A recurrent neural network regression model optimized for online learning. More...

#include <shark/Models/OnlineRNNet.h>

Inheritance diagram for shark::OnlineRNNet:

Public Member Functions
SHARK_EXPORT_SYMBOL	OnlineRNNet (RecurrentStructure *structure, bool computeGradient)
	structure The structure of the OnlineRNNet More...

std::string	name () const
	From INameable: return the class name. More...

SHARK_EXPORT_SYMBOL void	eval (RealMatrix const &pattern, RealMatrix &output, State &state) const
	Feeds a timestep of a time series to the model and calculates it's output. The batches must have size 1. More...

SHARK_EXPORT_SYMBOL void	eval (RealMatrix const &pattern, RealMatrix &output) const
	It is forbidding to call eval without a state object. More...

std::size_t	inputSize () const
	obtain the input dimension More...

std::size_t	outputSize () const
	obtain the output dimension More...

SHARK_EXPORT_SYMBOL void	weightedParameterDerivative (RealMatrix const &pattern, RealMatrix const &coefficients, State const &state, RealVector &gradient) const
	calculates the weighted sum of gradients w.r.t the parameters More...

RealVector	parameterVector () const
	get internal parameters of the model More...

void	setParameterVector (RealVector const &newParameters)
	set internal parameters of the model More...

std::size_t	numberOfParameters () const
	number of parameters of the network More...

boost::shared_ptr< State >	createState () const
	Creates an internal state of the model. More...

void	setOutputActivation (State &state, RealVector const &activation)
	This Method sets the activation of the output neurons. More...

Public Member Functions inherited from shark::AbstractModel< RealVector, RealVector >
	AbstractModel ()

virtual	~AbstractModel ()

const Features &	features () const

virtual void	updateFeatures ()

bool	hasFirstParameterDerivative () const
	Returns true when the first parameter derivative is implemented. More...

bool	hasSecondParameterDerivative () const
	Returns true when the second parameter derivative is implemented. More...

bool	hasFirstInputDerivative () const
	Returns true when the first input derivative is implemented. More...

bool	hasSecondInputDerivative () const
	Returns true when the second parameter derivative is implemented. More...

bool	isSequential () const

virtual void	read (InArchive &archive)
	From ISerializable, reads a model from an archive. More...

virtual void	write (OutArchive &archive) const
	writes a model to an archive More...

virtual void	eval (BatchInputType const &patterns, BatchOutputType &outputs) const
	Standard interface for evaluating the response of the model to a batch of patterns. More...

virtual void	eval (BatchInputType const &patterns, BatchOutputType &outputs, State &state) const=0
	Standard interface for evaluating the response of the model to a batch of patterns. More...

virtual void	eval (InputType const &pattern, OutputType &output) const
	Standard interface for evaluating the response of the model to a single pattern. More...

Data< OutputType >	operator() (Data< InputType > const &patterns) const
	Model evaluation as an operator for a whole dataset. This is a convenience function. More...

OutputType	operator() (InputType const &pattern) const
	Model evaluation as an operator for a single pattern. This is a convenience function. More...

BatchOutputType	operator() (BatchInputType const &patterns) const
	Model evaluation as an operator for a single pattern. This is a convenience function. More...

virtual void	weightedParameterDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, State const &state, RealVector &derivative) const
	calculates the weighted sum of derivatives w.r.t the parameters. More...

virtual void	weightedParameterDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, Batch< RealMatrix >::type const &errorHessian, State const &state, RealVector &derivative, RealMatrix &hessian) const
	calculates the weighted sum of derivatives w.r.t the parameters More...

virtual void	weightedInputDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, State const &state, BatchInputType &derivative) const
	calculates the weighted sum of derivatives w.r.t the inputs More...

virtual void	weightedInputDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, typename Batch< RealMatrix >::type const &errorHessian, State const &state, RealMatrix &derivative, Batch< RealMatrix >::type &hessian) const
	calculates the weighted sum of derivatives w.r.t the inputs More...

virtual void	weightedDerivatives (BatchInputType const &patterns, BatchOutputType const &coefficients, State const &state, RealVector &parameterDerivative, BatchInputType &inputDerivative) const
	calculates weighted input and parameter derivative at the same time More...

Public Member Functions inherited from shark::IParameterizable
virtual	~IParameterizable ()

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Protected Attributes
RecurrentStructure *	mpe_structure
	the topology of the network. More...

bool	m_computeGradient
	stores whether the network should compute a gradient More...

Protected Attributes inherited from shark::AbstractModel< RealVector, RealVector >
Features	m_features

Additional Inherited Members
Public Types inherited from shark::AbstractModel< RealVector, RealVector >
enum	Feature

typedef RealVector	InputType
	Defines the input type of the model. More...

typedef RealVector	OutputType
	Defines the output type of the model. More...

typedef Batch< InputType >::type	BatchInputType
	defines the batch type of the input type. More...

typedef Batch< OutputType >::type	BatchOutputType
	defines the batch type of the output type More...

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Detailed Description

A recurrent neural network regression model optimized for online learning.

The OnlineRNNet can only process a single input at a time. Internally it stores the last activation as well as the derivatives which get updated over the course of the sequence. Instead of feeding in the whole sequence, the inputs must be given one after another. However if the whole sequence is available in advance, this implementation is not advisable, since it is a lot slower than RNNet which is targeted to whole sequences.

All network state is stored in the State structure which can be created by createState() which has to be supplied to eval. A new time sequence is started by generating a new state object. When the network is created the user has to decide whether gradients are needed. In this case additional ressources are allocated in the state object on creation and eval makes sure that the gradient is properly updated between steps, this is costly. It is possible to skip steps updating the parameters, e.g. when no reward signal is available.

Note that eval can only work with batches of size one and eval without a state object can not be called.

Definition at line 62 of file OnlineRNNet.h.

Constructor & Destructor Documentation

◆ OnlineRNNet()

SHARK_EXPORT_SYMBOL shark::OnlineRNNet::OnlineRNNet	(	RecurrentStructure *	structure,
		bool	computeGradient
	)

structure The structure of the OnlineRNNet

creates a configured neural network

computeGradient Whether the network will be used to compute gradients

Member Function Documentation

◆ createState()

boost::shared_ptr<State> shark::OnlineRNNet::createState ( ) const

inlinevirtual

Creates an internal state of the model.

The state is needed when the derivatives are to be calculated. Eval can store a state which is then reused to speed up the calculations of the derivatives. This also allows eval to be evaluated in parallel!

Reimplemented from shark::AbstractModel< RealVector, RealVector >.

Definition at line 152 of file OnlineRNNet.h.

References mpe_structure.

◆ eval() [1/2]

SHARK_EXPORT_SYMBOL void shark::OnlineRNNet::eval	(	RealMatrix const &	pattern,
		RealMatrix &	output,
		State &	state
	)		const

Feeds a timestep of a time series to the model and calculates it's output. The batches must have size 1.

Parameters

pattern	Input patterns for the network.
output	Used to store the outputs of the network.
state	the current state of the RNN that is updated by eval

Referenced by shark::GruauPole::balanceFit(), shark::GruauPole::generalFit(), shark::GruauPole::gruauFit(), and name().

◆ eval() [2/2]

SHARK_EXPORT_SYMBOL void shark::OnlineRNNet::eval	(	RealMatrix const &	pattern,
		RealMatrix &	output
	)		const

inline

It is forbidding to call eval without a state object.

Definition at line 108 of file OnlineRNNet.h.

References SHARKEXCEPTION.

◆ inputSize()

std::size_t shark::OnlineRNNet::inputSize ( ) const

inline

obtain the input dimension

Definition at line 114 of file OnlineRNNet.h.

References shark::RecurrentStructure::inputs(), and mpe_structure.

◆ name()

std::string shark::OnlineRNNet::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 95 of file OnlineRNNet.h.

References eval(), and SHARK_EXPORT_SYMBOL.

◆ numberOfParameters()

std::size_t shark::OnlineRNNet::numberOfParameters ( ) const

inlinevirtual

number of parameters of the network

Reimplemented from shark::IParameterizable.

Definition at line 148 of file OnlineRNNet.h.

References mpe_structure, and shark::RecurrentStructure::parameters().

Referenced by shark::GruauPole::GruauPole(), and shark::NonMarkovPole::NonMarkovPole().

◆ outputSize()

std::size_t shark::OnlineRNNet::outputSize ( ) const

inline

obtain the output dimension

Definition at line 119 of file OnlineRNNet.h.

References mpe_structure, shark::RecurrentStructure::outputs(), SHARK_EXPORT_SYMBOL, and weightedParameterDerivative().

Referenced by setOutputActivation().

◆ parameterVector()

RealVector shark::OnlineRNNet::parameterVector ( ) const

inlinevirtual

get internal parameters of the model

Reimplemented from shark::IParameterizable.

Definition at line 139 of file OnlineRNNet.h.

References mpe_structure, and shark::RecurrentStructure::parameterVector().

◆ setOutputActivation()

void shark::OnlineRNNet::setOutputActivation	(	State &	state,
		RealVector const &	activation
	)

inline

This Method sets the activation of the output neurons.

This is usefull when teacher forcing is used. When the network is trained to predict a timeseries and diverges from the sequence at an early stage, the resulting gradient might not be very helpfull. In this case, teacher forcing can be applied to prevent diverging. However, the network might become unstable, when teacher-forcing is turned off because there is no force which prevents it from diverging anymore.

Parameters

state	The current state of the network
activation	Input patterns for the network.

Definition at line 168 of file OnlineRNNet.h.

References mpe_structure, shark::RecurrentStructure::numberOfUnits(), outputSize(), remora::subrange(), and shark::State::toState().

◆ setParameterVector()

void shark::OnlineRNNet::setParameterVector ( RealVector const & newParameters )

inlinevirtual

set internal parameters of the model

Reimplemented from shark::IParameterizable.

Definition at line 143 of file OnlineRNNet.h.

References mpe_structure, and shark::RecurrentStructure::setParameterVector().

Referenced by shark::GruauPole::balanceFit(), shark::GruauPole::generalFit(), and shark::GruauPole::gruauFit().

◆ weightedParameterDerivative()

SHARK_EXPORT_SYMBOL void shark::OnlineRNNet::weightedParameterDerivative	(	RealMatrix const &	pattern,
		RealMatrix const &	coefficients,
		State const &	state,
		RealVector &	gradient
	)		const

calculates the weighted sum of gradients w.r.t the parameters

Uses an iterative update scheme to calculate the gradient at timestep t from the gradient at timestep t-1 using forward propagation. This Methods requires O(n^3) Memory and O(n^4) computations, where n is the number of neurons. So if the network is very large, RNNet should be used!

Parameters

pattern	the pattern to evaluate
coefficients	the oefficients which are used to calculate the weighted sum
gradient	the calculated gradient
state	the current state of the RNN

Referenced by outputSize().

Member Data Documentation

◆ m_computeGradient

bool shark::OnlineRNNet::m_computeGradient

protected

stores whether the network should compute a gradient

Definition at line 182 of file OnlineRNNet.h.

◆ mpe_structure

RecurrentStructure* shark::OnlineRNNet::mpe_structure

protected

the topology of the network.

Definition at line 179 of file OnlineRNNet.h.

Referenced by createState(), inputSize(), numberOfParameters(), outputSize(), parameterVector(), setOutputActivation(), and setParameterVector().

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

include/shark/Models/OnlineRNNet.h

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ OnlineRNNet()

Member Function Documentation

◆ createState()

◆ eval() [1/2]

◆ eval() [2/2]

◆ inputSize()

◆ name()

◆ numberOfParameters()

◆ outputSize()

◆ parameterVector()

◆ setOutputActivation()

◆ setParameterVector()

◆ weightedParameterDerivative()

Member Data Documentation

◆ m_computeGradient

◆ mpe_structure