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,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:
: <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,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: ␛[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,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:
: <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,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 : ␛[1mTrain all methods␛[0m
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: 0.728 sec
BDT : [dataset] : Evaluation of BDT on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.00681 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 = 22.9693
: --------------------------------------------------------------
: 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 | 0.928727 0.922709 0.0149056 0.00160571 90226.2 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.691264 0.819181 0.0141439 0.00143518 94423.5 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.595997 0.788938 0.0142676 0.00145409 93651 0
: 4 | 0.510819 0.791288 0.0138245 0.0010389 93855.8 1
: 5 Minimum Test error found - save the configuration
: 5 | 0.459059 0.761623 0.0141999 0.00139323 93701.1 0
: 6 | 0.403914 0.770949 0.0138446 0.00109568 94125.7 1
: 7 | 0.350193 0.763591 0.0138038 0.00111482 94570 2
: 8 | 0.30281 0.762167 0.0139221 0.00109292 93536.8 3
: 9 Minimum Test error found - save the configuration
: 9 | 0.255788 0.74998 0.0141031 0.00139767 94447.6 0
: 10 | 0.230627 0.812798 0.0139846 0.00106799 92903.6 1
:
: Elapsed time for training with 1600 events: 0.156 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: 0.00555 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 = 151.443
: --------------------------------------------------------------
: 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 | 2.62096 0.914133 0.3039 0.0226567 4266.77 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.911569 0.750351 0.295212 0.0217608 4388.36 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.710932 0.6265 0.293864 0.02129 4402.47 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.658319 0.615431 0.289601 0.0210134 4467.81 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.623086 0.609849 0.287458 0.0207732 4499.69 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.605007 0.57578 0.267796 0.0219645 4881.4 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.578265 0.561753 0.277984 0.0208726 4667.24 0
: 8 | 0.546576 0.582299 0.290419 0.0198139 4434.5 1
: 9 Minimum Test error found - save the configuration
: 9 | 0.522003 0.528338 0.292957 0.0210264 4412.89 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.488524 0.516719 0.294355 0.0215861 4399.33 0
:
: Elapsed time for training with 1600 events: 2.93 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: 0.104 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 : 1.097e-02
: 2 : vars : 1.044e-02
: 3 : vars : 1.009e-02
: 4 : vars : 9.033e-03
: 5 : vars : 8.736e-03
: 6 : vars : 8.611e-03
: 7 : vars : 8.507e-03
: 8 : vars : 8.457e-03
: 9 : vars : 8.251e-03
: 10 : vars : 8.208e-03
: 11 : vars : 8.193e-03
: 12 : vars : 8.113e-03
: 13 : vars : 8.033e-03
: 14 : vars : 8.013e-03
: 15 : vars : 7.950e-03
: 16 : vars : 7.948e-03
: 17 : vars : 7.779e-03
: 18 : vars : 7.659e-03
: 19 : vars : 7.569e-03
: 20 : vars : 7.502e-03
: 21 : vars : 7.472e-03
: 22 : vars : 7.411e-03
: 23 : vars : 7.349e-03
: 24 : vars : 7.238e-03
: 25 : vars : 7.237e-03
: 26 : vars : 7.234e-03
: 27 : vars : 7.141e-03
: 28 : vars : 7.103e-03
: 29 : vars : 6.975e-03
: 30 : vars : 6.962e-03
: 31 : vars : 6.921e-03
: 32 : vars : 6.911e-03
: 33 : vars : 6.895e-03
: 34 : vars : 6.892e-03
: 35 : vars : 6.770e-03
: 36 : vars : 6.755e-03
: 37 : vars : 6.723e-03
: 38 : vars : 6.616e-03
: 39 : vars : 6.610e-03
: 40 : vars : 6.559e-03
: 41 : vars : 6.549e-03
: 42 : vars : 6.466e-03
: 43 : vars : 6.414e-03
: 44 : vars : 6.410e-03
: 45 : vars : 6.403e-03
: 46 : vars : 6.401e-03
: 47 : vars : 6.399e-03
: 48 : vars : 6.392e-03
: 49 : vars : 6.390e-03
: 50 : vars : 6.370e-03
: 51 : vars : 6.364e-03
: 52 : vars : 6.345e-03
: 53 : vars : 6.277e-03
: 54 : vars : 6.276e-03
: 55 : vars : 6.257e-03
: 56 : vars : 6.240e-03
: 57 : vars : 6.176e-03
: 58 : vars : 6.145e-03
: 59 : vars : 6.064e-03
: 60 : vars : 6.043e-03
: 61 : vars : 6.040e-03
: 62 : vars : 6.035e-03
: 63 : vars : 6.035e-03
: 64 : vars : 6.021e-03
: 65 : vars : 5.999e-03
: 66 : vars : 5.984e-03
: 67 : vars : 5.973e-03
: 68 : vars : 5.892e-03
: 69 : vars : 5.847e-03
: 70 : vars : 5.841e-03
: 71 : vars : 5.771e-03
: 72 : vars : 5.749e-03
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: 89 : vars : 5.209e-03
: 90 : vars : 5.159e-03
: 91 : vars : 5.157e-03
: 92 : vars : 5.149e-03
: 93 : vars : 5.128e-03
: 94 : vars : 5.107e-03
: 95 : vars : 5.072e-03
: 96 : vars : 5.059e-03
: 97 : vars : 5.020e-03
: 98 : vars : 5.005e-03
: 99 : vars : 4.999e-03
: 100 : vars : 4.977e-03
: 101 : vars : 4.949e-03
: 102 : vars : 4.937e-03
: 103 : vars : 4.891e-03
: 104 : vars : 4.887e-03
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: 106 : vars : 4.850e-03
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: 109 : vars : 4.771e-03
: 110 : vars : 4.750e-03
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: 125 : vars : 4.275e-03
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: 127 : vars : 4.251e-03
: 128 : vars : 4.162e-03
: 129 : vars : 4.107e-03
: 130 : vars : 4.064e-03
: 131 : vars : 4.049e-03
: 132 : vars : 4.044e-03
: 133 : vars : 4.028e-03
: 134 : vars : 4.000e-03
: 135 : vars : 3.987e-03
: 136 : vars : 3.986e-03
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: 139 : vars : 3.916e-03
: 140 : vars : 3.913e-03
: 141 : vars : 3.911e-03
: 142 : vars : 3.882e-03
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: 145 : vars : 3.807e-03
: 146 : vars : 3.792e-03
: 147 : vars : 3.791e-03
: 148 : vars : 3.779e-03
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: 150 : vars : 3.760e-03
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: 152 : vars : 3.650e-03
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: 185 : vars : 2.408e-03
: 186 : vars : 2.318e-03
: 187 : vars : 2.311e-03
: 188 : vars : 2.222e-03
: 189 : vars : 2.217e-03
: 190 : vars : 2.155e-03
: 191 : vars : 2.084e-03
: 192 : vars : 1.990e-03
: 193 : vars : 1.944e-03
: 194 : vars : 1.882e-03
: 195 : vars : 1.541e-03
: 196 : vars : 1.400e-03
: 197 : vars : 1.249e-03
: 198 : vars : 1.237e-03
: 199 : vars : 9.053e-04
: 200 : vars : 7.024e-04
: 201 : vars : 0.000e+00
: 202 : vars : 0.000e+00
: 203 : vars : 0.000e+00
: 204 : vars : 0.000e+00
: 205 : vars : 0.000e+00
: 206 : vars : 0.000e+00
: 207 : vars : 0.000e+00
: 208 : vars : 0.000e+00
: 209 : vars : 0.000e+00
: 210 : vars : 0.000e+00
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: 214 : vars : 0.000e+00
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: 221 : vars : 0.000e+00
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: 225 : vars : 0.000e+00
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: 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
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: 240 : vars : 0.000e+00
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: 244 : vars : 0.000e+00
: 245 : vars : 0.000e+00
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: 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= 4.7292
TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 7.94322
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 8.26524
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 6.28115
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: 0.0019 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: 0.00121 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: 0.0308 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 TMVA_CNN_CPU : 0.828
: dataset BDT : 0.732
: dataset TMVA_DNN_CPU : 0.706
: -------------------------------------------------------------------------------------------------------------------
:
: 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 : 0.095 (0.318) 0.415 (0.553) 0.830 (0.863)
: dataset BDT : 0.045 (0.178) 0.270 (0.582) 0.605 (0.764)
: dataset TMVA_DNN_CPU : 0.085 (0.208) 0.285 (0.609) 0.607 (0.803)
: -------------------------------------------------------------------------------------------------------------------
:
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