AI-MXNet
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examples/calculator.pl view on Meta::CPAN
batch_size => $batch_size,
data => $validation_data,
label => $validation_label,
));
}
## the network model
sub nn_fc {
my $data = mx->sym->Variable('data');
my $ln = mx->sym->exp(mx->sym->FullyConnected(
data => mx->sym->log($data),
num_hidden => 1,
));
my $wide = mx->sym->Concat($data, $ln);
my $fc = mx->sym->FullyConnected(
$wide,
num_hidden => 1
);
return mx->sym->MAERegressionOutput(data => $fc, name => 'softmax');
}
lib/AI/MXNet/Callback.pm view on Meta::CPAN
$period = max(1, $period);
return sub {
my ($iter_no, $sym, $arg, $aux) = @_;
if(($iter_no + 1) % $period == 0)
{
$mod->save_checkpoint($prefix, $iter_no + 1, $save_optimizer_states);
}
}
}
=head2 log_train_metric
Callback to log the training evaluation result every period.
Parameters
----------
$period : Int
The number of batches after which to log the training evaluation metric.
$auto_reset : Bool
Whether to reset the metric after the logging.
Returns
-------
$callback : sub ref
The callback function that can be passed as iter_epoch_callback to fit.
=cut
method log_train_metric(Int $period, Int $auto_reset=0)
{
return sub {
my ($param) = @_;
if($param->nbatch % $period == 0 and defined $param->eval_metric)
{
my $name_value = $param->eval_metric->get_name_value;
while(my ($name, $value) = each %{ $name_value })
{
AI::MXNet::Logging->info(
"Iter[%d] Batch[%d] Train-%s=%f",
lib/AI/MXNet/Callback.pm view on Meta::CPAN
}
}
package AI::MXNet::Speedometer;
use Mouse;
use Time::HiRes qw/time/;
extends 'AI::MXNet::Callback';
=head1 NAME
AI::MXNet::Speedometer - A callback that logs training speed
=cut
=head1 DESCRIPTION
Calculate and log training speed periodically.
Parameters
----------
batch_size: int
batch_size of data
frequent: int
How many batches between calculations.
Defaults to calculating & logging every 50 batches.
auto_reset: Bool
Reset the metric after each log, defaults to true.
=cut
has 'batch_size' => (is => 'ro', isa => 'Int', required => 1);
has 'frequent' => (is => 'ro', isa => 'Int', default => 50);
has 'init' => (is => 'rw', isa => 'Int', default => 0);
has 'tic' => (is => 'rw', isa => 'Num', default => 0);
has 'last_count' => (is => 'rw', isa => 'Int', default => 0);
has 'auto_reset' => (is => 'ro', isa => 'Bool', default => 1);
method call(AI::MXNet::BatchEndParam $param)
lib/AI/MXNet/Callback.pm view on Meta::CPAN
{
my $count = $param->nbatch;
my $filled_len = int(0.5 + $self->length * $count / $self->total);
my $percents = int(100.0 * $count / $self->total) + 1;
my $prog_bar = ('=' x $filled_len) . ('-' x ($self->length - $filled_len));
print "[$prog_bar] $percents%\r";
}
*slice = \&call;
# Just logs the eval metrics at the end of an epoch.
package AI::MXNet::LogValidationMetricsCallback;
use Mouse;
extends 'AI::MXNet::Callback';
=head1 NAME
AI::MXNet::LogValidationMetricsCallback - A callback to log the eval metrics at the end of an epoch.
=cut
method call(AI::MXNet::BatchEndParam $param)
{
return unless defined $param->eval_metric;
my $name_value = $param->eval_metric->get_name_value;
while(my ($name, $value) = each %{ $name_value })
{
AI::MXNet::Logging->info(
"Epoch[%d] Validation-%s=%f",
lib/AI/MXNet/Executor/Group.pm view on Meta::CPAN
Indicate whether the executors should be bind for training. When not doing training,
the memory for gradients will not be allocated.
inputs_need_grad : Bool
Indicate whether the gradients for the input data should be computed. This is currently
not used. It will be useful for implementing composition of modules.
shared_group : AI::MXNet::DataParallelExecutorGroup
Default is undef. This is used in bucketing. When not undef, it should be a executor
group corresponding to a different bucket. In other words, it will correspond to a different
symbol with the same set of parameters (e.g. unrolled RNNs with different lengths).
In this case the memory regions of the parameters will be shared.
logger : Logger
Default is AI::MXNet::Logging->get_logger.
fixed_param_names: Maybe[ArrayRef[Str]]
Indicate parameters to be fixed during training. Parameters in this array ref will not allocate
space for gradient, nor do gradient calculation.
grad_req : ArrayRef[GradReq]|HashRef[GradReq]|GradReq
Requirement for gradient accumulation. Can be 'write', 'add', or 'null'
(default to 'write').
Can be specified globally (str) or for each argument (array ref, hash ref).
state_names: Maybe[ArrayRef[Str]]
=cut
has 'symbol' => (is => 'ro', isa => 'AI::MXNet::Symbol', required => 1);
has 'contexts' => (is => 'ro', isa => 'ArrayRef[AI::MXNet::Context]', required => 1);
has 'workload' => (is => 'ro', isa => 'ArrayRef[Num]', default => sub { [] });
has 'data_shapes' => (is => 'rw', isa => 'ArrayRef[NameShape|AI::MXNet::DataDesc]', required => 1);
has 'label_shapes' => (is => 'rw', isa => 'Maybe[ArrayRef[NameShape|AI::MXNet::DataDesc]]');
has 'param_names' => (is => 'ro', isa => 'ArrayRef[Str]', required => 1);
has 'for_training' => (is => 'ro', isa => 'Bool', required => 1);
has 'inputs_need_grad' => (is => 'ro', isa => 'Bool', default => 0);
has 'shared_group' => (is => 'ro', isa => 'Maybe[AI::MXNet::DataParallelExecutorGroup]');
has 'logger' => (is => 'ro', default => sub { AI::MXNet::Logging->get_logger });
has 'fixed_param_names' => (is => 'rw', isa => 'Maybe[ArrayRef[Str]]');
has 'state_names' => (is => 'rw', isa => 'Maybe[ArrayRef[Str]]');
has 'grad_req' => (is => 'rw', isa => 'ArrayRef[GradReq]|HashRef[GradReq]|GradReq', default=>'write');
has '_p' => (is => 'rw', init_arg => undef);
sub BUILD
{
my $self = shift;
my $p = AI::MXNet::DataParallelExecutorGroup::_private->new;
$p->arg_names($self->symbol->list_arguments);
$p->aux_names($self->symbol->list_auxiliary_states);
lib/AI/MXNet/Initializer.pm view on Meta::CPAN
=cut
=head2 new
Parameters
----------
param: HashRef[AI::MXNet::NDArray]
default_init: Initializer
default initializer when a name is not found in the param hash ref.
verbose: bool
log the names when initializing.
=cut
package AI::MXNet::Load;
use Mouse;
extends 'AI::MXNet::Initializer';
has 'param' => (is => "rw", isa => 'HashRef[AI::MXNet::NDArray]', required => 1);
has 'default_init' => (is => "rw", isa => "AI::MXNet::Initializer");
has 'verbose' => (is => "rw", isa => "Int", default => 0);
lib/AI/MXNet/KVStoreServer.pm view on Meta::CPAN
Initialize a new KVStoreServer.
Parameters
----------
kvstore : KVStore
=cut
has 'kvstore' => (is => 'ro', isa => 'AI::MXNet::KVStore', required => 1);
has 'handle' => (is => 'ro', isa => 'KVStoreHandle', default => sub { shift->kvstore->handle }, lazy => 1);
has 'init_logging' => (is => 'rw', isa => 'Int', default => 0);
# return the server controller
method _controller()
{
return sub {
my ($cmd_id, $cmd_body) = @_;
if (not $self->init_logging)
{
## TODO write logging
$self->init_logging(1);
}
if($cmd_id == 0)
{
my $optimizer = Storable::thaw(MIME::Base64::decode_base64($cmd_body));
$self->kvstore->set_optimizer($optimizer);
}
else
{
my $rank = $self->kvstore->rank;
print("server $rank, unknown command ($cmd_id, $cmd_body)\n");
lib/AI/MXNet/Logging.pm view on Meta::CPAN
package AI::MXNet::Logging;
## TODO
use Mouse;
sub warning { shift; warn sprintf(shift, @_) . "\n" };
*debug = *info = *warning;
sub get_logger { __PACKAGE__->new }
1;
lib/AI/MXNet/Metric.pm view on Meta::CPAN
"shape mismatch: (@$label_shape) vs. (@$pred_shape)"
);
$label = $label->as_in_context($pred->context)->reshape([$label->size]);
$pred = AI::MXNet::NDArray->pick($pred, $label->astype('int32'), { axis => $self->axis });
if(defined $self->ignore_label)
{
my $ignore = ($label == $self->ignore_label);
$num -= $ignore->sum->asscalar;
$pred = $pred*(1-$ignore) + $ignore;
}
$loss -= $pred->maximum(1e-10)->log->sum->asscalar;
$num += $pred->size;
}, $labels, $preds);
$self->sum_metric($self->sum_metric + $loss);
$self->num_inst($self->num_inst + $num);
}
method get()
{
return ($self->name, exp($self->sum_metric / $self->num_inst));
}
lib/AI/MXNet/Metric.pm view on Meta::CPAN
my ($label, $pred) = @_;
$label = $label->aspdl->flat;
$pred = $pred->aspdl;
my $label_shape = $label->shape->at(0);
my $pred_shape = $pred->shape->at(-1);
confess(
"Size of label $label_shape and
.first dimension of pred $pred_shape do not match"
) unless $label_shape == $pred_shape;
my $prob = $pred->index($label);
$self->sum_metric($self->sum_metric + (-($prob + $self->eps)->log)->sum);
$self->num_inst($self->num_inst + $label_shape);
}, $labels, $preds);
}
package AI::MXNet::PearsonCorrelation;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::EvalMetric';
has '+name' => (default => 'pearson-correlation');
lib/AI/MXNet/Module.pm view on Meta::CPAN
=cut
extends 'AI::MXNet::Module::Base';
has '_symbol' => (is => 'ro', init_arg => 'symbol', isa => 'AI::MXNet::Symbol', required => 1);
has '_data_names' => (is => 'ro', init_arg => 'data_names', isa => 'ArrayRef[Str]');
has '_label_names' => (is => 'ro', init_arg => 'label_names', isa => 'Maybe[ArrayRef[Str]]');
has 'work_load_list' => (is => 'rw', isa => 'Maybe[ArrayRef[Int]]');
has 'fixed_param_names' => (is => 'rw', isa => 'Maybe[ArrayRef[Str]]');
has 'state_names' => (is => 'rw', isa => 'Maybe[ArrayRef[Str]]');
has 'logger' => (is => 'ro', default => sub { AI::MXNet::Logging->get_logger });
has '_p' => (is => 'rw', init_arg => undef);
has 'context' => (
is => 'ro',
isa => 'AI::MXNet::Context|ArrayRef[AI::MXNet::Context]',
default => sub { AI::MXNet::Context->cpu }
);
around BUILDARGS => sub {
my $orig = shift;
my $class = shift;
lib/AI/MXNet/Module.pm view on Meta::CPAN
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : array ref of str
Default is ['data'] for a typical model used in image classification.
label_names : array ref of str
Default is ['softmax_label'] for a typical model used in image
classification.
logger : Logger
Default is AI::MXNet::Logging.
context : Context or list of Context
Default is cpu(0).
work_load_list : array ref of number
Default is undef, indicating an uniform workload.
fixed_param_names: array ref of str
Default is undef, indicating no network parameters are fixed.
=cut
method load(
lib/AI/MXNet/Module.pm view on Meta::CPAN
)
{
# force rebinding is typically used when one want to switch from
# training to prediction phase.
if($force_rebind)
{
$self->_reset_bind();
}
if($self->binded)
{
$self->logger->warning('Already binded, ignoring bind()');
return;
}
$self->for_training($for_training);
$self->inputs_need_grad($inputs_need_grad);
$self->binded(1);
$self->_p->_grad_req($grad_req);
if(not $for_training)
{
assert(not $inputs_need_grad);
lib/AI/MXNet/Module.pm view on Meta::CPAN
symbol => $self->_symbol,
contexts => $self->_p->_context,
workload => $self->_p->_work_load_list,
data_shapes => $self->_p->_data_shapes,
label_shapes => $self->_p->_label_shapes,
param_names => $self->_p->_param_names,
state_names => $state_names,
for_training => $for_training,
inputs_need_grad => $inputs_need_grad,
shared_group => $shared_group,
logger => $self->logger,
fixed_param_names => $self->_p->_fixed_param_names,
grad_req => $grad_req
)
);
if($shared_module)
{
$self->params_initialized(1);
$self->_p->_arg_params($shared_module->_p->_arg_params);
$self->_p->_aux_params($shared_module->_p->_aux_params);
}
lib/AI/MXNet/Module.pm view on Meta::CPAN
method init_optimizer(
Str|AI::MXNet::KVStore :$kvstore='local',
Optimizer :$optimizer='sgd',
HashRef :$optimizer_params={ learning_rate => 0.01 },
Bool :$force_init=0
)
{
assert($self->binded and $self->params_initialized);
if($self->optimizer_initialized and not $force_init)
{
$self->logger->warning('optimizer already initialized, ignoring...');
return;
}
if($self->_p->_params_dirty)
{
$self->_sync_params_from_devices;
}
my ($kvstore, $update_on_kvstore) = _create_kvstore(
$kvstore,
scalar(@{$self->_p->_context}),
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
this value might not be well defined.
When those intermediate-level API are implemented properly, the following
high-level API will be automatically available for a module:
- fit: train the module parameters on a data set
- predict: run prediction on a data set and collect outputs
- score: run prediction on a data set and evaluate performance
=cut
has 'logger' => (is => 'rw', default => sub { AI::MXNet::Logging->get_logger });
has '_symbol' => (is => 'rw', init_arg => 'symbol', isa => 'AI::MXNet::Symbol');
has [
qw/binded for_training inputs_need_grad
params_initialized optimizer_initialized/
] => (is => 'rw', isa => 'Bool', init_arg => undef, default => 0);
################################################################################
# High Level API
################################################################################
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
{
&{$callback}($batch_end_params);
}
}
$nbatch++;
}
# one epoch of training is finished
my $name_value = $eval_metric->get_name_value;
while(my ($name, $val) = each %{ $name_value })
{
$self->logger->info('Epoch[%d] Train-%s=%f', $epoch, $name, $val);
}
my $toc = time;
$self->logger->info('Epoch[%d] Time cost=%.3f', $epoch, ($toc-$tic));
# sync aux params across devices
my ($arg_params, $aux_params) = $self->get_params;
$self->set_params($arg_params, $aux_params);
if($epoch_end_callback)
{
for my $callback (@{ _as_list($epoch_end_callback) })
{
&{$callback}($epoch, $self->get_symbol, $arg_params, $aux_params);
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
my $res = $self->score(
$eval_data,
$validation_metric,
score_end_callback => $eval_end_callback,
batch_end_callback => $eval_batch_end_callback,
epoch => $epoch
);
#TODO: pull this into default
while(my ($name, $val) = each %{ $res })
{
$self->logger->info('Epoch[%d] Validation-%s=%f', $epoch, $name, $val);
}
}
# end of 1 epoch, reset the data-iter for another epoch
$train_data->reset;
}
}
################################################################################
# Symbol information
################################################################################
lib/AI/MXNet/Module/Bucketing.pm view on Meta::CPAN
=head2 new
Parameters
----------
$sym_gen : subref or any perl object that overloads &{} op
A sub when called with a bucket key, returns a list with triple
of ($symbol, $data_names, $label_names).
$default_bucket_key : str or anything else
The key for the default bucket.
$logger : Logger
$context : AI::MXNet::Context or array ref of AI::MXNet::Context objects
Default is cpu(0)
$work_load_list : array ref of Num
Default is undef, indicating uniform workload.
$fixed_param_names: arrayref of str
Default is undef, indicating no network parameters are fixed.
$state_names : arrayref of str
states are similar to data and label, but not provided by data iterator.
