Neural Net Training all values in output vector converge to one value

Lina_Lee: <p>Hi I am training my neural net model.<br>
Loss function was built by likelihood function.<br>
4hidden layers with 100 nodes. ReLU activation function.</p>
<p>output is vector of size 500.</p>
<p>As I train model more, the values in the output vector converges to same value.<br>
The vector in [ ] is yhat. values in yhat consists of one value after running some epochs. but they should be different for each observation.</p>
<p>The outut that I have is<br>
epoch 0, loss1 001.12 , loss2 034.93 , loss3 035.45, loss5 009.52 , totloss 081.01, acc 000.00<br>
[1.1405597 1.107288  1.1093471 1.1373975 1.1396397 1.1277186 1.130907<br>
1.1311299 1.1377169 1.1102827 1.1286004 1.1275425 1.1157365 1.1355797<br>
1.1156945 1.1289982 1.126752  1.1433362 1.1223617]<br>
epoch 1, loss1 001.10 , loss2 037.71 , loss3 040.34, loss5 011.70 , totloss 090.85, acc 000.05<br>
[1.1225772 1.161662  1.1216304 1.1504776 1.1397775 1.1806581 1.1605873<br>
1.143011  1.1311299 1.13676   1.1585436 1.1280268 1.1688657 1.1579617<br>
1.1458558 1.1458558 1.1302841 1.1462775 1.1340436]<br>
epoch 2, loss1 001.10 , loss2 036.14 , loss3 036.35, loss5 009.22 , totloss 082.82, acc 000.00<br>
[1.1699617 1.1569937 1.1845496 1.1844468 1.152382  1.2059082 1.1704608<br>
1.1789266 1.2139411 1.1711605 1.1610755 1.153051  1.1812706 1.1575742<br>
1.1693635 1.1706606 1.18301   1.1964973 1.1716609]<br>
epoch 3, loss1 001.10 , loss2 036.64 , loss3 036.14, loss5 008.84 , totloss 082.72, acc 000.05<br>
[1.2007099 1.2341156 1.2004987 1.2547344 1.2282869 1.2278485 1.2440958<br>
1.2400917 1.2177199 1.2093245 1.2228231 1.2310315 1.2310315 1.2358824<br>
1.2203218 1.2440958 1.2018733 1.2330129 1.1950296]<br>
epoch 4, loss1 001.09 , loss2 034.21 , loss3 037.43, loss5 010.75 , totloss 083.50, acc 000.32<br>
[1.3193738 1.3362886 1.2875427 1.3245302 1.2740083 1.3582877 1.3386464<br>
1.2861611 1.3156309 1.3644643 1.307931  1.2988677 1.3816202 1.3301677<br>
1.3039752 1.360662  1.2940054 1.2914636 1.2712704]<br>
epoch 5, loss1 001.10 , loss2 032.44 , loss3 034.00, loss5 008.73 , totloss 076.27, acc 000.42<br>
[1.4496078 1.4650714 1.3861591 1.454679  1.4223737 1.4980042 1.4084361<br>
1.4178045 1.3914201 1.4062761 1.3804265 1.4481595 1.4607198 1.4336894<br>
1.3866371 1.4380276 1.4158816 1.4435745 1.3863981]<br>
…<br>
epoch 96, loss1 001.08 , loss2 019.22 , loss3 019.23, loss5 000.64 , totloss 040.18, acc 001.00<br>
[4.0625    4.0585938 4.0703125 4.0585938 4.029297  4.0625    4.0664062<br>
4.0664062 4.0566406 4.0703125 4.0664062 4.0507812 4.0546875 4.0585938<br>
4.0664062 4.0664062 4.0585938 4.0664062 4.0566406]<br>
epoch 97, loss1 001.09 , loss2 018.49 , loss3 018.53, loss5 000.70 , totloss 038.81, acc 001.00<br>
[3.9902344 3.9726562 3.9902344 3.9902344 3.9882812 3.9960938 3.9921875<br>
4.001953  3.9824219 3.9960938 3.9960938 4.        4.        3.9882812<br>
3.9882812 3.9960938 3.9921875 3.9921875 3.9707031]<br>
epoch 98, loss1 001.09 , loss2 017.47 , loss3 019.78, loss5 004.69 , totloss 043.03, acc 000.95<br>
[4.0527344 4.0410156 4.0585938 4.0664062 4.048828  4.046875  4.0585938<br>
4.0410156 4.0546875 4.060547  4.0546875 4.0625    4.048828  4.0625<br>
4.0585938 4.064453  4.046875  4.0390625 4.060547 ]<br>
epoch 99, loss1 001.11 , loss2 022.12 , loss3 019.87, loss5 004.69 , totloss 047.78, acc 000.95<br>
[4.0234375 4.029297  4.0234375 4.0273438 4.0253906 4.029297  4.0273438<br>
4.017578  4.0273438 4.0214844 4.0214844 4.0234375 4.03125   4.0273438<br>
4.0273438 4.0253906 4.0234375 4.0195312 4.015625 ]<br>
epoch 100, loss1 001.09 , loss2 019.24 , loss3 019.26, loss5 000.69 , totloss 040.28, acc 001.00<br>
[3.9882812 3.9804688 3.9902344 3.9941406 3.9921875 3.9824219 3.9726562<br>
3.9863281 3.9707031 3.984375  3.9785156 3.9765625 3.9785156 3.9824219<br>
3.9746094 3.96875   3.9863281 3.9824219 3.9824219]<br>
…<br>
epoch 125, loss1 001.08 , loss2 019.14 , loss3 019.14, loss5 000.72 , totloss 040.07, acc 001.00<br>
[3.9492188 3.9492188 3.9511719 3.9492188 3.9492188 3.9511719 3.9511719<br>
3.9511719 3.9472656 3.9492188 3.9492188 3.9492188 3.9511719 3.9492188<br>
3.9472656 3.9472656 3.9472656 3.9492188 3.9492188]<br>
epoch 126, loss1 001.08 , loss2 018.86 , loss3 018.87, loss5 000.72 , totloss 039.53, acc 001.00<br>
[3.9472656 3.9453125 3.9453125 3.9433594 3.9453125 3.9453125 3.9453125<br>
3.9472656 3.9453125 3.9433594 3.9433594 3.9433594 3.9453125 3.9414062<br>
3.9414062 3.9433594 3.9433594 3.9472656 3.9472656]<br>
epoch 127, loss1 001.08 , loss2 019.23 , loss3 019.23, loss5 000.80 , totloss 040.33, acc 001.00<br>
[3.8457031 3.8457031 3.8457031 3.8457031 3.8457031 3.8476562 3.8457031<br>
3.8457031 3.84375   3.8457031 3.8457031 3.8476562 3.8457031 3.8457031<br>
3.8457031 3.8457031 3.8457031 3.8457031 3.84375  ]<br>
epoch 128, loss1 001.09 , loss2 019.86 , loss3 022.09, loss5 004.63 , totloss 047.67, acc 000.95<br>
[3.8085938 3.8085938 3.8085938 3.8085938 3.8085938 3.8085938 3.8066406<br>
3.8085938 3.8085938 3.8085938 3.8105469 3.8066406 3.8066406 3.8085938<br>
3.8085938 3.8066406 3.8085938 3.8105469 3.8085938]<br>
epoch 129, loss1 001.08 , loss2 019.39 , loss3 019.39, loss5 000.77 , totloss 040.63, acc 001.00<br>
[3.875     3.875     3.875     3.875     3.875     3.875     3.875<br>
3.875     3.875     3.8730469 3.875     3.875     3.875     3.875<br>
3.875     3.8769531 3.875     3.875     3.8730469]<br>
epoch 130, loss1 001.08 , loss2 018.86 , loss3 018.86, loss5 000.73 , totloss 039.53, acc 001.00<br>
[3.9335938 3.9316406 3.9316406 3.9316406 3.9316406 3.9335938 3.9316406<br>
3.9316406 3.9316406 3.9316406 3.9335938 3.9316406 3.9316406 3.9316406<br>
3.9335938 3.9316406 3.9316406 3.9316406 3.9335938]<br>
epoch 131, loss1 001.08 , loss2 018.85 , loss3 018.85, loss5 000.73 , totloss 039.51, acc 001.00<br>
[3.9335938 3.9335938 3.9335938 3.9335938 3.9335938 3.9335938 3.9335938<br>
3.9335938 3.9335938 3.9335938 3.9335938 3.9335938 3.9335938 3.9335938<br>
3.9335938 3.9316406 3.9335938 3.9335938 3.9335938]</p>
<p>Does anyone have any idea about possible reasons?<br>
The value itself is close to the real value though.</p>