DataSetInfo : [dataset] : Added class "Signal"
: Add Tree sgn of type Signal with 2000 events
DataSetInfo : [dataset] : Added class "Background"
: Add Tree bkg of type Background with 2000 events
Factory : Booking method: ␛[1mTMVA_LSTM␛[0m
:
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIERUNIFORM:ValidationSize=0.2:RandomSeed=1234:InputLayout=10|30:Layout=LSTM|10|30|10|0|1,RESHAPE|FLAT,DENSE|64|TANH,LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-2,Regularization=None,MaxEpochs=10Optimizer=ADAM,DropConfig=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=XAVIERUNIFORM:ValidationSize=0.2:RandomSeed=1234:InputLayout=10|30:Layout=LSTM|10|30|10|0|1,RESHAPE|FLAT,DENSE|64|TANH,LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-2,Regularization=None,MaxEpochs=10Optimizer=ADAM,DropConfig=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: "10|30" [The Layout of the input]
: Layout: "LSTM|10|30|10|0|1,RESHAPE|FLAT,DENSE|64|TANH,LINEAR" [Layout of the network.]
: ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
: WeightInitialization: "XAVIERUNIFORM" [Weight initialization strategy]
: RandomSeed: "1234" [Random seed used for weight initialization and batch shuffling]
: ValidationSize: "0.2" [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%)]
: Architecture: "CPU" [Which architecture to perform the training on.]
: TrainingStrategy: "LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-2,Regularization=None,MaxEpochs=10Optimizer=ADAM,DropConfig=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]
: Will now use the CPU architecture with BLAS and IMT support !
Factory : Booking method: ␛[1mTMVA_DNN␛[0m
:
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:RandomSeed=0:InputLayout=1|1|300:Layout=DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,MaxEpochs=20DropConfig=0.0+0.+0.+0.,Optimizer=ADAM: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:RandomSeed=0:InputLayout=1|1|300:Layout=DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,MaxEpochs=20DropConfig=0.0+0.+0.+0.,Optimizer=ADAM: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|1|300" [The Layout of the input]
: Layout: "DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR" [Layout of the network.]
: ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
: WeightInitialization: "XAVIER" [Weight initialization strategy]
: RandomSeed: "0" [Random seed used for weight initialization and batch shuffling]
: Architecture: "CPU" [Which architecture to perform the training on.]
: TrainingStrategy: "LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,MaxEpochs=20DropConfig=0.0+0.+0.+0.,Optimizer=ADAM" [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]
: 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 !
Running with nthreads = 4
--- RNNClassification : Using input file: time_data_t10_d30.root
number of variables is 300
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prepared DATA LOADER
Building recurrent keras model using a LSTM layer
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
reshape (Reshape) (None, 10, 30) 0
lstm (LSTM) (None, 10, 10) 1640
flatten (Flatten) (None, 100) 0
dense (Dense) (None, 64) 6464
dense_1 (Dense) (None, 2) 130
=================================================================
Total params: 8,234
Trainable params: 8,234
Non-trainable params: 0
_________________________________________________________________
Factory : Booking method: ␛[1mPyKeras_LSTM␛[0m
:
: Setting up tf.keras
: Using TensorFlow version 2
: Use Keras version from TensorFlow : tf.keras
: Applying GPU option: gpu_options.allow_growth=True
: Loading Keras Model
: Loaded model from file: model_LSTM.h5
Factory : Booking method: ␛[1mBDTG␛[0m
:
: the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad
: --> change to new default NegWeightTreatment=Pray
: Rebuilding Dataset dataset
: Building event vectors for type 2 Signal
: Dataset[dataset] : create input formulas for tree sgn
: Using variable vars_time0[0] from array expression vars_time0 of size 30
: Using variable vars_time1[0] from array expression vars_time1 of size 30
: Using variable vars_time2[0] from array expression vars_time2 of size 30
: Using variable vars_time3[0] from array expression vars_time3 of size 30
: Using variable vars_time4[0] from array expression vars_time4 of size 30
: Using variable vars_time5[0] from array expression vars_time5 of size 30
: Using variable vars_time6[0] from array expression vars_time6 of size 30
: Using variable vars_time7[0] from array expression vars_time7 of size 30
: Using variable vars_time8[0] from array expression vars_time8 of size 30
: Using variable vars_time9[0] from array expression vars_time9 of size 30
: Building event vectors for type 2 Background
: Dataset[dataset] : create input formulas for tree bkg
: Using variable vars_time0[0] from array expression vars_time0 of size 30
: Using variable vars_time1[0] from array expression vars_time1 of size 30
: Using variable vars_time2[0] from array expression vars_time2 of size 30
: Using variable vars_time3[0] from array expression vars_time3 of size 30
: Using variable vars_time4[0] from array expression vars_time4 of size 30
: Using variable vars_time5[0] from array expression vars_time5 of size 30
: Using variable vars_time6[0] from array expression vars_time6 of size 30
: Using variable vars_time7[0] from array expression vars_time7 of size 30
: Using variable vars_time8[0] from array expression vars_time8 of size 30
: Using variable vars_time9[0] from array expression vars_time9 of size 30
DataSetFactory : [dataset] : Number of events in input trees
:
:
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 1600
: Signal -- testing events : 400
: Signal -- training and testing events: 2000
: Background -- training events : 1600
: Background -- testing events : 400
: Background -- training and testing events: 2000
:
Factory : ␛[1mTrain all methods␛[0m
Factory : Train method: TMVA_LSTM for Classification
:
: Start of deep neural network training on CPU using MT, nthreads = 4
:
: ***** Deep Learning Network *****
DEEP NEURAL NETWORK: Depth = 4 Input = ( 10, 1, 30 ) Batch size = 100 Loss function = C
Layer 0 LSTM Layer: (NInput = 30, NState = 10, NTime = 10 ) Output = ( 100 , 10 , 10 )
Layer 1 RESHAPE Layer Input = ( 1 , 10 , 10 ) Output = ( 1 , 100 , 100 )
Layer 2 DENSE Layer: ( Input = 100 , Width = 64 ) Output = ( 1 , 100 , 64 ) Activation Function = Tanh
Layer 3 DENSE Layer: ( Input = 64 , Width = 1 ) Output = ( 1 , 100 , 1 ) Activation Function = Identity
: Using 2560 events for training and 640 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 = 0.72429
: --------------------------------------------------------------
: 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 | 0.703777 0.701171 0.632086 0.041325 4231.83 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.693082 0.694744 0.652739 0.0424882 4096.67 0
: 3 | 0.693378 0.700637 0.633074 0.0408161 4221.13 1
: 4 | 0.690082 0.697304 0.612887 0.0416509 4376.47 2
: 5 | 0.688247 0.696985 0.610203 0.0408132 4390.67 3
: 6 | 0.684968 0.694799 0.595361 0.0410712 4510.27 4
: 7 Minimum Test error found - save the configuration
: 7 | 0.682341 0.694492 0.595738 0.0406836 4504.06 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.681392 0.693064 0.595283 0.0407793 4508.54 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.678542 0.690725 0.59469 0.0405449 4511.45 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.66649 0.681498 0.590291 0.0401438 4544.24 0
:
: Elapsed time for training with 3200 events: 6.17 sec
: Evaluate deep neural network on CPU using batches with size = 100
:
TMVA_LSTM : [dataset] : Evaluation of TMVA_LSTM on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.212 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_TMVA_LSTM.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_TMVA_LSTM.class.C␛[0m
Factory : Training finished
:
Factory : Train method: TMVA_DNN for Classification
:
: Start of deep neural network training on CPU using MT, nthreads = 4
:
: ***** Deep Learning Network *****
DEEP NEURAL NETWORK: Depth = 4 Input = ( 1, 1, 300 ) Batch size = 256 Loss function = C
Layer 0 DENSE Layer: ( Input = 300 , Width = 64 ) Output = ( 1 , 256 , 64 ) Activation Function = Tanh
Layer 1 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 256 , 64 ) Activation Function = Tanh
Layer 2 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 256 , 64 ) Activation Function = Tanh
Layer 3 DENSE Layer: ( Input = 64 , Width = 1 ) Output = ( 1 , 256 , 1 ) Activation Function = Identity
: Using 2560 events for training and 640 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 = 0.790979
: --------------------------------------------------------------
: 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 | 0.720806 0.689941 0.195702 0.0159864 14244.7 0
: 2 | 0.68008 0.690574 0.194338 0.0154904 14313.9 1
: 3 | 0.67905 0.708329 0.194267 0.0155588 14325.1 2
: 4 Minimum Test error found - save the configuration
: 4 | 0.682846 0.68524 0.194922 0.015841 14295.2 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.667835 0.679177 0.194078 0.0157115 14352.5 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.67105 0.664136 0.192312 0.0156531 14491.2 0
: 7 | 0.66763 0.679178 0.191216 0.0152183 14545.6 1
: 8 | 0.677284 0.67243 0.191524 0.0152478 14522.6 2
: 9 | 0.665479 0.683727 0.194782 0.0153148 14264.4 3
: 10 | 0.674968 0.683778 0.193052 0.0154637 14415.4 4
: 11 | 0.660376 0.680061 0.191457 0.0148754 14497.5 5
: 12 | 0.656872 0.667585 0.1914 0.0148463 14499.8 6
: 13 Minimum Test error found - save the configuration
: 13 | 0.649992 0.663633 0.190788 0.0150165 14564.4 0
: 14 Minimum Test error found - save the configuration
: 14 | 0.646061 0.659387 0.196884 0.0159547 14149.2 0
: 15 | 0.639855 0.687434 0.193783 0.0156285 14369.5 1
: 16 | 0.646477 0.678213 0.192136 0.0150344 14454.9 2
: 17 Minimum Test error found - save the configuration
: 17 | 0.643465 0.658796 0.190595 0.0152939 14603.4 0
: 18 | 0.637952 0.670749 0.190042 0.0147207 14601.8 1
: 19 | 0.646537 0.675896 0.18984 0.0147651 14622.3 2
: 20 | 0.654984 0.684533 0.189742 0.0147304 14627.6 3
:
: Elapsed time for training with 3200 events: 3.87 sec
: Evaluate deep neural network on CPU using batches with size = 256
:
TMVA_DNN : [dataset] : Evaluation of TMVA_DNN on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.0992 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_TMVA_DNN.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_TMVA_DNN.class.C␛[0m
Factory : Training finished
:
Factory : Train method: PyKeras_LSTM for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ PyKeras_LSTM ] :␛[0m
:
: Keras is a high-level API for the Theano and Tensorflow packages.
