Definition at line 89 of file MethodDL.h.

Public Member Functions
	MethodDL (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption)
	Constructor.

	MethodDL (DataSetInfo &theData, const TString &theWeightFile)
	Constructor.

virtual	~MethodDL ()
	Virtual Destructor.

void	AddWeightsXMLTo (void *parent) const

const Ranking *	CreateRanking ()

TString	GetArchitectureString () const

size_t	GetBatchDepth () const

size_t	GetBatchHeight () const

TString	GetBatchLayoutString () const

size_t	GetBatchSize () const

size_t	GetBatchWidth () const

const DeepNetImpl_t &	GetDeepNet () const

TString	GetErrorStrategyString () const

size_t	GetInputDepth () const

size_t	GetInputDim () const

size_t	GetInputHeight () const

TString	GetInputLayoutString () const

std::vector< size_t >	GetInputShape () const

size_t	GetInputWidth () const

KeyValueVector_t &	GetKeyValueSettings ()

const KeyValueVector_t &	GetKeyValueSettings () const

TString	GetLayoutString () const

DNN::ELossFunction	GetLossFunction () const

virtual const std::vector< Float_t > &	GetMulticlassValues ()

Double_t	GetMvaValue (Double_t err=nullptr, Double_t errUpper=nullptr)

DNN::EOutputFunction	GetOutputFunction () const

virtual const std::vector< Float_t > &	GetRegressionValues ()

std::vector< TTrainingSettings > &	GetTrainingSettings ()

const std::vector< TTrainingSettings > &	GetTrainingSettings () const

TString	GetTrainingStrategyString () const

DNN::EInitialization	GetWeightInitialization () const

TString	GetWeightInitializationString () const

Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
	Check the type of analysis the deep learning network can do.

virtual TClass *	IsA () const

KeyValueVector_t	ParseKeyValueString (TString parseString, TString blockDelim, TString tokenDelim)
	Function for parsing the training settings, provided as a string in a key-value form.

void	ReadWeightsFromStream (std::istream &)

virtual void	ReadWeightsFromStream (std::istream &)=0
	Methods for writing and reading weights.

virtual void	ReadWeightsFromStream (TFile &)
	Methods for writing and reading weights.

void	ReadWeightsFromXML (void *wghtnode)

void	SetArchitectureString (TString architectureString)

void	SetBatchDepth (size_t batchDepth)

void	SetBatchHeight (size_t batchHeight)

void	SetBatchSize (size_t batchSize)

void	SetBatchWidth (size_t batchWidth)

void	SetErrorStrategyString (TString errorStrategy)

void	SetInputDepth (int inputDepth)
	Setters.

void	SetInputHeight (int inputHeight)

void	SetInputShape (std::vector< size_t > inputShape)

void	SetInputWidth (int inputWidth)

void	SetLayoutString (TString layoutString)

void	SetOutputFunction (DNN::EOutputFunction outputFunction)

void	SetTrainingStrategyString (TString trainingStrategyString)

void	SetWeightInitialization (DNN::EInitialization weightInitialization)

void	SetWeightInitializationString (TString weightInitializationString)

virtual void	Streamer (TBuffer &)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void	Train ()
	Methods for training the deep learning network.

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=nullptr, Double_t *errUpper=nullptr)

const char *	GetName () const

UInt_t	GetNEvents () const

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=nullptr, PDF pdfB=nullptr) 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=nullptr, PDF pdfB=nullptr) 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

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

virtual void	TestClassification ()
	initialization

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 ()

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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.

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

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

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

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

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

const char *	GetName () const override
	Returns name of object.

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

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

TClass *	IsA () const override

Bool_t	IsSortable () const override

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

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

void	Print (Option_t *option="") const override
	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.

void	Streamer (TBuffer &) override
	Stream an object of class TObject.

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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 with: `gROOT->SetSelectedPad(c1)`.

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=nullptr)
	Execute method on this object with the given parameter string, e.g.

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=nullptr)
	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)

Bool_t	IsDestructed () const
	IsDestructed.

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 (the base implementation is no-op).

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, void vp)
	Only called by placement new when throwing an exception.

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

void	operator delete[] (void ptr, void vp)
	Only called by placement new[] when throwing an exception.

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.

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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=nullptr, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.

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

Static Public Member Functions
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TMVA::MethodBase
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TMVA::IMethod
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TMVA::Configurable
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TNamed
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TObject
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

static Longptr_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.

Protected Member Functions
void	GetHelpMessage () const

virtual std::vector< Double_t >	GetMvaValues (Long64_t firstEvt, Long64_t lastEvt, Bool_t logProgress)
	Evaluate the DeepNet on a vector of input values stored in the TMVA Event class Here we will evaluate using a default batch size and the same architecture used for Training.

Protected Member Functions inherited from TMVA::MethodBase
virtual std::vector< Double_t >	GetDataMvaValues (DataSet *data=nullptr, Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the given Data type

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 ()

Private Types
using	ArchitectureImpl_t = TMVA::DNN::TCpu< Float_t >

using	DeepNetImpl_t = TMVA::DNN::TDeepNet< ArchitectureImpl_t >

enum	ERecurrentLayerType { kLayerRNN = 0 , kLayerLSTM = 1 , kLayerGRU = 2 }

using	HostBufferImpl_t = typename ArchitectureImpl_t::HostBuffer_t

using	KeyValueVector_t = std::vector< std::map< TString, TString > >

using	MatrixImpl_t = typename ArchitectureImpl_t::Matrix_t

using	ScalarImpl_t = typename ArchitectureImpl_t::Scalar_t

using	TensorImpl_t = typename ArchitectureImpl_t::Tensor_t

Private Member Functions
template<typename Architecture_t , typename Layer_t >
void	CreateDeepNet (DNN::TDeepNet< Architecture_t, Layer_t > &deepNet, std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &nets)
	After calling the ProcesOptions(), all of the options are parsed, so using the parsed options, and given the architecture and the type of the layers, we build the Deep Network passed as a reference in the function.

void	DeclareOptions ()
	The option handling methods.

void	FillInputTensor ()
	Get the input event tensor for evaluation Internal function to fill the fXInput tensor with the correct shape from TMVA current Event class.

UInt_t	GetNumValidationSamples ()
	parce the validation string and return the number of event data used for validation

void	Init ()
	default initializations

void	ParseBatchLayout ()
	Parse the input layout.

template<typename Architecture_t , typename Layer_t >
void	ParseBatchNormLayer (DNN::TDeepNet< Architecture_t, Layer_t > &deepNet, std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &nets, TString layerString, TString delim)
	Pases the layer string and creates the appropriate reshape layer.

template<typename Architecture_t , typename Layer_t >
void	ParseConvLayer (DNN::TDeepNet< Architecture_t, Layer_t > &deepNet, std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &nets, TString layerString, TString delim)
	Pases the layer string and creates the appropriate convolutional layer.

template<typename Architecture_t , typename Layer_t >
void	ParseDenseLayer (DNN::TDeepNet< Architecture_t, Layer_t > &deepNet, std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &nets, TString layerString, TString delim)
	Pases the layer string and creates the appropriate dense layer.

void	ParseInputLayout ()
	Parse the input layout.

template<typename Architecture_t , typename Layer_t >
void	ParseMaxPoolLayer (DNN::TDeepNet< Architecture_t, Layer_t > &deepNet, std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &nets, TString layerString, TString delim)
	Pases the layer string and creates the appropriate max pool layer.

template<typename Architecture_t , typename Layer_t >
void	ParseRecurrentLayer (ERecurrentLayerType type, DNN::TDeepNet< Architecture_t, Layer_t > &deepNet, std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &nets, TString layerString, TString delim)
	Pases the layer string and creates the appropriate rnn layer.

template<typename Architecture_t , typename Layer_t >
void	ParseReshapeLayer (DNN::TDeepNet< Architecture_t, Layer_t > &deepNet, std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &nets, TString layerString, TString delim)
	Pases the layer string and creates the appropriate reshape layer.

template<typename Architecture_t >
std::vector< Double_t >	PredictDeepNet (Long64_t firstEvt, Long64_t lastEvt, size_t batchSize, Bool_t logProgress)
	perform prediction of the deep neural network using batches (called by GetMvaValues)

void	ProcessOptions ()

template<typename Architecture_t >
void	TrainDeepNet ()
	train of deep neural network using the defined architecture

Private Attributes
TString	fArchitectureString
	The string defining the architecture: CPU or GPU.

size_t	fBatchDepth
	The depth of the batch used to train the deep net.

size_t	fBatchHeight
	The height of the batch used to train the deep net.

TString	fBatchLayoutString
	The string defining the layout of the batch.

size_t	fBatchWidth
	The width of the batch used to train the deep net.

bool	fBuildNet
	Flag to control whether to build fNet, the stored network used for the evaluation.

TString	fErrorStrategy
	The string defining the error strategy for training.

TString	fInputLayoutString
	The string defining the layout of the input.

std::vector< size_t >	fInputShape
	Contains the batch size (no.

TString	fLayoutString
	The string defining the layout of the deep net.

DNN::ELossFunction	fLossFunction
	The loss function.

std::unique_ptr< DeepNetImpl_t >	fNet

TString	fNumValidationString
	The string defining the number (or percentage) of training data used for validation.

DNN::EOutputFunction	fOutputFunction
	The output function for making the predictions.

size_t	fRandomSeed
	The random seed used to initialize the weights and shuffling batches (default is zero)

bool	fResume

KeyValueVector_t	fSettings
	Map for the training strategy.

std::vector< TTrainingSettings >	fTrainingSettings
	The vector defining each training strategy.

TString	fTrainingStrategyString
	The string defining the training strategy.

DNN::EInitialization	fWeightInitialization
	The initialization method.

TString	fWeightInitializationString
	The string defining the weight initialization method.

TensorImpl_t	fXInput

HostBufferImpl_t	fXInputBuffer

std::unique_ptr< MatrixImpl_t >	fYHat

Additional Inherited Members
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 = (1ULL << ( 0 )) , kOverwrite = (1ULL << ( 1 )) , kWriteDelete = (1ULL << ( 2 )) }

enum	EDeprecatedStatusBits { kObjInCanvas = (1ULL << ( 3 )) }

enum	EStatusBits { kCanDelete = (1ULL << ( 0 )) , kMustCleanup = (1ULL << ( 3 )) , kIsReferenced = (1ULL << ( 4 )) , kHasUUID = (1ULL << ( 5 )) , kCannotPick = (1ULL << ( 6 )) , kNoContextMenu = (1ULL << ( 8 )) , kInvalidObject = (1ULL << ( 13 )) }

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = (1ULL << ( 3 )) }

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

UInt_t	fBackgroundClass

bool	fExitFromTraining = false

std::vector< TString > *	fInputVars

IPythonInteractive *	fInteractive = nullptr
	temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)

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

DataSet *	fTmpData = nullptr
	temporary event when testing on a different DataSet than the own one

const Event *	fTmpEvent

Protected Attributes inherited from TMVA::Configurable
MsgLogger *	fLogger
	! message logger

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

#include <TMVA/MethodDL.h>

Inheritance diagram for TMVA::MethodDL:

[legend]

Member Typedef Documentation

◆ ArchitectureImpl_t

using TMVA::MethodDL::ArchitectureImpl_t = TMVA::DNN::TCpu<Float_t>

private

Definition at line 103 of file MethodDL.h.

◆ DeepNetImpl_t

using TMVA::MethodDL::DeepNetImpl_t = TMVA::DNN::TDeepNet<ArchitectureImpl_t>

private

Definition at line 106 of file MethodDL.h.

◆ HostBufferImpl_t

using TMVA::MethodDL::HostBufferImpl_t = typename ArchitectureImpl_t::HostBuffer_t

private

Definition at line 110 of file MethodDL.h.

◆ KeyValueVector_t

using TMVA::MethodDL::KeyValueVector_t = std::vector<std::map<TString, TString> >

private

Definition at line 93 of file MethodDL.h.

◆ MatrixImpl_t

using TMVA::MethodDL::MatrixImpl_t = typename ArchitectureImpl_t::Matrix_t

private

Definition at line 107 of file MethodDL.h.

◆ ScalarImpl_t

using TMVA::MethodDL::ScalarImpl_t = typename ArchitectureImpl_t::Scalar_t

private

Definition at line 109 of file MethodDL.h.

◆ TensorImpl_t

using TMVA::MethodDL::TensorImpl_t = typename ArchitectureImpl_t::Tensor_t

private

Definition at line 108 of file MethodDL.h.

Member Enumeration Documentation

◆ ERecurrentLayerType

enum TMVA::MethodDL::ERecurrentLayerType

private

Enumerator
kLayerRNN
kLayerLSTM
kLayerGRU

Definition at line 153 of file MethodDL.h.

Constructor & Destructor Documentation

◆ MethodDL() [1/2]

TMVA::MethodDL::MethodDL	(	const TString &	jobName,
		const TString &	methodTitle,
		DataSetInfo &	theData,
		const TString &	theOption
	)

Constructor.

Standard constructor.

Definition at line 1019 of file MethodDL.cxx.

◆ MethodDL() [2/2]

TMVA::MethodDL::MethodDL	(	DataSetInfo &	theData,
		const TString &	theWeightFile
	)

Constructor.

Constructor from a weight file.

Definition at line 1032 of file MethodDL.cxx.

◆ ~MethodDL()

TMVA::MethodDL::~MethodDL ( )

virtual

Virtual Destructor.

Destructor.

