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: 8234 (32.16 KB)
Trainable params: 8234 (32.16 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
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 = 0.704769
: --------------------------------------------------------------
: 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 | 0.699544 0.68801 0.632475 0.0418389 4232.73 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.691849 0.684867 0.615583 0.0404007 4346.44 0
: 3 | 0.687092 0.688008 0.577617 0.039721 4647.74 1
: 4 Minimum Test error found - save the configuration
: 4 | 0.675711 0.68058 0.559112 0.0400108 4816.01 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.663111 0.665767 0.547972 0.040502 4926.39 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.645617 0.645601 0.534752 0.0393853 5046.76 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.628355 0.624678 0.530516 0.0399871 5096.54 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.608285 0.595707 0.523115 0.0402431 5177.36 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.579185 0.569992 0.534106 0.0398363 5057.97 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.559794 0.546354 0.521042 0.0392971 5189.47 0
:
: Elapsed time for training with 3200 events: 5.63 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: 0.203 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 = 0.857812
: --------------------------------------------------------------
: 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 | 0.743751 0.70755 0.195339 0.0155642 14240 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.683666 0.694624 0.19425 0.0152132 14298.8 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.67496 0.679133 0.193242 0.0152813 14385.2 0
: 4 | 0.667509 0.685864 0.191939 0.0148792 14458.4 1
: 5 Minimum Test error found - save the configuration
: 5 | 0.664477 0.672249 0.195125 0.0152745 14234 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.660775 0.663714 0.192276 0.0151982 14456.9 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.657925 0.662036 0.192 0.0149883 14462.4 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.652607 0.652784 0.193435 0.0154948 14386.8 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.650198 0.644288 0.194289 0.0168664 14428.8 0
: 10 | 0.643776 0.652844 0.1949 0.0147707 14212 1
: 11 | 0.635001 0.646668 0.191351 0.0146606 14488.6 2
: 12 | 0.635094 0.657332 0.191021 0.0144716 14500.2 3
: 13 | 0.640766 0.657064 0.191653 0.0147892 14474.4 4
: 14 Minimum Test error found - save the configuration
: 14 | 0.632083 0.644169 0.191321 0.0147686 14499.9 0
: 15 | 0.634135 0.661477 0.192437 0.0147685 14408.9 1
: 16 | 0.638604 0.657789 0.191218 0.0147291 14505.1 2
: 17 | 0.638459 0.65517 0.190997 0.0146422 14516.2 3
: 18 | 0.62911 0.668496 0.191614 0.014786 14477.4 4
: 19 | 0.626112 0.671028 0.192324 0.015004 14437.2 5
: 20 Minimum Test error found - save the configuration
: 20 | 0.63029 0.637235 0.191772 0.0150616 14487 0
:
: 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: 0.101 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: 8234 (32.16 KB)
Trainable params: 8234 (32.16 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
: Option SaveBestOnly: Only model weights with smallest validation loss will be stored
Epoch 1/10
1/26 [>.............................] - ETA: 41s - loss: 0.6781 - accuracy: 0.6100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
9/26 [=========>....................] - ETA: 0s - loss: 0.6914 - accuracy: 0.5456 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.6966 - accuracy: 0.5205
Epoch 1: val_loss improved from inf to 0.69219, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 3s 35ms/step - loss: 0.6931 - accuracy: 0.5340 - val_loss: 0.6922 - val_accuracy: 0.5406
Epoch 2/10
1/26 [>.............................] - ETA: 0s - loss: 0.6681 - accuracy: 0.6100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.6792 - accuracy: 0.5727␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.6754 - accuracy: 0.5815
Epoch 2: val_loss improved from 0.69219 to 0.67187, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.6736 - accuracy: 0.5918 - val_loss: 0.6719 - val_accuracy: 0.5719
