paddlespeech.text.models.ernie_linear.ernie_linear_updater module

class paddlespeech.text.models.ernie_linear.ernie_linear_updater.ErnieLinearEvaluator(model: Layer, criterion: Layer, dataloader: DataLoader, output_dir=None)

Bases: StandardEvaluator

Attributes:

name

Methods

__call__([trainer])	Main action of the extention.
finalize(trainer)	Action that is executed when training is done.
initialize(trainer)	Action that is executed once to get the corect trainer state.
on_error(trainer, exc, tb)	Handles the error raised during training before finalization.

evaluate
evaluate_core

evaluate_core(batch)

models: Dict[str, Layer]

class paddlespeech.text.models.ernie_linear.ernie_linear_updater.ErnieLinearUpdater(model: Layer, criterion: Layer, scheduler: LRScheduler, optimizer: Optimizer, dataloader: DataLoader, output_dir=None)

Bases: StandardUpdater

Attributes:

updates_per_epoch

Number of updater per epoch, determined by the length of the dataloader.

Methods

new_epoch()	Start a new epoch.
read_batch()	Read a batch from the data loader, auto renew when data is exhausted.
set_state_dict(state_dict)	Set state dict for a Updater.
state_dict()	State dict of a Updater, model, optimizer and updater state are included.
update_core(batch)	A simple case for a training step.

load
save
update

update_core(batch)

A simple case for a training step. Basic assumptions are: Single model; Single optimizer; A batch from the dataloader is just the input of the model; The model return a single loss, or a dict containing serval losses. Parameters updates at every batch, no gradient accumulation.