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TMVA_CNN_Classification.py File Reference
Tutorials » Machine Learning tutorials

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TMVA Classification Example Using a Convolutional Neural Network

This is an example of using a CNN in TMVA. We do classification using a toy image data set that is generated when running the example macro

Running with nthreads = 4
DataSetInfo : [dataset] : Added class "Signal"
: Add Tree sig_tree of type Signal with 1000 events
DataSetInfo : [dataset] : Added class "Background"
: Add Tree bkg_tree of type Background with 1000 events
Factory : Booking method: ␛[1mBDT␛[0m
:
: Rebuilding Dataset dataset
: Building event vectors for type 2 Signal
: Dataset[dataset] : create input formulas for tree sig_tree
: Using variable vars[0] from array expression vars of size 256
: Building event vectors for type 2 Background
: Dataset[dataset] : create input formulas for tree bkg_tree
: Using variable vars[0] from array expression vars of size 256
DataSetFactory : [dataset] : Number of events in input trees
:
:
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 800
: Signal -- testing events : 200
: Signal -- training and testing events: 1000
: Background -- training events : 800
: Background -- testing events : 200
: Background -- training and testing events: 1000
:
Factory : Booking method: ␛[1mTMVA_DNN_CPU␛[0m
:
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:Layout=DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.,MaxEpochs=10:Architecture=CPU"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:Layout=DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.,MaxEpochs=10:Architecture=CPU"
: The following options are set:
: - By User:
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: Layout: "DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR" [Layout of the network.]
: ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
: WeightInitialization: "XAVIER" [Weight initialization strategy]
: Architecture: "CPU" [Which architecture to perform the training on.]
: TrainingStrategy: "LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.,MaxEpochs=10" [Defines the training strategies.]
: - Default:
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: InputLayout: "0|0|0" [The Layout of the input]
: BatchLayout: "0|0|0" [The Layout of the batch]
: RandomSeed: "0" [Random seed used for weight initialization and batch shuffling]
: ValidationSize: "20%" [Part of the training data to use for validation. Specify as 0.2 or 20% to use a fifth of the data set as validation set. Specify as 100 to use exactly 100 events. (Default: 20%)]
: Will now use the CPU architecture with BLAS and IMT support !
Factory : Booking method: ␛[1mTMVA_CNN_CPU␛[0m
:
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:InputLayout=1|16|16:Layout=CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0,MaxEpochs=10:Architecture=CPU"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:InputLayout=1|16|16:Layout=CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0,MaxEpochs=10:Architecture=CPU"
: The following options are set:
: - By User:
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: InputLayout: "1|16|16" [The Layout of the input]
: Layout: "CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR" [Layout of the network.]
: ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
: WeightInitialization: "XAVIER" [Weight initialization strategy]
: Architecture: "CPU" [Which architecture to perform the training on.]
: TrainingStrategy: "LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0,MaxEpochs=10" [Defines the training strategies.]
: - Default:
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: BatchLayout: "0|0|0" [The Layout of the batch]
: RandomSeed: "0" [Random seed used for weight initialization and batch shuffling]
: ValidationSize: "20%" [Part of the training data to use for validation. Specify as 0.2 or 20% to use a fifth of the data set as validation set. Specify as 100 to use exactly 100 events. (Default: 20%)]
: Will now use the CPU architecture with BLAS and IMT support !
Factory : ␛[1mTrain all methods␛[0m
Factory : Train method: BDT for Classification
:
BDT : #events: (reweighted) sig: 800 bkg: 800
: #events: (unweighted) sig: 800 bkg: 800
: Training 400 Decision Trees ... patience please
: Elapsed time for training with 1600 events: 1.4 sec
BDT : [dataset] : Evaluation of BDT on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.0141 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.class.C␛[0m
: TMVA_CNN_ClassificationOutput.root:/dataset/Method_BDT/BDT
Factory : Training finished
:
Factory : Train method: TMVA_DNN_CPU for Classification
:
: Start of deep neural network training on CPU using MT, nthreads = 4
:
: ***** Deep Learning Network *****
DEEP NEURAL NETWORK: Depth = 8 Input = ( 1, 1, 256 ) Batch size = 100 Loss function = C
Layer 0 DENSE Layer: ( Input = 256 , Width = 100 ) Output = ( 1 , 100 , 100 ) Activation Function = Relu
Layer 1 BATCH NORM Layer: Input/Output = ( 100 , 100 , 1 ) Norm dim = 100 axis = -1
Layer 2 DENSE Layer: ( Input = 100 , Width = 100 ) Output = ( 1 , 100 , 100 ) Activation Function = Relu
Layer 3 BATCH NORM Layer: Input/Output = ( 100 , 100 , 1 ) Norm dim = 100 axis = -1
Layer 4 DENSE Layer: ( Input = 100 , Width = 100 ) Output = ( 1 , 100 , 100 ) Activation Function = Relu
Layer 5 BATCH NORM Layer: Input/Output = ( 100 , 100 , 1 ) Norm dim = 100 axis = -1
Layer 6 DENSE Layer: ( Input = 100 , Width = 100 ) Output = ( 1 , 100 , 100 ) Activation Function = Relu
Layer 7 DENSE Layer: ( Input = 100 , Width = 1 ) Output = ( 1 , 100 , 1 ) Activation Function = Identity
: Using 1280 events for training and 320 for testing
: Compute initial loss on the validation data
: Training phase 1 of 1: Optimizer ADAM (beta1=0.9,beta2=0.999,eps=1e-07) Learning rate = 0.001 regularization 0 minimum error = 43.0589
: --------------------------------------------------------------
: Epoch | Train Err. Val. Err. t(s)/epoch t(s)/Loss nEvents/s Conv. Steps
: --------------------------------------------------------------
: Start epoch iteration ...
: 1 Minimum Test error found - save the configuration
: 1 | 0.932488 1.2527 0.105314 0.0105951 12669.1 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.677594 1.23311 0.104922 0.0104005 12695.6 0
: 3 | 0.574937 1.38763 0.104628 0.0100086 12682.5 1
: 4 | 0.499837 1.8571 0.105002 0.0101234 12647.7 2
: 5 | 0.444238 1.87116 0.107988 0.0105073 12310.1 3
: 6 | 0.380996 1.74179 0.134588 0.0107704 9691.64 4
: 7 | 0.338267 1.96232 0.107533 0.00997432 12300.3 5
: 8 | 0.28944 1.90496 0.105579 0.00995037 12548.5 6
:
: Elapsed time for training with 1600 events: 0.897 sec
: Evaluate deep neural network on CPU using batches with size = 100
:
TMVA_DNN_CPU : [dataset] : Evaluation of TMVA_DNN_CPU on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.0523 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.class.C␛[0m
Factory : Training finished
:
Factory : Train method: TMVA_CNN_CPU for Classification
:
: Start of deep neural network training on CPU using MT, nthreads = 4
:
: ***** Deep Learning Network *****
DEEP NEURAL NETWORK: Depth = 7 Input = ( 1, 16, 16 ) Batch size = 100 Loss function = C
Layer 0 CONV LAYER: ( W = 16 , H = 16 , D = 10 ) Filter ( W = 3 , H = 3 ) Output = ( 100 , 10 , 10 , 256 ) Activation Function = Relu
Layer 1 BATCH NORM Layer: Input/Output = ( 10 , 256 , 100 ) Norm dim = 10 axis = 1
Layer 2 CONV LAYER: ( W = 16 , H = 16 , D = 10 ) Filter ( W = 3 , H = 3 ) Output = ( 100 , 10 , 10 , 256 ) Activation Function = Relu
Layer 3 POOL Layer: ( W = 15 , H = 15 , D = 10 ) Filter ( W = 2 , H = 2 ) Output = ( 100 , 10 , 10 , 225 )
Layer 4 RESHAPE Layer Input = ( 10 , 15 , 15 ) Output = ( 1 , 100 , 2250 )
Layer 5 DENSE Layer: ( Input = 2250 , Width = 100 ) Output = ( 1 , 100 , 100 ) Activation Function = Relu
Layer 6 DENSE Layer: ( Input = 100 , Width = 1 ) Output = ( 1 , 100 , 1 ) Activation Function = Identity
: Using 1280 events for training and 320 for testing
: Compute initial loss on the validation data
: Training phase 1 of 1: Optimizer ADAM (beta1=0.9,beta2=0.999,eps=1e-07) Learning rate = 0.001 regularization 0 minimum error = 127.636
: --------------------------------------------------------------
: Epoch | Train Err. Val. Err. t(s)/epoch t(s)/Loss nEvents/s Conv. Steps
: --------------------------------------------------------------
: Start epoch iteration ...
: 1 Minimum Test error found - save the configuration
: 1 | 2.55997 1.60892 0.771301 0.0670039 1703.83 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.981753 0.730916 0.779342 0.0711751 1694.52 0
: 3 | 0.740921 0.734262 0.796542 0.069434 1650.37 1
: 4 Minimum Test error found - save the configuration
: 4 | 0.697824 0.697007 0.771522 0.0693114 1708.89 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.675924 0.693747 0.796508 0.0672177 1645.43 0
: 6 | 0.6668 0.694096 0.779626 0.069259 1689.27 1
: 7 Minimum Test error found - save the configuration
: 7 | 0.647536 0.685804 0.77967 0.0707074 1692.61 0
: 8 | 0.637668 0.6879 0.806972 0.0700978 1628.5 1
: 9 Minimum Test error found - save the configuration
: 9 | 0.620557 0.678213 0.817108 0.0756234 1618.37 0
: 10 | 0.603514 0.690045 0.788529 0.0705711 1671.41 1
:
: Elapsed time for training with 1600 events: 7.97 sec
: Evaluate deep neural network on CPU using batches with size = 100
:
TMVA_CNN_CPU : [dataset] : Evaluation of TMVA_CNN_CPU on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.363 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.class.C␛[0m
Factory : Training finished
:
: Ranking input variables (method specific)...
BDT : Ranking result (top variable is best ranked)
: --------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------
: 1 : vars : 9.508e-03
: 2 : vars : 8.589e-03
: 3 : vars : 8.501e-03
: 4 : vars : 8.290e-03
: 5 : vars : 8.266e-03
: 6 : vars : 7.958e-03
: 7 : vars : 7.576e-03
: 8 : vars : 7.534e-03
: 9 : vars : 7.384e-03
: 10 : vars : 7.301e-03
: 11 : vars : 7.283e-03
: 12 : vars : 7.170e-03
: 13 : vars : 7.013e-03
: 14 : vars : 6.991e-03
: 15 : vars : 6.912e-03
: 16 : vars : 6.775e-03
: 17 : vars : 6.636e-03
: 18 : vars : 6.449e-03
: 19 : vars : 6.437e-03
: 20 : vars : 6.437e-03
: 21 : vars : 6.414e-03
: 22 : vars : 6.404e-03
: 23 : vars : 6.380e-03
: 24 : vars : 6.305e-03
: 25 : vars : 6.288e-03
: 26 : vars : 6.254e-03
: 27 : vars : 6.226e-03
: 28 : vars : 6.111e-03
: 29 : vars : 6.089e-03
: 30 : vars : 5.939e-03
: 31 : vars : 5.938e-03
: 32 : vars : 5.932e-03
: 33 : vars : 5.885e-03
: 34 : vars : 5.884e-03
: 35 : vars : 5.857e-03
: 36 : vars : 5.776e-03
: 37 : vars : 5.749e-03
: 38 : vars : 5.741e-03
: 39 : vars : 5.704e-03
: 40 : vars : 5.698e-03
: 41 : vars : 5.697e-03
: 42 : vars : 5.693e-03
: 43 : vars : 5.669e-03
: 44 : vars : 5.609e-03
: 45 : vars : 5.573e-03
: 46 : vars : 5.519e-03
: 47 : vars : 5.510e-03
: 48 : vars : 5.408e-03
: 49 : vars : 5.324e-03
: 50 : vars : 5.295e-03
: 51 : vars : 5.283e-03
: 52 : vars : 5.262e-03
: 53 : vars : 5.247e-03
: 54 : vars : 5.219e-03
: 55 : vars : 5.202e-03
: 56 : vars : 5.201e-03
: 57 : vars : 5.199e-03
: 58 : vars : 5.133e-03
: 59 : vars : 5.095e-03
: 60 : vars : 5.092e-03
: 61 : vars : 5.078e-03
: 62 : vars : 5.066e-03
: 63 : vars : 5.062e-03
: 64 : vars : 5.038e-03
: 65 : vars : 5.017e-03
: 66 : vars : 5.016e-03
: 67 : vars : 4.988e-03
: 68 : vars : 4.964e-03
: 69 : vars : 4.948e-03
: 70 : vars : 4.910e-03
: 71 : vars : 4.856e-03
: 72 : vars : 4.800e-03
: 73 : vars : 4.797e-03
: 74 : vars : 4.741e-03
: 75 : vars : 4.720e-03
: 76 : vars : 4.712e-03
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: 79 : vars : 4.687e-03
: 80 : vars : 4.685e-03
: 81 : vars : 4.653e-03
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: 84 : vars : 4.602e-03
: 85 : vars : 4.589e-03
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: 93 : vars : 4.423e-03
: 94 : vars : 4.412e-03
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: 99 : vars : 4.364e-03
: 100 : vars : 4.359e-03
: 101 : vars : 4.348e-03
: 102 : vars : 4.333e-03
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: 111 : vars : 4.159e-03
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: 121 : vars : 3.934e-03
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: 123 : vars : 3.919e-03
: 124 : vars : 3.848e-03
: 125 : vars : 3.834e-03
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: 127 : vars : 3.815e-03
: 128 : vars : 3.815e-03
: 129 : vars : 3.799e-03
: 130 : vars : 3.772e-03
: 131 : vars : 3.767e-03
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: 133 : vars : 3.745e-03
: 134 : vars : 3.726e-03
: 135 : vars : 3.722e-03
: 136 : vars : 3.718e-03
: 137 : vars : 3.710e-03
: 138 : vars : 3.709e-03
: 139 : vars : 3.708e-03
: 140 : vars : 3.698e-03
: 141 : vars : 3.690e-03
: 142 : vars : 3.688e-03
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: 146 : vars : 3.610e-03
: 147 : vars : 3.606e-03
: 148 : vars : 3.595e-03
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: 150 : vars : 3.532e-03
: 151 : vars : 3.509e-03
: 152 : vars : 3.475e-03
: 153 : vars : 3.453e-03
: 154 : vars : 3.431e-03
: 155 : vars : 3.426e-03
: 156 : vars : 3.423e-03
: 157 : vars : 3.420e-03
: 158 : vars : 3.408e-03
: 159 : vars : 3.395e-03
: 160 : vars : 3.388e-03
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: 165 : vars : 3.276e-03
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: 178 : vars : 3.033e-03
: 179 : vars : 2.983e-03
: 180 : vars : 2.967e-03
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: 182 : vars : 2.953e-03
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: 184 : vars : 2.920e-03
: 185 : vars : 2.914e-03
: 186 : vars : 2.851e-03
: 187 : vars : 2.829e-03
: 188 : vars : 2.817e-03
: 189 : vars : 2.816e-03
: 190 : vars : 2.806e-03
: 191 : vars : 2.772e-03
: 192 : vars : 2.766e-03
: 193 : vars : 2.759e-03
: 194 : vars : 2.668e-03
: 195 : vars : 2.667e-03
: 196 : vars : 2.627e-03
: 197 : vars : 2.599e-03
: 198 : vars : 2.598e-03
: 199 : vars : 2.597e-03
: 200 : vars : 2.581e-03
: 201 : vars : 2.572e-03
: 202 : vars : 2.564e-03
: 203 : vars : 2.553e-03
: 204 : vars : 2.545e-03
: 205 : vars : 2.437e-03
: 206 : vars : 2.379e-03
: 207 : vars : 2.378e-03
: 208 : vars : 2.372e-03
: 209 : vars : 2.351e-03
: 210 : vars : 2.319e-03
: 211 : vars : 2.306e-03
: 212 : vars : 2.252e-03
: 213 : vars : 2.199e-03
: 214 : vars : 2.199e-03
: 215 : vars : 2.172e-03
: 216 : vars : 2.158e-03
: 217 : vars : 2.151e-03
: 218 : vars : 2.148e-03
: 219 : vars : 2.141e-03
: 220 : vars : 2.093e-03
: 221 : vars : 2.090e-03
: 222 : vars : 2.060e-03
: 223 : vars : 2.033e-03
: 224 : vars : 1.998e-03
: 225 : vars : 1.964e-03
: 226 : vars : 1.962e-03
: 227 : vars : 1.958e-03
: 228 : vars : 1.953e-03
: 229 : vars : 1.872e-03
: 230 : vars : 1.819e-03
: 231 : vars : 1.783e-03
: 232 : vars : 1.782e-03
: 233 : vars : 1.747e-03
: 234 : vars : 1.701e-03
: 235 : vars : 1.667e-03
: 236 : vars : 1.592e-03
: 237 : vars : 1.583e-03
: 238 : vars : 1.516e-03
: 239 : vars : 1.426e-03
: 240 : vars : 1.382e-03
: 241 : vars : 1.343e-03
: 242 : vars : 1.305e-03
: 243 : vars : 1.278e-03
: 244 : vars : 8.622e-04
: 245 : vars : 7.212e-04
: 246 : vars : 0.000e+00
: 247 : vars : 0.000e+00
: 248 : vars : 0.000e+00
