--------[19_09_2019 18:04:11]-------- Random Grid Search Search 1 of 500 momentum0.99, features=[64, 64, 64], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1 [18:05:27] INIT Loss(val): 0.139415 Accuarcy: 0.086344 [18:07:54] Epoch 2: Loss(train): 0.077418 Loss(val): 0.077792 [18:08:22] Epoch 4: Loss(train): 0.068747 Loss(val): 0.069827 [18:08:53] Epoch 6: Loss(train): 0.066216 Loss(val): 0.067672 [18:09:24] Epoch 8: Loss(train): 0.064400 Loss(val): 0.065635 [18:09:56] Epoch 10: Loss(train): 0.062374 Loss(val): 0.062887 [18:10:24] Epoch 12: Loss(train): 0.062486 Loss(val): 0.061977 [18:10:52] Epoch 14: Loss(train): 0.060712 Loss(val): 0.060443 [18:11:20] Epoch 16: Loss(train): 0.059088 Loss(val): 0.059083 [18:11:51] Epoch 18: Loss(train): 0.058101 Loss(val): 0.058331 [18:12:19] Epoch 20: Loss(train): 0.057148 Loss(val): 0.057508 [18:12:51] Epoch 22: Loss(train): 0.055926 Loss(val): 0.056367 [18:13:19] Epoch 24: Loss(train): 0.055365 Loss(val): 0.055971 [18:13:47] Epoch 26: Loss(train): 0.054611 Loss(val): 0.055201 [18:14:15] Epoch 28: Loss(train): 0.053842 Loss(val): 0.054555 [18:14:44] Epoch 30: Loss(train): 0.053042 Loss(val): 0.053763 [18:15:19] Epoch 32: Loss(train): 0.052779 Loss(val): 0.053499 [18:15:47] Epoch 34: Loss(train): 0.052054 Loss(val): 0.052898 [18:16:15] Epoch 36: Loss(train): 0.051787 Loss(val): 0.052560 [18:16:43] Epoch 38: Loss(train): 0.051411 Loss(val): 0.052254 [18:17:11] Epoch 40: Loss(train): 0.051067 Loss(val): 0.051975 [18:17:14] FINAL(40) Loss(val): 0.051975 Accuarcy: 0.638027 Search 2 of 500 momentum0.98, features=[64, 64, 64], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.001 [18:17:28] INIT Loss(val): 0.153462 Accuarcy: 0.102959 [18:17:59] Epoch 2: Loss(train): 0.075035 Loss(val): 0.075419 [18:18:28] Epoch 4: Loss(train): 0.070219 Loss(val): 0.071059 [18:18:56] Epoch 6: Loss(train): 0.066796 Loss(val): 0.068081 [18:19:26] Epoch 8: Loss(train): 0.064382 Loss(val): 0.066310 [18:19:55] Epoch 10: Loss(train): 0.062338 Loss(val): 0.063341 [18:20:24] Epoch 12: Loss(train): 0.062264 Loss(val): 0.062503 [18:20:52] Epoch 14: Loss(train): 0.060864 Loss(val): 0.061372 [18:21:21] Epoch 16: Loss(train): 0.059382 Loss(val): 0.060050 [18:21:49] Epoch 18: Loss(train): 0.058139 Loss(val): 0.058792 [18:22:18] Epoch 20: Loss(train): 0.057172 Loss(val): 0.057924 [18:22:48] Epoch 22: Loss(train): 0.056341 Loss(val): 0.057077 [18:23:17] Epoch 24: Loss(train): 0.055905 Loss(val): 0.056701 [18:23:45] Epoch 26: Loss(train): 0.055191 Loss(val): 0.056047 [18:24:14] Epoch 28: Loss(train): 0.054708 Loss(val): 0.055567 [18:24:43] Epoch 30: Loss(train): 0.054421 Loss(val): 0.055375 [18:25:11] Epoch 32: Loss(train): 0.054032 Loss(val): 0.054960 [18:25:41] Epoch 34: Loss(train): 0.053697 Loss(val): 0.054623 [18:26:10] Epoch 36: Loss(train): 0.053191 Loss(val): 0.054077 [18:26:39] Epoch 38: Loss(train): 0.052736 Loss(val): 0.053705 [18:27:08] Epoch 40: Loss(train): 0.052341 Loss(val): 0.053276 [18:27:11] FINAL(40) Loss(val): 0.053276 Accuarcy: 0.613299 Search 3 of 500 momentum0.94, features=[32, 64, 128], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=1.0 [18:27:25] INIT Loss(val): 0.162784 Accuarcy: 0.093435 [18:27:57] Epoch 2: Loss(train): 0.074868 Loss(val): 0.075268 [18:28:26] Epoch 4: Loss(train): 0.068138 Loss(val): 0.068649 [18:28:55] Epoch 6: Loss(train): 0.064977 Loss(val): 0.065487 [18:29:24] Epoch 8: Loss(train): 0.063515 Loss(val): 0.063438 [18:29:53] Epoch 10: Loss(train): 0.063088 Loss(val): 0.062578 [18:30:22] Epoch 12: Loss(train): 0.061802 Loss(val): 0.061483 [18:30:51] Epoch 14: Loss(train): 0.060184 Loss(val): 0.060016 [18:31:21] Epoch 16: Loss(train): 0.058834 Loss(val): 0.058876 [18:31:50] Epoch 18: Loss(train): 0.057796 Loss(val): 0.057980 [18:32:19] Epoch 20: Loss(train): 0.057109 Loss(val): 0.057453 [18:32:48] Epoch 22: Loss(train): 0.056280 Loss(val): 0.056685 [18:33:17] Epoch 24: Loss(train): 0.055541 Loss(val): 0.055949 [18:33:46] Epoch 26: Loss(train): 0.054714 Loss(val): 0.055165 [18:34:15] Epoch 28: Loss(train): 0.054159 Loss(val): 0.054650 [18:34:45] Epoch 30: Loss(train): 0.053510 Loss(val): 0.054028 [18:35:15] Epoch 32: Loss(train): 0.053212 Loss(val): 0.053752 [18:35:44] Epoch 34: Loss(train): 0.052627 Loss(val): 0.053269 [18:36:14] Epoch 36: Loss(train): 0.052508 Loss(val): 0.053079 [18:36:43] Epoch 38: Loss(train): 0.051985 Loss(val): 0.052550 [18:37:13] Epoch 40: Loss(train): 0.051785 Loss(val): 0.052311 [18:37:16] FINAL(40) Loss(val): 0.052311 Accuarcy: 0.634133 Search 4 of 500 momentum0.98, features=[32, 32, 32], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.3 [18:37:31] INIT Loss(val): 0.134779 Accuarcy: 0.095527 [18:38:04] Epoch 2: Loss(train): 0.078241 Loss(val): 0.079199 [18:38:33] Epoch 4: Loss(train): 0.070412 Loss(val): 0.072017 [18:39:04] Epoch 6: Loss(train): 0.066808 Loss(val): 0.068458 [18:39:34] Epoch 8: Loss(train): 0.063924 Loss(val): 0.065181 [18:40:05] Epoch 10: Loss(train): 0.064103 Loss(val): 0.063963 [18:40:35] Epoch 12: Loss(train): 0.062526 Loss(val): 0.062825 [18:41:05] Epoch 14: Loss(train): 0.060927 Loss(val): 0.061415 [18:41:35] Epoch 16: Loss(train): 0.059985 Loss(val): 0.060562 [18:42:05] Epoch 18: Loss(train): 0.058566 Loss(val): 0.059268 [18:42:36] Epoch 20: Loss(train): 0.057687 Loss(val): 0.058355 [18:43:05] Epoch 22: Loss(train): 0.056681 Loss(val): 0.057413 [18:43:36] Epoch 24: Loss(train): 0.055931 Loss(val): 0.056701 [18:44:07] Epoch 26: Loss(train): 0.055389 Loss(val): 0.056172 [18:44:37] Epoch 28: Loss(train): 0.054791 Loss(val): 0.055668 [18:45:07] Epoch 30: Loss(train): 0.054420 Loss(val): 0.055324 [18:45:38] Epoch 32: Loss(train): 0.053756 Loss(val): 0.054584 [18:46:09] Epoch 34: Loss(train): 0.053215 Loss(val): 0.054125 [18:46:39] Epoch 36: Loss(train): 0.052708 Loss(val): 0.053595 [18:47:10] Epoch 38: Loss(train): 0.052368 Loss(val): 0.053279 [18:47:40] Epoch 40: Loss(train): 0.051784 Loss(val): 0.052760 [18:47:44] FINAL(40) Loss(val): 0.052760 Accuarcy: 0.623980 Search 5 of 500 momentum0.96, features=[32, 64, 128], dropout_rate=0.8 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.01 [18:47:58] INIT Loss(val): 0.138354 Accuarcy: 0.100289 [18:48:33] Epoch 2: Loss(train): 0.075274 Loss(val): 0.075726 [18:49:04] Epoch 4: Loss(train): 0.067656 Loss(val): 0.068969 [18:49:35] Epoch 6: Loss(train): 0.064109 Loss(val): 0.065550 [18:50:05] Epoch 8: Loss(train): 0.062007 Loss(val): 0.063224 [18:50:35] Epoch 10: Loss(train): 0.060712 Loss(val): 0.060455 [18:51:06] Epoch 12: Loss(train): 0.060152 Loss(val): 0.059586 [18:51:38] Epoch 14: Loss(train): 0.058922 Loss(val): 0.058455 [18:52:10] Epoch 16: Loss(train): 0.057797 Loss(val): 0.057439 [18:52:41] Epoch 18: Loss(train): 0.056685 Loss(val): 0.056471 [18:53:11] Epoch 20: Loss(train): 0.056109 Loss(val): 0.055995 [18:53:43] Epoch 22: Loss(train): 0.055229 Loss(val): 0.055187 [18:54:14] Epoch 24: Loss(train): 0.054637 Loss(val): 0.054621 [18:54:46] Epoch 26: Loss(train): 0.054068 Loss(val): 0.054110 [18:55:17] Epoch 28: Loss(train): 0.053452 Loss(val): 0.053568 [18:55:47] Epoch 30: Loss(train): 0.052814 Loss(val): 0.053036 [18:56:18] Epoch 32: Loss(train): 0.052657 Loss(val): 0.052841 [18:56:55] Epoch 34: Loss(train): 0.052147 Loss(val): 0.052422 [18:57:28] Epoch 36: Loss(train): 0.051882 Loss(val): 0.052147 [18:58:00] Epoch 38: Loss(train): 0.051624 Loss(val): 0.051983 [18:58:33] Epoch 40: Loss(train): 0.051157 Loss(val): 0.051610 [18:58:36] FINAL(40) Loss(val): 0.051610 Accuarcy: 0.654694 Search 6 of 500 momentum0.9, features=[64, 64, 64], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.1 [18:58:51] INIT Loss(val): 0.137227 Accuarcy: 0.089082 [18:59:25] Epoch 2: Loss(train): 0.076255 Loss(val): 0.077085 [18:59:58] Epoch 4: Loss(train): 0.069647 Loss(val): 0.070416 [19:00:30] Epoch 6: Loss(train): 0.066060 Loss(val): 0.066870 [19:01:03] Epoch 8: Loss(train): 0.063875 Loss(val): 0.064680 [19:01:36] Epoch 10: Loss(train): 0.061816 Loss(val): 0.062256 [19:02:07] Epoch 12: Loss(train): 0.062256 Loss(val): 0.061819 [19:02:39] Epoch 14: Loss(train): 0.060937 Loss(val): 0.060642 [19:03:10] Epoch 16: Loss(train): 0.059295 Loss(val): 0.059192 [19:03:43] Epoch 18: Loss(train): 0.058088 Loss(val): 0.058086 [19:04:15] Epoch 20: Loss(train): 0.057366 Loss(val): 0.057583 [19:04:48] Epoch 22: Loss(train): 0.056409 Loss(val): 0.056883 [19:05:20] Epoch 24: Loss(train): 0.055725 Loss(val): 0.056264 [19:05:54] Epoch 26: Loss(train): 0.055122 Loss(val): 0.055765 [19:06:26] Epoch 28: Loss(train): 0.054427 Loss(val): 0.055090 [19:06:58] Epoch 30: Loss(train): 0.053994 Loss(val): 0.054728 [19:07:30] Epoch 32: Loss(train): 0.053254 Loss(val): 0.054063 [19:08:02] Epoch 34: Loss(train): 0.052780 Loss(val): 0.053672 [19:08:34] Epoch 36: Loss(train): 0.052507 Loss(val): 0.053308 [19:09:06] Epoch 38: Loss(train): 0.052088 Loss(val): 0.052931 [19:09:38] Epoch 40: Loss(train): 0.051752 Loss(val): 0.052681 [19:09:41] FINAL(40) Loss(val): 0.052681 Accuarcy: 0.620153 Search 7 of 500 momentum0.96, features=[64, 64, 64], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1 [19:09:56] INIT Loss(val): 0.129805 Accuarcy: 0.096088 [19:10:31] Epoch 2: Loss(train): 0.078690 Loss(val): 0.079806 [19:11:03] Epoch 4: Loss(train): 0.068390 Loss(val): 0.069582 [19:11:34] Epoch 6: Loss(train): 0.065949 Loss(val): 0.067527 [19:12:06] Epoch 8: Loss(train): 0.063865 Loss(val): 0.065614 [19:12:37] Epoch 10: Loss(train): 0.061512 Loss(val): 0.062654 [19:13:09] Epoch 12: Loss(train): 0.061305 Loss(val): 0.061241 [19:13:41] Epoch 14: Loss(train): 0.060556 Loss(val): 0.060605 [19:14:14] Epoch 16: Loss(train): 0.059072 Loss(val): 0.059439 [19:14:46] Epoch 18: Loss(train): 0.057793 Loss(val): 0.058227 [19:15:19] Epoch 20: Loss(train): 0.057246 Loss(val): 0.057806 [19:15:51] Epoch 22: Loss(train): 0.056266 Loss(val): 0.056918 [19:16:24] Epoch 24: Loss(train): 0.055712 Loss(val): 0.056347 [19:16:56] Epoch 26: Loss(train): 0.055176 Loss(val): 0.055786 [19:17:28] Epoch 28: Loss(train): 0.054897 Loss(val): 0.055550 [19:18:01] Epoch 30: Loss(train): 0.054363 Loss(val): 0.055041 [19:18:35] Epoch 32: Loss(train): 0.053887 Loss(val): 0.054487 [19:19:08] Epoch 34: Loss(train): 0.053423 Loss(val): 0.054010 [19:19:41] Epoch 36: Loss(train): 0.053153 Loss(val): 0.053742 [19:20:13] Epoch 38: Loss(train): 0.052864 Loss(val): 0.053482 [19:20:46] Epoch 40: Loss(train): 0.052327 Loss(val): 0.052950 [19:20:49] FINAL(40) Loss(val): 0.052950 Accuarcy: 0.619643 Search 8 of 500 momentum0.94, features=[32, 64, 128], dropout_rate=0.8 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.1 [19:21:05] INIT Loss(val): 0.126856 Accuarcy: 0.097840 [19:21:42] Epoch 2: Loss(train): 0.076776 Loss(val): 0.077285 [19:22:15] Epoch 4: Loss(train): 0.069062 Loss(val): 0.070340 [19:22:48] Epoch 6: Loss(train): 0.066273 Loss(val): 0.068127 [19:23:22] Epoch 8: Loss(train): 0.063386 Loss(val): 0.064870 [19:23:55] Epoch 10: Loss(train): 0.062181 Loss(val): 0.061876 [19:24:29] Epoch 12: Loss(train): 0.060695 Loss(val): 0.060278 [19:25:03] Epoch 14: Loss(train): 0.059365 Loss(val): 0.059288 [19:25:36] Epoch 16: Loss(train): 0.058426 Loss(val): 0.058403 [19:26:09] Epoch 18: Loss(train): 0.057446 Loss(val): 0.057615 [19:26:41] Epoch 20: Loss(train): 0.056655 Loss(val): 0.056790 [19:27:15] Epoch 22: Loss(train): 0.056013 Loss(val): 0.056254 [19:27:49] Epoch 24: Loss(train): 0.055537 Loss(val): 0.055781 [19:28:21] Epoch 26: Loss(train): 0.055158 Loss(val): 0.055334 [19:28:54] Epoch 28: Loss(train): 0.054319 Loss(val): 0.054558 [19:29:27] Epoch 30: Loss(train): 0.054050 Loss(val): 0.054258 [19:30:00] Epoch 32: Loss(train): 0.053499 Loss(val): 0.053745 [19:30:33] Epoch 34: Loss(train): 0.053151 Loss(val): 0.053429 [19:31:07] Epoch 36: Loss(train): 0.052711 Loss(val): 0.052953 [19:31:40] Epoch 38: Loss(train): 0.052423 Loss(val): 0.052699 [19:32:14] Epoch 40: Loss(train): 0.052046 Loss(val): 0.052357 [19:32:18] FINAL(40) Loss(val): 0.052357 Accuarcy: 0.630391 Search 9 of 500 momentum0.94, features=[32, 32, 32], dropout_rate=0.8 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003 [19:32:34] INIT Loss(val): 0.131642 Accuarcy: 0.090017 [19:33:10] Epoch 2: Loss(train): 0.076535 Loss(val): 0.077012 [19:33:44] Epoch 4: Loss(train): 0.068991 Loss(val): 0.070012 [19:34:17] Epoch 6: Loss(train): 0.066681 Loss(val): 0.068178 [19:34:52] Epoch 8: Loss(train): 0.063801 Loss(val): 0.065194 [19:35:27] Epoch 10: Loss(train): 0.062749 Loss(val): 0.062579 [19:36:03] Epoch 12: Loss(train): 0.061396 Loss(val): 0.061188 [19:36:36] Epoch 14: Loss(train): 0.059828 Loss(val): 0.059840 [19:37:09] Epoch 16: Loss(train): 0.058267 Loss(val): 0.058412 [19:37:42] Epoch 18: Loss(train): 0.056997 Loss(val): 0.057319 [19:38:17] Epoch 20: Loss(train): 0.056212 Loss(val): 0.056592 [19:38:51] Epoch 22: Loss(train): 0.055699 Loss(val): 0.056120 [19:39:25] Epoch 24: Loss(train): 0.055265 Loss(val): 0.055732 [19:40:00] Epoch 26: Loss(train): 0.054956 Loss(val): 0.055544 [19:40:35] Epoch 28: Loss(train): 0.054316 Loss(val): 0.054904 [19:41:09] Epoch 30: Loss(train): 0.053904 Loss(val): 0.054533 [19:41:42] Epoch 32: Loss(train): 0.053655 Loss(val): 0.054287 [19:42:16] Epoch 34: Loss(train): 0.053000 Loss(val): 0.053676 [19:42:50] Epoch 36: Loss(train): 0.052240 Loss(val): 0.053027 [19:43:24] Epoch 38: Loss(train): 0.051914 Loss(val): 0.052626 [19:43:59] Epoch 40: Loss(train): 0.051464 Loss(val): 0.052201 [19:44:02] FINAL(40) Loss(val): 0.052201 Accuarcy: 0.634864 Search 10 of 500 momentum0.94, features=[32, 32, 32], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.3 [19:44:18] INIT Loss(val): 0.147838 Accuarcy: 0.096224 [19:44:56] Epoch 2: Loss(train): 0.073673 Loss(val): 0.074037 [19:45:29] Epoch 4: Loss(train): 0.066077 Loss(val): 0.066764 [19:46:04] Epoch 6: Loss(train): 0.063749 Loss(val): 0.064495 [19:46:39] Epoch 8: Loss(train): 0.061897 Loss(val): 0.062830 [19:47:13] Epoch 10: Loss(train): 0.059758 Loss(val): 0.059846 [19:47:47] Epoch 12: Loss(train): 0.060317 Loss(val): 0.059947 [19:48:20] Epoch 14: Loss(train): 0.059522 Loss(val): 0.059213 [19:48:55] Epoch 16: Loss(train): 0.058307 Loss(val): 0.058215 [19:49:29] Epoch 18: Loss(train): 0.057684 Loss(val): 0.057823 [19:50:03] Epoch 20: Loss(train): 0.056430 Loss(val): 0.056712 [19:50:37] Epoch 22: Loss(train): 0.055679 Loss(val): 0.056159 [19:51:12] Epoch 24: Loss(train): 0.055052 Loss(val): 0.055606 [19:51:53] Epoch 26: Loss(train): 0.054097 Loss(val): 0.054759 [19:52:54] Epoch 28: Loss(train): 0.053659 Loss(val): 0.054303 [19:53:57] Epoch 30: Loss(train): 0.053202 Loss(val): 0.053859 [19:55:01] Epoch 32: Loss(train): 0.052676 Loss(val): 0.053317 [19:56:05] Epoch 34: Loss(train): 0.052394 Loss(val): 0.053083 [19:57:09] Epoch 36: Loss(train): 0.051938 Loss(val): 0.052596 [19:58:14] Epoch 38: Loss(train): 0.051952 Loss(val): 0.052589 [19:59:18] Epoch 40: Loss(train): 0.051624 Loss(val): 0.052260 [19:59:27] FINAL(40) Loss(val): 0.052260 Accuarcy: 0.640425 Search 11 of 500 momentum0.94, features=[32, 64, 128], dropout_rate=0.8 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.01 [20:00:05] INIT Loss(val): 0.128097 Accuarcy: 0.100561 [20:01:16] Epoch 2: Loss(train): 0.076811 Loss(val): 0.077396 [20:02:46] Epoch 4: Loss(train): 0.067419 Loss(val): 0.068027 [20:03:52] Epoch 6: Loss(train): 0.064314 Loss(val): 0.064847 [20:05:02] Epoch 8: Loss(train): 0.062622 Loss(val): 0.063239 [20:06:05] Epoch 10: Loss(train): 0.062587 Loss(val): 0.061918 [20:07:18] Epoch 12: Loss(train): 0.062067 Loss(val): 0.061365 [20:08:35] Epoch 14: Loss(train): 0.060435 Loss(val): 0.059998 [20:09:40] Epoch 16: Loss(train): 0.059291 Loss(val): 0.059136 [20:10:47] Epoch 18: Loss(train): 0.058127 Loss(val): 0.058230 [20:11:56] Epoch 20: Loss(train): 0.057044 Loss(val): 0.057275 [20:13:07] Epoch 22: Loss(train): 0.056308 Loss(val): 0.056648 [20:14:12] Epoch 24: Loss(train): 0.055546 Loss(val): 0.055942 [20:15:17] Epoch 26: Loss(train): 0.054920 Loss(val): 0.055446 [20:16:23] Epoch 28: Loss(train): 0.054230 Loss(val): 0.054767 [20:17:29] Epoch 30: Loss(train): 0.053606 Loss(val): 0.054120 [20:18:38] Epoch 32: Loss(train): 0.053080 Loss(val): 0.053653 [20:19:46] Epoch 34: Loss(train): 0.052750 Loss(val): 0.053295 [20:20:56] Epoch 36: Loss(train): 0.052336 Loss(val): 0.052997 [20:22:03] Epoch 38: Loss(train): 0.052112 Loss(val): 0.052736 [20:23:12] Epoch 40: Loss(train): 0.051778 Loss(val): 0.052454 [20:23:21] FINAL(40) Loss(val): 0.052454 Accuarcy: 0.635867 Search 12 of 500 momentum0.98, features=[64, 64, 64], dropout_rate=0.4 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.003 [20:24:00] INIT Loss(val): 0.148256 Accuarcy: 0.087296 [20:25:12] Epoch 2: Loss(train): 0.079662 Loss(val): 0.080293 [20:26:18] Epoch 4: Loss(train): 0.070002 Loss(val): 0.070702 [20:27:29] Epoch 6: Loss(train): 0.067117 Loss(val): 0.068561 [20:28:37] Epoch 8: Loss(train): 0.064559 Loss(val): 0.066135 [20:29:44] Epoch 10: Loss(train): 0.062571 Loss(val): 0.062898 [20:30:51] Epoch 12: Loss(train): 0.061983 Loss(val): 0.062073 [20:31:58] Epoch 14: Loss(train): 0.060905 Loss(val): 0.061123 [20:33:04] Epoch 16: Loss(train): 0.059509 Loss(val): 0.059778 [20:34:11] Epoch 18: Loss(train): 0.058631 Loss(val): 0.058973 [20:35:21] Epoch 20: Loss(train): 0.057901 Loss(val): 0.058287 [20:36:31] Epoch 22: Loss(train): 0.057311 Loss(val): 0.057726 [20:37:40] Epoch 24: Loss(train): 0.056381 Loss(val): 0.056801 [20:38:48] Epoch 26: Loss(train): 0.055594 Loss(val): 0.056158 [20:39:55] Epoch 28: Loss(train): 0.055280 Loss(val): 0.055773 [20:41:04] Epoch 30: Loss(train): 0.054724 Loss(val): 0.055232 [20:42:15] Epoch 32: Loss(train): 0.054286 Loss(val): 0.054696 [20:43:23] Epoch 34: Loss(train): 0.054172 Loss(val): 0.054566 [20:44:34] Epoch 36: Loss(train): 0.053554 Loss(val): 0.053958 [20:45:45] Epoch 38: Loss(train): 0.053425 Loss(val): 0.053765 Early stopping with Loss(train) 0.054845 at epoch 38 (Accuracy: 0.584490) Search 13 of 500 momentum0.99, features=[32, 32, 32], dropout_rate=0.4 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.003 [20:46:34] INIT Loss(val): 0.140461 Accuarcy: 0.104388 [20:47:52] Epoch 2: Loss(train): 0.076866 Loss(val): 0.076988 [20:49:05] Epoch 4: Loss(train): 0.068130 Loss(val): 0.069167 [20:50:14] Epoch 6: Loss(train): 0.064605 Loss(val): 0.065783 [20:51:26] Epoch 8: Loss(train): 0.062285 Loss(val): 0.063043 [20:52:39] Epoch 10: Loss(train): 0.061567 Loss(val): 0.061103 [20:53:52] Epoch 12: Loss(train): 0.060784 Loss(val): 0.060254 [20:55:15] Epoch 14: Loss(train): 0.059443 Loss(val): 0.059142 [20:56:25] Epoch 16: Loss(train): 0.058249 Loss(val): 0.058051 [20:57:36] Epoch 18: Loss(train): 0.057259 Loss(val): 0.057268 [20:58:51] Epoch 20: Loss(train): 0.056265 Loss(val): 0.056417 [21:00:38] Epoch 22: Loss(train): 0.055420 Loss(val): 0.055663 [21:02:37] Epoch 24: Loss(train): 0.054498 Loss(val): 0.054781 [21:04:27] Epoch 26: Loss(train): 0.054194 Loss(val): 0.054544 [21:06:17] Epoch 28: Loss(train): 0.053792 Loss(val): 0.054235 [21:07:45] Epoch 30: Loss(train): 0.053412 Loss(val): 0.053891 [21:09:00] Epoch 32: Loss(train): 0.052879 Loss(val): 0.053408 [21:10:13] Epoch 34: Loss(train): 0.052690 Loss(val): 0.053264 [21:11:49] Epoch 36: Loss(train): 0.052348 Loss(val): 0.052886 [21:13:17] Epoch 38: Loss(train): 0.052192 Loss(val): 0.052790 [21:14:31] Epoch 40: Loss(train): 0.051947 Loss(val): 0.052624 [21:14:41] FINAL(40) Loss(val): 0.052624 Accuarcy: 0.635612 Search 14 of 500 momentum0.94, features=[64, 64, 64], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.3 [21:15:24] INIT Loss(val): 0.140948 Accuarcy: 0.090221 [21:16:49] Epoch 2: Loss(train): 0.073946 Loss(val): 0.074002 [21:18:12] Epoch 4: Loss(train): 0.067837 Loss(val): 0.068280 [21:19:25] Epoch 6: Loss(train): 0.065246 Loss(val): 0.066273 [21:20:41] Epoch 8: Loss(train): 0.062865 Loss(val): 0.064018 [21:21:56] Epoch 10: Loss(train): 0.060457 Loss(val): 0.061356 [21:23:07] Epoch 12: Loss(train): 0.060426 Loss(val): 0.060503 [21:24:19] Epoch 14: Loss(train): 0.059488 Loss(val): 0.059690 [21:25:30] Epoch 16: Loss(train): 0.058704 Loss(val): 0.059006 [21:26:42] Epoch 18: Loss(train): 0.057890 Loss(val): 0.058259 [21:27:56] Epoch 20: Loss(train): 0.056893 Loss(val): 0.057368 [21:29:09] Epoch 22: Loss(train): 0.056089 Loss(val): 0.056659 [21:30:30] Epoch 24: Loss(train): 0.055805 Loss(val): 0.056313 [21:32:29] Epoch 26: Loss(train): 0.055030 Loss(val): 0.055562 [21:34:07] Epoch 28: Loss(train): 0.054475 Loss(val): 0.055053 [21:35:34] Epoch 30: Loss(train): 0.053939 Loss(val): 0.054514 [21:37:03] Epoch 32: Loss(train): 0.053544 Loss(val): 0.054175 [21:38:43] Epoch 34: Loss(train): 0.053206 Loss(val): 0.053755 [21:40:20] Epoch 36: Loss(train): 0.052790 Loss(val): 0.053353 [21:41:49] Epoch 38: Loss(train): 0.052477 Loss(val): 0.053059 [21:43:08] Epoch 40: Loss(train): 0.052117 Loss(val): 0.052704 [21:43:19] FINAL(40) Loss(val): 0.052704 Accuarcy: 0.630085 Search 15 of 500 momentum0.98, features=[96, 192, 192], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=1.0 [21:44:07] INIT Loss(val): 0.128062 Accuarcy: 0.099609 [21:45:40] Epoch 2: Loss(train): 0.076486 Loss(val): 0.076834 [21:47:06] Epoch 4: Loss(train): 0.069786 Loss(val): 0.070414 [21:48:20] Epoch 6: Loss(train): 0.066293 Loss(val): 0.067163 [21:49:39] Epoch 8: Loss(train): 0.064205 Loss(val): 0.065383 [21:51:00] Epoch 10: Loss(train): 0.062865 Loss(val): 0.063068 [21:52:18] Epoch 12: Loss(train): 0.062064 Loss(val): 0.062093 [21:53:34] Epoch 14: Loss(train): 0.060184 Loss(val): 0.060345 [21:54:52] Epoch 16: Loss(train): 0.058316 Loss(val): 0.058735 [21:56:08] Epoch 18: Loss(train): 0.057090 Loss(val): 0.057527 [21:57:26] Epoch 20: Loss(train): 0.056003 Loss(val): 0.056649 [21:58:44] Epoch 22: Loss(train): 0.055251 Loss(val): 0.056051 [22:00:01] Epoch 24: Loss(train): 0.054980 Loss(val): 0.055802 [22:01:34] Epoch 26: Loss(train): 0.054432 Loss(val): 0.055166 [22:03:15] Epoch 28: Loss(train): 0.054014 Loss(val): 0.054775 [22:04:48] Epoch 30: Loss(train): 0.053364 Loss(val): 0.054182 [22:06:32] Epoch 32: Loss(train): 0.053011 Loss(val): 0.053829 [22:08:16] Epoch 34: Loss(train): 0.052472 Loss(val): 0.053219 [22:09:45] Epoch 36: Loss(train): 0.052156 Loss(val): 0.052837 [22:11:13] Epoch 38: Loss(train): 0.051691 Loss(val): 0.052349 [22:12:40] Epoch 40: Loss(train): 0.051328 Loss(val): 0.051960 [22:12:53] FINAL(40) Loss(val): 0.051960 Accuarcy: 0.644507 Search 16 of 500 momentum0.96, features=[96, 192, 192], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(7, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=1.0 [22:13:50] INIT Loss(val): 0.155339 Accuarcy: 0.101888 [22:15:19] Epoch 2: Loss(train): 0.076373 Loss(val): 0.077512 [22:16:45] Epoch 4: Loss(train): 0.069962 Loss(val): 0.071391 [22:18:07] Epoch 6: Loss(train): 0.066614 Loss(val): 0.067830 [22:19:30] Epoch 8: Loss(train): 0.063646 Loss(val): 0.064866 [22:20:51] Epoch 10: Loss(train): 0.061357 Loss(val): 0.061409 [22:22:11] Epoch 12: Loss(train): 0.060890 Loss(val): 0.060584 [22:23:32] Epoch 14: Loss(train): 0.060370 Loss(val): 0.060146 [22:24:51] Epoch 16: Loss(train): 0.058836 Loss(val): 0.059034 [22:26:09] Epoch 18: Loss(train): 0.058080 Loss(val): 0.058426 [22:27:31] Epoch 20: Loss(train): 0.056827 Loss(val): 0.057453 [22:28:50] Epoch 22: Loss(train): 0.055707 Loss(val): 0.056433 [22:30:10] Epoch 24: Loss(train): 0.055046 Loss(val): 0.055827 [22:31:48] Epoch 26: Loss(train): 0.054370 Loss(val): 0.055342 [22:33:53] Epoch 28: Loss(train): 0.053903 Loss(val): 0.054847 [22:35:30] Epoch 30: Loss(train): 0.053405 Loss(val): 0.054422 [22:37:03] Epoch 32: Loss(train): 0.053015 Loss(val): 0.054073 [22:38:31] Epoch 34: Loss(train): 0.052485 Loss(val): 0.053563 [22:40:20] Epoch 36: Loss(train): 0.052231 Loss(val): 0.053308 [22:41:44] Epoch 38: Loss(train): 0.051928 Loss(val): 0.053000 [22:43:16] Epoch 40: Loss(train): 0.051724 Loss(val): 0.052842 [22:43:29] FINAL(40) Loss(val): 0.052842 Accuarcy: 0.618367 Search 17 of 500 momentum0.9, features=[96, 192, 192], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(7, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.3 [22:44:29] INIT Loss(val): 0.140444 Accuarcy: 0.087840 [22:45:59] Epoch 2: Loss(train): 0.077613 Loss(val): 0.077837 [22:47:22] Epoch 4: Loss(train): 0.069080 Loss(val): 0.069612 [22:48:55] Epoch 6: Loss(train): 0.065800 Loss(val): 0.066876 [22:50:25] Epoch 8: Loss(train): 0.064667 Loss(val): 0.066089 [22:51:52] Epoch 10: Loss(train): 0.062540 Loss(val): 0.063536 [22:53:13] Epoch 12: Loss(train): 0.062173 Loss(val): 0.062304 [22:54:37] Epoch 14: Loss(train): 0.060266 Loss(val): 0.060558 [22:56:00] Epoch 16: Loss(train): 0.059095 Loss(val): 0.059716 Early stopping with Loss(train) 0.060400 at epoch 17 (Accuracy: 0.520408) Search 18 of 500 momentum0.9, features=[32, 32, 32], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.01 [22:57:35] INIT Loss(val): 0.122866 Accuarcy: 0.100969 [22:59:04] Epoch 2: Loss(train): 0.083609 Loss(val): 0.083896 [23:00:25] Epoch 4: Loss(train): 0.071636 Loss(val): 0.072329 [23:01:49] Epoch 6: Loss(train): 0.068079 Loss(val): 0.068947 [23:03:15] Epoch 8: Loss(train): 0.064902 Loss(val): 0.065714 [23:05:29] Epoch 10: Loss(train): 0.064068 Loss(val): 0.063601 [23:07:25] Epoch 12: Loss(train): 0.062458 Loss(val): 0.062049 [23:09:11] Epoch 14: Loss(train): 0.061018 Loss(val): 0.060821 [23:11:11] Epoch 16: Loss(train): 0.059970 Loss(val): 0.060042 [23:12:45] Epoch 18: Loss(train): 0.059173 Loss(val): 0.059317 [23:14:32] Epoch 20: Loss(train): 0.058569 Loss(val): 0.058824 [23:16:06] Epoch 22: Loss(train): 0.057706 Loss(val): 0.058048 [23:17:38] Epoch 24: Loss(train): 0.057017 Loss(val): 0.057352 [23:19:03] Epoch 26: Loss(train): 0.056235 Loss(val): 0.056608 [23:20:39] Epoch 28: Loss(train): 0.055231 Loss(val): 0.055639 [23:22:06] Epoch 30: Loss(train): 0.054434 Loss(val): 0.054796 [23:23:38] Epoch 32: Loss(train): 0.053868 Loss(val): 0.054278 [23:25:03] Epoch 34: Loss(train): 0.053368 Loss(val): 0.053715 [23:26:29] Epoch 36: Loss(train): 0.052716 Loss(val): 0.053143 [23:27:56] Epoch 38: Loss(train): 0.052115 Loss(val): 0.052569 [23:29:22] Epoch 40: Loss(train): 0.051541 Loss(val): 0.052065 [23:29:35] FINAL(40) Loss(val): 0.052065 Accuarcy: 0.634830 Search 19 of 500 momentum0.94, features=[96, 192, 192], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.03 [23:30:26] INIT Loss(val): 0.144806 Accuarcy: 0.112262 [23:31:59] Epoch 2: Loss(train): 0.074827 Loss(val): 0.075901 [23:33:26] Epoch 4: Loss(train): 0.068091 Loss(val): 0.069344 [23:35:18] Epoch 6: Loss(train): 0.065056 Loss(val): 0.066292 [23:37:15] Epoch 8: Loss(train): 0.063074 Loss(val): 0.064072 [23:39:25] Epoch 10: Loss(train): 0.063645 Loss(val): 0.063041 [23:41:05] Epoch 12: Loss(train): 0.061954 Loss(val): 0.061517 [23:42:54] Epoch 14: Loss(train): 0.060315 Loss(val): 0.060035 [23:44:37] Epoch 16: Loss(train): 0.058829 Loss(val): 0.058677 [23:46:22] Epoch 18: Loss(train): 0.057952 Loss(val): 0.058050 [23:48:02] Epoch 20: Loss(train): 0.056611 Loss(val): 0.056781 [23:49:36] Epoch 22: Loss(train): 0.055679 Loss(val): 0.056027 [23:51:07] Epoch 24: Loss(train): 0.054832 Loss(val): 0.055280 [23:52:42] Epoch 26: Loss(train): 0.053982 Loss(val): 0.054524 [23:54:12] Epoch 28: Loss(train): 0.053434 Loss(val): 0.054000 [23:55:44] Epoch 30: Loss(train): 0.052832 Loss(val): 0.053443 [23:57:20] Epoch 32: Loss(train): 0.052402 Loss(val): 0.053045 [23:58:49] Epoch 34: Loss(train): 0.051839 Loss(val): 0.052468 [00:00:16] Epoch 36: Loss(train): 0.051443 Loss(val): 0.052053 [00:01:42] Epoch 38: Loss(train): 0.051110 Loss(val): 0.051693 [00:03:09] Epoch 40: Loss(train): 0.050843 Loss(val): 0.051443 [00:03:22] FINAL(40) Loss(val): 0.051443 Accuarcy: 0.653452 Search 20 of 500 momentum0.9, features=[64, 64, 64], dropout_rate=0.4 kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.003 [00:04:16] INIT Loss(val): 0.149194 Accuarcy: 0.082381 [00:05:57] Epoch 2: Loss(train): 0.075874 Loss(val): 0.075935 [00:07:50] Epoch 4: Loss(train): 0.069149 Loss(val): 0.069925 [00:09:56] Epoch 6: Loss(train): 0.066218 Loss(val): 0.067506 [00:11:34] Epoch 8: Loss(train): 0.064170 Loss(val): 0.064543 [00:13:19] Epoch 10: Loss(train): 0.062819 Loss(val): 0.062660 [00:14:51] Epoch 12: Loss(train): 0.061025 Loss(val): 0.061116 [00:16:24] Epoch 14: Loss(train): 0.059640 Loss(val): 0.059809 [00:17:59] Epoch 16: Loss(train): 0.058324 Loss(val): 0.058535 [00:19:29] Epoch 18: Loss(train): 0.057796 Loss(val): 0.057942 [00:21:10] Epoch 20: Loss(train): 0.056837 Loss(val): 0.057051 [00:22:49] Epoch 22: Loss(train): 0.056581 Loss(val): 0.056687 [00:24:27] Epoch 24: Loss(train): 0.055684 Loss(val): 0.055845 [00:26:04] Epoch 26: Loss(train): 0.055280 Loss(val): 0.055467 [00:27:38] Epoch 28: Loss(train): 0.054666 Loss(val): 0.054960 [00:29:12] Epoch 30: Loss(train): 0.054681 Loss(val): 0.054927 [00:30:50] Epoch 32: Loss(train): 0.053786 Loss(val): 0.054179 [00:32:24] Epoch 34: Loss(train): 0.053478 Loss(val): 0.053842 [00:33:57] Epoch 36: Loss(train): 0.052924 Loss(val): 0.053352 [00:35:35] Epoch 38: Loss(train): 0.052522 Loss(val): 0.053011 [00:37:10] Epoch 40: Loss(train): 0.051971 Loss(val): 0.052540 [00:37:21] FINAL(40) Loss(val): 0.052540 Accuarcy: 0.630085 Search 21 of 500 momentum0.92, features=[96, 192, 192], dropout_rate=0.4 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=1.0 [00:38:18] INIT Loss(val): 0.129994 Accuarcy: 0.091718 [00:39:56] Epoch 2: Loss(train): 0.078515 Loss(val): 0.079490 [00:41:28] Epoch 4: Loss(train): 0.070014 Loss(val): 0.071300 [00:43:03] Epoch 6: Loss(train): 0.066612 Loss(val): 0.067780 [00:44:45] Epoch 8: Loss(train): 0.063762 Loss(val): 0.065018 [00:46:24] Epoch 10: Loss(train): 0.061744 Loss(val): 0.062313 [00:48:00] Epoch 12: Loss(train): 0.061314 Loss(val): 0.060970 [00:49:35] Epoch 14: Loss(train): 0.059673 Loss(val): 0.059553 [00:51:11] Epoch 16: Loss(train): 0.058347 Loss(val): 0.058497 [00:52:47] Epoch 18: Loss(train): 0.057153 Loss(val): 0.057509 [00:54:24] Epoch 20: Loss(train): 0.056539 Loss(val): 0.057007 [00:55:59] Epoch 22: Loss(train): 0.055904 Loss(val): 0.056457 [00:57:37] Epoch 24: Loss(train): 0.055228 Loss(val): 0.055896 [00:59:20] Epoch 26: Loss(train): 0.054506 Loss(val): 0.055190 [01:01:02] Epoch 28: Loss(train): 0.054147 Loss(val): 0.054805 [01:02:39] Epoch 30: Loss(train): 0.053628 Loss(val): 0.054319 [01:04:21] Epoch 32: Loss(train): 0.053111 Loss(val): 0.053844 [01:06:04] Epoch 34: Loss(train): 0.052740 Loss(val): 0.053401 [01:07:48] Epoch 36: Loss(train): 0.052391 Loss(val): 0.053115 [01:09:25] Epoch 38: Loss(train): 0.052113 Loss(val): 0.052774 [01:11:02] Epoch 40: Loss(train): 0.051565 Loss(val): 0.052283 [01:11:16] FINAL(40) Loss(val): 0.052283 Accuarcy: 0.628997 Search 22 of 500 momentum0.9, features=[32, 32, 32], dropout_rate=0.8 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.01 [01:12:17] INIT Loss(val): 0.135674 Accuarcy: 0.100561 [01:13:58] Epoch 2: Loss(train): 0.077077 Loss(val): 0.077243 [01:15:36] Epoch 4: Loss(train): 0.068678 Loss(val): 0.069657 [01:17:13] Epoch 6: Loss(train): 0.065798 Loss(val): 0.067142 [01:18:52] Epoch 8: Loss(train): 0.063817 Loss(val): 0.065166 [01:20:27] Epoch 10: Loss(train): 0.062389 Loss(val): 0.062729 [01:22:02] Epoch 12: Loss(train): 0.061790 Loss(val): 0.061513 [01:23:42] Epoch 14: Loss(train): 0.060073 Loss(val): 0.060047 [01:25:21] Epoch 16: Loss(train): 0.059103 Loss(val): 0.059342 [01:27:01] Epoch 18: Loss(train): 0.057784 Loss(val): 0.058103 [01:28:43] Epoch 20: Loss(train): 0.056717 Loss(val): 0.057255 [01:30:23] Epoch 22: Loss(train): 0.056179 Loss(val): 0.056797 [01:32:05] Epoch 24: Loss(train): 0.055371 Loss(val): 0.056182 [01:33:42] Epoch 26: Loss(train): 0.054835 Loss(val): 0.055637 [01:35:21] Epoch 28: Loss(train): 0.054415 Loss(val): 0.055158 [01:37:00] Epoch 30: Loss(train): 0.053935 Loss(val): 0.054692 [01:38:45] Epoch 32: Loss(train): 0.053190 Loss(val): 0.054009 [01:40:29] Epoch 34: Loss(train): 0.052750 Loss(val): 0.053599 [01:42:10] Epoch 36: Loss(train): 0.052327 Loss(val): 0.053117 [01:44:00] Epoch 38: Loss(train): 0.051865 Loss(val): 0.052722 [01:45:43] Epoch 40: Loss(train): 0.051399 Loss(val): 0.052158 [01:45:57] FINAL(40) Loss(val): 0.052158 Accuarcy: 0.637602 Search 23 of 500 momentum0.94, features=[32, 32, 32], dropout_rate=0.4 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003 [01:46:59] INIT Loss(val): 0.148260 Accuarcy: 0.107823 [01:48:51] Epoch 2: Loss(train): 0.076951 Loss(val): 0.077701 [01:50:32] Epoch 4: Loss(train): 0.069506 Loss(val): 0.070086 [01:52:13] Epoch 6: Loss(train): 0.066102 Loss(val): 0.066808 [01:53:53] Epoch 8: Loss(train): 0.063157 Loss(val): 0.064062 [01:55:33] Epoch 10: Loss(train): 0.061524 Loss(val): 0.062082 [01:57:10] Epoch 12: Loss(train): 0.061474 Loss(val): 0.061203 [01:58:53] Epoch 14: Loss(train): 0.061059 Loss(val): 0.060495 [02:00:30] Epoch 16: Loss(train): 0.060032 Loss(val): 0.059752 [02:02:10] Epoch 18: Loss(train): 0.058489 Loss(val): 0.058475 [02:03:58] Epoch 20: Loss(train): 0.057222 Loss(val): 0.057490 [02:05:39] Epoch 22: Loss(train): 0.056035 Loss(val): 0.056433 [02:07:23] Epoch 24: Loss(train): 0.055428 Loss(val): 0.055924 [02:09:03] Epoch 26: Loss(train): 0.054762 Loss(val): 0.055473 [02:10:45] Epoch 28: Loss(train): 0.054114 Loss(val): 0.054938 [02:12:36] Epoch 30: Loss(train): 0.053423 Loss(val): 0.054303 [02:14:22] Epoch 32: Loss(train): 0.053115 Loss(val): 0.054204 [02:16:12] Epoch 34: Loss(train): 0.052551 Loss(val): 0.053640 [02:18:02] Epoch 36: Loss(train): 0.052369 Loss(val): 0.053541 [02:19:47] Epoch 38: Loss(train): 0.052099 Loss(val): 0.053308 [02:21:40] Epoch 40: Loss(train): 0.051823 Loss(val): 0.052996 [02:21:56] FINAL(40) Loss(val): 0.052996 Accuarcy: 0.616395 Search 24 of 500 momentum0.99, features=[32, 64, 128], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003 [02:23:02] INIT Loss(val): 0.133748 Accuarcy: 0.106752 [02:24:59] Epoch 2: Loss(train): 0.084285 Loss(val): 0.085462 [02:26:52] Epoch 4: Loss(train): 0.073009 Loss(val): 0.074089 [02:28:38] Epoch 6: Loss(train): 0.069297 Loss(val): 0.071221 [02:30:26] Epoch 8: Loss(train): 0.066733 Loss(val): 0.068690 [02:32:11] Epoch 10: Loss(train): 0.064559 Loss(val): 0.066219 [02:33:54] Epoch 12: Loss(train): 0.064015 Loss(val): 0.064132 [02:35:39] Epoch 14: Loss(train): 0.062375 Loss(val): 0.062579 [02:37:22] Epoch 16: Loss(train): 0.060784 Loss(val): 0.061219 [02:39:04] Epoch 18: Loss(train): 0.059065 Loss(val): 0.059696 [02:40:53] Epoch 20: Loss(train): 0.057731 Loss(val): 0.058328 [02:42:53] Epoch 22: Loss(train): 0.056679 Loss(val): 0.057394 [02:45:15] Epoch 24: Loss(train): 0.056036 Loss(val): 0.056692 [02:47:22] Epoch 26: Loss(train): 0.055356 Loss(val): 0.055885 [02:49:12] Epoch 28: Loss(train): 0.054560 Loss(val): 0.055035 [02:50:58] Epoch 30: Loss(train): 0.054176 Loss(val): 0.054564 [02:52:50] Epoch 32: Loss(train): 0.053846 Loss(val): 0.054231 [02:54:38] Epoch 34: Loss(train): 0.053485 Loss(val): 0.053779 [02:56:28] Epoch 36: Loss(train): 0.052997 Loss(val): 0.053439 [02:58:15] Epoch 38: Loss(train): 0.052470 Loss(val): 0.052942 [03:00:02] Epoch 40: Loss(train): 0.052162 Loss(val): 0.052635 [03:00:19] FINAL(40) Loss(val): 0.052635 Accuarcy: 0.628929 Search 25 of 500 momentum0.96, features=[64, 64, 64], dropout_rate=0.4 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.03 [03:01:32] INIT Loss(val): 0.139732 Accuarcy: 0.089677 [03:03:33] Epoch 2: Loss(train): 0.082942 Loss(val): 0.083400 [03:05:23] Epoch 4: Loss(train): 0.068298 Loss(val): 0.068648 [03:07:10] Epoch 6: Loss(train): 0.065199 Loss(val): 0.066157 [03:09:11] Epoch 8: Loss(train): 0.062757 Loss(val): 0.063605 [03:11:01] Epoch 10: Loss(train): 0.060662 Loss(val): 0.061181 [03:12:45] Epoch 12: Loss(train): 0.061178 Loss(val): 0.060557 [03:14:29] Epoch 14: Loss(train): 0.060727 Loss(val): 0.060099 [03:16:15] Epoch 16: Loss(train): 0.059698 Loss(val): 0.059351 [03:18:07] Epoch 18: Loss(train): 0.059191 Loss(val): 0.058946 [03:19:58] Epoch 20: Loss(train): 0.058253 Loss(val): 0.058241 [03:21:48] Epoch 22: Loss(train): 0.057497 Loss(val): 0.057644 [03:23:39] Epoch 24: Loss(train): 0.056404 Loss(val): 0.056670 [03:25:28] Epoch 26: Loss(train): 0.055838 Loss(val): 0.056263 [03:27:20] Epoch 28: Loss(train): 0.055152 Loss(val): 0.055671 [03:29:10] Epoch 30: Loss(train): 0.054532 Loss(val): 0.055117 [03:31:03] Epoch 32: Loss(train): 0.054211 Loss(val): 0.054901 [03:32:55] Epoch 34: Loss(train): 0.053131 Loss(val): 0.053860 [03:34:49] Epoch 36: Loss(train): 0.052709 Loss(val): 0.053471 [03:36:40] Epoch 38: Loss(train): 0.052348 Loss(val): 0.053128 [03:38:31] Epoch 40: Loss(train): 0.052056 Loss(val): 0.052836 [03:38:48] FINAL(40) Loss(val): 0.052836 Accuarcy: 0.626956 Search 26 of 500 momentum0.98, features=[64, 64, 64], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.3 [03:40:00] INIT Loss(val): 0.166028 Accuarcy: 0.109320 [03:42:03] Epoch 2: Loss(train): 0.078286 Loss(val): 0.079137 [03:43:50] Epoch 4: Loss(train): 0.069890 Loss(val): 0.070590 [03:45:42] Epoch 6: Loss(train): 0.068108 Loss(val): 0.069192 Early stopping with Loss(train) 0.069478 at epoch 6 (Accuracy: 0.431769) Search 27 of 500 momentum0.99, features=[32, 64, 128], dropout_rate=0.4 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.001 [03:47:05] INIT Loss(val): 0.130044 Accuarcy: 0.093027 [03:49:03] Epoch 2: Loss(train): 0.073057 Loss(val): 0.073623 [03:50:54] Epoch 4: Loss(train): 0.068406 Loss(val): 0.069279 [03:52:42] Epoch 6: Loss(train): 0.064816 Loss(val): 0.065600 [03:54:34] Epoch 8: Loss(train): 0.062026 Loss(val): 0.062644 [03:56:22] Epoch 10: Loss(train): 0.061487 Loss(val): 0.060742 [03:58:22] Epoch 12: Loss(train): 0.060632 Loss(val): 0.059967 [04:00:24] Epoch 14: Loss(train): 0.058923 Loss(val): 0.058691 [04:02:21] Epoch 16: Loss(train): 0.057931 Loss(val): 0.057902 [04:04:14] Epoch 18: Loss(train): 0.057200 Loss(val): 0.057431 [04:06:08] Epoch 20: Loss(train): 0.055906 Loss(val): 0.056321 [04:08:02] Epoch 22: Loss(train): 0.055524 Loss(val): 0.056040 [04:09:56] Epoch 24: Loss(train): 0.054580 Loss(val): 0.055210 [04:11:47] Epoch 26: Loss(train): 0.054290 Loss(val): 0.054957 [04:13:45] Epoch 28: Loss(train): 0.053581 Loss(val): 0.054256 [04:15:39] Epoch 30: Loss(train): 0.053228 Loss(val): 0.053937 [04:17:38] Epoch 32: Loss(train): 0.052785 Loss(val): 0.053508 Early stopping with Loss(train) 0.054120 at epoch 33 (Accuracy: 0.584745) Search 28 of 500 momentum0.9, features=[96, 192, 192], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003 [04:19:49] INIT Loss(val): 0.136011 Accuarcy: 0.099609 [04:21:53] Epoch 2: Loss(train): 0.077656 Loss(val): 0.077949 [04:23:48] Epoch 4: Loss(train): 0.068895 Loss(val): 0.069556 [04:25:40] Epoch 6: Loss(train): 0.066168 Loss(val): 0.067473 [04:27:36] Epoch 8: Loss(train): 0.063354 Loss(val): 0.064709 [04:29:29] Epoch 10: Loss(train): 0.061640 Loss(val): 0.062868 [04:31:19] Epoch 12: Loss(train): 0.061320 Loss(val): 0.060932 [04:33:10] Epoch 14: Loss(train): 0.059866 Loss(val): 0.059400 [04:35:01] Epoch 16: Loss(train): 0.058214 Loss(val): 0.058128 [04:36:51] Epoch 18: Loss(train): 0.057005 Loss(val): 0.057073 [04:38:50] Epoch 20: Loss(train): 0.056297 Loss(val): 0.056596 [04:40:46] Epoch 22: Loss(train): 0.055755 Loss(val): 0.056088 [04:42:41] Epoch 24: Loss(train): 0.055030 Loss(val): 0.055492 [04:44:38] Epoch 26: Loss(train): 0.054414 Loss(val): 0.054884 [04:46:31] Epoch 28: Loss(train): 0.053946 Loss(val): 0.054396 [04:48:26] Epoch 30: Loss(train): 0.053415 Loss(val): 0.053909 [04:50:21] Epoch 32: Loss(train): 0.052741 Loss(val): 0.053254 [04:52:18] Epoch 34: Loss(train): 0.052312 Loss(val): 0.052819 [04:54:15] Epoch 36: Loss(train): 0.051820 Loss(val): 0.052377 Early stopping with Loss(train) 0.053078 at epoch 37 (Accuracy: 0.614864) Search 29 of 500 momentum0.98, features=[32, 32, 32], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.03 [04:56:34] INIT Loss(val): 0.127762 Accuarcy: 0.093554 [04:58:39] Epoch 2: Loss(train): 0.081097 Loss(val): 0.081242 [05:00:37] Epoch 4: Loss(train): 0.070298 Loss(val): 0.070567 [05:02:37] Epoch 6: Loss(train): 0.065919 Loss(val): 0.066522 [05:04:42] Epoch 8: Loss(train): 0.063405 Loss(val): 0.064249 [05:06:38] Epoch 10: Loss(train): 0.063253 Loss(val): 0.062682 [05:08:34] Epoch 12: Loss(train): 0.061738 Loss(val): 0.061292 [05:10:33] Epoch 14: Loss(train): 0.060479 Loss(val): 0.060325 [05:12:27] Epoch 16: Loss(train): 0.059348 Loss(val): 0.059426 [05:14:21] Epoch 18: Loss(train): 0.058353 Loss(val): 0.058594 [05:16:16] Epoch 20: Loss(train): 0.057237 Loss(val): 0.057736 [05:18:12] Epoch 22: Loss(train): 0.056678 Loss(val): 0.057289 [05:20:15] Epoch 24: Loss(train): 0.055750 Loss(val): 0.056493 [05:22:22] Epoch 26: Loss(train): 0.055023 Loss(val): 0.055779 [05:24:29] Epoch 28: Loss(train): 0.054617 Loss(val): 0.055446 [05:26:34] Epoch 30: Loss(train): 0.053968 Loss(val): 0.054875 [05:28:41] Epoch 32: Loss(train): 0.053587 Loss(val): 0.054505 [05:30:46] Epoch 34: Loss(train): 0.053049 Loss(val): 0.054103 [05:32:47] Epoch 36: Loss(train): 0.052663 Loss(val): 0.053695 [05:34:43] Epoch 38: Loss(train): 0.052114 Loss(val): 0.053180 [05:37:04] Epoch 40: Loss(train): 0.051749 Loss(val): 0.052826 [05:37:22] FINAL(40) Loss(val): 0.052826 Accuarcy: 0.624507 Early stopping with Loss(train) 0.052970 at epoch 40 (Accuracy: 0.595935) Search 30 of 500 momentum0.98, features=[96, 192, 192], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.01 [05:39:00] INIT Loss(val): 0.127742 Accuarcy: 0.095663 [05:41:51] Epoch 2: Loss(train): 0.081031 Loss(val): 0.081951 [05:45:05] Epoch 4: Loss(train): 0.070746 Loss(val): 0.071572 [05:48:05] Epoch 6: Loss(train): 0.067815 Loss(val): 0.069164 [05:50:04] Epoch 8: Loss(train): 0.065377 Loss(val): 0.066656 [05:52:08] Epoch 10: Loss(train): 0.063210 Loss(val): 0.064106 [05:54:41] Epoch 12: Loss(train): 0.062753 Loss(val): 0.062455 [05:56:39] Epoch 14: Loss(train): 0.061486 Loss(val): 0.061452 [05:58:53] Epoch 16: Loss(train): 0.060042 Loss(val): 0.060332 [06:00:49] Epoch 18: Loss(train): 0.059365 Loss(val): 0.059779 [06:02:54] Epoch 20: Loss(train): 0.058556 Loss(val): 0.059076 [06:04:54] Epoch 22: Loss(train): 0.057655 Loss(val): 0.058139 [06:06:56] Epoch 24: Loss(train): 0.056971 Loss(val): 0.057441 [06:08:55] Epoch 26: Loss(train): 0.055996 Loss(val): 0.056477 [06:10:58] Epoch 28: Loss(train): 0.055765 Loss(val): 0.056300 [06:14:14] Epoch 30: Loss(train): 0.054864 Loss(val): 0.055330 [06:16:31] Epoch 32: Loss(train): 0.054358 Loss(val): 0.054817 [06:19:21] Epoch 34: Loss(train): 0.053667 Loss(val): 0.054224 [06:21:55] Epoch 36: Loss(train): 0.053111 Loss(val): 0.053710 [06:24:00] Epoch 38: Loss(train): 0.052701 Loss(val): 0.053255 [06:26:17] Epoch 40: Loss(train): 0.052049 Loss(val): 0.052678 [06:26:41] FINAL(40) Loss(val): 0.052678 Accuarcy: 0.631378 Search 31 of 500 momentum0.9, features=[32, 64, 128], dropout_rate=0.8 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.03 [06:28:02] INIT Loss(val): 0.133576 Accuarcy: 0.099745 [06:30:17] Epoch 2: Loss(train): 0.080279 Loss(val): 0.080963 [06:32:18] Epoch 4: Loss(train): 0.070567 Loss(val): 0.071757 [06:34:19] Epoch 6: Loss(train): 0.066493 Loss(val): 0.067333 [06:36:21] Epoch 8: Loss(train): 0.064128 Loss(val): 0.064883 [06:38:18] Epoch 10: Loss(train): 0.062089 Loss(val): 0.062799 [06:40:16] Epoch 12: Loss(train): 0.060965 Loss(val): 0.060560 [06:42:14] Epoch 14: Loss(train): 0.060438 Loss(val): 0.059971 [06:44:43] Epoch 16: Loss(train): 0.059131 Loss(val): 0.058957 [06:48:03] Epoch 18: Loss(train): 0.057784 Loss(val): 0.057753 [06:50:43] Epoch 20: Loss(train): 0.057066 Loss(val): 0.057209 [06:53:15] Epoch 22: Loss(train): 0.055903 Loss(val): 0.056190 [06:55:38] Epoch 24: Loss(train): 0.055119 Loss(val): 0.055490 [06:57:56] Epoch 26: Loss(train): 0.054542 Loss(val): 0.054945 [07:00:08] Epoch 28: Loss(train): 0.054099 Loss(val): 0.054588 [07:02:13] Epoch 30: Loss(train): 0.053754 Loss(val): 0.054166 [07:04:20] Epoch 32: Loss(train): 0.053401 Loss(val): 0.053892 [07:06:25] Epoch 34: Loss(train): 0.053173 Loss(val): 0.053717 [07:08:27] Epoch 36: Loss(train): 0.052537 Loss(val): 0.053207 [07:10:29] Epoch 38: Loss(train): 0.052195 Loss(val): 0.052842 [07:12:29] Epoch 40: Loss(train): 0.051708 Loss(val): 0.052489 [07:12:47] FINAL(40) Loss(val): 0.052489 Accuarcy: 0.625255 Search 32 of 500 momentum0.99, features=[32, 32, 32], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003 [07:14:30] INIT Loss(val): 0.133619 Accuarcy: 0.084439 [07:18:07] Epoch 2: Loss(train): 0.073850 Loss(val): 0.074187 [07:20:36] Epoch 4: Loss(train): 0.067530 Loss(val): 0.068399 [07:23:14] Epoch 6: Loss(train): 0.064794 Loss(val): 0.065898 [07:25:46] Epoch 8: Loss(train): 0.062768 Loss(val): 0.063800 [07:27:54] Epoch 10: Loss(train): 0.062472 Loss(val): 0.062035 [07:30:10] Epoch 12: Loss(train): 0.060418 Loss(val): 0.060405 [07:32:26] Epoch 14: Loss(train): 0.059070 Loss(val): 0.059311 [07:34:35] Epoch 16: Loss(train): 0.057910 Loss(val): 0.058325 [07:36:44] Epoch 18: Loss(train): 0.056905 Loss(val): 0.057580 [07:38:54] Epoch 20: Loss(train): 0.056036 Loss(val): 0.056788 [07:40:57] Epoch 22: Loss(train): 0.055506 Loss(val): 0.056173 [07:43:06] Epoch 24: Loss(train): 0.054936 Loss(val): 0.055625 [07:45:27] Epoch 26: Loss(train): 0.054225 Loss(val): 0.054838 [07:48:13] Epoch 28: Loss(train): 0.053781 Loss(val): 0.054315 [07:51:16] Epoch 30: Loss(train): 0.053474 Loss(val): 0.053908 [07:54:02] Epoch 32: Loss(train): 0.052798 Loss(val): 0.053247 [07:56:36] Epoch 34: Loss(train): 0.052396 Loss(val): 0.052880 [07:59:01] Epoch 36: Loss(train): 0.052076 Loss(val): 0.052543 [08:01:20] Epoch 38: Loss(train): 0.051783 Loss(val): 0.052259 [08:03:37] Epoch 40: Loss(train): 0.051529 Loss(val): 0.052001 [08:03:57] FINAL(40) Loss(val): 0.052001 Accuarcy: 0.645969 Search 33 of 500 momentum0.96, features=[32, 64, 128], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=1.0 [08:05:24] INIT Loss(val): 0.178517 Accuarcy: 0.112415 [08:07:42] Epoch 2: Loss(train): 0.073139 Loss(val): 0.073297 [08:09:50] Epoch 4: Loss(train): 0.068176 Loss(val): 0.068788 [08:11:55] Epoch 6: Loss(train): 0.065703 Loss(val): 0.066643 [08:14:03] Epoch 8: Loss(train): 0.063960 Loss(val): 0.064976 [08:16:06] Epoch 10: Loss(train): 0.062551 Loss(val): 0.063301 [08:18:47] Epoch 12: Loss(train): 0.063731 Loss(val): 0.062842 [08:21:02] Epoch 14: Loss(train): 0.061109 Loss(val): 0.060589 [08:23:14] Epoch 16: Loss(train): 0.059993 Loss(val): 0.059475 [08:25:26] Epoch 18: Loss(train): 0.058808 Loss(val): 0.058376 [08:27:50] Epoch 20: Loss(train): 0.057739 Loss(val): 0.057466 [08:29:59] Epoch 22: Loss(train): 0.057158 Loss(val): 0.056976 [08:32:06] Epoch 24: Loss(train): 0.056492 