why does loss have negative numbers

[WeiYangBin]

I use my own data

[-INFO-] 2020-08-18 17:03:03,229 31422  train.<module> line:80  epoch 22, step 880, loss -102.3357 , accuracy 0.8097
[-INFO-] 2020-08-18 17:03:08,052 31422  train.<module> line:80  epoch 23, step 900, loss -55.5771 , accuracy 0.8884
[-INFO-] 2020-08-18 17:03:12,845 31422  train.<module> line:80  epoch 23, step 920, loss -109.6482 , accuracy 0.8704
[-INFO-] 2020-08-18 17:03:17,668 31422  train.<module> line:80  epoch 24, step 940, loss 6.0771 , accuracy 0.9116
[-INFO-] 2020-08-18 17:03:22,638 31422  train.<module> line:80  epoch 24, step 960, loss -25.0925 , accuracy 0.9362
[-INFO-] 2020-08-18 17:03:27,504 31422  train.<module> line:80  epoch 25, step 980, loss 3.5970 , accuracy 0.9642
[-INFO-] 2020-08-18 17:03:32,433 31422  train.<module> line:80  epoch 25, step 1000, loss -180.6551 , accuracy 0.8611
[-INFO-] 2020-08-18 17:03:37,352 31422  train.<module> line:80  epoch 26, step 1020, loss -185.5950 , accuracy 0.9019
[-INFO-] 2020-08-18 17:03:42,195 31422  train.<module> line:80  epoch 26, step 1040, loss -26.1356 , accuracy 0.9035
[-INFO-] 2020-08-18 17:03:47,002 31422  train.<module> line:80  epoch 27, step 1060, loss -60.3808 , accuracy 0.9388
[-INFO-] 2020-08-18 17:03:52,013 31422  train.<module> line:80  epoch 27, step 1080, loss -368.6034 , accuracy 0.7799
[-INFO-] 2020-08-18 17:03:56,875 31422  train.<module> line:80  epoch 28, step 1100, loss -136.8763 , accuracy 0.8971
[-INFO-] 2020-08-18 17:04:01,815 31422  train.<module> line:80  epoch 28, step 1120, loss 4.8511 , accuracy 0.9637
[-INFO-] 2020-08-18 17:04:06,698 31422  train.<module> line:80  epoch 29, step 1140, loss -145.3242 , accuracy 0.8888
[-INFO-] 2020-08-18 17:04:11,561 31422  train.<module> line:80  epoch 29, step 1160, loss -140.1508 , accuracy 0.9304
[-INFO-] 2020-08-18 17:04:16,441 31422  train.<module> line:80  epoch 30, step 1180, loss -191.4668 , accuracy 0.9232
[-INFO-] 2020-08-18 17:04:21,178 31422  train.<module> line:80  epoch 30, step 1200, loss -193.8787 , accuracy 0.8962
[-INFO-] 2020-08-18 17:04:26,084 31422  train.<module> line:80  epoch 31, step 1220, loss -158.3943 , accuracy 0.9505
[-INFO-] 2020-08-18 17:04:30,918 31422  train.<module> line:80  epoch 31, step 1240, loss -118.8575 , accuracy 0.8672
[-INFO-] 2020-08-18 17:04:35,754 31422  train.<module> line:80  epoch 32, step 1260, loss -367.0381 , accuracy 0.7739
[-INFO-] 2020-08-18 17:04:40,663 31422  train.<module> line:80  epoch 32, step 1280, loss 4.8814 , accuracy 0.9808
[-INFO-] 2020-08-18 17:04:40,716 31422  train.<module> line:84  model saved
[-INFO-] 2020-08-18 17:04:45,581 31422  train.<module> line:80  epoch 33, step 1300, loss -83.2353 , accuracy 0.9721
[-INFO-] 2020-08-18 17:04:50,272 31422  train.<module> line:80  epoch 33, step 1320, loss -386.9019 , accuracy 0.7725
[-INFO-] 2020-08-18 17:04:55,229 31422  train.<module> line:80  epoch 34, step 1340, loss -42.3887 , accuracy 0.9648
[-INFO-] 2020-08-18 17:05:00,131 31422  train.<module> line:80  epoch 34, step 1360, loss 4.1282 , accuracy 0.9734
[-INFO-] 2020-08-18 17:05:05,001 31422  train.<module> line:80  epoch 35, step 1380, loss -139.4714 , accuracy 0.9339
[-INFO-] 2020-08-18 17:05:09,776 31422  train.<module> line:80  epoch 35, step 1400, loss 2.0760 , accuracy 0.9880
[-INFO-] 2020-08-18 17:05:09,833 31422  train.<module> line:84  model saved
[-INFO-] 2020-08-18 17:05:14,682 31422  train.<module> line:80  epoch 36, step 1420, loss -151.9778 , accuracy 0.9138
[-INFO-] 2020-08-18 17:05:19,641 31422  train.<module> line:80  epoch 36, step 1440, loss -94.4556 , accuracy 0.8666
[-INFO-] 2020-08-18 17:05:24,617 31422  train.<module> line:80  epoch 37, step 1460, loss -201.3645 , accuracy 0.9187
[-INFO-] 2020-08-18 17:05:29,423 31422  train.<module> line:80  epoch 37, step 1480, loss -142.1119 , accuracy 0.9106
[-INFO-] 2020-08-18 17:05:34,226 31422  train.<module> line:80  epoch 38, step 1500, loss -349.6097 , accuracy 0.8306
[-INFO-] 2020-08-18 17:05:39,113 31422  train.<module> line:80  epoch 38, step 1520, loss 2.9786 , accuracy 0.9374