AI-MXNet
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lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
And also the following richer information after binded:
- state information
- binded: bool, indicating whether the memory buffers needed for computation
has been allocated.
- for_training: whether the module is binded for training (if binded).
- params_initialized: bool, indicating whether the parameters of this modules
has been initialized.
- optimizer_initialized: bool, indicating whether an optimizer is defined
and initialized.
- inputs_need_grad: bool, indicating whether gradients with respect to the
input data is needed. Might be useful when implementing composition of modules.
- input/output information
- data_shapes: am array ref of [name, shape]. In theory, since the memory is allocated,
we could directly provide the data arrays. But in the case of data parallelization,
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.
- backward(out_grads=): backward operation.
- update(): update parameters according to installed optimizer.
- get_outputs(): get outputs of the previous forward operation.
- get_input_grads(): get the gradients with respect to the inputs computed
in the previous backward operation.
- update_metric(metric, labels): update performance metric for the previous forward
computed results.
- other properties (mostly for backward compatability)
- symbol: the underlying symbolic graph for this module (if any)
This property is not necessarily constant. For example, for AI::MXNet::Module::Bucketing,
this property is simply the *current* symbol being used. For other modules,
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
################################################################################
=head2 forward_backward
A convenient function that calls both forward and backward.
=cut
method forward_backward(AI::MXNet::DataBatch $data_batch)
{
$self->forward($data_batch, is_train => 1);
$self->backward();
}
=head2 score
Run prediction on eval_data and evaluate the performance according to
eval_metric.
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=,
Maybe[Callback]|ArrayRef[Callback] :$batch_end_callback=,
Maybe[Callback]|ArrayRef[Callback] :$score_end_callback=,
Bool :$reset=1,
Int :$epoch=0
)
{
assert($self->binded and $self->params_initialized);
$eval_data->reset if $reset;
if(not blessed $eval_metric or not $eval_metric->isa('AI::MXNet::EvalMetric'))
{
$eval_metric = AI::MXNet::Metric->create($eval_metric);
}
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
The objects in the results are AI::MXNet::NDArray`s. If you need to work with pdl array,
just call ->aspdl() on each AI::MXNet::NDArray.
=cut
method predict(
AI::MXNet::DataIter $eval_data,
Maybe[Int] :$num_batch=, Bool :$merge_batches=1, Bool :$reset=1, Bool :$always_output_list=0
)
{
assert($self->binded and $self->params_initialized);
$eval_data->reset() if $reset;
my @output_list;
my $nbatch = 0;
while(my $eval_batch = <$eval_data>)
{
last if defined $num_batch and $nbatch == $num_batch;
$self->forward($eval_batch, is_train => 0);
my $pad = $eval_batch->pad;
my $outputs = [map { $_->slice([0, $_->shape->[0]-($pad//0)-1])->copy } @{ $self->get_outputs }];
push @output_list, $outputs;
}
return () unless @output_list;
if($merge_batches)
{
my $num_outputs = @{ $output_list[0] };
for my $out (@output_list)
{
unless(@{ $out } == $num_outputs)
{
confess('Cannot merge batches, as num of outputs is not the same '
.'in mini-batches. Maybe bucketing is used?');
}
}
my @output_list2;
for my $i (0..$num_outputs-1)
{
push @output_list2,
AI::MXNet::NDArray->concatenate([map { $_->[$i] } @output_list]);
}
if($num_outputs == 1 and not $always_output_list)
{
return $output_list2[0];
}
return @output_list2;
}
return @output_list;
}
=head2 fit
Train the module parameters.
Parameters
----------
$train_data : AI::MXNet::DataIter
:$eval_data= : Maybe[AI::MXNet::DataIter]
If not undef, it will be used as a validation set to evaluate the performance
after each epoch.
:$eval_metric='acc' : str or AI::MXNet::EvalMetric subclass object.
Default is 'accuracy'. The performance measure used to display during training.
Other possible predefined metrics are:
'ce' (CrossEntropy), 'f1', 'mae', 'mse', 'rmse', 'top_k_accuracy'
:$epoch_end_callback= : Maybe[Callback]|ArrayRef[Callback] function or array ref of functions.
Each callback will be called with the current $epoch, $symbol, $arg_params
and $aux_params.
:$batch_end_callback= : Maybe[Callback]|ArrayRef[Callback] function or array ref of functions.
Each callback will be called with a AI::MXNet::BatchEndParam.
:$kvstore='local' : str or AI::MXNet::KVStore
Default is 'local'.
:$optimizer : str or AI::MXNet::Optimizer
Default is 'sgd'
:$optimizer_params : hash ref
Default { learning_rate => 0.01 }.
The parameters for the optimizer constructor.
