Training loop abstraction¶
Chainer provides a standard implementation of the training loops under the chainer.training
module. It is built on top of many other core features of Chainer, including Variable and Function, Link/Chain/ChainList, Optimizer, Dataset, and Reporter/Summary. Compared to the training loop abstraction of other machine learning tool kits, Chainer’s training framework aims at maximal flexibility, while keeps the simplicity for the typical usages. Most components are pluggable, and users can overwrite the definition.
The core of the training loop abstraction is Trainer
, which implements the training loop itself. The training loop consists of two parts: one is Updater
, which actually updates the parameters to train, and the other is Extension
for arbitrary functionalities other than the parameter update.
Updater and some extensions use chainer.dataset
and Iterator
to scan the datasets and load mini batches. The trainer also uses Reporter
to collect the observed values, and some extensions use DictSummary
to accumulate them and computes the statistics.
You can find many examples for the usage of this training utilities from the official examples. You can also search the extension implementations from Trainer extensions.
Trainer¶
chainer.training.Trainer |
The standard training loop in Chainer. |
Updater¶
chainer.training.Updater |
Interface of updater objects for trainers. |
chainer.training.StandardUpdater |
Standard implementation of Updater. |
chainer.training.ParallelUpdater |
Implementation of a parallel GPU Updater. |
chainer.training.updaters.MultiprocessParallelUpdater |
Implementation of a multiprocess parallel GPU Updater. |
Extension¶
chainer.training.Extension |
Base class of trainer extensions. |
chainer.training.make_extension |
Decorator to make given functions into trainer extensions. |
Trigger¶
Trigger is a callable object to decide when to process some specific event within the training loop. It takes a Trainer object as the argument, and returns True if some event should be fired.
It is mainly used to determine when to call an extension. It is also used to determine when to quit the training loop.
chainer.training.get_trigger |
Gets a trigger object. |