File size: 222,979 Bytes
e3ae526 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 |
2022-11-06 20:36:44,639 ----------------------------------------------------------------------------------------------------
2022-11-06 20:36:44,639 Model: "SequenceTagger(
(embeddings): StackedEmbeddings(
(list_embedding_0): FlairEmbeddings(
(lm): LanguageModel(
(drop): Dropout(p=0.1, inplace=False)
(encoder): Embedding(962, 100)
(rnn): LSTM(100, 1024)
(decoder): Linear(in_features=1024, out_features=962, bias=True)
)
)
(list_embedding_1): FlairEmbeddings(
(lm): LanguageModel(
(drop): Dropout(p=0.1, inplace=False)
(encoder): Embedding(962, 100)
(rnn): LSTM(100, 1024)
(decoder): Linear(in_features=1024, out_features=962, bias=True)
)
)
)
(dropout): Dropout(p=0.005334913013756493, inplace=False)
(word_dropout): WordDropout(p=0.05)
(locked_dropout): LockedDropout(p=0.5)
(embedding2nn): Linear(in_features=2048, out_features=2048, bias=True)
(rnn): LSTM(2048, 256, batch_first=True, bidirectional=True)
(linear): Linear(in_features=512, out_features=20, bias=True)
(loss_function): ViterbiLoss()
(crf): CRF()
)"
2022-11-06 20:36:44,639 ----------------------------------------------------------------------------------------------------
2022-11-06 20:36:44,639 Corpus: "Corpus: 5496 train + 672 dev + 892 test sentences"
2022-11-06 20:36:44,639 ----------------------------------------------------------------------------------------------------
2022-11-06 20:36:44,640 Parameters:
2022-11-06 20:36:44,640 - learning_rate: "0.100000"
2022-11-06 20:36:44,640 - mini_batch_size: "16"
2022-11-06 20:36:44,640 - patience: "3"
2022-11-06 20:36:44,640 - anneal_factor: "0.5"
2022-11-06 20:36:44,640 - max_epochs: "150"
2022-11-06 20:36:44,640 - shuffle: "True"
2022-11-06 20:36:44,640 - train_with_dev: "True"
2022-11-06 20:36:44,640 - batch_growth_annealing: "False"
2022-11-06 20:36:44,640 ----------------------------------------------------------------------------------------------------
2022-11-06 20:36:44,640 Model training base path: "pos-tests/uk.flairembeddings.champ"
2022-11-06 20:36:44,640 ----------------------------------------------------------------------------------------------------
2022-11-06 20:36:44,640 Device: cuda:0
2022-11-06 20:36:44,640 ----------------------------------------------------------------------------------------------------
2022-11-06 20:36:44,641 Embeddings storage mode: cpu
2022-11-06 20:36:44,641 ----------------------------------------------------------------------------------------------------
2022-11-06 20:36:52,658 epoch 1 - iter 38/386 - loss 1.88961718 - samples/sec: 75.87 - lr: 0.100000
2022-11-06 20:36:58,417 epoch 1 - iter 76/386 - loss 1.47768008 - samples/sec: 105.63 - lr: 0.100000
2022-11-06 20:37:05,566 epoch 1 - iter 114/386 - loss 1.19063825 - samples/sec: 85.09 - lr: 0.100000
2022-11-06 20:37:11,577 epoch 1 - iter 152/386 - loss 1.02974511 - samples/sec: 101.19 - lr: 0.100000
2022-11-06 20:37:18,761 epoch 1 - iter 190/386 - loss 0.92486845 - samples/sec: 84.67 - lr: 0.100000
2022-11-06 20:37:27,941 epoch 1 - iter 228/386 - loss 0.81813485 - samples/sec: 66.25 - lr: 0.100000
2022-11-06 20:37:35,445 epoch 1 - iter 266/386 - loss 0.77842545 - samples/sec: 81.05 - lr: 0.100000
2022-11-06 20:37:43,156 epoch 1 - iter 304/386 - loss 0.72645892 - samples/sec: 78.89 - lr: 0.100000
2022-11-06 20:37:51,456 epoch 1 - iter 342/386 - loss 0.68857673 - samples/sec: 73.28 - lr: 0.100000
2022-11-06 20:37:59,557 epoch 1 - iter 380/386 - loss 0.65020291 - samples/sec: 75.08 - lr: 0.100000
2022-11-06 20:38:00,729 ----------------------------------------------------------------------------------------------------
2022-11-06 20:38:00,729 EPOCH 1 done: loss 0.6450 - lr 0.100000
2022-11-06 20:38:14,564 Evaluating as a multi-label problem: False
2022-11-06 20:38:14,679 TEST : loss 0.17086097598075867 - f1-score (micro avg) 0.9512
2022-11-06 20:38:14,794 BAD EPOCHS (no improvement): 0
2022-11-06 20:38:14,951 ----------------------------------------------------------------------------------------------------
2022-11-06 20:38:20,209 epoch 2 - iter 38/386 - loss 0.35452940 - samples/sec: 115.73 - lr: 0.100000
2022-11-06 20:38:26,172 epoch 2 - iter 76/386 - loss 0.35270834 - samples/sec: 102.02 - lr: 0.100000
2022-11-06 20:38:31,703 epoch 2 - iter 114/386 - loss 0.35270777 - samples/sec: 109.98 - lr: 0.100000
2022-11-06 20:38:37,311 epoch 2 - iter 152/386 - loss 0.34728297 - samples/sec: 108.47 - lr: 0.100000
2022-11-06 20:38:42,764 epoch 2 - iter 190/386 - loss 0.34372065 - samples/sec: 111.57 - lr: 0.100000
2022-11-06 20:38:48,305 epoch 2 - iter 228/386 - loss 0.34270320 - samples/sec: 109.78 - lr: 0.100000
2022-11-06 20:38:54,041 epoch 2 - iter 266/386 - loss 0.33850648 - samples/sec: 106.07 - lr: 0.100000
2022-11-06 20:38:59,745 epoch 2 - iter 304/386 - loss 0.33432568 - samples/sec: 106.65 - lr: 0.100000
2022-11-06 20:39:05,211 epoch 2 - iter 342/386 - loss 0.33065647 - samples/sec: 111.30 - lr: 0.100000
2022-11-06 20:39:10,663 epoch 2 - iter 380/386 - loss 0.32681839 - samples/sec: 111.58 - lr: 0.100000
2022-11-06 20:39:11,364 ----------------------------------------------------------------------------------------------------
2022-11-06 20:39:11,365 EPOCH 2 done: loss 0.3262 - lr 0.100000
2022-11-06 20:39:20,976 Evaluating as a multi-label problem: False
2022-11-06 20:39:21,093 TEST : loss 0.13997453451156616 - f1-score (micro avg) 0.9582
2022-11-06 20:39:21,207 BAD EPOCHS (no improvement): 0
2022-11-06 20:39:21,434 ----------------------------------------------------------------------------------------------------
2022-11-06 20:39:26,741 epoch 3 - iter 38/386 - loss 0.29294011 - samples/sec: 114.64 - lr: 0.100000
2022-11-06 20:39:31,872 epoch 3 - iter 76/386 - loss 0.29017001 - samples/sec: 118.58 - lr: 0.100000
2022-11-06 20:39:37,423 epoch 3 - iter 114/386 - loss 0.29170149 - samples/sec: 109.59 - lr: 0.100000
2022-11-06 20:39:42,974 epoch 3 - iter 152/386 - loss 0.29094573 - samples/sec: 109.60 - lr: 0.100000
2022-11-06 20:39:48,865 epoch 3 - iter 190/386 - loss 0.28629286 - samples/sec: 103.25 - lr: 0.100000
2022-11-06 20:39:54,264 epoch 3 - iter 228/386 - loss 0.28390933 - samples/sec: 112.68 - lr: 0.100000
2022-11-06 20:39:59,891 epoch 3 - iter 266/386 - loss 0.28294971 - samples/sec: 108.12 - lr: 0.100000
2022-11-06 20:40:05,418 epoch 3 - iter 304/386 - loss 0.28193462 - samples/sec: 110.08 - lr: 0.100000
2022-11-06 20:40:11,108 epoch 3 - iter 342/386 - loss 0.28075670 - samples/sec: 106.91 - lr: 0.100000
2022-11-06 20:40:16,699 epoch 3 - iter 380/386 - loss 0.28035510 - samples/sec: 108.80 - lr: 0.100000
2022-11-06 20:40:17,449 ----------------------------------------------------------------------------------------------------
2022-11-06 20:40:17,449 EPOCH 3 done: loss 0.2802 - lr 0.100000
2022-11-06 20:40:27,148 Evaluating as a multi-label problem: False
2022-11-06 20:40:27,264 TEST : loss 0.12084240466356277 - f1-score (micro avg) 0.9638
2022-11-06 20:40:27,378 BAD EPOCHS (no improvement): 0
2022-11-06 20:40:27,583 ----------------------------------------------------------------------------------------------------
2022-11-06 20:40:32,829 epoch 4 - iter 38/386 - loss 0.25615401 - samples/sec: 115.99 - lr: 0.100000
2022-11-06 20:40:38,449 epoch 4 - iter 76/386 - loss 0.25978411 - samples/sec: 108.25 - lr: 0.100000
2022-11-06 20:40:43,530 epoch 4 - iter 114/386 - loss 0.25883124 - samples/sec: 119.73 - lr: 0.100000
2022-11-06 20:40:49,225 epoch 4 - iter 152/386 - loss 0.25933191 - samples/sec: 106.82 - lr: 0.100000
2022-11-06 20:40:54,682 epoch 4 - iter 190/386 - loss 0.25994752 - samples/sec: 111.48 - lr: 0.100000
2022-11-06 20:41:00,117 epoch 4 - iter 228/386 - loss 0.25830164 - samples/sec: 111.94 - lr: 0.100000
2022-11-06 20:41:06,007 epoch 4 - iter 266/386 - loss 0.25880376 - samples/sec: 103.27 - lr: 0.100000
2022-11-06 20:41:11,863 epoch 4 - iter 304/386 - loss 0.25683624 - samples/sec: 103.89 - lr: 0.100000
2022-11-06 20:41:17,322 epoch 4 - iter 342/386 - loss 0.25543810 - samples/sec: 111.44 - lr: 0.100000
2022-11-06 20:41:22,801 epoch 4 - iter 380/386 - loss 0.25652313 - samples/sec: 111.03 - lr: 0.100000
2022-11-06 20:41:23,731 ----------------------------------------------------------------------------------------------------
2022-11-06 20:41:23,732 EPOCH 4 done: loss 0.2567 - lr 0.100000
2022-11-06 20:41:35,645 Evaluating as a multi-label problem: False
2022-11-06 20:41:35,761 TEST : loss 0.10487399250268936 - f1-score (micro avg) 0.9673
2022-11-06 20:41:35,875 BAD EPOCHS (no improvement): 0
2022-11-06 20:41:36,092 ----------------------------------------------------------------------------------------------------
2022-11-06 20:41:41,723 epoch 5 - iter 38/386 - loss 0.22752191 - samples/sec: 108.07 - lr: 0.100000
2022-11-06 20:41:47,194 epoch 5 - iter 76/386 - loss 0.23877064 - samples/sec: 111.20 - lr: 0.100000
2022-11-06 20:41:53,184 epoch 5 - iter 114/386 - loss 0.23723243 - samples/sec: 101.55 - lr: 0.100000
2022-11-06 20:41:58,276 epoch 5 - iter 152/386 - loss 0.23492548 - samples/sec: 120.16 - lr: 0.100000
2022-11-06 20:42:04,003 epoch 5 - iter 190/386 - loss 0.23384590 - samples/sec: 106.22 - lr: 0.100000
2022-11-06 20:42:09,511 epoch 5 - iter 228/386 - loss 0.23679768 - samples/sec: 110.44 - lr: 0.100000
2022-11-06 20:42:15,048 epoch 5 - iter 266/386 - loss 0.23705954 - samples/sec: 109.88 - lr: 0.100000
2022-11-06 20:42:20,836 epoch 5 - iter 304/386 - loss 0.23739395 - samples/sec: 105.09 - lr: 0.100000
2022-11-06 20:42:26,380 epoch 5 - iter 342/386 - loss 0.23844616 - samples/sec: 109.72 - lr: 0.100000
2022-11-06 20:42:31,800 epoch 5 - iter 380/386 - loss 0.23836010 - samples/sec: 112.25 - lr: 0.100000
2022-11-06 20:42:32,565 ----------------------------------------------------------------------------------------------------
2022-11-06 20:42:32,566 EPOCH 5 done: loss 0.2380 - lr 0.100000
2022-11-06 20:42:42,191 Evaluating as a multi-label problem: False
2022-11-06 20:42:42,309 TEST : loss 0.10361631959676743 - f1-score (micro avg) 0.9666
2022-11-06 20:42:42,424 BAD EPOCHS (no improvement): 0
2022-11-06 20:42:42,637 ----------------------------------------------------------------------------------------------------
2022-11-06 20:42:48,102 epoch 6 - iter 38/386 - loss 0.22899429 - samples/sec: 111.34 - lr: 0.100000
2022-11-06 20:42:53,390 epoch 6 - iter 76/386 - loss 0.22634441 - samples/sec: 115.06 - lr: 0.100000
2022-11-06 20:42:58,802 epoch 6 - iter 114/386 - loss 0.22738383 - samples/sec: 112.40 - lr: 0.100000
2022-11-06 20:43:04,281 epoch 6 - iter 152/386 - loss 0.22831817 - samples/sec: 111.04 - lr: 0.100000
2022-11-06 20:43:09,553 epoch 6 - iter 190/386 - loss 0.22899114 - samples/sec: 115.40 - lr: 0.100000
2022-11-06 20:43:15,176 epoch 6 - iter 228/386 - loss 0.22503611 - samples/sec: 108.19 - lr: 0.100000
2022-11-06 20:43:20,596 epoch 6 - iter 266/386 - loss 0.22562923 - samples/sec: 112.24 - lr: 0.100000
2022-11-06 20:43:26,484 epoch 6 - iter 304/386 - loss 0.22385177 - samples/sec: 103.31 - lr: 0.100000
2022-11-06 20:43:32,694 epoch 6 - iter 342/386 - loss 0.22503902 - samples/sec: 97.96 - lr: 0.100000
2022-11-06 20:43:38,008 epoch 6 - iter 380/386 - loss 0.22506621 - samples/sec: 114.49 - lr: 0.100000
2022-11-06 20:43:38,867 ----------------------------------------------------------------------------------------------------
2022-11-06 20:43:38,867 EPOCH 6 done: loss 0.2247 - lr 0.100000
2022-11-06 20:43:48,534 Evaluating as a multi-label problem: False
2022-11-06 20:43:48,652 TEST : loss 0.10305152833461761 - f1-score (micro avg) 0.9663
2022-11-06 20:43:48,766 BAD EPOCHS (no improvement): 0
2022-11-06 20:43:48,980 ----------------------------------------------------------------------------------------------------
2022-11-06 20:43:54,333 epoch 7 - iter 38/386 - loss 0.22529638 - samples/sec: 113.68 - lr: 0.100000
2022-11-06 20:44:00,048 epoch 7 - iter 76/386 - loss 0.21567440 - samples/sec: 106.44 - lr: 0.100000
2022-11-06 20:44:05,556 epoch 7 - iter 114/386 - loss 0.22089919 - samples/sec: 110.46 - lr: 0.100000
2022-11-06 20:44:11,230 epoch 7 - iter 152/386 - loss 0.22059846 - samples/sec: 107.20 - lr: 0.100000
2022-11-06 20:44:16,903 epoch 7 - iter 190/386 - loss 0.22095491 - samples/sec: 107.25 - lr: 0.100000
2022-11-06 20:44:22,101 epoch 7 - iter 228/386 - loss 0.21976408 - samples/sec: 117.04 - lr: 0.100000
2022-11-06 20:44:27,485 epoch 7 - iter 266/386 - loss 0.22107223 - samples/sec: 112.99 - lr: 0.100000
2022-11-06 20:44:33,198 epoch 7 - iter 304/386 - loss 0.21947713 - samples/sec: 106.49 - lr: 0.100000
2022-11-06 20:44:38,728 epoch 7 - iter 342/386 - loss 0.21871513 - samples/sec: 110.01 - lr: 0.100000
2022-11-06 20:44:44,565 epoch 7 - iter 380/386 - loss 0.21872143 - samples/sec: 104.22 - lr: 0.100000
2022-11-06 20:44:45,335 ----------------------------------------------------------------------------------------------------
2022-11-06 20:44:45,336 EPOCH 7 done: loss 0.2184 - lr 0.100000
2022-11-06 20:44:55,019 Evaluating as a multi-label problem: False
2022-11-06 20:44:55,137 TEST : loss 0.09460901468992233 - f1-score (micro avg) 0.9693
2022-11-06 20:44:55,250 BAD EPOCHS (no improvement): 0
2022-11-06 20:44:55,463 ----------------------------------------------------------------------------------------------------
2022-11-06 20:45:01,495 epoch 8 - iter 38/386 - loss 0.21493772 - samples/sec: 100.86 - lr: 0.100000
2022-11-06 20:45:07,230 epoch 8 - iter 76/386 - loss 0.21122181 - samples/sec: 106.09 - lr: 0.100000
2022-11-06 20:45:12,511 epoch 8 - iter 114/386 - loss 0.20912935 - samples/sec: 115.20 - lr: 0.100000
2022-11-06 20:45:18,013 epoch 8 - iter 152/386 - loss 0.20730821 - samples/sec: 110.57 - lr: 0.100000
2022-11-06 20:45:23,128 epoch 8 - iter 190/386 - loss 0.20626902 - samples/sec: 118.92 - lr: 0.100000
2022-11-06 20:45:28,405 epoch 8 - iter 228/386 - loss 0.20860099 - samples/sec: 115.29 - lr: 0.100000
2022-11-06 20:45:33,414 epoch 8 - iter 266/386 - loss 0.20959349 - samples/sec: 121.46 - lr: 0.100000
2022-11-06 20:45:39,424 epoch 8 - iter 304/386 - loss 0.20963436 - samples/sec: 101.22 - lr: 0.100000
2022-11-06 20:45:45,212 epoch 8 - iter 342/386 - loss 0.20919203 - samples/sec: 105.10 - lr: 0.100000
2022-11-06 20:45:50,812 epoch 8 - iter 380/386 - loss 0.20887056 - samples/sec: 108.64 - lr: 0.100000
2022-11-06 20:45:51,741 ----------------------------------------------------------------------------------------------------
2022-11-06 20:45:51,742 EPOCH 8 done: loss 0.2089 - lr 0.100000
2022-11-06 20:46:01,411 Evaluating as a multi-label problem: False
2022-11-06 20:46:01,529 TEST : loss 0.09638751298189163 - f1-score (micro avg) 0.969
2022-11-06 20:46:01,643 BAD EPOCHS (no improvement): 0
2022-11-06 20:46:01,851 ----------------------------------------------------------------------------------------------------
2022-11-06 20:46:07,202 epoch 9 - iter 38/386 - loss 0.21385281 - samples/sec: 113.71 - lr: 0.100000
2022-11-06 20:46:12,883 epoch 9 - iter 76/386 - loss 0.20570144 - samples/sec: 107.08 - lr: 0.100000
2022-11-06 20:46:18,474 epoch 9 - iter 114/386 - loss 0.19932819 - samples/sec: 108.82 - lr: 0.100000
2022-11-06 20:46:24,012 epoch 9 - iter 152/386 - loss 0.19956175 - samples/sec: 109.85 - lr: 0.100000
2022-11-06 20:46:29,555 epoch 9 - iter 190/386 - loss 0.20140471 - samples/sec: 109.74 - lr: 0.100000
2022-11-06 20:46:35,896 epoch 9 - iter 228/386 - loss 0.20212131 - samples/sec: 95.93 - lr: 0.100000
2022-11-06 20:46:41,010 epoch 9 - iter 266/386 - loss 0.20129877 - samples/sec: 118.97 - lr: 0.100000
2022-11-06 20:46:46,315 epoch 9 - iter 304/386 - loss 0.20214050 - samples/sec: 114.68 - lr: 0.100000
2022-11-06 20:46:51,766 epoch 9 - iter 342/386 - loss 0.20216261 - samples/sec: 111.61 - lr: 0.100000
2022-11-06 20:46:57,274 epoch 9 - iter 380/386 - loss 0.20182060 - samples/sec: 110.45 - lr: 0.100000
2022-11-06 20:46:58,137 ----------------------------------------------------------------------------------------------------
2022-11-06 20:46:58,137 EPOCH 9 done: loss 0.2015 - lr 0.100000
2022-11-06 20:47:07,877 Evaluating as a multi-label problem: False
2022-11-06 20:47:07,993 TEST : loss 0.09079930186271667 - f1-score (micro avg) 0.9707
2022-11-06 20:47:08,107 BAD EPOCHS (no improvement): 0
2022-11-06 20:47:08,312 ----------------------------------------------------------------------------------------------------
2022-11-06 20:47:14,371 epoch 10 - iter 38/386 - loss 0.18753357 - samples/sec: 100.43 - lr: 0.100000
2022-11-06 20:47:19,847 epoch 10 - iter 76/386 - loss 0.18895331 - samples/sec: 111.09 - lr: 0.100000
2022-11-06 20:47:25,633 epoch 10 - iter 114/386 - loss 0.19191244 - samples/sec: 105.14 - lr: 0.100000
2022-11-06 20:47:31,164 epoch 10 - iter 152/386 - loss 0.19083482 - samples/sec: 109.99 - lr: 0.100000
2022-11-06 20:47:37,079 epoch 10 - iter 190/386 - loss 0.19443441 - samples/sec: 102.84 - lr: 0.100000
2022-11-06 20:47:42,890 epoch 10 - iter 228/386 - loss 0.19279406 - samples/sec: 104.70 - lr: 0.100000
2022-11-06 20:47:48,533 epoch 10 - iter 266/386 - loss 0.19340536 - samples/sec: 107.79 - lr: 0.100000
2022-11-06 20:47:53,622 epoch 10 - iter 304/386 - loss 0.19422661 - samples/sec: 119.56 - lr: 0.100000
2022-11-06 20:47:58,679 epoch 10 - iter 342/386 - loss 0.19472214 - samples/sec: 120.31 - lr: 0.100000
2022-11-06 20:48:04,250 epoch 10 - iter 380/386 - loss 0.19471480 - samples/sec: 109.20 - lr: 0.100000
2022-11-06 20:48:05,211 ----------------------------------------------------------------------------------------------------
2022-11-06 20:48:05,211 EPOCH 10 done: loss 0.1945 - lr 0.100000
2022-11-06 20:48:14,925 Evaluating as a multi-label problem: False
2022-11-06 20:48:15,042 TEST : loss 0.09166789054870605 - f1-score (micro avg) 0.97
2022-11-06 20:48:15,155 BAD EPOCHS (no improvement): 0
2022-11-06 20:48:15,371 ----------------------------------------------------------------------------------------------------
2022-11-06 20:48:20,625 epoch 11 - iter 38/386 - loss 0.19465953 - samples/sec: 115.82 - lr: 0.100000
2022-11-06 20:48:26,212 epoch 11 - iter 76/386 - loss 0.18636815 - samples/sec: 108.88 - lr: 0.100000
2022-11-06 20:48:31,875 epoch 11 - iter 114/386 - loss 0.18495775 - samples/sec: 107.43 - lr: 0.100000
2022-11-06 20:48:37,842 epoch 11 - iter 152/386 - loss 0.18408789 - samples/sec: 101.94 - lr: 0.100000
2022-11-06 20:48:44,103 epoch 11 - iter 190/386 - loss 0.18540037 - samples/sec: 97.16 - lr: 0.100000
2022-11-06 20:48:49,724 epoch 11 - iter 228/386 - loss 0.18697837 - samples/sec: 108.22 - lr: 0.100000
2022-11-06 20:48:55,206 epoch 11 - iter 266/386 - loss 0.18772444 - samples/sec: 110.97 - lr: 0.100000
2022-11-06 20:49:00,750 epoch 11 - iter 304/386 - loss 0.18780846 - samples/sec: 109.74 - lr: 0.100000
2022-11-06 20:49:05,829 epoch 11 - iter 342/386 - loss 0.18780680 - samples/sec: 119.77 - lr: 0.100000
2022-11-06 20:49:11,200 epoch 11 - iter 380/386 - loss 0.18794317 - samples/sec: 113.29 - lr: 0.100000
2022-11-06 20:49:11,957 ----------------------------------------------------------------------------------------------------
2022-11-06 20:49:11,957 EPOCH 11 done: loss 0.1881 - lr 0.100000
2022-11-06 20:49:21,692 Evaluating as a multi-label problem: False
2022-11-06 20:49:21,810 TEST : loss 0.08823293447494507 - f1-score (micro avg) 0.9713
2022-11-06 20:49:21,925 BAD EPOCHS (no improvement): 0
2022-11-06 20:49:22,138 ----------------------------------------------------------------------------------------------------
2022-11-06 20:49:27,810 epoch 12 - iter 38/386 - loss 0.19030824 - samples/sec: 107.26 - lr: 0.100000
2022-11-06 20:49:33,408 epoch 12 - iter 76/386 - loss 0.19090830 - samples/sec: 108.69 - lr: 0.100000
2022-11-06 20:49:39,084 epoch 12 - iter 114/386 - loss 0.18845799 - samples/sec: 107.18 - lr: 0.100000
2022-11-06 20:49:44,918 epoch 12 - iter 152/386 - loss 0.18810649 - samples/sec: 104.27 - lr: 0.100000
2022-11-06 20:49:50,979 epoch 12 - iter 190/386 - loss 0.18715379 - samples/sec: 100.38 - lr: 0.100000
2022-11-06 20:49:56,472 epoch 12 - iter 228/386 - loss 0.18446952 - samples/sec: 110.75 - lr: 0.100000
2022-11-06 20:50:01,831 epoch 12 - iter 266/386 - loss 0.18390291 - samples/sec: 113.51 - lr: 0.100000
2022-11-06 20:50:07,542 epoch 12 - iter 304/386 - loss 0.18496511 - samples/sec: 106.53 - lr: 0.100000
2022-11-06 20:50:13,032 epoch 12 - iter 342/386 - loss 0.18602052 - samples/sec: 110.81 - lr: 0.100000
2022-11-06 20:50:18,370 epoch 12 - iter 380/386 - loss 0.18607219 - samples/sec: 113.98 - lr: 0.100000
2022-11-06 20:50:19,126 ----------------------------------------------------------------------------------------------------
2022-11-06 20:50:19,126 EPOCH 12 done: loss 0.1864 - lr 0.100000
2022-11-06 20:50:28,390 Evaluating as a multi-label problem: False
2022-11-06 20:50:28,509 TEST : loss 0.09221376478672028 - f1-score (micro avg) 0.9697
2022-11-06 20:50:28,623 BAD EPOCHS (no improvement): 0
2022-11-06 20:50:28,837 ----------------------------------------------------------------------------------------------------
2022-11-06 20:50:34,166 epoch 13 - iter 38/386 - loss 0.18837518 - samples/sec: 114.18 - lr: 0.100000
2022-11-06 20:50:39,865 epoch 13 - iter 76/386 - loss 0.18305753 - samples/sec: 106.86 - lr: 0.100000
2022-11-06 20:50:45,311 epoch 13 - iter 114/386 - loss 0.17754302 - samples/sec: 111.72 - lr: 0.100000
2022-11-06 20:50:50,952 epoch 13 - iter 152/386 - loss 0.17535643 - samples/sec: 107.84 - lr: 0.100000
2022-11-06 20:50:56,810 epoch 13 - iter 190/386 - loss 0.17662633 - samples/sec: 103.85 - lr: 0.100000
2022-11-06 20:51:02,296 epoch 13 - iter 228/386 - loss 0.17879492 - samples/sec: 110.88 - lr: 0.100000
2022-11-06 20:51:08,204 epoch 13 - iter 266/386 - loss 0.17749818 - samples/sec: 102.98 - lr: 0.100000
2022-11-06 20:51:13,932 epoch 13 - iter 304/386 - loss 0.17909369 - samples/sec: 106.20 - lr: 0.100000
2022-11-06 20:51:19,654 epoch 13 - iter 342/386 - loss 0.17936686 - samples/sec: 106.33 - lr: 0.100000
2022-11-06 20:51:25,470 epoch 13 - iter 380/386 - loss 0.17907693 - samples/sec: 104.60 - lr: 0.100000
2022-11-06 20:51:26,408 ----------------------------------------------------------------------------------------------------
2022-11-06 20:51:26,409 EPOCH 13 done: loss 0.1789 - lr 0.100000
2022-11-06 20:51:35,303 Evaluating as a multi-label problem: False
2022-11-06 20:51:35,420 TEST : loss 0.08666753023862839 - f1-score (micro avg) 0.9711
2022-11-06 20:51:35,535 BAD EPOCHS (no improvement): 0
2022-11-06 20:51:35,751 ----------------------------------------------------------------------------------------------------
2022-11-06 20:51:41,328 epoch 14 - iter 38/386 - loss 0.17659370 - samples/sec: 109.11 - lr: 0.100000
2022-11-06 20:51:46,724 epoch 14 - iter 76/386 - loss 0.17717785 - samples/sec: 112.74 - lr: 0.100000
2022-11-06 20:51:52,363 epoch 14 - iter 114/386 - loss 0.17727583 - samples/sec: 107.88 - lr: 0.100000
2022-11-06 20:51:57,835 epoch 14 - iter 152/386 - loss 0.17558755 - samples/sec: 111.18 - lr: 0.100000
2022-11-06 20:52:03,438 epoch 14 - iter 190/386 - loss 0.17647551 - samples/sec: 108.57 - lr: 0.100000
2022-11-06 20:52:09,148 epoch 14 - iter 228/386 - loss 0.17614281 - samples/sec: 106.54 - lr: 0.100000
2022-11-06 20:52:14,747 epoch 14 - iter 266/386 - loss 0.17762340 - samples/sec: 108.66 - lr: 0.100000
2022-11-06 20:52:20,348 epoch 14 - iter 304/386 - loss 0.17659061 - samples/sec: 108.60 - lr: 0.100000
2022-11-06 20:52:26,079 epoch 14 - iter 342/386 - loss 0.17538245 - samples/sec: 106.17 - lr: 0.100000
2022-11-06 20:52:31,932 epoch 14 - iter 380/386 - loss 0.17530091 - samples/sec: 103.93 - lr: 0.100000
2022-11-06 20:52:32,726 ----------------------------------------------------------------------------------------------------
2022-11-06 20:52:32,727 EPOCH 14 done: loss 0.1751 - lr 0.100000
2022-11-06 20:52:42,073 Evaluating as a multi-label problem: False
2022-11-06 20:52:42,190 TEST : loss 0.08278612792491913 - f1-score (micro avg) 0.9732
2022-11-06 20:52:42,302 BAD EPOCHS (no improvement): 0
2022-11-06 20:52:42,515 ----------------------------------------------------------------------------------------------------
2022-11-06 20:52:47,798 epoch 15 - iter 38/386 - loss 0.16309852 - samples/sec: 115.19 - lr: 0.100000
2022-11-06 20:52:53,422 epoch 15 - iter 76/386 - loss 0.16417836 - samples/sec: 108.17 - lr: 0.100000
2022-11-06 20:52:59,582 epoch 15 - iter 114/386 - loss 0.16900543 - samples/sec: 98.75 - lr: 0.100000
2022-11-06 20:53:04,615 epoch 15 - iter 152/386 - loss 0.16798509 - samples/sec: 120.89 - lr: 0.100000
2022-11-06 20:53:10,430 epoch 15 - iter 190/386 - loss 0.17086367 - samples/sec: 104.61 - lr: 0.100000
2022-11-06 20:53:16,529 epoch 15 - iter 228/386 - loss 0.16985321 - samples/sec: 99.75 - lr: 0.100000
2022-11-06 20:53:21,908 epoch 15 - iter 266/386 - loss 0.17032852 - samples/sec: 113.10 - lr: 0.100000
2022-11-06 20:53:27,343 epoch 15 - iter 304/386 - loss 0.17092036 - samples/sec: 111.92 - lr: 0.100000
2022-11-06 20:53:33,186 epoch 15 - iter 342/386 - loss 0.17242412 - samples/sec: 104.12 - lr: 0.100000
2022-11-06 20:53:38,393 epoch 15 - iter 380/386 - loss 0.17357857 - samples/sec: 116.85 - lr: 0.100000
2022-11-06 20:53:39,106 ----------------------------------------------------------------------------------------------------
2022-11-06 20:53:39,107 EPOCH 15 done: loss 0.1732 - lr 0.100000
2022-11-06 20:53:51,076 Evaluating as a multi-label problem: False
2022-11-06 20:53:51,192 TEST : loss 0.08690010011196136 - f1-score (micro avg) 0.972
2022-11-06 20:53:51,306 BAD EPOCHS (no improvement): 0
2022-11-06 20:53:51,510 ----------------------------------------------------------------------------------------------------
2022-11-06 20:53:56,589 epoch 16 - iter 38/386 - loss 0.17036775 - samples/sec: 119.81 - lr: 0.100000
2022-11-06 20:54:02,420 epoch 16 - iter 76/386 - loss 0.16920502 - samples/sec: 104.34 - lr: 0.100000
2022-11-06 20:54:07,695 epoch 16 - iter 114/386 - loss 0.16618744 - samples/sec: 115.32 - lr: 0.100000
2022-11-06 20:54:13,031 epoch 16 - iter 152/386 - loss 0.16660212 - samples/sec: 114.03 - lr: 0.100000
2022-11-06 20:54:18,443 epoch 16 - iter 190/386 - loss 0.16844597 - samples/sec: 112.40 - lr: 0.100000
2022-11-06 20:54:23,874 epoch 16 - iter 228/386 - loss 0.16658228 - samples/sec: 112.01 - lr: 0.100000
2022-11-06 20:54:29,566 epoch 16 - iter 266/386 - loss 0.16713623 - samples/sec: 106.89 - lr: 0.100000
2022-11-06 20:54:35,356 epoch 16 - iter 304/386 - loss 0.16775392 - samples/sec: 105.07 - lr: 0.100000
2022-11-06 20:54:40,973 epoch 16 - iter 342/386 - loss 0.16753092 - samples/sec: 108.31 - lr: 0.100000
2022-11-06 20:54:47,190 epoch 16 - iter 380/386 - loss 0.16711554 - samples/sec: 97.84 - lr: 0.100000
2022-11-06 20:54:48,047 ----------------------------------------------------------------------------------------------------
2022-11-06 20:54:48,047 EPOCH 16 done: loss 0.1675 - lr 0.100000
2022-11-06 20:54:57,695 Evaluating as a multi-label problem: False
2022-11-06 20:54:57,813 TEST : loss 0.08330953121185303 - f1-score (micro avg) 0.9734
2022-11-06 20:54:57,930 BAD EPOCHS (no improvement): 0
2022-11-06 20:54:58,141 ----------------------------------------------------------------------------------------------------
2022-11-06 20:55:03,292 epoch 17 - iter 38/386 - loss 0.17502829 - samples/sec: 118.14 - lr: 0.100000
2022-11-06 20:55:08,797 epoch 17 - iter 76/386 - loss 0.16843116 - samples/sec: 110.51 - lr: 0.100000
2022-11-06 20:55:13,948 epoch 17 - iter 114/386 - loss 0.16803005 - samples/sec: 118.11 - lr: 0.100000
2022-11-06 20:55:19,380 epoch 17 - iter 152/386 - loss 0.16644258 - samples/sec: 111.98 - lr: 0.100000
2022-11-06 20:55:25,262 epoch 17 - iter 190/386 - loss 0.16591449 - samples/sec: 103.42 - lr: 0.100000
2022-11-06 20:55:31,113 epoch 17 - iter 228/386 - loss 0.16655165 - samples/sec: 103.97 - lr: 0.100000
2022-11-06 20:55:36,772 epoch 17 - iter 266/386 - loss 0.16720170 - samples/sec: 107.52 - lr: 0.100000
2022-11-06 20:55:42,603 epoch 17 - iter 304/386 - loss 0.16818979 - samples/sec: 104.32 - lr: 0.100000
2022-11-06 20:55:48,158 epoch 17 - iter 342/386 - loss 0.16742200 - samples/sec: 109.51 - lr: 0.100000
2022-11-06 20:55:53,675 epoch 17 - iter 380/386 - loss 0.16757147 - samples/sec: 110.28 - lr: 0.100000
2022-11-06 20:55:54,502 ----------------------------------------------------------------------------------------------------
2022-11-06 20:55:54,503 EPOCH 17 done: loss 0.1678 - lr 0.100000
2022-11-06 20:56:04,246 Evaluating as a multi-label problem: False
2022-11-06 20:56:04,363 TEST : loss 0.08121314644813538 - f1-score (micro avg) 0.9744
2022-11-06 20:56:04,477 BAD EPOCHS (no improvement): 1
2022-11-06 20:56:04,688 ----------------------------------------------------------------------------------------------------
2022-11-06 20:56:09,972 epoch 18 - iter 38/386 - loss 0.16529975 - samples/sec: 115.16 - lr: 0.100000
2022-11-06 20:56:15,051 epoch 18 - iter 76/386 - loss 0.16989333 - samples/sec: 119.80 - lr: 0.100000
2022-11-06 20:56:20,209 epoch 18 - iter 114/386 - loss 0.16695045 - samples/sec: 117.94 - lr: 0.100000
2022-11-06 20:56:26,104 epoch 18 - iter 152/386 - loss 0.16782753 - samples/sec: 103.19 - lr: 0.100000
2022-11-06 20:56:31,985 epoch 18 - iter 190/386 - loss 0.16581193 - samples/sec: 103.44 - lr: 0.100000
2022-11-06 20:56:37,824 epoch 18 - iter 228/386 - loss 0.16465238 - samples/sec: 104.19 - lr: 0.100000
2022-11-06 20:56:43,581 epoch 18 - iter 266/386 - loss 0.16401545 - samples/sec: 105.68 - lr: 0.100000
2022-11-06 20:56:49,303 epoch 18 - iter 304/386 - loss 0.16283744 - samples/sec: 106.32 - lr: 0.100000
2022-11-06 20:56:54,990 epoch 18 - iter 342/386 - loss 0.16172837 - samples/sec: 106.96 - lr: 0.100000
2022-11-06 20:57:00,441 epoch 18 - iter 380/386 - loss 0.16220310 - samples/sec: 111.62 - lr: 0.100000
2022-11-06 20:57:01,181 ----------------------------------------------------------------------------------------------------
2022-11-06 20:57:01,182 EPOCH 18 done: loss 0.1623 - lr 0.100000
2022-11-06 20:57:10,902 Evaluating as a multi-label problem: False
2022-11-06 20:57:11,019 TEST : loss 0.07836408168077469 - f1-score (micro avg) 0.9745
2022-11-06 20:57:11,133 BAD EPOCHS (no improvement): 0
2022-11-06 20:57:11,347 ----------------------------------------------------------------------------------------------------
2022-11-06 20:57:17,197 epoch 19 - iter 38/386 - loss 0.16558504 - samples/sec: 104.03 - lr: 0.100000
2022-11-06 20:57:22,661 epoch 19 - iter 76/386 - loss 0.16480656 - samples/sec: 111.34 - lr: 0.100000
2022-11-06 20:57:28,015 epoch 19 - iter 114/386 - loss 0.16165750 - samples/sec: 113.62 - lr: 0.100000
2022-11-06 20:57:33,088 epoch 19 - iter 152/386 - loss 0.16171112 - samples/sec: 119.94 - lr: 0.100000
2022-11-06 20:57:38,973 epoch 19 - iter 190/386 - loss 0.16295973 - samples/sec: 103.36 - lr: 0.100000
2022-11-06 20:57:44,532 epoch 19 - iter 228/386 - loss 0.16345664 - samples/sec: 109.43 - lr: 0.100000
2022-11-06 20:57:50,631 epoch 19 - iter 266/386 - loss 0.16293156 - samples/sec: 99.74 - lr: 0.100000
2022-11-06 20:57:56,147 epoch 19 - iter 304/386 - loss 0.16175446 - samples/sec: 110.30 - lr: 0.100000
2022-11-06 20:58:02,000 epoch 19 - iter 342/386 - loss 0.16181308 - samples/sec: 103.93 - lr: 0.100000
2022-11-06 20:58:07,419 epoch 19 - iter 380/386 - loss 0.16159155 - samples/sec: 112.28 - lr: 0.100000
2022-11-06 20:58:08,189 ----------------------------------------------------------------------------------------------------
2022-11-06 20:58:08,189 EPOCH 19 done: loss 0.1617 - lr 0.100000
2022-11-06 20:58:17,915 Evaluating as a multi-label problem: False
2022-11-06 20:58:18,032 TEST : loss 0.08216149359941483 - f1-score (micro avg) 0.9747
2022-11-06 20:58:18,146 BAD EPOCHS (no improvement): 0
2022-11-06 20:58:18,357 ----------------------------------------------------------------------------------------------------
2022-11-06 20:58:24,010 epoch 20 - iter 38/386 - loss 0.15493718 - samples/sec: 107.64 - lr: 0.100000
2022-11-06 20:58:29,441 epoch 20 - iter 76/386 - loss 0.15938559 - samples/sec: 112.01 - lr: 0.100000
2022-11-06 20:58:35,738 epoch 20 - iter 114/386 - loss 0.16028978 - samples/sec: 96.61 - lr: 0.100000
2022-11-06 20:58:41,385 epoch 20 - iter 152/386 - loss 0.15622031 - samples/sec: 107.72 - lr: 0.100000
2022-11-06 20:58:46,596 epoch 20 - iter 190/386 - loss 0.15824790 - samples/sec: 116.77 - lr: 0.100000
2022-11-06 20:58:51,920 epoch 20 - iter 228/386 - loss 0.15992430 - samples/sec: 114.27 - lr: 0.100000
2022-11-06 20:58:57,417 epoch 20 - iter 266/386 - loss 0.16045659 - samples/sec: 110.66 - lr: 0.100000
2022-11-06 20:59:02,733 epoch 20 - iter 304/386 - loss 0.15924395 - samples/sec: 114.45 - lr: 0.100000
2022-11-06 20:59:08,115 epoch 20 - iter 342/386 - loss 0.15996715 - samples/sec: 113.03 - lr: 0.100000
2022-11-06 20:59:14,095 epoch 20 - iter 380/386 - loss 0.15988839 - samples/sec: 101.72 - lr: 0.100000
2022-11-06 20:59:14,920 ----------------------------------------------------------------------------------------------------
2022-11-06 20:59:14,920 EPOCH 20 done: loss 0.1600 - lr 0.100000
2022-11-06 20:59:24,629 Evaluating as a multi-label problem: False
2022-11-06 20:59:24,747 TEST : loss 0.08073458075523376 - f1-score (micro avg) 0.9737
2022-11-06 20:59:24,861 BAD EPOCHS (no improvement): 0
2022-11-06 20:59:25,069 ----------------------------------------------------------------------------------------------------
2022-11-06 20:59:30,496 epoch 21 - iter 38/386 - loss 0.15646384 - samples/sec: 112.12 - lr: 0.100000
2022-11-06 20:59:36,689 epoch 21 - iter 76/386 - loss 0.15833186 - samples/sec: 98.22 - lr: 0.100000
2022-11-06 20:59:42,302 epoch 21 - iter 114/386 - loss 0.15639674 - samples/sec: 108.39 - lr: 0.100000
2022-11-06 20:59:47,388 epoch 21 - iter 152/386 - loss 0.15523675 - samples/sec: 119.62 - lr: 0.100000
2022-11-06 20:59:52,743 epoch 21 - iter 190/386 - loss 0.15815249 - samples/sec: 113.59 - lr: 0.100000
2022-11-06 20:59:57,973 epoch 21 - iter 228/386 - loss 0.15968102 - samples/sec: 116.33 - lr: 0.100000
2022-11-06 21:00:03,286 epoch 21 - iter 266/386 - loss 0.15926273 - samples/sec: 114.49 - lr: 0.100000
2022-11-06 21:00:08,837 epoch 21 - iter 304/386 - loss 0.15918787 - samples/sec: 109.61 - lr: 0.100000
2022-11-06 21:00:14,457 epoch 21 - iter 342/386 - loss 0.15886766 - samples/sec: 108.23 - lr: 0.100000
2022-11-06 21:00:20,472 epoch 21 - iter 380/386 - loss 0.15755869 - samples/sec: 101.14 - lr: 0.100000
2022-11-06 21:00:21,352 ----------------------------------------------------------------------------------------------------
2022-11-06 21:00:21,352 EPOCH 21 done: loss 0.1575 - lr 0.100000
2022-11-06 21:00:31,068 Evaluating as a multi-label problem: False
2022-11-06 21:00:31,185 TEST : loss 0.08205189555883408 - f1-score (micro avg) 0.9743
2022-11-06 21:00:31,301 BAD EPOCHS (no improvement): 0
2022-11-06 21:00:31,505 ----------------------------------------------------------------------------------------------------
2022-11-06 21:00:37,354 epoch 22 - iter 38/386 - loss 0.15864763 - samples/sec: 104.03 - lr: 0.100000
2022-11-06 21:00:43,136 epoch 22 - iter 76/386 - loss 0.15493977 - samples/sec: 105.21 - lr: 0.100000
2022-11-06 21:00:48,831 epoch 22 - iter 114/386 - loss 0.15076336 - samples/sec: 106.82 - lr: 0.100000
2022-11-06 21:00:54,557 epoch 22 - iter 152/386 - loss 0.15246751 - samples/sec: 106.24 - lr: 0.100000
2022-11-06 21:01:00,455 epoch 22 - iter 190/386 - loss 0.15165248 - samples/sec: 103.15 - lr: 0.100000
2022-11-06 21:01:05,621 epoch 22 - iter 228/386 - loss 0.15177154 - samples/sec: 117.75 - lr: 0.100000
2022-11-06 21:01:10,922 epoch 22 - iter 266/386 - loss 0.15051751 - samples/sec: 114.76 - lr: 0.100000
2022-11-06 21:01:16,760 epoch 22 - iter 304/386 - loss 0.15118076 - samples/sec: 104.21 - lr: 0.100000
2022-11-06 21:01:22,266 epoch 22 - iter 342/386 - loss 0.15055201 - samples/sec: 110.50 - lr: 0.100000
2022-11-06 21:01:27,611 epoch 22 - iter 380/386 - loss 0.15119519 - samples/sec: 113.82 - lr: 0.100000
2022-11-06 21:01:28,340 ----------------------------------------------------------------------------------------------------
2022-11-06 21:01:28,340 EPOCH 22 done: loss 0.1515 - lr 0.100000
2022-11-06 21:01:38,050 Evaluating as a multi-label problem: False
2022-11-06 21:01:38,168 TEST : loss 0.07914499193429947 - f1-score (micro avg) 0.9744
2022-11-06 21:01:38,282 BAD EPOCHS (no improvement): 0
2022-11-06 21:01:38,489 ----------------------------------------------------------------------------------------------------
2022-11-06 21:01:44,087 epoch 23 - iter 38/386 - loss 0.13755212 - samples/sec: 108.70 - lr: 0.100000
2022-11-06 21:01:49,886 epoch 23 - iter 76/386 - loss 0.14831372 - samples/sec: 104.90 - lr: 0.100000
2022-11-06 21:01:55,850 epoch 23 - iter 114/386 - loss 0.15013172 - samples/sec: 102.00 - lr: 0.100000
2022-11-06 21:02:01,257 epoch 23 - iter 152/386 - loss 0.15083360 - samples/sec: 112.52 - lr: 0.100000
2022-11-06 21:02:06,555 epoch 23 - iter 190/386 - loss 0.15422736 - samples/sec: 114.84 - lr: 0.100000
2022-11-06 21:02:11,963 epoch 23 - iter 228/386 - loss 0.15444813 - samples/sec: 112.47 - lr: 0.100000
2022-11-06 21:02:17,169 epoch 23 - iter 266/386 - loss 0.15525693 - samples/sec: 116.88 - lr: 0.100000
2022-11-06 21:02:22,061 epoch 23 - iter 304/386 - loss 0.15494546 - samples/sec: 124.37 - lr: 0.100000
2022-11-06 21:02:27,705 epoch 23 - iter 342/386 - loss 0.15405838 - samples/sec: 107.79 - lr: 0.100000
2022-11-06 21:02:33,764 epoch 23 - iter 380/386 - loss 0.15553576 - samples/sec: 100.39 - lr: 0.100000
2022-11-06 21:02:34,599 ----------------------------------------------------------------------------------------------------
2022-11-06 21:02:34,599 EPOCH 23 done: loss 0.1554 - lr 0.100000
2022-11-06 21:02:44,235 Evaluating as a multi-label problem: False
2022-11-06 21:02:44,352 TEST : loss 0.07991506159305573 - f1-score (micro avg) 0.9745
2022-11-06 21:02:44,466 BAD EPOCHS (no improvement): 1
2022-11-06 21:02:44,683 ----------------------------------------------------------------------------------------------------
2022-11-06 21:02:50,503 epoch 24 - iter 38/386 - loss 0.14697074 - samples/sec: 104.54 - lr: 0.100000
2022-11-06 21:02:56,444 epoch 24 - iter 76/386 - loss 0.15070964 - samples/sec: 102.39 - lr: 0.100000
2022-11-06 21:03:01,829 epoch 24 - iter 114/386 - loss 0.14717350 - samples/sec: 112.97 - lr: 0.100000
2022-11-06 21:03:07,250 epoch 24 - iter 152/386 - loss 0.14756800 - samples/sec: 112.23 - lr: 0.100000
2022-11-06 21:03:12,903 epoch 24 - iter 190/386 - loss 0.14806885 - samples/sec: 107.63 - lr: 0.100000
2022-11-06 21:03:18,413 epoch 24 - iter 228/386 - loss 0.14679408 - samples/sec: 110.39 - lr: 0.100000
2022-11-06 21:03:24,214 epoch 24 - iter 266/386 - loss 0.14714592 - samples/sec: 104.87 - lr: 0.100000
2022-11-06 21:03:29,178 epoch 24 - iter 304/386 - loss 0.14747206 - samples/sec: 122.56 - lr: 0.100000
2022-11-06 21:03:34,142 epoch 24 - iter 342/386 - loss 0.14818296 - samples/sec: 122.56 - lr: 0.100000
2022-11-06 21:03:39,739 epoch 24 - iter 380/386 - loss 0.14974964 - samples/sec: 108.71 - lr: 0.100000
2022-11-06 21:03:40,537 ----------------------------------------------------------------------------------------------------
2022-11-06 21:03:40,537 EPOCH 24 done: loss 0.1506 - lr 0.100000
2022-11-06 21:03:50,243 Evaluating as a multi-label problem: False
2022-11-06 21:03:50,360 TEST : loss 0.07870050519704819 - f1-score (micro avg) 0.9741
2022-11-06 21:03:50,475 BAD EPOCHS (no improvement): 0
2022-11-06 21:03:50,687 ----------------------------------------------------------------------------------------------------
2022-11-06 21:03:56,233 epoch 25 - iter 38/386 - loss 0.13940087 - samples/sec: 109.72 - lr: 0.100000
2022-11-06 21:04:01,975 epoch 25 - iter 76/386 - loss 0.14896394 - samples/sec: 105.96 - lr: 0.100000
2022-11-06 21:04:07,889 epoch 25 - iter 114/386 - loss 0.14707969 - samples/sec: 102.85 - lr: 0.100000
2022-11-06 21:04:13,194 epoch 25 - iter 152/386 - loss 0.14597872 - samples/sec: 114.69 - lr: 0.100000
2022-11-06 21:04:18,624 epoch 25 - iter 190/386 - loss 0.14539814 - samples/sec: 112.04 - lr: 0.100000
2022-11-06 21:04:23,930 epoch 25 - iter 228/386 - loss 0.14700191 - samples/sec: 114.64 - lr: 0.100000
2022-11-06 21:04:29,612 epoch 25 - iter 266/386 - loss 0.14515688 - samples/sec: 107.08 - lr: 0.100000
2022-11-06 21:04:35,184 epoch 25 - iter 304/386 - loss 0.14791253 - samples/sec: 109.17 - lr: 0.100000
2022-11-06 21:04:40,665 epoch 25 - iter 342/386 - loss 0.14734839 - samples/sec: 111.00 - lr: 0.100000
2022-11-06 21:04:45,848 epoch 25 - iter 380/386 - loss 0.14822894 - samples/sec: 117.40 - lr: 0.100000
2022-11-06 21:04:46,703 ----------------------------------------------------------------------------------------------------
2022-11-06 21:04:46,703 EPOCH 25 done: loss 0.1481 - lr 0.100000
2022-11-06 21:04:58,721 Evaluating as a multi-label problem: False
2022-11-06 21:04:58,838 TEST : loss 0.07901712507009506 - f1-score (micro avg) 0.9746
2022-11-06 21:04:58,952 BAD EPOCHS (no improvement): 0
2022-11-06 21:04:59,165 ----------------------------------------------------------------------------------------------------
2022-11-06 21:05:04,673 epoch 26 - iter 38/386 - loss 0.14282227 - samples/sec: 110.46 - lr: 0.100000
2022-11-06 21:05:10,143 epoch 26 - iter 76/386 - loss 0.14009603 - samples/sec: 111.22 - lr: 0.100000
2022-11-06 21:05:15,479 epoch 26 - iter 114/386 - loss 0.14511417 - samples/sec: 114.01 - lr: 0.100000
2022-11-06 21:05:20,755 epoch 26 - iter 152/386 - loss 0.14774136 - samples/sec: 115.31 - lr: 0.100000
2022-11-06 21:05:26,550 epoch 26 - iter 190/386 - loss 0.14833681 - samples/sec: 104.98 - lr: 0.100000
2022-11-06 21:05:32,041 epoch 26 - iter 228/386 - loss 0.14767418 - samples/sec: 110.80 - lr: 0.100000
2022-11-06 21:05:37,997 epoch 26 - iter 266/386 - loss 0.14808159 - samples/sec: 102.14 - lr: 0.100000
2022-11-06 21:05:43,704 epoch 26 - iter 304/386 - loss 0.14801024 - samples/sec: 106.59 - lr: 0.100000
2022-11-06 21:05:49,439 epoch 26 - iter 342/386 - loss 0.14801693 - samples/sec: 106.06 - lr: 0.100000
2022-11-06 21:05:55,135 epoch 26 - iter 380/386 - loss 0.14740170 - samples/sec: 106.80 - lr: 0.100000
2022-11-06 21:05:55,861 ----------------------------------------------------------------------------------------------------
2022-11-06 21:05:55,861 EPOCH 26 done: loss 0.1475 - lr 0.100000
2022-11-06 21:06:05,533 Evaluating as a multi-label problem: False
2022-11-06 21:06:05,651 TEST : loss 0.07874496281147003 - f1-score (micro avg) 0.9749
2022-11-06 21:06:05,766 BAD EPOCHS (no improvement): 0
2022-11-06 21:06:05,968 ----------------------------------------------------------------------------------------------------
2022-11-06 21:06:11,873 epoch 27 - iter 38/386 - loss 0.13004283 - samples/sec: 103.03 - lr: 0.100000
2022-11-06 21:06:17,429 epoch 27 - iter 76/386 - loss 0.13711483 - samples/sec: 109.50 - lr: 0.100000
2022-11-06 21:06:22,939 epoch 27 - iter 114/386 - loss 0.13564053 - samples/sec: 110.41 - lr: 0.100000
2022-11-06 21:06:28,139 epoch 27 - iter 152/386 - loss 0.13669751 - samples/sec: 116.98 - lr: 0.100000
2022-11-06 21:06:34,190 epoch 27 - iter 190/386 - loss 0.13980698 - samples/sec: 100.54 - lr: 0.100000
2022-11-06 21:06:39,906 epoch 27 - iter 228/386 - loss 0.14014600 - samples/sec: 106.43 - lr: 0.100000
2022-11-06 21:06:45,374 epoch 27 - iter 266/386 - loss 0.14190313 - samples/sec: 111.25 - lr: 0.100000
2022-11-06 21:06:50,848 epoch 27 - iter 304/386 - loss 0.14403091 - samples/sec: 111.12 - lr: 0.100000
2022-11-06 21:06:56,487 epoch 27 - iter 342/386 - loss 0.14531144 - samples/sec: 107.89 - lr: 0.100000
2022-11-06 21:07:02,016 epoch 27 - iter 380/386 - loss 0.14536325 - samples/sec: 110.03 - lr: 0.100000
2022-11-06 21:07:02,857 ----------------------------------------------------------------------------------------------------
2022-11-06 21:07:02,858 EPOCH 27 done: loss 0.1455 - lr 0.100000
2022-11-06 21:07:11,706 Evaluating as a multi-label problem: False
2022-11-06 21:07:11,820 TEST : loss 0.07635033130645752 - f1-score (micro avg) 0.9757
2022-11-06 21:07:11,932 BAD EPOCHS (no improvement): 0
2022-11-06 21:07:12,133 ----------------------------------------------------------------------------------------------------
2022-11-06 21:07:17,911 epoch 28 - iter 38/386 - loss 0.13194440 - samples/sec: 105.31 - lr: 0.100000
2022-11-06 21:07:23,934 epoch 28 - iter 76/386 - loss 0.13987553 - samples/sec: 101.00 - lr: 0.100000
2022-11-06 21:07:29,141 epoch 28 - iter 114/386 - loss 0.13984912 - samples/sec: 116.84 - lr: 0.100000
2022-11-06 21:07:34,786 epoch 28 - iter 152/386 - loss 0.14119805 - samples/sec: 107.76 - lr: 0.100000
2022-11-06 21:07:40,664 epoch 28 - iter 190/386 - loss 0.14009221 - samples/sec: 103.49 - lr: 0.100000
2022-11-06 21:07:46,086 epoch 28 - iter 228/386 - loss 0.14194481 - samples/sec: 112.19 - lr: 0.100000
2022-11-06 21:07:51,402 epoch 28 - iter 266/386 - loss 0.14287215 - samples/sec: 114.43 - lr: 0.100000
2022-11-06 21:07:56,794 epoch 28 - iter 304/386 - loss 0.14232579 - samples/sec: 112.83 - lr: 0.100000
2022-11-06 21:08:02,371 epoch 28 - iter 342/386 - loss 0.14283220 - samples/sec: 109.07 - lr: 0.100000
2022-11-06 21:08:07,977 epoch 28 - iter 380/386 - loss 0.14188657 - samples/sec: 108.51 - lr: 0.100000
2022-11-06 21:08:08,829 ----------------------------------------------------------------------------------------------------
2022-11-06 21:08:08,829 EPOCH 28 done: loss 0.1414 - lr 0.100000
2022-11-06 21:08:17,807 Evaluating as a multi-label problem: False
2022-11-06 21:08:17,921 TEST : loss 0.07446986436843872 - f1-score (micro avg) 0.9765
2022-11-06 21:08:18,033 BAD EPOCHS (no improvement): 0
2022-11-06 21:08:18,240 ----------------------------------------------------------------------------------------------------
2022-11-06 21:08:23,510 epoch 29 - iter 38/386 - loss 0.14492015 - samples/sec: 115.46 - lr: 0.100000
2022-11-06 21:08:29,410 epoch 29 - iter 76/386 - loss 0.14047206 - samples/sec: 103.11 - lr: 0.100000
2022-11-06 21:08:34,873 epoch 29 - iter 114/386 - loss 0.13804250 - samples/sec: 111.34 - lr: 0.100000
2022-11-06 21:08:40,671 epoch 29 - iter 152/386 - loss 0.13884114 - samples/sec: 104.92 - lr: 0.100000
2022-11-06 21:08:45,997 epoch 29 - iter 190/386 - loss 0.13904629 - samples/sec: 114.23 - lr: 0.100000
2022-11-06 21:08:51,833 epoch 29 - iter 228/386 - loss 0.13886545 - samples/sec: 104.22 - lr: 0.100000
2022-11-06 21:08:57,787 epoch 29 - iter 266/386 - loss 0.14075134 - samples/sec: 102.17 - lr: 0.100000
2022-11-06 21:09:03,106 epoch 29 - iter 304/386 - loss 0.14174863 - samples/sec: 114.37 - lr: 0.100000
2022-11-06 21:09:08,622 epoch 29 - iter 342/386 - loss 0.14258607 - samples/sec: 110.29 - lr: 0.100000
2022-11-06 21:09:14,442 epoch 29 - iter 380/386 - loss 0.14217338 - samples/sec: 104.52 - lr: 0.100000
2022-11-06 21:09:15,384 ----------------------------------------------------------------------------------------------------
2022-11-06 21:09:15,385 EPOCH 29 done: loss 0.1419 - lr 0.100000
2022-11-06 21:09:24,917 Evaluating as a multi-label problem: False
2022-11-06 21:09:25,031 TEST : loss 0.07629775255918503 - f1-score (micro avg) 0.977
2022-11-06 21:09:25,143 BAD EPOCHS (no improvement): 1
2022-11-06 21:09:25,353 ----------------------------------------------------------------------------------------------------
2022-11-06 21:09:30,737 epoch 30 - iter 38/386 - loss 0.13435244 - samples/sec: 113.02 - lr: 0.100000
2022-11-06 21:09:36,689 epoch 30 - iter 76/386 - loss 0.13767440 - samples/sec: 102.20 - lr: 0.100000
2022-11-06 21:09:42,013 epoch 30 - iter 114/386 - loss 0.13696206 - samples/sec: 114.27 - lr: 0.100000
2022-11-06 21:09:47,791 epoch 30 - iter 152/386 - loss 0.13829190 - samples/sec: 105.27 - lr: 0.100000
2022-11-06 21:09:53,579 epoch 30 - iter 190/386 - loss 0.13687404 - samples/sec: 105.11 - lr: 0.100000
2022-11-06 21:09:58,847 epoch 30 - iter 228/386 - loss 0.13774599 - samples/sec: 115.47 - lr: 0.100000
2022-11-06 21:10:04,568 epoch 30 - iter 266/386 - loss 0.13901783 - samples/sec: 107.13 - lr: 0.100000
2022-11-06 21:10:10,503 epoch 30 - iter 304/386 - loss 0.13908626 - samples/sec: 102.49 - lr: 0.100000
2022-11-06 21:10:16,164 epoch 30 - iter 342/386 - loss 0.14021005 - samples/sec: 107.47 - lr: 0.100000
2022-11-06 21:10:21,615 epoch 30 - iter 380/386 - loss 0.14053641 - samples/sec: 111.59 - lr: 0.100000
2022-11-06 21:10:22,475 ----------------------------------------------------------------------------------------------------
2022-11-06 21:10:22,475 EPOCH 30 done: loss 0.1405 - lr 0.100000
2022-11-06 21:10:32,025 Evaluating as a multi-label problem: False
2022-11-06 21:10:32,139 TEST : loss 0.08012723922729492 - f1-score (micro avg) 0.9751
2022-11-06 21:10:32,251 BAD EPOCHS (no improvement): 0
2022-11-06 21:10:32,460 ----------------------------------------------------------------------------------------------------
2022-11-06 21:10:37,874 epoch 31 - iter 38/386 - loss 0.13605364 - samples/sec: 112.39 - lr: 0.100000
2022-11-06 21:10:43,072 epoch 31 - iter 76/386 - loss 0.13129275 - samples/sec: 117.05 - lr: 0.100000
2022-11-06 21:10:48,560 epoch 31 - iter 114/386 - loss 0.12929537 - samples/sec: 110.85 - lr: 0.100000
2022-11-06 21:10:54,309 epoch 31 - iter 152/386 - loss 0.13464118 - samples/sec: 105.81 - lr: 0.100000
2022-11-06 21:11:00,243 epoch 31 - iter 190/386 - loss 0.13449220 - samples/sec: 102.51 - lr: 0.100000
2022-11-06 21:11:05,899 epoch 31 - iter 228/386 - loss 0.13490655 - samples/sec: 107.54 - lr: 0.100000
2022-11-06 21:11:11,421 epoch 31 - iter 266/386 - loss 0.13724499 - samples/sec: 110.18 - lr: 0.100000
2022-11-06 21:11:16,750 epoch 31 - iter 304/386 - loss 0.13669783 - samples/sec: 114.14 - lr: 0.100000
2022-11-06 21:11:22,824 epoch 31 - iter 342/386 - loss 0.13557873 - samples/sec: 100.15 - lr: 0.100000
2022-11-06 21:11:28,281 epoch 31 - iter 380/386 - loss 0.13537335 - samples/sec: 111.48 - lr: 0.100000
2022-11-06 21:11:29,086 ----------------------------------------------------------------------------------------------------
2022-11-06 21:11:29,086 EPOCH 31 done: loss 0.1352 - lr 0.100000
2022-11-06 21:11:38,570 Evaluating as a multi-label problem: False
2022-11-06 21:11:38,683 TEST : loss 0.07867585122585297 - f1-score (micro avg) 0.9759
2022-11-06 21:11:38,795 BAD EPOCHS (no improvement): 0
2022-11-06 21:11:39,003 ----------------------------------------------------------------------------------------------------
2022-11-06 21:11:44,555 epoch 32 - iter 38/386 - loss 0.13232513 - samples/sec: 109.61 - lr: 0.100000
2022-11-06 21:11:49,687 epoch 32 - iter 76/386 - loss 0.13482951 - samples/sec: 118.55 - lr: 0.100000
2022-11-06 21:11:54,993 epoch 32 - iter 114/386 - loss 0.13304503 - samples/sec: 114.65 - lr: 0.100000
2022-11-06 21:12:01,031 epoch 32 - iter 152/386 - loss 0.13218221 - samples/sec: 100.74 - lr: 0.100000
2022-11-06 21:12:06,365 epoch 32 - iter 190/386 - loss 0.13670654 - samples/sec: 114.04 - lr: 0.100000
2022-11-06 21:12:11,993 epoch 32 - iter 228/386 - loss 0.13752981 - samples/sec: 108.10 - lr: 0.100000
2022-11-06 21:12:17,882 epoch 32 - iter 266/386 - loss 0.13855191 - samples/sec: 103.29 - lr: 0.100000
2022-11-06 21:12:23,269 epoch 32 - iter 304/386 - loss 0.13809400 - samples/sec: 112.93 - lr: 0.100000
2022-11-06 21:12:29,310 epoch 32 - iter 342/386 - loss 0.13822772 - samples/sec: 100.71 - lr: 0.100000
2022-11-06 21:12:34,964 epoch 32 - iter 380/386 - loss 0.13783689 - samples/sec: 107.59 - lr: 0.100000
2022-11-06 21:12:35,755 ----------------------------------------------------------------------------------------------------
2022-11-06 21:12:35,755 EPOCH 32 done: loss 0.1380 - lr 0.100000
2022-11-06 21:12:45,245 Evaluating as a multi-label problem: False
2022-11-06 21:12:45,358 TEST : loss 0.07559281587600708 - f1-score (micro avg) 0.9764
2022-11-06 21:12:45,470 BAD EPOCHS (no improvement): 1
2022-11-06 21:12:45,673 ----------------------------------------------------------------------------------------------------
2022-11-06 21:12:50,680 epoch 33 - iter 38/386 - loss 0.13040322 - samples/sec: 121.52 - lr: 0.100000
2022-11-06 21:12:56,389 epoch 33 - iter 76/386 - loss 0.13221927 - samples/sec: 106.56 - lr: 0.100000
2022-11-06 21:13:01,972 epoch 33 - iter 114/386 - loss 0.13390827 - samples/sec: 108.96 - lr: 0.100000
2022-11-06 21:13:07,263 epoch 33 - iter 152/386 - loss 0.13364135 - samples/sec: 114.98 - lr: 0.100000
2022-11-06 21:13:12,446 epoch 33 - iter 190/386 - loss 0.13402240 - samples/sec: 117.38 - lr: 0.100000
2022-11-06 21:13:18,032 epoch 33 - iter 228/386 - loss 0.13390599 - samples/sec: 108.91 - lr: 0.100000
2022-11-06 21:13:23,570 epoch 33 - iter 266/386 - loss 0.13400107 - samples/sec: 109.84 - lr: 0.100000
2022-11-06 21:13:29,197 epoch 33 - iter 304/386 - loss 0.13416777 - samples/sec: 108.10 - lr: 0.100000
2022-11-06 21:13:35,233 epoch 33 - iter 342/386 - loss 0.13495150 - samples/sec: 100.79 - lr: 0.100000
2022-11-06 21:13:41,146 epoch 33 - iter 380/386 - loss 0.13447871 - samples/sec: 102.87 - lr: 0.100000
2022-11-06 21:13:41,959 ----------------------------------------------------------------------------------------------------
2022-11-06 21:13:41,959 EPOCH 33 done: loss 0.1345 - lr 0.100000
2022-11-06 21:13:51,410 Evaluating as a multi-label problem: False
2022-11-06 21:13:51,526 TEST : loss 0.07484747469425201 - f1-score (micro avg) 0.9777
2022-11-06 21:13:51,639 BAD EPOCHS (no improvement): 0
2022-11-06 21:13:51,845 ----------------------------------------------------------------------------------------------------
2022-11-06 21:13:58,087 epoch 34 - iter 38/386 - loss 0.12716848 - samples/sec: 97.48 - lr: 0.100000
2022-11-06 21:14:03,926 epoch 34 - iter 76/386 - loss 0.13247563 - samples/sec: 104.19 - lr: 0.100000
2022-11-06 21:14:09,453 epoch 34 - iter 114/386 - loss 0.13110225 - samples/sec: 110.06 - lr: 0.100000
2022-11-06 21:14:14,599 epoch 34 - iter 152/386 - loss 0.13129140 - samples/sec: 118.22 - lr: 0.100000
2022-11-06 21:14:19,723 epoch 34 - iter 190/386 - loss 0.13341774 - samples/sec: 118.72 - lr: 0.100000
2022-11-06 21:14:25,406 epoch 34 - iter 228/386 - loss 0.13308897 - samples/sec: 107.05 - lr: 0.100000
2022-11-06 21:14:30,738 epoch 34 - iter 266/386 - loss 0.13388475 - samples/sec: 114.09 - lr: 0.100000
2022-11-06 21:14:36,691 epoch 34 - iter 304/386 - loss 0.13500382 - samples/sec: 102.19 - lr: 0.100000
2022-11-06 21:14:42,091 epoch 34 - iter 342/386 - loss 0.13561086 - samples/sec: 112.66 - lr: 0.100000
2022-11-06 21:14:47,903 epoch 34 - iter 380/386 - loss 0.13482125 - samples/sec: 104.67 - lr: 0.100000
2022-11-06 21:14:48,710 ----------------------------------------------------------------------------------------------------
2022-11-06 21:14:48,711 EPOCH 34 done: loss 0.1347 - lr 0.100000
2022-11-06 21:14:58,212 Evaluating as a multi-label problem: False
2022-11-06 21:14:58,326 TEST : loss 0.07798200100660324 - f1-score (micro avg) 0.9772
2022-11-06 21:14:58,438 BAD EPOCHS (no improvement): 1
2022-11-06 21:14:58,646 ----------------------------------------------------------------------------------------------------
2022-11-06 21:15:03,999 epoch 35 - iter 38/386 - loss 0.12669705 - samples/sec: 113.66 - lr: 0.100000
2022-11-06 21:15:09,226 epoch 35 - iter 76/386 - loss 0.12803323 - samples/sec: 116.39 - lr: 0.100000
2022-11-06 21:15:14,490 epoch 35 - iter 114/386 - loss 0.13240979 - samples/sec: 115.59 - lr: 0.100000
2022-11-06 21:15:19,801 epoch 35 - iter 152/386 - loss 0.13315680 - samples/sec: 114.53 - lr: 0.100000
2022-11-06 21:15:25,186 epoch 35 - iter 190/386 - loss 0.13257909 - samples/sec: 112.99 - lr: 0.100000
2022-11-06 21:15:31,186 epoch 35 - iter 228/386 - loss 0.13326997 - samples/sec: 101.37 - lr: 0.100000
2022-11-06 21:15:36,633 epoch 35 - iter 266/386 - loss 0.13333582 - samples/sec: 111.69 - lr: 0.100000
2022-11-06 21:15:42,519 epoch 35 - iter 304/386 - loss 0.13407603 - samples/sec: 103.34 - lr: 0.100000
2022-11-06 21:15:47,912 epoch 35 - iter 342/386 - loss 0.13477046 - samples/sec: 112.81 - lr: 0.100000
2022-11-06 21:15:54,149 epoch 35 - iter 380/386 - loss 0.13462222 - samples/sec: 97.53 - lr: 0.100000
2022-11-06 21:15:55,071 ----------------------------------------------------------------------------------------------------
2022-11-06 21:15:55,071 EPOCH 35 done: loss 0.1344 - lr 0.100000
2022-11-06 21:16:06,985 Evaluating as a multi-label problem: False
2022-11-06 21:16:07,099 TEST : loss 0.07759656012058258 - f1-score (micro avg) 0.9763
2022-11-06 21:16:07,211 BAD EPOCHS (no improvement): 0
2022-11-06 21:16:07,415 ----------------------------------------------------------------------------------------------------
2022-11-06 21:16:13,125 epoch 36 - iter 38/386 - loss 0.14084251 - samples/sec: 106.55 - lr: 0.100000
2022-11-06 21:16:18,426 epoch 36 - iter 76/386 - loss 0.13560492 - samples/sec: 114.77 - lr: 0.100000
2022-11-06 21:16:23,844 epoch 36 - iter 114/386 - loss 0.13425714 - samples/sec: 112.28 - lr: 0.100000
2022-11-06 21:16:29,425 epoch 36 - iter 152/386 - loss 0.13443528 - samples/sec: 108.98 - lr: 0.100000
2022-11-06 21:16:35,416 epoch 36 - iter 190/386 - loss 0.13544237 - samples/sec: 101.55 - lr: 0.100000
2022-11-06 21:16:40,715 epoch 36 - iter 228/386 - loss 0.13445681 - samples/sec: 114.79 - lr: 0.100000
2022-11-06 21:16:46,197 epoch 36 - iter 266/386 - loss 0.13370809 - samples/sec: 110.98 - lr: 0.100000
2022-11-06 21:16:52,085 epoch 36 - iter 304/386 - loss 0.13356780 - samples/sec: 103.30 - lr: 0.100000
2022-11-06 21:16:57,690 epoch 36 - iter 342/386 - loss 0.13324128 - samples/sec: 108.54 - lr: 0.100000
2022-11-06 21:17:03,316 epoch 36 - iter 380/386 - loss 0.13276034 - samples/sec: 108.12 - lr: 0.100000
2022-11-06 21:17:04,142 ----------------------------------------------------------------------------------------------------
2022-11-06 21:17:04,143 EPOCH 36 done: loss 0.1328 - lr 0.100000
2022-11-06 21:17:13,677 Evaluating as a multi-label problem: False
2022-11-06 21:17:13,791 TEST : loss 0.07676690816879272 - f1-score (micro avg) 0.9773
2022-11-06 21:17:13,903 BAD EPOCHS (no improvement): 0
2022-11-06 21:17:14,112 ----------------------------------------------------------------------------------------------------
2022-11-06 21:17:20,098 epoch 37 - iter 38/386 - loss 0.11773268 - samples/sec: 101.66 - lr: 0.100000
2022-11-06 21:17:25,549 epoch 37 - iter 76/386 - loss 0.12268172 - samples/sec: 111.60 - lr: 0.100000
2022-11-06 21:17:31,263 epoch 37 - iter 114/386 - loss 0.12196934 - samples/sec: 106.46 - lr: 0.100000
2022-11-06 21:17:36,935 epoch 37 - iter 152/386 - loss 0.12469575 - samples/sec: 107.26 - lr: 0.100000
2022-11-06 21:17:42,684 epoch 37 - iter 190/386 - loss 0.12632570 - samples/sec: 105.81 - lr: 0.100000
2022-11-06 21:17:48,000 epoch 37 - iter 228/386 - loss 0.12650076 - samples/sec: 114.42 - lr: 0.100000
2022-11-06 21:17:52,778 epoch 37 - iter 266/386 - loss 0.12751872 - samples/sec: 127.35 - lr: 0.100000
2022-11-06 21:17:58,664 epoch 37 - iter 304/386 - loss 0.12835283 - samples/sec: 103.34 - lr: 0.100000
2022-11-06 21:18:04,258 epoch 37 - iter 342/386 - loss 0.12751420 - samples/sec: 108.76 - lr: 0.100000
2022-11-06 21:18:10,061 epoch 37 - iter 380/386 - loss 0.12880953 - samples/sec: 104.82 - lr: 0.100000
2022-11-06 21:18:10,878 ----------------------------------------------------------------------------------------------------
2022-11-06 21:18:10,878 EPOCH 37 done: loss 0.1289 - lr 0.100000
2022-11-06 21:18:20,270 Evaluating as a multi-label problem: False
2022-11-06 21:18:20,386 TEST : loss 0.07300665974617004 - f1-score (micro avg) 0.978
2022-11-06 21:18:20,498 BAD EPOCHS (no improvement): 0
2022-11-06 21:18:20,713 ----------------------------------------------------------------------------------------------------
2022-11-06 21:18:25,955 epoch 38 - iter 38/386 - loss 0.12656382 - samples/sec: 116.08 - lr: 0.100000
2022-11-06 21:18:31,778 epoch 38 - iter 76/386 - loss 0.12664169 - samples/sec: 104.47 - lr: 0.100000
2022-11-06 21:18:37,249 epoch 38 - iter 114/386 - loss 0.12657923 - samples/sec: 111.19 - lr: 0.100000
2022-11-06 21:18:43,101 epoch 38 - iter 152/386 - loss 0.12572572 - samples/sec: 103.96 - lr: 0.100000
2022-11-06 21:18:49,038 epoch 38 - iter 190/386 - loss 0.12654161 - samples/sec: 102.46 - lr: 0.100000
2022-11-06 21:18:54,901 epoch 38 - iter 228/386 - loss 0.12749062 - samples/sec: 103.74 - lr: 0.100000
2022-11-06 21:19:00,492 epoch 38 - iter 266/386 - loss 0.12772509 - samples/sec: 108.81 - lr: 0.100000
2022-11-06 21:19:05,602 epoch 38 - iter 304/386 - loss 0.12844144 - samples/sec: 119.05 - lr: 0.100000
2022-11-06 21:19:10,460 epoch 38 - iter 342/386 - loss 0.12831778 - samples/sec: 125.23 - lr: 0.100000
2022-11-06 21:19:16,192 epoch 38 - iter 380/386 - loss 0.12827157 - samples/sec: 106.12 - lr: 0.100000
2022-11-06 21:19:16,916 ----------------------------------------------------------------------------------------------------
2022-11-06 21:19:16,916 EPOCH 38 done: loss 0.1286 - lr 0.100000
2022-11-06 21:19:26,445 Evaluating as a multi-label problem: False
2022-11-06 21:19:26,560 TEST : loss 0.07631804794073105 - f1-score (micro avg) 0.9765
2022-11-06 21:19:26,673 BAD EPOCHS (no improvement): 0
2022-11-06 21:19:26,873 ----------------------------------------------------------------------------------------------------
2022-11-06 21:19:32,474 epoch 39 - iter 38/386 - loss 0.12921879 - samples/sec: 108.64 - lr: 0.100000
2022-11-06 21:19:38,030 epoch 39 - iter 76/386 - loss 0.12550183 - samples/sec: 109.50 - lr: 0.100000
2022-11-06 21:19:43,651 epoch 39 - iter 114/386 - loss 0.12590097 - samples/sec: 108.22 - lr: 0.100000
2022-11-06 21:19:49,937 epoch 39 - iter 152/386 - loss 0.12453781 - samples/sec: 96.76 - lr: 0.100000
2022-11-06 21:19:55,059 epoch 39 - iter 190/386 - loss 0.12681373 - samples/sec: 118.78 - lr: 0.100000
2022-11-06 21:20:00,742 epoch 39 - iter 228/386 - loss 0.12644162 - samples/sec: 107.04 - lr: 0.100000
2022-11-06 21:20:06,147 epoch 39 - iter 266/386 - loss 0.12619254 - samples/sec: 112.55 - lr: 0.100000
2022-11-06 21:20:11,462 epoch 39 - iter 304/386 - loss 0.12768228 - samples/sec: 114.46 - lr: 0.100000
2022-11-06 21:20:16,796 epoch 39 - iter 342/386 - loss 0.12841007 - samples/sec: 114.06 - lr: 0.100000
2022-11-06 21:20:22,820 epoch 39 - iter 380/386 - loss 0.12930063 - samples/sec: 100.98 - lr: 0.100000
2022-11-06 21:20:23,811 ----------------------------------------------------------------------------------------------------
2022-11-06 21:20:23,811 EPOCH 39 done: loss 0.1297 - lr 0.100000
2022-11-06 21:20:34,264 Evaluating as a multi-label problem: False
2022-11-06 21:20:34,385 TEST : loss 0.07472442835569382 - f1-score (micro avg) 0.9769
2022-11-06 21:20:34,499 BAD EPOCHS (no improvement): 1
2022-11-06 21:20:34,700 ----------------------------------------------------------------------------------------------------
2022-11-06 21:20:40,752 epoch 40 - iter 38/386 - loss 0.12268551 - samples/sec: 100.55 - lr: 0.100000
2022-11-06 21:20:47,598 epoch 40 - iter 76/386 - loss 0.12238748 - samples/sec: 88.84 - lr: 0.100000
2022-11-06 21:20:53,304 epoch 40 - iter 114/386 - loss 0.12365842 - samples/sec: 106.63 - lr: 0.100000
2022-11-06 21:20:59,388 epoch 40 - iter 152/386 - loss 0.12125446 - samples/sec: 99.98 - lr: 0.100000
2022-11-06 21:21:05,617 epoch 40 - iter 190/386 - loss 0.12154079 - samples/sec: 97.67 - lr: 0.100000
2022-11-06 21:21:11,161 epoch 40 - iter 228/386 - loss 0.12124347 - samples/sec: 109.71 - lr: 0.100000
2022-11-06 21:21:17,542 epoch 40 - iter 266/386 - loss 0.12335241 - samples/sec: 95.34 - lr: 0.100000
2022-11-06 21:21:23,746 epoch 40 - iter 304/386 - loss 0.12514150 - samples/sec: 98.04 - lr: 0.100000
2022-11-06 21:21:29,748 epoch 40 - iter 342/386 - loss 0.12565763 - samples/sec: 101.36 - lr: 0.100000
2022-11-06 21:21:35,962 epoch 40 - iter 380/386 - loss 0.12620808 - samples/sec: 97.90 - lr: 0.100000
2022-11-06 21:21:36,822 ----------------------------------------------------------------------------------------------------
2022-11-06 21:21:36,822 EPOCH 40 done: loss 0.1260 - lr 0.100000
2022-11-06 21:21:47,770 Evaluating as a multi-label problem: False
2022-11-06 21:21:47,888 TEST : loss 0.0739121362566948 - f1-score (micro avg) 0.9773
2022-11-06 21:21:48,002 BAD EPOCHS (no improvement): 0
2022-11-06 21:21:48,214 ----------------------------------------------------------------------------------------------------
2022-11-06 21:21:54,367 epoch 41 - iter 38/386 - loss 0.12501690 - samples/sec: 98.87 - lr: 0.100000
2022-11-06 21:21:59,976 epoch 41 - iter 76/386 - loss 0.12864011 - samples/sec: 108.46 - lr: 0.100000
2022-11-06 21:22:05,669 epoch 41 - iter 114/386 - loss 0.12787936 - samples/sec: 106.86 - lr: 0.100000
2022-11-06 21:22:11,608 epoch 41 - iter 152/386 - loss 0.12923740 - samples/sec: 102.43 - lr: 0.100000
2022-11-06 21:22:17,559 epoch 41 - iter 190/386 - loss 0.12849211 - samples/sec: 102.21 - lr: 0.100000
2022-11-06 21:22:23,015 epoch 41 - iter 228/386 - loss 0.12815326 - samples/sec: 111.50 - lr: 0.100000
2022-11-06 21:22:28,588 epoch 41 - iter 266/386 - loss 0.12708207 - samples/sec: 110.00 - lr: 0.100000
2022-11-06 21:22:34,117 epoch 41 - iter 304/386 - loss 0.12568639 - samples/sec: 110.03 - lr: 0.100000
2022-11-06 21:22:40,037 epoch 41 - iter 342/386 - loss 0.12439832 - samples/sec: 102.75 - lr: 0.100000
2022-11-06 21:22:45,593 epoch 41 - iter 380/386 - loss 0.12537910 - samples/sec: 109.51 - lr: 0.100000
2022-11-06 21:22:46,444 ----------------------------------------------------------------------------------------------------
2022-11-06 21:22:46,444 EPOCH 41 done: loss 0.1253 - lr 0.100000
2022-11-06 21:22:56,038 Evaluating as a multi-label problem: False
2022-11-06 21:22:56,155 TEST : loss 0.0780162438750267 - f1-score (micro avg) 0.977
2022-11-06 21:22:56,269 BAD EPOCHS (no improvement): 0
2022-11-06 21:22:56,482 ----------------------------------------------------------------------------------------------------
2022-11-06 21:23:02,363 epoch 42 - iter 38/386 - loss 0.11149869 - samples/sec: 103.47 - lr: 0.100000
2022-11-06 21:23:07,895 epoch 42 - iter 76/386 - loss 0.11748343 - samples/sec: 109.98 - lr: 0.100000
2022-11-06 21:23:13,346 epoch 42 - iter 114/386 - loss 0.11649259 - samples/sec: 111.59 - lr: 0.100000
2022-11-06 21:23:18,275 epoch 42 - iter 152/386 - loss 0.11698177 - samples/sec: 123.44 - lr: 0.100000
2022-11-06 21:23:23,563 epoch 42 - iter 190/386 - loss 0.11887342 - samples/sec: 115.03 - lr: 0.100000
2022-11-06 21:23:29,173 epoch 42 - iter 228/386 - loss 0.11677996 - samples/sec: 108.44 - lr: 0.100000
2022-11-06 21:23:34,579 epoch 42 - iter 266/386 - loss 0.11824350 - samples/sec: 112.54 - lr: 0.100000
2022-11-06 21:23:40,055 epoch 42 - iter 304/386 - loss 0.12008683 - samples/sec: 111.08 - lr: 0.100000
2022-11-06 21:23:45,880 epoch 42 - iter 342/386 - loss 0.12062932 - samples/sec: 104.43 - lr: 0.100000
2022-11-06 21:23:51,979 epoch 42 - iter 380/386 - loss 0.12229146 - samples/sec: 99.74 - lr: 0.100000
2022-11-06 21:23:52,834 ----------------------------------------------------------------------------------------------------
2022-11-06 21:23:52,834 EPOCH 42 done: loss 0.1220 - lr 0.100000
2022-11-06 21:24:02,444 Evaluating as a multi-label problem: False
2022-11-06 21:24:02,562 TEST : loss 0.07854187488555908 - f1-score (micro avg) 0.976
2022-11-06 21:24:02,674 BAD EPOCHS (no improvement): 0
2022-11-06 21:24:02,884 ----------------------------------------------------------------------------------------------------
2022-11-06 21:24:08,148 epoch 43 - iter 38/386 - loss 0.11958608 - samples/sec: 115.61 - lr: 0.100000
2022-11-06 21:24:13,398 epoch 43 - iter 76/386 - loss 0.11836372 - samples/sec: 115.88 - lr: 0.100000
2022-11-06 21:24:19,459 epoch 43 - iter 114/386 - loss 0.12201423 - samples/sec: 100.36 - lr: 0.100000
2022-11-06 21:24:25,282 epoch 43 - iter 152/386 - loss 0.12318619 - samples/sec: 104.48 - lr: 0.100000
2022-11-06 21:24:30,615 epoch 43 - iter 190/386 - loss 0.12337366 - samples/sec: 114.06 - lr: 0.100000
2022-11-06 21:24:36,294 epoch 43 - iter 228/386 - loss 0.12486417 - samples/sec: 107.83 - lr: 0.100000
2022-11-06 21:24:41,939 epoch 43 - iter 266/386 - loss 0.12427762 - samples/sec: 107.77 - lr: 0.100000
2022-11-06 21:24:47,463 epoch 43 - iter 304/386 - loss 0.12320286 - samples/sec: 110.98 - lr: 0.100000
2022-11-06 21:24:53,194 epoch 43 - iter 342/386 - loss 0.12386685 - samples/sec: 106.14 - lr: 0.100000
2022-11-06 21:24:58,726 epoch 43 - iter 380/386 - loss 0.12403804 - samples/sec: 109.98 - lr: 0.100000
2022-11-06 21:24:59,622 ----------------------------------------------------------------------------------------------------
2022-11-06 21:24:59,622 EPOCH 43 done: loss 0.1240 - lr 0.100000
2022-11-06 21:25:09,220 Evaluating as a multi-label problem: False
2022-11-06 21:25:09,336 TEST : loss 0.0756213441491127 - f1-score (micro avg) 0.9772
2022-11-06 21:25:09,450 BAD EPOCHS (no improvement): 1
2022-11-06 21:25:09,661 ----------------------------------------------------------------------------------------------------
2022-11-06 21:25:14,975 epoch 44 - iter 38/386 - loss 0.11529806 - samples/sec: 114.50 - lr: 0.100000
2022-11-06 21:25:21,233 epoch 44 - iter 76/386 - loss 0.12011694 - samples/sec: 97.22 - lr: 0.100000
2022-11-06 21:25:27,381 epoch 44 - iter 114/386 - loss 0.11971319 - samples/sec: 98.93 - lr: 0.100000
2022-11-06 21:25:33,175 epoch 44 - iter 152/386 - loss 0.11990878 - samples/sec: 105.00 - lr: 0.100000
2022-11-06 21:25:38,881 epoch 44 - iter 190/386 - loss 0.12065174 - samples/sec: 106.62 - lr: 0.100000
2022-11-06 21:25:44,099 epoch 44 - iter 228/386 - loss 0.11906558 - samples/sec: 116.58 - lr: 0.100000
2022-11-06 21:25:49,404 epoch 44 - iter 266/386 - loss 0.12058134 - samples/sec: 115.19 - lr: 0.100000
2022-11-06 21:25:54,627 epoch 44 - iter 304/386 - loss 0.12038138 - samples/sec: 116.49 - lr: 0.100000
2022-11-06 21:25:59,973 epoch 44 - iter 342/386 - loss 0.12033364 - samples/sec: 113.80 - lr: 0.100000
2022-11-06 21:26:05,652 epoch 44 - iter 380/386 - loss 0.12198193 - samples/sec: 107.12 - lr: 0.100000
2022-11-06 21:26:06,529 ----------------------------------------------------------------------------------------------------
2022-11-06 21:26:06,530 EPOCH 44 done: loss 0.1220 - lr 0.100000
2022-11-06 21:26:16,050 Evaluating as a multi-label problem: False
2022-11-06 21:26:16,167 TEST : loss 0.07966844737529755 - f1-score (micro avg) 0.9765
2022-11-06 21:26:16,280 BAD EPOCHS (no improvement): 0
2022-11-06 21:26:16,483 ----------------------------------------------------------------------------------------------------
2022-11-06 21:26:22,198 epoch 45 - iter 38/386 - loss 0.11658074 - samples/sec: 106.47 - lr: 0.100000
2022-11-06 21:26:27,956 epoch 45 - iter 76/386 - loss 0.11810186 - samples/sec: 105.66 - lr: 0.100000
2022-11-06 21:26:33,511 epoch 45 - iter 114/386 - loss 0.11998494 - samples/sec: 109.51 - lr: 0.100000
2022-11-06 21:26:39,132 epoch 45 - iter 152/386 - loss 0.11772619 - samples/sec: 108.22 - lr: 0.100000
2022-11-06 21:26:44,693 epoch 45 - iter 190/386 - loss 0.11807594 - samples/sec: 109.41 - lr: 0.100000
2022-11-06 21:26:50,190 epoch 45 - iter 228/386 - loss 0.11906856 - samples/sec: 110.67 - lr: 0.100000
2022-11-06 21:26:55,645 epoch 45 - iter 266/386 - loss 0.12077577 - samples/sec: 111.52 - lr: 0.100000
2022-11-06 21:27:01,272 epoch 45 - iter 304/386 - loss 0.12018970 - samples/sec: 108.11 - lr: 0.100000
2022-11-06 21:27:06,688 epoch 45 - iter 342/386 - loss 0.12045177 - samples/sec: 112.33 - lr: 0.100000
2022-11-06 21:27:12,362 epoch 45 - iter 380/386 - loss 0.12097869 - samples/sec: 107.20 - lr: 0.100000
2022-11-06 21:27:13,161 ----------------------------------------------------------------------------------------------------
2022-11-06 21:27:13,161 EPOCH 45 done: loss 0.1209 - lr 0.100000
2022-11-06 21:27:22,937 Evaluating as a multi-label problem: False
2022-11-06 21:27:23,053 TEST : loss 0.07720887660980225 - f1-score (micro avg) 0.9767
2022-11-06 21:27:23,166 BAD EPOCHS (no improvement): 0
2022-11-06 21:27:23,377 ----------------------------------------------------------------------------------------------------
2022-11-06 21:27:29,128 epoch 46 - iter 38/386 - loss 0.11581804 - samples/sec: 105.81 - lr: 0.100000
2022-11-06 21:27:34,828 epoch 46 - iter 76/386 - loss 0.11872404 - samples/sec: 106.72 - lr: 0.100000
2022-11-06 21:27:39,690 epoch 46 - iter 114/386 - loss 0.11995258 - samples/sec: 125.14 - lr: 0.100000
2022-11-06 21:27:45,311 epoch 46 - iter 152/386 - loss 0.12179851 - samples/sec: 108.22 - lr: 0.100000
2022-11-06 21:27:51,300 epoch 46 - iter 190/386 - loss 0.12292524 - samples/sec: 101.57 - lr: 0.100000
2022-11-06 21:27:57,100 epoch 46 - iter 228/386 - loss 0.12271512 - samples/sec: 104.88 - lr: 0.100000
2022-11-06 21:28:02,842 epoch 46 - iter 266/386 - loss 0.12196746 - samples/sec: 105.95 - lr: 0.100000
2022-11-06 21:28:08,370 epoch 46 - iter 304/386 - loss 0.12271587 - samples/sec: 110.06 - lr: 0.100000
2022-11-06 21:28:13,125 epoch 46 - iter 342/386 - loss 0.12309745 - samples/sec: 127.93 - lr: 0.100000
2022-11-06 21:28:18,470 epoch 46 - iter 380/386 - loss 0.12348590 - samples/sec: 113.83 - lr: 0.100000
2022-11-06 21:28:19,306 ----------------------------------------------------------------------------------------------------
2022-11-06 21:28:19,307 EPOCH 46 done: loss 0.1233 - lr 0.100000
2022-11-06 21:28:31,227 Evaluating as a multi-label problem: False
2022-11-06 21:28:31,344 TEST : loss 0.07568249851465225 - f1-score (micro avg) 0.9774
2022-11-06 21:28:31,457 BAD EPOCHS (no improvement): 1
2022-11-06 21:28:31,669 ----------------------------------------------------------------------------------------------------
2022-11-06 21:28:37,650 epoch 47 - iter 38/386 - loss 0.11595339 - samples/sec: 101.73 - lr: 0.100000
2022-11-06 21:28:43,362 epoch 47 - iter 76/386 - loss 0.11709559 - samples/sec: 106.50 - lr: 0.100000
2022-11-06 21:28:49,223 epoch 47 - iter 114/386 - loss 0.11756609 - samples/sec: 103.79 - lr: 0.100000
2022-11-06 21:28:54,635 epoch 47 - iter 152/386 - loss 0.12033252 - samples/sec: 112.41 - lr: 0.100000
2022-11-06 21:29:00,052 epoch 47 - iter 190/386 - loss 0.12025189 - samples/sec: 112.30 - lr: 0.100000
2022-11-06 21:29:05,678 epoch 47 - iter 228/386 - loss 0.11965504 - samples/sec: 108.12 - lr: 0.100000
2022-11-06 21:29:11,257 epoch 47 - iter 266/386 - loss 0.11906688 - samples/sec: 109.05 - lr: 0.100000
2022-11-06 21:29:16,838 epoch 47 - iter 304/386 - loss 0.11868831 - samples/sec: 109.00 - lr: 0.100000
2022-11-06 21:29:21,901 epoch 47 - iter 342/386 - loss 0.12035505 - samples/sec: 120.16 - lr: 0.100000
2022-11-06 21:29:27,022 epoch 47 - iter 380/386 - loss 0.12021009 - samples/sec: 118.79 - lr: 0.100000
2022-11-06 21:29:27,884 ----------------------------------------------------------------------------------------------------
2022-11-06 21:29:27,884 EPOCH 47 done: loss 0.1203 - lr 0.100000
2022-11-06 21:29:37,446 Evaluating as a multi-label problem: False
2022-11-06 21:29:37,563 TEST : loss 0.07547062635421753 - f1-score (micro avg) 0.9775
2022-11-06 21:29:37,678 BAD EPOCHS (no improvement): 0
2022-11-06 21:29:37,876 ----------------------------------------------------------------------------------------------------
2022-11-06 21:29:43,653 epoch 48 - iter 38/386 - loss 0.12227377 - samples/sec: 105.33 - lr: 0.100000
2022-11-06 21:29:49,140 epoch 48 - iter 76/386 - loss 0.12056539 - samples/sec: 110.87 - lr: 0.100000
2022-11-06 21:29:54,836 epoch 48 - iter 114/386 - loss 0.11941017 - samples/sec: 106.80 - lr: 0.100000
2022-11-06 21:30:00,821 epoch 48 - iter 152/386 - loss 0.12095295 - samples/sec: 101.64 - lr: 0.100000
2022-11-06 21:30:06,274 epoch 48 - iter 190/386 - loss 0.12128609 - samples/sec: 111.55 - lr: 0.100000
2022-11-06 21:30:12,004 epoch 48 - iter 228/386 - loss 0.12113142 - samples/sec: 106.17 - lr: 0.100000
2022-11-06 21:30:17,705 epoch 48 - iter 266/386 - loss 0.12022908 - samples/sec: 106.71 - lr: 0.100000
2022-11-06 21:30:23,040 epoch 48 - iter 304/386 - loss 0.12018651 - samples/sec: 114.03 - lr: 0.100000
2022-11-06 21:30:28,146 epoch 48 - iter 342/386 - loss 0.12015658 - samples/sec: 119.16 - lr: 0.100000
2022-11-06 21:30:33,425 epoch 48 - iter 380/386 - loss 0.12050812 - samples/sec: 115.24 - lr: 0.100000
2022-11-06 21:30:34,660 ----------------------------------------------------------------------------------------------------
2022-11-06 21:30:34,660 EPOCH 48 done: loss 0.1204 - lr 0.100000
2022-11-06 21:30:43,888 Evaluating as a multi-label problem: False
2022-11-06 21:30:44,005 TEST : loss 0.07593300938606262 - f1-score (micro avg) 0.9779
2022-11-06 21:30:44,119 BAD EPOCHS (no improvement): 1
2022-11-06 21:30:44,324 ----------------------------------------------------------------------------------------------------
2022-11-06 21:30:49,608 epoch 49 - iter 38/386 - loss 0.11311434 - samples/sec: 115.17 - lr: 0.100000
2022-11-06 21:30:55,068 epoch 49 - iter 76/386 - loss 0.11893316 - samples/sec: 111.43 - lr: 0.100000
2022-11-06 21:31:00,591 epoch 49 - iter 114/386 - loss 0.11886508 - samples/sec: 110.13 - lr: 0.100000
2022-11-06 21:31:06,265 epoch 49 - iter 152/386 - loss 0.11676186 - samples/sec: 107.23 - lr: 0.100000
2022-11-06 21:31:11,933 epoch 49 - iter 190/386 - loss 0.11803846 - samples/sec: 107.32 - lr: 0.100000
2022-11-06 21:31:17,749 epoch 49 - iter 228/386 - loss 0.11884410 - samples/sec: 104.58 - lr: 0.100000
2022-11-06 21:31:23,644 epoch 49 - iter 266/386 - loss 0.11916669 - samples/sec: 103.21 - lr: 0.100000
2022-11-06 21:31:29,531 epoch 49 - iter 304/386 - loss 0.11847528 - samples/sec: 103.32 - lr: 0.100000
2022-11-06 21:31:34,769 epoch 49 - iter 342/386 - loss 0.11839289 - samples/sec: 116.15 - lr: 0.100000
2022-11-06 21:31:40,611 epoch 49 - iter 380/386 - loss 0.11785760 - samples/sec: 104.12 - lr: 0.100000
2022-11-06 21:31:41,537 ----------------------------------------------------------------------------------------------------
2022-11-06 21:31:41,537 EPOCH 49 done: loss 0.1180 - lr 0.100000
2022-11-06 21:31:50,354 Evaluating as a multi-label problem: False
2022-11-06 21:31:50,469 TEST : loss 0.07601259648799896 - f1-score (micro avg) 0.9772
2022-11-06 21:31:50,581 BAD EPOCHS (no improvement): 0
2022-11-06 21:31:50,792 ----------------------------------------------------------------------------------------------------
2022-11-06 21:31:56,264 epoch 50 - iter 38/386 - loss 0.10859661 - samples/sec: 111.21 - lr: 0.100000
2022-11-06 21:32:01,873 epoch 50 - iter 76/386 - loss 0.11903546 - samples/sec: 108.47 - lr: 0.100000
2022-11-06 21:32:07,333 epoch 50 - iter 114/386 - loss 0.11678697 - samples/sec: 111.42 - lr: 0.100000
2022-11-06 21:32:13,408 epoch 50 - iter 152/386 - loss 0.11600897 - samples/sec: 100.53 - lr: 0.100000
2022-11-06 21:32:19,137 epoch 50 - iter 190/386 - loss 0.11566727 - samples/sec: 106.17 - lr: 0.100000
2022-11-06 21:32:24,914 epoch 50 - iter 228/386 - loss 0.11646796 - samples/sec: 105.31 - lr: 0.100000
2022-11-06 21:32:30,880 epoch 50 - iter 266/386 - loss 0.11725206 - samples/sec: 101.97 - lr: 0.100000
2022-11-06 21:32:36,731 epoch 50 - iter 304/386 - loss 0.11755446 - samples/sec: 103.98 - lr: 0.100000
2022-11-06 21:32:42,356 epoch 50 - iter 342/386 - loss 0.11906673 - samples/sec: 108.14 - lr: 0.100000
2022-11-06 21:32:47,819 epoch 50 - iter 380/386 - loss 0.11887510 - samples/sec: 111.35 - lr: 0.100000
2022-11-06 21:32:48,663 ----------------------------------------------------------------------------------------------------
2022-11-06 21:32:48,664 EPOCH 50 done: loss 0.1194 - lr 0.100000
2022-11-06 21:32:57,909 Evaluating as a multi-label problem: False
2022-11-06 21:32:58,026 TEST : loss 0.07529637962579727 - f1-score (micro avg) 0.9777
2022-11-06 21:32:58,140 BAD EPOCHS (no improvement): 1
2022-11-06 21:32:58,345 ----------------------------------------------------------------------------------------------------
2022-11-06 21:33:03,192 epoch 51 - iter 38/386 - loss 0.11452134 - samples/sec: 125.55 - lr: 0.100000
2022-11-06 21:33:08,686 epoch 51 - iter 76/386 - loss 0.11590443 - samples/sec: 110.74 - lr: 0.100000
2022-11-06 21:33:14,440 epoch 51 - iter 114/386 - loss 0.11784341 - samples/sec: 105.72 - lr: 0.100000
2022-11-06 21:33:19,823 epoch 51 - iter 152/386 - loss 0.11705536 - samples/sec: 113.02 - lr: 0.100000
2022-11-06 21:33:26,045 epoch 51 - iter 190/386 - loss 0.11543878 - samples/sec: 97.76 - lr: 0.100000
2022-11-06 21:33:31,639 epoch 51 - iter 228/386 - loss 0.11612966 - samples/sec: 108.74 - lr: 0.100000
2022-11-06 21:33:37,603 epoch 51 - iter 266/386 - loss 0.11650269 - samples/sec: 102.00 - lr: 0.100000
2022-11-06 21:33:43,427 epoch 51 - iter 304/386 - loss 0.11665112 - samples/sec: 104.46 - lr: 0.100000
2022-11-06 21:33:49,009 epoch 51 - iter 342/386 - loss 0.11723027 - samples/sec: 108.98 - lr: 0.100000
2022-11-06 21:33:54,280 epoch 51 - iter 380/386 - loss 0.11702633 - samples/sec: 115.41 - lr: 0.100000
2022-11-06 21:33:55,304 ----------------------------------------------------------------------------------------------------
2022-11-06 21:33:55,304 EPOCH 51 done: loss 0.1173 - lr 0.100000
2022-11-06 21:34:04,866 Evaluating as a multi-label problem: False
2022-11-06 21:34:04,983 TEST : loss 0.0740566998720169 - f1-score (micro avg) 0.9782
2022-11-06 21:34:05,097 BAD EPOCHS (no improvement): 0
2022-11-06 21:34:05,303 ----------------------------------------------------------------------------------------------------
2022-11-06 21:34:10,727 epoch 52 - iter 38/386 - loss 0.11155641 - samples/sec: 112.18 - lr: 0.100000
2022-11-06 21:34:15,463 epoch 52 - iter 76/386 - loss 0.10635453 - samples/sec: 128.47 - lr: 0.100000
2022-11-06 21:34:21,886 epoch 52 - iter 114/386 - loss 0.10919505 - samples/sec: 94.71 - lr: 0.100000
2022-11-06 21:34:27,388 epoch 52 - iter 152/386 - loss 0.11221453 - samples/sec: 110.56 - lr: 0.100000
2022-11-06 21:34:33,430 epoch 52 - iter 190/386 - loss 0.11321872 - samples/sec: 100.69 - lr: 0.100000
2022-11-06 21:34:39,236 epoch 52 - iter 228/386 - loss 0.11335050 - samples/sec: 104.76 - lr: 0.100000
2022-11-06 21:34:44,401 epoch 52 - iter 266/386 - loss 0.11362461 - samples/sec: 117.80 - lr: 0.100000
2022-11-06 21:34:49,871 epoch 52 - iter 304/386 - loss 0.11499895 - samples/sec: 111.23 - lr: 0.100000
2022-11-06 21:34:55,416 epoch 52 - iter 342/386 - loss 0.11483124 - samples/sec: 109.70 - lr: 0.100000
2022-11-06 21:35:00,713 epoch 52 - iter 380/386 - loss 0.11433020 - samples/sec: 114.85 - lr: 0.100000
2022-11-06 21:35:01,535 ----------------------------------------------------------------------------------------------------
2022-11-06 21:35:01,535 EPOCH 52 done: loss 0.1147 - lr 0.100000
2022-11-06 21:35:11,116 Evaluating as a multi-label problem: False
2022-11-06 21:35:11,232 TEST : loss 0.07622171938419342 - f1-score (micro avg) 0.9777
2022-11-06 21:35:11,346 BAD EPOCHS (no improvement): 0
2022-11-06 21:35:11,555 ----------------------------------------------------------------------------------------------------
2022-11-06 21:35:16,892 epoch 53 - iter 38/386 - loss 0.11911110 - samples/sec: 114.04 - lr: 0.100000
2022-11-06 21:35:22,478 epoch 53 - iter 76/386 - loss 0.11354567 - samples/sec: 108.90 - lr: 0.100000
2022-11-06 21:35:27,479 epoch 53 - iter 114/386 - loss 0.11330347 - samples/sec: 121.65 - lr: 0.100000
2022-11-06 21:35:32,942 epoch 53 - iter 152/386 - loss 0.11457219 - samples/sec: 111.37 - lr: 0.100000
2022-11-06 21:35:38,294 epoch 53 - iter 190/386 - loss 0.11374010 - samples/sec: 113.67 - lr: 0.100000
2022-11-06 21:35:44,439 epoch 53 - iter 228/386 - loss 0.11472213 - samples/sec: 98.99 - lr: 0.100000
2022-11-06 21:35:49,935 epoch 53 - iter 266/386 - loss 0.11446083 - samples/sec: 110.69 - lr: 0.100000
2022-11-06 21:35:55,321 epoch 53 - iter 304/386 - loss 0.11480098 - samples/sec: 112.95 - lr: 0.100000
2022-11-06 21:36:01,025 epoch 53 - iter 342/386 - loss 0.11431744 - samples/sec: 106.65 - lr: 0.100000
2022-11-06 21:36:06,693 epoch 53 - iter 380/386 - loss 0.11353443 - samples/sec: 107.33 - lr: 0.100000
2022-11-06 21:36:07,519 ----------------------------------------------------------------------------------------------------
2022-11-06 21:36:07,519 EPOCH 53 done: loss 0.1142 - lr 0.100000
2022-11-06 21:36:17,026 Evaluating as a multi-label problem: False
2022-11-06 21:36:17,170 TEST : loss 0.07737206667661667 - f1-score (micro avg) 0.9773
2022-11-06 21:36:17,289 BAD EPOCHS (no improvement): 0
2022-11-06 21:36:17,516 ----------------------------------------------------------------------------------------------------
2022-11-06 21:36:23,135 epoch 54 - iter 38/386 - loss 0.11378117 - samples/sec: 108.31 - lr: 0.100000
2022-11-06 21:36:28,739 epoch 54 - iter 76/386 - loss 0.11354071 - samples/sec: 108.56 - lr: 0.100000
2022-11-06 21:36:34,885 epoch 54 - iter 114/386 - loss 0.11360766 - samples/sec: 98.97 - lr: 0.100000
2022-11-06 21:36:40,077 epoch 54 - iter 152/386 - loss 0.11208863 - samples/sec: 117.19 - lr: 0.100000
2022-11-06 21:36:45,443 epoch 54 - iter 190/386 - loss 0.11447499 - samples/sec: 113.37 - lr: 0.100000
2022-11-06 21:36:51,117 epoch 54 - iter 228/386 - loss 0.11376877 - samples/sec: 107.22 - lr: 0.100000
2022-11-06 21:36:56,600 epoch 54 - iter 266/386 - loss 0.11313088 - samples/sec: 110.96 - lr: 0.100000
2022-11-06 21:37:02,149 epoch 54 - iter 304/386 - loss 0.11250098 - samples/sec: 109.63 - lr: 0.100000
2022-11-06 21:37:07,918 epoch 54 - iter 342/386 - loss 0.11255859 - samples/sec: 105.45 - lr: 0.100000
2022-11-06 21:37:13,229 epoch 54 - iter 380/386 - loss 0.11233373 - samples/sec: 114.54 - lr: 0.100000
2022-11-06 21:37:13,950 ----------------------------------------------------------------------------------------------------
2022-11-06 21:37:13,950 EPOCH 54 done: loss 0.1124 - lr 0.100000
2022-11-06 21:37:23,552 Evaluating as a multi-label problem: False
2022-11-06 21:37:23,669 TEST : loss 0.07463442534208298 - f1-score (micro avg) 0.9784
2022-11-06 21:37:23,783 BAD EPOCHS (no improvement): 0
2022-11-06 21:37:23,992 ----------------------------------------------------------------------------------------------------
2022-11-06 21:37:29,904 epoch 55 - iter 38/386 - loss 0.11729128 - samples/sec: 102.92 - lr: 0.100000
2022-11-06 21:37:35,325 epoch 55 - iter 76/386 - loss 0.11251135 - samples/sec: 112.21 - lr: 0.100000
2022-11-06 21:37:41,114 epoch 55 - iter 114/386 - loss 0.10996077 - samples/sec: 105.09 - lr: 0.100000
2022-11-06 21:37:46,552 epoch 55 - iter 152/386 - loss 0.11022640 - samples/sec: 111.86 - lr: 0.100000
2022-11-06 21:37:52,451 epoch 55 - iter 190/386 - loss 0.10943894 - samples/sec: 103.13 - lr: 0.100000
2022-11-06 21:37:57,751 epoch 55 - iter 228/386 - loss 0.10922557 - samples/sec: 114.80 - lr: 0.100000
2022-11-06 21:38:03,543 epoch 55 - iter 266/386 - loss 0.11133408 - samples/sec: 105.03 - lr: 0.100000
2022-11-06 21:38:08,825 epoch 55 - iter 304/386 - loss 0.11169533 - samples/sec: 115.16 - lr: 0.100000
2022-11-06 21:38:13,916 epoch 55 - iter 342/386 - loss 0.11234160 - samples/sec: 119.52 - lr: 0.100000
2022-11-06 21:38:19,174 epoch 55 - iter 380/386 - loss 0.11184340 - samples/sec: 115.69 - lr: 0.100000
2022-11-06 21:38:20,110 ----------------------------------------------------------------------------------------------------
2022-11-06 21:38:20,110 EPOCH 55 done: loss 0.1120 - lr 0.100000
2022-11-06 21:38:29,735 Evaluating as a multi-label problem: False
2022-11-06 21:38:29,852 TEST : loss 0.07638020068407059 - f1-score (micro avg) 0.9794
2022-11-06 21:38:29,966 BAD EPOCHS (no improvement): 0
2022-11-06 21:38:30,172 ----------------------------------------------------------------------------------------------------
2022-11-06 21:38:36,103 epoch 56 - iter 38/386 - loss 0.11105095 - samples/sec: 102.58 - lr: 0.100000
2022-11-06 21:38:41,324 epoch 56 - iter 76/386 - loss 0.11292715 - samples/sec: 116.54 - lr: 0.100000
2022-11-06 21:38:46,970 epoch 56 - iter 114/386 - loss 0.11197317 - samples/sec: 107.75 - lr: 0.100000
2022-11-06 21:38:52,483 epoch 56 - iter 152/386 - loss 0.10861220 - samples/sec: 110.34 - lr: 0.100000
2022-11-06 21:38:58,226 epoch 56 - iter 190/386 - loss 0.11033258 - samples/sec: 105.94 - lr: 0.100000
2022-11-06 21:39:03,351 epoch 56 - iter 228/386 - loss 0.11314777 - samples/sec: 118.70 - lr: 0.100000
2022-11-06 21:39:08,750 epoch 56 - iter 266/386 - loss 0.11319016 - samples/sec: 112.69 - lr: 0.100000
2022-11-06 21:39:14,663 epoch 56 - iter 304/386 - loss 0.11310103 - samples/sec: 102.88 - lr: 0.100000
2022-11-06 21:39:19,683 epoch 56 - iter 342/386 - loss 0.11376647 - samples/sec: 121.19 - lr: 0.100000
2022-11-06 21:39:25,519 epoch 56 - iter 380/386 - loss 0.11373646 - samples/sec: 104.22 - lr: 0.100000
2022-11-06 21:39:26,221 ----------------------------------------------------------------------------------------------------
2022-11-06 21:39:26,221 EPOCH 56 done: loss 0.1136 - lr 0.100000
2022-11-06 21:39:38,259 Evaluating as a multi-label problem: False
2022-11-06 21:39:38,376 TEST : loss 0.07799383252859116 - f1-score (micro avg) 0.9774
2022-11-06 21:39:38,489 BAD EPOCHS (no improvement): 1
2022-11-06 21:39:38,695 ----------------------------------------------------------------------------------------------------
2022-11-06 21:39:43,986 epoch 57 - iter 38/386 - loss 0.10619651 - samples/sec: 115.01 - lr: 0.100000
2022-11-06 21:39:49,781 epoch 57 - iter 76/386 - loss 0.10675645 - samples/sec: 104.98 - lr: 0.100000
2022-11-06 21:39:55,535 epoch 57 - iter 114/386 - loss 0.10678482 - samples/sec: 105.72 - lr: 0.100000
2022-11-06 21:40:01,371 epoch 57 - iter 152/386 - loss 0.10370251 - samples/sec: 104.24 - lr: 0.100000
2022-11-06 21:40:06,881 epoch 57 - iter 190/386 - loss 0.10679265 - samples/sec: 110.41 - lr: 0.100000
2022-11-06 21:40:12,219 epoch 57 - iter 228/386 - loss 0.10708625 - samples/sec: 113.97 - lr: 0.100000
2022-11-06 21:40:17,514 epoch 57 - iter 266/386 - loss 0.10891728 - samples/sec: 114.88 - lr: 0.100000
2022-11-06 21:40:22,851 epoch 57 - iter 304/386 - loss 0.10862710 - samples/sec: 113.99 - lr: 0.100000
2022-11-06 21:40:28,460 epoch 57 - iter 342/386 - loss 0.11015940 - samples/sec: 109.29 - lr: 0.100000
2022-11-06 21:40:34,126 epoch 57 - iter 380/386 - loss 0.10992567 - samples/sec: 107.37 - lr: 0.100000
2022-11-06 21:40:34,921 ----------------------------------------------------------------------------------------------------
2022-11-06 21:40:34,921 EPOCH 57 done: loss 0.1101 - lr 0.100000
2022-11-06 21:40:44,609 Evaluating as a multi-label problem: False
2022-11-06 21:40:44,727 TEST : loss 0.07584260404109955 - f1-score (micro avg) 0.9782
2022-11-06 21:40:44,841 BAD EPOCHS (no improvement): 0
2022-11-06 21:40:45,039 ----------------------------------------------------------------------------------------------------
2022-11-06 21:40:50,995 epoch 58 - iter 38/386 - loss 0.10393475 - samples/sec: 102.16 - lr: 0.100000
2022-11-06 21:40:56,311 epoch 58 - iter 76/386 - loss 0.10775986 - samples/sec: 114.45 - lr: 0.100000
2022-11-06 21:41:01,589 epoch 58 - iter 114/386 - loss 0.10667533 - samples/sec: 115.27 - lr: 0.100000
2022-11-06 21:41:07,012 epoch 58 - iter 152/386 - loss 0.10719735 - samples/sec: 112.16 - lr: 0.100000
2022-11-06 21:41:12,616 epoch 58 - iter 190/386 - loss 0.10740467 - samples/sec: 108.57 - lr: 0.100000
2022-11-06 21:41:18,171 epoch 58 - iter 228/386 - loss 0.10698504 - samples/sec: 109.50 - lr: 0.100000
2022-11-06 21:41:23,781 epoch 58 - iter 266/386 - loss 0.10806348 - samples/sec: 108.45 - lr: 0.100000
2022-11-06 21:41:29,415 epoch 58 - iter 304/386 - loss 0.10804363 - samples/sec: 107.98 - lr: 0.100000
2022-11-06 21:41:34,923 epoch 58 - iter 342/386 - loss 0.10848937 - samples/sec: 110.44 - lr: 0.100000
2022-11-06 21:41:40,542 epoch 58 - iter 380/386 - loss 0.10878650 - samples/sec: 108.27 - lr: 0.100000
2022-11-06 21:41:41,317 ----------------------------------------------------------------------------------------------------
2022-11-06 21:41:41,317 EPOCH 58 done: loss 0.1089 - lr 0.100000
2022-11-06 21:41:51,029 Evaluating as a multi-label problem: False
2022-11-06 21:41:51,146 TEST : loss 0.07553786039352417 - f1-score (micro avg) 0.9777
2022-11-06 21:41:51,259 BAD EPOCHS (no improvement): 0
2022-11-06 21:41:51,476 ----------------------------------------------------------------------------------------------------
2022-11-06 21:41:57,194 epoch 59 - iter 38/386 - loss 0.10729746 - samples/sec: 106.42 - lr: 0.100000
2022-11-06 21:42:02,764 epoch 59 - iter 76/386 - loss 0.10706038 - samples/sec: 109.21 - lr: 0.100000
2022-11-06 21:42:08,164 epoch 59 - iter 114/386 - loss 0.10819463 - samples/sec: 112.65 - lr: 0.100000
2022-11-06 21:42:13,862 epoch 59 - iter 152/386 - loss 0.11092300 - samples/sec: 107.23 - lr: 0.100000
2022-11-06 21:42:19,449 epoch 59 - iter 190/386 - loss 0.11033596 - samples/sec: 108.88 - lr: 0.100000
2022-11-06 21:42:25,236 epoch 59 - iter 228/386 - loss 0.10859445 - samples/sec: 105.11 - lr: 0.100000
2022-11-06 21:42:30,916 epoch 59 - iter 266/386 - loss 0.10859639 - samples/sec: 107.12 - lr: 0.100000
2022-11-06 21:42:36,552 epoch 59 - iter 304/386 - loss 0.10838577 - samples/sec: 107.93 - lr: 0.100000
2022-11-06 21:42:41,937 epoch 59 - iter 342/386 - loss 0.10856409 - samples/sec: 112.96 - lr: 0.100000
2022-11-06 21:42:47,304 epoch 59 - iter 380/386 - loss 0.10888417 - samples/sec: 113.36 - lr: 0.100000
2022-11-06 21:42:48,120 ----------------------------------------------------------------------------------------------------
2022-11-06 21:42:48,121 EPOCH 59 done: loss 0.1092 - lr 0.100000
2022-11-06 21:42:57,722 Evaluating as a multi-label problem: False
2022-11-06 21:42:57,837 TEST : loss 0.07385119795799255 - f1-score (micro avg) 0.9784
2022-11-06 21:42:57,951 BAD EPOCHS (no improvement): 1
2022-11-06 21:42:58,161 ----------------------------------------------------------------------------------------------------
2022-11-06 21:43:03,698 epoch 60 - iter 38/386 - loss 0.10381911 - samples/sec: 109.91 - lr: 0.100000
2022-11-06 21:43:09,144 epoch 60 - iter 76/386 - loss 0.10904296 - samples/sec: 111.69 - lr: 0.100000
2022-11-06 21:43:14,641 epoch 60 - iter 114/386 - loss 0.10938936 - samples/sec: 111.54 - lr: 0.100000
2022-11-06 21:43:20,357 epoch 60 - iter 152/386 - loss 0.10910729 - samples/sec: 106.41 - lr: 0.100000
2022-11-06 21:43:26,035 epoch 60 - iter 190/386 - loss 0.10992253 - samples/sec: 107.58 - lr: 0.100000
2022-11-06 21:43:31,751 epoch 60 - iter 228/386 - loss 0.10991650 - samples/sec: 106.43 - lr: 0.100000
2022-11-06 21:43:37,812 epoch 60 - iter 266/386 - loss 0.11025661 - samples/sec: 100.37 - lr: 0.100000
2022-11-06 21:43:43,686 epoch 60 - iter 304/386 - loss 0.11020600 - samples/sec: 103.55 - lr: 0.100000
2022-11-06 21:43:48,655 epoch 60 - iter 342/386 - loss 0.11033435 - samples/sec: 122.44 - lr: 0.100000
2022-11-06 21:43:54,168 epoch 60 - iter 380/386 - loss 0.11091046 - samples/sec: 110.35 - lr: 0.100000
2022-11-06 21:43:54,941 ----------------------------------------------------------------------------------------------------
2022-11-06 21:43:54,941 EPOCH 60 done: loss 0.1108 - lr 0.100000
2022-11-06 21:44:04,122 Evaluating as a multi-label problem: False
2022-11-06 21:44:04,237 TEST : loss 0.07543539255857468 - f1-score (micro avg) 0.9787
2022-11-06 21:44:04,349 BAD EPOCHS (no improvement): 2
2022-11-06 21:44:04,562 ----------------------------------------------------------------------------------------------------
2022-11-06 21:44:10,584 epoch 61 - iter 38/386 - loss 0.12045508 - samples/sec: 101.02 - lr: 0.100000
2022-11-06 21:44:16,219 epoch 61 - iter 76/386 - loss 0.11946764 - samples/sec: 107.97 - lr: 0.100000
2022-11-06 21:44:21,669 epoch 61 - iter 114/386 - loss 0.11543090 - samples/sec: 111.62 - lr: 0.100000
2022-11-06 21:44:27,225 epoch 61 - iter 152/386 - loss 0.11605390 - samples/sec: 109.48 - lr: 0.100000
2022-11-06 21:44:33,223 epoch 61 - iter 190/386 - loss 0.11461676 - samples/sec: 101.42 - lr: 0.100000
2022-11-06 21:44:39,012 epoch 61 - iter 228/386 - loss 0.11382792 - samples/sec: 105.09 - lr: 0.100000
2022-11-06 21:44:44,401 epoch 61 - iter 266/386 - loss 0.11310712 - samples/sec: 113.76 - lr: 0.100000
2022-11-06 21:44:49,754 epoch 61 - iter 304/386 - loss 0.11212259 - samples/sec: 113.65 - lr: 0.100000
2022-11-06 21:44:55,345 epoch 61 - iter 342/386 - loss 0.11132594 - samples/sec: 108.80 - lr: 0.100000
2022-11-06 21:45:00,835 epoch 61 - iter 380/386 - loss 0.11213902 - samples/sec: 110.81 - lr: 0.100000
2022-11-06 21:45:01,644 ----------------------------------------------------------------------------------------------------
2022-11-06 21:45:01,645 EPOCH 61 done: loss 0.1122 - lr 0.100000
2022-11-06 21:45:10,326 Evaluating as a multi-label problem: False
2022-11-06 21:45:10,441 TEST : loss 0.07580757886171341 - f1-score (micro avg) 0.9781
2022-11-06 21:45:10,554 BAD EPOCHS (no improvement): 3
2022-11-06 21:45:10,759 ----------------------------------------------------------------------------------------------------
2022-11-06 21:45:16,107 epoch 62 - iter 38/386 - loss 0.11185314 - samples/sec: 113.78 - lr: 0.100000
2022-11-06 21:45:21,736 epoch 62 - iter 76/386 - loss 0.10673749 - samples/sec: 108.07 - lr: 0.100000
2022-11-06 21:45:27,662 epoch 62 - iter 114/386 - loss 0.10907345 - samples/sec: 102.66 - lr: 0.100000
2022-11-06 21:45:33,872 epoch 62 - iter 152/386 - loss 0.10767255 - samples/sec: 97.95 - lr: 0.100000
2022-11-06 21:45:39,107 epoch 62 - iter 190/386 - loss 0.10876200 - samples/sec: 116.22 - lr: 0.100000
2022-11-06 21:45:44,769 epoch 62 - iter 228/386 - loss 0.10956018 - samples/sec: 107.44 - lr: 0.100000
2022-11-06 21:45:50,459 epoch 62 - iter 266/386 - loss 0.11019035 - samples/sec: 106.91 - lr: 0.100000
2022-11-06 21:45:56,323 epoch 62 - iter 304/386 - loss 0.11093248 - samples/sec: 103.73 - lr: 0.100000
2022-11-06 21:46:01,923 epoch 62 - iter 342/386 - loss 0.11064050 - samples/sec: 108.64 - lr: 0.100000
2022-11-06 21:46:07,180 epoch 62 - iter 380/386 - loss 0.11057009 - samples/sec: 115.72 - lr: 0.100000
2022-11-06 21:46:08,019 ----------------------------------------------------------------------------------------------------
2022-11-06 21:46:08,019 EPOCH 62 done: loss 0.1102 - lr 0.100000
2022-11-06 21:46:17,339 Evaluating as a multi-label problem: False
2022-11-06 21:46:17,453 TEST : loss 0.07879139482975006 - f1-score (micro avg) 0.9781
2022-11-06 21:46:17,565 Epoch 62: reducing learning rate of group 0 to 5.0000e-02.
2022-11-06 21:46:17,565 BAD EPOCHS (no improvement): 4
2022-11-06 21:46:17,766 ----------------------------------------------------------------------------------------------------
2022-11-06 21:46:22,757 epoch 63 - iter 38/386 - loss 0.10658703 - samples/sec: 121.93 - lr: 0.050000
2022-11-06 21:46:27,969 epoch 63 - iter 76/386 - loss 0.09978209 - samples/sec: 116.72 - lr: 0.050000
2022-11-06 21:46:33,517 epoch 63 - iter 114/386 - loss 0.10149720 - samples/sec: 109.64 - lr: 0.050000
2022-11-06 21:46:39,162 epoch 63 - iter 152/386 - loss 0.10215730 - samples/sec: 107.77 - lr: 0.050000
2022-11-06 21:46:44,390 epoch 63 - iter 190/386 - loss 0.10305351 - samples/sec: 116.35 - lr: 0.050000
2022-11-06 21:46:50,527 epoch 63 - iter 228/386 - loss 0.10224865 - samples/sec: 99.11 - lr: 0.050000
2022-11-06 21:46:56,167 epoch 63 - iter 266/386 - loss 0.10338093 - samples/sec: 107.87 - lr: 0.050000
2022-11-06 21:47:02,026 epoch 63 - iter 304/386 - loss 0.10281964 - samples/sec: 103.82 - lr: 0.050000
2022-11-06 21:47:07,641 epoch 63 - iter 342/386 - loss 0.10326028 - samples/sec: 108.34 - lr: 0.050000
2022-11-06 21:47:13,252 epoch 63 - iter 380/386 - loss 0.10281205 - samples/sec: 108.42 - lr: 0.050000
2022-11-06 21:47:14,056 ----------------------------------------------------------------------------------------------------
2022-11-06 21:47:14,056 EPOCH 63 done: loss 0.1023 - lr 0.050000
2022-11-06 21:47:23,528 Evaluating as a multi-label problem: False
2022-11-06 21:47:23,642 TEST : loss 0.07534310221672058 - f1-score (micro avg) 0.9785
2022-11-06 21:47:23,755 BAD EPOCHS (no improvement): 0
2022-11-06 21:47:23,957 ----------------------------------------------------------------------------------------------------
2022-11-06 21:47:29,857 epoch 64 - iter 38/386 - loss 0.09442249 - samples/sec: 103.14 - lr: 0.050000
2022-11-06 21:47:35,255 epoch 64 - iter 76/386 - loss 0.09624885 - samples/sec: 112.69 - lr: 0.050000
2022-11-06 21:47:40,478 epoch 64 - iter 114/386 - loss 0.09654897 - samples/sec: 116.46 - lr: 0.050000
2022-11-06 21:47:46,285 epoch 64 - iter 152/386 - loss 0.09732478 - samples/sec: 104.76 - lr: 0.050000
2022-11-06 21:47:51,986 epoch 64 - iter 190/386 - loss 0.09745275 - samples/sec: 106.70 - lr: 0.050000
2022-11-06 21:47:57,364 epoch 64 - iter 228/386 - loss 0.09955387 - samples/sec: 113.11 - lr: 0.050000
2022-11-06 21:48:02,959 epoch 64 - iter 266/386 - loss 0.10073669 - samples/sec: 108.74 - lr: 0.050000
2022-11-06 21:48:08,329 epoch 64 - iter 304/386 - loss 0.10014693 - samples/sec: 113.27 - lr: 0.050000
2022-11-06 21:48:13,610 epoch 64 - iter 342/386 - loss 0.09955591 - samples/sec: 115.19 - lr: 0.050000
2022-11-06 21:48:19,482 epoch 64 - iter 380/386 - loss 0.10003809 - samples/sec: 103.60 - lr: 0.050000
2022-11-06 21:48:20,306 ----------------------------------------------------------------------------------------------------
2022-11-06 21:48:20,306 EPOCH 64 done: loss 0.1000 - lr 0.050000
2022-11-06 21:48:29,824 Evaluating as a multi-label problem: False
2022-11-06 21:48:29,939 TEST : loss 0.07464543730020523 - f1-score (micro avg) 0.9787
2022-11-06 21:48:30,052 BAD EPOCHS (no improvement): 0
2022-11-06 21:48:30,265 ----------------------------------------------------------------------------------------------------
2022-11-06 21:48:36,046 epoch 65 - iter 38/386 - loss 0.09887412 - samples/sec: 105.25 - lr: 0.050000
2022-11-06 21:48:41,537 epoch 65 - iter 76/386 - loss 0.10023593 - samples/sec: 110.80 - lr: 0.050000
2022-11-06 21:48:47,002 epoch 65 - iter 114/386 - loss 0.09668348 - samples/sec: 111.31 - lr: 0.050000
2022-11-06 21:48:52,703 epoch 65 - iter 152/386 - loss 0.09943600 - samples/sec: 106.71 - lr: 0.050000
2022-11-06 21:48:57,968 epoch 65 - iter 190/386 - loss 0.09869457 - samples/sec: 115.55 - lr: 0.050000
2022-11-06 21:49:03,341 epoch 65 - iter 228/386 - loss 0.09852745 - samples/sec: 113.21 - lr: 0.050000
2022-11-06 21:49:08,708 epoch 65 - iter 266/386 - loss 0.09876531 - samples/sec: 113.37 - lr: 0.050000
2022-11-06 21:49:14,273 epoch 65 - iter 304/386 - loss 0.09762860 - samples/sec: 109.29 - lr: 0.050000
2022-11-06 21:49:19,749 epoch 65 - iter 342/386 - loss 0.09794980 - samples/sec: 111.10 - lr: 0.050000
2022-11-06 21:49:25,372 epoch 65 - iter 380/386 - loss 0.09788718 - samples/sec: 108.17 - lr: 0.050000
2022-11-06 21:49:26,241 ----------------------------------------------------------------------------------------------------
2022-11-06 21:49:26,242 EPOCH 65 done: loss 0.0977 - lr 0.050000
2022-11-06 21:49:35,664 Evaluating as a multi-label problem: False
2022-11-06 21:49:35,779 TEST : loss 0.0754692405462265 - f1-score (micro avg) 0.9788
2022-11-06 21:49:35,891 BAD EPOCHS (no improvement): 0
2022-11-06 21:49:36,102 ----------------------------------------------------------------------------------------------------
2022-11-06 21:49:41,628 epoch 66 - iter 38/386 - loss 0.10147858 - samples/sec: 110.11 - lr: 0.050000
2022-11-06 21:49:47,280 epoch 66 - iter 76/386 - loss 0.10001964 - samples/sec: 107.63 - lr: 0.050000
2022-11-06 21:49:52,958 epoch 66 - iter 114/386 - loss 0.09909022 - samples/sec: 107.14 - lr: 0.050000
2022-11-06 21:49:58,085 epoch 66 - iter 152/386 - loss 0.09947914 - samples/sec: 118.65 - lr: 0.050000
2022-11-06 21:50:03,139 epoch 66 - iter 190/386 - loss 0.09872543 - samples/sec: 120.39 - lr: 0.050000
2022-11-06 21:50:09,214 epoch 66 - iter 228/386 - loss 0.09837586 - samples/sec: 100.13 - lr: 0.050000
2022-11-06 21:50:14,663 epoch 66 - iter 266/386 - loss 0.09818549 - samples/sec: 111.64 - lr: 0.050000
2022-11-06 21:50:20,201 epoch 66 - iter 304/386 - loss 0.09828617 - samples/sec: 109.85 - lr: 0.050000
2022-11-06 21:50:25,865 epoch 66 - iter 342/386 - loss 0.09844885 - samples/sec: 107.40 - lr: 0.050000
2022-11-06 21:50:31,143 epoch 66 - iter 380/386 - loss 0.09810462 - samples/sec: 115.26 - lr: 0.050000
2022-11-06 21:50:32,093 ----------------------------------------------------------------------------------------------------
2022-11-06 21:50:32,093 EPOCH 66 done: loss 0.0985 - lr 0.050000
2022-11-06 21:50:44,000 Evaluating as a multi-label problem: False
2022-11-06 21:50:44,114 TEST : loss 0.07432317733764648 - f1-score (micro avg) 0.9786
2022-11-06 21:50:44,226 BAD EPOCHS (no improvement): 1
2022-11-06 21:50:44,434 ----------------------------------------------------------------------------------------------------
2022-11-06 21:50:50,060 epoch 67 - iter 38/386 - loss 0.09547788 - samples/sec: 108.15 - lr: 0.050000
2022-11-06 21:50:55,291 epoch 67 - iter 76/386 - loss 0.09522140 - samples/sec: 116.30 - lr: 0.050000
2022-11-06 21:51:00,893 epoch 67 - iter 114/386 - loss 0.09466178 - samples/sec: 108.59 - lr: 0.050000
2022-11-06 21:51:06,833 epoch 67 - iter 152/386 - loss 0.09304239 - samples/sec: 102.40 - lr: 0.050000
2022-11-06 21:51:12,028 epoch 67 - iter 190/386 - loss 0.09333640 - samples/sec: 117.11 - lr: 0.050000
2022-11-06 21:51:17,096 epoch 67 - iter 228/386 - loss 0.09409673 - samples/sec: 120.04 - lr: 0.050000
2022-11-06 21:51:22,697 epoch 67 - iter 266/386 - loss 0.09443416 - samples/sec: 108.62 - lr: 0.050000
2022-11-06 21:51:28,403 epoch 67 - iter 304/386 - loss 0.09389862 - samples/sec: 106.60 - lr: 0.050000
2022-11-06 21:51:34,208 epoch 67 - iter 342/386 - loss 0.09431186 - samples/sec: 105.56 - lr: 0.050000
2022-11-06 21:51:39,915 epoch 67 - iter 380/386 - loss 0.09465824 - samples/sec: 106.59 - lr: 0.050000
2022-11-06 21:51:40,804 ----------------------------------------------------------------------------------------------------
2022-11-06 21:51:40,805 EPOCH 67 done: loss 0.0947 - lr 0.050000
2022-11-06 21:51:50,263 Evaluating as a multi-label problem: False
2022-11-06 21:51:50,378 TEST : loss 0.07349660992622375 - f1-score (micro avg) 0.9798
2022-11-06 21:51:50,492 BAD EPOCHS (no improvement): 0
2022-11-06 21:51:50,702 ----------------------------------------------------------------------------------------------------
2022-11-06 21:51:56,209 epoch 68 - iter 38/386 - loss 0.09137315 - samples/sec: 110.51 - lr: 0.050000
2022-11-06 21:52:01,826 epoch 68 - iter 76/386 - loss 0.09330429 - samples/sec: 108.30 - lr: 0.050000
2022-11-06 21:52:07,193 epoch 68 - iter 114/386 - loss 0.09387846 - samples/sec: 113.35 - lr: 0.050000
2022-11-06 21:52:12,840 epoch 68 - iter 152/386 - loss 0.09271632 - samples/sec: 107.72 - lr: 0.050000
2022-11-06 21:52:18,316 epoch 68 - iter 190/386 - loss 0.09315752 - samples/sec: 111.09 - lr: 0.050000
2022-11-06 21:52:23,742 epoch 68 - iter 228/386 - loss 0.09312014 - samples/sec: 112.13 - lr: 0.050000
2022-11-06 21:52:28,716 epoch 68 - iter 266/386 - loss 0.09332579 - samples/sec: 122.31 - lr: 0.050000
2022-11-06 21:52:34,044 epoch 68 - iter 304/386 - loss 0.09372537 - samples/sec: 114.16 - lr: 0.050000
2022-11-06 21:52:39,864 epoch 68 - iter 342/386 - loss 0.09456013 - samples/sec: 104.54 - lr: 0.050000
2022-11-06 21:52:45,686 epoch 68 - iter 380/386 - loss 0.09506871 - samples/sec: 104.47 - lr: 0.050000
2022-11-06 21:52:46,446 ----------------------------------------------------------------------------------------------------
2022-11-06 21:52:46,446 EPOCH 68 done: loss 0.0954 - lr 0.050000
2022-11-06 21:52:55,924 Evaluating as a multi-label problem: False
2022-11-06 21:52:56,039 TEST : loss 0.07435259222984314 - f1-score (micro avg) 0.9791
2022-11-06 21:52:56,152 BAD EPOCHS (no improvement): 1
2022-11-06 21:52:56,363 ----------------------------------------------------------------------------------------------------
2022-11-06 21:53:01,852 epoch 69 - iter 38/386 - loss 0.09120895 - samples/sec: 110.84 - lr: 0.050000
2022-11-06 21:53:07,266 epoch 69 - iter 76/386 - loss 0.09158650 - samples/sec: 112.38 - lr: 0.050000
2022-11-06 21:53:13,000 epoch 69 - iter 114/386 - loss 0.09354837 - samples/sec: 106.09 - lr: 0.050000
2022-11-06 21:53:18,752 epoch 69 - iter 152/386 - loss 0.09241557 - samples/sec: 106.54 - lr: 0.050000
2022-11-06 21:53:24,432 epoch 69 - iter 190/386 - loss 0.09405392 - samples/sec: 107.10 - lr: 0.050000
2022-11-06 21:53:29,942 epoch 69 - iter 228/386 - loss 0.09437177 - samples/sec: 110.42 - lr: 0.050000
2022-11-06 21:53:35,360 epoch 69 - iter 266/386 - loss 0.09345038 - samples/sec: 112.28 - lr: 0.050000
2022-11-06 21:53:40,749 epoch 69 - iter 304/386 - loss 0.09349443 - samples/sec: 112.88 - lr: 0.050000
2022-11-06 21:53:46,313 epoch 69 - iter 342/386 - loss 0.09350680 - samples/sec: 109.33 - lr: 0.050000
2022-11-06 21:53:51,756 epoch 69 - iter 380/386 - loss 0.09377817 - samples/sec: 111.77 - lr: 0.050000
2022-11-06 21:53:52,543 ----------------------------------------------------------------------------------------------------
2022-11-06 21:53:52,543 EPOCH 69 done: loss 0.0937 - lr 0.050000
2022-11-06 21:54:02,057 Evaluating as a multi-label problem: False
2022-11-06 21:54:02,171 TEST : loss 0.0757945254445076 - f1-score (micro avg) 0.9784
2022-11-06 21:54:02,284 BAD EPOCHS (no improvement): 0
2022-11-06 21:54:02,486 ----------------------------------------------------------------------------------------------------
2022-11-06 21:54:08,097 epoch 70 - iter 38/386 - loss 0.08890860 - samples/sec: 108.45 - lr: 0.050000
2022-11-06 21:54:13,612 epoch 70 - iter 76/386 - loss 0.09276883 - samples/sec: 110.30 - lr: 0.050000
2022-11-06 21:54:19,103 epoch 70 - iter 114/386 - loss 0.09169168 - samples/sec: 110.78 - lr: 0.050000
2022-11-06 21:54:24,503 epoch 70 - iter 152/386 - loss 0.09332129 - samples/sec: 112.65 - lr: 0.050000
2022-11-06 21:54:30,183 epoch 70 - iter 190/386 - loss 0.09109459 - samples/sec: 107.10 - lr: 0.050000
2022-11-06 21:54:35,735 epoch 70 - iter 228/386 - loss 0.09359296 - samples/sec: 109.56 - lr: 0.050000
2022-11-06 21:54:41,443 epoch 70 - iter 266/386 - loss 0.09350137 - samples/sec: 106.59 - lr: 0.050000
2022-11-06 21:54:46,719 epoch 70 - iter 304/386 - loss 0.09343522 - samples/sec: 115.31 - lr: 0.050000
2022-11-06 21:54:52,170 epoch 70 - iter 342/386 - loss 0.09365079 - samples/sec: 111.59 - lr: 0.050000
2022-11-06 21:54:57,826 epoch 70 - iter 380/386 - loss 0.09379290 - samples/sec: 107.55 - lr: 0.050000
2022-11-06 21:54:58,474 ----------------------------------------------------------------------------------------------------
2022-11-06 21:54:58,474 EPOCH 70 done: loss 0.0937 - lr 0.050000
2022-11-06 21:55:07,791 Evaluating as a multi-label problem: False
2022-11-06 21:55:07,905 TEST : loss 0.0764009952545166 - f1-score (micro avg) 0.9789
2022-11-06 21:55:08,017 BAD EPOCHS (no improvement): 0
2022-11-06 21:55:08,221 ----------------------------------------------------------------------------------------------------
2022-11-06 21:55:13,770 epoch 71 - iter 38/386 - loss 0.08554802 - samples/sec: 109.64 - lr: 0.050000
2022-11-06 21:55:19,301 epoch 71 - iter 76/386 - loss 0.09025236 - samples/sec: 110.00 - lr: 0.050000
2022-11-06 21:55:25,041 epoch 71 - iter 114/386 - loss 0.09217128 - samples/sec: 105.97 - lr: 0.050000
2022-11-06 21:55:30,809 epoch 71 - iter 152/386 - loss 0.09300167 - samples/sec: 105.47 - lr: 0.050000
2022-11-06 21:55:36,154 epoch 71 - iter 190/386 - loss 0.09282890 - samples/sec: 113.80 - lr: 0.050000
2022-11-06 21:55:41,243 epoch 71 - iter 228/386 - loss 0.09400901 - samples/sec: 119.54 - lr: 0.050000
2022-11-06 21:55:46,720 epoch 71 - iter 266/386 - loss 0.09346036 - samples/sec: 111.93 - lr: 0.050000
2022-11-06 21:55:52,245 epoch 71 - iter 304/386 - loss 0.09346459 - samples/sec: 110.11 - lr: 0.050000
2022-11-06 21:55:58,282 epoch 71 - iter 342/386 - loss 0.09398476 - samples/sec: 100.75 - lr: 0.050000
2022-11-06 21:56:03,625 epoch 71 - iter 380/386 - loss 0.09421206 - samples/sec: 113.87 - lr: 0.050000
2022-11-06 21:56:04,435 ----------------------------------------------------------------------------------------------------
2022-11-06 21:56:04,435 EPOCH 71 done: loss 0.0942 - lr 0.050000
2022-11-06 21:56:13,395 Evaluating as a multi-label problem: False
2022-11-06 21:56:13,509 TEST : loss 0.07746423035860062 - f1-score (micro avg) 0.9776
2022-11-06 21:56:13,620 BAD EPOCHS (no improvement): 1
2022-11-06 21:56:13,824 ----------------------------------------------------------------------------------------------------
2022-11-06 21:56:19,279 epoch 72 - iter 38/386 - loss 0.08836120 - samples/sec: 111.56 - lr: 0.050000
2022-11-06 21:56:25,113 epoch 72 - iter 76/386 - loss 0.09152661 - samples/sec: 104.27 - lr: 0.050000
2022-11-06 21:56:30,762 epoch 72 - iter 114/386 - loss 0.09266384 - samples/sec: 107.67 - lr: 0.050000
2022-11-06 21:56:36,310 epoch 72 - iter 152/386 - loss 0.09210641 - samples/sec: 109.66 - lr: 0.050000
2022-11-06 21:56:41,389 epoch 72 - iter 190/386 - loss 0.09198951 - samples/sec: 119.76 - lr: 0.050000
2022-11-06 21:56:46,984 epoch 72 - iter 228/386 - loss 0.09362383 - samples/sec: 108.73 - lr: 0.050000
2022-11-06 21:56:52,622 epoch 72 - iter 266/386 - loss 0.09271150 - samples/sec: 107.89 - lr: 0.050000
2022-11-06 21:56:58,105 epoch 72 - iter 304/386 - loss 0.09327678 - samples/sec: 110.95 - lr: 0.050000
2022-11-06 21:57:03,413 epoch 72 - iter 342/386 - loss 0.09330768 - samples/sec: 114.62 - lr: 0.050000
2022-11-06 21:57:09,064 epoch 72 - iter 380/386 - loss 0.09305626 - samples/sec: 107.65 - lr: 0.050000
2022-11-06 21:57:09,872 ----------------------------------------------------------------------------------------------------
2022-11-06 21:57:09,872 EPOCH 72 done: loss 0.0932 - lr 0.050000
2022-11-06 21:57:18,688 Evaluating as a multi-label problem: False
2022-11-06 21:57:18,802 TEST : loss 0.07610904425382614 - f1-score (micro avg) 0.9783
2022-11-06 21:57:18,912 BAD EPOCHS (no improvement): 0
2022-11-06 21:57:19,120 ----------------------------------------------------------------------------------------------------
2022-11-06 21:57:24,632 epoch 73 - iter 38/386 - loss 0.08300037 - samples/sec: 110.39 - lr: 0.050000
2022-11-06 21:57:30,262 epoch 73 - iter 76/386 - loss 0.08575533 - samples/sec: 108.06 - lr: 0.050000
2022-11-06 21:57:35,831 epoch 73 - iter 114/386 - loss 0.08793053 - samples/sec: 109.98 - lr: 0.050000
2022-11-06 21:57:41,583 epoch 73 - iter 152/386 - loss 0.09031249 - samples/sec: 105.76 - lr: 0.050000
2022-11-06 21:57:47,239 epoch 73 - iter 190/386 - loss 0.09171636 - samples/sec: 107.55 - lr: 0.050000
2022-11-06 21:57:52,452 epoch 73 - iter 228/386 - loss 0.09110892 - samples/sec: 117.66 - lr: 0.050000
2022-11-06 21:57:58,120 epoch 73 - iter 266/386 - loss 0.09069697 - samples/sec: 107.33 - lr: 0.050000
2022-11-06 21:58:03,045 epoch 73 - iter 304/386 - loss 0.09137763 - samples/sec: 123.52 - lr: 0.050000
2022-11-06 21:58:08,421 epoch 73 - iter 342/386 - loss 0.09163594 - samples/sec: 113.15 - lr: 0.050000
2022-11-06 21:58:14,624 epoch 73 - iter 380/386 - loss 0.09233790 - samples/sec: 98.07 - lr: 0.050000
2022-11-06 21:58:15,352 ----------------------------------------------------------------------------------------------------
2022-11-06 21:58:15,352 EPOCH 73 done: loss 0.0927 - lr 0.050000
2022-11-06 21:58:24,784 Evaluating as a multi-label problem: False
2022-11-06 21:58:24,897 TEST : loss 0.07682343572378159 - f1-score (micro avg) 0.9791
2022-11-06 21:58:25,009 BAD EPOCHS (no improvement): 0
2022-11-06 21:58:25,213 ----------------------------------------------------------------------------------------------------
2022-11-06 21:58:30,702 epoch 74 - iter 38/386 - loss 0.09071120 - samples/sec: 110.85 - lr: 0.050000
2022-11-06 21:58:36,204 epoch 74 - iter 76/386 - loss 0.09116074 - samples/sec: 110.56 - lr: 0.050000
2022-11-06 21:58:41,481 epoch 74 - iter 114/386 - loss 0.09187654 - samples/sec: 115.28 - lr: 0.050000
2022-11-06 21:58:46,655 epoch 74 - iter 152/386 - loss 0.09141319 - samples/sec: 117.58 - lr: 0.050000
2022-11-06 21:58:52,078 epoch 74 - iter 190/386 - loss 0.09142797 - samples/sec: 112.17 - lr: 0.050000
2022-11-06 21:58:57,689 epoch 74 - iter 228/386 - loss 0.08984326 - samples/sec: 108.42 - lr: 0.050000
2022-11-06 21:59:03,721 epoch 74 - iter 266/386 - loss 0.08897908 - samples/sec: 100.84 - lr: 0.050000
2022-11-06 21:59:08,915 epoch 74 - iter 304/386 - loss 0.08840306 - samples/sec: 117.13 - lr: 0.050000
2022-11-06 21:59:14,226 epoch 74 - iter 342/386 - loss 0.08851215 - samples/sec: 114.55 - lr: 0.050000
2022-11-06 21:59:19,603 epoch 74 - iter 380/386 - loss 0.08856454 - samples/sec: 113.13 - lr: 0.050000
2022-11-06 21:59:20,499 ----------------------------------------------------------------------------------------------------
2022-11-06 21:59:20,499 EPOCH 74 done: loss 0.0887 - lr 0.050000
2022-11-06 21:59:29,816 Evaluating as a multi-label problem: False
2022-11-06 21:59:29,929 TEST : loss 0.07699835300445557 - f1-score (micro avg) 0.9795
2022-11-06 21:59:30,041 BAD EPOCHS (no improvement): 0
2022-11-06 21:59:30,248 ----------------------------------------------------------------------------------------------------
2022-11-06 21:59:35,938 epoch 75 - iter 38/386 - loss 0.09010983 - samples/sec: 106.93 - lr: 0.050000
2022-11-06 21:59:41,512 epoch 75 - iter 76/386 - loss 0.08888721 - samples/sec: 109.14 - lr: 0.050000
2022-11-06 21:59:46,674 epoch 75 - iter 114/386 - loss 0.08627651 - samples/sec: 117.85 - lr: 0.050000
2022-11-06 21:59:51,815 epoch 75 - iter 152/386 - loss 0.08648960 - samples/sec: 118.34 - lr: 0.050000
2022-11-06 21:59:57,162 epoch 75 - iter 190/386 - loss 0.08750875 - samples/sec: 113.77 - lr: 0.050000
2022-11-06 22:00:02,844 epoch 75 - iter 228/386 - loss 0.08733463 - samples/sec: 107.06 - lr: 0.050000
2022-11-06 22:00:08,984 epoch 75 - iter 266/386 - loss 0.08776781 - samples/sec: 99.07 - lr: 0.050000
2022-11-06 22:00:14,611 epoch 75 - iter 304/386 - loss 0.08859041 - samples/sec: 108.11 - lr: 0.050000
2022-11-06 22:00:19,915 epoch 75 - iter 342/386 - loss 0.09008267 - samples/sec: 114.70 - lr: 0.050000
2022-11-06 22:00:25,381 epoch 75 - iter 380/386 - loss 0.09035732 - samples/sec: 111.29 - lr: 0.050000
2022-11-06 22:00:26,276 ----------------------------------------------------------------------------------------------------
2022-11-06 22:00:26,277 EPOCH 75 done: loss 0.0905 - lr 0.050000
2022-11-06 22:00:35,742 Evaluating as a multi-label problem: False
2022-11-06 22:00:35,857 TEST : loss 0.07650885730981827 - f1-score (micro avg) 0.9794
2022-11-06 22:00:35,969 BAD EPOCHS (no improvement): 1
2022-11-06 22:00:36,169 ----------------------------------------------------------------------------------------------------
2022-11-06 22:00:41,371 epoch 76 - iter 38/386 - loss 0.08535642 - samples/sec: 116.98 - lr: 0.050000
2022-11-06 22:00:46,936 epoch 76 - iter 76/386 - loss 0.08909989 - samples/sec: 109.30 - lr: 0.050000
2022-11-06 22:00:52,273 epoch 76 - iter 114/386 - loss 0.08858041 - samples/sec: 114.00 - lr: 0.050000
2022-11-06 22:00:57,019 epoch 76 - iter 152/386 - loss 0.09111687 - samples/sec: 128.16 - lr: 0.050000
2022-11-06 22:01:01,932 epoch 76 - iter 190/386 - loss 0.09184363 - samples/sec: 123.84 - lr: 0.050000
2022-11-06 22:01:07,359 epoch 76 - iter 228/386 - loss 0.09209636 - samples/sec: 112.10 - lr: 0.050000
2022-11-06 22:01:12,927 epoch 76 - iter 266/386 - loss 0.09302243 - samples/sec: 109.24 - lr: 0.050000
2022-11-06 22:01:18,650 epoch 76 - iter 304/386 - loss 0.09212630 - samples/sec: 106.30 - lr: 0.050000
2022-11-06 22:01:24,682 epoch 76 - iter 342/386 - loss 0.09211552 - samples/sec: 100.85 - lr: 0.050000
2022-11-06 22:01:30,494 epoch 76 - iter 380/386 - loss 0.09221232 - samples/sec: 104.67 - lr: 0.050000
2022-11-06 22:01:31,413 ----------------------------------------------------------------------------------------------------
2022-11-06 22:01:31,413 EPOCH 76 done: loss 0.0920 - lr 0.050000
2022-11-06 22:01:43,220 Evaluating as a multi-label problem: False
2022-11-06 22:01:43,334 TEST : loss 0.07444333285093307 - f1-score (micro avg) 0.9793
2022-11-06 22:01:43,445 BAD EPOCHS (no improvement): 2
2022-11-06 22:01:43,657 ----------------------------------------------------------------------------------------------------
2022-11-06 22:01:49,340 epoch 77 - iter 38/386 - loss 0.08895759 - samples/sec: 107.07 - lr: 0.050000
2022-11-06 22:01:54,722 epoch 77 - iter 76/386 - loss 0.08888293 - samples/sec: 113.03 - lr: 0.050000
2022-11-06 22:02:00,637 epoch 77 - iter 114/386 - loss 0.09145351 - samples/sec: 102.84 - lr: 0.050000
2022-11-06 22:02:05,857 epoch 77 - iter 152/386 - loss 0.09274616 - samples/sec: 116.55 - lr: 0.050000
2022-11-06 22:02:10,767 epoch 77 - iter 190/386 - loss 0.09381220 - samples/sec: 123.90 - lr: 0.050000
2022-11-06 22:02:16,246 epoch 77 - iter 228/386 - loss 0.09315648 - samples/sec: 111.03 - lr: 0.050000
2022-11-06 22:02:21,442 epoch 77 - iter 266/386 - loss 0.09256458 - samples/sec: 118.03 - lr: 0.050000
2022-11-06 22:02:27,103 epoch 77 - iter 304/386 - loss 0.09210060 - samples/sec: 107.46 - lr: 0.050000
2022-11-06 22:02:32,763 epoch 77 - iter 342/386 - loss 0.09166523 - samples/sec: 107.48 - lr: 0.050000
2022-11-06 22:02:38,387 epoch 77 - iter 380/386 - loss 0.09144354 - samples/sec: 108.15 - lr: 0.050000
2022-11-06 22:02:39,265 ----------------------------------------------------------------------------------------------------
2022-11-06 22:02:39,266 EPOCH 77 done: loss 0.0915 - lr 0.050000
2022-11-06 22:02:48,671 Evaluating as a multi-label problem: False
2022-11-06 22:02:48,785 TEST : loss 0.07569604367017746 - f1-score (micro avg) 0.9792
2022-11-06 22:02:48,897 BAD EPOCHS (no improvement): 3
2022-11-06 22:02:49,105 ----------------------------------------------------------------------------------------------------
2022-11-06 22:02:54,891 epoch 78 - iter 38/386 - loss 0.08376892 - samples/sec: 105.17 - lr: 0.050000
2022-11-06 22:03:00,207 epoch 78 - iter 76/386 - loss 0.08734464 - samples/sec: 114.43 - lr: 0.050000
2022-11-06 22:03:05,970 epoch 78 - iter 114/386 - loss 0.08772487 - samples/sec: 105.54 - lr: 0.050000
2022-11-06 22:03:11,595 epoch 78 - iter 152/386 - loss 0.08741174 - samples/sec: 108.15 - lr: 0.050000
2022-11-06 22:03:17,144 epoch 78 - iter 190/386 - loss 0.08621154 - samples/sec: 109.62 - lr: 0.050000
2022-11-06 22:03:22,343 epoch 78 - iter 228/386 - loss 0.08552517 - samples/sec: 117.01 - lr: 0.050000
2022-11-06 22:03:27,433 epoch 78 - iter 266/386 - loss 0.08659634 - samples/sec: 119.53 - lr: 0.050000
2022-11-06 22:03:32,676 epoch 78 - iter 304/386 - loss 0.08698140 - samples/sec: 116.03 - lr: 0.050000
2022-11-06 22:03:38,658 epoch 78 - iter 342/386 - loss 0.08706887 - samples/sec: 101.69 - lr: 0.050000
2022-11-06 22:03:44,138 epoch 78 - iter 380/386 - loss 0.08741793 - samples/sec: 111.00 - lr: 0.050000
2022-11-06 22:03:44,857 ----------------------------------------------------------------------------------------------------
2022-11-06 22:03:44,857 EPOCH 78 done: loss 0.0875 - lr 0.050000
2022-11-06 22:03:54,244 Evaluating as a multi-label problem: False
2022-11-06 22:03:54,358 TEST : loss 0.07991818338632584 - f1-score (micro avg) 0.9787
2022-11-06 22:03:54,471 BAD EPOCHS (no improvement): 0
2022-11-06 22:03:54,685 ----------------------------------------------------------------------------------------------------
2022-11-06 22:03:59,993 epoch 79 - iter 38/386 - loss 0.08928639 - samples/sec: 114.62 - lr: 0.050000
2022-11-06 22:04:05,218 epoch 79 - iter 76/386 - loss 0.08696352 - samples/sec: 116.44 - lr: 0.050000
2022-11-06 22:04:10,821 epoch 79 - iter 114/386 - loss 0.08802286 - samples/sec: 108.57 - lr: 0.050000
2022-11-06 22:04:16,869 epoch 79 - iter 152/386 - loss 0.08901779 - samples/sec: 100.58 - lr: 0.050000
2022-11-06 22:04:22,263 epoch 79 - iter 190/386 - loss 0.08795694 - samples/sec: 112.78 - lr: 0.050000
2022-11-06 22:04:27,565 epoch 79 - iter 228/386 - loss 0.08824170 - samples/sec: 114.73 - lr: 0.050000
2022-11-06 22:04:32,608 epoch 79 - iter 266/386 - loss 0.08800000 - samples/sec: 120.63 - lr: 0.050000
2022-11-06 22:04:37,722 epoch 79 - iter 304/386 - loss 0.08685455 - samples/sec: 118.97 - lr: 0.050000
2022-11-06 22:04:43,314 epoch 79 - iter 342/386 - loss 0.08722570 - samples/sec: 108.77 - lr: 0.050000
2022-11-06 22:04:49,093 epoch 79 - iter 380/386 - loss 0.08745041 - samples/sec: 105.27 - lr: 0.050000
2022-11-06 22:04:50,042 ----------------------------------------------------------------------------------------------------
2022-11-06 22:04:50,042 EPOCH 79 done: loss 0.0875 - lr 0.050000
2022-11-06 22:04:59,310 Evaluating as a multi-label problem: False
2022-11-06 22:04:59,424 TEST : loss 0.07551249861717224 - f1-score (micro avg) 0.9797
2022-11-06 22:04:59,535 BAD EPOCHS (no improvement): 0
2022-11-06 22:04:59,744 ----------------------------------------------------------------------------------------------------
2022-11-06 22:05:04,922 epoch 80 - iter 38/386 - loss 0.09880198 - samples/sec: 117.52 - lr: 0.050000
2022-11-06 22:05:11,034 epoch 80 - iter 76/386 - loss 0.09604834 - samples/sec: 99.52 - lr: 0.050000
2022-11-06 22:05:16,842 epoch 80 - iter 114/386 - loss 0.09443440 - samples/sec: 104.74 - lr: 0.050000
2022-11-06 22:05:22,343 epoch 80 - iter 152/386 - loss 0.09369188 - samples/sec: 110.59 - lr: 0.050000
2022-11-06 22:05:27,484 epoch 80 - iter 190/386 - loss 0.09212382 - samples/sec: 118.33 - lr: 0.050000
2022-11-06 22:05:32,960 epoch 80 - iter 228/386 - loss 0.09101292 - samples/sec: 111.09 - lr: 0.050000
2022-11-06 22:05:38,305 epoch 80 - iter 266/386 - loss 0.09099899 - samples/sec: 113.82 - lr: 0.050000
2022-11-06 22:05:44,025 epoch 80 - iter 304/386 - loss 0.09116268 - samples/sec: 106.36 - lr: 0.050000
2022-11-06 22:05:49,444 epoch 80 - iter 342/386 - loss 0.09095614 - samples/sec: 112.25 - lr: 0.050000
2022-11-06 22:05:54,693 epoch 80 - iter 380/386 - loss 0.09117192 - samples/sec: 115.91 - lr: 0.050000
2022-11-06 22:05:55,455 ----------------------------------------------------------------------------------------------------
2022-11-06 22:05:55,455 EPOCH 80 done: loss 0.0910 - lr 0.050000
2022-11-06 22:06:04,822 Evaluating as a multi-label problem: False
2022-11-06 22:06:04,939 TEST : loss 0.07765697687864304 - f1-score (micro avg) 0.9791
2022-11-06 22:06:05,051 BAD EPOCHS (no improvement): 1
2022-11-06 22:06:05,262 ----------------------------------------------------------------------------------------------------
2022-11-06 22:06:10,885 epoch 81 - iter 38/386 - loss 0.08081290 - samples/sec: 108.20 - lr: 0.050000
2022-11-06 22:06:16,437 epoch 81 - iter 76/386 - loss 0.08337375 - samples/sec: 109.57 - lr: 0.050000
2022-11-06 22:06:22,013 epoch 81 - iter 114/386 - loss 0.08302250 - samples/sec: 109.10 - lr: 0.050000
2022-11-06 22:06:27,395 epoch 81 - iter 152/386 - loss 0.08480407 - samples/sec: 113.93 - lr: 0.050000
2022-11-06 22:06:33,387 epoch 81 - iter 190/386 - loss 0.08609702 - samples/sec: 101.51 - lr: 0.050000
2022-11-06 22:06:39,214 epoch 81 - iter 228/386 - loss 0.08554252 - samples/sec: 104.41 - lr: 0.050000
2022-11-06 22:06:44,743 epoch 81 - iter 266/386 - loss 0.08462515 - samples/sec: 110.01 - lr: 0.050000
2022-11-06 22:06:50,380 epoch 81 - iter 304/386 - loss 0.08538954 - samples/sec: 107.91 - lr: 0.050000
2022-11-06 22:06:55,651 epoch 81 - iter 342/386 - loss 0.08586110 - samples/sec: 115.41 - lr: 0.050000
2022-11-06 22:07:00,762 epoch 81 - iter 380/386 - loss 0.08543691 - samples/sec: 119.04 - lr: 0.050000
2022-11-06 22:07:01,391 ----------------------------------------------------------------------------------------------------
2022-11-06 22:07:01,391 EPOCH 81 done: loss 0.0858 - lr 0.050000
2022-11-06 22:07:10,242 Evaluating as a multi-label problem: False
2022-11-06 22:07:10,356 TEST : loss 0.07825277745723724 - f1-score (micro avg) 0.9793
2022-11-06 22:07:10,468 BAD EPOCHS (no improvement): 0
2022-11-06 22:07:10,670 ----------------------------------------------------------------------------------------------------
2022-11-06 22:07:16,234 epoch 82 - iter 38/386 - loss 0.08629919 - samples/sec: 109.36 - lr: 0.050000
2022-11-06 22:07:22,344 epoch 82 - iter 76/386 - loss 0.08451248 - samples/sec: 99.56 - lr: 0.050000
2022-11-06 22:07:28,020 epoch 82 - iter 114/386 - loss 0.08580786 - samples/sec: 107.17 - lr: 0.050000
2022-11-06 22:07:33,590 epoch 82 - iter 152/386 - loss 0.08754692 - samples/sec: 109.21 - lr: 0.050000
2022-11-06 22:07:38,703 epoch 82 - iter 190/386 - loss 0.08805859 - samples/sec: 118.99 - lr: 0.050000
2022-11-06 22:07:44,067 epoch 82 - iter 228/386 - loss 0.08781350 - samples/sec: 113.42 - lr: 0.050000
2022-11-06 22:07:49,722 epoch 82 - iter 266/386 - loss 0.08812045 - samples/sec: 107.57 - lr: 0.050000
2022-11-06 22:07:55,087 epoch 82 - iter 304/386 - loss 0.08978244 - samples/sec: 113.38 - lr: 0.050000
2022-11-06 22:08:00,793 epoch 82 - iter 342/386 - loss 0.08986460 - samples/sec: 106.62 - lr: 0.050000
2022-11-06 22:08:05,834 epoch 82 - iter 380/386 - loss 0.08970849 - samples/sec: 120.69 - lr: 0.050000
2022-11-06 22:08:06,579 ----------------------------------------------------------------------------------------------------
2022-11-06 22:08:06,579 EPOCH 82 done: loss 0.0899 - lr 0.050000
2022-11-06 22:08:15,556 Evaluating as a multi-label problem: False
2022-11-06 22:08:15,670 TEST : loss 0.077557772397995 - f1-score (micro avg) 0.9795
2022-11-06 22:08:15,782 BAD EPOCHS (no improvement): 1
2022-11-06 22:08:15,988 ----------------------------------------------------------------------------------------------------
2022-11-06 22:08:21,824 epoch 83 - iter 38/386 - loss 0.08818104 - samples/sec: 104.27 - lr: 0.050000
2022-11-06 22:08:27,128 epoch 83 - iter 76/386 - loss 0.08980860 - samples/sec: 114.68 - lr: 0.050000
2022-11-06 22:08:32,632 epoch 83 - iter 114/386 - loss 0.08931832 - samples/sec: 110.53 - lr: 0.050000
2022-11-06 22:08:37,949 epoch 83 - iter 152/386 - loss 0.08688520 - samples/sec: 114.43 - lr: 0.050000
2022-11-06 22:08:43,185 epoch 83 - iter 190/386 - loss 0.08696988 - samples/sec: 116.17 - lr: 0.050000
2022-11-06 22:08:48,329 epoch 83 - iter 228/386 - loss 0.08623230 - samples/sec: 118.28 - lr: 0.050000
2022-11-06 22:08:54,180 epoch 83 - iter 266/386 - loss 0.08715665 - samples/sec: 103.96 - lr: 0.050000
2022-11-06 22:08:59,938 epoch 83 - iter 304/386 - loss 0.08655812 - samples/sec: 105.64 - lr: 0.050000
2022-11-06 22:09:05,407 epoch 83 - iter 342/386 - loss 0.08648025 - samples/sec: 111.23 - lr: 0.050000
2022-11-06 22:09:11,416 epoch 83 - iter 380/386 - loss 0.08651596 - samples/sec: 101.25 - lr: 0.050000
2022-11-06 22:09:12,286 ----------------------------------------------------------------------------------------------------
2022-11-06 22:09:12,286 EPOCH 83 done: loss 0.0868 - lr 0.050000
2022-11-06 22:09:21,669 Evaluating as a multi-label problem: False
2022-11-06 22:09:21,783 TEST : loss 0.07837608456611633 - f1-score (micro avg) 0.9794
2022-11-06 22:09:21,895 BAD EPOCHS (no improvement): 2
2022-11-06 22:09:22,100 ----------------------------------------------------------------------------------------------------
2022-11-06 22:09:27,084 epoch 84 - iter 38/386 - loss 0.08085540 - samples/sec: 122.10 - lr: 0.050000
2022-11-06 22:09:32,394 epoch 84 - iter 76/386 - loss 0.08316482 - samples/sec: 114.55 - lr: 0.050000
2022-11-06 22:09:37,928 epoch 84 - iter 114/386 - loss 0.08390017 - samples/sec: 109.92 - lr: 0.050000
2022-11-06 22:09:43,188 epoch 84 - iter 152/386 - loss 0.08477912 - samples/sec: 115.65 - lr: 0.050000
2022-11-06 22:09:48,647 epoch 84 - iter 190/386 - loss 0.08779591 - samples/sec: 111.44 - lr: 0.050000
2022-11-06 22:09:54,140 epoch 84 - iter 228/386 - loss 0.08604904 - samples/sec: 110.75 - lr: 0.050000
2022-11-06 22:09:59,696 epoch 84 - iter 266/386 - loss 0.08526498 - samples/sec: 109.50 - lr: 0.050000
2022-11-06 22:10:05,344 epoch 84 - iter 304/386 - loss 0.08543825 - samples/sec: 107.71 - lr: 0.050000
2022-11-06 22:10:11,119 epoch 84 - iter 342/386 - loss 0.08616034 - samples/sec: 105.33 - lr: 0.050000
2022-11-06 22:10:16,300 epoch 84 - iter 380/386 - loss 0.08594080 - samples/sec: 117.42 - lr: 0.050000
2022-11-06 22:10:17,085 ----------------------------------------------------------------------------------------------------
2022-11-06 22:10:17,085 EPOCH 84 done: loss 0.0859 - lr 0.050000
2022-11-06 22:10:26,482 Evaluating as a multi-label problem: False
2022-11-06 22:10:26,595 TEST : loss 0.07908571511507034 - f1-score (micro avg) 0.9791
2022-11-06 22:10:26,707 BAD EPOCHS (no improvement): 3
2022-11-06 22:10:26,916 ----------------------------------------------------------------------------------------------------
2022-11-06 22:10:32,295 epoch 85 - iter 38/386 - loss 0.08409247 - samples/sec: 113.11 - lr: 0.050000
2022-11-06 22:10:37,764 epoch 85 - iter 76/386 - loss 0.08732911 - samples/sec: 111.25 - lr: 0.050000
2022-11-06 22:10:42,541 epoch 85 - iter 114/386 - loss 0.08752875 - samples/sec: 127.34 - lr: 0.050000
2022-11-06 22:10:47,789 epoch 85 - iter 152/386 - loss 0.08668668 - samples/sec: 115.92 - lr: 0.050000
2022-11-06 22:10:53,048 epoch 85 - iter 190/386 - loss 0.08746045 - samples/sec: 115.67 - lr: 0.050000
2022-11-06 22:10:58,323 epoch 85 - iter 228/386 - loss 0.08632880 - samples/sec: 115.32 - lr: 0.050000
2022-11-06 22:11:03,801 epoch 85 - iter 266/386 - loss 0.08607122 - samples/sec: 111.06 - lr: 0.050000
2022-11-06 22:11:10,221 epoch 85 - iter 304/386 - loss 0.08680703 - samples/sec: 94.74 - lr: 0.050000
2022-11-06 22:11:15,817 epoch 85 - iter 342/386 - loss 0.08675650 - samples/sec: 108.71 - lr: 0.050000
2022-11-06 22:11:21,365 epoch 85 - iter 380/386 - loss 0.08631388 - samples/sec: 109.66 - lr: 0.050000
2022-11-06 22:11:22,266 ----------------------------------------------------------------------------------------------------
2022-11-06 22:11:22,267 EPOCH 85 done: loss 0.0860 - lr 0.050000
2022-11-06 22:11:31,819 Evaluating as a multi-label problem: False
2022-11-06 22:11:31,935 TEST : loss 0.07693811506032944 - f1-score (micro avg) 0.9796
2022-11-06 22:11:32,049 Epoch 85: reducing learning rate of group 0 to 2.5000e-02.
2022-11-06 22:11:32,049 BAD EPOCHS (no improvement): 4
2022-11-06 22:11:32,261 ----------------------------------------------------------------------------------------------------
2022-11-06 22:11:38,275 epoch 86 - iter 38/386 - loss 0.09401872 - samples/sec: 101.17 - lr: 0.025000
2022-11-06 22:11:43,699 epoch 86 - iter 76/386 - loss 0.09432225 - samples/sec: 112.17 - lr: 0.025000
2022-11-06 22:11:49,718 epoch 86 - iter 114/386 - loss 0.08964112 - samples/sec: 101.77 - lr: 0.025000
2022-11-06 22:11:54,809 epoch 86 - iter 152/386 - loss 0.08763708 - samples/sec: 119.50 - lr: 0.025000
2022-11-06 22:12:00,232 epoch 86 - iter 190/386 - loss 0.08731319 - samples/sec: 112.18 - lr: 0.025000
2022-11-06 22:12:05,644 epoch 86 - iter 228/386 - loss 0.08663491 - samples/sec: 112.41 - lr: 0.025000
2022-11-06 22:12:11,665 epoch 86 - iter 266/386 - loss 0.08581371 - samples/sec: 101.05 - lr: 0.025000
2022-11-06 22:12:17,252 epoch 86 - iter 304/386 - loss 0.08535848 - samples/sec: 108.88 - lr: 0.025000
2022-11-06 22:12:22,510 epoch 86 - iter 342/386 - loss 0.08557678 - samples/sec: 115.69 - lr: 0.025000
2022-11-06 22:12:28,286 epoch 86 - iter 380/386 - loss 0.08575220 - samples/sec: 105.33 - lr: 0.025000
2022-11-06 22:12:29,093 ----------------------------------------------------------------------------------------------------
2022-11-06 22:12:29,093 EPOCH 86 done: loss 0.0860 - lr 0.025000
2022-11-06 22:12:38,762 Evaluating as a multi-label problem: False
2022-11-06 22:12:38,878 TEST : loss 0.07507522404193878 - f1-score (micro avg) 0.9797
2022-11-06 22:12:38,992 BAD EPOCHS (no improvement): 1
2022-11-06 22:12:39,205 ----------------------------------------------------------------------------------------------------
2022-11-06 22:12:44,844 epoch 87 - iter 38/386 - loss 0.08439559 - samples/sec: 107.91 - lr: 0.025000
2022-11-06 22:12:50,265 epoch 87 - iter 76/386 - loss 0.08054664 - samples/sec: 112.21 - lr: 0.025000
2022-11-06 22:12:55,855 epoch 87 - iter 114/386 - loss 0.08111561 - samples/sec: 108.83 - lr: 0.025000
2022-11-06 22:13:01,318 epoch 87 - iter 152/386 - loss 0.08025030 - samples/sec: 111.36 - lr: 0.025000
2022-11-06 22:13:06,932 epoch 87 - iter 190/386 - loss 0.08014814 - samples/sec: 108.37 - lr: 0.025000
2022-11-06 22:13:12,282 epoch 87 - iter 228/386 - loss 0.07974041 - samples/sec: 113.70 - lr: 0.025000
2022-11-06 22:13:17,692 epoch 87 - iter 266/386 - loss 0.07913362 - samples/sec: 112.45 - lr: 0.025000
2022-11-06 22:13:23,418 epoch 87 - iter 304/386 - loss 0.07971830 - samples/sec: 106.25 - lr: 0.025000
2022-11-06 22:13:28,893 epoch 87 - iter 342/386 - loss 0.08018386 - samples/sec: 111.12 - lr: 0.025000
2022-11-06 22:13:34,703 epoch 87 - iter 380/386 - loss 0.08080162 - samples/sec: 104.70 - lr: 0.025000
2022-11-06 22:13:35,597 ----------------------------------------------------------------------------------------------------
2022-11-06 22:13:35,597 EPOCH 87 done: loss 0.0809 - lr 0.025000
2022-11-06 22:13:47,565 Evaluating as a multi-label problem: False
2022-11-06 22:13:47,681 TEST : loss 0.0789749026298523 - f1-score (micro avg) 0.9794
2022-11-06 22:13:47,795 BAD EPOCHS (no improvement): 0
2022-11-06 22:13:47,998 ----------------------------------------------------------------------------------------------------
2022-11-06 22:13:53,872 epoch 88 - iter 38/386 - loss 0.07960722 - samples/sec: 103.57 - lr: 0.025000
2022-11-06 22:13:59,254 epoch 88 - iter 76/386 - loss 0.08122546 - samples/sec: 113.04 - lr: 0.025000
2022-11-06 22:14:04,434 epoch 88 - iter 114/386 - loss 0.07933891 - samples/sec: 117.44 - lr: 0.025000
2022-11-06 22:14:09,961 epoch 88 - iter 152/386 - loss 0.07896805 - samples/sec: 110.07 - lr: 0.025000
2022-11-06 22:14:16,177 epoch 88 - iter 190/386 - loss 0.08116421 - samples/sec: 98.16 - lr: 0.025000
2022-11-06 22:14:21,535 epoch 88 - iter 228/386 - loss 0.08035744 - samples/sec: 113.55 - lr: 0.025000
2022-11-06 22:14:26,609 epoch 88 - iter 266/386 - loss 0.08058892 - samples/sec: 119.89 - lr: 0.025000
2022-11-06 22:14:32,263 epoch 88 - iter 304/386 - loss 0.08057870 - samples/sec: 107.59 - lr: 0.025000
2022-11-06 22:14:37,985 epoch 88 - iter 342/386 - loss 0.08112992 - samples/sec: 106.32 - lr: 0.025000
2022-11-06 22:14:43,722 epoch 88 - iter 380/386 - loss 0.08050531 - samples/sec: 106.03 - lr: 0.025000
2022-11-06 22:14:44,574 ----------------------------------------------------------------------------------------------------
2022-11-06 22:14:44,574 EPOCH 88 done: loss 0.0806 - lr 0.025000
2022-11-06 22:14:54,171 Evaluating as a multi-label problem: False
2022-11-06 22:14:54,289 TEST : loss 0.07823394984006882 - f1-score (micro avg) 0.9803
2022-11-06 22:14:54,403 BAD EPOCHS (no improvement): 0
2022-11-06 22:14:54,614 ----------------------------------------------------------------------------------------------------
2022-11-06 22:15:00,149 epoch 89 - iter 38/386 - loss 0.08881669 - samples/sec: 109.94 - lr: 0.025000
2022-11-06 22:15:05,572 epoch 89 - iter 76/386 - loss 0.08617115 - samples/sec: 112.18 - lr: 0.025000
2022-11-06 22:15:11,757 epoch 89 - iter 114/386 - loss 0.08675312 - samples/sec: 98.35 - lr: 0.025000
2022-11-06 22:15:17,611 epoch 89 - iter 152/386 - loss 0.08471759 - samples/sec: 103.91 - lr: 0.025000
2022-11-06 22:15:23,183 epoch 89 - iter 190/386 - loss 0.08466736 - samples/sec: 109.18 - lr: 0.025000
2022-11-06 22:15:29,113 epoch 89 - iter 228/386 - loss 0.08438120 - samples/sec: 102.59 - lr: 0.025000
2022-11-06 22:15:34,144 epoch 89 - iter 266/386 - loss 0.08433213 - samples/sec: 120.91 - lr: 0.025000
2022-11-06 22:15:39,249 epoch 89 - iter 304/386 - loss 0.08327686 - samples/sec: 119.17 - lr: 0.025000
2022-11-06 22:15:44,762 epoch 89 - iter 342/386 - loss 0.08302938 - samples/sec: 110.35 - lr: 0.025000
2022-11-06 22:15:49,944 epoch 89 - iter 380/386 - loss 0.08333523 - samples/sec: 117.41 - lr: 0.025000
2022-11-06 22:15:50,759 ----------------------------------------------------------------------------------------------------
2022-11-06 22:15:50,760 EPOCH 89 done: loss 0.0834 - lr 0.025000
2022-11-06 22:16:00,390 Evaluating as a multi-label problem: False
2022-11-06 22:16:00,506 TEST : loss 0.07800823450088501 - f1-score (micro avg) 0.9801
2022-11-06 22:16:00,619 BAD EPOCHS (no improvement): 1
2022-11-06 22:16:00,827 ----------------------------------------------------------------------------------------------------
2022-11-06 22:16:06,217 epoch 90 - iter 38/386 - loss 0.07277550 - samples/sec: 112.90 - lr: 0.025000
2022-11-06 22:16:12,219 epoch 90 - iter 76/386 - loss 0.07701800 - samples/sec: 101.35 - lr: 0.025000
2022-11-06 22:16:17,987 epoch 90 - iter 114/386 - loss 0.07742446 - samples/sec: 105.47 - lr: 0.025000
2022-11-06 22:16:23,938 epoch 90 - iter 152/386 - loss 0.07881106 - samples/sec: 102.22 - lr: 0.025000
2022-11-06 22:16:29,683 epoch 90 - iter 190/386 - loss 0.07802511 - samples/sec: 105.90 - lr: 0.025000
2022-11-06 22:16:35,320 epoch 90 - iter 228/386 - loss 0.07783453 - samples/sec: 107.92 - lr: 0.025000
2022-11-06 22:16:40,941 epoch 90 - iter 266/386 - loss 0.07841678 - samples/sec: 108.23 - lr: 0.025000
2022-11-06 22:16:46,376 epoch 90 - iter 304/386 - loss 0.07848769 - samples/sec: 111.93 - lr: 0.025000
2022-11-06 22:16:51,326 epoch 90 - iter 342/386 - loss 0.07989413 - samples/sec: 122.91 - lr: 0.025000
2022-11-06 22:16:56,567 epoch 90 - iter 380/386 - loss 0.07990854 - samples/sec: 116.09 - lr: 0.025000
2022-11-06 22:16:57,414 ----------------------------------------------------------------------------------------------------
2022-11-06 22:16:57,414 EPOCH 90 done: loss 0.0798 - lr 0.025000
2022-11-06 22:17:07,040 Evaluating as a multi-label problem: False
2022-11-06 22:17:07,155 TEST : loss 0.0782727301120758 - f1-score (micro avg) 0.9797
2022-11-06 22:17:07,269 BAD EPOCHS (no improvement): 0
2022-11-06 22:17:07,484 ----------------------------------------------------------------------------------------------------
2022-11-06 22:17:13,293 epoch 91 - iter 38/386 - loss 0.07733783 - samples/sec: 104.74 - lr: 0.025000
2022-11-06 22:17:18,780 epoch 91 - iter 76/386 - loss 0.07882994 - samples/sec: 110.88 - lr: 0.025000
2022-11-06 22:17:24,473 epoch 91 - iter 114/386 - loss 0.07801837 - samples/sec: 106.85 - lr: 0.025000
2022-11-06 22:17:29,980 epoch 91 - iter 152/386 - loss 0.07769611 - samples/sec: 110.48 - lr: 0.025000
2022-11-06 22:17:35,570 epoch 91 - iter 190/386 - loss 0.07877727 - samples/sec: 108.83 - lr: 0.025000
2022-11-06 22:17:41,695 epoch 91 - iter 228/386 - loss 0.08001718 - samples/sec: 99.31 - lr: 0.025000
2022-11-06 22:17:47,341 epoch 91 - iter 266/386 - loss 0.08042378 - samples/sec: 107.74 - lr: 0.025000
2022-11-06 22:17:52,607 epoch 91 - iter 304/386 - loss 0.08061679 - samples/sec: 115.53 - lr: 0.025000
2022-11-06 22:17:58,187 epoch 91 - iter 342/386 - loss 0.08023143 - samples/sec: 109.03 - lr: 0.025000
2022-11-06 22:18:02,883 epoch 91 - iter 380/386 - loss 0.08028607 - samples/sec: 129.55 - lr: 0.025000
2022-11-06 22:18:03,642 ----------------------------------------------------------------------------------------------------
2022-11-06 22:18:03,643 EPOCH 91 done: loss 0.0802 - lr 0.025000
2022-11-06 22:18:13,097 Evaluating as a multi-label problem: False
2022-11-06 22:18:13,212 TEST : loss 0.07592158764600754 - f1-score (micro avg) 0.9801
2022-11-06 22:18:13,325 BAD EPOCHS (no improvement): 1
2022-11-06 22:18:13,537 ----------------------------------------------------------------------------------------------------
2022-11-06 22:18:19,331 epoch 92 - iter 38/386 - loss 0.08023383 - samples/sec: 105.02 - lr: 0.025000
2022-11-06 22:18:24,596 epoch 92 - iter 76/386 - loss 0.07880003 - samples/sec: 115.55 - lr: 0.025000
2022-11-06 22:18:30,071 epoch 92 - iter 114/386 - loss 0.07966319 - samples/sec: 111.10 - lr: 0.025000
2022-11-06 22:18:35,373 epoch 92 - iter 152/386 - loss 0.07820587 - samples/sec: 114.74 - lr: 0.025000
2022-11-06 22:18:40,814 epoch 92 - iter 190/386 - loss 0.07957457 - samples/sec: 111.81 - lr: 0.025000
2022-11-06 22:18:46,314 epoch 92 - iter 228/386 - loss 0.08100660 - samples/sec: 110.60 - lr: 0.025000
2022-11-06 22:18:51,788 epoch 92 - iter 266/386 - loss 0.08021248 - samples/sec: 111.15 - lr: 0.025000
2022-11-06 22:18:57,733 epoch 92 - iter 304/386 - loss 0.08025273 - samples/sec: 102.32 - lr: 0.025000
2022-11-06 22:19:03,667 epoch 92 - iter 342/386 - loss 0.08017579 - samples/sec: 102.51 - lr: 0.025000
2022-11-06 22:19:09,758 epoch 92 - iter 380/386 - loss 0.08044134 - samples/sec: 99.87 - lr: 0.025000
2022-11-06 22:19:10,656 ----------------------------------------------------------------------------------------------------
2022-11-06 22:19:10,656 EPOCH 92 done: loss 0.0804 - lr 0.025000
2022-11-06 22:19:19,504 Evaluating as a multi-label problem: False
2022-11-06 22:19:19,620 TEST : loss 0.07693848758935928 - f1-score (micro avg) 0.9794
2022-11-06 22:19:19,734 BAD EPOCHS (no improvement): 2
2022-11-06 22:19:19,946 ----------------------------------------------------------------------------------------------------
2022-11-06 22:19:25,255 epoch 93 - iter 38/386 - loss 0.07236939 - samples/sec: 114.63 - lr: 0.025000
2022-11-06 22:19:30,943 epoch 93 - iter 76/386 - loss 0.07614718 - samples/sec: 106.95 - lr: 0.025000
2022-11-06 22:19:36,710 epoch 93 - iter 114/386 - loss 0.07729937 - samples/sec: 105.48 - lr: 0.025000
2022-11-06 22:19:42,344 epoch 93 - iter 152/386 - loss 0.07799549 - samples/sec: 107.97 - lr: 0.025000
2022-11-06 22:19:48,026 epoch 93 - iter 190/386 - loss 0.07765939 - samples/sec: 107.06 - lr: 0.025000
2022-11-06 22:19:53,694 epoch 93 - iter 228/386 - loss 0.07913237 - samples/sec: 107.33 - lr: 0.025000
2022-11-06 22:19:59,951 epoch 93 - iter 266/386 - loss 0.07940271 - samples/sec: 97.22 - lr: 0.025000
2022-11-06 22:20:05,381 epoch 93 - iter 304/386 - loss 0.08045973 - samples/sec: 112.05 - lr: 0.025000
2022-11-06 22:20:10,874 epoch 93 - iter 342/386 - loss 0.08003574 - samples/sec: 110.75 - lr: 0.025000
2022-11-06 22:20:16,619 epoch 93 - iter 380/386 - loss 0.07979854 - samples/sec: 105.88 - lr: 0.025000
2022-11-06 22:20:17,372 ----------------------------------------------------------------------------------------------------
2022-11-06 22:20:17,373 EPOCH 93 done: loss 0.0799 - lr 0.025000
2022-11-06 22:20:26,550 Evaluating as a multi-label problem: False
2022-11-06 22:20:26,665 TEST : loss 0.07909691333770752 - f1-score (micro avg) 0.9797
2022-11-06 22:20:26,778 BAD EPOCHS (no improvement): 3
2022-11-06 22:20:26,979 ----------------------------------------------------------------------------------------------------
2022-11-06 22:20:32,580 epoch 94 - iter 38/386 - loss 0.07446764 - samples/sec: 108.63 - lr: 0.025000
2022-11-06 22:20:37,820 epoch 94 - iter 76/386 - loss 0.07660519 - samples/sec: 116.10 - lr: 0.025000
2022-11-06 22:20:43,411 epoch 94 - iter 114/386 - loss 0.07713177 - samples/sec: 108.80 - lr: 0.025000
2022-11-06 22:20:48,823 epoch 94 - iter 152/386 - loss 0.07876191 - samples/sec: 112.40 - lr: 0.025000
2022-11-06 22:20:54,987 epoch 94 - iter 190/386 - loss 0.07891465 - samples/sec: 98.70 - lr: 0.025000
2022-11-06 22:21:00,715 epoch 94 - iter 228/386 - loss 0.08002992 - samples/sec: 106.19 - lr: 0.025000
2022-11-06 22:21:06,057 epoch 94 - iter 266/386 - loss 0.08085213 - samples/sec: 113.89 - lr: 0.025000
2022-11-06 22:21:11,648 epoch 94 - iter 304/386 - loss 0.08032064 - samples/sec: 108.79 - lr: 0.025000
2022-11-06 22:21:17,202 epoch 94 - iter 342/386 - loss 0.08072521 - samples/sec: 109.52 - lr: 0.025000
2022-11-06 22:21:22,895 epoch 94 - iter 380/386 - loss 0.08026007 - samples/sec: 106.87 - lr: 0.025000
2022-11-06 22:21:23,735 ----------------------------------------------------------------------------------------------------
2022-11-06 22:21:23,735 EPOCH 94 done: loss 0.0799 - lr 0.025000
2022-11-06 22:21:33,272 Evaluating as a multi-label problem: False
2022-11-06 22:21:33,386 TEST : loss 0.07736696302890778 - f1-score (micro avg) 0.98
2022-11-06 22:21:33,499 Epoch 94: reducing learning rate of group 0 to 1.2500e-02.
2022-11-06 22:21:33,499 BAD EPOCHS (no improvement): 4
2022-11-06 22:21:33,708 ----------------------------------------------------------------------------------------------------
2022-11-06 22:21:39,107 epoch 95 - iter 38/386 - loss 0.08174345 - samples/sec: 112.71 - lr: 0.012500
2022-11-06 22:21:44,470 epoch 95 - iter 76/386 - loss 0.07654626 - samples/sec: 113.43 - lr: 0.012500
2022-11-06 22:21:50,355 epoch 95 - iter 114/386 - loss 0.07662746 - samples/sec: 103.36 - lr: 0.012500
2022-11-06 22:21:55,951 epoch 95 - iter 152/386 - loss 0.07713288 - samples/sec: 108.71 - lr: 0.012500
2022-11-06 22:22:01,525 epoch 95 - iter 190/386 - loss 0.07768088 - samples/sec: 109.15 - lr: 0.012500
2022-11-06 22:22:07,333 epoch 95 - iter 228/386 - loss 0.07691802 - samples/sec: 104.74 - lr: 0.012500
2022-11-06 22:22:12,698 epoch 95 - iter 266/386 - loss 0.07735595 - samples/sec: 113.38 - lr: 0.012500
2022-11-06 22:22:17,950 epoch 95 - iter 304/386 - loss 0.07722152 - samples/sec: 115.84 - lr: 0.012500
2022-11-06 22:22:23,779 epoch 95 - iter 342/386 - loss 0.07755445 - samples/sec: 104.34 - lr: 0.012500
2022-11-06 22:22:29,040 epoch 95 - iter 380/386 - loss 0.07761664 - samples/sec: 115.64 - lr: 0.012500
2022-11-06 22:22:29,870 ----------------------------------------------------------------------------------------------------
2022-11-06 22:22:29,870 EPOCH 95 done: loss 0.0775 - lr 0.012500
2022-11-06 22:22:39,433 Evaluating as a multi-label problem: False
2022-11-06 22:22:39,548 TEST : loss 0.07741290330886841 - f1-score (micro avg) 0.9799
2022-11-06 22:22:39,660 BAD EPOCHS (no improvement): 0
2022-11-06 22:22:39,868 ----------------------------------------------------------------------------------------------------
2022-11-06 22:22:45,073 epoch 96 - iter 38/386 - loss 0.07749330 - samples/sec: 116.90 - lr: 0.012500
2022-11-06 22:22:50,290 epoch 96 - iter 76/386 - loss 0.07728624 - samples/sec: 116.60 - lr: 0.012500
2022-11-06 22:22:55,456 epoch 96 - iter 114/386 - loss 0.07852333 - samples/sec: 117.77 - lr: 0.012500
2022-11-06 22:23:00,968 epoch 96 - iter 152/386 - loss 0.07840505 - samples/sec: 110.38 - lr: 0.012500
2022-11-06 22:23:06,683 epoch 96 - iter 190/386 - loss 0.07797949 - samples/sec: 107.23 - lr: 0.012500
2022-11-06 22:23:12,055 epoch 96 - iter 228/386 - loss 0.07937941 - samples/sec: 113.25 - lr: 0.012500
2022-11-06 22:23:17,534 epoch 96 - iter 266/386 - loss 0.07834560 - samples/sec: 111.03 - lr: 0.012500
2022-11-06 22:23:23,499 epoch 96 - iter 304/386 - loss 0.07818574 - samples/sec: 101.98 - lr: 0.012500
2022-11-06 22:23:29,172 epoch 96 - iter 342/386 - loss 0.07823185 - samples/sec: 107.23 - lr: 0.012500
2022-11-06 22:23:34,750 epoch 96 - iter 380/386 - loss 0.07889026 - samples/sec: 109.06 - lr: 0.012500
2022-11-06 22:23:35,658 ----------------------------------------------------------------------------------------------------
2022-11-06 22:23:35,659 EPOCH 96 done: loss 0.0789 - lr 0.012500
2022-11-06 22:23:45,210 Evaluating as a multi-label problem: False
2022-11-06 22:23:45,326 TEST : loss 0.07733169943094254 - f1-score (micro avg) 0.9796
2022-11-06 22:23:45,439 BAD EPOCHS (no improvement): 1
2022-11-06 22:23:45,649 ----------------------------------------------------------------------------------------------------
2022-11-06 22:23:50,915 epoch 97 - iter 38/386 - loss 0.08544884 - samples/sec: 115.57 - lr: 0.012500
2022-11-06 22:23:56,576 epoch 97 - iter 76/386 - loss 0.08138444 - samples/sec: 107.45 - lr: 0.012500
2022-11-06 22:24:01,972 epoch 97 - iter 114/386 - loss 0.07902100 - samples/sec: 112.74 - lr: 0.012500
2022-11-06 22:24:07,363 epoch 97 - iter 152/386 - loss 0.07840143 - samples/sec: 112.84 - lr: 0.012500
2022-11-06 22:24:12,825 epoch 97 - iter 190/386 - loss 0.07712703 - samples/sec: 111.37 - lr: 0.012500
2022-11-06 22:24:18,787 epoch 97 - iter 228/386 - loss 0.07678814 - samples/sec: 102.77 - lr: 0.012500
2022-11-06 22:24:24,633 epoch 97 - iter 266/386 - loss 0.07676126 - samples/sec: 104.05 - lr: 0.012500
2022-11-06 22:24:30,835 epoch 97 - iter 304/386 - loss 0.07801854 - samples/sec: 98.08 - lr: 0.012500
2022-11-06 22:24:36,203 epoch 97 - iter 342/386 - loss 0.07840287 - samples/sec: 113.33 - lr: 0.012500
2022-11-06 22:24:41,478 epoch 97 - iter 380/386 - loss 0.07841984 - samples/sec: 115.32 - lr: 0.012500
2022-11-06 22:24:42,301 ----------------------------------------------------------------------------------------------------
2022-11-06 22:24:42,301 EPOCH 97 done: loss 0.0783 - lr 0.012500
2022-11-06 22:24:54,117 Evaluating as a multi-label problem: False
2022-11-06 22:24:54,232 TEST : loss 0.07772345840930939 - f1-score (micro avg) 0.98
2022-11-06 22:24:54,344 BAD EPOCHS (no improvement): 2
2022-11-06 22:24:54,551 ----------------------------------------------------------------------------------------------------
2022-11-06 22:25:00,111 epoch 98 - iter 38/386 - loss 0.07439980 - samples/sec: 109.44 - lr: 0.012500
2022-11-06 22:25:05,686 epoch 98 - iter 76/386 - loss 0.07645508 - samples/sec: 109.11 - lr: 0.012500
2022-11-06 22:25:11,184 epoch 98 - iter 114/386 - loss 0.07708381 - samples/sec: 110.65 - lr: 0.012500
2022-11-06 22:25:16,337 epoch 98 - iter 152/386 - loss 0.08009789 - samples/sec: 118.07 - lr: 0.012500
2022-11-06 22:25:21,314 epoch 98 - iter 190/386 - loss 0.07936476 - samples/sec: 122.22 - lr: 0.012500
2022-11-06 22:25:26,767 epoch 98 - iter 228/386 - loss 0.07906153 - samples/sec: 111.56 - lr: 0.012500
2022-11-06 22:25:32,821 epoch 98 - iter 266/386 - loss 0.07918697 - samples/sec: 100.50 - lr: 0.012500
2022-11-06 22:25:38,261 epoch 98 - iter 304/386 - loss 0.07882593 - samples/sec: 111.82 - lr: 0.012500
2022-11-06 22:25:43,440 epoch 98 - iter 342/386 - loss 0.07839498 - samples/sec: 117.47 - lr: 0.012500
2022-11-06 22:25:49,435 epoch 98 - iter 380/386 - loss 0.07899311 - samples/sec: 101.47 - lr: 0.012500
2022-11-06 22:25:50,290 ----------------------------------------------------------------------------------------------------
2022-11-06 22:25:50,290 EPOCH 98 done: loss 0.0790 - lr 0.012500
2022-11-06 22:25:59,749 Evaluating as a multi-label problem: False
2022-11-06 22:25:59,865 TEST : loss 0.07763128727674484 - f1-score (micro avg) 0.9803
2022-11-06 22:25:59,979 BAD EPOCHS (no improvement): 3
2022-11-06 22:26:00,189 ----------------------------------------------------------------------------------------------------
2022-11-06 22:26:06,142 epoch 99 - iter 38/386 - loss 0.07254806 - samples/sec: 102.21 - lr: 0.012500
2022-11-06 22:26:11,686 epoch 99 - iter 76/386 - loss 0.07331350 - samples/sec: 109.74 - lr: 0.012500
2022-11-06 22:26:17,577 epoch 99 - iter 114/386 - loss 0.07299872 - samples/sec: 103.26 - lr: 0.012500
2022-11-06 22:26:23,460 epoch 99 - iter 152/386 - loss 0.07443824 - samples/sec: 103.39 - lr: 0.012500
2022-11-06 22:26:28,808 epoch 99 - iter 190/386 - loss 0.07449089 - samples/sec: 113.75 - lr: 0.012500
2022-11-06 22:26:33,908 epoch 99 - iter 228/386 - loss 0.07509719 - samples/sec: 119.29 - lr: 0.012500
2022-11-06 22:26:39,140 epoch 99 - iter 266/386 - loss 0.07561839 - samples/sec: 116.29 - lr: 0.012500
2022-11-06 22:26:44,840 epoch 99 - iter 304/386 - loss 0.07630032 - samples/sec: 106.71 - lr: 0.012500
2022-11-06 22:26:49,959 epoch 99 - iter 342/386 - loss 0.07606443 - samples/sec: 118.84 - lr: 0.012500
2022-11-06 22:26:55,370 epoch 99 - iter 380/386 - loss 0.07638476 - samples/sec: 113.33 - lr: 0.012500
2022-11-06 22:26:56,359 ----------------------------------------------------------------------------------------------------
2022-11-06 22:26:56,359 EPOCH 99 done: loss 0.0766 - lr 0.012500
2022-11-06 22:27:05,824 Evaluating as a multi-label problem: False
2022-11-06 22:27:05,939 TEST : loss 0.07717061787843704 - f1-score (micro avg) 0.9801
2022-11-06 22:27:06,052 BAD EPOCHS (no improvement): 0
2022-11-06 22:27:06,257 ----------------------------------------------------------------------------------------------------
2022-11-06 22:27:11,941 epoch 100 - iter 38/386 - loss 0.08360850 - samples/sec: 107.04 - lr: 0.012500
2022-11-06 22:27:17,484 epoch 100 - iter 76/386 - loss 0.07889395 - samples/sec: 109.75 - lr: 0.012500
2022-11-06 22:27:23,204 epoch 100 - iter 114/386 - loss 0.08078996 - samples/sec: 106.34 - lr: 0.012500
2022-11-06 22:27:28,890 epoch 100 - iter 152/386 - loss 0.08157095 - samples/sec: 106.99 - lr: 0.012500
2022-11-06 22:27:34,405 epoch 100 - iter 190/386 - loss 0.08048207 - samples/sec: 110.31 - lr: 0.012500
2022-11-06 22:27:39,771 epoch 100 - iter 228/386 - loss 0.07920505 - samples/sec: 113.35 - lr: 0.012500
2022-11-06 22:27:45,192 epoch 100 - iter 266/386 - loss 0.07736238 - samples/sec: 112.23 - lr: 0.012500
2022-11-06 22:27:50,358 epoch 100 - iter 304/386 - loss 0.07652413 - samples/sec: 117.75 - lr: 0.012500
2022-11-06 22:27:55,824 epoch 100 - iter 342/386 - loss 0.07592693 - samples/sec: 111.31 - lr: 0.012500
2022-11-06 22:28:01,279 epoch 100 - iter 380/386 - loss 0.07600602 - samples/sec: 111.51 - lr: 0.012500
2022-11-06 22:28:02,152 ----------------------------------------------------------------------------------------------------
2022-11-06 22:28:02,152 EPOCH 100 done: loss 0.0759 - lr 0.012500
2022-11-06 22:28:11,635 Evaluating as a multi-label problem: False
2022-11-06 22:28:11,753 TEST : loss 0.07758809626102448 - f1-score (micro avg) 0.9803
2022-11-06 22:28:11,866 BAD EPOCHS (no improvement): 0
2022-11-06 22:28:12,070 ----------------------------------------------------------------------------------------------------
2022-11-06 22:28:17,706 epoch 101 - iter 38/386 - loss 0.07388983 - samples/sec: 107.96 - lr: 0.012500
2022-11-06 22:28:23,437 epoch 101 - iter 76/386 - loss 0.07406827 - samples/sec: 106.16 - lr: 0.012500
2022-11-06 22:28:28,733 epoch 101 - iter 114/386 - loss 0.07275898 - samples/sec: 114.86 - lr: 0.012500
2022-11-06 22:28:34,583 epoch 101 - iter 152/386 - loss 0.07457340 - samples/sec: 104.00 - lr: 0.012500
2022-11-06 22:28:39,730 epoch 101 - iter 190/386 - loss 0.07526331 - samples/sec: 118.19 - lr: 0.012500
2022-11-06 22:28:45,750 epoch 101 - iter 228/386 - loss 0.07519061 - samples/sec: 101.05 - lr: 0.012500
2022-11-06 22:28:51,489 epoch 101 - iter 266/386 - loss 0.07529028 - samples/sec: 105.99 - lr: 0.012500
2022-11-06 22:28:56,953 epoch 101 - iter 304/386 - loss 0.07552666 - samples/sec: 111.34 - lr: 0.012500
2022-11-06 22:29:01,707 epoch 101 - iter 342/386 - loss 0.07603837 - samples/sec: 127.98 - lr: 0.012500
2022-11-06 22:29:06,807 epoch 101 - iter 380/386 - loss 0.07630310 - samples/sec: 119.28 - lr: 0.012500
2022-11-06 22:29:07,609 ----------------------------------------------------------------------------------------------------
2022-11-06 22:29:07,609 EPOCH 101 done: loss 0.0765 - lr 0.012500
2022-11-06 22:29:17,153 Evaluating as a multi-label problem: False
2022-11-06 22:29:17,268 TEST : loss 0.07733462750911713 - f1-score (micro avg) 0.9801
2022-11-06 22:29:17,380 BAD EPOCHS (no improvement): 1
2022-11-06 22:29:17,589 ----------------------------------------------------------------------------------------------------
2022-11-06 22:29:22,875 epoch 102 - iter 38/386 - loss 0.08028161 - samples/sec: 115.12 - lr: 0.012500
2022-11-06 22:29:28,787 epoch 102 - iter 76/386 - loss 0.07647839 - samples/sec: 102.89 - lr: 0.012500
2022-11-06 22:29:34,241 epoch 102 - iter 114/386 - loss 0.07459047 - samples/sec: 111.53 - lr: 0.012500
2022-11-06 22:29:39,930 epoch 102 - iter 152/386 - loss 0.07461720 - samples/sec: 106.95 - lr: 0.012500
2022-11-06 22:29:45,892 epoch 102 - iter 190/386 - loss 0.07589447 - samples/sec: 102.02 - lr: 0.012500
2022-11-06 22:29:51,280 epoch 102 - iter 228/386 - loss 0.07462336 - samples/sec: 112.92 - lr: 0.012500
2022-11-06 22:29:56,688 epoch 102 - iter 266/386 - loss 0.07534371 - samples/sec: 112.75 - lr: 0.012500
2022-11-06 22:30:02,427 epoch 102 - iter 304/386 - loss 0.07531681 - samples/sec: 106.00 - lr: 0.012500
2022-11-06 22:30:08,110 epoch 102 - iter 342/386 - loss 0.07515869 - samples/sec: 107.02 - lr: 0.012500
2022-11-06 22:30:13,062 epoch 102 - iter 380/386 - loss 0.07486919 - samples/sec: 122.85 - lr: 0.012500
2022-11-06 22:30:13,878 ----------------------------------------------------------------------------------------------------
2022-11-06 22:30:13,878 EPOCH 102 done: loss 0.0751 - lr 0.012500
2022-11-06 22:30:23,042 Evaluating as a multi-label problem: False
2022-11-06 22:30:23,157 TEST : loss 0.07761669158935547 - f1-score (micro avg) 0.9801
2022-11-06 22:30:23,269 BAD EPOCHS (no improvement): 0
2022-11-06 22:30:23,477 ----------------------------------------------------------------------------------------------------
2022-11-06 22:30:29,131 epoch 103 - iter 38/386 - loss 0.07445893 - samples/sec: 107.61 - lr: 0.012500
2022-11-06 22:30:34,529 epoch 103 - iter 76/386 - loss 0.07775874 - samples/sec: 112.69 - lr: 0.012500
2022-11-06 22:30:39,927 epoch 103 - iter 114/386 - loss 0.07897023 - samples/sec: 112.71 - lr: 0.012500
2022-11-06 22:30:45,537 epoch 103 - iter 152/386 - loss 0.07848129 - samples/sec: 108.44 - lr: 0.012500
2022-11-06 22:30:51,233 epoch 103 - iter 190/386 - loss 0.07676500 - samples/sec: 106.79 - lr: 0.012500
2022-11-06 22:30:56,738 epoch 103 - iter 228/386 - loss 0.07555006 - samples/sec: 110.51 - lr: 0.012500
2022-11-06 22:31:02,068 epoch 103 - iter 266/386 - loss 0.07606481 - samples/sec: 114.13 - lr: 0.012500
2022-11-06 22:31:07,290 epoch 103 - iter 304/386 - loss 0.07608797 - samples/sec: 116.48 - lr: 0.012500
2022-11-06 22:31:13,424 epoch 103 - iter 342/386 - loss 0.07538846 - samples/sec: 99.72 - lr: 0.012500
2022-11-06 22:31:19,479 epoch 103 - iter 380/386 - loss 0.07560503 - samples/sec: 100.47 - lr: 0.012500
2022-11-06 22:31:20,161 ----------------------------------------------------------------------------------------------------
2022-11-06 22:31:20,162 EPOCH 103 done: loss 0.0753 - lr 0.012500
2022-11-06 22:31:29,000 Evaluating as a multi-label problem: False
2022-11-06 22:31:29,114 TEST : loss 0.07814455777406693 - f1-score (micro avg) 0.9797
2022-11-06 22:31:29,226 BAD EPOCHS (no improvement): 1
2022-11-06 22:31:29,435 ----------------------------------------------------------------------------------------------------
2022-11-06 22:31:35,248 epoch 104 - iter 38/386 - loss 0.06466635 - samples/sec: 104.67 - lr: 0.012500
2022-11-06 22:31:40,662 epoch 104 - iter 76/386 - loss 0.06932147 - samples/sec: 112.38 - lr: 0.012500
2022-11-06 22:31:46,456 epoch 104 - iter 114/386 - loss 0.07324214 - samples/sec: 104.99 - lr: 0.012500
2022-11-06 22:31:52,249 epoch 104 - iter 152/386 - loss 0.07342158 - samples/sec: 105.01 - lr: 0.012500
2022-11-06 22:31:57,692 epoch 104 - iter 190/386 - loss 0.07451835 - samples/sec: 111.76 - lr: 0.012500
2022-11-06 22:32:03,008 epoch 104 - iter 228/386 - loss 0.07500854 - samples/sec: 114.46 - lr: 0.012500
2022-11-06 22:32:08,916 epoch 104 - iter 266/386 - loss 0.07423502 - samples/sec: 102.95 - lr: 0.012500
2022-11-06 22:32:14,693 epoch 104 - iter 304/386 - loss 0.07465836 - samples/sec: 105.30 - lr: 0.012500
2022-11-06 22:32:20,353 epoch 104 - iter 342/386 - loss 0.07547961 - samples/sec: 107.48 - lr: 0.012500
2022-11-06 22:32:26,022 epoch 104 - iter 380/386 - loss 0.07525945 - samples/sec: 107.31 - lr: 0.012500
2022-11-06 22:32:26,914 ----------------------------------------------------------------------------------------------------
2022-11-06 22:32:26,914 EPOCH 104 done: loss 0.0754 - lr 0.012500
2022-11-06 22:32:36,375 Evaluating as a multi-label problem: False
2022-11-06 22:32:36,490 TEST : loss 0.07854548841714859 - f1-score (micro avg) 0.9799
2022-11-06 22:32:36,604 BAD EPOCHS (no improvement): 2
2022-11-06 22:32:36,873 ----------------------------------------------------------------------------------------------------
2022-11-06 22:32:42,092 epoch 105 - iter 38/386 - loss 0.07411338 - samples/sec: 116.62 - lr: 0.012500
2022-11-06 22:32:47,358 epoch 105 - iter 76/386 - loss 0.07593740 - samples/sec: 115.52 - lr: 0.012500
2022-11-06 22:32:52,835 epoch 105 - iter 114/386 - loss 0.07967368 - samples/sec: 111.94 - lr: 0.012500
2022-11-06 22:32:58,386 epoch 105 - iter 152/386 - loss 0.07724821 - samples/sec: 109.58 - lr: 0.012500
2022-11-06 22:33:04,547 epoch 105 - iter 190/386 - loss 0.07662746 - samples/sec: 98.74 - lr: 0.012500
2022-11-06 22:33:10,019 epoch 105 - iter 228/386 - loss 0.07663282 - samples/sec: 111.18 - lr: 0.012500
2022-11-06 22:33:15,439 epoch 105 - iter 266/386 - loss 0.07641141 - samples/sec: 112.23 - lr: 0.012500
2022-11-06 22:33:21,105 epoch 105 - iter 304/386 - loss 0.07529226 - samples/sec: 107.37 - lr: 0.012500
2022-11-06 22:33:27,073 epoch 105 - iter 342/386 - loss 0.07538330 - samples/sec: 101.92 - lr: 0.012500
2022-11-06 22:33:32,649 epoch 105 - iter 380/386 - loss 0.07513659 - samples/sec: 109.10 - lr: 0.012500
2022-11-06 22:33:33,521 ----------------------------------------------------------------------------------------------------
2022-11-06 22:33:33,521 EPOCH 105 done: loss 0.0751 - lr 0.012500
2022-11-06 22:33:43,099 Evaluating as a multi-label problem: False
2022-11-06 22:33:43,215 TEST : loss 0.07858723402023315 - f1-score (micro avg) 0.9794
2022-11-06 22:33:43,330 BAD EPOCHS (no improvement): 0
2022-11-06 22:33:43,625 ----------------------------------------------------------------------------------------------------
2022-11-06 22:33:49,689 epoch 106 - iter 38/386 - loss 0.06727736 - samples/sec: 100.35 - lr: 0.012500
2022-11-06 22:33:54,733 epoch 106 - iter 76/386 - loss 0.06934926 - samples/sec: 120.61 - lr: 0.012500
2022-11-06 22:34:00,089 epoch 106 - iter 114/386 - loss 0.07156163 - samples/sec: 113.60 - lr: 0.012500
2022-11-06 22:34:05,485 epoch 106 - iter 152/386 - loss 0.07347557 - samples/sec: 112.73 - lr: 0.012500
2022-11-06 22:34:10,897 epoch 106 - iter 190/386 - loss 0.07486589 - samples/sec: 112.42 - lr: 0.012500
2022-11-06 22:34:16,610 epoch 106 - iter 228/386 - loss 0.07406187 - samples/sec: 106.49 - lr: 0.012500
2022-11-06 22:34:22,290 epoch 106 - iter 266/386 - loss 0.07504310 - samples/sec: 107.09 - lr: 0.012500
2022-11-06 22:34:27,902 epoch 106 - iter 304/386 - loss 0.07553583 - samples/sec: 108.40 - lr: 0.012500
2022-11-06 22:34:33,820 epoch 106 - iter 342/386 - loss 0.07586313 - samples/sec: 102.79 - lr: 0.012500
2022-11-06 22:34:39,531 epoch 106 - iter 380/386 - loss 0.07565249 - samples/sec: 106.52 - lr: 0.012500
2022-11-06 22:34:40,287 ----------------------------------------------------------------------------------------------------
2022-11-06 22:34:40,287 EPOCH 106 done: loss 0.0757 - lr 0.012500
2022-11-06 22:34:49,940 Evaluating as a multi-label problem: False
2022-11-06 22:34:50,056 TEST : loss 0.0764971524477005 - f1-score (micro avg) 0.9797
2022-11-06 22:34:50,168 BAD EPOCHS (no improvement): 1
2022-11-06 22:34:50,465 ----------------------------------------------------------------------------------------------------
2022-11-06 22:34:55,872 epoch 107 - iter 38/386 - loss 0.07793043 - samples/sec: 112.54 - lr: 0.012500
2022-11-06 22:35:01,477 epoch 107 - iter 76/386 - loss 0.07886203 - samples/sec: 108.53 - lr: 0.012500
2022-11-06 22:35:06,360 epoch 107 - iter 114/386 - loss 0.07789126 - samples/sec: 124.61 - lr: 0.012500
2022-11-06 22:35:11,369 epoch 107 - iter 152/386 - loss 0.07771686 - samples/sec: 121.44 - lr: 0.012500
2022-11-06 22:35:17,286 epoch 107 - iter 190/386 - loss 0.07679357 - samples/sec: 102.82 - lr: 0.012500
2022-11-06 22:35:22,885 epoch 107 - iter 228/386 - loss 0.07780927 - samples/sec: 108.65 - lr: 0.012500
2022-11-06 22:35:28,831 epoch 107 - iter 266/386 - loss 0.07691362 - samples/sec: 102.30 - lr: 0.012500
2022-11-06 22:35:34,328 epoch 107 - iter 304/386 - loss 0.07767365 - samples/sec: 110.68 - lr: 0.012500
2022-11-06 22:35:39,973 epoch 107 - iter 342/386 - loss 0.07674244 - samples/sec: 107.75 - lr: 0.012500
2022-11-06 22:35:45,758 epoch 107 - iter 380/386 - loss 0.07682198 - samples/sec: 105.16 - lr: 0.012500
2022-11-06 22:35:46,691 ----------------------------------------------------------------------------------------------------
2022-11-06 22:35:46,691 EPOCH 107 done: loss 0.0768 - lr 0.012500
2022-11-06 22:35:58,826 Evaluating as a multi-label problem: False
2022-11-06 22:35:58,940 TEST : loss 0.07759314030408859 - f1-score (micro avg) 0.9797
2022-11-06 22:35:59,054 BAD EPOCHS (no improvement): 2
2022-11-06 22:35:59,288 ----------------------------------------------------------------------------------------------------
2022-11-06 22:36:04,886 epoch 108 - iter 38/386 - loss 0.07772745 - samples/sec: 108.70 - lr: 0.012500
2022-11-06 22:36:10,217 epoch 108 - iter 76/386 - loss 0.07956371 - samples/sec: 114.12 - lr: 0.012500
2022-11-06 22:36:15,669 epoch 108 - iter 114/386 - loss 0.07712381 - samples/sec: 111.56 - lr: 0.012500
2022-11-06 22:36:20,706 epoch 108 - iter 152/386 - loss 0.07614295 - samples/sec: 120.78 - lr: 0.012500
2022-11-06 22:36:26,206 epoch 108 - iter 190/386 - loss 0.07566320 - samples/sec: 110.62 - lr: 0.012500
2022-11-06 22:36:31,915 epoch 108 - iter 228/386 - loss 0.07528023 - samples/sec: 106.54 - lr: 0.012500
2022-11-06 22:36:37,490 epoch 108 - iter 266/386 - loss 0.07548655 - samples/sec: 109.12 - lr: 0.012500
2022-11-06 22:36:43,171 epoch 108 - iter 304/386 - loss 0.07645383 - samples/sec: 107.09 - lr: 0.012500
2022-11-06 22:36:48,792 epoch 108 - iter 342/386 - loss 0.07723662 - samples/sec: 108.22 - lr: 0.012500
2022-11-06 22:36:54,537 epoch 108 - iter 380/386 - loss 0.07675000 - samples/sec: 105.89 - lr: 0.012500
2022-11-06 22:36:55,359 ----------------------------------------------------------------------------------------------------
2022-11-06 22:36:55,359 EPOCH 108 done: loss 0.0769 - lr 0.012500
2022-11-06 22:37:05,010 Evaluating as a multi-label problem: False
2022-11-06 22:37:05,125 TEST : loss 0.07701174914836884 - f1-score (micro avg) 0.9797
2022-11-06 22:37:05,238 BAD EPOCHS (no improvement): 3
2022-11-06 22:37:05,447 ----------------------------------------------------------------------------------------------------
2022-11-06 22:37:11,421 epoch 109 - iter 38/386 - loss 0.07682666 - samples/sec: 101.86 - lr: 0.012500
2022-11-06 22:37:16,860 epoch 109 - iter 76/386 - loss 0.07576946 - samples/sec: 111.86 - lr: 0.012500
2022-11-06 22:37:23,112 epoch 109 - iter 114/386 - loss 0.07493292 - samples/sec: 97.29 - lr: 0.012500
2022-11-06 22:37:28,787 epoch 109 - iter 152/386 - loss 0.07609361 - samples/sec: 107.20 - lr: 0.012500
2022-11-06 22:37:33,989 epoch 109 - iter 190/386 - loss 0.07586317 - samples/sec: 116.94 - lr: 0.012500
2022-11-06 22:37:39,671 epoch 109 - iter 228/386 - loss 0.07565590 - samples/sec: 107.06 - lr: 0.012500
2022-11-06 22:37:44,772 epoch 109 - iter 266/386 - loss 0.07547549 - samples/sec: 119.27 - lr: 0.012500
2022-11-06 22:37:49,966 epoch 109 - iter 304/386 - loss 0.07521647 - samples/sec: 117.13 - lr: 0.012500
2022-11-06 22:37:55,223 epoch 109 - iter 342/386 - loss 0.07531028 - samples/sec: 115.74 - lr: 0.012500
2022-11-06 22:38:00,867 epoch 109 - iter 380/386 - loss 0.07562079 - samples/sec: 107.78 - lr: 0.012500
2022-11-06 22:38:01,854 ----------------------------------------------------------------------------------------------------
2022-11-06 22:38:01,854 EPOCH 109 done: loss 0.0754 - lr 0.012500
2022-11-06 22:38:11,582 Evaluating as a multi-label problem: False
2022-11-06 22:38:11,697 TEST : loss 0.07831114530563354 - f1-score (micro avg) 0.9796
2022-11-06 22:38:11,810 Epoch 109: reducing learning rate of group 0 to 6.2500e-03.
2022-11-06 22:38:11,811 BAD EPOCHS (no improvement): 4
2022-11-06 22:38:12,018 ----------------------------------------------------------------------------------------------------
2022-11-06 22:38:17,685 epoch 110 - iter 38/386 - loss 0.08174241 - samples/sec: 107.37 - lr: 0.006250
2022-11-06 22:38:23,300 epoch 110 - iter 76/386 - loss 0.07864406 - samples/sec: 108.34 - lr: 0.006250
2022-11-06 22:38:28,712 epoch 110 - iter 114/386 - loss 0.07705464 - samples/sec: 112.40 - lr: 0.006250
2022-11-06 22:38:34,063 epoch 110 - iter 152/386 - loss 0.07474375 - samples/sec: 113.69 - lr: 0.006250
2022-11-06 22:38:39,781 epoch 110 - iter 190/386 - loss 0.07452670 - samples/sec: 106.40 - lr: 0.006250
2022-11-06 22:38:45,050 epoch 110 - iter 228/386 - loss 0.07418272 - samples/sec: 115.45 - lr: 0.006250
2022-11-06 22:38:49,859 epoch 110 - iter 266/386 - loss 0.07402425 - samples/sec: 126.51 - lr: 0.006250
2022-11-06 22:38:55,578 epoch 110 - iter 304/386 - loss 0.07402912 - samples/sec: 106.36 - lr: 0.006250
2022-11-06 22:39:01,063 epoch 110 - iter 342/386 - loss 0.07401083 - samples/sec: 110.92 - lr: 0.006250
2022-11-06 22:39:06,947 epoch 110 - iter 380/386 - loss 0.07465410 - samples/sec: 103.39 - lr: 0.006250
2022-11-06 22:39:07,954 ----------------------------------------------------------------------------------------------------
2022-11-06 22:39:07,954 EPOCH 110 done: loss 0.0747 - lr 0.006250
2022-11-06 22:39:17,722 Evaluating as a multi-label problem: False
2022-11-06 22:39:17,837 TEST : loss 0.07766838371753693 - f1-score (micro avg) 0.9796
2022-11-06 22:39:17,950 BAD EPOCHS (no improvement): 0
2022-11-06 22:39:18,158 ----------------------------------------------------------------------------------------------------
2022-11-06 22:39:23,273 epoch 111 - iter 38/386 - loss 0.07438457 - samples/sec: 118.96 - lr: 0.006250
2022-11-06 22:39:28,793 epoch 111 - iter 76/386 - loss 0.07338315 - samples/sec: 110.20 - lr: 0.006250
2022-11-06 22:39:34,098 epoch 111 - iter 114/386 - loss 0.07331771 - samples/sec: 114.68 - lr: 0.006250
2022-11-06 22:39:39,860 epoch 111 - iter 152/386 - loss 0.07467265 - samples/sec: 105.58 - lr: 0.006250
2022-11-06 22:39:45,521 epoch 111 - iter 190/386 - loss 0.07408059 - samples/sec: 107.46 - lr: 0.006250
2022-11-06 22:39:51,422 epoch 111 - iter 228/386 - loss 0.07348917 - samples/sec: 103.08 - lr: 0.006250
2022-11-06 22:39:57,321 epoch 111 - iter 266/386 - loss 0.07367028 - samples/sec: 103.12 - lr: 0.006250
2022-11-06 22:40:02,596 epoch 111 - iter 304/386 - loss 0.07349484 - samples/sec: 115.33 - lr: 0.006250
2022-11-06 22:40:08,382 epoch 111 - iter 342/386 - loss 0.07372678 - samples/sec: 105.14 - lr: 0.006250
2022-11-06 22:40:13,289 epoch 111 - iter 380/386 - loss 0.07366151 - samples/sec: 123.97 - lr: 0.006250
2022-11-06 22:40:14,051 ----------------------------------------------------------------------------------------------------
2022-11-06 22:40:14,051 EPOCH 111 done: loss 0.0736 - lr 0.006250
2022-11-06 22:40:23,690 Evaluating as a multi-label problem: False
2022-11-06 22:40:23,804 TEST : loss 0.07827350497245789 - f1-score (micro avg) 0.9799
2022-11-06 22:40:23,916 BAD EPOCHS (no improvement): 0
2022-11-06 22:40:24,123 ----------------------------------------------------------------------------------------------------
2022-11-06 22:40:29,099 epoch 112 - iter 38/386 - loss 0.07457649 - samples/sec: 122.30 - lr: 0.006250
2022-11-06 22:40:34,672 epoch 112 - iter 76/386 - loss 0.07390976 - samples/sec: 109.16 - lr: 0.006250
2022-11-06 22:40:40,401 epoch 112 - iter 114/386 - loss 0.07481698 - samples/sec: 106.17 - lr: 0.006250
2022-11-06 22:40:46,367 epoch 112 - iter 152/386 - loss 0.07362836 - samples/sec: 101.98 - lr: 0.006250
2022-11-06 22:40:52,448 epoch 112 - iter 190/386 - loss 0.07372337 - samples/sec: 100.02 - lr: 0.006250
2022-11-06 22:40:57,973 epoch 112 - iter 228/386 - loss 0.07390448 - samples/sec: 110.11 - lr: 0.006250
2022-11-06 22:41:03,671 epoch 112 - iter 266/386 - loss 0.07383284 - samples/sec: 106.77 - lr: 0.006250
2022-11-06 22:41:09,516 epoch 112 - iter 304/386 - loss 0.07359920 - samples/sec: 104.07 - lr: 0.006250
2022-11-06 22:41:14,542 epoch 112 - iter 342/386 - loss 0.07330693 - samples/sec: 121.05 - lr: 0.006250
2022-11-06 22:41:19,768 epoch 112 - iter 380/386 - loss 0.07359435 - samples/sec: 116.41 - lr: 0.006250
2022-11-06 22:41:20,506 ----------------------------------------------------------------------------------------------------
2022-11-06 22:41:20,506 EPOCH 112 done: loss 0.0736 - lr 0.006250
2022-11-06 22:41:30,178 Evaluating as a multi-label problem: False
2022-11-06 22:41:30,292 TEST : loss 0.0784047469496727 - f1-score (micro avg) 0.9796
2022-11-06 22:41:30,405 BAD EPOCHS (no improvement): 0
2022-11-06 22:41:30,603 ----------------------------------------------------------------------------------------------------
2022-11-06 22:41:36,132 epoch 113 - iter 38/386 - loss 0.06745935 - samples/sec: 110.06 - lr: 0.006250
2022-11-06 22:41:41,642 epoch 113 - iter 76/386 - loss 0.07312497 - samples/sec: 110.41 - lr: 0.006250
2022-11-06 22:41:47,251 epoch 113 - iter 114/386 - loss 0.07487375 - samples/sec: 108.46 - lr: 0.006250
2022-11-06 22:41:53,374 epoch 113 - iter 152/386 - loss 0.07448522 - samples/sec: 99.34 - lr: 0.006250
2022-11-06 22:41:58,986 epoch 113 - iter 190/386 - loss 0.07481588 - samples/sec: 108.39 - lr: 0.006250
2022-11-06 22:42:04,661 epoch 113 - iter 228/386 - loss 0.07481104 - samples/sec: 107.19 - lr: 0.006250
2022-11-06 22:42:09,868 epoch 113 - iter 266/386 - loss 0.07450630 - samples/sec: 116.85 - lr: 0.006250
2022-11-06 22:42:15,444 epoch 113 - iter 304/386 - loss 0.07432014 - samples/sec: 109.09 - lr: 0.006250
2022-11-06 22:42:20,828 epoch 113 - iter 342/386 - loss 0.07418859 - samples/sec: 113.00 - lr: 0.006250
2022-11-06 22:42:26,222 epoch 113 - iter 380/386 - loss 0.07414788 - samples/sec: 112.77 - lr: 0.006250
2022-11-06 22:42:27,047 ----------------------------------------------------------------------------------------------------
2022-11-06 22:42:27,047 EPOCH 113 done: loss 0.0740 - lr 0.006250
2022-11-06 22:42:36,351 Evaluating as a multi-label problem: False
2022-11-06 22:42:36,466 TEST : loss 0.0782204121351242 - f1-score (micro avg) 0.9798
2022-11-06 22:42:36,579 BAD EPOCHS (no improvement): 1
2022-11-06 22:42:36,779 ----------------------------------------------------------------------------------------------------
2022-11-06 22:42:42,018 epoch 114 - iter 38/386 - loss 0.06587890 - samples/sec: 116.14 - lr: 0.006250
2022-11-06 22:42:48,243 epoch 114 - iter 76/386 - loss 0.06948695 - samples/sec: 97.72 - lr: 0.006250
2022-11-06 22:42:53,714 epoch 114 - iter 114/386 - loss 0.06935897 - samples/sec: 111.20 - lr: 0.006250
2022-11-06 22:42:59,267 epoch 114 - iter 152/386 - loss 0.06830812 - samples/sec: 109.55 - lr: 0.006250
2022-11-06 22:43:05,012 epoch 114 - iter 190/386 - loss 0.06990073 - samples/sec: 105.90 - lr: 0.006250
2022-11-06 22:43:10,273 epoch 114 - iter 228/386 - loss 0.07031777 - samples/sec: 115.63 - lr: 0.006250
2022-11-06 22:43:15,874 epoch 114 - iter 266/386 - loss 0.07140795 - samples/sec: 108.60 - lr: 0.006250
2022-11-06 22:43:21,628 epoch 114 - iter 304/386 - loss 0.07147415 - samples/sec: 105.72 - lr: 0.006250
2022-11-06 22:43:27,261 epoch 114 - iter 342/386 - loss 0.07159018 - samples/sec: 107.99 - lr: 0.006250
2022-11-06 22:43:32,736 epoch 114 - iter 380/386 - loss 0.07193801 - samples/sec: 111.14 - lr: 0.006250
2022-11-06 22:43:33,684 ----------------------------------------------------------------------------------------------------
2022-11-06 22:43:33,684 EPOCH 114 done: loss 0.0717 - lr 0.006250
2022-11-06 22:43:42,618 Evaluating as a multi-label problem: False
2022-11-06 22:43:42,733 TEST : loss 0.07809162139892578 - f1-score (micro avg) 0.9798
2022-11-06 22:43:42,846 BAD EPOCHS (no improvement): 0
2022-11-06 22:43:43,053 ----------------------------------------------------------------------------------------------------
2022-11-06 22:43:48,689 epoch 115 - iter 38/386 - loss 0.08079929 - samples/sec: 107.96 - lr: 0.006250
2022-11-06 22:43:54,157 epoch 115 - iter 76/386 - loss 0.07503846 - samples/sec: 111.25 - lr: 0.006250
2022-11-06 22:43:59,851 epoch 115 - iter 114/386 - loss 0.07552664 - samples/sec: 106.83 - lr: 0.006250
2022-11-06 22:44:05,412 epoch 115 - iter 152/386 - loss 0.07313925 - samples/sec: 109.41 - lr: 0.006250
2022-11-06 22:44:10,998 epoch 115 - iter 190/386 - loss 0.07399548 - samples/sec: 108.90 - lr: 0.006250
2022-11-06 22:44:17,159 epoch 115 - iter 228/386 - loss 0.07418077 - samples/sec: 98.73 - lr: 0.006250
2022-11-06 22:44:22,490 epoch 115 - iter 266/386 - loss 0.07427701 - samples/sec: 114.12 - lr: 0.006250
2022-11-06 22:44:27,871 epoch 115 - iter 304/386 - loss 0.07325423 - samples/sec: 113.05 - lr: 0.006250
2022-11-06 22:44:33,096 epoch 115 - iter 342/386 - loss 0.07281653 - samples/sec: 116.43 - lr: 0.006250
2022-11-06 22:44:38,839 epoch 115 - iter 380/386 - loss 0.07338386 - samples/sec: 105.93 - lr: 0.006250
2022-11-06 22:44:39,763 ----------------------------------------------------------------------------------------------------
2022-11-06 22:44:39,763 EPOCH 115 done: loss 0.0737 - lr 0.006250
2022-11-06 22:44:49,211 Evaluating as a multi-label problem: False
2022-11-06 22:44:49,326 TEST : loss 0.07869032025337219 - f1-score (micro avg) 0.9797
2022-11-06 22:44:49,438 BAD EPOCHS (no improvement): 1
2022-11-06 22:44:49,645 ----------------------------------------------------------------------------------------------------
2022-11-06 22:44:54,808 epoch 116 - iter 38/386 - loss 0.06467089 - samples/sec: 117.87 - lr: 0.006250
2022-11-06 22:45:00,285 epoch 116 - iter 76/386 - loss 0.06757483 - samples/sec: 111.06 - lr: 0.006250
2022-11-06 22:45:05,972 epoch 116 - iter 114/386 - loss 0.07024725 - samples/sec: 106.98 - lr: 0.006250
2022-11-06 22:45:11,528 epoch 116 - iter 152/386 - loss 0.07050239 - samples/sec: 109.48 - lr: 0.006250
2022-11-06 22:45:16,928 epoch 116 - iter 190/386 - loss 0.07115237 - samples/sec: 112.66 - lr: 0.006250
2022-11-06 22:45:23,168 epoch 116 - iter 228/386 - loss 0.07166118 - samples/sec: 97.49 - lr: 0.006250
2022-11-06 22:45:28,503 epoch 116 - iter 266/386 - loss 0.07279125 - samples/sec: 114.04 - lr: 0.006250
2022-11-06 22:45:33,863 epoch 116 - iter 304/386 - loss 0.07294731 - samples/sec: 113.50 - lr: 0.006250
2022-11-06 22:45:39,513 epoch 116 - iter 342/386 - loss 0.07319292 - samples/sec: 107.66 - lr: 0.006250
2022-11-06 22:45:45,244 epoch 116 - iter 380/386 - loss 0.07311641 - samples/sec: 106.16 - lr: 0.006250
2022-11-06 22:45:46,149 ----------------------------------------------------------------------------------------------------
2022-11-06 22:45:46,149 EPOCH 116 done: loss 0.0731 - lr 0.006250
2022-11-06 22:45:55,775 Evaluating as a multi-label problem: False
2022-11-06 22:45:55,890 TEST : loss 0.07787258923053741 - f1-score (micro avg) 0.9799
2022-11-06 22:45:56,003 BAD EPOCHS (no improvement): 2
2022-11-06 22:45:56,208 ----------------------------------------------------------------------------------------------------
2022-11-06 22:46:01,914 epoch 117 - iter 38/386 - loss 0.07456506 - samples/sec: 106.63 - lr: 0.006250
2022-11-06 22:46:07,149 epoch 117 - iter 76/386 - loss 0.07301545 - samples/sec: 116.21 - lr: 0.006250
2022-11-06 22:46:12,710 epoch 117 - iter 114/386 - loss 0.07214790 - samples/sec: 109.40 - lr: 0.006250
2022-11-06 22:46:18,389 epoch 117 - iter 152/386 - loss 0.07349199 - samples/sec: 107.12 - lr: 0.006250
2022-11-06 22:46:24,211 epoch 117 - iter 190/386 - loss 0.07438933 - samples/sec: 104.48 - lr: 0.006250
2022-11-06 22:46:29,483 epoch 117 - iter 228/386 - loss 0.07472042 - samples/sec: 115.41 - lr: 0.006250
2022-11-06 22:46:35,736 epoch 117 - iter 266/386 - loss 0.07482219 - samples/sec: 97.28 - lr: 0.006250
2022-11-06 22:46:41,063 epoch 117 - iter 304/386 - loss 0.07409200 - samples/sec: 114.21 - lr: 0.006250
2022-11-06 22:46:46,457 epoch 117 - iter 342/386 - loss 0.07417828 - samples/sec: 112.77 - lr: 0.006250
2022-11-06 22:46:51,990 epoch 117 - iter 380/386 - loss 0.07410059 - samples/sec: 109.95 - lr: 0.006250
2022-11-06 22:46:52,769 ----------------------------------------------------------------------------------------------------
2022-11-06 22:46:52,769 EPOCH 117 done: loss 0.0740 - lr 0.006250
2022-11-06 22:47:02,415 Evaluating as a multi-label problem: False
2022-11-06 22:47:02,530 TEST : loss 0.07921639084815979 - f1-score (micro avg) 0.9794
2022-11-06 22:47:02,643 BAD EPOCHS (no improvement): 3
2022-11-06 22:47:02,849 ----------------------------------------------------------------------------------------------------
2022-11-06 22:47:08,496 epoch 118 - iter 38/386 - loss 0.07675392 - samples/sec: 107.75 - lr: 0.006250
2022-11-06 22:47:14,016 epoch 118 - iter 76/386 - loss 0.07527668 - samples/sec: 110.22 - lr: 0.006250
2022-11-06 22:47:18,927 epoch 118 - iter 114/386 - loss 0.07337067 - samples/sec: 123.87 - lr: 0.006250
2022-11-06 22:47:24,183 epoch 118 - iter 152/386 - loss 0.07340006 - samples/sec: 115.75 - lr: 0.006250
2022-11-06 22:47:29,668 epoch 118 - iter 190/386 - loss 0.07289468 - samples/sec: 110.90 - lr: 0.006250
2022-11-06 22:47:35,382 epoch 118 - iter 228/386 - loss 0.07318162 - samples/sec: 106.47 - lr: 0.006250
2022-11-06 22:47:40,963 epoch 118 - iter 266/386 - loss 0.07385145 - samples/sec: 109.01 - lr: 0.006250
2022-11-06 22:47:46,511 epoch 118 - iter 304/386 - loss 0.07402890 - samples/sec: 109.63 - lr: 0.006250
2022-11-06 22:47:52,857 epoch 118 - iter 342/386 - loss 0.07374341 - samples/sec: 95.87 - lr: 0.006250
2022-11-06 22:47:58,354 epoch 118 - iter 380/386 - loss 0.07366664 - samples/sec: 110.66 - lr: 0.006250
2022-11-06 22:47:59,163 ----------------------------------------------------------------------------------------------------
2022-11-06 22:47:59,163 EPOCH 118 done: loss 0.0738 - lr 0.006250
2022-11-06 22:48:11,151 Evaluating as a multi-label problem: False
2022-11-06 22:48:11,266 TEST : loss 0.07913336902856827 - f1-score (micro avg) 0.9798
2022-11-06 22:48:11,380 Epoch 118: reducing learning rate of group 0 to 3.1250e-03.
2022-11-06 22:48:11,380 BAD EPOCHS (no improvement): 4
2022-11-06 22:48:11,579 ----------------------------------------------------------------------------------------------------
2022-11-06 22:48:17,130 epoch 119 - iter 38/386 - loss 0.08441339 - samples/sec: 109.62 - lr: 0.003125
2022-11-06 22:48:23,068 epoch 119 - iter 76/386 - loss 0.07742126 - samples/sec: 102.45 - lr: 0.003125
2022-11-06 22:48:28,362 epoch 119 - iter 114/386 - loss 0.07506273 - samples/sec: 114.92 - lr: 0.003125
2022-11-06 22:48:33,790 epoch 119 - iter 152/386 - loss 0.07328483 - samples/sec: 112.06 - lr: 0.003125
2022-11-06 22:48:39,044 epoch 119 - iter 190/386 - loss 0.07163464 - samples/sec: 115.78 - lr: 0.003125
2022-11-06 22:48:44,770 epoch 119 - iter 228/386 - loss 0.07048723 - samples/sec: 106.24 - lr: 0.003125
2022-11-06 22:48:50,324 epoch 119 - iter 266/386 - loss 0.07111069 - samples/sec: 109.54 - lr: 0.003125
2022-11-06 22:48:55,700 epoch 119 - iter 304/386 - loss 0.07159411 - samples/sec: 113.16 - lr: 0.003125
2022-11-06 22:49:01,783 epoch 119 - iter 342/386 - loss 0.07142949 - samples/sec: 100.01 - lr: 0.003125
2022-11-06 22:49:06,925 epoch 119 - iter 380/386 - loss 0.07080463 - samples/sec: 118.30 - lr: 0.003125
2022-11-06 22:49:07,684 ----------------------------------------------------------------------------------------------------
2022-11-06 22:49:07,684 EPOCH 119 done: loss 0.0707 - lr 0.003125
2022-11-06 22:49:17,305 Evaluating as a multi-label problem: False
2022-11-06 22:49:17,420 TEST : loss 0.07869245857000351 - f1-score (micro avg) 0.9798
2022-11-06 22:49:17,533 BAD EPOCHS (no improvement): 0
2022-11-06 22:49:17,804 ----------------------------------------------------------------------------------------------------
2022-11-06 22:49:23,240 epoch 120 - iter 38/386 - loss 0.07003334 - samples/sec: 111.93 - lr: 0.003125
2022-11-06 22:49:28,407 epoch 120 - iter 76/386 - loss 0.07241340 - samples/sec: 117.74 - lr: 0.003125
2022-11-06 22:49:34,114 epoch 120 - iter 114/386 - loss 0.07110733 - samples/sec: 106.59 - lr: 0.003125
2022-11-06 22:49:39,401 epoch 120 - iter 152/386 - loss 0.07101024 - samples/sec: 115.06 - lr: 0.003125
2022-11-06 22:49:44,739 epoch 120 - iter 190/386 - loss 0.07154690 - samples/sec: 113.95 - lr: 0.003125
2022-11-06 22:49:51,008 epoch 120 - iter 228/386 - loss 0.07106011 - samples/sec: 97.04 - lr: 0.003125
2022-11-06 22:49:56,441 epoch 120 - iter 266/386 - loss 0.07079481 - samples/sec: 111.97 - lr: 0.003125
2022-11-06 22:50:01,937 epoch 120 - iter 304/386 - loss 0.07116823 - samples/sec: 110.70 - lr: 0.003125
2022-11-06 22:50:07,609 epoch 120 - iter 342/386 - loss 0.07116696 - samples/sec: 107.25 - lr: 0.003125
2022-11-06 22:50:13,211 epoch 120 - iter 380/386 - loss 0.07097452 - samples/sec: 108.59 - lr: 0.003125
2022-11-06 22:50:14,248 ----------------------------------------------------------------------------------------------------
2022-11-06 22:50:14,248 EPOCH 120 done: loss 0.0710 - lr 0.003125
2022-11-06 22:50:23,902 Evaluating as a multi-label problem: False
2022-11-06 22:50:24,018 TEST : loss 0.07862657308578491 - f1-score (micro avg) 0.9795
2022-11-06 22:50:24,131 BAD EPOCHS (no improvement): 1
2022-11-06 22:50:24,426 ----------------------------------------------------------------------------------------------------
2022-11-06 22:50:30,014 epoch 121 - iter 38/386 - loss 0.07008204 - samples/sec: 108.91 - lr: 0.003125
2022-11-06 22:50:35,466 epoch 121 - iter 76/386 - loss 0.07665982 - samples/sec: 111.58 - lr: 0.003125
2022-11-06 22:50:41,151 epoch 121 - iter 114/386 - loss 0.07515720 - samples/sec: 107.01 - lr: 0.003125
2022-11-06 22:50:46,784 epoch 121 - iter 152/386 - loss 0.07385046 - samples/sec: 107.98 - lr: 0.003125
2022-11-06 22:50:52,336 epoch 121 - iter 190/386 - loss 0.07283816 - samples/sec: 109.58 - lr: 0.003125
2022-11-06 22:50:57,812 epoch 121 - iter 228/386 - loss 0.07269037 - samples/sec: 111.10 - lr: 0.003125
2022-11-06 22:51:02,881 epoch 121 - iter 266/386 - loss 0.07269133 - samples/sec: 120.01 - lr: 0.003125
2022-11-06 22:51:08,459 epoch 121 - iter 304/386 - loss 0.07326572 - samples/sec: 109.06 - lr: 0.003125
2022-11-06 22:51:14,108 epoch 121 - iter 342/386 - loss 0.07365154 - samples/sec: 107.69 - lr: 0.003125
2022-11-06 22:51:20,029 epoch 121 - iter 380/386 - loss 0.07361047 - samples/sec: 102.75 - lr: 0.003125
2022-11-06 22:51:20,813 ----------------------------------------------------------------------------------------------------
2022-11-06 22:51:20,813 EPOCH 121 done: loss 0.0738 - lr 0.003125
2022-11-06 22:51:30,343 Evaluating as a multi-label problem: False
2022-11-06 22:51:30,457 TEST : loss 0.07829055935144424 - f1-score (micro avg) 0.9796
2022-11-06 22:51:30,571 BAD EPOCHS (no improvement): 2
2022-11-06 22:51:30,867 ----------------------------------------------------------------------------------------------------
2022-11-06 22:51:36,365 epoch 122 - iter 38/386 - loss 0.07587148 - samples/sec: 110.68 - lr: 0.003125
2022-11-06 22:51:42,507 epoch 122 - iter 76/386 - loss 0.07060888 - samples/sec: 99.04 - lr: 0.003125
2022-11-06 22:51:48,139 epoch 122 - iter 114/386 - loss 0.07060573 - samples/sec: 108.01 - lr: 0.003125
2022-11-06 22:51:53,462 epoch 122 - iter 152/386 - loss 0.07077425 - samples/sec: 114.28 - lr: 0.003125
2022-11-06 22:51:59,291 epoch 122 - iter 190/386 - loss 0.07107700 - samples/sec: 104.36 - lr: 0.003125
2022-11-06 22:52:04,979 epoch 122 - iter 228/386 - loss 0.07241594 - samples/sec: 106.95 - lr: 0.003125
2022-11-06 22:52:10,324 epoch 122 - iter 266/386 - loss 0.07267496 - samples/sec: 113.82 - lr: 0.003125
2022-11-06 22:52:16,139 epoch 122 - iter 304/386 - loss 0.07311927 - samples/sec: 104.63 - lr: 0.003125
2022-11-06 22:52:21,712 epoch 122 - iter 342/386 - loss 0.07384208 - samples/sec: 109.15 - lr: 0.003125
2022-11-06 22:52:26,791 epoch 122 - iter 380/386 - loss 0.07386006 - samples/sec: 119.79 - lr: 0.003125
2022-11-06 22:52:27,544 ----------------------------------------------------------------------------------------------------
2022-11-06 22:52:27,544 EPOCH 122 done: loss 0.0742 - lr 0.003125
2022-11-06 22:52:37,264 Evaluating as a multi-label problem: False
2022-11-06 22:52:37,379 TEST : loss 0.07821225374937057 - f1-score (micro avg) 0.9796
2022-11-06 22:52:37,492 BAD EPOCHS (no improvement): 3
2022-11-06 22:52:37,769 ----------------------------------------------------------------------------------------------------
2022-11-06 22:52:43,801 epoch 123 - iter 38/386 - loss 0.07069385 - samples/sec: 100.86 - lr: 0.003125
2022-11-06 22:52:49,323 epoch 123 - iter 76/386 - loss 0.07225030 - samples/sec: 110.17 - lr: 0.003125
2022-11-06 22:52:55,191 epoch 123 - iter 114/386 - loss 0.07087317 - samples/sec: 103.67 - lr: 0.003125
2022-11-06 22:53:00,530 epoch 123 - iter 152/386 - loss 0.07098515 - samples/sec: 113.94 - lr: 0.003125
2022-11-06 22:53:05,949 epoch 123 - iter 190/386 - loss 0.07226604 - samples/sec: 112.26 - lr: 0.003125
2022-11-06 22:53:11,126 epoch 123 - iter 228/386 - loss 0.07236956 - samples/sec: 117.52 - lr: 0.003125
2022-11-06 22:53:17,082 epoch 123 - iter 266/386 - loss 0.07276559 - samples/sec: 102.15 - lr: 0.003125
2022-11-06 22:53:22,656 epoch 123 - iter 304/386 - loss 0.07227973 - samples/sec: 109.12 - lr: 0.003125
2022-11-06 22:53:28,107 epoch 123 - iter 342/386 - loss 0.07257929 - samples/sec: 111.61 - lr: 0.003125
2022-11-06 22:53:33,469 epoch 123 - iter 380/386 - loss 0.07230997 - samples/sec: 113.46 - lr: 0.003125
2022-11-06 22:53:34,176 ----------------------------------------------------------------------------------------------------
2022-11-06 22:53:34,176 EPOCH 123 done: loss 0.0724 - lr 0.003125
2022-11-06 22:53:43,848 Evaluating as a multi-label problem: False
2022-11-06 22:53:43,963 TEST : loss 0.07795599102973938 - f1-score (micro avg) 0.9795
2022-11-06 22:53:44,077 Epoch 123: reducing learning rate of group 0 to 1.5625e-03.
