Execution Time22.05s

Test: TMVA-DNN-MethodDL-Adam-Optimization-Cpu (Passed)
Build: PR-4624-i686-ubuntu18-gcc7-opt (sft-ubuntu-1804-i386-2) on 2019-11-14 21:02:05
Repository revision: dfb00e27dc8e66d7a36bd3789ae6ae978a535876

Test Timing: Passed
Processors1

Show Command Line
Display graphs:

Test output
Testing Method DL with Adam Optimizer for CPU backend: 
Info in <TFile::OpenFromCache>: using local cache copy of http://root.cern.ch/files/tmva_class_example.root [./files/tmva_class_example.root]
create data set info dataset
DataSetInfo              : [dataset] : Added class "Signal"
                         : Add Tree TreeS of type Signal with 6000 events
DataSetInfo              : [dataset] : Added class "Background"
                         : Add Tree TreeB of type Background with 6000 events
Factory                  : Booking method: [NON-XML-CHAR-0x1B][1mDL_ADAM_CPU[NON-XML-CHAR-0x1B][0m
                         : 
                         : Parsing option string: 
                         : ... "!H:V:ErrorStrategy=SUMOFSQUARES:WeightInitialization=XAVIERUNIFORM:InputLayout=1|1|4:BatchLayout=256|1|4:Layout=RESHAPE|1|1|4|FLAT,DENSE|128|TANH,DENSE|128|TANH,DENSE|128|TANH,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-2,Optimizer=ADAM,Momentum=0.9,Repetitions=1,ConvergenceSteps=20,BatchSize=256,TestRepetitions=10,WeightDecay=1e-4,Regularization=L2,DropConfig=0.0+0.5+0.5+0.5,MaxEpochs=100: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=SUMOFSQUARES:WeightInitialization=XAVIERUNIFORM:InputLayout=1|1|4:BatchLayout=256|1|4:Layout=RESHAPE|1|1|4|FLAT,DENSE|128|TANH,DENSE|128|TANH,DENSE|128|TANH,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-2,Optimizer=ADAM,Momentum=0.9,Repetitions=1,ConvergenceSteps=20,BatchSize=256,TestRepetitions=10,WeightDecay=1e-4,Regularization=L2,DropConfig=0.0+0.5+0.5+0.5,MaxEpochs=100:Architecture=CPU"
                         : The following options are set:
                         : - By User:
                         :     V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
                         :     H: "False" [Print method-specific help message]
                         :     InputLayout: "1|1|4" [The Layout of the input]
                         :     BatchLayout: "256|1|4" [The Layout of the batch]
                         :     Layout: "RESHAPE|1|1|4|FLAT,DENSE|128|TANH,DENSE|128|TANH,DENSE|128|TANH,DENSE|1|LINEAR" [Layout of the network.]
                         :     ErrorStrategy: "SUMOFSQUARES" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
                         :     WeightInitialization: "XAVIERUNIFORM" [Weight initialization strategy]
                         :     Architecture: "CPU" [Which architecture to perform the training on.]
                         :     TrainingStrategy: "LearningRate=1e-2,Optimizer=ADAM,Momentum=0.9,Repetitions=1,ConvergenceSteps=20,BatchSize=256,TestRepetitions=10,WeightDecay=1e-4,Regularization=L2,DropConfig=0.0+0.5+0.5+0.5,MaxEpochs=100" [Defines the training strategies.]
                         : - Default:
                         :     VerbosityLevel: "Default" [Verbosity level]
                         :     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)"]
                         :     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)]
                         :     RandomSeed: "0" [Random seed used for weight initialization and batch shuffling]
                         :     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 use now the CPU architecture !
Factory                  : [NON-XML-CHAR-0x1B][1mTrain all methods[NON-XML-CHAR-0x1B][0m
                         : Building event vectors for type 2 Signal
                         : Dataset[dataset] :  create input formulas for tree TreeS
                         : Building event vectors for type 2 Background
                         : Dataset[dataset] :  create input formulas for tree TreeB
DataSetFactory           : [dataset] : Number of events in input trees
                         : 
                         : 
                         : Number of training and testing events
                         : ---------------------------------------------------------------------------
                         : Signal     -- training events            : 1000
                         : Signal     -- testing events             : 5000
                         : Signal     -- training and testing events: 6000
                         : Background -- training events            : 1000
                         : Background -- testing events             : 5000
                         : Background -- training and testing events: 6000
                         : 
DataSetInfo              : Correlation matrix (Signal):
                         : ----------------------------------------
                         :           myvar1  myvar2    var3    var4
                         :  myvar1:  +1.000  +0.038  +0.748  +0.922
                         :  myvar2:  +0.038  +1.000  -0.058  +0.128
                         :    var3:  +0.748  -0.058  +1.000  +0.831
                         :    var4:  +0.922  +0.128  +0.831  +1.000
                         : ----------------------------------------
DataSetInfo              : Correlation matrix (Background):
                         : ----------------------------------------
                         :           myvar1  myvar2    var3    var4
                         :  myvar1:  +1.000  -0.021  +0.783  +0.931
                         :  myvar2:  -0.021  +1.000  -0.162  +0.057
                         :    var3:  +0.783  -0.162  +1.000  +0.841
                         :    var4:  +0.931  +0.057  +0.841  +1.000
                         : ----------------------------------------
DataSetFactory           : [dataset] :  
                         : 
Factory                  : [dataset] : Create Transformation "I" with events from all classes.
                         : 
                         : Transformation, Variable selection : 
                         : Input : variable 'myvar1' <---> Output : variable 'myvar1'
                         : Input : variable 'myvar2' <---> Output : variable 'myvar2'
                         : Input : variable 'var3' <---> Output : variable 'var3'
                         : Input : variable 'var4' <---> Output : variable 'var4'
Factory                  : [dataset] : Create Transformation "D" with events from all classes.
                         : 
                         : Transformation, Variable selection : 
                         : Input : variable 'myvar1' <---> Output : variable 'myvar1'
                         : Input : variable 'myvar2' <---> Output : variable 'myvar2'
                         : Input : variable 'var3' <---> Output : variable 'var3'
                         : Input : variable 'var4' <---> Output : variable 'var4'
Factory                  : [dataset] : Create Transformation "P" with events from all classes.
                         : 
                         : Transformation, Variable selection : 
                         : Input : variable 'myvar1' <---> Output : variable 'myvar1'
                         : Input : variable 'myvar2' <---> Output : variable 'myvar2'
                         : Input : variable 'var3' <---> Output : variable 'var3'
                         : Input : variable 'var4' <---> Output : variable 'var4'
Factory                  : [dataset] : Create Transformation "G" with events from all classes.
                         : 
                         : Transformation, Variable selection : 
                         : Input : variable 'myvar1' <---> Output : variable 'myvar1'
                         : Input : variable 'myvar2' <---> Output : variable 'myvar2'
                         : Input : variable 'var3' <---> Output : variable 'var3'
                         : Input : variable 'var4' <---> Output : variable 'var4'
Factory                  : [dataset] : Create Transformation "D" with events from all classes.
                         : 
                         : Transformation, Variable selection : 
                         : Input : variable 'myvar1' <---> Output : variable 'myvar1'
                         : Input : variable 'myvar2' <---> Output : variable 'myvar2'
                         : Input : variable 'var3' <---> Output : variable 'var3'
                         : Input : variable 'var4' <---> Output : variable 'var4'
TFHandler_Factory        : Variable        Mean        RMS   [        Min        Max ]
                         : -----------------------------------------------------------
                         :   myvar1:  -0.062775     1.7187   [    -9.3380     7.6931 ]
                         :   myvar2:   0.056495     1.0784   [    -3.2551     4.0291 ]
                         :     var3:  -0.020366     1.0633   [    -5.2777     4.6430 ]
                         :     var4:    0.13214     1.2464   [    -5.6007     4.6744 ]
                         : -----------------------------------------------------------
                         : Preparing the Decorrelation transformation...
TFHandler_Factory        : Variable        Mean        RMS   [        Min        Max ]
                         : -----------------------------------------------------------
                         :   myvar1:   -0.17586     1.0000   [    -5.6401     4.8529 ]
                         :   myvar2:   0.026952     1.0000   [    -2.9292     3.7065 ]
                         :     var3:   -0.11549     1.0000   [    -4.1792     3.5180 ]
                         :     var4:    0.34819     1.0000   [    -3.3363     3.3963 ]
                         : -----------------------------------------------------------
                         : Preparing the Principle Component (PCA) transformation...
TFHandler_Factory        : Variable        Mean        RMS   [        Min        Max ]
                         : -----------------------------------------------------------
                         :   myvar1:   -0.11433     2.2714   [    -11.272     9.0916 ]
                         :   myvar2: -0.0070834     1.0934   [    -3.9875     3.3836 ]
                         :     var3:   0.011107    0.57824   [    -2.0171     2.1958 ]
                         :     var4: -0.0094450    0.33437   [    -1.0176     1.0617 ]
                         : -----------------------------------------------------------
                         : Preparing the Gaussian transformation...
                         : Preparing the Decorrelation transformation...
TFHandler_Factory        : Variable        Mean        RMS   [        Min        Max ]
                         : -----------------------------------------------------------
                         :   myvar1:  -0.054412     1.0000   [    -3.0924     8.1350 ]
                         :   myvar2: -0.0021417     1.0000   [    -4.5913     5.6461 ]
                         :     var3: -0.0051998     1.0000   [    -3.1457     4.6043 ]
                         :     var4:   0.074624     1.0000   [    -3.4587     5.9397 ]
                         : -----------------------------------------------------------
                         : Ranking input variables (method unspecific)...
IdTransformation         : Ranking result (top variable is best ranked)
                         : -------------------------------------
                         : Rank : Variable     : Separation
                         : -------------------------------------
                         :    1 : Variable 4   : 2.843e-01
                         :    2 : Variable 3   : 1.756e-01
                         :    3 : myvar1       : 1.018e-01
                         :    4 : Expression 2 : 3.860e-02
                         : -------------------------------------
Factory                  : Train method: DL_ADAM_CPU for Classification
                         : 
                         : Start of deep neural network training on CPU using (for ROOT-IMT) nthreads = 1
                         : 
                         : *****   Deep Learning Network *****
DEEP NEURAL NETWORK:   Depth = 5  Input = ( 1, 1, 4 )  Batch size = 256  Loss function = R
	Layer 0	 RESHAPE Layer 	 Input = ( 1 , 1 , 4 ) 	Output = ( 1 , 256 , 4 ) 
	Layer 1	 DENSE Layer: 	 ( Input =     4 , Width =   128 ) 	Output = (  1 ,   256 ,   128 ) 	 Activation Function = Tanh	 Dropout prob. = 0.5
	Layer 2	 DENSE Layer: 	 ( Input =   128 , Width =   128 ) 	Output = (  1 ,   256 ,   128 ) 	 Activation Function = Tanh	 Dropout prob. = 0.5
	Layer 3	 DENSE Layer: 	 ( Input =   128 , Width =   128 ) 	Output = (  1 ,   256 ,   128 ) 	 Activation Function = Tanh	 Dropout prob. = 0.5
	Layer 4	 DENSE Layer: 	 ( Input =   128 , Width =     1 ) 	Output = (  1 ,   256 ,     1 ) 	 Activation Function = Identity
                         : Using 1600 events for training and 400 for testing
                         : Training phase 1 of 1:  Optimizer ADAM Learning rate = 0.01 regularization 2 minimum error = 0.620555
                         : --------------------------------------------------------------
                         :      Epoch |   Train Err.   Val. Err.  t(s)/epoch   t(s)/Loss   nEvents/s Conv. Steps
                         : --------------------------------------------------------------
                         :         10 Minimum Test error found - save the configuration 
                         :         10 |     0.221857    0.267231    0.195849   0.0703078     8134.79           0
                         :         20 Minimum Test error found - save the configuration 
                         :         20 |     0.215945    0.258719    0.201746   0.0696757     7885.89           0
                         :         30 |     0.218108    0.259695    0.211275   0.0695135     7517.48          10
                         :         40 |     0.216581    0.262143    0.193551   0.0664349     8217.96          20
                         :         50 Minimum Test error found - save the configuration 
                         :         50 |     0.207981    0.247656    0.195599    0.069131     8140.52           0
                         :         60 |     0.210303    0.257313    0.198027   0.0679164     8031.97          10
                         :         70 Minimum Test error found - save the configuration 
                         :         70 |      0.20463    0.246084    0.197657   0.0696663     8054.93           0
                         :         80 Minimum Test error found - save the configuration 
                         :         80 |     0.204174    0.239524    0.196224   0.0689596     8112.92           0
                         :         90 Minimum Test error found - save the configuration 
                         :         90 |     0.188437    0.211067    0.200059   0.0717422     7963.31           0
                         :        100 Minimum Test error found - save the configuration 
                         :        100 |     0.174174    0.201368    0.198518   0.0705274     8022.33           0
                         : 
                         : Elapsed time for training with 2000 events: 19.9 sec         
                         : Evaluate deep neural network on CPU using batches with size = 256
                         : 
DL_ADAM_CPU              : [dataset] : Evaluation of DL_ADAM_CPU on training sample (2000 events)
                         : Elapsed time for evaluation of 2000 events: 0.102 sec       
                         : Creating xml weight file: [NON-XML-CHAR-0x1B][0;36mdataset/weights/TMVAClassification_DL_ADAM_CPU.weights.xml[NON-XML-CHAR-0x1B][0m
                         : Creating standalone class: [NON-XML-CHAR-0x1B][0;36mdataset/weights/TMVAClassification_DL_ADAM_CPU.class.C[NON-XML-CHAR-0x1B][0m
Factory                  : Training finished
                         : 
                         : Ranking input variables (method specific)...
                         : No variable ranking supplied by classifier: DL_ADAM_CPU
Factory                  : === Destroy and recreate all methods via weight files for testing ===
                         : 
                         : Reading weight file: [NON-XML-CHAR-0x1B][0;36mdataset/weights/TMVAClassification_DL_ADAM_CPU.weights.xml[NON-XML-CHAR-0x1B][0m
==> Wrote root file: TMVA_DNN_ADAM_CPU.root
==> TMVAClassification is done!