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_resnet110_v1', 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)
[07:54:30] 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=0.242238 val=0.220900 loss=2.397356 time: 63.080625
INFO:root:[Epoch 1] train=0.233172 val=0.327900 loss=2.083092 time: 60.887869
INFO:root:[Epoch 2] train=0.228317 val=0.391300 loss=2.007521 time: 61.251401
INFO:root:[Epoch 3] train=0.225998 val=0.415300 loss=1.920607 time: 60.934551
INFO:root:[Epoch 4] train=0.217320 val=0.474900 loss=1.873631 time: 58.989378
INFO:root:[Epoch 5] train=0.212781 val=0.497700 loss=1.803222 time: 59.018027
INFO:root:[Epoch 6] train=0.205770 val=0.542900 loss=1.745561 time: 60.376061
INFO:root:[Epoch 7] train=0.198665 val=0.626400 loss=1.658939 time: 59.034685
INFO:root:[Epoch 8] train=0.190825 val=0.602900 loss=1.573668 time: 61.290957
INFO:root:[Epoch 9] train=0.184618 val=0.686700 loss=1.515964 time: 61.232361
INFO:root:[Epoch 10] train=0.179505 val=0.715900 loss=1.441919 time: 61.400654
INFO:root:[Epoch 11] train=0.174099 val=0.711900 loss=1.437851 time: 61.616406
INFO:root:[Epoch 12] train=0.170355 val=0.728800 loss=1.392158 time: 62.196309
INFO:root:[Epoch 13] train=0.167632 val=0.740800 loss=1.388337 time: 59.896543
INFO:root:[Epoch 14] train=0.165722 val=0.794600 loss=1.335102 time: 58.524192
INFO:root:[Epoch 15] train=0.162744 val=0.775800 loss=1.344843 time: 58.702788
INFO:root:[Epoch 16] train=0.160645 val=0.713300 loss=1.317592 time: 58.880275
INFO:root:[Epoch 17] train=0.158254 val=0.755200 loss=1.306653 time: 58.850903
INFO:root:[Epoch 18] train=0.157600 val=0.789400 loss=1.254112 time: 58.601548
INFO:root:[Epoch 19] train=0.155943 val=0.809700 loss=1.278181 time: 58.675076
INFO:root:[Epoch 20] train=0.155334 val=0.820800 loss=1.310428 time: 59.280778
INFO:root:[Epoch 21] train=0.153215 val=0.831000 loss=1.262612 time: 58.764067
INFO:root:[Epoch 22] train=0.151602 val=0.815100 loss=1.228986 time: 59.587080
INFO:root:[Epoch 23] train=0.149553 val=0.804100 loss=1.253048 time: 59.487419
INFO:root:[Epoch 24] train=0.149383 val=0.798900 loss=1.228097 time: 59.228999
INFO:root:[Epoch 25] train=0.149150 val=0.811800 loss=1.235092 time: 63.391444
INFO:root:[Epoch 26] train=0.146518 val=0.822300 loss=1.213245 time: 62.913781
INFO:root:[Epoch 27] train=0.145553 val=0.766200 loss=1.181609 time: 61.322266
INFO:root:[Epoch 28] train=0.143119 val=0.787900 loss=1.162240 time: 58.843295
INFO:root:[Epoch 29] train=0.145046 val=0.836500 loss=1.231352 time: 62.784313
INFO:root:[Epoch 30] train=0.143093 val=0.803500 loss=1.194633 time: 61.905679
INFO:root:[Epoch 31] train=0.143671 val=0.836100 loss=1.212082 time: 62.021349
INFO:root:[Epoch 32] train=0.144107 val=0.833700 loss=1.202641 time: 62.529104
INFO:root:[Epoch 33] train=0.141685 val=0.844400 loss=1.170645 time: 60.654518
INFO:root:[Epoch 34] train=0.141106 val=0.840400 loss=1.174972 time: 60.472315
INFO:root:[Epoch 35] train=0.141675 val=0.843200 loss=1.184875 time: 61.254956
INFO:root:[Epoch 36] train=0.140414 val=0.841300 loss=1.180513 time: 59.696808
INFO:root:[Epoch 37] train=0.139060 val=0.854400 loss=1.158247 time: 59.505459
INFO:root:[Epoch 38] train=0.138837 val=0.846200 loss=1.147944 time: 58.738553
INFO:root:[Epoch 39] train=0.138518 val=0.844000 loss=1.142267 time: 58.893544
INFO:root:[Epoch 40] train=0.137453 val=0.834100 loss=1.158834 time: 58.969470
INFO:root:[Epoch 41] train=0.137410 val=0.867900 loss=1.165239 time: 60.233509
INFO:root:[Epoch 42] train=0.137250 val=0.853400 loss=1.142253 time: 59.229732
INFO:root:[Epoch 43] train=0.137565 val=0.855800 loss=1.162683 time: 59.204245
INFO:root:[Epoch 44] train=0.134439 val=0.840300 loss=1.110868 time: 59.022679
INFO:root:[Epoch 45] train=0.135614 val=0.855800 loss=1.141849 time: 58.643078
INFO:root:[Epoch 46] train=0.134913 val=0.870500 loss=1.127129 time: 59.543111
INFO:root:[Epoch 47] train=0.135085 val=0.850300 loss=1.119843 time: 59.253978
INFO:root:[Epoch 48] train=0.133738 val=0.826300 loss=1.112249 time: 59.239847
INFO:root:[Epoch 49] train=0.132549 val=0.837300 loss=1.109174 time: 59.327098
INFO:root:[Epoch 50] train=0.132307 val=0.842100 loss=1.091485 time: 58.898659
INFO:root:[Epoch 51] train=0.130999 val=0.866500 loss=1.097421 time: 58.720758
INFO:root:[Epoch 52] train=0.133331 val=0.878800 loss=1.132106 time: 59.554577
INFO:root:[Epoch 53] train=0.131435 val=0.863000 loss=1.107168 time: 59.139966
INFO:root:[Epoch 54] train=0.130053 val=0.854200 loss=1.084869 time: 61.259725
INFO:root:[Epoch 55] train=0.132013 val=0.868400 loss=1.113725 time: 61.495592
INFO:root:[Epoch 56] train=0.133008 val=0.859400 loss=1.140805 time: 61.657673
INFO:root:[Epoch 57] train=0.129760 val=0.862000 loss=1.094977 time: 61.394334
INFO:root:[Epoch 58] train=0.128535 val=0.881700 loss=1.072434 time: 59.802488
INFO:root:[Epoch 59] train=0.130163 val=0.883500 loss=1.097498 time: 60.192508
INFO:root:[Epoch 60] train=0.129944 val=0.831700 loss=1.092586 time: 58.484096
INFO:root:[Epoch 61] train=0.131106 val=0.858500 loss=1.136102 time: 59.258931
INFO:root:[Epoch 62] train=0.128874 val=0.864600 loss=1.078408 time: 58.753677
INFO:root:[Epoch 63] train=0.130176 val=0.856300 loss=1.099756 time: 59.018871
INFO:root:[Epoch 64] train=0.128093 val=0.858000 loss=1.078363 time: 58.859003
INFO:root:[Epoch 65] train=0.128187 val=0.891000 loss=1.091953 time: 58.796630
INFO:root:[Epoch 66] train=0.129891 val=0.886900 loss=1.095246 time: 59.388491
INFO:root:[Epoch 67] train=0.127407 val=0.848900 loss=1.083766 time: 59.557701
INFO:root:[Epoch 68] train=0.129645 val=0.856000 loss=1.109543 time: 58.393125
INFO:root:[Epoch 69] train=0.128853 val=0.884500 loss=1.096850 time: 59.034910
INFO:root:[Epoch 70] train=0.127855 val=0.873000 loss=1.090857 time: 59.318612
INFO:root:[Epoch 71] train=0.126191 val=0.848900 loss=1.066400 time: 59.128266
INFO:root:[Epoch 72] train=0.128033 val=0.880400 loss=1.100324 time: 61.904291
INFO:root:[Epoch 73] train=0.127245 val=0.833100 loss=1.096644 time: 63.075434
INFO:root:[Epoch 74] train=0.127896 val=0.852700 loss=1.099957 time: 59.438477
INFO:root:[Epoch 75] train=0.127622 val=0.878000 loss=1.091679 time: 59.827521
INFO:root:[Epoch 76] train=0.126162 val=0.889400 loss=1.063257 time: 60.712116
INFO:root:[Epoch 77] train=0.125850 val=0.865400 loss=1.080494 time: 61.372342
INFO:root:[Epoch 78] train=0.127018 val=0.885800 loss=1.081872 time: 61.901690
INFO:root:[Epoch 79] train=0.125884 val=0.855300 loss=1.062933 time: 59.213468
INFO:root:[Epoch 80] train=0.125786 val=0.883100 loss=1.075041 time: 59.317363
INFO:root:[Epoch 81] train=0.124769 val=0.851600 loss=1.072455 time: 59.768333
INFO:root:[Epoch 82] train=0.129102 val=0.874300 loss=1.124595 time: 59.687409
INFO:root:[Epoch 83] train=0.124477 val=0.891400 loss=1.052346 time: 59.711187
INFO:root:[Epoch 84] train=0.125630 val=0.862400 loss=1.079818 time: 59.106864
INFO:root:[Epoch 85] train=0.124588 val=0.856900 loss=1.061077 time: 59.112554
INFO:root:[Epoch 86] train=0.124346 val=0.857100 loss=1.065626 time: 59.781603
INFO:root:[Epoch 87] train=0.126415 val=0.879900 loss=1.092143 time: 59.696571
INFO:root:[Epoch 88] train=0.124963 val=0.870000 loss=1.073957 time: 59.031554
INFO:root:[Epoch 89] train=0.125749 val=0.873900 loss=1.080777 time: 59.405808
INFO:root:[Epoch 90] train=0.123657 val=0.860700 loss=1.036622 time: 59.623487
INFO:root:[Epoch 91] train=0.122161 val=0.891300 loss=1.028440 time: 58.932886
INFO:root:[Epoch 92] train=0.124402 val=0.874300 loss=1.067268 time: 62.474635
INFO:root:[Epoch 93] train=0.124735 val=0.885200 loss=1.078311 time: 62.452552
INFO:root:[Epoch 94] train=0.124049 val=0.886400 loss=1.079958 time: 61.995099
INFO:root:[Epoch 95] train=0.124431 val=0.887400 loss=1.066440 time: 59.779371
INFO:root:[Epoch 96] train=0.123990 val=0.890300 loss=1.064617 time: 59.969076
INFO:root:[Epoch 97] train=0.120636 val=0.886800 loss=1.022923 time: 59.862376
INFO:root:[Epoch 98] train=0.124520 val=0.900600 loss=1.084171 time: 59.252154
INFO:root:[Epoch 99] train=0.125653 val=0.883100 loss=1.089204 time: 59.320652
INFO:root:[Epoch 100] train=0.112727 val=0.925400 loss=1.007580 time: 59.133555
INFO:root:[Epoch 101] train=0.106928 val=0.930700 loss=0.953305 time: 58.466971
INFO:root:[Epoch 102] train=0.106657 val=0.930300 loss=0.975978 time: 58.800662
INFO:root:[Epoch 103] train=0.104206 val=0.936800 loss=0.955567 time: 61.483798
INFO:root:[Epoch 104] train=0.103072 val=0.932000 loss=0.955251 time: 61.772316
INFO:root:[Epoch 105] train=0.102809 val=0.931700 loss=0.949214 time: 62.246561
INFO:root:[Epoch 106] train=0.101134 val=0.933700 loss=0.926932 time: 61.547667
INFO:root:[Epoch 107] train=0.101199 val=0.933600 loss=0.941697 time: 61.517561
INFO:root:[Epoch 108] train=0.099805 val=0.940400 loss=0.924546 time: 61.108278
INFO:root:[Epoch 109] train=0.100201 val=0.938000 loss=0.936385 time: 59.196965
