https://github.com/nahumsa/variational-autoencoder
Notebooks with examples using variational autoencoders.
https://github.com/nahumsa/variational-autoencoder
Last synced: about 2 months ago
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Notebooks with examples using variational autoencoders.
- Host: GitHub
- URL: https://github.com/nahumsa/variational-autoencoder
- Owner: nahumsa
- Created: 2019-12-16T04:02:58.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-19T17:16:54.000Z (over 2 years ago)
- Last Synced: 2025-02-16T10:13:37.075Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 28.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Variational Autoencoder
In order to use the code on this repository, install the dependencies
using conda:
- conda env create -f environment.yml
- source activate VAE_env
To put this environtmen on your jupyter notebook environment you need to type the following comands:
- conda install jupyter
- conda install nb_conda
- conda install ipykernel
- python -m ipykernel install --user --name VAE_env
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Examples using a (Beta-)Variational autoencoder.
- [MNIST with Beta-Variational Autoencoder using Tensorflow](https://github.com/nahumsa/Variational-Autoencoder/blob/master/Beta-VAE%20MNIST%20Tensorflow.ipynb)
- [MNIST with Beta-Variational Autoencoder using Keras.](https://github.com/nahumsa/Variational-Autoencoder/blob/master/VAE%20with%20Keras.ipynb)
- [Dsprites with Variational Autoencoder using Keras](https://github.com/nahumsa/Variational-Autoencoder/blob/master/VAE%20with%20Keras%20-%20Dsprites.ipynb)
- [Dsprites traversals with Variational Autoencoder using Keras](https://github.com/nahumsa/Variational-Autoencoder/blob/master/BETA-VAE%20with%20Keras%20Dsprites%20-%20Traversals.ipynb)
- [Dsprites positions with Variational Autoencoder using Keras](https://github.com/nahumsa/Variational-Autoencoder/blob/master/BETA-VAE%20with%20Keras%20Dsprites%20Positions.ipynb)
- [Representing Qubits using Beta-Variational autoencoder](https://github.com/nahumsa/Variational-Autoencoder/blob/master/B-VAE%20to%20represent%20Qubits.ipynb)