INFO:root:Namespace(batch_size=32, drop_rate=0.0, lr=0.1, lr_decay=0.1, lr_decay_epoch='150,225', lr_decay_period=0, mode='hybrid', model='cifar_resnext29_16x64d', momentum=0.9, num_epochs=300, num_gpus=4, num_workers=4, resume_from=None, save_dir='params', save_period=10, save_plot_dir='.', wd=0.0005)
[22:30:21] 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=0.254587 val=0.387000 loss=2.388983 time: 121.264746
INFO:root:[Epoch 1] train=0.439343 val=0.498100 loss=1.529739 time: 103.425415
INFO:root:[Epoch 2] train=0.562280 val=0.598100 loss=1.218344 time: 103.901607
INFO:root:[Epoch 3] train=0.668690 val=0.701300 loss=0.943388 time: 104.204033
INFO:root:[Epoch 4] train=0.733614 val=0.705900 loss=0.765795 time: 104.512723
INFO:root:[Epoch 5] train=0.767628 val=0.743900 loss=0.673113 time: 103.943775
INFO:root:[Epoch 6] train=0.790785 val=0.788400 loss=0.609754 time: 104.364944
INFO:root:[Epoch 7] train=0.804547 val=0.774100 loss=0.564139 time: 103.743787
INFO:root:[Epoch 8] train=0.818029 val=0.780400 loss=0.530973 time: 103.840765
INFO:root:[Epoch 9] train=0.827985 val=0.800800 loss=0.501325 time: 104.030176
INFO:root:[Epoch 10] train=0.836018 val=0.784600 loss=0.480235 time: 103.590893
INFO:root:[Epoch 11] train=0.842408 val=0.806600 loss=0.457477 time: 103.796863
INFO:root:[Epoch 12] train=0.847857 val=0.823200 loss=0.444729 time: 103.777773
INFO:root:[Epoch 13] train=0.850741 val=0.811300 loss=0.432450 time: 103.446299
INFO:root:[Epoch 14] train=0.856070 val=0.822200 loss=0.421495 time: 103.735004
INFO:root:[Epoch 15] train=0.856771 val=0.814700 loss=0.416995 time: 103.764821
INFO:root:[Epoch 16] train=0.863101 val=0.846500 loss=0.401943 time: 103.950046
INFO:root:[Epoch 17] train=0.866306 val=0.811900 loss=0.394671 time: 103.414543
INFO:root:[Epoch 18] train=0.865264 val=0.824600 loss=0.392417 time: 103.754372
INFO:root:[Epoch 19] train=0.870773 val=0.788800 loss=0.378488 time: 103.423100
INFO:root:[Epoch 20] train=0.873057 val=0.841000 loss=0.373006 time: 103.344018
INFO:root:[Epoch 21] train=0.874700 val=0.820600 loss=0.367266 time: 104.097478
INFO:root:[Epoch 22] train=0.875020 val=0.840200 loss=0.363705 time: 103.767939
INFO:root:[Epoch 23] train=0.879087 val=0.781300 loss=0.353888 time: 103.779761
INFO:root:[Epoch 24] train=0.878365 val=0.833200 loss=0.351732 time: 103.589624
INFO:root:[Epoch 25] train=0.882913 val=0.834300 loss=0.346682 time: 103.737566
INFO:root:[Epoch 26] train=0.882993 val=0.838100 loss=0.342275 time: 103.437846
INFO:root:[Epoch 27] train=0.884956 val=0.820500 loss=0.335301 time: 103.849993
INFO:root:[Epoch 28] train=0.886839 val=0.833500 loss=0.334601 time: 103.640695
INFO:root:[Epoch 29] train=0.886659 val=0.858400 loss=0.330526 time: 104.008021
INFO:root:[Epoch 30] train=0.887179 val=0.821100 loss=0.329138 time: 103.403264
INFO:root:[Epoch 31] train=0.889343 val=0.830000 loss=0.324099 time: 103.638965
INFO:root:[Epoch 32] train=0.889704 val=0.865800 loss=0.325065 time: 103.855956
INFO:root:[Epoch 33] train=0.890204 val=0.870200 loss=0.319664 time: 103.886632
INFO:root:[Epoch 34] train=0.893950 val=0.834800 loss=0.314336 time: 103.427017
INFO:root:[Epoch 35] train=0.891106 val=0.840300 loss=0.315513 time: 103.824211
INFO:root:[Epoch 36] train=0.896635 val=0.859600 loss=0.303954 time: 103.479729
INFO:root:[Epoch 37] train=0.893129 val=0.802800 loss=0.313049 time: 103.580083
INFO:root:[Epoch 38] train=0.892107 val=0.841400 loss=0.312927 time: 103.752292
INFO:root:[Epoch 39] train=0.893309 val=0.834500 loss=0.311141 time: 103.539993
INFO:root:[Epoch 40] train=0.893810 val=0.828000 loss=0.306463 time: 103.402920
INFO:root:[Epoch 41] train=0.894451 val=0.862900 loss=0.307286 time: 103.549328
INFO:root:[Epoch 42] train=0.896354 val=0.855800 loss=0.301753 time: 103.455883
INFO:root:[Epoch 43] train=0.896975 val=0.849800 loss=0.301644 time: 103.554921
INFO:root:[Epoch 44] train=0.894511 val=0.806200 loss=0.307605 time: 103.360553
INFO:root:[Epoch 45] train=0.899499 val=0.846700 loss=0.294207 time: 103.313168
INFO:root:[Epoch 46] train=0.898037 val=0.860900 loss=0.299756 time: 103.517361
INFO:root:[Epoch 47] train=0.898377 val=0.862000 loss=0.297062 time: 103.589144
INFO:root:[Epoch 48] train=0.900140 val=0.830300 loss=0.292924 time: 103.467623
INFO:root:[Epoch 49] train=0.899760 val=0.848300 loss=0.294213 time: 103.654468
INFO:root:[Epoch 50] train=0.897656 val=0.844900 loss=0.294228 time: 103.474528
INFO:root:[Epoch 51] train=0.899179 val=0.827000 loss=0.292572 time: 103.574239
INFO:root:[Epoch 52] train=0.901162 val=0.855100 loss=0.291084 time: 103.488753
INFO:root:[Epoch 53] train=0.901522 val=0.865300 loss=0.286054 time: 103.662927
INFO:root:[Epoch 54] train=0.900641 val=0.873800 loss=0.294568 time: 104.065435
INFO:root:[Epoch 55] train=0.901162 val=0.852900 loss=0.290041 time: 104.018101
INFO:root:[Epoch 56] train=0.900601 val=0.832300 loss=0.288417 time: 103.554592
INFO:root:[Epoch 57] train=0.902764 val=0.854100 loss=0.285997 time: 103.412689
INFO:root:[Epoch 58] train=0.902925 val=0.858300 loss=0.284951 time: 103.539226
INFO:root:[Epoch 59] train=0.900942 val=0.830900 loss=0.288738 time: 103.363522
INFO:root:[Epoch 60] train=0.901963 val=0.856300 loss=0.287156 time: 103.510250
INFO:root:[Epoch 61] train=0.902344 val=0.863100 loss=0.289372 time: 103.404736
INFO:root:[Epoch 62] train=0.901583 val=0.810400 loss=0.287537 time: 103.892159
INFO:root:[Epoch 63] train=0.903305 val=0.872500 loss=0.286246 time: 103.464158
INFO:root:[Epoch 64] train=0.904768 val=0.864600 loss=0.283525 time: 103.395466
INFO:root:[Epoch 65] train=0.901943 val=0.848100 loss=0.283435 time: 103.292471
INFO:root:[Epoch 66] train=0.903966 val=0.870100 loss=0.280134 time: 103.501338
INFO:root:[Epoch 67] train=0.904407 val=0.814200 loss=0.281932 time: 103.113713
INFO:root:[Epoch 68] train=0.904307 val=0.844400 loss=0.280998 time: 103.485421
INFO:root:[Epoch 69] train=0.903906 val=0.832800 loss=0.279185 time: 103.562079
INFO:root:[Epoch 70] train=0.902724 val=0.840300 loss=0.283664 time: 103.388610
INFO:root:[Epoch 71] train=0.903486 val=0.879500 loss=0.282173 time: 103.625482
INFO:root:[Epoch 72] train=0.904046 val=0.864900 loss=0.282390 time: 103.397816
INFO:root:[Epoch 73] train=0.904868 val=0.878700 loss=0.280434 time: 103.488379
INFO:root:[Epoch 74] train=0.905509 val=0.834700 loss=0.274315 time: 103.341583
INFO:root:[Epoch 75] train=0.903486 val=0.844400 loss=0.282614 time: 103.475355
INFO:root:[Epoch 76] train=0.902945 val=0.840600 loss=0.283449 time: 103.529373
INFO:root:[Epoch 77] train=0.907192 val=0.849700 loss=0.272645 time: 103.437257
INFO:root:[Epoch 78] train=0.904647 val=0.858100 loss=0.279580 time: 103.769725
INFO:root:[Epoch 79] train=0.907292 val=0.868700 loss=0.273265 time: 103.469408
INFO:root:[Epoch 80] train=0.904708 val=0.849000 loss=0.278242 time: 103.898952
INFO:root:[Epoch 81] train=0.905749 val=0.847400 loss=0.276294 time: 103.686139
INFO:root:[Epoch 82] train=0.906030 val=0.849100 loss=0.278005 time: 103.559281
INFO:root:[Epoch 83] train=0.905108 val=0.864800 loss=0.278900 time: 103.616832
INFO:root:[Epoch 84] train=0.905329 val=0.847300 loss=0.276838 time: 103.433567
INFO:root:[Epoch 85] train=0.906130 val=0.841500 loss=0.272787 time: 103.621358
INFO:root:[Epoch 86] train=0.906110 val=0.853900 loss=0.272904 time: 103.435923
INFO:root:[Epoch 87] train=0.905529 val=0.864200 loss=0.276882 time: 103.552488
INFO:root:[Epoch 88] train=0.906631 val=0.840900 loss=0.274616 time: 103.356642
INFO:root:[Epoch 89] train=0.907071 val=0.864400 loss=0.275974 time: 103.243311
INFO:root:[Epoch 90] train=0.904627 val=0.833600 loss=0.279042 time: 104.094970
INFO:root:[Epoch 91] train=0.908514 val=0.836800 loss=0.268027 time: 103.458088
INFO:root:[Epoch 92] train=0.905829 val=0.860300 loss=0.273092 time: 103.292117
INFO:root:[Epoch 93] train=0.906591 val=0.882100 loss=0.275857 time: 103.783212
INFO:root:[Epoch 94] train=0.904107 val=0.873000 loss=0.275648 time: 103.563151
INFO:root:[Epoch 95] train=0.905649 val=0.842500 loss=0.276547 time: 103.544921
INFO:root:[Epoch 96] train=0.907953 val=0.853700 loss=0.271525 time: 103.376798
INFO:root:[Epoch 97] train=0.907031 val=0.873500 loss=0.272714 time: 103.468992
INFO:root:[Epoch 98] train=0.907833 val=0.867300 loss=0.272067 time: 103.748251
INFO:root:[Epoch 99] train=0.905869 val=0.824200 loss=0.272581 time: 103.474701
INFO:root:[Epoch 100] train=0.905909 val=0.868300 loss=0.274028 time: 103.588264
INFO:root:[Epoch 101] train=0.909555 val=0.865100 loss=0.268548 time: 103.513400
INFO:root:[Epoch 102] train=0.905849 val=0.810300 loss=0.274839 time: 103.436362
INFO:root:[Epoch 103] train=0.907893 val=0.854100 loss=0.269338 time: 103.835910
INFO:root:[Epoch 104] train=0.907332 val=0.847700 loss=0.269113 time: 103.515598
INFO:root:[Epoch 105] train=0.907272 val=0.876100 loss=0.270832 time: 103.724531
INFO:root:[Epoch 106] train=0.906891 val=0.875100 loss=0.271531 time: 103.325866
INFO:root:[Epoch 107] train=0.907953 val=0.838600 loss=0.267824 time: 103.499441
INFO:root:[Epoch 108] train=0.910196 val=0.857100 loss=0.263647 time: 104.127290
INFO:root:[Epoch 109] train=0.907071 val=0.875300 loss=0.273198 time: 103.994966
INFO:root:[Epoch 110] train=0.907332 val=0.852400 loss=0.270959 time: 103.589658
INFO:root:[Epoch 111] train=0.907031 val=0.820600 loss=0.271932 time: 103.545918
INFO:root:[Epoch 112] train=0.907452 val=0.884600 loss=0.272753 time: 103.794986
INFO:root:[Epoch 113] train=0.907091 val=0.810700 loss=0.267760 time: 103.310645
INFO:root:[Epoch 114] train=0.908313 val=0.834500 loss=0.268844 time: 103.591113
INFO:root:[Epoch 115] train=0.906731 val=0.862200 loss=0.271513 time: 103.228637
INFO:root:[Epoch 116] train=0.906891 val=0.813200 loss=0.269692 time: 103.400879
INFO:root:[Epoch 117] train=0.907813 val=0.865500 loss=0.270568 time: 103.458353
INFO:root:[Epoch 118] train=0.908514 val=0.849200 loss=0.266793 time: 104.020767
INFO:root:[Epoch 119] train=0.908794 val=0.832000 loss=0.269655 time: 103.415648
INFO:root:[Epoch 120] train=0.910877 val=0.875600 loss=0.261564 time: 103.936444
INFO:root:[Epoch 121] train=0.906130 val=0.857700 loss=0.275799 time: 103.727601
INFO:root:[Epoch 122] train=0.910537 val=0.831500 loss=0.266594 time: 103.609164
INFO:root:[Epoch 123] train=0.908333 val=0.847200 loss=0.268781 time: 103.598833
INFO:root:[Epoch 124] train=0.911699 val=0.799300 loss=0.261249 time: 103.459525
INFO:root:[Epoch 125] train=0.908173 val=0.860200 loss=0.267932 time: 104.341629
INFO:root:[Epoch 126] train=0.908934 val=0.839200 loss=0.268208 time: 103.378668
INFO:root:[Epoch 127] train=0.908373 val=0.826400 loss=0.267601 time: 103.775731
INFO:root:[Epoch 128] train=0.909034 val=0.851100 loss=0.264878 time: 103.515166
INFO:root:[Epoch 129] train=0.909295 val=0.810400 loss=0.265358 time: 103.615375
INFO:root:[Epoch 130] train=0.906991 val=0.870500 loss=0.271668 time: 103.460095
INFO:root:[Epoch 131] train=0.908854 val=0.836300 loss=0.267808 time: 103.647874
INFO:root:[Epoch 132] train=0.911659 val=0.874200 loss=0.261029 time: 103.500904
INFO:root:[Epoch 133] train=0.908213 val=0.837000 loss=0.269997 time: 103.580026
INFO:root:[Epoch 134] train=0.909435 val=0.855700 loss=0.264578 time: 103.649168
INFO:root:[Epoch 135] train=0.908153 val=0.844100 loss=0.268067 time: 103.568678
INFO:root:[Epoch 136] train=0.908073 val=0.860000 loss=0.271620 time: 103.567421
INFO:root:[Epoch 137] train=0.910437 val=0.845900 loss=0.264084 time: 103.473432
INFO:root:[Epoch 138] train=0.910677 val=0.869700 loss=0.264191 time: 103.750709
INFO:root:[Epoch 139] train=0.909575 val=0.826200 loss=0.263769 time: 103.652110
INFO:root:[Epoch 140] train=0.907412 val=0.837700 loss=0.269058 time: 103.430987
INFO:root:[Epoch 141] train=0.909235 val=0.864400 loss=0.264999 time: 103.632106
INFO:root:[Epoch 142] train=0.905429 val=0.859600 loss=0.269804 time: 103.394616
INFO:root:[Epoch 143] train=0.909595 val=0.814700 loss=0.262598 time: 103.581142
INFO:root:[Epoch 144] train=0.911679 val=0.853700 loss=0.259079 time: 103.729933
INFO:root:[Epoch 145] train=0.907933 val=0.829600 loss=0.269130 time: 103.561234
INFO:root:[Epoch 146] train=0.909034 val=0.842800 loss=0.267386 time: 103.796646
INFO:root:[Epoch 147] train=0.911138 val=0.848500 loss=0.262869 time: 103.333398
INFO:root:[Epoch 148] train=0.910958 val=0.847000 loss=0.263077 time: 104.119929
