This repository contains code for LAMBC, an optimizer that trains with layer-wise adaptive learning rate. LAMBC improves upon LAMB by clipping the trust ratio which stabilizes its magnitude and prevent extreme values.

Experiments on image classification tasks (CIFAR-10 and down-sampled ImageNet-64) validate LAMBC's improvement over LAMB across different batch sizes and clipping bounds.

LAMBC requires the following:

• Linux
• Python 3.6 or later
• PyTorch 1.2 or later
• CUDA 10 or later

git clone https://github.com/jeffrey-fong/LAMBC.git
cd LAMBC
python main.py

LAMBC can be used just like any other PyTorch optimizers.

optimizer = Lambc(model.parameters(), lr=0.001, weight_decay=0.0, betas=(.9, .999), adam=False, clip=True, clip_bound=1.0)

The boolean clip parameter decides whether clipping is enabled. LAMBC will perform just like the normal LAMB when the parameter is set to False. The clip_bound parameter determines the clipping bound for the trust ratio. In our experiments, the bound value of 1.0 provided the optimal performance (see figure above). However, we emphasize that the bound value will likely change according to the model used and the task complexity. Therefore, you will still need to optimize the hyperparameters depending on your specific problem.

We would like to acknowledge that the LAMBC optimizer code was adapted from this repository.

If you apply LAMBC to your research and project, please cite our work (Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping):

@article{fong2020lambc,
  title={Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping},
  author={Fong, Jeffrey and Chen, Siwei and Chen, Kaiqi},
  journal={arXiv preprint arXiv:2011.13584},
  year={2020}
}