View source on GitHub
|
tensorflow_datasets (tfds) defines a collection of datasets ready-to-use with TensorFlow.
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 atfds.core.DatasetBuilderby nametfds.load: convenience method to construct a builder, download the data, and create an input pipeline, returning atf.data.Dataset.
Documentation:
- These API docs
- Available datasets
- Colab tutorial
- Add a dataset
Modules
core module: API to define datasets.
decode module: Decoder public API.
download module: tfds.download.DownloadManager API.
features module: tfds.features.FeatureConnector API defining feature types.
testing module: Testing utilities.
units module: Defines convenience constants/functions for converting various units.
visualization module: Visualizer utils.
Classes
class GenerateMode: Enum for how to treat pre-existing downloads and data.
class ImageFolder: Generic image classification dataset created from manual directory.
class ReadConfig: Configures input reading pipeline.
class Split: Enum for dataset splits.
class TranslateFolder: Generic text translation dataset created from manual directory.
Functions
as_numpy(...): Converts a tf.data.Dataset to an iterable of NumPy arrays.
builder(...): Fetches a tfds.core.DatasetBuilder by string name.
builder_cls(...): Fetches a tfds.core.DatasetBuilder class by string name.
disable_progress_bar(...): Disabled Tqdm progress bar.
is_dataset_on_gcs(...): If the dataset is available on the GCS bucket gs://tfds-data/datasets.
list_builders(...): Returns the string names of all tfds.core.DatasetBuilders.
load(...): Loads the named dataset into a tf.data.Dataset.
show_examples(...): Visualize images (and labels) from an image classification dataset.
show_statistics(...): Display the datasets statistics on a Colab/Jupyter notebook.