INFO:root:[Epoch 149] train=0.907752 val=0.854700 loss=0.268654 time: 103.724993
INFO:root:[Epoch 150] train=0.959235 val=0.943900 loss=0.129270 time: 104.133253
INFO:root:[Epoch 151] train=0.974379 val=0.947300 loss=0.086325 time: 104.000512
INFO:root:[Epoch 152] train=0.979447 val=0.949000 loss=0.070776 time: 103.773251
INFO:root:[Epoch 153] train=0.983534 val=0.952400 loss=0.058530 time: 103.880674
INFO:root:[Epoch 154] train=0.985897 val=0.950600 loss=0.051479 time: 103.605982
INFO:root:[Epoch 155] train=0.988982 val=0.953100 loss=0.043408 time: 103.931591
INFO:root:[Epoch 156] train=0.990184 val=0.953700 loss=0.039316 time: 104.160892
INFO:root:[Epoch 157] train=0.992348 val=0.951900 loss=0.033763 time: 103.583744
INFO:root:[Epoch 158] train=0.993149 val=0.952900 loss=0.031273 time: 103.555459
INFO:root:[Epoch 159] train=0.994812 val=0.953800 loss=0.026798 time: 103.958829
INFO:root:[Epoch 160] train=0.995453 val=0.951800 loss=0.024424 time: 103.941102
INFO:root:[Epoch 161] train=0.996054 val=0.953600 loss=0.022179 time: 103.784477
INFO:root:[Epoch 162] train=0.996895 val=0.957000 loss=0.019369 time: 104.260732
INFO:root:[Epoch 163] train=0.996895 val=0.955700 loss=0.019152 time: 103.563078
INFO:root:[Epoch 164] train=0.997536 val=0.954400 loss=0.017106 time: 103.718253
INFO:root:[Epoch 165] train=0.997416 val=0.952400 loss=0.016594 time: 104.378000
INFO:root:[Epoch 166] train=0.997556 val=0.953000 loss=0.016058 time: 104.202289
INFO:root:[Epoch 167] train=0.997696 val=0.954900 loss=0.015912 time: 103.627378
INFO:root:[Epoch 168] train=0.997837 val=0.955000 loss=0.015245 time: 103.814240
INFO:root:[Epoch 169] train=0.998458 val=0.955400 loss=0.013716 time: 103.607377
INFO:root:[Epoch 170] train=0.998538 val=0.953200 loss=0.013282 time: 103.752795
INFO:root:[Epoch 171] train=0.998718 val=0.957000 loss=0.012619 time: 103.599325
INFO:root:[Epoch 172] train=0.999018 val=0.953900 loss=0.011557 time: 103.554273
INFO:root:[Epoch 173] train=0.998638 val=0.952600 loss=0.011909 time: 103.624299
INFO:root:[Epoch 174] train=0.998518 val=0.954300 loss=0.012073 time: 103.788840
INFO:root:[Epoch 175] train=0.998357 val=0.954500 loss=0.012912 time: 103.896904
INFO:root:[Epoch 176] train=0.998458 val=0.955400 loss=0.012411 time: 104.193541
INFO:root:[Epoch 177] train=0.998518 val=0.953500 loss=0.011786 time: 103.590558
INFO:root:[Epoch 178] train=0.998898 val=0.953900 loss=0.011831 time: 103.822768
INFO:root:[Epoch 179] train=0.998978 val=0.953800 loss=0.011586 time: 103.693007
INFO:root:[Epoch 180] train=0.998898 val=0.955500 loss=0.010775 time: 103.684890
INFO:root:[Epoch 181] train=0.998177 val=0.950800 loss=0.013390 time: 103.715364
INFO:root:[Epoch 182] train=0.998377 val=0.950500 loss=0.013545 time: 103.621375
INFO:root:[Epoch 183] train=0.998057 val=0.956400 loss=0.013993 time: 103.812442
INFO:root:[Epoch 184] train=0.998277 val=0.953800 loss=0.013584 time: 103.694686
INFO:root:[Epoch 185] train=0.998137 val=0.954100 loss=0.014003 time: 103.450503
INFO:root:[Epoch 186] train=0.998077 val=0.950200 loss=0.014452 time: 103.915686
INFO:root:[Epoch 187] train=0.998097 val=0.948500 loss=0.014375 time: 103.779391
INFO:root:[Epoch 188] train=0.997696 val=0.951500 loss=0.015270 time: 103.778690
INFO:root:[Epoch 189] train=0.997456 val=0.949500 loss=0.016610 time: 103.818416
INFO:root:[Epoch 190] train=0.995573 val=0.946400 loss=0.021897 time: 103.752133
INFO:root:[Epoch 191] train=0.996194 val=0.950900 loss=0.021320 time: 104.084825
INFO:root:[Epoch 192] train=0.996214 val=0.942000 loss=0.020785 time: 103.746345
INFO:root:[Epoch 193] train=0.995132 val=0.949600 loss=0.023955 time: 103.647671
INFO:root:[Epoch 194] train=0.994952 val=0.947500 loss=0.024472 time: 103.824220
INFO:root:[Epoch 195] train=0.994291 val=0.940000 loss=0.025564 time: 104.133986
INFO:root:[Epoch 196] train=0.994351 val=0.940300 loss=0.025463 time: 103.660429
INFO:root:[Epoch 197] train=0.992889 val=0.937900 loss=0.030648 time: 103.837014
INFO:root:[Epoch 198] train=0.993269 val=0.937300 loss=0.028870 time: 103.602703
INFO:root:[Epoch 199] train=0.994932 val=0.939600 loss=0.023338 time: 103.951013
INFO:root:[Epoch 200] train=0.993990 val=0.936400 loss=0.026946 time: 103.976498
INFO:root:[Epoch 201] train=0.994892 val=0.945200 loss=0.024664 time: 103.963886
INFO:root:[Epoch 202] train=0.995393 val=0.938600 loss=0.022013 time: 103.811518
INFO:root:[Epoch 203] train=0.993810 val=0.944100 loss=0.027894 time: 103.685897
INFO:root:[Epoch 204] train=0.994531 val=0.939200 loss=0.024683 time: 103.834687
INFO:root:[Epoch 205] train=0.993830 val=0.942400 loss=0.027544 time: 103.872908
INFO:root:[Epoch 206] train=0.993610 val=0.941500 loss=0.027769 time: 103.921927
INFO:root:[Epoch 207] train=0.993610 val=0.937000 loss=0.028291 time: 103.827445
INFO:root:[Epoch 208] train=0.992708 val=0.928400 loss=0.029583 time: 103.590898
INFO:root:[Epoch 209] train=0.990585 val=0.933000 loss=0.035556 time: 103.725861
INFO:root:[Epoch 210] train=0.993930 val=0.932300 loss=0.026753 time: 103.759951
INFO:root:[Epoch 211] train=0.991947 val=0.940500 loss=0.032021 time: 103.696488
INFO:root:[Epoch 212] train=0.995453 val=0.929200 loss=0.022530 time: 103.698880
INFO:root:[Epoch 213] train=0.994491 val=0.944800 loss=0.024732 time: 103.911080
INFO:root:[Epoch 214] train=0.995333 val=0.927400 loss=0.021047 time: 103.869023
INFO:root:[Epoch 215] train=0.994311 val=0.937300 loss=0.025440 time: 103.897502
INFO:root:[Epoch 216] train=0.993950 val=0.938700 loss=0.026410 time: 103.966858
INFO:root:[Epoch 217] train=0.994872 val=0.940800 loss=0.023910 time: 103.854320
INFO:root:[Epoch 218] train=0.993429 val=0.940900 loss=0.027073 time: 103.810853
INFO:root:[Epoch 219] train=0.994732 val=0.930500 loss=0.024030 time: 103.803955
INFO:root:[Epoch 220] train=0.993790 val=0.926400 loss=0.026230 time: 103.910739
INFO:root:[Epoch 221] train=0.992628 val=0.933500 loss=0.028737 time: 104.070239
INFO:root:[Epoch 222] train=0.993910 val=0.941800 loss=0.026577 time: 103.779724
INFO:root:[Epoch 223] train=0.993129 val=0.926500 loss=0.026603 time: 103.954274
INFO:root:[Epoch 224] train=0.993089 val=0.944800 loss=0.027920 time: 103.918148
INFO:root:[Epoch 225] train=0.998297 val=0.954900 loss=0.011871 time: 104.072223
INFO:root:[Epoch 226] train=0.999459 val=0.956100 loss=0.006712 time: 103.897754
INFO:root:[Epoch 227] train=0.999619 val=0.957800 loss=0.005767 time: 104.588201
INFO:root:[Epoch 228] train=0.999760 val=0.959100 loss=0.005018 time: 104.705413
INFO:root:[Epoch 229] train=0.999840 val=0.958800 loss=0.004448 time: 103.917291
INFO:root:[Epoch 230] train=0.999720 val=0.960500 loss=0.004263 time: 104.412160
INFO:root:[Epoch 231] train=0.999920 val=0.959500 loss=0.003876 time: 104.084162
INFO:root:[Epoch 232] train=0.999940 val=0.960000 loss=0.003572 time: 103.709347
INFO:root:[Epoch 233] train=0.999880 val=0.959800 loss=0.003618 time: 103.950503
INFO:root:[Epoch 234] train=0.999960 val=0.960600 loss=0.003267 time: 104.281652
INFO:root:[Epoch 235] train=0.999900 val=0.960900 loss=0.003460 time: 104.229717
INFO:root:[Epoch 236] train=0.999920 val=0.960900 loss=0.003199 time: 104.015668
INFO:root:[Epoch 237] train=0.999940 val=0.959800 loss=0.003136 time: 104.091710
INFO:root:[Epoch 238] train=0.999900 val=0.960700 loss=0.003084 time: 104.339262
INFO:root:[Epoch 239] train=0.999940 val=0.960600 loss=0.002839 time: 104.327343
INFO:root:[Epoch 240] train=0.999980 val=0.960400 loss=0.002683 time: 103.749881
INFO:root:[Epoch 241] train=0.999960 val=0.961300 loss=0.002883 time: 104.167455
INFO:root:[Epoch 242] train=0.999980 val=0.960800 loss=0.002793 time: 104.025073
INFO:root:[Epoch 243] train=0.999980 val=0.960600 loss=0.002781 time: 104.016920
INFO:root:[Epoch 244] train=0.999980 val=0.961900 loss=0.002742 time: 104.247525
INFO:root:[Epoch 245] train=0.999940 val=0.960900 loss=0.002715 time: 104.087641
INFO:root:[Epoch 246] train=0.999940 val=0.960900 loss=0.002670 time: 103.957845
INFO:root:[Epoch 247] train=1.000000 val=0.961700 loss=0.002582 time: 103.922248
INFO:root:[Epoch 248] train=1.000000 val=0.961000 loss=0.002615 time: 104.118095
INFO:root:[Epoch 249] train=0.999920 val=0.960900 loss=0.002668 time: 103.849005
INFO:root:[Epoch 250] train=0.999960 val=0.962600 loss=0.002598 time: 104.371074
INFO:root:[Epoch 251] train=0.999960 val=0.962600 loss=0.002568 time: 104.139870
INFO:root:[Epoch 252] train=0.999980 val=0.962300 loss=0.002523 time: 103.767191
INFO:root:[Epoch 253] train=0.999980 val=0.962600 loss=0.002518 time: 103.997379
INFO:root:[Epoch 254] train=1.000000 val=0.962100 loss=0.002448 time: 103.982121
INFO:root:[Epoch 255] train=0.999980 val=0.960800 loss=0.002565 time: 103.837445
INFO:root:[Epoch 256] train=1.000000 val=0.960700 loss=0.002411 time: 103.977963
INFO:root:[Epoch 257] train=1.000000 val=0.961300 loss=0.002460 time: 104.025536
INFO:root:[Epoch 258] train=0.999960 val=0.961600 loss=0.002470 time: 104.618283
INFO:root:[Epoch 259] train=0.999980 val=0.962900 loss=0.002443 time: 104.345475
INFO:root:[Epoch 260] train=1.000000 val=0.961500 loss=0.002391 time: 104.010877
INFO:root:[Epoch 261] train=1.000000 val=0.960600 loss=0.002475 time: 104.029811
INFO:root:[Epoch 262] train=1.000000 val=0.962300 loss=0.002433 time: 104.472499
INFO:root:[Epoch 263] train=1.000000 val=0.962000 loss=0.002408 time: 104.543709
INFO:root:[Epoch 264] train=0.999980 val=0.961300 loss=0.002461 time: 104.156145
INFO:root:[Epoch 265] train=1.000000 val=0.961600 loss=0.002513 time: 103.930792
INFO:root:[Epoch 266] train=1.000000 val=0.961000 loss=0.002479 time: 104.319599
INFO:root:[Epoch 267] train=0.999980 val=0.962800 loss=0.002479 time: 104.132848
INFO:root:[Epoch 268] train=1.000000 val=0.962100 loss=0.002458 time: 103.867289
INFO:root:[Epoch 269] train=1.000000 val=0.961800 loss=0.002508 time: 104.012984
INFO:root:[Epoch 270] train=0.999980 val=0.962100 loss=0.002487 time: 103.957049
INFO:root:[Epoch 271] train=1.000000 val=0.961600 loss=0.002447 time: 103.797082
INFO:root:[Epoch 272] train=1.000000 val=0.962000 loss=0.002541 time: 103.946082
INFO:root:[Epoch 273] train=0.999980 val=0.962500 loss=0.002498 time: 104.055490
INFO:root:[Epoch 274] train=0.999980 val=0.961600 loss=0.002529 time: 104.211465
INFO:root:[Epoch 275] train=1.000000 val=0.962400 loss=0.002522 time: 103.909869
INFO:root:[Epoch 276] train=0.999980 val=0.962000 loss=0.002515 time: 104.234693
INFO:root:[Epoch 277] train=1.000000 val=0.962100 loss=0.002565 time: 103.929072
INFO:root:[Epoch 278] train=1.000000 val=0.962400 loss=0.002514 time: 104.020122
INFO:root:[Epoch 279] train=0.999980 val=0.962400 loss=0.002495 time: 103.911172
INFO:root:[Epoch 280] train=1.000000 val=0.961100 loss=0.002455 time: 103.898999
INFO:root:[Epoch 281] train=0.999980 val=0.962000 loss=0.002606 time: 104.183787
INFO:root:[Epoch 282] train=1.000000 val=0.962900 loss=0.002440 time: 104.267682
INFO:root:[Epoch 283] train=0.999980 val=0.963300 loss=0.002542 time: 104.787247
INFO:root:[Epoch 284] train=1.000000 val=0.962600 loss=0.002590 time: 104.024889
INFO:root:[Epoch 285] train=1.000000 val=0.962600 loss=0.002591 time: 104.100350
INFO:root:[Epoch 286] train=0.999980 val=0.962100 loss=0.002576 time: 104.554198
INFO:root:[Epoch 287] train=0.999980 val=0.961400 loss=0.002618 time: 104.385201
INFO:root:[Epoch 288] train=1.000000 val=0.961900 loss=0.002500 time: 104.293501
INFO:root:[Epoch 289] train=1.000000 val=0.962500 loss=0.002540 time: 104.035520
INFO:root:[Epoch 290] train=0.999980 val=0.962500 loss=0.002664 time: 104.304873
INFO:root:[Epoch 291] train=0.999980 val=0.963000 loss=0.002663 time: 104.811107
INFO:root:[Epoch 292] train=1.000000 val=0.963300 loss=0.002567 time: 104.471037
INFO:root:[Epoch 293] train=1.000000 val=0.962600 loss=0.002617 time: 104.824323
INFO:root:[Epoch 294] train=0.999980 val=0.962700 loss=0.002515 time: 104.124167
INFO:root:[Epoch 295] train=1.000000 val=0.963100 loss=0.002696 time: 104.517815
INFO:root:[Epoch 296] train=1.000000 val=0.962700 loss=0.002600 time: 104.122015
INFO:root:[Epoch 297] train=0.999980 val=0.962300 loss=0.002652 time: 104.020578
INFO:root:[Epoch 298] train=1.000000 val=0.962400 loss=0.002636 time: 104.146440
INFO:root:[Epoch 299] train=1.000000 val=0.962000 loss=0.002706 time: 104.270134