ClassificationKeras.py File Reference

Namespaces
namespace	ClassificationKeras

Detailed Description

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

 
from ROOT import TMVA, TFile, TTree, TCut
from subprocess import call
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_Classification_Keras..root', 'RECREATE')
factory = TMVA.Factory('TMVAClassification', output,
                       '!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Classification')
 
# Load data
TFile.SetCacheFileDir(".")
data = TFile.Open("http://root.cern.ch/files/tmva_class_example.root", "CACHEREAD")
if data is None:
    raise FileNotFoundError("Input file cannot be downloaded - exit")
signal = data.Get('TreeS')
background = data.Get('TreeB')
 
dataloader = TMVA.DataLoader('dataset')
for branch in signal.GetListOfBranches():
    dataloader.AddVariable(branch.GetName())
 
dataloader.AddSignalTree(signal, 1.0)
dataloader.AddBackgroundTree(background, 1.0)
dataloader.PrepareTrainingAndTestTree(TCut(''),
                                      'nTrain_Signal=4000:nTrain_Background=4000:SplitMode=Random:NormMode=NumEvents:!V')
 
# Generate model
 
# Define model
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=4))
model.add(Dense(2, 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('modelClassification.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=modelClassification.h5:FilenameTrainedModel=trainedModelClassification.h5:NumEpochs=20:BatchSize=32')
 
# Run training, test and evaluation
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()

Date

2017

Author

TMVA Team

Definition in file ClassificationKeras.py.