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

The mean squared logarithmic error performance function measures the network's performance according to the mean of squared logarithmic errors. More...

#include <mean_squared_logarithmic_error.hpp>

Public Member Functions

 MeanSquaredLogarithmicError ()
 Create the MeanSquaredLogarithmicError 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 mean squared logarithmic error function. 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>
class mlpack::ann::MeanSquaredLogarithmicError< InputDataType, OutputDataType >

The mean squared logarithmic error performance function measures the network's performance according to the mean of squared logarithmic errors.

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 mean_squared_logarithmic_error.hpp.

Constructor & Destructor Documentation

◆ MeanSquaredLogarithmicError()

Create the MeanSquaredLogarithmicError 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 vector.
outputThe calculated error.

◆ Forward()

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

Computes the mean squared logarithmic error function.

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

◆ OutputParameter() [1/2]

OutputDataType & OutputParameter ( )
inline

Modify the output parameter.

Definition at line 66 of file mean_squared_logarithmic_error.hpp.

◆ OutputParameter() [2/2]

OutputDataType & OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 64 of file mean_squared_logarithmic_error.hpp.

◆ serialize()

void serialize ( Archive &  ar,
const unsigned int   
)

Serialize the layer.


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