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Artificial Neural Networks made easy with the FANN library

, 28 Aug 2013
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
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><DT
><A
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>fann_print_parameters</A
>&nbsp;--&nbsp;Prints all of the parameters and options of the ANN.</DT
><DT
><A
HREF="r954.html"
>fann_get_training_algorithm</A
>&nbsp;--&nbsp;Retrieve training algorithm from a network.</DT
><DT
><A
HREF="r972.html"
>fann_set_training_algorithm</A
>&nbsp;--&nbsp;Set a network's training algorithm.</DT
><DT
><A
HREF="r993.html"
>fann_get_learning_rate</A
>&nbsp;--&nbsp;Retrieve learning rate from a network.</DT
><DT
><A
HREF="r1007.html"
>fann_set_learning_rate</A
>&nbsp;--&nbsp;Set a network's learning rate.</DT
><DT
><A
HREF="r1024.html"
>fann_get_activation_function_hidden</A
>&nbsp;--&nbsp;Get the activation function used in the hidden layers.</DT
><DT
><A
HREF="r1040.html"
>fann_set_activation_function_hidden</A
>&nbsp;--&nbsp;Set the activation function for the hidden layers.</DT
><DT
><A
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>fann_get_activation_function_output</A
>&nbsp;--&nbsp;Get the activation function of the output layer.</DT
><DT
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>fann_set_activation_function_output</A
>&nbsp;--&nbsp;Set the activation function for the output layer.</DT
><DT
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>fann_get_activation_steepness_hidden</A
>&nbsp;--&nbsp;Retrieve the steepness of the activation function of the hidden layers.</DT
><DT
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>fann_set_activation_steepness_hidden</A
>&nbsp;--&nbsp;Set the steepness of the activation function of the hidden layers.</DT
><DT
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>fann_get_activation_steepness_output</A
>&nbsp;--&nbsp;Retrieve the steepness of the activation function of the output layer.</DT
><DT
><A
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>fann_set_activation_steepness_output</A
>&nbsp;--&nbsp;Set the steepness of the activation function of the output layer.</DT
><DT
><A
HREF="r1170.html"
>fann_set_train_error_function</A
>&nbsp;--&nbsp;Sets the training error function to be used.</DT
><DT
><A
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>fann_get_train_error_function</A
>&nbsp;--&nbsp;Gets the training error function to be used.</DT
><DT
><A
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>fann_get_quickprop_decay</A
>&nbsp;--&nbsp;Get the decay parameter used by the quickprop training.</DT
><DT
><A
HREF="r1224.html"
>fann_set_quickprop_decay</A
>&nbsp;--&nbsp;Set the decay parameter used by the quickprop training.</DT
><DT
><A
HREF="r1242.html"
>fann_get_quickprop_mu</A
>&nbsp;--&nbsp;Get the mu factor used by quickprop training.</DT
><DT
><A
HREF="r1257.html"
>fann_set_quickprop_mu</A
>&nbsp;--&nbsp;Set the mu factor used by quickprop training.</DT
><DT
><A
HREF="r1275.html"
>fann_get_rprop_increase_factor</A
>&nbsp;--&nbsp;Get the increase factor used by RPROP training.</DT
><DT
><A
HREF="r1290.html"
>fann_set_rprop_increase_factor</A
>&nbsp;--&nbsp;Get the increase factor used by RPROP training.</DT
><DT
><A
HREF="r1308.html"
>fann_get_rprop_decrease_factor</A
>&nbsp;--&nbsp;Get the decrease factor used by RPROP training.</DT
><DT
><A
HREF="r1323.html"
>fann_set_rprop_decrease_factor</A
>&nbsp;--&nbsp;Set the decrease factor used by RPROP training.</DT
><DT
><A
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>fann_get_rprop_delta_min</A
>&nbsp;--&nbsp;Get the minimum step-size used by RPROP training.</DT
><DT
><A
HREF="r1356.html"
>fann_set_rprop_delta_min</A
>&nbsp;--&nbsp;Set the minimum step-size used by RPROP training.</DT
><DT
><A
HREF="r1374.html"
>fann_get_rprop_delta_max</A
>&nbsp;--&nbsp;Get the maximum step-size used by RPROP training.</DT
><DT
><A
HREF="r1389.html"
>fann_set_rprop_delta_max</A
>&nbsp;--&nbsp;Set the maximum step-size used by RPROP training.</DT
><DT
><A
HREF="r1407.html"
>fann_get_num_input</A
>&nbsp;--&nbsp;Get the number of neurons in the input layer.</DT
><DT
><A
HREF="r1422.html"
>fann_get_num_output</A
>&nbsp;--&nbsp;Get number of neurons in the output layer.</DT
><DT
><A
HREF="r1437.html"
>fann_get_total_neurons</A
>&nbsp;--&nbsp;Get the total number of neurons in a network.</DT
><DT
><A
HREF="r1452.html"
>fann_get_total_connections</A
>&nbsp;--&nbsp;Get the total number of connections in a network.</DT
><DT
><A
HREF="r1467.html"
>fann_get_decimal_point</A
>&nbsp;--&nbsp;Get the position of the decimal point.</DT
><DT
><A
HREF="r1483.html"
>fann_get_multiplier</A
>&nbsp;--&nbsp;Get the multiplier.</DT
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