INFO:root:Namespace(batch_size=64, 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_wideresnet40_8', momentum=0.9, num_epochs=200, num_gpus=2, num_workers=2, resume_from=None, save_dir='params', save_period=10, save_plot_dir='.', wd=0.0005)
[20:38:50] 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=0.476302 val=0.598800 loss=1.443515 time: 45.898591
INFO:root:[Epoch 1] train=0.691546 val=0.725200 loss=0.873744 time: 42.230497
INFO:root:[Epoch 2] train=0.767688 val=0.732300 loss=0.671316 time: 42.343659
INFO:root:[Epoch 3] train=0.803706 val=0.707000 loss=0.571761 time: 42.076353
INFO:root:[Epoch 4] train=0.822276 val=0.778000 loss=0.515836 time: 42.234004
INFO:root:[Epoch 5] train=0.837800 val=0.809300 loss=0.473916 time: 42.290411
INFO:root:[Epoch 6] train=0.850100 val=0.827500 loss=0.439616 time: 42.231269
INFO:root:[Epoch 7] train=0.857792 val=0.814800 loss=0.421665 time: 42.121221
INFO:root:[Epoch 8] train=0.865044 val=0.841800 loss=0.401713 time: 42.270418
INFO:root:[Epoch 9] train=0.868429 val=0.848800 loss=0.384855 time: 42.402341
INFO:root:[Epoch 10] train=0.874639 val=0.812500 loss=0.370893 time: 42.216523
INFO:root:[Epoch 11] train=0.876783 val=0.832100 loss=0.361717 time: 42.317246
INFO:root:[Epoch 12] train=0.878946 val=0.829100 loss=0.351601 time: 42.236991
INFO:root:[Epoch 13] train=0.884896 val=0.837200 loss=0.338937 time: 42.156110
INFO:root:[Epoch 14] train=0.887881 val=0.854500 loss=0.332707 time: 42.399788
INFO:root:[Epoch 15] train=0.890064 val=0.855600 loss=0.321776 time: 42.227620
INFO:root:[Epoch 16] train=0.892067 val=0.839700 loss=0.318966 time: 42.137775
INFO:root:[Epoch 17] train=0.892248 val=0.811400 loss=0.315582 time: 42.020999
INFO:root:[Epoch 18] train=0.895292 val=0.863300 loss=0.309926 time: 42.200590
INFO:root:[Epoch 19] train=0.897756 val=0.866300 loss=0.303675 time: 42.325874
INFO:root:[Epoch 20] train=0.898678 val=0.842800 loss=0.296958 time: 42.088936
INFO:root:[Epoch 21] train=0.899058 val=0.846500 loss=0.297367 time: 42.187479
INFO:root:[Epoch 22] train=0.899379 val=0.845500 loss=0.292278 time: 42.047068
INFO:root:[Epoch 23] train=0.902364 val=0.804000 loss=0.287766 time: 42.239910
INFO:root:[Epoch 24] train=0.903265 val=0.856200 loss=0.285258 time: 42.044796
INFO:root:[Epoch 25] train=0.903025 val=0.869700 loss=0.284536 time: 42.179846
INFO:root:[Epoch 26] train=0.904988 val=0.861500 loss=0.280433 time: 42.015146
INFO:root:[Epoch 27] train=0.904207 val=0.872200 loss=0.283251 time: 42.396142
INFO:root:[Epoch 28] train=0.906751 val=0.855500 loss=0.276069 time: 42.244164
INFO:root:[Epoch 29] train=0.905549 val=0.849900 loss=0.277549 time: 42.048852
INFO:root:[Epoch 30] train=0.906771 val=0.854700 loss=0.271572 time: 42.164427
INFO:root:[Epoch 31] train=0.909976 val=0.866100 loss=0.267549 time: 42.259323
INFO:root:[Epoch 32] train=0.907853 val=0.871700 loss=0.270957 time: 42.087081
INFO:root:[Epoch 33] train=0.909415 val=0.826100 loss=0.268795 time: 42.112518
INFO:root:[Epoch 34] train=0.909014 val=0.864300 loss=0.266842 time: 42.085460
INFO:root:[Epoch 35] train=0.909014 val=0.864700 loss=0.264742 time: 42.121698
INFO:root:[Epoch 36] train=0.910136 val=0.863700 loss=0.261699 time: 42.173026
INFO:root:[Epoch 37] train=0.911138 val=0.880700 loss=0.262382 time: 42.242649
INFO:root:[Epoch 38] train=0.909435 val=0.853400 loss=0.264262 time: 42.140792
INFO:root:[Epoch 39] train=0.911498 val=0.832400 loss=0.257764 time: 42.232321
INFO:root:[Epoch 40] train=0.911398 val=0.845300 loss=0.259688 time: 42.168666
INFO:root:[Epoch 41] train=0.910837 val=0.858200 loss=0.259622 time: 42.175675
INFO:root:[Epoch 42] train=0.913361 val=0.846100 loss=0.256284 time: 42.172025
INFO:root:[Epoch 43] train=0.914243 val=0.890200 loss=0.254390 time: 42.342459
INFO:root:[Epoch 44] train=0.913401 val=0.870800 loss=0.254443 time: 42.200440
INFO:root:[Epoch 45] train=0.913922 val=0.865900 loss=0.254884 time: 42.042654
INFO:root:[Epoch 46] train=0.913381 val=0.860500 loss=0.257285 time: 42.210280
INFO:root:[Epoch 47] train=0.913021 val=0.861600 loss=0.255337 time: 42.166932
INFO:root:[Epoch 48] train=0.915224 val=0.872800 loss=0.248502 time: 42.147548
INFO:root:[Epoch 49] train=0.913602 val=0.824100 loss=0.251589 time: 42.260018
INFO:root:[Epoch 50] train=0.915284 val=0.884200 loss=0.252099 time: 42.211380
INFO:root:[Epoch 51] train=0.913722 val=0.873100 loss=0.254660 time: 42.201839
INFO:root:[Epoch 52] train=0.913602 val=0.852200 loss=0.251401 time: 42.659653
INFO:root:[Epoch 53] train=0.916306 val=0.888500 loss=0.245559 time: 42.165497
INFO:root:[Epoch 54] train=0.915385 val=0.872400 loss=0.247453 time: 42.085088
INFO:root:[Epoch 55] train=0.914363 val=0.797600 loss=0.250533 time: 42.166940
INFO:root:[Epoch 56] train=0.913922 val=0.842600 loss=0.252985 time: 42.120806
INFO:root:[Epoch 57] train=0.913782 val=0.878300 loss=0.250330 time: 42.087673
INFO:root:[Epoch 58] train=0.915264 val=0.843700 loss=0.247809 time: 42.152510
INFO:root:[Epoch 59] train=0.916967 val=0.891200 loss=0.245026 time: 42.391626
INFO:root:[Epoch 60] train=0.962560 val=0.944000 loss=0.111421 time: 42.311076
INFO:root:[Epoch 61] train=0.974960 val=0.945700 loss=0.073659 time: 42.242499
INFO:root:[Epoch 62] train=0.981631 val=0.943900 loss=0.057145 time: 42.227373
INFO:root:[Epoch 63] train=0.984075 val=0.938200 loss=0.050825 time: 42.228992
INFO:root:[Epoch 64] train=0.983674 val=0.945900 loss=0.050020 time: 42.317205
INFO:root:[Epoch 65] train=0.985717 val=0.940200 loss=0.046083 time: 42.483492
INFO:root:[Epoch 66] train=0.986078 val=0.938600 loss=0.043047 time: 42.241663
INFO:root:[Epoch 67] train=0.984696 val=0.936200 loss=0.047460 time: 42.164494
INFO:root:[Epoch 68] train=0.985076 val=0.935700 loss=0.046108 time: 42.351168
INFO:root:[Epoch 69] train=0.982652 val=0.919800 loss=0.051860 time: 42.292412
INFO:root:[Epoch 70] train=0.982712 val=0.929000 loss=0.052084 time: 42.185853
INFO:root:[Epoch 71] train=0.983153 val=0.930100 loss=0.051815 time: 42.249864
INFO:root:[Epoch 72] train=0.980889 val=0.931700 loss=0.058213 time: 42.196212
INFO:root:[Epoch 73] train=0.982131 val=0.926600 loss=0.054688 time: 42.334945
INFO:root:[Epoch 74] train=0.979087 val=0.923200 loss=0.062297 time: 42.224657
INFO:root:[Epoch 75] train=0.980088 val=0.930300 loss=0.061950 time: 42.343746
INFO:root:[Epoch 76] train=0.981110 val=0.919700 loss=0.058863 time: 42.316310
INFO:root:[Epoch 77] train=0.977324 val=0.936400 loss=0.069435 time: 42.142142
INFO:root:[Epoch 78] train=0.979207 val=0.923700 loss=0.062369 time: 42.256794
INFO:root:[Epoch 79] train=0.978966 val=0.927000 loss=0.063179 time: 42.369067
INFO:root:[Epoch 80] train=0.977284 val=0.931800 loss=0.067306 time: 42.086987
INFO:root:[Epoch 81] train=0.979547 val=0.923700 loss=0.064090 time: 42.263097
INFO:root:[Epoch 82] train=0.977344 val=0.928100 loss=0.067344 time: 42.285950
INFO:root:[Epoch 83] train=0.978085 val=0.916000 loss=0.067055 time: 42.229627
INFO:root:[Epoch 84] train=0.976923 val=0.931100 loss=0.070651 time: 42.205252
INFO:root:[Epoch 85] train=0.977825 val=0.913400 loss=0.066624 time: 42.348780
INFO:root:[Epoch 86] train=0.975601 val=0.923000 loss=0.071688 time: 42.350603
INFO:root:[Epoch 87] train=0.977424 val=0.940700 loss=0.068050 time: 42.282161
INFO:root:[Epoch 88] train=0.979507 val=0.924300 loss=0.063647 time: 42.188025
INFO:root:[Epoch 89] train=0.977985 val=0.926300 loss=0.067604 time: 42.132257
INFO:root:[Epoch 90] train=0.979507 val=0.913400 loss=0.061807 time: 42.224421
INFO:root:[Epoch 91] train=0.978446 val=0.917300 loss=0.065600 time: 42.217626
INFO:root:[Epoch 92] train=0.978546 val=0.922000 loss=0.067114 time: 42.310878
INFO:root:[Epoch 93] train=0.978425 val=0.918000 loss=0.064742 time: 42.156946
INFO:root:[Epoch 94] train=0.979107 val=0.905700 loss=0.061897 time: 42.543226
INFO:root:[Epoch 95] train=0.976462 val=0.913900 loss=0.070348 time: 42.193728
INFO:root:[Epoch 96] train=0.978906 val=0.922400 loss=0.066130 time: 42.329325
INFO:root:[Epoch 97] train=0.979848 val=0.925400 loss=0.062698 time: 42.133957
INFO:root:[Epoch 98] train=0.979247 val=0.915800 loss=0.063833 time: 42.100119
INFO:root:[Epoch 99] train=0.979768 val=0.929000 loss=0.062592 time: 42.185596
INFO:root:[Epoch 100] train=0.978486 val=0.925500 loss=0.064317 time: 42.268944
INFO:root:[Epoch 101] train=0.977364 val=0.924700 loss=0.067796 time: 42.157291
INFO:root:[Epoch 102] train=0.977744 val=0.925200 loss=0.068441 time: 42.231722
INFO:root:[Epoch 103] train=0.977885 val=0.923800 loss=0.067436 time: 42.189025
INFO:root:[Epoch 104] train=0.979127 val=0.924600 loss=0.062431 time: 42.182323
INFO:root:[Epoch 105] train=0.980649 val=0.924700 loss=0.059420 time: 42.219483
INFO:root:[Epoch 106] train=0.979768 val=0.926800 loss=0.061916 time: 42.272263
INFO:root:[Epoch 107] train=0.979888 val=0.933400 loss=0.062954 time: 42.117058
INFO:root:[Epoch 108] train=0.980449 val=0.927700 loss=0.062778 time: 42.294972
INFO:root:[Epoch 109] train=0.980809 val=0.914300 loss=0.059531 time: 42.205245
INFO:root:[Epoch 110] train=0.979828 val=0.917500 loss=0.061893 time: 42.227349
INFO:root:[Epoch 111] train=0.978365 val=0.930000 loss=0.066345 time: 42.341012
INFO:root:[Epoch 112] train=0.979667 val=0.919100 loss=0.062881 time: 42.255664
INFO:root:[Epoch 113] train=0.979507 val=0.916700 loss=0.062973 time: 42.380544
INFO:root:[Epoch 114] train=0.978185 val=0.922200 loss=0.065768 time: 42.357505
INFO:root:[Epoch 115] train=0.980549 val=0.928400 loss=0.062132 time: 42.279506
INFO:root:[Epoch 116] train=0.980909 val=0.917700 loss=0.059102 time: 42.288382
INFO:root:[Epoch 117] train=0.980469 val=0.925500 loss=0.063363 time: 42.233501
INFO:root:[Epoch 118] train=0.979247 val=0.931300 loss=0.063623 time: 42.342618
INFO:root:[Epoch 119] train=0.979908 val=0.927300 loss=0.060788 time: 42.175171
INFO:root:[Epoch 120] train=0.994692 val=0.952000 loss=0.019682 time: 42.570270
INFO:root:[Epoch 121] train=0.997796 val=0.955000 loss=0.009556 time: 42.422057
INFO:root:[Epoch 122] train=0.998998 val=0.956700 loss=0.006178 time: 42.513492
INFO:root:[Epoch 123] train=0.998798 val=0.957600 loss=0.005016 time: 42.884045
INFO:root:[Epoch 124] train=0.999339 val=0.958000 loss=0.004182 time: 42.433888
INFO:root:[Epoch 125] train=0.999219 val=0.958000 loss=0.003984 time: 42.429669
INFO:root:[Epoch 126] train=0.999439 val=0.958300 loss=0.003566 time: 42.480016
INFO:root:[Epoch 127] train=0.999399 val=0.956100 loss=0.003525 time: 42.314623
INFO:root:[Epoch 128] train=0.999639 val=0.958900 loss=0.002962 time: 42.443832
INFO:root:[Epoch 129] train=0.999559 val=0.958400 loss=0.003070 time: 42.285468
INFO:root:[Epoch 130] train=0.999619 val=0.960100 loss=0.002699 time: 42.362187
INFO:root:[Epoch 131] train=0.999760 val=0.959600 loss=0.002353 time: 42.285986
INFO:root:[Epoch 132] train=0.999539 val=0.959800 loss=0.002659 time: 42.235578
INFO:root:[Epoch 133] train=0.999720 val=0.958500 loss=0.002306 time: 42.336942
INFO:root:[Epoch 134] train=0.999800 val=0.959700 loss=0.002274 time: 42.272216
INFO:root:[Epoch 135] train=0.999780 val=0.959200 loss=0.002308 time: 42.286930
INFO:root:[Epoch 136] train=0.999740 val=0.958700 loss=0.002277 time: 42.220672
INFO:root:[Epoch 137] train=0.999619 val=0.958200 loss=0.002582 time: 42.299017
INFO:root:[Epoch 138] train=0.999860 val=0.958100 loss=0.002016 time: 42.235040
INFO:root:[Epoch 139] train=0.999880 val=0.959600 loss=0.002000 time: 42.311057
INFO:root:[Epoch 140] train=0.999740 val=0.958900 loss=0.002188 time: 42.192445
INFO:root:[Epoch 141] train=0.999740 val=0.956700 loss=0.002213 time: 42.285608
INFO:root:[Epoch 142] train=0.999700 val=0.958800 loss=0.002528 time: 42.439785
INFO:root:[Epoch 143] train=0.999960 val=0.959300 loss=0.002020 time: 42.220488
INFO:root:[Epoch 144] train=0.999840 val=0.958000 loss=0.001993 time: 42.282910
INFO:root:[Epoch 145] train=0.999880 val=0.960300 loss=0.001979 time: 42.390648
INFO:root:[Epoch 146] train=0.999900 val=0.959100 loss=0.001855 time: 42.281874
INFO:root:[Epoch 147] train=0.999720 val=0.958000 loss=0.002487 time: 42.361354
INFO:root:[Epoch 148] train=0.999639 val=0.959500 loss=0.002765 time: 42.569374
INFO:root:[Epoch 149] train=0.999399 val=0.956000 loss=0.003425 time: 42.388227
INFO:root:[Epoch 150] train=0.999499 val=0.956400 loss=0.003565 time: 42.387783
INFO:root:[Epoch 151] train=0.999579 val=0.957800 loss=0.002939 time: 42.228279
INFO:root:[Epoch 152] train=0.999659 val=0.953900 loss=0.002708 time: 42.278586
INFO:root:[Epoch 153] train=0.999299 val=0.957500 loss=0.004000 time: 42.446565
INFO:root:[Epoch 154] train=0.999519 val=0.953400 loss=0.003258 time: 42.353672
INFO:root:[Epoch 155] train=0.999219 val=0.954100 loss=0.004127 time: 42.189157
INFO:root:[Epoch 156] train=0.999499 val=0.956400 loss=0.003822 time: 42.253225
INFO:root:[Epoch 157] train=0.999439 val=0.954900 loss=0.003528 time: 42.333342
INFO:root:[Epoch 158] train=0.999199 val=0.955600 loss=0.005148 time: 42.324099
INFO:root:[Epoch 159] train=0.999439 val=0.955200 loss=0.003660 time: 42.214964
INFO:root:[Epoch 160] train=0.999499 val=0.957700 loss=0.003417 time: 42.314951
INFO:root:[Epoch 161] train=0.999800 val=0.958200 loss=0.002330 time: 42.331939
INFO:root:[Epoch 162] train=0.999800 val=0.959500 loss=0.002309 time: 42.270923
INFO:root:[Epoch 163] train=0.999800 val=0.959000 loss=0.002118 time: 42.333505
INFO:root:[Epoch 164] train=0.999900 val=0.959100 loss=0.001911 time: 42.239333
INFO:root:[Epoch 165] train=0.999659 val=0.959100 loss=0.002372 time: 42.172201
INFO:root:[Epoch 166] train=0.999860 val=0.959000 loss=0.002074 time: 42.256329
INFO:root:[Epoch 167] train=0.999980 val=0.958400 loss=0.001862 time: 42.197917
INFO:root:[Epoch 168] train=0.999960 val=0.959300 loss=0.001827 time: 42.370424
INFO:root:[Epoch 169] train=0.999960 val=0.959500 loss=0.001892 time: 42.286333
INFO:root:[Epoch 170] train=0.999940 val=0.958700 loss=0.001818 time: 42.292847
INFO:root:[Epoch 171] train=0.999920 val=0.959600 loss=0.001767 time: 42.295761
INFO:root:[Epoch 172] train=0.999880 val=0.959300 loss=0.001887 time: 42.630956
INFO:root:[Epoch 173] train=0.999940 val=0.959000 loss=0.001734 time: 42.560009
INFO:root:[Epoch 174] train=0.999940 val=0.959500 loss=0.001700 time: 42.248701
INFO:root:[Epoch 175] train=0.999940 val=0.959900 loss=0.001708 time: 42.331849
INFO:root:[Epoch 176] train=0.999920 val=0.959800 loss=0.001749 time: 42.452900
INFO:root:[Epoch 177] train=0.999960 val=0.959800 loss=0.001786 time: 42.347811
INFO:root:[Epoch 178] train=0.999880 val=0.959100 loss=0.001764 time: 42.291487
INFO:root:[Epoch 179] train=0.999960 val=0.959300 loss=0.001709 time: 42.487573
INFO:root:[Epoch 180] train=0.999920 val=0.959800 loss=0.001663 time: 42.272434
INFO:root:[Epoch 181] train=0.999980 val=0.959800 loss=0.001645 time: 42.516465
INFO:root:[Epoch 182] train=0.999960 val=0.959500 loss=0.001719 time: 42.190712
INFO:root:[Epoch 183] train=0.999980 val=0.958600 loss=0.001652 time: 42.274032
INFO:root:[Epoch 184] train=0.999920 val=0.958900 loss=0.001655 time: 42.428305
INFO:root:[Epoch 185] train=0.999960 val=0.959800 loss=0.001652 time: 42.434277
INFO:root:[Epoch 186] train=0.999940 val=0.959700 loss=0.001679 time: 42.423691
INFO:root:[Epoch 187] train=1.000000 val=0.959500 loss=0.001535 time: 42.361517
INFO:root:[Epoch 188] train=0.999960 val=0.959700 loss=0.001589 time: 42.529857
INFO:root:[Epoch 189] train=1.000000 val=0.960100 loss=0.001554 time: 42.469446
INFO:root:[Epoch 190] train=0.999940 val=0.960200 loss=0.001609 time: 42.498894
INFO:root:[Epoch 191] train=0.999940 val=0.959700 loss=0.001677 time: 42.246150
INFO:root:[Epoch 192] train=0.999980 val=0.959300 loss=0.001556 time: 42.563770
INFO:root:[Epoch 193] train=0.999960 val=0.959700 loss=0.001614 time: 42.471868
INFO:root:[Epoch 194] train=0.999960 val=0.959700 loss=0.001588 time: 42.527478
INFO:root:[Epoch 195] train=0.999960 val=0.960200 loss=0.001559 time: 42.285410
INFO:root:[Epoch 196] train=0.999980 val=0.960100 loss=0.001558 time: 42.437583
INFO:root:[Epoch 197] train=1.000000 val=0.960500 loss=0.001547 time: 42.478795
INFO:root:[Epoch 198] train=0.999960 val=0.960700 loss=0.001567 time: 42.504768
INFO:root:[Epoch 199] train=0.999960 val=0.959700 loss=0.001677 time: 42.364387