42useLikelihoodKDE =
False
58 ROOT.Warning(
"TMVA_Higgs_Classification",
"Skip using Keras since tensorflow is not available")
61outputFile =
TFile.Open(
"Higgs_ClassificationOutput.root",
"RECREATE")
63 "TMVA_Higgs_Classification", outputFile, V=
False, ROC=
True, Silent=
False, Color=
True, AnalysisType=
"Classification"
101backgroundWeight = 1.0
124 mycuts, mycutb, nTrain_Signal=7000, nTrain_Background=7000, SplitMode=
"Random", NormMode=
"NumEvents", V=
False
139 TransformOutput=
True,
140 PDFInterpol=
"Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10",
153 TransformOutput=
False,
173 PDFInterpolMVAPdf=
"Spline2",
188 BoostType=
"AdaBoost",
191 BaggedSampleFraction=0.5,
192 SeparationType=
"GiniIndex",
278 "LearningRate=1e-3,Momentum=0.9,"
279 "ConvergenceSteps=10,BatchSize=128,TestRepetitions=1,"
280 "MaxEpochs=20,WeightDecay=1e-4,Regularization=None,"
281 "Optimizer=ADAM,ADAM_beta1=0.9,ADAM_beta2=0.999,ADAM_eps=1.E-7,"
282 "DropConfig=0.0+0.0+0.0+0."
294 dnnMethodName =
"DNN_GPU"
304 ErrorStrategy=
"CROSSENTROPY",
306 WeightInitialization=
"XAVIER",
308 BatchLayout=
"1|128|7",
309 Layout=
"DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|1|LINEAR",
310 TrainingStrategy=training1,
316 ROOT.Info(
"TMVA_Higgs_Classification",
"Building Deep Learning keras model")
329 model.compile(loss=
"binary_crossentropy", optimizer=
Adam(learning_rate=0.001), weighted_metrics=[
"accuracy"])
337 ROOT.Info(
"TMVA_Higgs_Classification",
"Booking Deep Learning keras model")
345 FilenameModel=
"model_higgs.h5",
346 FilenameTrainedModel=
"trained_model_higgs.h5",
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
This is the main MVA steering class.