Click here to Skip to main content
15,867,488 members
Articles / Artificial Intelligence

Artificial Neural Networks made easy with the FANN library

Rate me:
Please Sign up or sign in to vote.
4.93/5 (46 votes)
28 Aug 2013CPOL24 min read 193.7K   10.6K   206  
Neural networks are typically associated with specialised applications, developed only by select groups of experts. This misconception has had a highly negative effect on its popularity. Hopefully, the FANN library will help fill this gap.
/*
Fast Artificial Neural Network Library (fann)
Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk)

This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
*/

#ifndef __fann_activation_h__
#define __fann_activation_h__
/* internal include file, not to be included directly
 */

/* The possible activation functions.
   They are described with functions,
   where x is the input to the activation function,
   y is the output,
   s is the steepness and
   d is the derivation.
 */

enum {
	/* Linear activation function.
	   span: -inf < y < inf
	   y = x*s, d = 1*s
	   Can NOT be used in fixed point.
	*/
	FANN_LINEAR = 0,

	/* Threshold activation function.
	   x < 0 -> y = 0, x >= 0 -> y = 1
	   Can NOT be used during training.
	*/
	FANN_THRESHOLD,

	/* Threshold activation function.
	   x < 0 -> y = 0, x >= 0 -> y = 1
	   Can NOT be used during training.
	*/
	FANN_THRESHOLD_SYMMETRIC,

	/* Sigmoid activation function.
	   One of the most used activation functions.
	   span: 0 < y < 1
	   y = 1/(1 + exp(-2*s*x)), d = 2*s*y*(1 - y)
	*/
	FANN_SIGMOID,

	/* Stepwise linear approximation to sigmoid.
	   Faster than sigmoid but a bit less precise.
	*/
	FANN_SIGMOID_STEPWISE, /* (default) */


	/* Symmetric sigmoid activation function, aka. tanh.
	   One of the most used activation functions.
	   span: -1 < y < 1
	   y = tanh(s*x) = 2/(1 + exp(-2*s*x)) - 1, d = s*(1-(y*y))
	*/
	FANN_SIGMOID_SYMMETRIC,
	
	/* Stepwise linear approximation to symmetric sigmoid.
	   Faster than symmetric sigmoid but a bit less precise.
	*/
	FANN_SIGMOID_SYMMETRIC_STEPWISE,

	/* Gausian activation function.
	   0 when x = -inf, 1 when x = 0 and 0 when x = inf
	   span: 0 < y < 1
	   y = exp(-x*s*x*s), d = -2*x*y*s
	*/
	FANN_GAUSSIAN,

	/* Stepwise linear approximation to gaussian.
	   Faster than gaussian but a bit less precise.
	   NOT implemented yet.
	*/
	FANN_GAUSSIAN_STEPWISE,

	/* Fast (sigmoid like) activation function defined by David Elliott
	   span: 0 < y < 1
	   y = ((x*s) / 2) / (1 + |x*s|) + 0.5, d = s*1/(2*(1+|x|)*(1+|x|))
	   NOT implemented yet.
	*/
	FANN_ELLIOT,

	/* Fast (symmetric sigmoid like) activation function defined by David Elliott
	   span: -1 < y < 1   
	   y = (x*s) / (1 + |x*s|), d = s*1/((1+|x|)*(1+|x|))
	   NOT implemented yet.
	*/
	FANN_ELLIOT_SYMMETRIC
};

static char const * const FANN_ACTIVATION_NAMES[] = {
	"FANN_LINEAR",
	"FANN_THRESHOLD",
	"FANN_THRESHOLD_SYMMETRIC",
	"FANN_SIGMOID",
	"FANN_SIGMOID_STEPWISE",
	"FANN_SIGMOID_SYMMETRIC",
	"FANN_SIGMOID_SYMMETRIC_STEPWISE",
	"FANN_GAUSSIAN",
	"FANN_GAUSSIAN_STEPWISE",
	"FANN_ELLIOT",
	"FANN_ELLIOT_SYMMETRIC"
};

/* Implementation of the activation functions
 */

/* stepwise linear functions used for some of the activation functions */

/* defines used for the stepwise linear functions */

#define fann_linear_func(v1, r1, v2, r2, value) ((((r2-r1) * (value-v1))/(v2-v1)) + r1)
#define fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6, min, max, value) (value < v5 ? (value < v3 ? (value < v2 ? (value < v1 ? min : fann_linear_func(v1, r1, v2, r2, value)) : fann_linear_func(v2, r2, v3, r3, value)) : (value < v4 ? fann_linear_func(v3, r3, v4, r4, value) : fann_linear_func(v4, r4, v5, r5, value))) : (value < v6 ? fann_linear_func(v5, r5, v6, r6, value) : max))

/* FANN_LINEAR */
#define fann_linear(steepness, value) fann_mult(steepness, value)
#define fann_linear_derive(steepness, value) (steepness)

/* FANN_SIGMOID */
#define fann_sigmoid(steepness, value) (1.0f/(1.0f + exp(-2.0f * steepness * value)))
#define fann_sigmoid_derive(steepness, value) (2.0f * steepness * value * (1.0f - value)) /* the plus is a trick to the derived function, to avoid getting stuck on flat spots */

/* FANN_SIGMOID_SYMMETRIC */
#define fann_sigmoid_symmetric(steepness, value) (2.0f/(1.0f + exp(-2.0f * steepness * value)) - 1.0f)
#define fann_sigmoid_symmetric_derive(steepness, value) steepness * (1.0f - (value*value))

/* FANN_GAUSSIAN */
#define fann_gaussian(steepness, value) (exp(-value * steepness * value * steepness))

#endif

By viewing downloads associated with this article you agree to the Terms of Service and the article's licence.

If a file you wish to view isn't highlighted, and is a text file (not binary), please let us know and we'll add colourisation support for it.

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


Written By
Publisher
Poland Poland
Software Developer's Journal (formerly Software 2.0) is a magazine for professional programmers and developers publishing news from the software world and practical articles presenting very interesting ready programming solutions. To read more

Comments and Discussions