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MulticlassKeras.py File Reference

Namespaces

namespace  MulticlassKeras
 

Detailed Description

View in nbviewer Open in SWAN 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 tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.optimizers import SGD
# Setup TMVA
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(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()
A specialized string object used for TTree selections.
Definition TCut.h:25
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition TFile.cxx:3997
This is the main MVA steering class.
Definition Factory.h:80
static void PyInitialize()
Initialize Python interpreter.
static Tools & Instance()
Definition Tools.cxx:75
Date
2017
Author
TMVA Team

Definition in file MulticlassKeras.py.