CrossEntropyError< InputDataType, OutputDataType > Class Template Reference

The cross-entropy performance function measures the network's performance according to the cross-entropy between the input and target distributions. More...

Public Member Functions
	CrossEntropyError (const double eps=1e-10)
	Create the CrossEntropyError 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...
OutputDataType &	Delta () const
	Get the delta. More...
OutputDataType &	Delta ()
	Modify the delta. More...
double	Eps () const
	Get the epsilon. More...
double &	Eps ()
	Modify the epsilon. More...
template<typename InputType , typename TargetType >
double	Forward (const InputType &&input, const TargetType &&target)
InputDataType &	InputParameter () const
	Get the input parameter. More...
InputDataType &	InputParameter ()
	Modify the input parameter. 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::CrossEntropyError< InputDataType, OutputDataType >

The cross-entropy performance function measures the network's performance according to the cross-entropy between the input and target distributions.

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 cross_entropy_error.hpp.

Constructor & Destructor Documentation

CrossEntropyError

(

const double

eps = 1e-10

)

Create the CrossEntropyError object.

Parameters

eps	The minimum value used for computing logarithms and denominators in a numerically stable way.

Member Function Documentation

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.

OutputDataType& Delta ( ) const

inline

Get the delta.

Definition at line 76 of file cross_entropy_error.hpp.

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 78 of file cross_entropy_error.hpp.

double Eps ( ) const

inline

Get the epsilon.

Definition at line 81 of file cross_entropy_error.hpp.

double& Eps ( )

inline

Modify the epsilon.

Definition at line 83 of file cross_entropy_error.hpp.

double Forward	(	const InputType &&	input,
		const TargetType &&	target
	)

InputDataType& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 66 of file cross_entropy_error.hpp.

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 68 of file cross_entropy_error.hpp.

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 71 of file cross_entropy_error.hpp.

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 73 of file cross_entropy_error.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/cross_entropy_error.hpp