Table Of Contents
Table Of Contents


class mxnet.module.SequentialModule(logger=<module 'logging' from '/var/lib/jenkins/miniconda3/envs/mxnet-docs/lib/python3.7/logging/'>)[source]

A SequentialModule is a container module that can chain multiple modules together.


Building a computation graph with this kind of imperative container is less flexible and less efficient than the symbolic graph. So, this should be only used as a handy utility.

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

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


__init__([logger]) Initialize self.
add(module, **kwargs) Add a module to the chain.
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 with respect to the inputs of the module.
get_outputs([merge_multi_context]) Gets outputs from a 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 parameters.
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 installed optimizer and the gradient computed in the previous forward-backward cycle.
update_metric(eval_metric, labels[, pre_sliced]) Evaluates and accumulates evaluation metric on outputs of the last forward computation.


data_names A list of names for data required by this module.
data_shapes Gets data shapes.
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.