TensorFlow linear regression model not working












2















I am building a linear regression model that maps a numpy array of ones into a numpy array of fives,



i.e. [1.0,1.0,1.0,1.0] ---> [5.0,5.0,5.0,5.0]



My network is shown below where you can see that the x placeholders correspond to the inputs and the y placeholders correspond to the outputs. However my model is just converging to 1.0s instead:



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)


The results unfortunately converge to a numpy array of ones instead of 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 ]]
[[0.87468034 0.954388 0.92551994 0.9658495 ]]
[[0.8751687 0.95459515 0.92583597 0.9660106 ]]
[[0.8756549 0.95480096 0.9261504 0.9661705 ]]
[[0.87613887 0.9550055 0.92646295 0.9663294 ]]
[[0.8766207 0.9552089 0.926774 0.9664874]]
[[0.8771005 0.955411 0.92708343 0.9666444 ]]
[[0.87757826 0.955612 0.92739123 0.9668004 ]]
[[0.8780539 0.95581174 0.9276973 0.9669553 ]]
[[0.87852734 0.9560103 0.92800176 0.9671094 ]]
[[0.8789989 0.95620775 0.9283046 0.9672624 ]]
[[0.8794682 0.9564039 0.9286059 0.9674145]]
[[0.8799355 0.956599 0.92890567 0.9675656 ]]
[[0.8804008 0.9567929 0.92920375 0.96771586]]
[[0.8808639 0.95698565 0.9295003 0.9678651 ]]
[[0.88132507 0.9571773 0.92979527 0.96801347]]
[[0.8817841 0.9573677 0.93008864 0.9681608 ]]
[[0.8822412 0.9575571 0.9303806 0.9683073]]
[[0.8826963 0.9577454 0.9306708 0.968453 ]]
[[0.8831493 0.9579325 0.9309596 0.9685976]]
[[0.8836004 0.9581185 0.93124694 0.9687414 ]]
[[0.8840494 0.95830345 0.9315326 0.9688843 ]]
[[0.8844965 0.9584873 0.9318169 0.9690263]]
[[0.8849416 0.95867014 0.93209964 0.9691674 ]]
[[0.8853847 0.9588518 0.9323809 0.9693077]]
[[0.8858257 0.9590325 0.9326607 0.9694471]]
[[0.8862649 0.959212 0.93293905 0.96958566]]
[[0.8867021 0.9593906 0.9332158 0.9697233]]
[[0.8871373 0.959568 0.93349123 0.9698602 ]]
[[0.88757056 0.95974445 0.9337651 0.96999615]]
[[0.88800204 0.9599199 0.9340376 0.9701313 ]]
[[0.88843143 0.96009433 0.93430877 0.9702656 ]]
[[0.8888589 0.9602677 0.93457836 0.9703992 ]]
[[0.8892847 0.96044004 0.93484664 0.9705319 ]]
[[0.88970834 0.96061146 0.93511343 0.9706637 ]]
[[0.89013016 0.9607818 0.9353789 0.9707948 ]]
[[0.89055014 0.9609512 0.93564296 0.97092515]]
[[0.8909682 0.96111965 0.9359056 0.9710547 ]]
[[0.89138436 0.961287 0.93616694 0.97118336]]
[[0.8917987 0.96145356 0.9364268 0.97131133]]
[[0.8922111 0.9616191 0.9366853 0.97143847]]
[[0.8926217 0.9617836 0.9369426 0.97156477]]
[[0.8930305 0.96194726 0.9371984 0.9716905 ]]
[[0.8934374 0.96210986 0.9374529 0.97181535]]
[[0.8938426 0.96227163 0.9377062 0.97193944]]
[[0.8942458 0.9624324 0.93795806 0.97206277]]
[[0.89464736 0.9625923 0.9382085 0.97218543]]
[[0.89504695 0.96275127 0.93845785 0.97230726]]
[[0.89544487 0.96290934 0.93870574 0.97242844]]
