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

The reconstruction loss performance function measures the network's performance equal to the negative log probability of the target with the input distribution. More...

#include <reconstruction_loss.hpp>

Public Member Functions

 ReconstructionLoss ()
 Create the ReconstructionLoss object. More...
 
template<typename InputType , typename TargetType , typename OutputType >
void Backward (const InputType &input, const TargetType &target, OutputType &output)
 Ordinary feed backward pass of a neural network. More...
 
template<typename InputType , typename TargetType >
InputType::elem_type Forward (const InputType &input, const TargetType &target)
 Computes the reconstruction loss. More...
 
OutputDataType & OutputParameter ()
 Modify the output parameter. More...
 
OutputDataType & 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, typename DistType = BernoulliDistribution<InputDataType>>
class mlpack::ann::ReconstructionLoss< InputDataType, OutputDataType, DistType >

The reconstruction loss performance function measures the network's performance equal to the negative log probability of the target with the input distribution.

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).
DistTypeThe type of distribution parametrized by the input.

Definition at line 37 of file reconstruction_loss.hpp.

Constructor & Destructor Documentation

◆ ReconstructionLoss()

Create the ReconstructionLoss object.

Member Function Documentation

◆ Backward()

void Backward ( const InputType &  input,
const TargetType &  target,
OutputType &  output 
)

Ordinary feed backward pass of a neural network.

Parameters
inputThe propagated input activation.
targetThe target matrix.
outputThe calculated error.

◆ Forward()

InputType::elem_type Forward ( const InputType &  input,
const TargetType &  target 
)

Computes the reconstruction loss.

Parameters
inputInput data used for evaluating the specified function.
targetThe target matrix.

◆ OutputParameter() [1/2]

OutputDataType & OutputParameter ( )
inline

Modify the output parameter.

Definition at line 70 of file reconstruction_loss.hpp.

◆ OutputParameter() [2/2]

OutputDataType & OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 68 of file reconstruction_loss.hpp.

◆ serialize()

void serialize ( Archive &  ar,
const unsigned int   
)

Serialize the layer.


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