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 (by YanaiEliyahu)
DemonRangerOptimizer
Quasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay (by JRC1995)
| AdasOptimizer | DemonRangerOptimizer | |
|---|---|---|
| 2 | 1 | |
| 85 | 23 | |
| - | - | |
| 5.8 | 0.0 | |
| over 3 years ago | over 3 years ago | |
| C++ | Python | |
| MIT License | - |
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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.
AdasOptimizer
Posts with mentions or reviews of AdasOptimizer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-15.
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[R] AdasOptimizer Update: Cifar-100+MobileNetV2 Adas generalizes with Adas 15% better and 9x faster than Adam
I think too, I was comfortable with posting this because it was the same code for each optimizer. https://github.com/YanaiEliyahu/AdasOptimizer/blob/master/misc/cifar-100-mobilenetv2/model_with_training.py.txt if you care to find what I did wrong, go for it.
- Optimizer obsoletes step-size scheduling, 100% on MNIST's training set 11 epochs
DemonRangerOptimizer
Posts with mentions or reviews of DemonRangerOptimizer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-15.
-
[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?
When comparing AdasOptimizer and DemonRangerOptimizer you can also consider the following projects:
ML-Optimizers-JAX - Toy implementations of some popular ML optimizers using Python/JAX
pytorch-optimizer - torch-optimizer -- collection of optimizers for Pytorch
imagenette - A smaller subset of 10 easily classified classes from Imagenet, and a little more French
tensorflow - An Open Source Machine Learning Framework for Everyone
Gradient-Centralization-TensorFlow - Instantly improve your training performance of TensorFlow models with just 2 lines of code!
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
RAdam - On the Variance of the Adaptive Learning Rate and Beyond
Caffe - Caffe: a fast open framework for deep learning.
AdasOptimizer vs ML-Optimizers-JAX
DemonRangerOptimizer vs pytorch-optimizer
AdasOptimizer vs imagenette
DemonRangerOptimizer vs ML-Optimizers-JAX
AdasOptimizer vs pytorch-optimizer
DemonRangerOptimizer vs imagenette
AdasOptimizer vs tensorflow
DemonRangerOptimizer vs Gradient-Centralization-TensorFlow
AdasOptimizer vs Pytorch
DemonRangerOptimizer vs RAdam
AdasOptimizer vs Caffe