INFO:root:Namespace(batch_size=128, drop_rate=0.0, lr=0.1, lr_decay=0.1, lr_decay_epoch='100,150', lr_decay_period=0, mode='hybrid', model='cifar_resnet20_v2', momentum=0.9, num_epochs=200, num_gpus=1, num_workers=2, resume_from=None, save_dir='params', save_period=10, save_plot_dir='.', wd=0.0001)
[17:11:36] 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=0.473778 val=0.531800 loss=1.421917 time: 16.654408
INFO:root:[Epoch 1] train=0.662981 val=0.636300 loss=0.939930 time: 15.970724
INFO:root:[Epoch 2] train=0.731130 val=0.660200 loss=0.766965 time: 16.039982
INFO:root:[Epoch 3] train=0.765585 val=0.747200 loss=0.672715 time: 15.848485
INFO:root:[Epoch 4] train=0.786979 val=0.760600 loss=0.612521 time: 16.015854
INFO:root:[Epoch 5] train=0.802544 val=0.781600 loss=0.567590 time: 15.905040
INFO:root:[Epoch 6] train=0.814022 val=0.774300 loss=0.533449 time: 15.987603
INFO:root:[Epoch 7] train=0.823257 val=0.785600 loss=0.508393 time: 16.158545
INFO:root:[Epoch 8] train=0.833173 val=0.785600 loss=0.486528 time: 15.905881
INFO:root:[Epoch 9] train=0.840966 val=0.794500 loss=0.463939 time: 16.217334
INFO:root:[Epoch 10] train=0.843029 val=0.801100 loss=0.448957 time: 15.900644
INFO:root:[Epoch 11] train=0.851462 val=0.812200 loss=0.432708 time: 16.027359
INFO:root:[Epoch 12] train=0.853606 val=0.816600 loss=0.422710 time: 16.010183
INFO:root:[Epoch 13] train=0.858874 val=0.829100 loss=0.411532 time: 15.973541
INFO:root:[Epoch 14] train=0.859215 val=0.830600 loss=0.403417 time: 16.184268
INFO:root:[Epoch 15] train=0.865865 val=0.827300 loss=0.387795 time: 16.217068
INFO:root:[Epoch 16] train=0.869852 val=0.843000 loss=0.380951 time: 15.697000
INFO:root:[Epoch 17] train=0.871534 val=0.806600 loss=0.372860 time: 15.943041
INFO:root:[Epoch 18] train=0.872696 val=0.822200 loss=0.366303 time: 16.069994
INFO:root:[Epoch 19] train=0.875060 val=0.829200 loss=0.359868 time: 15.812849
INFO:root:[Epoch 20] train=0.878285 val=0.815200 loss=0.351070 time: 15.891168
INFO:root:[Epoch 21] train=0.880329 val=0.828200 loss=0.344715 time: 15.913901
INFO:root:[Epoch 22] train=0.881110 val=0.831900 loss=0.339557 time: 15.792625
INFO:root:[Epoch 23] train=0.883113 val=0.842900 loss=0.335409 time: 16.102293
INFO:root:[Epoch 24] train=0.885958 val=0.806600 loss=0.330549 time: 16.089784
INFO:root:[Epoch 25] train=0.887019 val=0.840800 loss=0.325508 time: 15.982189
INFO:root:[Epoch 26] train=0.887179 val=0.839100 loss=0.323022 time: 15.980385
INFO:root:[Epoch 27] train=0.889643 val=0.830800 loss=0.318727 time: 16.183260
INFO:root:[Epoch 28] train=0.890204 val=0.827500 loss=0.315027 time: 15.927914
INFO:root:[Epoch 29] train=0.891787 val=0.826200 loss=0.313510 time: 16.439603
INFO:root:[Epoch 30] train=0.891106 val=0.844000 loss=0.311379 time: 16.161455
INFO:root:[Epoch 31] train=0.893930 val=0.852600 loss=0.303473 time: 15.861997
INFO:root:[Epoch 32] train=0.894351 val=0.849600 loss=0.302952 time: 16.162878
INFO:root:[Epoch 33] train=0.895933 val=0.836800 loss=0.300107 time: 16.074366
INFO:root:[Epoch 34] train=0.897115 val=0.844600 loss=0.292832 time: 15.770090
INFO:root:[Epoch 35] train=0.900741 val=0.847800 loss=0.287452 time: 15.912691
INFO:root:[Epoch 36] train=0.899499 val=0.840900 loss=0.290508 time: 16.146277
