VRClassReward< InputDataType, OutputDataType > Class Template Reference

Implementation of the variance reduced classification reinforcement layer. More...

Public Member Functions
	VRClassReward (const double scale=1, const bool sizeAverage=true)
	Create the VRClassReward object. More...
template<class LayerType , class... Args>
void	Add (Args...args)
void	Add (LayerTypes<> layer)
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...
template<typename InputType , typename TargetType >
double	Forward (const InputType &input, const TargetType &target)
	Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...
std::vector< LayerTypes<> > &	Model ()
	Get the network modules. More...
OutputDataType &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. More...
double	Scale () const
	Get the value of scale parameter. More...
template<typename Archive >
void	serialize (Archive &, const unsigned int)
	Serialize the layer. More...
bool	SizeAverage () const
	Get the value of parameter sizeAverage. More...

Detailed Description

template<typename InputDataType, typename OutputDataType>
class mlpack::ann::VRClassReward< InputDataType, OutputDataType >

Implementation of the variance reduced classification reinforcement layer.

This layer is meant to be used in combination with the reinforce normal layer (ReinforceNormalLayer), which expects that an reward: (1 for success, 0 otherwise).

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 79 of file layer_types.hpp.

Constructor & Destructor Documentation

VRClassReward	(	const double	scale = 1,
		const bool	sizeAverage = true
	)

Create the VRClassReward object.

Parameters

scale	Parameter used to scale the reward.
sizeAverage	Take the average over all batches.

Member Function Documentation

void Add ( Args...  args )

inline

Definition at line 92 of file vr_class_reward.hpp.

void Add ( LayerTypes<>  layer )

inline

Definition at line 99 of file vr_class_reward.hpp.

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

Ordinary feed backward pass of a neural network.

The negative log likelihood layer expectes that the input contains log-probabilities for each class. The layer also 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.

OutputDataType& Delta ( ) const

inline

Get the delta.

Definition at line 82 of file vr_class_reward.hpp.

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 84 of file vr_class_reward.hpp.

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

Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f.

Parameters

input	Input data that contains the log-probabilities for each class.
target	The target vector, that contains the class index in the range between 1 and the number of classes.

std::vector<LayerTypes<> >& Model ( )

inline

Get the network modules.

Definition at line 102 of file vr_class_reward.hpp.

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 77 of file vr_class_reward.hpp.

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 79 of file vr_class_reward.hpp.

double Scale ( ) const

inline

Get the value of scale parameter.

Definition at line 108 of file vr_class_reward.hpp.

void serialize	(	Archive &	,
		const unsigned	int
	)

Serialize the layer.

bool SizeAverage ( ) const

inline

Get the value of parameter sizeAverage.

Definition at line 105 of file vr_class_reward.hpp.

---

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

• src/mlpack/methods/ann/layer/layer_types.hpp
• src/mlpack/methods/ann/layer/vr_class_reward.hpp