espnet.nets.chainer_backend.transformer.training.CustomUpdater

source

class espnet.nets.chainer_backend.transformer.training.CustomUpdater(train_iter, optimizer, converter, device, accum_grad=1)

Bases: StandardUpdater

Custom updater for chainer.

• Parameters:
  • train_iter (iterator | dict *[*str , iterator ]) – Dataset iterator for the training dataset. It can also be a dictionary that maps strings to iterators. If this is just an iterator, then the iterator is registered by the name 'main'.
  • optimizer (optimizer | dict *[*str , optimizer ]) – Optimizer to update parameters. It can also be a dictionary that maps strings to optimizers. If this is just an optimizer, then the optimizer is registered by the name 'main'.
  • converter (espnet.asr.chainer_backend.asr.CustomConverter) – Converter function to build input arrays. Each batch extracted by the main iterator and the device option are passed to this function. chainer.dataset.concat_examples() is used by default.
  • device (int or dict) – The destination device info to send variables. In the case of cpu or single gpu, device=-1 or 0, respectively. In the case of multi-gpu, device={“main”:0, “sub_1”: 1, …}.
  • accum_grad (int) – The number of gradient accumulation. if set to 2, the network parameters will be updated once in twice, i.e. actual batchsize will be doubled.

Initialize Custom Updater.

update()

Update step for Custom Updater.

update_core()

Process main update routine for Custom Updater.