MulticlassKeras.py File Reference

Namespaces
namespace	MulticlassKeras

Detailed Description

This tutorial shows how to do multiclass classification in TMVA with neural networks trained with keras.

 
from ROOT import TMVA, TFile, TTree, TCut, gROOT
from os.path import isfile
 
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.regularizers import l2
from 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', W_regularizer=l2(1e-5), input_dim=4))
model.add(Dense(4, activation='softmax'))
 
# Set loss and optimizer
model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy',])
 
# Store model to file
model.save('model.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=model.h5:NumEpochs=20:BatchSize=32')
 
# Run TMVA
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()

Date

2017

Author

TMVA Team

Definition in file MulticlassKeras.py.