: This method wraps the training and predictions steps of the Keras
: Python package for TMVA, so that dataloading, preprocessing and
: evaluation can be done within the TMVA system. To use this Keras
: interface, you have to generate a model with Keras first. Then,
: this model can be loaded and trained in TMVA.
:
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
: Split TMVA training data in 2560 training events and 640 validation events
: Training Model Summary
saved recurrent model model_LSTM.h5
Booking Keras model LSTM
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
reshape (Reshape) (None, 10, 30) 0
lstm (LSTM) (None, 10, 10) 1640
flatten (Flatten) (None, 100) 0
dense (Dense) (None, 64) 6464
dense_1 (Dense) (None, 2) 130
=================================================================
Total params: 8,234
Trainable params: 8,234
Non-trainable params: 0
_________________________________________________________________
: Option SaveBestOnly: Only model weights with smallest validation loss will be stored
Epoch 1/10
1/26 [>.............................] - ETA: 45s - loss: 0.7077 - accuracy: 0.5000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
8/26 [========>.....................] - ETA: 0s - loss: 0.7028 - accuracy: 0.5063 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
15/26 [================>.............] - ETA: 0s - loss: 0.7004 - accuracy: 0.5100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
24/26 [==========================>...] - ETA: 0s - loss: 0.6975 - accuracy: 0.5154
Epoch 1: val_loss improved from inf to 0.68954, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 3s 49ms/step - loss: 0.6970 - accuracy: 0.5184 - val_loss: 0.6895 - val_accuracy: 0.5328
Epoch 2/10
1/26 [>.............................] - ETA: 0s - loss: 0.6602 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
9/26 [=========>....................] - ETA: 0s - loss: 0.6768 - accuracy: 0.5911␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
17/26 [==================>...........] - ETA: 0s - loss: 0.6804 - accuracy: 0.5776␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - ETA: 0s - loss: 0.6757 - accuracy: 0.5793
Epoch 2: val_loss improved from 0.68954 to 0.67106, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.6757 - accuracy: 0.5793 - val_loss: 0.6711 - val_accuracy: 0.5781
Epoch 3/10
1/26 [>.............................] - ETA: 0s - loss: 0.6624 - accuracy: 0.6100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.6612 - accuracy: 0.6140␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.6621 - accuracy: 0.6074
Epoch 3: val_loss improved from 0.67106 to 0.65467, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.6584 - accuracy: 0.6156 - val_loss: 0.6547 - val_accuracy: 0.6219
Epoch 4/10
1/26 [>.............................] - ETA: 0s - loss: 0.6528 - accuracy: 0.5900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.6385 - accuracy: 0.6220␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.6349 - accuracy: 0.6400
Epoch 4: val_loss improved from 0.65467 to 0.64147, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.6338 - accuracy: 0.6344 - val_loss: 0.6415 - val_accuracy: 0.6406
Epoch 5/10
1/26 [>.............................] - ETA: 0s - loss: 0.6514 - accuracy: 0.6400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.6280 - accuracy: 0.6664␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.6191 - accuracy: 0.6740
Epoch 5: val_loss improved from 0.64147 to 0.62445, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.6153 - accuracy: 0.6777 - val_loss: 0.6244 - val_accuracy: 0.6672
Epoch 6/10
1/26 [>.............................] - ETA: 0s - loss: 0.6158 - accuracy: 0.6200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.5950 - accuracy: 0.6808␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.5921 - accuracy: 0.6843
Epoch 6: val_loss improved from 0.62445 to 0.59720, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5898 - accuracy: 0.6859 - val_loss: 0.5972 - val_accuracy: 0.6797
Epoch 7/10
1/26 [>.............................] - ETA: 0s - loss: 0.5201 - accuracy: 0.7700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.5654 - accuracy: 0.6983␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.5722 - accuracy: 0.6983
Epoch 7: val_loss improved from 0.59720 to 0.57471, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5722 - accuracy: 0.6988 - val_loss: 0.5747 - val_accuracy: 0.6844
Epoch 8/10
1/26 [>.............................] - ETA: 0s - loss: 0.5643 - accuracy: 0.6700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.5640 - accuracy: 0.7080␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.5583 - accuracy: 0.7255
Epoch 8: val_loss improved from 0.57471 to 0.55953, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5533 - accuracy: 0.7316 - val_loss: 0.5595 - val_accuracy: 0.7031
Epoch 9/10
