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Experiment 1

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John Martinsson edited this page Nov 28, 2016 · 3 revisions

Results

Mon, 28 Nov 2016 18:44:11 +0000
mini-batch:  0 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 12s - loss: 0.2765 - fbeta_score: 0.0153 - val_loss: 0.2321 - val_fbeta_score: 0.0000e+00
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.2728 - fbeta_score: 0.0135 - val_loss: 0.2360 - val_fbeta_score: 0.0233
Epoch 3/25
1000/1000 [==============================] - 11s - loss: 0.2619 - fbeta_score: 0.0052 - val_loss: 0.2329 - val_fbeta_score: 0.0065
Epoch 4/25
1000/1000 [==============================] - 11s - loss: 0.2570 - fbeta_score: 0.0055 - val_loss: 0.2669 - val_fbeta_score: 0.0155
Epoch 5/25
1000/1000 [==============================] - 11s - loss: 0.2551 - fbeta_score: 0.0058 - val_loss: 0.2370 - val_fbeta_score: 0.0094
Epoch 6/25
1000/1000 [==============================] - 12s - loss: 0.2510 - fbeta_score: 0.0013 - val_loss: 0.2377 - val_fbeta_score: 0.0048
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.2496 - fbeta_score: 0.0015 - val_loss: 0.2370 - val_fbeta_score: 0.0053
Epoch 8/25
1000/1000 [==============================] - 12s - loss: 0.2478 - fbeta_score: 0.0000e+00 - val_loss: 0.2336 - val_fbeta_score: 0.0081
Epoch 9/25
1000/1000 [==============================] - 12s - loss: 0.2473 - fbeta_score: 0.0015 - val_loss: 0.2302 - val_fbeta_score: 0.0103
Epoch 10/25
1000/1000 [==============================] - 11s - loss: 0.2456 - fbeta_score: 0.0059 - val_loss: 0.2255 - val_fbeta_score: 0.0023
Epoch 11/25
1000/1000 [==============================] - 12s - loss: 0.2397 - fbeta_score: 0.0089 - val_loss: 0.2317 - val_fbeta_score: 0.0142
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.2377 - fbeta_score: 0.0136 - val_loss: 0.2330 - val_fbeta_score: 0.0191
Epoch 13/25
1000/1000 [==============================] - 12s - loss: 0.2350 - fbeta_score: 0.0216 - val_loss: 0.2344 - val_fbeta_score: 0.0216
Epoch 14/25
1000/1000 [==============================] - 12s - loss: 0.2317 - fbeta_score: 0.0270 - val_loss: 0.2320 - val_fbeta_score: 0.0159
Epoch 15/25
1000/1000 [==============================] - 12s - loss: 0.2301 - fbeta_score: 0.0365 - val_loss: 0.2276 - val_fbeta_score: 0.0231
Epoch 16/25
1000/1000 [==============================] - 11s - loss: 0.2282 - fbeta_score: 0.0339 - val_loss: 0.2348 - val_fbeta_score: 0.0293
Epoch 17/25
1000/1000 [==============================] - 11s - loss: 0.2248 - fbeta_score: 0.0400 - val_loss: 0.2354 - val_fbeta_score: 0.0536
Epoch 18/25
1000/1000 [==============================] - 11s - loss: 0.2210 - fbeta_score: 0.0531 - val_loss: 0.2442 - val_fbeta_score: 0.0510
Epoch 19/25
1000/1000 [==============================] - 12s - loss: 0.2178 - fbeta_score: 0.0634 - val_loss: 0.2300 - val_fbeta_score: 0.0204
Epoch 20/25
1000/1000 [==============================] - 11s - loss: 0.2123 - fbeta_score: 0.0601 - val_loss: 0.2529 - val_fbeta_score: 0.0675
Epoch 21/25
1000/1000 [==============================] - 11s - loss: 0.2114 - fbeta_score: 0.0789 - val_loss: 0.2550 - val_fbeta_score: 0.0800
Epoch 22/25
1000/1000 [==============================] - 11s - loss: 0.2066 - fbeta_score: 0.0851 - val_loss: 0.2497 - val_fbeta_score: 0.0453
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.2054 - fbeta_score: 0.0991 - val_loss: 0.2654 - val_fbeta_score: 0.0476
Epoch 24/25
1000/1000 [==============================] - 11s - loss: 0.2036 - fbeta_score: 0.1110 - val_loss: 0.2530 - val_fbeta_score: 0.0442
Epoch 25/25
1000/1000 [==============================] - 12s - loss: 0.1971 - fbeta_score: 0.1180 - val_loss: 0.2647 - val_fbeta_score: 0.0274
mini-batch:  1 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.2344 - fbeta_score: 0.0335 - val_loss: 0.2370 - val_fbeta_score: 0.0479
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.2258 - fbeta_score: 0.0425 - val_loss: 0.2471 - val_fbeta_score: 0.0364
Epoch 3/25
1000/1000 [==============================] - 11s - loss: 0.2182 - fbeta_score: 0.0741 - val_loss: 0.2544 - val_fbeta_score: 0.0491
Epoch 4/25
1000/1000 [==============================] - 12s - loss: 0.2145 - fbeta_score: 0.0868 - val_loss: 0.2900 - val_fbeta_score: 0.1045
Epoch 5/25
1000/1000 [==============================] - 12s - loss: 0.2095 - fbeta_score: 0.1025 - val_loss: 0.2611 - val_fbeta_score: 0.0982
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.2067 - fbeta_score: 0.1093 - val_loss: 0.2774 - val_fbeta_score: 0.1367
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.2015 - fbeta_score: 0.1136 - val_loss: 0.2811 - val_fbeta_score: 0.1242
Epoch 8/25
1000/1000 [==============================] - 12s - loss: 0.1982 - fbeta_score: 0.1512 - val_loss: 0.2780 - val_fbeta_score: 0.1254
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1954 - fbeta_score: 0.1503 - val_loss: 0.2680 - val_fbeta_score: 0.1161
Epoch 10/25
1000/1000 [==============================] - 11s - loss: 0.1913 - fbeta_score: 0.1654 - val_loss: 0.2711 - val_fbeta_score: 0.0816
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1896 - fbeta_score: 0.1773 - val_loss: 0.2738 - val_fbeta_score: 0.1189
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.1850 - fbeta_score: 0.1665 - val_loss: 0.2649 - val_fbeta_score: 0.1454
Epoch 13/25
1000/1000 [==============================] - 11s - loss: 0.1807 - fbeta_score: 0.2183 - val_loss: 0.2796 - val_fbeta_score: 0.1077
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1761 - fbeta_score: 0.2250 - val_loss: 0.2674 - val_fbeta_score: 0.1299
Epoch 15/25
1000/1000 [==============================] - 12s - loss: 0.1758 - fbeta_score: 0.2558 - val_loss: 0.2965 - val_fbeta_score: 0.0964
Epoch 16/25
1000/1000 [==============================] - 11s - loss: 0.1716 - fbeta_score: 0.2391 - val_loss: 0.2694 - val_fbeta_score: 0.1135
Epoch 17/25
1000/1000 [==============================] - 12s - loss: 0.1688 - fbeta_score: 0.2596 - val_loss: 0.2919 - val_fbeta_score: 0.1397
Epoch 18/25
1000/1000 [==============================] - 11s - loss: 0.1667 - fbeta_score: 0.2576 - val_loss: 0.2946 - val_fbeta_score: 0.1716
Epoch 19/25
1000/1000 [==============================] - 12s - loss: 0.1671 - fbeta_score: 0.2766 - val_loss: 0.3378 - val_fbeta_score: 0.1545
Epoch 20/25
1000/1000 [==============================] - 12s - loss: 0.1610 - fbeta_score: 0.2872 - val_loss: 0.2648 - val_fbeta_score: 0.1156
Epoch 21/25
1000/1000 [==============================] - 12s - loss: 0.1581 - fbeta_score: 0.2763 - val_loss: 0.3077 - val_fbeta_score: 0.1345
Epoch 22/25
1000/1000 [==============================] - 11s - loss: 0.1587 - fbeta_score: 0.2905 - val_loss: 0.5699 - val_fbeta_score: 0.0654
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.1553 - fbeta_score: 0.3050 - val_loss: 0.2729 - val_fbeta_score: 0.1316
Epoch 24/25
1000/1000 [==============================] - 12s - loss: 0.1539 - fbeta_score: 0.3071 - val_loss: 0.2683 - val_fbeta_score: 0.1467
Epoch 25/25
1000/1000 [==============================] - 11s - loss: 0.1508 - fbeta_score: 0.3340 - val_loss: 0.2682 - val_fbeta_score: 0.1371
mini-batch:  2 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.2183 - fbeta_score: 0.1182 - val_loss: 0.2663 - val_fbeta_score: 0.1176
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.2032 - fbeta_score: 0.1207 - val_loss: 0.2419 - val_fbeta_score: 0.0936
Epoch 3/25
1000/1000 [==============================] - 12s - loss: 0.1912 - fbeta_score: 0.1928 - val_loss: 0.2586 - val_fbeta_score: 0.1055
Epoch 4/25
1000/1000 [==============================] - 11s - loss: 0.1897 - fbeta_score: 0.2135 - val_loss: 0.2361 - val_fbeta_score: 0.1209
Epoch 5/25
1000/1000 [==============================] - 11s - loss: 0.1839 - fbeta_score: 0.2216 - val_loss: 0.2471 - val_fbeta_score: 0.1461
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.1808 - fbeta_score: 0.2233 - val_loss: 0.2578 - val_fbeta_score: 0.1285
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.1742 - fbeta_score: 0.2549 - val_loss: 0.2679 - val_fbeta_score: 0.2122
Epoch 8/25
1000/1000 [==============================] - 11s - loss: 0.1723 - fbeta_score: 0.2477 - val_loss: 0.2416 - val_fbeta_score: 0.1174
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1673 - fbeta_score: 0.2589 - val_loss: 0.2820 - val_fbeta_score: 0.0996
Epoch 10/25
1000/1000 [==============================] - 11s - loss: 0.1612 - fbeta_score: 0.2812 - val_loss: 0.2486 - val_fbeta_score: 0.1786
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1593 - fbeta_score: 0.3000 - val_loss: 0.2411 - val_fbeta_score: 0.1283
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.1561 - fbeta_score: 0.3198 - val_loss: 0.2570 - val_fbeta_score: 0.1405
Epoch 13/25
1000/1000 [==============================] - 11s - loss: 0.1521 - fbeta_score: 0.3394 - val_loss: 0.2649 - val_fbeta_score: 0.1600
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1485 - fbeta_score: 0.3600 - val_loss: 0.2547 - val_fbeta_score: 0.2058
Epoch 15/25
1000/1000 [==============================] - 12s - loss: 0.1475 - fbeta_score: 0.3530 - val_loss: 0.2394 - val_fbeta_score: 0.1660
Epoch 16/25
1000/1000 [==============================] - 11s - loss: 0.1466 - fbeta_score: 0.3528 - val_loss: 0.2476 - val_fbeta_score: 0.1437
Epoch 17/25
1000/1000 [==============================] - 12s - loss: 0.1415 - fbeta_score: 0.3590 - val_loss: 0.2583 - val_fbeta_score: 0.1940
Epoch 18/25
1000/1000 [==============================] - 12s - loss: 0.1408 - fbeta_score: 0.3799 - val_loss: 0.2329 - val_fbeta_score: 0.1527
Epoch 19/25
1000/1000 [==============================] - 11s - loss: 0.1392 - fbeta_score: 0.3829 - val_loss: 0.2387 - val_fbeta_score: 0.1535
Epoch 20/25
