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_resnet56_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)
[05:57:09] 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=0.245553 val=0.117000 loss=2.409948 time: 31.946900
INFO:root:[Epoch 1] train=0.238411 val=0.245400 loss=2.218438 time: 30.912946
INFO:root:[Epoch 2] train=0.231879 val=0.350300 loss=2.051030 time: 31.329821
INFO:root:[Epoch 3] train=0.225405 val=0.387000 loss=1.951583 time: 32.690079
INFO:root:[Epoch 4] train=0.218668 val=0.415100 loss=1.892617 time: 32.299360
INFO:root:[Epoch 5] train=0.214707 val=0.465100 loss=1.861088 time: 31.183751
INFO:root:[Epoch 6] train=0.213340 val=0.481200 loss=1.808338 time: 30.840700
INFO:root:[Epoch 7] train=0.209233 val=0.462800 loss=1.797550 time: 33.150284
INFO:root:[Epoch 8] train=0.210896 val=0.471400 loss=1.739224 time: 32.504408
INFO:root:[Epoch 9] train=0.206424 val=0.541900 loss=1.720819 time: 33.367087
INFO:root:[Epoch 10] train=0.202786 val=0.514100 loss=1.702099 time: 32.950647
INFO:root:[Epoch 11] train=0.202490 val=0.618300 loss=1.652141 time: 30.359903
INFO:root:[Epoch 12] train=0.198080 val=0.598000 loss=1.647376 time: 30.672132
INFO:root:[Epoch 13] train=0.196400 val=0.590500 loss=1.608576 time: 30.729964
INFO:root:[Epoch 14] train=0.193088 val=0.633400 loss=1.605630 time: 30.787885
INFO:root:[Epoch 15] train=0.191081 val=0.623800 loss=1.565649 time: 31.014481
INFO:root:[Epoch 16] train=0.186632 val=0.675800 loss=1.518770 time: 30.868783
INFO:root:[Epoch 17] train=0.181103 val=0.702400 loss=1.471338 time: 31.230373
INFO:root:[Epoch 18] train=0.177404 val=0.653400 loss=1.464596 time: 30.877100
INFO:root:[Epoch 19] train=0.175727 val=0.739400 loss=1.409699 time: 31.213190
INFO:root:[Epoch 20] train=0.172065 val=0.692200 loss=1.404940 time: 31.139366
INFO:root:[Epoch 21] train=0.169576 val=0.747900 loss=1.370056 time: 30.864038
INFO:root:[Epoch 22] train=0.167682 val=0.739300 loss=1.347631 time: 30.860088
INFO:root:[Epoch 23] train=0.167337 val=0.762500 loss=1.378107 time: 31.216135
INFO:root:[Epoch 24] train=0.162850 val=0.798900 loss=1.307181 time: 31.606931
INFO:root:[Epoch 25] train=0.163209 val=0.760300 loss=1.344565 time: 32.721770
INFO:root:[Epoch 26] train=0.160833 val=0.750100 loss=1.345579 time: 30.693892
INFO:root:[Epoch 27] train=0.160245 val=0.731100 loss=1.296400 time: 30.838795
INFO:root:[Epoch 28] train=0.158744 val=0.751800 loss=1.290251 time: 31.371371
INFO:root:[Epoch 29] train=0.157582 val=0.771300 loss=1.290177 time: 31.325232
INFO:root:[Epoch 30] train=0.156261 val=0.803800 loss=1.271116 time: 31.389458
INFO:root:[Epoch 31] train=0.156132 val=0.718600 loss=1.260258 time: 31.174085
INFO:root:[Epoch 32] train=0.154519 val=0.801900 loss=1.258166 time: 30.744816
INFO:root:[Epoch 33] train=0.154254 val=0.792100 loss=1.290150 time: 30.880132
INFO:root:[Epoch 34] train=0.152598 val=0.806500 loss=1.241911 time: 31.171973
INFO:root:[Epoch 35] train=0.152203 val=0.809500 loss=1.245495 time: 31.096713
INFO:root:[Epoch 36] train=0.151904 val=0.820900 loss=1.247614 time: 31.344034
INFO:root:[Epoch 37] train=0.150370 val=0.816000 loss=1.253540 time: 31.172642
INFO:root:[Epoch 38] train=0.149135 val=0.809100 loss=1.237089 time: 31.069434
INFO:root:[Epoch 39] train=0.149186 val=0.828000 loss=1.267902 time: 30.877094
INFO:root:[Epoch 40] train=0.146728 val=0.818900 loss=1.203774 time: 31.084999
INFO:root:[Epoch 41] train=0.146269 val=0.820600 loss=1.199334 time: 31.323668
INFO:root:[Epoch 42] train=0.145809 val=0.820900 loss=1.208963 time: 30.842863
INFO:root:[Epoch 43] train=0.145413 val=0.807700 loss=1.219242 time: 30.939761
INFO:root:[Epoch 44] train=0.144875 val=0.832500 loss=1.193650 time: 30.837562
INFO:root:[Epoch 45] train=0.143804 val=0.859900 loss=1.173192 time: 30.739808
INFO:root:[Epoch 46] train=0.143595 val=0.841000 loss=1.195748 time: 30.773082
INFO:root:[Epoch 47] train=0.143669 val=0.799300 loss=1.208282 time: 30.834301
INFO:root:[Epoch 48] train=0.142517 val=0.826400 loss=1.186448 time: 30.922496
INFO:root:[Epoch 49] train=0.142579 val=0.822600 loss=1.178959 time: 31.025083
INFO:root:[Epoch 50] train=0.141051 val=0.808100 loss=1.191332 time: 31.111820
INFO:root:[Epoch 51] train=0.141846 val=0.819800 loss=1.199076 time: 30.764131
INFO:root:[Epoch 52] train=0.141648 val=0.814900 loss=1.168170 time: 30.695599
INFO:root:[Epoch 53] train=0.141216 val=0.863400 loss=1.175379 time: 31.006623
INFO:root:[Epoch 54] train=0.140323 val=0.839900 loss=1.154492 time: 30.955142
INFO:root:[Epoch 55] train=0.139381 val=0.831000 loss=1.148360 time: 32.984278
INFO:root:[Epoch 56] train=0.138994 val=0.849400 loss=1.160823 time: 33.001503
INFO:root:[Epoch 57] train=0.140566 val=0.836800 loss=1.201909 time: 33.044491
INFO:root:[Epoch 58] train=0.138634 val=0.838600 loss=1.168567 time: 33.045428
INFO:root:[Epoch 59] train=0.138564 val=0.834800 loss=1.165159 time: 31.546316
INFO:root:[Epoch 60] train=0.138115 val=0.828200 loss=1.165692 time: 31.283575
INFO:root:[Epoch 61] train=0.138122 val=0.825900 loss=1.168394 time: 30.608252
INFO:root:[Epoch 62] train=0.138629 val=0.818500 loss=1.169235 time: 30.952843
INFO:root:[Epoch 63] train=0.136910 val=0.854700 loss=1.137464 time: 31.098293
INFO:root:[Epoch 64] train=0.137270 val=0.831200 loss=1.160658 time: 31.029511
INFO:root:[Epoch 65] train=0.136064 val=0.861600 loss=1.156136 time: 31.085042
INFO:root:[Epoch 66] train=0.134585 val=0.828200 loss=1.123742 time: 31.411698
INFO:root:[Epoch 67] train=0.135009 val=0.844700 loss=1.120106 time: 31.210982
INFO:root:[Epoch 68] train=0.135449 val=0.851400 loss=1.160330 time: 31.096433
INFO:root:[Epoch 69] train=0.134636 val=0.870600 loss=1.141314 time: 31.253979
INFO:root:[Epoch 70] train=0.134817 val=0.825600 loss=1.134043 time: 31.063519
INFO:root:[Epoch 71] train=0.135079 val=0.876800 loss=1.137628 time: 30.986733
INFO:root:[Epoch 72] train=0.132562 val=0.863900 loss=1.095483 time: 31.179994
INFO:root:[Epoch 73] train=0.133476 val=0.851500 loss=1.115028 time: 31.085719
INFO:root:[Epoch 74] train=0.133272 val=0.847100 loss=1.111313 time: 33.000150
INFO:root:[Epoch 75] train=0.133632 val=0.861700 loss=1.135198 time: 33.046472
INFO:root:[Epoch 76] train=0.133163 val=0.857800 loss=1.124500 time: 33.212258
INFO:root:[Epoch 77] train=0.133315 val=0.870900 loss=1.134876 time: 33.051252
INFO:root:[Epoch 78] train=0.133359 val=0.865700 loss=1.142806 time: 31.311565
INFO:root:[Epoch 79] train=0.131960 val=0.852400 loss=1.136241 time: 30.540375
INFO:root:[Epoch 80] train=0.132135 val=0.881300 loss=1.119883 time: 31.482614
INFO:root:[Epoch 81] train=0.130376 val=0.849000 loss=1.103480 time: 30.932882
INFO:root:[Epoch 82] train=0.131050 val=0.857000 loss=1.090886 time: 30.977002
INFO:root:[Epoch 83] train=0.130395 val=0.880800 loss=1.086011 time: 31.119477
INFO:root:[Epoch 84] train=0.128654 val=0.866200 loss=1.075941 time: 30.904126
INFO:root:[Epoch 85] train=0.131059 val=0.841600 loss=1.123064 time: 30.280454
INFO:root:[Epoch 86] train=0.132094 val=0.826400 loss=1.133341 time: 30.988851
INFO:root:[Epoch 87] train=0.129839 val=0.870900 loss=1.077238 time: 31.017762
INFO:root:[Epoch 88] train=0.129108 val=0.858200 loss=1.103612 time: 30.941011
INFO:root:[Epoch 89] train=0.128681 val=0.874900 loss=1.085935 time: 31.164044
INFO:root:[Epoch 90] train=0.128887 val=0.883900 loss=1.094172 time: 31.071828
INFO:root:[Epoch 91] train=0.130465 val=0.857100 loss=1.133841 time: 31.089112
INFO:root:[Epoch 92] train=0.129402 val=0.876500 loss=1.086432 time: 31.023612
INFO:root:[Epoch 93] train=0.130975 val=0.866100 loss=1.127814 time: 31.031830
INFO:root:[Epoch 94] train=0.127593 val=0.866400 loss=1.077333 time: 30.985571
INFO:root:[Epoch 95] train=0.128692 val=0.872600 loss=1.095819 time: 32.514032
INFO:root:[Epoch 96] train=0.130665 val=0.878400 loss=1.131716 time: 32.455270
INFO:root:[Epoch 97] train=0.129796 val=0.833900 loss=1.105962 time: 32.505983
INFO:root:[Epoch 98] train=0.130630 val=0.885400 loss=1.119874 time: 32.549109
INFO:root:[Epoch 99] train=0.128632 val=0.842600 loss=1.091804 time: 32.555449
INFO:root:[Epoch 100] train=0.119389 val=0.912900 loss=1.038020 time: 32.247959
INFO:root:[Epoch 101] train=0.114615 val=0.920700 loss=1.021112 time: 31.029578
INFO:root:[Epoch 102] train=0.112626 val=0.921400 loss=0.990237 time: 31.145803
INFO:root:[Epoch 103] train=0.113190 val=0.920900 loss=1.008554 time: 31.437591
INFO:root:[Epoch 104] train=0.111599 val=0.923700 loss=0.987625 time: 31.359448
INFO:root:[Epoch 105] train=0.110624 val=0.915700 loss=0.986249 time: 31.242637
INFO:root:[Epoch 106] train=0.108930 val=0.926900 loss=0.985460 time: 31.002346
INFO:root:[Epoch 107] train=0.111466 val=0.919700 loss=1.008411 time: 31.501196
INFO:root:[Epoch 108] train=0.110174 val=0.929300 loss=0.990791 time: 31.358528
INFO:root:[Epoch 109] train=0.107474 val=0.925800 loss=0.973110 time: 31.098554
INFO:root:[Epoch 110] train=0.107544 val=0.921500 loss=0.969292 time: 31.250888
INFO:root:[Epoch 111] train=0.105689 val=0.921900 loss=0.944871 time: 30.902959
INFO:root:[Epoch 112] train=0.107920 val=0.923500 loss=0.984998 time: 30.825752
INFO:root:[Epoch 113] train=0.106089 val=0.922100 loss=0.952240 time: 31.307659
INFO:root:[Epoch 114] train=0.107446 val=0.927200 loss=0.989055 time: 31.204028
INFO:root:[Epoch 115] train=0.106558 val=0.930300 loss=0.953049 time: 30.770218
INFO:root:[Epoch 116] train=0.106760 val=0.924300 loss=0.965808 time: 30.991448
INFO:root:[Epoch 117] train=0.104227 val=0.927400 loss=0.941937 time: 31.246711
INFO:root:[Epoch 118] train=0.105593 val=0.924000 loss=0.959822 time: 30.942686
INFO:root:[Epoch 119] train=0.104126 val=0.923400 loss=0.934548 time: 31.235559
INFO:root:[Epoch 120] train=0.104170 val=0.927500 loss=0.935571 time: 31.108390
INFO:root:[Epoch 121] train=0.104662 val=0.926600 loss=0.953423 time: 32.360254
INFO:root:[Epoch 122] train=0.102823 val=0.929200 loss=0.934000 time: 31.067274
INFO:root:[Epoch 123] train=0.103175 val=0.926900 loss=0.935702 time: 31.101682
INFO:root:[Epoch 124] train=0.104141 val=0.929000 loss=0.941833 time: 31.096692
INFO:root:[Epoch 125] train=0.102558 val=0.924500 loss=0.923643 time: 31.159402
INFO:root:[Epoch 126] train=0.102265 val=0.923700 loss=0.941147 time: 30.838422
INFO:root:[Epoch 127] train=0.104924 val=0.927300 loss=0.970425 time: 31.247428
INFO:root:[Epoch 128] train=0.102918 val=0.928000 loss=0.936552 time: 31.112553
INFO:root:[Epoch 129] train=0.100400 val=0.925100 loss=0.923556 time: 31.122125
INFO:root:[Epoch 130] train=0.097987 val=0.926000 loss=0.902171 time: 31.351420
INFO:root:[Epoch 131] train=0.102225 val=0.930300 loss=0.932076 time: 31.209896
INFO:root:[Epoch 132] train=0.102828 val=0.928000 loss=0.953167 time: 30.977682
INFO:root:[Epoch 133] train=0.100437 val=0.924400 loss=0.925563 time: 30.930332
INFO:root:[Epoch 134] train=0.101271 val=0.923900 loss=0.931190 time: 32.972881
INFO:root:[Epoch 135] train=0.101539 val=0.928000 loss=0.923193 time: 32.959522
INFO:root:[Epoch 136] train=0.100941 val=0.925900 loss=0.923246 time: 33.035794
INFO:root:[Epoch 137] train=0.099520 val=0.930500 loss=0.911130 time: 31.273552
INFO:root:[Epoch 138] train=0.097472 val=0.928100 loss=0.900860 time: 31.192741
INFO:root:[Epoch 139] train=0.096280 val=0.925400 loss=0.880021 time: 30.557854
INFO:root:[Epoch 140] train=0.100861 val=0.931800 loss=0.936867 time: 30.982085
INFO:root:[Epoch 141] train=0.099566 val=0.928500 loss=0.918870 time: 31.205555
INFO:root:[Epoch 142] train=0.101553 val=0.923900 loss=0.944290 time: 30.907724
INFO:root:[Epoch 143] train=0.099493 val=0.930800 loss=0.921257 time: 33.108743
INFO:root:[Epoch 144] train=0.102908 val=0.930300 loss=0.959346 time: 33.166672
INFO:root:[Epoch 145] train=0.103861 val=0.925500 loss=0.963164 time: 31.126696
INFO:root:[Epoch 146] train=0.099628 val=0.929300 loss=0.923344 time: 31.061267
INFO:root:[Epoch 147] train=0.099542 val=0.921800 loss=0.917899 time: 30.807081
INFO:root:[Epoch 148] train=0.100501 val=0.932100 loss=0.925660 time: 32.434849
INFO:root:[Epoch 149] train=0.100281 val=0.926200 loss=0.928853 time: 31.483564
INFO:root:[Epoch 150] train=0.099725 val=0.932800 loss=0.944187 time: 31.510724
INFO:root:[Epoch 151] train=0.095489 val=0.934100 loss=0.898410 time: 31.192080
INFO:root:[Epoch 152] train=0.096626 val=0.933900 loss=0.918057 time: 31.020529
INFO:root:[Epoch 153] train=0.096483 val=0.932600 loss=0.912193 time: 30.776595
INFO:root:[Epoch 154] train=0.097400 val=0.934900 loss=0.927416 time: 31.248380
INFO:root:[Epoch 155] train=0.093699 val=0.933500 loss=0.889435 time: 31.097425
INFO:root:[Epoch 156] train=0.092672 val=0.934300 loss=0.880543 time: 32.847086
INFO:root:[Epoch 157] train=0.094926 val=0.933300 loss=0.907687 time: 32.615067
INFO:root:[Epoch 158] train=0.095737 val=0.936500 loss=0.911872 time: 32.684478
INFO:root:[Epoch 159] train=0.097649 val=0.934300 loss=0.930986 time: 32.622373
INFO:root:[Epoch 160] train=0.093836 val=0.936000 loss=0.898200 time: 32.605075
INFO:root:[Epoch 161] train=0.096165 val=0.935800 loss=0.916353 time: 32.943762
INFO:root:[Epoch 162] train=0.093223 val=0.936600 loss=0.880325 time: 32.828555
INFO:root:[Epoch 163] train=0.094413 val=0.938200 loss=0.900386 time: 31.749761
INFO:root:[Epoch 164] train=0.094779 val=0.934100 loss=0.905692 time: 33.359275
INFO:root:[Epoch 165] train=0.093326 val=0.933600 loss=0.888379 time: 33.232528
INFO:root:[Epoch 166] train=0.095951 val=0.934900 loss=0.917850 time: 33.047994
INFO:root:[Epoch 167] train=0.093223 val=0.934300 loss=0.890872 time: 32.959855
INFO:root:[Epoch 168] train=0.093768 val=0.934900 loss=0.896927 time: 31.891189
INFO:root:[Epoch 169] train=0.095873 val=0.933200 loss=0.917749 time: 31.451100
INFO:root:[Epoch 170] train=0.093879 val=0.938400 loss=0.893192 time: 32.229775
INFO:root:[Epoch 171] train=0.093711 val=0.935800 loss=0.892548 time: 32.379835
INFO:root:[Epoch 172] train=0.095438 val=0.936200 loss=0.916385 time: 30.875024
INFO:root:[Epoch 173] train=0.092972 val=0.934700 loss=0.883934 time: 30.933900
INFO:root:[Epoch 174] train=0.093114 val=0.934400 loss=0.895221 time: 31.312036
INFO:root:[Epoch 175] train=0.093994 val=0.936900 loss=0.903956 time: 31.170646
INFO:root:[Epoch 176] train=0.096197 val=0.937000 loss=0.923305 time: 31.167003
INFO:root:[Epoch 177] train=0.094184 val=0.933800 loss=0.905254 time: 31.401820
INFO:root:[Epoch 178] train=0.094535 val=0.934500 loss=0.909958 time: 33.425636
INFO:root:[Epoch 179] train=0.093224 val=0.937100 loss=0.884142 time: 33.354227
INFO:root:[Epoch 180] train=0.094406 val=0.937000 loss=0.904240 time: 33.455952
INFO:root:[Epoch 181] train=0.092677 val=0.937600 loss=0.889786 time: 31.564631
INFO:root:[Epoch 182] train=0.094566 val=0.935900 loss=0.910457 time: 31.335675
