NAME
AI::MXNet::Module::Bucketing
SYNOPSIS
This is alpha release.
Please refer to t dir for examples.DESCRIPTION
Implements the AI::MXNet::Module::Base API, and allows multiple
symbols to be used depending on the `bucket_key` provided by each different
mini-batch of datanew
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, $label_names).
default_bucket_key : str or anything else
The key for the default bucket.
logger : Logger
context : Context or arrayref of Context
Default `cpu()`
work_load_list : array ref of Num
Default undef, indicating uniform workload.
fixed_param_names: arrayref of str
Default undef, indicating no network parameters are fixed.
state_names : arrayref of str
states are similar to data and label, but not provided by data iterator.
Instead they are initialized to 0 and can be set by set_states()data_names
A list of names for data required by this module.output_names
A list of names for the outputs of this module.data_shapes
Get data shapes.
Returns
-------
An array ref of AI::MXNet::DataDesc objects.label_shapes
Get label shapes.
Returns
-------
An array ref of AI::MXNet::DataDesc objects. The return value could be undef if
the module does not need labels, or if the module is not binded for
training (in this case, label information is not available).
An array ref of AI::MXNet::DataDesc objects.output_shapes
Get output shapes.
Returns
-------
An array ref of AI::MXNet::DataDesc objects.get_params
Get current parameters.
Returns
-------
List of ($arg_params, $aux_params), each a hash ref of name to parameters (
AI::MXNet::NDArray) mapping.set_params
Assign parameter and aux state values.
Parameters
----------
arg_params : HashRef[AI::MXNet::NDArray]
aux_params : HashRef[AI::MXNet::NDArray]
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.init_params
Initialize parameters.
Parameters
----------
initializer : AI::MXNet::Initializer, default AI::MXNet::Initalizer->Uniform->(scale => 0.01)
arg_params : HashRef
Default undef. Existing parameters. This has higher priority than `initializer`.
aux_params : HashRef
Default undef. Existing auxiliary states. This has higher priority than `initializer`.
allow_missing : Bool
Allow missing values in `arg_params` and `aux_params` (if not undef). In this case,
missing values will be filled with `initializer` Default 0
force_init : Bool
Default 0bind
Binding for a AI::MXNet::Module::Bucketing means setting up the buckets and bind the
executor for the default bucket key. Executors corresponding to other keys are
binded afterwards with 'switch_bucket'.
Parameters
----------
data_shapes : ArrayRef[AI::MXNet::DataDesc|NameShape]
This should correspond to the symbol for the default bucket.
label_shapes : Maybe[ArrayRef[AI::MXNet::DataDesc|NameShape]]
This should correspond to the symbol for the default bucket.
for_training : Bool
Default is 1.
inputs_need_grad : Bool
Default is 0.
force_rebind : Bool
Default is 0.
shared_module : AI::MXNet::Module::Bucketing
Default is undef. This value is currently not used.
grad_req : str, array ref of str, hash ref of str to str
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).
bucket_key : str
bucket key for binding. by default use the default_bucket_keyswitch_bucket
Switch to a different bucket. This will change $self->_curr_module.
Parameters
----------
bucket_key : str (or any perl object that overloads "" op)
The key of the target bucket.
data_shapes : ArrayRef[AI::MXNet::DataDesc|NameShape]
Typically `data_batch.provide_data`.
label_shapes : Maybe[ArrayRef[AI::MXNet::DataDesc|NameShape]]
Typically `data_batch.provide_label`.init_optimizer
Install and initialize optimizers.
Parameters
----------
kvstore : str or AI::MXNet::KVStore object
Default 'local'
optimizer : str or AI::MXNet::Optimizer object
Default 'sgd'
optimizer_params : hash ref
Default: { learning_rate => 0.01 }
force_init : Bool
Default 0, indicating whether we should force re-initializing the
optimizer in the case an optimizer is already installed.forward
Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : Bool
Default is undef, in which case 'is_train' is taken from $self->for_training.backward
Backward computation.
Parameters
----------
out_grads : Maybe[ArrayRef[AI::MXNet::NDArray]|AI::MXNet::NDArray]
Default: undefupdate
Update parameters according to installed optimizer and the gradient computed
in the previous forward-backward cycle.get_outputs
Get outputs from a previous forward computation.
Parameters
----------
merge_multi_context : Bool
Default is 1. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A 1 value indicates that we
should merge the collected results so that they look like from a single
executor.
Returns
-------
If 'merge_multi_context' is 1, it is like [out1, out2]. Otherwise, it
is like [[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]. All the output
elements are pdl objects.get_input_grads
Get the gradients with respect to the inputs of the module.
Parameters
----------
merge_multi_context : Bool
Default is 1. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A 1 value indicates that we
should merge the collected results so that they look like from a single
executor.
Returns
-------
If 'merge_multi_context' is 1, it is like [grad1, grad2]. Otherwise, it
is like [[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]. All the output
elements are AI::MXNet::NDArray objects.update_metric Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : AI::MXNet::EvalMetric
labels : ArrayRef[AI::MXNet::NDArray]
Typically $data_batch->label.symbol
The symbol of the current bucket being used.get_symbol
The symbol of the default bucket being used.install_monitor
Install monitor on all executors.
Paramters
---------
AI::MXNet::Monitor