一般当学习率回到base_lr时使训练结束。 tensorflow CLR接口www.tensorflow.org/addons/api_docs/python/tfa/optimizers/CyclicalLearningRate 在CLR的基础上,"1cycle"是在整个训练过程中只有一个cycle,学习率首先从初始值上升至max_lr,之后从max_lr下降至低于初始值的大小。
def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.", UserWarning) if self.last_epoch == 0: return [group['lr'] for group in self.optimizer.param_groups] elif (self....
import mindspore.ops as P import mindspore.common.dtype as mstype from mindspore import context from mindspore.nn.learning_rate_schedule import LearningRateSchedule class CosineDecayLR(LearningRateSchedule): def __init__(self, min_lr, max_lr, decay_steps): super(CosineDecayLR, self).__init__...
( learning_rate=lr, T_max=step_each_epoch * epochs, ) self.update_specified = False class CosineWarmup(LinearWarmup): """ Cosine learning rate decay with warmup [0, warmup_epoch): linear warmup [warmup_epoch, epochs): cosine decay Args: lr(float): initial learning rate step_each_...
Corollary 3.1 : Higherηgives higher initial learning speed whenLt≫L~(η), while we shall decayηasLtgets closer toL~(η). We get an excellent fit of the above loss curve using our parameters{α,β,L~(0)}and the learning rate history: ...
plt.xlabel('Step')plt.ylabel('Learning Rate')plt.title('Learning Rate Schedules')plt.legend()plt.show() 得到的图像如下: WarmupCosineLR 并且注意到,虽然后面的decay阶段从图像上看似乎差异不大,但是实际上我手动算了一下,仍然会有0.4%左右的误差。
接着作者引出了学习率策略并解释A common learning rate schedule is to use a constant learning rate and divide it by a fixed constant in (approximately) regular intervals. 注意:对数轴模糊了余弦函数的典型形状 意思是说,当年比较好的模型在训练时虽然用了SGD优化器,但它们的学习率策略是阶梯下降的。