Version 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.