defone_cycle(y1=0.0,y2=1.0,steps=100):returnlambda x:((1-math.cos(x*math.pi/steps))/2)*(y2-y1)+y1
這份trainer.py目前使用的是學習率衰減 (learning rate decay),但你希望改成餘弦退火學習率調度 (Cosine Annealing LR Scheduler)來讓學習率變化更平滑。我已修改如下: 變更點: 移除update_lr(),不再手動減半學習率。 使用CosineAnnealingLR: 對G 和 D的 Adam 優化器加上CosineAnnealingLR。 T_max設為args.epoch...
公式也很简单,直接改造一下CosineDecayLR源码即可。 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, deca...
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_epoch(int): steps each epoch epochs...
0.0,learning_rate)classWarmUpCosineDecayScheduler(keras.callbacks.Callback):""" 继承Callback,实现对学习率的调度 """def__init__(self,learning_rate_base,total_steps,global_step_init=0,warmup_learning_rate=0.0,warmup_steps=0,hold_base_rate_steps=0,verbose=0):super...
( 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_...
self.update_specified=FalseclassCosineWarmup(LinearWarmup):"""Cosine learning rate decay with warm...
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 ...
CosineDecayLR的解决(规避)方案 方案1 根据@用什么名字没那么重要的建议,直接clip数值更合适,不会出现误差问题。 代码如下: importmindspore.opsasPimportmindspore.common.dtypeasmstypefrommindsporeimportcontextfrommindspore.nn.learning_rate_scheduleimportLearningRateScheduleclassCosineDecayLR(LearningRateSchedule):def_...
learning_rate=lr, T_max=step_each_epoch *epochs, ) self.update_specified=FalseclassCosineWarmup(LinearWarmup):"""Cosine learning rate decay with warmup [0, warmup_epoch): linear warmup [warmup_epoch, epochs): cosine decay Args: