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.
We recommend using paysage with Anaconda3. Simply,
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.
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
pytorch. If you have a CUDA enabled version of pytorch, you can change the setting in
gpu to run on the GPU.
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.