AI-MXNet
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examples/lstm_bucketing.pl view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use PDL;
use AI::MXNet qw(mx);
use AI::MXNet::Function::Parameters;
use Getopt::Long qw(HelpMessage);
GetOptions(
'num-layers=i' => \(my $num_layers = 2 ),
'num-hidden=i' => \(my $num_hidden = 200 ),
'num-embed=i' => \(my $num_embed = 200 ),
'gpus=s' => \(my $gpus ),
'kv-store=s' => \(my $kv_store = 'device'),
'num-epoch=i' => \(my $num_epoch = 25 ),
'lr=f' => \(my $lr = 0.01 ),
'optimizer=s' => \(my $optimizer = 'sgd' ),
'mom=f' => \(my $mom = 0 ),
'wd=f' => \(my $wd = 0.00001 ),
'batch-size=i' => \(my $batch_size = 32 ),
'disp-batches=i' => \(my $disp_batches = 50 ),
'chkp-prefix=s' => \(my $chkp_prefix = 'lstm_' ),
'chkp-epoch=i' => \(my $chkp_epoch = 0 ),
'help' => sub { HelpMessage(0) },
) or HelpMessage(1);
=head1 NAME
lstm_bucketing.pl - Example of training LSTM RNN on Penn Tree Bank data using high level RNN interface
=head1 SYNOPSIS
--num-layers number of stacked RNN layers, default=2
--num-hidden hidden layer size, default=200
--num-embed embedding layer size, default=200
--gpus list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu.
Increase batch size when using multiple gpus for best performance.
--kv-store key-value store type, default='device'
--num-epochs max num of epochs, default=25
--lr initial learning rate, default=0.01
--optimizer the optimizer type, default='sgd'
--mom momentum for sgd, default=0.0
--wd weight decay for sgd, default=0.00001
--batch-size the batch size type, default=32
--disp-batches show progress for every n batches, default=50
--chkp-prefix prefix for checkpoint files, default='lstm_'
--chkp-epoch save checkpoint after this many epoch, default=0 (saving checkpoints is disabled)
=cut
func tokenize_text($fname, :$vocab=, :$invalid_label=-1, :$start_label=0)
{
open(F, $fname) or die "Can't open $fname: $!";
my @lines = map { my $l = [split(/ /)]; shift(@$l); $l } (<F>);
my $sentences;
($sentences, $vocab) = mx->rnn->encode_sentences(
\@lines,
vocab => $vocab,
invalid_label => $invalid_label,
start_label => $start_label
);
return ($sentences, $vocab);
}
my $buckets = [10, 20, 30, 40, 50, 60];
my $start_label = 1;
my $invalid_label = 0;
my ($train_sentences, $vocabulary) = tokenize_text(
'./data/ptb.train.txt', start_label => $start_label,
invalid_label => $invalid_label
);
my ($validation_sentences) = tokenize_text(
'./data/ptb.test.txt', vocab => $vocabulary,
start_label => $start_label, invalid_label => $invalid_label
);
my $data_train = mx->rnn->BucketSentenceIter(
$train_sentences, $batch_size, buckets => $buckets,
invalid_label => $invalid_label
);
my $data_val = mx->rnn->BucketSentenceIter(
$validation_sentences, $batch_size, buckets => $buckets,
invalid_label => $invalid_label
);
my $stack = mx->rnn->SequentialRNNCell();
for my $i (0..$num_layers-1)
{
$stack->add(mx->rnn->LSTMCell(num_hidden => $num_hidden, prefix => "lstm_l${i}_"));
}
my $sym_gen = sub {
my $seq_len = shift;
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_len, inputs => $embed, merge_outputs => 1);
my $pred = mx->sym->Reshape($outputs, shape => [-1, $num_hidden]);
$pred = mx->sym->FullyConnected(data => $pred, num_hidden => scalar(keys %$vocabulary), name => 'pred');
$label = mx->sym->Reshape($label, shape => [-1]);
$pred = mx->sym->SoftmaxOutput(data => $pred, label => $label, name => 'softmax');
return ($pred, ['data'], ['softmax_label']);
};
my $contexts;
if(defined $gpus)
{
$contexts = [map { mx->gpu($_) } split(/,/, $gpus)];
}
else
{
$contexts = mx->cpu(0);
}
my $model = mx->mod->BucketingModule(
sym_gen => $sym_gen,
default_bucket_key => $data_train->default_bucket_key,
context => $contexts
);
$model->fit(
$data_train,
eval_data => $data_val,
eval_metric => mx->metric->Perplexity($invalid_label),
kvstore => $kv_store,
optimizer => $optimizer,
optimizer_params => {
learning_rate => $lr,
momentum => $mom,
wd => $wd,
},
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)) : ())
);
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