The most basic
dataset implementation is an array.
Both NumPy and CuPy arrays can be used directly as datasets.
In many cases, though, the simple arrays are not enough to write the training procedure. In order to cover most of such cases, Chainer provides many built-in implementations of datasets.
These built-in datasets are divided into two groups.
One is a group of general datasets.
Most of them are wrapper of other datasets to introduce some structures (e.g., tuple or dict) to each data point.
The other one is a group of concrete, popular datasets.
These concrete examples use the downloading utilities in the
chainer.dataset module to cache downloaded and converted datasets.
General datasets are further divided into four types.
The second one is
ConcatenatedDataset represents a concatenation of existing datasets. It can be used to merge datasets and make a larger dataset.
SubDataset represents a subset of an existing dataset. It can be used to separate a dataset for hold-out validation or cross validation. Convenient functions to make random splits are also provided.
The third one is
TransformDataset, which wraps around a dataset by applying a function to data indexed from the underlying dataset.
It can be used to modify behavior of a dataset that is already prepared.
Dataset which concatenates some base datasets.
This dataset wraps some base datasets and works as a concatenated dataset. For example, if a base dataset with 10 samples and another base dataset with 20 samples are given, this dataset works as a dataset which has 30 samples.
Parameters: datasets – The underlying datasets. Each dataset has to support
||Subset of a base dataset.|
||Splits a dataset into two subsets.|
||Splits a dataset into two subsets randomly.|
||Creates a set of training/test splits for cross validation.|
||Creates a set of training/test splits for cross validation randomly.|
||Dataset that indexes the base dataset and transforms the data.|
||Dataset of images built from a list of paths to image files.|
||Gets the MNIST dataset.|
||Gets the Fashion-MNIST dataset.|
||Gets the CIFAR-10 dataset.|
||Gets the CIFAR-100 dataset.|
||Gets the Penn Tree Bank dataset as long word sequences.|
||Gets the Penn Tree Bank word vocabulary.|
||Gets the SVHN dataset.|