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from import LRScheduler

[文档] class InverseSquareRootSchedule(LRScheduler): """ Decay the LR based on the inverse square root of the update number. We also support a warmup phase where we linearly increase the learning rate from some initial learning rate until the configured learning rate. Thereafter we decay proportional to the number of updates, with a decay factor set to align with the configured learning rate. Args: warmup_steps(int): The number of warmup steps. A super parameter. learning_rate(float, optional): The learning rate. It is a python float number. Defaults to 1.0. last_epoch(int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate. verbose(bool, optional): If ``True``, prints a message to stdout for each update. Defaults to ``False``. """ def __init__(self, warmup_steps, learning_rate=1.0, last_epoch=-1, verbose=False): self.warmup_steps = warmup_steps warmup_end_lr = learning_rate self.warmup_init_lr = 0.0 self.lr_step = (warmup_end_lr - self.warmup_init_lr) / self.warmup_steps self.decay_factor = warmup_end_lr * (self.warmup_steps**0.5) super(InverseSquareRootSchedule, self).__init__(learning_rate, last_epoch, verbose)
[文档] def get_lr(self): if self.last_epoch < self.warmup_steps: return self.warmup_init_lr + self.last_epoch * self.lr_step else: return self.decay_factor * (self.last_epoch**-0.5)