mlpack::ann::Recurrent< InputDataType, OutputDataType, CustomLayers > Class Template Reference

Implementation of the RecurrentLayer class. More...

#include <recurrent.hpp>

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.
	Recurrent (const Recurrent &)
	Copy constructor.
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	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	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...
template<typename eT >
void	Gradient (const arma::Mat< eT > &input, const arma::Mat< eT > &error, arma::Mat< eT > &)
std::vector< LayerTypes< CustomLayers... > > &	Model ()
	Get the model modules.
bool	Deterministic () const
	The value of the deterministic parameter.
bool &	Deterministic ()
	Modify the value of the deterministic parameter.
OutputDataType const &	Parameters () const
	Get the parameters.
OutputDataType &	Parameters ()
	Modify the parameters.
OutputDataType const &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
OutputDataType const &	Delta () const
	Get the delta.
OutputDataType &	Delta ()
	Modify the delta.
OutputDataType const &	Gradient () const
	Get the gradient.
OutputDataType &	Gradient ()
	Modify the gradient.
size_t const &	Rho () const
	Get the number of steps to backpropagate through time.
size_t	InputShape () const
	Get the shape of the input.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer.

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).

Constructor & Destructor Documentation

Recurrent()

template<typename InputDataType , typename OutputDataType , typename... CustomLayers>

template<typename StartModuleType , typename InputModuleType , typename FeedbackModuleType , typename TransferModuleType >

mlpack::ann::Recurrent< InputDataType, OutputDataType, CustomLayers >::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()

template<typename InputDataType , typename OutputDataType , typename... CustomLayers>

template<typename eT >

void mlpack::ann::Recurrent< InputDataType, OutputDataType, CustomLayers >::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.

Forward()

template<typename InputDataType , typename OutputDataType , typename... CustomLayers>

template<typename eT >

void mlpack::ann::Recurrent< InputDataType, OutputDataType, CustomLayers >::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.

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

• src/mlpack/methods/ann/layer/layer_types.hpp
• src/mlpack/methods/ann/layer/recurrent.hpp
• src/mlpack/methods/ann/layer/recurrent_impl.hpp