TMVA_CNN_Classification.py File Reference

Detailed Description

TMVA Classification Example Using a Convolutional Neural Network

This is an example of using a CNN in TMVA. We do classification using a toy image data set that is generated when running the example macro

Running with nthreads  = 4
DataSetInfo              : [dataset] : Added class "Signal"
                         : Add Tree sig_tree of type Signal with 1000 events
DataSetInfo              : [dataset] : Added class "Background"
                         : Add Tree bkg_tree of type Background with 1000 events
Factory                  : Booking method: ␛[1mBDT␛[0m
                         : 
                         : Rebuilding Dataset dataset
                         : Building event vectors for type 2 Signal
                         : Dataset[dataset] :  create input formulas for tree sig_tree
                         : Using variable vars[0] from array expression vars of size 256
                         : Building event vectors for type 2 Background
                         : Dataset[dataset] :  create input formulas for tree bkg_tree
                         : Using variable vars[0] from array expression vars of size 256
DataSetFactory           : [dataset] : Number of events in input trees
                         : 
                         : 
                         : Number of training and testing events
                         : ---------------------------------------------------------------------------
                         : Signal     -- training events            : 800
                         : Signal     -- testing events             : 200
                         : Signal     -- training and testing events: 1000
                         : Background -- training events            : 800
                         : Background -- testing events             : 200
                         : Background -- training and testing events: 1000
                         : 
Factory                  : Booking method: ␛[1mTMVA_DNN_CPU␛[0m
                         : 
                         : Parsing option string: 
                         : ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:Layout=DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.,MaxEpochs=10: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=XAVIER:Layout=DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.,MaxEpochs=10: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]
                         :     Layout: "DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR" [Layout of the network.]
                         :     ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
                         :     WeightInitialization: "XAVIER" [Weight initialization strategy]
                         :     Architecture: "CPU" [Which architecture to perform the training on.]
                         :     TrainingStrategy: "LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.,MaxEpochs=10" [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)]
                         :     InputLayout: "0|0|0" [The Layout of the input]
                         :     BatchLayout: "0|0|0" [The Layout of the batch]
                         :     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 now use the CPU architecture with BLAS and IMT support !
Factory                  : Booking method: ␛[1mTMVA_CNN_CPU␛[0m
                         : 
                         : Parsing option string: 
                         : ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:InputLayout=1|16|16:Layout=CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0,MaxEpochs=10: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=XAVIER:InputLayout=1|16|16:Layout=CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0,MaxEpochs=10: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: "1|16|16" [The Layout of the input]
                         :     Layout: "CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR" [Layout of the network.]
                         :     ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
                         :     WeightInitialization: "XAVIER" [Weight initialization strategy]
                         :     Architecture: "CPU" [Which architecture to perform the training on.]
                         :     TrainingStrategy: "LearningRate=1e-3,Momentum=0.9,Repetitions=1,ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0,MaxEpochs=10" [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]
                         :     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 now use the CPU architecture with BLAS and IMT support !
Factory                  : ␛[1mTrain all methods␛[0m
Factory                  : Train method: BDT for Classification
                         : 
BDT                      : #events: (reweighted) sig: 800 bkg: 800
                         : #events: (unweighted) sig: 800 bkg: 800
                         : Training 400 Decision Trees ... patience please
                         : Elapsed time for training with 1600 events: 1.28 sec         
BDT                      : [dataset] : Evaluation of BDT on training sample (1600 events)
BDT                      : [dataset] : Evaluation of BDT on training sample (1600 events)
                         : Elapsed time for evaluation of 1600 events: 0.0141 sec       
                         : Elapsed time for evaluation of 1600 events: 0.0143 sec       
                         : Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.weights.xml␛[0m
                         : Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.class.C␛[0m
                         : TMVA_CNN_ClassificationOutput.root:/dataset/Method_BDT/BDT
Factory                  : Training finished
                         : 
Factory                  : Train method: TMVA_DNN_CPU for Classification
                         : 
                         : Start of deep neural network training on CPU using MT,  nthreads = 4
                         : 
                         : *****   Deep Learning Network *****
DEEP NEURAL NETWORK:   Depth = 8  Input = ( 1, 1, 256 )  Batch size = 100  Loss function = C
   Layer 0   DENSE Layer:   ( Input =   256 , Width =   100 )  Output = (  1 ,   100 ,   100 )   Activation Function = Relu
   Layer 1   BATCH NORM Layer:    Input/Output = ( 100 , 100 , 1 )    Norm dim =   100  axis = -1
 
   Layer 2   DENSE Layer:   ( Input =   100 , Width =   100 )  Output = (  1 ,   100 ,   100 )   Activation Function = Relu
   Layer 3   BATCH NORM Layer:    Input/Output = ( 100 , 100 , 1 )    Norm dim =   100  axis = -1
 
   Layer 4   DENSE Layer:   ( Input =   100 , Width =   100 )  Output = (  1 ,   100 ,   100 )   Activation Function = Relu
   Layer 5   BATCH NORM Layer:    Input/Output = ( 100 , 100 , 1 )    Norm dim =   100  axis = -1
 
   Layer 6   DENSE Layer:   ( Input =   100 , Width =   100 )  Output = (  1 ,   100 ,   100 )   Activation Function = Relu
   Layer 7   DENSE Layer:   ( Input =   100 , Width =     1 )  Output = (  1 ,   100 ,     1 )   Activation Function = Identity
                         : Using 1280 events for training and 320 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 = 173.246
                         : --------------------------------------------------------------
                         :      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 |     0.934705    0.933465    0.103862   0.0102902     12824.3           0
                         :          2 Minimum Test error found - save the configuration 
                         :          2 |     0.677803    0.803412    0.103558   0.0101733     12850.1           0
                         :          3 Minimum Test error found - save the configuration 
                         :          3 |     0.571694    0.738735    0.104045   0.0103754       12811           0
                         :          4 Minimum Test error found - save the configuration 
                         :          4 |     0.531039    0.667816    0.104009   0.0101912     12790.7           0
                         :          5 |     0.439783     0.67379    0.103466  0.00983466     12816.2           1
                         :          6 |     0.391407    0.669859     0.10319  0.00984199     12855.2           2
                         :          7 |     0.344927    0.671823    0.107007  0.00983351     12349.1           3
                         :          8 Minimum Test error found - save the configuration 
                         :          8 |     0.291353    0.660555      0.1037   0.0101109     12822.1           0
                         :          9 Minimum Test error found - save the configuration 
                         :          9 |     0.247179    0.651609    0.104037   0.0101859     12786.2           0
                         :         10 |     0.216492     0.67669    0.103658   0.0098743     12795.4           1
                         : 
                         : Elapsed time for training with 1600 events: 1.06 sec         
TMVA_DNN_CPU             : [dataset] : Evaluation of TMVA_DNN_CPU on training sample (1600 events)
                         : Evaluate deep neural network on CPU using batches with size = 100
                         : 
TMVA_DNN_CPU             : [dataset] : Evaluation of TMVA_DNN_CPU on training sample (1600 events)
                         : Elapsed time for evaluation of 1600 events: 0.0513 sec       
                         : Elapsed time for evaluation of 1600 events: 0.0533 sec       
                         : Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.weights.xml␛[0m
                         : Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.class.C␛[0m
Factory                  : Training finished
                         : 
Factory                  : Train method: TMVA_CNN_CPU for Classification
                         : 
                         : Start of deep neural network training on CPU using MT,  nthreads = 4
                         : 
                         : *****   Deep Learning Network *****
DEEP NEURAL NETWORK:   Depth = 7  Input = ( 1, 16, 16 )  Batch size = 100  Loss function = C
   Layer 0   CONV LAYER:   ( W = 16 ,  H = 16 ,  D = 10 )    Filter ( W = 3 ,  H = 3 )    Output = ( 100 , 10 , 10 , 256 )     Activation Function = Relu
   Layer 1   BATCH NORM Layer:    Input/Output = ( 10 , 256 , 100 )   Norm dim =    10  axis = 1
 
   Layer 2   CONV LAYER:   ( W = 16 ,  H = 16 ,  D = 10 )    Filter ( W = 3 ,  H = 3 )    Output = ( 100 , 10 , 10 , 256 )     Activation Function = Relu
   Layer 3   POOL Layer:   ( W = 15 ,  H = 15 ,  D = 10 )    Filter ( W = 2 ,  H = 2 )    Output = ( 100 , 10 , 10 , 225 ) 
   Layer 4   RESHAPE Layer     Input = ( 10 , 15 , 15 )  Output = ( 1 , 100 , 2250 ) 
   Layer 5   DENSE Layer:   ( Input =  2250 , Width =   100 )  Output = (  1 ,   100 ,   100 )   Activation Function = Relu
   Layer 6   DENSE Layer:   ( Input =   100 , Width =     1 )  Output = (  1 ,   100 ,     1 )   Activation Function = Identity
                         : Using 1280 events for training and 320 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 = 110.269
                         : --------------------------------------------------------------
                         :      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 |      2.16326    0.921664    0.800492   0.0647171     1630.93           0
                         :          2 Minimum Test error found - save the configuration 
                         :          2 |     0.883703    0.701432    0.792259   0.0638987     1647.54           0
                         :          3 Minimum Test error found - save the configuration 
                         :          3 |     0.686762    0.687493    0.792095    0.065538     1651.63           0
                         :          4 Minimum Test error found - save the configuration 
                         :          4 |     0.649921    0.675041    0.795459   0.0642615     1641.14           0
                         :          5 Minimum Test error found - save the configuration 
                         :          5 |     0.631668    0.658641    0.799744   0.0637712     1630.49           0
                         :          6 |      0.60877    0.674484    0.791833   0.0624751     1645.28           1
                         :          7 Minimum Test error found - save the configuration 
                         :          7 |      0.59883     0.62676    0.790309   0.0639276     1652.03           0
                         :          8 Minimum Test error found - save the configuration 
                         :          8 |     0.568673    0.605838    0.793006   0.0635105     1644.97           0
                         :          9 Minimum Test error found - save the configuration 
                         :          9 |     0.530585    0.589471    0.787811   0.0637526     1657.33           0
                         :         10 Minimum Test error found - save the configuration 
                         :         10 |     0.508897    0.570803    0.793547   0.0684225     1654.89           0
                         : 
                         : Elapsed time for training with 1600 events: 8.01 sec         
TMVA_CNN_CPU             : [dataset] : Evaluation of TMVA_CNN_CPU on training sample (1600 events)
                         : Evaluate deep neural network on CPU using batches with size = 100
                         : 
TMVA_CNN_CPU             : [dataset] : Evaluation of TMVA_CNN_CPU on training sample (1600 events)
                         : Elapsed time for evaluation of 1600 events: 0.339 sec       
                         : Elapsed time for evaluation of 1600 events: 0.347 sec       
                         : Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.weights.xml␛[0m
                         : Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.class.C␛[0m
Factory                  : Training finished
                         : 
                         : Ranking input variables (method specific)...
BDT                      : Ranking result (top variable is best ranked)
                         : --------------------------------------
                         : Rank : Variable  : Variable Importance
                         : --------------------------------------
                         :    1 : vars      : 9.005e-03
                         :    2 : vars      : 8.721e-03
                         :    3 : vars      : 8.718e-03
                         :    4 : vars      : 8.623e-03
                         :    5 : vars      : 8.512e-03
                         :    6 : vars      : 8.498e-03
                         :    7 : vars      : 8.329e-03
                         :    8 : vars      : 8.171e-03
                         :    9 : vars      : 7.928e-03
                         :   10 : vars      : 7.852e-03
                         :   11 : vars      : 7.625e-03
                         :   12 : vars      : 7.578e-03
                         :   13 : vars      : 7.238e-03
                         :   14 : vars      : 7.164e-03
                         :   15 : vars      : 7.119e-03
                         :   16 : vars      : 7.035e-03
                         :   17 : vars      : 7.023e-03
                         :   18 : vars      : 6.967e-03
                         :   19 : vars      : 6.910e-03
                         :   20 : vars      : 6.856e-03
                         :   21 : vars      : 6.611e-03
                         :   22 : vars      : 6.609e-03
                         :   23 : vars      : 6.600e-03
                         :   24 : vars      : 6.492e-03
                         :   25 : vars      : 6.464e-03
                         :   26 : vars      : 6.432e-03
                         :   27 : vars      : 6.405e-03
                         :   28 : vars      : 6.250e-03
                         :   29 : vars      : 6.227e-03
                         :   30 : vars      : 6.177e-03
                         :   31 : vars      : 6.145e-03
                         :   32 : vars      : 6.140e-03
                         :   33 : vars      : 6.123e-03
                         :   34 : vars      : 6.029e-03
                         :   35 : vars      : 5.990e-03
                         :   36 : vars      : 5.970e-03
                         :   37 : vars      : 5.912e-03
                         :   38 : vars      : 5.892e-03
                         :   39 : vars      : 5.875e-03
                         :   40 : vars      : 5.823e-03
                         :   41 : vars      : 5.715e-03
                         :   42 : vars      : 5.682e-03
                         :   43 : vars      : 5.640e-03
                         :   44 : vars      : 5.628e-03
                         :   45 : vars      : 5.504e-03
                         :   46 : vars      : 5.464e-03
                         :   47 : vars      : 5.430e-03
                         :   48 : vars      : 5.428e-03
                         :   49 : vars      : 5.422e-03
                         :   50 : vars      : 5.413e-03
                         :   51 : vars      : 5.403e-03
                         :   52 : vars      : 5.376e-03
                         :   53 : vars      : 5.369e-03
                         :   54 : vars      : 5.368e-03
                         :   55 : vars      : 5.345e-03
                         :   56 : vars      : 5.237e-03
                         :   57 : vars      : 5.210e-03
                         :   58 : vars      : 5.202e-03
                         :   59 : vars      : 5.151e-03
                         :   60 : vars      : 5.142e-03
                         :   61 : vars      : 5.133e-03
                         :   62 : vars      : 5.119e-03
                         :   63 : vars      : 5.115e-03
                         :   64 : vars      : 5.083e-03
                         :   65 : vars      : 5.072e-03
                         :   66 : vars      : 5.064e-03
                         :   67 : vars      : 5.041e-03
                         :   68 : vars      : 5.034e-03
                         :   69 : vars      : 5.026e-03
                         :   70 : vars      : 5.001e-03
                         :   71 : vars      : 4.943e-03
                         :   72 : vars      : 4.937e-03
                         :   73 : vars      : 4.935e-03
                         :   74 : vars      : 4.909e-03
                         :   75 : vars      : 4.860e-03
                         :   76 : vars      : 4.819e-03
                         :   77 : vars      : 4.816e-03
                         :   78 : vars      : 4.806e-03
                         :   79 : vars      : 4.802e-03
                         :   80 : vars      : 4.791e-03
                         :   81 : vars      : 4.770e-03
                         :   82 : vars      : 4.759e-03
                         :   83 : vars      : 4.672e-03
                         :   84 : vars      : 4.672e-03
                         :   85 : vars      : 4.656e-03
                         :   86 : vars      : 4.645e-03
                         :   87 : vars      : 4.629e-03
                         :   88 : vars      : 4.618e-03
                         :   89 : vars      : 4.574e-03
                         :   90 : vars      : 4.565e-03
                         :   91 : vars      : 4.557e-03
                         :   92 : vars      : 4.526e-03
                         :   93 : vars      : 4.520e-03
                         :   94 : vars      : 4.453e-03
                         :   95 : vars      : 4.428e-03
                         :   96 : vars      : 4.425e-03
                         :   97 : vars      : 4.411e-03
                         :   98 : vars      : 4.396e-03
                         :   99 : vars      : 4.374e-03
                         :  100 : vars      : 4.370e-03
                         :  101 : vars      : 4.302e-03
                         :  102 : vars      : 4.290e-03
                         :  103 : vars      : 4.277e-03
                         :  104 : vars      : 4.255e-03
                         :  105 : vars      : 4.241e-03
                         :  106 : vars      : 4.192e-03
                         :  107 : vars      : 4.187e-03
                         :  108 : vars      : 4.184e-03
                         :  109 : vars      : 4.164e-03
                         :  110 : vars      : 4.164e-03
                         :  111 : vars      : 4.152e-03
                         :  112 : vars      : 4.149e-03
                         :  113 : vars      : 4.148e-03
                         :  114 : vars      : 4.138e-03
                         :  115 : vars      : 4.094e-03
                         :  116 : vars      : 4.092e-03
                         :  117 : vars      : 4.080e-03
                         :  118 : vars      : 4.080e-03
                         :  119 : vars      : 4.048e-03
                         :  120 : vars      : 4.012e-03
                         :  121 : vars      : 4.011e-03
                         :  122 : vars      : 4.004e-03
                         :  123 : vars      : 4.004e-03
                         :  124 : vars      : 3.993e-03
                         :  125 : vars      : 3.958e-03
                         :  126 : vars      : 3.957e-03
                         :  127 : vars      : 3.957e-03
                         :  128 : vars      : 3.933e-03
                         :  129 : vars      : 3.929e-03
                         :  130 : vars      : 3.910e-03
                         :  131 : vars      : 3.904e-03
                         :  132 : vars      : 3.904e-03
                         :  133 : vars      : 3.890e-03
                         :  134 : vars      : 3.875e-03
                         :  135 : vars      : 3.861e-03
                         :  136 : vars      : 3.839e-03
                         :  137 : vars      : 3.826e-03
                         :  138 : vars      : 3.774e-03
                         :  139 : vars      : 3.759e-03
                         :  140 : vars      : 3.755e-03
                         :  141 : vars      : 3.752e-03
                         :  142 : vars      : 3.735e-03
                         :  143 : vars      : 3.718e-03
                         :  144 : vars      : 3.618e-03
                         :  145 : vars      : 3.614e-03
                         :  146 : vars      : 3.599e-03
                         :  147 : vars      : 3.583e-03
                         :  148 : vars      : 3.581e-03
                         :  149 : vars      : 3.573e-03
                         :  150 : vars      : 3.526e-03
                         :  151 : vars      : 3.491e-03
                         :  152 : vars      : 3.483e-03
                         :  153 : vars      : 3.482e-03
                         :  154 : vars      : 3.434e-03
                         :  155 : vars      : 3.414e-03
                         :  156 : vars      : 3.404e-03
                         :  157 : vars      : 3.377e-03
                         :  158 : vars      : 3.366e-03
                         :  159 : vars      : 3.347e-03
                         :  160 : vars      : 3.341e-03
                         :  161 : vars      : 3.336e-03
                         :  162 : vars      : 3.263e-03
                         :  163 : vars      : 3.250e-03
                         :  164 : vars      : 3.196e-03
                         :  165 : vars      : 3.193e-03
                         :  166 : vars      : 3.192e-03
                         :  167 : vars      : 3.168e-03
                         :  168 : vars      : 3.153e-03
                         :  169 : vars      : 3.116e-03
                         :  170 : vars      : 3.114e-03
                         :  171 : vars      : 3.099e-03
                         :  172 : vars      : 3.098e-03
                         :  173 : vars      : 3.097e-03
                         :  174 : vars      : 3.035e-03
                         :  175 : vars      : 3.015e-03
                         :  176 : vars      : 3.004e-03
                         :  177 : vars      : 2.990e-03
                         :  178 : vars      : 2.976e-03
                         :  179 : vars      : 2.972e-03
                         :  180 : vars      : 2.928e-03
                         :  181 : vars      : 2.911e-03
                         :  182 : vars      : 2.900e-03
                         :  183 : vars      : 2.893e-03
                         :  184 : vars      : 2.861e-03
                         :  185 : vars      : 2.794e-03
                         :  186 : vars      : 2.769e-03
                         :  187 : vars      : 2.767e-03
                         :  188 : vars      : 2.757e-03
                         :  189 : vars      : 2.698e-03
                         :  190 : vars      : 2.672e-03
                         :  191 : vars      : 2.663e-03
                         :  192 : vars      : 2.645e-03
                         :  193 : vars      : 2.624e-03
                         :  194 : vars      : 2.616e-03
                         :  195 : vars      : 2.577e-03
                         :  196 : vars      : 2.573e-03
                         :  197 : vars      : 2.569e-03
                         :  198 : vars      : 2.527e-03
                         :  199 : vars      : 2.499e-03
                         :  200 : vars      : 2.492e-03
                         :  201 : vars      : 2.446e-03
                         :  202 : vars      : 2.440e-03
                         :  203 : vars      : 2.438e-03
                         :  204 : vars      : 2.414e-03
                         :  205 : vars      : 2.396e-03
                         :  206 : vars      : 2.391e-03
                         :  207 : vars      : 2.354e-03
                         :  208 : vars      : 2.321e-03
                         :  209 : vars      : 2.313e-03
                         :  210 : vars      : 2.306e-03
                         :  211 : vars      : 2.284e-03
                         :  212 : vars      : 2.281e-03
                         :  213 : vars      : 2.236e-03
                         :  214 : vars      : 2.234e-03
                         :  215 : vars      : 2.198e-03
                         :  216 : vars      : 2.195e-03
                         :  217 : vars      : 2.179e-03
                         :  218 : vars      : 2.104e-03
                         :  219 : vars      : 2.102e-03
                         :  220 : vars      : 2.055e-03
                         :  221 : vars      : 2.035e-03
                         :  222 : vars      : 2.017e-03
                         :  223 : vars      : 1.999e-03
                         :  224 : vars      : 1.997e-03
                         :  225 : vars      : 1.994e-03
                         :  226 : vars      : 1.927e-03
                         :  227 : vars      : 1.863e-03
                         :  228 : vars      : 1.847e-03
                         :  229 : vars      : 1.823e-03
                         :  230 : vars      : 1.767e-03
                         :  231 : vars      : 1.756e-03
                         :  232 : vars      : 1.676e-03
                         :  233 : vars      : 1.671e-03
                         :  234 : vars      : 1.541e-03
                         :  235 : vars      : 1.333e-03
                         :  236 : vars      : 8.409e-04
                         :  237 : vars      : 0.000e+00
                         :  238 : vars      : 0.000e+00
                         :  239 : vars      : 0.000e+00
                         :  240 : vars      : 0.000e+00
                         :  241 : vars      : 0.000e+00
                         :  242 : vars      : 0.000e+00
                         :  243 : vars      : 0.000e+00
                         :  244 : vars      : 0.000e+00
                         :  245 : vars      : 0.000e+00
                         :  246 : vars      : 0.000e+00
                         :  247 : vars      : 0.000e+00
                         :  248 : vars      : 0.000e+00
                         :  249 : vars      : 0.000e+00
                         :  250 : vars      : 0.000e+00
                         :  251 : vars      : 0.000e+00
                         :  252 : vars      : 0.000e+00
                         :  253 : vars      : 0.000e+00
                         :  254 : vars      : 0.000e+00
                         :  255 : vars      : 0.000e+00
                         :  256 : vars      : 0.000e+00
                         : --------------------------------------
                         : No variable ranking supplied by classifier: TMVA_DNN_CPU
                         : No variable ranking supplied by classifier: TMVA_CNN_CPU
TH1.Print Name  = TrainingHistory_TMVA_DNN_CPU_trainingError, Entries= 0, Total sum= 4.64638
TH1.Print Name  = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 7.14775
TH1.Print Name  = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 7.83107
TH1.Print Name  = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 6.71163
Factory                  : === Destroy and recreate all methods via weight files for testing ===
                         : 
                         : Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.weights.xml␛[0m
                         : Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_DNN_CPU.weights.xml␛[0m
                         : Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_TMVA_CNN_CPU.weights.xml␛[0m
Factory                  : ␛[1mTest all methods␛[0m
Factory                  : Test method: BDT for Classification performance
                         : 
BDT                      : [dataset] : Evaluation of BDT on testing sample (400 events)
BDT                      : [dataset] : Evaluation of BDT on testing sample (400 events)
                         : Elapsed time for evaluation of 400 events: 0.00406 sec       
                         : Elapsed time for evaluation of 400 events: 0.00425 sec       
Factory                  : Test method: TMVA_DNN_CPU for Classification performance
                         : 
TMVA_DNN_CPU             : [dataset] : Evaluation of TMVA_DNN_CPU on testing sample (400 events)
                         : Evaluate deep neural network on CPU using batches with size = 400
                         : 
TMVA_DNN_CPU             : [dataset] : Evaluation of TMVA_DNN_CPU on testing sample (400 events)
                         : Elapsed time for evaluation of 400 events: 0.0125 sec       
                         : Elapsed time for evaluation of 400 events: 0.0145 sec       
Factory                  : Test method: TMVA_CNN_CPU for Classification performance
                         : 
TMVA_CNN_CPU             : [dataset] : Evaluation of TMVA_CNN_CPU on testing sample (400 events)
                         : Evaluate deep neural network on CPU using batches with size = 400
                         : 
TMVA_CNN_CPU             : [dataset] : Evaluation of TMVA_CNN_CPU on testing sample (400 events)
                         : Elapsed time for evaluation of 400 events: 0.084 sec       
                         : Elapsed time for evaluation of 400 events: 0.0946 sec       
Factory                  : ␛[1mEvaluate all methods␛[0m
Factory                  : Evaluate classifier: BDT
                         : 
BDT                      : [dataset] : Loop over test events and fill histograms with classifier response...
                         : 
                         : Dataset[dataset] :  variable plots are not produces ! The number of variables is 256 , it is larger than 200
Factory                  : Evaluate classifier: TMVA_DNN_CPU
                         : 
TMVA_DNN_CPU             : [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 256 , it is larger than 200
Factory                  : Evaluate classifier: TMVA_CNN_CPU
                         : 
TMVA_CNN_CPU             : [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 256 , it is larger than 200
                         : 
                         : Evaluation results ranked by best signal efficiency and purity (area)
                         : -------------------------------------------------------------------------------------------------------------------
                         : DataSet       MVA                       
                         : Name:         Method:          ROC-integ
                         : dataset       TMVA_CNN_CPU   : 0.788
                         : dataset       BDT            : 0.752
                         : dataset       TMVA_DNN_CPU   : 0.728
                         : -------------------------------------------------------------------------------------------------------------------
                         : 
                         : 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              TMVA_CNN_CPU   : 0.120 (0.200)       0.465 (0.508)      0.725 (0.744)
                         : dataset              BDT            : 0.085 (0.305)       0.385 (0.686)      0.665 (0.869)
                         : dataset              TMVA_DNN_CPU   : 0.065 (0.215)       0.368 (0.650)      0.613 (0.845)
                         : -------------------------------------------------------------------------------------------------------------------
                         : 
Dataset:dataset          : Created tree 'TestTree' with 400 events
                         : 
Dataset:dataset          : Created tree 'TrainTree' with 1600 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 Convolutional Neural Network
 
 
## Helper function to create input images data
## we create a signal and background 2D histograms from 2d gaussians
## with a location (means in X and Y)  different for each event
## The difference between signal and background is in the gaussian width.
## The width for the background gaussian is slightly larger than the signal width by few % values
 
import importlib.util
import os
 
import ROOT
 
opt = [1, 1, 1, 1, 1]
useTMVACNN = opt[0] if len(opt) > 0  else False
useKerasCNN = opt[1] if len(opt) > 1 else False
useTMVADNN = opt[2] if len(opt) > 2 else False
useTMVABDT = opt[3] if len(opt) > 3 else False
usePyTorchCNN = opt[4] if len(opt) > 4 else False
 
TMVA = ROOT.TMVA
TFile = ROOT.TFile
 
TMVA.Tools.Instance()
 
def MakeImagesTree(n, nh, nw):
    # image size (nh x nw)
    ntot = nh * nw
    fileOutName = "images_data_16x16.root"
    nRndmEvts = 10000  # number of events we use to fill each image
    delta_sigma = 0.1  # 5% difference in the sigma
    pixelNoise = 5
 
    sX1 = 3
    sY1 = 3
    sX2 = sX1 + delta_sigma
    sY2 = sY1 - delta_sigma
    h1 = ROOT.TH2D("h1", "h1", nh, 0, 10, nw, 0, 10)
    h2 = ROOT.TH2D("h2", "h2", nh, 0, 10, nw, 0, 10)
    f1 = ROOT.TF2("f1", "xygaus")
    f2 = ROOT.TF2("f2", "xygaus")
    sgn = ROOT.TTree("sig_tree", "signal_tree")
    bkg = ROOT.TTree("bkg_tree", "background_tree")
 
    f = TFile(fileOutName, "RECREATE")
    x1 = ROOT.std.vector["float"](ntot)
    x2 = ROOT.std.vector["float"](ntot)
 
    # create signal and background trees with a single branch
    # an std::vector<float> of size nh x nw containing the image data
    bkg.Branch("vars", "std::vector<float>", x1)
    sgn.Branch("vars", "std::vector<float>", x2)
 
    sgn.SetDirectory(f)
    bkg.SetDirectory(f)
 
    f1.SetParameters(1, 5, sX1, 5, sY1)
    f2.SetParameters(1, 5, sX2, 5, sY2)
    ROOT.gRandom.SetSeed(0)
    ROOT.Info("TMVA_CNN_Classification", "Filling ROOT tree \n")
    for i in range(n):
        if i % 1000 == 0:
            print("Generating image event ...", i)
 
        h1.Reset()
        h2.Reset()
        # generate random means in range [3,7] to be not too much on the border
        f1.SetParameter(1, ROOT.gRandom.Uniform(3, 7))
        f1.SetParameter(3, ROOT.gRandom.Uniform(3, 7))
        f2.SetParameter(1, ROOT.gRandom.Uniform(3, 7))
        f2.SetParameter(3, ROOT.gRandom.Uniform(3, 7))
 
        h1.FillRandom(f1, nRndmEvts)
        h2.FillRandom(f2, nRndmEvts)
 
        for k in range(nh):
            for l in range(nw):
                m = k * nw + l
                # add some noise in each bin
                x1[m] = h1.GetBinContent(k + 1, l + 1) + ROOT.gRandom.Gaus(0, pixelNoise)
                x2[m] = h2.GetBinContent(k + 1, l + 1) + ROOT.gRandom.Gaus(0, pixelNoise)
 
        sgn.Fill()
        bkg.Fill()
 
    sgn.Write()
    bkg.Write()
 
    print("Signal and background tree with images data written to the file %s", f.GetName())
    sgn.Print()
    bkg.Print()
    f.Close()
 
hasGPU = "tmva-gpu" in ROOT.gROOT.GetConfigFeatures()
hasCPU = "tmva-cpu" in ROOT.gROOT.GetConfigFeatures()
 
nevt = 1000    # use a larger value to get better results
 
if (not hasCPU and not hasGPU) :
    ROOT.Warning("TMVA_CNN_Classificaton","ROOT is not supporting tmva-cpu and tmva-gpu skip using TMVA-DNN and TMVA-CNN")
    useTMVACNN = False
    useTMVADNN = False
 
if "tmva-pymva" not in ROOT.gROOT.GetConfigFeatures():
    useKerasCNN = False
    usePyTorchCNN = False
else:
    TMVA.PyMethodBase.PyInitialize()
 
if not useTMVACNN:
    ROOT.Warning(
        "TMVA_CNN_Classificaton",
        "TMVA is not build with GPU or CPU multi-thread support. Cannot use TMVA Deep Learning for CNN",
    )
 
writeOutputFile = True
 
num_threads = 4  # use max 4 threads
max_epochs = 10  # maximum number of epochs used for training
 
 
# do enable MT running
if "imt" in ROOT.gROOT.GetConfigFeatures():
    ROOT.EnableImplicitMT(num_threads)
    ROOT.gSystem.Setenv("OMP_NUM_THREADS", "1")  # switch OFF MT in OpenBLAS
    print("Running with nthreads  = {}".format(ROOT.GetThreadPoolSize()))
else:
    print("Running in serial mode since ROOT does not support MT")
 
 
 
 
outputFile = None
if writeOutputFile:
    outputFile = TFile.Open("TMVA_CNN_ClassificationOutput.root", "RECREATE")
 
 
## Create TMVA 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
#   weight files 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
 
# - note that we disable any pre-transformation of the input variables and we avoid computing correlations between
# input variables
 
 
factory = TMVA.Factory(
    "TMVA_CNN_Classification",
    outputFile,
    V=False,
    ROC=True,
    Silent=False,
    Color=True,
    AnalysisType="Classification",
    Transformations=None,
    Correlations=False,
)
 
 
## Declare DataLoader(s)
 
# The next step is to declare the DataLoader class that deals with input variables
 
# Define the input variables that shall be used for the MVA training
# note that you may also use variable expressions, which can be parsed by TTree::Draw( "expression" )]
 
# In this case the input data consists of an image of 16x16 pixels. Each single pixel is a branch in a ROOT TTree
 
loader = TMVA.DataLoader("dataset")
 
 
## Setup Dataset(s)
 
# Define input data file and signal and background trees
 
 
imgSize = 16 * 16
inputFileName = "images_data_16x16.root"
 
# if the input file does not exist create it
if ROOT.gSystem.AccessPathName(inputFileName):
    MakeImagesTree(nevt, 16, 16)
 
inputFile = TFile.Open(inputFileName)
if inputFile is None:
    ROOT.Warning("TMVA_CNN_Classification", "Error opening input file %s - exit", inputFileName.Data())
 
 
# inputFileName = "tmva_class_example.root"
 
 
# --- Register the training and test trees
 
signalTree = inputFile.Get("sig_tree")
backgroundTree = inputFile.Get("bkg_tree")
 
nEventsSig = signalTree.GetEntries()
nEventsBkg = backgroundTree.GetEntries()
 
# global event weights per tree (see below for setting event-wise weights)
signalWeight = 1.0
backgroundWeight = 1.0
 
# You can add an arbitrary number of signal or background trees
loader.AddSignalTree(signalTree, signalWeight)
loader.AddBackgroundTree(backgroundTree, backgroundWeight)
 
## add event variables (image)
## use new method (from ROOT 6.20 to add a variable array for all image data)
loader.AddVariablesArray("vars", imgSize)
 
# Set individual event weights (the variables must exist in the original TTree)
#    for signal    : factory->SetSignalWeightExpression    ("weight1*weight2");
#    for background: factory->SetBackgroundWeightExpression("weight1*weight2");
# loader->SetBackgroundWeightExpression( "weight" );
 
# Apply additional cuts on the signal and background samples (can be different)
mycuts = ""  # for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
mycutb = ""  # for example: TCut mycutb = "abs(var1)<0.5";
 
# Tell the factory how to use the training and testing events
# If no numbers of events are given, half of the events in the tree are used
# for training, and the other half for testing:
#    loader.PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
# It is possible also to specify the number of training and testing events,
# note we disable the computation of the correlation matrix of the input variables
 
nTrainSig = 0.8 * nEventsSig
nTrainBkg = 0.8 * nEventsBkg
 
# build the string options for DataLoader::PrepareTrainingAndTestTree
 
loader.PrepareTrainingAndTestTree(
    mycuts,
    mycutb,
    nTrain_Signal=nTrainSig,
    nTrain_Background=nTrainBkg,
    SplitMode="Random",
    SplitSeed=100,
    NormMode="NumEvents",
    V=False,
    CalcCorrelations=False,
)
 
 
# DataSetInfo              : [dataset] : Added class "Signal"
#    : Add Tree sig_tree of type Signal with 10000 events
#    DataSetInfo              : [dataset] : Added class "Background"
#        : Add Tree bkg_tree of type Background with 10000 events
 
# signalTree.Print();
 
# Booking Methods
 
# Here we book the TMVA methods. We book a Boosted Decision Tree method (BDT)
 
 
# Boosted Decision Trees
if useTMVABDT:
    factory.BookMethod(
        loader,
        TMVA.Types.kBDT,
        "BDT",
        V=False,
        NTrees=400,
        MinNodeSize="2.5%",
        MaxDepth=2,
        BoostType="AdaBoost",
        AdaBoostBeta=0.5,
        UseBaggedBoost=True,
        BaggedSampleFraction=0.5,
        SeparationType="GiniIndex",
        nCuts=20,
    )
 
 
#### Booking Deep Neural Network
 
# Here we book the DNN of TMVA. See the example TMVA_Higgs_Classification.C for a detailed description of the
# options
 
if useTMVADNN:
    layoutString = ROOT.TString(
        "DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR"
    )
 
    # Training strategies
    # one can catenate several training strings with different parameters (e.g. learning rates or regularizations
    # parameters) The training string must be concatenated with the `|` delimiter
    trainingString1 = ROOT.TString(
        "LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
        "ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,"
        "WeightDecay=1e-4,Regularization=None,"
        "Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0."
    )  # + "|" + trainingString2 + ...
    trainingString1 += ",MaxEpochs=" + str(max_epochs)
 
    # Build now the full DNN Option string
    dnnMethodName = "TMVA_DNN_CPU"
 
    # use GPU if available
    dnnOptions = "CPU"
    if hasGPU :
        dnnOptions = "GPU"
        dnnMethodName = "TMVA_DNN_GPU"
 
    factory.BookMethod(
        loader,
        TMVA.Types.kDL,
        dnnMethodName,
        H=False,
        V=True,
        ErrorStrategy="CROSSENTROPY",
        VarTransform=None,
        WeightInitialization="XAVIER",
        Layout=layoutString,
        TrainingStrategy=trainingString1,
        Architecture=dnnOptions
    )
 
