TensorFlow Probability

This package collects tools for probabilistic reasoning in TensorFlow. It is intended to serve as a hub for development of modeling tools, inference algorithms, useful models, and general statistical computation. Taking advantage of the TensorFlow ecosystem allows straightforward combination of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.

Contents of this package currently include:

• Sampling algorithms E.g., tfp.metropolis_hastings, tfp.hmc, tfp.monte_carlo.
• Example models (tfp.examples): implementations of common probability models in TensorFlow using tools from this package and from tf.contrib.distributions.

Contents of this repository should be considered under active development; interfaces may change at any time. We welcome external contributions! See Contributing for details.

Installation

The easiest way to install TensorFlow Probability is using pip:

pip install --upgrade tensorflow-probability       # for Python 2.7
pip3 install --upgrade tensorflow-probability      # for Python 3.n
pip install --upgrade tensorflow-probability-gpu   # for Python 2.7 and GPU
pip3 install --upgrade tensorflow-probability-gpu  # for Python 3.n and GPU

TensorFlow Probability does not currently contain any GPU-specific code; the primary difference between these packages is that tensorflow-probability-gpu depends on a GPU-enabled version of TensorFlow.

You can also install from source. This requires the Bazel build system.

git clone https://github.com/tensorflow/probability.git
cd probability
bazel build :pip_pkg
./bazel-bin/pip_pkg /tmp/tensorflow_probability_pkg
pip install /tmp/tensorflow_probability_pkg/*.whl

Examples

It is often easiest to learn by example. The examples/ directory contains reference implementations of common probabilistic models and demonstrates idiomatic styles for building probability models in TensorFlow. Example code may be run directly from the command line, e.g.,

python -m tensorflow_probability.examples.weight_uncertainty.mnist_deep_nn

to train a Bayesian deep network to classify MNIST digits. See the examples directory for the list of example implementations.

Usage

After you've installed tensorflow_probability, functions can be accessed as:

import tensorflow_probability as tfp

Contributing

We're eager to collaborate with you! Feel free to open an issue on GitHub and/or send us your pull requests. See our contribution doc for more details.