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

#include <ebl_normalization.h>

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

List of all members.

Public Member Functions
	subtractive_norm_module (idxdim &kerdim, int nf, bool mirror=false, bool global_norm=false, parameter< T, Tstate > *p=NULL, const char *name="subtractive_norm", bool across_features=true, double cgauss=2.0, bool fsum_div=false, float fsum_split=1.0)
virtual	~subtractive_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 subtractive_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
parameter< T, Tstate > *	param
layers< T, Tstate >	convmean
convolution_module< T, Tstate > *	meanconv
module_1_1< T, Tstate > *	padding
idx< T >	w
	weights
diff_module< T, Tstate >	difmod
	difference module
Tstate	inmean
bool	global_norm
	global norm first
int	nfeatures
idxdim	kerdim
bool	across_features
	Norm across feats.
bool	mirror
	mirror input or not.
Friends
class	contrast_norm_module_gui
class	contrast_norm_module

---

Detailed Description

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

Subtractive normalization operation using a weighted expectation over a local neighborhood. An input set of feature maps is locally normalized to be zero mean.

---

Constructor & Destructor Documentation

template<typename T , class Tstate >

ebl::subtractive_norm_module< T, Tstate >::subtractive_norm_module	(	idxdim &	kerdim,
		int	nf,
		bool	mirror = false,
		bool	global_norm = false,
		parameter< T, Tstate > *	p = NULL,
		const char *	name = "subtractive_norm",
		bool	across_features = true,
		double	cgauss = 2.0,
		bool	fsum_div = false,
		float	fsum_split = 1.0
	)

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.

normalize the kernel

feature sum module to sum along features this might be implemented by making the table in above conv module all to all connection, but that would be very inefficient

---

Member Function Documentation

template<typename T , class Tstate >

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

second-derivative backward propagation from out to in

in - mean

sum_j (w_j * in_j)

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

template<typename T , class Tstate >

subtractive_norm_module< T, Tstate > * ebl::subtractive_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::subtractive_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::subtractive_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 >.

---

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