AI::MXNet(perlapi):如何适应动物园模型

下面的代码没有抱怨,但它不适合()。fit()会立即返回。我的数据集是一个RecordIO格式的图像文件(使用im2rec构建),我确信它是有效的。

我已经从这里修改了代码 https://codehex.hateblo.jp/entry/2017/09/12/160149 ,其中原始作者以象征性方式创建模型(而不是从ModelZoo)。

第二个问题是从一组图像创建数据加载器的最佳实践是什么。是我正在做的(使用 mx->io->ImageRecordIter )好吗?

use strict;
use warnings;

use AI::MXNet qw/mx/;

use AI::MXNet::Gluon qw/gluon/; # needed for nn-> (which must be replaced by gluon->nn->...)
use AI::MXNet::Gluon::NN qw(nn); # for nn->
use AI::MXNet::Gluon::ModelZoo::Vision::VGG;
use AI::MXNet::Monitor;

my $ctx = mx->cpu(0);

my $vgg = AI::MXNet::Gluon::ModelZoo::Vision->get_vgg(
    11, # num layers
    (
        'classes' => 26, # latin alphabet recognition
        'root' => 'abc', # where to save the models
        'ctx' => $ctx # context
    )
);
die "get the model" unless defined $vgg;

my $batch_size = 4;
# num channels, width, height our png training images of letters:
my $img_shape = [3, 256, 256];
my $training_file = 'training.bin';
my $train_dataiter = mx->io->ImageRecordIter({
    'path_imgrec' => $training_file,
    'path_imglist' => 'training.lst',
    # num channels, width, height
    'data_shape' => $img_shape,
    'batch_size' => $batch_size,
    'label_width' => 1, # dimensionality of labels, for us is 1 (i.e. just the letter name)
});
die "mx->io->ImageRecordIter()" unless defined $train_dataiter;

$vgg->init_params(initializer => mx->init->Xavier(magnitude => 2));
$vgg->init_optimizer(optimizer => 'sgd', optimizer_params => {learning_rate => 0.1});
$vgg->initialize();

print "$0 : fitting ...\n";
$vgg->fit(
    $train_dataiter,
    'num_epoch' => 1000
);