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		close(FILE);
		
		return @image,@palette;
	}

1;


__END__





=head1 NAME

AI::NeuralNet::BackProp - A simple back-prop neural net that uses Delta's and Hebbs' rule.

=head1 SYNOPSIS

use AI::NeuralNet::BackProp;
	
	# Create a new network with 1 layer, 5 inputs, and 5 outputs.
	my $net = new AI::NeuralNet::BackProp(1,5,5);
	
	# Add a small amount of randomness to the network
	$net->random(0.001);

	# Demonstrate a simple learn() call
	my @inputs = ( 0,0,1,1,1 );
	my @ouputs = ( 1,0,1,0,1 );
	
	print $net->learn(\@inputs, \@outputs),"\n";

	# Create a data set to learn
	my @set = (
		[ 2,2,3,4,1 ], [ 1,1,1,1,1 ],
		[ 1,1,1,1,1 ], [ 0,0,0,0,0 ],
		[ 1,1,1,0,0 ], [ 0,0,0,1,1 ]	
	);
	
	# Demo learn_set()
	my $f = $net->learn_set(\@set);
	print "Forgetfulness: $f unit\n";
	
	# Crunch a bunch of strings and return array refs
	my $phrase1 = $net->crunch("I love neural networks!");
	my $phrase2 = $net->crunch("Jay Lenno is wierd.");
	my $phrase3 = $net->crunch("The rain in spain...");
	my $phrase4 = $net->crunch("Tired of word crunching yet?");

	# Make a data set from the array refs
	my @phrases = (
		$phrase1, $phrase2,
		$phrase3, $phrase4
	);

	# Learn the data set	
	$net->learn_set(\@phrases);
	
	# Run a test phrase through the network
	my $test_phrase = $net->crunch("I love neural networking!");
	my $result = $net->run($test_phrase);
	
	# Get this, it prints "Jay Leno is  networking!" ...  LOL!
	print $net->uncrunch($result),"\n";



=head1 UPDATES

This is version 0.89. In this version I have included a new feature, output range limits, as
well as automatic crunching of run() and learn*() inputs. Included in the examples directory
are seven new practical-use example scripts. Also implemented in this version is a much cleaner 
learning function for individual neurons which is more accurate than previous verions and is 
based on the LMS rule. See range() for information on output range limits. I have also updated 
the load() and save() methods so that they do not depend on Storable anymore. In this version 
you also have the choice between three network topologies, two not as stable, and the third is 
the default which has been in use for the previous four versions.


=head1 DESCRIPTION

AI::NeuralNet::BackProp implements a nerual network similar to a feed-foward,
back-propagtion network; learning via a mix of a generalization
of the Delta rule and a disection of Hebbs rule. The actual 
neruons of the network are implemented via the AI::NeuralNet::BackProp::neuron package.
	
You constuct a new network via the new constructor:
	
	my $net = new AI::NeuralNet::BackProp(2,3,1);
		

The new() constructor accepts two arguments and one optional argument, $layers, $size, 
and $outputs is optional (in this example, $layers is 2, $size is 3, and $outputs is 1).

$layers specifies the number of layers, including the input
and the output layer, to use in each neural grouping. A new
neural grouping is created for each pattern learned. Layers
is typically set to 2. Each layer has $size neurons in it.
Each neuron's output is connected to one input of every neuron
in the layer below it. 
	
This diagram illustrates a simple network, created with a call
to "new AI::NeuralNet::BackProp(2,2,2)" (2 layers, 2 neurons/layer, 2 outputs).
	                             	
     input
     /  \
    O    O
    |\  /|
    | \/ |
    | /\ |
    |/  \|
    O    O
     \  /
    mapper

In this diagram, each neuron is connected to one input of every
neuron in the layer below it, but there are not connections
between neurons in the same layer. Weights of the connection
are controlled by the neuron it is connected to, not the connecting
neuron. (E.g. the connecting neuron has no idea how much weight
its output has when it sends it, it just sends its output and the

BackProp.pm  view on Meta::CPAN

=item AI::NeuralNet::BackProp::neuron

AI::NeuralNet::BackProp::neuron is the worker package for AI::NeuralNet::BackProp.
It implements the actual neurons of the nerual network.
AI::NeuralNet::BackProp::neuron is not designed to be created directly, as
it is used internally by AI::NeuralNet::BackProp.

=item AI::NeuralNet::BackProp::_run

=item AI::NeuralNet::BackProp::_map

These two packages, _run and _map are used to insert data into
the network and used to get data from the network. The _run and _map packages 
are connected to the neurons so that the neurons think that the IO packages are
just another neuron, sending data on. But the IO packs. are special packages designed
with the same methods as neurons, just meant for specific IO purposes. You will
never need to call any of the IO packs. directly. Instead, they are called whenever
you use the run() or learn() methods of your network.
        



=head1 BUGS

This is an alpha release of C<AI::NeuralNet::BackProp>, and that holding true, I am sure 
there are probably bugs in here which I just have not found yet. If you find bugs in this module, I would 
appreciate it greatly if you could report them to me at F<E<lt>jdb@wcoil.comE<gt>>,
or, even better, try to patch them yourself and figure out why the bug is being buggy, and
send me the patched code, again at F<E<lt>jdb@wcoil.comE<gt>>. 



=head1 AUTHOR

Josiah Bryan F<E<lt>jdb@wcoil.comE<gt>>

Copyright (c) 2000 Josiah Bryan. All rights reserved. This program is free software; 
you can redistribute it and/or modify it under the same terms as Perl itself.

The C<AI::NeuralNet::BackProp> and related modules are free software. THEY COME WITHOUT WARRANTY OF ANY KIND.

                                                             




=head1 THANKS

Below is a list of people that have helped, made suggestions, patches, etc. No particular order.

		Tobias Bronx, tobiasb@odin.funcom.com
		Pat Trainor, ptrainor@title14.com
		Steve Purkis, spurkis@epn.nu
		Rodin Porrata, rodin@ursa.llnl.gov
		Daniel Macks dmacks@sas.upenn.edu

Tobias was a great help with the initial releases, and helped with learning options and a great
many helpful suggestions. Rodin has gave me some great ideas for the new internals, as well
as disabling Storable. Steve is the author of AI::Perceptron, and gave some good suggestions for 
weighting the neurons. Daniel was a great help with early beta testing of the module and related 
ideas. Pat has been a great help for running the module through the works. Pat is the author of 
the new Inter game, a in-depth strategy game. He is using a group of neural networks internally 
which provides a good test bed for coming up with new ideas for the network. Thankyou for all of
your help, everybody.


=head1 DOWNLOAD

You can always download the latest copy of AI::NeuralNet::BackProp
from http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl


=head1 MAILING LIST

A mailing list has been setup for AI::NeuralNet::BackProp for discussion of AI and 
neural net related topics as they pertain to AI::NeuralNet::BackProp. I will also 
announce in the group each time a new release of AI::NeuralNet::BackProp is available.

The list address is at: ai-neuralnet-backprop@egroups.com 

To subscribe, send a blank email to: ai-neuralnet-backprop-subscribe@egroups.com  


=cut