/ 
AI-NNFlex-0.16
River stage zero No dependents
/ AI::NNFlex

NAME

AI::NNFlex - A customisable neural network simulator

SYNOPSIS

 use AI::NNFlex;

 my $network = AI::NNFlex->new(config parameter=>value);

 $network->add_layer(nodes=>x,activationfunction=>'function');

 $network->init(); 



 use AI::NNFlex::Dataset;

 my $dataset = AI::NNFlex::Dataset->new([
			[INPUTARRAY],[TARGETOUTPUT],
			[INPUTARRAY],[TARGETOUTPUT]]);

 my $sqrError = 10;

 while ($sqrError >0.01)

 {

	$sqrError = $dataset->learn($network);

 }

 $network->lesion({'nodes'=>PROBABILITY,'connections'=>PROBABILITY});

 $network->dump_state(filename=>'badgers.wts');

 $network->load_state(filename=>'badgers.wts');

 my $outputsRef = $dataset->run($network);

DESCRIPTION

 AI::NNFlex is intended to be a highly flexible, modular NN framework.
 It's written entirely in native perl, so there are essentially no
 prereq's. The following modular divisions are made:

	* NNFlex.pm
		the core module. Contains methods to construct and
		lesion the network

	* feedforward.pm
		the network type module. Feedforward is the only type
		currently defined, but others may be created and
		imported at runtime

	* <learning>.pm
		the learning algorithm. Currently the options are
			backprop - standard vanilla backprop
			momentum - backprop with momentum


	* <activation>.pm
		node activation function. Currently the options are
		tanh, linear & sigmoid.

	* Dataset.pm
		methods for constructing a set of input/output data
		and applying to a network.

 The code should be simple enough to use for teaching
 purposes, but a simpler implementation of a simple backprop
 network is included in the example file bp.pl. This is
 derived from Phil Brierleys freely available java code
 at www.philbrierley.com.

 AI::NNFlex leans towards teaching NN and cognitive modelling
 applications. Future modules are likely to include more
 biologically plausible nets like DeVries & Principes
 Gamma model.

CONSTRUCTOR

AI::NNFlex

 new ( parameter => value );
	
	randomweights=>MAXIMUM VALUE FOR INITIAL WEIGHT

	learningalgorithm=>The AI::NNFlex module to import for
		training the net

	networktype=>The AI::NNFlex module to import for flowing
		activation

	debug=>[LIST OF CODES FOR MODULES TO DEBUG]

	learningrate=>the learning rate of the network

	momentum=>the momentum value (momentum learning only)

AI::NNFlex::Dataset

new ( [[INPUT VALUES],[OUTPUT VALUES],[INPUT VALUES],[OUTPUT VALUES],..])

INPUT VALUES

These should be comma separated values. They can be applied
to the network with ::run or ::learn

OUTPUT VALUES

These are the intended or target output values. Comma separated
These will be used by ::learn

METHODS

This is a short list of the main methods. For details on all
available methods, please see individual pod pages below, and
in individual imported modules.

AI::NNFlex

add_layer

 Syntax:

 $network->add_layer(	nodes=>NUMBER OF NODES IN LAYER,
			persistentactivation=>RETAIN ACTIVATION BETWEEN PASSES,
			decay=>RATE OF ACTIVATION DECAY PER PASS,
			randomactivation=>MAXIMUM STARTING ACTIVATION,
			threshold=>NYI,
			activationfunction=>"ACTIVATION FUNCTION",
			randomweights=>MAX VALUE OF STARTING WEIGHTS);

init

Syntax:

$network->init();

Initialises connections between nodes, sets initial weights and
loads external components

lesion

$network->lesion ({'nodes'=>PROBABILITY,'connections'=>PROBABILITY})

Damages the network.

PROBABILITY

A value between 0 and 1, denoting the probability of a given node
or connection being damaged.

Note: this method may be called on a per network, per node or per
layer basis using the appropriate object.

AN::NNFlex::Dataset

learn

$dataset->learn($network)

'Teaches' the network the dataset using the networks defined learning
algorithm.
Returns sqrError;

run

$dataset->run($network)

Runs the dataset through the network and returns a reference to an array of
output patterns.

EXAMPLES

See the code in ./examples. For any given version of NNFlex, xor.pl will
contain the latest functionality.

PREREQs

None. NNFlex should run OK on any version of Perl 5 >.

ACKNOWLEDGEMENTS

Phil Brierley, for his excellent free java code, that solved my backprop
problem

Dr Martin Le Voi, for help with concepts of NN in the early stages

Dr David Plaut, for help with the project that this code was originally
intended for.

Graciliano M.Passos for suggestions & improved code (see SEE ALSO).

SEE ALSO

AI::NNEasy - Developed by Graciliano M.Passos 
Shares some common code with NNFlex. Much faster, and more suitable for
backprop projects with large datasets.

TODO

Lots of things:

clean up the perldocs some more
write gamma modules
write BPTT modules
write a perceptron learning module
speed it up
write a tk gui

CHANGES

v0.11 introduces the lesion method, png support in the draw module
 and datasets.
v0.12 fixes a bug in reinforce.pm & adds a reflector in feedforward->run
 to make $network->run($dataset) work.
v0.13 introduces the momentum learning algorithm and fixes a bug that
 allowed training to proceed even if the node activation function module
 can't be loaded
v0.14 fixes momentum and backprop so they are no longer nailed to tanh hidden
 units only.
v0.15 fixes a bug in feedforward, and reduces the debug overhead
v0.16 changes some underlying addressing of weights, to simplify and speed

COPYRIGHT

Copyright (c) 2004-2005 Charles Colbourn. All rights reserved. This program
is free software; you can redistribute it and/or modify it under the same
terms as Perl itself.

CONTACT

charlesc@nnflex.g0n.net