androidMotionLogger/julia/logs/log_20_09_2019.log

--------[20_09_2019 13:25:28]--------
Random Grid Search

Search 1 of 500
momentum0.96, features=[96, 192, 192], dropout_rate=0.6
kernel=Tuple{Int64,Int64}[(7, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.03

[13:26:39] INIT Loss(val): 0.141007 Accuarcy: 0.117381
[13:29:14] Epoch   2: Loss(train): 0.088968 Loss(val): 0.087040
[13:30:22] Epoch   4: Loss(train): 0.078608 Loss(val): 0.076951
[13:31:29] Epoch   6: Loss(train): 0.076482 Loss(val): 0.074798
[13:32:37] Epoch   8: Loss(train): 0.074540 Loss(val): 0.072936
[13:33:44] Epoch  10: Loss(train): 0.073011 Loss(val): 0.071681
[13:34:52] Epoch  12: Loss(train): 0.071422 Loss(val): 0.070381
[13:36:01] Epoch  14: Loss(train): 0.070299 Loss(val): 0.069362
[13:37:10] Epoch  16: Loss(train): 0.069983 Loss(val): 0.069102
[13:38:18] Epoch  18: Loss(train): 0.069733 Loss(val): 0.068830
[13:39:27] Epoch  20: Loss(train): 0.068419 Loss(val): 0.067956
[13:40:35] Epoch  22: Loss(train): 0.068390 Loss(val): 0.067870
[13:41:44] Epoch  24: Loss(train): 0.068358 Loss(val): 0.067915
[13:42:53] Epoch  26: Loss(train): 0.068200 Loss(val): 0.067803
[13:44:01] Epoch  28: Loss(train): 0.067958 Loss(val): 0.067655
[13:45:10] Epoch  30: Loss(train): 0.067752 Loss(val): 0.067437
[13:46:18] Epoch  32: Loss(train): 0.067372 Loss(val): 0.067225
[13:47:28] Epoch  34: Loss(train): 0.067107 Loss(val): 0.067024
[13:48:38] Epoch  36: Loss(train): 0.066756 Loss(val): 0.066762
[13:49:50] Epoch  38: Loss(train): 0.066352 Loss(val): 0.066501
[13:50:59] Epoch  40: Loss(train): 0.066226 Loss(val): 0.066400
[13:51:06] FINAL(40) Loss(val): 0.066400 Accuarcy: 0.621310

Search 2 of 500
momentum0.99, features=[32, 32, 32], 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

[13:51:25] INIT Loss(val): 0.116734 Accuarcy: 0.086190
[13:52:24] Epoch   2: Loss(train): 0.111209 Loss(val): 0.112811
[13:52:52] Epoch   4: Loss(train): 0.070398 Loss(val): 0.072423
[13:53:22] Epoch   6: Loss(train): 0.068424 Loss(val): 0.070707
[13:53:52] Epoch   8: Loss(train): 0.067158 Loss(val): 0.069694
[13:54:21] Epoch  10: Loss(train): 0.066686 Loss(val): 0.069351
[13:54:50] Epoch  12: Loss(train): 0.066295 Loss(val): 0.069087
[13:55:20] Epoch  14: Loss(train): 0.066166 Loss(val): 0.069011
[13:55:49] Epoch  16: Loss(train): 0.066101 Loss(val): 0.068977
[13:56:18] Epoch  18: Loss(train): 0.066013 Loss(val): 0.068943
[13:56:46] Epoch  20: Loss(train): 0.065961 Loss(val): 0.068933
[13:57:14] Epoch  22: Loss(train): 0.065942 Loss(val): 0.068932
Early stopping with Loss(train) 0.065942 at epoch 22 (Accuracy: 0.091786)

Search 3 of 500
momentum0.98, features=[32, 64, 128], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.3

[13:57:28] INIT Loss(val): 0.158934 Accuarcy: 0.094031
[13:59:04] Epoch   2: Loss(train): 0.079493 Loss(val): 0.079803
[14:00:10] Epoch   4: Loss(train): 0.071139 Loss(val): 0.070190
[14:01:15] Epoch   6: Loss(train): 0.057562 Loss(val): 0.059708
[14:02:20] Epoch   8: Loss(train): 0.042016 Loss(val): 0.042341
[14:03:25] Epoch  10: Loss(train): 0.035010 Loss(val): 0.034012
[14:04:32] Epoch  12: Loss(train): 0.031262 Loss(val): 0.030622
[14:05:40] Epoch  14: Loss(train): 0.031100 Loss(val): 0.031548
[14:06:43] Epoch  16: Loss(train): 0.027940 Loss(val): 0.026886
[14:07:49] Epoch  18: Loss(train): 0.026650 Loss(val): 0.026341
[14:08:55] Epoch  20: Loss(train): 0.025911 Loss(val): 0.024959
[14:09:59] Epoch  22: Loss(train): 0.023871 Loss(val): 0.022819
[14:11:04] Epoch  24: Loss(train): 0.022636 Loss(val): 0.021778
[14:12:08] Epoch  26: Loss(train): 0.020553 Loss(val): 0.020178
[14:13:13] Epoch  28: Loss(train): 0.020170 Loss(val): 0.019662
[14:14:21] Epoch  30: Loss(train): 0.020618 Loss(val): 0.019873
[14:15:29] Epoch  32: Loss(train): 0.021250 Loss(val): 0.020526
[14:16:36] Epoch  34: Loss(train): 0.020209 Loss(val): 0.019461
[14:17:50] Epoch  36: Loss(train): 0.019272 Loss(val): 0.018871
[14:19:04] Epoch  38: Loss(train): 0.019579 Loss(val): 0.019146
[14:20:18] Epoch  40: Loss(train): 0.020248 Loss(val): 0.019587
[14:20:28] FINAL(40) Loss(val): 0.019587 Accuarcy: 0.607517

Search 4 of 500
momentum0.98, features=[32, 32, 32], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003

[14:20:51] INIT Loss(val): 0.128640 Accuarcy: 0.094643
[14:21:39] Epoch   2: Loss(train): 0.056761 Loss(val): 0.056647
[14:22:08] Epoch   4: Loss(train): 0.050836 Loss(val): 0.050852
[14:22:37] Epoch   6: Loss(train): 0.048547 Loss(val): 0.048590
[14:23:06] Epoch   8: Loss(train): 0.047363 Loss(val): 0.047409
[14:23:35] Epoch  10: Loss(train): 0.046506 Loss(val): 0.046528
[14:24:05] Epoch  12: Loss(train): 0.046048 Loss(val): 0.046113
[14:24:34] Epoch  14: Loss(train): 0.045531 Loss(val): 0.045707
[14:25:05] Epoch  16: Loss(train): 0.045208 Loss(val): 0.045390
[14:25:35] Epoch  18: Loss(train): 0.044927 Loss(val): 0.045131
[14:26:06] Epoch  20: Loss(train): 0.044599 Loss(val): 0.044836
[14:26:36] Epoch  22: Loss(train): 0.044361 Loss(val): 0.044653
[14:27:05] Epoch  24: Loss(train): 0.044173 Loss(val): 0.044473
[14:27:36] Epoch  26: Loss(train): 0.043907 Loss(val): 0.044196
[14:28:06] Epoch  28: Loss(train): 0.043688 Loss(val): 0.044030
[14:28:36] Epoch  30: Loss(train): 0.043458 Loss(val): 0.043854
[14:29:07] Epoch  32: Loss(train): 0.043282 Loss(val): 0.043654
[14:29:37] Epoch  34: Loss(train): 0.043138 Loss(val): 0.043506
[14:30:07] Epoch  36: Loss(train): 0.042960 Loss(val): 0.043359
[14:30:38] Epoch  38: Loss(train): 0.042856 Loss(val): 0.043255
Early stopping with Loss(train) 0.044059 at epoch 38 (Accuracy: 0.575901)

Search 5 of 500
momentum0.94, features=[64, 64, 64], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003

[14:30:53] INIT Loss(val): 0.119816 Accuarcy: 0.091463
[14:34:37] Epoch   2: Loss(train): 0.068172 Loss(val): 0.066521
[14:38:47] Epoch   4: Loss(train): 0.061371 Loss(val): 0.060421
[14:44:35] Epoch   6: Loss(train): 0.058998 Loss(val): 0.058359
[14:50:21] Epoch   8: Loss(train): 0.057713 Loss(val): 0.057312
[14:54:55] Epoch  10: Loss(train): 0.056964 Loss(val): 0.056694
[14:59:13] Epoch  12: Loss(train): 0.056202 Loss(val): 0.056133
[15:03:30] Epoch  14: Loss(train): 0.055774 Loss(val): 0.055825
[15:07:46] Epoch  16: Loss(train): 0.055417 Loss(val): 0.055541
[15:09:41] Epoch  18: Loss(train): 0.055157 Loss(val): 0.055343
[15:11:36] Epoch  20: Loss(train): 0.054948 Loss(val): 0.055184
[15:14:40] Epoch  22: Loss(train): 0.054736 Loss(val): 0.055040
[15:17:10] Epoch  24: Loss(train): 0.054518 Loss(val): 0.054851
[15:19:29] Epoch  26: Loss(train): 0.054388 Loss(val): 0.054757
[15:21:38] Epoch  28: Loss(train): 0.054141 Loss(val): 0.054547
[15:23:56] Epoch  30: Loss(train): 0.054011 Loss(val): 0.054442
[15:25:57] Epoch  32: Loss(train): 0.053802 Loss(val): 0.054259
[15:27:59] Epoch  34: Loss(train): 0.053625 Loss(val): 0.054112
[15:30:03] Epoch  36: Loss(train): 0.053405 Loss(val): 0.053942
[15:32:05] Epoch  38: Loss(train): 0.053237 Loss(val): 0.053814
Early stopping with Loss(train) 0.054171 at epoch 38 (Accuracy: 0.534439)

Search 6 of 500
momentum0.94, features=[96, 192, 192], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.1

[15:32:54] INIT Loss(val): 0.138280 Accuarcy: 0.103418
[15:34:06] Epoch   2: Loss(train): 0.083781 Loss(val): 0.084471
[15:35:16] Epoch   4: Loss(train): 0.076121 Loss(val): 0.077370
[15:36:23] Epoch   6: Loss(train): 0.074109 Loss(val): 0.073338
[15:37:30] Epoch   8: Loss(train): 0.070528 Loss(val): 0.070370
[15:38:36] Epoch  10: Loss(train): 0.067579 Loss(val): 0.067635
[15:39:43] Epoch  12: Loss(train): 0.065495 Loss(val): 0.065627
[15:40:51] Epoch  14: Loss(train): 0.064575 Loss(val): 0.064573
[15:42:01] Epoch  16: Loss(train): 0.062974 Loss(val): 0.063009
[15:43:23] Epoch  18: Loss(train): 0.061456 Loss(val): 0.061671
[15:44:49] Epoch  20: Loss(train): 0.060464 Loss(val): 0.060732
[15:46:07] Epoch  22: Loss(train): 0.059721 Loss(val): 0.060011
[15:47:29] Epoch  24: Loss(train): 0.058930 Loss(val): 0.059211
[15:48:42] Epoch  26: Loss(train): 0.058132 Loss(val): 0.058528
[15:50:06] Epoch  28: Loss(train): 0.057695 Loss(val): 0.058175
[15:51:18] Epoch  30: Loss(train): 0.056704 Loss(val): 0.057436
[15:52:28] Epoch  32: Loss(train): 0.056475 Loss(val): 0.057144
[15:53:48] Epoch  34: Loss(train): 0.055826 Loss(val): 0.056582
Early stopping with Loss(train) 0.056777 at epoch 35 (Accuracy: 0.632602)

Search 7 of 500
momentum0.9, 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=0.003

[15:54:46] INIT Loss(val): 0.146946 Accuarcy: 0.092534
[15:59:34] Epoch   2: Loss(train): 0.080755 Loss(val): 0.080996
[16:04:53] Epoch   4: Loss(train): 0.072572 Loss(val): 0.072945
[16:10:04] Epoch   6: Loss(train): 0.069757 Loss(val): 0.070359
[16:16:05] Epoch   8: Loss(train): 0.068311 Loss(val): 0.069053
[16:18:27] Epoch  10: Loss(train): 0.067540 Loss(val): 0.068310
[16:20:27] Epoch  12: Loss(train): 0.066874 Loss(val): 0.067751
[16:21:51] Epoch  14: Loss(train): 0.066258 Loss(val): 0.067226
[16:23:24] Epoch  16: Loss(train): 0.065859 Loss(val): 0.066875
[16:24:42] Epoch  18: Loss(train): 0.065511 Loss(val): 0.066519
[16:26:03] Epoch  20: Loss(train): 0.065213 Loss(val): 0.066262
[16:27:34] Epoch  22: Loss(train): 0.064957 Loss(val): 0.066055
[16:28:56] Epoch  24: Loss(train): 0.064687 Loss(val): 0.065852
[16:30:15] Epoch  26: Loss(train): 0.064495 Loss(val): 0.065710
[16:31:42] Epoch  28: Loss(train): 0.064351 Loss(val): 0.065567
[16:33:03] Epoch  30: Loss(train): 0.064181 Loss(val): 0.065445
Early stopping with Loss(train) 0.067298 at epoch 31 (Accuracy: 0.446395)

Search 8 of 500
momentum0.96, features=[32, 32, 32], dropout_rate=0.6
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003

[16:34:07] INIT Loss(val): 0.114290 Accuarcy: 0.088316
[16:34:46] Epoch   2: Loss(train): 0.064952 Loss(val): 0.064766
[16:35:23] Epoch   4: Loss(train): 0.055647 Loss(val): 0.055572
[16:36:02] Epoch   6: Loss(train): 0.052844 Loss(val): 0.052606
[16:36:38] Epoch   8: Loss(train): 0.050985 Loss(val): 0.050845
[16:37:12] Epoch  10: Loss(train): 0.049845 Loss(val): 0.049633
[16:37:47] Epoch  12: Loss(train): 0.049152 Loss(val): 0.048910
[16:38:22] Epoch  14: Loss(train): 0.048669 Loss(val): 0.048351
[16:38:57] Epoch  16: Loss(train): 0.048184 Loss(val): 0.047886
[16:39:31] Epoch  18: Loss(train): 0.047724 Loss(val): 0.047523
[16:40:06] Epoch  20: Loss(train): 0.047366 Loss(val): 0.047177
[16:40:42] Epoch  22: Loss(train): 0.047168 Loss(val): 0.046973
[16:41:17] Epoch  24: Loss(train): 0.046858 Loss(val): 0.046676
[16:41:52] Epoch  26: Loss(train): 0.046654 Loss(val): 0.046519
[16:42:27] Epoch  28: Loss(train): 0.046444 Loss(val): 0.046329
[16:43:03] Epoch  30: Loss(train): 0.046292 Loss(val): 0.046197
[16:43:39] Epoch  32: Loss(train): 0.046148 Loss(val): 0.046083
[16:44:14] Epoch  34: Loss(train): 0.045966 Loss(val): 0.045960
[16:44:50] Epoch  36: Loss(train): 0.045872 Loss(val): 0.045834
[16:45:40] Epoch  38: Loss(train): 0.045775 Loss(val): 0.045743
Early stopping with Loss(train) 0.048965 at epoch 38 (Accuracy: 0.502738)

Search 9 of 500
momentum0.9, features=[32, 64, 128], dropout_rate=0.6
kernel=Tuple{Int64,Int64}[(7, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.3

[16:46:09] INIT Loss(val): 0.128704 Accuarcy: 0.095255
[16:47:37] Epoch   2: Loss(train): 0.068261 Loss(val): 0.066425
[16:48:59] Epoch   4: Loss(train): 0.059768 Loss(val): 0.058380
[16:49:56] Epoch   6: Loss(train): 0.056013 Loss(val): 0.054921
[16:50:56] Epoch   8: Loss(train): 0.052184 Loss(val): 0.051936
[16:52:12] Epoch  10: Loss(train): 0.050930 Loss(val): 0.050823
[16:53:14] Epoch  12: Loss(train): 0.050832 Loss(val): 0.051081
[16:54:05] Epoch  14: Loss(train): 0.047966 Loss(val): 0.048353
[16:54:58] Epoch  16: Loss(train): 0.046510 Loss(val): 0.046471
[16:55:53] Epoch  18: Loss(train): 0.045391 Loss(val): 0.045162
Early stopping with Loss(train) 0.047088 at epoch 19 (Accuracy: 0.514779)

Search 10 of 500
momentum0.99, features=[32, 32, 32], dropout_rate=0.6
kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1

[16:56:41] INIT Loss(val): 0.155156 Accuarcy: 0.107092
[16:57:23] Epoch   2: Loss(train): 0.082692 Loss(val): 0.082872
[16:58:03] Epoch   4: Loss(train): 0.070038 Loss(val): 0.068951
[16:58:43] Epoch   6: Loss(train): 0.052024 Loss(val): 0.051050
[16:59:23] Epoch   8: Loss(train): 0.042558 Loss(val): 0.042033
[17:00:01] Epoch  10: Loss(train): 0.033804 Loss(val): 0.032005
[17:00:41] Epoch  12: Loss(train): 0.028679 Loss(val): 0.028131
[17:01:25] Epoch  14: Loss(train): 0.026270 Loss(val): 0.025863
[17:02:07] Epoch  16: Loss(train): 0.025516 Loss(val): 0.024859
[17:02:46] Epoch  18: Loss(train): 0.024369 Loss(val): 0.024071
[17:03:26] Epoch  20: Loss(train): 0.022684 Loss(val): 0.022117
[17:04:06] Epoch  22: Loss(train): 0.022049 Loss(val): 0.020969
[17:04:45] Epoch  24: Loss(train): 0.021334 Loss(val): 0.020421
[17:05:26] Epoch  26: Loss(train): 0.021068 Loss(val): 0.020358
[17:06:06] Epoch  28: Loss(train): 0.021064 Loss(val): 0.020323
[17:06:45] Epoch  30: Loss(train): 0.021924 Loss(val): 0.020800
[17:07:25] Epoch  32: Loss(train): 0.022128 Loss(val): 0.021133
[17:08:05] Epoch  34: Loss(train): 0.021443 Loss(val): 0.020385
[17:08:44] Epoch  36: Loss(train): 0.023132 Loss(val): 0.021738
[17:09:23] Epoch  38: Loss(train): 0.024656 Loss(val): 0.023247
[17:10:02] Epoch  40: Loss(train): 0.022748 Loss(val): 0.021955
[17:10:07] FINAL(40) Loss(val): 0.021955 Accuarcy: 0.579932

Search 11 of 500
momentum0.99, features=[32, 64, 128], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003

[17:10:22] INIT Loss(val): 0.131512 Accuarcy: 0.094524
[17:11:17] Epoch   2: Loss(train): 0.067090 Loss(val): 0.065792
[17:12:12] Epoch   4: Loss(train): 0.061073 Loss(val): 0.059899
[17:13:05] Epoch   6: Loss(train): 0.059854 Loss(val): 0.059079
[17:14:02] Epoch   8: Loss(train): 0.059035 Loss(val): 0.058605
[17:14:57] Epoch  10: Loss(train): 0.058039 Loss(val): 0.057734
[17:15:54] Epoch  12: Loss(train): 0.056114 Loss(val): 0.055818
[17:17:03] Epoch  14: Loss(train): 0.055465 Loss(val): 0.055276
[17:18:32] Epoch  16: Loss(train): 0.055368 Loss(val): 0.055433
[17:19:59] Epoch  18: Loss(train): 0.055454 Loss(val): 0.055520
[17:21:32] Epoch  20: Loss(train): 0.055490 Loss(val): 0.055287
[17:22:53] Epoch  22: Loss(train): 0.054614 Loss(val): 0.054396
[17:23:49] Epoch  24: Loss(train): 0.053453 Loss(val): 0.053415
[17:25:15] Epoch  26: Loss(train): 0.053183 Loss(val): 0.053106
[17:26:20] Epoch  28: Loss(train): 0.053469 Loss(val): 0.053248
[17:27:23] Epoch  30: Loss(train): 0.054454 Loss(val): 0.053836
[17:28:23] Epoch  32: Loss(train): 0.054597 Loss(val): 0.053841
[17:29:24] Epoch  34: Loss(train): 0.053575 Loss(val): 0.053139
[17:30:23] Epoch  36: Loss(train): 0.052531 Loss(val): 0.052516
[17:31:27] Epoch  38: Loss(train): 0.052137 Loss(val): 0.052324
[17:32:28] Epoch  40: Loss(train): 0.051984 Loss(val): 0.052194
[17:32:35] FINAL(40) Loss(val): 0.052194 Accuarcy: 0.657959

