lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F > Class Template Reference

#include <LutNetworkFixedPointRegression2Outputs.h>

Inheritance diagram for lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >:

Public Types
typedef LutNeuronLayerFixedPoint< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I >	LutLayer1
typedef LutNeuronLayerFixedPoint< layer2_input_I, layer2_input_F, layer1_neurons, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I >	LutLayer2
typedef LutNeuronLayerFixedPoint< layer3_input_I, layer3_input_F, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, 1, output0_I >	LutLayer3_0
typedef LutNeuronLayerFixedPoint< layer3_input_I, layer3_input_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, 1, output1_I >	LutLayer3_1

Public Member Functions
int	getCalibratedHwPt () override
auto &	getPtCalibrationArray ()
void	load (const std::string &filename) override
	LutNetworkFixedPointRegression2Outputs ()
void	run (std::vector< float > &inputs, float noHitVal, std::vector< double > &nnResult) override
void	runWithInterpolation ()
void	save (const std::string &filename) override
	~LutNetworkFixedPointRegression2Outputs () override
Public Member Functions inherited from lutNN::LutNetworkFixedPointRegressionBase
virtual	~LutNetworkFixedPointRegressionBase ()

Public Attributes
LutLayer1	lutLayer1
LutLayer2	lutLayer2
LutLayer3_0	lutLayer3_0
LutLayer3_1	lutLayer3_1

Static Public Attributes
static constexpr int	layer2_input_F = layer1_lut_F
static constexpr int	layer3_input_F = layer2_lut_F
static constexpr unsigned int	noHitCntShift = layer1_output_I

Private Attributes
std::array< ap_ufixed< LutLayer1::input_W, input_I, AP_TRN, AP_SAT >, inputSize >	inputArray
ap_uint< layer2_input_I >	layer1Bias
std::string	name = "LutNetworkFixedPointRegression2Outputs"
std::array< ap_uint< 9 >, 1<<(output0_I+output0_F)>	ptCalibrationArray

Detailed Description

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>
class lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >

Definition at line 48 of file LutNetworkFixedPointRegression2Outputs.h.

Member Typedef Documentation

LutLayer1

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

typedef LutNeuronLayerFixedPoint<input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I> lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutLayer1

Definition at line 60 of file LutNetworkFixedPointRegression2Outputs.h.

LutLayer2

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

typedef LutNeuronLayerFixedPoint<layer2_input_I, layer2_input_F, layer1_neurons, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I> lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutLayer2

Definition at line 77 of file LutNetworkFixedPointRegression2Outputs.h.

LutLayer3_0

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

typedef LutNeuronLayerFixedPoint<layer3_input_I, layer3_input_F, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, 1, output0_I> lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutLayer3_0

Definition at line 89 of file LutNetworkFixedPointRegression2Outputs.h.

LutLayer3_1

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

typedef LutNeuronLayerFixedPoint<layer3_input_I, layer3_input_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, 1, output1_I> lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutLayer3_1

Definition at line 99 of file LutNetworkFixedPointRegression2Outputs.h.

Constructor & Destructor Documentation

LutNetworkFixedPointRegression2Outputs()

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutNetworkFixedPointRegression2Outputs ( )

inline

Definition at line 50 of file LutNetworkFixedPointRegression2Outputs.h.

References gather_cfg::cout, lutNN::layer2_neurons, lutNN::layer3_0_inputCnt, lutNN::layer3_1_inputCnt, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer1, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer2, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_0, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_1, and lutNN::LutNeuronLayerFixedPoint< input_I, input_F, inputSize, lut_I, lut_F, neurons, output_I >::setName().

                                             {
      static_assert(layer2_neurons == (layer3_0_inputCnt + layer3_1_inputCnt));

      std::cout << "LutNetworkFixedPoint" << std::endl;
      lutLayer1.setName("lutLayer1");
      lutLayer2.setName("lutLayer2");
      lutLayer3_0.setName("lutLayer3_0");
      lutLayer3_1.setName("lutLayer3_1");
    };

~LutNetworkFixedPointRegression2Outputs()

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::~LutNetworkFixedPointRegression2Outputs ( )

inlineoverride

Definition at line 60 of file LutNetworkFixedPointRegression2Outputs.h.

{};

Member Function Documentation

getCalibratedHwPt()

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

int lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::getCalibratedHwPt ( )

inlineoverridevirtual

Implements lutNN::LutNetworkFixedPointRegressionBase.

Definition at line 154 of file LutNetworkFixedPointRegression2Outputs.h.

References lutNN::LutNeuronLayerFixedPoint< input_I, input_F, inputSize, lut_I, lut_F, neurons, output_I >::getLutOutSum(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_0, lutNN::output0_F, and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::ptCalibrationArray.

                                     {
      auto lutAddr = ap_ufixed<output0_I + output0_F + output0_F, output0_I + output0_F, AP_RND_CONV, AP_SAT>(
          lutLayer3_0.getLutOutSum()[0]);
      lutAddr = lutAddr << output0_F;
      //std::cout<<"lutLayer3_0.getLutOutSum()[0] "<<lutLayer3_0.getLutOutSum()[0]<<" lutAddr.to_uint() "<<lutAddr.to_uint()<<" ptCalibrationArray[lutAddr] "<<ptCalibrationArray[lutAddr.to_uint()]<<std::endl;
      return ptCalibrationArray[lutAddr.to_uint()].to_uint();
    }

getPtCalibrationArray()

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

auto& lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::getPtCalibrationArray ( )

inline

Definition at line 226 of file LutNetworkFixedPointRegression2Outputs.h.

References lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::ptCalibrationArray.

{ return ptCalibrationArray; }

load()

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

void lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::load ( const std::string &  filename )

inlineoverridevirtual

Implements lutNN::LutNetworkFixedPointRegressionBase.

Definition at line 191 of file LutNetworkFixedPointRegression2Outputs.h.

References a, CHECK_VAR, corrVsCorr::filename, B2GTnPMonitor_cfi::item, submitPVResolutionJobs::key, lutNN::LutNeuronLayerFixedPoint< input_I, input_F, inputSize, lut_I, lut_F, neurons, output_I >::load(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer1, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer2, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_0, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_1, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::name, lutNN::output0_F, lutNN::output0_I, lutNN::output1_F, lutNN::output1_I, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::ptCalibrationArray, contentValuesCheck::ss, str, AlCaHLTBitMon_QueryRunRegistry::string, and compare::tree.

                                                  {
      // Create an empty property tree object.
      boost::property_tree::ptree tree;

      boost::property_tree::read_xml(filename, tree);

      CHECK_VAR(tree, name, output0_I)
      CHECK_VAR(tree, name, output0_F)
      CHECK_VAR(tree, name, output1_I)
      CHECK_VAR(tree, name, output1_F)

      lutLayer1.load(tree, name);
      lutLayer2.load(tree, name);
      lutLayer3_0.load(tree, name);
      lutLayer3_1.load(tree, name);

      std::string key = "LutNetworkFixedPointRegression2Outputs.ptCalibrationArray";
      int size = ptCalibrationArray.size();
      CHECK_VAR(tree, key, size)

      auto str = tree.get<std::string>(key + ".values");

      std::stringstream ss(str);
      std::string item;

      for (auto& a : ptCalibrationArray) {
        if (std::getline(ss, item, ',')) {
          a = std::stoul(item, nullptr, 10);
        } else {
          throw std::runtime_error(
              "LutNetworkFixedPointRegression2Outputs::read: number of items get from file is smaller than lut size");
        }
      }
    }

run()

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

void lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::run ( std::vector< float > &  inputs, float  noHitVal, std::vector< double > &  nnResult  )

inlineoverridevirtual

Implements lutNN::LutNetworkFixedPointRegressionBase.

