++ed by:
PABLROD FARACO PERLOVER SSCAFFIDI EGOR

12 PAUSE users
5 non-PAUSE users.

Sergey V. Kolychev

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 mostly achieved, few deprecated
    and not often used features left unported for now.

SEE ALSO

    http://mxnet.io/
    https://github.com/dmlc/mxnet/tree/master/perl-package
    Function::Parameters, Mouse

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