mxnet.module.Module¶

class mxnet.module.Module(symbol, data_names=('data', ), label_names=('softmax_label', ), 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]¶

Module is a basic module that wrap a Symbol. It is functionally the same as the FeedForward model, except under the module API.

Parameters

symbol (Symbol) –
data_names (list of str) – Defaults to (‘data’) for a typical model used in image classification.
label_names (list of str) – Defaults to (‘softmax_label’) for a typical model used in image classification.
logger (Logger) – Defaults to logging.
context (Context or list of Context) – Defaults to mx.cpu().
work_load_list (list of number) – Default None, indicating uniform workload.
fixed_param_names (list of str) – Default 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__(symbol, data_names=('data', ), label_names=('softmax_label', ), 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__`(symbol[, data_names, label_names, …])	Initialize self.
`backward`([out_grads])	Backward computation.
`bind`(data_shapes[, label_shapes, …])	Binds the symbols to construct executors.
`borrow_optimizer`(shared_module)	Borrows optimizer from a shared module.
`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 of the 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 the parameters and auxiliary states.
`install_monitor`(mon)	Installs monitor on all executors.
`iter_predict`(eval_data[, num_batch, reset, …])	Iterates over predictions.
`load`(prefix, epoch[, load_optimizer_states])	Creates a model from previously saved checkpoint.
`load_optimizer_states`(fname)	Loads optimizer (updater) state from a file.
`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.
`reshape`(data_shapes[, label_shapes])	Reshapes the module for new input shapes.
`save_checkpoint`(prefix, epoch[, …])	Saves current progress to checkpoint.
`save_optimizer_states`(fname)	Saves optimizer (updater) state to a file.
`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 parameter and aux state values.
`set_states`([states, value])	Sets value for states.
`update`()	Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch.
`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`	Gets data shapes.
`label_names`	A list of names for labels required by this module.
`label_shapes`	Gets label shapes.
`output_names`	A list of names for the outputs of this module.
`output_shapes`	Gets output shapes.
`symbol`	Gets the symbol associated with this module.