pytorch中lr_scheduler的使用

torch.optim.lr_scheduler.StepLR

• 代码

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)

# lr_scheduler.StepLR()
# Assuming optimizer uses lr = 0.05 for all groups
# lr = 0.05     if epoch < 30
# lr = 0.005    if 30 <= epoch < 60
# lr = 0.0005   if 60 <= epoch < 90

scheduler = lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
plt.figure()
x = list(range(100))
y = []
for epoch in range(100):
    scheduler.step()
    lr = scheduler.get_lr()
    print(epoch, scheduler.get_lr()[0])
    y.append(scheduler.get_lr()[0])

plt.plot(x, y)

0 30<=epoch<60, lr = 0.005
60<=epoch<90, lr = 0.0005

torch.optim.lr_scheduler.MultiStepLR

与StepLR相比，MultiStepLR可以设置指定的区间

• 代码

# ---------------------------------------------------------------
# 可以指定区间
# lr_scheduler.MultiStepLR()
#  Assuming optimizer uses lr = 0.05 for all groups
# lr = 0.05     if epoch < 30
# lr = 0.005    if 30 <= epoch < 80
#  lr = 0.0005   if epoch >= 80
print()
plt.figure()
y.clear()
scheduler = lr_scheduler.MultiStepLR(optimizer, [30, 80], 0.1)
for epoch in range(100):
    scheduler.step()
    print(epoch, 'lr={:.6f}'.format(scheduler.get_lr()[0]))
    y.append(scheduler.get_lr()[0])

plt.plot(x, y)
plt.show()

torch.optim.lr_scheduler.ExponentialLR

指数衰减

• 代码

scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
print()
plt.figure()
y.clear()
for epoch in range(100):
    scheduler.step()
    print(epoch, 'lr={:.6f}'.format(scheduler.get_lr()[0]))
    y.append(scheduler.get_lr()[0])

plt.plot(x, y)
plt.show()