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_v1', 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)
[04:56:42] 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=0.467608 val=0.558900 loss=1.452417 time: 16.450441
INFO:root:[Epoch 1] train=0.654387 val=0.690600 loss=0.976340 time: 16.356752
INFO:root:[Epoch 2] train=0.725621 val=0.734700 loss=0.779339 time: 16.056174
INFO:root:[Epoch 3] train=0.763502 val=0.713000 loss=0.679259 time: 16.238353
INFO:root:[Epoch 4] train=0.789323 val=0.793400 loss=0.609874 time: 16.442251
INFO:root:[Epoch 5] train=0.807192 val=0.785100 loss=0.559083 time: 16.484005
INFO:root:[Epoch 6] train=0.819010 val=0.794000 loss=0.523631 time: 16.147346
INFO:root:[Epoch 7] train=0.827464 val=0.779500 loss=0.498826 time: 16.130143
INFO:root:[Epoch 8] train=0.837740 val=0.807400 loss=0.470464 time: 15.896262
INFO:root:[Epoch 9] train=0.844752 val=0.813400 loss=0.449489 time: 16.079025
INFO:root:[Epoch 10] train=0.850901 val=0.824300 loss=0.430169 time: 15.932847
INFO:root:[Epoch 11] train=0.856410 val=0.828000 loss=0.418624 time: 16.387527
INFO:root:[Epoch 12] train=0.861378 val=0.787100 loss=0.400076 time: 16.224660
INFO:root:[Epoch 13] train=0.863862 val=0.834000 loss=0.392679 time: 16.697373
INFO:root:[Epoch 14] train=0.868510 val=0.832800 loss=0.376483 time: 16.135893
INFO:root:[Epoch 15] train=0.871014 val=0.841100 loss=0.368428 time: 16.362697
INFO:root:[Epoch 16] train=0.875661 val=0.817100 loss=0.357952 time: 16.043023
INFO:root:[Epoch 17] train=0.878746 val=0.851700 loss=0.349465 time: 16.245746
INFO:root:[Epoch 18] train=0.878926 val=0.848800 loss=0.347273 time: 16.143231
INFO:root:[Epoch 19] train=0.882812 val=0.815000 loss=0.339310 time: 16.401147
INFO:root:[Epoch 20] train=0.885717 val=0.825000 loss=0.329579 time: 16.532947
INFO:root:[Epoch 21] train=0.888081 val=0.838400 loss=0.324417 time: 16.554947
INFO:root:[Epoch 22] train=0.890284 val=0.829700 loss=0.317602 time: 16.048430
INFO:root:[Epoch 23] train=0.889663 val=0.844900 loss=0.315706 time: 16.743092
INFO:root:[Epoch 24] train=0.891927 val=0.826100 loss=0.307264 time: 16.160159
INFO:root:[Epoch 25] train=0.893109 val=0.825800 loss=0.306528 time: 16.093714
INFO:root:[Epoch 26] train=0.897716 val=0.855700 loss=0.295618 time: 16.103249
INFO:root:[Epoch 27] train=0.895793 val=0.867300 loss=0.299040 time: 16.360766
INFO:root:[Epoch 28] train=0.897997 val=0.860800 loss=0.292082 time: 16.581570
INFO:root:[Epoch 29] train=0.898618 val=0.853200 loss=0.287514 time: 16.220623
INFO:root:[Epoch 30] train=0.900080 val=0.850200 loss=0.283724 time: 16.470374
INFO:root:[Epoch 31] train=0.900861 val=0.849500 loss=0.280451 time: 16.008697
INFO:root:[Epoch 32] train=0.902885 val=0.857700 loss=0.276403 time: 16.205060
INFO:root:[Epoch 33] train=0.902744 val=0.860700 loss=0.278247 time: 16.298109
INFO:root:[Epoch 34] train=0.904607 val=0.859700 loss=0.276091 time: 16.410761
INFO:root:[Epoch 35] train=0.905649 val=0.869000 loss=0.270065 time: 16.546314
INFO:root:[Epoch 36] train=0.906651 val=0.838500 loss=0.264497 time: 16.140385
INFO:root:[Epoch 37] train=0.908013 val=0.855100 loss=0.264962 time: 16.278032
INFO:root:[Epoch 38] train=0.907692 val=0.852800 loss=0.265545 time: 15.835175
INFO:root:[Epoch 39] train=0.908574 val=0.859200 loss=0.262060 time: 16.372809
INFO:root:[Epoch 40] train=0.907792 val=0.869300 loss=0.260299 time: 16.384937
INFO:root:[Epoch 41] train=0.910577 val=0.846300 loss=0.252985 time: 16.003968
INFO:root:[Epoch 42] train=0.911999 val=0.856100 loss=0.250454 time: 16.324400
INFO:root:[Epoch 43] train=0.910517 val=0.837000 loss=0.256553 time: 16.228765
INFO:root:[Epoch 44] train=0.912520 val=0.839700 loss=0.249225 time: 16.311597
INFO:root:[Epoch 45] train=0.912039 val=0.866500 loss=0.248236 time: 16.151795
INFO:root:[Epoch 46] train=0.916326 val=0.862400 loss=0.241464 time: 16.359800
INFO:root:[Epoch 47] train=0.913421 val=0.839400 loss=0.246433 time: 16.258186
INFO:root:[Epoch 48] train=0.914303 val=0.873700 loss=0.241282 time: 16.438929
INFO:root:[Epoch 49] train=0.913762 val=0.867600 loss=0.244878 time: 16.202172
INFO:root:[Epoch 50] train=0.915224 val=0.876300 loss=0.241208 time: 16.433206
INFO:root:[Epoch 51] train=0.916346 val=0.867100 loss=0.240494 time: 16.058994
INFO:root:[Epoch 52] train=0.916146 val=0.854500 loss=0.236560 time: 15.866137
INFO:root:[Epoch 53] train=0.914403 val=0.857000 loss=0.242893 time: 16.038805
INFO:root:[Epoch 54] train=0.918009 val=0.856300 loss=0.234128 time: 16.309281
INFO:root:[Epoch 55] train=0.919712 val=0.879100 loss=0.229098 time: 16.240462
INFO:root:[Epoch 56] train=0.917228 val=0.849100 loss=0.231750 time: 16.190122
INFO:root:[Epoch 57] train=0.918650 val=0.843600 loss=0.233665 time: 16.303913
INFO:root:[Epoch 58] train=0.916627 val=0.832700 loss=0.233659 time: 16.283458
INFO:root:[Epoch 59] train=0.921034 val=0.855400 loss=0.223950 time: 16.117105
INFO:root:[Epoch 60] train=0.919231 val=0.870400 loss=0.232705 time: 16.396026
INFO:root:[Epoch 61] train=0.920272 val=0.860100 loss=0.225832 time: 16.196655
INFO:root:[Epoch 62] train=0.920353 val=0.858300 loss=0.226656 time: 16.350908
INFO:root:[Epoch 63] train=0.921154 val=0.864000 loss=0.224867 time: 16.189870
INFO:root:[Epoch 64] train=0.920813 val=0.835600 loss=0.224640 time: 16.348761
INFO:root:[Epoch 65] train=0.921735 val=0.850400 loss=0.225492 time: 16.382515
INFO:root:[Epoch 66] train=0.920473 val=0.858600 loss=0.224568 time: 16.273062
INFO:root:[Epoch 67] train=0.922436 val=0.871700 loss=0.221823 time: 16.322385
INFO:root:[Epoch 68] train=0.922055 val=0.879300 loss=0.220531 time: 16.278263
INFO:root:[Epoch 69] train=0.923778 val=0.869400 loss=0.216848 time: 16.315413
INFO:root:[Epoch 70] train=0.921995 val=0.868600 loss=0.220863 time: 16.322680
INFO:root:[Epoch 71] train=0.921855 val=0.873400 loss=0.220862 time: 16.565508
INFO:root:[Epoch 72] train=0.923798 val=0.877100 loss=0.216296 time: 16.541918
INFO:root:[Epoch 73] train=0.922035 val=0.792800 loss=0.218908 time: 16.697990
INFO:root:[Epoch 74] train=0.922115 val=0.869100 loss=0.218806 time: 16.166203
INFO:root:[Epoch 75] train=0.923518 val=0.869800 loss=0.217753 time: 16.411241
INFO:root:[Epoch 76] train=0.923918 val=0.853900 loss=0.216461 time: 16.014224
INFO:root:[Epoch 77] train=0.924900 val=0.884900 loss=0.214963 time: 16.011093
INFO:root:[Epoch 78] train=0.924980 val=0.848800 loss=0.210703 time: 16.326828
INFO:root:[Epoch 79] train=0.925020 val=0.867400 loss=0.214989 time: 16.261787
INFO:root:[Epoch 80] train=0.924760 val=0.876800 loss=0.215274 time: 16.542028
INFO:root:[Epoch 81] train=0.923738 val=0.843500 loss=0.214462 time: 16.303494
INFO:root:[Epoch 82] train=0.923217 val=0.867500 loss=0.215110 time: 16.124388
INFO:root:[Epoch 83] train=0.923417 val=0.864900 loss=0.215067 time: 16.352117
INFO:root:[Epoch 84] train=0.928185 val=0.808200 loss=0.207275 time: 16.186363
INFO:root:[Epoch 85] train=0.924419 val=0.888100 loss=0.215107 time: 16.583211
INFO:root:[Epoch 86] train=0.926182 val=0.878500 loss=0.212300 time: 16.495654
INFO:root:[Epoch 87] train=0.927143 val=0.880200 loss=0.207058 time: 16.531153
INFO:root:[Epoch 88] train=0.924379 val=0.878300 loss=0.213139 time: 16.118172
INFO:root:[Epoch 89] train=0.928345 val=0.879700 loss=0.205894 time: 16.144841
INFO:root:[Epoch 90] train=0.925741 val=0.865000 loss=0.212142 time: 15.858166
INFO:root:[Epoch 91] train=0.928265 val=0.873200 loss=0.207647 time: 16.104458
INFO:root:[Epoch 92] train=0.927083 val=0.855800 loss=0.205763 time: 15.929814
INFO:root:[Epoch 93] train=0.925881 val=0.869800 loss=0.210061 time: 16.376977
INFO:root:[Epoch 94] train=0.927784 val=0.874600 loss=0.204307 time: 16.620931
INFO:root:[Epoch 95] train=0.925781 val=0.864700 loss=0.209660 time: 16.640068
INFO:root:[Epoch 96] train=0.927224 val=0.846300 loss=0.207704 time: 16.361441
INFO:root:[Epoch 97] train=0.926743 val=0.867600 loss=0.207614 time: 16.469640
INFO:root:[Epoch 98] train=0.929046 val=0.880800 loss=0.203933 time: 16.563624
INFO:root:[Epoch 99] train=0.927644 val=0.880000 loss=0.206193 time: 16.211096
INFO:root:[Epoch 100] train=0.956150 val=0.910900 loss=0.130355 time: 16.177213
INFO:root:[Epoch 101] train=0.967728 val=0.912400 loss=0.098303 time: 16.130494
INFO:root:[Epoch 102] train=0.970913 val=0.915600 loss=0.088207 time: 16.293974
INFO:root:[Epoch 103] train=0.972296 val=0.916700 loss=0.082703 time: 16.584502
INFO:root:[Epoch 104] train=0.975140 val=0.915100 loss=0.076201 time: 16.779471
INFO:root:[Epoch 105] train=0.975561 val=0.915600 loss=0.071510 time: 16.022058
INFO:root:[Epoch 106] train=0.976843 val=0.915900 loss=0.068931 time: 16.562019
INFO:root:[Epoch 107] train=0.978245 val=0.916500 loss=0.065845 time: 16.338162
INFO:root:[Epoch 108] train=0.979347 val=0.916300 loss=0.062411 time: 16.794200
INFO:root:[Epoch 109] train=0.979888 val=0.916300 loss=0.059620 time: 16.104220
INFO:root:[Epoch 110] train=0.980248 val=0.913900 loss=0.059137 time: 16.015758
INFO:root:[Epoch 111] train=0.981270 val=0.917800 loss=0.056056 time: 16.221114
INFO:root:[Epoch 112] train=0.981931 val=0.913700 loss=0.053818 time: 16.566824
INFO:root:[Epoch 113] train=0.982492 val=0.915300 loss=0.051643 time: 16.148926
INFO:root:[Epoch 114] train=0.982833 val=0.916500 loss=0.051974 time: 16.359837
INFO:root:[Epoch 115] train=0.984335 val=0.915700 loss=0.047573 time: 16.519790
INFO:root:[Epoch 116] train=0.984675 val=0.915900 loss=0.047519 time: 16.343573
INFO:root:[Epoch 117] train=0.984455 val=0.916300 loss=0.045932 time: 16.183020
INFO:root:[Epoch 118] train=0.984916 val=0.918600 loss=0.045467 time: 16.182452
INFO:root:[Epoch 119] train=0.985938 val=0.917600 loss=0.042671 time: 16.414871
INFO:root:[Epoch 120] train=0.986558 val=0.917500 loss=0.041902 time: 16.651437
INFO:root:[Epoch 121] train=0.986999 val=0.917600 loss=0.039851 time: 16.034811
INFO:root:[Epoch 122] train=0.986979 val=0.916800 loss=0.040490 time: 16.219050
INFO:root:[Epoch 123] train=0.987841 val=0.917000 loss=0.038223 time: 16.553145
INFO:root:[Epoch 124] train=0.987780 val=0.915700 loss=0.037487 time: 16.349275
INFO:root:[Epoch 125] train=0.988782 val=0.917100 loss=0.036567 time: 16.431462
INFO:root:[Epoch 126] train=0.988842 val=0.917900 loss=0.037135 time: 15.919887
INFO:root:[Epoch 127] train=0.988361 val=0.914500 loss=0.036855 time: 16.229873
INFO:root:[Epoch 128] train=0.988502 val=0.916100 loss=0.035451 time: 16.402407
INFO:root:[Epoch 129] train=0.989203 val=0.917100 loss=0.034171 time: 16.537166
INFO:root:[Epoch 130] train=0.988742 val=0.915500 loss=0.035210 time: 16.375344
INFO:root:[Epoch 131] train=0.989543 val=0.913000 loss=0.032436 time: 16.531819
INFO:root:[Epoch 132] train=0.989343 val=0.915500 loss=0.033613 time: 16.402074
INFO:root:[Epoch 133] train=0.990104 val=0.916500 loss=0.032223 time: 16.196118
INFO:root:[Epoch 134] train=0.990184 val=0.913600 loss=0.032011 time: 16.410753
INFO:root:[Epoch 135] train=0.990885 val=0.915200 loss=0.029078 time: 16.725836
INFO:root:[Epoch 136] train=0.990184 val=0.916700 loss=0.030508 time: 16.777744
INFO:root:[Epoch 137] train=0.990705 val=0.915200 loss=0.030126 time: 16.128791
INFO:root:[Epoch 138] train=0.990625 val=0.912900 loss=0.030048 time: 16.330201
INFO:root:[Epoch 139] train=0.990865 val=0.916700 loss=0.029754 time: 16.463360
INFO:root:[Epoch 140] train=0.990745 val=0.915600 loss=0.028997 time: 16.098539
INFO:root:[Epoch 141] train=0.990405 val=0.918000 loss=0.028905 time: 16.550459
INFO:root:[Epoch 142] train=0.990705 val=0.917400 loss=0.028378 time: 16.113878
INFO:root:[Epoch 143] train=0.991627 val=0.915500 loss=0.027477 time: 16.164229
INFO:root:[Epoch 144] train=0.991386 val=0.917500 loss=0.027728 time: 16.309255
INFO:root:[Epoch 145] train=0.991747 val=0.917800 loss=0.027467 time: 16.245961
INFO:root:[Epoch 146] train=0.991006 val=0.915300 loss=0.027045 time: 16.322504
INFO:root:[Epoch 147] train=0.991767 val=0.915600 loss=0.026531 time: 16.368130
INFO:root:[Epoch 148] train=0.992027 val=0.917000 loss=0.025971 time: 16.062262
INFO:root:[Epoch 149] train=0.991827 val=0.917700 loss=0.025582 time: 16.347462
INFO:root:[Epoch 150] train=0.993450 val=0.919400 loss=0.022140 time: 16.209898
INFO:root:[Epoch 151] train=0.994391 val=0.921000 loss=0.019395 time: 16.244192
INFO:root:[Epoch 152] train=0.995052 val=0.921100 loss=0.018588 time: 16.407324
INFO:root:[Epoch 153] train=0.995072 val=0.920300 loss=0.018771 time: 16.284558
INFO:root:[Epoch 154] train=0.995813 val=0.919300 loss=0.017195 time: 16.660882
INFO:root:[Epoch 155] train=0.994792 val=0.920500 loss=0.017725 time: 16.424143
INFO:root:[Epoch 156] train=0.995112 val=0.920700 loss=0.017788 time: 16.352637
INFO:root:[Epoch 157] train=0.995553 val=0.920900 loss=0.017411 time: 16.687143
INFO:root:[Epoch 158] train=0.994972 val=0.919700 loss=0.018388 time: 16.410315
INFO:root:[Epoch 159] train=0.995613 val=0.919900 loss=0.016741 time: 17.046810
