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: 8234 (32.16 KB)
Trainable params: 8234 (32.16 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
(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 = 0.706511
: --------------------------------------------------------------
: 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 | 0.701708 0.69817 0.652208 0.0430245 4103.85 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.693475 0.695759 0.660752 0.0417971 4039.07 0
: 3 | 0.687753 0.698077 0.658556 0.0416252 4052.32 1
: 4 Minimum Test error found - save the configuration
: 4 | 0.678409 0.691142 0.638542 0.0411255 4184.69 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.666726 0.677079 0.627646 0.0412981 4263.68 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.648882 0.656136 0.599676 0.0400878 4467.57 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.628427 0.646569 0.593822 0.0399642 4513.79 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.613436 0.630771 0.596476 0.0404368 4496.09 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.58733 0.613073 0.608133 0.040516 4404.38 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.56648 0.591079 0.604132 0.0403027 4433.96 0
: 11 Minimum Test error found - save the configuration
: 11 | 0.551819 0.582653 0.59773 0.0399924 4482.39 0
: 12 | 0.539671 0.588457 0.588352 0.039286 4553.19 1
: 13 | 0.526355 0.586975 0.568633 0.0381541 4712.72 2
: 14 Minimum Test error found - save the configuration
: 14 | 0.514895 0.556662 0.554544 0.0387423 4846.83 0
: 15 Minimum Test error found - save the configuration
: 15 | 0.495319 0.536454 0.544486 0.0383503 4939.38 0
: 16 Minimum Test error found - save the configuration
: 16 | 0.479113 0.536217 0.535457 0.0382094 5027.68 0
: 17 Minimum Test error found - save the configuration
: 17 | 0.471206 0.519126 0.532107 0.0382202 5061.89 0
: 18 | 0.455934 0.519491 0.532372 0.0380526 5057.46 1
: 19 Minimum Test error found - save the configuration
: 19 | 0.443945 0.503518 0.533845 0.0382866 5044.81 0
: 20 Minimum Test error found - save the configuration
: 20 | 0.43639 0.500718 0.539201 0.0384222 4992.22 0
:
: Elapsed time for training with 3200 events: 11.8 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: 0.205 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 = 1.04941
: --------------------------------------------------------------
: 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 | 0.869958 0.725553 0.201207 0.0162921 13844.2 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.738276 0.725224 0.201176 0.0162335 13842.2 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.708737 0.698406 0.2011 0.0165245 13869.6 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.688126 0.665377 0.193568 0.0156066 14385.2 0
: 5 | 0.678687 0.681129 0.192416 0.0152716 14451.5 1
: 6 | 0.667565 0.680621 0.192483 0.0153285 14450.7 2
: 7 | 0.65751 0.67596 0.192848 0.0152565 14415.1 3
: 8 | 0.657112 0.679343 0.193272 0.0153278 14386.5 4
: 9 | 0.649843 0.675151 0.199081 0.0154285 13939.4 5
: 10 | 0.658999 0.668509 0.194365 0.0152984 14296.3 6
: 11 | 0.648508 0.682569 0.193279 0.0152966 14383.5 7
: 12 | 0.647254 0.674864 0.192638 0.0152186 14429.1 8
: 13 | 0.638651 0.690955 0.192711 0.0152971 14429.5 9
: 14 | 0.632114 0.666363 0.194642 0.0152683 14271.9 10
: 15 | 0.644693 0.667062 0.19469 0.0163687 14356.1 11
:
: Elapsed time for training with 3200 events: 2.95 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: 0.105 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: 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 1/20
1/26 [>.............................] - ETA: 43s - loss: 0.7340 - accuracy: 0.5400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
8/26 [========>.....................] - ETA: 0s - loss: 0.7145 - accuracy: 0.5125 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
16/26 [=================>............] - ETA: 0s - loss: 0.7054 - accuracy: 0.5238␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
24/26 [==========================>...] - ETA: 0s - loss: 0.7019 - accuracy: 0.5250
Epoch 1: val_loss improved from inf to 0.69184, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 3s 38ms/step - loss: 0.7012 - accuracy: 0.5223 - val_loss: 0.6918 - val_accuracy: 0.5281
Epoch 2/20
1/26 [>.............................] - ETA: 0s - loss: 0.6811 - accuracy: 0.5500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
7/26 [=======>......................] - ETA: 0s - loss: 0.6884 - accuracy: 0.5500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
15/26 [================>.............] - ETA: 0s - loss: 0.6857 - accuracy: 0.5500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.6818 - accuracy: 0.5664
Epoch 2: val_loss improved from 0.69184 to 0.68245, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 11ms/step - loss: 0.6793 - accuracy: 0.5738 - val_loss: 0.6825 - val_accuracy: 0.5641
Epoch 3/20
1/26 [>.............................] - ETA: 0s - loss: 0.6624 - accuracy: 0.6100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
7/26 [=======>......................] - ETA: 0s - loss: 0.6633 - accuracy: 0.6143␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.6707 - accuracy: 0.5967␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.6657 - accuracy: 0.6055
Epoch 3: val_loss improved from 0.68245 to 0.67275, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 12ms/step - loss: 0.6687 - accuracy: 0.5930 - val_loss: 0.6727 - val_accuracy: 0.5781
Epoch 4/20
1/26 [>.............................] - ETA: 0s - loss: 0.6343 - accuracy: 0.6300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
8/26 [========>.....................] - ETA: 0s - loss: 0.6552 - accuracy: 0.6225␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
14/26 [===============>..............] - ETA: 0s - loss: 0.6555 - accuracy: 0.6164␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.6524 - accuracy: 0.6195
Epoch 4: val_loss improved from 0.67275 to 0.66059, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 11ms/step - loss: 0.6549 - accuracy: 0.6109 - val_loss: 0.6606 - val_accuracy: 0.6156
Epoch 5/20
1/26 [>.............................] - ETA: 0s - loss: 0.6337 - accuracy: 0.6400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
8/26 [========>.....................] - ETA: 0s - loss: 0.6514 - accuracy: 0.6100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
17/26 [==================>...........] - ETA: 0s - loss: 0.6351 - accuracy: 0.6412␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
24/26 [==========================>...] - ETA: 0s - loss: 0.6382 - accuracy: 0.6383
Epoch 5: val_loss improved from 0.66059 to 0.63905, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 10ms/step - loss: 0.6370 - accuracy: 0.6363 - val_loss: 0.6391 - val_accuracy: 0.6281
Epoch 6/20
1/26 [>.............................] - ETA: 0s - loss: 0.5953 - accuracy: 0.7300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.6092 - accuracy: 0.6790␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
18/26 [===================>..........] - ETA: 0s - loss: 0.6121 - accuracy: 0.6683
Epoch 6: val_loss improved from 0.63905 to 0.62025, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.6125 - accuracy: 0.6648 - val_loss: 0.6202 - val_accuracy: 0.6656
Epoch 7/20
1/26 [>.............................] - ETA: 0s - loss: 0.5801 - accuracy: 0.7300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.5870 - accuracy: 0.6930␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
19/26 [====================>.........] - ETA: 0s - loss: 0.5832 - accuracy: 0.6995
Epoch 7: val_loss improved from 0.62025 to 0.61529, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.5858 - accuracy: 0.6980 - val_loss: 0.6153 - val_accuracy: 0.6406
Epoch 8/20
1/26 [>.............................] - ETA: 0s - loss: 0.5015 - accuracy: 0.7300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
9/26 [=========>....................] - ETA: 0s - loss: 0.5797 - accuracy: 0.6911␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
17/26 [==================>...........] - ETA: 0s - loss: 0.5749 - accuracy: 0.7012␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - ETA: 0s - loss: 0.5702 - accuracy: 0.7047
Epoch 8: val_loss improved from 0.61529 to 0.58292, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.5702 - accuracy: 0.7047 - val_loss: 0.5829 - val_accuracy: 0.7000
Epoch 9/20
1/26 [>.............................] - ETA: 0s - loss: 0.5262 - accuracy: 0.7000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.5485 - accuracy: 0.7180␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
18/26 [===================>..........] - ETA: 0s - loss: 0.5388 - accuracy: 0.7294␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - ETA: 0s - loss: 0.5372 - accuracy: 0.7309
Epoch 9: val_loss improved from 0.58292 to 0.54673, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.5372 - accuracy: 0.7309 - val_loss: 0.5467 - val_accuracy: 0.7375
Epoch 10/20
1/26 [>.............................] - ETA: 0s - loss: 0.5478 - accuracy: 0.7200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5131 - accuracy: 0.7582␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.5088 - accuracy: 0.7540
Epoch 10: val_loss improved from 0.54673 to 0.52583, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.5097 - accuracy: 0.7480 - val_loss: 0.5258 - val_accuracy: 0.7500
Epoch 11/20
1/26 [>.............................] - ETA: 0s - loss: 0.4824 - accuracy: 0.7200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.4900 - accuracy: 0.7627␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.4841 - accuracy: 0.7690
Epoch 11: val_loss improved from 0.52583 to 0.50905, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4942 - accuracy: 0.7594 - val_loss: 0.5090 - val_accuracy: 0.7609
Epoch 12/20
1/26 [>.............................] - ETA: 0s - loss: 0.4493 - accuracy: 0.7800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.4866 - accuracy: 0.7510␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.4725 - accuracy: 0.7690
Epoch 12: val_loss improved from 0.50905 to 0.48350, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4697 - accuracy: 0.7738 - val_loss: 0.4835 - val_accuracy: 0.7906
Epoch 13/20
1/26 [>.............................] - ETA: 0s - loss: 0.4898 - accuracy: 0.7500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.4677 - accuracy: 0.7582␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.4613 - accuracy: 0.7680
Epoch 13: val_loss improved from 0.48350 to 0.48221, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 9ms/step - loss: 0.4495 - accuracy: 0.7801 - val_loss: 0.4822 - val_accuracy: 0.7906
Epoch 14/20
1/26 [>.............................] - ETA: 0s - loss: 0.3689 - accuracy: 0.8600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.4453 - accuracy: 0.7990␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.4566 - accuracy: 0.7910
Epoch 14: val_loss improved from 0.48221 to 0.47486, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4562 - accuracy: 0.7867 - val_loss: 0.4749 - val_accuracy: 0.7922
Epoch 15/20
1/26 [>.............................] - ETA: 0s - loss: 0.4179 - accuracy: 0.8100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.4133 - accuracy: 0.8150␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.4225 - accuracy: 0.7973
Epoch 15: val_loss improved from 0.47486 to 0.45124, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4258 - accuracy: 0.7992 - val_loss: 0.4512 - val_accuracy: 0.7922
Epoch 16/20
1/26 [>.............................] - ETA: 0s - loss: 0.4175 - accuracy: 0.7900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.4055 - accuracy: 0.8055␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.4176 - accuracy: 0.7995
Epoch 16: val_loss improved from 0.45124 to 0.44947, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4215 - accuracy: 0.7996 - val_loss: 0.4495 - val_accuracy: 0.8047
Epoch 17/20
1/26 [>.............................] - ETA: 0s - loss: 0.4596 - accuracy: 0.8300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3961 - accuracy: 0.8283␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.4167 - accuracy: 0.8082
Epoch 17: val_loss improved from 0.44947 to 0.43981, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4200 - accuracy: 0.8074 - val_loss: 0.4398 - val_accuracy: 0.7953
Epoch 18/20
1/26 [>.............................] - ETA: 0s - loss: 0.3658 - accuracy: 0.8300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.4273 - accuracy: 0.8045␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.4145 - accuracy: 0.8080
Epoch 18: val_loss improved from 0.43981 to 0.43832, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4121 - accuracy: 0.8113 - val_loss: 0.4383 - val_accuracy: 0.8094
Epoch 19/20
1/26 [>.............................] - ETA: 0s - loss: 0.3486 - accuracy: 0.8200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
9/26 [=========>....................] - ETA: 0s - loss: 0.3886 - accuracy: 0.8178␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
17/26 [==================>...........] - ETA: 0s - loss: 0.4146 - accuracy: 0.8047␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
25/26 [===========================>..] - ETA: 0s - loss: 0.4050 - accuracy: 0.8104
Epoch 19: val_loss did not improve from 0.43832
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4032 - accuracy: 0.8121 - val_loss: 0.4421 - val_accuracy: 0.7984
Epoch 20/20
1/26 [>.............................] - ETA: 0s - loss: 0.4000 - accuracy: 0.7800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.4009 - accuracy: 0.8064␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.3917 - accuracy: 0.8164
Epoch 20: val_loss did not improve from 0.43832
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3920 - accuracy: 0.8160 - val_loss: 0.4400 - val_accuracy: 0.7969
: 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: 7.19 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: 0.286 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: 1.67 sec
BDTG : [dataset] : Evaluation of BDTG on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.0187 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 : 2.192e-02
: 2 : vars_time7 : 2.038e-02
: 3 : vars_time8 : 1.908e-02
: 4 : vars_time7 : 1.901e-02
: 5 : vars_time9 : 1.895e-02
: 6 : vars_time6 : 1.821e-02
: 7 : vars_time6 : 1.818e-02
: 8 : vars_time9 : 1.786e-02
: 9 : vars_time8 : 1.679e-02
: 10 : vars_time8 : 1.634e-02
: 11 : vars_time7 : 1.631e-02
: 12 : vars_time8 : 1.571e-02
: 13 : vars_time9 : 1.558e-02
: 14 : vars_time0 : 1.500e-02
: 15 : vars_time0 : 1.470e-02
: 16 : vars_time8 : 1.435e-02
: 17 : vars_time1 : 1.389e-02
: 18 : vars_time7 : 1.381e-02
: 19 : vars_time5 : 1.381e-02
: 20 : vars_time9 : 1.315e-02
: 21 : vars_time9 : 1.286e-02
: 22 : vars_time4 : 1.228e-02
: 23 : vars_time5 : 1.223e-02
: 24 : vars_time5 : 1.151e-02
: 25 : vars_time6 : 1.137e-02
: 26 : vars_time8 : 1.122e-02
: 27 : vars_time8 : 1.098e-02
: 28 : vars_time8 : 1.076e-02
: 29 : vars_time9 : 1.073e-02
: 30 : vars_time6 : 1.058e-02
: 31 : vars_time7 : 1.030e-02
: 32 : vars_time0 : 1.015e-02
: 33 : vars_time0 : 9.995e-03
: 34 : vars_time4 : 9.881e-03
: 35 : vars_time9 : 9.708e-03
: 36 : vars_time9 : 9.664e-03
: 37 : vars_time6 : 9.345e-03
: 38 : vars_time6 : 9.322e-03
: 39 : vars_time8 : 9.189e-03
: 40 : vars_time9 : 9.172e-03
: 41 : vars_time6 : 9.143e-03
: 42 : vars_time1 : 8.999e-03
: 43 : vars_time6 : 8.916e-03
: 44 : vars_time9 : 8.730e-03
: 45 : vars_time6 : 8.129e-03
: 46 : vars_time4 : 8.043e-03
: 47 : vars_time1 : 7.960e-03
: 48 : vars_time7 : 7.941e-03
: 49 : vars_time6 : 7.755e-03
: 50 : vars_time7 : 7.687e-03
: 51 : vars_time6 : 7.632e-03
: 52 : vars_time8 : 7.623e-03
: 53 : vars_time5 : 7.535e-03
: 54 : vars_time1 : 7.518e-03
: 55 : vars_time6 : 7.289e-03
: 56 : vars_time1 : 7.239e-03
: 57 : vars_time7 : 7.198e-03
: 58 : vars_time4 : 7.131e-03
: 59 : vars_time4 : 7.107e-03
: 60 : vars_time0 : 7.073e-03
: 61 : vars_time8 : 6.985e-03
: 62 : vars_time9 : 6.905e-03
: 63 : vars_time1 : 6.899e-03
: 64 : vars_time0 : 6.818e-03
: 65 : vars_time9 : 6.735e-03
: 66 : vars_time7 : 6.720e-03
: 67 : vars_time6 : 6.714e-03
: 68 : vars_time7 : 6.706e-03
: 69 : vars_time3 : 6.667e-03
: 70 : vars_time0 : 6.656e-03
: 71 : vars_time7 : 6.451e-03
: 72 : vars_time6 : 6.422e-03
: 73 : vars_time5 : 6.412e-03
: 74 : vars_time9 : 6.390e-03
: 75 : vars_time8 : 6.275e-03
: 76 : vars_time8 : 6.264e-03
: 77 : vars_time5 : 6.233e-03
: 78 : vars_time1 : 6.219e-03
: 79 : vars_time0 : 6.114e-03
: 80 : vars_time9 : 6.024e-03
: 81 : vars_time1 : 5.917e-03
: 82 : vars_time7 : 5.860e-03
: 83 : vars_time1 : 5.832e-03
: 84 : vars_time0 : 5.792e-03
: 85 : vars_time6 : 5.725e-03
: 86 : vars_time9 : 5.708e-03
: 87 : vars_time8 : 5.706e-03
: 88 : vars_time9 : 5.706e-03
: 89 : vars_time4 : 5.671e-03
: 90 : vars_time5 : 5.543e-03
: 91 : vars_time3 : 5.531e-03
: 92 : vars_time8 : 5.356e-03
: 93 : vars_time9 : 5.295e-03
: 94 : vars_time0 : 5.289e-03
: 95 : vars_time4 : 5.256e-03
: 96 : vars_time9 : 5.248e-03
: 97 : vars_time0 : 5.236e-03
: 98 : vars_time7 : 4.975e-03
: 99 : vars_time5 : 4.928e-03
: 100 : vars_time2 : 4.812e-03
: 101 : vars_time2 : 4.784e-03
: 102 : vars_time0 : 4.589e-03
: 103 : vars_time7 : 4.452e-03
: 104 : vars_time7 : 4.272e-03
: 105 : vars_time8 : 4.233e-03
: 106 : vars_time4 : 4.163e-03
: 107 : vars_time1 : 4.128e-03
: 108 : vars_time1 : 3.937e-03
: 109 : vars_time2 : 3.906e-03
: 110 : vars_time7 : 3.715e-03
: 111 : vars_time9 : 3.549e-03
: 112 : vars_time6 : 3.511e-03
: 113 : vars_time5 : 3.443e-03
: 114 : vars_time4 : 3.391e-03
: 115 : vars_time1 : 3.008e-03
: 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
: 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
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: --------------------------------------------
TH1.Print Name = TrainingHistory_TMVA_LSTM_trainingError, Entries= 0, Total sum= 11.3873
TH1.Print Name = TrainingHistory_TMVA_LSTM_valError, Entries= 0, Total sum= 12.0281
TH1.Print Name = TrainingHistory_TMVA_DNN_trainingError, Entries= 0, Total sum= 10.186
TH1.Print Name = TrainingHistory_TMVA_DNN_valError, Entries= 0, Total sum= 10.2571
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'accuracy', Entries= 0, Total sum= 14.4285
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'loss', Entries= 0, Total sum= 10.5006
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_accuracy', Entries= 0, Total sum= 14.3391
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_loss', Entries= 0, Total sum= 10.8483
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: 0.0517 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: 0.0213 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: 0.238 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: 0.00586 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 : 0.889
: dataset BDTG : 0.851
: dataset TMVA_LSTM : 0.844
: dataset TMVA_DNN : 0.587
: -------------------------------------------------------------------------------------------------------------------
:
: 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 : 0.195 (0.275) 0.698 (0.681) 0.880 (0.888)
: dataset BDTG : 0.150 (0.290) 0.572 (0.676) 0.838 (0.891)
: dataset TMVA_LSTM : 0.195 (0.175) 0.540 (0.590) 0.840 (0.855)
: dataset TMVA_DNN : 0.027 (0.030) 0.170 (0.193) 0.425 (0.426)
: -------------------------------------------------------------------------------------------------------------------
:
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