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Artificial Neural Networks made easy with the FANN library

, 28 Aug 2013 CPOL
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.
fann-1.2.0
debian
changelog
compat
control
copyright
docs
libfann1-dev.dirs
libfann1-dev.examples
libfann1-dev.files
libfann1-dev.install
libfann1.dirs
libfann1.files
libfann1.install
rules
doc
fann_doc_complete_1.0.pdf
Makefile
html
src
include
Makefile.in
Makefile.am
Makefile.in
COPYING
Makefile.am
win32_dll
examples
makefile
README
Makefile.in
configure
AUTHORS
COPYING
ChangeLog
INSTALL
Makefile.am
NEWS
TODO
aclocal.m4
config.guess
config.sub
configure.in
depcomp
fann.pc.in
fann.spec.in
install-sh
ltmain.sh
missing
mkinstalldirs
benchmarks
datasets
building.test
building.train
diabetes.test
diabetes.train
gene.test
gene.train
mushroom.test
mushroom.train
robot.test
robot.train
soybean.test
soybean.train
thyroid.test
thyroid.train
two-spiral.train
pumadyn-32fm.test
pumadyn-32fm.train
two-spiral.test
parity8.train
parity8.test
parity13.test
parity13.train
Makefile
README
benchmark.sh
benchmarks.pdf
gnuplot
performance.cc
quality.cc
.cvsignore
examples
Makefile
xor.data
python
README
examples
libfann.i
makefile.gnu
makefile.msvc
libfann.pyc
MSVC++
libfann.dsp
all.dsw
simple_test.dsp
simple_train.dsp
steepness_train.dsp
xor_test.dsp
xor_train.dsp
config.in
changelog
compat
control
copyright
docs
libfann1-dev.dirs
libfann1-dev.examples
libfann1-dev.files
libfann1-dev.install
libfann1.dirs
libfann1.files
libfann1.install
rules
fann_doc_complete_1.0.pdf
Makefile
Makefile.in
Makefile.am
Makefile.in
COPYING
Makefile.am
makefile
README
Makefile.in
configure
AUTHORS
COPYING
ChangeLog
INSTALL
Makefile.am
NEWS
TODO
aclocal.m4
config.guess
config.sub
configure.in
depcomp
fann.pc.in
fann.spec.in
install-sh
ltmain.sh
missing
mkinstalldirs
building.test
building.train
diabetes.test
diabetes.train
gene.test
gene.train
mushroom.test
mushroom.train
robot.test
robot.train
soybean.test
soybean.train
thyroid.test
thyroid.train
two-spiral.train
pumadyn-32fm.test
pumadyn-32fm.train
two-spiral.test
parity8.train
parity8.test
parity13.test
parity13.train
Makefile
README
benchmark.sh
benchmarks.pdf
gnuplot
performance.cc
quality.cc
.cvsignore
Makefile
xor.data
README
libfann.i
makefile.gnu
makefile.msvc
libfann.pyc
libfann.dsp
all.dsw
simple_test.dsp
simple_train.dsp
steepness_train.dsp
xor_test.dsp
xor_train.dsp
config.in
bin
fanndoubled.dll
fanndoubled.lib
fanndoubleMTd.dll
fanndoubleMTd.lib
fannfixedd.dll
fannfixedd.lib
fannfixedMTd.dll
fannfixedMTd.lib
fannfloatd.dll
fannfloatd.lib
fannfloatMTd.dll
fannfloatMTd.lib
fanndouble.dll
fanndouble.lib
fanndoubleMT.dll
fanndoubleMT.lib
fannfixed.dll
fannfixed.lib
fannfixedMT.dll
fannfixedMT.lib
fannfloat.dll
fannfloat.lib
fannfloatMT.dll
fannfloatMT.lib
VS.NET2003
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<HTML
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>Fixed Point Usage</TITLE
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>Chapter 3. Fixed Point Usage</H1
><P
>&#13;      It is possible to run the ANN with fixed point numbers (internally represented as integers). This option is only intended for use on computers with no
      floating point processor, for example, the iPAQ, but a minor performance enhancement can also be seen on most modern computers
      [<A
HREF="b3048.html#bib.IDS_2000"
><I
>IDS, 2000</I
></A
>].
    </P
><DIV
CLASS="section"
><H1
CLASS="section"
><A
NAME="fixed.train"
>3.1. Training a Fixed Point ANN</A
></H1
><P
>&#13;        The ANN cannot be trained in fixed point, which is why the training part is basically the same as for floating point numbers. The only difference is that
	you should save the ANN as fixed point. This is done by the <A
HREF="r494.html"
><CODE
CLASS="function"
>fann_save_to_fixed</CODE
></A
>
	function. This function saves a fixed point version of the ANN, but it also does some analysis, in order to find out where the decimal point should be.
	The result of this analysis is returned from the function.
      </P
><P
>&#13;	The decimal point returned from the function is an indicator of, how many bits is used for the fractional part of the fixed point numbers. If this number
	is negative, there will most likely be integer overflow when running the library with fixed point numbers and this should be avoided. Furthermore, if
	the decimal point is too low (e.g. lower than 5), it is probably not a good idea to use the fixed point version.
      </P
><P
>&#13;	Please note, that the inputs to networks that should be used in fixed point should be between -1 and 1.
      </P
><DIV
CLASS="example"
><A
NAME="example.train_fixed"
></A
><P
><B
>Example 3-1. An example of a program written to support training in both fixed point and floating point numbers</B
></P
><PRE
CLASS="programlisting"
>&#13;
#include "fann.h"
#include &#60;stdio.h&#62;

int main()
{
	fann_type *calc_out;
	const float connection_rate = 1;
	const float learning_rate = 0.7;
	const unsigned int num_input = 2;
	const unsigned int num_output = 1;
	const unsigned int num_layers = 3;
	const unsigned int num_neurons_hidden = 4;
	const float desired_error = 0.001;
	const unsigned int max_iterations = 20000;
	const unsigned int iterations_between_reports = 100;
	struct fann *ann;
	struct fann_train_data *data;
	
	unsigned int i = 0;
	unsigned int decimal_point;

	printf("Creating network.\n");

	ann = fann_create(connection_rate, learning_rate, num_layers,
		num_input,
		num_neurons_hidden,
		num_output);

	printf("Training network.\n");

	data = fann_read_train_from_file("xor.data");

	fann_train_on_data(ann, data, max_iterations, iterations_between_reports, desired_error);

	printf("Testing network.\n");

	for(i = 0; i &#60; data-&#62;num_data; i++){
		calc_out = fann_run(ann, data-&#62;input[i]);
		printf("XOR test (%f,%f) -&#62; %f, should be %f, difference=%f\n",
		data-&#62;input[i][0], data-&#62;input[i][1], *calc_out, data-&#62;output[i][0], fann_abs(*calc_out - data-&#62;output[i][0]));
	}
	
	printf("Saving network.\n");

	fann_save(ann, "xor_float.net");

	decimal_point = fann_save_to_fixed(ann, "xor_fixed.net");
	fann_save_train_to_fixed(data, "xor_fixed.data", decimal_point);
	
	printf("Cleaning up.\n");
	fann_destroy_train(data);
	fann_destroy(ann);
	
	return 0;
}

	</PRE
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>Running a Fixed Point ANN</TD
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>

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