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torch.optim.lr_scheduler.LambdaLR与OneCycleLR

作者：羊村懒王 | 2024-03-28 03:53:17

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torch.optim.lr_scheduler.lambdalr

目录

LambdaLR 

输出

OneCycleLR

输出

LambdaLR 

函数接口：

LambdaLR(optimizer, lr_lambda, last_epoch=-1, verbose=False)

更新策略：

new\_lr = \lambda \times initial\_lr

 其中 new \_lr是得到的新的学习率，initial\_lr是初始的学习率， λ是通过参数lr_lambda和epoch得到的。

参数：

optimizer （Optimizer）：要更改学习率的优化器；

lr_lambda（function or list）：可以为一个lambda函数，也可以传入列表；

last_epoch （int）：最后一个epoch的index，如果是训练了很多个epoch后中断了，继续训练，这个值就等于加载的模型的epoch。默认为-1表示从头开始训练，即从epoch=1开始。

verbose(bool)：如果未True，则打印输出信息对于每次更新，反之亦然。默认为False。

  1. import torch
  2. import torch.nn as nn
  3. from torch.optim.lr_scheduler import LambdaLR
  4.  
  5. initial_lr = 0.1
  6.  
  7. class model(nn.Module):
  8.     def __init__(self):
  9.         super().__init__()
  10.         self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3)
  11.  
  12.     def forward(self, x):
  13.         pass
  14.  
  15. net_1 = model()
  16.  
  17. optimizer_1 = torch.optim.SGD(net_1.parameters(), lr = initial_lr)
  18. print(optimizer_1)
  19. scheduler_1 = LambdaLR(optimizer_1, lr_lambda=lambda epoch: 1/(epoch+1))
  20.  
  21. print("初始化的学习率：", optimizer_1.defaults['lr'])
  22.  
  23. for epoch in range(1, 11):
  24.     # train
  25.  
  26.     optimizer_1.zero_grad()
  27.     optimizer_1.step()
  28.     print("*-------------------------------------------------------*")
  29.     print("更新前，epoch = %d lr = %f" % (epoch, optimizer_1.param_groups[0]['lr']))
  30.     scheduler_1.step()
  31.     print("更新后，epoch = %d lr = %f" % (epoch + 1, optimizer_1.param_groups[0]['lr']))
  32.     print("更新后，epoch = %d lr = %f" % (epoch + 1, (1/(epoch+1))*initial_lr))
  33.     print("*-------------------------------------------------------*")

输出

  1. SGD (
  2. Parameter Group 0
  3.     dampening: 0
  4.     lr: 0.1
  5.     momentum: 0
  6.     nesterov: False
  7.     weight_decay: 0
  8. )
  9. 初始化的学习率： 0.1
  10. *-------------------------------------------------------*
  11. 更新前，epoch = 1 lr = 0.100000
  12. 更新后，epoch = 2 lr = 0.050000
  13. 更新后，epoch = 2 lr = 0.050000
  14. *-------------------------------------------------------*
  15. *-------------------------------------------------------*
  16. 更新前，epoch = 2 lr = 0.050000
  17. 更新后，epoch = 3 lr = 0.033333
  18. 更新后，epoch = 3 lr = 0.033333
  19. *-------------------------------------------------------*
  20. *-------------------------------------------------------*
  21. 更新前，epoch = 3 lr = 0.033333
  22. 更新后，epoch = 4 lr = 0.025000
  23. 更新后，epoch = 4 lr = 0.025000
  24. *-------------------------------------------------------*
  25. *-------------------------------------------------------*
  26. 更新前，epoch = 4 lr = 0.025000
  27. 更新后，epoch = 5 lr = 0.020000
  28. 更新后，epoch = 5 lr = 0.020000
  29. *-------------------------------------------------------*
  30. *-------------------------------------------------------*
  31. 更新前，epoch = 5 lr = 0.020000
  32. 更新后，epoch = 6 lr = 0.016667
  33. 更新后，epoch = 6 lr = 0.016667
  34. *-------------------------------------------------------*
  35. *-------------------------------------------------------*
  36. 更新前，epoch = 6 lr = 0.016667
  37. 更新后，epoch = 7 lr = 0.014286
  38. 更新后，epoch = 7 lr = 0.014286
  39. *-------------------------------------------------------*
  40. *-------------------------------------------------------*
  41. 更新前，epoch = 7 lr = 0.014286
  42. 更新后，epoch = 8 lr = 0.012500
  43. 更新后，epoch = 8 lr = 0.012500
  44. *-------------------------------------------------------*
  45. *-------------------------------------------------------*
  46. 更新前，epoch = 8 lr = 0.012500
  47. 更新后，epoch = 9 lr = 0.011111
  48. 更新后，epoch = 9 lr = 0.011111
  49. *-------------------------------------------------------*
  50. *-------------------------------------------------------*
  51. 更新前，epoch = 9 lr = 0.011111
  52. 更新后，epoch = 10 lr = 0.010000
  53. 更新后，epoch = 10 lr = 0.010000
  54. *-------------------------------------------------------*
  55. *-------------------------------------------------------*
  56. 更新前，epoch = 10 lr = 0.010000
  57. 更新后，epoch = 11 lr = 0.009091
  58. 更新后，epoch = 11 lr = 0.009091
  59. *-------------------------------------------------------*

OneCycleLR

  1. import cv2
  2. import torch.nn as nn
  3. import torch
  4. from torchvision.models import AlexNet
  5. import matplotlib.pyplot as plt
  6. from torch.optim.lr_scheduler import LambdaLR
  7. import math
  8.  
  9. total_steps = 300
  10.  
  11. # 自定义函数OneCycleLR
  12. def one_cycle(y1=0.0, y2=1.0, steps=100):
  13.     # lambda function for sinusoidal ramp from y1 to y2
  14.     return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
  15.  
  16. lf = one_cycle(1, 0.2, total_steps)  # cosine 1->hyp['lrf']
  17.  
  18. lr = 0.001
  19. # pytorch函数OneCycleLR
  20. lrs = []
  21. steps = []
  22. model = AlexNet(num_classes=20)
  23. optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
  24.  
  25. scheduler =torch.optim.lr_scheduler.OneCycleLR(optimizer,max_lr=0.001,total_steps=total_steps, 
  26.                                                verbose=False)
  27.  
  28. # 自定义函数OneCycleLR与LambdaLR结合使用
  29. lrs1 = []
  30. steps1 = []
  31. model1 = AlexNet(num_classes=20)
  32. optimizer1 = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
  33. scheduler1 = LambdaLR(optimizer1, lr_lambda=lf)
  34.  
  35.  
  36. for epoch in range(total_steps):
  37.     scheduler1.step()
  38.     lrs1.append(scheduler1.get_lr()[0])
  39.     steps1.append(epoch)
  40.     
  41.     scheduler.step()
  42.     lrs.append(scheduler.get_lr()[0])
  43.     steps.append(epoch)
  44.     
  45.     print("custom OneCycleLR: ",scheduler1.get_lr()[0],
  46.           "pytorch OneCycleLR: ",scheduler.get_lr()[0])
  47.     
  48. plt.figure()
  49. plt.legend()
  50. plt.plot(steps, lrs, color='black', marker='',label='pytorch')
  51.  
  52. plt.plot(steps1, lrs1, color='red', marker='',label='custom')
  53.  
  54. plt.savefig("OneCycleLR.png")

