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ROperator_Selu.hxx
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1#ifndef TMVA_SOFIE_ROPERATOR_SELU
2#define TMVA_SOFIE_ROPERATOR_SELU
3
5#include "TMVA/ROperator.hxx"
6#include "TMVA/RModel.hxx"
7
8#include <sstream>
9
10namespace TMVA{
11namespace Experimental{
12namespace SOFIE{
13
14template <typename T>
15class ROperator_Selu final : public ROperator
16{
17
18private:
19
20 std::string fNX;
21 std::string fNY;
22 std::vector<size_t> fShape;
23
24public:
26 ROperator_Selu(std::string nameX, std::string nameY):
27 fNX(UTILITY::Clean_name(nameX)), fNY(UTILITY::Clean_name(nameY)){}
28
29 std::vector<ETensorType> TypeInference(std::vector<ETensorType> input){
30 return input;
31 }
32
33 std::vector<std::vector<size_t>> ShapeInference(std::vector<std::vector<size_t>> input){
34 auto ret = input; //suggest copy to compiler
35 return ret;
36 }
37
38 void Initialize(RModel& model){
39 if (model.CheckIfTensorAlreadyExist(fNX) == false){ //input must be a graph input, or already initialized intermediate tensor
40 throw std::runtime_error("TMVA SOFIE Selu Op Input Tensor is not found in model");
41 }
42 fShape = model.GetTensorShape(fNX);
44 }
45
46
47 std::string Generate(std::string OpName){
48 OpName = "op_" + OpName;
49 if (fShape.empty()){
50 throw std::runtime_error("TMVA SOFIE Operator Selu called to Generate without being initialized first");
51 }
52 std::stringstream out;
53 int length = 1;
54 for(auto& i: fShape){
55 length *= i;
56 }
57 out << "\t" << "for (int id = 0; id < " << length << " ; id++){\n";
58 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";
59 out << "\t}\n";
60 return out.str();
61 }
62
63 std::vector<std::string> GetStdLibs() { return { std::string("cmath") };}
64};
65
66}//SOFIE
67}//Experimental
68}//TMVA
69
70
71#endif //TMVA_SOFIE_ROPERATOR_SELU
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void input
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h length
const ETensorType & GetTensorType(std::string name)
Definition RModel.cxx:94
void AddIntermediateTensor(std::string tensor_name, ETensorType type, std::vector< Dim > dim_shape)
Definition RModel.cxx:203
bool CheckIfTensorAlreadyExist(std::string tensor_name)
Definition RModel.cxx:122
const std::vector< size_t > & GetTensorShape(std::string name)
Definition RModel.cxx:56
std::vector< std::vector< size_t > > ShapeInference(std::vector< std::vector< size_t > > input)
std::vector< std::string > GetStdLibs()
std::string Generate(std::string OpName)
ROperator_Selu(std::string nameX, std::string nameY)
std::vector< ETensorType > TypeInference(std::vector< ETensorType > input)
create variable transformations