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

Public Member Functions
	GRU ()
	Create the GRU object. More...

	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	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 > &, 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...

size_t	InSize () const
	Get the number of input units. More...

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

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

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

size_t	OutSize () const
	Get the number of output units. More...

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

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

void	ResetCell (const size_t size)

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

size_t &	Rho ()
	Modify the maximum number of steps to backpropagate through time (BPTT). 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
>

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).

Definition at line 58 of file gru.hpp.

Constructor & Destructor Documentation

◆ GRU() [1/2]

GRU ( )

Create the GRU object.

◆ GRU() [2/2]

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()

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 140 of file gru.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 142 of file gru.hpp.

◆ Deterministic() [1/2]

bool Deterministic ( ) const

inline

The value of the deterministic parameter.

Definition at line 120 of file gru.hpp.

◆ Deterministic() [2/2]

bool& Deterministic ( )

inline

Modify the value of the deterministic parameter.

Definition at line 122 of file gru.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 > &	,
		arma::Mat< eT > &
	)

◆ Gradient() [2/3]

OutputDataType const& Gradient ( ) const

inline

Get the gradient.

Definition at line 145 of file gru.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 147 of file gru.hpp.

◆ InputShape()

size_t InputShape ( ) const

inline

Get the shape of the input.

Definition at line 159 of file gru.hpp.

References GRU< InputDataType, OutputDataType >::serialize().

◆ InSize()

size_t InSize ( ) const

inline

Get the number of input units.

Definition at line 153 of file gru.hpp.

◆ Model()

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

inline

Get the model modules.

Definition at line 150 of file gru.hpp.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 135 of file gru.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 137 of file gru.hpp.

◆ OutSize()

size_t OutSize ( ) const

inline

Get the number of output units.

Definition at line 156 of file gru.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 130 of file gru.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 132 of file gru.hpp.

◆ ResetCell()

void ResetCell ( const size_t size )

◆ Rho() [1/2]

size_t Rho ( ) const

inline

Get the maximum number of steps to backpropagate through time (BPTT).

Definition at line 125 of file gru.hpp.

◆ Rho() [2/2]

size_t& Rho ( )

inline

Modify the maximum number of steps to backpropagate through time (BPTT).

Definition at line 127 of file gru.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by GRU< InputDataType, OutputDataType >::InputShape().

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

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

Public Member Functions

Detailed Description

template<typenameInputDataType=arma::mat,typenameOutputDataType=arma::mat> class mlpack::ann::GRU< InputDataType, OutputDataType >

Constructor & Destructor Documentation

◆ GRU() [1/2]

◆ GRU() [2/2]

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()

◆ InSize()

◆ Model()

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ OutSize()

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ ResetCell()

◆ Rho() [1/2]

◆ Rho() [2/2]

◆ serialize()

template
<
typename
InputDataType
=
arma::mat
,
typename
OutputDataType
=
arma::mat
>

class mlpack::ann::GRU< InputDataType, OutputDataType >