AI-MXNet
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examples/cudnn_lstm_bucketing.pl view on Meta::CPAN
--model-prefix prefix for checkpoint files for loading/saving, default='lstm_'
--load-epoch load from epoch
--stack-rnn stack rnn to reduce communication overhead (1,0 default 0)
--bidirectional whether to use bidirectional layers (1,0 default 0)
--dropout dropout probability (1.0 - keep probability), default 0
=cut
$bidirectional = $bidirectional ? 1 : 0;
$stack_rnn = $stack_rnn ? 1 : 0;
func tokenize_text($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 $invalid_label = 0;
func get_data($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,
layout => $layout
examples/lstm_bucketing.pl view on Meta::CPAN
--lr initial learning rate, default=0.01
--optimizer the optimizer type, default='sgd'
--mom momentum for sgd, default=0.0
--wd weight decay for sgd, default=0.00001
--batch-size the batch size type, default=32
--disp-batches show progress for every n batches, default=50
--chkp-prefix prefix for checkpoint files, default='lstm_'
--chkp-epoch save checkpoint after this many epoch, default=0 (saving checkpoints is disabled)
=cut
func tokenize_text($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 => $invalid_label
);
lib/AI/MXNet/IO.pm view on Meta::CPAN
method provide_label()
{
return [map {
my ($k, $v) = @{ $_ };
my $shape = $v->shape;
$shape->[0] = $self->batch_size;
AI::MXNet::DataDesc->new(name => $k, shape => $shape, dtype => $v->dtype)
} @{ $self->label }];
}
# Ignore roll over data and set to start
method hard_reset()
{
$self->cursor(-$self->batch_size);
}
method reset()
{
if($self->last_batch_handle eq 'roll_over' and $self->cursor > $self->num_data)
{
$self->cursor(-$self->batch_size + ($self->cursor%$self->num_data)%$self->batch_size);
lib/AI/MXNet/IO.pm view on Meta::CPAN
{
my $name = $iter_meta{ $data_iter }{__name__};
no strict 'refs';
{
*{__PACKAGE__."::$name"} = $data_iter;
}
}
}
}
# Initialize the io in startups
__PACKAGE__->_init_io_module;
1;
lib/AI/MXNet/Image.pm view on Meta::CPAN
Created with tools/im2rec.py or with custom script.
Format: index\t[one or more label separated by \t]\trelative_path_from_root
imglist: array ref
a list of image with the label(s)
each item is a list [imagelabel: float or array ref of float, imgpath]
path_root : str
Root folder of image files
path_imgidx : str
Path to image index file. Needed for partition and shuffling when using .rec source.
shuffle : bool
Whether to shuffle all images at the start of each iteration.
Can be slow for HDD.
part_index : int
Partition index
num_parts : int
Total number of partitions.
data_name='data' Str
label_name='softmax_label' Str
kwargs : hash ref with any additional arguments for augmenters
=cut
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
Parameters
----------
$eval_data : AI::MXNet::DataIter
$eval_metric : AI::MXNet::EvalMetric
:$num_batch= : Maybe[Int]
Number of batches to run. Default is undef, indicating run until the AI::MXNet::DataIter
finishes.
:$batch_end_callback= : Maybe[Callback]
Could also be a array ref of functions.
:$reset=1 : Bool
Default 1, indicating whether we should reset $eval_data before starting
evaluating.
$epoch=0 : Int
Default is 0. For compatibility, this will be passed to callbacks (if any). During
training, this will correspond to the training epoch number.
=cut
method score(
AI::MXNet::DataIter $eval_data,
EvalMetric $eval_metric,
Maybe[Int] :$num_batch=,
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
=head2 iter_predict
Iterate over predictions.
Parameters
----------
$eval_data : AI::MXNet::DataIter
:$num_batch= : Maybe[Int]
Default is undef, indicating running all the batches in the data iterator.
:$reset=1 : bool
Default is 1, indicating whether we should reset the data iter before start
doing prediction.
