pytorch-optimizer
DemonRangerOptimizer
| pytorch-optimizer | DemonRangerOptimizer | |
|---|---|---|
| 3 | 1 | |
| 2,946 | 23 | |
| - | - | |
| 3.1 | 0.0 | |
| about 1 month ago | over 3 years ago | |
| Python | Python | |
| Apache License 2.0 | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pytorch-optimizer
-
[D]: Implementation: Deconvolutional Paragraph Representation Learning
The specific implementation is from (here)[https://github.com/jettify/pytorch-optimizer] since pytorch doesn't have it directly.
- VQGAN+CLIP : "RAdam" from torch_optimizer could not be imported ?
- [R] AdasOptimizer Update: Cifar-100+MobileNetV2 Adas generalizes with Adas 15% better and 9x faster than Adam
DemonRangerOptimizer
-
[R] AdasOptimizer Update: Cifar-100+MobileNetV2 Adas generalizes with Adas 15% better and 9x faster than Adam
The results are interesting, but in terms of novelty of the main theory - isn't it almost identical to Baydin et al.? https://arxiv.org/pdf/1703.04782.pdf It seems the difference may be in some implementation details, like using a running average for the past gradient. If it's useful, I implemented a bunch of optimizers with options to synergize different techniques (https://github.com/JRC1995/DemonRangerOptimizer) including hypergradient updates for stuffs (and taking into account decorrelated weight decay and per-parameter lrs for hypergradient lr) when I was bored before practically abandoning it all together. I didn't really run any experiments with it though, but some people tried although they may not have got any particularly striking results.
What are some alternatives?
sam - SAM: Sharpness-Aware Minimization (PyTorch)
ML-Optimizers-JAX - Toy implementations of some popular ML optimizers using Python/JAX
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
AdasOptimizer - ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance
imagenette - A smaller subset of 10 easily classified classes from Imagenet, and a little more French
simple-sam - Sharpness-Aware Minimization for Efficiently Improving Generalization
Gradient-Centralization-TensorFlow - Instantly improve your training performance of TensorFlow models with just 2 lines of code!
RAdam - On the Variance of the Adaptive Learning Rate and Beyond
PythonPID_Tuner - Python PID Tuner - Based on a FOPDT model obtained using a Open Loop Process Reaction Curve