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TMVA_CNN_Classification.C File Reference
Tutorials » TMVA tutorials

Detailed Description

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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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.:Architecture=CPU"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.:Architecture=CPU"
: The following options are set:
: - By User:
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: 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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0." [Defines the training strategies.]
: - Default:
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: 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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0:Architecture=CPU"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0:Architecture=CPU"
: The following options are set:
: - By User:
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: InputLayout: "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,MaxEpochs=10,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0" [Defines the training strategies.]
: - Default:
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: BatchLayout: "0|0|0" [The Layout of the batch]
: 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 !
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
reshape (Reshape) (None, 16, 16, 1) 0
conv2d (Conv2D) (None, 16, 16, 10) 100
batch_normalization (Batch (None, 16, 16, 10) 40
Normalization)
conv2d_1 (Conv2D) (None, 16, 16, 10) 910
max_pooling2d (MaxPooling2 (None, 15, 15, 10) 0
D)
flatten (Flatten) (None, 2250) 0
dense (Dense) (None, 256) 576256
dense_1 (Dense) (None, 2) 514
=================================================================
Total params: 577820 (2.20 MB)
Trainable params: 577800 (2.20 MB)
Non-trainable params: 20 (80.00 Byte)
_________________________________________________________________
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 : Booking method: ␛[1mPyTorch␛[0m
:
: Using PyTorch - setting special configuration options
: Using PyTorch version 2
: Setup PyTorch Model for training
: Executing user initialization code from /home/sftnight/build/workspace/root-makedoc-v632/rootspi/rdoc/src/v6-32-00-patches.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: PyTorchModelCNN.pt
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 200 Decision Trees ... patience please
: Elapsed time for training with 1600 events: 0.92 sec
BDT : [dataset] : Evaluation of BDT on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.017 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 = 20.7014
: --------------------------------------------------------------
: 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 | 0.905303 0.874533 0.18125 0.016734 7294.1 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.705465 0.787148 0.179483 0.0155336 7319.35 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.590074 0.715658 0.179471 0.0155064 7318.64 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.534285 0.702733 0.179838 0.0156703 7309.61 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.49621 0.668084 0.180275 0.0158906 7299.95 0
: 6 | 0.446924 0.70173 0.179454 0.0149805 7296 1
: 7 | 0.422794 0.668243 0.179863 0.0150046 7278.98 2
: 8 | 0.360381 0.698093 0.178082 0.0148501 7351.52 3
: 9 Minimum Test error found - save the configuration
: 9 | 0.313036 0.664967 0.180209 0.0158448 7300.87 0
: 10 | 0.281233 0.697551 0.179573 0.0149272 7288.37 1
:
: Elapsed time for training with 1600 events: 1.83 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: 0.0793 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 = 148.341
: --------------------------------------------------------------
: 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 | 2.83293 1.02439 1.54989 0.120528 839.537 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.96017 0.802735 1.53158 0.119286 849.684 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.767107 0.771736 1.52576 0.120307 853.819 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.719783 0.717977 1.53085 0.121489 851.452 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.707696 0.714968 1.51488 0.117386 858.678 0
: 6 | 0.680172 0.721918 1.5224 0.116598 853.607 1
: 7 | 0.677788 0.719315 1.51837 0.115458 855.362 2
: 8 Minimum Test error found - save the configuration
: 8 | 0.669998 0.692828 1.52125 0.118219 855.293 0
: 9 | 0.654588 0.693428 1.508 0.115413 861.708 1
: 10 | 0.641201 0.696572 1.51447 0.116524 858.4 2
:
: Elapsed time for training with 1600 events: 15.4 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: 0.621 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: 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
custom objects for loading model : {'optimizer': <class 'torch.optim.adam.Adam'>, 'criterion': BCELoss(), 'train_func': <function fit at 0x7f664c990ca0>, 'predict_func': <function predict at 0x7f664c990dc0>}
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
reshape (Reshape) (None, 16, 16, 1) 0
conv2d (Conv2D) (None, 16, 16, 10) 100
batch_normalization (Batch (None, 16, 16, 10) 40
Normalization)
conv2d_1 (Conv2D) (None, 16, 16, 10) 910
max_pooling2d (MaxPooling2 (None, 15, 15, 10) 0
D)
flatten (Flatten) (None, 2250) 0
dense (Dense) (None, 256) 576256
dense_1 (Dense) (None, 2) 514
=================================================================
Total params: 577820 (2.20 MB)
Trainable params: 577800 (2.20 MB)
Non-trainable params: 20 (80.00 Byte)
_________________________________________________________________
: Option SaveBestOnly: Only model weights with smallest validation loss will be stored
Epoch 1/10
1/13 [=>............................] - ETA: 10s - loss: 0.7256 - accuracy: 0.5700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
5/13 [==========>...................] - ETA: 0s - loss: 2.1267 - accuracy: 0.5260 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/13 [======================>.......] - ETA: 0s - loss: 1.6344 - accuracy: 0.5160
Epoch 1: val_loss improved from inf to 1.06710, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 1s 47ms/step - loss: 1.4778 - accuracy: 0.5195 - val_loss: 1.0671 - val_accuracy: 0.5281
Epoch 2/10
1/13 [=>............................] - ETA: 0s - loss: 0.8484 - accuracy: 0.4500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.7588 - accuracy: 0.4883␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.7319 - accuracy: 0.5227
Epoch 2: val_loss improved from 1.06710 to 0.74125, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 18ms/step - loss: 0.7263 - accuracy: 0.5250 - val_loss: 0.7413 - val_accuracy: 0.5219
Epoch 3/10
1/13 [=>............................] - ETA: 0s - loss: 0.6876 - accuracy: 0.5200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.6812 - accuracy: 0.5583␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.6764 - accuracy: 0.5927
Epoch 3: val_loss improved from 0.74125 to 0.68728, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 18ms/step - loss: 0.6763 - accuracy: 0.5836 - val_loss: 0.6873 - val_accuracy: 0.5625
Epoch 4/10
1/13 [=>............................] - ETA: 0s - loss: 0.6665 - accuracy: 0.5700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.6652 - accuracy: 0.6167␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.6645 - accuracy: 0.6064
Epoch 4: val_loss improved from 0.68728 to 0.68011, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 18ms/step - loss: 0.6632 - accuracy: 0.6148 - val_loss: 0.6801 - val_accuracy: 0.5500
Epoch 5/10
1/13 [=>............................] - ETA: 0s - loss: 0.6441 - accuracy: 0.7000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.6475 - accuracy: 0.6683␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.6458 - accuracy: 0.6645
Epoch 5: val_loss did not improve from 0.68011
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 15ms/step - loss: 0.6452 - accuracy: 0.6711 - val_loss: 0.6826 - val_accuracy: 0.5406
Epoch 6/10
1/13 [=>............................] - ETA: 0s - loss: 0.6108 - accuracy: 0.6700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.6301 - accuracy: 0.6733␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.6344 - accuracy: 0.6691
Epoch 6: val_loss improved from 0.68011 to 0.65956, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 19ms/step - loss: 0.6312 - accuracy: 0.6781 - val_loss: 0.6596 - val_accuracy: 0.6000
Epoch 7/10
1/13 [=>............................] - ETA: 0s - loss: 0.6018 - accuracy: 0.7500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.6038 - accuracy: 0.7433␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.6122 - accuracy: 0.7173
Epoch 7: val_loss did not improve from 0.65956
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 15ms/step - loss: 0.6113 - accuracy: 0.7195 - val_loss: 0.6671 - val_accuracy: 0.6062
Epoch 8/10
