NAME

AI::Nerl::Network - 3-layer neural network with backpropagation

SYNOPSIS

 use AI::Nerl::Network;
 use PDL;
   
 my $x = pdl([0,0,1,1],
             [0,1,0,1],
             [1,0,0,1]);
 my $y = pdl([1,1,0,1]);
 my $nn = AI::Nerl::Network->new(
            l1 => 3,       # 3 inputs
            l2 => 18,      # 18 hidden neurons
            l3 => 1,       # 1 output
            alpha => .3,   # learning rate
            lambda => .01, # 'squashing' parameter
 );
 $nn->train($x,$y, passes=>45);
 my ($cost,$num_correct) = $nn->cost($x,$y);
 #$nn wasn't programmed with this input. could be anything:
 print $nn->run(pdl([0,0,0])); 

DESCRIPTION

METHODS

train($x,$y, %params)

Train with backpropagation using $x as input & $y as target. $x and $y are both pdls. If there are multiple cases, each one will occupy a column (dimension 2) of the pdl. If your dimensions are off, you will experience an pdl error of some sort.

%params

passes

number of passes.

run($x)

 $output = $nn->run($x);

cost($x,$y)

 ($cost,$num_correct) = $nn->cost($x,$y);

Calculate the 'cost' of the network. This is basically the difference between the actual output ($nn->run($x)) and the the target output($y), added to the sum of the neural weights if you're penalizing weights with lambda. The cost should Always decrease after being trained with ($x,$y).

This function returns both the cost, and the number of "correct" responses if using output neurons for classification.

SEE ALSO

http://en.wikipedia.org/wiki/Feedforward_neural_network#Multi-layer_perceptron

http://en.wikipedia.org/wiki/Backpropagation

AUTHOR

Zach Morgan <zpmorgan@gmail.com>

COPYRIGHT

Copyright 2012 by Zach Morgan

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