doc/v632/MulticlassKeras_8py.html

from ROOT import TMVA, TFile, TTree, TCut, gROOT

from os.path import isfile


from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Activation

from tensorflow.keras.optimizers import SGD


# Setup TMVA

TMVA.Tools.Instance()

TMVA.PyMethodBase.PyInitialize()


output = TFile.Open('TMVA.root', 'RECREATE')

factory = TMVA.Factory('TMVAClassification', output,

    '!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=multiclass')


# Load data

if not isfile('tmva_example_multiple_background.root'):

    createDataMacro = str(gROOT.GetTutorialDir()) + '/tmva/createData.C'

    print(createDataMacro)

    gROOT.ProcessLine('.L {}'.format(createDataMacro))

    gROOT.ProcessLine('create_MultipleBackground(4000)')


data = TFile.Open('tmva_example_multiple_background.root')

signal = data.Get('TreeS')

background0 = data.Get('TreeB0')

background1 = data.Get('TreeB1')

background2 = data.Get('TreeB2')


dataloader = TMVA.DataLoader('dataset')

for branch in signal.GetListOfBranches():

    dataloader.AddVariable(branch.GetName())


dataloader.AddTree(signal, 'Signal')

dataloader.AddTree(background0, 'Background_0')

dataloader.AddTree(background1, 'Background_1')

dataloader.AddTree(background2, 'Background_2')

dataloader.PrepareTrainingAndTestTree(TCut(''),

        'SplitMode=Random:NormMode=NumEvents:!V')


# Generate model


# Define model

model = Sequential()

model.add(Dense(32, activation='relu', input_dim=4))

model.add(Dense(4, activation='softmax'))


# Set loss and optimizer

model.compile(loss='categorical_crossentropy', optimizer=SGD(learning_rate=0.01), weighted_metrics=['accuracy',])


# Store model to file

model.save('modelMultiClass.h5')

model.summary()


# Book methods

factory.BookMethod(dataloader, TMVA.Types.kFisher, 'Fisher',

        '!H:!V:Fisher:VarTransform=D,G')

factory.BookMethod(dataloader, TMVA.Types.kPyKeras, 'PyKeras',

                   'H:!V:VarTransform=D,G:FilenameModel=modelMultiClass.h5:FilenameTrainedModel=trainedModelMultiClass.h5:NumEpochs=20:BatchSize=32')


# Run TMVA

factory.TrainAllMethods()

factory.TestAllMethods()

factory.EvaluateAllMethods()

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358

format
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 Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t format
Definition TGWin32VirtualXProxy.cxx:249

ROOT::Detail::TRangeCast
Definition TCollection.h:311

TCut
A specialized string object used for TTree selections.
Definition TCut.h:25

TMVA::DataLoader
Definition DataLoader.h:50

TMVA::Factory
This is the main MVA steering class.
Definition Factory.h:80