1/26 [>.............................] - ETA: 0s - loss: 0.5179 - accuracy: 0.7700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5285 - accuracy: 0.7509␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.5305 - accuracy: 0.7450
Epoch 9: val_loss did not improve from 0.55953
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.5280 - accuracy: 0.7437 - val_loss: 0.5647 - val_accuracy: 0.7172
Epoch 10/10
1/26 [>.............................] - ETA: 0s - loss: 0.4783 - accuracy: 0.7500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5278 - accuracy: 0.7373␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.5265 - accuracy: 0.7391
Epoch 10: val_loss improved from 0.55953 to 0.55313, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5260 - accuracy: 0.7406 - val_loss: 0.5531 - val_accuracy: 0.7078
: Getting training history for item:0 name = 'loss'
: Getting training history for item:1 name = 'accuracy'
: Getting training history for item:2 name = 'val_loss'
: Getting training history for item:3 name = 'val_accuracy'
: Elapsed time for training with 3200 events: 6 sec
: Setting up tf.keras
: Using TensorFlow version 2
: Use Keras version from TensorFlow : tf.keras
: Applying GPU option: gpu_options.allow_growth=True
: Disabled TF eager execution when evaluating model
: Loading Keras Model
: Loaded model from file: trained_model_LSTM.h5
PyKeras_LSTM : [dataset] : Evaluation of PyKeras_LSTM on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.272 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_PyKeras_LSTM.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_PyKeras_LSTM.class.C␛[0m
Factory : Training finished
:
Factory : Train method: BDTG for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ BDTG ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: Boosted Decision Trees are a collection of individual decision
: trees which form a multivariate classifier by (weighted) majority
: vote of the individual trees. Consecutive decision trees are
: trained using the original training data set with re-weighted
: events. By default, the AdaBoost method is employed, which gives
: events that were misclassified in the previous tree a larger
: weight in the training of the following tree.
:
: Decision trees are a sequence of binary splits of the data sample
: using a single discriminant variable at a time. A test event
: ending up after the sequence of left-right splits in a final
: ("leaf") node is classified as either signal or background
: depending on the majority type of training events in that node.
:
: ␛[1m--- Performance optimisation:␛[0m
:
: By the nature of the binary splits performed on the individual
: variables, decision trees do not deal well with linear correlations
: between variables (they need to approximate the linear split in
: the two dimensional space by a sequence of splits on the two
: variables individually). Hence decorrelation could be useful
: to optimise the BDT performance.
:
: ␛[1m--- Performance tuning via configuration options:␛[0m
:
: The two most important parameters in the configuration are the
: minimal number of events requested by a leaf node as percentage of the
: number of training events (option "MinNodeSize" replacing the actual number
: of events "nEventsMin" as given in earlier versions
: If this number is too large, detailed features
: in the parameter space are hard to be modelled. If it is too small,
: the risk to overtrain rises and boosting seems to be less effective
: typical values from our current experience for best performance
: are between 0.5(%) and 10(%)
:
: The default minimal number is currently set to
: max(20, (N_training_events / N_variables^2 / 10))
: and can be changed by the user.
:
: The other crucial parameter, the pruning strength ("PruneStrength"),
: is also related to overtraining. It is a regularisation parameter
: that is used when determining after the training which splits
: are considered statistically insignificant and are removed. The
: user is advised to carefully watch the BDT screen output for
: the comparison between efficiencies obtained on the training and
: the independent test sample. They should be equal within statistical
: errors, in order to minimize statistical fluctuations in different samples.
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
BDTG : #events: (reweighted) sig: 1600 bkg: 1600
: #events: (unweighted) sig: 1600 bkg: 1600
: Training 100 Decision Trees ... patience please
: Elapsed time for training with 3200 events: 1.69 sec
BDTG : [dataset] : Evaluation of BDTG on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.0181 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_BDTG.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_BDTG.class.C␛[0m
: data_RNN_CPU.root:/dataset/Method_BDT/BDTG
Factory : Training finished
:
: Ranking input variables (method specific)...