INFO:root:[Epoch 110] train=0.099126 val=0.938800 loss=0.921309 time: 59.441393
INFO:root:[Epoch 111] train=0.099599 val=0.936100 loss=0.917937 time: 59.560577
INFO:root:[Epoch 112] train=0.096267 val=0.938600 loss=0.889083 time: 59.220432
INFO:root:[Epoch 113] train=0.099362 val=0.938400 loss=0.937416 time: 58.877195
INFO:root:[Epoch 114] train=0.098127 val=0.935300 loss=0.918052 time: 59.105405
INFO:root:[Epoch 115] train=0.099621 val=0.940600 loss=0.942286 time: 63.255857
INFO:root:[Epoch 116] train=0.098751 val=0.939700 loss=0.926748 time: 59.482377
INFO:root:[Epoch 117] train=0.097927 val=0.940600 loss=0.921494 time: 60.160936
INFO:root:[Epoch 118] train=0.096604 val=0.942800 loss=0.909736 time: 59.378185
INFO:root:[Epoch 119] train=0.097123 val=0.939400 loss=0.921825 time: 59.894703
INFO:root:[Epoch 120] train=0.098140 val=0.939700 loss=0.936591 time: 58.941106
INFO:root:[Epoch 121] train=0.097677 val=0.939100 loss=0.927663 time: 59.256206
INFO:root:[Epoch 122] train=0.094638 val=0.937500 loss=0.890885 time: 59.290198
INFO:root:[Epoch 123] train=0.094944 val=0.935700 loss=0.905077 time: 58.714115
INFO:root:[Epoch 124] train=0.093604 val=0.941300 loss=0.878118 time: 59.993170
INFO:root:[Epoch 125] train=0.098796 val=0.940800 loss=0.934407 time: 62.803082
INFO:root:[Epoch 126] train=0.096950 val=0.940100 loss=0.914856 time: 61.884139
INFO:root:[Epoch 127] train=0.093079 val=0.941600 loss=0.886969 time: 60.024640
INFO:root:[Epoch 128] train=0.095297 val=0.946600 loss=0.901044 time: 60.001615
INFO:root:[Epoch 129] train=0.096123 val=0.939800 loss=0.915845 time: 59.190057
INFO:root:[Epoch 130] train=0.092471 val=0.937500 loss=0.878877 time: 58.843306
INFO:root:[Epoch 131] train=0.092988 val=0.942700 loss=0.885288 time: 58.813085
INFO:root:[Epoch 132] train=0.095407 val=0.939100 loss=0.905891 time: 59.114792
INFO:root:[Epoch 133] train=0.095787 val=0.941000 loss=0.913477 time: 59.172804
INFO:root:[Epoch 134] train=0.092711 val=0.940300 loss=0.888874 time: 60.436311
INFO:root:[Epoch 135] train=0.092447 val=0.933300 loss=0.887779 time: 59.546219
INFO:root:[Epoch 136] train=0.091987 val=0.938600 loss=0.882076 time: 59.523210
INFO:root:[Epoch 137] train=0.090649 val=0.940700 loss=0.864204 time: 59.625267
INFO:root:[Epoch 138] train=0.094511 val=0.941300 loss=0.917605 time: 59.827567
INFO:root:[Epoch 139] train=0.091255 val=0.941200 loss=0.872139 time: 62.791230
INFO:root:[Epoch 140] train=0.090364 val=0.939600 loss=0.866623 time: 61.099221
INFO:root:[Epoch 141] train=0.095390 val=0.940500 loss=0.912321 time: 59.850853
INFO:root:[Epoch 142] train=0.094788 val=0.937000 loss=0.914073 time: 60.583221
INFO:root:[Epoch 143] train=0.097140 val=0.942600 loss=0.931589 time: 59.794355
INFO:root:[Epoch 144] train=0.091840 val=0.941400 loss=0.887670 time: 59.946050
INFO:root:[Epoch 145] train=0.095949 val=0.938700 loss=0.923641 time: 59.235390
INFO:root:[Epoch 146] train=0.088325 val=0.940000 loss=0.851564 time: 59.286095
INFO:root:[Epoch 147] train=0.092434 val=0.940300 loss=0.890419 time: 60.890470
INFO:root:[Epoch 148] train=0.092050 val=0.936700 loss=0.892130 time: 61.732326
INFO:root:[Epoch 149] train=0.092008 val=0.939900 loss=0.883573 time: 59.748664
INFO:root:[Epoch 150] train=0.088852 val=0.945700 loss=0.872177 time: 61.227113
INFO:root:[Epoch 151] train=0.085134 val=0.946400 loss=0.840192 time: 61.449733
INFO:root:[Epoch 152] train=0.086262 val=0.945400 loss=0.853869 time: 59.496900
INFO:root:[Epoch 153] train=0.085295 val=0.947400 loss=0.850386 time: 59.253738
INFO:root:[Epoch 154] train=0.086997 val=0.947700 loss=0.869461 time: 63.056826
INFO:root:[Epoch 155] train=0.089246 val=0.945900 loss=0.882534 time: 61.200576
INFO:root:[Epoch 156] train=0.084354 val=0.947000 loss=0.840150 time: 61.736857
INFO:root:[Epoch 157] train=0.086358 val=0.946700 loss=0.855900 time: 59.388718
INFO:root:[Epoch 158] train=0.086125 val=0.944900 loss=0.865194 time: 59.213965
INFO:root:[Epoch 159] train=0.086912 val=0.947100 loss=0.866481 time: 59.928949
INFO:root:[Epoch 160] train=0.085496 val=0.946000 loss=0.851484 time: 62.740787
INFO:root:[Epoch 161] train=0.085360 val=0.948300 loss=0.849564 time: 59.561446
INFO:root:[Epoch 162] train=0.087973 val=0.947700 loss=0.875761 time: 59.848342
INFO:root:[Epoch 163] train=0.085467 val=0.944700 loss=0.851516 time: 59.852623
INFO:root:[Epoch 164] train=0.085907 val=0.947200 loss=0.860074 time: 59.480276
INFO:root:[Epoch 165] train=0.083357 val=0.947000 loss=0.832707 time: 59.392923
INFO:root:[Epoch 166] train=0.085931 val=0.944900 loss=0.859658 time: 59.383577
INFO:root:[Epoch 167] train=0.085442 val=0.943500 loss=0.853260 time: 59.122558
INFO:root:[Epoch 168] train=0.085498 val=0.948000 loss=0.849531 time: 59.327736
INFO:root:[Epoch 169] train=0.084474 val=0.946200 loss=0.849618 time: 59.527943
INFO:root:[Epoch 170] train=0.082132 val=0.945700 loss=0.826326 time: 59.356289
INFO:root:[Epoch 171] train=0.086004 val=0.944500 loss=0.865513 time: 58.817744
INFO:root:[Epoch 172] train=0.087485 val=0.945600 loss=0.878306 time: 59.137968
INFO:root:[Epoch 173] train=0.084052 val=0.946100 loss=0.848395 time: 60.066247
INFO:root:[Epoch 174] train=0.085192 val=0.948300 loss=0.859278 time: 59.644967
INFO:root:[Epoch 175] train=0.081634 val=0.946500 loss=0.817492 time: 58.843238
INFO:root:[Epoch 176] train=0.082031 val=0.947400 loss=0.828341 time: 59.047713
INFO:root:[Epoch 177] train=0.083071 val=0.945800 loss=0.840850 time: 59.198931
INFO:root:[Epoch 178] train=0.081062 val=0.945800 loss=0.817745 time: 59.205945
INFO:root:[Epoch 179] train=0.085324 val=0.944100 loss=0.851139 time: 60.046059
INFO:root:[Epoch 180] train=0.087814 val=0.944700 loss=0.881536 time: 62.595908
INFO:root:[Epoch 181] train=0.085135 val=0.945500 loss=0.851721 time: 61.518451
INFO:root:[Epoch 182] train=0.082489 val=0.944900 loss=0.829544 time: 59.722726
INFO:root:[Epoch 183] train=0.081682 val=0.947400 loss=0.827193 time: 59.606229
INFO:root:[Epoch 184] train=0.082964 val=0.946900 loss=0.838643 time: 59.662186
INFO:root:[Epoch 185] train=0.085424 val=0.946600 loss=0.857998 time: 59.774950
INFO:root:[Epoch 186] train=0.079190 val=0.947100 loss=0.805394 time: 59.438497
INFO:root:[Epoch 187] train=0.083584 val=0.947200 loss=0.847614 time: 59.432208
INFO:root:[Epoch 188] train=0.086068 val=0.947300 loss=0.868507 time: 59.174148
INFO:root:[Epoch 189] train=0.087006 val=0.946500 loss=0.870217 time: 59.087540
INFO:root:[Epoch 190] train=0.083415 val=0.945200 loss=0.839081 time: 59.103906
INFO:root:[Epoch 191] train=0.083962 val=0.946000 loss=0.850490 time: 62.825694
INFO:root:[Epoch 192] train=0.083385 val=0.945700 loss=0.845683 time: 59.725898
INFO:root:[Epoch 193] train=0.080823 val=0.948100 loss=0.815933 time: 59.464731
INFO:root:[Epoch 194] train=0.084906 val=0.946500 loss=0.860601 time: 59.238033
INFO:root:[Epoch 195] train=0.087125 val=0.945500 loss=0.877971 time: 59.157629
INFO:root:[Epoch 196] train=0.083001 val=0.947700 loss=0.835697 time: 59.113989
INFO:root:[Epoch 197] train=0.084735 val=0.945600 loss=0.852830 time: 58.998327
INFO:root:[Epoch 198] train=0.085131 val=0.945300 loss=0.860332 time: 58.976646
INFO:root:[Epoch 199] train=0.083447 val=0.947600 loss=0.846203 time: 62.903021
INFO:root:[Epoch 200] train=0.023162 val=0.950800 loss=0.022998 time: 58.954006
INFO:root:[Epoch 201] train=0.022574 val=0.950800 loss=0.019215 time: 59.038637
INFO:root:[Epoch 202] train=0.019346 val=0.950300 loss=0.015423 time: 59.539583
INFO:root:[Epoch 203] train=0.019746 val=0.950900 loss=0.015101 time: 59.482560
INFO:root:[Epoch 204] train=0.019071 val=0.949600 loss=0.014324 time: 59.211551
INFO:root:[Epoch 205] train=0.017829 val=0.950200 loss=0.012933 time: 59.182305
INFO:root:[Epoch 206] train=0.018483 val=0.950700 loss=0.012859 time: 59.014192
INFO:root:[Epoch 207] train=0.017510 val=0.951100 loss=0.011896 time: 59.148775
INFO:root:[Epoch 208] train=0.016758 val=0.950500 loss=0.011660 time: 58.638624
INFO:root:[Epoch 209] train=0.016219 val=0.949800 loss=0.010793 time: 59.404674
INFO:root:[Epoch 210] train=0.016328 val=0.950700 loss=0.010436 time: 62.915420
INFO:root:[Epoch 211] train=0.015648 val=0.950100 loss=0.010013 time: 62.756830
INFO:root:[Epoch 212] train=0.015289 val=0.950100 loss=0.010107 time: 60.684336
INFO:root:[Epoch 213] train=0.014527 val=0.950700 loss=0.009168 time: 59.282319
INFO:root:[Epoch 214] train=0.014558 val=0.949800 loss=0.009502 time: 58.750260
INFO:root:[Epoch 215] train=0.013813 val=0.950400 loss=0.008776 time: 58.166798
INFO:root:[Epoch 216] train=0.013248 val=0.951200 loss=0.008222 time: 58.657114
INFO:root:[Epoch 217] train=0.012503 val=0.950100 loss=0.007718 time: 58.531915
INFO:root:[Epoch 218] train=0.012401 val=0.950500 loss=0.007265 time: 58.886904
INFO:root:[Epoch 219] train=0.012619 val=0.952000 loss=0.007456 time: 62.958607