public WeightTensor CreateWeightTensor(int row, int column, int deviceId, bool cleanWeights = false)
{
var k = buffer.GetOrAdd(row, x => new ConcurrentDictionary <int, WeightTensorList>());
var mList = k.GetOrAdd(column, x => new WeightTensorList());
WeightTensor r;
// lock (locker)
// {
// if (mList.index == mList.WeightTensors.Count)
// {
r = new WeightTensor(row, column, deviceId);
mList.WeightTensors.Add(r);
//}
//else
//{
// r = mList.WeightTensors[mList.index];
// r.ClearGradient();
//}
//mList.index++;
// }
if (cleanWeights)
{
r.ClearWeight();
}
return(r);
}
public WeightTensor CreateWeightTensor(int row, int column, int deviceId, bool cleanWeights = false)
{
WeightTensor r = new WeightTensor(row, column, deviceId);
if (cleanWeights)
{
r.ClearWeight();
}
weights.Add(r);
return(r);
}
public WeightTensor CreateWeightTensor(long[] sizes, int deviceId, bool cleanWeights = false, string name = "")
{
WeightTensor r = new WeightTensor(sizes, deviceId, name);
if (cleanWeights)
{
r.ClearWeight();
}
weights.Add(r);
return(r);
}
public WeightTensor CreateWeightTensor(int row, int column, int deviceId, bool cleanWeights = false, string name = "", bool isTrainable = false)
{
WeightTensor r = new WeightTensor(new long[2] {
row, column
}, deviceId, name: name, isTrainable: isTrainable);
if (cleanWeights)
{
r.ClearWeight();
}
weights.Add(r);
return(r);
}