: No variable ranking supplied by classifier: TMVA_LSTM
: No variable ranking supplied by classifier: TMVA_DNN
: No variable ranking supplied by classifier: PyKeras_LSTM
BDTG : Ranking result (top variable is best ranked)
: --------------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------------
: 1 : vars_time9 : 2.116e-02
: 2 : vars_time0 : 2.110e-02
: 3 : vars_time6 : 2.106e-02
: 4 : vars_time7 : 2.075e-02
: 5 : vars_time8 : 2.073e-02
: 6 : vars_time7 : 2.072e-02
: 7 : vars_time8 : 2.056e-02
: 8 : vars_time8 : 1.995e-02
: 9 : vars_time9 : 1.954e-02
: 10 : vars_time7 : 1.932e-02
: 11 : vars_time7 : 1.853e-02
: 12 : vars_time9 : 1.772e-02
: 13 : vars_time8 : 1.741e-02
: 14 : vars_time0 : 1.728e-02
: 15 : vars_time6 : 1.711e-02
: 16 : vars_time5 : 1.688e-02
: 17 : vars_time6 : 1.682e-02
: 18 : vars_time0 : 1.618e-02
: 19 : vars_time5 : 1.412e-02
: 20 : vars_time9 : 1.351e-02
: 21 : vars_time8 : 1.348e-02
: 22 : vars_time0 : 1.332e-02
: 23 : vars_time9 : 1.329e-02
: 24 : vars_time5 : 1.324e-02
: 25 : vars_time9 : 1.290e-02
: 26 : vars_time7 : 1.277e-02
: 27 : vars_time4 : 1.267e-02
: 28 : vars_time7 : 1.225e-02
: 29 : vars_time9 : 1.197e-02
: 30 : vars_time0 : 1.080e-02
: 31 : vars_time9 : 1.059e-02
: 32 : vars_time6 : 1.037e-02
: 33 : vars_time7 : 1.032e-02
: 34 : vars_time7 : 1.013e-02
: 35 : vars_time9 : 1.009e-02
: 36 : vars_time5 : 1.004e-02
: 37 : vars_time8 : 9.931e-03
: 38 : vars_time6 : 9.860e-03
: 39 : vars_time5 : 9.753e-03
: 40 : vars_time8 : 9.669e-03
: 41 : vars_time7 : 9.599e-03
: 42 : vars_time6 : 9.176e-03
: 43 : vars_time7 : 9.126e-03
: 44 : vars_time7 : 8.952e-03
: 45 : vars_time0 : 8.937e-03
: 46 : vars_time8 : 8.903e-03
: 47 : vars_time6 : 8.864e-03
: 48 : vars_time7 : 8.445e-03
: 49 : vars_time4 : 8.210e-03
: 50 : vars_time4 : 8.136e-03
: 51 : vars_time1 : 8.064e-03
: 52 : vars_time8 : 7.845e-03
: 53 : vars_time6 : 7.753e-03
: 54 : vars_time7 : 7.734e-03
: 55 : vars_time6 : 7.450e-03
: 56 : vars_time7 : 7.404e-03
: 57 : vars_time8 : 7.340e-03
: 58 : vars_time5 : 7.320e-03
: 59 : vars_time6 : 7.311e-03
: 60 : vars_time3 : 7.291e-03
: 61 : vars_time6 : 7.251e-03
: 62 : vars_time5 : 7.178e-03
: 63 : vars_time5 : 7.014e-03
: 64 : vars_time6 : 6.964e-03
: 65 : vars_time9 : 6.916e-03
: 66 : vars_time0 : 6.820e-03
: 67 : vars_time7 : 6.716e-03
: 68 : vars_time2 : 6.685e-03
: 69 : vars_time6 : 6.444e-03
: 70 : vars_time9 : 6.442e-03
: 71 : vars_time6 : 6.361e-03
: 72 : vars_time9 : 5.957e-03
: 73 : vars_time5 : 5.723e-03
: 74 : vars_time8 : 5.688e-03
: 75 : vars_time3 : 5.560e-03
: 76 : vars_time5 : 5.523e-03
: 77 : vars_time4 : 5.506e-03
: 78 : vars_time8 : 5.248e-03
: 79 : vars_time1 : 5.166e-03
: 80 : vars_time4 : 5.050e-03
: 81 : vars_time7 : 5.049e-03
: 82 : vars_time8 : 5.037e-03
: 83 : vars_time3 : 4.974e-03
: 84 : vars_time8 : 4.918e-03
: 85 : vars_time9 : 4.713e-03
: 86 : vars_time4 : 4.674e-03
: 87 : vars_time9 : 4.640e-03
: 88 : vars_time0 : 4.590e-03
: 89 : vars_time4 : 4.522e-03
: 90 : vars_time6 : 4.492e-03
: 91 : vars_time6 : 4.362e-03
: 92 : vars_time2 : 4.335e-03
: 93 : vars_time6 : 4.323e-03
: 94 : vars_time8 : 4.322e-03
: 95 : vars_time9 : 4.288e-03
: 96 : vars_time5 : 4.269e-03
: 97 : vars_time4 : 4.200e-03
: 98 : vars_time9 : 4.145e-03
: 99 : vars_time1 : 4.134e-03
: 100 : vars_time2 : 4.037e-03
: 101 : vars_time6 : 4.002e-03
: 102 : vars_time4 : 3.961e-03
