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output.txt
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Iter: 000, Train Loss: 11.3093, Train MAPE: 0.3090, Train RMSE: 13.8657
Iter: 001, Train Loss: 9.9089, Train MAPE: 0.2768, Train RMSE: 12.8453
Iter: 002, Train Loss: 7.9891, Train MAPE: 0.2280, Train RMSE: 11.1854
Iter: 003, Train Loss: 6.2738, Train MAPE: 0.1734, Train RMSE: 9.5803
Iter: 004, Train Loss: 5.8669, Train MAPE: 0.1909, Train RMSE: 10.2112
Iter: 005, Train Loss: 6.0799, Train MAPE: 0.2024, Train RMSE: 10.1809
Iter: 006, Train Loss: 6.2161, Train MAPE: 0.1741, Train RMSE: 9.4664
Iter: 007, Train Loss: 6.5820, Train MAPE: 0.2171, Train RMSE: 10.1644
Iter: 008, Train Loss: 6.2362, Train MAPE: 0.2011, Train RMSE: 10.1554
Iter: 009, Train Loss: 4.6429, Train MAPE: 0.1256, Train RMSE: 7.7695
Iter: 010, Train Loss: 4.4202, Train MAPE: 0.1096, Train RMSE: 7.2617
Iter: 011, Train Loss: 4.8260, Train MAPE: 0.1357, Train RMSE: 8.9275
Iter: 012, Train Loss: 5.3298, Train MAPE: 0.1281, Train RMSE: 9.7416
Iter: 013, Train Loss: 5.4591, Train MAPE: 0.1294, Train RMSE: 11.0492
Iter: 014, Train Loss: 4.7797, Train MAPE: 0.1290, Train RMSE: 8.8247
Iter: 015, Train Loss: 4.7558, Train MAPE: 0.1235, Train RMSE: 8.9098
Iter: 016, Train Loss: 5.5097, Train MAPE: 0.1707, Train RMSE: 9.0848
Iter: 017, Train Loss: 4.7166, Train MAPE: 0.1526, Train RMSE: 8.2826
Iter: 018, Train Loss: 4.4400, Train MAPE: 0.1344, Train RMSE: 7.6936
Iter: 019, Train Loss: 4.8326, Train MAPE: 0.1543, Train RMSE: 8.5566
Iter: 020, Train Loss: 4.5626, Train MAPE: 0.1449, Train RMSE: 8.0064
Iter: 021, Train Loss: 4.5385, Train MAPE: 0.1261, Train RMSE: 7.8011
Iter: 022, Train Loss: 5.0940, Train MAPE: 0.1523, Train RMSE: 9.0825
Iter: 023, Train Loss: 4.5583, Train MAPE: 0.1362, Train RMSE: 8.2423
Iter: 024, Train Loss: 4.4201, Train MAPE: 0.1233, Train RMSE: 8.5477
Iter: 025, Train Loss: 4.5891, Train MAPE: 0.1187, Train RMSE: 8.5469
Iter: 026, Train Loss: 4.1914, Train MAPE: 0.1198, Train RMSE: 7.9621
Iter: 027, Train Loss: 4.3677, Train MAPE: 0.1147, Train RMSE: 8.3404
Iter: 028, Train Loss: 3.9826, Train MAPE: 0.1185, Train RMSE: 7.8184
Iter: 029, Train Loss: 4.0075, Train MAPE: 0.0972, Train RMSE: 7.5756
Iter: 030, Train Loss: 4.2151, Train MAPE: 0.1135, Train RMSE: 7.6528
Iter: 031, Train Loss: 5.9389, Train MAPE: 0.1797, Train RMSE: 9.4705
Iter: 032, Train Loss: 4.9189, Train MAPE: 0.1600, Train RMSE: 9.2691
Iter: 033, Train Loss: 4.1612, Train MAPE: 0.1255, Train RMSE: 7.7217
Iter: 034, Train Loss: 4.0333, Train MAPE: 0.1061, Train RMSE: 7.3989
Iter: 035, Train Loss: 4.0551, Train MAPE: 0.1123, Train RMSE: 7.5821
Iter: 036, Train Loss: 3.6661, Train MAPE: 0.0929, Train RMSE: 6.7998
Iter: 037, Train Loss: 4.1870, Train MAPE: 0.1163, Train RMSE: 7.7691
Iter: 038, Train Loss: 3.9458, Train MAPE: 0.1029, Train RMSE: 7.5382
Iter: 039, Train Loss: 5.6533, Train MAPE: 0.1600, Train RMSE: 10.4313
Iter: 040, Train Loss: 4.0492, Train MAPE: 0.1081, Train RMSE: 7.7115
Iter: 041, Train Loss: 4.3223, Train MAPE: 0.1093, Train RMSE: 8.3834
Iter: 042, Train Loss: 4.2862, Train MAPE: 0.1177, Train RMSE: 8.4419
Iter: 043, Train Loss: 4.7086, Train MAPE: 0.1264, Train RMSE: 9.4754
Iter: 044, Train Loss: 3.9532, Train MAPE: 0.1028, Train RMSE: 7.8617
Iter: 045, Train Loss: 4.3920, Train MAPE: 0.1295, Train RMSE: 8.5590
Iter: 046, Train Loss: 4.4974, Train MAPE: 0.1332, Train RMSE: 8.4829
Iter: 047, Train Loss: 3.5599, Train MAPE: 0.1017, Train RMSE: 7.1271
Iter: 048, Train Loss: 4.4049, Train MAPE: 0.1311, Train RMSE: 8.4668
Iter: 049, Train Loss: 4.4729, Train MAPE: 0.1365, Train RMSE: 8.4295
Iter: 050, Train Loss: 4.1584, Train MAPE: 0.1183, Train RMSE: 7.8569
Iter: 051, Train Loss: 4.4192, Train MAPE: 0.1268, Train RMSE: 8.5047
Iter: 052, Train Loss: 4.0730, Train MAPE: 0.0971, Train RMSE: 7.9654
Iter: 053, Train Loss: 4.7897, Train MAPE: 0.1301, Train RMSE: 9.7464
Iter: 054, Train Loss: 4.5013, Train MAPE: 0.1207, Train RMSE: 9.0476
Iter: 055, Train Loss: 4.1844, Train MAPE: 0.1084, Train RMSE: 8.5840
Iter: 056, Train Loss: 4.1742, Train MAPE: 0.1182, Train RMSE: 7.8409
Iter: 057, Train Loss: 3.9978, Train MAPE: 0.1174, Train RMSE: 7.5921
Iter: 058, Train Loss: 3.7989, Train MAPE: 0.0931, Train RMSE: 6.9648
Iter: 059, Train Loss: 3.9230, Train MAPE: 0.1035, Train RMSE: 7.3412
Iter: 060, Train Loss: 3.9047, Train MAPE: 0.1163, Train RMSE: 7.3034
Iter: 061, Train Loss: 4.2704, Train MAPE: 0.1324, Train RMSE: 7.8845
Iter: 062, Train Loss: 4.0690, Train MAPE: 0.1148, Train RMSE: 7.6698
Iter: 063, Train Loss: 4.1588, Train MAPE: 0.1276, Train RMSE: 7.9339
Iter: 064, Train Loss: 4.1399, Train MAPE: 0.1279, Train RMSE: 7.9099
Iter: 065, Train Loss: 4.3842, Train MAPE: 0.1225, Train RMSE: 8.3570
Iter: 066, Train Loss: 3.8690, Train MAPE: 0.1083, Train RMSE: 7.6462
Iter: 067, Train Loss: 4.4726, Train MAPE: 0.1199, Train RMSE: 9.0526
Iter: 068, Train Loss: 4.1959, Train MAPE: 0.1052, Train RMSE: 8.2005
Iter: 069, Train Loss: 3.8561, Train MAPE: 0.0961, Train RMSE: 7.9256
Iter: 070, Train Loss: 3.8125, Train MAPE: 0.0994, Train RMSE: 7.4914
Iter: 071, Train Loss: 3.7900, Train MAPE: 0.1077, Train RMSE: 7.1588
Iter: 072, Train Loss: 4.1112, Train MAPE: 0.1215, Train RMSE: 7.6908
Iter: 073, Train Loss: 4.9307, Train MAPE: 0.1703, Train RMSE: 8.9265
Iter: 074, Train Loss: 4.3863, Train MAPE: 0.1342, Train RMSE: 7.8373
Iter: 075, Train Loss: 4.3439, Train MAPE: 0.1249, Train RMSE: 7.4584
Iter: 076, Train Loss: 4.3185, Train MAPE: 0.1318, Train RMSE: 7.7502
Iter: 077, Train Loss: 4.3125, Train MAPE: 0.1274, Train RMSE: 8.0020
Iter: 078, Train Loss: 4.1982, Train MAPE: 0.1211, Train RMSE: 8.1814
Iter: 079, Train Loss: 4.3002, Train MAPE: 0.1322, Train RMSE: 8.5658
Iter: 080, Train Loss: 4.2666, Train MAPE: 0.1254, Train RMSE: 7.8688
Iter: 081, Train Loss: 4.1317, Train MAPE: 0.1026, Train RMSE: 7.8624
Iter: 082, Train Loss: 4.1220, Train MAPE: 0.1084, Train RMSE: 7.7653
Iter: 083, Train Loss: 4.2914, Train MAPE: 0.1216, Train RMSE: 8.2026
Iter: 084, Train Loss: 4.6397, Train MAPE: 0.1305, Train RMSE: 8.9458
Iter: 085, Train Loss: 4.9890, Train MAPE: 0.1326, Train RMSE: 8.5797
Iter: 086, Train Loss: 4.0521, Train MAPE: 0.1171, Train RMSE: 7.9177
Iter: 087, Train Loss: 4.5701, Train MAPE: 0.1329, Train RMSE: 8.2248
Iter: 088, Train Loss: 4.0663, Train MAPE: 0.1117, Train RMSE: 8.2145
Iter: 089, Train Loss: 3.9511, Train MAPE: 0.1130, Train RMSE: 7.6768
Iter: 090, Train Loss: 4.1971, Train MAPE: 0.1250, Train RMSE: 7.7863
Iter: 091, Train Loss: 3.9842, Train MAPE: 0.1200, Train RMSE: 7.6076
Iter: 092, Train Loss: 4.4630, Train MAPE: 0.1428, Train RMSE: 8.4301
Iter: 093, Train Loss: 3.9484, Train MAPE: 0.0997, Train RMSE: 7.4957
Iter: 094, Train Loss: 3.9499, Train MAPE: 0.1086, Train RMSE: 7.5753
Iter: 095, Train Loss: 3.8772, Train MAPE: 0.1021, Train RMSE: 7.7059
Iter: 096, Train Loss: 4.2246, Train MAPE: 0.1133, Train RMSE: 8.1904
Iter: 097, Train Loss: 4.7527, Train MAPE: 0.1361, Train RMSE: 8.7148
Iter: 098, Train Loss: 4.4129, Train MAPE: 0.1253, Train RMSE: 8.0133
Iter: 099, Train Loss: 4.1602, Train MAPE: 0.1217, Train RMSE: 7.7075
Iter: 100, Train Loss: 3.9293, Train MAPE: 0.1074, Train RMSE: 7.3535
Iter: 101, Train Loss: 3.8776, Train MAPE: 0.0987, Train RMSE: 7.2410
Iter: 102, Train Loss: 3.9684, Train MAPE: 0.1027, Train RMSE: 7.5125
Iter: 103, Train Loss: 3.6518, Train MAPE: 0.0929, Train RMSE: 7.0316
Iter: 104, Train Loss: 4.2913, Train MAPE: 0.1289, Train RMSE: 8.2657
Iter: 105, Train Loss: 3.8114, Train MAPE: 0.1009, Train RMSE: 7.3860
Iter: 106, Train Loss: 3.9928, Train MAPE: 0.1077, Train RMSE: 7.9427
Iter: 107, Train Loss: 4.1571, Train MAPE: 0.1080, Train RMSE: 8.3117
Iter: 108, Train Loss: 4.0974, Train MAPE: 0.1104, Train RMSE: 8.0760
Iter: 109, Train Loss: 3.9079, Train MAPE: 0.1095, Train RMSE: 7.7355
Iter: 110, Train Loss: 3.5277, Train MAPE: 0.0829, Train RMSE: 6.6911
Iter: 111, Train Loss: 4.6482, Train MAPE: 0.1491, Train RMSE: 8.2127
Iter: 112, Train Loss: 4.3678, Train MAPE: 0.1322, Train RMSE: 8.2478
Iter: 113, Train Loss: 3.9397, Train MAPE: 0.1185, Train RMSE: 7.5409
Iter: 114, Train Loss: 3.9940, Train MAPE: 0.1293, Train RMSE: 7.6023
Iter: 115, Train Loss: 3.7743, Train MAPE: 0.1118, Train RMSE: 7.4503
Iter: 116, Train Loss: 4.0689, Train MAPE: 0.1201, Train RMSE: 8.0619
Iter: 117, Train Loss: 4.1013, Train MAPE: 0.1121, Train RMSE: 8.3741
Iter: 118, Train Loss: 3.8201, Train MAPE: 0.0978, Train RMSE: 7.7136
Iter: 119, Train Loss: 3.9807, Train MAPE: 0.1041, Train RMSE: 8.3120
Iter: 120, Train Loss: 3.7300, Train MAPE: 0.0939, Train RMSE: 7.4814
Iter: 121, Train Loss: 3.6030, Train MAPE: 0.1047, Train RMSE: 7.1602
Iter: 122, Train Loss: 3.4682, Train MAPE: 0.0972, Train RMSE: 6.5257
Iter: 123, Train Loss: 3.9435, Train MAPE: 0.1245, Train RMSE: 7.5465
Iter: 124, Train Loss: 4.0835, Train MAPE: 0.1213, Train RMSE: 7.9009
Iter: 125, Train Loss: 3.6530, Train MAPE: 0.1006, Train RMSE: 6.9833
Iter: 126, Train Loss: 3.7720, Train MAPE: 0.1050, Train RMSE: 7.5083
Iter: 127, Train Loss: 3.9701, Train MAPE: 0.1125, Train RMSE: 8.0939
Iter: 128, Train Loss: 3.8704, Train MAPE: 0.1078, Train RMSE: 7.8963
Iter: 129, Train Loss: 3.7997, Train MAPE: 0.0994, Train RMSE: 7.5865
Iter: 130, Train Loss: 4.1697, Train MAPE: 0.1279, Train RMSE: 8.4410
Iter: 131, Train Loss: 3.8823, Train MAPE: 0.1151, Train RMSE: 7.3743
Iter: 132, Train Loss: 3.9500, Train MAPE: 0.1096, Train RMSE: 7.8738
Iter: 133, Train Loss: 3.8763, Train MAPE: 0.1139, Train RMSE: 7.5972
Iter: 134, Train Loss: 4.1387, Train MAPE: 0.1235, Train RMSE: 8.0907
Iter: 135, Train Loss: 3.5307, Train MAPE: 0.0942, Train RMSE: 7.0196
Iter: 136, Train Loss: 3.7828, Train MAPE: 0.1007, Train RMSE: 7.4420
Iter: 137, Train Loss: 3.9404, Train MAPE: 0.1202, Train RMSE: 7.8987
Iter: 138, Train Loss: 3.9811, Train MAPE: 0.1057, Train RMSE: 7.9751
Iter: 139, Train Loss: 3.7323, Train MAPE: 0.1128, Train RMSE: 7.7843
Iter: 140, Train Loss: 4.0401, Train MAPE: 0.1184, Train RMSE: 7.9533
Iter: 141, Train Loss: 3.7823, Train MAPE: 0.0958, Train RMSE: 7.4204
Iter: 142, Train Loss: 3.9661, Train MAPE: 0.1005, Train RMSE: 7.6011
Iter: 143, Train Loss: 3.6268, Train MAPE: 0.0946, Train RMSE: 7.3064
Iter: 144, Train Loss: 3.6962, Train MAPE: 0.0948, Train RMSE: 7.1141
Iter: 145, Train Loss: 3.8963, Train MAPE: 0.1064, Train RMSE: 7.2718
Iter: 146, Train Loss: 4.1055, Train MAPE: 0.1213, Train RMSE: 7.3040
Iter: 147, Train Loss: 3.9824, Train MAPE: 0.1300, Train RMSE: 7.7998
Iter: 148, Train Loss: 3.7175, Train MAPE: 0.0998, Train RMSE: 7.1845
Iter: 149, Train Loss: 3.7844, Train MAPE: 0.1048, Train RMSE: 7.3411
Iter: 150, Train Loss: 3.9859, Train MAPE: 0.1102, Train RMSE: 8.0040
Iter: 151, Train Loss: 3.6532, Train MAPE: 0.1020, Train RMSE: 7.0939
Iter: 152, Train Loss: 3.8540, Train MAPE: 0.1025, Train RMSE: 7.7125
Iter: 153, Train Loss: 3.7760, Train MAPE: 0.1025, Train RMSE: 7.3869
Iter: 154, Train Loss: 3.9341, Train MAPE: 0.1070, Train RMSE: 7.7377
