class torch.optim.lr_scheduler.ExponentialLR

参考链接: class torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma, 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.ExponentialLR

import 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 = True

class Dataset4cxq(Dataset):
    def __init__(self, length):
        self.length = length

    def __len__(self):
        return self.length
        
    def __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 * Celsius
            return 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_f



if __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   False
        collate_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 # False
    model.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 * Celsius
    model.train()
    cost_function = torch.nn.MSELoss()
    epochs = 100001  # 100001
    epochs = 10001  # 100001
    print('\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.ExponentialLR(optimizer, gamma=0.99999)
    
    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=False
        total_loss = 0.0
        for cnt, data in enumerate(dataloader4cxq, 0):
            Celsius, Fahrenheit = data
            Celsius, 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()

控制台下结果输出:

Windows PowerShell
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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 1012 毫秒。
(base) 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' '50126' '--' 'c:\Users\chenxuqi\Desktop\News4cxq\测试学习率调度器\test10.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:1.128552079200744629     bias:2.950278997421264648       

epoch:  1000/10001      weight:1.248196125030517578     bias:4.879709720611572266       

epoch:  1500/10001      weight:1.319370269775390625     bias:6.787014484405517578       

epoch:  2000/10001      weight:1.432388424873352051     bias:8.678206443786621094

epoch:  2500/10001      weight:1.466557621955871582     bias:10.553906440734863281

epoch:  3000/10001      weight:1.534925937652587891     bias:12.413959503173828125

epoch:  3500/10001      weight:1.678856253623962402     bias:14.257437705993652344

epoch:  4000/10001      weight:1.757018804550170898     bias:16.082637786865234375

epoch:  4500/10001      weight:1.768605709075927734     bias:17.888135910034179688

epoch:  5000/10001      weight:1.743626236915588379     bias:19.671495437622070312

epoch:  5500/10001      weight:1.757284045219421387     bias:21.429470062255859375

epoch:  6000/10001      weight:1.789364457130432129     bias:23.157424926757812500

epoch:  6500/10001      weight:1.826124668121337891     bias:24.847982406616210938

epoch:  7000/10001      weight:1.756658554077148438     bias:26.489616394042968750

epoch:  7500/10001      weight:1.809312343597412109     bias:28.062467575073242188

epoch:  8000/10001      weight:1.825970530509948730     bias:29.528060913085937500

epoch:  8500/10001      weight:1.800836801528930664     bias:30.803880691528320312

epoch:  9000/10001      weight:1.800451874732971191     bias:31.699626922607421875

epoch:  9500/10001      weight:1.799751043319702148     bias:31.989871978759765625

epoch: 10000/10001      weight:1.800003290176391602     bias:31.999866485595703125

运行结果截图展示: