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A deep generative model library in Theano and Lasagne
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Tars
datasets initial commit May 17, 2016
docs Add rtfd-requirements.txt May 12, 2017
examples
plot initial commit May 17, 2016
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.travis.yml
README.md Update README.md Nov 11, 2018
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setup.py

README.md

Tars

Build Status Python Version Documentation Status

[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:

  • Various distributions

    • 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 models

    • Autoencoder
    • VAE
      • Conditional VAE
      • Importance weighted autoencoder
      • JMVAE
      • Multiple latent layers
    • GAN, Conditional GAN
    • VAE-GAN, conditional VAE-GAN
    • VAE-RNN
      • Variational RNN
      • DRAW, Convolutional DRAW
  • 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).

Installation

$ git clone https://github.com/masa-su/Tars.git
$ pip install -e Tars --process-dependency-links

or

$ 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:

  • Theano
  • Lasagne
  • progressbar2
  • matplotlib
  • sklearn

Examples

Please go to the examples directory and try to run some examples.

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