ActivationFunctions.hxx

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// @(#)root/tmva/tmva/dnn:$Id$
// Author: Simon Pfreundschuh 19/07/16
 
/*************************************************************************
 * 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 activation functions for multi-threaded //
 // CPU architectures using Roots TThreadExecutor and BLAS.            //
 ///////////////////////////////////////////////////////////////////
 
#include "TMVA/DNN/Architectures/Cpu.h"
#include <math.h>
 
#ifdef R__HAS_VDT
#include "vdt/tanh.h"
#define TANH_IMPL_X   vdt::fast_tanhf(x)
#else 
#define TANH_IMPL_X    tanh(x)
#endif
 
 
namespace TMVA
{
namespace DNN
{
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::ActivationFunctionForward(Tensor_t & X, EActivationFunction activFunct, 
                                             const ActivationDescriptor_t /* activationDescr */,  
                                             const double /* coef */, const AFloat /*alpha */, const AFloat /*beta*/)
{
   // scaling and translation is not yet implemented
   TMVA::DNN::evaluate<TCpu<AFloat>>( X, activFunct);
}
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::ActivationFunctionBackward(Tensor_t & dX, const Tensor_t & /* Y */,  
                                                const Tensor_t & dY, const Tensor_t & X,
                                                EActivationFunction activFunct,
                                                const ActivationDescriptor_t /* activationDescr */,
                                                const AFloat /* alpha */, const AFloat /* beta */)
{
   // scaling and translation not yet implemented
   // output tensor (Y) could also be used to speed up derivative calculation
   // compute dx = f'(x)
   TMVA::DNN::evaluateDerivative<TCpu<AFloat>>(dX, activFunct, X); 
    // Compute element-wise product.  dx = f'(x) * dY
   Hadamard(dX, dY);
}
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::IdentityDerivative(TCpuTensor<AFloat> & B,
                                      const TCpuTensor<AFloat> &/*A*/)
{
   auto f = [](AFloat) {return 1.0;};
   B.Map(f);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::Relu(TCpuTensor<AFloat> & B)
{
   auto f = [](AFloat x) {return (x < 0.0) ? 0.0 : x;};
   B.Map(f);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::ReluDerivative(TCpuTensor<AFloat> & B,
                                               const TCpuTensor<AFloat> &A)
{
   auto f = [](AFloat x) {return (x < 0.0) ? 0.0 : 1.0;};
   B.MapFrom(f, A);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::Sigmoid(TCpuTensor<AFloat> & B)
{
   auto f = [](AFloat x) {return 1.0 / (1.0 + exp(-x));};
   B.Map(f);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::SigmoidDerivative(TCpuTensor<AFloat> & B,
                                     const TCpuTensor<AFloat> &A)
{
   auto f = [](AFloat x) {
      AFloat sig = 1.0 / (1.0 + exp(-x));
      return sig * (1.0 - sig);
   };
   B.MapFrom(f, A);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::Tanh(TCpuTensor<AFloat> & B)
{
   auto f = [](AFloat x) {return TANH_IMPL_X;};
   B.Map(f);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::TanhDerivative(TCpuTensor<AFloat> & B,
                                  const TCpuTensor<AFloat> &A)
{
   auto f = [](AFloat x) {
      AFloat t = TANH_IMPL_X;
      return 1 - t * t;
   };
   B.MapFrom(f, A);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::SymmetricRelu(TCpuTensor<AFloat> & B)
{
   auto f = [](AFloat x) {return fabs(x);};
   B.Map(f);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::SymmetricReluDerivative(TCpuTensor<AFloat> & B,
                                           const TCpuTensor<AFloat> &A)
{
   auto f = [](AFloat x) {
      return (x < 0.0) ? -1.0 : 1.0;
   };
   B.MapFrom(f, A);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::SoftSign(TCpuTensor<AFloat> & B)
{
   auto f = [](AFloat x) {return x / (1 + fabs(x));};
   B.Map(f);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::SoftSignDerivative(TCpuTensor<AFloat> & B,
                                      const TCpuTensor<AFloat> &A)
{
   auto f = [](AFloat x) {
      x = 1.0 + fabs(x);
      x = 1.0 / (x * x);
      return x;
   };
   B.MapFrom(f, A);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::Gauss(TCpuTensor<AFloat> & B)
{
   auto f = [](AFloat x) {return exp(- x * x);};
   B.Map(f);
}
 
//______________________________________________________________________________
template<typename AFloat>
void TCpu<AFloat>::GaussDerivative(TCpuTensor<AFloat> & B,
                                   const TCpuTensor<AFloat> &A)
{
   auto f = [](AFloat x) {return - 2.0 * x * exp(- x * x);};
   B.MapFrom(f, A);
}
 
} // namespace DNN
} // namespace TMVA