Convolutional Model for 2D image data. More...

#include <shark/Models/ConvolutionalModel.h>

Inheritance diagram for shark::Conv2DModel< VectorType, ActivationFunction >:

Public Types
typedef base_type::BatchOutputType	BatchOutputType

typedef base_type::BatchInputType	BatchInputType

typedef base_type::ParameterVectorType	ParameterVectorType

Public Types inherited from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >
enum	Feature { HAS_FIRST_PARAMETER_DERIVATIVE = 1 , HAS_FIRST_INPUT_DERIVATIVE = 4 }

typedef InputTypeT	InputType
	Defines the input type of the model.

typedef OutputTypeT	OutputType
	Defines the output type of the model.

typedef AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >	ModelBaseType
	Defines the BaseType used by the model (this type). Useful for creating derived models.

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

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

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Public Types inherited from shark::IParameterizable< VectorType >
typedef VectorType	ParameterVectorType

Public Member Functions
	Conv2DModel ()
	Default Constructor; use setStructure later.

	Conv2DModel (Shape const &imageShape, Shape const &filterShape, Padding type=Padding::ZeroPad)
	Sets the structure by setting the dimensionalities of image and filters.

std::string	name () const
	returns the name of the object

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

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

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

ActivationFunction &	activationFunction ()
	Returns the activation function.

ParameterVectorType	parameterVector () const
	Obtain the parameter vector.

void	setParameterVector (ParameterVectorType const &newParameters)
	Set the new parameters of the model.

size_t	numberOfParameters () const
	Return the number of parameters.

void	setStructure (Shape const &imageShape, Shape const &filterShape, Padding type=Padding::ZeroPad)
	Sets the structure by setting the shape of image and filters.

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

void	eval (BatchInputType const &inputs, BatchOutputType &outputs, State &state) const
	Evaluate the model.

void	weightedParameterDerivative (BatchInputType const &inputs, 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 inputs of the last computed batch.

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

void	read (InArchive &archive)
	From ISerializable.

void	write (OutArchive &archive) const
	From ISerializable.

Public Member Functions inherited from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >
	AbstractModel ()

virtual	~AbstractModel ()

const Features &	features () const

virtual void	updateFeatures ()

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

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

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

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

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

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

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

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

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

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

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

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

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

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Additional Inherited Members
Protected Attributes inherited from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >
Features	m_features

Detailed Description

template<class VectorType = RealVector, class ActivationFunction = LinearNeuron>
class shark::Conv2DModel< VectorType, ActivationFunction >

Convolutional Model for 2D image data.

: This model computes the result of \( y = f(x) = g(\text{convolution}(w, x) + b) \), where g is an arbitrary activation function Neuron activation functions and convolution is the convolution of the input image x with the filters w. b is a vector with one entry for each filter which is then applied to each response above

The image is allowed to have several channels and are linearized to a single vector of size width * height * numChannels. This is done by itnerleaving channels, i.e. for a pixel all channels are stored contiguously. Then the pixels are stored in a row-major scheme.

For handling edge condition, the Conv2D model handles two different convolution modes:

Padding::Valid: The output is only computed on patches which are fully inside the unpadded image as a linearized vector in the same format of size (width - filter_width+1) * (height - filter_height+1) * numFilters.

Padding::ZeroPad The output input is padded with zeros and the output has the same size as the input of size width * height * numFilters.

Definition at line 66 of file ConvolutionalModel.h.

Member Typedef Documentation

◆ BatchInputType

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

typedef base_type::BatchInputType shark::Conv2DModel< VectorType, ActivationFunction >::BatchInputType

Definition at line 76 of file ConvolutionalModel.h.

◆ BatchOutputType

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

typedef base_type::BatchOutputType shark::Conv2DModel< VectorType, ActivationFunction >::BatchOutputType

Definition at line 75 of file ConvolutionalModel.h.

◆ ParameterVectorType

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

typedef base_type::ParameterVectorType shark::Conv2DModel< VectorType, ActivationFunction >::ParameterVectorType

Definition at line 77 of file ConvolutionalModel.h.

Constructor & Destructor Documentation

◆ Conv2DModel() [1/2]

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

shark::Conv2DModel< VectorType, ActivationFunction >::Conv2DModel ( )

inline

Default Constructor; use setStructure later.

Definition at line 80 of file ConvolutionalModel.h.

References shark::AbstractModel< VectorType, VectorType, VectorType >::HAS_FIRST_INPUT_DERIVATIVE, shark::AbstractModel< VectorType, VectorType, VectorType >::HAS_FIRST_PARAMETER_DERIVATIVE, and shark::AbstractModel< VectorType, VectorType, VectorType >::m_features.

◆ Conv2DModel() [2/2]

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

shark::Conv2DModel< VectorType, ActivationFunction >::Conv2DModel	(	Shape const &	imageShape,
		Shape const &	filterShape,
		Padding	type = `Padding::ZeroPad`
	)

inline

Sets the structure by setting the dimensionalities of image and filters.

imageShape Shape of the image imHeight x imWidth x channel
filterShape Shape of the filter matrix numFilters x fiHeight x fiWidth x channel
type Type of convolution padding to perform

Definition at line 90 of file ConvolutionalModel.h.

References shark::AbstractModel< VectorType, VectorType, VectorType >::HAS_FIRST_INPUT_DERIVATIVE, shark::AbstractModel< VectorType, VectorType, VectorType >::HAS_FIRST_PARAMETER_DERIVATIVE, shark::AbstractModel< VectorType, VectorType, VectorType >::m_features, and shark::Conv2DModel< VectorType, ActivationFunction >::setStructure().

Member Function Documentation

◆ activationFunction() [1/2]

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

ActivationFunction & shark::Conv2DModel< VectorType, ActivationFunction >::activationFunction ( )

inline

Returns the activation function.

Definition at line 120 of file ConvolutionalModel.h.

◆ activationFunction() [2/2]

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

ActivationFunction const & shark::Conv2DModel< VectorType, ActivationFunction >::activationFunction ( ) const

inline

Returns the activation function.

Definition at line 115 of file ConvolutionalModel.h.

◆ createState()

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

boost::shared_ptr< State > shark::Conv2DModel< VectorType, 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< InputTypeT, OutputTypeT, ParameterVectorType >.

Definition at line 162 of file ConvolutionalModel.h.

◆ eval()

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

void shark::Conv2DModel< VectorType, ActivationFunction >::eval	(	BatchInputType const &	inputs,
		BatchOutputType &	outputs,
		State &	state
	)		const

inlinevirtual

Evaluate the model.

Implements shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >.

Definition at line 169 of file ConvolutionalModel.h.

References shark::Conv2DModel< VectorType, ActivationFunction >::inputShape(), shark::Shape::numElements(), shark::Conv2DModel< VectorType, ActivationFunction >::outputShape(), SIZE_CHECK, shark::State::toState(), and shark::Valid.

◆ inputShape()

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

Shape shark::Conv2DModel< VectorType, ActivationFunction >::inputShape ( ) const

inlinevirtual

Returns the expected shape of the input.

Implements shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >.

Definition at line 102 of file ConvolutionalModel.h.

Referenced by shark::Conv2DModel< VectorType, ActivationFunction >::eval(), shark::Conv2DModel< VectorType, ActivationFunction >::weightedInputDerivative(), and shark::Conv2DModel< VectorType, ActivationFunction >::weightedParameterDerivative().

◆ name()

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

std::string shark::Conv2DModel< VectorType, ActivationFunction >::name ( ) const

inlinevirtual

returns the name of the object

Reimplemented from shark::INameable.

Definition at line 98 of file ConvolutionalModel.h.

◆ numberOfParameters()

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

size_t shark::Conv2DModel< VectorType, ActivationFunction >::numberOfParameters ( ) const

inlinevirtual

Return the number of parameters.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 138 of file ConvolutionalModel.h.

Referenced by shark::Conv2DModel< VectorType, ActivationFunction >::setParameterVector(), and shark::Conv2DModel< VectorType, ActivationFunction >::weightedParameterDerivative().

◆ outputShape()

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

Shape shark::Conv2DModel< VectorType, ActivationFunction >::outputShape ( ) const

inlinevirtual

Returns the shape of the output.

Implements shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >.

