Input Convex Neural Networks (ICNNs)
This repository is by Brandon Amos, Leonard Xu, and J. Zico Kolter and contains the TensorFlow source code to reproduce the experiments in our ICML 2017 paper Input Convex Neural Networks.
If you find this repository helpful in your publications, please consider citing our paper.
@InProceedings{amos2017icnn,
title = {Input Convex Neural Networks},
author = {Brandon Amos and Lei Xu and J. Zico Kolter},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {146--155},
year = {2017},
volume = {70},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
}
Setup and Dependencies
- Python/numpy
- TensorFlow (we used r10)
- OpenAI Gym + Mujoco (for the RL experiments)
Libraries
lib
βββ bundle_entropy.py - Optimize a function over the [0,1] box with the bundle entropy method.
(Development is still in-progress and we are still
fixing some numerical issues here.)
Synthetic Classification
This image shows FICNN (top) and PICNN (bottom) classification of synthetic non-convex decision boundaries.
synthetic-cls
βββ icnn.py - Main script.
βββ legend.py - Create a figure of just the legend.
βββ make-tile.sh - Make the tile of images.
βββ run.sh - Run all experiments on 4 GPUs.
Multi-Label Classification
(These are currently slightly inconsistent with our paper and we plan on synchronizing our paper and code.)
multi-label-cls
βββ bibsonomy.py - Loads the Bibsonomy datasets.
βββ ebundle-vs-gd.py - Compare ebundle and gradient descent.
βββ ff.py - Train a feed-forward net baseline.
βββ icnn_ebundle.py - Train an ICNN with the bundle entropy method.
βββ icnn.back.py - Train an ICNN with gradient descent and back differentiation.
βββ icnn.plot.py - Plot the results from any multi-label cls experiment.
Image Completion
This image shows the test set completions on the Olivetti faces dataset over the first few iterations of training a PICNN with the bundle entropy method for 5 iterations.
completion
βββ icnn.back.py - Train an ICNN with gradient descent and back differentiation.
βββ icnn_ebundle.py - Train an ICNN with the bundle entropy method.
βββ icnn.plot.py - Plot the results from any image completion experiment.
βββ olivetti.py - Loads the Olivetti faces dataset.
Reinforcement Learning
Training
From the RL directory, run a single experiment with:
python src/main.py --model ICNN --env InvertedPendulum-v1 --outdir output \
--total 100000 --train 100 --test 1 --tfseed 0 --npseed 0 --gymseed 0
- Use
--model
to select a model from[DDPG, NAF, ICNN]
. - Use
--env
to select a task. TaskList - View all of the parameters with
python main.py -h
.
Output
The TensorBoard summary is on by default. Use --summary False
to
turn it off. The TensorBoard summary includes (1) average Q value, (2)
loss function, and (3) average reward for each training minibatch.
The testing total rewards are logged to log.txt.
Each line is [training_timesteps] [testing_episode_total_reward]
.
Settings
To reproduce our experiments, run the scripts in the RL directory.
Acknowledgments
The DDPG portions of our RL code are from Simon Ramstedt's SimonRamstedt/ddpg repository.
Licensing
Unless otherwise stated, the source code is copyright Carnegie Mellon University and licensed under the Apache 2.0 License. Portions from the following third party sources have been modified and are included in this repository. These portions are noted in the source files and are copyright their respective authors with the licenses listed.
Project | License |
---|---|
SimonRamstedt/ddpg | MIT |