SoftMarginLoss< InputDataType, OutputDataType > Class Template Reference

Public Member Functions

 SoftMarginLoss (const bool reduction=true)
 Create the SoftMarginLoss object. More...

 
template
<
typename
PredictionType
,
typename
TargetType
,
typename
LossType
>
void Backward (const PredictionType &prediction, const TargetType &target, LossType &loss)
 Ordinary feed backward pass of a neural network. More...

 
template
<
typename
PredictionType
,
typename
TargetType
>
PredictionType::elem_type Forward (const PredictionType &prediction, const TargetType &target)
 Computes the Soft Margin Loss function. More...

 
OutputDataType & OutputParameter () const
 Get the output parameter. More...

 
OutputDataType & OutputParameter ()
 Modify the output parameter. More...

 
bool Reduction () const
 Get the type of reduction used. More...

 
bool & Reduction ()
 Modify the type of reduction used. More...

 
template
<
typename
Archive
>
void serialize (Archive &ar, const uint32_t version)
 Serialize the layer. More...

 

Detailed Description


template
<
typename
InputDataType
=
arma::mat
,
typename
OutputDataType
=
arma::mat
>

class mlpack::ann::SoftMarginLoss< InputDataType, OutputDataType >

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 34 of file soft_margin_loss.hpp.

Constructor & Destructor Documentation

◆ SoftMarginLoss()

SoftMarginLoss ( const bool  reduction = true)

Create the SoftMarginLoss object.

Parameters
reductionSpecifies the reduction to apply to the output. If false, 'mean' reduction is used, where sum of the output will be divided by the number of elements in the output. If true, 'sum' reduction is used and the output will be summed. It is set to true by default.

Member Function Documentation

◆ Backward()

void Backward ( const PredictionType &  prediction,
const TargetType &  target,
LossType &  loss 
)

Ordinary feed backward pass of a neural network.

Parameters
predictionPredictions used for evaluating the specified loss function.
targetThe target vector.
lossThe calculated error.

◆ Forward()

PredictionType::elem_type Forward ( const PredictionType &  prediction,
const TargetType &  target 
)

Computes the Soft Margin Loss function.

Parameters
predictionPredictions used for evaluating the specified loss function.
targetThe target vector with same shape as input.

◆ OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 73 of file soft_margin_loss.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )
inline

Modify the output parameter.

Definition at line 75 of file soft_margin_loss.hpp.

◆ Reduction() [1/2]

bool Reduction ( ) const
inline

Get the type of reduction used.

Definition at line 78 of file soft_margin_loss.hpp.

◆ Reduction() [2/2]

bool& Reduction ( )
inline

Modify the type of reduction used.

Definition at line 80 of file soft_margin_loss.hpp.

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

◆ serialize()

void serialize ( Archive &  ar,
const uint32_t  version 
)

The documentation for this class was generated from the following file:
  • /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp