AI-NNFlex

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CHANGES  view on Meta::CPAN

Removed feedforward_pdl.pm from the distribution - it shouldn't have
been there in the first place!

Fixed the lesion subs so they expect parameter=>value pairs instead
of an anonymous hash (left over from when I didn't know you could do
that).

Fixed an error - random weights was bounded by 1, not the parameter
'randomweights'. Its now positive only. Some benchmarking needed as
it appears that positive random starting weights rather than a mix
of positive and negative make the network quicker to converge, at
least with momentum.

weights now defaults to rand(1) instead of 0 - at least for backprop
type nets, a default 0 weight will never work. For other types of nets
the random weights can be overridden with the 'fixedweights' parameter.

Fixed load_state to correctly read weights from the bias node

CHANGES  view on Meta::CPAN

Implemented a rounding network property in output

Fixed a bug that allowed activation to flow through a node 
even if it was inactive

Altered the syntax for output to be param=>value instead of
an anonymous hash

As per Scott Fahlmans comments about neural net benchmarking,
(Fahlman S.E., (1988) 'An empirical study of learning speed in back-propagation networks'. Tech. Rep. CMU-CS-88-162, Carnegie Mellon University, Pittsburgh, PA.) , I've started using a more realistic benchmark than xor.
The 'cars' subfolder in examples contains the learning code
for this, drawn from
 ftp://ftp.ics.uci.edu/pub/machine-learning-databases/car/





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lib/AI/NNFlex.pm  view on Meta::CPAN

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

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

 my $outputsRef = $network->output(layer=>2,round=>1);


=head1 DESCRIPTION

AI::NNFlex is a base class for constructing your own neural network modules. To implement a neural network, start with the documentation for AI::NNFlex::Backprop, included in this distribution

=head1 CONSTRUCTOR 

=head2 AI::NNFlex->new ( parameter => value );
	

randomweights=>MAXIMUM VALUE FOR INITIAL WEIGHT

fixedweights=>WEIGHT TO USE FOR ALL CONNECTIONS

lib/AI/NNFlex/Feedforward.pm  view on Meta::CPAN

				$_->{'activation'} =$inputPattern[$counter];
				if (scalar @debug> 0)
				{$network->dbug("Applying ".$inputPattern[$counter]." to $_",3);}
			 
			}
		}
		$counter++;
	}
	

	# Now flow activation through the network starting with the second layer
	foreach my $layer (@{$network->{'layers'}})
	{
		if ($layer eq $network->{'layers'}->[0]){next}

		foreach my $node (@{$layer->{'nodes'}})
		{
			my $totalActivation;
			# Set the node to 0 if not persistent
			if (!($node->{'persistentactivation'}))
			{

lib/AI/NNFlex/Reinforce.pm  view on Meta::CPAN

sub learn
{

	my $network = shift;

	my @layers = @{$network->{'layers'}};

	# no connections westwards from input, so no weights to adjust
	shift @layers;

	# reverse to start with the last layer first
	foreach my $layer (reverse @layers)
	{
		my @nodes = @{$layer->{'nodes'}};

		foreach my $node (@nodes)
		{
			my @westNodes = @{$node->{'connectedNodesWest'}->{'nodes'}};
			my @westWeights = @{$node->{'connectedNodesWest'}->{'weights'}};
			my $connectedNodeCounter=0;
			foreach my $westNode (@westNodes)