[[0.89584094 0.9630664 0.9389525 0.97254884]]
[[0.89623517 0.9632227 0.93919784 0.9726686 ]]
[[0.8966277 0.96337795 0.939442 0.97278756]]
[[0.89701843 0.96353257 0.93968475 0.9729058 ]]
[[0.8974075 0.9636861 0.9399264 0.9730234]]
[[0.89779466 0.9638388 0.94016665 0.97314024]]
[[0.8981802 0.9639906 0.94040585 0.9732564 ]]
[[0.8985639 0.96414167 0.9406436 0.9733718 ]]
[[0.89894587 0.9642917 0.94088024 0.9734867 ]]
[[0.8993262 0.96444106 0.9411157 0.9736008 ]]
[[0.89970475 0.9645894 0.9413498 0.9737143 ]]
[[0.90008163 0.9647371 0.9415828 0.973827 ]]
[[0.90045685 0.9648838 0.9418145 0.9739391 ]]
[[0.90083027 0.9650298 0.9420451 0.9740505 ]]
[[0.9012021 0.9651748 0.9422744 0.97416127]]
[[0.90157217 0.96531916 0.9425026 0.9742715 ]]
[[0.9019405 0.96546257 0.9427296 0.9743809 ]]
[[0.90230733 0.96560526 0.94295543 0.9744898 ]]
[[0.9026724 0.9657471 0.9431801 0.97459793]]
[[0.9030359 0.96588814 0.9434036 0.9747055 ]]
[[0.90339774 0.9660284 0.9436259 0.97481245]]
[[0.90375787 0.9661679 0.9438472 0.9749188 ]]
[[0.9041164 0.96630657 0.9440673 0.9750245 ]]
[[0.9044733 0.96644455 0.9442862 0.9751296 ]]
[[0.9048286 0.9665817 0.944504 0.97523403]]
[[0.90518236 0.9667182 0.9447208 0.97533786]]
[[0.9055344 0.9668538 0.94493645 0.9754411 ]]
[[0.90588486 0.9669886 0.94515085 0.97554374]]
[[0.90623385 0.96712273 0.9453643 0.97564584]]
[[0.90658116 0.9672561 0.9455766 0.97574735]]
[[0.90692693 0.96738887 0.9457878 0.9758482 ]]
[[0.90727115 0.9675207 0.945998 0.97594845]]
[[0.9076137 0.9676519 0.94620705 0.9760482 ]]
[[0.9079548 0.9677824 0.9464151 0.97614735]]
[[0.9082944 0.96791214 0.9466221 0.97624594]]
[[0.9086324 0.9680411 0.94682795 0.97634387]]
[[0.90896887 0.9681694 0.94703275 0.9764414 ]]
[[0.9093037 0.96829706 0.9472366 0.97653824]]
[[0.9096372 0.9684239 0.94743943 0.9766346 ]]
[[0.90996915 0.96855015 0.9476412 0.9767304 ]]
[[0.91029954 0.9686756 0.9478419 0.9768256 ]]
[[0.9106285 0.9688005 0.9480416 0.97692037]]
[[0.91095597 0.9689247 0.94824034 0.9770145 ]]
[[0.91128194 0.96904814 0.948438 0.9771081 ]]
[[0.9116064 0.96917087 0.94863474 0.9772012 ]]
[[0.9119294 0.96929306 0.9488303 0.9772938 ]]
[[0.91225094 0.9694145 0.94902503 0.97738576]]
[[0.9125711 0.96953523 0.94921875 0.97747725]]
[[0.91288966 0.9696554 0.94941163 0.97756827]]
[[0.9132069 0.9697749 0.9496034 0.9776588]]
[[0.91352266 0.96989375 0.9497942 0.97774875]]
[[0.913837 0.97001195 0.949984 0.9778382 ]]
[[0.91414994 0.9701295 0.9501729 0.9779272 ]]
[[0.91446143 0.97024643 0.95036095 0.9780156 ]]
[[0.91477156 0.9703627 0.9505479 0.97810364]]
[[0.9150802 0.97047836 0.95073396 0.97819114]]
[[0.9153876 0.97059345 0.9509191 0.97827804]]
[[0.9156934 0.9707079 0.9511034 0.97836465]]
[[0.915998 0.9708217 0.9512866 0.9784506]]
[[0.9163012 0.9709348 0.95146906 0.9785362 ]]
