INFO:root:Namespace(batch_size=128, drop_rate=0.0, 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=200, num_gpus=1, num_workers=8, resume_from=None, save_dir='params', save_period=10, save_plot_dir='.', wd=0.0005)
[20:20:39] 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=0.411478 val=0.527100 loss=1.604757 time: 76.786019
INFO:root:[Epoch 1] train=0.629507 val=0.590100 loss=1.035245 time: 75.563017
INFO:root:[Epoch 2] train=0.736338 val=0.735200 loss=0.758177 time: 75.613648
INFO:root:[Epoch 3] train=0.784876 val=0.744000 loss=0.619980 time: 76.333943
INFO:root:[Epoch 4] train=0.814844 val=0.786400 loss=0.540382 time: 76.635575
INFO:root:[Epoch 5] train=0.832492 val=0.820800 loss=0.485153 time: 76.804371
INFO:root:[Epoch 6] train=0.845913 val=0.825600 loss=0.451254 time: 76.885624
INFO:root:[Epoch 7] train=0.853165 val=0.780000 loss=0.427118 time: 76.895997
INFO:root:[Epoch 8] train=0.860817 val=0.769300 loss=0.405323 time: 76.840270
INFO:root:[Epoch 9] train=0.867107 val=0.769900 loss=0.390695 time: 76.790104
INFO:root:[Epoch 10] train=0.872175 val=0.805000 loss=0.374486 time: 76.762068
INFO:root:[Epoch 11] train=0.875381 val=0.795700 loss=0.360864 time: 76.709388
INFO:root:[Epoch 12] train=0.880429 val=0.850500 loss=0.352070 time: 77.074128
INFO:root:[Epoch 13] train=0.883273 val=0.857600 loss=0.341508 time: 77.121983
INFO:root:[Epoch 14] train=0.887941 val=0.849600 loss=0.330904 time: 76.818573
INFO:root:[Epoch 15] train=0.890304 val=0.817900 loss=0.325029 time: 77.043508
INFO:root:[Epoch 16] train=0.892308 val=0.791000 loss=0.313694 time: 76.693376
INFO:root:[Epoch 17] train=0.894191 val=0.777500 loss=0.306659 time: 76.842998
INFO:root:[Epoch 18] train=0.895653 val=0.823800 loss=0.307914 time: 76.805647
INFO:root:[Epoch 19] train=0.897857 val=0.858700 loss=0.298387 time: 77.060187
INFO:root:[Epoch 20] train=0.897516 val=0.867900 loss=0.300664 time: 77.008439
INFO:root:[Epoch 21] train=0.900401 val=0.826100 loss=0.289358 time: 77.042503
INFO:root:[Epoch 22] train=0.901823 val=0.858300 loss=0.288290 time: 76.800276
INFO:root:[Epoch 23] train=0.902364 val=0.851400 loss=0.284713 time: 76.954841
INFO:root:[Epoch 24] train=0.904687 val=0.838700 loss=0.278676 time: 76.529180
INFO:root:[Epoch 25] train=0.905329 val=0.763500 loss=0.278139 time: 76.302079
INFO:root:[Epoch 26] train=0.907171 val=0.868300 loss=0.273437 time: 76.856555
INFO:root:[Epoch 27] train=0.907151 val=0.846300 loss=0.269411 time: 76.452647
INFO:root:[Epoch 28] train=0.907332 val=0.863100 loss=0.269037 time: 76.337997
INFO:root:[Epoch 29] train=0.907973 val=0.875300 loss=0.271785 time: 76.097357
INFO:root:[Epoch 30] train=0.907873 val=0.854700 loss=0.267503 time: 76.004329
INFO:root:[Epoch 31] train=0.911659 val=0.868600 loss=0.258179 time: 75.868145
INFO:root:[Epoch 32] train=0.910657 val=0.849900 loss=0.259154 time: 75.937060
INFO:root:[Epoch 33] train=0.909255 val=0.871600 loss=0.264340 time: 76.000189
INFO:root:[Epoch 34] train=0.911378 val=0.838000 loss=0.258368 time: 75.873708
INFO:root:[Epoch 35] train=0.912680 val=0.875600 loss=0.257033 time: 76.430120
INFO:root:[Epoch 36] train=0.912079 val=0.841200 loss=0.257123 time: 75.945894
INFO:root:[Epoch 37] train=0.913181 val=0.859000 loss=0.255132 time: 75.877126
INFO:root:[Epoch 38] train=0.912620 val=0.818100 loss=0.254776 time: 76.022744
INFO:root:[Epoch 39] train=0.913041 val=0.815800 loss=0.252586 time: 75.859214
INFO:root:[Epoch 40] train=0.914964 val=0.859500 loss=0.251124 time: 75.850141
INFO:root:[Epoch 41] train=0.916046 val=0.863400 loss=0.249114 time: 75.871451
INFO:root:[Epoch 42] train=0.915465 val=0.858800 loss=0.249315 time: 75.820667
INFO:root:[Epoch 43] train=0.916166 val=0.849500 loss=0.246926 time: 76.143404
INFO:root:[Epoch 44] train=0.915625 val=0.835700 loss=0.249089 time: 76.076772
INFO:root:[Epoch 45] train=0.914363 val=0.869800 loss=0.251399 time: 76.052775
INFO:root:[Epoch 46] train=0.916687 val=0.881300 loss=0.244185 time: 76.242505
INFO:root:[Epoch 47] train=0.916747 val=0.881400 loss=0.246097 time: 76.049461
INFO:root:[Epoch 48] train=0.917448 val=0.843400 loss=0.244519 time: 76.060408
INFO:root:[Epoch 49] train=0.915224 val=0.834600 loss=0.245817 time: 75.966492
INFO:root:[Epoch 50] train=0.917007 val=0.830800 loss=0.241525 time: 75.881734
INFO:root:[Epoch 51] train=0.916747 val=0.875200 loss=0.242560 time: 75.812014
INFO:root:[Epoch 52] train=0.917288 val=0.830900 loss=0.242016 time: 75.983203
INFO:root:[Epoch 53] train=0.918409 val=0.889000 loss=0.240282 time: 75.955918
INFO:root:[Epoch 54] train=0.918830 val=0.859900 loss=0.236284 time: 76.060130
INFO:root:[Epoch 55] train=0.917188 val=0.879100 loss=0.241967 time: 75.697196
INFO:root:[Epoch 56] train=0.921154 val=0.890100 loss=0.234695 time: 76.201006
INFO:root:[Epoch 57] train=0.917608 val=0.854300 loss=0.240529 time: 75.804280
INFO:root:[Epoch 58] train=0.920252 val=0.875000 loss=0.234271 time: 75.804553
INFO:root:[Epoch 59] train=0.920012 val=0.872500 loss=0.236529 time: 75.983403
INFO:root:[Epoch 60] train=0.966827 val=0.942100 loss=0.101373 time: 76.089979
INFO:root:[Epoch 61] train=0.979948 val=0.944400 loss=0.061611 time: 76.021462
INFO:root:[Epoch 62] train=0.984896 val=0.945800 loss=0.049037 time: 76.037481
INFO:root:[Epoch 63] train=0.987200 val=0.939100 loss=0.039862 time: 76.169953
INFO:root:[Epoch 64] train=0.988281 val=0.938700 loss=0.036921 time: 76.013017
INFO:root:[Epoch 65] train=0.987059 val=0.939400 loss=0.040287 time: 75.957039
INFO:root:[Epoch 66] train=0.988201 val=0.937700 loss=0.036855 time: 75.833351
INFO:root:[Epoch 67] train=0.987821 val=0.937700 loss=0.038640 time: 75.984682
INFO:root:[Epoch 68] train=0.986719 val=0.936400 loss=0.040600 time: 75.928329
INFO:root:[Epoch 69] train=0.986258 val=0.928200 loss=0.043452 time: 75.811647
INFO:root:[Epoch 70] train=0.985857 val=0.929200 loss=0.043097 time: 76.016374
INFO:root:[Epoch 71] train=0.984816 val=0.936800 loss=0.046955 time: 75.878043
INFO:root:[Epoch 72] train=0.983854 val=0.936800 loss=0.049381 time: 76.013218
INFO:root:[Epoch 73] train=0.983233 val=0.929400 loss=0.050601 time: 75.922495
INFO:root:[Epoch 74] train=0.980449 val=0.927900 loss=0.058597 time: 75.938987
INFO:root:[Epoch 75] train=0.981490 val=0.922900 loss=0.055975 time: 75.833478
INFO:root:[Epoch 76] train=0.982011 val=0.927200 loss=0.057181 time: 76.059116
INFO:root:[Epoch 77] train=0.980789 val=0.931800 loss=0.058010 time: 75.929221
INFO:root:[Epoch 78] train=0.980349 val=0.925600 loss=0.060537 time: 75.994185
INFO:root:[Epoch 79] train=0.979627 val=0.917300 loss=0.062715 time: 76.078748
INFO:root:[Epoch 80] train=0.979507 val=0.928900 loss=0.060999 time: 75.953045
INFO:root:[Epoch 81] train=0.979367 val=0.923600 loss=0.060491 time: 76.087807
INFO:root:[Epoch 82] train=0.980429 val=0.924800 loss=0.060002 time: 75.837559
INFO:root:[Epoch 83] train=0.980288 val=0.914400 loss=0.059440 time: 76.019928
INFO:root:[Epoch 84] train=0.979808 val=0.928500 loss=0.061940 time: 75.927077
INFO:root:[Epoch 85] train=0.978105 val=0.915100 loss=0.064863 time: 75.994716
INFO:root:[Epoch 86] train=0.979647 val=0.905600 loss=0.061025 time: 76.001382
INFO:root:[Epoch 87] train=0.979627 val=0.913500 loss=0.062888 time: 75.944417
INFO:root:[Epoch 88] train=0.977644 val=0.918800 loss=0.066185 time: 76.084952
INFO:root:[Epoch 89] train=0.979908 val=0.922200 loss=0.059964 time: 75.942107
INFO:root:[Epoch 90] train=0.980529 val=0.925800 loss=0.058984 time: 75.876464
INFO:root:[Epoch 91] train=0.980789 val=0.924800 loss=0.059944 time: 76.016670
INFO:root:[Epoch 92] train=0.980449 val=0.904800 loss=0.059417 time: 75.926422
INFO:root:[Epoch 93] train=0.981871 val=0.924600 loss=0.058919 time: 75.973004
INFO:root:[Epoch 94] train=0.980349 val=0.908700 loss=0.060447 time: 75.734221
INFO:root:[Epoch 95] train=0.979247 val=0.921400 loss=0.063762 time: 76.717672
INFO:root:[Epoch 96] train=0.980449 val=0.920000 loss=0.058659 time: 76.933074
INFO:root:[Epoch 97] train=0.979868 val=0.924200 loss=0.062879 time: 76.752609
INFO:root:[Epoch 98] train=0.982111 val=0.914300 loss=0.056383 time: 76.951226
INFO:root:[Epoch 99] train=0.980308 val=0.925800 loss=0.059644 time: 76.882280
INFO:root:[Epoch 100] train=0.980569 val=0.915200 loss=0.059703 time: 76.887065
INFO:root:[Epoch 101] train=0.979067 val=0.927600 loss=0.064281 time: 77.007686
INFO:root:[Epoch 102] train=0.979407 val=0.927700 loss=0.061703 time: 76.933822
INFO:root:[Epoch 103] train=0.980649 val=0.923100 loss=0.059933 time: 76.808674
INFO:root:[Epoch 104] train=0.980108 val=0.926300 loss=0.058719 time: 77.071546
INFO:root:[Epoch 105] train=0.980188 val=0.918100 loss=0.057562 time: 77.181934
INFO:root:[Epoch 106] train=0.980168 val=0.913500 loss=0.059322 time: 76.888077
INFO:root:[Epoch 107] train=0.980108 val=0.919400 loss=0.058681 time: 77.103371
INFO:root:[Epoch 108] train=0.980028 val=0.921300 loss=0.060165 time: 77.013135
INFO:root:[Epoch 109] train=0.980669 val=0.919500 loss=0.058246 time: 77.025441
INFO:root:[Epoch 110] train=0.980449 val=0.919800 loss=0.058611 time: 76.977444
INFO:root:[Epoch 111] train=0.979587 val=0.927100 loss=0.061315 time: 77.013220
INFO:root:[Epoch 112] train=0.980609 val=0.916500 loss=0.057548 time: 77.135965
INFO:root:[Epoch 113] train=0.981130 val=0.911000 loss=0.056765 time: 77.002316
INFO:root:[Epoch 114] train=0.980990 val=0.923800 loss=0.058057 time: 77.008551
INFO:root:[Epoch 115] train=0.981070 val=0.922800 loss=0.057741 time: 76.895302
INFO:root:[Epoch 116] train=0.982853 val=0.917300 loss=0.055407 time: 76.512581
INFO:root:[Epoch 117] train=0.982031 val=0.931200 loss=0.054952 time: 76.624840
INFO:root:[Epoch 118] train=0.982472 val=0.924400 loss=0.053854 time: 76.927078
INFO:root:[Epoch 119] train=0.980369 val=0.925700 loss=0.060546 time: 76.613975
INFO:root:[Epoch 120] train=0.995473 val=0.951800 loss=0.017130 time: 76.297625
INFO:root:[Epoch 121] train=0.998377 val=0.954600 loss=0.008383 time: 76.173693
INFO:root:[Epoch 122] train=0.998918 val=0.955100 loss=0.005754 time: 76.301186
INFO:root:[Epoch 123] train=0.999058 val=0.956400 loss=0.004932 time: 76.145753
INFO:root:[Epoch 124] train=0.999599 val=0.956700 loss=0.003664 time: 76.190890
INFO:root:[Epoch 125] train=0.999479 val=0.957300 loss=0.003594 time: 76.317236
INFO:root:[Epoch 126] train=0.999700 val=0.957600 loss=0.003018 time: 76.165440
INFO:root:[Epoch 127] train=0.999659 val=0.958500 loss=0.002722 time: 76.290953
INFO:root:[Epoch 128] train=0.999760 val=0.956900 loss=0.002554 time: 75.975890
INFO:root:[Epoch 129] train=0.999760 val=0.957200 loss=0.002560 time: 75.996817
INFO:root:[Epoch 130] train=0.999840 val=0.958200 loss=0.002329 time: 75.981942
INFO:root:[Epoch 131] train=0.999740 val=0.958500 loss=0.002146 time: 75.948230
INFO:root:[Epoch 132] train=0.999920 val=0.957500 loss=0.002016 time: 76.025905
INFO:root:[Epoch 133] train=0.999880 val=0.957300 loss=0.002047 time: 75.983742
INFO:root:[Epoch 134] train=0.999780 val=0.959900 loss=0.002196 time: 76.254936
INFO:root:[Epoch 135] train=0.999920 val=0.959000 loss=0.001905 time: 76.107590
INFO:root:[Epoch 136] train=0.999820 val=0.958900 loss=0.002059 time: 75.972149
INFO:root:[Epoch 137] train=0.999940 val=0.959700 loss=0.001879 time: 76.046919
INFO:root:[Epoch 138] train=0.999900 val=0.959300 loss=0.001978 time: 75.984901
INFO:root:[Epoch 139] train=0.999840 val=0.959100 loss=0.001987 time: 75.963612
INFO:root:[Epoch 140] train=0.999980 val=0.958500 loss=0.001798 time: 75.975422
INFO:root:[Epoch 141] train=0.999900 val=0.959000 loss=0.001810 time: 75.936846
INFO:root:[Epoch 142] train=0.999780 val=0.961100 loss=0.002075 time: 76.212732
INFO:root:[Epoch 143] train=1.000000 val=0.960500 loss=0.001819 time: 76.014351
INFO:root:[Epoch 144] train=0.999900 val=0.959100 loss=0.001976 time: 75.999465
INFO:root:[Epoch 145] train=0.999940 val=0.959900 loss=0.001803 time: 75.985080
INFO:root:[Epoch 146] train=0.999900 val=0.959700 loss=0.002020 time: 75.953398
INFO:root:[Epoch 147] train=0.999960 val=0.959900 loss=0.001705 time: 75.995894
INFO:root:[Epoch 148] train=1.000000 val=0.959100 loss=0.001683 time: 75.896147
INFO:root:[Epoch 149] train=0.999960 val=0.959200 loss=0.001765 time: 76.245607
INFO:root:[Epoch 150] train=1.000000 val=0.959500 loss=0.001668 time: 76.110133
INFO:root:[Epoch 151] train=0.999980 val=0.958900 loss=0.001692 time: 75.975034
INFO:root:[Epoch 152] train=0.999860 val=0.958900 loss=0.001939 time: 76.198540
INFO:root:[Epoch 153] train=1.000000 val=0.959400 loss=0.001717 time: 75.967747
INFO:root:[Epoch 154] train=0.999960 val=0.960000 loss=0.001759 time: 75.964865
INFO:root:[Epoch 155] train=0.999940 val=0.960000 loss=0.001850 time: 76.222487
INFO:root:[Epoch 156] train=0.999900 val=0.959400 loss=0.001981 time: 76.149271
INFO:root:[Epoch 157] train=0.999900 val=0.960100 loss=0.001914 time: 76.022040
INFO:root:[Epoch 158] train=1.000000 val=0.960300 loss=0.001774 time: 76.077219
INFO:root:[Epoch 159] train=0.999960 val=0.959900 loss=0.001780 time: 76.047673
INFO:root:[Epoch 160] train=1.000000 val=0.961200 loss=0.001651 time: 76.125563
INFO:root:[Epoch 161] train=0.999980 val=0.960500 loss=0.001693 time: 75.949454
INFO:root:[Epoch 162] train=0.999980 val=0.961500 loss=0.001747 time: 76.100046
INFO:root:[Epoch 163] train=0.999960 val=0.960400 loss=0.001726 time: 76.019905
INFO:root:[Epoch 164] train=0.999960 val=0.960200 loss=0.001761 time: 76.111566
INFO:root:[Epoch 165] train=0.999960 val=0.960100 loss=0.001660 time: 76.005345
INFO:root:[Epoch 166] train=1.000000 val=0.960100 loss=0.001655 time: 76.099242
INFO:root:[Epoch 167] train=0.999980 val=0.960500 loss=0.001669 time: 75.997092
INFO:root:[Epoch 168] train=0.999980 val=0.960000 loss=0.001732 time: 76.135871
INFO:root:[Epoch 169] train=1.000000 val=0.960700 loss=0.001647 time: 76.006681
INFO:root:[Epoch 170] train=0.999960 val=0.960100 loss=0.001665 time: 76.194753
INFO:root:[Epoch 171] train=0.999960 val=0.960000 loss=0.001709 time: 76.121263
INFO:root:[Epoch 172] train=1.000000 val=0.959600 loss=0.001631 time: 76.140113
INFO:root:[Epoch 173] train=0.999940 val=0.959300 loss=0.001748 time: 76.131179
INFO:root:[Epoch 174] train=0.999980 val=0.959500 loss=0.001645 time: 75.978573
INFO:root:[Epoch 175] train=0.999960 val=0.960400 loss=0.001713 time: 76.084595
INFO:root:[Epoch 176] train=0.999960 val=0.960100 loss=0.001684 time: 75.883304
INFO:root:[Epoch 177] train=1.000000 val=0.959000 loss=0.001623 time: 75.925456
INFO:root:[Epoch 178] train=0.999960 val=0.960300 loss=0.001710 time: 76.248084
INFO:root:[Epoch 179] train=1.000000 val=0.959800 loss=0.001640 time: 76.075536
INFO:root:[Epoch 180] train=1.000000 val=0.960700 loss=0.001657 time: 76.063507
INFO:root:[Epoch 181] train=0.999980 val=0.960100 loss=0.001655 time: 76.048741
INFO:root:[Epoch 182] train=1.000000 val=0.960100 loss=0.001634 time: 75.938718
INFO:root:[Epoch 183] train=0.999980 val=0.959600 loss=0.001663 time: 76.003034
INFO:root:[Epoch 184] train=0.999940 val=0.959800 loss=0.001691 time: 75.936472
INFO:root:[Epoch 185] train=1.000000 val=0.959400 loss=0.001661 time: 76.081705
INFO:root:[Epoch 186] train=0.999960 val=0.959800 loss=0.001690 time: 76.635968
INFO:root:[Epoch 187] train=0.999940 val=0.959200 loss=0.001771 time: 77.288599
INFO:root:[Epoch 188] train=0.999980 val=0.959500 loss=0.001654 time: 77.126302
INFO:root:[Epoch 189] train=1.000000 val=0.959200 loss=0.001558 time: 76.942405
INFO:root:[Epoch 190] train=1.000000 val=0.959500 loss=0.001612 time: 77.025101
INFO:root:[Epoch 191] train=1.000000 val=0.959200 loss=0.001645 time: 76.961548
INFO:root:[Epoch 192] train=1.000000 val=0.959600 loss=0.001690 time: 77.117101
INFO:root:[Epoch 193] train=0.999980 val=0.959700 loss=0.001660 time: 77.017935
INFO:root:[Epoch 194] train=1.000000 val=0.959600 loss=0.001679 time: 77.034686
INFO:root:[Epoch 195] train=1.000000 val=0.960700 loss=0.001633 time: 77.031159
INFO:root:[Epoch 196] train=1.000000 val=0.959400 loss=0.001658 time: 76.995886
INFO:root:[Epoch 197] train=1.000000 val=0.959300 loss=0.001637 time: 77.059258
INFO:root:[Epoch 198] train=0.999980 val=0.959000 loss=0.001667 time: 76.986565
INFO:root:[Epoch 199] train=1.000000 val=0.959400 loss=0.001666 time: 77.023471