Running with nthreads = 4
--- RNNClassification : Using input file: time_data_t10_d30.root
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
number of variables is 300
vars_time0[0],vars_time0[1],vars_time0[2],vars_time0[3],vars_time0[4],vars_time0[5],vars_time0[6],vars_time0[7],vars_time0[8],vars_time0[9],vars_time0[10],vars_time0[11],vars_time0[12],vars_time0[13],vars_time0[14],vars_time0[15],vars_time0[16],vars_time0[17],vars_time0[18],vars_time0[19],vars_time0[20],vars_time0[21],vars_time0[22],vars_time0[23],vars_time0[24],vars_time0[25],vars_time0[26],vars_time0[27],vars_time0[28],vars_time0[29],vars_time1[0],vars_time1[1],vars_time1[2],vars_time1[3],vars_time1[4],vars_time1[5],vars_time1[6],vars_time1[7],vars_time1[8],vars_time1[9],vars_time1[10],vars_time1[11],vars_time1[12],vars_time1[13],vars_time1[14],vars_time1[15],vars_time1[16],vars_time1[17],vars_time1[18],vars_time1[19],vars_time1[20],vars_time1[21],vars_time1[22],vars_time1[23],vars_time1[24],vars_time1[25],vars_time1[26],vars_time1[27],vars_time1[28],vars_time1[29],vars_time2[0],vars_time2[1],vars_time2[2],vars_time2[3],vars_time2[4],vars_time2[5],vars_time2[6],vars_time2[7],vars_time2[8],vars_time2[9],vars_time2[10],vars_time2[11],vars_time2[12],vars_time2[13],vars_time2[14],vars_time2[15],vars_time2[16],vars_time2[17],vars_time2[18],vars_time2[19],vars_time2[20],vars_time2[21],vars_time2[22],vars_time2[23],vars_time2[24],vars_time2[25],vars_time2[26],vars_time2[27],vars_time2[28],vars_time2[29],vars_time3[0],vars_time3[1],vars_time3[2],vars_time3[3],vars_time3[4],vars_time3[5],vars_time3[6],vars_time3[7],vars_time3[8],vars_time3[9],vars_time3[10],vars_time3[11],vars_time3[12],vars_time3[13],vars_time3[14],vars_time3[15],vars_time3[16],vars_time3[17],vars_time3[18],vars_time3[19],vars_time3[20],vars_time3[21],vars_time3[22],vars_time3[23],vars_time3[24],vars_time3[25],vars_time3[26],vars_time3[27],vars_time3[28],vars_time3[29],vars_time4[0],vars_time4[1],vars_time4[2],vars_time4[3],vars_time4[4],vars_time4[5],vars_time4[6],vars_time4[7],vars_time4[8],vars_time4[9],vars_time4[10],vars_time4[11],vars_time4[12],vars_time4[13],vars_time4[14],vars_time4[15],vars_time4[16],vars_time4[17],vars_time4[18],vars_time4[19],vars_time4[20],vars_time4[21],vars_time4[22],vars_time4[23],vars_time4[24],vars_time4[25],vars_time4[26],vars_time4[27],vars_time4[28],vars_time4[29],vars_time5[0],vars_time5[1],vars_time5[2],vars_time5[3],vars_time5[4],vars_time5[5],vars_time5[6],vars_time5[7],vars_time5[8],vars_time5[9],vars_time5[10],vars_time5[11],vars_time5[12],vars_time5[13],vars_time5[14],vars_time5[15],vars_time5[16],vars_time5[17],vars_time5[18],vars_time5[19],vars_time5[20],vars_time5[21],vars_time5[22],vars_time5[23],vars_time5[24],vars_time5[25],vars_time5[26],vars_time5[27],vars_time5[28],vars_time5[29],vars_time6[0],vars_time6[1],vars_time6[2],vars_time6[3],vars_time6[4],vars_time6[5],vars_time6[6],vars_time6[7],vars_time6[8],vars_time6[9],vars_time6[10],vars_time6[11],vars_time6[12],vars_time6[13],vars_time6[14],vars_time6[15],vars_time6[16],vars_time6[17],vars_time6[18],vars_time6[19],vars_time6[20],vars_time6[21],vars_time6[22],vars_time6[23],vars_time6[24],vars_time6[25],vars_time6[26],vars_time6[27],vars_time6[28],vars_time6[29],vars_time7[0],vars_time7[1],vars_time7[2],vars_time7[3],vars_time7[4],vars_time7[5],vars_time7[6],vars_time7[7],vars_time7[8],vars_time7[9],vars_time7[10],vars_time7[11],vars_time7[12],vars_time7[13],vars_time7[14],vars_time7[15],vars_time7[16],vars_time7[17],vars_time7[18],vars_time7[19],vars_time7[20],vars_time7[21],vars_time7[22],vars_time7[23],vars_time7[24],vars_time7[25],vars_time7[26],vars_time7[27],vars_time7[28],vars_time7[29],vars_time8[0],vars_time8[1],vars_time8[2],vars_time8[3],vars_time8[4],vars_time8[5],vars_time8[6],vars_time8[7],vars_time8[8],vars_time8[9],vars_time8[10],vars_time8[11],vars_time8[12],vars_time8[13],vars_time8[14],vars_time8[15],vars_time8[16],vars_time8[17],vars_time8[18],vars_time8[19],vars_time8[20],vars_time8[21],vars_time8[22],vars_time8[23],vars_time8[24],vars_time8[25],vars_time8[26],vars_time8[27],vars_time8[28],vars_time8[29],vars_time9[0],vars_time9[1],vars_time9[2],vars_time9[3],vars_time9[4],vars_time9[5],vars_time9[6],vars_time9[7],vars_time9[8],vars_time9[9],vars_time9[10],vars_time9[11],vars_time9[12],vars_time9[13],vars_time9[14],vars_time9[15],vars_time9[16],vars_time9[17],vars_time9[18],vars_time9[19],vars_time9[20],vars_time9[21],vars_time9[22],vars_time9[23],vars_time9[24],vars_time9[25],vars_time9[26],vars_time9[27],vars_time9[28],vars_time9[29],
prepared DATA LOADER
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=20,Optimizer=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=20,Optimizer=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=20,Optimizer=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 !
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
_________________________________________________________________
(TString) "python3"[7]
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 = 0.700094
: --------------------------------------------------------------
: 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 | 0.700709 0.692299 0.623396 0.0426751 4305 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.690056 0.687369 0.639616 0.0416902 4181.12 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.686013 0.679618 0.631097 0.0415708 4240.7 0
: 4 | 0.67465 0.683242 0.631131 0.0415672 4240.43 1
: 5 Minimum Test error found - save the configuration
: 5 | 0.671099 0.669527 0.620431 0.0414438 4317.88 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.663713 0.668589 0.615615 0.0410969 4351.48 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.666177 0.665694 0.612747 0.0413578 4375.3 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.656895 0.657509 0.613028 0.0412902 4372.63 0
: 9 | 0.650408 0.657942 0.606333 0.040653 4419.46 1
: 10 Minimum Test error found - save the configuration
: 10 | 0.643387 0.653443 0.600846 0.040675 4462.92 0
: 11 Minimum Test error found - save the configuration
: 11 | 0.639277 0.648563 0.594632 0.0406354 4512.67 0
: 12 Minimum Test error found - save the configuration
: 12 | 0.63139 0.645344 0.597337 0.040871 4492.63 0
: 13 Minimum Test error found - save the configuration
: 13 | 0.621743 0.641308 0.598503 0.0407855 4482.56 0
: 14 | 0.61437 0.648109 0.599804 0.0406355 4470.92 1
: 15 Minimum Test error found - save the configuration
: 15 | 0.614278 0.637531 0.600425 0.0405603 4465.36 0
: 16 | 0.599434 0.643228 0.59317 0.0405208 4523.67 1
: 17 Minimum Test error found - save the configuration
: 17 | 0.595871 0.635024 0.595234 0.0405871 4507.37 0
: 18 Minimum Test error found - save the configuration
: 18 | 0.582108 0.630024 0.594482 0.0405054 4512.82 0
: 19 Minimum Test error found - save the configuration
: 19 | 0.589276 0.615802 0.593176 0.0404628 4523.14 0
: 20 | 0.584229 0.635687 0.594027 0.0404508 4516.09 1
:
: Elapsed time for training with 3200 events: 12.2 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: 0.214 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 = 0.964367
: --------------------------------------------------------------
: 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 | 0.783334 0.713449 0.195181 0.0159846 14286 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.700764 0.687005 0.195123 0.0161962 14307.5 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.686679 0.678477 0.192593 0.0155649 14461 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.67781 0.670898 0.197937 0.0155491 14036.1 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.673342 0.655336 0.192539 0.0154964 14459.8 0
: 6 | 0.671756 0.669186 0.191729 0.0152767 14508.2 1
: 7 | 0.668845 0.657326 0.193566 0.015257 14357.1 2
: 8 | 0.673777 0.669189 0.191475 0.0151887 14521.8 3
: 9 | 0.680693 0.680579 0.191882 0.015182 14487.8 4
: 10 | 0.673359 0.669519 0.192838 0.0154035 14427.9 5
: 11 | 0.673077 0.682805 0.191241 0.0151158 14535.1 6
: 12 | 0.674114 0.6642 0.190449 0.0150433 14594.7 7
: 13 Minimum Test error found - save the configuration
: 13 | 0.668262 0.652884 0.192238 0.0156588 14497.8 0
: 14 | 0.661937 0.65306 0.195788 0.015185 14174.7 1
: 15 Minimum Test error found - save the configuration
: 15 | 0.661247 0.652646 0.196205 0.0156553 14178.9 0
: 16 | 0.662874 0.662853 0.193287 0.0156432 14410.9 1
: 17 | 0.664485 0.660968 0.192117 0.0152737 14476.1 2
: 18 Minimum Test error found - save the configuration
: 18 | 0.657952 0.652078 0.192568 0.0155527 14462 0
: 19 Minimum Test error found - save the configuration
: 19 | 0.653683 0.650256 0.19209 0.0155414 14500.3 0
: 20 Minimum Test error found - save the configuration
: 20 | 0.65008 0.631316 0.193097 0.0155612 14419.6 0
:
: Elapsed time for training with 3200 events: 3.88 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
:
: Split TMVA training data in 2560 training events and 640 validation events
: Training Model Summary
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 1/20
1/26 [>.............................] - ETA: 46s - loss: 0.7005 - accuracy: 0.5400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
9/26 [=========>....................] - ETA: 0s - loss: 0.6953 - accuracy: 0.5267 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
18/26 [===================>..........] - ETA: 0s - loss: 0.6939 - accuracy: 0.5306
Epoch 1: val_loss improved from inf to 0.68238, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 3s 49ms/step - loss: 0.6943 - accuracy: 0.5270 - val_loss: 0.6824 - val_accuracy: 0.5500
Epoch 2/20
1/26 [>.............................] - ETA: 0s - loss: 0.6916 - accuracy: 0.5800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.6794 - accuracy: 0.5840␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.6731 - accuracy: 0.5911␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
25/26 [===========================>..] - ETA: 0s - loss: 0.6695 - accuracy: 0.5956
Epoch 2: val_loss improved from 0.68238 to 0.65888, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 10ms/step - loss: 0.6696 - accuracy: 0.5957 - val_loss: 0.6589 - val_accuracy: 0.6187
Epoch 3/20
1/26 [>.............................] - ETA: 0s - loss: 0.6618 - accuracy: 0.6200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
8/26 [========>.....................] - ETA: 0s - loss: 0.6559 - accuracy: 0.6125␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
16/26 [=================>............] - ETA: 0s - loss: 0.6411 - accuracy: 0.6463␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
24/26 [==========================>...] - ETA: 0s - loss: 0.6390 - accuracy: 0.6446
Epoch 3: val_loss improved from 0.65888 to 0.62293, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 10ms/step - loss: 0.6386 - accuracy: 0.6438 - val_loss: 0.6229 - val_accuracy: 0.6781
Epoch 4/20
1/26 [>.............................] - ETA: 0s - loss: 0.5962 - accuracy: 0.6800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.6082 - accuracy: 0.6760␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
18/26 [===================>..........] - ETA: 0s - loss: 0.6017 - accuracy: 0.6811
Epoch 4: val_loss improved from 0.62293 to 0.61297, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.5979 - accuracy: 0.6887 - val_loss: 0.6130 - val_accuracy: 0.6750
Epoch 5/20
1/26 [>.............................] - ETA: 0s - loss: 0.6438 - accuracy: 0.6000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5839 - accuracy: 0.6964␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.5739 - accuracy: 0.7071
Epoch 5: val_loss improved from 0.61297 to 0.57862, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5716 - accuracy: 0.7086 - val_loss: 0.5786 - val_accuracy: 0.6891
Epoch 6/20
1/26 [>.............................] - ETA: 0s - loss: 0.5660 - accuracy: 0.7100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5471 - accuracy: 0.7200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.5392 - accuracy: 0.7233
Epoch 6: val_loss improved from 0.57862 to 0.53005, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.5390 - accuracy: 0.7262 - val_loss: 0.5301 - val_accuracy: 0.7469
Epoch 7/20
1/26 [>.............................] - ETA: 0s - loss: 0.5171 - accuracy: 0.7300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5024 - accuracy: 0.7600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.5057 - accuracy: 0.7605
Epoch 7: val_loss improved from 0.53005 to 0.52048, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5039 - accuracy: 0.7617 - val_loss: 0.5205 - val_accuracy: 0.7672
Epoch 8/20
1/26 [>.............................] - ETA: 0s - loss: 0.5327 - accuracy: 0.7600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.4694 - accuracy: 0.7920␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
18/26 [===================>..........] - ETA: 0s - loss: 0.4771 - accuracy: 0.7767
Epoch 8: val_loss improved from 0.52048 to 0.48989, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.4811 - accuracy: 0.7719 - val_loss: 0.4899 - val_accuracy: 0.7672
Epoch 9/20
1/26 [>.............................] - ETA: 0s - loss: 0.5525 - accuracy: 0.7100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.4866 - accuracy: 0.7691␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.4757 - accuracy: 0.7730
Epoch 9: val_loss improved from 0.48989 to 0.47349, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.4677 - accuracy: 0.7781 - val_loss: 0.4735 - val_accuracy: 0.7750
Epoch 10/20
1/26 [>.............................] - ETA: 0s - loss: 0.3495 - accuracy: 0.8400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.4430 - accuracy: 0.8010␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.4463 - accuracy: 0.7905
Epoch 10: val_loss improved from 0.47349 to 0.46204, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.4454 - accuracy: 0.7906 - val_loss: 0.4620 - val_accuracy: 0.7859
Epoch 11/20
1/26 [>.............................] - ETA: 0s - loss: 0.4586 - accuracy: 0.7700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.4221 - accuracy: 0.8090␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.4473 - accuracy: 0.7874
Epoch 11: val_loss improved from 0.46204 to 0.46050, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.4354 - accuracy: 0.7984 - val_loss: 0.4605 - val_accuracy: 0.7688
Epoch 12/20
1/26 [>.............................] - ETA: 0s - loss: 0.4211 - accuracy: 0.8200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.4146 - accuracy: 0.8010␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.4158 - accuracy: 0.8063
Epoch 12: val_loss did not improve from 0.46050
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4231 - accuracy: 0.7969 - val_loss: 0.4647 - val_accuracy: 0.7766
Epoch 13/20
1/26 [>.............................] - ETA: 0s - loss: 0.4879 - accuracy: 0.7600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.4014 - accuracy: 0.8164␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.4105 - accuracy: 0.8114
Epoch 13: val_loss improved from 0.46050 to 0.43183, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4088 - accuracy: 0.8137 - val_loss: 0.4318 - val_accuracy: 0.7937
Epoch 14/20
1/26 [>.............................] - ETA: 0s - loss: 0.3873 - accuracy: 0.8200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
9/26 [=========>....................] - ETA: 0s - loss: 0.4025 - accuracy: 0.8189␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.3992 - accuracy: 0.8184
Epoch 14: val_loss did not improve from 0.43183
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4026 - accuracy: 0.8180 - val_loss: 0.4495 - val_accuracy: 0.7906
Epoch 15/20
1/26 [>.............................] - ETA: 0s - loss: 0.4066 - accuracy: 0.8500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3959 - accuracy: 0.8325␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.3882 - accuracy: 0.8318
Epoch 15: val_loss did not improve from 0.43183
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3942 - accuracy: 0.8285 - val_loss: 0.4417 - val_accuracy: 0.7984
Epoch 16/20
1/26 [>.............................] - ETA: 0s - loss: 0.4368 - accuracy: 0.7900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.3933 - accuracy: 0.8236␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.3872 - accuracy: 0.8255
Epoch 16: val_loss improved from 0.43183 to 0.42462, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.3874 - accuracy: 0.8266 - val_loss: 0.4246 - val_accuracy: 0.7937
Epoch 17/20
1/26 [>.............................] - ETA: 0s - loss: 0.3932 - accuracy: 0.8600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.3845 - accuracy: 0.8300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.3815 - accuracy: 0.8295
Epoch 17: val_loss improved from 0.42462 to 0.41628, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.3806 - accuracy: 0.8313 - val_loss: 0.4163 - val_accuracy: 0.8047
Epoch 18/20
1/26 [>.............................] - ETA: 0s - loss: 0.3572 - accuracy: 0.8200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.3892 - accuracy: 0.8140␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.3885 - accuracy: 0.8232
Epoch 18: val_loss did not improve from 0.41628
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.3868 - accuracy: 0.8258 - val_loss: 0.4183 - val_accuracy: 0.8047
Epoch 19/20
1/26 [>.............................] - ETA: 0s - loss: 0.3827 - accuracy: 0.8000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.3826 - accuracy: 0.8291␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.3772 - accuracy: 0.8382
Epoch 19: val_loss did not improve from 0.41628
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3727 - accuracy: 0.8375 - val_loss: 0.4209 - val_accuracy: 0.7984
Epoch 20/20
1/26 [>.............................] - ETA: 0s - loss: 0.3890 - accuracy: 0.8300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.3531 - accuracy: 0.8427␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.3694 - accuracy: 0.8279
Epoch 20: val_loss improved from 0.41628 to 0.41424, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.3633 - accuracy: 0.8371 - val_loss: 0.4142 - val_accuracy: 0.8062
: 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: 8.47 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: 0.306 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
:
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: 1.65 sec
BDTG : [dataset] : Evaluation of BDTG on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.0175 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_time8 : 2.278e-02
: 2 : vars_time9 : 2.267e-02
: 3 : vars_time7 : 2.097e-02
: 4 : vars_time8 : 2.081e-02
: 5 : vars_time8 : 2.055e-02
: 6 : vars_time9 : 2.035e-02
: 7 : vars_time6 : 1.958e-02
: 8 : vars_time7 : 1.957e-02
: 9 : vars_time8 : 1.943e-02
: 10 : vars_time8 : 1.794e-02
: 11 : vars_time9 : 1.693e-02
: 12 : vars_time8 : 1.682e-02
: 13 : vars_time7 : 1.616e-02
: 14 : vars_time0 : 1.611e-02
: 15 : vars_time9 : 1.609e-02
: 16 : vars_time9 : 1.584e-02
: 17 : vars_time8 : 1.530e-02
: 18 : vars_time7 : 1.510e-02
: 19 : vars_time0 : 1.393e-02
: 20 : vars_time8 : 1.305e-02
: 21 : vars_time4 : 1.278e-02
: 22 : vars_time6 : 1.261e-02
: 23 : vars_time8 : 1.252e-02
: 24 : vars_time6 : 1.244e-02
: 25 : vars_time6 : 1.240e-02
: 26 : vars_time7 : 1.223e-02
: 27 : vars_time9 : 1.142e-02
: 28 : vars_time5 : 1.108e-02
: 29 : vars_time6 : 1.067e-02
: 30 : vars_time6 : 1.055e-02
: 31 : vars_time4 : 1.026e-02
: 32 : vars_time7 : 1.011e-02
: 33 : vars_time6 : 1.008e-02
: 34 : vars_time4 : 1.001e-02
: 35 : vars_time7 : 9.782e-03
: 36 : vars_time5 : 9.731e-03
: 37 : vars_time7 : 9.480e-03
: 38 : vars_time8 : 9.407e-03
: 39 : vars_time6 : 9.373e-03
: 40 : vars_time8 : 9.227e-03
: 41 : vars_time9 : 8.705e-03
: 42 : vars_time5 : 8.676e-03
: 43 : vars_time4 : 8.362e-03
: 44 : vars_time8 : 8.304e-03
: 45 : vars_time9 : 8.296e-03
: 46 : vars_time9 : 8.153e-03
: 47 : vars_time7 : 8.151e-03
: 48 : vars_time8 : 7.925e-03
: 49 : vars_time7 : 7.921e-03
: 50 : vars_time9 : 7.798e-03
: 51 : vars_time3 : 7.772e-03
: 52 : vars_time1 : 7.758e-03
: 53 : vars_time4 : 7.690e-03
: 54 : vars_time0 : 7.503e-03
: 55 : vars_time5 : 7.492e-03
: 56 : vars_time8 : 7.386e-03
: 57 : vars_time8 : 7.280e-03
: 58 : vars_time1 : 7.182e-03
: 59 : vars_time7 : 7.057e-03
: 60 : vars_time8 : 6.932e-03
: 61 : vars_time1 : 6.905e-03
: 62 : vars_time5 : 6.895e-03
: 63 : vars_time6 : 6.829e-03
: 64 : vars_time0 : 6.819e-03
: 65 : vars_time6 : 6.809e-03
: 66 : vars_time7 : 6.543e-03
: 67 : vars_time4 : 6.483e-03
: 68 : vars_time9 : 6.483e-03
: 69 : vars_time9 : 6.446e-03
: 70 : vars_time6 : 6.295e-03
: 71 : vars_time2 : 6.121e-03
: 72 : vars_time3 : 6.029e-03
: 73 : vars_time9 : 5.934e-03
: 74 : vars_time0 : 5.846e-03
: 75 : vars_time7 : 5.725e-03
: 76 : vars_time4 : 5.666e-03
: 77 : vars_time6 : 5.448e-03
: 78 : vars_time4 : 5.437e-03
: 79 : vars_time9 : 5.345e-03
: 80 : vars_time7 : 5.312e-03
: 81 : vars_time3 : 5.303e-03
: 82 : vars_time6 : 5.054e-03
: 83 : vars_time9 : 5.044e-03
: 84 : vars_time2 : 5.044e-03
: 85 : vars_time5 : 4.989e-03
: 86 : vars_time8 : 4.960e-03
: 87 : vars_time3 : 4.915e-03
: 88 : vars_time5 : 4.858e-03
: 89 : vars_time3 : 4.844e-03
: 90 : vars_time3 : 4.829e-03
: 91 : vars_time0 : 4.785e-03
: 92 : vars_time0 : 4.746e-03
: 93 : vars_time0 : 4.723e-03
: 94 : vars_time7 : 4.546e-03
: 95 : vars_time9 : 4.457e-03
: 96 : vars_time1 : 4.340e-03