[[0.9166029 0.97104746 0.9516505 0.97862124]]
[[0.9169033 0.9711594 0.95183104 0.9787058 ]]
[[0.91720235 0.97127086 0.9520107 0.97879 ]]
[[0.9175001 0.97138166 0.95218956 0.9788736 ]]
[[0.91779643 0.97149175 0.95236737 0.97895676]]
[[0.91809154 0.97160137 0.9525444 0.9790396 ]]
[[0.91838527 0.97171044 0.9527205 0.97912186]]
[[0.9186776 0.9718188 0.95289576 0.9792036 ]]
[[0.9189687 0.9719268 0.95307016 0.979285 ]]
[[0.9192584 0.97203404 0.9532437 0.97936594]]
[[0.9195469 0.97214085 0.95341635 0.9794464 ]]
[[0.91983396 0.97224694 0.9535881 0.97952646]]
[[0.9201199 0.97235256 0.95375913 0.9796061 ]]
[[0.9204044 0.9724576 0.9539292 0.97968525]]
[[0.92068774 0.97256213 0.9540984 0.979764 ]]
[[0.9209698 0.97266597 0.9542669 0.97984225]]
[[0.92125046 0.97276944 0.9544344 0.9799201 ]]
[[0.92152995 0.9728723 0.9546012 0.97999763]]
[[0.9218081 0.97297466 0.9547672 0.98007464]]
[[0.92208517 0.97307634 0.95493233 0.9801513 ]]
[[0.92236084 0.9731776 0.95509666 0.9802274 ]]
[[0.9226354 0.97327834 0.9552602 0.9803033 ]]
[[0.9229086 0.9733785 0.955423 0.9803786]]
[[0.9231806 0.97347826 0.9555848 0.9804536 ]]
[[0.9234514 0.9735773 0.95574594 0.9805282 ]]
[[0.923721 0.97367597 0.9559064 0.9806024 ]]
[[0.9239894 0.9737741 0.9560659 0.98067605]]
[[0.9242567 0.97387165 0.9562247 0.9807494 ]]
[[0.92452264 0.9739688 0.95638275 0.9808224 ]]
[[0.9247874 0.97406536 0.95654 0.980895 ]]
[[0.92505103 0.9741615 0.95669645 0.9809672 ]]
[[0.9253135 0.9742571 0.95685214 0.98103905]]
[[0.9255748 0.9743522 0.95700717 0.98111045]]
[[0.92583483 0.9744468 0.95716137 0.98118144]]
[[0.92609376 0.97454095 0.95731485 0.9812522 ]]
[[0.92635155 0.9746346 0.95746744 0.9813224 ]]
[[0.9266082 0.9747277 0.9576195 0.9813923]]
[[0.9268636 0.9748205 0.9577707 0.98146194]]
[[0.927118 0.9749127 0.9579212 0.98153114]]
[[0.92737114 0.9750044 0.9580711 0.9816 ]]
[[0.9276232 0.9750957 0.9582201 0.9816684]]
[[0.9278742 0.9751864 0.9583685 0.9817364]]
[[0.92812407 0.97527677 0.9585161 0.98180425]]
[[0.92837274 0.9753667 0.958663 0.98187155]]
[[0.9286204 0.97545606 0.95880926 0.9819386 ]]
[[0.92886686 0.9755451 0.9589548 0.98200524]]
[[0.92911226 0.9756336 0.9590996 0.9820716 ]]
[[0.92935663 0.97572166 0.9592438 0.98213756]]
[[0.9295999 0.9758093 0.9593872 0.98220325]]
[[0.92984205 0.9758965 0.95952994 0.98226845]]
[[0.93008316 0.9759832 0.959672 0.9823334 ]]
[[0.9303231 0.9760695 0.9598134 0.9823981]]
[[0.93056214 0.9761553 0.95995414 0.9824623 ]]
[[0.9308 0.9762407 0.9600942 0.9825263]]
[[0.9310368 0.9763258 0.96023357 0.9825899 ]]
[[0.9312727 0.9764104 0.96037227 0.98265314]]
[[0.9315074 0.9764945 0.9605103 0.9827161]]
[[0.93174106 0.97657824 0.96064776 0.9827787 ]]
[[0.93197376 0.9766616 0.96078455 0.982841 ]]
[[0.93220544 0.9767444 0.9609206 0.98290306]]
[[0.93243605 0.97682697 0.9610561 0.9829647 ]]
[[0.9326657 0.976909 0.9611909 0.98302597]]
[[0.93289423 0.9769907 0.96132505 0.98308706]]
[[0.93312186 0.97707194 0.9614586 0.98314774]]
[[0.9333484 0.9771527 0.9615916 0.9832081]]
[[0.933574 0.97723323 0.9617239 0.9832683 ]]
[[0.9337986 0.9773132 0.9618556 0.9833281]]
[[0.93402225 0.977393 0.9619866 0.9833876 ]]
[[0.9342449 0.9774721 0.962117 0.9834468]]
[[0.93446654 0.97755104 0.96224695 0.98350567]]
[[0.9346872 0.97762954 0.9623761 0.98356426]]
[[0.93490684 0.9777076 0.96250474 0.9836225 ]]
[[0.93512565 0.97778535 0.9626328 0.9836805 ]]
[[0.93534344 0.9778626 0.96276015 0.9837382 ]]
[[0.9355602 0.97793955 0.962887 0.98379564]]
[[0.9357761 0.97801614 0.96301323 0.98385274]]
[[0.93599105 0.9780924 0.9631388 0.98390967]]
[[0.9362051 0.9781682 0.9632639 0.9839662]]
[[0.9364182 0.9782437 0.9633884 0.9840224]]
[[0.9366302 0.97831875 0.9635123 0.9840784 ]]
[[0.93684137 0.9783934 0.9636356 0.984134 ]]
[[0.9370517 0.9784678 0.9637584 0.98418945]]
[[0.9372611 0.9785418 0.96388066 0.98424464]]
[[0.93746954 0.9786154 0.96400225 0.98429954]]
[[0.93767715 0.9786887 0.96412337 0.98435414]]
[[0.9378838 0.9787617 0.9642438 0.98440844]]
[[0.9380895 0.9788342 0.96436375 0.9844625 ]]
[[0.9382944 0.9789065 0.9644832 0.9845163]]
[[0.9384984 0.97897834 0.9646021 0.98456985]]
[[0.93870133 0.97904986 0.9647203 0.98462313]]
[[0.9389036 0.9791211 0.9648381 0.98467606]]
[[0.9391049 0.9791919 0.9649553 0.98472875]]
[[0.9393053 0.97926235 0.9650719 0.98478127]]
[[0.939505 0.97933257 0.9651881 0.98483354]]
[[0.9397036 0.9794024 0.9653037 0.98488545]]
[[0.93990153 0.9794718 0.9654188 0.98493713]]
[[0.9400985 0.979541 0.9655334 0.9849886]]
[[0.9402946 0.97960985 0.9656474 0.98503983]]
[[0.94048995 0.9796784 0.9657609 0.98509073]]
[[0.94068444 0.97974646 0.965874 0.9851415 ]]
[[0.94087803 0.97981435 0.96598643 0.98519194]]
[[0.9410709 0.9798819 0.9660985 0.98524207]]
[[0.9412627 0.9799492 0.9662099 0.985292 ]]
[[0.94145393 0.98001605 0.9663208 0.9853418 ]]
[[0.9416442 0.9800826 0.96643126 0.98539126]]
[[0.94183373 0.98014885 0.9665413 0.98544055]]
[[0.94202244 0.98021483 0.9666508 0.98548955]]
[[0.94221026 0.98028046 0.96675974 0.98553824]]
[[0.94239736 0.9803458 0.9668683 0.98558676]]
[[0.9425836 0.98041075 0.9669763 0.98563504]]
[[0.9427691 0.9804755 0.96708375 0.98568314]]
[[0.94295377 0.9805399 0.96719074 0.98573095]]
[[0.9431377 0.98060405 0.96729726 0.98577857]]
[[0.94332075 0.9806678 0.96740335 0.98582596]]
[[0.94350314 0.9807313 0.9675089 0.9858731 ]]
[[0.94368464 0.9807946 0.96761405 0.9859201 ]]
[[0.9438655 0.9808575 0.9677187 0.9859666]]
[[0.94404536 0.9809201 0.9678229 0.9860131 ]]
[[0.94422466 0.9809825 0.9679267 0.98605937]]
[[0.9444031 0.9810445 0.96802986 0.9861055 ]]
[[0.94458085 0.9811063 0.9681327 0.9861513 ]]
[[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 ]]