1/13 [=>............................] - ETA: 0s - loss: 0.6229 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.5963 - accuracy: 0.7150␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.5894 - accuracy: 0.7336
Epoch 8: val_loss improved from 0.65956 to 0.65290, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 18ms/step - loss: 0.5879 - accuracy: 0.7344 - val_loss: 0.6529 - val_accuracy: 0.6219
Epoch 9/10
1/13 [=>............................] - ETA: 0s - loss: 0.5921 - accuracy: 0.7200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.5651 - accuracy: 0.7283␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.5868 - accuracy: 0.6800
Epoch 9: val_loss did not improve from 0.65290
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 14ms/step - loss: 0.5856 - accuracy: 0.6859 - val_loss: 0.7493 - val_accuracy: 0.5188
Epoch 10/10
1/13 [=>............................] - ETA: 0s - loss: 0.6377 - accuracy: 0.5600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
7/13 [===============>..............] - ETA: 0s - loss: 0.5596 - accuracy: 0.7086␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/13 [==========================>...] - ETA: 0s - loss: 0.5536 - accuracy: 0.7283
Epoch 10: val_loss improved from 0.65290 to 0.62709, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 33ms/step - loss: 0.5500 - accuracy: 0.7367 - val_loss: 0.6271 - val_accuracy: 0.6844
: 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: 3.67 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: 0.214 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
:
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: 22.9 sec
PyTorch : [dataset] : Evaluation of PyTorch on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.422 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
:
: Ranking input variables (method specific)...
BDT : Ranking result (top variable is best ranked)
: --------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------
: 1 : vars : 1.193e-02
: 2 : vars : 1.046e-02
: 3 : vars : 1.022e-02
: 4 : vars : 9.499e-03
: 5 : vars : 8.957e-03
: 6 : vars : 8.927e-03
: 7 : vars : 8.827e-03
: 8 : vars : 8.503e-03
: 9 : vars : 8.402e-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: PyKeras
: No variable ranking supplied by classifier: PyTorch
TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_trainingError, Entries= 0, Total sum= 5.0557
TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 7.17874
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 9.31143
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 7.55587
TH1.Print Name = TrainingHistory_PyKeras_'accuracy', Entries= 0, Total sum= 6.46875
TH1.Print Name = TrainingHistory_PyKeras_'loss', Entries= 0, Total sum= 7.15486
TH1.Print Name = TrainingHistory_PyKeras_'val_accuracy', Entries= 0, Total sum= 5.73438
TH1.Print Name = TrainingHistory_PyKeras_'val_loss', Entries= 0, Total sum= 7.21435
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_PyKeras.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_PyTorch.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: 0.00571 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: 0.0187 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: 0.159 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: 0.175 sec
Factory : Test method: PyTorch for Classification performance
:
: Setup PyTorch Model for training
: Executing user initialization code from /home/sftnight/build/workspace/root-makedoc-v632/rootspi/rdoc/src/v6-32-00-patches.build/tutorials/tmva/PyTorch_Generate_CNN_Model.py
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: 1.010
[1, 8] train loss: 0.727
[1, 12] train loss: 0.703
[1] val loss: 0.751
[2, 4] train loss: 0.703
[2, 8] train loss: 0.695
[2, 12] train loss: 0.693
[2] val loss: 0.687
[3, 4] train loss: 0.687
[3, 8] train loss: 0.682
[3, 12] train loss: 0.682
[3] val loss: 0.680
[4, 4] train loss: 0.668
[4, 8] train loss: 0.655
[4, 12] train loss: 0.661
[4] val loss: 0.703
[5, 4] train loss: 0.646
[5, 8] train loss: 0.626
[5, 12] train loss: 0.628
[5] val loss: 0.715
[6, 4] train loss: 0.593
[6, 8] train loss: 0.558
[6, 12] train loss: 0.542
[6] val loss: 0.755
[7, 4] train loss: 0.525
[7, 8] train loss: 0.506
[7, 12] train loss: 0.518
[7] val loss: 0.582
[8, 4] train loss: 0.517
[8, 8] train loss: 0.431
[8, 12] train loss: 0.423
[8] val loss: 0.619
[9, 4] train loss: 0.424
[9, 8] train loss: 0.381
[9, 12] train loss: 0.401
[9] val loss: 0.798
[10, 4] train loss: 0.427
[10, 8] train loss: 0.354
[10, 12] train loss: 0.304
[10] val loss: 0.807
Finished Training on 10 Epochs!
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: 0.119 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: 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
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
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset PyKeras : 0.724
: dataset BDT : 0.719
: dataset TMVA_DNN_CPU : 0.708
: dataset PyTorch : 0.703
: dataset TMVA_CNN_CPU : 0.571
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset PyKeras : 0.035 (0.139) 0.305 (0.422) 0.585 (0.719)
: dataset BDT : 0.085 (0.307) 0.346 (0.575) 0.620 (0.768)
: dataset TMVA_DNN_CPU : 0.045 (0.157) 0.270 (0.528) 0.555 (0.829)
: dataset PyTorch : 0.055 (0.105) 0.279 (0.473) 0.625 (0.697)
: dataset TMVA_CNN_CPU : 0.015 (0.057) 0.162 (0.196) 0.331 (0.406)
: -------------------------------------------------------------------------------------------------------------------
:
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
///
///
void MakeImagesTree(int n, int nh, int nw)
{
// image size (nh x nw)
const int ntot = nh * nw;
const TString fileOutName = TString::Format("images_data_%dx%d.root", nh, nw);
TFile f(fileOutName, "RECREATE");
const int nRndmEvts = 10000; // number of events we use to fill each image
double delta_sigma = 0.1; // 5% difference in the sigma
double pixelNoise = 5;
double sX1 = 3;
double sY1 = 3;
double sX2 = sX1 + delta_sigma;
double sY2 = sY1 - delta_sigma;
TH2D h1("h1", "h1", nh, 0, 10, nw, 0, 10);
TH2D h2("h2", "h2", nh, 0, 10, nw, 0, 10);
TF2 f1("f1", "xygaus");
TF2 f2("f2", "xygaus");
TTree sgn("sig_tree", "signal_tree");
TTree bkg("bkg_tree", "background_tree");
std::vector<float> x1(ntot);
std::vector<float> x2(ntot);
// create signal and background trees with a single branch
// an std::vector<float> of size nh x nw containing the image data
std::vector<float> *px1 = &x1;
std::vector<float> *px2 = &x2;
bkg.Branch("vars", "std::vector<float>", &px1);
sgn.Branch("vars", "std::vector<float>", &px2);
// std::cout << "create tree " << std::endl;
sgn.SetDirectory(&f);
bkg.SetDirectory(&f);
f1.SetParameters(1, 5, sX1, 5, sY1);
f2.SetParameters(1, 5, sX2, 5, sY2);
gRandom->SetSeed(0);
std::cout << "Filling ROOT tree " << std::endl;
for (int i = 0; i < n; ++i) {
if (i % 1000 == 0)
std::cout << "Generating image event ... " << i << std::endl;
h1.Reset();
h2.Reset();
// generate random means in range [3,7] to be not too much on the border
f1.SetParameter(1, gRandom->Uniform(3, 7));
f1.SetParameter(3, gRandom->Uniform(3, 7));
f2.SetParameter(1, gRandom->Uniform(3, 7));
f2.SetParameter(3, gRandom->Uniform(3, 7));
h1.FillRandom("f1", nRndmEvts);
h2.FillRandom("f2", nRndmEvts);
for (int k = 0; k < nh; ++k) {
for (int l = 0; l < nw; ++l) {
int m = k * nw + l;
// add some noise in each bin
x1[m] = h1.GetBinContent(k + 1, l + 1) + gRandom->Gaus(0, pixelNoise);
x2[m] = h2.GetBinContent(k + 1, l + 1) + gRandom->Gaus(0, pixelNoise);
}
}
sgn.Fill();
bkg.Fill();
}
sgn.Write();
bkg.Write();
Info("MakeImagesTree", "Signal and background tree with images data written to the file %s", f.GetName());
sgn.Print();
bkg.Print();
f.Close();
}
/// @brief Run the TMVA CNN Classification example
/// @param nevts : number of signal/background events. Use by default a low value (1000)
/// but increase to at least 5000 to get a good result
/// @param opt : vector of bool with method used (default all on if available). The order is:
/// - TMVA CNN
/// - Keras CNN
/// - TMVA DNN
/// - TMVA BDT
/// - PyTorch CNN
void TMVA_CNN_Classification(int nevts = 1000, std::vector<bool> opt = {1, 1, 1, 1, 1})
{
int imgSize = 16 * 16;
TString inputFileName = "images_data_16x16.root";
bool fileExist = !gSystem->AccessPathName(inputFileName);