:$eval_end_callback= : Maybe[Callback]|ArrayRef[Callback] function or array ref of functions
These will be called at the end of each full evaluation, with the metrics over
the entire evaluation set.
:$eval_batch_end_callback : Maybe[Callback]|ArrayRef[Callback] function or array ref of functions
These will be called at the end of each minibatch during evaluation
:$initializer= : Initializer
Will be called to initialize the module parameters if not already initialized.
:$arg_params= : hash ref
Default undef, if not undef, must be an existing parameters from a trained
model or loaded from a checkpoint (previously saved model). In this case,
the value here will be used to initialize the module parameters, unless they
are already initialized by the user via a call to init_params or fit.
$arg_params have higher priority than the $initializer.
:$aux_params= : hash ref
Default is undef. This is similar to the $arg_params, except for auxiliary states.
:$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=,
EvalMetric :$eval_metric='acc',
Maybe[Callback]|ArrayRef[Callback] :$epoch_end_callback=,
Maybe[Callback]|ArrayRef[Callback] :$batch_end_callback=,
Str :$kvstore='local',
Optimizer :$optimizer='sgd',
HashRef :$optimizer_params={ learning_rate => 0.01 },
Maybe[Callback]|ArrayRef[Callback] :$eval_end_callback=,
Maybe[Callback]|ArrayRef[Callback] :$eval_batch_end_callback=,
AI::MXNet::Initializer :$initializer=AI::MXNet::Initializer->Uniform(scale => 0.01),
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
=head2 get_input_grads
The gradients to the inputs, computed in the previous backward computation.
Parameters
----------
$merge_multi_context=1 : Bool
=cut
method get_input_grads(Bool $merge_multi_context=1) { confess("NotImplemented") }
=head2 update
Update parameters according to the installed optimizer and the gradients computed
in the previous forward-backward batch.
=cut
method update() { confess("NotImplemented") }
=head2 update_metric
Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
$eval_metric : EvalMetric
$labels : ArrayRef[AI::MXNet::NDArray]
Typically $data_batch->label.
=cut
method update_metric(EvalMetric $eval_metric, ArrayRef[AI::MXNet::NDArray] $labels)
{
confess("NotImplemented")
}
################################################################################
# module setup
################################################################################
=head2 bind
Binds the symbols in order to construct the executors. This is necessary
before the computations can be performed.
Parameters
----------
$data_shapes : ArrayRef[AI::MXNet::DataDesc]
Typically is $data_iter->provide_data.
:$label_shapes= : Maybe[ArrayRef[AI::MXNet::DataDesc]]
Typically is $data_iter->provide_label.
:$for_training=1 : Bool
Default is 1. Whether the executors should be bind for training.
:$inputs_need_grad=0 : Bool
Default is 0. Whether the gradients to the input data need to be computed.
Typically this is not needed. But this might be needed when implementing composition
of modules.
:$force_rebind=0 : Bool
Default is 0. This function does nothing if the executors are already
binded. But with this as 1, the executors will be forced to rebind.
:$shared_module= : A subclass of AI::MXNet::Module::Base
Default is undef. This is used in bucketing. When not undef, the shared module
essentially corresponds to a different bucket -- a module with different symbol
but with the same sets of parameters (e.g. unrolled RNNs with different lengths).
:$grad_req='write' : Str|ArrayRef[Str]|HashRef[Str]
Requirement for gradient accumulation. Can be 'write', 'add', or 'null'
(defaults to 'write').
Can be specified globally (str) or for each argument (array ref, hash ref).
=cut
method bind(
ArrayRef[AI::MXNet::DataDesc] $data_shapes,
Maybe[ArrayRef[AI::MXNet::DataDesc]] :$label_shapes=,
Bool :$for_training=1,
Bool :$inputs_need_grad=0,
Bool :$force_rebind=0,
Maybe[AI::MXNet::BaseModule] :$shared_module=,
Str|ArrayRef[Str]|HashRef[Str] :$grad_req='write'
)
{
confess("NotImplemented")
}
=head2 init_optimizer
Install and initialize optimizers.
Parameters
----------
:$kvstore='local' : str or KVStore
:$optimizer='sgd' : str or Optimizer
:$optimizer_params={ learning_rate => 0.01 } : hash ref
:$force_init=0 : Bool
=cut
method init_optimizer(
Str :$kvstore='local',
Optimizer :$optimizer='sgd',
HashRef :$optimizer_params={ learning_rate => 0.01 },
Bool :$force_init=0
)
{
confess("NotImplemented")
}
################################################################################
# misc
################################################################################
=head2 symbol
The symbol associated with this module.
Except for AI::MXNet::Module, for other types of modules (e.g. AI::MXNet::Module::Bucketing), this
property might not be a constant throughout its life time. Some modules might
not even be associated with any symbols.
=cut
method symbol()
{
return $self->_symbol;
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