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

#!/usr/bin/perl

use Modern::Perl;
use PDL;
use PDL::NiceSlice;
use PDL::IO::FITS;
use PDL::Constants 'E';
use lib 'lib';
use lib '../../lib';
use AI::Nerl;

use FindBin qw($Bin); 
chdir $Bin;

unless (-e "t10k-labels-idx1-ubyte.fits"){ die <<"NODATA";}
pull this data by running get_digits.sh
convert it to FITS by running idx_to_fits.pl
NODATA


my $images = rfits('t10k-images-idx3-ubyte.fits');
my $labels = rfits('t10k-labels-idx1-ubyte.fits');
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");
   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);

my $out_neurons = grandom(28*28,10) * .01;