鸢尾花数据集分类问题（2）https://developer.aliyun.com/article/1540969
6.循环寻找最优解
lr = 0.1 # 学习率为0.1 train_loss_results = [] # 将每轮的loss记录在此列表中，为后续画loss曲线提供数据 test_acc = [] # 将每轮的acc记录在此列表中，为后续画acc曲线提供数据 epoch = 500 # 循环500轮 loss_all = 0 # 每轮分4个step，loss_all记录四个step生成的4个loss的和 for epoch in range(epoch): #数据集级别的循环，每个epoch循环一次数据集 for step, (x_train, y_train) in enumerate(train_db): #batch级别的循环 ，每个step循环一个batch with tf.GradientTape() as tape: # with结构记录梯度信息 # y = tf.matmul(x_train, w1) + b1 # 神经网络乘加运算 y = tf.matmul(tf.cast(x_train,dtype=tf.float32), tf.cast(w1,dtype=tf.float32)) + tf.cast(b1,dtype=tf.float32) # 神经网络乘加运算 y = tf.nn.softmax(y) # 使输出y符合概率分布（此操作后与独热码同量级，可相减求loss） y_ = tf.one_hot(y_train, depth=3) # 将标签值转换为独热码格式，方便计算loss和accuracy loss = tf.reduce_mean(tf.square(y_ - y)) # 采用均方误差损失函数mse = mean(sum(y-out)^2) loss_all += loss.numpy() # 将每个step计算出的loss累加，为后续求loss平均值提供数据，这样计算的loss更准确 # 计算loss对各个参数的梯度 grads = tape.gradient(loss, [w1, b1]) # 实现梯度更新 w1 = w1 - lr * w1_grad b = b - lr * b_grad w1.assign_sub(lr * grads[0]) # 参数w1自更新 b1.assign_sub(lr * grads[1]) # 参数b自更新 # 每个epoch，打印loss信息 print("Epoch {}, loss: {}".format(epoch, loss_all/4)) train_loss_results.append(loss_all / 4) # 将4个step的loss求平均记录在此变量中 loss_all = 0 # loss_all归零，为记录下一个epoch的loss做准备
Epoch 0, loss: 0.018348709330894053 Epoch 1, loss: 0.01834014558698982 Epoch 2, loss: 0.018331602681428194 Epoch 3, loss: 0.018323074793443084 Epoch 4, loss: 0.018314557150006294 Epoch 5, loss: 0.018306054058484733 Epoch 6, loss: 0.01829756808001548 Epoch 7, loss: 0.018289093393832445 Epoch 8, loss: 0.018280635005794466 Epoch 9, loss: 0.01827219035476446 Epoch 10, loss: 0.01826376060489565 Epoch 11, loss: 0.018255344009958208 Epoch 12, loss: 0.01824694511014968 Epoch 13, loss: 0.018238553777337074 Epoch 14, loss: 0.018230177694931626 Epoch 15, loss: 0.01822182268369943 Epoch 16, loss: 0.018213476752862334 Epoch 17, loss: 0.01820514490827918 Epoch 18, loss: 0.01819682738278061 Epoch 19, loss: 0.018188522895798087 Epoch 20, loss: 0.018180232029408216 Epoch 21, loss: 0.01817195222247392 Epoch 22, loss: 0.018163689645007253 Epoch 23, loss: 0.01815544522833079 Epoch 24, loss: 0.01814720663242042 Epoch 25, loss: 0.018138986080884933 Epoch 26, loss: 0.01813077600672841 Epoch 27, loss: 0.018122584326192737 Epoch 28, loss: 0.01811440079472959 Epoch 29, loss: 0.018106236471794546 Epoch 30, loss: 0.018098083091899753 Epoch 31, loss: 0.018089941586367786 Epoch 32, loss: 0.018081813119351864 Epoch 33, loss: 0.018073701881803572 Epoch 34, loss: 0.01806559960823506 Epoch 35, loss: 0.018057511537335813 Epoch 36, loss: 0.01804943708702922 Epoch 37, loss: 0.018041377421468496 Epoch 38, loss: 0.018033328698948026 Epoch 39, loss: 0.018025295110419393 Epoch 40, loss: 0.018017273396253586 Epoch 41, loss: 0.018009266234003007 Epoch 42, loss: 0.018001268268562853 Epoch 43, loss: 0.01799328823108226 Epoch 44, loss: 0.01798531913664192 Epoch 45, loss: 0.017977362498641014 Epoch 46, loss: 0.01796942122746259 Epoch 47, loss: 0.017961489502340555 Epoch 48, loss: 0.017953572561964393 Epoch 49, loss: 0.017945665982551873 Epoch 50, loss: 0.01793777325656265 Epoch 51, loss: 0.017929895548149943 Epoch 52, loss: 0.01792202692013234 Epoch 53, loss: 0.01791416946798563 Epoch 54, loss: 0.017906329594552517 Epoch 55, loss: 0.017898502526804805 Epoch 56, loss: 0.017890685354359448 Epoch 57, loss: 0.017882882384583354 Epoch 58, loss: 0.017875090707093477 Epoch 59, loss: 0.017867310787551105 Epoch 60, loss: 0.01785954600200057 Epoch 61, loss: 0.01785179285798222 Epoch 62, loss: 0.0178440521704033 Epoch 63, loss: 0.01783632324077189 Epoch 64, loss: 0.01782860280945897 Epoch 65, loss: 0.017820901703089476 Epoch 66, loss: 0.01781320560257882 Epoch 67, loss: 0.017805526731535792 Epoch 68, loss: 0.017797861131839454 Epoch 69, loss: 0.01779020589310676 Epoch 70, loss: 0.017782564624212682 Epoch 71, loss: 0.017774931038729846 Epoch 72, loss: 0.017767311888746917 Epoch 73, loss: 0.01775970368180424 Epoch 74, loss: 0.017752105719409883 Epoch 75, loss: 0.017744520911946893 Epoch 76, loss: 0.017736950423568487 Epoch 77, loss: 0.01772939204238355 Epoch 78, loss: 0.01772184018045664 Epoch 79, loss: 0.017714308574795723 Epoch 80, loss: 0.017706783837638795 Epoch 81, loss: 0.01769926946144551 Epoch 82, loss: 0.017691769637167454 Epoch 83, loss: 0.017684280988760293 Epoch 84, loss: 0.017676800955086946 Epoch 85, loss: 0.01766933756880462 Epoch 86, loss: 0.017661882215179503 Epoch 87, loss: 0.017654439667239785 Epoch 88, loss: 0.017647008411586285 Epoch 89, loss: 0.017639590427279472 Epoch 90, loss: 0.017632179893553257 Epoch 91, loss: 0.01762478763703257 Epoch 92, loss: 0.01761740050278604 Epoch 93, loss: 0.0176100266398862 Epoch 94, loss: 0.01760266872588545 Epoch 95, loss: 0.017595318146049976 Epoch 96, loss: 0.0175879776943475 Epoch 97, loss: 0.01758064958266914 Epoch 98, loss: 0.017573332996107638 Epoch 99, loss: 0.017566024092957377 Epoch 100, loss: 0.017558730207383633 Epoch 101, loss: 0.017551449011079967 Epoch 102, loss: 0.017544178059324622 Epoch 103, loss: 0.01753691746853292 Epoch 104, loss: 0.01752966921776533 Epoch 105, loss: 0.01752243028022349 Epoch 106, loss: 0.017515201005153358 Epoch 107, loss: 0.017507984535768628 Epoch 108, loss: 0.017500781221315265 Epoch 109, loss: 0.017493583145551383 Epoch 110, loss: 0.017486400436609983 Epoch 111, loss: 0.017479229834862053 Epoch 112, loss: 0.017472067032940686 Epoch 113, loss: 0.01746491272933781 Epoch 114, loss: 0.017457775538787246 Epoch 115, loss: 0.01745064160786569 Epoch 116, loss: 0.017443525372073054 Epoch 117, loss: 0.01743642147630453 Epoch 118, loss: 0.01742932270281017 Epoch 119, loss: 0.017422234057448804 Epoch 120, loss: 0.017415160895325243 Epoch 121, loss: 0.017408096930012107 Epoch 122, loss: 0.017401042976416647 Epoch 123, loss: 0.017394001595675945 Epoch 124, loss: 0.01738697080872953 Epoch 125, loss: 0.01737994607537985 Epoch 126, loss: 0.01737293624319136 Epoch 127, loss: 0.01736593304667622 Epoch 128, loss: 0.01735894614830613 Epoch 129, loss: 0.017351965652778745 Epoch 130, loss: 0.017344993189908564 Epoch 131, loss: 0.017338032950647175 Epoch 132, loss: 0.017331089940853417 Epoch 133, loss: 0.01732415158767253 Epoch 134, loss: 0.017317220801487565 Epoch 135, loss: 0.0173103054985404 Epoch 136, loss: 0.01730339613277465 Epoch 137, loss: 0.01729650003835559 Epoch 138, loss: 0.017289610113948584 Epoch 139, loss: 0.017282731248997152 Epoch 140, loss: 0.01727586518973112 Epoch 141, loss: 0.017269007512368262 Epoch 142, loss: 0.017262160661630332 Epoch 143, loss: 0.017255326150916517 Epoch 144, loss: 0.01724849990569055 Epoch 145, loss: 0.01724168413784355 Epoch 146, loss: 0.01723487861454487 Epoch 147, loss: 0.017228084267117083 Epoch 148, loss: 0.01722129958216101 Epoch 149, loss: 0.01721451699268073 Epoch 150, loss: 0.017207755357958376 Epoch 151, loss: 0.01720099721569568 Epoch 152, loss: 0.0171942503657192 Epoch 153, loss: 0.017187514924444258 Epoch 154, loss: 0.017180791590362787 Epoch 155, loss: 0.01717407302930951 Epoch 156, loss: 0.017167365411296487 Epoch 157, loss: 0.017160667572170496 Epoch 158, loss: 0.017153977882117033 Epoch 159, loss: 0.017147303326055408 Epoch 160, loss: 0.01714063237886876 Epoch 161, loss: 0.017133974004536867 Epoch 162, loss: 0.017127325758337975 Epoch 163, loss: 0.01712068449705839 Epoch 164, loss: 0.017114059766754508 Epoch 165, loss: 0.017107439343817532 Epoch 166, loss: 0.0171008255565539 Epoch 167, loss: 0.01709422725252807 Epoch 168, loss: 0.017087630345486104 Epoch 169, loss: 0.017081054509617388 Epoch 170, loss: 0.017074481584131718 Epoch 171, loss: 0.017067916807718575 Epoch 172, loss: 0.017061361926607788 Epoch 173, loss: 0.01705481717363 Epoch 174, loss: 0.01704828254878521 Epoch 175, loss: 0.01704175816848874 Epoch 176, loss: 0.01703524007461965 Epoch 177, loss: 0.017028734902851284 Epoch 178, loss: 0.017022235435433686 Epoch 179, loss: 0.017015748424455523 Epoch 180, loss: 0.017009268747642636 Epoch 181, loss: 0.01700279803480953 Epoch 182, loss: 0.016996338381431997 Epoch 183, loss: 0.016989889089018106 Epoch 184, loss: 0.016983451205305755 Epoch 185, loss: 0.01697701681405306 Epoch 186, loss: 0.016970591619610786 Epoch 187, loss: 0.01696417632047087 Epoch 188, loss: 0.016957771498709917 Epoch 189, loss: 0.01695137470960617 Epoch 190, loss: 0.01694498595315963 Epoch 191, loss: 0.016938610235229135 Epoch 192, loss: 0.016932239173911512 Epoch 193, loss: 0.016925878240726888 Epoch 194, loss: 0.01691952720284462 Epoch 195, loss: 0.016913186525925994 Epoch 196, loss: 0.016906854696571827 Epoch 197, loss: 0.016900531016290188 Epoch 198, loss: 0.016894210246391594 Epoch 199, loss: 0.016887904377654195 Epoch 200, loss: 0.016881612362340093 Epoch 201, loss: 