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: 1.28 sec
BDT : [dataset] : Evaluation of BDT on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.0147 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 = 76.4805
: --------------------------------------------------------------
: 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 | 0.941975 1.08404 0.0155097 0.00153488 85868.9 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.701396 0.975008 0.0148741 0.00132143 88543.5 0
: 3 | 0.601356 1.13492 0.0153403 0.00106981 84089.5 1
: 4 | 0.548989 1.06418 0.0149603 0.00105918 86323.9 2
: 5 | 0.494702 1.06044 0.0155513 0.00102585 82613.6 3
: 6 | 0.438378 1.14054 0.0157251 0.0010251 81632.7 4
: 7 | 0.387263 1.22797 0.0151609 0.00107396 85185 5
: 8 | 0.345567 1.23591 0.0147969 0.00111346 87697.2 6
:
: Elapsed time for training with 1600 events: 0.135 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: 0.00517 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 = 45.8087
: --------------------------------------------------------------
: 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 | 3.04288 1.36031 0.340448 0.0469653 4088.82 0
: 2 Minimum Test error found - save the configuration
: 2 | 1.15787 1.058 0.340786 0.0301917 3863.56 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.81182 0.793155 0.321515 0.0304234 4122.41 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.736493 0.704795 0.327568 0.0331167 4075.38 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.69793 0.701004 1.03764 0.0300301 1190.93 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.690846 0.693624 0.323299 0.0312781 4109.29 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.678865 0.690503 0.310929 0.0296432 4266.13 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.671517 0.687812 0.309275 0.0298973 4295.25 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.663078 0.676388 0.323473 0.0284939 4068.08 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.655225 0.668616 0.313133 0.0313319 4258.33 0
:
: Elapsed time for training with 1600 events: 3.99 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: 0.172 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 : 8.962e-03
: 2 : vars : 8.381e-03
: 3 : vars : 8.021e-03
: 4 : vars : 7.681e-03
: 5 : vars : 7.653e-03
: 6 : vars : 7.620e-03
: 7 : vars : 7.598e-03
: 8 : vars : 7.572e-03
: 9 : vars : 7.486e-03
: 10 : vars : 7.314e-03
: 11 : vars : 7.249e-03
: 12 : vars : 7.173e-03
: 13 : vars : 6.931e-03
: 14 : vars : 6.601e-03
: 15 : vars : 6.575e-03
: 16 : vars : 6.569e-03
: 17 : vars : 6.559e-03
: 18 : vars : 6.447e-03
: 19 : vars : 6.426e-03
: 20 : vars : 6.417e-03
: 21 : vars : 6.331e-03
: 22 : vars : 6.315e-03
: 23 : vars : 6.312e-03
: 24 : vars : 6.203e-03
: 25 : vars : 6.140e-03
: 26 : vars : 6.124e-03
: 27 : vars : 6.019e-03
: 28 : vars : 6.000e-03
: 29 : vars : 5.989e-03
: 30 : vars : 5.917e-03
: 31 : vars : 5.899e-03
: 32 : vars : 5.893e-03
: 33 : vars : 5.878e-03
: 34 : vars : 5.874e-03
: 35 : vars : 5.869e-03
: 36 : vars : 5.868e-03
: 37 : vars : 5.820e-03
: 38 : vars : 5.790e-03
: 39 : vars : 5.759e-03
: 40 : vars : 5.759e-03
: 41 : vars : 5.754e-03
: 42 : vars : 5.739e-03
: 43 : vars : 5.734e-03
: 44 : vars : 5.694e-03
: 45 : vars : 5.660e-03
: 46 : vars : 5.622e-03
: 47 : vars : 5.616e-03
: 48 : vars : 5.611e-03
: 49 : vars : 5.606e-03
: 50 : vars : 5.546e-03
: 51 : vars : 5.495e-03
: 52 : vars : 5.485e-03
: 53 : vars : 5.478e-03
: 54 : vars : 5.454e-03
: 55 : vars : 5.435e-03
: 56 : vars : 5.359e-03
: 57 : vars : 5.352e-03
: 58 : vars : 5.351e-03
: 59 : vars : 5.349e-03
: 60 : vars : 5.246e-03
: 61 : vars : 5.229e-03
: 62 : vars : 5.224e-03
: 63 : vars : 5.217e-03
: 64 : vars : 5.194e-03
: 65 : vars : 5.193e-03
: 66 : vars : 5.191e-03
: 67 : vars : 5.129e-03
: 68 : vars : 5.123e-03
: 69 : vars : 5.104e-03
: 70 : vars : 5.076e-03
: 71 : vars : 5.067e-03
: 72 : vars : 5.057e-03
: 73 : vars : 5.051e-03
: 74 : vars : 4.986e-03
: 75 : vars : 4.968e-03
: 76 : vars : 4.919e-03
: 77 : vars : 4.895e-03
: 78 : vars : 4.893e-03
: 79 : vars : 4.871e-03
: 80 : vars : 4.842e-03
: 81 : vars : 4.837e-03
: 82 : vars : 4.822e-03
: 83 : vars : 4.817e-03
: 84 : vars : 4.790e-03
: 85 : vars : 4.785e-03
: 86 : vars : 4.769e-03
: 87 : vars : 4.757e-03
: 88 : vars : 4.721e-03
: 89 : vars : 4.679e-03
: 90 : vars : 4.646e-03
: 91 : vars : 4.621e-03
: 92 : vars : 4.544e-03
: 93 : vars : 4.531e-03
: 94 : vars : 4.523e-03
: 95 : vars : 4.518e-03
: 96 : vars : 4.504e-03
: 97 : vars : 4.457e-03
: 98 : vars : 4.451e-03
: 99 : vars : 4.451e-03
: 100 : vars : 4.442e-03
: 101 : vars : 4.428e-03
: 102 : vars : 4.408e-03
: 103 : vars : 4.401e-03
: 104 : vars : 4.371e-03
: 105 : vars : 4.371e-03
: 106 : vars : 4.334e-03
: 107 : vars : 4.331e-03
: 108 : vars : 4.328e-03
: 109 : vars : 4.318e-03
: 110 : vars : 4.293e-03
: 111 : vars : 4.214e-03
: 112 : vars : 4.190e-03
: 113 : vars : 4.160e-03
: 114 : vars : 4.151e-03
: 115 : vars : 4.141e-03
: 116 : vars : 4.138e-03
: 117 : vars : 4.134e-03
: 118 : vars : 4.132e-03
: 119 : vars : 4.131e-03
: 120 : vars : 4.103e-03
: 121 : vars : 4.093e-03
: 122 : vars : 4.081e-03
: 123 : vars : 4.081e-03
: 124 : vars : 4.051e-03
: 125 : vars : 4.005e-03
: 126 : vars : 4.005e-03
: 127 : vars : 4.002e-03
: 128 : vars : 3.997e-03
: 129 : vars : 3.946e-03
: 130 : vars : 3.913e-03
: 131 : vars : 3.889e-03
: 132 : vars : 3.809e-03
: 133 : vars : 3.804e-03
: 134 : vars : 3.789e-03
: 135 : vars : 3.780e-03
: 136 : vars : 3.766e-03
: 137 : vars : 3.738e-03
: 138 : vars : 3.730e-03
: 139 : vars : 3.691e-03
: 140 : vars : 3.673e-03
: 141 : vars : 3.646e-03
: 142 : vars : 3.628e-03
: 143 : vars : 3.600e-03
: 144 : vars : 3.583e-03
: 145 : vars : 3.578e-03
: 146 : vars : 3.549e-03
: 147 : vars : 3.536e-03
: 148 : vars : 3.535e-03
: 149 : vars : 3.506e-03
: 150 : vars : 3.500e-03
: 151 : vars : 3.486e-03
: 152 : vars : 3.465e-03
: 153 : vars : 3.465e-03
: 154 : vars : 3.405e-03
: 155 : vars : 3.396e-03
: 156 : vars : 3.380e-03
: 157 : vars : 3.350e-03
: 158 : vars : 3.346e-03
: 159 : vars : 3.334e-03
: 160 : vars : 3.312e-03
: 161 : vars : 3.275e-03
: 162 : vars : 3.274e-03
: 163 : vars : 3.268e-03
: 164 : vars : 3.262e-03
: 165 : vars : 3.249e-03
: 166 : vars : 3.241e-03
: 167 : vars : 3.221e-03
: 168 : vars : 3.206e-03
: 169 : vars : 3.194e-03
: 170 : vars : 3.162e-03
: 171 : vars : 3.160e-03
: 172 : vars : 3.159e-03
: 173 : vars : 3.152e-03
: 174 : vars : 3.146e-03
: 175 : vars : 3.131e-03
