Linear Prediction with optional activation function. More...

#include <shark/Models/LinearModel.h>

Inheritance diagram for shark::LinearModel< InputType, ActivationFunction >:

Public Types
typedef base_type::BatchInputType	BatchInputType

typedef base_type::BatchOutputType	BatchOutputType

typedef base_type::ParameterVectorType	ParameterVectorType

Public Member Functions
	LinearModel ()
	CDefault Constructor; use setStructure later. More...

	LinearModel (Shape const &inputs, Shape const &outputs=1, bool offset=false)
	Constructor creating a model with given dimensionalities and optional offset term. More...

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

	LinearModel (MatrixType const &matrix, VectorType const &offset=VectorType())
	Construction from matrix (and vector) More...

bool	hasOffset () const
	check for the presence of an offset term More...

Shape	inputShape () const
	Returns the expected shape of the input. More...

Shape	outputShape () const
	Returns the shape of the output. More...

ParameterVectorType	parameterVector () const
	obtain the parameter vector More...

void	setParameterVector (ParameterVectorType const &newParameters)
	overwrite the parameter vector More...

size_t	numberOfParameters () const
	return the number of parameter More...

void	setStructure (Shape const &inputs, Shape const &outputs=1, bool offset=false)
	overwrite structure and parameters More...

void	setStructure (MatrixType const &matrix, VectorType const &offset=VectorType())
	overwrite structure and parameters More...

MatrixType const &	matrix () const
	return a copy of the matrix in dense format More...

MatrixType &	matrix ()

VectorType const &	offset () const
	return the offset More...

VectorType &	offset ()

ActivationFunction const &	activationFunction () const
	Returns the activation function. More...

ActivationFunction &	activationFunction ()
	Returns the activation function. More...

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

void	eval (BatchInputType const &inputs, BatchOutputType &outputs) const
	Evaluate the model: output = matrix * input + offset. More...

void	eval (InputType const &input, VectorType &output) const

void	eval (BatchInputType const &inputs, BatchOutputType &outputs, State &state) const
	Evaluate the model: output = matrix * input + offset. More...

void	weightedParameterDerivative (BatchInputType const &patterns, BatchOutputType const &outputs, BatchOutputType const &coefficients, State const &state, ParameterVectorType &gradient) const
	Calculates the first derivative w.r.t the parameters and summing them up over all patterns of the last computed batch. More...

void	weightedInputDerivative (BatchInputType const &patterns, BatchOutputType const &outputs, BatchOutputType const &coefficients, State const &state, MatrixType &derivative) const
	Calculates the first derivative w.r.t the inputs and summs them up over all patterns of the last computed batch. More...

void	weightedDerivatives (BatchInputType const &patterns, BatchOutputType const &outputs, BatchOutputType const &coefficients, State const &state, ParameterVectorType &parameterDerivative, MatrixType &inputDerivative) const

void	read (InArchive &archive)
	From ISerializable. More...

void	write (OutArchive &archive) const
	From ISerializable. More...

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 ()

Detailed Description

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>
class shark::LinearModel< InputType, ActivationFunction >

Linear Prediction with optional activation function.

: This model computes the result of \( y = f(x) = g(A x + b) \), where g is an arbitrary activation function. By default g is the identity and the model is a simple linear model. Otherwise, this is known as a generalized linear model. There are two important special cases: The output may be a single number, and the offset term b may be dropped.

The class allows for dense and sparse input vector types. However it assumes that the weight matrix and the ouputs are dense. There are some cases where this is not good behavior. Check for example Normalizer for a class which is designed for sparse inputs and outputs.

Definition at line 57 of file LinearModel.h.

Member Typedef Documentation

◆ BatchInputType

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

typedef base_type::BatchInputType shark::LinearModel< InputType, ActivationFunction >::BatchInputType

Definition at line 73 of file LinearModel.h.

◆ BatchOutputType

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

typedef base_type::BatchOutputType shark::LinearModel< InputType, ActivationFunction >::BatchOutputType

Definition at line 74 of file LinearModel.h.

◆ ParameterVectorType

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

typedef base_type::ParameterVectorType shark::LinearModel< InputType, ActivationFunction >::ParameterVectorType

Definition at line 75 of file LinearModel.h.

Constructor & Destructor Documentation

◆ LinearModel() [1/3]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

shark::LinearModel< InputType, ActivationFunction >::LinearModel ( )

inline

CDefault Constructor; use setStructure later.

Definition at line 78 of file LinearModel.h.

◆ LinearModel() [2/3]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

shark::LinearModel< InputType, ActivationFunction >::LinearModel	(	Shape const &	inputs,
		Shape const &	outputs = `1`,
		bool	offset = `false`
	)

inline

Constructor creating a model with given dimensionalities and optional offset term.

Definition at line 85 of file LinearModel.h.

◆ LinearModel() [3/3]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

shark::LinearModel< InputType, ActivationFunction >::LinearModel	(	MatrixType const &	matrix,
		VectorType const &	offset = `VectorType()`
	)

inline

Construction from matrix (and vector)

Definition at line 101 of file LinearModel.h.

Member Function Documentation

◆ activationFunction() [1/2]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

ActivationFunction const& shark::LinearModel< InputType, ActivationFunction >::activationFunction ( ) const

inline

Returns the activation function.

Definition at line 174 of file LinearModel.h.

◆ activationFunction() [2/2]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

ActivationFunction& shark::LinearModel< InputType, ActivationFunction >::activationFunction ( )

inline

Returns the activation function.

Definition at line 179 of file LinearModel.h.

◆ createState()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

boost::shared_ptr<State> shark::LinearModel< InputType, ActivationFunction >::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< InputType, blas::vector< InputType::value_type, InputType::device_type >, blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 183 of file LinearModel.h.

◆ eval() [1/3]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::eval	(	BatchInputType const &	inputs,
		BatchOutputType &	outputs
	)		const

inlinevirtual

Evaluate the model: output = matrix * input + offset.

Reimplemented from shark::AbstractModel< InputType, blas::vector< InputType::value_type, InputType::device_type >, blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 190 of file LinearModel.h.

◆ eval() [2/3]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::eval	(	InputType const &	input,
		VectorType &	output
	)		const

inline

Definition at line 200 of file LinearModel.h.

◆ eval() [3/3]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::eval	(	BatchInputType const &	inputs,
		BatchOutputType &	outputs,
		State &	state
	)		const

inlinevirtual

Evaluate the model: output = matrix * input + offset.

Implements shark::AbstractModel< InputType, blas::vector< InputType::value_type, InputType::device_type >, blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 210 of file LinearModel.h.

◆ hasOffset()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

bool shark::LinearModel< InputType, ActivationFunction >::hasOffset ( ) const

inline

check for the presence of an offset term

Definition at line 113 of file LinearModel.h.

◆ inputShape()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

Shape shark::LinearModel< InputType, ActivationFunction >::inputShape ( ) const

inlinevirtual

Returns the expected shape of the input.

Implements shark::AbstractModel< InputType, blas::vector< InputType::value_type, InputType::device_type >, blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 118 of file LinearModel.h.

Referenced by shark::LinearSAGTrainer< InputType, LabelType >::numberOfParameters(), shark::LinearModel< VectorType >::setParameterVector(), shark::LinearModel< VectorType >::weightedDerivatives(), and shark::LinearModel< VectorType >::weightedParameterDerivative().

◆ matrix() [1/2]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

MatrixType const& shark::LinearModel< InputType, ActivationFunction >::matrix ( ) const

inline

return a copy of the matrix in dense format

Definition at line 157 of file LinearModel.h.

Referenced by main().

◆ matrix() [2/2]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

MatrixType& shark::LinearModel< InputType, ActivationFunction >::matrix ( )

inline

Definition at line 161 of file LinearModel.h.

◆ name()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

std::string shark::LinearModel< InputType, ActivationFunction >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 97 of file LinearModel.h.

◆ numberOfParameters()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

size_t shark::LinearModel< InputType, ActivationFunction >::numberOfParameters ( ) const

inlinevirtual

return the number of parameter

Reimplemented from shark::IParameterizable< blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 140 of file LinearModel.h.

Referenced by shark::LinearModel< VectorType >::weightedParameterDerivative().

◆ offset() [1/2]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

VectorType const& shark::LinearModel< InputType, ActivationFunction >::offset ( ) const

inline

return the offset

Definition at line 166 of file LinearModel.h.

Referenced by main(), and shark::LinearModel< VectorType >::setStructure().

◆ offset() [2/2]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

VectorType& shark::LinearModel< InputType, ActivationFunction >::offset ( )

inline

Definition at line 169 of file LinearModel.h.

◆ outputShape()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

Shape shark::LinearModel< InputType, ActivationFunction >::outputShape ( ) const

inlinevirtual

Returns the shape of the output.

Implements shark::AbstractModel< InputType, blas::vector< InputType::value_type, InputType::device_type >, blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 122 of file LinearModel.h.

Referenced by shark::LinearSAGTrainer< InputType, LabelType >::numberOfParameters(), shark::LinearModel< VectorType >::setParameterVector(), shark::PCA::train(), shark::LinearModel< VectorType >::weightedDerivatives(), and shark::LinearModel< VectorType >::weightedParameterDerivative().

◆ parameterVector()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

ParameterVectorType shark::LinearModel< InputType, ActivationFunction >::parameterVector ( ) const

inlinevirtual

obtain the parameter vector

Reimplemented from shark::IParameterizable< blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 127 of file LinearModel.h.

◆ read()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::read ( InArchive & archive )

inlinevirtual

From ISerializable.

Reimplemented from shark::AbstractModel< InputType, blas::vector< InputType::value_type, InputType::device_type >, blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 304 of file LinearModel.h.

◆ setParameterVector()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::setParameterVector ( ParameterVectorType const & newParameters )

inline

overwrite the parameter vector

Definition at line 132 of file LinearModel.h.

Referenced by main().

◆ setStructure() [1/2]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::setStructure	(	Shape const &	inputs,
		Shape const &	outputs = `1`,
		bool	offset = `false`
	)

inline

overwrite structure and parameters

Definition at line 145 of file LinearModel.h.

Referenced by shark::LinearSAGTrainer< InputType, LabelType >::numberOfParameters(), and shark::NormalizeComponentsZCA::train().

◆ setStructure() [2/2]

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::setStructure	(	MatrixType const &	matrix,
		VectorType const &	offset = `VectorType()`
	)

inline

overwrite structure and parameters

Definition at line 151 of file LinearModel.h.

◆ weightedDerivatives()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::weightedDerivatives	(	BatchInputType const &	patterns,
		BatchOutputType const &	outputs,
		BatchOutputType const &	coefficients,
		State const &	state,
		ParameterVectorType &	parameterDerivative,
		MatrixType &	inputDerivative
	)		const

inline

Definition at line 267 of file LinearModel.h.

◆ weightedInputDerivative()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::weightedInputDerivative	(	BatchInputType const &	patterns,
		BatchOutputType const &	outputs,
		BatchOutputType const &	coefficients,
		State const &	state,
		MatrixType &	derivative
	)		const

inline

Calculates the first derivative w.r.t the inputs and summs them up over all patterns of the last computed batch.

Definition at line 249 of file LinearModel.h.

◆ weightedParameterDerivative()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::weightedParameterDerivative	(	BatchInputType const &	patterns,
		BatchOutputType const &	outputs,
		BatchOutputType const &	coefficients,
		State const &	state,
		ParameterVectorType &	gradient
	)		const

inline

Calculates the first derivative w.r.t the parameters and summing them up over all patterns of the last computed batch.

Definition at line 221 of file LinearModel.h.

◆ write()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>

void shark::LinearModel< InputType, ActivationFunction >::write ( OutArchive & archive ) const

inlinevirtual

From ISerializable.

Reimplemented from shark::AbstractModel< InputType, blas::vector< InputType::value_type, InputType::device_type >, blas::vector< InputType::value_type, InputType::device_type > >.

Definition at line 311 of file LinearModel.h.

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

include/shark/Models/LinearModel.h

Public Types

Public Member Functions

Detailed Description

template<class InputType = RealVector, class ActivationFunction = LinearNeuron> class shark::LinearModel< InputType, ActivationFunction >

Member Typedef Documentation

◆ BatchInputType

◆ BatchOutputType

◆ ParameterVectorType

Constructor & Destructor Documentation

◆ LinearModel() [1/3]

◆ LinearModel() [2/3]

◆ LinearModel() [3/3]

Member Function Documentation

◆ activationFunction() [1/2]

◆ activationFunction() [2/2]

◆ createState()

◆ eval() [1/3]

◆ eval() [2/3]

◆ eval() [3/3]

◆ hasOffset()

◆ inputShape()

◆ matrix() [1/2]

◆ matrix() [2/2]

◆ name()

◆ numberOfParameters()

◆ offset() [1/2]

◆ offset() [2/2]

◆ outputShape()

◆ parameterVector()

◆ read()

◆ setParameterVector()

◆ setStructure() [1/2]

◆ setStructure() [2/2]

◆ weightedDerivatives()

◆ weightedInputDerivative()

◆ weightedParameterDerivative()

◆ write()

template<class InputType = RealVector, class ActivationFunction = LinearNeuron>
class shark::LinearModel< InputType, ActivationFunction >