In [9]:

    

import numpy as np
import tensorflow as tf

# Model parameters
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [1,2,3,4]
y_train = [0,-1,-2,-3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
  sess.run([loss, train], {x:x_train, y:y_train})
  #print('%d: %s' % (i, t))
# evaluate training accuracy
  curr_m, curr_W, curr_b, curr_loss  = sess.run([linear_model, W, b, loss], {x:x_train, y:y_train})
  print("i: %d -> W: %s b: %s, result: % loss: %s"%(i, curr_W, curr_b, curr_m, curr_loss))


    

    




i: 0 -> W: [-0.21999997] b: [-0.456], result: [-0.676      -0.89599991 -1.11599994 -1.33599985] loss: 4.01814
i: 1 -> W: [-0.39679998] b: [-0.49552], result: [-0.89231998 -1.28911996 -1.68591988 -2.0827198 ] loss: 1.81987
i: 2 -> W: [-0.45961601] b: [-0.4965184], result: [-0.95613444 -1.41575038 -1.87536645 -2.3349824 ] loss: 1.54482
i: 3 -> W: [-0.48454273] b: [-0.48487374], result: [-0.9694165  -1.45395923 -1.93850195 -2.42304468] loss: 1.48251
i: 4 -> W: [-0.49684232] b: [-0.46917531], result: [-0.9660176  -1.46285999 -1.95970225 -2.45654464] loss: 1.4444
i: 5 -> W: [-0.50490189] b: [-0.45227283], result: [-0.95717472 -1.46207666 -1.96697855 -2.47188044] loss: 1.4097
i: 6 -> W: [-0.5115062] b: [-0.43511063], result: [-0.94661683 -1.45812297 -1.96962929 -2.48113537] loss: 1.3761
i: 7 -> W: [-0.51758033] b: [-0.41800055], result: [-0.93558085 -1.45316124 -1.97074163 -2.48832178] loss: 1.34334
i: 8 -> W: [-0.52343202] b: [-0.40104443], result: [-0.92447644 -1.4479084  -1.97134042 -2.49477243] loss: 1.31135
i: 9 -> W: [-0.52916396] b: [-0.38427448], result: [-0.91343844 -1.4426024  -1.97176635 -2.50093031] loss: 1.28013
i: 10 -> W: [-0.53481066] b: [-0.36769974], result: [-0.9025104  -1.43732107 -1.97213173 -2.50694227] loss: 1.24966
i: 11 -> W: [-0.54038435] b: [-0.35132164], result: [-0.89170599 -1.43209028 -1.97247481 -2.51285911] loss: 1.21991
i: 12 -> W: [-0.54588938] b: [-0.33513904], result: [-0.88102841 -1.42691779 -1.97280717 -2.51869655] loss: 1.19086
i: 13 -> W: [-0.55132794] b: [-0.31915003], result: [-0.87047797 -1.42180586 -1.9731338  -2.52446175] loss: 1.16251
i: 14 -> W: [-0.55670118] b: [-0.30335245], result: [-0.86005366 -1.41675484 -1.97345603 -2.53015709] loss: 1.13483
i: 15 -> W: [-0.56200999] b: [-0.28774402], result: [-0.84975398 -1.41176403 -1.97377396 -2.53578401] loss: 1.10782
i: 16 -> W: [-0.5672552] b: [-0.27232251], result: [-0.83957767 -1.40683293 -1.97408807 -2.54134321] loss: 1.08144
i: 17 -> W: [-0.57243758] b: [-0.25708568], result: [-0.82952327 -1.40196085 -1.97439849 -2.5468359 ] loss: 1.05569
i: 18 -> W: [-0.57755792] b: [-0.24203131], result: [-0.81958926 -1.39714718 -1.9747051  -2.55226302] loss: 1.03056
i: 19 -> W: [-0.58261693] b: [-0.22715722], result: [-0.80977416 -1.39239109 -1.97500801 -2.55762482] loss: 1.00603
i: 20 -> W: [-0.58761531] b: [-0.21246126], result: [-0.8000766  -1.38769186 -1.97530723 -2.56292248] loss: 0.982074
i: 21 -> W: [-0.59255385] b: [-0.1979413], result: [-0.79049516 -1.38304901 -1.97560287 -2.56815672] loss: 0.958693
i: 22 -> W: [-0.59743327] b: [-0.18359523], result: [-0.78102851 -1.37846172 -1.97589493 -2.57332826] loss: 0.935869
i: 23 -> W: [-0.60225427] b: [-0.16942096], result: [-0.77167523 -1.3739295  -1.97618377 -2.57843804] loss: 0.913588
i: 24 -> W: [-0.60701752] b: [-0.15541643], result: [-0.76243395 -1.36945152 -1.97646904 -2.58348656] loss: 0.891837
i: 25 -> W: [-0.61172372] b: [-0.14157961], result: [-0.75330335 -1.36502707 -1.97675085 -2.58847451] loss: 0.870604
i: 26 -> W: [-0.61637354] b: [-0.1279085], result: [-0.74428201 -1.36065555 -1.97702909 -2.59340262] loss: 0.849877
i: 27 -> W: [-0.62096775] b: [-0.11440112], result: [-0.73536885 -1.35633659 -1.97730434 -2.59827209] loss: 0.829644
i: 28 -> W: [-0.62550688] b: [-0.10105548], result: [-0.72656238 -1.35206926 -1.97757614 -2.6030829 ] loss: 0.809892
i: 29 -> W: [-0.62999165] b: [-0.08786967], result: [-0.71786129 -1.34785295 -1.9778446  -2.60783625] loss: 0.79061
i: 30 -> W: [-0.63442272] b: [-0.07484177], result: [-0.70926452 -1.34368718 -1.97810984 -2.61253262] loss: 0.771787
i: 31 -> W: [-0.63880074] b: [-0.06196988], result: [-0.70077062 -1.33957136 -1.9783721  -2.61717296] loss: 0.753412
i: 32 -> W: [-0.64312631] b: [-0.04925214], result: [-0.69237846 -1.33550477 -1.97863114 -2.62175727] loss: 0.735476
i: 33 -> W: [-0.64740008] b: [-0.03668671], result: [-0.6840868  -1.33148682 -1.97888684 -2.62628698] loss: 0.717965
i: 34 -> W: [-0.65162271] b: [-0.02427176], result: [-0.6758945  -1.32751715 -1.9791398  -2.63076258] loss: 0.700872
i: 35 -> W: [-0.65579474] b: [-0.01200548], result: [-0.66780025 -1.32359493 -1.97938967 -2.63518453] loss: 0.684186
i: 36 -> W: [-0.65991682] b: [ 0.00011391], result: [-0.65980291 -1.31971967 -1.97963643 -2.63955331] loss: 0.667897
i: 37 -> W: [-0.66398954] b: [ 0.01208815], result: [-0.65190136 -1.31589091 -1.97988045 -2.64387012] loss: 0.651996
i: 38 -> W: [-0.66801345] b: [ 0.02391901], result: [-0.64409447 -1.31210792 -1.98012125 -2.64813471] loss: 0.636473
i: 39 -> W: [-0.6719892] b: [ 0.03560818], result: [-0.63638103 -1.30837023 -1.98035944 -2.65234852] loss: 0.62132
i: 40 -> W: [-0.67591733] b: [ 0.04715736], result: [-0.62875998 -1.30467725 -1.98059452 -2.65651202] loss: 0.606528
i: 41 -> W: [-0.67979842] b: [ 0.05856824], result: [-0.62123019 -1.30102861 -1.98082709 -2.66062546] loss: 0.592088
i: 42 -> W: [-0.68363303] b: [ 0.06984247], result: [-0.61379057 -1.2974236  -1.98105657 -2.66468954] loss: 0.577992
i: 43 -> W: [-0.68742174] b: [ 0.08098167], result: [-0.60644007 -1.29386187 -1.98128343 -2.66870522] loss: 0.564231
i: 44 -> W: [-0.69116503] b: [ 0.09198748], result: [-0.59917754 -1.29034257 -1.98150766 -2.67267275] loss: 0.550798
i: 45 -> W: [-0.6948635] b: [ 0.10286149], result: [-0.59200203 -1.28686547 -1.98172891 -2.67659259] loss: 0.537684
i: 46 -> W: [-0.69851768] b: [ 0.11360528], result: [-0.58491242 -1.2834301  -1.98194766 -2.68046546] loss: 0.524883
i: 47 -> W: [-0.70212811] b: [ 0.12422039], result: [-0.57790774 -1.28003585 -1.98216391 -2.68429208] loss: 0.512387
i: 48 -> W: [-0.70569533] b: [ 0.13470837], result: [-0.57098699 -1.27668226 -1.98237753 -2.68807292] loss: 0.500188
i: 49 -> W: [-0.70921981] b: [ 0.14507076], result: [-0.56414902 -1.27336884 -1.98258853 -2.69180846] loss: 0.48828
i: 50 -> W: [-0.7127021] b: [ 0.15530905], result: [-0.55739307 -1.27009511 -1.98279727 -2.69549942] loss: 0.476655
i: 51 -> W: [-0.71614265] b: [ 0.16542475], result: [-0.55071789 -1.2668606  -1.98300326 -2.69914579] loss: 0.465307
i: 52 -> W: [-0.71954203] b: [ 0.1754193], result: [-0.5441227  -1.26366472 -1.98320675 -2.70274878] loss: 0.454229
i: 53 -> W: [-0.72290069] b: [ 0.18529417], result: [-0.53760654 -1.26050723 -1.98340797 -2.7063086 ] loss: 0.443415
i: 54 -> W: [-0.72621912] b: [ 0.19505078], result: [-0.53116834 -1.2573874  -1.98360658 -2.70982575] loss: 0.432858
i: 55 -> W: [-0.72949779] b: [ 0.20469053], result: [-0.52480727 -1.25430501 -1.98380268 -2.7133007 ] loss: 0.422553
i: 56 -> W: [-0.73273724] b: [ 0.21421485], result: [-0.51852238 -1.25125968 -1.98399687 -2.71673417] loss: 0.412493
i: 57 -> W: [-0.73593783] b: [ 0.22362511], result: [-0.51231271 -1.2482506  -1.98418844 -2.72012615] loss: 0.402672
i: 58 -> W: [-0.73910016] b: [ 0.23292267], result: [-0.50617748 -1.24527764 -1.98437774 -2.72347784] loss: 0.393085
i: 59 -> W: [-0.74222463] b: [ 0.24210888], result: [-0.50011575 -1.24234033 -1.98456502 -2.72678971] loss: 0.383727
i: 60 -> W: [-0.74531162] b: [ 0.25118509], result: [-0.49412653 -1.23943818 -1.98474967 -2.73006129] loss: 0.374591
i: 61 -> W: [-0.74836165] b: [ 0.26015261], result: [-0.48820904 -1.23657072 -1.98493242 -2.73329401] loss: 0.365673
i: 62 -> W: [-0.7513752] b: [ 0.26901272], result: [-0.48236248 -1.23373771 -1.98511291 -2.7364881 ] loss: 0.356967
i: 63 -> W: [-0.75435263] b: [ 0.27776673], result: [-0.47658589 -1.23093855 -1.98529124 -2.73964381] loss: 0.348468
i: 64 -> W: [-0.75729442] b: [ 0.28641593], result: [-0.47087848 -1.2281729  -1.98546731 -2.74276161] loss: 0.340172
i: 65 -> W: [-0.76020098] b: [ 0.29496154], result: [-0.46523944 -1.22544038 -1.98564136 -2.74584246] loss: 0.332073
i: 66 -> W: [-0.76307267] b: [ 0.30340481], result: [-0.45966786 -1.22274053 -1.98581314 -2.74888587] loss: 0.324167
i: 67 -> W: [-0.76591003] b: [ 0.31174695], result: [-0.45416307 -1.2200731  -1.98598301 -2.75189304] loss: 0.31645
i: 68 -> W: [-0.76871341] b: [ 0.3199892], result: [-0.44872421 -1.21743762 -1.98615098 -2.75486445] loss: 0.308916
i: 69 -> W: [-0.77148318] b: [ 0.32813275], result: [-0.44335043 -1.21483362 -1.9863168  -2.7578001 ] loss: 0.301561
i: 70 -> W: [-0.77421981] b: [ 0.33617878], result: [-0.43804103 -1.21226084 -1.98648071 -2.76070046] loss: 0.294382
i: 71 -> W: [-0.77692366] b: [ 0.34412843], result: [-0.43279523 -1.20971894 -1.9866426  -2.76356626] loss: 0.287373
i: 72 -> W: [-0.77959514] b: [ 0.35198289], result: [-0.42761225 -1.20720744 -1.98680258 -2.76639771] loss: 0.280531
i: 73 -> W: [-0.78223461] b: [ 0.3597433], result: [-0.42249131 -1.20472598 -1.98696041 -2.76919508] loss: 0.273853
i: 74 -> W: [-0.78484249] b: [ 0.36741075], result: [-0.41743174 -1.2022742  -1.98711669 -2.7719593 ] loss: 0.267333
i: 75 -> W: [-0.78741914] b: [ 0.37498638], result: [-0.41243276 -1.19985187 -1.98727107 -2.77469015] loss: 0.260968
i: 76 -> W: [-0.78996491] b: [ 0.38247129], result: [-0.40749362 -1.19745851 -1.98742342 -2.77738833] loss: 0.254755
i: 77 -> W: [-0.79248023] b: [ 0.38986656], result: [-0.40261367 -1.19509387 -1.9875741  -2.78005433] loss: 0.24869
i: 78 -> W: [-0.79496539] b: [ 0.39717329], result: [-0.3977921  -1.19275749 -1.98772299 -2.78268814] loss: 0.242769
i: 79 -> W: [-0.7974208] b: [ 0.40439251], result: [-0.39302829 -1.19044912 -1.98786998 -2.78529072] loss: 0.236989
i: 80 -> W: [-0.79984683] b: [ 0.41152528], result: [-0.38832155 -1.18816841 -1.98801517 -2.78786206] loss: 0.231347
i: 81 -> W: [-0.80224377] b: [ 0.41857263], result: [-0.38367113 -1.18591487 -1.9881587  -2.79040241] loss: 0.225839
i: 82 -> W: [-0.80461204] b: [ 0.42553559], result: [-0.37907645 -1.18368852 -1.98830044 -2.79291248] loss: 0.220463
i: 83 -> W: [-0.80695194] b: [ 0.43241516], result: [-0.37453678 -1.18148875 -1.98844063 -2.79539251] loss: 0.215214
i: 84 -> W: [-0.80926383] b: [ 0.43921232], result: [-0.3700515  -1.17931533 -1.98857927 -2.79784298] loss: 0.21009
i: 85 -> W: [-0.81154799] b: [ 0.4459281], result: [-0.3656199  -1.17716789 -1.98871589 -2.80026388] loss: 0.205088
i: 86 -> W: [-0.81380481] b: [ 0.45256343], result: [-0.36124137 -1.17504621 -1.98885095 -2.8026557 ] loss: 0.200206
i: 87 -> W: [-0.81603462] b: [ 0.45911932], result: [-0.3569153  -1.17294991 -1.98898458 -2.80501914] loss: 0.195439
i: 88 -> W: [-0.81823772] b: [ 0.46559671], result: [-0.35264102 -1.17087877 -1.98911643 -2.80735421] loss: 0.190786
i: 89 -> W: [-0.82041442] b: [ 0.47199652], result: [-0.34841791 -1.1688323  -1.98924661 -2.80966115] loss: 0.186244
i: 90 -> W: [-0.82256508] b: [ 0.47831967], result: [-0.3442454  -1.16681051 -1.98937559 -2.81194067] loss: 0.18181
i: 91 -> W: [-0.82468998] b: [ 0.48456711], result: [-0.34012288 -1.1648128  -1.98950291 -2.81419277] loss: 0.177481
i: 92 -> W: [-0.82678944] b: [ 0.49073973], result: [-0.33604971 -1.16283917 -1.98962867 -2.81641793] loss: 0.173256
i: 93 -> W: [-0.82886374] b: [ 0.49683845], result: [-0.33202529 -1.16088903 -1.98975289 -2.81861639] loss: 0.169131
i: 94 -> W: [-0.83091319] b: [ 0.50286412], result: [-0.32804906 -1.15896225 -1.98987556 -2.82078862] loss: 0.165104
i: 95 -> W: [-0.83293808] b: [ 0.50881761], result: [-0.32412046 -1.15705848 -1.98999643 -2.82293463] loss: 0.161174
i: 96 -> W: [-0.83493876] b: [ 0.51469982], result: [-0.32023895 -1.15517771 -1.99011648 -2.82505512] loss: 0.157337
i: 97 -> W: [-0.83691549] b: [ 0.52051157], result: [-0.31640393 -1.15331936 -1.99023485 -2.82715034] loss: 0.153591
i: 98 -> W: [-0.8388685] b: [ 0.52625376], result: [-0.31261474 -1.1514833  -1.99035168 -2.82922029] loss: 0.149934
i: 99 -> W: [-0.84079814] b: [ 0.53192717], result: [-0.30887097 -1.14966917 -1.99046731 -2.83126545] loss: 0.146364
i: 100 -> W: [-0.84270465] b: [ 0.53753263], result: [-0.30517203 -1.14787674 -1.99058127 -2.83328605] loss: 0.14288
i: 101 -> W: [-0.8445884] b: [ 0.54307097], result: [-0.30151743 -1.14610577 -1.99069428 -2.83528256] loss: 0.139478
i: 102 -> W: [-0.84644955] b: [ 0.54854298], result: [-0.29790658 -1.14435613 -1.99080563 -2.83725524] loss: 0.136157
i: 103 -> W: [-0.84828842] b: [ 0.55394948], result: [-0.29433894 -1.14262736 -1.99091566 -2.83920431] loss: 0.132916
i: 104 -> W: [-0.85010529] b: [ 0.55929118], result: [-0.2908141  -1.14091945 -1.99102473 -2.84113002] loss: 0.129751
i: 105 -> W: [-0.85190034] b: [ 0.56456894], result: [-0.2873314  -1.13923168 -1.99113202 -2.84303236] loss: 0.126662
i: 106 -> W: [-0.85367393] b: [ 0.56978351], result: [-0.28389043 -1.13756442 -1.99123836 -2.84491229] loss: 0.123647
i: 107 -> W: [-0.85542625] b: [ 0.57493562], result: [-0.28049064 -1.13591695 -1.99134302 -2.84676933] loss: 0.120703
i: 108 -> W: [-0.85715765] b: [ 0.58002603], result: [-0.27713162 -1.13428926 -1.99144685 -2.84860468] loss: 0.117829
i: 109 -> W: [-0.85886824] b: [ 0.58505547], result: [-0.27381277 -1.13268101 -1.99154937 -2.85041761] loss: 0.115024
i: 110 -> W: [-0.86055839] b: [ 0.59002471], result: [-0.27053368 -1.13109207 -1.99165034 -2.85220885] loss: 0.112286
i: 111 -> W: [-0.86222833] b: [ 0.5949344], result: [-0.26729393 -1.12952232 -1.99175048 -2.85397887] loss: 0.109612
i: 112 -> W: [-0.86387825] b: [ 0.59978533], result: [-0.26409292 -1.12797117 -1.99184942 -2.85572767] loss: 0.107003
i: 113 -> W: [-0.86550838] b: [ 0.60457814], result: [-0.26093024 -1.12643862 -1.99194705 -2.85745525] loss: 0.104455
i: 114 -> W: [-0.86711895] b: [ 0.60931355], result: [-0.25780541 -1.12492442 -1.9920435  -2.85916233] loss: 0.101968
i: 115 -> W: [-0.86871028] b: [ 0.61399227], result: [-0.25471801 -1.12342834 -1.99213862 -2.8608489 ] loss: 0.0995407
i: 116 -> W: [-0.87028253] b: [ 0.61861497], result: [-0.25166756 -1.12195015 -1.99223256 -2.86251521] loss: 0.0971708
