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Project description

Implementation of for large batch, large learning rate training.

Bonus: TensorboardX logging (example below).

Try the sample

git clone
cd pytorch-lamb
pip install -e .
tensorboard --logdir=runs

Sample results

At --lr=.1, the Adam optimizer is unable to train. With a little weight decay, LAMB avoids diverging!

Green: python --batch-size=512 --lr=.1 --wd=0 --log-interval=30 --optimizer=lamb

Blue: python --batch-size=512 --lr=.1 --wd=.01 --log-interval=30 --optimizer=lamb

r1 is the L2 norm of the weights. You can see in the green plot that some of the weights start to run away, which leads to divergence. This is why weight decay helps.

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