9、ChainedScheduler 10、ConstantLR 三、自定义学习率调整策略 四、参考内容 学习率是深度学习训练中至关重要的参数,很多时候一个合适的学习率才能发挥出模型的较大潜力。学习率的选择是深度学习中一个困扰人们许久的问题,学习率设置过小,极大降低收敛速度,增加训练时间;学习率太大,可能导致参数在最优解两侧来回振荡...
# 需要導入模塊: from torch.optim import lr_scheduler [as 別名]# 或者: from torch.optim.lr_scheduler importMultiStepLR[as 別名]defget_lr_scheduler(optimizer_conf, scheduler_name, optimizer, initial_epoch=-1):ifscheduler_name =='multistep':returnlr_scheduler.MultiStepLR(optimizer, optimizer_con...
Python PyTorch ConstantLR用法及代码示例本文简要介绍python语言中 torch.optim.lr_scheduler.ConstantLR 的用法。 用法: class torch.optim.lr_scheduler.ConstantLR(optimizer, factor=0.3333333333333333, total_iters=5, last_epoch=- 1, verbose=False) 参数: optimizer(Optimizer) -包装优化器。 factor(float) -...
optim.lr_scheduler import _LRScheduler class ConstantLR(_LRScheduler): def __init__(self, optimizer, last_epoch=-1): super(ConstantLR, self).__init__(optimizer, last_epoch) def get_lr(self): return [base_lr for base_lr in self.base_lrs] class PolynomialLR(_LRScheduler): def __...
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] or DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION[name] if name == SchedulerType.CONSTANT: return wrap_check_needless_num_warmup_steps(schedule_func(optimizer, **lr_scheduler_kwargs)) if name == SchedulerType.PIECEWISE_CONSTANT: if name == DiffusersSchedu...
def step(self, epoch=None, metrics=None): if type(self.after_scheduler) != ReduceLROnPlateau: if self.finished and self.after_scheduler: if epoch is None: self.after_scheduler.step(None) else: self.after_scheduler.step(epoch - self.total_epoch) self._last_lr = self.after_scheduler.get...
if type(self.after_scheduler) != ReduceLROnPlateau: if self.finished and self.after_scheduler: return self.after_scheduler.step(epoch) else: return super(GradualWarmupScheduler, self).step(epoch) else: self.step_ReduceLROnPlateau(metrics, epoch) class WarmupMultiStepLR(torch.o...
FunctionType): state_dict['lr_lambdas'][idx] = fn.__dict__.copy() return state_dict def load_state_dict(self, state_dict): """Loads the schedulers state.When saving or loading the scheduler, please make sure to also save or load the state of the optimizer....
class _LRScheduler(object): def __init__(self, optimizer, last_epoch=-1, verbose=False): # Attach optimizer if not isinstance(optimizer, Optimizer): raise TypeError('{} is not an Optimizer'.format( type(optimizer).__name__)) self.optimizer = optimizer # Initialize epoch and base learnin...
If you’re a Constant Contact customer, you can create and schedule your holiday emails in advance. You can also use tools like Facebook’s native post scheduler, which lets you schedule your posts ahead of time. Try to incorporate a number of time-saving tools to schedule posts before thi...