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

#include <ebl_nonlinearity.h>

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

List of all members.

Public Member Functions
	tanh_shrink_module (parameter< T, Tstate > *p, intg nf, bool diags=false)
virtual	~tanh_shrink_module ()
	Destructor.
virtual void	fprop (Tstate &in, Tstate &out)
	forward
virtual void	bprop (Tstate &in, Tstate &out)
	backward
virtual void	bbprop (Tstate &in, Tstate &out)
	2nd deriv backward
virtual tanh_shrink_module< T, Tstate > *	copy ()
	Returns a deep copy of this module.
virtual std::string	describe ()
	Returns a string describing this module and its parameters.
Protected Attributes
intg	nfeatures
Tstate	abuf
Tstate	tbuf
Tstate	bbuf
diag_module< T, Tstate > *	alpha
diag_module< T, Tstate > *	beta
tanh_module< T, Tstate >	mtanh
diff_module< T, Tstate >	difmod
	difference module
bool	diags
	Use coefficients or not.

---

Detailed Description

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

A smoothed shrinkage module using (x - tanh(x)) that parametrizes the steepnes of the shrinkage operator. This function is useful for learning since there is always gradients flowing through it.

---

Constructor & Destructor Documentation

template<typename T , class Tstate >

ebl::tanh_shrink_module< T, Tstate >::tanh_shrink_module	(	parameter< T, Tstate > *	p,
		intg	nf,
		bool	diags = false
	)

Constructor.

Parameters:

nf	The number of features.
diags	If true, alpha and beta coefficients are learned such that the output is: a * x - tanh(b * x)

---

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

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