1000/1000 [==============================] - 12s - loss: 0.1390 - fbeta_score: 0.3829 - val_loss: 0.2519 - val_fbeta_score: 0.1609
Epoch 21/25
1000/1000 [==============================] - 11s - loss: 0.1308 - fbeta_score: 0.4193 - val_loss: 0.2474 - val_fbeta_score: 0.1481
Epoch 22/25
1000/1000 [==============================] - 11s - loss: 0.1326 - fbeta_score: 0.4147 - val_loss: 0.2530 - val_fbeta_score: 0.1236
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.1298 - fbeta_score: 0.4202 - val_loss: 0.2483 - val_fbeta_score: 0.1650
Epoch 24/25
1000/1000 [==============================] - 11s - loss: 0.1296 - fbeta_score: 0.4108 - val_loss: 0.2456 - val_fbeta_score: 0.1676
Epoch 25/25
1000/1000 [==============================] - 11s - loss: 0.1245 - fbeta_score: 0.4418 - val_loss: 0.2558 - val_fbeta_score: 0.1693
mini-batch:  3 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.1918 - fbeta_score: 0.2164 - val_loss: 0.2235 - val_fbeta_score: 0.1117
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.1731 - fbeta_score: 0.2686 - val_loss: 0.2327 - val_fbeta_score: 0.1379
Epoch 3/25
1000/1000 [==============================] - 12s - loss: 0.1628 - fbeta_score: 0.3078 - val_loss: 0.2419 - val_fbeta_score: 0.1658
Epoch 4/25
1000/1000 [==============================] - 11s - loss: 0.1585 - fbeta_score: 0.3127 - val_loss: 0.2399 - val_fbeta_score: 0.1451
Epoch 5/25
1000/1000 [==============================] - 12s - loss: 0.1532 - fbeta_score: 0.3443 - val_loss: 0.2434 - val_fbeta_score: 0.1630
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.1497 - fbeta_score: 0.3684 - val_loss: 0.2398 - val_fbeta_score: 0.1220
Epoch 7/25
1000/1000 [==============================] - 12s - loss: 0.1456 - fbeta_score: 0.3589 - val_loss: 0.2565 - val_fbeta_score: 0.1849
Epoch 8/25
1000/1000 [==============================] - 11s - loss: 0.1417 - fbeta_score: 0.3877 - val_loss: 0.2375 - val_fbeta_score: 0.1517
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1383 - fbeta_score: 0.3904 - val_loss: 0.2573 - val_fbeta_score: 0.1922
Epoch 10/25
1000/1000 [==============================] - 12s - loss: 0.1372 - fbeta_score: 0.4045 - val_loss: 0.2456 - val_fbeta_score: 0.1643
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1378 - fbeta_score: 0.3933 - val_loss: 0.2378 - val_fbeta_score: 0.1397
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.1341 - fbeta_score: 0.4246 - val_loss: 0.2729 - val_fbeta_score: 0.1562
Epoch 13/25
1000/1000 [==============================] - 12s - loss: 0.1288 - fbeta_score: 0.4195 - val_loss: 0.2388 - val_fbeta_score: 0.1971
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1271 - fbeta_score: 0.4403 - val_loss: 0.2450 - val_fbeta_score: 0.2557
Epoch 15/25
1000/1000 [==============================] - 12s - loss: 0.1270 - fbeta_score: 0.4430 - val_loss: 0.2390 - val_fbeta_score: 0.1934
Epoch 16/25
1000/1000 [==============================] - 11s - loss: 0.1255 - fbeta_score: 0.4507 - val_loss: 0.2340 - val_fbeta_score: 0.2163
Epoch 17/25
1000/1000 [==============================] - 11s - loss: 0.1228 - fbeta_score: 0.4380 - val_loss: 0.2202 - val_fbeta_score: 0.1211
Epoch 18/25
1000/1000 [==============================] - 12s - loss: 0.1207 - fbeta_score: 0.4491 - val_loss: 0.2271 - val_fbeta_score: 0.1802
Epoch 19/25
1000/1000 [==============================] - 11s - loss: 0.1206 - fbeta_score: 0.4479 - val_loss: 0.8743 - val_fbeta_score: 0.0900
Epoch 20/25
1000/1000 [==============================] - 12s - loss: 0.1358 - fbeta_score: 0.3992 - val_loss: 0.2966 - val_fbeta_score: 0.1288
Epoch 21/25
1000/1000 [==============================] - 11s - loss: 0.1262 - fbeta_score: 0.4222 - val_loss: 0.2322 - val_fbeta_score: 0.2167
Epoch 22/25
1000/1000 [==============================] - 11s - loss: 0.1207 - fbeta_score: 0.4561 - val_loss: 0.2424 - val_fbeta_score: 0.2112
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.1199 - fbeta_score: 0.4631 - val_loss: 0.2723 - val_fbeta_score: 0.2276
Epoch 24/25
1000/1000 [==============================] - 11s - loss: 0.1186 - fbeta_score: 0.4423 - val_loss: 0.2247 - val_fbeta_score: 0.1704
Epoch 25/25
1000/1000 [==============================] - 11s - loss: 0.1164 - fbeta_score: 0.4640 - val_loss: 0.2375 - val_fbeta_score: 0.1864
mini-batch:  4 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.1836 - fbeta_score: 0.2795 - val_loss: 0.2483 - val_fbeta_score: 0.1552
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.1717 - fbeta_score: 0.2833 - val_loss: 0.2251 - val_fbeta_score: 0.1243
Epoch 3/25
1000/1000 [==============================] - 11s - loss: 0.1656 - fbeta_score: 0.2940 - val_loss: 0.2310 - val_fbeta_score: 0.1541
Epoch 4/25
1000/1000 [==============================] - 11s - loss: 0.1627 - fbeta_score: 0.3102 - val_loss: 0.2274 - val_fbeta_score: 0.1882
Epoch 5/25
1000/1000 [==============================] - 11s - loss: 0.1557 - fbeta_score: 0.3271 - val_loss: 0.2524 - val_fbeta_score: 0.1049
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.1500 - fbeta_score: 0.3544 - val_loss: 0.2273 - val_fbeta_score: 0.1558
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.1478 - fbeta_score: 0.3733 - val_loss: 0.2340 - val_fbeta_score: 0.2017
Epoch 8/25
1000/1000 [==============================] - 11s - loss: 0.1451 - fbeta_score: 0.3829 - val_loss: 0.2287 - val_fbeta_score: 0.1410
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1382 - fbeta_score: 0.3768 - val_loss: 0.2217 - val_fbeta_score: 0.1698
Epoch 10/25
1000/1000 [==============================] - 11s - loss: 0.1361 - fbeta_score: 0.4094 - val_loss: 0.2320 - val_fbeta_score: 0.1257
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1362 - fbeta_score: 0.3982 - val_loss: 0.2443 - val_fbeta_score: 0.1232
Epoch 12/25
1000/1000 [==============================] - 12s - loss: 0.1330 - fbeta_score: 0.4084 - val_loss: 0.2422 - val_fbeta_score: 0.1920
Epoch 13/25
1000/1000 [==============================] - 11s - loss: 0.1308 - fbeta_score: 0.4330 - val_loss: 0.2435 - val_fbeta_score: 0.1514
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1297 - fbeta_score: 0.4146 - val_loss: 0.2246 - val_fbeta_score: 0.1847
Epoch 15/25
1000/1000 [==============================] - 11s - loss: 0.1270 - fbeta_score: 0.4215 - val_loss: 0.2318 - val_fbeta_score: 0.1764
Epoch 16/25
1000/1000 [==============================] - 11s - loss: 0.1229 - fbeta_score: 0.4540 - val_loss: 0.2389 - val_fbeta_score: 0.1828
Epoch 17/25
1000/1000 [==============================] - 12s - loss: 0.1237 - fbeta_score: 0.4570 - val_loss: 0.2368 - val_fbeta_score: 0.1641
Epoch 18/25
1000/1000 [==============================] - 11s - loss: 0.1233 - fbeta_score: 0.4285 - val_loss: 0.2408 - val_fbeta_score: 0.2113
Epoch 19/25
1000/1000 [==============================] - 11s - loss: 0.1215 - fbeta_score: 0.4332 - val_loss: 0.2196 - val_fbeta_score: 0.1177
Epoch 20/25
1000/1000 [==============================] - 12s - loss: 0.1187 - fbeta_score: 0.4522 - val_loss: 0.2225 - val_fbeta_score: 0.1303
Epoch 21/25
1000/1000 [==============================] - 11s - loss: 0.1173 - fbeta_score: 0.4769 - val_loss: 0.2181 - val_fbeta_score: 0.1569
Epoch 22/25
1000/1000 [==============================] - 11s - loss: 0.1176 - fbeta_score: 0.4518 - val_loss: 0.2178 - val_fbeta_score: 0.1069
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.1151 - fbeta_score: 0.4711 - val_loss: 0.2242 - val_fbeta_score: 0.1677
Epoch 24/25
1000/1000 [==============================] - 11s - loss: 0.1130 - fbeta_score: 0.4844 - val_loss: 0.2236 - val_fbeta_score: 0.1452
Epoch 25/25
1000/1000 [==============================] - 11s - loss: 0.1111 - fbeta_score: 0.4831 - val_loss: 0.2192 - val_fbeta_score: 0.1806
mini-batch:  5 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.1590 - fbeta_score: 0.3290 - val_loss: 0.2371 - val_fbeta_score: 0.1104
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.1473 - fbeta_score: 0.3433 - val_loss: 0.2252 - val_fbeta_score: 0.1430
Epoch 3/25
1000/1000 [==============================] - 12s - loss: 0.1410 - fbeta_score: 0.3749 - val_loss: 0.2113 - val_fbeta_score: 0.1863
Epoch 4/25
1000/1000 [==============================] - 12s - loss: 0.1370 - fbeta_score: 0.3869 - val_loss: 0.2120 - val_fbeta_score: 0.1611
Epoch 5/25
1000/1000 [==============================] - 11s - loss: 0.1341 - fbeta_score: 0.3725 - val_loss: 0.2235 - val_fbeta_score: 0.1915
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.1286 - fbeta_score: 0.4144 - val_loss: 0.2142 - val_fbeta_score: 0.2112
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.1272 - fbeta_score: 0.4080 - val_loss: 0.2242 - val_fbeta_score: 0.1852
Epoch 8/25
1000/1000 [==============================] - 11s - loss: 0.1235 - fbeta_score: 0.4212 - val_loss: 0.2321 - val_fbeta_score: 0.1634
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1236 - fbeta_score: 0.4204 - val_loss: 0.2133 - val_fbeta_score: 0.1781
Epoch 10/25
1000/1000 [==============================] - 12s - loss: 0.1249 - fbeta_score: 0.4165 - val_loss: 0.2628 - val_fbeta_score: 0.2025
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1186 - fbeta_score: 0.4373 - val_loss: 0.2121 - val_fbeta_score: 0.1756
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.1200 - fbeta_score: 0.4372 - val_loss: 0.2324 - val_fbeta_score: 0.1387
Epoch 13/25
1000/1000 [==============================] - 11s - loss: 0.1180 - fbeta_score: 0.4205 - val_loss: 0.2243 - val_fbeta_score: 0.1854
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1151 - fbeta_score: 0.4405 - val_loss: 0.2374 - val_fbeta_score: 0.1485
Epoch 15/25
1000/1000 [==============================] - 11s - loss: 0.1147 - fbeta_score: 0.4418 - val_loss: 0.2293 - val_fbeta_score: 0.1832
Epoch 16/25
1000/1000 [==============================] - 12s - loss: 0.1127 - fbeta_score: 0.4401 - val_loss: 0.2282 - val_fbeta_score: 0.2009
Epoch 17/25
1000/1000 [==============================] - 12s - loss: 0.1108 - fbeta_score: 0.4497 - val_loss: 0.2347 - val_fbeta_score: 0.1354
Epoch 18/25
1000/1000 [==============================] - 11s - loss: 0.1095 - fbeta_score: 0.4637 - val_loss: 0.2321 - val_fbeta_score: 0.1519
Epoch 19/25
1000/1000 [==============================] - 12s - loss: 0.1088 - fbeta_score: 0.4922 - val_loss: 0.2323 - val_fbeta_score: 0.1721
Epoch 20/25
1000/1000 [==============================] - 11s - loss: 0.1067 - fbeta_score: 0.4910 - val_loss: 0.2359 - val_fbeta_score: 0.2261
Epoch 21/25
1000/1000 [==============================] - 11s - loss: 0.1035 - fbeta_score: 0.4751 - val_loss: 0.2329 - val_fbeta_score: 0.1706
Epoch 22/25
1000/1000 [==============================] - 12s - loss: 0.1027 - fbeta_score: 0.4915 - val_loss: 0.2397 - val_fbeta_score: 0.1909
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.1036 - fbeta_score: 0.4843 - val_loss: 0.2350 - val_fbeta_score: 0.1382
Epoch 24/25
1000/1000 [==============================] - 11s - loss: 0.1031 - fbeta_score: 0.5056 - val_loss: 0.2473 - val_fbeta_score: 0.2386
Epoch 25/25
1000/1000 [==============================] - 12s - loss: 0.1029 - fbeta_score: 0.5049 - val_loss: 0.2280 - val_fbeta_score: 0.2180
mini-batch:  6 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.1717 - fbeta_score: 0.2744 - val_loss: 0.2293 - val_fbeta_score: 0.1899
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.1549 - fbeta_score: 0.3041 - val_loss: 0.2621 - val_fbeta_score: 0.2567
Epoch 3/25
1000/1000 [==============================] - 11s - loss: 0.1468 - fbeta_score: 0.3629 - val_loss: 0.2293 - val_fbeta_score: 0.1810
Epoch 4/25
1000/1000 [==============================] - 11s - loss: 0.1449 - fbeta_score: 0.3551 - val_loss: 0.2785 - val_fbeta_score: 0.1593
Epoch 5/25
1000/1000 [==============================] - 11s - loss: 0.1406 - fbeta_score: 0.3940 - val_loss: 0.2527 - val_fbeta_score: 0.1872
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.1377 - fbeta_score: 0.3888 - val_loss: 0.2664 - val_fbeta_score: 0.1929
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.1324 - fbeta_score: 0.3876 - val_loss: 0.2510 - val_fbeta_score: 0.2548
Epoch 8/25
1000/1000 [==============================] - 12s - loss: 0.1304 - fbeta_score: 0.4039 - val_loss: 0.2484 - val_fbeta_score: 0.2631
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1275 - fbeta_score: 0.4096 - val_loss: 0.2399 - val_fbeta_score: 0.2586
Epoch 10/25
1000/1000 [==============================] - 11s - loss: 0.1239 - fbeta_score: 0.4195 - val_loss: 0.2347 - val_fbeta_score: 0.2436
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1209 - fbeta_score: 0.4462 - val_loss: 0.2454 - val_fbeta_score: 0.2315
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.1199 - fbeta_score: 0.4540 - val_loss: 0.2447 - val_fbeta_score: 0.1666
Epoch 13/25
1000/1000 [==============================] - 11s - loss: 0.1177 - fbeta_score: 0.4415 - val_loss: 0.2402 - val_fbeta_score: 0.2329
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1135 - fbeta_score: 0.4524 - val_loss: 0.2514 - val_fbeta_score: 0.2137
Epoch 15/25
1000/1000 [==============================] - 11s - loss: 0.1123 - fbeta_score: 0.4723 - val_loss: 0.2336 - val_fbeta_score: 0.2362
Epoch 16/25
1000/1000 [==============================] - 12s - loss: 0.1107 - fbeta_score: 0.4618 - val_loss: 0.2461 - val_fbeta_score: 0.2002
Epoch 17/25
1000/1000 [==============================] - 12s - loss: 0.1090 - fbeta_score: 0.4661 - val_loss: 0.2391 - val_fbeta_score: 0.2238
Epoch 18/25
1000/1000 [==============================] - 11s - loss: 0.1082 - fbeta_score: 0.4937 - val_loss: 0.2482 - val_fbeta_score: 0.2125
Epoch 19/25
1000/1000 [==============================] - 11s - loss: 0.1049 - fbeta_score: 0.4748 - val_loss: 0.2368 - val_fbeta_score: 0.2023
Epoch 20/25
1000/1000 [==============================] - 12s - loss: 0.1066 - fbeta_score: 0.4880 - val_loss: 0.2469 - val_fbeta_score: 0.1991
Epoch 21/25
1000/1000 [==============================] - 11s - loss: 0.1039 - fbeta_score: 0.5009 - val_loss: 0.2364 - val_fbeta_score: 0.2038
Epoch 22/25
1000/1000 [==============================] - 11s - loss: 0.1045 - fbeta_score: 0.4881 - val_loss: 0.2304 - val_fbeta_score: 0.1981
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.1049 - fbeta_score: 0.5115 - val_loss: 0.2521 - val_fbeta_score: 0.2423
Epoch 24/25
1000/1000 [==============================] - 11s - loss: 0.1023 - fbeta_score: 0.5107 - val_loss: 0.2492 - val_fbeta_score: 0.2234
Epoch 25/25
1000/1000 [==============================] - 12s - loss: 0.1000 - fbeta_score: 0.5105 - val_loss: 0.2513 - val_fbeta_score: 0.2295
mini-batch:  7 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.1511 - fbeta_score: 0.3870 - val_loss: 0.2423 - val_fbeta_score: 0.1941
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.1424 - fbeta_score: 0.3952 - val_loss: 0.2375 - val_fbeta_score: 0.1951
Epoch 3/25
1000/1000 [==============================] - 12s - loss: 0.1345 - fbeta_score: 0.4140 - val_loss: 0.2276 - val_fbeta_score: 0.2556
Epoch 4/25
1000/1000 [==============================] - 11s - loss: 0.1323 - fbeta_score: 0.4275 - val_loss: 0.2226 - val_fbeta_score: 0.2064
Epoch 5/25
1000/1000 [==============================] - 11s - loss: 0.1277 - fbeta_score: 0.4316 - val_loss: 0.2358 - val_fbeta_score: 0.2216
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.1256 - fbeta_score: 0.4414 - val_loss: 0.2342 - val_fbeta_score: 0.2536
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.1213 - fbeta_score: 0.4549 - val_loss: 0.2286 - val_fbeta_score: 0.2231
Epoch 8/25
1000/1000 [==============================] - 11s - loss: 0.1203 - fbeta_score: 0.4496 - val_loss: 0.2453 - val_fbeta_score: 0.1861
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1194 - fbeta_score: 0.4500 - val_loss: 0.2362 - val_fbeta_score: 0.2498
Epoch 10/25
1000/1000 [==============================] - 11s - loss: 0.1168 - fbeta_score: 0.4666 - val_loss: 0.2369 - val_fbeta_score: 0.2497
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1163 - fbeta_score: 0.4574 - val_loss: 0.2272 - val_fbeta_score: 0.2092
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.1162 - fbeta_score: 0.4512 - val_loss: 0.2374 - val_fbeta_score: 0.2143
Epoch 13/25
1000/1000 [==============================] - 11s - loss: 0.1119 - fbeta_score: 0.4693 - val_loss: 0.2300 - val_fbeta_score: 0.2124
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1104 - fbeta_score: 0.4795 - val_loss: 0.2346 - val_fbeta_score: 0.2264
Epoch 15/25
1000/1000 [==============================] - 11s - loss: 0.1087 - fbeta_score: 0.4810 - val_loss: 0.2447 - val_fbeta_score: 0.2243
Epoch 16/25
1000/1000 [==============================] - 11s - loss: 0.1074 - fbeta_score: 0.4801 - val_loss: 0.2598 - val_fbeta_score: 0.1993
Epoch 17/25
1000/1000 [==============================] - 12s - loss: 0.1062 - fbeta_score: 0.4857 - val_loss: 0.2567 - val_fbeta_score: 0.2230
Epoch 18/25
1000/1000 [==============================] - 12s - loss: 0.1053 - fbeta_score: 0.4892 - val_loss: 0.2582 - val_fbeta_score: 0.2311
Epoch 19/25
1000/1000 [==============================] - 12s - loss: 0.1044 - fbeta_score: 0.5144 - val_loss: 0.2503 - val_fbeta_score: 0.2521
Epoch 20/25
1000/1000 [==============================] - 12s - loss: 0.1051 - fbeta_score: 0.5083 - val_loss: 0.2437 - val_fbeta_score: 0.2553
Epoch 21/25
1000/1000 [==============================] - 12s - loss: 0.1029 - fbeta_score: 0.5022 - val_loss: 0.2467 - val_fbeta_score: 0.2467
Epoch 22/25
1000/1000 [==============================] - 12s - loss: 0.1028 - fbeta_score: 0.4960 - val_loss: 0.2454 - val_fbeta_score: 0.2717
Epoch 23/25
1000/1000 [==============================] - 12s - loss: 0.1040 - fbeta_score: 0.4954 - val_loss: 0.2478 - val_fbeta_score: 0.2533
Epoch 24/25
1000/1000 [==============================] - 12s - loss: 0.1027 - fbeta_score: 0.4976 - val_loss: 0.2474 - val_fbeta_score: 0.2443
Epoch 25/25
1000/1000 [==============================] - 11s - loss: 0.1019 - fbeta_score: 0.4953 - val_loss: 0.2447 - val_fbeta_score: 0.2287
mini-batch:  8 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.1445 - fbeta_score: 0.3961 - val_loss: 0.2201 - val_fbeta_score: 0.1552
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.1375 - fbeta_score: 0.3955 - val_loss: 0.2271 - val_fbeta_score: 0.2099
Epoch 3/25
1000/1000 [==============================] - 12s - loss: 0.1314 - fbeta_score: 0.3990 - val_loss: 0.2252 - val_fbeta_score: 0.1751
Epoch 4/25
1000/1000 [==============================] - 11s - loss: 0.1274 - fbeta_score: 0.4165 - val_loss: 0.2260 - val_fbeta_score: 0.1650
Epoch 5/25
1000/1000 [==============================] - 11s - loss: 0.1239 - fbeta_score: 0.4115 - val_loss: 0.2375 - val_fbeta_score: 0.2033
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.1231 - fbeta_score: 0.4284 - val_loss: 0.2458 - val_fbeta_score: 0.2145
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.1206 - fbeta_score: 0.4255 - val_loss: 0.2570 - val_fbeta_score: 0.1619
Epoch 8/25
1000/1000 [==============================] - 11s - loss: 0.1175 - fbeta_score: 0.4361 - val_loss: 0.2695 - val_fbeta_score: 0.2170
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1169 - fbeta_score: 0.4287 - val_loss: 0.4156 - val_fbeta_score: 0.0790
Epoch 10/25
1000/1000 [==============================] - 11s - loss: 0.1139 - fbeta_score: 0.4518 - val_loss: 0.2306 - val_fbeta_score: 0.1644
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1120 - fbeta_score: 0.4613 - val_loss: 0.2583 - val_fbeta_score: 0.1588
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.1106 - fbeta_score: 0.4521 - val_loss: 0.2500 - val_fbeta_score: 0.2294
