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TMVA_CNN_Classification.py File Reference

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namespace  TMVA_CNN_Classification
 

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

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 2
: 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 0x7fee85bc1820>, 'predict_func': <function predict at 0x7fee85bc18b0>}
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 (MaxPooling2 (None, 8, 8, 10) 0
D)
flatten (Flatten) (None, 640) 0
dense (Dense) (None, 64) 41024
dense_1 (Dense) (None, 2) 130
=================================================================
Total params: 42164 (164.70 KB)
Trainable params: 42164 (164.70 KB)
Non-trainable params: 0 (0.00 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 : ␛[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: 1.79 sec
BDT : [dataset] : Evaluation of BDT on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.0421 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 | 0.878195 0.952076 0.18284 0.0162537 7203.48 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.677155 0.850495 0.182016 0.0171033 7276.56 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.614541 0.781907 0.183102 0.0160072 7181.56 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.535788 0.772875 0.181678 0.0159369 7240.22 0
: 5 | 0.487129 0.784808 0.180684 0.0151983 7251.39 1
: 6 | 0.435332 0.795604 0.180478 0.0151297 7257.4 2
: 7 | 0.387087 0.77577 0.183224 0.0157813 7166.64 3
: 8 | 0.332298 0.774405 0.180225 0.0152216 7272.56 4
: 9 | 0.28438 0.841246 0.180424 0.0154335 7273.16 5
: 10 | 0.256234 0.875934 0.183807 0.0151163 7113.6 6
:
: Elapsed time for training with 1600 events: 1.86 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: 0.0794 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 | 2.26561 1.26566 1.56521 0.124126 832.709 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.802278 0.764696 1.55261 0.123338 839.587 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.718661 0.700742 1.55242 0.124535 840.405 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.688676 0.696206 1.55692 0.123002 836.868 0
: 5 | 0.681639 0.710277 1.5438 0.117411 841.283 1
: 6 Minimum Test error found - save the configuration
: 6 | 0.679707 0.685444 1.52869 0.119093 851.307 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.664508 0.675794 1.53501 0.120042 848.073 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.651955 0.672914 1.53112 0.121769 851.457 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.647983 0.664562 1.53044 0.118905 850.14 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.649466 0.651671 1.54672 0.130357 847.24 0
:
: Elapsed time for training with 1600 events: 15.6 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: 0.647 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: 23.5 sec
PyTorch : [dataset] : Evaluation of PyTorch on training sample (1600 events)
: Elapsed time for evaluation of 1600 events: 0.437 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: 1.278
[1, 8] train loss: 0.735
[1, 12] train loss: 0.712
[1] val loss: 0.740
[2, 4] train loss: 0.702
[2, 8] train loss: 0.688
[2, 12] train loss: 0.692
[2] val loss: 0.693
[3, 4] train loss: 0.686
[3, 8] train loss: 0.680
[3, 12] train loss: 0.677
[3] val loss: 0.708
[4, 4] train loss: 0.667
[4, 8] train loss: 0.656
[4, 12] train loss: 0.652
[4] val loss: 0.742
[5, 4] train loss: 0.624
[5, 8] train loss: 0.646
[5, 12] train loss: 0.643
[5] val loss: 0.613
[6, 4] train loss: 0.606
[6, 8] train loss: 0.617
[6, 12] train loss: 0.615
[6] val loss: 0.634
[7, 4] train loss: 0.561
[7, 8] train loss: 0.559
[7, 12] train loss: 0.522
[7] val loss: 0.794
[8, 4] train loss: 0.463
[8, 8] train loss: 0.454