Search 12 of 500
momentum0.98, features=[64, 64, 64], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1

[17:32:56] INIT Loss(val): 0.152510 Accuarcy: 0.094932
[17:34:22] Epoch   2: Loss(train): 0.076679 Loss(val): 0.078237
[17:35:44] Epoch   4: Loss(train): 0.059022 Loss(val): 0.060126
[17:40:56] Epoch   6: Loss(train): 0.050557 Loss(val): 0.051844
[17:49:29] Epoch   8: Loss(train): 0.045205 Loss(val): 0.045557
[18:00:08] Epoch  10: Loss(train): 0.040994 Loss(val): 0.041337
[18:08:11] Epoch  12: Loss(train): 0.038653 Loss(val): 0.038964
[18:15:40] Epoch  14: Loss(train): 0.036438 Loss(val): 0.036957
[18:25:46] Epoch  16: Loss(train): 0.035700 Loss(val): 0.035639
[18:35:47] Epoch  18: Loss(train): 0.034637 Loss(val): 0.034805
[18:43:48] Epoch  20: Loss(train): 0.031340 Loss(val): 0.031079
[18:51:14] Epoch  22: Loss(train): 0.029792 Loss(val): 0.029546
[19:02:50] Epoch  24: Loss(train): 0.028174 Loss(val): 0.027946
[19:11:45] Epoch  26: Loss(train): 0.027586 Loss(val): 0.027259
[19:19:33] Epoch  28: Loss(train): 0.027456 Loss(val): 0.027579
[19:28:45] Epoch  30: Loss(train): 0.027956 Loss(val): 0.027891
[19:40:09] Epoch  32: Loss(train): 0.028770 Loss(val): 0.028286
Early stopping with Loss(train) 0.028975 at epoch 33 (Accuracy: 0.584439)

Search 13 of 500
momentum0.99, features=[64, 64, 64], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.001

[19:46:57] INIT Loss(val): 0.145452 Accuarcy: 0.113588
[19:50:44] Epoch   2: Loss(train): 0.089347 Loss(val): 0.088077
[19:54:42] Epoch   4: Loss(train): 0.063581 Loss(val): 0.064125
[19:59:21] Epoch   6: Loss(train): 0.061616 Loss(val): 0.062470
[20:06:13] Epoch   8: Loss(train): 0.061005 Loss(val): 0.061986
[20:11:36] Epoch  10: Loss(train): 0.060453 Loss(val): 0.061392
[20:16:11] Epoch  12: Loss(train): 0.059992 Loss(val): 0.060942
[20:20:27] Epoch  14: Loss(train): 0.059627 Loss(val): 0.060763
[20:24:29] Epoch  16: Loss(train): 0.059573 Loss(val): 0.060785
[20:28:31] Epoch  18: Loss(train): 0.059532 Loss(val): 0.060666
[20:34:27] Epoch  20: Loss(train): 0.058798 Loss(val): 0.059861
[20:41:03] Epoch  22: Loss(train): 0.058062 Loss(val): 0.059021
[20:46:14] Epoch  24: Loss(train): 0.057693 Loss(val): 0.058506
[20:50:45] Epoch  26: Loss(train): 0.057584 Loss(val): 0.058362
[20:54:59] Epoch  28: Loss(train): 0.057410 Loss(val): 0.058215
[20:59:00] Epoch  30: Loss(train): 0.057174 Loss(val): 0.057987
[21:03:40] Epoch  32: Loss(train): 0.057070 Loss(val): 0.057808
[21:10:36] Epoch  34: Loss(train): 0.057235 Loss(val): 0.057774
[21:16:08] Epoch  36: Loss(train): 0.057474 Loss(val): 0.057816
[21:21:06] Epoch  38: Loss(train): 0.057530 Loss(val): 0.057807
[21:25:37] Epoch  40: Loss(train): 0.057105 Loss(val): 0.057592
[21:26:04] FINAL(40) Loss(val): 0.057592 Accuarcy: 0.591735

Search 14 of 500
momentum0.9, features=[32, 32, 32], dropout_rate=0.6
kernel=Tuple{Int64,Int64}[(7, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.01

[21:26:59] INIT Loss(val): 0.122847 Accuarcy: 0.101803
[21:29:56] Epoch   2: Loss(train): 0.064045 Loss(val): 0.063516
[21:33:02] Epoch   4: Loss(train): 0.056780 Loss(val): 0.055908
[21:36:53] Epoch   6: Loss(train): 0.054376 Loss(val): 0.053519
[21:42:25] Epoch   8: Loss(train): 0.052633 Loss(val): 0.051863
[21:47:10] Epoch  10: Loss(train): 0.051527 Loss(val): 0.050836
[21:51:13] Epoch  12: Loss(train): 0.050602 Loss(val): 0.049957
[21:54:56] Epoch  14: Loss(train): 0.050027 Loss(val): 0.049464
[21:58:20] Epoch  16: Loss(train): 0.049429 Loss(val): 0.048945
[22:01:33] Epoch  18: Loss(train): 0.048902 Loss(val): 0.048480
[22:04:43] Epoch  20: Loss(train): 0.048485 Loss(val): 0.048132
[22:07:56] Epoch  22: Loss(train): 0.048165 Loss(val): 0.047822
[22:11:08] Epoch  24: Loss(train): 0.047914 Loss(val): 0.047628
[22:14:30] Epoch  26: Loss(train): 0.047709 Loss(val): 0.047426
[22:17:49] Epoch  28: Loss(train): 0.047584 Loss(val): 0.047305
[22:21:12] Epoch  30: Loss(train): 0.047354 Loss(val): 0.047143
Early stopping with Loss(train) 0.050260 at epoch 31 (Accuracy: 0.503571)

Search 15 of 500
momentum0.98, features=[32, 64, 128], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.001

[22:23:39] INIT Loss(val): 0.218097 Accuarcy: 0.093946
[22:31:36] Epoch   2: Loss(train): 0.072229 Loss(val): 0.072008
[22:42:38] Epoch   4: Loss(train): 0.066523 Loss(val): 0.066956
[22:51:18] Epoch   6: Loss(train): 0.064689 Loss(val): 0.065551
[22:59:03] Epoch   8: Loss(train): 0.063820 Loss(val): 0.064851
[23:08:26] Epoch  10: Loss(train): 0.063226 Loss(val): 0.064352
[23:17:54] Epoch  12: Loss(train): 0.062768 Loss(val): 0.063999
[23:26:17] Epoch  14: Loss(train): 0.062558 Loss(val): 0.063801
[23:34:03] Epoch  16: Loss(train): 0.062267 Loss(val): 0.063589
[23:44:32] Epoch  18: Loss(train): 0.061969 Loss(val): 0.063301
[23:53:31] Epoch  20: Loss(train): 0.061649 Loss(val): 0.062996
[00:01:31] Epoch  22: Loss(train): 0.061327 Loss(val): 0.062696
[00:11:05] Epoch  24: Loss(train): 0.060975 Loss(val): 0.062371
[00:20:49] Epoch  26: Loss(train): 0.060806 Loss(val): 0.062130
[00:29:09] Epoch  28: Loss(train): 0.060544 Loss(val): 0.061854
[00:37:04] Epoch  30: Loss(train): 0.060328 Loss(val): 0.061611
[00:48:06] Epoch  32: Loss(train): 0.060126 Loss(val): 0.061430
[00:57:20] Epoch  34: Loss(train): 0.059966 Loss(val): 0.061301
[01:05:24] Epoch  36: Loss(train): 0.059804 Loss(val): 0.061190
[01:16:04] Epoch  38: Loss(train): 0.059661 Loss(val): 0.061111
[01:25:57] Epoch  40: Loss(train): 0.059557 Loss(val): 0.061046
[01:27:07] FINAL(40) Loss(val): 0.061046 Accuarcy: 0.573963

Search 16 of 500
momentum0.94, features=[64, 64, 64], dropout_rate=0.8
kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.001

[01:29:00] INIT Loss(val): 0.151834 Accuarcy: 0.108827
[01:33:32] Epoch   2: Loss(train): 0.110386 Loss(val): 0.113633
[01:38:13] Epoch   4: Loss(train): 0.106996 Loss(val): 0.110476
[01:44:49] Epoch   6: Loss(train): 0.100696 Loss(val): 0.104549
[01:52:04] Epoch   8: Loss(train): 0.094171 Loss(val): 0.098184
[01:58:26] Epoch  10: Loss(train): 0.089803 Loss(val): 0.093782
[02:03:44] Epoch  12: Loss(train): 0.087074 Loss(val): 0.091006
[02:08:33] Epoch  14: Loss(train): 0.085113 Loss(val): 0.088931
[02:13:20] Epoch  16: Loss(train): 0.083500 Loss(val): 0.087246
[02:21:19] Epoch  18: Loss(train): 0.081911 Loss(val): 0.085563
[02:28:13] Epoch  20: Loss(train): 0.080556 Loss(val): 0.084120
[02:34:03] Epoch  22: Loss(train): 0.079362 Loss(val): 0.082816
[02:39:08] Epoch  24: Loss(train): 0.078311 Loss(val): 0.081671
[02:43:58] Epoch  26: Loss(train): 0.077400 Loss(val): 0.080661
[02:49:40] Epoch  28: Loss(train): 0.076570 Loss(val): 0.079715
[02:57:52] Epoch  30: Loss(train): 0.075883 Loss(val): 0.078951
[03:04:07] Epoch  32: Loss(train): 0.075348 Loss(val): 0.078346
[03:09:39] Epoch  34: Loss(train): 0.074812 Loss(val): 0.077732
[03:14:37] Epoch  36: Loss(train): 0.074405 Loss(val): 0.077256
[03:19:32] Epoch  38: Loss(train): 0.074032 Loss(val): 0.076837
[03:27:14] Epoch  40: Loss(train): 0.073680 Loss(val): 0.076428
[03:28:09] FINAL(40) Loss(val): 0.076428 Accuarcy: 0.274915

Search 17 of 500
momentum0.9, features=[64, 64, 64], dropout_rate=0.4
kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.03

[03:29:53] INIT Loss(val): 0.131165 Accuarcy: 0.093435
[03:41:05] Epoch   2: Loss(train): 0.068231 Loss(val): 0.066406
[03:51:07] Epoch   4: Loss(train): 0.064709 Loss(val): 0.062722
[04:06:03] Epoch   6: Loss(train): 0.062242 Loss(val): 0.060431
[04:17:27] Epoch   8: Loss(train): 0.060373 Loss(val): 0.058780
[04:28:16] Epoch  10: Loss(train): 0.059085 Loss(val): 0.057533
[04:43:05] Epoch  12: Loss(train): 0.058318 Loss(val): 0.056778
[04:53:42] Epoch  14: Loss(train): 0.057390 Loss(val): 0.055989
[05:06:46] Epoch  16: Loss(train): 0.056481 Loss(val): 0.055205
[05:19:57] Epoch  18: Loss(train): 0.055879 Loss(val): 0.054648
[05:30:13] Epoch  20: Loss(train): 0.055364 Loss(val): 0.054170
[05:45:34] Epoch  22: Loss(train): 0.054641 Loss(val): 0.053574
[05:56:57] Epoch  24: Loss(train): 0.054031 Loss(val): 0.053062
[06:07:12] Epoch  26: Loss(train): 0.053433 Loss(val): 0.052634
Early stopping with Loss(train) 0.054989 at epoch 27 (Accuracy: 0.557653)

Search 18 of 500
momentum0.92, 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=0.001

[06:15:45] INIT Loss(val): 0.118689 Accuarcy: 0.101990
[06:19:38] Epoch   2: Loss(train): 0.068195 Loss(val): 0.069483
[06:23:52] Epoch   4: Loss(train): 0.062983 Loss(val): 0.063813
[06:28:34] Epoch   6: Loss(train): 0.060712 Loss(val): 0.061474
[06:34:45] Epoch   8: Loss(train): 0.059224 Loss(val): 0.060002
[06:39:41] Epoch  10: Loss(train): 0.058208 Loss(val): 0.059001
[06:44:28] Epoch  12: Loss(train): 0.057323 Loss(val): 0.058152
[06:48:47] Epoch  14: Loss(train): 0.056700 Loss(val): 0.057535
[06:52:56] Epoch  16: Loss(train): 0.056110 Loss(val): 0.056953
[06:57:00] Epoch  18: Loss(train): 0.055600 Loss(val): 0.056486
[07:01:11] Epoch  20: Loss(train): 0.055162 Loss(val): 0.056064
[07:07:25] Epoch  22: Loss(train): 0.054831 Loss(val): 0.055762
[07:12:41] Epoch  24: Loss(train): 0.054532 Loss(val): 0.055485
[07:17:21] Epoch  26: Loss(train): 0.054293 Loss(val): 0.055247
[07:21:44] Epoch  28: Loss(train): 0.054086 Loss(val): 0.055038
[07:25:57] Epoch  30: Loss(train): 0.053895 Loss(val): 0.054867
[07:30:12] Epoch  32: Loss(train): 0.053734 Loss(val): 0.054717
[07:35:30] Epoch  34: Loss(train): 0.053598 Loss(val): 0.054584
[07:41:27] Epoch  36: Loss(train): 0.053484 Loss(val): 0.054487
[07:46:25] Epoch  38: Loss(train): 0.053388 Loss(val): 0.054393
[07:50:59] Epoch  40: Loss(train): 0.053292 Loss(val): 0.054313
[07:51:26] FINAL(40) Loss(val): 0.054313 Accuarcy: 0.432704

Search 19 of 500
momentum0.9, features=[96, 192, 192], dropout_rate=0.4
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=1.0

[07:52:19] INIT Loss(val): 0.174489 Accuarcy: 0.104456
[08:05:37] Epoch   2: Loss(train): 0.102096 Loss(val): 0.102915
[08:21:51] Epoch   4: Loss(train): 0.066749 Loss(val): 0.065971
[08:35:13] Epoch   6: Loss(train): 0.055856 Loss(val): 0.055964
[08:52:20] Epoch   8: Loss(train): 0.047794 Loss(val): 0.047813
[09:05:42] Epoch  10: Loss(train): 0.041983 Loss(val): 0.042211
[09:23:11] Epoch  12: Loss(train): 0.037630 Loss(val): 0.038138
[09:36:57] Epoch  14: Loss(train): 0.034115 Loss(val): 0.034479
[09:55:56] Epoch  16: Loss(train): 0.032334 Loss(val): 0.032522
[10:09:49] Epoch  18: Loss(train): 0.030284 Loss(val): 0.030510
[10:29:29] Epoch  20: Loss(train): 0.028992 Loss(val): 0.029348
[10:43:10] Epoch  22: Loss(train): 0.028101 Loss(val): 0.028161
[11:02:49] Epoch  24: Loss(train): 0.027532 Loss(val): 0.027852
Early stopping with Loss(train) 0.028088 at epoch 25 (Accuracy: 0.567806)

Search 20 of 500
momentum0.9, features=[96, 192, 192], dropout_rate=0.6
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.3

[11:12:09] INIT Loss(val): 0.158469 Accuarcy: 0.092806
[11:25:25] Epoch   2: Loss(train): 0.092062 Loss(val): 0.091566
[11:38:25] Epoch   4: Loss(train): 0.081868 Loss(val): 0.081678
[11:49:01] Epoch   6: Loss(train): 0.076118 Loss(val): 0.075529
[12:04:29] Epoch   8: Loss(train): 0.073110 Loss(val): 0.072846
[12:16:23] Epoch  10: Loss(train): 0.069156 Loss(val): 0.069085
[12:30:09] Epoch  12: Loss(train): 0.066856 Loss(val): 0.067039
[12:44:01] Epoch  14: Loss(train): 0.065261 Loss(val): 0.065455
[12:54:52] Epoch  16: Loss(train): 0.063509 Loss(val): 0.064036
[13:10:55] Epoch  18: Loss(train): 0.061315 Loss(val): 0.061605
[13:22:43] Epoch  20: Loss(train): 0.059638 Loss(val): 0.059675
[13:36:46] Epoch  22: Loss(train): 0.058390 Loss(val): 0.058582
[13:50:21] Epoch  24: Loss(train): 0.057187 Loss(val): 0.057440
[14:01:25] Epoch  26: Loss(train): 0.056083 Loss(val): 0.056261
[14:12:57] Epoch  28: Loss(train): 0.055120 Loss(val): 0.055430
[14:25:54] Epoch  30: Loss(train): 0.054959 Loss(val): 0.055094
[14:40:39] Epoch  32: Loss(train): 0.054236 Loss(val): 0.054509
[14:52:26] Epoch  34: Loss(train): 0.053323 Loss(val): 0.053676
[15:04:28] Epoch  36: Loss(train): 0.052686 Loss(val): 0.053072
[15:18:03] Epoch  38: Loss(train): 0.051935 Loss(val): 0.052419
[15:29:22] Epoch  40: Loss(train): 0.051245 Loss(val): 0.051794
[15:30:12] FINAL(40) Loss(val): 0.051794 Accuarcy: 0.634048

Search 21 of 500
momentum0.99, features=[96, 192, 192], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1

[15:31:35] INIT Loss(val): 0.245048 Accuarcy: 0.072585
[15:49:17] Epoch   2: Loss(train): 0.106915 Loss(val): 0.105428
[16:05:12] Epoch   4: Loss(train): 0.089091 Loss(val): 0.088908
[16:23:07] Epoch   6: Loss(train): 0.075282 Loss(val): 0.074277
[16:40:46] Epoch   8: Loss(train): 0.062850 Loss(val): 0.062624
[16:58:29] Epoch  10: Loss(train): 0.056189 Loss(val): 0.056083
[17:17:16] Epoch  12: Loss(train): 0.049655 Loss(val): 0.049626
[17:34:08] Epoch  14: Loss(train): 0.045409 Loss(val): 0.045518
[17:54:19] Epoch  16: Loss(train): 0.042162 Loss(val): 0.042254
[18:10:02] Epoch  18: Loss(train): 0.039818 Loss(val): 0.040065
[18:30:42] Epoch  20: Loss(train): 0.038585 Loss(val): 0.038927
[18:46:52] Epoch  22: Loss(train): 0.038263 Loss(val): 0.038389
[19:07:10] Epoch  24: Loss(train): 0.034990 Loss(val): 0.035632
[19:25:45] Epoch  26: Loss(train): 0.034374 Loss(val): 0.035082
[19:43:48] Epoch  28: Loss(train): 0.033463 Loss(val): 0.033584
[20:03:44] Epoch  30: Loss(train): 0.032233 Loss(val): 0.032437
[20:20:20] Epoch  32: Loss(train): 0.033065 Loss(val): 0.032645
[20:41:38] Epoch  34: Loss(train): 0.032210 Loss(val): 0.032403
[20:58:04] Epoch  36: Loss(train): 0.031725 Loss(val): 0.032099
[21:18:34] Epoch  38: Loss(train): 0.030471 Loss(val): 0.030934
[21:36:54] Epoch  40: Loss(train): 0.030312 Loss(val): 0.030813
[21:38:37] FINAL(40) Loss(val): 0.030813 Accuarcy: 0.600442

Search 22 of 500
momentum0.98, features=[96, 192, 192], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=1.0

[21:41:08] INIT Loss(val): 0.164662 Accuarcy: 0.097636
[21:57:03] Epoch   2: Loss(train): 20.748877 Loss(val): 20.759123
[22:12:28] Epoch   4: Loss(train): 27.568346 Loss(val): 27.566154
[22:30:34] Epoch   6: Loss(train): 28.220102 Loss(val): 28.217909
[22:48:10] Epoch   8: Loss(train): 67.155548 Loss(val): 67.165779
[23:07:08] Epoch  10: Loss(train): 67.068855 Loss(val): 67.079079
[23:23:42] Epoch  12: Loss(train): 66.994072 Loss(val): 67.004318
[23:43:22] Epoch  14: Loss(train): 66.931145 Loss(val): 66.941414
[23:59:21] Epoch  16: Loss(train): 66.875031 Loss(val): 66.885284
[00:19:03] Epoch  18: Loss(train): 66.825371 Loss(val): 66.835571
[00:36:41] Epoch  20: Loss(train): 66.781197 Loss(val): 66.791435
[00:54:54] Epoch  22: Loss(train): 66.741692 Loss(val): 66.751991
[01:15:18] Epoch  24: Loss(train): 66.706535 Loss(val): 66.716827
[01:32:32] Epoch  26: Loss(train): 66.675232 Loss(val): 66.685478
[01:54:11] Epoch  28: Loss(train): 66.647171 Loss(val): 66.657463
[02:12:09] Epoch  30: Loss(train): 66.621796 Loss(val): 66.632065
[02:32:33] Epoch  32: Loss(train): 66.598854 Loss(val): 66.609077
[02:53:21] Epoch  34: Loss(train): 66.578270 Loss(val): 66.588493
[03:10:47] Epoch  36: Loss(train): 66.560196 Loss(val): 66.570473
[03:33:05] Epoch  38: Loss(train): 66.544395 Loss(val): 66.554642
[03:51:21] Epoch  40: Loss(train): 66.530609 Loss(val): 66.540833
[03:53:19] FINAL(40) Loss(val): 66.540833 Accuarcy: 0.080357

Search 23 of 500
momentum0.99, features=[64, 64, 64], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.01

[03:56:53] INIT Loss(val): 0.156261 Accuarcy: 0.086173
[04:05:45] Epoch   2: Loss(train): 0.079105 Loss(val): 0.076430
[04:12:50] Epoch   4: Loss(train): 0.067922 Loss(val): 0.066296
[04:19:22] Epoch   6: Loss(train): 0.062268 Loss(val): 0.061061
[04:29:48] Epoch   8: Loss(train): 0.059802 Loss(val): 0.059280
[04:39:00] Epoch  10: Loss(train): 0.059741 Loss(val): 0.059402
[04:46:13] Epoch  12: Loss(train): 0.058659 Loss(val): 0.058159
[04:52:49] Epoch  14: Loss(train): 0.057998 Loss(val): 0.057408
[05:03:34] Epoch  16: Loss(train): 0.057697 Loss(val): 0.057173
[05:12:41] Epoch  18: Loss(train): 0.058854 Loss(val): 0.058166
[05:19:47] Epoch  20: Loss(train): 0.056203 Loss(val): 0.056039
[05:26:30] Epoch  22: Loss(train): 0.056706 Loss(val): 0.056652
[05:37:03] Epoch  24: Loss(train): 0.057241 Loss(val): 0.057240
[05:45:12] Epoch  26: Loss(train): 0.056111 Loss(val): 0.055896
[05:52:35] Epoch  28: Loss(train): 0.054545 Loss(val): 0.054455
[05:59:21] Epoch  30: Loss(train): 0.053666 Loss(val): 0.053996
[06:06:18] Epoch  32: Loss(train): 0.055091 Loss(val): 0.055624
[06:13:18] Epoch  34: Loss(train): 0.054184 Loss(val): 0.054805
[06:20:29] Epoch  36: Loss(train): 0.052324 Loss(val): 0.052880
[06:30:01] Epoch  38: Loss(train): 0.051825 Loss(val): 0.052219
[06:38:57] Epoch  40: Loss(train): 0.050568 Loss(val): 0.051316
[06:39:40] FINAL(40) Loss(val): 0.051316 Accuarcy: 0.649643

Search 24 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=0.03

[06:41:03] INIT Loss(val): 0.188385 Accuarcy: 0.083690
[06:52:54] Epoch   2: Loss(train): 0.072349 Loss(val): 0.071118
[07:08:32] Epoch   4: Loss(train): 0.064639 Loss(val): 0.063467
[07:21:35] Epoch   6: Loss(train): 0.061816 Loss(val): 0.060563
[07:37:04] Epoch   8: Loss(train): 0.058359 Loss(val): 0.057653
[07:50:18] Epoch  10: Loss(train): 0.057401 Loss(val): 0.056310
[08:04:59] Epoch  12: Loss(train): 0.056189 Loss(val): 0.055490
[08:19:04] Epoch  14: Loss(train): 0.054788 Loss(val): 0.054267
[08:33:18] Epoch  16: Loss(train): 0.054138 Loss(val): 0.053642
[08:48:22] Epoch  18: Loss(train): 0.053486 Loss(val): 0.052964
[09:01:45] Epoch  20: Loss(train): 0.053176 Loss(val): 0.052770
[09:19:46] Epoch  22: Loss(train): 0.052395 Loss(val): 0.052135
[09:32:18] Epoch  24: Loss(train): 0.052084 Loss(val): 0.051754
[09:50:48] Epoch  26: Loss(train): 0.051454 Loss(val): 0.051278
[10:03:51] Epoch  28: Loss(train): 0.051378 Loss(val): 0.051049
[10:22:26] Epoch  30: Loss(train): 0.051123 Loss(val): 0.050865
[10:35:48] Epoch  32: Loss(train): 0.050678 Loss(val): 0.050489
[10:54:01] Epoch  34: Loss(train): 0.050485 Loss(val): 0.050431
[11:07:16] Epoch  36: Loss(train): 0.050130 Loss(val): 0.050041
[11:25:31] Epoch  38: Loss(train): 0.050138 Loss(val): 0.049954
[11:39:24] Epoch  40: Loss(train): 0.049971 Loss(val): 0.049950
[11:40:54] FINAL(40) Loss(val): 0.049950 Accuarcy: 0.656633

Search 25 of 500
momentum0.94, 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

[11:43:33] INIT Loss(val): 0.206394 Accuarcy: 0.087228
[11:52:00] Epoch   2: Loss(train): 0.101289 Loss(val): 0.103436
[11:59:34] Epoch   4: Loss(train): 0.080375 Loss(val): 0.080668
[12:05:42] Epoch   6: Loss(train): 0.071989 Loss(val): 0.073021
[12:11:22] Epoch   8: Loss(train): 0.067130 Loss(val): 0.066621
[12:17:03] Epoch  10: Loss(train): 0.062482 Loss(val): 0.062284
[12:26:05] Epoch  12: Loss(train): 0.059126 Loss(val): 0.059354
[12:33:42] Epoch  14: Loss(train): 0.054605 Loss(val): 0.055075
[12:40:10] Epoch  16: Loss(train): 0.052491 Loss(val): 0.053066
[12:46:25] Epoch  18: Loss(train): 0.050310 Loss(val): 0.050966
[12:52:38] Epoch  20: Loss(train): 0.048141 Loss(val): 0.048782
[12:59:55] Epoch  22: Loss(train): 0.046192 Loss(val): 0.047045
[13:06:18] Epoch  24: Loss(train): 0.044195 Loss(val): 0.045356
[13:12:35] Epoch  26: Loss(train): 0.043286 Loss(val): 0.044734
[13:18:26] Epoch  28: Loss(train): 0.042307 Loss(val): 0.043518
[13:24:05] Epoch  30: Loss(train): 0.041010 Loss(val): 0.042333
[13:31:27] Epoch  32: Loss(train): 0.040678 Loss(val): 0.042079
[13:38:43] Epoch  34: Loss(train): 0.039944 Loss(val): 0.041404
[13:45:18] Epoch  36: Loss(train): 0.039505 Loss(val): 0.040942
[13:51:09] Epoch  38: Loss(train): 0.039001 Loss(val): 0.040541
[13:56:48] Epoch  40: Loss(train): 0.038627 Loss(val): 0.040171
[13:57:14] FINAL(40) Loss(val): 0.040171 Accuarcy: 0.615595

Search 26 of 500
momentum0.99, features=[64, 64, 64], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.03

[13:58:10] INIT Loss(val): 0.150567 Accuarcy: 0.075561
[14:05:36] Epoch   2: Loss(train): 0.109244 Loss(val): 0.109046
[14:13:33] Epoch   4: Loss(train): 0.069782 Loss(val): 0.068761
[14:21:46] Epoch   6: Loss(train): 0.057850 Loss(val): 0.057034
[14:32:51] Epoch   8: Loss(train): 0.053643 Loss(val): 0.053023
[14:41:22] Epoch  10: Loss(train): 0.051602 Loss(val): 0.051370
[14:49:15] Epoch  12: Loss(train): 0.051565 Loss(val): 0.051038
[14:56:52] Epoch  14: Loss(train): 0.050527 Loss(val): 0.050176
[15:08:13] Epoch  16: Loss(train): 0.047978 Loss(val): 0.047917
[15:16:48] Epoch  18: Loss(train): 0.046836 Loss(val): 0.046800
[15:24:29] Epoch  20: Loss(train): 0.049216 Loss(val): 0.049681
[15:34:18] Epoch  22: Loss(train): 0.044710 Loss(val): 0.045216
[15:43:20] Epoch  24: Loss(train): 0.046563 Loss(val): 0.047376
[15:51:33] Epoch  26: Loss(train): 0.042391 Loss(val): 0.042913
[15:59:26] Epoch  28: Loss(train): 0.041383 Loss(val): 0.041802
[16:09:13] Epoch  30: Loss(train): 0.040634 Loss(val): 0.041065
[16:18:52] Epoch  32: Loss(train): 0.039923 Loss(val): 0.040496
[16:27:03] Epoch  34: Loss(train): 0.040688 Loss(val): 0.042053
[16:34:46] Epoch  36: Loss(train): 0.038398 Loss(val): 0.039507
[16:45:25] Epoch  38: Loss(train): 0.037850 Loss(val): 0.038930
[16:53:54] Epoch  40: Loss(train): 0.038935 Loss(val): 0.039747
[16:54:57] FINAL(40) Loss(val): 0.039747 Accuarcy: 0.614592

Search 27 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=0.03

[16:56:34] INIT Loss(val): 0.134213 Accuarcy: 0.091514
[17:02:28] Epoch   2: Loss(train): 0.090860 Loss(val): 0.090545
[17:08:42] Epoch   4: Loss(train): 0.078672 Loss(val): 0.076943
[17:16:48] Epoch   6: Loss(train): 0.069592 Loss(val): 0.068936
[17:23:32] Epoch   8: Loss(train): 0.066346 Loss(val): 0.065861
[17:29:47] Epoch  10: Loss(train): 0.064795 Loss(val): 0.064491
[17:35:36] Epoch  12: Loss(train): 0.063546 Loss(val): 0.063492
[17:43:11] Epoch  14: Loss(train): 0.062957 Loss(val): 0.062895
[17:50:18] Epoch  16: Loss(train): 0.062168 Loss(val): 0.062227
[17:56:49] Epoch  18: Loss(train): 0.061885 Loss(val): 0.061934
[18:02:49] Epoch  20: Loss(train): 0.061532 Loss(val): 0.061760
Early stopping with Loss(train) 0.061861 at epoch 20 (Accuracy: 0.608146)

Search 28 of 500
momentum0.9, features=[64, 64, 64], dropout_rate=0.4
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.003

[18:04:05] INIT Loss(val): 0.128106 Accuarcy: 0.102738
[18:12:15] Epoch   2: Loss(train): 0.074689 Loss(val): 0.075390
[18:24:22] Epoch   4: Loss(train): 0.068198 Loss(val): 0.068720
[18:33:08] Epoch   6: Loss(train): 0.066180 Loss(val): 0.066643
[18:40:59] Epoch   8: Loss(train): 0.064997 Loss(val): 0.065444
[18:53:03] Epoch  10: Loss(train): 0.064207 Loss(val): 0.064619
[19:02:48] Epoch  12: Loss(train): 0.063483 Loss(val): 0.063943
[19:10:44] Epoch  14: Loss(train): 0.063037 Loss(val): 0.063462
[19:21:21] Epoch  16: Loss(train): 0.062571 Loss(val): 0.063062
[19:32:27] Epoch  18: Loss(train): 0.062316 Loss(val): 0.062758
[19:41:00] Epoch  20: Loss(train): 0.061978 Loss(val): 0.062450
[19:49:41] Epoch  22: Loss(train): 0.061717 Loss(val): 0.062215
[20:02:18] Epoch  24: Loss(train): 0.061504 Loss(val): 0.062028
[20:11:17] Epoch  26: Loss(train): 0.061352 Loss(val): 0.061835
[20:19:18] Epoch  28: Loss(train): 0.061151 Loss(val): 0.061692
[20:31:40] Epoch  30: Loss(train): 0.061015 Loss(val): 0.061562
[20:41:43] Epoch  32: Loss(train): 0.060934 Loss(val): 0.061451
[20:49:44] Epoch  34: Loss(train): 0.060828 Loss(val): 0.061372
[21:01:36] Epoch  36: Loss(train): 0.060729 Loss(val): 0.061282
[21:11:39] Epoch  38: Loss(train): 0.060642 Loss(val): 0.061227
[21:20:03] Epoch  40: Loss(train): 0.060570 Loss(val): 0.061159
[21:20:56] FINAL(40) Loss(val): 0.061159 Accuarcy: 0.525493

Search 29 of 500
momentum0.92, features=[32, 32, 32], dropout_rate=0.4
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=1.0

[21:22:36] INIT Loss(val): 0.137907 Accuarcy: 0.095374
[21:31:00] Epoch   2: Loss(train): 0.065825 Loss(val): 0.066324
[21:40:00] Epoch   4: Loss(train): 0.058934 Loss(val): 0.057341
[21:47:02] Epoch   6: Loss(train): 0.051880 Loss(val): 0.052709
[21:52:58] Epoch   8: Loss(train): 0.044297 Loss(val): 0.045645
[21:58:54] Epoch  10: Loss(train): 0.041006 Loss(val): 0.042122
[22:04:54] Epoch  12: Loss(train): 0.040873 Loss(val): 0.042592
[22:10:53] Epoch  14: Loss(train): 0.037040 Loss(val): 0.038307
[22:17:16] Epoch  16: Loss(train): 0.039913 Loss(val): 0.042390
[22:24:29] Epoch  18: Loss(train): 0.031755 Loss(val): 0.033696
[22:32:00] Epoch  20: Loss(train): 0.030639 Loss(val): 0.030783
[22:39:06] Epoch  22: Loss(train): 0.028381 Loss(val): 0.028629
[22:45:35] Epoch  24: Loss(train): 0.027468 Loss(val): 0.027725
[22:51:38] Epoch  26: Loss(train): 0.025523 Loss(val): 0.025599
[23:00:27] Epoch  28: Loss(train): 0.025676 Loss(val): 0.025638
[23:07:41] Epoch  30: Loss(train): 0.025141 Loss(val): 0.025392
[23:14:21] Epoch  32: Loss(train): 0.024193 Loss(val): 0.024012
[23:20:33] Epoch  34: Loss(train): 0.023470 Loss(val): 0.023429
[23:28:02] Epoch  36: Loss(train): 0.022927 Loss(val): 0.022909
[23:36:20] Epoch  38: Loss(train): 0.022175 Loss(val): 0.022574
[23:43:15] Epoch  40: Loss(train): 0.021259 Loss(val): 0.021576
[23:43:50] FINAL(40) Loss(val): 0.021576 Accuarcy: 0.537568

Search 30 of 500
momentum0.96, features=[96, 192, 192], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=1.0

[23:44:59] INIT Loss(val): 0.157926 Accuarcy: 0.096905
[00:07:35] Epoch   2: Loss(train): 0.083815 Loss(val): 0.085143
[00:30:44] Epoch   4: Loss(train): 0.066922 Loss(val): 0.068693
[00:54:18] Epoch   6: Loss(train): 0.062800 Loss(val): 0.064619
[01:19:21] Epoch   8: Loss(train): 0.055423 Loss(val): 0.056209
[01:45:26] Epoch  10: Loss(train): 0.051950 Loss(val): 0.052295
[02:10:56] Epoch  12: Loss(train): 0.048478 Loss(val): 0.050152
[02:34:46] Epoch  14: Loss(train): 0.049978 Loss(val): 0.049653
[03:01:13] Epoch  16: Loss(train): 0.043648 Loss(val): 0.045337
[03:29:02] Epoch  18: Loss(train): 0.045327 Loss(val): 0.048296
[03:56:01] Epoch  20: Loss(train): 0.045312 Loss(val): 0.048873
[04:21:49] Epoch  22: Loss(train): 0.040705 Loss(val): 0.040873
[04:45:42] Epoch  24: Loss(train): 0.046981 Loss(val): 0.049900
[05:12:34] Epoch  26: Loss(train): 0.038865 Loss(val): 0.040929
[05:38:34] Epoch  28: Loss(train): 0.030650 Loss(val): 0.030977
[06:00:05] Epoch  30: Loss(train): 0.030971 Loss(val): 0.032735
[06:25:37] Epoch  32: Loss(train): 0.032108 Loss(val): 0.030270
[06:50:02] Epoch  34: Loss(train): 0.029535 Loss(val): 0.028256
[07:13:34] Epoch  36: Loss(train): 0.025526 Loss(val): 0.024374
[07:38:32] Epoch  38: Loss(train): 0.025246 Loss(val): 0.023842
[08:03:25] Epoch  40: Loss(train): 0.024390 Loss(val): 0.023580
[08:05:38] FINAL(40) Loss(val): 0.023580 Accuarcy: 0.518469

Search 31 of 500
momentum0.98, features=[64, 64, 64], dropout_rate=0.6
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.001

[08:08:47] INIT Loss(val): 0.136395 Accuarcy: 0.102109
[08:16:59] Epoch   2: Loss(train): 0.083085 Loss(val): 0.084971
[08:28:48] Epoch   4: Loss(train): 0.070578 Loss(val): 0.071192
[08:38:26] Epoch   6: Loss(train): 0.067818 Loss(val): 0.068311
[08:47:17] Epoch   8: Loss(train): 0.066219 Loss(val): 0.066654
[08:58:49] Epoch  10: Loss(train): 0.065115 Loss(val): 0.065624
[09:08:46] Epoch  12: Loss(train): 0.064355 Loss(val): 0.064817
[09:17:31] Epoch  14: Loss(train): 0.063803 Loss(val): 0.064206
[09:28:50] Epoch  16: Loss(train): 0.063242 Loss(val): 0.063749
[09:38:48] Epoch  18: Loss(train): 0.062930 Loss(val): 0.063295
[09:47:45] Epoch  20: Loss(train): 0.062665 Loss(val): 0.063019
[09:58:15] Epoch  22: Loss(train): 0.062384 Loss(val): 0.062730
[10:09:26] Epoch  24: Loss(train): 0.062142 Loss(val): 0.062500
[10:18:33] Epoch  26: Loss(train): 0.061943 Loss(val): 0.062314
[10:30:07] Epoch  28: Loss(train): 0.061784 Loss(val): 0.062140
[10:40:47] Epoch  30: Loss(train): 0.061690 Loss(val): 0.062006
[10:50:03] Epoch  32: Loss(train): 0.061544 Loss(val): 0.061859
[11:02:31] Epoch  34: Loss(train): 0.061405 Loss(val): 0.061765
[11:13:54] Epoch  36: Loss(train): 0.061310 Loss(val): 0.061659
Early stopping with Loss(train) 0.065227 at epoch 36 (Accuracy: 0.477653)

Search 32 of 500
momentum0.92, 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=0.001

[11:16:41] INIT Loss(val): 0.156245 Accuarcy: 0.081514
[11:35:09] Epoch   2: Loss(train): 0.075818 Loss(val): 0.075976
[11:54:19] Epoch   4: Loss(train): 0.069581 Loss(val): 0.069867
[12:16:03] Epoch   6: Loss(train): 0.067491 Loss(val): 0.067784
[12:33:26] Epoch   8: Loss(train): 0.066309 Loss(val): 0.066613
[12:55:22] Epoch  10: Loss(train): 0.065570 Loss(val): 0.065932
[13:16:01] Epoch  12: Loss(train): 0.064942 Loss(val): 0.065361
[13:34:16] Epoch  14: Loss(train): 0.064421 Loss(val): 0.064918
[13:50:08] Epoch  16: Loss(train): 0.064045 Loss(val): 0.064583
[14:10:29] Epoch  18: Loss(train): 0.063691 Loss(val): 0.064300
[14:27:48] Epoch  20: Loss(train): 0.063414 Loss(val): 0.064062
[14:47:52] Epoch  22: Loss(train): 0.063163 Loss(val): 0.063851
[15:05:02] Epoch  24: Loss(train): 0.062957 Loss(val): 0.063692
[15:25:31] Epoch  26: Loss(train): 0.062760 Loss(val): 0.063534
[15:44:27] Epoch  28: Loss(train): 0.062596 Loss(val): 0.063417
[16:02:11] Epoch  30: Loss(train): 0.062460 Loss(val): 0.063311
[16:22:44] Epoch  32: Loss(train): 0.062348 Loss(val): 0.063210
[16:40:25] Epoch  34: Loss(train): 0.062251 Loss(val): 0.063149
[17:00:11] Epoch  36: Loss(train): 0.062162 Loss(val): 0.063097
[17:19:44] Epoch  38: Loss(train): 0.062111 Loss(val): 0.063032
[17:37:13] Epoch  40: Loss(train): 0.062047 Loss(val): 0.062991
[17:39:26] FINAL(40) Loss(val): 0.062991 Accuarcy: 0.500272

Search 33 of 500
momentum0.94, features=[64, 64, 64], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.001

[17:44:54] INIT Loss(val): 0.129508 Accuarcy: 0.087432
[18:04:53] Epoch   2: Loss(train): 0.072966 Loss(val): 0.074566
[18:29:05] Epoch   4: Loss(train): 0.067318 Loss(val): 0.068274
[18:53:29] Epoch   6: Loss(train): 0.064862 Loss(val): 0.065613
[19:14:04] Epoch   8: Loss(train): 0.063560 Loss(val): 0.064150
[19:39:54] Epoch  10: Loss(train): 0.062483 Loss(val): 0.063086
[20:05:05] Epoch  12: Loss(train): 0.061835 Loss(val): 0.062398
[20:28:37] Epoch  14: Loss(train): 0.061281 Loss(val): 0.061852
[20:50:12] Epoch  16: Loss(train): 0.060817 Loss(val): 0.061377
[21:16:16] Epoch  18: Loss(train): 0.060441 Loss(val): 0.061011
[21:35:17] Epoch  20: Loss(train): 0.060146 Loss(val): 0.060714
[21:58:43] Epoch  22: Loss(train): 0.059869 Loss(val): 0.060446
[22:20:18] Epoch  24: Loss(train): 0.059664 Loss(val): 0.060259
[22:42:47] Epoch  26: Loss(train): 0.059494 Loss(val): 0.060083
[23:06:12] Epoch  28: Loss(train): 0.059329 Loss(val): 0.059940
Early stopping with Loss(train) 0.062314 at epoch 29 (Accuracy: 0.436071)

Search 34 of 500
momentum0.94, features=[64, 64, 64], 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

[23:23:32] INIT Loss(val): 0.161144 Accuarcy: 0.107738
[23:47:42] Epoch   2: Loss(train): 0.079291 Loss(val): 0.079141
[00:12:49] Epoch   4: Loss(train): 0.067550 Loss(val): 0.066014
[00:39:19] Epoch   6: Loss(train): 0.063289 Loss(val): 0.061791
[01:05:04] Epoch   8: Loss(train): 0.060796 Loss(val): 0.059554
[01:31:19] Epoch  10: Loss(train): 0.059412 Loss(val): 0.058274
[01:59:17] Epoch  12: Loss(train): 0.058747 Loss(val): 0.057489
[02:28:58] Epoch  14: Loss(train): 0.058412 Loss(val): 0.057003
[02:58:46] Epoch  16: Loss(train): 0.057484 Loss(val): 0.056236
[03:28:32] Epoch  18: Loss(train): 0.057060 Loss(val): 0.055838
[03:57:59] Epoch  20: Loss(train): 0.056660 Loss(val): 0.055544
[04:26:58] Epoch  22: Loss(train): 0.056213 Loss(val): 0.055160
[04:56:08] Epoch  24: Loss(train): 0.056014 Loss(val): 0.054934
[05:21:16] Epoch  26: Loss(train): 0.055638 Loss(val): 0.054669
[05:48:43] Epoch  28: Loss(train): 0.055279 Loss(val): 0.054408
[06:13:58] Epoch  30: Loss(train): 0.055161 Loss(val): 0.054242
[06:41:24] Epoch  32: Loss(train): 0.054907 Loss(val): 0.054066
[07:08:20] Epoch  34: Loss(train): 0.054621 Loss(val): 0.053844
[07:35:37] Epoch  36: Loss(train): 0.054479 Loss(val): 0.053736
[08:03:00] Epoch  38: Loss(train): 0.054394 Loss(val): 0.053631
[08:29:53] Epoch  40: Loss(train): 0.054193 Loss(val): 0.053513
[08:32:42] FINAL(40) Loss(val): 0.053513 Accuarcy: 0.610051

Search 35 of 500
momentum0.94, features=[64, 64, 64], dropout_rate=0.3
kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1

[08:36:22] INIT Loss(val): 0.123761 Accuarcy: 0.091531
[08:57:47] Epoch   2: Loss(train): 0.065259 Loss(val): 0.065832
[09:19:41] Epoch   4: Loss(train): 0.059903 Loss(val): 0.058281
[09:44:11] Epoch   6: Loss(train): 0.056566 Loss(val): 0.054878
[10:10:43] Epoch   8: Loss(train): 0.053432 Loss(val): 0.052172
[10:35:42] Epoch  10: Loss(train): 0.051702 Loss(val): 0.050658
[10:57:51] Epoch  12: Loss(train): 0.050034 Loss(val): 0.049270
[11:24:31] Epoch  14: Loss(train): 0.048701 Loss(val): 0.048126
[11:51:11] Epoch  16: Loss(train): 0.047392 Loss(val): 0.047040
[12:17:09] Epoch  18: Loss(train): 0.046799 Loss(val): 0.046500
[12:40:51] Epoch  20: Loss(train): 0.045793 Loss(val): 0.045674
[13:05:28] Epoch  22: Loss(train): 0.045341 Loss(val): 0.045252
[13:25:44] Epoch  24: Loss(train): 0.044809 Loss(val): 0.044760
[13:50:57] Epoch  26: Loss(train): 0.044566 Loss(val): 0.044537
[14:14:24] Epoch  28: Loss(train): 0.043879 Loss(val): 0.043859
[14:36:11] Epoch  30: Loss(train): 0.043258 Loss(val): 0.043268
[15:00:39] Epoch  32: Loss(train): 0.042763 Loss(val): 0.042793
[15:25:03] Epoch  34: Loss(train): 0.042519 Loss(val): 0.042491
[15:48:44] Epoch  36: Loss(train): 0.042028 Loss(val): 0.041963
[16:10:14] Epoch  38: Loss(train): 0.041775 Loss(val): 0.041745
[16:34:21] Epoch  40: Loss(train): 0.041268 Loss(val): 0.041277
[16:37:30] FINAL(40) Loss(val): 0.041277 Accuarcy: 0.626071

Search 36 of 500
momentum0.94, features=[32, 32, 32], dropout_rate=0.4
kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.3

[16:42:54] INIT Loss(val): 0.113207 Accuarcy: 0.083673
[17:05:24] Epoch   2: Loss(train): 0.071602 Loss(val): 0.071321
[17:30:50] Epoch   4: Loss(train): 0.060653 Loss(val): 0.061529
[17:55:40] Epoch   6: Loss(train): 0.050651 Loss(val): 0.051918
[18:18:57] Epoch   8: Loss(train): 0.046450 Loss(val): 0.047496
[18:46:06] Epoch  10: Loss(train): 0.042359 Loss(val): 0.043354
[19:13:08] Epoch  12: Loss(train): 0.038272 Loss(val): 0.038781
[19:39:21] Epoch  14: Loss(train): 0.039356 Loss(val): 0.039668
[20:03:45] Epoch  16: Loss(train): 0.036349 Loss(val): 0.036034
[20:27:40] Epoch  18: Loss(train): 0.034223 Loss(val): 0.034151
[20:55:17] Epoch  20: Loss(train): 0.032276 Loss(val): 0.032745
[21:16:30] Epoch  22: Loss(train): 0.029983 Loss(val): 0.030295
[21:42:49] Epoch  24: Loss(train): 0.028562 Loss(val): 0.028459
[22:06:22] Epoch  26: Loss(train): 0.027147 Loss(val): 0.026862
[22:29:11] Epoch  28: Loss(train): 0.025605 Loss(val): 0.025301
[22:54:25] Epoch  30: Loss(train): 0.025205 Loss(val): 0.025065
[23:19:33] Epoch  32: Loss(train): 0.024206 Loss(val): 0.024281
[23:44:35] Epoch  34: Loss(train): 0.023469 Loss(val): 0.023604
[00:07:42] Epoch  36: Loss(train): 0.022483 Loss(val): 0.022621
[00:31:31] Epoch  38: Loss(train): 0.022307 Loss(val): 0.022504
[00:57:02] Epoch  40: Loss(train): 0.021628 Loss(val): 0.021826
[01:00:15] FINAL(40) Loss(val): 0.021826 Accuarcy: 0.606854

Search 37 of 500
momentum0.92, features=[64, 64, 64], dropout_rate=0.8
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=1.0

[01:05:55] INIT Loss(val): 0.118011 Accuarcy: 0.093418
[01:28:59] Epoch   2: Loss(train): 0.071947 Loss(val): 0.071658
[01:54:44] Epoch   4: Loss(train): 0.057903 Loss(val): 0.058991
[02:20:57] Epoch   6: Loss(train): 0.048510 Loss(val): 0.047729
[02:46:19] Epoch   8: Loss(train): 0.042900 Loss(val): 0.043739
[03:11:28] Epoch  10: Loss(train): 0.036034 Loss(val): 0.035340
[03:38:58] Epoch  12: Loss(train): 0.035133 Loss(val): 0.036576
[04:06:04] Epoch  14: Loss(train): 0.031527 Loss(val): 0.032155
[04:33:18] Epoch  16: Loss(train): 0.032142 Loss(val): 0.033449
[04:56:07] Epoch  18: Loss(train): 0.029012 Loss(val): 0.028829
[05:20:51] Epoch  20: Loss(train): 0.027290 Loss(val): 0.027411
[05:43:24] Epoch  22: Loss(train): 0.026393 Loss(val): 0.025477
[06:09:01] Epoch  24: Loss(train): 0.025136 Loss(val): 0.024777
[06:34:51] Epoch  26: Loss(train): 0.023614 Loss(val): 0.022565
[07:00:12] Epoch  28: Loss(train): 0.023541 Loss(val): 0.023284
[07:24:35] Epoch  30: Loss(train): 0.023083 Loss(val): 0.022527
[07:48:19] Epoch  32: Loss(train): 0.022078 Loss(val): 0.021276
[08:12:51] Epoch  34: Loss(train): 0.021870 Loss(val): 0.021672
[08:36:53] Epoch  36: Loss(train): 0.021393 Loss(val): 0.021033
[09:00:56] Epoch  38: Loss(train): 0.021274 Loss(val): 0.020903
[09:27:55] Epoch  40: Loss(train): 0.022459 Loss(val): 0.021333
[09:31:11] FINAL(40) Loss(val): 0.021333 Accuarcy: 0.573265

Search 38 of 500
momentum0.92, features=[96, 192, 192], 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

[09:40:13] INIT Loss(val): 0.177381 Accuarcy: 0.082194
[10:02:06] Epoch   2: Loss(train): 0.100145 Loss(val): 0.101442
[10:28:32] Epoch   4: Loss(train): 0.090080 Loss(val): 0.090478
[10:54:33] Epoch   6: Loss(train): 0.086961 Loss(val): 0.087106
[11:18:52] Epoch   8: Loss(train): 0.085055 Loss(val): 0.085218
[11:42:03] Epoch  10: Loss(train): 0.084008 Loss(val): 0.084082
[12:08:51] Epoch  12: Loss(train): 0.083061 Loss(val): 0.083309
[12:34:18] Epoch  14: Loss(train): 0.082237 Loss(val): 0.082573
[12:54:57] Epoch  16: Loss(train): 0.082143 Loss(val): 0.082355
[13:18:12] Epoch  18: Loss(train): 0.081369 Loss(val): 0.081832
[13:40:38] Epoch  20: Loss(train): 0.081240 Loss(val): 0.081622
[14:05:08] Epoch  22: Loss(train): 0.080846 Loss(val): 0.081271
[14:28:44] Epoch  24: Loss(train): 0.080775 Loss(val): 0.081209
Early stopping with Loss(train) 0.087498 at epoch 24 (Accuracy: 0.458163)

Search 39 of 500
momentum0.94, features=[64, 64, 64], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.003

[14:32:44] INIT Loss(val): 0.131732 Accuarcy: 0.093946
[14:43:04] Epoch   2: Loss(train): 0.081255 Loss(val): 0.079859
[14:57:19] Epoch   4: Loss(train): 0.068332 Loss(val): 0.067841
[15:09:13] Epoch   6: Loss(train): 0.064528 Loss(val): 0.064704
[15:21:16] Epoch   8: Loss(train): 0.062921 Loss(val): 0.063261
[15:34:46] Epoch  10: Loss(train): 0.062030 Loss(val): 0.062498
[15:45:50] Epoch  12: Loss(train): 0.061265 Loss(val): 0.061891
[16:00:28] Epoch  14: Loss(train): 0.060731 Loss(val): 0.061435
[16:12:25] Epoch  16: Loss(train): 0.060351 Loss(val): 0.061083
[16:24:49] Epoch  18: Loss(train): 0.060006 Loss(val): 0.060862
[16:37:32] Epoch  20: Loss(train): 0.059765 Loss(val): 0.060644
[16:48:56] Epoch  22: Loss(train): 0.059551 Loss(val): 0.060483
[17:01:56] Epoch  24: Loss(train): 0.059328 Loss(val): 0.060275
[17:14:41] Epoch  26: Loss(train): 0.059153 Loss(val): 0.060095
[17:26:03] Epoch  28: Loss(train): 0.058965 Loss(val): 0.059951
[17:40:07] Epoch  30: Loss(train): 0.058867 Loss(val): 0.059819
[17:52:28] Epoch  32: Loss(train): 0.058637 Loss(val): 0.059653
[18:05:08] Epoch  34: Loss(train): 0.058543 Loss(val): 0.059542
[18:19:54] Epoch  36: Loss(train): 0.058402 Loss(val): 0.059439
[18:30:58] Epoch  38: Loss(train): 0.058293 Loss(val): 0.059341
Early stopping with Loss(train) 0.059225 at epoch 38 (Accuracy: 0.544405)

Search 40 of 500
momentum0.92, features=[32, 32, 32], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(5, 1), (5, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1

[18:34:47] INIT Loss(val): 0.124025 Accuarcy: 0.088776
[18:47:33] Epoch   2: Loss(train): 0.057543 Loss(val): 0.058215
[18:57:06] Epoch   4: Loss(train): 0.051879 Loss(val): 0.052554
[19:05:25] Epoch   6: Loss(train): 0.048303 Loss(val): 0.048884
[19:19:12] Epoch   8: Loss(train): 0.047418 Loss(val): 0.046097
[19:28:58] Epoch  10: Loss(train): 0.046238 Loss(val): 0.044915
[19:37:28] Epoch  12: Loss(train): 0.044154 Loss(val): 0.043053
[19:51:17] Epoch  14: Loss(train): 0.042655 Loss(val): 0.041790
[20:01:13] Epoch  16: Loss(train): 0.041199 Loss(val): 0.040503
[20:09:50] Epoch  18: Loss(train): 0.040215 Loss(val): 0.039571
[20:23:19] Epoch  20: Loss(train): 0.039205 Loss(val): 0.038591
[20:33:47] Epoch  22: Loss(train): 0.038505 Loss(val): 0.037907
[20:42:15] Epoch  24: Loss(train): 0.037870 Loss(val): 0.037287
[20:50:49] Epoch  26: Loss(train): 0.037377 Loss(val): 0.036808
[20:59:19] Epoch  28: Loss(train): 0.036958 Loss(val): 0.036429
[21:08:32] Epoch  30: Loss(train): 0.036559 Loss(val): 0.036016
[21:21:13] Epoch  32: Loss(train): 0.036171 Loss(val): 0.035656
[21:30:26] Epoch  34: Loss(train): 0.035821 Loss(val): 0.035373
[21:39:16] Epoch  36: Loss(train): 0.035724 Loss(val): 0.035267
[21:51:46] Epoch  38: Loss(train): 0.035684 Loss(val): 0.035258
[22:01:20] Epoch  40: Loss(train): 0.035597 Loss(val): 0.035128
[22:02:18] FINAL(40) Loss(val): 0.035128 Accuarcy: 0.636701

Search 41 of 500
momentum0.98, features=[64, 64, 64], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.003

[22:03:51] INIT Loss(val): 0.116976 Accuarcy: 0.093078
[22:29:47] Epoch   2: Loss(train): 0.064875 Loss(val): 0.063949
[22:57:17] Epoch   4: Loss(train): 0.059843 Loss(val): 0.058989
[23:24:07] Epoch   6: Loss(train): 0.057889 Loss(val): 0.057287
[23:50:54] Epoch   8: Loss(train): 0.056778 Loss(val): 0.056329
[00:17:41] Epoch  10: Loss(train): 0.056209 Loss(val): 0.055885
[00:43:47] Epoch  12: Loss(train): 0.055611 Loss(val): 0.055446
[01:11:43] Epoch  14: Loss(train): 0.055447 Loss(val): 0.055392
[01:40:33] Epoch  16: Loss(train): 0.055111 Loss(val): 0.055106
[02:09:24] Epoch  18: Loss(train): 0.054887 Loss(val): 0.054910
[02:39:30] Epoch  20: Loss(train): 0.054918 Loss(val): 0.054874
[03:09:24] Epoch  22: Loss(train): 0.054555 Loss(val): 0.054594
[03:38:37] Epoch  24: Loss(train): 0.054447 Loss(val): 0.054464
[04:07:54] Epoch  26: Loss(train): 0.054165 Loss(val): 0.054282
[04:34:20] Epoch  28: Loss(train): 0.053983 Loss(val): 0.054111
[05:01:39] Epoch  30: Loss(train): 0.053737 Loss(val): 0.053914
[05:27:48] Epoch  32: Loss(train): 0.053491 Loss(val): 0.053724
[05:55:10] Epoch  34: Loss(train): 0.053195 Loss(val): 0.053495
[06:23:22] Epoch  36: Loss(train): 0.052964 Loss(val): 0.053314
[06:51:28] Epoch  38: Loss(train): 0.052682 Loss(val): 0.053067
[07:19:10] Epoch  40: Loss(train): 0.052387 Loss(val): 0.052851
[07:23:01] FINAL(40) Loss(val): 0.052851 Accuarcy: 0.607602

Search 42 of 500
momentum0.96, features=[32, 64, 128], dropout_rate=0.1
kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.001

[07:29:16] INIT Loss(val): 0.176981 Accuarcy: 0.089626
[08:08:38] Epoch   2: Loss(train): 0.084734 Loss(val): 0.084191
[08:51:50] Epoch   4: Loss(train): 0.074645 Loss(val): 0.074708
[09:34:55] Epoch   6: Loss(train): 0.071603 Loss(val): 0.072190
[10:19:23] Epoch   8: Loss(train): 0.070126 Loss(val): 0.070948
[11:06:15] Epoch  10: Loss(train): 0.069323 Loss(val): 0.070290