0.016875318135134876 Epoch 202, loss: 0.01686903869267553 Epoch 203, loss: 0.016862768796272576 Epoch 204, loss: 0.01685650448780507 Epoch 205, loss: 0.016850251937285066 Epoch 206, loss: 0.016844003926962614 Epoch 207, loss: 0.016837768722325563 Epoch 208, loss: 0.01683154294732958 Epoch 209, loss: 0.016825323225930333 Epoch 210, loss: 0.016819112235680223 Epoch 211, loss: 0.0168129145167768 Epoch 212, loss: 0.016806717729195952 Epoch 213, loss: 0.016800534911453724 Epoch 214, loss: 0.016794355935417116 Epoch 215, loss: 0.016788186854682863 Epoch 216, loss: 0.016782031278125942 Epoch 217, loss: 0.016775880940258503 Epoch 218, loss: 0.01676973991561681 Epoch 219, loss: 0.016763601452112198 Epoch 220, loss: 0.01675747858826071 Epoch 221, loss: 0.016751361661590636 Epoch 222, loss: 0.016745252651162446 Epoch 223, loss: 0.01673915423452854 Epoch 224, loss: 0.01673306548036635 Epoch 225, loss: 0.01672698266338557 Epoch 226, loss: 0.016720909159630537 Epoch 227, loss: 0.016714839730411768 Epoch 228, loss: 0.016708783456124365 Epoch 229, loss: 0.016702735447324812 Epoch 230, loss: 0.016696687205694616 Epoch 231, loss: 0.016690657357685268 Epoch 232, loss: 0.016684631700627506 Epoch 233, loss: 0.016678615822456777 Epoch 234, loss: 0.016672609141096473 Epoch 235, loss: 0.016666610725224018 Epoch 236, loss: 0.01666061650030315 Epoch 237, loss: 0.016654630890116096 Epoch 238, loss: 0.016648655757308006 Epoch 239, loss: 0.016642686328850687 Epoch 240, loss: 0.016636730288155377 Epoch 241, loss: 0.01663077261764556 Epoch 242, loss: 0.01662483369000256 Epoch 243, loss: 0.016618896857835352 Epoch 244, loss: 0.016612968873232603 Epoch 245, loss: 0.016607051366008818 Epoch 246, loss: 0.01660113874822855 Epoch 247, loss: 0.016595232766121626 Epoch 248, loss: 0.016589339822530746 Epoch 249, loss: 0.016583450604230165 Epoch 250, loss: 0.016577572794631124 Epoch 251, loss: 0.0165716998744756 Epoch 252, loss: 0.01656583370640874 Epoch 253, loss: 0.0165599777828902 Epoch 254, loss: 0.016554133966565132 Epoch 255, loss: 0.016548291314393282 Epoch 256, loss: 0.016542457044124603 Epoch 257, loss: 0.016536633833311498 Epoch 258, loss: 0.016530817258171737 Epoch 259, loss: 0.01652501046191901 Epoch 260, loss: 0.016519207507371902 Epoch 261, loss: 0.016513409093022346 Epoch 262, loss: 0.016507624299265444 Epoch 263, loss: 0.016501848469488323 Epoch 264, loss: 0.016496076830662787 Epoch 265, loss: 0.016490311361849308 Epoch 266, loss: 0.01648456056136638 Epoch 267, loss: 0.016478811507113278 Epoch 268, loss: 0.016473067924380302 Epoch 269, loss: 0.016467337496578693 Epoch 270, loss: 0.016461612773127854 Epoch 271, loss: 0.01645589538384229 Epoch 272, loss: 0.016450183582492173 Epoch 273, loss: 0.016444484004750848 Epoch 274, loss: 0.016438791411928833 Epoch 275, loss: 0.016433099750429392 Epoch 276, loss: 0.016427419031970203 Epoch 277, loss: 0.016421747859567404 Epoch 278, loss: 0.016416081925854087 Epoch 279, loss: 0.016410424723289907 Epoch 280, loss: 0.016404774389229715 Epoch 281, loss: 0.016399133251979947 Epoch 282, loss: 0.01639349735341966 Epoch 283, loss: 0.01638787181582302 Epoch 284, loss: 0.016382248955778778 Epoch 285, loss: 0.01637663773726672 Epoch 286, loss: 0.01637103164102882 Epoch 287, loss: 0.016365430550649762 Epoch 288, loss: 0.01635984191671014 Epoch 289, loss: 0.016354259103536606 Epoch 290, loss: 0.01634867547545582 Epoch 291, loss: 0.016343110240995884 Epoch 292, loss: 0.01633754454087466 Epoch 293, loss: 0.016331990715116262 Epoch 294, loss: 0.016326445154845715 Epoch 295, loss: 0.01632090308703482 Epoch 296, loss: 0.016315370216034353 Epoch 297, loss: 0.016309840604662895 Epoch 298, loss: 0.016304321703501046 Epoch 299, loss: 0.016298808972351253 Epoch 300, loss: 0.016293306602165103 Epoch 301, loss: 0.01628780399914831 Epoch 302, loss: 0.016282318392768502 Epoch 303, loss: 0.016276830923743546 Epoch 304, loss: 0.016271355096250772 Epoch 305, loss: 0.016265882644802332 Epoch 306, loss: 0.016260426375083625 Epoch 307, loss: 0.016254968708381057 Epoch 308, loss: 0.01624951616395265 Epoch 309, loss: 0.016244074911810458 Epoch 310, loss: 0.016238643671385944 Epoch 311, loss: 0.01623321371152997 Epoch 312, loss: 0.016227793879806995 Epoch 313, loss: 0.01622237788978964 Epoch 314, loss: 0.01621697621885687 Epoch 315, loss: 0.016211574315093458 Epoch 316, loss: 0.016206182190217078 Epoch 317, loss: 0.016200798097997904 Epoch 318, loss: 0.016195413190871477 Epoch 319, loss: 0.016190045629628003 Epoch 320, loss: 0.016184675972908735 Epoch 321, loss: 0.01617931795772165 Epoch 322, loss: 0.01617396529763937 Epoch 323, loss: 0.016168618807569146 Epoch 324, loss: 0.016163283959031105 Epoch 325, loss: 0.01615795132238418 Epoch 326, loss: 0.016152626019902527 Epoch 327, loss: 0.016147307120263577 Epoch 328, loss: 0.01614199543837458 Epoch 329, loss: 0.01613669202197343 Epoch 330, loss: 0.016131390701048076 Epoch 331, loss: 0.016126098460517824 Epoch 332, loss: 0.016120814019814134 Epoch 333, loss: 0.016115537495352328 Epoch 334, loss: 0.016110264230519533 Epoch 335, loss: 0.016104997717775404 Epoch 336, loss: 0.01609973842278123 Epoch 337, loss: 0.016094490187242627 Epoch 338, loss: 0.016089246841147542 Epoch 339, loss: 0.016084003378637135 Epoch 340, loss: 0.016078770509921014 Epoch 341, loss: 0.01607354322914034 Epoch 342, loss: 0.016068323981016874 Epoch 343, loss: 0.016063116141594946 Epoch 344, loss: 0.016057906206697226 Epoch 345, loss: 0.01605270802974701 Epoch 346, loss: 0.016047520330175757 Epoch 347, loss: 0.01604233030229807 Epoch 348, loss: 0.016037149005569518 Epoch 349, loss: 0.01603197434451431 Epoch 350, loss: 0.01602680841460824 Epoch 351, loss: 0.016021644696593285 Epoch 352, loss: 0.016016490990296006 Epoch 353, loss: 0.016011342289857566 Epoch 354, loss: 0.016006200574338436 Epoch 355, loss: 