class chainer.dataset.Iterator[source]

Base class of all dataset iterators.

Iterator iterates over the dataset, yielding a minibatch at each iteration. Minibatch is a list of examples. Each implementation should implement an iterator protocol (e.g., the __next__() method).

Note that, even if the iterator supports setting the batch size, it does not guarantee that each batch always contains the same number of examples. For example, if you let the iterator to stop at the end of the sweep, the last batch may contain a fewer number of examples.

The interface between the iterator and the underlying dataset is not fixed, and up to the implementation.

Each implementation should provide the following attributes (not needed to be writable).

  • batch_size: Number of examples within each minibatch.
  • epoch: Number of completed sweeps over the dataset.
  • epoch_detail: Floating point number version of the epoch. For example, if the iterator is at the middle of the dataset at the third epoch, then this value is 2.5.
  • previous_epoch_detail: The value of epoch_detail at the previous iteration. This value is None before the first iteration.
  • is_new_epoch: True if the epoch count was incremented at the last update.

Each implementation should also support serialization to resume/suspend the iteration.



Returns the next batch.

This is a part of the iterator protocol of Python. It may raise the StopIteration exception when it stops the iteration.


Returns self.


Finalizes the iterator and possibly releases the resources.

This method does nothing by default. Implementation may override it to better handle the internal resources.


Python2 alternative of __next__.

It calls __next__() by default.


Serializes the internal state of the iterator.

This is a method to support serializer protocol of Chainer.


It should only serialize the internal state that changes over the iteration. It should not serializes what is set manually by users such as the batch size.