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

{
	my $network = shift;

	my $outputPatternRef = shift;
	my @outputPattern = @$outputPatternRef;

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

	if (scalar @debug > 0)
	{$network->dbug ("Output pattern @outputPattern received by Backprop",4);}


	my $outputLayer = $network->{'layers'}->[-1]->{'nodes'};

	if (scalar @$outputLayer != scalar @outputPattern)
	{	
		$network->dbug ("Wrong number of output values, net has ".scalar @$outputLayer." nodes",0);
		return 0;
	}

	# Now calculate the error
	my $counter=0;
	foreach (@$outputLayer)
	{	
		my $value = $_->{'activation'} - $outputPattern[$counter];


		if ($_->{'errorfunction'})
		{
			my $errorfunction = $_->{'errorfunction'};
			$value = $network->$errorfunction($value);
		}
		
		$_->{'error'} = $value;
		$counter++;
		if (scalar @debug > 0)
		{$network->dbug ("Error on output node $_ = ".$_->{'error'},4);}
	}


}


########################################################
# AI::NNFlex::Backprop::learn
########################################################
sub learn
{

	my $network = shift;

	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
	$network->hiddenToOutput;
	if (scalar @{$network->{'layers'}} > 2) 
	{
		$network->hiddenOrInputToHidden;
	}

	# calculate network sqErr
	my $Err = $network->RMSErr($outputPatternRef);
	return $Err;	
}


#########################################################
# AI::NNFlex::Backprop::hiddenToOutput
#########################################################
sub hiddenToOutput
{
	my $network = shift;

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

	my $outputLayer = $network->{'layers'}->[-1]->{'nodes'};

	foreach my $node (@$outputLayer)
	{
		my $connectedNodeCounter=0;
		foreach my $connectedNode (@{$node->{'connectedNodesWest'}->{'nodes'}})
		{
			my $momentum = 0;
			if ($network->{'momentum'})
			{

				if ($node->{'connectedNodesWest'}->{'lastdelta'}->[$connectedNodeCounter])
				{
					$momentum = ($network->{'momentum'})*($node->{'connectedNodesWest'}->{'lastdelta'}->[$connectedNodeCounter]);
				}
			}
			if (scalar @debug > 0)
			{$network->dbug("Learning rate is ".$network->{'learningrate'},4);}
			my $deltaW = (($network->{'learningrate'}) * ($node->{'error'}) * ($connectedNode->{'activation'}));
			$deltaW = $deltaW+$momentum;
			$node->{'connectedNodesWest'}->{'lastdelta'}->[$connectedNodeCounter] = $deltaW;
			
			if (scalar @debug > 0)
			{$network->dbug("Applying delta $deltaW on hiddenToOutput $connectedNode to $node",4);}
			# 
			$node->{'connectedNodesWest'}->{'weights'}->[$connectedNodeCounter] -= $deltaW;
			$connectedNodeCounter++;
		}
			

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

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

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

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

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

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

=head1 DESCRIPTION

AI::NNFlex::Backprop is a class to generate feedforward, backpropagation neural nets. It inherits various constructs from AI::NNFlex & AI::NNFlex::Feedforward, but is documented here as a standalone.

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

Full documentation for AI::NNFlex::Dataset can be found in the modules own perldoc. It's documented here for convenience only.

=head1 CONSTRUCTOR 

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

Parameters:

	
	randomweights=>MAXIMUM VALUE FOR INITIAL WEIGHT

	fixedweights=>WEIGHT TO USE FOR ALL CONNECTIONS

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

	learningrate=>the learning rate of the network

	momentum=>the momentum value (momentum learning only)

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

	fahlmanconstant=>0.1
		


The following parameters are optional:

 randomweights

 fixedweights

 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

=head2 OUTPUT VALUES
	
These are the intended or target output values. Comma separated. These will be used by ::learn


=head1 METHODS

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

=head2 AI::NNFlex::Backprop

=head2 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",
			errorfunction=>'ERROR TRANSFORMATION FUNCTION',
			randomweights=>MAX VALUE OF STARTING WEIGHTS);


The activation function must be defined in AI::NNFlex::Mathlib. Valid predefined activation functions are tanh & linear.

The error transformation function defines a transform that is done on the error value. It must be a valid function in AI::NNFlex::Mathlib. Using a non linear transformation function on the error value can sometimes speed up training.

The following parameters are optional:

 persistentactivation

 decay

 randomactivation

 threshold

 errorfunction

 randomweights



=head2 init

 Syntax:

 $network->init();