share|improve this question





























    2















    I am building a linear regression model that maps a numpy array of ones into a numpy array of fives,



    i.e. [1.0,1.0,1.0,1.0] ---> [5.0,5.0,5.0,5.0]



    My network is shown below where you can see that the x placeholders correspond to the inputs and the y placeholders correspond to the outputs. However my model is just converging to 1.0s instead:



    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)


    The results unfortunately converge to a numpy array of ones instead of 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 ]]
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    [[0.9116064 0.96917087 0.94863474 0.9772012 ]]
    [[0.9119294 0.96929306 0.9488303 0.9772938 ]]
    [[0.91225094 0.9694145 0.94902503 0.97738576]]
    [[0.9125711 0.96953523 0.94921875 0.97747725]]
    [[0.91288966 0.9696554 0.94941163 0.97756827]]
    [[0.9132069 0.9697749 0.9496034 0.9776588]]
    [[0.91352266 0.96989375 0.9497942 0.97774875]]
    [[0.913837 0.97001195 0.949984 0.9778382 ]]
    [[0.91414994 0.9701295 0.9501729 0.9779272 ]]
    [[0.91446143 0.97024643 0.95036095 0.9780156 ]]
    [[0.91477156 0.9703627 0.9505479 0.97810364]]
    [[0.9150802 0.97047836 0.95073396 0.97819114]]
    [[0.9153876 0.97059345 0.9509191 0.97827804]]
    [[0.9156934 0.9707079 0.9511034 0.97836465]]
    [[0.915998 0.9708217 0.9512866 0.9784506]]
    [[0.9163012 0.9709348 0.95146906 0.9785362 ]]
    [[0.9166029 0.97104746 0.9516505 0.97862124]]
    [[0.9169033 0.9711594 0.95183104 0.9787058 ]]
    [[0.91720235 0.97127086 0.9520107 0.97879 ]]
    [[0.9175001 0.97138166 0.95218956 0.9788736 ]]
    [[0.91779643 0.97149175 0.95236737 0.97895676]]
    [[0.91809154 0.97160137 0.9525444 0.9790396 ]]
    [[0.91838527 0.97171044 0.9527205 0.97912186]]
    [[0.9186776 0.9718188 0.95289576 0.9792036 ]]
    [[0.9189687 0.9719268 0.95307016 0.979285 ]]
    [[0.9192584 0.97203404 0.9532437 0.97936594]]
    [[0.9195469 0.97214085 0.95341635 0.9794464 ]]
    [[0.91983396 0.97224694 0.9535881 0.97952646]]
    [[0.9201199 0.97235256 0.95375913 0.9796061 ]]
    [[0.9204044 0.9724576 0.9539292 0.97968525]]
    [[0.92068774 0.97256213 0.9540984 0.979764 ]]
    [[0.9209698 0.97266597 0.9542669 0.97984225]]
    [[0.92125046 0.97276944 0.9544344 0.9799201 ]]
    [[0.92152995 0.9728723 0.9546012 0.97999763]]
    [[0.9218081 0.97297466 0.9547672 0.98007464]]
    [[0.92208517 0.97307634 0.95493233 0.9801513 ]]
    [[0.92236084 0.9731776 0.95509666 0.9802274 ]]
    [[0.9226354 0.97327834 0.9552602 0.9803033 ]]
    [[0.9229086 0.9733785 0.955423 0.9803786]]
    [[0.9231806 0.97347826 0.9555848 0.9804536 ]]
    [[0.9234514 0.9735773 0.95574594 0.9805282 ]]
    [[0.923721 0.97367597 0.9559064 0.9806024 ]]
    [[0.9239894 0.9737741 0.9560659 0.98067605]]
    [[0.9242567 0.97387165 0.9562247 0.9807494 ]]
    [[0.92452264 0.9739688 0.95638275 0.9808224 ]]
    [[0.9247874 0.97406536 0.95654 0.980895 ]]
    [[0.92505103 0.9741615 0.95669645 0.9809672 ]]
    [[0.9253135 0.9742571 0.95685214 0.98103905]]
    [[0.9255748 0.9743522 0.95700717 0.98111045]]
    [[0.92583483 0.9744468 0.95716137 0.98118144]]
    [[0.92609376 0.97454095 0.95731485 0.9812522 ]]
    [[0.92635155 0.9746346 0.95746744 0.9813224 ]]
    [[0.9266082 0.9747277 0.9576195 0.9813923]]
    [[0.9268636 0.9748205 0.9577707 0.98146194]]
    [[0.927118 0.9749127 0.9579212 0.98153114]]
    [[0.92737114 0.9750044 0.9580711 0.9816 ]]
    [[0.9276232 0.9750957 0.9582201 0.9816684]]
    [[0.9278742 0.9751864 0.9583685 0.9817364]]
    [[0.92812407 0.97527677 0.9585161 0.98180425]]
    [[0.92837274 0.9753667 0.958663 0.98187155]]
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    [[0.92886686 0.9755451 0.9589548 0.98200524]]
    [[0.92911226 0.9756336 0.9590996 0.9820716 ]]
    [[0.92935663 0.97572166 0.9592438 0.98213756]]
    [[0.9295999 0.9758093 0.9593872 0.98220325]]
    [[0.92984205 0.9758965 0.95952994 0.98226845]]
    [[0.93008316 0.9759832 0.959672 0.9823334 ]]
    [[0.9303231 0.9760695 0.9598134 0.9823981]]
    [[0.93056214 0.9761553 0.95995414 0.9824623 ]]
    [[0.9308 0.9762407 0.9600942 0.9825263]]
    [[0.9310368 0.9763258 0.96023357 0.9825899 ]]
    [[0.9312727 0.9764104 0.96037227 0.98265314]]
    [[0.9315074 0.9764945 0.9605103 0.9827161]]
    [[0.93174106 0.97657824 0.96064776 0.9827787 ]]
    [[0.93197376 0.9766616 0.96078455 0.982841 ]]
    [[0.93220544 0.9767444 0.9609206 0.98290306]]
    [[0.93243605 0.97682697 0.9610561 0.9829647 ]]
    [[0.9326657 0.976909 0.9611909 0.98302597]]
    [[0.93289423 0.9769907 0.96132505 0.98308706]]
    [[0.93312186 0.97707194 0.9614586 0.98314774]]
    [[0.9333484 0.9771527 0.9615916 0.9832081]]
    [[0.933574 0.97723323 0.9617239 0.9832683 ]]
    [[0.9337986 0.9773132 0.9618556 0.9833281]]
    [[0.93402225 0.977393 0.9619866 0.9833876 ]]
    [[0.9342449 0.9774721 0.962117 0.9834468]]
    [[0.93446654 0.97755104 0.96224695 0.98350567]]
    [[0.9346872 0.97762954 0.9623761 0.98356426]]
    [[0.93490684 0.9777076 0.96250474 0.9836225 ]]
    [[0.93512565 0.97778535 0.9626328 0.9836805 ]]
    [[0.93534344 0.9778626 0.96276015 0.9837382 ]]
    [[0.9355602 0.97793955 0.962887 0.98379564]]
    [[0.9357761 0.97801614 0.96301323 0.98385274]]
    [[0.93599105 0.9780924 0.9631388 0.98390967]]
    [[0.9362051 0.9781682 0.9632639 0.9839662]]
    [[0.9364182 0.9782437 0.9633884 0.9840224]]
    [[0.9366302 0.97831875 0.9635123 0.9840784 ]]
    [[0.93684137 0.9783934 0.9636356 0.984134 ]]
    [[0.9370517 0.9784678 0.9637584 0.98418945]]
    [[0.9372611 0.9785418 0.96388066 0.98424464]]
    [[0.93746954 0.9786154 0.96400225 0.98429954]]
    [[0.93767715 0.9786887 0.96412337 0.98435414]]
    [[0.9378838 0.9787617 0.9642438 0.98440844]]
    [[0.9380895 0.9788342 0.96436375 0.9844625 ]]
    [[0.9382944 0.9789065 0.9644832 0.9845163]]
    [[0.9384984 0.97897834 0.9646021 0.98456985]]
    [[0.93870133 0.97904986 0.9647203 0.98462313]]
    [[0.9389036 0.9791211 0.9648381 0.98467606]]
    [[0.9391049 0.9791919 0.9649553 0.98472875]]
    [[0.9393053 0.97926235 0.9650719 0.98478127]]
    [[0.939505 0.97933257 0.9651881 0.98483354]]
    [[0.9397036 0.9794024 0.9653037 0.98488545]]
    [[0.93990153 0.9794718 0.9654188 0.98493713]]
    [[0.9400985 0.979541 0.9655334 0.9849886]]
    [[0.9402946 0.97960985 0.9656474 0.98503983]]
    [[0.94048995 0.9796784 0.9657609 0.98509073]]
    [[0.94068444 0.97974646 0.965874 0.9851415 ]]
    [[0.94087803 0.97981435 0.96598643 0.98519194]]
    [[0.9410709 0.9798819 0.9660985 0.98524207]]
    [[0.9412627 0.9799492 0.9662099 0.985292 ]]
    [[0.94145393 0.98001605 0.9663208 0.9853418 ]]
    [[0.9416442 0.9800826 0.96643126 0.98539126]]
    [[0.94183373 0.98014885 0.9665413 0.98544055]]
    [[0.94202244 0.98021483 0.9666508 0.98548955]]
    [[0.94221026 0.98028046 0.96675974 0.98553824]]
    [[0.94239736 0.9803458 0.9668683 0.98558676]]
    [[0.9425836 0.98041075 0.9669763 0.98563504]]
    [[0.9427691 0.9804755 0.96708375 0.98568314]]
    [[0.94295377 0.9805399 0.96719074 0.98573095]]
    [[0.9431377 0.98060405 0.96729726 0.98577857]]
    [[0.94332075 0.9806678 0.96740335 0.98582596]]
    [[0.94350314 0.9807313 0.9675089 0.9858731 ]]
    [[0.94368464 0.9807946 0.96761405 0.9859201 ]]
    [[0.9438655 0.9808575 0.9677187 0.9859666]]
    [[0.94404536 0.9809201 0.9678229 0.9860131 ]]
    [[0.94422466 0.9809825 0.9679267 0.98605937]]
    [[0.9444031 0.9810445 0.96802986 0.9861055 ]]
    [[0.94458085 0.9811063 0.9681327 0.9861513 ]]
    [[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 ]]