Definition at line 125 of file LutNetworkFixedPointRegression2Outputs.h.

References lutNN::LutNeuronLayerFixedPoint< input_I, input_F, inputSize, lut_I, lut_F, neurons, output_I >::getLutOutSum(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::inputArray, PixelMapPlotter::inputs, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::layer1Bias, LogTrace, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_0, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_1, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::noHitCntShift, and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::runWithInterpolation().

                                                                                             {
      unsigned int noHitsCnt = 0;
      for (unsigned int iInput = 0; iInput < inputs.size(); iInput++) {
        inputArray[iInput] = inputs[iInput];
        if (inputs[iInput] == noHitVal)
          noHitsCnt++;
      }

      unsigned int bias = (noHitsCnt << noHitCntShift);

      //layer1Bias switches the input of the layer2 (i.e. output of the layer1) do different regions in the LUTs
      //depending on the  number of layers without hits
      layer1Bias = bias;

      runWithInterpolation();

      //output0_I goes to the declaration of the lutLayer3_0, but it does not matter, as it is used only for the outputArray
      //auto layer3_0_out = ap_ufixed<output0_I+output0_F, output0_I, AP_RND_CONV, AP_SAT>(lutLayer3_0.getLutOutSum()[0]); //TODO should be AP_RND_CONV rather, but it affect the rate
      //auto layer3_1_out = ap_fixed <output1_I+output1_F, output1_I, AP_RND_CONV, AP_SAT>(lutLayer3_1.getLutOutSum()[0]); //here layer3_0_out has size 1
      auto layer3_0_out = lutLayer3_0.getLutOutSum()[0];  //here layer3_0_out has size 1
      auto layer3_1_out = lutLayer3_1.getLutOutSum()[0];  //here layer3_0_out has size 1

      nnResult[0] = layer3_0_out.to_float();
      nnResult[1] = layer3_1_out.to_float();
      LogTrace("l1tOmtfEventPrint") << "layer3_0_out[0] " << layer3_0_out[0] << " layer3_1_out[0] " << layer3_1_out[0]
                                    << std::endl;
    }

runWithInterpolation()

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

void lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::runWithInterpolation ( )

inline

Definition at line 102 of file LutNetworkFixedPointRegression2Outputs.h.

References filterCSVwithJSON::copy, lutNN::LutNeuronLayerFixedPoint< input_I, input_F, inputSize, lut_I, lut_F, neurons, output_I >::getOutWithOffset(), mps_fire::i, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::inputArray, lutNN::layer1_neurons, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::layer1Bias, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer1, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer2, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_0, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_1, and lutNN::LutNeuronLayerFixedPoint< input_I, input_F, inputSize, lut_I, lut_F, neurons, output_I >::runWithInterpolation().

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::run().

                                {
      lutLayer1.runWithInterpolation(inputArray);
      auto& layer1Out = lutLayer1.getOutWithOffset();

      std::array<ap_ufixed<layer2_input_I + layer2_input_F, layer2_input_I, AP_TRN, AP_SAT>, layer1_neurons>
          layer1OutWithBias;
      for (unsigned int i = 0; i < layer1Out.size(); i++) {
        layer1OutWithBias[i] = layer1Out[i] + layer1Bias;
      }

      lutLayer2.runWithInterpolation(layer1OutWithBias);
      auto& layer2Out = lutLayer2.getOutWithOffset();

      typename LutLayer3_0::inputArrayType lutLayer3_0_input;
      std::copy(layer2Out.begin(), layer2Out.begin() + lutLayer3_0_input.size(), lutLayer3_0_input.begin());

      typename LutLayer3_1::inputArrayType lutLayer3_1_input;
      std::copy(layer2Out.begin() + lutLayer3_0_input.size(), layer2Out.end(), lutLayer3_1_input.begin());

      lutLayer3_0.runWithInterpolation(lutLayer3_0_input);
      lutLayer3_1.runWithInterpolation(lutLayer3_1_input);
    }

save()

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

void lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::save ( const std::string &  filename )

inlineoverridevirtual

Implements lutNN::LutNetworkFixedPointRegressionBase.

Definition at line 162 of file LutNetworkFixedPointRegression2Outputs.h.

References a, corrVsCorr::filename, submitPVResolutionJobs::key, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer1, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer2, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_0, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_1, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::name, lutNN::output0_F, lutNN::output0_I, lutNN::output1_F, lutNN::output1_I, lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::ptCalibrationArray, PUT_VAR, lutNN::LutNeuronLayerFixedPoint< input_I, input_F, inputSize, lut_I, lut_F, neurons, output_I >::save(), AlCaHLTBitMon_QueryRunRegistry::string, compare::tree, and MCScenario_CRAFT1_22X::write_xml().

                                                  {
      // Create an empty property tree object.
      boost::property_tree::ptree tree;

      PUT_VAR(tree, name, output0_I)
      PUT_VAR(tree, name, output0_F)
      PUT_VAR(tree, name, output1_I)
      PUT_VAR(tree, name, output1_F)

      lutLayer1.save(tree, name);
      lutLayer2.save(tree, name);
      lutLayer3_0.save(tree, name);
      lutLayer3_1.save(tree, name);

      int size = ptCalibrationArray.size();
      std::string key = "LutNetworkFixedPointRegression2Outputs.ptCalibrationArray";
      PUT_VAR(tree, key, size)
      std::ostringstream ostr;
      for (auto& a : ptCalibrationArray) {
        ostr << a.to_uint() << ", ";
      }
      tree.put(key + ".values", ostr.str());

      boost::property_tree::write_xml(filename,
                                      tree,
                                      std::locale(),
                                      boost::property_tree::xml_parser::xml_writer_make_settings<std::string>(' ', 2));
    }

Member Data Documentation

inputArray

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

std::array<ap_ufixed<LutLayer1::input_W, input_I, AP_TRN, AP_SAT>, inputSize> lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::inputArray

private

Definition at line 229 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::run(), and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::runWithInterpolation().

layer1Bias

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

ap_uint<layer2_input_I> lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::layer1Bias

private

Definition at line 230 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::run(), and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::runWithInterpolation().

layer2_input_F

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

constexpr int lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::layer2_input_F = layer1_lut_F

static

Definition at line 68 of file LutNetworkFixedPointRegression2Outputs.h.

layer3_input_F

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

constexpr int lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::layer3_input_F = layer2_lut_F

static

Definition at line 80 of file LutNetworkFixedPointRegression2Outputs.h.

lutLayer1

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

LutLayer1 lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer1

Definition at line 64 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::load(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutNetworkFixedPointRegression2Outputs(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::runWithInterpolation(), and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::save().

lutLayer2

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

LutLayer2 lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer2

Definition at line 78 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::load(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutNetworkFixedPointRegression2Outputs(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::runWithInterpolation(), and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::save().

lutLayer3_0

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

LutLayer3_0 lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_0

Definition at line 90 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::getCalibratedHwPt(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::load(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutNetworkFixedPointRegression2Outputs(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::run(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::runWithInterpolation(), and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::save().

lutLayer3_1

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

LutLayer3_1 lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::lutLayer3_1

Definition at line 100 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::load(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::LutNetworkFixedPointRegression2Outputs(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::run(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::runWithInterpolation(), and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::save().

