Skip to main content

A framework to explore the latent space of autoencoders implemented in torch

Project description


Test Package Documentation Status PyPI

A framework to explore the latent space of convolutional autoencoders implemented in pytorch.


Compose your decoder and your encoder into the lightcone autoencoder:

from lightcone.models import AutoEncoder

model = AutoEncoder(encoder=your_encoder, decoder=your_decoder)

After model has been training, the latent space can be explored in a Jupyter-Notebook as follows


Jupyter Dash

Make sure to install and activate the Jupyter notebook extenstion

jupyter nbextension install --py jupyter_dash
jupyter nbextension enable --py jupyter_dash

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lightcone-0.1.0.tar.gz (7.1 kB view hashes)

Uploaded Source

Built Distribution

lightcone-0.1.0-py3-none-any.whl (7.5 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page