12fromtypingimportList,Optional1314fromtorchimportnn1516fromlabmlimportexperiment17fromlabml.configsimportoption18fromlabml\_nn.experiments.cifar10importCIFAR10Configs19fromlabml\_nn.resnetimportResNetBase

22classConfigs(CIFAR10Configs):

31n\_blocks:List[int]=[3,3,3]

33n\_channels:List[int]=[16,32,64]

35bottlenecks:Optional[List[int]]=None

37first\_kernel\_size:int=3

40@option(Configs.model)41def\_resnet(c:Configs):

46base=ResNetBase(c.n\_blocks,c.n\_channels,c.bottlenecks,img\_channels=3,first\_kernel\_size=c.first\_kernel\_size)

48classification=nn.Linear(c.n\_channels[-1],10)

51model=nn.Sequential(base,classification)

53returnmodel.to(c.device)

56defmain():

58experiment.create(name='resnet',comment='cifar10')

60conf=Configs()

62experiment.configs(conf,{63'bottlenecks':[8,16,16],64'n\_blocks':[6,6,6],6566'optimizer.optimizer':'Adam',67'optimizer.learning\_rate':2.5e-4,6869'epochs':500,70'train\_batch\_size':256,7172'train\_dataset':'cifar10\_train\_augmented',73'valid\_dataset':'cifar10\_valid\_no\_augment',74})

76experiment.add\_pytorch\_models({'model':conf.model})

78withexperiment.start():79conf.run()

83if\_\_name\_\_=='\_\_main\_\_':84main()