深度学习优化器：《Lookahead Optimizer: k steps forward, 1 step back》

深度学习优化器：《Lookahead Optimizer: k steps forward, 1 step back》

项目地址：

https://github.com/michaelrzhang/lookahead

pytorch版本：

https://github.com/michaelrzhang/lookahead/blob/master/lookahead_pytorch.py

论文地址：

https://arxiv.org/abs/1907.08610

使用方法：（pytorch）

optimizer = # {any optimizer} e.g. torch.optim.Adam
if args.lookahead:
    optimizer = Lookahead(optimizer, la_steps=args.la_steps, la_alpha=args.la_alpha)

We found that evaluation performance is typically better using the slow weights. This can be done in PyTorch with something like this in your eval loop:

if args.lookahead:
    optimizer._backup_and_load_cache()
    val_loss = eval_func(model)
    optimizer._clear_and_load_backup()

@article{zhang2019lookahead,

title={Lookahead Optimizer: k steps forward, 1 step back},

author={Zhang, Michael R and Lucas, James and Hinton, Geoffrey and Ba, Jimmy},

journal={arXiv preprint arXiv:1907.08610},

year={2019}

}