TensorFlow Datasets
Table of Contents
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)
* [List of datasets](https://github.com/tensorflow/datasets/tree/master/docs/datasets.md)
* [Try it in Colab](https://colab.research.google.com/github/tensorflow/datasets/blob/master/docs/overview.ipynb)
* [API docs](https://www.tensorflow.org/datasets/api_docs/python/tfds)
* [Add a dataset](https://github.com/tensorflow/datasets/tree/master/docs/add_dataset.md)
### Installation
```sh
pip install tensorflow-datasets
# Requires TF 1.12+ to be installed.
# Some datasets require additional libraries; see setup.py extras_require
pip install tensorflow
# or:
pip install tensorflow-gpu
Usage
import tensorflow_datasets as tfds
import tensorflow as tf
# tfds works in both Eager and Graph modes
tf.enable_eager_execution()
# See available datasets
print(tfds.list_builders())
# Construct a tf.data.Dataset
ds_train, ds_test = tfds.load(name="mnist", split=["train", "test"])
# 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.
DatasetBuilder
All datasets are implemented as subclasses of
DatasetBuilder
and
tfds.load
is a thin convenience wrapper.
DatasetInfo
documents the dataset.
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=tfds.Split.TRAIN)
# Get the `DatasetInfo` object, which contains useful information about the
# dataset and its features
info = mnist_builder.info
print(info)
tfds.core.DatasetInfo(
name='mnist',
version=1.0.0,
description='The MNIST database of handwritten digits.',
urls=[u'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={
u'test': <tfds.core.SplitInfo num_examples=10000>,
u'train': <tfds.core.SplitInfo num_examples=60000>
},
supervised_keys=(u'image', u'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}
}
"""',
)
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.
train_ds = tfds.load("mnist", split=tfds.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:
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.
Want a certain dataset?
Adding a dataset is really straightforward by following
our guide.
Request a dataset by opening a
Dataset request GitHub issue.
And vote on the current
set of requests
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.
tensorflow/datasets is Apache 2.0 licensed. See the LICENSE file.