python - TensorFlow 线性回归模型不起作用

Question

我正在构建一个线性回归模型，将一个 numpy 数组映射到一个 numpy 数组，

即[1.0,1.0,1.0,1.0] ---> [5.0,5.0,5.0,5.0]

我的网络如下所示，您可以看到 x 占位符对应于输入，y 占位符对应于输出。但是，我的模型只是收敛到 1.0:

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense

g= tf.Graph()
with g.as_default():

    x = tf.placeholder(dtype=tf.float32, shape = (None,4))
    y = tf.placeholder(dtype=tf.float32, shape = (None,4))

    model = tf.keras.Sequential([
    Dense(units=4, activation=tf.nn.relu),
    Dense(units=4, activation=tf.nn.sigmoid)
    ])


    pred = model(x)    
    loss = tf.reduce_mean(tf.square(pred - y))

    train_op = tf.train.AdamOptimizer().minimize(loss)

    init_op = tf.group(tf.global_variables_initializer(),
                         tf.local_variables_initializer())



with tf.Session(graph=g) as sess:
        sess.run(init_op)
        for step in range(1000):
            _ , lossy, predicted = sess.run([train_op,loss,pred], feed_dict = {x:np.ones(shape=(1,4)),
                                           y:5*np.ones(shape=(1,4))})        

            print(predicted)

不幸的是，结果收敛到 numpy 的 ones 数组而不是 fives:

[[0.51713973 0.59164494 0.5563706  0.61163014]]
[[0.5176364  0.5928199  0.5572325  0.61297345]]
[[0.51813626 0.59399694 0.5580971  0.614318  ]]
[[0.518639  0.595176  0.5589645 0.615664 ]]
[[0.5191449  0.5963571  0.5598347  0.61701125]]
[[0.51965386 0.59754026 0.56070775 0.61835986]]
[[0.6156333  0.7611001  0.69670683 0.79359496]]
[[0.61654085 0.76225615 0.6978007  0.7947457 ]]
[[0.6174505  0.76340926 0.6988941  0.7958921 ]]
[[0.61836195 0.7645594  0.69998693 0.7970343 ]]
[[0.61927533 0.7657065  0.70107937 0.79817224]]
[[0.6201906  0.7668506  0.70217115 0.7993058 ]]
[[0.6211078 0.7679917 0.7032624 0.8004351]]
[[0.62202674 0.7691298  0.7043529  0.80156004]]
[[0.6229476  0.77026474 0.70544285 0.80268055]]
[[0.62387013 0.77139646 0.706532   0.8037967 ]]
[[0.6247945  0.7725251  0.70762056 0.80490845]]
[[0.6257205  0.77365047 0.70870817 0.80601573]]
[[0.62664825 0.7747727  0.70979506 0.8071186 ]]
[[0.6275776  0.77589166 0.7108811  0.8082169 ]]
[[0.6285086  0.77700734 0.7119662  0.80931073]]
[[0.6294413 0.7781197 0.7130505 0.8104002]]
[[0.63037556 0.7792287  0.7141337  0.81148493]]
[[0.63131136 0.7803343  0.715216   0.81256527]]
[[0.6322487 0.7814366 0.7162972 0.813641 ]]
[[0.63318753 0.78253555 0.7173774  0.81471217]]
[[0.6341278  0.78363097 0.71845657 0.8157787 ]]
[[0.63506955 0.7847229  0.7195346  0.81684065]]
[[0.6360127  0.78581136 0.72061133 0.817898  ]]
[[0.6369573  0.78689635 0.72168696 0.8189507 ]]
[[0.63790315 0.78797776 0.7227614  0.8199988 ]]
[[0.6388504 0.7890556 0.7238345 0.8210421]]
[[0.6397989  0.79012996 0.7249064  0.8220809 ]]
[[0.64074874 0.79120064 0.72597694 0.82311493]]
[[0.64169973 0.7922677  0.72704613 0.8241443 ]]
[[0.64265203 0.793331   0.7281139  0.825169  ]]
[[0.6436055 0.7943908 0.7291803 0.826189 ]]
[[0.64456004 0.7954469  0.7302452  0.82720417]]
[[0.6455158  0.7964993  0.7313086  0.82821476]]
[[0.6464725  0.7975479  0.7323705  0.82922053]]
[[0.6474304  0.79859275 0.7334308  0.8302216 ]]
[[0.6483893  0.79963386 0.7344896  0.83121794]]
[[0.6493493  0.8006713  0.7355467  0.83220947]]
[[0.65031016 0.80170476 0.7366023  0.8331962 ]]
[[0.65127194 0.80273455 0.7376562  0.8341783 ]]
[[0.65223473 0.8037604  0.7387082  0.83515555]]
[[0.6531983  0.80478245 0.7397586  0.836128  ]]
[[0.6541628  0.8058007  0.74080724 0.8370958 ]]
[[0.6551281  0.8068149  0.74185413 0.8380587 ]]
[[0.65609425 0.8078254  0.7428992  0.83901685]]
[[0.65706116 0.80883193 0.7439424  0.8399703 ]]
[[0.6580287  0.8098345  0.7449836  0.84091884]]
[[0.65899706 0.81083316 0.746023   0.8418626 ]]
[[0.65996605 0.8118279  0.74706054 0.84280175]]
[[0.6609357  0.8128186  0.748096   0.84373593]]
[[0.66190594 0.81380534 0.7491295  0.8446654 ]]
[[0.66287684 0.8147882  0.750161   0.84559   ]]
[[0.6638481  0.815767   0.7511904  0.84650993]]
[[0.6648201  0.8167418  0.75221777 0.84742504]]
[[0.6657925 0.8177126 0.753243  0.8483353]]
[[0.66676533 0.8186794  0.7542662  0.8492409 ]]
[[0.6677386  0.81964207 0.75528723 0.8501416 ]]
[[0.6687124  0.8206008  0.75630605 0.8510376 ]]
[[0.66968644 0.8215554  0.75732267 0.8519288 ]]
[[0.6706608  0.82250595 0.7583371  0.8528153 ]]
[[0.6716356  0.8234524  0.75934917 0.85369694]]
[[0.6726106  0.8243949  0.76035905 0.85457385]]
[[0.6735859  0.82533324 0.7613666  0.85544604]]
[[0.6745613 0.8262675 0.7623719 0.8563134]]
[[0.7172588 0.8633007 0.8040014 0.8899244]]
[[0.71821445 0.86405045 0.8048828  0.8905892 ]]
[[0.7191691  0.8647962  0.80576116 0.8912497 ]]
[[0.7201225  0.86553806 0.80663645 0.89190614]]
[[0.7210749  0.8662759  0.80750865 0.89255834]]
[[0.72202617 0.8670098  0.8083777  0.89320654]]
[[0.72297627 0.8677398  0.8092437  0.89385056]]
