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Christian Kastner
chrisk-guest at moszumanska.debian.org
Sat Oct 4 21:10:47 UTC 2014
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chrisk-guest pushed a commit to tag Version2_0_0
in repository libfann.
commit a9e0c3ed1db9d9869998530c7c189bd845825b07
Author: Steffen Nissen <lukesky at diku.dk>
Date: Tue Dec 13 00:13:26 2005 +0000
*** empty log message ***
---
doc/advancedusage.txt | 84 +
doc/fann.xml | 4590 +-----------------------------------------------
doc/gettingstarted.txt | 92 +
doc/installation.txt | 117 ++
doc/theory.txt | 33 +
5 files changed, 328 insertions(+), 4588 deletions(-)
diff --git a/doc/advancedusage.txt b/doc/advancedusage.txt
new file mode 100644
index 0000000..17c1d6d
--- /dev/null
+++ b/doc/advancedusage.txt
@@ -0,0 +1,84 @@
+Section: Advanced Usage
+
+This section describes some of the low-level functions and how they can be used to obtain more control of the fann library. For a full list of functions, please see the Reference Manual, which has an explanation of all the fann library functions. Also feel free to take a look at the source code.
+
+This section describes different procedures, which can help to get more power out of the fann library.
+
+Topic: Adjusting Parameters
+
+Several different parameters exists in an ANN, these parameters are given defaults in the fann library, but they can be adjusted at runtime. There is no sense in adjusting most of these parameters after the training, since it would invalidate the training, but it does make sense to adjust some of the parameters during training, as will be described in Training and Testing. Generally speaking, these are parameters that should be adjusted before training.
+
+The training algorithm is one of the most important parameters. The default training algorithm is FANN_TRAIN_RPROP, but this may not always be the best choice. See <fann_set_training_algorithm> for more information about the different training algorithms.
+
+The training algorithms have several different parameters which can be set. For FANN_TRAIN_INCREMENTAL, FANN_TRAIN_BATCH, FANN_TRAIN_QUICKPROP the most important parameter is the learning rate, but unfortunately this is also a parameter which is hard to find a reasonable default for. I (SN) have several times ended up using 0.7, but it is a good idea to test several different learning rates when training a network. It is also worth noting that the activation function has a profound effec [...]
+
+The initial weights are random values between -0.1 and 0.1, if other weights are preferred, the weights can be altered by the <fann_randomize_weights> or <fann_init_weights> function.
+
+In [Thimm and Fiesler, High-Order and Multilayer Perceptron Initialization, 1997], Thimm and Fiesler state that, "An (sic) fixed weight variance of 0.2, which corresponds to a weight range of [-0.77, 0.77], gave the best mean performance for all the applications tested in this study. This performance is similar or better as compared to those of the other weight initialization methods."
+
+The standard activation function is the sigmoid activation function, but it is also possible to use other functions. A list of the currently available activation functions is available in the <fann_activationfunc_enum> section. The activation function can be set for a single neuron using the <fann_set_activation_function> function and for a group of neurons by the <fann_set_activation_function_hidden> and the <fann_set_activation_function_output> functions. Likewise the steepness paramet [...]
+
+FANN distinguishes between the hidden layers and the output layer, to allow more flexibility. This is especially a good idea for users wanting discrete output from the network, since they can set the activation function for the output to threshold. Please note, that it is not possible to train a network when using the threshold activation function, due to the fact, that it is not differentiable.
+
+Topic: Network Design
+
+When creating a network it is necessary to define how many layers, neurons and connections it should have. If the network become too large, the ANN will have difficulties learning and when it does learn it will tend to over-fit resulting in poor generalization. If the network becomes too small, it will not be able to represent the rules needed to learn the problem and it will never gain a sufficiently low error rate.
+
+The number of hidden layers is also important. Generally speaking, if the problem is simple it is often enough to have one or two hidden layers, but as the problems get more complex, so does the need for more layers.
+
+One way of getting a large network which is not too complex, is to adjust the connection_rate parameter given to <fann_create_sparse>. If this parameter is 0.5, the constructed network will have the same amount of neurons, but only half as many connections. It is difficult to say which problems this approach is useful for, but if you have a problem which can be solved by a fully connected network, then it would be a good idea to see if it still works after removing half the connections.
+
+Version 2.0 of the FANN library introduces a new way of creating ANNs, where the neurons are added one by one to the ANN, until it has build an optimal ANN. This approach uses the Cascade2 algorithm and is described in detail in the <Cascade Training> section.
+
+Topic: Understanding the Error Value
+
+The mean square error value is calculated while the ANN is being trained. Some functions are implemented, to use and manipulate this error value. The <fann_get_MSE> function returns the error value and the <fann_reset_MSE> resets the error value. The following explains how the mean square error value is calculated, to give an idea of the value's ability to reveal the quality of the training.
+
+If d is the desired output of an output neuron and y is the actual output of the neuron, the square error is (d *-* y) squared. If two output neurons exists, then the mean square error for these two neurons is the average of the two square errors.
+
+When training with the <fann_train_on_file> function, an error value is printed. This error value is the mean square error for all the training data. Meaning that it is the average of all the square errors in each of the training pairs.
+
+Topic: Training and Testing
+
+Normally it will be sufficient to use the <fann_train_on_file> 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.
+
+A highly simplified version of the <fann_train_on_file> function:
+
+(code)
+struct fann_train_data *data = fann_read_train_from_file(filename);
+for(i = 1 ; i <= max_epochs ; i++) {
+ error = fann_train_epoch(ann, data);
+ if ( error < desired_error ) {
+ break;
+ }
+}
+fann_destroy_train(data);
+(end)
+
+
+This piece of code introduces the <fann_train_epoch> function, which trains the ANN for one epoch and also returns the mean square error. The <struct fann_train_data> structure is also introduced, this structure is a container for the training data. 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.
+
+Test all of the data in a file and calculates the mean square error:
+
+(code)
+struct fann_train_data *data = fann_read_train_from_file(filename);
+fann_reset_MSE(ann);
+fann_test_data(ann, data);
+printf("Mean Square Error: %f\n", fann_get_MSE(ann));
+fann_destroy_train(data);
+(end)
+
+
+This piece of code introduces another useful function: <fann_test_data> function, which updates the mean square error.
+
+Topic: Avoid Over-Fitting
+
+With the knowledge of how to train and test an ANN, a new approach to training can be introduced. If too much training is applied to a set of data, the ANN will eventually over-fit, meaning that it will be fitted precisely to this set of training data and thereby loosing generalization. It is often a good idea to test, how good an ANN performs on data that it has not seen before. Testing with data not seen before, can be done while training, to see how much training is required in order [...]
+
+Topic: Adjusting Parameters During Training
+
+If a very low mean square error is required it can sometimes be a good idea to gradually decrease the learning rate during training, in order to make the adjusting of weights more subtle. If more precision is required, it might also be a good idea to use double precision floats instead of standard floats.
+
+The threshold activation function is faster than the sigmoid function, but since it is not possible to train with this function, you may wish to consider an alternate approach.
+
+While training the ANN you could slightly increase the steepness parameter of the sigmoid function. This would make the sigmoid function more steep and make it look more like the threshold function. After this training session you could set the activation function to the threshold function and the ANN would work with this activation function. This approach will not work on all kinds of problems, but has been successfully tested on the XOR function.
diff --git a/doc/fann.xml b/doc/fann.xml
index e27db3e..b9e1a63 100644
--- a/doc/fann.xml
+++ b/doc/fann.xml
@@ -3,7 +3,7 @@
<!DOCTYPE book PUBLIC "-//OASIS//DTD DocBook XML V4.1.2//EN" "docbook/xml-dtd-4.1.2/docbookx.dtd">
<book>
<bookinfo id="bookinfo">
- <title>Fast Artificial Neural Network Library</title>
+ <title>Fast Artificial Neural Network Library - PHP Extension</title>
<authorgroup id="authors">
<author>
<firstname>Steffen</firstname>
@@ -18,4121 +18,6 @@
<year>2004</year>
</copyright>
</bookinfo>
- <chapter id="intro">
- <title>Introduction</title>
- <para>
- fann - Fast Artificial Neural Network Library is written in ANSI C. The library implements multilayer
- feedforward ANNs, up to 150 times faster than other libraries. FANN supports execution in fixed point, for fast
- execution on systems like the iPAQ.
- </para>
- <section id="intro.dl">
- <title id="intro.dl.title">Getting FANN</title>
-
- <para>
- Copies of FANN can be obtained from our SourceForge project page, located at
- <ulink url="http://www.sourceforge.net/projects/fann/">http://www.sourceforge.net/projects/fann/</ulink>
- </para>
- <para>
- You can currently get FANN as source code (<filename>fann-*.tar.bz2</filename>), Debian packages
- (<filename>fann-*.deb</filename>), or RPM's (<filename>fann-*.rpm</filename>).
- </para>
- <para>
- FANN is available under the terms of the
- <ulink url="http://www.fsf.org/copyleft/lesser.html">GNU Lesser General Public License</ulink>.
- </para>
- </section>
- <section id="intro.install">
- <title>Installation</title>
- <section id="intro.install.rpm">
- <title>RPMs</title>
- <para>
- RPMs are a simple way to manage packages, and is used on many common Linux distributions such as
- <ulink url="http://www.redhat.com">Red Hat</ulink>, <ulink url="http://www.mandrake.com/">Mandrake</ulink>,
- and <ulink url="http://www.suse.com/">SuSE</ulink>.
- </para>
- <para>
- Two separate packages exist; fann, the runtime library, and fann-devel, the development library and
- header files.
- </para>
- <para>
- After downloading FANN, simply run (as root) the following command: <command>rpm -ivh $PATH_TO_RPM</command>
- </para>
- </section>
- <section id="intro.install.deb">
- <title>DEBs</title>
- <para>
- DEBs are packages for the <ulink url="http://www.debian.org">Debian</ulink> Linux distribution.
- Two separate packages exists libfann1 and libfann1-dev, where libfann1 is the runtime library and
- libfann1-dev is the development library.
- </para>
- <para>
- Fann is included in the testing distribution of Debian, so testing users can simply run (as root) the following command: <command>apt-get install libfann1 libfann1-dev</command>.
- </para>
- <para>
- After downloading the FANN DEB package, simply run (as root) the following command: <command>dpkg -i $PATH_TO_DEB</command>
- </para>
- </section>
- <section id="intro.install.win32">
- <title>Windows</title>
- <para>
- FANN >= 1.1.0 includes a Microsoft Visual C++ 6.0 project file, which can be used to compile FANN for Windows.
- To build the library and examples with MSVC++ 6.0:
- </para>
- <!-- Thanks to Koen Tanghe for this part. -->
- <para>
- First, navigate to the MSVC++ directory in the FANN distribution and open the <filename>all.dsw</filename> workspace.
- In the Visual Studio menu bar, choose "Build" -> "Batch build...", select the project configurations
- that you would like to build (by default, all are selected), and press "rebuild all"
- </para>
- <para>
- When the build process is complete, the library and examples can be found in the <filename class="directory">MSVC++\Debug</filename> and
- <filename class="directory">MSVC++\Release</filename> directories and the release versions of the examples are automatically copied into
- the <filename class="directory">examples</filename> where they are supposed to be run.
- </para>
- <!-- /Koen -->
- </section>
- <section id="intro.install.src">
- <title id="intro.install.src.title">Compiling from source</title>
- <para>
- Compiling FANN from source code entails the standard GNU autotools technique. First, configure the package as
- you want it by typing (in the FANN directory), <command>./configure</command> If you need help choosing the
- options you would like to use, try <command>./configure --help</command>
- </para>
- <para>
- Next, you have to actually compile the library. To do this, simply type <command>make</command>
- </para>
- <para>
- Finally, to install the library, type <command>make install</command>. Odds are you will have to
- be root to install, so you may need to <command>su</command> to root before installing. Please
- remember to log out of the root account immediately after <command>make install</command> finishes.
- </para>
- <para>
- Some people have experienced problems with compiling the library with some compilers, especially windows compilers which can not use GNU autotools. Please look through the <ulink url="http://sourceforge.net/forum/forum.php?forum_id=323465">help forum</ulink> and the <ulink url="http://sourceforge.net/mailarchive/forum.php?forum=fann-general">mailing list</ulink> archives for info on how these problems was solved. If you do not find any information here, feel free to ask questions.
-
- </para>
- </section>
- </section>
- <section id="intro.start">
- <title id="intro.start.title">Getting Started</title>
- <para>
- An ANN is normally run in two different modes, a training mode and an execution mode. Although it is
- possible to do this in the same program, using different programs is recommended.
- </para>
- <para>
- There are several reasons to why it is usually a good idea to write the training and execution in two
- different programs, but the most obvious is the fact that a typical ANN system is only trained once, while it
- is executed many times.
- </para>
- <section id="intro.start.train">
- <title id="intro.start.train.title">Training</title>
- <para>
- The following is a simple program which trains an ANN with a data set and then saves the ANN to a file.
- </para>
- <example id="example.simple_train">
- <title id="example.simple_train.title">Simple training example</title>
- <programlisting>
-<![CDATA[
-#include "fann.h"
-
-int main()
-{
- 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 = 0.0001;
- const unsigned int max_iterations = 500000;
- const unsigned int iterations_between_reports = 1000;
-
- struct fann *ann = fann_create(connection_rate, learning_rate, num_layers,
- num_input, num_neurons_hidden, num_output);
-
- fann_train_on_file(ann, "xor.data", max_iterations,
- iterations_between_reports, desired_error);
-
- fann_save(ann, "xor_float.net");
-
- fann_destroy(ann);
-
- return 0;
-}
-]]>
- </programlisting>
- </example>
- <para>
- The file xor.data, used to train the xor function:
- <literallayout class="monospaced" id="file_contents.xor.data">
-4 2 1
-0 0
-0
-0 1
-1
-1 0
-1
-1 1
-0
- </literallayout> The first line consists of three numbers: The first is the number of training pairs in the file, the second is the number of inputs and
- the third is the number of outputs. The rest of the file is the actual training data, consisting of one line with inputs, one with outputs etc.
- </para>
- <para>
- This example introduces several fundamental functions, namely <link linkend="api.fann_create"><function>fann_create</function></link>,
- <link linkend="api.fann_train_on_file"><function>fann_train_on_file</function></link>,
- <link linkend="api.fann_save"><function>fann_save</function></link>, and <link linkend="api.fann_destroy"><function>fann_destroy</function></link>.
- </para>
- </section>
- <section id="intro.start.execution">
- <title id="intro.start.execution.title">Execution</title>
- <para>
- The following example shows a simple program which executes a single input on the ANN. The program introduces two new functions
- (<link linkend="api.fann_create_from_file"><function>fann_create_from_file</function></link> and
- <link linkend="api.fann_run"><function>fann_run</function></link>) which were not used in the training procedure, as well as the <type>fann_type</type>
- type.
- </para>
- <example id="example.simple_exec">
- <title id="example.simple_exec.title">Simple execution example</title>
- <programlisting>
-<![CDATA[
-#include <stdio.h>
-#include "floatfann.h"
-
-int main()
-{
- fann_type *calc_out;
- fann_type input[2];
-
- struct fann *ann = fann_create_from_file("xor_float.net");
-
- input[0] = 0;
- input[1] = 1;
- calc_out = fann_run(ann, input);
-
- printf("xor test (%f,%f) -> %f\n",
- input[0], input[1], *calc_out);
-
- fann_destroy(ann);
- return 0;
-}
-]]>
- </programlisting>
- </example>
- </section>
- </section>
- <section id="intro.help">
- <title id="intro.help.title">Getting Help</title>
-
- <para>
- If after reading the documentation you are still having problems, or have a question that is not covered in the documentation,
- please consult the fann-general mailing list. Archives and subscription information are available
- <ulink url="http://lists.sourceforge.net/lists/listinfo/fann-general">here</ulink>.
- </para>
- </section>
- </chapter>
- <chapter id="adv">
- <title id="adv.title">Advanced Usage</title>
- <para>
- This section describes some of the low-level functions and how they can be used to obtain more control of the fann library. For a full list of functions,
- lease see the <link linkend="api">API Reference</link>, which has an explanation of all the fann library functions. Also feel free to take a look at
- the source code.
- </para>
- <para>
- This section describes different procedures, which can help to get more power out of the fann library:
- <link linkend="adv.adj" endterm="adv.adj.title" />, <link linkend="adv.design" endterm="adv.design.title" />,
- <link linkend="adv.errval" endterm="adv.errval.title" />, and <link linkend="adv.train_test" endterm="adv.train_test.title" />.
- </para>
-
- <section id="adv.adj">
- <title id="adv.adj.title">Adjusting Parameters</title>
-
- <para>
- Several different parameters exists in an ANN, these parameters are given defaults in the fann library, but they can be adjusted at runtime. There is no
- sense in adjusting most of these parameters after the training, since it would invalidate the training, but it does make sense to adjust some of the
- parameters during training, as will be described in <link linkend="adv.train_test" endterm="adv.train_test.title" />. Generally speaking,
- these are parameters that should be adjusted before training.
- </para>
- <para>
- The learning rate is one of the most important parameters, but unfortunately it is also a parameter which is hard to find a reasonable default for. I
- (SN) have several times ended up using 0.7, but it is a good idea to test several different learning rates when training a network. It is also worth
- noting that the activation function has a profound effect on the optimal learning rate [<xref linkend="bib.thimm_1997" endterm="bib.thimm_1997.abbrev"/>].
- The learning rate can be set when creating the network, but it can also be set by the
- <link linkend="api.fann_set_learning_rate"><function>fann_set_learning_rate</function></link> function.
- </para>
- <para>
- The initial weights are random values between -0.1 and 0.1, if other weights are preferred, the weights can be altered by the
- <link linkend="api.fann_randomize_weights"><function>fann_randomize_weights</function></link> or
- <link linkend="api.fann_init_weights"><function>fann_init_weights</function></link> function.
- </para>
- <para>
- In [<xref linkend="bib.fiesler_1997" endterm="bib.fiesler_1997.abbrev"/>], Thimm and Fiesler state that, "An <emphasis>(sic)</emphasis> fixed weight
- variance of 0.2, which corresponds to a weight range of [-0.77, 0.77], gave the best mean performance for all the applications tested in this study. This
- performance is similar or better as compared to those of the other weight initialization methods."
- </para>
- <para>
- The standard activation function is the sigmoid activation function, but it is also possible to use the threshold activation function. A list of the
- currently available activation functions is available in the <link linkend="api.sec.constants.activation" endterm="api.sec.constants.activation.title"/>
- section. The activation functions are chosen using the
- <link linkend="api.fann_set_activation_function_hidden"><function>fann_set_activation_function_hidden</function></link> and
- <link linkend="api.fann_set_activation_function_output"><function>fann_set_activation_function_output</function></link> functions.
- </para>
- <para>
- These two functions set the activation function for the hidden layers and for the output layer. Likewise the steepness parameter used in the sigmoid
- function can be adjusted with the
- <link linkend="api.fann_set_activation_steepness_hidden"><function>fann_set_activation_steepness_hidden</function></link> and
- <link linkend="api.fann_set_activation_steepness_output"><function>fann_set_activation_steepness_output</function></link> functions.
- </para>
- <para>
- FANN distinguishes between the hidden layers and the output layer, to allow more flexibility. This is especially a good idea for users wanting discrete
- output from the network, since they can set the activation function for the output to threshold. Please note, that it is not possible to train a network
- when using the threshold activation function, due to the fact, that it is not differentiable.
- </para>
- </section>
-
- <section id="adv.design">
- <title id="adv.design.title">Network Design</title>
-
- <para>
- When creating a network it is necessary to define how many layers, neurons and connections it should have. If the network become too large, the ANN will
- have difficulties learning and when it does learn it will tend to over-fit resulting in poor generalization. If the network becomes too small, it will
- not be able to represent the rules needed to learn the problem and it will never gain a sufficiently low error rate.
- </para>
- <para>
- The number of hidden layers is also important. Generally speaking, if the problem is simple it is often enough to have one or two hidden layers, but as
- the problems get more complex, so does the need for more layers.
- </para>
- <para>
- One way of getting a large network which is not too complex, is to adjust the connection_rate parameter given to
- <link linkend="api.fann_create"><function>fann_create</function></link>. If this parameter is 0.5, the constructed network will have the same amount of
- neurons, but only half as many connections. It is difficult to say which problems this approach is useful for, but if you have a problem which can be
- solved by a fully connected network, then it would be a good idea to see if it still works after removing half the connections.
- </para>
- </section>
-
- <section id="adv.errval">
- <title id="adv.errval.title">Understanding the Error Value</title>
-
- <para>
- The mean square error value is calculated while the ANN is being trained. Some functions are implemented, to use and manipulate this error value. The
- <link linkend="api.fann_get_MSE"><function>fann_get_MSE</function></link> function returns the error value and the
- <link linkend="api.fann_reset_MSE"><function>fann_reset_MSE</function></link> resets the error value. The following explains how the mean square error
- value is calculated, to give an idea of the value's ability to reveal the quality of the training.
- </para>
- <para>
- If <emphasis>d</emphasis> is the desired output of an output neuron and <emphasis>y</emphasis> is the actual output of the neuron, the square error is
- (d - y) squared. If two output neurons exists, then the mean square error for these two neurons is the average of the two square errors.
- </para>
- <para>
- When training with the <link linkend="api.fann_train_on_file"><function>fann_train_on_file</function></link> function, an error value is printed. This
- error value is the mean square error for all the training data. Meaning that it is the average of all the square errors in each of the training pairs.
- </para>
- </section>
-
- <section id="adv.train_test">
- <title id="adv.train_test.title">Training and Testing</title>
-
- <para>
- Normally it will be sufficient to use the <link linkend="api.fann_train_on_file"><function>fann_train_on_file</function></link> 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.
- </para>
- <example id="example.train_on_file_internals">
- <title id="example.train_on_file_internals.title">
- The internals of the <function>fann_train_on_file</function> function, without writing the status line.
- </title>
- <programlisting>
-<![CDATA[
-struct fann_train_data *data = fann_read_train_from_file(filename);
-for(i = 1 ; i <= max_epochs ; i++) {
- fann_reset_MSE(ann);
- for (j = 0 ; j != data->num_data ; j++) {
- fann_train(ann, data->input[j], data->output[j]);
- }
- if ( fann_get_MSE(ann) < desired_error ) {
- break;
- }
-}
-fann_destroy_train(data);
-]]>
- </programlisting>
- </example>
- <para>
- This piece of code introduces the <link linkend="api.fann_train"><function>fann_train</function></link> function, which trains the ANN for one iteration
- with one pair of inputs and outputs and also updates the mean square error. The
- <link linkend="api.struct.fann_train_data"><type>fann_train_data</type></link> 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.
- </para>
- <example id="example.calc_mse">
- <title id="example.calc_mse.title">Test all of the data in a file and calculates the mean square error.</title>
- <programlisting>
-<![CDATA[
-struct fann_train_data *data = fann_read_train_from_file(filename);
-fann_reset_MSE(ann);
-for(i = 0 ; i != data->num_data ; i++ ) {
- fann_test(ann, data->input[i], data->output[i]);
-}
-printf("Mean Square Error: %f\n", fann_get_MSE(ann));
-fann_destroy_train(data);
-]]>
- </programlisting>
- </example>
- <para>
- This piece of code introduces another useful function: <link linkend="api.fann_test"><function>fann_test</function></link> 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.
- </para>
- </section>
- <section id="adv.over_fit">
- <title id="adv.over_fit.title">Avoid Over-Fitting</title>
-
- <para>
- With the knowledge of how to train and test an ANN, a new approach to training can be introduced. If too much training is applied to a set of data, the
- ANN will eventually over-fit, meaning that it will be fitted precisely to this set of training data and thereby loosing generalization. It is often a
- good idea to test, how good an ANN performs on data that it has not seen before. Testing with data not seen before, can be done while training, to see
- how much training is required in order to perform well without over-fitting. The testing can either be done by hand, or an automatic test can be applied,
- which stops the training when the mean square error of the test data is not improving anymore.
- </para>
- </section>
- <section id="adv.adj_train">
- <title id="adv.adj_train.title">Adjusting Parameters During Training</title>
-
- <para>
- If a very low mean square error is required it can sometimes be a good idea to gradually decrease the learning rate during training, in order to make the
- adjusting of weights more subtle. If more precision is required, it might also be a good idea to use double precision floats instead of standard floats.
- </para>
- <para>
- The threshold activation function is faster than the sigmoid function, but since it is not possible to train with this function, you may wish to consider
- an alternate approach:
- </para>
- <para>
- While training the ANN you could slightly increase the steepness parameter of the sigmoid function. This would make the sigmoid function more steep and
- make it look more like the threshold function. After this training session you could set the activation function to the threshold function and the ANN
- would work with this activation function. This approach will not work on all kinds of problems, and has been successfully tested on the XOR function.
- </para>
- </section>
- </chapter>
- <chapter id="fixed">
- <title id="fixed.title">Fixed Point Usage</title>
-
- <para>
- 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
- [<xref linkend="bib.IDS_2000" endterm="bib.IDS_2000.abbrev"/>].
- </para>
-
- <section id="fixed.train">
- <title id="fixed.train.title">Training a Fixed Point ANN</title>
-
- <para>
- 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 <link linkend="api.fann_save_to_fixed"><function>fann_save_to_fixed</function></link>
- 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.
- </para>
- <para>
- 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.
- </para>
- <para>
- Please note, that the inputs to networks that should be used in fixed point should be between -1 and 1.
- </para>
- <example id="example.train_fixed">
- <title id="example.train_fixed.title">An example of a program written to support training in both fixed point and floating point numbers</title>
- <programlisting>
-<![CDATA[
-#include "fann.h"
-#include <stdio.h>
-
-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 < data->num_data; i++){
- calc_out = fann_run(ann, data->input[i]);
- printf("XOR test (%f,%f) -> %f, should be %f, difference=%f\n",
- data->input[i][0], data->input[i][1], *calc_out, data->output[i][0], fann_abs(*calc_out - data->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;
-}
-]]>
- </programlisting>
- </example>
- </section>
- <section id="fixed.run">
- <title id="fixed.run.title">Running a Fixed Point ANN</title>
-
- <para>
- Running a fixed point ANN is done much like running an ordinary ANN. The difference is that the inputs and outputs should be in fixed point
- representation. Furthermore the inputs should be restricted to be between -<parameter>multiplier</parameter> and <parameter>multiplier</parameter> to
- avoid integer overflow, where the <parameter>multiplier</parameter> is the value returned from
- <link linkend="api.fann_get_multiplier"><function>fann_get_multiplier</function></link>. This multiplier is the value that a floating point number should
- be multiplied with, in order to be a fixed point number, likewise the output of the ANN should be divided by this multiplier in order to be between zero
- and one.
- </para>
- <para>
- To help using fixed point numbers, another function is provided.
- <link linkend="api.fann_get_decimal_point"><function>fann_get_decimal_point</function></link> which returns the decimal point. The decimal point is the
- position dividing the integer and fractional part of the fixed point number and is useful for doing operations on the fixed point inputs and outputs.
- </para>
- <example id="example.exec_fixed">
- <title id="example.exec_fixed.title">An example of a program written to support both fixed point and floating point numbers</title>
- <programlisting>
-<![CDATA[
-#include <time.h>
-#include <sys/time.h>
-#include <stdio.h>
-
-#include "fann.h"
-
-int main()
-{
- fann_type *calc_out;
- unsigned int i;
- int ret = 0;
-
- struct fann *ann;
- struct fann_train_data *data;
-
- printf("Creating network.\n");
-
-#ifdef FIXEDFANN
- ann = fann_create_from_file("xor_fixed.net");
-#else
- ann = fann_create_from_file("xor_float.net");
-#endif
-
- if(!ann){
- printf("Error creating ann --- ABORTING.\n");
- return 0;
- }
-
- printf("Testing network.\n");
-
-#ifdef FIXEDFANN
- data = fann_read_train_from_file("xor_fixed.data");
-#else
- data = fann_read_train_from_file("xor.data");
-#endif
-
- for(i = 0; i < data->num_data; i++){
- fann_reset_MSE(ann);
- calc_out = fann_test(ann, data->input[i], data->output[i]);
-#ifdef FIXEDFANN
- printf("XOR test (%d, %d) -> %d, should be %d, difference=%f\n",
- data->input[i][0], data->input[i][1], *calc_out, data->output[i][0], (float)fann_abs(*calc_out - data->output[i][0])/fann_get_multiplier(ann));
-
- if((float)fann_abs(*calc_out - data->output[i][0])/fann_get_multiplier(ann) > 0.1){
- printf("Test failed\n");
- ret = -1;
- }
-#else
- printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n",
- data->input[i][0], data->input[i][1], *calc_out, data->output[i][0], (float)fann_abs(*calc_out - data->output[i][0]));
-#endif
- }
-
- printf("Cleaning up.\n");
- fann_destroy_train(data);
- fann_destroy(ann);
-
- return ret;
-}
-]]>
- </programlisting>
- </example>
- </section>
- <section id="fixed.precision">
- <title id="fixed.precision.title">Precision of a Fixed Point ANN</title>
-
- <para>
- The fixed point ANN is not as precise as a floating point ANN, furthermore it approximates the sigmoid function by a stepwise linear function. Therefore,
- it is always a good idea to test the fixed point ANN after loading it from a file. This can be done by calculating the mean square error as described
- <link linkend="example.calc_mse">earlier</link>. There is, however, one problem with this approach: The training data stored in the file is in floating
- point format. Therefore, it is possible to save this data in a fixed point format from within the floating point program. This is done by the function
- <link linkend="api.fann_save_train_to_fixed"><function>fann_save_train_to_fixed</function></link>. Please note that this function takes the decimal point
- as an argument, meaning that the decimal point should be calculated first by using the
- <link linkend="api.fann_save_to_fixed"><function>fann_save_to_fixed</function></link> function.
- </para>
- </section>
- </chapter>
- <chapter id="theory">
- <title id="theory.title">Neural Network Theory</title>
- <para>
- This section will briefly explain the theory of neural networks (hereafter known as NN) and artificial neural
- networks (hereafter known as ANN). For a more in depth explanation of these concepts please consult the
- literature; [<xref linkend="bib.hassoun_1995" endterm="bib.hassoun_1995.abbrev" />] has good coverage of most
- concepts of ANN and [<xref linkend="bib.hertz_1991" endterm="bib.hertz_1991.abbrev" />] describes the mathematics
- of ANN very thoroughly, while [<xref linkend="bib.anderson_1995" endterm="bib.anderson_1995.abbrev" />] has a
- more psychological and physiological approach to NN and ANN. For the pragmatic I (SN) could recommend
- [<xref linkend="bib.tettamanzi_2001" endterm="bib.tettamanzi_2001.abbrev" />], which has a short and easily
- understandable introduction to NN and ANN.
