NAME

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

SYNOPSIS

    ## 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 http://neuralnetworksanddeeplearning.com/chap1.html

    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
    GetMNIST_ubyte();

    my $train_dataiter = mx->io->MNISTIter({
        image=>"data/train-images-idx3-ubyte",
        label=>"data/train-labels-idx1-ubyte",
        data_shape=>[1, 28, 28],
        batch_size=>$batch_size, shuffle=>1, flat=>0, silent=>0, seed=>10});
    my $val_dataiter = mx->io->MNISTIter({
        image=>"data/t10k-images-idx3-ubyte",
        label=>"data/t10k-labels-idx1-ubyte",
        data_shape=>[1, 28, 28],
        batch_size=>$batch_size, shuffle=>1, flat=>0, silent=>0});

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

DESCRIPTION

    Perl interface to MXNet machine learning library.

BUGS AND INCOMPATIBILITIES

Parity with Python inteface is not yet achieved. Pod mostly contains Python documentation taken as is. This is WIP.

SEE ALSO

http://mxnet.io/ https://github.com/dmlc/mxnet/tree/master/perl-package

AUTHOR

Sergey Kolychev, <sergeykolychev.github@gmail.com>

COPYRIGHT & LICENSE

Copyright (C) 2017 by Sergey Kolychev <sergeykolychev.github@gmail.com>

This library is licensed under Apache 2.0 license https://www.apache.org/licenses/LICENSE-2.0