// if file does not exists create it
if (!fileExist) {
MakeImagesTree(nevts, 16, 16);
}
bool useTMVACNN = (opt.size() > 0) ? opt[0] : false;
bool useKerasCNN = (opt.size() > 1) ? opt[1] : false;
bool useTMVADNN = (opt.size() > 2) ? opt[2] : false;
bool useTMVABDT = (opt.size() > 3) ? opt[3] : false;
bool usePyTorchCNN = (opt.size() > 4) ? opt[4] : false;
#ifndef R__HAS_TMVACPU
#ifndef R__HAS_TMVAGPU
Warning("TMVA_CNN_Classification",
"TMVA is not build with GPU or CPU multi-thread support. Cannot use TMVA Deep Learning for CNN");
useTMVACNN = false;
#endif
#endif
bool writeOutputFile = true;
#ifdef R__USE_IMT
int num_threads = 4; // use by default 4 threads if value is not set before
// switch off MT in OpenBLAS to avoid conflict with tbb
gSystem->Setenv("OMP_NUM_THREADS", "1");
// do enable MT running
if (num_threads >= 0) {
ROOT::EnableImplicitMT(num_threads);
}
#endif
TMVA::Tools::Instance();
std::cout << "Running with nthreads = " << ROOT::GetThreadPoolSize() << std::endl;
#ifdef R__HAS_PYMVA
gSystem->Setenv("KERAS_BACKEND", "tensorflow");
// for using Keras
TMVA::PyMethodBase::PyInitialize();
#else
useKerasCNN = false;
usePyTorchCNN = false;
#endif
TFile *outputFile = nullptr;
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
***/
TMVA::Factory factory(
"TMVA_CNN_Classification", outputFile,
"!V:ROC:!Silent:Color:AnalysisType=Classification:Transformations=None:!Correlations");
/***
## 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
**/
TMVA::DataLoader loader("dataset");
/***
## Setup Dataset(s)
Define input data file and signal and background trees
**/
std::unique_ptr<TFile> inputFile{TFile::Open(inputFileName)};
if (!inputFile) {
Error("TMVA_CNN_Classification", "Error opening input file %s - exit", inputFileName.Data());
return;
}
// --- Register the training and test trees
auto signalTree = inputFile->Get<TTree>("sig_tree");
auto backgroundTree = inputFile->Get<TTree>("bkg_tree");
if (!signalTree) {
Error("TMVA_CNN_Classification", "Could not find signal tree in file '%s'", inputFileName.Data());
return;
}
if (!backgroundTree) {
Error("TMVA_CNN_Classification", "Could not find background tree in file '%s'", inputFileName.Data());
return;
}
int nEventsSig = signalTree->GetEntries();
int nEventsBkg = backgroundTree->GetEntries();
// global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 1.0;
Double_t 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)
TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
TCut 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
int nTrainSig = 0.8 * nEventsSig;
int nTrainBkg = 0.8 * nEventsBkg;
// build the string options for DataLoader::PrepareTrainingAndTestTree
TString prepareOptions = TString::Format(
"nTrain_Signal=%d:nTrain_Background=%d:SplitMode=Random:SplitSeed=100:NormMode=NumEvents:!V:!CalcCorrelations",
nTrainSig, nTrainBkg);
loader.PrepareTrainingAndTestTree(mycuts, mycutb, prepareOptions);
/***
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
**/
/****
# 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:NTrees=200:MinNodeSize=2.5%:MaxDepth=2:BoostType=AdaBoost:AdaBoostBeta=0.5:"
"UseBaggedBoost: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) {
TString layoutString(
"Layout=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 concatenates with the `|` delimiter
TString trainingString1("LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
"ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,"
"MaxEpochs=10,WeightDecay=1e-4,Regularization=None,"
"Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.");
TString trainingStrategyString("TrainingStrategy=");
trainingStrategyString += trainingString1; // + "|" + trainingString2 + ....
// Build now the full DNN Option string
TString dnnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:"
"WeightInitialization=XAVIER");
dnnOptions.Append(":");
dnnOptions.Append(layoutString);
dnnOptions.Append(":");
dnnOptions.Append(trainingStrategyString);
TString dnnMethodName = "TMVA_DNN_CPU";
// use GPU if available
#ifdef R__HAS_TMVAGPU
dnnOptions += ":Architecture=GPU";
dnnMethodName = "TMVA_DNN_GPU";
#elif defined(R__HAS_TMVACPU)
dnnOptions += ":Architecture=CPU";
#endif
factory.BookMethod(&loader, TMVA::Types::kDL, dnnMethodName, 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) {
TString inputLayoutString("InputLayout=1|16|16");
// Batch Layout
TString layoutString("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");
// Training strategies.
TString trainingString1("LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
"ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,"
"MaxEpochs=10,WeightDecay=1e-4,Regularization=None,"
"Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0");
TString trainingStrategyString("TrainingStrategy=");
trainingStrategyString +=
trainingString1; // + "|" + trainingString2 + "|" + trainingString3; for concatenating more training strings
// Build full CNN Options.
TString cnnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:"
"WeightInitialization=XAVIER");
cnnOptions.Append(":");
cnnOptions.Append(inputLayoutString);
cnnOptions.Append(":");
cnnOptions.Append(layoutString);
cnnOptions.Append(":");
cnnOptions.Append(trainingStrategyString);
//// New DL (CNN)
TString cnnMethodName = "TMVA_CNN_CPU";
// use GPU if available
#ifdef R__HAS_TMVAGPU
cnnOptions += ":Architecture=GPU";
cnnMethodName = "TMVA_CNN_GPU";
#else
cnnOptions += ":Architecture=CPU";
cnnMethodName = "TMVA_CNN_CPU";
#endif
factory.BookMethod(&loader, TMVA::Types::kDL, cnnMethodName, cnnOptions);
}
/**
### Book Convolutional Neural Network in Keras using a generated model
**/
#ifdef R__HAS_PYMVA
// The next section uses Python packages, execute it only if PyMVA is available
TString tmva_python_exe{TMVA::Python_Executable()};
TString python_exe = tmva_python_exe.IsNull() ? "python" : tmva_python_exe;
if (useKerasCNN) {
Info("TMVA_CNN_Classification", "Building convolutional keras model");
// create python script which can be executed
// create 2 conv2d layer + maxpool + dense
TMacro m;
m.AddLine("import tensorflow");
m.AddLine("from tensorflow.keras.models import Sequential");
m.AddLine("from tensorflow.keras.optimizers import Adam");
m.AddLine(
"from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape, BatchNormalization");
m.AddLine("");
m.AddLine("model = Sequential() ");
m.AddLine("model.add(Reshape((16, 16, 1), input_shape = (256, )))");
m.AddLine("model.add(Conv2D(10, kernel_size = (3, 3), kernel_initializer = 'glorot_normal',activation = "
"'relu', padding = 'same'))");
m.AddLine("model.add(BatchNormalization())");
m.AddLine("model.add(Conv2D(10, kernel_size = (3, 3), kernel_initializer = 'glorot_normal',activation = "
"'relu', padding = 'same'))");
// m.AddLine("model.add(BatchNormalization())");
m.AddLine("model.add(MaxPooling2D(pool_size = (2, 2), strides = (1,1))) ");
m.AddLine("model.add(Flatten())");
m.AddLine("model.add(Dense(256, activation = 'relu')) ");
m.AddLine("model.add(Dense(2, activation = 'sigmoid')) ");
m.AddLine("model.compile(loss = 'binary_crossentropy', optimizer = Adam(learning_rate = 0.001), weighted_metrics = ['accuracy'])");
m.AddLine("model.save('model_cnn.h5')");
m.AddLine("model.summary()");
m.SaveSource("make_cnn_model.py");
// execute
gSystem->Exec(python_exe + " make_cnn_model.py");
if (gSystem->AccessPathName("model_cnn.h5")) {
Warning("TMVA_CNN_Classification", "Error creating Keras model file - skip using Keras");
} else {
// book PyKeras method only if Keras model could be created
Info("TMVA_CNN_Classification", "Booking tf.Keras CNN model");
factory.BookMethod(
&loader, TMVA::Types::kPyKeras, "PyKeras",
"H:!V:VarTransform=None:FilenameModel=model_cnn.h5:tf.keras:"
"FilenameTrainedModel=trained_model_cnn.h5:NumEpochs=10:BatchSize=100:"
"GpuOptions=allow_growth=True"); // needed for RTX NVidia card and to avoid TF allocates all GPU memory
}
}
if (usePyTorchCNN) {
Info("TMVA_CNN_Classification", "Using Convolutional PyTorch Model");
TString pyTorchFileName = gROOT->GetTutorialDir() + TString("/tmva/PyTorch_Generate_CNN_Model.py");
// check that pytorch can be imported and file defining the model and used later when booking the method is
// existing
if (gSystem->Exec(python_exe + " -c 'import torch'") || gSystem->AccessPathName(pyTorchFileName)) {
Warning("TMVA_CNN_Classification", "PyTorch is not installed or model building file is not existing - skip using PyTorch");
} else {
// book PyTorch method only if PyTorch model could be created
Info("TMVA_CNN_Classification", "Booking PyTorch CNN model");
TString methodOpt = "H:!V:VarTransform=None:FilenameModel=PyTorchModelCNN.pt:"
"FilenameTrainedModel=PyTorchTrainedModelCNN.pt:NumEpochs=10:BatchSize=100";
methodOpt += TString(":UserCode=") + pyTorchFileName;
factory.BookMethod(&loader, TMVA::Types::kPyTorch, "PyTorch", methodOpt);
}
}
#endif
//// ## Train Methods
factory.TrainAllMethods();
/// ## Test and Evaluate Methods
factory.TestAllMethods();
factory.EvaluateAllMethods();
/// ## Plot ROC Curve
auto c1 = factory.GetROCCurve(&loader);
c1->Draw();
// close outputfile to save output file
outputFile->Close();
}
f
#define f(i)
Definition RSha256.hxx:104
Double_t
double Double_t
Definition RtypesCore.h:59
Info
void Info(const char *location, const char *msgfmt,...)
Use this function for informational messages.
Definition TError.cxx:218
Error
void Error(const char *location, const char *msgfmt,...)
Use this function in case an error occurred.
Definition TError.cxx:185
Warning
void Warning(const char *location, const char *msgfmt,...)
Use this function in warning situations.
Definition TError.cxx:229
x2
Option_t Option_t TPoint TPoint const char x2
Definition TGWin32VirtualXProxy.cxx:70
x1
Option_t Option_t TPoint TPoint const char x1
Definition TGWin32VirtualXProxy.cxx:70
gROOT
#define gROOT
Definition TROOT.h:406
gRandom
R__EXTERN TRandom * gRandom
Definition TRandom.h:62
gSystem
R__EXTERN TSystem * gSystem
Definition TSystem.h:555
TCut
A specialized string object used for TTree selections.
Definition TCut.h:25
TF1::SetParameters
virtual void SetParameters(const Double_t *params)
Definition TF1.h:670
TF1::SetParameter
virtual void SetParameter(Int_t param, Double_t value)
Definition TF1.h:660
TF2
A 2-Dim function with parameters.
Definition TF2.h:29
TFile
A ROOT file is an on-disk file, usually with extension .root, that stores objects in a file-system-li...
Definition TFile.h:53
TFile::Open
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition TFile.cxx:4089
TFile::Close
void Close(Option_t *option="") override
Close a file.
Definition TFile.cxx:950
TH1F::Reset
void Reset(Option_t *option="") override
Reset.
Definition TH1.cxx:10278
TH1::FillRandom
virtual void FillRandom(const char *fname, Int_t ntimes=5000, TRandom *rng=nullptr)
Fill histogram following distribution in function fname.
Definition TH1.cxx:3519
TH1::GetBinContent
virtual Double_t GetBinContent(Int_t bin) const
Return content of bin number bin.
Definition TH1.cxx:5029
TH2D
2-D histogram with a double per channel (see TH1 documentation)
Definition TH2.h:357
TMVA::Factory
This is the main MVA steering class.
Definition Factory.h:80
TMVA::PyMethodBase::PyInitialize
static void PyInitialize()
Initialize Python interpreter.
Definition PyMethodBase.cxx:153
TMVA::Tools::Instance
static Tools & Instance()
Definition Tools.cxx:71
TMacro
Class supporting a collection of lines with C++ code.
Definition TMacro.h:31
TRandom::Gaus
virtual Double_t Gaus(Double_t mean=0, Double_t sigma=1)
Samples a random number from the standard Normal (Gaussian) Distribution with the given mean and sigm...
Definition TRandom.cxx:275
TRandom::SetSeed
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
Definition TRandom.cxx:615
TRandom::Uniform
virtual Double_t Uniform(Double_t x1=1)
Returns a uniform deviate on the interval (0, x1).
Definition TRandom.cxx:682
TString
Basic string class.
Definition TString.h:139
TString::Data
const char * Data() const
Definition TString.h:376
TString::IsNull
Bool_t IsNull() const
Definition TString.h:414
TString::Format
static TString Format(const char *fmt,...)
Static method which formats a string using a printf style format descriptor and return a TString.
Definition TString.cxx:2378
TSystem::Exec
virtual Int_t Exec(const char *shellcmd)
Execute a command.
Definition TSystem.cxx:653
TSystem::AccessPathName
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
Definition TSystem.cxx:1296
TSystem::Setenv
virtual void Setenv(const char *name, const char *value)
Set environment variable.
Definition TSystem.cxx:1649
TTree
A TTree represents a columnar dataset.
Definition TTree.h:79
TTree::GetEntries
virtual Long64_t GetEntries() const
Definition TTree.h:463
c1
return c1
Definition legend1.C:41
n
const Int_t n
Definition legend1.C:16
h1
TH1F * h1
Definition legend1.C:5
f1
TF1 * f1
Definition legend1.C:11
ROOT::EnableImplicitMT
void EnableImplicitMT(UInt_t numthreads=0)
Enable ROOT's implicit multi-threading for all objects and methods that provide an internal paralleli...
Definition TROOT.cxx:539
ROOT::GetThreadPoolSize
UInt_t GetThreadPoolSize()
Returns the size of ROOT's thread pool.
Definition TROOT.cxx:577
TMVA_CNN_Classification
Definition TMVA_CNN_Classification.py:1
TMVA::Python_Executable
TString Python_Executable()
Function to find current Python executable used by ROOT If "Python3" is installed,...
Definition PyMethodBase.cxx:43
m
TMarker m
Definition textangle.C:8
l
TLine l
Definition textangle.C:4
Author
Lorenzo Moneta

Definition in file TMVA_CNN_Classification.C.