AI::MXNet - Perl interface to MXNet machine learning library


    ## Convolutional NN for recognizing hand-written digits in MNIST dataset
    ## It's considered "Hello, World" for Neural Networks
    ## For more info about the MNIST problem please refer to L<>

    use strict;
    use warnings;
    use AI::MXNet qw(mx);
    use AI::MXNet::TestUtils qw(GetMNIST_ubyte);
    use Test::More tests => 1;

    # symbol net
    my $batch_size = 100;

    ### model
    my $data = mx->symbol->Variable('data');
    my $conv1= mx->symbol->Convolution(data => $data, name => 'conv1', num_filter => 32, kernel => [3,3], stride => [2,2]);
    my $bn1  = mx->symbol->BatchNorm(data => $conv1, name => "bn1");
    my $act1 = mx->symbol->Activation(data => $bn1, name => 'relu1', act_type => "relu");
    my $mp1  = mx->symbol->Pooling(data => $act1, name => 'mp1', kernel => [2,2], stride =>[2,2], pool_type=>'max');

    my $conv2= mx->symbol->Convolution(data => $mp1, name => 'conv2', num_filter => 32, kernel=>[3,3], stride=>[2,2]);
    my $bn2  = mx->symbol->BatchNorm(data => $conv2, name=>"bn2");
    my $act2 = mx->symbol->Activation(data => $bn2, name=>'relu2', act_type=>"relu");
    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');

    # check data

    my $train_dataiter = mx->io->MNISTIter({
        data_shape=>[1, 28, 28],
        batch_size=>$batch_size, shuffle=>1, flat=>0, silent=>0, seed=>10});
    my $val_dataiter = mx->io->MNISTIter({
        data_shape=>[1, 28, 28],
        batch_size=>$batch_size, shuffle=>1, flat=>0, silent=>0});

    my $n_epoch = 1;
    my $mod = mx->mod->new(symbol => $softmax);
        eval_data => $val_dataiter,
        optimizer_params=>{learning_rate=>0.01, momentum=> 0.9},
    my $res = $mod->score($val_dataiter, mx->metric->create('acc'));
    ok($res->{accuracy} > 0.8);

    ## Gluon MNIST example

    my $net = nn->Sequential();
    $net->name_scope(sub {
        $net->add(nn->Dense(128, activation=>'relu'));
        $net->add(nn->Dense(64, activation=>'relu'));

    # data
    sub transformer
        my ($data, $label) = @_;
        $data = $data->reshape([-1])->astype('float32')/255;
        return ($data, $label);
    my $train_data = gluon->data->DataLoader(
        gluon->data->vision->MNIST('./data', train=>1, transform => \&transformer),
        batch_size=>$batch_size, shuffle=>1, last_batch=>'discard'

    ## training
    sub train
        my ($epochs, $ctx) = @_;
        # Collect all parameters from net and its children, then initialize them.
        $net->initialize(mx->init->Xavier(magnitude=>2.24), ctx=>$ctx);
        # Trainer is for updating parameters with gradient.
        my $trainer = gluon->Trainer($net->collect_params(), 'sgd', { learning_rate => $lr, momentum => $momentum });
        my $metric = mx->metric->Accuracy();
        my $loss = gluon->loss->SoftmaxCrossEntropyLoss();

        for my $epoch (0..$epochs-1)
            # reset data iterator and metric at begining of epoch.
            enumerate(sub {
                my ($i, $d) = @_;
                my ($data, $label) = @$d;
                $data = $data->as_in_context($ctx);
                $label = $label->as_in_context($ctx);
                # Start recording computation graph with record() section.
                # Recorded graphs can then be differentiated with backward.
                my $output;
                autograd->record(sub {
                    $output = $net->($data);
                    my $L = $loss->($output, $label);
                # take a gradient step with batch_size equal to data.shape[0]
                # update metric at last.
                $metric->update([$label], [$output]);

                if($i % $log_interval == 0 and $i > 0)
                    my ($name, $acc) = $metric->get();
                    print "[Epoch $epoch Batch $i] Training: $name=$acc\n";
            }, \@{ $train_data });

            my ($name, $acc) = $metric->get();
            print "[Epoch $epoch] Training: $name=$acc\n";

            my ($val_name, $val_acc) = test($ctx);
            print "[Epoch $epoch] Validation: $val_name=$val_acc\n"

    train($epochs, $cuda ? mx->gpu(0) : mx->cpu);


    Perl interface to MXNet machine learning library.
    MXNet supports the Perl programming language.
    The MXNet Perl package brings flexible and efficient GPU computing and
    state-of-art deep learning to Perl.
    It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl.
    It also lets you construct and customize the state-of-art deep learning models in Perl,
    and apply them to tasks, such as image classification and data science challenges.

    One important thing to internalize is that Perl interface is written to be as close as possible to the Python’s API,
    so most, if not all of Python’s documentation and examples should just work in Perl after making few changes
    in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ;
    and in 99% of cases that’s all that is needed there.
    In addition please refer to very detailed L<MXNet Python API Documentation|>.

    AI::MXNet supports new imperative PyTorch like Gluon MXNet interface.
    Please get acquainted with this new interface at L<Dive into Deep Learning|>.

    For specific Perl Gluon usage please refer to Perl examples and tests directories on github,
    but be assured that the Python and Perl usage are extremely close in order to make the use
    of the Python Gluon docs and examples as easy as possible.

    AI::MXNet is seamlessly glued with L<PDL|>, the C++ level state can be easily initialized from PDL
    and the results can be transferred to PDL objects in order to allow you to use all the glory and power of the PDL!


    Parity with Python interface is mostly achieved, few deprecated
    and not often used features left unported for now.


    L<Function::Parameters|>, L<Mouse|>


    Sergey Kolychev, <>


    This library is licensed under Apache 2.0 license L<>