public static Tensor4[] GetRandBatch(Tensor3[] x, int[] y, int batchSize, NeuralNet net)
{
Tensor4 batchX = new Tensor4(x[0].width, x[0].height, x[0].deep, batchSize);
Tensor4 batchY = new Tensor4(net.output.width, net.output.height, net.output.deep, batchSize);
int[] indexes = new int[batchSize];
int index = 0;
while (true)
{
indexes[index] = (int)(rand.NextDouble() * x.Length);
for (int i = 0; i < index; i++)
{
if (indexes[i] == indexes[index])
{
break;
}
if (i == index - 1)
{
index++;
break;
}
}
if (index == 0)
{
index++;
}
if (index == indexes.Length)
{
break;
}
}
for (int i = 0; i < batchSize; i++)
{
for (int zInd = 0; zInd < x[indexes[i]].deep; zInd++)
{
for (int yInd = 0; yInd < x[indexes[i]].height; yInd++)
{
for (int xInd = 0; xInd < x[indexes[i]].width; xInd++)
{
batchX[i, zInd, yInd, xInd] = x[indexes[i]][zInd, yInd, xInd];
}
}
}
Tensor3 Y = new Tensor3(net.output.width, net.output.height, net.output.deep);
if (net.output.width != 1)
{
Y[0, 0, y[indexes[i]]] = 1.0;
}
else
{
Y[y[indexes[i]], 0, 0] = 1.0;
}
for (int zInd = 0; zInd < Y.deep; zInd++)
{
for (int yInd = 0; yInd < Y.height; yInd++)
{
for (int xInd = 0; xInd < Y.width; xInd++)
{
batchY[i, zInd, yInd, xInd] = Y[zInd, yInd, xInd];
}
}
}
}
return(new Tensor4[] { batchX, batchY });
}
public int Calculation(Tensor3 inp, int layNum = 0, double thres = 0.4)
{
Tensor4 input = inp.ToTensor4();
return(CalculationBase(input, layNum, thres)[0]);
}
public int Calculation(Tensor3 input)
{
return(CalculationBase(input.ToTensor4()));
}
