Probabilistic modeling with Tensorflow made easy
InferPy: Deep Probabilistic Modeling Made Easy
InferPy is a high-level API for deep probabilistic modeling 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.
InferPy is to Edward what Keras is to Tensorflow
InferPy's aim is to be to Edward what Keras is to Tensorflow. Edward is a general purpose probabilistic programing language, like Tensorflow is a general computational engine. But this generality comes a at price. Edward's API is verbose and is based on distributions over Tensor objects, which are n-dimensional arrays with complex semantics operations. Probability distributions over Tensors are powerful abstractions but it is not easy to operate with them. InferPy's API is no so general like Edward's API but still covers a wide range of powerful and widely used probabilistic models, which can contain complex probability constructs containing deep neural networks.
For more details about InferPy, check the online documentation.
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