INFO:root:[Epoch 37] train=0.900962 val=0.837200 loss=0.283798 time: 15.854546
INFO:root:[Epoch 38] train=0.899479 val=0.859600 loss=0.287392 time: 15.927529
INFO:root:[Epoch 39] train=0.899940 val=0.860000 loss=0.287186 time: 15.914080
INFO:root:[Epoch 40] train=0.901803 val=0.856000 loss=0.280833 time: 16.013161
INFO:root:[Epoch 41] train=0.902684 val=0.850700 loss=0.282991 time: 15.860476
INFO:root:[Epoch 42] train=0.902905 val=0.853500 loss=0.278550 time: 15.687512
INFO:root:[Epoch 43] train=0.904547 val=0.852900 loss=0.272673 time: 16.008318
INFO:root:[Epoch 44] train=0.905088 val=0.840300 loss=0.272384 time: 15.968120
INFO:root:[Epoch 45] train=0.904507 val=0.839800 loss=0.272916 time: 16.056292
INFO:root:[Epoch 46] train=0.903666 val=0.860700 loss=0.274570 time: 15.785026
INFO:root:[Epoch 47] train=0.907051 val=0.867000 loss=0.265050 time: 15.912398
INFO:root:[Epoch 48] train=0.907372 val=0.862600 loss=0.267280 time: 15.871592
INFO:root:[Epoch 49] train=0.907232 val=0.874600 loss=0.264495 time: 15.568886
INFO:root:[Epoch 50] train=0.907853 val=0.850300 loss=0.265047 time: 16.092443
INFO:root:[Epoch 51] train=0.908013 val=0.845900 loss=0.262836 time: 16.378566
INFO:root:[Epoch 52] train=0.905569 val=0.833600 loss=0.267682 time: 16.311263
INFO:root:[Epoch 53] train=0.909115 val=0.860200 loss=0.262439 time: 16.152548
INFO:root:[Epoch 54] train=0.909115 val=0.859400 loss=0.262034 time: 15.619929
INFO:root:[Epoch 55] train=0.909415 val=0.856700 loss=0.257824 time: 16.011269
INFO:root:[Epoch 56] train=0.908614 val=0.860100 loss=0.260817 time: 15.901550
INFO:root:[Epoch 57] train=0.909595 val=0.852600 loss=0.256846 time: 15.925056
INFO:root:[Epoch 58] train=0.912200 val=0.862300 loss=0.253358 time: 16.086088
INFO:root:[Epoch 59] train=0.909535 val=0.848200 loss=0.256873 time: 15.988465
INFO:root:[Epoch 60] train=0.912159 val=0.851800 loss=0.249408 time: 16.443826
INFO:root:[Epoch 61] train=0.912200 val=0.799500 loss=0.250228 time: 16.157079
INFO:root:[Epoch 62] train=0.910757 val=0.859700 loss=0.253065 time: 15.687431
INFO:root:[Epoch 63] train=0.913642 val=0.831000 loss=0.248203 time: 16.003610
INFO:root:[Epoch 64] train=0.913341 val=0.862800 loss=0.247360 time: 16.319250
INFO:root:[Epoch 65] train=0.911318 val=0.875100 loss=0.253427 time: 15.999571
INFO:root:[Epoch 66] train=0.915605 val=0.860700 loss=0.242025 time: 16.099908
INFO:root:[Epoch 67] train=0.913782 val=0.859900 loss=0.247369 time: 15.975207
INFO:root:[Epoch 68] train=0.913181 val=0.860300 loss=0.248045 time: 15.919691
INFO:root:[Epoch 69] train=0.912941 val=0.858600 loss=0.246639 time: 16.263304
INFO:root:[Epoch 70] train=0.916446 val=0.859000 loss=0.241425 time: 15.783331
INFO:root:[Epoch 71] train=0.916667 val=0.847000 loss=0.242097 time: 15.813170
INFO:root:[Epoch 72] train=0.916987 val=0.874300 loss=0.238561 time: 16.389371
INFO:root:[Epoch 73] train=0.915685 val=0.842200 loss=0.242403 time: 15.711219
INFO:root:[Epoch 74] train=0.917328 val=0.864900 loss=0.238257 time: 16.010814
INFO:root:[Epoch 75] train=0.915885 val=0.863300 loss=0.241545 time: 16.422986
INFO:root:[Epoch 76] train=0.917007 val=0.845200 loss=0.238506 time: 16.213622
INFO:root:[Epoch 77] train=0.917007 val=0.869900 loss=0.237861 time: 15.938805
INFO:root:[Epoch 78] train=0.916546 val=0.880000 loss=0.238254 time: 15.937255
INFO:root:[Epoch 79] train=0.917167 val=0.870700 loss=0.236266 time: 15.743145
INFO:root:[Epoch 80] train=0.917127 val=0.881000 loss=0.236218 time: 16.029499
INFO:root:[Epoch 81] train=0.918349 val=0.834500 loss=0.234039 time: 15.552893
INFO:root:[Epoch 82] train=0.915264 val=0.850800 loss=0.242030 time: 15.970758
INFO:root:[Epoch 83] train=0.918490 val=0.853500 loss=0.232192 time: 15.698742
INFO:root:[Epoch 84] train=0.918149 val=0.861600 loss=0.231921 time: 15.870953
INFO:root:[Epoch 85] train=0.918089 val=0.872600 loss=0.235236 time: 15.722489
INFO:root:[Epoch 86] train=0.917127 val=0.851700 loss=0.235453 time: 16.036183
INFO:root:[Epoch 87] train=0.918830 val=0.866700 loss=0.233496 time: 15.943092
INFO:root:[Epoch 88] train=0.919371 val=0.863300 loss=0.226290 time: 16.012324
INFO:root:[Epoch 89] train=0.918450 val=0.864300 loss=0.231929 time: 16.032231
INFO:root:[Epoch 90] train=0.917268 val=0.844800 loss=0.234731 time: 15.900228
INFO:root:[Epoch 91] train=0.919531 val=0.870200 loss=0.229942 time: 16.201020
INFO:root:[Epoch 92] train=0.919551 val=0.859000 loss=0.230054 time: 16.065759
INFO:root:[Epoch 93] train=0.920673 val=0.869800 loss=0.225873 time: 16.058874
INFO:root:[Epoch 94] train=0.920693 val=0.868800 loss=0.228585 time: 15.984202
INFO:root:[Epoch 95] train=0.919010 val=0.858700 loss=0.226100 time: 15.720023
INFO:root:[Epoch 96] train=0.919952 val=0.864500 loss=0.226924 time: 15.869648
INFO:root:[Epoch 97] train=0.918890 val=0.859800 loss=0.231327 time: 16.202316
INFO:root:[Epoch 98] train=0.921875 val=0.870200 loss=0.227176 time: 16.104815
INFO:root:[Epoch 99] train=0.921074 val=0.876600 loss=0.227925 time: 16.539034
INFO:root:[Epoch 100] train=0.952103 val=0.911600 loss=0.137906 time: 15.967225
INFO:root:[Epoch 101] train=0.963662 val=0.915300 loss=0.110873 time: 15.989537
INFO:root:[Epoch 102] train=0.966967 val=0.916200 loss=0.098315 time: 15.927925
INFO:root:[Epoch 103] train=0.969050 val=0.918400 loss=0.091461 time: 15.548570
INFO:root:[Epoch 104] train=0.970893 val=0.917800 loss=0.086838 time: 16.009611
INFO:root:[Epoch 105] train=0.972256 val=0.916400 loss=0.082620 time: 15.806460
INFO:root:[Epoch 106] train=0.973277 val=0.918100 loss=0.079679 time: 15.937810
INFO:root:[Epoch 107] train=0.974720 val=0.917200 loss=0.075910 time: 15.907912
INFO:root:[Epoch 108] train=0.975781 val=0.917900 loss=0.072079 time: 15.922338
INFO:root:[Epoch 109] train=0.975901 val=0.917100 loss=0.069891 time: 16.183245
INFO:root:[Epoch 110] train=0.978305 val=0.917900 loss=0.065461 time: 16.452564
INFO:root:[Epoch 111] train=0.976883 val=0.914700 loss=0.066974 time: 16.352750
INFO:root:[Epoch 112] train=0.979046 val=0.916700 loss=0.062247 time: 16.158751
INFO:root:[Epoch 113] train=0.979688 val=0.917700 loss=0.060793 time: 16.048735
INFO:root:[Epoch 114] train=0.980108 val=0.916900 loss=0.059619 time: 15.889942
INFO:root:[Epoch 115] train=0.980769 val=0.917600 loss=0.057712 time: 16.020323
INFO:root:[Epoch 116] train=0.981130 val=0.916100 loss=0.056297 time: 15.826916
INFO:root:[Epoch 117] train=0.982612 val=0.915200 loss=0.054060 time: 15.860108
INFO:root:[Epoch 118] train=0.982392 val=0.918800 loss=0.052621 time: 15.972913
INFO:root:[Epoch 119] train=0.983454 val=0.916900 loss=0.050272 time: 15.870861
INFO:root:[Epoch 120] train=0.982572 val=0.918400 loss=0.052021 time: 15.981322
INFO:root:[Epoch 121] train=0.983193 val=0.917700 loss=0.050076 time: 15.932643
INFO:root:[Epoch 122] train=0.982873 val=0.916900 loss=0.049416 time: 16.187437
INFO:root:[Epoch 123] train=0.984235 val=0.915900 loss=0.047914 time: 15.924309
INFO:root:[Epoch 124] train=0.985417 val=0.915500 loss=0.044629 time: 15.889800
INFO:root:[Epoch 125] train=0.984135 val=0.917200 loss=0.046232 time: 15.861483
INFO:root:[Epoch 126] train=0.985256 val=0.916900 loss=0.044056 time: 15.961840
INFO:root:[Epoch 127] train=0.985597 val=0.916400 loss=0.043740 time: 15.967920
INFO:root:[Epoch 128] train=0.986458 val=0.915100 loss=0.041962 time: 16.113220
INFO:root:[Epoch 129] train=0.985136 val=0.913600 loss=0.044535 time: 16.244609
INFO:root:[Epoch 130] train=0.986258 val=0.916600 loss=0.041582 time: 15.860055
INFO:root:[Epoch 131] train=0.985437 val=0.916000 loss=0.043263 time: 16.708170
INFO:root:[Epoch 132] train=0.986238 val=0.916900 loss=0.041396 time: 15.960449
INFO:root:[Epoch 133] train=0.987420 val=0.915000 loss=0.038510 time: 16.300295
INFO:root:[Epoch 134] train=0.986739 val=0.916400 loss=0.040290 time: 16.184877
INFO:root:[Epoch 135] train=0.987580 val=0.913700 loss=0.037330 time: 15.862560
INFO:root:[Epoch 136] train=0.987500 val=0.915500 loss=0.037601 time: 15.994493
INFO:root:[Epoch 137] train=0.987821 val=0.914800 loss=0.037902 time: 16.285368
INFO:root:[Epoch 138] train=0.987440 val=0.914000 loss=0.038328 time: 16.001405
INFO:root:[Epoch 139] train=0.987440 val=0.915900 loss=0.037580 time: 15.885465
INFO:root:[Epoch 140] train=0.987740 val=0.915600 loss=0.038125 time: 15.907133
INFO:root:[Epoch 141] train=0.988462 val=0.915100 loss=0.035570 time: 16.313690
INFO:root:[Epoch 142] train=0.988922 val=0.913600 loss=0.034044 time: 16.189604
INFO:root:[Epoch 143] train=0.988181 val=0.914900 loss=0.035568 time: 16.335108
INFO:root:[Epoch 144] train=0.988542 val=0.914600 loss=0.035180 time: 16.088714
INFO:root:[Epoch 145] train=0.989724 val=0.914200 loss=0.032734 time: 16.195358
INFO:root:[Epoch 146] train=0.989002 val=0.915800 loss=0.034197 time: 16.568501
INFO:root:[Epoch 147] train=0.988802 val=0.914000 loss=0.034255 time: 16.133934
INFO:root:[Epoch 148] train=0.989243 val=0.916600 loss=0.033354 time: 16.061861
INFO:root:[Epoch 149] train=0.988822 val=0.915200 loss=0.034188 time: 15.903247
INFO:root:[Epoch 150] train=0.991286 val=0.918900 loss=0.028237 time: 15.728926
INFO:root:[Epoch 151] train=0.992708 val=0.919500 loss=0.023837 time: 15.678112
INFO:root:[Epoch 152] train=0.993069 val=0.919800 loss=0.023435 time: 15.749915
INFO:root:[Epoch 153] train=0.993369 val=0.920200 loss=0.022944 time: 15.732052
INFO:root:[Epoch 154] train=0.993810 val=0.919600 loss=0.021752 time: 16.182789
INFO:root:[Epoch 155] train=0.993970 val=0.919400 loss=0.021135 time: 16.088237
INFO:root:[Epoch 156] train=0.993990 val=0.919200 loss=0.020748 time: 16.077363
INFO:root:[Epoch 157] train=0.994491 val=0.918700 loss=0.021554 time: 15.861220
INFO:root:[Epoch 158] train=0.994251 val=0.920200 loss=0.020727 time: 16.176383
INFO:root:[Epoch 159] train=0.993369 val=0.919700 loss=0.021763 time: 15.864910
INFO:root:[Epoch 160] train=0.994131 val=0.919200 loss=0.021029 time: 16.222810
INFO:root:[Epoch 161] train=0.994211 val=0.919500 loss=0.020060 time: 16.221357
INFO:root:[Epoch 162] train=0.995112 val=0.919700 loss=0.018511 time: 15.919566
INFO:root:[Epoch 163] train=0.994331 val=0.919300 loss=0.020469 time: 16.035535
INFO:root:[Epoch 164] train=0.994852 val=0.919100 loss=0.019250 time: 16.516820
INFO:root:[Epoch 165] train=0.994471 val=0.919000 loss=0.019289 time: 16.024760
INFO:root:[Epoch 166] train=0.994732 val=0.919000 loss=0.019335 time: 16.011142
INFO:root:[Epoch 167] train=0.994792 val=0.917900 loss=0.018873 time: 16.317844
INFO:root:[Epoch 168] train=0.994511 val=0.919600 loss=0.020316 time: 15.862381
INFO:root:[Epoch 169] train=0.995132 val=0.919500 loss=0.018424 time: 15.999997
INFO:root:[Epoch 170] train=0.995333 val=0.920600 loss=0.017942 time: 15.984909
INFO:root:[Epoch 171] train=0.995012 val=0.918800 loss=0.019537 time: 16.102351
INFO:root:[Epoch 172] train=0.995132 val=0.919400 loss=0.018411 time: 16.040717
INFO:root:[Epoch 173] train=0.995212 val=0.919100 loss=0.018004 time: 15.963812
INFO:root:[Epoch 174] train=0.995733 val=0.919200 loss=0.017417 time: 16.420958
INFO:root:[Epoch 175] train=0.995092 val=0.918400 loss=0.018736 time: 16.329392
INFO:root:[Epoch 176] train=0.995453 val=0.918600 loss=0.018213 time: 16.549562
INFO:root:[Epoch 177] train=0.995252 val=0.917900 loss=0.018165 time: 16.324187
INFO:root:[Epoch 178] train=0.995272 val=0.919700 loss=0.017816 time: 16.078192
INFO:root:[Epoch 179] train=0.995172 val=0.919800 loss=0.018228 time: 16.217224
INFO:root:[Epoch 180] train=0.994832 val=0.920300 loss=0.018255 time: 16.036796
INFO:root:[Epoch 181] train=0.995533 val=0.918300 loss=0.017272 time: 16.026442
INFO:root:[Epoch 182] train=0.995713 val=0.918700 loss=0.017200 time: 16.277630
INFO:root:[Epoch 183] train=0.995873 val=0.919700 loss=0.016814 time: 16.313480
INFO:root:[Epoch 184] train=0.995333 val=0.918600 loss=0.018480 time: 16.377030
INFO:root:[Epoch 185] train=0.995473 val=0.917800 loss=0.017227 time: 16.256062
INFO:root:[Epoch 186] train=0.995393 val=0.919000 loss=0.017147 time: 16.052741
INFO:root:[Epoch 187] train=0.995633 val=0.918300 loss=0.017107 time: 16.100180
INFO:root:[Epoch 188] train=0.995553 val=0.918500 loss=0.017438 time: 15.658685
INFO:root:[Epoch 189] train=0.995333 val=0.920200 loss=0.017224 time: 15.916636
INFO:root:[Epoch 190] train=0.995633 val=0.920400 loss=0.016989 time: 15.869403
INFO:root:[Epoch 191] train=0.996074 val=0.920500 loss=0.016783 time: 16.155280
INFO:root:[Epoch 192] train=0.995393 val=0.917800 loss=0.017149 time: 16.271315
INFO:root:[Epoch 193] train=0.995913 val=0.917800 loss=0.016804 time: 15.724539
INFO:root:[Epoch 194] train=0.995553 val=0.917800 loss=0.016823 time: 16.430179
INFO:root:[Epoch 195] train=0.995713 val=0.918900 loss=0.016928 time: 16.196840
INFO:root:[Epoch 196] train=0.995613 val=0.918400 loss=0.016705 time: 16.159641
INFO:root:[Epoch 197] train=0.995513 val=0.917800 loss=0.016586 time: 16.272789
INFO:root:[Epoch 198] train=0.995793 val=0.918800 loss=0.017248 time: 16.816678
INFO:root:[Epoch 199] train=0.995653 val=0.918900 loss=0.016605 time: 15.778410