TMVA_CNN_Classification.py File Reference

Namespaces
namespace	TMVA_CNN_Classification

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

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                  : Booking method: ␛[1mPyTorch␛[0m
                         : 
                         : Using PyTorch - setting special configuration options 
                         : Using PyTorch version 1
                         :  Setup PyTorch Model for training
                         :  Executing user initialization code from  /home/sftnight/build/workspace/root-makedoc-master/rootspi/rdoc/src/master.build/tutorials/tmva/PyTorch_Generate_CNN_Model.py
Running with nthreads  =  4
running Torch code defining the model....
The PyTorch CNN model is created and saved as PyTorchModelCNN.pt
                         : Loaded pytorch train function: 
                         : Loaded pytorch optimizer: 
                         : Loaded pytorch loss function: 
                         : Loaded pytorch predict function: 
                         : Loaded model from file: PyTorchModelCNN.pt
custom objects for loading model :  {'optimizer': <class 'torch.optim.adam.Adam'>, 'criterion': BCELoss(), 'train_func': <function fit at 0x7f7bcf9e0040>, 'predict_func': <function predict at 0x7f7bcf9e00d0>}
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 reshape (Reshape)           (None, 16, 16, 1)         0         
                                                                 
 conv2d (Conv2D)             (None, 16, 16, 10)        100       
                                                                 
 conv2d_1 (Conv2D)           (None, 16, 16, 10)        910       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 8, 8, 10)         0         
 )                                                               
                                                                 
 flatten (Flatten)           (None, 640)               0         
                                                                 
 dense (Dense)               (None, 64)                41024     
                                                                 
 dense_1 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 42,164
Trainable params: 42,164
Non-trainable params: 0
_________________________________________________________________
Factory                  : Booking method: ␛[1mPyKeras␛[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_cnn.h5
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: 1.66 sec         
BDT                      : [dataset] : Evaluation of BDT on training sample (1600 events)
                         : Elapsed time for evaluation of 1600 events: 0.0402 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 = inf
                         : --------------------------------------------------------------
                         :      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 |      1.20998     1.05391    0.180705   0.0159379        7283           0
                         :          2 Minimum Test error found - save the configuration 
                         :          2 |     0.724207    0.929779    0.179681   0.0156885     7317.41           0
                         :          3 Minimum Test error found - save the configuration 
                         :          3 |     0.633575    0.859455    0.179518    0.015633     7322.19           0
                         :          4 Minimum Test error found - save the configuration 
                         :          4 |     0.561124    0.761845    0.184553   0.0158233     7111.98           0
                         :          5 Minimum Test error found - save the configuration 
                         :          5 |     0.483289    0.725782    0.179832   0.0157844     7314.95           0
                         :          6 |     0.421968    0.742091    0.178994   0.0150177     7318.13           1
                         :          7 |     0.406054    0.836856    0.179571   0.0151418     7297.96           2
                         :          8 |     0.349026    0.772862    0.179291   0.0150292     7305.42           3
                         :          9 |     0.285144    0.756626     0.18152   0.0154451     7225.65           4
                         :         10 |     0.276912    0.730814    0.182177   0.0151286     7183.53           5
                         : 
                         : Elapsed time for training with 1600 events: 1.85 sec         
                         : 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: 0.0797 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 = inf
                         : --------------------------------------------------------------
                         :      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 |      2.72363    0.782774     1.46189    0.116153     891.707           0
                         :          2 Minimum Test error found - save the configuration 
                         :          2 |     0.813984    0.739156     1.43998    0.113416     904.595           0
                         :          3 |     0.745331    0.776283     1.44198    0.110265     901.096           1
                         :          4 Minimum Test error found - save the configuration 
                         :          4 |     0.704638    0.717018     1.44163    0.113153     903.289           0
                         :          5 Minimum Test error found - save the configuration 
                         :          5 |     0.659612    0.675979     1.44032    0.113058     904.115           0
                         :          6 Minimum Test error found - save the configuration 
                         :          6 |     0.613388    0.673139     1.44186    0.113481     903.357           0
                         :          7 Minimum Test error found - save the configuration 
                         :          7 |     0.586098    0.654411     1.43594    0.112096     906.453           0
                         :          8 Minimum Test error found - save the configuration 
                         :          8 |     0.565368     0.63537     1.42963    0.112273     910.915           0
                         :          9 |     0.526173    0.642534     1.43061    0.109998      908.67           1
                         :         10 |     0.533928    0.660939     1.43842    0.110836     903.899           2
                         : 
                         : Elapsed time for training with 1600 events: 14.5 sec         
                         : 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: 0.585 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
                         : 
Factory                  : Train method: PyTorch for Classification
                         : 
                         : 
                         : ␛[1m================================================================␛[0m
                         : ␛[1mH e l p   f o r   M V A   m e t h o d   [ PyTorch ] :␛[0m
                         : 
                         : PyTorch is a scientific computing package supporting
                         : automatic differentiation. This method wraps the training
                         : and predictions steps of the PyTorch Python package for
                         : TMVA, so that dataloading, preprocessing and evaluation
                         : can be done within the TMVA system. To use this PyTorch
                         : interface, you need to generatea model with PyTorch first.
                         : Then, this model can be loaded and trained in TMVA.
                         : 
                         : 
                         : <Suppress this message by specifying "!H" in the booking option>
                         : ␛[1m================================================================␛[0m
                         : 
                         : Split TMVA training data in 1280 training events and 320 validation events
                         : Print Training Model Architecture
                         : Option SaveBestOnly: Only model weights with smallest validation loss will be stored
                         : Elapsed time for training with 1600 events: 22.1 sec         
PyTorch                  : [dataset] : Evaluation of PyTorch on training sample (1600 events)
                         : Elapsed time for evaluation of 1600 events: 0.417 sec       
                         : Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_PyTorch.weights.xml␛[0m
                         : Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_PyTorch.class.C␛[0m
Factory                  : Training finished
                         : 
Factory                  : Train method: PyKeras for Classification
                         : 
                         : 
                         : ␛[1m================================================================␛[0m
                         : ␛[1mH e l p   f o r   M V A   m e t h o d   [ PyKeras ] :␛[0m
                         : 
                         : Keras is a high-level API for the Theano and Tensorflow packages.
                         : This method wraps the training and predictions steps of the Keras
                         : Python package for TMVA, so that dataloading, preprocessing and
                         : evaluation can be done within the TMVA system. To use this Keras
                         : interface, you have to generate a model with Keras first. Then,
                         : this model can be loaded and trained in TMVA.
                         : 
                         : 
                         : <Suppress this message by specifying "!H" in the booking option>
                         : ␛[1m================================================================␛[0m
                         : 
                         : Split TMVA training data in 1280 training events and 320 validation events
                         : Training Model Summary
RecursiveScriptModule(
  original_name=Sequential
  (0): RecursiveScriptModule(original_name=Reshape)
  (1): RecursiveScriptModule(original_name=Conv2d)
  (2): RecursiveScriptModule(original_name=ReLU)
  (3): RecursiveScriptModule(original_name=BatchNorm2d)
  (4): RecursiveScriptModule(original_name=Conv2d)
  (5): RecursiveScriptModule(original_name=ReLU)
  (6): RecursiveScriptModule(original_name=MaxPool2d)
  (7): RecursiveScriptModule(original_name=Flatten)
  (8): RecursiveScriptModule(original_name=Linear)
  (9): RecursiveScriptModule(original_name=ReLU)
  (10): RecursiveScriptModule(original_name=Linear)
  (11): RecursiveScriptModule(original_name=Sigmoid)
)
[1, 4] train loss: 1.027
[1, 8] train loss: 0.736
[1, 12] train loss: 0.715
[1] val loss: 0.709
[2, 4] train loss: 0.693
[2, 8] train loss: 0.687
[2, 12] train loss: 0.700
[2] val loss: 0.689
[3, 4] train loss: 0.677
[3, 8] train loss: 0.687
[3, 12] train loss: 0.677
[3] val loss: 0.684
[4, 4] train loss: 0.660
[4, 8] train loss: 0.665
[4, 12] train loss: 0.658
[4] val loss: 0.732
[5, 4] train loss: 0.656
[5, 8] train loss: 0.636
[5, 12] train loss: 0.658
[5] val loss: 0.716
[6, 4] train loss: 0.595
[6, 8] train loss: 0.605
[6, 12] train loss: 0.605
[6] val loss: 0.692
[7, 4] train loss: 0.578
[7, 8] train loss: 0.547
[7, 12] train loss: 0.593
[7] val loss: 0.675
[8, 4] train loss: 0.539
[8, 8] train loss: 0.539
[8, 12] train loss: 0.481
[8] val loss: 0.693
[9, 4] train loss: 0.458
[9, 8] train loss: 0.403
[9, 12] train loss: 0.377
[9] val loss: 0.647
[10, 4] train loss: 0.411
[10, 8] train loss: 0.409
[10, 12] train loss: 0.525
[10] val loss: 0.933
Finished Training on 10 Epochs!
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 reshape (Reshape)           (None, 16, 16, 1)         0         
                                                                 
 conv2d (Conv2D)             (None, 16, 16, 10)        100       
                                                                 
 conv2d_1 (Conv2D)           (None, 16, 16, 10)        910       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 8, 8, 10)         0         
 )                                                               
                                                                 
 flatten (Flatten)           (None, 640)               0         
                                                                 
 dense (Dense)               (None, 64)                41024     
                                                                 
 dense_1 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 42,164
Trainable params: 42,164
Non-trainable params: 0
_________________________________________________________________
                         : Option SaveBestOnly: Only model weights with smallest validation loss will be stored
Epoch 1/10
 
 1/13 [=>............................] - ETA: 9s - loss: 0.8127 - accuracy: 0.4800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 5/13 [==========>...................] - ETA: 0s - loss: 0.7558 - accuracy: 0.4920␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.7290 - accuracy: 0.4955
Epoch 1: val_loss improved from inf to 0.69625, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 1s 54ms/step - loss: 0.7241 - accuracy: 0.5023 - val_loss: 0.6962 - val_accuracy: 0.4812
Epoch 2/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.6940 - accuracy: 0.4900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 7/13 [===============>..............] - ETA: 0s - loss: 0.6897 - accuracy: 0.5500
Epoch 2: val_loss did not improve from 0.69625
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 12ms/step - loss: 0.6927 - accuracy: 0.5336 - val_loss: 0.7038 - val_accuracy: 0.4719
Epoch 3/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.6865 - accuracy: 0.5300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 8/13 [=================>............] - ETA: 0s - loss: 0.6928 - accuracy: 0.5400
Epoch 3: val_loss improved from 0.69625 to 0.68637, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 13ms/step - loss: 0.6897 - accuracy: 0.5344 - val_loss: 0.6864 - val_accuracy: 0.5312
Epoch 4/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.6814 - accuracy: 0.5200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 7/13 [===============>..............] - ETA: 0s - loss: 0.6798 - accuracy: 0.5757
Epoch 4: val_loss improved from 0.68637 to 0.68222, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 15ms/step - loss: 0.6809 - accuracy: 0.5539 - val_loss: 0.6822 - val_accuracy: 0.5969
Epoch 5/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.6809 - accuracy: 0.6000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 8/13 [=================>............] - ETA: 0s - loss: 0.6676 - accuracy: 0.6087
Epoch 5: val_loss improved from 0.68222 to 0.67945, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 14ms/step - loss: 0.6715 - accuracy: 0.5961 - val_loss: 0.6794 - val_accuracy: 0.5594
Epoch 6/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.6643 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 8/13 [=================>............] - ETA: 0s - loss: 0.6640 - accuracy: 0.6125
Epoch 6: val_loss improved from 0.67945 to 0.67893, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 15ms/step - loss: 0.6628 - accuracy: 0.6070 - val_loss: 0.6789 - val_accuracy: 0.6000
Epoch 7/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.6484 - accuracy: 0.7000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 8/13 [=================>............] - ETA: 0s - loss: 0.6435 - accuracy: 0.6775
Epoch 7: val_loss improved from 0.67893 to 0.66773, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 15ms/step - loss: 0.6466 - accuracy: 0.6602 - val_loss: 0.6677 - val_accuracy: 0.6094
Epoch 8/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.6469 - accuracy: 0.6800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 8/13 [=================>............] - ETA: 0s - loss: 0.6314 - accuracy: 0.6875
Epoch 8: val_loss did not improve from 0.66773
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 13ms/step - loss: 0.6245 - accuracy: 0.6969 - val_loss: 0.6756 - val_accuracy: 0.5938
Epoch 9/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.5547 - accuracy: 0.7800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 8/13 [=================>............] - ETA: 0s - loss: 0.6069 - accuracy: 0.7075
Epoch 9: val_loss improved from 0.66773 to 0.66753, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 14ms/step - loss: 0.6018 - accuracy: 0.7109 - val_loss: 0.6675 - val_accuracy: 0.5750
Epoch 10/10
 
 1/13 [=>............................] - ETA: 0s - loss: 0.5905 - accuracy: 0.7300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
 7/13 [===============>..............] - ETA: 0s - loss: 0.5827 - accuracy: 0.7214
Epoch 10: val_loss improved from 0.66753 to 0.64470, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 15ms/step - loss: 0.5678 - accuracy: 0.7437 - val_loss: 0.6447 - val_accuracy: 0.6344
                         : 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 1600 events: 3.68 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_cnn.h5
PyKeras                  : [dataset] : Evaluation of PyKeras on training sample (1600 events)
                         : Elapsed time for evaluation of 1600 events: 0.152 sec       
                         : Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_PyKeras.weights.xml␛[0m
                         : Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_PyKeras.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      : 9.186e-03
                         :    2 : vars      : 8.519e-03
                         :    3 : vars      : 8.403e-03
                         :    4 : vars      : 8.362e-03
                         :    5 : vars      : 8.307e-03
                         :    6 : vars      : 7.765e-03
                         :    7 : vars      : 7.681e-03
                         :    8 : vars      : 7.622e-03
                         :    9 : vars      : 7.418e-03
                         :   10 : vars      : 7.244e-03
                         :   11 : vars      : 7.054e-03
                         :   12 : vars      : 6.970e-03
                         :   13 : vars      : 6.941e-03
                         :   14 : vars      : 6.821e-03
                         :   15 : vars      : 6.802e-03
                         :   16 : vars      : 6.780e-03
                         :   17 : vars      : 6.650e-03
                         :   18 : vars      : 6.584e-03
                         :   19 : vars      : 6.506e-03
                         :   20 : vars      : 6.450e-03
                         :   21 : vars      : 6.447e-03
                         :   22 : vars      : 6.290e-03
                         :   23 : vars      : 6.284e-03
                         :   24 : vars      : 6.273e-03
                         :   25 : vars      : 6.260e-03
                         :   26 : vars      : 6.257e-03
                         :   27 : vars      : 6.248e-03
                         :   28 : vars      : 6.242e-03
                         :   29 : vars      : 6.198e-03
                         :   30 : vars      : 6.173e-03
                         :   31 : vars      : 6.147e-03
                         :   32 : vars      : 6.089e-03
                         :   33 : vars      : 5.931e-03
                         :   34 : vars      : 5.911e-03
                         :   35 : vars      : 5.907e-03
                         :   36 : vars      : 5.904e-03
                         :   37 : vars      : 5.878e-03
                         :   38 : vars      : 5.847e-03
                         :   39 : vars      : 5.826e-03
                         :   40 : vars      : 5.819e-03
                         :   41 : vars      : 5.818e-03
                         :   42 : vars      : 5.794e-03
                         :   43 : vars      : 5.743e-03
                         :   44 : vars      : 5.714e-03
                         :   45 : vars      : 5.704e-03
                         :   46 : vars      : 5.659e-03
                         :   47 : vars      : 5.652e-03
                         :   48 : vars      : 5.634e-03
                         :   49 : vars      : 5.617e-03
                         :   50 : vars      : 5.616e-03
                         :   51 : vars      : 5.603e-03
                         :   52 : vars      : 5.602e-03
                         :   53 : vars      : 5.590e-03
                         :   54 : vars      : 5.566e-03
                         :   55 : vars      : 5.534e-03
                         :   56 : vars      : 5.524e-03
                         :   57 : vars      : 5.521e-03
                         :   58 : vars      : 5.479e-03
                         :   59 : vars      : 5.438e-03
                         :   60 : vars      : 5.435e-03
                         :   61 : vars      : 5.419e-03
                         :   62 : vars      : 5.309e-03
                         :   63 : vars      : 5.293e-03
                         :   64 : vars      : 5.285e-03
                         :   65 : vars      : 5.265e-03
                         :   66 : vars      : 5.253e-03
                         :   67 : vars      : 5.247e-03
                         :   68 : vars      : 5.246e-03
                         :   69 : vars      : 5.217e-03
                         :   70 : vars      : 5.197e-03
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                         : --------------------------------------
                         : No variable ranking supplied by classifier: TMVA_DNN_CPU
                         : No variable ranking supplied by classifier: TMVA_CNN_CPU
