Yogi, introduced by Zaheer et al. (in a paper titled "Adaptive Methods for Nonconvex Optimization" ), proposes a simple yet profound change to the update rule of the second moment.
The Yogi Optimizer represents a crucial philosophical shift in adaptive optimization: yogi optimizer
You don't need to implement Yogi from scratch. It is available in major deep learning frameworks. Yogi, introduced by Zaheer et al
The default epsilon for Yogi is typically 1e-3 (compared to 1e-7 for Adam). Do not change this without reason, as it interacts with the additive update rule. yogi optimizer
import tensorflow as tf
The name is actually an acronym derived from the mechanics of the update: Y et O ther G radient I nformation.
import torch import torch_optimizer as optim