Backpropagation learning fp. More...

Inheritance diagram for BPLearningFeaturePlane:

Public Member Functions
	~BPLearningFeaturePlane ()
void	read (QDataStream inStream, QProgressDialog progress, int *index)
void	save (QDataStream outStream, QProgressDialog progress, int *index)
bool	isLearning ()
Protected Member Functions
	BPLearningFeaturePlane (Network net, int l, QString name, int w, int h, int angles, int speeds, int alpha, int speed, bool isWTA, bool visible, Neuron neuron, NotifyStrategyAbstract notify, bool learning)
void	applyGatingControl ()
void	run ()
void	step ()
void	updateWeights (Matrix *errorMatrix)
void	computeActivations ()
float	nonlinearity (Matrix totalActivation, OutputImage workOutput)
Protected Attributes
Matrix *	activations
Matrix *	desiredOutcome
Matrix *	errorMatrix
vector< Matrix * >	backErrorMatrix
bool	learning
float	ita
Friends
class	GatingUnits
class	FeaturePlaneFactory

Detailed Description

Backpropagation learning fp.

Constructor & Destructor Documentation

BPLearningFeaturePlane::~BPLearningFeaturePlane ( )

BPLearningFeaturePlane::BPLearningFeaturePlane	(	Network *	net,
		int	l,
		QString *	name,
		int	w,
		int	h,
		int	angles,
		int	speeds,
		int	alpha,
		int	speed,
		bool	isWTA,
		bool	visible,
		Neuron *	neuron,
		NotifyStrategyAbstract *	notify,
		bool	learning
	)		`[protected]`

Constructor

Parameters:

net	- network, this FP will be added to the end of the list of feature planes
l	- layer index
name	- name of the feature plane
w	- width
h	- height
angles	- number of discrete angles (e.g. speeds, orientations)
speeds	- number of speeds (e.g. speed bands, scale bands)
alpha	- alpha
speed	- speed of this fp
isWTA	- participates in Selective Tuning
visible	- should the FP be presented in the UI
neuron	- neuron strategy (what is the output nonlinearity, or the ODE that defines response)
notify	- notification strategy (deprecated, on the way out)
learning	- are weights adjustable through learning?