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

    bn_size : Int, default 4
        Multiplicative factor for number of bottle neck layers.
        (i.e. bn_size * k features in the bottleneck layer)
    dropout : float, default 0
        Rate of dropout after each dense layer.
    classes : int, default 1000
        Number of classification classes.
=cut
has [qw/num_init_features
        growth_rate/] => (is => 'ro', isa => 'Int', required => 1);
has 'block_config'    => (is => 'ro', isa => 'ArrayRef[Int]', required => 1);
has 'bn_size'         => (is => 'ro', isa => 'Int', default => 4);
has 'dropout'         => (is => 'ro', isa => 'Num', default => 0);
has 'classes'         => (is => 'ro', isa => 'Int', default => 1000);
method python_constructor_arguments(){ [qw/num_init_features growth_rate block_config bn_size dropout classes/] }

sub BUILD
{
    my $self = shift;
    $self->name_scope(sub {
        $self->features(nn->HybridSequential(prefix=>''));

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

    channels : array ref of Int
        Numbers of channels in each block. Length should be one larger than layers list.
    classes : int, default 1000
        Number of classification classes.
    thumbnail : bool, default 0
        Enable thumbnail.
=cut

has 'block'     => (is => 'ro', isa => 'Str', required => 1);
has ['layers',
    'channels'] => (is => 'ro', isa => 'ArrayRef[Int]', required => 1);
has 'classes'   => (is => 'ro', isa => 'Int', default => 1000);
has 'thumbnail' => (is => 'ro', isa => 'Bool', default => 0);
method python_constructor_arguments() { [qw/block layers channels classes thumbnail/] }
func _conv3x3($channels, $stride, $in_channels)
{
    return nn->Conv2D(
        $channels, kernel_size=>3, strides=>$stride, padding=>1,
        use_bias=>0, in_channels=>$in_channels
    );
}