# 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.