InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy ==========================================================
InferPy is a high-level API for probabilistic modeling with deep neural networks written in Python and capable of running on top of Tensorflow. InferPy's API is strongly inspired by Keras and it has a focus on enabling flexible data processing, easy-to-code probabilistic modeling, scalable inference, and robust model validation.
Use InferPy if you need a probabilistic programming language that:
- Allows easy and fast prototyping of hierarchical probabilistic models with a simple and user-friendly API inspired by Keras.
- Automatically creates computational efficient batched models without the need to deal with complex tensor operations and theoretical concepts.
- Run seamlessly on CPU and GPU by relying on Tensorflow, without having to learn how to use Tensorflow.
- Defines probabilistic models with complex probabilistic constructs containing deep neural networks.
A set of examples can be found in the Probabilistic Model Zoo section.
notes/getting30s notes/gettingGuiding notes/requirements notes/installation
notes/guidemodels notes/guidedeepmodels notes/guideinference notes/guidebayesian notes/guidedata notes/advancedsetup
notes/probzoo VAE with MNIST in Edward and Inferpy <notes/vae_mnist> notes/bayesianNN notes/mixture
modules/inferpy modules/inferpy.contextmanager modules/inferpy.data modules/inferpy.exceptions modules/inferpy.inference modules/inferpy.models modules/inferpy.util
notes/contact