Loss(val): 0.056414 [08:34:28] Epoch 26: Loss(train): 0.055811 Loss(val): 0.055825 [08:36:36] Epoch 28: Loss(train): 0.055204 Loss(val): 0.055333 [08:38:46] Epoch 30: Loss(train): 0.054898 Loss(val): 0.055027 [08:41:09] Epoch 32: Loss(train): 0.053986 Loss(val): 0.054217 [08:43:25] Epoch 34: Loss(train): 0.053655 Loss(val): 0.053866 [08:45:34] Epoch 36: Loss(train): 0.052755 Loss(val): 0.053041 [08:47:51] Epoch 38: Loss(train): 0.052027 Loss(val): 0.052424 [08:50:00] Epoch 40: Loss(train): 0.051380 Loss(val): 0.051832 [08:50:21] FINAL(40) Loss(val): 0.051832 Accuarcy: 0.644133 Search 34 of 500 momentum0.9, features=[96, 192, 192], dropout_rate=0.4 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.3 [08:51:40] INIT Loss(val): 0.144004 Accuarcy: 0.099796 [08:53:56] Epoch 2: Loss(train): 0.076873 Loss(val): 0.077967 [08:56:01] Epoch 4: Loss(train): 0.069047 Loss(val): 0.070184 [08:58:14] Epoch 6: Loss(train): 0.066172 Loss(val): 0.067933 [09:00:35] Epoch 8: Loss(train): 0.063611 Loss(val): 0.064377 [09:02:47] Epoch 10: Loss(train): 0.063118 Loss(val): 0.062959 [09:05:01] Epoch 12: Loss(train): 0.060982 Loss(val): 0.061159 [09:07:14] Epoch 14: Loss(train): 0.059704 Loss(val): 0.060033 [09:09:23] Epoch 16: Loss(train): 0.058530 Loss(val): 0.059018 [09:11:38] Epoch 18: Loss(train): 0.057384 Loss(val): 0.057913 [09:14:04] Epoch 20: Loss(train): 0.056447 Loss(val): 0.057108 [09:16:16] Epoch 22: Loss(train): 0.055736 Loss(val): 0.056406 [09:18:40] Epoch 24: Loss(train): 0.055298 Loss(val): 0.056005 [09:21:01] Epoch 26: Loss(train): 0.054855 Loss(val): 0.055544 [09:23:14] Epoch 28: Loss(train): 0.054428 Loss(val): 0.055165 [09:25:34] Epoch 30: Loss(train): 0.053971 Loss(val): 0.054723 Early stopping with Loss(train) 0.055224 at epoch 31 (Accuracy: 0.570884) Search 35 of 500 momentum0.98, features=[32, 64, 128], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(7, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.01 [09:28:11] INIT Loss(val): 0.131561 Accuarcy: 0.088435 [09:30:33] Epoch 2: Loss(train): 0.078600 Loss(val): 0.079560 [09:32:44] Epoch 4: Loss(train): 0.069469 Loss(val): 0.070441 [09:34:53] Epoch 6: Loss(train): 0.066407 Loss(val): 0.067910 [09:37:13] Epoch 8: Loss(train): 0.063834 Loss(val): 0.065309 [09:39:54] Epoch 10: Loss(train): 0.063342 Loss(val): 0.063286 [09:42:15] Epoch 12: Loss(train): 0.061538 Loss(val): 0.061831 [09:44:29] Epoch 14: Loss(train): 0.060098 Loss(val): 0.060590 [09:46:46] Epoch 16: Loss(train): 0.058880 Loss(val): 0.059626 [09:48:56] Epoch 18: Loss(train): 0.058013 Loss(val): 0.058851 [09:51:21] Epoch 20: Loss(train): 0.057582 Loss(val): 0.058371 Early stopping with Loss(train) 0.059074 at epoch 20 (Accuracy: 0.533912) Search 36 of 500 momentum0.98, features=[32, 64, 128], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.001 [09:53:01] INIT Loss(val): 0.128501 Accuarcy: 0.093844 [09:55:31] Epoch 2: Loss(train): 0.078614 Loss(val): 0.078914 [09:57:57] Epoch 4: Loss(train): 0.069358 Loss(val): 0.069975 [10:00:16] Epoch 6: Loss(train): 0.065321 Loss(val): 0.066424 [10:02:44] Epoch 8: Loss(train): 0.063663 Loss(val): 0.065086 [10:05:02] Epoch 10: Loss(train): 0.061664 Loss(val): 0.062794 [10:07:20] Epoch 12: Loss(train): 0.060420 Loss(val): 0.059941 [10:09:34] Epoch 14: Loss(train): 0.059115 Loss(val): 0.058971 [10:11:45] Epoch 16: Loss(train): 0.058384 Loss(val): 0.058312 [10:13:59] Epoch 18: Loss(train): 0.057044 Loss(val): 0.057162 [10:16:13] Epoch 20: Loss(train): 0.056068 Loss(val): 0.056346 [10:18:34] Epoch 22: Loss(train): 0.055208 Loss(val): 0.055646 [10:20:58] Epoch 24: Loss(train): 0.054296 Loss(val): 0.054775 [10:23:15] Epoch 26: Loss(train): 0.053540 Loss(val): 0.054122 [10:25:39] Epoch 28: Loss(train): 0.052913 Loss(val): 0.053533 [10:27:58] Epoch 30: Loss(train): 0.052410 Loss(val): 0.052975 [10:30:17] Epoch 32: Loss(train): 0.051984 Loss(val): 0.052642 [10:32:35] Epoch 34: Loss(train): 0.051636 Loss(val): 0.052200 [10:35:02] Epoch 36: Loss(train): 0.051358 Loss(val): 0.051943 [10:37:34] Epoch 38: Loss(train): 0.051226 Loss(val): 0.051788 [10:40:00] Epoch 40: Loss(train): 0.050862 Loss(val): 0.051429 [10:40:21] FINAL(40) Loss(val): 0.051429 Accuarcy: 0.651922 Search 37 of 500 momentum0.94, features=[64, 64, 64], dropout_rate=0.8 kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1 [10:41:50] INIT Loss(val): 0.135753 Accuarcy: 0.104099 [10:44:26] Epoch 2: Loss(train): 0.079794 Loss(val): 0.080364 [10:46:44] Epoch 4: Loss(train): 0.069623 Loss(val): 0.070324 [10:49:04] Epoch 6: Loss(train): 0.064974 Loss(val): 0.065927 [10:51:28] Epoch 8: Loss(train): 0.062599 Loss(val): 0.063265 [10:53:40] Epoch 10: Loss(train): 0.062145 Loss(val): 0.061630 [10:55:54] Epoch 12: Loss(train): 0.061773 Loss(val): 0.061062 [10:58:12] Epoch 14: Loss(train): 0.060630 Loss(val): 0.060278 [11:00:38] Epoch 16: Loss(train): 0.059063 Loss(val): 0.059083 [11:03:04] Epoch 18: Loss(train): 0.057864 Loss(val): 0.058049 [11:05:33] Epoch 20: Loss(train): 0.056927 Loss(val): 0.057260 [11:08:05] Epoch 22: Loss(train): 0.055935 Loss(val): 0.056480 [11:10:24] Epoch 24: Loss(train): 0.054968 Loss(val): 0.055692 [11:12:51] Epoch 26: Loss(train): 0.054661 Loss(val): 0.055324 [11:15:24] Epoch 28: Loss(train): 0.054079 Loss(val): 0.054774 [11:17:53] Epoch 30: Loss(train): 0.053640 Loss(val): 0.054396 [11:20:23] Epoch 32: Loss(train): 0.053329 Loss(val): 0.054070 [11:22:48] Epoch 34: Loss(train): 0.052933 Loss(val): 0.053679 [11:25:08] Epoch 36: Loss(train): 0.052513 Loss(val): 0.053309 [11:27:32] Epoch 38: Loss(train): 0.052231 Loss(val): 0.053066 [11:29:55] Epoch 40: Loss(train): 0.051719 Loss(val): 0.052561 [11:30:19] FINAL(40) Loss(val): 0.052561 Accuarcy: 0.624575 Search 38 of 500 momentum0.96, features=[64, 64, 64], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(7, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.001 [11:31:48] INIT Loss(val): 0.147992 Accuarcy: 0.094031 [11:34:20] Epoch 2: Loss(train): 0.081095 Loss(val): 0.081899 [11:36:38] Epoch 4: Loss(train): 0.071199 Loss(val): 0.072058 [11:39:04] Epoch 6: Loss(train): 0.066202 Loss(val): 0.067400 [11:41:36] Epoch 8: Loss(train): 0.063791 Loss(val): 0.065201 [11:44:05] Epoch 10: Loss(train): 0.061899 Loss(val): 0.062050 [11:46:25] Epoch 12: Loss(train): 0.061027 Loss(val): 0.060768 [11:48:46] Epoch 14: Loss(train): 0.059124 Loss(val): 0.059066 [11:51:10] Epoch 16: Loss(train): 0.057615 Loss(val): 0.057749 [11:53:41] Epoch 18: Loss(train): 0.056756 Loss(val): 0.056945 [11:56:19] Epoch 20: Loss(train): 0.056063 Loss(val): 0.056301 [11:58:45] Epoch 22: Loss(train): 0.055472 Loss(val): 0.055713 [12:01:20] Epoch 24: Loss(train): 0.054792 Loss(val): 0.055112