INFO:root:[Epoch 183] train=0.094163 val=0.937400 loss=0.906886 time: 31.470773
INFO:root:[Epoch 184] train=0.096383 val=0.935600 loss=0.926090 time: 31.611580
INFO:root:[Epoch 185] train=0.091797 val=0.933400 loss=0.867345 time: 31.303591
INFO:root:[Epoch 186] train=0.093197 val=0.936600 loss=0.890929 time: 31.462399
INFO:root:[Epoch 187] train=0.094792 val=0.936600 loss=0.909964 time: 31.304985
INFO:root:[Epoch 188] train=0.091743 val=0.933100 loss=0.885973 time: 31.621666
INFO:root:[Epoch 189] train=0.092689 val=0.937400 loss=0.877523 time: 31.302109
INFO:root:[Epoch 190] train=0.090557 val=0.935100 loss=0.871286 time: 31.475441
INFO:root:[Epoch 191] train=0.093419 val=0.938600 loss=0.895070 time: 33.231300
INFO:root:[Epoch 192] train=0.093009 val=0.936400 loss=0.891070 time: 32.787014
INFO:root:[Epoch 193] train=0.091732 val=0.933800 loss=0.888578 time: 30.878043
INFO:root:[Epoch 194] train=0.095330 val=0.937600 loss=0.918983 time: 32.273140
INFO:root:[Epoch 195] train=0.095334 val=0.934900 loss=0.908604 time: 31.704738
INFO:root:[Epoch 196] train=0.089722 val=0.938900 loss=0.868906 time: 31.468515
INFO:root:[Epoch 197] train=0.091567 val=0.937000 loss=0.874226 time: 31.321301
INFO:root:[Epoch 198] train=0.092314 val=0.934300 loss=0.880295 time: 31.701340
INFO:root:[Epoch 199] train=0.093597 val=0.937900 loss=0.901062 time: 31.347433
INFO:root:[Epoch 200] train=0.040203 val=0.941100 loss=0.054403 time: 31.469229
INFO:root:[Epoch 201] train=0.038527 val=0.941000 loss=0.045609 time: 31.357336
INFO:root:[Epoch 202] train=0.037634 val=0.941900 loss=0.041740 time: 31.612705
INFO:root:[Epoch 203] train=0.036394 val=0.939800 loss=0.039322 time: 31.290005
INFO:root:[Epoch 204] train=0.035586 val=0.941600 loss=0.037468 time: 31.185529
INFO:root:[Epoch 205] train=0.034641 val=0.940700 loss=0.034903 time: 31.310957
INFO:root:[Epoch 206] train=0.033263 val=0.941400 loss=0.032618 time: 31.434949
INFO:root:[Epoch 207] train=0.032400 val=0.942000 loss=0.030910 time: 31.417430
INFO:root:[Epoch 208] train=0.031070 val=0.939600 loss=0.029129 time: 31.318435
INFO:root:[Epoch 209] train=0.031394 val=0.940500 loss=0.029171 time: 31.932187
INFO:root:[Epoch 210] train=0.031545 val=0.940500 loss=0.028801 time: 31.310368
INFO:root:[Epoch 211] train=0.030711 val=0.941700 loss=0.027229 time: 31.726441
INFO:root:[Epoch 212] train=0.030118 val=0.940800 loss=0.026277 time: 32.414606
INFO:root:[Epoch 213] train=0.030874 val=0.940900 loss=0.027009 time: 31.463490
INFO:root:[Epoch 214] train=0.029724 val=0.941200 loss=0.025320 time: 31.123005
INFO:root:[Epoch 215] train=0.028417 val=0.941500 loss=0.023741 time: 31.331209
INFO:root:[Epoch 216] train=0.028014 val=0.940100 loss=0.023827 time: 31.713582
INFO:root:[Epoch 217] train=0.027493 val=0.940700 loss=0.022860 time: 31.625681
INFO:root:[Epoch 218] train=0.027684 val=0.940900 loss=0.022762 time: 33.263472
INFO:root:[Epoch 219] train=0.026287 val=0.941100 loss=0.020992 time: 31.165117