adamr 0.0.2

Adam with weight recovery optimizer for pytorch

AdamR

Adam with weight Recovery optimizer

TL;DR

AdamW tends to decay parameters towards zero, which makes the model "forget" the pretrained parameters during finetuning. Instead, AdamWR tries to recover parameters towards pretrained values during finetuning.

Have a try

Just like other PyTorch optimizers,

from adamr import AdamR
from xxx import SomeModel, SomeData, SomeDevice, SomeLoss

model = SomeModel()
dataloader = SomeData()
model.to(SomeDevice)

adamr = AdamR(
   model.parameters(),
   lr=1e-5,
   betas=(0.9, 0.998), # Adam's beta parameters
   eps=1e-8,
   weight_recovery=0.1
   )

loss_fn = SomeLoss()

for x, y in dataloader:
   adamwr.zero_grad()
   y_bar = model(x)
   loss = loss_fn(y_bar, y)
   loss.backward()
   adamr.step()

Algorithm

TODO: improve the readability

Here is a paper snippet: