tensorflow-datasets-3.2.0.tar.gz

# TensorFlow Datasets TensorFlow Datasets provides many public datasets as `tf.data.Datasets`. [](https://storage.googleapis.com/tfds-kokoro-public/kokoro-build.html) [](https://badge.fury.io/py/tensorflow-datasets) [](https://www.tensorflow.org/datasets/api_docs/python/tfds) * [List of datasets](https://www.tensorflow.org/datasets/catalog/overview) * Getting started: * [Introduction](https://www.tensorflow.org/datasets/overview) ([Try it in Colab](https://colab.research.google.com/github/tensorflow/datasets/blob/master/docs/overview.ipynb)) * [End-to-end example with Keras](https://colab.research.google.com/github/tensorflow/datasets/blob/master/docs/keras_example.ipynb) * Features & performances: * [Using splits and slicing API](https://www.tensorflow.org/datasets/splits) * [Performance advice](https://www.tensorflow.org/datasets/performances) * [Datasets versioning](https://www.tensorflow.org/datasets/datasets_versioning) * [Feature decoding](https://www.tensorflow.org/datasets/decode) * [Store your dataset on GCS](https://www.tensorflow.org/datasets/gcs) * Add your dataset: * [Add a dataset](https://www.tensorflow.org/datasets/add_dataset) * [Add a huge dataset with Beam (>>100GiB)](https://www.tensorflow.org/datasets/beam_datasets) * [API docs](https://www.tensorflow.org/datasets/api_docs/python/tfds) Note: [`tf.data`](https://www.tensorflow.org/guide/data) is a builtin library in TensorFlow which builds efficient data pipelines. [TFDS](https://www.tensorflow.org/datasets) (this library) uses `tf.data` to build an input pipeline when you load a dataset. **Table of Contents** * [Installation](#installation) * [Usage](#usage) * [`DatasetBuilder`](#datasetbuilder) * [NumPy usage with `tfds.as_numpy`](#numpy-usage-with-tfdsas_numpy) * [Citation](#citation) * [Want a certain dataset?](#want-a-certain-dataset) * [*Disclaimers*](#disclaimers) ### Installation ```sh pip install tensorflow-datasets # Requires TF 1.15+ to be installed. # Some datasets require additional libraries; see setup.py extras_require pip install tensorflow # or: pip install tensorflow-gpu ``` Join [our Google group](https://groups.google.com/forum/#!forum/tensorflow-datasets-public-announce) to receive updates on the project. ### Usage ```python import tensorflow_datasets as tfds import tensorflow as tf # Here we assume Eager mode is enabled (TF2), but tfds also works in Graph mode. # Construct a tf.data.Dataset ds_train = tfds.load('mnist', split='train', shuffle_files=True) # Build your input pipeline ds_train = ds_train.shuffle(1000).batch(128).prefetch(10) for features in ds_train.take(1): image, label = features['image'], features['label'] ``` Try it interactively in a [Colab notebook](https://colab.research.google.com/github/tensorflow/datasets/blob/master/docs/overview.ipynb). ### `DatasetBuilder` All datasets are implemented as subclasses of `tfds.core.DatasetBuilder`. TFDS has two entry points: * `tfds.builder`: Returns the `tfds.core.DatasetBuilder` instance, giving control over `builder.download_and_prepare()` and `builder.as_dataset()`. * `tfds.load`: Convenience wrapper which hides the `download_and_prepare` and `as_dataset` calls, and directly returns the `tf.data.Dataset`. ```python import tensorflow_datasets as tfds # The following is the equivalent of the `load` call above. # You can fetch the DatasetBuilder class by string mnist_builder = tfds.builder('mnist') # Download the dataset mnist_builder.download_and_prepare() # Construct a tf.data.Dataset ds = mnist_builder.as_dataset(split='train') # Get the `DatasetInfo` object, which contains useful information about the # dataset and its features info = mnist_builder.info print(info) ``` This will print the dataset info content: ```python tfds.core.DatasetInfo( name='mnist', version=3.0.1, description='The MNIST database of handwritten digits.', homepage='http://yann.lecun.com/exdb/mnist/', features=FeaturesDict({ 'image': Image(shape=(28, 28, 1), dtype=tf.uint8), 'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10), }), total_num_examples=70000, splits={ 'test': 10000, 'train': 60000, }, supervised_keys=('image', 'label'), citation="""@article{lecun2010mnist, title={MNIST handwritten digit database}, author={LeCun, Yann and Cortes, Corinna and Burges, CJ}, journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist}, volume={2}, year={2010} }""", redistribution_info=, ) ``` You can also get details about the classes (number of classes and their names). ```python info = tfds.builder('cats_vs_dogs').info info.features['label'].num_classes # 2 info.features['label'].names # ['cat', 'dog'] info.features['label'].int2str(1) # "dog" info.features['label'].str2int('cat') # 0 ``` ### NumPy Usage with `tfds.as_numpy` As a convenience for users that want simple NumPy arrays in their programs, you can use `tfds.as_numpy` to return a generator that yields NumPy array records out of a `tf.data.Dataset`. This allows you to build high-performance input pipelines with `tf.data` but use whatever you'd like for your model components. ```python train_ds = tfds.load("mnist", split="train") train_ds = train_ds.shuffle(1024).batch(128).repeat(5).prefetch(10) for example in tfds.as_numpy(train_ds): numpy_images, numpy_labels = example["image"], example["label"] ``` You can also use `tfds.as_numpy` in conjunction with `batch_size=-1` to get the full dataset in NumPy arrays from the returned `tf.Tensor` object: ```python train_ds = tfds.load("mnist", split=tfds.Split.TRAIN, batch_size=-1) numpy_ds = tfds.as_numpy(train_ds) numpy_images, numpy_labels = numpy_ds["image"], numpy_ds["label"] ``` Note that the library still requires `tensorflow` as an internal dependency. ### Citation Please include the following citation when using `tensorflow-datasets` for a paper, in addition to any citation specific to the used datasets. ``` @misc{TFDS, title = {{TensorFlow Datasets}, A collection of ready-to-use datasets}, howpublished = {\url{https://www.tensorflow.org/datasets}}, } ``` ## Want a certain dataset? Adding a dataset is really straightforward by following [our guide](https://github.com/tensorflow/datasets/tree/master/docs/add_dataset.md). Request a dataset by opening a [Dataset request GitHub issue](https://github.com/tensorflow/datasets/issues/new?assignees=&labels=dataset+request&template=dataset-request.md&title=%5Bdata+request%5D+%3Cdataset+name%3E). And vote on the current [set of requests](https://github.com/tensorflow/datasets/labels/dataset%20request) by adding a thumbs-up reaction to the issue. #### *Disclaimers* *This is a utility library that downloads and prepares public datasets. We do* *not host or distribute these datasets, vouch for their quality or fairness, or* *claim that you have license to use the dataset. It is your responsibility to* *determine whether you have permission to use the dataset under the dataset's* *license.* *If you're a dataset owner and wish to update any part of it (description,* *citation, etc.), or do not want your dataset to be included in this* *library, please get in touch through a GitHub issue. Thanks for your* *contribution to the ML community!* *If you're interested in learning more about responsible AI practices, including* *fairness, please see Google AI's [Responsible AI Practices](https://ai.google/education/responsible-ai-practices).* *`tensorflow/datasets` is Apache 2.0 licensed. See the [`LICENSE`](LICENSE) file.*