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.