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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
Factory : Booking method: ␛[1mLikelihood␛[0m
:
Factory : Booking method: ␛[1mFisher␛[0m
:
Factory : Booking method: ␛[1mBDT␛[0m
:
: Rebuilding Dataset dataset
: Building event vectors for type 2 Signal
: Dataset[dataset] : create input formulas for tree sig_tree
: Building event vectors for type 2 Background
: Dataset[dataset] : create input formulas for tree bkg_tree
DataSetFactory : [dataset] : Number of events in input trees
:
:
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 7000
: Signal -- testing events : 3000
: Signal -- training and testing events: 10000
: Background -- training events : 7000
: Background -- testing events : 3000
: Background -- training and testing events: 10000
:
DataSetInfo : Correlation matrix (Signal):
: ----------------------------------------------------------------
: m_jj m_jjj m_lv m_jlv m_bb m_wbb m_wwbb
: m_jj: +1.000 +0.777 +0.010 +0.107 +0.036 +0.517 +0.532
: m_jjj: +0.777 +1.000 +0.006 +0.083 +0.157 +0.682 +0.669
: m_lv: +0.010 +0.006 +1.000 +0.111 -0.026 +0.011 +0.023
: m_jlv: +0.107 +0.083 +0.111 +1.000 +0.325 +0.550 +0.555
: m_bb: +0.036 +0.157 -0.026 +0.325 +1.000 +0.463 +0.347
: m_wbb: +0.517 +0.682 +0.011 +0.550 +0.463 +1.000 +0.912
: m_wwbb: +0.532 +0.669 +0.023 +0.555 +0.347 +0.912 +1.000
: ----------------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: ----------------------------------------------------------------
: m_jj m_jjj m_lv m_jlv m_bb m_wbb m_wwbb
: m_jj: +1.000 +0.804 +0.017 +0.125 +0.007 +0.381 +0.394
: m_jjj: +0.804 +1.000 +0.025 +0.159 +0.153 +0.535 +0.520
: m_lv: +0.017 +0.025 +1.000 +0.114 +0.042 +0.064 +0.069
: m_jlv: +0.125 +0.159 +0.114 +1.000 +0.286 +0.592 +0.542
: m_bb: +0.007 +0.153 +0.042 +0.286 +1.000 +0.623 +0.441
: m_wbb: +0.381 +0.535 +0.064 +0.592 +0.623 +1.000 +0.878
: m_wwbb: +0.394 +0.520 +0.069 +0.542 +0.441 +0.878 +1.000
: ----------------------------------------------------------------
DataSetFactory : [dataset] :
:
Factory : Booking method: ␛[1mDNN_CPU␛[0m
:
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=G:WeightInitialization=XAVIER:InputLayout=1|1|7:BatchLayout=1|128|7:Layout=DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,ConvergenceSteps=10,BatchSize=128,TestRepetitions=1,MaxEpochs=30,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,ADAM_beta1=0.9,ADAM_beta2=0.999,ADAM_eps=1.E-7,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=G:WeightInitialization=XAVIER:InputLayout=1|1|7:BatchLayout=1|128|7:Layout=DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,ConvergenceSteps=10,BatchSize=128,TestRepetitions=1,MaxEpochs=30,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,ADAM_beta1=0.9,ADAM_beta2=0.999,ADAM_eps=1.E-7,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: "G" [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|1|7" [The Layout of the input]
: BatchLayout: "1|128|7" [The Layout of the batch]
: Layout: "DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,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,ConvergenceSteps=10,BatchSize=128,TestRepetitions=1,MaxEpochs=30,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,ADAM_beta1=0.9,ADAM_beta2=0.999,ADAM_eps=1.E-7,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)]
: 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%)]
DNN_CPU : [dataset] : Create Transformation "G" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'm_jj' <---> Output : variable 'm_jj'
: Input : variable 'm_jjj' <---> Output : variable 'm_jjj'
: Input : variable 'm_lv' <---> Output : variable 'm_lv'
: Input : variable 'm_jlv' <---> Output : variable 'm_jlv'
: Input : variable 'm_bb' <---> Output : variable 'm_bb'
: Input : variable 'm_wbb' <---> Output : variable 'm_wbb'
: Input : variable 'm_wwbb' <---> Output : variable 'm_wwbb'
: Will now use the CPU architecture with BLAS and IMT support !
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 64) 512
dense_1 (Dense) (None, 64) 4160
dense_2 (Dense) (None, 64) 4160
dense_3 (Dense) (None, 64) 4160
dense_4 (Dense) (None, 2) 130
=================================================================
Total params: 13122 (51.26 KB)
Trainable params: 13122 (51.26 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
(TString) "python3"[7]
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: Higgs_model.h5
Factory : ␛[1mTrain all methods␛[0m
Factory : [dataset] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'm_jj' <---> Output : variable 'm_jj'
: Input : variable 'm_jjj' <---> Output : variable 'm_jjj'
: Input : variable 'm_lv' <---> Output : variable 'm_lv'
: Input : variable 'm_jlv' <---> Output : variable 'm_jlv'
: Input : variable 'm_bb' <---> Output : variable 'm_bb'
: Input : variable 'm_wbb' <---> Output : variable 'm_wbb'
: Input : variable 'm_wwbb' <---> Output : variable 'm_wwbb'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 1.0352 0.65399 [ 0.14661 13.098 ]
: m_jjj: 1.0218 0.36964 [ 0.34201 7.3920 ]
: m_lv: 1.0497 0.16065 [ 0.26679 3.6823 ]
: m_jlv: 1.0126 0.39935 [ 0.38441 6.5831 ]
: m_bb: 0.98070 0.53223 [ 0.093482 7.8598 ]
: m_wbb: 1.0338 0.35968 [ 0.38503 4.5425 ]
: m_wwbb: 0.96049 0.31009 [ 0.43228 4.0728 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: -------------------------------
: Rank : Variable : Separation
: -------------------------------
: 1 : m_bb : 9.114e-02
: 2 : m_wwbb : 4.330e-02
: 3 : m_wbb : 4.241e-02
: 4 : m_jjj : 2.875e-02
: 5 : m_jlv : 1.905e-02
: 6 : m_jj : 3.432e-03
: 7 : m_lv : 2.855e-03
: -------------------------------
Factory : Train method: Likelihood for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ Likelihood ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: The maximum-likelihood classifier models the data with probability
: density functions (PDF) reproducing the signal and background
: distributions of the input variables. Correlations among the
: variables are ignored.
:
: ␛[1m--- Performance optimisation:␛[0m
:
: Required for good performance are decorrelated input variables
: (PCA transformation via the option "VarTransform=Decorrelate"
: may be tried). Irreducible non-linear correlations may be reduced
: by precombining strongly correlated input variables, or by simply
: removing one of the variables.
:
: ␛[1m--- Performance tuning via configuration options:␛[0m
:
: High fidelity PDF estimates are mandatory, i.e., sufficient training
: statistics is required to populate the tails of the distributions
: It would be a surprise if the default Spline or KDE kernel parameters
: provide a satisfying fit to the data. The user is advised to properly
: tune the events per bin and smooth options in the spline cases
: individually per variable. If the KDE kernel is used, the adaptive
: Gaussian kernel may lead to artefacts, so please always also try
: the non-adaptive one.
:
: All tuning parameters must be adjusted individually for each input
: variable!
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
: Filling reference histograms
: Building PDF out of reference histograms
: Elapsed time for training with 14000 events: 0.122 sec
Likelihood : [dataset] : Evaluation of Likelihood on training sample (14000 events)
: Elapsed time for evaluation of 14000 events: 0.0188 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_Likelihood.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_Likelihood.class.C␛[0m
: Higgs_ClassificationOutput.root:/dataset/Method_Likelihood/Likelihood
Factory : Training finished
:
Factory : Train method: Fisher for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ Fisher ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: Fisher discriminants select events by distinguishing the mean
: values of the signal and background distributions in a trans-
: formed variable space where linear correlations are removed.
:
: (More precisely: the "linear discriminator" determines
: an axis in the (correlated) hyperspace of the input
: variables such that, when projecting the output classes
: (signal and background) upon this axis, they are pushed
: as far as possible away from each other, while events
: of a same class are confined in a close vicinity. The
: linearity property of this classifier is reflected in the
: metric with which "far apart" and "close vicinity" are
: determined: the covariance matrix of the discriminating
: variable space.)
:
: ␛[1m--- Performance optimisation:␛[0m
:
: Optimal performance for Fisher discriminants is obtained for
: linearly correlated Gaussian-distributed variables. Any deviation
: from this ideal reduces the achievable separation power. In
: particular, no discrimination at all is achieved for a variable
: that has the same sample mean for signal and background, even if
: the shapes of the distributions are very different. Thus, Fisher
: discriminants often benefit from suitable transformations of the
: input variables. For example, if a variable x in [-1,1] has a
: a parabolic signal distributions, and a uniform background
: distributions, their mean value is zero in both cases, leading
: to no separation. The simple transformation x -> |x| renders this
: variable powerful for the use in a Fisher discriminant.
:
: ␛[1m--- Performance tuning via configuration options:␛[0m
:
: <None>
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
Fisher : Results for Fisher coefficients:
: -----------------------
: Variable: Coefficient:
: -----------------------
: m_jj: -0.051
: m_jjj: +0.187
: m_lv: +0.037
: m_jlv: +0.065
: m_bb: -0.207
: m_wbb: +0.532
: m_wwbb: -0.743
: (offset): +0.125
: -----------------------
: Elapsed time for training with 14000 events: 0.0117 sec
Fisher : [dataset] : Evaluation of Fisher on training sample (14000 events)
: Elapsed time for evaluation of 14000 events: 0.00197 sec
: <CreateMVAPdfs> Separation from histogram (PDF): 0.085 (0.000)
: Dataset[dataset] : Evaluation of Fisher on training sample
: Creating xml weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_Fisher.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_Fisher.class.C␛[0m
Factory : Training finished
:
Factory : Train method: BDT for Classification
:
BDT : #events: (reweighted) sig: 7000 bkg: 7000
: #events: (unweighted) sig: 7000 bkg: 7000
: Training 200 Decision Trees ... patience please
: Elapsed time for training with 14000 events: 0.694 sec
BDT : [dataset] : Evaluation of BDT on training sample (14000 events)
: Elapsed time for evaluation of 14000 events: 0.115 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_BDT.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_BDT.class.C␛[0m
: Higgs_ClassificationOutput.root:/dataset/Method_BDT/BDT
Factory : Training finished
:
Factory : Train method: DNN_CPU for Classification
:
: Preparing the Gaussian transformation...
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 0.0042139 0.99787 [ -3.2801 5.7307 ]
: m_jjj: 0.0043508 0.99784 [ -3.2805 5.7307 ]
: m_lv: 0.0051611 1.0008 [ -3.2813 5.7307 ]
: m_jlv: 0.0044388 0.99830 [ -3.2803 5.7307 ]
: m_bb: 0.0041864 0.99765 [ -3.2793 5.7307 ]
: m_wbb: 0.0046426 0.99950 [ -3.2802 5.7307 ]
: m_wwbb: 0.0044594 0.99873 [ -3.2802 5.7307 ]
: -----------------------------------------------------------
: Start of deep neural network training on CPU using MT, nthreads = 1
:
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 0.0042139 0.99787 [ -3.2801 5.7307 ]
: m_jjj: 0.0043508 0.99784 [ -3.2805 5.7307 ]
: m_lv: 0.0051611 1.0008 [ -3.2813 5.7307 ]
: m_jlv: 0.0044388 0.99830 [ -3.2803 5.7307 ]
: m_bb: 0.0041864 0.99765 [ -3.2793 5.7307 ]
: m_wbb: 0.0046426 0.99950 [ -3.2802 5.7307 ]
: m_wwbb: 0.0044594 0.99873 [ -3.2802 5.7307 ]
: -----------------------------------------------------------
: ***** Deep Learning Network *****
DEEP NEURAL NETWORK: Depth = 5 Input = ( 1, 1, 7 ) Batch size = 128 Loss function = C
Layer 0 DENSE Layer: ( Input = 7 , Width = 64 ) Output = ( 1 , 128 , 64 ) Activation Function = Tanh
Layer 1 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 128 , 64 ) Activation Function = Tanh
Layer 2 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 128 , 64 ) Activation Function = Tanh
Layer 3 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 128 , 64 ) Activation Function = Tanh
Layer 4 DENSE Layer: ( Input = 64 , Width = 1 ) Output = ( 1 , 128 , 1 ) Activation Function = Identity
: Using 11200 events for training and 2800 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 = 0.768335
: --------------------------------------------------------------
: 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 | 0.655615 0.622132 0.226974 0.022674 54508.2 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.604763 0.590802 0.225892 0.0227121 54808.6 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.58572 0.581279 0.22626 0.0230775 54808 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.576148 0.577643 0.239896 0.0228503 51307.2 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.571624 0.575825 0.227081 0.0229114 54542.8 0
: 6 | 0.567871 0.584648 0.227683 0.0227983 54352.5 1
: 7 Minimum Test error found - save the configuration
: 7 | 0.564975 0.57346 0.227911 0.0230064 54347.3 0
: 8 | 0.56161 0.576771 0.22905 0.0229232 54025 1
: 9 | 0.561295 0.574335 0.259041 0.0230307 47184.5 2
: 10 | 0.558677 0.576457 0.229837 0.0230742 53858.9 3
: 11 Minimum Test error found - save the configuration
: 11 | 0.558247 0.572953 0.230222 0.0233112 53820.2 0
: 12 | 0.556658 0.578676 0.231923 0.0237689 53498.9 1
: 13 | 0.552351 0.574511 0.23082 0.0231159 53614.7 2
: 14 | 0.553159 0.577472 0.230638 0.023183 53679 3
: 15 Minimum Test error found - save the configuration
: 15 | 0.552827 0.572514 0.231871 0.0233553 53406 0
: 16 | 0.549466 0.577819 0.231191 0.023252 53554.2 1
: 17 | 0.548002 0.577899 0.232982 0.0233871 53131.1 2
: 18 | 0.545078 0.577224 0.232197 0.0231652 53274.1 3
: 19 | 0.543651 0.577518 0.23603 0.0236885 52443.8 4
: 20 | 0.542186 0.578073 0.232457 0.0235931 53317 5
: 21 | 0.540483 0.582209 0.233115 0.0233396 53085.4 6
: 22 | 0.539472 0.576185 0.233583 0.0233758 52976.3 7
: 23 | 0.538213 0.580327 0.233091 0.0234084 53108.8 8
: 24 | 0.53595 0.579247 0.233412 0.0234319 53033.7 9
: 25 | 0.535542 0.577413 0.284811 0.0234185 42602.7 10
: 26 | 0.533672 0.579921 0.244321 0.023581 50448.6 11
:
: Elapsed time for training with 14000 events: 6.19 sec
: Evaluate deep neural network on CPU using batches with size = 128
:
DNN_CPU : [dataset] : Evaluation of DNN_CPU on training sample (14000 events)
: Elapsed time for evaluation of 14000 events: 0.123 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_DNN_CPU.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_DNN_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 11200 training events and 2800 validation events
: Training Model Summary
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 64) 512
dense_1 (Dense) (None, 64) 4160
dense_2 (Dense) (None, 64) 4160
dense_3 (Dense) (None, 64) 4160
dense_4 (Dense) (None, 2) 130
=================================================================
Total params: 13122 (51.26 KB)
Trainable params: 13122 (51.26 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
: Option SaveBestOnly: Only model weights with smallest validation loss will be stored
Epoch 1/20
1/112 [..............................] - ETA: 1:06 - loss: 0.7152 - accuracy: 0.4900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
22/112 [====>.........................] - ETA: 0s - loss: 0.6948 - accuracy: 0.5050 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
46/112 [===========>..................] - ETA: 0s - loss: 0.6912 - accuracy: 0.5057␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
70/112 [=================>............] - ETA: 0s - loss: 0.6892 - accuracy: 0.5180␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
95/112 [========================>.....] - ETA: 0s - loss: 0.6844 - accuracy: 0.5347
Epoch 1: val_loss improved from inf to 0.65743, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 1s 5ms/step - loss: 0.6806 - accuracy: 0.5455 - val_loss: 0.6574 - val_accuracy: 0.6050
Epoch 2/20
1/112 [..............................] - ETA: 0s - loss: 0.6844 - accuracy: 0.5300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
31/112 [=======>......................] - ETA: 0s - loss: 0.6569 - accuracy: 0.6016␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
61/112 [===============>..............] - ETA: 0s - loss: 0.6556 - accuracy: 0.6016␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
90/112 [=======================>......] - ETA: 0s - loss: 0.6525 - accuracy: 0.6096
Epoch 2: val_loss improved from 0.65743 to 0.63019, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.6491 - accuracy: 0.6147 - val_loss: 0.6302 - val_accuracy: 0.6429
Epoch 3/20
1/112 [..............................] - ETA: 0s - loss: 0.6567 - accuracy: 0.5900␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
30/112 [=======>......................] - ETA: 0s - loss: 0.6309 - accuracy: 0.6473␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
60/112 [===============>..............] - ETA: 0s - loss: 0.6330 - accuracy: 0.6438␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
89/112 [======================>.......] - ETA: 0s - loss: 0.6319 - accuracy: 0.6437
Epoch 3: val_loss improved from 0.63019 to 0.62085, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 3ms/step - loss: 0.6315 - accuracy: 0.6459 - val_loss: 0.6208 - val_accuracy: 0.6507
Epoch 4/20
1/112 [..............................] - ETA: 0s - loss: 0.5759 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
29/112 [======>.......................] - ETA: 0s - loss: 0.6290 - accuracy: 0.6462␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
57/112 [==============>...............] - ETA: 0s - loss: 0.6279 - accuracy: 0.6439␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
85/112 [=====================>........] - ETA: 0s - loss: 0.6240 - accuracy: 0.6512
Epoch 4: val_loss improved from 0.62085 to 0.61638, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 3ms/step - loss: 0.6264 - accuracy: 0.6496 - val_loss: 0.6164 - val_accuracy: 0.6646
Epoch 5/20
1/112 [..............................] - ETA: 0s - loss: 0.6316 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
27/112 [======>.......................] - ETA: 0s - loss: 0.6232 - accuracy: 0.6526␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
55/112 [=============>................] - ETA: 0s - loss: 0.6187 - accuracy: 0.6607␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
71/112 [==================>...........] - ETA: 0s - loss: 0.6213 - accuracy: 0.6546␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
100/112 [=========================>....] - ETA: 0s - loss: 0.6215 - accuracy: 0.6558
Epoch 5: val_loss improved from 0.61638 to 0.61382, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 3ms/step - loss: 0.6209 - accuracy: 0.6566 - val_loss: 0.6138 - val_accuracy: 0.6671
Epoch 6/20
1/112 [..............................] - ETA: 0s - loss: 0.6392 - accuracy: 0.6100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
28/112 [======>.......................] - ETA: 0s - loss: 0.6128 - accuracy: 0.6664␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
56/112 [==============>...............] - ETA: 0s - loss: 0.6124 - accuracy: 0.6643␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
86/112 [======================>.......] - ETA: 0s - loss: 0.6120 - accuracy: 0.6641
Epoch 6: val_loss did not improve from 0.61382
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.6139 - accuracy: 0.6636 - val_loss: 0.6197 - val_accuracy: 0.6607
Epoch 7/20
1/112 [..............................] - ETA: 0s - loss: 0.5867 - accuracy: 0.6700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
31/112 [=======>......................] - ETA: 0s - loss: 0.6118 - accuracy: 0.6652␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
60/112 [===============>..............] - ETA: 0s - loss: 0.6109 - accuracy: 0.6632␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
89/112 [======================>.......] - ETA: 0s - loss: 0.6116 - accuracy: 0.6638
Epoch 7: val_loss did not improve from 0.61382
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.6094 - accuracy: 0.6671 - val_loss: 0.6251 - val_accuracy: 0.6507
Epoch 8/20
1/112 [..............................] - ETA: 0s - loss: 0.5878 - accuracy: 0.7000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
31/112 [=======>......................] - ETA: 0s - loss: 0.6202 - accuracy: 0.6523␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
62/112 [===============>..............] - ETA: 0s - loss: 0.6089 - accuracy: 0.6629␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
92/112 [=======================>......] - ETA: 0s - loss: 0.6089 - accuracy: 0.6634
Epoch 8: val_loss improved from 0.61382 to 0.60844, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.6091 - accuracy: 0.6633 - val_loss: 0.6084 - val_accuracy: 0.6600
Epoch 9/20
1/112 [..............................] - ETA: 0s - loss: 0.6306 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
31/112 [=======>......................] - ETA: 0s - loss: 0.6017 - accuracy: 0.6713␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
60/112 [===============>..............] - ETA: 0s - loss: 0.6052 - accuracy: 0.6682␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
89/112 [======================>.......] - ETA: 0s - loss: 0.6007 - accuracy: 0.6721
Epoch 9: val_loss did not improve from 0.60844
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5998 - accuracy: 0.6728 - val_loss: 0.6105 - val_accuracy: 0.6514
Epoch 10/20
1/112 [..............................] - ETA: 0s - loss: 0.5928 - accuracy: 0.6800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
29/112 [======>.......................] - ETA: 0s - loss: 0.6032 - accuracy: 0.6728␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
58/112 [==============>...............] - ETA: 0s - loss: 0.5982 - accuracy: 0.6774␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
88/112 [======================>.......] - ETA: 0s - loss: 0.6014 - accuracy: 0.6756
Epoch 10: val_loss improved from 0.60844 to 0.60188, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 3ms/step - loss: 0.6003 - accuracy: 0.6750 - val_loss: 0.6019 - val_accuracy: 0.6682
Epoch 11/20
1/112 [..............................] - ETA: 0s - loss: 0.6199 - accuracy: 0.6800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
33/112 [=======>......................] - ETA: 0s - loss: 0.5883 - accuracy: 0.6797␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
65/112 [================>.............] - ETA: 0s - loss: 0.5972 - accuracy: 0.6748␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
95/112 [========================>.....] - ETA: 0s - loss: 0.5998 - accuracy: 0.6734
Epoch 11: val_loss did not improve from 0.60188
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5982 - accuracy: 0.6755 - val_loss: 0.6048 - val_accuracy: 0.6721
Epoch 12/20
1/112 [..............................] - ETA: 0s - loss: 0.5577 - accuracy: 0.7400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
32/112 [=======>......................] - ETA: 0s - loss: 0.5976 - accuracy: 0.6803␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
63/112 [===============>..............] - ETA: 0s - loss: 0.5938 - accuracy: 0.6794␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
89/112 [======================>.......] - ETA: 0s - loss: 0.5903 - accuracy: 0.6817
Epoch 12: val_loss improved from 0.60188 to 0.59969, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 3ms/step - loss: 0.5915 - accuracy: 0.6796 - val_loss: 0.5997 - val_accuracy: 0.6743
Epoch 13/20
1/112 [..............................] - ETA: 0s - loss: 0.6179 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
31/112 [=======>......................] - ETA: 0s - loss: 0.5975 - accuracy: 0.6719␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
61/112 [===============>..............] - ETA: 0s - loss: 0.5903 - accuracy: 0.6789␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
91/112 [=======================>......] - ETA: 0s - loss: 0.5899 - accuracy: 0.6792
Epoch 13: val_loss improved from 0.59969 to 0.59953, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5894 - accuracy: 0.6794 - val_loss: 0.5995 - val_accuracy: 0.6725
Epoch 14/20
1/112 [..............................] - ETA: 0s - loss: 0.5218 - accuracy: 0.7300␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
32/112 [=======>......................] - ETA: 0s - loss: 0.5818 - accuracy: 0.6906␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