    share|improve this question



























      2












      2








      2








      I am building a linear regression model that maps a numpy array of ones into a numpy array of fives,



      i.e. [1.0,1.0,1.0,1.0] ---> [5.0,5.0,5.0,5.0]



      My network is shown below where you can see that the x placeholders correspond to the inputs and the y placeholders correspond to the outputs. However my model is just converging to 1.0s instead:



      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)


      The results unfortunately converge to a numpy array of ones instead of 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 ]]
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      [[0.92984205 0.9758965 0.95952994 0.98226845]]
      [[0.93008316 0.9759832 0.959672 0.9823334 ]]
      [[0.9303231 0.9760695 0.9598134 0.9823981]]
      [[0.93056214 0.9761553 0.95995414 0.9824623 ]]
      [[0.9308 0.9762407 0.9600942 0.9825263]]
      [[0.9310368 0.9763258 0.96023357 0.9825899 ]]
      [[0.9312727 0.9764104 0.96037227 0.98265314]]
      [[0.9315074 0.9764945 0.9605103 0.9827161]]
      [[0.93174106 0.97657824 0.96064776 0.9827787 ]]
      [[0.93197376 0.9766616 0.96078455 0.982841 ]]
      [[0.93220544 0.9767444 0.9609206 0.98290306]]
      [[0.93243605 0.97682697 0.9610561 0.9829647 ]]
      [[0.9326657 0.976909 0.9611909 0.98302597]]
      [[0.93289423 0.9769907 0.96132505 0.98308706]]
      [[0.93312186 0.97707194 0.9614586 0.98314774]]
      [[0.9333484 0.9771527 0.9615916 0.9832081]]
      [[0.933574 0.97723323 0.9617239 0.9832683 ]]
      [[0.9337986 0.9773132 0.9618556 0.9833281]]
      [[0.93402225 0.977393 0.9619866 0.9833876 ]]
      [[0.9342449 0.9774721 0.962117 0.9834468]]
      [[0.93446654 0.97755104 0.96224695 0.98350567]]
      [[0.9346872 0.97762954 0.9623761 0.98356426]]
      [[0.93490684 0.9777076 0.96250474 0.9836225 ]]
      [[0.93512565 0.97778535 0.9626328 0.9836805 ]]
      [[0.93534344 0.9778626 0.96276015 0.9837382 ]]
      [[0.9355602 0.97793955 0.962887 0.98379564]]
      [[0.9357761 0.97801614 0.96301323 0.98385274]]
      [[0.93599105 0.9780924 0.9631388 0.98390967]]
      [[0.9362051 0.9781682 0.9632639 0.9839662]]
      [[0.9364182 0.9782437 0.9633884 0.9840224]]
      [[0.9366302 0.97831875 0.9635123 0.9840784 ]]
      [[0.93684137 0.9783934 0.9636356 0.984134 ]]
      [[0.9370517 0.9784678 0.9637584 0.98418945]]
      [[0.9372611 0.9785418 0.96388066 0.98424464]]
      [[0.93746954 0.9786154 0.96400225 0.98429954]]
      [[0.93767715 0.9786887 0.96412337 0.98435414]]
      [[0.9378838 0.9787617 0.9642438 0.98440844]]
      [[0.9380895 0.9788342 0.96436375 0.9844625 ]]
      [[0.9382944 0.9789065 0.9644832 0.9845163]]
      [[0.9384984 0.97897834 0.9646021 0.98456985]]
      [[0.93870133 0.97904986 0.9647203 0.98462313]]
      [[0.9389036 0.9791211 0.9648381 0.98467606]]
      [[0.9391049 0.9791919 0.9649553 0.98472875]]
      [[0.9393053 0.97926235 0.9650719 0.98478127]]
      [[0.939505 0.97933257 0.9651881 0.98483354]]
      [[0.9397036 0.9794024 0.9653037 0.98488545]]
      [[0.93990153 0.9794718 0.9654188 0.98493713]]
      [[0.9400985 0.979541 0.9655334 0.9849886]]
      [[0.9402946 0.97960985 0.9656474 0.98503983]]
      [[0.94048995 0.9796784 0.9657609 0.98509073]]
      [[0.94068444 0.97974646 0.965874 0.9851415 ]]
      [[0.94087803 0.97981435 0.96598643 0.98519194]]
      [[0.9410709 0.9798819 0.9660985 0.98524207]]
      [[0.9412627 0.9799492 0.9662099 0.985292 ]]
      [[0.94145393 0.98001605 0.9663208 0.9853418 ]]
      [[0.9416442 0.9800826 0.96643126 0.98539126]]
      [[0.94183373 0.98014885 0.9665413 0.98544055]]
      [[0.94202244 0.98021483 0.9666508 0.98548955]]
      [[0.94221026 0.98028046 0.96675974 0.98553824]]
      [[0.94239736 0.9803458 0.9668683 0.98558676]]
      [[0.9425836 0.98041075 0.9669763 0.98563504]]
      [[0.9427691 0.9804755 0.96708375 0.98568314]]
      [[0.94295377 0.9805399 0.96719074 0.98573095]]
      [[0.9431377 0.98060405 0.96729726 0.98577857]]
      [[0.94332075 0.9806678 0.96740335 0.98582596]]
      [[0.94350314 0.9807313 0.9675089 0.9858731 ]]
      [[0.94368464 0.9807946 0.96761405 0.9859201 ]]
      [[0.9438655 0.9808575 0.9677187 0.9859666]]
      [[0.94404536 0.9809201 0.9678229 0.9860131 ]]
      [[0.94422466 0.9809825 0.9679267 0.98605937]]
      [[0.9444031 0.9810445 0.96802986 0.9861055 ]]
      [[0.94458085 0.9811063 0.9681327 0.9861513 ]]
      [[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 ]]









