public void TrainWithTeach(Matrix x, int y, double norm = 1e-3, double moment = 0.0, double lambda = 0.0, Optimizer optimizer = Optimizer.SGD)
{
Tensor4 X = new Tensor4(x.width, x.height, 1, 1);
X.elements = x.elements;
Tensor4 Y = new Tensor4(output.width, output.height, output.deep, 1);
if (y != -1)
{
Y[0, 0, 0, y] = 1.0;
}
BackwardBase(X, Y, norm, moment, lambda, optimizer);
}
public void TrainWithTeach(Vector x, int y, double val, double norm = 1e-3, double moment = 0.0, double lambda = 0.0, Optimizer optimizer = Optimizer.SGD)
{
Tensor4 X = new Tensor4(x.Length, 1, 1, 1);
X.elements = x.elements;
Tensor4 Y = new Tensor4(output.width, output.height, output.deep, 1);
if (y != -1)
{
Y[0, 0, 0, y] = 1.0;
}
for (int i = 0; i < layers.Count; i++)
{
if (layers[i] is Hand)
{
layers[i].output.elements[layers[i].output.elements.Length - 1] = val;
break;
}
}
BackwardBase(X, Y, norm, moment, lambda, optimizer);
}
public void TrainWithTeach(Tensor4 x, Tensor4 y, double norm = 1e-3, double moment = 0.0, double lambda = 0.0, Optimizer optimizer = Optimizer.SGD)
{
BackwardBase(x, y, norm, moment, lambda, optimizer);
}
public void TrainWithTeach(Tensor4 x, int[] y, double norm = 1e-3, double moment = 0.0, double lambda = 0.0, Optimizer optimizer = Optimizer.SGD)
{
Tensor4 Y = new Tensor4(output.width, output.height, output.deep, output.bs);
if (output.width != 1 && output.height * output.deep == 1)
{
for (int i = 0; i < y.Length; i++)
{
if (y[i] != -1)
{
Y[i, 0, 0, y[i]] = 1.0;
}
}
}
else if (output.deep != 1 && output.height * output.width == 1)
{
for (int i = 0; i < y.Length; i++)
{
if (y[i] != -1)
{
Y[i, y[i], 0, 0] = 1.0;
}
else
{
throw new Exception();
}
}
}
BackwardBase(x, Y, norm, moment, lambda, optimizer);
}
void BackwardBase(Tensor4 x, Tensor4 y, double norm, double moment, double lambda, Optimizer optimizer)
{
Tensor4[] grads, weights;
Vector E;
Matrix A;
Vector b;
switch (optimizer)
{
#region SGD
case Optimizer.SGD:
CalcDelts(x, y);
CalcGrads(lambda);
Train(norm, moment);
break;
#endregion
#region ConjGrads
case Optimizer.ConjGrads:
CalcDelts(x, y);
grads = CalcGrads(lambda);
weights = GetWeights();
if (lastGrads == null)
{
lastGrads = new Tensor4[layers.Count];
lastB = new Tensor4[layers.Count];
for (int i = 0; i < lastGrads.Length; i++)
{
if (grads[i] != null)
{
lastGrads[i] = new Tensor4(grads[i].width, grads[i].height, grads[i].deep, grads[i].bs);
lastB[i] = new Tensor4(grads[i].width, grads[i].height, grads[i].deep, grads[i].bs);
}
}
}
for (int i = 0; i < layers.Count; i++)
{
if (grads[i] != null)
{
double lG_l2 = lastGrads[i].EuclidNorm();
double w = (lG_l2 != 0.0) ? grads[i].EuclidNorm() / lG_l2 : 0.0;
lastB[i] = grads[i] + w * lastB[i];
weights[i] -= norm * lastB[i];
}
}
lastGrads = grads;
SetWeights(weights);
break;
#endregion
#region Adam
case Optimizer.Adam:
CalcDelts(x, y);
grads = CalcGrads(lambda);
weights = GetWeights();
if (m == null || v == null)
{
m = new Tensor4[grads.Length];
v = new Tensor4[grads.Length];
for (int i = 0; i < grads.Length; i++)
{
if (grads[i] != null)
{
m[i] = new Tensor4(grads[i].width, grads[i].height, grads[i].deep, grads[i].bs);
v[i] = new Tensor4(grads[i].width, grads[i].height, grads[i].deep, grads[i].bs);
}
}
}
Parallel.For(0, grads.Length, i =>
{ //for (int i = 1; i < grads.Length; i++)
if (grads[i] != null)
{
m[i] = b1 * m[i] + (1.0 - b1) * grads[i];
v[i] = b2 * v[i] + (1.0 - b2) * (grads[i] * grads[i]);
var M = m[i] / (1.0 - b1);
var V = v[i] / (1.0 - b2);
weights[i] -= norm * M / (V.ElementsPow(0.5) + e);
}
});
SetWeights(weights);
break;
#endregion
#region Marat
case Optimizer.Marat:
CalcDelts(x, y);
grads = CalcGrads(lambda);
weights = GetWeights();
int d = x.bs;
E = CalcErrRootMSEBase2(x, y);
//*
for (int i = 0; i < weights.Length; i++)
{
if (grads[i] != null)
{
if (d < weights[i].dhw)
{
throw new Exception("Недостаточно обучающей выборки.");
}
A = new Matrix(weights[i].dhw, weights[i].dhw);
b = new Vector(weights[i].dhw);
var w = layers[i].WeightsNeurons();
for (int n = 0; n < w.Length; n++)
{
}
}
}
//*
//*/
/*
* k = 0;
* for (int i = 0; i < weights.Length; i++)
* {
* if (grads[i] != null)
* {
* for (int j = 0; j < weights[i].dhw; j++)
* {
* weights[i].elements[j] = a[k++];
* }
* }
* }
* //*/
SetWeights(weights);
break;
#endregion
#region Taylor
case Optimizer.Taylor:
CalcDelts(x, y);
grads = CalcGrads(lambda);
weights = GetWeights();
E = CalcErrRootMSEBase2(x, y);
if (E.Length != 1)
{
throw new Exception("Ты пидор");
}
for (int i = 1; i < grads.Length; i++)
{ //for (int i = 1; i < grads.Length; i++)
if (grads[i] != null)
{
for (int j = 0; j < grads[i].elements.Length; j++)
{
var grad = E[0] / grads[i].elements[j];
if (!Double.IsNaN(grad) && !Double.IsInfinity(grad))
{
weights[i] -= norm * grad;
}
else
{
}
}
}
}
;
SetWeights(weights);
//Train(norm, moment);
break;
#endregion
#region NonIteratorLinear
case Optimizer.NonIteratorLinear:
int wN = layers[layers.Count - 1].weights.height;
var input = x; //[k, 0, 0, j]
for (int i = 0; i < layers[layers.Count - 1].weights.width; i++)
{
A = new Matrix(wN, wN);
b = new Vector(wN);
for (int j = 0; j < b.Length; j++)
{
for (int k = 0; k < input.bs; k++)
{
b[j] += y[k, 0, 0, i] * input[k, 0, 0, j];
}
}
for (int q = 0; q < wN; q++)
{
for (int n = 0; n < wN; n++)
{
for (int k = 0; k < input.bs; k++)
{
A[q, n] += input[k, 0, 0, n] * input[k, 0, 0, q];
}
}
}
//A.Transpose();
var result = matlib.SolvingSystems.SolvingSLAY(A, b);
for (int j = 0; j < wN; j++)
{
layers[layers.Count - 1].weights[0, 0, j, i] = result[j];
}
}
break;
#endregion
}
//if(lambda != 0.0)
//Regularization(lambda);
}