public double[] GetDenseWeights(double[] weights)
{
if (!prepared)
{
Prepare();
}
int rows = maps * Corners.Length;
int columns = InputShape.Aggregate(1, (acc, val) => acc * val);
int kernelSize = KernelShape.Aggregate(1, (acc, val) => acc * val);
Matrix <double> mat = Matrix <double> .Build.Dense(rows, columns);
for (int m = 0; m < maps; m++)
{
for (int i = 0; i < Corners.Length; i++)
{
var c = Corners[i];
foreach (var o in Offsets)
{
var l = Location(c, o, InputShape);
if (l < 0)
{
continue;
}
var k = Location(null, o, KernelShape);
mat[m * Corners.Length + i, l] = weights[k + m * kernelSize];
}
}
}
return(mat.ToRowMajorArray());
}
public override void Prepare()
{
if (!layerPrepared)
{
convolutionEngine.Prepare();
kernelSize = KernelShape.Aggregate(1, (acc, val) => acc * val);
if (Bias == null)
{
kernelSize++;
}
if (Weights == null)
{
return;
}
PrepareWeightsWindows();
double BiasScale = GetOutputScale();
int maps = (MapCount == null) ? 1 : MapCount.Aggregate(1, (acc, val) => acc * val);
if (HotIndices == null)
{
HotIndices = Vector <double> .Build.Dense(Corners.Length) + 1;
}
if (Bias != null)
{
biasVectors = new IVector[maps];
ParallelProcessInEnv(maps, (env, taskIndex, mapIndex) =>
{
biasVectors[mapIndex] = Factory.GetPlainVector(HotIndices * Bias[mapIndex], EVectorFormat.dense, Source.GetOutputScale() * WeightsScale);
});
}
else
{
biasVectors = new IVector[maps];
ParallelProcessInEnv(maps, (env, taskIndex, mapIndex) =>
{
biasVectors[mapIndex] = Factory.GetPlainVector(HotIndices * Weights[(mapIndex + 1) * kernelSize - 1], EVectorFormat.dense, Source.GetOutputScale() * WeightsScale);
});
}
layerPrepared = true;
}
}
public override void Prepare()
{
if (layerPrepared)
{
return;
}
convolutionEngine.Prepare();
kernelSize = KernelShape.Aggregate(1, (acc, val) => acc * val);
if (Bias == null)
{
kernelSize++;
}
if (Weights == null)
{
return;
}
PrepareWeightsWindows();
biasVectors = null;
layerPrepared = true;
}