How to decay your Learning Rate (PyTorch)
PyTorch implementation of ABEL LRScheduler based on weight-norm. If you find this work interesting, do consider starring the repository. If you use this in your research, don't forget to cite!
Installation
WIP - not available on PyPi yet.
pip install abel-pytorch
Usage
import torch
from torch import nn, optim
from abel import ABEL
model = resnet18()
optim = optim.SGD(model.parameters(), 1e-3)
scheduler = ABEL(optim, 0.9)
for i, (images, labels) in enumerate(trainloader):
# forward pass...
optim.step()
scheduler.step()
Cite original paper:
@article{lewkowycz2021decay,
title={How to decay your learning rate},
author={Lewkowycz, Aitor},
journal={arXiv preprint arXiv:2103.12682},
year={2021}
}
Cite this work:
@misc{abel2021pytorch,
author = {Vaibhav Balloli},
title = {A PyTorch implementation of ABEL},
year = {2021},
howpublished = {\url{https://github.com/tourdeml/abel-pytorch}}
}
Metadata
Release files for abel-pytorch 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| abel-pytorch-0.0.1.tar.gz | 3.1 kB | Without provenance | Details for abel-pytorch-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| abel_pytorch-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for abel_pytorch-0.0.1-py3-none-any.whl |
Total release size: 7.3 kB
Release files / abel-pytorch-0.0.1.tar.gz
| Download URL | abel-pytorch-0.0.1.tar.gz |
|---|---|
| Size | 3.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
aa8b99d0c6d4983c083150b2b5379ddf0593d80f2b7934e1995019f75b53c67b
|
|
BLAKE2b-256 checksum How to use checksums |
f130f98adc949f38fdb1e67fb5a13078c983ec17a188f800186aab3b0675014b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.2
|
Release files / abel_pytorch-0.0.1-py3-none-any.whl
| Download URL | abel_pytorch-0.0.1-py3-none-any.whl |
|---|---|
| Size | 4.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c456d8dbbd3116379c2fa3775d7dc42e7c2846d784a267e4c11e76809126de26
|
|
BLAKE2b-256 checksum How to use checksums |
a022dace821001bfe52bf0d9c96ae3c8f9c826d9888992480b38d77530a9e9c9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.2
|
Security for abel-pytorch 0.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.