start_factor(float) -我们在第一个 epoch 中乘以学习率(base_lr)的数字。在接下来的 epoch 中,乘法因子向 end_factor 变化。默认值:1./3。 end_factor(float) -我们在线性变化过程结束时乘以学习率(base_lr)的数字。默认值:1.0。 total_iters(int) -乘法因子达到1的迭代次数。默认值:5。 last_epoch(...
---> 23 lr_scheduler = torch.optim.lr_scheduler.LinearLR( 24 optimizer, start_factor=warmup_factor, total_iters=warmup_iters 25 ) AttributeError: module 'torch.optim.lr_scheduler' has no attribute 'LinearLR'
(), lr=lr) # Store scheduler learing rates for different `total_iters` start_factor = 0.1 end_factor = 1. lr_dict = dict() for ti in [3, 5, 10, 25, 50, 100]: sch = LinearLR( opt, start_factor=start_factor, end_factor=end_factor, total_iters=ti, last_epoch=-1 ) lr_...