i: 117 -> W: [-0.87183601] b: [ 0.6231823], result: [-0.24865371 -1.12048972 -1.99232578 -2.86416173] loss: 0.0948574
i: 118 -> W: [-0.87337089] b: [ 0.6276949], result: [-0.24567598 -1.11904693 -1.99241781 -2.8657887 ] loss: 0.092599
i: 119 -> W: [-0.87488735] b: [ 0.63215351], result: [-0.24273384 -1.11762118 -1.99250841 -2.86739588] loss: 0.0903944
i: 120 -> W: [-0.87638563] b: [ 0.63655871], result: [-0.23982692 -1.11621261 -1.99259806 -2.86898375] loss: 0.0882424
i: 121 -> W: [-0.87786603] b: [ 0.64091116], result: [-0.23695487 -1.11482096 -1.99268699 -2.87055302] loss: 0.0861415
i: 122 -> W: [-0.87932861] b: [ 0.64521146], result: [-0.23411715 -1.11344576 -1.99277425 -2.87210298] loss: 0.0840906
i: 123 -> W: [-0.88077372] b: [ 0.64946026], result: [-0.23131347 -1.11208725 -1.99286079 -2.87363458] loss: 0.0820887
i: 124 -> W: [-0.88220155] b: [ 0.65365815], result: [-0.2285434  -1.11074495 -1.99294639 -2.87514806] loss: 0.0801343
i: 125 -> W: [-0.88361228] b: [ 0.6578058], result: [-0.22580647 -1.10941875 -1.99303114 -2.87664318] loss: 0.0782265
i: 126 -> W: [-0.88500607] b: [ 0.6619038], result: [-0.22310227 -1.10810828 -1.99311447 -2.87812042] loss: 0.0763641
i: 127 -> W: [-0.88638318] b: [ 0.66595268], result: [-0.22043049 -1.10681367 -1.99319696 -2.87958002] loss: 0.074546
i: 128 -> W: [-0.88774377] b: [ 0.66995311], result: [-0.21779066 -1.10553443 -1.99327826 -2.88102198] loss: 0.0727712
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i: 390 -> W: [-0.99522096] b: [ 0.9859491], result: [-0.00927186 -1.00449276 -1.9997139  -2.9949348 ] loss: 0.00013189
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i: 392 -> W: [-0.99533474] b: [ 0.9862836], result: [-0.00905114 -1.00438595 -1.99972081 -2.99505544] loss: 0.000125686
i: 393 -> W: [-0.99539059] b: [ 0.98644787], result: [-0.00894272 -1.00433326 -1.99972391 -2.99511456] loss: 0.000122693
i: 394 -> W: [-0.99544579] b: [ 0.98661017], result: [-0.00883561 -1.0042814  -1.99972725 -2.99517298] loss: 0.000119773
i: 395 -> W: [-0.99550033] b: [ 0.98677051], result: [-0.00872982 -1.00423014 -1.99973047 -2.99523067] loss: 0.000116923
i: 396 -> W: [-0.99555427] b: [ 0.98692894], result: [-0.00862533 -1.0041796  -1.99973392 -2.99528813] loss: 0.000114138
i: 397 -> W: [-0.9956075] b: [ 0.98708546], result: [-0.00852203 -1.00412953 -1.99973714 -2.99534464] loss: 0.00011142
i: 398 -> W: [-0.99566007] b: [ 0.98724014], result: [-0.00841993 -1.00408006 -1.99974012 -2.99540019] loss: 0.000108768
i: 399 -> W: [-0.99571204] b: [ 0.98739296], result: [-0.00831908 -1.00403118 -1.99974322 -2.99545527] loss: 0.000106178
i: 400 -> W: [-0.99576342] b: [ 0.98754394], result: [-0.00821948 -1.0039829  -1.99974644 -2.99550962] loss: 0.000103651
i: 401 -> W: [-0.99581414] b: [ 0.98769313], result: [-0.00812101 -1.0039351  -1.99974942 -2.99556351] loss: 0.000101181
i: 402 -> W: [-0.99586427] b: [ 0.98784053], result: [-0.00802374 -1.00388801 -1.99975216 -2.99561644] loss: 9.87741e-05
i: 403 -> W: [-0.9959138] b: [ 0.98798615], result: [-0.00792766 -1.0038414  -1.99975538 -2.99566913] loss: 9.64204e-05
i: 404 -> W: [-0.99596274] b: [ 0.98813003], result: [-0.00783271 -1.00379539 -1.99975824 -2.99572086] loss: 9.41257e-05
i: 405 -> W: [-0.99601108] b: [ 0.98827219], result: [-0.00773889 -1.00374997 -1.9997611  -2.99577212] loss: 9.18846e-05
i: 406 -> W: [-0.99605888] b: [ 0.98841262], result: [-0.00764626 -1.00370514 -1.99976397 -2.99582291] loss: 8.96972e-05
i: 407 -> W: [-0.99610609] b: [ 0.98855138], result: [-0.00755471 -1.0036608  -1.99976683 -2.99587297] loss: 8.75618e-05
i: 408 -> W: [-0.9961527] b: [ 0.98868847], result: [-0.00746423 -1.00361693 -1.99976969 -2.99592233] loss: 8.54774e-05
i: 409 -> W: [-0.99619877] b: [ 0.98882395], result: [-0.00737482 -1.00357366 -1.99977255 -2.9959712 ] loss: 8.3442e-05
i: 410 -> W: [-0.99624431] b: [ 0.98895782], result: [-0.00728649 -1.00353074 -1.99977493 -2.99601936] loss: 8.14552e-05
i: 411 -> W: [-0.99628931] b: [ 0.98909003], result: [-0.00719929 -1.00348854 -1.99977803 -2.99606729] loss: 7.95152e-05
i: 412 -> W: [-0.99633372] b: [ 0.98922068], result: [-0.00711304 -1.00344682 -1.99978065 -2.99611425] loss: 7.7623e-05
i: 413 -> W: [-0.99637759] b: [ 0.98934978], result: [-0.0070278  -1.00340533 -1.99978304 -2.99616051] loss: 7.57751e-05
i: 414 -> W: [-0.99642098] b: [ 0.98947734], result: [-0.00694364 -1.00336456 -1.99978566 -2.99620652] loss: 7.39709e-05
i: 415 -> W: [-0.99646384] b: [ 0.98960334], result: [-0.00686049 -1.00332427 -1.99978828 -2.99625206] loss: 7.2209e-05
i: 416 -> W: [-0.99650621] b: [ 0.98972785], result: [-0.00677836 -1.00328457 -1.99979079 -2.99629688] loss: 7.04914e-05
i: 417 -> W: [-0.99654806] b: [ 0.98985088], result: [-0.00669718 -1.00324523 -1.99979317 -2.99634123] loss: 6.88131e-05
i: 418 -> W: [-0.99658942] b: [ 0.98997241], result: [-0.00661701 -1.00320649 -1.99979591 -2.99638534] loss: 6.71739e-05
i: 419 -> W: [-0.99663025] b: [ 0.99009252], result: [-0.00653774 -1.00316799 -1.9997983  -2.99642849] loss: 6.55745e-05
i: 420 -> W: [-0.9966706] b: [ 0.99021119], result: [-0.00645941 -1.00312996 -1.99980044 -2.99647117] loss: 6.40132e-05
i: 421 -> W: [-0.99671048] b: [ 0.99032843], result: [-0.00638205 -1.00309253 -1.99980295 -2.99651337] loss: 6.24897e-05
i: 422 -> W: [-0.99674988] b: [ 0.99044424], result: [-0.00630563 -1.00305557 -1.99980545 -2.99655533] loss: 6.10012e-05
i: 423 -> W: [-0.9967888] b: [ 0.99055868], result: [-0.00623012 -1.00301886 -1.99980783 -2.99659657] loss: 5.95481e-05
i: 424 -> W: [-0.99682724] b: [ 0.99067175], result: [-0.00615549 -1.00298274 -1.9998101  -2.99663734] loss: 5.81303e-05
i: 425 -> W: [-0.99686521] b: [ 0.99078345], result: [-0.00608176 -1.00294697 -1.99981213 -2.9966774 ] loss: 5.67474e-05
i: 426 -> W: [-0.99690276] b: [ 0.99089384], result: [-0.00600892 -1.00291169 -1.99981451 -2.99671721] loss: 5.53962e-05
i: 427 -> W: [-0.9969399] b: [ 0.99100292], result: [-0.00593698 -1.00287688 -1.99981678 -2.99675655] loss: 5.40777e-05
i: 428 -> W: [-0.99697655] b: [ 0.99111068], result: [-0.00586587 -1.00284243 -1.99981892 -2.99679565] loss: 5.27885e-05
i: 429 -> W: [-0.99701273] b: [ 0.99121714], result: [-0.0057956  -1.00280833 -1.99982119 -2.9968338 ] loss: 5.15325e-05
i: 430 -> W: [-0.9970485] b: [ 0.99132234], result: [-0.00572616 -1.00277472 -1.99982333 -2.99687171] loss: 5.03053e-05
i: 431 -> W: [-0.99708384] b: [ 0.99142623], result: [-0.00565761 -1.00274146 -1.99982524 -2.99690914] loss: 4.91081e-05
i: 432 -> W: [-0.99711877] b: [ 0.99152887], result: [-0.0055899  -1.00270867 -1.9998275  -2.99694633] loss: 4.79385e-05
i: 433 -> W: [-0.99715328] b: [ 0.99163032], result: [-0.00552297 -1.00267625 -1.99982953 -2.99698281] loss: 4.67979e-05
i: 434 -> W: [-0.99718738] b: [ 0.99173057], result: [-0.00545681 -1.00264418 -1.99983156 -2.99701881] loss: 4.56843e-05
i: 435 -> W: [-0.99722105] b: [ 0.99182957], result: [-0.00539148 -1.00261259 -1.99983358 -2.99705458] loss: 4.45969e-05
i: 436 -> W: [-0.99725431] b: [ 0.99192744], result: [-0.00532687 -1.00258112 -1.99983549 -2.99708986] loss: 4.35337e-05
i: 437 -> W: [-0.99728721] b: [ 0.99202412], result: [-0.00526309 -1.00255036 -1.9998374  -2.99712467] loss: 4.24984e-05
i: 438 -> W: [-0.9973197] b: [ 0.99211961], result: [-0.00520009 -1.00251985 -1.99983954 -2.99715924] loss: 4.14862e-05
i: 439 -> W: [-0.99735177] b: [ 0.99221396], result: [-0.0051378  -1.00248957 -1.99984145 -2.9971931 ] loss: 4.04988e-05
i: 440 -> W: [-0.99738348] b: [ 0.99230719], result: [-0.00507629 -1.00245976 -1.99984312 -2.99722672] loss: 3.95349e-05
i: 441 -> W: [-0.99741483] b: [ 0.99239928], result: [-0.00501555 -1.00243044 -1.99984527 -2.99726009] loss: 3.85938e-05
i: 442 -> W: [-0.99744576] b: [ 0.99249029], result: [-0.00495547 -1.00240123 -1.99984694 -2.99729276] loss: 3.76752e-05
i: 443 -> W: [-0.99747634] b: [ 0.99258024], result: [-0.0048961  -1.0023725  -1.99984884 -2.99732518] loss: 3.67781e-05
i: 444 -> W: [-0.99750656] b: [ 0.99266911], result: [-0.00483745 -1.00234401 -1.99985051 -2.99735713] loss: 3.59025e-05
i: 445 -> W: [-0.99753642] b: [ 0.9927569], result: [-0.00477952 -1.002316   -1.99985242 -2.99738884] loss: 3.50476e-05
i: 446 -> W: [-0.99756593] b: [ 0.99284363], result: [-0.0047223  -1.00228822 -1.99985409 -2.99742007] loss: 3.42134e-05
i: 447 -> W: [-0.99759507] b: [ 0.99292934], result: [-0.00466573 -1.0022608  -1.99985588 -2.99745083] loss: 3.33993e-05
i: 448 -> W: [-0.99762392] b: [ 0.99301398], result: [-0.00460994 -1.00223386 -1.99985778 -2.99748182] loss: 3.26032e-05
i: 449 -> W: [-0.99765235] b: [ 0.99309766], result: [-0.00455469 -1.00220704 -1.99985945 -2.99751186] loss: 3.18268e-05
i: 450 -> W: [-0.99768049] b: [ 0.99318033], result: [-0.00450015 -1.00218058 -1.99986124 -2.99754167] loss: 3.10689e-05
i: 451 -> W: [-0.99770826] b: [ 0.99326199], result: [-0.00444627 -1.00215459 -1.99986267 -2.99757099] loss: 3.03305e-05
i: 452 -> W: [-0.99773574] b: [ 0.9933427], result: [-0.00439304 -1.00212884 -1.99986458 -2.99760032] loss: 2.96076e-05
i: 453 -> W: [-0.9977628] b: [ 0.99342245], result: [-0.00434035 -1.00210309 -1.99986601 -2.99762869] loss: 2.89027e-05
i: 454 -> W: [-0.99778962] b: [ 0.99350119], result: [-0.00428843 -1.00207806 -1.99986768 -2.9976573 ] loss: 2.82147e-05
i: 455 -> W: [-0.99781609] b: [ 0.99357903], result: [-0.00423706 -1.00205314 -1.99986923 -2.99768543] loss: 2.75424e-05
i: 456 -> W: [-0.99784225] b: [ 0.99365592], result: [-0.00418633 -1.00202858 -1.99987078 -2.99771309] loss: 2.68872e-05
i: 457 -> W: [-0.99786806] b: [ 0.99373192], result: [-0.00413615 -1.00200415 -1.99987221 -2.99774027] loss: 2.6247e-05
i: 458 -> W: [-0.99789363] b: [ 0.99380696], result: [-0.00408667 -1.0019803  -1.999874   -2.99776745] loss: 2.56227e-05
i: 459 -> W: [-0.99791884] b: [ 0.99388111], result: [-0.00403774 -1.00195658 -1.99987543 -2.99779415] loss: 2.50128e-05
i: 460 -> W: [-0.99794376] b: [ 0.99395436], result: [-0.0039894  -1.0019331  -1.99987674 -2.99782062] loss: 2.44171e-05
i: 461 -> W: [-0.99796838] b: [ 0.99402678], result: [-0.0039416  -1.00190997 -1.99987829 -2.9978466 ] loss: 2.38361e-05
i: 462 -> W: [-0.99799269] b: [ 0.99409831], result: [-0.00389439 -1.00188708 -1.99987972 -2.99787235] loss: 2.32687e-05
i: 463 -> W: [-0.99801677] b: [ 0.994169], result: [-0.00384778 -1.00186455 -1.99988127 -2.9978981 ] loss: 2.2714e-05
i: 464 -> W: [-0.9980405] b: [ 0.99423885], result: [-0.00380164 -1.00184214 -1.9998827  -2.99792314] loss: 2.21731e-05
i: 465 -> W: [-0.99806398] b: [ 0.99430788], result: [-0.00375611 -1.00182009 -1.99988401 -2.99794817] loss: 2.16445e-05
i: 466 -> W: [-0.99808717] b: [ 0.99437606], result: [-0.0037111  -1.00179827 -1.99988544 -2.99797249] loss: 2.113e-05
i: 467 -> W: [-0.99811012] b: [ 0.99444342], result: [-0.0036667  -1.00177681 -1.99988699 -2.99799705] loss: 2.06263e-05
i: 468 -> W: [-0.99813271] b: [ 0.99450999], result: [-0.00362271 -1.00175548 -1.99988818 -2.99802089] loss: 2.01351e-05
i: 469 -> W: [-0.99815506] b: [ 0.99457574], result: [-0.00357932 -1.00173438 -1.99988937 -2.99804449] loss: 1.96558e-05
i: 470 -> W: [-0.99817717] b: [ 0.99464071], result: [-0.00353646 -1.00171363 -1.99989092 -2.99806786] loss: 1.91882e-05
i: 471 -> W: [-0.99819899] b: [ 0.9947049], result: [-0.00349408 -1.00169301 -1.999892   -2.99809098] loss: 1.87309e-05
i: 472 -> W: [-0.99822056] b: [ 0.99476832], result: [-0.00345224 -1.00167274 -1.99989343 -2.99811387] loss: 1.82849e-05
i: 473 -> W: [-0.9982419] b: [ 0.99483097], result: [-0.00341094 -1.00165284 -1.99989474 -2.99813652] loss: 1.785e-05
i: 474 -> W: [-0.99826294] b: [ 0.9948929], result: [-0.00337005 -1.00163293 -1.99989605 -2.99815893] loss: 1.7424e-05
i: 475 -> W: [-0.99828374] b: [ 0.99495405], result: [-0.00332969 -1.00161338 -1.999897   -2.99818087] loss: 1.70097e-05
i: 476 -> W: [-0.99830431] b: [ 0.99501449], result: [-0.00328982 -1.00159407 -1.99989843 -2.9982028 ] loss: 1.66042e-05
i: 477 -> W: [-0.99832463] b: [ 0.99507421], result: [-0.00325042 -1.00157499 -1.99989963 -2.99822426] loss: 1.62092e-05
i: 478 -> W: [-0.99834472] b: [ 0.99513322], result: [-0.0032115  -1.00155616 -1.99990106 -2.99824572] loss: 1.58227e-05
i: 479 -> W: [-0.99836451] b: [ 0.99519151], result: [-0.00317299 -1.00153756 -1.99990201 -2.99826646] loss: 1.54468e-05
i: 480 -> W: [-0.99838412] b: [ 0.99524909], result: [-0.00313503 -1.0015192  -1.99990344 -2.99828744] loss: 1.50786e-05
i: 481 -> W: [-0.99840343] b: [ 0.99530602], result: [-0.00309741 -1.00150084 -1.99990416 -2.9983077 ] loss: 1.47196e-05
i: 482 -> W: [-0.99842256] b: [ 0.99536222], result: [-0.00306034 -1.00148296 -1.99990535 -2.99832797] loss: 1.43695e-05
i: 483 -> W: [-0.99844146] b: [ 0.99541777], result: [-0.00302368 -1.00146508 -1.99990654 -2.998348  ] loss: 1.4027e-05
i: 484 -> W: [-0.99846017] b: [ 0.99547267], result: [ -2.98750401e-03  -1.00144768e+00  -1.99990773e+00  -2.99836802e+00] loss: 1.36928e-05
i: 485 -> W: [-0.99847859] b: [ 0.99552691], result: [ -2.95168161e-03  -1.00143027e+00  -1.99990880e+00  -2.99838734e+00] loss: 1.33671e-05
i: 486 -> W: [-0.99849683] b: [ 0.99558049], result: [ -2.91633606e-03  -1.00141311e+00  -1.99991012e+00  -2.99840689e+00] loss: 1.3048e-05
i: 487 -> W: [-0.99851483] b: [ 0.99563342], result: [ -2.88140774e-03  -1.00139618e+00  -1.99991107e+00  -2.99842596e+00] loss: 1.27373e-05
i: 488 -> W: [-0.99853259] b: [ 0.9956857], result: [ -2.84689665e-03  -1.00137949e+00  -1.99991214e+00  -2.99844456e+00] loss: 1.24349e-05
i: 489 -> W: [-0.99855018] b: [ 0.99573737], result: [ -2.81280279e-03  -1.00136304e+00  -1.99991322e+00  -2.99846339e+00] loss: 1.21384e-05
i: 490 -> W: [-0.99856752] b: [ 0.9957884], result: [ -2.77912617e-03  -1.00134659e+00  -1.99991417e+00  -2.99848175e+00] loss: 1.18493e-05
i: 491 -> W: [-0.99858469] b: [ 0.99583882], result: [ -2.74586678e-03  -1.00133061e+00  -1.99991512e+00  -2.99849987e+00] loss: 1.15679e-05
i: 492 -> W: [-0.99860168] b: [ 0.99588865], result: [ -2.71302462e-03  -1.00131464e+00  -1.99991632e+00  -2.99851799e+00] loss: 1.12921e-05
i: 493 -> W: [-0.99861842] b: [ 0.99593788], result: [ -2.68054008e-03  -1.00129890e+00  -1.99991751e+00  -2.99853587e+00] loss: 1.10229e-05
i: 494 -> W: [-0.99863493] b: [ 0.99598652], result: [ -2.64841318e-03  -1.00128341e+00  -1.99991846e+00  -2.99855328e+00] loss: 1.07609e-05
i: 495 -> W: [-0.99865127] b: [ 0.99603462], result: [ -2.61664391e-03  -1.00126791e+00  -1.99991918e+00  -2.99857044e+00] loss: 1.05046e-05
i: 496 -> W: [-0.99866742] b: [ 0.99608213], result: [ -2.58529186e-03  -1.00125265e+00  -1.99992013e+00  -2.99858761e+00] loss: 1.02541e-05
i: 497 -> W: [-0.99868339] b: [ 0.99612904], result: [ -2.55435705e-03  -1.00123775e+00  -1.99992108e+00  -2.99860454e+00] loss: 1.00103e-05
i: 498 -> W: [-0.99869919] b: [ 0.99617541], result: [ -2.52377987e-03  -1.00122297e+00  -1.99992216e+00  -2.99862146e+00] loss: 9.77154e-06
i: 499 -> W: [-0.99871475] b: [ 0.99622124], result: [ -2.49350071e-03  -1.00120831e+00  -1.99992299e+00  -2.99863768e+00] loss: 9.5394e-06
i: 500 -> W: [-0.99873012] b: [ 0.99626648], result: [ -2.46363878e-03  -1.00119376e+00  -1.99992383e+00  -2.99865389e+00] loss: 9.3124e-06
i: 501 -> W: [-0.99874538] b: [ 0.99631119], result: [ -2.43419409e-03  -1.00117958e+00  -1.99992490e+00  -2.99867034e+00] loss: 9.09034e-06
i: 502 -> W: [-0.9987604] b: [ 0.99635535], result: [ -2.40504742e-03  -1.00116539e+00  -1.99992585e+00  -2.99868631e+00] loss: 8.87366e-06
i: 503 -> W: [-0.99877524] b: [ 0.99639899], result: [ -2.37625837e-03  -1.00115156e+00  -1.99992681e+00  -2.99870205e+00] loss: 8.66273e-06
i: 504 -> W: [-0.99878991] b: [ 0.99644214], result: [ -2.34776735e-03  -1.00113773e+00  -1.99992776e+00  -2.99871755e+00] loss: 8.45635e-06
i: 505 -> W: [-0.99880439] b: [ 0.99648476], result: [ -2.31963396e-03  -1.00112402e+00  -1.99992847e+00  -2.99873281e+00] loss: 8.25503e-06
i: 506 -> W: [-0.9988187] b: [ 0.99652684], result: [ -2.29185820e-03  -1.00111055e+00  -1.99992931e+00  -2.99874783e+00] loss: 8.05888e-06
i: 507 -> W: [-0.99883282] b: [ 0.99656844], result: [ -2.26438046e-03  -1.00109720e+00  -1.99993014e+00  -2.99876285e+00] loss: 7.8667e-06
i: 508 -> W: [-0.99884683] b: [ 0.99660951], result: [ -2.23731995e-03  -1.00108409e+00  -1.99993110e+00  -2.99877787e+00] loss: 7.67921e-06
i: 509 -> W: [-0.9988606] b: [ 0.9966501], result: [ -2.21049786e-03  -1.00107110e+00  -1.99993169e+00  -2.99879217e+00] loss: 7.49706e-06
i: 510 -> W: [-0.99887425] b: [ 0.99669021], result: [ -2.18403339e-03  -1.00105834e+00  -1.99993253e+00  -2.99880672e+00] loss: 7.31857e-06
i: 511 -> W: [-0.99888772] b: [ 0.99672985], result: [ -2.15786695e-03  -1.00104558e+00  -1.99993324e+00  -2.99882102e+00] loss: 7.14409e-06
i: 512 -> W: [-0.99890107] b: [ 0.99676901], result: [ -2.13205814e-03  -1.00103307e+00  -1.99993420e+00  -2.99883533e+00] loss: 6.9737e-06
i: 513 -> W: [-0.99891424] b: [ 0.99680769], result: [ -2.10654736e-03  -1.00102079e+00  -1.99993503e+00  -2.99884939e+00] loss: 6.80767e-06
i: 514 -> W: [-0.99892724] b: [ 0.9968459], result: [ -2.08133459e-03  -1.00100851e+00  -1.99993587e+00  -2.99886298e+00] loss: 6.64597e-06
i: 515 -> W: [-0.99894005] b: [ 0.99688369], result: [ -2.05636024e-03  -1.00099635e+00  -1.99993658e+00  -2.99887657e+00] loss: 6.48745e-06
i: 516 -> W: [-0.99895275] b: [ 0.996921], result: [ -2.03174353e-03  -1.00098443e+00  -1.99993706e+00  -2.99888992e+00] loss: 6.33332e-06
i: 517 -> W: [-0.99896532] b: [ 0.99695784], result: [ -2.00748444e-03  -1.00097275e+00  -1.99993825e+00  -2.99890351e+00] loss: 6.18233e-06
i: 518 -> W: [-0.99897766] b: [ 0.99699426], result: [ -1.98340416e-03  -1.00096107e+00  -1.99993873e+00  -2.99891639e+00] loss: 6.03551e-06
i: 519 -> W: [-0.99898994] b: [ 0.99703026], result: [ -1.95968151e-03  -1.00094962e+00  -1.99993944e+00  -2.99892950e+00] loss: 5.89177e-06
i: 520 -> W: [-0.99900204] b: [ 0.99706584], result: [ -1.93619728e-03  -1.00093818e+00  -1.99994040e+00  -2.99894238e+00] loss: 5.75116e-06
i: 521 -> W: [-0.99901396] b: [ 0.99710101], result: [ -1.91295147e-03  -1.00092697e+00  -1.99994087e+00  -2.99895477e+00] loss: 5.61466e-06
i: 522 -> W: [-0.99902576] b: [ 0.9971357], result: [ -1.89006329e-03  -1.00091577e+00  -1.99994159e+00  -2.99896741e+00] loss: 5.48062e-06
i: 523 -> W: [-0.99903744] b: [ 0.99716997], result: [ -1.86747313e-03  -1.00090492e+00  -1.99994230e+00  -2.99897981e+00] loss: 5.35045e-06
i: 524 -> W: [-0.99904895] b: [ 0.99720389], result: [ -1.84506178e-03  -1.00089407e+00  -1.99994302e+00  -2.99899197e+00] loss: 5.22299e-06
i: 525 -> W: [-0.99906033] b: [ 0.99723738], result: [ -1.82294846e-03  -1.00088334e+00  -1.99994349e+00  -2.99900389e+00] loss: 5.09887e-06
i: 526 -> W: [-0.9990716] b: [ 0.99727046], result: [ -1.80113316e-03  -1.00087273e+00  -1.99994433e+00  -2.99901581e+00] loss: 4.97747e-06
i: 527 -> W: [-0.99908274] b: [ 0.99730313], result: [ -1.77961588e-03  -1.00086236e+00  -1.99994504e+00  -2.99902773e+00] loss: 4.85903e-06
i: 528 -> W: [-0.99909371] b: [ 0.99733543], result: [ -1.75827742e-03  -1.00085199e+00  -1.99994564e+00  -2.99903941e+00] loss: 4.74311e-06
i: 529 -> W: [-0.99910456] b: [ 0.99736732], result: [ -1.73723698e-03  -1.00084186e+00  -1.99994636e+00  -2.99905086e+00] loss: 4.63047e-06
i: 530 -> W: [-0.99911529] b: [ 0.99739885], result: [ -1.71643496e-03  -1.00083172e+00  -1.99994707e+00  -2.99906230e+00] loss: 4.52e-06
i: 531 -> W: [-0.9991259] b: [ 0.99743003], result: [ -1.69587135e-03  -1.00082183e+00  -1.99994755e+00  -2.99907351e+00] loss: 4.41253e-06
i: 532 -> W: [-0.99913639] b: [ 0.99746078], result: [ -1.67560577e-03  -1.00081205e+00  -1.99994826e+00  -2.99908471e+00] loss: 4.30752e-06
i: 533 -> W: [-0.9991467] b: [ 0.99749118], result: [ -1.65551901e-03  -1.00080228e+00  -1.99994898e+00  -2.99909568e+00] loss: 4.2048e-06
i: 534 -> W: [-0.99915689] b: [ 0.99752122], result: [ -1.63567066e-03  -1.00079250e+00  -1.99994946e+00  -2.99910641e+00] loss: 4.10454e-06
i: 535 -> W: [-0.99916703] b: [ 0.9975509], result: [ -1.61612034e-03  -1.00078321e+00  -1.99995017e+00  -2.99911714e+00] loss: 4.00719e-06
i: 536 -> W: [-0.99917698] b: [ 0.99758023], result: [ -1.59674883e-03  -1.00077367e+00  -1.99995065e+00  -2.99912763e+00] loss: 3.91164e-06
i: 537 -> W: [-0.99918687] b: [ 0.9976092], result: [ -1.57767534e-03  -1.00076461e+00  -1.99995136e+00  -2.99913836e+00] loss: 3.81848e-06
i: 538 -> W: [-0.99919659] b: [ 0.99763787], result: [ -1.55872107e-03  -1.00075531e+00  -1.99995196e+00  -2.99914837e+00] loss: 3.72769e-06
i: 539 -> W: [-0.99920619] b: [ 0.99766618], result: [ -1.54000521e-03  -1.00074625e+00  -1.99995255e+00  -2.99915862e+00] loss: 3.63868e-06
i: 540 -> W: [-0.99921572] b: [ 0.99769413], result: [ -1.52158737e-03  -1.00073731e+00  -1.99995315e+00  -2.99916887e+00] loss: 3.55182e-06
i: 541 -> W: [-0.99922508] b: [ 0.99772173], result: [ -1.50334835e-03  -1.00072837e+00  -1.99995351e+00  -2.99917865e+00] loss: 3.46736e-06
i: 542 -> W: [-0.99923438] b: [ 0.99774903], result: [ -1.48534775e-03  -1.00071979e+00  -1.99995399e+00  -2.99918842e+00] loss: 3.38512e-06
i: 543 -> W: [-0.99924356] b: [ 0.99777597], result: [ -1.46758556e-03  -1.00071120e+00  -1.99995470e+00  -2.99919820e+00] loss: 3.30455e-06
i: 544 -> W: [-0.99925262] b: [ 0.99780262], result: [ -1.45000219e-03  -1.00070262e+00  -1.99995530e+00  -2.99920797e+00] loss: 3.22548e-06
i: 545 -> W: [-0.99926156] b: [ 0.99782896], result: [ -1.43259764e-03  -1.00069416e+00  -1.99995565e+00  -2.99921727e+00] loss: 3.14882e-06
i: 546 -> W: [-0.99927044] b: [ 0.99785495], result: [ -1.41549110e-03  -1.00068593e+00  -1.99995637e+00  -2.99922681e+00] loss: 3.07384e-06
i: 547 -> W: [-0.99927914] b: [ 0.99788064], result: [ -1.39850378e-03  -1.00067759e+00  -1.99995685e+00  -2.99923587e+00] loss: 3.00069e-06
i: 548 -> W: [-0.99928778] b: [ 0.99790603], result: [ -1.38175488e-03  -1.00066948e+00  -1.99995732e+00  -2.99924517e+00] loss: 2.92904e-06
i: 549 -> W: [-0.99929631] b: [ 0.99793112], result: [ -1.36518478e-03  -1.00066149e+00  -1.99995792e+00  -2.99925423e+00] loss: 2.85925e-06
i: 550 -> W: [-0.99930471] b: [ 0.99795592], result: [ -1.34879351e-03  -1.00065351e+00  -1.99995816e+00  -2.99926281e+00] loss: 2.79151e-06
i: 551 -> W: [-0.99931306] b: [ 0.99798036], result: [ -1.33270025e-03  -1.00064576e+00  -1.99995875e+00  -2.99927187e+00] loss: 2.72497e-06
i: 552 -> W: [-0.99932128] b: [ 0.99800456], result: [ -1.31672621e-03  -1.00063801e+00  -1.99995935e+00  -2.99928045e+00] loss: 2.66022e-06
i: 553 -> W: [-0.99932945] b: [ 0.99802846], result: [ -1.30099058e-03  -1.00063038e+00  -1.99995971e+00  -2.99928927e+00] loss: 2.59671e-06
i: 554 -> W: [-0.99933749] b: [ 0.99805206], result: [ -1.28543377e-03  -1.00062299e+00  -1.99996042e+00  -2.99929786e+00] loss: 2.53502e-06
i: 555 -> W: [-0.99934542] b: [ 0.99807537], result: [ -1.27005577e-03  -1.00061548e+00  -1.99996102e+00  -2.99930620e+00] loss: 2.47473e-06
i: 556 -> W: [-0.99935323] b: [ 0.99809843], result: [ -1.25479698e-03  -1.00060797e+00  -1.99996138e+00  -2.99931455e+00] loss: 2.41548e-06
i: 557 -> W: [-0.99936098] b: [ 0.9981212], result: [ -1.23977661e-03  -1.00060081e+00  -1.99996161e+00  -2.99932265e+00] loss: 2.3583e-06
i: 558 -> W: [-0.99936867] b: [ 0.99814367], result: [ -1.22499466e-03  -1.00059366e+00  -1.99996233e+00  -2.99933100e+00] loss: 2.30203e-06
i: 559 -> W: [-0.99937618] b: [ 0.99816591], result: [ -1.21027231e-03  -1.00058651e+00  -1.99996257e+00  -2.99933887e+00] loss: 2.24725e-06
i: 560 -> W: [-0.99938363] b: [ 0.9981879], result: [ -1.19572878e-03  -1.00057936e+00  -1.99996293e+00  -2.99934673e+00] loss: 2.19355e-06
i: 561 -> W: [-0.99939102] b: [ 0.9982096], result: [ -1.18142366e-03  -1.00057244e+00  -1.99996340e+00  -2.99935436e+00] loss: 2.14164e-06
i: 562 -> W: [-0.99939835] b: [ 0.99823105], result: [ -1.16729736e-03  -1.00056565e+00  -1.99996412e+00  -2.99936247e+00] loss: 2.09027e-06
i: 563 -> W: [-0.99940556] b: [ 0.99825227], result: [ -1.15329027e-03  -1.00055885e+00  -1.99996436e+00  -2.99937010e+00] loss: 2.04044e-06
i: 564 -> W: [-0.99941266] b: [ 0.99827319], result: [ -1.13946199e-03  -1.00055218e+00  -1.99996495e+00  -2.99937749e+00] loss: 1.99202e-06
i: 565 -> W: [-0.99941969] b: [ 0.99829388], result: [ -1.12581253e-03  -1.00054550e+00  -1.99996519e+00  -2.99938488e+00] loss: 1.94461e-06
i: 566 -> W: [-0.99942666] b: [ 0.99831432], result: [ -1.11234188e-03  -1.00053906e+00  -1.99996567e+00  -2.99939227e+00] loss: 1.89841e-06
i: 567 -> W: [-0.99943352] b: [ 0.99833453], result: [ -1.09899044e-03  -1.00053251e+00  -1.99996603e+00  -2.99939966e+00] loss: 1.8529e-06
i: 568 -> W: [-0.99944031] b: [ 0.99835449], result: [ -1.08581781e-03  -1.00052619e+00  -1.99996662e+00  -2.99940681e+00] loss: 1.80886e-06
i: 569 -> W: [-0.99944699] b: [ 0.99837422], result: [ -1.07276440e-03  -1.00051975e+00  -1.99996686e+00  -2.99941373e+00] loss: 1.76578e-06
i: 570 -> W: [-0.9994536] b: [ 0.99839371], result: [ -1.05988979e-03  -1.00051355e+00  -1.99996710e+00  -2.99942064e+00] loss: 1.72384e-06
i: 571 -> W: [-0.99946016] b: [ 0.99841297], result: [ -1.04719400e-03  -1.00050735e+00  -1.99996746e+00  -2.99942780e+00] loss: 1.6825e-06
i: 572 -> W: [-0.99946666] b: [ 0.99843198], result: [ -1.03467703e-03  -1.00050139e+00  -1.99996805e+00  -2.99943471e+00] loss: 1.64253e-06
i: 573 -> W: [-0.99947304] b: [ 0.99845076], result: [ -1.02227926e-03  -1.00049531e+00  -1.99996829e+00  -2.99944139e+00] loss: 1.60345e-06
i: 574 -> W: [-0.99947935] b: [ 0.99846929], result: [ -1.01006031e-03  -1.00048947e+00  -1.99996877e+00  -2.99944806e+00] loss: 1.56542e-06
i: 575 -> W: [-0.99948561] b: [ 0.99848759], result: [ -9.98020172e-04  -1.00048363e+00  -1.99996936e+00  -2.99945498e+00] loss: 1.52793e-06
i: 576 -> W: [-0.99949175] b: [ 0.99850571], result: [ -9.86039639e-04  -1.00047779e+00  -1.99996960e+00  -2.99946117e+00] loss: 1.49182e-06
i: 577 -> W: [-0.99949783] b: [ 0.99852359], result: [ -9.74237919e-04  -1.00047207e+00  -1.99996984e+00  -2.99946785e+00] loss: 1.45608e-06
i: 578 -> W: [-0.99950385] b: [ 0.9985413], result: [ -9.62555408e-04  -1.00046635e+00  -1.99997020e+00  -2.99947405e+00] loss: 1.42151e-06
i: 579 -> W: [-0.99950981] b: [ 0.99855876], result: [ -9.51051712e-04  -1.00046086e+00  -1.99997067e+00  -2.99948049e+00] loss: 1.38765e-06
i: 580 -> W: [-0.99951565] b: [ 0.99857605], result: [ -9.39607620e-04  -1.00045526e+00  -1.99997103e+00  -2.99948645e+00] loss: 1.3547e-06
i: 581 -> W: [-0.99952149] b: [ 0.99859309], result: [ -9.28401947e-04  -1.00044990e+00  -1.99997139e+00  -2.99949288e+00] loss: 1.32232e-06
i: 582 -> W: [-0.99952722] b: [ 0.99860996], result: [ -9.17255878e-04  -1.00044441e+00  -1.99997163e+00  -2.99949884e+00] loss: 1.29082e-06
i: 583 -> W: [-0.99953288] b: [ 0.99862659], result: [ -9.06288624e-04  -1.00043917e+00  -1.99997199e+00  -2.99950504e+00] loss: 1.25999e-06
i: 584 -> W: [-0.99953848] b: [ 0.99864304], result: [ -8.95440578e-04  -1.00043392e+00  -1.99997246e+00  -2.99951077e+00] loss: 1.23021e-06
i: 585 -> W: [-0.99954402] b: [ 0.99865931], result: [ -8.84711742e-04  -1.00042868e+00  -1.99997258e+00  -2.99951673e+00] loss: 1.20078e-06
i: 586 -> W: [-0.99954951] b: [ 0.99867535], result: [ -8.74161720e-04  -1.00042367e+00  -1.99997330e+00  -2.99952269e+00] loss: 1.1722e-06
i: 587 -> W: [-0.99955487] b: [ 0.9986912], result: [ -8.63671303e-04  -1.00041854e+00  -1.99997342e+00  -2.99952841e+00] loss: 1.14421e-06
i: 588 -> W: [-0.99956018] b: [ 0.99870688], result: [ -8.53300095e-04  -1.00041342e+00  -1.99997377e+00  -2.99953389e+00] loss: 1.11698e-06
i: 589 -> W: [-0.99956542] b: [ 0.99872237], result: [ -8.43048096e-04  -1.00040841e+00  -1.99997401e+00  -2.99953938e+00] loss: 1.09038e-06
i: 590 -> W: [-0.99957061] b: [ 0.99873769], result: [ -8.32915306e-04  -1.00040352e+00  -1.99997413e+00  -2.99954462e+00] loss: 1.06462e-06
i: 591 -> W: [-0.99957579] b: [ 0.99875277], result: [ -8.23020935e-04  -1.00039887e+00  -1.99997449e+00  -2.99955034e+00] loss: 1.03931e-06
i: 592 -> W: [-0.99958086] b: [ 0.99876773], result: [ -8.13126564e-04  -1.00039399e+00  -1.99997485e+00  -2.99955559e+00] loss: 1.01454e-06
i: 593 -> W: [-0.99958593] b: [ 0.99878246], result: [ -8.03470612e-04  -1.00038934e+00  -1.99997544e+00  -2.99956131e+00] loss: 9.90201e-07
i: 594 -> W: [-0.99959087] b: [ 0.99879706], result: [ -7.93814659e-04  -1.00038469e+00  -1.99997556e+00  -2.99956656e+00] loss: 9.66599e-07
i: 595 -> W: [-0.99959576] b: [ 0.99881148], result: [ -7.84277916e-04  -1.00038004e+00  -1.99997592e+00  -2.99957156e+00] loss: 9.43661e-07
