Definition at line 34 of file MethodPyKeras.h.

Public Types
enum	EBackendType { kUndefined = -1 , kTensorFlow = 0 , kTheano = 1 , kCNTK = 2 }
	enumeration defining the used Keras backend More...

Public Types inherited from TMVA::MethodBase
enum	EWeightFileType { kROOT =0 , kTEXT }

Public Types inherited from TObject
enum	{ kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff }

enum	{ kSingleKey = BIT(0) , kOverwrite = BIT(1) , kWriteDelete = BIT(2) }

enum	EDeprecatedStatusBits { kObjInCanvas = BIT(3) }

enum	EStatusBits { kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) , kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13) }

Public Member Functions
	MethodPyKeras (const TString &jobName, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")

	MethodPyKeras (DataSetInfo &dsi, const TString &theWeightFile)

	~MethodPyKeras ()

virtual void	AddWeightsXMLTo (void *) const

const Ranking *	CreateRanking ()

void	DeclareOptions ()

void	GetHelpMessage () const

EBackendType	GetKerasBackend ()
	Get the Keras backend (can be: TensorFlow, Theano or CNTK)

TString	GetKerasBackendName ()

std::vector< Float_t > &	GetMulticlassValues ()

Double_t	GetMvaValue (Double_t errLower, Double_t errUpper)

std::vector< Double_t >	GetMvaValues (Long64_t firstEvt, Long64_t lastEvt, Bool_t logProgress)
	get all the MVA values for the events of the current Data type

std::vector< Float_t > &	GetRegressionValues ()

Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t)

void	Init ()
	Initialization function called from MethodBase::SetupMethod() Note that option string are not yet filled with their values.

void	ProcessOptions ()
	Function processing the options This is called only when creating the method before training not when readinf from XML file.

void	ReadModelFromFile ()

virtual void	ReadWeightsFromStream (std::istream &)

virtual void	ReadWeightsFromStream (TFile &)

virtual void	ReadWeightsFromXML (void *)

virtual void	TestClassification ()
	initialization

void	Train ()

Bool_t	UseTFKeras () const

Public Member Functions inherited from TMVA::PyMethodBase
	PyMethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")

	PyMethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile)

virtual	~PyMethodBase ()

PyObject *	Eval (TString code)
	Evaluate Python code.

Public Member Functions inherited from TMVA::MethodBase
	MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
	standard constructor

	MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile)
	constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles

virtual	~MethodBase ()
	destructor

void	AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)

TDirectory *	BaseDir () const
	returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored

virtual void	CheckSetup ()
	check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)

DataSet *	Data () const

DataSetInfo &	DataInfo () const

virtual void	DeclareCompatibilityOptions ()
	options that are used ONLY for the READER to ensure backward compatibility they are hence without any effect (the reader is only reading the training options that HAD been used at the training of the .xml weight file at hand

void	DisableWriting (Bool_t setter)

Bool_t	DoMulticlass () const

Bool_t	DoRegression () const

void	ExitFromTraining ()

Types::EAnalysisType	GetAnalysisType () const

UInt_t	GetCurrentIter ()

virtual Double_t	GetEfficiency (const TString &, Types::ETreeType, Double_t &err)
	fill background efficiency (resp.

const Event *	GetEvent () const

const Event *	GetEvent (const TMVA::Event *ev) const

const Event *	GetEvent (Long64_t ievt) const

const Event *	GetEvent (Long64_t ievt, Types::ETreeType type) const

const std::vector< TMVA::Event * > &	GetEventCollection (Types::ETreeType type)
	returns the event collection (i.e.

TFile *	GetFile () const

const TString &	GetInputLabel (Int_t i) const

const char *	GetInputTitle (Int_t i) const

const TString &	GetInputVar (Int_t i) const

TMultiGraph *	GetInteractiveTrainingError ()

const TString &	GetJobName () const

virtual Double_t	GetKSTrainingVsTest (Char_t SorB, TString opt="X")

virtual Double_t	GetMaximumSignificance (Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
	plot significance, \( \frac{S}{\sqrt{S^2 + B^2}} \), curve for given number of signal and background events; returns cut for maximum significance also returned via reference is the maximum significance

UInt_t	GetMaxIter ()

Double_t	GetMean (Int_t ivar) const

const TString &	GetMethodName () const

Types::EMVA	GetMethodType () const

TString	GetMethodTypeName () const

virtual TMatrixD	GetMulticlassConfusionMatrix (Double_t effB, Types::ETreeType type)
	Construct a confusion matrix for a multiclass classifier.

virtual std::vector< Float_t >	GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)

virtual std::vector< Float_t >	GetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity)

Double_t	GetMvaValue (const TMVA::Event const ev, Double_t err=0, Double_t *errUpper=0)

const char *	GetName () const

UInt_t	GetNEvents () const
	temporary event when testing on a different DataSet than the own one

UInt_t	GetNTargets () const

UInt_t	GetNvar () const

UInt_t	GetNVariables () const

virtual Double_t	GetProba (const Event *ev)

virtual Double_t	GetProba (Double_t mvaVal, Double_t ap_sig)
	compute likelihood ratio

const TString	GetProbaName () const

virtual Double_t	GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
	compute rarity:

virtual void	GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const

const std::vector< Float_t > &	GetRegressionValues (const TMVA::Event *const ev)

Double_t	GetRMS (Int_t ivar) const

virtual Double_t	GetROCIntegral (PDF pdfS=0, PDF pdfB=0) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification

virtual Double_t	GetROCIntegral (TH1D histS, TH1D histB) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification

virtual Double_t	GetSeparation (PDF pdfS=0, PDF pdfB=0) const
	compute "separation" defined as

virtual Double_t	GetSeparation (TH1 , TH1 ) const
	compute "separation" defined as

Double_t	GetSignalReferenceCut () const

Double_t	GetSignalReferenceCutOrientation () const

virtual Double_t	GetSignificance () const
	compute significance of mean difference

const Event *	GetTestingEvent (Long64_t ievt) const

Double_t	GetTestTime () const

const TString &	GetTestvarName () const

virtual Double_t	GetTrainingEfficiency (const TString &)

const Event *	GetTrainingEvent (Long64_t ievt) const

virtual const std::vector< Float_t > &	GetTrainingHistory (const char *)

UInt_t	GetTrainingROOTVersionCode () const

TString	GetTrainingROOTVersionString () const
	calculates the ROOT version string from the training version code on the fly

UInt_t	GetTrainingTMVAVersionCode () const

TString	GetTrainingTMVAVersionString () const
	calculates the TMVA version string from the training version code on the fly

Double_t	GetTrainTime () const

TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true)

const TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const

TString	GetWeightFileName () const
	retrieve weight file name

Double_t	GetXmax (Int_t ivar) const

Double_t	GetXmin (Int_t ivar) const

Bool_t	HasMVAPdfs () const

void	InitIPythonInteractive ()

Bool_t	IsModelPersistence () const

virtual Bool_t	IsSignalLike ()
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event would be selected as signal or background

virtual Bool_t	IsSignalLike (Double_t mvaVal)
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would be selected as signal or background

Bool_t	IsSilentFile () const

virtual void	MakeClass (const TString &classFileName=TString("")) const
	create reader class for method (classification only at present)

TDirectory *	MethodBaseDir () const
	returns the ROOT directory where all instances of the corresponding MVA method are stored

virtual std::map< TString, Double_t >	OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
	call the Optimizer with the set of parameters and ranges that are meant to be tuned.

void	PrintHelpMessage () const
	prints out method-specific help method

void	ProcessSetup ()
	process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)

void	ReadStateFromFile ()
	Function to write options and weights to file.

