Linear regression with pytorch

''' def func(*args,**kwargs):print(args)#當不知道傳入函數的參數的個數時可以用args，輸出的args是以元祖的形式print(kwargs)#不知道參數名稱和值的時候 傳入的是詞典func(1,2,4,5,x=3,y=5)(1, 2, 4, 5) {'x': 3, 'y': 5}'''class Foobar:def __init__(self):passdef __call__(self, *args, **kwargs):print('hello'+str(args[0]))#所以同理 在call這個函數里面會實現forward這個函數foobar=Foobar() foobar(1,2,3)#hello1 import os import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"import matplotlib.pyplot as plt import torch #數據集是個三乘一的矩陣 x_data=torch.Tensor([[1.0],[2.0],[3.0]]) y_data=torch.Tensor([[2.0],[4.0],[6.0]])loss_list=[] epoch_list=[]class LinearModel(torch.nn.Module):#init和forward必須重載 backward不一定要自己實現 module對象會自動根據計算圖去實現backwarddef __init__(self):super(LinearModel,self).__init__()self.linear=torch.nn.Linear(1,1)#確定w和b 可以直接完成y_hat=xw+b的計算def forward(self,x):#重載forwardy_pre=self.linear(x)#由init可知實現forward的計算return y_pre model =LinearModel()#實例化criterion=torch.nn.MSELoss(size_average=False)#MSEloss是損失函數 求loss optimizer=torch.optim.Adam(model.parameters(),lr=0.01)#優化方法 分析優化器對哪些tensor進行優化或用梯度下降更新w的值for epoch in range(100):epoch_list.append(epoch)y_pre=model(x_data)loss=criterion(y_pre,y_data)loss_list.append(loss.item())print(epoch,loss.item())optimizer.zero_grad()#在backward求導數前必須權重清零loss.backward()optimizer.step()#更新wprint('w=',model.linear.weight.item()) print('b=',model.linear.bias.item())x_test=torch.Tensor([4.0]) y_test=model(x_test) print('y_pre',y_test.data)plt.plot(epoch_list,loss_list) plt.ylabel('Loss') plt.xlabel('epoch') plt.show()

當用Adam這個方法時且epoch=100時可以發現效果很不好 理想的w=2，b=0，y_pre=8

1 39.28014373779297 2 38.61161804199219 3 37.94915008544922 4 37.29282760620117 5 36.642757415771484 6 35.999019622802734 7 35.36170959472656 8 34.73091125488281 9 34.106685638427734 10 33.489131927490234 11 32.8783073425293 12 32.27427673339844 13 31.677106857299805 14 31.08685302734375 15 30.503564834594727 16 29.927288055419922 17 29.358070373535156 18 28.795944213867188 19 28.240938186645508 20 27.69308853149414 21 27.15241241455078 22 26.618927001953125 23 26.092649459838867 24 25.57358169555664 25 25.06173324584961 26 24.557104110717773 27 24.0596866607666 28 23.56947135925293 29 23.086450576782227 30 22.610599517822266 31 22.14190673828125 32 21.680343627929688 33 21.22588348388672 34 20.77849769592285 35 20.338153839111328 36 19.904815673828125 37 19.478439331054688 38 19.058988571166992 39 18.64641761779785 40 18.240680694580078 41 17.84173011779785 42 17.449512481689453 43 17.063976287841797 44 16.685068130493164 45 16.312732696533203 46 15.946911811828613 47 15.587545394897461 48 15.234573364257812 49 14.887937545776367 50 14.547571182250977 51 14.21341323852539 52 13.885400772094727 53 13.563462257385254 54 13.247535705566406 55 12.937556266784668 56 12.633451461791992 57 12.335158348083496 58 12.042604446411133 59 11.755722045898438 60 11.474445343017578 61 11.198698043823242 62 10.928417205810547 63 10.663528442382812 64 10.40396499633789 65 10.149651527404785 66 9.90052318572998 67 9.656505584716797 68 9.417531967163086 69 9.1835298538208 70 8.954428672790527 71 8.730156898498535 72 8.510649681091309 73 8.295831680297852 74 8.085638999938965 75 7.879994869232178 76 7.678836822509766 77 7.48209285736084 78 7.289695739746094 79 7.101578712463379 80 6.917667865753174 81 6.73790168762207 82 6.562210559844971 83 6.390528678894043 84 6.2227888107299805 85 6.058926582336426 86 5.898874759674072 87 5.742569923400879 88 5.589945316314697 89 5.4409379959106445 90 5.295485973358154 91 5.153524398803711 92 5.014989852905273 93 4.879822731018066 94 4.747958183288574 95 4.619339466094971 96 4.493905067443848 97 4.371591567993164 98 4.252344608306885 99 4.136101722717285 w= 1.2186882495880127 b= 0.59620201587677 y_pre tensor([5.4710])

epoch=1000時，

1 73.64485931396484 2 72.73275756835938 3 71.82670593261719 4 70.92681121826172 5 70.03315734863281 6 69.14585876464844 7 68.26498413085938 8 67.390625 9 66.52285766601562 10 65.66177368164062 11 64.80744934082031 12 63.95995330810547 13 63.11935806274414 14 62.28571319580078 15 61.45909881591797 16 60.639556884765625 17 59.827152252197266 18 59.021915435791016 19 58.22390365600586 20 57.43315124511719 21 56.649696350097656 22 55.873565673828125 23 55.10478591918945 24 54.343387603759766 25 53.589385986328125 26 52.842796325683594 27 52.10362243652344 28 51.371883392333984 29 50.64757537841797 30 49.93070983886719 31 49.221275329589844 32 48.51927947998047 33 47.82469177246094 34 47.13752365112305 35 46.45774841308594 36 45.78535842895508 37 45.120323181152344 38 44.46263122558594 39 43.812255859375 40 43.16917037963867 41 42.533348083496094 42 41.904762268066406 43 41.28337860107422 44 40.669158935546875 45 40.06208419799805 46 39.46210861206055 47 38.86919403076172 48 38.283302307128906 49 37.70439910888672 50 37.13243103027344 51 36.56737518310547 52 36.00917053222656 53 35.457786560058594 54 34.913177490234375 55 34.37528610229492 56 33.844078063964844 57 33.31950378417969 58 32.80152130126953 59 32.290069580078125 60 31.785110473632812 61 31.28660011291504 62 30.794479370117188 63 30.30870246887207 64 29.829219818115234 65 29.355979919433594 66 28.888935089111328 67 28.428035736083984 68 27.97323226928711 69 27.52446746826172 70 27.081693649291992 71 26.644861221313477 72 26.21392250061035 73 25.788818359375 74 25.36949920654297 75 24.95591926574707 76 24.548019409179688 77 24.145755767822266 78 23.749069213867188 79 23.35791778564453 80 22.972244262695312 81 22.59199333190918 82 22.217117309570312 83 21.847570419311523 84 21.483293533325195 85 21.12424087524414 86 20.770357131958008 87 20.421592712402344 88 20.07790184020996 89 19.739225387573242 90 19.405517578125 91 19.07672882080078 92 18.7528076171875 93 18.433700561523438 94 18.119361877441406 95 17.809741973876953 96 17.504789352416992 97 17.204452514648438 98 16.90868377685547 99 16.617433547973633 100 16.330656051635742 101 16.048295974731445 102 15.77031135559082 103 15.496650695800781 104 15.227263450622559 105 14.962108612060547 106 14.701131820678711 107 14.444283485412598 108 14.191522598266602 109 13.942800521850586 110 13.698066711425781 111 13.457279205322266 112 13.220386505126953 113 12.987346649169922 114 12.758111953735352 115 12.532634735107422 116 12.310872077941895 117 12.092779159545898 118 11.878307342529297 119 11.667415618896484 120 11.460057258605957 121 11.256189346313477 122 11.055767059326172 123 10.858745574951172 124 10.665084838867188 125 10.474738121032715 126 10.287666320800781 127 10.103822708129883 128 9.923168182373047 129 9.745658874511719 130 9.571252822875977 131 9.399909973144531 132 9.231588363647461 133 9.066245079040527 134 8.90384292602539 135 8.744339942932129 136 8.587697982788086 137 8.43387508392334 138 8.282833099365234 139 8.134530067443848 140 7.988931655883789 141 7.845998764038086 142 7.705689430236816 143 7.567971229553223 144 7.432806015014648 145 7.300151348114014 146 7.1699748039245605 147 7.042239189147949 148 6.916906833648682 149 6.793943405151367 150 6.673313140869141 151 6.554978370666504 152 6.438908576965332 153 6.325067043304443 154 6.213418483734131 155 6.103930473327637 156 5.996567726135254 157 5.891299247741699 158 5.788090705871582 159 5.6869096755981445 160 5.5877251625061035 161 5.49050235748291 162 5.395212173461914 163 5.301821708679199 164 5.21030330657959 165 5.1206207275390625 166 5.032748222351074 167 4.946656227111816 168 4.862311840057373 169 4.779687881469727 170 4.698754787445068 171 4.619483947753906 172 4.541847229003906 173 4.465815544128418 174 4.391365051269531 175 4.3184638023376465 176 4.247086524963379 177 4.177209377288818 178 4.108800888061523 179 4.041836738586426 180 3.976295232772827 181 3.9121463298797607 182 3.8493666648864746 183 3.787931442260742 184 3.727816104888916 185 