 
### Book Convolutional Neural Network in TMVA
 
# For building a CNN one needs to define
 
# -  Input Layout :  number of channels (in this case = 1)  | image height | image width
# -  Batch Layout :  batch size | number of channels | image size = (height*width)
 
# Then one add Convolutional layers and MaxPool layers.
 
# -  For Convolutional layer the option string has to be:
# - CONV | number of units | filter height | filter width | stride height | stride width | padding height | paddig
# width | activation function
 
# - note in this case we are using a filer 3x3 and padding=1 and stride=1 so we get the output dimension of the
# conv layer equal to the input
 
# - note we use after the first convolutional layer a batch normalization layer. This seems to help significantly the
# convergence
 
# - For the MaxPool layer:
# - MAXPOOL  | pool height | pool width | stride height | stride width
 
# The RESHAPE layer is needed to flatten the output before the Dense layer
 
# Note that to run the CNN is required to have CPU  or GPU support
 
 
if useTMVACNN:
    # Training strategies.
    trainingString1 = ROOT.TString(
        "LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
        "ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,"
        "WeightDecay=1e-4,Regularization=None,"
        "Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0"
    )
    trainingString1 += ",MaxEpochs=" + str(max_epochs)
 
    ## New DL (CNN)
    cnnMethodName = "TMVA_CNN_CPU"
    cnnOptions = "CPU"
    # use GPU if available
    if hasGPU:
        cnnOptions = "GPU"
        cnnMethodName = "TMVA_CNN_GPU"
 
    factory.BookMethod(
        loader,
        TMVA.Types.kDL,
        cnnMethodName,
        H=False,
        V=True,
        ErrorStrategy="CROSSENTROPY",
        VarTransform=None,
        WeightInitialization="XAVIER",
        InputLayout="1|16|16",
        Layout="CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR",
        TrainingStrategy=trainingString1,
        Architecture=cnnOptions,
    )
 
 
### Book Convolutional Neural Network in Keras using a generated model
 
 
if usePyTorchCNN:
    ROOT.Info("TMVA_CNN_Classification", "Using Convolutional PyTorch Model")
    pyTorchFileName = str(ROOT.gROOT.GetTutorialDir())
    pyTorchFileName += "/machine_learning/PyTorch_Generate_CNN_Model.py"
    # check that pytorch can be imported and file defining the model exists
    torch_spec = importlib.util.find_spec("torch")
    if torch_spec is not None and os.path.exists(pyTorchFileName):
        #cmd = str(ROOT.TMVA.Python_Executable()) + "  " + pyTorchFileName
        #os.system(cmd)
        #import PyTorch_Generate_CNN_Model
        ROOT.Info("TMVA_CNN_Classification", "Booking PyTorch CNN model")
        factory.BookMethod(
            loader,
            TMVA.Types.kPyTorch,
            "PyTorch",
            H=True,
            V=False,
            VarTransform=None,
            FilenameModel="PyTorchModelCNN.pt",
            FilenameTrainedModel="PyTorchTrainedModelCNN.pt",
            NumEpochs=max_epochs,
            BatchSize=100,
            UserCode=str(pyTorchFileName)
        )
    else:
        ROOT.Warning(
            "TMVA_CNN_Classification",
            "PyTorch is not installed or model building file is not existing - skip using PyTorch",
        )
 
if useKerasCNN:
    ROOT.Info("TMVA_CNN_Classification", "Building convolutional keras model")
    # create python script which can be executed
    # create 2 conv2d layer + maxpool + dense
    # from keras.initializers import TruncatedNormal
    # from keras import initializations
    from tensorflow.keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Reshape
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.optimizers import Adam
 
    # from keras.callbacks import ReduceLROnPlateau
    model = Sequential()
    model.add(Reshape((16, 16, 1), input_shape=(256,)))
    model.add(Conv2D(10, kernel_size=(3, 3), kernel_initializer="TruncatedNormal", activation="relu", padding="same"))
    model.add(Conv2D(10, kernel_size=(3, 3), kernel_initializer="TruncatedNormal", activation="relu", padding="same"))
    # stride for maxpool is equal to pool size
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(64, activation="tanh"))
    # model.add(Dropout(0.2))
    model.add(Dense(2, activation="sigmoid"))
    model.compile(loss="binary_crossentropy", optimizer=Adam(learning_rate=0.001), weighted_metrics=["accuracy"])
    model.save("model_cnn.keras")
    model.summary()
 
    if not os.path.exists("model_cnn.keras"):
        raise FileNotFoundError("Error creating Keras model file - skip using Keras")
    else:
        # book PyKeras method only if Keras model could be created
        ROOT.Info("TMVA_CNN_Classification", "Booking convolutional keras model")
        factory.BookMethod(
            loader,
            TMVA.Types.kPyKeras,
            "PyKeras",
            H=True,
            V=False,
            VarTransform=None,
            FilenameModel="model_cnn.keras",
            FilenameTrainedModel="trained_model_cnn.keras",
            NumEpochs=max_epochs,
            BatchSize=100,
            GpuOptions="allow_growth=True",
        )  # needed for RTX NVidia card and to avoid TF allocates all GPU memory
 
 
 
## Train Methods
 
factory.TrainAllMethods()
 
## Test and Evaluate Methods
 
factory.TestAllMethods()
 
factory.EvaluateAllMethods()
 
## Plot ROC Curve
 
c1 = factory.GetROCCurve(loader)
c1.Draw()
 
# close outputfile to save output file
outputFile.Close()

Author

Harshal Shende

Definition in file TMVA_CNN_Classification.py.