PyTorch学习率调整策略通过torch.optim.lr_scheduler接口实现。PyTorch提供的学习率调整策略分为三大类，分别是

a. 有序调整：等间隔调整(Step)，按需调整学习率(MultiStep)，指数衰减调整(Exponential)和 余弦退火CosineAnnealing。
b. 自适应调整：自适应调整学习率 ReduceLROnPlateau。
c. 自定义调整：自定义调整学习率 LambdaLR
  1. torch.optim.lr_scheduler.LambdaLr lamda计算
  2. torch.optim.lr_scheduler.StepLR 等间隔梯度下降
  3. torch.optim.lr_scheduler.MultiStepLR 指定step_list梯度下降
  4. torch.optim.lr_scheduler.ExponentialLR 指数下降
  5. torch.optim.lr_sheduler.CosineAnnealingLR 余弦下降
  6. torch.optim.lr_scheduler.ReduceLROnPlateau
  7. torch.optim.lr_scheduler.CyclicLR
  8. lr_scheduler.CosineAnnealingWarmRestarts 
# 准备环境
import torch
import torch.optim as optim
from torch.optim import lr_scheduler
from torchvision.models import AlexNet
import matplotlib.pyplot as plt

model = AlexNet(num_classes=2)
optimizer = optim.SGD(params=model.parameters(), lr=0.05)

torch.optim.lr_scheduler.LambdaLr

可以为不同参数组，设定不同的学习率

指定lamda匿名函数，lr=base_lr∗lmbda(self.last_epoch)

''' 参数 lr_lambda 给定一个lambda函数，将epoch作用于该函数，生成scale*init_lr，可以用来定义自己的lamda函数 Args: optimizer (Optimizer): Wrapped optimizer. lr_lambda (function or list): A function which computes a multiplicative factor given an integer parameter epoch, or a list of such functions, one for each group in optimizer.param_groups. last_epoch (int): The index of last epoch. Default: -1. '''

# epoch//30 * init_lr（0.05）
lambda1 = lambda epoch: epoch // 30
# lambda2 = lambda epoch: 0.95 ** epoch
    
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda= lambda1)
plt.figure()
x = list(range(100))
y = []
for epoch in range(100):
    scheduler.step()
    lr = scheduler.get_lr()
    y.append(scheduler.get_lr()[0])

plt.plot(x, y)

2. lr_scheduler.StepLR/MultiStepLR

两者都是阶梯递降学习率，其中StepLR 需要指定[step_size,gamma]，step_size为下降频率，gamma为decay比率，而MultiStepLR 则指定一个step_list，每个指定的step则乘以 衰减系数，milestones=[30,60,80]

# milestones = [step for step in step_list] gamma 衰减系数

#scheduler = lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[30,60,80],gamma= 0.5)
plt.figure()
x = list(range(100))
y = []
for epoch in range(100):
    scheduler.step()
    lr = scheduler.get_lr()
    y.append(scheduler.get_lr()[0])

plt.plot(x, y)

4 torch.optim.lr_scheduler.ExponentialLR

指数衰减 gamma 衰减系数

scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.8)

5. torch.optim.lr_sheduler.CosineAnnealingLR

Args:

  1. T_max： 每次cosine的epoch数，既 cos函数 周期
  2. eta_min: 每个周期衰减的最小学习率，一般设置为1e-5之类的小学习率

scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=20,eta_min = 0)

6. torch.optim.lr_scheduler.ReduceLROnPlateau

7.自定义调整学习率:CyclicLR https://arxiv.org/pdf/1506.01186.pdf


scheduler = lr_scheduler.CyclicLR(optimizer,base_lr=0.05,max_lr=0.1,step_size_up=10,step_size_down=10)

plt.figure()
x = list(range(100))
y = []
for epoch in range(100):
    scheduler.step()
    lr = scheduler.get_lr()
    y.append(scheduler.get_lr()[0])

plt.plot(x, y)

lr_scheduler.CosineAnnealingWarmRestarts

https://arxiv.org/abs/1608.03983

动态调整cos周期的 温和的cos 学习调整函数

其实这个有很多变体，还有动态调整 base_lr ，类似于T0的调整策略，
base_lr = base_lr* lr_mul lr_mul 为base_lr的调整步长

# https://arxiv.org/abs/1608.03983
# To 初始周期
# T_mult 每次循环 周期改变倍数 T_0 = T_0*T_mult
''' def __init__(self, optimizer, T_0, T_mult=1, eta_min=0, last_epoch=-1): if T_0 <= 0 or not isinstance(T_0, int): raise ValueError("Expected positive integer T_0, but got {}".format(T_0)) if T_mult < 1 or not isinstance(T_mult, int): raise ValueError("Expected integer T_mul >= 1, but got {}".format(T_mul)) self.T_0 = T_0 self.T_i = T_0 self.T_mult = T_mult self.eta_min = eta_min super(CosineAnnealingWarmRestarts, self).__init__(optimizer, last_epoch) self.T_cur = last_epoch '''

scheduler = lr_scheduler.CosineAnnealingWarmRestarts(optimizer,T_0=10,T_mult=2,eta_min=0)

plt.figure()
x = list(range(100))
y = []
for epoch in range(100):
    scheduler.step()
    lr = scheduler.get_lr()
    y.append(scheduler.get_lr()[0])

plt.plot(x, y)