INFO:root:Namespace(batch_size=128, drop_rate=0.0, logging_dir='logs', lr=0.1, lr_decay=0.1, lr_decay_epoch='100,150', lr_decay_period=0, mode='hybrid', model='cifar_resnet56_v2', momentum=0.9, num_epochs=220, num_gpus=1, num_workers=2, resume_from=None, save_dir='params', save_period=10, save_plot_dir='.', wd=0.0001)
[18:12:07] 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=0.219255 val=0.459500 loss=1.870001 time: 32.818569
INFO:root:[Epoch 1] train=0.197690 val=0.479600 loss=1.630761 time: 31.544907
INFO:root:[Epoch 2] train=0.186175 val=0.669700 loss=1.511333 time: 31.684400
INFO:root:[Epoch 3] train=0.178580 val=0.715700 loss=1.433864 time: 30.070204
INFO:root:[Epoch 4] train=0.173693 val=0.689500 loss=1.421203 time: 31.093816
INFO:root:[Epoch 5] train=0.168734 val=0.773700 loss=1.364895 time: 30.470547
INFO:root:[Epoch 6] train=0.165434 val=0.701600 loss=1.316816 time: 30.240383
INFO:root:[Epoch 7] train=0.161605 val=0.751600 loss=1.287635 time: 30.412922
INFO:root:[Epoch 8] train=0.160571 val=0.784500 loss=1.296630 time: 32.275829
INFO:root:[Epoch 9] train=0.158900 val=0.787900 loss=1.286895 time: 32.263896
INFO:root:[Epoch 10] train=0.155784 val=0.791300 loss=1.284705 time: 30.554201
INFO:root:[Epoch 11] train=0.155481 val=0.812000 loss=1.284073 time: 30.696090
INFO:root:[Epoch 12] train=0.154278 val=0.818700 loss=1.263096 time: 30.394676
INFO:root:[Epoch 13] train=0.153672 val=0.814100 loss=1.289455 time: 30.219075
INFO:root:[Epoch 14] train=0.151400 val=0.807800 loss=1.250173 time: 30.873957
INFO:root:[Epoch 15] train=0.150006 val=0.841700 loss=1.228736 time: 30.557053
INFO:root:[Epoch 16] train=0.148753 val=0.795400 loss=1.224341 time: 30.061354
INFO:root:[Epoch 17] train=0.146498 val=0.784100 loss=1.216148 time: 30.353804
INFO:root:[Epoch 18] train=0.147822 val=0.810600 loss=1.239882 time: 31.400574
INFO:root:[Epoch 19] train=0.145030 val=0.821900 loss=1.194228 time: 31.980506
INFO:root:[Epoch 20] train=0.144422 val=0.817800 loss=1.196083 time: 31.875226
INFO:root:[Epoch 21] train=0.144501 val=0.834400 loss=1.218434 time: 30.549700
INFO:root:[Epoch 22] train=0.143286 val=0.830100 loss=1.176954 time: 30.689143
INFO:root:[Epoch 23] train=0.142218 val=0.842900 loss=1.177174 time: 30.609638
INFO:root:[Epoch 24] train=0.140876 val=0.826500 loss=1.165247 time: 30.647755
INFO:root:[Epoch 25] train=0.140109 val=0.782900 loss=1.146735 time: 30.504727
INFO:root:[Epoch 26] train=0.139637 val=0.828800 loss=1.116086 time: 30.622359
INFO:root:[Epoch 27] train=0.139055 val=0.856100 loss=1.157480 time: 30.833572
INFO:root:[Epoch 28] train=0.139812 val=0.813800 loss=1.181379 time: 30.544462
INFO:root:[Epoch 29] train=0.138000 val=0.830400 loss=1.151236 time: 31.387619
INFO:root:[Epoch 30] train=0.137454 val=0.863400 loss=1.138104 time: 30.634535
INFO:root:[Epoch 31] train=0.136268 val=0.861400 loss=1.129530 time: 31.000422
INFO:root:[Epoch 32] train=0.135181 val=0.846100 loss=1.126849 time: 31.793985
INFO:root:[Epoch 33] train=0.136533 val=0.859500 loss=1.142540 time: 31.693240
INFO:root:[Epoch 34] train=0.135385 val=0.867200 loss=1.129858 time: 32.343223
INFO:root:[Epoch 35] train=0.136537 val=0.828700 loss=1.153196 time: 30.115682
INFO:root:[Epoch 36] train=0.136090 val=0.865500 loss=1.135462 time: 30.631741
INFO:root:[Epoch 37] train=0.135393 val=0.849700 loss=1.125216 time: 30.514136
INFO:root:[Epoch 38] train=0.135161 val=0.842600 loss=1.110564 time: 30.392942
INFO:root:[Epoch 39] train=0.134474 val=0.863100 loss=1.135216 time: 30.752823
INFO:root:[Epoch 40] train=0.134015 val=0.850800 loss=1.112120 time: 30.629396
INFO:root:[Epoch 41] train=0.133541 val=0.859700 loss=1.122387 time: 30.464485
INFO:root:[Epoch 42] train=0.133410 val=0.822200 loss=1.113672 time: 30.643799
INFO:root:[Epoch 43] train=0.134129 val=0.873100 loss=1.137374 time: 30.379665
INFO:root:[Epoch 44] train=0.132592 val=0.849000 loss=1.111461 time: 30.877643
INFO:root:[Epoch 45] train=0.131988 val=0.885200 loss=1.098053 time: 31.691911
INFO:root:[Epoch 46] train=0.133075 val=0.834300 loss=1.117272 time: 31.804437
INFO:root:[Epoch 47] train=0.131155 val=0.875800 loss=1.095229 time: 31.853171
INFO:root:[Epoch 48] train=0.131331 val=0.861600 loss=1.102157 time: 31.453815
INFO:root:[Epoch 49] train=0.132824 val=0.831900 loss=1.125982 time: 31.769461
INFO:root:[Epoch 50] train=0.133319 val=0.860700 loss=1.147263 time: 32.237520
INFO:root:[Epoch 51] train=0.133234 val=0.843400 loss=1.133793 time: 32.819375
INFO:root:[Epoch 52] train=0.131771 val=0.867700 loss=1.098428 time: 30.928885
INFO:root:[Epoch 53] train=0.130816 val=0.869200 loss=1.078340 time: 30.603062
INFO:root:[Epoch 54] train=0.131506 val=0.861400 loss=1.128231 time: 32.367083
INFO:root:[Epoch 55] train=0.129665 val=0.871800 loss=1.085897 time: 30.535030
INFO:root:[Epoch 56] train=0.129454 val=0.872100 loss=1.067391 time: 30.444107
INFO:root:[Epoch 57] train=0.130153 val=0.848600 loss=1.104423 time: 30.205999
INFO:root:[Epoch 58] train=0.129747 val=0.874300 loss=1.101904 time: 30.341524
INFO:root:[Epoch 59] train=0.130396 val=0.874100 loss=1.110506 time: 30.298577
INFO:root:[Epoch 60] train=0.130085 val=0.869000 loss=1.101037 time: 30.270200
INFO:root:[Epoch 61] train=0.130172 val=0.879600 loss=1.106189 time: 30.556505
INFO:root:[Epoch 62] train=0.129341 val=0.879800 loss=1.091534 time: 30.113472
INFO:root:[Epoch 63] train=0.127130 val=0.839200 loss=1.053031 time: 30.307434
INFO:root:[Epoch 64] train=0.129420 val=0.873100 loss=1.093496 time: 30.430434
INFO:root:[Epoch 65] train=0.129179 val=0.865500 loss=1.105830 time: 30.461817
INFO:root:[Epoch 66] train=0.129056 val=0.895700 loss=1.079639 time: 31.944078
INFO:root:[Epoch 67] train=0.129066 val=0.810700 loss=1.087664 time: 31.805271
INFO:root:[Epoch 68] train=0.127960 val=0.861600 loss=1.093769 time: 30.347351
INFO:root:[Epoch 69] train=0.127690 val=0.871200 loss=1.071206 time: 30.301856
INFO:root:[Epoch 70] train=0.128143 val=0.887400 loss=1.067996 time: 32.440559
INFO:root:[Epoch 71] train=0.128478 val=0.877600 loss=1.082754 time: 30.519329
INFO:root:[Epoch 72] train=0.127976 val=0.859900 loss=1.075222 time: 30.222759
INFO:root:[Epoch 73] train=0.127575 val=0.866100 loss=1.071188 time: 30.676507
INFO:root:[Epoch 74] train=0.128322 val=0.881100 loss=1.097619 time: 30.648760
INFO:root:[Epoch 75] train=0.126879 val=0.843000 loss=1.082663 time: 30.505135
INFO:root:[Epoch 76] train=0.127397 val=0.878500 loss=1.074099 time: 30.321088
INFO:root:[Epoch 77] train=0.127271 val=0.879700 loss=1.084677 time: 30.477291
INFO:root:[Epoch 78] train=0.126429 val=0.891400 loss=1.067379 time: 32.866642
INFO:root:[Epoch 79] train=0.126798 val=0.864500 loss=1.083632 time: 31.107375
INFO:root:[Epoch 80] train=0.127147 val=0.859500 loss=1.094247 time: 30.587663
INFO:root:[Epoch 81] train=0.126870 val=0.861700 loss=1.087240 time: 30.488004
INFO:root:[Epoch 82] train=0.126566 val=0.882900 loss=1.068868 time: 30.211129
INFO:root:[Epoch 83] train=0.128329 val=0.887300 loss=1.094429 time: 30.897031
INFO:root:[Epoch 84] train=0.126228 val=0.874300 loss=1.069318 time: 31.741651
INFO:root:[Epoch 85] train=0.127125 val=0.863700 loss=1.086897 time: 30.524610
INFO:root:[Epoch 86] train=0.127384 val=0.885000 loss=1.099724 time: 32.645069
INFO:root:[Epoch 87] train=0.127167 val=0.865600 loss=1.076439 time: 32.841936
INFO:root:[Epoch 88] train=0.127342 val=0.861700 loss=1.079192 time: 32.284434
INFO:root:[Epoch 89] train=0.127470 val=0.878100 loss=1.087514 time: 30.572668
INFO:root:[Epoch 90] train=0.125588 val=0.890700 loss=1.055233 time: 32.145016
INFO:root:[Epoch 91] train=0.125598 val=0.875300 loss=1.069850 time: 30.638660
INFO:root:[Epoch 92] train=0.126184 val=0.895900 loss=1.063976 time: 30.804195
INFO:root:[Epoch 93] train=0.122965 val=0.890600 loss=1.036225 time: 31.911598
INFO:root:[Epoch 94] train=0.126545 val=0.883600 loss=1.064704 time: 30.534046
INFO:root:[Epoch 95] train=0.124086 val=0.874900 loss=1.045289 time: 31.308117
INFO:root:[Epoch 96] train=0.125084 val=0.864500 loss=1.067994 time: 31.348533
INFO:root:[Epoch 97] train=0.127090 val=0.878600 loss=1.083587 time: 32.338923
INFO:root:[Epoch 98] train=0.126752 val=0.867300 loss=1.094614 time: 32.732575
INFO:root:[Epoch 99] train=0.126527 val=0.896000 loss=1.083123 time: 30.855850
INFO:root:[Epoch 100] train=0.114550 val=0.919600 loss=1.004417 time: 30.835947
INFO:root:[Epoch 101] train=0.107750 val=0.922900 loss=0.938300 time: 31.689465
INFO:root:[Epoch 102] train=0.107164 val=0.926300 loss=0.959022 time: 30.479158
INFO:root:[Epoch 103] train=0.109204 val=0.921300 loss=0.987139 time: 31.038078
INFO:root:[Epoch 104] train=0.106902 val=0.923600 loss=0.960252 time: 30.616778
INFO:root:[Epoch 105] train=0.104721 val=0.928700 loss=0.940315 time: 30.812559
INFO:root:[Epoch 106] train=0.106026 val=0.927600 loss=0.970526 time: 30.972366
INFO:root:[Epoch 107] train=0.102627 val=0.928600 loss=0.918395 time: 30.755998
INFO:root:[Epoch 108] train=0.103786 val=0.929100 loss=0.949245 time: 30.697260
INFO:root:[Epoch 109] train=0.103403 val=0.929200 loss=0.925664 time: 30.869129
INFO:root:[Epoch 110] train=0.102245 val=0.928800 loss=0.936776 time: 30.321777
INFO:root:[Epoch 111] train=0.105066 val=0.930500 loss=0.976877 time: 32.530797
INFO:root:[Epoch 112] train=0.100795 val=0.933300 loss=0.909781 time: 30.722336
INFO:root:[Epoch 113] train=0.101274 val=0.932600 loss=0.914753 time: 30.827227
INFO:root:[Epoch 114] train=0.102897 val=0.929900 loss=0.941488 time: 30.959703
INFO:root:[Epoch 115] train=0.102617 val=0.930300 loss=0.931630 time: 30.596147
INFO:root:[Epoch 116] train=0.101655 val=0.930800 loss=0.932068 time: 31.202390
INFO:root:[Epoch 117] train=0.099023 val=0.928700 loss=0.899164 time: 31.703786
INFO:root:[Epoch 118] train=0.103228 val=0.930400 loss=0.949609 time: 30.836153
INFO:root:[Epoch 119] train=0.101892 val=0.930000 loss=0.952693 time: 32.657241
INFO:root:[Epoch 120] train=0.101902 val=0.928800 loss=0.939741 time: 32.506535
INFO:root:[Epoch 121] train=0.102290 val=0.932600 loss=0.942471 time: 32.894138
INFO:root:[Epoch 122] train=0.100442 val=0.929900 loss=0.929777 time: 30.699552
INFO:root:[Epoch 123] train=0.101897 val=0.930300 loss=0.942702 time: 30.755304
INFO:root:[Epoch 124] train=0.101414 val=0.928600 loss=0.931805 time: 30.583111
INFO:root:[Epoch 125] train=0.097280 val=0.930500 loss=0.883449 time: 30.865642
INFO:root:[Epoch 126] train=0.098704 val=0.925900 loss=0.912417 time: 30.622941
INFO:root:[Epoch 127] train=0.098173 val=0.935300 loss=0.909616 time: 31.494010
INFO:root:[Epoch 128] train=0.100698 val=0.933800 loss=0.937801 time: 30.519974
INFO:root:[Epoch 129] train=0.097867 val=0.932800 loss=0.904702 time: 30.380886
INFO:root:[Epoch 130] train=0.098579 val=0.930000 loss=0.920755 time: 30.379462
INFO:root:[Epoch 131] train=0.098345 val=0.935800 loss=0.908259 time: 30.772894
INFO:root:[Epoch 132] train=0.100040 val=0.929800 loss=0.934270 time: 31.184171
INFO:root:[Epoch 133] train=0.096754 val=0.932700 loss=0.907192 time: 30.503713
INFO:root:[Epoch 134] train=0.096469 val=0.927900 loss=0.901778 time: 32.041323
INFO:root:[Epoch 135] train=0.098226 val=0.932300 loss=0.910633 time: 31.845125
INFO:root:[Epoch 136] train=0.099461 val=0.931200 loss=0.921410 time: 30.799442
INFO:root:[Epoch 137] train=0.100097 val=0.934100 loss=0.928555 time: 30.526141
INFO:root:[Epoch 138] train=0.096788 val=0.933000 loss=0.895830 time: 31.045861
INFO:root:[Epoch 139] train=0.094844 val=0.933300 loss=0.878650 time: 30.827856
INFO:root:[Epoch 140] train=0.096788 val=0.933900 loss=0.907762 time: 30.684544
INFO:root:[Epoch 141] train=0.095654 val=0.934300 loss=0.888225 time: 30.762350
INFO:root:[Epoch 142] train=0.099785 val=0.930900 loss=0.934542 time: 31.052811
INFO:root:[Epoch 143] train=0.097864 val=0.937000 loss=0.917298 time: 31.435860
INFO:root:[Epoch 144] train=0.095251 val=0.932700 loss=0.884937 time: 30.452293
INFO:root:[Epoch 145] train=0.098242 val=0.934000 loss=0.922480 time: 30.940249
INFO:root:[Epoch 146] train=0.097069 val=0.937500 loss=0.903764 time: 30.648387
INFO:root:[Epoch 147] train=0.095272 val=0.933300 loss=0.889892 time: 30.806662
INFO:root:[Epoch 148] train=0.098294 val=0.934000 loss=0.923114 time: 30.872846
INFO:root:[Epoch 149] train=0.095725 val=0.934000 loss=0.893726 time: 30.723855
INFO:root:[Epoch 150] train=0.095984 val=0.936900 loss=0.902769 time: 30.815698
INFO:root:[Epoch 151] train=0.092426 val=0.935700 loss=0.880826 time: 30.702808
INFO:root:[Epoch 152] train=0.094841 val=0.935300 loss=0.906956 time: 30.944867
INFO:root:[Epoch 153] train=0.093658 val=0.937900 loss=0.902832 time: 30.760642
INFO:root:[Epoch 154] train=0.091409 val=0.937900 loss=0.876602 time: 30.733613
INFO:root:[Epoch 155] train=0.091971 val=0.939600 loss=0.885749 time: 30.873834
INFO:root:[Epoch 156] train=0.092210 val=0.939900 loss=0.875866 time: 32.839207
INFO:root:[Epoch 157] train=0.089685 val=0.936700 loss=0.859422 time: 32.911991
INFO:root:[Epoch 158] train=0.091765 val=0.937500 loss=0.880337 time: 30.815673
INFO:root:[Epoch 159] train=0.089316 val=0.938300 loss=0.860186 time: 30.521792
INFO:root:[Epoch 160] train=0.091593 val=0.940300 loss=0.886104 time: 30.759400
INFO:root:[Epoch 161] train=0.092896 val=0.939300 loss=0.892338 time: 30.633905
INFO:root:[Epoch 162] train=0.090258 val=0.939600 loss=0.870170 time: 30.818215
INFO:root:[Epoch 163] train=0.090092 val=0.939300 loss=0.863778 time: 32.848244
INFO:root:[Epoch 164] train=0.092860 val=0.937500 loss=0.901241 time: 30.867904
INFO:root:[Epoch 165] train=0.091160 val=0.937300 loss=0.873334 time: 31.320880
INFO:root:[Epoch 166] train=0.089438 val=0.940400 loss=0.866489 time: 30.668391
INFO:root:[Epoch 167] train=0.091175 val=0.939700 loss=0.877444 time: 30.774601
INFO:root:[Epoch 168] train=0.093210 val=0.939000 loss=0.895723 time: 30.794822
INFO:root:[Epoch 169] train=0.092122 val=0.942400 loss=0.885550 time: 30.717761
INFO:root:[Epoch 170] train=0.091148 val=0.941200 loss=0.880292 time: 30.927549
INFO:root:[Epoch 171] train=0.089523 val=0.941800 loss=0.855518 time: 30.659112
INFO:root:[Epoch 172] train=0.089553 val=0.940200 loss=0.863626 time: 30.930390
INFO:root:[Epoch 173] train=0.090622 val=0.941300 loss=0.871129 time: 30.985705
INFO:root:[Epoch 174] train=0.091117 val=0.942400 loss=0.874960 time: 31.833263
INFO:root:[Epoch 175] train=0.087437 val=0.940100 loss=0.844462 time: 32.537114
INFO:root:[Epoch 176] train=0.094656 val=0.938100 loss=0.924685 time: 32.605171
INFO:root:[Epoch 177] train=0.093136 val=0.938400 loss=0.902887 time: 32.695540
INFO:root:[Epoch 178] train=0.088782 val=0.939600 loss=0.856888 time: 30.823923
INFO:root:[Epoch 179] train=0.091202 val=0.939300 loss=0.884202 time: 30.837315
INFO:root:[Epoch 180] train=0.087687 val=0.940400 loss=0.839438 time: 30.394162
INFO:root:[Epoch 181] train=0.090203 val=0.940400 loss=0.878511 time: 30.682723
INFO:root:[Epoch 182] train=0.088792 val=0.940900 loss=0.858363 time: 30.603953
