LogCoshLoss< InputDataType, OutputDataType > Class Template Reference

The Log-Hyperbolic-Cosine loss function is often used to improve variational auto encoder. More...

Public Member Functions
	LogCoshLoss (const double a=1.0)
	Create the Log-Hyperbolic-Cosine object with the specified parameters. More...
double	A () const
	Get the value of hyperparameter a. More...
double &	A ()
	Modify the value of hyperparameter a. More...
template
void	Backward (const InputType &input, const TargetType &target, OutputType &output)
	Ordinary feed backward pass of a neural network. More...
template
InputType::elem_type	Forward (const InputType &input, const TargetType &target)
	Computes the Log-Hyperbolic-Cosine loss function. More...
OutputDataType &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. More...
template
void	serialize (Archive &ar, const unsigned int)
	Serialize the loss function. More...

Detailed Description

template
class mlpack::ann::LogCoshLoss< InputDataType, OutputDataType >

The Log-Hyperbolic-Cosine loss function is often used to improve variational auto encoder.

This function is the log of hyperbolic cosine of difference between true values and predicted values.

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 35 of file log_cosh_loss.hpp.

Constructor & Destructor Documentation

LogCoshLoss()

LogCoshLoss

(

const double

a = 1.0

)

Create the Log-Hyperbolic-Cosine object with the specified parameters.

Parameters

a	A double type value for smoothening loss function. It must be positive a real number, Sharpness of loss function is directly proportional to a. It can also act as a scaling factor hence making the loss function more sensitive to small losses around the origin. Default value = 1.0.

Member Function Documentation

A() [1/2]

double A ( ) const

inline

Get the value of hyperparameter a.

Definition at line 79 of file log_cosh_loss.hpp.

A() [2/2]

double& A ( )

inline

Modify the value of hyperparameter a.

Definition at line 81 of file log_cosh_loss.hpp.

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

Backward()

void Backward	(	const InputType &	input,
		const TargetType &	target,
		OutputType &	output
	)

Ordinary feed backward pass of a neural network.

Parameters

input	The propagated input activation.
target	The target vector.
output	The calculated error.

Forward()

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

Computes the Log-Hyperbolic-Cosine loss function.

Parameters

input	Input data used for evaluating the specified function.
target	Target data to compare with.

OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 74 of file log_cosh_loss.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 76 of file log_cosh_loss.hpp.

serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the loss function.

Referenced by LogCoshLoss< InputDataType, OutputDataType >::A().

---

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

• /home/jenkins-mlpack/mlpack.org/_src/mlpack-3.4.2/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp