Neuron activation layer. More...

#include <shark/Models/NeuronLayers.h>

Inheritance diagram for shark::NeuronLayer< NeuronType, VectorType >:

Public Types
typedef base_type::BatchInputType	BatchInputType

typedef base_type::BatchOutputType	BatchOutputType

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
	NeuronLayer (Shape const &shape=Shape())

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

NeuronType const &	neuron () const

NeuronType &	neuron ()

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

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

ParameterVectorType	parameterVector () const
	obtain the parameter vector

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

size_t	numberOfParameters () const
	return the number of parameter

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

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

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

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

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 &derivative) 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 (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 NeuronType, class VectorType = RealVector>
class shark::NeuronLayer< NeuronType, VectorType >

Neuron activation layer.

Applies a nonlinear activation function to the given input. Various choices for activations are given in Neuron activation functions.

Definition at line 255 of file NeuronLayers.h.

Member Typedef Documentation

◆ BatchInputType

template<class NeuronType , class VectorType = RealVector>

typedef base_type::BatchInputType shark::NeuronLayer< NeuronType, VectorType >::BatchInputType

Definition at line 262 of file NeuronLayers.h.

◆ BatchOutputType

template<class NeuronType , class VectorType = RealVector>

typedef base_type::BatchOutputType shark::NeuronLayer< NeuronType, VectorType >::BatchOutputType

Definition at line 263 of file NeuronLayers.h.

◆ ParameterVectorType

template<class NeuronType , class VectorType = RealVector>

typedef base_type::ParameterVectorType shark::NeuronLayer< NeuronType, VectorType >::ParameterVectorType

Definition at line 264 of file NeuronLayers.h.

Constructor & Destructor Documentation

◆ NeuronLayer()

template<class NeuronType , class VectorType = RealVector>

shark::NeuronLayer< NeuronType, VectorType >::NeuronLayer ( Shape const & shape = Shape() )

inline

Definition at line 266 of file NeuronLayers.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.

Member Function Documentation

◆ createState()

template<class NeuronType , class VectorType = RealVector>

boost::shared_ptr< State > shark::NeuronLayer< NeuronType, VectorType >::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 301 of file NeuronLayers.h.

◆ eval() [1/3]

template<class NeuronType , class VectorType = RealVector>

void shark::NeuronLayer< NeuronType, VectorType >::eval	(	BatchInputType const &	patterns,
		BatchOutputType &	outputs
	)		const

inlinevirtual

Standard interface for evaluating the response of the model to a batch of patterns.

Parameters

patterns	the inputs of the model
outputs	the predictions or response of the model to every pattern

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

Definition at line 307 of file NeuronLayers.h.

References shark::Shape::numElements(), and SIZE_CHECK.

◆ eval() [2/3]

template<class NeuronType , class VectorType = RealVector>

void shark::NeuronLayer< NeuronType, VectorType >::eval	(	BatchInputType const &	patterns,
		BatchOutputType &	outputs,
		State &	state
	)		const

inlinevirtual

Standard interface for evaluating the response of the model to a batch of patterns.

Parameters

patterns	the inputs of the model
outputs	the predictions or response of the model to every pattern
state	intermediate results stored by eval which can be reused for derivative computation.

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

Definition at line 320 of file NeuronLayers.h.

References shark::Shape::numElements(), SIZE_CHECK, and shark::State::toState().

◆ eval() [3/3]

template<class NeuronType , class VectorType = RealVector>

void shark::NeuronLayer< NeuronType, VectorType >::eval	(	VectorType const &	input,
		VectorType &	output
	)		const

inline

Definition at line 314 of file NeuronLayers.h.

References shark::Shape::numElements(), and SIZE_CHECK.

◆ inputShape()

template<class NeuronType , class VectorType = RealVector>

Shape shark::NeuronLayer< NeuronType, VectorType >::inputShape ( ) const

inlinevirtual

Returns the expected shape of the input.

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

Definition at line 278 of file NeuronLayers.h.

◆ name()

template<class NeuronType , class VectorType = RealVector>

std::string shark::NeuronLayer< NeuronType, VectorType >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 272 of file NeuronLayers.h.

◆ neuron() [1/2]

template<class NeuronType , class VectorType = RealVector>

NeuronType & shark::NeuronLayer< NeuronType, VectorType >::neuron ( )

inline

Definition at line 276 of file NeuronLayers.h.

◆ neuron() [2/2]

template<class NeuronType , class VectorType = RealVector>

NeuronType const & shark::NeuronLayer< NeuronType, VectorType >::neuron ( ) const

inline

Definition at line 275 of file NeuronLayers.h.

◆ numberOfParameters()

template<class NeuronType , class VectorType = RealVector>

size_t shark::NeuronLayer< NeuronType, VectorType >::numberOfParameters ( ) const

inlinevirtual

return the number of parameter

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 297 of file NeuronLayers.h.

◆ outputShape()

template<class NeuronType , class VectorType = RealVector>

Shape shark::NeuronLayer< NeuronType, VectorType >::outputShape ( ) const

inlinevirtual

Returns the shape of the output.

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

Definition at line 282 of file NeuronLayers.h.

◆ parameterVector()

template<class NeuronType , class VectorType = RealVector>

ParameterVectorType shark::NeuronLayer< NeuronType, VectorType >::parameterVector ( ) const

inlinevirtual

obtain the parameter vector

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 287 of file NeuronLayers.h.

◆ read()

template<class NeuronType , class VectorType = RealVector>

void shark::NeuronLayer< NeuronType, VectorType >::read ( InArchive & archive )

inlinevirtual

From ISerializable.

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

Definition at line 356 of file NeuronLayers.h.

◆ setParameterVector()

template<class NeuronType , class VectorType = RealVector>

void shark::NeuronLayer< NeuronType, VectorType >::setParameterVector ( ParameterVectorType const & newParameters )

inlinevirtual

overwrite the parameter vector

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 292 of file NeuronLayers.h.

References SIZE_CHECK.

◆ weightedInputDerivative()

template<class NeuronType , class VectorType = RealVector>

void shark::NeuronLayer< NeuronType, VectorType >::weightedInputDerivative	(	BatchInputType const &	inputs,
		BatchOutputType const &	outputs,
		BatchOutputType const &	coefficients,
		State const &	state,
		BatchInputType &	derivative
	)		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 339 of file NeuronLayers.h.

References SIZE_CHECK, and shark::State::toState().

◆ weightedParameterDerivative()

template<class NeuronType , class VectorType = RealVector>

void shark::NeuronLayer< NeuronType, VectorType >::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 328 of file NeuronLayers.h.

References SIZE_CHECK.

◆ write()

template<class NeuronType , class VectorType = RealVector>

void shark::NeuronLayer< NeuronType, VectorType >::write ( OutArchive & archive ) const

inlinevirtual

From ISerializable.

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

Definition at line 358 of file NeuronLayers.h.

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

include/shark/Models/NeuronLayers.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ BatchInputType

◆ BatchOutputType

◆ ParameterVectorType

Constructor & Destructor Documentation

◆ NeuronLayer()

Member Function Documentation

◆ createState()

◆ eval() [1/3]

◆ eval() [2/3]

◆ eval() [3/3]

◆ inputShape()

◆ name()

◆ neuron() [1/2]

◆ neuron() [2/2]

◆ numberOfParameters()

◆ outputShape()

◆ parameterVector()

◆ read()

◆ setParameterVector()

◆ weightedInputDerivative()

◆ weightedParameterDerivative()

◆ write()