INFO:root:Namespace(batch_size=64, drop_rate=0.0, logging_dir='logs', lr=0.1, lr_decay=0.2, lr_decay_epoch='60,120,160', lr_decay_period=0, mode='hybrid', model='cifar_wideresnet28_10', momentum=0.9, num_epochs=220, num_gpus=2, num_workers=2, resume_from=None, save_dir='params', save_period=10, save_plot_dir='.', wd=0.0005)
[05:26:33] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:107: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
INFO:root:[Epoch 0] train=0.224028 val=0.482900 loss=1.950176 time: 45.404497
INFO:root:[Epoch 1] train=0.203862 val=0.557200 loss=1.660156 time: 43.756065
INFO:root:[Epoch 2] train=0.187612 val=0.579300 loss=1.542175 time: 43.969779
INFO:root:[Epoch 3] train=0.178039 val=0.731800 loss=1.459369 time: 44.066053
INFO:root:[Epoch 4] train=0.171639 val=0.640900 loss=1.401079 time: 43.656998
INFO:root:[Epoch 5] train=0.165530 val=0.751400 loss=1.335215 time: 43.943576
INFO:root:[Epoch 6] train=0.161095 val=0.777600 loss=1.307159 time: 43.915971
INFO:root:[Epoch 7] train=0.158209 val=0.797400 loss=1.282708 time: 43.857640
INFO:root:[Epoch 8] train=0.155871 val=0.805800 loss=1.263017 time: 43.875153
INFO:root:[Epoch 9] train=0.153694 val=0.805400 loss=1.263226 time: 43.764914
INFO:root:[Epoch 10] train=0.152623 val=0.823900 loss=1.254204 time: 43.920997
INFO:root:[Epoch 11] train=0.151281 val=0.770800 loss=1.239575 time: 43.766306
INFO:root:[Epoch 12] train=0.149837 val=0.803200 loss=1.252495 time: 43.733004
INFO:root:[Epoch 13] train=0.148700 val=0.801700 loss=1.240201 time: 43.763433
INFO:root:[Epoch 14] train=0.148559 val=0.774600 loss=1.252127 time: 43.767890
INFO:root:[Epoch 15] train=0.145441 val=0.815500 loss=1.198048 time: 43.709632
INFO:root:[Epoch 16] train=0.145757 val=0.838500 loss=1.192184 time: 43.864982
INFO:root:[Epoch 17] train=0.145823 val=0.845400 loss=1.223695 time: 43.915633
INFO:root:[Epoch 18] train=0.144334 val=0.806800 loss=1.207271 time: 43.672931
INFO:root:[Epoch 19] train=0.143259 val=0.811400 loss=1.187466 time: 43.883082
INFO:root:[Epoch 20] train=0.141098 val=0.843800 loss=1.150255 time: 43.795635
INFO:root:[Epoch 21] train=0.140260 val=0.819900 loss=1.152821 time: 43.725700
INFO:root:[Epoch 22] train=0.141992 val=0.824500 loss=1.196738 time: 43.577678
INFO:root:[Epoch 23] train=0.142988 val=0.816100 loss=1.196122 time: 43.762801
INFO:root:[Epoch 24] train=0.141329 val=0.843800 loss=1.191809 time: 43.638755
INFO:root:[Epoch 25] train=0.139784 val=0.748500 loss=1.178604 time: 43.871025
INFO:root:[Epoch 26] train=0.140494 val=0.829500 loss=1.186559 time: 43.703132
INFO:root:[Epoch 27] train=0.140647 val=0.841100 loss=1.191448 time: 43.786672
INFO:root:[Epoch 28] train=0.139157 val=0.849000 loss=1.168991 time: 43.840091
INFO:root:[Epoch 29] train=0.140631 val=0.842900 loss=1.186458 time: 43.766028
INFO:root:[Epoch 30] train=0.139029 val=0.833000 loss=1.165622 time: 43.852212
INFO:root:[Epoch 31] train=0.138767 val=0.836200 loss=1.155995 time: 43.732333
INFO:root:[Epoch 32] train=0.138544 val=0.842400 loss=1.163079 time: 43.667506
INFO:root:[Epoch 33] train=0.137689 val=0.843600 loss=1.148468 time: 43.742535
INFO:root:[Epoch 34] train=0.138903 val=0.816400 loss=1.193902 time: 43.718015
INFO:root:[Epoch 35] train=0.138003 val=0.825900 loss=1.154242 time: 43.683018
INFO:root:[Epoch 36] train=0.136440 val=0.851900 loss=1.139542 time: 43.897084
INFO:root:[Epoch 37] train=0.136914 val=0.825100 loss=1.133238 time: 43.630694
INFO:root:[Epoch 38] train=0.136836 val=0.873400 loss=1.131935 time: 43.857248
INFO:root:[Epoch 39] train=0.137555 val=0.809600 loss=1.175605 time: 43.794267
INFO:root:[Epoch 40] train=0.136295 val=0.850700 loss=1.118762 time: 43.837638
INFO:root:[Epoch 41] train=0.136783 val=0.854000 loss=1.144005 time: 43.716118
INFO:root:[Epoch 42] train=0.135036 val=0.855300 loss=1.102903 time: 43.672684
INFO:root:[Epoch 43] train=0.136433 val=0.835400 loss=1.145881 time: 43.712345
INFO:root:[Epoch 44] train=0.135950 val=0.837100 loss=1.130419 time: 43.756850
INFO:root:[Epoch 45] train=0.136228 val=0.850300 loss=1.141692 time: 43.785824
INFO:root:[Epoch 46] train=0.137601 val=0.849400 loss=1.150258 time: 43.741758
INFO:root:[Epoch 47] train=0.137341 val=0.838500 loss=1.145149 time: 43.797335
INFO:root:[Epoch 48] train=0.134622 val=0.847200 loss=1.141895 time: 43.743194
INFO:root:[Epoch 49] train=0.134767 val=0.834100 loss=1.123249 time: 43.732773
INFO:root:[Epoch 50] train=0.135278 val=0.853900 loss=1.132183 time: 43.662964
INFO:root:[Epoch 51] train=0.135708 val=0.798600 loss=1.144136 time: 43.727210
INFO:root:[Epoch 52] train=0.133476 val=0.847000 loss=1.126734 time: 43.761667
INFO:root:[Epoch 53] train=0.135667 val=0.812700 loss=1.155953 time: 43.639298
INFO:root:[Epoch 54] train=0.134328 val=0.856500 loss=1.127569 time: 43.875562
INFO:root:[Epoch 55] train=0.133239 val=0.847700 loss=1.126387 time: 43.782314
INFO:root:[Epoch 56] train=0.133251 val=0.859900 loss=1.116511 time: 44.046250
INFO:root:[Epoch 57] train=0.134686 val=0.880500 loss=1.135306 time: 43.888070
INFO:root:[Epoch 58] train=0.135259 val=0.874900 loss=1.121738 time: 43.728234
INFO:root:[Epoch 59] train=0.134543 val=0.852800 loss=1.127853 time: 44.324278
INFO:root:[Epoch 60] train=0.116162 val=0.925400 loss=1.020859 time: 43.870405
INFO:root:[Epoch 61] train=0.110403 val=0.934700 loss=0.979393 time: 43.896148
INFO:root:[Epoch 62] train=0.107239 val=0.938400 loss=0.951903 time: 43.911494
INFO:root:[Epoch 63] train=0.107406 val=0.938200 loss=0.953969 time: 43.816657
INFO:root:[Epoch 64] train=0.105068 val=0.930800 loss=0.936802 time: 43.948915
INFO:root:[Epoch 65] train=0.104760 val=0.935600 loss=0.927543 time: 43.966811
INFO:root:[Epoch 66] train=0.102414 val=0.935100 loss=0.910531 time: 44.036777
INFO:root:[Epoch 67] train=0.104553 val=0.930300 loss=0.930896 time: 43.845578
INFO:root:[Epoch 68] train=0.105366 val=0.931300 loss=0.955404 time: 43.907139
INFO:root:[Epoch 69] train=0.104517 val=0.931000 loss=0.950189 time: 43.826949
INFO:root:[Epoch 70] train=0.105356 val=0.922300 loss=0.956281 time: 43.712623
INFO:root:[Epoch 71] train=0.104993 val=0.931700 loss=0.945013 time: 44.048209
INFO:root:[Epoch 72] train=0.103608 val=0.937000 loss=0.932521 time: 43.758734
INFO:root:[Epoch 73] train=0.102414 val=0.936300 loss=0.915012 time: 43.826120
INFO:root:[Epoch 74] train=0.102838 val=0.936600 loss=0.918806 time: 43.660199
INFO:root:[Epoch 75] train=0.104584 val=0.937200 loss=0.941230 time: 43.811344
INFO:root:[Epoch 76] train=0.101229 val=0.925400 loss=0.892476 time: 43.671403
INFO:root:[Epoch 77] train=0.105875 val=0.910700 loss=0.962411 time: 43.707539
INFO:root:[Epoch 78] train=0.106314 val=0.935200 loss=0.974011 time: 43.840018
INFO:root:[Epoch 79] train=0.102516 val=0.929400 loss=0.917352 time: 43.675487
INFO:root:[Epoch 80] train=0.101977 val=0.927600 loss=0.904969 time: 43.674932
INFO:root:[Epoch 81] train=0.103365 val=0.930600 loss=0.939250 time: 43.773001
INFO:root:[Epoch 82] train=0.102345 val=0.927000 loss=0.915088 time: 43.814480
INFO:root:[Epoch 83] train=0.101881 val=0.930900 loss=0.934927 time: 43.828586
INFO:root:[Epoch 84] train=0.102396 val=0.939100 loss=0.924201 time: 43.896295
INFO:root:[Epoch 85] train=0.103161 val=0.909800 loss=0.925075 time: 43.820630
INFO:root:[Epoch 86] train=0.100367 val=0.933700 loss=0.912042 time: 43.700018
INFO:root:[Epoch 87] train=0.103252 val=0.918900 loss=0.953636 time: 43.739809
INFO:root:[Epoch 88] train=0.102109 val=0.932400 loss=0.925660 time: 43.592337
INFO:root:[Epoch 89] train=0.101817 val=0.927700 loss=0.925779 time: 43.783274
INFO:root:[Epoch 90] train=0.102045 val=0.932100 loss=0.928197 time: 43.805511
INFO:root:[Epoch 91] train=0.102572 val=0.938600 loss=0.927186 time: 43.826640
INFO:root:[Epoch 92] train=0.103002 val=0.914600 loss=0.930079 time: 43.776383
INFO:root:[Epoch 93] train=0.100217 val=0.935600 loss=0.883303 time: 43.855833
INFO:root:[Epoch 94] train=0.101103 val=0.916000 loss=0.924378 time: 43.700180
INFO:root:[Epoch 95] train=0.099922 val=0.931400 loss=0.913929 time: 43.854016
INFO:root:[Epoch 96] train=0.102974 val=0.914900 loss=0.940440 time: 43.682389
INFO:root:[Epoch 97] train=0.102630 val=0.927600 loss=0.924116 time: 43.840972
INFO:root:[Epoch 98] train=0.100024 val=0.923900 loss=0.915474 time: 43.816698
INFO:root:[Epoch 99] train=0.100165 val=0.923500 loss=0.915940 time: 43.753183
INFO:root:[Epoch 100] train=0.097830 val=0.926700 loss=0.892739 time: 43.745879
INFO:root:[Epoch 101] train=0.099152 val=0.924800 loss=0.894760 time: 43.785433
INFO:root:[Epoch 102] train=0.102018 val=0.931700 loss=0.929307 time: 43.690580
INFO:root:[Epoch 103] train=0.098963 val=0.912700 loss=0.886989 time: 43.790448
INFO:root:[Epoch 104] train=0.100319 val=0.933400 loss=0.915405 time: 43.677340
INFO:root:[Epoch 105] train=0.099235 val=0.928000 loss=0.894513 time: 43.678516
INFO:root:[Epoch 106] train=0.100635 val=0.919800 loss=0.923268 time: 43.740569
INFO:root:[Epoch 107] train=0.100350 val=0.920400 loss=0.912862 time: 43.728286
INFO:root:[Epoch 108] train=0.096401 val=0.928100 loss=0.866769 time: 43.768286
INFO:root:[Epoch 109] train=0.100882 val=0.931900 loss=0.919562 time: 43.702568
INFO:root:[Epoch 110] train=0.099113 val=0.929100 loss=0.897342 time: 43.806651
INFO:root:[Epoch 111] train=0.096812 val=0.923300 loss=0.875620 time: 43.886957
INFO:root:[Epoch 112] train=0.099052 val=0.928000 loss=0.904198 time: 44.164926
INFO:root:[Epoch 113] train=0.099973 val=0.931300 loss=0.914912 time: 43.859646
INFO:root:[Epoch 114] train=0.096703 val=0.925600 loss=0.878660 time: 44.274045
INFO:root:[Epoch 115] train=0.097395 val=0.930500 loss=0.904776 time: 43.978522
INFO:root:[Epoch 116] train=0.099933 val=0.935700 loss=0.921428 time: 43.677167
INFO:root:[Epoch 117] train=0.095826 val=0.925400 loss=0.878971 time: 43.929503
INFO:root:[Epoch 118] train=0.099232 val=0.914000 loss=0.915271 time: 43.866561
INFO:root:[Epoch 119] train=0.100242 val=0.928100 loss=0.925861 time: 43.784599
INFO:root:[Epoch 120] train=0.082034 val=0.956700 loss=0.803938 time: 44.105316
INFO:root:[Epoch 121] train=0.080893 val=0.956400 loss=0.812654 time: 43.884287
INFO:root:[Epoch 122] train=0.077295 val=0.958100 loss=0.777416 time: 44.024491
INFO:root:[Epoch 123] train=0.078603 val=0.957500 loss=0.795025 time: 43.748974
INFO:root:[Epoch 124] train=0.074229 val=0.956500 loss=0.757268 time: 43.953431
INFO:root:[Epoch 125] train=0.076171 val=0.960600 loss=0.784139 time: 43.914249
INFO:root:[Epoch 126] train=0.075428 val=0.958000 loss=0.775389 time: 44.008703
INFO:root:[Epoch 127] train=0.078837 val=0.957700 loss=0.810879 time: 43.915574
INFO:root:[Epoch 128] train=0.072120 val=0.959100 loss=0.750031 time: 43.786689
INFO:root:[Epoch 129] train=0.071974 val=0.955700 loss=0.746879 time: 43.857830
INFO:root:[Epoch 130] train=0.072601 val=0.962600 loss=0.760772 time: 43.935692
INFO:root:[Epoch 131] train=0.075279 val=0.957100 loss=0.778647 time: 43.784460
INFO:root:[Epoch 132] train=0.077176 val=0.960400 loss=0.801950 time: 43.794439
INFO:root:[Epoch 133] train=0.075443 val=0.958100 loss=0.785649 time: 43.695788
INFO:root:[Epoch 134] train=0.073633 val=0.958800 loss=0.762868 time: 43.745360
INFO:root:[Epoch 135] train=0.069724 val=0.958800 loss=0.738409 time: 43.843373
INFO:root:[Epoch 136] train=0.073968 val=0.962600 loss=0.769277 time: 43.808938
INFO:root:[Epoch 137] train=0.075210 val=0.958300 loss=0.776008 time: 43.947658
INFO:root:[Epoch 138] train=0.073671 val=0.961800 loss=0.769878 time: 43.881146
INFO:root:[Epoch 139] train=0.069927 val=0.957300 loss=0.739570 time: 43.919593
INFO:root:[Epoch 140] train=0.075210 val=0.958400 loss=0.783583 time: 43.833205
INFO:root:[Epoch 141] train=0.072627 val=0.962000 loss=0.758373 time: 43.893913
INFO:root:[Epoch 142] train=0.072021 val=0.960600 loss=0.765970 time: 44.015451
INFO:root:[Epoch 143] train=0.070325 val=0.959100 loss=0.740337 time: 43.875555
INFO:root:[Epoch 144] train=0.072209 val=0.959900 loss=0.751636 time: 43.695948
INFO:root:[Epoch 145] train=0.073541 val=0.953500 loss=0.767479 time: 44.140878
INFO:root:[Epoch 146] train=0.073098 val=0.958100 loss=0.761980 time: 43.846367
INFO:root:[Epoch 147] train=0.072718 val=0.957500 loss=0.759321 time: 44.059663
INFO:root:[Epoch 148] train=0.071087 val=0.959100 loss=0.751651 time: 43.829104
INFO:root:[Epoch 149] train=0.073446 val=0.959000 loss=0.766064 time: 43.835579
INFO:root:[Epoch 150] train=0.071303 val=0.963800 loss=0.749838 time: 43.913551
INFO:root:[Epoch 151] train=0.070615 val=0.956700 loss=0.743360 time: 44.003987
INFO:root:[Epoch 152] train=0.071255 val=0.961200 loss=0.748598 time: 43.837179
INFO:root:[Epoch 153] train=0.071300 val=0.962400 loss=0.741817 time: 43.862939
INFO:root:[Epoch 154] train=0.069790 val=0.960900 loss=0.745153 time: 43.959822
INFO:root:[Epoch 155] train=0.073371 val=0.961200 loss=0.769262 time: 43.893749
INFO:root:[Epoch 156] train=0.071871 val=0.958400 loss=0.752689 time: 43.920340
INFO:root:[Epoch 157] train=0.070317 val=0.959000 loss=0.744270 time: 43.853702
INFO:root:[Epoch 158] train=0.074140 val=0.961500 loss=0.771337 time: 43.830757
INFO:root:[Epoch 159] train=0.067506 val=0.959800 loss=0.725508 time: 43.864356
INFO:root:[Epoch 160] train=0.066694 val=0.966600 loss=0.726758 time: 44.067668
INFO:root:[Epoch 161] train=0.066227 val=0.968400 loss=0.724356 time: 43.960288
INFO:root:[Epoch 162] train=0.067256 val=0.967200 loss=0.736341 time: 43.838951
INFO:root:[Epoch 163] train=0.064104 val=0.966900 loss=0.706350 time: 43.799392
INFO:root:[Epoch 164] train=0.064534 val=0.968800 loss=0.710283 time: 43.885271
INFO:root:[Epoch 165] train=0.069093 val=0.964800 loss=0.752712 time: 43.865782
INFO:root:[Epoch 166] train=0.065105 val=0.966600 loss=0.717501 time: 43.814032
INFO:root:[Epoch 167] train=0.061174 val=0.968600 loss=0.686523 time: 43.892916
INFO:root:[Epoch 168] train=0.060534 val=0.966400 loss=0.679314 time: 43.829902
INFO:root:[Epoch 169] train=0.061369 val=0.968100 loss=0.689038 time: 43.929710
INFO:root:[Epoch 170] train=0.062135 val=0.967800 loss=0.694851 time: 44.015651
INFO:root:[Epoch 171] train=0.063368 val=0.970400 loss=0.701404 time: 44.028950
INFO:root:[Epoch 172] train=0.063164 val=0.966900 loss=0.704561 time: 43.846140
INFO:root:[Epoch 173] train=0.061143 val=0.968000 loss=0.687217 time: 43.835616
INFO:root:[Epoch 174] train=0.063702 val=0.966200 loss=0.706506 time: 43.852980
INFO:root:[Epoch 175] train=0.064422 val=0.967600 loss=0.716260 time: 43.984680
INFO:root:[Epoch 176] train=0.061153 val=0.969600 loss=0.689911 time: 43.877254
INFO:root:[Epoch 177] train=0.063256 val=0.968500 loss=0.708040 time: 44.072231
INFO:root:[Epoch 178] train=0.060937 val=0.968800 loss=0.684849 time: 43.825771
INFO:root:[Epoch 179] train=0.060025 val=0.969700 loss=0.673871 time: 43.991530
INFO:root:[Epoch 180] train=0.061735 val=0.970000 loss=0.690235 time: 43.933797
INFO:root:[Epoch 181] train=0.060249 val=0.967700 loss=0.679948 time: 43.837749
INFO:root:[Epoch 182] train=0.061656 val=0.968600 loss=0.687342 time: 43.936497
INFO:root:[Epoch 183] train=0.062514 val=0.969100 loss=0.699228 time: 43.964869
INFO:root:[Epoch 184] train=0.061100 val=0.969700 loss=0.683754 time: 43.831334
INFO:root:[Epoch 185] train=0.061888 val=0.969300 loss=0.692570 time: 43.903310
INFO:root:[Epoch 186] train=0.060488 val=0.969400 loss=0.674395 time: 43.870052
INFO:root:[Epoch 187] train=0.062224 val=0.966600 loss=0.695844 time: 43.786967
INFO:root:[Epoch 188] train=0.061204 val=0.967500 loss=0.693224 time: 44.149295
INFO:root:[Epoch 189] train=0.061116 val=0.968100 loss=0.685428 time: 43.830188
INFO:root:[Epoch 190] train=0.061348 val=0.969200 loss=0.692704 time: 43.897022
INFO:root:[Epoch 191] train=0.060985 val=0.968500 loss=0.684653 time: 44.077839
INFO:root:[Epoch 192] train=0.061518 val=0.968000 loss=0.684874 time: 43.950105
INFO:root:[Epoch 193] train=0.059019 val=0.967400 loss=0.668455 time: 43.829805
INFO:root:[Epoch 194] train=0.063035 val=0.969600 loss=0.705532 time: 43.925745
INFO:root:[Epoch 195] train=0.063073 val=0.969300 loss=0.703320 time: 44.149888
INFO:root:[Epoch 196] train=0.062028 val=0.967500 loss=0.697395 time: 43.987637
INFO:root:[Epoch 197] train=0.060524 val=0.967700 loss=0.678957 time: 43.868462
INFO:root:[Epoch 198] train=0.061551 val=0.967100 loss=0.696643 time: 43.895014
INFO:root:[Epoch 199] train=0.060431 val=0.968600 loss=0.679901 time: 43.955945
INFO:root:[Epoch 200] train=0.003315 val=0.970700 loss=0.006844 time: 44.081731
INFO:root:[Epoch 201] train=0.002543 val=0.970900 loss=0.004786 time: 44.046536
INFO:root:[Epoch 202] train=0.002467 val=0.970000 loss=0.004247 time: 44.052110
INFO:root:[Epoch 203] train=0.002096 val=0.970900 loss=0.003707 time: 43.843403
INFO:root:[Epoch 204] train=0.001636 val=0.971500 loss=0.003242 time: 44.080545
INFO:root:[Epoch 205] train=0.002157 val=0.970800 loss=0.003383 time: 43.855552
INFO:root:[Epoch 206] train=0.001648 val=0.970100 loss=0.002982 time: 43.881498
INFO:root:[Epoch 207] train=0.001474 val=0.970300 loss=0.002765 time: 44.019800
INFO:root:[Epoch 208] train=0.001484 val=0.971000 loss=0.002626 time: 43.884180
INFO:root:[Epoch 209] train=0.001513 val=0.970500 loss=0.002633 time: 43.934078
INFO:root:[Epoch 210] train=0.001477 val=0.970100 loss=0.002507 time: 43.913250
INFO:root:[Epoch 211] train=0.001188 val=0.971300 loss=0.002311 time: 43.869159
INFO:root:[Epoch 212] train=0.001201 val=0.971600 loss=0.002243 time: 44.102835
INFO:root:[Epoch 213] train=0.001071 val=0.971400 loss=0.002141 time: 43.977917
INFO:root:[Epoch 214] train=0.001269 val=0.970800 loss=0.002266 time: 43.967461
INFO:root:[Epoch 215] train=0.001118 val=0.971100 loss=0.002091 time: 44.008376
INFO:root:[Epoch 216] train=0.001069 val=0.970800 loss=0.002051 time: 43.863586
INFO:root:[Epoch 217] train=0.000923 val=0.971700 loss=0.001934 time: 44.153939
INFO:root:[Epoch 218] train=0.001139 val=0.971200 loss=0.002097 time: 43.828339
INFO:root:[Epoch 219] train=0.001223 val=0.969900 loss=0.002034 time: 43.972796