GRLNeuro Class Reference

Class to represent the GRL Neuro. More...

#include <GRLNeuro.h>

Collaboration diagram for GRLNeuro:

Classes
struct	Parameters
	Struct to keep neurotrigger parameters. More...

Public Member Functions
	GRLNeuro ()
	Default constructor.
virtual	~GRLNeuro ()
	Default destructor.
void	initialize (const Parameters &p)
	Set parameters and get some network independent parameters.
GRLMLP &	operator[] (unsigned index)
	Set parameters and get some network independent parameters. More...
const GRLMLP &	operator[] (unsigned index) const
	return const reference to a neural network
unsigned	nSectors () const
	return number of neural networks
float	relu (float x)
	ReLu activation function.
float	float_to_fixed (float num, int m, int n)
	change the percision of number, m = number of integer bits, n = number of decimal
float	mysigmiod (float num)
	discrete sigmoid activation function (1024 bins)
void	save (const std::string &filename, const std::string &arrayname="MLPs")
	Save MLPs to file. More...
bool	load (unsigned isector, const std::string &wfilename, const std::string &bfilename)
	Load MLPs from file. More...
float	runMLP (unsigned isector, const std::vector< float > &input)
	Run an expert MLP. More...

Private Attributes
std::vector< GRLMLP >	m_MLPs = {}
	List of networks.

Detailed Description

Class to represent the GRL Neuro.

The Neurotrigger consists of one or several Multi Layer Perceptrons. The input values are calculated from ECLTRG cluster and a 2D track estimate. The output is a scaled estimate of the judgement.

See also

GRLNeuro Modules:

GRLTrainerModule for preparing training data and training,

GRLNeuro for loading trained networks and using them.

Definition at line 35 of file GRLNeuro.h.

Member Function Documentation

load()

bool load	(	unsigned	isector,
		const std::string &	wfilename,
		const std::string &	bfilename
	)

Load MLPs from file.

Parameters

filename	name of the TFile to read from
arrayname	name of the TObjArray holding the MLPs in the file

Returns

true if the MLPs were loaded correctly

Definition at line 154 of file GRLNeuro.cc.

{
  if (weightfilename.size() < 1) {
    B2ERROR("Could not load Neurotrigger weights from database!");
    return false;
  } else if (biasfilename.size() < 1) {
    B2ERROR("Could not load Neurotrigger bias from database!");
    return false;
  } else {
    std::ifstream wfile(weightfilename);
    if (!wfile.is_open()) {
      B2WARNING("Could not open file " << weightfilename);
      return false;
    } else {
      std::ifstream bfile(biasfilename);
      if (!bfile.is_open()) {
        B2WARNING("Could not open file " << biasfilename);
        return false;
      } else {
        GRLMLP& expert = m_MLPs[isector];
        std::vector<float> warray;
        std::vector<float> barray;
        warray.clear();
        barray.clear();
 
        float element;
        while (wfile >> element) {
          warray.push_back(element);
        }
        while (bfile >> element) {
          barray.push_back(element);
        }
 
        if (warray.size() != expert.nWeightsCal()) {
          B2ERROR("Number of weights is not equal to registered architecture!");
          return false;
        } else expert.setWeights(warray);
        if (barray.size() != expert.nBiasCal()) {
          B2ERROR("Number of bias is not equal to registered architecture!");
          return false;
        }
        //change the precision based in FPGA (hls4ml)
        for (int it = 0; it < warray.size(); it++) {
          if (it < 380)      warray[it] = float_to_fixed(warray[it], 2, 13);
          else if (it < 780) warray[it] = float_to_fixed(warray[it], 1, 13);
          else            warray[it] = float_to_fixed(warray[it], 2, 11);
        }
 
        for (int it = 0; it < barray.size(); it++) {
          if (it < 20)      barray[it] = float_to_fixed(barray[it], 4, 2);
          else if (it < 40) barray[it] = float_to_fixed(barray[it], 4, 3);
          else           barray[it] = float_to_fixed(barray[it], 0, 2);
        }
 
        expert.setWeights(warray);
        expert.setBias(barray);
        return true;
      }
    }
  }
}

operator[]()

GRLMLP& operator[] ( unsigned  index )

inline

Set parameters and get some network independent parameters.

return reference to a neural network

Definition at line 82 of file GRLNeuro.h.

{ return m_MLPs[index]; }

runMLP()

float runMLP	(	unsigned	isector,
		const std::vector< float > &	input
	)

Run an expert MLP.

Parameters

isector	index of the MLP
input	vector of input values

Returns

output values (classifier)

Definition at line 87 of file GRLNeuro.cc.

save()

void save	(	const std::string &	filename,
		const std::string &	arrayname = "MLPs"
	)

Save MLPs to file.

Parameters

filename	name of the TFile to write to
arrayname	name of the TObjArray holding the MLPs in the file

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The documentation for this class was generated from the following files:

• trg/grl/include/GRLNeuro.h
• trg/grl/src/GRLNeuro.cc