- </para>
- <section id="theory.neural_networks">
- <title id="theory.neural_networks.title">Neural Networks</title>
- <para>
- The human brain is a highly complicated machine capable of solving very complex problems. Although we have
- a good understanding of some of the basic operations that drive the brain, we are still far from understanding
- everything there is to know about the brain.
- </para>
- <para>
- In order to understand ANN, you will need to have a basic knowledge of how the internals of the brain work.
- The brain is part of the central nervous system and consists of a very large NN. The NN is actually quite
- complicated, so the following discussion shall be relegated to the details needed to understand ANN, in order
- to simplify the explanation.
- </para>
- <para>
- The NN is a network consisting of connected neurons. The center of the neuron is called the nucleus. The
- nucleus is connected to other nucleuses by means of the dendrites and the axon. This connection is called a
- synaptic connection.
- </para>
- <para>
- The neuron can fire electric pulses through its synaptic connections, which is received at the dendrites of
- other neurons.
- </para>
- <para>
- When a neuron receives enough electric pulses through its dendrites, it activates and fires a pulse through
- its axon, which is then received by other neurons. In this way information can propagate through the NN. The
- synaptic connections change throughout the lifetime of a neuron and the amount of incoming pulses needed to
- activate a neuron (the threshold) also change. This behavior allows the NN to learn.
- </para>
- <para>
- The human brain consists of around 10^11 neurons which are highly interconnected with around 10^15
- connections [<xref linkend="bib.tettamanzi_2001" endterm="bib.tettamanzi_2001.abbrev" />]. These neurons
- activates in parallel as an effect to internal and external sources. The brain is connected to the rest of the
- nervous system, which allows it to receive information by means of the five senses and also allows it to
- control the muscles.
- </para>
- </section>
- <section id="theory.artificial_neural_networks">
- <title id="theory.artificial_neural_networks.title">Artificial Neural Networks</title>
- <para>
- It is not possible (at the moment) to make an artificial brain, but it is possible to make simplified
- artificial neurons and artificial neural networks. These ANNs can be made in many different ways and can try to
- mimic the brain in many different ways.
- </para>
- <para>
- ANNs are not intelligent, but they are good for recognizing patterns and making simple rules for complex
- problems. They also have excellent training capabilities which is why they are often used in artificial
- intelligence research.
- </para>
- <para>
- ANNs are good at generalizing from a set of training data. E.g. this means an ANN given data about a set of
- animals connected to a fact telling if they are mammals or not, is able to predict whether an animal outside
- the original set is a mammal from its data. This is a very desirable feature of ANNs, because you do not need
- to know the characteristics defining a mammal, the ANN will find out by itself.
- </para>
- </section>
- <section id="theory.training">
- <title id="theory.training.title">Training an ANN</title>
- <para>
- When training an ANN with a set of input and output data, we wish to adjust the weights in the ANN, to make
- the ANN give the same outputs as seen in the training data. On the other hand, we do not want to make the ANN
- too specific, making it give precise results for the training data, but incorrect results for all other data.
- When this happens, we say that the ANN has been over-fitted.
- </para>
- <para>
- The training process can be seen as an optimization problem, where we wish to minimize the mean square
- error of the entire set of training data. This problem can be solved in many different ways, ranging from
- standard optimization heuristics like simulated annealing, through more special optimization techniques like
- genetic algorithms to specialized gradient descent algorithms like backpropagation.
- </para>
- <para>
- The most used algorithm is the backpropagation algorithm, but this algorithm has some limitations
- concerning, the extent of adjustment to the weights in each iteration. This problem has been solved in more
- advanced algorithms like RPROP [<xref linkend="bib.riedmiller_1993" endterm="bib.riedmiller_1993.abbrev" />]
- and quickprop [<xref linkend="bib.fahlman_1988" endterm="bib.fahlman_1988.abbrev" />].
- </para>
- </section>
- </chapter>
- <chapter id="api">
- <title id="api.title">API Reference</title>
- <para>This is a list of all functions and structures in FANN.</para>
- <section id="api.sec.create_destroy">
- <title id="api.sec.create_destroy.title">Creation, Destruction, and Execution</title>
- <refentry id="api.fann_create">
- <refnamediv>
- <refname>fann_create</refname>
- <refpurpose>Create a new artificial neural network, and return a pointer to it.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann *</type>
- <methodname>fann_create</methodname>
- <methodparam>
- <type>float</type>
- <parameter>connection_rate</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>learning_rate</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>num_layers</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>...</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_create</function> will create a new artificial neural network, and return
- a pointer to it. The <parameter>connection_rate</parameter> controls how many
- connections there will be in the network. If the connection rate is set to 1, the
- network will be fully connected, but if it is set to 0.5 only half of the connections
- will be set.
- </para>
- <para>
- The <parameter>num_layers</parameter> is the number of layers including the input and
- output layer. This parameter is followed by one parameter for each layer telling how
- many neurons there should be in the layer.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_create_array">
- <refnamediv>
- <refname>fann_create_array</refname>
- <refpurpose>Create a new artificial neural network, and return a pointer to it.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann *</type>
- <methodname>fann_create_array</methodname>
- <methodparam>
- <type>float</type>
- <parameter>connection_rate</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>learning_rate</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>num_layers</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int *</type>
- <parameter>neurons_per_layer</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_create_array</function> will create a new artificial neural network, and return a pointer to
- it. It is the same as <function>fann_create</function>, only it accepts an array as its final parameter
- instead of variable arguments.
- </para>
- <para>
- <example id="example.api.fann_create_array">
- <title id="example.api.fann_create_array.title"><function>fann_create_array</function> example</title>
- <programlisting>
-<![CDATA[
-unsigned int neurons_per_layer[3] = {2, 3, 1};
-
-// The following two calls have identical results
-struct fann * ann = fann_create_array(1.0f, 0.7f, 3, neurons_per_layer);
-struct fann * ann2 = fann_create(1.0f, 0.7f, 3, 2, 3, 1);
-
-fann_destroy(ann);
-fann_destroy(ann2);
-]]>
- </programlisting>
- </example>
- </para>
- <para>This function appears in FANN >= 1.0.5.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_create_shortcut">
- <refnamediv>
- <refname>fann_create_shortcut</refname>
- <refpurpose>Create a new artificial neural network with shortcut connections, and return a pointer to it.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann *</type>
- <methodname>fann_create_shortcut</methodname>
- <methodparam>
- <type>float</type>
- <parameter>learning_rate</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>num_layers</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>...</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_create_shortcut</function> will create a new artificial neural network, and return
- a pointer to it. The network will be fully connected, and will furthermore have all shortcut
- connections connected.
- </para>
- <para>
- Shortcut connections are connections that skip layers. A fully connected network with shortcut
- connections, is a network where all neurons are connected to all neurons in later layers.
- Including direct connections from the input layer to the output layer.
- </para>
- <para>
- The <parameter>num_layers</parameter> is the number of layers including the input and
- output layer. This parameter is followed by one parameter for each layer telling how
- many neurons there should be in the layer.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_create_shortcut_array">
- <refnamediv>
- <refname>fann_create_shortcut_array</refname>
- <refpurpose>Create a new artificial neural network with shortcut connections, and return a pointer to it.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann *</type>
- <methodname>fann_create_shortcut_array</methodname>
- <methodparam>
- <type>float</type>
- <parameter>learning_rate</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>num_layers</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int *</type>
- <parameter>neurons_per_layer</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_create_shortcut_array</function> will create a new artificial neural network, and return a pointer to
- it. It is the same as <function>fann_create_shortcut</function>, only it accepts an array as its final parameter
- instead of variable arguments.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_destroy">
- <refnamediv>
- <refname>fann_destroy</refname>
- <refpurpose>Destroy an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_destroy</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_destroy</function> will destroy an artificial neural network, properly freeing all associate
- memory.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_run">
- <refnamediv>
- <refname>fann_run</refname>
- <refpurpose>Run (execute) an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>fann_type *</type>
- <methodname>fann_run</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type *</type>
- <parameter>input</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_run</function> will run <parameter>input</parameter> through <parameter>ann</parameter>,
- returning an array of outputs, the number of which being equal to the number of neurons in the output
- layer.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_randomize_weights">
- <refnamediv>
- <refname>fann_randomize_weights</refname>
- <refpurpose>Give each connection a random weight.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_randomize_weights</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type</type>
- <parameter>min_weight</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type</type>
- <parameter>max_height</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Randomizes the weight of each connection in <parameter>ann</parameter>, effectively resetting the network.
- </para>
- <para>
- See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
- <link linkend="api.fann_init_weights"><function>fann_init_weights</function></link>
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_init_weights">
- <refnamediv>
- <refname>fann_init_weights</refname>
- <refpurpose>Initialize the weight of each connection.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_init_weights</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>train_data</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- This function behaves similarly to <link linkend="api.fann_randomize_weights"><function>fann_randomize_weights</function></link>.
- It will use the algorithm developed by Derrick Nguyen and Bernard Widrow
- [<link linkend="bib.nguyen_1990" endterm="bib.nguyen_1990.abbrev" />] to set the weights in such a way as to speed up training.
- This technique is not always successful, and in some cases can be <emphasis>less</emphasis> efficient than a purely random
- initialization.
- </para>
- <para>
- The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second
- argument, <parameter>data</parameter>, which is the training data that will be used to train the network.
- </para>
- <para>
- See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
- <link linkend="api.fann_randomize_weights"><function>fann_randomize_weights</function></link>
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_print_connections">
- <refnamediv>
- <refname>fann_print_connections</refname>
- <refpurpose>Prints the connections of an ann.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_print_connections</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_print_connections</function> will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.
- </para>
- <para>
- The output from fann_print_connections on a small (2 2 1) network trained on the xor problem:
- <literallayout class="monospaced" id="api.fann_print_connections.output">
-Layer / Neuron 012345
-L 1 / N 3 ddb...
-L 1 / N 4 bbb...
-L 2 / N 6 ...cda
- </literallayout> This network have five real neurons and two bias neurons. This gives a total of seven neurons named from 0 to 6. The connections between these neurons can be seen in the matrix. <constant>"."</constant> is a place where there is no connection, while a character tells how strong the connection is on a scale from a-z. The two real neurons in the hidden layer (neuron <constant>3</constant> and <constant>4</constant> in layer <constant>1</constant>) has connection from th [...]
- </para>
- <para> To simplify the matrix output neurons is not visible as neurons that connections can come from, and input and bias neurons are not visible as neurons that connections can go to.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.io">
- <title id="api.sec.io.title">Input/Output</title>
- <refentry id="api.fann_save">
- <refnamediv>
- <refname>fann_save</refname>
- <refpurpose>Save an ANN to a file.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_save</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>const char *</type>
- <parameter>configuration_file</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_save</function> will attempt to save <parameter>ann</parameter> to the file located at
- <parameter>configuration_file</parameter>
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_save_to_fixed">
- <refnamediv>
- <refname>fann_save_to_fixed</refname>
- <refpurpose>Save an ANN to a fixed-point file.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_save_to_fixed</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>const char *</type>
- <parameter>configuration_file</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_save_to_fixed</function> will attempt to save <parameter>ann</parameter> to the file located at
- <parameter>configuration_file</parameter> as a fixed-point network.
-
- </para>
- <para>
- This is useful for training a network in floating points,
- and then later executing it in fixed point.
- </para>
- <para>
- The function returns the bit position of the fix point, which
- can be used to find out how accurate the fixed point network will be.
- A high value indicates high precision, and a low value indicates low
- precision.
- </para>
- <para>
- A negative value indicates very low precision, and a very
- strong possibility for overflow.
- (the actual fix point will be set to 0, since a negative
- fix point does not make sense).
- </para>
- <para>
- Generally, a fix point lower than 6 is bad, and should be avoided.
- The best way to avoid this, is to have less connections to each neuron,
- or just less neurons in each layer.
- </para>
- <para>
- The fixed point use of this network is only intended for use on machines that
- have no floating point processor, like an iPAQ. On normal computers the floating
- point version is actually faster.
- </para>
-
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_create_from_file">
- <refnamediv>
- <refname>fann_create_from_file</refname>
- <refpurpose>Load an ANN from a file.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann *</type>
- <methodname>fann_create_from_file</methodname>
- <methodparam>
- <type>const char *</type>
- <parameter>configuration_file</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_create_from_file</function>will attempt to load an artificial neural network from a file.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.train_algo">
- <title id="api.sec.train_algo.title">Training</title>
- <refentry id="api.fann_train">
- <refnamediv>
- <refname>fann_train</refname>
- <refpurpose>Train an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_train</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type *</type>
- <parameter>input</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type *</type>
- <parameter>output</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_train</function> will train one iteration with a set of inputs, and a set of desired
- outputs. The training will be done by the standard backpropagation algorithm.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_test">
- <refnamediv>
- <refname>fann_test</refname>
- <refpurpose>Tests an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>fann_type *</type>
- <methodname>fann_test</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type *</type>
- <parameter>input</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type *</type>
- <parameter>desired_output</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Test with a set of inputs, and a set of desired outputs. This operation updates the mean square error,
- but does not change the network in any way.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_MSE">
- <refnamediv>
- <refname>fann_get_MSE</refname>
- <refpurpose>Return the mean square error of an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_MSE</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Reads the mean square error from the network. This value is calculated during training or testing, and can therefore sometimes be a bit off if the weights have been changed since the last calculation of the value.</para>
- <para>This function appears in FANN >= 1.1.0. (before this
- <link linkend="api.fann_get_error"><function>fann_get_error</function></link> is used)</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_reset_MSE">
- <refnamediv>
- <refname>fann_reset_MSE</refname>
- <refpurpose>Reset the mean square error of an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_reset_MSE</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Resets the mean square error from the network.
- </para>
- <para>
- This function appears in FANN >= 1.1.0. (before this
- <link linkend="api.fann_reset_error"><function>fann_reset_error</function></link> is used)
- </para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.train_data">
- <title id="api.sec.train_data.title">Training Data</title>
- <refentry id="api.fann_read_train_from_file">
- <refnamediv>
- <refname>fann_read_train_from_file</refname>
- <refpurpose>Read training data from a file.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann_train_data *</type>
- <methodname>fann_read_train_from_file</methodname>
- <methodparam>
- <type>char *</type>
- <parameter>filename</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_read_train_from_file</function>will load training data from a file.
- The file should be formatted in the following way:
- </para>
- <programlisting>
-<![CDATA[
- num_train_data num_input num_output
- inputdata seperated by space
- outputdata seperated by space
-
- .
- .
- .
-
- inputdata seperated by space
- outputdata seperated by space
-]]>
- </programlisting>
- <para>
- An example of a <link linkend="file_contents.xor.data">properly formatted file</link> is
- provided in the Introduction.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_save_train">
- <refnamediv>
- <refname>fann_save_train</refname>
- <refpurpose>Save training data.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_save_train</methodname>
- <methodparam>
- <type>struct data *</type>
- <parameter>train_data</parameter>
- </methodparam>
- <methodparam>
- <type>FILE *</type>
- <parameter>filename</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Save <parameter>train_data</parameter> to <parameter>filename</parameter>.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_save_train_to_fixed">
- <refnamediv>
- <refname>fann_save_train_to_fixed</refname>
- <refpurpose>Save training data as fixed point.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_save_to_fixed</methodname>
- <methodparam>
- <type>struct data *</type>
- <parameter>train_data</parameter>
- </methodparam>
- <methodparam>
- <type>FILE *</type>
- <parameter>filename</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>decimal_point</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Save <parameter>train_data</parameter> as fixed point to <parameter>filename</parameter>.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_destroy_train">
- <refnamediv>
- <refname>fann_destroy_train</refname>
- <refpurpose>Destroy training data.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_destroy_train_data</methodname>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>train_data</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Destroy the training data stored in <parameter>train_data</parameter>, freeing the associated memory.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_train_epoch">
- <refnamediv>
- <refname>fann_train_epoch</refname>
- <refpurpose>Trains one epoch.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_train_epoch</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Train one epoch with the training data stored in <parameter>data</parameter>. One epoch is
- where all of the training data is considered exactly once.
- </para>
- <para>
- This function returns the MSE error as it is calculated either before or during the actual training.
- This is not the actual MSE after the training epoch, but since calculating this will require to go
- through the entire training set once more, it is more than adequate to use this value during training.
- </para>
- <para>
- The training algorithm used by this function is chosen by the
- <link linkend="api.fann_set_training_algorithm"><function>fann_set_training_algorithm</function></link>
- function. The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_test_data">
- <refnamediv>
- <refname>fann_test_data</refname>
- <refpurpose>Calculates the mean square error for a set of data.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_test_data</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Calculates the mean square error for a set of data.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_train_on_data">
- <refnamediv>
- <refname>fann_train_on_data</refname>
- <refpurpose>Train an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_train_on_data</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>max_epochs</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>epochs_between_reports</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>desired_error</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Trains
- <parameter>ann</parameter>using
- <parameter>data</parameter>until
- <parameter>desired_error</parameter>is reached, or until
- <parameter>max_epochs</parameter>is surpassed.</para>
- <para>
- The training algorithm used by this function is chosen by the
- <link linkend="api.fann_set_training_algorithm"><function>fann_set_training_algorithm</function></link>
- function. The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_train_on_data_callback">
- <refnamediv>
- <refname>fann_train_on_data_callback</refname>
- <refpurpose>Train an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_train_on_data_callback</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>max_epochs</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>epochs_between_reports</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>desired_error</parameter>
- </methodparam>
- <methodparam>
- <type>int</type>
- <parameter>(*callback)(unsigned int epochs, float error)</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Trains <parameter>ann</parameter> using <parameter>data</parameter> until
- <parameter>desired_error</parameter> is reached, or until <parameter>max_epochs</parameter>
- is surpassed.
- </para>
- <para>
- This function behaves identically to
- <link linkend="api.fann_train_on_data"><function>fann_train_on_data</function></link>, except that
- <function>fann_train_on_data_callback</function>allows you to specify a function to be called every
- <parameter>epochs_between_reports</parameter>instead of using the default reporting mechanism.
- If the callback function returns -1 the training will terminate.
- </para>
- <para>
- The callback function is very useful in GUI applications or in other applications which
- do not wish to report the progress on standard output. Furthermore the callback function
- can be used to stop the training at non standard stop criteria (see
- <xref linkend="adv.train_test" endterm="adv.train_test.title"/>.)
- </para>
- <para>This function appears in FANN >= 1.0.5.</para>
- <para>
- The training algorithm used by this function is chosen by the
- <link linkend="api.fann_set_training_algorithm"><function>fann_set_training_algorithm</function></link>
- function. The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
- </para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_train_on_file">
- <refnamediv>
- <refname>fann_train_on_file</refname>
- <refpurpose>Train an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_train_on_file</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>char *</type>
- <parameter>filename</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>max_epochs</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>epochs_between_reports</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>desired_error</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Trains <parameter>ann</parameter> using the data in <parameter>filename</parameter> until
- <parameter>desired_error</parameter> is reached, or until <parameter>max_epochs</parameter> is surpassed.
- </para>
- <para>
- The training algorithm used by this function is chosen by the
- <link linkend="api.fann_set_training_algorithm"><function>fann_set_training_algorithm</function></link>
- function. The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_train_on_file_callback">
- <refnamediv>
- <refname>fann_train_on_file_callback</refname>
- <refpurpose>Train an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_train_on_file_callback</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>char *</type>
- <parameter>filename</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>max_epochs</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>epochs_between_reports</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>desired_error</parameter>
- </methodparam>
- <methodparam>
- <type>int</type>
- <parameter>(*callback)(unsigned int epochs, float error)</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Trains <parameter>ann</parameter> using the data in <parameter>filename</parameter> until
- <parameter>desired_error</parameter> is reached, or until <parameter>max_epochs</parameter> is surpassed.
- </para>
- <para>
- This function behaves identically to
- <link linkend="api.fann_train_on_file"><function>fann_train_on_file</function></link>, except that
- <function>fann_train_on_file_callback</function> allows you to specify a function to be called every
- <parameter>epochs_between_reports</parameter> instead of using the default reporting mechanism.
- The callback function works as described in
- <link linkend="api.fann_train_on_data_callback"><function>fann_train_on_data_callback</function></link>
- </para>
- <para>
- The training algorithm used by this function is chosen by the
- <link linkend="api.fann_set_training_algorithm"><function>fann_set_training_algorithm</function></link>
- function. The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
- </para>
- <para>This function appears in FANN >= 1.0.5.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_shuffle_train_data">
- <refnamediv>
- <refname>fann_shuffle_train_data</refname>
- <refpurpose>Shuffle the training data.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_shuffle_train_data</methodname>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_shuffle_train_data</function>will randomize the order of the training data contained in
- <parameter>data</parameter>.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_merge_train_data">
- <refnamediv>
- <refname>fann_merge_train_data</refname>
- <refpurpose>Merge two sets of training data.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann_train_data *</type>
- <methodname>fann_merge_train_data</methodname>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data1</parameter>
- </methodparam>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data2</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_merge_train_data</function>will return a single set of training data which contains all data
- from <parameter>data1</parameter> and <parameter>data2</parameter>.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_duplicate_train_data">
- <refnamediv>
- <refname>fann_duplicate_train_data</refname>
- <refpurpose>Copies a set of training data.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann_train_data *</type>
- <methodname>fann_duplicate_train_data</methodname>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_duplicate_train_data</function>will return a copy of <parameter>data</parameter>.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.options">
- <title id="api.sec.options.title">Options</title>
- <refentry id="api.fann_print_parameters">
- <refnamediv>
- <refname>fann_print_parameters</refname>
- <refpurpose>Prints all of the parameters and options of the ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_print_parameters</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Prints all the parameters of the network, for easy viewing of all the values.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_training_algorithm">
- <refnamediv>
- <refname>fann_get_training_algorithm</refname>
- <refpurpose>Retrieve training algorithm from a network.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_training_algorithm</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the training algorithm (as described in <link linkend="api.sec.constants.training">Training algorithms</link>) for a given network.</para>
- <para>
- The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_training_algorithm">
- <refnamediv>
- <refname>fann_set_training_algorithm</refname>
- <refpurpose>Set a network's training algorithm.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_training_algorithm</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>training_algorithm</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Set the training algorithm (as described in <link linkend="api.sec.constants.training">Training algorithms</link>) of a network.</para>
- <para>
- The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_learning_rate">
- <refnamediv>
- <refname>fann_get_learning_rate</refname>
- <refpurpose>Retrieve learning rate from a network.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_learning_rate</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the learning rate for a given network.</para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_learning_rate">
- <refnamediv>
- <refname>fann_set_learning_rate</refname>
- <refpurpose>Set a network's learning rate.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_learning_rate</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>learning_rate</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Set the learning rate of a network.</para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_activation_function_hidden">
- <refnamediv>
- <refname>fann_get_activation_function_hidden</refname>
- <refpurpose>Get the activation function used in the hidden layers.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_activation_function_hidden</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the activation function used in the hidden layers.</para>
- <para>
- See <link linkend="api.sec.constants.activation" endterm="api.sec.constants.activation.title"/>
- for details on the activation functions.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_activation_function_hidden">
- <refnamediv>
- <refname>fann_set_activation_function_hidden</refname>
- <refpurpose>Set the activation function for the hidden layers.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type></type>
- <methodname>fann_set_activation_function_hidden</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>activation_function</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Set the activation function used in the hidden layers to
- <parameter>activation_function</parameter>.
- </para>
- <para>
- See <link linkend="api.sec.constants.activation" endterm="api.sec.constants.activation.title"/>
- for details on the activation functions.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_activation_function_output">
- <refnamediv>
- <refname>fann_get_activation_function_output</refname>
- <refpurpose>Get the activation function of the output layer.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_activation_function_output</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the activation function of the output layer.</para>
- <para>
- See <link linkend="api.sec.constants.activation" endterm="api.sec.constants.activation.title"/>
- for details on the activation functions.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_activation_function_output">
- <refnamediv>
- <refname>fann_set_activation_function_output</refname>
- <refpurpose>Set the activation function for the output layer.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_activation_function_output</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>activation_function</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Set the activation function of the output layer to
- <parameter>activation_function</parameter>.
- </para>
- <para>
- See <link linkend="api.sec.constants.activation" endterm="api.sec.constants.activation.title"/>
- for details on the activation functions.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_activation_steepness_hidden">
- <refnamediv>
- <refname>fann_get_activation_steepness_hidden</refname>
- <refpurpose>Retrieve the steepness of the activation function of the hidden layers.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>fann_type</type>
- <methodname>fann_get_activation_steepness_hidden</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the steepness of the activation function of the hidden layers.</para>
- <para>
- The steepness defaults to 0.5 and a larger steepness will make the slope of the
- activation function more steep, while a smaller steepness will make the slope less
- steep. A large steepness is well suited for classification problems while a small
- steepness is well suited for function approximation.
- </para>
- <para>This function appears in FANN >= 1.2.0. and replaces the <methodname>fann_get_activation_hidden_steepness</methodname> function from FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_activation_steepness_hidden">
- <refnamediv>
- <refname>fann_set_activation_steepness_hidden</refname>
- <refpurpose>Set the steepness of the activation function of the hidden layers.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_activation_steepness_hidden</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type</type>
- <parameter>steepness</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Set the steepness of the activation function of the hidden layers of
- <parameter>ann</parameter> to
- <parameter>steepness</parameter>.
- </para>
- <para>
- The steepness defaults to 0.5 and a larger steepness will make the slope of the
- activation function more steep, while a smaller steepness will make the slope less
- steep. A large steepness is well suited for classification problems while a small
- steepness is well suited for function approximation.
- </para>
- <para>This function appears in FANN >= 1.2.0. and replaces the <methodname>fann_set_activation_hidden_steepness</methodname> function from FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_activation_steepness_output">
- <refnamediv>
- <refname>fann_get_activation_steepness_output</refname>
- <refpurpose>Retrieve the steepness of the activation function of the output layer.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>fann_type</type>
- <methodname>fann_get_activation_steepness_output</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the steepness of the activation function of the hidden layers.</para>
- <para>
- The steepness defaults to 0.5 and a larger steepness will make the slope of the
- activation function more steep, while a smaller steepness will make the slope less
- steep. A large steepness is well suited for classification problems while a small
- steepness is well suited for function approximation.
- </para>
- <para>This function appears in FANN >= 1.2.0. and replaces the <methodname>fann_get_activation_output_steepness</methodname> function from FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_activation_steepness_output">
- <refnamediv>
- <refname>fann_set_activation_steepness_output</refname>
- <refpurpose>Set the steepness of the activation function of the output layer.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_activation_steepness_output</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type</type>
- <parameter>steepness</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Set the steepness of the activation function of the hidden layers of
- <parameter>ann</parameter> to <parameter>steepness</parameter>.
- </para>
- <para>
- The steepness defaults to 0.5 and a larger steepness will make the slope of the
- activation function more steep, while a smaller steepness will make the slope less
- steep. A large steepness is well suited for classification problems while a small
- steepness is well suited for function approximation.
- </para>
- <para>This function appears in FANN >= 1.2.0. and replaces the <methodname>fann_set_activation_output_steepness</methodname> function from FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_train_error_function">
- <refnamediv>
- <refname>fann_set_train_error_function</refname>
- <refpurpose>Sets the training error function to be used.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_train_error_function</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>train_error_function</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Set the training error function (as described in <link linkend="api.sec.constants.errorfunc">Training Error Functions</link>) of a network.</para>
- <para>
- The default training error function is <link linkend="api.sec.constants.errorfunc"><constant>FANN_ERRORFUNC_TANH</constant></link>.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_train_error_function">
- <refnamediv>
- <refname>fann_get_train_error_function</refname>
- <refpurpose>Gets the training error function to be used.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_train_error_function</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Get the training error function (as described in <link linkend="api.sec.constants.errorfunc">Training Error Functions</link>) of a network.</para>
- <para>
- The default training error function is <link linkend="api.sec.constants.errorfunc"><constant>FANN_ERRORFUNC_TANH</constant></link>.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_quickprop_decay">
- <refnamediv>
- <refname>fann_get_quickprop_decay</refname>
- <refpurpose>Get the decay parameter used by the quickprop training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_quickprop_decay</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The decay is a small negative valued number which is the factor that the weights
- should become smaller in each iteration. This is used to make sure that the
- weights do not become too high during training.
- </para>
- <para>
- The default value for this parameter is -0.0001.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_quickprop_decay">
- <refnamediv>
- <refname>fann_set_quickprop_decay</refname>
- <refpurpose>Set the decay parameter used by the quickprop training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_quickprop_decay</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>quickprop_decay</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The decay is a small negative valued number which is the factor that the weights
- should become smaller in each iteration. This is used to make sure that the
- weights do not become too high during training.
- </para>
- <para>
- The default value for this parameter is -0.0001.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_quickprop_mu">
- <refnamediv>
- <refname>fann_get_quickprop_mu</refname>
- <refpurpose>Get the mu factor used by quickprop training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_quickprop_mu</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The mu factor is used to increase and decrease the step-size during quickprop
- training. The mu factor should always be above 1, since it would otherwise
- decrease the step-size when it was suppose to increase it.
- </para>
- <para>
- The default value for this parameter is 1.75.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_quickprop_mu">
- <refnamediv>
- <refname>fann_set_quickprop_mu</refname>
- <refpurpose>Set the mu factor used by quickprop training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_quickprop_mu</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>quickprop_mu</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The mu factor is used to increase and decrease the step-size during quickprop
- training. The mu factor should always be above 1, since it would otherwise
- decrease the step-size when it was suppose to increase it.
- </para>
- <para>
- The default value for this parameter is 1.75.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_rprop_increase_factor">
- <refnamediv>
- <refname>fann_get_rprop_increase_factor</refname>
- <refpurpose>Get the increase factor used by RPROP training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_rprop_increase_factor</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The increase factor is a value larger than 1, which is used to increase the
- step-size during RPROP training.
- </para>
- <para>
- The default value for this parameter is 1.2.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_rprop_increase_factor">
- <refnamediv>
- <refname>fann_set_rprop_increase_factor</refname>
- <refpurpose>Get the increase factor used by RPROP training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_rprop_increase_factor</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>rprop_increase_factor</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The increase factor is a value larger than 1, which is used to increase the
- step-size during RPROP training.
- </para>
- <para>
- The default value for this parameter is 1.2.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_rprop_decrease_factor">
- <refnamediv>
- <refname>fann_get_rprop_decrease_factor</refname>
- <refpurpose>Get the decrease factor used by RPROP training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_rprop_decrease_factor</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The increase factor is a value smaller than 1, which is used to decrease the
- step-size during RPROP training.
