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_wideresnet40_8', 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)
[17:44:32] 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=0.219952 val=0.499700 loss=1.911323 time: 50.333600
INFO:root:[Epoch 1] train=0.196054 val=0.601000 loss=1.626948 time: 44.503168
INFO:root:[Epoch 2] train=0.182511 val=0.591800 loss=1.501126 time: 44.675566
INFO:root:[Epoch 3] train=0.173603 val=0.724400 loss=1.437783 time: 44.708228
INFO:root:[Epoch 4] train=0.168937 val=0.783300 loss=1.357568 time: 44.626345
INFO:root:[Epoch 5] train=0.164118 val=0.709200 loss=1.325904 time: 44.559828
INFO:root:[Epoch 6] train=0.160492 val=0.728100 loss=1.329188 time: 44.512022
INFO:root:[Epoch 7] train=0.158414 val=0.789300 loss=1.295253 time: 44.582459
INFO:root:[Epoch 8] train=0.155017 val=0.835200 loss=1.275585 time: 44.539583
INFO:root:[Epoch 9] train=0.153184 val=0.745900 loss=1.271881 time: 44.608600
INFO:root:[Epoch 10] train=0.152124 val=0.766100 loss=1.251192 time: 44.442901
INFO:root:[Epoch 11] train=0.149554 val=0.817600 loss=1.211089 time: 44.334974
INFO:root:[Epoch 12] train=0.150028 val=0.807100 loss=1.249768 time: 44.583720
INFO:root:[Epoch 13] train=0.148734 val=0.840900 loss=1.228246 time: 44.646010
INFO:root:[Epoch 14] train=0.146763 val=0.715600 loss=1.192878 time: 44.514535
INFO:root:[Epoch 15] train=0.145825 val=0.778300 loss=1.219239 time: 44.546136
INFO:root:[Epoch 16] train=0.145761 val=0.828000 loss=1.210629 time: 44.443538
INFO:root:[Epoch 17] train=0.144053 val=0.796800 loss=1.205256 time: 44.450171
INFO:root:[Epoch 18] train=0.145520 val=0.827900 loss=1.226314 time: 44.567337
INFO:root:[Epoch 19] train=0.143489 val=0.825800 loss=1.200091 time: 44.470397
INFO:root:[Epoch 20] train=0.143270 val=0.828400 loss=1.188118 time: 44.599792
INFO:root:[Epoch 21] train=0.141960 val=0.837000 loss=1.181068 time: 44.481488
INFO:root:[Epoch 22] train=0.141312 val=0.836400 loss=1.180597 time: 44.908391
INFO:root:[Epoch 23] train=0.141344 val=0.843200 loss=1.164266 time: 44.778766
INFO:root:[Epoch 24] train=0.140260 val=0.840000 loss=1.187572 time: 44.488770
INFO:root:[Epoch 25] train=0.140548 val=0.837500 loss=1.174908 time: 44.374629
INFO:root:[Epoch 26] train=0.140432 val=0.862100 loss=1.163799 time: 44.683164
INFO:root:[Epoch 27] train=0.141085 val=0.850100 loss=1.196683 time: 44.499467
INFO:root:[Epoch 28] train=0.138807 val=0.825600 loss=1.162478 time: 44.625142
INFO:root:[Epoch 29] train=0.138499 val=0.801700 loss=1.144171 time: 44.358693
INFO:root:[Epoch 30] train=0.138438 val=0.864900 loss=1.162258 time: 44.863405
INFO:root:[Epoch 31] train=0.137710 val=0.854000 loss=1.148588 time: 44.428713
INFO:root:[Epoch 32] train=0.137682 val=0.861300 loss=1.147325 time: 44.499194
INFO:root:[Epoch 33] train=0.138892 val=0.831800 loss=1.180611 time: 44.657292
INFO:root:[Epoch 34] train=0.137187 val=0.839900 loss=1.136938 time: 44.535849
INFO:root:[Epoch 35] train=0.137715 val=0.814400 loss=1.161840 time: 44.512356
INFO:root:[Epoch 36] train=0.136947 val=0.821700 loss=1.157288 time: 44.466854
INFO:root:[Epoch 37] train=0.136305 val=0.871700 loss=1.135245 time: 44.680869
INFO:root:[Epoch 38] train=0.136958 val=0.879900 loss=1.142114 time: 44.620641
INFO:root:[Epoch 39] train=0.135367 val=0.859200 loss=1.129142 time: 44.465682
INFO:root:[Epoch 40] train=0.136329 val=0.845800 loss=1.146607 time: 44.908323
INFO:root:[Epoch 41] train=0.136909 val=0.846200 loss=1.166316 time: 44.487509
INFO:root:[Epoch 42] train=0.136581 val=0.842300 loss=1.146179 time: 44.538083
INFO:root:[Epoch 43] train=0.136021 val=0.852400 loss=1.144136 time: 44.688010
INFO:root:[Epoch 44] train=0.135983 val=0.859600 loss=1.135039 time: 44.525790
INFO:root:[Epoch 45] train=0.134901 val=0.858600 loss=1.115644 time: 44.776671
INFO:root:[Epoch 46] train=0.135264 val=0.833600 loss=1.119030 time: 44.549735
INFO:root:[Epoch 47] train=0.135538 val=0.862400 loss=1.126625 time: 44.606788
INFO:root:[Epoch 48] train=0.134393 val=0.869500 loss=1.099569 time: 44.411887
INFO:root:[Epoch 49] train=0.134599 val=0.836700 loss=1.139589 time: 44.575031
INFO:root:[Epoch 50] train=0.133923 val=0.847800 loss=1.106126 time: 44.660111
INFO:root:[Epoch 51] train=0.134629 val=0.879100 loss=1.132829 time: 44.443515
INFO:root:[Epoch 52] train=0.133755 val=0.860600 loss=1.109944 time: 44.526823
INFO:root:[Epoch 53] train=0.133950 val=0.822200 loss=1.119002 time: 44.543048
INFO:root:[Epoch 54] train=0.134842 val=0.818900 loss=1.143671 time: 44.612078
INFO:root:[Epoch 55] train=0.135053 val=0.849200 loss=1.124428 time: 44.412301
INFO:root:[Epoch 56] train=0.135530 val=0.841900 loss=1.146221 time: 44.552198
INFO:root:[Epoch 57] train=0.134341 val=0.862500 loss=1.137281 time: 44.603089
INFO:root:[Epoch 58] train=0.136704 val=0.872500 loss=1.161794 time: 44.631266
INFO:root:[Epoch 59] train=0.134500 val=0.794600 loss=1.141307 time: 44.588937
INFO:root:[Epoch 60] train=0.114588 val=0.931300 loss=0.990636 time: 44.887503
INFO:root:[Epoch 61] train=0.112462 val=0.930800 loss=1.005789 time: 44.481476
INFO:root:[Epoch 62] train=0.110676 val=0.926200 loss=0.993342 time: 44.502753
INFO:root:[Epoch 63] train=0.107924 val=0.937500 loss=0.957788 time: 44.513521
INFO:root:[Epoch 64] train=0.107914 val=0.928900 loss=0.975841 time: 44.425685
INFO:root:[Epoch 65] train=0.107593 val=0.933200 loss=0.964817 time: 44.607276
INFO:root:[Epoch 66] train=0.105680 val=0.938100 loss=0.942959 time: 44.729374
INFO:root:[Epoch 67] train=0.103622 val=0.931000 loss=0.923338 time: 44.662202
INFO:root:[Epoch 68] train=0.105746 val=0.934700 loss=0.943117 time: 44.532003
INFO:root:[Epoch 69] train=0.106058 val=0.927500 loss=0.954020 time: 44.624376
INFO:root:[Epoch 70] train=0.105985 val=0.934500 loss=0.942049 time: 44.451976
INFO:root:[Epoch 71] train=0.104007 val=0.929400 loss=0.930195 time: 44.445717
INFO:root:[Epoch 72] train=0.104497 val=0.938700 loss=0.934264 time: 44.541913
INFO:root:[Epoch 73] train=0.105069 val=0.922400 loss=0.938123 time: 44.424444
INFO:root:[Epoch 74] train=0.106287 val=0.906100 loss=0.955408 time: 44.463745
INFO:root:[Epoch 75] train=0.103898 val=0.927100 loss=0.932675 time: 44.492891
INFO:root:[Epoch 76] train=0.104724 val=0.927600 loss=0.937691 time: 44.416522
INFO:root:[Epoch 77] train=0.102859 val=0.932400 loss=0.920061 time: 44.401079
INFO:root:[Epoch 78] train=0.104117 val=0.916900 loss=0.926575 time: 44.398926
INFO:root:[Epoch 79] train=0.105024 val=0.929500 loss=0.941869 time: 44.504310
INFO:root:[Epoch 80] train=0.105740 val=0.929700 loss=0.956447 time: 44.657966
INFO:root:[Epoch 81] train=0.105047 val=0.928300 loss=0.936405 time: 44.561295
INFO:root:[Epoch 82] train=0.105193 val=0.930100 loss=0.956249 time: 44.575764
INFO:root:[Epoch 83] train=0.104791 val=0.919600 loss=0.944940 time: 44.482587
INFO:root:[Epoch 84] train=0.105037 val=0.924900 loss=0.941363 time: 44.516811
INFO:root:[Epoch 85] train=0.105564 val=0.930000 loss=0.944679 time: 44.364732
INFO:root:[Epoch 86] train=0.105927 val=0.930500 loss=0.958116 time: 44.725673
INFO:root:[Epoch 87] train=0.104070 val=0.934700 loss=0.938443 time: 44.450152
INFO:root:[Epoch 88] train=0.104992 val=0.931100 loss=0.942266 time: 44.533992
INFO:root:[Epoch 89] train=0.102374 val=0.926500 loss=0.931593 time: 44.533597
INFO:root:[Epoch 90] train=0.103414 val=0.934000 loss=0.935008 time: 44.640390
INFO:root:[Epoch 91] train=0.101622 val=0.930200 loss=0.920266 time: 44.598008
INFO:root:[Epoch 92] train=0.102127 val=0.925000 loss=0.908047 time: 44.538098
INFO:root:[Epoch 93] train=0.105060 val=0.928000 loss=0.953411 time: 44.422655
INFO:root:[Epoch 94] train=0.102144 val=0.939400 loss=0.913084 time: 44.629947
INFO:root:[Epoch 95] train=0.102745 val=0.931500 loss=0.932592 time: 44.774911
INFO:root:[Epoch 96] train=0.100174 val=0.934300 loss=0.912847 time: 44.513267
INFO:root:[Epoch 97] train=0.101826 val=0.931600 loss=0.909913 time: 44.441829
INFO:root:[Epoch 98] train=0.100915 val=0.917700 loss=0.918969 time: 44.474735
INFO:root:[Epoch 99] train=0.099388 val=0.935500 loss=0.898805 time: 44.631557
INFO:root:[Epoch 100] train=0.103451 val=0.930400 loss=0.927197 time: 44.796891
INFO:root:[Epoch 101] train=0.104547 val=0.929500 loss=0.956820 time: 44.695773
INFO:root:[Epoch 102] train=0.101163 val=0.923400 loss=0.910012 time: 44.567052
INFO:root:[Epoch 103] train=0.101352 val=0.934600 loss=0.913389 time: 44.434458
INFO:root:[Epoch 104] train=0.101890 val=0.936800 loss=0.934862 time: 44.699506
INFO:root:[Epoch 105] train=0.101161 val=0.931300 loss=0.923195 time: 44.564047
INFO:root:[Epoch 106] train=0.101639 val=0.926600 loss=0.930891 time: 44.467655
INFO:root:[Epoch 107] train=0.100907 val=0.929400 loss=0.902967 time: 44.532271
INFO:root:[Epoch 108] train=0.101302 val=0.925600 loss=0.920878 time: 44.579312
INFO:root:[Epoch 109] train=0.099281 val=0.929200 loss=0.897988 time: 44.685882
INFO:root:[Epoch 110] train=0.099944 val=0.927200 loss=0.908495 time: 44.657195
INFO:root:[Epoch 111] train=0.100013 val=0.931200 loss=0.905222 time: 44.479635
INFO:root:[Epoch 112] train=0.098970 val=0.932600 loss=0.899952 time: 44.679129
INFO:root:[Epoch 113] train=0.101789 val=0.926300 loss=0.935051 time: 44.666291
INFO:root:[Epoch 114] train=0.101870 val=0.931800 loss=0.921534 time: 44.515806
INFO:root:[Epoch 115] train=0.099542 val=0.922100 loss=0.915626 time: 44.530868
INFO:root:[Epoch 116] train=0.099592 val=0.926100 loss=0.905271 time: 44.494026
INFO:root:[Epoch 117] train=0.100550 val=0.933200 loss=0.930654 time: 44.493510
INFO:root:[Epoch 118] train=0.097971 val=0.931800 loss=0.878442 time: 44.805136
INFO:root:[Epoch 119] train=0.099139 val=0.935500 loss=0.900521 time: 44.645547
INFO:root:[Epoch 120] train=0.084566 val=0.955800 loss=0.829487 time: 44.665405
INFO:root:[Epoch 121] train=0.084725 val=0.955800 loss=0.842166 time: 44.555359
INFO:root:[Epoch 122] train=0.079352 val=0.956600 loss=0.798337 time: 44.919173
INFO:root:[Epoch 123] train=0.079169 val=0.956900 loss=0.802848 time: 44.701911
INFO:root:[Epoch 124] train=0.078216 val=0.958300 loss=0.798856 time: 44.855626
INFO:root:[Epoch 125] train=0.077079 val=0.957900 loss=0.780546 time: 44.544173
INFO:root:[Epoch 126] train=0.075780 val=0.960700 loss=0.776312 time: 44.884551
INFO:root:[Epoch 127] train=0.077664 val=0.961000 loss=0.803397 time: 44.776361
INFO:root:[Epoch 128] train=0.071682 val=0.960900 loss=0.744951 time: 44.702604
INFO:root:[Epoch 129] train=0.073215 val=0.957800 loss=0.759718 time: 44.666271
INFO:root:[Epoch 130] train=0.075261 val=0.958100 loss=0.782108 time: 44.618448
INFO:root:[Epoch 131] train=0.073107 val=0.960500 loss=0.757863 time: 44.622597
INFO:root:[Epoch 132] train=0.072496 val=0.960000 loss=0.755354 time: 44.779354
INFO:root:[Epoch 133] train=0.072202 val=0.958500 loss=0.753734 time: 44.602366
INFO:root:[Epoch 134] train=0.070668 val=0.962000 loss=0.740264 time: 44.766044
INFO:root:[Epoch 135] train=0.076137 val=0.956900 loss=0.786903 time: 44.569443
INFO:root:[Epoch 136] train=0.073168 val=0.958900 loss=0.758902 time: 44.763457
INFO:root:[Epoch 137] train=0.071819 val=0.955300 loss=0.749593 time: 44.670271
INFO:root:[Epoch 138] train=0.075248 val=0.961400 loss=0.786526 time: 44.551978
INFO:root:[Epoch 139] train=0.072755 val=0.956500 loss=0.759692 time: 44.556657
INFO:root:[Epoch 140] train=0.068970 val=0.961900 loss=0.728794 time: 44.546638
INFO:root:[Epoch 141] train=0.073508 val=0.956800 loss=0.768233 time: 44.519467
INFO:root:[Epoch 142] train=0.070283 val=0.960500 loss=0.734898 time: 44.867760
INFO:root:[Epoch 143] train=0.072504 val=0.959100 loss=0.763397 time: 44.719276
INFO:root:[Epoch 144] train=0.069574 val=0.957600 loss=0.726738 time: 44.650144
INFO:root:[Epoch 145] train=0.070700 val=0.958800 loss=0.744716 time: 44.520609
INFO:root:[Epoch 146] train=0.072711 val=0.963000 loss=0.757401 time: 44.775141
INFO:root:[Epoch 147] train=0.073497 val=0.957300 loss=0.770637 time: 44.646810
INFO:root:[Epoch 148] train=0.070272 val=0.961000 loss=0.743098 time: 44.841553
INFO:root:[Epoch 149] train=0.072157 val=0.960700 loss=0.762067 time: 44.593877
INFO:root:[Epoch 150] train=0.070523 val=0.960100 loss=0.745106 time: 44.575095
INFO:root:[Epoch 151] train=0.071508 val=0.960800 loss=0.756283 time: 44.660100
INFO:root:[Epoch 152] train=0.074359 val=0.957000 loss=0.772720 time: 44.701235
INFO:root:[Epoch 153] train=0.071334 val=0.956900 loss=0.749584 time: 44.702948
INFO:root:[Epoch 154] train=0.074159 val=0.955200 loss=0.773810 time: 44.626395
INFO:root:[Epoch 155] train=0.068205 val=0.960500 loss=0.720357 time: 44.591217
INFO:root:[Epoch 156] train=0.073909 val=0.959500 loss=0.773207 time: 44.646934
INFO:root:[Epoch 157] train=0.070473 val=0.954500 loss=0.739542 time: 44.708304
INFO:root:[Epoch 158] train=0.074764 val=0.958900 loss=0.774178 time: 44.812476
INFO:root:[Epoch 159] train=0.071215 val=0.960500 loss=0.743392 time: 44.794825
INFO:root:[Epoch 160] train=0.065291 val=0.964100 loss=0.713678 time: 44.871889
INFO:root:[Epoch 161] train=0.066994 val=0.964600 loss=0.731250 time: 44.941477
INFO:root:[Epoch 162] train=0.064875 val=0.965000 loss=0.716591 time: 44.719177
INFO:root:[Epoch 163] train=0.062745 val=0.967000 loss=0.697327 time: 44.880890
INFO:root:[Epoch 164] train=0.065155 val=0.965900 loss=0.714578 time: 44.901800
INFO:root:[Epoch 165] train=0.064205 val=0.965100 loss=0.716234 time: 44.696204
INFO:root:[Epoch 166] train=0.064623 val=0.966500 loss=0.713468 time: 44.862581
INFO:root:[Epoch 167] train=0.067641 val=0.966600 loss=0.743825 time: 44.795763
INFO:root:[Epoch 168] train=0.061804 val=0.968400 loss=0.691600 time: 44.773496
INFO:root:[Epoch 169] train=0.063832 val=0.967900 loss=0.710245 time: 44.539008
INFO:root:[Epoch 170] train=0.062624 val=0.965400 loss=0.701733 time: 44.701418
INFO:root:[Epoch 171] train=0.064269 val=0.966300 loss=0.710349 time: 44.647466
INFO:root:[Epoch 172] train=0.063289 val=0.968800 loss=0.701586 time: 44.920712
INFO:root:[Epoch 173] train=0.063094 val=0.968000 loss=0.702079 time: 44.911269
INFO:root:[Epoch 174] train=0.062003 val=0.967100 loss=0.691606 time: 44.884998
INFO:root:[Epoch 175] train=0.060127 val=0.968300 loss=0.682300 time: 44.871903
INFO:root:[Epoch 176] train=0.059594 val=0.967400 loss=0.674029 time: 44.654919
INFO:root:[Epoch 177] train=0.062851 val=0.966500 loss=0.699677 time: 44.746648
INFO:root:[Epoch 178] train=0.061650 val=0.967900 loss=0.689541 time: 44.733453
INFO:root:[Epoch 179] train=0.061984 val=0.967000 loss=0.692201 time: 44.865347
INFO:root:[Epoch 180] train=0.066708 val=0.965800 loss=0.738385 time: 44.799499
INFO:root:[Epoch 181] train=0.062511 val=0.967600 loss=0.695493 time: 44.830229
INFO:root:[Epoch 182] train=0.060508 val=0.968300 loss=0.682841 time: 44.647044
INFO:root:[Epoch 183] train=0.062838 val=0.968800 loss=0.703241 time: 44.895789
INFO:root:[Epoch 184] train=0.061530 val=0.967200 loss=0.691147 time: 44.646259
INFO:root:[Epoch 185] train=0.060471 val=0.966100 loss=0.675965 time: 44.801462
INFO:root:[Epoch 186] train=0.061417 val=0.968900 loss=0.692746 time: 44.973913
INFO:root:[Epoch 187] train=0.061330 val=0.968100 loss=0.685756 time: 44.927295
INFO:root:[Epoch 188] train=0.060357 val=0.969300 loss=0.684316 time: 45.001905
INFO:root:[Epoch 189] train=0.060796 val=0.967600 loss=0.684419 time: 44.890638
INFO:root:[Epoch 190] train=0.061426 val=0.967700 loss=0.688514 time: 44.856087
INFO:root:[Epoch 191] train=0.063213 val=0.967600 loss=0.709457 time: 44.717411
INFO:root:[Epoch 192] train=0.060780 val=0.968800 loss=0.684689 time: 44.878537
INFO:root:[Epoch 193] train=0.060682 val=0.967800 loss=0.686652 time: 45.085745
INFO:root:[Epoch 194] train=0.061645 val=0.966800 loss=0.688792 time: 44.752851
INFO:root:[Epoch 195] train=0.060157 val=0.967700 loss=0.683282 time: 44.785503
INFO:root:[Epoch 196] train=0.058517 val=0.967600 loss=0.664722 time: 44.906520
INFO:root:[Epoch 197] train=0.061482 val=0.969500 loss=0.690279 time: 45.030602
INFO:root:[Epoch 198] train=0.061508 val=0.966200 loss=0.689769 time: 44.729063
INFO:root:[Epoch 199] train=0.062565 val=0.966300 loss=0.700144 time: 44.717595
INFO:root:[Epoch 200] train=0.003756 val=0.969700 loss=0.007651 time: 44.838128
INFO:root:[Epoch 201] train=0.002762 val=0.970700 loss=0.005396 time: 45.108403
INFO:root:[Epoch 202] train=0.002115 val=0.970500 loss=0.004385 time: 44.760510
INFO:root:[Epoch 203] train=0.002133 val=0.971900 loss=0.004160 time: 44.773640
INFO:root:[Epoch 204] train=0.002057 val=0.971800 loss=0.003682 time: 44.895405
INFO:root:[Epoch 205] train=0.001916 val=0.972000 loss=0.003455 time: 44.902360
INFO:root:[Epoch 206] train=0.001858 val=0.971700 loss=0.003377 time: 44.704216
INFO:root:[Epoch 207] train=0.001609 val=0.972700 loss=0.002937 time: 44.801580
INFO:root:[Epoch 208] train=0.001501 val=0.972200 loss=0.002848 time: 45.256155
INFO:root:[Epoch 209] train=0.001282 val=0.971800 loss=0.002544 time: 44.770879
INFO:root:[Epoch 210] train=0.001567 val=0.971000 loss=0.002778 time: 44.828537
INFO:root:[Epoch 211] train=0.001272 val=0.972700 loss=0.002438 time: 44.855664
INFO:root:[Epoch 212] train=0.001291 val=0.972100 loss=0.002470 time: 44.741077
INFO:root:[Epoch 213] train=0.001223 val=0.972100 loss=0.002303 time: 44.886231
INFO:root:[Epoch 214] train=0.001106 val=0.972000 loss=0.002190 time: 44.969022
INFO:root:[Epoch 215] train=0.001371 val=0.972200 loss=0.002318 time: 44.759269
INFO:root:[Epoch 216] train=0.001122 val=0.971700 loss=0.002116 time: 44.857690
INFO:root:[Epoch 217] train=0.001295 val=0.972500 loss=0.002173 time: 44.875287
INFO:root:[Epoch 218] train=0.001134 val=0.971000 loss=0.002113 time: 44.851217
INFO:root:[Epoch 219] train=0.001251 val=0.971600 loss=0.002157 time: 44.751287