INFO:root:[Epoch 160] train=0.995533 val=0.919700 loss=0.017069 time: 16.429122
INFO:root:[Epoch 161] train=0.995813 val=0.920200 loss=0.017167 time: 16.332429
INFO:root:[Epoch 162] train=0.995353 val=0.919900 loss=0.017084 time: 16.456290
INFO:root:[Epoch 163] train=0.995873 val=0.919600 loss=0.016640 time: 16.665655
INFO:root:[Epoch 164] train=0.995653 val=0.919600 loss=0.016259 time: 16.050839
INFO:root:[Epoch 165] train=0.995753 val=0.918600 loss=0.016862 time: 16.829157
INFO:root:[Epoch 166] train=0.995092 val=0.919300 loss=0.017366 time: 16.362975
INFO:root:[Epoch 167] train=0.996074 val=0.919200 loss=0.015932 time: 16.330289
INFO:root:[Epoch 168] train=0.995613 val=0.920600 loss=0.016429 time: 16.468876
INFO:root:[Epoch 169] train=0.995733 val=0.918600 loss=0.016015 time: 16.367891
INFO:root:[Epoch 170] train=0.996014 val=0.920400 loss=0.016110 time: 16.517901
INFO:root:[Epoch 171] train=0.995813 val=0.919900 loss=0.015776 time: 16.618844
INFO:root:[Epoch 172] train=0.995793 val=0.919900 loss=0.016175 time: 15.867436
INFO:root:[Epoch 173] train=0.995733 val=0.920300 loss=0.016245 time: 16.072907
INFO:root:[Epoch 174] train=0.995693 val=0.921100 loss=0.015880 time: 16.472676
INFO:root:[Epoch 175] train=0.995833 val=0.920700 loss=0.016177 time: 16.623861
INFO:root:[Epoch 176] train=0.996655 val=0.920800 loss=0.014725 time: 16.229118
INFO:root:[Epoch 177] train=0.995873 val=0.920400 loss=0.015045 time: 16.098111
INFO:root:[Epoch 178] train=0.995653 val=0.919900 loss=0.016459 time: 16.755926
INFO:root:[Epoch 179] train=0.996554 val=0.921000 loss=0.014774 time: 16.293465
INFO:root:[Epoch 180] train=0.996174 val=0.919600 loss=0.015274 time: 16.279559
INFO:root:[Epoch 181] train=0.996154 val=0.921200 loss=0.014858 time: 16.449700
INFO:root:[Epoch 182] train=0.996114 val=0.921300 loss=0.014924 time: 16.454417
INFO:root:[Epoch 183] train=0.995813 val=0.920700 loss=0.016039 time: 16.150658
INFO:root:[Epoch 184] train=0.996014 val=0.921400 loss=0.015651 time: 16.849808
INFO:root:[Epoch 185] train=0.996074 val=0.919600 loss=0.014586 time: 16.272660
INFO:root:[Epoch 186] train=0.996034 val=0.920500 loss=0.015126 time: 16.393122
INFO:root:[Epoch 187] train=0.996274 val=0.919500 loss=0.014665 time: 16.591407
INFO:root:[Epoch 188] train=0.996014 val=0.920800 loss=0.015428 time: 16.372325
INFO:root:[Epoch 189] train=0.996554 val=0.920200 loss=0.014343 time: 16.306489
INFO:root:[Epoch 190] train=0.996274 val=0.920200 loss=0.014633 time: 16.593350
INFO:root:[Epoch 191] train=0.996174 val=0.920600 loss=0.014503 time: 16.105995
INFO:root:[Epoch 192] train=0.995893 val=0.920500 loss=0.015252 time: 16.766473
INFO:root:[Epoch 193] train=0.996595 val=0.921300 loss=0.014449 time: 16.456559
INFO:root:[Epoch 194] train=0.996014 val=0.919700 loss=0.014672 time: 16.537375
INFO:root:[Epoch 195] train=0.996514 val=0.920100 loss=0.013929 time: 16.744001
INFO:root:[Epoch 196] train=0.996835 val=0.919700 loss=0.013776 time: 16.412151
INFO:root:[Epoch 197] train=0.996114 val=0.920000 loss=0.014396 time: 16.445592
INFO:root:[Epoch 198] train=0.996314 val=0.920600 loss=0.014762 time: 16.834647
INFO:root:[Epoch 199] train=0.996374 val=0.920000 loss=0.014310 time: 16.754802