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Note: this repository has not been under active development for quite some time

Paysage

Paysage is library for unsupervised learning and probabilistic generative models written in Python. The library is still in the early stages and is not yet stable, so new features will be added frequently.

Currently, paysage can be used to train things like:

Using advanced mean field and Markov Chain Monte Carlo methods.

Physics-inspired machine learning

Installation:

We recommend using paysage with Anaconda3. Simply,

  1. Clone this git repo
  2. Move into the directory with setup.py
  3. Run “pip install -e .”

Running the examples requires a file mnist.h5 containing the MNIST dataset of handwritten images. The script download_mnist.py in the mnist/ folder will fetch the file from the web.

Using PyTorch

Paysage uses one of two backends for performing computations. By default, computations are performed using numpy/numexpr/numba on the CPU. If you have installed PyTorch, then you can switch to the pytorch backend by changing the setting in paysage/backends/config.json to pytorch. If you have a CUDA enabled version of pytorch, you can change the setting in paysage/backends/config.json from cpu to gpu to run on the GPU.

System Dependencies

About the name:

Boltzmann machines encode information in an "energy landscape" where highly probable states have low energy and lowly probable states have high energy. The name "landscape" was already taken, but the French translation "paysage" was not.