: 103 : vars_time1 : 3.898e-03
: 104 : vars_time8 : 3.872e-03
: 105 : vars_time6 : 3.692e-03
: 106 : vars_time8 : 3.035e-03
: 107 : vars_time1 : 1.589e-03
: 108 : vars_time0 : 0.000e+00
: 109 : vars_time0 : 0.000e+00
: 110 : vars_time0 : 0.000e+00
: 111 : vars_time0 : 0.000e+00
: 112 : vars_time0 : 0.000e+00
: 113 : vars_time0 : 0.000e+00
: 114 : vars_time0 : 0.000e+00
: 115 : vars_time0 : 0.000e+00
: 116 : vars_time0 : 0.000e+00
: 117 : vars_time0 : 0.000e+00
: 118 : vars_time0 : 0.000e+00
: 119 : vars_time0 : 0.000e+00
: 120 : vars_time0 : 0.000e+00
: 121 : vars_time0 : 0.000e+00
: 122 : vars_time0 : 0.000e+00
: 123 : vars_time0 : 0.000e+00
: 124 : vars_time0 : 0.000e+00
: 125 : vars_time0 : 0.000e+00
: 126 : vars_time0 : 0.000e+00
: 127 : vars_time0 : 0.000e+00
: 128 : vars_time0 : 0.000e+00
: 129 : vars_time0 : 0.000e+00
: 130 : vars_time1 : 0.000e+00
: 131 : vars_time1 : 0.000e+00
: 132 : vars_time1 : 0.000e+00
: 133 : vars_time1 : 0.000e+00
: 134 : vars_time1 : 0.000e+00
: 135 : vars_time1 : 0.000e+00
: 136 : vars_time1 : 0.000e+00
: 137 : vars_time1 : 0.000e+00
: 138 : vars_time1 : 0.000e+00
: 139 : vars_time1 : 0.000e+00
: 140 : vars_time1 : 0.000e+00
: 141 : vars_time1 : 0.000e+00
: 142 : vars_time1 : 0.000e+00
: 143 : vars_time1 : 0.000e+00
: 144 : vars_time1 : 0.000e+00
: 145 : vars_time1 : 0.000e+00
: 146 : vars_time1 : 0.000e+00
: 147 : vars_time1 : 0.000e+00
: 148 : vars_time1 : 0.000e+00
: 149 : vars_time1 : 0.000e+00
: 150 : vars_time1 : 0.000e+00
: 151 : vars_time1 : 0.000e+00
: 152 : vars_time1 : 0.000e+00
: 153 : vars_time1 : 0.000e+00
: 154 : vars_time1 : 0.000e+00
: 155 : vars_time2 : 0.000e+00
: 156 : vars_time2 : 0.000e+00
: 157 : vars_time2 : 0.000e+00
: 158 : vars_time2 : 0.000e+00
: 159 : vars_time2 : 0.000e+00
: 160 : vars_time2 : 0.000e+00
: 161 : vars_time2 : 0.000e+00
: 162 : vars_time2 : 0.000e+00
: 163 : vars_time2 : 0.000e+00
: 164 : vars_time2 : 0.000e+00
: 165 : vars_time2 : 0.000e+00
: 166 : vars_time2 : 0.000e+00
: 167 : vars_time2 : 0.000e+00
: 168 : vars_time2 : 0.000e+00
: 169 : vars_time2 : 0.000e+00
: 170 : vars_time2 : 0.000e+00
: 171 : vars_time2 : 0.000e+00
: 172 : vars_time2 : 0.000e+00
: 173 : vars_time2 : 0.000e+00
: 174 : vars_time2 : 0.000e+00
: 175 : vars_time2 : 0.000e+00
: 176 : vars_time2 : 0.000e+00
: 177 : vars_time2 : 0.000e+00
: 178 : vars_time2 : 0.000e+00
: 179 : vars_time2 : 0.000e+00
: 180 : vars_time2 : 0.000e+00
: 181 : vars_time2 : 0.000e+00
: 182 : vars_time3 : 0.000e+00
: 183 : vars_time3 : 0.000e+00
: 184 : vars_time3 : 0.000e+00
: 185 : vars_time3 : 0.000e+00
: 186 : vars_time3 : 0.000e+00
: 187 : vars_time3 : 0.000e+00
: 188 : vars_time3 : 0.000e+00
: 189 : vars_time3 : 0.000e+00
: 190 : vars_time3 : 0.000e+00
: 191 : vars_time3 : 0.000e+00
: 192 : vars_time3 : 0.000e+00
: 193 : vars_time3 : 0.000e+00
: 194 : vars_time3 : 0.000e+00
: 195 : vars_time3 : 0.000e+00
: 196 : vars_time3 : 0.000e+00
: 197 : vars_time3 : 0.000e+00
: 198 : vars_time3 : 0.000e+00
: 199 : vars_time3 : 0.000e+00
: 200 : vars_time3 : 0.000e+00
: 201 : vars_time3 : 0.000e+00
: 202 : vars_time3 : 0.000e+00
: 203 : vars_time3 : 0.000e+00
: 204 : vars_time3 : 0.000e+00
: 205 : vars_time3 : 0.000e+00
: 206 : vars_time3 : 0.000e+00
: 207 : vars_time3 : 0.000e+00
: 208 : vars_time3 : 0.000e+00
: 209 : vars_time4 : 0.000e+00
