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TMVA_RNN_Classification.C File Reference
Tutorials » TMVA tutorials

Detailed Description

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TMVA Classification Example Using a Recurrent Neural Network

This is an example of using a RNN in TMVA. We do classification using a toy time dependent data set that is generated when running this example macro

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 = 0.719114
: --------------------------------------------------------------
: 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 | 0.702189 0.708581 0.332165 0.0241588 8116.72 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.691782 0.690943 0.363487 0.0245512 7376.03 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.690623 0.686006 0.367701 0.0239523 7272.75 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.68855 0.684817 0.361207 0.0238085 7409.64 0
: 5 | 0.688793 0.685543 0.354298 0.0235644 7558.94 1
: 6 Minimum Test error found - save the configuration
: 6 | 0.683405 0.677033 0.376137 0.0234938 7089.31 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.673865 0.652272 0.346352 0.0235586 7744.9 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.622217 0.607017 0.346484 0.0232193 7733.6 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.592548 0.568648 0.343863 0.0235235 7804.22 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.55959 0.552087 0.348143 0.023388 7698.11 0
: 11 Minimum Test error found - save the configuration
: 11 | 0.549633 0.537144 0.342434 0.0232282 7831.95 0
: 12 Minimum Test error found - save the configuration
: 12 | 0.549283 0.527369 0.336842 0.0228626 7962.31 0
: 13 Minimum Test error found - save the configuration
: 13 | 0.521819 0.516917 0.326376 0.022423 8224.96 0
: 14 Minimum Test error found - save the configuration
: 14 | 0.507248 0.508653 0.290841 0.0231713 9339.86 0
: 15 Minimum Test error found - save the configuration
: 15 | 0.497391 0.491631 0.280534 0.0219416 9667.72 0
: 16 Minimum Test error found - save the configuration
: 16 | 0.489098 0.480656 0.283516 0.0221453 9564.97 0
: 17 Minimum Test error found - save the configuration
: 17 | 0.483679 0.471965 0.284919 0.0222035 9515.99 0
: 18 | 0.468133 0.477116 0.283124 0.0218254 9567.61 1
: 19 Minimum Test error found - save the configuration
: 19 | 0.461435 0.46042 0.28264 0.0223416 9604.37 0
: 20 | 0.456852 0.462187 0.284773 0.0221328 9518.73 1
:
: Elapsed time for training with 3200 events: 6.57 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: 0.117 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 = 0.834491
: --------------------------------------------------------------
: 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 | 0.761916 0.726175 0.0530194 0.00621378 54694.3 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.690589 0.712397 0.0506315 0.00580382 57107.5 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.68073 0.668438 0.0503714 0.0057461 57366.6 0
: 4 | 0.673892 0.670162 0.049112 0.00539374 58556.8 1
: 5 | 0.663523 0.690303 0.0490627 0.00538583 58612.3 2
: 6 Minimum Test error found - save the configuration
: 6 | 0.657219 0.665319 0.0494109 0.00569937 58565.8 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.649965 0.652613 0.049543 0.0056926 58380.3 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.641614 0.651642 0.0490257 0.00564863 59017.4 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.640332 0.641095 0.04923 0.00563335 58720.2 0
: 10 | 0.645908 0.662234 0.0492023 0.00543608 58492.6 1
: 11 Minimum Test error found - save the configuration
: 11 | 0.646441 0.640022 0.0490728 0.00568114 58997.5 0
: 12 | 0.644968 0.659915 0.0485607 0.00538312 59290.1 1
: 13 | 0.645267 0.644955 0.0483577 0.0053761 59560.4 2
: 14 Minimum Test error found - save the configuration
: 14 | 0.646807 0.631749 0.0486666 0.00564209 59501 0
: 15 | 0.637992 0.674223 0.0483303 0.00534066 59549.2 1
: 16 | 0.65371 0.663103 0.0477236 0.00525078 60273.9 2
: 17 | 0.645885 0.637939 0.047242 0.00521504 60913.3 3
: 18 | 0.649942 0.643171 0.0470232 0.00520334 61214.9 4
: 19 Minimum Test error found - save the configuration
: 19 | 0.650728 0.629074 0.0474563 0.00558937 61146.2 0
: 20 | 0.631408 0.642376 0.0516876 0.00923005 60295.5 1
:
: Elapsed time for training with 3200 events: 1.01 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: 0.0365 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: 38s - loss: 0.7459 - accuracy: 0.5000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.7172 - accuracy: 0.5091 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.7063 - accuracy: 0.5218
Epoch 1: val_loss improved from inf to 0.67549, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 2s 35ms/step - loss: 0.7021 - accuracy: 0.5348 - val_loss: 0.6755 - val_accuracy: 0.5797
Epoch 2/20
1/26 [>.............................] - ETA: 0s - loss: 0.6610 - accuracy: 0.6300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/26 [==========>...................] - ETA: 0s - loss: 0.6510 - accuracy: 0.6480␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
20/26 [======================>.......] - ETA: 0s - loss: 0.6467 - accuracy: 0.6445
Epoch 2: val_loss improved from 0.67549 to 0.62130, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.6401 - accuracy: 0.6543 - val_loss: 0.6213 - val_accuracy: 0.6750
Epoch 3/20
1/26 [>.............................] - ETA: 0s - loss: 0.5887 - accuracy: 0.6900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5872 - accuracy: 0.6973␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
21/26 [=======================>......] - ETA: 0s - loss: 0.5747 - accuracy: 0.7005
Epoch 3: val_loss improved from 0.62130 to 0.56693, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5706 - accuracy: 0.7070 - val_loss: 0.5669 - val_accuracy: 0.7172
Epoch 4/20
1/26 [>.............................] - ETA: 0s - loss: 0.5491 - accuracy: 0.7200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.5302 - accuracy: 0.7527␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.5287 - accuracy: 0.7441
Epoch 4: val_loss improved from 0.56693 to 0.52587, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.5233 - accuracy: 0.7457 - val_loss: 0.5259 - val_accuracy: 0.7391
Epoch 5/20
1/26 [>.............................] - ETA: 0s - loss: 0.4868 - accuracy: 0.7700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.5034 - accuracy: 0.7517␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.4890 - accuracy: 0.7661
Epoch 5: val_loss improved from 0.52587 to 0.48946, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.4891 - accuracy: 0.7660 - val_loss: 0.4895 - val_accuracy: 0.7797
Epoch 6/20
1/26 [>.............................] - ETA: 0s - loss: 0.4515 - accuracy: 0.8300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.4584 - accuracy: 0.7917␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.4603 - accuracy: 0.7883
Epoch 6: val_loss improved from 0.48946 to 0.48283, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.4633 - accuracy: 0.7848 - val_loss: 0.4828 - val_accuracy: 0.7594
Epoch 7/20
1/26 [>.............................] - ETA: 0s - loss: 0.4391 - accuracy: 0.7800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.4271 - accuracy: 0.8075␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.4381 - accuracy: 0.8000
Epoch 7: val_loss improved from 0.48283 to 0.45073, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.4379 - accuracy: 0.8012 - val_loss: 0.4507 - val_accuracy: 0.7969
Epoch 8/20
1/26 [>.............................] - ETA: 0s - loss: 0.4052 - accuracy: 0.7900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/26 [===========>..................] - ETA: 0s - loss: 0.4119 - accuracy: 0.8191␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.4278 - accuracy: 0.8127
Epoch 8: val_loss improved from 0.45073 to 0.45044, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4225 - accuracy: 0.8152 - val_loss: 0.4504 - val_accuracy: 0.7797
Epoch 9/20
1/26 [>.............................] - ETA: 0s - loss: 0.4706 - accuracy: 0.7500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.4179 - accuracy: 0.8117␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.4061 - accuracy: 0.8174
Epoch 9: val_loss improved from 0.45044 to 0.44390, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4097 - accuracy: 0.8160 - val_loss: 0.4439 - val_accuracy: 0.7891
Epoch 10/20
1/26 [>.............................] - ETA: 0s - loss: 0.3748 - accuracy: 0.8600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.4022 - accuracy: 0.8242␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/26 [========================>.....] - ETA: 0s - loss: 0.4057 - accuracy: 0.8164
Epoch 10: val_loss improved from 0.44390 to 0.42977, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 8ms/step - loss: 0.4052 - accuracy: 0.8176 - val_loss: 0.4298 - val_accuracy: 0.7984
Epoch 11/20
1/26 [>.............................] - ETA: 0s - loss: 0.3962 - accuracy: 0.8500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.4011 - accuracy: 0.8192␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3945 - accuracy: 0.8291
Epoch 11: val_loss improved from 0.42977 to 0.42471, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3931 - accuracy: 0.8293 - val_loss: 0.4247 - val_accuracy: 0.7984
Epoch 12/20
1/26 [>.............................] - ETA: 0s - loss: 0.3373 - accuracy: 0.8500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3724 - accuracy: 0.8425␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3822 - accuracy: 0.8352
Epoch 12: val_loss improved from 0.42471 to 0.42203, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3833 - accuracy: 0.8340 - val_loss: 0.4220 - val_accuracy: 0.8125
Epoch 13/20
1/26 [>.............................] - ETA: 0s - loss: 0.3483 - accuracy: 0.8600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3759 - accuracy: 0.8342␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3754 - accuracy: 0.8370
Epoch 13: val_loss improved from 0.42203 to 0.41117, saving model to trained_model_LSTM.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3788 - accuracy: 0.8348 - val_loss: 0.4112 - val_accuracy: 0.8094
Epoch 14/20
1/26 [>.............................] - ETA: 0s - loss: 0.3339 - accuracy: 0.8700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3684 - accuracy: 0.8458␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3731 - accuracy: 0.8383
Epoch 14: val_loss did not improve from 0.41117
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3733 - accuracy: 0.8406 - val_loss: 0.4325 - val_accuracy: 0.8078
Epoch 15/20
1/26 [>.............................] - ETA: 0s - loss: 0.4070 - accuracy: 0.8300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3675 - accuracy: 0.8450␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3646 - accuracy: 0.8383
Epoch 15: val_loss did not improve from 0.41117
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3674 - accuracy: 0.8367 - val_loss: 0.4338 - val_accuracy: 0.8031
Epoch 16/20
1/26 [>.............................] - ETA: 0s - loss: 0.3147 - accuracy: 0.8900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3682 - accuracy: 0.8458␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3616 - accuracy: 0.8470
Epoch 16: val_loss did not improve from 0.41117
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 7ms/step - loss: 0.3643 - accuracy: 0.8434 - val_loss: 0.4197 - val_accuracy: 0.8094
Epoch 17/20
1/26 [>.............................] - ETA: 0s - loss: 0.3726 - accuracy: 0.8300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3627 - accuracy: 0.8400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3572 - accuracy: 0.8413
Epoch 17: val_loss did not improve from 0.41117
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 6ms/step - loss: 0.3589 - accuracy: 0.8414 - val_loss: 0.4467 - val_accuracy: 0.7937
Epoch 18/20
1/26 [>.............................] - ETA: 0s - loss: 0.2923 - accuracy: 0.8900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3492 - accuracy: 0.8483␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3550 - accuracy: 0.8457
Epoch 18: val_loss did not improve from 0.41117
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 6ms/step - loss: 0.3544 - accuracy: 0.8469 - val_loss: 0.4243 - val_accuracy: 0.8156
Epoch 19/20
1/26 [>.............................] - ETA: 0s - loss: 0.2650 - accuracy: 0.9100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3319 - accuracy: 0.8708␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3442 - accuracy: 0.8565
Epoch 19: val_loss did not improve from 0.41117
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 6ms/step - loss: 0.3488 - accuracy: 0.8520 - val_loss: 0.4246 - val_accuracy: 0.8125
Epoch 20/20
1/26 [>.............................] - ETA: 0s - loss: 0.3434 - accuracy: 0.8500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/26 [============>.................] - ETA: 0s - loss: 0.3518 - accuracy: 0.8533␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
23/26 [=========================>....] - ETA: 0s - loss: 0.3450 - accuracy: 0.8578
Epoch 20: val_loss did not improve from 0.41117
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/26 [==============================] - 0s 6ms/step - loss: 0.3441 - accuracy: 0.8578 - val_loss: 0.4242 - val_accuracy: 0.8141
: Getting training history for item:0 name = 'loss'
: Getting training history for item:1 name = 'accuracy'
: Getting training history for item:2 name = 'val_loss'
: Getting training history for item:3 name = 'val_accuracy'
: Elapsed time for training with 3200 events: 6.07 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: 0.265 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: 1.46 sec
BDTG : [dataset] : Evaluation of BDTG on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.0184 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 : 2.312e-02
: 2 : vars_time7 : 2.147e-02
: 3 : vars_time7 : 2.016e-02
: 4 : vars_time9 : 1.940e-02
: 5 : vars_time9 : 1.895e-02
: 6 : vars_time6 : 1.762e-02
: 7 : vars_time8 : 1.747e-02
: 8 : vars_time8 : 1.625e-02
: 9 : vars_time8 : 1.596e-02
: 10 : vars_time9 : 1.557e-02
: 11 : vars_time7 : 1.523e-02
: 12 : vars_time9 : 1.505e-02
: 13 : vars_time7 : 1.490e-02
: 14 : vars_time9 : 1.473e-02
: 15 : vars_time9 : 1.461e-02
: 16 : vars_time8 : 1.460e-02
: 17 : vars_time6 : 1.447e-02
: 18 : vars_time9 : 1.424e-02
: 19 : vars_time8 : 1.399e-02
: 20 : vars_time8 : 1.395e-02
: 21 : vars_time7 : 1.356e-02
: 22 : vars_time5 : 1.254e-02
: 23 : vars_time9 : 1.242e-02
: 24 : vars_time5 : 1.216e-02
: 25 : vars_time0 : 1.214e-02
: 26 : vars_time7 : 1.191e-02
: 27 : vars_time6 : 1.143e-02
: 28 : vars_time1 : 1.132e-02
: 29 : vars_time7 : 1.105e-02
: 30 : vars_time6 : 1.103e-02
: 31 : vars_time0 : 1.073e-02
: 32 : vars_time7 : 1.062e-02
: 33 : vars_time7 : 1.056e-02
: 34 : vars_time8 : 1.012e-02
: 35 : vars_time7 : 1.010e-02
: 36 : vars_time6 : 1.001e-02
: 37 : vars_time9 : 9.906e-03
: 38 : vars_time1 : 9.763e-03
: 39 : vars_time8 : 9.563e-03
: 40 : vars_time0 : 9.464e-03
: 41 : vars_time8 : 9.177e-03
: 42 : vars_time8 : 8.952e-03
: 43 : vars_time1 : 8.920e-03
: 44 : vars_time8 : 8.629e-03
: 45 : vars_time8 : 8.511e-03
: 46 : vars_time7 : 8.155e-03
: 47 : vars_time5 : 8.109e-03
: 48 : vars_time6 : 7.898e-03
: 49 : vars_time5 : 7.894e-03
: 50 : vars_time7 : 7.836e-03
: 51 : vars_time1 : 7.786e-03
: 52 : vars_time8 : 7.769e-03
: 53 : vars_time6 : 7.608e-03
: 54 : vars_time1 : 7.469e-03
: 55 : vars_time0 : 7.441e-03
: 56 : vars_time7 : 7.370e-03
: 57 : vars_time9 : 7.298e-03
: 58 : vars_time9 : 7.262e-03
: 59 : vars_time0 : 7.234e-03
: 60 : vars_time5 : 7.133e-03
: 61 : vars_time2 : 7.124e-03
: 62 : vars_time9 : 7.050e-03
: 63 : vars_time6 : 7.042e-03
: 64 : vars_time4 : 6.902e-03
: 65 : vars_time9 : 6.763e-03
: 66 : vars_time0 : 6.760e-03
: 67 : vars_time6 : 6.631e-03
: 68 : vars_time5 : 6.594e-03
: 69 : vars_time6 : 6.543e-03
: 70 : vars_time7 : 6.446e-03
: 71 : vars_time0 : 6.403e-03
: 72 : vars_time4 : 6.390e-03
: 73 : vars_time8 : 6.366e-03
: 74 : vars_time7 : 6.220e-03
: 75 : vars_time9 : 6.191e-03
: 76 : vars_time4 : 6.174e-03
