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: 1.34 sec
BDT : [dataset] : Evaluation of BDT on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.0143 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 = 37.6866
: --------------------------------------------------------------
: 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 | 0.88928 0.913412 0.0148371 0.00142326 89459.9 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.667224 0.717007 0.0149795 0.00122945 87272.4 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.588492 0.682504 0.0139839 0.00125627 94283.1 0
: 4 | 0.493283 0.749467 0.0144612 0.000967277 88928.6 1
: 5 | 0.45542 0.708523 0.0134922 0.000911704 95385.7 2
: 6 | 0.392388 0.770107 0.0142979 0.00107306 90738.1 3
: 7 Minimum Test error found - save the configuration
: 7 | 0.337808 0.666771 0.0148454 0.00127579 88432.6 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.26266 0.664686 0.0147099 0.00129929 89481.6 0
: 9 | 0.230575 0.665832 0.0139394 0.000925325 92208.1 1
: 10 Minimum Test error found - save the configuration
: 10 | 0.181053 0.579972 0.0142634 0.00125513 92249 0
:
: Elapsed time for training with 1600 events: 0.326 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: 0.00451 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 = 80.4984
: --------------------------------------------------------------
: 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 | 2.86637 1.54795 0.326882 0.0385075 4161.26 0
: 2 Minimum Test error found - save the configuration
: 2 | 1.1252 1.08521 0.322523 0.0308852 4114.69 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.851767 0.846733 0.30594 0.0309868 4364.37 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.740546 0.713377 0.341331 0.0305481 3861.21 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.684241 0.678182 0.331922 0.0316849 3996.84 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.666133 0.67719 0.305163 0.0305524 4369.82 0
: 7 | 0.652947 0.687088 0.337345 0.0334067 3948.18 1
: 8 Minimum Test error found - save the configuration
: 8 | 0.647821 0.663086 0.341854 0.0320355 3873.24 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.629438 0.65248 0.308769 0.0317996 4332.61 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.615326 0.645519 0.319398 0.0307987 4158.01 0
:
: Elapsed time for training with 1600 events: 3.28 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: 0.167 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 : 8.798e-03
: 2 : vars : 8.624e-03
: 3 : vars : 8.599e-03
: 4 : vars : 8.537e-03
: 5 : vars : 8.018e-03
: 6 : vars : 7.918e-03
: 7 : vars : 7.791e-03
: 8 : vars : 7.566e-03
: 9 : vars : 7.440e-03
: 10 : vars : 7.192e-03
: 11 : vars : 6.922e-03
: 12 : vars : 6.870e-03
: 13 : vars : 6.791e-03
: 14 : vars : 6.726e-03
: 15 : vars : 6.621e-03
: 16 : vars : 6.551e-03
: 17 : vars : 6.475e-03
: 18 : vars : 6.460e-03
: 19 : vars : 6.417e-03
: 20 : vars : 6.235e-03
: 21 : vars : 6.210e-03
: 22 : vars : 6.107e-03
: 23 : vars : 6.099e-03
: 24 : vars : 6.072e-03
: 25 : vars : 6.065e-03
: 26 : vars : 6.056e-03
: 27 : vars : 6.053e-03
: 28 : vars : 6.047e-03
: 29 : vars : 6.031e-03
: 30 : vars : 6.029e-03
: 31 : vars : 6.016e-03
: 32 : vars : 6.015e-03
: 33 : vars : 5.822e-03
: 34 : vars : 5.789e-03
: 35 : vars : 5.774e-03
: 36 : vars : 5.758e-03
: 37 : vars : 5.747e-03
: 38 : vars : 5.705e-03
: 39 : vars : 5.683e-03
: 40 : vars : 5.665e-03
: 41 : vars : 5.643e-03