[8, 12] train loss: 0.540
[8] val loss: 0.536
[9, 4] train loss: 0.402
[9, 8] train loss: 0.518
[9, 12] train loss: 0.477
[9] val loss: 0.631
[10, 4] train loss: 0.458
[10, 8] train loss: 0.499
[10, 12] train loss: 0.436
[10] val loss: 0.680
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 (MaxPooling2 (None, 8, 8, 10) 0
D)
flatten (Flatten) (None, 640) 0
dense (Dense) (None, 64) 41024
dense_1 (Dense) (None, 2) 130
=================================================================
Total params: 42164 (164.70 KB)
Trainable params: 42164 (164.70 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
: Option SaveBestOnly: Only model weights with smallest validation loss will be stored
Epoch 1/10
1/13 [=>............................] - ETA: 9s - loss: 0.9128 - accuracy: 0.5600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.7663 - accuracy: 0.4717␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/13 [==========================>...] - ETA: 0s - loss: 0.7448 - accuracy: 0.4942
Epoch 1: val_loss improved from inf to 0.69840, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 2s 63ms/step - loss: 0.7418 - accuracy: 0.4953 - val_loss: 0.6984 - val_accuracy: 0.5063
Epoch 2/10
1/13 [=>............................] - ETA: 0s - loss: 0.6960 - accuracy: 0.5300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
7/13 [===============>..............] - ETA: 0s - loss: 0.7046 - accuracy: 0.5171␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - ETA: 0s - loss: 0.6984 - accuracy: 0.5203
Epoch 2: val_loss improved from 0.69840 to 0.69796, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 16ms/step - loss: 0.6984 - accuracy: 0.5203 - val_loss: 0.6980 - val_accuracy: 0.4844
Epoch 3/10
1/13 [=>............................] - ETA: 0s - loss: 0.6936 - accuracy: 0.4900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.6865 - accuracy: 0.5417␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.6882 - accuracy: 0.5336
Epoch 3: val_loss improved from 0.69796 to 0.68400, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 16ms/step - loss: 0.6873 - accuracy: 0.5414 - val_loss: 0.6840 - val_accuracy: 0.5500
Epoch 4/10
1/13 [=>............................] - ETA: 0s - loss: 0.6850 - accuracy: 0.5700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
5/13 [==========>...................] - ETA: 0s - loss: 0.6800 - accuracy: 0.5760␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/13 [======================>.......] - ETA: 0s - loss: 0.6784 - accuracy: 0.5910
Epoch 4: val_loss improved from 0.68400 to 0.68383, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 19ms/step - loss: 0.6778 - accuracy: 0.5953 - val_loss: 0.6838 - val_accuracy: 0.5406
Epoch 5/10
1/13 [=>............................] - ETA: 0s - loss: 0.6709 - accuracy: 0.6200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
5/13 [==========>...................] - ETA: 0s - loss: 0.6757 - accuracy: 0.5820␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
10/13 [======================>.......] - ETA: 0s - loss: 0.6723 - accuracy: 0.6050
Epoch 5: val_loss improved from 0.68383 to 0.67881, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 19ms/step - loss: 0.6694 - accuracy: 0.6133 - val_loss: 0.6788 - val_accuracy: 0.5500
Epoch 6/10
1/13 [=>............................] - ETA: 0s - loss: 0.6557 - accuracy: 0.6100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.6537 - accuracy: 0.6417␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.6525 - accuracy: 0.6445
Epoch 6: val_loss improved from 0.67881 to 0.67050, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 17ms/step - loss: 0.6557 - accuracy: 0.6352 - val_loss: 0.6705 - val_accuracy: 0.5750
Epoch 7/10
1/13 [=>............................] - ETA: 0s - loss: 0.6260 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
