mlpack 3.4.2
Public Member Functions | List of all members
L1Loss< InputDataType, OutputDataType > Class Template Reference

The L1 loss is a loss function that measures the mean absolute error (MAE) between each element in the input x and target y. More...

#include <l1_loss.hpp>

Public Member Functions

 L1Loss (const bool mean=true)
 Create the L1Loss 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 L1 Loss function. More...
 
bool & Mean ()
 Set the value of reduction type. More...
 
bool Mean () const
 Get the value of reduction type. 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::L1Loss< InputDataType, OutputDataType >

The L1 loss is a loss function that measures the mean absolute error (MAE) between each element in the input x and target y.

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 l1_loss.hpp.

Constructor & Destructor Documentation

◆ L1Loss()

L1Loss ( const bool  mean = true)

Create the L1Loss object.

Parameters
meanReduction type. If true, it returns the mean of the loss. Else, it returns the sum.

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 L1 Loss function.

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

◆ Mean() [1/2]

bool & Mean ( )
inline

Set the value of reduction type.

Definition at line 74 of file l1_loss.hpp.

◆ Mean() [2/2]

bool Mean ( ) const
inline

Get the value of reduction type.

Definition at line 72 of file l1_loss.hpp.

◆ OutputParameter() [1/2]

OutputDataType & OutputParameter ( )
inline

Modify the output parameter.

Definition at line 69 of file l1_loss.hpp.

◆ OutputParameter() [2/2]

OutputDataType & OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 67 of file l1_loss.hpp.

◆ serialize()

void serialize ( Archive &  ar,
const unsigned int   
)

Serialize the layer.


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