This class describes a neural network.

There are facilities to train the network and use the output.

The input layer is made of inactive neurons (returning the optionally normalized input) and output neurons are linear. The type of hidden neurons is free, the default being sigmoids. (One should still try to pass normalized inputs, e.g. between [0.,1])

The basic input is a TTree and two (training and test) TEventLists. Input and output neurons are assigned a value computed for each event with the same possibilities as for TTree::Draw(). Events may be weighted individually or via TTree::SetWeight(). 6 learning methods are available: kStochastic, kBatch, kSteepestDescent, kRibierePolak, kFletcherReeves and kBFGS.

This implementation, written by C. Delaere, is inspired from the mlpfit package from J.Schwindling et al. with some extensions:

the algorithms are globally the same
in TMultilayerPerceptron, there is no limitation on the number of layers/neurons, while MLPFIT was limited to 2 hidden layers
TMultilayerPerceptron allows you to save the network in a root file, and provides more export functionalities
TMultilayerPerceptron gives more flexibility regarding the normalization of inputs/outputs
TMultilayerPerceptron provides, thanks to Andrea Bocci, the possibility to use cross-entropy errors, which allows to train a network for pattern classification based on Bayesian posterior probability.

Introduction

Neural Networks are more and more used in various fields for data analysis and classification, both for research and commercial institutions. Some randomly chosen examples are:

image analysis
financial movements predictions and analysis
sales forecast and product shipping optimisation
in particles physics: mainly for classification tasks (signal over background discrimination)

More than 50% of neural networks are multilayer perceptrons. This implementation of multilayer perceptrons is inspired from the MLPfit package originally written by Jerome Schwindling. MLPfit remains one of the fastest tool for neural networks studies, and this ROOT add-on will not try to compete on that. A clear and flexible Object Oriented implementation has been chosen over a faster but more difficult to maintain code. Nevertheless, the time penalty does not exceed a factor 2.

The MLP

The multilayer perceptron is a simple feed-forward network with the following structure:

It is made of neurons characterized by a bias and weighted links between them (let's call those links synapses). The input neurons receive the inputs, normalize them and forward them to the first hidden layer.

Each neuron in any subsequent layer first computes a linear combination of the outputs of the previous layer. The output of the neuron is then function of that combination with f being linear for output neurons or a sigmoid for hidden layers. This is useful because of two theorems:

A linear combination of sigmoids can approximate any continuous function.
Trained with output = 1 for the signal and 0 for the background, the approximated function of inputs X is the probability of signal, knowing X.

Learning methods

The aim of all learning methods is to minimize the total error on a set of weighted examples. The error is defined as the sum in quadrature, divided by two, of the error on each individual output neuron. In all methods implemented, one needs to compute the first derivative of that error with respect to the weights. Exploiting the well-known properties of the derivative, especially the derivative of compound functions, one can write:

for a neuron: product of the local derivative with the weighted sum on the outputs of the derivatives.
for a synapse: product of the input with the local derivative of the output neuron.

This computation is called back-propagation of the errors. A loop over all examples is called an epoch. Six learning methods are implemented.

Stochastic minimization:

is the most trivial learning method. This is the Robbins-Monro stochastic approximation applied to multilayer perceptrons. The weights are updated after each example according to the formula: $w_{ij}(t+1) = w_{ij}(t) + \Delta w_{ij}(t)$

with

$\Delta w_{ij}(t) = - \eta(d e_p / d w_{ij} + \delta) + \epsilon \Delta w_{ij}(t-1)$

The parameters for this method are Eta, EtaDecay, Delta and Epsilon.

Steepest descent with fixed step size (batch learning):

It is the same as the stochastic minimization, but the weights are updated after considering all the examples, with the total derivative dEdw. The parameters for this method are Eta, EtaDecay, Delta and Epsilon.

Steepest descent algorithm:

Weights are set to the minimum along the line defined by the gradient. The only parameter for this method is Tau. Lower tau = higher precision = slower search. A value Tau = 3 seems reasonable.

Conjugate gradients with the Polak-Ribiere updating formula:

Weights are set to the minimum along the line defined by the conjugate gradient. Parameters are Tau and Reset, which defines the epochs where the direction is reset to the steepest descent.

Conjugate gradients with the Fletcher-Reeves updating formula:

Weights are set to the minimum along the line defined by the conjugate gradient. Parameters are Tau and Reset, which defines the epochs where the direction is reset to the steepest descent.

Broyden, Fletcher, Goldfarb, Shanno (BFGS) method:

Implies the computation of a NxN matrix computation, but seems more powerful at least for less than 300 weights. Parameters are Tau and Reset, which defines the epochs where the direction is reset to the steepest descent.

How to use it...

TMLP is build from 3 classes: TNeuron, TSynapse and TMultiLayerPerceptron. Only TMultiLayerPerceptron should be used explicitly by the user.

TMultiLayerPerceptron will take examples from a TTree given in the constructor. The network is described by a simple string: The input/output layers are defined by giving the expression for each neuron, separated by comas. Hidden layers are just described by the number of neurons. The layers are separated by colons. In addition, input/output layer formulas can be preceded by '@' (e.g "@out") if one wants to also normalize the data from the TTree. Input and outputs are taken from the TTree given as second argument. Expressions are evaluated as for TTree::Draw(), arrays are expended in distinct neurons, one for each index. This can only be done for fixed-size arrays. If the formula ends with "!", softmax functions are used for the output layer. One defines the training and test datasets by TEventLists.

Example:

TMultiLayerPerceptron("x,y:10:5:f",inputTree);

TMultiLayerPerceptron::TMultiLayerPerceptron

TMultiLayerPerceptron()

Default constructor.

Definition TMultiLayerPerceptron.cxx:264

Both the TTree and the TEventLists can be defined in the constructor, or later with the suited setter method. The lists used for training and test can be defined either explicitly, or via a string containing the formula to be used to define them, exactly as for a TCut.

The learning method is defined using the TMultiLayerPerceptron::SetLearningMethod() . Learning methods are :

A weight can be assigned to events, either in the constructor, either with TMultiLayerPerceptron::SetEventWeight(). In addition, the TTree weight is taken into account.

Finally, one starts the training with TMultiLayerPerceptron::Train(Int_t nepoch, Option_t* options). The first argument is the number of epochs while option is a string that can contain: "text" (simple text output) , "graph" (evoluting graphical training curves), "update=X" (step for the text/graph output update) or "+" (will skip the randomisation and start from the previous values). All combinations are available.

Example:

net.Train(100,"text, graph, update=10");

When the neural net is trained, it can be used directly ( TMultiLayerPerceptron::Evaluate() ) or exported to a standalone C++ code ( TMultiLayerPerceptron::Export() ).

Finally, note that even if this implementation is inspired from the mlpfit code, the feature lists are not exactly matching:

mlpfit hybrid learning method is not implemented
output neurons can be normalized, this is not the case for mlpfit
the neural net is exported in C++, FORTRAN or PYTHON
the drawResult() method allows a fast check of the learning procedure

In addition, the paw version of mlpfit had additional limitations on the number of neurons, hidden layers and inputs/outputs that does not apply to TMultiLayerPerceptron.

Definition at line 26 of file TMultiLayerPerceptron.h.

Public Types
enum	EDataSet { kTraining , kTest }

enum	ELearningMethod { kStochastic , kBatch , kSteepestDescent , kRibierePolak , kFletcherReeves , kBFGS }

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 Member Functions
	TMultiLayerPerceptron ()
	Default constructor.

	TMultiLayerPerceptron (const char layout, const char weight, TTree data, TEventList training, TEventList test, TNeuron::ENeuronType type=TNeuron::kSigmoid, const char extF="", const char *extD="")
	The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

	TMultiLayerPerceptron (const char layout, const char weight, TTree data=nullptr, const char training="Entry$%2==0", const char test="", TNeuron::ENeuronType type=TNeuron::kSigmoid, const char extF="", const char *extD="")
	The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

	TMultiLayerPerceptron (const char layout, TTree data, TEventList training, TEventList test, TNeuron::ENeuronType type=TNeuron::kSigmoid, const char extF="", const char extD="")
	The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

	TMultiLayerPerceptron (const char layout, TTree data=nullptr, const char training="Entry$%2==0", const char test="", TNeuron::ENeuronType type=TNeuron::kSigmoid, const char extF="", const char extD="")
	The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

	~TMultiLayerPerceptron () override
	Destructor.

void	ComputeDEDw () const
	Compute the DEDw = sum on all training events of dedw for each weight normalized by the number of events.

void	Draw (Option_t *option="") override
	Draws the network structure.

void	DrawResult (Int_t index=0, Option_t *option="test") const
	Draws the neural net output It produces an histogram with the output for the two datasets.

Bool_t	DumpWeights (Option_t *filename="-") const
	Dumps the weights to a text file.

Double_t	Evaluate (Int_t index, Double_t *params) const
	Returns the Neural Net for a given set of input parameters #parameters must equal #input neurons.

void	Export (Option_t filename="NNfunction", Option_t language="C++") const
	Exports the NN as a function for any non-ROOT-dependant code Supported languages are: only C++ , FORTRAN and Python (yet) This feature is also useful if you want to plot the NN as a function (TF1 or TF2).

Double_t	GetDelta () const

Double_t	GetEpsilon () const

Double_t	GetError (Int_t event) const
	Error on the output for a given event.

Double_t	GetError (TMultiLayerPerceptron::EDataSet set) const
	Error on the whole dataset.

Double_t	GetEta () const

Double_t	GetEtaDecay () const

TMultiLayerPerceptron::ELearningMethod	GetLearningMethod () const

Int_t	GetReset () const

TString	GetStructure () const

Double_t	GetTau () const

TNeuron::ENeuronType	GetType () const

TClass *	IsA () const override

Bool_t	LoadWeights (Option_t *filename="")
	Loads the weights from a text file conforming to the format defined by DumpWeights.

void	Randomize () const
	Randomize the weights.

Double_t	Result (Int_t event, Int_t index=0) const
	Computes the output for a given event.

void	SetData (TTree *)
	Set the data source.

void	SetDelta (Double_t delta)
	Sets Delta - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

void	SetEpsilon (Double_t eps)
	Sets Epsilon - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

void	SetEta (Double_t eta)
	Sets Eta - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

void	SetEtaDecay (Double_t ed)
	Sets EtaDecay - Eta *= EtaDecay at each epoch (look at the constructor for the complete description of learning methods and parameters)

void	SetEventWeight (const char *)
	Set the event weight.

void	SetLearningMethod (TMultiLayerPerceptron::ELearningMethod method)
	Sets the learning method.

void	SetReset (Int_t reset)
	Sets number of epochs between two resets of the search direction to the steepest descent.

void	SetTau (Double_t tau)
	Sets Tau - used in line search (look at the constructor for the complete description of learning methods and parameters)

void	SetTestDataSet (const char *test)
	Sets the Test dataset.

void	SetTestDataSet (TEventList *test)
	Sets the Test dataset.

void	SetTrainingDataSet (const char *train)
	Sets the Training dataset.

void	SetTrainingDataSet (TEventList *train)
	Sets the Training dataset.

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

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void	Train (Int_t nEpoch, Option_t *option="text", Double_t minE=0)
	Train the network.

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	Clear (Option_t *="")

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 abstract method.

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

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

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.

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

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

virtual Bool_t	IsSortable () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

virtual void	ls (Option_t *option="") const
	The ls function lists the contents of a class on stdout.

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 void	Print (Option_t *option="") const
	This method must be overridden when a class wants to print itself.

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 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	AttachData ()
	Connects the TTree to Neurons in input and output layers.

void	BFGSDir (TMatrixD &, Double_t *)
	Computes the direction for the BFGS algorithm as the product between the Hessian estimate (bfgsh) and the dir.

void	BuildNetwork ()
	Instantiates the network from the description.

void	ConjugateGradientsDir (Double_t *, Double_t)
	Sets the search direction to conjugate gradient direction beta should be:

Double_t	DerivDir (Double_t *)
	scalar product between gradient and direction = derivative along direction

bool	GetBFGSH (TMatrixD &, TMatrixD &, TMatrixD &)
	Computes the hessian matrix using the BFGS update algorithm.

Double_t	GetCrossEntropy () const
	Cross entropy error for a softmax output neuron, for a given event.

Double_t	GetCrossEntropyBinary () const
	Cross entropy error for sigmoid output neurons, for a given event.

void	GetEntry (Int_t) const
	Load an entry into the network.

Double_t	GetSumSquareError () const
	Error on the output for a given event.

Bool_t	LineSearch (Double_t , Double_t )
	Search along the line defined by direction.

void	MLP_Batch (Double_t *)
	One step for the batch (stochastic) method.

void	MLP_Stochastic (Double_t *)
	One step for the stochastic method buffer should contain the previous dw vector and will be updated.

void	SetGammaDelta (TMatrixD &, TMatrixD &, Double_t *)
	Sets the gamma $(g_{(t+1)}-g_{(t)})$ and delta $(w_{(t+1)}-w_{(t)})$ vectors Gamma is computed here, so ComputeDEDw cannot have been called before, and delta is a direct translation of buffer into a TMatrixD.

void	SteepestDir (Double_t *)
	Sets the search direction to steepest descent.

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 Member Functions
	TMultiLayerPerceptron (const TMultiLayerPerceptron &)

void	BuildFirstLayer (TString &)
	Instantiates the neurons in input Inputs are normalised and the type is set to kOff (simple forward of the formula value)

void	BuildHiddenLayers (TString &)
	Builds hidden layers.

void	BuildLastLayer (TString &, Int_t)
	Builds the output layer Neurons are linear combinations of input, by default.

void	BuildOneHiddenLayer (const TString &sNumNodes, Int_t &layer, Int_t &prevStart, Int_t &prevStop, Bool_t lastLayer)
	Builds a hidden layer, updates the number of layers.

void	ExpandStructure ()
	Expand the structure of the first layer.

void	MLP_Line (Double_t , Double_t , Double_t)
	Sets the weights to a point along a line Weights are set to [origin + (dist * dir)].

TMultiLayerPerceptron &	operator= (const TMultiLayerPerceptron &)

void	Shuffle (Int_t *, Int_t) const
	Shuffle the Int_t index[n] in input.

Private Attributes
Int_t	fCurrentTree
	! index of the current tree in a chain

Double_t	fCurrentTreeWeight
	! weight of the current tree in a chain

TTree *	fData
	! pointer to the tree used as datasource

Double_t	fDelta
	! Delta - used in stochastic minimisation - Default=0.

Double_t	fEpsilon
	! Epsilon - used in stochastic minimisation - Default=0.

Double_t	fEta
	! Eta - used in stochastic minimisation - Default=0.1

Double_t	fEtaDecay
	! EtaDecay - Eta *= EtaDecay at each epoch - Default=1.

TTreeFormula *	fEventWeight
	! formula representing the event weight

TString	fextD
	String containing the derivative name.

TString	fextF
	String containing the function name.

TObjArray	fFirstLayer
	Collection of the input neurons; subset of fNetwork.

Double_t	fLastAlpha
	! internal parameter used in line search

TObjArray	fLastLayer
	Collection of the output neurons; subset of fNetwork.

ELearningMethod	fLearningMethod
	! The Learning Method

TTreeFormulaManager *	fManager
	! TTreeFormulaManager for the weight and neurons

TObjArray	fNetwork
	Collection of all the neurons in the network.

TNeuron::ENeuronType	fOutType
	Type of output neurons.

Int_t	fReset
	! number of epochs between two resets of the search direction to the steepest descent - Default=50

TString	fStructure
	String containing the network structure.

TObjArray	fSynapses
	Collection of all the synapses in the network.

Double_t	fTau
	! Tau - used in line search - Default=3.

TEventList *	fTest
	! EventList defining the events in the test dataset

Bool_t	fTestOwner
	! internal flag whether one has to delete fTest or not

TEventList *	fTraining
	! EventList defining the events in the training dataset

Bool_t	fTrainingOwner
	! internal flag whether one has to delete fTraining or not

TNeuron::ENeuronType	fType
	Type of hidden neurons.

TString	fWeight
	String containing the event weight.

Friends
class	TMLPAnalyzer

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

#include <TMultiLayerPerceptron.h>

Inheritance diagram for TMultiLayerPerceptron:

[legend]

Member Enumeration Documentation

◆ EDataSet

enum TMultiLayerPerceptron::EDataSet

Enumerator
kTraining
kTest

Definition at line 32 of file TMultiLayerPerceptron.h.

◆ ELearningMethod

enum TMultiLayerPerceptron::ELearningMethod

Enumerator
kStochastic
kBatch
kSteepestDescent
kRibierePolak
kFletcherReeves
kBFGS

Definition at line 30 of file TMultiLayerPerceptron.h.

Constructor & Destructor Documentation

◆ TMultiLayerPerceptron() [1/6]

TMultiLayerPerceptron::TMultiLayerPerceptron ( )

Default constructor.

Definition at line 264 of file TMultiLayerPerceptron.cxx.

◆ TMultiLayerPerceptron() [2/6]

TMultiLayerPerceptron::TMultiLayerPerceptron	(	const char *	layout,
		TTree *	data = `nullptr`,
		const char *	training = `"Entry$%2==0"`,
		const char *	test = `""`,
		TNeuron::ENeuronType	type = `TNeuron::kSigmoid`,
		const char *	extF = `""`,
		const char *	extD = `""`
	)

The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

Hidden layers are just described by the number of neurons. The layers are separated by colons.

Ex: "x,y:10:5:f"

The output can be prepended by '@' if the variable has to be normalized. The output can be followed by '!' to use Softmax neurons for the output layer only.

Ex: "x,y:10:5:c1,c2,c3!"

Input and outputs are taken from the TTree given as second argument. training and test are two cuts (see TTreeFormula) defining events to be used during the neural net training and testing.

Example: "Entry$%2", "(Entry$+1)%2".

Both the TTree and the cut can be defined in the constructor, or later with the suited setter method.

Definition at line 446 of file TMultiLayerPerceptron.cxx.

◆ TMultiLayerPerceptron() [3/6]

TMultiLayerPerceptron::TMultiLayerPerceptron	(	const char *	layout,
		const char *	weight,
		TTree *	data = `nullptr`,
		const char *	training = `"Entry$%2==0"`,
		const char *	test = `""`,
		TNeuron::ENeuronType	type = `TNeuron::kSigmoid`,
		const char *	extF = `""`,
		const char *	extD = `""`
	)

The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

Hidden layers are just described by the number of neurons. The layers are separated by colons.

Ex: "x,y:10:5:f"

The output can be prepended by '@' if the variable has to be normalized. The output can be followed by '!' to use Softmax neurons for the output layer only.

Ex: "x,y:10:5:c1,c2,c3!"

Input and outputs are taken from the TTree given as second argument. training and test are two cuts (see TTreeFormula) defining events to be used during the neural net training and testing.

Example: "Entry$%2", "(Entry$+1)%2".

Both the TTree and the cut can be defined in the constructor, or later with the suited setter method.

Definition at line 524 of file TMultiLayerPerceptron.cxx.

◆ TMultiLayerPerceptron() [4/6]

TMultiLayerPerceptron::TMultiLayerPerceptron	(	const char *	layout,
		TTree *	data,
		TEventList *	training,
		TEventList *	test,
		TNeuron::ENeuronType	type = `TNeuron::kSigmoid`,
		const char *	extF = `""`,
		const char *	extD = `""`
	)

The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

Hidden layers are just described by the number of neurons. The layers are separated by colons.

Ex: "x,y:10:5:f"

The output can be prepended by '@' if the variable has to be normalized. The output can be followed by '!' to use Softmax neurons for the output layer only.

Ex: "x,y:10:5:c1,c2,c3!"

Input and outputs are taken from the TTree given as second argument. training and test are the two TEventLists defining events to be used during the neural net training. Both the TTree and the TEventLists can be defined in the constructor, or later with the suited setter method.

Definition at line 318 of file TMultiLayerPerceptron.cxx.

◆ TMultiLayerPerceptron() [5/6]

TMultiLayerPerceptron::TMultiLayerPerceptron	(	const char *	layout,
		const char *	weight,
		TTree *	data,
		TEventList *	training,
		TEventList *	test,
		TNeuron::ENeuronType	type = `TNeuron::kSigmoid`,
		const char *	extF = `""`,
		const char *	extD = `""`
	)

The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

Hidden layers are just described by the number of neurons. The layers are separated by colons.

Ex: "x,y:10:5:f"

The output can be prepended by '@' if the variable has to be normalized. The output can be followed by '!' to use Softmax neurons for the output layer only.

Ex: "x,y:10:5:c1,c2,c3!"

Input and outputs are taken from the TTree given as second argument. training and test are the two TEventLists defining events to be used during the neural net training. Both the TTree and the TEventLists can be defined in the constructor, or later with the suited setter method.

Definition at line 380 of file TMultiLayerPerceptron.cxx.

◆ ~TMultiLayerPerceptron()

TMultiLayerPerceptron::~TMultiLayerPerceptron ( )

override

Destructor.

Definition at line 581 of file TMultiLayerPerceptron.cxx.

◆ TMultiLayerPerceptron() [6/6]

TMultiLayerPerceptron::TMultiLayerPerceptron ( const TMultiLayerPerceptron & )

private

Member Function Documentation

◆ AttachData()

void TMultiLayerPerceptron::AttachData ( )

protected

Connects the TTree to Neurons in input and output layers.

The formulas associated to each neuron are created and reported to the network formula manager. By default, the branch is not normalised since this would degrade performance for classification jobs. Normalisation can be requested by putting '@' in front of the formula.

Definition at line 1266 of file TMultiLayerPerceptron.cxx.

◆ BFGSDir()

void TMultiLayerPerceptron::BFGSDir	(	TMatrixD &	bfgsh,
		Double_t *	dir
	)

protected

Computes the direction for the BFGS algorithm as the product between the Hessian estimate (bfgsh) and the dir.

Definition at line 2494 of file TMultiLayerPerceptron.cxx.

◆ BuildFirstLayer()

void TMultiLayerPerceptron::BuildFirstLayer ( TString & input )

private

Instantiates the neurons in input Inputs are normalised and the type is set to kOff (simple forward of the formula value)

Definition at line 1401 of file TMultiLayerPerceptron.cxx.

◆ BuildHiddenLayers()

void TMultiLayerPerceptron::BuildHiddenLayers ( TString & hidden )

private

Builds hidden layers.

Definition at line 1419 of file TMultiLayerPerceptron.cxx.

◆ BuildLastLayer()

void TMultiLayerPerceptron::BuildLastLayer	(	TString &	output,
		Int_t	prev
	)

private

Builds the output layer Neurons are linear combinations of input, by default.

If the structure ends with "!", neurons are set up for classification, ie. with a sigmoid (1 neuron) or softmax (more neurons) activation function.

Definition at line 1483 of file TMultiLayerPerceptron.cxx.

