from torch.optim.lr_scheduler import StepLRscheduler = StepLR(optimizer, step_size = 4, # Period of learning rate decay gamma = 0.5) # Multiplicative factor of learning rate decay 2、MultiStepLR MultiStepLR -类似于StepLR -也通过乘法因子降低了学习率，但在可以自定义修改学习率的时间节点。
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...
公式也很简单,直接改造一下CosineDecayLR源码即可。 importmindspore.opsasPimportmindspore.common.dtypeasmstypefrommindsporeimportcontextfrommindspore.nn.learning_rate_scheduleimportLearningRateScheduleclassCosineDecayLR(LearningRateSchedule):def__init__(self, min_lr, max_lr, decay_steps):super(CosineDecayLR, ...
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...
init_decay_epochs(int)- Number of initial decay epochs. min_decay_lr(float or iterable of floats)- Learning rate at the end of decay. restart_interval(int)- Restart interval for fixed cycles. Set to None to disable cycles. Default: None. ...
如何在 PyTorch 中设定学习率衰减(learning rate decay) 很多时候我们要对学习率(learning rate)进行衰减,下面的代码示范了如何每30个epoch按10%的速率衰减: 什么是param_groups? optimizer通过param_group来管理参数组.param_group中保存了参数组及其对应的学习率,动量等等.所以我们可以通过更改param_group[‘lr’]的...
GPU:Cos(pi) 无精度误差,Sin(pi)有精度误差。(Pytorch也是同样的情况。) Ascend:Sin(pi) 无精度误差,Cos(pi)有精度误差。 因此才会有CosineDecayLR出现1e-6数量级的负数出现,考虑到是硬件平台的差异,目前可以用以下方式规避 importmindspore.opsasPimportmindspore.common.dtypeasmstypefrommindsporeimportcontextfrom...
decay_steps, # 对应\eta_{min}^i / \eta_{max}^i alpha=0.0, name=None ) def decayed_learning_rate(step): step = min(step, decay_steps) cosine_decay = 0.5 * (1 + cos(pi * step / decay_steps)) decayed = (1 - alpha) * cosine_decay + alpha return initial_learning_rate * ...
注意在训练开始之前,pytorch似乎会提前调用一次lr_scheduler.step()方法 """ifcurrent_epoch <= warmup_epoch: alpha =float(current_epoch) / (warmup_epoch)# warmup过程中lr倍率因子大小从warmup_factor -> 1returnwarmup_factor * (1- alpha) + alpha# 对于alpha的一个线性变换,alpha是关于x的一个反...
def build_lr_scheduler(self): """Build cosine learning rate scheduler.""" self.G_scheduler = lr_scheduler.CosineAnnealingLR(self.G_optimizer, T_max=self.train_config.total_step - self.train_config.warmup_step) Example #23Source File: networks.py From EvolutionaryGAN-pytorch with MIT License...