Epoch 13/25
1000/1000 [==============================] - 11s - loss: 0.1082 - fbeta_score: 0.4655 - val_loss: 0.2323 - val_fbeta_score: 0.2180
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1081 - fbeta_score: 0.4566 - val_loss: 0.2349 - val_fbeta_score: 0.2006
Epoch 15/25
1000/1000 [==============================] - 11s - loss: 0.1076 - fbeta_score: 0.4584 - val_loss: 0.3077 - val_fbeta_score: 0.1704
Epoch 16/25
1000/1000 [==============================] - 11s - loss: 0.1080 - fbeta_score: 0.4573 - val_loss: 0.2213 - val_fbeta_score: 0.1786
Epoch 17/25
1000/1000 [==============================] - 11s - loss: 0.1074 - fbeta_score: 0.4865 - val_loss: 0.2498 - val_fbeta_score: 0.1792
Epoch 18/25
1000/1000 [==============================] - 11s - loss: 0.1045 - fbeta_score: 0.4726 - val_loss: 0.2187 - val_fbeta_score: 0.2020
Epoch 19/25
1000/1000 [==============================] - 11s - loss: 0.1048 - fbeta_score: 0.4865 - val_loss: 0.2207 - val_fbeta_score: 0.1927
Epoch 20/25
1000/1000 [==============================] - 11s - loss: 0.1049 - fbeta_score: 0.4730 - val_loss: 0.2324 - val_fbeta_score: 0.1219
Epoch 21/25
1000/1000 [==============================] - 11s - loss: 0.1041 - fbeta_score: 0.4808 - val_loss: 0.2266 - val_fbeta_score: 0.1500
Epoch 22/25
1000/1000 [==============================] - 12s - loss: 0.1067 - fbeta_score: 0.4667 - val_loss: 0.2284 - val_fbeta_score: 0.1960
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.1022 - fbeta_score: 0.4815 - val_loss: 0.2124 - val_fbeta_score: 0.1917
Epoch 24/25
1000/1000 [==============================] - 11s - loss: 0.1018 - fbeta_score: 0.4768 - val_loss: 0.2321 - val_fbeta_score: 0.2391
Epoch 25/25
1000/1000 [==============================] - 11s - loss: 0.1006 - fbeta_score: 0.4925 - val_loss: 0.2287 - val_fbeta_score: 0.1789
mini-batch:  9 / 10
Train on 1000 samples, validate on 433 samples
Epoch 1/25
1000/1000 [==============================] - 11s - loss: 0.1533 - fbeta_score: 0.3358 - val_loss: 0.2169 - val_fbeta_score: 0.1264
Epoch 2/25
1000/1000 [==============================] - 11s - loss: 0.1383 - fbeta_score: 0.3513 - val_loss: 0.2173 - val_fbeta_score: 0.2042
Epoch 3/25
1000/1000 [==============================] - 11s - loss: 0.1362 - fbeta_score: 0.3815 - val_loss: 0.2232 - val_fbeta_score: 0.1757
Epoch 4/25
1000/1000 [==============================] - 12s - loss: 0.1283 - fbeta_score: 0.4037 - val_loss: 0.2113 - val_fbeta_score: 0.2216
Epoch 5/25
1000/1000 [==============================] - 12s - loss: 0.1250 - fbeta_score: 0.4207 - val_loss: 0.2211 - val_fbeta_score: 0.2349
Epoch 6/25
1000/1000 [==============================] - 11s - loss: 0.1221 - fbeta_score: 0.4200 - val_loss: 0.2192 - val_fbeta_score: 0.2269
Epoch 7/25
1000/1000 [==============================] - 11s - loss: 0.1194 - fbeta_score: 0.4165 - val_loss: 0.2272 - val_fbeta_score: 0.2031
Epoch 8/25
1000/1000 [==============================] - 11s - loss: 0.1195 - fbeta_score: 0.4245 - val_loss: 0.2373 - val_fbeta_score: 0.2150
Epoch 9/25
1000/1000 [==============================] - 11s - loss: 0.1162 - fbeta_score: 0.4416 - val_loss: 0.2404 - val_fbeta_score: 0.2391
Epoch 10/25
1000/1000 [==============================] - 11s - loss: 0.1138 - fbeta_score: 0.4475 - val_loss: 0.2401 - val_fbeta_score: 0.2443
Epoch 11/25
1000/1000 [==============================] - 11s - loss: 0.1132 - fbeta_score: 0.4466 - val_loss: 0.2290 - val_fbeta_score: 0.2417
Epoch 12/25
1000/1000 [==============================] - 11s - loss: 0.1120 - fbeta_score: 0.4400 - val_loss: 0.2323 - val_fbeta_score: 0.2387
Epoch 13/25
1000/1000 [==============================] - 11s - loss: 0.1104 - fbeta_score: 0.4594 - val_loss: 0.2204 - val_fbeta_score: 0.2528
Epoch 14/25
1000/1000 [==============================] - 11s - loss: 0.1080 - fbeta_score: 0.4734 - val_loss: 0.2274 - val_fbeta_score: 0.2327
Epoch 15/25
1000/1000 [==============================] - 11s - loss: 0.1070 - fbeta_score: 0.4600 - val_loss: 0.2315 - val_fbeta_score: 0.2171
Epoch 16/25
1000/1000 [==============================] - 11s - loss: 0.1051 - fbeta_score: 0.4664 - val_loss: 0.2222 - val_fbeta_score: 0.2388
Epoch 17/25
1000/1000 [==============================] - 11s - loss: 0.1035 - fbeta_score: 0.4912 - val_loss: 0.2362 - val_fbeta_score: 0.2636
Epoch 18/25
1000/1000 [==============================] - 11s - loss: 0.1035 - fbeta_score: 0.4823 - val_loss: 0.2195 - val_fbeta_score: 0.2568
Epoch 19/25
1000/1000 [==============================] - 11s - loss: 0.1018 - fbeta_score: 0.4776 - val_loss: 0.2123 - val_fbeta_score: 0.2388
Epoch 20/25
1000/1000 [==============================] - 11s - loss: 0.1012 - fbeta_score: 0.4853 - val_loss: 0.2155 - val_fbeta_score: 0.2413
Epoch 21/25
1000/1000 [==============================] - 11s - loss: 0.1003 - fbeta_score: 0.4972 - val_loss: 0.2242 - val_fbeta_score: 0.2177
Epoch 22/25
1000/1000 [==============================] - 11s - loss: 0.1014 - fbeta_score: 0.4807 - val_loss: 0.2108 - val_fbeta_score: 0.1861
Epoch 23/25
1000/1000 [==============================] - 11s - loss: 0.1018 - fbeta_score: 0.4970 - val_loss: 0.2171 - val_fbeta_score: 0.2319
Epoch 24/25
1000/1000 [==============================] - 11s - loss: 0.1016 - fbeta_score: 0.4915 - val_loss: 0.2148 - val_fbeta_score: 0.1888
Epoch 25/25
1000/1000 [==============================] - 11s - loss: 0.0990 - fbeta_score: 0.4881 - val_loss: 0.2124 - val_fbeta_score: 0.2262
Mon, 28 Nov 2016 19:36:01 +0000
The weights have been saved in: ./weights/2016_11_28_18:44:10_cuberun.h5

Training Set Predictions

Predicted Ground Truth
,12 ,12
,7,9,10,18
,1,2,8
,1 ,1
,18
,1,2,8
,1 ,1,14
,4,7
,6
,10 ,7,10
,10 ,10,12,18
,1,10
,10 ,10,18
,7
,1,12
,15,17
,6
,13 ,13
,8 ,8
,1,7,11,12
,0 ,0
,2,8,10,14,15
,12
,8 ,8
,15 ,4,15
,10 ,1,10
,10 ,9,10
,18 ,18
,8,10,12,14
,13 ,13,14
,1,10
,2,10,14
,2 ,2,4,10
,16 ,16
,6 ,6
,2,7,8
,2,8,10,14
,8 ,8
,4,8,12
,2,3,7
,12 ,1,12
,1,10
,11
,2 ,2,3
,1,8,10
,6,9
,1,10
,14
,10 ,8,10
,11
,8,10,14
,9
,1 ,1,10
,10 ,5,6,10
,17 ,17
,4,6,10
,9
,13 ,13
,4,6,7,9,10
,9,12,18
,1 ,1
,8
,4,6,10
,10,12,14
,1 ,1
,4,8,10,14
,8,10,12,14
,2,4,6,7,9,10
,5,6,10
,1,2,11
,1
,1,7,12
,1,10,11
,0 ,0
,9,10,18
,2,4,6,7,9,10
,1,7,12
,6 ,6,10
,3 ,3
,8 ,8
,0,2
,9,10,13,18
,10,13,14
,1,7,10
,10 ,10
,2 ,2
,1 ,1
,13 ,13,14
,5,12
,10 ,10,18
,9 ,9
,2,6,8
,8,11
,10 ,10
,6 ,5,6,10
,9,10,13,18
,1 ,1
,7 ,7,10
,5 ,5,12
,8,10
,10 ,10,18
,7,9,10,18
,0,5
,9 ,9
,7,10
,9,10,13,18
,8 ,8,11
,0,2
,10,12
,1 ,1
,1,11
,4
,13,14,16
,14
,1 ,1
,9,10,16
,10
,10 ,2,10
,1,10
,10 ,10
,9 ,9
,1 ,1
,1
,9 ,0,9
,1,2,8
,6 ,6,12
,2,6,8
,2,10
,6,12
,7,10
,11,12
,6,10
,4,8,10,14
,9
,6,12
,1,10
,2,8,10,14,15
,10 ,8,10,14
,8
,2,3
,9 ,9
,13 ,13,14
,10 ,4,8,10
,18 ,18
,10,15
,5,6,10
,10 ,10
,0,5
,1,10,11
,1,2,7,12
,1 ,1,12
,6 ,6
,16
,10 ,1,7,10
,10 ,6,10
,10
,10 ,7,10
,7
,7
,1,10,11
,1
,1,6,14
,9 ,9
,17
,6 ,6
,10 ,6,10
,6 ,6
,1,14
,9,12,18
,7,15
,1,11
,17
,8,10,14
,8,9
,10
,13 ,13,14,16
,18
,15 ,15
,1,6,14
,2 ,2
,4,8,10,14
,8,10,14
,10 ,6,10
,1,12
,4,8,10,15
,11 ,11
,17
,9 ,9
,3
,1,8,10
,14
,1,7,11,12
,11 ,1,10,11
,10 ,10
,8 ,1,2,8
,10
,1,2,5,9
,9 ,9,10
,18 ,18
,1,7,11,12
,15 ,15
,6
,1,10,12
,10
,10
,0,9
,18 ,18
,13 ,13,14
,10,12,18
,9 ,9
,8 ,8
,15 ,4,15
,7
,10,13,14
,10 ,10
,10
,10 ,10,18
,6 ,6,9
,15 ,15,17
,1,12
,8
,18 ,18
,1 ,1,17
,1,10
,18 ,7,18
,1,12
,18 ,18
,9
,2,8,10,14
,10 ,1,10
,10 ,9,10,18
,9,10
,1,2,5,9
,17
,1
,9,10,16
,11 ,1,2,11
,4,6,7,9,10
,3,5,7,8
,11 ,1,11
,10 ,2,10
,8 ,8,9
,11 ,1,11
,11 ,1,11
,8,10,14
,14
,4,8,12
,4,15
,17
,1
,3,5,7,8
,5 ,5,6,10
,4,8,10
,1,2,7,12
,8 ,8,11
,4,8,10,14
,8,10,14
,4,8,10,15
,4,6,7,9,10
,1 ,1
,10 ,10
,1 ,1
,12 ,6,12
,18
,6,10
,1
,2,5,9
,7 ,7
,10 ,1,10,12
,6 ,6,7,10
,2,10
,15 ,4,15
,7 ,4,7
,2,4,6,7,9,10
,6
,5,6,10
,0,7,8
,12 ,12
,8
,6 ,6
,10 ,7,10
,17 ,1,17
,1 ,1
,10,12,14
,2,3,7
,6 ,6

Test Set Predictions

Predicted Ground Truth
,9,14
,8 ,8,9,11
,2,7
,8 ,2,8
,2,10
,6,7,8,10,14
,1
,2,5,9
,6
,1
,9
,10 ,6
,10 ,3
,8,10,14
,10,15
,1,12
,7 ,7,16
,8,10,14
,0,8,14
,0,8,14
,1,2
,2,9
,7
,9
,1 ,1
,4,6,9,10
,1 ,1,7,11,12
,1
,12
,13
,5,10,12
,2,8,10
,2 ,1,10
,1,14
,7
,2,8,10
,1 ,1
,8 ,2,8
,2,9
,1 ,1
,18 ,18
,7,8,9
,5,6,12
,8 ,1,2,8
,9,10
,16
,8,10,14
,2
,8 ,8,10,14
,1 ,1
,10
,8,9
,1 ,1
,1,12
,8,10,14
,18 ,18
,11 ,1,10,11,12
,15
,8,9,11
,6 ,6
,1,11,12
,9
,10,15
,1 ,1,14
,6
,3
,2,14
,6 ,1,8,9
,9 ,9
,6 ,6
,0
,7
,7
,17
,9 ,9,14
,4,11
,10 ,6,10,18
,8 ,2,8
,2 ,1,10
,1,12
,8,10,14
,0,7
,12
,3,8
,6,10
,7 ,7
,7,12
,1,17
,1 ,1,2
,10 ,10
,1 ,1
,1 ,1,14
,1,2,10
,4,6,9,10
,11
,8,10,14
,9,13
,9 ,1
,10 ,10,17
,6 ,6,12
,1,9,11
,5,6,10,12
,1 ,1
,3,8
,1
,10 ,8,10,14,15
,15
,6,7,8,10,14
,8 ,1,2,8
,2,8,14
,11
,10,15
,1,10,18
,1,2
,1,2,10
,2,6,8
,2,5,9
,15
,1 ,1,2
,18
,2
,5,6,10,12
,2,10
,1,5,9
,18 ,15
,9 ,0,9
,6,10
,2 ,1,2,11
,8 ,8
,9,13
,1,11,12
,8 ,8,10
,10 ,1,10
,6 ,6,10
,10
,5
,10 ,8,10
,18 ,18
,1
,10 ,8,10,14
,1
,6 ,4,6,9,10
,18 ,18
,2,8,14
,4
,11
,0,8
,9,11
,10,18
,1,10,18
,1
,1
,8,10,15
,8,10,15
,8 ,8,10,14,15
,9 ,9,16
,8,10,15
,1
,6 ,4,6
,4
,11
,1
,1 ,1
,11
,1,7,12
,1 ,1
,2
,11
,8,10,15
,1 ,1
,9,13
,6,12
,7
,1,9,11
,2,6,7
,0,9
,9 ,9
,10 ,3,8
,18 ,18
,9 ,2,6,7
,8 ,8
,9,14
,1
,12,14
,8,10
,6,13,14
,1
,7 ,3,8
,7 ,7
,6
,9
,2,7
,11
,1,17
,1,10
,1,14
,11
,1,2,8
,1,10,11
,1,2
,7 ,7
,6 ,0,8
,10 ,4,6,9,10
,3
,2,6,14
,1
,8,10,15
,8 ,2,8,10
,17
,18 ,8,10
,1
,1
,1
,9
,0
,1,7,11,12
,6
,6 ,6
,12
,1,7,12
,8,10,14
,2,6,8
,7 ,7
,2
,11 ,1,10,11,12
,6,13,14
,9
,10
,1,11,12
,2 ,1
,8 ,1,8,9
,5
,0,9
,1
,1 ,1
,1 ,1,9
,10,15
,11
,4,6,9,10
,5,10,12
,10,15
,8 ,0,7,8
,2 ,2
,1 ,1
,2,8
,2 ,8,10,14
,12,14
,9 ,9,14
,8 ,7,8
,6 ,6
,9 ,4,6
,1,11,12
,13
,9 ,6,9
,9,14
,1 ,1
,10 ,10
,1,7,12
,18
,1
,10 ,8,10,14
,6 ,4,6
,1,10
,6,10,18
,1,2,8
,7,16
,8 ,2,8,10
,8 ,8
,1,5,9
,8,10,14
,7
,10,15
,6 ,5,6,12
,2,8,14
,10
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