8000 GitHub - dmosta/video_generator: This is implementation of convolutional variational autoencoder in TensorFlow library and it is used for video generation.
[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to content

This is implementation of convolutional variational autoencoder in TensorFlow library and it is used for video generation.

License

Notifications You must be signed in to change notification settings

dmosta/video_generator

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

video_generator

This is implementation of convolutional variational autoencoder in TensorFlow library and it is used for video generation.

Also this is my code for the Siraj Raval's coding challenge "How to Generate Images - Intro to Deep Learning #14".

You can find more about it here

Overview

For more info about video generation see ipython notebook and for implementation details of conv-variational autoencoder see conv_vae.py. Data set is created by using ffmpeg tool.

Dependencies

  • TensorFlow r1.0
  • pillow
  • matplotlib
  • numpy
  • sklearn

All dependencies can be installed by pip.

Credits

Credits go to these guys:

  • Kevin Frans url

  • Arthur Juliani url

About

This is implementation of convolutional variational autoencoder in TensorFlow library and it is used for video generation.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.4%
  • Python 0.6%
0