AI-NeuralNet-BackProp

 view release on metacpan or  search on metacpan

BackProp.pm  view on Meta::CPAN

#!/usr/bin/perl	

# $Id: BackProp.pm,v 0.89 2000/08/12 01:05:27 josiah Exp $
#
# Copyright (c) 2000  Josiah Bryan  USA
#
# See AUTHOR section in pod text below for usage and distribution rights.   
# See UPDATES section in pod text below for info on what has changed in this release.
#

BEGIN {
	$AI::NeuralNet::BackProp::VERSION = "0.89";
}

#
# name:   AI::NeuralNet::BackProp
#
# author: Josiah Bryan 

BackProp.pm  view on Meta::CPAN

Level 0 ($level = 0) : Default, no debugging information printed. All printing is 
left to calling script.

Level 1 ($level = 1) : This causes ALL debugging information for the network to be dumped
as the network runs. In this mode, it is a good idea to pipe your STDIO to a file, especially
for large programs.

Level 2 ($level = 2) : A slightly-less verbose form of debugging, not as many internal 
data dumps.

Level 3 ($level = 3) : JUST prints weight mapping as weights change.

Level 4 ($level = 4) : JUST prints the benchmark info for EACH learn loop iteteration, not just
learning as a whole. Also prints the percentage difference for each loop between current network
results and desired results, as well as learning gradient ('incremenet').   

Level 4 is useful for seeing if you need to give a smaller learning incrememnt to learn() .
I used level 4 debugging quite often in creating the letters.pl example script and the small_1.pl
example script.

Toggles debuging off when called with no arguments. 

BackProp.pm  view on Meta::CPAN

Yet with the allowance of 0s, it requires one of two factors to learn correctly. Either you
must enable randomness with $net->random(0.0001) (Any values work [other than 0], see random() ), 
or you must set an error-minimum with the 'error => 5' option (you can use some other error value 
as well). 

When randomness is enabled (that is, when you call random() with a value other than 0), it interjects
a bit of randomness into the output of every neuron in the network, except for the input and output
neurons. The randomness is interjected with rand()*$rand, where $rand is the value that was
passed to random() call. This assures the network that it will never have a pure 0 internally. It is
bad to have a pure 0 internally because the weights cannot change a 0 when multiplied by a 0, the
product stays a 0. Yet when a weight is multiplied by 0.00001, eventually with enough weight, it will
be able to learn. With a 0 value instead of 0.00001 or whatever, then it would never be able
to add enough weight to get anything other than a 0. 

The second option to allow for 0s is to enable a maximum error with the 'error' option in
learn() , learn_set() , and learn_set_rand() . This allows the network to not worry about
learning an output perfectly. 

For accuracy reasons, it is recomended that you work with 0s using the random() method.

docs.htm  view on Meta::CPAN

<DD>
Toggles debugging off if called with $level = 0 or no arguments. There are four levels
of debugging.
<P>Level 0 ($level = 0) : Default, no debugging information printed. All printing is 
left to calling script.</P>
<P>Level 1 ($level = 1) : This causes ALL debugging information for the network to be dumped
as the network runs. In this mode, it is a good idea to pipe your STDIO to a file, especially
for large programs.</P>
<P>Level 2 ($level = 2) : A slightly-less verbose form of debugging, not as many internal 
data dumps.</P>
<P>Level 3 ($level = 3) : JUST prints weight mapping as weights change.</P>
<P>Level 4 ($level = 4) : JUST prints the benchmark info for EACH learn loop iteteration, not just
learning as a whole. Also prints the percentage difference for each loop between current network
results and desired results, as well as learning gradient ('incremenet').</P>
<P>Level 4 is useful for seeing if you need to give a smaller learning incrememnt to <A HREF="#item_learn"><CODE>learn()</CODE></A> .
I used level 4 debugging quite often in creating the letters.pl example script and the small_1.pl
example script.</P>
<P>Toggles debuging off when called with no arguments.</P>
<P></P>
<DT><STRONG><A NAME="item_save">$net-&gt;save($filename);</A></STRONG><BR>
<DD>

docs.htm  view on Meta::CPAN

and 0.42, where no 0s were allowed because the learning would never finish learning completly
with a 0 in the input.
<P>Yet with the allowance of 0s, it requires one of two factors to learn correctly. Either you
must enable randomness with $net-&gt;<A HREF="#item_random"><CODE>random(0.0001)</CODE></A> (Any values work [other than 0], see <A HREF="#item_random"><CODE>random()</CODE></A> ), 
or you must set an error-minimum with the 'error =&gt; 5' option (you can use some other error value 
as well).</P>
<P>When randomness is enabled (that is, when you call <A HREF="#item_random"><CODE>random()</CODE></A> with a value other than 0), it interjects
a bit of randomness into the output of every neuron in the network, except for the input and output
neurons. The randomness is interjected with rand()*$rand, where $rand is the value that was
passed to <A HREF="#item_random"><CODE>random()</CODE></A> call. This assures the network that it will never have a pure 0 internally. It is
bad to have a pure 0 internally because the weights cannot change a 0 when multiplied by a 0, the
product stays a 0. Yet when a weight is multiplied by 0.00001, eventually with enough weight, it will
be able to learn. With a 0 value instead of 0.00001 or whatever, then it would never be able
to add enough weight to get anything other than a 0.</P>
<P>The second option to allow for 0s is to enable a maximum error with the 'error' option in
<A HREF="#item_learn"><CODE>learn()</CODE></A> , <A HREF="#item_learn_set"><CODE>learn_set()</CODE></A> , and <A HREF="#item_learn_set_rand"><CODE>learn_set_rand()</CODE></A> . This allows the network to not worry about
learning an output perfectly.</P>
<P>For accuracy reasons, it is recomended that you work with 0s using the <A HREF="#item_random"><CODE>random()</CODE></A> method.</P>
<P>If anyone has any thoughts/arguments/suggestions for using 0s in the network, let me know
at <A HREF="mailto:jdb@wcoil.com.">jdb@wcoil.com.</A></P>
<P></P></DL>