androidMotionLogger/julia/logs/log_19_09_2019.log

--------[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