      share|improve this question
















      I am building a linear regression model that maps a numpy array of ones into a numpy array of fives,



      i.e. [1.0,1.0,1.0,1.0] ---> [5.0,5.0,5.0,5.0]



      My network is shown below where you can see that the x placeholders correspond to the inputs and the y placeholders correspond to the outputs. However my model is just converging to 1.0s instead:



      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)


      The results unfortunately converge to a numpy array of ones instead of 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 ]]
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      [[0.94332075 0.9806678 0.96740335 0.98582596]]
      [[0.94350314 0.9807313 0.9675089 0.9858731 ]]
      [[0.94368464 0.9807946 0.96761405 0.9859201 ]]
      [[0.9438655 0.9808575 0.9677187 0.9859666]]
      [[0.94404536 0.9809201 0.9678229 0.9860131 ]]
      [[0.94422466 0.9809825 0.9679267 0.98605937]]
      [[0.9444031 0.9810445 0.96802986 0.9861055 ]]
      [[0.94458085 0.9811063 0.9681327 0.9861513 ]]
      [[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 ]]






      python tensorflow deep-learning linear-regression






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      edited Dec 31 '18 at 0:08







      Mellow

















      asked Dec 30 '18 at 23:53









      MellowMellow

      526




      526
























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          You should not use the sigmoid activation function. Use RELU instead.
          Because the sigmoid function confine the number to range(-1, 1).



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





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            You should not use the sigmoid activation function. Use RELU instead.
            Because the sigmoid function confine the number to range(-1, 1).



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





            share|improve this answer






























              1














              You should not use the sigmoid activation function. Use RELU instead.
              Because the sigmoid function confine the number to range(-1, 1).



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





              share|improve this answer




























                1












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                You should not use the sigmoid activation function. Use RELU instead.
                Because the sigmoid function confine the number to range(-1, 1).



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





                share|improve this answer















                You should not use the sigmoid activation function. Use RELU instead.
                Because the sigmoid function confine the number to range(-1, 1).



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






                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Dec 31 '18 at 4:56









                Shayan Salehian

                71211




                71211










                answered Dec 31 '18 at 1:26









                WEN WENWEN WEN

                8128




                8128






























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