tl;dr: pytorch的torch.optim.lr_scheduler.OneCycleLR就很不错,能兼顾warmup和余弦学习率,也不用下载额外的包 importtorchfromtorch.optim.lr_schedulerimportCosineAnnealingLR, CosineAnnealingWarmRestartsimportmatplotlib.pyplotaspltfromtimmimportschedulerastimm_schedulerfromtimm.scheduler.schedulerimportSchedulerastimm...
from torch.optim.lr_scheduler import CosineAnnealingWarmRestartsscheduler = CosineAnnealingWarmRestarts(optimizer, T_0 = 8,# Number of iterations for the first restart T_mult = 1, # A factor increases TiTi after a restart eta_min = 1e-4) # Minimum learning rate 这个计划调度于2017...
Describe the bug It's unclear if this is a bug, an intentional design decision, or part of a design trade-off I don't fully understand. Let me explain with an example. I'm using the cosine LR scheduler and my script uses a warm up LR (1e-5), number of warm up epochs (20), ...
timm库中封装了很好用的学习率调度器,可以方便的实现学习率的预热和余弦退火,对其简单的使用方法如下图所示: 可以看到,使用timm库比自己实现或使用pytorch库里的学习率调度,要简单方便很多。 timm库中的cosin…
As an example of how ChainedScheduler would look like for the case where two different schedulers need to have synchronized and updated states that are to be used by the next scheduler: ` `` >>> warmup_scheduler = WarmUpScheduler(
rWySp/pytorch-cosine-annealing-with-warmup 代码Issues0Pull Requests0Wiki统计流水线 服务 加入Gitee 与超过 1200万 开发者一起发现、参与优秀开源项目,私有仓库也完全免费 :) 免费加入 已有帐号?立即登录 该仓库未声明开源许可证文件(LICENSE),使用请关注具体项目描述及其代码上游依赖。
开发者ID:iBelieveCJM,项目名称:Tricks-of-Semi-supervisedDeepLeanring-Pytorch,代码行数:23,代码来源:main.py 示例2: get_scheduler ▲点赞 6▼ # 需要导入模块: from torch.optim import lr_scheduler [as 别名]# 或者: from torch.optim.lr_scheduler importCosineAnnealingLR[as 别名]defget_scheduler(opti...
steps] scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=config.steps, gamma=config.gamma) elif config.lr_scheduler == 'exp-warmup': lr_lambda = exp_warmup(config.rampup_length, config.rampdown_length, config.epochs) scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda...
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...
在本文末尾的附录中会包含用于可视化PyTorch学习率调度器的Python代码。 1、StepLR 在每个预定义的训练步骤数之后,StepLR通过乘法因子降低学习率。 from torch.optim.lr_scheduler import StepLR scheduler = StepLR(optimizer, step_size = 4, # Period of learning rate decay ...