: 77 : vars_time0 : 6.043e-03
: 78 : vars_time5 : 5.917e-03
: 79 : vars_time1 : 5.897e-03
: 80 : vars_time8 : 5.790e-03
: 81 : vars_time4 : 5.776e-03
: 82 : vars_time7 : 5.731e-03
: 83 : vars_time9 : 5.714e-03
: 84 : vars_time3 : 5.701e-03
: 85 : vars_time5 : 5.647e-03
: 86 : vars_time8 : 5.605e-03
: 87 : vars_time8 : 5.598e-03
: 88 : vars_time3 : 5.571e-03
: 89 : vars_time5 : 5.368e-03
: 90 : vars_time8 : 5.364e-03
: 91 : vars_time7 : 5.318e-03
: 92 : vars_time3 : 5.263e-03
: 93 : vars_time5 : 5.252e-03
: 94 : vars_time0 : 5.108e-03
: 95 : vars_time6 : 5.082e-03
: 96 : vars_time6 : 4.886e-03
: 97 : vars_time1 : 4.884e-03
: 98 : vars_time0 : 4.712e-03
: 99 : vars_time5 : 4.600e-03
: 100 : vars_time7 : 4.367e-03
: 101 : vars_time5 : 4.284e-03
: 102 : vars_time4 : 4.273e-03
: 103 : vars_time5 : 4.269e-03
: 104 : vars_time4 : 4.235e-03
: 105 : vars_time5 : 4.120e-03
: 106 : vars_time8 : 4.100e-03
: 107 : vars_time6 : 4.019e-03
: 108 : vars_time1 : 3.868e-03
: 109 : vars_time9 : 3.832e-03
: 110 : vars_time4 : 3.826e-03
: 111 : vars_time7 : 3.796e-03
: 112 : vars_time5 : 3.604e-03
: 113 : vars_time0 : 3.364e-03
: 114 : vars_time9 : 3.034e-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
: 148 : vars_time1 : 0.000e+00
: 149 : vars_time1 : 0.000e+00
: 150 : vars_time1 : 0.000e+00
: 151 : vars_time1 : 0.000e+00
: 152 : vars_time1 : 0.000e+00
: 153 : vars_time1 : 0.000e+00
: 154 : vars_time1 : 0.000e+00
: 155 : vars_time1 : 0.000e+00
: 156 : vars_time2 : 0.000e+00
: 157 : vars_time2 : 0.000e+00
: 158 : vars_time2 : 0.000e+00
: 159 : vars_time2 : 0.000e+00
: 160 : vars_time2 : 0.000e+00
: 161 : vars_time2 : 0.000e+00
: 162 : vars_time2 : 0.000e+00
: 163 : vars_time2 : 0.000e+00
: 164 : vars_time2 : 0.000e+00
: 165 : vars_time2 : 0.000e+00
: 166 : vars_time2 : 0.000e+00
: 167 : vars_time2 : 0.000e+00
: 168 : vars_time2 : 0.000e+00
: 169 : vars_time2 : 0.000e+00
: 170 : vars_time2 : 0.000e+00
: 171 : vars_time2 : 0.000e+00
: 172 : vars_time2 : 0.000e+00
: 173 : vars_time2 : 0.000e+00
: 174 : vars_time2 : 0.000e+00
: 175 : vars_time2 : 0.000e+00
: 176 : vars_time2 : 0.000e+00
: 177 : vars_time2 : 0.000e+00
: 178 : vars_time2 : 0.000e+00
: 179 : vars_time2 : 0.000e+00
: 180 : vars_time2 : 0.000e+00
: 181 : vars_time2 : 0.000e+00
: 182 : vars_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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: 205 : vars_time3 : 0.000e+00
: 206 : vars_time3 : 0.000e+00
: 207 : vars_time3 : 0.000e+00
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: 209 : vars_time3 : 0.000e+00
: 210 : vars_time3 : 0.000e+00
: 211 : vars_time3 : 0.000e+00
: 212 : vars_time4 : 0.000e+00
: 213 : vars_time4 : 0.000e+00
: 214 : vars_time4 : 0.000e+00
: 215 : vars_time4 : 0.000e+00
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: 225 : vars_time4 : 0.000e+00
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: 230 : vars_time4 : 0.000e+00
: 231 : vars_time4 : 0.000e+00
: 232 : vars_time4 : 0.000e+00
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: 234 : vars_time4 : 0.000e+00
: 235 : vars_time5 : 0.000e+00
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: 250 : vars_time6 : 0.000e+00
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: 259 : vars_time6 : 0.000e+00
: 260 : vars_time6 : 0.000e+00
: 261 : vars_time6 : 0.000e+00
: 262 : vars_time6 : 0.000e+00
: 263 : vars_time6 : 0.000e+00
: 264 : vars_time6 : 0.000e+00
: 265 : vars_time6 : 0.000e+00
: 266 : vars_time6 : 0.000e+00
: 267 : vars_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_time7 : 0.000e+00
: 277 : vars_time7 : 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_time8 : 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= 11.5781
TH1.Print Name = TrainingHistory_TMVA_LSTM_valError, Entries= 0, Total sum= 11.447
TH1.Print Name = TrainingHistory_TMVA_DNN_trainingError, Entries= 0, Total sum= 13.1588
TH1.Print Name = TrainingHistory_TMVA_DNN_valError, Entries= 0, Total sum= 13.2069
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'accuracy', Entries= 0, Total sum= 15.8594
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'loss', Entries= 0, Total sum= 8.73046
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_accuracy', Entries= 0, Total sum= 15.4906
TH1.Print Name = TrainingHistory_PyKeras_LSTM_'val_loss', Entries= 0, Total sum= 9.40035
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: 0.0338 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: 0.0107 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: 0.219 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: 0.00475 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 : 0.866
: dataset BDTG : 0.833
: dataset TMVA_LSTM : 0.820
: dataset TMVA_DNN : 0.661
: -------------------------------------------------------------------------------------------------------------------
:
: 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 : 0.235 (0.292) 0.616 (0.711) 0.848 (0.893)
: dataset BDTG : 0.112 (0.324) 0.490 (0.718) 0.799 (0.899)
: dataset TMVA_LSTM : 0.105 (0.195) 0.452 (0.579) 0.785 (0.833)
: dataset TMVA_DNN : 0.045 (0.050) 0.243 (0.284) 0.542 (0.588)
: -------------------------------------------------------------------------------------------------------------------
:
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
/***
# TMVA Classification Example Using a Recurrent Neural Network
This is an example of using a RNN in TMVA.
We do the classification using a toy data set containing a time series of data sample ntimes
and with dimension ndim that is generated when running the provided function `MakeTimeData (nevents, ntime, ndim)`
**/
#include<TROOT.h>
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/DataSetInfo.h"
#include "TMVA/Config.h"
#include "TMVA/MethodDL.h"
#include "TFile.h"
#include "TTree.h"
/// Helper function to generate the time data set
/// make some time data but not of fixed length.
/// use a poisson with mu = 5 and truncated at 10
///
void MakeTimeData(int n, int ntime, int ndim )
{
// const int ntime = 10;
// const int ndim = 30; // number of dim/time
TString fname = TString::Format("time_data_t%d_d%d.root", ntime, ndim);
std::vector<TH1 *> v1(ntime);
std::vector<TH1 *> v2(ntime);
int i = 0;
for (int i = 0; i < ntime; ++i) {
v1[i] = new TH1D(TString::Format("h1_%d", i), "h1", ndim, 0, 10);
v2[i] = new TH1D(TString::Format("h2_%d", i), "h2", ndim, 0, 10);
}
auto f1 = new TF1("f1", "gaus");
auto f2 = new TF1("f2", "gaus");
TFile f(fname, "RECREATE");
TTree sgn("sgn", "sgn");
TTree bkg("bkg", "bkg");
std::vector<std::vector<float>> x1(ntime);
std::vector<std::vector<float>> x2(ntime);
for (int i = 0; i < ntime; ++i) {
x1[i] = std::vector<float>(ndim);
x2[i] = std::vector<float>(ndim);
}
for (auto i = 0; i < ntime; i++) {
bkg.Branch(Form("vars_time%d", i), "std::vector<float>", &x1[i]);
sgn.Branch(Form("vars_time%d", i), "std::vector<float>", &x2[i]);
}
sgn.SetDirectory(&f);
bkg.SetDirectory(&f);
gRandom->SetSeed(0);
std::vector<double> mean1(ntime);
std::vector<double> mean2(ntime);
std::vector<double> sigma1(ntime);
std::vector<double> sigma2(ntime);
for (int j = 0; j < ntime; ++j) {
mean1[j] = 5. + 0.2 * sin(TMath::Pi() * j / double(ntime));
mean2[j] = 5. + 0.2 * cos(TMath::Pi() * j / double(ntime));
sigma1[j] = 4 + 0.3 * sin(TMath::Pi() * j / double(ntime));
sigma2[j] = 4 + 0.3 * cos(TMath::Pi() * j / double(ntime));
}
for (int i = 0; i < n; ++i) {
if (i % 1000 == 0)
std::cout << "Generating event ... " << i << std::endl;
for (int j = 0; j < ntime; ++j) {
auto h1 = v1[j];
auto h2 = v2[j];
h1->Reset();
h2->Reset();
f1->SetParameters(1, mean1[j], sigma1[j]);
f2->SetParameters(1, mean2[j], sigma2[j]);
h1->FillRandom("f1", 1000);
h2->FillRandom("f2", 1000);
for (int k = 0; k < ndim; ++k) {
// std::cout << j*10+k << " ";
x1[j][k] = h1->GetBinContent(k + 1) + gRandom->Gaus(0, 10);
x2[j][k] = h2->GetBinContent(k + 1) + gRandom->Gaus(0, 10);
}
}
// std::cout << std::endl;
sgn.Fill();
bkg.Fill();
if (n == 1) {
auto c1 = new TCanvas();
c1->Divide(ntime, 2);
for (int j = 0; j < ntime; ++j) {
c1->cd(j + 1);
v1[j]->Draw();
}
for (int j = 0; j < ntime; ++j) {
c1->cd(ntime + j + 1);
v2[j]->Draw();
}
gPad->Update();
}
}
if (n > 1) {
sgn.Write();
bkg.Write();
sgn.Print();
bkg.Print();
f.Close();
}
}
/// macro for performing a classification using a Recurrent Neural Network
/// @param nevts = 2000 Number of events used. (increase for better classification results)
/// @param use_type
/// use_type = 0 use Simple RNN network
/// use_type = 1 use LSTM network
/// use_type = 2 use GRU
/// use_type = 3 build 3 different networks with RNN, LSTM and GRU
void TMVA_RNN_Classification(int nevts = 2000, int use_type = 1)
{
const int ninput = 30;
const int ntime = 10;
const int batchSize = 100;
const int maxepochs = 20;
int nTotEvts = nevts; // total events to be generated for signal or background
bool useKeras = true;
bool useTMVA_RNN = true;
bool useTMVA_DNN = true;
bool useTMVA_BDT = false;
std::vector<std::string> rnn_types = {"RNN", "LSTM", "GRU"};
std::vector<bool> use_rnn_type = {1, 1, 1};
if (use_type >=0 && use_type < 3) {
use_rnn_type = {0,0,0};
use_rnn_type[use_type] = 1;
}
bool useGPU = true; // use GPU for TMVA if available
#ifndef R__HAS_TMVAGPU
useGPU = false;
#ifndef R__HAS_TMVACPU
Warning("TMVA_RNN_Classification", "TMVA is not build with GPU or CPU multi-thread support. Cannot use TMVA Deep Learning for RNN");
useTMVA_RNN = false;
#endif
#endif
TString archString = (useGPU) ? "GPU" : "CPU";
bool writeOutputFile = true;
const char *rnn_type = "RNN";
#ifdef R__HAS_PYMVA
TMVA::PyMethodBase::PyInitialize();
#else
useKeras = false;
#endif
#ifdef R__USE_IMT
int num_threads = 4; // use max 4 threads
// switch off MT in OpenBLAS to avoid conflict with tbb
gSystem->Setenv("OMP_NUM_THREADS", "1");
// do enable MT running
if (num_threads >= 0) {
ROOT::EnableImplicitMT(num_threads);
}
#endif
TMVA::Config::Instance();
std::cout << "Running with nthreads = " << ROOT::GetThreadPoolSize() << std::endl;
TString inputFileName = "time_data_t10_d30.root";
bool fileExist = !gSystem->AccessPathName(inputFileName);
// if file does not exists create it
if (!fileExist) {
MakeTimeData(nTotEvts,ntime, ninput);
}
auto inputFile = TFile::Open(inputFileName);
if (!inputFile) {
Error("TMVA_RNN_Classification", "Error opening input file %s - exit", inputFileName.Data());
return;
}
std::cout << "--- RNNClassification : Using input file: " << inputFile->GetName() << std::endl;
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName(TString::Format("data_RNN_%s.root", archString.Data()));
TFile *outputFile = nullptr;
if (writeOutputFile) outputFile = TFile::Open(outfileName, "RECREATE");
/**
## Declare Factory
Create the Factory class. Later you can choose the methods
whose performance you'd like to investigate.
The factory is the major TMVA object you have to interact with. Here is the list of parameters you need to
pass
- The first argument is the base of the name of all the output
weightfiles in the directory weight/ that will be created with the
method parameters
- The second argument is the output file for the training results
- The third argument is a string option defining some general configuration for the TMVA session.
For example all TMVA output can be suppressed by removing the "!" (not) in front of the "Silent" argument in
the option string
**/
// Creating the factory object
TMVA::Factory *factory = new TMVA::Factory("TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=None:!Correlations:"
"AnalysisType=Classification:ModelPersistence");
TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset");
TTree *signalTree = (TTree *)inputFile->Get("sgn");
TTree *background = (TTree *)inputFile->Get("bkg");
const int nvar = ninput * ntime;
/// add variables - use new AddVariablesArray function
for (auto i = 0; i < ntime; i++) {
dataloader->AddVariablesArray(Form("vars_time%d", i), ninput);
}
dataloader->AddSignalTree(signalTree, 1.0);
dataloader->AddBackgroundTree(background, 1.0);
// check given input
auto &datainfo = dataloader->GetDataSetInfo();
auto vars = datainfo.GetListOfVariables();
std::cout << "number of variables is " << vars.size() << std::endl;
for (auto &v : vars)
std::cout << v << ",";
std::cout << std::endl;
int nTrainSig = 0.8 * nTotEvts;
int nTrainBkg = 0.8 * nTotEvts;
// build the string options for DataLoader::PrepareTrainingAndTestTree
TString prepareOptions = TString::Format("nTrain_Signal=%d:nTrain_Background=%d:SplitMode=Random:SplitSeed=100:NormMode=NumEvents:!V:!CalcCorrelations", nTrainSig, nTrainBkg);
// Apply additional cuts on the signal and background samples (can be different)
TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
TCut mycutb = "";
dataloader->PrepareTrainingAndTestTree(mycuts, mycutb, prepareOptions);
std::cout << "prepared DATA LOADER " << std::endl;
/**
## Book TMVA recurrent models
Book the different types of recurrent models in TMVA (SimpleRNN, LSTM or GRU)
**/
if (useTMVA_RNN) {
for (int i = 0; i < 3; ++i) {
if (!use_rnn_type[i])
continue;
const char *rnn_type = rnn_types[i].c_str();
/// define the inputlayout string for RNN
/// the input data should be organize as following:
//// input layout for RNN: time x ndim
TString inputLayoutString = TString::Format("InputLayout=%d|%d", ntime, ninput);
/// Define RNN layer layout
/// it should be LayerType (RNN or LSTM or GRU) | number of units | number of inputs | time steps | remember output (typically no=0 | return full sequence
TString rnnLayout = TString::Format("%s|10|%d|%d|0|1", rnn_type, ninput, ntime);
/// add after RNN a reshape layer (needed top flatten the output) and a dense layer with 64 units and a last one
/// Note the last layer is linear because when using Crossentropy a Sigmoid is applied already
TString layoutString = TString("Layout=") + rnnLayout + TString(",RESHAPE|FLAT,DENSE|64|TANH,LINEAR");
/// Defining Training strategies. Different training strings can be concatenate. Use however only one
TString trainingString1 = TString::Format("LearningRate=1e-3,Momentum=0.0,Repetitions=1,"
"ConvergenceSteps=5,BatchSize=%d,TestRepetitions=1,"
"WeightDecay=1e-2,Regularization=None,MaxEpochs=%d,"
"Optimizer=ADAM,DropConfig=0.0+0.+0.+0.",
batchSize,maxepochs);
TString trainingStrategyString("TrainingStrategy=");
trainingStrategyString += trainingString1; // + "|" + trainingString2
/// Define the full RNN Noption string adding the final options for all network
TString rnnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:"
"WeightInitialization=XAVIERUNIFORM:ValidationSize=0.2:RandomSeed=1234");
rnnOptions.Append(":");
rnnOptions.Append(inputLayoutString);
rnnOptions.Append(":");
rnnOptions.Append(layoutString);
rnnOptions.Append(":");
rnnOptions.Append(trainingStrategyString);
rnnOptions.Append(":");
rnnOptions.Append(TString::Format("Architecture=%s", archString.Data()));
TString rnnName = "TMVA_" + TString(rnn_type);
factory->BookMethod(dataloader, TMVA::Types::kDL, rnnName, rnnOptions);
}
}
/**
## Book TMVA fully connected dense layer models
**/
if (useTMVA_DNN) {
// Method DL with Dense Layer
TString inputLayoutString = TString::Format("InputLayout=1|1|%d", ntime * ninput);
TString layoutString("Layout=DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR");
// Training strategies.
TString trainingString1("LearningRate=1e-3,Momentum=0.0,Repetitions=1,"
"ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,"
"WeightDecay=1e-4,Regularization=None,MaxEpochs=20"
"DropConfig=0.0+0.+0.+0.,Optimizer=ADAM");
TString trainingStrategyString("TrainingStrategy=");
trainingStrategyString += trainingString1; // + "|" + trainingString2
// General Options.
TString dnnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:"
"WeightInitialization=XAVIER:RandomSeed=0");
dnnOptions.Append(":");
dnnOptions.Append(inputLayoutString);
dnnOptions.Append(":");
dnnOptions.Append(layoutString);
dnnOptions.Append(":");
dnnOptions.Append(trainingStrategyString);
dnnOptions.Append(":");
dnnOptions.Append(archString);
TString dnnName = "TMVA_DNN";
factory->BookMethod(dataloader, TMVA::Types::kDL, dnnName, dnnOptions);
}
/**
## Book Keras recurrent models
Book the different types of recurrent models in Keras (SimpleRNN, LSTM or GRU)
**/
if (useKeras) {
for (int i = 0; i < 3; i++) {
if (use_rnn_type[i]) {
TString modelName = TString::Format("model_%s.h5", rnn_types[i].c_str());
TString trainedModelName = TString::Format("trained_model_%s.h5", rnn_types[i].c_str());
Info("TMVA_RNN_Classification", "Building recurrent keras model using a %s layer", rnn_types[i].c_str());
// create python script which can be executed
// create 2 conv2d layer + maxpool + dense
TMacro m;
m.AddLine("import tensorflow");
m.AddLine("from tensorflow.keras.models import Sequential");
m.AddLine("from tensorflow.keras.optimizers import Adam");
m.AddLine("from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, SimpleRNN, GRU, LSTM, Reshape, "
"BatchNormalization");
m.AddLine("");
m.AddLine("model = Sequential() ");
m.AddLine("model.add(Reshape((10, 30), input_shape = (10*30, )))");
// add recurrent neural network depending on type / Use option to return the full output
if (rnn_types[i] == "LSTM")
m.AddLine("model.add(LSTM(units=10, return_sequences=True) )");
else if (rnn_types[i] == "GRU")
m.AddLine("model.add(GRU(units=10, return_sequences=True) )");
else
m.AddLine("model.add(SimpleRNN(units=10, return_sequences=True) )");
// m.AddLine("model.add(BatchNormalization())");
m.AddLine("model.add(Flatten())"); // needed if returning the full time output sequence
m.AddLine("model.add(Dense(64, activation = 'tanh')) ");
m.AddLine("model.add(Dense(2, activation = 'sigmoid')) ");
m.AddLine(
"model.compile(loss = 'binary_crossentropy', optimizer = Adam(learning_rate = 0.001), weighted_metrics = ['accuracy'])");
m.AddLine(TString::Format("modelName = '%s'", modelName.Data()));
m.AddLine("model.save(modelName)");
m.AddLine("model.summary()");
m.SaveSource("make_rnn_model.py");
// execute python script to make the model
auto ret = (TString *)gROOT->ProcessLine("TMVA::Python_Executable()");
TString python_exe = (ret) ? *(ret) : "python";
gSystem->Exec(python_exe + " make_rnn_model.py");
if (gSystem->AccessPathName(modelName)) {
Warning("TMVA_RNN_Classification", "Error creating Keras recurrent model file - Skip using Keras");
useKeras = false;
} else {
// book PyKeras method only if Keras model could be created
Info("TMVA_RNN_Classification", "Booking Keras %s model", rnn_types[i].c_str());
factory->BookMethod(dataloader, TMVA::Types::kPyKeras,
TString::Format("PyKeras_%s", rnn_types[i].c_str()),
TString::Format("!H:!V:VarTransform=None:FilenameModel=%s:tf.keras:"
"FilenameTrainedModel=%s:GpuOptions=allow_growth=True:"
"NumEpochs=%d:BatchSize=%d",
modelName.Data(), trainedModelName.Data(), maxepochs, batchSize));
}
}
}
}
// use BDT in case not using Keras or TMVA DL
if (!useKeras || !useTMVA_BDT)
useTMVA_BDT = true;
/**
## Book TMVA BDT
**/
if (useTMVA_BDT) {
factory->BookMethod(dataloader, TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=100:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:"
"BaggedSampleFraction=0.5:nCuts=20:"
"MaxDepth=2");
}
/// Train all methods
factory->TrainAllMethods();
std::cout << "nthreads = " << ROOT::GetThreadPoolSize() << std::endl;
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// check method
// plot ROC curve
auto c1 = factory->GetROCCurve(dataloader);
c1->Draw();
if (outputFile) outputFile->Close();
}
f
#define f(i)
Definition RSha256.hxx:104
TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358
Info
void Info(const char *location, const char *msgfmt,...)
Use this function for informational messages.
Definition TError.cxx:218
Error
void Error(const char *location, const char *msgfmt,...)
Use this function in case an error occurred.
Definition TError.cxx:185
Warning
void Warning(const char *location, const char *msgfmt,...)
Use this function in warning situations.
Definition TError.cxx:229
x2
Option_t Option_t TPoint TPoint const char x2
Definition TGWin32VirtualXProxy.cxx:70
x1
Option_t Option_t TPoint TPoint const char x1
Definition TGWin32VirtualXProxy.cxx:70
gROOT
#define gROOT
Definition TROOT.h:406
gRandom
R__EXTERN TRandom * gRandom
Definition TRandom.h:62
Form
char * Form(const char *fmt,...)
Formats a string in a circular formatting buffer.
Definition TString.cxx:2489
gSystem
R__EXTERN TSystem * gSystem
Definition TSystem.h:572
gPad
#define gPad
Definition TVirtualPad.h:308
TCanvas
The Canvas class.
Definition TCanvas.h:23
TCut
A specialized string object used for TTree selections.
Definition TCut.h:25
TF1
1-Dim function class
Definition TF1.h:233
TF1::SetParameters
virtual void SetParameters(const Double_t *params)
Definition TF1.h:677
TFile
A ROOT file is an on-disk file, usually with extension .root, that stores objects in a file-system-li...
Definition TFile.h:53
TFile::Open
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition TFile.cxx:4094
TH1D
1-D histogram with a double per channel (see TH1 documentation)
Definition TH1.h:670
TH1F::Reset
void Reset(Option_t *option="") override
Reset.
Definition TH1.cxx:10331
TH1::FillRandom
virtual void FillRandom(const char *fname, Int_t ntimes=5000, TRandom *rng=nullptr)
Fill histogram following distribution in function fname.
Definition TH1.cxx:3519
TH1::GetBinContent
virtual Double_t GetBinContent(Int_t bin) const
Return content of bin number bin.
Definition TH1.cxx:5082
TMVA::Config::Instance
static Config & Instance()
static function: returns TMVA instance
Definition Config.cxx:98
TMVA::Factory
This is the main MVA steering class.
Definition Factory.h:80
TMVA::Factory::TrainAllMethods
void TrainAllMethods()
Iterates through all booked methods and calls training.
Definition Factory.cxx:1114
TMVA::Factory::BookMethod
MethodBase * BookMethod(DataLoader *loader, TString theMethodName, TString methodTitle, TString theOption="")
Book a classifier or regression method.
Definition Factory.cxx:352
TMVA::Factory::TestAllMethods
void TestAllMethods()
Evaluates all booked methods on the testing data and adds the output to the Results in the corresponi...
Definition Factory.cxx:1271
TMVA::Factory::EvaluateAllMethods
void EvaluateAllMethods(void)
Iterates over all MVAs that have been booked, and calls their evaluation methods.
Definition Factory.cxx:1376
TMVA::Factory::GetROCCurve
TGraph * GetROCCurve(DataLoader *loader, TString theMethodName, Bool_t setTitles=kTRUE, UInt_t iClass=0, Types::ETreeType type=Types::kTesting)
Argument iClass specifies the class to generate the ROC curve in a multiclass setting.
Definition Factory.cxx:912
TMVA::PyMethodBase::PyInitialize
static void PyInitialize()
Initialize Python interpreter.
Definition PyMethodBase.cxx:153
TMacro
Class supporting a collection of lines with C++ code.
Definition TMacro.h:31
TRandom::Gaus
virtual Double_t Gaus(Double_t mean=0, Double_t sigma=1)
Samples a random number from the standard Normal (Gaussian) Distribution with the given mean and sigm...
Definition TRandom.cxx:275
TRandom::SetSeed
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
Definition TRandom.cxx:615
TString
Basic string class.
Definition TString.h:139
TString::Format
static TString Format(const char *fmt,...)
Static method which formats a string using a printf style format descriptor and return a TString.
Definition TString.cxx:2378
TSystem::Exec
virtual Int_t Exec(const char *shellcmd)
Execute a command.
Definition TSystem.cxx:653
TSystem::AccessPathName
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
Definition TSystem.cxx:1296
TSystem::Setenv
virtual void Setenv(const char *name, const char *value)
Set environment variable.
Definition TSystem.cxx:1649
TTree
A TTree represents a columnar dataset.
Definition TTree.h:79
c1
return c1
Definition legend1.C:41
n
const Int_t n
Definition legend1.C:16
h1
TH1F * h1
Definition legend1.C:5
f1
TF1 * f1
Definition legend1.C:11
ROOT::EnableImplicitMT
void EnableImplicitMT(UInt_t numthreads=0)
Enable ROOT's implicit multi-threading for all objects and methods that provide an internal paralleli...
Definition TROOT.cxx:539
ROOT::GetThreadPoolSize
UInt_t GetThreadPoolSize()
Returns the size of ROOT's thread pool.
Definition TROOT.cxx:577
TMVA_RNN_Classification
Definition TMVA_RNN_Classification.py:1
TMVA_SOFIE_GNN_Parser.h2
h2
Definition TMVA_SOFIE_GNN_Parser.py:188
TMath::Pi
constexpr Double_t Pi()
Definition TMath.h:37
v2
@ v2
Definition rootcling_impl.cxx:3702
v
@ v
Definition rootcling_impl.cxx:3699
v1
@ v1
Definition rootcling_impl.cxx:3701
m
TMarker m
Definition textangle.C:8
Author
Lorenzo Moneta

Definition in file TMVA_RNN_Classification.C.