AI-Nerl

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

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;
say 't10k data loaded';

my $nerl = AI::Nerl->new(
   # type => image,dims=>[28,28],...
   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;

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

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),

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

      #arn $out_neurons x $img->transpose if $pass > 1;; #(t,10)
      $a = $a((0));
      my $label = $labels(($i));
      my $d= $id(($label)) - $a;
      $d = -$d * $a * (1-$a); #(t,10)
      $delta += $d->transpose x $img;
      if (rand() < 1.002){
         warn $d;
         warn $a;
         warn "$label -> " . $a->maximum_ind;
         say "\n"x 2;
      }
      if ($pass%250==0 and $i<5){
         warn $a;
         warn $d;
         warn "$label -> " . $a->maximum_ind;
      }
   }
   $delta /= $images->dim(0);
   $delta *= .2;
   $out_neurons -= $delta;

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

use PDL::Graphics2D;

if(!defined($dims[1])){
   $img_pdl = $img_pdl->squeeze;
}
elsif ($dims[1]==28){ #images
   @dims = (28**2,$dims[0]);
   $img_pdl = $img_pdl->reshape(@dims)->transpose();
   #imag2d($img_pdl(3000)->reshape(28,28)/256);
}
say "out: " . join ',',$img_pdl->dims;

$img_pdl->wfits($img_filename . '.fits');