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
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.
||Interface of updater objects for trainers.|
||Standard implementation of Updater.|
||Implementation of a parallel GPU Updater.|
||Implementation of a multiprocess parallel GPU Updater.|
||Base class of trainer extensions.|
||Decorator to make given functions into trainer extensions.|
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.
||Gets a trigger object.|