Logo ROOT  
Reference Guide
 
Loading...
Searching...
No Matches
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_multiclass.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(learning_rate=0.01), weighted_metrics=['accuracy',])
# Store model to file
model.save('modelMultiClass.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=modelMultiClass.h5:FilenameTrainedModel=trainedModelMultiClass.h5:NumEpochs=20:BatchSize=32')
# Run TMVA
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t format
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:4053
This is the main MVA steering class.
Definition Factory.h:80
static void PyInitialize()
Initialize Python interpreter.
static Tools & Instance()
Definition Tools.cxx:71
Date
2017
Author
TMVA Team

Definition in file MulticlassKeras.py.