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Author
Contributors
Last Commit
Apr. 3, 2017
Created
May. 17, 2016

Tars

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Tars is the deep generative models library. It has the following features:

  • Various distributions
    • Gaussian, Bernoulli, Laplace, Gamma, Beta, Dirichlet, Bernoulli, Categorical, and so on.
    • 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.

Latest Releases
v0.0.2
 Mar. 24 2017
v0.0.4a1
 Mar. 24 2017
v0.0.3
 Mar. 24 2017
v0.0.4a2
 Mar. 24 2017
v0.0.4a1
 Feb. 28 2017