chainer.training
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 ~chainer.training.Trainer, which implements the training loop itself. The training loop consists of two parts: one is ~chainer.training.Updater, which actually updates the parameters to train, and the other is ~chainer.training.Extension for arbitrary functionalities other than the parameter update.
Updater and some extensions use chainer.dataset and ~chainer.dataset.Iterator to scan the datasets and load mini-batches. The trainer also uses ~chainer.Reporter to collect the observed values, and some extensions use ~chainer.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 extensions.
chainer.training.Trainer
chainer.training.Updater chainer.training.updaters.StandardUpdater chainer.training.updaters.ParallelUpdater chainer.training.updaters.MultiprocessParallelUpdater
An extension is a callable object that can perform arbitrary actions during the training loop. Extensions can be registered to Trainer by using Trainer.extend method, and they are invoked when the Trigger <triggers> condition is satisfied.
In addition to the built-in extensions listed below, you can define your own extension by implementing Extension or using the make_extension decorator. See ../guides/extensions for details.
chainer.training.Extension chainer.training.make_extension
These extensions provide features to collect additional metrics. The typical use case is to use ~chainer.training.extensions.Evaluator to perform evaluation with a validation dataset to compute validation loss/accuracy.
chainer.training.extensions.Evaluator chainer.training.extensions.MicroAverage
chainer.training.extensions.FailOnNonNumber chainer.training.extensions.ParameterStatistics
chainer.training.extensions.observe_lr chainer.training.extensions.observe_value
These extensions provide features to adjust optimizer behavior. The typical use case is to change the learning rate of the optimizer over time.
chainer.training.extensions.ExponentialShift chainer.training.extensions.LinearShift
These extensions provide features to perform reporting of metrics and various statistics to the console or files.
chainer.training.extensions.PrintReport chainer.training.extensions.ProgressBar
chainer.training.extensions.LogReport
chainer.training.extensions.PlotReport chainer.training.extensions.VariableStatisticsPlot
chainer.training.extensions.dump_graph
These extensions provide features to take snapshots of models.
chainer.training.extensions.snapshot chainer.training.extensions.snapshot_object
A 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 chainer.training.triggers.BestValueTrigger chainer.training.triggers.EarlyStoppingTrigger chainer.training.triggers.IntervalTrigger chainer.training.triggers.ManualScheduleTrigger chainer.training.triggers.MaxValueTrigger chainer.training.triggers.MinValueTrigger chainer.training.triggers.TimeTrigger