ebl::contrast_norm_module< T, Tstate > Class Template Reference

#include <ebl_normalization.h>

Inheritance diagram for ebl::contrast_norm_module< T, Tstate >:

List of all members.

Public Member Functions
	contrast_norm_module (idxdim &kerdim, int nf, bool mirror=false, bool threshold=true, bool global_norm=false, parameter< T, Tstate > *p=NULL, const char *name="contrast_norm", bool across_features=true, bool learn_mean=false, double cnorm=2.0, bool fsum_div=false, float fsum_split=1.0, double epsilon=1e-6)
virtual	~contrast_norm_module ()
	destructor
virtual void	fprop (Tstate &in, Tstate &out)
	forward propagation from in to out
virtual void	bprop (Tstate &in, Tstate &out)
	backward propagation from out to in
virtual void	bbprop (Tstate &in, Tstate &out)
	second-derivative backward propagation from out to in
virtual void	dump_fprop (Tstate &in, Tstate &out)
virtual contrast_norm_module < T, Tstate > *	copy (parameter< T, Tstate > *p=NULL)
virtual bool	optimize_fprop (Tstate &in, Tstate &out)
virtual std::string	describe ()
	Returns a string describing this module and its parameters.
Protected Attributes
subtractive_norm_module< T, Tstate >	subnorm
divisive_norm_module< T, Tstate >	divnorm
Tstate	tmp
bool	global_norm
	global norm first
bool	learn_mean
	Learn mean weighting.
Friends
class	contrast_norm_module_gui

---

Detailed Description

template<typename T, class Tstate = bbstate_idx<T>>
class ebl::contrast_norm_module< T, Tstate >

Local contrast normalization operation using a weighted expectation over a local neighborhood. An input set of feature maps is locally normalized to be zero mean and unit standard deviation.

---

Constructor & Destructor Documentation

template<typename T , class Tstate >

ebl::contrast_norm_module< T, Tstate >::contrast_norm_module	(	idxdim &	kerdim,
		int	nf,
		bool	mirror = false,
		bool	threshold = true,
		bool	global_norm = false,
		parameter< T, Tstate > *	p = NULL,
		const char *	name = "contrast_norm",
		bool	across_features = true,
		bool	learn_mean = false,
		double	cnorm = 2.0,
		bool	fsum_div = false,
		float	fsum_split = 1.0,
		double	epsilon = 1e-6
	)

Parameters:

kerdim	The kernel dimensions.
nf	The number of feature maps input to this module.
mirror	Use mirroring of the input to pad border if true, or use zero-padding otherwise (default).
global_norm	If true, apply global normalization first.
p	If specified, parameter p holds learned weights.
across_features	If true, normalize across feature dimensions in addition to spatial dimensions.
learn_mean	If true, learn mean weighting.
cgauss	Gaussian kernel coefficient.

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Member Function Documentation

template<typename T , class Tstate >

contrast_norm_module< T, Tstate > * ebl::contrast_norm_module< T, Tstate >::copy

(

parameter< T, Tstate > *

p = NULL

)

[virtual]

Returns a deep copy of this module.

Parameters:

p	If NULL, reuse current parameter space, otherwise allocate new weights on parameter 'p'.

Reimplemented from ebl::module_1_1< T, Tstate >.

template<typename T , class Tstate >

void ebl::contrast_norm_module< T, Tstate >::dump_fprop	(	Tstate &	in,
		Tstate &	out
	)		[virtual]

Calls fprop and then dumps internal buffers, inputs and outputs into files. This can be useful for debugging.

Reimplemented from ebl::module_1_1< T, Tstate >.

template<typename T , class Tstate >

bool ebl::contrast_norm_module< T, Tstate >::optimize_fprop	(	Tstate &	in,
		Tstate &	out
	)		[virtual]

Pre-determine the order of hidden buffers to use only 2 buffers in order to reduce memory footprint. This returns true if outputs is actually put in out, false if it's in in.

Reimplemented from ebl::module_1_1< T, Tstate >.

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

• /home/rex/ebltrunk/core/libeblearn/include/ebl_normalization.h
• /home/rex/ebltrunk/core/libeblearn/include/ebl_normalization.hpp