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: BDT : : 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: TMVA_DNN_CPU : : 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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.:Architecture=CPU" : The following options are set: : - By User: : : - 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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.: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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0." [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: TMVA_CNN_CPU : : 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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0:Architecture=CPU" : The following options are set: : - By User: : : - 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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0: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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0" [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 : Train all methods Factory : Train method: BDT for Classification : BDT : #events: (reweighted) sig: 800 bkg: 800 : #events: (unweighted) sig: 800 bkg: 800 : Training 200 Decision Trees ... patience please : Elapsed time for training with 1600 events: 0.684 sec BDT : [dataset] : Evaluation of BDT on training sample (1600 events) BDT : [dataset] : Evaluation of BDT on training sample (1600 events) : Elapsed time for evaluation of 1600 events: 0.00688 sec : Elapsed time for evaluation of 1600 events: 0.007 sec : Creating xml weight file: dataset/weights/TMVA_CNN_Classification_BDT.weights.xml : Creating standalone class: dataset/weights/TMVA_CNN_Classification_BDT.class.C : 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 = 66.2467 : -------------------------------------------------------------- : 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 | 0.89151 0.800326 0.0815616 0.00601026 15883.2 0 : 2 Minimum Test error found - save the configuration : 2 | 0.688696 0.796484 0.0811883 0.00592606 15944.3 0 : 3 Minimum Test error found - save the configuration : 3 | 0.582026 0.772563 0.081188 0.00588572 15935.8 0 : 4 | 0.532395 0.796619 0.0809363 0.00566263 15941.8 1 : 5 Minimum Test error found - save the configuration : 5 | 0.461875 0.746318 0.0810744 0.00593384 15970.1 0 : 6 Minimum Test error found - save the configuration : 6 | 0.43044 0.723082 0.081183 0.00586905 15933.3 0 : 7 | 0.358105 0.736452 0.081544 0.00559542 15800.2 1 : 8 | 0.297718 0.723595 0.0813468 0.00605844 15938.7 2 : 9 Minimum Test error found - save the configuration : 9 | 0.262659 0.684458 0.0812503 0.00592678 15931.3 0 : 10 | 0.227925 0.744205 0.0810539 0.00559902 15903.5 1 : : Elapsed time for training with 1600 events: 0.827 sec TMVA_DNN_CPU : [dataset] : Evaluation of TMVA_DNN_CPU on training sample (1600 events) : 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: 0.0287 sec : Elapsed time for evaluation of 1600 events: 0.0306 sec : Creating xml weight file: dataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.weights.xml : Creating standalone class: dataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.class.C 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 = 75.6915 : -------------------------------------------------------------- : 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 | 1.84979 1.1089 0.674068 0.0510701 1926.17 0 : 2 Minimum Test error found - save the configuration : 2 | 0.908151 0.811589 0.670292 0.0502337 1935.3 0 : 3 Minimum Test error found - save the configuration : 3 | 0.694934 0.711321 0.673047 0.0507679 1928.4 0 : 4 Minimum Test error found - save the configuration : 4 | 0.665876 0.683104 0.690399 0.0506048 1875.6 0 : 5 Minimum Test error found - save the configuration : 5 | 0.637218 0.649807 0.669251 0.0501971 1938.44 0 : 6 Minimum Test error found - save the configuration : 6 | 0.616457 0.636264 0.678856 0.0500503 1908.38 0 : 7 | 0.585497 0.64324 0.68015 0.0527848 1912.76 1 : 8 Minimum Test error found - save the configuration : 8 | 0.585584 0.618608 0.677677 0.0518098 1917.34 0 : 9 Minimum Test error found - save the configuration : 9 | 0.531354 0.568109 0.680426 0.0506978 1905.58 0 : 10 | 0.496706 0.56951 0.671143 0.0498233 1931.37 1 : : Elapsed time for training with 1600 events: 6.82 sec TMVA_CNN_CPU : [dataset] : Evaluation of TMVA_CNN_CPU on training sample (1600 events) : 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: 0.263 sec : Elapsed time for evaluation of 1600 events: 0.27 sec : Creating xml weight file: dataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.weights.xml : Creating standalone class: dataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.class.C Factory : Training finished : : Ranking input variables (method specific)... BDT : Ranking result (top variable is best ranked) : -------------------------------------- : Rank : Variable : Variable Importance : -------------------------------------- : 1 : vars : 1.184e-02 : 2 : vars : 1.126e-02 : 3 : vars : 1.027e-02 : 4 : vars : 9.743e-03 : 5 : vars : 9.562e-03 : 6 : vars : 8.753e-03 : 7 : vars : 8.648e-03 : 8 : vars : 8.617e-03 : 9 : vars : 8.197e-03 : 10 : vars : 8.080e-03 : 11 : vars : 8.073e-03 : 12 : vars : 8.041e-03 : 13 : vars : 8.008e-03 : 14 : vars : 7.986e-03 : 15 : vars : 7.979e-03 : 16 : vars : 7.735e-03 : 17 : vars : 7.722e-03 : 18 : vars : 7.514e-03 : 19 : vars : 7.513e-03 : 20 : vars : 7.491e-03 : 21 : vars : 7.385e-03 : 22 : vars : 7.365e-03 : 23 : vars : 7.252e-03 : 24 : vars : 7.183e-03 : 25 : vars : 7.105e-03 : 26 : vars : 7.086e-03 : 27 : vars : 7.082e-03 : 28 : vars : 6.999e-03 : 29 : vars : 6.996e-03 : 30 : vars : 6.962e-03 : 31 : vars : 6.883e-03 : 32 : vars : 6.854e-03 : 33 : vars : 6.840e-03 : 34 : vars : 6.793e-03 : 35 : vars : 6.790e-03 : 36 : vars : 6.724e-03 : 37 : vars : 6.652e-03 : 38 : vars : 6.602e-03 : 39 : vars : 6.550e-03 : 40 : vars : 6.485e-03 : 41 : vars : 6.474e-03 : 42 : vars : 6.447e-03 : 43 : vars : 6.410e-03 : 44 : vars : 6.275e-03 : 45 : vars : 6.225e-03 : 46 : vars : 6.217e-03 : 47 : vars : 6.128e-03 : 48 : vars : 6.127e-03 : 49 : vars : 6.120e-03 : 50 : vars : 6.094e-03 : 51 : vars : 6.014e-03 : 52 : vars : 6.014e-03 : 53 : vars : 6.003e-03 : 54 : vars : 5.948e-03 : 55 : vars : 5.921e-03 : 56 : vars : 5.919e-03 : 57 : vars : 5.868e-03 : 58 : vars : 5.784e-03 : 59 : vars : 5.782e-03 : 60 : vars : 5.727e-03 : 61 : vars : 5.714e-03 : 62 : vars : 5.652e-03 : 63 : vars : 5.628e-03 : 64 : vars : 5.597e-03 : 65 : vars : 5.594e-03 : 66 : vars : 5.584e-03 : 67 : vars : 5.580e-03 : 68 : vars : 5.531e-03 : 69 : vars : 5.452e-03 : 70 : vars : 5.441e-03 : 71 : vars : 5.427e-03 : 72 : vars : 5.416e-03 : 73 : vars : 5.331e-03 : 74 : vars : 5.321e-03 : 75 : vars : 5.301e-03 : 76 : vars : 5.230e-03 : 77 : vars : 5.227e-03 : 78 : vars : 5.212e-03 : 79 : vars : 5.184e-03 : 80 : vars : 5.172e-03 : 81 : vars : 5.140e-03 : 82 : vars : 5.119e-03 : 83 : vars : 5.077e-03 : 84 : vars : 5.076e-03 : 85 : vars : 5.038e-03 : 86 : vars : 5.007e-03 : 87 : vars : 5.004e-03 : 88 : vars : 4.983e-03 : 89 : vars : 4.979e-03 : 90 : vars : 4.960e-03 : 91 : vars : 4.950e-03 : 92 : vars : 4.890e-03 : 93 : vars : 4.867e-03 : 94 : vars : 4.838e-03 : 95 : vars : 4.836e-03 : 96 : vars : 4.833e-03 : 97 : vars : 4.804e-03 : 98 : vars : 4.778e-03 : 99 : vars : 4.773e-03 : 100 : vars : 4.756e-03 : 101 : vars : 4.752e-03 : 102 : vars : 4.726e-03 : 103 : vars : 4.682e-03 : 104 : vars : 4.661e-03 : 105 : vars : 4.612e-03 : 106 : vars : 4.590e-03 : 107 : vars : 4.560e-03 : 108 : vars : 4.555e-03 : 109 : vars : 4.551e-03 : 110 : vars : 4.522e-03 : 111 : vars : 4.507e-03 : 112 : vars : 4.501e-03 : 113 : vars : 4.433e-03 : 114 : vars : 4.432e-03 : 115 : vars : 4.373e-03 : 116 : vars : 4.369e-03 : 117 : vars : 4.361e-03 : 118 : vars : 4.344e-03 : 119 : vars : 4.342e-03 : 120 : vars : 4.341e-03 : 121 : vars : 4.302e-03 : 122 : vars : 4.293e-03 : 123 : vars : 4.214e-03 : 124 : vars : 4.194e-03 : 125 : vars : 4.182e-03 : 126 : vars : 4.173e-03 : 127 : vars : 4.161e-03 : 128 : vars : 4.151e-03 : 129 : vars : 4.074e-03 : 130 : vars : 4.063e-03 : 131 : vars : 4.063e-03 : 132 : vars : 4.049e-03 : 133 : vars : 4.033e-03 : 134 : vars : 3.971e-03 : 135 : vars : 3.970e-03 : 136 : vars : 3.965e-03 : 137 : vars : 3.890e-03 : 138 : vars : 3.756e-03 : 139 : vars : 3.719e-03 : 140 : vars : 3.700e-03 : 141 : vars : 3.692e-03 : 142 : vars : 3.616e-03 : 143 : vars : 3.615e-03 : 144 : vars : 3.583e-03 : 145 : vars : 3.553e-03 : 146 : vars : 3.527e-03 : 147 : vars : 3.519e-03 : 148 : vars : 3.481e-03 : 149 : vars : 3.478e-03 : 150 : vars : 3.449e-03 : 151 : vars : 3.436e-03 : 152 : vars : 3.396e-03 : 153 : vars : 3.390e-03 : 154 : vars : 3.389e-03 : 155 : vars : 3.365e-03 : 156 : vars : 3.350e-03 : 157 : vars : 3.345e-03 : 158 : vars : 3.330e-03 : 159 : vars : 3.330e-03 : 160 : vars : 3.317e-03 : 161 : vars : 3.302e-03 : 162 : vars : 3.287e-03 : 163 : vars : 3.272e-03 : 164 : vars : 3.220e-03 : 165 : vars : 3.201e-03 : 166 : vars : 3.192e-03 : 167 : vars : 3.179e-03 : 168 : vars : 3.158e-03 : 169 : vars : 3.105e-03 : 170 : vars : 3.054e-03 : 171 : vars : 3.053e-03 : 172 : vars : 3.039e-03 : 173 : vars : 3.023e-03 : 174 : vars : 3.004e-03 : 175 : vars : 2.977e-03 : 176 : vars : 2.828e-03 : 177 : vars : 2.812e-03 : 178 : vars : 2.770e-03 : 179 : vars : 2.741e-03 : 180 : vars : 2.741e-03 : 181 : vars : 2.733e-03 : 182 : vars : 2.725e-03 : 183 : vars : 2.725e-03 : 184 : vars : 2.648e-03 : 185 : vars : 2.609e-03 : 186 : vars : 2.572e-03 : 187 : vars : 2.507e-03 : 188 : vars : 2.434e-03 : 189 : vars : 2.432e-03 : 190 : vars : 2.400e-03 : 191 : vars : 2.319e-03 : 192 : vars : 2.235e-03 : 193 : vars : 2.188e-03 : 194 : vars : 2.177e-03 : 195 : vars : 2.121e-03 : 196 : vars : 2.116e-03 : 197 : vars : 2.115e-03 : 198 : vars : 2.070e-03 : 199 : vars : 2.027e-03 : 200 : vars : 1.770e-03 : 201 : vars : 1.722e-03 : 202 : vars : 1.689e-03 : 203 : vars : 1.651e-03 : 204 : vars : 1.529e-03 : 205 : vars : 9.766e-04 : 206 : vars : 8.962e-04 : 207 : vars : 7.786e-04 : 208 : vars : 7.665e-04 : 209 : vars : 7.076e-04 : 210 : vars : 4.402e-04 : 211 : vars : 2.366e-04 : 212 : vars : 0.000e+00 : 213 : vars : 0.000e+00 : 214 : vars : 0.000e+00 : 215 : vars : 0.000e+00 : 216 : vars : 0.000e+00 : 217 : vars : 0.000e+00 : 218 : vars : 0.000e+00 : 219 : vars : 0.000e+00 : 220 : vars : 0.000e+00 : 221 : vars : 0.000e+00 : 222 : vars : 0.000e+00 : 223 : vars : 0.000e+00 : 224 : vars : 0.000e+00 : 225 : vars : 0.000e+00 : 226 : vars : 0.000e+00 : 227 : vars : 0.000e+00 : 228 : vars : 0.000e+00 : 229 : vars : 0.000e+00 : 230 : vars : 0.000e+00 : 231 : vars : 0.000e+00 : 232 : vars : 0.000e+00 : 233 : vars : 0.000e+00 : 234 : vars : 0.000e+00 : 235 : vars : 0.000e+00 : 236 : vars : 0.000e+00 : 237 : vars : 0.000e+00 : 238 : vars : 0.000e+00 : 239 : vars : 0.000e+00 : 240 : vars : 0.000e+00 : 241 : vars : 0.000e+00 : 242 : vars : 0.000e+00 : 243 : vars : 0.000e+00 : 244 : vars : 0.000e+00 : 245 : vars : 0.000e+00 : 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= 4.73335 TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 7.5241 TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 7.57156 TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 7.00045 Factory : === Destroy and recreate all methods via weight files for testing === : : Reading weight file: dataset/weights/TMVA_CNN_Classification_BDT.weights.xml : Reading weight file: dataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.weights.xml : Reading weight file: dataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.weights.xml Factory : Test all methods Factory : Test method: BDT for Classification performance : BDT : [dataset] : Evaluation of BDT on testing sample (400 events) BDT : [dataset] : Evaluation of BDT on testing sample (400 events) : Elapsed time for evaluation of 400 events: 0.00188 sec : Elapsed time for evaluation of 400 events: 0.00199 sec Factory : Test method: TMVA_DNN_CPU for Classification performance : TMVA_DNN_CPU : [dataset] : Evaluation of TMVA_DNN_CPU on testing sample (400 events) : 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: 0.00693 sec : Elapsed time for evaluation of 400 events: 0.0088 sec Factory : Test method: TMVA_CNN_CPU for Classification performance : TMVA_CNN_CPU : [dataset] : Evaluation of TMVA_CNN_CPU on testing sample (400 events) : 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: 0.0684 sec : Elapsed time for evaluation of 400 events: 0.0786 sec Factory : Evaluate all methods 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 TMVA_CNN_CPU : 0.782 : dataset TMVA_DNN_CPU : 0.725 : dataset BDT : 0.687 : ------------------------------------------------------------------------------------------------------------------- : : 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 TMVA_CNN_CPU : 0.115 (0.145) 0.395 (0.535) 0.713 (0.780) : dataset TMVA_DNN_CPU : 0.010 (0.240) 0.335 (0.685) 0.630 (0.867) : dataset BDT : 0.060 (0.235) 0.257 (0.588) 0.585 (0.787) : ------------------------------------------------------------------------------------------------------------------- : Dataset:dataset : Created tree 'TestTree' with 400 events : Dataset:dataset : Created tree 'TrainTree' with 1600 events : Factory : Thank you for using TMVA! : For citation information, please visit: http://tmva.sf.net/citeTMVA.html