63/112 [===============>..............] - ETA: 0s - loss: 0.5889 - accuracy: 0.6830␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
95/112 [========================>.....] - ETA: 0s - loss: 0.5901 - accuracy: 0.6815
Epoch 14: val_loss improved from 0.59953 to 0.59193, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5882 - accuracy: 0.6824 - val_loss: 0.5919 - val_accuracy: 0.6807
Epoch 15/20
1/112 [..............................] - ETA: 0s - loss: 0.5709 - accuracy: 0.6700␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
32/112 [=======>......................] - ETA: 0s - loss: 0.5868 - accuracy: 0.6753␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
65/112 [================>.............] - ETA: 0s - loss: 0.5860 - accuracy: 0.6789␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
96/112 [========================>.....] - ETA: 0s - loss: 0.5891 - accuracy: 0.6787
Epoch 15: val_loss improved from 0.59193 to 0.58750, saving model to Higgs_trained_model.h5
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5882 - accuracy: 0.6787 - val_loss: 0.5875 - val_accuracy: 0.6857
Epoch 16/20
1/112 [..............................] - ETA: 0s - loss: 0.5383 - accuracy: 0.7100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
34/112 [========>.....................] - ETA: 0s - loss: 0.5798 - accuracy: 0.6871␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
67/112 [================>.............] - ETA: 0s - loss: 0.5794 - accuracy: 0.6901␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
100/112 [=========================>....] - ETA: 0s - loss: 0.5817 - accuracy: 0.6875
Epoch 16: val_loss did not improve from 0.58750
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5835 - accuracy: 0.6849 - val_loss: 0.5906 - val_accuracy: 0.6764
Epoch 17/20
1/112 [..............................] - ETA: 0s - loss: 0.5960 - accuracy: 0.6600␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
29/112 [======>.......................] - ETA: 0s - loss: 0.5867 - accuracy: 0.6721␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
54/112 [=============>................] - ETA: 0s - loss: 0.5886 - accuracy: 0.6767␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
80/112 [====================>.........] - ETA: 0s - loss: 0.5820 - accuracy: 0.6861␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
108/112 [===========================>..] - ETA: 0s - loss: 0.5829 - accuracy: 0.6831
Epoch 17: val_loss did not improve from 0.58750
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5829 - accuracy: 0.6838 - val_loss: 0.5905 - val_accuracy: 0.6825
Epoch 18/20
1/112 [..............................] - ETA: 0s - loss: 0.5272 - accuracy: 0.7500␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
30/112 [=======>......................] - ETA: 0s - loss: 0.5739 - accuracy: 0.6980␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
59/112 [==============>...............] - ETA: 0s - loss: 0.5818 - accuracy: 0.6905␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
88/112 [======================>.......] - ETA: 0s - loss: 0.5839 - accuracy: 0.6882
Epoch 18: val_loss did not improve from 0.58750
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5799 - accuracy: 0.6902 - val_loss: 0.5912 - val_accuracy: 0.6789
Epoch 19/20
1/112 [..............................] - ETA: 0s - loss: 0.5717 - accuracy: 0.7100␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
30/112 [=======>......................] - ETA: 0s - loss: 0.5688 - accuracy: 0.6943␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
59/112 [==============>...............] - ETA: 0s - loss: 0.5760 - accuracy: 0.6868␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
88/112 [======================>.......] - ETA: 0s - loss: 0.5804 - accuracy: 0.6824
Epoch 19: val_loss did not improve from 0.58750
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5788 - accuracy: 0.6862 - val_loss: 0.5888 - val_accuracy: 0.6811
Epoch 20/20
1/112 [..............................] - ETA: 0s - loss: 0.6073 - accuracy: 0.6400␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
30/112 [=======>......................] - ETA: 0s - loss: 0.5867 - accuracy: 0.6800␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
59/112 [==============>...............] - ETA: 0s - loss: 0.5820 - accuracy: 0.6831␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
89/112 [======================>.......] - ETA: 0s - loss: 0.5789 - accuracy: 0.6884
Epoch 20: val_loss did not improve from 0.58750
␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
112/112 [==============================] - 0s 2ms/step - loss: 0.5787 - accuracy: 0.6889 - val_loss: 0.5921 - val_accuracy: 0.6754
: 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 14000 events: 6.36 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: Higgs_trained_model.h5
PyKeras : [dataset] : Evaluation of PyKeras on training sample (14000 events)
: Elapsed time for evaluation of 14000 events: 0.293 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_PyKeras.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_PyKeras.class.C␛[0m
Factory : Training finished
:
: Ranking input variables (method specific)...
Likelihood : Ranking result (top variable is best ranked)
: -------------------------------------
: Rank : Variable : Delta Separation
: -------------------------------------
: 1 : m_bb : 5.483e-02
: 2 : m_wbb : 4.416e-02
: 3 : m_wwbb : 4.201e-02
: 4 : m_jjj : 4.949e-03
: 5 : m_lv : -2.642e-03
: 6 : m_jj : -5.516e-03
: 7 : m_jlv : -1.042e-02
: -------------------------------------
Fisher : Ranking result (top variable is best ranked)
: ---------------------------------
: Rank : Variable : Discr. power
: ---------------------------------
: 1 : m_bb : 1.180e-02
: 2 : m_wwbb : 7.816e-03
: 3 : m_wbb : 2.085e-03
: 4 : m_jlv : 5.619e-04
: 5 : m_jjj : 2.327e-04
: 6 : m_lv : 3.319e-05
: 7 : m_jj : 1.479e-05
: ---------------------------------
BDT : Ranking result (top variable is best ranked)
: ----------------------------------------
: Rank : Variable : Variable Importance
: ----------------------------------------
: 1 : m_bb : 2.045e-01
: 2 : m_wwbb : 1.687e-01
: 3 : m_jlv : 1.638e-01
: 4 : m_jjj : 1.413e-01
: 5 : m_wbb : 1.356e-01
: 6 : m_jj : 1.080e-01
: 7 : m_lv : 7.813e-02
: ----------------------------------------
: No variable ranking supplied by classifier: DNN_CPU
: No variable ranking supplied by classifier: PyKeras
TH1.Print Name = TrainingHistory_DNN_CPU_trainingError, Entries= 0, Total sum= 14.5333
TH1.Print Name = TrainingHistory_DNN_CPU_valError, Entries= 0, Total sum= 15.0733
TH1.Print Name = TrainingHistory_PyKeras_'accuracy', Entries= 0, Total sum= 13.2837
TH1.Print Name = TrainingHistory_PyKeras_'loss', Entries= 0, Total sum= 12.1004
TH1.Print Name = TrainingHistory_PyKeras_'val_accuracy', Entries= 0, Total sum= 13.3011
TH1.Print Name = TrainingHistory_PyKeras_'val_loss', Entries= 0, Total sum= 12.1409
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_Likelihood.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_Fisher.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_BDT.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_DNN_CPU.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVA_Higgs_Classification_PyKeras.weights.xml␛[0m
Factory : ␛[1mTest all methods␛[0m
Factory : Test method: Likelihood for Classification performance
:
Likelihood : [dataset] : Evaluation of Likelihood on testing sample (6000 events)
: Elapsed time for evaluation of 6000 events: 0.00949 sec
Factory : Test method: Fisher for Classification performance
:
Fisher : [dataset] : Evaluation of Fisher on testing sample (6000 events)
: Elapsed time for evaluation of 6000 events: 0.000996 sec
: Dataset[dataset] : Evaluation of Fisher on testing sample
Factory : Test method: BDT for Classification performance
:
BDT : [dataset] : Evaluation of BDT on testing sample (6000 events)
: Elapsed time for evaluation of 6000 events: 0.0482 sec
Factory : Test method: DNN_CPU for Classification performance
:
: Evaluate deep neural network on CPU using batches with size = 1000
:
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 0.029995 0.98065 [ -3.1064 5.7307 ]
: m_jjj: 0.030151 0.98464 [ -2.9982 5.7307 ]
: m_lv: 0.011982 1.0066 [ -3.2274 5.7307 ]
: m_jlv: 0.0049774 1.0015 [ -3.0644 5.7307 ]
: m_bb: -0.036143 1.0111 [ -5.7307 5.7307 ]
: m_wbb: -0.0056377 1.0239 [ -3.0260 5.7307 ]
: m_wwbb: 0.0023364 1.0091 [ -3.1905 5.7307 ]
: -----------------------------------------------------------
DNN_CPU : [dataset] : Evaluation of DNN_CPU on testing sample (6000 events)
: Elapsed time for evaluation of 6000 events: 0.0896 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: Higgs_trained_model.h5
PyKeras : [dataset] : Evaluation of PyKeras on testing sample (6000 events)
: Elapsed time for evaluation of 6000 events: 0.172 sec
Factory : ␛[1mEvaluate all methods␛[0m
Factory : Evaluate classifier: Likelihood
:
Likelihood : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_Likelihood : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 1.0368 0.66752 [ 0.16310 16.132 ]
: m_jjj: 1.0272 0.38070 [ 0.41899 8.9401 ]
: m_lv: 1.0522 0.17017 [ 0.29757 3.2605 ]
: m_jlv: 1.0135 0.40315 [ 0.41660 5.8195 ]
: m_bb: 0.96616 0.53867 [ 0.080986 8.2551 ]
: m_wbb: 1.0344 0.37776 [ 0.42068 6.4013 ]
: m_wwbb: 0.96122 0.31782 [ 0.44118 4.5350 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: Fisher
:
Fisher : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Also filling probability and rarity histograms (on request)...