INFO:root:[Epoch 183] train=0.087951 val=0.942200 loss=0.849135 time: 30.595757
INFO:root:[Epoch 184] train=0.092301 val=0.940600 loss=0.888819 time: 30.465251
INFO:root:[Epoch 185] train=0.089625 val=0.938900 loss=0.860925 time: 30.729040
INFO:root:[Epoch 186] train=0.089308 val=0.940600 loss=0.858474 time: 30.975687
INFO:root:[Epoch 187] train=0.090710 val=0.942100 loss=0.876949 time: 32.586791
INFO:root:[Epoch 188] train=0.092458 val=0.939400 loss=0.889712 time: 32.484966
INFO:root:[Epoch 189] train=0.090096 val=0.938600 loss=0.873197 time: 32.753017
INFO:root:[Epoch 190] train=0.088198 val=0.940500 loss=0.857175 time: 30.706950
INFO:root:[Epoch 191] train=0.093070 val=0.938200 loss=0.894848 time: 30.847335
INFO:root:[Epoch 192] train=0.090959 val=0.942100 loss=0.885444 time: 30.955712
INFO:root:[Epoch 193] train=0.090038 val=0.940500 loss=0.879325 time: 30.927235
INFO:root:[Epoch 194] train=0.085402 val=0.943100 loss=0.830450 time: 31.584736
INFO:root:[Epoch 195] train=0.090083 val=0.941400 loss=0.881930 time: 30.717058
INFO:root:[Epoch 196] train=0.090296 val=0.939400 loss=0.882995 time: 30.585093
INFO:root:[Epoch 197] train=0.089577 val=0.941800 loss=0.860873 time: 30.655229
INFO:root:[Epoch 198] train=0.086353 val=0.941300 loss=0.835114 time: 30.720644
INFO:root:[Epoch 199] train=0.090160 val=0.940700 loss=0.878110 time: 30.845694
INFO:root:[Epoch 200] train=0.039094 val=0.944900 loss=0.047074 time: 30.765232
INFO:root:[Epoch 201] train=0.038356 val=0.945100 loss=0.042275 time: 30.871049
INFO:root:[Epoch 202] train=0.034878 val=0.945500 loss=0.036586 time: 30.957158
INFO:root:[Epoch 203] train=0.034719 val=0.945200 loss=0.034924 time: 30.891974
INFO:root:[Epoch 204] train=0.034210 val=0.945200 loss=0.033441 time: 32.429174
INFO:root:[Epoch 205] train=0.033059 val=0.946400 loss=0.031843 time: 31.035927
INFO:root:[Epoch 206] train=0.033364 val=0.945700 loss=0.031698 time: 30.947111
INFO:root:[Epoch 207] train=0.031043 val=0.945400 loss=0.028975 time: 30.914665
INFO:root:[Epoch 208] train=0.031454 val=0.944400 loss=0.028724 time: 30.679496
INFO:root:[Epoch 209] train=0.032157 val=0.945700 loss=0.029231 time: 30.517693
INFO:root:[Epoch 210] train=0.030602 val=0.943000 loss=0.026901 time: 31.023809
INFO:root:[Epoch 211] train=0.029656 val=0.943100 loss=0.026310 time: 30.608368
INFO:root:[Epoch 212] train=0.029951 val=0.945100 loss=0.025719 time: 32.381828
INFO:root:[Epoch 213] train=0.029089 val=0.944600 loss=0.024801 time: 30.842030
INFO:root:[Epoch 214] train=0.028458 val=0.943300 loss=0.024522 time: 30.965086
INFO:root:[Epoch 215] train=0.027106 val=0.946000 loss=0.022265 time: 31.727007
INFO:root:[Epoch 216] train=0.028122 val=0.946000 loss=0.022991 time: 32.232413
INFO:root:[Epoch 217] train=0.026861 val=0.943000 loss=0.022033 time: 32.068817
INFO:root:[Epoch 218] train=0.027470 val=0.943400 loss=0.022271 time: 32.452847
INFO:root:[Epoch 219] train=0.025434 val=0.945000 loss=0.020138 time: 30.839950