Epoch 3/10
1/26 [>.............................] - ETA: 0s - loss: 0.6460 - accuracy: 0.6900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.6528 - accuracy: 0.6509␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.6556 - accuracy: 0.6400
Epoch 3: val_loss improved from 0.67187 to 0.64929, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.6539 - accuracy: 0.6367 - val_loss: 0.6493 - val_accuracy: 0.6438
Epoch 4/10
1/26 [>.............................] - ETA: 0s - loss: 0.6410 - accuracy: 0.6400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.6340 - accuracy: 0.6545␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.6275 - accuracy: 0.6562
Epoch 4: val_loss improved from 0.64929 to 0.63355, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.6306 - accuracy: 0.6516 - val_loss: 0.6336 - val_accuracy: 0.6469
Epoch 5/10
1/26 [>.............................] - ETA: 0s - loss: 0.6605 - accuracy: 0.6200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.6080 - accuracy: 0.6836␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.6113 - accuracy: 0.6725
Epoch 5: val_loss improved from 0.63355 to 0.60258, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.6051 - accuracy: 0.6828 - val_loss: 0.6026 - val_accuracy: 0.6859
Epoch 6/10
1/26 [>.............................] - ETA: 0s - loss: 0.5551 - accuracy: 0.7400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.5757 - accuracy: 0.7220␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.5843 - accuracy: 0.7032
Epoch 6: val_loss improved from 0.60258 to 0.58152, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.5802 - accuracy: 0.7008 - val_loss: 0.5815 - val_accuracy: 0.6906
Epoch 7/10
1/26 [>.............................] - ETA: 0s - loss: 0.5443 - accuracy: 0.7200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5530 - accuracy: 0.7273␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.5576 - accuracy: 0.7243
Epoch 7: val_loss improved from 0.58152 to 0.56570, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5558 - accuracy: 0.7262 - val_loss: 0.5657 - val_accuracy: 0.7016
Epoch 8/10
1/26 [>.............................] - ETA: 0s - loss: 0.5531 - accuracy: 0.7300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5284 - accuracy: 0.7436␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.5314 - accuracy: 0.7338
Epoch 8: val_loss improved from 0.56570 to 0.54208, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5284 - accuracy: 0.7352 - val_loss: 0.5421 - val_accuracy: 0.7109
Epoch 9/10
1/26 [>.............................] - ETA: 0s - loss: 0.5923 - accuracy: 0.7100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5088 - accuracy: 0.7573␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.5092 - accuracy: 0.7500
Epoch 9: val_loss improved from 0.54208 to 0.53354, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5088 - accuracy: 0.7512 - val_loss: 0.5335 - val_accuracy: 0.7266
Epoch 10/10
1/26 [>.............................] - ETA: 0s - loss: 0.5188 - accuracy: 0.7700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.4993 - accuracy: 0.7667␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.5010 - accuracy: 0.7596
Epoch 10: val_loss improved from 0.53354 to 0.51497, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.4963 - accuracy: 0.7633 - val_loss: 0.5150 - val_accuracy: 0.7406
: 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: 4.51 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: 0.244 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: 1.62 sec
BDTG : [dataset] : Evaluation of BDTG on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.018 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_time7 : 2.294e-02
: 2 : vars_time7 : 2.221e-02
: 3 : vars_time8 : 2.103e-02
: 4 : vars_time9 : 1.919e-02
: 5 : vars_time5 : 1.776e-02
: 6 : vars_time9 : 1.772e-02
: 7 : vars_time8 : 1.733e-02
: 8 : vars_time6 : 1.647e-02
: 9 : vars_time6 : 1.619e-02
: 10 : vars_time8 : 1.590e-02
: 11 : vars_time9 : 1.575e-02
: 12 : vars_time9 : 1.574e-02
: 13 : vars_time9 : 1.545e-02
: 14 : vars_time8 : 1.533e-02
: 15 : vars_time0 : 1.521e-02
: 16 : vars_time7 : 1.520e-02
: 17 : vars_time6 : 1.457e-02
: 18 : vars_time7 : 1.457e-02
: 19 : vars_time6 : 1.432e-02
: 20 : vars_time5 : 1.345e-02
: 21 : vars_time6 : 1.338e-02
: 22 : vars_time9 : 1.239e-02
: 23 : vars_time7 : 1.226e-02
: 24 : vars_time6 : 1.190e-02
: 25 : vars_time9 : 1.154e-02
: 26 : vars_time5 : 1.135e-02
: 27 : vars_time8 : 1.122e-02
: 28 : vars_time8 : 1.112e-02
: 29 : vars_time0 : 1.107e-02
: 30 : vars_time7 : 1.102e-02
: 31 : vars_time9 : 1.098e-02
: 32 : vars_time9 : 1.087e-02
: 33 : vars_time0 : 1.056e-02
: 34 : vars_time8 : 1.047e-02
: 35 : vars_time7 : 1.032e-02
: 36 : vars_time8 : 1.026e-02
: 37 : vars_time5 : 1.022e-02
: 38 : vars_time7 : 1.013e-02
: 39 : vars_time7 : 1.010e-02
: 40 : vars_time9 : 9.900e-03
: 41 : vars_time7 : 9.758e-03
: 42 : vars_time7 : 9.727e-03
: 43 : vars_time8 : 9.429e-03
: 44 : vars_time0 : 9.333e-03
: 45 : vars_time6 : 9.192e-03
: 46 : vars_time6 : 9.181e-03
: 47 : vars_time8 : 9.162e-03
: 48 : vars_time6 : 9.039e-03
: 49 : vars_time9 : 8.987e-03
: 50 : vars_time8 : 8.963e-03
: 51 : vars_time7 : 8.705e-03
: 52 : vars_time8 : 8.649e-03
: 53 : vars_time5 : 8.548e-03
: 54 : vars_time8 : 8.208e-03
: 55 : vars_time7 : 7.653e-03
: 56 : vars_time9 : 7.510e-03
: 57 : vars_time6 : 7.471e-03
: 58 : vars_time7 : 7.329e-03
: 59 : vars_time1 : 7.306e-03
: 60 : vars_time9 : 7.267e-03
: 61 : vars_time7 : 7.256e-03
: 62 : vars_time6 : 7.167e-03
: 63 : vars_time1 : 7.161e-03
: 64 : vars_time1 : 7.118e-03
: 65 : vars_time8 : 7.019e-03
: 66 : vars_time9 : 6.816e-03
: 67 : vars_time5 : 6.789e-03
: 68 : vars_time1 : 6.788e-03
: 69 : vars_time0 : 6.786e-03
: 70 : vars_time0 : 6.717e-03
: 71 : vars_time4 : 6.431e-03
: 72 : vars_time5 : 6.336e-03
: 73 : vars_time4 : 6.182e-03
: 74 : vars_time6 : 6.011e-03
: 75 : vars_time8 : 5.859e-03
: 76 : vars_time1 : 5.837e-03
: 77 : vars_time0 : 5.823e-03
: 78 : vars_time1 : 5.782e-03
: 79 : vars_time0 : 5.776e-03
: 80 : vars_time4 : 5.763e-03
: 81 : vars_time5 : 5.735e-03
: 82 : vars_time9 : 5.620e-03
: 83 : vars_time8 : 5.575e-03
: 84 : vars_time5 : 5.360e-03
: 85 : vars_time1 : 5.332e-03
: 86 : vars_time9 : 5.329e-03
: 87 : vars_time9 : 5.314e-03
: 88 : vars_time9 : 5.223e-03
: 89 : vars_time9 : 5.012e-03
: 90 : vars_time3 : 4.994e-03
: 91 : vars_time9 : 4.960e-03
: 92 : vars_time8 : 4.905e-03
: 93 : vars_time7 : 4.904e-03
: 94 : vars_time6 : 4.727e-03
: 95 : vars_time1 : 4.685e-03
: 96 : vars_time1 : 4.656e-03
: 97 : vars_time9 : 4.630e-03
: 98 : vars_time6 : 4.506e-03
: 99 : vars_time7 : 4.490e-03
: 100 : vars_time5 : 4.489e-03
: 101 : vars_time8 : 4.477e-03
: 102 : vars_time1 : 4.371e-03
: 103 : vars_time7 : 4.346e-03
: 104 : vars_time4 : 4.333e-03
: 105 : vars_time7 : 4.332e-03
: 106 : vars_time4 : 4.203e-03
: 107 : vars_time5 : 4.002e-03
: 108 : vars_time1 : 3.972e-03
: 109 : vars_time7 : 3.744e-03
: 110 : vars_time8 : 3.564e-03
: 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_time0 : 0.000e+00
: 131 : vars_time0 : 0.000e+00
: 132 : vars_time0 : 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_time2 : 0.000e+00
: 153 : vars_time2 : 0.000e+00
: 154 : vars_time2 : 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_time3 : 0.000e+00
: 210 : vars_time3 : 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_time4 : 0.000e+00
: 231 : vars_time4 : 0.000e+00
: 232 : vars_time4 : 0.000e+00
: 233 : vars_time4 : 0.000e+00
: 234 : vars_time4 : 0.000e+00
: 235 : vars_time4 : 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_time5 : 0.000e+00
: 250 : vars_time5 : 0.000e+00
: 251 : vars_time5 : 0.000e+00
: 252 : vars_time5 : 0.000e+00
: 253 : vars_time5 : 0.000e+00
: 254 : vars_time5 : 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_time6 : 0.000e+00
: 261 : vars_time6 : 0.000e+00
: 262 : vars_time6 : 0.000e+00
: 263 : vars_time6 : 0.000e+00
: 264 : vars_time6 : 0.000e+00
: 265 : vars_time6 : 0.000e+00
: 266 : vars_time6 : 0.000e+00
: 267 : vars_time6 : 0.000e+00
: 268 : vars_time6 : 0.000e+00
: 269 : vars_time6 : 0.000e+00
: 270 : vars_time6 : 0.000e+00
: 271 : vars_time7 : 0.000e+00
: 272 : vars_time7 : 0.000e+00
: 273 : vars_time7 : 0.000e+00
: 274 : vars_time7 : 0.000e+00
: 275 : vars_time7 : 0.000e+00
: 276 : vars_time7 : 0.000e+00
: 277 : vars_time7 : 0.000e+00
: 278 : vars_time7 : 0.000e+00
: 279 : vars_time7 : 0.000e+00
: 280 : vars_time7 : 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_time8 : 0.000e+00
: 288 : vars_time8 : 0.000e+00
: 289 : vars_time8 : 0.000e+00
: 290 : vars_time8 : 0.000e+00
: 291 : vars_time8 : 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= 6.43854
TH1.Print Name = TrainingHistory_TMVA_LSTM_valError, Entries= 0, Total sum= 6.38956
TH1.Print Name = TrainingHistory_TMVA_DNN_trainingError, Entries= 0, Total sum= 13.0393
TH1.Print Name = TrainingHistory_TMVA_DNN_valError, Entries= 0, Total sum= 13.2715
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'accuracy', Entries= 0, Total sum= 6.77344
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'loss', Entries= 0, Total sum= 5.92593
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_accuracy', Entries= 0, Total sum= 6.65938
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_loss', Entries= 0, Total sum= 5.98731
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: 0.0476 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: 0.02 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: 0.215 sec
Factory : Test method: BDTG for Classification performance
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BDTG : [dataset] : Evaluation of BDTG on testing sample (800 events)
: Elapsed time for evaluation of 800 events: 0.00554 sec
Factory : ␛[1mEvaluate all methods␛[0m
Factory : Evaluate classifier: TMVA_LSTM
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TMVA_LSTM : [dataset] : Loop over test events and fill histograms with classifier response...
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: Evaluate deep neural network on CPU using batches with size = 1000
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: Dataset[dataset] : variable plots are not produces ! The number of variables is 300 , it is larger than 200
Factory : Evaluate classifier: TMVA_DNN
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TMVA_DNN : [dataset] : Loop over test events and fill histograms with classifier response...
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: Evaluate deep neural network on CPU using batches with size = 1000
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: Dataset[dataset] : variable plots are not produces ! The number of variables is 300 , it is larger than 200
Factory : Evaluate classifier: PyKeras_LSTM
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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
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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)
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: DataSet MVA
: Name: Method: ROC-integ
: dataset BDTG : 0.843
: dataset PyKeras_LSTM : 0.833
: dataset TMVA_LSTM : 0.788
: dataset TMVA_DNN : 0.673
: -------------------------------------------------------------------------------------------------------------------
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: Testing efficiency compared to training efficiency (overtraining check)
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: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset BDTG : 0.238 (0.295) 0.535 (0.643) 0.795 (0.885)
: dataset PyKeras_LSTM : 0.165 (0.165) 0.500 (0.527) 0.793 (0.800)
: dataset TMVA_LSTM : 0.095 (0.142) 0.425 (0.420) 0.724 (0.741)
: dataset TMVA_DNN : 0.035 (0.080) 0.277 (0.318) 0.528 (0.570)
: -------------------------------------------------------------------------------------------------------------------
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Dataset:dataset : Created tree 'TestTree' with 800 events
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Dataset:dataset : Created tree 'TrainTree' with 3200 events
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Factory : ␛[1mThank you for using TMVA!␛[0m
: ␛[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html␛[0m
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