Table Of Contents
Table Of Contents

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.