Definition at line 1045 of file MethodDL.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

void TMVA::MethodDL::AddWeightsXMLTo ( void * parent ) const

virtual

Implements TMVA::MethodBase.

Definition at line 2051 of file MethodDL.cxx.

◆ Class()

static TClass * TMVA::MethodDL::Class ( )

static

Returns: TClass describing this class

◆ Class_Name()

static const char * TMVA::MethodDL::Class_Name ( )

static

Returns: Name of this class

◆ Class_Version()

static constexpr Version_t TMVA::MethodDL::Class_Version ( )

inlinestaticconstexpr

Returns: Version of this class

Definition at line 212 of file MethodDL.h.

◆ CreateDeepNet()

template<typename Architecture_t , typename Layer_t >

void TMVA::MethodDL::CreateDeepNet	(	DNN::TDeepNet< Architecture_t, Layer_t > &	deepNet,
		std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &	nets
	)

private

After calling the ProcesOptions(), all of the options are parsed, so using the parsed options, and given the architecture and the type of the layers, we build the Deep Network passed as a reference in the function.

Create a deep net based on the layout string.

Definition at line 529 of file MethodDL.cxx.

◆ CreateRanking()

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

virtual

Implements TMVA::MethodBase.

Definition at line 2335 of file MethodDL.cxx.

◆ DeclareOptions()

void TMVA::MethodDL::DeclareOptions ( )

privatevirtual

The option handling methods.

Implements TMVA::MethodBase.

Definition at line 167 of file MethodDL.cxx.

◆ DeclFileName()

static const char * TMVA::MethodDL::DeclFileName ( )

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 212 of file MethodDL.h.

◆ FillInputTensor()

void TMVA::MethodDL::FillInputTensor ( )

private

Get the input event tensor for evaluation Internal function to fill the fXInput tensor with the correct shape from TMVA current Event class.

Definition at line 1704 of file MethodDL.cxx.

◆ GetArchitectureString()

TString TMVA::MethodDL::GetArchitectureString ( ) const

inline

Definition at line 278 of file MethodDL.h.

◆ GetBatchDepth()

size_t TMVA::MethodDL::GetBatchDepth ( ) const

inline

Definition at line 262 of file MethodDL.h.

◆ GetBatchHeight()

size_t TMVA::MethodDL::GetBatchHeight ( ) const

inline

Definition at line 263 of file MethodDL.h.

◆ GetBatchLayoutString()

TString TMVA::MethodDL::GetBatchLayoutString ( ) const

inline

Definition at line 273 of file MethodDL.h.

◆ GetBatchSize()

size_t TMVA::MethodDL::GetBatchSize ( ) const

inline

Definition at line 261 of file MethodDL.h.

◆ GetBatchWidth()

size_t TMVA::MethodDL::GetBatchWidth ( ) const

inline

Definition at line 264 of file MethodDL.h.

◆ GetDeepNet()

const DeepNetImpl_t & TMVA::MethodDL::GetDeepNet ( ) const

inline

Definition at line 266 of file MethodDL.h.

◆ GetErrorStrategyString()

TString TMVA::MethodDL::GetErrorStrategyString ( ) const

inline

Definition at line 275 of file MethodDL.h.

◆ GetHelpMessage()

void TMVA::MethodDL::GetHelpMessage ( ) const

protectedvirtual

Implements TMVA::IMethod.

Definition at line 2342 of file MethodDL.cxx.

◆ GetInputDepth()

size_t TMVA::MethodDL::GetInputDepth ( ) const

inline

Definition at line 255 of file MethodDL.h.

◆ GetInputDim()

size_t TMVA::MethodDL::GetInputDim ( ) const

inline

Definition at line 258 of file MethodDL.h.

◆ GetInputHeight()

size_t TMVA::MethodDL::GetInputHeight ( ) const

inline

Definition at line 256 of file MethodDL.h.

◆ GetInputLayoutString()

TString TMVA::MethodDL::GetInputLayoutString ( ) const

inline

Definition at line 272 of file MethodDL.h.

◆ GetInputShape()

std::vector< size_t > TMVA::MethodDL::GetInputShape ( ) const

inline

Definition at line 259 of file MethodDL.h.

◆ GetInputWidth()

size_t TMVA::MethodDL::GetInputWidth ( ) const

inline

Definition at line 257 of file MethodDL.h.

◆ GetKeyValueSettings() [1/2]

KeyValueVector_t & TMVA::MethodDL::GetKeyValueSettings ( )

inline

Definition at line 283 of file MethodDL.h.

◆ GetKeyValueSettings() [2/2]

const KeyValueVector_t & TMVA::MethodDL::GetKeyValueSettings ( ) const

inline

Definition at line 282 of file MethodDL.h.

