Function load

tensorflow_datasets/core/load.py:523–687

Loads the named dataset into a `tf.data.Dataset`. `tfds.load` is a convenience method that: 1. Fetch the `tfds.core.DatasetBuilder` by name: ```python builder = tfds.builder(name, data_dir=data_dir, **builder_kwargs) ``` 2. Generate the data (when `download=True`): ```

(
    name: str,
    *,
    split: Tree[splits_lib.SplitArg] | None = None,
    data_dir: epath.PathLike | None = None,
    batch_size: int | None = None,
    shuffle_files: bool = False,
    download: bool = True,
    as_supervised: bool = False,
    decoders: TreeDict[decode.partial_decode.DecoderArg] | None = None,
    read_config: read_config_lib.ReadConfig | None = None,
    with_info: bool = False,
    builder_kwargs: dict[str, Any] | None = None,
    download_and_prepare_kwargs: dict[str, Any] | None = None,
    as_dataset_kwargs: dict[str, Any] | None = None,
    try_gcs: bool = False,
    file_format: str | file_adapters.FileFormat | None = None,
)

@tfds_logging.load()
def load(
    name: str,
    *,
    split: Tree[splits_lib.SplitArg] | None = None,
    data_dir: epath.PathLike | None = None,
    batch_size: int | None = None,
    shuffle_files: bool = False,
    download: bool = True,
    as_supervised: bool = False,
    decoders: TreeDict[decode.partial_decode.DecoderArg] | None = None,
    read_config: read_config_lib.ReadConfig | None = None,
    with_info: bool = False,
    builder_kwargs: dict[str, Any] | None = None,
    download_and_prepare_kwargs: dict[str, Any] | None = None,
    as_dataset_kwargs: dict[str, Any] | None = None,
    try_gcs: bool = False,
    file_format: str | file_adapters.FileFormat | None = None,
):
  # pylint: disable=line-too-long
  """Loads the named dataset into a `tf.data.Dataset`.

  `tfds.load` is a convenience method that:

  1. Fetch the `tfds.core.DatasetBuilder` by name:

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

  2. Generate the data (when `download=True`):

     ```python
     builder.download_and_prepare(**download_and_prepare_kwargs)
     ```

  3. Load the `tf.data.Dataset` object:

     ```python
     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.Dataset`s or `tf.Tensor`s,
  you can pass the return value to `tfds.as_numpy`.

  **Warning**: calling this function might potentially trigger the download
  of hundreds of GiB to disk. Refer to the `download` argument.

  Args: