AI-MXNet
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examples/cudnn_lstm_bucketing.pl view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use AI::MXNet qw(mx);
use AI::MXNet::Function::Parameters;
use AI::MXNet::Base;
use Getopt::Long qw(HelpMessage);
GetOptions(
'test' => \(my $do_test ),
'num-layers=i' => \(my $num_layers = 2 ),
'num-hidden=i' => \(my $num_hidden = 256 ),
'num-embed=i' => \(my $num_embed = 256 ),
'num-seq=i' => \(my $seq_size = 32 ),
'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 = 'adam' ),
'mom=f' => \(my $mom = 0 ),
examples/cudnn_lstm_bucketing.pl view on Meta::CPAN
'dropout=f', => \(my $dropout = 0 ),
'help' => sub { HelpMessage(0) },
) or HelpMessage(1);
=head1 NAME
char_lstm.pl - Example of training char LSTM RNN on tiny shakespeare using high level RNN interface
=head1 SYNOPSIS
--test Whether to test or train (default 0)
--num-layers number of stacked RNN layers, default=2
--num-hidden hidden layer size, default=200
--num-seq sequence size, default=32
--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='adam'
--mom momentum for sgd, default=0.0
examples/cudnn_lstm_bucketing.pl view on Meta::CPAN
my $start_label = 1;
my $invalid_label = 0;
func get_data($layout)
{
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,
layout => $layout
);
my $data_val = mx->rnn->BucketSentenceIter(
$validation_sentences, $batch_size, buckets => $buckets,
invalid_label => $invalid_label,
examples/cudnn_lstm_bucketing.pl view on Meta::CPAN
wd => $wd,
},
begin_epoch => $load_epoch,
initializer => mx->init->Xavier(factor_type => "in", magnitude => 2.34),
num_epoch => $num_epoch,
batch_end_callback => mx->callback->Speedometer($batch_size, $disp_batches),
($model_prefix ? (epoch_end_callback => mx->rnn->do_rnn_checkpoint($cell, $model_prefix, 1)) : ())
);
};
my $test = sub {
assert($model_prefix, "Must specifiy path to load from");
my (undef, $data_val, $vocab) = get_data('NT');
my $stack;
if($stack_rnn)
{
$stack = mx->rnn->SequentialRNNCell();
for my $i (0..$num_layers-1)
{
my $cell = mx->rnn->LSTMCell(num_hidden => $num_hidden, prefix => "lstm_${i}l0_");
if($bidirectional)
examples/cudnn_lstm_bucketing.pl view on Meta::CPAN
mx->metric->Perplexity($invalid_label),
batch_end_callback=>mx->callback->Speedometer($batch_size, 5)
);
};
if($num_layers >= 4 and split(/,/,$gpus) >= 4 and not $stack_rnn)
{
print("WARNING: stack-rnn is recommended to train complex model on multiple GPUs\n");
}
if($do_test)
{
# Demonstrates how to load a model trained with CuDNN RNN and predict
# with non-fused MXNet symbol
$test->();
}
else
{
$train->();
}
examples/lstm_bucketing.pl view on Meta::CPAN
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
);
lib/AI/MXNet.pm view on Meta::CPAN
=head1 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');
lib/AI/MXNet/Contrib/AutoGrad.pm view on Meta::CPAN
};
}
method train_section(CodeRef $sub)
{
my $prev = __PACKAGE__->set_is_training(1);
$sub->();
__PACKAGE__->set_is_training(0) unless $prev;
}
method test_section(CodeRef $sub)
{
my $prev = __PACKAGE__->set_is_training(0);
$sub->();
__PACKAGE__->set_is_training(1) if $prev;
}
1;
lib/AI/MXNet/IO.pm view on Meta::CPAN
=head1 DESCRIPTION
Predefined NDArray iterator. Accepts PDL or AI::MXNet::NDArray object as an input.
Parameters
----------
data: Maybe[AcceptableInput|HashRef[AcceptableInput]|ArrayRef[AcceptableInput]].
NDArrayIter supports single or multiple data and label.
label: Maybe[AcceptableInput|HashRef[AcceptableInput]|ArrayRef[AcceptableInput]].
Same as data, but is not given to the model during testing.
batch_size=1: Int
Batch Size
shuffle=0: Bool
Whether to shuffle the data
last_batch_handle='pad': 'pad', 'discard' or 'roll_over'
How to handle the last batch
Note
----
This iterator will pad, discard or roll over the last batch if
lib/AI/MXNet/IO.pm view on Meta::CPAN
sub DEMOLISH
{
check_call(AI::MXNetCAPI::DataIterFree(shift->handle));
}
=head2 debug_skip_load
Set the iterator to simply return always first batch.
Notes
-----
This can be used to test the speed of network without taking
the loading delay into account.
=cut
method debug_skip_load()
{
$self->_debug_skip_load(1);
AI::MXNet::Logging->info('Set debug_skip_load to be true, will simply return first batch');
}
method reset()
lib/AI/MXNet/Module.pm view on Meta::CPAN
ArrayRef[AI::MXNet::NDArray] $labels
)
{
$self->_p->_exec_group->update_metric($eval_metric, $labels);
}
=head2 _sync_params_from_devices
Synchronize parameters from devices to CPU. This function should be called after
calling 'update' that updates the parameters on the devices, before one can read the
latest parameters from $self->_arg_params and $self->_aux_params.
=cut
method _sync_params_from_devices()
{
$self->_p->_exec_group->get_params($self->_p->_arg_params, $self->_p->_aux_params);
$self->_p->_params_dirty(0);
}
method save_optimizer_states(Str $fname)
{
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
the data arrays might not be of the same shape as viewed from the external world.
- label_shapes: an array ref of [name, shape]. This might be [] if the module does
not need labels (e.g. it does not contains a loss function at the top), or a module
is not binded for training.
- output_shapes: an array ref of [name, shape] for outputs of the module.
- parameters (for modules with parameters)
- get_params(): return an array ($arg_params, $aux_params). Each of those
is a hash ref of name to NDArray mapping. Those NDArrays always on
CPU. The actual parameters used for computing might be on other devices (GPUs),
this function will retrieve (a copy of) the latest parameters. Therefore, modifying
- get_params($arg_params, $aux_params): assign parameters to the devices
doing the computation.
- init_params(...): a more flexible interface to assign or initialize the parameters.
- setup
- bind(): prepare environment for computation.
- init_optimizer(): install optimizer for parameter updating.
- computation
- forward(data_batch): forward operation.
lib/AI/MXNet/Module/Bucketing.pm view on Meta::CPAN
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 => $invalid_label
);
lib/AI/MXNet/Optimizer.pm view on Meta::CPAN
/
($acc_g + $self->epsilon)->sqrt
*
$grad;
$acc_delta .= $self->rho * $acc_delta + (1 - $self->rho) * $current_delta * $current_delta;
$weight -= $current_delta + $wd * $weight;
}
__PACKAGE__->register;
# For test use
package AI::MXNet::Test;
use Mouse;
extends 'AI::MXNet::Optimizer';
# Create a state to duplicate weight
method create_state(Index $index, AI::MXNet::NDArray $weight)
{
return AI::MXNet::NDArray->zeros(
$weight->shape,
lib/AI/MXNet/TestUtils.pm view on Meta::CPAN
use Scalar::Util qw(blessed);
use AI::MXNet::Function::Parameters;
use Exporter;
use base qw(Exporter);
@AI::MXNet::TestUtils::EXPORT_OK = qw(same reldiff almost_equal GetMNIST_ubyte
GetCifar10 pdl_maximum pdl_minimum mlp2 conv
check_consistency zip assert enumerate same_array dies_like);
use constant default_numerical_threshold => 1e-6;
=head1 NAME
AI::MXNet::TestUtils - Convenience subs used in tests.
=head2 same
Test if two pdl arrays are the same
Parameters
----------
a : pdl
b : pdl
=cut
lib/AI/MXNet/TestUtils.pm view on Meta::CPAN
return $softmax;
}
=head2 check_consistency
Check symbol gives the same output for different running context
Parameters
----------
sym : Symbol or list of Symbols
symbol(s) to run the consistency test
ctx_list : list
running context. See example for more detail.
scale : float, optional
standard deviation of the inner normal distribution. Used in initialization
grad_req : str or list of str or dict of str to str
gradient requirement.
=cut
my %dtypes = (
float32 => 0,
lib/AI/MXNet/TestUtils.pm view on Meta::CPAN
my $gt = $ground_truth;
if(not defined $gt)
{
$gt = { %{ $exe_list[$max_idx]->output_dict } };
if($grad_req ne 'null')
{
%{$gt} = (%{$gt}, %{ $exe_list[$max_idx]->grad_dict });
}
}
# test
for my $exe (@exe_list)
{
$exe->forward(0);
}
enumerate(sub {
my ($i, $exe) = @_;
if($i == $max_idx)
{
return;
}
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