def configure_optimizers(self): optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate) scheduler = partial(transformers.get_cosine_schedule_with_warmup, optimizer=optimizer, num_cycles=0.5) return [optimizer], [{'scheduler': scheduler}] Then we define a callback that will p...
# 需要导入模块: from torch.optim import lr_scheduler [as 别名]# 或者: from torch.optim.lr_scheduler importLambdaLR[as 别名]defget_cosine_with_hard_restarts_schedule_with_warmup( optimizer, num_warmup_steps, num_training_steps, num_cycles=1.0, last_epoch=-1):""" Create a schedule with ...
Let me explain with an example. I'm using the cosine LR scheduler and my script uses a warm up LR (1e-5), number of warm up epochs (20), base LR (1e-3), min LR (1e-5) and total epochs (300). For this let's assume 1 cycle. I expect to start at min LR = 1e-5 th...
def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=.5, last_epoch=-1): """ Create a schedule with a learning rate that decreases following the values of the cosine function between 0 and `pi * cycles` after a warmup period during which it...
warmup_learning_rate=0.0, warmup_steps=0, hold_base_rate_steps=0): """Cosine decay schedule with warm up period. Cosine annealing learning rate as described in: Loshchilov and Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts. ...
warmup step 上图中的0-10epoch阶段就是一个warmup操作,学习率缓慢增加,10之后就是常规的学习率递减算法原理上很简单,接下来从代码上进行分析,warmup可以有两种构成方式:对已有的scheduler类进行包装重构 直接编写新的类 对于第一种情况,我们以CosineAnnealingLR类为例...
对于超过warmup范围,直接使用CosineAnnealingLR类,比较简单 对于warmup范围类,使用当前重构类的step()函数,因为也是继承于**_LRScheduler**类,所以step()同样是运用到get_lr() def get_lr(self): if self.last_epoch > self.warmup_epoch: # 超过warmup范围,使用CosineAnnealingLR类的get_lr() return self....
torch.optim.lr_scheduler.CosineAnnealingWarmRestarts 跟余弦退火类似,只是在学习率上升时使用热启动。这个 Scheduler 在各种比赛中也经常用到。文档链接 T_0: 20, T_mult: 1, eta_min: 0.001 torch.optim.lr_scheduler.CyclicLR 学习率在两个边界之间以固定频率变化,该 Scheduler 的 step 函数应该在每个 iterat...
如果我们在 1.1.0 及之后的版本仍然将学习率的调整(即 scheduler.step())放在 optimizer’s update(即 optimizer.step())之前,那么 learning rate schedule 的第一个值将会被跳过。所以如果某个代码是在 1.1.0 之前的版本下开发,但现在移植到 1.1.0及之后的版本运行,发现效果变差,需要检查一下是否将scheduler....
class CosineAnnealingWarmRestarts(_LRScheduler): r"""Set the learning rate of each parameter group using a cosine annealing schedule, where :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` is the number of epochs since the last restart and :math:`T_{i}` is the...