What are extensions in chainer how to use them

This recipe explains what are extensions in chainer how to use them

Recipe Objective - What are extensions in chainer, how to use them?

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 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.

chainer.training.Extension - Base class of trainer extensions.

chainer.training.make_extension - Decorator to make given functions into trainer extensions.

Example -

You can make a new Extension by writing a simple function which takes a Trainer object as its argument. For example, when you want to reduce the learning rate periodically during training, an drop_lr extension can be written as follows:

import math
import numpy as np
import chainer
from chainer.training import extensions
def drop_lr(trainer):
 trainer.updater.get_optimizer('main').lr *= 0.1

Then you can add this function to a Trainer object via extend() method.

trainer.extend(drop_lr, trigger=(10, 'epoch'))

It lowers the learning rate every 10 epochs by multiplying 0.1 with the current learning rate.

In this way, we can use extensions in chainer.

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