Repository for rotation projects with Liam Paninski/John Cunningham.
Dependencies: Python 2.7 Standard Scientific Computing Packages: (Scipy, Numpy, Matplotlib, Pandas) scikit-learn scikit-image scikit-video tensorflow-gpu (1.6) prettytensor progressbar imageio moviepy
It's recommended to install the above in a separate anaconda virtual environment.
This package contains code that compresses video data (when used with joint information derived from DeepLabCut, https://arxiv.org/abs/1804.03142). The main body of training code can be called by running:
$$ python ConvVAE_feed_queue_select.py
This will save a trained model in a folder specified by the variable "train_foldername", in the script mentioned above.
Then, analysis can be done by:
- Generating a video corresponding to the reconstruction, via $$ python video_maker_queue.py and
- Exploring the latent space via $$ python noise_explorer.py