[[0.7239251  0.8684658  0.81010664 0.8944905 ]]
[[0.7248728  0.86918783 0.8109664  0.8951264 ]]
[[0.72581923 0.8699059  0.811823   0.8957583 ]]
[[0.7267645  0.8706201  0.81267655 0.8963861 ]]
[[0.72770846 0.8713304  0.8135269  0.8970099 ]]
[[0.7286511 0.8720368 0.8143741 0.8976297]]
[[0.7295924  0.8727393  0.81521827 0.89824563]]
[[0.73053235 0.87343794 0.8160591  0.8988575 ]]
[[0.73147094 0.87413275 0.816897   0.89946544]]
[[0.7324081 0.8748237 0.8177316 0.9000695]]
[[0.7333439  0.87551075 0.818563   0.90066963]]
[[0.73427826 0.876194   0.81939125 0.9012659 ]]
[[0.7352112  0.87687343 0.82021636 0.9018583 ]]
[[0.7361426  0.87754905 0.82103825 0.90244687]]
[[0.73707247 0.8782209  0.8218571  0.9030316 ]]
[[0.73800087 0.878889   0.8226726  0.9036125 ]]
[[0.7389278  0.87955326 0.82348496 0.9041897 ]]
[[0.7398531  0.8802138  0.82429415 0.9047631 ]]
[[0.74077684 0.88087064 0.8251     0.9053327 ]]
[[0.7416989  0.8815237  0.8259028  0.90589863]]
[[0.7426194 0.882173  0.8267024 0.9064608]]
[[0.7435383  0.8828187  0.82749873 0.9070193 ]]
[[0.7444555  0.8834607  0.8282919  0.90757424]]
[[0.745371   0.884099   0.82908183 0.90812546]]
[[0.74628484 0.8847336  0.8298686  0.90867305]]
[[0.7471969 0.8853646 0.8306521 0.909217 ]]
[[0.7481073  0.88599193 0.83143246 0.9097574 ]]
[[0.749016  0.8866157 0.8322095 0.9102941]]
[[0.7499228  0.8872358  0.83298343 0.91082746]]
[[0.7508279  0.8878523  0.8337541  0.91135716]]
[[0.7517311  0.88846534 0.8345216  0.9118833 ]]
[[0.7526325  0.8890747  0.83528584 0.912406  ]]
[[0.7535321  0.8896805  0.83604693 0.91292536]]
[[0.7544298  0.8902828  0.83680475 0.91344106]]
[[0.7553256  0.8908816  0.83755946 0.9139535 ]]
[[0.7562196  0.8914768  0.8383109  0.91446245]]
[[0.75711167 0.8920687  0.8390592  0.9149679 ]]
[[0.7580018  0.8926569  0.8398042  0.91547006]]
[[0.75889   0.8932418 0.840546  0.915969 ]]
[[0.7597762  0.89382327 0.84128463 0.91646445]]
[[0.7606604  0.89440125 0.84202003 0.9169566 ]]
[[0.7615427  0.8949758  0.8427523  0.91744554]]
[[0.76242286 0.89554685 0.84348136 0.91793114]]
[[0.76330113 0.89611465 0.8442072  0.9184134 ]]
[[0.7641773  0.89667904 0.84492993 0.9188925 ]]
[[0.76505154 0.8972401  0.8456494  0.9193683 ]]
[[0.76592356 0.8977977  0.8463657  0.91984105]]
[[0.7667936 0.8983522 0.8470789 0.9203106]]
[[0.7676616 0.8989032 0.8477889 0.9207769]]
[[0.7685274  0.89945096 0.84849566 0.9212401 ]]
[[0.7693911 0.8999955 0.8491993 0.9217002]]
[[0.7702527  0.90053666 0.8498998  0.9221571 ]]
[[0.77111214 0.9010747  0.85059714 0.922611  ]]
[[0.77196944 0.90160936 0.8512913  0.9230617 ]]
[[0.7728246  0.9021409  0.85198236 0.9235095 ]]
[[0.7736775  0.90266925 0.8526702  0.9239543 ]]
[[0.77452826 0.9031944  0.85335505 0.924396  ]]
[[0.7753768 0.9037164 0.8540367 0.9248347]]
[[0.7762232  0.9042352  0.85471517 0.9252705 ]]
[[0.7770673  0.90475094 0.85539055 0.92570335]]
[[0.77790916 0.9052634  0.8560629  0.9261332 ]]
[[0.77874887 0.90577286 0.85673195 0.92656016]]
[[0.7795862  0.90627927 0.8573981  0.9269843 ]]
[[0.7804213 0.9067825 0.858061  0.9274055]]
[[0.7812542  0.9072827  0.85872096 0.9278238 ]]
[[0.7820847  0.90777993 0.85937774 0.9282393 ]]
[[0.782913  0.9082741 0.8600315 0.928652 ]]
[[0.783739   0.9087652  0.8606822  0.92906183]]
[[0.78456265 0.90925336 0.8613298  0.929469  ]]
[[0.78538394 0.90973854 0.86197436 0.9298733 ]]
[[0.7862029  0.91022074 0.8626159  0.9302748 ]]
[[0.7870195 0.9107    0.8632543 0.9306737]]
[[0.7878338  0.9111763  0.86388975 0.93106973]]
[[0.78864574 0.91164976 0.86452216 0.93146324]]
[[0.78945535 0.9121202  0.8651515  0.93185395]]
[[0.7902625 0.9125879 0.8657779 0.9322421]]
[[0.79106736 0.9130525  0.86640126 0.93262756]]
[[0.79186976 0.9135145  0.8670217  0.93301034]]
[[0.7926698  0.9139735  0.86763906 0.93339056]]
[[0.7934674  0.9144297  0.8682535  0.93376815]]
[[0.79426265 0.9148831  0.86886495 0.93414325]]
[[0.79505545 0.91533375 0.86947346 0.93451566]]
[[0.7958458  0.9157816  0.870079   0.93488574]]
[[0.7966338  0.9162267  0.8706816  0.93525314]]
[[0.79741925 0.9166691  0.87128115 0.93561804]]
[[0.7982023  0.9171086  0.87187797 0.9359805 ]]
[[0.7989829  0.91754556 0.87247175 0.9363405 ]]
[[0.7997611 0.9179797 0.8730627 0.9366981]]
[[0.8005369 0.9184112 0.8736506 0.9370531]]
[[0.8013101  0.91884005 0.8742358  0.93740577]]
[[0.80208087 0.9192663  0.874818   0.93775606]]
[[0.8028492 0.9196898 0.8753973 0.9381039]]
[[0.80361503 0.9201107  0.8759739  0.93844944]]
[[0.8043784  0.920529   0.8765475  0.93879265]]
[[0.8051393  0.9209448  0.87711823 0.93913347]]
[[0.80589765 0.92135787 0.8776862  0.93947196]]
[[0.80665356 0.92176855 0.8782513  0.93980825]]
[[0.80740696 0.9221765  0.8788136  0.9401421 ]]
[[0.80815786 0.9225821  0.8793731  0.94047374]]
[[0.8089062  0.9229851  0.87992984 0.9408031 ]]
[[0.8096521  0.9233855  0.88048375 0.9411302 ]]
[[0.8103955 0.9237835 0.8810349 0.9414551]]
[[0.8111363 0.9241791 0.8815832 0.9417779]]
[[0.81187475 0.92457217 0.8821289  0.9420984 ]]
[[0.81261057 0.9249628  0.88267165 0.9424168 ]]
[[0.81334394 0.92535096 0.8832118  0.94273293]]
[[0.8140747 0.9257367 0.8837492 0.9430469]]
[[0.814803   0.9261201  0.8842839  0.94335884]]
[[0.81552875 0.92650115 0.8848158  0.9436686 ]]
[[0.816252   0.9268798  0.88534504 0.9439762 ]]
[[0.81697273 0.92725605 0.8858717  0.9442818 ]]
[[0.81769097 0.92763    0.8863955  0.9445853 ]]
[[0.8184066  0.92800164 0.8869168  0.94488674]]
[[0.8191197 0.928371  0.8874354 0.9451862]]
[[0.8198303  0.92873794 0.88795125 0.94548357]]
[[0.82053834 0.92910266 0.88846457 0.9457789 ]]
[[0.8212439 0.9294652 0.8889752 0.9460723]]
[[0.8219469 0.9298253 0.8894833 0.9463636]]
[[0.8226474  0.9301833  0.8899887  0.94665307]]
[[0.82334536 0.930539   0.8904915  0.94694054]]
[[0.8240408  0.9308926  0.89099187 0.947226  ]]
[[0.8247337 0.9312439 0.8914895 0.9475095]]
[[0.8254241  0.93159294 0.89198464 0.9477912 ]]
[[0.82611185 0.93193996 0.8924773  0.948071  ]]
[[0.8267972  0.9322848  0.8929673  0.94834894]]
[[0.82747996 0.93262744 0.89345473 0.94862485]]
[[0.82816017 0.93296796 0.89393973 0.94889903]]
[[0.8288378  0.93330634 0.89442223 0.9491713 ]]
[[0.8295131  0.9336427  0.89490217 0.94944173]]
[[0.8301857  0.9339769  0.89537966 0.9497103 ]]
[[0.8308559  0.93430907 0.8958547  0.94997716]]
[[0.83152354 0.93463904 0.8963272  0.9502422 ]]
[[0.83218867 0.9349671  0.8967973  0.95050544]]
[[0.8328512  0.9352931  0.89726496 0.95076704]]
[[0.83351135 0.9356171  0.8977302  0.9510267 ]]
[[0.83416885 0.935939   0.898193   0.95128465]]
[[0.8348239  0.936259   0.8986533  0.95154095]]
[[0.83547646 0.93657684 0.89911133 0.9517955 ]]
[[0.83612657 0.93689287 0.8995669  0.9520483 ]]
[[0.8367741  0.93720686 0.90002006 0.9522995 ]]
[[0.8374192 0.937519  0.9004709 0.9525489]]
[[0.8380617  0.93782914 0.9009194  0.9527967 ]]
[[0.8387018  0.9381373  0.9013655  0.95304286]]
[[0.8393393  0.93844366 0.90180933 0.9532873 ]]
[[0.83997434 0.9387481  0.90225077 0.95353013]]
[[0.840607   0.9390507  0.90268993 0.9537715 ]]
[[0.8412371  0.93935126 0.9031268  0.9540111 ]]
[[0.84186476 0.93965    0.9035613  0.9542491 ]]
[[0.84248984 0.939947   0.9039936  0.95448554]]
[[0.8431125  0.9402421  0.90442365 0.95472044]]
[[0.8437328 0.9405354 0.9048513 0.9549538]]
[[0.8443506  0.94082683 0.9052768  0.9551855 ]]
[[0.8449659  0.9411165  0.9057001  0.95541567]]
[[0.8455787  0.9414044  0.9061211  0.95564437]]
[[0.84618914 0.9416905  0.9065399  0.95587164]]
[[0.846797   0.9419748  0.90695643 0.95609725]]
[[0.8474025  0.94225746 0.90737087 0.9563215 ]]
[[0.8480056  0.9425382  0.90778303 0.95654416]]
[[0.84860617 0.94281733 0.90819305 0.9567654 ]]
[[0.8492044  0.94309473 0.90860105 0.95698524]]
[[0.84980017 0.9433704  0.90900666 0.9572035 ]]
[[0.8503936  0.9436444  0.9094103  0.95742035]]
[[0.8509845  0.9439166  0.90981174 0.9576358 ]]
[[0.8515731  0.94418734 0.9102111  0.9578499 ]]
[[0.85215926 0.9444563  0.9106083  0.9580626 ]]
[[0.85274297 0.9447236  0.91100335 0.95827377]]
[[0.8533243 0.9449892 0.9113964 0.9584836]]
[[0.8539032 0.9452532 0.9117873 0.958692 ]]
[[0.85447973 0.94551563 0.91217613 0.95889914]]
[[0.8550539  0.94577646 0.91256297 0.95910496]]
[[0.85562575 0.9460357  0.9129477  0.9593093 ]]
[[0.8561953  0.9462933  0.9133304  0.95951235]]
[[0.85676235 0.94654936 0.9137111  0.9597142 ]]
[[0.85732704 0.9468038  0.91408974 0.95991445]]
[[0.8578894  0.9470567  0.91446644 0.96011364]]
[[0.8584494  0.94730806 0.9148411  0.96031135]]
[[0.8590071 0.9475579 0.9152137 0.960508 ]]
[[0.8595625  0.9478062  0.9155845  0.96070325]]
[[0.86011547 0.948053   0.9159532  0.96089727]]
[[0.8606662  0.94829834 0.91631997 0.9610899 ]]
[[0.8612146  0.9485422  0.91668475 0.9612813 ]]
[[0.8617606  0.94878453 0.91704774 0.9614716 ]]
[[0.86230445 0.9490253  0.9174087  0.9616606 ]]
[[0.86284584 0.94926465 0.9177677  0.96184844]]
[[0.8633851  0.94950265 0.9181249  0.96203494]]
[[0.86392194 0.94973904 0.91848016 0.9622203 ]]
[[0.8644566 0.9499741 0.9188335 0.9624045]]
[[0.8649889  0.95020777 0.919185   0.96258736]]
[[0.86551905 0.9504399  0.9195346  0.9627692 ]]
[[0.8660469  0.9506707  0.91988236 0.9629498 ]]
[[0.8665724  0.95090014 0.9202283  0.96312934]]
[[0.86709577 0.9511282  0.92057234 0.9633076 ]]
[[0.86761683 0.9513548  0.92091465 0.96348476]]
[[0.86813563 0.95158005 0.9212551  0.9636607 ]]
[[0.8686523 0.9518039 0.9215937 0.9638357]]
[[0.8691666  0.9520265  0.9219306  0.96400946]]
[[0.86967885 0.9522477  0.9222656  0.9641821 ]]
[[0.87018883 0.9524676  0.9225989  0.9643537 ]]
[[0.8706966  0.95268613 0.9229304  0.9645242 ]]
[[0.8712022  0.95290345 0.92326015 0.9646936 ]]
[[0.87170553 0.95311934 0.9235882  0.9648619 ]]
[[0.87220675 0.953334   0.92391443 0.96502906]]
[[0.8727058  0.95354736 0.9242389  0.9651953 ]]
[[0.8732026  0.95375943 0.92456174 0.96536046]]
[[0.8736974 0.9539702 0.9248829 0.9655245]]
[[0.87419    0.9541798  0.92520225 0.9656875 ]]
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[[0.9438655 0.9808575 0.9677187 0.9859666]]
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[[0.94422466 0.9809825  0.9679267  0.98605937]]
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[[0.94475776 0.9811678  0.968235   0.9861968 ]]
[[0.944934   0.981229   0.96833694 0.9862422 ]]
[[0.9451094 0.9812898 0.9684383 0.9862874]]
[[0.94528407 0.9813504  0.9685393  0.9863323 ]]
[[0.94545805 0.9814108  0.96863985 0.98637706]]
[[0.9456314 0.9814709 0.96874   0.9864216]]
[[0.94580376 0.9815307  0.9688395  0.9864659 ]]
[[0.9459756 0.9815902 0.9689388 0.98651  ]]
[[0.94614667 0.98164946 0.96903753 0.98655397]]
[[0.94631696 0.9817084  0.96913594 0.9865976 ]]
[[0.94648653 0.9817671  0.9692338  0.9866411 ]]

Answer 1

您不应使用 sigmoid 激活函数。请改用 RELU。因为 sigmoid 函数将数字限制在 range(-1, 1) 范围内。

model = tf.keras.Sequential([
            Dense(units=4, activation=tf.nn.relu),
            Dense(units=4, activation=tf.nn.relu)
        ])