: 176 : vars : 3.105e-03
: 177 : vars : 3.078e-03
: 178 : vars : 3.077e-03
: 179 : vars : 3.076e-03
: 180 : vars : 2.998e-03
: 181 : vars : 2.961e-03
: 182 : vars : 2.960e-03
: 183 : vars : 2.921e-03
: 184 : vars : 2.908e-03
: 185 : vars : 2.891e-03
: 186 : vars : 2.884e-03
: 187 : vars : 2.874e-03
: 188 : vars : 2.834e-03
: 189 : vars : 2.828e-03
: 190 : vars : 2.749e-03
: 191 : vars : 2.746e-03
: 192 : vars : 2.740e-03
: 193 : vars : 2.679e-03
: 194 : vars : 2.604e-03
: 195 : vars : 2.576e-03
: 196 : vars : 2.565e-03
: 197 : vars : 2.538e-03
: 198 : vars : 2.517e-03
: 199 : vars : 2.510e-03
: 200 : vars : 2.500e-03
: 201 : vars : 2.492e-03
: 202 : vars : 2.482e-03
: 203 : vars : 2.459e-03
: 204 : vars : 2.442e-03
: 205 : vars : 2.413e-03
: 206 : vars : 2.395e-03
: 207 : vars : 2.349e-03
: 208 : vars : 2.261e-03
: 209 : vars : 2.251e-03
: 210 : vars : 2.241e-03
: 211 : vars : 2.241e-03
: 212 : vars : 2.237e-03
: 213 : vars : 2.210e-03
: 214 : vars : 2.144e-03
: 215 : vars : 2.087e-03
: 216 : vars : 2.085e-03
: 217 : vars : 2.063e-03
: 218 : vars : 2.059e-03
: 219 : vars : 2.023e-03
: 220 : vars : 2.015e-03
: 221 : vars : 1.943e-03
: 222 : vars : 1.914e-03
: 223 : vars : 1.912e-03
: 224 : vars : 1.911e-03
: 225 : vars : 1.880e-03
: 226 : vars : 1.865e-03
: 227 : vars : 1.815e-03
: 228 : vars : 1.789e-03
: 229 : vars : 1.693e-03
: 230 : vars : 1.663e-03
: 231 : vars : 1.654e-03
: 232 : vars : 1.650e-03
: 233 : vars : 1.627e-03
: 234 : vars : 1.621e-03
: 235 : vars : 1.615e-03
: 236 : vars : 1.584e-03
: 237 : vars : 1.558e-03
: 238 : vars : 1.511e-03
: 239 : vars : 1.383e-03
: 240 : vars : 8.918e-04
: 241 : vars : 7.801e-04
: 242 : vars : 5.765e-04
: 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= 4.45963
TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 8.92301
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 9.80652
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 8.03421
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: 0.00346 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: 0.000857 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: 0.0322 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 : 0.775
: dataset TMVA_CNN_CPU : 0.643
: dataset TMVA_DNN_CPU : 0.577
: -------------------------------------------------------------------------------------------------------------------
:
: 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 : 0.032 (0.335) 0.351 (0.701) 0.695 (0.879)
: dataset TMVA_CNN_CPU : 0.035 (0.030) 0.235 (0.292) 0.485 (0.533)
: dataset TMVA_DNN_CPU : 0.035 (0.035) 0.123 (0.217) 0.368 (0.460)
: -------------------------------------------------------------------------------------------------------------------
:
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 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
if tf_spec is None:
useKerasCNN = False
print("TMVA_CNN_Classificaton","Skip using Keras since tensorflow is not installed")
else:
import tensorflow
if torch_spec is None:
usePyTorchCNN = False
print("TMVA_CNN_Classificaton","Skip using PyTorch since torch is not installed")
else:
import torch
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 += "/tmva/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.