name

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

std::string lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::name = "LutNetworkFixedPointRegression2Outputs"

private

Definition at line 236 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by ElectronMVAID.ElectronMVAID::__call__(), FWLite.ElectronMVAID::__call__(), dirstructure.Directory::__create_pie_image(), DisplayManager.DisplayManager::__del__(), dqm_interfaces.DirID::__eq__(), dirstructure.Directory::__get_full_path(), dirstructure.Comparison::__get_img_name(), dirstructure.Comparison::__make_image(), core.autovars.NTupleVariable::__repr__(), core.autovars.NTupleObjectType::__repr__(), core.autovars.NTupleObject::__repr__(), core.autovars.NTupleCollection::__repr__(), dirstructure.Directory::__repr__(), dqm_interfaces.DirID::__repr__(), dirstructure.Comparison::__repr__(), config.Service::__setattr__(), config.CFG::__str__(), counter.Counter::__str__(), average.Average::__str__(), FWLite.WorkingPoints::_reformat_cut_definitions(), core.autovars.NTupleObjectType::addSubObjects(), core.autovars.NTupleObjectType::addVariables(), core.autovars.NTupleObjectType::allVars(), dataset.CMSDataset::buildListOfFiles(), dataset.LocalDataset::buildListOfFiles(), dataset.CMSDataset::buildListOfFilesDBS(), dirstructure.Directory::calcStats(), validation.Sample::digest(), python.rootplot.utilities.Hist::divide(), python.rootplot.utilities.Hist::divide_wilson(), DisplayManager.DisplayManager::Draw(), core.autovars.NTupleVariable::fillBranch(), core.autovars.NTupleObject::fillBranches(), core.autovars.NTupleCollection::fillBranchesScalar(), core.autovars.NTupleCollection::fillBranchesVector(), core.autovars.NTupleCollection::get_cpp_declaration(), core.autovars.NTupleCollection::get_cpp_wrapper_class(), core.autovars.NTupleCollection::get_py_wrapper_class(), utils.StatisticalTest::get_status(), production_tasks.Task::getname(), dataset.CMSDataset::getPrimaryDatasetEntries(), dataset.PrivateDataset::getPrimaryDatasetEntries(), VIDSelectorBase.VIDSelectorBase::initialize(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::load(), core.autovars.NTupleVariable::makeBranch(), core.autovars.NTupleObject::makeBranches(), core.autovars.NTupleCollection::makeBranchesScalar(), core.autovars.NTupleCollection::makeBranchesVector(), dirstructure.Directory::print_report(), dataset.BaseDataset::printInfo(), dataset.Dataset::printInfo(), production_tasks.MonitorJobs::run(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::save(), python.rootplot.utilities.Hist::TGraph(), python.rootplot.utilities.Hist::TH1F(), counter.Counter::write(), and average.Average::write().

noHitCntShift

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

constexpr unsigned int lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::noHitCntShift = layer1_output_I

static

Definition at line 66 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::run().

ptCalibrationArray

template<int input_I, int input_F, std::size_t inputSize, int layer1_lut_I, int layer1_lut_F, int layer1_neurons, int layer1_output_I, int layer2_input_I, int layer2_lut_I, int layer2_lut_F, int layer2_neurons, int layer3_input_I, int layer3_0_inputCnt, int layer3_0_lut_I, int layer3_0_lut_F, int output0_I, int output0_F, int layer3_1_inputCnt, int layer3_1_lut_I, int layer3_1_lut_F, int output1_I, int output1_F>

std::array<ap_uint<9>, 1 << (output0_I + output0_F)> lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::ptCalibrationArray

private

Definition at line 234 of file LutNetworkFixedPointRegression2Outputs.h.

Referenced by lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::getCalibratedHwPt(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::getPtCalibrationArray(), lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::load(), and lutNN::LutNetworkFixedPointRegression2Outputs< input_I, input_F, inputSize, layer1_lut_I, layer1_lut_F, layer1_neurons, layer1_output_I, layer2_input_I, layer2_lut_I, layer2_lut_F, layer2_neurons, layer3_input_I, layer3_0_inputCnt, layer3_0_lut_I, layer3_0_lut_F, output0_I, output0_F, layer3_1_inputCnt, layer3_1_lut_I, layer3_1_lut_F, output1_I, output1_F >::save().