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_resnet56_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) [18:06:47] 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=0.467728 val=0.525200 loss=1.439675 time: 31.947065 INFO:root:[Epoch 1] train=0.665905 val=0.649700 loss=0.935496 time: 30.635085 INFO:root:[Epoch 2] train=0.738341 val=0.722900 loss=0.750481 time: 31.030238 INFO:root:[Epoch 3] train=0.775721 val=0.768600 loss=0.646242 time: 32.162692 INFO:root:[Epoch 4] train=0.800841 val=0.752200 loss=0.579040 time: 31.387381 INFO:root:[Epoch 5] train=0.816607 val=0.788100 loss=0.528245 time: 30.658829 INFO:root:[Epoch 6] train=0.831711 val=0.809600 loss=0.487326 time: 31.563556 INFO:root:[Epoch 7] train=0.840405 val=0.809600 loss=0.460945 time: 31.305872 INFO:root:[Epoch 8] train=0.850120 val=0.825900 loss=0.434033 time: 31.091245 INFO:root:[Epoch 9] train=0.855990 val=0.810200 loss=0.414678 time: 31.011519 INFO:root:[Epoch 10] train=0.863301 val=0.842900 loss=0.395262 time: 30.924553 INFO:root:[Epoch 11] train=0.869992 val=0.808100 loss=0.379183 time: 31.619977 INFO:root:[Epoch 12] train=0.871494 val=0.851600 loss=0.371174 time: 31.602543 INFO:root:[Epoch 13] train=0.877664 val=0.844600 loss=0.351017 time: 32.059724 INFO:root:[Epoch 14] train=0.881430 val=0.848300 loss=0.345085 time: 31.584109 INFO:root:[Epoch 15] train=0.885096 val=0.850600 loss=0.332060 time: 30.812576 INFO:root:[Epoch 16] train=0.887260 val=0.857100 loss=0.325576 time: 31.221223 INFO:root:[Epoch 17] train=0.889503 val=0.851100 loss=0.314382 time: 31.478571 INFO:root:[Epoch 18] train=0.893850 val=0.848700 loss=0.306226 time: 31.054700 INFO:root:[Epoch 19] train=0.896114 val=0.850400 loss=0.298996 time: 32.473672 INFO:root:[Epoch 20] train=0.900100 val=0.852400 loss=0.292024 time: 30.763343 INFO:root:[Epoch 21] train=0.900321 val=0.863500 loss=0.287530 time: 31.019295 INFO:root:[Epoch 22] train=0.901242 val=0.860900 loss=0.285588 time: 30.696782 INFO:root:[Epoch 23] train=0.903185 val=0.865000 loss=0.276014 time: 31.544998 INFO:root:[Epoch 24] train=0.903265 val=0.879400 loss=0.276601 time: 31.514833 INFO:root:[Epoch 25] train=0.906070 val=0.863000 loss=0.267613 time: 31.931569 INFO:root:[Epoch 26] train=0.909655 val=0.863800 loss=0.259897 time: 31.649990 INFO:root:[Epoch 27] train=0.909275 val=0.858100 loss=0.259620 time: 33.131546 INFO:root:[Epoch 28] train=0.910817 val=0.880000 loss=0.254877 time: 31.161205 INFO:root:[Epoch 29] train=0.913842 val=0.870200 loss=0.246291 time: 30.541413 INFO:root:[Epoch 30] train=0.912580 val=0.867000 loss=0.248649 time: 31.125802 INFO:root:[Epoch 31] train=0.915244 val=0.850700 loss=0.241436 time: 31.425678 INFO:root:[Epoch 32] train=0.916767 val=0.857400 loss=0.236274 time: 31.081656 INFO:root:[Epoch 33] train=0.915705 val=0.880800 loss=0.239126 time: 31.378237 INFO:root:[Epoch 34] train=0.919671 val=0.846000 loss=0.230344 time: 32.052306 INFO:root:[Epoch 35] train=0.918269 val=0.859100 loss=0.233794 time: 31.836306 INFO:root:[Epoch 36] train=0.919511 val=0.855500 loss=0.228524 time: 32.753836 INFO:root:[Epoch 37] train=0.921254 val=0.856800 loss=0.225132 time: 31.444240 INFO:root:[Epoch 38] train=0.921274 val=0.850500 loss=0.227316 time: 30.477969 INFO:root:[Epoch 39] train=0.922616 val=0.864400 loss=0.220100 time: 30.807691 INFO:root:[Epoch 40] train=0.924159 val=0.872500 loss=0.220127 time: 31.227414 INFO:root:[Epoch 41] train=0.923117 val=0.880100 loss=0.215417 time: 32.044902 INFO:root:[Epoch 42] train=0.925080 val=0.885400 loss=0.217759 time: 31.051053 INFO:root:[Epoch 43] train=0.925661 val=0.891600 loss=0.214898 time: 32.330430 INFO:root:[Epoch 44] train=0.928546 val=0.876000 loss=0.207055 time: 30.761047 INFO:root:[Epoch 45] train=0.926062 val=0.868400 loss=0.207357 time: 31.118433 INFO:root:[Epoch 46] train=0.927224 val=0.879500 loss=0.208420 time: 33.257146 INFO:root:[Epoch 47] train=0.926222 val=0.865500 loss=0.210348 time: 31.250050 INFO:root:[Epoch 48] train=0.929127 val=0.877500 loss=0.204552 time: 32.082116 INFO:root:[Epoch 49] train=0.929046 val=0.868300 loss=0.203476 time: 30.965697 INFO:root:[Epoch 50] train=0.929267 val=0.891900 loss=0.202882 time: 31.783733 INFO:root:[Epoch 51] train=0.929347 val=0.859100 loss=0.201421 time: 30.000828 INFO:root:[Epoch 52] train=0.929107 val=0.881700 loss=0.199854 time: 30.797817 INFO:root:[Epoch 53] train=0.931390 val=0.886400 loss=0.193539 time: 31.726819 INFO:root:[Epoch 54] train=0.929768 val=0.892600 loss=0.197360 time: 31.322584 INFO:root:[Epoch 55] train=0.930749 val=0.875700 loss=0.196415 time: 31.054195 INFO:root:[Epoch 56] train=0.933433 val=0.871000 loss=0.190217 time: 31.439690 INFO:root:[Epoch 57] train=0.931991 val=0.884300 loss=0.194039 time: 31.096726 INFO:root:[Epoch 58] train=0.934155 val=0.886600 loss=0.188513 time: 30.491559 INFO:root:[Epoch 59] train=0.934235 val=0.888000 loss=0.190254 time: 31.364253 INFO:root:[Epoch 60] train=0.933674 val=0.882700 loss=0.189417 time: 31.078221 INFO:root:[Epoch 61] train=0.933974 val=0.872000 loss=0.188467 time: 31.763475 INFO:root:[Epoch 62] train=0.934495 val=0.876200 loss=0.186582 time: 31.798704 INFO:root:[Epoch 63] train=0.934175 val=0.857500 loss=0.186759 time: 33.328237 INFO:root:[Epoch 64] train=0.934796 val=0.877400 loss=0.186084 time: 30.784464 INFO:root:[Epoch 65] train=0.936739 val=0.877100 loss=0.180773 time: 31.464115 INFO:root:[Epoch 66] train=0.936298 val=0.880700 loss=0.182346 time: 31.265029 INFO:root:[Epoch 67] train=0.936338 val=0.865700 loss=0.183194 time: 30.965736 INFO:root:[Epoch 68] train=0.935276 val=0.856100 loss=0.183650 time: 30.860743 INFO:root:[Epoch 69] train=0.938101 val=0.880300 loss=0.177803 time: 31.718144 INFO:root:[Epoch 70] train=0.937240 val=0.872500 loss=0.178784 time: 30.942141 INFO:root:[Epoch 71] train=0.936178 val=0.897200 loss=0.180370 time: 32.318561 INFO:root:[Epoch 72] train=0.936979 val=0.886300 loss=0.179168 time: 30.833666 INFO:root:[Epoch 73] train=0.936579 val=0.884000 loss=0.181646 time: 31.079922 INFO:root:[Epoch 74] train=0.939243 val=0.882100 loss=0.174626 time: 31.569324 INFO:root:[Epoch 75] train=0.937700 val=0.871300 loss=0.178140 time: 31.196596 INFO:root:[Epoch 76] train=0.939323 val=0.866800 loss=0.175846 time: 32.030756 INFO:root:[Epoch 77] train=0.937720 val=0.886100 loss=0.176912 time: 31.806217 INFO:root:[Epoch 78] train=0.938902 val=0.889100 loss=0.174321 time: 31.435611 INFO:root:[Epoch 79] train=0.937981 val=0.881100 loss=0.176156 time: 31.142543 INFO:root:[Epoch 80] train=0.938722 val=0.886900 loss=0.172115 time: 31.865659 INFO:root:[Epoch 81] train=0.937540 val=0.867300 loss=0.175443 time: 32.325207 INFO:root:[Epoch 82] train=0.939483 val=0.870100 loss=0.171235 time: 32.362474 INFO:root:[Epoch 83] train=0.939724 val=0.868800 loss=0.172031 time: 30.578072 INFO:root:[Epoch 84] train=0.939683 val=0.875200 loss=0.172248 time: 32.115087 INFO:root:[Epoch 85] train=0.939183 val=0.869300 loss=0.173412 time: 31.579502 INFO:root:[Epoch 86] train=0.939183 val=0.879900 loss=0.171998 time: 31.236259 INFO:root:[Epoch 87] train=0.940845 val=0.872500 loss=0.167698 time: 32.366493 INFO:root:[Epoch 88] train=0.940204 val=0.881400 loss=0.168291 time: 31.349943 INFO:root:[Epoch 89] train=0.941086 val=0.880500 loss=0.168917 time: 31.507586 INFO:root:[Epoch 90] train=0.941867 val=0.884200 loss=0.166523 time: 30.559509 INFO:root:[Epoch 91] train=0.940585 val=0.876800 loss=0.168587 time: 32.429560 INFO:root:[Epoch 92] train=0.943009 val=0.890200 loss=0.163274 time: 31.409625 INFO:root:[Epoch 93] train=0.942388 val=0.862200 loss=0.164428 time: 31.713080 INFO:root:[Epoch 94] train=0.941066 val=0.898200 loss=0.166689 time: 30.773256 INFO:root:[Epoch 95] train=0.942608 val=0.879400 loss=0.164612 time: 31.350914 INFO:root:[Epoch 96] train=0.943229 val=0.890300 loss=0.163641 time: 31.205965 INFO:root:[Epoch 97] train=0.940084 val=0.880800 loss=0.167767 time: 30.818263 INFO:root:[Epoch 98] train=0.943990 val=0.897300 loss=0.161650 time: 32.306657 INFO:root:[Epoch 99] train=0.940665 val=0.883900 loss=0.167311 time: 30.858424 INFO:root:[Epoch 100] train=0.972035 val=0.925100 loss=0.084212 time: 31.622666 INFO:root:[Epoch 101] train=0.981851 val=0.927500 loss=0.056504 time: 31.898541 INFO:root:[Epoch 102] train=0.985757 val=0.929500 loss=0.045336 time: 31.943790 INFO:root:[Epoch 103] train=0.988021 val=0.928400 loss=0.038965 time: 31.353610 INFO:root:[Epoch 104] train=0.988582 val=0.930300 loss=0.037162 time: 31.911643 INFO:root:[Epoch 105] train=0.989944 val=0.930300 loss=0.031997 time: 31.305322 INFO:root:[Epoch 106] train=0.991426 val=0.931100 loss=0.028387 time: 30.947674 INFO:root:[Epoch 107] train=0.992007 val=0.931100 loss=0.026956 time: 30.918825 INFO:root:[Epoch 108] train=0.992428 val=0.932100 loss=0.024614 time: 31.719487 INFO:root:[Epoch 109] train=0.992969 val=0.932600 loss=0.023467 time: 30.216904 INFO:root:[Epoch 110] train=0.993229 val=0.932400 loss=0.021864 time: 31.303299 INFO:root:[Epoch 111] train=0.994171 val=0.930000 loss=0.020410 time: 31.174907 INFO:root:[Epoch 112] train=0.994992 val=0.933300 loss=0.017790 time: 31.351473 INFO:root:[Epoch 113] train=0.994531 val=0.932600 loss=0.018054 time: 30.442219 INFO:root:[Epoch 114] train=0.994451 val=0.931600 loss=0.018307 time: 32.086569 INFO:root:[Epoch 115] train=0.995693 val=0.932200 loss=0.015849 time: 33.026262 INFO:root:[Epoch 116] train=0.994872 val=0.932200 loss=0.016374 time: 31.968570 INFO:root:[Epoch 117] train=0.996154 val=0.933200 loss=0.014580 time: 31.393448 INFO:root:[Epoch 118] train=0.995833 val=0.932700 loss=0.014798 time: 32.401295 INFO:root:[Epoch 119] train=0.996114 val=0.930600 loss=0.013265 