0.01600106677506119 Epoch 356, loss: 0.015995933092199266 Epoch 357, loss: 0.015990809304639697 Epoch 358, loss: 0.015985695994459093 Epoch 359, loss: 0.015980583033524454 Epoch 360, loss: 0.01597548311110586 Epoch 361, loss: 0.01597038039471954 Epoch 362, loss: 0.015965290600433946 Epoch 363, loss: 0.01596020522993058 Epoch 364, loss: 0.015955125214532018 Epoch 365, loss: 0.01595005253329873 Epoch 366, loss: 0.0159449860220775 Epoch 367, loss: 0.015939930453896523 Epoch 368, loss: 0.01593487267382443 Epoch 369, loss: 0.015929824323393404 Epoch 370, loss: 0.015924783772788942 Epoch 371, loss: 0.01591974718030542 Epoch 372, loss: 0.01591471757274121 Epoch 373, loss: 0.01590969110839069 Epoch 374, loss: 0.015904674073681235 Epoch 375, loss: 0.015899666235782206 Epoch 376, loss: 0.015894662123173475 Epoch 377, loss: 0.01588966406416148 Epoch 378, loss: 0.015884671825915575 Epoch 379, loss: 0.01587968470994383 Epoch 380, loss: 0.015874700155109167 Epoch 381, loss: 0.015869727358222008 Epoch 382, loss: 0.015864760149270296 Epoch 383, loss: 0.015859795035794377 Epoch 384, loss: 0.015854840632528067 Epoch 385, loss: 0.015849886927753687 Epoch 386, loss: 0.015844940091483295 Epoch 387, loss: 0.015840004081837833 Epoch 388, loss: 0.01583507191389799 Epoch 389, loss: 0.015830145333893597 Epoch 390, loss: 0.01582522422540933 Epoch 391, loss: 0.0158203081227839 Epoch 392, loss: 0.015815396909601986 Epoch 393, loss: 0.015810495242476463 Epoch 394, loss: 0.015805594623088837 Epoch 395, loss: 0.01580070413183421 Epoch 396, loss: 0.01579581806436181 Epoch 397, loss: 0.01579094084445387 Epoch 398, loss: 0.01578606979455799 Epoch 399, loss: 0.01578119967598468 Epoch 400, loss: 0.01577633630950004 Epoch 401, loss: 0.015771481674164534 Epoch 402, loss: 0.015766629367135465 Epoch 403, loss: 0.015761783928610384 Epoch 404, loss: 0.015756942448206246 Epoch 405, loss: 0.015752111445181072 Epoch 406, loss: 0.01574728370178491 Epoch 407, loss: 0.015742462361231446 Epoch 408, loss: 0.01573764579370618 Epoch 409, loss: 0.01573283178731799 Epoch 410, loss: 0.015728031750768423 Epoch 411, loss: 0.015723232878372073 Epoch 412, loss: 0.015718435985036194 Epoch 413, loss: 0.015713648172095418 Epoch 414, loss: 0.015708868973888457 Epoch 415, loss: 0.015704086748883128 Epoch 416, loss: 0.015699317445978522 Epoch 417, loss: 0.015694554429501295 Epoch 418, loss: 0.015689792577177286 Epoch 419, loss: 0.01568503910675645 Epoch 420, loss: 0.01568028738256544 Epoch 421, loss: 0.01567555032670498 Epoch 422, loss: 0.01567080896347761 Epoch 423, loss: 0.015666080871596932 Epoch 424, loss: 0.015661356854252517 Epoch 425, loss: 0.015656631090678275 Epoch 426, loss: 0.015651918249204755 Epoch 427, loss: 0.015647205989807844 Epoch 428, loss: 0.01564250630326569 Epoch 429, loss: 0.015637808246538043 Epoch 430, loss: 0.01563311368227005 Epoch 431, loss: 0.01562842621933669 Epoch 432, loss: 0.015623745857737958 Epoch 433, loss: 0.015619067242369056 Epoch 434, loss: 0.015614393167197704 Epoch 435, loss: 0.015609726426191628 Epoch 436, loss: 0.015605070278979838 Epoch 437, loss: 0.015600414481014013 Epoch 438, loss: 0.015595762757584453 Epoch 439, loss: 0.015591120114549994 Epoch 440, loss: 0.015586481895297766 Epoch 441, loss: 0.015581847401335835 Epoch 442, loss: 0.015577218844555318 Epoch 443, loss: 0.015572597738355398 Epoch 444, loss: 0.015567979775369167 Epoch 445, loss: 0.015563365770503879 Epoch 446, loss: 0.015558761078864336 Epoch 447, loss: 0.015554160112515092 Epoch 448, loss: 0.015549563453532755 Epoch 449, loss: 0.015544973313808441 Epoch 450, loss: 0.015540387947112322 Epoch 451, loss: 0.015535812941379845 Epoch 452, loss: 0.015531235025264323 Epoch 453, loss: 0.015526664792560041 Epoch 454, loss: 0.015522102243267 Epoch 455, loss: 0.01551753783132881 Epoch 456, loss: 0.015512991114519536 Epoch 457, loss: 0.01550843648146838 Epoch 458, loss: 0.015503892907872796 Epoch 459, loss: 0.015499353175982833 Epoch 460, loss: 0.015494818333536386 Epoch 461, loss: 0.015490289777517319 Epoch 462, loss: 0.015485770301893353 Epoch 463, loss: 0.015481252688914537 Epoch 464, loss: 0.015476739266887307 Epoch 465, loss: 0.01547223306261003 Epoch 466, loss: 0.01546773046720773 Epoch 467, loss: 0.01546323299407959 Epoch 468, loss: 0.01545873936265707 Epoch 469, loss: 0.015454252483323216 Epoch 470, loss: 0.015449768863618374 Epoch 471, loss: 0.015445296070538461 Epoch 472, loss: 0.015440825140103698 Epoch 473, loss: 0.015436352114193141 Epoch 474, loss: 0.015431895037181675 Epoch 475, loss: 0.015427436213940382 Epoch 476, loss: 0.015422984608449042 Epoch 477, loss: 0.015418535564094782 Epoch 478, loss: 0.015414097812026739 Epoch 479, loss: 0.015409659245051444 Epoch 480, loss: 0.015405226848088205 Epoch 481, loss: 0.01540080236736685 Epoch 482, loss: 0.015396383474580944 Epoch 483, loss: 0.0153919622534886 Epoch 484, loss: 0.01538755011279136 Epoch 485, loss: 0.015383146819658577 Epoch 486, loss: 0.015378746087662876 Epoch 487, loss: 0.015374345588497818 Epoch 488, loss: 0.015369954984635115 Epoch 489, loss: 0.015365565428510308 Epoch 490, loss: 0.015361182391643524 Epoch 491, loss: 0.01535680890083313 Epoch 492, loss: 0.015352435759268701 Epoch 493, loss: 0.015348068787716329 Epoch 494, loss: 0.015343705890700221 Epoch 495, loss: 0.015339347766712308 Epoch 496, loss: 0.01533499569632113 Epoch 497, loss: 0.015330647234804928 Epoch 498, loss: 0.015326304011978209 Epoch 499, loss: 0.015321968006901443
上边这个cell的第10行，如果只是写y = tf.matmul(x_train, w1) + b1，会报错：InvalidArgumentError: cannot compute MatMul as input #1(zero-based) was expected to be a double tensor but is a float tensor [Op:MatMul]，看错误原因是说不应该用float类型而应该用double类型，但是测试过把它转换成tf.double也会报错，转换成tf.float32后运行成功。不过好像没有tf.double类型，具体原因我也不知道是啥。
鸢尾花数据集分类问题（4）https://developer.aliyun.com/article/1540971
