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....
LR_scheduler及warmup底层原理和代码分析 LR_schedulerLR_scheduler是用于调节学习率lr的,在代码中,我们经常看到这样的一行代码scheduler.step()通过这行代码来实现lr的更新的,那么其中的底层原理是什么呢?我们就进去看看在pytorch代码中,各种类型scheduler大多基于**_LRScheduler**类我们...
3warmup机制 由于刚开始训练时,模型的权重(weights)是随机初始化的,此时若选择一个较大的学习率,可能带来模型的不稳定(振荡),选择Warmup预热学习率的方式,可以使得开始训练的几个epoches或者一些steps内学习率较小,在预热的小学习率下,模型可以慢慢趋于稳定,等模型相对稳定后再选择预先设置的学习率进行训练,使得模型...
iter (int): iteration at which to calculate the warmup factor. warmup_iters (int): the number of warmup iterations. warmup_factor (float): the base warmup factor (the meaning changes according to the method used). Returns: float: the effective warmup factor at the given iteration. ""...
scheduler=lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=20, T_mult=2, eta_min=0.01) 8. LambdaLR LambdaLR其实没有固定的学习率曲线,名字中的lambda指的是可以将学习率自定义为一个有关epoch的lambda函数,比如下面我们定义了一个指数函数,实现了ExponentialLR的功能。
def __init__(self, optimizer, warmup_steps: int = 0, last_epoch: int = -1): self.warmup_steps = warmup_steps super().__init__(optimizer, last_epoch=last_epoch) def get_lr(self): if self.last_epoch < self.warmup_steps: return [(self.last_epoch + 1) / (self.warmup_step...
Run any example with a warmup scheduler, observe the the effective LR is 0 for the first step, unnecessarily wasting compute. See similar discussion for this issue on torchtunepytorch/torchtune#2010. See the code at (current_step)/float(max(1,num_warmup_steps)) ...
self.warmup_lr = warmup_lr self.warmup_mode = warmup_mode self.N = n_epochs * n_iters self.warmup_N = warmup_epochs * n_iters 开发者ID:osmr,项目名称:imgclsmob,代码行数:33,代码来源:lr_scheduler.py 注:本文中的mxnet.lr_scheduler.LRScheduler方法示例由纯净天空整理自Github/MSDocs等开...
return super(GradualWarmupScheduler, self).step(epoch) else: self.step_ReduceLROnPlateau(metrics, epoch) class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler): def __init__( self, optimizer, milestones, gamma=0.1, warmup_factor=1.0 / 3, warmup_iters=500, ...
warmup_epochs=args.warmup_epochs, total_epochs=total_epochsor[args.epochs] * args.num_lrs, steps_per_epoch=args.train_data_size // args.batch_size, init_lr=args.init_lr, max_lr=args.max_lr, final_lr=args.final_lr )ifargs.scheduler =='none':returnMockLR(optimizer=optimizer, lr=arg...