LibSPN

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.

Why LibSPN?

Several reasons:

• 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

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
• Built-in visualizations
  • SPN graph structure visualization
  • Data/distribution visualizations
• SPN Inference
  • SPN/MPN value calculation
  • Gradient calculation
  • Inferring MPE state
• SPN Learning
  • Expectation Maximization
  • Gradient Descent
• Other
  • Generating random sub-sets of all partitions of a set using repeated sampling or enumeration

Documentation

Installation instructions and complete documentation can be found at http://www.libspn.org

Papers using LibSPN

• Deep Convolutional Sum-Product Networks for Probabilistic Image Representations Jos van de Wolfshaar, Andrzej Pronobis (2019). Code can be found in the this experimental branch and the experiments are reproduced here
• 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)