Definition at line 106 of file ConvolutionalModel.h.

References shark::Valid.

Referenced by shark::Conv2DModel< VectorType, ActivationFunction >::eval(), main(), shark::Conv2DModel< VectorType, ActivationFunction >::weightedInputDerivative(), and shark::Conv2DModel< VectorType, ActivationFunction >::weightedParameterDerivative().

◆ parameterVector()

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

ParameterVectorType shark::Conv2DModel< VectorType, ActivationFunction >::parameterVector ( ) const

inlinevirtual

Obtain the parameter vector.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 125 of file ConvolutionalModel.h.

◆ read()

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

void shark::Conv2DModel< VectorType, ActivationFunction >::read ( InArchive & archive )

inlinevirtual

From ISerializable.

Reimplemented from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >.

Definition at line 281 of file ConvolutionalModel.h.

◆ setParameterVector()

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

void shark::Conv2DModel< VectorType, ActivationFunction >::setParameterVector ( ParameterVectorType const & newParameters )

inlinevirtual

Set the new parameters of the model.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 130 of file ConvolutionalModel.h.

References shark::Conv2DModel< VectorType, ActivationFunction >::numberOfParameters(), and SIZE_CHECK.

◆ setStructure()

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

void shark::Conv2DModel< VectorType, ActivationFunction >::setStructure	(	Shape const &	imageShape,
		Shape const &	filterShape,
		Padding	type = `Padding::ZeroPad`
	)

inline

Sets the structure by setting the shape of image and filters.

imageShape Shape of the image imHeight x imWidth x channel
filterShape Shape of the filter matrix numFilters x fiHeight x fiWidth
type Type of convolution padding to perform

Definition at line 147 of file ConvolutionalModel.h.

Referenced by shark::Conv2DModel< VectorType, ActivationFunction >::Conv2DModel().

◆ weightedInputDerivative()

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

void shark::Conv2DModel< VectorType, ActivationFunction >::weightedInputDerivative	(	BatchInputType const &	inputs,
		BatchOutputType const &	outputs,
		BatchOutputType const &	coefficients,
		State const &	state,
		BatchInputType &	derivatives
	)		const

inlinevirtual

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

Reimplemented from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >.

Definition at line 251 of file ConvolutionalModel.h.

References shark::Conv2DModel< VectorType, ActivationFunction >::inputShape(), shark::Conv2DModel< VectorType, ActivationFunction >::outputShape(), SIZE_CHECK, shark::State::toState(), and shark::Valid.

◆ weightedParameterDerivative()

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

void shark::Conv2DModel< VectorType, ActivationFunction >::weightedParameterDerivative	(	BatchInputType const &	inputs,
		BatchOutputType const &	outputs,
		BatchOutputType const &	coefficients,
		State const &	state,
		ParameterVectorType &	gradient
	)		const

inlinevirtual

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

Reimplemented from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >.

Definition at line 191 of file ConvolutionalModel.h.

References shark::CHWN, shark::Conv2DModel< VectorType, ActivationFunction >::inputShape(), shark::NHWC, shark::Conv2DModel< VectorType, ActivationFunction >::numberOfParameters(), shark::Conv2DModel< VectorType, ActivationFunction >::outputShape(), SIZE_CHECK, shark::State::toState(), and shark::Valid.

◆ write()

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

void shark::Conv2DModel< VectorType, ActivationFunction >::write ( OutArchive & archive ) const

inlinevirtual

From ISerializable.

Reimplemented from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >.

Definition at line 294 of file ConvolutionalModel.h.

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

include/shark/Models/ConvolutionalModel.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ BatchInputType

◆ BatchOutputType

◆ ParameterVectorType

Constructor & Destructor Documentation

◆ Conv2DModel() [1/2]

◆ Conv2DModel() [2/2]

Member Function Documentation

◆ activationFunction() [1/2]

◆ activationFunction() [2/2]

◆ createState()

◆ eval()

◆ inputShape()

◆ name()

◆ numberOfParameters()

◆ outputShape()

◆ parameterVector()

◆ read()

◆ setParameterVector()

◆ setStructure()

◆ weightedInputDerivative()

◆ weightedParameterDerivative()

◆ write()