MulticlassKeras.py

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#!/usr/bin/env python
## \file
## \ingroup tutorial_tmva_keras
## \notebook -nodraw
## This tutorial shows how to do multiclass classification in TMVA with neural
## networks trained with keras.
##
## \macro_code
##
## \date 2017
## \author TMVA Team
 
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_multiclass.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()