Module: tfds

tensorflow_datasets (tfds) defines a collection of datasets ready-to-use with TensorFlow.

Defined in __init__.py.

Each dataset is defined as a tfds.core.DatasetBuilder, which encapsulates the logic to download the dataset and construct an input pipeline, as well as contains the dataset documentation (version, splits, number of examples, etc.).

The main library entrypoints are:

• tfds.builder: fetch a tfds.core.DatasetBuilder by name
• tfds.load: convenience method to construct a builder, download the data, and create an input pipeline, returning a tf.data.Dataset.

Documentation:

• These API docs
• Available datasets
• Colab tutorial
• Add a dataset

Modules

core module: API to define datasets.

download module: tfds.download.DownloadManager API.

features module: tfds.features.FeatureConnector API defining feature types.

file_adapter module: tfds.file_adapter.FileFormatAdapters for GeneratorBasedBuilder.

units module: Defines convenience constants/functions for converting various units.

testing module: Testing utilities.

Classes

class GenerateMode: Enum for how to treat pre-existing downloads and data.

class percent: Syntactic sugar for defining slice subsplits: tfds.percent[75:-5].

class Split: Enum for dataset splits.

Functions

as_numpy(...): Converts a tf.data.Dataset to an iterable of NumPy arrays.

builder(...): Fetches a tfds.core.DatasetBuilder by string name.

list_builders(...): Returns the string names of all tfds.core.DatasetBuilders.

load(...): Loads the named dataset into a tf.data.Dataset.

disable_progress_bar(...): Disabled Tqdm progress bar.

is_dataset_on_gcs(...): If the dataset is available on the GCS bucket gs://tfds-data/datasets.