: 97 : vars_time1 : 4.233e-03
: 98 : vars_time5 : 4.142e-03
: 99 : vars_time4 : 4.079e-03
: 100 : vars_time8 : 4.016e-03
: 101 : vars_time6 : 4.015e-03
: 102 : vars_time7 : 3.939e-03
: 103 : vars_time7 : 3.904e-03
: 104 : vars_time7 : 3.892e-03
: 105 : vars_time4 : 3.882e-03
: 106 : vars_time0 : 3.828e-03
: 107 : vars_time7 : 3.824e-03
: 108 : vars_time4 : 3.820e-03
: 109 : vars_time3 : 3.769e-03
: 110 : vars_time0 : 3.536e-03
: 111 : vars_time1 : 3.375e-03
: 112 : vars_time2 : 3.348e-03
: 113 : vars_time0 : 3.101e-03
: 114 : vars_time8 : 2.009e-03
: 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_time0 : 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
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: 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_time1 : 0.000e+00
: 156 : vars_time1 : 0.000e+00
: 157 : vars_time1 : 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_time2 : 0.000e+00
: 183 : vars_time2 : 0.000e+00
: 184 : vars_time2 : 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
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: 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_time4 : 0.000e+00
: 209 : vars_time4 : 0.000e+00
: 210 : vars_time4 : 0.000e+00
: 211 : vars_time4 : 0.000e+00
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: 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_time5 : 0.000e+00
: 228 : vars_time5 : 0.000e+00
: 229 : vars_time5 : 0.000e+00
: 230 : vars_time5 : 0.000e+00
: 231 : vars_time5 : 0.000e+00
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: 248 : vars_time5 : 0.000e+00
: 249 : vars_time6 : 0.000e+00
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: 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_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_time7 : 0.000e+00
: 275 : vars_time7 : 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= 12.7751
TH1.Print Name = TrainingHistory_TMVA_LSTM_valError, Entries= 0, Total sum= 13.0959
TH1.Print Name = TrainingHistory_TMVA_DNN_trainingError, Entries= 0, Total sum= 13.5181
TH1.Print Name = TrainingHistory_TMVA_DNN_valError, Entries= 0, Total sum= 13.314
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'accuracy', Entries= 0, Total sum= 15.2059
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'loss', Entries= 0, Total sum= 9.56386
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_accuracy', Entries= 0, Total sum= 14.9891
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_loss', Entries= 0, Total sum= 9.97435
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
nthreads = 4
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: 0.0511 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: 0.0207 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: 0.232 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: 0.00689 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 PyKeras_LSTM : 0.876
: dataset BDTG : 0.837
: dataset TMVA_LSTM : 0.702
: dataset TMVA_DNN : 0.653
: -------------------------------------------------------------------------------------------------------------------
:
: 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 PyKeras_LSTM : 0.325 (0.310) 0.615 (0.720) 0.873 (0.884)
: dataset BDTG : 0.165 (0.305) 0.590 (0.651) 0.815 (0.856)
: dataset TMVA_LSTM : 0.019 (0.053) 0.297 (0.301) 0.575 (0.596)
: dataset TMVA_DNN : 0.032 (0.043) 0.230 (0.262) 0.515 (0.513)
: -------------------------------------------------------------------------------------------------------------------
:
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