Skip to main content

Adam-atan2 for Pytorch

Project description

Adam-atan2 - Pytorch

Implementation of the proposed Adam-atan2 optimizer in Pytorch

A multi-million dollar paper out of google deepmind proposes a small change to Adam update rule (using atan2) to remove the epsilon altogether for numerical stability and scale invariance

Install

$ pip install adam-atan2-pytorch

Usage

import torch
from torch import nn

# toy model

model = nn.Linear(10, 1)

# import AdamAtan2 and instantiate with parameters

from adam_atan2_pytorch import AdamAtan2

opt = AdamAtan2(model.parameters(), lr = 1e-4)

# forward and backwards

for _ in range(100):
  loss = model(torch.randn(10))
  loss.backward()

  # optimizer step

  opt.step()
  opt.zero_grad()

Citations

@inproceedings{Everett2024ScalingEA,
    title   = {Scaling Exponents Across Parameterizations and Optimizers},
    author  = {Katie Everett and Lechao Xiao and Mitchell Wortsman and Alex Alemi and Roman Novak and Peter J. Liu and Izzeddin Gur and Jascha Narain Sohl-Dickstein and Leslie Pack Kaelbling and Jaehoon Lee and Jeffrey Pennington},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:271051056}
}

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

adam_atan2_pytorch-0.0.6.tar.gz (417.8 kB view details)

Uploaded Source

Built Distribution

adam_atan2_pytorch-0.0.6-py3-none-any.whl (6.2 kB view details)

Uploaded Python 3

File details

Details for the file adam_atan2_pytorch-0.0.6.tar.gz.

File metadata

  • Download URL: adam_atan2_pytorch-0.0.6.tar.gz
  • Upload date:
  • Size: 417.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for adam_atan2_pytorch-0.0.6.tar.gz
Algorithm Hash digest
SHA256 1de9cd3a10c0e3c85782108ef53fd588679f6cc68eefd4ca37df1fd1d288a5b3
MD5 b80ae5f46cb9d3af94679a35a5a61798
BLAKE2b-256 efe11b68323692babc5bf748fd33d0436fba16b5af4fe95624dc1eb273d5d76a

See more details on using hashes here.

File details

Details for the file adam_atan2_pytorch-0.0.6-py3-none-any.whl.

File metadata

File hashes

Hashes for adam_atan2_pytorch-0.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 f43340b82a0dc2d3b83f9077690e6cb8f1af650404f46e5195e31afe46c173d9
MD5 e911c8ee392767926a92c245a95b51fe
BLAKE2b-256 2ea546ebc570c236f91a7c1ecfc54f549cdcca8c5b4672956863b9323b95c54f

See more details on using hashes here.

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