[New] We are developing a PyTorch version of Tars made with more sophisticated API (which is named Pixyz). It is released now!
Tars is a deep generative models library. It has the following features:
- Gaussian, Bernoulli, Laplace, Gamma, Beta, Dirichlet, Bernoulli, Categorical, and so on.
- Hierarchical latent distributions (New!).
- We can draw samples from these distributions by the reparameterization trick .
Various lower bounds
- The evidence lower bound (ELBO, which is the same as the original lower bound)
- The importance sampling lower bound
- The variational R'enyi bound
Note: Some of the implementations of the above models have not yet been released in this version. If you want to use such models, please use the old version (v0.0.2).
For a more detailed explanation of this library, please refer to this page (in Japanese).
$ git clone https://github.com/masa-su/Tars.git $ pip install -e Tars --process-dependency-links
$ pip install -e git://github.com/masa-su/Tars --process-dependency-links
When you execute this command, the following packages will be automatically installed in your environment:
Please go to the examples directory and try to run some examples.