输出

  1. custom OneCycleLR:  0.000999978067746205 pytorch OneCycleLR:  4.029901011425041e-05
  2. custom OneCycleLR:  0.0009999122733899382 pytorch OneCycleLR:  4.1195667927633396e-05
  3. custom OneCycleLR:  0.0009998026241462925 pytorch OneCycleLR:  4.268885631616862e-05
  4. custom OneCycleLR:  0.0009996491320395434 pytorch OneCycleLR:  4.477671495306198e-05
  5. custom OneCycleLR:  0.0009994518139018296 pytorch OneCycleLR:  4.745664262643999e-05
  6. custom OneCycleLR:  0.0009992106913713087 pytorch OneCycleLR:  5.072530048013703e-05
  7. custom OneCycleLR:  0.0009989257908897833 pytorch OneCycleLR:  5.4578616173493744e-05
  8. custom OneCycleLR:  0.0009985971436998018 pytorch OneCycleLR:  5.901178895498596e-05
  9. custom OneCycleLR:  0.000998224785841232 pytorch OneCycleLR:  6.40192956433644e-05
  10. custom OneCycleLR:  0.0009978087581473092 pytorch OneCycleLR:  6.959489750884987e-05
  11. custom OneCycleLR:  0.0009973491062401586 pytorch OneCycleLR:  7.573164804581384e-05
  12. custom OneCycleLR:  0.0009968458805257913 pytorch OneCycleLR:  8.242190162726e-05
  13. custom OneCycleLR:  0.0009962991361885775 pytorch OneCycleLR:  8.965732303032237e-05
  14. custom OneCycleLR:  0.0009957089331851954 pytorch OneCycleLR:  9.742889782091613e-05
  15. custom OneCycleLR:  0.0009950753362380552 pytorch OneCycleLR:  0.00010572694358459967
  16. custom OneCycleLR:  0.000994398414828202 pytorch OneCycleLR:  0.00011454112198965667
  17. custom OneCycleLR:  0.0009936782431876968 pytorch OneCycleLR:  0.00012386045166737076
  18. custom OneCycleLR:  0.0009929149002914754 pytorch OneCycleLR:  0.0001336733218934436
  19. custom OneCycleLR:  0.0009921084698486888 pytorch OneCycleLR:  0.00014396750705351078
  20. custom OneCycleLR:  0.0009912590402935224 pytorch OneCycleLR:  0.0001547301818747355
  21. custom OneCycleLR:  0.000990366704775499 pytorch OneCycleLR:  0.0001659479374045014
  22. custom OneCycleLR:  0.0009894315611492642 pytorch OneCycleLR:  0.0001776067977162975
  23. custom OneCycleLR:  0.0009884537119638544 pytorch OneCycleLR:  0.00018969223732198301
  24. custom OneCycleLR:  0.0009874332644514525 pytorch OneCycleLR:  0.0002021891992687357
  25. custom OneCycleLR:  0.0009863703305156273 pytorch OneCycleLR:  0.00021508211389814147
  26. custom OneCycleLR:  0.0009852650267190633 pytorch OneCycleLR:  0.00022835491824404845
  27. custom OneCycleLR:  0.0009841174742707772 pytorch OneCycleLR:  0.000241991076045024
  28. custom OneCycleLR:  0.000982927799012827 pytorch OneCycleLR:  0.00025597359834647615
  29. custom OneCycleLR:  0.0009816961314065109 pytorch OneCycleLR:  0.0002702850646667766
  30. custom OneCycleLR:  0.0009804226065180616 pytorch OneCycleLR:  0.0002849076447010104
  31. custom OneCycleLR:  0.0009791073640038343 pytorch OneCycleLR:  0.0002998231205353166
  32. custom OneCycleLR:  0.0009777505480949925 pytorch OneCycleLR:  0.0003150129093441395
  33. custom OneCycleLR:  0.0009763523075816903 pytorch OneCycleLR:  0.0003304580865421162
  34. custom OneCycleLR:  0.0009749127957967566 pytorch OneCycleLR:  0.00034613940936175237
  35. custom OneCycleLR:  0.0009734321705988807 pytorch OneCycleLR:  0.00036203734082751525
  36. custom OneCycleLR:  0.0009719105943553006 pytorch OneCycleLR:  0.00037813207409647157
  37. custom OneCycleLR:  0.000970348233923998 pytorch OneCycleLR:  0.00039440355713514844
  38. custom OneCycleLR:  0.0009687452606354002 pytorch OneCycleLR:  0.00041083151770186885
  39. custom OneCycleLR:  0.0009671018502735925 pytorch OneCycleLR:  0.00042739548860344146
  40. custom OneCycleLR:  0.0009654181830570403 pytorch OneCycleLR:  0.00044407483319473405
  41. custom OneCycleLR:  0.0009636944436188274 pytorch OneCycleLR:  0.00046084877108936394
  42. custom OneCycleLR:  0.0009619308209864078 pytorch OneCycleLR:  0.0004776964040494711
  43. custom OneCycleLR:  0.0009601275085608774 pytorch OneCycleLR:  0.0004945967420223218
  44. custom OneCycleLR:  0.0009582847040957652 pytorch OneCycleLR:  0.0005115287292912982
  45. custom OneCycleLR:  0.0009564026096753472 pytorch OneCycleLR:  0.0005284712707087017
  46. custom OneCycleLR:  0.0009544814316924859 pytorch OneCycleLR:  0.0005454032579776784
  47. custom OneCycleLR:  0.0009525213808259969 pytorch OneCycleLR:  0.0005623035959505287
  48. custom OneCycleLR:  0.0009505226720175455 pytorch OneCycleLR:  0.0005791512289106361
  49. custom OneCycleLR:  0.0009484855244480758 pytorch OneCycleLR:  0.000595925166805266
  50. custom OneCycleLR:  0.0009464101615137755 pytorch OneCycleLR:  0.0006126045113965584
  51. custom OneCycleLR:  0.0009442968108015775 pytorch OneCycleLR:  0.000629168482298131
  52. custom OneCycleLR:  0.0009421457040642026 pytorch OneCycleLR:  0.0006455964428648515
  53. custom OneCycleLR:  0.0009399570771947457 pytorch OneCycleLR:  0.0006618679259035283
  54. custom OneCycleLR:  0.0009377311702008061 pytorch OneCycleLR:  0.0006779626591724849
  55. custom OneCycleLR:  0.0009354682271781696 pytorch OneCycleLR:  0.0006938605906382474
  56. custom OneCycleLR:  0.0009331684962840398 pytorch OneCycleLR:  0.0007095419134578837
  57. custom OneCycleLR:  0.0009308322297098248 pytorch OneCycleLR:  0.0007249870906558605
  58. custom OneCycleLR:  0.0009284596836534817 pytorch OneCycleLR:  0.0007401768794646835
  59. custom OneCycleLR:  0.0009260511182914218 pytorch OneCycleLR:  0.0007550923552989896
  60. custom OneCycleLR:  0.000923606797749979 pytorch OneCycleLR:  0.0007697149353332234
  61. custom OneCycleLR:  0.0009211269900764458 pytorch OneCycleLR:  0.0007840264016535239
  62. custom OneCycleLR:  0.0009186119672096785 pytorch OneCycleLR:  0.0007980089239549761
  63. custom OneCycleLR:  0.0009160620049502761 pytorch OneCycleLR:  0.0008116450817559515
  64. custom OneCycleLR:  0.000913477382930336 pytorch OneCycleLR:  0.0008249178861018585
  65. custom OneCycleLR:  0.0009108583845827883 pytorch OneCycleLR:  0.0008378108007312642
  66. custom OneCycleLR:  0.0009082052971103158 pytorch OneCycleLR:  0.000850307762678017
  67. custom OneCycleLR:  0.0009055184114538569 pytorch OneCycleLR:  0.0008623932022837024
  68. custom OneCycleLR:  0.0009027980222607026 pytorch OneCycleLR:  0.0008740520625954986
  69. custom OneCycleLR:  0.0009000444278521839 pytorch OneCycleLR:  0.0008852698181252645
  70. custom OneCycleLR:  0.0008972579301909578 pytorch OneCycleLR:  0.0008960324929464892
  71. custom OneCycleLR:  0.0008944388348478938 pytorch OneCycleLR:  0.0009063266781065563
  72. custom OneCycleLR:  0.0008915874509685647 pytorch OneCycleLR:  0.0009161395483326291
  73. custom OneCycleLR:  0.0008887040912393449 pytorch OneCycleLR:  0.0009254588780103433
  74. custom OneCycleLR:  0.0008857890718531214 pytorch OneCycleLR:  0.0009342730564154004
  75. custom OneCycleLR:  0.000882842712474619 pytorch OneCycleLR:  0.0009425711021790838