- </para>
- <para>
- The default value for this parameter is 0.5.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_rprop_decrease_factor">
- <refnamediv>
- <refname>fann_set_rprop_decrease_factor</refname>
- <refpurpose>Set the decrease factor used by RPROP training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_rprop_decrease_factor</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>rprop_decrease_factor</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The increase factor is a value smaller than 1, which is used to decrease the
- step-size during RPROP training.
- </para>
- <para>
- The default value for this parameter is 0.5.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_rprop_delta_min">
- <refnamediv>
- <refname>fann_get_rprop_delta_min</refname>
- <refpurpose>Get the minimum step-size used by RPROP training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_rprop_delta_min</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The minimum step-size is a small positive number determining how small the minimum step may be.
- </para>
- <para>
- The default value for this parameter is 0.0.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_rprop_delta_min">
- <refnamediv>
- <refname>fann_set_rprop_delta_min</refname>
- <refpurpose>Set the minimum step-size used by RPROP training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_rprop_delta_min</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>rprop_delta_min</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The minimum step-size is a small positive number determining how small the minimum step may be.
- </para>
- <para>
- The default value for this parameter is 0.0.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_rprop_delta_max">
- <refnamediv>
- <refname>fann_get_rprop_delta_max</refname>
- <refpurpose>Get the maximum step-size used by RPROP training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_rprop_delta_max</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The maximum step-size is a small positive number determining how small the minimum step may be.
- </para>
- <para>
- The default value for this parameter is 50.0.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_rprop_delta_max">
- <refnamediv>
- <refname>fann_set_rprop_delta_max</refname>
- <refpurpose>Set the maximum step-size used by RPROP training.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_rprop_delta_max</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>float</type>
- <parameter>rprop_delta_max</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- The maximum step-size is a small positive number determining how small the minimum step may be.
- </para>
- <para>
- The default value for this parameter is 50.0.
- </para>
- <para>This function appears in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_num_input">
- <refnamediv>
- <refname>fann_get_num_input</refname>
- <refpurpose>Get the number of neurons in the input layer.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_num_input</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the number of neurons in the input layer of
- <parameter>ann</parameter>.</para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_num_output">
- <refnamediv>
- <refname>fann_get_num_output</refname>
- <refpurpose>Get number of neurons in the output layer.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_num_output</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Return the number of neurons in the output layer of
- <parameter>ann</parameter>.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_total_neurons">
- <refnamediv>
- <refname>fann_get_total_neurons</refname>
- <refpurpose>Get the total number of neurons in a network.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_total_neurons</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Return the total number of neurons in
- <parameter>ann</parameter>. This number includes the bias neurons.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_total_connections">
- <refnamediv>
- <refname>fann_get_total_connections</refname>
- <refpurpose>Get the total number of connections in a network.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_total_connections</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Return the total number of connections in <parameter>ann</parameter>.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_decimal_point">
- <refnamediv>
- <refname>fann_get_decimal_point</refname>
- <refpurpose>Get the position of the decimal point.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_decimal_point</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Return the position of the decimal point in <parameter>ann</parameter>.
- </para>
- <para>
- This function is only available when the ANN is in fixed point mode.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_multiplier">
- <refnamediv>
- <refname>fann_get_multiplier</refname>
- <refpurpose>Get the multiplier.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type></type>
- <methodname>fann_get_multiplier</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Return the multiplier that fix point data in <parameter>ann</parameter>is multiplied with.
- </para>
- <para>
- This function is only available when the ANN is in fixed point mode.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.errors">
- <title id="api.sec.errors.title">Error Handling</title>
- <refentry id="api.fann_get_errno">
- <refnamediv>
- <refname>fann_get_errno</refname>
- <refpurpose>Return the numerical representation of the last error.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>unsigned int</type>
- <methodname>fann_get_errno</methodname>
- <methodparam>
- <type>struct fann_error *</type>
- <parameter>errdat</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Returns the numerical representation of the last error. The error codes are defined in
- <filename>fann_errno.h</filename>.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_errstr">
- <refnamediv>
- <refname>fann_get_errstr</refname>
- <refpurpose>Return the last error.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>char *</type>
- <methodname>fann_get_errstr</methodname>
- <methodparam>
- <type>struct fann_error *</type>
- <parameter>errdat</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Returns the last error.</para>
- <para>
- Note: This will reset the network's error- any subsequent calls to <function>fann_get_errno</function> or
- <function>fann_get_errstr</function> will yield 0 and NULL, respectively.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_reset_errno">
- <refnamediv>
- <refname>fann_reset_errno</refname>
- <refpurpose>Reset the last error number.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_reset_errno</methodname>
- <methodparam>
- <type>struct fann_error *</type>
- <parameter>errdat</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Reset the last error number.</para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_reset_errstr">
- <refnamediv>
- <refname>fann_reset_errstr</refname>
- <refpurpose>Reset the last error string.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_reset_errstr</methodname>
- <methodparam>
- <type>struct fann_error *</type>
- <parameter>errdat</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Reset the last error string.</para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_error_log">
- <refnamediv>
- <refname>fann_set_error_log</refname>
- <refpurpose>Set the error log to a file descriptor.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_error_log</methodname>
- <methodparam>
- <type>struct fann_error *</type>
- <parameter>errdat</parameter>
- </methodparam>
- <methodparam>
- <type>FILE *</type>
- <parameter>log</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Set the error log to <parameter>log</parameter>.
- </para>
- <para>The error log defaults to stderr.</para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_print_error">
- <refnamediv>
- <refname>fann_print_error</refname>
- <refpurpose>Print the last error to the error log.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_print_error_log</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Prints the network's last error to the error log.</para>
- <para>The error log defaults to stderr.</para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.struct">
- <title id="api.sec.struct.title">Data Structures</title>
- <refentry id="api.struct.fann">
- <refnamediv>
- <refname>struct fann</refname>
- <refpurpose>Describes a neural network.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- This structure is subject to change at any time. If you need to use the values contained herein, please
- see the <link linkend="api.sec.options">Options</link> functions. If these functions do not fulfill your
- needs, please open a feature request on our SourceForge
- <ulink url="http://www.sourceforge.net/projects/fann">project page</ulink>.
- </para>
- <variablelist>
- <title>Properties</title>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>errno_f</varname>
- </term>
- <listitem>
- <para>The type of error that last occurred.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>FILE *</type>
- <varname>error_log</varname>
- </term>
- <listitem>
- <para>Where to log error messages.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>char *</type>
- <varname>errstr</varname>
- </term>
- <listitem>
- <para>A string representation of the last error.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>learning_rate</varname>
- </term>
- <listitem>
- <para>The learning rate of the network.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>connection_rate</varname>
- </term>
- <listitem>
- <para>The connection rate of the network. Between 0 and 1, 1 meaning fully connected.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>shortcut_connections</varname>
- </term>
- <listitem>
- <para>
- Is 1 if shortcut connections are used in the ann otherwise 0
- Shortcut connections are connections that skip layers.
- A fully connected ann with shortcut connections is an ann where
- neurons have connections to all neurons in all later layers.
- </para>
- <para>
- ANNs with shortcut connections are created by <link linkend="api.fann_create_shortcut"><function>fann_create_shortcut</function></link>.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>struct fann_layer *</type>
- <varname>first_layer</varname>
- </term>
- <listitem>
- <para>
- Pointer to the first layer (input layer) in an array of all the layers, including the input and
- output layer.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>struct fann_layer *</type>
- <varname>last_layer</varname>
- </term>
- <listitem>
- <para>
- Pointer to the layer past the last layer in an array of all the layers, including the input and
- output layer.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>total_neurons</varname>
- </term>
- <listitem>
- <para>
- Total number of neurons. Very useful, because the actual neurons are allocated in one long
- array.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>num_input</varname>
- </term>
- <listitem>
- <para>Number of input neurons (not calculating bias)</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>num_output</varname>
- </term>
- <listitem>
- <para>Number of output neurons (not calculating bias)</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>train_errors</varname>
- </term>
- <listitem>
- <para>
- Used to contain the error deltas used during training Is allocated during first training session,
- which means that if we do not train, it is never allocated.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>activation_function_output</varname>
- </term>
- <listitem>
- <para>Used to choose which activation function to use in the output layer.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>activation_function_hidden</varname>
- </term>
- <listitem>
- <para>Used to choose which activation function to use in the hidden layers.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>activation_steepness_hidden</varname>
- </term>
- <listitem>
- <para>Parameters for the activation function in the hidden layers.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>activation_steepness_output</varname>
- </term>
- <listitem>
- <para>Parameters for the activation function in the output layer.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>training_algorithm</varname>
- </term>
- <listitem>
- <para>
- Training algorithm used when calling fann_train_on_... and <link linkend="api.fann_train_epoch"><function>fann_train_epoch</function></link>.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>decimal point</varname>
- </term>
- <listitem>
- <para>
- <emphasis>Fixed point only.</emphasis> The decimal point, used for shifting the fix point in fixed point
- integer operations.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>multiplier</varname>
- </term>
- <listitem>
- <para>
- <emphasis>Fixed point only.</emphasis> The multiplier, used for multiplying the fix point in fixed point
- integer operations. Only used in special cases, since the decimal_point is much faster.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>activation_results_hidden</varname>
- </term>
- <listitem>
- <para>
- An array of six members used by some activation functions to hold results for the hidden
- layer(s).
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>activation_values_hidden</varname>
- </term>
- <listitem>
- <para>
- An array of six members used by some activation functions to hold values for the hidden
- layer(s).
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>activation_results_output</varname>
- </term>
- <listitem>
- <para>
- An array of six members used by some activation functions to hold results for the output
- layer.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>activation_values_output</varname>
- </term>
- <listitem>
- <para>
- An array of six members used by some activation functions to hold values for the output
- layer.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>total_connections</varname>
- </term>
- <listitem>
- <para>
- Total number of connections. Very useful, because the actual connections are allocated in one
- long array.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>output</varname>
- </term>
- <listitem>
- <para>Used to store outputs in.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>num_MSE</varname>
- </term>
- <listitem>
- <para>The number of data used to calculate the mean square error.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>MSE_value</varname>
- </term>
- <listitem>
- <para>The total error value. The real mean square error is MSE_value/num_MSE.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>train_error_function</varname>
- </term>
- <listitem>
- <para>When using this, training is usually faster.
- Makes the error used for calculating the slopes
- higher when the difference is higher.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>quickprop_decay</varname>
- </term>
- <listitem>
- <para>Decay is used to make the weights not go so high.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>quickprop_mu</varname>
- </term>
- <listitem>
- <para>Mu is a factor used to increase and decrease the step-size.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>rprop_increase_factor</varname>
- </term>
- <listitem>
- <para>Tells how much the step-size should increase during learning.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>rprop_decrease_factor</varname>
- </term>
- <listitem>
- <para>Tells how much the step-size should decrease during learning.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>rprop_delta_min</varname>
- </term>
- <listitem>
- <para>The minimum step-size.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>float</type>
- <varname>rprop_delta_max</varname>
- </term>
- <listitem>
- <para>The maximum step-size.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>train_slopes</varname>
- </term>
- <listitem>
- <para>
- Used to contain the slope errors used during batch training
- Is allocated during first training session,
- which means that if we do not train, it is never allocated.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>prev_steps</varname>
- </term>
- <listitem>
- <para>
- The previous step taken by the quickprop/rprop procedures.
- Not allocated if not used.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>prev_train_slopes</varname>
- </term>
- <listitem>
- <para>
- The slope values used by the quickprop/rprop procedures.
- Not allocated if not used.
- </para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- <refentry id="api.struct.fann_train_data">
- <refnamediv>
- <refname>struct fann_train_data</refname>
- <refpurpose>Describes a set of training data.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- This structure is subject to change at any time. If you need to use the values contained herein, please
- see the <link linkend="api.sec.train_data">Training Data</link> functions. If these functions do not
- fulfill your needs, please open a feature request on our SourceForge
- <ulink url="http://www.sourceforge.net/projects/fann">project page</ulink>.
- </para>
- <variablelist>
- <title>Properties</title>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>errno_f</varname>
- </term>
- <listitem>
- <para>The type of error that last occurred.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>FILE *</type>
- <varname>error_log</varname>
- </term>
- <listitem>
- <para>Where to log error messages.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>char *</type>
- <varname>errstr</varname>
- </term>
- <listitem>
- <para>A string representation of the last error.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>num_data</varname>
- </term>
- <listitem>
- <para>The number of sets of data in the array.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>num_input</varname>
- </term>
- <listitem>
- <para>The number of inputs per set of data.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>num_output</varname>
- </term>
- <listitem>
- <para>The number of outputs per set of data.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type **</type>
- <varname>input</varname>
- </term>
- <listitem>
- <para>
- An array of <varname>num_data</varname> elements, each of which contain an array of
- <varname>num_input</varname> elements, which represent every item of input data.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type **</type>
- <varname>input</varname>
- </term>
- <listitem>
- <para>
- An array of <varname>num_data</varname> elements, each of which contain an array of
- <varname>num_output</varname> elements, which represent every item of output data.
- </para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- <refentry id="api.struct.fann_error">
- <refnamediv>
- <refname>struct fann_error</refname>
- <refpurpose>Describes an error.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- This structure is subject to change at any time. If you need to use the values contained herein, please
- see the <link linkend="api.sec.errors">Error Handling</link> functions. If these functions do not
- fulfill your needs, please open a feature request on our SourceForge
- <ulink url="http://www.sourceforge.net/projects/fann">project page</ulink>.
- </para>
- <para>
- You may notice that this structure is identical to the first three properties of the
- <link linkend="api.struct.fann"><type>fann</type></link> and
- <link linkend="api.struct.fann_train_data"><type>fann_train_data</type></link> structures. This is so you can cast
- each of those structures to <type>struct fann_error *</type> when calling the
- <link linkend="api.sec.errors">Error Handling</link> functions.
- </para>
- <variablelist>
- <title>Properties</title>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>errno_f</varname>
- </term>
- <listitem>
- <para>The type of error that last occurred.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>FILE *</type>
- <varname>error_log</varname>
- </term>
- <listitem>
- <para>Where to log error messages.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>char *</type>
- <varname>errstr</varname>
- </term>
- <listitem>
- <para>A string representation of the last error.</para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- <refentry id="api.struct.fann_neuron">
- <refnamediv>
- <refname>struct fann_neuron</refname>
- <refpurpose>Describes an individual neuron.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- This structure is subject to change at any time. If you require direct
- access to the contents of this structure, you may want to consider contacting
- the <ulink url="mailto:fann-general at lists.sourceforge.net">FANN development
- team</ulink>.
- </para>
- <variablelist>
- <title>Properties</title>
- <varlistentry>
- <term>
- <type>fann_type *</type>
- <varname>weights</varname>
- </term>
- <listitem>
- <para>This property is not yet documented.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>struct fann_neuron **</type>
- <varname>connected_neurons</varname>
- </term>
- <listitem>
- <para>This property is not yet documented.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>unsigned int</type>
- <varname>num_connections</varname>
- </term>
- <listitem>
- <para>This property is not yet documented.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>fann_type</type>
- <varname>value</varname>
- </term>
- <listitem>
- <para>This property is not yet documented.</para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- <refentry id="api.struct.fann_layer">
- <refnamediv>
- <refname>struct fann_layer</refname>
- <refpurpose>Describes a layer in a network.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- This structure is subject to change at any time. If you require direct
- access to the contents of this structure, you may want to consider contacting
- the <ulink url="mailto:fann-general at lists.sourceforge.net">FANN development
- team</ulink>.
- </para>
- <variablelist>
- <title>Properties</title>
- <varlistentry>
- <term>
- <type>struct fann_neuron *</type>
- <varname>first_neuron</varname>
- </term>
- <listitem>
- <para>
- A pointer to the first neuron in the layer. When allocated, all the
- neurons in all the layers are actually in one long array, this is
- because we want to easily clear all the neurons at once.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>
- <type>struct fann_neuron *</type>
- <varname>last_neuron</varname>
- </term>
- <listitem>
- <para>
- A pointer to the neuron past the last neuron in the layer
- the number of neurons is <varname>last_neuron</varname>
- - <varname>first_neuron</varname>
- </para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.constants">
- <title id="api.sec.constants.title">Constants</title>
-
- <refentry id="api.sec.constants.training">
- <refnamediv>
- <refname id="api.sec.constants.training.title">Training algorithms</refname>
- <refpurpose>Constants representing training algorithms.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- These constants represent the training algorithms available within the fann library.
- The list will grow over time, but probably not shrink.
- </para>
- <para>
- The training algorithm used by this function is chosen by the
- <link linkend="api.fann_set_training_algorithm"><function>fann_set_training_algorithm</function></link>
- function. The default training algorithm is <constant>FANN_TRAIN_RPROP</constant>.
- </para>
- <variablelist>
- <title>Constants</title>
- <varlistentry>
- <term>FANN_TRAIN_INCREMENTAL</term>
- <listitem>
- <para>
- Standard backpropagation algorithm, where the weights are updated after each training
- pattern. This means that the weights are updated many times during a single epoch.
- For this reason some problems, will train very fast with this algorithm, while other more
- advanced problems will not train very well.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_TRAIN_BATCH</term>
- <listitem>
- <para>
- Standard backpropagation algorithm, where the weights are updated after calculating
- the mean square error for the whole training set. This means that the weights are only updated
- once during a epoch. For this reason some problems, will train slower with this algorithm.
- But since the mean square error is calculated more correctly than in incremental training,
- some problems will reach a better solutions with this algorithm.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_TRAIN_RPROP</term>
- <listitem>
- <para>
- A more advanced batch training algorithm which achieves good results for many problems.
- The RPROP training algorithm is adaptive, and does therefore not use the learning_rate.
- Some other parameters can however be set to change the way the RPROP algorithm works,
- but it is only recommended for users with insight in how the RPROP training algorithm works.
- </para>
- <para>
- The RPROP training algorithm is described in
- [<xref linkend="bib.riedmiller_1993" endterm="bib.riedmiller_1993.abbrev" />], but the
- actual learning algorithm used here is the iRPROP- training algorithm
- [<xref linkend="bib.igel_2000" endterm="bib.igel_2000.abbrev" />] which is an variety
- of the standard RPROP training algorithm.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_TRAIN_QUICKPROP</term>
- <listitem>
- <para>
- A more advanced batch training algorithm which achieves good results for many problems.
- The quickprop training algorithm uses the learning_rate parameter along with other more
- advanced parameters, but it is only recommended to change these advanced parameters, for
- users with insight in how the quickprop training algorithm works.
- </para>
- <para>
- The quickprop training algorithm is described in [<xref linkend="bib.fahlman_1988" endterm="bib.fahlman_1988.abbrev" />].
- </para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- <refentry id="api.sec.constants.activation">
- <refnamediv>
- <refname id="api.sec.constants.activation.title">Activation Functions</refname>
- <refpurpose>Constants representing activation functions.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- These constants represent the activation functions available within the fann library.
- The list will grow over time, but probably not shrink.
- </para>
- <variablelist>
- <title>Constants</title>
- <varlistentry>
- <term>FANN_THRESHOLD</term>
- <listitem>
- <para>
- <emphasis>Execution only</emphasis> - Threshold activation function.
- </para>
- <para> This activation function gives output that is either 0 or 1.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_THRESHOLD_SYMMETRIC</term>
- <listitem>
- <para>
- <emphasis>Execution only</emphasis> - Threshold activation function.
- </para>
- <para> This activation function gives output that is either -1 or 1.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_LINEAR</term>
- <listitem>
- <para>
- <emphasis>Can not be used in fixed point</emphasis> - Linear activation function.
- </para>
- <para> This activation function gives output that is unbounded.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_SIGMOID</term>
- <listitem>
- <para>
- Sigmoid activation function. One of the most used activation functions.
- </para>
- <para> This activation function gives output that is between 0 and 1.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_SIGMOID_STEPWISE</term>
- <listitem>
- <para>
- Stepwise linear approximation to sigmoid. Faster than sigmoid but a bit less precise.
- </para>
- <para> This activation function gives output that is between 0 and 1.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_SIGMOID_SYMMETRIC</term>
- <listitem>
- <para>
- Symmetric sigmoid activation function, AKA tanh. One of the most used activation functions.
- </para>
- <para> This activation function gives output that is between -1 and 1.</para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_SIGMOID_SYMMETRIC_STEPWISE</term>
- <listitem>
- <para>
- Stepwise linear approximation to symmetric sigmoid. Faster than symmetric sigmoid but a bit less precise.
- </para>
- <para> This activation function gives output that is between -1 and 1.</para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- <refentry id="api.sec.constants.errorfunc">
- <refnamediv>
- <refname id="api.sec.constants.errorfunc.title">Training Error Functions</refname>
- <refpurpose>Constants representing errors functions.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- These constants represent the error functions used when calculating the error during training.
- </para>
- <para>
- The training error function used is chosen by the
- <link linkend="api.fann_set_train_error_function"><function>fann_set_train_error_function</function></link>
- function. The default training error function is <constant>FANN_ERRORFUNC_TANH</constant>.
- </para>
- <variablelist>
- <title>Constants</title>
- <varlistentry>
- <term>FANN_ERRORFUNC_LINEAR</term>
- <listitem>
- <para>
- The basic linear error function which simply calculates the error as the difference
- between the real output and the desired output.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_ERRORFUNC_TANH</term>
- <listitem>
- <para>
- The tanh error function is an error function that makes large deviations
- stand out, by altering the error value used when training the network.
- The idea behind this is that it is worse to have 1 output that misses the target
- by 100%, than having 10 outputs that misses the target by 10%.
- </para>
- <para>
- This is the default error function and it is usually better. It can however
- give poor results with high learning rates.
- </para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- <refentry id="api.sec.constants.error">
- <refnamediv>
- <refname id="api.sec.constants.error.title">Error Codes</refname>
- <refpurpose>Constants representing errors.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <para>
- These constants represent the various errors possible in fann, as
- defined by <filename>fann_errno.h</filename>.
- </para>
- <variablelist>
- <title>Constants</title>
- <varlistentry>
- <term>FANN_E_NO_ERROR</term>
- <listitem>
- <para>
- No error.
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_OPEN_CONFIG_R</term>
- <listitem>
- <para>
- Unable to open configuration file for reading
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_OPEN_CONFIG_W</term>
- <listitem>
- <para>
- Unable to open configuration file for writing
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_WRONG_CONFIG_VERSION</term>
- <listitem>
- <para>
- Wrong version of configuration file
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_READ_CONFIG</term>
- <listitem>
- <para>
- Error reading info from configuration file
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_READ_NEURON</term>
- <listitem>
- <para>
- Error reading neuron info from configuration file
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_READ_CONNECTIONS</term>
- <listitem>
- <para>
- Error reading connections from configuration file
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_WRONG_NUM_CONNECTIONS</term>
- <listitem>
- <para>
- Number of connections not equal to the number expected
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_OPEN_TD_W</term>
- <listitem>
- <para>
- Unable to open train data file for writing
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_OPEN_TD_R</term>
- <listitem>
- <para>
- Unable to open train data file for reading
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_READ_TD</term>
- <listitem>
- <para>
- Error reading training data from file
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_ALLOCATE_MEM</term>
- <listitem>
- <para>
- Unable to allocate memory
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_TRAIN_ACTIVATION</term>
- <listitem>
- <para>
- Unable to train with the selected activation function
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_CANT_USE_ACTIVATION</term>
- <listitem>
- <para>
- Unable to use the selected activation function
- </para>
- </listitem>
- </varlistentry>
- <varlistentry>
- <term>FANN_E_TRAIN_DATA_MISMATCH</term>
- <listitem>
- <para>
- Irreconcilable differences between two fann_train_data structures
- </para>
- </listitem>
- </varlistentry>
- </variablelist>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.internal">
- <title id="api.sec.internal.title">Internal Functions</title>
- <section id="api.sec.create_destroy.internal">
- <title id="api.sec.create_destroy.internal.title">Creation And Destruction</title>
- <refentry id="api.fann_allocate_structure">
- <refnamediv>
- <refname>fann_allocate_structure</refname>
- <refpurpose>Allocate the core elements of a
- <type>struct fann</type>.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann *</type>
- <methodname>fann_allocate_structure</methodname>
- <methodparam>
- <type>float</type>
- <parameter>learning_rate</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>num_layers</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_allocate_structure</function>is used internally to create a
- <type>struct fann</type>.</para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.io.internal">
- <title id="api.sec.io.internal.title">Input/Output</title>
- <refentry id="api.fann_save_internal">
- <refnamediv>
- <refname>fann_save_internal</refname>
- <refpurpose>Save an ANN to a file.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>int</type>
- <methodname>fann_save_internal</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>const char *</type>
- <parameter>configuration_file</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>save_as_fixed</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_save_internal_fd</function> is used internally to save an ANN to a file.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_save_internal_fd">
- <refnamediv>
- <refname>fann_save_internal_fd</refname>
- <refpurpose>Save an ANN to a file descriptor.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>int</type>
- <methodname>fann_save_internal_fd</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>FILE *</type>
- <parameter>conf</parameter>
- </methodparam>
- <methodparam>
- <type>const char *</type>
- <parameter>configuration_file</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>save_as_fixed</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_save_internal_fd</function> is used internally to save an ANN to a location pointed to by
- <parameter>conf</parameter>. <parameter>configuration_file</parameter> is the name of the file, used only
- for debugging purposes.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_create_from_fd">
- <refnamediv>
- <refname>fann_create_from_fd</refname>
- <refpurpose>Load an ANN from a file descriptor.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann *</type>
- <methodname>fann_create_from_fd</methodname>
- <methodparam>
- <type>FILE *</type>
- <parameter>conf</parameter>
- </methodparam>
- <methodparam>
- <type>const char *</type>
- <parameter>configuration_file</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_create_from_fd</function> will load an ANN from a file descriptor.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.train_data.internal">
- <title id="api.sec.train_data.internal.title">Training Data</title>
- <refentry id="api.fann_save_train_internal">
- <refnamediv>
- <refname>fann_save_train_internal</refname>
- <refpurpose>Save training data to a file.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_save_train_internal</methodname>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data</parameter>
- </methodparam>
- <methodparam>
- <type>char *</type>
- <parameter>filename</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>save_as_fixed</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>decimal_point</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Saves the data in <parameter>data</parameter> to <parameter>filename</parameter>.
- <parameter>save_as_fixed</parameter> is either TRUE or FALSE. <parameter>decimal_point</parameter> tells
- FANN where the decimal point may be if using fixed point math.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_save_train_internal_fd">
- <refnamediv>
- <refname>fann_save_train_internal_fd</refname>
- <refpurpose>Save training data to a file descriptor.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_save_train_internal_fd</methodname>
- <methodparam>
- <type>struct fann_train_data *</type>
- <parameter>data</parameter>
- </methodparam>
- <methodparam>
- <type>FILE *</type>
- <parameter>file</parameter>
- </methodparam>
- <methodparam>
- <type>char *</type>
- <parameter>filename</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>save_as_fixed</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>decimal_point</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Saves the data in <parameter>data</parameter> to <parameter>file</parameter>.
- <parameter>save_as_fixed</parameter> is either TRUE or FALSE. <parameter>decimal_point</parameter> tells
- FANN where the decimal point may be if using fixed point math.
- </para>
- <para>
- <parameter>filename</parameter> is used for debugging output only.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_read_train_from_fd">
- <refnamediv>
- <refname>fann_read_train_from_fd</refname>
- <refpurpose>Read training data from a file descriptor.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>struct fann_train_data *</type>
- <methodname>fann_read_train_from_file</methodname>
- <methodparam>
- <type>FILE *</type>
- <parameter>file</parameter>
- </methodparam>
- <methodparam>
- <type>char *</type>
- <parameter>filename</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- <function>fann_read_train_from_file</function> will load training data from the file descriptor
- <parameter>file</parameter>.
- </para>
- <para>
- <parameter>filename</parameter> is used for debugging output only.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.io.errors">
- <title id="api.sec.io.errors.title">Error Handling</title>
- <refentry id="api.fann_error">
- <refnamediv>
- <refname>fann_error</refname>
- <refpurpose>Throw an internal error.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_error</methodname>
- <methodparam>
- <type>struct fann_error *</type>
- <parameter>errdat</parameter>
- </methodparam>
- <methodparam>
- <type>unsigned int</type>
- <parameter>errno</parameter>
- </methodparam>
- <methodparam>
- <parameter>...</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- This will set the network's error to correspond to <parameter>errno</parameter>. The variable arguments
- depend (both in type and quantity) on <parameter>errno</parameter>. Possible <parameter>errno</parameter>
- values are defined in <filename>fann_errno.h</filename>.
- </para>
- <para>This function appears in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.options.internal">
- <title id="api.sec.options.internal.title">Options</title>
- <refentry id="api.fann_update_stepwise_hidden">
- <refnamediv>
- <refname>fann_update_stepwise_hidden</refname>
- <refpurpose>Adjust the stepwise function in the hidden layers.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_update_stepwise_hidden</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Update the stepwise function in the hidden layers of <parameter>ann</parameter>.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_update_stepwise_output">
- <refnamediv>
- <refname>fann_update_stepwise_output</refname>
- <refpurpose>Adjust the stepwise functions in the output layer.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_update_stepwise_output</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Update the stepwise function in the output layer of <parameter>ann</parameter>.
- </para>
- <para>This function appears in FANN >= 1.0.0.</para>
- </refsect1>
- </refentry>
- </section>
- </section>
- <section id="api.sec.deprecated">
- <title id="api.sec.deprecated.title">Deprecated Functions</title>
- <section id="api.sec.error.deprecated">
- <title id="api.sec.error.deprecated.title">Mean Square Error</title>
- <refentry id="api.fann_get_error">
- <refnamediv>
- <refname>fann_get_error</refname>
- <refpurpose>Return the mean square error of an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>float</type>
- <methodname>fann_get_error</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- This function is deprecated and will be removed in a future version. Use
- <link linkend="api.fann_get_MSE"><function>fann_get_MSE</function></link> instead.
- </para>
- <para>This function appears in FANN >= 1.0.0, but is deprecated in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_reset_error">
- <refnamediv>
- <refname>fann_reset_error</refname>
- <refpurpose>Reset the mean square error of an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_reset_error</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- This function is deprecated and will be removed in a future version. Use
- <link linkend="api.fann_reset_MSE"><function>fann_reset_MSE</function></link> instead.
- </para>
- <para>This function appears in FANN >= 1.0.0, but is deprecated in FANN >= 1.1.0.</para>
- </refsect1>
- </refentry>
- </section>
- <section id="api.sec.steepness.deprecated">
- <title id="api.sec.steepness.deprecated.title">Get and set activation function steepness.</title>
- <refentry id="api.fann_get_activation_hidden_steepness">
- <refnamediv>
- <refname>fann_get_activation_hidden_steepness</refname>
- <refpurpose>Retrieve the steepness of the activation function of the hidden layers.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>fann_type</type>
- <methodname>fann_get_activation_hidden_steepness</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the steepness of the activation function of the hidden layers.</para>
- <para>
- The steepness defaults to 0.5 and a larger steepness will make the slope of the
- activation function more steep, while a smaller steepness will make the slope less
- steep. A large steepness is well suited for classification problems while a small
- steepness is well suited for function approximation.
- </para>
- <para>
- This function is deprecated and will be removed in a future version. Use
- <link linkend="api.fann_get_activation_steepness_hidden"><function>fann_get_activation_steepness_hidden</function></link> instead.
- </para>
- <para>This function appears in FANN >= 1.0.0. and is deprecated in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_activation_hidden_steepness">
- <refnamediv>
- <refname>fann_set_activation_hidden_steepness</refname>
- <refpurpose>Set the steepness of the activation function of the hidden layers.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_activation_hidden_steepness</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type</type>
- <parameter>steepness</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Set the steepness of the activation function of the hidden layers of
- <parameter>ann</parameter> to
- <parameter>steepness</parameter>.
- </para>
- <para>
- The steepness defaults to 0.5 and a larger steepness will make the slope of the
- activation function more steep, while a smaller steepness will make the slope less
- steep. A large steepness is well suited for classification problems while a small
- steepness is well suited for function approximation.
- </para>
- <para>
- This function is deprecated and will be removed in a future version. Use
- <link linkend="api.fann_set_activation_steepness_hidden"><function>fann_set_activation_steepness_hidden</function></link> instead.
- </para>
- <para>This function appears in FANN >= 1.0.0. and is deprecated in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_get_activation_output_steepness">
- <refnamediv>
- <refname>fann_get_activation_output_steepness</refname>
- <refpurpose>Retrieve the steepness of the activation function of the output layer.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>fann_type</type>
- <methodname>fann_get_activation_output_steepness</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- </methodsynopsis>
- <para>Return the steepness of the activation function of the output layer.</para>
- <para>
- The steepness defaults to 0.5 and a larger steepness will make the slope of the
- activation function more steep, while a smaller steepness will make the slope less
- steep. A large steepness is well suited for classification problems while a small
- steepness is well suited for function approximation.
- </para>
- <para>
- This function is deprecated and will be removed in a future version. Use
- <link linkend="api.fann_get_activation_steepness_output"><function>fann_get_activation_steepness_output</function></link> instead.
- </para>
- <para>This function appears in FANN >= 1.0.0. and is deprecated in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- <refentry id="api.fann_set_activation_output_steepness">
- <refnamediv>
- <refname>fann_set_activation_output_steepness</refname>
- <refpurpose>Set the steepness of the activation function of the hidden layers.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
- <methodsynopsis>
- <type>void</type>
- <methodname>fann_set_activation_output_steepness</methodname>
- <methodparam>
- <type>struct fann *</type>
- <parameter>ann</parameter>
- </methodparam>
- <methodparam>
- <type>fann_type</type>
- <parameter>steepness</parameter>
- </methodparam>
- </methodsynopsis>
- <para>
- Set the steepness of the activation function of the hidden layers of
- <parameter>ann</parameter> to <parameter>steepness</parameter>.
- </para>
- <para>
- The steepness defaults to 0.5 and a larger steepness will make the slope of the
- activation function more steep, while a smaller steepness will make the slope less
- steep. A large steepness is well suited for classification problems while a small
- steepness is well suited for function approximation.
- </para>
- <para>
- This function is deprecated and will be removed in a future version. Use
- <link linkend="api.fann_set_activation_steepness_output"><function>fann_set_activation_steepness_output</function></link> instead.
- </para>
- <para>This function appears in FANN >= 1.0.0. and is deprecated in FANN >= 1.2.0.</para>
- </refsect1>
- </refentry>
- </section>
- </section>
- </chapter>
<chapter id="php">
<title id="php.title">PHP Extension</title>
<para>These functions allow you to interact with the FANN library from PHP.</para>
@@ -4140,7 +25,7 @@ L 2 / N 6 ...cda
<ulink url="http://fann.sf.net/">FANN</ulink> library, version 1.1.0 or later.</para>
<para>
This extension supports the same activation functions as the library, a list of which can
- be found in the <link linkend="api.sec.constants.activation" endterm="api.sec.constants.activation.title"/> section.
+ be found in the reference manual for the C library.
</para>
<section id="php.install">
<title id="php.instal.title">Installation</title>
@@ -4466,7 +351,6 @@ else
<parameter>ann</parameter>, effectively resetting the network.
</para>
<para>
- See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
<link linkend="function.fann_init_weights"><function>fann_init_weights</function></link>
</para>
<para>This function appears in FANN-PHP >= 0.1.0.</para>
@@ -4501,7 +385,6 @@ else
argument, <parameter>data</parameter>, which is the training data that will be used to train the network.
</para>
<para>
- See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
<link linkend="function.fann_randomize_weights"><function>fann_randomize_weights</function></link>
</para>
<para>This function appears in FANN-PHP >= 0.1.0.</para>
@@ -4897,268 +780,8 @@ else
</refentry>
</section>
</chapter>
- <chapter id="python">
- <title id="python.title">Python Bindings</title>
- <para>These functions allow you to interact with the FANN library from Python.</para>
- <para>This extension requires the
- <ulink url="http://fann.sf.net/">FANN</ulink> library, version 1.1.0 or later.</para>
- <para>This python binding is provided by Vincenzo Di Massa (hawk.it at tiscalinet.it) and updated by Gil Megidish (gil at megidish.net)</para>
- <section id="python.install">
- <title id="python.install.title">Python Install</title>
- <para>
- Make sure to make and install the fann library first.
- Make sure that you have swig and python development files installed.
- Perhaps change the include directory of python.
- Then run 'make' to compile in the python directory.
- </para>
- <para>
-Copy the generated _fann.so and fann.py files to python modules or into working directory.
- </para>
- <para>
-After the install, just import fann and all the C functions will be available to your python code.
- </para>
- </section>
- </chapter>
- <chapter id="delphi">
- <title id="delphi.title">Delphi Bindings</title>
- <para>These functions allow you to interact with the FANN library from Delphi.</para>
- <para>This extension requires the
- <ulink url="http://fann.sf.net/">FANN</ulink> library, version 1.2.0 or later.</para>
- <para>This extension can be downloaded from the
- <ulink url="http://fann.sf.net/">FANN</ulink> library download section.</para>
- <para>This Delphi binding is provided by Maur�cio Pereira Maia (mauricio at uaisol.com.br)</para>
- <section id="delphi.install">
- <title id="delphi.install.title">Delphi Install</title>
- <para>
- Make sure to make and install the fann library first.
-
- Put the file fannfloat.dll in your PATH. (If you want to use the fixed version you should define FIXEDFANN on fann.pas).
- Include fann.pas in your project and in your unit uses clause, and have fun!
- See the XorConsole sample for more details.
- </para>
- </section>
- <section id="delphi.tfann">
- <title id="delphi.tfann.title">TFannNetwork</title>
- <para>
-TFannNetwork is a Delphi component that encapsulates the Fann Library.
-You do not have to install TFannNetwork to use Fann on Delphi,
-but it will make the library more Delphi friendly.
-Currently it has only a small subset of all the library functions, but I
-hope that will change in the near future.
- </para>
- <para>
-To install TFannNetwork you should follow all the previous steps and copy the FannNetwork.pas and Fann.dcr to your Delphi Library PATH. Choose Component/Install Component. In the Unit file name field, click on Browse and point to the fannnetwork.pas file. By default Delphi will install in the Borland User Components package, it might be changed using Package file name field or Into new package page. Click on Ok. A confirmation dialog will be shown asking if you want to build the package. [...]
- </para>
- </section>
- <section id="delphi.problems">
- <title id="delphi.problems.title">Known Problems</title>
- <para>
-If you are getting in trouble to use your own compiled FANN DLL with Delphi that might be
-because of the C++ naming mangle that changes between C++ compilers.
-You will need to make a TDUMP on your dll and changes all the name directives on fann.pas to
-the correct function names on your dll.
- </para>
- </section>
- </chapter>
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- <abbrev id="bib.anderson_1995.abbrev">Anderson, 1995</abbrev>
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- </author>
- <pubdate>1995</pubdate>
- <title id="bib.anderson_1995.title">An Introduction to Neural Networks</title>
- <publishername>The MIT Press</publishername>
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- <abbrev id="bib.heller_2002.abbrev">Heller, 2002</abbrev>
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- </biblioentry>
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- <author>
- <firstname>Y.</firstname>
- <surname>LeCun</surname>
- </author>
- <author>
- <firstname>J.</firstname>
- <surname>Denker</surname>
- </author>
- <author>
- <firstname>S.</firstname>
- <surname>Solla</surname>
- </author>
- <author>
- <firstname>R.E.</firstname>
- <surname>Howard</surname>
- </author>
- <author>
- <firstname>L.D.</firstname>
- <surname>Jackel</surname>
- </author>
- <pubdate>1990</pubdate>
- <title id="bib.lecun_1990.title">Advances in Neural Information Processing Systems II</title>
- </biblioentry>
<biblioentry id="bib.nguyen_1990">
<abbrev id="bib.nguyen_1990.abbrev">Nguyen and Widrow, 1990</abbrev>
<title id="bib.nguyen_1990.title">Reinforcement Learning</title>
@@ -5178,215 +801,6 @@ the correct function names on your dll.
<ulink url="http://www.cs.montana.edu/~clemens/nguyen-widrow.pdf">http://www.cs.montana.edu/~clemens/nguyen-widrow.pdf</ulink>
</releaseinfo>
</biblioentry>
- <biblioentry id="bib.nissen_2003">
- <abbrev id="bib.nissen_2003.abbrev">Nissen et al., 2003</abbrev>
- <author>
- <firstname>S.</firstname>
- <surname>Nissen</surname>
- </author>
- <author>
- <firstname>J.</firstname>
- <surname>Damkjær</surname>
- </author>
- <author>
- <firstname>J.</firstname>
- <surname>Hansson</surname>
- </author>
- <author>
- <firstname>S.</firstname>
- <surname>Larsen</surname>
- </author>
- <author>
- <firstname>S.</firstname>
- <surname>Jensen</surname>
- </author>
- <pubdate>2003</pubdate>
- <title id="bib.nissen_2003.title">Real-time image processing of an ipaq based robot with fuzzy logic (fuzzy)</title>
- <releaseinfo>
- <ulink url="http://www.hamster.dk/~purple/robot/fuzzy/weblog/">
- http://www.hamster.dk/~purple/robot/fuzzy/weblog/</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.nissen_2002">
- <abbrev id="bib.nissen_2002.abbrev">Nissen et al., 2002</abbrev>
- <author>
- <firstname>S.</firstname>
- <surname>Nissen</surname>
- </author>
- <author>
- <firstname>S.</firstname>
- <surname>Larsen</surname>
- </author>
- <author>
- <firstname>S.</firstname>
- <surname>Jensen</surname>
- </author>
- <pubdate>2003</pubdate>
- <title id="bib.nissen_2002.title">Real-time image processing of an iPAQ based robot (iBOT)</title>
- <releaseinfo>
- <ulink url="http://www.hamster.dk/~purple/robot/iBOT/report.pdf">
- http://www.hamster.dk/~purple/robot/iBOT/report.pdf</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.OSDN_2003">
- <abbrev id="bib.OSDN_2003.abbrev">OSDN, 2003</abbrev>
- <pubdate>2003</pubdate>
- <title id="bib.OSDN_2003.title">SourceForge.net</title>
- <releaseinfo>
- <ulink url="http://sourceforge.net/">http://sourceforge.net/</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.pendleton_1993">
- <abbrev id="bib.pendleton_1993.abbrev">Pendleton, 1993</abbrev>
- <author>
- <firstname>R.C.</firstname>
- <surname>Pendleton</surname>
- </author>
- <pubdate>1993</pubdate>
- <title id="bib.pendleton_1993.title">Doing it Fast</title>
- <releaseinfo>
- <ulink url="http://www.gameprogrammer.com/4-fixed.html">http://www.gameprogrammer.com/4-fixed.html</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.prechelt_1994">
- <abbrev id="bib.prechelt_1994.abbrev">Prechelt, 1994</abbrev>
- <author>
- <firstname>L.</firstname>
- <surname>Prechelt</surname>
- </author>
- <pubdate>1994</pubdate>
- <title id="bib.prechelt_1994.title">Proben1</title>
- <subtitle>A set of neural network benchmark problems and benchmarking rules</subtitle>
- </biblioentry>
- <biblioentry id="bib.riedmiller_1993">
- <abbrev id="bib.riedmiller_1993.abbrev">Riedmiller and Braun, 1993</abbrev>
- <author>
- <firstname></firstname>
- <surname>Riedmiller</surname>
- </author>
- <author>
- <firstname></firstname>
- <surname>Braun</surname>
- </author>
- <pubdate>1993</pubdate>
- <title id="bib.riedmiller_1993.title">A direct adaptive method for faster backpropagation learning: The RPROP algorithm</title>
- <pagenums>586-591</pagenums>
- <releaseinfo>
- <ulink url="http://citeseer.nj.nec.com/riedmiller93direct.html">
- http://citeseer.nj.nec.com/riedmiller93direct.html</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.sarle_2002">
- <abbrev id="bib.sarle_2002.abbrev">Sarle, 2002</abbrev>
- <author>
- <firstname>W.S.</firstname>
- <surname>Sarle</surname>
- </author>
- <pubdate></pubdate>
- <title id="bib.sarle_2002.title">Neural Network FAQ</title>
- <releaseinfo>
- <ulink url="ftp://ftp.sas.com/pub/neural/FAQ2.html#A_binary">ftp://ftp.sas.com/pub/neural/FAQ2.html#A_binary</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.pemstein">
- <abbrev id="bib.pemstein.abbrev">Pemstein, 2002</abbrev>
- <author>
- <firstname>Dan</firstname>
- <surname>Pemstein</surname>
- </author>
- <pubdate>2002</pubdate>
- <title id="bib.pemstein_2002.title">ANN++</title>
- <releaseinfo>
- <ulink url="http://savannah.nongnu.org/projects/annpp/">http://savannah.nongnu.org/projects/annpp/</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.tettamanzi_2001">
- <abbrev id="bib.tettamanzi_2001.abbrev">Tettamanzi and Tomassini, 2001</abbrev>
- <author>
- <firstname>A.</firstname>
- <surname>Tettamanzi</surname>
- </author>
- <author>
- <firstname>M.</firstname>
- <surname>Tomassini</surname>
- </author>
- <pubdate></pubdate>
- <title id="bib.tettamanzi_2001.title">Soft Computing</title>
- <publishername>Springer-Verlag</publishername>
- </biblioentry>
- <biblioentry id="bib.fiesler_1997">
- <abbrev id="bib.fiesler_1997.abbrev">Thimm and Fiesler, High-Order and Multilayer Perceptron Initialization, 1997</abbrev>
- <author>
- <firstname>Georg</firstname>
- <surname>Thimm</surname>
- </author>
- <author>
- <firstname>Emile</firstname>
- <surname>Fiesler</surname>
- </author>
- <pubdate>March 1997</pubdate>
- <title id="bib.fiesler_1997.title">High-Order and Multilayer Perceptron Initialization</title>
- <publishername>IEEE Transactions on Neural Networks</publishername>
- <volumenum>8</volumenum>
- <issuenum>2</issuenum>
- <pagenums>249-259</pagenums>
- <releaseinfo>
- <ulink url="http://citeseer.ist.psu.edu/thimm96high.html">http://citeseer.ist.psu.edu/thimm96high.html</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.thimm_1997">
- <abbrev id="bib.thimm_1997.abbrev">Thimm and Fiesler, 1997</abbrev>
- <author>
- <firstname>G</firstname>
- <surname>Thimm</surname>
- </author>
- <author>
- <firstname>E</firstname>
- <surname>Fiesler</surname>
- </author>
- <pubdate>1997</pubdate>
- <title id="bib.thimm_1997.title">Optimal Setting of Weights, Learning Rate, and Gain</title>
- <releaseinfo>
- <ulink url="http://citeseer.ist.psu.edu/thimm97optimal.html">http://citeseer.ist.psu.edu/thimm97optimal.html</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.van_rossum_2003">
- <abbrev id="bib.van_rossum_2003.abbrev">van Rossum, 2003</abbrev>
- <author>
- <firstname>P.</firstname>
- <surname>van Rossum</surname>
- </author>
- <pubdate>2003</pubdate>
- <title id="bib.van_rossum_2003.title">Lightweight neural network</title>
- <releaseinfo>
- <ulink url="http://lwneuralnet.sourceforge.net/">http://lwneuralnet.sourceforge.net/</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.van_waveren_2001">
- <abbrev id="bib.van_waveren_2001.abbrev">van Waveren, 2001</abbrev>
- <author>
- <firstname>J.P.</firstname>
- <surname>van Waveren</surname>
- </author>
- <pubdate>2001</pubdate>
- <title id="bib.van_waveren_2001.title">The quake III arena bot</title>
- <releaseinfo>
- <ulink url="http://www.kbs.twi.tudelft.nl/Publications/MSc/2001-VanWaveren-MSc.html">
- http://www.kbs.twi.tudelft.nl/Publications/MSc/2001-VanWaveren-MSc.html</ulink>
- </releaseinfo>
- </biblioentry>
- <biblioentry id="bib.zell_2003">
- <abbrev id="bib.zell_2003.abbrev">Zell, 2003</abbrev>
- <author>
- <firstname>A.</firstname>
- <surname>Zell</surname>
- </author>
- <pubdate>2003</pubdate>
- <title id="bib.zell_2003.title">Stuttgart neural network simulator</title>
- <releaseinfo>
- <ulink url="http://www-ra.informatik.uni-tuebingen.de/SNNS/">http://www-ra.informatik.uni-tuebingen.de/SNNS/</ulink>
- </releaseinfo>
- </biblioentry>
</bibliography>
</book>
<!-- Keep this comment at the end of the file
diff --git a/doc/gettingstarted.txt b/doc/gettingstarted.txt
new file mode 100644
index 0000000..59cba5e
--- /dev/null
+++ b/doc/gettingstarted.txt
@@ -0,0 +1,92 @@
+Section: Getting Started
+
+An ANN is normally run in two different modes, a training mode and an execution mode. Although it is possible to do this in the same program, using different programs is recommended.
+
+There are several reasons to why it is usually a good idea to write the training and execution in two different programs, but the most obvious is the fact that a typical ANN system is only trained once, while it is executed many times.
+
+Topic: Training
+
+The following is a simple program which trains an ANN with a data set and then saves the ANN to a file.
+
+Simple training example:
+
+(code)
+#include "fann.h"
+
+int main()
+{
+ const unsigned int num_input = 2;
+ const unsigned int num_output = 1;
+ const unsigned int num_layers = 3;
+ const unsigned int num_neurons_hidden = 3;
+ const float desired_error = (const float) 0.001;
+ const unsigned int max_epochs = 500000;
+ const unsigned int epochs_between_reports = 1000;
+
+ struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output);
+
+ fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
+ fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
+
+ fann_train_on_file(ann, "xor.data", max_epochs, epochs_between_reports, desired_error);
+
+ fann_save(ann, "xor_float.net");
+
+ fann_destroy(ann);
+
+ return 0;
+}
+(end)
+
+
+The file xor.data, used to train the xor function:
+
+(code)
+4 2 1
+-1 -1
+-1
+-1 1
+1
+1 -1
+1
+1 1
+-1
+(end)
+
+
+The first line consists of three numbers: The first is the number of training pairs in the file, the second is the number of inputs and the third is the number of outputs. The rest of the file is the actual training data, consisting of one line with inputs, one with outputs etc.
+
+This example introduces several fundamental functions, namely <fann_create_standard>, <fann_train_on_file>, <fann_save>, and <fann_destroy>.
+
+Topic: Execution
+
+The following example shows a simple program which executes a single input on the ANN. The program introduces two new functions (<fann_create_from_file> and <fann_run>) which were not used in the training procedure, as well as the <fann_type> type.
+
+Simple execution example:
+
+(code)
+#include <stdio.h>
+#include "floatfann.h"
+
+int main()
+{
+ fann_type *calc_out;
+ fann_type input[2];
+
+ struct fann *ann = fann_create_from_file("xor_float.net");
+
+ input[0] = -1;
+ input[1] = 1;
+ calc_out = fann_run(ann, input);
+
+ printf("xor test (%f,%f) -> %f\n", input[0], input[1], calc_out[0]);
+
+ fann_destroy(ann);
+ return 0;
+}
+(end)
+
+
+Topic: Getting Help
+
+If after reading the documentation you are still having problems, or have a question that is not covered in the documentation, please consult the fann-general mailing list. Archives and subscription information are available at http://lists.sourceforge.net/mailman/listinfo/fann-general.
diff --git a/doc/installation.txt b/doc/installation.txt
new file mode 100644
index 0000000..a0887f2
--- /dev/null
+++ b/doc/installation.txt
@@ -0,0 +1,117 @@
+Section: Installing FANN
+
+Copies of FANN can be obtained from our SourceForge page, located at http://fann.sf.net
+
+You can currently get FANN as source code (fann-*.tar.bz2), Debian packages (fann-*.deb), or RPM's (fann-*.rpm).
+
+FANN is available under the terms of the GNU Lesser General Public License.
+
+FANN bindings to several other programming languages is also available from: http://fann.sf.net
+
+Group: C Library Installation
+
+Topic: Windows Installation
+
+FANN >= 1.1.0 includes a Microsoft Visual C++ 6.0 project file, which can be used to compile FANN for Windows. To build the library and examples with MSVC++ 6.0.
+
+First, navigate to the MSVC++ directory in the FANN distribution and open the all.dsw workspace. In the Visual Studio menu bar, choose "Build" -> "Batch build...", select the project configurations that you would like to build (by default, all are selected), and press "rebuild all"
+
+When the build process is complete, the library and examples can be found in the MSVC++\Debug and MSVC++\Release directories and the release versions of the examples are automatically copied into the examples where they are supposed to be run.
+
+Topic: RPM Installation
+
+RPMs are a simple way to manage packages, and is used on many common Linux distributions such as Red Hat, Mandrake, and SuSE.
+
+Two separate packages exist; fann, the runtime library, and fann-devel, the development library and header files.
+
+After downloading FANN, simply run (as root) the following command: rpm -ivh $PATH_TO_RPM
+
+Topic: DEB Installation
+
+DEBs are packages for the Debian Linux distribution. Two separate packages exists libfann1 and libfann1-dev, where libfann1 is the runtime library and libfann1-dev is the development library.
+
+Fann is included in the stable distribution of Debian, so Debian users can simply run (as root) the following command: apt-get install libfann1 libfann1-dev.
+
+After downloading the FANN DEB package, simply run (as root) the following command: dpkg -i $PATH_TO_DEB
+
+Topic: Compiling from source
+
+Compiling FANN from source code entails the standard GNU autotools technique. First, configure the package as you want it by typing (in the FANN directory), ./configure If you need help choosing the options you would like to use, try ./configure --help
+
+Next, you have to actually compile the library. To do this, simply type make
+
+Finally, to install the library, type make install. Odds are you will have to be root to install, so you may need to su to root before installing. Please remember to log out of the root account immediately after make install finishes.
+
+Some people have experienced problems with compiling the library with some compilers, especially windows compilers which can not use GNU autotools. Please look through the help forum and the mailing list archives for info on how these problems was solved. If you do not find any information here, feel free to ask questions.
+
+Group: Python Bindings
+
+These functions allow you to interact with the FANN library from Python.
+
+This extension requires the FANN library, version 1.1.0 or later.
+
+This python binding is provided by Vincenzo Di Massa (hawk.it at tiscalinet.it) and updated by Gil Megidish (gil at megidish.net)
+
+Topic: Python Install
+
+Make sure to make and install the fann library first. Make sure that you have swig and python development files installed. Perhaps change the include directory of python. Then run 'make' to compile in the python directory.
+
+Copy the generated _fann.so and fann.py files to python modules or into working directory.
+
+After the install, just import fann and all the C functions will be available to your python code.
+
+Group: PHP Extension
+
+These functions allow you to interact with the FANN library from PHP.
+
+This extension requires the FANN library, version 1.1.0 or later.
+
+This extension supports the same activation functions as the library, a list of which can be found in the reference manual for the C library.
+
+Topic: Installation Using PEAR
+
+The easiest way to install FANN-PHP is to use PEAR- if you have a fairly recent version of PHP installed, simply run pear install fann. Note that if there are no stable releases of FANN-PHP, you may have to specify the URI for the package, which can be obtained from http://pecl.php.net/fann.
+
+If you cannot install FANN-PHP using PEAR, you can try following the (obsolete) instructions at http://www.cs.utexas.edu/users/UTCS/online-docs/php/pear/faq.install-pecl.html.
+
+If you use one of these methods, you'll need to either dl('fann.so') or add it to your php.ini
+
+If you use either of the above methods, you will probably need to be root.
+
+Topic: Compiling into PHP
+
+Please only use this method if using the methods outlined in Using PEAR have failed.
+
+If you wish to compile FANN-PHP into PHP itself, you can. First, uncompress the package into the ext subdirectory of your copy of the PHP source code, and rename the directory to ext/fann (from fann-x.x.x).
+
+Next, you must rebuild the configure script- to do so, run ./buildconf from the PHP source directory.
+
+From here on, the procedure is similar to when you built PHP originally- run ./configure with your desired options, plus --with-fann.
+
+Finally, run make and make install. Note that you will probably need to be root for make install to work.
+
+This method may require flex and bison to work- more information can be obtained at http://www.php.net/anoncvs.php
+
+Group: Delphi Bindings
+
+These functions allow you to interact with the FANN library from Delphi.
+
+This extension requires the FANN library, version 1.2.0 or later.
+
+This extension can be downloaded from the FANN library download section.
+
+This Delphi binding is provided by Maur�cio Pereira Maia (mauricio at uaisol.com.br)
+
+Topic: Delphi Install
+
+Make sure to make and install the fann library first. Put the file fannfloat.dll in your PATH. (If you want to use the fixed version you should define FIXEDFANN on fann.pas). Include fann.pas in your project and in your unit uses clause, and have fun! See the XorConsole sample for more details.
+
+Topic: TFannNetwork
+
+TFannNetwork is a Delphi component that encapsulates the Fann Library. You do not have to install TFannNetwork to use Fann on Delphi, but it will make the library more Delphi friendly. Currently it has only a small subset of all the library functions, but I hope that will change in the near future.
+
+To install TFannNetwork you should follow all the previous steps and copy the FannNetwork.pas and Fann.dcr to your Delphi Library PATH. Choose Component/Install Component. In the Unit file name field, click on Browse and point to the fannnetwork.pas file. By default Delphi will install in the Borland User Components package, it might be changed using Package file name field or Into new package page. Click on Ok. A confirmation dialog will be shown asking if you want to build the package. [...]
+
+Topic: Known Problems
+
+If you are getting in trouble to use your own compiled FANN DLL with Delphi that might be because of the C++ naming mangle that changes between C++ compilers. You will need to make a TDUMP on your dll and changes all the name directives on fann.pas to the correct function names on your dll.
diff --git a/doc/theory.txt b/doc/theory.txt
new file mode 100644
index 0000000..a4ac5dc
--- /dev/null
+++ b/doc/theory.txt
@@ -0,0 +1,33 @@
+Section: Neural Network Theory
+
+This section will briefly explain the theory of neural networks (hereafter known as NN) and artificial neural networks (hereafter known as ANN). For a more in depth explanation of these concepts please consult the literature; [Hassoun, 1995] has good coverage of most concepts of ANN and [Hertz et al., 1991] describes the mathematics of ANN very thoroughly, while [Anderson, 1995] has a more psychological and physiological approach to NN and ANN. For the pragmatic I (SN) could recommend [T [...]
+
+Topic: Neural Networks
+
+The human brain is a highly complicated machine capable of solving very complex problems. Although we have a good understanding of some of the basic operations that drive the brain, we are still far from understanding everything there is to know about the brain.
+
+In order to understand ANN, you will need to have a basic knowledge of how the internals of the brain work. The brain is part of the central nervous system and consists of a very large NN. The NN is actually quite complicated, so the following discussion shall be relegated to the details needed to understand ANN, in order to simplify the explanation.
+
+The NN is a network consisting of connected neurons. The center of the neuron is called the nucleus. The nucleus is connected to other nucleuses by means of the dendrites and the axon. This connection is called a synaptic connection.
+
+The neuron can fire electric pulses through its synaptic connections, which is received at the dendrites of other neurons.
+
+When a neuron receives enough electric pulses through its dendrites, it activates and fires a pulse through its axon, which is then received by other neurons. In this way information can propagate through the NN. The synaptic connections change throughout the lifetime of a neuron and the amount of incoming pulses needed to activate a neuron (the threshold) also change. This behavior allows the NN to learn.
+
+The human brain consists of around 10^11 neurons which are highly interconnected with around 10^15 connections [Tettamanzi and Tomassini, 2001]. These neurons activates in parallel as an effect to internal and external sources. The brain is connected to the rest of the nervous system, which allows it to receive information by means of the five senses and also allows it to control the muscles.
+
+Topic: Artificial Neural Networks
+
+It is not possible (at the moment) to make an artificial brain, but it is possible to make simplified artificial neurons and artificial neural networks. These ANNs can be made in many different ways and can try to mimic the brain in many different ways.
+
+ANNs are not intelligent, but they are good for recognizing patterns and making simple rules for complex problems. They also have excellent training capabilities which is why they are often used in artificial intelligence research.
+
+ANNs are good at generalizing from a set of training data. E.g. this means an ANN given data about a set of animals connected to a fact telling if they are mammals or not, is able to predict whether an animal outside the original set is a mammal from its data. This is a very desirable feature of ANNs, because you do not need to know the characteristics defining a mammal, the ANN will find out by itself.
+
+Topic: Training an ANN
+
+When training an ANN with a set of input and output data, we wish to adjust the weights in the ANN, to make the ANN give the same outputs as seen in the training data. On the other hand, we do not want to make the ANN too specific, making it give precise results for the training data, but incorrect results for all other data. When this happens, we say that the ANN has been over-fitted.
+
+The training process can be seen as an optimization problem, where we wish to minimize the mean square error of the entire set of training data. This problem can be solved in many different ways, ranging from standard optimization heuristics like simulated annealing, through more special optimization techniques like genetic algorithms to specialized gradient descent algorithms like backpropagation.
+
+The most used algorithm is the backpropagation algorithm, but this algorithm has some limitations concerning, the extent of adjustment to the weights in each iteration. This problem has been solved in more advanced algorithms like RPROP [Riedmiller and Braun, 1993] and quickprop [Fahlman, 1988].
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