INFO:root:Namespace(batch_size=128, drop_rate=0.0, lr=0.1, lr_decay=0.2, lr_decay_epoch='60,120,160', lr_decay_period=0, mode='hybrid', model='cifar_wideresnet16_10', momentum=0.9, num_epochs=200, num_gpus=1, num_workers=8, resume_from=None, save_dir='params', save_period=10, save_plot_dir='.', wd=0.0005)
[17:59:44] 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=0.452885 val=0.526300 loss=1.505485 time: 41.090768
INFO:root:[Epoch 1] train=0.660437 val=0.698300 loss=0.958634 time: 39.838578
INFO:root:[Epoch 2] train=0.746174 val=0.712600 loss=0.729965 time: 40.082485
INFO:root:[Epoch 3] train=0.789924 val=0.779200 loss=0.603674 time: 40.096536
INFO:root:[Epoch 4] train=0.817728 val=0.773900 loss=0.527407 time: 39.976265
INFO:root:[Epoch 5] train=0.834916 val=0.785400 loss=0.480524 time: 40.050292
INFO:root:[Epoch 6] train=0.848858 val=0.811600 loss=0.441215 time: 40.130361
INFO:root:[Epoch 7] train=0.858253 val=0.817000 loss=0.414508 time: 40.236865
INFO:root:[Epoch 8] train=0.862901 val=0.829900 loss=0.397598 time: 40.143710
INFO:root:[Epoch 9] train=0.870493 val=0.797500 loss=0.380865 time: 40.025119
INFO:root:[Epoch 10] train=0.876442 val=0.821300 loss=0.360702 time: 40.063477
INFO:root:[Epoch 11] train=0.878486 val=0.816800 loss=0.350798 time: 40.097237
INFO:root:[Epoch 12] train=0.882011 val=0.836600 loss=0.345096 time: 40.226307
INFO:root:[Epoch 13] train=0.885477 val=0.815100 loss=0.331985 time: 40.104483
INFO:root:[Epoch 14] train=0.886518 val=0.801900 loss=0.327884 time: 40.172089
INFO:root:[Epoch 15] train=0.890545 val=0.828400 loss=0.318842 time: 40.169616
INFO:root:[Epoch 16] train=0.893930 val=0.842600 loss=0.308963 time: 40.402603
INFO:root:[Epoch 17] train=0.895453 val=0.854700 loss=0.305085 time: 40.410649
INFO:root:[Epoch 18] train=0.896474 val=0.815600 loss=0.300487 time: 40.229836
INFO:root:[Epoch 19] train=0.897316 val=0.794900 loss=0.297329 time: 40.310620
INFO:root:[Epoch 20] train=0.900421 val=0.818200 loss=0.290443 time: 40.414653
INFO:root:[Epoch 21] train=0.898838 val=0.833500 loss=0.293897 time: 40.376354
INFO:root:[Epoch 22] train=0.900881 val=0.867700 loss=0.287505 time: 40.344028
INFO:root:[Epoch 23] train=0.903966 val=0.865300 loss=0.282892 time: 40.211933
INFO:root:[Epoch 24] train=0.904367 val=0.867300 loss=0.278961 time: 40.345888
INFO:root:[Epoch 25] train=0.905228 val=0.857900 loss=0.277730 time: 40.267902
INFO:root:[Epoch 26] train=0.906070 val=0.852900 loss=0.275180 time: 40.149287
INFO:root:[Epoch 27] train=0.904868 val=0.875100 loss=0.273633 time: 40.279364
INFO:root:[Epoch 28] train=0.907752 val=0.836600 loss=0.269623 time: 40.252290
INFO:root:[Epoch 29] train=0.907772 val=0.871700 loss=0.268052 time: 40.201291
INFO:root:[Epoch 30] train=0.908113 val=0.770100 loss=0.265632 time: 40.295592
INFO:root:[Epoch 31] train=0.908033 val=0.859400 loss=0.264517 time: 40.174424
INFO:root:[Epoch 32] train=0.908574 val=0.871800 loss=0.270672 time: 40.252384
INFO:root:[Epoch 33] train=0.908233 val=0.878600 loss=0.267298 time: 40.223646
INFO:root:[Epoch 34] train=0.911759 val=0.791000 loss=0.259774 time: 40.259261
INFO:root:[Epoch 35] train=0.908233 val=0.843500 loss=0.264976 time: 40.256903
INFO:root:[Epoch 36] train=0.911338 val=0.828900 loss=0.259541 time: 40.177868
INFO:root:[Epoch 37] train=0.912240 val=0.838600 loss=0.258554 time: 40.256668
INFO:root:[Epoch 38] train=0.913301 val=0.879400 loss=0.252139 time: 40.284456
INFO:root:[Epoch 39] train=0.914643 val=0.872700 loss=0.250713 time: 40.143083
INFO:root:[Epoch 40] train=0.912079 val=0.849700 loss=0.259664 time: 40.160038
INFO:root:[Epoch 41] train=0.912921 val=0.836800 loss=0.255025 time: 40.649544
INFO:root:[Epoch 42] train=0.913742 val=0.837900 loss=0.255020 time: 41.088382
INFO:root:[Epoch 43] train=0.912861 val=0.876500 loss=0.255571 time: 41.072700
INFO:root:[Epoch 44] train=0.912881 val=0.844900 loss=0.252378 time: 40.977554
INFO:root:[Epoch 45] train=0.912981 val=0.868500 loss=0.252966 time: 40.976509
INFO:root:[Epoch 46] train=0.914083 val=0.872300 loss=0.252068 time: 40.948708
INFO:root:[Epoch 47] train=0.915004 val=0.827700 loss=0.248730 time: 41.084691
INFO:root:[Epoch 48] train=0.916326 val=0.836200 loss=0.247089 time: 41.114538
INFO:root:[Epoch 49] train=0.915705 val=0.859000 loss=0.245918 time: 40.987998
INFO:root:[Epoch 50] train=0.913341 val=0.859500 loss=0.253281 time: 41.018335
INFO:root:[Epoch 51] train=0.915064 val=0.867800 loss=0.248310 time: 40.942216
INFO:root:[Epoch 52] train=0.914002 val=0.850700 loss=0.248857 time: 41.044087
INFO:root:[Epoch 53] train=0.914623 val=0.843700 loss=0.246441 time: 41.031204
INFO:root:[Epoch 54] train=0.915365 val=0.839400 loss=0.245162 time: 41.041178
INFO:root:[Epoch 55] train=0.916647 val=0.815800 loss=0.243110 time: 41.044522
INFO:root:[Epoch 56] train=0.915905 val=0.852500 loss=0.245756 time: 41.177765
INFO:root:[Epoch 57] train=0.914744 val=0.874700 loss=0.244910 time: 40.963608
INFO:root:[Epoch 58] train=0.917808 val=0.867600 loss=0.241898 time: 41.067286
INFO:root:[Epoch 59] train=0.916887 val=0.840300 loss=0.243665 time: 41.058361
INFO:root:[Epoch 60] train=0.964583 val=0.940000 loss=0.110462 time: 41.110763
INFO:root:[Epoch 61] train=0.979006 val=0.942100 loss=0.069053 time: 41.006274
INFO:root:[Epoch 62] train=0.984054 val=0.940800 loss=0.052775 time: 40.931876
INFO:root:[Epoch 63] train=0.986839 val=0.943600 loss=0.045191 time: 41.103805
INFO:root:[Epoch 64] train=0.987901 val=0.942100 loss=0.041042 time: 40.972444
INFO:root:[Epoch 65] train=0.990605 val=0.941400 loss=0.035486 time: 40.937173
INFO:root:[Epoch 66] train=0.990264 val=0.941200 loss=0.034055 time: 41.041673
INFO:root:[Epoch 67] train=0.991486 val=0.940800 loss=0.031631 time: 40.906663
INFO:root:[Epoch 68] train=0.990385 val=0.941700 loss=0.033839 time: 40.984285
INFO:root:[Epoch 69] train=0.991607 val=0.937700 loss=0.030639 time: 41.045866
INFO:root:[Epoch 70] train=0.989263 val=0.933600 loss=0.037054 time: 41.117827
INFO:root:[Epoch 71] train=0.986298 val=0.929400 loss=0.043109 time: 41.018928
INFO:root:[Epoch 72] train=0.987260 val=0.928500 loss=0.042760 time: 41.015058
INFO:root:[Epoch 73] train=0.986799 val=0.930400 loss=0.043653 time: 40.960628
INFO:root:[Epoch 74] train=0.984996 val=0.934900 loss=0.048099 time: 41.172306
INFO:root:[Epoch 75] train=0.987340 val=0.927500 loss=0.042228 time: 40.868697
INFO:root:[Epoch 76] train=0.983814 val=0.919500 loss=0.051048 time: 40.924271
INFO:root:[Epoch 77] train=0.982592 val=0.915600 loss=0.055859 time: 41.084815
INFO:root:[Epoch 78] train=0.981671 val=0.922600 loss=0.057411 time: 40.548730
INFO:root:[Epoch 79] train=0.982432 val=0.921300 loss=0.057072 time: 40.887219
INFO:root:[Epoch 80] train=0.979968 val=0.924800 loss=0.063078 time: 41.059592
INFO:root:[Epoch 81] train=0.981150 val=0.916300 loss=0.060413 time: 41.595970
INFO:root:[Epoch 82] train=0.982131 val=0.910900 loss=0.055381 time: 40.858116
INFO:root:[Epoch 83] train=0.980489 val=0.922500 loss=0.060907 time: 40.817080
INFO:root:[Epoch 84] train=0.983193 val=0.921300 loss=0.054832 time: 40.876421
INFO:root:[Epoch 85] train=0.982692 val=0.910400 loss=0.055187 time: 40.877991
INFO:root:[Epoch 86] train=0.981270 val=0.922600 loss=0.058762 time: 40.745024
INFO:root:[Epoch 87] train=0.981270 val=0.921000 loss=0.058360 time: 40.719026
INFO:root:[Epoch 88] train=0.983514 val=0.925700 loss=0.052850 time: 40.652221
INFO:root:[Epoch 89] train=0.983393 val=0.903800 loss=0.052685 time: 40.246643
INFO:root:[Epoch 90] train=0.982672 val=0.919600 loss=0.056549 time: 40.373135
INFO:root:[Epoch 91] train=0.981931 val=0.914900 loss=0.056973 time: 40.425586
INFO:root:[Epoch 92] train=0.983554 val=0.925500 loss=0.052064 time: 40.194414
INFO:root:[Epoch 93] train=0.983153 val=0.910600 loss=0.053379 time: 40.250663
INFO:root:[Epoch 94] train=0.981110 val=0.910000 loss=0.059779 time: 40.382052
INFO:root:[Epoch 95] train=0.980469 val=0.910800 loss=0.060333 time: 40.078893
INFO:root:[Epoch 96] train=0.982812 val=0.915800 loss=0.053778 time: 40.123154
INFO:root:[Epoch 97] train=0.982893 val=0.930400 loss=0.054217 time: 40.400787
INFO:root:[Epoch 98] train=0.984615 val=0.916500 loss=0.048070 time: 40.256018
INFO:root:[Epoch 99] train=0.985357 val=0.900200 loss=0.047654 time: 40.305305
INFO:root:[Epoch 100] train=0.983854 val=0.924600 loss=0.051676 time: 40.198760
INFO:root:[Epoch 101] train=0.983173 val=0.902000 loss=0.054179 time: 40.307047
INFO:root:[Epoch 102] train=0.983193 val=0.916600 loss=0.053435 time: 40.278483
INFO:root:[Epoch 103] train=0.983033 val=0.919800 loss=0.054895 time: 40.374098
INFO:root:[Epoch 104] train=0.984375 val=0.910600 loss=0.050057 time: 40.405598
INFO:root:[Epoch 105] train=0.982392 val=0.917700 loss=0.056191 time: 40.368729
INFO:root:[Epoch 106] train=0.984335 val=0.927000 loss=0.052149 time: 40.401544
INFO:root:[Epoch 107] train=0.983714 val=0.921900 loss=0.051251 time: 40.233444
INFO:root:[Epoch 108] train=0.984034 val=0.930400 loss=0.051889 time: 40.242075
INFO:root:[Epoch 109] train=0.982833 val=0.922100 loss=0.055353 time: 40.258028
INFO:root:[Epoch 110] train=0.983754 val=0.916000 loss=0.052091 time: 40.224135
INFO:root:[Epoch 111] train=0.983173 val=0.925700 loss=0.054331 time: 40.219215
INFO:root:[Epoch 112] train=0.986819 val=0.924400 loss=0.043551 time: 40.320031
INFO:root:[Epoch 113] train=0.983874 val=0.916500 loss=0.050389 time: 40.405449
INFO:root:[Epoch 114] train=0.982572 val=0.918100 loss=0.054731 time: 40.339697
INFO:root:[Epoch 115] train=0.984856 val=0.913100 loss=0.049172 time: 40.264746
INFO:root:[Epoch 116] train=0.983914 val=0.920800 loss=0.053165 time: 40.231099
INFO:root:[Epoch 117] train=0.983293 val=0.927600 loss=0.055161 time: 40.260970
INFO:root:[Epoch 118] train=0.982893 val=0.901300 loss=0.052994 time: 40.179638
INFO:root:[Epoch 119] train=0.984876 val=0.931500 loss=0.049038 time: 40.460463
INFO:root:[Epoch 120] train=0.996575 val=0.950600 loss=0.015862 time: 40.376320
INFO:root:[Epoch 121] train=0.998958 val=0.952800 loss=0.007856 time: 40.425187
INFO:root:[Epoch 122] train=0.999539 val=0.953300 loss=0.005822 time: 40.298849
INFO:root:[Epoch 123] train=0.999459 val=0.953200 loss=0.005292 time: 40.300383
INFO:root:[Epoch 124] train=0.999820 val=0.953800 loss=0.004274 time: 40.265657
INFO:root:[Epoch 125] train=0.999639 val=0.954200 loss=0.004138 time: 40.284036
INFO:root:[Epoch 126] train=0.999800 val=0.955400 loss=0.003730 time: 40.391626
INFO:root:[Epoch 127] train=0.999700 val=0.953400 loss=0.003774 time: 40.253561
INFO:root:[Epoch 128] train=0.999840 val=0.955500 loss=0.003384 time: 40.393407
INFO:root:[Epoch 129] train=0.999900 val=0.954100 loss=0.002976 time: 40.575016
INFO:root:[Epoch 130] train=0.999920 val=0.955000 loss=0.002939 time: 40.268593
INFO:root:[Epoch 131] train=0.999960 val=0.955400 loss=0.002841 time: 40.397584
INFO:root:[Epoch 132] train=0.999980 val=0.956100 loss=0.002875 time: 40.418379
INFO:root:[Epoch 133] train=0.999940 val=0.956100 loss=0.002637 time: 40.305007
INFO:root:[Epoch 134] train=0.999940 val=0.956000 loss=0.002760 time: 40.218800
INFO:root:[Epoch 135] train=0.999940 val=0.954400 loss=0.002742 time: 40.309353
INFO:root:[Epoch 136] train=0.999980 val=0.955000 loss=0.002537 time: 40.463141
INFO:root:[Epoch 137] train=0.999920 val=0.956000 loss=0.002596 time: 40.312790
INFO:root:[Epoch 138] train=0.999960 val=0.956000 loss=0.002568 time: 40.313195
INFO:root:[Epoch 139] train=0.999980 val=0.956900 loss=0.002483 time: 40.563648
INFO:root:[Epoch 140] train=0.999960 val=0.956200 loss=0.002511 time: 40.340726
INFO:root:[Epoch 141] train=1.000000 val=0.955000 loss=0.002450 time: 40.239937
INFO:root:[Epoch 142] train=0.999980 val=0.956000 loss=0.002589 time: 40.311189
INFO:root:[Epoch 143] train=1.000000 val=0.956200 loss=0.002389 time: 40.347612
INFO:root:[Epoch 144] train=0.999980 val=0.956100 loss=0.002482 time: 40.265009
INFO:root:[Epoch 145] train=1.000000 val=0.956400 loss=0.002404 time: 40.384379
INFO:root:[Epoch 146] train=0.999960 val=0.955400 loss=0.002448 time: 40.425565
INFO:root:[Epoch 147] train=0.999960 val=0.956500 loss=0.002605 time: 40.396537
INFO:root:[Epoch 148] train=0.999960 val=0.956000 loss=0.002630 time: 40.193232
INFO:root:[Epoch 149] train=1.000000 val=0.956400 loss=0.002499 time: 40.461458
INFO:root:[Epoch 150] train=0.999980 val=0.956600 loss=0.002556 time: 40.388512
INFO:root:[Epoch 151] train=0.999960 val=0.955300 loss=0.002553 time: 40.458123
INFO:root:[Epoch 152] train=1.000000 val=0.955700 loss=0.002506 time: 40.357268
INFO:root:[Epoch 153] train=0.999960 val=0.956000 loss=0.002647 time: 40.514898
INFO:root:[Epoch 154] train=1.000000 val=0.955600 loss=0.002561 time: 40.424350
INFO:root:[Epoch 155] train=1.000000 val=0.955300 loss=0.002483 time: 40.312543
INFO:root:[Epoch 156] train=0.999980 val=0.955000 loss=0.002563 time: 40.408589
INFO:root:[Epoch 157] train=1.000000 val=0.956700 loss=0.002472 time: 40.297699
INFO:root:[Epoch 158] train=1.000000 val=0.955600 loss=0.002500 time: 40.403031
INFO:root:[Epoch 159] train=0.999960 val=0.956800 loss=0.002633 time: 40.364763
INFO:root:[Epoch 160] train=1.000000 val=0.956200 loss=0.002443 time: 40.524344
INFO:root:[Epoch 161] train=1.000000 val=0.957600 loss=0.002493 time: 40.388657
INFO:root:[Epoch 162] train=0.999980 val=0.957500 loss=0.002386 time: 40.429811
INFO:root:[Epoch 163] train=0.999980 val=0.956600 loss=0.002437 time: 40.335319
INFO:root:[Epoch 164] train=0.999960 val=0.956300 loss=0.002405 time: 40.401859
INFO:root:[Epoch 165] train=1.000000 val=0.956500 loss=0.002420 time: 40.375349
INFO:root:[Epoch 166] train=1.000000 val=0.958000 loss=0.002404 time: 40.412515
INFO:root:[Epoch 167] train=1.000000 val=0.956600 loss=0.002329 time: 40.281207
INFO:root:[Epoch 168] train=1.000000 val=0.957400 loss=0.002374 time: 40.467407
INFO:root:[Epoch 169] train=1.000000 val=0.956800 loss=0.002320 time: 40.330910
INFO:root:[Epoch 170] train=1.000000 val=0.957500 loss=0.002392 time: 40.345917
INFO:root:[Epoch 171] train=1.000000 val=0.957400 loss=0.002385 time: 40.324143
INFO:root:[Epoch 172] train=1.000000 val=0.957600 loss=0.002289 time: 40.398934
INFO:root:[Epoch 173] train=1.000000 val=0.957500 loss=0.002412 time: 40.441342
INFO:root:[Epoch 174] train=1.000000 val=0.957800 loss=0.002383 time: 40.331205
INFO:root:[Epoch 175] train=1.000000 val=0.958800 loss=0.002423 time: 40.504648
INFO:root:[Epoch 176] train=1.000000 val=0.957000 loss=0.002421 time: 40.448477
INFO:root:[Epoch 177] train=0.999980 val=0.958100 loss=0.002482 time: 40.385350
INFO:root:[Epoch 178] train=1.000000 val=0.957300 loss=0.002353 time: 40.385279
INFO:root:[Epoch 179] train=0.999980 val=0.957200 loss=0.002365 time: 40.439954
INFO:root:[Epoch 180] train=1.000000 val=0.957900 loss=0.002460 time: 40.460447
INFO:root:[Epoch 181] train=1.000000 val=0.957100 loss=0.002392 time: 40.565180
INFO:root:[Epoch 182] train=1.000000 val=0.957200 loss=0.002406 time: 40.444778
INFO:root:[Epoch 183] train=1.000000 val=0.958100 loss=0.002343 time: 40.390881
INFO:root:[Epoch 184] train=0.999980 val=0.957300 loss=0.002402 time: 40.446874
INFO:root:[Epoch 185] train=1.000000 val=0.957500 loss=0.002455 time: 40.544557
INFO:root:[Epoch 186] train=1.000000 val=0.957300 loss=0.002376 time: 40.596416
INFO:root:[Epoch 187] train=1.000000 val=0.958600 loss=0.002441 time: 40.604706
INFO:root:[Epoch 188] train=1.000000 val=0.958500 loss=0.002330 time: 40.586975
INFO:root:[Epoch 189] train=1.000000 val=0.957400 loss=0.002380 time: 40.484769
INFO:root:[Epoch 190] train=1.000000 val=0.957800 loss=0.002401 time: 40.317702
INFO:root:[Epoch 191] train=1.000000 val=0.958500 loss=0.002355 time: 40.606100
INFO:root:[Epoch 192] train=1.000000 val=0.957800 loss=0.002399 time: 40.452688
INFO:root:[Epoch 193] train=1.000000 val=0.958000 loss=0.002367 time: 40.404095
INFO:root:[Epoch 194] train=1.000000 val=0.957700 loss=0.002366 time: 40.410737
INFO:root:[Epoch 195] train=1.000000 val=0.957600 loss=0.002344 time: 40.431707
INFO:root:[Epoch 196] train=1.000000 val=0.957700 loss=0.002410 time: 40.553392
INFO:root:[Epoch 197] train=1.000000 val=0.958000 loss=0.002332 time: 40.469864
INFO:root:[Epoch 198] train=1.000000 val=0.957500 loss=0.002392 time: 40.288922
INFO:root:[Epoch 199] train=0.999980 val=0.958400 loss=0.002476 time: 40.522717