void	ReadStateFromStream (std::istream &tf)
	read the header from the weight files of the different MVA methods

void	ReadStateFromStream (TFile &rf)
	write reference MVA distributions (and other information) to a ROOT type weight file

void	ReadStateFromXMLString (const char *xmlstr)
	for reading from memory

void	RerouteTransformationHandler (TransformationHandler *fTargetTransformation)

virtual void	Reset ()

virtual void	SetAnalysisType (Types::EAnalysisType type)

void	SetBaseDir (TDirectory *methodDir)

void	SetFile (TFile *file)

void	SetMethodBaseDir (TDirectory *methodDir)

void	SetMethodDir (TDirectory *methodDir)

void	SetModelPersistence (Bool_t status)

void	SetSignalReferenceCut (Double_t cut)

void	SetSignalReferenceCutOrientation (Double_t cutOrientation)

void	SetSilentFile (Bool_t status)

void	SetTestTime (Double_t testTime)

void	SetTestvarName (const TString &v="")

void	SetTrainTime (Double_t trainTime)

virtual void	SetTuneParameters (std::map< TString, Double_t > tuneParameters)
	set the tuning parameters according to the argument This is just a dummy .

void	SetupMethod ()
	setup of methods

virtual void	TestMulticlass ()
	test multiclass classification

virtual void	TestRegression (Double_t &bias, Double_t &biasT, Double_t &dev, Double_t &devT, Double_t &rms, Double_t &rmsT, Double_t &mInf, Double_t &mInfT, Double_t &corr, Types::ETreeType type)
	calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample

bool	TrainingEnded ()

void	TrainMethod ()

virtual void	WriteEvaluationHistosToFile (Types::ETreeType treetype)
	writes all MVA evaluation histograms to file

virtual void	WriteMonitoringHistosToFile () const
	write special monitoring histograms to file dummy implementation here --------------—

void	WriteStateToFile () const
	write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created

Public Member Functions inherited from TMVA::IMethod
	IMethod ()

virtual	~IMethod ()

Public Member Functions inherited from TMVA::Configurable
	Configurable (const TString &theOption="")
	constructor

virtual	~Configurable ()
	default destructor

void	AddOptionsXMLTo (void *parent) const
	write options to XML file

template<class T >
void	AddPreDefVal (const T &)

template<class T >
void	AddPreDefVal (const TString &optname, const T &)

void	CheckForUnusedOptions () const
	checks for unused options in option string

template<class T >
TMVA::OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc)

template<class T >
OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc="")

template<class T >
TMVA::OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc)

template<class T >
OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="")

const char *	GetConfigDescription () const

const char *	GetConfigName () const

const TString &	GetOptions () const

MsgLogger &	Log () const

virtual void	ParseOptions ()
	options parser

void	PrintOptions () const
	prints out the options set in the options string and the defaults

void	ReadOptionsFromStream (std::istream &istr)
	read option back from the weight file

void	ReadOptionsFromXML (void *node)

void	SetConfigDescription (const char *d)

void	SetConfigName (const char *n)

void	SetMsgType (EMsgType t)

void	SetOptions (const TString &s)

void	WriteOptionsToStream (std::ostream &o, const TString &prefix) const
	write options to output stream (e.g. in writing the MVA weight files

Public Member Functions inherited from TNamed
	TNamed ()

	TNamed (const char name, const char title)

	TNamed (const TNamed &named)
	TNamed copy ctor.

	TNamed (const TString &name, const TString &title)

virtual	~TNamed ()
	TNamed destructor.

virtual void	Clear (Option_t *option="")
	Set name and title to empty strings ("").

virtual TObject *	Clone (const char *newname="") const
	Make a clone of an object using the Streamer facility.

virtual Int_t	Compare (const TObject *obj) const
	Compare two TNamed objects.

virtual void	Copy (TObject &named) const
	Copy this to obj.

virtual void	FillBuffer (char *&buffer)
	Encode TNamed into output buffer.

virtual const char *	GetTitle () const
	Returns title of object.

virtual ULong_t	Hash () const
	Return hash value for this object.

virtual Bool_t	IsSortable () const

virtual void	ls (Option_t *option="") const
	List TNamed name and title.

TNamed &	operator= (const TNamed &rhs)
	TNamed assignment operator.

virtual void	Print (Option_t *option="") const
	Print TNamed name and title.

virtual void	SetName (const char *name)
	Set the name of the TNamed.

virtual void	SetNameTitle (const char name, const char title)
	Set all the TNamed parameters (name and title).

virtual void	SetTitle (const char *title="")
	Set the title of the TNamed.

virtual Int_t	Sizeof () const
	Return size of the TNamed part of the TObject.

Public Member Functions inherited from TObject
	TObject ()
	TObject constructor.

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

void	AbstractMethod (const char *method) const
	Use this method to implement an "abstract" method that you don't want to leave purely abstract.

virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad.

virtual void	Browse (TBrowser *b)
	Browse object. May be overridden for another default action.

ULong_t	CheckedHash ()
	Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.

virtual const char *	ClassName () const
	Returns name of class to which the object belongs.

virtual void	Delete (Option_t *option="")
	Delete this object.

virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object.

virtual void	Draw (Option_t *option="")
	Default Draw method for all objects.

virtual void	DrawClass () const
	Draw class inheritance tree of the class to which this object belongs.

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current selected pad for instance with: `gROOT->SetSelectedPad(gPad)`.

virtual void	Dump () const
	Dump contents of object on stdout.

virtual void	Error (const char method, const char msgfmt,...) const
	Issue error message.

virtual void	Execute (const char method, const char params, Int_t *error=0)
	Execute method on this object with the given parameter string, e.g.

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=0)
	Execute method on this object with parameters stored in the TObjArray.

virtual void	ExecuteEvent (Int_t event, Int_t px, Int_t py)
	Execute action corresponding to an event at (px,py).

virtual void	Fatal (const char method, const char msgfmt,...) const
	Issue fatal error message.

virtual TObject *	FindObject (const char *name) const
	Must be redefined in derived classes.

virtual TObject *	FindObject (const TObject *obj) const
	Must be redefined in derived classes.

virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object.

virtual const char *	GetIconName () const
	Returns mime type name of object.

virtual char *	GetObjectInfo (Int_t px, Int_t py) const
	Returns string containing info about the object at position (px,py).

virtual Option_t *	GetOption () const

virtual UInt_t	GetUniqueID () const
	Return the unique object id.

virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out.

Bool_t	HasInconsistentHash () const
	Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.

virtual void	Info (const char method, const char msgfmt,...) const
	Issue info message.

virtual Bool_t	InheritsFrom (const char *classname) const
	Returns kTRUE if object inherits from class "classname".

virtual Bool_t	InheritsFrom (const TClass *cl) const
	Returns kTRUE if object inherits from TClass cl.

virtual void	Inspect () const
	Dump contents of this object in a graphics canvas.

void	InvertBit (UInt_t f)

virtual Bool_t	IsEqual (const TObject *obj) const
	Default equal comparison (objects are equal if they have the same address in memory).

virtual Bool_t	IsFolder () const
	Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

void	MayNotUse (const char *method) const
	Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary).

virtual Bool_t	Notify ()
	This method must be overridden to handle object notification.

void	Obsolete (const char method, const char asOfVers, const char *removedFromVers) const
	Use this method to declare a method obsolete.

void	operator delete (void *ptr)
	Operator delete.

void	operator delete[] (void *ptr)
	Operator delete [].

void *	operator new (size_t sz)

void *	operator new (size_t sz, void *vp)

void *	operator new[] (size_t sz)

void *	operator new[] (size_t sz, void *vp)

TObject &	operator= (const TObject &rhs)
	TObject assignment operator.

virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself.

virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list.

virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory.

virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list.

void	ResetBit (UInt_t f)

virtual void	SaveAs (const char filename="", Option_t option="") const
	Save this object in the file specified by filename.

virtual void	SavePrimitive (std::ostream &out, Option_t *option="")
	Save a primitive as a C++ statement(s) on output stream "out".

void	SetBit (UInt_t f)

void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f.

virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object.

virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id.

virtual void	SysError (const char method, const char msgfmt,...) const
	Issue system error message.

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

virtual void	UseCurrentStyle ()
	Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.

virtual void	Warning (const char method, const char msgfmt,...) const
	Issue warning message.

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
	Write this object to the current directory.

Private Member Functions
UInt_t	GetNumValidationSamples ()
	Validation of the ValidationSize option.

void	SetupKerasModel (Bool_t loadTrainedModel)

Private Attributes
UInt_t	fBatchSize {0}

Bool_t	fContinueTraining

TString	fFilenameModel

TString	fFilenameTrainedModel

TString	fGpuOptions

TString	fKerasString

TString	fLearningRateSchedule

bool	fModelIsSetup = false

UInt_t	fNOutputs {0}

UInt_t	fNumEpochs {0}

Int_t	fNumThreads {0}

TString	fNumValidationString

UInt_t	fNVars {0}

std::vector< float >	fOutput

Bool_t	fSaveBestOnly

TString	fTensorBoard

Int_t	fTriesEarlyStopping

TString	fUserCodeName

Bool_t	fUseTFKeras { kFALSE}

float *	fVals = nullptr

Int_t	fVerbose

Additional Inherited Members
Static Public Member Functions inherited from TMVA::PyMethodBase
static TString	Py_GetProgramName ()
	Get program name from Python interpreter.

static void	PyFinalize ()

static void	PyInitialize ()
	Initialize Python interpreter.

static int	PyIsInitialized ()
	Check Python interpreter initialization status.

static void	PySetProgramName (TString name)
	Set program name for Python interpeter.

static void	Serialize (TString file, PyObject *classifier)
	Serialize Python object.

static Int_t	UnSerialize (TString file, PyObject **obj)
	Unserialize Python object.

Static Public Member Functions inherited from TObject
static Long_t	GetDtorOnly ()
	Return destructor only flag.

static Bool_t	GetObjectStat ()
	Get status of object stat flag.

static void	SetDtorOnly (void *obj)
	Set destructor only flag.

static void	SetObjectStat (Bool_t stat)
	Turn on/off tracking of objects in the TObjectTable.

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

const Event *	fTmpEvent

TrainingHistory	fTrainHistory

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = BIT(3) }

Protected Member Functions inherited from TMVA::PyMethodBase
void	PyRunString (TString code, TString errorMessage="Failed to run python code", int start=Py_single_input)
	Execute Python code from string.

Protected Member Functions inherited from TMVA::MethodBase
const TString &	GetInternalVarName (Int_t ivar) const

const TString &	GetOriginalVarName (Int_t ivar) const

const TString &	GetWeightFileDir () const

Bool_t	HasTrainingTree () const

Bool_t	Help () const

Bool_t	IgnoreEventsWithNegWeightsInTraining () const

Bool_t	IsConstructedFromWeightFile () const

Bool_t	IsNormalised () const

virtual void	MakeClassSpecific (std::ostream &, const TString &="") const

virtual void	MakeClassSpecificHeader (std::ostream &, const TString &="") const

void	NoErrorCalc (Double_t const err, Double_t const errUpper)

void	SetNormalised (Bool_t norm)

void	SetWeightFileDir (TString fileDir)
	set directory of weight file

void	SetWeightFileName (TString)
	set the weight file name (depreciated)

void	Statistics (Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
	calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE

Bool_t	TxtWeightsOnly () const

Bool_t	Verbose () const

Protected Member Functions inherited from TMVA::Configurable
void	EnableLooseOptions (Bool_t b=kTRUE)

const TString &	GetReferenceFile () const

Bool_t	LooseOptionCheckingEnabled () const

void	ResetSetFlag ()
	resets the IsSet flag for all declare options to be called before options are read from stream

void	WriteOptionsReferenceToFile ()
	write complete options to output stream

Protected Member Functions inherited from TObject
virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected).

void	MakeZombie ()

Protected Attributes inherited from TMVA::PyMethodBase
PyObject *	fClassifier

PyObject *	fLocalNS

PyObject *	fModule

PyObject *	fPyReturn

Protected Attributes inherited from TMVA::MethodBase
Types::EAnalysisType	fAnalysisType

UInt_t	fBackgroundClass

bool	fExitFromTraining = false

std::vector< TString > *	fInputVars

IPythonInteractive *	fInteractive = nullptr

UInt_t	fIPyCurrentIter = 0

UInt_t	fIPyMaxIter = 0

std::vector< Float_t > *	fMulticlassReturnVal

Int_t	fNbins

Int_t	fNbinsH

Int_t	fNbinsMVAoutput

Ranking *	fRanking

std::vector< Float_t > *	fRegressionReturnVal

Results *	fResults

UInt_t	fSignalClass

Protected Attributes inherited from TMVA::Configurable
MsgLogger *	fLogger

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

Static Protected Attributes inherited from TMVA::PyMethodBase
static PyObject *	fGlobalNS = NULL

static PyObject *	fMain = NULL

static PyObject *	fModulePickle = NULL

static PyObject *	fPickleDumps = NULL

static PyObject *	fPickleLoads = NULL

#include <TMVA/MethodPyKeras.h>

Inheritance diagram for TMVA::MethodPyKeras:

[legend]

Member Enumeration Documentation

◆ EBackendType

enum TMVA::MethodPyKeras::EBackendType

enumeration defining the used Keras backend

Enumerator
kUndefined
kTensorFlow
kTheano
kCNTK

Definition at line 74 of file MethodPyKeras.h.

Constructor & Destructor Documentation

◆ MethodPyKeras() [1/2]

MethodPyKeras::MethodPyKeras	(	const TString &	jobName,
		const TString &	methodTitle,
		DataSetInfo &	dsi,
		const TString &	theOption = `""`
	)

Definition at line 38 of file MethodPyKeras.cxx.

◆ MethodPyKeras() [2/2]

MethodPyKeras::MethodPyKeras	(	DataSetInfo &	dsi,
		const TString &	theWeightFile
	)

Definition at line 51 of file MethodPyKeras.cxx.

◆ ~MethodPyKeras()

MethodPyKeras::~MethodPyKeras ( )

Definition at line 65 of file MethodPyKeras.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

virtual void TMVA::MethodPyKeras::AddWeightsXMLTo ( void * ) const

inlinevirtual

Implements TMVA::PyMethodBase.

Definition at line 65 of file MethodPyKeras.h.

◆ CreateRanking()

const Ranking * TMVA::MethodPyKeras::CreateRanking ( )

inlinevirtual

Implements TMVA::PyMethodBase.

Definition at line 63 of file MethodPyKeras.h.

◆ DeclareOptions()

void MethodPyKeras::DeclareOptions ( )

virtual

Implements TMVA::PyMethodBase.

Definition at line 77 of file MethodPyKeras.cxx.

◆ GetHelpMessage()

void MethodPyKeras::GetHelpMessage ( ) const

virtual

Implements TMVA::IMethod.

Definition at line 759 of file MethodPyKeras.cxx.

◆ GetKerasBackend()

MethodPyKeras::EBackendType MethodPyKeras::GetKerasBackend ( )

Get the Keras backend (can be: TensorFlow, Theano or CNTK)

Definition at line 772 of file MethodPyKeras.cxx.

◆ GetKerasBackendName()

TString MethodPyKeras::GetKerasBackendName ( )

Definition at line 797 of file MethodPyKeras.cxx.

◆ GetMulticlassValues()

std::vector< Float_t > & MethodPyKeras::GetMulticlassValues ( )

virtual

Reimplemented from TMVA::MethodBase.

Definition at line 739 of file MethodPyKeras.cxx.

◆ GetMvaValue()

Double_t MethodPyKeras::GetMvaValue	(	Double_t *	errLower,
		Double_t *	errUpper
	)

virtual

Implements TMVA::PyMethodBase.

Definition at line 630 of file MethodPyKeras.cxx.

◆ GetMvaValues()

std::vector< Double_t > MethodPyKeras::GetMvaValues	(	Long64_t	firstEvt,
		Long64_t	lastEvt,
		Bool_t	logProgress
	)

virtual

get all the MVA values for the events of the current Data type

Implements TMVA::PyMethodBase.

Definition at line 650 of file MethodPyKeras.cxx.

◆ GetNumValidationSamples()

UInt_t TMVA::MethodPyKeras::GetNumValidationSamples ( )

private

Validation of the ValidationSize option.

Allowed formats are 20%, 0.2 and 100 etc.

20% and 0.2 selects 20% of the training set as validation data.
100 selects 100 events as the validation data.

Returns: number of samples in validation set

Definition at line 108 of file MethodPyKeras.cxx.

◆ GetRegressionValues()

std::vector< Float_t > & MethodPyKeras::GetRegressionValues ( )

virtual

Reimplemented from TMVA::MethodBase.

Definition at line 711 of file MethodPyKeras.cxx.

◆ HasAnalysisType()

Bool_t MethodPyKeras::HasAnalysisType	(	Types::EAnalysisType	type,
		UInt_t	numberClasses,
		UInt_t
	)

virtual

Implements TMVA::PyMethodBase.

Definition at line 68 of file MethodPyKeras.cxx.

◆ Init()

void MethodPyKeras::Init ( void )

virtual

Initialization function called from MethodBase::SetupMethod() Note that option string are not yet filled with their values.

This is done before ProcessOption method or after reading from XML file

Implements TMVA::PyMethodBase.

Definition at line 382 of file MethodPyKeras.cxx.

◆ ProcessOptions()

void MethodPyKeras::ProcessOptions ( )

virtual

Function processing the options This is called only when creating the method before training not when readinf from XML file.

Called from MethodBase::ProcessSetup that is called from Factory::BookMethod

Implements TMVA::PyMethodBase.

Definition at line 163 of file MethodPyKeras.cxx.

◆ ReadModelFromFile()

void MethodPyKeras::ReadModelFromFile ( )

virtual

Implements TMVA::PyMethodBase.

Definition at line 756 of file MethodPyKeras.cxx.

◆ ReadWeightsFromStream() [1/2]

virtual void TMVA::MethodPyKeras::ReadWeightsFromStream ( std::istream & )

inlinevirtual

Implements TMVA::PyMethodBase.

Definition at line 67 of file MethodPyKeras.h.

◆ ReadWeightsFromStream() [2/2]

virtual void TMVA::MethodPyKeras::ReadWeightsFromStream ( TFile & )

inlinevirtual

Reimplemented from TMVA::PyMethodBase.

Definition at line 68 of file MethodPyKeras.h.

◆ ReadWeightsFromXML()

virtual void TMVA::MethodPyKeras::ReadWeightsFromXML ( void * )

inlinevirtual

Implements TMVA::PyMethodBase.

Definition at line 66 of file MethodPyKeras.h.

◆ SetupKerasModel()

void MethodPyKeras::SetupKerasModel ( Bool_t loadTrainedModel )

private

Definition at line 176 of file MethodPyKeras.cxx.

◆ TestClassification()

void MethodPyKeras::TestClassification ( )

virtual

initialization

Reimplemented from TMVA::MethodBase.

Definition at line 626 of file MethodPyKeras.cxx.

◆ Train()

void MethodPyKeras::Train ( void )

virtual

Implements TMVA::PyMethodBase.

Definition at line 398 of file MethodPyKeras.cxx.

◆ UseTFKeras()

Bool_t TMVA::MethodPyKeras::UseTFKeras ( ) const

inline

Definition at line 80 of file MethodPyKeras.h.

Member Data Documentation

◆ fBatchSize

UInt_t TMVA::MethodPyKeras::fBatchSize {0}

private

Definition at line 85 of file MethodPyKeras.h.

◆ fContinueTraining

Bool_t TMVA::MethodPyKeras::fContinueTraining

private

Definition at line 90 of file MethodPyKeras.h.

◆ fFilenameModel

TString TMVA::MethodPyKeras::fFilenameModel

private

Definition at line 84 of file MethodPyKeras.h.

◆ fFilenameTrainedModel

TString TMVA::MethodPyKeras::fFilenameTrainedModel

private

Definition at line 105 of file MethodPyKeras.h.

◆ fGpuOptions

TString TMVA::MethodPyKeras::fGpuOptions

private

Definition at line 96 of file MethodPyKeras.h.

◆ fKerasString

TString TMVA::MethodPyKeras::fKerasString

private

Definition at line 98 of file MethodPyKeras.h.

◆ fLearningRateSchedule

TString TMVA::MethodPyKeras::fLearningRateSchedule

private

Definition at line 93 of file MethodPyKeras.h.

◆ fModelIsSetup

bool TMVA::MethodPyKeras::fModelIsSetup = false

private

Definition at line 100 of file MethodPyKeras.h.

◆ fNOutputs

UInt_t TMVA::MethodPyKeras::fNOutputs {0}

private

Definition at line 104 of file MethodPyKeras.h.

◆ fNumEpochs

UInt_t TMVA::MethodPyKeras::fNumEpochs {0}

private

Definition at line 86 of file MethodPyKeras.h.

◆ fNumThreads

Int_t TMVA::MethodPyKeras::fNumThreads {0}

private

Definition at line 87 of file MethodPyKeras.h.

◆ fNumValidationString

TString TMVA::MethodPyKeras::fNumValidationString

private

Definition at line 95 of file MethodPyKeras.h.

◆ fNVars

UInt_t TMVA::MethodPyKeras::fNVars {0}

private

Definition at line 103 of file MethodPyKeras.h.

◆ fOutput

std::vector<float> TMVA::MethodPyKeras::fOutput

private

Definition at line 102 of file MethodPyKeras.h.

◆ fSaveBestOnly

Bool_t TMVA::MethodPyKeras::fSaveBestOnly

private

Definition at line 91 of file MethodPyKeras.h.

◆ fTensorBoard

TString TMVA::MethodPyKeras::fTensorBoard

private

Definition at line 94 of file MethodPyKeras.h.

◆ fTriesEarlyStopping

Int_t TMVA::MethodPyKeras::fTriesEarlyStopping

private

Definition at line 92 of file MethodPyKeras.h.

◆ fUserCodeName

TString TMVA::MethodPyKeras::fUserCodeName

private

Definition at line 97 of file MethodPyKeras.h.

◆ fUseTFKeras

Bool_t TMVA::MethodPyKeras::fUseTFKeras { kFALSE}

private

Definition at line 89 of file MethodPyKeras.h.

◆ fVals

float* TMVA::MethodPyKeras::fVals = nullptr

private

Definition at line 101 of file MethodPyKeras.h.

◆ fVerbose

Int_t TMVA::MethodPyKeras::fVerbose

private

Definition at line 88 of file MethodPyKeras.h.

Libraries for TMVA::MethodPyKeras:

[legend]

The documentation for this class was generated from the following files:

tmva/pymva/inc/TMVA/MethodPyKeras.h
tmva/pymva/src/MethodPyKeras.cxx

Public Types

Public Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Member Enumeration Documentation

◆ EBackendType

Constructor & Destructor Documentation

◆ MethodPyKeras() [1/2]

◆ MethodPyKeras() [2/2]

◆ ~MethodPyKeras()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ CreateRanking()

◆ DeclareOptions()

◆ GetHelpMessage()

◆ GetKerasBackend()

◆ GetKerasBackendName()

◆ GetMulticlassValues()

◆ GetMvaValue()

◆ GetMvaValues()

◆ GetNumValidationSamples()

◆ GetRegressionValues()

◆ HasAnalysisType()

◆ Init()

◆ ProcessOptions()

◆ ReadModelFromFile()

◆ ReadWeightsFromStream() [1/2]

◆ ReadWeightsFromStream() [2/2]

◆ ReadWeightsFromXML()

◆ SetupKerasModel()

◆ TestClassification()

◆ Train()

◆ UseTFKeras()

Member Data Documentation

◆ fBatchSize

◆ fContinueTraining

◆ fFilenameModel

◆ fFilenameTrainedModel

◆ fGpuOptions

◆ fKerasString

◆ fLearningRateSchedule

◆ fModelIsSetup

◆ fNOutputs

◆ fNumEpochs

◆ fNumThreads

◆ fNumValidationString

◆ fNVars

◆ fOutput

◆ fSaveBestOnly

◆ fTensorBoard

◆ fTriesEarlyStopping

◆ fUserCodeName

◆ fUseTFKeras

◆ fVals

◆ fVerbose