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examples/char_lstm.pl  view on Meta::CPAN

    }
    $stack->add($cell);
}

my $data  = mx->sym->Variable('data');
my $label = mx->sym->Variable('softmax_label');
my $embed = mx->sym->Embedding(
        data => $data, input_dim => scalar(keys %vocabulary),
        output_dim => $num_embed, name => 'embed'
);
$stack->reset;
my ($outputs, $states) = $stack->unroll($seq_size, inputs => $embed, merge_outputs => 1);
my $pred  = mx->sym->Reshape($outputs, shape => [-1, $num_hidden*(1+($bidirectional ? 1 : 0))]);
$pred     = mx->sym->FullyConnected(data => $pred, num_hidden => $data_iter->vocab_size, name => 'pred');
$label    = mx->sym->Reshape($label, shape => [-1]);
my $net   = mx->sym->SoftmaxOutput(data => $pred, label => $label, name => 'softmax');

my $contexts;
if(defined $gpus)
{
    $contexts = [map { mx->gpu($_) } split(/,/, $gpus)];
}
else
{
    $contexts = mx->cpu(0);
}

my $model = mx->mod->Module(
    symbol  => $net,
    context => $contexts
);
$model->fit(
    $data_iter,
    eval_metric         => mx->metric->Perplexity,
    kvstore             => $kv_store,
    optimizer           => $optimizer,
    optimizer_params    => {
                                learning_rate => $lr,
                                momentum      => $mom,
                                wd            => $wd,
                                clip_gradient => 5,
                                rescale_grad  => 1/$batch_size,
                                lr_scheduler  => AI::MXNet::FactorScheduler->new(step => 1000, factor => 0.99)
                        },
    initializer         => mx->init->Xavier(factor_type => "in", magnitude => 2.34),
    num_epoch           => $num_epoch,
    batch_end_callback  => mx->callback->Speedometer($batch_size, $disp_batches),
    ($chkp_epoch ? (epoch_end_callback  => [mx->rnn->do_rnn_checkpoint($stack, $chkp_prefix, $chkp_epoch), \&sample]) : ())
);

sub sample {
    return if not $sample_size;
    $model->reshape(data_shapes=>[['data',[1, $seq_size]]], label_shapes=>[['softmax_label',[1, $seq_size]]]);
    my $input = mx->nd->array($fdata->slice([0, $seq_size-1]))->reshape([1, $seq_size]);
    $| = 1;
    for (0..$sample_size-1)
    {
        $model->forward(mx->io->DataBatch(data=>[$input]), is_train => 0);
        my $prob = $model->get_outputs(0)->[0][0]->at($seq_size-1)->aspdl;
        my $next_char = Math::Random::Discrete->new($prob->reshape(-1)->unpdl, [0..scalar(keys %vocabulary)-1])->rand;
        print "$reverse_vocab{$next_char}";
        $input->at(0)->slice([0, $seq_size-2]) .= $input->at(0)->slice([1, $seq_size-1])->copy;
        $input->at(0)->at($seq_size-1) .= $next_char;
    }
    $model->reshape(data_shapes=>[['data',[$batch_size, $seq_size]]], label_shapes=>[['softmax_label',[$batch_size, $seq_size]]]);
}