time: 31.551088 INFO:root:[Epoch 120] train=0.996494 val=0.931800 loss=0.012904 time: 31.155517 INFO:root:[Epoch 121] train=0.996174 val=0.931500 loss=0.012687 time: 31.645764 INFO:root:[Epoch 122] train=0.996214 val=0.931200 loss=0.013069 time: 32.148686 INFO:root:[Epoch 123] train=0.996675 val=0.931600 loss=0.011504 time: 30.604300 INFO:root:[Epoch 124] train=0.996595 val=0.934100 loss=0.012133 time: 31.224355 INFO:root:[Epoch 125] train=0.996895 val=0.932300 loss=0.011771 time: 31.304577 INFO:root:[Epoch 126] train=0.997236 val=0.933600 loss=0.010070 time: 30.574855 INFO:root:[Epoch 127] train=0.997196 val=0.931300 loss=0.010089 time: 34.473024 INFO:root:[Epoch 128] train=0.997496 val=0.934700 loss=0.010250 time: 31.149298 INFO:root:[Epoch 129] train=0.997075 val=0.932800 loss=0.010080 time: 32.333220 INFO:root:[Epoch 130] train=0.997256 val=0.932700 loss=0.009559 time: 31.727610 INFO:root:[Epoch 131] train=0.997336 val=0.933500 loss=0.009622 time: 30.575943 INFO:root:[Epoch 132] train=0.997837 val=0.933700 loss=0.008077 time: 31.272966 INFO:root:[Epoch 133] train=0.997917 val=0.933800 loss=0.007892 time: 31.728893 INFO:root:[Epoch 134] train=0.997636 val=0.933800 loss=0.008697 time: 31.272703 INFO:root:[Epoch 135] train=0.997696 val=0.931800 loss=0.008657 time: 31.404738 INFO:root:[Epoch 136] train=0.997356 val=0.934100 loss=0.009091 time: 30.356279 INFO:root:[Epoch 137] train=0.997897 val=0.933800 loss=0.008036 time: 30.900738 INFO:root:[Epoch 138] train=0.997556 val=0.932700 loss=0.008633 time: 30.726372 INFO:root:[Epoch 139] train=0.998057 val=0.932300 loss=0.007971 time: 30.906380 INFO:root:[Epoch 140] train=0.997636 val=0.934300 loss=0.008275 time: 31.657294 INFO:root:[Epoch 141] train=0.998157 val=0.933300 loss=0.007616 time: 31.844876 INFO:root:[Epoch 142] train=0.997676 val=0.932000 loss=0.008090 time: 31.608844 INFO:root:[Epoch 143] train=0.997756 val=0.933900 loss=0.007906 time: 31.377925 INFO:root:[Epoch 144] train=0.997877 val=0.934200 loss=0.007446 time: 31.901409 INFO:root:[Epoch 145] train=0.998117 val=0.930900 loss=0.007127 time: 31.580801 INFO:root:[Epoch 146] train=0.998397 val=0.931300 loss=0.006503 time: 31.133694 INFO:root:[Epoch 147] train=0.998257 val=0.932200 loss=0.006585 time: 31.152757 INFO:root:[Epoch 148] train=0.998598 val=0.933100 loss=0.006121 time: 30.042501 INFO:root:[Epoch 149] train=0.997877 val=0.932600 loss=0.007363 time: 31.065120 INFO:root:[Epoch 150] train=0.998397 val=0.934200 loss=0.006182 time: 32.205377 INFO:root:[Epoch 151] train=0.998738 val=0.934100 loss=0.005649 time: 30.728213 INFO:root:[Epoch 152] train=0.998938 val=0.934300 loss=0.004865 time: 31.909617 INFO:root:[Epoch 153] train=0.999179 val=0.934300 loss=0.004781 time: 30.872140 INFO:root:[Epoch 154] train=0.998738 val=0.936100 loss=0.005064 time: 31.639743 INFO:root:[Epoch 155] train=0.998998 val=0.935900 loss=0.004364 time: 30.607561 INFO:root:[Epoch 156] train=0.998798 val=0.934800 loss=0.004984 time: 30.178677 INFO:root:[Epoch 157] train=0.999119 val=0.936700 loss=0.004384 time: 31.323469 INFO:root:[Epoch 158] train=0.999038 val=0.935800 loss=0.004479 time: 31.271315 INFO:root:[Epoch 159] train=0.998938 val=0.936300 loss=0.004515 time: 30.812507 INFO:root:[Epoch 160] train=0.999038 val=0.934600 loss=0.004354 time: 30.884004 INFO:root:[Epoch 161] train=0.999159 val=0.936400 loss=0.004215 time: 30.715355 INFO:root:[Epoch 162] train=0.998898 val=0.936000 loss=0.004444 time: 31.824414 INFO:root:[Epoch 163] train=0.998918 val=0.935000 loss=0.004626 time: 31.414063 INFO:root:[Epoch 164] train=0.999299 val=0.935000 loss=0.004092 time: 30.262378 INFO:root:[Epoch 165] train=0.999179 val=0.936300 loss=0.004167 time: 31.849886 INFO:root:[Epoch 166] train=0.999279 val=0.936100 loss=0.003612 time: 31.603546 INFO:root:[Epoch 167] train=0.999459 val=0.936300 loss=0.003732 time: 31.465542 INFO:root:[Epoch 168] train=0.999379 val=0.936300 loss=0.003770 time: 31.188303 INFO:root:[Epoch 169] train=0.999239 val=0.936100 loss=0.003794 time: 31.089124 INFO:root:[Epoch 170] train=0.999459 val=0.935700 loss=0.003396 time: 31.388350 INFO:root:[Epoch 171] train=0.999359 val=0.935600 loss=0.003691 time: 32.827049 INFO:root:[Epoch 172] train=0.999239 val=0.935200 loss=0.003709 time: 31.598875 INFO:root:[Epoch 173] train=0.999319 val=0.934700 loss=0.003427 time: 32.131952 INFO:root:[Epoch 174] train=0.999299 val=0.935100 loss=0.003561 time: 30.900928 INFO:root:[Epoch 175] train=0.999099 val=0.935000 loss=0.003818 time: 31.525527 INFO:root:[Epoch 176] train=0.999339 val=0.936000 loss=0.003524 time: 31.142746 INFO:root:[Epoch 177] train=0.999339 val=0.935700 loss=0.003472 time: 31.873135 INFO:root:[Epoch 178] train=0.999319 val=0.934900 loss=0.003588 time: 32.084193 INFO:root:[Epoch 179] train=0.999359 val=0.936600 loss=0.003412 time: 31.296588 INFO:root:[Epoch 180] train=0.999319 val=0.936700 loss=0.003444 time: 32.177694 INFO:root:[Epoch 181] train=0.999379 val=0.936300 loss=0.003593 time: 31.272216 INFO:root:[Epoch 182] train=0.999259 val=0.936000 loss=0.003537 time: 31.046682 INFO:root:[Epoch 183] train=0.999379 val=0.935700 loss=0.003565 time: 31.544269 INFO:root:[Epoch 184] train=0.999399 val=0.935800 loss=0.003412 time: 31.109906 INFO:root:[Epoch 185] train=0.999439 val=0.936000 loss=0.003489 time: 31.077428 INFO:root:[Epoch 186] train=0.999359 val=0.936200 loss=0.003427 time: 31.489723 INFO:root:[Epoch 187] train=0.999519 val=0.936000 loss=0.003194 time: 32.042740 INFO:root:[Epoch 188] train=0.999619 val=0.936100 loss=0.002863 time: 32.045007 INFO:root:[Epoch 189] train=0.999419 val=0.936800 loss=0.003137 time: 32.094956 INFO:root:[Epoch 190] train=0.999199 val=0.935600 loss=0.003727 time: 32.089412 INFO:root:[Epoch 191] train=0.999319 val=0.936100 loss=0.003496 time: 32.292182 INFO:root:[Epoch 192] train=0.999379 val=0.935700 loss=0.003347 time: 32.235887 INFO:root:[Epoch 193] train=0.999339 val=0.935800 loss=0.003387 time: 32.369994 INFO:root:[Epoch 194] train=0.999459 val=0.935200 loss=0.003037 time: 31.461487 INFO:root:[Epoch 195] train=0.999619 val=0.935500 loss=0.002782 time: 31.053144 INFO:root:[Epoch 196] train=0.999459 val=0.936300 loss=0.003363 time: 32.471730 INFO:root:[Epoch 197] train=0.999639 val=0.935500 loss=0.002895 time: 30.571156 INFO:root:[Epoch 198] train=0.999619 val=0.935200 loss=0.002920 time: 30.528603 INFO:root:[Epoch 199] train=0.999339 val=0.936800 loss=0.003261 time: 31.708269