Paysage

Latest version: v0.2.1

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0.1

Paysage v0.1 is the first release of our library for unsupervised learning and probabilistic generative models written in Python and PyTorch. Currently, paysage can be used to train things like:

- Bernoulli Restricted Boltzmann Machines
- Gaussian Restricted Boltzmann Machines
- Hopfield Models
- Deep Boltzmann Machines

All of these models can be trained using advanced Monte Carlo methods designed for efficiently exploring complex energy landscapes. Deep Boltzmann machines are trained using a greedy layerwise algorithm. Restricted Boltzmann machines with Bernoulli layers can also be trained using an advanced mean-field algorithm called the Thouless-Anderson-Palmer (TAP) approximation.

Training can be performed on a CPU or using a GPU -- to use the GPU, change the settings in `paysage\backends\config.json` to `backend: pytorch` and `processor: gpu`. Make sure that you have a CUDA enabled version of PyTorch installed and running already.

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