Implementation of the RecurrentLayer class. More...

Public Member Functions
	Recurrent ()
	Default constructor—this will create a Recurrent object that can't be used, so be careful! Make sure to set all the parameters before use. More...

	Recurrent (const Recurrent &)
	Copy constructor. More...

template < typename StartModuleType , typename InputModuleType , typename FeedbackModuleType , typename TransferModuleType >
	Recurrent (const StartModuleType &start, const InputModuleType &input, const FeedbackModuleType &feedback, const TransferModuleType &transfer, const size_t rho)
	Create the Recurrent object using the specified modules. More...

template < typename eT >
void	Backward (const arma::Mat< eT > &, const arma::Mat< eT > &gy, arma::Mat< eT > &g)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards trough f. More...

OutputDataType const &	Delta () const
	Get the delta. More...

OutputDataType &	Delta ()
	Modify the delta. More...

bool	Deterministic () const
	The value of the deterministic parameter. More...

bool &	Deterministic ()
	Modify the value of the deterministic parameter. More...

template < typename eT >
void	Forward (const arma::Mat< eT > &input, arma::Mat< eT > &output)
	Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...

template < typename eT >
void	Gradient (const arma::Mat< eT > &input, const arma::Mat< eT > &error, arma::Mat< eT > &)

OutputDataType const &	Gradient () const
	Get the gradient. More...

OutputDataType &	Gradient ()
	Modify the gradient. More...

size_t	InputShape () const
	Get the shape of the input. More...

std::vector< LayerTypes< CustomLayers... > > &	Model ()
	Get the model modules. More...

OutputDataType const &	OutputParameter () const
	Get the output parameter. More...

OutputDataType &	OutputParameter ()
	Modify the output parameter. More...

OutputDataType const &	Parameters () const
	Get the parameters. More...

OutputDataType &	Parameters ()
	Modify the parameters. More...

size_t const &	Rho () const
	Get the number of steps to backpropagate through time. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat, typename... CustomLayers>
class mlpack::ann::Recurrent< InputDataType, OutputDataType, CustomLayers >

Implementation of the RecurrentLayer class.

Recurrent layers can be used similarly to feed-forward layers.

Template Parameters

InputDataType	Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataType	Type of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).

Definition at line 159 of file layer_types.hpp.

Constructor & Destructor Documentation

◆ Recurrent() [1/3]

Recurrent ( )

Default constructor—this will create a Recurrent object that can't be used, so be careful! Make sure to set all the parameters before use.

◆ Recurrent() [2/3]

Recurrent ( const Recurrent< InputDataType, OutputDataType, CustomLayers > & )

Copy constructor.

◆ Recurrent() [3/3]

Recurrent	(	const StartModuleType &	start,
		const InputModuleType &	input,
		const FeedbackModuleType &	feedback,
		const TransferModuleType &	transfer,
		const size_t	rho
	)

Create the Recurrent object using the specified modules.

Parameters

start	The start module.
input	The input module.
feedback	The feedback module.
transfer	The transfer module.
rho	Maximum number of steps to backpropagate through time (BPTT).

Member Function Documentation

◆ Backward()

void Backward	(	const arma::Mat< eT > &	,
		const arma::Mat< eT > &	gy,
		arma::Mat< eT > &	g
	)

Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards trough f.

Using the results from the feed forward pass.

Parameters

*	(input) The propagated input activation.
gy	The backpropagated error.
g	The calculated gradient.

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 130 of file recurrent.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 132 of file recurrent.hpp.

◆ Deterministic() [1/2]

bool Deterministic ( ) const

inline

The value of the deterministic parameter.

Definition at line 115 of file recurrent.hpp.

◆ Deterministic() [2/2]

bool& Deterministic ( )

inline

Modify the value of the deterministic parameter.

Definition at line 117 of file recurrent.hpp.

◆ Forward()

void Forward	(	const arma::Mat< eT > &	input,
		arma::Mat< eT > &	output
	)

Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f.

Parameters

input	Input data used for evaluating the specified function.
output	Resulting output activation.

◆ Gradient() [1/3]

void Gradient	(	const arma::Mat< eT > &	input,
		const arma::Mat< eT > &	error,
		arma::Mat< eT > &
	)

◆ Gradient() [2/3]

OutputDataType const& Gradient ( ) const

inline

Get the gradient.

Definition at line 135 of file recurrent.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 137 of file recurrent.hpp.

◆ InputShape()

size_t InputShape ( ) const

Get the shape of the input.

Referenced by Recurrent< InputDataType, OutputDataType, CustomLayers >::Rho().

◆ Model()

std::vector<LayerTypes<CustomLayers...> >& Model ( )

inline

Get the model modules.

Definition at line 112 of file recurrent.hpp.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 125 of file recurrent.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 127 of file recurrent.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 120 of file recurrent.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 122 of file recurrent.hpp.

◆ Rho()

size_t const& Rho ( ) const

inline

Get the number of steps to backpropagate through time.

Definition at line 140 of file recurrent.hpp.

References Recurrent< InputDataType, OutputDataType, CustomLayers >::InputShape(), and Recurrent< InputDataType, OutputDataType, CustomLayers >::serialize().

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by Recurrent< InputDataType, OutputDataType, CustomLayers >::Rho().

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/layer_types.hpp
/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/recurrent.hpp

Public Member Functions

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat, typename... CustomLayers> class mlpack::ann::Recurrent< InputDataType, OutputDataType, CustomLayers >

Constructor & Destructor Documentation

◆ Recurrent() [1/3]

◆ Recurrent() [2/3]

◆ Recurrent() [3/3]

Member Function Documentation

◆ Backward()

◆ Delta() [1/2]

◆ Delta() [2/2]

◆ Deterministic() [1/2]

◆ Deterministic() [2/2]

◆ Forward()

◆ Gradient() [1/3]

◆ Gradient() [2/3]

◆ Gradient() [3/3]

◆ InputShape()

◆ Model()

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ Rho()

◆ serialize()

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat, typename... CustomLayers>
class mlpack::ann::Recurrent< InputDataType, OutputDataType, CustomLayers >