Table Of Contents
Table Of Contents

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.