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AI::Perceptron(3)     User Contributed Perl Documentation    AI::Perceptron(3)

NAME
       AI::Perceptron -	example	of a node in a neural network.

SYNOPSIS
	use AI::Perceptron;

	my $p =	AI::Perceptron->new
		  ->num_inputs(	2 )
		  ->learning_rate( 0.04	)
		  ->threshold( 0.02 )
		  ->weights([ 0.1, 0.2 ]);

	my @inputs  = (	1.3, -0.45 );	# input	can be any number
	my $target  = 1;		# output is always -1 or 1
	my $current = $p->compute_output( @inputs );

	print "current output: $current, target: $target\n";

	$p->add_examples( [ $target, @inputs ] );

	$p->max_iterations( 10 )->train	or
	  warn "couldn't train in 10 iterations!";

	print "training	until it gets it right\n";
	$p->max_iterations( -1 )->train; # watch out for infinite loops

DESCRIPTION
       This module is meant to show how	a single node of a neural network
       works.

       Training	is done	by the Stochastic Approximation	of the Gradient-
       Descent model.

MODEL
       Model of	a Perceptron

		     +---------------+
	X[1] o------ |W[1]	T    |
	X[2] o------ |W[2] +---------+	       +-------------------+
	 .	     | .   |   ___   |_________|    __	Squarewave |_______\  Output
	 .	     | .   |   \     |	  S    | __|	Generator  |	   /
	 .	     | .   |   /__   |	       +-------------------+
	X[n] o------ |W[n] |   Sum   |
		     +-----+---------+

		    S  =  T + Sum( W[i]*X[i] )	as i goes from 1 -> n
	       Output  =  1 if S > 0; else -1

       Where "X[n]" are	the perceptron's inputs, "W[n]"	are the	Weights	that
       get applied to the corresponding	input, and "T" is the Threshold.

       The squarewave generator	just turns the result into a positive or
       negative	number.

       So in summary, when you feed the	perceptron some	numeric	inputs you get
       either a	positive or negative output depending on the input's weights
       and a threshold.

TRAINING
       Usually you have	to train a perceptron before it	will give you the
       outputs you expect.  This is done by giving the perceptron a set	of
       examples	containing the output you want for some	given inputs:

	   -1 => -1, -1
	   -1 =>  1, -1
	   -1 => -1,  1
	    1 =>  1,  1

       If you've ever studied boolean logic, you should	recognize that as the
       truth table for an "AND"	gate (ok so we're using	-1 instead of the
       commonly	used 0,	same thing really).

       You train the perceptron	by iterating over the examples and adjusting
       the weights and threshold by some value until the perceptron's output
       matches the expected output of each example:

	   while some examples are incorrectly classified
	       update weights for each example that fails

       The value each weight is	adjusted by is calculated as follows:

	   delta[i] = learning_rate * (expected_output - output) * input[i]

       Which is	know as	a negative feedback loop - it uses the current output
       as an input to determine	what the next output will be.

       Also, note that this means you can get stuck in an infinite loop.  It's
       not a bad idea to set the maximum number	of iterations to prevent that.

CONSTRUCTOR
       new( [%args] )
	   Creates a new perceptron with the following default properties:

	       num_inputs    = 1
	       learning_rate = 0.01
	       threshold     = 0.0
	       weights	     = empty list

	   Ideally you should use the accessors	to set the properties, but for
	   backwards compatability you can still use the following arguments:

	       Inputs => $number_of_inputs  (positive int)
	       N      => $learning_rate	    (float)
	       W      => [ @weights ]	    (floats)

	   The number of elements in W must be equal to	the number of inputs
	   plus	one.  This is because older version of AI::Perceptron combined
	   the threshold and the weights a single list where W[0] was the
	   threshold and W[1] was the first weight.  Great idea, eh? :)
	   That's why it's DEPRECATED.

ACCESSORS
       num_inputs( [ $int ] )
	   Set/get the perceptron's number of inputs.

       learning_rate( [	$float ] )
	   Set/get the perceptron's number of inputs.

       weights(	[ \@weights ] )
	   Set/get the perceptron's weights (floats).

	   For backwards compatability,	returns	a list containing the
	   threshold as	the first element in list context:

	     ($threshold, @weights) = $p->weights;

	   This	usage is DEPRECATED.

       threshold( [ $float ] )
	   Set/get the perceptron's number of inputs.

       training_examples( [ \@examples ] )
	   Set/get the perceptron's list of training examples.	This should be
	   a list of arrayrefs of the form:

	       [ $expected_result => @inputs ]

       max_iterations( [ $int ]	)
	   Set/get the perceptron's number of inputs, a	negative value implies
	   no maximum.

METHODS
       compute_output( @inputs )
	   Computes and	returns	the perceptron's output	(either	-1 or 1) for
	   the given inputs.  See the above model for more details.

       add_examples( @training_examples	)
	   Adds	the @training_examples to to current list of examples.	See
	   training_examples() for more	details.

       train( [	@training_examples ] )
	   Uses	the Stochastic Approximation of	the Gradient-Descent model to
	   adjust the perceptron's weights until all training examples are
	   classified correctly.

	   @training_examples can be passed for	convenience.  These are	passed
	   to add_examples().  If you want to re-train the perceptron with an
	   entirely new	set of examples, reset the training_examples().

AUTHOR
       Steve Purkis <spurkis@epn.nu>

COPYRIGHT
       Copyright (c) 1999-2003 Steve Purkis.  All rights reserved.

       This package is free software; you can redistribute it and/or modify it
       under the same terms as Perl itself.

REFERENCES
       Machine Learning, by Tom	M. Mitchell.

THANKS
       Himanshu	Garg <himanshu@gdit.iiit.net> for his bug-report and feedback.
       Many others for their feedback.

SEE ALSO
       Statistics::LTU,	AI::jNeural, AI::NeuralNet::BackProp,
       AI::NeuralNet::Kohonen

perl v5.32.0			  2020-08-09		     AI::Perceptron(3)

NAME | SYNOPSIS | DESCRIPTION | MODEL | TRAINING | CONSTRUCTOR | ACCESSORS | METHODS | AUTHOR | COPYRIGHT | REFERENCES | THANKS | SEE ALSO

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