class torch.optim.lr_scheduler.StepLR

参考链接: class torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1, verbose=False)
配套代码下载链接: 测试学习率调度器.zip

实验代码展示:

# torch.optim.lr_scheduler.StepLRimport matplotlib.pyplot as plt
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torch import nn
from torch.autograd import Function
import random
import os
seed = 20200910
os.environ['PYTHONHASHSEED'] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.
np.random.seed(seed)  # Numpy module.
random.seed(seed)  # Python random module.
torch.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = Trueclass Dataset4cxq(Dataset):def __init__(self, length):self.length = lengthdef __len__(self):return self.lengthdef __getitem__(self, index):if type(index) != type(2) and type(index) != (slice):raise  TypeError('索引类型错误,程序退出...')# index 是单个数if type(index) == type(2):if index >= self.length or index < -1 * self.length:# print("索引越界,程序退出...")raise IndexError("索引越界,程序退出...")elif index < 0:index = index + self.length Celsius = torch.randn(1,1,dtype=torch.float).item()Fahrenheit = 32.0 + 1.8 * Celsiusreturn Celsius, Fahrenheit def collate_fn4cxq(batch):list_c = []list_f = []for c, f in batch:list_c.append(c)list_f.append(f)list_c = torch.tensor(list_c)list_f = torch.tensor(list_f)return list_c, list_fif __name__ == "__main__":my_dataset = Dataset4cxq(32)# for c,f in my_dataset:#     print(type(c),type(f))dataloader4cxq = torch.utils.data.DataLoader(dataset=my_dataset, batch_size=8,# batch_size=2,drop_last=True,# drop_last=False,shuffle=True,  #  True   False# shuffle=False,  #  True   Falsecollate_fn=collate_fn4cxq,# collate_fn=None,)# for cnt, data in enumerate(dataloader4cxq, 0):#     # pass#     sample4cxq, label4cxq = data#     print('sample4cxq的类型: ',type(sample4cxq),'\tlabel4cxq的类型: ',type(label4cxq))#     print('迭代次数:', cnt, '  sample4cxq:', sample4cxq, '  label4cxq:', label4cxq)print('开始创建模型'.center(80,'-'))model = torch.nn.Linear(in_features=1, out_features=1, bias=True)  # True # Falsemodel.cuda()# optimizer = torch.optim.Adam(model.parameters(), lr=0.001)optimizer = torch.optim.Adam(model.parameters(), lr=0.001)# 模拟华氏度与摄氏度之间的转换  # Fahrenheit = 32 + 1.8 * Celsiusmodel.train()cost_function = torch.nn.MSELoss()epochs = 100001  # 100001epochs = 10001  # 100001print('\n')print('开始训练模型'.center(80,'-'))list4delta = list()list4epoch = list()# scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=(lambda epoch: 0.99 ** (epoch//1000)))scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=500, gamma=0.9)for epoch in range(epochs):# with torch.no_grad():#     Celsius = torch.randn(10,1,dtype=torch.float).cuda()#     Fahrenheit = 32.0 + 1.8 * Celsius#     Fahrenheit = Fahrenheit.cuda()# Celsius = torch.randn(1,1,dtype=torch.float,requires_grad=False).cuda()  # requires_grad=False  True# Fahrenheit = 32.0 + 1.8 * Celsius# Fahrenheit = Fahrenheit.cuda()        # requires_grad=Falsetotal_loss = 0.0for cnt, data in enumerate(dataloader4cxq, 0):Celsius, Fahrenheit = dataCelsius, Fahrenheit = Celsius.cuda().view(-1,1), Fahrenheit.cuda().view(-1,1)output = model(Celsius)loss = cost_function(output, Fahrenheit)total_loss += loss.item()optimizer.zero_grad()loss.backward()optimizer.step()scheduler.step()if epoch % 100 == 0:  # if epoch % 1000 == 0:list4delta.append(total_loss)list4epoch.append(epoch)if epoch % 500 == 0:info = '\nepoch:{0:>6}/{1:<6}\t'.format(epoch,epochs)for k, v in model.state_dict().items():info += str(k)+ ':' + '{0:<.18f}'.format(v.item()) + '\t'# info += str(k)+ ':' + str(v.item()) + '\t'print(info)fig, ax = plt.subplots() # ax.plot(10*np.random.randn(100),10*np.random.randn(100),'o')ax.plot(list4epoch, list4delta, 'r.-', markersize=8)ax.set_title("Visualization For My Model's Errors")plt.show()

控制台下输出:

Windows PowerShell
版权所有 (C) Microsoft Corporation。保留所有权利。尝试新的跨平台 PowerShell https://aka.ms/pscore6加载个人及系统配置文件用了 796 毫秒。
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试学习率调度器> conda activate pytorch_1.7.1_cu102
(pytorch_1.7.1_cu102) PS C:\Users\chenxuqi\Desktop\News4cxq\测试学习率调度器>  & 'D:\Anaconda3\envs\pytorch_1.7.1_cu102\python.exe' 'c:\Users\chenxuqi\.vscode\extensions\ms-python.python-2021.1.502429796\pythonFiles\lib\python\debugpy\launcher' '49758' '--' 'c:\Users\chenxuqi\Desktop\News4cxq\测试学习率调度器\test08.py'
-------------------------------------开始创建模型--------------------------------------------------------------------------开始训练模型-------------------------------------epoch:     0/10001      weight:0.962383031845092773     bias:0.980020046234130859       epoch:   500/10001      weight:1.129075884819030762     bias:2.954752445220947266       epoch:  1000/10001      weight:1.238264322280883789     bias:4.707053184509277344       epoch:  1500/10001      weight:1.298225045204162598     bias:6.279161930084228516       epoch:  2000/10001      weight:1.386581063270568848     bias:7.694698333740234375epoch:  2500/10001      weight:1.414126753807067871     bias:8.970099449157714844epoch:  3000/10001      weight:1.462724089622497559     bias:10.119626998901367188epoch:  3500/10001      weight:1.554199934005737305     bias:11.155819892883300781epoch:  4000/10001      weight:1.609407067298889160     bias:12.089537620544433594epoch:  4500/10001      weight:1.631286978721618652     bias:12.930944442749023438epoch:  5000/10001      weight:1.631085872650146484     bias:13.689365386962890625epoch:  5500/10001      weight:1.646203756332397461     bias:14.372797966003417969epoch:  6000/10001      weight:1.667070984840393066     bias:14.988748550415039062epoch:  6500/10001      weight:1.690698742866516113     bias:15.543690681457519531epoch:  7000/10001      weight:1.677183747291564941     bias:16.043390274047851562epoch:  7500/10001      weight:1.694453477859497070     bias:16.493432998657226562epoch:  8000/10001      weight:1.713040113449096680     bias:16.898756027221679688epoch:  8500/10001      weight:1.715337395668029785     bias:17.264890670776367188epoch:  9000/10001      weight:1.727315664291381836     bias:17.592933654785156250epoch:  9500/10001      weight:1.720250487327575684     bias:17.890419006347656250epoch: 10000/10001      weight:1.725156664848327637     bias:18.157394409179687500

运行结果展示:

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