  76. custom OneCycleLR:  0.0008798653362053463 pytorch OneCycleLR:  0.0009503426769696777
  77. custom OneCycleLR:  0.0008768572695481627 pytorch OneCycleLR:  0.00095757809837274
  78. custom OneCycleLR:  0.0008738188423714755 pytorch OneCycleLR:  0.0009642683519541861
  79. custom OneCycleLR:  0.0008707503878730643 pytorch OneCycleLR:  0.0009704051024911501
  80. custom OneCycleLR:  0.0008676522425435433 pytorch OneCycleLR:  0.0009759807043566356
  81. custom OneCycleLR:  0.0008645247461294607 pytorch OneCycleLR:  0.000980988211045014
  82. custom OneCycleLR:  0.0008613682415960422 pytorch OneCycleLR:  0.0009854213838265064
  83. custom OneCycleLR:  0.0008581830750895803 pytorch OneCycleLR:  0.0009892746995198629
  84. custom OneCycleLR:  0.0008549695958994758 pytorch OneCycleLR:  0.00099254335737356
  85. custom OneCycleLR:  0.0008517281564199351 pytorch OneCycleLR:  0.000995223285046938
  86. custom OneCycleLR:  0.0008484591121113242 pytorch OneCycleLR:  0.0009973111436838314
  87. custom OneCycleLR:  0.0008451628214611906 pytorch OneCycleLR:  0.0009988043320723666
  88. custom OneCycleLR:  0.00084183964594495 pytorch OneCycleLR:  0.0009997009898857496
  89. custom OneCycleLR:  0.0008384899499862462 pytorch OneCycleLR:  0.001
  90. custom OneCycleLR:  0.0008351141009169893 pytorch OneCycleLR:  0.0009999440511289331
  91. custom OneCycleLR:  0.0008317124689370715 pytorch OneCycleLR:  0.0009997762170368871
  92. custom OneCycleLR:  0.0008282854270737727 pytorch OneCycleLR:  0.0009994965352845243
  93. custom OneCycleLR:  0.0008248333511408524 pytorch OneCycleLR:  0.000999105068463608
  94. custom OneCycleLR:  0.0008213566196973377 pytorch OneCycleLR:  0.0009986019041829956
  95. custom OneCycleLR:  0.0008178556140060108 pytorch OneCycleLR:  0.0009979871550490316
  96. custom OneCycleLR:  0.0008143307179915986 pytorch OneCycleLR:  0.000997260958640346
  97. custom OneCycleLR:  0.0008107823181986711 pytorch OneCycleLR:  0.000996423477477066
  98. custom OneCycleLR:  0.0008072108037492522 pytorch OneCycleLR:  0.0009954748989844438
  99. custom OneCycleLR:  0.0008036165663001484 pytorch OneCycleLR:  0.0009944154354509117
  100. custom OneCycleLR:  0.0007999999999999999 pytorch OneCycleLR:  0.0009932453239805729
  101. custom OneCycleLR:  0.0007963615014460563 pytorch OneCycleLR:  0.0009919648264401376
  102. custom OneCycleLR:  0.0007927014696406861 pytorch OneCycleLR:  0.0009905742294003194
  103. custom OneCycleLR:  0.0007890203059476216 pytorch OneCycleLR:  0.0009890738440717015
  104. custom OneCycleLR:  0.0007853184140479448 pytorch OneCycleLR:  0.0009874640062350875
  105. custom OneCycleLR:  0.0007815961998958187 pytorch OneCycleLR:  0.0009857450761663572
  106. custom OneCycleLR:  0.000777854071673971 pytorch OneCycleLR:  0.0009839174385558363
  107. custom OneCycleLR:  0.000774092439748931 pytorch OneCycleLR:  0.0009819815024222052
  108. custom OneCycleLR:  0.000770311716626029 pytorch OneCycleLR:  0.0009799377010209615
  109. custom OneCycleLR:  0.0007665123169041606 pytorch OneCycleLR:  0.0009777864917474587
  110. custom OneCycleLR:  0.00076269465723032 pytorch OneCycleLR:  0.0009755283560345441
  111. custom OneCycleLR:  0.0007588591562539122 pytorch OneCycleLR:  0.0009731637992448144
  112. custom OneCycleLR:  0.0007550062345808412 pytorch OneCycleLR:  0.0009706933505575182
  113. custom OneCycleLR:  0.0007511363147273869 pytorch OneCycleLR:  0.0009681175628501272
  114. custom OneCycleLR:  0.0007472498210738712 pytorch OneCycleLR:  0.0009654370125746047
  115. custom OneCycleLR:  0.00074334717981812 pytorch OneCycleLR:  0.0009626522996283973
  116. custom OneCycleLR:  0.0007394288189287261 pytorch OneCycleLR:  0.0009597640472201802
  117. custom OneCycleLR:  0.0007354951680981166 pytorch OneCycleLR:  0.000956772901730385
  118. custom OneCycleLR:  0.0007315466586954334 pytorch OneCycleLR:  0.0009536795325665432
  119. custom OneCycleLR:  0.000727583723719228 pytorch OneCycleLR:  0.0009504846320134736
  120. custom OneCycleLR:  0.0007236067977499789 pytorch OneCycleLR:  0.000947188915078353
  121. custom OneCycleLR:  0.0007196163169024347 pytorch OneCycleLR:  0.000943793119330699
  122. custom OneCycleLR:  0.0007156127187777885 pytorch OneCycleLR:  0.0009402980047373052
  123. custom OneCycleLR:  0.0007115964424156918 pytorch OneCycleLR:  0.0009367043534921636
  124. custom OneCycleLR:  0.0007075679282461063 pytorch OneCycleLR:  0.0009330129698414117
  125. custom OneCycleLR:  0.0007035276180410084 pytorch OneCycleLR:  0.0009292246799033457
  126. custom OneCycleLR:  0.0006994759548659419 pytorch OneCycleLR:  0.0009253403314835384
  127. custom OneCycleLR:  0.0006954133830314323 pytorch OneCycleLR:  0.0009213607938851022
  128. custom OneCycleLR:  0.0006913403480442624 pytorch OneCycleLR:  0.0009172869577141438
  129. custom OneCycleLR:  0.000687257296558617 pytorch OneCycleLR:  0.0009131197346804487
  130. custom OneCycleLR:  0.0006831646763271038 pytorch OneCycleLR:  0.0009088600573934443
  131. custom OneCycleLR:  0.0006790629361516505 pytorch OneCycleLR:  0.0009045088791534849
  132. custom OneCycleLR:  0.0006749525258342899 pytorch OneCycleLR:  0.0009000671737385071
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  285. custom OneCycleLR:  0.00020492466376194484 pytorch OneCycleLR:  1.0930155928298623e-05
  286. custom OneCycleLR:  0.00020429106681480458 pytorch OneCycleLR:  9.429770599680518e-06
  287. custom OneCycleLR:  0.0002037008638114225 pytorch OneCycleLR:  8.039173559862363e-06
  288. custom OneCycleLR:  0.0002031541194742088 pytorch OneCycleLR:  6.758676019427176e-06
  289. custom OneCycleLR:  0.00020265089375984147 pytorch OneCycleLR:  5.588564549088189e-06
  290. custom OneCycleLR:  0.00020219124185269065 pytorch OneCycleLR:  4.529101015556186e-06
  291. custom OneCycleLR:  0.000201775214158768 pytorch OneCycleLR:  3.580522522934006e-06
  292. custom OneCycleLR:  0.00020140285630019817 pytorch OneCycleLR:  2.7430413596540916e-06
  293. custom OneCycleLR:  0.00020107420911021678 pytorch OneCycleLR:  2.016844950968456e-06
  294. custom OneCycleLR:  0.0002007893086286914 pytorch OneCycleLR:  1.4020958170042977e-06
  295. custom OneCycleLR:  0.0002005481860981705 pytorch OneCycleLR:  8.989315363919581e-07
  296. custom OneCycleLR:  0.00020035086796045665 pytorch OneCycleLR:  5.074647154757879e-07
  297. custom OneCycleLR:  0.00020019737585370734 pytorch OneCycleLR:  2.277829631129057e-07
  298. custom OneCycleLR:  0.0002000877266100618 pytorch OneCycleLR:  5.994887106690402e-08
  299. custom OneCycleLR:  0.00020002193225379506 pytorch OneCycleLR:  4e-09
  300. custom OneCycleLR:  0.00019999999999999996 pytorch OneCycleLR:  5.994887106690402e-08

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