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Library for learning and inference with Sum-product Networks
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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


Installation instructions and complete documentation can be found at

Papers using LibSPN

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