This repository hosts an independent LibSPN implementation utilizing TensorFlow 1.x. For an alpha version of LibSPN integrated with Keras and compatible with Tensorflow 2.x, see libspn-keras.
LibSPN is a library for learning and inference with Sum-Product Networks. LibSPN is integrated with TensorFlow.
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.
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
LibSPN requires installing tensorflow and tensorflow-probability first. The table below shows
which version of each you'd need if you want to be specific:
tensorflow |
tensorflow-probability |
|---|---|
| 1.14 | 0.7.0 |
| 1.13 | 0.6.0 |
| 1.12 | 0.5.0 |
| 1.11 | 0.4.0 |
First, install tensorflow or tensorflow-gpu:
pip install tensorflow-gpuThen, install tensorflow-probability:
pip install tensorflow-probabilityLibSPN is also available on pypi:
pip install 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
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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
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Loading and saving of structure and weights of learned models
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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
- 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)