3.6689963340759277 186 3.6114494800567627 187 3.555152416229248 188 3.5000805854797363 189 3.446211576461792 190 3.393522262573242 191 3.341992139816284 192 3.291597366333008 193 3.2423181533813477 194 3.194131851196289 195 3.147017478942871 196 3.100956916809082 197 3.0559258460998535 198 3.0119051933288574 199 2.9688799381256104 200 2.926823139190674 201 2.885720729827881 202 2.8455519676208496 203 2.80629825592041 204 2.76794171333313 205 2.730464458465576 206 2.6938467025756836 207 2.658074378967285 208 2.6231274604797363 209 2.5889902114868164 210 2.555644989013672 211 2.5230770111083984 212 2.4912686347961426 213 2.460205554962158 214 2.4298691749572754 215 2.400247573852539 216 2.371324062347412 217 2.3430824279785156 218 2.3155088424682617 219 2.2885916233062744 220 2.2623133659362793 221 2.236661672592163 222 2.211623191833496 223 2.187182664871216 224 2.163328170776367 225 2.140047550201416 226 2.1173276901245117 227 2.0951552391052246 228 2.0735201835632324 229 2.0524063110351562 230 2.031805992126465 231 2.0117058753967285 232 1.99209463596344 233 1.9729598760604858 234 1.9542920589447021 235 1.9360814094543457 236 1.918316125869751 237 1.9009848833084106 238 1.8840787410736084 239 1.8675884008407593 240 1.8515033721923828 241 1.8358139991760254 242 1.8205106258392334 243 1.8055843114852905 244 1.791025996208191 245 1.7768267393112183 246 1.7629787921905518 247 1.7494722604751587 248 1.7363014221191406 249 1.7234545946121216 250 1.7109260559082031 251 1.6987075805664062 252 1.686791181564331 253 1.6751692295074463 254 1.6638338565826416 255 1.652780294418335 256 1.6419994831085205 257 1.6314845085144043 258 1.6212302446365356 259 1.611228585243225 260 1.6014745235443115 261 1.5919599533081055 262 1.5826785564422607 263 1.573627233505249 264 1.564797043800354 265 1.5561835765838623 266 1.5477813482284546 267 1.539583444595337 268 1.531585454940796 269 1.5237834453582764 270 1.516170859336853 271 1.508741855621338 272 1.5014925003051758 273 1.4944188594818115 274 1.4875149726867676 275 1.4807758331298828 276 1.4741991758346558 277 1.4677786827087402 278 1.461510419845581 279 1.4553906917572021 280 1.4494149684906006 281 1.4435794353485107 282 1.4378809928894043 283 1.432314157485962 284 1.4268763065338135 285 1.4215648174285889 286 1.416374683380127 287 1.4113030433654785 288 1.4063470363616943 289 1.4015024900436401 290 1.3967658281326294 291 1.3921360969543457 292 1.3876075744628906 293 1.3831793069839478 294 1.3788474798202515 295 1.3746098279953003 296 1.3704642057418823 297 1.3664060831069946 298 1.3624346256256104 299 1.3585468530654907 300 1.3547394275665283 301 1.3510115146636963 302 1.3473589420318604 303 1.343780517578125 304 1.3402751684188843 305 1.3368384838104248 306 1.3334698677062988 307 1.3301657438278198 308 1.3269264698028564 309 1.3237487077713013 310 1.3206310272216797 311 1.317570686340332 312 1.3145663738250732 313 1.3116172552108765 314 1.3087208271026611 315 1.3058750629425049 316 1.30307936668396 317 1.3003323078155518 318 1.297631025314331 319 1.29497492313385 320 1.2923626899719238 321 1.289792776107788 322 1.2872636318206787 323 1.2847744226455688 324 1.2823233604431152 325 1.2799098491668701 326 1.2775323390960693 327 1.2751896381378174 328 1.2728803157806396 329 1.2706034183502197 330 1.2683587074279785 331 1.2661442756652832 332 1.2639589309692383 333 1.2618035078048706 334 1.2596746683120728 335 1.257573127746582 336 1.2554970979690552 337 1.253446102142334 338 1.2514185905456543 339 1.2494145631790161 340 1.247434139251709 341 1.2454745769500732 342 1.243537187576294 343 1.2416186332702637 344 1.2397209405899048 345 1.2378422021865845 346 1.2359806299209595 347 1.2341375350952148 348 1.2323123216629028 349 1.2305020093917847 350 1.2287085056304932 351 1.2269303798675537 352 1.2251675128936768 353 1.2234179973602295 354 1.221683144569397 355 1.2199612855911255 356 1.218252182006836 357 1.2165557146072388 358 1.2148706912994385 359 1.2131974697113037 360 1.211535096168518 361 1.2098841667175293 362 1.2082427740097046 