i: 596 -> W: [-0.99960059] b: [ 0.99882573], result: [ -7.74860382e-04  -1.00037551e+00  -1.99997592e+00  -2.99957657e+00] loss: 9.2129e-07
i: 597 -> W: [-0.99960542] b: [ 0.9988398], result: [ -7.65621662e-04  -1.00037098e+00  -1.99997640e+00  -2.99958181e+00] loss: 8.99239e-07
i: 598 -> W: [-0.99961013] b: [ 0.99885368], result: [ -7.56442547e-04  -1.00036657e+00  -1.99997663e+00  -2.99958682e+00] loss: 8.77841e-07
i: 599 -> W: [-0.99961478] b: [ 0.99886739], result: [ -7.47382641e-04  -1.00036216e+00  -1.99997699e+00  -2.99959183e+00] loss: 8.56873e-07
i: 600 -> W: [-0.99961936] b: [ 0.99888098], result: [ -7.38382339e-04  -1.00035775e+00  -1.99997699e+00  -2.99959660e+00] loss: 8.36456e-07
i: 601 -> W: [-0.99962395] b: [ 0.99889439], result: [ -7.29560852e-04  -1.00035357e+00  -1.99997735e+00  -2.99960136e+00] loss: 8.16698e-07
i: 602 -> W: [-0.99962848] b: [ 0.99890763], result: [ -7.20858574e-04  -1.00034928e+00  -1.99997783e+00  -2.99960637e+00] loss: 7.97071e-07
i: 603 -> W: [-0.99963289] b: [ 0.99892074], result: [ -7.12156296e-04  -1.00034499e+00  -1.99997807e+00  -2.99961090e+00] loss: 7.78065e-07
i: 604 -> W: [-0.99963731] b: [ 0.99893367], result: [ -7.03632832e-04  -1.00034094e+00  -1.99997818e+00  -2.99961567e+00] loss: 7.59524e-07
i: 605 -> W: [-0.99964166] b: [ 0.99894643], result: [ -6.95228577e-04  -1.00033689e+00  -1.99997854e+00  -2.99962020e+00] loss: 7.41544e-07
i: 606 -> W: [-0.99964595] b: [ 0.99895906], result: [ -6.86883926e-04  -1.00033283e+00  -1.99997878e+00  -2.99962473e+00] loss: 7.23865e-07
i: 607 -> W: [-0.99965018] b: [ 0.99897152], result: [ -6.78658485e-04  -1.00032878e+00  -1.99997902e+00  -2.99962926e+00] loss: 7.06562e-07
i: 608 -> W: [-0.99965435] b: [ 0.99898386], result: [ -6.70492649e-04  -1.00032485e+00  -1.99997926e+00  -2.99963355e+00] loss: 6.898e-07
i: 609 -> W: [-0.99965852] b: [ 0.99899602], result: [ -6.62505627e-04  -1.00032103e+00  -1.99997950e+00  -2.99963808e+00] loss: 6.7338e-07
i: 610 -> W: [-0.99966264] b: [ 0.99900806], result: [ -6.54578209e-04  -1.00031722e+00  -1.99997985e+00  -2.99964237e+00] loss: 6.57402e-07
i: 611 -> W: [-0.99966669] b: [ 0.99901992], result: [ -6.46770000e-04  -1.00031352e+00  -1.99998021e+00  -2.99964690e+00] loss: 6.41676e-07
i: 612 -> W: [-0.99967062] b: [ 0.99903166], result: [ -6.38961792e-04  -1.00030959e+00  -1.99998033e+00  -2.99965096e+00] loss: 6.26335e-07
i: 613 -> W: [-0.99967456] b: [ 0.99904329], result: [ -6.31272793e-04  -1.00030589e+00  -1.99998045e+00  -2.99965501e+00] loss: 6.11476e-07
i: 614 -> W: [-0.99967849] b: [ 0.99905473], result: [ -6.23762608e-04  -1.00030231e+00  -1.99998069e+00  -2.99965930e+00] loss: 5.96924e-07
i: 615 -> W: [-0.99968237] b: [ 0.99906605], result: [ -6.16312027e-04  -1.00029874e+00  -1.99998093e+00  -2.99966335e+00] loss: 5.8278e-07
i: 616 -> W: [-0.99968618] b: [ 0.99907726], result: [ -6.08921051e-04  -1.00029516e+00  -1.99998116e+00  -2.99966741e+00] loss: 5.68879e-07
i: 617 -> W: [-0.99968994] b: [ 0.99908829], result: [ -6.01649284e-04  -1.00029159e+00  -1.99998140e+00  -2.99967146e+00] loss: 5.55289e-07
i: 618 -> W: [-0.99969363] b: [ 0.9990992], result: [ -5.94437122e-04  -1.00028801e+00  -1.99998164e+00  -2.99967527e+00] loss: 5.42089e-07
i: 619 -> W: [-0.99969733] b: [ 0.99910998], result: [ -5.87344170e-04  -1.00028467e+00  -1.99998212e+00  -2.99967933e+00] loss: 5.29162e-07
i: 620 -> W: [-0.9997009] b: [ 0.99912065], result: [ -5.80251217e-04  -1.00028110e+00  -1.99998188e+00  -2.99968290e+00] loss: 5.16585e-07
i: 621 -> W: [-0.99970448] b: [ 0.9991312], result: [ -5.73277473e-04  -1.00027776e+00  -1.99998236e+00  -2.99968672e+00] loss: 5.04253e-07
i: 622 -> W: [-0.999708] b: [ 0.99914163], result: [ -5.66363335e-04  -1.00027442e+00  -1.99998236e+00  -2.99969029e+00] loss: 4.92303e-07
i: 623 -> W: [-0.99971151] b: [ 0.99915189], result: [ -5.59628010e-04  -1.00027108e+00  -1.99998260e+00  -2.99969411e+00] loss: 4.80541e-07
i: 624 -> W: [-0.99971497] b: [ 0.99916202], result: [ -5.52952290e-04  -1.00026798e+00  -1.99998307e+00  -2.99969792e+00] loss: 4.69108e-07
i: 625 -> W: [-0.99971837] b: [ 0.99917203], result: [ -5.46336174e-04  -1.00026464e+00  -1.99998307e+00  -2.99970150e+00] loss: 4.57909e-07
i: 626 -> W: [-0.99972177] b: [ 0.99918193], result: [ -5.39839268e-04  -1.00026155e+00  -1.99998331e+00  -2.99970508e+00] loss: 4.47091e-07
i: 627 -> W: [-0.9997251] b: [ 0.9991917], result: [ -5.33401966e-04  -1.00025845e+00  -1.99998355e+00  -2.99970865e+00] loss: 4.36466e-07
i: 628 -> W: [-0.99972838] b: [ 0.99920136], result: [ -5.27024269e-04  -1.00025535e+00  -1.99998379e+00  -2.99971223e+00] loss: 4.26031e-07
i: 629 -> W: [-0.9997316] b: [ 0.99921095], result: [ -5.20646572e-04  -1.00025225e+00  -1.99998391e+00  -2.99971533e+00] loss: 4.15998e-07
i: 630 -> W: [-0.99973482] b: [ 0.99922037], result: [ -5.14447689e-04  -1.00024927e+00  -1.99998403e+00  -2.99971890e+00] loss: 4.0606e-07
i: 631 -> W: [-0.99973798] b: [ 0.99922973], result: [ -5.08248806e-04  -1.00024629e+00  -1.99998426e+00  -2.99972224e+00] loss: 3.96371e-07
i: 632 -> W: [-0.99974114] b: [ 0.99923897], result: [ -5.02169132e-04  -1.00024331e+00  -1.99998450e+00  -2.99972558e+00] loss: 3.86918e-07
i: 633 -> W: [-0.99974424] b: [ 0.99924809], result: [ -4.96149063e-04  -1.00024033e+00  -1.99998474e+00  -2.99972892e+00] loss: 3.77639e-07
i: 634 -> W: [-0.99974728] b: [ 0.99925709], result: [ -4.90188599e-04  -1.00023746e+00  -1.99998474e+00  -2.99973202e+00] loss: 3.68722e-07
i: 635 -> W: [-0.99975032] b: [ 0.99926597], result: [ -4.84347343e-04  -1.00023460e+00  -1.99998498e+00  -2.99973536e+00] loss: 3.59894e-07
i: 636 -> W: [-0.9997533] b: [ 0.99927473], result: [ -4.78565693e-04  -1.00023186e+00  -1.99998522e+00  -2.99973845e+00] loss: 3.5141e-07
i: 637 -> W: [-0.99975628] b: [ 0.99928343], result: [ -4.72843647e-04  -1.00022912e+00  -1.99998534e+00  -2.99974155e+00] loss: 3.43086e-07
i: 638 -> W: [-0.9997592] b: [ 0.99929202], result: [ -4.67181206e-04  -1.00022638e+00  -1.99998558e+00  -2.99974489e+00] loss: 3.34794e-07
i: 639 -> W: [-0.99976206] b: [ 0.99930048], result: [ -4.61578369e-04  -1.00022364e+00  -1.99998569e+00  -2.99974775e+00] loss: 3.26901e-07
i: 640 -> W: [-0.99976492] b: [ 0.99930882], result: [ -4.56094742e-04  -1.00022101e+00  -1.99998593e+00  -2.99975085e+00] loss: 3.19142e-07
i: 641 -> W: [-0.99976772] b: [ 0.99931711], result: [ -4.50611115e-04  -1.00021839e+00  -1.99998593e+00  -2.99975371e+00] loss: 3.116e-07
i: 642 -> W: [-0.99977052] b: [ 0.99932528], result: [ -4.45246696e-04  -1.00021577e+00  -1.99998617e+00  -2.99975681e+00] loss: 3.04132e-07
i: 643 -> W: [-0.99977326] b: [ 0.99933338], result: [ -4.39882278e-04  -1.00021315e+00  -1.99998641e+00  -2.99975967e+00] loss: 2.96869e-07
i: 644 -> W: [-0.99977601] b: [ 0.99934137], result: [ -4.34637070e-04  -1.00021064e+00  -1.99998677e+00  -2.99976254e+00] loss: 2.89844e-07
i: 645 -> W: [-0.99977869] b: [ 0.99934924], result: [ -4.29451466e-04  -1.00020814e+00  -1.99998677e+00  -2.99976540e+00] loss: 2.82965e-07
i: 646 -> W: [-0.99978131] b: [ 0.99935704], result: [ -4.24265862e-04  -1.00020552e+00  -1.99998689e+00  -2.99976826e+00] loss: 2.76115e-07
i: 647 -> W: [-0.99978393] b: [ 0.99936473], result: [ -4.19199467e-04  -1.00020313e+00  -1.99998701e+00  -2.99977112e+00] loss: 2.69547e-07
i: 648 -> W: [-0.9997865] b: [ 0.99937236], result: [ -4.14133072e-04  -1.00020063e+00  -1.99998724e+00  -2.99977350e+00] loss: 2.63222e-07
i: 649 -> W: [-0.99978906] b: [ 0.99937987], result: [ -4.09185886e-04  -1.00019825e+00  -1.99998736e+00  -2.99977636e+00] loss: 2.56907e-07
i: 650 -> W: [-0.99979162] b: [ 0.99938732], result: [ -4.04298306e-04  -1.00019598e+00  -1.99998760e+00  -2.99977922e+00] loss: 2.50761e-07
i: 651 -> W: [-0.99979413] b: [ 0.99939466], result: [ -3.99470329e-04  -1.00019360e+00  -1.99998784e+00  -2.99978185e+00] loss: 2.44794e-07
i: 652 -> W: [-0.99979657] b: [ 0.99940193], result: [ -3.94642353e-04  -1.00019121e+00  -1.99998772e+00  -2.99978447e+00] loss: 2.38909e-07
i: 653 -> W: [-0.99979901] b: [ 0.99940908], result: [ -3.89933586e-04  -1.00018895e+00  -1.99998796e+00  -2.99978685e+00] loss: 2.33325e-07
i: 654 -> W: [-0.99980146] b: [ 0.99941617], result: [ -3.85284424e-04  -1.00018668e+00  -1.99998832e+00  -2.99978971e+00] loss: 2.27651e-07
i: 655 -> W: [-0.99980378] b: [ 0.99942315], result: [ -3.80635262e-04  -1.00018442e+00  -1.99998820e+00  -2.99979210e+00] loss: 2.22255e-07
i: 656 -> W: [-0.99980611] b: [ 0.99943006], result: [ -3.76045704e-04  -1.00018215e+00  -1.99998820e+00  -2.99979448e+00] loss: 2.16966e-07
i: 657 -> W: [-0.99980843] b: [ 0.99943686], result: [ -3.71575356e-04  -1.00018001e+00  -1.99998856e+00  -2.99979687e+00] loss: 2.11864e-07
i: 658 -> W: [-0.99981076] b: [ 0.99944359], result: [ -3.67164612e-04  -1.00017786e+00  -1.99998879e+00  -2.99979949e+00] loss: 2.06774e-07
i: 659 -> W: [-0.99981302] b: [ 0.99945027], result: [ -3.62753868e-04  -1.00017571e+00  -1.99998879e+00  -2.99980187e+00] loss: 2.01845e-07
i: 660 -> W: [-0.99981529] b: [ 0.99945682], result: [ -3.58462334e-04  -1.00017381e+00  -1.99998903e+00  -2.99980426e+00] loss: 1.97139e-07
i: 661 -> W: [-0.99981749] b: [ 0.99946332], result: [ -3.54170799e-04  -1.00017166e+00  -1.99998927e+00  -2.99980664e+00] loss: 1.92407e-07
i: 662 -> W: [-0.99981964] b: [ 0.99946976], result: [ -3.49879265e-04  -1.00016952e+00  -1.99998903e+00  -2.99980879e+00] loss: 1.87833e-07
i: 663 -> W: [-0.99982184] b: [ 0.99947608], result: [ -3.45766544e-04  -1.00016761e+00  -1.99998939e+00  -2.99981117e+00] loss: 1.83415e-07
i: 664 -> W: [-0.99982399] b: [ 0.99948233], result: [ -3.41653824e-04  -1.00016570e+00  -1.99998951e+00  -2.99981356e+00] loss: 1.79055e-07
i: 665 -> W: [-0.99982607] b: [ 0.99948853], result: [ -3.37541103e-04  -1.00016356e+00  -1.99998975e+00  -2.99981570e+00] loss: 1.74755e-07
i: 666 -> W: [-0.99982816] b: [ 0.99949467], result: [ -3.33487988e-04  -1.00016165e+00  -1.99998987e+00  -2.99981785e+00] loss: 1.70626e-07
i: 667 -> W: [-0.99983019] b: [ 0.99950075], result: [ -3.29434872e-04  -1.00015962e+00  -1.99998975e+00  -2.99981999e+00] loss: 1.66514e-07
i: 668 -> W: [-0.99983221] b: [ 0.99950671], result: [ -3.25500965e-04  -1.00015771e+00  -1.99998999e+00  -2.99982214e+00] loss: 1.62559e-07
i: 669 -> W: [-0.99983424] b: [ 0.99951261], result: [ -3.21626663e-04  -1.00015593e+00  -1.99998999e+00  -2.99982429e+00] loss: 1.58732e-07
i: 670 -> W: [-0.99983621] b: [ 0.99951845], result: [ -3.17752361e-04  -1.00015402e+00  -1.99999022e+00  -2.99982643e+00] loss: 1.5491e-07
i: 671 -> W: [-0.99983817] b: [ 0.99952424], result: [ -3.13937664e-04  -1.00015211e+00  -1.99999034e+00  -2.99982834e+00] loss: 1.51255e-07
i: 672 -> W: [-0.99984014] b: [ 0.99952996], result: [ -3.10182571e-04  -1.00015032e+00  -1.99999034e+00  -2.99983072e+00] loss: 1.47558e-07
i: 673 -> W: [-0.99984205] b: [ 0.99953556], result: [ -3.06487083e-04  -1.00014853e+00  -1.99999046e+00  -2.99983263e+00] loss: 1.44101e-07
i: 674 -> W: [-0.99984396] b: [ 0.9995411], result: [ -3.02851200e-04  -1.00014687e+00  -1.99999070e+00  -2.99983478e+00] loss: 1.40674e-07
i: 675 -> W: [-0.9998458] b: [ 0.99954659], result: [ -2.99215317e-04  -1.00014496e+00  -1.99999094e+00  -2.99983668e+00] loss: 1.37297e-07
i: 676 -> W: [-0.99984765] b: [ 0.99955201], result: [ -2.95639038e-04  -1.00014329e+00  -1.99999094e+00  -2.99983859e+00] loss: 1.34069e-07
i: 677 -> W: [-0.99984944] b: [ 0.99955738], result: [ -2.92062759e-04  -1.00014150e+00  -1.99999106e+00  -2.99984026e+00] loss: 1.3092e-07
i: 678 -> W: [-0.99985123] b: [ 0.99956268], result: [ -2.88546085e-04  -1.00013971e+00  -1.99999094e+00  -2.99984217e+00] loss: 1.27772e-07
i: 679 -> W: [-0.99985301] b: [ 0.99956793], result: [ -2.85089016e-04  -1.00013804e+00  -1.99999094e+00  -2.99984407e+00] loss: 1.24727e-07
i: 680 -> W: [-0.9998548] b: [ 0.99957311], result: [ -2.81691551e-04  -1.00013649e+00  -1.99999130e+00  -2.99984598e+00] loss: 1.21778e-07
i: 681 -> W: [-0.99985653] b: [ 0.99957824], result: [ -2.78294086e-04  -1.00013483e+00  -1.99999142e+00  -2.99984789e+00] loss: 1.18837e-07
i: 682 -> W: [-0.99985826] b: [ 0.9995833], result: [ -2.74956226e-04  -1.00013328e+00  -1.99999142e+00  -2.99984980e+00] loss: 1.15998e-07
i: 683 -> W: [-0.99985999] b: [ 0.99958831], result: [ -2.71677971e-04  -1.00013161e+00  -1.99999166e+00  -2.99985170e+00] loss: 1.13191e-07
i: 684 -> W: [-0.99986166] b: [ 0.99959326], result: [ -2.68399715e-04  -1.00013006e+00  -1.99999166e+00  -2.99985337e+00] loss: 1.10523e-07
i: 685 -> W: [-0.99986333] b: [ 0.99959815], result: [ -2.65181065e-04  -1.00012851e+00  -1.99999177e+00  -2.99985504e+00] loss: 1.07916e-07
i: 686 -> W: [-0.999865] b: [ 0.99960297], result: [ -2.62022018e-04  -1.00012708e+00  -1.99999189e+00  -2.99985695e+00] loss: 1.05333e-07
i: 687 -> W: [-0.9998666] b: [ 0.99960774], result: [ -2.58862972e-04  -1.00012541e+00  -1.99999213e+00  -2.99985862e+00] loss: 1.02788e-07
i: 688 -> W: [-0.99986821] b: [ 0.99961245], result: [ -2.55763531e-04  -1.00012398e+00  -1.99999225e+00  -2.99986029e+00] loss: 1.00365e-07
i: 689 -> W: [-0.99986976] b: [ 0.9996171], result: [ -2.52664089e-04  -1.00012243e+00  -1.99999213e+00  -2.99986196e+00] loss: 9.79459e-08
i: 690 -> W: [-0.99987131] b: [ 0.99962169], result: [ -2.49624252e-04  -1.00012088e+00  -1.99999237e+00  -2.99986362e+00] loss: 9.55803e-08
i: 691 -> W: [-0.99987286] b: [ 0.99962622], result: [ -2.46644020e-04  -1.00011945e+00  -1.99999237e+00  -2.99986529e+00] loss: 9.33051e-08
i: 692 -> W: [-0.99987441] b: [ 0.99963069], result: [ -2.43723392e-04  -1.00011814e+00  -1.99999261e+00  -2.99986696e+00] loss: 9.11109e-08
i: 693 -> W: [-0.9998759] b: [ 0.9996351], result: [ -2.40802765e-04  -1.00011671e+00  -1.99999249e+00  -2.99986839e+00] loss: 8.89831e-08
i: 694 -> W: [-0.99987739] b: [ 0.99963945], result: [ -2.37941742e-04  -1.00011539e+00  -1.99999261e+00  -2.99987006e+00] loss: 8.68707e-08
i: 695 -> W: [-0.99987888] b: [ 0.99964374], result: [ -2.35140324e-04  -1.00011396e+00  -1.99999285e+00  -2.99987173e+00] loss: 8.47829e-08
i: 696 -> W: [-0.99988031] b: [ 0.99964803], result: [ -2.32279301e-04  -1.00011253e+00  -1.99999285e+00  -2.99987316e+00] loss: 8.27567e-08
i: 697 -> W: [-0.99988174] b: [ 0.99965227], result: [ -2.29477882e-04  -1.00011122e+00  -1.99999297e+00  -2.99987459e+00] loss: 8.08072e-08
i: 698 -> W: [-0.99988317] b: [ 0.99965644], result: [ -2.26736069e-04  -1.00010991e+00  -1.99999309e+00  -2.99987626e+00] loss: 7.88489e-08
i: 699 -> W: [-0.99988455] b: [ 0.99966055], result: [ -2.23994255e-04  -1.00010848e+00  -1.99999309e+00  -2.99987769e+00] loss: 7.69487e-08
i: 700 -> W: [-0.99988592] b: [ 0.9996646], result: [ -2.21312046e-04  -1.00010729e+00  -1.99999309e+00  -2.99987912e+00] loss: 7.51492e-08
i: 701 -> W: [-0.99988729] b: [ 0.9996686], result: [ -2.18689442e-04  -1.00010598e+00  -1.99999332e+00  -2.99988055e+00] loss: 7.33685e-08
i: 702 -> W: [-0.99988866] b: [ 0.99967259], result: [ -2.16066837e-04  -1.00010467e+00  -1.99999332e+00  -2.99988198e+00] loss: 7.16124e-08
i: 703 -> W: [-0.99988997] b: [ 0.99967653], result: [ -2.13444233e-04  -1.00010347e+00  -1.99999356e+00  -2.99988341e+00] loss: 6.98991e-08
i: 704 -> W: [-0.99989128] b: [ 0.9996804], result: [ -2.10881233e-04  -1.00010216e+00  -1.99999344e+00  -2.99988461e+00] loss: 6.82669e-08
i: 705 -> W: [-0.99989259] b: [ 0.99968421], result: [ -2.08377838e-04  -1.00010097e+00  -1.99999344e+00  -2.99988604e+00] loss: 6.66471e-08
i: 706 -> W: [-0.9998939] b: [ 0.99968797], result: [ -2.05934048e-04  -1.00009990e+00  -1.99999380e+00  -2.99988770e+00] loss: 6.50369e-08
i: 707 -> W: [-0.99989516] b: [ 0.99969172], result: [ -2.03430653e-04  -1.00009859e+00  -1.99999380e+00  -2.99988890e+00] loss: 6.34856e-08
i: 708 -> W: [-0.99989641] b: [ 0.99969542], result: [ -2.00986862e-04  -1.00009739e+00  -1.99999368e+00  -2.99989033e+00] loss: 6.19493e-08
i: 709 -> W: [-0.99989766] b: [ 0.99969906], result: [ -1.98602676e-04  -1.00009632e+00  -1.99999380e+00  -2.99989152e+00] loss: 6.05272e-08
i: 710 -> W: [-0.99989891] b: [ 0.99970263], result: [ -1.96278095e-04  -1.00009513e+00  -1.99999404e+00  -2.99989295e+00] loss: 5.90698e-08
i: 711 -> W: [-0.9999001] b: [ 0.99970621], result: [ -1.93893909e-04  -1.00009394e+00  -1.99999404e+00  -2.99989414e+00] loss: 5.76604e-08
i: 712 -> W: [-0.99990129] b: [ 0.99970973], result: [ -1.91569328e-04  -1.00009286e+00  -1.99999416e+00  -2.99989557e+00] loss: 5.62617e-08
i: 713 -> W: [-0.99990243] b: [ 0.99971318], result: [ -1.89244747e-04  -1.00009167e+00  -1.99999404e+00  -2.99989653e+00] loss: 5.49596e-08
i: 714 -> W: [-0.99990362] b: [ 0.99971664], result: [ -1.86979771e-04  -1.00009060e+00  -1.99999416e+00  -2.99989796e+00] loss: 5.36165e-08
i: 715 -> W: [-0.99990475] b: [ 0.99972004], result: [ -1.84714794e-04  -1.00008941e+00  -1.99999428e+00  -2.99989891e+00] loss: 5.2365e-08
i: 716 -> W: [-0.99990588] b: [ 0.99972337], result: [ -1.82509422e-04  -1.00008845e+00  -1.99999428e+00  -2.99990010e+00] loss: 5.11459e-08
i: 717 -> W: [-0.99990702] b: [ 0.99972665], result: [ -1.80363655e-04  -1.00008738e+00  -1.99999440e+00  -2.99990129e+00] loss: 4.99405e-08
i: 718 -> W: [-0.99990815] b: [ 0.99972993], result: [ -1.78217888e-04  -1.00008631e+00  -1.99999452e+00  -2.99990273e+00] loss: 4.87031e-08
i: 719 -> W: [-0.99990922] b: [ 0.99973315], result: [ -1.76072121e-04  -1.00008535e+00  -1.99999452e+00  -2.99990368e+00] loss: 4.75945e-08
i: 720 -> W: [-0.9999103] b: [ 0.99973637], result: [ -1.73926353e-04  -1.00008416e+00  -1.99999452e+00  -2.99990487e+00] loss: 4.64132e-08
i: 721 -> W: [-0.99991137] b: [ 0.99973953], result: [ -1.71840191e-04  -1.00008321e+00  -1.99999464e+00  -2.99990606e+00] loss: 4.53056e-08
i: 722 -> W: [-0.99991244] b: [ 0.99974263], result: [ -1.69813633e-04  -1.00008225e+00  -1.99999464e+00  -2.99990702e+00] loss: 4.42771e-08
i: 723 -> W: [-0.99991351] b: [ 0.99974573], result: [ -1.67787075e-04  -1.00008130e+00  -1.99999487e+00  -2.99990845e+00] loss: 4.31705e-08
i: 724 -> W: [-0.99991453] b: [ 0.99974877], result: [ -1.65760517e-04  -1.00008035e+00  -1.99999475e+00  -2.99990940e+00] loss: 4.21679e-08
i: 725 -> W: [-0.99991554] b: [ 0.99975175], result: [ -1.63793564e-04  -1.00007939e+00  -1.99999475e+00  -2.99991035e+00] loss: 4.11954e-08
i: 726 -> W: [-0.99991655] b: [ 0.99975473], result: [ -1.61826611e-04  -1.00007844e+00  -1.99999499e+00  -2.99991155e+00] loss: 4.01897e-08
i: 727 -> W: [-0.99991757] b: [ 0.99975765], result: [ -1.59919262e-04  -1.00007749e+00  -1.99999511e+00  -2.99991274e+00] loss: 3.92167e-08
i: 728 -> W: [-0.99991852] b: [ 0.99976057], result: [ -1.57952309e-04  -1.00007653e+00  -1.99999499e+00  -2.99991345e+00] loss: 3.83214e-08
i: 729 -> W: [-0.99991953] b: [ 0.99976343], result: [ -1.56104565e-04  -1.00007558e+00  -1.99999523e+00  -2.99991465e+00] loss: 3.73888e-08
i: 730 -> W: [-0.99992049] b: [ 0.99976623], result: [ -1.54256821e-04  -1.00007474e+00  -1.99999535e+00  -2.99991560e+00] loss: 3.65269e-08
i: 731 -> W: [-0.99992144] b: [ 0.99976903], result: [ -1.52409077e-04  -1.00007391e+00  -1.99999547e+00  -2.99991679e+00] loss: 3.56353e-08
i: 732 -> W: [-0.99992239] b: [ 0.99977183], result: [ -1.50561333e-04  -1.00007296e+00  -1.99999547e+00  -2.99991775e+00] loss: 3.47776e-08
i: 733 -> W: [-0.99992329] b: [ 0.99977458], result: [ -1.48713589e-04  -1.00007200e+00  -1.99999535e+00  -2.99991846e+00] loss: 3.39703e-08
i: 734 -> W: [-0.99992424] b: [ 0.99977726], result: [ -1.46985054e-04  -1.00007129e+00  -1.99999547e+00  -2.99991965e+00] loss: 3.31626e-08
i: 735 -> W: [-0.99992514] b: [ 0.99977994], result: [ -1.45196915e-04  -1.00007033e+00  -1.99999547e+00  -2.99992061e+00] loss: 3.23528e-08
i: 736 -> W: [-0.99992603] b: [ 0.99978256], result: [ -1.43468380e-04  -1.00006950e+00  -1.99999547e+00  -2.99992156e+00] loss: 3.15866e-08
i: 737 -> W: [-0.99992692] b: [ 0.99978518], result: [ -1.41739845e-04  -1.00006866e+00  -1.99999547e+00  -2.99992251e+00] loss: 3.08296e-08
i: 738 -> W: [-0.99992782] b: [ 0.99978775], result: [ -1.40070915e-04  -1.00006795e+00  -1.99999571e+00  -2.99992347e+00] loss: 3.01126e-08
i: 739 -> W: [-0.99992865] b: [ 0.99979031], result: [ -1.38342381e-04  -1.00006700e+00  -1.99999559e+00  -2.99992418e+00] loss: 2.93947e-08
i: 740 -> W: [-0.99992955] b: [ 0.99979281], result: [ -1.36733055e-04  -1.00006628e+00  -1.99999595e+00  -2.99992537e+00] loss: 2.86743e-08
i: 741 -> W: [-0.99993038] b: [ 0.99979532], result: [ -1.35064125e-04  -1.00006545e+00  -1.99999583e+00  -2.99992609e+00] loss: 2.80055e-08
i: 742 -> W: [-0.99993122] b: [ 0.99979776], result: [ -1.33454800e-04  -1.00006461e+00  -1.99999571e+00  -2.99992704e+00] loss: 2.73258e-08
i: 743 -> W: [-0.99993205] b: [ 0.99980021], result: [ -1.31845474e-04  -1.00006390e+00  -1.99999595e+00  -2.99992800e+00] loss: 2.66667e-08
i: 744 -> W: [-0.99993289] b: [ 0.99980259], result: [ -1.30295753e-04  -1.00006318e+00  -1.99999619e+00  -2.99992895e+00] loss: 2.60313e-08
i: 745 -> W: [-0.99993366] b: [ 0.99980497], result: [ -1.28686428e-04  -1.00006235e+00  -1.99999595e+00  -2.99992967e+00] loss: 2.54105e-08
i: 746 -> W: [-0.99993443] b: [ 0.9998073], result: [ -1.27136707e-04  -1.00006151e+00  -1.99999595e+00  -2.99993038e+00] loss: 2.48106e-08
i: 747 -> W: [-0.99993527] b: [ 0.99980962], result: [ -1.25646591e-04  -1.00006092e+00  -1.99999630e+00  -2.99993134e+00] loss: 2.42263e-08
i: 748 -> W: [-0.99993604] b: [ 0.99981189], result: [ -1.24156475e-04  -1.00006020e+00  -1.99999619e+00  -2.99993229e+00] loss: 2.36383e-08
i: 749 -> W: [-0.99993682] b: [ 0.99981415], result: [ -1.22666359e-04  -1.00005949e+00  -1.99999630e+00  -2.99993324e+00] loss: 2.30557e-08
i: 750 -> W: [-0.99993753] b: [ 0.99981636], result: [ -1.21176243e-04  -1.00005865e+00  -1.99999619e+00  -2.99993372e+00] loss: 2.25313e-08
i: 751 -> W: [-0.99993831] b: [ 0.99981856], result: [ -1.19745731e-04  -1.00005805e+00  -1.99999642e+00  -2.99993467e+00] loss: 2.19898e-08
i: 752 -> W: [-0.99993902] b: [ 0.99982077], result: [ -1.18255615e-04  -1.00005722e+00  -1.99999642e+00  -2.99993539e+00] loss: 2.1446e-08
i: 753 -> W: [-0.99993974] b: [ 0.99982291], result: [ -1.16825104e-04  -1.00005651e+00  -1.99999642e+00  -2.99993610e+00] loss: 2.09365e-08
i: 754 -> W: [-0.99994045] b: [ 0.99982506], result: [ -1.15394592e-04  -1.00005579e+00  -1.99999642e+00  -2.99993682e+00] loss: 2.0433e-08
i: 755 -> W: [-0.99994117] b: [ 0.99982715], result: [ -1.14023685e-04  -1.00005519e+00  -1.99999642e+00  -2.99993753e+00] loss: 1.99625e-08
i: 756 -> W: [-0.99994189] b: [ 0.99982923], result: [ -1.12652779e-04  -1.00005460e+00  -1.99999642e+00  -2.99993825e+00] loss: 1.94975e-08
i: 757 -> W: [-0.9999426] b: [ 0.99983126], result: [ -1.11341476e-04  -1.00005388e+00  -1.99999666e+00  -2.99993920e+00] loss: 1.90076e-08
i: 758 -> W: [-0.99994326] b: [ 0.99983329], result: [ -1.09970570e-04  -1.00005317e+00  -1.99999642e+00  -2.99993968e+00] loss: 1.85716e-08
i: 759 -> W: [-0.99994397] b: [ 0.99983525], result: [ -1.08718872e-04  -1.00005269e+00  -1.99999666e+00  -2.99994063e+00] loss: 1.81316e-08
i: 760 -> W: [-0.99994463] b: [ 0.99983722], result: [ -1.07407570e-04  -1.00005198e+00  -1.99999666e+00  -2.99994135e+00] loss: 1.76889e-08
i: 761 -> W: [-0.99994528] b: [ 0.99983919], result: [ -1.06096268e-04  -1.00005138e+00  -1.99999678e+00  -2.99994183e+00] loss: 1.72908e-08
i: 762 -> W: [-0.99994594] b: [ 0.99984109], result: [ -1.04844570e-04  -1.00005078e+00  -1.99999678e+00  -2.99994278e+00] loss: 1.68559e-08
i: 763 -> W: [-0.99994659] b: [ 0.999843], result: [ -1.03592873e-04  -1.00005019e+00  -1.99999678e+00  -2.99994326e+00] loss: 1.64804e-08
i: 764 -> W: [-0.99994725] b: [ 0.99984485], result: [ -1.02400780e-04  -1.00004959e+00  -1.99999690e+00  -2.99994421e+00] loss: 1.60673e-08
i: 765 -> W: [-0.99994785] b: [ 0.9998467], result: [ -1.01149082e-04  -1.00004900e+00  -1.99999690e+00  -2.99994469e+00] loss: 1.57008e-08
i: 766 -> W: [-0.9999485] b: [ 0.99984854], result: [ -9.99569893e-05  -1.00004840e+00  -1.99999690e+00  -2.99994540e+00] loss: 1.53244e-08
i: 767 -> W: [-0.9999491] b: [ 0.99985033], result: [ -9.87648964e-05  -1.00004792e+00  -1.99999714e+00  -2.99994612e+00] loss: 1.49626e-08
i: 768 -> W: [-0.99994969] b: [ 0.99985212], result: [ -9.75728035e-05  -1.00004721e+00  -1.99999690e+00  -2.99994659e+00] loss: 1.46107e-08
i: 769 -> W: [-0.99995029] b: [ 0.99985391], result: [ -9.63807106e-05  -1.00004673e+00  -1.99999690e+00  -2.99994731e+00] loss: 1.42588e-08
i: 770 -> W: [-0.99995089] b: [ 0.99985564], result: [ -9.52482224e-05  -1.00004613e+00  -1.99999702e+00  -2.99994802e+00] loss: 1.39109e-08
i: 771 -> W: [-0.99995148] b: [ 0.99985737], result: [ -9.41157341e-05  -1.00004554e+00  -1.99999714e+00  -2.99994850e+00] loss: 1.35917e-08
i: 772 -> W: [-0.99995208] b: [ 0.99985909], result: [ -9.29832458e-05  -1.00004506e+00  -1.99999714e+00  -2.99994922e+00] loss: 1.32635e-08
i: 773 -> W: [-0.99995267] b: [ 0.99986076], result: [ -9.19103622e-05  -1.00004458e+00  -1.99999714e+00  -2.99994993e+00] loss: 1.29503e-08
i: 774 -> W: [-0.99995321] b: [ 0.99986243], result: [ -9.07778740e-05  -1.00004399e+00  -1.99999714e+00  -2.99995041e+00] loss: 1.2643e-08
i: 775 -> W: [-0.99995375] b: [ 0.9998641], result: [ -8.96453857e-05  -1.00004339e+00  -1.99999714e+00  -2.99995089e+00] loss: 1.23396e-08
i: 776 -> W: [-0.99995434] b: [ 0.99986571], result: [ -8.86321068e-05  -1.00004292e+00  -1.99999738e+00  -2.99995160e+00] loss: 1.20467e-08
i: 777 -> W: [-0.99995488] b: [ 0.99986732], result: [ -8.75592232e-05  -1.00004244e+00  -1.99999726e+00  -2.99995232e+00] loss: 1.17489e-08
i: 778 -> W: [-0.99995542] b: [ 0.99986893], result: [ -8.64863396e-05  -1.00004196e+00  -1.99999738e+00  -2.99995279e+00] loss: 1.1476e-08
i: 779 -> W: [-0.99995595] b: [ 0.99987048], result: [ -8.54730606e-05  -1.00004148e+00  -1.99999738e+00  -2.99995327e+00] loss: 1.12172e-08
i: 780 -> W: [-0.99995649] b: [ 0.99987203], result: [ -8.44597816e-05  -1.00004101e+00  -1.99999738e+00  -2.99995399e+00] loss: 1.09393e-08
i: 781 -> W: [-0.99995703] b: [ 0.99987358], result: [ -8.34465027e-05  -1.00004053e+00  -1.99999738e+00  -2.99995446e+00] loss: 1.06867e-08
i: 782 -> W: [-0.99995756] b: [ 0.99987507], result: [ -8.24928284e-05  -1.00004005e+00  -1.99999762e+00  -2.99995518e+00] loss: 1.04242e-08
i: 783 -> W: [-0.99995804] b: [ 0.99987656], result: [ -8.14795494e-05  -1.00003958e+00  -1.99999762e+00  -2.99995565e+00] loss: 1.01775e-08
i: 784 -> W: [-0.99995852] b: [ 0.99987805], result: [ -8.04662704e-05  -1.00003898e+00  -1.99999750e+00  -2.99995613e+00] loss: 9.92513e-09
i: 785 -> W: [-0.99995899] b: [ 0.99987954], result: [ -7.94529915e-05  -1.00003839e+00  -1.99999738e+00  -2.99995637e+00] loss: 9.69672e-09
i: 786 -> W: [-0.99995953] b: [ 0.99988097], result: [ -7.85589218e-05  -1.00003815e+00  -1.99999762e+00  -2.99995708e+00] loss: 9.47411e-09
i: 787 -> W: [-0.99996001] b: [ 0.9998824], result: [ -7.76052475e-05  -1.00003767e+00  -1.99999762e+00  -2.99995756e+00] loss: 9.24832e-09
i: 788 -> W: [-0.99996048] b: [ 0.99988383], result: [ -7.66515732e-05  -1.00003719e+00  -1.99999762e+00  -2.99995804e+00] loss: 9.02527e-09
i: 789 -> W: [-0.99996096] b: [ 0.9998852], result: [ -7.57575035e-05  -1.00003672e+00  -1.99999774e+00  -2.99995852e+00] loss: 8.81342e-09
i: 790 -> W: [-0.99996144] b: [ 0.99988657], result: [ -7.48634338e-05  -1.00003624e+00  -1.99999785e+00  -2.99995923e+00] loss: 8.58461e-09
i: 791 -> W: [-0.99996185] b: [ 0.99988794], result: [ -7.39097595e-05  -1.00003576e+00  -1.99999762e+00  -2.99995947e+00] loss: 8.39009e-09
i: 792 -> W: [-0.99996233] b: [ 0.99988925], result: [ -7.30752945e-05  -1.00003541e+00  -1.99999774e+00  -2.99995995e+00] loss: 8.203e-09
i: 793 -> W: [-0.99996281] b: [ 0.99989057], result: [ -7.22408295e-05  -1.00003505e+00  -1.99999785e+00  -2.99996066e+00] loss: 7.99923e-09
i: 794 -> W: [-0.99996322] b: [ 0.99989188], result: [ -7.13467598e-05  -1.00003457e+00  -1.99999774e+00  -2.99996090e+00] loss: 7.81948e-09
i: 795 -> W: [-0.9999637] b: [ 0.99989319], result: [ -7.05122948e-05  -1.00003421e+00  -1.99999785e+00  -2.99996161e+00] loss: 7.62056e-09
i: 796 -> W: [-0.99996412] b: [ 0.9998945], result: [ -6.96182251e-05  -1.00003374e+00  -1.99999774e+00  -2.99996185e+00] loss: 7.44515e-09
i: 797 -> W: [-0.99996454] b: [ 0.99989575], result: [ -6.87837601e-05  -1.00003338e+00  -1.99999785e+00  -2.99996233e+00] loss: 7.26898e-09
i: 798 -> W: [-0.99996495] b: [ 0.999897], result: [ -6.79492950e-05  -1.00003290e+00  -1.99999785e+00  -2.99996281e+00] loss: 7.08758e-09
i: 799 -> W: [-0.99996537] b: [ 0.99989825], result: [ -6.71148300e-05  -1.00003242e+00  -1.99999785e+00  -2.99996328e+00] loss: 6.90848e-09
i: 800 -> W: [-0.99996579] b: [ 0.99989945], result: [ -6.63399696e-05  -1.00003219e+00  -1.99999809e+00  -2.99996376e+00] loss: 6.75391e-09
i: 801 -> W: [-0.9999662] b: [ 0.99990064], result: [ -6.55651093e-05  -1.00003171e+00  -1.99999809e+00  -2.99996424e+00] loss: 6.5869e-09
i: 802 -> W: [-0.99996662] b: [ 0.99990183], result: [ -6.47902489e-05  -1.00003147e+00  -1.99999809e+00  -2.99996471e+00] loss: 6.43695e-09
i: 803 -> W: [-0.99996698] b: [ 0.99990302], result: [ -6.39557838e-05  -1.00003099e+00  -1.99999785e+00  -2.99996495e+00] loss: 6.28393e-09
i: 804 -> W: [-0.9999674] b: [ 0.99990416], result: [ -6.32405281e-05  -1.00003064e+00  -1.99999809e+00  -2.99996543e+00] loss: 6.13675e-09
i: 805 -> W: [-0.99996781] b: [ 0.99990529], result: [ -6.25252724e-05  -1.00003028e+00  -1.99999809e+00  -2.99996591e+00] loss: 5.99227e-09
i: 806 -> W: [-0.99996817] b: [ 0.99990642], result: [ -6.17504120e-05  -1.00002992e+00  -1.99999821e+00  -2.99996614e+00] loss: 5.8578e-09
i: 807 -> W: [-0.99996853] b: [ 0.99990755], result: [ -6.09755516e-05  -1.00002956e+00  -1.99999809e+00  -2.99996662e+00] loss: 5.70981e-09
i: 808 -> W: [-0.99996889] b: [ 0.99990869], result: [ -6.02006912e-05  -1.00002909e+00  -1.99999785e+00  -2.99996686e+00] loss: 5.57306e-09
i: 809 -> W: [-0.9999693] b: [ 0.99990976], result: [ -5.95450401e-05  -1.00002885e+00  -1.99999821e+00  -2.99996758e+00] loss: 5.43243e-09
i: 810 -> W: [-0.99996966] b: [ 0.99991083], result: [ -5.88297844e-05  -1.00002849e+00  -1.99999809e+00  -2.99996781e+00] loss: 5.31229e-09
i: 811 -> W: [-0.99997002] b: [ 0.9999119], result: [ -5.81145287e-05  -1.00002813e+00  -1.99999821e+00  -2.99996805e+00] loss: 5.19266e-09
i: 812 -> W: [-0.99997038] b: [ 0.99991298], result: [ -5.73992729e-05  -1.00002778e+00  -1.99999809e+00  -2.99996853e+00] loss: 5.06025e-09
i: 813 -> W: [-0.99997073] b: [ 0.99991399], result: [ -5.67436218e-05  -1.00002742e+00  -1.99999833e+00  -2.99996901e+00] loss: 4.93503e-09
i: 814 -> W: [-0.99997109] b: [ 0.999915], result: [ -5.60879707e-05  -1.00002718e+00  -1.99999821e+00  -2.99996948e+00] loss: 4.81912e-09
i: 815 -> W: [-0.99997145] b: [ 0.99991602], result: [ -5.54323196e-05  -1.00002694e+00  -1.99999833e+00  -2.99996972e+00] loss: 4.71819e-09
i: 816 -> W: [-0.99997181] b: [ 0.99991703], result: [ -5.47766685e-05  -1.00002658e+00  -1.99999833e+00  -2.99997020e+00] loss: 4.59814e-09
i: 817 -> W: [-0.99997211] b: [ 0.99991804], result: [ -5.40614128e-05  -1.00002623e+00  -1.99999833e+00  -2.99997044e+00] loss: 4.48725e-09
i: 818 -> W: [-0.99997246] b: [ 0.999919], result: [ -5.34653664e-05  -1.00002599e+00  -1.99999857e+00  -2.99997091e+00] loss: 4.38201e-09
i: 819 -> W: [-0.99997276] b: [ 0.99991995], result: [ -5.28097153e-05  -1.00002551e+00  -1.99999833e+00  -2.99997115e+00] loss: 4.2747e-09
i: 820 -> W: [-0.99997312] b: [ 0.9999209], result: [ -5.22136688e-05  -1.00002527e+00  -1.99999857e+00  -2.99997163e+00] loss: 4.17197e-09
i: 821 -> W: [-0.99997342] b: [ 0.99992186], result: [ -5.15580177e-05  -1.00002503e+00  -1.99999857e+00  -2.99997187e+00] loss: 4.07846e-09
i: 822 -> W: [-0.99997371] b: [ 0.99992281], result: [ -5.09023666e-05  -1.00002456e+00  -1.99999833e+00  -2.99997211e+00] loss: 3.97502e-09
i: 823 -> W: [-0.99997407] b: [ 0.99992371], result: [ -5.03659248e-05  -1.00002444e+00  -1.99999857e+00  -2.99997258e+00] loss: 3.88774e-09
i: 824 -> W: [-0.99997437] b: [ 0.9999246], result: [ -4.97698784e-05  -1.00002408e+00  -1.99999857e+00  -2.99997282e+00] loss: 3.79768e-09
i: 825 -> W: [-0.99997467] b: [ 0.99992549], result: [ -4.91738319e-05  -1.00002384e+00  -1.99999845e+00  -2.99997330e+00] loss: 3.70195e-09
i: 826 -> W: [-0.99997497] b: [ 0.99992639], result: [ -4.85777855e-05  -1.00002360e+00  -1.99999857e+00  -2.99997354e+00] loss: 3.61934e-09
i: 827 -> W: [-0.99997526] b: [ 0.99992728], result: [ -4.79817390e-05  -1.00002325e+00  -1.99999857e+00  -2.99997377e+00] loss: 3.53247e-09
i: 828 -> W: [-0.99997556] b: [ 0.99992818], result: [ -4.73856926e-05  -1.00002289e+00  -1.99999833e+00  -2.99997401e+00] loss: 3.44741e-09
i: 829 -> W: [-0.99997586] b: [ 0.99992901], result: [ -4.68492508e-05  -1.00002265e+00  -1.99999857e+00  -2.99997449e+00] loss: 3.36071e-09
i: 830 -> W: [-0.99997616] b: [ 0.99992985], result: [ -4.63128090e-05  -1.00002241e+00  -1.99999857e+00  -2.99997473e+00] loss: 3.28788e-09
i: 831 -> W: [-0.99997646] b: [ 0.99993068], result: [ -4.57763672e-05  -1.00002217e+00  -1.99999881e+00  -2.99997520e+00] loss: 3.20335e-09
i: 832 -> W: [-0.99997669] b: [ 0.99993151], result: [ -4.51803207e-05  -1.00002193e+00  -1.99999857e+00  -2.99997520e+00] loss: 3.13925e-09
i: 833 -> W: [-0.99997699] b: [ 0.99993235], result: [ -4.46438789e-05  -1.00002170e+00  -1.99999857e+00  -2.99997568e+00] loss: 3.05724e-09
i: 834 -> W: [-0.99997729] b: [ 0.99993318], result: [ -4.41074371e-05  -1.00002146e+00  -1.99999857e+00  -2.99997592e+00] loss: 2.9878e-09
i: 835 -> W: [-0.99997759] b: [ 0.99993396], result: [ -4.36306000e-05  -1.00002122e+00  -1.99999881e+00  -2.99997640e+00] loss: 2.91243e-09
i: 836 -> W: [-0.99997783] b: [ 0.99993473], result: [ -4.30941582e-05  -1.00002098e+00  -1.99999881e+00  -2.99997663e+00] loss: 2.84465e-09
i: 837 -> W: [-0.99997807] b: [ 0.99993551], result: [ -4.25577164e-05  -1.00002062e+00  -1.99999869e+00  -2.99997663e+00] loss: 2.78412e-09
i: 838 -> W: [-0.99997836] b: [ 0.99993628], result: [ -4.20808792e-05  -1.00002050e+00  -1.99999881e+00  -2.99997711e+00] loss: 2.7165e-09
i: 839 -> W: [-0.9999786] b: [ 0.99993706], result: [ -4.15444374e-05  -1.00002015e+00  -1.99999881e+00  -2.99997735e+00] loss: 2.64625e-09
i: 840 -> W: [-0.99997884] b: [ 0.99993783], result: [ -4.10079956e-05  -1.00001979e+00  -1.99999881e+00  -2.99997759e+00] loss: 2.57694e-09
i: 841 -> W: [-0.99997908] b: [ 0.99993855], result: [ -4.05311584e-05  -1.00001955e+00  -1.99999881e+00  -2.99997783e+00] loss: 2.51805e-09
i: 842 -> W: [-0.99997932] b: [ 0.99993926], result: [ -4.00543213e-05  -1.00001931e+00  -1.99999881e+00  -2.99997807e+00] loss: 2.45984e-09
i: 843 -> W: [-0.99997956] b: [ 0.99993998], result: [ -3.95774841e-05  -1.00001907e+00  -1.99999881e+00  -2.99997830e+00] loss: 2.40232e-09
i: 844 -> W: [-0.99997979] b: [ 0.99994069], result: [ -3.91006470e-05  -1.00001884e+00  -1.99999881e+00  -2.99997854e+00] loss: 2.34547e-09
i: 845 -> W: [-0.99998003] b: [ 0.99994141], result: [ -3.86238098e-05  -1.00001860e+00  -1.99999881e+00  -2.99997878e+00] loss: 2.28931e-09
i: 846 -> W: [-0.99998027] b: [ 0.99994212], result: [ -3.81469727e-05  -1.00001836e+00  -1.99999881e+00  -2.99997902e+00] loss: 2.23383e-09
i: 847 -> W: [-0.99998051] b: [ 0.99994284], result: [ -3.76701355e-05  -1.00001812e+00  -1.99999881e+00  -2.99997926e+00] loss: 2.17904e-09
i: 848 -> W: [-0.99998075] b: [ 0.99994349], result: [ -3.72529030e-05  -1.00001800e+00  -1.99999881e+00  -2.99997950e+00] loss: 2.13364e-09
i: 849 -> W: [-0.99998099] b: [ 0.99994415], result: [ -3.68356705e-05  -1.00001788e+00  -1.99999881e+00  -2.99997973e+00] loss: 2.08873e-09
i: 850 -> W: [-0.99998122] b: [ 0.99994481], result: [ -3.64184380e-05  -1.00001764e+00  -1.99999893e+00  -2.99997997e+00] loss: 2.03982e-09
i: 851 -> W: [-0.99998146] b: [ 0.99994546], result: [ -3.60012054e-05  -1.00001740e+00  -1.99999905e+00  -2.99998045e+00] loss: 1.98213e-09
i: 852 -> W: [-0.99998164] b: [ 0.99994612], result: [ -3.55243683e-05  -1.00001717e+00  -1.99999881e+00  -2.99998045e+00] loss: 1.94029e-09
i: 853 -> W: [-0.99998188] b: [ 0.99994677], result: [ -3.51071358e-05  -1.00001693e+00  -1.99999881e+00  -2.99998069e+00] loss: 1.89343e-09
i: 854 -> W: [-0.99998212] b: [ 0.99994743], result: [ -3.46899033e-05  -1.00001681e+00  -1.99999893e+00  -2.99998093e+00] loss: 1.85086e-09
i: 855 -> W: [-0.99998236] b: [ 0.99994808], result: [ -3.42726707e-05  -1.00001669e+00  -1.99999905e+00  -2.99998140e+00] loss: 1.79989e-09
i: 856 -> W: [-0.99998254] b: [ 0.99994874], result: [ -3.37958336e-05  -1.00001633e+00  -1.99999881e+00  -2.99998140e+00] loss: 1.75614e-09
i: 857 -> W: [-0.99998277] b: [ 0.99994934], result: [ -3.34382057e-05  -1.00001621e+00  -1.99999893e+00  -2.99998188e+00] loss: 1.71044e-09
i: 858 -> W: [-0.99998295] b: [ 0.99994993], result: [ -3.30209732e-05  -1.00001597e+00  -1.99999905e+00  -2.99998188e+00] loss: 1.67479e-09
i: 859 -> W: [-0.99998313] b: [ 0.99995053], result: [ -3.26037407e-05  -1.00001574e+00  -1.99999893e+00  -2.99998188e+00] loss: 1.64009e-09
i: 860 -> W: [-0.99998337] b: [ 0.99995112], result: [ -3.22461128e-05  -1.00001562e+00  -1.99999905e+00  -2.99998236e+00] loss: 1.59587e-09
i: 861 -> W: [-0.99998355] b: [ 0.99995172], result: [ -3.18288803e-05  -1.00001538e+00  -1.99999893e+00  -2.99998236e+00] loss: 1.56199e-09
i: 862 -> W: [-0.99998379] b: [ 0.99995232], result: [ -3.14712524e-05  -1.00001526e+00  -1.99999905e+00  -2.99998283e+00] loss: 1.51886e-09
i: 863 -> W: [-0.99998397] b: [ 0.99995291], result: [ -3.10540199e-05  -1.00001502e+00  -1.99999893e+00  -2.99998283e+00] loss: 1.48579e-09
i: 864 -> W: [-0.9999842] b: [ 0.99995345], result: [ -3.07559967e-05  -1.00001502e+00  -1.99999905e+00  -2.99998331e+00] loss: 1.45099e-09
i: 865 -> W: [-0.99998438] b: [ 0.99995399], result: [ -3.03983688e-05  -1.00001478e+00  -1.99999928e+00  -2.99998355e+00] loss: 1.41371e-09
i: 866 -> W: [-0.99998456] b: [ 0.99995452], result: [ -3.00407410e-05  -1.00001454e+00  -1.99999928e+00  -2.99998379e+00] loss: 1.37732e-09
i: 867 -> W: [-0.99998474] b: [ 0.99995506], result: [ -2.96831131e-05  -1.00001442e+00  -1.99999917e+00  -2.99998379e+00] loss: 1.35269e-09
i: 868 -> W: [-0.99998492] b: [ 0.99995559], result: [ -2.93254852e-05  -1.00001431e+00  -1.99999905e+00  -2.99998403e+00] loss: 1.3207e-09
i: 869 -> W: [-0.9999851] b: [ 0.99995613], result: [ -2.89678574e-05  -1.00001407e+00  -1.99999905e+00  -2.99998426e+00] loss: 1.28553e-09
i: 870 -> W: [-0.99998528] b: [ 0.99995667], result: [ -2.86102295e-05  -1.00001383e+00  -1.99999928e+00  -2.99998450e+00] loss: 1.25044e-09
i: 871 -> W: [-0.99998546] b: [ 0.9999572], result: [ -2.82526016e-05  -1.00001371e+00  -1.99999917e+00  -2.99998474e+00] loss: 1.21968e-09
i: 872 -> W: [-0.99998564] b: [ 0.99995774], result: [ -2.78949738e-05  -1.00001359e+00  -1.99999905e+00  -2.99998474e+00] loss: 1.19655e-09
i: 873 -> W: [-0.99998581] b: [ 0.99995822], result: [ -2.75969505e-05  -1.00001335e+00  -1.99999928e+00  -2.99998498e+00] loss: 1.16598e-09
i: 874 -> W: [-0.99998599] b: [ 0.99995869], result: [ -2.72989273e-05  -1.00001335e+00  -1.99999928e+00  -2.99998522e+00] loss: 1.14251e-09
i: 875 -> W: [-0.99998611] b: [ 0.99995917], result: [ -2.69412994e-05  -1.00001311e+00  -1.99999905e+00  -2.99998522e+00] loss: 1.1172e-09
i: 876 -> W: [-0.99998629] b: [ 0.99995965], result: [ -2.66432762e-05  -1.00001287e+00  -1.99999928e+00  -2.99998546e+00] loss: 1.08765e-09
i: 877 -> W: [-0.99998647] b: [ 0.99996012], result: [ -2.63452530e-05  -1.00001287e+00  -1.99999928e+00  -2.99998569e+00] loss: 1.06498e-09
i: 878 -> W: [-0.99998659] b: [ 0.9999606], result: [ -2.59876251e-05  -1.00001264e+00  -1.99999905e+00  -2.99998569e+00] loss: 1.04058e-09
i: 879 -> W: [-0.99998677] b: [ 0.99996108], result: [ -2.56896019e-05  -1.00001240e+00  -1.99999928e+00  -2.99998593e+00] loss: 1.01204e-09
i: 880 -> W: [-0.99998695] b: [ 0.99996156], result: [ -2.53915787e-05  -1.00001240e+00  -1.99999928e+00  -2.99998617e+00] loss: 9.9017e-10
i: 881 -> W: [-0.99998707] b: [ 0.99996203], result: [ -2.50339508e-05  -1.00001216e+00  -1.99999905e+00  -2.99998617e+00] loss: 9.66679e-10
i: 882 -> W: [-0.99998724] b: [ 0.99996251], result: [ -2.47359276e-05  -1.00001192e+00  -1.99999928e+00  -2.99998641e+00] loss: 9.39171e-10
i: 883 -> W: [-0.99998742] b: [ 0.99996293], result: [ -2.44975090e-05  -1.00001192e+00  -1.99999940e+00  -2.99998665e+00] loss: 9.20853e-10
i: 884 -> W: [-0.99998754] b: [ 0.9999634], result: [ -2.41398811e-05  -1.00001168e+00  -1.99999917e+00  -2.99998665e+00] loss: 8.98172e-10
i: 885 -> W: [-0.99998772] b: [ 0.99996382], result: [ -2.39014626e-05  -1.00001168e+00  -1.99999952e+00  -2.99998713e+00] loss: 8.73744e-10
i: 886 -> W: [-0.99998784] b: [ 0.99996424], result: [ -2.36034393e-05  -1.00001144e+00  -1.99999928e+00  -2.99998713e+00] loss: 8.54357e-10
i: 887 -> W: [-0.99998796] b: [ 0.99996465], result: [ -2.33054161e-05  -1.00001121e+00  -1.99999905e+00  -2.99998713e+00] loss: 8.35374e-10
i: 888 -> W: [-0.99998814] b: [ 0.99996507], result: [ -2.30669975e-05  -1.00001121e+00  -1.99999940e+00  -2.99998760e+00] loss: 8.11713e-10
i: 889 -> W: [-0.99998826] b: [ 0.99996549], result: [ -2.27689743e-05  -1.00001097e+00  -1.99999928e+00  -2.99998760e+00] loss: 7.92923e-10
i: 890 -> W: [-0.99998838] b: [ 0.99996591], result: [ -2.24709511e-05  -1.00001085e+00  -1.99999928e+00  -2.99998760e+00] loss: 7.7684e-10
i: 891 -> W: [-0.99998856] b: [ 0.99996632], result: [ -2.22325325e-05  -1.00001073e+00  -1.99999928e+00  -2.99998784e+00] loss: 7.57755e-10
i: 892 -> W: [-0.99998868] b: [ 0.99996674], result: [ -2.19345093e-05  -1.00001061e+00  -1.99999940e+00  -2.99998808e+00] loss: 7.36151e-10
i: 893 -> W: [-0.99998879] b: [ 0.99996716], result: [ -2.16364861e-05  -1.00001049e+00  -1.99999928e+00  -2.99998808e+00] loss: 7.20807e-10
i: 894 -> W: [-0.99998891] b: [ 0.99996758], result: [ -2.13384628e-05  -1.00001025e+00  -1.99999905e+00  -2.99998808e+00] loss: 7.03452e-10
i: 895 -> W: [-0.99998909] b: [ 0.99996793], result: [ -2.11596489e-05  -1.00001025e+00  -1.99999940e+00  -2.99998856e+00] loss: 6.84157e-10
i: 896 -> W: [-0.99998921] b: [ 0.99996829], result: [ -2.09212303e-05  -1.00001013e+00  -1.99999928e+00  -2.99998856e+00] loss: 6.7185e-10
i: 897 -> W: [-0.99998933] b: [ 0.99996865], result: [ -2.06828117e-05  -1.00001001e+00  -1.99999940e+00  -2.99998856e+00] loss: 6.59373e-10
i: 898 -> W: [-0.99998945] b: [ 0.99996901], result: [ -2.04443932e-05  -1.00000989e+00  -1.99999928e+00  -2.99998879e+00] loss: 6.4195e-10
