ROperator_Selu.hxx

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#ifndef TMVA_SOFIE_ROPERATOR_SELU
#define TMVA_SOFIE_ROPERATOR_SELU
 
#include "TMVA/SOFIE_common.hxx"
#include "TMVA/ROperator.hxx"
#include "TMVA/RModel.hxx"
 
#include <sstream>
 
namespace TMVA{
namespace Experimental{
namespace SOFIE{
 
template <typename T>
class ROperator_Selu final : public ROperator
{
 
private:
 
   std::string fNX;
   std::string fNY;
   std::vector<size_t> fShape;
 
public:
   ROperator_Selu(){}
   ROperator_Selu(std::string nameX, std::string nameY):
      fNX(UTILITY::Clean_name(nameX)), fNY(UTILITY::Clean_name(nameY)){}
 
   std::vector<ETensorType> TypeInference(std::vector<ETensorType> input){
      return input;
   }
 
   std::vector<std::vector<size_t>> ShapeInference(std::vector<std::vector<size_t>> input){
      auto ret = input; //suggest copy to compiler
      return ret;
   }
 
   void Initialize(RModel& model){
      if (model.CheckIfTensorAlreadyExist(fNX) == false){   //input must be a graph input, or already initialized intermediate tensor
         throw std::runtime_error("TMVA SOFIE Selu Op Input Tensor is not found in model");
      }
      fShape = model.GetTensorShape(fNX);
      model.AddIntermediateTensor(fNY, model.GetTensorType(fNX), fShape);
   }
 
 
   std::string Generate(std::string OpName){
      OpName = "op_" + OpName;
      if (fShape.empty()){
         throw std::runtime_error("TMVA SOFIE Transpose Selu called to Generate without being initialized first");
      }
      std::stringstream out;
      int length = 1;
      for(auto& i: fShape){
         length *= i;
      }
      out << "\t" << "for (int id = 0; id < " << length << " ; id++){\n";
      out << "\t\t" << "tensor_" << fNY << "[id] = 1.0507009873554804934193349852946 * (std::max(float(0.0), tensor_"  << fNX << "[id]) + std::min(0.0, 1.6732632423543772848170429916717 * (std::exp(" << "tensor_" << fNX << "[id]" <<")-1)));\n";
      out << "\t}\n";
      return out.str();
   }
 
};
 
}//SOFIE
}//Experimental
}//TMVA
 
 
#endif //TMVA_SOFIE_ROPERATOR_SELU