Iter: 155, Train Loss: 3.8101, Train MAPE: 0.1153, Train RMSE: 7.4048
Iter: 156, Train Loss: 3.5742, Train MAPE: 0.1076, Train RMSE: 7.2922
Iter: 157, Train Loss: 3.6594, Train MAPE: 0.0958, Train RMSE: 7.3727
Iter: 158, Train Loss: 3.4672, Train MAPE: 0.0906, Train RMSE: 6.8378
Iter: 159, Train Loss: 3.4433, Train MAPE: 0.0883, Train RMSE: 6.9834
Iter: 160, Train Loss: 3.8488, Train MAPE: 0.1135, Train RMSE: 7.4563
Iter: 161, Train Loss: 4.1185, Train MAPE: 0.1132, Train RMSE: 7.8179
Iter: 162, Train Loss: 3.9569, Train MAPE: 0.1149, Train RMSE: 7.8866
Iter: 163, Train Loss: 3.7313, Train MAPE: 0.0999, Train RMSE: 7.3574
Iter: 164, Train Loss: 3.8684, Train MAPE: 0.1019, Train RMSE: 7.7220
Iter: 165, Train Loss: 4.1040, Train MAPE: 0.1188, Train RMSE: 8.0913
Iter: 166, Train Loss: 3.6540, Train MAPE: 0.0991, Train RMSE: 7.4382
Iter: 167, Train Loss: 3.9407, Train MAPE: 0.1205, Train RMSE: 7.9925
Iter: 168, Train Loss: 3.7521, Train MAPE: 0.1071, Train RMSE: 7.6940
Iter: 169, Train Loss: 3.8999, Train MAPE: 0.1160, Train RMSE: 7.6999
Iter: 170, Train Loss: 4.1106, Train MAPE: 0.1315, Train RMSE: 7.9827
Iter: 171, Train Loss: 4.0033, Train MAPE: 0.1223, Train RMSE: 7.6411
Iter: 172, Train Loss: 3.5962, Train MAPE: 0.0902, Train RMSE: 7.0147
Iter: 173, Train Loss: 3.8479, Train MAPE: 0.1144, Train RMSE: 8.1644
Iter: 174, Train Loss: 4.5930, Train MAPE: 0.1305, Train RMSE: 8.4816
Iter: 175, Train Loss: 4.2185, Train MAPE: 0.1094, Train RMSE: 8.1930
Iter: 176, Train Loss: 3.9487, Train MAPE: 0.1036, Train RMSE: 7.9995
Iter: 177, Train Loss: 4.0720, Train MAPE: 0.1170, Train RMSE: 8.0646
Iter: 178, Train Loss: 3.9369, Train MAPE: 0.1032, Train RMSE: 7.2976
Iter: 179, Train Loss: 3.9014, Train MAPE: 0.1033, Train RMSE: 7.3028
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Iter: 181, Train Loss: 3.6680, Train MAPE: 0.0998, Train RMSE: 6.9439
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Epoch: 001, Inference Time: 236.1354 secs
Epoch: 001, Train Loss: 4.0422, Train MAPE: 0.1138, Train RMSE: 7.7395, Valid Loss: 3.4219, Valid MAPE: 0.0988, Valid RMSE: 6.3415, Training Time: 2513.7937/epoch
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Iter: 325, Train Loss: 3.4765, Train MAPE: 0.0869, Train RMSE: 6.6514
Iter: 326, Train Loss: 4.1436, Train MAPE: 0.1244, Train RMSE: 7.7084
Iter: 327, Train Loss: 2.9875, Train MAPE: 0.0745, Train RMSE: 6.0958
Iter: 328, Train Loss: 3.4436, Train MAPE: 0.0955, Train RMSE: 6.7477
Iter: 329, Train Loss: 3.6612, Train MAPE: 0.1050, Train RMSE: 7.1728
Iter: 330, Train Loss: 3.8410, Train MAPE: 0.1096, Train RMSE: 7.3669
Iter: 331, Train Loss: 4.0358, Train MAPE: 0.1232, Train RMSE: 7.9149
Iter: 332, Train Loss: 3.5130, Train MAPE: 0.0961, Train RMSE: 6.8216
Iter: 333, Train Loss: 3.5575, Train MAPE: 0.1012, Train RMSE: 7.1028
Iter: 334, Train Loss: 3.4378, Train MAPE: 0.0959, Train RMSE: 7.1171
Iter: 335, Train Loss: 3.5228, Train MAPE: 0.1032, Train RMSE: 7.1442
Iter: 336, Train Loss: 3.5872, Train MAPE: 0.1091, Train RMSE: 7.2218
Iter: 337, Train Loss: 3.7886, Train MAPE: 0.1201, Train RMSE: 7.6083
Iter: 338, Train Loss: 3.7482, Train MAPE: 0.1109, Train RMSE: 7.3479
Iter: 339, Train Loss: 3.3536, Train MAPE: 0.0897, Train RMSE: 6.7036
Iter: 340, Train Loss: 3.5978, Train MAPE: 0.1071, Train RMSE: 7.3376
Iter: 341, Train Loss: 3.3888, Train MAPE: 0.0880, Train RMSE: 6.7238
Iter: 342, Train Loss: 3.3392, Train MAPE: 0.0905, Train RMSE: 6.7932
Iter: 343, Train Loss: 3.6038, Train MAPE: 0.0995, Train RMSE: 7.3154
Iter: 344, Train Loss: 3.7466, Train MAPE: 0.1026, Train RMSE: 7.3269
Iter: 345, Train Loss: 4.0501, Train MAPE: 0.1282, Train RMSE: 8.2309
Iter: 346, Train Loss: 3.4525, Train MAPE: 0.0915, Train RMSE: 6.7948
Iter: 347, Train Loss: 3.6381, Train MAPE: 0.1003, Train RMSE: 6.7868
Iter: 348, Train Loss: 3.4223, Train MAPE: 0.0836, Train RMSE: 6.3756
Iter: 349, Train Loss: 3.4859, Train MAPE: 0.1041, Train RMSE: 7.1022
Iter: 350, Train Loss: 3.4133, Train MAPE: 0.0991, Train RMSE: 6.7655
Iter: 351, Train Loss: 3.1378, Train MAPE: 0.0833, Train RMSE: 6.3696
Iter: 352, Train Loss: 3.2584, Train MAPE: 0.0861, Train RMSE: 6.4605
Iter: 353, Train Loss: 3.8852, Train MAPE: 0.1108, Train RMSE: 7.4345
Iter: 354, Train Loss: 3.3904, Train MAPE: 0.0952, Train RMSE: 6.8631
Iter: 355, Train Loss: 3.8254, Train MAPE: 0.1072, Train RMSE: 7.5168
Iter: 356, Train Loss: 3.5918, Train MAPE: 0.0921, Train RMSE: 6.7343
Iter: 357, Train Loss: 3.7210, Train MAPE: 0.1068, Train RMSE: 7.1685
Iter: 358, Train Loss: 3.5428, Train MAPE: 0.1038, Train RMSE: 7.0509
Iter: 359, Train Loss: 3.6385, Train MAPE: 0.1114, Train RMSE: 7.3329
Iter: 360, Train Loss: 3.7694, Train MAPE: 0.1157, Train RMSE: 7.6891
Iter: 361, Train Loss: 3.2128, Train MAPE: 0.0786, Train RMSE: 6.3352
Iter: 362, Train Loss: 3.5437, Train MAPE: 0.0975, Train RMSE: 7.2151
Iter: 363, Train Loss: 3.5710, Train MAPE: 0.1020, Train RMSE: 7.1270
Iter: 364, Train Loss: 3.4453, Train MAPE: 0.0897, Train RMSE: 6.6422
Iter: 365, Train Loss: 3.3764, Train MAPE: 0.0878, Train RMSE: 6.3937
Iter: 366, Train Loss: 3.6863, Train MAPE: 0.1080, Train RMSE: 7.4230
Iter: 367, Train Loss: 3.4316, Train MAPE: 0.0924, Train RMSE: 6.8902
Iter: 368, Train Loss: 3.5058, Train MAPE: 0.1004, Train RMSE: 7.1593
Iter: 369, Train Loss: 3.9952, Train MAPE: 0.1163, Train RMSE: 7.6032
Iter: 370, Train Loss: 3.5789, Train MAPE: 0.1004, Train RMSE: 7.0601
Iter: 371, Train Loss: 3.0428, Train MAPE: 0.0685, Train RMSE: 5.6795
Iter: 372, Train Loss: 3.3894, Train MAPE: 0.0920, Train RMSE: 6.7425
Iter: 373, Train Loss: 3.3415, Train MAPE: 0.0882, Train RMSE: 6.7311
Iter: 374, Train Loss: 3.1721, Train MAPE: 0.0819, Train RMSE: 6.2839
Epoch: 002, Inference Time: 207.3449 secs
Epoch: 002, Train Loss: 3.6121, Train MAPE: 0.1012, Train RMSE: 7.1191, Valid Loss: 3.3569, Valid MAPE: 0.1012, Valid RMSE: 6.2453, Training Time: 2685.8135/epoch
Iter: 000, Train Loss: 3.5957, Train MAPE: 0.1059, Train RMSE: 7.3896
Iter: 001, Train Loss: 3.7583, Train MAPE: 0.1126, Train RMSE: 7.4460
Iter: 002, Train Loss: 3.8051, Train MAPE: 0.1167, Train RMSE: 7.3342
Iter: 003, Train Loss: 3.2972, Train MAPE: 0.0851, Train RMSE: 6.4525
Iter: 004, Train Loss: 3.5301, Train MAPE: 0.0928, Train RMSE: 6.9167
Iter: 005, Train Loss: 3.7765, Train MAPE: 0.1071, Train RMSE: 7.2119
Iter: 006, Train Loss: 3.2233, Train MAPE: 0.0763, Train RMSE: 6.1367
Iter: 007, Train Loss: 3.3059, Train MAPE: 0.0826, Train RMSE: 6.5225
Iter: 008, Train Loss: 3.6724, Train MAPE: 0.1134, Train RMSE: 7.2777
Iter: 009, Train Loss: 3.4066, Train MAPE: 0.1014, Train RMSE: 6.7455
Iter: 010, Train Loss: 3.3103, Train MAPE: 0.0904, Train RMSE: 6.4871
Iter: 011, Train Loss: 3.4512, Train MAPE: 0.1015, Train RMSE: 6.9505
Iter: 012, Train Loss: 3.3705, Train MAPE: 0.0919, Train RMSE: 6.6544
Iter: 013, Train Loss: 3.4893, Train MAPE: 0.0917, Train RMSE: 6.8473
Iter: 014, Train Loss: 3.3649, Train MAPE: 0.0834, Train RMSE: 6.6896
Iter: 015, Train Loss: 3.2246, Train MAPE: 0.0875, Train RMSE: 6.3635
Iter: 016, Train Loss: 3.2598, Train MAPE: 0.0860, Train RMSE: 6.4480
Iter: 017, Train Loss: 3.2032, Train MAPE: 0.0892, Train RMSE: 6.4020
Iter: 018, Train Loss: 3.4525, Train MAPE: 0.0952, Train RMSE: 6.7673
Iter: 019, Train Loss: 3.2340, Train MAPE: 0.0888, Train RMSE: 6.6123
Iter: 020, Train Loss: 3.5984, Train MAPE: 0.1071, Train RMSE: 7.3005
Iter: 021, Train Loss: 3.3986, Train MAPE: 0.0909, Train RMSE: 6.9304
Iter: 022, Train Loss: 3.5428, Train MAPE: 0.0992, Train RMSE: 6.9675
Iter: 023, Train Loss: 3.3139, Train MAPE: 0.0895, Train RMSE: 6.6318
Iter: 024, Train Loss: 3.6373, Train MAPE: 0.1006, Train RMSE: 7.1577
Iter: 025, Train Loss: 3.4578, Train MAPE: 0.0934, Train RMSE: 6.8497
Iter: 026, Train Loss: 3.3920, Train MAPE: 0.1011, Train RMSE: 6.8962
Iter: 027, Train Loss: 3.3385, Train MAPE: 0.0909, Train RMSE: 6.7582
Iter: 028, Train Loss: 3.1039, Train MAPE: 0.0790, Train RMSE: 6.0172
Iter: 029, Train Loss: 3.5745, Train MAPE: 0.0969, Train RMSE: 7.0914
Iter: 030, Train Loss: 3.2328, Train MAPE: 0.0843, Train RMSE: 6.5013
Iter: 031, Train Loss: 3.6392, Train MAPE: 0.1046, Train RMSE: 7.3569
Iter: 032, Train Loss: 3.3832, Train MAPE: 0.0870, Train RMSE: 6.5465