Table Of Contents
Table Of Contents

mxnet.module.PythonModule

class mxnet.module.PythonModule(data_names, label_names, output_names, logger=<module 'logging' from '/var/lib/jenkins/miniconda3/envs/mxnet-docs/lib/python3.7/logging/__init__.py'>)[source]

A convenient module class that implements many of the module APIs as empty functions.

Parameters
  • data_names (list of str) – Names of the data expected by the module.

  • label_names (list of str) – Names of the labels expected by the module. Could be None if the module does not need labels.

  • output_names (list of str) – Names of the outputs.

__init__(data_names, label_names, output_names, logger=<module 'logging' from '/var/lib/jenkins/miniconda3/envs/mxnet-docs/lib/python3.7/logging/__init__.py'>)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(data_names, label_names, output_names)

Initialize self.

backward([out_grads])

Backward computation.

bind(data_shapes[, label_shapes, …])

Binds the symbols to construct executors.

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 to the inputs, computed in the previous backward computation.

get_outputs([merge_multi_context])

Gets outputs of the previous forward computation.

get_params()

Gets parameters, those are potentially copies of the the actual parameters used to do computation on the device.

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_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 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

A list of (name, shape) pairs specifying the data inputs to this module.

label_shapes

A list of (name, shape) pairs specifying the label inputs to this module.

output_names

A list of names for the outputs of this module.

output_shapes

A list of (name, shape) pairs specifying the outputs of this module.

symbol

Gets the symbol associated with this module.