: 249 : vars : 0.000e+00
: 250 : vars : 0.000e+00
: 251 : vars : 0.000e+00
: 252 : vars : 0.000e+00
: 253 : vars : 0.000e+00
: 254 : vars : 0.000e+00
: 255 : vars : 0.000e+00
: 256 : vars : 0.000e+00
: --------------------------------------
: No variable ranking supplied by classifier: TMVA_DNN_CPU
: No variable ranking supplied by classifier: TMVA_CNN_CPU
TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_trainingError, Entries= 0, Total sum= 4.1378
TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 13.2108
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 8.83246
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 7.90091
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.weights.xml␛[0m
Factory : ␛[1mTest all methods␛[0m
Factory : Test method: BDT for Classification performance
:
BDT : [dataset] : Evaluation of BDT on testing sample (400 events)
: Elapsed time for evaluation of 400 events: 0.00368 sec
Factory : Test method: TMVA_DNN_CPU for Classification performance
:
: Evaluate deep neural network on CPU using batches with size = 400
:
TMVA_DNN_CPU : [dataset] : Evaluation of TMVA_DNN_CPU on testing sample (400 events)
: Elapsed time for evaluation of 400 events: 0.0129 sec
Factory : Test method: TMVA_CNN_CPU for Classification performance
:
: Evaluate deep neural network on CPU using batches with size = 400
:
TMVA_CNN_CPU : [dataset] : Evaluation of TMVA_CNN_CPU on testing sample (400 events)
: Elapsed time for evaluation of 400 events: 0.0951 sec
Factory : ␛[1mEvaluate all methods␛[0m
Factory : Evaluate classifier: BDT
:
BDT : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Dataset[dataset] : variable plots are not produces ! The number of variables is 256 , it is larger than 200
Factory : Evaluate classifier: TMVA_DNN_CPU
:
TMVA_DNN_CPU : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Evaluate deep neural network on CPU using batches with size = 1000
:
: Dataset[dataset] : variable plots are not produces ! The number of variables is 256 , it is larger than 200
Factory : Evaluate classifier: TMVA_CNN_CPU
:
TMVA_CNN_CPU : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Evaluate deep neural network on CPU using batches with size = 1000
:
: Dataset[dataset] : variable plots are not produces ! The number of variables is 256 , it is larger than 200
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset BDT : 0.785
: dataset TMVA_CNN_CPU : 0.602
: dataset TMVA_DNN_CPU : 0.570
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset BDT : 0.065 (0.400) 0.417 (0.775) 0.735 (0.891)
: dataset TMVA_CNN_CPU : 0.005 (0.050) 0.220 (0.300) 0.423 (0.561)
: dataset TMVA_DNN_CPU : 0.008 (0.032) 0.175 (0.228) 0.445 (0.483)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:dataset : Created tree 'TestTree' with 400 events
:
Dataset:dataset : Created tree 'TrainTree' with 1600 events
:
Factory : ␛[1mThank you for using TMVA!␛[0m
: ␛[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html␛[0m
# TMVA Classification Example Using a Convolutional Neural Network
## Helper function to create input images data
## we create a signal and background 2D histograms from 2d gaussians
## with a location (means in X and Y) different for each event
## The difference between signal and background is in the gaussian width.
## The width for the background gaussian is slightly larger than the signal width by few % values
import ROOT
import os
import importlib.util
opt = [1, 1, 1, 1, 1]
useTMVACNN = opt[0] if len(opt) > 0 else False
useKerasCNN = opt[1] if len(opt) > 1 else False
useTMVADNN = opt[2] if len(opt) > 2 else False
useTMVABDT = opt[3] if len(opt) > 3 else False
usePyTorchCNN = opt[4] if len(opt) > 4 else False
TMVA = ROOT.TMVA
TFile = ROOT.TFile
TMVA.Tools.Instance()
def MakeImagesTree(n, nh, nw):
# image size (nh x nw)
ntot = nh * nw
fileOutName = "images_data_16x16.root"
nRndmEvts = 10000 # number of events we use to fill each image
delta_sigma = 0.1 # 5% difference in the sigma
pixelNoise = 5
sX1 = 3
sY1 = 3
sX2 = sX1 + delta_sigma
sY2 = sY1 - delta_sigma
h1 = ROOT.TH2D("h1", "h1", nh, 0, 10, nw, 0, 10)
h2 = ROOT.TH2D("h2", "h2", nh, 0, 10, nw, 0, 10)
f1 = ROOT.TF2("f1", "xygaus")
f2 = ROOT.TF2("f2", "xygaus")
sgn = ROOT.TTree("sig_tree", "signal_tree")
bkg = ROOT.TTree("bkg_tree", "background_tree")
f = TFile(fileOutName, "RECREATE")
x1 = ROOT.std.vector["float"](ntot)
x2 = ROOT.std.vector["float"](ntot)
# create signal and background trees with a single branch
# an std::vector<float> of size nh x nw containing the image data
bkg.Branch("vars", "std::vector<float>", x1)
sgn.Branch("vars", "std::vector<float>", x2)
sgn.SetDirectory(f)
bkg.SetDirectory(f)
f1.SetParameters(1, 5, sX1, 5, sY1)
f2.SetParameters(1, 5, sX2, 5, sY2)
ROOT.gRandom.SetSeed(0)
ROOT.Info("TMVA_CNN_Classification", "Filling ROOT tree \n")
for i in range(n):
if i % 1000 == 0:
print("Generating image event ...", i)
h1.Reset()
h2.Reset()
# generate random means in range [3,7] to be not too much on the border
f1.SetParameter(1, ROOT.gRandom.Uniform(3, 7))
f1.SetParameter(3, ROOT.gRandom.Uniform(3, 7))
f2.SetParameter(1, ROOT.gRandom.Uniform(3, 7))
f2.SetParameter(3, ROOT.gRandom.Uniform(3, 7))
h1.FillRandom(f1, nRndmEvts)
h2.FillRandom(f2, nRndmEvts)
for k in range(nh):
for l in range(nw):
m = k * nw + l
# add some noise in each bin
x1[m] = h1.GetBinContent(k + 1, l + 1) + ROOT.gRandom.Gaus(0, pixelNoise)
x2[m] = h2.GetBinContent(k + 1, l + 1) + ROOT.gRandom.Gaus(0, pixelNoise)
sgn.Fill()
bkg.Fill()
sgn.Write()
bkg.Write()
print("Signal and background tree with images data written to the file %s", f.GetName())
sgn.Print()
bkg.Print()
f.Close()
hasGPU = "tmva-gpu" in ROOT.gROOT.GetConfigFeatures()
hasCPU = "tmva-cpu" in ROOT.gROOT.GetConfigFeatures()
nevt = 1000 # use a larger value to get better results
if (not hasCPU and not hasGPU) :
ROOT.Warning("TMVA_CNN_Classificaton","ROOT is not supporting tmva-cpu and tmva-gpu skip using TMVA-DNN and TMVA-CNN")
useTMVACNN = False
useTMVADNN = False
if not "tmva-pymva" in ROOT.gROOT.GetConfigFeatures():
useKerasCNN = False
usePyTorchCNN = False
else:
TMVA.PyMethodBase.PyInitialize()
if not useTMVACNN:
ROOT.Warning(
"TMVA_CNN_Classificaton",
"TMVA is not build with GPU or CPU multi-thread support. Cannot use TMVA Deep Learning for CNN",
)
writeOutputFile = True
num_threads = 4 # use max 4 threads
max_epochs = 10 # maximum number of epochs used for training
# do enable MT running
if "imt" in ROOT.gROOT.GetConfigFeatures():
ROOT.EnableImplicitMT(num_threads)
ROOT.gSystem.Setenv("OMP_NUM_THREADS", "1") # switch OFF MT in OpenBLAS
print("Running with nthreads = {}".format(ROOT.GetThreadPoolSize()))
else:
print("Running in serial mode since ROOT does not support MT")
outputFile = None
if writeOutputFile:
outputFile = TFile.Open("TMVA_CNN_ClassificationOutput.root", "RECREATE")
## Create TMVA Factory
# Create the Factory class. Later you can choose the methods
# whose performance you'd like to investigate.
# The factory is the major TMVA object you have to interact with. Here is the list of parameters you need to pass
# - The first argument is the base of the name of all the output
# weight files in the directory weight/ that will be created with the
# method parameters
# - The second argument is the output file for the training results
# - The third argument is a string option defining some general configuration for the TMVA session.
# For example all TMVA output can be suppressed by removing the "!" (not) in front of the "Silent" argument in the
# option string
# - note that we disable any pre-transformation of the input variables and we avoid computing correlations between
# input variables
factory = TMVA.Factory(
"TMVA_CNN_Classification",
outputFile,
V=False,
ROC=True,
Silent=False,
Color=True,
AnalysisType="Classification",
Transformations=None,
Correlations=False,
)
## Declare DataLoader(s)
# The next step is to declare the DataLoader class that deals with input variables
# Define the input variables that shall be used for the MVA training
# note that you may also use variable expressions, which can be parsed by TTree::Draw( "expression" )]
# In this case the input data consists of an image of 16x16 pixels. Each single pixel is a branch in a ROOT TTree
loader = TMVA.DataLoader("dataset")
## Setup Dataset(s)
# Define input data file and signal and background trees
imgSize = 16 * 16
inputFileName = "images_data_16x16.root"
# if the input file does not exist create it
if ROOT.gSystem.AccessPathName(inputFileName):
MakeImagesTree(nevt, 16, 16)
inputFile = TFile.Open(inputFileName)
if inputFile is None:
ROOT.Warning("TMVA_CNN_Classification", "Error opening input file %s - exit", inputFileName.Data())
# inputFileName = "tmva_class_example.root"
# --- Register the training and test trees
signalTree = inputFile.Get("sig_tree")
backgroundTree = inputFile.Get("bkg_tree")
nEventsSig = signalTree.GetEntries()
nEventsBkg = backgroundTree.GetEntries()
# global event weights per tree (see below for setting event-wise weights)
signalWeight = 1.0
backgroundWeight = 1.0
# You can add an arbitrary number of signal or background trees
loader.AddSignalTree(signalTree, signalWeight)
loader.AddBackgroundTree(backgroundTree, backgroundWeight)
## add event variables (image)
## use new method (from ROOT 6.20 to add a variable array for all image data)
loader.AddVariablesArray("vars", imgSize)
# Set individual event weights (the variables must exist in the original TTree)
# for signal : factory->SetSignalWeightExpression ("weight1*weight2");
# for background: factory->SetBackgroundWeightExpression("weight1*weight2");
# loader->SetBackgroundWeightExpression( "weight" );
# Apply additional cuts on the signal and background samples (can be different)
mycuts = "" # for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
mycutb = "" # for example: TCut mycutb = "abs(var1)<0.5";
# Tell the factory how to use the training and testing events
# If no numbers of events are given, half of the events in the tree are used
# for training, and the other half for testing:
# loader.PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
# It is possible also to specify the number of training and testing events,
# note we disable the computation of the correlation matrix of the input variables
nTrainSig = 0.8 * nEventsSig
nTrainBkg = 0.8 * nEventsBkg
# build the string options for DataLoader::PrepareTrainingAndTestTree
loader.PrepareTrainingAndTestTree(
mycuts,
mycutb,
nTrain_Signal=nTrainSig,
nTrain_Background=nTrainBkg,
SplitMode="Random",
SplitSeed=100,
NormMode="NumEvents",
V=False,
CalcCorrelations=False,
)
# DataSetInfo : [dataset] : Added class "Signal"
# : Add Tree sig_tree of type Signal with 10000 events
# DataSetInfo : [dataset] : Added class "Background"
# : Add Tree bkg_tree of type Background with 10000 events
# signalTree.Print();
# Booking Methods
# Here we book the TMVA methods. We book a Boosted Decision Tree method (BDT)
# Boosted Decision Trees
if useTMVABDT:
factory.BookMethod(
loader,
TMVA.Types.kBDT,
"BDT",
V=False,
NTrees=400,
MinNodeSize="2.5%",
MaxDepth=2,
BoostType="AdaBoost",
AdaBoostBeta=0.5,
UseBaggedBoost=True,
BaggedSampleFraction=0.5,
SeparationType="GiniIndex",
nCuts=20,
)
#### Booking Deep Neural Network
# Here we book the DNN of TMVA. See the example TMVA_Higgs_Classification.C for a detailed description of the
# options
if useTMVADNN:
layoutString = ROOT.TString(
"DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR"
)
# Training strategies
# one can catenate several training strings with different parameters (e.g. learning rates or regularizations
# parameters) The training string must be concatenated with the `|` delimiter
trainingString1 = ROOT.TString(
"LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
"ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,"
"WeightDecay=1e-4,Regularization=None,"
"Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0."
) # + "|" + trainingString2 + ...
trainingString1 += ",MaxEpochs=" + str(max_epochs)
# Build now the full DNN Option string
dnnMethodName = "TMVA_DNN_CPU"
# use GPU if available
dnnOptions = "CPU"
if hasGPU :
dnnOptions = "GPU"
dnnMethodName = "TMVA_DNN_GPU"
factory.BookMethod(
loader,
TMVA.Types.kDL,
dnnMethodName,
H=False,
V=True,
ErrorStrategy="CROSSENTROPY",
VarTransform=None,
WeightInitialization="XAVIER",
Layout=layoutString,
TrainingStrategy=trainingString1,
Architecture=dnnOptions
)
### Book Convolutional Neural Network in TMVA
# For building a CNN one needs to define
# - Input Layout : number of channels (in this case = 1) | image height | image width
# - Batch Layout : batch size | number of channels | image size = (height*width)
# Then one add Convolutional layers and MaxPool layers.
# - For Convolutional layer the option string has to be:
# - CONV | number of units | filter height | filter width | stride height | stride width | padding height | paddig
# width | activation function
# - note in this case we are using a filer 3x3 and padding=1 and stride=1 so we get the output dimension of the
# conv layer equal to the input
# - note we use after the first convolutional layer a batch normalization layer. This seems to help significantly the
# convergence
# - For the MaxPool layer:
# - MAXPOOL | pool height | pool width | stride height | stride width
# The RESHAPE layer is needed to flatten the output before the Dense layer
# Note that to run the CNN is required to have CPU or GPU support
if useTMVACNN:
# Training strategies.
trainingString1 = ROOT.TString(
"LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
"ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,"
"WeightDecay=1e-4,Regularization=None,"
"Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0"
)
trainingString1 += ",MaxEpochs=" + str(max_epochs)
## New DL (CNN)
cnnMethodName = "TMVA_CNN_CPU"
cnnOptions = "CPU"
# use GPU if available
if hasGPU:
cnnOptions = "GPU"
cnnMethodName = "TMVA_CNN_GPU"
factory.BookMethod(
loader,
TMVA.Types.kDL,
cnnMethodName,
H=False,
V=True,
ErrorStrategy="CROSSENTROPY",
VarTransform=None,
WeightInitialization="XAVIER",
InputLayout="1|16|16",
Layout="CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR",
TrainingStrategy=trainingString1,
Architecture=cnnOptions,
)
### Book Convolutional Neural Network in Keras using a generated model
if usePyTorchCNN:
ROOT.Info("TMVA_CNN_Classification", "Using Convolutional PyTorch Model")
pyTorchFileName = str(ROOT.gROOT.GetTutorialDir())
pyTorchFileName += "/machine_learning/PyTorch_Generate_CNN_Model.py"
# check that pytorch can be imported and file defining the model exists
torch_spec = importlib.util.find_spec("torch")
if torch_spec is not None and os.path.exists(pyTorchFileName):
#cmd = str(ROOT.TMVA.Python_Executable()) + " " + pyTorchFileName
#os.system(cmd)
#import PyTorch_Generate_CNN_Model
ROOT.Info("TMVA_CNN_Classification", "Booking PyTorch CNN model")
factory.BookMethod(
loader,
TMVA.Types.kPyTorch,
"PyTorch",
H=True,
V=False,
VarTransform=None,
FilenameModel="PyTorchModelCNN.pt",
FilenameTrainedModel="PyTorchTrainedModelCNN.pt",
NumEpochs=max_epochs,
BatchSize=100,
UserCode=str(pyTorchFileName)
)
else:
ROOT.Warning(
"TMVA_CNN_Classification",
"PyTorch is not installed or model building file is not existing - skip using PyTorch",
)
if useKerasCNN:
ROOT.Info("TMVA_CNN_Classification", "Building convolutional keras model")
# create python script which can be executed
# create 2 conv2d layer + maxpool + dense
import tensorflow
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
# from keras.initializers import TruncatedNormal
# from keras import initializations
from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape
# from keras.callbacks import ReduceLROnPlateau
model = Sequential()
model.add(Reshape((16, 16, 1), input_shape=(256,)))
model.add(Conv2D(10, kernel_size=(3, 3), kernel_initializer="TruncatedNormal", activation="relu", padding="same"))
model.add(Conv2D(10, kernel_size=(3, 3), kernel_initializer="TruncatedNormal", activation="relu", padding="same"))
# stride for maxpool is equal to pool size
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64, activation="tanh"))
# model.add(Dropout(0.2))
model.add(Dense(2, activation="sigmoid"))
model.compile(loss="binary_crossentropy", optimizer=Adam(learning_rate=0.001), weighted_metrics=["accuracy"])
model.save("model_cnn.h5")
model.summary()
if not os.path.exists("model_cnn.h5"):
raise FileNotFoundError("Error creating Keras model file - skip using Keras")
else:
# book PyKeras method only if Keras model could be created
ROOT.Info("TMVA_CNN_Classification", "Booking convolutional keras model")
factory.BookMethod(
loader,
TMVA.Types.kPyKeras,
"PyKeras",
H=True,
V=False,
VarTransform=None,
FilenameModel="model_cnn.h5",
FilenameTrainedModel="trained_model_cnn.h5",
NumEpochs=max_epochs,
BatchSize=100,
GpuOptions="allow_growth=True",
) # needed for RTX NVidia card and to avoid TF allocates all GPU memory
## Train Methods
factory.TrainAllMethods()
## Test and Evaluate Methods
factory.TestAllMethods()
factory.EvaluateAllMethods()
## Plot ROC Curve
c1 = factory.GetROCCurve(loader)
c1.Draw()
# close outputfile to save output file
outputFile.Close()
TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358
len
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 UChar_t len
Definition TGWin32VirtualXProxy.cxx:249
format
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
Definition TGWin32VirtualXProxy.cxx:249
TFile
A ROOT file is an on-disk file, usually with extension .root, that stores objects in a file-system-li...
Definition TFile.h:131
TMVA::Factory
This is the main MVA steering class.
Definition Factory.h:80
Author
Harshal Shende

Definition in file TMVA_CNN_Classification.py.