TFHandler_Fisher : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 1.0368 0.66752 [ 0.16310 16.132 ]
: m_jjj: 1.0272 0.38070 [ 0.41899 8.9401 ]
: m_lv: 1.0522 0.17017 [ 0.29757 3.2605 ]
: m_jlv: 1.0135 0.40315 [ 0.41660 5.8195 ]
: m_bb: 0.96616 0.53867 [ 0.080986 8.2551 ]
: m_wbb: 1.0344 0.37776 [ 0.42068 6.4013 ]
: m_wwbb: 0.96122 0.31782 [ 0.44118 4.5350 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: BDT
:
BDT : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_BDT : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 1.0368 0.66752 [ 0.16310 16.132 ]
: m_jjj: 1.0272 0.38070 [ 0.41899 8.9401 ]
: m_lv: 1.0522 0.17017 [ 0.29757 3.2605 ]
: m_jlv: 1.0135 0.40315 [ 0.41660 5.8195 ]
: m_bb: 0.96616 0.53867 [ 0.080986 8.2551 ]
: m_wbb: 1.0344 0.37776 [ 0.42068 6.4013 ]
: m_wwbb: 0.96122 0.31782 [ 0.44118 4.5350 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: DNN_CPU
:
DNN_CPU : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Evaluate deep neural network on CPU using batches with size = 1000
:
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 0.0042139 0.99787 [ -3.2801 5.7307 ]
: m_jjj: 0.0043508 0.99784 [ -3.2805 5.7307 ]
: m_lv: 0.0051611 1.0008 [ -3.2813 5.7307 ]
: m_jlv: 0.0044388 0.99830 [ -3.2803 5.7307 ]
: m_bb: 0.0041864 0.99765 [ -3.2793 5.7307 ]
: m_wbb: 0.0046426 0.99950 [ -3.2802 5.7307 ]
: m_wwbb: 0.0044594 0.99873 [ -3.2802 5.7307 ]
: -----------------------------------------------------------
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 0.029995 0.98065 [ -3.1064 5.7307 ]
: m_jjj: 0.030151 0.98464 [ -2.9982 5.7307 ]
: m_lv: 0.011982 1.0066 [ -3.2274 5.7307 ]
: m_jlv: 0.0049774 1.0015 [ -3.0644 5.7307 ]
: m_bb: -0.036143 1.0111 [ -5.7307 5.7307 ]
: m_wbb: -0.0056377 1.0239 [ -3.0260 5.7307 ]
: m_wwbb: 0.0023364 1.0091 [ -3.1905 5.7307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: PyKeras
:
PyKeras : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_PyKeras : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: m_jj: 1.0368 0.66752 [ 0.16310 16.132 ]
: m_jjj: 1.0272 0.38070 [ 0.41899 8.9401 ]
: m_lv: 1.0522 0.17017 [ 0.29757 3.2605 ]
: m_jlv: 1.0135 0.40315 [ 0.41660 5.8195 ]
: m_bb: 0.96616 0.53867 [ 0.080986 8.2551 ]
: m_wbb: 1.0344 0.37776 [ 0.42068 6.4013 ]
: m_wwbb: 0.96122 0.31782 [ 0.44118 4.5350 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset DNN_CPU : 0.766
: dataset BDT : 0.758
: dataset PyKeras : 0.754
: dataset Likelihood : 0.699
: dataset Fisher : 0.654
: -------------------------------------------------------------------------------------------------------------------
:
: 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 DNN_CPU : 0.118 (0.141) 0.416 (0.436) 0.679 (0.696)
: dataset BDT : 0.080 (0.095) 0.394 (0.394) 0.674 (0.685)
: dataset PyKeras : 0.100 (0.106) 0.399 (0.403) 0.665 (0.672)
: dataset Likelihood : 0.058 (0.076) 0.309 (0.338) 0.584 (0.590)
: dataset Fisher : 0.017 (0.014) 0.128 (0.141) 0.500 (0.529)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:dataset : Created tree 'TestTree' with 6000 events
:
Dataset:dataset : Created tree 'TrainTree' with 14000 events
:
Factory : ␛[1mThank you for using TMVA!␛[0m
: ␛[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html␛[0m
