CosineEmbeddingLoss< InputDataType, OutputDataType > Class Template Reference

Cosine Embedding Loss function is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning. More...

Public Member Functions
	CosineEmbeddingLoss (const double margin=0.0, const bool similarity=true, const bool takeMean=false)
	Create the CosineEmbeddingLoss 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...
OutputDataType &	Delta () const
	Get the delta. More...
OutputDataType &	Delta ()
	Modify the delta. More...
template < typename PredictionType , typename TargetType >
PredictionType::elem_type	Forward (const PredictionType &prediction, const TargetType &target)
	Ordinary feed forward pass of a neural network. More...
InputDataType &	InputParameter () const
	Get the input parameter. More...
InputDataType &	InputParameter ()
	Modify the input parameter. More...
double	Margin () const
	Get the value of margin. More...
double &	Margin ()
	Modify the value of takeMean. More...
OutputDataType &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. More...
template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...
bool	Similarity () const
	Get the value of similarity hyperparameter. More...
bool &	Similarity ()
	Modify the value of takeMean. More...
bool	TakeMean () const
	Get the value of takeMean. More...
bool &	TakeMean ()
	Modify the value of takeMean. More...

Detailed Description

template

<

typename

InputDataType

=

arma::mat

,

typename

OutputDataType

=

arma::mat

>

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

Cosine Embedding Loss function is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning.

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 39 of file cosine_embedding_loss.hpp.

Constructor & Destructor Documentation

CosineEmbeddingLoss()

CosineEmbeddingLoss	(	const double	margin = 0.0,
		const bool	similarity = true,
		const bool	takeMean = false
	)

Create the CosineEmbeddingLoss object.

Parameters

margin	Increases cosine distance in case of dissimilarity. Refer definition of cosine-embedding-loss above.
similarity	Determines whether to use similarity or dissimilarity for comparision.
takeMean	Boolean variable to specify whether to take mean or not. Specifies reduction method i.e. sum or mean corresponding to 0 and 1 respectively. Default value = 0.

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.

Delta() [1/2]

OutputDataType& Delta ( ) const

inline

Get the delta.

Definition at line 92 of file cosine_embedding_loss.hpp.

Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 94 of file cosine_embedding_loss.hpp.

Forward()

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

Ordinary feed forward pass of a neural network.

Parameters

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

InputParameter() [1/2]

InputDataType& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 82 of file cosine_embedding_loss.hpp.

InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 84 of file cosine_embedding_loss.hpp.

Margin() [1/2]

double Margin ( ) const

inline

Get the value of margin.

Definition at line 102 of file cosine_embedding_loss.hpp.

Margin() [2/2]

double& Margin ( )

inline

Modify the value of takeMean.

Definition at line 104 of file cosine_embedding_loss.hpp.

OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 87 of file cosine_embedding_loss.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 89 of file cosine_embedding_loss.hpp.

serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by CosineEmbeddingLoss< InputDataType, OutputDataType >::Similarity().

Similarity() [1/2]

bool Similarity ( ) const

inline

Get the value of similarity hyperparameter.

Definition at line 107 of file cosine_embedding_loss.hpp.

Similarity() [2/2]

bool& Similarity ( )

inline

Modify the value of takeMean.

Definition at line 109 of file cosine_embedding_loss.hpp.

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

TakeMean() [1/2]

bool TakeMean ( ) const

inline

Get the value of takeMean.

Definition at line 97 of file cosine_embedding_loss.hpp.

TakeMean() [2/2]

bool& TakeMean ( )

inline

Modify the value of takeMean.

Definition at line 99 of file cosine_embedding_loss.hpp.

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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/cosine_embedding_loss.hpp