◆ GetLayoutString()

TString TMVA::MethodDL::GetLayoutString ( ) const

inline

Definition at line 274 of file MethodDL.h.

◆ GetLossFunction()

DNN::ELossFunction TMVA::MethodDL::GetLossFunction ( ) const

inline

Definition at line 270 of file MethodDL.h.

◆ GetMulticlassValues()

virtual const std::vector< Float_t > & TMVA::MethodDL::GetMulticlassValues ( )

virtual

Reimplemented from TMVA::MethodBase.

◆ GetMvaValue()

Double_t TMVA::MethodDL::GetMvaValue	(	Double_t *	err = `nullptr`,
		Double_t *	errUpper = `nullptr`
	)

virtual

Implements TMVA::MethodBase.

Definition at line 1772 of file MethodDL.cxx.

◆ GetMvaValues()

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

protectedvirtual

Evaluate the DeepNet on a vector of input values stored in the TMVA Event class Here we will evaluate using a default batch size and the same architecture used for Training.

Reimplemented from TMVA::MethodBase.

Definition at line 2022 of file MethodDL.cxx.

◆ GetNumValidationSamples()

UInt_t TMVA::MethodDL::GetNumValidationSamples ( )

private

parce the validation string and return the number of event data used for validation

◆ GetOutputFunction()

DNN::EOutputFunction TMVA::MethodDL::GetOutputFunction ( ) const

inline

Definition at line 269 of file MethodDL.h.

◆ GetRegressionValues()

virtual const std::vector< Float_t > & TMVA::MethodDL::GetRegressionValues ( )

virtual

Reimplemented from TMVA::MethodBase.

◆ GetTrainingSettings() [1/2]

std::vector< TTrainingSettings > & TMVA::MethodDL::GetTrainingSettings ( )

inline

Definition at line 281 of file MethodDL.h.

◆ GetTrainingSettings() [2/2]

const std::vector< TTrainingSettings > & TMVA::MethodDL::GetTrainingSettings ( ) const

inline

Definition at line 280 of file MethodDL.h.

◆ GetTrainingStrategyString()

TString TMVA::MethodDL::GetTrainingStrategyString ( ) const

inline

Definition at line 276 of file MethodDL.h.

◆ GetWeightInitialization()

DNN::EInitialization TMVA::MethodDL::GetWeightInitialization ( ) const

inline

Definition at line 268 of file MethodDL.h.

◆ GetWeightInitializationString()

TString TMVA::MethodDL::GetWeightInitializationString ( ) const

inline

Definition at line 277 of file MethodDL.h.

◆ HasAnalysisType()

Bool_t TMVA::MethodDL::HasAnalysisType	(	Types::EAnalysisType	type,
		UInt_t	numberClasses,
		UInt_t	numberTargets
	)

virtual

Check the type of analysis the deep learning network can do.

What kind of analysis type can handle the CNN.

Implements TMVA::IMethod.

Definition at line 1091 of file MethodDL.cxx.

◆ Init()

void TMVA::MethodDL::Init ( void )

privatevirtual

default initializations

Implements TMVA::MethodBase.

Definition at line 432 of file MethodDL.cxx.

◆ IsA()

virtual TClass * TMVA::MethodDL::IsA ( ) const

inlinevirtual

Returns: TClass describing current object

Reimplemented from TMVA::MethodBase.

Definition at line 212 of file MethodDL.h.

◆ ParseBatchLayout()

void TMVA::MethodDL::ParseBatchLayout ( )

private

Parse the input layout.

Definition at line 482 of file MethodDL.cxx.

◆ ParseBatchNormLayer()

template<typename Architecture_t , typename Layer_t >

void TMVA::MethodDL::ParseBatchNormLayer	(	DNN::TDeepNet< Architecture_t, Layer_t > &	deepNet,
		std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &	nets,
		TString	layerString,
		TString	delim
	)

private

Pases the layer string and creates the appropriate reshape layer.

Definition at line 890 of file MethodDL.cxx.

◆ ParseConvLayer()

template<typename Architecture_t , typename Layer_t >

void TMVA::MethodDL::ParseConvLayer	(	DNN::TDeepNet< Architecture_t, Layer_t > &	deepNet,
		std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &	nets,
		TString	layerString,
		TString	delim
	)

private

Pases the layer string and creates the appropriate convolutional layer.

Definition at line 669 of file MethodDL.cxx.

◆ ParseDenseLayer()

template<typename Architecture_t , typename Layer_t >

void TMVA::MethodDL::ParseDenseLayer	(	DNN::TDeepNet< Architecture_t, Layer_t > &	deepNet,
		std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &	nets,
		TString	layerString,
		TString	delim
	)

private

Pases the layer string and creates the appropriate dense layer.

Definition at line 583 of file MethodDL.cxx.

◆ ParseInputLayout()

void TMVA::MethodDL::ParseInputLayout ( )

private

Parse the input layout.

Definition at line 439 of file MethodDL.cxx.

◆ ParseKeyValueString()

auto TMVA::MethodDL::ParseKeyValueString	(	TString	parseString,
		TString	blockDelim,
		TString	tokenDelim
	)

Function for parsing the training settings, provided as a string in a key-value form.

Parse key value pairs in blocks -> return vector of blocks with map of key value pairs.

Definition at line 1052 of file MethodDL.cxx.

◆ ParseMaxPoolLayer()

template<typename Architecture_t , typename Layer_t >

void TMVA::MethodDL::ParseMaxPoolLayer	(	DNN::TDeepNet< Architecture_t, Layer_t > &	deepNet,
		std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &	nets,
		TString	layerString,
		TString	delim
	)

private

Pases the layer string and creates the appropriate max pool layer.

Definition at line 768 of file MethodDL.cxx.

◆ ParseRecurrentLayer()

template<typename Architecture_t , typename Layer_t >

void TMVA::MethodDL::ParseRecurrentLayer	(	ERecurrentLayerType	type,
		DNN::TDeepNet< Architecture_t, Layer_t > &	deepNet,
		std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &	nets,
		TString	layerString,
		TString	delim
	)

private

Pases the layer string and creates the appropriate rnn layer.

Definition at line 931 of file MethodDL.cxx.

◆ ParseReshapeLayer()

template<typename Architecture_t , typename Layer_t >

void TMVA::MethodDL::ParseReshapeLayer	(	DNN::TDeepNet< Architecture_t, Layer_t > &	deepNet,
		std::vector< DNN::TDeepNet< Architecture_t, Layer_t > > &	nets,
		TString	layerString,
		TString	delim
	)

private

Pases the layer string and creates the appropriate reshape layer.

Definition at line 829 of file MethodDL.cxx.

◆ PredictDeepNet()

template<typename Architecture_t >

std::vector< Double_t > TMVA::MethodDL::PredictDeepNet	(	Long64_t	firstEvt,
		Long64_t	lastEvt,
		size_t	batchSize,
		Bool_t	logProgress
	)

private

perform prediction of the deep neural network using batches (called by GetMvaValues)

Evaluate the DeepNet on a vector of input values stored in the TMVA Event class.

Definition at line 1828 of file MethodDL.cxx.

◆ ProcessOptions()

void TMVA::MethodDL::ProcessOptions ( )

privatevirtual

Implements TMVA::MethodBase.

Definition at line 219 of file MethodDL.cxx.

◆ ReadWeightsFromStream() [1/3]

void TMVA::MethodDL::ReadWeightsFromStream ( std::istream & )

virtual

Implements TMVA::MethodBase.

Definition at line 2330 of file MethodDL.cxx.

◆ ReadWeightsFromStream() [2/3]

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

virtual

Methods for writing and reading weights.

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [3/3]

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

inlinevirtual

Methods for writing and reading weights.

Reimplemented from TMVA::MethodBase.

Definition at line 266 of file MethodBase.h.

◆ ReadWeightsFromXML()

void TMVA::MethodDL::ReadWeightsFromXML ( void * wghtnode )

virtual

Implements TMVA::MethodBase.

Definition at line 2112 of file MethodDL.cxx.

◆ SetArchitectureString()

void TMVA::MethodDL::SetArchitectureString ( TString architectureString )

inline

Definition at line 307 of file MethodDL.h.

◆ SetBatchDepth()

void TMVA::MethodDL::SetBatchDepth ( size_t batchDepth )

inline

Definition at line 292 of file MethodDL.h.

◆ SetBatchHeight()

void TMVA::MethodDL::SetBatchHeight ( size_t batchHeight )

inline

Definition at line 293 of file MethodDL.h.

◆ SetBatchSize()

void TMVA::MethodDL::SetBatchSize ( size_t batchSize )

inline

Definition at line 291 of file MethodDL.h.

◆ SetBatchWidth()

void TMVA::MethodDL::SetBatchWidth ( size_t batchWidth )

inline

Definition at line 294 of file MethodDL.h.

◆ SetErrorStrategyString()

void TMVA::MethodDL::SetErrorStrategyString ( TString errorStrategy )

inline

Definition at line 301 of file MethodDL.h.

