Package: adamp
Slowing Down the Weight Norm Increase in Momentum-based OptimizersOfficial PyTorch implementation of AdamP and SGDP optimizers |Paper|Project pageByeongho Heo*, Sanghyuk Chun*, Seong Joon Oh, Dongyoon Han, Sangdoo Yun, Youngjung Uh, Jung-Woo Ha.* indicates equal contributionClova AI Research, NAVER Corp.AbstractNormalization techniques, such as batch normalization (BN), have led to significant improvements in deep neural network performances. Prior studies have analyzed the benefits of the resulting scale invariance of the weights for the gradient descent (GD) optimizers: it leads to a stabilized training due to the auto-tuning of step sizes. However, we show that, combined with the momentum-based algorithms, the scale invariance tends to induce an excessive growth of the weight norms. This in turn overly suppresses the effective step sizes during training, potentially leading to sub-optimal performances in deep neural networks. We analyze this phenomenon both theoretically and empirically. We propose a simple and effective solution: at each iteration of momentum-based GD optimizers (e.g., SGD or Adam) applied on scale-invariant weights (e.g., Conv weights preceding a BN layer), we remove the radial component (i.e., parallel to the weight vector) from the update vector. Intuitively, this operation prevents the unnecessary update along the radial direction that only increases the weight norm without contributing to the loss minimization. We verify that the modified optimizers SGDP and AdamP successfully regularize the norm growth and improve the performance of a broad set of models. Our experiments cover tasks including image classification and retrieval, object detection, robustness benchmarks, and audio classification.How does it work?Please visit ourproject page.UpdatesJun 19, 2020: built-in cosine similarity and fix warning (v0.3.0)Jun 19, 2020: nesterov update (v0.2.0)Jun 15, 2020: Initial upload (v0.1.0)Getting StartedInstallationpip3 install adampUsageUsage is exactly same astorch.optimlibrary!fromadampimportAdamP# define your paramsoptimizer=AdamP(params,lr=0.001,betas=(0.9,0.999),weight_decay=1e-2)fromadampimportSGDP# define your paramsoptimizer=SGDP(params,lr=0.1,weight_decay=1e-5,momentum=0.9,nesterov=True)ArgumentsSGDPandAdamPshare arguments withtorch.optim.SGDandtorch.optim.Adam.
There are two additional hyperparameters; we recommend using the default values.delta: threhold that determines whether a set of parameters is scale invariant or not (default: 0.1)wd_ratio: relative weight decay applied onscale-invariantparameters compared to that applied onscale-variantparameters (default: 0.1)BothSGDPandAdamPsupport Nesterov momentum.nesterov: enables Nesterov momentum (default: False)LicenseThis project is distributed under MIT license.Copyright (c) 2020-present NAVER Corp.

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THE SOFTWARE.How to cite@article{heo2020adamp,
    title={Slowing Down the Weight Norm Increase in Momentum-based Optimizers},
    author={Heo, Byeongho and Chun, Sanghyuk and Oh, Seong Joon and Han, Dongyoon and Yun, Sangdoo and Uh, Youngjung and Ha, Jung-Woo},
    year={2020},
    journal={arXiv preprint arXiv:2006.08217},
}