AI-MXNet

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examples/calculator.pl  view on Meta::CPAN

    my $sym = nn_fc();

    ## call as ./calculator.pl 1 to just print model and exit
    if($ARGV[0]) {
        my @dsz = @{$train_iter->data->[0][1]->shape};
        my @lsz = @{$train_iter->label->[0][1]->shape};
        my $shape = {
            data          => [ $batch_size, splice @dsz,  1 ],
            softmax_label => [ $batch_size, splice @lsz, 1 ],
        };
        print mx->viz->plot_network($sym, shape => $shape)->graph->as_png;
        exit;
    }

    my $model = mx->mod->Module(
        symbol => $sym,
        context => mx->cpu(),
    );
    $model->fit($train_iter,
        eval_data => $eval_iter,
        optimizer => 'adam',

examples/mnist.pl  view on Meta::CPAN

    my $win = Gtk2::Window->new('toplevel');
    $win->signal_connect(delete_event => sub { Gtk2->main_quit() });
    $win->add($hbox);
    $win->show_all();
    Gtk2->main();
}

sub show_network {
    my($viz) = @_;
    my $load = Gtk2::Gdk::PixbufLoader->new();
    $load->write($viz->graph->as_png);
    $load->close();
    my $img = Gtk2::Image->new_from_pixbuf($load->get_pixbuf());
    my $sw = Gtk2::ScrolledWindow->new(undef, undef);
    $sw->add_with_viewport($img);
    my $win = Gtk2::Window->new('toplevel');
    $win->signal_connect(delete_event => sub { Gtk2->main_quit() });
    $win->add($sw);
    $win->show_all();
    Gtk2->main();
}

examples/plot_network.pl  view on Meta::CPAN

my $mp2  = mx->symbol->Pooling(data => $act2, name => 'mp2', kernel=>[2,2], stride=>[2,2], pool_type=>'max');


my $fl   = mx->symbol->Flatten(data => $mp2, name=>"flatten");
my $fc1  = mx->symbol->FullyConnected(data => $fl,  name=>"fc1", num_hidden=>30);
my $act3 = mx->symbol->Activation(data => $fc1, name=>'relu3', act_type=>"relu");
my $fc2  = mx->symbol->FullyConnected(data => $act3, name=>'fc2', num_hidden=>10);
my $softmax = mx->symbol->SoftmaxOutput(data => $fc2, name => 'softmax');

## creates the image file in working directory, you need GraphViz installed for this to work
mx->viz->plot_network($softmax, save_format => 'png')->render("network.png");

lib/AI/MXNet/Visualization.pm  view on Meta::CPAN

    my $mp2  = mx->symbol->Pooling(data => $act2, name => 'mp2', kernel=>[2,2], stride=>[2,2], pool_type=>'max');


    my $fl   = mx->symbol->Flatten(data => $mp2, name=>"flatten");
    my $fc1  = mx->symbol->FullyConnected(data => $fl,  name=>"fc1", num_hidden=>30);
    my $act3 = mx->symbol->Activation(data => $fc1, name=>'relu3', act_type=>"relu");
    my $fc2  = mx->symbol->FullyConnected(data => $act3, name=>'fc2', num_hidden=>10);
    my $softmax = mx->symbol->SoftmaxOutput(data => $fc2, name => 'softmax');

    ## creates the image file working directory
    mx->viz->plot_network($softmax, save_format => 'png')->render("network.png"); 

=head1 DESCRIPTION

     Vizualization support for Perl interface to MXNet machine learning library

=head1 Class methods

=head2 print_summary

    convert symbol for detail information

lib/AI/MXNet/Visualization.pm  view on Meta::CPAN

    }
    return $dot;
}

package AI::MXNet::Visualization::PythonGraphviz;
use Mouse;
use AI::MXNet::Types;
has 'format' => (
    is => 'ro',
    isa => enum([qw/debug canon text ps hpgl pcl mif
                    pic gd gd2 gif jpeg png wbmp cmapx
                    imap vdx vrml vtx mp fig svg svgz
                    plain/]
    )
);
has 'graph' => (is => 'ro', isa => 'GraphViz');

method render($output=)
{
    my $method = 'as_' . $self->format;
    return $self->graph->$method($output);