10、ConstantLR 三、自定义学习率调整策略 四、参考内容 学习率是深度学习训练中至关重要的参数,很多时候一个合适的学习率才能发挥出模型的较大潜力。学习率的选择是深度学习中一个困扰人们许久的问题,学习率设置过小,极大降低收敛速度,增加训练时间;学习率太大,可能导致参数在最优解两侧来回振荡。当我们选定一个合...
scheduler=lr_scheduler.ChainedScheduler([lr_scheduler.LinearLR(optimizer,start_factor=1,end_factor=0.5,total_iters=10),lr_scheduler.ExponentialLR(optimizer, gamma=0.95)]) 11.ConstantLR ConstantLRConstantLR非常简单,在total_iters轮内将optimizer里面指定的学习率乘以factor,total_iters轮外恢复原学习率。 sc...
torch.optim.lr_scheduler.ConstantLR(optimizer,factor=0.3333333333333333,total_iters=5,last_epoch=-1,verbose=False) 在到达total_iters之前,学习率为初始值的factor倍,之后则恢复原lr值。 def plot_constantlr(): plt.clf() optim = torch.optim.Adam(model.parameters(), lr=initial_lr) scheduler = lr_s...
ChainedScheduler与SequentialLR相似，但允许连续调整学习率，提供更平滑的调整过程。12. **ConstantLR ConstantLR在固定迭代次数内保持学习率不变，之后恢复初始设置，适用于某些特定训练阶段。13. **ReduceLROnPlateau ReduceLROnPlateau策略通过监控验证集性能调整学习率，依据loss或accuracy变化，通过factor参数...
ConstantLR：在total_iters前保持固定学习率，之后恢复原值。LinearLR：线性增长学习率，从start_factor到end_factor。MultiplicativeLR：根据lr_lambda的函数调整前一步的lr，与LambdaLR不同。SequentialLR：多个调度器按顺序应用，milestones定义分界点，初始lr可能因OneCycleLR而改变。CosineAnnealingWarmRestarts...
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=args.lr_decay, patience=args.patience)elifargs.scheduler =='constant': scheduler = lr_scheduler.LambdaLR(optimizer,lambdax:1)elifargs.scheduler =='cosine': scheduler = lr_scheduler.CosineAnnealingLR(optimizer, args.T_max,...
scheduler == 'constant': scheduler = lr_scheduler.LambdaLR(optimizer, lambda x: 1) elif args.scheduler == 'cosine': scheduler = lr_scheduler.CosineAnnealingLR(optimizer, args.T_max, args.min_lr) return scheduler Example #30Source File: models.py From GANimation with GNU General Public ...
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=args.lr_decay, patience=args.patience)elifargs.scheduler =='constant': scheduler = lr_scheduler.LambdaLR(optimizer,lambdax:1)elifargs.scheduler =='cosine': scheduler = lr_scheduler.CosineAnnealingLR(optimizer, args.T_max,...
import torch model = torch.nn.Linear(2,2) optim = torch.optim.SGD(model.parameters(), lr=0.1) lr_scheduler = torch.optim.lr_scheduler.SequentialLR(optim, [torch.optim.lr_scheduler.ConstantLR(optim), torch.optim.lr_scheduler.StepLR(optim, step_size=10)], [1]) lr_scheduler.optimizer ...
class ConstantLR(_LRScheduler): def __init__(self, optimizer, last_epoch=-1): super(ConstantLR, self).__init__(optimizer, last_epoch) def get_lr(self): return [base_lr for base_lr in self.base_lrs] class PolynomialLR(_LRScheduler): def __init__(self, optimizer, max_iter, powe...