◆ SetInputDepth()

void TMVA::MethodDL::SetInputDepth ( int inputDepth )

inline

Setters.

Definition at line 286 of file MethodDL.h.

◆ SetInputHeight()

void TMVA::MethodDL::SetInputHeight ( int inputHeight )

inline

Definition at line 287 of file MethodDL.h.

◆ SetInputShape()

void TMVA::MethodDL::SetInputShape ( std::vector< size_t > inputShape )

inline

Definition at line 289 of file MethodDL.h.

◆ SetInputWidth()

void TMVA::MethodDL::SetInputWidth ( int inputWidth )

inline

Definition at line 288 of file MethodDL.h.

◆ SetLayoutString()

void TMVA::MethodDL::SetLayoutString ( TString layoutString )

inline

Definition at line 308 of file MethodDL.h.

◆ SetOutputFunction()

void TMVA::MethodDL::SetOutputFunction ( DNN::EOutputFunction outputFunction )

inline

Definition at line 300 of file MethodDL.h.

◆ SetTrainingStrategyString()

void TMVA::MethodDL::SetTrainingStrategyString ( TString trainingStrategyString )

inline

Definition at line 302 of file MethodDL.h.

◆ SetWeightInitialization()

void TMVA::MethodDL::SetWeightInitialization ( DNN::EInitialization weightInitialization )

inline

Definition at line 296 of file MethodDL.h.

◆ SetWeightInitializationString()

void TMVA::MethodDL::SetWeightInitializationString ( TString weightInitializationString )

inline

Definition at line 303 of file MethodDL.h.

◆ Streamer()

virtual void TMVA::MethodDL::Streamer ( TBuffer & )

virtual

Reimplemented from TMVA::MethodBase.

◆ StreamerNVirtual()

void TMVA::MethodDL::StreamerNVirtual ( TBuffer & ClassDef_StreamerNVirtual_b )

inline

Definition at line 212 of file MethodDL.h.

◆ Train()

void TMVA::MethodDL::Train ( void )

virtual

Methods for training the deep learning network.

Implements TMVA::MethodBase.

Definition at line 1659 of file MethodDL.cxx.

◆ TrainDeepNet()

template<typename Architecture_t >

void TMVA::MethodDL::TrainDeepNet

private

train of deep neural network using the defined architecture

Implementation of architecture specific train method.

Definition at line 1164 of file MethodDL.cxx.

Member Data Documentation

◆ fArchitectureString

TString TMVA::MethodDL::fArchitectureString

private

The string defining the architecture: CPU or GPU.

Definition at line 198 of file MethodDL.h.

◆ fBatchDepth

size_t TMVA::MethodDL::fBatchDepth

private

The depth of the batch used to train the deep net.

Definition at line 182 of file MethodDL.h.

◆ fBatchHeight

size_t TMVA::MethodDL::fBatchHeight

private

The height of the batch used to train the deep net.

Definition at line 183 of file MethodDL.h.

◆ fBatchLayoutString

TString TMVA::MethodDL::fBatchLayoutString

private

The string defining the layout of the batch.

Definition at line 193 of file MethodDL.h.

◆ fBatchWidth

size_t TMVA::MethodDL::fBatchWidth

private

The width of the batch used to train the deep net.

Definition at line 184 of file MethodDL.h.

◆ fBuildNet

bool TMVA::MethodDL::fBuildNet

private

Flag to control whether to build fNet, the stored network used for the evaluation.

Definition at line 201 of file MethodDL.h.

◆ fErrorStrategy

TString TMVA::MethodDL::fErrorStrategy

private

The string defining the error strategy for training.

Definition at line 195 of file MethodDL.h.

◆ fInputLayoutString

TString TMVA::MethodDL::fInputLayoutString

private

The string defining the layout of the input.

Definition at line 192 of file MethodDL.h.

◆ fInputShape

std::vector<size_t> TMVA::MethodDL::fInputShape

private

Contains the batch size (no.

of images in the batch), input depth (no. channels) and further input dimensions of the data (image height, width ...)

Definition at line 178 of file MethodDL.h.

◆ fLayoutString

TString TMVA::MethodDL::fLayoutString

private

The string defining the layout of the deep net.

Definition at line 194 of file MethodDL.h.

◆ fLossFunction

DNN::ELossFunction TMVA::MethodDL::fLossFunction

private

The loss function.

Definition at line 190 of file MethodDL.h.

◆ fNet

std::unique_ptr<DeepNetImpl_t> TMVA::MethodDL::fNet

private

Definition at line 209 of file MethodDL.h.

◆ fNumValidationString

TString TMVA::MethodDL::fNumValidationString

private

The string defining the number (or percentage) of training data used for validation.

Definition at line 199 of file MethodDL.h.

◆ fOutputFunction

DNN::EOutputFunction TMVA::MethodDL::fOutputFunction

private

The output function for making the predictions.

Definition at line 189 of file MethodDL.h.

◆ fRandomSeed

size_t TMVA::MethodDL::fRandomSeed

private

The random seed used to initialize the weights and shuffling batches (default is zero)

Definition at line 186 of file MethodDL.h.

◆ fResume

bool TMVA::MethodDL::fResume

private

Definition at line 200 of file MethodDL.h.

◆ fSettings

KeyValueVector_t TMVA::MethodDL::fSettings

private

Map for the training strategy.

Definition at line 203 of file MethodDL.h.

◆ fTrainingSettings

std::vector<TTrainingSettings> TMVA::MethodDL::fTrainingSettings

private

The vector defining each training strategy.

Definition at line 204 of file MethodDL.h.

◆ fTrainingStrategyString

TString TMVA::MethodDL::fTrainingStrategyString

private

The string defining the training strategy.

Definition at line 196 of file MethodDL.h.

◆ fWeightInitialization

DNN::EInitialization TMVA::MethodDL::fWeightInitialization

private

The initialization method.

Definition at line 188 of file MethodDL.h.

◆ fWeightInitializationString

TString TMVA::MethodDL::fWeightInitializationString

private

The string defining the weight initialization method.

Definition at line 197 of file MethodDL.h.

◆ fXInput

TensorImpl_t TMVA::MethodDL::fXInput

private

Definition at line 206 of file MethodDL.h.

◆ fXInputBuffer

HostBufferImpl_t TMVA::MethodDL::fXInputBuffer

private

Definition at line 207 of file MethodDL.h.

◆ fYHat

std::unique_ptr<MatrixImpl_t> TMVA::MethodDL::fYHat

private

Definition at line 208 of file MethodDL.h.

Libraries for TMVA::MethodDL:

[legend]

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

tmva/tmva/inc/TMVA/MethodDL.h
tmva/tmva/src/MethodDL.cxx

Public Member Functions

Static Public Member Functions

Protected Member Functions

Private Types

Private Member Functions

Private Attributes

Additional Inherited Members

Member Typedef Documentation

◆ ArchitectureImpl_t

◆ DeepNetImpl_t

◆ HostBufferImpl_t

◆ KeyValueVector_t

◆ MatrixImpl_t

◆ ScalarImpl_t

◆ TensorImpl_t

Member Enumeration Documentation

◆ ERecurrentLayerType

Constructor & Destructor Documentation

◆ MethodDL() [1/2]

◆ MethodDL() [2/2]

◆ ~MethodDL()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ CreateDeepNet()

◆ CreateRanking()

◆ DeclareOptions()

◆ DeclFileName()

◆ FillInputTensor()

◆ GetArchitectureString()

◆ GetBatchDepth()

◆ GetBatchHeight()

◆ GetBatchLayoutString()

◆ GetBatchSize()

◆ GetBatchWidth()

◆ GetDeepNet()

◆ GetErrorStrategyString()

◆ GetHelpMessage()

◆ GetInputDepth()

◆ GetInputDim()

◆ GetInputHeight()

◆ GetInputLayoutString()

◆ GetInputShape()

◆ GetInputWidth()

◆ GetKeyValueSettings() [1/2]

◆ GetKeyValueSettings() [2/2]

◆ GetLayoutString()

◆ GetLossFunction()

◆ GetMulticlassValues()

◆ GetMvaValue()

◆ GetMvaValues()

◆ GetNumValidationSamples()

◆ GetOutputFunction()

◆ GetRegressionValues()

◆ GetTrainingSettings() [1/2]

◆ GetTrainingSettings() [2/2]

◆ GetTrainingStrategyString()

◆ GetWeightInitialization()

◆ GetWeightInitializationString()

◆ HasAnalysisType()

◆ Init()

◆ IsA()

◆ ParseBatchLayout()

◆ ParseBatchNormLayer()

◆ ParseConvLayer()

◆ ParseDenseLayer()

◆ ParseInputLayout()

◆ ParseKeyValueString()

◆ ParseMaxPoolLayer()

◆ ParseRecurrentLayer()

◆ ParseReshapeLayer()

◆ PredictDeepNet()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/3]

◆ ReadWeightsFromStream() [2/3]

◆ ReadWeightsFromStream() [3/3]

◆ ReadWeightsFromXML()

◆ SetArchitectureString()