: 42 : vars : 5.641e-03
: 43 : vars : 5.631e-03
: 44 : vars : 5.630e-03
: 45 : vars : 5.603e-03
: 46 : vars : 5.589e-03
: 47 : vars : 5.490e-03
: 48 : vars : 5.457e-03
: 49 : vars : 5.438e-03
: 50 : vars : 5.431e-03
: 51 : vars : 5.395e-03
: 52 : vars : 5.359e-03
: 53 : vars : 5.299e-03
: 54 : vars : 5.297e-03
: 55 : vars : 5.296e-03
: 56 : vars : 5.287e-03
: 57 : vars : 5.264e-03
: 58 : vars : 5.255e-03
: 59 : vars : 5.200e-03
: 60 : vars : 5.190e-03
: 61 : vars : 5.181e-03
: 62 : vars : 5.160e-03
: 63 : vars : 5.117e-03
: 64 : vars : 5.095e-03
: 65 : vars : 5.074e-03
: 66 : vars : 5.055e-03
: 67 : vars : 5.036e-03
: 68 : vars : 5.027e-03
: 69 : vars : 5.013e-03
: 70 : vars : 5.008e-03
: 71 : vars : 5.003e-03
: 72 : vars : 4.917e-03
: 73 : vars : 4.908e-03
: 74 : vars : 4.860e-03
: 75 : vars : 4.845e-03
: 76 : vars : 4.842e-03
: 77 : vars : 4.811e-03
: 78 : vars : 4.790e-03
: 79 : vars : 4.716e-03
: 80 : vars : 4.694e-03
: 81 : vars : 4.678e-03
: 82 : vars : 4.650e-03
: 83 : vars : 4.643e-03
: 84 : vars : 4.640e-03
: 85 : vars : 4.615e-03
: 86 : vars : 4.597e-03
: 87 : vars : 4.580e-03
: 88 : vars : 4.548e-03
: 89 : vars : 4.541e-03
: 90 : vars : 4.535e-03
: 91 : vars : 4.526e-03
: 92 : vars : 4.518e-03
: 93 : vars : 4.511e-03
: 94 : vars : 4.511e-03
: 95 : vars : 4.505e-03
: 96 : vars : 4.489e-03
: 97 : vars : 4.442e-03
: 98 : vars : 4.441e-03
: 99 : vars : 4.419e-03
: 100 : vars : 4.418e-03
: 101 : vars : 4.418e-03
: 102 : vars : 4.397e-03
: 103 : vars : 4.383e-03
: 104 : vars : 4.365e-03
: 105 : vars : 4.359e-03
: 106 : vars : 4.352e-03
: 107 : vars : 4.334e-03
: 108 : vars : 4.332e-03
: 109 : vars : 4.301e-03
: 110 : vars : 4.286e-03
: 111 : vars : 4.269e-03
: 112 : vars : 4.243e-03
: 113 : vars : 4.233e-03
: 114 : vars : 4.211e-03
: 115 : vars : 4.198e-03
: 116 : vars : 4.193e-03
: 117 : vars : 4.175e-03
: 118 : vars : 4.142e-03
: 119 : vars : 4.137e-03
: 120 : vars : 4.131e-03
: 121 : vars : 4.115e-03
: 122 : vars : 4.070e-03
: 123 : vars : 4.040e-03
: 124 : vars : 4.035e-03
: 125 : vars : 4.027e-03
: 126 : vars : 4.010e-03
: 127 : vars : 4.002e-03
: 128 : vars : 3.996e-03
: 129 : vars : 3.933e-03
: 130 : vars : 3.910e-03
: 131 : vars : 3.900e-03
: 132 : vars : 3.883e-03
: 133 : vars : 3.870e-03
: 134 : vars : 3.855e-03
: 135 : vars : 3.816e-03
: 136 : vars : 3.768e-03
: 137 : vars : 3.767e-03
: 138 : vars : 3.728e-03
: 139 : vars : 3.704e-03
: 140 : vars : 3.654e-03
: 141 : vars : 3.625e-03
: 142 : vars : 3.619e-03
: 143 : vars : 3.585e-03
: 144 : vars : 3.585e-03
: 145 : vars : 3.581e-03
: 146 : vars : 3.554e-03
: 147 : vars : 3.507e-03
: 148 : vars : 3.504e-03
: 149 : vars : 3.501e-03
: 150 : vars : 3.492e-03
: 151 : vars : 3.470e-03
: 152 : vars : 3.454e-03
: 153 : vars : 3.440e-03
: 154 : vars : 3.414e-03
: 155 : vars : 3.383e-03
: 156 : vars : 3.308e-03
: 157 : vars : 3.253e-03
: 158 : vars : 3.215e-03
: 159 : vars : 3.210e-03
: 160 : vars : 3.201e-03
: 161 : vars : 3.184e-03
: 162 : vars : 3.177e-03
: 163 : vars : 3.173e-03
: 164 : vars : 3.161e-03
: 165 : vars : 3.152e-03
: 166 : vars : 3.096e-03
: 167 : vars : 3.078e-03
: 168 : vars : 3.037e-03
: 169 : vars : 3.032e-03
: 170 : vars : 3.014e-03
: 171 : vars : 3.014e-03
: 172 : vars : 3.004e-03
: 173 : vars : 2.968e-03
: 174 : vars : 2.968e-03
: 175 : vars : 2.966e-03
: 176 : vars : 2.956e-03
: 177 : vars : 2.942e-03
: 178 : vars : 2.914e-03
: 179 : vars : 2.893e-03
: 180 : vars : 2.888e-03
: 181 : vars : 2.887e-03
: 182 : vars : 2.869e-03
: 183 : vars : 2.847e-03
: 184 : vars : 2.836e-03
: 185 : vars : 2.801e-03
: 186 : vars : 2.762e-03
: 187 : vars : 2.748e-03
: 188 : vars : 2.742e-03
: 189 : vars : 2.722e-03
: 190 : vars : 2.713e-03
: 191 : vars : 2.701e-03
: 192 : vars : 2.698e-03
: 193 : vars : 2.683e-03
: 194 : vars : 2.644e-03
: 195 : vars : 2.638e-03
: 196 : vars : 2.634e-03
: 197 : vars : 2.634e-03
: 198 : vars : 2.618e-03
: 199 : vars : 2.605e-03
: 200 : vars : 2.577e-03
: 201 : vars : 2.572e-03
: 202 : vars : 2.554e-03
: 203 : vars : 2.530e-03
: 204 : vars : 2.500e-03
: 205 : vars : 2.489e-03
: 206 : vars : 2.481e-03
: 207 : vars : 2.469e-03
: 208 : vars : 2.459e-03
: 209 : vars : 2.385e-03
: 210 : vars : 2.366e-03
: 211 : vars : 2.313e-03
: 212 : vars : 2.300e-03
: 213 : vars : 2.294e-03
: 214 : vars : 2.293e-03
: 215 : vars : 2.260e-03
: 216 : vars : 2.241e-03
: 217 : vars : 2.228e-03
: 218 : vars : 2.201e-03
: 219 : vars : 2.146e-03
: 220 : vars : 2.069e-03
: 221 : vars : 2.038e-03
: 222 : vars : 2.004e-03
: 223 : vars : 1.972e-03
: 224 : vars : 1.923e-03
: 225 : vars : 1.899e-03
: 226 : vars : 1.894e-03
: 227 : vars : 1.874e-03
: 228 : vars : 1.823e-03
: 229 : vars : 1.787e-03
: 230 : vars : 1.776e-03
: 231 : vars : 1.753e-03
: 232 : vars : 1.747e-03
: 233 : vars : 1.676e-03
: 234 : vars : 1.674e-03
: 235 : vars : 1.672e-03
: 236 : vars : 1.654e-03
: 237 : vars : 1.519e-03
: 238 : vars : 1.491e-03
: 239 : vars : 1.481e-03
: 240 : vars : 1.372e-03
: 241 : vars : 1.311e-03
: 242 : vars : 1.239e-03
: 243 : vars : 1.018e-03
: 244 : vars : 1.000e-03
: 245 : vars : 7.316e-04
: 246 : vars : 6.427e-04
: 247 : vars : 5.459e-04
: 248 : vars : 2.654e-04
: 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= 4.49818
TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 7.11828
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 9.47979
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 8.19681
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: 0.00357 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: 0.00082 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: 0.0361 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_DNN_CPU : 0.761
: dataset BDT : 0.759
: dataset TMVA_CNN_CPU : 0.656
: -------------------------------------------------------------------------------------------------------------------
:
: 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_DNN_CPU : 0.025 (0.355) 0.395 (0.685) 0.645 (0.875)
: dataset BDT : 0.115 (0.445) 0.376 (0.704) 0.679 (0.896)
: dataset TMVA_CNN_CPU : 0.025 (0.085) 0.242 (0.262) 0.515 (0.538)
: -------------------------------------------------------------------------------------------------------------------
:
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
useKerasCNN = False
useKerasCNN = True
opt = [1, 1, 1, 1, 1]
useTMVACNN = opt[0]
if len(opt) > 0
else False
useKerasCNN = opt[1]
if len(opt) > 1
else useKerasCNN
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
if useKerasCNN:
try:
import tensorflow
except:
ROOT.Warning(
"TMVA_CNN_Classification",
"Skip using Keras since tensorflow cannot be imported")
useKerasCNN = False
if torch_spec is None:
usePyTorchCNN = False
print("TMVA_CNN_Classificaton","Skip using PyTorch since torch is not installed")
else:
try:
import torch
except:
ROOT.Warning(
"TMVA_CNN_Classification",
"Skip using PyTorch since it cannot be imported")
usePyTorchCNN = False
import ROOT
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.