AI-NNFlex

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CHANGES  view on Meta::CPAN

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






#############################################################

examples/xor_minimal.pl  view on Meta::CPAN

# Example demonstrating XOR with momentum backprop learning
# and minimal set of parameters (using default values)

use strict;
use AI::NNFlex::Backprop;
use AI::NNFlex::Dataset;

# Create the network 

my $network = AI::NNFlex::Backprop->new( learningrate=>.1,
				bias=>1,
				momentum=>0.6,

lib/AI/NNFlex.pm  view on Meta::CPAN

The constructor implements a fairly generalised network object with a number of parameters.


The following parameters are optional:
 randomweights
 fixedweights
 debug
 round


(Note, if randomweights is not specified the network will default to a random value from 0 to 1.

=head1 METHODS

This is a short list of the main methods implemented in AI::NNFlex. 

=head2 AI::NNFlex

=head3 add_layer

 Syntax:

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

	my $outputPatternRef = shift;

	# if this is an incorrect dataset call translate it
	if ($outputPatternRef =~/Dataset/)
	{
		return ($outputPatternRef->learn($network))
	}


	# Set a default value on the Fahlman constant
	if (!$network->{'fahlmanconstant'})
	{
		$network->{'fahlmanconstant'} = 0.1;
	}

	my @outputPattern = @$outputPatternRef;

	$network->calc_error($outputPatternRef);

	#calculate & apply dWs

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

 debug

 round

 momentum

 fahlmanconstant


If randomweights is not specified the network will default to a random value from 0 to 1.

If momentum is not specified the network will default to vanilla (non momentum) backprop.

The Fahlman constant modifies the slope of the error curve. 0.1 is the standard value for everything, and speeds the network up immensely. If no Fahlman constant is set, the network will default to 0.1

=head2 AI::NNFlex::Dataset

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

=head2 INPUT VALUES

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

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

		$node->{'activation'} = $network->$activationfunction($node->{'activation'});

	}

	return $network->output;
}

#######################################################
# AI::NNFlex::Hopfield::output
#######################################################
# This needs to be overloaded, because the default
# nnflex output method returns only the rightmost layer
#######################################################
sub output
{
	my $network = shift;

	my @array;
	foreach my $node (@{$network->{'nodes'}})
	{
		unshift @array,$node->{'activation'};

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

	round=>0 or 1 - 1 sets the network to round output values to
		nearest of 1, -1 or 0


The following parameters are optional:
 randomweights
 fixedweights
 debug
 round

(Note, if randomweights is not specified the network will default to a random value from 0 to 1.


=head1 METHODS

This is a short list of the main methods implemented in AI::NNFlex. Subclasses may implement other methods.

=head2 AI::NNFlex

=head3 add_layer