tfds.load

Loads the named dataset into a tf.data.Dataset.

tfds.load(
    name: str,
    *,
    split: Optional[Tree[splits_lib.SplitArg]] = None,
    data_dir: Optional[str] = None,
    batch_size: tfds.typing.Dim = None,
    shuffle_files: bool = False,
    download: bool = True,
    as_supervised: bool = False,
    decoders: Optional[TreeDict[decode.partial_decode.DecoderArg]] = None,
    read_config: Optional[tfds.ReadConfig] = None,
    with_info: bool = False,
    builder_kwargs: Optional[Dict[str, Any]] = None,
    download_and_prepare_kwargs: Optional[Dict[str, Any]] = None,
    as_dataset_kwargs: Optional[Dict[str, Any]] = None,
    try_gcs: bool = False
)

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate early stopping Effective Tensorflow 2 Estimators Transfer learning and fine-tuning Use TPUs	TensorFlow Datasets Data augmentation Custom training: walkthrough Load text Training a neural network on MNIST with Keras

tfds.load is a convenience method that:

Fetch the tfds.core.DatasetBuilder by name:

builder = tfds.builder(name, data_dir=data_dir, **builder_kwargs)

Generate the data (when download=True):

builder.download_and_prepare(**download_and_prepare_kwargs)

Load the tf.data.Dataset object:

ds = builder.as_dataset(
    split=split,
    as_supervised=as_supervised,
    shuffle_files=shuffle_files,
    read_config=read_config,
    decoders=decoders,
    **as_dataset_kwargs,
)

See: https://www.tensorflow.org/datasets/overview#load_a_dataset for more examples.

If you'd like NumPy arrays instead of tf.data.Datasets or tf.Tensors, you can pass the return value to tfds.as_numpy.

Args
`name`	`str`, the registered name of the `DatasetBuilder` (the snake case version of the class name). The config and version can also be specified in the name as follows: `'dataset_name[/config_name][:version]'`. For example, `'movielens/25m-ratings'` (for the latest version of `'25m-ratings'`), `'movielens:0.1.0'` (for the default config and version 0.1.0), or`'movielens/25m-ratings:0.1.0'`. Note that only the latest version can be generated, but old versions can be read if they are present on disk. For convenience, the `name` parameter can contain comma-separated keyword arguments for the builder. For example, `'foo_bar/a=True,b=3'` would use the `FooBar` dataset passing the keyword arguments `a=True` and `b=3` (for builders with configs, it would be `'foo_bar/zoo/a=True,b=3'` to use the `'zoo'` config and pass to the builder keyword arguments `a=True` and `b=3`).
`split`	Which split of the data to load (e.g. `'train'`, `'test'`, `['train', 'test']`, `'train[80%:]'`,...). See our split API guide. If `None`, will return all splits in a `Dict[Split, tf.data.Dataset]`
`data_dir`	`str`, directory to read/write data. Defaults to the value of the environment variable TFDS_DATA_DIR, if set, otherwise falls back to datasets are stored.
`batch_size`	`int`, if set, add a batch dimension to examples. Note that variable length features will be 0-padded. If `batch_size=-1`, will return the full dataset as `tf.Tensor`s.
`shuffle_files`	`bool`, whether to shuffle the input files. Defaults to `False`.
`download`	`bool` (optional), whether to call `tfds.core.DatasetBuilder.download_and_prepare` before calling `tf.DatasetBuilder.as_dataset`. If `False`, data is expected to be in `data_dir`. If `True` and the data is already in `data_dir`, when data_dir is a Placer path.
`as_supervised`	`bool`, if `True`, the returned `tf.data.Dataset` will have a 2-tuple structure `(input, label)` according to `builder.info.supervised_keys`. If `False`, the default, the returned `tf.data.Dataset` will have a dictionary with all the features.
`decoders`	Nested dict of `Decoder` objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info.
`read_config`	`tfds.ReadConfig`, Additional options to configure the input pipeline (e.g. seed, num parallel reads,...).
`with_info`	`bool`, if `True`, `tfds.load` will return the tuple (`tf.data.Dataset`, `tfds.core.DatasetInfo`), the latter containing the info associated with the builder.
`builder_kwargs`	`dict` (optional), keyword arguments to be passed to the `tfds.core.DatasetBuilder` constructor. `data_dir` will be passed through by default.
`download_and_prepare_kwargs`	`dict` (optional) keyword arguments passed to `tfds.core.DatasetBuilder.download_and_prepare` if `download=True`. Allow to control where to download and extract the cached data. If not set, cache_dir and manual_dir will automatically be deduced from data_dir.
`as_dataset_kwargs`	`dict` (optional), keyword arguments passed to `tfds.core.DatasetBuilder.as_dataset`.
`try_gcs`	`bool`, if True, tfds.load will see if the dataset exists on the public GCS bucket before building it locally.

Returns
`ds`	`tf.data.Dataset`, the dataset requested, or if `split` is None, a `dict<key: tfds.Split, value: tf.data.Dataset>`. If `batch_size=-1`, these will be full datasets as `tf.Tensor`s.
`ds_info`	`tfds.core.DatasetInfo`, if `with_info` is True, then `tfds.load` will return a tuple `(ds, ds_info)` containing dataset information (version, features, splits, num_examples,...). Note that the `ds_info` object documents the entire dataset, regardless of the `split` requested. Split-specific information is available in `ds_info.splits`.

tfds.load

Used in the notebooks

Args

Returns