx, s])
{
i = y + dy;
j = x + dx;
}
}
}
var max = input[i, j, s];
returnObj[y1, x1, s] = max;
map[i, j, s] = true;
}
}
}
;
//------------------------------------------------------------------------------------------
// Обратный проход
if (this.ApplyBackprop)
{
int i = 0;
Runnable bp = new Runnable();
bp.Run = delegate()
{
for (int n = 0; n < input.D; n++)
{
for (int y = 0; y < input.H; y++)
{
for (int x = 0; x < input.W; x++)
{
input.DifData[input.W * y + x + input.W * input.H * n] = map[y, x, n]? returnObj.DifData[i++]:0;
}
}
}
};
Backprop.Add(bp);
}
return(returnObj);
}
/// <summary>
/// Свертка
/// </summary>
/// <param name="input">Тензор входа</param>
/// <param name="filters">Фильтры</param>
public NNValue Convolution(NNValue input, NNValue[] filters, int padX, int padY)
{
int outpH, outpW, outpD = filters.Length;
outpH = input.H - filters[0].H + 1 + padY;
outpW = input.W - filters[0].W + 1 + padX;
if ((outpW < 1) || (outpH < 1))
{
throw new Exception("Недостаточная размерность выхода");
}
NNValue returnObj = new NNValue(outpH, outpW, outpD);
// Parallel.For(0, outpD, new ParallelOptions() { MaxDegreeOfParallelism = 2}, s =>
for (int s = 0; s < outpD; s++)
{
for (int y = -padY; y < outpH; y++)
{
for (int x = -padX; x < outpW; x++)
{
for (int z = 0; z < input.D; z++)
{
for (int dy = 0; dy < filters[0].H; dy++)
{
int y1 = y + dy;
for (int dx = 0; dx < filters[0].W; dx++)
{
int x1 = x + dx;
if ((x1 > -1) && (y1 > -1) && (x1 < outpW) && (y1 < outpH))
{
returnObj[y1, x1, s] += input[y + dy, x + dx, z] * filters[s][dy, dx, z];
}
}
}
}
}
}
}
//------------------------------------------------------------------------------------------
// Обратный проход
if (this.ApplyBackprop)
{
Runnable bp = new Runnable();
bp.Run = delegate()
{
Parallel.For(0, outpD, new ParallelOptions()
{
MaxDegreeOfParallelism = 2
}, d =>
{
for (int z = 0; z < input.D; z++)
{
for (int y = 0; y < filters[0].H; y++)
{
for (int x = 0; x < filters[0].W; x++)
{
for (int a = -padY; a < outpH; a++)
{
for (int b = -padX; b < outpW; b++)
{
int x1 = b + x;
int y1 = a + y;
if ((y1 > -1) && (x1 > -1) && (y1 < outpH) && (x1 < outpW))
{
double delt = returnObj.DifData[outpW * y1 + x1 + outpW * outpH * d];
filters[d].DifData[filters[0].W * y + x + filters[0].W * filters[0].H * z] +=
delt * input[a + y, b + x, z];
}
// --------------- ------------- ------------ ---------------//
}
}
}
}
}
});
Parallel.For(0, input.D, new ParallelOptions()
{
MaxDegreeOfParallelism = 2
}, n =>
{
for (int y = -padY; y < input.H; y++)
{
for (int x = -padX; x < input.W; x++)
{
for (int i = 0; i < outpD; i++)
{
for (int dy = 0; dy < filters[0].H; dy++)
{
var dyy = y - dy;
for (int dx = 0; dx < filters[0].W; dx++)
{
var dxx = x - dx;
if ((dyy > -1) && (dxx > -1) && (dyy < outpH) && (dxx < outpW))
{
if ((dxx > -1) && (dyy > -1) && (dyy < outpH) && (dxx < outpW))
{
double delt = returnObj.DifData[outpW * (y - dy) + (x - dx) + outpW * outpH * i];
input.DifData[input.W * y + x + input.W * input.H * n] += delt * filters[i][dy, dx, n];
}
}
}
}
}
}
}
});
};
Backprop.Add(bp);
}
return(returnObj);
}
public NNValue smul(double s, NNValue m)
{
NNValue returnObj = smul(m, s);
return(returnObj);
}
public NNValue smul(NNValue m, double s)
{
NNValue m2 = NNValue.Uniform(m.H, m.W, s);
return(Elmul(m, m2));
}
public NNValue Subtract(NNValue m1, NNValue m2)
{
return(Add(m1, Neg(m2)));
}
public NNValue OneMinus(NNValue m)
{
NNValue ones = NNValue.Ones(m.H, m.W);
return(Subtract(ones, m));
}