AI-Nerl

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

   scale_input => 1/256,
);

$nerl->init_network(l1 => 784, l3=>10, l2=>7);#method=batch,hidden=>12345,etc

my $prev_nerl = $nerl;
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!";

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

my $y = identity(10)->range($labels->transpose)->sever;
$y *= 2;
$y -= 1;
say 't10k data loaded';

my $nerl = AI::Nerl->new(
   # type => image,dims=>[28,28],...
   scale_input => 1/256,
#   train_x => $images(0:99),
#   train_y => $y(0:99),
#   test_x => $images(8000:8999),
#   test_y => $y(8000:8999),
#   cv_x => $images(9000:9999),
#   cv_y => $y(9000:9999),
);

$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");

lib/AI/Nerl.pm  view on Meta::CPAN

use PDL;
use AI::Nerl::Network;

# ABSTRACT: Neural networks with backpropagation.

# main_module

our $VERSION = .03;

#A Nerl is a mechanism to build neural networks?
#Give it training,test, and cv data?
#it settles on a learning rate and stuff?
#or maybe it's also a language for guided training?
#or maybe a visual gui thing?
#Not exactly sure. Maybe I'm tinkering with forces better left alone.
#That's a great excuse for failing horribly.


=head1 AI::Nerl - A sort of stackable neural network builder thing.

=head1 SYNOPSIS

lib/AI/Nerl.pm  view on Meta::CPAN

   isa => 'Num',
   default => 30,
);

has [qw/ train_x 
         train_y /] => (
   is => 'ro',
   isa => 'PDL',
   required => 0, #training can be done manually.
);
has [qw/ test_x cv_x
         test_y cv_y /] => (
   is => 'ro',
   isa => 'PDL',
   required => 0,
);

has network => (
   required=>0,
   is => 'rw',
   isa => 'AI::Nerl::Network',
);