chainer.training.extensions.WarmupShift

class chainer.training.extensions.WarmupShift(attr, warmup_start, warmup_iter, init, optimizer=None)

Trainer extension to gradually initialize an optimizer attribute.

This extension changes an optimizer attribute evenly at the beginning of one training.

For example, suppose that this extension is called at every iteration, and warmup_start = x , init = y, warmup_iter = t. Then this extension will set the corresponding attribute to from x to y evenly in first t iterations.

This extension is also called before the training loop starts by default.

Parameters

• attr (str) – Name of the optimizer attribute to adjust.

• warmup_start (float) – the value of the attr at the beginning of one training.

• init (float) – the value of the attr after warm up iterations.

• warmup_iter (int) – the number of the iterations in which the attr changes from warmup_start to init.

• optimizer (Optimizer) – Target optimizer object. If it is None, the main optimizer of the trainer is used.

Methods

__call__(trainer)

Invokes the extension.

Implementations should override this operator. This method is called at iterations which the corresponding trigger accepts.

Parameters

trainer (Trainer) – Trainer object that calls this operator.

finalize()

Finalizes the extension.

This method is called at the end of the training loop.

initialize(trainer)

Initializes up the trainer state.

This method is called before entering the training loop. An extension that modifies the state of Trainer can override this method to initialize it.

When the trainer has been restored from a snapshot, this method has to recover an appropriate part of the state of the trainer.

For example, ExponentialShift extension changes the optimizer’s hyperparameter at each invocation. Note that the hyperparameter is not saved to the snapshot; it is the responsibility of the extension to recover the hyperparameter. The ExponentialShift extension recovers it in its initialize method if it has been loaded from a snapshot, or just setting the initial value otherwise.

Parameters

trainer (Trainer) – Trainer object that runs the training loop.

on_error(trainer, exc, tb)

Handles the error raised during training before finalization.

This method is called when an exception is thrown during the training loop, before finalize. An extension that needs different error handling from finalize, can override this method to handle errors.

Parameters

• trainer (Trainer) – Trainer object that runs the training loop.

• exc (Exception) – arbitrary exception thrown during update loop.

• tb (traceback) – traceback object of the exception

serialize(serializer)

Serializes the extension state.

It is called when a trainer that owns this extension is serialized. It serializes nothing by default.

__eq__(value, /)

Return self==value.

__ne__(value, /)

Return self!=value.

__lt__(value, /)

Return self<value.

__le__(value, /)

Return self<=value.

__gt__(value, /)

Return self>value.

__ge__(value, /)

Return self>=value.

Attributes

default_name

Default name of the extension.

It is the name of the class by default. Implementation can override this property, or provide a class attribute to hide it.

name = None

priority = 100

trigger = (1, 'iteration')