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lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN

                        while( my ($i, $label_index) = each @top_for_image ) {
                            my $class_index = $includes_background_class ? $label_index : $label_index + 1;
                            push @tr, [ $h->td(
                                    $i + 1,
                                    $class_index,
                                    $labels[$class_index],
                                    $probabilities_batched->at($label_index,$batch_idx),
                            ) ];

                        }
                        $h->table([$h->tr(@tr)])
                    },
                )
        })
    );
    IPerl->display($html);
} else {
    for my $batch_idx (0..$#image_names) {
        my $image_name = $image_names[$batch_idx];
        my @top_for_image = $top_lists[$batch_idx]->list;
        my @td;
        say "Image name: `$image_name`";
        my $header = [ ('Rank', 'Label No', 'Label', 'Prob') ];
        my @rows;
        while( my ($i, $label_index) = each @top_for_image ) {
            my $class_index = $includes_background_class ? $label_index : $label_index + 1;
            push @rows, [ (
                    $i + 1,
                    $class_index,
                    $labels[$class_index],
                    $probabilities_batched->at($label_index,$batch_idx),
            ) ];
        }
        say generate_table( rows => [ $header, @rows ], header_row => 1 );
        print "\n";
    }
}

my $p_approx_batched = $probabilities_batched->sumover->approx(1, 1e-5);
p $p_approx_batched;
say "All probabilities sum up to approximately 1" if $p_approx_batched->all->sclr;

use Filesys::DiskUsage qw/du/;

my $total = du( { 'human-readable' => 1, dereference => 1 },
    $model_archive_path, $model_base, $labels_path );

say "Disk space usage: $total"; undef;

my @solid_channel_uris = (
    'https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/Solid_red.svg/480px-Solid_red.svg.png',
    'https://upload.wikimedia.org/wikipedia/commons/thumb/1/1d/Green_00FF00_9x9.svg/480px-Green_00FF00_9x9.svg.png',
    'https://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Solid_blue.svg/480px-Solid_blue.svg.png',
);
undef;

__END__

=pod

=encoding UTF-8

=head1 NAME

AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubMobileNetV2Model - Using TensorFlow to do image classification using a pre-trained model

=head1 SYNOPSIS

The following tutorial is based on the L<Image Classification with TensorFlow Hub notebook|https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/image_classification.ipynb>. It uses a pre-trained model based on the I<MobileNet V2> arch...

Please look at the L<SECURITY note|https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md> regarding running models as models are programs. You can also used C<saved_model_cli scan> to check for L<security-sensitive "denylisted ops"|https:/...

If you would like to visualise a model, you can use L<Netron|https://github.com/lutzroeder/netron> on the C<.pb> file.

=head1 COLOPHON

The following document is either a POD file which can additionally be run as a Perl script or a Jupyter Notebook which can be run in L<IPerl|https://p3rl.org/Devel::IPerl> (viewable online at L<nbviewer|https://nbviewer.org/github/EntropyOrg/perl-AI-...

If you are running the code, you may optionally install the L<C<tensorflow> Python package|https://www.tensorflow.org/install/pip> in order to access the C<saved_model_cli> command, but this is only used for informational purposes.

=head1 TUTORIAL

=head2 Load the library

First, we need to load the C<AI::TensorFlow::Libtensorflow> library and more helpers. We then create an C<AI::TensorFlow::Libtensorflow::Status> object and helper function to make sure that the calls to the C<libtensorflow> C library are working prop...

  use strict;
  use warnings;
  use utf8;
  use constant IN_IPERL => !! $ENV{PERL_IPERL_RUNNING};
  no if IN_IPERL, warnings => 'redefine'; # fewer messages when re-running cells
  
  use feature qw(say state);
  use Syntax::Construct qw(each-array);
  
  use lib::projectroot qw(lib);
  
  BEGIN {
      if( IN_IPERL ) {
          $ENV{TF_CPP_MIN_LOG_LEVEL} = 3;
      }
      require AI::TensorFlow::Libtensorflow;
  }
  
  use URI ();
  use HTTP::Tiny ();
  use Path::Tiny qw(path);
  
  use File::Which ();
  
  use List::Util ();
  
  use Data::Printer ( output => 'stderr', return_value => 'void', filters => ['PDL'] );
  use Data::Printer::Filter::PDL ();
  use Text::Table::Tiny qw(generate_table);
  
  use Imager;
  
  my $s = AI::TensorFlow::Libtensorflow::Status->New;
  sub AssertOK {
      die "Status $_[0]: " . $_[0]->Message