                         : No variable ranking supplied by classifier: PyTorch
                         : No variable ranking supplied by classifier: PyKeras
TH1.Print Name  = TrainingHistory_TMVA_DNN_CPU_trainingError, Entries= 0, Total sum= 5.35128
TH1.Print Name  = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 8.17002
TH1.Print Name  = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 8.47215
TH1.Print Name  = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 6.9576
TH1.Print Name  = TrainingHistory_PyKeras_'accuracy', Entries= 0, Total sum= 6.13906
TH1.Print Name  = TrainingHistory_PyKeras_'loss', Entries= 0, Total sum= 6.56255
TH1.Print Name  = TrainingHistory_PyKeras_'val_accuracy', Entries= 0, Total sum= 5.65312
TH1.Print Name  = TrainingHistory_PyKeras_'val_loss', Entries= 0, Total sum= 6.78261
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
                         : Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_PyTorch.weights.xml␛[0m
                         : Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_PyKeras.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)
                         : Elapsed time for evaluation of 400 events: 0.0112 sec       
Factory                  : Test method: TMVA_DNN_CPU for Classification performance
                         : 
                         : 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: 0.0188 sec       
Factory                  : Test method: TMVA_CNN_CPU for Classification performance
                         : 
                         : 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: 0.155 sec       
Factory                  : Test method: PyTorch for Classification performance
                         : 
                         :  Setup PyTorch Model for training
                         :  Executing user initialization code from  /home/sftnight/build/workspace/root-makedoc-master/rootspi/rdoc/src/master.build/tutorials/tmva/PyTorch_Generate_CNN_Model.py
running Torch code defining the model....
The PyTorch CNN model is created and saved as PyTorchModelCNN.pt
                         : Loaded pytorch train function: 
                         : Loaded pytorch optimizer: 
                         : Loaded pytorch loss function: 
                         : Loaded pytorch predict function: 
                         : Loaded model from file: PyTorchTrainedModelCNN.pt
PyTorch                  : [dataset] : Evaluation of PyTorch on testing sample (400 events)
                         : Elapsed time for evaluation of 400 events: 0.122 sec       
Factory                  : Test method: PyKeras 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_cnn.h5
PyKeras                  : [dataset] : Evaluation of PyKeras on testing sample (400 events)
                         : Elapsed time for evaluation of 400 events: 0.0969 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
Factory                  : Evaluate classifier: PyTorch
                         : 
PyTorch                  : [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: PyKeras
                         : 
PyKeras                  : [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
                         : 
                         : Evaluation results ranked by best signal efficiency and purity (area)
                         : -------------------------------------------------------------------------------------------------------------------
                         : DataSet       MVA                       
                         : Name:         Method:          ROC-integ
                         : dataset       PyTorch        : 0.834
                         : dataset       BDT            : 0.781
                         : dataset       TMVA_CNN_CPU   : 0.718
                         : dataset       PyKeras        : 0.641
                         : dataset       TMVA_DNN_CPU   : 0.603
                         : -------------------------------------------------------------------------------------------------------------------
                         : 
                         : 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              PyTorch        : 0.045 (0.305)       0.585 (0.713)      0.790 (0.926)
                         : dataset              BDT            : 0.135 (0.325)       0.365 (0.752)      0.751 (0.901)
                         : dataset              TMVA_CNN_CPU   : 0.035 (0.115)       0.315 (0.476)      0.638 (0.742)
                         : dataset              PyKeras        : 0.030 (0.075)       0.189 (0.361)      0.470 (0.713)
                         : dataset              TMVA_DNN_CPU   : 0.025 (0.105)       0.200 (0.402)      0.409 (0.681)
                         : -------------------------------------------------------------------------------------------------------------------
                         : 
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
custom objects for loading model :  {'optimizer': <class 'torch.optim.adam.Adam'>, 'criterion': BCELoss(), 'train_func': <function fit at 0x7f7b44045a60>, 'predict_func': <function predict at 0x7f7b44045c10>}

