Join< InputDataType, OutputDataType > Class Template Reference

Implementation of the Join module class. More...

Public Member Functions
	Join ()
	Create the Join object. 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...
template < typename InputType , typename OutputType >
void	Forward (const InputType &&input, OutputType &&output)
	Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...
InputDataType const &	InputParameter () const
	Get the input parameter. More...
InputDataType &	InputParameter ()
	Modify the input parameter. More...
OutputDataType const &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. 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::Join< InputDataType, OutputDataType >

Implementation of the Join module class.

The Join class accumulates the output of various modules.

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 33 of file join.hpp.

Constructor & Destructor Documentation

Join()

Join

(

)

Create the Join object.

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 74 of file join.hpp.

Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 76 of file join.hpp.

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

Forward()

void Forward	(	const InputType &&	input,
		OutputType &&	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.

InputParameter() [1/2]

InputDataType const& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 64 of file join.hpp.

InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 66 of file join.hpp.

OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 69 of file join.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 71 of file join.hpp.

serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

Referenced by Join< InputDataType, OutputDataType >::Delta().

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

• src/mlpack/methods/ann/layer/join.hpp