GRU< InputDataType, OutputDataType > Class Template Reference

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...
	~GRU ()
	Delete the GRU and the layers it holds. More...
template < typename eT >
void	Backward (const arma::Mat< eT > &&, 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 (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 (arma::Mat< eT > &&input, arma::Mat< eT > &&, arma::Mat< eT > &&)
OutputDataType const &	Gradient () const
	Get the gradient. More...
OutputDataType &	Gradient ()
	Modify the gradient. 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...
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 unsigned int)
	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 57 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).

~GRU()

~GRU

(

)

Delete the GRU and the layers it holds.

Member Function Documentation

Backward()

void Backward	(	const arma::Mat< eT > &&	,
		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 144 of file gru.hpp.

Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 146 of file gru.hpp.

Deterministic() [1/2]

bool Deterministic ( ) const

inline

The value of the deterministic parameter.

Definition at line 124 of file gru.hpp.

Deterministic() [2/2]

bool& Deterministic ( )

inline

Modify the value of the deterministic parameter.

Definition at line 126 of file gru.hpp.

Forward()

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

Gradient() [2/3]

OutputDataType const& Gradient ( ) const

inline

Get the gradient.

Definition at line 149 of file gru.hpp.

Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 151 of file gru.hpp.

Model()

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

inline

Get the model modules.

Definition at line 154 of file gru.hpp.

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

OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 139 of file gru.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 141 of file gru.hpp.

Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 134 of file gru.hpp.

Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 136 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 129 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 131 of file gru.hpp.

serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

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

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

• /home/jenkins-mlpack/mlpack.org/_src/mlpack-3.2.1/src/mlpack/methods/ann/layer/gru.hpp