mlpack::ann::GRU< InputDataType, OutputDataType > Class Template Reference

An implementation of a gru network layer. More...

#include <gru.hpp>

Public Member Functions
	GRU ()
	Create the GRU object.
	GRU (const size_t inSize, const size_t outSize, const size_t rho=std::numeric_limits< size_t >::max())
	Create the GRU layer object using the specified parameters. 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 > &, arma::Mat< eT > &)
void	ResetCell (const size_t size)
bool	Deterministic () const
	The value of the deterministic parameter.
bool &	Deterministic ()
	Modify the value of the deterministic parameter.
size_t	Rho () const
	Get the maximum number of steps to backpropagate through time (BPTT).
size_t &	Rho ()
	Modify the maximum number of steps to backpropagate through time (BPTT).
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.
std::vector< LayerTypes<> > &	Model ()
	Get the model modules.
size_t	InSize () const
	Get the number of input units.
size_t	OutSize () const
	Get the number of output units.
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>
class mlpack::ann::GRU< InputDataType, OutputDataType >

An implementation of a gru network layer.

This cell can be used in RNN networks.

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

GRU()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::GRU< InputDataType, OutputDataType >::GRU	(	const size_t	inSize,
		const size_t	outSize,
		const size_t	rho = std::numeric_limits<size_t>::max()
	)

Create the GRU layer object using the specified parameters.

Parameters

inSize	The number of input units.
outSize	The number of output units.
rho	Maximum number of steps to backpropagate through time (BPTT).

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::GRU< InputDataType, OutputDataType >::Backward	(	const arma::Mat< eT > &	input,
		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 >

template<typename eT >

void mlpack::ann::GRU< InputDataType, OutputDataType >::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/gru.hpp
• src/mlpack/methods/ann/layer/gru_impl.hpp