--------[18_09_2019 15:33:43]-------- Random Grid Search Search 1 of 500 momentum0.92, features=[32, 32, 32], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1 [15:34:55] INIT Loss(val): 0.125317 [15:36:49] Epoch 2: Loss(train): 0.075287 Loss(val): 0.075063 [15:37:26] Epoch 4: Loss(train): 0.068476 Loss(val): 0.069180 [15:37:55] Epoch 6: Loss(train): 0.065515 Loss(val): 0.066542 [15:38:22] Epoch 8: Loss(train): 0.063886 Loss(val): 0.064420 [15:38:53] Epoch 10: Loss(train): 0.063473 Loss(val): 0.062857 [15:39:23] Epoch 12: Loss(train): 0.061911 Loss(val): 0.061451 [15:39:52] Epoch 14: Loss(train): 0.060242 Loss(val): 0.059912 [15:40:22] Epoch 16: Loss(train): 0.058877 Loss(val): 0.058791 [15:40:51] Epoch 18: Loss(train): 0.058132 Loss(val): 0.058110 [15:41:19] Epoch 20: Loss(train): 0.057336 Loss(val): 0.057570 [15:41:49] Epoch 22: Loss(train): 0.056594 Loss(val): 0.056818 Early stopping at 0 Search 2 of 500 momentum0.94, features=[64, 64, 64], dropout_rate=0.6 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.01 [15:41:55] INIT Loss(val): 0.129147 [15:42:23] Epoch 2: Loss(train): 0.078632 Loss(val): 0.078414 [15:42:54] Epoch 4: Loss(train): 0.070202 Loss(val): 0.070887 [15:43:22] Epoch 6: Loss(train): 0.066139 Loss(val): 0.066908 [15:43:51] Epoch 8: Loss(train): 0.063795 Loss(val): 0.063834 [15:44:19] Epoch 10: Loss(train): 0.063544 Loss(val): 0.062947 [15:44:48] Epoch 12: Loss(train): 0.061362 Loss(val): 0.060898 [15:45:18] Epoch 14: Loss(train): 0.060353 Loss(val): 0.060166 [15:45:51] Epoch 16: Loss(train): 0.058804 Loss(val): 0.059007 [15:46:21] Epoch 18: Loss(train): 0.057630 Loss(val): 0.057931 [15:46:51] Epoch 20: Loss(train): 0.056580 Loss(val): 0.056976 [15:47:19] Epoch 22: Loss(train): 0.055983 Loss(val): 0.056418 [15:47:48] Epoch 24: Loss(train): 0.055619 Loss(val): 0.055914 [15:48:20] Epoch 26: Loss(train): 0.054729 Loss(val): 0.055086 [15:48:52] Epoch 28: Loss(train): 0.054222 Loss(val): 0.054575 [15:49:27] FINAL(30) Loss(val): 0.054254 [15:49:30] FINAL(30) Loss(val): 0.054254 Search 3 of 500 momentum0.94, features=[64, 64, 64], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.001 [15:49:31] INIT Loss(val): 0.126161 [15:49:59] Epoch 2: Loss(train): 0.077885 Loss(val): 0.078426 [15:50:34] Epoch 4: Loss(train): 0.070761 Loss(val): 0.071715 [15:51:04] Epoch 6: Loss(train): 0.067634 Loss(val): 0.068910 [15:51:33] Epoch 8: Loss(train): 0.065403 Loss(val): 0.066819 [15:52:02] Epoch 10: Loss(train): 0.064137 Loss(val): 0.064249 [15:52:30] Epoch 12: Loss(train): 0.062744 Loss(val): 0.062782 [15:53:00] Epoch 14: Loss(train): 0.061248 Loss(val): 0.061450 [15:53:29] Epoch 16: Loss(train): 0.059991 Loss(val): 0.060319 [15:53:58] Epoch 18: Loss(train): 0.058670 Loss(val): 0.059030 [15:54:29] Epoch 20: Loss(train): 0.057977 Loss(val): 0.058384 [15:55:01] Epoch 22: Loss(train): 0.056949 Loss(val): 0.057303 [15:55:30] Epoch 24: Loss(train): 0.056464 Loss(val): 0.056771 [15:55:59] Epoch 26: Loss(train): 0.055319 Loss(val): 0.055677 [15:56:29] Epoch 28: Loss(train): 0.055020 Loss(val): 0.055222 [15:56:59] FINAL(30) Loss(val): 0.054447 [15:57:02] FINAL(30) Loss(val): 0.054447 Search 4 of 500 momentum0.94, features=[96, 192, 192], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(5, 1), (3, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.01 [15:57:03] INIT Loss(val): 0.143514 [15:57:29] Epoch 2: Loss(train): 0.077462 Loss(val): 0.078461 [15:57:59] Epoch 4: Loss(train): 0.070687 Loss(val): 0.072219 [15:58:30] Epoch 6: Loss(train): 0.067443 Loss(val): 0.068943 [15:58:58] Epoch 8: Loss(train): 0.064753 Loss(val): 0.064823 [15:59:28] Epoch 10: Loss(train): 0.063935 Loss(val): 0.063728 [15:59:57] Epoch 12: Loss(train): 0.061962 Loss(val): 0.061995 [16:00:27] Epoch 14: Loss(train): 0.061007 Loss(val): 0.061087 [16:00:57] Epoch 16: Loss(train): 0.059649 Loss(val): 0.059899 [16:01:26] Epoch 18: Loss(train): 0.059178 Loss(val): 0.059439 [16:01:56] Epoch 20: Loss(train): 0.058152 Loss(val): 0.058421 [16:02:25] Epoch 22: Loss(train): 0.057042 Loss(val): 0.057437 [16:02:55] Epoch 24: Loss(train): 0.056095 Loss(val): 0.056463 [16:03:25] Epoch 26: Loss(train): 0.055047 Loss(val): 0.055456 [16:03:54] Epoch 28: Loss(train): 0.054421 Loss(val): 0.054925 [16:04:24] FINAL(30) Loss(val): 0.054328 [16:04:27] FINAL(30) Loss(val): 0.054328 Search 5 of 500 momentum0.94, features=[32, 32, 32], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(3, 1), (3, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.03 [16:04:27] INIT Loss(val): 0.142138 [16:04:55] Epoch 2: Loss(train): 0.078344 Loss(val): 0.079304 [16:05:24] Epoch 4: Loss(train): 0.069910 Loss(val): 0.070895 [16:05:54] Epoch 6: Loss(train): 0.066428 Loss(val): 0.067457 [16:06:23] Epoch 8: Loss(train): 0.063501 Loss(val): 0.064552 [16:06:53] Epoch 10: Loss(train): 0.062729 Loss(val): 0.062416 [16:07:23] Epoch 12: Loss(train): 0.061756 Loss(val): 0.061395 [16:07:53] Epoch 14: Loss(train): 0.059968 Loss(val): 0.059828 [16:08:22] Epoch 16: Loss(train): 0.059002 Loss(val): 0.058909 [16:08:52] Epoch 18: Loss(train): 0.058062 Loss(val): 0.058228 [16:09:22] Epoch 20: Loss(train): 0.057697 Loss(val): 0.057969 [16:09:52] Epoch 22: Loss(train): 0.057117 Loss(val): 0.057452 [16:10:22] Epoch 24: Loss(train): 0.056149 Loss(val): 0.056525 [16:10:53] Epoch 26: Loss(train): 0.055586 Loss(val): 0.055998 [16:11:23] Epoch 28: Loss(train): 0.054670 Loss(val): 0.055118 [16:11:53] FINAL(30) Loss(val): 0.054470 [16:11:56] FINAL(30) Loss(val): 0.054470 Search 6 of 500 momentum0.9, features=[64, 64, 64], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(7, 1), (5, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(2, 1), (2, 1)], learning_rate=0.1 [16:11:57] INIT Loss(val): 0.130219 [16:12:25] Epoch 2: Loss(train): 0.077388 Loss(val): 0.077279 [16:12:55] Epoch 4: Loss(train): 0.068726 Loss(val): 0.069355 [16:13:26] Epoch 6: Loss(train): 0.065969 Loss(val): 0.066996 [16:13:56] Epoch 8: Loss(train): 0.064049 Loss(val): 0.065168 [16:14:26] Epoch 10: Loss(train): 0.062873 Loss(val): 0.062917 [16:14:56] Epoch 12: Loss(train): 0.063614 Loss(val): 0.063174 [16:15:26] Epoch 14: Loss(train): 0.061988 Loss(val): 0.061926 [16:15:56] Epoch 16: Loss(train): 0.060946 Loss(val): 0.061109 Early stopping at 0 Search 7 of 500 momentum0.92, features=[96, 192, 192], dropout_rate=0.1 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (3, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.01 [16:16:01] INIT Loss(val): 0.158887 [16:16:29] Epoch 2: Loss(train): 0.077085 Loss(val): 0.077593 [16:16:59] Epoch 4: Loss(train): 0.068976 Loss(val): 0.070250 [16:17:28] Epoch 6: Loss(train): 0.065539 Loss(val): 0.066987 [16:17:58] Epoch 8: Loss(train): 0.062972 Loss(val): 0.064571 [16:18:29] Epoch 10: Loss(train): 0.060879 Loss(val): 0.061406 [16:18:58] Epoch 12: Loss(train): 0.060074 Loss(val): 0.060140 [16:19:28] Epoch 14: Loss(train): 0.058958 Loss(val): 0.059320 [16:19:58] Epoch 16: Loss(train): 0.057612 Loss(val): 0.058114 [16:20:29] Epoch 18: Loss(train): 0.056968 Loss(val): 0.057745 [16:20:59] Epoch 20: Loss(train): 0.056104 Loss(val): 0.056927 [16:21:30] Epoch 22: Loss(train): 0.055559 Loss(val): 0.056354 [16:22:20] Epoch 24: Loss(train): 0.055063 Loss(val): 0.055935 [16:23:07] Epoch 26: Loss(train): 0.054734 Loss(val): 0.055537 [16:23:56] Epoch 28: Loss(train): 0.054243 Loss(val): 0.055037 [16:24:42] FINAL(30) Loss(val): 0.054857 [16:24:45] FINAL(30) Loss(val): 0.054857 Search 8 of 500 momentum0.96, features=[64, 64, 64], 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 [16:24:47] INIT Loss(val): 0.138277 [16:25:30] Epoch 2: Loss(train): 0.079859 Loss(val): 0.080827 [16:26:20] Epoch 4: Loss(train): 0.069111 Loss(val): 0.070549 [16:27:11] Epoch 6: Loss(train): 0.065782 Loss(val): 0.067275 [16:28:05] Epoch 8: Loss(train): 0.062852 Loss(val): 0.064066 [16:29:03] Epoch 10: Loss(train): 0.061824 Loss(val): 0.061692 [16:29:56] Epoch 12: Loss(train): 0.061533 Loss(val): 0.060959 [16:30:49] Epoch 14: Loss(train): 0.060054 Loss(val): 0.059820 [16:31:44] Epoch 16: Loss(train): 0.058679 Loss(val): 0.058628 [16:32:37] Epoch 18: Loss(train): 0.057629 Loss(val): 0.057877 [16:33:29] Epoch 20: Loss(train): 0.056418 Loss(val): 0.056854 [16:34:21] Epoch 22: Loss(train): 0.056054 Loss(val): 0.056538 [16:35:13] Epoch 24: Loss(train): 0.055056 Loss(val): 0.055656 [16:36:01] Epoch 26: Loss(train): 0.054567 Loss(val): 0.055133 [16:36:50] Epoch 28: Loss(train): 0.053922 Loss(val): 0.054542 [16:37:37] FINAL(30) Loss(val): 0.053926 [16:37:42] FINAL(30) Loss(val): 0.053926 Search 9 of 500 momentum0.9, features=[32, 64, 128], dropout_rate=0.3 kernel=Tuple{Int64,Int64}[(7, 1), (7, 1), (2, 6)], pooldims=Tuple{Int64,Int64}[(3, 1), (3, 1)], learning_rate=0.1 [16:37:43] INIT Loss(val): 0.149463 [16:38:18] Epoch 2: Loss(train): 0.076049 Loss(val): 0.076722 [16:39:03] Epoch 4: Loss(train): 0.068973 Loss(val): 0.069592 [16:39:53] Epoch 6: Loss(train): 0.065072 Loss(val): 0.066030 [16:40:37] Epoch 8: Loss(train): 0.063404 Loss(val): 0.064415