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use strict;
use warnings;
use AI::MXNet::Function::Parameters;
package AI::MXNet::Gluon::ModelZoo::Vision::VGG;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
use AI::MXNet::Base;

=head1 NAME 

    AI::MXNet::Gluon::ModelZoo::Vision::VGG - VGG model from the "Very Deep Convolutional Networks for Large-Scale Image Recognition"
=cut

=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);

sub BUILD
{
    my $self = shift;
    assert(@{ $self->layers } == @{ $self->filters });
    $self->name_scope(sub {
        $self->features($self->_make_features());
        $self->features->add(nn->Dense(4096, activation=>'relu',
                                       weight_initializer=>'normal',
                                       bias_initializer=>'zeros'));
        $self->features->add(nn->Dropout(rate=>0.5));
        $self->features->add(nn->Dense(4096, activation=>'relu',
                                       weight_initializer=>'normal',