yell/boltzmann-machines
TensorFlow implementation of Restricted and Deep Boltzmann Machines with sklearn compatibility and examples on MNIST and CIFAR-10.
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This repository provides generic and flexible implementations of Restricted Boltzmann Machines (RBM) and Deep Boltzmann Machines (DBM) using TensorFlow. It reproduces experiments from academic papers on deep learning and energy-based models, supporting variants like CD-k, PCD, and annealed importance sampling. The library offers a scikit-learn compatible API with extensive features for training and sampling.
Frequently asked
- What is yell/boltzmann-machines?
- TensorFlow implementation of Restricted and Deep Boltzmann Machines with sklearn compatibility and examples on MNIST and CIFAR-10.
- Is boltzmann-machines open source?
- Yes — yell/boltzmann-machines is open source, released under the MIT license.
- What language is boltzmann-machines written in?
- yell/boltzmann-machines is primarily written in Jupyter Notebook.
- How popular is boltzmann-machines?
- yell/boltzmann-machines has 851 stars on GitHub.
- Where can I find boltzmann-machines?
- yell/boltzmann-machines is on GitHub at https://github.com/yell/boltzmann-machines.