Notebook

TMVA_SOFIE_RSofieReader¶

This macro provides an example of using a trained model with Keras and make inference using SOFIE with the RSofieReader class This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before to generate the trained Keras model

Execute in this order:

root TMVA_Higgs_Classification.C
root TMVA_SOFIE_RSofieReader.C

Author: Lorenzo Moneta
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, March 27, 2023 at 09:59 AM.

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using namespace TMVA::Experimental;

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RSofieReader model("Higgs_trained_model.h5");

for debugging RSofieReader model("Higgs_trained_model.h5", {}, true);

the input shape for this model is a tensor with shape (1,7)

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std::vector<float> input = {0.1,0.2,0.3,0.4,0.5,0.6,0.7};

predict model on a single event (takes a std::vector)

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auto output = model.Compute(input);

std::cout << "Event prediction = " << output[0] << std::endl;

predict model now on a input file using RDataFrame

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std::string inputFileName = "Higgs_data.root";
std::string inputFile = "http://root.cern.ch/files/" + inputFileName;


ROOT::RDataFrame df1("sig_tree", inputFile);

auto h1 = df1.Define("DNN_Values", Compute<7, float>(model),
                         {"m_jj", "m_jjj", "m_lv", "m_jlv", "m_bb", "m_wbb", "m_wwbb"})
               .Define("y","DNN_Values[0]")
               .Histo1D({"h_sig", "", 100, 0, 1}, "y");

ROOT::RDataFrame df2("bkg_tree", inputFile);
auto h2 = df2.Define("DNN_Values", Compute<7, float>(model),
                         {"m_jj", "m_jjj", "m_lv", "m_jlv", "m_bb", "m_wbb", "m_wwbb"})
               .Define("y","DNN_Values[0]")
               .Histo1D({"h_bkg", "", 100, 0, 1}, "y");

h1->SetLineColor(kRed);
h2->SetLineColor(kBlue);

auto c1 = new TCanvas();
gStyle->SetOptStat(0);

h2->DrawClone();
h1->DrawClone("SAME");
c1->BuildLegend();