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
: 77 : vars : 4.706e-03
: 78 : vars : 4.692e-03
: 79 : vars : 4.687e-03
: 80 : vars : 4.685e-03
: 81 : vars : 4.653e-03
: 82 : vars : 4.647e-03
: 83 : vars : 4.643e-03
: 84 : vars : 4.602e-03
: 85 : vars : 4.589e-03
: 86 : vars : 4.574e-03
: 87 : vars : 4.573e-03
: 88 : vars : 4.517e-03
: 89 : vars : 4.503e-03
: 90 : vars : 4.490e-03
: 91 : vars : 4.486e-03
: 92 : vars : 4.452e-03
: 93 : vars : 4.423e-03
: 94 : vars : 4.412e-03
: 95 : vars : 4.409e-03
: 96 : vars : 4.407e-03
: 97 : vars : 4.371e-03
: 98 : vars : 4.365e-03
: 99 : vars : 4.364e-03
: 100 : vars : 4.359e-03
: 101 : vars : 4.348e-03
: 102 : vars : 4.333e-03
: 103 : vars : 4.330e-03
: 104 : vars : 4.298e-03
: 105 : vars : 4.293e-03
: 106 : vars : 4.263e-03
: 107 : vars : 4.232e-03
: 108 : vars : 4.220e-03
: 109 : vars : 4.185e-03
: 110 : vars : 4.177e-03
: 111 : vars : 4.159e-03
: 112 : vars : 4.145e-03
: 113 : vars : 4.089e-03
: 114 : vars : 4.061e-03
: 115 : vars : 4.056e-03
: 116 : vars : 4.027e-03
: 117 : vars : 3.990e-03
: 118 : vars : 3.980e-03
: 119 : vars : 3.978e-03
: 120 : vars : 3.965e-03
: 121 : vars : 3.934e-03
: 122 : vars : 3.923e-03
: 123 : vars : 3.919e-03
: 124 : vars : 3.848e-03
: 125 : vars : 3.834e-03
: 126 : vars : 3.819e-03
: 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
: 132 : vars : 3.745e-03
: 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
: 143 : vars : 3.683e-03
: 144 : vars : 3.679e-03
: 145 : vars : 3.664e-03
: 146 : vars : 3.610e-03
: 147 : vars : 3.606e-03
: 148 : vars : 3.595e-03
: 149 : vars : 3.539e-03
: 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
: 161 : vars : 3.342e-03
: 162 : vars : 3.323e-03
: 163 : vars : 3.319e-03
: 164 : vars : 3.298e-03
: 165 : vars : 3.276e-03
: 166 : vars : 3.257e-03
: 167 : vars : 3.252e-03
: 168 : vars : 3.245e-03
: 169 : vars : 3.189e-03
: 170 : vars : 3.147e-03
: 171 : vars : 3.147e-03
: 172 : vars : 3.125e-03
: 173 : vars : 3.078e-03
: 174 : vars : 3.077e-03
: 175 : vars : 3.073e-03
: 176 : vars : 3.070e-03
: 177 : vars : 3.066e-03
: 178 : vars : 3.033e-03
: 179 : vars : 2.983e-03
: 180 : vars : 2.967e-03
: 181 : vars : 2.955e-03
: 182 : vars : 2.953e-03
: 183 : vars : 2.947e-03
: 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
import ROOT
import os
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
ntot = nh * nw
fileOutName = "images_data_16x16.root"
nRndmEvts = 10000
delta_sigma = 0.1
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)
f =
TFile(fileOutName,
"RECREATE")
ROOT.Info(
"TMVA_CNN_Classification",
"Filling ROOT tree \n")
if i % 1000 == 0:
print("Generating image event ...", i)
m = k * nw + l
print(
"Signal and background tree with images data written to the file %s",
f.GetName())
nevt = 1000
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
useKerasCNN = False
usePyTorchCNN = False
else:
if not useTMVACNN:
"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
max_epochs = 10
else:
print("Running in serial mode since ROOT does not support MT")
outputFile = None
if writeOutputFile:
outputFile =
TFile.Open(
"TMVA_CNN_ClassificationOutput.root",
"RECREATE")
"TMVA_CNN_Classification",
outputFile,
V=False,
ROC=True,
Silent=False,
Color=True,
AnalysisType="Classification",
Transformations=None,
Correlations=False,
)
imgSize = 16 * 16
inputFileName = "images_data_16x16.root"
if inputFile is None:
signalWeight = 1.0
backgroundWeight = 1.0
mycuts = ""
mycutb = ""
nTrainSig = 0.8 * nEventsSig
nTrainBkg = 0.8 * nEventsBkg
mycuts,
mycutb,
nTrain_Signal=nTrainSig,
nTrain_Background=nTrainBkg,
SplitMode="Random",
SplitSeed=100,
NormMode="NumEvents",
V=False,
CalcCorrelations=False,
)
if useTMVABDT:
loader,
"BDT",
V=False,
NTrees=400,
MinNodeSize="2.5%",
MaxDepth=2,
BoostType="AdaBoost",
AdaBoostBeta=0.5,
UseBaggedBoost=True,
BaggedSampleFraction=0.5,
SeparationType="GiniIndex",
nCuts=20,
)
if useTMVADNN:
"DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR"
)
"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."
)
trainingString1 += ",MaxEpochs=" + str(max_epochs)
dnnMethodName = "TMVA_DNN_CPU"
dnnOptions = "CPU"
if hasGPU :
dnnOptions = "GPU"
dnnMethodName = "TMVA_DNN_GPU"
loader,
dnnMethodName,
H=False,
V=True,
ErrorStrategy="CROSSENTROPY",
VarTransform=None,
WeightInitialization="XAVIER",
Layout=layoutString,
TrainingStrategy=trainingString1,
Architecture=dnnOptions
)
if useTMVACNN:
"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)
cnnMethodName = "TMVA_CNN_CPU"
cnnOptions = "CPU"
if hasGPU:
cnnOptions = "GPU"
cnnMethodName = "TMVA_CNN_GPU"
loader,
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,
)
if usePyTorchCNN:
ROOT.Info(
"TMVA_CNN_Classification",
"Using Convolutional PyTorch Model")
pyTorchFileName += "/machine_learning/PyTorch_Generate_CNN_Model.py"
ROOT.Info(
"TMVA_CNN_Classification",
"Booking PyTorch CNN model")
loader,
"PyTorch",
H=True,
V=False,
VarTransform=None,
FilenameModel="PyTorchModelCNN.pt",
FilenameTrainedModel="PyTorchTrainedModelCNN.pt",
NumEpochs=max_epochs,
BatchSize=100,
UserCode=str(pyTorchFileName)
)
else:
"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")
import tensorflow
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"))
model.compile(loss=
"binary_crossentropy", optimizer=
Adam(learning_rate=0.001), weighted_metrics=[
"accuracy"])
else:
ROOT.Info(
"TMVA_CNN_Classification",
"Booking convolutional keras model")
loader,
"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",
)
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
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
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 ROOT file is an on-disk file, usually with extension .root, that stores objects in a file-system-li...
This is the main MVA steering class.