◆ BuildNetwork()

void TMultiLayerPerceptron::BuildNetwork ( )

protected

Instantiates the network from the description.

Definition at line 1370 of file TMultiLayerPerceptron.cxx.

◆ BuildOneHiddenLayer()

void TMultiLayerPerceptron::BuildOneHiddenLayer	(	const TString &	sNumNodes,
		Int_t &	layer,
		Int_t &	prevStart,
		Int_t &	prevStop,
		Bool_t	lastLayer
	)

private

Builds a hidden layer, updates the number of layers.

Definition at line 1438 of file TMultiLayerPerceptron.cxx.

◆ Class()

static TClass * TMultiLayerPerceptron::Class ( )

static

Returns: TClass describing this class

◆ Class_Name()

static const char * TMultiLayerPerceptron::Class_Name ( )

static

Returns: Name of this class

◆ Class_Version()

static constexpr Version_t TMultiLayerPerceptron::Class_Version ( )

inlinestaticconstexpr

Returns: Version of this class

Definition at line 151 of file TMultiLayerPerceptron.h.

◆ ComputeDEDw()

void TMultiLayerPerceptron::ComputeDEDw ( ) const

Compute the DEDw = sum on all training events of dedw for each weight normalized by the number of events.

Definition at line 1163 of file TMultiLayerPerceptron.cxx.

◆ ConjugateGradientsDir()

void TMultiLayerPerceptron::ConjugateGradientsDir	(	Double_t *	dir,
		Double_t	beta
	)

protected

Sets the search direction to conjugate gradient direction beta should be:

$||g_{(t+1)}||^2 / ||g_{(t)}||^2$ (Fletcher-Reeves)

$g_{(t+1)} (g_{(t+1)}-g_{(t)}) / ||g_{(t)}||^2$ (Ribiere-Polak)

Definition at line 2379 of file TMultiLayerPerceptron.cxx.

◆ DeclFileName()

static const char * TMultiLayerPerceptron::DeclFileName ( )

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 151 of file TMultiLayerPerceptron.h.

◆ DerivDir()

Double_t TMultiLayerPerceptron::DerivDir ( Double_t * dir )

protected

scalar product between gradient and direction = derivative along direction

Definition at line 2470 of file TMultiLayerPerceptron.cxx.

◆ Draw()

void TMultiLayerPerceptron::Draw ( Option_t * option = "" )

overridevirtual

Draws the network structure.

Neurons are depicted by a blue disk, and synapses by lines connecting neurons. The line width is proportional to the weight.

Reimplemented from TObject.

Definition at line 2524 of file TMultiLayerPerceptron.cxx.

◆ DrawResult()

void TMultiLayerPerceptron::DrawResult	(	Int_t	index = `0`,
		Option_t *	option = `"test"`
	)		const

Draws the neural net output It produces an histogram with the output for the two datasets.

Index is the number of the desired output neuron. "option" can contain:

test or train to select a dataset
comp to produce a X-Y comparison plot
nocanv to not create a new TCanvas for the plot

Definition at line 1533 of file TMultiLayerPerceptron.cxx.

◆ DumpWeights()

Bool_t TMultiLayerPerceptron::DumpWeights ( Option_t * filename = "-" ) const

Dumps the weights to a text file.

Set filename to "-" (default) to dump to the standard output

Definition at line 1607 of file TMultiLayerPerceptron.cxx.

◆ Evaluate()

Double_t TMultiLayerPerceptron::Evaluate	(	Int_t	index,
		Double_t *	params
	)		const

Returns the Neural Net for a given set of input parameters #parameters must equal #input neurons.

Definition at line 1713 of file TMultiLayerPerceptron.cxx.

◆ ExpandStructure()

void TMultiLayerPerceptron::ExpandStructure ( )

private

Expand the structure of the first layer.

Definition at line 1324 of file TMultiLayerPerceptron.cxx.

◆ Export()

void TMultiLayerPerceptron::Export	(	Option_t *	filename = `"NNfunction"`,
		Option_t *	language = `"C++"`
	)		const

Exports the NN as a function for any non-ROOT-dependant code Supported languages are: only C++ , FORTRAN and Python (yet) This feature is also useful if you want to plot the NN as a function (TF1 or TF2).

Definition at line 1738 of file TMultiLayerPerceptron.cxx.

◆ GetBFGSH()

bool TMultiLayerPerceptron::GetBFGSH	(	TMatrixD &	bfgsh,
		TMatrixD &	gamma,
		TMatrixD &	delta
	)

protected

Computes the hessian matrix using the BFGS update algorithm.

from gamma (g_{(t+1)}-g_{(t)}) and delta (w_{(t+1)}-w_{(t)}). It returns true if such a direction could not be found (if gamma and delta are orthogonal).

Definition at line 2405 of file TMultiLayerPerceptron.cxx.

◆ GetCrossEntropy()

Double_t TMultiLayerPerceptron::GetCrossEntropy ( ) const

protected

Cross entropy error for a softmax output neuron, for a given event.

Definition at line 1142 of file TMultiLayerPerceptron.cxx.

◆ GetCrossEntropyBinary()

Double_t TMultiLayerPerceptron::GetCrossEntropyBinary ( ) const

protected

Cross entropy error for sigmoid output neurons, for a given event.

Definition at line 1111 of file TMultiLayerPerceptron.cxx.

◆ GetDelta()

Double_t TMultiLayerPerceptron::GetDelta ( ) const

inline

Definition at line 78 of file TMultiLayerPerceptron.h.

◆ GetEntry()

void TMultiLayerPerceptron::GetEntry ( Int_t entry ) const

protected

Load an entry into the network.

Definition at line 759 of file TMultiLayerPerceptron.cxx.

◆ GetEpsilon()

Double_t TMultiLayerPerceptron::GetEpsilon ( ) const

inline

Definition at line 77 of file TMultiLayerPerceptron.h.

◆ GetError() [1/2]

Double_t TMultiLayerPerceptron::GetError ( Int_t event ) const

Error on the output for a given event.

Definition at line 1046 of file TMultiLayerPerceptron.cxx.

◆ GetError() [2/2]

Double_t TMultiLayerPerceptron::GetError ( TMultiLayerPerceptron::EDataSet set ) const

Error on the whole dataset.

Definition at line 1075 of file TMultiLayerPerceptron.cxx.

◆ GetEta()

Double_t TMultiLayerPerceptron::GetEta ( ) const

inline

Definition at line 76 of file TMultiLayerPerceptron.h.

◆ GetEtaDecay()

Double_t TMultiLayerPerceptron::GetEtaDecay ( ) const

inline

Definition at line 79 of file TMultiLayerPerceptron.h.

◆ GetLearningMethod()

TMultiLayerPerceptron::ELearningMethod TMultiLayerPerceptron::GetLearningMethod ( ) const

inline

Definition at line 80 of file TMultiLayerPerceptron.h.

◆ GetReset()

Int_t TMultiLayerPerceptron::GetReset ( ) const

inline

Definition at line 82 of file TMultiLayerPerceptron.h.

◆ GetStructure()

TString TMultiLayerPerceptron::GetStructure ( ) const

inline

Definition at line 83 of file TMultiLayerPerceptron.h.

◆ GetSumSquareError()

Double_t TMultiLayerPerceptron::GetSumSquareError ( ) const

protected

Error on the output for a given event.

Definition at line 1098 of file TMultiLayerPerceptron.cxx.

◆ GetTau()

Double_t TMultiLayerPerceptron::GetTau ( ) const

inline

Definition at line 81 of file TMultiLayerPerceptron.h.

◆ GetType()

TNeuron::ENeuronType TMultiLayerPerceptron::GetType ( ) const

inline

Definition at line 84 of file TMultiLayerPerceptron.h.

◆ IsA()

TClass * TMultiLayerPerceptron::IsA ( ) const

inlineoverridevirtual

Returns: TClass describing current object

Reimplemented from TObject.

Definition at line 151 of file TMultiLayerPerceptron.h.

◆ LineSearch()

bool TMultiLayerPerceptron::LineSearch	(	Double_t *	direction,
		Double_t *	buffer
	)

protected

Search along the line defined by direction.

buffer is not used but is updated with the new dw so that it can be used by a later stochastic step. It returns true if the line search fails.

Definition at line 2274 of file TMultiLayerPerceptron.cxx.

◆ LoadWeights()

Bool_t TMultiLayerPerceptron::LoadWeights ( Option_t * filename = "" )

Loads the weights from a text file conforming to the format defined by DumpWeights.

Definition at line 1657 of file TMultiLayerPerceptron.cxx.

◆ MLP_Batch()

void TMultiLayerPerceptron::MLP_Batch ( Double_t * buffer )

protected

One step for the batch (stochastic) method.

DEDw should have been updated before calling this.

Definition at line 2203 of file TMultiLayerPerceptron.cxx.

◆ MLP_Line()

void TMultiLayerPerceptron::MLP_Line	(	Double_t *	origin,
		Double_t *	dir,
		Double_t	dist
	)

private

Sets the weights to a point along a line Weights are set to [origin + (dist * dir)].

Definition at line 2231 of file TMultiLayerPerceptron.cxx.

◆ MLP_Stochastic()

void TMultiLayerPerceptron::MLP_Stochastic ( Double_t * buffer )

protected

One step for the stochastic method buffer should contain the previous dw vector and will be updated.

Definition at line 2158 of file TMultiLayerPerceptron.cxx.

◆ operator=()

TMultiLayerPerceptron & TMultiLayerPerceptron::operator= ( const TMultiLayerPerceptron & )

private

◆ Randomize()

void TMultiLayerPerceptron::Randomize ( ) const

Randomize the weights.

Definition at line 1239 of file TMultiLayerPerceptron.cxx.

◆ Result()

Double_t TMultiLayerPerceptron::Result	(	Int_t	event,
		Int_t	index = `0`
	)		const

Computes the output for a given event.

Look at the output neuron designed by index.

Definition at line 1033 of file TMultiLayerPerceptron.cxx.

◆ SetData()

void TMultiLayerPerceptron::SetData ( TTree * data )

Set the data source.

Definition at line 590 of file TMultiLayerPerceptron.cxx.

◆ SetDelta()

void TMultiLayerPerceptron::SetDelta ( Double_t delta )

Sets Delta - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

Definition at line 720 of file TMultiLayerPerceptron.cxx.

◆ SetEpsilon()

void TMultiLayerPerceptron::SetEpsilon ( Double_t eps )

Sets Epsilon - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

Definition at line 710 of file TMultiLayerPerceptron.cxx.

◆ SetEta()

void TMultiLayerPerceptron::SetEta ( Double_t eta )

Sets Eta - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

Definition at line 700 of file TMultiLayerPerceptron.cxx.

◆ SetEtaDecay()

void TMultiLayerPerceptron::SetEtaDecay ( Double_t ed )

Sets EtaDecay - Eta *= EtaDecay at each epoch (look at the constructor for the complete description of learning methods and parameters)

Definition at line 730 of file TMultiLayerPerceptron.cxx.

◆ SetEventWeight()

void TMultiLayerPerceptron::SetEventWeight ( const char * branch )

Set the event weight.

Definition at line 606 of file TMultiLayerPerceptron.cxx.

◆ SetGammaDelta()

void TMultiLayerPerceptron::SetGammaDelta	(	TMatrixD &	gamma,
		TMatrixD &	delta,
		Double_t *	buffer
	)

protected

Sets the gamma $(g_{(t+1)}-g_{(t)})$ and delta $(w_{(t+1)}-w_{(t)})$ vectors Gamma is computed here, so ComputeDEDw cannot have been called before, and delta is a direct translation of buffer into a TMatrixD.

Definition at line 2431 of file TMultiLayerPerceptron.cxx.

◆ SetLearningMethod()

void TMultiLayerPerceptron::SetLearningMethod ( TMultiLayerPerceptron::ELearningMethod method )

Sets the learning method.

Available methods are: kStochastic, kBatch, kSteepestDescent, kRibierePolak, kFletcherReeves and kBFGS. (look at the constructor for the complete description of learning methods and parameters)

Definition at line 690 of file TMultiLayerPerceptron.cxx.

◆ SetReset()

void TMultiLayerPerceptron::SetReset ( Int_t reset )

Sets number of epochs between two resets of the search direction to the steepest descent.

(look at the constructor for the complete description of learning methods and parameters)

Definition at line 751 of file TMultiLayerPerceptron.cxx.

◆ SetTau()

void TMultiLayerPerceptron::SetTau ( Double_t tau )

Sets Tau - used in line search (look at the constructor for the complete description of learning methods and parameters)

Definition at line 740 of file TMultiLayerPerceptron.cxx.

◆ SetTestDataSet() [1/2]

void TMultiLayerPerceptron::SetTestDataSet ( const char * test )

Sets the Test dataset.

Those events will not be used for the minimization but for control. Note that the tree must be already defined.

Definition at line 666 of file TMultiLayerPerceptron.cxx.

◆ SetTestDataSet() [2/2]

void TMultiLayerPerceptron::SetTestDataSet ( TEventList * test )

Sets the Test dataset.

Those events will not be used for the minimization but for control

Definition at line 633 of file TMultiLayerPerceptron.cxx.

◆ SetTrainingDataSet() [1/2]

void TMultiLayerPerceptron::SetTrainingDataSet ( const char * train )

Sets the Training dataset.

Those events will be used for the minimization. Note that the tree must be already defined.

Definition at line 645 of file TMultiLayerPerceptron.cxx.

◆ SetTrainingDataSet() [2/2]

void TMultiLayerPerceptron::SetTrainingDataSet ( TEventList * train )

Sets the Training dataset.

Those events will be used for the minimization

Definition at line 622 of file TMultiLayerPerceptron.cxx.

◆ Shuffle()

void TMultiLayerPerceptron::Shuffle	(	Int_t *	index,
		Int_t	n
	)		const

private

Shuffle the Int_t index[n] in input.

Input:

index: the array to shuffle
n: the size of the array

Output:

index: the shuffled indexes

This method is used for stochastic training

Definition at line 2139 of file TMultiLayerPerceptron.cxx.

◆ SteepestDir()

void TMultiLayerPerceptron::SteepestDir ( Double_t * dir )

protected

Sets the search direction to steepest descent.

Definition at line 2253 of file TMultiLayerPerceptron.cxx.

◆ Streamer()

void TMultiLayerPerceptron::Streamer ( TBuffer & R__b )

overridevirtual

Stream an object of class TObject.

Reimplemented from TObject.

◆ StreamerNVirtual()

void TMultiLayerPerceptron::StreamerNVirtual ( TBuffer & ClassDef_StreamerNVirtual_b )

inline

Definition at line 151 of file TMultiLayerPerceptron.h.

◆ Train()

void TMultiLayerPerceptron::Train	(	Int_t	nEpoch,
		Option_t *	option = `"text"`,
		Double_t	minE = `0`
	)

Train the network.

nEpoch is the number of iterations. option can contain:

"text" (simple text output)
"graph" (evoluting graphical training curves)
"update=X" (step for the text/graph output update)
"+" will skip the randomisation and start from the previous values.
"current" (draw in the current canvas)
"minErrorTrain" (stop when NN error on the training sample gets below minE
"minErrorTest" (stop when NN error on the test sample gets below minE All combinations are available.

Definition at line 788 of file TMultiLayerPerceptron.cxx.

Friends And Related Symbol Documentation

◆ TMLPAnalyzer

friend class TMLPAnalyzer

friend

Definition at line 27 of file TMultiLayerPerceptron.h.

Member Data Documentation

◆ fCurrentTree

Int_t TMultiLayerPerceptron::fCurrentTree

private

! index of the current tree in a chain

Definition at line 124 of file TMultiLayerPerceptron.h.

◆ fCurrentTreeWeight

Double_t TMultiLayerPerceptron::fCurrentTreeWeight

private

! weight of the current tree in a chain

Definition at line 125 of file TMultiLayerPerceptron.h.

◆ fData

TTree* TMultiLayerPerceptron::fData

private

! pointer to the tree used as datasource

Definition at line 123 of file TMultiLayerPerceptron.h.

◆ fDelta

Double_t TMultiLayerPerceptron::fDelta

private

! Delta - used in stochastic minimisation - Default=0.

Definition at line 143 of file TMultiLayerPerceptron.h.

◆ fEpsilon

Double_t TMultiLayerPerceptron::fEpsilon

private

! Epsilon - used in stochastic minimisation - Default=0.

Definition at line 142 of file TMultiLayerPerceptron.h.

◆ fEta

Double_t TMultiLayerPerceptron::fEta

private

! Eta - used in stochastic minimisation - Default=0.1

Definition at line 141 of file TMultiLayerPerceptron.h.

◆ fEtaDecay

Double_t TMultiLayerPerceptron::fEtaDecay

private

! EtaDecay - Eta *= EtaDecay at each epoch - Default=1.

Definition at line 144 of file TMultiLayerPerceptron.h.

◆ fEventWeight

TTreeFormula* TMultiLayerPerceptron::fEventWeight

private

! formula representing the event weight

Definition at line 139 of file TMultiLayerPerceptron.h.

◆ fextD

TString TMultiLayerPerceptron::fextD

private

String containing the derivative name.

Definition at line 135 of file TMultiLayerPerceptron.h.

◆ fextF

TString TMultiLayerPerceptron::fextF

private

String containing the function name.

Definition at line 134 of file TMultiLayerPerceptron.h.

◆ fFirstLayer

TObjArray TMultiLayerPerceptron::fFirstLayer

private

Collection of the input neurons; subset of fNetwork.

Definition at line 127 of file TMultiLayerPerceptron.h.

◆ fLastAlpha

Double_t TMultiLayerPerceptron::fLastAlpha

private

! internal parameter used in line search

Definition at line 146 of file TMultiLayerPerceptron.h.

◆ fLastLayer

TObjArray TMultiLayerPerceptron::fLastLayer

private

Collection of the output neurons; subset of fNetwork.

Definition at line 128 of file TMultiLayerPerceptron.h.

◆ fLearningMethod

ELearningMethod TMultiLayerPerceptron::fLearningMethod

private

! The Learning Method

Definition at line 138 of file TMultiLayerPerceptron.h.

◆ fManager

TTreeFormulaManager* TMultiLayerPerceptron::fManager

private

! TTreeFormulaManager for the weight and neurons

Definition at line 140 of file TMultiLayerPerceptron.h.

◆ fNetwork

TObjArray TMultiLayerPerceptron::fNetwork

private

Collection of all the neurons in the network.

Definition at line 126 of file TMultiLayerPerceptron.h.

◆ fOutType

TNeuron::ENeuronType TMultiLayerPerceptron::fOutType

private

Type of output neurons.

Definition at line 133 of file TMultiLayerPerceptron.h.

◆ fReset

Int_t TMultiLayerPerceptron::fReset

private

! number of epochs between two resets of the search direction to the steepest descent - Default=50

Definition at line 147 of file TMultiLayerPerceptron.h.

◆ fStructure

TString TMultiLayerPerceptron::fStructure

private

String containing the network structure.

Definition at line 130 of file TMultiLayerPerceptron.h.

◆ fSynapses

TObjArray TMultiLayerPerceptron::fSynapses

private

Collection of all the synapses in the network.

Definition at line 129 of file TMultiLayerPerceptron.h.

◆ fTau

Double_t TMultiLayerPerceptron::fTau

private

! Tau - used in line search - Default=3.

Definition at line 145 of file TMultiLayerPerceptron.h.

◆ fTest

TEventList* TMultiLayerPerceptron::fTest

private

! EventList defining the events in the test dataset

Definition at line 137 of file TMultiLayerPerceptron.h.

◆ fTestOwner

Bool_t TMultiLayerPerceptron::fTestOwner

private

! internal flag whether one has to delete fTest or not

Definition at line 149 of file TMultiLayerPerceptron.h.

◆ fTraining

TEventList* TMultiLayerPerceptron::fTraining

private

! EventList defining the events in the training dataset

Definition at line 136 of file TMultiLayerPerceptron.h.

◆ fTrainingOwner

Bool_t TMultiLayerPerceptron::fTrainingOwner

private

! internal flag whether one has to delete fTraining or not

Definition at line 148 of file TMultiLayerPerceptron.h.

◆ fType

TNeuron::ENeuronType TMultiLayerPerceptron::fType

private

Type of hidden neurons.

Definition at line 132 of file TMultiLayerPerceptron.h.

◆ fWeight

TString TMultiLayerPerceptron::fWeight

private

String containing the event weight.

Definition at line 131 of file TMultiLayerPerceptron.h.

Libraries for TMultiLayerPerceptron:

[legend]

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

math/mlp/inc/TMultiLayerPerceptron.h
math/mlp/src/TMultiLayerPerceptron.cxx

Introduction

The MLP

Learning methods

Stochastic minimization:

Steepest descent with fixed step size (batch learning):

Steepest descent algorithm:

Conjugate gradients with the Polak-Ribiere updating formula:

Conjugate gradients with the Fletcher-Reeves updating formula:

Broyden, Fletcher, Goldfarb, Shanno (BFGS) method:

How to use it...

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Friends

Additional Inherited Members

Member Enumeration Documentation

◆ EDataSet

◆ ELearningMethod

Constructor & Destructor Documentation

◆ TMultiLayerPerceptron() [1/6]

◆ TMultiLayerPerceptron() [2/6]

◆ TMultiLayerPerceptron() [3/6]

◆ TMultiLayerPerceptron() [4/6]

◆ TMultiLayerPerceptron() [5/6]

◆ ~TMultiLayerPerceptron()

◆ TMultiLayerPerceptron() [6/6]

Member Function Documentation

◆ AttachData()

◆ BFGSDir()

◆ BuildFirstLayer()

◆ BuildHiddenLayers()

◆ BuildLastLayer()

◆ BuildNetwork()

◆ BuildOneHiddenLayer()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ ComputeDEDw()

◆ ConjugateGradientsDir()

◆ DeclFileName()

◆ DerivDir()

◆ Draw()

◆ DrawResult()

◆ DumpWeights()

◆ Evaluate()

◆ ExpandStructure()

◆ Export()

◆ GetBFGSH()

◆ GetCrossEntropy()

◆ GetCrossEntropyBinary()

◆ GetDelta()

◆ GetEntry()

◆ GetEpsilon()

◆ GetError() [1/2]

◆ GetError() [2/2]

◆ GetEta()

◆ GetEtaDecay()

◆ GetLearningMethod()

◆ GetReset()

◆ GetStructure()

◆ GetSumSquareError()

◆ GetTau()

◆ GetType()

◆ IsA()

◆ LineSearch()

◆ LoadWeights()

◆ MLP_Batch()

◆ MLP_Line()

◆ MLP_Stochastic()

◆ operator=()

◆ Randomize()

◆ Result()

◆ SetData()

◆ SetDelta()

◆ SetEpsilon()

◆ SetEta()

◆ SetEtaDecay()