 
 
# 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 ROOT
 
#switch off MT in OpenMP (BLAS)
ROOT.gSystem.Setenv("OMP_NUM_THREADS", "1")
 
TMVA = ROOT.TMVA
TFile = ROOT.TFile
 
 
import os
import importlib
 
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 = ROOT.gSystem.GetFromPipe("root-config --has-tmva-gpu") == "yes"
hasCPU = ROOT.gSystem.GetFromPipe("root-config --has-tmva-cpu") == "yes"
 
nevt = 1000    # use a larger value to get better results
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
 
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 ROOT.gSystem.GetFromPipe("root-config --has-tmva-pymva") != "yes":
    useKerasCNN = False
    usePyTorchCNN = False
else:
    TMVA.PyMethodBase.PyInitialize()
 
tf_spec = importlib.util.find_spec("tensorflow")
if tf_spec is None:
    useKerasCNN = False
    ROOT.Warning("TMVA_CNN_Classificaton","Skip using Keras since tensorflow is not installed")
 
torch_spec = importlib.util.find_spec("torch")
if torch_spec is None:
    usePyTorchCNN = False
    ROOT.Warning("TMVA_CNN_Classificaton","Skip using PyTorch since torch is not installed")
 
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 default threads
max_epochs = 10  # maximum number of epochs used for training
 
 
# do enable MT running
if num_threads >= 0:
    ROOT.EnableImplicitMT(num_threads)
 
print("Running with nthreads  = ", ROOT.GetThreadPoolSize())
 
 
 
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 += "/tmva/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
    import tensorflow
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.optimizers import Adam
 
    # from keras.initializers import TruncatedNormal
    # from keras import initializations
    from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape
 
    # 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.h5")
    model.summary()
 
    if not os.path.exists("model_cnn.h5"):
        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.h5",
            FilenameTrainedModel="trained_model_cnn.h5",
            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.