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 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()