Regularization.cxx

Go to the documentation of this file.

// @(#)root/tmva/tmva/dnn:$Id$
// Author: Simon Pfreundschuh 21/07/16
 
/*************************************************************************
 * Copyright (C) 2016, Simon Pfreundschuh                                *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/
 
///////////////////////////////////////////////////////////////////////
// Implementation of the regularization functionals and gradients    //
// for the multi-threaded CPU implementation using Roots TThreadExecutor. //
///////////////////////////////////////////////////////////////////////
 
#include "TMVA/DNN/Architectures/Reference.h"
 
namespace TMVA
{
namespace DNN
{
 
//______________________________________________________________________________
template<typename AFloat>
AFloat TCpu<AFloat>::L1Regularization(const TCpuMatrix<AFloat> &Weights)
{
   const AFloat  *data = Weights.GetRawDataPointer();
 
   size_t nElements =  Weights.GetNoElements();
   size_t nSteps = TCpuMatrix<AFloat>::GetNWorkItems(nElements);
 
   std::vector<AFloat> temp(nElements/nSteps + 1);
 
   auto f = [&data, &temp, nElements, nSteps](UInt_t workerID)
   {
      size_t iMax = std::min(workerID+nSteps, nElements);
      size_t iWorker = workerID/nSteps; 
      for (size_t i = workerID; i < iMax; ++i) {
         temp[iWorker] += fabs(data[i]);
      }
   };
 
   auto reduction = [](const std::vector<AFloat> & v )
   {
      return std::accumulate(v.begin(),v.end(),AFloat{});
   };
   // auto reduction = [](AFloat sum1, AFloat sum2)
   // {
   //    return sum1 + sum2;
   // };
   Weights.GetThreadExecutor().Foreach(f, ROOT::TSeqI(0,nElements,nSteps) ); 
   return Weights.GetThreadExecutor().Reduce(temp, reduction);
}
 
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::AddL1RegularizationGradients(
    TCpuMatrix<AFloat> & B,
    const TCpuMatrix<AFloat> & A,
    AFloat weightDecay)
{
         AFloat  *dataB     =  B.GetRawDataPointer();
   const AFloat  *dataA      = A.GetRawDataPointer();
 
   size_t nElements =  B.GetNoElements();
   R__ASSERT(A.GetNoElements() == nElements);
   size_t nSteps = TCpuMatrix<AFloat>::GetNWorkItems(nElements);
   
 
 
   auto f = [&dataA, &dataB, weightDecay, nElements, nSteps](UInt_t workerID)
   {
      size_t iMax = std::min(workerID+nSteps, nElements);
      for (size_t i = workerID; i < iMax; ++i) {
         AFloat sign = (dataA[i] < 0.0) ? -1.0 : 1.0;
         dataB[i] += weightDecay * sign;
      }
      return 0;
   };
 
   if (nSteps < nElements) {
#ifdef DL_USE_MTE
      B.GetThreadExecutor().Foreach(f, ROOT::TSeqI(0,nElements, nSteps));
#else
      for (size_t i = 0;  i < nElements; i+=nSteps)
         f(i);
#endif
   } else  {
      f(0); 
   }
}
 
//______________________________________________________________________________
template<typename AFloat>
AFloat TCpu<AFloat>::L2Regularization(const TCpuMatrix<AFloat> &Weights)
{
   const AFloat  *data = Weights.GetRawDataPointer();
 
   size_t nElements =  Weights.GetNoElements();
   size_t nSteps = TCpuMatrix<AFloat>::GetNWorkItems(nElements);
 
   std::vector<AFloat> temp(nElements/nSteps + 1);
 
   auto f = [&data, &temp, nElements, nSteps](UInt_t workerID)
   {
      size_t iMax = std::min(workerID+nSteps, nElements);
      size_t iWorker = workerID/nSteps;
 
      for (size_t i = workerID; i < iMax; ++i) {
         temp[iWorker] += data[i] * data[i];
      }
   };
 
   auto reduction = [](const std::vector<AFloat> & v )
   {
      return std::accumulate(v.begin(),v.end(),AFloat{});
   };
   // auto reduction = [](AFloat sum1, AFloat sum2)
   // {
   //    return sum1 + sum2;
   // };
 
   Weights.GetThreadExecutor().Foreach(f, ROOT::TSeqI(0,nElements,nSteps) ); 
   return Weights.GetThreadExecutor().Reduce(temp, reduction);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::AddL2RegularizationGradients(
    TCpuMatrix<AFloat> & B,
    const TCpuMatrix<AFloat> & A,
    AFloat weightDecay)
{
         AFloat  *dataB     =  B.GetRawDataPointer();
   const AFloat  *dataA      = A.GetRawDataPointer();
 
      size_t nElements =  B.GetNoElements();
   R__ASSERT(A.GetNoElements() == nElements);
   size_t nSteps = TCpuMatrix<AFloat>::GetNWorkItems(nElements);
 
   auto f = [&dataA, &dataB, weightDecay, nElements, nSteps](UInt_t workerID)
   {
      size_t iMax = std::min(workerID+nSteps, nElements);
      for (size_t i = workerID; i < iMax; ++i) {
         dataB[i] += 2.0 * weightDecay * dataA[i];
      }
      return 0;
   };
 
   if (nSteps < nElements) {
#ifdef DL_USE_MTE
      B.GetThreadExecutor().Foreach(f, ROOT::TSeqI(0,nElements, nSteps));
#else
      for (size_t i = 0;  i < nElements; i+=nSteps)
         f(i);
#endif
   } else {
      f(0);
   }
}
 
 
} // namespace DNN
} // namespace TMVA