LibSPN is a TensorFlow-based library for building and training Sum-Product Networks.
LibSPN is a library for learning and inference with Sum-Product Networks. LibSPN is integrated with TensorFlow.
What are SPNs?
Sum-Product Networks (SPNs) are a probabilistic deep architecture with solid theoretical foundations, which demonstrated state-of-the-art performance in several domains. Yet, surprisingly, there are no mature, general-purpose SPN implementations that would serve as a platform for the community of machine learning researchers centered around SPNs. LibSPN is a new general-purpose Python library, which aims to become such a platform. The library is designed to make it straightforward and effortless to apply various SPN architectures to large-scale datasets and problems. The library achieves scalability and efficiency, thanks to a tight coupling with TensorFlow, a framework already used by a large community of researchers and developers in multiple domains.
- LibSPN is a general-purpose library with a generic interface and tools for generating SPN structure, making it easy to apply SPNs to any domain/problem
- LibSPN offers a simple Python interface for building or generating networks, learning, and inference, facilitating prototyping (e.g. in Jupyter) and enabling simple integration of SPNs with other software
- LibSPN is integrated with TensorFlow, making it possible to combine SPNs with other deep learning methods
- LibSPN uses concepts that should sound familiar to TensorFlow users (e.g. tensors, variables, feeding, queues, batching, TensorBoard etc.)
- LibSPN leverages the power of TensorFlow to efficiently perform parallel computations on (multiple) GPU devices
- LibSPN is extendable, making it easy to add custom operations and graph nodes
LibSPN requires installing
tensorflow-probability first. The table below shows
which version of each you'd need if you want to be specific:
pip install tensorflow-gpu
pip install tensorflow-probability
LibSPN is also available on
pip install libspn
Features of LibSPN
Simple interface for manual creation of custom network architectures
- Automatic SPN validity checking and scope calculation
- Adding explicit latent variables to sums/mixtures
- Weight sharing
Integration with TensorFlow
- SPN graph is converted to TensorFlow graph realizing specific algorithms/computations
- Inputs to the network come from TensorFlow feeds or any TensorFlow tensors
SPN structure generation and learning
- Dense random SPN generator
- Simple naive Bayes mixture model generator
Loading and saving of structure and weights of learned models
Simple interface for random data generation, data loading and batching
- Random data sampling from Gaussian Mixtures
- Using TensorFlow queues for data loading, shuffling and batching
- SPN graph structure visualization
- Data/distribution visualizations
- SPN/MPN value calculation
- Gradient calculation
- Inferring MPE state
Papers using LibSPN
- Deep Convolutional Sum-Product Networks for Probabilistic Image Representations Jos van de Wolfshaar, Andrzej Pronobis (2019).
- From Pixels to Buildings: End-to-end Probabilistic Deep Networks for Large-scale Semantic Mapping Kaiyu Zheng, Andrzej Pronobis (2018)
- Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps Kaiyu Zheng, Andrzej Pronobis, Rajesh P. N. Rao (2018)
- Learning Deep Generative Spatial Models For Mobile Robots Andrzej Pronobis, Rajesh P. N. Rao (2017)
- Learning Semantic Maps With Topological Reasoning Kaiyu Zheng, Andrzej Pronobis, Rajesh P. N. Rao (2017)
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size libspn-0.1.3-py3-none-any.whl (167.5 kB)||File type Wheel||Python version py3||Upload date||Hashes View hashes|
|Filename, size libspn-0.1.3.tar.gz (12.0 MB)||File type Source||Python version None||Upload date||Hashes View hashes|