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BVAE-tf

Disentangled Variational Auto-Encoder in TensorFlow (Beta-VAE)

⭐ 💥 THE UNLICENSE 💥 ⭐

Example Reconstructed Image

alt text

What has been done

  • darknet 19 (fully convolutional & fast) encoder and decoder
  • Custom keras sampling layer for sampling the distribution of variational autoencoders
  • Custom loss in sampling layer for latent space regularization
    • Options are no reg, vae reg (kl divergence), or bvae reg (beta*kl-divergence)
    • You can also set a target capacity for dimension usage of the latent space
  • Simple interface for setting up your own VAE or B-VAE

Enviroment Setup

I am using conda to ensure the enviroment is easy to install

  1. Install Anaconda or Miniconda (the python 3 version) for your platform
  2. Recreate the conda environment from the yml: conda env create -f environment.yml
  3. Active the enviroment
    1. Windows: go to cmd and activate bvae-tf
    2. Linux: source activate bvae-tf
  4. If you want to use CPU only, run pip uninstall tensorflow-gpu followed by pip install tensorflow==1.4.0 after you activate the environment.

If you do not want to / cannot use conda, I am using tensorflow 1.4.0; see the environment.yml for more package info.

Demo

For a simple overfitting demonstration, run ae.py in your terminal. This will cause the autoencoder to run on the included demo image.

Note: The demo takes a few minutes on my 1060 6GB, so it will take a while on a CPU...