What are the training tools in chainer

This recipe explains what are the training tools in chainer

Recipe Objective - What are the training tools in chainer?

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.

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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.

class chainer.training.Trainer(updater, stop_trigger=None, out='result', extensions=None)

This is the standard training loop in Chainer.

Trainer is an implementation of a training loop. Users can invoke the training by calling the run() method.

Each iteration of the training loop proceeds as follows.

Update of the parameters. It includes the mini-batch loading, forward and backward computations, and an execution of the update formula. These are all done by the update object held by the trainer.

Invocation of trainer extensions in the descending order of their priorities. A trigger object is attached to each extension, and it decides at each iteration whether the extension should be executed. Trigger objects are callable objects that take the trainer object as the argument and return a boolean value indicating whether the extension should be called or not.

Extensions are callable objects that take the trainer object as the argument. There are three ways to define custom extensions: inheriting the Extension class, decorating functions by make_extension(), and defining any callable including lambda functions. See Extension for more details on custom extensions and how to configure them.

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