RegressionKeras.py File Reference

Detailed Description

This tutorial shows how to do regression 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_Regression_Keras.root', 'RECREATE')
factory = TMVA.Factory('TMVARegression', output,
        '!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Regression')
 
# Load data
if not isfile('tmva_reg_example.root'):
    call(['curl', '-L', '-O', 'http://root.cern/files/tmva_reg_example.root'])
 
data = TFile.Open('tmva_reg_example.root')
tree = data.Get('TreeR')
 
dataloader = TMVA.DataLoader('dataset')
for branch in tree.GetListOfBranches():
    name = branch.GetName()
    if name != 'fvalue':
        dataloader.AddVariable(name)
dataloader.AddTarget('fvalue')
 
dataloader.AddRegressionTree(tree, 1.0)
#use only 1000 events since evaluation is very slow (especially on MacOS). Increase it to get meaningful results
dataloader.PrepareTrainingAndTestTree(TCut(''),
        'nTrain_Regression=1000:SplitMode=Random:NormMode=NumEvents:!V')
 
# Generate model
 
# Define model
model = Sequential()
model.add(Dense(64, activation='tanh', input_dim=2))
model.add(Dense(1, activation='linear'))
 
# Set loss and optimizer
model.compile(loss='mean_squared_error', optimizer=SGD(learning_rate=0.01), weighted_metrics=[])
 
# Store model to file
model.save('modelRegression.h5')
model.summary()
 
# Book methods
factory.BookMethod(dataloader, TMVA.Types.kPyKeras, 'PyKeras',
        'H:!V:VarTransform=D,G:FilenameModel=modelRegression.h5:FilenameTrainedModel=trainedModelRegression.h5:NumEpochs=20:BatchSize=32')
factory.BookMethod(dataloader, TMVA.Types.kBDT, 'BDTG',
        '!H:!V:VarTransform=D,G:NTrees=1000:BoostType=Grad:Shrinkage=0.1:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=4')
 
# Run TMVA
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()

Date

2017

Author

TMVA Team

Definition in file RegressionKeras.py.