PyTorch + Lucid = Lucent
The wonderful Lucid library adapted for the wonderful PyTorch!
Lucent is not affiliated with Lucid or OpenAI's Clarity team, although we would love to be! Credit is due to the original Lucid authors, we merely adapted the code for PyTorch and we take the blame for all issues and bugs found here.
Lucent is still in pre-alpha phase and can be installed locally with the following command:
pip install torch-lucent
In the spirit of Lucid, get up and running with Lucent immediately, thanks to Google's Colab!
You can also clone this repository and run the notebooks locally with Jupyter.
import torch from lucent.optvis import render from lucent.modelzoo import inceptionv1 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = inceptionv1(pretrained=True) model.to(device).eval() render.render_vis(model, "mixed4a:476")
Here, we have tried to recreate some of the Lucid notebooks! You can also check out the lucent-notebooks repo to clone all the notebooks.
- Feature Visualization
- The Building Blocks of Interpretability
- Using Artiﬁcial Intelligence to Augment Human Intelligence
- Visualizing Representations: Deep Learning and Human Beings
- Differentiable Image Parameterizations
- Activation Atlas
- Lessons from a year of Distill ML Research (Shan Carter, OpenVisConf)
- Machine Learning for Visualization (Ian Johnson, OpenVisConf)
#circuits on the Distill slack!
License and Disclaimer
You may use this software under the Apache 2.0 License. See LICENSE.