i: 899 -> W: [-0.99998957] b: [ 0.99996936], result: [ -2.02059746e-05  -1.00000978e+00  -1.99999940e+00  -2.99998903e+00] loss: 6.24471e-10
i: 900 -> W: [-0.99998969] b: [ 0.99996972], result: [ -1.99675560e-05  -1.00000966e+00  -1.99999928e+00  -2.99998903e+00] loss: 6.12733e-10
i: 901 -> W: [-0.99998981] b: [ 0.99997008], result: [ -1.97291374e-05  -1.00000954e+00  -1.99999940e+00  -2.99998903e+00] loss: 6.00824e-10
i: 902 -> W: [-0.99998993] b: [ 0.99997044], result: [ -1.94907188e-05  -1.00000942e+00  -1.99999928e+00  -2.99998927e+00] loss: 5.84198e-10
i: 903 -> W: [-0.99999005] b: [ 0.99997079], result: [ -1.92523003e-05  -1.00000930e+00  -1.99999940e+00  -2.99998951e+00] loss: 5.67514e-10
i: 904 -> W: [-0.99999017] b: [ 0.99997115], result: [ -1.90138817e-05  -1.00000918e+00  -1.99999928e+00  -2.99998951e+00] loss: 5.56344e-10
i: 905 -> W: [-0.99999028] b: [ 0.99997151], result: [ -1.87754631e-05  -1.00000906e+00  -1.99999940e+00  -2.99998951e+00] loss: 5.45004e-10
i: 906 -> W: [-0.9999904] b: [ 0.99997187], result: [ -1.85370445e-05  -1.00000894e+00  -1.99999928e+00  -2.99998975e+00] loss: 5.29173e-10
i: 907 -> W: [-0.99999052] b: [ 0.99997222], result: [ -1.82986259e-05  -1.00000882e+00  -1.99999940e+00  -2.99998999e+00] loss: 5.13285e-10
i: 908 -> W: [-0.99999064] b: [ 0.99997258], result: [ -1.80602074e-05  -1.00000870e+00  -1.99999928e+00  -2.99998999e+00] loss: 5.02684e-10
i: 909 -> W: [-0.99999076] b: [ 0.99997288], result: [ -1.78813934e-05  -1.00000858e+00  -1.99999952e+00  -2.99999022e+00] loss: 4.89194e-10
i: 910 -> W: [-0.99999088] b: [ 0.99997318], result: [ -1.77025795e-05  -1.00000858e+00  -1.99999940e+00  -2.99999046e+00] loss: 4.78355e-10
i: 911 -> W: [-0.999991] b: [ 0.99997348], result: [ -1.75237656e-05  -1.00000858e+00  -1.99999952e+00  -2.99999046e+00] loss: 4.71928e-10
i: 912 -> W: [-0.99999112] b: [ 0.99997377], result: [ -1.73449516e-05  -1.00000846e+00  -1.99999952e+00  -2.99999070e+00] loss: 4.5917e-10
i: 913 -> W: [-0.99999118] b: [ 0.99997407], result: [ -1.71065331e-05  -1.00000834e+00  -1.99999952e+00  -2.99999070e+00] loss: 4.48953e-10
i: 914 -> W: [-0.9999913] b: [ 0.99997437], result: [ -1.69277191e-05  -1.00000823e+00  -1.99999940e+00  -2.99999094e+00] loss: 4.36643e-10
i: 915 -> W: [-0.99999142] b: [ 0.99997467], result: [ -1.67489052e-05  -1.00000811e+00  -1.99999952e+00  -2.99999094e+00] loss: 4.28546e-10
i: 916 -> W: [-0.99999154] b: [ 0.99997497], result: [ -1.65700912e-05  -1.00000811e+00  -1.99999976e+00  -2.99999118e+00] loss: 4.18154e-10
i: 917 -> W: [-0.9999916] b: [ 0.99997526], result: [ -1.63316727e-05  -1.00000787e+00  -1.99999952e+00  -2.99999118e+00] loss: 4.06672e-10
i: 918 -> W: [-0.99999171] b: [ 0.99997556], result: [ -1.61528587e-05  -1.00000787e+00  -1.99999964e+00  -2.99999142e+00] loss: 3.96614e-10
i: 919 -> W: [-0.99999177] b: [ 0.99997586], result: [ -1.59144402e-05  -1.00000763e+00  -1.99999928e+00  -2.99999118e+00] loss: 3.89807e-10
i: 920 -> W: [-0.99999189] b: [ 0.99997616], result: [ -1.57356262e-05  -1.00000763e+00  -1.99999952e+00  -2.99999142e+00] loss: 3.79714e-10
i: 921 -> W: [-0.99999201] b: [ 0.99997646], result: [ -1.55568123e-05  -1.00000763e+00  -1.99999976e+00  -2.99999166e+00] loss: 3.69912e-10
i: 922 -> W: [-0.99999207] b: [ 0.99997675], result: [ -1.53183937e-05  -1.00000739e+00  -1.99999940e+00  -2.99999142e+00] loss: 3.63304e-10
i: 923 -> W: [-0.99999219] b: [ 0.99997705], result: [ -1.51395798e-05  -1.00000739e+00  -1.99999952e+00  -2.99999166e+00] loss: 3.53694e-10
i: 924 -> W: [-0.99999231] b: [ 0.99997735], result: [ -1.49607658e-05  -1.00000727e+00  -1.99999952e+00  -2.99999189e+00] loss: 3.42641e-10
i: 925 -> W: [-0.99999237] b: [ 0.99997765], result: [ -1.47223473e-05  -1.00000715e+00  -1.99999952e+00  -2.99999189e+00] loss: 3.33845e-10
i: 926 -> W: [-0.99999249] b: [ 0.99997789], result: [ -1.46031380e-05  -1.00000715e+00  -1.99999976e+00  -2.99999213e+00] loss: 3.2637e-10
i: 927 -> W: [-0.99999255] b: [ 0.99997818], result: [ -1.43647194e-05  -1.00000691e+00  -1.99999940e+00  -2.99999189e+00] loss: 3.20217e-10
i: 928 -> W: [-0.99999267] b: [ 0.99997842], result: [ -1.42455101e-05  -1.00000691e+00  -1.99999964e+00  -2.99999237e+00] loss: 3.09075e-10
i: 929 -> W: [-0.99999273] b: [ 0.99997866], result: [ -1.40666962e-05  -1.00000679e+00  -1.99999940e+00  -2.99999237e+00] loss: 3.02606e-10
i: 930 -> W: [-0.99999285] b: [ 0.9999789], result: [ -1.39474869e-05  -1.00000679e+00  -1.99999964e+00  -2.99999237e+00] loss: 2.99039e-10
i: 931 -> W: [-0.99999291] b: [ 0.99997914], result: [ -1.37686729e-05  -1.00000668e+00  -1.99999964e+00  -2.99999237e+00] loss: 2.92477e-10
i: 932 -> W: [-0.99999297] b: [ 0.99997938], result: [ -1.35898590e-05  -1.00000656e+00  -1.99999964e+00  -2.99999237e+00] loss: 2.86008e-10
i: 933 -> W: [-0.99999309] b: [ 0.99997962], result: [ -1.34706497e-05  -1.00000656e+00  -1.99999964e+00  -2.99999285e+00] loss: 2.75733e-10
i: 934 -> W: [-0.99999315] b: [ 0.99997985], result: [ -1.32918358e-05  -1.00000644e+00  -1.99999964e+00  -2.99999285e+00] loss: 2.69399e-10
i: 935 -> W: [-0.99999321] b: [ 0.99998009], result: [ -1.31130219e-05  -1.00000632e+00  -1.99999940e+00  -2.99999285e+00] loss: 2.63384e-10
i: 936 -> W: [-0.99999332] b: [ 0.99998033], result: [ -1.29938126e-05  -1.00000632e+00  -1.99999964e+00  -2.99999285e+00] loss: 2.60044e-10
i: 937 -> W: [-0.99999338] b: [ 0.99998057], result: [ -1.28149986e-05  -1.00000620e+00  -1.99999964e+00  -2.99999285e+00] loss: 2.53937e-10
i: 938 -> W: [-0.99999344] b: [ 0.99998081], result: [ -1.26361847e-05  -1.00000608e+00  -1.99999964e+00  -2.99999285e+00] loss: 2.47923e-10
i: 939 -> W: [-0.99999356] b: [ 0.99998105], result: [ -1.25169754e-05  -1.00000608e+00  -1.99999964e+00  -2.99999332e+00] loss: 2.3833e-10
i: 940 -> W: [-0.99999362] b: [ 0.99998128], result: [ -1.23381615e-05  -1.00000596e+00  -1.99999964e+00  -2.99999332e+00] loss: 2.32451e-10
i: 941 -> W: [-0.99999368] b: [ 0.99998152], result: [ -1.21593475e-05  -1.00000584e+00  -1.99999940e+00  -2.99999332e+00] loss: 2.26891e-10
i: 942 -> W: [-0.9999938] b: [ 0.99998176], result: [ -1.20401382e-05  -1.00000584e+00  -1.99999964e+00  -2.99999332e+00] loss: 2.23778e-10
i: 943 -> W: [-0.99999386] b: [ 0.999982], result: [ -1.18613243e-05  -1.00000572e+00  -1.99999964e+00  -2.99999332e+00] loss: 2.18126e-10
i: 944 -> W: [-0.99999392] b: [ 0.99998224], result: [ -1.16825104e-05  -1.00000560e+00  -1.99999964e+00  -2.99999332e+00] loss: 2.12566e-10
i: 945 -> W: [-0.99999404] b: [ 0.99998242], result: [ -1.16229057e-05  -1.00000572e+00  -1.99999976e+00  -2.99999380e+00] loss: 2.06317e-10
i: 946 -> W: [-0.9999941] b: [ 0.99998266], result: [ -1.14440918e-05  -1.00000548e+00  -1.99999976e+00  -2.99999380e+00] loss: 1.9952e-10
i: 947 -> W: [-0.99999416] b: [ 0.99998289], result: [ -1.12652779e-05  -1.00000548e+00  -1.99999952e+00  -2.99999380e+00] loss: 1.9563e-10
i: 948 -> W: [-0.99999422] b: [ 0.99998307], result: [ -1.11460686e-05  -1.00000536e+00  -1.99999952e+00  -2.99999380e+00] loss: 1.91665e-10
i: 949 -> W: [-0.99999428] b: [ 0.99998325], result: [ -1.10268593e-05  -1.00000525e+00  -1.99999952e+00  -2.99999380e+00] loss: 1.87757e-10
i: 950 -> W: [-0.9999944] b: [ 0.99998343], result: [ -1.09672546e-05  -1.00000536e+00  -1.99999988e+00  -2.99999428e+00] loss: 1.81814e-10
i: 951 -> W: [-0.99999446] b: [ 0.99998367], result: [ -1.07884407e-05  -1.00000525e+00  -1.99999964e+00  -2.99999428e+00] loss: 1.76772e-10
i: 952 -> W: [-0.99999452] b: [ 0.99998385], result: [ -1.06692314e-05  -1.00000525e+00  -1.99999976e+00  -2.99999428e+00] loss: 1.74143e-10
i: 953 -> W: [-0.99999458] b: [ 0.99998403], result: [ -1.05500221e-05  -1.00000513e+00  -1.99999976e+00  -2.99999428e+00] loss: 1.70377e-10
i: 954 -> W: [-0.99999464] b: [ 0.9999842], result: [ -1.04308128e-05  -1.00000501e+00  -1.99999952e+00  -2.99999428e+00] loss: 1.66839e-10
i: 955 -> W: [-0.9999947] b: [ 0.99998438], result: [ -1.03116035e-05  -1.00000501e+00  -1.99999964e+00  -2.99999428e+00] loss: 1.64267e-10
i: 956 -> W: [-0.99999475] b: [ 0.99998456], result: [ -1.01923943e-05  -1.00000501e+00  -1.99999976e+00  -2.99999452e+00] loss: 1.5908e-10
i: 957 -> W: [-0.99999481] b: [ 0.99998474], result: [ -1.00731850e-05  -1.00000489e+00  -1.99999976e+00  -2.99999452e+00] loss: 1.55485e-10
i: 958 -> W: [-0.99999487] b: [ 0.99998492], result: [ -9.95397568e-06  -1.00000477e+00  -1.99999976e+00  -2.99999452e+00] loss: 1.51946e-10
i: 959 -> W: [-0.99999493] b: [ 0.9999851], result: [ -9.83476639e-06  -1.00000477e+00  -1.99999964e+00  -2.99999475e+00] loss: 1.471e-10
i: 960 -> W: [-0.99999499] b: [ 0.99998528], result: [ -9.71555710e-06  -1.00000477e+00  -1.99999976e+00  -2.99999475e+00] loss: 1.44698e-10
i: 961 -> W: [-0.99999505] b: [ 0.99998546], result: [ -9.59634781e-06  -1.00000465e+00  -1.99999976e+00  -2.99999475e+00] loss: 1.41274e-10
i: 962 -> W: [-0.99999511] b: [ 0.99998564], result: [ -9.47713852e-06  -1.00000453e+00  -1.99999952e+00  -2.99999475e+00] loss: 1.38076e-10
i: 963 -> W: [-0.99999517] b: [ 0.99998581], result: [ -9.35792923e-06  -1.00000453e+00  -1.99999964e+00  -2.99999475e+00] loss: 1.35731e-10
i: 964 -> W: [-0.99999523] b: [ 0.99998599], result: [ -9.23871994e-06  -1.00000453e+00  -1.99999976e+00  -2.99999499e+00] loss: 1.30999e-10
i: 965 -> W: [-0.99999529] b: [ 0.99998617], result: [ -9.11951065e-06  -1.00000441e+00  -1.99999976e+00  -2.99999499e+00] loss: 1.27745e-10
i: 966 -> W: [-0.99999535] b: [ 0.99998635], result: [ -9.00030136e-06  -1.00000429e+00  -1.99999976e+00  -2.99999499e+00] loss: 1.24547e-10
i: 967 -> W: [-0.99999541] b: [ 0.99998653], result: [ -8.88109207e-06  -1.00000429e+00  -1.99999964e+00  -2.99999523e+00] loss: 1.20156e-10
i: 968 -> W: [-0.99999547] b: [ 0.99998671], result: [ -8.76188278e-06  -1.00000429e+00  -1.99999976e+00  -2.99999523e+00] loss: 1.17982e-10
i: 969 -> W: [-0.99999553] b: [ 0.99998689], result: [ -8.64267349e-06  -1.00000417e+00  -1.99999976e+00  -2.99999523e+00] loss: 1.14898e-10
i: 970 -> W: [-0.99999559] b: [ 0.99998707], result: [ -8.52346420e-06  -1.00000405e+00  -1.99999952e+00  -2.99999523e+00] loss: 1.12042e-10
i: 971 -> W: [-0.99999565] b: [ 0.99998719], result: [ -8.46385956e-06  -1.00000405e+00  -1.99999976e+00  -2.99999547e+00] loss: 1.08642e-10
i: 972 -> W: [-0.99999571] b: [ 0.99998736], result: [ -8.34465027e-06  -1.00000405e+00  -1.99999976e+00  -2.99999547e+00] loss: 1.06638e-10
i: 973 -> W: [-0.99999577] b: [ 0.99998754], result: [ -8.22544098e-06  -1.00000405e+00  -1.99999976e+00  -2.99999547e+00] loss: 1.04663e-10
i: 974 -> W: [-0.99999583] b: [ 0.99998772], result: [ -8.10623169e-06  -1.00000393e+00  -1.99999988e+00  -2.99999571e+00] loss: 9.96181e-11
i: 975 -> W: [-0.99999589] b: [ 0.9999879], result: [ -7.98702240e-06  -1.00000381e+00  -1.99999976e+00  -2.99999571e+00] loss: 9.68186e-11
i: 976 -> W: [-0.99999595] b: [ 0.99998802], result: [ -7.92741776e-06  -1.00000381e+00  -1.99999976e+00  -2.99999571e+00] loss: 9.587e-11
i: 977 -> W: [-0.99999601] b: [ 0.99998814], result: [ -7.86781311e-06  -1.00000381e+00  -2.00000000e+00  -2.99999595e+00] loss: 9.28821e-11
i: 978 -> W: [-0.99999601] b: [ 0.99998832], result: [ -7.68899918e-06  -1.00000370e+00  -1.99999976e+00  -2.99999571e+00] loss: 9.12515e-11
i: 979 -> W: [-0.99999607] b: [ 0.99998844], result: [ -7.62939453e-06  -1.00000370e+00  -1.99999964e+00  -2.99999571e+00] loss: 9.04095e-11
i: 980 -> W: [-0.99999613] b: [ 0.99998856], result: [ -7.56978989e-06  -1.00000370e+00  -1.99999976e+00  -2.99999595e+00] loss: 8.74429e-11
i: 981 -> W: [-0.99999619] b: [ 0.99998868], result: [ -7.51018524e-06  -1.00000370e+00  -1.99999988e+00  -2.99999619e+00] loss: 8.46256e-11
i: 982 -> W: [-0.99999619] b: [ 0.99998879], result: [ -7.39097595e-06  -1.00000358e+00  -1.99999976e+00  -2.99999595e+00] loss: 8.39009e-11
i: 983 -> W: [-0.99999624] b: [ 0.99998891], result: [ -7.33137131e-06  -1.00000358e+00  -1.99999988e+00  -2.99999619e+00] loss: 8.11049e-11
i: 984 -> W: [-0.99999624] b: [ 0.99998903], result: [ -7.21216202e-06  -1.00000346e+00  -1.99999976e+00  -2.99999595e+00] loss: 8.04512e-11
i: 985 -> W: [-0.9999963] b: [ 0.99998915], result: [ -7.15255737e-06  -1.00000346e+00  -1.99999988e+00  -2.99999619e+00] loss: 7.76765e-11
i: 986 -> W: [-0.99999636] b: [ 0.99998927], result: [ -7.09295273e-06  -1.00000346e+00  -1.99999976e+00  -2.99999619e+00] loss: 7.68701e-11
i: 987 -> W: [-0.99999642] b: [ 0.99998939], result: [ -7.03334808e-06  -1.00000346e+00  -1.99999988e+00  -2.99999619e+00] loss: 7.59854e-11
i: 988 -> W: [-0.99999648] b: [ 0.99998951], result: [ -6.97374344e-06  -1.00000346e+00  -2.00000000e+00  -2.99999642e+00] loss: 7.33742e-11
i: 989 -> W: [-0.99999648] b: [ 0.99998963], result: [ -6.85453415e-06  -1.00000334e+00  -1.99999988e+00  -2.99999619e+00] loss: 7.26921e-11
i: 990 -> W: [-0.99999654] b: [ 0.99998975], result: [ -6.79492950e-06  -1.00000334e+00  -1.99999976e+00  -2.99999642e+00] loss: 7.0159e-11
i: 991 -> W: [-0.9999966] b: [ 0.99998987], result: [ -6.73532486e-06  -1.00000334e+00  -1.99999988e+00  -2.99999666e+00] loss: 6.76614e-11
i: 992 -> W: [-0.9999966] b: [ 0.99998999], result: [ -6.61611557e-06  -1.00000322e+00  -1.99999976e+00  -2.99999642e+00] loss: 6.69793e-11
i: 993 -> W: [-0.99999666] b: [ 0.99999011], result: [ -6.55651093e-06  -1.00000322e+00  -1.99999988e+00  -2.99999666e+00] loss: 6.45031e-11
i: 994 -> W: [-0.99999666] b: [ 0.99999022], result: [ -6.43730164e-06  -1.00000310e+00  -1.99999976e+00  -2.99999642e+00] loss: 6.3892e-11
i: 995 -> W: [-0.99999672] b: [ 0.99999034], result: [ -6.37769699e-06  -1.00000310e+00  -1.99999988e+00  -2.99999666e+00] loss: 6.14371e-11
i: 996 -> W: [-0.99999672] b: [ 0.99999046], result: [ -6.25848770e-06  -1.00000298e+00  -1.99999976e+00  -2.99999642e+00] loss: 6.08971e-11
i: 997 -> W: [-0.99999678] b: [ 0.99999058], result: [ -6.19888306e-06  -1.00000298e+00  -1.99999988e+00  -2.99999666e+00] loss: 5.84635e-11
i: 998 -> W: [-0.99999684] b: [ 0.9999907], result: [ -6.13927841e-06  -1.00000298e+00  -1.99999976e+00  -2.99999666e+00] loss: 5.77707e-11
i: 999 -> W: [-0.9999969] b: [ 0.99999082], result: [ -6.07967377e-06  -1.00000298e+00  -1.99999988e+00  -2.99999666e+00] loss: 5.69997e-11

In [11]:

    

import tensorflow as tf
# NumPy is often used to load, manipulate and preprocess data.
import numpy as np

# Declare list of features. We only have one real-valued feature. There are many
# other types of columns that are more complicated and useful.
features = [tf.contrib.layers.real_valued_column("x", dimension=1)]

# An estimator is the front end to invoke training (fitting) and evaluation
# (inference). There are many predefined types like linear regression,
# logistic regression, linear classification, logistic classification, and
# many neural network classifiers and regressors. The following code
# provides an estimator that does linear regression.
estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

# TensorFlow provides many helper methods to read and set up data sets.
# Here we use `numpy_input_fn`. We have to tell the function how many batches
# of data (num_epochs) we want and how big each batch should be.
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x}, y, batch_size=4,
                                              num_epochs=1000)

# We can invoke 1000 training steps by invoking the `fit` method and passing the
# training data set.
estimator.fit(input_fn=input_fn, steps=1000)

# Here we evaluate how well our model did. In a real example, we would want
# to use a separate validation and testing data set to avoid overfitting.
print(estimator.evaluate(input_fn=input_fn))


    

    




INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': None, '_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_task_type': None, '_environment': 'local', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fa9a004f8d0>, '_tf_config': gpu_options {
  per_process_gpu_memory_fraction: 1.0
}
, '_num_worker_replicas': 0, '_task_id': 0, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_evaluation_master': '', '_keep_checkpoint_every_n_hours': 10000, '_master': ''}
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp1i3Q9f
WARNING:tensorflow:Rank of input Tensor (1) should be the same as output_rank (2) for column. Will attempt to expand dims. It is highly recommended that you resize your input, as this behavior may change.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:615: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmp1i3Q9f/model.ckpt.
INFO:tensorflow:loss = 4.25, step = 1
INFO:tensorflow:global_step/sec: 833.667
INFO:tensorflow:loss = 0.111102, step = 101 (0.122 sec)
INFO:tensorflow:global_step/sec: 581.277
INFO:tensorflow:loss = 0.014385, step = 201 (0.172 sec)
INFO:tensorflow:global_step/sec: 653.761
INFO:tensorflow:loss = 0.00126467, step = 301 (0.152 sec)
INFO:tensorflow:global_step/sec: 413.469
INFO:tensorflow:loss = 0.000593631, step = 401 (0.249 sec)
INFO:tensorflow:global_step/sec: 249.173
INFO:tensorflow:loss = 0.000150793, step = 501 (0.397 sec)
INFO:tensorflow:global_step/sec: 664.752
INFO:tensorflow:loss = 3.29077e-05, step = 601 (0.150 sec)
INFO:tensorflow:global_step/sec: 424.258
INFO:tensorflow:loss = 9.83954e-06, step = 701 (0.245 sec)
INFO:tensorflow:global_step/sec: 843.519
INFO:tensorflow:loss = 2.03328e-06, step = 801 (0.107 sec)
INFO:tensorflow:global_step/sec: 1352.54
INFO:tensorflow:loss = 4.00023e-07, step = 901 (0.076 sec)
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmp1i3Q9f/model.ckpt.
INFO:tensorflow:Loss for final step: 9.64454e-08.
WARNING:tensorflow:Rank of input Tensor (1) should be the same as output_rank (2) for column. Will attempt to expand dims. It is highly recommended that you resize your input, as this behavior may change.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:615: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-05-10-16:52:29
INFO:tensorflow:Restoring parameters from /tmp/tmp1i3Q9f/model.ckpt-1000
INFO:tensorflow:Finished evaluation at 2017-05-10-16:52:30
INFO:tensorflow:Saving dict for global step 1000: global_step = 1000, loss = 8.04845e-08
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
{'loss': 8.0484533e-08, 'global_step': 1000}

In [12]:

    

import numpy as np
import tensorflow as tf
# Declare list of features, we only have one real-valued feature
def model(features, labels, mode):
  # Build a linear model and predict values
  W = tf.get_variable("W", [1], dtype=tf.float64)
  b = tf.get_variable("b", [1], dtype=tf.float64)
  y = W*features['x'] + b
  # Loss sub-graph
  loss = tf.reduce_sum(tf.square(y - labels))
  # Training sub-graph
  global_step = tf.train.get_global_step()
  optimizer = tf.train.GradientDescentOptimizer(0.01)
  train = tf.group(optimizer.minimize(loss),
                   tf.assign_add(global_step, 1))
  # ModelFnOps connects subgraphs we built to the
  # appropriate functionality.
  return tf.contrib.learn.ModelFnOps(
      mode=mode, predictions=y,
      loss=loss,
      train_op=train)

estimator = tf.contrib.learn.Estimator(model_fn=model)
# define our data set
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000)

# train
estimator.fit(input_fn=input_fn, steps=1000)
# evaluate our model
print(estimator.evaluate(input_fn=input_fn, steps=10))


    

    




INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': None, '_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_task_type': None, '_environment': 'local', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fa99b79b090>, '_tf_config': gpu_options {
  per_process_gpu_memory_fraction: 1.0
}
, '_num_worker_replicas': 0, '_task_id': 0, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_evaluation_master': '', '_keep_checkpoint_every_n_hours': 10000, '_master': ''}
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpLX7JxZ
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpLX7JxZ/model.ckpt.
INFO:tensorflow:loss = 3.92993585929, step = 1
INFO:tensorflow:global_step/sec: 892.753
INFO:tensorflow:loss = 0.00571298107409, step = 101 (0.117 sec)
INFO:tensorflow:global_step/sec: 824.526
INFO:tensorflow:loss = 0.000308272265102, step = 201 (0.122 sec)
INFO:tensorflow:global_step/sec: 794.818
INFO:tensorflow:loss = 0.000240151319896, step = 301 (0.126 sec)
INFO:tensorflow:global_step/sec: 452.359
INFO:tensorflow:loss = 2.35446504905e-05, step = 401 (0.245 sec)
INFO:tensorflow:global_step/sec: 243.072
INFO:tensorflow:loss = 2.93038449258e-06, step = 501 (0.384 sec)
INFO:tensorflow:global_step/sec: 463.956
INFO:tensorflow:loss = 1.23636430862e-07, step = 601 (0.216 sec)
INFO:tensorflow:global_step/sec: 798.333
INFO:tensorflow:loss = 1.25080109866e-08, step = 701 (0.125 sec)
INFO:tensorflow:global_step/sec: 1051.45
INFO:tensorflow:loss = 1.68746586891e-09, step = 801 (0.095 sec)
INFO:tensorflow:global_step/sec: 1296.31
INFO:tensorflow:loss = 1.05971893941e-10, step = 901 (0.077 sec)
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpLX7JxZ/model.ckpt.
INFO:tensorflow:Loss for final step: 1.22567005008e-11.
INFO:tensorflow:Starting evaluation at 2017-05-10-16:53:00
INFO:tensorflow:Restoring parameters from /tmp/tmpLX7JxZ/model.ckpt-1000
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Finished evaluation at 2017-05-10-16:53:00
INFO:tensorflow:Saving dict for global step 1000: global_step = 1000, loss = 1.20893e-11
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
{'loss': 1.2089327e-11, 'global_step': 1000}