=cut
method iter_predict(AI::MXNet::DataIter $eval_data, Maybe[Int] :$num_batch=, Bool :$reset=1)
{
assert($self->binded and $self->params_initialized);
if($reset)
{
$eval_data->reset;
}
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
Run prediction and collect the outputs.
Parameters
----------
$eval_data : AI::MXNet::DataIter
:$num_batch= : Maybe[Int]
Default is undef, indicating running all the batches in the data iterator.
:$merge_batches=1 : Bool
Default is 1.
:$reset=1 : Bool
Default is 1, indicating whether we should reset the data iter before start
doing prediction.
:$always_output_list=0 : Bool
Default is 0, see the doc for return values.
Returns
-------
When $merge_batches is 1 (by default), the return value will be an array ref
[$out1, $out2, $out3] where each element is concatenation of the outputs for
all the mini-batches. If $always_output_list` also is 0 (by default),
then in the case of a single output, $out1 is returned in stead of [$out1].
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
:$allow_missing=0 : Bool
Default is 0. Indicates whether we allow missing parameters when $arg_params
and $aux_params are not undefined. If this is 1, then the missing parameters
will be initialized via the $initializer.
:$force_rebind=0 : Bool
Default is 0. Whether to force rebinding the executors if already binded.
:$force_init=0 : Bool
Default is 0. Indicates whether we should force initialization even if the
parameters are already initialized.
:$begin_epoch=0 : Int
Default is 0. Indicates the starting epoch. Usually, if we are resuming from a
checkpoint saved at a previous training phase at epoch N, then we should specify
this value as N+1.
:$num_epoch : Int
Number of epochs for the training.
=cut
method fit(
AI::MXNet::DataIter $train_data,
Maybe[AI::MXNet::DataIter] :$eval_data=,
lib/AI/MXNet/Module/Bucketing.pm view on Meta::CPAN
=encoding UTF-8
=head1 NAME
AI::MXNet::Module::Bucketing
=head1 SYNOPSIS
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/Monitor.pm view on Meta::CPAN
=cut
method install(AI::MXNet::Executor $exe)
{
$exe->set_monitor_callback($self->stat_helper);
push @{ $self->exes }, $exe;
}
=head2 tic
start collecting stats for current batch.
Call before forward
=cut
method tic()
{
if ($self->step % $self->interval == 0)
{
for my $exe (@{ $self->exes })
{
$_->wait_to_read for @{ $exe->arg_arrays };
lib/AI/MXNet/NDArray.pm view on Meta::CPAN
return $pdl;
}
=head2 _slice
Returns sliced NDArray that shares memory with the current one.
Parameters
----------
start : int
Starting index of slice.
stop : int
Finishing index of slice.
=cut
method _slice (
Index $start,
Index $stop
)
{
confess("start $start > stop $stop") if $start > $stop;
my $handle = check_call(
AI::MXNetCAPI::NDArraySlice(
$self->handle,
$start,
$stop
)
);
return __PACKAGE__->new(handle => $handle, writable => $self->writable);
}
=head2 _at
Returns a sub NDArray that shares memory with current one.
lib/AI/MXNet/NDArray.pm view on Meta::CPAN
return $ret
}
=head2 arange
Similar function in the MXNet ndarray as numpy.arange
See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html.
Parameters
----------
:$start=0 : number, optional
Start of interval. The interval includes this value. The default start value is 0.
$stop= : number, optional
End of interval. The interval does not include this value.
:$step=1 : number, optional
Spacing between the values
:$repeat=1 : number, optional
The repeating time of all elements.
E.g repeat=3, the element a will be repeated three times --> a, a, a.
:$ctx : Context, optional
The context of the NDArray, defaultw to current default context.
:$dtype : data type, optional
The value type of the NDArray, defaults to float32
Returns
-------
$out : NDArray
The created NDArray
=cut
method arange(Index :$start=0, Index :$stop=, Index :$step=1, Index :$repeat=1,
AI::MXNet::Context :$ctx=AI::MXNet::Context->current_ctx, Dtype :$dtype='float32')
{
return __PACKAGE__->_arange({
start => $start,
(defined $stop ? (stop => $stop) : ()),
step => $step,
repeat => $repeat,
dtype => $dtype,
ctx => "$ctx"
});
}
=head2 load
lib/AI/MXNet/NDArray/Base.pm view on Meta::CPAN
=head1 NAME
AI::MXNet::NDArray::Base
=cut
=head1 DESCRIPTION
This module provides a convenient interface to a C++ functions
that work with NDArray.
Essentially it loads them up during the lib startup into the Perl space.
=cut
my %function_meta;
method function_meta($code)
{
return $function_meta{$code};
}
method function_meta_hash()
{
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
AI::MXNet::Symbol->can('uniform'), AI::MXNet::Symbol->can('Variable') etc.
Use AI::MXNet::Symbol->can('Variable') if you want to directly
feed the input as states.
@kwargs :
more keyword arguments passed to func. For example
mean, std, dtype, etc.
Returns
-------
$states : ArrayRef[AI::MXNet::Symbol]
starting states for first RNN step
=cut
method begin_state(CodeRef :$func=AI::MXNet::Symbol->can('zeros'), @kwargs)
{
assert(
(not $self->_modified),
"After applying modifier cells (e.g. DropoutCell) the base "
."cell cannot be called directly. Call the modifier cell instead."
);
my @states;
lib/AI/MXNet/RNN/IO.pm view on Meta::CPAN
$sentences : array ref of array refs of str
A array ref of sentences to encode. Each sentence
should be a array ref of string tokens.
:$vocab : undef or hash ref of str -> int
Optional input Vocabulary
:$invalid_label : int, default -1
Index for invalid token, like <end-of-sentence>
:$invalid_key : str, default '\n'
Key for invalid token. Uses '\n' for end
of sentence by default.
:$start_label=0 : int
lowest index.
Returns
-------
$result : array ref of array refs of int
encoded sentences
$vocab : hash ref of str -> int
result vocabulary
=cut
method encode_sentences(
ArrayRef[ArrayRef] $sentences,
Maybe[HashRef] :$vocab=,
Int :$invalid_label=-1,
Str :$invalid_key="\n",
Int :$start_label=0
)
{
my $idx = $start_label;
my $new_vocab;
if(not defined $vocab)
{
$vocab = { $invalid_key => $invalid_label };
$new_vocab = 1;
}
else
{
$new_vocab = 0;
}
lib/AI/MXNet/Symbol.pm view on Meta::CPAN
$attr->{__layout__} = $__layout__ if defined $__layout__;
while(my ($k, $v) = each %{ $kwargs })
{
if($k =~ /^__/ and $k =~ /__$/)
{
$attr->{$k} = "$v";
}
else
{
confess("Attribute name=$k is not supported.".
' Additional attributes must start and end with double underscores,'.
' e.g, __yourattr__'
);
}
}
$ret->_set_attr(%{ $attr });
return $ret;
}
=head2 var
lib/AI/MXNet/Symbol.pm view on Meta::CPAN
return __PACKAGE__->_ones({ shape => $shape, dtype => $dtype, name => $name, ($__layout__ ? (__layout__ => $__layout__) : ()) });
}
=head2 arange
Simlar function in the MXNet ndarray as numpy.arange
See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html.
Parameters
----------
start : number
Start of interval. The interval includes this value. The default start value is 0.
stop : number, optional
End of interval. The interval does not include this value.
step : number, optional
Spacing between values
repeat : int, optional
"The repeating time of all elements.
E.g repeat=3, the element a will be repeated three times --> a, a, a.
dtype : type, optional
The value type of the NDArray, default to np.float32
Returns
-------
out : Symbol
The created Symbol
=cut
method arange(Index :$start=0, Index :$stop=, Num :$step=1.0, Index :$repeat=1, Maybe[Str] :$name=, Dtype :$dtype='float32')
{
return __PACKAGE__->_arange({
start => $start, (defined $stop ? (stop => $stop) : ()),
step => $step, repeat => $repeat, name => $name, dtype => $dtype
});
}
sub _parse_arguments
{
my $type = shift;
my @args = @_;
my $type_c = find_type_constraint($type);
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