mlpack 3.4.2
Public Member Functions | List of all members
Join< InputDataType, OutputDataType > Class Template Reference

Implementation of the Join module class. More...

#include <join.hpp>

Public Member Functions

 Join ()
 Create the Join object. 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 & Delta ()
 Modify the delta. More...
 
OutputDataType const & Delta () const
 Get 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...
 
OutputDataType & OutputParameter ()
 Modify the output parameter. More...
 
OutputDataType const & OutputParameter () const
 Get 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
InputDataTypeType of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataTypeType 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 > &  ,
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.
gyThe backpropagated error.
gThe calculated gradient.

◆ Delta() [1/2]

OutputDataType & Delta ( )
inline

Modify the delta.

Definition at line 71 of file join.hpp.

◆ Delta() [2/2]

OutputDataType const & Delta ( ) const
inline

Get the delta.

Definition at line 69 of file join.hpp.

◆ 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
inputInput data used for evaluating the specified function.
outputResulting output activation.

◆ OutputParameter() [1/2]

OutputDataType & OutputParameter ( )
inline

Modify the output parameter.

Definition at line 66 of file join.hpp.

◆ OutputParameter() [2/2]

OutputDataType const & OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 64 of file join.hpp.

◆ serialize()

void serialize ( Archive &  ar,
const unsigned int   
)

Serialize the layer.


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