: 210 : vars_time4 : 0.000e+00
: 211 : vars_time4 : 0.000e+00
: 212 : vars_time4 : 0.000e+00
: 213 : vars_time4 : 0.000e+00
: 214 : vars_time4 : 0.000e+00
: 215 : vars_time4 : 0.000e+00
: 216 : vars_time4 : 0.000e+00
: 217 : vars_time4 : 0.000e+00
: 218 : vars_time4 : 0.000e+00
: 219 : vars_time4 : 0.000e+00
: 220 : vars_time4 : 0.000e+00
: 221 : vars_time4 : 0.000e+00
: 222 : vars_time4 : 0.000e+00
: 223 : vars_time4 : 0.000e+00
: 224 : vars_time4 : 0.000e+00
: 225 : vars_time4 : 0.000e+00
: 226 : vars_time4 : 0.000e+00
: 227 : vars_time4 : 0.000e+00
: 228 : vars_time4 : 0.000e+00
: 229 : vars_time4 : 0.000e+00
: 230 : vars_time5 : 0.000e+00
: 231 : vars_time5 : 0.000e+00
: 232 : vars_time5 : 0.000e+00
: 233 : vars_time5 : 0.000e+00
: 234 : vars_time5 : 0.000e+00
: 235 : vars_time5 : 0.000e+00
: 236 : vars_time5 : 0.000e+00
: 237 : vars_time5 : 0.000e+00
: 238 : vars_time5 : 0.000e+00
: 239 : vars_time5 : 0.000e+00
: 240 : vars_time5 : 0.000e+00
: 241 : vars_time5 : 0.000e+00
: 242 : vars_time5 : 0.000e+00
: 243 : vars_time5 : 0.000e+00
: 244 : vars_time5 : 0.000e+00
: 245 : vars_time5 : 0.000e+00
: 246 : vars_time5 : 0.000e+00
: 247 : vars_time5 : 0.000e+00
: 248 : vars_time5 : 0.000e+00
: 249 : vars_time6 : 0.000e+00
: 250 : vars_time6 : 0.000e+00
: 251 : vars_time6 : 0.000e+00
: 252 : vars_time6 : 0.000e+00
: 253 : vars_time6 : 0.000e+00
: 254 : vars_time6 : 0.000e+00
: 255 : vars_time6 : 0.000e+00
: 256 : vars_time6 : 0.000e+00
: 257 : vars_time6 : 0.000e+00
: 258 : vars_time6 : 0.000e+00
: 259 : vars_time6 : 0.000e+00
: 260 : vars_time7 : 0.000e+00
: 261 : vars_time7 : 0.000e+00
: 262 : vars_time7 : 0.000e+00
: 263 : vars_time7 : 0.000e+00
: 264 : vars_time7 : 0.000e+00
: 265 : vars_time7 : 0.000e+00
: 266 : vars_time7 : 0.000e+00
: 267 : vars_time7 : 0.000e+00
: 268 : vars_time7 : 0.000e+00
: 269 : vars_time7 : 0.000e+00
: 270 : vars_time7 : 0.000e+00
: 271 : vars_time7 : 0.000e+00
: 272 : vars_time7 : 0.000e+00
: 273 : vars_time7 : 0.000e+00
: 274 : vars_time8 : 0.000e+00
: 275 : vars_time8 : 0.000e+00
: 276 : vars_time8 : 0.000e+00
: 277 : vars_time8 : 0.000e+00
: 278 : vars_time8 : 0.000e+00
: 279 : vars_time8 : 0.000e+00
: 280 : vars_time8 : 0.000e+00
: 281 : vars_time8 : 0.000e+00
: 282 : vars_time8 : 0.000e+00
: 283 : vars_time8 : 0.000e+00
: 284 : vars_time8 : 0.000e+00
: 285 : vars_time8 : 0.000e+00
: 286 : vars_time8 : 0.000e+00
: 287 : vars_time9 : 0.000e+00
: 288 : vars_time9 : 0.000e+00
: 289 : vars_time9 : 0.000e+00
: 290 : vars_time9 : 0.000e+00
: 291 : vars_time9 : 0.000e+00
: 292 : vars_time9 : 0.000e+00
: 293 : vars_time9 : 0.000e+00
: 294 : vars_time9 : 0.000e+00
: 295 : vars_time9 : 0.000e+00
: 296 : vars_time9 : 0.000e+00
: 297 : vars_time9 : 0.000e+00
: 298 : vars_time9 : 0.000e+00
: 299 : vars_time9 : 0.000e+00
: 300 : vars_time9 : 0.000e+00
: --------------------------------------------
TH1.Print Name = TrainingHistory_TMVA_LSTM_trainingError, Entries= 0, Total sum= 6.8623
TH1.Print Name = TrainingHistory_TMVA_LSTM_valError, Entries= 0, Total sum= 6.94542
TH1.Print Name = TrainingHistory_TMVA_DNN_trainingError, Entries= 0, Total sum= 13.2696
TH1.Print Name = TrainingHistory_TMVA_DNN_valError, Entries= 0, Total sum= 13.5628
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'accuracy', Entries= 0, Total sum= 6.62617
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'loss', Entries= 0, Total sum= 6.04949
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_accuracy', Entries= 0, Total sum= 6.53281
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_loss', Entries= 0, Total sum= 6.13042
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_TMVA_LSTM.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_TMVA_DNN.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_PyKeras_LSTM.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_BDTG.weights.xml␛[0m
Factory : ␛[1mTest all methods␛[0m
Factory : Test method: TMVA_LSTM for Classification performance
:
: Evaluate deep neural network on CPU using batches with size = 800
:
TMVA_LSTM : [dataset] : Evaluation of TMVA_LSTM on testing sample (800 events)
: Elapsed time for evaluation of 800 events: 0.0493 sec
Factory : Test method: TMVA_DNN for Classification performance
:
: Evaluate deep neural network on CPU using batches with size = 800
:
TMVA_DNN : [dataset] : Evaluation of TMVA_DNN on testing sample (800 events)
: Elapsed time for evaluation of 800 events: 0.0201 sec
Factory : Test method: PyKeras_LSTM for Classification performance
:
: Setting up tf.keras
: Using TensorFlow version 2
: Use Keras version from TensorFlow : tf.keras
: Applying GPU option: gpu_options.allow_growth=True
: Disabled TF eager execution when evaluating model
: Loading Keras Model
: Loaded model from file: trained_model_LSTM.h5
PyKeras_LSTM : [dataset] : Evaluation of PyKeras_LSTM on testing sample (800 events)
: Elapsed time for evaluation of 800 events: 0.22 sec
Factory : Test method: BDTG for Classification performance
:
BDTG : [dataset] : Evaluation of BDTG on testing sample (800 events)
: Elapsed time for evaluation of 800 events: 0.00614 sec
Factory : ␛[1mEvaluate all methods␛[0m
Factory : Evaluate classifier: TMVA_LSTM
:
TMVA_LSTM : [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 300 , it is larger than 200
Factory : Evaluate classifier: TMVA_DNN
:
TMVA_DNN : [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 300 , it is larger than 200
Factory : Evaluate classifier: PyKeras_LSTM
:
PyKeras_LSTM : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Dataset[dataset] : variable plots are not produces ! The number of variables is 300 , it is larger than 200
Factory : Evaluate classifier: BDTG
:
BDTG : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Dataset[dataset] : variable plots are not produces ! The number of variables is 300 , it is larger than 200
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset BDTG : 0.852
: dataset PyKeras_LSTM : 0.832
: dataset TMVA_LSTM : 0.661
: dataset TMVA_DNN : 0.654
: -------------------------------------------------------------------------------------------------------------------
:
: 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 BDTG : 0.085 (0.260) 0.550 (0.717) 0.825 (0.897)
: dataset PyKeras_LSTM : 0.120 (0.159) 0.475 (0.544) 0.834 (0.810)
: dataset TMVA_LSTM : 0.010 (0.047) 0.223 (0.274) 0.535 (0.582)
: dataset TMVA_DNN : 0.035 (0.038) 0.260 (0.271) 0.499 (0.569)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:dataset : Created tree 'TestTree' with 800 events
:
Dataset:dataset : Created tree 'TrainTree' with 3200 events
:
Factory : ␛[1mThank you for using TMVA!␛[0m
: ␛[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html␛[0m
nthreads = 4