LinearLrWarmup¶ classpaddle.optimizer.lr_scheduler.LinearLrWarmup(learing_rate,warmup_steps,start_lr,end_lr,last_epoch=- 1,verbose=False)¶ 该接口提供一种学习率优化策略-线性学习率热身(warm up)对学习率进行初步调整。在正常调整学习率之前,先逐步增大学习率。
代码验证如下: importtorchfromtransformersimportget_cosine_schedule_with_warmupfromdeepspeed.runtime.lr_schedulesimportWarmupCosineLRimportmatplotlib.pyplotasplt# Assuming 'model' is predefinedoptimizer1=torch.optim.Adam(model.parameters(),lr=1.0)scheduler1=WarmupCosineLR(optimizer1,total_num_steps=20,warm...
warm_up策略.png 可以看出开始的时候,程序执行块1而不执行块2,自变量为iteration,因变量是学习率lr,起初iteration为0,lr为cfg.lr_warmup_init;随着iteration递增,lr以固定斜率线性增长,当iteration等于设定的cfg.lr_warmup_until,lr达到最大值args.lr,即初始学习率。 继而程序不执行块1开始执行块2,cfg.lr_steps...
lr_warmup_steps (int): number of warmup steps lr_decay_steps (int): number of decay steps lr_decay_style (str): decay style for learning rate start_wd (float): initial weight decay end_wd (float): final weight decay wd_incr_steps (int): number of weight decay increment steps wd_...
# 需要导入模块: from torch.optim import lr_scheduler [as 别名]# 或者: from torch.optim.lr_scheduler importCyclicLR[as 别名]def__init__(self, optimizer, lr, warmup_steps, momentum, decay):# cyclic paramsself.optimizer = optimizer
warmup_steps=warmup_steps, hold_base_rate_steps=0) for param_group in optimizer.param_groups: param_group['lr'] = lrreturn lrdef cosine_decay_with_warmup(global_step, learning_rate_base, total_steps, warmup_learning_rate=0.0,
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...
warmup def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1): def lr_lambda(current_step: int): if current_step < num_warmup_steps: return float(current_step) / float(max(1.0, num_warmup_steps)) ...
TRAIN.WARMUP_LR, warmup_t=warmup_steps, cycle_limit=1, t_in_epochs=False, warmup_prefix=config.TRAIN.LR_SCHEDULER.WARMUP_PREFIX, ) elif config.TRAIN.LR_SCHEDULER.NAME == 'linear': lr_scheduler = LinearLRScheduler( optimizer, t_initial=num_steps, lr_min_rate=0.01, ...
WarmUp Learning Rate cfg.SOLVER.BASE_LR=0.001cfg.SOLVER.STEPS=(300,400)cfg.SOLVER.MAX_ITER=500 detectron2中lr曲线 设置最大迭代次数为500次, 前面300按照一个线性增长,然后300, 400分别下降这样的一个策略,结合代码 build_lr_scheduler # ->箭头表示函数输出返回值类型 ...