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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.zip
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
fann_win32_dll-1_2_0.zip
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.zip
VS.NET2003
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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>Training and Testing</TITLE
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><DIV
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><H1
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><A
NAME="adv.train_test"
>2.4. Training and Testing</A
></H1
><P
>&#13;        Normally it will be sufficient to use the <A
HREF="r806.html"
><CODE
CLASS="function"
>fann_train_on_file</CODE
></A
> training function, but
	sometimes you want to have more control and you will have to write a custom training loop. This could be because you would like another stop criteria,
	or because you would like to adjust some of the parameters during training. Another stop criteria than the value of the combined mean square error could
	be that each of the training pairs should have a mean square error lower than a given value.
      </P
><DIV
CLASS="example"
><A
NAME="example.train_on_file_internals"
></A
><P
><B
>Example 2-1. 
	  The internals of the <CODE
CLASS="function"
>fann_train_on_file</CODE
> function, without writing the status line.
	</B
></P
><PRE
CLASS="programlisting"
>&#13;
struct fann_train_data *data = fann_read_train_from_file(filename);
for(i = 1 ; i &#60;= max_epochs ; i++) {
  fann_reset_MSE(ann);
  for (j = 0 ; j != data-&#62;num_data ; j++) {
    fann_train(ann, data-&#62;input[j], data-&#62;output[j]);
  }
  if ( fann_get_MSE(ann) &#60; desired_error ) {
    break;
  }
}
fann_destroy_train(data);

        </PRE
></DIV
><P
>&#13;	This piece of code introduces the <A
HREF="r536.html"
><CODE
CLASS="function"
>fann_train</CODE
></A
> function, which trains the ANN for one iteration
	with one pair of inputs and outputs and also updates the mean square error. The
	<A
HREF="r1837.html"
><SPAN
CLASS="type"
>fann_train_data</SPAN
></A
> structure is also introduced, this structure is a container for the
	training data in the file described in figure 10. The structure can be used to train the ANN, but it can also be used to test the ANN with data which it
	has not been trained with.
      </P
><DIV
CLASS="example"
><A
NAME="example.calc_mse"
></A
><P
><B
>Example 2-2. Test all of the data in a file and calculates the mean square error.</B
></P
><PRE
CLASS="programlisting"
>&#13;
struct fann_train_data *data = fann_read_train_from_file(filename);
fann_reset_MSE(ann);
for(i = 0 ; i != data-&#62;num_data ; i++ ) {
  fann_test(ann, data-&#62;input[i], data-&#62;output[i]);
}
printf("Mean Square Error: %f\n", fann_get_MSE(ann));
fann_destroy_train(data);

	</PRE
></DIV
><P
>&#13;	This piece of code introduces another useful function: <A
HREF="r557.html"
><CODE
CLASS="function"
>fann_test</CODE
></A
> function, which takes an input
	array and a desired output array as the parameters and returns the calculated output. It also updates the mean square error.
      </P
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