Instead they are initialized to 0 and can be set by set_states()
=cut
lib/AI/MXNet/Module/Bucketing.pm view on Meta::CPAN
{
($arg_params, $aux_params) = $self->get_params;
}
# force rebinding is typically used when one want to switch from
# training to prediction phase.
$self->_reset_bind if $force_rebind;
if($self->binded)
{
$self->logger->warning('Already binded, ignoring bind()');
return;
}
assert((not defined $shared_module), 'shared_module for BucketingModule is not supported');
$self->for_training($for_training);
$self->inputs_need_grad($inputs_need_grad);
$self->binded(1);
my ($symbol, $data_names, $label_names) = &{$self->_sym_gen}($bucket_key//$self->_default_bucket_key);
my $module = AI::MXNet::Module->new(
symbol => $symbol,
data_names => $data_names,
label_names => $label_names,
logger => $self->logger,
context => $self->_context,
work_load_list => $self->_work_load_list,
state_names => $self->_state_names,
fixed_param_names => $self->_fixed_param_names
);
$module->bind(
data_shapes => $data_shapes,
label_shapes => $label_shapes,
for_training => $for_training,
inputs_need_grad => $inputs_need_grad,
lib/AI/MXNet/Module/Bucketing.pm view on Meta::CPAN
)
{
assert($self->binded, 'call bind before switching bucket');
if(not exists $self->_buckets->{ $bucket_key })
{
my ($symbol, $data_names, $label_names) = &{$self->_sym_gen}($bucket_key);
my $module = AI::MXNet::Module->new(
symbol => $symbol,
data_names => $data_names,
label_names => $label_names,
logger => $self->logger,
context => $self->_context,
work_load_list => $self->_work_load_list
);
$module->bind(
data_shapes => $data_shapes,
label_shapes => $label_shapes,
for_training => $self->_curr_module->for_training,
inputs_need_grad => $self->_curr_module->inputs_need_grad,
force_rebind => 0,
shared_module => $self->_buckets->{ $self->_default_bucket_key },
lib/AI/MXNet/Module/Bucketing.pm view on Meta::CPAN
method init_optimizer(
Str :$kvstore='local',
Optimizer :$optimizer='sgd',
HashRef :$optimizer_params={ learning_rate => 0.01 },
Bool :$force_init=0
)
{
assert($self->binded and $self->params_initialized);
if($self->optimizer_initialized and not $force_init)
{
$self->logger->warning('optimizer already initialized, ignoring.');
return;
}
$self->_curr_module->init_optimizer(
kvstore => $kvstore,
optimizer => $optimizer,
optimizer_params => $optimizer_params,
force_init => $force_init
);
for my $mod (values %{ $self->_buckets })
lib/AI/MXNet/Visualization.pm view on Meta::CPAN
Parameters
----------
symbol: AI::MXNet::Symbol
symbol to be visualized
shape: hashref
hashref of shapes, str->shape (arrayref[int]), given input shapes
line_length: int
total length of printed lines
positions: arrayref[float]
relative or absolute positions of log elements in each line
Returns
------
nothing
=cut
method print_summary(
AI::MXNet::Symbol $symbol,
Maybe[HashRef[Shape]] $shape=,
Int $line_length=120,
ArrayRef[Num] $positions=[.44, .64, .74, 1]
lib/AI/MXNet/Visualization.pm view on Meta::CPAN
unless defined $out_shapes;
@shape_dict{ @{ $interals->list_outputs } } = @{ $out_shapes };
}
my $conf = decode_json($symbol->tojson);
my $nodes = $conf->{nodes};
my %heads = map { $_ => 1 } @{ $conf->{heads}[0] };
if($positions->[-1] <= 1)
{
$positions = [map { int($line_length * $_) } @{ $positions }];
}
# header names for the different log elements
my $to_display = ['Layer (type)', 'Output Shape', 'Param #', 'Previous Layer'];
my $print_row = sub { my ($fields, $positions) = @_;
my $line = '';
enumerate(sub {
my ($i, $field) = @_;
$line .= $field//'';
$line = substr($line, 0, $positions->[$i]);
$line .= ' ' x ($positions->[$i] - length($line));
}, $fields);
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