PoissonNLLLoss< InputDataType, OutputDataType > Class Template Reference

Implementation of the Poisson negative log likelihood loss. More...

Public Member Functions
	PoissonNLLLoss (const bool logInput=true, const bool full=false, const typename InputDataType::elem_type eps=1e-08, const bool mean=true)
	Create the PoissonNLLLoss 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...
InputDataType::elem_type	Eps () const
	Get the value of eps. More...
InputDataType::elem_type &	Eps ()
	Modify the value of eps. More...
template<typename InputType , typename TargetType >
InputDataType::elem_type	Forward (const InputType &input, const TargetType &target)
	Computes the Poisson negative log likelihood Loss. More...
bool	Full () const
	Get the value of full. More...
bool &	Full ()
	Modify the value of full. More...
InputDataType &	InputParameter () const
	Get the input parameter. More...
InputDataType &	InputParameter ()
	Modify the input parameter. More...
bool	LogInput () const
	Get the value of logInput. More...
bool &	LogInput ()
	Modify the value of logInput. More...
bool	Mean () const
	Get the value of mean. More...
bool &	Mean ()
	Modify the value of mean. More...
OutputDataType &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify 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::PoissonNLLLoss< InputDataType, OutputDataType >

Implementation of the Poisson negative log likelihood loss.

This loss function expects input for each class. It also expects a class index, in the range between 1 and the number of classes, as target when calling the Forward function.

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 36 of file poisson_nll_loss.hpp.

Constructor & Destructor Documentation

PoissonNLLLoss	(	const bool	logInput = true,
		const bool	full = false,
		const typename InputDataType::elem_type	eps = 1e-08,
		const bool	mean = true
	)

Create the PoissonNLLLoss object.

Parameters

logInput	If true the loss is computed as , if false then the loss is .
full	Boolean value that determines whether to include Stirling's approximation term.
eps	A small value to prevent 0 in denominators and logarithms.
mean	When true, mean loss is computed otherwise total loss.

Member Function Documentation

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

Ordinary feed backward pass of a neural network.

The Poisson Negative Log Likelihood loss function expects the input for each class. It expects a class index, in the range between 1 and the number of classes, as target when calling the Forward function.

Parameters

input	The propagated input activation.
target	The target vector, that contains the class index in the range between 1 and the number of classes.
output	The calculated error.

InputDataType::elem_type Eps ( ) const

inline

Get the value of eps.

eps is a small value required to prevent 0 in logarithms and denominators.

Definition at line 108 of file poisson_nll_loss.hpp.

InputDataType::elem_type& Eps ( )

inline

Modify the value of eps.

eps is a small value required to prevent 0 in logarithms and denominators.

Definition at line 111 of file poisson_nll_loss.hpp.

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

Computes the Poisson negative log likelihood Loss.

Parameters

input	Input data used for evaluating the specified function.
target	The target vector, that contains the class index in the range between 1 and the number of classes.

bool Full ( ) const

inline

Get the value of full.

full is a boolean value that determines whether to include Stirling's approximation term.

Definition at line 101 of file poisson_nll_loss.hpp.

bool& Full ( )

inline

Modify the value of full.

full is a boolean value that determines whether to include Stirling's approximation term.

Definition at line 104 of file poisson_nll_loss.hpp.

InputDataType& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 83 of file poisson_nll_loss.hpp.

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 85 of file poisson_nll_loss.hpp.

bool LogInput ( ) const

inline

Get the value of logInput.

logInput is a boolean value that tells if logits are given as input.

Definition at line 94 of file poisson_nll_loss.hpp.

bool& LogInput ( )

inline

Modify the value of logInput.

logInput is a boolean value that tells if logits are given as input.

Definition at line 97 of file poisson_nll_loss.hpp.

bool Mean ( ) const

inline

Get the value of mean.

It's a boolean value that tells if mean of the total loss has to be taken.

Definition at line 115 of file poisson_nll_loss.hpp.

bool& Mean ( )

inline

Modify the value of mean.

It's a boolean value that tells if mean of the total loss has to be taken.

Definition at line 118 of file poisson_nll_loss.hpp.

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 88 of file poisson_nll_loss.hpp.

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 90 of file poisson_nll_loss.hpp.

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

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

• src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp