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

InputDataType	Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataType	Type 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

reduction

Specifies 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

prediction	Predictions used for evaluating the specified loss function.
target	The target vector.
loss	The calculated error.

Forward()

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

Computes the Soft Margin Loss function.

Parameters

prediction	Predictions used for evaluating the specified loss function.
target	The 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
	)

Serialize the layer.

Referenced by SoftMarginLoss< InputDataType, OutputDataType >::Reduction().

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

• /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp