mxnet.module.BucketingModule¶

class mxnet.module.BucketingModule(sym_gen, default_bucket_key=None, logger=<module 'logging' from '/var/lib/jenkins/miniconda3/envs/mxnet-docs/lib/python3.7/logging/__init__.py'>, context=cpu(0), work_load_list=None, fixed_param_names=None, state_names=None, group2ctxs=None, compression_params=None)[source]¶

This module helps to deal efficiently with varying-length inputs.

Parameters

sym_gen (function) – A function when called with a bucket key, returns a triple (symbol, data_names, label_names).
default_bucket_key (str (or any python object)) – The key for the default bucket.
logger (Logger) –
context (Context or list of Context) – Defaults to mx.cpu()
work_load_list (list of number) – Defaults to None, indicating uniform workload.
fixed_param_names (list of str) – Defaults to None, indicating no network parameters are fixed.
state_names (list 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()
group2ctxs (dict of str to context or list of context,) – or list of dict of str to context Default is None. Mapping the ctx_group attribute to the context assignment.
compression_params (dict) – Specifies type of gradient compression and additional arguments depending on the type of compression being used. For example, 2bit compression requires a threshold. Arguments would then be {‘type’:‘2bit’, ‘threshold’:0.5} See mxnet.KVStore.set_gradient_compression method for more details on gradient compression.

__init__(sym_gen, default_bucket_key=None, logger=<module 'logging' from '/var/lib/jenkins/miniconda3/envs/mxnet-docs/lib/python3.7/logging/__init__.py'>, context=cpu(0), work_load_list=None, fixed_param_names=None, state_names=None, group2ctxs=None, compression_params=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(sym_gen[, default_bucket_key, …])	Initialize self.
`backward`([out_grads])	Backward computation.
`bind`(data_shapes[, label_shapes, …])	Binding for a BucketingModule means setting up the buckets and binding the executor for the default bucket key.
`fit`(train_data[, eval_data, eval_metric, …])	Trains the module parameters.
`forward`(data_batch[, is_train])	Forward computation.
`forward_backward`(data_batch)	A convenient function that calls both `forward` and `backward`.
`get_input_grads`([merge_multi_context])	Gets the gradients with respect to the inputs of the module.
`get_outputs`([merge_multi_context])	Gets outputs from a previous forward computation.
`get_params`()	Gets current parameters.
`get_states`([merge_multi_context])	Gets states from all devices.
`init_optimizer`([kvstore, optimizer, …])	Installs and initializes optimizers.
`init_params`([initializer, arg_params, …])	Initializes parameters.
`install_monitor`(mon)	Installs monitor on all executors
`iter_predict`(eval_data[, num_batch, reset, …])	Iterates over predictions.
`load_params`(fname)	Loads model parameters from file.
`predict`(eval_data[, num_batch, …])	Runs prediction and collects the outputs.
`prepare`(data_batch[, sparse_row_id_fn])	Prepares the module for processing a data batch.
`save_params`(fname)	Saves model parameters to file.
`score`(eval_data, eval_metric[, num_batch, …])	Runs prediction on `eval_data` and evaluates the performance according to the given `eval_metric`.
`set_params`(arg_params, aux_params[, …])	Assigns parameters and aux state values.
`set_states`([states, value])	Sets value for states.
`switch_bucket`(bucket_key, data_shapes[, …])	Switches to a different bucket.
`update`()	Updates parameters according to installed optimizer and the gradient computed in the previous forward-backward cycle.
`update_metric`(eval_metric, labels[, pre_sliced])	Evaluates and accumulates evaluation metric on outputs of the last forward computation.

Attributes

`data_names`	A list of names for data required by this module.
`data_shapes`	Get data shapes.
`label_shapes`	Get label shapes.
`output_names`	A list of names for the outputs of this module.
`output_shapes`	Gets output shapes.
`symbol`	The symbol of the current bucket being used.