363 1.2066116333007812 364 1.204990029335022 365 1.2033777236938477 366 1.2017743587493896 367 1.2001795768737793 368 1.1985926628112793 369 1.1970137357711792 370 1.195443034172058 371 1.1938796043395996 372 1.192323088645935 373 1.1907728910446167 374 1.1892297267913818 375 1.187692642211914 376 1.1861613988876343 377 1.1846365928649902 378 1.183117389678955 379 1.1816024780273438 380 1.180093765258789 381 1.1785897016525269 382 1.177091121673584 383 1.1755962371826172 384 1.1741057634353638 385 1.1726197004318237 386 1.171137809753418 387 1.1696598529815674 388 1.1681857109069824 389 1.1667149066925049 390 1.165247917175293 391 1.1637840270996094 392 1.1623235940933228 393 1.1608657836914062 394 1.1594107151031494 395 1.157958745956421 396 1.1565097570419312 397 1.1550630331039429 398 1.1536190509796143 399 1.152176856994629 400 1.1507372856140137 401 1.1493000984191895 402 1.147864580154419 403 1.1464306116104126 404 1.1449998617172241 405 1.1435694694519043 406 1.1421420574188232 407 1.1407157182693481 408 1.1392905712127686 409 1.1378672122955322 410 1.13644540309906 411 1.1350250244140625 412 1.133606195449829 413 1.132188081741333 414 1.1307710409164429 415 1.1293559074401855 416 1.1279412508010864 417 1.1265281438827515 418 1.1251156330108643 419 1.123704433441162 420 1.1222939491271973 421 1.1208841800689697 422 1.119475245475769 423 1.1180672645568848 424 1.1166596412658691 425 1.1152534484863281 426 1.113847017288208 427 1.112441897392273 428 1.1110377311706543 429 1.1096336841583252 430 1.1082301139831543 431 1.106826901435852 432 1.1054246425628662 433 1.1040222644805908 434 1.1026207208633423 435 1.101219892501831 436 1.0998187065124512 437 1.0984185934066772 438 1.0970187187194824 439 1.0956188440322876 440 1.09421968460083 441 1.0928208827972412 442 1.0914225578308105 443 1.0900238752365112 444 1.0886261463165283 445 1.0872280597686768 446 1.0858312845230103 447 1.0844335556030273 448 1.0830364227294922 449 1.0816398859024048 450 1.0802432298660278 451 1.07884681224823 452 1.0774507522583008 453 1.0760548114776611 454 1.074658989906311 455 1.0732632875442505 456 1.071867823600769 457 1.070472240447998 458 1.0690771341323853 459 1.0676822662353516 460 1.0662869215011597 461 1.0648924112319946 462 1.0634981393814087 463 1.0621037483215332 464 1.060708999633789 465 1.0593152046203613 466 1.0579206943511963 467 1.0565274953842163 468 1.0551329851150513 469 1.0537396669387817 470 1.0523457527160645 471 1.050952672958374 472 1.0495595932006836 473 1.0481665134429932 474 1.046773076057434 475 1.045379877090454 476 1.0439872741699219 477 1.0425946712493896 478 1.0412020683288574 479 1.0398097038269043 480 1.038417100906372 481 1.0370252132415771 482 1.0356335639953613 483 1.0342413187026978 484 1.032849669456482 485 1.0314579010009766 486 1.0300660133361816 487 1.0286747217178345 488 1.0272836685180664 489 1.025892734527588 490 1.0245015621185303 491 1.0231103897094727 492 1.0217199325561523 493 1.0203297138214111 494 1.0189392566680908 495 1.0175485610961914 496 1.016158938407898 497 1.0147688388824463 498 1.0133793354034424 499 1.0119895935058594 500 1.0106004476547241 501 1.0092110633850098 502 1.007822036743164 503 1.0064334869384766 504 1.005044937133789 505 1.0036563873291016 506 1.0022687911987305 507 1.0008801221847534 508 0.9994925856590271 509 0.9981048107147217 510 0.9967176914215088 511 0.9953306317329407 512 0.9939436912536621 513 0.9925569891929626 514 0.991170346736908 515 0.9897842407226562 516 0.9883979558944702 517 0.9870126247406006 518 0.9856271743774414 519 0.9842422008514404 520 0.9828568696975708 521 0.9814721345901489 522 0.9800871014595032 523 0.9787031412124634 524 0.977319061756134 525 0.9759358167648315 526 0.9745523929595947 527 0.9731693267822266 528 0.9717866778373718 529 0.9704040288925171 530 0.9690216183662415 531 0.9676398634910583 532 0.9662580490112305 533 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當用SGD且epcho=100

當用SGD且epcho=1000

總結

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