view release on metacpan or  search on metacpan

lib/AI/MXNet/Gluon/ModelZoo/Vision.pm  view on Meta::CPAN

use AI::MXNet::Gluon::ModelZoo::Vision::ResNet;
use AI::MXNet::Gluon::ModelZoo::Vision::SqueezeNet;
use AI::MXNet::Gluon::ModelZoo::Vision::VGG;

sub import
{
    my ($class, $short_name) = @_;
    if($short_name)
    {
        $short_name =~ s/[^\w:]//g;
        if(length $short_name)
        {
            my $short_name_package =<<"EOP";
            package $short_name;
            \@${short_name}::ISA = ('AI::MXNet::Gluon::ModelZoo::Vision');
            1;
EOP
            eval $short_name_package;
        }
    }
}

lib/AI/MXNet/Gluon/ModelZoo/Vision/VGG.pm  view on Meta::CPAN

=head1 DESCRIPTION

    VGG model from the "Very Deep Convolutional Networks for Large-Scale Image Recognition"
    <https://arxiv.org/abs/1409.1556> paper.

    Parameters
    ----------
    layers : array ref of Int
        Numbers of layers in each feature block.
    filters : array ref of Int
        Numbers of filters in each feature block. List length should match the layers.
    classes : Int, default 1000
        Number of classification classes.
    batch_norm : Bool, default 0
        Use batch normalization.
=cut
method python_constructor_arguments() { [qw/layers filters classes batch_norm/] }
has ['layers',
     'filters']   => (is => 'ro', isa => 'ArrayRef[Int]', required => 1);
has  'classes'    => (is => 'ro', isa => 'Int', default => 1000);
has  'batch_norm' => (is => 'ro', isa => 'Bool', default => 0);