2022-11-06 22:53:44,077 BAD EPOCHS (no improvement): 4
2022-11-06 22:53:44,282 ----------------------------------------------------------------------------------------------------
2022-11-06 22:53:50,236 epoch 124 - iter 38/386 - loss 0.07453479 - samples/sec: 102.19 - lr: 0.001563
2022-11-06 22:53:55,507 epoch 124 - iter 76/386 - loss 0.07387122 - samples/sec: 115.43 - lr: 0.001563
2022-11-06 22:54:00,969 epoch 124 - iter 114/386 - loss 0.07305453 - samples/sec: 111.37 - lr: 0.001563
2022-11-06 22:54:07,025 epoch 124 - iter 152/386 - loss 0.07218372 - samples/sec: 100.45 - lr: 0.001563
2022-11-06 22:54:12,843 epoch 124 - iter 190/386 - loss 0.07196633 - samples/sec: 104.56 - lr: 0.001563
2022-11-06 22:54:18,154 epoch 124 - iter 228/386 - loss 0.07159715 - samples/sec: 114.53 - lr: 0.001563
2022-11-06 22:54:23,388 epoch 124 - iter 266/386 - loss 0.07198522 - samples/sec: 116.24 - lr: 0.001563
2022-11-06 22:54:29,116 epoch 124 - iter 304/386 - loss 0.07176734 - samples/sec: 106.21 - lr: 0.001563
2022-11-06 22:54:34,933 epoch 124 - iter 342/386 - loss 0.07178848 - samples/sec: 104.56 - lr: 0.001563
2022-11-06 22:54:40,217 epoch 124 - iter 380/386 - loss 0.07159231 - samples/sec: 115.14 - lr: 0.001563
2022-11-06 22:54:40,898 ----------------------------------------------------------------------------------------------------
2022-11-06 22:54:40,898 EPOCH 124 done: loss 0.0716 - lr 0.001563
2022-11-06 22:54:50,281 Evaluating as a multi-label problem: False
2022-11-06 22:54:50,395 TEST : loss 0.0781024768948555 - f1-score (micro avg) 0.9795
2022-11-06 22:54:50,508 BAD EPOCHS (no improvement): 1
2022-11-06 22:54:50,716 ----------------------------------------------------------------------------------------------------
2022-11-06 22:54:56,516 epoch 125 - iter 38/386 - loss 0.06603111 - samples/sec: 104.91 - lr: 0.001563
2022-11-06 22:55:02,113 epoch 125 - iter 76/386 - loss 0.07181574 - samples/sec: 108.70 - lr: 0.001563
2022-11-06 22:55:07,785 epoch 125 - iter 114/386 - loss 0.07095853 - samples/sec: 107.24 - lr: 0.001563
2022-11-06 22:55:13,685 epoch 125 - iter 152/386 - loss 0.06870419 - samples/sec: 103.11 - lr: 0.001563
2022-11-06 22:55:19,200 epoch 125 - iter 190/386 - loss 0.06850812 - samples/sec: 110.30 - lr: 0.001563
2022-11-06 22:55:24,478 epoch 125 - iter 228/386 - loss 0.06907321 - samples/sec: 115.27 - lr: 0.001563
2022-11-06 22:55:29,890 epoch 125 - iter 266/386 - loss 0.07039809 - samples/sec: 112.39 - lr: 0.001563
2022-11-06 22:55:35,805 epoch 125 - iter 304/386 - loss 0.07069692 - samples/sec: 102.84 - lr: 0.001563
2022-11-06 22:55:41,538 epoch 125 - iter 342/386 - loss 0.07081859 - samples/sec: 106.11 - lr: 0.001563
2022-11-06 22:55:46,898 epoch 125 - iter 380/386 - loss 0.07078155 - samples/sec: 113.51 - lr: 0.001563
2022-11-06 22:55:47,710 ----------------------------------------------------------------------------------------------------
2022-11-06 22:55:47,710 EPOCH 125 done: loss 0.0713 - lr 0.001563
2022-11-06 22:55:56,543 Evaluating as a multi-label problem: False
2022-11-06 22:55:56,659 TEST : loss 0.0782727301120758 - f1-score (micro avg) 0.9793
2022-11-06 22:55:56,772 BAD EPOCHS (no improvement): 2
2022-11-06 22:55:56,972 ----------------------------------------------------------------------------------------------------
2022-11-06 22:56:02,610 epoch 126 - iter 38/386 - loss 0.07739603 - samples/sec: 107.91 - lr: 0.001563
2022-11-06 22:56:08,430 epoch 126 - iter 76/386 - loss 0.07644368 - samples/sec: 104.53 - lr: 0.001563
2022-11-06 22:56:13,909 epoch 126 - iter 114/386 - loss 0.07472416 - samples/sec: 111.03 - lr: 0.001563
2022-11-06 22:56:19,773 epoch 126 - iter 152/386 - loss 0.07530828 - samples/sec: 103.74 - lr: 0.001563
2022-11-06 22:56:25,801 epoch 126 - iter 190/386 - loss 0.07487976 - samples/sec: 100.92 - lr: 0.001563
2022-11-06 22:56:31,148 epoch 126 - iter 228/386 - loss 0.07432315 - samples/sec: 113.77 - lr: 0.001563
2022-11-06 22:56:36,614 epoch 126 - iter 266/386 - loss 0.07569476 - samples/sec: 111.29 - lr: 0.001563
2022-11-06 22:56:42,041 epoch 126 - iter 304/386 - loss 0.07498686 - samples/sec: 112.11 - lr: 0.001563
2022-11-06 22:56:47,304 epoch 126 - iter 342/386 - loss 0.07493999 - samples/sec: 115.59 - lr: 0.001563
2022-11-06 22:56:53,067 epoch 126 - iter 380/386 - loss 0.07474151 - samples/sec: 105.56 - lr: 0.001563
2022-11-06 22:56:54,028 ----------------------------------------------------------------------------------------------------
2022-11-06 22:56:54,028 EPOCH 126 done: loss 0.0750 - lr 0.001563
2022-11-06 22:57:03,343 Evaluating as a multi-label problem: False
2022-11-06 22:57:03,458 TEST : loss 0.07818238437175751 - f1-score (micro avg) 0.9794
2022-11-06 22:57:03,571 BAD EPOCHS (no improvement): 3
2022-11-06 22:57:03,777 ----------------------------------------------------------------------------------------------------
2022-11-06 22:57:08,565 epoch 127 - iter 38/386 - loss 0.07566822 - samples/sec: 127.10 - lr: 0.001563
2022-11-06 22:57:13,988 epoch 127 - iter 76/386 - loss 0.07792866 - samples/sec: 112.19 - lr: 0.001563
2022-11-06 22:57:19,657 epoch 127 - iter 114/386 - loss 0.07648305 - samples/sec: 107.29 - lr: 0.001563
2022-11-06 22:57:25,348 epoch 127 - iter 152/386 - loss 0.07462793 - samples/sec: 106.89 - lr: 0.001563
2022-11-06 22:57:31,158 epoch 127 - iter 190/386 - loss 0.07373016 - samples/sec: 104.70 - lr: 0.001563
2022-11-06 22:57:36,849 epoch 127 - iter 228/386 - loss 0.07276951 - samples/sec: 106.90 - lr: 0.001563
2022-11-06 22:57:42,857 epoch 127 - iter 266/386 - loss 0.07281459 - samples/sec: 101.26 - lr: 0.001563
2022-11-06 22:57:48,385 epoch 127 - iter 304/386 - loss 0.07252182 - samples/sec: 110.05 - lr: 0.001563
2022-11-06 22:57:53,642 epoch 127 - iter 342/386 - loss 0.07222995 - samples/sec: 115.71 - lr: 0.001563
2022-11-06 22:57:59,416 epoch 127 - iter 380/386 - loss 0.07296185 - samples/sec: 105.37 - lr: 0.001563
2022-11-06 22:58:00,242 ----------------------------------------------------------------------------------------------------
2022-11-06 22:58:00,242 EPOCH 127 done: loss 0.0729 - lr 0.001563
2022-11-06 22:58:09,831 Evaluating as a multi-label problem: False
2022-11-06 22:58:09,945 TEST : loss 0.07828076183795929 - f1-score (micro avg) 0.9793
2022-11-06 22:58:10,058 Epoch 127: reducing learning rate of group 0 to 7.8125e-04.
2022-11-06 22:58:10,059 BAD EPOCHS (no improvement): 4
2022-11-06 22:58:10,266 ----------------------------------------------------------------------------------------------------
2022-11-06 22:58:15,464 epoch 128 - iter 38/386 - loss 0.06787719 - samples/sec: 117.08 - lr: 0.000781
2022-11-06 22:58:20,646 epoch 128 - iter 76/386 - loss 0.06892049 - samples/sec: 117.40 - lr: 0.000781
2022-11-06 22:58:26,003 epoch 128 - iter 114/386 - loss 0.06771139 - samples/sec: 113.54 - lr: 0.000781
2022-11-06 22:58:32,117 epoch 128 - iter 152/386 - loss 0.06817545 - samples/sec: 99.51 - lr: 0.000781
2022-11-06 22:58:37,740 epoch 128 - iter 190/386 - loss 0.06973778 - samples/sec: 108.18 - lr: 0.000781
2022-11-06 22:58:43,253 epoch 128 - iter 228/386 - loss 0.06980534 - samples/sec: 110.34 - lr: 0.000781
2022-11-06 22:58:49,035 epoch 128 - iter 266/386 - loss 0.07035821 - samples/sec: 105.22 - lr: 0.000781
2022-11-06 22:58:54,939 epoch 128 - iter 304/386 - loss 0.07045187 - samples/sec: 103.03 - lr: 0.000781
2022-11-06 22:59:00,489 epoch 128 - iter 342/386 - loss 0.07072151 - samples/sec: 109.62 - lr: 0.000781
2022-11-06 22:59:05,643 epoch 128 - iter 380/386 - loss 0.07044890 - samples/sec: 118.04 - lr: 0.000781
2022-11-06 22:59:06,646 ----------------------------------------------------------------------------------------------------
2022-11-06 22:59:06,646 EPOCH 128 done: loss 0.0703 - lr 0.000781
2022-11-06 22:59:18,654 Evaluating as a multi-label problem: False
2022-11-06 22:59:18,768 TEST : loss 0.07819060236215591 - f1-score (micro avg) 0.9793
2022-11-06 22:59:18,881 BAD EPOCHS (no improvement): 0
2022-11-06 22:59:19,088 ----------------------------------------------------------------------------------------------------
2022-11-06 22:59:24,916 epoch 129 - iter 38/386 - loss 0.07275281 - samples/sec: 104.40 - lr: 0.000781
2022-11-06 22:59:29,978 epoch 129 - iter 76/386 - loss 0.07363556 - samples/sec: 120.18 - lr: 0.000781
2022-11-06 22:59:35,388 epoch 129 - iter 114/386 - loss 0.07430612 - samples/sec: 112.45 - lr: 0.000781
2022-11-06 22:59:40,922 epoch 129 - iter 152/386 - loss 0.07419195 - samples/sec: 109.93 - lr: 0.000781
2022-11-06 22:59:46,863 epoch 129 - iter 190/386 - loss 0.07384624 - samples/sec: 102.40 - lr: 0.000781
2022-11-06 22:59:52,430 epoch 129 - iter 228/386 - loss 0.07418418 - samples/sec: 109.27 - lr: 0.000781
2022-11-06 22:59:58,146 epoch 129 - iter 266/386 - loss 0.07356684 - samples/sec: 106.43 - lr: 0.000781
2022-11-06 23:00:03,937 epoch 129 - iter 304/386 - loss 0.07393466 - samples/sec: 105.05 - lr: 0.000781
2022-11-06 23:00:09,128 epoch 129 - iter 342/386 - loss 0.07341780 - samples/sec: 117.19 - lr: 0.000781
2022-11-06 23:00:14,745 epoch 129 - iter 380/386 - loss 0.07290983 - samples/sec: 108.30 - lr: 0.000781
2022-11-06 23:00:15,530 ----------------------------------------------------------------------------------------------------
2022-11-06 23:00:15,530 EPOCH 129 done: loss 0.0730 - lr 0.000781
2022-11-06 23:00:25,171 Evaluating as a multi-label problem: False
2022-11-06 23:00:25,286 TEST : loss 0.07834754884243011 - f1-score (micro avg) 0.9793
2022-11-06 23:00:25,398 BAD EPOCHS (no improvement): 1
2022-11-06 23:00:25,604 ----------------------------------------------------------------------------------------------------
2022-11-06 23:00:31,190 epoch 130 - iter 38/386 - loss 0.07424163 - samples/sec: 108.93 - lr: 0.000781
2022-11-06 23:00:37,401 epoch 130 - iter 76/386 - loss 0.07589137 - samples/sec: 97.93 - lr: 0.000781
2022-11-06 23:00:42,682 epoch 130 - iter 114/386 - loss 0.07505534 - samples/sec: 115.21 - lr: 0.000781
2022-11-06 23:00:48,121 epoch 130 - iter 152/386 - loss 0.07513815 - samples/sec: 111.85 - lr: 0.000781
2022-11-06 23:00:53,956 epoch 130 - iter 190/386 - loss 0.07341735 - samples/sec: 104.26 - lr: 0.000781
2022-11-06 23:00:59,379 epoch 130 - iter 228/386 - loss 0.07319684 - samples/sec: 112.18 - lr: 0.000781
2022-11-06 23:01:04,834 epoch 130 - iter 266/386 - loss 0.07297365 - samples/sec: 111.50 - lr: 0.000781
2022-11-06 23:01:10,560 epoch 130 - iter 304/386 - loss 0.07221610 - samples/sec: 106.24 - lr: 0.000781
2022-11-06 23:01:16,153 epoch 130 - iter 342/386 - loss 0.07171895 - samples/sec: 108.77 - lr: 0.000781
2022-11-06 23:01:21,459 epoch 130 - iter 380/386 - loss 0.07161407 - samples/sec: 114.65 - lr: 0.000781
2022-11-06 23:01:22,369 ----------------------------------------------------------------------------------------------------
2022-11-06 23:01:22,369 EPOCH 130 done: loss 0.0716 - lr 0.000781
2022-11-06 23:01:31,982 Evaluating as a multi-label problem: False
2022-11-06 23:01:32,097 TEST : loss 0.07843586802482605 - f1-score (micro avg) 0.9793
2022-11-06 23:01:32,209 BAD EPOCHS (no improvement): 2
2022-11-06 23:01:32,410 ----------------------------------------------------------------------------------------------------
2022-11-06 23:01:38,329 epoch 131 - iter 38/386 - loss 0.06891126 - samples/sec: 102.79 - lr: 0.000781
2022-11-06 23:01:44,462 epoch 131 - iter 76/386 - loss 0.07240625 - samples/sec: 99.19 - lr: 0.000781
2022-11-06 23:01:49,997 epoch 131 - iter 114/386 - loss 0.06907713 - samples/sec: 109.91 - lr: 0.000781
2022-11-06 23:01:55,666 epoch 131 - iter 152/386 - loss 0.06824256 - samples/sec: 107.30 - lr: 0.000781
2022-11-06 23:02:00,565 epoch 131 - iter 190/386 - loss 0.06781263 - samples/sec: 124.19 - lr: 0.000781
2022-11-06 23:02:06,017 epoch 131 - iter 228/386 - loss 0.06909911 - samples/sec: 111.59 - lr: 0.000781
2022-11-06 23:02:11,619 epoch 131 - iter 266/386 - loss 0.06976854 - samples/sec: 108.59 - lr: 0.000781
2022-11-06 23:02:17,071 epoch 131 - iter 304/386 - loss 0.07020506 - samples/sec: 111.58 - lr: 0.000781
2022-11-06 23:02:22,544 epoch 131 - iter 342/386 - loss 0.07057120 - samples/sec: 111.14 - lr: 0.000781
2022-11-06 23:02:28,026 epoch 131 - iter 380/386 - loss 0.07018851 - samples/sec: 110.98 - lr: 0.000781
2022-11-06 23:02:28,830 ----------------------------------------------------------------------------------------------------
2022-11-06 23:02:28,830 EPOCH 131 done: loss 0.0703 - lr 0.000781
2022-11-06 23:02:38,525 Evaluating as a multi-label problem: False
2022-11-06 23:02:38,640 TEST : loss 0.07843144983053207 - f1-score (micro avg) 0.9793
2022-11-06 23:02:38,753 BAD EPOCHS (no improvement): 3
2022-11-06 23:02:38,953 ----------------------------------------------------------------------------------------------------
2022-11-06 23:02:44,737 epoch 132 - iter 38/386 - loss 0.06893361 - samples/sec: 105.20 - lr: 0.000781
2022-11-06 23:02:50,682 epoch 132 - iter 76/386 - loss 0.07274241 - samples/sec: 102.32 - lr: 0.000781
2022-11-06 23:02:56,233 epoch 132 - iter 114/386 - loss 0.07379979 - samples/sec: 109.59 - lr: 0.000781
2022-11-06 23:03:01,739 epoch 132 - iter 152/386 - loss 0.07395336 - samples/sec: 110.49 - lr: 0.000781
2022-11-06 23:03:07,196 epoch 132 - iter 190/386 - loss 0.07211236 - samples/sec: 111.49 - lr: 0.000781
2022-11-06 23:03:12,542 epoch 132 - iter 228/386 - loss 0.07159589 - samples/sec: 113.79 - lr: 0.000781
2022-11-06 23:03:18,250 epoch 132 - iter 266/386 - loss 0.07132845 - samples/sec: 106.58 - lr: 0.000781
2022-11-06 23:03:23,643 epoch 132 - iter 304/386 - loss 0.07088838 - samples/sec: 112.80 - lr: 0.000781
2022-11-06 23:03:29,147 epoch 132 - iter 342/386 - loss 0.07061821 - samples/sec: 110.53 - lr: 0.000781
2022-11-06 23:03:34,744 epoch 132 - iter 380/386 - loss 0.07135944 - samples/sec: 108.68 - lr: 0.000781
2022-11-06 23:03:35,383 ----------------------------------------------------------------------------------------------------
2022-11-06 23:03:35,383 EPOCH 132 done: loss 0.0712 - lr 0.000781
2022-11-06 23:03:45,044 Evaluating as a multi-label problem: False
2022-11-06 23:03:45,159 TEST : loss 0.07832777500152588 - f1-score (micro avg) 0.9793
2022-11-06 23:03:45,272 Epoch 132: reducing learning rate of group 0 to 3.9063e-04.
2022-11-06 23:03:45,273 BAD EPOCHS (no improvement): 4
2022-11-06 23:03:45,481 ----------------------------------------------------------------------------------------------------
2022-11-06 23:03:51,549 epoch 133 - iter 38/386 - loss 0.07133258 - samples/sec: 100.26 - lr: 0.000391
2022-11-06 23:03:57,506 epoch 133 - iter 76/386 - loss 0.07133234 - samples/sec: 102.12 - lr: 0.000391
2022-11-06 23:04:03,027 epoch 133 - iter 114/386 - loss 0.07265932 - samples/sec: 110.20 - lr: 0.000391
2022-11-06 23:04:08,538 epoch 133 - iter 152/386 - loss 0.07271682 - samples/sec: 110.38 - lr: 0.000391
2022-11-06 23:04:14,070 epoch 133 - iter 190/386 - loss 0.07173817 - samples/sec: 109.97 - lr: 0.000391
2022-11-06 23:04:19,366 epoch 133 - iter 228/386 - loss 0.07111288 - samples/sec: 114.87 - lr: 0.000391
2022-11-06 23:04:24,572 epoch 133 - iter 266/386 - loss 0.07163658 - samples/sec: 116.86 - lr: 0.000391
2022-11-06 23:04:30,386 epoch 133 - iter 304/386 - loss 0.07191561 - samples/sec: 104.63 - lr: 0.000391
2022-11-06 23:04:35,605 epoch 133 - iter 342/386 - loss 0.07226248 - samples/sec: 116.57 - lr: 0.000391
2022-11-06 23:04:41,353 epoch 133 - iter 380/386 - loss 0.07225441 - samples/sec: 105.83 - lr: 0.000391
2022-11-06 23:04:42,215 ----------------------------------------------------------------------------------------------------
2022-11-06 23:04:42,215 EPOCH 133 done: loss 0.0724 - lr 0.000391
2022-11-06 23:04:51,832 Evaluating as a multi-label problem: False
2022-11-06 23:04:51,947 TEST : loss 0.07831750810146332 - f1-score (micro avg) 0.9792
2022-11-06 23:04:52,060 BAD EPOCHS (no improvement): 1
2022-11-06 23:04:52,265 ----------------------------------------------------------------------------------------------------
2022-11-06 23:04:57,502 epoch 134 - iter 38/386 - loss 0.07272210 - samples/sec: 116.20 - lr: 0.000391
2022-11-06 23:05:03,859 epoch 134 - iter 76/386 - loss 0.07188465 - samples/sec: 95.69 - lr: 0.000391
2022-11-06 23:05:09,578 epoch 134 - iter 114/386 - loss 0.07221689 - samples/sec: 106.38 - lr: 0.000391
2022-11-06 23:05:14,935 epoch 134 - iter 152/386 - loss 0.07164910 - samples/sec: 113.54 - lr: 0.000391
2022-11-06 23:05:20,362 epoch 134 - iter 190/386 - loss 0.07186012 - samples/sec: 112.11 - lr: 0.000391
2022-11-06 23:05:25,922 epoch 134 - iter 228/386 - loss 0.07095586 - samples/sec: 109.41 - lr: 0.000391
2022-11-06 23:05:31,321 epoch 134 - iter 266/386 - loss 0.07005834 - samples/sec: 112.67 - lr: 0.000391
2022-11-06 23:05:36,532 epoch 134 - iter 304/386 - loss 0.06974718 - samples/sec: 116.76 - lr: 0.000391
2022-11-06 23:05:41,664 epoch 134 - iter 342/386 - loss 0.07038373 - samples/sec: 118.55 - lr: 0.000391
2022-11-06 23:05:47,300 epoch 134 - iter 380/386 - loss 0.07138653 - samples/sec: 107.94 - lr: 0.000391
2022-11-06 23:05:48,096 ----------------------------------------------------------------------------------------------------
2022-11-06 23:05:48,096 EPOCH 134 done: loss 0.0713 - lr 0.000391
2022-11-06 23:05:57,677 Evaluating as a multi-label problem: False
2022-11-06 23:05:57,792 TEST : loss 0.07832799106836319 - f1-score (micro avg) 0.9793
2022-11-06 23:05:57,905 BAD EPOCHS (no improvement): 2
2022-11-06 23:05:58,112 ----------------------------------------------------------------------------------------------------
2022-11-06 23:06:03,825 epoch 135 - iter 38/386 - loss 0.07625152 - samples/sec: 106.50 - lr: 0.000391
2022-11-06 23:06:09,420 epoch 135 - iter 76/386 - loss 0.07596756 - samples/sec: 108.74 - lr: 0.000391
2022-11-06 23:06:14,938 epoch 135 - iter 114/386 - loss 0.07537203 - samples/sec: 110.23 - lr: 0.000391
2022-11-06 23:06:20,851 epoch 135 - iter 152/386 - loss 0.07465357 - samples/sec: 102.88 - lr: 0.000391
2022-11-06 23:06:26,437 epoch 135 - iter 190/386 - loss 0.07282803 - samples/sec: 108.92 - lr: 0.000391
2022-11-06 23:06:32,254 epoch 135 - iter 228/386 - loss 0.07223191 - samples/sec: 104.57 - lr: 0.000391
2022-11-06 23:06:37,417 epoch 135 - iter 266/386 - loss 0.07261668 - samples/sec: 117.82 - lr: 0.000391
2022-11-06 23:06:43,327 epoch 135 - iter 304/386 - loss 0.07227402 - samples/sec: 102.93 - lr: 0.000391
2022-11-06 23:06:48,605 epoch 135 - iter 342/386 - loss 0.07149145 - samples/sec: 115.28 - lr: 0.000391
2022-11-06 23:06:53,388 epoch 135 - iter 380/386 - loss 0.07197639 - samples/sec: 127.19 - lr: 0.000391
2022-11-06 23:06:54,256 ----------------------------------------------------------------------------------------------------
2022-11-06 23:06:54,256 EPOCH 135 done: loss 0.0719 - lr 0.000391
2022-11-06 23:07:03,834 Evaluating as a multi-label problem: False
2022-11-06 23:07:03,950 TEST : loss 0.07829069346189499 - f1-score (micro avg) 0.9793
2022-11-06 23:07:04,062 BAD EPOCHS (no improvement): 3
2022-11-06 23:07:04,267 ----------------------------------------------------------------------------------------------------
2022-11-06 23:07:09,727 epoch 136 - iter 38/386 - loss 0.07134941 - samples/sec: 111.46 - lr: 0.000391
2022-11-06 23:07:15,291 epoch 136 - iter 76/386 - loss 0.07170014 - samples/sec: 109.33 - lr: 0.000391
2022-11-06 23:07:21,563 epoch 136 - iter 114/386 - loss 0.07030659 - samples/sec: 96.99 - lr: 0.000391
2022-11-06 23:07:27,126 epoch 136 - iter 152/386 - loss 0.07200756 - samples/sec: 109.34 - lr: 0.000391
2022-11-06 23:07:32,758 epoch 136 - iter 190/386 - loss 0.07243107 - samples/sec: 108.02 - lr: 0.000391
2022-11-06 23:07:38,121 epoch 136 - iter 228/386 - loss 0.07266476 - samples/sec: 113.44 - lr: 0.000391
2022-11-06 23:07:43,764 epoch 136 - iter 266/386 - loss 0.07246653 - samples/sec: 107.81 - lr: 0.000391
2022-11-06 23:07:49,451 epoch 136 - iter 304/386 - loss 0.07247297 - samples/sec: 106.96 - lr: 0.000391
2022-11-06 23:07:55,084 epoch 136 - iter 342/386 - loss 0.07289621 - samples/sec: 107.99 - lr: 0.000391
2022-11-06 23:08:00,375 epoch 136 - iter 380/386 - loss 0.07291326 - samples/sec: 115.00 - lr: 0.000391
2022-11-06 23:08:01,172 ----------------------------------------------------------------------------------------------------
2022-11-06 23:08:01,172 EPOCH 136 done: loss 0.0726 - lr 0.000391
2022-11-06 23:08:10,258 Evaluating as a multi-label problem: False
2022-11-06 23:08:10,372 TEST : loss 0.07828548550605774 - f1-score (micro avg) 0.9793
2022-11-06 23:08:10,485 Epoch 136: reducing learning rate of group 0 to 1.9531e-04.
2022-11-06 23:08:10,486 BAD EPOCHS (no improvement): 4
2022-11-06 23:08:10,684 ----------------------------------------------------------------------------------------------------
2022-11-06 23:08:16,120 epoch 137 - iter 38/386 - loss 0.07268823 - samples/sec: 111.92 - lr: 0.000195
2022-11-06 23:08:21,492 epoch 137 - iter 76/386 - loss 0.07101301 - samples/sec: 113.26 - lr: 0.000195
2022-11-06 23:08:26,957 epoch 137 - iter 114/386 - loss 0.06977042 - samples/sec: 111.31 - lr: 0.000195
2022-11-06 23:08:32,736 epoch 137 - iter 152/386 - loss 0.07180263 - samples/sec: 105.27 - lr: 0.000195
2022-11-06 23:08:38,291 epoch 137 - iter 190/386 - loss 0.07341440 - samples/sec: 109.51 - lr: 0.000195
2022-11-06 23:08:43,944 epoch 137 - iter 228/386 - loss 0.07287976 - samples/sec: 107.61 - lr: 0.000195
2022-11-06 23:08:49,582 epoch 137 - iter 266/386 - loss 0.07302229 - samples/sec: 107.91 - lr: 0.000195
2022-11-06 23:08:55,206 epoch 137 - iter 304/386 - loss 0.07313939 - samples/sec: 108.17 - lr: 0.000195
2022-11-06 23:09:01,352 epoch 137 - iter 342/386 - loss 0.07256527 - samples/sec: 98.97 - lr: 0.000195
2022-11-06 23:09:07,148 epoch 137 - iter 380/386 - loss 0.07234924 - samples/sec: 104.95 - lr: 0.000195
2022-11-06 23:09:07,904 ----------------------------------------------------------------------------------------------------
2022-11-06 23:09:07,904 EPOCH 137 done: loss 0.0723 - lr 0.000195
2022-11-06 23:09:17,011 Evaluating as a multi-label problem: False
2022-11-06 23:09:17,126 TEST : loss 0.07833118736743927 - f1-score (micro avg) 0.9793
2022-11-06 23:09:17,239 BAD EPOCHS (no improvement): 1
2022-11-06 23:09:17,437 ----------------------------------------------------------------------------------------------------
2022-11-06 23:09:23,143 epoch 138 - iter 38/386 - loss 0.07121465 - samples/sec: 106.64 - lr: 0.000195
2022-11-06 23:09:28,758 epoch 138 - iter 76/386 - loss 0.07053113 - samples/sec: 108.34 - lr: 0.000195
2022-11-06 23:09:34,135 epoch 138 - iter 114/386 - loss 0.07217032 - samples/sec: 113.14 - lr: 0.000195
2022-11-06 23:09:39,673 epoch 138 - iter 152/386 - loss 0.07049193 - samples/sec: 109.84 - lr: 0.000195
2022-11-06 23:09:45,545 epoch 138 - iter 190/386 - loss 0.07228868 - samples/sec: 103.61 - lr: 0.000195
2022-11-06 23:09:51,097 epoch 138 - iter 228/386 - loss 0.07213669 - samples/sec: 109.56 - lr: 0.000195
2022-11-06 23:09:56,773 epoch 138 - iter 266/386 - loss 0.07246026 - samples/sec: 107.17 - lr: 0.000195
2022-11-06 23:10:02,194 epoch 138 - iter 304/386 - loss 0.07150123 - samples/sec: 112.22 - lr: 0.000195
2022-11-06 23:10:07,597 epoch 138 - iter 342/386 - loss 0.07211538 - samples/sec: 112.60 - lr: 0.000195
2022-11-06 23:10:13,032 epoch 138 - iter 380/386 - loss 0.07293704 - samples/sec: 111.93 - lr: 0.000195
2022-11-06 23:10:13,786 ----------------------------------------------------------------------------------------------------
2022-11-06 23:10:13,786 EPOCH 138 done: loss 0.0726 - lr 0.000195
2022-11-06 23:10:25,774 Evaluating as a multi-label problem: False
2022-11-06 23:10:25,889 TEST : loss 0.07832229882478714 - f1-score (micro avg) 0.9792
2022-11-06 23:10:26,002 BAD EPOCHS (no improvement): 2
2022-11-06 23:10:26,210 ----------------------------------------------------------------------------------------------------
2022-11-06 23:10:31,431 epoch 139 - iter 38/386 - loss 0.07244392 - samples/sec: 116.55 - lr: 0.000195
2022-11-06 23:10:36,809 epoch 139 - iter 76/386 - loss 0.06971478 - samples/sec: 113.11 - lr: 0.000195
2022-11-06 23:10:42,330 epoch 139 - iter 114/386 - loss 0.06955680 - samples/sec: 110.18 - lr: 0.000195
2022-11-06 23:10:47,907 epoch 139 - iter 152/386 - loss 0.07193651 - samples/sec: 109.09 - lr: 0.000195
2022-11-06 23:10:53,595 epoch 139 - iter 190/386 - loss 0.07346354 - samples/sec: 106.96 - lr: 0.000195
2022-11-06 23:10:59,145 epoch 139 - iter 228/386 - loss 0.07351255 - samples/sec: 109.60 - lr: 0.000195
2022-11-06 23:11:04,574 epoch 139 - iter 266/386 - loss 0.07280062 - samples/sec: 112.06 - lr: 0.000195
2022-11-06 23:11:10,865 epoch 139 - iter 304/386 - loss 0.07154111 - samples/sec: 96.69 - lr: 0.000195
2022-11-06 23:11:16,382 epoch 139 - iter 342/386 - loss 0.07190465 - samples/sec: 110.27 - lr: 0.000195
2022-11-06 23:11:21,840 epoch 139 - iter 380/386 - loss 0.07176361 - samples/sec: 111.46 - lr: 0.000195
2022-11-06 23:11:22,626 ----------------------------------------------------------------------------------------------------
2022-11-06 23:11:22,626 EPOCH 139 done: loss 0.0717 - lr 0.000195
2022-11-06 23:11:32,319 Evaluating as a multi-label problem: False
2022-11-06 23:11:32,434 TEST : loss 0.07830418646335602 - f1-score (micro avg) 0.9792
2022-11-06 23:11:32,547 BAD EPOCHS (no improvement): 3
2022-11-06 23:11:32,756 ----------------------------------------------------------------------------------------------------
2022-11-06 23:11:38,308 epoch 140 - iter 38/386 - loss 0.07596932 - samples/sec: 109.59 - lr: 0.000195
2022-11-06 23:11:43,612 epoch 140 - iter 76/386 - loss 0.07530102 - samples/sec: 114.70 - lr: 0.000195
2022-11-06 23:11:48,989 epoch 140 - iter 114/386 - loss 0.07424606 - samples/sec: 113.14 - lr: 0.000195
2022-11-06 23:11:54,700 epoch 140 - iter 152/386 - loss 0.07306686 - samples/sec: 106.52 - lr: 0.000195
2022-11-06 23:12:00,523 epoch 140 - iter 190/386 - loss 0.07428603 - samples/sec: 104.47 - lr: 0.000195
2022-11-06 23:12:05,472 epoch 140 - iter 228/386 - loss 0.07433713 - samples/sec: 122.93 - lr: 0.000195
2022-11-06 23:12:11,266 epoch 140 - iter 266/386 - loss 0.07343813 - samples/sec: 105.00 - lr: 0.000195
2022-11-06 23:12:17,102 epoch 140 - iter 304/386 - loss 0.07382207 - samples/sec: 104.24 - lr: 0.000195
2022-11-06 23:12:22,701 epoch 140 - iter 342/386 - loss 0.07336550 - samples/sec: 108.65 - lr: 0.000195
2022-11-06 23:12:28,060 epoch 140 - iter 380/386 - loss 0.07330513 - samples/sec: 113.51 - lr: 0.000195
2022-11-06 23:12:28,798 ----------------------------------------------------------------------------------------------------
2022-11-06 23:12:28,798 EPOCH 140 done: loss 0.0733 - lr 0.000195
2022-11-06 23:12:38,412 Evaluating as a multi-label problem: False
2022-11-06 23:12:38,528 TEST : loss 0.07832666486501694 - f1-score (micro avg) 0.9793
2022-11-06 23:12:38,641 Epoch 140: reducing learning rate of group 0 to 9.7656e-05.
2022-11-06 23:12:38,641 BAD EPOCHS (no improvement): 4
2022-11-06 23:12:38,846 ----------------------------------------------------------------------------------------------------
2022-11-06 23:12:38,846 ----------------------------------------------------------------------------------------------------
2022-11-06 23:12:38,846 learning rate too small - quitting training!
2022-11-06 23:12:38,846 ----------------------------------------------------------------------------------------------------
2022-11-06 23:12:38,988 ----------------------------------------------------------------------------------------------------
2022-11-06 23:12:38,988 Testing using last state of model ...
2022-11-06 23:12:48,641 Evaluating as a multi-label problem: False
2022-11-06 23:12:48,755 0.9793 0.9793 0.9793 0.9793
2022-11-06 23:12:48,755
Results:
- F-score (micro) 0.9793
- F-score (macro) 0.9275
- Accuracy 0.9793
By class:
precision recall f1-score support
NOUN 0.9857 0.9851 0.9854 4549
PUNCT 0.9984 1.0000 0.9992 3097
ADJ 0.9772 0.9852 0.9812 1959
ADP 0.9956 0.9968 0.9962 1584
VERB 0.9891 0.9910 0.9900 1552
ADV 0.9630 0.9118 0.9367 714
CCONJ 0.9685 0.9746 0.9715 630
PROPN 0.9279 0.9472 0.9375 625
DET 0.9729 0.9698 0.9713 629
PRON 0.9706 0.9631 0.9669 515
PART 0.9235 0.8693 0.8956 375
NUM 0.9722 0.9804 0.9763 357
SCONJ 0.8768 0.9577 0.9154 260
AUX 0.8906 0.9500 0.9194 120
X 0.9833 0.9593 0.9712 123
SYM 1.0000 0.7059 0.8276 17
INTJ 0.5556 0.5000 0.5263 10
accuracy 0.9793 17116
macro avg 0.9383 0.9204 0.9275 17116
weighted avg 0.9794 0.9793 0.9792 17116
2022-11-06 23:12:48,755 ----------------------------------------------------------------------------------------------------
|