Ranger - a synergistic optimizer combining RAdam (Rectified Adam) and LookAhead in one codebase.
Latest version 9.3.19 - full refactoring for slow weights and one pass handling (vs two before). Refactor should eliminate any random save/load issues regarding memory.
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Beta Version - Ranger913A.py:
For anyone who wants to try this out early, this version changes from RAdam to using calibrated anistropic adaptive learning rate per this paper:
https://arxiv.org/abs/1908.00700v2
"Empirical studies support our observation of the anisotropic A-LR and show that the proposed methods outperform existing AGMs and generalize even better than S-Momentum in multiple deep learning tasks."
Initial testing looks very good for training stabilization. Any feedback in comparsion with current Ranger (9.3.19) is welcome!
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Medium article with more info:
https://medium.com/@lessw/new-deep-learning-optimizer-ranger-synergistic-combination-of-radam-lookahead-for-the-best-of-2dc83f79a48d
Multiple updates: 1 - Ranger is the optimizer we used to beat the high scores for 12 different categories on the FastAI leaderboards! (Previous records all held with AdamW optimizer).
2 - Highly recommend combining Ranger with: Mish activation function, and flat+ cosine anneal training curve.
3 - Based on that, also found .95 is better than .90 for beta1 (momentum) param (ala betas=(0.95, 0.999)).
Fixes: 1 - Differential Group learning rates now supported. This was fix in RAdam and ported here thanks to @sholderbach. 2 - save and then load may leave first run weights stranded in memory, slowing down future runs = fixed.
Clone the repo, cd into it and install it in editable mode (-e option).
That way, these is no more need to re-install the package after modification.
git clone https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
cd Ranger-Deep-Learning-Optimizer
pip install -e . from ranger import Ranger # this is from ranger.py
from ranger import RangerVA # this is from ranger913A.py
from ranger import RangerQH # this is from rangerqh.py
# Define your model
model = ...
# Each of the Ranger, RangerVA, RangerQH have different parameters.
optimizer = Ranger(model.parameters(), **kwargs)Usage and notebook to test are available here: https://github.com/lessw2020/Ranger-Mish-ImageWoof-5