6/13 [============>.................] - ETA: 0s - loss: 0.6307 - accuracy: 0.6950␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/13 [==========================>...] - ETA: 0s - loss: 0.6351 - accuracy: 0.6742
Epoch 7: val_loss improved from 0.67050 to 0.66346, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 16ms/step - loss: 0.6323 - accuracy: 0.6844 - val_loss: 0.6635 - val_accuracy: 0.5688
Epoch 8/10
1/13 [=>............................] - ETA: 0s - loss: 0.6193 - accuracy: 0.6200␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
7/13 [===============>..............] - ETA: 0s - loss: 0.6323 - accuracy: 0.6343␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - ETA: 0s - loss: 0.6248 - accuracy: 0.6555
Epoch 8: val_loss did not improve from 0.66346
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 13ms/step - loss: 0.6248 - accuracy: 0.6555 - val_loss: 0.6680 - val_accuracy: 0.5969
Epoch 9/10
1/13 [=>............................] - ETA: 0s - loss: 0.5964 - accuracy: 0.6900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
5/13 [==========>...................] - ETA: 0s - loss: 0.6142 - accuracy: 0.6760␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
11/13 [========================>.....] - ETA: 0s - loss: 0.6092 - accuracy: 0.7036
Epoch 9: val_loss improved from 0.66346 to 0.62907, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 17ms/step - loss: 0.6056 - accuracy: 0.7133 - val_loss: 0.6291 - val_accuracy: 0.6625
Epoch 10/10
1/13 [=>............................] - ETA: 0s - loss: 0.6117 - accuracy: 0.6900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
7/13 [===============>..............] - ETA: 0s - loss: 0.5846 - accuracy: 0.7329␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - ETA: 0s - loss: 0.5747 - accuracy: 0.7617
Epoch 10: val_loss improved from 0.62907 to 0.59210, saving model to trained_model_cnn.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
13/13 [==============================] - 0s 15ms/step - loss: 0.5747 - accuracy: 0.7617 - val_loss: 0.5921 - val_accuracy: 0.7094
: 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: 3.55 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: 0.169 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 : 9.169e-03
: 2 : vars : 8.732e-03
: 3 : vars : 7.920e-03
: 4 : vars : 7.823e-03
: 5 : vars : 7.810e-03
: 6 : vars : 7.655e-03
: 7 : vars : 7.637e-03
: 8 : vars : 7.554e-03
: 9 : vars : 7.510e-03
: 10 : vars : 7.449e-03
: 11 : vars : 7.428e-03
: 12 : vars : 7.209e-03
: 13 : vars : 6.952e-03
: 14 : vars : 6.912e-03
: 15 : vars : 6.840e-03
: 16 : vars : 6.839e-03
: 17 : vars : 6.800e-03
: 18 : vars : 6.776e-03
: 19 : vars : 6.747e-03
: 20 : vars : 6.739e-03
: 21 : vars : 6.599e-03
: 22 : vars : 6.590e-03
: 23 : vars : 6.581e-03
: 24 : vars : 6.508e-03
: 25 : vars : 6.474e-03
: 26 : vars : 6.370e-03
: 27 : vars : 6.323e-03
: 28 : vars : 6.315e-03
: 29 : vars : 6.247e-03
: 30 : vars : 6.210e-03
: 31 : vars : 6.121e-03
: 32 : vars : 6.056e-03
: 33 : vars : 6.051e-03
: 34 : vars : 6.015e-03
: 35 : vars : 6.003e-03
: 36 : vars : 5.971e-03
: 37 : vars : 5.926e-03
: 38 : vars : 5.835e-03
: 39 : vars : 5.821e-03
: 40 : vars : 5.796e-03
: 41 : vars : 5.793e-03
: 42 : vars : 5.744e-03
: 43 : vars : 5.705e-03
: 44 : vars : 5.698e-03
: 45 : vars : 5.692e-03
: 46 : vars : 5.657e-03
: 47 : vars : 5.555e-03
: 48 : vars : 5.551e-03
: 49 : vars : 5.519e-03
: 50 : vars : 5.469e-03
: 51 : vars : 5.396e-03
: 52 : vars : 5.357e-03
: 53 : vars : 5.348e-03
: 54 : vars : 5.347e-03
: 55 : vars : 5.339e-03
: 56 : vars : 5.307e-03
: 57 : vars : 5.302e-03
: 58 : vars : 5.283e-03
: 59 : vars : 5.283e-03
: 60 : vars : 5.236e-03
: 61 : vars : 5.224e-03
: 62 : vars : 5.221e-03
: 63 : vars : 5.192e-03
: 64 : vars : 5.191e-03
: 65 : vars : 5.182e-03
: 66 : vars : 5.175e-03
: 67 : vars : 5.138e-03
: 68 : vars : 5.128e-03
: 69 : vars : 5.119e-03
: 70 : vars : 5.079e-03
: 71 : vars : 5.078e-03
: 72 : vars : 4.967e-03
: 73 : vars : 4.951e-03
: 74 : vars : 4.935e-03
: 75 : vars : 4.923e-03
: 76 : vars : 4.887e-03
: 77 : vars : 4.871e-03
: 78 : vars : 4.851e-03
: 79 : vars : 4.844e-03
: 80 : vars : 4.804e-03
: 81 : vars : 4.788e-03
: 82 : vars : 4.746e-03
: 83 : vars : 4.728e-03
: 84 : vars : 4.705e-03
: 85 : vars : 4.660e-03
: 86 : vars : 4.620e-03
: 87 : vars : 4.601e-03
: 88 : vars : 4.595e-03
: 89 : vars : 4.551e-03
: 90 : vars : 4.513e-03
: 91 : vars : 4.467e-03
: 92 : vars : 4.460e-03
: 93 : vars : 4.429e-03
: 94 : vars : 4.423e-03
: 95 : vars : 4.423e-03
: 96 : vars : 4.409e-03
: 97 : vars : 4.399e-03
: 98 : vars : 4.391e-03
: 99 : vars : 4.388e-03
: 100 : vars : 4.340e-03
: 101 : vars : 4.307e-03
: 102 : vars : 4.276e-03
: 103 : vars : 4.272e-03
: 104 : vars : 4.251e-03
: 105 : vars : 4.240e-03
: 106 : vars : 4.240e-03
: 107 : vars : 4.218e-03
: 108 : vars : 4.183e-03
: 109 : vars : 4.176e-03
: 110 : vars : 4.158e-03
: 111 : vars : 4.136e-03
: 112 : vars : 4.105e-03
: 113 : vars : 4.095e-03
: 114 : vars : 4.082e-03
: 115 : vars : 4.075e-03
: 116 : vars : 4.072e-03
: 117 : vars : 4.063e-03
: 118 : vars : 4.062e-03
: 119 : vars : 4.054e-03
: 120 : vars : 4.052e-03
: 121 : vars : 4.033e-03
: 122 : vars : 3.981e-03
: 123 : vars : 3.963e-03
: 124 : vars : 3.923e-03
: 125 : vars : 3.907e-03
: 126 : vars : 3.862e-03
: 127 : vars : 3.835e-03
: 128 : vars : 3.832e-03
: 129 : vars : 3.824e-03
: 130 : vars : 3.823e-03
: 131 : vars : 3.785e-03
: 132 : vars : 3.776e-03
: 133 : vars : 3.776e-03
: 134 : vars : 3.774e-03
: 135 : vars : 3.757e-03
: 136 : vars : 3.729e-03
: 137 : vars : 3.721e-03
: 138 : vars : 3.705e-03
: 139 : vars : 3.640e-03
: 140 : vars : 3.620e-03
: 141 : vars : 3.615e-03
: 142 : vars : 3.584e-03
: 143 : vars : 3.584e-03
: 144 : vars : 3.566e-03
: 145 : vars : 3.534e-03
: 146 : vars : 3.527e-03
: 147 : vars : 3.492e-03
: 148 : vars : 3.489e-03
: 149 : vars : 3.466e-03
: 150 : vars : 3.455e-03
: 151 : vars : 3.418e-03
: 152 : vars : 3.408e-03
: 153 : vars : 3.387e-03
: 154 : vars : 3.377e-03
: 155 : vars : 3.362e-03
: 156 : vars : 3.352e-03
: 157 : vars : 3.351e-03
: 158 : vars : 3.350e-03
: 159 : vars : 3.310e-03
: 160 : vars : 3.302e-03
: 161 : vars : 3.296e-03
: 162 : vars : 3.220e-03
: 163 : vars : 3.210e-03
: 164 : vars : 3.151e-03
: 165 : vars : 3.150e-03
: 166 : vars : 3.144e-03
: 167 : vars : 3.142e-03
: 168 : vars : 3.122e-03
: 169 : vars : 3.112e-03
: 170 : vars : 3.088e-03
: 171 : vars : 3.084e-03
: 172 : vars : 3.048e-03
: 173 : vars : 3.046e-03
: 174 : vars : 3.008e-03
: 175 : vars : 3.000e-03
: 176 : vars : 2.968e-03
: 177 : vars : 2.961e-03
: 178 : vars : 2.943e-03
: 179 : vars : 2.919e-03
: 180 : vars : 2.904e-03
: 181 : vars : 2.881e-03
: 182 : vars : 2.878e-03
: 183 : vars : 2.863e-03
: 184 : vars : 2.843e-03
: 185 : vars : 2.755e-03
: 186 : vars : 2.739e-03
: 187 : vars : 2.735e-03
: 188 : vars : 2.702e-03
: 189 : vars : 2.691e-03
: 190 : vars : 2.678e-03
: 191 : vars : 2.654e-03
: 192 : vars : 2.609e-03
: 193 : vars : 2.606e-03
: 194 : vars : 2.597e-03
: 195 : vars : 2.592e-03
: 196 : vars : 2.579e-03
: 197 : vars : 2.573e-03
: 198 : vars : 2.559e-03
: 199 : vars : 2.531e-03
: 200 : vars : 2.524e-03
: 201 : vars : 2.510e-03
: 202 : vars : 2.446e-03
: 203 : vars : 2.444e-03
: 204 : vars : 2.432e-03
: 205 : vars : 2.418e-03
: 206 : vars : 2.396e-03
: 207 : vars : 2.368e-03
: 208 : vars : 2.324e-03
: 209 : vars : 2.322e-03
: 210 : vars : 2.299e-03
: 211 : vars : 2.262e-03
: 212 : vars : 2.232e-03
: 213 : vars : 2.198e-03
: 214 : vars : 2.193e-03
: 215 : vars : 2.183e-03
: 216 : vars : 2.178e-03
: 217 : vars : 2.170e-03
: 218 : vars : 2.094e-03
: 219 : vars : 1.993e-03
: 220 : vars : 1.992e-03
: 221 : vars : 1.973e-03
: 222 : vars : 1.939e-03
: 223 : vars : 1.926e-03
: 224 : vars : 1.902e-03
: 225 : vars : 1.864e-03
: 226 : vars : 1.744e-03
: 227 : vars : 1.739e-03
: 228 : vars : 1.735e-03
: 229 : vars : 1.729e-03
: 230 : vars : 1.729e-03
: 231 : vars : 1.725e-03
: 232 : vars : 1.718e-03
: 233 : vars : 1.679e-03
: 234 : vars : 1.646e-03
: 235 : vars : 1.573e-03
: 236 : vars : 1.443e-03
: 237 : vars : 1.303e-03
: 238 : vars : 1.260e-03
: 239 : vars : 1.213e-03
: 240 : vars : 1.203e-03
: 241 : vars : 1.174e-03
: 242 : vars : 1.108e-03
: 243 : vars : 9.220e-04
: 244 : vars : 9.143e-04
: 245 : vars : 6.798e-04
: 246 : vars : 0.000e+00
: 247 : vars : 0.000e+00
: 248 : vars : 0.000e+00
: 249 : vars : 0.000e+00
: 250 : vars : 0.000e+00
: 251 : vars : 0.000e+00
: 252 : vars : 0.000e+00
: 253 : vars : 0.000e+00
: 254 : vars : 0.000e+00
: 255 : vars : 0.000e+00
: 256 : vars : 0.000e+00
: --------------------------------------
: No variable ranking supplied by classifier: TMVA_DNN_CPU
: No variable ranking supplied by classifier: TMVA_CNN_CPU
: 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= 4.88814
TH1.Print Name = TrainingHistory_TMVA_DNN_CPU_valError, Entries= 0, Total sum= 8.20512
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_trainingError, Entries= 0, Total sum= 8.45049
TH1.Print Name = TrainingHistory_TMVA_CNN_CPU_valError, Entries= 0, Total sum= 7.48796
TH1.Print Name = TrainingHistory_PyKeras_'accuracy', Entries= 0, Total sum= 6.21563
TH1.Print Name = TrainingHistory_PyKeras_'loss', Entries= 0, Total sum= 6.56773
TH1.Print Name = TrainingHistory_PyKeras_'val_accuracy', Entries= 0, Total sum= 5.74375
TH1.Print Name = TrainingHistory_PyKeras_'val_loss', Entries= 0, Total sum= 6.66611
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: 0.0107 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: 0.0243 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: 0.124 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: 0.097 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 : 0.804
: dataset PyKeras : 0.800
: dataset BDT : 0.711
: dataset TMVA_CNN_CPU : 0.670
: dataset TMVA_DNN_CPU : 0.591
: -------------------------------------------------------------------------------------------------------------------
:
: 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 : 0.190 (0.202) 0.495 (0.665) 0.750 (0.826)
: dataset PyKeras : 0.095 (0.185) 0.495 (0.581) 0.775 (0.810)
: dataset BDT : 0.065 (0.335) 0.350 (0.638) 0.593 (0.860)
: dataset TMVA_CNN_CPU : 0.045 (0.085) 0.285 (0.356) 0.505 (0.604)
: dataset TMVA_DNN_CPU : 0.035 (0.080) 0.145 (0.235) 0.355 (0.547)
: -------------------------------------------------------------------------------------------------------------------
:
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 0x7fee403bc3a0>, 'predict_func': <function predict at 0x7fedcc785c10>}
# 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
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:
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()
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t UChar_t len
A ROOT file is composed of a header, followed by consecutive data records (TKey instances) with a wel...
Definition TFile.h:54
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:4070
This is the main MVA steering class.
Definition Factory.h:80
static void PyInitialize()
Initialize Python interpreter.
static Tools & Instance()
Definition Tools.cxx:71
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:537
UInt_t GetThreadPoolSize()
Returns the size of ROOT's thread pool.
Definition TROOT.cxx:575
Author
Harshal Shende

Definition in file TMVA_CNN_Classification.py.