AI-Nerl

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LICENSE  view on Meta::CPAN

    b) cause the whole of any work that you distribute or publish, that
    in whole or in part contains the Program or any part thereof, either
    with or without modifications, to be licensed at no charge to all
    third parties under the terms of this General Public License (except
    that you may choose to grant warranty protection to some or all
    third parties, at your option).

    c) If the modified program normally reads commands interactively when
    run, you must cause it, when started running for such interactive use
    in the simplest and most usual way, to print or display an
    announcement including an appropriate copyright notice and a notice
    that there is no warranty (or else, saying that you provide a
    warranty) and that users may redistribute the program under these
    conditions, and telling the user how to view a copy of this General
    Public License.

    d) You may charge a fee for the physical act of transferring a
    copy, and you may at your option offer warranty protection in
    exchange for a fee.

examples/digits/deep_digits.pl  view on Meta::CPAN

my $prev_cost = 10000;
my $passes=0;

for(1..3000){
   my @test = ($images(9000:9999)->sever,$y(9000:9999)->sever);
   my $n = int rand(8000);
   my $m = $n+499;
   my @train = ($images->slice("$n:$m")->copy, $y->slice("$n:$m")->copy);
   $nerl->train(@train,passes=>10);
   my ($cost, $nc) = $nerl->cost( @test );
   print "cost:$cost\n,num correct: $nc / 1000\n";
#   $nerl->network->show_neuron(1);
   $passes++;
   if ($cost < $prev_cost or $passes<10){
      $prev_cost = $cost;
      $prev_nerl = $nerl;
   } else { # use $nerl as basis for $nerl
      $passes=0;

      print "New layer!";
      $prev_cost = 1000;
      $nerl = AI::Nerl->new(
         basis => $prev_nerl,
         l2 => int(rand(12))+5,
      );
      $nerl->init_network();
      $prev_nerl = $nerl;
      #die $nerl->network->theta1->slice("1:2") . $nerl->network->theta2->slice("1:2");
   }


   #print "example output, images 0 to 4\n";
   #print "Labels: " . $y(0:4) . "\n";
   #print $nerl->run($images(0:4));
#   $nerl->network->show_neuron($_) for (0..4);

}

examples/digits/digits.pl  view on Meta::CPAN

$nerl->init_network(l1 => 784, l3=>10, l2=>80,alpha=>.45);#method=batch,hidden=>12345,etc

for(1..300){
   my $n = int rand(8000);
   my $m = $n+999;
   my $ix = $images->slice("$n:$m");
   my $iy = $y->slice("$n:$m");
   $nerl->network->train($ix,$iy,passes=>5);
   my ($cost,$nc) =  $nerl->network->cost($images(9000:9999),$y(9000:9999));
   print "cost:$cost\n,num correct: $nc / 1000\n";
   print "example output, images 0 to 4\n";
   print "Labels: " . $y(0:4) . "\n";
   print $nerl->network->run($images(0:4));
   $nerl->network->show_neuron($_) for (0..4);
}

__END__
#my $label_targets = identity(10)->($labels);
my $id = identity(10);
$images = $images(10:11);
show784($images(0));
show784($images(1));
$labels = $labels(10:11);

lib/AI/Nerl/Network.pm  view on Meta::CPAN

 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])); 

=head1 DESCRIPTION

=head1 METHODS

=head2 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,

t/nerl.t  view on Meta::CPAN

      train_y => $AND,
   );
   $AND_nerl->build_network;
   my $AND_output = $AND_nerl->run($x);

}

#task: mod 3
#in: 8 bits from (n=0..255);
#out: 1 output: (n%3 != 0)
my $x = map{split '',sprintf("%b",$_)} 0..255;
$x = pdl($x)->transpose;
my $y = pdl map{$_%3 ? 1 : 0} 0..255;
$y = identity(3)->range($y->transpose);

my $nerl = AI::Nerl->new(
   train_x => $x,
   train_y => $y,
   l2 => 4,
);