[2019-08-22 23:48:18,621] TRAIN Iter 40020: lr = 0.433302,	loss = 2.730011,	Top-1 err = 0.443848,	Top-5 err = 0.218359,	data_time = 0.050142,	train_time = 4.685253
[2019-08-22 23:48:24,911] TRAIN Iter 40040: lr = 0.433268,	loss = 2.924795,	Top-1 err = 0.452344,	Top-5 err = 0.221240,	data_time = 0.050405,	train_time = 0.314492
[2019-08-22 23:48:31,079] TRAIN Iter 40060: lr = 0.433235,	loss = 2.871212,	Top-1 err = 0.452197,	Top-5 err = 0.222559,	data_time = 0.050336,	train_time = 0.308369
[2019-08-22 23:48:46,275] TRAIN Iter 40080: lr = 0.433202,	loss = 2.943947,	Top-1 err = 0.453369,	Top-5 err = 0.224561,	data_time = 0.050395,	train_time = 0.759797
[2019-08-22 23:49:03,304] TRAIN Iter 40100: lr = 0.433168,	loss = 2.805028,	Top-1 err = 0.451807,	Top-5 err = 0.218311,	data_time = 0.355979,	train_time = 0.851429
[2019-08-22 23:49:13,517] TRAIN Iter 40120: lr = 0.433135,	loss = 2.835135,	Top-1 err = 0.453467,	Top-5 err = 0.226709,	data_time = 0.050497,	train_time = 0.510606
[2019-08-22 23:49:31,863] TRAIN Iter 40140: lr = 0.433102,	loss = 2.870939,	Top-1 err = 0.455566,	Top-5 err = 0.224170,	data_time = 0.050451,	train_time = 0.917295
[2019-08-22 23:49:39,428] TRAIN Iter 40160: lr = 0.433068,	loss = 2.890429,	Top-1 err = 0.463965,	Top-5 err = 0.227441,	data_time = 0.050101,	train_time = 0.378254
[2019-08-22 23:50:01,569] TRAIN Iter 40180: lr = 0.433035,	loss = 2.919677,	Top-1 err = 0.459180,	Top-5 err = 0.219971,	data_time = 0.050296,	train_time = 1.107052
[2019-08-22 23:50:21,591] TRAIN Iter 40200: lr = 0.433002,	loss = 2.849628,	Top-1 err = 0.460156,	Top-5 err = 0.227637,	data_time = 0.103386,	train_time = 1.001071
[2019-08-22 23:50:29,677] TRAIN Iter 40220: lr = 0.432968,	loss = 2.829119,	Top-1 err = 0.453711,	Top-5 err = 0.221240,	data_time = 0.050480,	train_time = 0.404277
[2019-08-22 23:50:52,507] TRAIN Iter 40240: lr = 0.432935,	loss = 2.860531,	Top-1 err = 0.459326,	Top-5 err = 0.224951,	data_time = 0.109894,	train_time = 1.141513
[2019-08-22 23:51:09,710] TRAIN Iter 40260: lr = 0.432902,	loss = 2.844932,	Top-1 err = 0.461914,	Top-5 err = 0.225684,	data_time = 0.050576,	train_time = 0.860132
[2019-08-22 23:51:19,322] TRAIN Iter 40280: lr = 0.432868,	loss = 2.812153,	Top-1 err = 0.460938,	Top-5 err = 0.228223,	data_time = 0.050220,	train_time = 0.480588
[2019-08-22 23:51:40,096] TRAIN Iter 40300: lr = 0.432835,	loss = 2.932000,	Top-1 err = 0.457764,	Top-5 err = 0.231396,	data_time = 0.050307,	train_time = 1.038693
[2019-08-22 23:51:46,857] TRAIN Iter 40320: lr = 0.432802,	loss = 2.957402,	Top-1 err = 0.462646,	Top-5 err = 0.231396,	data_time = 0.050201,	train_time = 0.338027
[2019-08-22 23:52:08,899] TRAIN Iter 40340: lr = 0.432768,	loss = 2.909377,	Top-1 err = 0.471387,	Top-5 err = 0.230469,	data_time = 0.050476,	train_time = 1.102082
[2019-08-22 23:52:31,009] TRAIN Iter 40360: lr = 0.432735,	loss = 2.876770,	Top-1 err = 0.460156,	Top-5 err = 0.224854,	data_time = 0.120751,	train_time = 1.105496
[2019-08-22 23:52:38,004] TRAIN Iter 40380: lr = 0.432702,	loss = 2.772464,	Top-1 err = 0.461572,	Top-5 err = 0.228027,	data_time = 0.050447,	train_time = 0.349722
[2019-08-22 23:52:59,414] TRAIN Iter 40400: lr = 0.432668,	loss = 2.965831,	Top-1 err = 0.465430,	Top-5 err = 0.225928,	data_time = 0.050476,	train_time = 1.070463
[2019-08-22 23:53:19,281] TRAIN Iter 40420: lr = 0.432635,	loss = 2.925372,	Top-1 err = 0.462695,	Top-5 err = 0.232764,	data_time = 0.528595,	train_time = 0.993348
[2019-08-22 23:53:29,134] TRAIN Iter 40440: lr = 0.432602,	loss = 2.999497,	Top-1 err = 0.459619,	Top-5 err = 0.227100,	data_time = 0.050516,	train_time = 0.492666
[2019-08-22 23:53:50,026] TRAIN Iter 40460: lr = 0.432568,	loss = 2.946469,	Top-1 err = 0.455127,	Top-5 err = 0.225635,	data_time = 0.050176,	train_time = 1.044578
[2019-08-22 23:53:58,154] TRAIN Iter 40480: lr = 0.432535,	loss = 2.823046,	Top-1 err = 0.467773,	Top-5 err = 0.229199,	data_time = 0.050397,	train_time = 0.406359
[2019-08-22 23:54:17,633] TRAIN Iter 40500: lr = 0.432502,	loss = 2.802237,	Top-1 err = 0.463281,	Top-5 err = 0.226367,	data_time = 0.050260,	train_time = 0.973971
[2019-08-22 23:54:37,890] TRAIN Iter 40520: lr = 0.432468,	loss = 2.891133,	Top-1 err = 0.462988,	Top-5 err = 0.230078,	data_time = 0.097645,	train_time = 1.012839
[2019-08-22 23:54:48,951] TRAIN Iter 40540: lr = 0.432435,	loss = 2.956400,	Top-1 err = 0.465967,	Top-5 err = 0.229053,	data_time = 0.050623,	train_time = 0.552995
[2019-08-22 23:55:08,966] TRAIN Iter 40560: lr = 0.432402,	loss = 2.911584,	Top-1 err = 0.460547,	Top-5 err = 0.227295,	data_time = 0.050335,	train_time = 1.000759
[2019-08-22 23:55:24,452] TRAIN Iter 40580: lr = 0.432368,	loss = 2.802012,	Top-1 err = 0.454834,	Top-5 err = 0.227393,	data_time = 0.050165,	train_time = 0.774264
[2019-08-22 23:55:36,978] TRAIN Iter 40600: lr = 0.432335,	loss = 2.861167,	Top-1 err = 0.465625,	Top-5 err = 0.230859,	data_time = 0.050475,	train_time = 0.626273
[2019-08-22 23:55:59,420] TRAIN Iter 40620: lr = 0.432302,	loss = 2.859533,	Top-1 err = 0.460400,	Top-5 err = 0.230664,	data_time = 0.050547,	train_time = 1.122092
[2019-08-22 23:56:06,451] TRAIN Iter 40640: lr = 0.432268,	loss = 2.982180,	Top-1 err = 0.464893,	Top-5 err = 0.230518,	data_time = 0.050299,	train_time = 0.351531
[2019-08-22 23:56:29,109] TRAIN Iter 40660: lr = 0.432235,	loss = 2.952045,	Top-1 err = 0.461768,	Top-5 err = 0.228467,	data_time = 0.050254,	train_time = 1.132906
[2019-08-22 23:56:45,454] TRAIN Iter 40680: lr = 0.432202,	loss = 2.963435,	Top-1 err = 0.463037,	Top-5 err = 0.228809,	data_time = 0.050555,	train_time = 0.817256
[2019-08-22 23:56:55,630] TRAIN Iter 40700: lr = 0.432168,	loss = 2.832747,	Top-1 err = 0.462451,	Top-5 err = 0.231250,	data_time = 0.050164,	train_time = 0.508757
[2019-08-22 23:57:16,038] TRAIN Iter 40720: lr = 0.432135,	loss = 2.918461,	Top-1 err = 0.471240,	Top-5 err = 0.231299,	data_time = 0.050342,	train_time = 1.020376
[2019-08-22 23:57:34,238] TRAIN Iter 40740: lr = 0.432102,	loss = 2.814511,	Top-1 err = 0.461084,	Top-5 err = 0.223828,	data_time = 0.095207,	train_time = 0.910020
[2019-08-22 23:57:45,103] TRAIN Iter 40760: lr = 0.432068,	loss = 2.886750,	Top-1 err = 0.461865,	Top-5 err = 0.229150,	data_time = 0.050421,	train_time = 0.543206
[2019-08-22 23:58:08,650] TRAIN Iter 40780: lr = 0.432035,	loss = 2.906490,	Top-1 err = 0.455811,	Top-5 err = 0.224414,	data_time = 0.050437,	train_time = 1.177373
[2019-08-22 23:58:15,620] TRAIN Iter 40800: lr = 0.432002,	loss = 2.917836,	Top-1 err = 0.460840,	Top-5 err = 0.226465,	data_time = 0.050152,	train_time = 0.348494
[2019-08-22 23:58:39,522] TRAIN Iter 40820: lr = 0.431968,	loss = 2.923328,	Top-1 err = 0.467529,	Top-5 err = 0.230957,	data_time = 0.050578,	train_time = 1.195055
[2019-08-22 23:58:55,131] TRAIN Iter 40840: lr = 0.431935,	loss = 2.944259,	Top-1 err = 0.466553,	Top-5 err = 0.230078,	data_time = 0.050349,	train_time = 0.780429
[2019-08-22 23:59:08,394] TRAIN Iter 40860: lr = 0.431902,	loss = 2.989414,	Top-1 err = 0.462939,	Top-5 err = 0.230957,	data_time = 0.050215,	train_time = 0.663129
[2019-08-22 23:59:30,528] TRAIN Iter 40880: lr = 0.431868,	loss = 3.009044,	Top-1 err = 0.465625,	Top-5 err = 0.233740,	data_time = 0.050320,	train_time = 1.106718
[2019-08-22 23:59:42,249] TRAIN Iter 40900: lr = 0.431835,	loss = 2.814265,	Top-1 err = 0.468066,	Top-5 err = 0.233398,	data_time = 0.050338,	train_time = 0.586052
[2019-08-22 23:59:59,288] TRAIN Iter 40920: lr = 0.431802,	loss = 2.828671,	Top-1 err = 0.465039,	Top-5 err = 0.228711,	data_time = 0.050406,	train_time = 0.851903
[2019-08-23 00:00:21,102] TRAIN Iter 40940: lr = 0.431768,	loss = 2.950464,	Top-1 err = 0.463818,	Top-5 err = 0.231641,	data_time = 0.050552,	train_time = 1.090673
[2019-08-23 00:00:27,587] TRAIN Iter 40960: lr = 0.431735,	loss = 2.954866,	Top-1 err = 0.468945,	Top-5 err = 0.235059,	data_time = 0.050379,	train_time = 0.324260
[2019-08-23 00:00:49,858] TRAIN Iter 40980: lr = 0.431702,	loss = 3.028795,	Top-1 err = 0.467822,	Top-5 err = 0.235547,	data_time = 0.050304,	train_time = 1.113519
[2019-08-23 00:01:02,719] TRAIN Iter 41000: lr = 0.431668,	loss = 2.921680,	Top-1 err = 0.463525,	Top-5 err = 0.231641,	data_time = 0.050290,	train_time = 0.643038
[2019-08-23 00:01:18,884] TRAIN Iter 41020: lr = 0.431635,	loss = 2.930208,	Top-1 err = 0.467383,	Top-5 err = 0.234570,	data_time = 0.050331,	train_time = 0.808237
[2019-08-23 00:01:40,945] TRAIN Iter 41040: lr = 0.431602,	loss = 2.945507,	Top-1 err = 0.470264,	Top-5 err = 0.233643,	data_time = 0.050247,	train_time = 1.103064
[2019-08-23 00:01:53,955] TRAIN Iter 41060: lr = 0.431568,	loss = 2.899787,	Top-1 err = 0.462109,	Top-5 err = 0.228467,	data_time = 0.050644,	train_time = 0.650486
[2019-08-23 00:02:10,680] TRAIN Iter 41080: lr = 0.431535,	loss = 2.971538,	Top-1 err = 0.463379,	Top-5 err = 0.229053,	data_time = 0.050143,	train_time = 0.836205
[2019-08-23 00:02:32,872] TRAIN Iter 41100: lr = 0.431502,	loss = 2.895216,	Top-1 err = 0.468213,	Top-5 err = 0.235840,	data_time = 0.050447,	train_time = 1.109615
[2019-08-23 00:02:39,477] TRAIN Iter 41120: lr = 0.431468,	loss = 2.873761,	Top-1 err = 0.464795,	Top-5 err = 0.227148,	data_time = 0.050560,	train_time = 0.330245
[2019-08-23 00:03:02,169] TRAIN Iter 41140: lr = 0.431435,	loss = 2.947660,	Top-1 err = 0.459277,	Top-5 err = 0.225635,	data_time = 0.050343,	train_time = 1.134588
[2019-08-23 00:03:16,174] TRAIN Iter 41160: lr = 0.431402,	loss = 2.875225,	Top-1 err = 0.466553,	Top-5 err = 0.233740,	data_time = 0.126899,	train_time = 0.700223
[2019-08-23 00:03:32,778] TRAIN Iter 41180: lr = 0.431368,	loss = 2.971373,	Top-1 err = 0.466553,	Top-5 err = 0.232959,	data_time = 0.050489,	train_time = 0.830207
[2019-08-23 00:03:54,797] TRAIN Iter 41200: lr = 0.431335,	loss = 2.891624,	Top-1 err = 0.464014,	Top-5 err = 0.228125,	data_time = 0.049984,	train_time = 1.100903
[2019-08-23 00:04:04,230] TRAIN Iter 41220: lr = 0.431302,	loss = 2.882048,	Top-1 err = 0.460205,	Top-5 err = 0.227686,	data_time = 2.168739,	train_time = 0.471657
[2019-08-23 00:04:22,850] TRAIN Iter 41240: lr = 0.431268,	loss = 2.888989,	Top-1 err = 0.465723,	Top-5 err = 0.232910,	data_time = 0.049881,	train_time = 0.930993
[2019-08-23 00:05:05,798] TRAIN Iter 41260: lr = 0.431235,	loss = 2.945352,	Top-1 err = 0.468331,	Top-5 err = 0.231402,	data_time = 0.050582,	train_time = 2.147358
[2019-08-23 00:05:12,608] TRAIN Iter 41280: lr = 0.431202,	loss = 2.897203,	Top-1 err = 0.460156,	Top-5 err = 0.229150,	data_time = 0.050640,	train_time = 0.340485
[2019-08-23 00:05:26,991] TRAIN Iter 41300: lr = 0.431168,	loss = 2.873933,	Top-1 err = 0.462402,	Top-5 err = 0.224609,	data_time = 0.050672,	train_time = 0.719163
[2019-08-23 00:05:39,685] TRAIN Iter 41320: lr = 0.431135,	loss = 2.901296,	Top-1 err = 0.457861,	Top-5 err = 0.226025,	data_time = 2.967834,	train_time = 0.634682
[2019-08-23 00:05:47,009] TRAIN Iter 41340: lr = 0.431102,	loss = 2.846115,	Top-1 err = 0.462061,	Top-5 err = 0.224658,	data_time = 0.050290,	train_time = 0.366175
[2019-08-23 00:06:02,424] TRAIN Iter 41360: lr = 0.431068,	loss = 2.932395,	Top-1 err = 0.459277,	Top-5 err = 0.222412,	data_time = 0.050436,	train_time = 0.770741
[2019-08-23 00:06:10,227] TRAIN Iter 41380: lr = 0.431035,	loss = 2.913747,	Top-1 err = 0.460840,	Top-5 err = 0.227148,	data_time = 0.050512,	train_time = 0.390153
[2019-08-23 00:06:24,622] TRAIN Iter 41400: lr = 0.431002,	loss = 2.872805,	Top-1 err = 0.461182,	Top-5 err = 0.222803,	data_time = 0.050879,	train_time = 0.719723
[2019-08-23 00:06:38,528] TRAIN Iter 41420: lr = 0.430968,	loss = 2.896882,	Top-1 err = 0.451270,	Top-5 err = 0.219775,	data_time = 0.050585,	train_time = 0.695297
[2019-08-23 00:06:45,727] TRAIN Iter 41440: lr = 0.430935,	loss = 2.782202,	Top-1 err = 0.455566,	Top-5 err = 0.224365,	data_time = 0.093007,	train_time = 0.359917
[2019-08-23 00:06:58,310] TRAIN Iter 41460: lr = 0.430902,	loss = 2.968291,	Top-1 err = 0.462988,	Top-5 err = 0.225781,	data_time = 0.050434,	train_time = 0.629127
[2019-08-23 00:07:13,379] TRAIN Iter 41480: lr = 0.430868,	loss = 2.776797,	Top-1 err = 0.461230,	Top-5 err = 0.229102,	data_time = 0.050624,	train_time = 0.753460
[2019-08-23 00:07:20,212] TRAIN Iter 41500: lr = 0.430835,	loss = 2.862957,	Top-1 err = 0.454053,	Top-5 err = 0.224072,	data_time = 0.051021,	train_time = 0.341632
[2019-08-23 00:07:34,906] TRAIN Iter 41520: lr = 0.430802,	loss = 2.909614,	Top-1 err = 0.458350,	Top-5 err = 0.222754,	data_time = 0.050629,	train_time = 0.734685
[2019-08-23 00:07:41,113] TRAIN Iter 41540: lr = 0.430768,	loss = 2.906435,	Top-1 err = 0.461865,	Top-5 err = 0.229297,	data_time = 0.050384,	train_time = 0.310312
[2019-08-23 00:08:04,061] TRAIN Iter 41560: lr = 0.430735,	loss = 2.815347,	Top-1 err = 0.455176,	Top-5 err = 0.225635,	data_time = 0.050327,	train_time = 1.147408
[2019-08-23 00:08:19,765] TRAIN Iter 41580: lr = 0.430702,	loss = 2.891327,	Top-1 err = 0.460986,	Top-5 err = 0.225439,	data_time = 0.050733,	train_time = 0.785202
[2019-08-23 00:08:26,602] TRAIN Iter 41600: lr = 0.430668,	loss = 2.933750,	Top-1 err = 0.463770,	Top-5 err = 0.227246,	data_time = 0.050714,	train_time = 0.341795
[2019-08-23 00:08:41,891] TRAIN Iter 41620: lr = 0.430635,	loss = 2.918160,	Top-1 err = 0.460889,	Top-5 err = 0.224512,	data_time = 0.050336,	train_time = 0.764439
[2019-08-23 00:08:57,223] TRAIN Iter 41640: lr = 0.430602,	loss = 2.929903,	Top-1 err = 0.460889,	Top-5 err = 0.226270,	data_time = 0.050447,	train_time = 0.766598
[2019-08-23 00:09:03,825] TRAIN Iter 41660: lr = 0.430568,	loss = 2.926004,	Top-1 err = 0.461475,	Top-5 err = 0.229687,	data_time = 0.050163,	train_time = 0.330102
[2019-08-23 00:09:23,198] TRAIN Iter 41680: lr = 0.430535,	loss = 2.911783,	Top-1 err = 0.464307,	Top-5 err = 0.227051,	data_time = 0.050344,	train_time = 0.968623
[2019-08-23 00:09:29,494] TRAIN Iter 41700: lr = 0.430502,	loss = 2.983825,	Top-1 err = 0.457471,	Top-5 err = 0.226367,	data_time = 0.050228,	train_time = 0.314790
[2019-08-23 00:09:48,277] TRAIN Iter 41720: lr = 0.430468,	loss = 2.796459,	Top-1 err = 0.460156,	Top-5 err = 0.224072,	data_time = 0.050308,	train_time = 0.939128
[2019-08-23 00:10:03,871] TRAIN Iter 41740: lr = 0.430435,	loss = 2.815515,	Top-1 err = 0.461914,	Top-5 err = 0.227100,	data_time = 0.050360,	train_time = 0.779676
[2019-08-23 00:10:10,389] TRAIN Iter 41760: lr = 0.430402,	loss = 2.962876,	Top-1 err = 0.466260,	Top-5 err = 0.227393,	data_time = 0.147779,	train_time = 0.325899
[2019-08-23 00:10:27,073] TRAIN Iter 41780: lr = 0.430368,	loss = 2.914405,	Top-1 err = 0.455615,	Top-5 err = 0.227686,	data_time = 0.050588,	train_time = 0.834198
[2019-08-23 00:10:41,298] TRAIN Iter 41800: lr = 0.430335,	loss = 3.003731,	Top-1 err = 0.458887,	Top-5 err = 0.226660,	data_time = 0.118663,	train_time = 0.711223
[2019-08-23 00:10:48,029] TRAIN Iter 41820: lr = 0.430302,	loss = 2.939929,	Top-1 err = 0.457520,	Top-5 err = 0.222119,	data_time = 0.050971,	train_time = 0.336547
[2019-08-23 00:11:05,460] TRAIN Iter 41840: lr = 0.430268,	loss = 2.857169,	Top-1 err = 0.457617,	Top-5 err = 0.225635,	data_time = 0.050344,	train_time = 0.871534
[2019-08-23 00:11:11,861] TRAIN Iter 41860: lr = 0.430235,	loss = 2.963479,	Top-1 err = 0.458740,	Top-5 err = 0.226416,	data_time = 0.050666,	train_time = 0.320041
[2019-08-23 00:11:30,751] TRAIN Iter 41880: lr = 0.430202,	loss = 2.927954,	Top-1 err = 0.465283,	Top-5 err = 0.235742,	data_time = 0.050328,	train_time = 0.944465
[2019-08-23 00:11:48,124] TRAIN Iter 41900: lr = 0.430168,	loss = 2.896961,	Top-1 err = 0.468359,	Top-5 err = 0.232715,	data_time = 0.050517,	train_time = 0.868668
[2019-08-23 00:11:54,696] TRAIN Iter 41920: lr = 0.430135,	loss = 2.834684,	Top-1 err = 0.460986,	Top-5 err = 0.227881,	data_time = 0.050546,	train_time = 0.328591
[2019-08-23 00:12:11,780] TRAIN Iter 41940: lr = 0.430102,	loss = 2.773765,	Top-1 err = 0.462109,	Top-5 err = 0.227881,	data_time = 0.050355,	train_time = 0.854171
[2019-08-23 00:12:28,866] TRAIN Iter 41960: lr = 0.430068,	loss = 2.890959,	Top-1 err = 0.457910,	Top-5 err = 0.227832,	data_time = 1.491186,	train_time = 0.854294
[2019-08-23 00:12:35,708] TRAIN Iter 41980: lr = 0.430035,	loss = 2.873768,	Top-1 err = 0.465283,	Top-5 err = 0.231592,	data_time = 0.050306,	train_time = 0.342091
[2019-08-23 00:12:54,564] TRAIN Iter 42000: lr = 0.430002,	loss = 2.945041,	Top-1 err = 0.464990,	Top-5 err = 0.231982,	data_time = 0.050509,	train_time = 0.942782
[2019-08-23 00:13:00,989] TRAIN Iter 42020: lr = 0.429968,	loss = 3.105084,	Top-1 err = 0.462354,	Top-5 err = 0.230420,	data_time = 0.050463,	train_time = 0.321220
[2019-08-23 00:13:19,877] TRAIN Iter 42040: lr = 0.429935,	loss = 2.785820,	Top-1 err = 0.459863,	Top-5 err = 0.228662,	data_time = 0.050351,	train_time = 0.944366
[2019-08-23 00:13:37,949] TRAIN Iter 42060: lr = 0.429902,	loss = 2.916722,	Top-1 err = 0.464404,	Top-5 err = 0.226074,	data_time = 0.050456,	train_time = 0.903631
[2019-08-23 00:13:44,296] TRAIN Iter 42080: lr = 0.429868,	loss = 2.958612,	Top-1 err = 0.466260,	Top-5 err = 0.232666,	data_time = 0.050459,	train_time = 0.317292
[2019-08-23 00:14:02,994] TRAIN Iter 42100: lr = 0.429835,	loss = 2.878477,	Top-1 err = 0.460742,	Top-5 err = 0.225977,	data_time = 0.050344,	train_time = 0.934917
[2019-08-23 00:14:21,973] TRAIN Iter 42120: lr = 0.429802,	loss = 2.906970,	Top-1 err = 0.460303,	Top-5 err = 0.229736,	data_time = 0.120890,	train_time = 0.948916
[2019-08-23 00:14:28,664] TRAIN Iter 42140: lr = 0.429768,	loss = 2.851742,	Top-1 err = 0.462061,	Top-5 err = 0.229883,	data_time = 0.050588,	train_time = 0.334535
[2019-08-23 00:14:46,556] TRAIN Iter 42160: lr = 0.429735,	loss = 2.940969,	Top-1 err = 0.462939,	Top-5 err = 0.232227,	data_time = 0.050391,	train_time = 0.894603
[2019-08-23 00:14:52,838] TRAIN Iter 42180: lr = 0.429702,	loss = 2.848695,	Top-1 err = 0.460791,	Top-5 err = 0.227734,	data_time = 0.050216,	train_time = 0.314069
[2019-08-23 00:15:11,325] TRAIN Iter 42200: lr = 0.429668,	loss = 2.881160,	Top-1 err = 0.468506,	Top-5 err = 0.230420,	data_time = 0.050312,	train_time = 0.924355
[2019-08-23 00:15:28,866] TRAIN Iter 42220: lr = 0.429635,	loss = 2.902802,	Top-1 err = 0.459033,	Top-5 err = 0.222607,	data_time = 0.050562,	train_time = 0.877059
[2019-08-23 00:15:35,370] TRAIN Iter 42240: lr = 0.429602,	loss = 2.987260,	Top-1 err = 0.462500,	Top-5 err = 0.235937,	data_time = 0.050125,	train_time = 0.325158
[2019-08-23 00:15:55,043] TRAIN Iter 42260: lr = 0.429568,	loss = 2.826375,	Top-1 err = 0.459473,	Top-5 err = 0.227979,	data_time = 0.050363,	train_time = 0.983661
[2019-08-23 00:16:14,162] TRAIN Iter 42280: lr = 0.429535,	loss = 2.872514,	Top-1 err = 0.472852,	Top-5 err = 0.233350,	data_time = 0.911646,	train_time = 0.955900
[2019-08-23 00:16:20,773] TRAIN Iter 42300: lr = 0.429502,	loss = 2.897951,	Top-1 err = 0.465820,	Top-5 err = 0.230908,	data_time = 0.050334,	train_time = 0.330542
[2019-08-23 00:16:39,949] TRAIN Iter 42320: lr = 0.429468,	loss = 2.969280,	Top-1 err = 0.466113,	Top-5 err = 0.234229,	data_time = 0.050122,	train_time = 0.958812
[2019-08-23 00:16:46,296] TRAIN Iter 42340: lr = 0.429435,	loss = 2.810978,	Top-1 err = 0.460840,	Top-5 err = 0.228223,	data_time = 0.050407,	train_time = 0.317304
[2019-08-23 00:17:04,924] TRAIN Iter 42360: lr = 0.429402,	loss = 2.848748,	Top-1 err = 0.469775,	Top-5 err = 0.233203,	data_time = 0.050279,	train_time = 0.931399
[2019-08-23 00:17:21,823] TRAIN Iter 42380: lr = 0.429368,	loss = 2.900755,	Top-1 err = 0.472119,	Top-5 err = 0.231836,	data_time = 0.050284,	train_time = 0.844945
[2019-08-23 00:17:28,227] TRAIN Iter 42400: lr = 0.429335,	loss = 2.856886,	Top-1 err = 0.459570,	Top-5 err = 0.227734,	data_time = 0.050308,	train_time = 0.320171
[2019-08-23 00:17:46,161] TRAIN Iter 42420: lr = 0.429302,	loss = 2.915606,	Top-1 err = 0.460938,	Top-5 err = 0.228711,	data_time = 0.050358,	train_time = 0.896708
[2019-08-23 00:18:04,214] TRAIN Iter 42440: lr = 0.429268,	loss = 2.980114,	Top-1 err = 0.468945,	Top-5 err = 0.232764,	data_time = 0.072881,	train_time = 0.902625
[2019-08-23 00:18:10,826] TRAIN Iter 42460: lr = 0.429235,	loss = 2.764644,	Top-1 err = 0.461377,	Top-5 err = 0.228760,	data_time = 0.049978,	train_time = 0.330609
[2019-08-23 00:18:29,768] TRAIN Iter 42480: lr = 0.429202,	loss = 2.847048,	Top-1 err = 0.468066,	Top-5 err = 0.234229,	data_time = 0.049930,	train_time = 0.947071
[2019-08-23 00:18:35,811] TRAIN Iter 42500: lr = 0.429168,	loss = 2.929581,	Top-1 err = 0.457324,	Top-5 err = 0.225342,	data_time = 0.049828,	train_time = 0.302155
[2019-08-23 00:19:26,881] TRAIN Iter 42520: lr = 0.429135,	loss = 2.886374,	Top-1 err = 0.476314,	Top-5 err = 0.235919,	data_time = 0.050440,	train_time = 2.553464
[2019-08-23 00:19:40,510] TRAIN Iter 42540: lr = 0.429102,	loss = 2.889961,	Top-1 err = 0.458154,	Top-5 err = 0.222705,	data_time = 0.050649,	train_time = 0.681416
[2019-08-23 00:19:48,145] TRAIN Iter 42560: lr = 0.429068,	loss = 2.886285,	Top-1 err = 0.458057,	Top-5 err = 0.226807,	data_time = 0.050227,	train_time = 0.381764
[2019-08-23 00:20:00,784] TRAIN Iter 42580: lr = 0.429035,	loss = 2.845581,	Top-1 err = 0.457764,	Top-5 err = 0.226855,	data_time = 0.050664,	train_time = 0.631937
[2019-08-23 00:20:07,741] TRAIN Iter 42600: lr = 0.429002,	loss = 2.794066,	Top-1 err = 0.456104,	Top-5 err = 0.225635,	data_time = 0.050867,	train_time = 0.347839
[2019-08-23 00:20:22,362] TRAIN Iter 42620: lr = 0.428968,	loss = 2.823192,	Top-1 err = 0.460840,	Top-5 err = 0.229102,	data_time = 0.050122,	train_time = 0.731017
[2019-08-23 00:20:37,858] TRAIN Iter 42640: lr = 0.428935,	loss = 2.854590,	Top-1 err = 0.457861,	Top-5 err = 0.229004,	data_time = 0.050546,	train_time = 0.774786
[2019-08-23 00:20:44,789] TRAIN Iter 42660: lr = 0.428902,	loss = 2.888286,	Top-1 err = 0.462305,	Top-5 err = 0.225635,	data_time = 0.050753,	train_time = 0.346515
[2019-08-23 00:20:59,049] TRAIN Iter 42680: lr = 0.428868,	loss = 2.855910,	Top-1 err = 0.457178,	Top-5 err = 0.222852,	data_time = 0.050516,	train_time = 0.712982
[2019-08-23 00:21:14,149] TRAIN Iter 42700: lr = 0.428835,	loss = 2.797280,	Top-1 err = 0.452832,	Top-5 err = 0.221387,	data_time = 0.095635,	train_time = 0.754985
[2019-08-23 00:21:21,086] TRAIN Iter 42720: lr = 0.428802,	loss = 2.879223,	Top-1 err = 0.459912,	Top-5 err = 0.226367,	data_time = 0.050328,	train_time = 0.346850
[2019-08-23 00:21:35,733] TRAIN Iter 42740: lr = 0.428768,	loss = 2.844420,	Top-1 err = 0.456250,	Top-5 err = 0.224268,	data_time = 0.050167,	train_time = 0.732338
[2019-08-23 00:21:42,390] TRAIN Iter 42760: lr = 0.428735,	loss = 2.878338,	Top-1 err = 0.459229,	Top-5 err = 0.227051,	data_time = 0.050192,	train_time = 0.332840
[2019-08-23 00:21:58,372] TRAIN Iter 42780: lr = 0.428702,	loss = 2.894950,	Top-1 err = 0.469141,	Top-5 err = 0.228418,	data_time = 0.050386,	train_time = 0.799075
[2019-08-23 00:22:16,564] TRAIN Iter 42800: lr = 0.428668,	loss = 2.901648,	Top-1 err = 0.464502,	Top-5 err = 0.225830,	data_time = 0.050397,	train_time = 0.909578
[2019-08-23 00:22:23,914] TRAIN Iter 42820: lr = 0.428635,	loss = 2.770189,	Top-1 err = 0.454102,	Top-5 err = 0.224805,	data_time = 0.050760,	train_time = 0.367493
[2019-08-23 00:22:38,160] TRAIN Iter 42840: lr = 0.428602,	loss = 2.932148,	Top-1 err = 0.459180,	Top-5 err = 0.225635,	data_time = 0.050402,	train_time = 0.712292
[2019-08-23 00:22:53,431] TRAIN Iter 42860: lr = 0.428568,	loss = 2.866525,	Top-1 err = 0.457861,	Top-5 err = 0.223877,	data_time = 0.050840,	train_time = 0.763559
[2019-08-23 00:23:00,616] TRAIN Iter 42880: lr = 0.428535,	loss = 2.861842,	Top-1 err = 0.461133,	Top-5 err = 0.221973,	data_time = 0.050423,	train_time = 0.359224
[2019-08-23 00:23:14,424] TRAIN Iter 42900: lr = 0.428502,	loss = 2.881305,	Top-1 err = 0.450146,	Top-5 err = 0.218555,	data_time = 0.050306,	train_time = 0.690386
[2019-08-23 00:23:22,198] TRAIN Iter 42920: lr = 0.428468,	loss = 2.852931,	Top-1 err = 0.461475,	Top-5 err = 0.229687,	data_time = 0.050768,	train_time = 0.388694
[2019-08-23 00:23:35,180] TRAIN Iter 42940: lr = 0.428435,	loss = 2.850767,	Top-1 err = 0.459326,	Top-5 err = 0.226416,	data_time = 0.050696,	train_time = 0.649078
[2019-08-23 00:23:49,980] TRAIN Iter 42960: lr = 0.428402,	loss = 2.874587,	Top-1 err = 0.459814,	Top-5 err = 0.230176,	data_time = 0.050214,	train_time = 0.739987
[2019-08-23 00:23:56,726] TRAIN Iter 42980: lr = 0.428368,	loss = 2.989530,	Top-1 err = 0.463281,	Top-5 err = 0.228662,	data_time = 0.050348,	train_time = 0.337298
[2019-08-23 00:24:12,604] TRAIN Iter 43000: lr = 0.428335,	loss = 2.850276,	Top-1 err = 0.455859,	Top-5 err = 0.222656,	data_time = 0.050644,	train_time = 0.793843
[2019-08-23 00:24:27,013] TRAIN Iter 43020: lr = 0.428302,	loss = 2.852575,	Top-1 err = 0.458887,	Top-5 err = 0.228320,	data_time = 0.135169,	train_time = 0.720458
[2019-08-23 00:24:34,633] TRAIN Iter 43040: lr = 0.428268,	loss = 2.926215,	Top-1 err = 0.454785,	Top-5 err = 0.220947,	data_time = 0.050668,	train_time = 0.380993
[2019-08-23 00:24:49,033] TRAIN Iter 43060: lr = 0.428235,	loss = 2.966021,	Top-1 err = 0.461377,	Top-5 err = 0.232178,	data_time = 0.050723,	train_time = 0.719975
[2019-08-23 00:24:56,128] TRAIN Iter 43080: lr = 0.428202,	loss = 2.816241,	Top-1 err = 0.461816,	Top-5 err = 0.226025,	data_time = 0.050384,	train_time = 0.354729
[2019-08-23 00:25:11,343] TRAIN Iter 43100: lr = 0.428168,	loss = 2.928047,	Top-1 err = 0.456641,	Top-5 err = 0.223682,	data_time = 0.050839,	train_time = 0.760751
[2019-08-23 00:25:27,821] TRAIN Iter 43120: lr = 0.428135,	loss = 2.951479,	Top-1 err = 0.465527,	Top-5 err = 0.234033,	data_time = 0.050743,	train_time = 0.823903
[2019-08-23 00:25:34,604] TRAIN Iter 43140: lr = 0.428102,	loss = 2.898161,	Top-1 err = 0.460400,	Top-5 err = 0.225195,	data_time = 0.050576,	train_time = 0.339108
[2019-08-23 00:25:50,345] TRAIN Iter 43160: lr = 0.428068,	loss = 2.825325,	Top-1 err = 0.464551,	Top-5 err = 0.228027,	data_time = 0.050176,	train_time = 0.787067
[2019-08-23 00:26:06,030] TRAIN Iter 43180: lr = 0.428035,	loss = 2.823191,	Top-1 err = 0.460303,	Top-5 err = 0.224121,	data_time = 0.124275,	train_time = 0.784219
[2019-08-23 00:26:12,694] TRAIN Iter 43200: lr = 0.428002,	loss = 2.991866,	Top-1 err = 0.461035,	Top-5 err = 0.224902,	data_time = 0.050632,	train_time = 0.333165
[2019-08-23 00:26:26,962] TRAIN Iter 43220: lr = 0.427968,	loss = 2.856380,	Top-1 err = 0.459814,	Top-5 err = 0.225195,	data_time = 0.050495,	train_time = 0.713377
[2019-08-23 00:26:34,151] TRAIN Iter 43240: lr = 0.427935,	loss = 2.829121,	Top-1 err = 0.463965,	Top-5 err = 0.228857,	data_time = 0.050332,	train_time = 0.359464
[2019-08-23 00:26:49,354] TRAIN Iter 43260: lr = 0.427902,	loss = 2.842999,	Top-1 err = 0.457959,	Top-5 err = 0.229590,	data_time = 0.050413,	train_time = 0.760148
[2019-08-23 00:27:05,949] TRAIN Iter 43280: lr = 0.427868,	loss = 2.884678,	Top-1 err = 0.457422,	Top-5 err = 0.225830,	data_time = 0.050342,	train_time = 0.829733
[2019-08-23 00:27:12,948] TRAIN Iter 43300: lr = 0.427835,	loss = 2.938657,	Top-1 err = 0.458398,	Top-5 err = 0.224121,	data_time = 0.050839,	train_time = 0.349928
[2019-08-23 00:27:28,686] TRAIN Iter 43320: lr = 0.427802,	loss = 2.795282,	Top-1 err = 0.461914,	Top-5 err = 0.227100,	data_time = 0.050372,	train_time = 0.786901
[2019-08-23 00:27:44,603] TRAIN Iter 43340: lr = 0.427768,	loss = 2.817363,	Top-1 err = 0.460840,	Top-5 err = 0.223193,	data_time = 0.375722,	train_time = 0.795806
[2019-08-23 00:27:51,072] TRAIN Iter 43360: lr = 0.427735,	loss = 2.950269,	Top-1 err = 0.463721,	Top-5 err = 0.229687,	data_time = 0.050245,	train_time = 0.323456
[2019-08-23 00:28:07,501] TRAIN Iter 43380: lr = 0.427702,	loss = 2.992227,	Top-1 err = 0.460352,	Top-5 err = 0.224463,	data_time = 0.050445,	train_time = 0.821409
[2019-08-23 00:28:14,229] TRAIN Iter 43400: lr = 0.427668,	loss = 2.912579,	Top-1 err = 0.464600,	Top-5 err = 0.231396,	data_time = 0.050242,	train_time = 0.336391
[2019-08-23 00:28:30,695] TRAIN Iter 43420: lr = 0.427635,	loss = 2.897377,	Top-1 err = 0.456396,	Top-5 err = 0.229395,	data_time = 0.050375,	train_time = 0.823300
[2019-08-23 00:28:47,524] TRAIN Iter 43440: lr = 0.427602,	loss = 2.969998,	Top-1 err = 0.461816,	Top-5 err = 0.227930,	data_time = 0.050640,	train_time = 0.841452
[2019-08-23 00:28:54,282] TRAIN Iter 43460: lr = 0.427568,	loss = 2.853882,	Top-1 err = 0.462695,	Top-5 err = 0.229004,	data_time = 0.115943,	train_time = 0.337848
[2019-08-23 00:29:11,246] TRAIN Iter 43480: lr = 0.427535,	loss = 2.882565,	Top-1 err = 0.464111,	Top-5 err = 0.229053,	data_time = 0.050379,	train_time = 0.848198
[2019-08-23 00:29:28,291] TRAIN Iter 43500: lr = 0.427502,	loss = 2.838043,	Top-1 err = 0.459131,	Top-5 err = 0.228271,	data_time = 4.368772,	train_time = 0.852259
[2019-08-23 00:29:35,133] TRAIN Iter 43520: lr = 0.427468,	loss = 2.912052,	Top-1 err = 0.462061,	Top-5 err = 0.235156,	data_time = 0.050300,	train_time = 0.342077
[2019-08-23 00:29:52,747] TRAIN Iter 43540: lr = 0.427435,	loss = 2.870420,	Top-1 err = 0.460352,	Top-5 err = 0.228467,	data_time = 0.050436,	train_time = 0.880693
[2019-08-23 00:29:59,429] TRAIN Iter 43560: lr = 0.427402,	loss = 2.938204,	Top-1 err = 0.465234,	Top-5 err = 0.226416,	data_time = 0.050190,	train_time = 0.334059
[2019-08-23 00:30:17,241] TRAIN Iter 43580: lr = 0.427368,	loss = 2.869709,	Top-1 err = 0.460840,	Top-5 err = 0.226221,	data_time = 0.050444,	train_time = 0.890631
[2019-08-23 00:30:37,056] TRAIN Iter 43600: lr = 0.427335,	loss = 2.901774,	Top-1 err = 0.465625,	Top-5 err = 0.230615,	data_time = 0.050126,	train_time = 0.990703
[2019-08-23 00:30:43,439] TRAIN Iter 43620: lr = 0.427302,	loss = 3.016721,	Top-1 err = 0.462451,	Top-5 err = 0.234375,	data_time = 0.050740,	train_time = 0.319164
[2019-08-23 00:31:03,297] TRAIN Iter 43640: lr = 0.427268,	loss = 2.918960,	Top-1 err = 0.465479,	Top-5 err = 0.229053,	data_time = 0.050147,	train_time = 0.992883
[2019-08-23 00:31:23,305] TRAIN Iter 43660: lr = 0.427235,	loss = 2.757437,	Top-1 err = 0.463184,	Top-5 err = 0.229785,	data_time = 5.896186,	train_time = 1.000370
[2019-08-23 00:31:29,888] TRAIN Iter 43680: lr = 0.427202,	loss = 2.994076,	Top-1 err = 0.462549,	Top-5 err = 0.228369,	data_time = 0.050294,	train_time = 0.329160
[2019-08-23 00:31:50,969] TRAIN Iter 43700: lr = 0.427168,	loss = 2.844489,	Top-1 err = 0.458398,	Top-5 err = 0.225586,	data_time = 0.050048,	train_time = 1.054037
[2019-08-23 00:31:57,842] TRAIN Iter 43720: lr = 0.427135,	loss = 2.942169,	Top-1 err = 0.463135,	Top-5 err = 0.224756,	data_time = 0.050049,	train_time = 0.343647
[2019-08-23 00:32:14,412] TRAIN Iter 43740: lr = 0.427102,	loss = 2.906546,	Top-1 err = 0.466260,	Top-5 err = 0.234229,	data_time = 0.049970,	train_time = 0.828456
[2019-08-23 00:33:02,751] TRAIN Iter 43760: lr = 0.427068,	loss = 2.871439,	Top-1 err = 0.460805,	Top-5 err = 0.230687,	data_time = 0.105812,	train_time = 2.416946
[2019-08-23 00:33:08,929] TRAIN Iter 43780: lr = 0.427035,	loss = 2.917830,	Top-1 err = 0.462646,	Top-5 err = 0.228223,	data_time = 0.050875,	train_time = 0.308891
[2019-08-23 00:33:27,026] TRAIN Iter 43800: lr = 0.427002,	loss = 2.935344,	Top-1 err = 0.464209,	Top-5 err = 0.228906,	data_time = 0.050509,	train_time = 0.904819
[2019-08-23 00:33:34,722] TRAIN Iter 43820: lr = 0.426968,	loss = 2.948221,	Top-1 err = 0.455176,	Top-5 err = 0.224170,	data_time = 0.050312,	train_time = 0.384767
[2019-08-23 00:33:48,059] TRAIN Iter 43840: lr = 0.426935,	loss = 2.950997,	Top-1 err = 0.458984,	Top-5 err = 0.224951,	data_time = 0.101558,	train_time = 0.666828
[2019-08-23 00:34:01,735] TRAIN Iter 43860: lr = 0.426902,	loss = 2.955794,	Top-1 err = 0.456689,	Top-5 err = 0.225049,	data_time = 0.050799,	train_time = 0.683826
[2019-08-23 00:34:08,375] TRAIN Iter 43880: lr = 0.426868,	loss = 2.844172,	Top-1 err = 0.459131,	Top-5 err = 0.223438,	data_time = 0.050776,	train_time = 0.331963
[2019-08-23 00:34:25,671] TRAIN Iter 43900: lr = 0.426835,	loss = 2.861826,	Top-1 err = 0.446240,	Top-5 err = 0.219238,	data_time = 0.050550,	train_time = 0.864782
[2019-08-23 00:34:41,390] TRAIN Iter 43920: lr = 0.426802,	loss = 2.754879,	Top-1 err = 0.455371,	Top-5 err = 0.224805,	data_time = 0.050651,	train_time = 0.785910
[2019-08-23 00:34:47,815] TRAIN Iter 43940: lr = 0.426768,	loss = 2.776197,	Top-1 err = 0.450098,	Top-5 err = 0.218701,	data_time = 0.050479,	train_time = 0.321266
[2019-08-23 00:35:03,248] TRAIN Iter 43960: lr = 0.426735,	loss = 2.827025,	Top-1 err = 0.458936,	Top-5 err = 0.224170,	data_time = 0.050415,	train_time = 0.771629
[2019-08-23 00:35:10,698] TRAIN Iter 43980: lr = 0.426702,	loss = 2.821820,	Top-1 err = 0.461963,	Top-5 err = 0.227100,	data_time = 0.050180,	train_time = 0.372510
[2019-08-23 00:35:23,867] TRAIN Iter 44000: lr = 0.426668,	loss = 2.923578,	Top-1 err = 0.456348,	Top-5 err = 0.223291,	data_time = 0.050488,	train_time = 0.658419
[2019-08-23 00:35:40,001] TRAIN Iter 44020: lr = 0.426635,	loss = 2.885605,	Top-1 err = 0.453174,	Top-5 err = 0.224756,	data_time = 0.050163,	train_time = 0.806703
[2019-08-23 00:35:46,528] TRAIN Iter 44040: lr = 0.426602,	loss = 2.922725,	Top-1 err = 0.460400,	Top-5 err = 0.224756,	data_time = 0.050611,	train_time = 0.326303
[2019-08-23 00:36:01,871] TRAIN Iter 44060: lr = 0.426568,	loss = 2.899801,	Top-1 err = 0.461914,	Top-5 err = 0.225684,	data_time = 0.050625,	train_time = 0.767172
[2019-08-23 00:36:18,231] TRAIN Iter 44080: lr = 0.426535,	loss = 2.847500,	Top-1 err = 0.460938,	Top-5 err = 0.227148,	data_time = 0.050784,	train_time = 0.817949
[2019-08-23 00:36:24,489] TRAIN Iter 44100: lr = 0.426502,	loss = 2.745794,	Top-1 err = 0.457031,	Top-5 err = 0.225098,	data_time = 0.050316,	train_time = 0.312923
[2019-08-23 00:36:40,109] TRAIN Iter 44120: lr = 0.426468,	loss = 2.902354,	Top-1 err = 0.459570,	Top-5 err = 0.222461,	data_time = 0.050431,	train_time = 0.780947
[2019-08-23 00:36:46,878] TRAIN Iter 44140: lr = 0.426435,	loss = 2.915205,	Top-1 err = 0.464258,	Top-5 err = 0.230957,	data_time = 0.050694,	train_time = 0.338466
[2019-08-23 00:37:02,114] TRAIN Iter 44160: lr = 0.426402,	loss = 2.801250,	Top-1 err = 0.461426,	Top-5 err = 0.224121,	data_time = 0.050486,	train_time = 0.761789
[2019-08-23 00:37:18,152] TRAIN Iter 44180: lr = 0.426368,	loss = 2.947554,	Top-1 err = 0.457568,	Top-5 err = 0.226709,	data_time = 0.118588,	train_time = 0.801869
[2019-08-23 00:37:24,457] TRAIN Iter 44200: lr = 0.426335,	loss = 2.974970,	Top-1 err = 0.460498,	Top-5 err = 0.225488,	data_time = 0.050742,	train_time = 0.315260
[2019-08-23 00:37:41,441] TRAIN Iter 44220: lr = 0.426302,	loss = 2.945359,	Top-1 err = 0.455322,	Top-5 err = 0.225977,	data_time = 0.050300,	train_time = 0.849166
[2019-08-23 00:37:54,109] TRAIN Iter 44240: lr = 0.426268,	loss = 2.835140,	Top-1 err = 0.457178,	Top-5 err = 0.229590,	data_time = 0.100764,	train_time = 0.633407
[2019-08-23 00:38:02,635] TRAIN Iter 44260: lr = 0.426235,	loss = 2.923392,	Top-1 err = 0.462012,	Top-5 err = 0.228857,	data_time = 0.050456,	train_time = 0.426295
[2019-08-23 00:38:19,072] TRAIN Iter 44280: lr = 0.426202,	loss = 2.857328,	Top-1 err = 0.456689,	Top-5 err = 0.225439,	data_time = 0.050338,	train_time = 0.821808
[2019-08-23 00:38:25,774] TRAIN Iter 44300: lr = 0.426168,	loss = 2.812307,	Top-1 err = 0.457129,	Top-5 err = 0.220996,	data_time = 0.050326,	train_time = 0.335066
[2019-08-23 00:38:41,551] TRAIN Iter 44320: lr = 0.426135,	loss = 2.826630,	Top-1 err = 0.458154,	Top-5 err = 0.224951,	data_time = 0.050429,	train_time = 0.788878
[2019-08-23 00:38:57,110] TRAIN Iter 44340: lr = 0.426102,	loss = 2.919021,	Top-1 err = 0.458447,	Top-5 err = 0.225439,	data_time = 0.050538,	train_time = 0.777915
[2019-08-23 00:39:03,481] TRAIN Iter 44360: lr = 0.426068,	loss = 2.867706,	Top-1 err = 0.452539,	Top-5 err = 0.223096,	data_time = 0.050276,	train_time = 0.318522
[2019-08-23 00:39:20,995] TRAIN Iter 44380: lr = 0.426035,	loss = 2.938156,	Top-1 err = 0.453906,	Top-5 err = 0.224805,	data_time = 0.050771,	train_time = 0.875716
[2019-08-23 00:39:35,043] TRAIN Iter 44400: lr = 0.426002,	loss = 2.941694,	Top-1 err = 0.462061,	Top-5 err = 0.230518,	data_time = 0.050324,	train_time = 0.702370
[2019-08-23 00:39:42,874] TRAIN Iter 44420: lr = 0.425968,	loss = 2.866168,	Top-1 err = 0.456250,	Top-5 err = 0.226270,	data_time = 0.050862,	train_time = 0.391546
[2019-08-23 00:39:59,809] TRAIN Iter 44440: lr = 0.425935,	loss = 2.908929,	Top-1 err = 0.458301,	Top-5 err = 0.222607,	data_time = 0.050644,	train_time = 0.846722
[2019-08-23 00:40:06,751] TRAIN Iter 44460: lr = 0.425902,	loss = 2.974020,	Top-1 err = 0.464160,	Top-5 err = 0.231396,	data_time = 0.050435,	train_time = 0.347107
[2019-08-23 00:40:22,166] TRAIN Iter 44480: lr = 0.425868,	loss = 3.114710,	Top-1 err = 0.460059,	Top-5 err = 0.227539,	data_time = 0.050527,	train_time = 0.770708
[2019-08-23 00:40:38,068] TRAIN Iter 44500: lr = 0.425835,	loss = 2.967026,	Top-1 err = 0.459814,	Top-5 err = 0.227930,	data_time = 0.050835,	train_time = 0.795124
[2019-08-23 00:40:44,582] TRAIN Iter 44520: lr = 0.425802,	loss = 2.879233,	Top-1 err = 0.454492,	Top-5 err = 0.225439,	data_time = 0.050139,	train_time = 0.325676
[2019-08-23 00:41:00,709] TRAIN Iter 44540: lr = 0.425768,	loss = 2.908604,	Top-1 err = 0.458594,	Top-5 err = 0.226855,	data_time = 0.050321,	train_time = 0.806345
[2019-08-23 00:41:15,691] TRAIN Iter 44560: lr = 0.425735,	loss = 2.860761,	Top-1 err = 0.464453,	Top-5 err = 0.232666,	data_time = 0.050356,	train_time = 0.749067
[2019-08-23 00:41:22,095] TRAIN Iter 44580: lr = 0.425702,	loss = 2.735533,	Top-1 err = 0.464062,	Top-5 err = 0.230908,	data_time = 0.050514,	train_time = 0.320185
[2019-08-23 00:41:40,568] TRAIN Iter 44600: lr = 0.425668,	loss = 2.906503,	Top-1 err = 0.457813,	Top-5 err = 0.225342,	data_time = 0.050708,	train_time = 0.923636
[2019-08-23 00:41:47,017] TRAIN Iter 44620: lr = 0.425635,	loss = 2.902853,	Top-1 err = 0.466260,	Top-5 err = 0.230469,	data_time = 0.050524,	train_time = 0.322462
[2019-08-23 00:42:03,380] TRAIN Iter 44640: lr = 0.425602,	loss = 2.946260,	Top-1 err = 0.464893,	Top-5 err = 0.234473,	data_time = 0.050375,	train_time = 0.818109
[2019-08-23 00:42:21,804] TRAIN Iter 44660: lr = 0.425568,	loss = 2.758474,	Top-1 err = 0.462598,	Top-5 err = 0.226465,	data_time = 0.050785,	train_time = 0.921210
[2019-08-23 00:42:28,019] TRAIN Iter 44680: lr = 0.425535,	loss = 2.825243,	Top-1 err = 0.455127,	Top-5 err = 0.229297,	data_time = 0.050326,	train_time = 0.310740
[2019-08-23 00:42:45,200] TRAIN Iter 44700: lr = 0.425502,	loss = 2.923786,	Top-1 err = 0.457324,	Top-5 err = 0.226807,	data_time = 0.050343,	train_time = 0.859009
[2019-08-23 00:43:01,442] TRAIN Iter 44720: lr = 0.425468,	loss = 3.024619,	Top-1 err = 0.466113,	Top-5 err = 0.228613,	data_time = 0.080788,	train_time = 0.812118
[2019-08-23 00:43:07,990] TRAIN Iter 44740: lr = 0.425435,	loss = 2.987967,	Top-1 err = 0.464355,	Top-5 err = 0.225391,	data_time = 0.050673,	train_time = 0.327354
[2019-08-23 00:43:25,065] TRAIN Iter 44760: lr = 0.425402,	loss = 2.869855,	Top-1 err = 0.461084,	Top-5 err = 0.231982,	data_time = 0.050455,	train_time = 0.853762
[2019-08-23 00:43:31,226] TRAIN Iter 44780: lr = 0.425368,	loss = 2.856472,	Top-1 err = 0.461230,	Top-5 err = 0.226367,	data_time = 0.050461,	train_time = 0.307994
[2019-08-23 00:43:50,012] TRAIN Iter 44800: lr = 0.425335,	loss = 2.859248,	Top-1 err = 0.461670,	Top-5 err = 0.227930,	data_time = 0.050217,	train_time = 0.939294
[2019-08-23 00:44:08,924] TRAIN Iter 44820: lr = 0.425302,	loss = 2.981508,	Top-1 err = 0.456543,	Top-5 err = 0.227979,	data_time = 0.050413,	train_time = 0.945608
[2019-08-23 00:44:15,099] TRAIN Iter 44840: lr = 0.425268,	loss = 2.914056,	Top-1 err = 0.462793,	Top-5 err = 0.224316,	data_time = 0.050143,	train_time = 0.308722
[2019-08-23 00:44:33,526] TRAIN Iter 44860: lr = 0.425235,	loss = 2.990137,	Top-1 err = 0.464844,	Top-5 err = 0.226953,	data_time = 0.050513,	train_time = 0.921364
[2019-08-23 00:44:50,988] TRAIN Iter 44880: lr = 0.425202,	loss = 2.920231,	Top-1 err = 0.460449,	Top-5 err = 0.229443,	data_time = 0.132732,	train_time = 0.873051
[2019-08-23 00:44:57,634] TRAIN Iter 44900: lr = 0.425168,	loss = 2.899793,	Top-1 err = 0.457617,	Top-5 err = 0.228662,	data_time = 0.050364,	train_time = 0.332329
[2019-08-23 00:45:18,294] TRAIN Iter 44920: lr = 0.425135,	loss = 2.850819,	Top-1 err = 0.459033,	Top-5 err = 0.224805,	data_time = 0.050532,	train_time = 1.032945
[2019-08-23 00:45:24,643] TRAIN Iter 44940: lr = 0.425102,	loss = 2.965508,	Top-1 err = 0.461035,	Top-5 err = 0.229004,	data_time = 0.050941,	train_time = 0.317439
[2019-08-23 00:45:44,265] TRAIN Iter 44960: lr = 0.425068,	loss = 2.950057,	Top-1 err = 0.462012,	Top-5 err = 0.231250,	data_time = 0.049997,	train_time = 0.981082
[2019-08-23 00:46:02,823] TRAIN Iter 44980: lr = 0.425035,	loss = 2.858547,	Top-1 err = 0.460303,	Top-5 err = 0.231152,	data_time = 0.049905,	train_time = 0.927916
[2019-08-23 00:46:08,717] TRAIN Iter 45000: lr = 0.425002,	loss = 2.912016,	Top-1 err = 0.466455,	Top-5 err = 0.229639,	data_time = 0.049936,	train_time = 0.294674
[2019-08-23 00:47:01,666] TRAIN Iter 45020: lr = 0.424968,	loss = 2.892362,	Top-1 err = 0.468452,	Top-5 err = 0.231289,	data_time = 0.050390,	train_time = 2.647418
[2019-08-23 00:47:09,627] TRAIN Iter 45040: lr = 0.424935,	loss = 2.788629,	Top-1 err = 0.459180,	Top-5 err = 0.223584,	data_time = 0.050361,	train_time = 0.398068
[2019-08-23 00:47:25,417] TRAIN Iter 45060: lr = 0.424902,	loss = 2.948648,	Top-1 err = 0.454053,	Top-5 err = 0.221436,	data_time = 0.050184,	train_time = 0.789458
[2019-08-23 00:47:35,764] TRAIN Iter 45080: lr = 0.424868,	loss = 2.891141,	Top-1 err = 0.451807,	Top-5 err = 0.221240,	data_time = 0.050431,	train_time = 0.517340
[2019-08-23 00:47:43,006] TRAIN Iter 45100: lr = 0.424835,	loss = 2.947247,	Top-1 err = 0.458691,	Top-5 err = 0.222852,	data_time = 0.050905,	train_time = 0.362099
[2019-08-23 00:47:55,294] TRAIN Iter 45120: lr = 0.424802,	loss = 2.834407,	Top-1 err = 0.455566,	Top-5 err = 0.219873,	data_time = 0.050356,	train_time = 0.614384
[2019-08-23 00:48:08,298] TRAIN Iter 45140: lr = 0.424768,	loss = 2.857975,	Top-1 err = 0.452783,	Top-5 err = 0.222705,	data_time = 0.132924,	train_time = 0.650160
[2019-08-23 00:48:15,914] TRAIN Iter 45160: lr = 0.424735,	loss = 2.857715,	Top-1 err = 0.455420,	Top-5 err = 0.222852,	data_time = 0.050261,	train_time = 0.380783
[2019-08-23 00:48:28,913] TRAIN Iter 45180: lr = 0.424702,	loss = 2.831934,	Top-1 err = 0.459326,	Top-5 err = 0.224512,	data_time = 0.050591,	train_time = 0.649932
[2019-08-23 00:48:36,814] TRAIN Iter 45200: lr = 0.424668,	loss = 2.828638,	Top-1 err = 0.453564,	Top-5 err = 0.222607,	data_time = 0.114994,	train_time = 0.395032
[2019-08-23 00:48:49,810] TRAIN Iter 45220: lr = 0.424635,	loss = 2.798921,	Top-1 err = 0.456641,	Top-5 err = 0.220947,	data_time = 0.050380,	train_time = 0.649820
[2019-08-23 00:49:03,083] TRAIN Iter 45240: lr = 0.424602,	loss = 2.801342,	Top-1 err = 0.455713,	Top-5 err = 0.223877,	data_time = 0.100460,	train_time = 0.663616
[2019-08-23 00:49:11,603] TRAIN Iter 45260: lr = 0.424568,	loss = 2.809164,	Top-1 err = 0.452734,	Top-5 err = 0.222998,	data_time = 0.050483,	train_time = 0.425999
[2019-08-23 00:49:25,337] TRAIN Iter 45280: lr = 0.424535,	loss = 2.952845,	Top-1 err = 0.458008,	Top-5 err = 0.224268,	data_time = 0.050352,	train_time = 0.686673
[2019-08-23 00:49:40,161] TRAIN Iter 45300: lr = 0.424502,	loss = 2.824681,	Top-1 err = 0.450684,	Top-5 err = 0.218945,	data_time = 1.472758,	train_time = 0.741200
[2019-08-23 00:49:47,761] TRAIN Iter 45320: lr = 0.424468,	loss = 2.787508,	Top-1 err = 0.449951,	Top-5 err = 0.223877,	data_time = 0.050441,	train_time = 0.379964
[2019-08-23 00:50:01,341] TRAIN Iter 45340: lr = 0.424435,	loss = 2.909835,	Top-1 err = 0.459521,	Top-5 err = 0.221680,	data_time = 0.113372,	train_time = 0.678965
[2019-08-23 00:50:08,361] TRAIN Iter 45360: lr = 0.424402,	loss = 2.899543,	Top-1 err = 0.462012,	Top-5 err = 0.226270,	data_time = 0.139754,	train_time = 0.351001
[2019-08-23 00:50:24,655] TRAIN Iter 45380: lr = 0.424368,	loss = 2.937137,	Top-1 err = 0.459521,	Top-5 err = 0.222363,	data_time = 0.050620,	train_time = 0.814690
[2019-08-23 00:50:39,187] TRAIN Iter 45400: lr = 0.424335,	loss = 2.845566,	Top-1 err = 0.455420,	Top-5 err = 0.223779,	data_time = 0.050624,	train_time = 0.726597
[2019-08-23 00:50:46,056] TRAIN Iter 45420: lr = 0.424302,	loss = 2.868975,	Top-1 err = 0.460205,	Top-5 err = 0.225195,	data_time = 0.050353,	train_time = 0.343416
[2019-08-23 00:50:59,036] TRAIN Iter 45440: lr = 0.424268,	loss = 2.894824,	Top-1 err = 0.460107,	Top-5 err = 0.227637,	data_time = 0.050238,	train_time = 0.649016
[2019-08-23 00:51:13,041] TRAIN Iter 45460: lr = 0.424235,	loss = 2.824143,	Top-1 err = 0.459424,	Top-5 err = 0.223828,	data_time = 4.188547,	train_time = 0.700226
[2019-08-23 00:51:20,260] TRAIN Iter 45480: lr = 0.424202,	loss = 2.828088,	Top-1 err = 0.456201,	Top-5 err = 0.228711,	data_time = 0.050432,	train_time = 0.360909
[2019-08-23 00:51:35,932] TRAIN Iter 45500: lr = 0.424168,	loss = 2.893888,	Top-1 err = 0.458350,	Top-5 err = 0.226758,	data_time = 0.050162,	train_time = 0.783600
[2019-08-23 00:51:42,760] TRAIN Iter 45520: lr = 0.424135,	loss = 2.804151,	Top-1 err = 0.452783,	Top-5 err = 0.221143,	data_time = 0.050801,	train_time = 0.341357
[2019-08-23 00:51:59,087] TRAIN Iter 45540: lr = 0.424102,	loss = 2.888427,	Top-1 err = 0.451904,	Top-5 err = 0.223193,	data_time = 0.050746,	train_time = 0.816377
[2019-08-23 00:52:13,067] TRAIN Iter 45560: lr = 0.424068,	loss = 2.892160,	Top-1 err = 0.458545,	Top-5 err = 0.227393,	data_time = 0.050497,	train_time = 0.698976
[2019-08-23 00:52:20,146] TRAIN Iter 45580: lr = 0.424035,	loss = 2.895775,	Top-1 err = 0.452734,	Top-5 err = 0.227197,	data_time = 0.050461,	train_time = 0.353947
[2019-08-23 00:52:33,288] TRAIN Iter 45600: lr = 0.424002,	loss = 2.865771,	Top-1 err = 0.458008,	Top-5 err = 0.227588,	data_time = 0.050533,	train_time = 0.657071
[2019-08-23 00:52:48,196] TRAIN Iter 45620: lr = 0.423968,	loss = 2.869570,	Top-1 err = 0.463672,	Top-5 err = 0.225391,	data_time = 5.323348,	train_time = 0.745394
[2019-08-23 00:52:55,265] TRAIN Iter 45640: lr = 0.423935,	loss = 2.885796,	Top-1 err = 0.455420,	Top-5 err = 0.222607,	data_time = 0.050121,	train_time = 0.353425
[2019-08-23 00:53:10,869] TRAIN Iter 45660: lr = 0.423902,	loss = 2.831867,	Top-1 err = 0.453564,	Top-5 err = 0.223047,	data_time = 0.050806,	train_time = 0.780175
[2019-08-23 00:53:18,093] TRAIN Iter 45680: lr = 0.423868,	loss = 2.882933,	Top-1 err = 0.461768,	Top-5 err = 0.228076,	data_time = 0.050870,	train_time = 0.361183
[2019-08-23 00:53:32,347] TRAIN Iter 45700: lr = 0.423835,	loss = 2.937296,	Top-1 err = 0.460840,	Top-5 err = 0.228711,	data_time = 0.050633,	train_time = 0.712712
[2019-08-23 00:53:46,878] TRAIN Iter 45720: lr = 0.423802,	loss = 2.816733,	Top-1 err = 0.458789,	Top-5 err = 0.227100,	data_time = 0.050828,	train_time = 0.726510
[2019-08-23 00:53:53,874] TRAIN Iter 45740: lr = 0.423768,	loss = 2.930837,	Top-1 err = 0.462305,	Top-5 err = 0.229687,	data_time = 0.050298,	train_time = 0.349791
[2019-08-23 00:54:09,996] TRAIN Iter 45760: lr = 0.423735,	loss = 2.865246,	Top-1 err = 0.459814,	Top-5 err = 0.228613,	data_time = 0.050208,	train_time = 0.806119
[2019-08-23 00:54:23,660] TRAIN Iter 45780: lr = 0.423702,	loss = 2.886751,	Top-1 err = 0.462451,	Top-5 err = 0.227588,	data_time = 1.502274,	train_time = 0.683182
[2019-08-23 00:54:31,280] TRAIN Iter 45800: lr = 0.423668,	loss = 2.846522,	Top-1 err = 0.456738,	Top-5 err = 0.229297,	data_time = 0.050179,	train_time = 0.380956
[2019-08-23 00:54:47,948] TRAIN Iter 45820: lr = 0.423635,	loss = 2.907631,	Top-1 err = 0.458887,	Top-5 err = 0.227197,	data_time = 0.050426,	train_time = 0.833401
[2019-08-23 00:54:54,820] TRAIN Iter 45840: lr = 0.423602,	loss = 2.890107,	Top-1 err = 0.463232,	Top-5 err = 0.233398,	data_time = 0.050664,	train_time = 0.343575
[2019-08-23 00:55:09,761] TRAIN Iter 45860: lr = 0.423568,	loss = 2.963733,	Top-1 err = 0.462012,	Top-5 err = 0.232520,	data_time = 0.050450,	train_time = 0.747061
[2019-08-23 00:55:25,829] TRAIN Iter 45880: lr = 0.423535,	loss = 2.967847,	Top-1 err = 0.460547,	Top-5 err = 0.228613,	data_time = 0.050268,	train_time = 0.803378
[2019-08-23 00:55:33,038] TRAIN Iter 45900: lr = 0.423502,	loss = 2.865877,	Top-1 err = 0.458057,	Top-5 err = 0.225830,	data_time = 0.050519,	train_time = 0.360434
[2019-08-23 00:55:50,062] TRAIN Iter 45920: lr = 0.423468,	loss = 2.821985,	Top-1 err = 0.461572,	Top-5 err = 0.230713,	data_time = 0.050338,	train_time = 0.851198
[2019-08-23 00:56:06,973] TRAIN Iter 45940: lr = 0.423435,	loss = 2.851015,	Top-1 err = 0.456934,	Top-5 err = 0.227783,	data_time = 1.699357,	train_time = 0.845540
[2019-08-23 00:56:14,189] TRAIN Iter 45960: lr = 0.423402,	loss = 2.914814,	Top-1 err = 0.459277,	Top-5 err = 0.224414,	data_time = 0.050370,	train_time = 0.360766
[2019-08-23 00:56:30,732] TRAIN Iter 45980: lr = 0.423368,	loss = 2.910655,	Top-1 err = 0.459375,	Top-5 err = 0.226611,	data_time = 0.050387,	train_time = 0.827144
[2019-08-23 00:56:38,218] TRAIN Iter 46000: lr = 0.423335,	loss = 2.857764,	Top-1 err = 0.459229,	Top-5 err = 0.228467,	data_time = 0.050811,	train_time = 0.374264
[2019-08-23 00:56:57,513] TRAIN Iter 46020: lr = 0.423302,	loss = 2.832588,	Top-1 err = 0.453760,	Top-5 err = 0.221387,	data_time = 0.050362,	train_time = 0.964728
[2019-08-23 00:57:16,019] TRAIN Iter 46040: lr = 0.423268,	loss = 3.056893,	Top-1 err = 0.457422,	Top-5 err = 0.229834,	data_time = 0.050381,	train_time = 0.925290
[2019-08-23 00:57:22,636] TRAIN Iter 46060: lr = 0.423235,	loss = 2.896181,	Top-1 err = 0.459082,	Top-5 err = 0.222510,	data_time = 0.050168,	train_time = 0.330874
[2019-08-23 00:57:41,108] TRAIN Iter 46080: lr = 0.423202,	loss = 2.913693,	Top-1 err = 0.465088,	Top-5 err = 0.232324,	data_time = 0.050613,	train_time = 0.923560
[2019-08-23 00:57:59,504] TRAIN Iter 46100: lr = 0.423168,	loss = 2.977658,	Top-1 err = 0.451562,	Top-5 err = 0.220264,	data_time = 3.728517,	train_time = 0.919780
[2019-08-23 00:58:06,273] TRAIN Iter 46120: lr = 0.423135,	loss = 2.951160,	Top-1 err = 0.457080,	Top-5 err = 0.224561,	data_time = 0.050360,	train_time = 0.338449
[2019-08-23 00:58:24,266] TRAIN Iter 46140: lr = 0.423102,	loss = 2.906699,	Top-1 err = 0.459131,	Top-5 err = 0.225830,	data_time = 0.122537,	train_time = 0.899632
[2019-08-23 00:58:30,818] TRAIN Iter 46160: lr = 0.423068,	loss = 2.930024,	Top-1 err = 0.463135,	Top-5 err = 0.227197,	data_time = 0.050591,	train_time = 0.327607
[2019-08-23 00:58:49,925] TRAIN Iter 46180: lr = 0.423035,	loss = 2.942090,	Top-1 err = 0.459229,	Top-5 err = 0.227930,	data_time = 0.050378,	train_time = 0.955309
[2019-08-23 00:59:10,120] TRAIN Iter 46200: lr = 0.423002,	loss = 2.869809,	Top-1 err = 0.459863,	Top-5 err = 0.225781,	data_time = 0.049895,	train_time = 1.009741
[2019-08-23 00:59:16,869] TRAIN Iter 46220: lr = 0.422968,	loss = 2.853845,	Top-1 err = 0.459619,	Top-5 err = 0.225830,	data_time = 0.050101,	train_time = 0.337434
[2019-08-23 00:59:34,886] TRAIN Iter 46240: lr = 0.422935,	loss = 2.880918,	Top-1 err = 0.459375,	Top-5 err = 0.226562,	data_time = 0.049886,	train_time = 0.900827
[2019-08-23 00:59:50,664] TRAIN Iter 46260: lr = 0.422902,	loss = 3.170185,	Top-1 err = 0.464976,	Top-5 err = 0.228734,	data_time = 0.007057,	train_time = 0.788902
[2019-08-23 01:00:35,239] TRAIN Iter 46280: lr = 0.422868,	loss = 2.926346,	Top-1 err = 0.465088,	Top-5 err = 0.225049,	data_time = 0.050396,	train_time = 2.228754
[2019-08-23 01:00:49,723] TRAIN Iter 46300: lr = 0.422835,	loss = 2.857315,	Top-1 err = 0.456592,	Top-5 err = 0.221924,	data_time = 0.606878,	train_time = 0.724137
[2019-08-23 01:00:57,545] TRAIN Iter 46320: lr = 0.422802,	loss = 2.862559,	Top-1 err = 0.455469,	Top-5 err = 0.225781,	data_time = 0.050467,	train_time = 0.391109
[2019-08-23 01:01:11,156] TRAIN Iter 46340: lr = 0.422768,	loss = 2.853418,	Top-1 err = 0.451025,	Top-5 err = 0.216260,	data_time = 0.050636,	train_time = 0.680540
[2019-08-23 01:01:19,655] TRAIN Iter 46360: lr = 0.422735,	loss = 2.824203,	Top-1 err = 0.454248,	Top-5 err = 0.225830,	data_time = 0.050794,	train_time = 0.424893
[2019-08-23 01:01:30,871] TRAIN Iter 46380: lr = 0.422702,	loss = 2.827571,	Top-1 err = 0.457959,	Top-5 err = 0.222217,	data_time = 0.050337,	train_time = 0.560809
[2019-08-23 01:01:44,880] TRAIN Iter 46400: lr = 0.422668,	loss = 2.866474,	Top-1 err = 0.456396,	Top-5 err = 0.220850,	data_time = 0.050282,	train_time = 0.700449
[2019-08-23 01:01:52,081] TRAIN Iter 46420: lr = 0.422635,	loss = 2.931636,	Top-1 err = 0.453857,	Top-5 err = 0.220068,	data_time = 0.050562,	train_time = 0.360031
[2019-08-23 01:02:05,181] TRAIN Iter 46440: lr = 0.422602,	loss = 2.856792,	Top-1 err = 0.454688,	Top-5 err = 0.223584,	data_time = 0.050304,	train_time = 0.654979
[2019-08-23 01:02:20,243] TRAIN Iter 46460: lr = 0.422568,	loss = 2.890462,	Top-1 err = 0.456104,	Top-5 err = 0.224707,	data_time = 0.050647,	train_time = 0.753064
[2019-08-23 01:02:27,083] TRAIN Iter 46480: lr = 0.422535,	loss = 2.795525,	Top-1 err = 0.454980,	Top-5 err = 0.220117,	data_time = 0.050306,	train_time = 0.341979
[2019-08-23 01:02:43,407] TRAIN Iter 46500: lr = 0.422502,	loss = 2.950709,	Top-1 err = 0.454590,	Top-5 err = 0.223633,	data_time = 0.050837,	train_time = 0.816207
[2019-08-23 01:02:57,452] TRAIN Iter 46520: lr = 0.422468,	loss = 2.872242,	Top-1 err = 0.461426,	Top-5 err = 0.227881,	data_time = 0.050756,	train_time = 0.702253
[2019-08-23 01:03:04,582] TRAIN Iter 46540: lr = 0.422435,	loss = 2.875239,	Top-1 err = 0.454541,	Top-5 err = 0.221777,	data_time = 0.050660,	train_time = 0.356453
[2019-08-23 01:03:17,989] TRAIN Iter 46560: lr = 0.422402,	loss = 2.817472,	Top-1 err = 0.457275,	Top-5 err = 0.222363,	data_time = 0.050530,	train_time = 0.670326
[2019-08-23 01:03:24,946] TRAIN Iter 46580: lr = 0.422368,	loss = 2.885594,	Top-1 err = 0.456689,	Top-5 err = 0.223584,	data_time = 0.106349,	train_time = 0.347838
[2019-08-23 01:03:38,660] TRAIN Iter 46600: lr = 0.422335,	loss = 2.876823,	Top-1 err = 0.450293,	Top-5 err = 0.223730,	data_time = 0.050541,	train_time = 0.685720
[2019-08-23 01:03:54,053] TRAIN Iter 46620: lr = 0.422302,	loss = 2.872740,	Top-1 err = 0.456152,	Top-5 err = 0.227490,	data_time = 0.050608,	train_time = 0.769606
[2019-08-23 01:04:00,973] TRAIN Iter 46640: lr = 0.422268,	loss = 2.858531,	Top-1 err = 0.452490,	Top-5 err = 0.220068,	data_time = 0.050553,	train_time = 0.345978
[2019-08-23 01:04:14,821] TRAIN Iter 46660: lr = 0.422235,	loss = 2.821165,	Top-1 err = 0.453369,	Top-5 err = 0.218945,	data_time = 0.050653,	train_time = 0.692423
[2019-08-23 01:04:31,096] TRAIN Iter 46680: lr = 0.422202,	loss = 2.850949,	Top-1 err = 0.459229,	Top-5 err = 0.225781,	data_time = 0.050775,	train_time = 0.813714
[2019-08-23 01:04:38,197] TRAIN Iter 46700: lr = 0.422168,	loss = 2.909291,	Top-1 err = 0.458838,	Top-5 err = 0.225684,	data_time = 0.050655,	train_time = 0.355030
[2019-08-23 01:04:51,618] TRAIN Iter 46720: lr = 0.422135,	loss = 2.943888,	Top-1 err = 0.454883,	Top-5 err = 0.224561,	data_time = 0.050261,	train_time = 0.671048
[2019-08-23 01:04:58,738] TRAIN Iter 46740: lr = 0.422102,	loss = 2.890037,	Top-1 err = 0.456152,	Top-5 err = 0.222607,	data_time = 0.050771,	train_time = 0.355981
[2019-08-23 01:05:14,195] TRAIN Iter 46760: lr = 0.422068,	loss = 2.932836,	Top-1 err = 0.452637,	Top-5 err = 0.220898,	data_time = 0.050573,	train_time = 0.772828
[2019-08-23 01:05:29,573] TRAIN Iter 46780: lr = 0.422035,	loss = 2.826560,	Top-1 err = 0.451660,	Top-5 err = 0.224316,	data_time = 0.050325,	train_time = 0.768880
[2019-08-23 01:05:37,168] TRAIN Iter 46800: lr = 0.422002,	loss = 2.916867,	Top-1 err = 0.457373,	Top-5 err = 0.221484,	data_time = 0.122554,	train_time = 0.379777
[2019-08-23 01:05:48,987] TRAIN Iter 46820: lr = 0.421968,	loss = 2.879691,	Top-1 err = 0.454639,	Top-5 err = 0.226221,	data_time = 0.050327,	train_time = 0.590917
[2019-08-23 01:06:04,690] TRAIN Iter 46840: lr = 0.421935,	loss = 2.835790,	Top-1 err = 0.450098,	Top-5 err = 0.218066,	data_time = 0.050159,	train_time = 0.785108
[2019-08-23 01:06:11,669] TRAIN Iter 46860: lr = 0.421902,	loss = 2.887820,	Top-1 err = 0.459375,	Top-5 err = 0.223438,	data_time = 0.050659,	train_time = 0.348940
[2019-08-23 01:06:27,649] TRAIN Iter 46880: lr = 0.421868,	loss = 2.903781,	Top-1 err = 0.462939,	Top-5 err = 0.223828,	data_time = 0.050683,	train_time = 0.798977
[2019-08-23 01:06:34,571] TRAIN Iter 46900: lr = 0.421835,	loss = 2.894845,	Top-1 err = 0.454834,	Top-5 err = 0.222168,	data_time = 0.050409,	train_time = 0.346094
[2019-08-23 01:06:49,130] TRAIN Iter 46920: lr = 0.421802,	loss = 2.815003,	Top-1 err = 0.458643,	Top-5 err = 0.223486,	data_time = 0.050375,	train_time = 0.727926
[2019-08-23 01:07:03,990] TRAIN Iter 46940: lr = 0.421768,	loss = 2.833526,	Top-1 err = 0.453369,	Top-5 err = 0.221973,	data_time = 0.050332,	train_time = 0.742989
[2019-08-23 01:07:10,579] TRAIN Iter 46960: lr = 0.421735,	loss = 2.876640,	Top-1 err = 0.455273,	Top-5 err = 0.223389,	data_time = 0.050715,	train_time = 0.329425
[2019-08-23 01:07:26,325] TRAIN Iter 46980: lr = 0.421702,	loss = 2.914557,	Top-1 err = 0.455615,	Top-5 err = 0.222656,	data_time = 0.050492,	train_time = 0.787302
[2019-08-23 01:07:43,166] TRAIN Iter 47000: lr = 0.421668,	loss = 2.834335,	Top-1 err = 0.456982,	Top-5 err = 0.222266,	data_time = 0.080046,	train_time = 0.842030
[2019-08-23 01:07:49,954] TRAIN Iter 47020: lr = 0.421635,	loss = 2.878782,	Top-1 err = 0.461523,	Top-5 err = 0.228418,	data_time = 0.050505,	train_time = 0.339409
[2019-08-23 01:08:06,041] TRAIN Iter 47040: lr = 0.421602,	loss = 2.813344,	Top-1 err = 0.458057,	Top-5 err = 0.222461,	data_time = 0.050361,	train_time = 0.804334
[2019-08-23 01:08:12,722] TRAIN Iter 47060: lr = 0.421568,	loss = 3.006907,	Top-1 err = 0.455957,	Top-5 err = 0.225928,	data_time = 0.050601,	train_time = 0.334027
[2019-08-23 01:08:28,433] TRAIN Iter 47080: lr = 0.421535,	loss = 2.949833,	Top-1 err = 0.460254,	Top-5 err = 0.227246,	data_time = 0.050205,	train_time = 0.785552
[2019-08-23 01:08:43,952] TRAIN Iter 47100: lr = 0.421502,	loss = 2.746769,	Top-1 err = 0.452783,	Top-5 err = 0.224854,	data_time = 0.050784,	train_time = 0.775897
[2019-08-23 01:08:51,320] TRAIN Iter 47120: lr = 0.421468,	loss = 2.963589,	Top-1 err = 0.458105,	Top-5 err = 0.227832,	data_time = 0.050274,	train_time = 0.368384
[2019-08-23 01:09:07,621] TRAIN Iter 47140: lr = 0.421435,	loss = 2.928239,	Top-1 err = 0.451953,	Top-5 err = 0.225098,	data_time = 0.050430,	train_time = 0.815061
[2019-08-23 01:09:24,492] TRAIN Iter 47160: lr = 0.421402,	loss = 2.938773,	Top-1 err = 0.452686,	Top-5 err = 0.222949,	data_time = 0.150347,	train_time = 0.843556
[2019-08-23 01:09:31,372] TRAIN Iter 47180: lr = 0.421368,	loss = 2.912938,	Top-1 err = 0.458350,	Top-5 err = 0.228955,	data_time = 0.050504,	train_time = 0.343961
[2019-08-23 01:09:49,754] TRAIN Iter 47200: lr = 0.421335,	loss = 2.849098,	Top-1 err = 0.452344,	Top-5 err = 0.228760,	data_time = 0.050422,	train_time = 0.919101
[2019-08-23 01:09:56,682] TRAIN Iter 47220: lr = 0.421302,	loss = 2.872946,	Top-1 err = 0.453125,	Top-5 err = 0.226270,	data_time = 0.050429,	train_time = 0.346374
[2019-08-23 01:10:12,396] TRAIN Iter 47240: lr = 0.421268,	loss = 2.744598,	Top-1 err = 0.462549,	Top-5 err = 0.224756,	data_time = 0.050331,	train_time = 0.785667
[2019-08-23 01:10:29,417] TRAIN Iter 47260: lr = 0.421235,	loss = 2.963273,	Top-1 err = 0.460986,	Top-5 err = 0.228125,	data_time = 0.050369,	train_time = 0.851054
[2019-08-23 01:10:35,844] TRAIN Iter 47280: lr = 0.421202,	loss = 2.944704,	Top-1 err = 0.457373,	Top-5 err = 0.223535,	data_time = 0.050821,	train_time = 0.321326
[2019-08-23 01:10:53,640] TRAIN Iter 47300: lr = 0.421168,	loss = 2.837292,	Top-1 err = 0.458350,	Top-5 err = 0.226318,	data_time = 0.050650,	train_time = 0.889816
[2019-08-23 01:11:10,711] TRAIN Iter 47320: lr = 0.421135,	loss = 2.960331,	Top-1 err = 0.464795,	Top-5 err = 0.229736,	data_time = 0.050478,	train_time = 0.853514
[2019-08-23 01:11:18,719] TRAIN Iter 47340: lr = 0.421102,	loss = 2.931090,	Top-1 err = 0.455615,	Top-5 err = 0.226367,	data_time = 0.050366,	train_time = 0.400397
[2019-08-23 01:11:38,549] TRAIN Iter 47360: lr = 0.421068,	loss = 2.879755,	Top-1 err = 0.458984,	Top-5 err = 0.224951,	data_time = 0.050575,	train_time = 0.991491
[2019-08-23 01:11:44,974] TRAIN Iter 47380: lr = 0.421035,	loss = 2.821561,	Top-1 err = 0.461426,	Top-5 err = 0.225488,	data_time = 0.050659,	train_time = 0.321229
[2019-08-23 01:12:04,125] TRAIN Iter 47400: lr = 0.421002,	loss = 2.845911,	Top-1 err = 0.455420,	Top-5 err = 0.225146,	data_time = 0.050457,	train_time = 0.957526
[2019-08-23 01:12:23,070] TRAIN Iter 47420: lr = 0.420968,	loss = 2.977340,	Top-1 err = 0.462695,	Top-5 err = 0.229980,	data_time = 0.050458,	train_time = 0.947253
[2019-08-23 01:12:29,797] TRAIN Iter 47440: lr = 0.420935,	loss = 2.860168,	Top-1 err = 0.462988,	Top-5 err = 0.225635,	data_time = 0.050292,	train_time = 0.336338
[2019-08-23 01:12:49,182] TRAIN Iter 47460: lr = 0.420902,	loss = 2.787265,	Top-1 err = 0.454053,	Top-5 err = 0.228027,	data_time = 0.050029,	train_time = 0.969219
[2019-08-23 01:13:09,235] TRAIN Iter 47480: lr = 0.420868,	loss = 2.900311,	Top-1 err = 0.458398,	Top-5 err = 0.225244,	data_time = 0.049899,	train_time = 1.002622
[2019-08-23 01:13:15,195] TRAIN Iter 47500: lr = 0.420835,	loss = 3.005328,	Top-1 err = 0.463818,	Top-5 err = 0.233789,	data_time = 0.049882,	train_time = 0.298003
[2019-08-23 01:14:08,217] TRAIN Iter 47520: lr = 0.420802,	loss = 2.952263,	Top-1 err = 0.460536,	Top-5 err = 0.223568,	data_time = 0.050457,	train_time = 2.651082
[2019-08-23 01:14:15,905] TRAIN Iter 47540: lr = 0.420768,	loss = 2.863267,	Top-1 err = 0.454736,	Top-5 err = 0.226807,	data_time = 0.094010,	train_time = 0.384361
[2019-08-23 01:14:28,751] TRAIN Iter 47560: lr = 0.420735,	loss = 2.786425,	Top-1 err = 0.449219,	Top-5 err = 0.220117,	data_time = 0.050819,	train_time = 0.642323
[2019-08-23 01:14:38,239] TRAIN Iter 47580: lr = 0.420702,	loss = 2.896337,	Top-1 err = 0.457520,	Top-5 err = 0.222949,	data_time = 0.050822,	train_time = 0.474372
[2019-08-23 01:14:45,359] TRAIN Iter 47600: lr = 0.420668,	loss = 2.790093,	Top-1 err = 0.452979,	Top-5 err = 0.219873,	data_time = 0.050373,	train_time = 0.355981
[2019-08-23 01:15:01,189] TRAIN Iter 47620: lr = 0.420635,	loss = 2.811756,	Top-1 err = 0.444092,	Top-5 err = 0.217090,	data_time = 0.050560,	train_time = 0.791512
[2019-08-23 01:15:08,601] TRAIN Iter 47640: lr = 0.420602,	loss = 2.806959,	Top-1 err = 0.453516,	Top-5 err = 0.218896,	data_time = 0.050578,	train_time = 0.370582
[2019-08-23 01:15:21,613] TRAIN Iter 47660: lr = 0.420568,	loss = 2.833480,	Top-1 err = 0.455615,	Top-5 err = 0.225732,	data_time = 0.050473,	train_time = 0.650574
[2019-08-23 01:15:36,599] TRAIN Iter 47680: lr = 0.420535,	loss = 2.733279,	Top-1 err = 0.451318,	Top-5 err = 0.217139,	data_time = 0.050669,	train_time = 0.749264
[2019-08-23 01:15:43,464] TRAIN Iter 47700: lr = 0.420502,	loss = 2.812591,	Top-1 err = 0.447998,	Top-5 err = 0.217871,	data_time = 0.050597,	train_time = 0.343239
[2019-08-23 01:15:57,877] TRAIN Iter 47720: lr = 0.420468,	loss = 2.867524,	Top-1 err = 0.453369,	Top-5 err = 0.218896,	data_time = 0.050580,	train_time = 0.720621
[2019-08-23 01:16:13,447] TRAIN Iter 47740: lr = 0.420435,	loss = 2.927644,	Top-1 err = 0.454590,	Top-5 err = 0.218066,	data_time = 0.050606,	train_time = 0.778489
[2019-08-23 01:16:20,672] TRAIN Iter 47760: lr = 0.420402,	loss = 2.817375,	Top-1 err = 0.455664,	Top-5 err = 0.225977,	data_time = 0.118011,	train_time = 0.361243
[2019-08-23 01:16:33,547] TRAIN Iter 47780: lr = 0.420368,	loss = 2.918513,	Top-1 err = 0.451904,	Top-5 err = 0.220166,	data_time = 0.050339,	train_time = 0.643734
[2019-08-23 01:16:40,784] TRAIN Iter 47800: lr = 0.420335,	loss = 3.005808,	Top-1 err = 0.453418,	Top-5 err = 0.222998,	data_time = 0.050384,	train_time = 0.361834
[2019-08-23 01:16:54,118] TRAIN Iter 47820: lr = 0.420302,	loss = 2.869005,	Top-1 err = 0.452393,	Top-5 err = 0.218750,	data_time = 0.050313,	train_time = 0.666702
[2019-08-23 01:17:10,020] TRAIN Iter 47840: lr = 0.420268,	loss = 2.807757,	Top-1 err = 0.450732,	Top-5 err = 0.217969,	data_time = 0.050451,	train_time = 0.795106
[2019-08-23 01:17:16,619] TRAIN Iter 47860: lr = 0.420235,	loss = 2.927644,	Top-1 err = 0.456250,	Top-5 err = 0.221484,	data_time = 0.132323,	train_time = 0.329870
[2019-08-23 01:17:31,417] TRAIN Iter 47880: lr = 0.420202,	loss = 2.864410,	Top-1 err = 0.454395,	Top-5 err = 0.224658,	data_time = 0.050808,	train_time = 0.739915
[2019-08-23 01:17:43,308] TRAIN Iter 47900: lr = 0.420168,	loss = 2.804094,	Top-1 err = 0.452637,	Top-5 err = 0.222656,	data_time = 0.988486,	train_time = 0.594528
[2019-08-23 01:17:52,793] TRAIN Iter 47920: lr = 0.420135,	loss = 2.803182,	Top-1 err = 0.452979,	Top-5 err = 0.220752,	data_time = 0.050623,	train_time = 0.474255
[2019-08-23 01:18:08,652] TRAIN Iter 47940: lr = 0.420102,	loss = 2.810867,	Top-1 err = 0.449902,	Top-5 err = 0.219189,	data_time = 0.050523,	train_time = 0.792937
[2019-08-23 01:18:15,957] TRAIN Iter 47960: lr = 0.420068,	loss = 2.819669,	Top-1 err = 0.454004,	Top-5 err = 0.225635,	data_time = 0.050752,	train_time = 0.365194
[2019-08-23 01:18:30,856] TRAIN Iter 47980: lr = 0.420035,	loss = 2.881206,	Top-1 err = 0.451758,	Top-5 err = 0.224365,	data_time = 0.050368,	train_time = 0.744972
[2019-08-23 01:18:46,505] TRAIN Iter 48000: lr = 0.420002,	loss = 2.870484,	Top-1 err = 0.456299,	Top-5 err = 0.223340,	data_time = 0.050553,	train_time = 0.782445
[2019-08-23 01:18:53,871] TRAIN Iter 48020: lr = 0.419968,	loss = 2.770486,	Top-1 err = 0.458252,	Top-5 err = 0.223926,	data_time = 0.050567,	train_time = 0.368242
[2019-08-23 01:19:07,252] TRAIN Iter 48040: lr = 0.419935,	loss = 2.849763,	Top-1 err = 0.459473,	Top-5 err = 0.229639,	data_time = 0.050548,	train_time = 0.669043
[2019-08-23 01:19:16,889] TRAIN Iter 48060: lr = 0.419902,	loss = 2.829069,	Top-1 err = 0.455859,	Top-5 err = 0.223389,	data_time = 1.182682,	train_time = 0.481825
[2019-08-23 01:19:28,341] TRAIN Iter 48080: lr = 0.419868,	loss = 2.789196,	Top-1 err = 0.455518,	Top-5 err = 0.221533,	data_time = 0.050214,	train_time = 0.572608
[2019-08-23 01:19:43,747] TRAIN Iter 48100: lr = 0.419835,	loss = 2.836141,	Top-1 err = 0.451465,	Top-5 err = 0.220215,	data_time = 0.050349,	train_time = 0.770304
[2019-08-23 01:19:51,141] TRAIN Iter 48120: lr = 0.419802,	loss = 2.823053,	Top-1 err = 0.447607,	Top-5 err = 0.220361,	data_time = 0.050173,	train_time = 0.369660
[2019-08-23 01:20:06,714] TRAIN Iter 48140: lr = 0.419768,	loss = 2.832602,	Top-1 err = 0.457275,	Top-5 err = 0.227295,	data_time = 0.050627,	train_time = 0.778639
[2019-08-23 01:20:22,147] TRAIN Iter 48160: lr = 0.419735,	loss = 2.910845,	Top-1 err = 0.456982,	Top-5 err = 0.226416,	data_time = 0.050745,	train_time = 0.771617
[2019-08-23 01:20:29,397] TRAIN Iter 48180: lr = 0.419702,	loss = 2.901169,	Top-1 err = 0.456934,	Top-5 err = 0.223535,	data_time = 0.050508,	train_time = 0.362491
[2019-08-23 01:20:44,197] TRAIN Iter 48200: lr = 0.419668,	loss = 2.828161,	Top-1 err = 0.456885,	Top-5 err = 0.226172,	data_time = 0.050493,	train_time = 0.739980
[2019-08-23 01:20:55,462] TRAIN Iter 48220: lr = 0.419635,	loss = 2.853535,	Top-1 err = 0.460107,	Top-5 err = 0.227490,	data_time = 3.997891,	train_time = 0.563271
[2019-08-23 01:21:08,762] TRAIN Iter 48240: lr = 0.419602,	loss = 2.950611,	Top-1 err = 0.461523,	Top-5 err = 0.223779,	data_time = 0.050585,	train_time = 0.664985
[2019-08-23 01:21:24,951] TRAIN Iter 48260: lr = 0.419568,	loss = 2.810846,	Top-1 err = 0.455029,	Top-5 err = 0.225049,	data_time = 0.050486,	train_time = 0.809416
[2019-08-23 01:21:32,908] TRAIN Iter 48280: lr = 0.419535,	loss = 3.023153,	Top-1 err = 0.459570,	Top-5 err = 0.228711,	data_time = 0.050366,	train_time = 0.397826
[2019-08-23 01:21:47,431] TRAIN Iter 48300: lr = 0.419502,	loss = 2.853832,	Top-1 err = 0.450977,	Top-5 err = 0.223584,	data_time = 0.169636,	train_time = 0.726140
[2019-08-23 01:22:03,459] TRAIN Iter 48320: lr = 0.419468,	loss = 2.790291,	Top-1 err = 0.455811,	Top-5 err = 0.221973,	data_time = 0.050656,	train_time = 0.801414
[2019-08-23 01:22:10,423] TRAIN Iter 48340: lr = 0.419435,	loss = 2.865939,	Top-1 err = 0.462451,	Top-5 err = 0.226270,	data_time = 0.050353,	train_time = 0.348166
[2019-08-23 01:22:25,087] TRAIN Iter 48360: lr = 0.419402,	loss = 2.844245,	Top-1 err = 0.459912,	Top-5 err = 0.221875,	data_time = 0.050208,	train_time = 0.733190
[2019-08-23 01:22:36,925] TRAIN Iter 48380: lr = 0.419368,	loss = 2.859924,	Top-1 err = 0.461133,	Top-5 err = 0.225146,	data_time = 2.453024,	train_time = 0.591890
[2019-08-23 01:22:48,073] TRAIN Iter 48400: lr = 0.419335,	loss = 2.899635,	Top-1 err = 0.458936,	Top-5 err = 0.226270,	data_time = 0.050776,	train_time = 0.557386
[2019-08-23 01:23:05,655] TRAIN Iter 48420: lr = 0.419302,	loss = 2.863381,	Top-1 err = 0.451709,	Top-5 err = 0.223633,	data_time = 0.050598,	train_time = 0.879077
[2019-08-23 01:23:12,758] TRAIN Iter 48440: lr = 0.419268,	loss = 2.814339,	Top-1 err = 0.454590,	Top-5 err = 0.220605,	data_time = 0.050362,	train_time = 0.355137
[2019-08-23 01:23:30,117] TRAIN Iter 48460: lr = 0.419235,	loss = 2.870705,	Top-1 err = 0.453711,	Top-5 err = 0.221094,	data_time = 0.050202,	train_time = 0.867946
[2019-08-23 01:23:49,188] TRAIN Iter 48480: lr = 0.419202,	loss = 2.798835,	Top-1 err = 0.462891,	Top-5 err = 0.225732,	data_time = 0.050534,	train_time = 0.953521
[2019-08-23 01:23:55,802] TRAIN Iter 48500: lr = 0.419168,	loss = 2.805684,	Top-1 err = 0.454639,	Top-5 err = 0.222949,	data_time = 0.050408,	train_time = 0.330703
[2019-08-23 01:24:12,808] TRAIN Iter 48520: lr = 0.419135,	loss = 2.850738,	Top-1 err = 0.456445,	Top-5 err = 0.219141,	data_time = 0.050478,	train_time = 0.850260
[2019-08-23 01:24:29,629] TRAIN Iter 48540: lr = 0.419102,	loss = 2.977631,	Top-1 err = 0.455713,	Top-5 err = 0.225586,	data_time = 7.854044,	train_time = 0.841056
[2019-08-23 01:24:37,573] TRAIN Iter 48560: lr = 0.419068,	loss = 2.808164,	Top-1 err = 0.457520,	Top-5 err = 0.226318,	data_time = 0.050513,	train_time = 0.397175
[2019-08-23 01:24:56,427] TRAIN Iter 48580: lr = 0.419035,	loss = 2.877927,	Top-1 err = 0.459814,	Top-5 err = 0.223584,	data_time = 0.050796,	train_time = 0.942717
[2019-08-23 01:25:03,385] TRAIN Iter 48600: lr = 0.419002,	loss = 2.871084,	Top-1 err = 0.455420,	Top-5 err = 0.221436,	data_time = 0.050617,	train_time = 0.347848
[2019-08-23 01:25:23,170] TRAIN Iter 48620: lr = 0.418968,	loss = 2.990026,	Top-1 err = 0.461523,	Top-5 err = 0.229248,	data_time = 0.050441,	train_time = 0.989232
[2019-08-23 01:25:43,646] TRAIN Iter 48640: lr = 0.418935,	loss = 2.920764,	Top-1 err = 0.461768,	Top-5 err = 0.224365,	data_time = 0.050492,	train_time = 1.023795
[2019-08-23 01:25:50,873] TRAIN Iter 48660: lr = 0.418902,	loss = 2.965979,	Top-1 err = 0.461572,	Top-5 err = 0.228564,	data_time = 0.050382,	train_time = 0.361364
[2019-08-23 01:26:08,271] TRAIN Iter 48680: lr = 0.418868,	loss = 2.866561,	Top-1 err = 0.461719,	Top-5 err = 0.225879,	data_time = 0.050830,	train_time = 0.869869
[2019-08-23 01:26:25,434] TRAIN Iter 48700: lr = 0.418835,	loss = 2.980293,	Top-1 err = 0.455762,	Top-5 err = 0.226611,	data_time = 7.628573,	train_time = 0.858143
[2019-08-23 01:26:34,025] TRAIN Iter 48720: lr = 0.418802,	loss = 2.857017,	Top-1 err = 0.464404,	Top-5 err = 0.229687,	data_time = 0.050005,	train_time = 0.429548
[2019-08-23 01:26:51,126] TRAIN Iter 48740: lr = 0.418768,	loss = 2.838466,	Top-1 err = 0.459375,	Top-5 err = 0.225586,	data_time = 0.049900,	train_time = 0.855015
[2019-08-23 01:26:57,097] TRAIN Iter 48760: lr = 0.418735,	loss = 2.959087,	Top-1 err = 0.458203,	Top-5 err = 0.225195,	data_time = 0.049866,	train_time = 0.298567
[2019-08-23 01:27:45,882] TRAIN Iter 48780: lr = 0.418702,	loss = 2.796949,	Top-1 err = 0.459802,	Top-5 err = 0.223671,	data_time = 0.050366,	train_time = 2.439231
[2019-08-23 01:27:58,603] TRAIN Iter 48800: lr = 0.418668,	loss = 2.854886,	Top-1 err = 0.451562,	Top-5 err = 0.222607,	data_time = 0.125917,	train_time = 0.636034
[2019-08-23 01:28:05,686] TRAIN Iter 48820: lr = 0.418635,	loss = 2.980934,	Top-1 err = 0.453174,	Top-5 err = 0.223047,	data_time = 0.050632,	train_time = 0.354118
[2019-08-23 01:28:17,592] TRAIN Iter 48840: lr = 0.418602,	loss = 2.911453,	Top-1 err = 0.448682,	Top-5 err = 0.219824,	data_time = 0.050582,	train_time = 0.595266
[2019-08-23 01:28:24,584] TRAIN Iter 48860: lr = 0.418568,	loss = 2.770533,	Top-1 err = 0.450049,	Top-5 err = 0.218457,	data_time = 0.050815,	train_time = 0.349605
[2019-08-23 01:28:38,602] TRAIN Iter 48880: lr = 0.418535,	loss = 2.808898,	Top-1 err = 0.451416,	Top-5 err = 0.216162,	data_time = 0.050304,	train_time = 0.700881
[2019-08-23 01:28:51,601] TRAIN Iter 48900: lr = 0.418502,	loss = 2.871925,	Top-1 err = 0.446631,	Top-5 err = 0.212354,	data_time = 0.050495,	train_time = 0.649929
[2019-08-23 01:28:58,853] TRAIN Iter 48920: lr = 0.418468,	loss = 2.776399,	Top-1 err = 0.453906,	Top-5 err = 0.220410,	data_time = 0.050513,	train_time = 0.362608
[2019-08-23 01:29:13,122] TRAIN Iter 48940: lr = 0.418435,	loss = 2.857189,	Top-1 err = 0.456494,	Top-5 err = 0.223730,	data_time = 0.050481,	train_time = 0.713436
[2019-08-23 01:29:28,131] TRAIN Iter 48960: lr = 0.418402,	loss = 2.886083,	Top-1 err = 0.455273,	Top-5 err = 0.220752,	data_time = 0.050617,	train_time = 0.750400
[2019-08-23 01:29:35,559] TRAIN Iter 48980: lr = 0.418368,	loss = 2.849884,	Top-1 err = 0.452441,	Top-5 err = 0.219336,	data_time = 0.119944,	train_time = 0.371413
[2019-08-23 01:29:49,718] TRAIN Iter 49000: lr = 0.418335,	loss = 2.935708,	Top-1 err = 0.454492,	Top-5 err = 0.225879,	data_time = 0.050399,	train_time = 0.707917
[2019-08-23 01:29:57,632] TRAIN Iter 49020: lr = 0.418302,	loss = 2.782435,	Top-1 err = 0.451221,	Top-5 err = 0.222705,	data_time = 0.050255,	train_time = 0.395686
[2019-08-23 01:30:10,368] TRAIN Iter 49040: lr = 0.418268,	loss = 2.835620,	Top-1 err = 0.459863,	Top-5 err = 0.225000,	data_time = 0.050281,	train_time = 0.636778
[2019-08-23 01:30:24,548] TRAIN Iter 49060: lr = 0.418235,	loss = 2.858227,	Top-1 err = 0.452002,	Top-5 err = 0.222119,	data_time = 0.050796,	train_time = 0.708978
[2019-08-23 01:30:31,616] TRAIN Iter 49080: lr = 0.418202,	loss = 2.798031,	Top-1 err = 0.449316,	Top-5 err = 0.220605,	data_time = 0.050570,	train_time = 0.353415
[2019-08-23 01:30:46,278] TRAIN Iter 49100: lr = 0.418168,	loss = 2.882062,	Top-1 err = 0.453516,	Top-5 err = 0.224170,	data_time = 0.050339,	train_time = 0.733063
[2019-08-23 01:30:58,718] TRAIN Iter 49120: lr = 0.418135,	loss = 2.785552,	Top-1 err = 0.450146,	Top-5 err = 0.220117,	data_time = 0.172866,	train_time = 0.621994
[2019-08-23 01:31:05,906] TRAIN Iter 49140: lr = 0.418102,	loss = 2.876167,	Top-1 err = 0.458984,	Top-5 err = 0.225928,	data_time = 0.050406,	train_time = 0.359386
[2019-08-23 01:31:21,361] TRAIN Iter 49160: lr = 0.418068,	loss = 2.808160,	Top-1 err = 0.454053,	Top-5 err = 0.218994,	data_time = 0.050375,	train_time = 0.772739
[2019-08-23 01:31:28,429] TRAIN Iter 49180: lr = 0.418035,	loss = 2.849884,	Top-1 err = 0.453320,	Top-5 err = 0.222607,	data_time = 0.050750,	train_time = 0.353390
[2019-08-23 01:31:42,745] TRAIN Iter 49200: lr = 0.418002,	loss = 2.819485,	Top-1 err = 0.454492,	Top-5 err = 0.224512,	data_time = 0.050531,	train_time = 0.715779
[2019-08-23 01:31:57,194] TRAIN Iter 49220: lr = 0.417968,	loss = 2.856793,	Top-1 err = 0.459131,	Top-5 err = 0.219873,	data_time = 0.050651,	train_time = 0.722426
[2019-08-23 01:32:04,529] TRAIN Iter 49240: lr = 0.417935,	loss = 2.867734,	Top-1 err = 0.457568,	Top-5 err = 0.226416,	data_time = 0.051035,	train_time = 0.366718
[2019-08-23 01:32:17,378] TRAIN Iter 49260: lr = 0.417902,	loss = 2.854136,	Top-1 err = 0.451514,	Top-5 err = 0.221826,	data_time = 0.050530,	train_time = 0.642444
[2019-08-23 01:32:32,173] TRAIN Iter 49280: lr = 0.417868,	loss = 2.881123,	Top-1 err = 0.457910,	Top-5 err = 0.226074,	data_time = 0.646618,	train_time = 0.739721
[2019-08-23 01:32:40,905] TRAIN Iter 49300: lr = 0.417835,	loss = 2.875770,	Top-1 err = 0.453906,	Top-5 err = 0.224658,	data_time = 0.050113,	train_time = 0.436597
[2019-08-23 01:32:56,177] TRAIN Iter 49320: lr = 0.417802,	loss = 2.900161,	Top-1 err = 0.454932,	Top-5 err = 0.224854,	data_time = 0.050332,	train_time = 0.763570
[2019-08-23 01:33:03,486] TRAIN Iter 49340: lr = 0.417768,	loss = 2.896737,	Top-1 err = 0.453174,	Top-5 err = 0.220215,	data_time = 0.050557,	train_time = 0.365474
[2019-08-23 01:33:17,529] TRAIN Iter 49360: lr = 0.417735,	loss = 2.856740,	Top-1 err = 0.452588,	Top-5 err = 0.221094,	data_time = 0.050389,	train_time = 0.702107
[2019-08-23 01:33:32,460] TRAIN Iter 49380: lr = 0.417702,	loss = 2.866983,	Top-1 err = 0.454346,	Top-5 err = 0.225439,	data_time = 0.050291,	train_time = 0.746529
[2019-08-23 01:33:39,369] TRAIN Iter 49400: lr = 0.417668,	loss = 2.880181,	Top-1 err = 0.461523,	Top-5 err = 0.225439,	data_time = 0.050690,	train_time = 0.345435
[2019-08-23 01:33:55,820] TRAIN Iter 49420: lr = 0.417635,	loss = 2.848951,	Top-1 err = 0.448779,	Top-5 err = 0.219482,	data_time = 0.050425,	train_time = 0.822560
[2019-08-23 01:34:08,470] TRAIN Iter 49440: lr = 0.417602,	loss = 2.800402,	Top-1 err = 0.451172,	Top-5 err = 0.219092,	data_time = 1.469069,	train_time = 0.632460
[2019-08-23 01:34:17,985] TRAIN Iter 49460: lr = 0.417568,	loss = 2.818504,	Top-1 err = 0.455273,	Top-5 err = 0.221191,	data_time = 0.050428,	train_time = 0.475731
[2019-08-23 01:34:34,285] TRAIN Iter 49480: lr = 0.417535,	loss = 2.900472,	Top-1 err = 0.454297,	Top-5 err = 0.220312,	data_time = 0.050543,	train_time = 0.815003
[2019-08-23 01:34:41,241] TRAIN Iter 49500: lr = 0.417502,	loss = 2.931577,	Top-1 err = 0.456348,	Top-5 err = 0.225195,	data_time = 0.050614,	train_time = 0.347777
[2019-08-23 01:34:57,389] TRAIN Iter 49520: lr = 0.417468,	loss = 2.895721,	Top-1 err = 0.458398,	Top-5 err = 0.225879,	data_time = 0.050193,	train_time = 0.807406
[2019-08-23 01:35:13,496] TRAIN Iter 49540: lr = 0.417435,	loss = 2.922037,	Top-1 err = 0.450391,	Top-5 err = 0.221680,	data_time = 0.050475,	train_time = 0.805318
[2019-08-23 01:35:20,383] TRAIN Iter 49560: lr = 0.417402,	loss = 2.870461,	Top-1 err = 0.455957,	Top-5 err = 0.228271,	data_time = 0.050383,	train_time = 0.344323
[2019-08-23 01:35:39,624] TRAIN Iter 49580: lr = 0.417368,	loss = 2.880177,	Top-1 err = 0.458252,	Top-5 err = 0.220557,	data_time = 0.050576,	train_time = 0.962052
[2019-08-23 01:35:52,686] TRAIN Iter 49600: lr = 0.417335,	loss = 2.908175,	Top-1 err = 0.463818,	Top-5 err = 0.227100,	data_time = 0.050372,	train_time = 0.653056
[2019-08-23 01:36:02,038] TRAIN Iter 49620: lr = 0.417302,	loss = 2.865761,	Top-1 err = 0.460547,	Top-5 err = 0.223584,	data_time = 0.050589,	train_time = 0.467623
[2019-08-23 01:36:15,893] TRAIN Iter 49640: lr = 0.417268,	loss = 2.855853,	Top-1 err = 0.454590,	Top-5 err = 0.217920,	data_time = 0.050115,	train_time = 0.692714
[2019-08-23 01:36:22,746] TRAIN Iter 49660: lr = 0.417235,	loss = 2.865241,	Top-1 err = 0.453174,	Top-5 err = 0.224854,	data_time = 0.050684,	train_time = 0.342636
[2019-08-23 01:36:38,382] TRAIN Iter 49680: lr = 0.417202,	loss = 2.823888,	Top-1 err = 0.453809,	Top-5 err = 0.218848,	data_time = 0.050463,	train_time = 0.781795
[2019-08-23 01:36:57,644] TRAIN Iter 49700: lr = 0.417168,	loss = 2.895802,	Top-1 err = 0.456152,	Top-5 err = 0.227539,	data_time = 0.050448,	train_time = 0.963059
[2019-08-23 01:37:04,759] TRAIN Iter 49720: lr = 0.417135,	loss = 2.826690,	Top-1 err = 0.463037,	Top-5 err = 0.226904,	data_time = 0.050453,	train_time = 0.355725
[2019-08-23 01:37:22,317] TRAIN Iter 49740: lr = 0.417102,	loss = 2.782849,	Top-1 err = 0.456641,	Top-5 err = 0.225098,	data_time = 0.050469,	train_time = 0.877927
[2019-08-23 01:37:36,021] TRAIN Iter 49760: lr = 0.417068,	loss = 2.875696,	Top-1 err = 0.456055,	Top-5 err = 0.228223,	data_time = 0.050266,	train_time = 0.685175
[2019-08-23 01:37:46,483] TRAIN Iter 49780: lr = 0.417035,	loss = 2.907426,	Top-1 err = 0.460010,	Top-5 err = 0.227686,	data_time = 0.050267,	train_time = 0.523056
[2019-08-23 01:38:04,216] TRAIN Iter 49800: lr = 0.417002,	loss = 2.794277,	Top-1 err = 0.460107,	Top-5 err = 0.227686,	data_time = 0.050400,	train_time = 0.886637
[2019-08-23 01:38:11,250] TRAIN Iter 49820: lr = 0.416968,	loss = 2.868060,	Top-1 err = 0.459473,	Top-5 err = 0.225439,	data_time = 0.050354,	train_time = 0.351678
[2019-08-23 01:38:31,451] TRAIN Iter 49840: lr = 0.416935,	loss = 2.862486,	Top-1 err = 0.453760,	Top-5 err = 0.224805,	data_time = 0.050114,	train_time = 1.010037
[2019-08-23 01:38:48,859] TRAIN Iter 49860: lr = 0.416902,	loss = 2.873553,	Top-1 err = 0.459766,	Top-5 err = 0.224512,	data_time = 0.050267,	train_time = 0.870395
[2019-08-23 01:38:56,007] TRAIN Iter 49880: lr = 0.416868,	loss = 2.838468,	Top-1 err = 0.461084,	Top-5 err = 0.228320,	data_time = 0.050464,	train_time = 0.357411
[2019-08-23 01:39:12,142] TRAIN Iter 49900: lr = 0.416835,	loss = 2.915437,	Top-1 err = 0.458936,	Top-5 err = 0.221191,	data_time = 0.050537,	train_time = 0.806713
[2019-08-23 01:39:26,795] TRAIN Iter 49920: lr = 0.416802,	loss = 2.873908,	Top-1 err = 0.459668,	Top-5 err = 0.227881,	data_time = 0.050602,	train_time = 0.732651
[2019-08-23 01:39:36,829] TRAIN Iter 49940: lr = 0.416768,	loss = 2.929682,	Top-1 err = 0.456885,	Top-5 err = 0.225439,	data_time = 0.050773,	train_time = 0.501684
[2019-08-23 01:39:56,171] TRAIN Iter 49960: lr = 0.416735,	loss = 2.843178,	Top-1 err = 0.453760,	Top-5 err = 0.223242,	data_time = 0.050051,	train_time = 0.967098
[2019-08-23 01:40:03,739] TRAIN Iter 49980: lr = 0.416702,	loss = 2.874287,	Top-1 err = 0.459717,	Top-5 err = 0.228174,	data_time = 0.049953,	train_time = 0.378361
[2019-08-23 01:40:19,923] TRAIN Iter 50000: lr = 0.416668,	loss = 2.926733,	Top-1 err = 0.454736,	Top-5 err = 0.219873,	data_time = 0.049969,	train_time = 0.809198
[2019-08-23 01:41:04,275] TEST Iter 50000: loss = 2.639694,	Top-1 err = 0.415380,	Top-5 err = 0.177160,	val_time = 44.315913
[2019-08-23 01:41:48,263] TRAIN Iter 50020: lr = 0.416635,	loss = 2.795295,	Top-1 err = 0.456749,	Top-5 err = 0.228398,	data_time = 0.050293,	train_time = 2.199381
[2019-08-23 01:41:55,419] TRAIN Iter 50040: lr = 0.416602,	loss = 2.851929,	Top-1 err = 0.461035,	Top-5 err = 0.225342,	data_time = 0.050360,	train_time = 0.357771
[2019-08-23 01:42:10,690] TRAIN Iter 50060: lr = 0.416568,	loss = 2.771440,	Top-1 err = 0.455029,	Top-5 err = 0.218359,	data_time = 0.050730,	train_time = 0.763528
[2019-08-23 01:42:19,262] TRAIN Iter 50080: lr = 0.416535,	loss = 2.768435,	Top-1 err = 0.451807,	Top-5 err = 0.216357,	data_time = 0.050924,	train_time = 0.428604
[2019-08-23 01:42:28,520] TRAIN Iter 50100: lr = 0.416502,	loss = 2.767445,	Top-1 err = 0.446777,	Top-5 err = 0.216602,	data_time = 0.050759,	train_time = 0.462887
[2019-08-23 01:42:41,443] TRAIN Iter 50120: lr = 0.416468,	loss = 2.844805,	Top-1 err = 0.449707,	Top-5 err = 0.222070,	data_time = 0.050844,	train_time = 0.646135
[2019-08-23 01:42:48,821] TRAIN Iter 50140: lr = 0.416435,	loss = 2.835351,	Top-1 err = 0.452539,	Top-5 err = 0.221533,	data_time = 0.050828,	train_time = 0.368879
[2019-08-23 01:43:03,898] TRAIN Iter 50160: lr = 0.416402,	loss = 2.888063,	Top-1 err = 0.454346,	Top-5 err = 0.222021,	data_time = 0.050955,	train_time = 0.753838
[2019-08-23 01:43:18,016] TRAIN Iter 50180: lr = 0.416368,	loss = 2.919380,	Top-1 err = 0.449756,	Top-5 err = 0.216260,	data_time = 0.050392,	train_time = 0.705865
[2019-08-23 01:43:25,456] TRAIN Iter 50200: lr = 0.416335,	loss = 2.869653,	Top-1 err = 0.445020,	Top-5 err = 0.220947,	data_time = 0.050777,	train_time = 0.372019
[2019-08-23 01:43:40,997] TRAIN Iter 50220: lr = 0.416302,	loss = 2.761436,	Top-1 err = 0.454932,	Top-5 err = 0.216553,	data_time = 0.050117,	train_time = 0.777000
[2019-08-23 01:43:47,882] TRAIN Iter 50240: lr = 0.416268,	loss = 2.929188,	Top-1 err = 0.457568,	Top-5 err = 0.224219,	data_time = 0.050509,	train_time = 0.344258
[2019-08-23 01:44:03,407] TRAIN Iter 50260: lr = 0.416235,	loss = 2.903163,	Top-1 err = 0.446338,	Top-5 err = 0.219238,	data_time = 0.050311,	train_time = 0.776214
[2019-08-23 01:44:18,825] TRAIN Iter 50280: lr = 0.416202,	loss = 2.861724,	Top-1 err = 0.457178,	Top-5 err = 0.225098,	data_time = 0.050654,	train_time = 0.770894
[2019-08-23 01:44:26,293] TRAIN Iter 50300: lr = 0.416168,	loss = 2.880232,	Top-1 err = 0.452295,	Top-5 err = 0.218555,	data_time = 0.050463,	train_time = 0.373420
[2019-08-23 01:44:39,871] TRAIN Iter 50320: lr = 0.416135,	loss = 2.858561,	Top-1 err = 0.454834,	Top-5 err = 0.222754,	data_time = 0.050572,	train_time = 0.678854
[2019-08-23 01:44:55,191] TRAIN Iter 50340: lr = 0.416102,	loss = 2.785704,	Top-1 err = 0.454492,	Top-5 err = 0.221631,	data_time = 0.099027,	train_time = 0.766005
[2019-08-23 01:45:02,414] TRAIN Iter 50360: lr = 0.416068,	loss = 2.825934,	Top-1 err = 0.452588,	Top-5 err = 0.218262,	data_time = 0.050306,	train_time = 0.361111
[2019-08-23 01:45:16,693] TRAIN Iter 50380: lr = 0.416035,	loss = 2.896414,	Top-1 err = 0.447852,	Top-5 err = 0.222217,	data_time = 0.050389,	train_time = 0.713932
[2019-08-23 01:45:24,085] TRAIN Iter 50400: lr = 0.416002,	loss = 2.858902,	Top-1 err = 0.456396,	Top-5 err = 0.225635,	data_time = 0.050976,	train_time = 0.369614
[2019-08-23 01:45:36,814] TRAIN Iter 50420: lr = 0.415968,	loss = 2.970919,	Top-1 err = 0.450537,	Top-5 err = 0.216650,	data_time = 0.050481,	train_time = 0.636399
[2019-08-23 01:45:52,236] TRAIN Iter 50440: lr = 0.415935,	loss = 2.822208,	Top-1 err = 0.453516,	Top-5 err = 0.222998,	data_time = 0.094322,	train_time = 0.771087
[2019-08-23 01:45:59,434] TRAIN Iter 50460: lr = 0.415902,	loss = 2.916329,	Top-1 err = 0.448584,	Top-5 err = 0.220752,	data_time = 0.050160,	train_time = 0.359884
[2019-08-23 01:46:14,936] TRAIN Iter 50480: lr = 0.415868,	loss = 2.879730,	Top-1 err = 0.451807,	Top-5 err = 0.218994,	data_time = 0.050402,	train_time = 0.775105
[2019-08-23 01:46:33,542] TRAIN Iter 50500: lr = 0.415835,	loss = 2.828725,	Top-1 err = 0.453418,	Top-5 err = 0.217773,	data_time = 0.051807,	train_time = 0.930296
[2019-08-23 01:46:40,682] TRAIN Iter 50520: lr = 0.415802,	loss = 2.831037,	Top-1 err = 0.452490,	Top-5 err = 0.223096,	data_time = 0.050524,	train_time = 0.356976
[2019-08-23 01:46:55,032] TRAIN Iter 50540: lr = 0.415768,	loss = 2.792414,	Top-1 err = 0.448633,	Top-5 err = 0.219287,	data_time = 0.050467,	train_time = 0.717468
[2019-08-23 01:47:02,309] TRAIN Iter 50560: lr = 0.415735,	loss = 2.868595,	Top-1 err = 0.450830,	Top-5 err = 0.220068,	data_time = 0.050735,	train_time = 0.363820
[2019-08-23 01:47:17,026] TRAIN Iter 50580: lr = 0.415702,	loss = 2.759780,	Top-1 err = 0.448389,	Top-5 err = 0.220410,	data_time = 0.050761,	train_time = 0.735870
[2019-08-23 01:47:32,462] TRAIN Iter 50600: lr = 0.415668,	loss = 2.879646,	Top-1 err = 0.450146,	Top-5 err = 0.222949,	data_time = 0.050916,	train_time = 0.771786
[2019-08-23 01:47:39,159] TRAIN Iter 50620: lr = 0.415635,	loss = 2.935439,	Top-1 err = 0.453418,	Top-5 err = 0.223047,	data_time = 0.050419,	train_time = 0.334819
[2019-08-23 01:47:54,633] TRAIN Iter 50640: lr = 0.415602,	loss = 2.920259,	Top-1 err = 0.450635,	Top-5 err = 0.220605,	data_time = 0.050529,	train_time = 0.773668
[2019-08-23 01:48:09,437] TRAIN Iter 50660: lr = 0.415568,	loss = 2.893681,	Top-1 err = 0.458643,	Top-5 err = 0.225439,	data_time = 0.050437,	train_time = 0.740199
[2019-08-23 01:48:16,877] TRAIN Iter 50680: lr = 0.415535,	loss = 2.870393,	Top-1 err = 0.456055,	Top-5 err = 0.221387,	data_time = 0.050334,	train_time = 0.372017
[2019-08-23 01:48:32,394] TRAIN Iter 50700: lr = 0.415502,	loss = 2.766817,	Top-1 err = 0.451025,	Top-5 err = 0.218213,	data_time = 0.050202,	train_time = 0.775794
[2019-08-23 01:48:39,456] TRAIN Iter 50720: lr = 0.415468,	loss = 2.838337,	Top-1 err = 0.452490,	Top-5 err = 0.218945,	data_time = 0.050424,	train_time = 0.353087
[2019-08-23 01:48:54,804] TRAIN Iter 50740: lr = 0.415435,	loss = 2.871375,	Top-1 err = 0.452051,	Top-5 err = 0.222754,	data_time = 0.050714,	train_time = 0.767386
[2019-08-23 01:49:10,612] TRAIN Iter 50760: lr = 0.415402,	loss = 2.920110,	Top-1 err = 0.448389,	Top-5 err = 0.222949,	data_time = 0.130172,	train_time = 0.790385
[2019-08-23 01:49:18,169] TRAIN Iter 50780: lr = 0.415368,	loss = 2.862065,	Top-1 err = 0.450781,	Top-5 err = 0.220459,	data_time = 0.050413,	train_time = 0.377841
[2019-08-23 01:49:35,473] TRAIN Iter 50800: lr = 0.415335,	loss = 2.770673,	Top-1 err = 0.450537,	Top-5 err = 0.223779,	data_time = 0.050642,	train_time = 0.865192
[2019-08-23 01:49:49,072] TRAIN Iter 50820: lr = 0.415302,	loss = 2.871942,	Top-1 err = 0.456348,	Top-5 err = 0.225000,	data_time = 0.050476,	train_time = 0.679955
[2019-08-23 01:49:58,615] TRAIN Iter 50840: lr = 0.415268,	loss = 2.813221,	Top-1 err = 0.445947,	Top-5 err = 0.220117,	data_time = 0.050305,	train_time = 0.477108
[2019-08-23 01:50:13,887] TRAIN Iter 50860: lr = 0.415235,	loss = 2.741398,	Top-1 err = 0.452783,	Top-5 err = 0.218457,	data_time = 0.050460,	train_time = 0.763626
[2019-08-23 01:50:23,971] TRAIN Iter 50880: lr = 0.415202,	loss = 2.800130,	Top-1 err = 0.447852,	Top-5 err = 0.219287,	data_time = 0.050295,	train_time = 0.504184
[2019-08-23 01:50:41,213] TRAIN Iter 50900: lr = 0.415168,	loss = 2.883481,	Top-1 err = 0.453760,	Top-5 err = 0.226660,	data_time = 0.050691,	train_time = 0.862091
[2019-08-23 01:50:54,646] TRAIN Iter 50920: lr = 0.415135,	loss = 2.838744,	Top-1 err = 0.452930,	Top-5 err = 0.226318,	data_time = 0.116779,	train_time = 0.671613
[2019-08-23 01:51:04,771] TRAIN Iter 50940: lr = 0.415102,	loss = 2.863801,	Top-1 err = 0.454590,	Top-5 err = 0.220312,	data_time = 0.050337,	train_time = 0.506235
[2019-08-23 01:51:21,147] TRAIN Iter 50960: lr = 0.415068,	loss = 2.850466,	Top-1 err = 0.456396,	Top-5 err = 0.223682,	data_time = 0.106616,	train_time = 0.818808
[2019-08-23 01:51:36,739] TRAIN Iter 50980: lr = 0.415035,	loss = 2.890905,	Top-1 err = 0.452783,	Top-5 err = 0.220605,	data_time = 0.050301,	train_time = 0.779584
[2019-08-23 01:51:46,513] TRAIN Iter 51000: lr = 0.415002,	loss = 2.879443,	Top-1 err = 0.459229,	Top-5 err = 0.225342,	data_time = 0.050133,	train_time = 0.488657
[2019-08-23 01:52:03,307] TRAIN Iter 51020: lr = 0.414968,	loss = 2.970058,	Top-1 err = 0.454199,	Top-5 err = 0.221631,	data_time = 0.050317,	train_time = 0.839699
[2019-08-23 01:52:12,353] TRAIN Iter 51040: lr = 0.414935,	loss = 2.953843,	Top-1 err = 0.451904,	Top-5 err = 0.224805,	data_time = 0.050359,	train_time = 0.452295
[2019-08-23 01:52:31,710] TRAIN Iter 51060: lr = 0.414902,	loss = 2.919633,	Top-1 err = 0.454004,	Top-5 err = 0.221436,	data_time = 0.050361,	train_time = 0.967823
[2019-08-23 01:52:49,759] TRAIN Iter 51080: lr = 0.414868,	loss = 2.871026,	Top-1 err = 0.457031,	Top-5 err = 0.225439,	data_time = 0.050559,	train_time = 0.902419
[2019-08-23 01:52:56,935] TRAIN Iter 51100: lr = 0.414835,	loss = 2.923870,	Top-1 err = 0.464307,	Top-5 err = 0.235107,	data_time = 0.050365,	train_time = 0.358785
[2019-08-23 01:53:16,852] TRAIN Iter 51120: lr = 0.414802,	loss = 2.867871,	Top-1 err = 0.452979,	Top-5 err = 0.224756,	data_time = 0.128378,	train_time = 0.995833
[2019-08-23 01:53:38,488] TRAIN Iter 51140: lr = 0.414768,	loss = 2.814028,	Top-1 err = 0.454395,	Top-5 err = 0.223340,	data_time = 6.621806,	train_time = 1.081800
[2019-08-23 01:53:45,806] TRAIN Iter 51160: lr = 0.414735,	loss = 2.909660,	Top-1 err = 0.456494,	Top-5 err = 0.227979,	data_time = 0.050561,	train_time = 0.365906
[2019-08-23 01:54:05,975] TRAIN Iter 51180: lr = 0.414702,	loss = 2.949990,	Top-1 err = 0.456055,	Top-5 err = 0.230566,	data_time = 0.050213,	train_time = 1.008444
[2019-08-23 01:54:12,683] TRAIN Iter 51200: lr = 0.414668,	loss = 2.878244,	Top-1 err = 0.455322,	Top-5 err = 0.226025,	data_time = 0.050545,	train_time = 0.335390
[2019-08-23 01:54:29,607] TRAIN Iter 51220: lr = 0.414635,	loss = 2.858060,	Top-1 err = 0.460156,	Top-5 err = 0.225342,	data_time = 0.050018,	train_time = 0.846163
[2019-08-23 01:54:48,741] TRAIN Iter 51240: lr = 0.414602,	loss = 2.934853,	Top-1 err = 0.458398,	Top-5 err = 0.225732,	data_time = 0.049899,	train_time = 0.956705
[2019-08-23 01:54:55,028] TRAIN Iter 51260: lr = 0.414568,	loss = 2.834752,	Top-1 err = 0.454834,	Top-5 err = 0.222949,	data_time = 0.049922,	train_time = 0.314312
[2019-08-23 01:55:44,923] TRAIN Iter 51280: lr = 0.414535,	loss = 2.821653,	Top-1 err = 0.455897,	Top-5 err = 0.223021,	data_time = 0.050727,	train_time = 2.494751
[2019-08-23 01:55:52,423] TRAIN Iter 51300: lr = 0.414502,	loss = 2.818945,	Top-1 err = 0.455273,	Top-5 err = 0.220654,	data_time = 0.112017,	train_time = 0.374976
[2019-08-23 01:56:06,424] TRAIN Iter 51320: lr = 0.414468,	loss = 2.791592,	Top-1 err = 0.452832,	Top-5 err = 0.220801,	data_time = 0.050764,	train_time = 0.700043
[2019-08-23 01:56:19,054] TRAIN Iter 51340: lr = 0.414435,	loss = 2.820068,	Top-1 err = 0.443408,	Top-5 err = 0.212793,	data_time = 0.120606,	train_time = 0.631464
[2019-08-23 01:56:26,405] TRAIN Iter 51360: lr = 0.414402,	loss = 2.689327,	Top-1 err = 0.448779,	Top-5 err = 0.218848,	data_time = 0.050475,	train_time = 0.367531
[2019-08-23 01:56:39,514] TRAIN Iter 51380: lr = 0.414368,	loss = 2.938365,	Top-1 err = 0.442871,	Top-5 err = 0.213135,	data_time = 0.050655,	train_time = 0.655459
[2019-08-23 01:56:52,395] TRAIN Iter 51400: lr = 0.414335,	loss = 2.817698,	Top-1 err = 0.454980,	Top-5 err = 0.219678,	data_time = 0.050360,	train_time = 0.644004
[2019-08-23 01:57:00,835] TRAIN Iter 51420: lr = 0.414302,	loss = 2.761937,	Top-1 err = 0.448779,	Top-5 err = 0.216895,	data_time = 0.050775,	train_time = 0.422033
[2019-08-23 01:57:14,963] TRAIN Iter 51440: lr = 0.414268,	loss = 2.810673,	Top-1 err = 0.451318,	Top-5 err = 0.218701,	data_time = 0.050963,	train_time = 0.706366
[2019-08-23 01:57:22,095] TRAIN Iter 51460: lr = 0.414235,	loss = 2.867872,	Top-1 err = 0.448193,	Top-5 err = 0.224072,	data_time = 0.050783,	train_time = 0.356576
[2019-08-23 01:57:35,084] TRAIN Iter 51480: lr = 0.414202,	loss = 2.904569,	Top-1 err = 0.447656,	Top-5 err = 0.218018,	data_time = 0.050137,	train_time = 0.649434
[2019-08-23 01:57:48,910] TRAIN Iter 51500: lr = 0.414168,	loss = 2.858094,	Top-1 err = 0.445166,	Top-5 err = 0.217188,	data_time = 0.143532,	train_time = 0.691314
[2019-08-23 01:57:55,840] TRAIN Iter 51520: lr = 0.414135,	loss = 2.768280,	Top-1 err = 0.445166,	Top-5 err = 0.221191,	data_time = 0.050480,	train_time = 0.346461
[2019-08-23 01:58:10,491] TRAIN Iter 51540: lr = 0.414102,	loss = 2.854608,	Top-1 err = 0.452393,	Top-5 err = 0.219971,	data_time = 0.050386,	train_time = 0.732552
[2019-08-23 01:58:21,254] TRAIN Iter 51560: lr = 0.414068,	loss = 2.868096,	Top-1 err = 0.449951,	Top-5 err = 0.218457,	data_time = 0.136237,	train_time = 0.538115
[2019-08-23 01:58:31,049] TRAIN Iter 51580: lr = 0.414035,	loss = 2.712122,	Top-1 err = 0.452881,	Top-5 err = 0.223438,	data_time = 0.051039,	train_time = 0.489735
[2019-08-23 01:58:46,314] TRAIN Iter 51600: lr = 0.414002,	loss = 2.884129,	Top-1 err = 0.446191,	Top-5 err = 0.217432,	data_time = 0.050793,	train_time = 0.763257
[2019-08-23 01:58:53,342] TRAIN Iter 51620: lr = 0.413968,	loss = 2.829717,	Top-1 err = 0.451318,	Top-5 err = 0.220508,	data_time = 0.050745,	train_time = 0.351362
[2019-08-23 01:59:07,422] TRAIN Iter 51640: lr = 0.413935,	loss = 2.814382,	Top-1 err = 0.446289,	Top-5 err = 0.219336,	data_time = 0.050485,	train_time = 0.704001
[2019-08-23 01:59:19,978] TRAIN Iter 51660: lr = 0.413902,	loss = 2.957782,	Top-1 err = 0.457227,	Top-5 err = 0.222803,	data_time = 0.621530,	train_time = 0.627775
[2019-08-23 01:59:28,224] TRAIN Iter 51680: lr = 0.413868,	loss = 2.841985,	Top-1 err = 0.450488,	Top-5 err = 0.219336,	data_time = 0.050363,	train_time = 0.412300
[2019-08-23 01:59:43,296] TRAIN Iter 51700: lr = 0.413835,	loss = 2.809698,	Top-1 err = 0.455859,	Top-5 err = 0.217676,	data_time = 0.050759,	train_time = 0.753590
[2019-08-23 01:59:56,792] TRAIN Iter 51720: lr = 0.413802,	loss = 2.869684,	Top-1 err = 0.449707,	Top-5 err = 0.219629,	data_time = 0.050299,	train_time = 0.674797
[2019-08-23 02:00:05,421] TRAIN Iter 51740: lr = 0.413768,	loss = 2.805349,	Top-1 err = 0.451074,	Top-5 err = 0.216406,	data_time = 0.050475,	train_time = 0.431392
[2019-08-23 02:00:19,918] TRAIN Iter 51760: lr = 0.413735,	loss = 2.810706,	Top-1 err = 0.456592,	Top-5 err = 0.222070,	data_time = 0.050698,	train_time = 0.724871
[2019-08-23 02:00:27,095] TRAIN Iter 51780: lr = 0.413702,	loss = 2.806345,	Top-1 err = 0.453906,	Top-5 err = 0.223633,	data_time = 0.050360,	train_time = 0.358843
[2019-08-23 02:00:42,345] TRAIN Iter 51800: lr = 0.413668,	loss = 2.831827,	Top-1 err = 0.455078,	Top-5 err = 0.228516,	data_time = 0.050989,	train_time = 0.762461
[2019-08-23 02:00:56,495] TRAIN Iter 51820: lr = 0.413635,	loss = 2.851757,	Top-1 err = 0.458350,	Top-5 err = 0.222070,	data_time = 1.437108,	train_time = 0.707517
[2019-08-23 02:01:04,686] TRAIN Iter 51840: lr = 0.413602,	loss = 2.914052,	Top-1 err = 0.453662,	Top-5 err = 0.224268,	data_time = 0.050211,	train_time = 0.409500
[2019-08-23 02:01:19,795] TRAIN Iter 51860: lr = 0.413568,	loss = 2.867004,	Top-1 err = 0.454834,	Top-5 err = 0.220898,	data_time = 0.050399,	train_time = 0.755458
[2019-08-23 02:01:32,472] TRAIN Iter 51880: lr = 0.413535,	loss = 2.803553,	Top-1 err = 0.450781,	Top-5 err = 0.221875,	data_time = 0.050976,	train_time = 0.633846
[2019-08-23 02:01:42,747] TRAIN Iter 51900: lr = 0.413502,	loss = 2.832695,	Top-1 err = 0.447510,	Top-5 err = 0.218994,	data_time = 0.050691,	train_time = 0.513724
[2019-08-23 02:01:57,652] TRAIN Iter 51920: lr = 0.413468,	loss = 2.954696,	Top-1 err = 0.448242,	Top-5 err = 0.219482,	data_time = 0.050885,	train_time = 0.745204
[2019-08-23 02:02:04,605] TRAIN Iter 51940: lr = 0.413435,	loss = 2.914130,	Top-1 err = 0.455176,	Top-5 err = 0.225635,	data_time = 0.151423,	train_time = 0.347654
[2019-08-23 02:02:20,514] TRAIN Iter 51960: lr = 0.413402,	loss = 2.864144,	Top-1 err = 0.453564,	Top-5 err = 0.223584,	data_time = 0.050283,	train_time = 0.795417
[2019-08-23 02:02:34,709] TRAIN Iter 51980: lr = 0.413368,	loss = 2.899328,	Top-1 err = 0.456543,	Top-5 err = 0.223389,	data_time = 0.128919,	train_time = 0.709779
[2019-08-23 02:02:42,954] TRAIN Iter 52000: lr = 0.413335,	loss = 2.954560,	Top-1 err = 0.455664,	Top-5 err = 0.224756,	data_time = 0.050740,	train_time = 0.412201
[2019-08-23 02:02:59,967] TRAIN Iter 52020: lr = 0.413302,	loss = 2.937011,	Top-1 err = 0.450830,	Top-5 err = 0.226123,	data_time = 0.050281,	train_time = 0.850624
[2019-08-23 02:03:10,588] TRAIN Iter 52040: lr = 0.413268,	loss = 2.904171,	Top-1 err = 0.453369,	Top-5 err = 0.224072,	data_time = 0.108671,	train_time = 0.531039
[2019-08-23 02:03:22,063] TRAIN Iter 52060: lr = 0.413235,	loss = 2.863752,	Top-1 err = 0.452295,	Top-5 err = 0.222852,	data_time = 0.050593,	train_time = 0.573739
[2019-08-23 02:03:39,097] TRAIN Iter 52080: lr = 0.413202,	loss = 2.940104,	Top-1 err = 0.458008,	Top-5 err = 0.224219,	data_time = 0.050775,	train_time = 0.851708
[2019-08-23 02:03:45,580] TRAIN Iter 52100: lr = 0.413168,	loss = 2.885205,	Top-1 err = 0.454395,	Top-5 err = 0.223828,	data_time = 0.050367,	train_time = 0.324118
[2019-08-23 02:04:03,564] TRAIN Iter 52120: lr = 0.413135,	loss = 2.818182,	Top-1 err = 0.455176,	Top-5 err = 0.221240,	data_time = 0.050382,	train_time = 0.899182
[2019-08-23 02:04:17,178] TRAIN Iter 52140: lr = 0.413102,	loss = 2.800535,	Top-1 err = 0.454199,	Top-5 err = 0.225342,	data_time = 0.158144,	train_time = 0.680693
[2019-08-23 02:04:26,139] TRAIN Iter 52160: lr = 0.413068,	loss = 2.835118,	Top-1 err = 0.444971,	Top-5 err = 0.218945,	data_time = 0.050455,	train_time = 0.448039
[2019-08-23 02:04:43,368] TRAIN Iter 52180: lr = 0.413035,	loss = 2.867686,	Top-1 err = 0.455225,	Top-5 err = 0.222803,	data_time = 0.050613,	train_time = 0.861415
[2019-08-23 02:05:00,704] TRAIN Iter 52200: lr = 0.413002,	loss = 2.929932,	Top-1 err = 0.458838,	Top-5 err = 0.225391,	data_time = 0.146509,	train_time = 0.866829
[2019-08-23 02:05:08,081] TRAIN Iter 52220: lr = 0.412968,	loss = 2.956037,	Top-1 err = 0.458594,	Top-5 err = 0.222412,	data_time = 0.050465,	train_time = 0.368810
[2019-08-23 02:05:24,888] TRAIN Iter 52240: lr = 0.412935,	loss = 2.893801,	Top-1 err = 0.457422,	Top-5 err = 0.218799,	data_time = 0.050472,	train_time = 0.840319
[2019-08-23 02:05:31,584] TRAIN Iter 52260: lr = 0.412902,	loss = 2.860502,	Top-1 err = 0.456689,	Top-5 err = 0.224316,	data_time = 0.050726,	train_time = 0.334808
[2019-08-23 02:05:49,276] TRAIN Iter 52280: lr = 0.412868,	loss = 2.877912,	Top-1 err = 0.456396,	Top-5 err = 0.223682,	data_time = 0.050790,	train_time = 0.884607
[2019-08-23 02:06:08,351] TRAIN Iter 52300: lr = 0.412835,	loss = 2.912491,	Top-1 err = 0.456299,	Top-5 err = 0.222119,	data_time = 0.050450,	train_time = 0.953699
[2019-08-23 02:06:15,048] TRAIN Iter 52320: lr = 0.412802,	loss = 2.794251,	Top-1 err = 0.458545,	Top-5 err = 0.224561,	data_time = 0.050172,	train_time = 0.334846
[2019-08-23 02:06:34,087] TRAIN Iter 52340: lr = 0.412768,	loss = 2.896734,	Top-1 err = 0.452539,	Top-5 err = 0.221582,	data_time = 0.050478,	train_time = 0.951938
[2019-08-23 02:06:52,580] TRAIN Iter 52360: lr = 0.412735,	loss = 2.847171,	Top-1 err = 0.460596,	Top-5 err = 0.223828,	data_time = 0.141171,	train_time = 0.924626
[2019-08-23 02:06:59,412] TRAIN Iter 52380: lr = 0.412702,	loss = 2.912881,	Top-1 err = 0.456787,	Top-5 err = 0.222266,	data_time = 0.050493,	train_time = 0.341608
[2019-08-23 02:07:19,965] TRAIN Iter 52400: lr = 0.412668,	loss = 2.916652,	Top-1 err = 0.455762,	Top-5 err = 0.221924,	data_time = 0.050472,	train_time = 1.027617
[2019-08-23 02:07:26,275] TRAIN Iter 52420: lr = 0.412635,	loss = 2.902012,	Top-1 err = 0.456396,	Top-5 err = 0.224902,	data_time = 0.050464,	train_time = 0.315517
[2019-08-23 02:07:46,407] TRAIN Iter 52440: lr = 0.412602,	loss = 2.948450,	Top-1 err = 0.454248,	Top-5 err = 0.227490,	data_time = 0.050170,	train_time = 1.006550
[2019-08-23 02:08:07,156] TRAIN Iter 52460: lr = 0.412568,	loss = 2.896368,	Top-1 err = 0.453564,	Top-5 err = 0.222070,	data_time = 0.049991,	train_time = 1.037479
[2019-08-23 02:08:13,394] TRAIN Iter 52480: lr = 0.412535,	loss = 2.855476,	Top-1 err = 0.458887,	Top-5 err = 0.223096,	data_time = 0.050182,	train_time = 0.311872
[2019-08-23 02:08:31,823] TRAIN Iter 52500: lr = 0.412502,	loss = 2.904792,	Top-1 err = 0.452881,	Top-5 err = 0.224561,	data_time = 0.049895,	train_time = 0.921418
[2019-08-23 02:08:44,403] TRAIN Iter 52520: lr = 0.412468,	loss = 3.333326,	Top-1 err = 0.450630,	Top-5 err = 0.223230,	data_time = 0.007106,	train_time = 0.629011
[2019-08-23 02:09:31,445] TRAIN Iter 52540: lr = 0.412435,	loss = 2.766305,	Top-1 err = 0.456836,	Top-5 err = 0.222656,	data_time = 0.050415,	train_time = 2.352094
[2019-08-23 02:09:47,290] TRAIN Iter 52560: lr = 0.412402,	loss = 2.872732,	Top-1 err = 0.451611,	Top-5 err = 0.217822,	data_time = 0.050415,	train_time = 0.792213
[2019-08-23 02:09:54,864] TRAIN Iter 52580: lr = 0.412368,	loss = 2.798982,	Top-1 err = 0.458252,	Top-5 err = 0.222021,	data_time = 0.050518,	train_time = 0.378679
[2019-08-23 02:10:07,088] TRAIN Iter 52600: lr = 0.412335,	loss = 2.785718,	Top-1 err = 0.451025,	Top-5 err = 0.219482,	data_time = 0.050440,	train_time = 0.611190
[2019-08-23 02:10:16,476] TRAIN Iter 52620: lr = 0.412302,	loss = 2.822618,	Top-1 err = 0.450586,	Top-5 err = 0.216992,	data_time = 0.858922,	train_time = 0.469398
[2019-08-23 02:10:26,166] TRAIN Iter 52640: lr = 0.412268,	loss = 2.814041,	Top-1 err = 0.451123,	Top-5 err = 0.221045,	data_time = 0.050436,	train_time = 0.484476
[2019-08-23 02:10:40,696] TRAIN Iter 52660: lr = 0.412235,	loss = 2.826756,	Top-1 err = 0.447314,	Top-5 err = 0.217480,	data_time = 0.050114,	train_time = 0.726506
[2019-08-23 02:10:47,647] TRAIN Iter 52680: lr = 0.412202,	loss = 2.809028,	Top-1 err = 0.444141,	Top-5 err = 0.209814,	data_time = 0.050474,	train_time = 0.347518
[2019-08-23 02:11:04,918] TRAIN Iter 52700: lr = 0.412168,	loss = 2.866299,	Top-1 err = 0.448779,	Top-5 err = 0.215625,	data_time = 0.050519,	train_time = 0.863559
[2019-08-23 02:11:18,610] TRAIN Iter 52720: lr = 0.412135,	loss = 2.757744,	Top-1 err = 0.445166,	Top-5 err = 0.216943,	data_time = 0.050319,	train_time = 0.684588
[2019-08-23 02:11:26,361] TRAIN Iter 52740: lr = 0.412102,	loss = 2.835690,	Top-1 err = 0.456055,	Top-5 err = 0.221143,	data_time = 0.050932,	train_time = 0.387493
[2019-08-23 02:11:40,446] TRAIN Iter 52760: lr = 0.412068,	loss = 2.875006,	Top-1 err = 0.447266,	Top-5 err = 0.219824,	data_time = 0.050300,	train_time = 0.704257
[2019-08-23 02:11:53,942] TRAIN Iter 52780: lr = 0.412035,	loss = 2.848847,	Top-1 err = 0.451465,	Top-5 err = 0.220752,	data_time = 0.570877,	train_time = 0.674767
[2019-08-23 02:12:02,153] TRAIN Iter 52800: lr = 0.412002,	loss = 2.843837,	Top-1 err = 0.447803,	Top-5 err = 0.213916,	data_time = 0.050354,	train_time = 0.410547
[2019-08-23 02:12:17,389] TRAIN Iter 52820: lr = 0.411968,	loss = 2.858956,	Top-1 err = 0.447412,	Top-5 err = 0.215234,	data_time = 0.050455,	train_time = 0.761816
[2019-08-23 02:12:25,329] TRAIN Iter 52840: lr = 0.411935,	loss = 2.944737,	Top-1 err = 0.446289,	Top-5 err = 0.216943,	data_time = 0.090966,	train_time = 0.396952
[2019-08-23 02:12:36,815] TRAIN Iter 52860: lr = 0.411902,	loss = 2.852206,	Top-1 err = 0.446729,	Top-5 err = 0.217578,	data_time = 0.050514,	train_time = 0.574283
[2019-08-23 02:12:52,718] TRAIN Iter 52880: lr = 0.411868,	loss = 2.890882,	Top-1 err = 0.446631,	Top-5 err = 0.217334,	data_time = 0.138667,	train_time = 0.795179
[2019-08-23 02:12:59,664] TRAIN Iter 52900: lr = 0.411835,	loss = 2.758359,	Top-1 err = 0.453125,	Top-5 err = 0.219922,	data_time = 0.050597,	train_time = 0.347285
[2019-08-23 02:13:16,559] TRAIN Iter 52920: lr = 0.411802,	loss = 2.871092,	Top-1 err = 0.448096,	Top-5 err = 0.219971,	data_time = 0.050360,	train_time = 0.844693
[2019-08-23 02:13:31,928] TRAIN Iter 52940: lr = 0.411768,	loss = 2.847314,	Top-1 err = 0.452686,	Top-5 err = 0.218213,	data_time = 4.319502,	train_time = 0.768478
[2019-08-23 02:13:39,003] TRAIN Iter 52960: lr = 0.411735,	loss = 2.882642,	Top-1 err = 0.456689,	Top-5 err = 0.224219,	data_time = 0.050594,	train_time = 0.353723
[2019-08-23 02:13:54,780] TRAIN Iter 52980: lr = 0.411702,	loss = 2.809860,	Top-1 err = 0.454736,	Top-5 err = 0.221045,	data_time = 0.050472,	train_time = 0.788813
[2019-08-23 02:14:02,549] TRAIN Iter 53000: lr = 0.411668,	loss = 2.901721,	Top-1 err = 0.454541,	Top-5 err = 0.221338,	data_time = 0.050565,	train_time = 0.388437
[2019-08-23 02:14:14,910] TRAIN Iter 53020: lr = 0.411635,	loss = 2.857836,	Top-1 err = 0.449756,	Top-5 err = 0.219775,	data_time = 0.050534,	train_time = 0.618054
[2019-08-23 02:14:30,506] TRAIN Iter 53040: lr = 0.411602,	loss = 2.751920,	Top-1 err = 0.445410,	Top-5 err = 0.214893,	data_time = 0.050835,	train_time = 0.779770
[2019-08-23 02:14:37,124] TRAIN Iter 53060: lr = 0.411568,	loss = 2.809439,	Top-1 err = 0.450684,	Top-5 err = 0.217139,	data_time = 0.050464,	train_time = 0.330924
[2019-08-23 02:14:52,922] TRAIN Iter 53080: lr = 0.411535,	loss = 2.868515,	Top-1 err = 0.448242,	Top-5 err = 0.220361,	data_time = 0.050553,	train_time = 0.789842
[2019-08-23 02:15:07,237] TRAIN Iter 53100: lr = 0.411502,	loss = 2.920571,	Top-1 err = 0.453467,	Top-5 err = 0.222803,	data_time = 0.050329,	train_time = 0.715747
[2019-08-23 02:15:14,573] TRAIN Iter 53120: lr = 0.411468,	loss = 2.903282,	Top-1 err = 0.453125,	Top-5 err = 0.220752,	data_time = 0.050601,	train_time = 0.366820
[2019-08-23 02:15:29,657] TRAIN Iter 53140: lr = 0.411435,	loss = 2.796297,	Top-1 err = 0.449365,	Top-5 err = 0.217139,	data_time = 0.050613,	train_time = 0.754151
[2019-08-23 02:15:36,822] TRAIN Iter 53160: lr = 0.411402,	loss = 2.788696,	Top-1 err = 0.451172,	Top-5 err = 0.222021,	data_time = 0.050630,	train_time = 0.358274
[2019-08-23 02:15:51,892] TRAIN Iter 53180: lr = 0.411368,	loss = 2.908525,	Top-1 err = 0.446973,	Top-5 err = 0.218555,	data_time = 0.050711,	train_time = 0.753465
[2019-08-23 02:16:07,435] TRAIN Iter 53200: lr = 0.411335,	loss = 2.853933,	Top-1 err = 0.458398,	Top-5 err = 0.220996,	data_time = 0.050433,	train_time = 0.777131
[2019-08-23 02:16:14,207] TRAIN Iter 53220: lr = 0.411302,	loss = 2.949871,	Top-1 err = 0.452734,	Top-5 err = 0.221631,	data_time = 0.050293,	train_time = 0.338598
[2019-08-23 02:16:29,156] TRAIN Iter 53240: lr = 0.411268,	loss = 2.916276,	Top-1 err = 0.451172,	Top-5 err = 0.225000,	data_time = 0.050459,	train_time = 0.747455
[2019-08-23 02:16:45,378] TRAIN Iter 53260: lr = 0.411235,	loss = 2.838629,	Top-1 err = 0.453857,	Top-5 err = 0.225781,	data_time = 0.050189,	train_time = 0.811087
[2019-08-23 02:16:52,218] TRAIN Iter 53280: lr = 0.411202,	loss = 2.805932,	Top-1 err = 0.452051,	Top-5 err = 0.223047,	data_time = 0.050495,	train_time = 0.341989
[2019-08-23 02:17:08,939] TRAIN Iter 53300: lr = 0.411168,	loss = 2.843659,	Top-1 err = 0.456738,	Top-5 err = 0.221387,	data_time = 0.050692,	train_time = 0.836029
[2019-08-23 02:17:15,693] TRAIN Iter 53320: lr = 0.411135,	loss = 2.785755,	Top-1 err = 0.451270,	Top-5 err = 0.224658,	data_time = 0.050272,	train_time = 0.337681
[2019-08-23 02:17:31,505] TRAIN Iter 53340: lr = 0.411102,	loss = 2.837238,	Top-1 err = 0.453223,	Top-5 err = 0.216113,	data_time = 0.050536,	train_time = 0.790579
[2019-08-23 02:17:47,369] TRAIN Iter 53360: lr = 0.411068,	loss = 2.846431,	Top-1 err = 0.459229,	Top-5 err = 0.224072,	data_time = 0.050355,	train_time = 0.793179
[2019-08-23 02:17:54,003] TRAIN Iter 53380: lr = 0.411035,	loss = 2.908526,	Top-1 err = 0.463232,	Top-5 err = 0.227734,	data_time = 0.050472,	train_time = 0.331680
[2019-08-23 02:18:11,364] TRAIN Iter 53400: lr = 0.411002,	loss = 2.767611,	Top-1 err = 0.457666,	Top-5 err = 0.225146,	data_time = 0.050798,	train_time = 0.868036
[2019-08-23 02:18:27,685] TRAIN Iter 53420: lr = 0.410968,	loss = 2.963859,	Top-1 err = 0.444336,	Top-5 err = 0.222266,	data_time = 0.050529,	train_time = 0.816037
[2019-08-23 02:18:34,930] TRAIN Iter 53440: lr = 0.410935,	loss = 2.854161,	Top-1 err = 0.456006,	Top-5 err = 0.223877,	data_time = 0.050343,	train_time = 0.362243
[2019-08-23 02:18:52,433] TRAIN Iter 53460: lr = 0.410902,	loss = 2.916832,	Top-1 err = 0.457422,	Top-5 err = 0.222314,	data_time = 0.050315,	train_time = 0.875126
[2019-08-23 02:18:59,059] TRAIN Iter 53480: lr = 0.410868,	loss = 2.867729,	Top-1 err = 0.456494,	Top-5 err = 0.223877,	data_time = 0.050414,	train_time = 0.331303
[2019-08-23 02:19:17,297] TRAIN Iter 53500: lr = 0.410835,	loss = 2.803471,	Top-1 err = 0.454688,	Top-5 err = 0.228564,	data_time = 0.050868,	train_time = 0.911902
[2019-08-23 02:19:33,408] TRAIN Iter 53520: lr = 0.410802,	loss = 2.877308,	Top-1 err = 0.455273,	Top-5 err = 0.222217,	data_time = 0.050659,	train_time = 0.805539
[2019-08-23 02:19:40,068] TRAIN Iter 53540: lr = 0.410768,	loss = 2.846100,	Top-1 err = 0.452979,	Top-5 err = 0.221729,	data_time = 0.050316,	train_time = 0.332964
[2019-08-23 02:19:59,498] TRAIN Iter 53560: lr = 0.410735,	loss = 2.819179,	Top-1 err = 0.455420,	Top-5 err = 0.224854,	data_time = 0.050401,	train_time = 0.971476
[2019-08-23 02:20:19,800] TRAIN Iter 53580: lr = 0.410702,	loss = 2.874727,	Top-1 err = 0.455078,	Top-5 err = 0.224658,	data_time = 0.050718,	train_time = 1.015098
[2019-08-23 02:20:26,111] TRAIN Iter 53600: lr = 0.410668,	loss = 2.787127,	Top-1 err = 0.452441,	Top-5 err = 0.222363,	data_time = 0.050347,	train_time = 0.315529
[2019-08-23 02:20:46,102] TRAIN Iter 53620: lr = 0.410635,	loss = 2.924698,	Top-1 err = 0.455322,	Top-5 err = 0.225879,	data_time = 0.050559,	train_time = 0.999516
[2019-08-23 02:20:53,390] TRAIN Iter 53640: lr = 0.410602,	loss = 2.862017,	Top-1 err = 0.454736,	Top-5 err = 0.222998,	data_time = 0.050365,	train_time = 0.364409
[2019-08-23 02:21:09,588] TRAIN Iter 53660: lr = 0.410568,	loss = 3.015776,	Top-1 err = 0.456299,	Top-5 err = 0.224268,	data_time = 0.050478,	train_time = 0.809878
[2019-08-23 02:21:27,675] TRAIN Iter 53680: lr = 0.410535,	loss = 2.777326,	Top-1 err = 0.457129,	Top-5 err = 0.219580,	data_time = 0.050333,	train_time = 0.904327
[2019-08-23 02:21:34,292] TRAIN Iter 53700: lr = 0.410502,	loss = 2.852768,	Top-1 err = 0.456494,	Top-5 err = 0.221436,	data_time = 0.050400,	train_time = 0.330869
[2019-08-23 02:21:53,697] TRAIN Iter 53720: lr = 0.410468,	loss = 2.918209,	Top-1 err = 0.453076,	Top-5 err = 0.224219,	data_time = 0.049899,	train_time = 0.970199
[2019-08-23 02:22:14,517] TRAIN Iter 53740: lr = 0.410435,	loss = 2.863958,	Top-1 err = 0.457959,	Top-5 err = 0.221338,	data_time = 0.049935,	train_time = 1.041007
[2019-08-23 02:22:20,595] TRAIN Iter 53760: lr = 0.410402,	loss = 2.968377,	Top-1 err = 0.451953,	Top-5 err = 0.223779,	data_time = 0.049873,	train_time = 0.303893
[2019-08-23 02:23:15,691] TRAIN Iter 53780: lr = 0.410368,	loss = 2.761502,	Top-1 err = 0.454831,	Top-5 err = 0.221312,	data_time = 0.050306,	train_time = 2.754801
[2019-08-23 02:23:23,546] TRAIN Iter 53800: lr = 0.410335,	loss = 2.947106,	Top-1 err = 0.457227,	Top-5 err = 0.225195,	data_time = 0.050515,	train_time = 0.392721
[2019-08-23 02:23:36,134] TRAIN Iter 53820: lr = 0.410302,	loss = 2.858047,	Top-1 err = 0.446875,	Top-5 err = 0.219531,	data_time = 0.050501,	train_time = 0.629393
[2019-08-23 02:23:45,538] TRAIN Iter 53840: lr = 0.410268,	loss = 2.851984,	Top-1 err = 0.446240,	Top-5 err = 0.221436,	data_time = 0.137630,	train_time = 0.470143
[2019-08-23 02:23:53,487] TRAIN Iter 53860: lr = 0.410235,	loss = 2.809593,	Top-1 err = 0.445361,	Top-5 err = 0.213086,	data_time = 0.050377,	train_time = 0.397474
[2019-08-23 02:24:06,641] TRAIN Iter 53880: lr = 0.410202,	loss = 2.807880,	Top-1 err = 0.445947,	Top-5 err = 0.218213,	data_time = 0.050652,	train_time = 0.657685
[2019-08-23 02:24:14,001] TRAIN Iter 53900: lr = 0.410168,	loss = 2.752504,	Top-1 err = 0.447119,	Top-5 err = 0.220947,	data_time = 0.050647,	train_time = 0.367980
[2019-08-23 02:24:28,417] TRAIN Iter 53920: lr = 0.410135,	loss = 2.643918,	Top-1 err = 0.436377,	Top-5 err = 0.213037,	data_time = 0.050320,	train_time = 0.720783
[2019-08-23 02:24:42,477] TRAIN Iter 53940: lr = 0.410102,	loss = 2.838478,	Top-1 err = 0.447852,	Top-5 err = 0.216602,	data_time = 0.050809,	train_time = 0.702994
[2019-08-23 02:24:50,165] TRAIN Iter 53960: lr = 0.410068,	loss = 2.802341,	Top-1 err = 0.448242,	Top-5 err = 0.214941,	data_time = 0.050586,	train_time = 0.384355
[2019-08-23 02:25:03,628] TRAIN Iter 53980: lr = 0.410035,	loss = 2.775040,	Top-1 err = 0.449023,	Top-5 err = 0.214844,	data_time = 0.050479,	train_time = 0.673154
[2019-08-23 02:25:13,670] TRAIN Iter 54000: lr = 0.410002,	loss = 2.945965,	Top-1 err = 0.449023,	Top-5 err = 0.218457,	data_time = 0.874061,	train_time = 0.502074
[2019-08-23 02:25:24,036] TRAIN Iter 54020: lr = 0.409968,	loss = 2.857349,	Top-1 err = 0.445166,	Top-5 err = 0.218164,	data_time = 0.050433,	train_time = 0.518270
[2019-08-23 02:25:39,239] TRAIN Iter 54040: lr = 0.409935,	loss = 2.877069,	Top-1 err = 0.447607,	Top-5 err = 0.218750,	data_time = 0.050523,	train_time = 0.760172
[2019-08-23 02:25:46,573] TRAIN Iter 54060: lr = 0.409902,	loss = 2.742247,	Top-1 err = 0.445117,	Top-5 err = 0.210352,	data_time = 0.050316,	train_time = 0.366680
[2019-08-23 02:26:01,449] TRAIN Iter 54080: lr = 0.409868,	loss = 2.845734,	Top-1 err = 0.452832,	Top-5 err = 0.222900,	data_time = 0.050663,	train_time = 0.743766
[2019-08-23 02:26:14,344] TRAIN Iter 54100: lr = 0.409835,	loss = 2.834722,	Top-1 err = 0.448242,	Top-5 err = 0.215479,	data_time = 0.569726,	train_time = 0.644756
[2019-08-23 02:26:22,082] TRAIN Iter 54120: lr = 0.409802,	loss = 2.817683,	Top-1 err = 0.441553,	Top-5 err = 0.217822,	data_time = 0.050414,	train_time = 0.386876
[2019-08-23 02:26:36,034] TRAIN Iter 54140: lr = 0.409768,	loss = 2.823843,	Top-1 err = 0.449219,	Top-5 err = 0.220361,	data_time = 0.050729,	train_time = 0.697566
[2019-08-23 02:26:45,096] TRAIN Iter 54160: lr = 0.409735,	loss = 2.871331,	Top-1 err = 0.447461,	Top-5 err = 0.216797,	data_time = 0.050741,	train_time = 0.453066
[2019-08-23 02:26:56,950] TRAIN Iter 54180: lr = 0.409702,	loss = 2.769052,	Top-1 err = 0.450146,	Top-5 err = 0.215869,	data_time = 0.050725,	train_time = 0.592697
[2019-08-23 02:27:13,028] TRAIN Iter 54200: lr = 0.409668,	loss = 2.922751,	Top-1 err = 0.446680,	Top-5 err = 0.222852,	data_time = 0.050505,	train_time = 0.803875
[2019-08-23 02:27:20,242] TRAIN Iter 54220: lr = 0.409635,	loss = 2.888144,	Top-1 err = 0.448242,	Top-5 err = 0.216113,	data_time = 0.147602,	train_time = 0.360698
[2019-08-23 02:27:35,143] TRAIN Iter 54240: lr = 0.409602,	loss = 2.847381,	Top-1 err = 0.444287,	Top-5 err = 0.219531,	data_time = 0.050561,	train_time = 0.745057
[2019-08-23 02:27:50,339] TRAIN Iter 54260: lr = 0.409568,	loss = 2.895346,	Top-1 err = 0.453955,	Top-5 err = 0.219873,	data_time = 0.100767,	train_time = 0.759790
[2019-08-23 02:27:57,466] TRAIN Iter 54280: lr = 0.409535,	loss = 2.830314,	Top-1 err = 0.451904,	Top-5 err = 0.219043,	data_time = 0.050642,	train_time = 0.356328
[2019-08-23 02:28:13,415] TRAIN Iter 54300: lr = 0.409502,	loss = 2.806014,	Top-1 err = 0.445605,	Top-5 err = 0.214990,	data_time = 0.050367,	train_time = 0.797442
[2019-08-23 02:28:22,670] TRAIN Iter 54320: lr = 0.409468,	loss = 2.870807,	Top-1 err = 0.450293,	Top-5 err = 0.219092,	data_time = 0.050924,	train_time = 0.462740
[2019-08-23 02:28:35,372] TRAIN Iter 54340: lr = 0.409435,	loss = 2.911155,	Top-1 err = 0.450146,	Top-5 err = 0.217725,	data_time = 0.050479,	train_time = 0.635067
[2019-08-23 02:28:51,114] TRAIN Iter 54360: lr = 0.409402,	loss = 2.861245,	Top-1 err = 0.453906,	Top-5 err = 0.221191,	data_time = 0.050565,	train_time = 0.787091
[2019-08-23 02:28:58,516] TRAIN Iter 54380: lr = 0.409368,	loss = 2.874640,	Top-1 err = 0.449756,	Top-5 err = 0.223047,	data_time = 0.171145,	train_time = 0.370067
[2019-08-23 02:29:12,166] TRAIN Iter 54400: lr = 0.409335,	loss = 2.878503,	Top-1 err = 0.449902,	Top-5 err = 0.222412,	data_time = 0.050696,	train_time = 0.682486
[2019-08-23 02:29:29,326] TRAIN Iter 54420: lr = 0.409302,	loss = 2.896218,	Top-1 err = 0.457178,	Top-5 err = 0.222607,	data_time = 0.050514,	train_time = 0.858011
[2019-08-23 02:29:36,616] TRAIN Iter 54440: lr = 0.409268,	loss = 2.862356,	Top-1 err = 0.452979,	Top-5 err = 0.217578,	data_time = 0.050468,	train_time = 0.364442
[2019-08-23 02:29:51,427] TRAIN Iter 54460: lr = 0.409235,	loss = 2.835536,	Top-1 err = 0.450635,	Top-5 err = 0.221240,	data_time = 0.050421,	train_time = 0.740558
[2019-08-23 02:30:00,113] TRAIN Iter 54480: lr = 0.409202,	loss = 2.833108,	Top-1 err = 0.454102,	Top-5 err = 0.220166,	data_time = 0.050512,	train_time = 0.434266
[2019-08-23 02:30:13,804] TRAIN Iter 54500: lr = 0.409168,	loss = 2.829968,	Top-1 err = 0.453760,	Top-5 err = 0.221777,	data_time = 0.050684,	train_time = 0.684543
[2019-08-23 02:30:29,151] TRAIN Iter 54520: lr = 0.409135,	loss = 2.878726,	Top-1 err = 0.447607,	Top-5 err = 0.222168,	data_time = 0.050665,	train_time = 0.767337
[2019-08-23 02:30:36,365] TRAIN Iter 54540: lr = 0.409102,	loss = 2.805026,	Top-1 err = 0.448193,	Top-5 err = 0.220850,	data_time = 0.050552,	train_time = 0.360690
[2019-08-23 02:30:52,672] TRAIN Iter 54560: lr = 0.409068,	loss = 2.811985,	Top-1 err = 0.444092,	Top-5 err = 0.217920,	data_time = 0.050657,	train_time = 0.815322
[2019-08-23 02:31:09,071] TRAIN Iter 54580: lr = 0.409035,	loss = 2.822053,	Top-1 err = 0.450146,	Top-5 err = 0.221191,	data_time = 0.104617,	train_time = 0.819948
[2019-08-23 02:31:16,116] TRAIN Iter 54600: lr = 0.409002,	loss = 2.901543,	Top-1 err = 0.452051,	Top-5 err = 0.222314,	data_time = 0.050614,	train_time = 0.352214
[2019-08-23 02:31:33,763] TRAIN Iter 54620: lr = 0.408968,	loss = 2.827841,	Top-1 err = 0.454053,	Top-5 err = 0.221631,	data_time = 0.050344,	train_time = 0.882327
[2019-08-23 02:31:46,543] TRAIN Iter 54640: lr = 0.408935,	loss = 2.898935,	Top-1 err = 0.453516,	Top-5 err = 0.219580,	data_time = 0.230273,	train_time = 0.639011
[2019-08-23 02:31:57,894] TRAIN Iter 54660: lr = 0.408902,	loss = 2.814442,	Top-1 err = 0.448242,	Top-5 err = 0.222021,	data_time = 0.050585,	train_time = 0.567511
[2019-08-23 02:32:13,403] TRAIN Iter 54680: lr = 0.408868,	loss = 2.921918,	Top-1 err = 0.456445,	Top-5 err = 0.224902,	data_time = 0.050467,	train_time = 0.775471
[2019-08-23 02:32:20,628] TRAIN Iter 54700: lr = 0.408835,	loss = 2.827709,	Top-1 err = 0.454932,	Top-5 err = 0.221045,	data_time = 0.050457,	train_time = 0.361225
[2019-08-23 02:32:35,752] TRAIN Iter 54720: lr = 0.408802,	loss = 2.880836,	Top-1 err = 0.450781,	Top-5 err = 0.221875,	data_time = 0.050465,	train_time = 0.756164
[2019-08-23 02:32:54,021] TRAIN Iter 54740: lr = 0.408768,	loss = 2.862413,	Top-1 err = 0.449170,	Top-5 err = 0.222363,	data_time = 0.050426,	train_time = 0.913436
[2019-08-23 02:33:00,805] TRAIN Iter 54760: lr = 0.408735,	loss = 2.827075,	Top-1 err = 0.457764,	Top-5 err = 0.223682,	data_time = 0.050355,	train_time = 0.339193
[2019-08-23 02:33:19,786] TRAIN Iter 54780: lr = 0.408702,	loss = 2.813161,	Top-1 err = 0.456055,	Top-5 err = 0.221533,	data_time = 0.050413,	train_time = 0.949070
[2019-08-23 02:33:33,448] TRAIN Iter 54800: lr = 0.408668,	loss = 2.839570,	Top-1 err = 0.451270,	Top-5 err = 0.220654,	data_time = 0.114416,	train_time = 0.683070
[2019-08-23 02:33:43,656] TRAIN Iter 54820: lr = 0.408635,	loss = 2.809421,	Top-1 err = 0.449902,	Top-5 err = 0.217822,	data_time = 0.050286,	train_time = 0.510376
[2019-08-23 02:34:02,431] TRAIN Iter 54840: lr = 0.408602,	loss = 2.912477,	Top-1 err = 0.449609,	Top-5 err = 0.219922,	data_time = 0.050641,	train_time = 0.938719
[2019-08-23 02:34:09,325] TRAIN Iter 54860: lr = 0.408568,	loss = 2.970437,	Top-1 err = 0.450439,	Top-5 err = 0.219141,	data_time = 0.050927,	train_time = 0.344707
[2019-08-23 02:34:28,287] TRAIN Iter 54880: lr = 0.408535,	loss = 2.898481,	Top-1 err = 0.455225,	Top-5 err = 0.222998,	data_time = 0.050479,	train_time = 0.948090
[2019-08-23 02:34:47,737] TRAIN Iter 54900: lr = 0.408502,	loss = 2.909817,	Top-1 err = 0.455176,	Top-5 err = 0.224951,	data_time = 0.050362,	train_time = 0.972506
[2019-08-23 02:34:54,732] TRAIN Iter 54920: lr = 0.408468,	loss = 2.831696,	Top-1 err = 0.455225,	Top-5 err = 0.222266,	data_time = 0.050389,	train_time = 0.349716
[2019-08-23 02:35:11,647] TRAIN Iter 54940: lr = 0.408435,	loss = 2.839807,	Top-1 err = 0.463428,	Top-5 err = 0.227930,	data_time = 0.050577,	train_time = 0.845741
[2019-08-23 02:35:27,699] TRAIN Iter 54960: lr = 0.408402,	loss = 2.798981,	Top-1 err = 0.454834,	Top-5 err = 0.221875,	data_time = 0.065813,	train_time = 0.802598
[2019-08-23 02:35:34,958] TRAIN Iter 54980: lr = 0.408368,	loss = 2.827114,	Top-1 err = 0.452734,	Top-5 err = 0.218555,	data_time = 0.050043,	train_time = 0.362901
[2019-08-23 02:35:50,623] TRAIN Iter 55000: lr = 0.408335,	loss = 2.834027,	Top-1 err = 0.454883,	Top-5 err = 0.225830,	data_time = 0.049904,	train_time = 0.783255
[2019-08-23 02:35:56,749] TRAIN Iter 55020: lr = 0.408302,	loss = 2.819870,	Top-1 err = 0.454980,	Top-5 err = 0.225439,	data_time = 0.049844,	train_time = 0.306289
[2019-08-23 02:36:47,191] TRAIN Iter 55040: lr = 0.408268,	loss = 2.809434,	Top-1 err = 0.463113,	Top-5 err = 0.228310,	data_time = 0.050532,	train_time = 2.522085
[2019-08-23 02:36:58,229] TRAIN Iter 55060: lr = 0.408235,	loss = 2.876755,	Top-1 err = 0.450342,	Top-5 err = 0.216748,	data_time = 0.132336,	train_time = 0.551865
[2019-08-23 02:37:07,177] TRAIN Iter 55080: lr = 0.408202,	loss = 2.848705,	Top-1 err = 0.446045,	Top-5 err = 0.215771,	data_time = 0.050537,	train_time = 0.447406
[2019-08-23 02:37:15,957] TRAIN Iter 55100: lr = 0.408168,	loss = 2.739542,	Top-1 err = 0.442236,	Top-5 err = 0.215723,	data_time = 0.124652,	train_time = 0.438979
[2019-08-23 02:37:23,429] TRAIN Iter 55120: lr = 0.408135,	loss = 2.897606,	Top-1 err = 0.450586,	Top-5 err = 0.220215,	data_time = 0.050711,	train_time = 0.373577
[2019-08-23 02:37:38,936] TRAIN Iter 55140: lr = 0.408102,	loss = 2.943242,	Top-1 err = 0.454688,	Top-5 err = 0.220459,	data_time = 0.050563,	train_time = 0.775327
[2019-08-23 02:37:50,865] TRAIN Iter 55160: lr = 0.408068,	loss = 2.818377,	Top-1 err = 0.447412,	Top-5 err = 0.216699,	data_time = 0.106592,	train_time = 0.596447
[2019-08-23 02:37:59,695] TRAIN Iter 55180: lr = 0.408035,	loss = 2.826720,	Top-1 err = 0.448877,	Top-5 err = 0.217432,	data_time = 0.118077,	train_time = 0.441509
[2019-08-23 02:38:14,001] TRAIN Iter 55200: lr = 0.408002,	loss = 2.769831,	Top-1 err = 0.445752,	Top-5 err = 0.213818,	data_time = 0.050864,	train_time = 0.715276
[2019-08-23 02:38:23,924] TRAIN Iter 55220: lr = 0.407968,	loss = 2.894241,	Top-1 err = 0.452344,	Top-5 err = 0.216064,	data_time = 1.720821,	train_time = 0.496109
[2019-08-23 02:38:36,169] TRAIN Iter 55240: lr = 0.407935,	loss = 2.895393,	Top-1 err = 0.451318,	Top-5 err = 0.216699,	data_time = 0.050319,	train_time = 0.612226
[2019-08-23 02:38:53,022] TRAIN Iter 55260: lr = 0.407902,	loss = 2.784893,	Top-1 err = 0.449414,	Top-5 err = 0.217725,	data_time = 0.051028,	train_time = 0.842666
[2019-08-23 02:39:01,522] TRAIN Iter 55280: lr = 0.407868,	loss = 2.832379,	Top-1 err = 0.447168,	Top-5 err = 0.216211,	data_time = 0.050757,	train_time = 0.424970
[2019-08-23 02:39:13,445] TRAIN Iter 55300: lr = 0.407835,	loss = 2.822389,	Top-1 err = 0.445996,	Top-5 err = 0.218408,	data_time = 0.050356,	train_time = 0.596149
[2019-08-23 02:39:26,553] TRAIN Iter 55320: lr = 0.407802,	loss = 2.822216,	Top-1 err = 0.446582,	Top-5 err = 0.219385,	data_time = 0.050272,	train_time = 0.655374
[2019-08-23 02:39:33,952] TRAIN Iter 55340: lr = 0.407768,	loss = 2.819577,	Top-1 err = 0.450342,	Top-5 err = 0.219336,	data_time = 0.050443,	train_time = 0.369920
[2019-08-23 02:39:48,777] TRAIN Iter 55360: lr = 0.407735,	loss = 2.786464,	Top-1 err = 0.443848,	Top-5 err = 0.214697,	data_time = 0.050385,	train_time = 0.741270
[2019-08-23 02:39:59,348] TRAIN Iter 55380: lr = 0.407702,	loss = 2.855240,	Top-1 err = 0.446289,	Top-5 err = 0.220459,	data_time = 2.081579,	train_time = 0.528538
[2019-08-23 02:40:09,444] TRAIN Iter 55400: lr = 0.407668,	loss = 2.843101,	Top-1 err = 0.448633,	Top-5 err = 0.217432,	data_time = 0.143630,	train_time = 0.504754
[2019-08-23 02:40:24,835] TRAIN Iter 55420: lr = 0.407635,	loss = 2.771117,	Top-1 err = 0.438672,	Top-5 err = 0.212744,	data_time = 0.050614,	train_time = 0.769569
[2019-08-23 02:40:33,166] TRAIN Iter 55440: lr = 0.407602,	loss = 2.785091,	Top-1 err = 0.453467,	Top-5 err = 0.221045,	data_time = 0.050938,	train_time = 0.416517
[2019-08-23 02:40:45,748] TRAIN Iter 55460: lr = 0.407568,	loss = 2.854227,	Top-1 err = 0.451270,	Top-5 err = 0.224707,	data_time = 0.146731,	train_time = 0.629070
[2019-08-23 02:40:59,852] TRAIN Iter 55480: lr = 0.407535,	loss = 2.845644,	Top-1 err = 0.449854,	Top-5 err = 0.216113,	data_time = 0.050808,	train_time = 0.705176
[2019-08-23 02:41:07,429] TRAIN Iter 55500: lr = 0.407502,	loss = 2.875603,	Top-1 err = 0.448437,	Top-5 err = 0.215381,	data_time = 0.050389,	train_time = 0.378872
[2019-08-23 02:41:20,514] TRAIN Iter 55520: lr = 0.407468,	loss = 2.907542,	Top-1 err = 0.449854,	Top-5 err = 0.216748,	data_time = 0.050897,	train_time = 0.654197
[2019-08-23 02:41:31,336] TRAIN Iter 55540: lr = 0.407435,	loss = 2.905716,	Top-1 err = 0.456348,	Top-5 err = 0.222314,	data_time = 2.853173,	train_time = 0.541117
[2019-08-23 02:41:42,261] TRAIN Iter 55560: lr = 0.407402,	loss = 2.831833,	Top-1 err = 0.451318,	Top-5 err = 0.219629,	data_time = 0.050640,	train_time = 0.546245
[2019-08-23 02:41:55,641] TRAIN Iter 55580: lr = 0.407368,	loss = 2.747601,	Top-1 err = 0.446240,	Top-5 err = 0.217383,	data_time = 0.050503,	train_time = 0.668947
[2019-08-23 02:42:03,241] TRAIN Iter 55600: lr = 0.407335,	loss = 2.819521,	Top-1 err = 0.446826,	Top-5 err = 0.216357,	data_time = 0.050478,	train_time = 0.380026
[2019-08-23 02:42:16,767] TRAIN Iter 55620: lr = 0.407302,	loss = 2.831038,	Top-1 err = 0.449854,	Top-5 err = 0.219385,	data_time = 0.050367,	train_time = 0.676270
[2019-08-23 02:42:32,934] TRAIN Iter 55640: lr = 0.407268,	loss = 2.828690,	Top-1 err = 0.452930,	Top-5 err = 0.217969,	data_time = 0.050838,	train_time = 0.808335
[2019-08-23 02:42:40,305] TRAIN Iter 55660: lr = 0.407235,	loss = 2.816956,	Top-1 err = 0.450146,	Top-5 err = 0.219531,	data_time = 0.050679,	train_time = 0.368551
[2019-08-23 02:42:53,738] TRAIN Iter 55680: lr = 0.407202,	loss = 2.858534,	Top-1 err = 0.451465,	Top-5 err = 0.220654,	data_time = 0.050625,	train_time = 0.671600
[2019-08-23 02:43:06,456] TRAIN Iter 55700: lr = 0.407168,	loss = 2.835528,	Top-1 err = 0.448682,	Top-5 err = 0.221143,	data_time = 4.942537,	train_time = 0.635891
[2019-08-23 02:43:15,322] TRAIN Iter 55720: lr = 0.407135,	loss = 2.948624,	Top-1 err = 0.453418,	Top-5 err = 0.224658,	data_time = 0.050146,	train_time = 0.443298
[2019-08-23 02:43:30,504] TRAIN Iter 55740: lr = 0.407102,	loss = 2.897224,	Top-1 err = 0.454883,	Top-5 err = 0.220117,	data_time = 0.050489,	train_time = 0.759095
[2019-08-23 02:43:38,054] TRAIN Iter 55760: lr = 0.407068,	loss = 2.922847,	Top-1 err = 0.453613,	Top-5 err = 0.218115,	data_time = 0.050812,	train_time = 0.377458
[2019-08-23 02:43:51,392] TRAIN Iter 55780: lr = 0.407035,	loss = 2.887225,	Top-1 err = 0.445410,	Top-5 err = 0.216504,	data_time = 0.050421,	train_time = 0.666875
[2019-08-23 02:44:04,916] TRAIN Iter 55800: lr = 0.407002,	loss = 2.788007,	Top-1 err = 0.454004,	Top-5 err = 0.221680,	data_time = 0.050689,	train_time = 0.676220
[2019-08-23 02:44:12,459] TRAIN Iter 55820: lr = 0.406968,	loss = 2.816485,	Top-1 err = 0.455762,	Top-5 err = 0.219385,	data_time = 0.050535,	train_time = 0.377133
[2019-08-23 02:44:27,786] TRAIN Iter 55840: lr = 0.406935,	loss = 2.831410,	Top-1 err = 0.458789,	Top-5 err = 0.222314,	data_time = 0.050428,	train_time = 0.766327
[2019-08-23 02:44:43,839] TRAIN Iter 55860: lr = 0.406902,	loss = 2.982000,	Top-1 err = 0.453564,	Top-5 err = 0.221631,	data_time = 8.206126,	train_time = 0.802641
[2019-08-23 02:44:51,102] TRAIN Iter 55880: lr = 0.406868,	loss = 2.931412,	Top-1 err = 0.451953,	Top-5 err = 0.220264,	data_time = 0.050402,	train_time = 0.363121
[2019-08-23 02:45:07,042] TRAIN Iter 55900: lr = 0.406835,	loss = 2.851339,	Top-1 err = 0.450830,	Top-5 err = 0.218750,	data_time = 0.050500,	train_time = 0.797019
[2019-08-23 02:45:14,396] TRAIN Iter 55920: lr = 0.406802,	loss = 2.803885,	Top-1 err = 0.449805,	Top-5 err = 0.220605,	data_time = 0.050579,	train_time = 0.367685
[2019-08-23 02:45:28,916] TRAIN Iter 55940: lr = 0.406768,	loss = 2.872644,	Top-1 err = 0.456348,	Top-5 err = 0.223779,	data_time = 0.050487,	train_time = 0.725979
[2019-08-23 02:45:45,859] TRAIN Iter 55960: lr = 0.406735,	loss = 2.901368,	Top-1 err = 0.454443,	Top-5 err = 0.224170,	data_time = 0.050851,	train_time = 0.847121
[2019-08-23 02:45:53,189] TRAIN Iter 55980: lr = 0.406702,	loss = 2.746203,	Top-1 err = 0.451172,	Top-5 err = 0.217627,	data_time = 0.050751,	train_time = 0.366497
[2019-08-23 02:46:08,652] TRAIN Iter 56000: lr = 0.406668,	loss = 2.813015,	Top-1 err = 0.451758,	Top-5 err = 0.226221,	data_time = 0.050551,	train_time = 0.773147
[2019-08-23 02:46:23,675] TRAIN Iter 56020: lr = 0.406635,	loss = 2.916434,	Top-1 err = 0.457520,	Top-5 err = 0.224170,	data_time = 6.602273,	train_time = 0.751098
[2019-08-23 02:46:31,301] TRAIN Iter 56040: lr = 0.406602,	loss = 2.858580,	Top-1 err = 0.452930,	Top-5 err = 0.221582,	data_time = 0.050453,	train_time = 0.381309
[2019-08-23 02:46:46,923] TRAIN Iter 56060: lr = 0.406568,	loss = 2.856795,	Top-1 err = 0.458154,	Top-5 err = 0.223291,	data_time = 0.050517,	train_time = 0.781073
[2019-08-23 02:46:53,801] TRAIN Iter 56080: lr = 0.406535,	loss = 2.852913,	Top-1 err = 0.452588,	Top-5 err = 0.217578,	data_time = 0.050435,	train_time = 0.343866
[2019-08-23 02:47:10,343] TRAIN Iter 56100: lr = 0.406502,	loss = 2.813564,	Top-1 err = 0.452393,	Top-5 err = 0.223730,	data_time = 0.050329,	train_time = 0.827090
[2019-08-23 02:47:25,881] TRAIN Iter 56120: lr = 0.406468,	loss = 2.873109,	Top-1 err = 0.454004,	Top-5 err = 0.221729,	data_time = 0.050659,	train_time = 0.776930
[2019-08-23 02:47:33,287] TRAIN Iter 56140: lr = 0.406435,	loss = 2.848169,	Top-1 err = 0.451318,	Top-5 err = 0.219727,	data_time = 0.050492,	train_time = 0.370249
[2019-08-23 02:47:51,081] TRAIN Iter 56160: lr = 0.406402,	loss = 2.894792,	Top-1 err = 0.449463,	Top-5 err = 0.220068,	data_time = 0.050535,	train_time = 0.889695
[2019-08-23 02:48:05,544] TRAIN Iter 56180: lr = 0.406368,	loss = 2.901607,	Top-1 err = 0.458154,	Top-5 err = 0.223779,	data_time = 4.945737,	train_time = 0.723136
[2019-08-23 02:48:15,005] TRAIN Iter 56200: lr = 0.406335,	loss = 2.795093,	Top-1 err = 0.452148,	Top-5 err = 0.219775,	data_time = 0.050407,	train_time = 0.473063
[2019-08-23 02:48:32,717] TRAIN Iter 56220: lr = 0.406302,	loss = 2.810216,	Top-1 err = 0.449707,	Top-5 err = 0.220898,	data_time = 0.154532,	train_time = 0.885568
[2019-08-23 02:48:39,606] TRAIN Iter 56240: lr = 0.406268,	loss = 2.826713,	Top-1 err = 0.454785,	Top-5 err = 0.221729,	data_time = 0.049943,	train_time = 0.344442
[2019-08-23 02:48:55,461] TRAIN Iter 56260: lr = 0.406235,	loss = 2.894314,	Top-1 err = 0.447363,	Top-5 err = 0.215527,	data_time = 0.049874,	train_time = 0.792747
[2019-08-23 02:49:35,531] TRAIN Iter 56280: lr = 0.406202,	loss = 2.889855,	Top-1 err = 0.449515,	Top-5 err = 0.220529,	data_time = 0.050823,	train_time = 2.003470
[2019-08-23 02:49:44,433] TRAIN Iter 56300: lr = 0.406168,	loss = 2.849072,	Top-1 err = 0.451953,	Top-5 err = 0.220068,	data_time = 0.050400,	train_time = 0.445057
[2019-08-23 02:49:58,573] TRAIN Iter 56320: lr = 0.406135,	loss = 2.839487,	Top-1 err = 0.450195,	Top-5 err = 0.222168,	data_time = 0.050615,	train_time = 0.707017
[2019-08-23 02:50:06,850] TRAIN Iter 56340: lr = 0.406102,	loss = 2.791417,	Top-1 err = 0.447119,	Top-5 err = 0.216797,	data_time = 0.050869,	train_time = 0.413835
[2019-08-23 02:50:19,391] TRAIN Iter 56360: lr = 0.406068,	loss = 2.746931,	Top-1 err = 0.443799,	Top-5 err = 0.213379,	data_time = 0.050560,	train_time = 0.627030
[2019-08-23 02:50:34,802] TRAIN Iter 56380: lr = 0.406035,	loss = 2.794417,	Top-1 err = 0.449658,	Top-5 err = 0.217529,	data_time = 0.050504,	train_time = 0.770547
[2019-08-23 02:50:43,206] TRAIN Iter 56400: lr = 0.406002,	loss = 2.776226,	Top-1 err = 0.444678,	Top-5 err = 0.210449,	data_time = 0.050702,	train_time = 0.420153
[2019-08-23 02:50:54,618] TRAIN Iter 56420: lr = 0.405968,	loss = 2.819286,	Top-1 err = 0.446680,	Top-5 err = 0.216602,	data_time = 0.050536,	train_time = 0.570581
[2019-08-23 02:51:07,038] TRAIN Iter 56440: lr = 0.405935,	loss = 2.769172,	Top-1 err = 0.444238,	Top-5 err = 0.218652,	data_time = 0.050694,	train_time = 0.621019
[2019-08-23 02:51:15,049] TRAIN Iter 56460: lr = 0.405902,	loss = 2.835498,	Top-1 err = 0.442090,	Top-5 err = 0.214014,	data_time = 0.050997,	train_time = 0.400533
[2019-08-23 02:51:26,755] TRAIN Iter 56480: lr = 0.405868,	loss = 2.801944,	Top-1 err = 0.451709,	Top-5 err = 0.218848,	data_time = 0.050591,	train_time = 0.585276
[2019-08-23 02:51:34,220] TRAIN Iter 56500: lr = 0.405835,	loss = 2.806453,	Top-1 err = 0.447607,	Top-5 err = 0.215088,	data_time = 0.050517,	train_time = 0.373213
[2019-08-23 02:51:48,868] TRAIN Iter 56520: lr = 0.405802,	loss = 2.869835,	Top-1 err = 0.451709,	Top-5 err = 0.220361,	data_time = 0.133351,	train_time = 0.732377
[2019-08-23 02:51:59,165] TRAIN Iter 56540: lr = 0.405768,	loss = 2.765297,	Top-1 err = 0.442236,	Top-5 err = 0.213379,	data_time = 0.050927,	train_time = 0.514845
[2019-08-23 02:52:06,920] TRAIN Iter 56560: lr = 0.405735,	loss = 2.748595,	Top-1 err = 0.446973,	Top-5 err = 0.215479,	data_time = 0.050876,	train_time = 0.387724
[2019-08-23 02:52:21,178] TRAIN Iter 56580: lr = 0.405702,	loss = 2.753263,	Top-1 err = 0.447559,	Top-5 err = 0.216113,	data_time = 0.050352,	train_time = 0.712905
[2019-08-23 02:52:37,234] TRAIN Iter 56600: lr = 0.405668,	loss = 2.814637,	Top-1 err = 0.440918,	Top-5 err = 0.210889,	data_time = 0.050465,	train_time = 0.802765
[2019-08-23 02:52:44,966] TRAIN Iter 56620: lr = 0.405635,	loss = 2.841261,	Top-1 err = 0.445850,	Top-5 err = 0.218115,	data_time = 0.050358,	train_time = 0.386599
[2019-08-23 02:52:58,878] TRAIN Iter 56640: lr = 0.405602,	loss = 2.826500,	Top-1 err = 0.443018,	Top-5 err = 0.217529,	data_time = 0.050777,	train_time = 0.695564
[2019-08-23 02:53:06,737] TRAIN Iter 56660: lr = 0.405568,	loss = 2.813464,	Top-1 err = 0.447217,	Top-5 err = 0.217041,	data_time = 0.050472,	train_time = 0.392950
[2019-08-23 02:53:19,910] TRAIN Iter 56680: lr = 0.405535,	loss = 2.873934,	Top-1 err = 0.444238,	Top-5 err = 0.217578,	data_time = 0.050637,	train_time = 0.658624
[2019-08-23 02:53:32,669] TRAIN Iter 56700: lr = 0.405502,	loss = 2.798724,	Top-1 err = 0.444922,	Top-5 err = 0.212158,	data_time = 0.051036,	train_time = 0.637972
[2019-08-23 02:53:40,179] TRAIN Iter 56720: lr = 0.405468,	loss = 2.812016,	Top-1 err = 0.449512,	Top-5 err = 0.222607,	data_time = 0.050411,	train_time = 0.375486
[2019-08-23 02:53:53,691] TRAIN Iter 56740: lr = 0.405435,	loss = 2.829110,	Top-1 err = 0.444727,	Top-5 err = 0.215137,	data_time = 0.050335,	train_time = 0.675561
[2019-08-23 02:54:07,880] TRAIN Iter 56760: lr = 0.405402,	loss = 2.922483,	Top-1 err = 0.448437,	Top-5 err = 0.216211,	data_time = 0.147333,	train_time = 0.709451
[2019-08-23 02:54:15,779] TRAIN Iter 56780: lr = 0.405368,	loss = 2.876952,	Top-1 err = 0.448145,	Top-5 err = 0.222754,	data_time = 0.050865,	train_time = 0.394918
[2019-08-23 02:54:30,430] TRAIN Iter 56800: lr = 0.405335,	loss = 2.837144,	Top-1 err = 0.445068,	Top-5 err = 0.217188,	data_time = 0.050390,	train_time = 0.732523
[2019-08-23 02:54:37,648] TRAIN Iter 56820: lr = 0.405302,	loss = 2.741205,	Top-1 err = 0.452002,	Top-5 err = 0.215381,	data_time = 0.050461,	train_time = 0.360895
[2019-08-23 02:54:52,455] TRAIN Iter 56840: lr = 0.405268,	loss = 2.844380,	Top-1 err = 0.442041,	Top-5 err = 0.217090,	data_time = 0.050575,	train_time = 0.740365
[2019-08-23 02:55:06,845] TRAIN Iter 56860: lr = 0.405235,	loss = 2.744165,	Top-1 err = 0.447852,	Top-5 err = 0.213672,	data_time = 0.050729,	train_time = 0.719467
[2019-08-23 02:55:13,896] TRAIN Iter 56880: lr = 0.405202,	loss = 2.825679,	Top-1 err = 0.451465,	Top-5 err = 0.217285,	data_time = 0.050784,	train_time = 0.352514
[2019-08-23 02:55:30,189] TRAIN Iter 56900: lr = 0.405168,	loss = 2.794615,	Top-1 err = 0.450684,	Top-5 err = 0.223047,	data_time = 0.050666,	train_time = 0.814629
[2019-08-23 02:55:43,635] TRAIN Iter 56920: lr = 0.405135,	loss = 2.851046,	Top-1 err = 0.444434,	Top-5 err = 0.215820,	data_time = 0.129746,	train_time = 0.672297
[2019-08-23 02:55:51,343] TRAIN Iter 56940: lr = 0.405102,	loss = 2.831864,	Top-1 err = 0.452588,	Top-5 err = 0.220459,	data_time = 0.050566,	train_time = 0.385372
[2019-08-23 02:56:07,115] TRAIN Iter 56960: lr = 0.405068,	loss = 2.830090,	Top-1 err = 0.443311,	Top-5 err = 0.211963,	data_time = 0.050592,	train_time = 0.788615
[2019-08-23 02:56:15,133] TRAIN Iter 56980: lr = 0.405035,	loss = 2.822070,	Top-1 err = 0.450635,	Top-5 err = 0.218262,	data_time = 0.050320,	train_time = 0.400895
[2019-08-23 02:56:29,361] TRAIN Iter 57000: lr = 0.405002,	loss = 2.965796,	Top-1 err = 0.455127,	Top-5 err = 0.223242,	data_time = 0.050900,	train_time = 0.711376
[2019-08-23 02:56:45,084] TRAIN Iter 57020: lr = 0.404968,	loss = 2.855198,	Top-1 err = 0.449365,	Top-5 err = 0.215674,	data_time = 0.050462,	train_time = 0.786138
[2019-08-23 02:56:52,554] TRAIN Iter 57040: lr = 0.404935,	loss = 2.810732,	Top-1 err = 0.452393,	Top-5 err = 0.219727,	data_time = 0.050320,	train_time = 0.373459
[2019-08-23 02:57:06,236] TRAIN Iter 57060: lr = 0.404902,	loss = 2.778241,	Top-1 err = 0.448193,	Top-5 err = 0.216943,	data_time = 0.050508,	train_time = 0.684091
[2019-08-23 02:57:22,845] TRAIN Iter 57080: lr = 0.404868,	loss = 2.849976,	Top-1 err = 0.454736,	Top-5 err = 0.217529,	data_time = 0.050631,	train_time = 0.830436
[2019-08-23 02:57:29,623] TRAIN Iter 57100: lr = 0.404835,	loss = 2.896794,	Top-1 err = 0.449268,	Top-5 err = 0.216650,	data_time = 0.050502,	train_time = 0.338886
[2019-08-23 02:57:46,408] TRAIN Iter 57120: lr = 0.404802,	loss = 2.850169,	Top-1 err = 0.449756,	Top-5 err = 0.221875,	data_time = 0.050483,	train_time = 0.839231
[2019-08-23 02:57:54,324] TRAIN Iter 57140: lr = 0.404768,	loss = 2.858064,	Top-1 err = 0.452197,	Top-5 err = 0.222607,	data_time = 0.050531,	train_time = 0.395799
[2019-08-23 02:58:07,368] TRAIN Iter 57160: lr = 0.404735,	loss = 2.843539,	Top-1 err = 0.453271,	Top-5 err = 0.218848,	data_time = 0.050610,	train_time = 0.652183
[2019-08-23 02:58:23,930] TRAIN Iter 57180: lr = 0.404702,	loss = 2.713391,	Top-1 err = 0.455811,	Top-5 err = 0.222363,	data_time = 0.050465,	train_time = 0.828049
[2019-08-23 02:58:31,162] TRAIN Iter 57200: lr = 0.404668,	loss = 2.800444,	Top-1 err = 0.449170,	Top-5 err = 0.217041,	data_time = 0.050327,	train_time = 0.361599
[2019-08-23 02:58:46,045] TRAIN Iter 57220: lr = 0.404635,	loss = 2.921648,	Top-1 err = 0.450098,	Top-5 err = 0.218506,	data_time = 0.050592,	train_time = 0.744170
[2019-08-23 02:59:01,701] TRAIN Iter 57240: lr = 0.404602,	loss = 2.784019,	Top-1 err = 0.454883,	Top-5 err = 0.226270,	data_time = 0.050869,	train_time = 0.782756
[2019-08-23 02:59:08,455] TRAIN Iter 57260: lr = 0.404568,	loss = 2.787525,	Top-1 err = 0.454346,	Top-5 err = 0.222217,	data_time = 0.050453,	train_time = 0.337664
[2019-08-23 02:59:24,931] TRAIN Iter 57280: lr = 0.404535,	loss = 2.900366,	Top-1 err = 0.452734,	Top-5 err = 0.212744,	data_time = 0.050700,	train_time = 0.823826
[2019-08-23 02:59:32,434] TRAIN Iter 57300: lr = 0.404502,	loss = 2.851333,	Top-1 err = 0.446631,	Top-5 err = 0.220605,	data_time = 0.147305,	train_time = 0.375100
[2019-08-23 02:59:46,844] TRAIN Iter 57320: lr = 0.404468,	loss = 2.850399,	Top-1 err = 0.453857,	Top-5 err = 0.221729,	data_time = 0.050546,	train_time = 0.720529
[2019-08-23 03:00:04,974] TRAIN Iter 57340: lr = 0.404435,	loss = 2.867203,	Top-1 err = 0.454053,	Top-5 err = 0.222217,	data_time = 0.050626,	train_time = 0.906457
[2019-08-23 03:00:11,687] TRAIN Iter 57360: lr = 0.404402,	loss = 2.828752,	Top-1 err = 0.450293,	Top-5 err = 0.223926,	data_time = 0.050624,	train_time = 0.335647
[2019-08-23 03:00:28,570] TRAIN Iter 57380: lr = 0.404368,	loss = 2.845239,	Top-1 err = 0.446729,	Top-5 err = 0.220068,	data_time = 0.050443,	train_time = 0.844130
[2019-08-23 03:00:45,492] TRAIN Iter 57400: lr = 0.404335,	loss = 2.893730,	Top-1 err = 0.452588,	Top-5 err = 0.223291,	data_time = 0.050090,	train_time = 0.846062
[2019-08-23 03:00:52,385] TRAIN Iter 57420: lr = 0.404302,	loss = 2.870899,	Top-1 err = 0.449805,	Top-5 err = 0.221143,	data_time = 0.050378,	train_time = 0.344641
[2019-08-23 03:01:11,400] TRAIN Iter 57440: lr = 0.404268,	loss = 2.799160,	Top-1 err = 0.450928,	Top-5 err = 0.219482,	data_time = 0.050241,	train_time = 0.950735
[2019-08-23 03:01:18,136] TRAIN Iter 57460: lr = 0.404235,	loss = 2.839737,	Top-1 err = 0.450977,	Top-5 err = 0.221289,	data_time = 0.166728,	train_time = 0.336822
[2019-08-23 03:01:36,141] TRAIN Iter 57480: lr = 0.404202,	loss = 2.821640,	Top-1 err = 0.451904,	Top-5 err = 0.221631,	data_time = 0.050026,	train_time = 0.900220
[2019-08-23 03:01:51,335] TRAIN Iter 57500: lr = 0.404168,	loss = 2.759590,	Top-1 err = 0.450635,	Top-5 err = 0.220703,	data_time = 0.049935,	train_time = 0.759661
[2019-08-23 03:01:57,408] TRAIN Iter 57520: lr = 0.404135,	loss = 2.897624,	Top-1 err = 0.453662,	Top-5 err = 0.224463,	data_time = 0.049941,	train_time = 0.303643
[2019-08-23 03:02:44,236] TRAIN Iter 57540: lr = 0.404102,	loss = 2.914921,	Top-1 err = 0.452956,	Top-5 err = 0.220178,	data_time = 0.050562,	train_time = 2.341385
[2019-08-23 03:02:52,072] TRAIN Iter 57560: lr = 0.404068,	loss = 2.845510,	Top-1 err = 0.454639,	Top-5 err = 0.218848,	data_time = 0.050531,	train_time = 0.391809
[2019-08-23 03:03:07,039] TRAIN Iter 57580: lr = 0.404035,	loss = 2.751159,	Top-1 err = 0.447803,	Top-5 err = 0.215039,	data_time = 0.051009,	train_time = 0.748349
[2019-08-23 03:03:19,271] TRAIN Iter 57600: lr = 0.404002,	loss = 2.881033,	Top-1 err = 0.447510,	Top-5 err = 0.215088,	data_time = 0.050539,	train_time = 0.611577
[2019-08-23 03:03:26,574] TRAIN Iter 57620: lr = 0.403968,	loss = 2.863420,	Top-1 err = 0.443506,	Top-5 err = 0.210645,	data_time = 0.161667,	train_time = 0.365117
[2019-08-23 03:03:41,221] TRAIN Iter 57640: lr = 0.403935,	loss = 2.771754,	Top-1 err = 0.439502,	Top-5 err = 0.213232,	data_time = 0.051032,	train_time = 0.732323
[2019-08-23 03:03:52,955] TRAIN Iter 57660: lr = 0.403902,	loss = 2.954846,	Top-1 err = 0.447119,	Top-5 err = 0.219238,	data_time = 0.050860,	train_time = 0.586715
[2019-08-23 03:04:00,956] TRAIN Iter 57680: lr = 0.403868,	loss = 2.808844,	Top-1 err = 0.442041,	Top-5 err = 0.210205,	data_time = 0.050441,	train_time = 0.400023
[2019-08-23 03:04:13,944] TRAIN Iter 57700: lr = 0.403835,	loss = 2.741583,	Top-1 err = 0.448145,	Top-5 err = 0.217822,	data_time = 0.138407,	train_time = 0.649403
[2019-08-23 03:04:22,026] TRAIN Iter 57720: lr = 0.403802,	loss = 2.777040,	Top-1 err = 0.436475,	Top-5 err = 0.213281,	data_time = 0.050597,	train_time = 0.404062
[2019-08-23 03:04:36,945] TRAIN Iter 57740: lr = 0.403768,	loss = 2.885023,	Top-1 err = 0.447656,	Top-5 err = 0.216895,	data_time = 0.050846,	train_time = 0.745934
[2019-08-23 03:04:50,959] TRAIN Iter 57760: lr = 0.403735,	loss = 2.937025,	Top-1 err = 0.442041,	Top-5 err = 0.215820,	data_time = 0.051049,	train_time = 0.700710
[2019-08-23 03:04:58,745] TRAIN Iter 57780: lr = 0.403702,	loss = 2.821480,	Top-1 err = 0.447510,	Top-5 err = 0.219189,	data_time = 0.050865,	train_time = 0.389274
[2019-08-23 03:05:12,460] TRAIN Iter 57800: lr = 0.403668,	loss = 2.885934,	Top-1 err = 0.448096,	Top-5 err = 0.218066,	data_time = 0.050683,	train_time = 0.685718
[2019-08-23 03:05:27,514] TRAIN Iter 57820: lr = 0.403635,	loss = 2.884699,	Top-1 err = 0.449756,	Top-5 err = 0.220801,	data_time = 0.112239,	train_time = 0.752708
[2019-08-23 03:05:35,325] TRAIN Iter 57840: lr = 0.403602,	loss = 2.948812,	Top-1 err = 0.443945,	Top-5 err = 0.216699,	data_time = 0.050322,	train_time = 0.390519
[2019-08-23 03:05:49,075] TRAIN Iter 57860: lr = 0.403568,	loss = 2.762355,	Top-1 err = 0.444629,	Top-5 err = 0.213086,	data_time = 0.050359,	train_time = 0.687497
[2019-08-23 03:05:57,081] TRAIN Iter 57880: lr = 0.403535,	loss = 2.764470,	Top-1 err = 0.454688,	Top-5 err = 0.215479,	data_time = 0.050388,	train_time = 0.400270
[2019-08-23 03:06:09,489] TRAIN Iter 57900: lr = 0.403502,	loss = 2.873204,	Top-1 err = 0.453027,	Top-5 err = 0.221289,	data_time = 0.050219,	train_time = 0.620368
[2019-08-23 03:06:24,710] TRAIN Iter 57920: lr = 0.403468,	loss = 2.897106,	Top-1 err = 0.452441,	Top-5 err = 0.218750,	data_time = 0.050826,	train_time = 0.761066
[2019-08-23 03:06:32,447] TRAIN Iter 57940: lr = 0.403435,	loss = 2.799036,	Top-1 err = 0.446387,	Top-5 err = 0.219971,	data_time = 0.050330,	train_time = 0.386792
[2019-08-23 03:06:44,301] TRAIN Iter 57960: lr = 0.403402,	loss = 2.828055,	Top-1 err = 0.445166,	Top-5 err = 0.218018,	data_time = 0.050594,	train_time = 0.592706
[2019-08-23 03:07:00,608] TRAIN Iter 57980: lr = 0.403368,	loss = 2.797530,	Top-1 err = 0.447070,	Top-5 err = 0.213379,	data_time = 0.050752,	train_time = 0.815346
[2019-08-23 03:07:08,531] TRAIN Iter 58000: lr = 0.403335,	loss = 2.900030,	Top-1 err = 0.450830,	Top-5 err = 0.217236,	data_time = 0.050928,	train_time = 0.396142
[2019-08-23 03:07:21,580] TRAIN Iter 58020: lr = 0.403302,	loss = 2.916258,	Top-1 err = 0.444238,	Top-5 err = 0.212451,	data_time = 0.050592,	train_time = 0.652404
[2019-08-23 03:07:30,176] TRAIN Iter 58040: lr = 0.403268,	loss = 2.897996,	Top-1 err = 0.453760,	Top-5 err = 0.221533,	data_time = 0.050485,	train_time = 0.429808
[2019-08-23 03:07:42,491] TRAIN Iter 58060: lr = 0.403235,	loss = 2.873083,	Top-1 err = 0.449951,	Top-5 err = 0.214160,	data_time = 0.050424,	train_time = 0.615744
[2019-08-23 03:07:58,401] TRAIN Iter 58080: lr = 0.403202,	loss = 2.714051,	Top-1 err = 0.446338,	Top-5 err = 0.218164,	data_time = 0.050450,	train_time = 0.795472
[2019-08-23 03:08:06,466] TRAIN Iter 58100: lr = 0.403168,	loss = 2.822914,	Top-1 err = 0.442822,	Top-5 err = 0.217871,	data_time = 0.050854,	train_time = 0.403231
[2019-08-23 03:08:17,555] TRAIN Iter 58120: lr = 0.403135,	loss = 2.897098,	Top-1 err = 0.449805,	Top-5 err = 0.219629,	data_time = 0.050584,	train_time = 0.554411
[2019-08-23 03:08:32,985] TRAIN Iter 58140: lr = 0.403102,	loss = 2.908722,	Top-1 err = 0.445166,	Top-5 err = 0.218848,	data_time = 0.050969,	train_time = 0.771495
[2019-08-23 03:08:40,290] TRAIN Iter 58160: lr = 0.403068,	loss = 2.926584,	Top-1 err = 0.453320,	Top-5 err = 0.220898,	data_time = 0.148150,	train_time = 0.365267
[2019-08-23 03:08:54,689] TRAIN Iter 58180: lr = 0.403035,	loss = 2.888641,	Top-1 err = 0.452148,	Top-5 err = 0.222754,	data_time = 0.050540,	train_time = 0.719916
[2019-08-23 03:09:02,373] TRAIN Iter 58200: lr = 0.403002,	loss = 2.708454,	Top-1 err = 0.446533,	Top-5 err = 0.220117,	data_time = 0.050431,	train_time = 0.384200
[2019-08-23 03:09:16,231] TRAIN Iter 58220: lr = 0.402968,	loss = 2.892008,	Top-1 err = 0.450488,	Top-5 err = 0.222070,	data_time = 0.050783,	train_time = 0.692876
[2019-08-23 03:09:32,490] TRAIN Iter 58240: lr = 0.402935,	loss = 2.856696,	Top-1 err = 0.454639,	Top-5 err = 0.224463,	data_time = 0.050659,	train_time = 0.812911
[2019-08-23 03:09:40,176] TRAIN Iter 58260: lr = 0.402902,	loss = 2.883824,	Top-1 err = 0.449170,	Top-5 err = 0.214941,	data_time = 0.050985,	train_time = 0.384329
[2019-08-23 03:09:54,780] TRAIN Iter 58280: lr = 0.402868,	loss = 2.666809,	Top-1 err = 0.446191,	Top-5 err = 0.215381,	data_time = 0.050548,	train_time = 0.730175
[2019-08-23 03:10:08,700] TRAIN Iter 58300: lr = 0.402835,	loss = 2.865405,	Top-1 err = 0.445898,	Top-5 err = 0.214844,	data_time = 0.050402,	train_time = 0.695999
[2019-08-23 03:10:16,216] TRAIN Iter 58320: lr = 0.402802,	loss = 2.915417,	Top-1 err = 0.452002,	Top-5 err = 0.219971,	data_time = 0.050952,	train_time = 0.375783
[2019-08-23 03:10:31,488] TRAIN Iter 58340: lr = 0.402768,	loss = 2.870368,	Top-1 err = 0.447949,	Top-5 err = 0.215576,	data_time = 0.050410,	train_time = 0.763549
[2019-08-23 03:10:39,189] TRAIN Iter 58360: lr = 0.402735,	loss = 2.786636,	Top-1 err = 0.450000,	Top-5 err = 0.216357,	data_time = 0.050362,	train_time = 0.385050
[2019-08-23 03:10:52,565] TRAIN Iter 58380: lr = 0.402702,	loss = 2.899313,	Top-1 err = 0.452637,	Top-5 err = 0.221191,	data_time = 0.050634,	train_time = 0.668812
[2019-08-23 03:11:08,886] TRAIN Iter 58400: lr = 0.402668,	loss = 2.954781,	Top-1 err = 0.451953,	Top-5 err = 0.220605,	data_time = 0.147966,	train_time = 0.815990
[2019-08-23 03:11:15,801] TRAIN Iter 58420: lr = 0.402635,	loss = 2.917638,	Top-1 err = 0.452441,	Top-5 err = 0.218848,	data_time = 0.050260,	train_time = 0.345743
[2019-08-23 03:11:31,914] TRAIN Iter 58440: lr = 0.402602,	loss = 2.765122,	Top-1 err = 0.455273,	Top-5 err = 0.220215,	data_time = 0.050522,	train_time = 0.805638
[2019-08-23 03:11:46,573] TRAIN Iter 58460: lr = 0.402568,	loss = 2.904851,	Top-1 err = 0.451465,	Top-5 err = 0.224805,	data_time = 0.050401,	train_time = 0.732927
[2019-08-23 03:11:54,524] TRAIN Iter 58480: lr = 0.402535,	loss = 2.794202,	Top-1 err = 0.453760,	Top-5 err = 0.222119,	data_time = 0.050518,	train_time = 0.397569
[2019-08-23 03:12:10,498] TRAIN Iter 58500: lr = 0.402502,	loss = 2.790290,	Top-1 err = 0.440576,	Top-5 err = 0.217041,	data_time = 0.050675,	train_time = 0.798701
[2019-08-23 03:12:17,602] TRAIN Iter 58520: lr = 0.402468,	loss = 2.820075,	Top-1 err = 0.444678,	Top-5 err = 0.214844,	data_time = 0.050803,	train_time = 0.355183
[2019-08-23 03:12:33,520] TRAIN Iter 58540: lr = 0.402435,	loss = 2.905434,	Top-1 err = 0.448730,	Top-5 err = 0.218506,	data_time = 0.050420,	train_time = 0.795849
[2019-08-23 03:12:50,887] TRAIN Iter 58560: lr = 0.402402,	loss = 2.833486,	Top-1 err = 0.456152,	Top-5 err = 0.222314,	data_time = 0.050081,	train_time = 0.868377
[2019-08-23 03:12:57,407] TRAIN Iter 58580: lr = 0.402368,	loss = 2.840051,	Top-1 err = 0.454443,	Top-5 err = 0.224854,	data_time = 0.050524,	train_time = 0.325989
[2019-08-23 03:13:15,344] TRAIN Iter 58600: lr = 0.402335,	loss = 2.760247,	Top-1 err = 0.448633,	Top-5 err = 0.218359,	data_time = 0.050329,	train_time = 0.896818
[2019-08-23 03:13:28,538] TRAIN Iter 58620: lr = 0.402302,	loss = 2.748532,	Top-1 err = 0.451904,	Top-5 err = 0.222119,	data_time = 0.050462,	train_time = 0.659663
[2019-08-23 03:13:39,428] TRAIN Iter 58640: lr = 0.402268,	loss = 2.870570,	Top-1 err = 0.450049,	Top-5 err = 0.219678,	data_time = 0.050602,	train_time = 0.544507
[2019-08-23 03:13:55,660] TRAIN Iter 58660: lr = 0.402235,	loss = 2.849250,	Top-1 err = 0.449707,	Top-5 err = 0.218994,	data_time = 0.050517,	train_time = 0.811609
[2019-08-23 03:14:02,602] TRAIN Iter 58680: lr = 0.402202,	loss = 2.781430,	Top-1 err = 0.450049,	Top-5 err = 0.217725,	data_time = 0.050403,	train_time = 0.347083
[2019-08-23 03:14:19,886] TRAIN Iter 58700: lr = 0.402168,	loss = 2.880623,	Top-1 err = 0.448730,	Top-5 err = 0.215723,	data_time = 0.050572,	train_time = 0.864171
[2019-08-23 03:14:36,977] TRAIN Iter 58720: lr = 0.402135,	loss = 2.933412,	Top-1 err = 0.454492,	Top-5 err = 0.223340,	data_time = 0.111347,	train_time = 0.854547
[2019-08-23 03:14:43,879] TRAIN Iter 58740: lr = 0.402102,	loss = 2.866132,	Top-1 err = 0.450830,	Top-5 err = 0.215967,	data_time = 0.050067,	train_time = 0.345055
[2019-08-23 03:14:59,563] TRAIN Iter 58760: lr = 0.402068,	loss = 2.810465,	Top-1 err = 0.449414,	Top-5 err = 0.221338,	data_time = 0.049964,	train_time = 0.784209
[2019-08-23 03:15:08,741] TRAIN Iter 58780: lr = 0.402035,	loss = 2.959852,	Top-1 err = 0.444708,	Top-5 err = 0.215249,	data_time = 0.007090,	train_time = 0.458872
[2019-08-23 03:15:54,426] TRAIN Iter 58800: lr = 0.402002,	loss = 2.827721,	Top-1 err = 0.449854,	Top-5 err = 0.220508,	data_time = 0.050606,	train_time = 2.284244
[2019-08-23 03:16:08,559] TRAIN Iter 58820: lr = 0.401968,	loss = 2.752644,	Top-1 err = 0.443945,	Top-5 err = 0.215967,	data_time = 0.050703,	train_time = 0.706611
[2019-08-23 03:16:16,624] TRAIN Iter 58840: lr = 0.401935,	loss = 2.818537,	Top-1 err = 0.437939,	Top-5 err = 0.211182,	data_time = 0.157159,	train_time = 0.403279
[2019-08-23 03:16:26,352] TRAIN Iter 58860: lr = 0.401902,	loss = 2.829846,	Top-1 err = 0.440186,	Top-5 err = 0.213232,	data_time = 0.050566,	train_time = 0.486358
[2019-08-23 03:16:40,053] TRAIN Iter 58880: lr = 0.401868,	loss = 2.814861,	Top-1 err = 0.444189,	Top-5 err = 0.211719,	data_time = 0.050723,	train_time = 0.685068
[2019-08-23 03:16:48,033] TRAIN Iter 58900: lr = 0.401835,	loss = 2.757900,	Top-1 err = 0.439941,	Top-5 err = 0.214062,	data_time = 0.050427,	train_time = 0.398970
[2019-08-23 03:17:00,845] TRAIN Iter 58920: lr = 0.401802,	loss = 2.860689,	Top-1 err = 0.445459,	Top-5 err = 0.216406,	data_time = 0.050433,	train_time = 0.640574
[2019-08-23 03:17:08,391] TRAIN Iter 58940: lr = 0.401768,	loss = 2.816926,	Top-1 err = 0.436914,	Top-5 err = 0.212500,	data_time = 0.050367,	train_time = 0.377296
[2019-08-23 03:17:23,551] TRAIN Iter 58960: lr = 0.401735,	loss = 2.778711,	Top-1 err = 0.442773,	Top-5 err = 0.213232,	data_time = 0.050854,	train_time = 0.757988
[2019-08-23 03:17:36,623] TRAIN Iter 58980: lr = 0.401702,	loss = 2.837526,	Top-1 err = 0.449414,	Top-5 err = 0.220361,	data_time = 0.050786,	train_time = 0.653596
[2019-08-23 03:17:43,432] TRAIN Iter 59000: lr = 0.401668,	loss = 2.917753,	Top-1 err = 0.450537,	Top-5 err = 0.221680,	data_time = 0.050552,	train_time = 0.340390
[2019-08-23 03:17:59,468] TRAIN Iter 59020: lr = 0.401635,	loss = 2.812993,	Top-1 err = 0.450830,	Top-5 err = 0.218311,	data_time = 0.050597,	train_time = 0.801784
[2019-08-23 03:18:15,096] TRAIN Iter 59040: lr = 0.401602,	loss = 2.820685,	Top-1 err = 0.445947,	Top-5 err = 0.218311,	data_time = 0.050696,	train_time = 0.781412
[2019-08-23 03:18:22,451] TRAIN Iter 59060: lr = 0.401568,	loss = 2.773045,	Top-1 err = 0.450146,	Top-5 err = 0.218457,	data_time = 0.050671,	train_time = 0.367705
[2019-08-23 03:18:35,687] TRAIN Iter 59080: lr = 0.401535,	loss = 2.777457,	Top-1 err = 0.444482,	Top-5 err = 0.220410,	data_time = 0.050906,	train_time = 0.661799
[2019-08-23 03:18:43,510] TRAIN Iter 59100: lr = 0.401502,	loss = 2.868093,	Top-1 err = 0.441748,	Top-5 err = 0.217090,	data_time = 0.050479,	train_time = 0.391162
[2019-08-23 03:18:57,603] TRAIN Iter 59120: lr = 0.401468,	loss = 2.818909,	Top-1 err = 0.442285,	Top-5 err = 0.217969,	data_time = 0.050622,	train_time = 0.704625
[2019-08-23 03:19:10,599] TRAIN Iter 59140: lr = 0.401435,	loss = 2.857459,	Top-1 err = 0.449707,	Top-5 err = 0.213574,	data_time = 0.050341,	train_time = 0.649795
[2019-08-23 03:19:17,768] TRAIN Iter 59160: lr = 0.401402,	loss = 2.870114,	Top-1 err = 0.450146,	Top-5 err = 0.213672,	data_time = 0.050716,	train_time = 0.358403
[2019-08-23 03:19:31,807] TRAIN Iter 59180: lr = 0.401368,	loss = 2.670355,	Top-1 err = 0.444092,	Top-5 err = 0.213721,	data_time = 0.050438,	train_time = 0.701936
[2019-08-23 03:19:46,746] TRAIN Iter 59200: lr = 0.401335,	loss = 2.769876,	Top-1 err = 0.445361,	Top-5 err = 0.212646,	data_time = 0.097615,	train_time = 0.746960
[2019-08-23 03:19:54,093] TRAIN Iter 59220: lr = 0.401302,	loss = 2.866321,	Top-1 err = 0.444775,	Top-5 err = 0.217334,	data_time = 0.050809,	train_time = 0.367337
[2019-08-23 03:20:08,431] TRAIN Iter 59240: lr = 0.401268,	loss = 2.861307,	Top-1 err = 0.445947,	Top-5 err = 0.217383,	data_time = 0.050354,	train_time = 0.716883
[2019-08-23 03:20:16,728] TRAIN Iter 59260: lr = 0.401235,	loss = 2.813184,	Top-1 err = 0.444531,	Top-5 err = 0.212158,	data_time = 0.050720,	train_time = 0.414819
[2019-08-23 03:20:29,200] TRAIN Iter 59280: lr = 0.401202,	loss = 2.888767,	Top-1 err = 0.451709,	Top-5 err = 0.221094,	data_time = 0.050535,	train_time = 0.623611
[2019-08-23 03:20:44,877] TRAIN Iter 59300: lr = 0.401168,	loss = 2.748640,	Top-1 err = 0.447900,	Top-5 err = 0.213867,	data_time = 0.050611,	train_time = 0.783804
[2019-08-23 03:20:53,086] TRAIN Iter 59320: lr = 0.401135,	loss = 2.915990,	Top-1 err = 0.442139,	Top-5 err = 0.215137,	data_time = 0.050531,	train_time = 0.410461
[2019-08-23 03:21:04,482] TRAIN Iter 59340: lr = 0.401102,	loss = 2.840231,	Top-1 err = 0.444482,	Top-5 err = 0.215186,	data_time = 0.050470,	train_time = 0.569798
[2019-08-23 03:21:19,771] TRAIN Iter 59360: lr = 0.401068,	loss = 2.854048,	Top-1 err = 0.447314,	Top-5 err = 0.215137,	data_time = 0.050483,	train_time = 0.764430
[2019-08-23 03:21:27,141] TRAIN Iter 59380: lr = 0.401035,	loss = 2.881106,	Top-1 err = 0.446191,	Top-5 err = 0.220459,	data_time = 0.050461,	train_time = 0.368479
[2019-08-23 03:21:42,175] TRAIN Iter 59400: lr = 0.401002,	loss = 2.795326,	Top-1 err = 0.448486,	Top-5 err = 0.219971,	data_time = 0.050904,	train_time = 0.751664
[2019-08-23 03:21:50,186] TRAIN Iter 59420: lr = 0.400968,	loss = 2.830503,	Top-1 err = 0.452197,	Top-5 err = 0.218359,	data_time = 0.050567,	train_time = 0.400534
[2019-08-23 03:22:03,587] TRAIN Iter 59440: lr = 0.400935,	loss = 2.863021,	Top-1 err = 0.453271,	Top-5 err = 0.221582,	data_time = 0.050699,	train_time = 0.670059
[2019-08-23 03:22:17,083] TRAIN Iter 59460: lr = 0.400902,	loss = 2.804539,	Top-1 err = 0.443506,	Top-5 err = 0.213721,	data_time = 0.050622,	train_time = 0.674768
[2019-08-23 03:22:24,393] TRAIN Iter 59480: lr = 0.400868,	loss = 2.826309,	Top-1 err = 0.447510,	Top-5 err = 0.211572,	data_time = 0.050502,	train_time = 0.365489
[2019-08-23 03:22:38,918] TRAIN Iter 59500: lr = 0.400835,	loss = 2.898637,	Top-1 err = 0.450244,	Top-5 err = 0.216650,	data_time = 0.050411,	train_time = 0.726251
[2019-08-23 03:22:53,634] TRAIN Iter 59520: lr = 0.400802,	loss = 2.827597,	Top-1 err = 0.451074,	Top-5 err = 0.223291,	data_time = 0.050287,	train_time = 0.735797
[2019-08-23 03:23:01,112] TRAIN Iter 59540: lr = 0.400768,	loss = 2.961861,	Top-1 err = 0.452979,	Top-5 err = 0.218799,	data_time = 0.050597,	train_time = 0.373874
[2019-08-23 03:23:15,778] TRAIN Iter 59560: lr = 0.400735,	loss = 2.888955,	Top-1 err = 0.451758,	Top-5 err = 0.219629,	data_time = 0.050360,	train_time = 0.733252
[2019-08-23 03:23:22,798] TRAIN Iter 59580: lr = 0.400702,	loss = 2.909044,	Top-1 err = 0.455371,	Top-5 err = 0.226025,	data_time = 0.050441,	train_time = 0.350982
[2019-08-23 03:23:39,162] TRAIN Iter 59600: lr = 0.400668,	loss = 2.789157,	Top-1 err = 0.451221,	Top-5 err = 0.221289,	data_time = 0.050476,	train_time = 0.818191
[2019-08-23 03:23:55,436] TRAIN Iter 59620: lr = 0.400635,	loss = 2.925888,	Top-1 err = 0.451416,	Top-5 err = 0.219434,	data_time = 0.050670,	train_time = 0.813720
[2019-08-23 03:24:02,632] TRAIN Iter 59640: lr = 0.400602,	loss = 2.829963,	Top-1 err = 0.445508,	Top-5 err = 0.219727,	data_time = 0.108469,	train_time = 0.359762
[2019-08-23 03:24:17,529] TRAIN Iter 59660: lr = 0.400568,	loss = 2.838500,	Top-1 err = 0.446582,	Top-5 err = 0.220166,	data_time = 0.050507,	train_time = 0.744824
[2019-08-23 03:24:32,709] TRAIN Iter 59680: lr = 0.400535,	loss = 2.800641,	Top-1 err = 0.450488,	Top-5 err = 0.220215,	data_time = 0.050839,	train_time = 0.759015
[2019-08-23 03:24:40,184] TRAIN Iter 59700: lr = 0.400502,	loss = 2.803867,	Top-1 err = 0.447607,	Top-5 err = 0.218018,	data_time = 0.050269,	train_time = 0.373735
[2019-08-23 03:24:55,107] TRAIN Iter 59720: lr = 0.400468,	loss = 2.885467,	Top-1 err = 0.453955,	Top-5 err = 0.221777,	data_time = 0.050314,	train_time = 0.746095
[2019-08-23 03:25:02,075] TRAIN Iter 59740: lr = 0.400435,	loss = 2.861913,	Top-1 err = 0.451270,	Top-5 err = 0.221777,	data_time = 0.050632,	train_time = 0.348426
[2019-08-23 03:25:19,463] TRAIN Iter 59760: lr = 0.400402,	loss = 2.950850,	Top-1 err = 0.450879,	Top-5 err = 0.218213,	data_time = 0.050588,	train_time = 0.869386
[2019-08-23 03:25:36,796] TRAIN Iter 59780: lr = 0.400368,	loss = 2.753531,	Top-1 err = 0.453271,	Top-5 err = 0.221680,	data_time = 0.050794,	train_time = 0.866607
[2019-08-23 03:25:43,798] TRAIN Iter 59800: lr = 0.400335,	loss = 2.845587,	Top-1 err = 0.447168,	Top-5 err = 0.219287,	data_time = 0.050358,	train_time = 0.350113
[2019-08-23 03:25:58,235] TRAIN Iter 59820: lr = 0.400302,	loss = 2.917624,	Top-1 err = 0.450879,	Top-5 err = 0.220703,	data_time = 0.050398,	train_time = 0.721839
[2019-08-23 03:26:14,873] TRAIN Iter 59840: lr = 0.400268,	loss = 2.845304,	Top-1 err = 0.447266,	Top-5 err = 0.216602,	data_time = 0.050502,	train_time = 0.831856
[2019-08-23 03:26:22,142] TRAIN Iter 59860: lr = 0.400235,	loss = 2.908810,	Top-1 err = 0.450391,	Top-5 err = 0.220947,	data_time = 0.050699,	train_time = 0.363438
[2019-08-23 03:26:39,016] TRAIN Iter 59880: lr = 0.400202,	loss = 2.903409,	Top-1 err = 0.446631,	Top-5 err = 0.216357,	data_time = 0.050460,	train_time = 0.843671
[2019-08-23 03:26:46,133] TRAIN Iter 59900: lr = 0.400168,	loss = 2.817644,	Top-1 err = 0.448633,	Top-5 err = 0.219092,	data_time = 0.050389,	train_time = 0.355852
[2019-08-23 03:27:04,449] TRAIN Iter 59920: lr = 0.400135,	loss = 2.888816,	Top-1 err = 0.449854,	Top-5 err = 0.220752,	data_time = 0.050319,	train_time = 0.915783
[2019-08-23 03:27:21,724] TRAIN Iter 59940: lr = 0.400102,	loss = 2.870776,	Top-1 err = 0.453906,	Top-5 err = 0.216797,	data_time = 0.050635,	train_time = 0.863746
[2019-08-23 03:27:28,737] TRAIN Iter 59960: lr = 0.400068,	loss = 2.733744,	Top-1 err = 0.444189,	Top-5 err = 0.213428,	data_time = 0.050694,	train_time = 0.350623
[2019-08-23 03:27:45,867] TRAIN Iter 59980: lr = 0.400035,	loss = 2.818360,	Top-1 err = 0.442383,	Top-5 err = 0.212891,	data_time = 0.050100,	train_time = 0.856490
[2019-08-23 03:28:01,629] TRAIN Iter 60000: lr = 0.400002,	loss = 2.837506,	Top-1 err = 0.448340,	Top-5 err = 0.220557,	data_time = 0.103320,	train_time = 0.788093
[2019-08-23 03:28:56,239] TEST Iter 60000: loss = 2.619609,	Top-1 err = 0.409020,	Top-5 err = 0.174300,	val_time = 54.571836
[2019-08-23 03:29:02,211] TRAIN Iter 60020: lr = 0.399968,	loss = 3.024686,	Top-1 err = 0.446094,	Top-5 err = 0.223438,	data_time = 0.049955,	train_time = 0.298583
[2019-08-23 03:29:48,273] TRAIN Iter 60040: lr = 0.399935,	loss = 2.928330,	Top-1 err = 0.457036,	Top-5 err = 0.225136,	data_time = 0.050916,	train_time = 2.303089
[2019-08-23 03:29:55,965] TRAIN Iter 60060: lr = 0.399902,	loss = 2.668608,	Top-1 err = 0.450781,	Top-5 err = 0.218555,	data_time = 0.114936,	train_time = 0.384567
[2019-08-23 03:30:10,080] TRAIN Iter 60080: lr = 0.399868,	loss = 2.780647,	Top-1 err = 0.445801,	Top-5 err = 0.216943,	data_time = 0.050442,	train_time = 0.705739
[2019-08-23 03:30:18,935] TRAIN Iter 60100: lr = 0.399835,	loss = 2.932257,	Top-1 err = 0.444287,	Top-5 err = 0.211768,	data_time = 0.050773,	train_time = 0.442757
[2019-08-23 03:30:26,684] TRAIN Iter 60120: lr = 0.399802,	loss = 2.822905,	Top-1 err = 0.448096,	Top-5 err = 0.213574,	data_time = 0.050393,	train_time = 0.387423
[2019-08-23 03:30:39,520] TRAIN Iter 60140: lr = 0.399768,	loss = 2.826437,	Top-1 err = 0.432080,	Top-5 err = 0.210840,	data_time = 0.146961,	train_time = 0.641789
[2019-08-23 03:30:47,423] TRAIN Iter 60160: lr = 0.399735,	loss = 2.717525,	Top-1 err = 0.441699,	Top-5 err = 0.207910,	data_time = 0.050331,	train_time = 0.395127
[2019-08-23 03:31:01,955] TRAIN Iter 60180: lr = 0.399702,	loss = 2.877924,	Top-1 err = 0.444141,	Top-5 err = 0.215479,	data_time = 0.050401,	train_time = 0.726589
[2019-08-23 03:31:16,260] TRAIN Iter 60200: lr = 0.399668,	loss = 2.806720,	Top-1 err = 0.445068,	Top-5 err = 0.221680,	data_time = 0.050611,	train_time = 0.715257
[2019-08-23 03:31:24,020] TRAIN Iter 60220: lr = 0.399635,	loss = 2.898000,	Top-1 err = 0.441504,	Top-5 err = 0.209961,	data_time = 0.126916,	train_time = 0.387993
[2019-08-23 03:31:39,150] TRAIN Iter 60240: lr = 0.399602,	loss = 2.805266,	Top-1 err = 0.447754,	Top-5 err = 0.218457,	data_time = 0.050735,	train_time = 0.756469
[2019-08-23 03:31:53,207] TRAIN Iter 60260: lr = 0.399568,	loss = 2.833251,	Top-1 err = 0.443115,	Top-5 err = 0.214600,	data_time = 0.107249,	train_time = 0.702820
[2019-08-23 03:32:00,790] TRAIN Iter 60280: lr = 0.399535,	loss = 2.715850,	Top-1 err = 0.441748,	Top-5 err = 0.213232,	data_time = 0.050454,	train_time = 0.379153
[2019-08-23 03:32:14,312] TRAIN Iter 60300: lr = 0.399502,	loss = 2.779875,	Top-1 err = 0.443555,	Top-5 err = 0.212012,	data_time = 0.050650,	train_time = 0.676098
[2019-08-23 03:32:21,941] TRAIN Iter 60320: lr = 0.399468,	loss = 2.895432,	Top-1 err = 0.444043,	Top-5 err = 0.213867,	data_time = 0.050503,	train_time = 0.381423
[2019-08-23 03:32:36,821] TRAIN Iter 60340: lr = 0.399435,	loss = 2.925987,	Top-1 err = 0.443213,	Top-5 err = 0.216699,	data_time = 0.050578,	train_time = 0.743994
[2019-08-23 03:32:51,005] TRAIN Iter 60360: lr = 0.399402,	loss = 2.817647,	Top-1 err = 0.451807,	Top-5 err = 0.221045,	data_time = 0.050466,	train_time = 0.709143
[2019-08-23 03:32:58,428] TRAIN Iter 60380: lr = 0.399368,	loss = 2.886909,	Top-1 err = 0.453662,	Top-5 err = 0.224170,	data_time = 0.050719,	train_time = 0.371176
[2019-08-23 03:33:14,631] TRAIN Iter 60400: lr = 0.399335,	loss = 2.855171,	Top-1 err = 0.442188,	Top-5 err = 0.219336,	data_time = 0.050902,	train_time = 0.810127
[2019-08-23 03:33:27,453] TRAIN Iter 60420: lr = 0.399302,	loss = 2.722515,	Top-1 err = 0.444629,	Top-5 err = 0.216113,	data_time = 0.050514,	train_time = 0.641074
[2019-08-23 03:33:34,446] TRAIN Iter 60440: lr = 0.399268,	loss = 2.847234,	Top-1 err = 0.446094,	Top-5 err = 0.214893,	data_time = 0.137085,	train_time = 0.349635
[2019-08-23 03:33:49,228] TRAIN Iter 60460: lr = 0.399235,	loss = 2.829218,	Top-1 err = 0.444824,	Top-5 err = 0.214600,	data_time = 0.050493,	train_time = 0.739097
[2019-08-23 03:33:56,973] TRAIN Iter 60480: lr = 0.399202,	loss = 2.858546,	Top-1 err = 0.445361,	Top-5 err = 0.218457,	data_time = 0.050472,	train_time = 0.387238
[2019-08-23 03:34:11,479] TRAIN Iter 60500: lr = 0.399168,	loss = 2.856439,	Top-1 err = 0.444727,	Top-5 err = 0.212842,	data_time = 0.050598,	train_time = 0.725257
[2019-08-23 03:34:24,154] TRAIN Iter 60520: lr = 0.399135,	loss = 2.869351,	Top-1 err = 0.446631,	Top-5 err = 0.216553,	data_time = 0.050495,	train_time = 0.633761
[2019-08-23 03:34:31,314] TRAIN Iter 60540: lr = 0.399102,	loss = 2.707856,	Top-1 err = 0.443408,	Top-5 err = 0.212158,	data_time = 0.050791,	train_time = 0.357961
[2019-08-23 03:34:46,506] TRAIN Iter 60560: lr = 0.399068,	loss = 2.818686,	Top-1 err = 0.445361,	Top-5 err = 0.218506,	data_time = 0.050658,	train_time = 0.759621
[2019-08-23 03:35:01,076] TRAIN Iter 60580: lr = 0.399035,	loss = 2.770109,	Top-1 err = 0.452100,	Top-5 err = 0.217676,	data_time = 0.050300,	train_time = 0.728482
[2019-08-23 03:35:08,412] TRAIN Iter 60600: lr = 0.399002,	loss = 2.786674,	Top-1 err = 0.449609,	Top-5 err = 0.217920,	data_time = 0.050338,	train_time = 0.366781
[2019-08-23 03:35:24,362] TRAIN Iter 60620: lr = 0.398968,	loss = 2.808829,	Top-1 err = 0.446436,	Top-5 err = 0.212842,	data_time = 0.050200,	train_time = 0.797460
[2019-08-23 03:35:32,819] TRAIN Iter 60640: lr = 0.398935,	loss = 2.766380,	Top-1 err = 0.442041,	Top-5 err = 0.216064,	data_time = 0.050633,	train_time = 0.422838
[2019-08-23 03:35:45,236] TRAIN Iter 60660: lr = 0.398902,	loss = 2.813853,	Top-1 err = 0.442188,	Top-5 err = 0.216406,	data_time = 0.050444,	train_time = 0.620838
[2019-08-23 03:36:01,075] TRAIN Iter 60680: lr = 0.398868,	loss = 2.867298,	Top-1 err = 0.450586,	Top-5 err = 0.221484,	data_time = 0.050404,	train_time = 0.791961
[2019-08-23 03:36:08,269] TRAIN Iter 60700: lr = 0.398835,	loss = 2.832739,	Top-1 err = 0.449854,	Top-5 err = 0.220850,	data_time = 0.050625,	train_time = 0.359639
[2019-08-23 03:36:22,791] TRAIN Iter 60720: lr = 0.398802,	loss = 2.794499,	Top-1 err = 0.440869,	Top-5 err = 0.210547,	data_time = 0.050570,	train_time = 0.726116
[2019-08-23 03:36:39,673] TRAIN Iter 60740: lr = 0.398768,	loss = 2.863947,	Top-1 err = 0.442383,	Top-5 err = 0.219238,	data_time = 0.050748,	train_time = 0.844099
[2019-08-23 03:36:47,109] TRAIN Iter 60760: lr = 0.398735,	loss = 2.761631,	Top-1 err = 0.448340,	Top-5 err = 0.221924,	data_time = 0.050556,	train_time = 0.371759
[2019-08-23 03:37:02,510] TRAIN Iter 60780: lr = 0.398702,	loss = 2.875563,	Top-1 err = 0.439502,	Top-5 err = 0.212891,	data_time = 0.050635,	train_time = 0.770063
[2019-08-23 03:37:11,186] TRAIN Iter 60800: lr = 0.398668,	loss = 2.831904,	Top-1 err = 0.440234,	Top-5 err = 0.214648,	data_time = 0.050480,	train_time = 0.433749
[2019-08-23 03:37:22,129] TRAIN Iter 60820: lr = 0.398635,	loss = 2.731706,	Top-1 err = 0.447803,	Top-5 err = 0.217041,	data_time = 0.050595,	train_time = 0.547150
[2019-08-23 03:37:36,649] TRAIN Iter 60840: lr = 0.398602,	loss = 2.842071,	Top-1 err = 0.448242,	Top-5 err = 0.217285,	data_time = 0.050293,	train_time = 0.725976
[2019-08-23 03:37:44,003] TRAIN Iter 60860: lr = 0.398568,	loss = 2.755277,	Top-1 err = 0.444922,	Top-5 err = 0.220850,	data_time = 0.128057,	train_time = 0.367683
[2019-08-23 03:37:57,700] TRAIN Iter 60880: lr = 0.398535,	loss = 2.808201,	Top-1 err = 0.445215,	Top-5 err = 0.215967,	data_time = 0.050738,	train_time = 0.684854
[2019-08-23 03:38:13,557] TRAIN Iter 60900: lr = 0.398502,	loss = 2.743415,	Top-1 err = 0.445703,	Top-5 err = 0.217627,	data_time = 0.051043,	train_time = 0.792848
[2019-08-23 03:38:20,484] TRAIN Iter 60920: lr = 0.398468,	loss = 2.833693,	Top-1 err = 0.447900,	Top-5 err = 0.214551,	data_time = 0.125038,	train_time = 0.346309
[2019-08-23 03:38:36,999] TRAIN Iter 60940: lr = 0.398435,	loss = 2.871911,	Top-1 err = 0.448682,	Top-5 err = 0.217969,	data_time = 0.050363,	train_time = 0.825735
[2019-08-23 03:38:45,160] TRAIN Iter 60960: lr = 0.398402,	loss = 2.844736,	Top-1 err = 0.450488,	Top-5 err = 0.218652,	data_time = 0.050665,	train_time = 0.408041
[2019-08-23 03:38:59,393] TRAIN Iter 60980: lr = 0.398368,	loss = 3.003728,	Top-1 err = 0.450049,	Top-5 err = 0.222314,	data_time = 0.050429,	train_time = 0.711608
[2019-08-23 03:39:13,621] TRAIN Iter 61000: lr = 0.398335,	loss = 2.830375,	Top-1 err = 0.445410,	Top-5 err = 0.215381,	data_time = 0.050302,	train_time = 0.711428
[2019-08-23 03:39:20,881] TRAIN Iter 61020: lr = 0.398302,	loss = 2.831579,	Top-1 err = 0.451465,	Top-5 err = 0.220166,	data_time = 0.140986,	train_time = 0.362947
[2019-08-23 03:39:35,845] TRAIN Iter 61040: lr = 0.398268,	loss = 2.827254,	Top-1 err = 0.448389,	Top-5 err = 0.219434,	data_time = 0.050657,	train_time = 0.748191
[2019-08-23 03:39:52,419] TRAIN Iter 61060: lr = 0.398235,	loss = 2.838490,	Top-1 err = 0.449951,	Top-5 err = 0.222949,	data_time = 0.051004,	train_time = 0.828708
[2019-08-23 03:39:59,196] TRAIN Iter 61080: lr = 0.398202,	loss = 2.862750,	Top-1 err = 0.455957,	Top-5 err = 0.222412,	data_time = 0.145270,	train_time = 0.338809
[2019-08-23 03:40:15,513] TRAIN Iter 61100: lr = 0.398168,	loss = 2.824594,	Top-1 err = 0.452588,	Top-5 err = 0.218213,	data_time = 0.050420,	train_time = 0.815879
[2019-08-23 03:40:23,282] TRAIN Iter 61120: lr = 0.398135,	loss = 2.700147,	Top-1 err = 0.443262,	Top-5 err = 0.213428,	data_time = 0.139099,	train_time = 0.388427
[2019-08-23 03:40:37,465] TRAIN Iter 61140: lr = 0.398102,	loss = 2.843621,	Top-1 err = 0.449854,	Top-5 err = 0.219043,	data_time = 0.050541,	train_time = 0.709142
[2019-08-23 03:40:55,033] TRAIN Iter 61160: lr = 0.398068,	loss = 2.758767,	Top-1 err = 0.444238,	Top-5 err = 0.214307,	data_time = 0.050551,	train_time = 0.878349
[2019-08-23 03:41:02,489] TRAIN Iter 61180: lr = 0.398035,	loss = 2.995202,	Top-1 err = 0.449951,	Top-5 err = 0.219238,	data_time = 0.050542,	train_time = 0.372796
[2019-08-23 03:41:18,348] TRAIN Iter 61200: lr = 0.398002,	loss = 2.683052,	Top-1 err = 0.456006,	Top-5 err = 0.222949,	data_time = 0.050504,	train_time = 0.792925
[2019-08-23 03:41:35,623] TRAIN Iter 61220: lr = 0.397968,	loss = 2.825875,	Top-1 err = 0.447949,	Top-5 err = 0.217090,	data_time = 0.065329,	train_time = 0.863776
[2019-08-23 03:41:42,608] TRAIN Iter 61240: lr = 0.397935,	loss = 2.808584,	Top-1 err = 0.445605,	Top-5 err = 0.215527,	data_time = 0.150951,	train_time = 0.349234
[2019-08-23 03:41:58,122] TRAIN Iter 61260: lr = 0.397902,	loss = 2.817350,	Top-1 err = 0.453516,	Top-5 err = 0.215967,	data_time = 0.049930,	train_time = 0.775672
[2019-08-23 03:42:04,268] TRAIN Iter 61280: lr = 0.397868,	loss = 2.794569,	Top-1 err = 0.449268,	Top-5 err = 0.218164,	data_time = 0.049883,	train_time = 0.307275
[2019-08-23 03:42:53,960] TRAIN Iter 61300: lr = 0.397835,	loss = 2.839406,	Top-1 err = 0.455439,	Top-5 err = 0.224890,	data_time = 0.050843,	train_time = 2.484583
[2019-08-23 03:43:06,136] TRAIN Iter 61320: lr = 0.397802,	loss = 2.827712,	Top-1 err = 0.443262,	Top-5 err = 0.217578,	data_time = 0.121276,	train_time = 0.608813
[2019-08-23 03:43:17,350] TRAIN Iter 61340: lr = 0.397768,	loss = 2.761806,	Top-1 err = 0.441895,	Top-5 err = 0.215527,	data_time = 0.050363,	train_time = 0.560655
[2019-08-23 03:43:27,754] TRAIN Iter 61360: lr = 0.397735,	loss = 2.799368,	Top-1 err = 0.444629,	Top-5 err = 0.219482,	data_time = 0.925776,	train_time = 0.520172
[2019-08-23 03:43:35,254] TRAIN Iter 61380: lr = 0.397702,	loss = 2.822545,	Top-1 err = 0.441113,	Top-5 err = 0.210010,	data_time = 0.050757,	train_time = 0.375006
[2019-08-23 03:43:47,547] TRAIN Iter 61400: lr = 0.397668,	loss = 2.775094,	Top-1 err = 0.445898,	Top-5 err = 0.212451,	data_time = 0.050829,	train_time = 0.614615
[2019-08-23 03:43:59,122] TRAIN Iter 61420: lr = 0.397635,	loss = 2.698457,	Top-1 err = 0.434912,	Top-5 err = 0.208447,	data_time = 0.050707,	train_time = 0.578764
[2019-08-23 03:44:07,751] TRAIN Iter 61440: lr = 0.397602,	loss = 2.888175,	Top-1 err = 0.444092,	Top-5 err = 0.216406,	data_time = 0.050775,	train_time = 0.431410
[2019-08-23 03:44:23,574] TRAIN Iter 61460: lr = 0.397568,	loss = 2.867410,	Top-1 err = 0.444873,	Top-5 err = 0.212451,	data_time = 0.050633,	train_time = 0.791164
[2019-08-23 03:44:31,797] TRAIN Iter 61480: lr = 0.397535,	loss = 2.788237,	Top-1 err = 0.442139,	Top-5 err = 0.213330,	data_time = 0.050893,	train_time = 0.411110
[2019-08-23 03:44:43,276] TRAIN Iter 61500: lr = 0.397502,	loss = 2.740578,	Top-1 err = 0.446582,	Top-5 err = 0.214893,	data_time = 0.050407,	train_time = 0.573947
[2019-08-23 03:44:57,873] TRAIN Iter 61520: lr = 0.397468,	loss = 2.781708,	Top-1 err = 0.437207,	Top-5 err = 0.213428,	data_time = 0.050477,	train_time = 0.729851
[2019-08-23 03:45:05,720] TRAIN Iter 61540: lr = 0.397435,	loss = 2.828526,	Top-1 err = 0.446387,	Top-5 err = 0.214453,	data_time = 0.163239,	train_time = 0.392297
[2019-08-23 03:45:19,208] TRAIN Iter 61560: lr = 0.397402,	loss = 2.806045,	Top-1 err = 0.443262,	Top-5 err = 0.214746,	data_time = 0.050648,	train_time = 0.674385
[2019-08-23 03:45:32,759] TRAIN Iter 61580: lr = 0.397368,	loss = 2.800565,	Top-1 err = 0.446094,	Top-5 err = 0.218604,	data_time = 0.050587,	train_time = 0.677563
[2019-08-23 03:45:40,460] TRAIN Iter 61600: lr = 0.397335,	loss = 2.867657,	Top-1 err = 0.443994,	Top-5 err = 0.214014,	data_time = 0.050420,	train_time = 0.384991
[2019-08-23 03:45:53,836] TRAIN Iter 61620: lr = 0.397302,	loss = 2.781250,	Top-1 err = 0.448633,	Top-5 err = 0.215527,	data_time = 0.050500,	train_time = 0.668823
[2019-08-23 03:46:03,964] TRAIN Iter 61640: lr = 0.397268,	loss = 2.795365,	Top-1 err = 0.446729,	Top-5 err = 0.215332,	data_time = 0.148793,	train_time = 0.506377
[2019-08-23 03:46:17,196] TRAIN Iter 61660: lr = 0.397235,	loss = 2.887521,	Top-1 err = 0.444482,	Top-5 err = 0.214941,	data_time = 0.050376,	train_time = 0.661581
[2019-08-23 03:46:28,483] TRAIN Iter 61680: lr = 0.397202,	loss = 2.845110,	Top-1 err = 0.442432,	Top-5 err = 0.214551,	data_time = 0.564969,	train_time = 0.564322
[2019-08-23 03:46:36,195] TRAIN Iter 61700: lr = 0.397168,	loss = 2.886494,	Top-1 err = 0.447998,	Top-5 err = 0.217188,	data_time = 0.050439,	train_time = 0.385604
[2019-08-23 03:46:49,946] TRAIN Iter 61720: lr = 0.397135,	loss = 2.762077,	Top-1 err = 0.438037,	Top-5 err = 0.211572,	data_time = 0.050976,	train_time = 0.687513
[2019-08-23 03:47:04,039] TRAIN Iter 61740: lr = 0.397102,	loss = 2.886761,	Top-1 err = 0.446631,	Top-5 err = 0.217871,	data_time = 0.050428,	train_time = 0.704648
[2019-08-23 03:47:13,308] TRAIN Iter 61760: lr = 0.397068,	loss = 2.848006,	Top-1 err = 0.444678,	Top-5 err = 0.215869,	data_time = 0.050685,	train_time = 0.463411
[2019-08-23 03:47:26,436] TRAIN Iter 61780: lr = 0.397035,	loss = 2.771742,	Top-1 err = 0.446387,	Top-5 err = 0.216699,	data_time = 0.050444,	train_time = 0.656423
[2019-08-23 03:47:34,197] TRAIN Iter 61800: lr = 0.397002,	loss = 2.747469,	Top-1 err = 0.452148,	Top-5 err = 0.219922,	data_time = 0.050632,	train_time = 0.388035
[2019-08-23 03:47:47,330] TRAIN Iter 61820: lr = 0.396968,	loss = 2.662081,	Top-1 err = 0.441699,	Top-5 err = 0.211670,	data_time = 0.050694,	train_time = 0.656641
[2019-08-23 03:48:01,605] TRAIN Iter 61840: lr = 0.396935,	loss = 2.800391,	Top-1 err = 0.445996,	Top-5 err = 0.214648,	data_time = 0.050343,	train_time = 0.713710
[2019-08-23 03:48:08,776] TRAIN Iter 61860: lr = 0.396902,	loss = 2.891480,	Top-1 err = 0.448145,	Top-5 err = 0.217578,	data_time = 0.126538,	train_time = 0.358554
[2019-08-23 03:48:23,337] TRAIN Iter 61880: lr = 0.396868,	loss = 2.824734,	Top-1 err = 0.440771,	Top-5 err = 0.213721,	data_time = 0.050363,	train_time = 0.728012
[2019-08-23 03:48:31,485] TRAIN Iter 61900: lr = 0.396835,	loss = 2.820312,	Top-1 err = 0.445605,	Top-5 err = 0.220410,	data_time = 0.134794,	train_time = 0.407408
[2019-08-23 03:48:45,037] TRAIN Iter 61920: lr = 0.396802,	loss = 2.827213,	Top-1 err = 0.449756,	Top-5 err = 0.218262,	data_time = 0.050851,	train_time = 0.677583
[2019-08-23 03:48:59,014] TRAIN Iter 61940: lr = 0.396768,	loss = 2.776940,	Top-1 err = 0.449707,	Top-5 err = 0.217480,	data_time = 0.050625,	train_time = 0.698819
[2019-08-23 03:49:09,417] TRAIN Iter 61960: lr = 0.396735,	loss = 2.831557,	Top-1 err = 0.446143,	Top-5 err = 0.218311,	data_time = 2.831740,	train_time = 0.520135
[2019-08-23 03:49:19,009] TRAIN Iter 61980: lr = 0.396702,	loss = 2.866863,	Top-1 err = 0.449316,	Top-5 err = 0.220410,	data_time = 0.050615,	train_time = 0.479587
[2019-08-23 03:49:34,393] TRAIN Iter 62000: lr = 0.396668,	loss = 2.894079,	Top-1 err = 0.451904,	Top-5 err = 0.220117,	data_time = 0.050570,	train_time = 0.769195
[2019-08-23 03:49:41,616] TRAIN Iter 62020: lr = 0.396635,	loss = 2.862670,	Top-1 err = 0.442920,	Top-5 err = 0.212646,	data_time = 0.050454,	train_time = 0.361108
[2019-08-23 03:49:55,850] TRAIN Iter 62040: lr = 0.396602,	loss = 2.884899,	Top-1 err = 0.455811,	Top-5 err = 0.223096,	data_time = 0.050793,	train_time = 0.711681
[2019-08-23 03:50:08,960] TRAIN Iter 62060: lr = 0.396568,	loss = 2.779848,	Top-1 err = 0.446924,	Top-5 err = 0.215234,	data_time = 0.050520,	train_time = 0.655524
[2019-08-23 03:50:16,899] TRAIN Iter 62080: lr = 0.396535,	loss = 2.791209,	Top-1 err = 0.451660,	Top-5 err = 0.221240,	data_time = 0.050727,	train_time = 0.396899
[2019-08-23 03:50:32,532] TRAIN Iter 62100: lr = 0.396502,	loss = 2.790636,	Top-1 err = 0.452051,	Top-5 err = 0.219385,	data_time = 0.050529,	train_time = 0.781673
[2019-08-23 03:50:42,008] TRAIN Iter 62120: lr = 0.396468,	loss = 2.821724,	Top-1 err = 0.451318,	Top-5 err = 0.221338,	data_time = 0.169837,	train_time = 0.473772
[2019-08-23 03:50:53,795] TRAIN Iter 62140: lr = 0.396435,	loss = 2.865660,	Top-1 err = 0.451465,	Top-5 err = 0.221289,	data_time = 0.050567,	train_time = 0.589339
[2019-08-23 03:51:08,286] TRAIN Iter 62160: lr = 0.396402,	loss = 2.860983,	Top-1 err = 0.449023,	Top-5 err = 0.218359,	data_time = 0.117805,	train_time = 0.724532
[2019-08-23 03:51:15,763] TRAIN Iter 62180: lr = 0.396368,	loss = 2.858350,	Top-1 err = 0.443115,	Top-5 err = 0.214941,	data_time = 0.050165,	train_time = 0.373819
[2019-08-23 03:51:30,400] TRAIN Iter 62200: lr = 0.396335,	loss = 2.849390,	Top-1 err = 0.450195,	Top-5 err = 0.217383,	data_time = 0.050379,	train_time = 0.731852
[2019-08-23 03:51:43,393] TRAIN Iter 62220: lr = 0.396302,	loss = 2.832297,	Top-1 err = 0.452686,	Top-5 err = 0.221240,	data_time = 0.438775,	train_time = 0.649646
[2019-08-23 03:51:53,961] TRAIN Iter 62240: lr = 0.396268,	loss = 2.785153,	Top-1 err = 0.446826,	Top-5 err = 0.214062,	data_time = 0.050479,	train_time = 0.528394
[2019-08-23 03:52:07,943] TRAIN Iter 62260: lr = 0.396235,	loss = 2.923267,	Top-1 err = 0.449121,	Top-5 err = 0.218604,	data_time = 0.050570,	train_time = 0.699073
[2019-08-23 03:52:16,509] TRAIN Iter 62280: lr = 0.396202,	loss = 2.836357,	Top-1 err = 0.449561,	Top-5 err = 0.219727,	data_time = 0.050338,	train_time = 0.428263
[2019-08-23 03:52:29,753] TRAIN Iter 62300: lr = 0.396168,	loss = 2.838631,	Top-1 err = 0.447803,	Top-5 err = 0.215674,	data_time = 0.050512,	train_time = 0.662204
[2019-08-23 03:52:45,478] TRAIN Iter 62320: lr = 0.396135,	loss = 2.807044,	Top-1 err = 0.443750,	Top-5 err = 0.217920,	data_time = 0.050381,	train_time = 0.786215
[2019-08-23 03:52:53,067] TRAIN Iter 62340: lr = 0.396102,	loss = 2.821706,	Top-1 err = 0.445801,	Top-5 err = 0.215479,	data_time = 0.050606,	train_time = 0.379429
[2019-08-23 03:53:05,781] TRAIN Iter 62360: lr = 0.396068,	loss = 2.842265,	Top-1 err = 0.444189,	Top-5 err = 0.214160,	data_time = 0.050524,	train_time = 0.635686
[2019-08-23 03:53:20,285] TRAIN Iter 62380: lr = 0.396035,	loss = 2.872319,	Top-1 err = 0.450244,	Top-5 err = 0.222412,	data_time = 1.156718,	train_time = 0.725220
[2019-08-23 03:53:28,407] TRAIN Iter 62400: lr = 0.396002,	loss = 2.723912,	Top-1 err = 0.447070,	Top-5 err = 0.219141,	data_time = 0.050665,	train_time = 0.406059
[2019-08-23 03:53:43,246] TRAIN Iter 62420: lr = 0.395968,	loss = 2.827711,	Top-1 err = 0.440967,	Top-5 err = 0.213867,	data_time = 0.050346,	train_time = 0.741935
[2019-08-23 03:53:54,962] TRAIN Iter 62440: lr = 0.395935,	loss = 2.736005,	Top-1 err = 0.449561,	Top-5 err = 0.220703,	data_time = 1.597404,	train_time = 0.585816
[2019-08-23 03:54:05,872] TRAIN Iter 62460: lr = 0.395902,	loss = 2.841776,	Top-1 err = 0.446777,	Top-5 err = 0.221045,	data_time = 0.050401,	train_time = 0.545445
[2019-08-23 03:54:22,601] TRAIN Iter 62480: lr = 0.395868,	loss = 2.820191,	Top-1 err = 0.454834,	Top-5 err = 0.222070,	data_time = 0.050189,	train_time = 0.836478
[2019-08-23 03:54:29,571] TRAIN Iter 62500: lr = 0.395835,	loss = 2.888146,	Top-1 err = 0.455273,	Top-5 err = 0.220410,	data_time = 0.050078,	train_time = 0.348461
[2019-08-23 03:54:44,678] TRAIN Iter 62520: lr = 0.395802,	loss = 2.845063,	Top-1 err = 0.447510,	Top-5 err = 0.215234,	data_time = 0.049934,	train_time = 0.755326
[2019-08-23 03:55:28,632] TRAIN Iter 62540: lr = 0.395768,	loss = 2.767708,	Top-1 err = 0.453119,	Top-5 err = 0.220445,	data_time = 0.133413,	train_time = 2.197697
[2019-08-23 03:55:38,292] TRAIN Iter 62560: lr = 0.395735,	loss = 2.821826,	Top-1 err = 0.454199,	Top-5 err = 0.220166,	data_time = 0.050482,	train_time = 0.482975
[2019-08-23 03:55:51,689] TRAIN Iter 62580: lr = 0.395702,	loss = 2.866814,	Top-1 err = 0.448730,	Top-5 err = 0.215527,	data_time = 0.050412,	train_time = 0.669830
[2019-08-23 03:56:00,120] TRAIN Iter 62600: lr = 0.395668,	loss = 2.840685,	Top-1 err = 0.442529,	Top-5 err = 0.212842,	data_time = 0.050889,	train_time = 0.421547
[2019-08-23 03:56:11,860] TRAIN Iter 62620: lr = 0.395635,	loss = 2.695421,	Top-1 err = 0.439648,	Top-5 err = 0.209912,	data_time = 0.050863,	train_time = 0.586946
[2019-08-23 03:56:22,868] TRAIN Iter 62640: lr = 0.395602,	loss = 2.734411,	Top-1 err = 0.435596,	Top-5 err = 0.204492,	data_time = 0.050882,	train_time = 0.550432
[2019-08-23 03:56:31,185] TRAIN Iter 62660: lr = 0.395568,	loss = 2.852571,	Top-1 err = 0.438672,	Top-5 err = 0.208154,	data_time = 0.050163,	train_time = 0.415812
[2019-08-23 03:56:43,708] TRAIN Iter 62680: lr = 0.395535,	loss = 2.828838,	Top-1 err = 0.439258,	Top-5 err = 0.208398,	data_time = 0.050586,	train_time = 0.626121
[2019-08-23 03:56:59,936] TRAIN Iter 62700: lr = 0.395502,	loss = 2.803608,	Top-1 err = 0.438477,	Top-5 err = 0.208789,	data_time = 0.050731,	train_time = 0.811422
[2019-08-23 03:57:07,375] TRAIN Iter 62720: lr = 0.395468,	loss = 2.856118,	Top-1 err = 0.446289,	Top-5 err = 0.211035,	data_time = 0.050731,	train_time = 0.371922
[2019-08-23 03:57:21,435] TRAIN Iter 62740: lr = 0.395435,	loss = 2.728295,	Top-1 err = 0.440527,	Top-5 err = 0.212012,	data_time = 0.050940,	train_time = 0.702990
[2019-08-23 03:57:30,259] TRAIN Iter 62760: lr = 0.395402,	loss = 2.863663,	Top-1 err = 0.443115,	Top-5 err = 0.214746,	data_time = 0.050359,	train_time = 0.441169
[2019-08-23 03:57:43,437] TRAIN Iter 62780: lr = 0.395368,	loss = 2.808523,	Top-1 err = 0.435791,	Top-5 err = 0.208887,	data_time = 0.050623,	train_time = 0.658863
[2019-08-23 03:57:56,214] TRAIN Iter 62800: lr = 0.395335,	loss = 2.781160,	Top-1 err = 0.443311,	Top-5 err = 0.215088,	data_time = 0.050352,	train_time = 0.638871
[2019-08-23 03:58:04,116] TRAIN Iter 62820: lr = 0.395302,	loss = 2.808732,	Top-1 err = 0.439990,	Top-5 err = 0.210400,	data_time = 0.050695,	train_time = 0.395082
[2019-08-23 03:58:16,771] TRAIN Iter 62840: lr = 0.395268,	loss = 2.793461,	Top-1 err = 0.441992,	Top-5 err = 0.214893,	data_time = 0.050389,	train_time = 0.632741
[2019-08-23 03:58:28,136] TRAIN Iter 62860: lr = 0.395235,	loss = 2.764667,	Top-1 err = 0.443018,	Top-5 err = 0.216797,	data_time = 0.149801,	train_time = 0.568201
[2019-08-23 03:58:35,759] TRAIN Iter 62880: lr = 0.395202,	loss = 2.893167,	Top-1 err = 0.442139,	Top-5 err = 0.218359,	data_time = 0.050486,	train_time = 0.381160
[2019-08-23 03:58:50,329] TRAIN Iter 62900: lr = 0.395168,	loss = 2.731366,	Top-1 err = 0.442090,	Top-5 err = 0.218262,	data_time = 0.050667,	train_time = 0.728480
[2019-08-23 03:58:58,044] TRAIN Iter 62920: lr = 0.395135,	loss = 2.743153,	Top-1 err = 0.444824,	Top-5 err = 0.215234,	data_time = 0.050430,	train_time = 0.385747
[2019-08-23 03:59:10,839] TRAIN Iter 62940: lr = 0.395102,	loss = 2.839965,	Top-1 err = 0.442627,	Top-5 err = 0.213184,	data_time = 0.050187,	train_time = 0.639731
[2019-08-23 03:59:26,802] TRAIN Iter 62960: lr = 0.395068,	loss = 2.720252,	Top-1 err = 0.442383,	Top-5 err = 0.211279,	data_time = 0.050801,	train_time = 0.798140
[2019-08-23 03:59:34,536] TRAIN Iter 62980: lr = 0.395035,	loss = 2.711372,	Top-1 err = 0.447852,	Top-5 err = 0.215576,	data_time = 0.050395,	train_time = 0.386676
[2019-08-23 03:59:46,430] TRAIN Iter 63000: lr = 0.395002,	loss = 2.857723,	Top-1 err = 0.443652,	Top-5 err = 0.213818,	data_time = 0.050757,	train_time = 0.594704
[2019-08-23 04:00:01,064] TRAIN Iter 63020: lr = 0.394968,	loss = 2.814960,	Top-1 err = 0.447949,	Top-5 err = 0.217920,	data_time = 0.050604,	train_time = 0.731679
[2019-08-23 04:00:08,920] TRAIN Iter 63040: lr = 0.394935,	loss = 2.889124,	Top-1 err = 0.450293,	Top-5 err = 0.220117,	data_time = 0.050789,	train_time = 0.392768
[2019-08-23 04:00:22,710] TRAIN Iter 63060: lr = 0.394902,	loss = 2.860879,	Top-1 err = 0.444922,	Top-5 err = 0.213428,	data_time = 0.050570,	train_time = 0.689471
[2019-08-23 04:00:30,250] TRAIN Iter 63080: lr = 0.394868,	loss = 2.917368,	Top-1 err = 0.441992,	Top-5 err = 0.215479,	data_time = 0.050360,	train_time = 0.376993
[2019-08-23 04:00:46,148] TRAIN Iter 63100: lr = 0.394835,	loss = 2.817843,	Top-1 err = 0.443799,	Top-5 err = 0.212109,	data_time = 0.050221,	train_time = 0.794895
[2019-08-23 04:01:01,258] TRAIN Iter 63120: lr = 0.394802,	loss = 2.850235,	Top-1 err = 0.448096,	Top-5 err = 0.221973,	data_time = 0.050344,	train_time = 0.755467
[2019-08-23 04:01:08,650] TRAIN Iter 63140: lr = 0.394768,	loss = 2.825269,	Top-1 err = 0.443994,	Top-5 err = 0.218018,	data_time = 0.050585,	train_time = 0.369589
[2019-08-23 04:01:22,941] TRAIN Iter 63160: lr = 0.394735,	loss = 2.879958,	Top-1 err = 0.446045,	Top-5 err = 0.217676,	data_time = 0.050833,	train_time = 0.714536
[2019-08-23 04:01:37,026] TRAIN Iter 63180: lr = 0.394702,	loss = 2.821854,	Top-1 err = 0.447852,	Top-5 err = 0.216211,	data_time = 0.050958,	train_time = 0.704243
[2019-08-23 04:01:44,562] TRAIN Iter 63200: lr = 0.394668,	loss = 2.891242,	Top-1 err = 0.443896,	Top-5 err = 0.215674,	data_time = 0.050765,	train_time = 0.376789
[2019-08-23 04:01:58,936] TRAIN Iter 63220: lr = 0.394635,	loss = 2.877624,	Top-1 err = 0.440137,	Top-5 err = 0.214307,	data_time = 0.050567,	train_time = 0.718707
[2019-08-23 04:02:06,593] TRAIN Iter 63240: lr = 0.394602,	loss = 2.776490,	Top-1 err = 0.441943,	Top-5 err = 0.222314,	data_time = 0.050705,	train_time = 0.382843
[2019-08-23 04:02:21,459] TRAIN Iter 63260: lr = 0.394568,	loss = 2.816446,	Top-1 err = 0.443994,	Top-5 err = 0.213330,	data_time = 0.050445,	train_time = 0.743276
[2019-08-23 04:02:36,905] TRAIN Iter 63280: lr = 0.394535,	loss = 2.793111,	Top-1 err = 0.439258,	Top-5 err = 0.211035,	data_time = 0.050625,	train_time = 0.772254
[2019-08-23 04:02:44,529] TRAIN Iter 63300: lr = 0.394502,	loss = 2.857371,	Top-1 err = 0.449072,	Top-5 err = 0.218994,	data_time = 0.050948,	train_time = 0.381230
[2019-08-23 04:02:59,497] TRAIN Iter 63320: lr = 0.394468,	loss = 2.963356,	Top-1 err = 0.445361,	Top-5 err = 0.218896,	data_time = 0.050950,	train_time = 0.748382
[2019-08-23 04:03:14,021] TRAIN Iter 63340: lr = 0.394435,	loss = 2.745225,	Top-1 err = 0.447559,	Top-5 err = 0.216699,	data_time = 0.050440,	train_time = 0.726165
[2019-08-23 04:03:21,528] TRAIN Iter 63360: lr = 0.394402,	loss = 2.898294,	Top-1 err = 0.450732,	Top-5 err = 0.215723,	data_time = 0.050714,	train_time = 0.375355
[2019-08-23 04:03:36,012] TRAIN Iter 63380: lr = 0.394368,	loss = 2.796173,	Top-1 err = 0.444971,	Top-5 err = 0.216016,	data_time = 0.050122,	train_time = 0.724164
[2019-08-23 04:03:44,004] TRAIN Iter 63400: lr = 0.394335,	loss = 2.804515,	Top-1 err = 0.443359,	Top-5 err = 0.217090,	data_time = 0.050450,	train_time = 0.399607
[2019-08-23 04:04:00,267] TRAIN Iter 63420: lr = 0.394302,	loss = 2.813604,	Top-1 err = 0.451953,	Top-5 err = 0.215625,	data_time = 0.050292,	train_time = 0.813106
[2019-08-23 04:04:16,337] TRAIN Iter 63440: lr = 0.394268,	loss = 2.782394,	Top-1 err = 0.446826,	Top-5 err = 0.217236,	data_time = 0.050568,	train_time = 0.803503
[2019-08-23 04:04:24,102] TRAIN Iter 63460: lr = 0.394235,	loss = 2.832681,	Top-1 err = 0.437793,	Top-5 err = 0.209717,	data_time = 0.050581,	train_time = 0.388240
[2019-08-23 04:04:39,822] TRAIN Iter 63480: lr = 0.394202,	loss = 2.953636,	Top-1 err = 0.452197,	Top-5 err = 0.221143,	data_time = 0.050855,	train_time = 0.785996
[2019-08-23 04:04:54,917] TRAIN Iter 63500: lr = 0.394168,	loss = 2.775884,	Top-1 err = 0.449268,	Top-5 err = 0.218555,	data_time = 0.050619,	train_time = 0.754708
[2019-08-23 04:05:02,421] TRAIN Iter 63520: lr = 0.394135,	loss = 2.797404,	Top-1 err = 0.448193,	Top-5 err = 0.221924,	data_time = 0.050886,	train_time = 0.375179
[2019-08-23 04:05:19,225] TRAIN Iter 63540: lr = 0.394102,	loss = 2.903930,	Top-1 err = 0.453271,	Top-5 err = 0.221973,	data_time = 0.140459,	train_time = 0.840221
[2019-08-23 04:05:26,405] TRAIN Iter 63560: lr = 0.394068,	loss = 2.834861,	Top-1 err = 0.445459,	Top-5 err = 0.219727,	data_time = 0.050499,	train_time = 0.358989
[2019-08-23 04:05:44,121] TRAIN Iter 63580: lr = 0.394035,	loss = 2.810243,	Top-1 err = 0.444482,	Top-5 err = 0.219531,	data_time = 0.050466,	train_time = 0.885747
[2019-08-23 04:06:00,319] TRAIN Iter 63600: lr = 0.394002,	loss = 2.857171,	Top-1 err = 0.449414,	Top-5 err = 0.219580,	data_time = 0.050260,	train_time = 0.809918
[2019-08-23 04:06:07,976] TRAIN Iter 63620: lr = 0.393968,	loss = 2.893973,	Top-1 err = 0.446143,	Top-5 err = 0.213867,	data_time = 0.050435,	train_time = 0.382836
[2019-08-23 04:06:23,549] TRAIN Iter 63640: lr = 0.393935,	loss = 2.820798,	Top-1 err = 0.444971,	Top-5 err = 0.216504,	data_time = 0.050501,	train_time = 0.778627
[2019-08-23 04:06:40,928] TRAIN Iter 63660: lr = 0.393902,	loss = 2.868520,	Top-1 err = 0.446191,	Top-5 err = 0.217529,	data_time = 0.050717,	train_time = 0.868931
[2019-08-23 04:06:48,159] TRAIN Iter 63680: lr = 0.393868,	loss = 2.781163,	Top-1 err = 0.446973,	Top-5 err = 0.214893,	data_time = 0.050692,	train_time = 0.361555
[2019-08-23 04:07:04,519] TRAIN Iter 63700: lr = 0.393835,	loss = 2.856825,	Top-1 err = 0.447119,	Top-5 err = 0.219189,	data_time = 0.050786,	train_time = 0.817954
[2019-08-23 04:07:12,094] TRAIN Iter 63720: lr = 0.393802,	loss = 2.782941,	Top-1 err = 0.443311,	Top-5 err = 0.218555,	data_time = 0.050605,	train_time = 0.378734
[2019-08-23 04:07:29,996] TRAIN Iter 63740: lr = 0.393768,	loss = 2.973181,	Top-1 err = 0.444043,	Top-5 err = 0.214355,	data_time = 0.049933,	train_time = 0.895095
[2019-08-23 04:07:45,596] TRAIN Iter 63760: lr = 0.393735,	loss = 2.806846,	Top-1 err = 0.447949,	Top-5 err = 0.216064,	data_time = 0.050031,	train_time = 0.779972
[2019-08-23 04:07:51,962] TRAIN Iter 63780: lr = 0.393702,	loss = 2.894929,	Top-1 err = 0.443604,	Top-5 err = 0.219678,	data_time = 0.049904,	train_time = 0.318318
[2019-08-23 04:08:45,541] TRAIN Iter 63800: lr = 0.393668,	loss = 2.855621,	Top-1 err = 0.452706,	Top-5 err = 0.220449,	data_time = 0.050443,	train_time = 2.678928
[2019-08-23 04:08:54,042] TRAIN Iter 63820: lr = 0.393635,	loss = 2.902034,	Top-1 err = 0.447803,	Top-5 err = 0.217139,	data_time = 0.050438,	train_time = 0.425017
[2019-08-23 04:09:07,072] TRAIN Iter 63840: lr = 0.393602,	loss = 2.920872,	Top-1 err = 0.433301,	Top-5 err = 0.210156,	data_time = 0.050423,	train_time = 0.651479
[2019-08-23 04:09:15,800] TRAIN Iter 63860: lr = 0.393568,	loss = 2.794174,	Top-1 err = 0.439014,	Top-5 err = 0.210059,	data_time = 0.050899,	train_time = 0.436407
[2019-08-23 04:09:23,524] TRAIN Iter 63880: lr = 0.393535,	loss = 2.912197,	Top-1 err = 0.441943,	Top-5 err = 0.216357,	data_time = 0.050372,	train_time = 0.386160
[2019-08-23 04:09:36,324] TRAIN Iter 63900: lr = 0.393502,	loss = 2.681860,	Top-1 err = 0.443066,	Top-5 err = 0.211816,	data_time = 0.051002,	train_time = 0.640007
[2019-08-23 04:09:49,953] TRAIN Iter 63920: lr = 0.393468,	loss = 2.849645,	Top-1 err = 0.438525,	Top-5 err = 0.208594,	data_time = 1.543527,	train_time = 0.681412
[2019-08-23 04:09:57,918] TRAIN Iter 63940: lr = 0.393435,	loss = 2.828792,	Top-1 err = 0.439746,	Top-5 err = 0.213086,	data_time = 0.050412,	train_time = 0.398260
[2019-08-23 04:10:12,007] TRAIN Iter 63960: lr = 0.393402,	loss = 2.883741,	Top-1 err = 0.436914,	Top-5 err = 0.212598,	data_time = 0.134691,	train_time = 0.704442
[2019-08-23 04:10:19,725] TRAIN Iter 63980: lr = 0.393368,	loss = 2.829464,	Top-1 err = 0.444092,	Top-5 err = 0.212793,	data_time = 0.050864,	train_time = 0.385847
[2019-08-23 04:10:34,234] TRAIN Iter 64000: lr = 0.393335,	loss = 2.816712,	Top-1 err = 0.443066,	Top-5 err = 0.213232,	data_time = 0.050275,	train_time = 0.725481
[2019-08-23 04:10:47,741] TRAIN Iter 64020: lr = 0.393302,	loss = 2.843955,	Top-1 err = 0.445850,	Top-5 err = 0.212012,	data_time = 0.050596,	train_time = 0.675313
[2019-08-23 04:10:55,316] TRAIN Iter 64040: lr = 0.393268,	loss = 2.802234,	Top-1 err = 0.442773,	Top-5 err = 0.212402,	data_time = 0.128227,	train_time = 0.378745
[2019-08-23 04:11:07,405] TRAIN Iter 64060: lr = 0.393235,	loss = 2.824688,	Top-1 err = 0.442041,	Top-5 err = 0.215479,	data_time = 0.050689,	train_time = 0.604408
[2019-08-23 04:11:19,288] TRAIN Iter 64080: lr = 0.393202,	loss = 2.861619,	Top-1 err = 0.446582,	Top-5 err = 0.212207,	data_time = 0.142646,	train_time = 0.594161
[2019-08-23 04:11:28,274] TRAIN Iter 64100: lr = 0.393168,	loss = 2.785921,	Top-1 err = 0.442383,	Top-5 err = 0.215967,	data_time = 0.050675,	train_time = 0.449266
[2019-08-23 04:11:43,678] TRAIN Iter 64120: lr = 0.393135,	loss = 2.700614,	Top-1 err = 0.445996,	Top-5 err = 0.214258,	data_time = 0.050407,	train_time = 0.770215
[2019-08-23 04:11:51,270] TRAIN Iter 64140: lr = 0.393102,	loss = 2.779321,	Top-1 err = 0.439941,	Top-5 err = 0.214600,	data_time = 0.161461,	train_time = 0.379551
[2019-08-23 04:12:04,719] TRAIN Iter 64160: lr = 0.393068,	loss = 2.765264,	Top-1 err = 0.441113,	Top-5 err = 0.210889,	data_time = 0.051085,	train_time = 0.672453
[2019-08-23 04:12:19,991] TRAIN Iter 64180: lr = 0.393035,	loss = 2.906334,	Top-1 err = 0.441553,	Top-5 err = 0.213916,	data_time = 0.050504,	train_time = 0.763594
[2019-08-23 04:12:27,845] TRAIN Iter 64200: lr = 0.393002,	loss = 2.864104,	Top-1 err = 0.441943,	Top-5 err = 0.212158,	data_time = 0.050471,	train_time = 0.392667
[2019-08-23 04:12:41,433] TRAIN Iter 64220: lr = 0.392968,	loss = 2.736264,	Top-1 err = 0.444141,	Top-5 err = 0.211035,	data_time = 0.050916,	train_time = 0.679380
[2019-08-23 04:12:56,929] TRAIN Iter 64240: lr = 0.392935,	loss = 2.948393,	Top-1 err = 0.440723,	Top-5 err = 0.213916,	data_time = 0.050937,	train_time = 0.774786
[2019-08-23 04:13:04,421] TRAIN Iter 64260: lr = 0.392902,	loss = 2.832067,	Top-1 err = 0.439453,	Top-5 err = 0.212451,	data_time = 0.050807,	train_time = 0.374610
[2019-08-23 04:13:18,523] TRAIN Iter 64280: lr = 0.392868,	loss = 2.844304,	Top-1 err = 0.448047,	Top-5 err = 0.216699,	data_time = 0.050737,	train_time = 0.705079
[2019-08-23 04:13:26,490] TRAIN Iter 64300: lr = 0.392835,	loss = 2.899135,	Top-1 err = 0.443701,	Top-5 err = 0.212354,	data_time = 0.050596,	train_time = 0.398314
[2019-08-23 04:13:38,857] TRAIN Iter 64320: lr = 0.392802,	loss = 2.873372,	Top-1 err = 0.439697,	Top-5 err = 0.216406,	data_time = 0.050699,	train_time = 0.618354
[2019-08-23 04:13:53,206] TRAIN Iter 64340: lr = 0.392768,	loss = 2.716307,	Top-1 err = 0.442285,	Top-5 err = 0.211719,	data_time = 0.050366,	train_time = 0.717439
[2019-08-23 04:14:00,774] TRAIN Iter 64360: lr = 0.392735,	loss = 2.884670,	Top-1 err = 0.454443,	Top-5 err = 0.220898,	data_time = 0.050659,	train_time = 0.378377
[2019-08-23 04:14:16,262] TRAIN Iter 64380: lr = 0.392702,	loss = 2.783086,	Top-1 err = 0.449072,	Top-5 err = 0.215186,	data_time = 0.050393,	train_time = 0.774386
[2019-08-23 04:14:29,500] TRAIN Iter 64400: lr = 0.392668,	loss = 2.833118,	Top-1 err = 0.441699,	Top-5 err = 0.214062,	data_time = 0.051017,	train_time = 0.661890
[2019-08-23 04:14:36,788] TRAIN Iter 64420: lr = 0.392635,	loss = 2.938524,	Top-1 err = 0.448145,	Top-5 err = 0.220898,	data_time = 0.050528,	train_time = 0.364365
[2019-08-23 04:14:51,106] TRAIN Iter 64440: lr = 0.392602,	loss = 2.846268,	Top-1 err = 0.440479,	Top-5 err = 0.212402,	data_time = 0.050365,	train_time = 0.715883
[2019-08-23 04:14:58,500] TRAIN Iter 64460: lr = 0.392568,	loss = 2.922678,	Top-1 err = 0.441309,	Top-5 err = 0.214062,	data_time = 0.050827,	train_time = 0.369708
[2019-08-23 04:15:13,625] TRAIN Iter 64480: lr = 0.392535,	loss = 2.731995,	Top-1 err = 0.442969,	Top-5 err = 0.210156,	data_time = 0.050835,	train_time = 0.756205
[2019-08-23 04:15:26,483] TRAIN Iter 64500: lr = 0.392502,	loss = 2.809983,	Top-1 err = 0.445605,	Top-5 err = 0.215869,	data_time = 0.050502,	train_time = 0.642910
[2019-08-23 04:15:33,852] TRAIN Iter 64520: lr = 0.392468,	loss = 2.855799,	Top-1 err = 0.442285,	Top-5 err = 0.215771,	data_time = 0.050694,	train_time = 0.368420
[2019-08-23 04:15:48,886] TRAIN Iter 64540: lr = 0.392435,	loss = 2.796694,	Top-1 err = 0.444580,	Top-5 err = 0.218359,	data_time = 0.050383,	train_time = 0.751699
[2019-08-23 04:16:02,697] TRAIN Iter 64560: lr = 0.392402,	loss = 2.918639,	Top-1 err = 0.446680,	Top-5 err = 0.218750,	data_time = 0.050929,	train_time = 0.690515
[2019-08-23 04:16:10,242] TRAIN Iter 64580: lr = 0.392368,	loss = 2.762191,	Top-1 err = 0.445020,	Top-5 err = 0.215039,	data_time = 0.050291,	train_time = 0.377279
[2019-08-23 04:16:25,704] TRAIN Iter 64600: lr = 0.392335,	loss = 2.878467,	Top-1 err = 0.448340,	Top-5 err = 0.216309,	data_time = 0.050575,	train_time = 0.773078
[2019-08-23 04:16:33,228] TRAIN Iter 64620: lr = 0.392302,	loss = 2.797173,	Top-1 err = 0.447314,	Top-5 err = 0.215771,	data_time = 0.050469,	train_time = 0.376165
[2019-08-23 04:16:48,106] TRAIN Iter 64640: lr = 0.392268,	loss = 2.797852,	Top-1 err = 0.445654,	Top-5 err = 0.212061,	data_time = 0.050690,	train_time = 0.743901
[2019-08-23 04:17:04,147] TRAIN Iter 64660: lr = 0.392235,	loss = 2.786056,	Top-1 err = 0.445703,	Top-5 err = 0.217236,	data_time = 0.050824,	train_time = 0.802040
[2019-08-23 04:17:11,454] TRAIN Iter 64680: lr = 0.392202,	loss = 2.765296,	Top-1 err = 0.443652,	Top-5 err = 0.216016,	data_time = 0.101901,	train_time = 0.365345
[2019-08-23 04:17:24,905] TRAIN Iter 64700: lr = 0.392168,	loss = 2.719556,	Top-1 err = 0.446045,	Top-5 err = 0.216455,	data_time = 0.050648,	train_time = 0.672501
[2019-08-23 04:17:39,214] TRAIN Iter 64720: lr = 0.392135,	loss = 2.795897,	Top-1 err = 0.444824,	Top-5 err = 0.214209,	data_time = 0.123722,	train_time = 0.715440
[2019-08-23 04:17:46,197] TRAIN Iter 64740: lr = 0.392102,	loss = 2.813196,	Top-1 err = 0.444824,	Top-5 err = 0.217676,	data_time = 0.050609,	train_time = 0.349177
[2019-08-23 04:18:02,336] TRAIN Iter 64760: lr = 0.392068,	loss = 2.833983,	Top-1 err = 0.447021,	Top-5 err = 0.217285,	data_time = 0.050631,	train_time = 0.806930
[2019-08-23 04:18:10,135] TRAIN Iter 64780: lr = 0.392035,	loss = 2.885652,	Top-1 err = 0.450195,	Top-5 err = 0.217822,	data_time = 0.050705,	train_time = 0.389903
[2019-08-23 04:18:23,638] TRAIN Iter 64800: lr = 0.392002,	loss = 2.869157,	Top-1 err = 0.446387,	Top-5 err = 0.219629,	data_time = 0.050495,	train_time = 0.675135
[2019-08-23 04:18:38,580] TRAIN Iter 64820: lr = 0.391968,	loss = 2.918137,	Top-1 err = 0.447461,	Top-5 err = 0.216748,	data_time = 0.050488,	train_time = 0.747109
[2019-08-23 04:18:45,446] TRAIN Iter 64840: lr = 0.391935,	loss = 2.820409,	Top-1 err = 0.438379,	Top-5 err = 0.209912,	data_time = 0.050471,	train_time = 0.343283
[2019-08-23 04:19:02,276] TRAIN Iter 64860: lr = 0.391902,	loss = 2.775382,	Top-1 err = 0.442041,	Top-5 err = 0.212305,	data_time = 0.131522,	train_time = 0.841502
[2019-08-23 04:19:16,684] TRAIN Iter 64880: lr = 0.391868,	loss = 2.768230,	Top-1 err = 0.445068,	Top-5 err = 0.215918,	data_time = 0.117864,	train_time = 0.720351
[2019-08-23 04:19:23,907] TRAIN Iter 64900: lr = 0.391835,	loss = 2.898248,	Top-1 err = 0.446289,	Top-5 err = 0.218164,	data_time = 0.050518,	train_time = 0.361158
[2019-08-23 04:19:37,991] TRAIN Iter 64920: lr = 0.391802,	loss = 2.811284,	Top-1 err = 0.447217,	Top-5 err = 0.215674,	data_time = 0.050935,	train_time = 0.704164
[2019-08-23 04:19:45,727] TRAIN Iter 64940: lr = 0.391768,	loss = 2.717360,	Top-1 err = 0.452686,	Top-5 err = 0.217969,	data_time = 0.050516,	train_time = 0.386799
[2019-08-23 04:19:58,790] TRAIN Iter 64960: lr = 0.391735,	loss = 2.892516,	Top-1 err = 0.445166,	Top-5 err = 0.222754,	data_time = 0.050939,	train_time = 0.653119
[2019-08-23 04:20:15,794] TRAIN Iter 64980: lr = 0.391702,	loss = 2.859721,	Top-1 err = 0.444336,	Top-5 err = 0.217285,	data_time = 0.050054,	train_time = 0.850186
[2019-08-23 04:20:22,499] TRAIN Iter 65000: lr = 0.391668,	loss = 2.806974,	Top-1 err = 0.449121,	Top-5 err = 0.218506,	data_time = 0.050100,	train_time = 0.335242
[2019-08-23 04:20:38,332] TRAIN Iter 65020: lr = 0.391635,	loss = 2.819471,	Top-1 err = 0.445312,	Top-5 err = 0.217383,	data_time = 0.049895,	train_time = 0.791642
[2019-08-23 04:20:49,495] TRAIN Iter 65040: lr = 0.391602,	loss = 3.062082,	Top-1 err = 0.449753,	Top-5 err = 0.214208,	data_time = 0.007050,	train_time = 0.558125
[2019-08-23 04:21:35,108] TRAIN Iter 65060: lr = 0.391568,	loss = 2.841668,	Top-1 err = 0.451709,	Top-5 err = 0.217822,	data_time = 0.050351,	train_time = 2.280669
[2019-08-23 04:21:48,482] TRAIN Iter 65080: lr = 0.391535,	loss = 2.758254,	Top-1 err = 0.444434,	Top-5 err = 0.210400,	data_time = 0.050896,	train_time = 0.668667
[2019-08-23 04:21:56,143] TRAIN Iter 65100: lr = 0.391502,	loss = 2.775282,	Top-1 err = 0.444775,	Top-5 err = 0.211475,	data_time = 0.050667,	train_time = 0.383062
[2019-08-23 04:22:06,297] TRAIN Iter 65120: lr = 0.391468,	loss = 2.807526,	Top-1 err = 0.435889,	Top-5 err = 0.213135,	data_time = 0.050994,	train_time = 0.507678
[2019-08-23 04:22:19,252] TRAIN Iter 65140: lr = 0.391435,	loss = 2.766884,	Top-1 err = 0.440820,	Top-5 err = 0.207910,	data_time = 0.050518,	train_time = 0.647698
[2019-08-23 04:22:26,799] TRAIN Iter 65160: lr = 0.391402,	loss = 2.779660,	Top-1 err = 0.441113,	Top-5 err = 0.213379,	data_time = 0.050813,	train_time = 0.377380
[2019-08-23 04:22:41,795] TRAIN Iter 65180: lr = 0.391368,	loss = 2.703718,	Top-1 err = 0.439111,	Top-5 err = 0.208154,	data_time = 0.050646,	train_time = 0.749756
[2019-08-23 04:22:50,315] TRAIN Iter 65200: lr = 0.391335,	loss = 2.790429,	Top-1 err = 0.433203,	Top-5 err = 0.209912,	data_time = 0.050320,	train_time = 0.425967
[2019-08-23 04:23:02,139] TRAIN Iter 65220: lr = 0.391302,	loss = 2.698749,	Top-1 err = 0.439893,	Top-5 err = 0.210352,	data_time = 0.050533,	train_time = 0.591204
[2019-08-23 04:23:15,072] TRAIN Iter 65240: lr = 0.391268,	loss = 2.768355,	Top-1 err = 0.435986,	Top-5 err = 0.205273,	data_time = 0.118442,	train_time = 0.646632
[2019-08-23 04:23:22,672] TRAIN Iter 65260: lr = 0.391235,	loss = 2.810112,	Top-1 err = 0.446191,	Top-5 err = 0.214502,	data_time = 0.050569,	train_time = 0.380010
[2019-08-23 04:23:35,448] TRAIN Iter 65280: lr = 0.391202,	loss = 2.747259,	Top-1 err = 0.441699,	Top-5 err = 0.211768,	data_time = 0.050479,	train_time = 0.638755
[2019-08-23 04:23:50,327] TRAIN Iter 65300: lr = 0.391168,	loss = 2.739602,	Top-1 err = 0.440430,	Top-5 err = 0.209180,	data_time = 0.050342,	train_time = 0.743952
[2019-08-23 04:23:57,765] TRAIN Iter 65320: lr = 0.391135,	loss = 2.767149,	Top-1 err = 0.438965,	Top-5 err = 0.212451,	data_time = 0.050333,	train_time = 0.371893
[2019-08-23 04:24:11,508] TRAIN Iter 65340: lr = 0.391102,	loss = 2.728198,	Top-1 err = 0.443652,	Top-5 err = 0.216211,	data_time = 0.050916,	train_time = 0.687127
[2019-08-23 04:24:19,637] TRAIN Iter 65360: lr = 0.391068,	loss = 2.747967,	Top-1 err = 0.438184,	Top-5 err = 0.204492,	data_time = 0.050675,	train_time = 0.406422
[2019-08-23 04:24:32,571] TRAIN Iter 65380: lr = 0.391035,	loss = 2.832674,	Top-1 err = 0.439551,	Top-5 err = 0.215234,	data_time = 0.050726,	train_time = 0.646684
[2019-08-23 04:24:44,907] TRAIN Iter 65400: lr = 0.391002,	loss = 2.973056,	Top-1 err = 0.440918,	Top-5 err = 0.211475,	data_time = 0.050860,	train_time = 0.616797
[2019-08-23 04:24:52,336] TRAIN Iter 65420: lr = 0.390968,	loss = 2.943969,	Top-1 err = 0.444238,	Top-5 err = 0.214062,	data_time = 0.050388,	train_time = 0.371411
[2019-08-23 04:25:06,194] TRAIN Iter 65440: lr = 0.390935,	loss = 2.833221,	Top-1 err = 0.445508,	Top-5 err = 0.217334,	data_time = 0.050293,	train_time = 0.692898
[2019-08-23 04:25:20,251] TRAIN Iter 65460: lr = 0.390902,	loss = 2.794280,	Top-1 err = 0.444580,	Top-5 err = 0.216016,	data_time = 0.050654,	train_time = 0.702833
[2019-08-23 04:25:27,677] TRAIN Iter 65480: lr = 0.390868,	loss = 2.773055,	Top-1 err = 0.437109,	Top-5 err = 0.209473,	data_time = 0.050432,	train_time = 0.371262
[2019-08-23 04:25:40,924] TRAIN Iter 65500: lr = 0.390835,	loss = 2.847701,	Top-1 err = 0.444189,	Top-5 err = 0.217969,	data_time = 0.050560,	train_time = 0.662338
[2019-08-23 04:25:48,520] TRAIN Iter 65520: lr = 0.390802,	loss = 2.803648,	Top-1 err = 0.442529,	Top-5 err = 0.210645,	data_time = 0.050804,	train_time = 0.379805
[2019-08-23 04:26:03,465] TRAIN Iter 65540: lr = 0.390768,	loss = 2.876576,	Top-1 err = 0.439014,	Top-5 err = 0.215234,	data_time = 0.050914,	train_time = 0.747245
[2019-08-23 04:26:17,640] TRAIN Iter 65560: lr = 0.390735,	loss = 2.762626,	Top-1 err = 0.442041,	Top-5 err = 0.214209,	data_time = 0.050556,	train_time = 0.708711
[2019-08-23 04:26:25,051] TRAIN Iter 65580: lr = 0.390702,	loss = 2.778750,	Top-1 err = 0.437158,	Top-5 err = 0.211670,	data_time = 0.050671,	train_time = 0.370536
[2019-08-23 04:26:38,538] TRAIN Iter 65600: lr = 0.390668,	loss = 2.879028,	Top-1 err = 0.448730,	Top-5 err = 0.218750,	data_time = 0.050527,	train_time = 0.674325
[2019-08-23 04:26:50,833] TRAIN Iter 65620: lr = 0.390635,	loss = 2.836552,	Top-1 err = 0.444336,	Top-5 err = 0.214844,	data_time = 0.050640,	train_time = 0.614755
[2019-08-23 04:26:58,556] TRAIN Iter 65640: lr = 0.390602,	loss = 2.767353,	Top-1 err = 0.440381,	Top-5 err = 0.215527,	data_time = 0.050397,	train_time = 0.386141
[2019-08-23 04:27:15,584] TRAIN Iter 65660: lr = 0.390568,	loss = 2.881206,	Top-1 err = 0.451465,	Top-5 err = 0.220410,	data_time = 0.050143,	train_time = 0.851391
[2019-08-23 04:27:23,388] TRAIN Iter 65680: lr = 0.390535,	loss = 2.780820,	Top-1 err = 0.443018,	Top-5 err = 0.211328,	data_time = 0.128392,	train_time = 0.390161
[2019-08-23 04:27:37,945] TRAIN Iter 65700: lr = 0.390502,	loss = 2.827407,	Top-1 err = 0.444385,	Top-5 err = 0.212061,	data_time = 0.050860,	train_time = 0.727846
[2019-08-23 04:27:53,044] TRAIN Iter 65720: lr = 0.390468,	loss = 2.808205,	Top-1 err = 0.443994,	Top-5 err = 0.211182,	data_time = 0.050520,	train_time = 0.754925
[2019-08-23 04:28:00,654] TRAIN Iter 65740: lr = 0.390435,	loss = 2.837770,	Top-1 err = 0.443799,	Top-5 err = 0.217822,	data_time = 0.050358,	train_time = 0.380483
[2019-08-23 04:28:15,947] TRAIN Iter 65760: lr = 0.390402,	loss = 2.737955,	Top-1 err = 0.448242,	Top-5 err = 0.216504,	data_time = 0.050380,	train_time = 0.764648
[2019-08-23 04:28:29,895] TRAIN Iter 65780: lr = 0.390368,	loss = 2.933173,	Top-1 err = 0.440234,	Top-5 err = 0.213574,	data_time = 0.050534,	train_time = 0.697369
[2019-08-23 04:28:36,988] TRAIN Iter 65800: lr = 0.390335,	loss = 2.855372,	Top-1 err = 0.440625,	Top-5 err = 0.214551,	data_time = 0.050871,	train_time = 0.354641
[2019-08-23 04:28:53,924] TRAIN Iter 65820: lr = 0.390302,	loss = 2.898926,	Top-1 err = 0.441553,	Top-5 err = 0.214697,	data_time = 0.050397,	train_time = 0.846789
[2019-08-23 04:29:01,776] TRAIN Iter 65840: lr = 0.390268,	loss = 2.827618,	Top-1 err = 0.437891,	Top-5 err = 0.216943,	data_time = 0.050955,	train_time = 0.392579
[2019-08-23 04:29:17,300] TRAIN Iter 65860: lr = 0.390235,	loss = 2.841360,	Top-1 err = 0.443652,	Top-5 err = 0.216650,	data_time = 0.050535,	train_time = 0.776195
[2019-08-23 04:29:31,860] TRAIN Iter 65880: lr = 0.390202,	loss = 2.920477,	Top-1 err = 0.443164,	Top-5 err = 0.213867,	data_time = 0.050481,	train_time = 0.727980
[2019-08-23 04:29:39,177] TRAIN Iter 65900: lr = 0.390168,	loss = 2.826247,	Top-1 err = 0.439844,	Top-5 err = 0.214111,	data_time = 0.050365,	train_time = 0.365847
[2019-08-23 04:29:55,140] TRAIN Iter 65920: lr = 0.390135,	loss = 2.824172,	Top-1 err = 0.445947,	Top-5 err = 0.212354,	data_time = 0.050383,	train_time = 0.798143
[2019-08-23 04:30:11,146] TRAIN Iter 65940: lr = 0.390102,	loss = 2.857961,	Top-1 err = 0.439648,	Top-5 err = 0.212061,	data_time = 0.050378,	train_time = 0.800279
[2019-08-23 04:30:18,115] TRAIN Iter 65960: lr = 0.390068,	loss = 2.785963,	Top-1 err = 0.449854,	Top-5 err = 0.217822,	data_time = 0.050529,	train_time = 0.348449
[2019-08-23 04:30:34,290] TRAIN Iter 65980: lr = 0.390035,	loss = 2.757573,	Top-1 err = 0.443262,	Top-5 err = 0.214209,	data_time = 0.050660,	train_time = 0.808699
[2019-08-23 04:30:41,738] TRAIN Iter 66000: lr = 0.390002,	loss = 2.806375,	Top-1 err = 0.442432,	Top-5 err = 0.214014,	data_time = 0.050321,	train_time = 0.372418
[2019-08-23 04:30:58,705] TRAIN Iter 66020: lr = 0.389968,	loss = 2.785423,	Top-1 err = 0.443018,	Top-5 err = 0.213867,	data_time = 0.050630,	train_time = 0.848321
[2019-08-23 04:31:12,937] TRAIN Iter 66040: lr = 0.389935,	loss = 2.808760,	Top-1 err = 0.449512,	Top-5 err = 0.220605,	data_time = 0.050525,	train_time = 0.711605
[2019-08-23 04:31:20,522] TRAIN Iter 66060: lr = 0.389902,	loss = 2.859627,	Top-1 err = 0.443018,	Top-5 err = 0.218359,	data_time = 0.140207,	train_time = 0.379237
[2019-08-23 04:31:34,685] TRAIN Iter 66080: lr = 0.389868,	loss = 2.887044,	Top-1 err = 0.444043,	Top-5 err = 0.216748,	data_time = 0.050306,	train_time = 0.708110
[2019-08-23 04:31:49,079] TRAIN Iter 66100: lr = 0.389835,	loss = 2.928613,	Top-1 err = 0.445947,	Top-5 err = 0.212744,	data_time = 0.050906,	train_time = 0.719700
[2019-08-23 04:31:55,792] TRAIN Iter 66120: lr = 0.389802,	loss = 2.811114,	Top-1 err = 0.442725,	Top-5 err = 0.214746,	data_time = 0.050277,	train_time = 0.335630
[2019-08-23 04:32:11,575] TRAIN Iter 66140: lr = 0.389768,	loss = 2.753322,	Top-1 err = 0.445215,	Top-5 err = 0.214990,	data_time = 0.050412,	train_time = 0.789162
[2019-08-23 04:32:19,037] TRAIN Iter 66160: lr = 0.389735,	loss = 2.809819,	Top-1 err = 0.450000,	Top-5 err = 0.218213,	data_time = 0.050489,	train_time = 0.373072
[2019-08-23 04:32:35,748] TRAIN Iter 66180: lr = 0.389702,	loss = 2.914006,	Top-1 err = 0.450684,	Top-5 err = 0.216455,	data_time = 0.050783,	train_time = 0.835538
[2019-08-23 04:32:51,614] TRAIN Iter 66200: lr = 0.389668,	loss = 2.750632,	Top-1 err = 0.451367,	Top-5 err = 0.217627,	data_time = 0.050503,	train_time = 0.793292
[2019-08-23 04:32:59,619] TRAIN Iter 66220: lr = 0.389635,	loss = 2.830462,	Top-1 err = 0.444775,	Top-5 err = 0.217773,	data_time = 0.050972,	train_time = 0.400225
[2019-08-23 04:33:16,367] TRAIN Iter 66240: lr = 0.389602,	loss = 2.825185,	Top-1 err = 0.445703,	Top-5 err = 0.213770,	data_time = 0.049999,	train_time = 0.837408
[2019-08-23 04:33:31,488] TRAIN Iter 66260: lr = 0.389568,	loss = 2.787209,	Top-1 err = 0.447510,	Top-5 err = 0.216895,	data_time = 0.049911,	train_time = 0.755999
[2019-08-23 04:33:37,557] TRAIN Iter 66280: lr = 0.389535,	loss = 2.792289,	Top-1 err = 0.450586,	Top-5 err = 0.222852,	data_time = 0.049935,	train_time = 0.303446
[2019-08-23 04:34:27,252] TRAIN Iter 66300: lr = 0.389502,	loss = 2.916382,	Top-1 err = 0.451599,	Top-5 err = 0.220669,	data_time = 0.050497,	train_time = 2.484722
[2019-08-23 04:34:34,806] TRAIN Iter 66320: lr = 0.389468,	loss = 2.621476,	Top-1 err = 0.441357,	Top-5 err = 0.213184,	data_time = 0.050499,	train_time = 0.377710
[2019-08-23 04:34:46,010] TRAIN Iter 66340: lr = 0.389435,	loss = 2.780249,	Top-1 err = 0.439648,	Top-5 err = 0.213721,	data_time = 0.050388,	train_time = 0.560186
[2019-08-23 04:34:59,235] TRAIN Iter 66360: lr = 0.389402,	loss = 2.750524,	Top-1 err = 0.437402,	Top-5 err = 0.209375,	data_time = 0.050900,	train_time = 0.661226
[2019-08-23 04:35:06,983] TRAIN Iter 66380: lr = 0.389368,	loss = 2.683386,	Top-1 err = 0.434717,	Top-5 err = 0.209229,	data_time = 0.050565,	train_time = 0.387396
[2019-08-23 04:35:21,113] TRAIN Iter 66400: lr = 0.389335,	loss = 2.770896,	Top-1 err = 0.433838,	Top-5 err = 0.211328,	data_time = 0.050447,	train_time = 0.706468
[2019-08-23 04:35:28,970] TRAIN Iter 66420: lr = 0.389302,	loss = 2.775419,	Top-1 err = 0.429395,	Top-5 err = 0.204590,	data_time = 0.050875,	train_time = 0.392840
[2019-08-23 04:35:43,156] TRAIN Iter 66440: lr = 0.389268,	loss = 2.789875,	Top-1 err = 0.440332,	Top-5 err = 0.215137,	data_time = 0.050371,	train_time = 0.709271
[2019-08-23 04:36:00,079] TRAIN Iter 66460: lr = 0.389235,	loss = 2.846499,	Top-1 err = 0.443262,	Top-5 err = 0.207520,	data_time = 0.050909,	train_time = 0.846163
[2019-08-23 04:36:08,143] TRAIN Iter 66480: lr = 0.389202,	loss = 2.871072,	Top-1 err = 0.442920,	Top-5 err = 0.213184,	data_time = 0.050417,	train_time = 0.403178
[2019-08-23 04:36:19,780] TRAIN Iter 66500: lr = 0.389168,	loss = 2.804301,	Top-1 err = 0.439551,	Top-5 err = 0.211377,	data_time = 0.050540,	train_time = 0.581849
[2019-08-23 04:36:32,636] TRAIN Iter 66520: lr = 0.389135,	loss = 2.884767,	Top-1 err = 0.436182,	Top-5 err = 0.211572,	data_time = 0.050431,	train_time = 0.642744
[2019-08-23 04:36:40,559] TRAIN Iter 66540: lr = 0.389102,	loss = 2.794009,	Top-1 err = 0.438623,	Top-5 err = 0.211279,	data_time = 0.050455,	train_time = 0.396177
[2019-08-23 04:36:53,416] TRAIN Iter 66560: lr = 0.389068,	loss = 2.840538,	Top-1 err = 0.440039,	Top-5 err = 0.212061,	data_time = 0.050715,	train_time = 0.642798
[2019-08-23 04:37:01,701] TRAIN Iter 66580: lr = 0.389035,	loss = 2.714447,	Top-1 err = 0.431348,	Top-5 err = 0.207275,	data_time = 0.152772,	train_time = 0.414237
[2019-08-23 04:37:14,444] TRAIN Iter 66600: lr = 0.389002,	loss = 2.785309,	Top-1 err = 0.436914,	Top-5 err = 0.210400,	data_time = 0.050344,	train_time = 0.637146
[2019-08-23 04:37:27,999] TRAIN Iter 66620: lr = 0.388968,	loss = 2.799425,	Top-1 err = 0.440771,	Top-5 err = 0.206445,	data_time = 0.050769,	train_time = 0.677729
[2019-08-23 04:37:35,914] TRAIN Iter 66640: lr = 0.388935,	loss = 2.936093,	Top-1 err = 0.443408,	Top-5 err = 0.212012,	data_time = 0.050429,	train_time = 0.395722
[2019-08-23 04:37:47,615] TRAIN Iter 66660: lr = 0.388902,	loss = 2.752510,	Top-1 err = 0.447168,	Top-5 err = 0.213135,	data_time = 0.050506,	train_time = 0.585076
[2019-08-23 04:38:00,260] TRAIN Iter 66680: lr = 0.388868,	loss = 2.669916,	Top-1 err = 0.447363,	Top-5 err = 0.215479,	data_time = 0.685839,	train_time = 0.632195
[2019-08-23 04:38:08,735] TRAIN Iter 66700: lr = 0.388835,	loss = 2.892326,	Top-1 err = 0.441699,	Top-5 err = 0.214258,	data_time = 0.050521,	train_time = 0.423767
[2019-08-23 04:38:21,887] TRAIN Iter 66720: lr = 0.388802,	loss = 2.866452,	Top-1 err = 0.440137,	Top-5 err = 0.215625,	data_time = 0.119270,	train_time = 0.657578
[2019-08-23 04:38:29,595] TRAIN Iter 66740: lr = 0.388768,	loss = 2.809617,	Top-1 err = 0.442529,	Top-5 err = 0.213525,	data_time = 0.050626,	train_time = 0.385392
[2019-08-23 04:38:43,871] TRAIN Iter 66760: lr = 0.388735,	loss = 2.888108,	Top-1 err = 0.443848,	Top-5 err = 0.213135,	data_time = 0.050572,	train_time = 0.713804
[2019-08-23 04:38:58,024] TRAIN Iter 66780: lr = 0.388702,	loss = 2.736618,	Top-1 err = 0.447461,	Top-5 err = 0.213525,	data_time = 0.050516,	train_time = 0.707602
[2019-08-23 04:39:05,146] TRAIN Iter 66800: lr = 0.388668,	loss = 2.931036,	Top-1 err = 0.445215,	Top-5 err = 0.217285,	data_time = 0.050508,	train_time = 0.356081
[2019-08-23 04:39:20,373] TRAIN Iter 66820: lr = 0.388635,	loss = 2.780675,	Top-1 err = 0.441699,	Top-5 err = 0.213916,	data_time = 0.050384,	train_time = 0.761340
[2019-08-23 04:39:34,508] TRAIN Iter 66840: lr = 0.388602,	loss = 2.875269,	Top-1 err = 0.445850,	Top-5 err = 0.214941,	data_time = 0.050601,	train_time = 0.706770
[2019-08-23 04:39:41,726] TRAIN Iter 66860: lr = 0.388568,	loss = 2.820723,	Top-1 err = 0.443018,	Top-5 err = 0.211719,	data_time = 0.050455,	train_time = 0.360854
[2019-08-23 04:39:56,103] TRAIN Iter 66880: lr = 0.388535,	loss = 2.744494,	Top-1 err = 0.438477,	Top-5 err = 0.215234,	data_time = 0.050486,	train_time = 0.718858
[2019-08-23 04:40:03,831] TRAIN Iter 66900: lr = 0.388502,	loss = 2.793671,	Top-1 err = 0.443457,	Top-5 err = 0.220312,	data_time = 0.050939,	train_time = 0.386370
[2019-08-23 04:40:17,146] TRAIN Iter 66920: lr = 0.388468,	loss = 2.726536,	Top-1 err = 0.439502,	Top-5 err = 0.213623,	data_time = 0.050566,	train_time = 0.665730
[2019-08-23 04:40:31,300] TRAIN Iter 66940: lr = 0.388435,	loss = 2.837428,	Top-1 err = 0.439502,	Top-5 err = 0.213916,	data_time = 0.050249,	train_time = 0.707677
[2019-08-23 04:40:38,890] TRAIN Iter 66960: lr = 0.388402,	loss = 2.817205,	Top-1 err = 0.445703,	Top-5 err = 0.214160,	data_time = 0.050796,	train_time = 0.379512
[2019-08-23 04:40:53,764] TRAIN Iter 66980: lr = 0.388368,	loss = 2.828766,	Top-1 err = 0.442334,	Top-5 err = 0.210498,	data_time = 0.050435,	train_time = 0.743681
[2019-08-23 04:41:07,505] TRAIN Iter 67000: lr = 0.388335,	loss = 2.904632,	Top-1 err = 0.443213,	Top-5 err = 0.214746,	data_time = 0.050489,	train_time = 0.687046
[2019-08-23 04:41:14,886] TRAIN Iter 67020: lr = 0.388302,	loss = 2.915273,	Top-1 err = 0.442578,	Top-5 err = 0.213037,	data_time = 0.050519,	train_time = 0.368996
[2019-08-23 04:41:30,030] TRAIN Iter 67040: lr = 0.388268,	loss = 2.835981,	Top-1 err = 0.451367,	Top-5 err = 0.215430,	data_time = 0.051018,	train_time = 0.757203
[2019-08-23 04:41:37,732] TRAIN Iter 67060: lr = 0.388235,	loss = 2.866994,	Top-1 err = 0.442578,	Top-5 err = 0.220654,	data_time = 0.050466,	train_time = 0.385080
[2019-08-23 04:41:52,880] TRAIN Iter 67080: lr = 0.388202,	loss = 2.855446,	Top-1 err = 0.442236,	Top-5 err = 0.216309,	data_time = 0.050490,	train_time = 0.757398
[2019-08-23 04:42:06,859] TRAIN Iter 67100: lr = 0.388168,	loss = 2.768696,	Top-1 err = 0.440674,	Top-5 err = 0.215430,	data_time = 0.050582,	train_time = 0.698917
[2019-08-23 04:42:14,791] TRAIN Iter 67120: lr = 0.388135,	loss = 2.833462,	Top-1 err = 0.445801,	Top-5 err = 0.213184,	data_time = 0.050334,	train_time = 0.396633
[2019-08-23 04:42:31,316] TRAIN Iter 67140: lr = 0.388102,	loss = 2.830993,	Top-1 err = 0.447803,	Top-5 err = 0.216504,	data_time = 0.050437,	train_time = 0.826235
[2019-08-23 04:42:46,823] TRAIN Iter 67160: lr = 0.388068,	loss = 2.790214,	Top-1 err = 0.443359,	Top-5 err = 0.214697,	data_time = 0.050530,	train_time = 0.775333
[2019-08-23 04:42:54,389] TRAIN Iter 67180: lr = 0.388035,	loss = 2.783103,	Top-1 err = 0.444287,	Top-5 err = 0.216748,	data_time = 0.050471,	train_time = 0.378277
[2019-08-23 04:43:10,066] TRAIN Iter 67200: lr = 0.388002,	loss = 2.725936,	Top-1 err = 0.437695,	Top-5 err = 0.212158,	data_time = 0.050426,	train_time = 0.783825
[2019-08-23 04:43:17,474] TRAIN Iter 67220: lr = 0.387968,	loss = 2.877365,	Top-1 err = 0.443359,	Top-5 err = 0.215869,	data_time = 0.050359,	train_time = 0.370381
[2019-08-23 04:43:33,390] TRAIN Iter 67240: lr = 0.387935,	loss = 2.796044,	Top-1 err = 0.440137,	Top-5 err = 0.208984,	data_time = 0.050407,	train_time = 0.795811
[2019-08-23 04:43:49,717] TRAIN Iter 67260: lr = 0.387902,	loss = 2.891572,	Top-1 err = 0.451562,	Top-5 err = 0.224902,	data_time = 0.050307,	train_time = 0.816336
[2019-08-23 04:43:56,985] TRAIN Iter 67280: lr = 0.387868,	loss = 2.834810,	Top-1 err = 0.447803,	Top-5 err = 0.218066,	data_time = 0.050449,	train_time = 0.363356
[2019-08-23 04:44:13,672] TRAIN Iter 67300: lr = 0.387835,	loss = 2.891745,	Top-1 err = 0.444629,	Top-5 err = 0.217188,	data_time = 0.050916,	train_time = 0.834363
[2019-08-23 04:44:31,631] TRAIN Iter 67320: lr = 0.387802,	loss = 2.794497,	Top-1 err = 0.437402,	Top-5 err = 0.212061,	data_time = 0.050426,	train_time = 0.897916
[2019-08-23 04:44:38,576] TRAIN Iter 67340: lr = 0.387768,	loss = 2.777815,	Top-1 err = 0.445264,	Top-5 err = 0.218848,	data_time = 0.050338,	train_time = 0.347237
[2019-08-23 04:44:55,026] TRAIN Iter 67360: lr = 0.387735,	loss = 2.804009,	Top-1 err = 0.445410,	Top-5 err = 0.214893,	data_time = 0.050424,	train_time = 0.822494
[2019-08-23 04:45:01,941] TRAIN Iter 67380: lr = 0.387702,	loss = 2.889150,	Top-1 err = 0.441797,	Top-5 err = 0.215820,	data_time = 0.133865,	train_time = 0.345745
[2019-08-23 04:45:20,810] TRAIN Iter 67400: lr = 0.387668,	loss = 2.858108,	Top-1 err = 0.446338,	Top-5 err = 0.217578,	data_time = 0.050555,	train_time = 0.943427
[2019-08-23 04:45:36,988] TRAIN Iter 67420: lr = 0.387635,	loss = 2.760404,	Top-1 err = 0.445947,	Top-5 err = 0.212500,	data_time = 0.050930,	train_time = 0.808901
[2019-08-23 04:45:44,092] TRAIN Iter 67440: lr = 0.387602,	loss = 2.863489,	Top-1 err = 0.443799,	Top-5 err = 0.212598,	data_time = 0.050406,	train_time = 0.355151
[2019-08-23 04:46:00,901] TRAIN Iter 67460: lr = 0.387568,	loss = 2.866038,	Top-1 err = 0.446338,	Top-5 err = 0.221191,	data_time = 0.050163,	train_time = 0.840471
[2019-08-23 04:46:17,715] TRAIN Iter 67480: lr = 0.387535,	loss = 2.882401,	Top-1 err = 0.454688,	Top-5 err = 0.223633,	data_time = 0.069724,	train_time = 0.840682
[2019-08-23 04:46:24,791] TRAIN Iter 67500: lr = 0.387502,	loss = 2.836572,	Top-1 err = 0.449023,	Top-5 err = 0.218457,	data_time = 0.050225,	train_time = 0.353792
[2019-08-23 04:46:41,531] TRAIN Iter 67520: lr = 0.387468,	loss = 2.760145,	Top-1 err = 0.441846,	Top-5 err = 0.214746,	data_time = 0.049915,	train_time = 0.836971
[2019-08-23 04:46:47,617] TRAIN Iter 67540: lr = 0.387435,	loss = 2.862195,	Top-1 err = 0.448584,	Top-5 err = 0.219629,	data_time = 0.049902,	train_time = 0.304273
[2019-08-23 04:47:40,212] TRAIN Iter 67560: lr = 0.387402,	loss = 2.737831,	Top-1 err = 0.447366,	Top-5 err = 0.215427,	data_time = 0.050588,	train_time = 2.629730
[2019-08-23 04:47:51,710] TRAIN Iter 67580: lr = 0.387368,	loss = 2.741619,	Top-1 err = 0.443457,	Top-5 err = 0.213721,	data_time = 0.050344,	train_time = 0.574893
[2019-08-23 04:47:59,820] TRAIN Iter 67600: lr = 0.387335,	loss = 2.751819,	Top-1 err = 0.435937,	Top-5 err = 0.207910,	data_time = 0.111573,	train_time = 0.405496
[2019-08-23 04:48:11,380] TRAIN Iter 67620: lr = 0.387302,	loss = 2.782207,	Top-1 err = 0.435059,	Top-5 err = 0.206201,	data_time = 0.050953,	train_time = 0.577996
[2019-08-23 04:48:19,141] TRAIN Iter 67640: lr = 0.387268,	loss = 2.725691,	Top-1 err = 0.429688,	Top-5 err = 0.201123,	data_time = 0.050919,	train_time = 0.388032
[2019-08-23 04:48:32,283] TRAIN Iter 67660: lr = 0.387235,	loss = 2.860451,	Top-1 err = 0.433398,	Top-5 err = 0.208008,	data_time = 0.050755,	train_time = 0.657044
[2019-08-23 04:48:45,537] TRAIN Iter 67680: lr = 0.387202,	loss = 2.780180,	Top-1 err = 0.436670,	Top-5 err = 0.204346,	data_time = 0.050871,	train_time = 0.662696
[2019-08-23 04:48:53,303] TRAIN Iter 67700: lr = 0.387168,	loss = 2.795223,	Top-1 err = 0.440381,	Top-5 err = 0.211719,	data_time = 0.050545,	train_time = 0.388274
[2019-08-23 04:49:06,554] TRAIN Iter 67720: lr = 0.387135,	loss = 2.851175,	Top-1 err = 0.438965,	Top-5 err = 0.215186,	data_time = 0.050668,	train_time = 0.662533
[2019-08-23 04:49:21,705] TRAIN Iter 67740: lr = 0.387102,	loss = 2.688792,	Top-1 err = 0.434131,	Top-5 err = 0.206250,	data_time = 0.126186,	train_time = 0.757542
[2019-08-23 04:49:29,164] TRAIN Iter 67760: lr = 0.387068,	loss = 2.806281,	Top-1 err = 0.434766,	Top-5 err = 0.206250,	data_time = 0.050867,	train_time = 0.372943
[2019-08-23 04:49:42,170] TRAIN Iter 67780: lr = 0.387035,	loss = 2.669765,	Top-1 err = 0.442139,	Top-5 err = 0.211182,	data_time = 0.050641,	train_time = 0.650307
[2019-08-23 04:49:49,414] TRAIN Iter 67800: lr = 0.387002,	loss = 2.694911,	Top-1 err = 0.441992,	Top-5 err = 0.210107,	data_time = 0.127611,	train_time = 0.362173
[2019-08-23 04:50:01,996] TRAIN Iter 67820: lr = 0.386968,	loss = 2.892467,	Top-1 err = 0.443213,	Top-5 err = 0.216650,	data_time = 0.050264,	train_time = 0.629074
[2019-08-23 04:50:17,078] TRAIN Iter 67840: lr = 0.386935,	loss = 2.755929,	Top-1 err = 0.438672,	Top-5 err = 0.214697,	data_time = 0.050535,	train_time = 0.754081
[2019-08-23 04:50:24,507] TRAIN Iter 67860: lr = 0.386902,	loss = 2.832116,	Top-1 err = 0.438574,	Top-5 err = 0.211572,	data_time = 0.050871,	train_time = 0.371452
[2019-08-23 04:50:37,641] TRAIN Iter 67880: lr = 0.386868,	loss = 2.786375,	Top-1 err = 0.440381,	Top-5 err = 0.214941,	data_time = 0.050322,	train_time = 0.656683
[2019-08-23 04:50:47,021] TRAIN Iter 67900: lr = 0.386835,	loss = 2.773733,	Top-1 err = 0.442773,	Top-5 err = 0.215674,	data_time = 0.148088,	train_time = 0.469001
[2019-08-23 04:50:59,658] TRAIN Iter 67920: lr = 0.386802,	loss = 2.805090,	Top-1 err = 0.445312,	Top-5 err = 0.218311,	data_time = 0.050553,	train_time = 0.631823
[2019-08-23 04:51:12,253] TRAIN Iter 67940: lr = 0.386768,	loss = 2.889523,	Top-1 err = 0.440674,	Top-5 err = 0.211914,	data_time = 0.050689,	train_time = 0.629742
[2019-08-23 04:51:19,699] TRAIN Iter 67960: lr = 0.386735,	loss = 2.817214,	Top-1 err = 0.441748,	Top-5 err = 0.216406,	data_time = 0.050533,	train_time = 0.372258
[2019-08-23 04:51:34,079] TRAIN Iter 67980: lr = 0.386702,	loss = 2.719791,	Top-1 err = 0.436621,	Top-5 err = 0.215088,	data_time = 0.050992,	train_time = 0.718989
[2019-08-23 04:51:49,447] TRAIN Iter 68000: lr = 0.386668,	loss = 2.715778,	Top-1 err = 0.437939,	Top-5 err = 0.209424,	data_time = 0.595243,	train_time = 0.768386
[2019-08-23 04:51:57,149] TRAIN Iter 68020: lr = 0.386635,	loss = 2.788507,	Top-1 err = 0.447461,	Top-5 err = 0.213379,	data_time = 0.162434,	train_time = 0.385075
[2019-08-23 04:52:10,992] TRAIN Iter 68040: lr = 0.386602,	loss = 2.786413,	Top-1 err = 0.441553,	Top-5 err = 0.215283,	data_time = 0.050848,	train_time = 0.692173
[2019-08-23 04:52:25,144] TRAIN Iter 68060: lr = 0.386568,	loss = 2.782216,	Top-1 err = 0.443164,	Top-5 err = 0.216846,	data_time = 0.050893,	train_time = 0.707546
[2019-08-23 04:52:34,805] TRAIN Iter 68080: lr = 0.386535,	loss = 2.792358,	Top-1 err = 0.442969,	Top-5 err = 0.215234,	data_time = 0.050362,	train_time = 0.483027
[2019-08-23 04:52:47,480] TRAIN Iter 68100: lr = 0.386502,	loss = 2.823181,	Top-1 err = 0.442529,	Top-5 err = 0.213086,	data_time = 0.050259,	train_time = 0.633780
[2019-08-23 04:52:54,993] TRAIN Iter 68120: lr = 0.386468,	loss = 2.731179,	Top-1 err = 0.440967,	Top-5 err = 0.211621,	data_time = 0.050502,	train_time = 0.375585
[2019-08-23 04:53:10,111] TRAIN Iter 68140: lr = 0.386435,	loss = 2.751660,	Top-1 err = 0.449072,	Top-5 err = 0.219531,	data_time = 0.050787,	train_time = 0.755917
[2019-08-23 04:53:24,527] TRAIN Iter 68160: lr = 0.386402,	loss = 2.747257,	Top-1 err = 0.442236,	Top-5 err = 0.213574,	data_time = 0.050472,	train_time = 0.720759
[2019-08-23 04:53:31,969] TRAIN Iter 68180: lr = 0.386368,	loss = 2.853713,	Top-1 err = 0.446436,	Top-5 err = 0.217041,	data_time = 0.050894,	train_time = 0.372097
[2019-08-23 04:53:45,639] TRAIN Iter 68200: lr = 0.386335,	loss = 2.781238,	Top-1 err = 0.441162,	Top-5 err = 0.213135,	data_time = 0.050292,	train_time = 0.683496
[2019-08-23 04:54:01,068] TRAIN Iter 68220: lr = 0.386302,	loss = 2.804645,	Top-1 err = 0.444434,	Top-5 err = 0.211621,	data_time = 0.050773,	train_time = 0.771440
[2019-08-23 04:54:08,608] TRAIN Iter 68240: lr = 0.386268,	loss = 2.719277,	Top-1 err = 0.444189,	Top-5 err = 0.212354,	data_time = 0.050825,	train_time = 0.376985
[2019-08-23 04:54:22,007] TRAIN Iter 68260: lr = 0.386235,	loss = 2.829389,	Top-1 err = 0.443262,	Top-5 err = 0.213037,	data_time = 0.050684,	train_time = 0.669911
[2019-08-23 04:54:29,420] TRAIN Iter 68280: lr = 0.386202,	loss = 2.822252,	Top-1 err = 0.442529,	Top-5 err = 0.212549,	data_time = 0.050654,	train_time = 0.370663
[2019-08-23 04:54:44,460] TRAIN Iter 68300: lr = 0.386168,	loss = 2.780296,	Top-1 err = 0.446582,	Top-5 err = 0.212549,	data_time = 0.050365,	train_time = 0.751962
[2019-08-23 04:54:59,362] TRAIN Iter 68320: lr = 0.386135,	loss = 2.802314,	Top-1 err = 0.437646,	Top-5 err = 0.208594,	data_time = 0.127078,	train_time = 0.745106
[2019-08-23 04:55:06,321] TRAIN Iter 68340: lr = 0.386102,	loss = 2.741544,	Top-1 err = 0.445117,	Top-5 err = 0.215234,	data_time = 0.051047,	train_time = 0.347949
[2019-08-23 04:55:22,793] TRAIN Iter 68360: lr = 0.386068,	loss = 2.820964,	Top-1 err = 0.447949,	Top-5 err = 0.221924,	data_time = 0.050655,	train_time = 0.823585
[2019-08-23 04:55:38,669] TRAIN Iter 68380: lr = 0.386035,	loss = 2.888643,	Top-1 err = 0.442285,	Top-5 err = 0.212939,	data_time = 0.050687,	train_time = 0.793750
[2019-08-23 04:55:45,932] TRAIN Iter 68400: lr = 0.386002,	loss = 2.803142,	Top-1 err = 0.436279,	Top-5 err = 0.211133,	data_time = 0.050551,	train_time = 0.363167
[2019-08-23 04:56:00,986] TRAIN Iter 68420: lr = 0.385968,	loss = 2.883558,	Top-1 err = 0.448047,	Top-5 err = 0.217383,	data_time = 0.050674,	train_time = 0.752668
[2019-08-23 04:56:09,225] TRAIN Iter 68440: lr = 0.385935,	loss = 2.857339,	Top-1 err = 0.437012,	Top-5 err = 0.211230,	data_time = 0.050671,	train_time = 0.411950
[2019-08-23 04:56:21,060] TRAIN Iter 68460: lr = 0.385902,	loss = 2.868833,	Top-1 err = 0.443848,	Top-5 err = 0.212695,	data_time = 0.050652,	train_time = 0.591749
[2019-08-23 04:56:37,559] TRAIN Iter 68480: lr = 0.385868,	loss = 2.802212,	Top-1 err = 0.439258,	Top-5 err = 0.211523,	data_time = 0.050892,	train_time = 0.824903
[2019-08-23 04:56:45,109] TRAIN Iter 68500: lr = 0.385835,	loss = 2.855070,	Top-1 err = 0.450146,	Top-5 err = 0.217676,	data_time = 0.050568,	train_time = 0.377480
[2019-08-23 04:56:58,521] TRAIN Iter 68520: lr = 0.385802,	loss = 2.855590,	Top-1 err = 0.443750,	Top-5 err = 0.215771,	data_time = 0.050274,	train_time = 0.670607
[2019-08-23 04:57:14,094] TRAIN Iter 68540: lr = 0.385768,	loss = 2.794509,	Top-1 err = 0.439795,	Top-5 err = 0.213623,	data_time = 0.050930,	train_time = 0.778645
[2019-08-23 04:57:21,019] TRAIN Iter 68560: lr = 0.385735,	loss = 2.809620,	Top-1 err = 0.450391,	Top-5 err = 0.216211,	data_time = 0.050476,	train_time = 0.346227
[2019-08-23 04:57:37,797] TRAIN Iter 68580: lr = 0.385702,	loss = 2.817677,	Top-1 err = 0.444482,	Top-5 err = 0.215674,	data_time = 0.050544,	train_time = 0.838884
[2019-08-23 04:57:44,733] TRAIN Iter 68600: lr = 0.385668,	loss = 2.860970,	Top-1 err = 0.440137,	Top-5 err = 0.212598,	data_time = 0.050534,	train_time = 0.346767
[2019-08-23 04:58:01,043] TRAIN Iter 68620: lr = 0.385635,	loss = 2.765959,	Top-1 err = 0.446045,	Top-5 err = 0.213721,	data_time = 0.050247,	train_time = 0.815478
[2019-08-23 04:58:16,613] TRAIN Iter 68640: lr = 0.385602,	loss = 2.824688,	Top-1 err = 0.442090,	Top-5 err = 0.214648,	data_time = 0.050464,	train_time = 0.778497
[2019-08-23 04:58:24,716] TRAIN Iter 68660: lr = 0.385568,	loss = 2.764584,	Top-1 err = 0.444238,	Top-5 err = 0.216504,	data_time = 0.050744,	train_time = 0.405110
[2019-08-23 04:58:40,907] TRAIN Iter 68680: lr = 0.385535,	loss = 2.802713,	Top-1 err = 0.446289,	Top-5 err = 0.216455,	data_time = 0.050699,	train_time = 0.809542
[2019-08-23 04:58:56,881] TRAIN Iter 68700: lr = 0.385502,	loss = 2.796806,	Top-1 err = 0.441650,	Top-5 err = 0.214453,	data_time = 0.050292,	train_time = 0.798691
[2019-08-23 04:59:03,514] TRAIN Iter 68720: lr = 0.385468,	loss = 2.780391,	Top-1 err = 0.443555,	Top-5 err = 0.214355,	data_time = 0.050867,	train_time = 0.331650
[2019-08-23 04:59:21,036] TRAIN Iter 68740: lr = 0.385435,	loss = 2.854332,	Top-1 err = 0.454150,	Top-5 err = 0.220508,	data_time = 0.050180,	train_time = 0.876062
[2019-08-23 04:59:28,001] TRAIN Iter 68760: lr = 0.385402,	loss = 2.843698,	Top-1 err = 0.443359,	Top-5 err = 0.211768,	data_time = 0.145148,	train_time = 0.348248
[2019-08-23 04:59:41,958] TRAIN Iter 68780: lr = 0.385368,	loss = 2.829187,	Top-1 err = 0.445654,	Top-5 err = 0.217383,	data_time = 0.049915,	train_time = 0.697851
[2019-08-23 05:00:29,165] TRAIN Iter 68800: lr = 0.385335,	loss = 2.848713,	Top-1 err = 0.445173,	Top-5 err = 0.218869,	data_time = 3.517833,	train_time = 2.360313
[2019-08-23 05:00:40,632] TRAIN Iter 68820: lr = 0.385302,	loss = 2.814428,	Top-1 err = 0.437549,	Top-5 err = 0.214844,	data_time = 0.050119,	train_time = 0.573348
[2019-08-23 05:00:50,639] TRAIN Iter 68840: lr = 0.385268,	loss = 2.850644,	Top-1 err = 0.440039,	Top-5 err = 0.209961,	data_time = 0.050408,	train_time = 0.500309
[2019-08-23 05:00:58,793] TRAIN Iter 68860: lr = 0.385235,	loss = 2.790335,	Top-1 err = 0.432080,	Top-5 err = 0.208447,	data_time = 0.050789,	train_time = 0.407713
[2019-08-23 05:01:08,704] TRAIN Iter 68880: lr = 0.385202,	loss = 2.787822,	Top-1 err = 0.440723,	Top-5 err = 0.210400,	data_time = 0.050912,	train_time = 0.495523
[2019-08-23 05:01:21,527] TRAIN Iter 68900: lr = 0.385168,	loss = 2.757966,	Top-1 err = 0.435986,	Top-5 err = 0.207715,	data_time = 0.050781,	train_time = 0.641140
[2019-08-23 05:01:29,633] TRAIN Iter 68920: lr = 0.385135,	loss = 2.717963,	Top-1 err = 0.438916,	Top-5 err = 0.210791,	data_time = 0.050599,	train_time = 0.405270
[2019-08-23 05:01:43,419] TRAIN Iter 68940: lr = 0.385102,	loss = 2.739534,	Top-1 err = 0.437842,	Top-5 err = 0.209912,	data_time = 0.050888,	train_time = 0.689292
[2019-08-23 05:01:55,352] TRAIN Iter 68960: lr = 0.385068,	loss = 2.816859,	Top-1 err = 0.440039,	Top-5 err = 0.213330,	data_time = 0.616261,	train_time = 0.596650
[2019-08-23 05:02:03,555] TRAIN Iter 68980: lr = 0.385035,	loss = 2.840920,	Top-1 err = 0.434180,	Top-5 err = 0.207178,	data_time = 0.050904,	train_time = 0.410109
[2019-08-23 05:02:17,962] TRAIN Iter 69000: lr = 0.385002,	loss = 2.774986,	Top-1 err = 0.438965,	Top-5 err = 0.212207,	data_time = 0.050881,	train_time = 0.720345
[2019-08-23 05:02:25,724] TRAIN Iter 69020: lr = 0.384968,	loss = 2.858641,	Top-1 err = 0.436475,	Top-5 err = 0.207471,	data_time = 0.050134,	train_time = 0.388101
[2019-08-23 05:02:38,409] TRAIN Iter 69040: lr = 0.384935,	loss = 2.846805,	Top-1 err = 0.442041,	Top-5 err = 0.216211,	data_time = 0.050917,	train_time = 0.634200
[2019-08-23 05:02:50,926] TRAIN Iter 69060: lr = 0.384902,	loss = 2.825358,	Top-1 err = 0.437744,	Top-5 err = 0.209619,	data_time = 0.155733,	train_time = 0.625836
[2019-08-23 05:02:58,474] TRAIN Iter 69080: lr = 0.384868,	loss = 2.816185,	Top-1 err = 0.443555,	Top-5 err = 0.214062,	data_time = 0.050740,	train_time = 0.377390
[2019-08-23 05:03:11,349] TRAIN Iter 69100: lr = 0.384835,	loss = 2.787602,	Top-1 err = 0.437842,	Top-5 err = 0.212354,	data_time = 0.050273,	train_time = 0.643733
[2019-08-23 05:03:25,066] TRAIN Iter 69120: lr = 0.384802,	loss = 2.937302,	Top-1 err = 0.443213,	Top-5 err = 0.214795,	data_time = 0.144860,	train_time = 0.685854
[2019-08-23 05:03:33,056] TRAIN Iter 69140: lr = 0.384768,	loss = 2.849681,	Top-1 err = 0.436279,	Top-5 err = 0.210010,	data_time = 0.050569,	train_time = 0.399463
[2019-08-23 05:03:46,840] TRAIN Iter 69160: lr = 0.384735,	loss = 2.703154,	Top-1 err = 0.436475,	Top-5 err = 0.209180,	data_time = 0.050653,	train_time = 0.689216
[2019-08-23 05:03:54,592] TRAIN Iter 69180: lr = 0.384702,	loss = 2.723955,	Top-1 err = 0.439160,	Top-5 err = 0.215723,	data_time = 0.050236,	train_time = 0.387553
[2019-08-23 05:04:09,468] TRAIN Iter 69200: lr = 0.384668,	loss = 2.720287,	Top-1 err = 0.442285,	Top-5 err = 0.211914,	data_time = 0.050883,	train_time = 0.743798
[2019-08-23 05:04:23,620] TRAIN Iter 69220: lr = 0.384635,	loss = 2.731839,	Top-1 err = 0.441113,	Top-5 err = 0.211572,	data_time = 0.050635,	train_time = 0.707571
[2019-08-23 05:04:31,386] TRAIN Iter 69240: lr = 0.384602,	loss = 2.894906,	Top-1 err = 0.441553,	Top-5 err = 0.209082,	data_time = 0.050756,	train_time = 0.388291
[2019-08-23 05:04:44,481] TRAIN Iter 69260: lr = 0.384568,	loss = 2.731448,	Top-1 err = 0.440527,	Top-5 err = 0.216895,	data_time = 0.050400,	train_time = 0.654759
[2019-08-23 05:04:57,014] TRAIN Iter 69280: lr = 0.384535,	loss = 2.715414,	Top-1 err = 0.439600,	Top-5 err = 0.206299,	data_time = 0.104073,	train_time = 0.626600
[2019-08-23 05:05:05,279] TRAIN Iter 69300: lr = 0.384502,	loss = 2.701951,	Top-1 err = 0.437012,	Top-5 err = 0.209082,	data_time = 0.124215,	train_time = 0.413246
[2019-08-23 05:05:19,183] TRAIN Iter 69320: lr = 0.384468,	loss = 2.807847,	Top-1 err = 0.438379,	Top-5 err = 0.209570,	data_time = 0.050376,	train_time = 0.695198
[2019-08-23 05:05:26,473] TRAIN Iter 69340: lr = 0.384435,	loss = 2.835025,	Top-1 err = 0.447705,	Top-5 err = 0.216699,	data_time = 0.050315,	train_time = 0.364498
[2019-08-23 05:05:41,866] TRAIN Iter 69360: lr = 0.384402,	loss = 2.790749,	Top-1 err = 0.439209,	Top-5 err = 0.216455,	data_time = 0.050738,	train_time = 0.769595
[2019-08-23 05:05:58,259] TRAIN Iter 69380: lr = 0.384368,	loss = 2.834945,	Top-1 err = 0.442822,	Top-5 err = 0.210303,	data_time = 0.125462,	train_time = 0.819675
[2019-08-23 05:06:05,883] TRAIN Iter 69400: lr = 0.384335,	loss = 2.736180,	Top-1 err = 0.438330,	Top-5 err = 0.212061,	data_time = 0.050540,	train_time = 0.381152
[2019-08-23 05:06:18,695] TRAIN Iter 69420: lr = 0.384302,	loss = 2.841640,	Top-1 err = 0.443945,	Top-5 err = 0.214014,	data_time = 0.050348,	train_time = 0.640580
[2019-08-23 05:06:33,112] TRAIN Iter 69440: lr = 0.384268,	loss = 2.837508,	Top-1 err = 0.441162,	Top-5 err = 0.215527,	data_time = 0.050967,	train_time = 0.720853
[2019-08-23 05:06:40,991] TRAIN Iter 69460: lr = 0.384235,	loss = 2.846680,	Top-1 err = 0.442627,	Top-5 err = 0.213379,	data_time = 0.050529,	train_time = 0.393948
[2019-08-23 05:06:54,999] TRAIN Iter 69480: lr = 0.384202,	loss = 2.844253,	Top-1 err = 0.441064,	Top-5 err = 0.212500,	data_time = 0.050750,	train_time = 0.700380
[2019-08-23 05:07:02,054] TRAIN Iter 69500: lr = 0.384168,	loss = 2.878420,	Top-1 err = 0.449365,	Top-5 err = 0.220752,	data_time = 0.050682,	train_time = 0.352731
[2019-08-23 05:07:17,327] TRAIN Iter 69520: lr = 0.384135,	loss = 2.859225,	Top-1 err = 0.438232,	Top-5 err = 0.212256,	data_time = 0.050383,	train_time = 0.763632
[2019-08-23 05:07:31,592] TRAIN Iter 69540: lr = 0.384102,	loss = 2.780495,	Top-1 err = 0.438037,	Top-5 err = 0.210156,	data_time = 0.050808,	train_time = 0.713226
[2019-08-23 05:07:38,593] TRAIN Iter 69560: lr = 0.384068,	loss = 2.800092,	Top-1 err = 0.439453,	Top-5 err = 0.209814,	data_time = 0.050477,	train_time = 0.350056
[2019-08-23 05:07:53,598] TRAIN Iter 69580: lr = 0.384035,	loss = 2.816729,	Top-1 err = 0.444189,	Top-5 err = 0.210205,	data_time = 0.114279,	train_time = 0.750209
[2019-08-23 05:08:07,052] TRAIN Iter 69600: lr = 0.384002,	loss = 2.888224,	Top-1 err = 0.440723,	Top-5 err = 0.209229,	data_time = 0.140237,	train_time = 0.672706
[2019-08-23 05:08:14,710] TRAIN Iter 69620: lr = 0.383968,	loss = 2.757601,	Top-1 err = 0.441602,	Top-5 err = 0.211670,	data_time = 0.050531,	train_time = 0.382883
[2019-08-23 05:08:30,468] TRAIN Iter 69640: lr = 0.383935,	loss = 2.796153,	Top-1 err = 0.439795,	Top-5 err = 0.213867,	data_time = 0.097147,	train_time = 0.787877
[2019-08-23 05:08:37,643] TRAIN Iter 69660: lr = 0.383902,	loss = 2.840232,	Top-1 err = 0.440918,	Top-5 err = 0.214697,	data_time = 0.050487,	train_time = 0.358762
[2019-08-23 05:08:54,464] TRAIN Iter 69680: lr = 0.383868,	loss = 2.818181,	Top-1 err = 0.443799,	Top-5 err = 0.214697,	data_time = 0.050735,	train_time = 0.840995
[2019-08-23 05:09:10,827] TRAIN Iter 69700: lr = 0.383835,	loss = 2.861495,	Top-1 err = 0.441846,	Top-5 err = 0.216357,	data_time = 0.050401,	train_time = 0.818146
[2019-08-23 05:09:17,762] TRAIN Iter 69720: lr = 0.383802,	loss = 2.859757,	Top-1 err = 0.448242,	Top-5 err = 0.221191,	data_time = 0.050716,	train_time = 0.346747
[2019-08-23 05:09:35,057] TRAIN Iter 69740: lr = 0.383768,	loss = 2.766991,	Top-1 err = 0.443896,	Top-5 err = 0.211279,	data_time = 0.050520,	train_time = 0.864742
[2019-08-23 05:09:50,182] TRAIN Iter 69760: lr = 0.383735,	loss = 2.824811,	Top-1 err = 0.443457,	Top-5 err = 0.213184,	data_time = 0.050396,	train_time = 0.756248
[2019-08-23 05:09:58,021] TRAIN Iter 69780: lr = 0.383702,	loss = 2.888839,	Top-1 err = 0.444727,	Top-5 err = 0.214160,	data_time = 0.050799,	train_time = 0.391896
[2019-08-23 05:10:13,520] TRAIN Iter 69800: lr = 0.383668,	loss = 2.833714,	Top-1 err = 0.440967,	Top-5 err = 0.213965,	data_time = 0.050673,	train_time = 0.774956
[2019-08-23 05:10:20,542] TRAIN Iter 69820: lr = 0.383635,	loss = 2.877039,	Top-1 err = 0.443213,	Top-5 err = 0.214453,	data_time = 0.050352,	train_time = 0.351069
[2019-08-23 05:10:37,854] TRAIN Iter 69840: lr = 0.383602,	loss = 2.793014,	Top-1 err = 0.443066,	Top-5 err = 0.212695,	data_time = 0.050566,	train_time = 0.865602
[2019-08-23 05:10:55,195] TRAIN Iter 69860: lr = 0.383568,	loss = 2.889530,	Top-1 err = 0.444336,	Top-5 err = 0.217480,	data_time = 0.050644,	train_time = 0.867042
[2019-08-23 05:11:02,459] TRAIN Iter 69880: lr = 0.383535,	loss = 2.844625,	Top-1 err = 0.448096,	Top-5 err = 0.216309,	data_time = 0.050552,	train_time = 0.363191
[2019-08-23 05:11:18,270] TRAIN Iter 69900: lr = 0.383502,	loss = 2.851001,	Top-1 err = 0.445947,	Top-5 err = 0.217139,	data_time = 0.050567,	train_time = 0.790516
[2019-08-23 05:11:34,932] TRAIN Iter 69920: lr = 0.383468,	loss = 2.845916,	Top-1 err = 0.444189,	Top-5 err = 0.215625,	data_time = 0.050628,	train_time = 0.833087
[2019-08-23 05:11:42,707] TRAIN Iter 69940: lr = 0.383435,	loss = 2.807730,	Top-1 err = 0.444141,	Top-5 err = 0.213184,	data_time = 0.050675,	train_time = 0.388758
[2019-08-23 05:12:00,100] TRAIN Iter 69960: lr = 0.383402,	loss = 2.854081,	Top-1 err = 0.439111,	Top-5 err = 0.212842,	data_time = 0.050477,	train_time = 0.869644
[2019-08-23 05:12:07,103] TRAIN Iter 69980: lr = 0.383368,	loss = 2.929645,	Top-1 err = 0.447656,	Top-5 err = 0.218701,	data_time = 0.050524,	train_time = 0.350101
[2019-08-23 05:12:25,171] TRAIN Iter 70000: lr = 0.383335,	loss = 2.875955,	Top-1 err = 0.448584,	Top-5 err = 0.220850,	data_time = 0.050198,	train_time = 0.903385
[2019-08-23 05:13:24,252] TEST Iter 70000: loss = 2.589505,	Top-1 err = 0.406620,	Top-5 err = 0.167880,	val_time = 59.031661
[2019-08-23 05:13:30,234] TRAIN Iter 70020: lr = 0.383302,	loss = 2.820316,	Top-1 err = 0.439600,	Top-5 err = 0.215039,	data_time = 0.049911,	train_time = 0.299053
[2019-08-23 05:13:36,214] TRAIN Iter 70040: lr = 0.383268,	loss = 2.764901,	Top-1 err = 0.437646,	Top-5 err = 0.209668,	data_time = 0.049865,	train_time = 0.298998
[2019-08-23 05:14:22,587] TRAIN Iter 70060: lr = 0.383235,	loss = 2.826545,	Top-1 err = 0.446253,	Top-5 err = 0.215323,	data_time = 0.050703,	train_time = 2.318648
[2019-08-23 05:14:31,051] TRAIN Iter 70080: lr = 0.383202,	loss = 2.907534,	Top-1 err = 0.442969,	Top-5 err = 0.215674,	data_time = 0.050493,	train_time = 0.423162
[2019-08-23 05:14:43,442] TRAIN Iter 70100: lr = 0.383168,	loss = 2.814235,	Top-1 err = 0.437061,	Top-5 err = 0.212109,	data_time = 0.050800,	train_time = 0.619532
[2019-08-23 05:14:52,097] TRAIN Iter 70120: lr = 0.383135,	loss = 2.831727,	Top-1 err = 0.436279,	Top-5 err = 0.207080,	data_time = 0.101493,	train_time = 0.432730
[2019-08-23 05:14:59,687] TRAIN Iter 70140: lr = 0.383102,	loss = 2.722048,	Top-1 err = 0.440820,	Top-5 err = 0.208838,	data_time = 0.113597,	train_time = 0.379499
[2019-08-23 05:15:15,012] TRAIN Iter 70160: lr = 0.383068,	loss = 2.759764,	Top-1 err = 0.435645,	Top-5 err = 0.211426,	data_time = 0.050646,	train_time = 0.766217
[2019-08-23 05:15:29,255] TRAIN Iter 70180: lr = 0.383035,	loss = 2.775305,	Top-1 err = 0.431836,	Top-5 err = 0.205518,	data_time = 0.050582,	train_time = 0.712138
[2019-08-23 05:15:36,943] TRAIN Iter 70200: lr = 0.383002,	loss = 2.761758,	Top-1 err = 0.438232,	Top-5 err = 0.208252,	data_time = 0.050549,	train_time = 0.384377
[2019-08-23 05:15:50,625] TRAIN Iter 70220: lr = 0.382968,	loss = 2.737393,	Top-1 err = 0.434424,	Top-5 err = 0.211377,	data_time = 0.050729,	train_time = 0.684094
[2019-08-23 05:15:59,161] TRAIN Iter 70240: lr = 0.382935,	loss = 2.766850,	Top-1 err = 0.435693,	Top-5 err = 0.206104,	data_time = 0.050469,	train_time = 0.426777
[2019-08-23 05:16:10,487] TRAIN Iter 70260: lr = 0.382902,	loss = 2.854127,	Top-1 err = 0.438623,	Top-5 err = 0.210303,	data_time = 0.050430,	train_time = 0.566306
[2019-08-23 05:16:24,267] TRAIN Iter 70280: lr = 0.382868,	loss = 2.854060,	Top-1 err = 0.441162,	Top-5 err = 0.211670,	data_time = 0.051133,	train_time = 0.689000
[2019-08-23 05:16:32,123] TRAIN Iter 70300: lr = 0.382835,	loss = 2.768931,	Top-1 err = 0.438965,	Top-5 err = 0.211768,	data_time = 0.050817,	train_time = 0.392776
[2019-08-23 05:16:45,494] TRAIN Iter 70320: lr = 0.382802,	loss = 2.710302,	Top-1 err = 0.436523,	Top-5 err = 0.208154,	data_time = 0.050753,	train_time = 0.668545
[2019-08-23 05:17:00,089] TRAIN Iter 70340: lr = 0.382768,	loss = 2.773591,	Top-1 err = 0.437695,	Top-5 err = 0.208545,	data_time = 0.050668,	train_time = 0.729733
[2019-08-23 05:17:08,165] TRAIN Iter 70360: lr = 0.382735,	loss = 2.867066,	Top-1 err = 0.438916,	Top-5 err = 0.209717,	data_time = 0.050508,	train_time = 0.403765
[2019-08-23 05:17:20,887] TRAIN Iter 70380: lr = 0.382702,	loss = 2.865933,	Top-1 err = 0.437500,	Top-5 err = 0.208008,	data_time = 0.050134,	train_time = 0.636064
[2019-08-23 05:17:28,434] TRAIN Iter 70400: lr = 0.382668,	loss = 2.651147,	Top-1 err = 0.433984,	Top-5 err = 0.208350,	data_time = 0.050737,	train_time = 0.377368
[2019-08-23 05:17:42,506] TRAIN Iter 70420: lr = 0.382635,	loss = 2.770390,	Top-1 err = 0.444287,	Top-5 err = 0.211865,	data_time = 0.050829,	train_time = 0.703585
[2019-08-23 05:17:57,357] TRAIN Iter 70440: lr = 0.382602,	loss = 2.790933,	Top-1 err = 0.441602,	Top-5 err = 0.212402,	data_time = 0.050920,	train_time = 0.742551
[2019-08-23 05:18:05,140] TRAIN Iter 70460: lr = 0.382568,	loss = 2.752080,	Top-1 err = 0.438330,	Top-5 err = 0.206250,	data_time = 0.050574,	train_time = 0.389126
[2019-08-23 05:18:17,983] TRAIN Iter 70480: lr = 0.382535,	loss = 2.835970,	Top-1 err = 0.440332,	Top-5 err = 0.209668,	data_time = 0.050689,	train_time = 0.642090
[2019-08-23 05:18:32,960] TRAIN Iter 70500: lr = 0.382502,	loss = 2.898302,	Top-1 err = 0.444678,	Top-5 err = 0.216162,	data_time = 0.050998,	train_time = 0.748873
[2019-08-23 05:18:40,949] TRAIN Iter 70520: lr = 0.382468,	loss = 2.789110,	Top-1 err = 0.437744,	Top-5 err = 0.210449,	data_time = 0.050546,	train_time = 0.399443
[2019-08-23 05:18:56,155] TRAIN Iter 70540: lr = 0.382435,	loss = 2.743099,	Top-1 err = 0.441846,	Top-5 err = 0.212793,	data_time = 0.050528,	train_time = 0.760277
[2019-08-23 05:19:03,579] TRAIN Iter 70560: lr = 0.382402,	loss = 2.857005,	Top-1 err = 0.440771,	Top-5 err = 0.212646,	data_time = 0.136142,	train_time = 0.371165
[2019-08-23 05:19:18,819] TRAIN Iter 70580: lr = 0.382368,	loss = 2.764135,	Top-1 err = 0.436816,	Top-5 err = 0.206494,	data_time = 0.050451,	train_time = 0.761974
[2019-08-23 05:19:32,334] TRAIN Iter 70600: lr = 0.382335,	loss = 2.790900,	Top-1 err = 0.429102,	Top-5 err = 0.211670,	data_time = 0.050554,	train_time = 0.675737
[2019-08-23 05:19:39,577] TRAIN Iter 70620: lr = 0.382302,	loss = 2.795098,	Top-1 err = 0.443604,	Top-5 err = 0.215820,	data_time = 0.050321,	train_time = 0.362139
[2019-08-23 05:19:54,646] TRAIN Iter 70640: lr = 0.382268,	loss = 2.811885,	Top-1 err = 0.447314,	Top-5 err = 0.216943,	data_time = 0.050440,	train_time = 0.753456
[2019-08-23 05:20:09,208] TRAIN Iter 70660: lr = 0.382235,	loss = 2.821779,	Top-1 err = 0.445215,	Top-5 err = 0.212500,	data_time = 0.050392,	train_time = 0.728057
[2019-08-23 05:20:16,903] TRAIN Iter 70680: lr = 0.382202,	loss = 2.859222,	Top-1 err = 0.438818,	Top-5 err = 0.210889,	data_time = 0.050256,	train_time = 0.384731
[2019-08-23 05:20:29,816] TRAIN Iter 70700: lr = 0.382168,	loss = 2.753449,	Top-1 err = 0.437061,	Top-5 err = 0.206787,	data_time = 0.050929,	train_time = 0.645656
[2019-08-23 05:20:37,034] TRAIN Iter 70720: lr = 0.382135,	loss = 2.841218,	Top-1 err = 0.447607,	Top-5 err = 0.215967,	data_time = 0.050522,	train_time = 0.360857
[2019-08-23 05:20:50,990] TRAIN Iter 70740: lr = 0.382102,	loss = 2.767184,	Top-1 err = 0.444824,	Top-5 err = 0.214551,	data_time = 0.050553,	train_time = 0.697812
[2019-08-23 05:21:05,315] TRAIN Iter 70760: lr = 0.382068,	loss = 2.885984,	Top-1 err = 0.437988,	Top-5 err = 0.213672,	data_time = 0.050712,	train_time = 0.716239
[2019-08-23 05:21:12,565] TRAIN Iter 70780: lr = 0.382035,	loss = 2.906266,	Top-1 err = 0.449316,	Top-5 err = 0.217285,	data_time = 0.050841,	train_time = 0.362478
[2019-08-23 05:21:30,300] TRAIN Iter 70800: lr = 0.382002,	loss = 2.825670,	Top-1 err = 0.445850,	Top-5 err = 0.211719,	data_time = 0.050309,	train_time = 0.886725
[2019-08-23 05:21:45,353] TRAIN Iter 70820: lr = 0.381968,	loss = 2.713168,	Top-1 err = 0.443652,	Top-5 err = 0.214502,	data_time = 0.050401,	train_time = 0.752628
[2019-08-23 05:21:52,894] TRAIN Iter 70840: lr = 0.381935,	loss = 2.786124,	Top-1 err = 0.440820,	Top-5 err = 0.208740,	data_time = 0.050631,	train_time = 0.377057
[2019-08-23 05:22:08,277] TRAIN Iter 70860: lr = 0.381902,	loss = 2.822686,	Top-1 err = 0.445996,	Top-5 err = 0.215430,	data_time = 0.050662,	train_time = 0.769121
[2019-08-23 05:22:15,223] TRAIN Iter 70880: lr = 0.381868,	loss = 2.845393,	Top-1 err = 0.443896,	Top-5 err = 0.215918,	data_time = 0.050622,	train_time = 0.347310
[2019-08-23 05:22:30,713] TRAIN Iter 70900: lr = 0.381835,	loss = 2.809914,	Top-1 err = 0.442529,	Top-5 err = 0.214648,	data_time = 0.050547,	train_time = 0.774454
[2019-08-23 05:22:47,177] TRAIN Iter 70920: lr = 0.381802,	loss = 2.700194,	Top-1 err = 0.440332,	Top-5 err = 0.209863,	data_time = 0.050321,	train_time = 0.823180
[2019-08-23 05:22:54,252] TRAIN Iter 70940: lr = 0.381768,	loss = 2.822412,	Top-1 err = 0.442383,	Top-5 err = 0.209668,	data_time = 0.050365,	train_time = 0.353753
[2019-08-23 05:23:10,940] TRAIN Iter 70960: lr = 0.381735,	loss = 2.811007,	Top-1 err = 0.442432,	Top-5 err = 0.210303,	data_time = 0.050662,	train_time = 0.834410
[2019-08-23 05:23:27,837] TRAIN Iter 70980: lr = 0.381702,	loss = 2.819705,	Top-1 err = 0.440381,	Top-5 err = 0.211377,	data_time = 0.050465,	train_time = 0.844826
[2019-08-23 05:23:35,261] TRAIN Iter 71000: lr = 0.381668,	loss = 2.904365,	Top-1 err = 0.445752,	Top-5 err = 0.218164,	data_time = 0.050560,	train_time = 0.371180
[2019-08-23 05:23:51,590] TRAIN Iter 71020: lr = 0.381635,	loss = 2.764210,	Top-1 err = 0.438525,	Top-5 err = 0.210107,	data_time = 0.050354,	train_time = 0.816453
[2019-08-23 05:23:58,886] TRAIN Iter 71040: lr = 0.381602,	loss = 2.739716,	Top-1 err = 0.444238,	Top-5 err = 0.216406,	data_time = 0.050575,	train_time = 0.364765
[2019-08-23 05:24:16,201] TRAIN Iter 71060: lr = 0.381568,	loss = 2.872179,	Top-1 err = 0.445312,	Top-5 err = 0.213623,	data_time = 0.050909,	train_time = 0.865757
[2019-08-23 05:24:34,295] TRAIN Iter 71080: lr = 0.381535,	loss = 2.835165,	Top-1 err = 0.441699,	Top-5 err = 0.217578,	data_time = 0.050362,	train_time = 0.904649
[2019-08-23 05:24:41,331] TRAIN Iter 71100: lr = 0.381502,	loss = 2.865403,	Top-1 err = 0.441992,	Top-5 err = 0.219727,	data_time = 0.050925,	train_time = 0.351823
[2019-08-23 05:24:58,028] TRAIN Iter 71120: lr = 0.381468,	loss = 2.770393,	Top-1 err = 0.442188,	Top-5 err = 0.212402,	data_time = 0.050436,	train_time = 0.834816
[2019-08-23 05:25:15,019] TRAIN Iter 71140: lr = 0.381435,	loss = 2.846817,	Top-1 err = 0.441309,	Top-5 err = 0.212500,	data_time = 0.050574,	train_time = 0.849551
[2019-08-23 05:25:21,976] TRAIN Iter 71160: lr = 0.381402,	loss = 2.859973,	Top-1 err = 0.448779,	Top-5 err = 0.220605,	data_time = 0.050529,	train_time = 0.347827
[2019-08-23 05:25:39,054] TRAIN Iter 71180: lr = 0.381368,	loss = 2.806235,	Top-1 err = 0.440576,	Top-5 err = 0.214746,	data_time = 0.050481,	train_time = 0.853855
[2019-08-23 05:25:46,424] TRAIN Iter 71200: lr = 0.381335,	loss = 2.823507,	Top-1 err = 0.440234,	Top-5 err = 0.209912,	data_time = 0.050517,	train_time = 0.368488
[2019-08-23 05:26:03,036] TRAIN Iter 71220: lr = 0.381302,	loss = 2.737161,	Top-1 err = 0.441260,	Top-5 err = 0.212402,	data_time = 0.050352,	train_time = 0.830581
[2019-08-23 05:26:21,049] TRAIN Iter 71240: lr = 0.381268,	loss = 2.714932,	Top-1 err = 0.445996,	Top-5 err = 0.216699,	data_time = 0.130903,	train_time = 0.900642
[2019-08-23 05:26:28,258] TRAIN Iter 71260: lr = 0.381235,	loss = 2.816237,	Top-1 err = 0.443750,	Top-5 err = 0.214893,	data_time = 0.050055,	train_time = 0.360443
[2019-08-23 05:26:42,800] TRAIN Iter 71280: lr = 0.381202,	loss = 2.831223,	Top-1 err = 0.438721,	Top-5 err = 0.209277,	data_time = 0.049914,	train_time = 0.727082
[2019-08-23 05:26:55,911] TRAIN Iter 71300: lr = 0.381168,	loss = 3.185638,	Top-1 err = 0.439620,	Top-5 err = 0.217265,	data_time = 0.007121,	train_time = 0.655545
[2019-08-23 05:27:42,031] TRAIN Iter 71320: lr = 0.381135,	loss = 2.737215,	Top-1 err = 0.440918,	Top-5 err = 0.209619,	data_time = 0.050504,	train_time = 2.305998
[2019-08-23 05:27:55,692] TRAIN Iter 71340: lr = 0.381102,	loss = 2.891556,	Top-1 err = 0.435596,	Top-5 err = 0.208301,	data_time = 0.050727,	train_time = 0.683021
[2019-08-23 05:28:03,753] TRAIN Iter 71360: lr = 0.381068,	loss = 2.720379,	Top-1 err = 0.430176,	Top-5 err = 0.205127,	data_time = 0.050380,	train_time = 0.403039
[2019-08-23 05:28:13,178] TRAIN Iter 71380: lr = 0.381035,	loss = 2.825498,	Top-1 err = 0.435791,	Top-5 err = 0.206592,	data_time = 0.050752,	train_time = 0.471251
[2019-08-23 05:28:26,326] TRAIN Iter 71400: lr = 0.381002,	loss = 2.748830,	Top-1 err = 0.434277,	Top-5 err = 0.212695,	data_time = 0.050348,	train_time = 0.657345
[2019-08-23 05:28:34,408] TRAIN Iter 71420: lr = 0.380968,	loss = 2.783157,	Top-1 err = 0.434473,	Top-5 err = 0.206836,	data_time = 0.050769,	train_time = 0.404088
[2019-08-23 05:28:47,274] TRAIN Iter 71440: lr = 0.380935,	loss = 2.774797,	Top-1 err = 0.433301,	Top-5 err = 0.208643,	data_time = 0.050624,	train_time = 0.643310
[2019-08-23 05:28:55,907] TRAIN Iter 71460: lr = 0.380902,	loss = 2.704610,	Top-1 err = 0.433350,	Top-5 err = 0.204785,	data_time = 0.100695,	train_time = 0.431621
[2019-08-23 05:29:09,613] TRAIN Iter 71480: lr = 0.380868,	loss = 2.830608,	Top-1 err = 0.432471,	Top-5 err = 0.206055,	data_time = 0.155489,	train_time = 0.685318
[2019-08-23 05:29:23,164] TRAIN Iter 71500: lr = 0.380835,	loss = 2.738721,	Top-1 err = 0.433252,	Top-5 err = 0.209180,	data_time = 0.050684,	train_time = 0.677500
[2019-08-23 05:29:31,404] TRAIN Iter 71520: lr = 0.380802,	loss = 2.806070,	Top-1 err = 0.437695,	Top-5 err = 0.207324,	data_time = 0.050663,	train_time = 0.411974
[2019-08-23 05:29:44,980] TRAIN Iter 71540: lr = 0.380768,	loss = 2.724040,	Top-1 err = 0.441309,	Top-5 err = 0.208496,	data_time = 0.050465,	train_time = 0.678808
[2019-08-23 05:29:57,089] TRAIN Iter 71560: lr = 0.380735,	loss = 2.742463,	Top-1 err = 0.444385,	Top-5 err = 0.214209,	data_time = 0.050224,	train_time = 0.605424
[2019-08-23 05:30:04,890] TRAIN Iter 71580: lr = 0.380702,	loss = 2.820014,	Top-1 err = 0.434912,	Top-5 err = 0.205566,	data_time = 0.050747,	train_time = 0.390027
[2019-08-23 05:30:19,219] TRAIN Iter 71600: lr = 0.380668,	loss = 2.905374,	Top-1 err = 0.441553,	Top-5 err = 0.210840,	data_time = 0.050837,	train_time = 0.716471
[2019-08-23 05:30:26,776] TRAIN Iter 71620: lr = 0.380635,	loss = 2.792374,	Top-1 err = 0.435937,	Top-5 err = 0.210303,	data_time = 0.050614,	train_time = 0.377822
[2019-08-23 05:30:41,172] TRAIN Iter 71640: lr = 0.380602,	loss = 2.844037,	Top-1 err = 0.447021,	Top-5 err = 0.215869,	data_time = 0.050435,	train_time = 0.719799
[2019-08-23 05:30:57,858] TRAIN Iter 71660: lr = 0.380568,	loss = 2.829042,	Top-1 err = 0.443848,	Top-5 err = 0.215674,	data_time = 0.050502,	train_time = 0.834272
[2019-08-23 05:31:06,125] TRAIN Iter 71680: lr = 0.380535,	loss = 2.828537,	Top-1 err = 0.442969,	Top-5 err = 0.212451,	data_time = 0.050967,	train_time = 0.413322
[2019-08-23 05:31:16,452] TRAIN Iter 71700: lr = 0.380502,	loss = 2.796347,	Top-1 err = 0.435303,	Top-5 err = 0.207178,	data_time = 0.050582,	train_time = 0.516381
[2019-08-23 05:31:31,293] TRAIN Iter 71720: lr = 0.380468,	loss = 2.791077,	Top-1 err = 0.437695,	Top-5 err = 0.212109,	data_time = 0.087852,	train_time = 0.741990
[2019-08-23 05:31:39,150] TRAIN Iter 71740: lr = 0.380435,	loss = 2.879034,	Top-1 err = 0.439355,	Top-5 err = 0.210596,	data_time = 0.050549,	train_time = 0.392874
[2019-08-23 05:31:52,135] TRAIN Iter 71760: lr = 0.380402,	loss = 2.775281,	Top-1 err = 0.444092,	Top-5 err = 0.214697,	data_time = 0.050681,	train_time = 0.649225
[2019-08-23 05:31:59,637] TRAIN Iter 71780: lr = 0.380368,	loss = 2.872464,	Top-1 err = 0.442480,	Top-5 err = 0.214648,	data_time = 0.050573,	train_time = 0.375063
[2019-08-23 05:32:12,987] TRAIN Iter 71800: lr = 0.380335,	loss = 2.888742,	Top-1 err = 0.439600,	Top-5 err = 0.212842,	data_time = 0.050415,	train_time = 0.667498
[2019-08-23 05:32:26,689] TRAIN Iter 71820: lr = 0.380302,	loss = 2.819937,	Top-1 err = 0.437646,	Top-5 err = 0.207959,	data_time = 0.050548,	train_time = 0.685108
[2019-08-23 05:32:34,010] TRAIN Iter 71840: lr = 0.380268,	loss = 2.811100,	Top-1 err = 0.439307,	Top-5 err = 0.206152,	data_time = 0.050647,	train_time = 0.365991
[2019-08-23 05:32:49,551] TRAIN Iter 71860: lr = 0.380235,	loss = 2.806746,	Top-1 err = 0.441162,	Top-5 err = 0.213623,	data_time = 0.050387,	train_time = 0.777058
[2019-08-23 05:33:03,632] TRAIN Iter 71880: lr = 0.380202,	loss = 2.764247,	Top-1 err = 0.439600,	Top-5 err = 0.207275,	data_time = 1.504171,	train_time = 0.704044
[2019-08-23 05:33:11,324] TRAIN Iter 71900: lr = 0.380168,	loss = 2.880267,	Top-1 err = 0.441846,	Top-5 err = 0.214355,	data_time = 0.051007,	train_time = 0.384562
[2019-08-23 05:33:23,875] TRAIN Iter 71920: lr = 0.380135,	loss = 2.771120,	Top-1 err = 0.437256,	Top-5 err = 0.207178,	data_time = 0.050507,	train_time = 0.627534
[2019-08-23 05:33:31,232] TRAIN Iter 71940: lr = 0.380102,	loss = 2.755818,	Top-1 err = 0.437842,	Top-5 err = 0.207520,	data_time = 0.050448,	train_time = 0.367826
[2019-08-23 05:33:47,724] TRAIN Iter 71960: lr = 0.380068,	loss = 2.712588,	Top-1 err = 0.433789,	Top-5 err = 0.208447,	data_time = 0.050507,	train_time = 0.824629
[2019-08-23 05:34:01,583] TRAIN Iter 71980: lr = 0.380035,	loss = 2.819205,	Top-1 err = 0.437598,	Top-5 err = 0.214307,	data_time = 0.050607,	train_time = 0.692898
[2019-08-23 05:34:08,964] TRAIN Iter 72000: lr = 0.380002,	loss = 2.807791,	Top-1 err = 0.444629,	Top-5 err = 0.214746,	data_time = 0.050578,	train_time = 0.369034
[2019-08-23 05:34:24,156] TRAIN Iter 72020: lr = 0.379968,	loss = 2.792097,	Top-1 err = 0.447949,	Top-5 err = 0.218945,	data_time = 0.050540,	train_time = 0.759596
[2019-08-23 05:34:38,175] TRAIN Iter 72040: lr = 0.379935,	loss = 2.892384,	Top-1 err = 0.444482,	Top-5 err = 0.212744,	data_time = 0.050742,	train_time = 0.700947
[2019-08-23 05:34:45,762] TRAIN Iter 72060: lr = 0.379902,	loss = 2.824315,	Top-1 err = 0.436963,	Top-5 err = 0.207275,	data_time = 0.050849,	train_time = 0.379318
[2019-08-23 05:35:01,241] TRAIN Iter 72080: lr = 0.379868,	loss = 2.709775,	Top-1 err = 0.443164,	Top-5 err = 0.215234,	data_time = 0.050551,	train_time = 0.773973
[2019-08-23 05:35:08,384] TRAIN Iter 72100: lr = 0.379835,	loss = 2.713415,	Top-1 err = 0.438330,	Top-5 err = 0.210547,	data_time = 0.050447,	train_time = 0.357096
[2019-08-23 05:35:24,542] TRAIN Iter 72120: lr = 0.379802,	loss = 2.845165,	Top-1 err = 0.445361,	Top-5 err = 0.212109,	data_time = 0.050612,	train_time = 0.807897
[2019-08-23 05:35:40,610] TRAIN Iter 72140: lr = 0.379768,	loss = 2.806769,	Top-1 err = 0.452002,	Top-5 err = 0.217041,	data_time = 0.050345,	train_time = 0.803385
[2019-08-23 05:35:47,779] TRAIN Iter 72160: lr = 0.379735,	loss = 2.671089,	Top-1 err = 0.440381,	Top-5 err = 0.207520,	data_time = 0.050559,	train_time = 0.358442
[2019-08-23 05:36:04,178] TRAIN Iter 72180: lr = 0.379702,	loss = 2.783206,	Top-1 err = 0.439893,	Top-5 err = 0.209326,	data_time = 0.050447,	train_time = 0.819908
[2019-08-23 05:36:20,435] TRAIN Iter 72200: lr = 0.379668,	loss = 2.775380,	Top-1 err = 0.435059,	Top-5 err = 0.206299,	data_time = 0.101510,	train_time = 0.812857
[2019-08-23 05:36:27,759] TRAIN Iter 72220: lr = 0.379635,	loss = 2.793836,	Top-1 err = 0.434033,	Top-5 err = 0.208838,	data_time = 0.122309,	train_time = 0.366206
[2019-08-23 05:36:43,134] TRAIN Iter 72240: lr = 0.379602,	loss = 2.849862,	Top-1 err = 0.437744,	Top-5 err = 0.209033,	data_time = 0.050345,	train_time = 0.768744
[2019-08-23 05:36:50,344] TRAIN Iter 72260: lr = 0.379568,	loss = 2.722848,	Top-1 err = 0.440332,	Top-5 err = 0.212158,	data_time = 0.050159,	train_time = 0.360476
[2019-08-23 05:37:07,373] TRAIN Iter 72280: lr = 0.379535,	loss = 2.882475,	Top-1 err = 0.437256,	Top-5 err = 0.210400,	data_time = 0.051262,	train_time = 0.851394
[2019-08-23 05:37:24,174] TRAIN Iter 72300: lr = 0.379502,	loss = 2.790478,	Top-1 err = 0.443262,	Top-5 err = 0.213477,	data_time = 0.050428,	train_time = 0.840080
[2019-08-23 05:37:31,379] TRAIN Iter 72320: lr = 0.379468,	loss = 2.833767,	Top-1 err = 0.448926,	Top-5 err = 0.219043,	data_time = 0.050478,	train_time = 0.360188
[2019-08-23 05:37:45,860] TRAIN Iter 72340: lr = 0.379435,	loss = 2.723634,	Top-1 err = 0.438770,	Top-5 err = 0.211914,	data_time = 0.050090,	train_time = 0.724041
[2019-08-23 05:38:03,340] TRAIN Iter 72360: lr = 0.379402,	loss = 2.829072,	Top-1 err = 0.439648,	Top-5 err = 0.211035,	data_time = 0.050780,	train_time = 0.874017
[2019-08-23 05:38:10,893] TRAIN Iter 72380: lr = 0.379368,	loss = 2.795788,	Top-1 err = 0.448975,	Top-5 err = 0.214941,	data_time = 0.050527,	train_time = 0.377638
[2019-08-23 05:38:26,323] TRAIN Iter 72400: lr = 0.379335,	loss = 2.874458,	Top-1 err = 0.444434,	Top-5 err = 0.213232,	data_time = 0.151787,	train_time = 0.771454
[2019-08-23 05:38:33,156] TRAIN Iter 72420: lr = 0.379302,	loss = 2.805427,	Top-1 err = 0.446729,	Top-5 err = 0.216699,	data_time = 0.050523,	train_time = 0.341675
[2019-08-23 05:38:50,716] TRAIN Iter 72440: lr = 0.379268,	loss = 2.849073,	Top-1 err = 0.444043,	Top-5 err = 0.215918,	data_time = 0.050338,	train_time = 0.877975
[2019-08-23 05:39:09,151] TRAIN Iter 72460: lr = 0.379235,	loss = 2.787068,	Top-1 err = 0.439746,	Top-5 err = 0.213525,	data_time = 0.050388,	train_time = 0.921712
[2019-08-23 05:39:16,408] TRAIN Iter 72480: lr = 0.379202,	loss = 2.852426,	Top-1 err = 0.447754,	Top-5 err = 0.219434,	data_time = 0.050517,	train_time = 0.362851
[2019-08-23 05:39:34,479] TRAIN Iter 72500: lr = 0.379168,	loss = 2.785962,	Top-1 err = 0.443799,	Top-5 err = 0.217969,	data_time = 0.164274,	train_time = 0.903494
[2019-08-23 05:39:50,404] TRAIN Iter 72520: lr = 0.379135,	loss = 2.671725,	Top-1 err = 0.441846,	Top-5 err = 0.212012,	data_time = 0.108289,	train_time = 0.796279
[2019-08-23 05:39:56,912] TRAIN Iter 72540: lr = 0.379102,	loss = 2.876143,	Top-1 err = 0.445068,	Top-5 err = 0.213916,	data_time = 0.049906,	train_time = 0.325353
[2019-08-23 05:40:48,344] TRAIN Iter 72560: lr = 0.379068,	loss = 2.799492,	Top-1 err = 0.446384,	Top-5 err = 0.213816,	data_time = 0.050790,	train_time = 2.571618
[2019-08-23 05:40:56,551] TRAIN Iter 72580: lr = 0.379035,	loss = 2.778895,	Top-1 err = 0.440869,	Top-5 err = 0.210059,	data_time = 0.050451,	train_time = 0.410288
[2019-08-23 05:41:09,122] TRAIN Iter 72600: lr = 0.379002,	loss = 2.903796,	Top-1 err = 0.437158,	Top-5 err = 0.202783,	data_time = 0.050984,	train_time = 0.628547
[2019-08-23 05:41:17,882] TRAIN Iter 72620: lr = 0.378968,	loss = 2.780512,	Top-1 err = 0.429639,	Top-5 err = 0.206445,	data_time = 0.050476,	train_time = 0.438003
[2019-08-23 05:41:25,374] TRAIN Iter 72640: lr = 0.378935,	loss = 2.791968,	Top-1 err = 0.434229,	Top-5 err = 0.206299,	data_time = 0.050695,	train_time = 0.374573
[2019-08-23 05:41:40,595] TRAIN Iter 72660: lr = 0.378902,	loss = 2.808559,	Top-1 err = 0.437939,	Top-5 err = 0.206299,	data_time = 0.050431,	train_time = 0.761055
[2019-08-23 05:41:48,812] TRAIN Iter 72680: lr = 0.378868,	loss = 2.739716,	Top-1 err = 0.440088,	Top-5 err = 0.211328,	data_time = 0.112736,	train_time = 0.410841
[2019-08-23 05:42:00,863] TRAIN Iter 72700: lr = 0.378835,	loss = 2.838174,	Top-1 err = 0.428809,	Top-5 err = 0.202148,	data_time = 0.050529,	train_time = 0.602506
[2019-08-23 05:42:14,033] TRAIN Iter 72720: lr = 0.378802,	loss = 2.763729,	Top-1 err = 0.436035,	Top-5 err = 0.215381,	data_time = 0.050440,	train_time = 0.658495
[2019-08-23 05:42:21,843] TRAIN Iter 72740: lr = 0.378768,	loss = 2.794840,	Top-1 err = 0.433252,	Top-5 err = 0.208691,	data_time = 0.153279,	train_time = 0.390502
[2019-08-23 05:42:33,914] TRAIN Iter 72760: lr = 0.378735,	loss = 2.789645,	Top-1 err = 0.437061,	Top-5 err = 0.208740,	data_time = 0.050659,	train_time = 0.603495
[2019-08-23 05:42:47,295] TRAIN Iter 72780: lr = 0.378702,	loss = 2.761127,	Top-1 err = 0.433203,	Top-5 err = 0.206543,	data_time = 0.114496,	train_time = 0.669080
[2019-08-23 05:42:55,132] TRAIN Iter 72800: lr = 0.378668,	loss = 2.774408,	Top-1 err = 0.435107,	Top-5 err = 0.203223,	data_time = 0.050757,	train_time = 0.391802
[2019-08-23 05:43:07,376] TRAIN Iter 72820: lr = 0.378635,	loss = 2.833959,	Top-1 err = 0.445410,	Top-5 err = 0.214258,	data_time = 0.050685,	train_time = 0.612171
[2019-08-23 05:43:15,025] TRAIN Iter 72840: lr = 0.378602,	loss = 2.774551,	Top-1 err = 0.438477,	Top-5 err = 0.209668,	data_time = 0.050420,	train_time = 0.382449
[2019-08-23 05:43:28,151] TRAIN Iter 72860: lr = 0.378568,	loss = 2.830358,	Top-1 err = 0.435059,	Top-5 err = 0.205957,	data_time = 0.050326,	train_time = 0.656303
[2019-08-23 05:43:43,212] TRAIN Iter 72880: lr = 0.378535,	loss = 2.861238,	Top-1 err = 0.437939,	Top-5 err = 0.209375,	data_time = 0.050499,	train_time = 0.753034
[2019-08-23 05:43:50,579] TRAIN Iter 72900: lr = 0.378502,	loss = 2.834139,	Top-1 err = 0.441016,	Top-5 err = 0.210547,	data_time = 0.050617,	train_time = 0.368330
[2019-08-23 05:44:05,609] TRAIN Iter 72920: lr = 0.378468,	loss = 2.811023,	Top-1 err = 0.443896,	Top-5 err = 0.213916,	data_time = 0.088896,	train_time = 0.751463
[2019-08-23 05:44:20,800] TRAIN Iter 72940: lr = 0.378435,	loss = 2.797706,	Top-1 err = 0.444873,	Top-5 err = 0.212012,	data_time = 0.050410,	train_time = 0.759542
[2019-08-23 05:44:29,372] TRAIN Iter 72960: lr = 0.378402,	loss = 2.752799,	Top-1 err = 0.435791,	Top-5 err = 0.212158,	data_time = 0.050415,	train_time = 0.428577
[2019-08-23 05:44:40,041] TRAIN Iter 72980: lr = 0.378368,	loss = 2.868825,	Top-1 err = 0.441699,	Top-5 err = 0.211523,	data_time = 0.050471,	train_time = 0.533425
[2019-08-23 05:44:47,813] TRAIN Iter 73000: lr = 0.378335,	loss = 2.843157,	Top-1 err = 0.437500,	Top-5 err = 0.210986,	data_time = 0.050859,	train_time = 0.388601
[2019-08-23 05:45:00,478] TRAIN Iter 73020: lr = 0.378302,	loss = 2.775214,	Top-1 err = 0.440479,	Top-5 err = 0.208154,	data_time = 0.050845,	train_time = 0.633239
[2019-08-23 05:45:13,680] TRAIN Iter 73040: lr = 0.378268,	loss = 2.732560,	Top-1 err = 0.435742,	Top-5 err = 0.210107,	data_time = 0.050576,	train_time = 0.660074
[2019-08-23 05:45:21,470] TRAIN Iter 73060: lr = 0.378235,	loss = 2.893348,	Top-1 err = 0.443359,	Top-5 err = 0.213574,	data_time = 0.050931,	train_time = 0.389491
[2019-08-23 05:45:35,298] TRAIN Iter 73080: lr = 0.378202,	loss = 2.793300,	Top-1 err = 0.434473,	Top-5 err = 0.208984,	data_time = 0.050403,	train_time = 0.691382
[2019-08-23 05:45:47,972] TRAIN Iter 73100: lr = 0.378168,	loss = 2.781470,	Top-1 err = 0.438232,	Top-5 err = 0.210205,	data_time = 0.050765,	train_time = 0.633671
[2019-08-23 05:45:56,281] TRAIN Iter 73120: lr = 0.378135,	loss = 2.826293,	Top-1 err = 0.442383,	Top-5 err = 0.213721,	data_time = 0.050526,	train_time = 0.415462
[2019-08-23 05:46:10,670] TRAIN Iter 73140: lr = 0.378102,	loss = 2.798224,	Top-1 err = 0.432080,	Top-5 err = 0.209521,	data_time = 0.050258,	train_time = 0.719408
[2019-08-23 05:46:17,768] TRAIN Iter 73160: lr = 0.378068,	loss = 2.728117,	Top-1 err = 0.436572,	Top-5 err = 0.207520,	data_time = 0.050724,	train_time = 0.354931
[2019-08-23 05:46:34,082] TRAIN Iter 73180: lr = 0.378035,	loss = 2.819971,	Top-1 err = 0.442090,	Top-5 err = 0.217725,	data_time = 0.050430,	train_time = 0.815652
[2019-08-23 05:46:45,289] TRAIN Iter 73200: lr = 0.378002,	loss = 2.768405,	Top-1 err = 0.433301,	Top-5 err = 0.207568,	data_time = 0.120027,	train_time = 0.560343
[2019-08-23 05:46:52,837] TRAIN Iter 73220: lr = 0.377968,	loss = 2.853833,	Top-1 err = 0.437793,	Top-5 err = 0.208496,	data_time = 0.050430,	train_time = 0.377407
[2019-08-23 05:47:09,573] TRAIN Iter 73240: lr = 0.377935,	loss = 2.781148,	Top-1 err = 0.438965,	Top-5 err = 0.208643,	data_time = 0.050683,	train_time = 0.836756
[2019-08-23 05:47:22,367] TRAIN Iter 73260: lr = 0.377902,	loss = 2.868639,	Top-1 err = 0.445459,	Top-5 err = 0.212402,	data_time = 0.143344,	train_time = 0.639684
[2019-08-23 05:47:30,172] TRAIN Iter 73280: lr = 0.377868,	loss = 2.781504,	Top-1 err = 0.438330,	Top-5 err = 0.213379,	data_time = 0.050825,	train_time = 0.390262
[2019-08-23 05:47:45,512] TRAIN Iter 73300: lr = 0.377835,	loss = 2.787003,	Top-1 err = 0.433203,	Top-5 err = 0.208008,	data_time = 0.050320,	train_time = 0.766987
[2019-08-23 05:47:53,081] TRAIN Iter 73320: lr = 0.377802,	loss = 2.805676,	Top-1 err = 0.439795,	Top-5 err = 0.211768,	data_time = 0.050624,	train_time = 0.378403
[2019-08-23 05:48:08,349] TRAIN Iter 73340: lr = 0.377768,	loss = 2.709229,	Top-1 err = 0.438916,	Top-5 err = 0.212549,	data_time = 0.050342,	train_time = 0.763407
[2019-08-23 05:48:23,147] TRAIN Iter 73360: lr = 0.377735,	loss = 2.849553,	Top-1 err = 0.446777,	Top-5 err = 0.215527,	data_time = 0.050405,	train_time = 0.739898
[2019-08-23 05:48:30,505] TRAIN Iter 73380: lr = 0.377702,	loss = 2.769727,	Top-1 err = 0.436719,	Top-5 err = 0.207813,	data_time = 0.050510,	train_time = 0.367865
[2019-08-23 05:48:45,736] TRAIN Iter 73400: lr = 0.377668,	loss = 2.768587,	Top-1 err = 0.442432,	Top-5 err = 0.212158,	data_time = 0.050446,	train_time = 0.761539
[2019-08-23 05:48:59,779] TRAIN Iter 73420: lr = 0.377635,	loss = 2.801560,	Top-1 err = 0.445801,	Top-5 err = 0.215430,	data_time = 0.281122,	train_time = 0.702138
[2019-08-23 05:49:08,141] TRAIN Iter 73440: lr = 0.377602,	loss = 2.826973,	Top-1 err = 0.440576,	Top-5 err = 0.213428,	data_time = 0.050444,	train_time = 0.418065
[2019-08-23 05:49:24,127] TRAIN Iter 73460: lr = 0.377568,	loss = 2.832096,	Top-1 err = 0.444434,	Top-5 err = 0.215039,	data_time = 0.125084,	train_time = 0.799296
[2019-08-23 05:49:31,819] TRAIN Iter 73480: lr = 0.377535,	loss = 2.821568,	Top-1 err = 0.440918,	Top-5 err = 0.211523,	data_time = 0.050863,	train_time = 0.384585
[2019-08-23 05:49:48,100] TRAIN Iter 73500: lr = 0.377502,	loss = 2.834534,	Top-1 err = 0.445215,	Top-5 err = 0.209521,	data_time = 0.050573,	train_time = 0.814051
[2019-08-23 05:50:04,441] TRAIN Iter 73520: lr = 0.377468,	loss = 2.712557,	Top-1 err = 0.442236,	Top-5 err = 0.210791,	data_time = 0.125018,	train_time = 0.817014
[2019-08-23 05:50:11,693] TRAIN Iter 73540: lr = 0.377435,	loss = 2.727308,	Top-1 err = 0.437988,	Top-5 err = 0.211768,	data_time = 0.050461,	train_time = 0.362625
[2019-08-23 05:50:28,669] TRAIN Iter 73560: lr = 0.377402,	loss = 2.867120,	Top-1 err = 0.440918,	Top-5 err = 0.213037,	data_time = 0.050650,	train_time = 0.848782
[2019-08-23 05:50:43,965] TRAIN Iter 73580: lr = 0.377368,	loss = 2.828095,	Top-1 err = 0.439893,	Top-5 err = 0.212451,	data_time = 0.646599,	train_time = 0.764790
[2019-08-23 05:50:51,944] TRAIN Iter 73600: lr = 0.377335,	loss = 2.816399,	Top-1 err = 0.440625,	Top-5 err = 0.213818,	data_time = 0.051041,	train_time = 0.398924
[2019-08-23 05:51:08,090] TRAIN Iter 73620: lr = 0.377302,	loss = 2.877240,	Top-1 err = 0.446973,	Top-5 err = 0.216406,	data_time = 0.050705,	train_time = 0.807286
[2019-08-23 05:51:15,142] TRAIN Iter 73640: lr = 0.377268,	loss = 2.796580,	Top-1 err = 0.440723,	Top-5 err = 0.214355,	data_time = 0.050596,	train_time = 0.352573
[2019-08-23 05:51:33,821] TRAIN Iter 73660: lr = 0.377235,	loss = 2.735828,	Top-1 err = 0.441406,	Top-5 err = 0.212451,	data_time = 0.050446,	train_time = 0.933947
[2019-08-23 05:51:50,787] TRAIN Iter 73680: lr = 0.377202,	loss = 2.840600,	Top-1 err = 0.447363,	Top-5 err = 0.215283,	data_time = 0.050501,	train_time = 0.848268
[2019-08-23 05:51:58,292] TRAIN Iter 73700: lr = 0.377168,	loss = 2.714769,	Top-1 err = 0.438623,	Top-5 err = 0.212256,	data_time = 0.050660,	train_time = 0.375257
[2019-08-23 05:52:16,130] TRAIN Iter 73720: lr = 0.377135,	loss = 2.823479,	Top-1 err = 0.436182,	Top-5 err = 0.217139,	data_time = 0.050337,	train_time = 0.891883
[2019-08-23 05:52:30,526] TRAIN Iter 73740: lr = 0.377102,	loss = 2.868768,	Top-1 err = 0.441895,	Top-5 err = 0.217920,	data_time = 0.972710,	train_time = 0.719784
[2019-08-23 05:52:38,374] TRAIN Iter 73760: lr = 0.377068,	loss = 2.829217,	Top-1 err = 0.442822,	Top-5 err = 0.207617,	data_time = 0.050091,	train_time = 0.392392
[2019-08-23 05:52:54,290] TRAIN Iter 73780: lr = 0.377035,	loss = 2.779067,	Top-1 err = 0.443115,	Top-5 err = 0.213281,	data_time = 0.056113,	train_time = 0.795755
[2019-08-23 05:53:00,602] TRAIN Iter 73800: lr = 0.377002,	loss = 2.806306,	Top-1 err = 0.441064,	Top-5 err = 0.208740,	data_time = 0.049871,	train_time = 0.315603
[2019-08-23 05:53:52,293] TRAIN Iter 73820: lr = 0.376968,	loss = 2.730752,	Top-1 err = 0.438075,	Top-5 err = 0.214589,	data_time = 0.050514,	train_time = 2.584541
[2019-08-23 05:54:05,756] TRAIN Iter 73840: lr = 0.376935,	loss = 2.770332,	Top-1 err = 0.434033,	Top-5 err = 0.206787,	data_time = 1.083790,	train_time = 0.673134
[2019-08-23 05:54:13,559] TRAIN Iter 73860: lr = 0.376902,	loss = 2.804094,	Top-1 err = 0.435791,	Top-5 err = 0.208350,	data_time = 0.050847,	train_time = 0.390154
[2019-08-23 05:54:22,648] TRAIN Iter 73880: lr = 0.376868,	loss = 2.727827,	Top-1 err = 0.431689,	Top-5 err = 0.204834,	data_time = 0.050730,	train_time = 0.454407
[2019-08-23 05:54:30,103] TRAIN Iter 73900: lr = 0.376835,	loss = 2.845018,	Top-1 err = 0.429883,	Top-5 err = 0.205859,	data_time = 0.133656,	train_time = 0.372741
[2019-08-23 05:54:43,815] TRAIN Iter 73920: lr = 0.376802,	loss = 2.751203,	Top-1 err = 0.427783,	Top-5 err = 0.202441,	data_time = 0.050408,	train_time = 0.685593
[2019-08-23 05:54:55,632] TRAIN Iter 73940: lr = 0.376768,	loss = 2.920360,	Top-1 err = 0.437256,	Top-5 err = 0.205811,	data_time = 0.050891,	train_time = 0.590820
[2019-08-23 05:55:05,066] TRAIN Iter 73960: lr = 0.376735,	loss = 2.888259,	Top-1 err = 0.436377,	Top-5 err = 0.208496,	data_time = 0.050909,	train_time = 0.471676
[2019-08-23 05:55:18,718] TRAIN Iter 73980: lr = 0.376702,	loss = 2.755992,	Top-1 err = 0.433936,	Top-5 err = 0.208594,	data_time = 0.050562,	train_time = 0.682620
[2019-08-23 05:55:32,940] TRAIN Iter 74000: lr = 0.376668,	loss = 2.911183,	Top-1 err = 0.440479,	Top-5 err = 0.207764,	data_time = 0.050991,	train_time = 0.711041
[2019-08-23 05:55:41,073] TRAIN Iter 74020: lr = 0.376635,	loss = 2.720998,	Top-1 err = 0.438086,	Top-5 err = 0.210254,	data_time = 0.051008,	train_time = 0.406650
[2019-08-23 05:55:53,879] TRAIN Iter 74040: lr = 0.376602,	loss = 2.819870,	Top-1 err = 0.441162,	Top-5 err = 0.207715,	data_time = 0.050337,	train_time = 0.640300
[2019-08-23 05:56:01,432] TRAIN Iter 74060: lr = 0.376568,	loss = 2.762400,	Top-1 err = 0.438281,	Top-5 err = 0.212598,	data_time = 0.050392,	train_time = 0.377642
[2019-08-23 05:56:15,716] TRAIN Iter 74080: lr = 0.376535,	loss = 2.792989,	Top-1 err = 0.429102,	Top-5 err = 0.209082,	data_time = 0.050477,	train_time = 0.714183
[2019-08-23 05:56:29,086] TRAIN Iter 74100: lr = 0.376502,	loss = 2.804429,	Top-1 err = 0.434668,	Top-5 err = 0.208252,	data_time = 0.050582,	train_time = 0.668492
[2019-08-23 05:56:36,900] TRAIN Iter 74120: lr = 0.376468,	loss = 2.693426,	Top-1 err = 0.432910,	Top-5 err = 0.203467,	data_time = 0.050620,	train_time = 0.390686
[2019-08-23 05:56:50,876] TRAIN Iter 74140: lr = 0.376435,	loss = 2.749358,	Top-1 err = 0.441748,	Top-5 err = 0.209961,	data_time = 0.050648,	train_time = 0.698767
[2019-08-23 05:57:03,681] TRAIN Iter 74160: lr = 0.376402,	loss = 2.829209,	Top-1 err = 0.435547,	Top-5 err = 0.209277,	data_time = 0.050547,	train_time = 0.640240
[2019-08-23 05:57:10,932] TRAIN Iter 74180: lr = 0.376368,	loss = 2.815579,	Top-1 err = 0.437646,	Top-5 err = 0.210596,	data_time = 0.050783,	train_time = 0.362529
[2019-08-23 05:57:27,592] TRAIN Iter 74200: lr = 0.376335,	loss = 2.752895,	Top-1 err = 0.432617,	Top-5 err = 0.207910,	data_time = 0.050737,	train_time = 0.832963
[2019-08-23 05:57:35,130] TRAIN Iter 74220: lr = 0.376302,	loss = 2.825358,	Top-1 err = 0.436328,	Top-5 err = 0.211670,	data_time = 0.126954,	train_time = 0.376891
[2019-08-23 05:57:48,194] TRAIN Iter 74240: lr = 0.376268,	loss = 2.861304,	Top-1 err = 0.434668,	Top-5 err = 0.206982,	data_time = 0.050691,	train_time = 0.653191
[2019-08-23 05:58:01,702] TRAIN Iter 74260: lr = 0.376235,	loss = 2.914184,	Top-1 err = 0.441797,	Top-5 err = 0.213037,	data_time = 0.050608,	train_time = 0.675369
[2019-08-23 05:58:10,934] TRAIN Iter 74280: lr = 0.376202,	loss = 2.934125,	Top-1 err = 0.443262,	Top-5 err = 0.210693,	data_time = 0.157904,	train_time = 0.461587
[2019-08-23 05:58:24,592] TRAIN Iter 74300: lr = 0.376168,	loss = 2.786258,	Top-1 err = 0.437207,	Top-5 err = 0.211523,	data_time = 0.050433,	train_time = 0.682925
[2019-08-23 05:58:38,132] TRAIN Iter 74320: lr = 0.376135,	loss = 2.802635,	Top-1 err = 0.439844,	Top-5 err = 0.212207,	data_time = 0.051018,	train_time = 0.676984
[2019-08-23 05:58:46,501] TRAIN Iter 74340: lr = 0.376102,	loss = 2.828740,	Top-1 err = 0.437061,	Top-5 err = 0.213135,	data_time = 0.050603,	train_time = 0.418395
[2019-08-23 05:59:01,002] TRAIN Iter 74360: lr = 0.376068,	loss = 2.793932,	Top-1 err = 0.440137,	Top-5 err = 0.210986,	data_time = 0.050361,	train_time = 0.725056
[2019-08-23 05:59:08,788] TRAIN Iter 74380: lr = 0.376035,	loss = 2.863812,	Top-1 err = 0.433252,	Top-5 err = 0.208350,	data_time = 0.050462,	train_time = 0.389298
[2019-08-23 05:59:20,954] TRAIN Iter 74400: lr = 0.376002,	loss = 2.751912,	Top-1 err = 0.437158,	Top-5 err = 0.208838,	data_time = 0.050779,	train_time = 0.608286
[2019-08-23 05:59:36,199] TRAIN Iter 74420: lr = 0.375968,	loss = 2.782918,	Top-1 err = 0.438623,	Top-5 err = 0.210205,	data_time = 0.170375,	train_time = 0.762227
[2019-08-23 05:59:43,820] TRAIN Iter 74440: lr = 0.375935,	loss = 2.712755,	Top-1 err = 0.436572,	Top-5 err = 0.212598,	data_time = 0.050605,	train_time = 0.381042
[2019-08-23 05:59:58,308] TRAIN Iter 74460: lr = 0.375902,	loss = 2.721910,	Top-1 err = 0.438428,	Top-5 err = 0.208496,	data_time = 0.050594,	train_time = 0.724358
[2019-08-23 06:00:11,225] TRAIN Iter 74480: lr = 0.375868,	loss = 2.905626,	Top-1 err = 0.439063,	Top-5 err = 0.212891,	data_time = 0.197392,	train_time = 0.645833
[2019-08-23 06:00:19,290] TRAIN Iter 74500: lr = 0.375835,	loss = 2.837358,	Top-1 err = 0.438818,	Top-5 err = 0.209619,	data_time = 0.050498,	train_time = 0.403269
[2019-08-23 06:00:35,252] TRAIN Iter 74520: lr = 0.375802,	loss = 2.711931,	Top-1 err = 0.435693,	Top-5 err = 0.210840,	data_time = 0.051046,	train_time = 0.798085
[2019-08-23 06:00:42,641] TRAIN Iter 74540: lr = 0.375768,	loss = 2.718559,	Top-1 err = 0.442725,	Top-5 err = 0.212305,	data_time = 0.050354,	train_time = 0.369413
[2019-08-23 06:00:56,242] TRAIN Iter 74560: lr = 0.375735,	loss = 2.767965,	Top-1 err = 0.436475,	Top-5 err = 0.208789,	data_time = 0.050489,	train_time = 0.680029
[2019-08-23 06:01:11,376] TRAIN Iter 74580: lr = 0.375702,	loss = 2.865386,	Top-1 err = 0.439551,	Top-5 err = 0.214404,	data_time = 0.050363,	train_time = 0.756719
[2019-08-23 06:01:19,336] TRAIN Iter 74600: lr = 0.375668,	loss = 2.859288,	Top-1 err = 0.440234,	Top-5 err = 0.212109,	data_time = 0.122408,	train_time = 0.397995
[2019-08-23 06:01:33,767] TRAIN Iter 74620: lr = 0.375635,	loss = 2.776730,	Top-1 err = 0.437158,	Top-5 err = 0.208789,	data_time = 0.050451,	train_time = 0.721537
[2019-08-23 06:01:49,027] TRAIN Iter 74640: lr = 0.375602,	loss = 2.776903,	Top-1 err = 0.443359,	Top-5 err = 0.212988,	data_time = 0.050763,	train_time = 0.762968
[2019-08-23 06:01:56,123] TRAIN Iter 74660: lr = 0.375568,	loss = 2.872271,	Top-1 err = 0.445312,	Top-5 err = 0.214062,	data_time = 0.050136,	train_time = 0.354770
[2019-08-23 06:02:12,773] TRAIN Iter 74680: lr = 0.375535,	loss = 2.830729,	Top-1 err = 0.447949,	Top-5 err = 0.213965,	data_time = 0.050632,	train_time = 0.832491
[2019-08-23 06:02:19,944] TRAIN Iter 74700: lr = 0.375502,	loss = 2.760116,	Top-1 err = 0.436230,	Top-5 err = 0.209570,	data_time = 0.050486,	train_time = 0.358504
[2019-08-23 06:02:35,939] TRAIN Iter 74720: lr = 0.375468,	loss = 2.774988,	Top-1 err = 0.439746,	Top-5 err = 0.213086,	data_time = 0.050614,	train_time = 0.799756
[2019-08-23 06:02:52,901] TRAIN Iter 74740: lr = 0.375435,	loss = 2.827771,	Top-1 err = 0.440283,	Top-5 err = 0.212207,	data_time = 0.050460,	train_time = 0.848064
[2019-08-23 06:03:00,157] TRAIN Iter 74760: lr = 0.375402,	loss = 2.883076,	Top-1 err = 0.437451,	Top-5 err = 0.210010,	data_time = 0.050316,	train_time = 0.362790
[2019-08-23 06:03:15,291] TRAIN Iter 74780: lr = 0.375368,	loss = 2.826146,	Top-1 err = 0.439014,	Top-5 err = 0.212500,	data_time = 0.050833,	train_time = 0.756714
[2019-08-23 06:03:32,468] TRAIN Iter 74800: lr = 0.375335,	loss = 2.791450,	Top-1 err = 0.444189,	Top-5 err = 0.210303,	data_time = 0.050522,	train_time = 0.858813
[2019-08-23 06:03:39,240] TRAIN Iter 74820: lr = 0.375302,	loss = 2.712774,	Top-1 err = 0.438965,	Top-5 err = 0.210205,	data_time = 0.050775,	train_time = 0.338585
[2019-08-23 06:03:57,812] TRAIN Iter 74840: lr = 0.375268,	loss = 2.836663,	Top-1 err = 0.442822,	Top-5 err = 0.210352,	data_time = 0.050250,	train_time = 0.928597
[2019-08-23 06:04:04,860] TRAIN Iter 74860: lr = 0.375235,	loss = 2.835103,	Top-1 err = 0.441162,	Top-5 err = 0.207910,	data_time = 0.050584,	train_time = 0.352360
[2019-08-23 06:04:20,010] TRAIN Iter 74880: lr = 0.375202,	loss = 2.795514,	Top-1 err = 0.439990,	Top-5 err = 0.207520,	data_time = 0.050717,	train_time = 0.757526
[2019-08-23 06:04:35,051] TRAIN Iter 74900: lr = 0.375168,	loss = 2.784428,	Top-1 err = 0.434277,	Top-5 err = 0.209668,	data_time = 0.050479,	train_time = 0.752000
[2019-08-23 06:04:43,315] TRAIN Iter 74920: lr = 0.375135,	loss = 2.818729,	Top-1 err = 0.438525,	Top-5 err = 0.212891,	data_time = 0.050276,	train_time = 0.413209
[2019-08-23 06:05:00,690] TRAIN Iter 74940: lr = 0.375102,	loss = 2.763453,	Top-1 err = 0.437842,	Top-5 err = 0.212109,	data_time = 0.050131,	train_time = 0.868740
[2019-08-23 06:05:17,107] TRAIN Iter 74960: lr = 0.375068,	loss = 2.756334,	Top-1 err = 0.440039,	Top-5 err = 0.210840,	data_time = 0.050692,	train_time = 0.820846
[2019-08-23 06:05:23,981] TRAIN Iter 74980: lr = 0.375035,	loss = 2.891715,	Top-1 err = 0.448926,	Top-5 err = 0.218652,	data_time = 0.050555,	train_time = 0.343695
[2019-08-23 06:05:41,277] TRAIN Iter 75000: lr = 0.375002,	loss = 2.816952,	Top-1 err = 0.437598,	Top-5 err = 0.211133,	data_time = 0.050145,	train_time = 0.864746
[2019-08-23 06:05:48,237] TRAIN Iter 75020: lr = 0.374968,	loss = 2.751026,	Top-1 err = 0.435449,	Top-5 err = 0.211475,	data_time = 0.050065,	train_time = 0.348010
[2019-08-23 06:06:05,039] TRAIN Iter 75040: lr = 0.374935,	loss = 2.788515,	Top-1 err = 0.441602,	Top-5 err = 0.209912,	data_time = 0.050087,	train_time = 0.840090
[2019-08-23 06:06:56,670] TRAIN Iter 75060: lr = 0.374902,	loss = 2.774990,	Top-1 err = 0.432632,	Top-5 err = 0.208632,	data_time = 0.097469,	train_time = 2.581517
[2019-08-23 06:07:04,559] TRAIN Iter 75080: lr = 0.374868,	loss = 2.836209,	Top-1 err = 0.437500,	Top-5 err = 0.211670,	data_time = 0.050318,	train_time = 0.394432
[2019-08-23 06:07:16,905] TRAIN Iter 75100: lr = 0.374835,	loss = 2.726852,	Top-1 err = 0.435596,	Top-5 err = 0.210107,	data_time = 0.050783,	train_time = 0.617267
[2019-08-23 06:07:25,334] TRAIN Iter 75120: lr = 0.374802,	loss = 2.747040,	Top-1 err = 0.431787,	Top-5 err = 0.202686,	data_time = 0.050656,	train_time = 0.421457
[2019-08-23 06:07:33,643] TRAIN Iter 75140: lr = 0.374768,	loss = 2.856087,	Top-1 err = 0.433936,	Top-5 err = 0.210986,	data_time = 0.050393,	train_time = 0.415424
[2019-08-23 06:07:45,293] TRAIN Iter 75160: lr = 0.374735,	loss = 2.715460,	Top-1 err = 0.442139,	Top-5 err = 0.211865,	data_time = 0.050538,	train_time = 0.582486
[2019-08-23 06:07:53,244] TRAIN Iter 75180: lr = 0.374702,	loss = 2.792022,	Top-1 err = 0.432080,	Top-5 err = 0.205713,	data_time = 0.050421,	train_time = 0.397560
[2019-08-23 06:08:06,667] TRAIN Iter 75200: lr = 0.374668,	loss = 2.724541,	Top-1 err = 0.429004,	Top-5 err = 0.203027,	data_time = 0.050587,	train_time = 0.671143
[2019-08-23 06:08:19,179] TRAIN Iter 75220: lr = 0.374635,	loss = 2.803417,	Top-1 err = 0.437207,	Top-5 err = 0.209033,	data_time = 0.124990,	train_time = 0.625548
[2019-08-23 06:08:28,640] TRAIN Iter 75240: lr = 0.374602,	loss = 2.782050,	Top-1 err = 0.434863,	Top-5 err = 0.212158,	data_time = 0.050432,	train_time = 0.473034
[2019-08-23 06:08:43,921] TRAIN Iter 75260: lr = 0.374568,	loss = 2.751252,	Top-1 err = 0.433105,	Top-5 err = 0.209180,	data_time = 0.050342,	train_time = 0.764056
[2019-08-23 06:08:51,001] TRAIN Iter 75280: lr = 0.374535,	loss = 2.696470,	Top-1 err = 0.435645,	Top-5 err = 0.210205,	data_time = 0.103598,	train_time = 0.354002
[2019-08-23 06:09:05,430] TRAIN Iter 75300: lr = 0.374502,	loss = 2.775638,	Top-1 err = 0.428564,	Top-5 err = 0.205273,	data_time = 0.050365,	train_time = 0.721394
[2019-08-23 06:09:18,928] TRAIN Iter 75320: lr = 0.374468,	loss = 2.836461,	Top-1 err = 0.437891,	Top-5 err = 0.207520,	data_time = 0.050789,	train_time = 0.674902
[2019-08-23 06:09:26,285] TRAIN Iter 75340: lr = 0.374435,	loss = 2.796277,	Top-1 err = 0.433057,	Top-5 err = 0.212109,	data_time = 0.134923,	train_time = 0.367843
[2019-08-23 06:09:38,619] TRAIN Iter 75360: lr = 0.374402,	loss = 2.774706,	Top-1 err = 0.438232,	Top-5 err = 0.208496,	data_time = 0.050468,	train_time = 0.616675
[2019-08-23 06:09:49,923] TRAIN Iter 75380: lr = 0.374368,	loss = 2.758222,	Top-1 err = 0.432617,	Top-5 err = 0.205518,	data_time = 0.095547,	train_time = 0.565192
[2019-08-23 06:09:58,895] TRAIN Iter 75400: lr = 0.374335,	loss = 2.700428,	Top-1 err = 0.434961,	Top-5 err = 0.207910,	data_time = 0.050852,	train_time = 0.448596
[2019-08-23 06:10:13,470] TRAIN Iter 75420: lr = 0.374302,	loss = 2.799942,	Top-1 err = 0.438037,	Top-5 err = 0.208594,	data_time = 0.051035,	train_time = 0.728716
[2019-08-23 06:10:21,605] TRAIN Iter 75440: lr = 0.374268,	loss = 2.844413,	Top-1 err = 0.440967,	Top-5 err = 0.211865,	data_time = 0.050538,	train_time = 0.406736
[2019-08-23 06:10:33,927] TRAIN Iter 75460: lr = 0.374235,	loss = 2.734213,	Top-1 err = 0.430762,	Top-5 err = 0.204248,	data_time = 0.050551,	train_time = 0.616073
[2019-08-23 06:10:49,203] TRAIN Iter 75480: lr = 0.374202,	loss = 2.771368,	Top-1 err = 0.439209,	Top-5 err = 0.211035,	data_time = 0.050371,	train_time = 0.763822
[2019-08-23 06:10:57,286] TRAIN Iter 75500: lr = 0.374168,	loss = 2.798728,	Top-1 err = 0.434375,	Top-5 err = 0.206689,	data_time = 0.050593,	train_time = 0.404134
[2019-08-23 06:11:11,178] TRAIN Iter 75520: lr = 0.374135,	loss = 2.851471,	Top-1 err = 0.438574,	Top-5 err = 0.210986,	data_time = 0.050515,	train_time = 0.694569
[2019-08-23 06:11:21,588] TRAIN Iter 75540: lr = 0.374102,	loss = 2.776871,	Top-1 err = 0.439160,	Top-5 err = 0.205615,	data_time = 1.602945,	train_time = 0.520483
[2019-08-23 06:11:33,567] TRAIN Iter 75560: lr = 0.374068,	loss = 2.867893,	Top-1 err = 0.438428,	Top-5 err = 0.213428,	data_time = 0.051089,	train_time = 0.598943
[2019-08-23 06:11:46,673] TRAIN Iter 75580: lr = 0.374035,	loss = 2.807181,	Top-1 err = 0.438965,	Top-5 err = 0.213477,	data_time = 0.050643,	train_time = 0.655255
[2019-08-23 06:11:54,249] TRAIN Iter 75600: lr = 0.374002,	loss = 2.713778,	Top-1 err = 0.439600,	Top-5 err = 0.211279,	data_time = 0.050596,	train_time = 0.378798
[2019-08-23 06:12:08,957] TRAIN Iter 75620: lr = 0.373968,	loss = 2.706349,	Top-1 err = 0.434180,	Top-5 err = 0.211377,	data_time = 0.050620,	train_time = 0.735419
[2019-08-23 06:12:24,349] TRAIN Iter 75640: lr = 0.373935,	loss = 2.795531,	Top-1 err = 0.438330,	Top-5 err = 0.214355,	data_time = 0.102191,	train_time = 0.769576
[2019-08-23 06:12:31,918] TRAIN Iter 75660: lr = 0.373902,	loss = 2.819150,	Top-1 err = 0.441943,	Top-5 err = 0.211768,	data_time = 0.050498,	train_time = 0.378430
[2019-08-23 06:12:45,643] TRAIN Iter 75680: lr = 0.373868,	loss = 2.890719,	Top-1 err = 0.444971,	Top-5 err = 0.209326,	data_time = 0.050397,	train_time = 0.686252
[2019-08-23 06:12:56,829] TRAIN Iter 75700: lr = 0.373835,	loss = 2.728496,	Top-1 err = 0.436719,	Top-5 err = 0.209668,	data_time = 0.050394,	train_time = 0.559284
[2019-08-23 06:13:09,064] TRAIN Iter 75720: lr = 0.373802,	loss = 2.778555,	Top-1 err = 0.430762,	Top-5 err = 0.206885,	data_time = 0.050489,	train_time = 0.611714
[2019-08-23 06:13:24,439] TRAIN Iter 75740: lr = 0.373768,	loss = 2.818996,	Top-1 err = 0.441895,	Top-5 err = 0.214111,	data_time = 0.050362,	train_time = 0.768753
[2019-08-23 06:13:32,793] TRAIN Iter 75760: lr = 0.373735,	loss = 2.725496,	Top-1 err = 0.438770,	Top-5 err = 0.206006,	data_time = 0.050330,	train_time = 0.417664
[2019-08-23 06:13:45,279] TRAIN Iter 75780: lr = 0.373702,	loss = 2.785484,	Top-1 err = 0.437793,	Top-5 err = 0.211865,	data_time = 0.050797,	train_time = 0.624284
[2019-08-23 06:14:01,079] TRAIN Iter 75800: lr = 0.373668,	loss = 2.952364,	Top-1 err = 0.439111,	Top-5 err = 0.214648,	data_time = 0.050855,	train_time = 0.789985
[2019-08-23 06:14:08,682] TRAIN Iter 75820: lr = 0.373635,	loss = 2.793795,	Top-1 err = 0.442334,	Top-5 err = 0.216602,	data_time = 0.050592,	train_time = 0.380171
[2019-08-23 06:14:22,860] TRAIN Iter 75840: lr = 0.373602,	loss = 2.888672,	Top-1 err = 0.435449,	Top-5 err = 0.213135,	data_time = 0.050503,	train_time = 0.708841
[2019-08-23 06:14:33,047] TRAIN Iter 75860: lr = 0.373568,	loss = 2.739265,	Top-1 err = 0.439063,	Top-5 err = 0.212305,	data_time = 0.050800,	train_time = 0.509353
[2019-08-23 06:14:45,612] TRAIN Iter 75880: lr = 0.373535,	loss = 2.706656,	Top-1 err = 0.439600,	Top-5 err = 0.208203,	data_time = 0.050200,	train_time = 0.628215
[2019-08-23 06:15:01,599] TRAIN Iter 75900: lr = 0.373502,	loss = 2.862075,	Top-1 err = 0.438770,	Top-5 err = 0.211621,	data_time = 0.050490,	train_time = 0.799370
[2019-08-23 06:15:09,330] TRAIN Iter 75920: lr = 0.373468,	loss = 2.750478,	Top-1 err = 0.440869,	Top-5 err = 0.209912,	data_time = 0.050561,	train_time = 0.386510
[2019-08-23 06:15:24,037] TRAIN Iter 75940: lr = 0.373435,	loss = 2.792464,	Top-1 err = 0.437744,	Top-5 err = 0.210791,	data_time = 0.050526,	train_time = 0.735341
[2019-08-23 06:15:39,497] TRAIN Iter 75960: lr = 0.373402,	loss = 2.749713,	Top-1 err = 0.435059,	Top-5 err = 0.209229,	data_time = 0.050417,	train_time = 0.772966
[2019-08-23 06:15:47,062] TRAIN Iter 75980: lr = 0.373368,	loss = 2.777010,	Top-1 err = 0.438135,	Top-5 err = 0.212842,	data_time = 0.050513,	train_time = 0.378279
[2019-08-23 06:16:00,945] TRAIN Iter 76000: lr = 0.373335,	loss = 2.730532,	Top-1 err = 0.440674,	Top-5 err = 0.211133,	data_time = 0.050532,	train_time = 0.694118
[2019-08-23 06:16:11,361] TRAIN Iter 76020: lr = 0.373302,	loss = 2.787131,	Top-1 err = 0.448779,	Top-5 err = 0.213574,	data_time = 0.177048,	train_time = 0.520787
[2019-08-23 06:16:23,194] TRAIN Iter 76040: lr = 0.373268,	loss = 2.732664,	Top-1 err = 0.436963,	Top-5 err = 0.211084,	data_time = 0.050553,	train_time = 0.591652
[2019-08-23 06:16:39,528] TRAIN Iter 76060: lr = 0.373235,	loss = 2.748881,	Top-1 err = 0.443457,	Top-5 err = 0.212549,	data_time = 0.130338,	train_time = 0.816667
[2019-08-23 06:16:46,819] TRAIN Iter 76080: lr = 0.373202,	loss = 2.947590,	Top-1 err = 0.443701,	Top-5 err = 0.217285,	data_time = 0.050730,	train_time = 0.364546
[2019-08-23 06:17:00,622] TRAIN Iter 76100: lr = 0.373168,	loss = 2.845901,	Top-1 err = 0.439063,	Top-5 err = 0.211133,	data_time = 0.050284,	train_time = 0.690117
[2019-08-23 06:17:16,735] TRAIN Iter 76120: lr = 0.373135,	loss = 2.855848,	Top-1 err = 0.438477,	Top-5 err = 0.211182,	data_time = 0.140128,	train_time = 0.805630
[2019-08-23 06:17:24,154] TRAIN Iter 76140: lr = 0.373102,	loss = 2.724802,	Top-1 err = 0.439355,	Top-5 err = 0.209814,	data_time = 0.050802,	train_time = 0.370943
[2019-08-23 06:17:40,763] TRAIN Iter 76160: lr = 0.373068,	loss = 2.776373,	Top-1 err = 0.436133,	Top-5 err = 0.209766,	data_time = 0.050474,	train_time = 0.830457
[2019-08-23 06:17:53,246] TRAIN Iter 76180: lr = 0.373035,	loss = 2.775413,	Top-1 err = 0.441846,	Top-5 err = 0.210205,	data_time = 0.532846,	train_time = 0.624107
[2019-08-23 06:18:03,813] TRAIN Iter 76200: lr = 0.373002,	loss = 2.776615,	Top-1 err = 0.437500,	Top-5 err = 0.209619,	data_time = 0.050481,	train_time = 0.528360
[2019-08-23 06:18:20,647] TRAIN Iter 76220: lr = 0.372968,	loss = 2.841245,	Top-1 err = 0.443604,	Top-5 err = 0.212646,	data_time = 0.050612,	train_time = 0.841674
[2019-08-23 06:18:27,624] TRAIN Iter 76240: lr = 0.372935,	loss = 2.786623,	Top-1 err = 0.437061,	Top-5 err = 0.212549,	data_time = 0.050341,	train_time = 0.348802
[2019-08-23 06:18:43,503] TRAIN Iter 76260: lr = 0.372902,	loss = 2.827264,	Top-1 err = 0.440381,	Top-5 err = 0.216504,	data_time = 0.050008,	train_time = 0.793977
[2019-08-23 06:18:59,353] TRAIN Iter 76280: lr = 0.372868,	loss = 2.895639,	Top-1 err = 0.445898,	Top-5 err = 0.211963,	data_time = 0.049980,	train_time = 0.792461
[2019-08-23 06:19:06,307] TRAIN Iter 76300: lr = 0.372835,	loss = 2.934146,	Top-1 err = 0.446680,	Top-5 err = 0.216846,	data_time = 0.049893,	train_time = 0.347673
[2019-08-23 06:19:55,630] TRAIN Iter 76320: lr = 0.372802,	loss = 2.768438,	Top-1 err = 0.446935,	Top-5 err = 0.216566,	data_time = 0.050575,	train_time = 2.466177
[2019-08-23 06:20:04,108] TRAIN Iter 76340: lr = 0.372768,	loss = 2.823282,	Top-1 err = 0.446143,	Top-5 err = 0.210889,	data_time = 0.051207,	train_time = 0.423878
[2019-08-23 06:20:16,790] TRAIN Iter 76360: lr = 0.372735,	loss = 2.729708,	Top-1 err = 0.428174,	Top-5 err = 0.205518,	data_time = 0.050677,	train_time = 0.634064
[2019-08-23 06:20:25,283] TRAIN Iter 76380: lr = 0.372702,	loss = 2.806314,	Top-1 err = 0.434863,	Top-5 err = 0.205176,	data_time = 0.050855,	train_time = 0.424662
[2019-08-23 06:20:33,025] TRAIN Iter 76400: lr = 0.372668,	loss = 2.773984,	Top-1 err = 0.430322,	Top-5 err = 0.201855,	data_time = 0.050490,	train_time = 0.387035
[2019-08-23 06:20:46,097] TRAIN Iter 76420: lr = 0.372635,	loss = 2.732272,	Top-1 err = 0.433789,	Top-5 err = 0.208496,	data_time = 0.050964,	train_time = 0.653609
[2019-08-23 06:20:58,925] TRAIN Iter 76440: lr = 0.372602,	loss = 2.709477,	Top-1 err = 0.426904,	Top-5 err = 0.202002,	data_time = 0.050527,	train_time = 0.641365
[2019-08-23 06:21:06,660] TRAIN Iter 76460: lr = 0.372568,	loss = 2.684204,	Top-1 err = 0.432031,	Top-5 err = 0.204785,	data_time = 0.050971,	train_time = 0.386766
[2019-08-23 06:21:20,796] TRAIN Iter 76480: lr = 0.372535,	loss = 2.717469,	Top-1 err = 0.430225,	Top-5 err = 0.206982,	data_time = 0.050901,	train_time = 0.706759
[2019-08-23 06:21:28,710] TRAIN Iter 76500: lr = 0.372502,	loss = 2.822355,	Top-1 err = 0.433447,	Top-5 err = 0.203857,	data_time = 0.050768,	train_time = 0.395716
[2019-08-23 06:21:40,836] TRAIN Iter 76520: lr = 0.372468,	loss = 2.793745,	Top-1 err = 0.438086,	Top-5 err = 0.212500,	data_time = 0.050456,	train_time = 0.606268
[2019-08-23 06:21:54,238] TRAIN Iter 76540: lr = 0.372435,	loss = 2.798092,	Top-1 err = 0.439063,	Top-5 err = 0.211230,	data_time = 0.050534,	train_time = 0.670072
[2019-08-23 06:22:02,124] TRAIN Iter 76560: lr = 0.372402,	loss = 2.753724,	Top-1 err = 0.435840,	Top-5 err = 0.205273,	data_time = 0.050918,	train_time = 0.394312
[2019-08-23 06:22:14,922] TRAIN Iter 76580: lr = 0.372368,	loss = 2.793255,	Top-1 err = 0.429834,	Top-5 err = 0.204736,	data_time = 0.050675,	train_time = 0.639871
[2019-08-23 06:22:27,944] TRAIN Iter 76600: lr = 0.372335,	loss = 2.817893,	Top-1 err = 0.434570,	Top-5 err = 0.209619,	data_time = 0.050644,	train_time = 0.651058
[2019-08-23 06:22:35,740] TRAIN Iter 76620: lr = 0.372302,	loss = 2.825371,	Top-1 err = 0.433301,	Top-5 err = 0.210645,	data_time = 0.050416,	train_time = 0.389822
[2019-08-23 06:22:50,213] TRAIN Iter 76640: lr = 0.372268,	loss = 2.771838,	Top-1 err = 0.436670,	Top-5 err = 0.211816,	data_time = 0.119394,	train_time = 0.723623
[2019-08-23 06:22:57,894] TRAIN Iter 76660: lr = 0.372235,	loss = 2.869504,	Top-1 err = 0.439697,	Top-5 err = 0.212256,	data_time = 0.051083,	train_time = 0.384007
[2019-08-23 06:23:11,848] TRAIN Iter 76680: lr = 0.372202,	loss = 2.759135,	Top-1 err = 0.437646,	Top-5 err = 0.204150,	data_time = 0.050384,	train_time = 0.697676
[2019-08-23 06:23:25,963] TRAIN Iter 76700: lr = 0.372168,	loss = 2.772500,	Top-1 err = 0.433936,	Top-5 err = 0.203076,	data_time = 0.128549,	train_time = 0.705765
[2019-08-23 06:23:33,480] TRAIN Iter 76720: lr = 0.372135,	loss = 2.730636,	Top-1 err = 0.435840,	Top-5 err = 0.209326,	data_time = 0.050590,	train_time = 0.375853
[2019-08-23 06:23:45,656] TRAIN Iter 76740: lr = 0.372102,	loss = 2.832832,	Top-1 err = 0.435742,	Top-5 err = 0.212549,	data_time = 0.050256,	train_time = 0.608782
[2019-08-23 06:23:58,424] TRAIN Iter 76760: lr = 0.372068,	loss = 2.857547,	Top-1 err = 0.433203,	Top-5 err = 0.207178,	data_time = 0.129715,	train_time = 0.638387
[2019-08-23 06:24:06,919] TRAIN Iter 76780: lr = 0.372035,	loss = 2.774295,	Top-1 err = 0.443066,	Top-5 err = 0.211377,	data_time = 0.050396,	train_time = 0.424727
[2019-08-23 06:24:21,899] TRAIN Iter 76800: lr = 0.372002,	loss = 2.835930,	Top-1 err = 0.434277,	Top-5 err = 0.208057,	data_time = 0.050291,	train_time = 0.748991
[2019-08-23 06:24:29,745] TRAIN Iter 76820: lr = 0.371968,	loss = 2.701665,	Top-1 err = 0.434619,	Top-5 err = 0.209277,	data_time = 0.050538,	train_time = 0.392292
[2019-08-23 06:24:43,943] TRAIN Iter 76840: lr = 0.371935,	loss = 2.792633,	Top-1 err = 0.436768,	Top-5 err = 0.207666,	data_time = 0.050434,	train_time = 0.709844
[2019-08-23 06:24:58,680] TRAIN Iter 76860: lr = 0.371902,	loss = 2.798935,	Top-1 err = 0.437109,	Top-5 err = 0.210742,	data_time = 0.050432,	train_time = 0.736841
[2019-08-23 06:25:06,345] TRAIN Iter 76880: lr = 0.371868,	loss = 2.791298,	Top-1 err = 0.440283,	Top-5 err = 0.212207,	data_time = 0.050594,	train_time = 0.383250
[2019-08-23 06:25:20,335] TRAIN Iter 76900: lr = 0.371835,	loss = 2.889780,	Top-1 err = 0.436426,	Top-5 err = 0.209473,	data_time = 0.050468,	train_time = 0.699496
[2019-08-23 06:25:34,282] TRAIN Iter 76920: lr = 0.371802,	loss = 2.828350,	Top-1 err = 0.446826,	Top-5 err = 0.215625,	data_time = 0.050499,	train_time = 0.697355
[2019-08-23 06:25:42,057] TRAIN Iter 76940: lr = 0.371768,	loss = 2.869404,	Top-1 err = 0.439111,	Top-5 err = 0.208350,	data_time = 0.051024,	train_time = 0.388733
[2019-08-23 06:25:55,499] TRAIN Iter 76960: lr = 0.371735,	loss = 2.772512,	Top-1 err = 0.443164,	Top-5 err = 0.210107,	data_time = 0.051247,	train_time = 0.672061
[2019-08-23 06:26:02,837] TRAIN Iter 76980: lr = 0.371702,	loss = 2.877367,	Top-1 err = 0.440088,	Top-5 err = 0.212012,	data_time = 0.050869,	train_time = 0.366870
[2019-08-23 06:26:18,153] TRAIN Iter 77000: lr = 0.371668,	loss = 2.842948,	Top-1 err = 0.438477,	Top-5 err = 0.209961,	data_time = 0.050864,	train_time = 0.765812
[2019-08-23 06:26:34,196] TRAIN Iter 77020: lr = 0.371635,	loss = 2.664962,	Top-1 err = 0.438281,	Top-5 err = 0.212158,	data_time = 0.050404,	train_time = 0.802136
[2019-08-23 06:26:41,406] TRAIN Iter 77040: lr = 0.371602,	loss = 2.809008,	Top-1 err = 0.439209,	Top-5 err = 0.213379,	data_time = 0.050702,	train_time = 0.360465
[2019-08-23 06:26:55,728] TRAIN Iter 77060: lr = 0.371568,	loss = 2.842297,	Top-1 err = 0.440674,	Top-5 err = 0.212109,	data_time = 0.050807,	train_time = 0.716078
[2019-08-23 06:27:10,406] TRAIN Iter 77080: lr = 0.371535,	loss = 2.873246,	Top-1 err = 0.444189,	Top-5 err = 0.215723,	data_time = 0.134227,	train_time = 0.733886
[2019-08-23 06:27:17,661] TRAIN Iter 77100: lr = 0.371502,	loss = 2.781255,	Top-1 err = 0.441504,	Top-5 err = 0.211377,	data_time = 0.050653,	train_time = 0.362747
[2019-08-23 06:27:32,877] TRAIN Iter 77120: lr = 0.371468,	loss = 2.756652,	Top-1 err = 0.435840,	Top-5 err = 0.207715,	data_time = 0.050521,	train_time = 0.760807
[2019-08-23 06:27:40,298] TRAIN Iter 77140: lr = 0.371435,	loss = 2.758332,	Top-1 err = 0.440771,	Top-5 err = 0.211621,	data_time = 0.050705,	train_time = 0.371024
[2019-08-23 06:27:55,492] TRAIN Iter 77160: lr = 0.371402,	loss = 2.823399,	Top-1 err = 0.435986,	Top-5 err = 0.211768,	data_time = 0.050322,	train_time = 0.759671
[2019-08-23 06:28:11,671] TRAIN Iter 77180: lr = 0.371368,	loss = 2.870964,	Top-1 err = 0.442236,	Top-5 err = 0.211572,	data_time = 0.050540,	train_time = 0.808947
[2019-08-23 06:28:18,857] TRAIN Iter 77200: lr = 0.371335,	loss = 2.725296,	Top-1 err = 0.439600,	Top-5 err = 0.208154,	data_time = 0.050568,	train_time = 0.359321
[2019-08-23 06:28:34,047] TRAIN Iter 77220: lr = 0.371302,	loss = 2.732714,	Top-1 err = 0.437305,	Top-5 err = 0.214697,	data_time = 0.050521,	train_time = 0.759487
[2019-08-23 06:28:50,189] TRAIN Iter 77240: lr = 0.371268,	loss = 2.879957,	Top-1 err = 0.439600,	Top-5 err = 0.213086,	data_time = 0.050391,	train_time = 0.807071
[2019-08-23 06:28:57,258] TRAIN Iter 77260: lr = 0.371235,	loss = 2.824713,	Top-1 err = 0.439844,	Top-5 err = 0.213330,	data_time = 0.050592,	train_time = 0.353411
[2019-08-23 06:29:13,926] TRAIN Iter 77280: lr = 0.371202,	loss = 2.853609,	Top-1 err = 0.442236,	Top-5 err = 0.217188,	data_time = 0.050579,	train_time = 0.833424
[2019-08-23 06:29:21,186] TRAIN Iter 77300: lr = 0.371168,	loss = 2.833290,	Top-1 err = 0.440479,	Top-5 err = 0.212549,	data_time = 0.097676,	train_time = 0.362978
[2019-08-23 06:29:37,473] TRAIN Iter 77320: lr = 0.371135,	loss = 2.797929,	Top-1 err = 0.436670,	Top-5 err = 0.208496,	data_time = 0.050349,	train_time = 0.814299
[2019-08-23 06:29:53,784] TRAIN Iter 77340: lr = 0.371102,	loss = 2.771151,	Top-1 err = 0.442285,	Top-5 err = 0.210352,	data_time = 0.050721,	train_time = 0.815568
[2019-08-23 06:30:00,668] TRAIN Iter 77360: lr = 0.371068,	loss = 2.769926,	Top-1 err = 0.438623,	Top-5 err = 0.211426,	data_time = 0.143376,	train_time = 0.344171
[2019-08-23 06:30:16,374] TRAIN Iter 77380: lr = 0.371035,	loss = 2.829303,	Top-1 err = 0.445850,	Top-5 err = 0.217725,	data_time = 0.050370,	train_time = 0.785302
[2019-08-23 06:30:33,895] TRAIN Iter 77400: lr = 0.371002,	loss = 2.800033,	Top-1 err = 0.437891,	Top-5 err = 0.212842,	data_time = 0.050794,	train_time = 0.876029
[2019-08-23 06:30:40,669] TRAIN Iter 77420: lr = 0.370968,	loss = 2.780833,	Top-1 err = 0.438818,	Top-5 err = 0.209814,	data_time = 0.050603,	train_time = 0.338680
[2019-08-23 06:30:58,889] TRAIN Iter 77440: lr = 0.370935,	loss = 2.713645,	Top-1 err = 0.435791,	Top-5 err = 0.211328,	data_time = 0.050537,	train_time = 0.911011
[2019-08-23 06:31:05,754] TRAIN Iter 77460: lr = 0.370902,	loss = 2.772792,	Top-1 err = 0.442578,	Top-5 err = 0.208398,	data_time = 0.050985,	train_time = 0.343211
[2019-08-23 06:31:23,284] TRAIN Iter 77480: lr = 0.370868,	loss = 2.776397,	Top-1 err = 0.436914,	Top-5 err = 0.209131,	data_time = 0.050414,	train_time = 0.876508
[2019-08-23 06:31:37,289] TRAIN Iter 77500: lr = 0.370835,	loss = 2.863564,	Top-1 err = 0.440918,	Top-5 err = 0.215820,	data_time = 0.050139,	train_time = 0.700202
[2019-08-23 06:31:44,095] TRAIN Iter 77520: lr = 0.370802,	loss = 2.810220,	Top-1 err = 0.441406,	Top-5 err = 0.212646,	data_time = 0.049967,	train_time = 0.340285
[2019-08-23 06:31:59,858] TRAIN Iter 77540: lr = 0.370768,	loss = 2.782541,	Top-1 err = 0.441113,	Top-5 err = 0.215039,	data_time = 0.049898,	train_time = 0.788161
[2019-08-23 06:32:12,332] TRAIN Iter 77560: lr = 0.370735,	loss = 2.848582,	Top-1 err = 0.441762,	Top-5 err = 0.211743,	data_time = 0.007060,	train_time = 0.623688
[2019-08-23 06:32:54,497] TRAIN Iter 77580: lr = 0.370702,	loss = 2.856783,	Top-1 err = 0.435498,	Top-5 err = 0.210596,	data_time = 0.050600,	train_time = 2.108250
[2019-08-23 06:33:08,462] TRAIN Iter 77600: lr = 0.370668,	loss = 2.696424,	Top-1 err = 0.436670,	Top-5 err = 0.210596,	data_time = 1.145425,	train_time = 0.698189
[2019-08-23 06:33:16,372] TRAIN Iter 77620: lr = 0.370635,	loss = 2.773246,	Top-1 err = 0.430615,	Top-5 err = 0.202832,	data_time = 0.050734,	train_time = 0.395502
[2019-08-23 06:33:29,037] TRAIN Iter 77640: lr = 0.370602,	loss = 2.691795,	Top-1 err = 0.434033,	Top-5 err = 0.205420,	data_time = 0.050934,	train_time = 0.633222
[2019-08-23 06:33:41,257] TRAIN Iter 77660: lr = 0.370568,	loss = 2.784460,	Top-1 err = 0.436328,	Top-5 err = 0.206201,	data_time = 0.110931,	train_time = 0.611004
[2019-08-23 06:33:51,997] TRAIN Iter 77680: lr = 0.370535,	loss = 2.756839,	Top-1 err = 0.432080,	Top-5 err = 0.200684,	data_time = 0.050576,	train_time = 0.536956
[2019-08-23 06:34:05,303] TRAIN Iter 77700: lr = 0.370502,	loss = 2.680333,	Top-1 err = 0.428955,	Top-5 err = 0.205615,	data_time = 0.050450,	train_time = 0.665306
[2019-08-23 06:34:13,876] TRAIN Iter 77720: lr = 0.370468,	loss = 2.719798,	Top-1 err = 0.429736,	Top-5 err = 0.201611,	data_time = 0.050704,	train_time = 0.428656
[2019-08-23 06:34:26,947] TRAIN Iter 77740: lr = 0.370435,	loss = 2.687074,	Top-1 err = 0.431592,	Top-5 err = 0.204199,	data_time = 0.172341,	train_time = 0.653502
[2019-08-23 06:34:39,417] TRAIN Iter 77760: lr = 0.370402,	loss = 2.711726,	Top-1 err = 0.430469,	Top-5 err = 0.206934,	data_time = 0.050695,	train_time = 0.623481
[2019-08-23 06:34:46,998] TRAIN Iter 77780: lr = 0.370368,	loss = 2.780536,	Top-1 err = 0.439355,	Top-5 err = 0.208105,	data_time = 0.050883,	train_time = 0.379076
[2019-08-23 06:34:59,167] TRAIN Iter 77800: lr = 0.370335,	loss = 2.834285,	Top-1 err = 0.432959,	Top-5 err = 0.207227,	data_time = 0.050893,	train_time = 0.608397
[2019-08-23 06:35:11,449] TRAIN Iter 77820: lr = 0.370302,	loss = 2.719003,	Top-1 err = 0.438281,	Top-5 err = 0.206396,	data_time = 0.129950,	train_time = 0.614090
[2019-08-23 06:35:19,430] TRAIN Iter 77840: lr = 0.370268,	loss = 2.757735,	Top-1 err = 0.436230,	Top-5 err = 0.209668,	data_time = 0.050316,	train_time = 0.399031
[2019-08-23 06:35:32,780] TRAIN Iter 77860: lr = 0.370235,	loss = 2.762320,	Top-1 err = 0.434766,	Top-5 err = 0.206494,	data_time = 0.050853,	train_time = 0.667514
[2019-08-23 06:35:40,489] TRAIN Iter 77880: lr = 0.370202,	loss = 2.784993,	Top-1 err = 0.434766,	Top-5 err = 0.209961,	data_time = 0.050382,	train_time = 0.385425
[2019-08-23 06:35:55,216] TRAIN Iter 77900: lr = 0.370168,	loss = 2.777007,	Top-1 err = 0.429541,	Top-5 err = 0.204590,	data_time = 0.050690,	train_time = 0.736339
[2019-08-23 06:36:10,031] TRAIN Iter 77920: lr = 0.370135,	loss = 2.866901,	Top-1 err = 0.436182,	Top-5 err = 0.209961,	data_time = 0.051102,	train_time = 0.740704
[2019-08-23 06:36:17,643] TRAIN Iter 77940: lr = 0.370102,	loss = 2.675840,	Top-1 err = 0.434229,	Top-5 err = 0.206006,	data_time = 0.050647,	train_time = 0.380583
[2019-08-23 06:36:30,926] TRAIN Iter 77960: lr = 0.370068,	loss = 2.788446,	Top-1 err = 0.434521,	Top-5 err = 0.205225,	data_time = 0.050622,	train_time = 0.664156
[2019-08-23 06:36:46,573] TRAIN Iter 77980: lr = 0.370035,	loss = 2.786683,	Top-1 err = 0.441650,	Top-5 err = 0.213477,	data_time = 0.050735,	train_time = 0.782344
[2019-08-23 06:36:53,617] TRAIN Iter 78000: lr = 0.370002,	loss = 2.872191,	Top-1 err = 0.438037,	Top-5 err = 0.210596,	data_time = 0.148679,	train_time = 0.352163
[2019-08-23 06:37:09,691] TRAIN Iter 78020: lr = 0.369968,	loss = 2.794380,	Top-1 err = 0.433594,	Top-5 err = 0.205127,	data_time = 0.050707,	train_time = 0.803700
[2019-08-23 06:37:18,052] TRAIN Iter 78040: lr = 0.369935,	loss = 2.794083,	Top-1 err = 0.441406,	Top-5 err = 0.212158,	data_time = 0.050598,	train_time = 0.418045
[2019-08-23 06:37:30,338] TRAIN Iter 78060: lr = 0.369902,	loss = 2.879056,	Top-1 err = 0.430371,	Top-5 err = 0.205713,	data_time = 0.126279,	train_time = 0.614286
[2019-08-23 06:37:45,803] TRAIN Iter 78080: lr = 0.369868,	loss = 2.785826,	Top-1 err = 0.434277,	Top-5 err = 0.208496,	data_time = 0.050415,	train_time = 0.773222
[2019-08-23 06:37:53,860] TRAIN Iter 78100: lr = 0.369835,	loss = 2.726596,	Top-1 err = 0.433203,	Top-5 err = 0.205078,	data_time = 0.050460,	train_time = 0.402838
[2019-08-23 06:38:05,792] TRAIN Iter 78120: lr = 0.369802,	loss = 2.856337,	Top-1 err = 0.429004,	Top-5 err = 0.206592,	data_time = 0.050608,	train_time = 0.596580
[2019-08-23 06:38:15,483] TRAIN Iter 78140: lr = 0.369768,	loss = 2.769947,	Top-1 err = 0.437402,	Top-5 err = 0.210352,	data_time = 0.050213,	train_time = 0.484517
[2019-08-23 06:38:27,704] TRAIN Iter 78160: lr = 0.369735,	loss = 2.801876,	Top-1 err = 0.432031,	Top-5 err = 0.205518,	data_time = 0.050299,	train_time = 0.611035
[2019-08-23 06:38:41,547] TRAIN Iter 78180: lr = 0.369702,	loss = 2.886086,	Top-1 err = 0.444775,	Top-5 err = 0.213525,	data_time = 0.050421,	train_time = 0.692165
[2019-08-23 06:38:49,299] TRAIN Iter 78200: lr = 0.369668,	loss = 2.860068,	Top-1 err = 0.447217,	Top-5 err = 0.216992,	data_time = 0.050766,	train_time = 0.387591
[2019-08-23 06:39:03,172] TRAIN Iter 78220: lr = 0.369635,	loss = 2.748748,	Top-1 err = 0.433545,	Top-5 err = 0.206396,	data_time = 0.050462,	train_time = 0.693632
[2019-08-23 06:39:20,254] TRAIN Iter 78240: lr = 0.369602,	loss = 2.882429,	Top-1 err = 0.437500,	Top-5 err = 0.213721,	data_time = 0.050229,	train_time = 0.854102
[2019-08-23 06:39:28,191] TRAIN Iter 78260: lr = 0.369568,	loss = 2.907953,	Top-1 err = 0.440723,	Top-5 err = 0.214355,	data_time = 0.050913,	train_time = 0.396817
[2019-08-23 06:39:41,821] TRAIN Iter 78280: lr = 0.369535,	loss = 2.708522,	Top-1 err = 0.436279,	Top-5 err = 0.212939,	data_time = 0.050364,	train_time = 0.681483
[2019-08-23 06:39:56,835] TRAIN Iter 78300: lr = 0.369502,	loss = 2.866679,	Top-1 err = 0.445020,	Top-5 err = 0.210986,	data_time = 0.050228,	train_time = 0.750666
[2019-08-23 06:40:04,830] TRAIN Iter 78320: lr = 0.369468,	loss = 2.878855,	Top-1 err = 0.435986,	Top-5 err = 0.208447,	data_time = 0.050819,	train_time = 0.399729
[2019-08-23 06:40:18,045] TRAIN Iter 78340: lr = 0.369435,	loss = 2.814238,	Top-1 err = 0.438135,	Top-5 err = 0.207617,	data_time = 0.050729,	train_time = 0.660754
[2019-08-23 06:40:26,039] TRAIN Iter 78360: lr = 0.369402,	loss = 2.866925,	Top-1 err = 0.438770,	Top-5 err = 0.213184,	data_time = 0.050756,	train_time = 0.399682
[2019-08-23 06:40:40,862] TRAIN Iter 78380: lr = 0.369368,	loss = 2.688093,	Top-1 err = 0.432861,	Top-5 err = 0.204834,	data_time = 0.104976,	train_time = 0.741163
[2019-08-23 06:40:54,461] TRAIN Iter 78400: lr = 0.369335,	loss = 2.834223,	Top-1 err = 0.441455,	Top-5 err = 0.219043,	data_time = 0.050434,	train_time = 0.679912
[2019-08-23 06:41:02,380] TRAIN Iter 78420: lr = 0.369302,	loss = 2.780122,	Top-1 err = 0.436084,	Top-5 err = 0.210059,	data_time = 0.050506,	train_time = 0.395955
[2019-08-23 06:41:16,937] TRAIN Iter 78440: lr = 0.369268,	loss = 2.822577,	Top-1 err = 0.438281,	Top-5 err = 0.209521,	data_time = 0.050608,	train_time = 0.727836
[2019-08-23 06:41:32,346] TRAIN Iter 78460: lr = 0.369235,	loss = 2.860016,	Top-1 err = 0.437402,	Top-5 err = 0.209473,	data_time = 0.050313,	train_time = 0.770392
[2019-08-23 06:41:40,093] TRAIN Iter 78480: lr = 0.369202,	loss = 2.764852,	Top-1 err = 0.431885,	Top-5 err = 0.210107,	data_time = 0.116774,	train_time = 0.387349
[2019-08-23 06:41:52,726] TRAIN Iter 78500: lr = 0.369168,	loss = 2.777929,	Top-1 err = 0.440918,	Top-5 err = 0.212451,	data_time = 0.050341,	train_time = 0.631628
[2019-08-23 06:42:01,410] TRAIN Iter 78520: lr = 0.369135,	loss = 2.822902,	Top-1 err = 0.436182,	Top-5 err = 0.209082,	data_time = 0.050789,	train_time = 0.434201
[2019-08-23 06:42:14,542] TRAIN Iter 78540: lr = 0.369102,	loss = 2.769808,	Top-1 err = 0.438184,	Top-5 err = 0.205420,	data_time = 0.050808,	train_time = 0.656598
[2019-08-23 06:42:29,557] TRAIN Iter 78560: lr = 0.369068,	loss = 2.806107,	Top-1 err = 0.439844,	Top-5 err = 0.205713,	data_time = 0.050890,	train_time = 0.750708
[2019-08-23 06:42:37,180] TRAIN Iter 78580: lr = 0.369035,	loss = 2.833186,	Top-1 err = 0.439502,	Top-5 err = 0.209082,	data_time = 0.050862,	train_time = 0.381149
[2019-08-23 06:42:51,044] TRAIN Iter 78600: lr = 0.369002,	loss = 2.934474,	Top-1 err = 0.443652,	Top-5 err = 0.211523,	data_time = 0.050108,	train_time = 0.693172
[2019-08-23 06:43:04,674] TRAIN Iter 78620: lr = 0.368968,	loss = 2.700831,	Top-1 err = 0.437451,	Top-5 err = 0.207568,	data_time = 0.050884,	train_time = 0.681511
[2019-08-23 06:43:11,898] TRAIN Iter 78640: lr = 0.368935,	loss = 2.773130,	Top-1 err = 0.436230,	Top-5 err = 0.210645,	data_time = 0.050740,	train_time = 0.361174
[2019-08-23 06:43:26,459] TRAIN Iter 78660: lr = 0.368902,	loss = 2.758773,	Top-1 err = 0.442480,	Top-5 err = 0.212207,	data_time = 0.050525,	train_time = 0.728046
[2019-08-23 06:43:33,681] TRAIN Iter 78680: lr = 0.368868,	loss = 2.690838,	Top-1 err = 0.443164,	Top-5 err = 0.215039,	data_time = 0.050607,	train_time = 0.361070
[2019-08-23 06:43:48,767] TRAIN Iter 78700: lr = 0.368835,	loss = 2.777847,	Top-1 err = 0.439941,	Top-5 err = 0.212305,	data_time = 0.050409,	train_time = 0.754295
[2019-08-23 06:44:02,928] TRAIN Iter 78720: lr = 0.368802,	loss = 2.708186,	Top-1 err = 0.436572,	Top-5 err = 0.208350,	data_time = 0.050464,	train_time = 0.708048
[2019-08-23 06:44:09,972] TRAIN Iter 78740: lr = 0.368768,	loss = 2.758545,	Top-1 err = 0.432959,	Top-5 err = 0.211719,	data_time = 0.050442,	train_time = 0.352154
[2019-08-23 06:44:25,821] TRAIN Iter 78760: lr = 0.368735,	loss = 2.809115,	Top-1 err = 0.434961,	Top-5 err = 0.210938,	data_time = 0.050107,	train_time = 0.792456
[2019-08-23 06:44:41,074] TRAIN Iter 78780: lr = 0.368702,	loss = 2.841653,	Top-1 err = 0.437598,	Top-5 err = 0.211426,	data_time = 0.160639,	train_time = 0.762618
[2019-08-23 06:44:47,763] TRAIN Iter 78800: lr = 0.368668,	loss = 2.769012,	Top-1 err = 0.443262,	Top-5 err = 0.214404,	data_time = 0.049934,	train_time = 0.334458
[2019-08-23 06:45:36,328] TRAIN Iter 78820: lr = 0.368635,	loss = 2.765768,	Top-1 err = 0.443901,	Top-5 err = 0.213629,	data_time = 0.050714,	train_time = 2.428227
[2019-08-23 06:45:45,355] TRAIN Iter 78840: lr = 0.368602,	loss = 2.717023,	Top-1 err = 0.433740,	Top-5 err = 0.204932,	data_time = 0.050831,	train_time = 0.451339
[2019-08-23 06:45:58,202] TRAIN Iter 78860: lr = 0.368568,	loss = 2.784251,	Top-1 err = 0.433447,	Top-5 err = 0.205127,	data_time = 0.050398,	train_time = 0.642319
[2019-08-23 06:46:09,090] TRAIN Iter 78880: lr = 0.368535,	loss = 2.716683,	Top-1 err = 0.431152,	Top-5 err = 0.208301,	data_time = 0.050851,	train_time = 0.544372
[2019-08-23 06:46:17,192] TRAIN Iter 78900: lr = 0.368502,	loss = 2.800359,	Top-1 err = 0.430762,	Top-5 err = 0.203174,	data_time = 0.164040,	train_time = 0.405073
[2019-08-23 06:46:30,666] TRAIN Iter 78920: lr = 0.368468,	loss = 2.823881,	Top-1 err = 0.436670,	Top-5 err = 0.210059,	data_time = 0.050714,	train_time = 0.673699
[2019-08-23 06:46:38,414] TRAIN Iter 78940: lr = 0.368435,	loss = 2.766989,	Top-1 err = 0.433887,	Top-5 err = 0.208154,	data_time = 0.050789,	train_time = 0.387404
[2019-08-23 06:46:52,453] TRAIN Iter 78960: lr = 0.368402,	loss = 2.749793,	Top-1 err = 0.427148,	Top-5 err = 0.201709,	data_time = 0.050863,	train_time = 0.701941
[2019-08-23 06:47:04,511] TRAIN Iter 78980: lr = 0.368368,	loss = 2.733211,	Top-1 err = 0.426074,	Top-5 err = 0.200439,	data_time = 0.135314,	train_time = 0.602893
[2019-08-23 06:47:12,146] TRAIN Iter 79000: lr = 0.368335,	loss = 2.859914,	Top-1 err = 0.429639,	Top-5 err = 0.203906,	data_time = 0.050732,	train_time = 0.381689
[2019-08-23 06:47:27,453] TRAIN Iter 79020: lr = 0.368302,	loss = 2.818405,	Top-1 err = 0.428662,	Top-5 err = 0.204443,	data_time = 0.050331,	train_time = 0.765341
[2019-08-23 06:47:40,578] TRAIN Iter 79040: lr = 0.368268,	loss = 2.778810,	Top-1 err = 0.432129,	Top-5 err = 0.205371,	data_time = 0.119020,	train_time = 0.656265
[2019-08-23 06:47:47,997] TRAIN Iter 79060: lr = 0.368235,	loss = 2.605904,	Top-1 err = 0.430518,	Top-5 err = 0.205957,	data_time = 0.050499,	train_time = 0.370938
[2019-08-23 06:48:02,505] TRAIN Iter 79080: lr = 0.368202,	loss = 2.787291,	Top-1 err = 0.429590,	Top-5 err = 0.203369,	data_time = 0.050856,	train_time = 0.725390
[2019-08-23 06:48:10,676] TRAIN Iter 79100: lr = 0.368168,	loss = 2.782778,	Top-1 err = 0.434375,	Top-5 err = 0.208057,	data_time = 0.050815,	train_time = 0.408499
[2019-08-23 06:48:22,585] TRAIN Iter 79120: lr = 0.368135,	loss = 2.803934,	Top-1 err = 0.433447,	Top-5 err = 0.207031,	data_time = 0.050888,	train_time = 0.595447
[2019-08-23 06:48:38,152] TRAIN Iter 79140: lr = 0.368102,	loss = 2.775187,	Top-1 err = 0.430664,	Top-5 err = 0.202783,	data_time = 0.050544,	train_time = 0.778333
[2019-08-23 06:48:45,988] TRAIN Iter 79160: lr = 0.368068,	loss = 2.699353,	Top-1 err = 0.432031,	Top-5 err = 0.206445,	data_time = 0.050656,	train_time = 0.391791
[2019-08-23 06:48:57,471] TRAIN Iter 79180: lr = 0.368035,	loss = 2.811764,	Top-1 err = 0.437061,	Top-5 err = 0.210059,	data_time = 0.051010,	train_time = 0.574128
[2019-08-23 06:49:10,213] TRAIN Iter 79200: lr = 0.368002,	loss = 2.776647,	Top-1 err = 0.434521,	Top-5 err = 0.205908,	data_time = 0.137408,	train_time = 0.637087
[2019-08-23 06:49:19,245] TRAIN Iter 79220: lr = 0.367968,	loss = 2.906083,	Top-1 err = 0.433545,	Top-5 err = 0.207031,	data_time = 0.051069,	train_time = 0.451584
[2019-08-23 06:49:34,827] TRAIN Iter 79240: lr = 0.367935,	loss = 2.799845,	Top-1 err = 0.432910,	Top-5 err = 0.208594,	data_time = 0.050533,	train_time = 0.779113
[2019-08-23 06:49:43,184] TRAIN Iter 79260: lr = 0.367902,	loss = 2.818361,	Top-1 err = 0.440967,	Top-5 err = 0.211328,	data_time = 0.050545,	train_time = 0.417833
[2019-08-23 06:49:54,422] TRAIN Iter 79280: lr = 0.367868,	loss = 2.804254,	Top-1 err = 0.439453,	Top-5 err = 0.210840,	data_time = 0.050809,	train_time = 0.561867
[2019-08-23 06:50:07,907] TRAIN Iter 79300: lr = 0.367835,	loss = 2.819550,	Top-1 err = 0.440869,	Top-5 err = 0.209766,	data_time = 0.164216,	train_time = 0.674259
[2019-08-23 06:50:16,095] TRAIN Iter 79320: lr = 0.367802,	loss = 2.715410,	Top-1 err = 0.439258,	Top-5 err = 0.206787,	data_time = 0.132752,	train_time = 0.409339
[2019-08-23 06:50:29,214] TRAIN Iter 79340: lr = 0.367768,	loss = 2.767961,	Top-1 err = 0.434912,	Top-5 err = 0.206836,	data_time = 0.050844,	train_time = 0.655929
[2019-08-23 06:50:41,984] TRAIN Iter 79360: lr = 0.367735,	loss = 2.854064,	Top-1 err = 0.437793,	Top-5 err = 0.208887,	data_time = 0.050757,	train_time = 0.638496
[2019-08-23 06:50:51,864] TRAIN Iter 79380: lr = 0.367702,	loss = 2.822019,	Top-1 err = 0.439844,	Top-5 err = 0.213232,	data_time = 0.146343,	train_time = 0.494000
[2019-08-23 06:51:05,284] TRAIN Iter 79400: lr = 0.367668,	loss = 2.819958,	Top-1 err = 0.438232,	Top-5 err = 0.209717,	data_time = 0.050466,	train_time = 0.670968
[2019-08-23 06:51:13,183] TRAIN Iter 79420: lr = 0.367635,	loss = 2.849773,	Top-1 err = 0.438574,	Top-5 err = 0.210107,	data_time = 0.050446,	train_time = 0.394920
[2019-08-23 06:51:27,474] TRAIN Iter 79440: lr = 0.367602,	loss = 2.647028,	Top-1 err = 0.435010,	Top-5 err = 0.205469,	data_time = 0.050556,	train_time = 0.714562
[2019-08-23 06:51:41,969] TRAIN Iter 79460: lr = 0.367568,	loss = 2.804017,	Top-1 err = 0.436279,	Top-5 err = 0.208936,	data_time = 0.050688,	train_time = 0.724737
[2019-08-23 06:51:49,688] TRAIN Iter 79480: lr = 0.367535,	loss = 2.852361,	Top-1 err = 0.433984,	Top-5 err = 0.210303,	data_time = 0.136274,	train_time = 0.385912
[2019-08-23 06:52:04,928] TRAIN Iter 79500: lr = 0.367502,	loss = 2.912262,	Top-1 err = 0.437988,	Top-5 err = 0.212891,	data_time = 0.050332,	train_time = 0.762024
[2019-08-23 06:52:19,836] TRAIN Iter 79520: lr = 0.367468,	loss = 2.902661,	Top-1 err = 0.440625,	Top-5 err = 0.214453,	data_time = 0.051005,	train_time = 0.745384
[2019-08-23 06:52:27,605] TRAIN Iter 79540: lr = 0.367435,	loss = 2.822863,	Top-1 err = 0.443359,	Top-5 err = 0.212988,	data_time = 0.050607,	train_time = 0.388410
[2019-08-23 06:52:41,350] TRAIN Iter 79560: lr = 0.367402,	loss = 2.763743,	Top-1 err = 0.435010,	Top-5 err = 0.207129,	data_time = 0.050349,	train_time = 0.687244
[2019-08-23 06:52:49,593] TRAIN Iter 79580: lr = 0.367368,	loss = 2.723232,	Top-1 err = 0.434033,	Top-5 err = 0.206982,	data_time = 0.050763,	train_time = 0.412110
[2019-08-23 06:53:04,950] TRAIN Iter 79600: lr = 0.367335,	loss = 2.887224,	Top-1 err = 0.441797,	Top-5 err = 0.213867,	data_time = 0.099602,	train_time = 0.767861
[2019-08-23 06:53:17,793] TRAIN Iter 79620: lr = 0.367302,	loss = 2.845974,	Top-1 err = 0.435840,	Top-5 err = 0.207764,	data_time = 0.050650,	train_time = 0.642143
[2019-08-23 06:53:25,459] TRAIN Iter 79640: lr = 0.367268,	loss = 2.762283,	Top-1 err = 0.442090,	Top-5 err = 0.211670,	data_time = 0.051018,	train_time = 0.383275
[2019-08-23 06:53:41,181] TRAIN Iter 79660: lr = 0.367235,	loss = 2.730359,	Top-1 err = 0.434912,	Top-5 err = 0.210791,	data_time = 0.050905,	train_time = 0.786066
[2019-08-23 06:53:55,290] TRAIN Iter 79680: lr = 0.367202,	loss = 2.792053,	Top-1 err = 0.434375,	Top-5 err = 0.209131,	data_time = 0.050512,	train_time = 0.705442
[2019-08-23 06:54:02,645] TRAIN Iter 79700: lr = 0.367168,	loss = 2.866970,	Top-1 err = 0.439160,	Top-5 err = 0.211572,	data_time = 0.050866,	train_time = 0.367724
[2019-08-23 06:54:17,925] TRAIN Iter 79720: lr = 0.367135,	loss = 2.798374,	Top-1 err = 0.432764,	Top-5 err = 0.212256,	data_time = 0.050396,	train_time = 0.764002
[2019-08-23 06:54:25,867] TRAIN Iter 79740: lr = 0.367102,	loss = 2.790108,	Top-1 err = 0.429932,	Top-5 err = 0.203076,	data_time = 0.050434,	train_time = 0.397093
[2019-08-23 06:54:40,966] TRAIN Iter 79760: lr = 0.367068,	loss = 2.820794,	Top-1 err = 0.439307,	Top-5 err = 0.210693,	data_time = 0.050261,	train_time = 0.754919
[2019-08-23 06:54:55,512] TRAIN Iter 79780: lr = 0.367035,	loss = 2.812793,	Top-1 err = 0.438184,	Top-5 err = 0.210645,	data_time = 0.050283,	train_time = 0.727311
[2019-08-23 06:55:02,939] TRAIN Iter 79800: lr = 0.367002,	loss = 2.828614,	Top-1 err = 0.441211,	Top-5 err = 0.214062,	data_time = 0.050934,	train_time = 0.371335
[2019-08-23 06:55:19,596] TRAIN Iter 79820: lr = 0.366968,	loss = 2.736601,	Top-1 err = 0.435400,	Top-5 err = 0.208496,	data_time = 0.050740,	train_time = 0.832800
[2019-08-23 06:55:33,437] TRAIN Iter 79840: lr = 0.366935,	loss = 2.816322,	Top-1 err = 0.433984,	Top-5 err = 0.208398,	data_time = 0.050652,	train_time = 0.692069
[2019-08-23 06:55:40,419] TRAIN Iter 79860: lr = 0.366902,	loss = 2.838609,	Top-1 err = 0.436377,	Top-5 err = 0.211426,	data_time = 0.050167,	train_time = 0.349073
[2019-08-23 06:55:56,038] TRAIN Iter 79880: lr = 0.366868,	loss = 2.794004,	Top-1 err = 0.439258,	Top-5 err = 0.209717,	data_time = 0.050540,	train_time = 0.780942
[2019-08-23 06:56:03,601] TRAIN Iter 79900: lr = 0.366835,	loss = 2.773993,	Top-1 err = 0.441406,	Top-5 err = 0.209570,	data_time = 0.050458,	train_time = 0.378133
[2019-08-23 06:56:19,559] TRAIN Iter 79920: lr = 0.366802,	loss = 2.741205,	Top-1 err = 0.441602,	Top-5 err = 0.208154,	data_time = 0.050561,	train_time = 0.797877
[2019-08-23 06:56:35,976] TRAIN Iter 79940: lr = 0.366768,	loss = 2.807483,	Top-1 err = 0.434912,	Top-5 err = 0.210107,	data_time = 0.050147,	train_time = 0.820860
[2019-08-23 06:56:43,154] TRAIN Iter 79960: lr = 0.366735,	loss = 2.757381,	Top-1 err = 0.439990,	Top-5 err = 0.212744,	data_time = 0.050730,	train_time = 0.358856
[2019-08-23 06:57:00,438] TRAIN Iter 79980: lr = 0.366702,	loss = 2.815288,	Top-1 err = 0.435986,	Top-5 err = 0.209326,	data_time = 0.050280,	train_time = 0.864189
[2019-08-23 06:57:16,848] TRAIN Iter 80000: lr = 0.366668,	loss = 2.848914,	Top-1 err = 0.439551,	Top-5 err = 0.211377,	data_time = 0.066390,	train_time = 0.820486
[2019-08-23 06:58:16,178] TEST Iter 80000: loss = 2.531389,	Top-1 err = 0.389880,	Top-5 err = 0.159760,	val_time = 59.290402
[2019-08-23 06:58:22,172] TRAIN Iter 80020: lr = 0.366635,	loss = 2.812045,	Top-1 err = 0.439111,	Top-5 err = 0.210986,	data_time = 0.049902,	train_time = 0.299687
[2019-08-23 06:58:28,138] TRAIN Iter 80040: lr = 0.366602,	loss = 2.766698,	Top-1 err = 0.432324,	Top-5 err = 0.206250,	data_time = 0.049858,	train_time = 0.298285
[2019-08-23 06:58:34,155] TRAIN Iter 80060: lr = 0.366568,	loss = 2.774500,	Top-1 err = 0.438135,	Top-5 err = 0.214746,	data_time = 0.049854,	train_time = 0.300830
[2019-08-23 06:59:18,265] TRAIN Iter 80080: lr = 0.366535,	loss = 2.729005,	Top-1 err = 0.432819,	Top-5 err = 0.209439,	data_time = 0.050439,	train_time = 2.205475
[2019-08-23 06:59:31,146] TRAIN Iter 80100: lr = 0.366502,	loss = 2.735371,	Top-1 err = 0.436279,	Top-5 err = 0.208252,	data_time = 0.050750,	train_time = 0.644032
[2019-08-23 06:59:39,478] TRAIN Iter 80120: lr = 0.366468,	loss = 2.853469,	Top-1 err = 0.431641,	Top-5 err = 0.202539,	data_time = 0.050993,	train_time = 0.416580
[2019-08-23 06:59:49,752] TRAIN Iter 80140: lr = 0.366435,	loss = 2.722497,	Top-1 err = 0.428809,	Top-5 err = 0.206396,	data_time = 0.050864,	train_time = 0.513710
[2019-08-23 06:59:56,917] TRAIN Iter 80160: lr = 0.366402,	loss = 2.706645,	Top-1 err = 0.427490,	Top-5 err = 0.200049,	data_time = 0.119228,	train_time = 0.358218
[2019-08-23 07:00:12,044] TRAIN Iter 80180: lr = 0.366368,	loss = 2.805408,	Top-1 err = 0.432959,	Top-5 err = 0.201172,	data_time = 0.050358,	train_time = 0.756333
[2019-08-23 07:00:27,258] TRAIN Iter 80200: lr = 0.366335,	loss = 2.730293,	Top-1 err = 0.433740,	Top-5 err = 0.205713,	data_time = 0.050404,	train_time = 0.760728
[2019-08-23 07:00:35,216] TRAIN Iter 80220: lr = 0.366302,	loss = 2.802455,	Top-1 err = 0.432617,	Top-5 err = 0.200244,	data_time = 0.050650,	train_time = 0.397883
[2019-08-23 07:00:48,095] TRAIN Iter 80240: lr = 0.366268,	loss = 2.735428,	Top-1 err = 0.427881,	Top-5 err = 0.202441,	data_time = 0.050365,	train_time = 0.643901
[2019-08-23 07:01:01,235] TRAIN Iter 80260: lr = 0.366235,	loss = 2.836639,	Top-1 err = 0.431543,	Top-5 err = 0.207617,	data_time = 0.050663,	train_time = 0.657021
[2019-08-23 07:01:09,460] TRAIN Iter 80280: lr = 0.366202,	loss = 2.905308,	Top-1 err = 0.432715,	Top-5 err = 0.208740,	data_time = 0.050974,	train_time = 0.411190
[2019-08-23 07:01:21,608] TRAIN Iter 80300: lr = 0.366168,	loss = 2.782507,	Top-1 err = 0.430957,	Top-5 err = 0.207178,	data_time = 0.050620,	train_time = 0.607428
[2019-08-23 07:01:28,974] TRAIN Iter 80320: lr = 0.366135,	loss = 2.746147,	Top-1 err = 0.435645,	Top-5 err = 0.204932,	data_time = 0.142602,	train_time = 0.368271
[2019-08-23 07:01:43,977] TRAIN Iter 80340: lr = 0.366102,	loss = 2.844069,	Top-1 err = 0.428516,	Top-5 err = 0.207764,	data_time = 0.141591,	train_time = 0.750131
[2019-08-23 07:01:55,789] TRAIN Iter 80360: lr = 0.366068,	loss = 2.746770,	Top-1 err = 0.434473,	Top-5 err = 0.206689,	data_time = 0.050433,	train_time = 0.590600
[2019-08-23 07:02:03,149] TRAIN Iter 80380: lr = 0.366035,	loss = 2.728150,	Top-1 err = 0.429443,	Top-5 err = 0.200586,	data_time = 0.050622,	train_time = 0.367984
[2019-08-23 07:02:18,846] TRAIN Iter 80400: lr = 0.366002,	loss = 2.719009,	Top-1 err = 0.431250,	Top-5 err = 0.207813,	data_time = 0.050560,	train_time = 0.784818
[2019-08-23 07:02:31,159] TRAIN Iter 80420: lr = 0.365968,	loss = 2.861452,	Top-1 err = 0.432080,	Top-5 err = 0.206250,	data_time = 0.050236,	train_time = 0.615625
[2019-08-23 07:02:38,907] TRAIN Iter 80440: lr = 0.365935,	loss = 2.805897,	Top-1 err = 0.433838,	Top-5 err = 0.207080,	data_time = 0.050574,	train_time = 0.387405
[2019-08-23 07:02:54,617] TRAIN Iter 80460: lr = 0.365902,	loss = 2.777919,	Top-1 err = 0.432178,	Top-5 err = 0.202979,	data_time = 0.050605,	train_time = 0.785495
[2019-08-23 07:03:02,553] TRAIN Iter 80480: lr = 0.365868,	loss = 2.811758,	Top-1 err = 0.433496,	Top-5 err = 0.205713,	data_time = 0.050541,	train_time = 0.396789
[2019-08-23 07:03:16,370] TRAIN Iter 80500: lr = 0.365835,	loss = 2.764878,	Top-1 err = 0.432617,	Top-5 err = 0.205322,	data_time = 0.050840,	train_time = 0.690801
[2019-08-23 07:03:31,926] TRAIN Iter 80520: lr = 0.365802,	loss = 2.775048,	Top-1 err = 0.438721,	Top-5 err = 0.213086,	data_time = 0.050542,	train_time = 0.777807
[2019-08-23 07:03:39,528] TRAIN Iter 80540: lr = 0.365768,	loss = 2.709452,	Top-1 err = 0.433057,	Top-5 err = 0.206592,	data_time = 0.050579,	train_time = 0.380084
[2019-08-23 07:03:52,342] TRAIN Iter 80560: lr = 0.365735,	loss = 2.713164,	Top-1 err = 0.430615,	Top-5 err = 0.205469,	data_time = 0.050530,	train_time = 0.640676
[2019-08-23 07:04:06,485] TRAIN Iter 80580: lr = 0.365702,	loss = 2.773058,	Top-1 err = 0.436572,	Top-5 err = 0.208643,	data_time = 0.121063,	train_time = 0.707151
[2019-08-23 07:04:13,892] TRAIN Iter 80600: lr = 0.365668,	loss = 2.795096,	Top-1 err = 0.430176,	Top-5 err = 0.207861,	data_time = 0.050577,	train_time = 0.370310
[2019-08-23 07:04:29,512] TRAIN Iter 80620: lr = 0.365635,	loss = 2.812509,	Top-1 err = 0.439502,	Top-5 err = 0.210107,	data_time = 0.050368,	train_time = 0.781012
[2019-08-23 07:04:37,404] TRAIN Iter 80640: lr = 0.365602,	loss = 2.701594,	Top-1 err = 0.441650,	Top-5 err = 0.211475,	data_time = 0.050561,	train_time = 0.394553
[2019-08-23 07:04:49,913] TRAIN Iter 80660: lr = 0.365568,	loss = 2.887679,	Top-1 err = 0.432129,	Top-5 err = 0.206055,	data_time = 0.050668,	train_time = 0.625436
[2019-08-23 07:05:04,208] TRAIN Iter 80680: lr = 0.365535,	loss = 2.782570,	Top-1 err = 0.439844,	Top-5 err = 0.206299,	data_time = 0.050546,	train_time = 0.714716
[2019-08-23 07:05:11,600] TRAIN Iter 80700: lr = 0.365502,	loss = 2.792742,	Top-1 err = 0.434863,	Top-5 err = 0.207129,	data_time = 0.050613,	train_time = 0.369624
[2019-08-23 07:05:27,268] TRAIN Iter 80720: lr = 0.365468,	loss = 2.827604,	Top-1 err = 0.440771,	Top-5 err = 0.210352,	data_time = 0.050230,	train_time = 0.783355
[2019-08-23 07:05:43,716] TRAIN Iter 80740: lr = 0.365435,	loss = 2.620127,	Top-1 err = 0.437939,	Top-5 err = 0.212354,	data_time = 0.050992,	train_time = 0.822369
[2019-08-23 07:05:50,924] TRAIN Iter 80760: lr = 0.365402,	loss = 2.800703,	Top-1 err = 0.432373,	Top-5 err = 0.206006,	data_time = 0.050303,	train_time = 0.360426
[2019-08-23 07:06:07,840] TRAIN Iter 80780: lr = 0.365368,	loss = 2.866711,	Top-1 err = 0.440381,	Top-5 err = 0.214844,	data_time = 0.050867,	train_time = 0.845761
[2019-08-23 07:06:16,005] TRAIN Iter 80800: lr = 0.365335,	loss = 2.814787,	Top-1 err = 0.438867,	Top-5 err = 0.209863,	data_time = 0.050680,	train_time = 0.408247
[2019-08-23 07:06:28,470] TRAIN Iter 80820: lr = 0.365302,	loss = 2.746784,	Top-1 err = 0.439990,	Top-5 err = 0.211865,	data_time = 0.050818,	train_time = 0.623249
[2019-08-23 07:06:43,846] TRAIN Iter 80840: lr = 0.365268,	loss = 2.719723,	Top-1 err = 0.432715,	Top-5 err = 0.203809,	data_time = 0.050623,	train_time = 0.768760
[2019-08-23 07:06:51,179] TRAIN Iter 80860: lr = 0.365235,	loss = 2.850101,	Top-1 err = 0.432666,	Top-5 err = 0.206006,	data_time = 0.050828,	train_time = 0.366650
[2019-08-23 07:07:06,324] TRAIN Iter 80880: lr = 0.365202,	loss = 2.911147,	Top-1 err = 0.438184,	Top-5 err = 0.208936,	data_time = 0.050522,	train_time = 0.757222
[2019-08-23 07:07:21,708] TRAIN Iter 80900: lr = 0.365168,	loss = 2.776915,	Top-1 err = 0.432764,	Top-5 err = 0.208203,	data_time = 0.050591,	train_time = 0.769176
[2019-08-23 07:07:28,982] TRAIN Iter 80920: lr = 0.365135,	loss = 2.840070,	Top-1 err = 0.437842,	Top-5 err = 0.210742,	data_time = 0.050514,	train_time = 0.363723
[2019-08-23 07:07:43,392] TRAIN Iter 80940: lr = 0.365102,	loss = 2.785157,	Top-1 err = 0.434375,	Top-5 err = 0.206885,	data_time = 0.050498,	train_time = 0.720447
[2019-08-23 07:07:51,357] TRAIN Iter 80960: lr = 0.365068,	loss = 2.710922,	Top-1 err = 0.441162,	Top-5 err = 0.210986,	data_time = 0.050916,	train_time = 0.398248
[2019-08-23 07:08:05,301] TRAIN Iter 80980: lr = 0.365035,	loss = 2.833666,	Top-1 err = 0.435254,	Top-5 err = 0.209766,	data_time = 0.050616,	train_time = 0.697187
[2019-08-23 07:08:20,769] TRAIN Iter 81000: lr = 0.365002,	loss = 2.796711,	Top-1 err = 0.436328,	Top-5 err = 0.208154,	data_time = 0.050838,	train_time = 0.773362
[2019-08-23 07:08:28,083] TRAIN Iter 81020: lr = 0.364968,	loss = 2.761656,	Top-1 err = 0.442139,	Top-5 err = 0.209668,	data_time = 0.050473,	train_time = 0.365711
[2019-08-23 07:08:42,692] TRAIN Iter 81040: lr = 0.364935,	loss = 2.876671,	Top-1 err = 0.437207,	Top-5 err = 0.212012,	data_time = 0.050514,	train_time = 0.730457
[2019-08-23 07:08:59,010] TRAIN Iter 81060: lr = 0.364902,	loss = 2.773775,	Top-1 err = 0.438916,	Top-5 err = 0.213867,	data_time = 0.050915,	train_time = 0.815857
[2019-08-23 07:09:06,429] TRAIN Iter 81080: lr = 0.364868,	loss = 2.808389,	Top-1 err = 0.434424,	Top-5 err = 0.204590,	data_time = 0.124582,	train_time = 0.370949
[2019-08-23 07:09:20,539] TRAIN Iter 81100: lr = 0.364835,	loss = 2.752997,	Top-1 err = 0.436670,	Top-5 err = 0.209521,	data_time = 0.050331,	train_time = 0.705470
[2019-08-23 07:09:28,121] TRAIN Iter 81120: lr = 0.364802,	loss = 2.798337,	Top-1 err = 0.439502,	Top-5 err = 0.210596,	data_time = 0.050765,	train_time = 0.379096
[2019-08-23 07:09:43,389] TRAIN Iter 81140: lr = 0.364768,	loss = 2.830731,	Top-1 err = 0.434814,	Top-5 err = 0.210303,	data_time = 0.050392,	train_time = 0.763403
[2019-08-23 07:09:58,046] TRAIN Iter 81160: lr = 0.364735,	loss = 2.804190,	Top-1 err = 0.441016,	Top-5 err = 0.211279,	data_time = 0.050339,	train_time = 0.732799
[2019-08-23 07:10:04,907] TRAIN Iter 81180: lr = 0.364702,	loss = 2.659221,	Top-1 err = 0.435840,	Top-5 err = 0.211133,	data_time = 0.130683,	train_time = 0.343052
[2019-08-23 07:10:20,107] TRAIN Iter 81200: lr = 0.364668,	loss = 2.838690,	Top-1 err = 0.433545,	Top-5 err = 0.209814,	data_time = 0.050274,	train_time = 0.759981
[2019-08-23 07:10:37,590] TRAIN Iter 81220: lr = 0.364635,	loss = 2.831485,	Top-1 err = 0.437891,	Top-5 err = 0.207422,	data_time = 0.050452,	train_time = 0.874152
[2019-08-23 07:10:44,575] TRAIN Iter 81240: lr = 0.364602,	loss = 2.767802,	Top-1 err = 0.440039,	Top-5 err = 0.213574,	data_time = 0.050423,	train_time = 0.349222
[2019-08-23 07:11:00,385] TRAIN Iter 81260: lr = 0.364568,	loss = 2.850378,	Top-1 err = 0.430615,	Top-5 err = 0.204053,	data_time = 0.050036,	train_time = 0.790474
[2019-08-23 07:11:07,506] TRAIN Iter 81280: lr = 0.364535,	loss = 2.797566,	Top-1 err = 0.431250,	Top-5 err = 0.206738,	data_time = 0.050086,	train_time = 0.356071
[2019-08-23 07:11:23,664] TRAIN Iter 81300: lr = 0.364502,	loss = 2.773111,	Top-1 err = 0.432617,	Top-5 err = 0.210645,	data_time = 0.049913,	train_time = 0.807891
[2019-08-23 07:12:09,954] TRAIN Iter 81320: lr = 0.364468,	loss = 2.840859,	Top-1 err = 0.441163,	Top-5 err = 0.214742,	data_time = 1.012230,	train_time = 2.314470
[2019-08-23 07:12:19,927] TRAIN Iter 81340: lr = 0.364435,	loss = 2.797711,	Top-1 err = 0.438623,	Top-5 err = 0.212793,	data_time = 0.050497,	train_time = 0.498649
[2019-08-23 07:12:35,231] TRAIN Iter 81360: lr = 0.364402,	loss = 2.727849,	Top-1 err = 0.427930,	Top-5 err = 0.200635,	data_time = 0.050553,	train_time = 0.765146
[2019-08-23 07:12:43,281] TRAIN Iter 81380: lr = 0.364368,	loss = 2.805135,	Top-1 err = 0.430322,	Top-5 err = 0.201270,	data_time = 0.050395,	train_time = 0.402520
[2019-08-23 07:12:51,141] TRAIN Iter 81400: lr = 0.364335,	loss = 2.819608,	Top-1 err = 0.433984,	Top-5 err = 0.203809,	data_time = 0.050635,	train_time = 0.392969
[2019-08-23 07:13:02,240] TRAIN Iter 81420: lr = 0.364302,	loss = 2.728024,	Top-1 err = 0.431543,	Top-5 err = 0.202441,	data_time = 0.050931,	train_time = 0.554942
[2019-08-23 07:13:10,094] TRAIN Iter 81440: lr = 0.364268,	loss = 2.763343,	Top-1 err = 0.432568,	Top-5 err = 0.202686,	data_time = 0.050708,	train_time = 0.392693
[2019-08-23 07:13:23,874] TRAIN Iter 81460: lr = 0.364235,	loss = 2.769287,	Top-1 err = 0.432373,	Top-5 err = 0.203027,	data_time = 0.050808,	train_time = 0.688983
[2019-08-23 07:13:35,635] TRAIN Iter 81480: lr = 0.364202,	loss = 2.775649,	Top-1 err = 0.428955,	Top-5 err = 0.203613,	data_time = 0.161378,	train_time = 0.588007
[2019-08-23 07:13:43,686] TRAIN Iter 81500: lr = 0.364168,	loss = 2.716099,	Top-1 err = 0.423877,	Top-5 err = 0.200830,	data_time = 0.050295,	train_time = 0.402569
[2019-08-23 07:13:58,310] TRAIN Iter 81520: lr = 0.364135,	loss = 2.770136,	Top-1 err = 0.429688,	Top-5 err = 0.201953,	data_time = 0.050678,	train_time = 0.731182
[2019-08-23 07:14:06,457] TRAIN Iter 81540: lr = 0.364102,	loss = 2.792969,	Top-1 err = 0.436328,	Top-5 err = 0.207617,	data_time = 0.050638,	train_time = 0.407298
[2019-08-23 07:14:17,684] TRAIN Iter 81560: lr = 0.364068,	loss = 2.734093,	Top-1 err = 0.425684,	Top-5 err = 0.200830,	data_time = 0.050648,	train_time = 0.561348
[2019-08-23 07:14:32,767] TRAIN Iter 81580: lr = 0.364035,	loss = 2.793670,	Top-1 err = 0.432959,	Top-5 err = 0.206738,	data_time = 0.050580,	train_time = 0.754160
[2019-08-23 07:14:40,580] TRAIN Iter 81600: lr = 0.364002,	loss = 2.773711,	Top-1 err = 0.425732,	Top-5 err = 0.201953,	data_time = 0.050360,	train_time = 0.390606
[2019-08-23 07:14:53,498] TRAIN Iter 81620: lr = 0.363968,	loss = 2.718604,	Top-1 err = 0.427100,	Top-5 err = 0.205078,	data_time = 0.174947,	train_time = 0.645896
[2019-08-23 07:15:07,258] TRAIN Iter 81640: lr = 0.363935,	loss = 2.780638,	Top-1 err = 0.430664,	Top-5 err = 0.207275,	data_time = 0.050616,	train_time = 0.688001
[2019-08-23 07:15:15,145] TRAIN Iter 81660: lr = 0.363902,	loss = 2.722775,	Top-1 err = 0.430176,	Top-5 err = 0.206396,	data_time = 0.050491,	train_time = 0.394315
[2019-08-23 07:15:27,429] TRAIN Iter 81680: lr = 0.363868,	loss = 2.707696,	Top-1 err = 0.432471,	Top-5 err = 0.206787,	data_time = 0.050246,	train_time = 0.614166
[2019-08-23 07:15:34,904] TRAIN Iter 81700: lr = 0.363835,	loss = 2.593827,	Top-1 err = 0.433154,	Top-5 err = 0.209473,	data_time = 0.150603,	train_time = 0.373752
[2019-08-23 07:15:54,003] TRAIN Iter 81720: lr = 0.363802,	loss = 2.773879,	Top-1 err = 0.432568,	Top-5 err = 0.204785,	data_time = 0.050499,	train_time = 0.954911
[2019-08-23 07:16:05,178] TRAIN Iter 81740: lr = 0.363768,	loss = 2.873911,	Top-1 err = 0.434668,	Top-5 err = 0.213525,	data_time = 0.050704,	train_time = 0.558761
[2019-08-23 07:16:13,146] TRAIN Iter 81760: lr = 0.363735,	loss = 2.830774,	Top-1 err = 0.436182,	Top-5 err = 0.209375,	data_time = 0.050630,	train_time = 0.398390
[2019-08-23 07:16:25,298] TRAIN Iter 81780: lr = 0.363702,	loss = 2.723338,	Top-1 err = 0.430469,	Top-5 err = 0.204883,	data_time = 0.050865,	train_time = 0.607572
[2019-08-23 07:16:40,380] TRAIN Iter 81800: lr = 0.363668,	loss = 2.763271,	Top-1 err = 0.435303,	Top-5 err = 0.207715,	data_time = 0.159690,	train_time = 0.754065
[2019-08-23 07:16:48,039] TRAIN Iter 81820: lr = 0.363635,	loss = 2.774869,	Top-1 err = 0.435986,	Top-5 err = 0.207129,	data_time = 0.152538,	train_time = 0.382956
[2019-08-23 07:17:00,265] TRAIN Iter 81840: lr = 0.363602,	loss = 2.790239,	Top-1 err = 0.434082,	Top-5 err = 0.205518,	data_time = 0.050276,	train_time = 0.611271
[2019-08-23 07:17:07,761] TRAIN Iter 81860: lr = 0.363568,	loss = 2.778828,	Top-1 err = 0.436865,	Top-5 err = 0.210059,	data_time = 0.050563,	train_time = 0.374804
[2019-08-23 07:17:22,074] TRAIN Iter 81880: lr = 0.363535,	loss = 2.779577,	Top-1 err = 0.437646,	Top-5 err = 0.208008,	data_time = 0.050511,	train_time = 0.715603
[2019-08-23 07:17:38,380] TRAIN Iter 81900: lr = 0.363502,	loss = 2.765584,	Top-1 err = 0.441504,	Top-5 err = 0.205078,	data_time = 0.050687,	train_time = 0.815316
[2019-08-23 07:17:45,451] TRAIN Iter 81920: lr = 0.363468,	loss = 2.702098,	Top-1 err = 0.434473,	Top-5 err = 0.207422,	data_time = 0.050358,	train_time = 0.353532
[2019-08-23 07:18:00,534] TRAIN Iter 81940: lr = 0.363435,	loss = 2.783732,	Top-1 err = 0.434717,	Top-5 err = 0.209180,	data_time = 0.050294,	train_time = 0.754137
[2019-08-23 07:18:12,908] TRAIN Iter 81960: lr = 0.363402,	loss = 2.805851,	Top-1 err = 0.437207,	Top-5 err = 0.212695,	data_time = 0.050789,	train_time = 0.618662
[2019-08-23 07:18:20,641] TRAIN Iter 81980: lr = 0.363368,	loss = 2.702357,	Top-1 err = 0.433740,	Top-5 err = 0.206396,	data_time = 0.050143,	train_time = 0.386645
[2019-08-23 07:18:34,727] TRAIN Iter 82000: lr = 0.363335,	loss = 2.736533,	Top-1 err = 0.439063,	Top-5 err = 0.211572,	data_time = 0.134326,	train_time = 0.704290
[2019-08-23 07:18:42,547] TRAIN Iter 82020: lr = 0.363302,	loss = 2.732080,	Top-1 err = 0.439990,	Top-5 err = 0.207227,	data_time = 0.050549,	train_time = 0.391001
[2019-08-23 07:18:56,865] TRAIN Iter 82040: lr = 0.363268,	loss = 2.745893,	Top-1 err = 0.440918,	Top-5 err = 0.211963,	data_time = 0.050402,	train_time = 0.715863
[2019-08-23 07:19:09,989] TRAIN Iter 82060: lr = 0.363235,	loss = 2.764795,	Top-1 err = 0.441064,	Top-5 err = 0.210742,	data_time = 0.128757,	train_time = 0.656204
[2019-08-23 07:19:17,815] TRAIN Iter 82080: lr = 0.363202,	loss = 2.801562,	Top-1 err = 0.431641,	Top-5 err = 0.209668,	data_time = 0.051011,	train_time = 0.391247
[2019-08-23 07:19:33,230] TRAIN Iter 82100: lr = 0.363168,	loss = 2.728787,	Top-1 err = 0.431689,	Top-5 err = 0.206543,	data_time = 0.050552,	train_time = 0.770753
[2019-08-23 07:19:46,178] TRAIN Iter 82120: lr = 0.363135,	loss = 2.742478,	Top-1 err = 0.433447,	Top-5 err = 0.204980,	data_time = 0.220150,	train_time = 0.647373
[2019-08-23 07:19:55,825] TRAIN Iter 82140: lr = 0.363102,	loss = 2.809126,	Top-1 err = 0.437207,	Top-5 err = 0.212744,	data_time = 0.050167,	train_time = 0.482316
[2019-08-23 07:20:12,695] TRAIN Iter 82160: lr = 0.363068,	loss = 2.831383,	Top-1 err = 0.437158,	Top-5 err = 0.208105,	data_time = 0.150331,	train_time = 0.843517
[2019-08-23 07:20:19,876] TRAIN Iter 82180: lr = 0.363035,	loss = 2.819975,	Top-1 err = 0.440088,	Top-5 err = 0.213477,	data_time = 0.050437,	train_time = 0.359014
[2019-08-23 07:20:34,834] TRAIN Iter 82200: lr = 0.363002,	loss = 2.831424,	Top-1 err = 0.443848,	Top-5 err = 0.214404,	data_time = 0.050540,	train_time = 0.747885
[2019-08-23 07:20:51,590] TRAIN Iter 82220: lr = 0.362968,	loss = 2.745180,	Top-1 err = 0.441064,	Top-5 err = 0.211279,	data_time = 0.050422,	train_time = 0.837782
[2019-08-23 07:20:58,711] TRAIN Iter 82240: lr = 0.362935,	loss = 2.889155,	Top-1 err = 0.439502,	Top-5 err = 0.211963,	data_time = 0.051066,	train_time = 0.356034
[2019-08-23 07:21:14,267] TRAIN Iter 82260: lr = 0.362902,	loss = 2.887608,	Top-1 err = 0.437451,	Top-5 err = 0.211865,	data_time = 0.050668,	train_time = 0.777807
[2019-08-23 07:21:30,573] TRAIN Iter 82280: lr = 0.362868,	loss = 2.742929,	Top-1 err = 0.435742,	Top-5 err = 0.210938,	data_time = 0.050252,	train_time = 0.815250
[2019-08-23 07:21:38,152] TRAIN Iter 82300: lr = 0.362835,	loss = 2.838633,	Top-1 err = 0.437109,	Top-5 err = 0.209863,	data_time = 0.050510,	train_time = 0.378949
[2019-08-23 07:21:53,456] TRAIN Iter 82320: lr = 0.362802,	loss = 2.791333,	Top-1 err = 0.436768,	Top-5 err = 0.205957,	data_time = 0.050440,	train_time = 0.765182
[2019-08-23 07:22:00,267] TRAIN Iter 82340: lr = 0.362768,	loss = 2.904160,	Top-1 err = 0.435010,	Top-5 err = 0.203760,	data_time = 0.050770,	train_time = 0.340508
[2019-08-23 07:22:17,983] TRAIN Iter 82360: lr = 0.362735,	loss = 2.745515,	Top-1 err = 0.433447,	Top-5 err = 0.204150,	data_time = 0.050612,	train_time = 0.885809
[2019-08-23 07:22:36,140] TRAIN Iter 82380: lr = 0.362702,	loss = 2.827526,	Top-1 err = 0.440283,	Top-5 err = 0.209229,	data_time = 0.050448,	train_time = 0.907839
[2019-08-23 07:22:43,020] TRAIN Iter 82400: lr = 0.362668,	loss = 2.823745,	Top-1 err = 0.435400,	Top-5 err = 0.206787,	data_time = 0.050214,	train_time = 0.343961
[2019-08-23 07:23:01,587] TRAIN Iter 82420: lr = 0.362635,	loss = 2.811131,	Top-1 err = 0.436719,	Top-5 err = 0.211719,	data_time = 0.050467,	train_time = 0.928375
[2019-08-23 07:23:18,265] TRAIN Iter 82440: lr = 0.362602,	loss = 2.709173,	Top-1 err = 0.432422,	Top-5 err = 0.209717,	data_time = 0.050499,	train_time = 0.833869
[2019-08-23 07:23:25,547] TRAIN Iter 82460: lr = 0.362568,	loss = 2.752917,	Top-1 err = 0.438672,	Top-5 err = 0.211084,	data_time = 0.050298,	train_time = 0.364080
[2019-08-23 07:23:42,348] TRAIN Iter 82480: lr = 0.362535,	loss = 2.719899,	Top-1 err = 0.441748,	Top-5 err = 0.211133,	data_time = 0.050675,	train_time = 0.840031
[2019-08-23 07:23:49,127] TRAIN Iter 82500: lr = 0.362502,	loss = 2.800431,	Top-1 err = 0.434668,	Top-5 err = 0.208105,	data_time = 0.109498,	train_time = 0.338958
[2019-08-23 07:24:06,422] TRAIN Iter 82520: lr = 0.362468,	loss = 2.758564,	Top-1 err = 0.436865,	Top-5 err = 0.205273,	data_time = 0.050099,	train_time = 0.864747
[2019-08-23 07:24:23,605] TRAIN Iter 82540: lr = 0.362435,	loss = 2.755723,	Top-1 err = 0.433203,	Top-5 err = 0.205127,	data_time = 0.049977,	train_time = 0.859116
[2019-08-23 07:24:29,993] TRAIN Iter 82560: lr = 0.362402,	loss = 2.830916,	Top-1 err = 0.432471,	Top-5 err = 0.204004,	data_time = 0.049978,	train_time = 0.319361
[2019-08-23 07:25:20,849] TRAIN Iter 82580: lr = 0.362368,	loss = 2.923246,	Top-1 err = 0.434333,	Top-5 err = 0.206421,	data_time = 0.050298,	train_time = 2.542797
[2019-08-23 07:25:28,754] TRAIN Iter 82600: lr = 0.362335,	loss = 2.695863,	Top-1 err = 0.431885,	Top-5 err = 0.211670,	data_time = 0.153058,	train_time = 0.395261
[2019-08-23 07:25:43,175] TRAIN Iter 82620: lr = 0.362302,	loss = 2.775130,	Top-1 err = 0.430322,	Top-5 err = 0.204590,	data_time = 0.050429,	train_time = 0.721044
[2019-08-23 07:25:54,234] TRAIN Iter 82640: lr = 0.362268,	loss = 2.751813,	Top-1 err = 0.430176,	Top-5 err = 0.203613,	data_time = 0.129033,	train_time = 0.552918
[2019-08-23 07:26:02,392] TRAIN Iter 82660: lr = 0.362235,	loss = 2.683140,	Top-1 err = 0.433643,	Top-5 err = 0.204492,	data_time = 0.050959,	train_time = 0.407871
[2019-08-23 07:26:13,625] TRAIN Iter 82680: lr = 0.362202,	loss = 2.722863,	Top-1 err = 0.429590,	Top-5 err = 0.200293,	data_time = 0.050871,	train_time = 0.561622
[2019-08-23 07:26:22,465] TRAIN Iter 82700: lr = 0.362168,	loss = 2.746415,	Top-1 err = 0.427588,	Top-5 err = 0.201807,	data_time = 0.050798,	train_time = 0.441988
[2019-08-23 07:26:33,892] TRAIN Iter 82720: lr = 0.362135,	loss = 2.744712,	Top-1 err = 0.430908,	Top-5 err = 0.203027,	data_time = 0.050603,	train_time = 0.571347
[2019-08-23 07:26:46,456] TRAIN Iter 82740: lr = 0.362102,	loss = 2.787919,	Top-1 err = 0.431396,	Top-5 err = 0.206738,	data_time = 0.113070,	train_time = 0.628188
[2019-08-23 07:26:54,453] TRAIN Iter 82760: lr = 0.362068,	loss = 2.630843,	Top-1 err = 0.426807,	Top-5 err = 0.202441,	data_time = 0.050644,	train_time = 0.399817
[2019-08-23 07:27:08,900] TRAIN Iter 82780: lr = 0.362035,	loss = 2.725499,	Top-1 err = 0.427344,	Top-5 err = 0.202051,	data_time = 0.050373,	train_time = 0.722334
[2019-08-23 07:27:21,583] TRAIN Iter 82800: lr = 0.362002,	loss = 2.787185,	Top-1 err = 0.428467,	Top-5 err = 0.203125,	data_time = 0.050920,	train_time = 0.634137
[2019-08-23 07:27:29,727] TRAIN Iter 82820: lr = 0.361968,	loss = 2.750996,	Top-1 err = 0.428906,	Top-5 err = 0.201367,	data_time = 0.050962,	train_time = 0.407206
[2019-08-23 07:27:41,792] TRAIN Iter 82840: lr = 0.361935,	loss = 2.755820,	Top-1 err = 0.428662,	Top-5 err = 0.207715,	data_time = 0.050646,	train_time = 0.603246
[2019-08-23 07:27:50,223] TRAIN Iter 82860: lr = 0.361902,	loss = 2.793949,	Top-1 err = 0.429004,	Top-5 err = 0.208838,	data_time = 0.050899,	train_time = 0.421528
[2019-08-23 07:28:04,041] TRAIN Iter 82880: lr = 0.361868,	loss = 2.729674,	Top-1 err = 0.434424,	Top-5 err = 0.205908,	data_time = 0.050896,	train_time = 0.690884
[2019-08-23 07:28:18,572] TRAIN Iter 82900: lr = 0.361835,	loss = 2.818836,	Top-1 err = 0.428516,	Top-5 err = 0.204102,	data_time = 0.050673,	train_time = 0.726536
[2019-08-23 07:28:26,304] TRAIN Iter 82920: lr = 0.361802,	loss = 2.863040,	Top-1 err = 0.434912,	Top-5 err = 0.207422,	data_time = 0.050655,	train_time = 0.386546
[2019-08-23 07:28:39,051] TRAIN Iter 82940: lr = 0.361768,	loss = 2.707789,	Top-1 err = 0.443066,	Top-5 err = 0.211328,	data_time = 0.050927,	train_time = 0.637359
[2019-08-23 07:28:52,733] TRAIN Iter 82960: lr = 0.361735,	loss = 2.729600,	Top-1 err = 0.428662,	Top-5 err = 0.206934,	data_time = 0.121167,	train_time = 0.684071
[2019-08-23 07:29:00,584] TRAIN Iter 82980: lr = 0.361702,	loss = 2.702305,	Top-1 err = 0.427832,	Top-5 err = 0.206641,	data_time = 0.050330,	train_time = 0.392554
[2019-08-23 07:29:14,326] TRAIN Iter 83000: lr = 0.361668,	loss = 2.780976,	Top-1 err = 0.433154,	Top-5 err = 0.205762,	data_time = 0.050370,	train_time = 0.687080
[2019-08-23 07:29:24,989] TRAIN Iter 83020: lr = 0.361635,	loss = 2.669522,	Top-1 err = 0.433252,	Top-5 err = 0.204248,	data_time = 0.050591,	train_time = 0.533154
[2019-08-23 07:29:36,214] TRAIN Iter 83040: lr = 0.361602,	loss = 2.765885,	Top-1 err = 0.428418,	Top-5 err = 0.202002,	data_time = 0.050982,	train_time = 0.561189
[2019-08-23 07:29:50,814] TRAIN Iter 83060: lr = 0.361568,	loss = 2.825282,	Top-1 err = 0.429736,	Top-5 err = 0.203027,	data_time = 0.050514,	train_time = 0.729983
[2019-08-23 07:29:57,847] TRAIN Iter 83080: lr = 0.361535,	loss = 2.838036,	Top-1 err = 0.433545,	Top-5 err = 0.207129,	data_time = 0.050870,	train_time = 0.351677
[2019-08-23 07:30:12,813] TRAIN Iter 83100: lr = 0.361502,	loss = 2.776845,	Top-1 err = 0.438281,	Top-5 err = 0.205469,	data_time = 0.050573,	train_time = 0.748263
[2019-08-23 07:30:27,280] TRAIN Iter 83120: lr = 0.361468,	loss = 2.865757,	Top-1 err = 0.430322,	Top-5 err = 0.206885,	data_time = 0.050797,	train_time = 0.723343
[2019-08-23 07:30:34,626] TRAIN Iter 83140: lr = 0.361435,	loss = 2.777617,	Top-1 err = 0.430322,	Top-5 err = 0.208350,	data_time = 0.050362,	train_time = 0.367283
[2019-08-23 07:30:48,785] TRAIN Iter 83160: lr = 0.361402,	loss = 2.644457,	Top-1 err = 0.434766,	Top-5 err = 0.205127,	data_time = 0.050624,	train_time = 0.707931
[2019-08-23 07:30:59,043] TRAIN Iter 83180: lr = 0.361368,	loss = 2.840651,	Top-1 err = 0.433838,	Top-5 err = 0.206250,	data_time = 0.236969,	train_time = 0.512907
[2019-08-23 07:31:10,333] TRAIN Iter 83200: lr = 0.361335,	loss = 2.735991,	Top-1 err = 0.441455,	Top-5 err = 0.211084,	data_time = 0.050553,	train_time = 0.564470
[2019-08-23 07:31:25,367] TRAIN Iter 83220: lr = 0.361302,	loss = 2.758855,	Top-1 err = 0.439209,	Top-5 err = 0.207959,	data_time = 0.050565,	train_time = 0.751666
[2019-08-23 07:31:32,874] TRAIN Iter 83240: lr = 0.361268,	loss = 2.763207,	Top-1 err = 0.433984,	Top-5 err = 0.206006,	data_time = 0.050431,	train_time = 0.375370
[2019-08-23 07:31:48,032] TRAIN Iter 83260: lr = 0.361235,	loss = 2.878507,	Top-1 err = 0.435107,	Top-5 err = 0.208740,	data_time = 0.050535,	train_time = 0.757863
[2019-08-23 07:32:02,385] TRAIN Iter 83280: lr = 0.361202,	loss = 2.760804,	Top-1 err = 0.433447,	Top-5 err = 0.206787,	data_time = 0.050949,	train_time = 0.717637
[2019-08-23 07:32:09,725] TRAIN Iter 83300: lr = 0.361168,	loss = 2.803374,	Top-1 err = 0.435254,	Top-5 err = 0.207422,	data_time = 0.050543,	train_time = 0.366962
[2019-08-23 07:32:25,144] TRAIN Iter 83320: lr = 0.361135,	loss = 2.839749,	Top-1 err = 0.437109,	Top-5 err = 0.208398,	data_time = 0.050827,	train_time = 0.770979
[2019-08-23 07:32:38,508] TRAIN Iter 83340: lr = 0.361102,	loss = 2.783617,	Top-1 err = 0.437354,	Top-5 err = 0.207861,	data_time = 0.050598,	train_time = 0.668166
[2019-08-23 07:32:46,187] TRAIN Iter 83360: lr = 0.361068,	loss = 2.824105,	Top-1 err = 0.432178,	Top-5 err = 0.203857,	data_time = 0.050463,	train_time = 0.383930
[2019-08-23 07:33:02,430] TRAIN Iter 83380: lr = 0.361035,	loss = 2.665604,	Top-1 err = 0.433398,	Top-5 err = 0.203418,	data_time = 0.050651,	train_time = 0.812134
[2019-08-23 07:33:09,253] TRAIN Iter 83400: lr = 0.361002,	loss = 2.773784,	Top-1 err = 0.435303,	Top-5 err = 0.205908,	data_time = 0.050863,	train_time = 0.341136
[2019-08-23 07:33:26,080] TRAIN Iter 83420: lr = 0.360968,	loss = 2.700030,	Top-1 err = 0.437158,	Top-5 err = 0.207031,	data_time = 0.050552,	train_time = 0.841325
[2019-08-23 07:33:42,230] TRAIN Iter 83440: lr = 0.360935,	loss = 2.874545,	Top-1 err = 0.437939,	Top-5 err = 0.208008,	data_time = 0.050816,	train_time = 0.807489
[2019-08-23 07:33:49,076] TRAIN Iter 83460: lr = 0.360902,	loss = 2.816378,	Top-1 err = 0.440381,	Top-5 err = 0.211572,	data_time = 0.050525,	train_time = 0.342286
[2019-08-23 07:34:05,566] TRAIN Iter 83480: lr = 0.360868,	loss = 2.778953,	Top-1 err = 0.440430,	Top-5 err = 0.209473,	data_time = 0.050445,	train_time = 0.824518
[2019-08-23 07:34:20,510] TRAIN Iter 83500: lr = 0.360835,	loss = 2.678217,	Top-1 err = 0.432227,	Top-5 err = 0.208398,	data_time = 2.817514,	train_time = 0.747145
[2019-08-23 07:34:27,799] TRAIN Iter 83520: lr = 0.360802,	loss = 2.750086,	Top-1 err = 0.442725,	Top-5 err = 0.207227,	data_time = 0.050700,	train_time = 0.364478
[2019-08-23 07:34:44,194] TRAIN Iter 83540: lr = 0.360768,	loss = 2.873154,	Top-1 err = 0.441016,	Top-5 err = 0.210986,	data_time = 0.050731,	train_time = 0.819719
[2019-08-23 07:34:50,956] TRAIN Iter 83560: lr = 0.360735,	loss = 2.856048,	Top-1 err = 0.440869,	Top-5 err = 0.210303,	data_time = 0.050453,	train_time = 0.338078
[2019-08-23 07:35:08,853] TRAIN Iter 83580: lr = 0.360702,	loss = 2.815919,	Top-1 err = 0.439746,	Top-5 err = 0.209473,	data_time = 0.050536,	train_time = 0.894851
[2019-08-23 07:35:26,251] TRAIN Iter 83600: lr = 0.360668,	loss = 2.819074,	Top-1 err = 0.435693,	Top-5 err = 0.205371,	data_time = 0.157624,	train_time = 0.869863
[2019-08-23 07:35:33,362] TRAIN Iter 83620: lr = 0.360635,	loss = 2.777114,	Top-1 err = 0.432129,	Top-5 err = 0.206836,	data_time = 0.050581,	train_time = 0.355556
[2019-08-23 07:35:50,066] TRAIN Iter 83640: lr = 0.360602,	loss = 2.829533,	Top-1 err = 0.435889,	Top-5 err = 0.212695,	data_time = 0.050412,	train_time = 0.835179
[2019-08-23 07:36:06,347] TRAIN Iter 83660: lr = 0.360568,	loss = 2.747963,	Top-1 err = 0.436719,	Top-5 err = 0.210254,	data_time = 0.050543,	train_time = 0.814012
[2019-08-23 07:36:13,842] TRAIN Iter 83680: lr = 0.360535,	loss = 2.782913,	Top-1 err = 0.442529,	Top-5 err = 0.214990,	data_time = 0.050690,	train_time = 0.374747
[2019-08-23 07:36:29,698] TRAIN Iter 83700: lr = 0.360502,	loss = 2.674287,	Top-1 err = 0.432715,	Top-5 err = 0.207666,	data_time = 0.050282,	train_time = 0.792802
[2019-08-23 07:36:36,472] TRAIN Iter 83720: lr = 0.360468,	loss = 2.815968,	Top-1 err = 0.440576,	Top-5 err = 0.214258,	data_time = 0.050699,	train_time = 0.338670
[2019-08-23 07:36:54,584] TRAIN Iter 83740: lr = 0.360435,	loss = 2.867493,	Top-1 err = 0.438770,	Top-5 err = 0.213037,	data_time = 0.050564,	train_time = 0.905580
[2019-08-23 07:37:13,565] TRAIN Iter 83760: lr = 0.360402,	loss = 2.814242,	Top-1 err = 0.434912,	Top-5 err = 0.211133,	data_time = 0.050125,	train_time = 0.949042
[2019-08-23 07:37:20,538] TRAIN Iter 83780: lr = 0.360368,	loss = 2.778097,	Top-1 err = 0.437744,	Top-5 err = 0.207813,	data_time = 0.050019,	train_time = 0.348639
[2019-08-23 07:37:35,932] TRAIN Iter 83800: lr = 0.360335,	loss = 2.804479,	Top-1 err = 0.437939,	Top-5 err = 0.212744,	data_time = 0.049839,	train_time = 0.769671
[2019-08-23 07:37:48,005] TRAIN Iter 83820: lr = 0.360302,	loss = 3.174400,	Top-1 err = 0.438870,	Top-5 err = 0.210244,	data_time = 0.007113,	train_time = 0.603634
[2019-08-23 07:38:34,714] TRAIN Iter 83840: lr = 0.360268,	loss = 2.735896,	Top-1 err = 0.429199,	Top-5 err = 0.203271,	data_time = 0.050199,	train_time = 2.335439
[2019-08-23 07:38:47,730] TRAIN Iter 83860: lr = 0.360235,	loss = 2.658045,	Top-1 err = 0.435010,	Top-5 err = 0.202344,	data_time = 0.050559,	train_time = 0.650779
[2019-08-23 07:38:55,880] TRAIN Iter 83880: lr = 0.360202,	loss = 2.673131,	Top-1 err = 0.423779,	Top-5 err = 0.202100,	data_time = 0.050514,	train_time = 0.407495
[2019-08-23 07:39:05,248] TRAIN Iter 83900: lr = 0.360168,	loss = 2.698149,	Top-1 err = 0.428467,	Top-5 err = 0.203809,	data_time = 0.050900,	train_time = 0.468370
[2019-08-23 07:39:17,250] TRAIN Iter 83920: lr = 0.360135,	loss = 2.738025,	Top-1 err = 0.430029,	Top-5 err = 0.199561,	data_time = 0.152598,	train_time = 0.600079
[2019-08-23 07:39:27,168] TRAIN Iter 83940: lr = 0.360102,	loss = 2.806200,	Top-1 err = 0.429443,	Top-5 err = 0.204736,	data_time = 0.050312,	train_time = 0.495908
[2019-08-23 07:39:40,160] TRAIN Iter 83960: lr = 0.360068,	loss = 2.870893,	Top-1 err = 0.429297,	Top-5 err = 0.205176,	data_time = 0.050536,	train_time = 0.649600
[2019-08-23 07:39:48,352] TRAIN Iter 83980: lr = 0.360035,	loss = 2.755262,	Top-1 err = 0.433057,	Top-5 err = 0.202100,	data_time = 0.050648,	train_time = 0.409597
[2019-08-23 07:40:01,384] TRAIN Iter 84000: lr = 0.360002,	loss = 2.782517,	Top-1 err = 0.428613,	Top-5 err = 0.201807,	data_time = 0.050863,	train_time = 0.651545
[2019-08-23 07:40:14,175] TRAIN Iter 84020: lr = 0.359968,	loss = 2.760867,	Top-1 err = 0.430420,	Top-5 err = 0.205762,	data_time = 0.050693,	train_time = 0.639573
[2019-08-23 07:40:21,927] TRAIN Iter 84040: lr = 0.359935,	loss = 2.772627,	Top-1 err = 0.428760,	Top-5 err = 0.208398,	data_time = 0.050544,	train_time = 0.387588
[2019-08-23 07:40:34,761] TRAIN Iter 84060: lr = 0.359902,	loss = 2.715462,	Top-1 err = 0.428613,	Top-5 err = 0.201123,	data_time = 0.050622,	train_time = 0.641659
[2019-08-23 07:40:47,201] TRAIN Iter 84080: lr = 0.359868,	loss = 2.705438,	Top-1 err = 0.430469,	Top-5 err = 0.204395,	data_time = 0.050913,	train_time = 0.621996
[2019-08-23 07:40:56,324] TRAIN Iter 84100: lr = 0.359835,	loss = 2.757320,	Top-1 err = 0.434473,	Top-5 err = 0.206006,	data_time = 0.050641,	train_time = 0.456124
[2019-08-23 07:41:09,252] TRAIN Iter 84120: lr = 0.359802,	loss = 2.793361,	Top-1 err = 0.429248,	Top-5 err = 0.204150,	data_time = 0.050482,	train_time = 0.646413
[2019-08-23 07:41:16,822] TRAIN Iter 84140: lr = 0.359768,	loss = 2.786966,	Top-1 err = 0.434424,	Top-5 err = 0.206152,	data_time = 0.050773,	train_time = 0.378470
[2019-08-23 07:41:30,408] TRAIN Iter 84160: lr = 0.359735,	loss = 2.746304,	Top-1 err = 0.430420,	Top-5 err = 0.200781,	data_time = 0.050732,	train_time = 0.679275
[2019-08-23 07:41:43,093] TRAIN Iter 84180: lr = 0.359702,	loss = 2.776660,	Top-1 err = 0.435010,	Top-5 err = 0.211084,	data_time = 0.050387,	train_time = 0.634229
[2019-08-23 07:41:50,961] TRAIN Iter 84200: lr = 0.359668,	loss = 2.768705,	Top-1 err = 0.424854,	Top-5 err = 0.200195,	data_time = 0.050772,	train_time = 0.393376
[2019-08-23 07:42:05,823] TRAIN Iter 84220: lr = 0.359635,	loss = 2.740649,	Top-1 err = 0.441895,	Top-5 err = 0.209033,	data_time = 0.050312,	train_time = 0.743085
[2019-08-23 07:42:18,228] TRAIN Iter 84240: lr = 0.359602,	loss = 2.782230,	Top-1 err = 0.423730,	Top-5 err = 0.198486,	data_time = 0.050734,	train_time = 0.620256
[2019-08-23 07:42:28,307] TRAIN Iter 84260: lr = 0.359568,	loss = 2.774983,	Top-1 err = 0.435352,	Top-5 err = 0.207178,	data_time = 0.147061,	train_time = 0.503939
[2019-08-23 07:42:41,879] TRAIN Iter 84280: lr = 0.359535,	loss = 2.741108,	Top-1 err = 0.427588,	Top-5 err = 0.202148,	data_time = 0.050483,	train_time = 0.678583
[2019-08-23 07:42:49,500] TRAIN Iter 84300: lr = 0.359502,	loss = 2.687714,	Top-1 err = 0.428418,	Top-5 err = 0.207471,	data_time = 0.050509,	train_time = 0.381038
[2019-08-23 07:43:02,937] TRAIN Iter 84320: lr = 0.359468,	loss = 2.739823,	Top-1 err = 0.436963,	Top-5 err = 0.210547,	data_time = 0.050414,	train_time = 0.671812
[2019-08-23 07:43:17,012] TRAIN Iter 84340: lr = 0.359435,	loss = 2.793666,	Top-1 err = 0.433887,	Top-5 err = 0.208057,	data_time = 0.050368,	train_time = 0.703729
[2019-08-23 07:43:24,513] TRAIN Iter 84360: lr = 0.359402,	loss = 2.802887,	Top-1 err = 0.428369,	Top-5 err = 0.203174,	data_time = 0.142636,	train_time = 0.375056
[2019-08-23 07:43:38,521] TRAIN Iter 84380: lr = 0.359368,	loss = 2.828635,	Top-1 err = 0.434521,	Top-5 err = 0.211035,	data_time = 0.050423,	train_time = 0.700395
[2019-08-23 07:43:51,737] TRAIN Iter 84400: lr = 0.359335,	loss = 2.888508,	Top-1 err = 0.434277,	Top-5 err = 0.209229,	data_time = 0.050481,	train_time = 0.660798
[2019-08-23 07:44:00,876] TRAIN Iter 84420: lr = 0.359302,	loss = 2.706579,	Top-1 err = 0.428418,	Top-5 err = 0.202930,	data_time = 0.050352,	train_time = 0.456896
[2019-08-23 07:44:14,888] TRAIN Iter 84440: lr = 0.359268,	loss = 2.771826,	Top-1 err = 0.434717,	Top-5 err = 0.204395,	data_time = 0.050540,	train_time = 0.700611
[2019-08-23 07:44:21,831] TRAIN Iter 84460: lr = 0.359235,	loss = 2.759887,	Top-1 err = 0.435059,	Top-5 err = 0.205859,	data_time = 0.050667,	train_time = 0.347134
[2019-08-23 07:44:36,539] TRAIN Iter 84480: lr = 0.359202,	loss = 2.708844,	Top-1 err = 0.430420,	Top-5 err = 0.208398,	data_time = 0.050567,	train_time = 0.735396
[2019-08-23 07:44:49,317] TRAIN Iter 84500: lr = 0.359168,	loss = 2.782805,	Top-1 err = 0.432324,	Top-5 err = 0.207471,	data_time = 0.137023,	train_time = 0.638859
[2019-08-23 07:44:56,783] TRAIN Iter 84520: lr = 0.359135,	loss = 2.850393,	Top-1 err = 0.438232,	Top-5 err = 0.212402,	data_time = 0.050539,	train_time = 0.373304
[2019-08-23 07:45:11,382] TRAIN Iter 84540: lr = 0.359102,	loss = 2.766579,	Top-1 err = 0.433838,	Top-5 err = 0.211768,	data_time = 0.050510,	train_time = 0.729926
[2019-08-23 07:45:25,982] TRAIN Iter 84560: lr = 0.359068,	loss = 2.806785,	Top-1 err = 0.431641,	Top-5 err = 0.202734,	data_time = 2.062334,	train_time = 0.729988
[2019-08-23 07:45:33,679] TRAIN Iter 84580: lr = 0.359035,	loss = 2.783090,	Top-1 err = 0.438037,	Top-5 err = 0.212012,	data_time = 0.050546,	train_time = 0.384856
[2019-08-23 07:45:48,703] TRAIN Iter 84600: lr = 0.359002,	loss = 2.817380,	Top-1 err = 0.435449,	Top-5 err = 0.208887,	data_time = 0.050640,	train_time = 0.751160
[2019-08-23 07:45:56,025] TRAIN Iter 84620: lr = 0.358968,	loss = 2.772753,	Top-1 err = 0.430566,	Top-5 err = 0.207861,	data_time = 0.050468,	train_time = 0.366083
[2019-08-23 07:46:11,178] TRAIN Iter 84640: lr = 0.358935,	loss = 2.799144,	Top-1 err = 0.427393,	Top-5 err = 0.207275,	data_time = 0.050500,	train_time = 0.757659
[2019-08-23 07:46:26,325] TRAIN Iter 84660: lr = 0.358902,	loss = 2.838560,	Top-1 err = 0.440479,	Top-5 err = 0.212500,	data_time = 0.050125,	train_time = 0.757299
[2019-08-23 07:46:33,980] TRAIN Iter 84680: lr = 0.358868,	loss = 2.688852,	Top-1 err = 0.434375,	Top-5 err = 0.207080,	data_time = 0.050790,	train_time = 0.382738
[2019-08-23 07:46:49,563] TRAIN Iter 84700: lr = 0.358835,	loss = 2.737880,	Top-1 err = 0.429736,	Top-5 err = 0.199072,	data_time = 0.050895,	train_time = 0.779129
[2019-08-23 07:47:05,684] TRAIN Iter 84720: lr = 0.358802,	loss = 2.712441,	Top-1 err = 0.432617,	Top-5 err = 0.207031,	data_time = 3.999604,	train_time = 0.806051
[2019-08-23 07:47:13,270] TRAIN Iter 84740: lr = 0.358768,	loss = 2.758252,	Top-1 err = 0.436084,	Top-5 err = 0.208838,	data_time = 0.050379,	train_time = 0.379292
[2019-08-23 07:47:29,958] TRAIN Iter 84760: lr = 0.358735,	loss = 2.807530,	Top-1 err = 0.434326,	Top-5 err = 0.206152,	data_time = 0.050535,	train_time = 0.834369
[2019-08-23 07:47:38,607] TRAIN Iter 84780: lr = 0.358702,	loss = 2.794186,	Top-1 err = 0.436328,	Top-5 err = 0.212598,	data_time = 0.140568,	train_time = 0.432432
[2019-08-23 07:47:54,412] TRAIN Iter 84800: lr = 0.358668,	loss = 2.794751,	Top-1 err = 0.435547,	Top-5 err = 0.208838,	data_time = 0.050451,	train_time = 0.790271
[2019-08-23 07:48:09,384] TRAIN Iter 84820: lr = 0.358635,	loss = 2.799759,	Top-1 err = 0.434033,	Top-5 err = 0.210840,	data_time = 0.050797,	train_time = 0.748553
[2019-08-23 07:48:18,027] TRAIN Iter 84840: lr = 0.358602,	loss = 2.742897,	Top-1 err = 0.433203,	Top-5 err = 0.207568,	data_time = 0.050279,	train_time = 0.432178
[2019-08-23 07:48:34,873] TRAIN Iter 84860: lr = 0.358568,	loss = 2.662136,	Top-1 err = 0.437939,	Top-5 err = 0.207080,	data_time = 0.050474,	train_time = 0.842286
[2019-08-23 07:48:49,354] TRAIN Iter 84880: lr = 0.358535,	loss = 2.711824,	Top-1 err = 0.434619,	Top-5 err = 0.209912,	data_time = 5.335959,	train_time = 0.724010
[2019-08-23 07:48:57,990] TRAIN Iter 84900: lr = 0.358502,	loss = 2.824822,	Top-1 err = 0.443018,	Top-5 err = 0.211035,	data_time = 0.050637,	train_time = 0.431768
[2019-08-23 07:49:15,849] TRAIN Iter 84920: lr = 0.358468,	loss = 2.732133,	Top-1 err = 0.432959,	Top-5 err = 0.210938,	data_time = 0.050695,	train_time = 0.892962
[2019-08-23 07:49:24,269] TRAIN Iter 84940: lr = 0.358435,	loss = 2.748075,	Top-1 err = 0.441162,	Top-5 err = 0.212891,	data_time = 0.050382,	train_time = 0.420989
[2019-08-23 07:49:41,153] TRAIN Iter 84960: lr = 0.358402,	loss = 2.747640,	Top-1 err = 0.440137,	Top-5 err = 0.212598,	data_time = 0.050534,	train_time = 0.844199
[2019-08-23 07:49:58,117] TRAIN Iter 84980: lr = 0.358368,	loss = 2.749882,	Top-1 err = 0.438379,	Top-5 err = 0.209277,	data_time = 0.050589,	train_time = 0.848187
[2019-08-23 07:50:04,950] TRAIN Iter 85000: lr = 0.358335,	loss = 2.729820,	Top-1 err = 0.433838,	Top-5 err = 0.207959,	data_time = 0.050487,	train_time = 0.341602
[2019-08-23 07:50:21,815] TRAIN Iter 85020: lr = 0.358302,	loss = 2.804044,	Top-1 err = 0.440723,	Top-5 err = 0.212939,	data_time = 0.050021,	train_time = 0.843240
[2019-08-23 07:50:38,938] TRAIN Iter 85040: lr = 0.358268,	loss = 2.758495,	Top-1 err = 0.435596,	Top-5 err = 0.210498,	data_time = 7.297270,	train_time = 0.856148
[2019-08-23 07:50:45,537] TRAIN Iter 85060: lr = 0.358235,	loss = 2.814791,	Top-1 err = 0.442822,	Top-5 err = 0.210352,	data_time = 0.049901,	train_time = 0.329934
[2019-08-23 07:51:33,017] TRAIN Iter 85080: lr = 0.358202,	loss = 2.775710,	Top-1 err = 0.434211,	Top-5 err = 0.206664,	data_time = 0.050675,	train_time = 2.373972
[2019-08-23 07:51:41,705] TRAIN Iter 85100: lr = 0.358168,	loss = 2.691411,	Top-1 err = 0.431836,	Top-5 err = 0.206396,	data_time = 0.050700,	train_time = 0.434388
[2019-08-23 07:51:50,679] TRAIN Iter 85120: lr = 0.358135,	loss = 2.839149,	Top-1 err = 0.433545,	Top-5 err = 0.206592,	data_time = 0.050147,	train_time = 0.448683
[2019-08-23 07:51:58,969] TRAIN Iter 85140: lr = 0.358102,	loss = 2.723738,	Top-1 err = 0.427832,	Top-5 err = 0.201318,	data_time = 0.137545,	train_time = 0.414488
[2019-08-23 07:52:07,638] TRAIN Iter 85160: lr = 0.358068,	loss = 2.678256,	Top-1 err = 0.431006,	Top-5 err = 0.201904,	data_time = 0.050576,	train_time = 0.433458
[2019-08-23 07:52:19,064] TRAIN Iter 85180: lr = 0.358035,	loss = 2.678103,	Top-1 err = 0.423340,	Top-5 err = 0.201074,	data_time = 0.050910,	train_time = 0.571267
[2019-08-23 07:52:26,847] TRAIN Iter 85200: lr = 0.358002,	loss = 2.688310,	Top-1 err = 0.429053,	Top-5 err = 0.206934,	data_time = 0.050539,	train_time = 0.389113
[2019-08-23 07:52:40,635] TRAIN Iter 85220: lr = 0.357968,	loss = 2.802732,	Top-1 err = 0.432617,	Top-5 err = 0.200732,	data_time = 0.050755,	train_time = 0.689428
[2019-08-23 07:52:53,972] TRAIN Iter 85240: lr = 0.357935,	loss = 2.747731,	Top-1 err = 0.428271,	Top-5 err = 0.199756,	data_time = 0.092606,	train_time = 0.666830
[2019-08-23 07:53:02,432] TRAIN Iter 85260: lr = 0.357902,	loss = 2.871006,	Top-1 err = 0.428906,	Top-5 err = 0.203076,	data_time = 0.051030,	train_time = 0.422974
[2019-08-23 07:53:15,193] TRAIN Iter 85280: lr = 0.357868,	loss = 2.817130,	Top-1 err = 0.431055,	Top-5 err = 0.205176,	data_time = 0.238969,	train_time = 0.638016
[2019-08-23 07:53:29,107] TRAIN Iter 85300: lr = 0.357835,	loss = 2.722593,	Top-1 err = 0.436328,	Top-5 err = 0.204395,	data_time = 4.117345,	train_time = 0.695688
[2019-08-23 07:53:38,022] TRAIN Iter 85320: lr = 0.357802,	loss = 2.805232,	Top-1 err = 0.428320,	Top-5 err = 0.202100,	data_time = 0.050649,	train_time = 0.445729
[2019-08-23 07:53:50,202] TRAIN Iter 85340: lr = 0.357768,	loss = 2.733922,	Top-1 err = 0.428662,	Top-5 err = 0.203027,	data_time = 0.050743,	train_time = 0.609007
[2019-08-23 07:54:01,218] TRAIN Iter 85360: lr = 0.357735,	loss = 2.770855,	Top-1 err = 0.433594,	Top-5 err = 0.205713,	data_time = 0.050125,	train_time = 0.550787
[2019-08-23 07:54:13,731] TRAIN Iter 85380: lr = 0.357702,	loss = 2.821890,	Top-1 err = 0.437061,	Top-5 err = 0.208984,	data_time = 0.050561,	train_time = 0.625607
[2019-08-23 07:54:27,548] TRAIN Iter 85400: lr = 0.357668,	loss = 2.840683,	Top-1 err = 0.431055,	Top-5 err = 0.204834,	data_time = 0.050484,	train_time = 0.690843
[2019-08-23 07:54:35,306] TRAIN Iter 85420: lr = 0.357635,	loss = 2.701402,	Top-1 err = 0.428516,	Top-5 err = 0.204248,	data_time = 0.050400,	train_time = 0.387881
[2019-08-23 07:54:49,073] TRAIN Iter 85440: lr = 0.357602,	loss = 2.716648,	Top-1 err = 0.434473,	Top-5 err = 0.205029,	data_time = 0.050640,	train_time = 0.688361
[2019-08-23 07:55:00,706] TRAIN Iter 85460: lr = 0.357568,	loss = 2.739816,	Top-1 err = 0.428760,	Top-5 err = 0.205615,	data_time = 3.462033,	train_time = 0.581619
[2019-08-23 07:55:09,031] TRAIN Iter 85480: lr = 0.357535,	loss = 2.812858,	Top-1 err = 0.437402,	Top-5 err = 0.208789,	data_time = 0.050834,	train_time = 0.416235
[2019-08-23 07:55:23,456] TRAIN Iter 85500: lr = 0.357502,	loss = 2.789029,	Top-1 err = 0.432764,	Top-5 err = 0.205811,	data_time = 0.050398,	train_time = 0.721211
[2019-08-23 07:55:31,866] TRAIN Iter 85520: lr = 0.357468,	loss = 2.731399,	Top-1 err = 0.431689,	Top-5 err = 0.207275,	data_time = 0.050337,	train_time = 0.420506
[2019-08-23 07:55:47,057] TRAIN Iter 85540: lr = 0.357435,	loss = 2.805303,	Top-1 err = 0.437695,	Top-5 err = 0.206104,	data_time = 0.050505,	train_time = 0.759536
[2019-08-23 07:55:59,288] TRAIN Iter 85560: lr = 0.357402,	loss = 2.706812,	Top-1 err = 0.427197,	Top-5 err = 0.205127,	data_time = 0.050887,	train_time = 0.611559
[2019-08-23 07:56:08,914] TRAIN Iter 85580: lr = 0.357368,	loss = 2.747150,	Top-1 err = 0.433594,	Top-5 err = 0.206982,	data_time = 0.050651,	train_time = 0.481240
[2019-08-23 07:56:24,096] TRAIN Iter 85600: lr = 0.357335,	loss = 2.729376,	Top-1 err = 0.431787,	Top-5 err = 0.205078,	data_time = 0.050473,	train_time = 0.759080
[2019-08-23 07:56:40,918] TRAIN Iter 85620: lr = 0.357302,	loss = 2.741900,	Top-1 err = 0.436279,	Top-5 err = 0.206543,	data_time = 8.010420,	train_time = 0.841095
[2019-08-23 07:56:49,087] TRAIN Iter 85640: lr = 0.357268,	loss = 2.744611,	Top-1 err = 0.428418,	Top-5 err = 0.204736,	data_time = 0.050428,	train_time = 0.408449
[2019-08-23 07:57:03,735] TRAIN Iter 85660: lr = 0.357235,	loss = 2.770284,	Top-1 err = 0.433545,	Top-5 err = 0.210840,	data_time = 0.050392,	train_time = 0.732368
[2019-08-23 07:57:11,618] TRAIN Iter 85680: lr = 0.357202,	loss = 2.720175,	Top-1 err = 0.427979,	Top-5 err = 0.202539,	data_time = 0.150555,	train_time = 0.394158
[2019-08-23 07:57:22,852] TRAIN Iter 85700: lr = 0.357168,	loss = 2.784621,	Top-1 err = 0.433594,	Top-5 err = 0.203857,	data_time = 0.050616,	train_time = 0.561704
[2019-08-23 07:57:36,129] TRAIN Iter 85720: lr = 0.357135,	loss = 2.846306,	Top-1 err = 0.434277,	Top-5 err = 0.205811,	data_time = 0.145128,	train_time = 0.663824
[2019-08-23 07:57:43,758] TRAIN Iter 85740: lr = 0.357102,	loss = 2.791103,	Top-1 err = 0.432812,	Top-5 err = 0.207178,	data_time = 0.050535,	train_time = 0.381451
[2019-08-23 07:57:58,459] TRAIN Iter 85760: lr = 0.357068,	loss = 2.763638,	Top-1 err = 0.438574,	Top-5 err = 0.213672,	data_time = 0.285601,	train_time = 0.735029
[2019-08-23 07:58:11,895] TRAIN Iter 85780: lr = 0.357035,	loss = 2.792958,	Top-1 err = 0.435010,	Top-5 err = 0.207422,	data_time = 3.867889,	train_time = 0.671754
[2019-08-23 07:58:19,725] TRAIN Iter 85800: lr = 0.357002,	loss = 2.843482,	Top-1 err = 0.436084,	Top-5 err = 0.208447,	data_time = 0.050458,	train_time = 0.391503
[2019-08-23 07:58:36,795] TRAIN Iter 85820: lr = 0.356968,	loss = 2.935594,	Top-1 err = 0.442041,	Top-5 err = 0.213574,	data_time = 0.050182,	train_time = 0.853488
[2019-08-23 07:58:43,954] TRAIN Iter 85840: lr = 0.356935,	loss = 2.770392,	Top-1 err = 0.434082,	Top-5 err = 0.208740,	data_time = 0.050558,	train_time = 0.357934
[2019-08-23 07:58:58,401] TRAIN Iter 85860: lr = 0.356902,	loss = 2.759402,	Top-1 err = 0.430713,	Top-5 err = 0.200488,	data_time = 0.050748,	train_time = 0.722314
[2019-08-23 07:59:10,513] TRAIN Iter 85880: lr = 0.356868,	loss = 2.805665,	Top-1 err = 0.433936,	Top-5 err = 0.212354,	data_time = 0.050401,	train_time = 0.605598
[2019-08-23 07:59:20,400] TRAIN Iter 85900: lr = 0.356835,	loss = 2.735151,	Top-1 err = 0.434277,	Top-5 err = 0.208594,	data_time = 0.050768,	train_time = 0.494322
[2019-08-23 07:59:35,674] TRAIN Iter 85920: lr = 0.356802,	loss = 2.836410,	Top-1 err = 0.437354,	Top-5 err = 0.211719,	data_time = 0.192047,	train_time = 0.763712
[2019-08-23 07:59:45,676] TRAIN Iter 85940: lr = 0.356768,	loss = 2.747793,	Top-1 err = 0.430615,	Top-5 err = 0.206689,	data_time = 0.150260,	train_time = 0.500065
[2019-08-23 07:59:57,199] TRAIN Iter 85960: lr = 0.356735,	loss = 2.833396,	Top-1 err = 0.432129,	Top-5 err = 0.204590,	data_time = 0.050381,	train_time = 0.576152
[2019-08-23 08:00:12,390] TRAIN Iter 85980: lr = 0.356702,	loss = 2.657424,	Top-1 err = 0.431445,	Top-5 err = 0.208447,	data_time = 0.050358,	train_time = 0.759515
[2019-08-23 08:00:19,947] TRAIN Iter 86000: lr = 0.356668,	loss = 2.819788,	Top-1 err = 0.439160,	Top-5 err = 0.207080,	data_time = 0.050551,	train_time = 0.377873
[2019-08-23 08:00:34,940] TRAIN Iter 86020: lr = 0.356635,	loss = 2.713471,	Top-1 err = 0.431641,	Top-5 err = 0.206006,	data_time = 0.050406,	train_time = 0.749635
[2019-08-23 08:00:49,991] TRAIN Iter 86040: lr = 0.356602,	loss = 2.798618,	Top-1 err = 0.439063,	Top-5 err = 0.207959,	data_time = 0.050619,	train_time = 0.752542
[2019-08-23 08:01:00,107] TRAIN Iter 86060: lr = 0.356568,	loss = 2.795127,	Top-1 err = 0.434668,	Top-5 err = 0.204199,	data_time = 0.050484,	train_time = 0.505773
[2019-08-23 08:01:16,203] TRAIN Iter 86080: lr = 0.356535,	loss = 2.705771,	Top-1 err = 0.437451,	Top-5 err = 0.211475,	data_time = 0.125653,	train_time = 0.804761
[2019-08-23 08:01:29,550] TRAIN Iter 86100: lr = 0.356502,	loss = 2.745709,	Top-1 err = 0.431787,	Top-5 err = 0.207861,	data_time = 4.549873,	train_time = 0.667367
[2019-08-23 08:01:38,146] TRAIN Iter 86120: lr = 0.356468,	loss = 2.764150,	Top-1 err = 0.439111,	Top-5 err = 0.210107,	data_time = 0.050699,	train_time = 0.429781
[2019-08-23 08:01:55,556] TRAIN Iter 86140: lr = 0.356435,	loss = 2.737857,	Top-1 err = 0.436523,	Top-5 err = 0.203564,	data_time = 0.050446,	train_time = 0.870460
[2019-08-23 08:02:03,029] TRAIN Iter 86160: lr = 0.356402,	loss = 2.894638,	Top-1 err = 0.439063,	Top-5 err = 0.213965,	data_time = 0.050469,	train_time = 0.373645
[2019-08-23 08:02:17,052] TRAIN Iter 86180: lr = 0.356368,	loss = 2.725187,	Top-1 err = 0.434131,	Top-5 err = 0.209570,	data_time = 0.050335,	train_time = 0.701156
[2019-08-23 08:02:32,971] TRAIN Iter 86200: lr = 0.356335,	loss = 2.824479,	Top-1 err = 0.428223,	Top-5 err = 0.206641,	data_time = 0.050494,	train_time = 0.795903
[2019-08-23 08:02:40,557] TRAIN Iter 86220: lr = 0.356302,	loss = 2.821631,	Top-1 err = 0.437646,	Top-5 err = 0.212939,	data_time = 0.050154,	train_time = 0.379301
[2019-08-23 08:02:59,720] TRAIN Iter 86240: lr = 0.356268,	loss = 2.900109,	Top-1 err = 0.440234,	Top-5 err = 0.208789,	data_time = 0.050527,	train_time = 0.958138
[2019-08-23 08:03:16,234] TRAIN Iter 86260: lr = 0.356235,	loss = 2.734627,	Top-1 err = 0.439209,	Top-5 err = 0.203662,	data_time = 8.651910,	train_time = 0.825669
[2019-08-23 08:03:23,200] TRAIN Iter 86280: lr = 0.356202,	loss = 2.730133,	Top-1 err = 0.433936,	Top-5 err = 0.204346,	data_time = 0.049969,	train_time = 0.348322
[2019-08-23 08:03:39,242] TRAIN Iter 86300: lr = 0.356168,	loss = 2.851413,	Top-1 err = 0.437158,	Top-5 err = 0.211084,	data_time = 0.050015,	train_time = 0.802048
[2019-08-23 08:03:45,297] TRAIN Iter 86320: lr = 0.356135,	loss = 2.699100,	Top-1 err = 0.429932,	Top-5 err = 0.210010,	data_time = 0.049831,	train_time = 0.302738
[2019-08-23 08:04:31,667] TRAIN Iter 86340: lr = 0.356102,	loss = 2.728245,	Top-1 err = 0.442791,	Top-5 err = 0.210599,	data_time = 0.050314,	train_time = 2.318518
[2019-08-23 08:04:41,242] TRAIN Iter 86360: lr = 0.356068,	loss = 2.803588,	Top-1 err = 0.434570,	Top-5 err = 0.205762,	data_time = 0.136971,	train_time = 0.478723
[2019-08-23 08:04:51,955] TRAIN Iter 86380: lr = 0.356035,	loss = 2.759697,	Top-1 err = 0.432812,	Top-5 err = 0.210840,	data_time = 0.134176,	train_time = 0.535601
[2019-08-23 08:05:01,645] TRAIN Iter 86400: lr = 0.356002,	loss = 2.771472,	Top-1 err = 0.422803,	Top-5 err = 0.196924,	data_time = 0.050845,	train_time = 0.484499
[2019-08-23 08:05:09,670] TRAIN Iter 86420: lr = 0.355968,	loss = 2.769211,	Top-1 err = 0.425928,	Top-5 err = 0.198340,	data_time = 0.050889,	train_time = 0.401252
[2019-08-23 08:05:24,446] TRAIN Iter 86440: lr = 0.355935,	loss = 2.849093,	Top-1 err = 0.430469,	Top-5 err = 0.206641,	data_time = 0.050522,	train_time = 0.738788
[2019-08-23 08:05:37,989] TRAIN Iter 86460: lr = 0.355902,	loss = 2.659768,	Top-1 err = 0.427686,	Top-5 err = 0.204297,	data_time = 0.050525,	train_time = 0.677127
[2019-08-23 08:05:45,980] TRAIN Iter 86480: lr = 0.355868,	loss = 2.805966,	Top-1 err = 0.432373,	Top-5 err = 0.205957,	data_time = 0.050970,	train_time = 0.399532
[2019-08-23 08:06:00,062] TRAIN Iter 86500: lr = 0.355835,	loss = 2.655854,	Top-1 err = 0.433545,	Top-5 err = 0.206445,	data_time = 0.050511,	train_time = 0.704096
[2019-08-23 08:06:10,122] TRAIN Iter 86520: lr = 0.355802,	loss = 2.812492,	Top-1 err = 0.425537,	Top-5 err = 0.203223,	data_time = 0.630589,	train_time = 0.502948
[2019-08-23 08:06:20,554] TRAIN Iter 86540: lr = 0.355768,	loss = 2.676660,	Top-1 err = 0.426123,	Top-5 err = 0.201758,	data_time = 0.050560,	train_time = 0.521624
[2019-08-23 08:06:34,326] TRAIN Iter 86560: lr = 0.355735,	loss = 2.720304,	Top-1 err = 0.424316,	Top-5 err = 0.200293,	data_time = 0.050606,	train_time = 0.688562
[2019-08-23 08:06:42,393] TRAIN Iter 86580: lr = 0.355702,	loss = 2.769060,	Top-1 err = 0.438330,	Top-5 err = 0.205859,	data_time = 0.050514,	train_time = 0.403340
[2019-08-23 08:06:54,426] TRAIN Iter 86600: lr = 0.355668,	loss = 2.772211,	Top-1 err = 0.435840,	Top-5 err = 0.207129,	data_time = 0.050469,	train_time = 0.601609
[2019-08-23 08:07:07,478] TRAIN Iter 86620: lr = 0.355635,	loss = 2.780667,	Top-1 err = 0.429053,	Top-5 err = 0.203564,	data_time = 0.050900,	train_time = 0.652595
[2019-08-23 08:07:15,462] TRAIN Iter 86640: lr = 0.355602,	loss = 2.664924,	Top-1 err = 0.427783,	Top-5 err = 0.201904,	data_time = 0.050478,	train_time = 0.399204
[2019-08-23 08:07:28,351] TRAIN Iter 86660: lr = 0.355568,	loss = 2.846989,	Top-1 err = 0.427930,	Top-5 err = 0.203076,	data_time = 0.050551,	train_time = 0.644431
[2019-08-23 08:07:40,759] TRAIN Iter 86680: lr = 0.355535,	loss = 2.676551,	Top-1 err = 0.436084,	Top-5 err = 0.205029,	data_time = 3.818772,	train_time = 0.620369
[2019-08-23 08:07:49,963] TRAIN Iter 86700: lr = 0.355502,	loss = 2.800093,	Top-1 err = 0.436230,	Top-5 err = 0.207324,	data_time = 0.050578,	train_time = 0.460212
[2019-08-23 08:08:04,483] TRAIN Iter 86720: lr = 0.355468,	loss = 2.709331,	Top-1 err = 0.428076,	Top-5 err = 0.201270,	data_time = 0.051071,	train_time = 0.725969
[2019-08-23 08:08:12,870] TRAIN Iter 86740: lr = 0.355435,	loss = 2.777273,	Top-1 err = 0.427539,	Top-5 err = 0.204687,	data_time = 0.050588,	train_time = 0.419352
[2019-08-23 08:08:25,299] TRAIN Iter 86760: lr = 0.355402,	loss = 2.765070,	Top-1 err = 0.422803,	Top-5 err = 0.202686,	data_time = 0.050666,	train_time = 0.621390
[2019-08-23 08:08:41,051] TRAIN Iter 86780: lr = 0.355368,	loss = 2.831236,	Top-1 err = 0.430762,	Top-5 err = 0.203125,	data_time = 0.050517,	train_time = 0.787626
[2019-08-23 08:08:49,359] TRAIN Iter 86800: lr = 0.355335,	loss = 2.681181,	Top-1 err = 0.430371,	Top-5 err = 0.204443,	data_time = 0.050622,	train_time = 0.415378
[2019-08-23 08:09:00,660] TRAIN Iter 86820: lr = 0.355302,	loss = 2.765469,	Top-1 err = 0.424072,	Top-5 err = 0.199658,	data_time = 0.050538,	train_time = 0.565004
[2019-08-23 08:09:13,947] TRAIN Iter 86840: lr = 0.355268,	loss = 2.794310,	Top-1 err = 0.433545,	Top-5 err = 0.206494,	data_time = 4.798353,	train_time = 0.664334
[2019-08-23 08:09:23,817] TRAIN Iter 86860: lr = 0.355235,	loss = 2.701071,	Top-1 err = 0.434229,	Top-5 err = 0.208643,	data_time = 0.050819,	train_time = 0.493505
[2019-08-23 08:09:36,102] TRAIN Iter 86880: lr = 0.355202,	loss = 2.729956,	Top-1 err = 0.428955,	Top-5 err = 0.202393,	data_time = 0.050838,	train_time = 0.614241
[2019-08-23 08:09:43,986] TRAIN Iter 86900: lr = 0.355168,	loss = 2.819572,	Top-1 err = 0.430566,	Top-5 err = 0.206055,	data_time = 0.050528,	train_time = 0.394161
[2019-08-23 08:09:57,503] TRAIN Iter 86920: lr = 0.355135,	loss = 2.682618,	Top-1 err = 0.426758,	Top-5 err = 0.200293,	data_time = 0.050327,	train_time = 0.675862
[2019-08-23 08:10:12,579] TRAIN Iter 86940: lr = 0.355102,	loss = 2.811400,	Top-1 err = 0.434326,	Top-5 err = 0.206592,	data_time = 0.050851,	train_time = 0.753758
[2019-08-23 08:10:20,115] TRAIN Iter 86960: lr = 0.355068,	loss = 2.724443,	Top-1 err = 0.429102,	Top-5 err = 0.210791,	data_time = 0.050541,	train_time = 0.376782
[2019-08-23 08:10:35,252] TRAIN Iter 86980: lr = 0.355035,	loss = 2.752702,	Top-1 err = 0.433154,	Top-5 err = 0.208350,	data_time = 0.050388,	train_time = 0.756837
[2019-08-23 08:10:47,783] TRAIN Iter 87000: lr = 0.355002,	loss = 2.660924,	Top-1 err = 0.429980,	Top-5 err = 0.207959,	data_time = 4.815621,	train_time = 0.626580
[2019-08-23 08:10:57,016] TRAIN Iter 87020: lr = 0.354968,	loss = 2.797358,	Top-1 err = 0.439795,	Top-5 err = 0.211670,	data_time = 0.050460,	train_time = 0.461590
[2019-08-23 08:11:10,793] TRAIN Iter 87040: lr = 0.354935,	loss = 2.813000,	Top-1 err = 0.436279,	Top-5 err = 0.204102,	data_time = 0.050158,	train_time = 0.688874
[2019-08-23 08:11:18,256] TRAIN Iter 87060: lr = 0.354902,	loss = 2.735036,	Top-1 err = 0.437988,	Top-5 err = 0.207031,	data_time = 0.050730,	train_time = 0.373115
[2019-08-23 08:11:33,382] TRAIN Iter 87080: lr = 0.354868,	loss = 2.781133,	Top-1 err = 0.432666,	Top-5 err = 0.208398,	data_time = 0.050393,	train_time = 0.756300
[2019-08-23 08:11:46,603] TRAIN Iter 87100: lr = 0.354835,	loss = 2.894877,	Top-1 err = 0.436719,	Top-5 err = 0.207910,	data_time = 0.050731,	train_time = 0.661048
[2019-08-23 08:11:53,670] TRAIN Iter 87120: lr = 0.354802,	loss = 2.731072,	Top-1 err = 0.430029,	Top-5 err = 0.206592,	data_time = 0.050504,	train_time = 0.353318
[2019-08-23 08:12:10,779] TRAIN Iter 87140: lr = 0.354768,	loss = 2.807068,	Top-1 err = 0.434424,	Top-5 err = 0.207031,	data_time = 0.050313,	train_time = 0.855432
[2019-08-23 08:12:28,936] TRAIN Iter 87160: lr = 0.354735,	loss = 2.739318,	Top-1 err = 0.433691,	Top-5 err = 0.208838,	data_time = 9.211928,	train_time = 0.907839
[2019-08-23 08:12:36,583] TRAIN Iter 87180: lr = 0.354702,	loss = 2.772350,	Top-1 err = 0.431641,	Top-5 err = 0.207324,	data_time = 0.050372,	train_time = 0.382310
[2019-08-23 08:12:50,038] TRAIN Iter 87200: lr = 0.354668,	loss = 2.729809,	Top-1 err = 0.437988,	Top-5 err = 0.209082,	data_time = 0.050389,	train_time = 0.672761
[2019-08-23 08:12:57,568] TRAIN Iter 87220: lr = 0.354635,	loss = 2.748450,	Top-1 err = 0.431152,	Top-5 err = 0.210889,	data_time = 0.050793,	train_time = 0.376501
[2019-08-23 08:13:11,614] TRAIN Iter 87240: lr = 0.354602,	loss = 2.753118,	Top-1 err = 0.440674,	Top-5 err = 0.212549,	data_time = 0.050498,	train_time = 0.702258
[2019-08-23 08:13:28,474] TRAIN Iter 87260: lr = 0.354568,	loss = 2.763547,	Top-1 err = 0.431250,	Top-5 err = 0.206787,	data_time = 0.050508,	train_time = 0.842979
[2019-08-23 08:13:35,521] TRAIN Iter 87280: lr = 0.354535,	loss = 2.754019,	Top-1 err = 0.426563,	Top-5 err = 0.198730,	data_time = 0.050232,	train_time = 0.352352
[2019-08-23 08:13:52,526] TRAIN Iter 87300: lr = 0.354502,	loss = 2.798551,	Top-1 err = 0.430615,	Top-5 err = 0.210742,	data_time = 0.050315,	train_time = 0.850238
[2019-08-23 08:14:09,437] TRAIN Iter 87320: lr = 0.354468,	loss = 2.723600,	Top-1 err = 0.434180,	Top-5 err = 0.208643,	data_time = 9.501368,	train_time = 0.845546
[2019-08-23 08:14:16,456] TRAIN Iter 87340: lr = 0.354435,	loss = 2.709306,	Top-1 err = 0.436670,	Top-5 err = 0.209766,	data_time = 0.050439,	train_time = 0.350907
[2019-08-23 08:14:35,590] TRAIN Iter 87360: lr = 0.354402,	loss = 2.750924,	Top-1 err = 0.438037,	Top-5 err = 0.206641,	data_time = 0.050333,	train_time = 0.956707
[2019-08-23 08:14:43,332] TRAIN Iter 87380: lr = 0.354368,	loss = 2.810375,	Top-1 err = 0.435547,	Top-5 err = 0.205029,	data_time = 0.050297,	train_time = 0.387080
[2019-08-23 08:14:58,182] TRAIN Iter 87400: lr = 0.354335,	loss = 2.735738,	Top-1 err = 0.432031,	Top-5 err = 0.207080,	data_time = 0.050665,	train_time = 0.742478
[2019-08-23 08:15:15,721] TRAIN Iter 87420: lr = 0.354302,	loss = 2.717864,	Top-1 err = 0.436279,	Top-5 err = 0.208105,	data_time = 0.050485,	train_time = 0.876950
[2019-08-23 08:15:22,912] TRAIN Iter 87440: lr = 0.354268,	loss = 2.813281,	Top-1 err = 0.432666,	Top-5 err = 0.208838,	data_time = 0.050588,	train_time = 0.359503
[2019-08-23 08:15:39,427] TRAIN Iter 87460: lr = 0.354235,	loss = 2.771988,	Top-1 err = 0.433301,	Top-5 err = 0.206934,	data_time = 0.050567,	train_time = 0.825763
[2019-08-23 08:15:56,532] TRAIN Iter 87480: lr = 0.354202,	loss = 2.760612,	Top-1 err = 0.432617,	Top-5 err = 0.204687,	data_time = 9.947285,	train_time = 0.855231
[2019-08-23 08:16:03,493] TRAIN Iter 87500: lr = 0.354168,	loss = 2.763047,	Top-1 err = 0.432227,	Top-5 err = 0.205957,	data_time = 0.050518,	train_time = 0.348046
[2019-08-23 08:16:24,548] TRAIN Iter 87520: lr = 0.354135,	loss = 2.777871,	Top-1 err = 0.435303,	Top-5 err = 0.211572,	data_time = 0.050017,	train_time = 1.052734
[2019-08-23 08:16:31,686] TRAIN Iter 87540: lr = 0.354102,	loss = 2.783357,	Top-1 err = 0.425488,	Top-5 err = 0.201318,	data_time = 0.134498,	train_time = 0.356885
[2019-08-23 08:16:46,782] TRAIN Iter 87560: lr = 0.354068,	loss = 2.793523,	Top-1 err = 0.438770,	Top-5 err = 0.211182,	data_time = 0.049920,	train_time = 0.754796
[2019-08-23 08:17:32,465] TRAIN Iter 87580: lr = 0.354035,	loss = 2.763399,	Top-1 err = 0.443191,	Top-5 err = 0.212716,	data_time = 0.050217,	train_time = 2.284101
[2019-08-23 08:17:39,980] TRAIN Iter 87600: lr = 0.354002,	loss = 2.785234,	Top-1 err = 0.436084,	Top-5 err = 0.206592,	data_time = 0.050424,	train_time = 0.375749
[2019-08-23 08:17:53,660] TRAIN Iter 87620: lr = 0.353968,	loss = 2.872953,	Top-1 err = 0.431104,	Top-5 err = 0.205273,	data_time = 0.143724,	train_time = 0.684007
[2019-08-23 08:18:02,183] TRAIN Iter 87640: lr = 0.353935,	loss = 2.700430,	Top-1 err = 0.427490,	Top-5 err = 0.199365,	data_time = 0.050533,	train_time = 0.426104
[2019-08-23 08:18:12,505] TRAIN Iter 87660: lr = 0.353902,	loss = 2.776134,	Top-1 err = 0.427783,	Top-5 err = 0.200244,	data_time = 0.051072,	train_time = 0.516090
[2019-08-23 08:18:28,536] TRAIN Iter 87680: lr = 0.353868,	loss = 2.748591,	Top-1 err = 0.425781,	Top-5 err = 0.205176,	data_time = 0.050662,	train_time = 0.801530
[2019-08-23 08:18:36,949] TRAIN Iter 87700: lr = 0.353835,	loss = 2.752539,	Top-1 err = 0.432422,	Top-5 err = 0.202002,	data_time = 0.050407,	train_time = 0.420632
[2019-08-23 08:18:46,474] TRAIN Iter 87720: lr = 0.353802,	loss = 2.760947,	Top-1 err = 0.429346,	Top-5 err = 0.198682,	data_time = 0.050273,	train_time = 0.476252
[2019-08-23 08:18:59,403] TRAIN Iter 87740: lr = 0.353768,	loss = 2.731998,	Top-1 err = 0.422363,	Top-5 err = 0.198193,	data_time = 0.050637,	train_time = 0.646431
[2019-08-23 08:19:07,025] TRAIN Iter 87760: lr = 0.353735,	loss = 2.706012,	Top-1 err = 0.419189,	Top-5 err = 0.198389,	data_time = 0.050452,	train_time = 0.381068
[2019-08-23 08:19:21,967] TRAIN Iter 87780: lr = 0.353702,	loss = 2.750147,	Top-1 err = 0.428955,	Top-5 err = 0.199316,	data_time = 0.050536,	train_time = 0.747117
[2019-08-23 08:19:29,930] TRAIN Iter 87800: lr = 0.353668,	loss = 2.658654,	Top-1 err = 0.431445,	Top-5 err = 0.200928,	data_time = 0.050719,	train_time = 0.398122
[2019-08-23 08:19:43,328] TRAIN Iter 87820: lr = 0.353635,	loss = 2.735721,	Top-1 err = 0.428467,	Top-5 err = 0.200244,	data_time = 0.050569,	train_time = 0.669873
[2019-08-23 08:19:55,431] TRAIN Iter 87840: lr = 0.353602,	loss = 2.808063,	Top-1 err = 0.430566,	Top-5 err = 0.202148,	data_time = 0.050334,	train_time = 0.605162
[2019-08-23 08:20:03,192] TRAIN Iter 87860: lr = 0.353568,	loss = 2.776655,	Top-1 err = 0.436768,	Top-5 err = 0.209961,	data_time = 0.050565,	train_time = 0.388025
[2019-08-23 08:20:17,456] TRAIN Iter 87880: lr = 0.353535,	loss = 2.746457,	Top-1 err = 0.432129,	Top-5 err = 0.205762,	data_time = 0.050702,	train_time = 0.713189
[2019-08-23 08:20:30,034] TRAIN Iter 87900: lr = 0.353502,	loss = 2.771719,	Top-1 err = 0.429736,	Top-5 err = 0.203955,	data_time = 0.050476,	train_time = 0.628851
[2019-08-23 08:20:37,651] TRAIN Iter 87920: lr = 0.353468,	loss = 2.713562,	Top-1 err = 0.429492,	Top-5 err = 0.202002,	data_time = 0.050393,	train_time = 0.380855
[2019-08-23 08:20:51,666] TRAIN Iter 87940: lr = 0.353435,	loss = 2.745640,	Top-1 err = 0.430908,	Top-5 err = 0.203662,	data_time = 0.050545,	train_time = 0.700708
[2019-08-23 08:20:58,961] TRAIN Iter 87960: lr = 0.353402,	loss = 2.712861,	Top-1 err = 0.431104,	Top-5 err = 0.208496,	data_time = 0.127257,	train_time = 0.364773
[2019-08-23 08:21:13,064] TRAIN Iter 87980: lr = 0.353368,	loss = 2.846071,	Top-1 err = 0.429004,	Top-5 err = 0.206543,	data_time = 0.050479,	train_time = 0.705127
[2019-08-23 08:21:27,571] TRAIN Iter 88000: lr = 0.353335,	loss = 2.874881,	Top-1 err = 0.425586,	Top-5 err = 0.200244,	data_time = 0.050527,	train_time = 0.725338
[2019-08-23 08:21:34,925] TRAIN Iter 88020: lr = 0.353302,	loss = 2.763109,	Top-1 err = 0.428906,	Top-5 err = 0.202783,	data_time = 0.050869,	train_time = 0.367657
[2019-08-23 08:21:48,912] TRAIN Iter 88040: lr = 0.353268,	loss = 2.727089,	Top-1 err = 0.435547,	Top-5 err = 0.203320,	data_time = 0.050818,	train_time = 0.699341
[2019-08-23 08:22:02,073] TRAIN Iter 88060: lr = 0.353235,	loss = 2.748801,	Top-1 err = 0.427637,	Top-5 err = 0.202295,	data_time = 0.050392,	train_time = 0.658043
[2019-08-23 08:22:09,339] TRAIN Iter 88080: lr = 0.353202,	loss = 2.817995,	Top-1 err = 0.433496,	Top-5 err = 0.204932,	data_time = 0.050627,	train_time = 0.363307
[2019-08-23 08:22:24,341] TRAIN Iter 88100: lr = 0.353168,	loss = 2.793091,	Top-1 err = 0.432129,	Top-5 err = 0.206348,	data_time = 0.050538,	train_time = 0.750070
[2019-08-23 08:22:31,561] TRAIN Iter 88120: lr = 0.353135,	loss = 2.798944,	Top-1 err = 0.430518,	Top-5 err = 0.209180,	data_time = 0.050364,	train_time = 0.360971
[2019-08-23 08:22:44,335] TRAIN Iter 88140: lr = 0.353102,	loss = 2.824957,	Top-1 err = 0.430420,	Top-5 err = 0.207568,	data_time = 0.050508,	train_time = 0.638680
[2019-08-23 08:23:00,343] TRAIN Iter 88160: lr = 0.353068,	loss = 2.763376,	Top-1 err = 0.431885,	Top-5 err = 0.204883,	data_time = 0.050438,	train_time = 0.800402
[2019-08-23 08:23:07,409] TRAIN Iter 88180: lr = 0.353035,	loss = 2.733564,	Top-1 err = 0.437012,	Top-5 err = 0.213135,	data_time = 0.050431,	train_time = 0.353274
[2019-08-23 08:23:22,585] TRAIN Iter 88200: lr = 0.353002,	loss = 2.836946,	Top-1 err = 0.427686,	Top-5 err = 0.203125,	data_time = 0.050847,	train_time = 0.758772
[2019-08-23 08:23:35,300] TRAIN Iter 88220: lr = 0.352968,	loss = 2.763701,	Top-1 err = 0.429541,	Top-5 err = 0.203564,	data_time = 1.362239,	train_time = 0.635756
[2019-08-23 08:23:42,830] TRAIN Iter 88240: lr = 0.352935,	loss = 2.751125,	Top-1 err = 0.430078,	Top-5 err = 0.203320,	data_time = 0.050438,	train_time = 0.376486
[2019-08-23 08:23:57,849] TRAIN Iter 88260: lr = 0.352902,	loss = 2.666101,	Top-1 err = 0.423633,	Top-5 err = 0.202441,	data_time = 0.050526,	train_time = 0.750922
[2019-08-23 08:24:05,422] TRAIN Iter 88280: lr = 0.352868,	loss = 2.897822,	Top-1 err = 0.439600,	Top-5 err = 0.209180,	data_time = 0.050499,	train_time = 0.378665
[2019-08-23 08:24:20,312] TRAIN Iter 88300: lr = 0.352835,	loss = 2.749795,	Top-1 err = 0.433984,	Top-5 err = 0.208496,	data_time = 0.050586,	train_time = 0.744456
[2019-08-23 08:24:35,788] TRAIN Iter 88320: lr = 0.352802,	loss = 2.884838,	Top-1 err = 0.434473,	Top-5 err = 0.207422,	data_time = 0.050245,	train_time = 0.773788
[2019-08-23 08:24:43,087] TRAIN Iter 88340: lr = 0.352768,	loss = 2.749871,	Top-1 err = 0.436914,	Top-5 err = 0.208887,	data_time = 0.050512,	train_time = 0.364938
[2019-08-23 08:24:57,872] TRAIN Iter 88360: lr = 0.352735,	loss = 2.776857,	Top-1 err = 0.432910,	Top-5 err = 0.206104,	data_time = 0.050539,	train_time = 0.739253
[2019-08-23 08:25:12,192] TRAIN Iter 88380: lr = 0.352702,	loss = 2.630512,	Top-1 err = 0.428516,	Top-5 err = 0.206982,	data_time = 2.567609,	train_time = 0.715966
[2019-08-23 08:25:19,956] TRAIN Iter 88400: lr = 0.352668,	loss = 2.801116,	Top-1 err = 0.433838,	Top-5 err = 0.207227,	data_time = 0.050548,	train_time = 0.388179
[2019-08-23 08:25:34,897] TRAIN Iter 88420: lr = 0.352635,	loss = 2.755755,	Top-1 err = 0.429004,	Top-5 err = 0.203076,	data_time = 0.123296,	train_time = 0.747066
[2019-08-23 08:25:42,074] TRAIN Iter 88440: lr = 0.352602,	loss = 2.805974,	Top-1 err = 0.432715,	Top-5 err = 0.203906,	data_time = 0.133262,	train_time = 0.358847
[2019-08-23 08:25:58,254] TRAIN Iter 88460: lr = 0.352568,	loss = 2.776676,	Top-1 err = 0.434131,	Top-5 err = 0.208252,	data_time = 0.050515,	train_time = 0.808949
[2019-08-23 08:26:15,350] TRAIN Iter 88480: lr = 0.352535,	loss = 2.788610,	Top-1 err = 0.435547,	Top-5 err = 0.209912,	data_time = 0.050190,	train_time = 0.854804
[2019-08-23 08:26:22,348] TRAIN Iter 88500: lr = 0.352502,	loss = 2.722517,	Top-1 err = 0.429492,	Top-5 err = 0.201318,	data_time = 0.050752,	train_time = 0.349880
[2019-08-23 08:26:38,320] TRAIN Iter 88520: lr = 0.352468,	loss = 2.746690,	Top-1 err = 0.431836,	Top-5 err = 0.205176,	data_time = 0.050308,	train_time = 0.798602
[2019-08-23 08:26:54,314] TRAIN Iter 88540: lr = 0.352435,	loss = 2.760857,	Top-1 err = 0.426953,	Top-5 err = 0.201074,	data_time = 3.428248,	train_time = 0.799677
[2019-08-23 08:27:01,400] TRAIN Iter 88560: lr = 0.352402,	loss = 2.794393,	Top-1 err = 0.430273,	Top-5 err = 0.210254,	data_time = 0.050375,	train_time = 0.354289
[2019-08-23 08:27:18,311] TRAIN Iter 88580: lr = 0.352368,	loss = 2.828169,	Top-1 err = 0.432324,	Top-5 err = 0.202832,	data_time = 0.050347,	train_time = 0.845527
[2019-08-23 08:27:25,242] TRAIN Iter 88600: lr = 0.352335,	loss = 2.846564,	Top-1 err = 0.434229,	Top-5 err = 0.209668,	data_time = 0.132710,	train_time = 0.346515
[2019-08-23 08:27:40,379] TRAIN Iter 88620: lr = 0.352302,	loss = 2.757733,	Top-1 err = 0.439355,	Top-5 err = 0.207666,	data_time = 0.050409,	train_time = 0.756851
[2019-08-23 08:27:57,145] TRAIN Iter 88640: lr = 0.352268,	loss = 2.821544,	Top-1 err = 0.435254,	Top-5 err = 0.206836,	data_time = 0.050465,	train_time = 0.838270
[2019-08-23 08:28:03,936] TRAIN Iter 88660: lr = 0.352235,	loss = 2.825223,	Top-1 err = 0.442480,	Top-5 err = 0.217188,	data_time = 0.050442,	train_time = 0.339546
[2019-08-23 08:28:20,814] TRAIN Iter 88680: lr = 0.352202,	loss = 2.676142,	Top-1 err = 0.434570,	Top-5 err = 0.208545,	data_time = 0.050347,	train_time = 0.843908
[2019-08-23 08:28:35,294] TRAIN Iter 88700: lr = 0.352168,	loss = 2.643775,	Top-1 err = 0.434277,	Top-5 err = 0.204102,	data_time = 0.823040,	train_time = 0.723965
[2019-08-23 08:28:42,697] TRAIN Iter 88720: lr = 0.352135,	loss = 2.813884,	Top-1 err = 0.441162,	Top-5 err = 0.208545,	data_time = 0.050526,	train_time = 0.370147
[2019-08-23 08:28:59,606] TRAIN Iter 88740: lr = 0.352102,	loss = 2.775281,	Top-1 err = 0.430225,	Top-5 err = 0.204199,	data_time = 0.050700,	train_time = 0.845412
[2019-08-23 08:29:06,409] TRAIN Iter 88760: lr = 0.352068,	loss = 2.633400,	Top-1 err = 0.437012,	Top-5 err = 0.207324,	data_time = 0.050195,	train_time = 0.340159
[2019-08-23 08:29:23,617] TRAIN Iter 88780: lr = 0.352035,	loss = 2.786171,	Top-1 err = 0.430029,	Top-5 err = 0.200781,	data_time = 0.050154,	train_time = 0.860367
[2019-08-23 08:29:41,520] TRAIN Iter 88800: lr = 0.352002,	loss = 2.814249,	Top-1 err = 0.434668,	Top-5 err = 0.213135,	data_time = 0.049914,	train_time = 0.895130
[2019-08-23 08:29:48,001] TRAIN Iter 88820: lr = 0.351968,	loss = 2.732283,	Top-1 err = 0.433496,	Top-5 err = 0.208887,	data_time = 0.049895,	train_time = 0.324055
[2019-08-23 08:30:38,097] TRAIN Iter 88840: lr = 0.351935,	loss = 2.837924,	Top-1 err = 0.444331,	Top-5 err = 0.217898,	data_time = 0.115520,	train_time = 2.504729
[2019-08-23 08:30:46,691] TRAIN Iter 88860: lr = 0.351902,	loss = 2.743632,	Top-1 err = 0.435010,	Top-5 err = 0.204980,	data_time = 0.128608,	train_time = 0.429670
[2019-08-23 08:30:57,827] TRAIN Iter 88880: lr = 0.351868,	loss = 2.783834,	Top-1 err = 0.432617,	Top-5 err = 0.203027,	data_time = 0.147260,	train_time = 0.556792
[2019-08-23 08:31:06,475] TRAIN Iter 88900: lr = 0.351835,	loss = 2.759260,	Top-1 err = 0.433594,	Top-5 err = 0.202637,	data_time = 0.050875,	train_time = 0.432375
[2019-08-23 08:31:14,520] TRAIN Iter 88920: lr = 0.351802,	loss = 2.821687,	Top-1 err = 0.429590,	Top-5 err = 0.206055,	data_time = 0.050358,	train_time = 0.402267
[2019-08-23 08:31:27,351] TRAIN Iter 88940: lr = 0.351768,	loss = 2.743127,	Top-1 err = 0.425684,	Top-5 err = 0.199268,	data_time = 0.050567,	train_time = 0.641497
[2019-08-23 08:31:38,181] TRAIN Iter 88960: lr = 0.351735,	loss = 2.723661,	Top-1 err = 0.434082,	Top-5 err = 0.202881,	data_time = 0.050785,	train_time = 0.541484
[2019-08-23 08:31:47,953] TRAIN Iter 88980: lr = 0.351702,	loss = 2.691639,	Top-1 err = 0.432324,	Top-5 err = 0.206348,	data_time = 0.050581,	train_time = 0.488587
[2019-08-23 08:32:01,477] TRAIN Iter 89000: lr = 0.351668,	loss = 2.749415,	Top-1 err = 0.429053,	Top-5 err = 0.205664,	data_time = 0.050708,	train_time = 0.676205
[2019-08-23 08:32:09,722] TRAIN Iter 89020: lr = 0.351635,	loss = 2.766439,	Top-1 err = 0.423242,	Top-5 err = 0.204150,	data_time = 0.113336,	train_time = 0.412234
[2019-08-23 08:32:21,640] TRAIN Iter 89040: lr = 0.351602,	loss = 2.721974,	Top-1 err = 0.420703,	Top-5 err = 0.196289,	data_time = 0.050546,	train_time = 0.595892
[2019-08-23 08:32:35,216] TRAIN Iter 89060: lr = 0.351568,	loss = 2.751383,	Top-1 err = 0.430127,	Top-5 err = 0.202930,	data_time = 0.050860,	train_time = 0.678748
[2019-08-23 08:32:42,901] TRAIN Iter 89080: lr = 0.351535,	loss = 2.637755,	Top-1 err = 0.424414,	Top-5 err = 0.204297,	data_time = 0.122199,	train_time = 0.384260
[2019-08-23 08:32:55,633] TRAIN Iter 89100: lr = 0.351502,	loss = 2.794102,	Top-1 err = 0.425586,	Top-5 err = 0.200732,	data_time = 0.050753,	train_time = 0.636583
[2019-08-23 08:33:07,457] TRAIN Iter 89120: lr = 0.351468,	loss = 2.697534,	Top-1 err = 0.426123,	Top-5 err = 0.202100,	data_time = 0.050444,	train_time = 0.591170
[2019-08-23 08:33:15,869] TRAIN Iter 89140: lr = 0.351435,	loss = 2.781225,	Top-1 err = 0.437549,	Top-5 err = 0.207080,	data_time = 0.050227,	train_time = 0.420609
[2019-08-23 08:33:28,838] TRAIN Iter 89160: lr = 0.351402,	loss = 2.735273,	Top-1 err = 0.428076,	Top-5 err = 0.200488,	data_time = 0.050585,	train_time = 0.648452
[2019-08-23 08:33:36,777] TRAIN Iter 89180: lr = 0.351368,	loss = 2.753497,	Top-1 err = 0.427295,	Top-5 err = 0.203613,	data_time = 0.050602,	train_time = 0.396934
[2019-08-23 08:33:51,368] TRAIN Iter 89200: lr = 0.351335,	loss = 2.793614,	Top-1 err = 0.425000,	Top-5 err = 0.202344,	data_time = 0.050788,	train_time = 0.729527
[2019-08-23 08:34:04,299] TRAIN Iter 89220: lr = 0.351302,	loss = 2.762511,	Top-1 err = 0.433936,	Top-5 err = 0.202002,	data_time = 0.299717,	train_time = 0.646497
[2019-08-23 08:34:12,080] TRAIN Iter 89240: lr = 0.351268,	loss = 2.815479,	Top-1 err = 0.431348,	Top-5 err = 0.202979,	data_time = 0.050676,	train_time = 0.389031
[2019-08-23 08:34:25,889] TRAIN Iter 89260: lr = 0.351235,	loss = 2.650447,	Top-1 err = 0.426855,	Top-5 err = 0.201123,	data_time = 0.050216,	train_time = 0.690439
[2019-08-23 08:34:38,004] TRAIN Iter 89280: lr = 0.351202,	loss = 2.748544,	Top-1 err = 0.434082,	Top-5 err = 0.206689,	data_time = 0.121537,	train_time = 0.605778
[2019-08-23 08:34:47,122] TRAIN Iter 89300: lr = 0.351168,	loss = 2.821097,	Top-1 err = 0.432227,	Top-5 err = 0.206250,	data_time = 0.050467,	train_time = 0.455838
[2019-08-23 08:34:59,851] TRAIN Iter 89320: lr = 0.351135,	loss = 2.799503,	Top-1 err = 0.435547,	Top-5 err = 0.203809,	data_time = 0.050317,	train_time = 0.636438
[2019-08-23 08:35:07,355] TRAIN Iter 89340: lr = 0.351102,	loss = 2.906346,	Top-1 err = 0.421631,	Top-5 err = 0.199463,	data_time = 0.050516,	train_time = 0.375215
[2019-08-23 08:35:22,910] TRAIN Iter 89360: lr = 0.351068,	loss = 2.818761,	Top-1 err = 0.425977,	Top-5 err = 0.202002,	data_time = 0.050447,	train_time = 0.777715
[2019-08-23 08:35:36,868] TRAIN Iter 89380: lr = 0.351035,	loss = 2.821089,	Top-1 err = 0.427344,	Top-5 err = 0.200586,	data_time = 1.087329,	train_time = 0.697920
[2019-08-23 08:35:44,531] TRAIN Iter 89400: lr = 0.351002,	loss = 2.734246,	Top-1 err = 0.428857,	Top-5 err = 0.203955,	data_time = 0.050845,	train_time = 0.383136
[2019-08-23 08:35:59,989] TRAIN Iter 89420: lr = 0.350968,	loss = 2.836862,	Top-1 err = 0.431982,	Top-5 err = 0.206836,	data_time = 0.050247,	train_time = 0.772859
[2019-08-23 08:36:12,395] TRAIN Iter 89440: lr = 0.350935,	loss = 2.714301,	Top-1 err = 0.432715,	Top-5 err = 0.207129,	data_time = 0.103690,	train_time = 0.620296
[2019-08-23 08:36:20,571] TRAIN Iter 89460: lr = 0.350902,	loss = 2.857436,	Top-1 err = 0.438770,	Top-5 err = 0.212646,	data_time = 0.050553,	train_time = 0.408762
[2019-08-23 08:36:35,979] TRAIN Iter 89480: lr = 0.350868,	loss = 2.713115,	Top-1 err = 0.432129,	Top-5 err = 0.207910,	data_time = 0.050194,	train_time = 0.770372
[2019-08-23 08:36:43,318] TRAIN Iter 89500: lr = 0.350835,	loss = 2.806875,	Top-1 err = 0.430566,	Top-5 err = 0.203418,	data_time = 0.050989,	train_time = 0.366965
[2019-08-23 08:36:58,402] TRAIN Iter 89520: lr = 0.350802,	loss = 2.787958,	Top-1 err = 0.430811,	Top-5 err = 0.206396,	data_time = 0.050505,	train_time = 0.754196
[2019-08-23 08:37:12,139] TRAIN Iter 89540: lr = 0.350768,	loss = 2.753411,	Top-1 err = 0.431738,	Top-5 err = 0.204932,	data_time = 0.050432,	train_time = 0.686829
[2019-08-23 08:37:19,723] TRAIN Iter 89560: lr = 0.350735,	loss = 2.772310,	Top-1 err = 0.428027,	Top-5 err = 0.201807,	data_time = 0.050501,	train_time = 0.379166
[2019-08-23 08:37:35,629] TRAIN Iter 89580: lr = 0.350702,	loss = 2.730500,	Top-1 err = 0.435156,	Top-5 err = 0.205664,	data_time = 0.050751,	train_time = 0.795307
[2019-08-23 08:37:50,204] TRAIN Iter 89600: lr = 0.350668,	loss = 2.704525,	Top-1 err = 0.431348,	Top-5 err = 0.207910,	data_time = 0.050503,	train_time = 0.728737
[2019-08-23 08:37:57,492] TRAIN Iter 89620: lr = 0.350635,	loss = 2.717941,	Top-1 err = 0.434033,	Top-5 err = 0.209570,	data_time = 0.050467,	train_time = 0.364362
[2019-08-23 08:38:12,381] TRAIN Iter 89640: lr = 0.350602,	loss = 2.747440,	Top-1 err = 0.430420,	Top-5 err = 0.206934,	data_time = 0.050705,	train_time = 0.744458
[2019-08-23 08:38:19,968] TRAIN Iter 89660: lr = 0.350568,	loss = 2.733628,	Top-1 err = 0.433740,	Top-5 err = 0.204443,	data_time = 0.050555,	train_time = 0.379327
[2019-08-23 08:38:33,752] TRAIN Iter 89680: lr = 0.350535,	loss = 2.775977,	Top-1 err = 0.428857,	Top-5 err = 0.200342,	data_time = 0.050534,	train_time = 0.689187
[2019-08-23 08:38:51,078] TRAIN Iter 89700: lr = 0.350502,	loss = 2.794231,	Top-1 err = 0.430225,	Top-5 err = 0.205859,	data_time = 0.050380,	train_time = 0.866275
[2019-08-23 08:38:58,506] TRAIN Iter 89720: lr = 0.350468,	loss = 2.733508,	Top-1 err = 0.431738,	Top-5 err = 0.205762,	data_time = 0.050526,	train_time = 0.371373
[2019-08-23 08:39:14,145] TRAIN Iter 89740: lr = 0.350435,	loss = 2.771264,	Top-1 err = 0.426367,	Top-5 err = 0.205078,	data_time = 0.050522,	train_time = 0.781969
[2019-08-23 08:39:29,374] TRAIN Iter 89760: lr = 0.350402,	loss = 2.713059,	Top-1 err = 0.426855,	Top-5 err = 0.203564,	data_time = 0.050687,	train_time = 0.761433
[2019-08-23 08:39:36,688] TRAIN Iter 89780: lr = 0.350368,	loss = 2.814403,	Top-1 err = 0.438086,	Top-5 err = 0.209326,	data_time = 0.050516,	train_time = 0.365661
[2019-08-23 08:39:53,908] TRAIN Iter 89800: lr = 0.350335,	loss = 2.739193,	Top-1 err = 0.430908,	Top-5 err = 0.204053,	data_time = 0.050488,	train_time = 0.861000
[2019-08-23 08:40:01,135] TRAIN Iter 89820: lr = 0.350302,	loss = 2.815326,	Top-1 err = 0.437842,	Top-5 err = 0.210498,	data_time = 0.050478,	train_time = 0.361327
[2019-08-23 08:40:15,230] TRAIN Iter 89840: lr = 0.350268,	loss = 2.788481,	Top-1 err = 0.436865,	Top-5 err = 0.205518,	data_time = 0.050599,	train_time = 0.704755
[2019-08-23 08:40:31,449] TRAIN Iter 89860: lr = 0.350235,	loss = 2.779536,	Top-1 err = 0.437061,	Top-5 err = 0.208887,	data_time = 0.050452,	train_time = 0.810894
[2019-08-23 08:40:38,589] TRAIN Iter 89880: lr = 0.350202,	loss = 2.897500,	Top-1 err = 0.431885,	Top-5 err = 0.204785,	data_time = 0.050457,	train_time = 0.356990
[2019-08-23 08:40:54,720] TRAIN Iter 89900: lr = 0.350168,	loss = 2.835576,	Top-1 err = 0.435986,	Top-5 err = 0.207275,	data_time = 0.050363,	train_time = 0.806541
[2019-08-23 08:41:10,649] TRAIN Iter 89920: lr = 0.350135,	loss = 2.734214,	Top-1 err = 0.431250,	Top-5 err = 0.202344,	data_time = 0.050493,	train_time = 0.796460
[2019-08-23 08:41:18,159] TRAIN Iter 89940: lr = 0.350102,	loss = 2.692573,	Top-1 err = 0.431494,	Top-5 err = 0.206348,	data_time = 0.050748,	train_time = 0.375476
[2019-08-23 08:41:34,085] TRAIN Iter 89960: lr = 0.350068,	loss = 2.820427,	Top-1 err = 0.431299,	Top-5 err = 0.207129,	data_time = 0.050606,	train_time = 0.796273
[2019-08-23 08:41:41,471] TRAIN Iter 89980: lr = 0.350035,	loss = 2.811037,	Top-1 err = 0.437500,	Top-5 err = 0.205664,	data_time = 0.050427,	train_time = 0.369319
[2019-08-23 08:41:58,667] TRAIN Iter 90000: lr = 0.350002,	loss = 2.754910,	Top-1 err = 0.434570,	Top-5 err = 0.208887,	data_time = 0.050435,	train_time = 0.859767
[2019-08-23 08:43:05,480] TEST Iter 90000: loss = 2.512459,	Top-1 err = 0.387800,	Top-5 err = 0.156380,	val_time = 66.685493
[2019-08-23 08:43:11,505] TRAIN Iter 90020: lr = 0.349968,	loss = 2.719961,	Top-1 err = 0.431885,	Top-5 err = 0.204785,	data_time = 0.049997,	train_time = 0.301236
[2019-08-23 08:43:17,630] TRAIN Iter 90040: lr = 0.349935,	loss = 2.786117,	Top-1 err = 0.434521,	Top-5 err = 0.206592,	data_time = 0.049977,	train_time = 0.306254
[2019-08-23 08:43:23,761] TRAIN Iter 90060: lr = 0.349902,	loss = 2.778873,	Top-1 err = 0.432373,	Top-5 err = 0.208154,	data_time = 0.049998,	train_time = 0.306509
[2019-08-23 08:43:30,599] TRAIN Iter 90080: lr = 0.349868,	loss = 3.231603,	Top-1 err = 0.436046,	Top-5 err = 0.205466,	data_time = 0.007029,	train_time = 0.341910
[2019-08-23 08:44:14,672] TRAIN Iter 90100: lr = 0.349835,	loss = 2.726063,	Top-1 err = 0.433203,	Top-5 err = 0.202686,	data_time = 0.050455,	train_time = 2.203606
[2019-08-23 08:44:27,791] TRAIN Iter 90120: lr = 0.349802,	loss = 2.802487,	Top-1 err = 0.428955,	Top-5 err = 0.206104,	data_time = 0.050462,	train_time = 0.655911
[2019-08-23 08:44:35,493] TRAIN Iter 90140: lr = 0.349768,	loss = 2.816706,	Top-1 err = 0.430176,	Top-5 err = 0.202197,	data_time = 0.165782,	train_time = 0.385109
[2019-08-23 08:44:47,986] TRAIN Iter 90160: lr = 0.349735,	loss = 2.700993,	Top-1 err = 0.426416,	Top-5 err = 0.198633,	data_time = 0.050716,	train_time = 0.624629
[2019-08-23 08:44:58,463] TRAIN Iter 90180: lr = 0.349702,	loss = 2.704804,	Top-1 err = 0.425781,	Top-5 err = 0.200488,	data_time = 2.578905,	train_time = 0.523851
[2019-08-23 08:45:07,787] TRAIN Iter 90200: lr = 0.349668,	loss = 2.699994,	Top-1 err = 0.426514,	Top-5 err = 0.202393,	data_time = 0.050519,	train_time = 0.466195
[2019-08-23 08:45:21,143] TRAIN Iter 90220: lr = 0.349635,	loss = 2.706223,	Top-1 err = 0.425684,	Top-5 err = 0.200195,	data_time = 0.050544,	train_time = 0.667758
[2019-08-23 08:45:28,618] TRAIN Iter 90240: lr = 0.349602,	loss = 2.770352,	Top-1 err = 0.421729,	Top-5 err = 0.197021,	data_time = 0.050637,	train_time = 0.373746
[2019-08-23 08:45:43,242] TRAIN Iter 90260: lr = 0.349568,	loss = 2.767609,	Top-1 err = 0.430664,	Top-5 err = 0.200049,	data_time = 0.050450,	train_time = 0.731197
[2019-08-23 08:45:56,454] TRAIN Iter 90280: lr = 0.349535,	loss = 2.801674,	Top-1 err = 0.432031,	Top-5 err = 0.207520,	data_time = 0.117381,	train_time = 0.660588
[2019-08-23 08:46:03,990] TRAIN Iter 90300: lr = 0.349502,	loss = 2.674005,	Top-1 err = 0.428223,	Top-5 err = 0.207373,	data_time = 0.050472,	train_time = 0.376790
[2019-08-23 08:46:17,564] TRAIN Iter 90320: lr = 0.349468,	loss = 2.716632,	Top-1 err = 0.430078,	Top-5 err = 0.204687,	data_time = 0.050642,	train_time = 0.678653
[2019-08-23 08:46:31,857] TRAIN Iter 90340: lr = 0.349435,	loss = 2.647871,	Top-1 err = 0.427490,	Top-5 err = 0.204150,	data_time = 4.279623,	train_time = 0.714650
[2019-08-23 08:46:39,075] TRAIN Iter 90360: lr = 0.349402,	loss = 2.783415,	Top-1 err = 0.421875,	Top-5 err = 0.200928,	data_time = 0.050586,	train_time = 0.360870
[2019-08-23 08:46:54,510] TRAIN Iter 90380: lr = 0.349368,	loss = 2.715417,	Top-1 err = 0.420898,	Top-5 err = 0.198047,	data_time = 0.050492,	train_time = 0.771738
[2019-08-23 08:47:01,997] TRAIN Iter 90400: lr = 0.349335,	loss = 2.761816,	Top-1 err = 0.429834,	Top-5 err = 0.201807,	data_time = 0.050443,	train_time = 0.374360
[2019-08-23 08:47:15,939] TRAIN Iter 90420: lr = 0.349302,	loss = 2.804377,	Top-1 err = 0.428418,	Top-5 err = 0.203467,	data_time = 0.050724,	train_time = 0.697092
[2019-08-23 08:47:32,829] TRAIN Iter 90440: lr = 0.349268,	loss = 2.841825,	Top-1 err = 0.428955,	Top-5 err = 0.204883,	data_time = 0.051032,	train_time = 0.844469
[2019-08-23 08:47:40,764] TRAIN Iter 90460: lr = 0.349235,	loss = 2.716123,	Top-1 err = 0.430322,	Top-5 err = 0.204736,	data_time = 0.050440,	train_time = 0.396753
[2019-08-23 08:47:52,370] TRAIN Iter 90480: lr = 0.349202,	loss = 2.775632,	Top-1 err = 0.423926,	Top-5 err = 0.199902,	data_time = 0.050764,	train_time = 0.580287
[2019-08-23 08:48:06,867] TRAIN Iter 90500: lr = 0.349168,	loss = 2.736974,	Top-1 err = 0.428125,	Top-5 err = 0.201172,	data_time = 6.504095,	train_time = 0.724823
[2019-08-23 08:48:14,357] TRAIN Iter 90520: lr = 0.349135,	loss = 2.772864,	Top-1 err = 0.435205,	Top-5 err = 0.210498,	data_time = 0.050744,	train_time = 0.374485
[2019-08-23 08:48:27,465] TRAIN Iter 90540: lr = 0.349102,	loss = 2.765937,	Top-1 err = 0.432471,	Top-5 err = 0.203369,	data_time = 0.050622,	train_time = 0.655397
[2019-08-23 08:48:34,557] TRAIN Iter 90560: lr = 0.349068,	loss = 2.835597,	Top-1 err = 0.432910,	Top-5 err = 0.206641,	data_time = 0.050853,	train_time = 0.354580
[2019-08-23 08:48:50,541] TRAIN Iter 90580: lr = 0.349035,	loss = 2.763231,	Top-1 err = 0.430469,	Top-5 err = 0.208301,	data_time = 0.050276,	train_time = 0.799152
[2019-08-23 08:49:06,751] TRAIN Iter 90600: lr = 0.349002,	loss = 2.728418,	Top-1 err = 0.426318,	Top-5 err = 0.200000,	data_time = 0.050303,	train_time = 0.810520
[2019-08-23 08:49:14,258] TRAIN Iter 90620: lr = 0.348968,	loss = 2.660350,	Top-1 err = 0.432617,	Top-5 err = 0.205420,	data_time = 0.050810,	train_time = 0.375350
[2019-08-23 08:49:27,988] TRAIN Iter 90640: lr = 0.348935,	loss = 2.713372,	Top-1 err = 0.433936,	Top-5 err = 0.210498,	data_time = 0.050483,	train_time = 0.686451
[2019-08-23 08:49:43,648] TRAIN Iter 90660: lr = 0.348902,	loss = 2.819209,	Top-1 err = 0.434131,	Top-5 err = 0.206445,	data_time = 8.156589,	train_time = 0.783013
[2019-08-23 08:49:51,095] TRAIN Iter 90680: lr = 0.348868,	loss = 2.847079,	Top-1 err = 0.428027,	Top-5 err = 0.201514,	data_time = 0.050588,	train_time = 0.372301
[2019-08-23 08:50:04,124] TRAIN Iter 90700: lr = 0.348835,	loss = 2.846690,	Top-1 err = 0.430762,	Top-5 err = 0.209717,	data_time = 0.050328,	train_time = 0.651465
[2019-08-23 08:50:11,374] TRAIN Iter 90720: lr = 0.348802,	loss = 2.727973,	Top-1 err = 0.429980,	Top-5 err = 0.208008,	data_time = 0.050851,	train_time = 0.362476
[2019-08-23 08:50:25,616] TRAIN Iter 90740: lr = 0.348768,	loss = 2.790246,	Top-1 err = 0.432520,	Top-5 err = 0.205273,	data_time = 0.050544,	train_time = 0.712066
[2019-08-23 08:50:38,859] TRAIN Iter 90760: lr = 0.348735,	loss = 2.726221,	Top-1 err = 0.427246,	Top-5 err = 0.205859,	data_time = 0.050538,	train_time = 0.662179
[2019-08-23 08:50:46,992] TRAIN Iter 90780: lr = 0.348702,	loss = 2.724183,	Top-1 err = 0.434961,	Top-5 err = 0.208203,	data_time = 0.050320,	train_time = 0.406615
[2019-08-23 08:51:01,144] TRAIN Iter 90800: lr = 0.348668,	loss = 2.794879,	Top-1 err = 0.428125,	Top-5 err = 0.201172,	data_time = 0.050680,	train_time = 0.707597
[2019-08-23 08:51:10,965] TRAIN Iter 90820: lr = 0.348635,	loss = 2.730639,	Top-1 err = 0.427979,	Top-5 err = 0.202393,	data_time = 0.701200,	train_time = 0.491009
[2019-08-23 08:51:23,112] TRAIN Iter 90840: lr = 0.348602,	loss = 2.694998,	Top-1 err = 0.427686,	Top-5 err = 0.202393,	data_time = 0.050250,	train_time = 0.607364
[2019-08-23 08:51:36,199] TRAIN Iter 90860: lr = 0.348568,	loss = 2.737374,	Top-1 err = 0.431104,	Top-5 err = 0.207324,	data_time = 0.144802,	train_time = 0.654312
[2019-08-23 08:51:44,495] TRAIN Iter 90880: lr = 0.348535,	loss = 2.647832,	Top-1 err = 0.429492,	Top-5 err = 0.204980,	data_time = 0.050516,	train_time = 0.414803
[2019-08-23 08:51:58,894] TRAIN Iter 90900: lr = 0.348502,	loss = 2.737524,	Top-1 err = 0.430615,	Top-5 err = 0.204736,	data_time = 0.050489,	train_time = 0.719938
[2019-08-23 08:52:12,741] TRAIN Iter 90920: lr = 0.348468,	loss = 2.655773,	Top-1 err = 0.430176,	Top-5 err = 0.207715,	data_time = 3.366768,	train_time = 0.692318
[2019-08-23 08:52:20,258] TRAIN Iter 90940: lr = 0.348435,	loss = 2.753096,	Top-1 err = 0.434570,	Top-5 err = 0.208594,	data_time = 0.050402,	train_time = 0.375859
[2019-08-23 08:52:35,872] TRAIN Iter 90960: lr = 0.348402,	loss = 2.816195,	Top-1 err = 0.429541,	Top-5 err = 0.206396,	data_time = 0.050403,	train_time = 0.780656
[2019-08-23 08:52:49,098] TRAIN Iter 90980: lr = 0.348368,	loss = 2.750473,	Top-1 err = 0.428125,	Top-5 err = 0.203564,	data_time = 0.130426,	train_time = 0.661285
[2019-08-23 08:52:58,060] TRAIN Iter 91000: lr = 0.348335,	loss = 2.631647,	Top-1 err = 0.431055,	Top-5 err = 0.208350,	data_time = 0.050439,	train_time = 0.448118
[2019-08-23 08:53:12,467] TRAIN Iter 91020: lr = 0.348302,	loss = 2.812552,	Top-1 err = 0.432910,	Top-5 err = 0.203125,	data_time = 0.050527,	train_time = 0.720312
[2019-08-23 08:53:20,692] TRAIN Iter 91040: lr = 0.348268,	loss = 2.713581,	Top-1 err = 0.434375,	Top-5 err = 0.207129,	data_time = 0.050445,	train_time = 0.411267
[2019-08-23 08:53:35,162] TRAIN Iter 91060: lr = 0.348235,	loss = 2.729152,	Top-1 err = 0.433203,	Top-5 err = 0.202148,	data_time = 0.050679,	train_time = 0.723495
[2019-08-23 08:53:48,698] TRAIN Iter 91080: lr = 0.348202,	loss = 2.678863,	Top-1 err = 0.435010,	Top-5 err = 0.207373,	data_time = 1.963999,	train_time = 0.676790
[2019-08-23 08:53:57,546] TRAIN Iter 91100: lr = 0.348168,	loss = 2.799343,	Top-1 err = 0.425781,	Top-5 err = 0.201367,	data_time = 0.050478,	train_time = 0.442353
[2019-08-23 08:54:13,971] TRAIN Iter 91120: lr = 0.348135,	loss = 2.715282,	Top-1 err = 0.429053,	Top-5 err = 0.204980,	data_time = 0.050580,	train_time = 0.821248
[2019-08-23 08:54:26,799] TRAIN Iter 91140: lr = 0.348102,	loss = 2.842253,	Top-1 err = 0.433887,	Top-5 err = 0.202393,	data_time = 0.225268,	train_time = 0.641392
[2019-08-23 08:54:35,968] TRAIN Iter 91160: lr = 0.348068,	loss = 2.793366,	Top-1 err = 0.431885,	Top-5 err = 0.206689,	data_time = 0.050625,	train_time = 0.458409
[2019-08-23 08:54:51,409] TRAIN Iter 91180: lr = 0.348035,	loss = 2.771546,	Top-1 err = 0.427002,	Top-5 err = 0.205566,	data_time = 0.169517,	train_time = 0.772030
[2019-08-23 08:54:58,735] TRAIN Iter 91200: lr = 0.348002,	loss = 2.761147,	Top-1 err = 0.426758,	Top-5 err = 0.206104,	data_time = 0.050587,	train_time = 0.366333
[2019-08-23 08:55:13,319] TRAIN Iter 91220: lr = 0.347968,	loss = 2.682609,	Top-1 err = 0.431348,	Top-5 err = 0.209229,	data_time = 0.050930,	train_time = 0.729160
[2019-08-23 08:55:26,175] TRAIN Iter 91240: lr = 0.347935,	loss = 2.764868,	Top-1 err = 0.428809,	Top-5 err = 0.204883,	data_time = 0.050541,	train_time = 0.642769
[2019-08-23 08:55:35,475] TRAIN Iter 91260: lr = 0.347902,	loss = 2.776211,	Top-1 err = 0.436865,	Top-5 err = 0.209424,	data_time = 0.050183,	train_time = 0.465003
[2019-08-23 08:55:53,069] TRAIN Iter 91280: lr = 0.347868,	loss = 2.696356,	Top-1 err = 0.430029,	Top-5 err = 0.203760,	data_time = 0.050017,	train_time = 0.879680
[2019-08-23 08:56:07,662] TRAIN Iter 91300: lr = 0.347835,	loss = 2.814656,	Top-1 err = 0.435645,	Top-5 err = 0.208545,	data_time = 0.049987,	train_time = 0.729659
[2019-08-23 08:56:15,210] TRAIN Iter 91320: lr = 0.347802,	loss = 2.708760,	Top-1 err = 0.435937,	Top-5 err = 0.205518,	data_time = 0.049922,	train_time = 0.377372
[2019-08-23 08:56:59,209] TRAIN Iter 91340: lr = 0.347768,	loss = 2.816048,	Top-1 err = 0.431915,	Top-5 err = 0.205499,	data_time = 2.018917,	train_time = 2.199919
[2019-08-23 08:57:09,091] TRAIN Iter 91360: lr = 0.347735,	loss = 2.768754,	Top-1 err = 0.430273,	Top-5 err = 0.206543,	data_time = 0.050511,	train_time = 0.494112
[2019-08-23 08:57:22,301] TRAIN Iter 91380: lr = 0.347702,	loss = 2.678224,	Top-1 err = 0.427930,	Top-5 err = 0.199561,	data_time = 0.050258,	train_time = 0.660471
[2019-08-23 08:57:35,087] TRAIN Iter 91400: lr = 0.347668,	loss = 2.703261,	Top-1 err = 0.425586,	Top-5 err = 0.198437,	data_time = 0.050853,	train_time = 0.639262
[2019-08-23 08:57:43,125] TRAIN Iter 91420: lr = 0.347635,	loss = 2.664476,	Top-1 err = 0.427783,	Top-5 err = 0.194580,	data_time = 0.148172,	train_time = 0.401922
[2019-08-23 08:57:55,866] TRAIN Iter 91440: lr = 0.347602,	loss = 2.746873,	Top-1 err = 0.426123,	Top-5 err = 0.200391,	data_time = 0.050732,	train_time = 0.637001
[2019-08-23 08:58:03,259] TRAIN Iter 91460: lr = 0.347568,	loss = 2.913679,	Top-1 err = 0.436816,	Top-5 err = 0.207178,	data_time = 0.143156,	train_time = 0.369651
[2019-08-23 08:58:18,779] TRAIN Iter 91480: lr = 0.347535,	loss = 2.640451,	Top-1 err = 0.424707,	Top-5 err = 0.200977,	data_time = 0.050421,	train_time = 0.775974
[2019-08-23 08:58:29,763] TRAIN Iter 91500: lr = 0.347502,	loss = 2.775596,	Top-1 err = 0.427002,	Top-5 err = 0.202295,	data_time = 0.050633,	train_time = 0.549194
[2019-08-23 08:58:37,816] TRAIN Iter 91520: lr = 0.347468,	loss = 2.740874,	Top-1 err = 0.422559,	Top-5 err = 0.200439,	data_time = 0.163041,	train_time = 0.402624
[2019-08-23 08:58:50,501] TRAIN Iter 91540: lr = 0.347435,	loss = 2.813810,	Top-1 err = 0.424561,	Top-5 err = 0.199854,	data_time = 0.050415,	train_time = 0.634258
[2019-08-23 08:59:03,666] TRAIN Iter 91560: lr = 0.347402,	loss = 2.742552,	Top-1 err = 0.430225,	Top-5 err = 0.202734,	data_time = 0.050554,	train_time = 0.658231
[2019-08-23 08:59:11,103] TRAIN Iter 91580: lr = 0.347368,	loss = 2.809235,	Top-1 err = 0.431494,	Top-5 err = 0.205762,	data_time = 0.050428,	train_time = 0.371800
[2019-08-23 08:59:27,376] TRAIN Iter 91600: lr = 0.347335,	loss = 2.800668,	Top-1 err = 0.428467,	Top-5 err = 0.208838,	data_time = 0.050370,	train_time = 0.813642
[2019-08-23 08:59:35,884] TRAIN Iter 91620: lr = 0.347302,	loss = 2.763085,	Top-1 err = 0.427881,	Top-5 err = 0.203564,	data_time = 0.050344,	train_time = 0.425381
[2019-08-23 08:59:47,676] TRAIN Iter 91640: lr = 0.347268,	loss = 2.748687,	Top-1 err = 0.433105,	Top-5 err = 0.203320,	data_time = 0.207962,	train_time = 0.589598
[2019-08-23 09:00:01,001] TRAIN Iter 91660: lr = 0.347235,	loss = 2.735879,	Top-1 err = 0.426318,	Top-5 err = 0.204199,	data_time = 0.050806,	train_time = 0.666230
[2019-08-23 09:00:09,289] TRAIN Iter 91680: lr = 0.347202,	loss = 2.728013,	Top-1 err = 0.418408,	Top-5 err = 0.196875,	data_time = 0.051067,	train_time = 0.414388
[2019-08-23 09:00:21,441] TRAIN Iter 91700: lr = 0.347168,	loss = 2.799988,	Top-1 err = 0.421729,	Top-5 err = 0.202539,	data_time = 0.050416,	train_time = 0.607597
[2019-08-23 09:00:35,403] TRAIN Iter 91720: lr = 0.347135,	loss = 2.749588,	Top-1 err = 0.427588,	Top-5 err = 0.200537,	data_time = 0.050313,	train_time = 0.698089
[2019-08-23 09:00:43,237] TRAIN Iter 91740: lr = 0.347102,	loss = 2.800762,	Top-1 err = 0.432666,	Top-5 err = 0.201660,	data_time = 0.050442,	train_time = 0.391668
[2019-08-23 09:00:56,832] TRAIN Iter 91760: lr = 0.347068,	loss = 2.753108,	Top-1 err = 0.430811,	Top-5 err = 0.203711,	data_time = 0.050560,	train_time = 0.679736
[2019-08-23 09:01:05,228] TRAIN Iter 91780: lr = 0.347035,	loss = 2.733963,	Top-1 err = 0.421777,	Top-5 err = 0.200732,	data_time = 0.209538,	train_time = 0.419772
[2019-08-23 09:01:18,546] TRAIN Iter 91800: lr = 0.347002,	loss = 2.739434,	Top-1 err = 0.432666,	Top-5 err = 0.206543,	data_time = 0.050713,	train_time = 0.665917
[2019-08-23 09:01:33,086] TRAIN Iter 91820: lr = 0.346968,	loss = 2.728223,	Top-1 err = 0.427979,	Top-5 err = 0.205322,	data_time = 0.050425,	train_time = 0.726976
[2019-08-23 09:01:40,930] TRAIN Iter 91840: lr = 0.346935,	loss = 2.744663,	Top-1 err = 0.434277,	Top-5 err = 0.209814,	data_time = 0.050428,	train_time = 0.392187
[2019-08-23 09:01:54,428] TRAIN Iter 91860: lr = 0.346902,	loss = 2.677042,	Top-1 err = 0.427246,	Top-5 err = 0.204541,	data_time = 0.050431,	train_time = 0.674894
[2019-08-23 09:02:10,441] TRAIN Iter 91880: lr = 0.346868,	loss = 2.737881,	Top-1 err = 0.431982,	Top-5 err = 0.209521,	data_time = 0.050637,	train_time = 0.800644
[2019-08-23 09:02:18,261] TRAIN Iter 91900: lr = 0.346835,	loss = 2.738336,	Top-1 err = 0.426416,	Top-5 err = 0.202002,	data_time = 0.051085,	train_time = 0.390972
[2019-08-23 09:02:31,931] TRAIN Iter 91920: lr = 0.346802,	loss = 2.791909,	Top-1 err = 0.426270,	Top-5 err = 0.201172,	data_time = 0.050615,	train_time = 0.683486
[2019-08-23 09:02:40,086] TRAIN Iter 91940: lr = 0.346768,	loss = 2.739844,	Top-1 err = 0.438721,	Top-5 err = 0.208398,	data_time = 0.162575,	train_time = 0.407733
[2019-08-23 09:02:55,037] TRAIN Iter 91960: lr = 0.346735,	loss = 2.704321,	Top-1 err = 0.432422,	Top-5 err = 0.204590,	data_time = 0.050478,	train_time = 0.747543
[2019-08-23 09:03:08,239] TRAIN Iter 91980: lr = 0.346702,	loss = 2.808294,	Top-1 err = 0.423975,	Top-5 err = 0.202051,	data_time = 0.050473,	train_time = 0.660089
[2019-08-23 09:03:16,005] TRAIN Iter 92000: lr = 0.346668,	loss = 2.754582,	Top-1 err = 0.428564,	Top-5 err = 0.204639,	data_time = 0.141357,	train_time = 0.388269
[2019-08-23 09:03:29,439] TRAIN Iter 92020: lr = 0.346635,	loss = 2.739181,	Top-1 err = 0.438574,	Top-5 err = 0.209570,	data_time = 0.050365,	train_time = 0.671698
[2019-08-23 09:03:44,585] TRAIN Iter 92040: lr = 0.346602,	loss = 2.779695,	Top-1 err = 0.428320,	Top-5 err = 0.205811,	data_time = 0.050496,	train_time = 0.757276
[2019-08-23 09:03:51,807] TRAIN Iter 92060: lr = 0.346568,	loss = 2.729460,	Top-1 err = 0.428174,	Top-5 err = 0.208252,	data_time = 0.050864,	train_time = 0.361076
[2019-08-23 09:04:07,601] TRAIN Iter 92080: lr = 0.346535,	loss = 2.664225,	Top-1 err = 0.435010,	Top-5 err = 0.207324,	data_time = 0.050557,	train_time = 0.789690
[2019-08-23 09:04:15,779] TRAIN Iter 92100: lr = 0.346502,	loss = 2.805673,	Top-1 err = 0.431641,	Top-5 err = 0.204687,	data_time = 0.050230,	train_time = 0.408894
[2019-08-23 09:04:28,040] TRAIN Iter 92120: lr = 0.346468,	loss = 2.861732,	Top-1 err = 0.440625,	Top-5 err = 0.210693,	data_time = 0.050378,	train_time = 0.613033
[2019-08-23 09:04:44,086] TRAIN Iter 92140: lr = 0.346435,	loss = 2.883894,	Top-1 err = 0.434570,	Top-5 err = 0.207813,	data_time = 0.050798,	train_time = 0.802263
[2019-08-23 09:04:51,478] TRAIN Iter 92160: lr = 0.346402,	loss = 2.748490,	Top-1 err = 0.432617,	Top-5 err = 0.207617,	data_time = 0.050618,	train_time = 0.369625
[2019-08-23 09:05:07,101] TRAIN Iter 92180: lr = 0.346368,	loss = 2.733651,	Top-1 err = 0.433301,	Top-5 err = 0.206738,	data_time = 0.050844,	train_time = 0.781134
[2019-08-23 09:05:22,442] TRAIN Iter 92200: lr = 0.346335,	loss = 2.863312,	Top-1 err = 0.432666,	Top-5 err = 0.203857,	data_time = 0.050466,	train_time = 0.767000
[2019-08-23 09:05:29,834] TRAIN Iter 92220: lr = 0.346302,	loss = 2.761893,	Top-1 err = 0.436523,	Top-5 err = 0.211182,	data_time = 0.050792,	train_time = 0.369606
[2019-08-23 09:05:46,400] TRAIN Iter 92240: lr = 0.346268,	loss = 2.692960,	Top-1 err = 0.427783,	Top-5 err = 0.205029,	data_time = 0.050543,	train_time = 0.828304
[2019-08-23 09:05:54,396] TRAIN Iter 92260: lr = 0.346235,	loss = 2.771902,	Top-1 err = 0.431689,	Top-5 err = 0.206934,	data_time = 0.050301,	train_time = 0.399766
[2019-08-23 09:06:08,229] TRAIN Iter 92280: lr = 0.346202,	loss = 2.694688,	Top-1 err = 0.433984,	Top-5 err = 0.207422,	data_time = 0.109291,	train_time = 0.691632
[2019-08-23 09:06:24,689] TRAIN Iter 92300: lr = 0.346168,	loss = 2.738725,	Top-1 err = 0.432129,	Top-5 err = 0.205225,	data_time = 0.050381,	train_time = 0.823013
[2019-08-23 09:06:32,050] TRAIN Iter 92320: lr = 0.346135,	loss = 2.708564,	Top-1 err = 0.428906,	Top-5 err = 0.200537,	data_time = 0.050459,	train_time = 0.368042
[2019-08-23 09:06:47,191] TRAIN Iter 92340: lr = 0.346102,	loss = 2.839895,	Top-1 err = 0.433203,	Top-5 err = 0.210986,	data_time = 0.050512,	train_time = 0.757027
[2019-08-23 09:07:04,425] TRAIN Iter 92360: lr = 0.346068,	loss = 2.712900,	Top-1 err = 0.430078,	Top-5 err = 0.205713,	data_time = 0.050501,	train_time = 0.861698
[2019-08-23 09:07:11,304] TRAIN Iter 92380: lr = 0.346035,	loss = 2.823218,	Top-1 err = 0.431934,	Top-5 err = 0.205713,	data_time = 0.050169,	train_time = 0.343923
[2019-08-23 09:07:27,737] TRAIN Iter 92400: lr = 0.346002,	loss = 2.822805,	Top-1 err = 0.437207,	Top-5 err = 0.204980,	data_time = 0.050332,	train_time = 0.821608
[2019-08-23 09:07:35,398] TRAIN Iter 92420: lr = 0.345968,	loss = 2.743339,	Top-1 err = 0.432031,	Top-5 err = 0.203809,	data_time = 0.050330,	train_time = 0.383050
[2019-08-23 09:07:50,197] TRAIN Iter 92440: lr = 0.345935,	loss = 2.718199,	Top-1 err = 0.427637,	Top-5 err = 0.203711,	data_time = 0.050423,	train_time = 0.739945
[2019-08-23 09:08:08,273] TRAIN Iter 92460: lr = 0.345902,	loss = 2.792540,	Top-1 err = 0.433105,	Top-5 err = 0.207227,	data_time = 0.050986,	train_time = 0.903782
[2019-08-23 09:08:15,561] TRAIN Iter 92480: lr = 0.345868,	loss = 2.741355,	Top-1 err = 0.434131,	Top-5 err = 0.204492,	data_time = 0.050168,	train_time = 0.364404
[2019-08-23 09:08:32,408] TRAIN Iter 92500: lr = 0.345835,	loss = 2.798198,	Top-1 err = 0.440234,	Top-5 err = 0.209033,	data_time = 0.050614,	train_time = 0.842296
[2019-08-23 09:08:49,659] TRAIN Iter 92520: lr = 0.345802,	loss = 2.717335,	Top-1 err = 0.425342,	Top-5 err = 0.200684,	data_time = 0.066231,	train_time = 0.862563
[2019-08-23 09:08:56,378] TRAIN Iter 92540: lr = 0.345768,	loss = 2.770038,	Top-1 err = 0.431201,	Top-5 err = 0.204980,	data_time = 0.050086,	train_time = 0.335917
[2019-08-23 09:09:13,187] TRAIN Iter 92560: lr = 0.345735,	loss = 2.842479,	Top-1 err = 0.422217,	Top-5 err = 0.202490,	data_time = 0.049912,	train_time = 0.840442
[2019-08-23 09:09:19,439] TRAIN Iter 92580: lr = 0.345702,	loss = 2.866296,	Top-1 err = 0.434961,	Top-5 err = 0.206348,	data_time = 0.049889,	train_time = 0.312602
[2019-08-23 09:10:11,555] TRAIN Iter 92600: lr = 0.345668,	loss = 2.728345,	Top-1 err = 0.441586,	Top-5 err = 0.210461,	data_time = 0.050436,	train_time = 2.605797
[2019-08-23 09:10:21,026] TRAIN Iter 92620: lr = 0.345635,	loss = 2.763101,	Top-1 err = 0.431641,	Top-5 err = 0.203320,	data_time = 0.050880,	train_time = 0.473515
[2019-08-23 09:10:30,855] TRAIN Iter 92640: lr = 0.345602,	loss = 2.749732,	Top-1 err = 0.419385,	Top-5 err = 0.194092,	data_time = 0.163663,	train_time = 0.491422
[2019-08-23 09:10:43,687] TRAIN Iter 92660: lr = 0.345568,	loss = 2.652766,	Top-1 err = 0.420898,	Top-5 err = 0.197852,	data_time = 0.051031,	train_time = 0.641598
[2019-08-23 09:10:51,293] TRAIN Iter 92680: lr = 0.345535,	loss = 2.699669,	Top-1 err = 0.425293,	Top-5 err = 0.201221,	data_time = 0.050708,	train_time = 0.380264
[2019-08-23 09:11:03,648] TRAIN Iter 92700: lr = 0.345502,	loss = 2.814245,	Top-1 err = 0.428271,	Top-5 err = 0.202783,	data_time = 0.050855,	train_time = 0.617770
[2019-08-23 09:11:17,414] TRAIN Iter 92720: lr = 0.345468,	loss = 2.799855,	Top-1 err = 0.430029,	Top-5 err = 0.200439,	data_time = 0.131848,	train_time = 0.688277
[2019-08-23 09:11:25,572] TRAIN Iter 92740: lr = 0.345435,	loss = 2.758461,	Top-1 err = 0.427148,	Top-5 err = 0.204639,	data_time = 0.050917,	train_time = 0.407890
[2019-08-23 09:11:39,147] TRAIN Iter 92760: lr = 0.345402,	loss = 2.768548,	Top-1 err = 0.423291,	Top-5 err = 0.201416,	data_time = 0.050723,	train_time = 0.678719
[2019-08-23 09:11:50,689] TRAIN Iter 92780: lr = 0.345368,	loss = 2.751857,	Top-1 err = 0.432324,	Top-5 err = 0.204687,	data_time = 0.050334,	train_time = 0.577092
[2019-08-23 09:11:58,993] TRAIN Iter 92800: lr = 0.345335,	loss = 2.827516,	Top-1 err = 0.428369,	Top-5 err = 0.200879,	data_time = 0.050328,	train_time = 0.415175
[2019-08-23 09:12:13,756] TRAIN Iter 92820: lr = 0.345302,	loss = 2.762593,	Top-1 err = 0.433301,	Top-5 err = 0.205664,	data_time = 0.050442,	train_time = 0.738139
[2019-08-23 09:12:21,825] TRAIN Iter 92840: lr = 0.345268,	loss = 2.780837,	Top-1 err = 0.422217,	Top-5 err = 0.198291,	data_time = 0.106786,	train_time = 0.403415
[2019-08-23 09:12:32,915] TRAIN Iter 92860: lr = 0.345235,	loss = 2.700932,	Top-1 err = 0.423779,	Top-5 err = 0.196533,	data_time = 0.050947,	train_time = 0.554490
[2019-08-23 09:12:46,867] TRAIN Iter 92880: lr = 0.345202,	loss = 2.758915,	Top-1 err = 0.435742,	Top-5 err = 0.204199,	data_time = 0.094541,	train_time = 0.697588
[2019-08-23 09:12:54,732] TRAIN Iter 92900: lr = 0.345168,	loss = 2.629306,	Top-1 err = 0.423877,	Top-5 err = 0.203320,	data_time = 0.050606,	train_time = 0.393229
[2019-08-23 09:13:06,436] TRAIN Iter 92920: lr = 0.345135,	loss = 2.914992,	Top-1 err = 0.431250,	Top-5 err = 0.203564,	data_time = 0.050731,	train_time = 0.585202
[2019-08-23 09:13:19,560] TRAIN Iter 92940: lr = 0.345102,	loss = 2.786718,	Top-1 err = 0.422363,	Top-5 err = 0.197949,	data_time = 0.050963,	train_time = 0.656152
[2019-08-23 09:13:28,649] TRAIN Iter 92960: lr = 0.345068,	loss = 2.766887,	Top-1 err = 0.425439,	Top-5 err = 0.203516,	data_time = 0.050890,	train_time = 0.454452
[2019-08-23 09:13:43,249] TRAIN Iter 92980: lr = 0.345035,	loss = 2.803984,	Top-1 err = 0.426221,	Top-5 err = 0.199805,	data_time = 0.050644,	train_time = 0.730004
[2019-08-23 09:13:51,046] TRAIN Iter 93000: lr = 0.345002,	loss = 2.724317,	Top-1 err = 0.429688,	Top-5 err = 0.201563,	data_time = 0.050728,	train_time = 0.389810
[2019-08-23 09:14:05,007] TRAIN Iter 93020: lr = 0.344968,	loss = 2.795074,	Top-1 err = 0.430518,	Top-5 err = 0.205078,	data_time = 0.050672,	train_time = 0.698068
[2019-08-23 09:14:17,142] TRAIN Iter 93040: lr = 0.344935,	loss = 2.638861,	Top-1 err = 0.430078,	Top-5 err = 0.204395,	data_time = 0.115505,	train_time = 0.606733
[2019-08-23 09:14:24,485] TRAIN Iter 93060: lr = 0.344902,	loss = 2.679375,	Top-1 err = 0.426074,	Top-5 err = 0.202441,	data_time = 0.050258,	train_time = 0.367120
[2019-08-23 09:14:38,603] TRAIN Iter 93080: lr = 0.344868,	loss = 2.722054,	Top-1 err = 0.430957,	Top-5 err = 0.206787,	data_time = 0.050817,	train_time = 0.705892
[2019-08-23 09:14:51,880] TRAIN Iter 93100: lr = 0.344835,	loss = 2.774771,	Top-1 err = 0.422168,	Top-5 err = 0.197998,	data_time = 0.050618,	train_time = 0.663818
[2019-08-23 09:15:01,620] TRAIN Iter 93120: lr = 0.344802,	loss = 2.806803,	Top-1 err = 0.422705,	Top-5 err = 0.199561,	data_time = 0.050822,	train_time = 0.487008
[2019-08-23 09:15:15,721] TRAIN Iter 93140: lr = 0.344768,	loss = 2.774116,	Top-1 err = 0.428809,	Top-5 err = 0.198437,	data_time = 0.050335,	train_time = 0.705026
[2019-08-23 09:15:23,453] TRAIN Iter 93160: lr = 0.344735,	loss = 2.691504,	Top-1 err = 0.428711,	Top-5 err = 0.204297,	data_time = 0.050415,	train_time = 0.386580
[2019-08-23 09:15:37,053] TRAIN Iter 93180: lr = 0.344702,	loss = 2.851128,	Top-1 err = 0.428857,	Top-5 err = 0.206641,	data_time = 0.050480,	train_time = 0.679968
[2019-08-23 09:15:52,532] TRAIN Iter 93200: lr = 0.344668,	loss = 2.741693,	Top-1 err = 0.433105,	Top-5 err = 0.201025,	data_time = 0.050540,	train_time = 0.773943
[2019-08-23 09:15:59,684] TRAIN Iter 93220: lr = 0.344635,	loss = 2.753395,	Top-1 err = 0.425000,	Top-5 err = 0.205664,	data_time = 0.050720,	train_time = 0.357590
[2019-08-23 09:16:14,153] TRAIN Iter 93240: lr = 0.344602,	loss = 2.771713,	Top-1 err = 0.421387,	Top-5 err = 0.203564,	data_time = 0.050417,	train_time = 0.723448
[2019-08-23 09:16:27,188] TRAIN Iter 93260: lr = 0.344568,	loss = 2.862113,	Top-1 err = 0.436230,	Top-5 err = 0.211133,	data_time = 0.134138,	train_time = 0.651716
[2019-08-23 09:16:36,673] TRAIN Iter 93280: lr = 0.344535,	loss = 2.716968,	Top-1 err = 0.429053,	Top-5 err = 0.204541,	data_time = 0.050598,	train_time = 0.474241
[2019-08-23 09:16:51,140] TRAIN Iter 93300: lr = 0.344502,	loss = 2.653895,	Top-1 err = 0.430811,	Top-5 err = 0.206299,	data_time = 0.050907,	train_time = 0.723329
[2019-08-23 09:16:58,818] TRAIN Iter 93320: lr = 0.344468,	loss = 2.686508,	Top-1 err = 0.422559,	Top-5 err = 0.196582,	data_time = 0.050613,	train_time = 0.383888
[2019-08-23 09:17:13,777] TRAIN Iter 93340: lr = 0.344435,	loss = 2.768051,	Top-1 err = 0.428271,	Top-5 err = 0.205664,	data_time = 0.050459,	train_time = 0.747973
[2019-08-23 09:17:28,460] TRAIN Iter 93360: lr = 0.344402,	loss = 2.790210,	Top-1 err = 0.430322,	Top-5 err = 0.202588,	data_time = 0.050499,	train_time = 0.734120
[2019-08-23 09:17:35,870] TRAIN Iter 93380: lr = 0.344368,	loss = 2.676497,	Top-1 err = 0.429834,	Top-5 err = 0.202490,	data_time = 0.050742,	train_time = 0.370461
[2019-08-23 09:17:50,697] TRAIN Iter 93400: lr = 0.344335,	loss = 2.866795,	Top-1 err = 0.429053,	Top-5 err = 0.202637,	data_time = 0.050346,	train_time = 0.741348
[2019-08-23 09:18:01,539] TRAIN Iter 93420: lr = 0.344302,	loss = 2.735712,	Top-1 err = 0.433301,	Top-5 err = 0.206592,	data_time = 0.050775,	train_time = 0.542100
[2019-08-23 09:18:14,573] TRAIN Iter 93440: lr = 0.344268,	loss = 2.691499,	Top-1 err = 0.431836,	Top-5 err = 0.201221,	data_time = 0.050680,	train_time = 0.651671
[2019-08-23 09:18:28,431] TRAIN Iter 93460: lr = 0.344235,	loss = 2.752469,	Top-1 err = 0.428906,	Top-5 err = 0.204834,	data_time = 0.050848,	train_time = 0.692895
[2019-08-23 09:18:35,870] TRAIN Iter 93480: lr = 0.344202,	loss = 2.727542,	Top-1 err = 0.433252,	Top-5 err = 0.211182,	data_time = 0.050374,	train_time = 0.371928
[2019-08-23 09:18:51,787] TRAIN Iter 93500: lr = 0.344168,	loss = 2.768088,	Top-1 err = 0.431152,	Top-5 err = 0.205859,	data_time = 0.050720,	train_time = 0.795871
[2019-08-23 09:19:06,045] TRAIN Iter 93520: lr = 0.344135,	loss = 2.849863,	Top-1 err = 0.433740,	Top-5 err = 0.208936,	data_time = 0.050105,	train_time = 0.712872
[2019-08-23 09:19:13,475] TRAIN Iter 93540: lr = 0.344102,	loss = 2.760149,	Top-1 err = 0.432275,	Top-5 err = 0.209326,	data_time = 0.050478,	train_time = 0.371492
[2019-08-23 09:19:28,613] TRAIN Iter 93560: lr = 0.344068,	loss = 2.796235,	Top-1 err = 0.439307,	Top-5 err = 0.207324,	data_time = 0.050313,	train_time = 0.756884
[2019-08-23 09:19:38,021] TRAIN Iter 93580: lr = 0.344035,	loss = 2.737552,	Top-1 err = 0.429688,	Top-5 err = 0.203174,	data_time = 0.050464,	train_time = 0.470396
[2019-08-23 09:19:51,421] TRAIN Iter 93600: lr = 0.344002,	loss = 2.743871,	Top-1 err = 0.433398,	Top-5 err = 0.204639,	data_time = 0.122093,	train_time = 0.669992
[2019-08-23 09:20:05,937] TRAIN Iter 93620: lr = 0.343968,	loss = 2.819301,	Top-1 err = 0.431445,	Top-5 err = 0.207715,	data_time = 0.050784,	train_time = 0.725764
[2019-08-23 09:20:13,467] TRAIN Iter 93640: lr = 0.343935,	loss = 2.741297,	Top-1 err = 0.428662,	Top-5 err = 0.204883,	data_time = 0.050636,	train_time = 0.376492
[2019-08-23 09:20:28,455] TRAIN Iter 93660: lr = 0.343902,	loss = 2.706476,	Top-1 err = 0.430762,	Top-5 err = 0.206445,	data_time = 0.050314,	train_time = 0.749384
[2019-08-23 09:20:44,690] TRAIN Iter 93680: lr = 0.343868,	loss = 2.712501,	Top-1 err = 0.429150,	Top-5 err = 0.203369,	data_time = 0.050449,	train_time = 0.811706
[2019-08-23 09:20:51,976] TRAIN Iter 93700: lr = 0.343835,	loss = 2.786532,	Top-1 err = 0.439551,	Top-5 err = 0.205322,	data_time = 0.050685,	train_time = 0.364319
[2019-08-23 09:21:07,227] TRAIN Iter 93720: lr = 0.343802,	loss = 2.756239,	Top-1 err = 0.432568,	Top-5 err = 0.206787,	data_time = 0.050319,	train_time = 0.762553
[2019-08-23 09:21:15,657] TRAIN Iter 93740: lr = 0.343768,	loss = 2.600968,	Top-1 err = 0.429053,	Top-5 err = 0.202393,	data_time = 0.050360,	train_time = 0.421482
[2019-08-23 09:21:32,139] TRAIN Iter 93760: lr = 0.343735,	loss = 2.870891,	Top-1 err = 0.435742,	Top-5 err = 0.207178,	data_time = 0.050387,	train_time = 0.824062
[2019-08-23 09:21:47,503] TRAIN Iter 93780: lr = 0.343702,	loss = 2.765084,	Top-1 err = 0.431348,	Top-5 err = 0.208105,	data_time = 0.050034,	train_time = 0.768193
[2019-08-23 09:21:54,850] TRAIN Iter 93800: lr = 0.343668,	loss = 2.810251,	Top-1 err = 0.426855,	Top-5 err = 0.204687,	data_time = 0.050032,	train_time = 0.367334
[2019-08-23 09:22:09,759] TRAIN Iter 93820: lr = 0.343635,	loss = 2.756356,	Top-1 err = 0.432373,	Top-5 err = 0.203906,	data_time = 0.049882,	train_time = 0.745459
[2019-08-23 09:22:55,401] TRAIN Iter 93840: lr = 0.343602,	loss = 2.692178,	Top-1 err = 0.435191,	Top-5 err = 0.205091,	data_time = 2.186089,	train_time = 2.282063
[2019-08-23 09:23:03,031] TRAIN Iter 93860: lr = 0.343568,	loss = 2.833045,	Top-1 err = 0.433691,	Top-5 err = 0.210645,	data_time = 0.127413,	train_time = 0.381484
[2019-08-23 09:23:15,880] TRAIN Iter 93880: lr = 0.343535,	loss = 2.834630,	Top-1 err = 0.420508,	Top-5 err = 0.200146,	data_time = 0.050171,	train_time = 0.642453
[2019-08-23 09:23:24,103] TRAIN Iter 93900: lr = 0.343502,	loss = 2.730097,	Top-1 err = 0.427148,	Top-5 err = 0.200732,	data_time = 0.114239,	train_time = 0.411124
[2019-08-23 09:23:35,263] TRAIN Iter 93920: lr = 0.343468,	loss = 2.716516,	Top-1 err = 0.425391,	Top-5 err = 0.197559,	data_time = 0.050483,	train_time = 0.557984
[2019-08-23 09:23:49,015] TRAIN Iter 93940: lr = 0.343435,	loss = 2.725515,	Top-1 err = 0.423145,	Top-5 err = 0.201416,	data_time = 0.050479,	train_time = 0.687561
[2019-08-23 09:23:57,088] TRAIN Iter 93960: lr = 0.343402,	loss = 2.772890,	Top-1 err = 0.422412,	Top-5 err = 0.194482,	data_time = 0.050617,	train_time = 0.403660
[2019-08-23 09:24:10,256] TRAIN Iter 93980: lr = 0.343368,	loss = 2.705971,	Top-1 err = 0.423633,	Top-5 err = 0.200000,	data_time = 0.050593,	train_time = 0.658404
[2019-08-23 09:24:23,808] TRAIN Iter 94000: lr = 0.343335,	loss = 2.731954,	Top-1 err = 0.425049,	Top-5 err = 0.198682,	data_time = 5.292794,	train_time = 0.677541
[2019-08-23 09:24:31,546] TRAIN Iter 94020: lr = 0.343302,	loss = 2.703236,	Top-1 err = 0.419971,	Top-5 err = 0.195459,	data_time = 0.050818,	train_time = 0.386897
[2019-08-23 09:24:44,895] TRAIN Iter 94040: lr = 0.343268,	loss = 2.658907,	Top-1 err = 0.430762,	Top-5 err = 0.203613,	data_time = 0.050991,	train_time = 0.667422
[2019-08-23 09:24:52,744] TRAIN Iter 94060: lr = 0.343235,	loss = 2.772356,	Top-1 err = 0.421240,	Top-5 err = 0.200098,	data_time = 0.050416,	train_time = 0.392432
[2019-08-23 09:25:04,717] TRAIN Iter 94080: lr = 0.343202,	loss = 2.880164,	Top-1 err = 0.424756,	Top-5 err = 0.204150,	data_time = 0.050426,	train_time = 0.598666
[2019-08-23 09:25:17,666] TRAIN Iter 94100: lr = 0.343168,	loss = 2.711365,	Top-1 err = 0.427100,	Top-5 err = 0.202588,	data_time = 2.188975,	train_time = 0.647424
[2019-08-23 09:25:25,568] TRAIN Iter 94120: lr = 0.343135,	loss = 2.730968,	Top-1 err = 0.427100,	Top-5 err = 0.201514,	data_time = 0.050348,	train_time = 0.395091
[2019-08-23 09:25:39,447] TRAIN Iter 94140: lr = 0.343102,	loss = 2.789980,	Top-1 err = 0.428027,	Top-5 err = 0.203906,	data_time = 0.050528,	train_time = 0.693909
[2019-08-23 09:25:52,331] TRAIN Iter 94160: lr = 0.343068,	loss = 2.868576,	Top-1 err = 0.428662,	Top-5 err = 0.200830,	data_time = 3.589229,	train_time = 0.644210
[2019-08-23 09:26:00,620] TRAIN Iter 94180: lr = 0.343035,	loss = 2.772994,	Top-1 err = 0.422705,	Top-5 err = 0.196826,	data_time = 0.050679,	train_time = 0.414450
[2019-08-23 09:26:13,948] TRAIN Iter 94200: lr = 0.343002,	loss = 2.935499,	Top-1 err = 0.432422,	Top-5 err = 0.203955,	data_time = 0.050594,	train_time = 0.666383
[2019-08-23 09:26:21,719] TRAIN Iter 94220: lr = 0.342968,	loss = 2.809215,	Top-1 err = 0.426660,	Top-5 err = 0.198633,	data_time = 0.050812,	train_time = 0.388530
[2019-08-23 09:26:35,399] TRAIN Iter 94240: lr = 0.342935,	loss = 2.850001,	Top-1 err = 0.431689,	Top-5 err = 0.204492,	data_time = 0.050684,	train_time = 0.683969
[2019-08-23 09:26:49,675] TRAIN Iter 94260: lr = 0.342902,	loss = 2.629921,	Top-1 err = 0.427100,	Top-5 err = 0.201221,	data_time = 0.050267,	train_time = 0.713786
[2019-08-23 09:26:57,513] TRAIN Iter 94280: lr = 0.342868,	loss = 2.765121,	Top-1 err = 0.436182,	Top-5 err = 0.207227,	data_time = 0.050815,	train_time = 0.391908
[2019-08-23 09:27:10,365] TRAIN Iter 94300: lr = 0.342835,	loss = 2.888355,	Top-1 err = 0.433740,	Top-5 err = 0.204736,	data_time = 0.050402,	train_time = 0.642566
[2019-08-23 09:27:23,686] TRAIN Iter 94320: lr = 0.342802,	loss = 2.831200,	Top-1 err = 0.418408,	Top-5 err = 0.200195,	data_time = 2.957723,	train_time = 0.666033
[2019-08-23 09:27:33,616] TRAIN Iter 94340: lr = 0.342768,	loss = 2.734510,	Top-1 err = 0.426367,	Top-5 err = 0.204932,	data_time = 0.050755,	train_time = 0.496504
[2019-08-23 09:27:47,420] TRAIN Iter 94360: lr = 0.342735,	loss = 2.717984,	Top-1 err = 0.428320,	Top-5 err = 0.201074,	data_time = 0.050478,	train_time = 0.690154
[2019-08-23 09:27:55,422] TRAIN Iter 94380: lr = 0.342702,	loss = 2.713516,	Top-1 err = 0.425049,	Top-5 err = 0.201074,	data_time = 0.050370,	train_time = 0.400098
[2019-08-23 09:28:07,232] TRAIN Iter 94400: lr = 0.342668,	loss = 2.792605,	Top-1 err = 0.427295,	Top-5 err = 0.203174,	data_time = 0.050307,	train_time = 0.590469
[2019-08-23 09:28:22,416] TRAIN Iter 94420: lr = 0.342635,	loss = 2.747447,	Top-1 err = 0.434814,	Top-5 err = 0.204492,	data_time = 1.924868,	train_time = 0.759200
[2019-08-23 09:28:29,869] TRAIN Iter 94440: lr = 0.342602,	loss = 2.805179,	Top-1 err = 0.428516,	Top-5 err = 0.202930,	data_time = 0.050504,	train_time = 0.372655
[2019-08-23 09:28:44,214] TRAIN Iter 94460: lr = 0.342568,	loss = 2.793511,	Top-1 err = 0.432715,	Top-5 err = 0.205029,	data_time = 0.050524,	train_time = 0.717210
[2019-08-23 09:28:54,507] TRAIN Iter 94480: lr = 0.342535,	loss = 2.705265,	Top-1 err = 0.432812,	Top-5 err = 0.203320,	data_time = 1.195078,	train_time = 0.514662
[2019-08-23 09:29:05,711] TRAIN Iter 94500: lr = 0.342502,	loss = 2.715795,	Top-1 err = 0.426807,	Top-5 err = 0.200244,	data_time = 0.050360,	train_time = 0.560196
[2019-08-23 09:29:20,366] TRAIN Iter 94520: lr = 0.342468,	loss = 2.581077,	Top-1 err = 0.427783,	Top-5 err = 0.198633,	data_time = 0.050449,	train_time = 0.732723
[2019-08-23 09:29:27,833] TRAIN Iter 94540: lr = 0.342435,	loss = 2.805318,	Top-1 err = 0.434668,	Top-5 err = 0.205225,	data_time = 0.050473,	train_time = 0.373316
[2019-08-23 09:29:42,688] TRAIN Iter 94560: lr = 0.342402,	loss = 2.766277,	Top-1 err = 0.429102,	Top-5 err = 0.202979,	data_time = 0.050671,	train_time = 0.742736
[2019-08-23 09:29:57,438] TRAIN Iter 94580: lr = 0.342368,	loss = 2.780553,	Top-1 err = 0.421875,	Top-5 err = 0.197852,	data_time = 0.653686,	train_time = 0.737498
[2019-08-23 09:30:04,786] TRAIN Iter 94600: lr = 0.342335,	loss = 2.748208,	Top-1 err = 0.428174,	Top-5 err = 0.203174,	data_time = 0.144551,	train_time = 0.367393
[2019-08-23 09:30:19,894] TRAIN Iter 94620: lr = 0.342302,	loss = 2.805410,	Top-1 err = 0.428955,	Top-5 err = 0.204248,	data_time = 0.050414,	train_time = 0.755354
[2019-08-23 09:30:33,887] TRAIN Iter 94640: lr = 0.342268,	loss = 2.734456,	Top-1 err = 0.432422,	Top-5 err = 0.201953,	data_time = 5.376667,	train_time = 0.699661
[2019-08-23 09:30:41,204] TRAIN Iter 94660: lr = 0.342235,	loss = 2.759674,	Top-1 err = 0.435254,	Top-5 err = 0.208594,	data_time = 0.050651,	train_time = 0.365814
[2019-08-23 09:30:57,125] TRAIN Iter 94680: lr = 0.342202,	loss = 2.776520,	Top-1 err = 0.433008,	Top-5 err = 0.208105,	data_time = 0.050556,	train_time = 0.796049
[2019-08-23 09:31:04,704] TRAIN Iter 94700: lr = 0.342168,	loss = 2.798968,	Top-1 err = 0.430518,	Top-5 err = 0.201953,	data_time = 0.050551,	train_time = 0.378949
[2019-08-23 09:31:21,080] TRAIN Iter 94720: lr = 0.342135,	loss = 2.719563,	Top-1 err = 0.427051,	Top-5 err = 0.202051,	data_time = 0.050465,	train_time = 0.818800
[2019-08-23 09:31:35,218] TRAIN Iter 94740: lr = 0.342102,	loss = 2.776891,	Top-1 err = 0.427979,	Top-5 err = 0.203516,	data_time = 0.050543,	train_time = 0.706893
[2019-08-23 09:31:42,609] TRAIN Iter 94760: lr = 0.342068,	loss = 2.797198,	Top-1 err = 0.433691,	Top-5 err = 0.206982,	data_time = 0.050986,	train_time = 0.369539
[2019-08-23 09:31:56,640] TRAIN Iter 94780: lr = 0.342035,	loss = 2.737614,	Top-1 err = 0.427100,	Top-5 err = 0.203857,	data_time = 0.050518,	train_time = 0.701501
[2019-08-23 09:32:10,398] TRAIN Iter 94800: lr = 0.342002,	loss = 2.666474,	Top-1 err = 0.430322,	Top-5 err = 0.210059,	data_time = 5.511049,	train_time = 0.687917
[2019-08-23 09:32:17,532] TRAIN Iter 94820: lr = 0.341968,	loss = 2.642258,	Top-1 err = 0.426709,	Top-5 err = 0.199707,	data_time = 0.050313,	train_time = 0.356678
[2019-08-23 09:32:32,759] TRAIN Iter 94840: lr = 0.341935,	loss = 2.755697,	Top-1 err = 0.428418,	Top-5 err = 0.204883,	data_time = 0.050507,	train_time = 0.761305
[2019-08-23 09:32:40,095] TRAIN Iter 94860: lr = 0.341902,	loss = 2.769957,	Top-1 err = 0.430322,	Top-5 err = 0.203076,	data_time = 0.050768,	train_time = 0.366826
[2019-08-23 09:32:55,318] TRAIN Iter 94880: lr = 0.341868,	loss = 2.786893,	Top-1 err = 0.430664,	Top-5 err = 0.204785,	data_time = 0.050190,	train_time = 0.761126
[2019-08-23 09:33:10,465] TRAIN Iter 94900: lr = 0.341835,	loss = 2.702823,	Top-1 err = 0.433740,	Top-5 err = 0.205225,	data_time = 0.050579,	train_time = 0.757332
[2019-08-23 09:33:17,943] TRAIN Iter 94920: lr = 0.341802,	loss = 2.796691,	Top-1 err = 0.433057,	Top-5 err = 0.206592,	data_time = 0.050558,	train_time = 0.373894
[2019-08-23 09:33:33,635] TRAIN Iter 94940: lr = 0.341768,	loss = 2.757377,	Top-1 err = 0.431689,	Top-5 err = 0.203564,	data_time = 0.050353,	train_time = 0.784568
[2019-08-23 09:33:48,459] TRAIN Iter 94960: lr = 0.341735,	loss = 2.757967,	Top-1 err = 0.435986,	Top-5 err = 0.213379,	data_time = 4.076911,	train_time = 0.741174
[2019-08-23 09:33:56,047] TRAIN Iter 94980: lr = 0.341702,	loss = 2.811819,	Top-1 err = 0.436084,	Top-5 err = 0.206055,	data_time = 0.050115,	train_time = 0.379387
[2019-08-23 09:34:12,870] TRAIN Iter 95000: lr = 0.341668,	loss = 2.812344,	Top-1 err = 0.430615,	Top-5 err = 0.204248,	data_time = 1.071527,	train_time = 0.841167
[2019-08-23 09:34:19,386] TRAIN Iter 95020: lr = 0.341635,	loss = 2.750389,	Top-1 err = 0.430371,	Top-5 err = 0.203809,	data_time = 0.050470,	train_time = 0.325762
[2019-08-23 09:34:37,384] TRAIN Iter 95040: lr = 0.341602,	loss = 2.791384,	Top-1 err = 0.436865,	Top-5 err = 0.209326,	data_time = 0.050066,	train_time = 0.899907
[2019-08-23 09:34:54,190] TRAIN Iter 95060: lr = 0.341568,	loss = 2.716378,	Top-1 err = 0.432324,	Top-5 err = 0.208203,	data_time = 0.049905,	train_time = 0.840272
[2019-08-23 09:35:00,405] TRAIN Iter 95080: lr = 0.341535,	loss = 2.757066,	Top-1 err = 0.430420,	Top-5 err = 0.204736,	data_time = 0.049909,	train_time = 0.310728
[2019-08-23 09:35:49,433] TRAIN Iter 95100: lr = 0.341502,	loss = 2.841185,	Top-1 err = 0.444281,	Top-5 err = 0.214002,	data_time = 0.050563,	train_time = 2.451410
[2019-08-23 09:35:57,743] TRAIN Iter 95120: lr = 0.341468,	loss = 2.813397,	Top-1 err = 0.434521,	Top-5 err = 0.206396,	data_time = 0.050656,	train_time = 0.415493
[2019-08-23 09:36:11,653] TRAIN Iter 95140: lr = 0.341435,	loss = 2.638609,	Top-1 err = 0.427393,	Top-5 err = 0.198584,	data_time = 0.050597,	train_time = 0.695457
[2019-08-23 09:36:20,847] TRAIN Iter 95160: lr = 0.341402,	loss = 2.757172,	Top-1 err = 0.428174,	Top-5 err = 0.202393,	data_time = 0.112149,	train_time = 0.459703
[2019-08-23 09:36:28,671] TRAIN Iter 95180: lr = 0.341368,	loss = 2.779552,	Top-1 err = 0.423486,	Top-5 err = 0.195557,	data_time = 0.050337,	train_time = 0.391198
[2019-08-23 09:36:41,034] TRAIN Iter 95200: lr = 0.341335,	loss = 2.705774,	Top-1 err = 0.416357,	Top-5 err = 0.196875,	data_time = 0.050361,	train_time = 0.618090
[2019-08-23 09:36:54,575] TRAIN Iter 95220: lr = 0.341302,	loss = 2.708356,	Top-1 err = 0.420215,	Top-5 err = 0.198389,	data_time = 0.050698,	train_time = 0.677069
[2019-08-23 09:37:02,521] TRAIN Iter 95240: lr = 0.341268,	loss = 2.683102,	Top-1 err = 0.416309,	Top-5 err = 0.193115,	data_time = 0.050909,	train_time = 0.397289
[2019-08-23 09:37:14,897] TRAIN Iter 95260: lr = 0.341235,	loss = 2.696174,	Top-1 err = 0.423975,	Top-5 err = 0.199121,	data_time = 0.050858,	train_time = 0.618756
[2019-08-23 09:37:23,125] TRAIN Iter 95280: lr = 0.341202,	loss = 2.769997,	Top-1 err = 0.424268,	Top-5 err = 0.198535,	data_time = 0.050656,	train_time = 0.411382
[2019-08-23 09:37:35,789] TRAIN Iter 95300: lr = 0.341168,	loss = 2.770732,	Top-1 err = 0.429443,	Top-5 err = 0.206250,	data_time = 0.050569,	train_time = 0.633196
[2019-08-23 09:37:50,875] TRAIN Iter 95320: lr = 0.341135,	loss = 2.770347,	Top-1 err = 0.417822,	Top-5 err = 0.196289,	data_time = 0.050759,	train_time = 0.754276
[2019-08-23 09:37:59,195] TRAIN Iter 95340: lr = 0.341102,	loss = 2.811997,	Top-1 err = 0.419922,	Top-5 err = 0.201416,	data_time = 0.051029,	train_time = 0.416004
[2019-08-23 09:38:11,081] TRAIN Iter 95360: lr = 0.341068,	loss = 2.694638,	Top-1 err = 0.424902,	Top-5 err = 0.198682,	data_time = 0.050343,	train_time = 0.594278
[2019-08-23 09:38:25,105] TRAIN Iter 95380: lr = 0.341035,	loss = 2.652566,	Top-1 err = 0.421143,	Top-5 err = 0.198682,	data_time = 0.127729,	train_time = 0.701206
[2019-08-23 09:38:33,243] TRAIN Iter 95400: lr = 0.341002,	loss = 2.771828,	Top-1 err = 0.424072,	Top-5 err = 0.199561,	data_time = 0.050354,	train_time = 0.406894
[2019-08-23 09:38:45,129] TRAIN Iter 95420: lr = 0.340968,	loss = 2.650319,	Top-1 err = 0.423975,	Top-5 err = 0.197217,	data_time = 0.050899,	train_time = 0.594277
[2019-08-23 09:38:52,891] TRAIN Iter 95440: lr = 0.340935,	loss = 2.736026,	Top-1 err = 0.429102,	Top-5 err = 0.202002,	data_time = 0.050406,	train_time = 0.388077
[2019-08-23 09:39:07,505] TRAIN Iter 95460: lr = 0.340902,	loss = 2.714839,	Top-1 err = 0.423486,	Top-5 err = 0.198486,	data_time = 0.050557,	train_time = 0.730692
[2019-08-23 09:39:21,522] TRAIN Iter 95480: lr = 0.340868,	loss = 2.691691,	Top-1 err = 0.425098,	Top-5 err = 0.198437,	data_time = 0.050900,	train_time = 0.700827
[2019-08-23 09:39:29,238] TRAIN Iter 95500: lr = 0.340835,	loss = 2.639797,	Top-1 err = 0.425537,	Top-5 err = 0.204053,	data_time = 0.050874,	train_time = 0.385773
[2019-08-23 09:39:45,090] TRAIN Iter 95520: lr = 0.340802,	loss = 2.792109,	Top-1 err = 0.423633,	Top-5 err = 0.203516,	data_time = 0.050494,	train_time = 0.792580
[2019-08-23 09:39:57,787] TRAIN Iter 95540: lr = 0.340768,	loss = 2.787541,	Top-1 err = 0.426367,	Top-5 err = 0.207910,	data_time = 0.160975,	train_time = 0.634846
[2019-08-23 09:40:05,305] TRAIN Iter 95560: lr = 0.340735,	loss = 2.731250,	Top-1 err = 0.426465,	Top-5 err = 0.205859,	data_time = 0.050385,	train_time = 0.375888
[2019-08-23 09:40:19,504] TRAIN Iter 95580: lr = 0.340702,	loss = 2.641140,	Top-1 err = 0.422803,	Top-5 err = 0.200635,	data_time = 0.050455,	train_time = 0.709930
[2019-08-23 09:40:27,668] TRAIN Iter 95600: lr = 0.340668,	loss = 2.764780,	Top-1 err = 0.426904,	Top-5 err = 0.204883,	data_time = 0.105500,	train_time = 0.408206
[2019-08-23 09:40:40,315] TRAIN Iter 95620: lr = 0.340635,	loss = 2.702579,	Top-1 err = 0.424414,	Top-5 err = 0.200586,	data_time = 0.050400,	train_time = 0.632333
[2019-08-23 09:40:55,153] TRAIN Iter 95640: lr = 0.340602,	loss = 2.702986,	Top-1 err = 0.431348,	Top-5 err = 0.201123,	data_time = 0.050678,	train_time = 0.741854
[2019-08-23 09:41:02,782] TRAIN Iter 95660: lr = 0.340568,	loss = 2.687072,	Top-1 err = 0.424463,	Top-5 err = 0.200732,	data_time = 0.050806,	train_time = 0.381472
[2019-08-23 09:41:17,079] TRAIN Iter 95680: lr = 0.340535,	loss = 2.757049,	Top-1 err = 0.428613,	Top-5 err = 0.204736,	data_time = 0.050500,	train_time = 0.714830
[2019-08-23 09:41:30,191] TRAIN Iter 95700: lr = 0.340502,	loss = 2.708044,	Top-1 err = 0.426123,	Top-5 err = 0.202393,	data_time = 0.050811,	train_time = 0.655585
[2019-08-23 09:41:38,155] TRAIN Iter 95720: lr = 0.340468,	loss = 2.690037,	Top-1 err = 0.431152,	Top-5 err = 0.202344,	data_time = 0.050500,	train_time = 0.398143
[2019-08-23 09:41:50,668] TRAIN Iter 95740: lr = 0.340435,	loss = 2.733100,	Top-1 err = 0.427686,	Top-5 err = 0.205371,	data_time = 0.050509,	train_time = 0.625645
[2019-08-23 09:41:58,602] TRAIN Iter 95760: lr = 0.340402,	loss = 2.671750,	Top-1 err = 0.429736,	Top-5 err = 0.200830,	data_time = 0.050305,	train_time = 0.396688
[2019-08-23 09:42:11,714] TRAIN Iter 95780: lr = 0.340368,	loss = 2.724022,	Top-1 err = 0.430127,	Top-5 err = 0.202686,	data_time = 0.050372,	train_time = 0.655587
[2019-08-23 09:42:26,397] TRAIN Iter 95800: lr = 0.340335,	loss = 2.750553,	Top-1 err = 0.427930,	Top-5 err = 0.203613,	data_time = 0.050625,	train_time = 0.734165
[2019-08-23 09:42:34,398] TRAIN Iter 95820: lr = 0.340302,	loss = 2.784646,	Top-1 err = 0.431055,	Top-5 err = 0.205859,	data_time = 0.050554,	train_time = 0.399995
[2019-08-23 09:42:48,376] TRAIN Iter 95840: lr = 0.340268,	loss = 2.758302,	Top-1 err = 0.425537,	Top-5 err = 0.201465,	data_time = 0.050439,	train_time = 0.698889
[2019-08-23 09:43:03,576] TRAIN Iter 95860: lr = 0.340235,	loss = 2.790940,	Top-1 err = 0.424902,	Top-5 err = 0.203857,	data_time = 0.126431,	train_time = 0.760010
[2019-08-23 09:43:10,914] TRAIN Iter 95880: lr = 0.340202,	loss = 2.791650,	Top-1 err = 0.431543,	Top-5 err = 0.205371,	data_time = 0.050557,	train_time = 0.366900
[2019-08-23 09:43:25,672] TRAIN Iter 95900: lr = 0.340168,	loss = 2.761206,	Top-1 err = 0.434131,	Top-5 err = 0.209424,	data_time = 0.050201,	train_time = 0.737882
[2019-08-23 09:43:33,047] TRAIN Iter 95920: lr = 0.340135,	loss = 2.772294,	Top-1 err = 0.431152,	Top-5 err = 0.204736,	data_time = 0.050440,	train_time = 0.368714
[2019-08-23 09:43:48,351] TRAIN Iter 95940: lr = 0.340102,	loss = 2.701781,	Top-1 err = 0.430273,	Top-5 err = 0.205957,	data_time = 0.050464,	train_time = 0.765205
[2019-08-23 09:44:02,048] TRAIN Iter 95960: lr = 0.340068,	loss = 2.701652,	Top-1 err = 0.428369,	Top-5 err = 0.202197,	data_time = 0.050438,	train_time = 0.684809
[2019-08-23 09:44:09,385] TRAIN Iter 95980: lr = 0.340035,	loss = 2.771187,	Top-1 err = 0.431152,	Top-5 err = 0.204150,	data_time = 0.050485,	train_time = 0.366829
[2019-08-23 09:44:25,335] TRAIN Iter 96000: lr = 0.340002,	loss = 2.712490,	Top-1 err = 0.424219,	Top-5 err = 0.199902,	data_time = 0.050399,	train_time = 0.797499
[2019-08-23 09:44:41,526] TRAIN Iter 96020: lr = 0.339968,	loss = 2.666689,	Top-1 err = 0.425146,	Top-5 err = 0.200098,	data_time = 0.050577,	train_time = 0.809516
[2019-08-23 09:44:48,500] TRAIN Iter 96040: lr = 0.339935,	loss = 2.729026,	Top-1 err = 0.429248,	Top-5 err = 0.204980,	data_time = 0.050472,	train_time = 0.348714
[2019-08-23 09:45:04,245] TRAIN Iter 96060: lr = 0.339902,	loss = 2.742823,	Top-1 err = 0.432080,	Top-5 err = 0.202686,	data_time = 0.050447,	train_time = 0.787221
[2019-08-23 09:45:11,880] TRAIN Iter 96080: lr = 0.339868,	loss = 2.702900,	Top-1 err = 0.435742,	Top-5 err = 0.206836,	data_time = 0.050629,	train_time = 0.381759
[2019-08-23 09:45:29,833] TRAIN Iter 96100: lr = 0.339835,	loss = 2.770902,	Top-1 err = 0.427100,	Top-5 err = 0.202295,	data_time = 0.050435,	train_time = 0.897593
[2019-08-23 09:45:45,679] TRAIN Iter 96120: lr = 0.339802,	loss = 2.762442,	Top-1 err = 0.423633,	Top-5 err = 0.200244,	data_time = 0.050424,	train_time = 0.792324
[2019-08-23 09:45:53,071] TRAIN Iter 96140: lr = 0.339768,	loss = 2.674979,	Top-1 err = 0.431055,	Top-5 err = 0.205615,	data_time = 0.115025,	train_time = 0.369561
[2019-08-23 09:46:08,326] TRAIN Iter 96160: lr = 0.339735,	loss = 2.753331,	Top-1 err = 0.426758,	Top-5 err = 0.200830,	data_time = 0.050531,	train_time = 0.762736
[2019-08-23 09:46:24,274] TRAIN Iter 96180: lr = 0.339702,	loss = 2.784518,	Top-1 err = 0.432422,	Top-5 err = 0.206201,	data_time = 0.050328,	train_time = 0.797412
[2019-08-23 09:46:31,285] TRAIN Iter 96200: lr = 0.339668,	loss = 2.747498,	Top-1 err = 0.428027,	Top-5 err = 0.201758,	data_time = 0.121625,	train_time = 0.350499
[2019-08-23 09:46:47,697] TRAIN Iter 96220: lr = 0.339635,	loss = 2.645111,	Top-1 err = 0.432080,	Top-5 err = 0.206592,	data_time = 0.050321,	train_time = 0.820620
[2019-08-23 09:46:55,821] TRAIN Iter 96240: lr = 0.339602,	loss = 2.813674,	Top-1 err = 0.437891,	Top-5 err = 0.208154,	data_time = 0.050257,	train_time = 0.406156
[2019-08-23 09:47:11,256] TRAIN Iter 96260: lr = 0.339568,	loss = 2.774827,	Top-1 err = 0.433691,	Top-5 err = 0.204443,	data_time = 0.050456,	train_time = 0.771743
[2019-08-23 09:47:28,040] TRAIN Iter 96280: lr = 0.339535,	loss = 2.789032,	Top-1 err = 0.427588,	Top-5 err = 0.205078,	data_time = 0.050121,	train_time = 0.839177
[2019-08-23 09:47:35,436] TRAIN Iter 96300: lr = 0.339502,	loss = 2.753296,	Top-1 err = 0.431885,	Top-5 err = 0.205664,	data_time = 0.049984,	train_time = 0.369784
[2019-08-23 09:47:49,118] TRAIN Iter 96320: lr = 0.339468,	loss = 2.803369,	Top-1 err = 0.431836,	Top-5 err = 0.208838,	data_time = 0.049908,	train_time = 0.684106
[2019-08-23 09:48:03,168] TRAIN Iter 96340: lr = 0.339435,	loss = 3.082743,	Top-1 err = 0.428612,	Top-5 err = 0.205263,	data_time = 0.007051,	train_time = 0.702473
[2019-08-23 09:48:46,401] TRAIN Iter 96360: lr = 0.339402,	loss = 2.658446,	Top-1 err = 0.427783,	Top-5 err = 0.198437,	data_time = 0.050193,	train_time = 2.161656
[2019-08-23 09:49:01,679] TRAIN Iter 96380: lr = 0.339368,	loss = 2.723719,	Top-1 err = 0.427246,	Top-5 err = 0.199268,	data_time = 0.050722,	train_time = 0.763850
[2019-08-23 09:49:10,048] TRAIN Iter 96400: lr = 0.339335,	loss = 2.643362,	Top-1 err = 0.416016,	Top-5 err = 0.194775,	data_time = 0.050654,	train_time = 0.418458
[2019-08-23 09:49:21,541] TRAIN Iter 96420: lr = 0.339302,	loss = 2.766638,	Top-1 err = 0.416602,	Top-5 err = 0.197070,	data_time = 0.050644,	train_time = 0.574649
[2019-08-23 09:49:31,415] TRAIN Iter 96440: lr = 0.339268,	loss = 2.800431,	Top-1 err = 0.423779,	Top-5 err = 0.199365,	data_time = 0.050494,	train_time = 0.493679
[2019-08-23 09:49:39,566] TRAIN Iter 96460: lr = 0.339235,	loss = 2.696345,	Top-1 err = 0.430371,	Top-5 err = 0.203369,	data_time = 0.050741,	train_time = 0.407546
[2019-08-23 09:49:52,806] TRAIN Iter 96480: lr = 0.339202,	loss = 2.789170,	Top-1 err = 0.422363,	Top-5 err = 0.198975,	data_time = 0.050507,	train_time = 0.661964
[2019-08-23 09:50:00,622] TRAIN Iter 96500: lr = 0.339168,	loss = 2.740507,	Top-1 err = 0.421875,	Top-5 err = 0.194727,	data_time = 0.050719,	train_time = 0.390798
[2019-08-23 09:50:15,728] TRAIN Iter 96520: lr = 0.339135,	loss = 2.720554,	Top-1 err = 0.424512,	Top-5 err = 0.198584,	data_time = 0.050330,	train_time = 0.755295
[2019-08-23 09:50:27,546] TRAIN Iter 96540: lr = 0.339102,	loss = 2.826074,	Top-1 err = 0.425684,	Top-5 err = 0.203369,	data_time = 2.848308,	train_time = 0.590850
[2019-08-23 09:50:35,401] TRAIN Iter 96560: lr = 0.339068,	loss = 2.683652,	Top-1 err = 0.420850,	Top-5 err = 0.197510,	data_time = 0.050234,	train_time = 0.392759
[2019-08-23 09:50:47,269] TRAIN Iter 96580: lr = 0.339035,	loss = 2.704596,	Top-1 err = 0.426563,	Top-5 err = 0.200684,	data_time = 0.050312,	train_time = 0.593395
[2019-08-23 09:50:58,347] TRAIN Iter 96600: lr = 0.339002,	loss = 2.718345,	Top-1 err = 0.425684,	Top-5 err = 0.203271,	data_time = 0.050330,	train_time = 0.553890
[2019-08-23 09:51:08,546] TRAIN Iter 96620: lr = 0.338968,	loss = 2.791886,	Top-1 err = 0.427686,	Top-5 err = 0.202295,	data_time = 0.050786,	train_time = 0.509932
[2019-08-23 09:51:21,186] TRAIN Iter 96640: lr = 0.338935,	loss = 2.708318,	Top-1 err = 0.428320,	Top-5 err = 0.201514,	data_time = 0.050696,	train_time = 0.631957
[2019-08-23 09:51:29,575] TRAIN Iter 96660: lr = 0.338902,	loss = 2.725261,	Top-1 err = 0.419238,	Top-5 err = 0.199902,	data_time = 0.050714,	train_time = 0.419460
[2019-08-23 09:51:40,762] TRAIN Iter 96680: lr = 0.338868,	loss = 2.690476,	Top-1 err = 0.427588,	Top-5 err = 0.203418,	data_time = 0.050863,	train_time = 0.559329
[2019-08-23 09:51:53,840] TRAIN Iter 96700: lr = 0.338835,	loss = 2.755393,	Top-1 err = 0.424658,	Top-5 err = 0.198877,	data_time = 0.216305,	train_time = 0.653861
[2019-08-23 09:52:02,145] TRAIN Iter 96720: lr = 0.338802,	loss = 2.701443,	Top-1 err = 0.424170,	Top-5 err = 0.202734,	data_time = 0.050723,	train_time = 0.415248
[2019-08-23 09:52:15,233] TRAIN Iter 96740: lr = 0.338768,	loss = 2.757458,	Top-1 err = 0.428125,	Top-5 err = 0.200684,	data_time = 0.050311,	train_time = 0.654407
[2019-08-23 09:52:26,913] TRAIN Iter 96760: lr = 0.338735,	loss = 2.697872,	Top-1 err = 0.431543,	Top-5 err = 0.200488,	data_time = 0.050845,	train_time = 0.583958
[2019-08-23 09:52:37,260] TRAIN Iter 96780: lr = 0.338702,	loss = 2.846834,	Top-1 err = 0.425879,	Top-5 err = 0.203271,	data_time = 0.050798,	train_time = 0.517329
[2019-08-23 09:52:51,258] TRAIN Iter 96800: lr = 0.338668,	loss = 2.720342,	Top-1 err = 0.421533,	Top-5 err = 0.196533,	data_time = 0.050403,	train_time = 0.699875
[2019-08-23 09:52:59,174] TRAIN Iter 96820: lr = 0.338635,	loss = 2.625619,	Top-1 err = 0.421924,	Top-5 err = 0.200684,	data_time = 0.050506,	train_time = 0.395810
[2019-08-23 09:53:12,689] TRAIN Iter 96840: lr = 0.338602,	loss = 2.753833,	Top-1 err = 0.430859,	Top-5 err = 0.203467,	data_time = 0.050820,	train_time = 0.675734
[2019-08-23 09:53:26,178] TRAIN Iter 96860: lr = 0.338568,	loss = 2.753726,	Top-1 err = 0.426465,	Top-5 err = 0.202734,	data_time = 2.992779,	train_time = 0.674438
[2019-08-23 09:53:33,931] TRAIN Iter 96880: lr = 0.338535,	loss = 2.786089,	Top-1 err = 0.430469,	Top-5 err = 0.201318,	data_time = 0.050456,	train_time = 0.387606
[2019-08-23 09:53:48,144] TRAIN Iter 96900: lr = 0.338502,	loss = 2.749917,	Top-1 err = 0.426758,	Top-5 err = 0.197900,	data_time = 0.050599,	train_time = 0.710670
[2019-08-23 09:53:56,317] TRAIN Iter 96920: lr = 0.338468,	loss = 2.702737,	Top-1 err = 0.430322,	Top-5 err = 0.207373,	data_time = 0.050496,	train_time = 0.408622
[2019-08-23 09:54:10,270] TRAIN Iter 96940: lr = 0.338435,	loss = 2.808170,	Top-1 err = 0.426318,	Top-5 err = 0.206689,	data_time = 0.050952,	train_time = 0.697633
[2019-08-23 09:54:23,728] TRAIN Iter 96960: lr = 0.338402,	loss = 2.713046,	Top-1 err = 0.426074,	Top-5 err = 0.205518,	data_time = 0.050883,	train_time = 0.672860
[2019-08-23 09:54:31,303] TRAIN Iter 96980: lr = 0.338368,	loss = 2.767448,	Top-1 err = 0.429297,	Top-5 err = 0.201611,	data_time = 0.050508,	train_time = 0.378763
[2019-08-23 09:54:44,945] TRAIN Iter 97000: lr = 0.338335,	loss = 2.777380,	Top-1 err = 0.430566,	Top-5 err = 0.203809,	data_time = 0.050516,	train_time = 0.682094
[2019-08-23 09:54:58,470] TRAIN Iter 97020: lr = 0.338302,	loss = 2.719380,	Top-1 err = 0.423193,	Top-5 err = 0.202783,	data_time = 1.471734,	train_time = 0.676239
[2019-08-23 09:55:05,582] TRAIN Iter 97040: lr = 0.338268,	loss = 2.760276,	Top-1 err = 0.424121,	Top-5 err = 0.198682,	data_time = 0.050295,	train_time = 0.355573
[2019-08-23 09:55:20,140] TRAIN Iter 97060: lr = 0.338235,	loss = 2.667038,	Top-1 err = 0.427441,	Top-5 err = 0.199609,	data_time = 0.141970,	train_time = 0.727868
[2019-08-23 09:55:29,742] TRAIN Iter 97080: lr = 0.338202,	loss = 2.735842,	Top-1 err = 0.432422,	Top-5 err = 0.203906,	data_time = 0.050891,	train_time = 0.480089
[2019-08-23 09:55:42,764] TRAIN Iter 97100: lr = 0.338168,	loss = 2.768138,	Top-1 err = 0.433203,	Top-5 err = 0.206250,	data_time = 0.050732,	train_time = 0.651105
[2019-08-23 09:55:57,802] TRAIN Iter 97120: lr = 0.338135,	loss = 2.782212,	Top-1 err = 0.431494,	Top-5 err = 0.200781,	data_time = 0.050524,	train_time = 0.751858
[2019-08-23 09:56:04,885] TRAIN Iter 97140: lr = 0.338102,	loss = 2.887159,	Top-1 err = 0.430811,	Top-5 err = 0.202832,	data_time = 0.050895,	train_time = 0.354133
[2019-08-23 09:56:20,202] TRAIN Iter 97160: lr = 0.338068,	loss = 2.758648,	Top-1 err = 0.431689,	Top-5 err = 0.202148,	data_time = 0.050546,	train_time = 0.765848
[2019-08-23 09:56:33,489] TRAIN Iter 97180: lr = 0.338035,	loss = 2.649045,	Top-1 err = 0.423047,	Top-5 err = 0.199316,	data_time = 0.143066,	train_time = 0.664355
[2019-08-23 09:56:41,256] TRAIN Iter 97200: lr = 0.338002,	loss = 2.827389,	Top-1 err = 0.424805,	Top-5 err = 0.202783,	data_time = 0.050997,	train_time = 0.388318
[2019-08-23 09:56:55,977] TRAIN Iter 97220: lr = 0.337968,	loss = 2.708586,	Top-1 err = 0.435107,	Top-5 err = 0.205615,	data_time = 0.050126,	train_time = 0.736043
[2019-08-23 09:57:05,444] TRAIN Iter 97240: lr = 0.337935,	loss = 2.666636,	Top-1 err = 0.430029,	Top-5 err = 0.198193,	data_time = 0.050555,	train_time = 0.473325
[2019-08-23 09:57:19,863] TRAIN Iter 97260: lr = 0.337902,	loss = 2.716394,	Top-1 err = 0.432031,	Top-5 err = 0.205371,	data_time = 0.050909,	train_time = 0.720921
[2019-08-23 09:57:33,003] TRAIN Iter 97280: lr = 0.337868,	loss = 2.767167,	Top-1 err = 0.435840,	Top-5 err = 0.206592,	data_time = 0.050486,	train_time = 0.657014
[2019-08-23 09:57:40,453] TRAIN Iter 97300: lr = 0.337835,	loss = 2.738263,	Top-1 err = 0.431152,	Top-5 err = 0.205225,	data_time = 0.050351,	train_time = 0.372484
[2019-08-23 09:57:55,391] TRAIN Iter 97320: lr = 0.337802,	loss = 2.802543,	Top-1 err = 0.434570,	Top-5 err = 0.206494,	data_time = 0.050863,	train_time = 0.746870
[2019-08-23 09:58:10,892] TRAIN Iter 97340: lr = 0.337768,	loss = 2.775920,	Top-1 err = 0.429102,	Top-5 err = 0.206152,	data_time = 0.165413,	train_time = 0.775023
[2019-08-23 09:58:20,308] TRAIN Iter 97360: lr = 0.337735,	loss = 2.725105,	Top-1 err = 0.435791,	Top-5 err = 0.205762,	data_time = 0.050597,	train_time = 0.470777
[2019-08-23 09:58:31,727] TRAIN Iter 97380: lr = 0.337702,	loss = 2.728114,	Top-1 err = 0.427539,	Top-5 err = 0.203223,	data_time = 0.050433,	train_time = 0.570970
[2019-08-23 09:58:41,360] TRAIN Iter 97400: lr = 0.337668,	loss = 2.726578,	Top-1 err = 0.427100,	Top-5 err = 0.203369,	data_time = 1.912102,	train_time = 0.481596
[2019-08-23 09:58:54,847] TRAIN Iter 97420: lr = 0.337635,	loss = 2.780161,	Top-1 err = 0.425146,	Top-5 err = 0.198877,	data_time = 0.050344,	train_time = 0.674333
[2019-08-23 09:59:10,684] TRAIN Iter 97440: lr = 0.337602,	loss = 2.732389,	Top-1 err = 0.428467,	Top-5 err = 0.205078,	data_time = 0.050372,	train_time = 0.791845
[2019-08-23 09:59:18,381] TRAIN Iter 97460: lr = 0.337568,	loss = 2.751260,	Top-1 err = 0.430225,	Top-5 err = 0.202979,	data_time = 0.136306,	train_time = 0.384829
[2019-08-23 09:59:31,952] TRAIN Iter 97480: lr = 0.337535,	loss = 2.673745,	Top-1 err = 0.427295,	Top-5 err = 0.200732,	data_time = 0.050812,	train_time = 0.678576
[2019-08-23 09:59:48,092] TRAIN Iter 97500: lr = 0.337502,	loss = 2.709842,	Top-1 err = 0.428320,	Top-5 err = 0.201123,	data_time = 1.879182,	train_time = 0.806956
[2019-08-23 09:59:54,945] TRAIN Iter 97520: lr = 0.337468,	loss = 2.746834,	Top-1 err = 0.428711,	Top-5 err = 0.205518,	data_time = 0.050349,	train_time = 0.342653
[2019-08-23 10:00:12,379] TRAIN Iter 97540: lr = 0.337435,	loss = 2.811414,	Top-1 err = 0.430664,	Top-5 err = 0.204150,	data_time = 0.049998,	train_time = 0.871677
[2019-08-23 10:00:23,213] TRAIN Iter 97560: lr = 0.337402,	loss = 2.698470,	Top-1 err = 0.427832,	Top-5 err = 0.200244,	data_time = 3.470298,	train_time = 0.541672
[2019-08-23 10:00:33,615] TRAIN Iter 97580: lr = 0.337368,	loss = 2.687830,	Top-1 err = 0.434814,	Top-5 err = 0.206396,	data_time = 0.050027,	train_time = 0.520123
[2019-08-23 10:01:18,283] TRAIN Iter 97600: lr = 0.337335,	loss = 2.835970,	Top-1 err = 0.435718,	Top-5 err = 0.204743,	data_time = 0.050511,	train_time = 2.233383
[2019-08-23 10:01:26,187] TRAIN Iter 97620: lr = 0.337302,	loss = 2.694817,	Top-1 err = 0.427051,	Top-5 err = 0.204150,	data_time = 0.050230,	train_time = 0.395135
[2019-08-23 10:01:38,559] TRAIN Iter 97640: lr = 0.337268,	loss = 2.801576,	Top-1 err = 0.422949,	Top-5 err = 0.197021,	data_time = 0.050839,	train_time = 0.618628
[2019-08-23 10:01:48,252] TRAIN Iter 97660: lr = 0.337235,	loss = 2.678636,	Top-1 err = 0.417187,	Top-5 err = 0.196826,	data_time = 0.128480,	train_time = 0.484608
[2019-08-23 10:01:56,306] TRAIN Iter 97680: lr = 0.337202,	loss = 2.768826,	Top-1 err = 0.420703,	Top-5 err = 0.198828,	data_time = 0.050449,	train_time = 0.402674
[2019-08-23 10:02:08,628] TRAIN Iter 97700: lr = 0.337168,	loss = 2.675681,	Top-1 err = 0.417432,	Top-5 err = 0.193262,	data_time = 0.050765,	train_time = 0.616126
[2019-08-23 10:02:16,780] TRAIN Iter 97720: lr = 0.337135,	loss = 2.643863,	Top-1 err = 0.425879,	Top-5 err = 0.199707,	data_time = 0.050989,	train_time = 0.407567
[2019-08-23 10:02:26,928] TRAIN Iter 97740: lr = 0.337102,	loss = 2.714613,	Top-1 err = 0.424707,	Top-5 err = 0.199219,	data_time = 0.141478,	train_time = 0.507368
[2019-08-23 10:02:42,102] TRAIN Iter 97760: lr = 0.337068,	loss = 2.719926,	Top-1 err = 0.423145,	Top-5 err = 0.199121,	data_time = 0.109017,	train_time = 0.758701
[2019-08-23 10:02:49,899] TRAIN Iter 97780: lr = 0.337035,	loss = 2.782254,	Top-1 err = 0.423242,	Top-5 err = 0.202783,	data_time = 0.050934,	train_time = 0.389839
[2019-08-23 10:03:02,174] TRAIN Iter 97800: lr = 0.337002,	loss = 2.636978,	Top-1 err = 0.418506,	Top-5 err = 0.197949,	data_time = 0.050742,	train_time = 0.613737
[2019-08-23 10:03:14,083] TRAIN Iter 97820: lr = 0.336968,	loss = 2.682637,	Top-1 err = 0.428564,	Top-5 err = 0.201660,	data_time = 0.099502,	train_time = 0.595415
[2019-08-23 10:03:25,306] TRAIN Iter 97840: lr = 0.336935,	loss = 2.719221,	Top-1 err = 0.427051,	Top-5 err = 0.202832,	data_time = 0.050467,	train_time = 0.561123
[2019-08-23 10:03:35,660] TRAIN Iter 97860: lr = 0.336902,	loss = 2.795432,	Top-1 err = 0.423877,	Top-5 err = 0.195605,	data_time = 0.050321,	train_time = 0.517734
[2019-08-23 10:03:45,078] TRAIN Iter 97880: lr = 0.336868,	loss = 2.787895,	Top-1 err = 0.424707,	Top-5 err = 0.199170,	data_time = 0.050757,	train_time = 0.470844
[2019-08-23 10:03:58,113] TRAIN Iter 97900: lr = 0.336835,	loss = 2.676729,	Top-1 err = 0.422705,	Top-5 err = 0.198047,	data_time = 0.050614,	train_time = 0.651774
[2019-08-23 10:04:09,448] TRAIN Iter 97920: lr = 0.336802,	loss = 2.745924,	Top-1 err = 0.430225,	Top-5 err = 0.199609,	data_time = 0.050344,	train_time = 0.566708
[2019-08-23 10:04:18,801] TRAIN Iter 97940: lr = 0.336768,	loss = 2.746384,	Top-1 err = 0.426074,	Top-5 err = 0.202783,	data_time = 0.050537,	train_time = 0.467630
[2019-08-23 10:04:33,337] TRAIN Iter 97960: lr = 0.336735,	loss = 2.784167,	Top-1 err = 0.425781,	Top-5 err = 0.203418,	data_time = 0.127274,	train_time = 0.726820
[2019-08-23 10:04:45,924] TRAIN Iter 97980: lr = 0.336702,	loss = 2.700097,	Top-1 err = 0.426025,	Top-5 err = 0.201563,	data_time = 0.122537,	train_time = 0.629301
[2019-08-23 10:04:55,011] TRAIN Iter 98000: lr = 0.336668,	loss = 2.727261,	Top-1 err = 0.422900,	Top-5 err = 0.202246,	data_time = 0.050619,	train_time = 0.454347
[2019-08-23 10:05:08,308] TRAIN Iter 98020: lr = 0.336635,	loss = 2.842425,	Top-1 err = 0.432764,	Top-5 err = 0.204883,	data_time = 0.050392,	train_time = 0.664854
[2019-08-23 10:05:15,629] TRAIN Iter 98040: lr = 0.336602,	loss = 2.749738,	Top-1 err = 0.429883,	Top-5 err = 0.204053,	data_time = 0.050576,	train_time = 0.366026
[2019-08-23 10:05:31,517] TRAIN Iter 98060: lr = 0.336568,	loss = 2.838995,	Top-1 err = 0.424951,	Top-5 err = 0.197510,	data_time = 0.050587,	train_time = 0.794352
[2019-08-23 10:05:45,641] TRAIN Iter 98080: lr = 0.336535,	loss = 2.670440,	Top-1 err = 0.430811,	Top-5 err = 0.202734,	data_time = 0.050770,	train_time = 0.706201
[2019-08-23 10:05:53,221] TRAIN Iter 98100: lr = 0.336502,	loss = 2.750630,	Top-1 err = 0.422168,	Top-5 err = 0.200537,	data_time = 0.113574,	train_time = 0.378973
[2019-08-23 10:06:05,362] TRAIN Iter 98120: lr = 0.336468,	loss = 2.728594,	Top-1 err = 0.415137,	Top-5 err = 0.193701,	data_time = 0.050505,	train_time = 0.607004
[2019-08-23 10:06:19,600] TRAIN Iter 98140: lr = 0.336435,	loss = 2.859661,	Top-1 err = 0.422363,	Top-5 err = 0.198682,	data_time = 0.050822,	train_time = 0.711918
[2019-08-23 10:06:27,632] TRAIN Iter 98160: lr = 0.336402,	loss = 2.777324,	Top-1 err = 0.426514,	Top-5 err = 0.200391,	data_time = 0.050367,	train_time = 0.401540
[2019-08-23 10:06:39,491] TRAIN Iter 98180: lr = 0.336368,	loss = 2.808244,	Top-1 err = 0.427930,	Top-5 err = 0.204932,	data_time = 0.050696,	train_time = 0.592962
[2019-08-23 10:06:46,850] TRAIN Iter 98200: lr = 0.336335,	loss = 2.755785,	Top-1 err = 0.426318,	Top-5 err = 0.201611,	data_time = 0.050489,	train_time = 0.367916
[2019-08-23 10:07:01,843] TRAIN Iter 98220: lr = 0.336302,	loss = 2.764596,	Top-1 err = 0.431152,	Top-5 err = 0.202344,	data_time = 0.050216,	train_time = 0.749654
[2019-08-23 10:07:17,902] TRAIN Iter 98240: lr = 0.336268,	loss = 2.721904,	Top-1 err = 0.431836,	Top-5 err = 0.205225,	data_time = 0.050871,	train_time = 0.802944
[2019-08-23 10:07:25,563] TRAIN Iter 98260: lr = 0.336235,	loss = 2.798777,	Top-1 err = 0.426221,	Top-5 err = 0.200830,	data_time = 0.050860,	train_time = 0.383009
[2019-08-23 10:07:35,850] TRAIN Iter 98280: lr = 0.336202,	loss = 2.662669,	Top-1 err = 0.428516,	Top-5 err = 0.204687,	data_time = 0.050792,	train_time = 0.514374
[2019-08-23 10:07:51,277] TRAIN Iter 98300: lr = 0.336168,	loss = 2.810462,	Top-1 err = 0.426758,	Top-5 err = 0.201904,	data_time = 0.050367,	train_time = 0.771303
[2019-08-23 10:07:58,821] TRAIN Iter 98320: lr = 0.336135,	loss = 2.794881,	Top-1 err = 0.429053,	Top-5 err = 0.205811,	data_time = 0.159937,	train_time = 0.377200
[2019-08-23 10:08:14,095] TRAIN Iter 98340: lr = 0.336102,	loss = 2.795796,	Top-1 err = 0.429541,	Top-5 err = 0.203076,	data_time = 0.050583,	train_time = 0.763676
[2019-08-23 10:08:22,134] TRAIN Iter 98360: lr = 0.336068,	loss = 2.671965,	Top-1 err = 0.430322,	Top-5 err = 0.207813,	data_time = 0.050879,	train_time = 0.401919
[2019-08-23 10:08:33,805] TRAIN Iter 98380: lr = 0.336035,	loss = 2.741635,	Top-1 err = 0.429004,	Top-5 err = 0.203076,	data_time = 0.050393,	train_time = 0.583545
[2019-08-23 10:08:47,606] TRAIN Iter 98400: lr = 0.336002,	loss = 2.809275,	Top-1 err = 0.423633,	Top-5 err = 0.202930,	data_time = 0.097914,	train_time = 0.690054
[2019-08-23 10:08:54,655] TRAIN Iter 98420: lr = 0.335968,	loss = 2.745248,	Top-1 err = 0.430029,	Top-5 err = 0.205469,	data_time = 0.050728,	train_time = 0.352414
[2019-08-23 10:09:11,238] TRAIN Iter 98440: lr = 0.335935,	loss = 2.707189,	Top-1 err = 0.422803,	Top-5 err = 0.199902,	data_time = 0.050602,	train_time = 0.829132
[2019-08-23 10:09:25,064] TRAIN Iter 98460: lr = 0.335902,	loss = 2.740047,	Top-1 err = 0.425830,	Top-5 err = 0.201270,	data_time = 0.050331,	train_time = 0.691278
[2019-08-23 10:09:32,150] TRAIN Iter 98480: lr = 0.335868,	loss = 2.767368,	Top-1 err = 0.428711,	Top-5 err = 0.203613,	data_time = 0.050763,	train_time = 0.354312
[2019-08-23 10:09:43,737] TRAIN Iter 98500: lr = 0.335835,	loss = 2.858209,	Top-1 err = 0.429150,	Top-5 err = 0.206934,	data_time = 0.050773,	train_time = 0.579321
[2019-08-23 10:09:51,350] TRAIN Iter 98520: lr = 0.335802,	loss = 2.803632,	Top-1 err = 0.426904,	Top-5 err = 0.205566,	data_time = 0.050353,	train_time = 0.380662
[2019-08-23 10:10:06,193] TRAIN Iter 98540: lr = 0.335768,	loss = 2.681180,	Top-1 err = 0.428711,	Top-5 err = 0.202002,	data_time = 0.050478,	train_time = 0.742130
[2019-08-23 10:10:18,159] TRAIN Iter 98560: lr = 0.335735,	loss = 2.677865,	Top-1 err = 0.422363,	Top-5 err = 0.199268,	data_time = 0.129134,	train_time = 0.598261
[2019-08-23 10:10:26,465] TRAIN Iter 98580: lr = 0.335702,	loss = 2.791339,	Top-1 err = 0.431836,	Top-5 err = 0.203760,	data_time = 0.050522,	train_time = 0.415294
[2019-08-23 10:10:42,784] TRAIN Iter 98600: lr = 0.335668,	loss = 2.738972,	Top-1 err = 0.426318,	Top-5 err = 0.202783,	data_time = 0.114481,	train_time = 0.815947
[2019-08-23 10:10:53,690] TRAIN Iter 98620: lr = 0.335635,	loss = 2.789940,	Top-1 err = 0.436816,	Top-5 err = 0.210156,	data_time = 0.116392,	train_time = 0.545287
[2019-08-23 10:11:04,139] TRAIN Iter 98640: lr = 0.335602,	loss = 2.712180,	Top-1 err = 0.432373,	Top-5 err = 0.208936,	data_time = 0.050627,	train_time = 0.522426
[2019-08-23 10:11:18,168] TRAIN Iter 98660: lr = 0.335568,	loss = 2.723560,	Top-1 err = 0.426904,	Top-5 err = 0.199170,	data_time = 0.050486,	train_time = 0.701445
[2019-08-23 10:11:25,265] TRAIN Iter 98680: lr = 0.335535,	loss = 2.775594,	Top-1 err = 0.427783,	Top-5 err = 0.205518,	data_time = 0.050966,	train_time = 0.354834
[2019-08-23 10:11:41,937] TRAIN Iter 98700: lr = 0.335502,	loss = 2.722758,	Top-1 err = 0.432129,	Top-5 err = 0.207178,	data_time = 0.050356,	train_time = 0.833589
[2019-08-23 10:11:54,028] TRAIN Iter 98720: lr = 0.335468,	loss = 2.709929,	Top-1 err = 0.426611,	Top-5 err = 0.204932,	data_time = 0.050483,	train_time = 0.604509
[2019-08-23 10:12:04,135] TRAIN Iter 98740: lr = 0.335435,	loss = 2.813931,	Top-1 err = 0.428125,	Top-5 err = 0.202734,	data_time = 0.050792,	train_time = 0.505365
[2019-08-23 10:12:18,380] TRAIN Iter 98760: lr = 0.335402,	loss = 2.851985,	Top-1 err = 0.426416,	Top-5 err = 0.202295,	data_time = 0.050569,	train_time = 0.712200
[2019-08-23 10:12:32,403] TRAIN Iter 98780: lr = 0.335368,	loss = 2.831469,	Top-1 err = 0.432373,	Top-5 err = 0.205859,	data_time = 0.063127,	train_time = 0.701174
[2019-08-23 10:12:41,680] TRAIN Iter 98800: lr = 0.335335,	loss = 2.768684,	Top-1 err = 0.426611,	Top-5 err = 0.203418,	data_time = 0.050090,	train_time = 0.463839
[2019-08-23 10:12:55,159] TRAIN Iter 98820: lr = 0.335302,	loss = 2.766294,	Top-1 err = 0.428174,	Top-5 err = 0.203271,	data_time = 0.123987,	train_time = 0.673920
[2019-08-23 10:13:03,466] TRAIN Iter 98840: lr = 0.335268,	loss = 2.721712,	Top-1 err = 0.432178,	Top-5 err = 0.207031,	data_time = 0.049927,	train_time = 0.415351
[2019-08-23 10:13:51,517] TRAIN Iter 98860: lr = 0.335235,	loss = 2.772707,	Top-1 err = 0.440042,	Top-5 err = 0.207662,	data_time = 0.050931,	train_time = 2.402532
[2019-08-23 10:14:04,363] TRAIN Iter 98880: lr = 0.335202,	loss = 2.746128,	Top-1 err = 0.422168,	Top-5 err = 0.201221,	data_time = 0.050635,	train_time = 0.642270
[2019-08-23 10:14:12,908] TRAIN Iter 98900: lr = 0.335168,	loss = 2.730928,	Top-1 err = 0.426318,	Top-5 err = 0.198779,	data_time = 0.050776,	train_time = 0.427248
[2019-08-23 10:14:23,651] TRAIN Iter 98920: lr = 0.335135,	loss = 2.635095,	Top-1 err = 0.422412,	Top-5 err = 0.195215,	data_time = 0.050875,	train_time = 0.537113
[2019-08-23 10:14:32,207] TRAIN Iter 98940: lr = 0.335102,	loss = 2.804948,	Top-1 err = 0.419922,	Top-5 err = 0.194580,	data_time = 0.050869,	train_time = 0.427775
[2019-08-23 10:14:43,331] TRAIN Iter 98960: lr = 0.335068,	loss = 2.666560,	Top-1 err = 0.418896,	Top-5 err = 0.192871,	data_time = 0.050464,	train_time = 0.556212
[2019-08-23 10:14:56,329] TRAIN Iter 98980: lr = 0.335035,	loss = 2.754503,	Top-1 err = 0.427979,	Top-5 err = 0.201709,	data_time = 0.050209,	train_time = 0.649842
[2019-08-23 10:15:04,274] TRAIN Iter 99000: lr = 0.335002,	loss = 2.625241,	Top-1 err = 0.424561,	Top-5 err = 0.196582,	data_time = 0.051109,	train_time = 0.397242
[2019-08-23 10:15:15,939] TRAIN Iter 99020: lr = 0.334968,	loss = 2.686231,	Top-1 err = 0.424609,	Top-5 err = 0.197803,	data_time = 0.050796,	train_time = 0.583248
[2019-08-23 10:15:27,678] TRAIN Iter 99040: lr = 0.334935,	loss = 2.783132,	Top-1 err = 0.425146,	Top-5 err = 0.200098,	data_time = 0.050925,	train_time = 0.586925
[2019-08-23 10:15:37,085] TRAIN Iter 99060: lr = 0.334902,	loss = 2.809576,	Top-1 err = 0.429102,	Top-5 err = 0.199512,	data_time = 0.050834,	train_time = 0.470328
[2019-08-23 10:15:50,004] TRAIN Iter 99080: lr = 0.334868,	loss = 2.681655,	Top-1 err = 0.423975,	Top-5 err = 0.197656,	data_time = 0.051285,	train_time = 0.645930
[2019-08-23 10:15:57,488] TRAIN Iter 99100: lr = 0.334835,	loss = 2.801582,	Top-1 err = 0.425342,	Top-5 err = 0.198633,	data_time = 0.141092,	train_time = 0.374203
[2019-08-23 10:16:10,212] TRAIN Iter 99120: lr = 0.334802,	loss = 2.717936,	Top-1 err = 0.423682,	Top-5 err = 0.194238,	data_time = 0.050758,	train_time = 0.636179
[2019-08-23 10:16:24,325] TRAIN Iter 99140: lr = 0.334768,	loss = 2.715671,	Top-1 err = 0.426270,	Top-5 err = 0.200195,	data_time = 0.106040,	train_time = 0.705654
[2019-08-23 10:16:32,572] TRAIN Iter 99160: lr = 0.334735,	loss = 2.741907,	Top-1 err = 0.423877,	Top-5 err = 0.203125,	data_time = 0.050957,	train_time = 0.412318
[2019-08-23 10:16:45,068] TRAIN Iter 99180: lr = 0.334702,	loss = 2.668036,	Top-1 err = 0.429102,	Top-5 err = 0.202979,	data_time = 0.050488,	train_time = 0.624811
[2019-08-23 10:16:56,548] TRAIN Iter 99200: lr = 0.334668,	loss = 2.718004,	Top-1 err = 0.419824,	Top-5 err = 0.195996,	data_time = 0.050956,	train_time = 0.573966
[2019-08-23 10:17:06,675] TRAIN Iter 99220: lr = 0.334635,	loss = 2.745082,	Top-1 err = 0.423047,	Top-5 err = 0.201660,	data_time = 0.050803,	train_time = 0.506334
[2019-08-23 10:17:20,368] TRAIN Iter 99240: lr = 0.334602,	loss = 2.778520,	Top-1 err = 0.426367,	Top-5 err = 0.199414,	data_time = 0.050699,	train_time = 0.684631
[2019-08-23 10:17:28,289] TRAIN Iter 99260: lr = 0.334568,	loss = 2.785361,	Top-1 err = 0.423047,	Top-5 err = 0.203271,	data_time = 0.050469,	train_time = 0.396048
[2019-08-23 10:17:39,265] TRAIN Iter 99280: lr = 0.334535,	loss = 2.692043,	Top-1 err = 0.420752,	Top-5 err = 0.197266,	data_time = 0.050661,	train_time = 0.548775
[2019-08-23 10:17:54,827] TRAIN Iter 99300: lr = 0.334502,	loss = 2.737918,	Top-1 err = 0.426807,	Top-5 err = 0.206348,	data_time = 0.050967,	train_time = 0.778062
[2019-08-23 10:18:02,739] TRAIN Iter 99320: lr = 0.334468,	loss = 2.689851,	Top-1 err = 0.421143,	Top-5 err = 0.200684,	data_time = 0.117276,	train_time = 0.395599
[2019-08-23 10:18:16,441] TRAIN Iter 99340: lr = 0.334435,	loss = 2.782497,	Top-1 err = 0.429736,	Top-5 err = 0.203223,	data_time = 0.050884,	train_time = 0.685087
[2019-08-23 10:18:28,555] TRAIN Iter 99360: lr = 0.334402,	loss = 2.697709,	Top-1 err = 0.422168,	Top-5 err = 0.201904,	data_time = 0.050666,	train_time = 0.605712
[2019-08-23 10:18:36,563] TRAIN Iter 99380: lr = 0.334368,	loss = 2.800696,	Top-1 err = 0.426465,	Top-5 err = 0.198389,	data_time = 0.050683,	train_time = 0.400359
[2019-08-23 10:18:50,911] TRAIN Iter 99400: lr = 0.334335,	loss = 2.645000,	Top-1 err = 0.430127,	Top-5 err = 0.204248,	data_time = 0.050506,	train_time = 0.717374
[2019-08-23 10:18:59,134] TRAIN Iter 99420: lr = 0.334302,	loss = 2.721974,	Top-1 err = 0.420410,	Top-5 err = 0.199170,	data_time = 0.050439,	train_time = 0.411126
[2019-08-23 10:19:13,169] TRAIN Iter 99440: lr = 0.334268,	loss = 2.885304,	Top-1 err = 0.428906,	Top-5 err = 0.201709,	data_time = 0.050908,	train_time = 0.701755
[2019-08-23 10:19:24,806] TRAIN Iter 99460: lr = 0.334235,	loss = 2.760102,	Top-1 err = 0.424756,	Top-5 err = 0.201025,	data_time = 0.050619,	train_time = 0.581834
[2019-08-23 10:19:32,292] TRAIN Iter 99480: lr = 0.334202,	loss = 2.704539,	Top-1 err = 0.420312,	Top-5 err = 0.197510,	data_time = 0.050842,	train_time = 0.374305
[2019-08-23 10:19:46,138] TRAIN Iter 99500: lr = 0.334168,	loss = 2.742247,	Top-1 err = 0.423486,	Top-5 err = 0.200586,	data_time = 0.050471,	train_time = 0.692282
[2019-08-23 10:20:00,161] TRAIN Iter 99520: lr = 0.334135,	loss = 2.777309,	Top-1 err = 0.424219,	Top-5 err = 0.201953,	data_time = 0.102524,	train_time = 0.701117
[2019-08-23 10:20:07,685] TRAIN Iter 99540: lr = 0.334102,	loss = 2.756448,	Top-1 err = 0.425195,	Top-5 err = 0.202588,	data_time = 0.050299,	train_time = 0.376100
[2019-08-23 10:20:22,231] TRAIN Iter 99560: lr = 0.334068,	loss = 2.729370,	Top-1 err = 0.424854,	Top-5 err = 0.200977,	data_time = 0.050535,	train_time = 0.727268
[2019-08-23 10:20:30,163] TRAIN Iter 99580: lr = 0.334035,	loss = 2.764176,	Top-1 err = 0.423633,	Top-5 err = 0.196387,	data_time = 0.050659,	train_time = 0.396604
[2019-08-23 10:20:42,833] TRAIN Iter 99600: lr = 0.334002,	loss = 2.728580,	Top-1 err = 0.425244,	Top-5 err = 0.203516,	data_time = 0.050355,	train_time = 0.633495
[2019-08-23 10:20:58,057] TRAIN Iter 99620: lr = 0.333968,	loss = 2.706646,	Top-1 err = 0.425195,	Top-5 err = 0.201904,	data_time = 0.050734,	train_time = 0.761177
[2019-08-23 10:21:05,840] TRAIN Iter 99640: lr = 0.333935,	loss = 2.810144,	Top-1 err = 0.427148,	Top-5 err = 0.208887,	data_time = 0.050163,	train_time = 0.389142
[2019-08-23 10:21:19,194] TRAIN Iter 99660: lr = 0.333902,	loss = 2.808506,	Top-1 err = 0.430908,	Top-5 err = 0.202490,	data_time = 0.050923,	train_time = 0.667649
[2019-08-23 10:21:32,745] TRAIN Iter 99680: lr = 0.333868,	loss = 2.714853,	Top-1 err = 0.427734,	Top-5 err = 0.200586,	data_time = 0.141067,	train_time = 0.677533
[2019-08-23 10:21:42,199] TRAIN Iter 99700: lr = 0.333835,	loss = 2.845866,	Top-1 err = 0.430908,	Top-5 err = 0.201563,	data_time = 0.050762,	train_time = 0.472718
[2019-08-23 10:21:54,923] TRAIN Iter 99720: lr = 0.333802,	loss = 2.794837,	Top-1 err = 0.425098,	Top-5 err = 0.200439,	data_time = 0.050598,	train_time = 0.636156
[2019-08-23 10:22:03,134] TRAIN Iter 99740: lr = 0.333768,	loss = 2.661821,	Top-1 err = 0.424658,	Top-5 err = 0.203369,	data_time = 0.050481,	train_time = 0.410539
[2019-08-23 10:22:15,309] TRAIN Iter 99760: lr = 0.333735,	loss = 2.844193,	Top-1 err = 0.431494,	Top-5 err = 0.206543,	data_time = 0.050673,	train_time = 0.608751
[2019-08-23 10:22:31,265] TRAIN Iter 99780: lr = 0.333702,	loss = 2.731718,	Top-1 err = 0.430957,	Top-5 err = 0.205859,	data_time = 0.050946,	train_time = 0.797774
[2019-08-23 10:22:39,054] TRAIN Iter 99800: lr = 0.333668,	loss = 2.785454,	Top-1 err = 0.427100,	Top-5 err = 0.204980,	data_time = 0.050512,	train_time = 0.389432
[2019-08-23 10:22:52,525] TRAIN Iter 99820: lr = 0.333635,	loss = 2.675147,	Top-1 err = 0.426953,	Top-5 err = 0.201611,	data_time = 0.050848,	train_time = 0.673553
[2019-08-23 10:23:10,006] TRAIN Iter 99840: lr = 0.333602,	loss = 2.746195,	Top-1 err = 0.435059,	Top-5 err = 0.210303,	data_time = 0.050519,	train_time = 0.874010
[2019-08-23 10:23:19,679] TRAIN Iter 99860: lr = 0.333568,	loss = 2.802871,	Top-1 err = 0.419971,	Top-5 err = 0.201514,	data_time = 0.050534,	train_time = 0.483647
[2019-08-23 10:23:33,710] TRAIN Iter 99880: lr = 0.333535,	loss = 2.626971,	Top-1 err = 0.429785,	Top-5 err = 0.202490,	data_time = 0.050524,	train_time = 0.701559
[2019-08-23 10:23:41,869] TRAIN Iter 99900: lr = 0.333502,	loss = 2.734656,	Top-1 err = 0.426563,	Top-5 err = 0.198779,	data_time = 0.050437,	train_time = 0.407903
[2019-08-23 10:23:55,782] TRAIN Iter 99920: lr = 0.333468,	loss = 2.651576,	Top-1 err = 0.427637,	Top-5 err = 0.202002,	data_time = 0.050184,	train_time = 0.695672
[2019-08-23 10:24:11,192] TRAIN Iter 99940: lr = 0.333435,	loss = 2.782269,	Top-1 err = 0.431934,	Top-5 err = 0.204785,	data_time = 0.050543,	train_time = 0.770452
[2019-08-23 10:24:18,608] TRAIN Iter 99960: lr = 0.333402,	loss = 2.755355,	Top-1 err = 0.423633,	Top-5 err = 0.207178,	data_time = 0.050532,	train_time = 0.370809
[2019-08-23 10:24:32,371] TRAIN Iter 99980: lr = 0.333368,	loss = 2.772287,	Top-1 err = 0.432227,	Top-5 err = 0.206885,	data_time = 0.050515,	train_time = 0.688133
[2019-08-23 10:24:48,012] TRAIN Iter 100000: lr = 0.333335,	loss = 2.818635,	Top-1 err = 0.430322,	Top-5 err = 0.201563,	data_time = 0.050543,	train_time = 0.782015
[2019-08-23 10:25:48,104] TEST Iter 100000: loss = 2.648072,	Top-1 err = 0.417220,	Top-5 err = 0.180120,	val_time = 60.049298
[2019-08-23 10:25:54,392] TRAIN Iter 100020: lr = 0.333302,	loss = 2.800487,	Top-1 err = 0.435937,	Top-5 err = 0.207959,	data_time = 0.050367,	train_time = 0.314402
[2019-08-23 10:26:00,864] TRAIN Iter 100040: lr = 0.333268,	loss = 2.853270,	Top-1 err = 0.435059,	Top-5 err = 0.204883,	data_time = 0.050048,	train_time = 0.323591
[2019-08-23 10:26:07,413] TRAIN Iter 100060: lr = 0.333235,	loss = 2.815468,	Top-1 err = 0.430762,	Top-5 err = 0.204590,	data_time = 0.157138,	train_time = 0.327431
[2019-08-23 10:26:15,587] TRAIN Iter 100080: lr = 0.333202,	loss = 2.777357,	Top-1 err = 0.431885,	Top-5 err = 0.204541,	data_time = 0.412774,	train_time = 0.408673
[2019-08-23 10:27:01,395] TRAIN Iter 100100: lr = 0.333168,	loss = 2.642125,	Top-1 err = 0.425167,	Top-5 err = 0.202822,	data_time = 0.051023,	train_time = 2.290374
[2019-08-23 10:27:08,979] TRAIN Iter 100120: lr = 0.333135,	loss = 2.720349,	Top-1 err = 0.432227,	Top-5 err = 0.205322,	data_time = 0.103465,	train_time = 0.379201
[2019-08-23 10:27:20,008] TRAIN Iter 100140: lr = 0.333102,	loss = 2.766012,	Top-1 err = 0.417920,	Top-5 err = 0.194775,	data_time = 0.050543,	train_time = 0.551403
[2019-08-23 10:27:28,111] TRAIN Iter 100160: lr = 0.333068,	loss = 2.775955,	Top-1 err = 0.417627,	Top-5 err = 0.196826,	data_time = 0.050558,	train_time = 0.405141
[2019-08-23 10:27:39,327] TRAIN Iter 100180: lr = 0.333035,	loss = 2.721828,	Top-1 err = 0.412939,	Top-5 err = 0.188086,	data_time = 0.050499,	train_time = 0.560808
[2019-08-23 10:27:54,339] TRAIN Iter 100200: lr = 0.333002,	loss = 2.708426,	Top-1 err = 0.423242,	Top-5 err = 0.199414,	data_time = 0.050891,	train_time = 0.750590
[2019-08-23 10:28:02,185] TRAIN Iter 100220: lr = 0.332968,	loss = 2.705617,	Top-1 err = 0.422656,	Top-5 err = 0.195703,	data_time = 0.050583,	train_time = 0.392250
[2019-08-23 10:28:14,459] TRAIN Iter 100240: lr = 0.332935,	loss = 2.746898,	Top-1 err = 0.422217,	Top-5 err = 0.199170,	data_time = 0.050627,	train_time = 0.613730
[2019-08-23 10:28:28,325] TRAIN Iter 100260: lr = 0.332902,	loss = 2.754040,	Top-1 err = 0.419531,	Top-5 err = 0.195850,	data_time = 0.050490,	train_time = 0.693255
[2019-08-23 10:28:36,008] TRAIN Iter 100280: lr = 0.332868,	loss = 2.721025,	Top-1 err = 0.416553,	Top-5 err = 0.197656,	data_time = 0.050425,	train_time = 0.384167
[2019-08-23 10:28:52,367] TRAIN Iter 100300: lr = 0.332835,	loss = 2.748062,	Top-1 err = 0.428711,	Top-5 err = 0.201611,	data_time = 0.050592,	train_time = 0.817932
[2019-08-23 10:29:00,908] TRAIN Iter 100320: lr = 0.332802,	loss = 2.860359,	Top-1 err = 0.422510,	Top-5 err = 0.196777,	data_time = 0.050346,	train_time = 0.427031
[2019-08-23 10:29:10,906] TRAIN Iter 100340: lr = 0.332768,	loss = 2.674387,	Top-1 err = 0.415918,	Top-5 err = 0.195459,	data_time = 0.050549,	train_time = 0.499881
[2019-08-23 10:29:27,991] TRAIN Iter 100360: lr = 0.332735,	loss = 2.661951,	Top-1 err = 0.417236,	Top-5 err = 0.199316,	data_time = 0.050346,	train_time = 0.854235
[2019-08-23 10:29:36,002] TRAIN Iter 100380: lr = 0.332702,	loss = 2.720337,	Top-1 err = 0.419727,	Top-5 err = 0.197754,	data_time = 0.145105,	train_time = 0.400515
[2019-08-23 10:29:47,565] TRAIN Iter 100400: lr = 0.332668,	loss = 2.698148,	Top-1 err = 0.422168,	Top-5 err = 0.197168,	data_time = 0.050402,	train_time = 0.578139
[2019-08-23 10:29:59,568] TRAIN Iter 100420: lr = 0.332635,	loss = 2.770458,	Top-1 err = 0.426904,	Top-5 err = 0.199121,	data_time = 0.164469,	train_time = 0.600125
[2019-08-23 10:30:07,731] TRAIN Iter 100440: lr = 0.332602,	loss = 2.764940,	Top-1 err = 0.428027,	Top-5 err = 0.197852,	data_time = 0.050526,	train_time = 0.408155
[2019-08-23 10:30:22,030] TRAIN Iter 100460: lr = 0.332568,	loss = 2.755207,	Top-1 err = 0.429443,	Top-5 err = 0.205371,	data_time = 0.050333,	train_time = 0.714932
[2019-08-23 10:30:28,963] TRAIN Iter 100480: lr = 0.332535,	loss = 2.766637,	Top-1 err = 0.422510,	Top-5 err = 0.194678,	data_time = 0.050742,	train_time = 0.346616
[2019-08-23 10:30:44,384] TRAIN Iter 100500: lr = 0.332502,	loss = 2.779242,	Top-1 err = 0.414648,	Top-5 err = 0.197559,	data_time = 0.050455,	train_time = 0.771044
[2019-08-23 10:31:00,053] TRAIN Iter 100520: lr = 0.332468,	loss = 2.714986,	Top-1 err = 0.424902,	Top-5 err = 0.205029,	data_time = 0.050809,	train_time = 0.783436
[2019-08-23 10:31:07,395] TRAIN Iter 100540: lr = 0.332435,	loss = 2.648015,	Top-1 err = 0.421533,	Top-5 err = 0.202490,	data_time = 0.050597,	train_time = 0.367092
[2019-08-23 10:31:20,801] TRAIN Iter 100560: lr = 0.332402,	loss = 2.761853,	Top-1 err = 0.428174,	Top-5 err = 0.200586,	data_time = 0.050219,	train_time = 0.670285
[2019-08-23 10:31:36,092] TRAIN Iter 100580: lr = 0.332368,	loss = 2.740313,	Top-1 err = 0.429834,	Top-5 err = 0.203809,	data_time = 1.705636,	train_time = 0.764546
[2019-08-23 10:31:43,705] TRAIN Iter 100600: lr = 0.332335,	loss = 2.660542,	Top-1 err = 0.420166,	Top-5 err = 0.192529,	data_time = 0.050431,	train_time = 0.380615
[2019-08-23 10:31:57,812] TRAIN Iter 100620: lr = 0.332302,	loss = 2.649344,	Top-1 err = 0.425439,	Top-5 err = 0.202393,	data_time = 0.050411,	train_time = 0.705365
[2019-08-23 10:32:05,364] TRAIN Iter 100640: lr = 0.332268,	loss = 2.775850,	Top-1 err = 0.426953,	Top-5 err = 0.202100,	data_time = 0.050462,	train_time = 0.377576
[2019-08-23 10:32:18,950] TRAIN Iter 100660: lr = 0.332235,	loss = 2.777554,	Top-1 err = 0.424365,	Top-5 err = 0.203174,	data_time = 0.050549,	train_time = 0.679270
[2019-08-23 10:32:34,497] TRAIN Iter 100680: lr = 0.332202,	loss = 2.709752,	Top-1 err = 0.424902,	Top-5 err = 0.200049,	data_time = 0.618328,	train_time = 0.777337
[2019-08-23 10:32:42,044] TRAIN Iter 100700: lr = 0.332168,	loss = 2.856879,	Top-1 err = 0.423730,	Top-5 err = 0.202686,	data_time = 0.050565,	train_time = 0.377350
[2019-08-23 10:32:56,991] TRAIN Iter 100720: lr = 0.332135,	loss = 2.702640,	Top-1 err = 0.426660,	Top-5 err = 0.201563,	data_time = 0.050547,	train_time = 0.747346
[2019-08-23 10:33:11,480] TRAIN Iter 100740: lr = 0.332102,	loss = 2.807051,	Top-1 err = 0.423633,	Top-5 err = 0.201660,	data_time = 0.118368,	train_time = 0.724432
[2019-08-23 10:33:19,032] TRAIN Iter 100760: lr = 0.332068,	loss = 2.703094,	Top-1 err = 0.427588,	Top-5 err = 0.198145,	data_time = 0.050548,	train_time = 0.377556
[2019-08-23 10:33:35,541] TRAIN Iter 100780: lr = 0.332035,	loss = 2.793542,	Top-1 err = 0.421045,	Top-5 err = 0.203613,	data_time = 0.050570,	train_time = 0.825449
[2019-08-23 10:33:43,010] TRAIN Iter 100800: lr = 0.332002,	loss = 2.745853,	Top-1 err = 0.424072,	Top-5 err = 0.200732,	data_time = 0.050836,	train_time = 0.373412
[2019-08-23 10:33:58,837] TRAIN Iter 100820: lr = 0.331968,	loss = 2.739876,	Top-1 err = 0.418994,	Top-5 err = 0.199805,	data_time = 0.204752,	train_time = 0.791339
[2019-08-23 10:34:14,303] TRAIN Iter 100840: lr = 0.331935,	loss = 2.799321,	Top-1 err = 0.424658,	Top-5 err = 0.202051,	data_time = 0.050454,	train_time = 0.773322
[2019-08-23 10:34:21,737] TRAIN Iter 100860: lr = 0.331902,	loss = 2.691537,	Top-1 err = 0.418652,	Top-5 err = 0.198486,	data_time = 0.050571,	train_time = 0.371642
[2019-08-23 10:34:37,125] TRAIN Iter 100880: lr = 0.331868,	loss = 2.784864,	Top-1 err = 0.424756,	Top-5 err = 0.203516,	data_time = 0.050550,	train_time = 0.769423
[2019-08-23 10:34:51,865] TRAIN Iter 100900: lr = 0.331835,	loss = 2.799132,	Top-1 err = 0.432520,	Top-5 err = 0.206982,	data_time = 0.050488,	train_time = 0.736970
[2019-08-23 10:34:59,014] TRAIN Iter 100920: lr = 0.331802,	loss = 2.810070,	Top-1 err = 0.428662,	Top-5 err = 0.202100,	data_time = 0.050327,	train_time = 0.357450
[2019-08-23 10:35:12,722] TRAIN Iter 100940: lr = 0.331768,	loss = 2.712853,	Top-1 err = 0.425537,	Top-5 err = 0.204395,	data_time = 0.050833,	train_time = 0.685375
[2019-08-23 10:35:19,564] TRAIN Iter 100960: lr = 0.331735,	loss = 2.917148,	Top-1 err = 0.427881,	Top-5 err = 0.202344,	data_time = 0.050502,	train_time = 0.342057
[2019-08-23 10:35:35,261] TRAIN Iter 100980: lr = 0.331702,	loss = 2.752591,	Top-1 err = 0.426074,	Top-5 err = 0.202832,	data_time = 0.050714,	train_time = 0.784860
[2019-08-23 10:35:52,695] TRAIN Iter 101000: lr = 0.331668,	loss = 2.711017,	Top-1 err = 0.433057,	Top-5 err = 0.209912,	data_time = 0.050532,	train_time = 0.871699
[2019-08-23 10:35:59,502] TRAIN Iter 101020: lr = 0.331635,	loss = 2.694626,	Top-1 err = 0.424414,	Top-5 err = 0.204346,	data_time = 0.050458,	train_time = 0.340329
[2019-08-23 10:36:15,985] TRAIN Iter 101040: lr = 0.331602,	loss = 2.676224,	Top-1 err = 0.424951,	Top-5 err = 0.199902,	data_time = 0.050397,	train_time = 0.824110
[2019-08-23 10:36:29,283] TRAIN Iter 101060: lr = 0.331568,	loss = 2.754494,	Top-1 err = 0.428613,	Top-5 err = 0.200928,	data_time = 0.050633,	train_time = 0.664915
[2019-08-23 10:36:37,953] TRAIN Iter 101080: lr = 0.331535,	loss = 2.768670,	Top-1 err = 0.426660,	Top-5 err = 0.202490,	data_time = 0.050761,	train_time = 0.433448
[2019-08-23 10:36:53,597] TRAIN Iter 101100: lr = 0.331502,	loss = 2.743300,	Top-1 err = 0.432715,	Top-5 err = 0.204004,	data_time = 0.416240,	train_time = 0.782229
[2019-08-23 10:37:00,451] TRAIN Iter 101120: lr = 0.331468,	loss = 2.737255,	Top-1 err = 0.428027,	Top-5 err = 0.203662,	data_time = 0.050475,	train_time = 0.342670
[2019-08-23 10:37:16,936] TRAIN Iter 101140: lr = 0.331435,	loss = 2.808511,	Top-1 err = 0.427539,	Top-5 err = 0.204346,	data_time = 0.050549,	train_time = 0.824252
[2019-08-23 10:37:32,079] TRAIN Iter 101160: lr = 0.331402,	loss = 2.786225,	Top-1 err = 0.428174,	Top-5 err = 0.205176,	data_time = 0.565855,	train_time = 0.757136
[2019-08-23 10:37:39,046] TRAIN Iter 101180: lr = 0.331368,	loss = 2.852758,	Top-1 err = 0.434326,	Top-5 err = 0.205518,	data_time = 0.050828,	train_time = 0.348318
[2019-08-23 10:37:57,272] TRAIN Iter 101200: lr = 0.331335,	loss = 2.721699,	Top-1 err = 0.428955,	Top-5 err = 0.205078,	data_time = 0.050392,	train_time = 0.911269
[2019-08-23 10:38:13,108] TRAIN Iter 101220: lr = 0.331302,	loss = 2.843928,	Top-1 err = 0.426514,	Top-5 err = 0.206250,	data_time = 0.050487,	train_time = 0.791807
[2019-08-23 10:38:20,955] TRAIN Iter 101240: lr = 0.331268,	loss = 2.724336,	Top-1 err = 0.429004,	Top-5 err = 0.199854,	data_time = 0.050140,	train_time = 0.392346
[2019-08-23 10:38:39,099] TRAIN Iter 101260: lr = 0.331235,	loss = 2.756591,	Top-1 err = 0.424365,	Top-5 err = 0.201904,	data_time = 0.210166,	train_time = 0.907154
[2019-08-23 10:38:45,782] TRAIN Iter 101280: lr = 0.331202,	loss = 2.889205,	Top-1 err = 0.433691,	Top-5 err = 0.209570,	data_time = 0.050648,	train_time = 0.334135
[2019-08-23 10:39:03,928] TRAIN Iter 101300: lr = 0.331168,	loss = 2.688857,	Top-1 err = 0.433691,	Top-5 err = 0.208643,	data_time = 0.049991,	train_time = 0.907303
[2019-08-23 10:39:21,404] TRAIN Iter 101320: lr = 0.331135,	loss = 2.675232,	Top-1 err = 0.424121,	Top-5 err = 0.200000,	data_time = 0.049977,	train_time = 0.873807
[2019-08-23 10:39:27,951] TRAIN Iter 101340: lr = 0.331102,	loss = 2.825293,	Top-1 err = 0.434961,	Top-5 err = 0.199268,	data_time = 0.049901,	train_time = 0.327334
[2019-08-23 10:40:18,158] TRAIN Iter 101360: lr = 0.331068,	loss = 2.885082,	Top-1 err = 0.434769,	Top-5 err = 0.209566,	data_time = 0.050946,	train_time = 2.510329
[2019-08-23 10:40:25,949] TRAIN Iter 101380: lr = 0.331035,	loss = 2.749181,	Top-1 err = 0.426172,	Top-5 err = 0.201025,	data_time = 0.134813,	train_time = 0.389513
[2019-08-23 10:40:38,004] TRAIN Iter 101400: lr = 0.331002,	loss = 2.725652,	Top-1 err = 0.415820,	Top-5 err = 0.193115,	data_time = 0.050613,	train_time = 0.602729
[2019-08-23 10:40:50,927] TRAIN Iter 101420: lr = 0.330968,	loss = 2.766377,	Top-1 err = 0.421826,	Top-5 err = 0.199121,	data_time = 0.050339,	train_time = 0.646130
[2019-08-23 10:40:58,614] TRAIN Iter 101440: lr = 0.330935,	loss = 2.686113,	Top-1 err = 0.418066,	Top-5 err = 0.194043,	data_time = 0.050483,	train_time = 0.384352
[2019-08-23 10:41:14,353] TRAIN Iter 101460: lr = 0.330902,	loss = 2.819224,	Top-1 err = 0.420117,	Top-5 err = 0.196045,	data_time = 0.051219,	train_time = 0.786940
[2019-08-23 10:41:29,886] TRAIN Iter 101480: lr = 0.330868,	loss = 2.791622,	Top-1 err = 0.427441,	Top-5 err = 0.200635,	data_time = 0.050506,	train_time = 0.776633
[2019-08-23 10:41:37,038] TRAIN Iter 101500: lr = 0.330835,	loss = 2.774743,	Top-1 err = 0.418018,	Top-5 err = 0.194336,	data_time = 0.050439,	train_time = 0.357564
[2019-08-23 10:41:52,741] TRAIN Iter 101520: lr = 0.330802,	loss = 2.684575,	Top-1 err = 0.422461,	Top-5 err = 0.194336,	data_time = 0.050875,	train_time = 0.785135
[2019-08-23 10:42:00,274] TRAIN Iter 101540: lr = 0.330768,	loss = 2.765295,	Top-1 err = 0.423730,	Top-5 err = 0.196436,	data_time = 0.050250,	train_time = 0.376676
[2019-08-23 10:42:13,955] TRAIN Iter 101560: lr = 0.330735,	loss = 2.835753,	Top-1 err = 0.422119,	Top-5 err = 0.193408,	data_time = 0.050816,	train_time = 0.684017
[2019-08-23 10:42:29,911] TRAIN Iter 101580: lr = 0.330702,	loss = 2.793198,	Top-1 err = 0.420703,	Top-5 err = 0.197266,	data_time = 0.050331,	train_time = 0.797776
[2019-08-23 10:42:37,148] TRAIN Iter 101600: lr = 0.330668,	loss = 2.712734,	Top-1 err = 0.416309,	Top-5 err = 0.195703,	data_time = 0.050584,	train_time = 0.361859
[2019-08-23 10:42:50,697] TRAIN Iter 101620: lr = 0.330635,	loss = 2.785719,	Top-1 err = 0.425146,	Top-5 err = 0.199219,	data_time = 0.050194,	train_time = 0.677408
[2019-08-23 10:43:07,063] TRAIN Iter 101640: lr = 0.330602,	loss = 2.723631,	Top-1 err = 0.422461,	Top-5 err = 0.195605,	data_time = 0.050660,	train_time = 0.818312
[2019-08-23 10:43:14,537] TRAIN Iter 101660: lr = 0.330568,	loss = 2.775645,	Top-1 err = 0.416504,	Top-5 err = 0.197656,	data_time = 0.050406,	train_time = 0.373667
[2019-08-23 10:43:27,128] TRAIN Iter 101680: lr = 0.330535,	loss = 2.747515,	Top-1 err = 0.423389,	Top-5 err = 0.200391,	data_time = 0.050359,	train_time = 0.629560
[2019-08-23 10:43:34,438] TRAIN Iter 101700: lr = 0.330502,	loss = 2.764654,	Top-1 err = 0.421289,	Top-5 err = 0.194434,	data_time = 0.050355,	train_time = 0.365475
[2019-08-23 10:43:50,185] TRAIN Iter 101720: lr = 0.330468,	loss = 2.781916,	Top-1 err = 0.420312,	Top-5 err = 0.197656,	data_time = 0.050412,	train_time = 0.787324
[2019-08-23 10:44:03,140] TRAIN Iter 101740: lr = 0.330435,	loss = 2.703677,	Top-1 err = 0.430908,	Top-5 err = 0.205566,	data_time = 0.050349,	train_time = 0.647733
[2019-08-23 10:44:09,860] TRAIN Iter 101760: lr = 0.330402,	loss = 2.719687,	Top-1 err = 0.421484,	Top-5 err = 0.198193,	data_time = 0.050659,	train_time = 0.335981
[2019-08-23 10:44:29,004] TRAIN Iter 101780: lr = 0.330368,	loss = 2.664637,	Top-1 err = 0.417822,	Top-5 err = 0.201416,	data_time = 0.050662,	train_time = 0.957202
[2019-08-23 10:44:39,400] TRAIN Iter 101800: lr = 0.330335,	loss = 2.717150,	Top-1 err = 0.422607,	Top-5 err = 0.199707,	data_time = 0.128975,	train_time = 0.519791
[2019-08-23 10:44:46,738] TRAIN Iter 101820: lr = 0.330302,	loss = 2.794980,	Top-1 err = 0.422852,	Top-5 err = 0.201709,	data_time = 0.050233,	train_time = 0.366885
[2019-08-23 10:44:59,141] TRAIN Iter 101840: lr = 0.330268,	loss = 2.740587,	Top-1 err = 0.424854,	Top-5 err = 0.203467,	data_time = 0.050391,	train_time = 0.620114
[2019-08-23 10:45:08,214] TRAIN Iter 101860: lr = 0.330235,	loss = 2.749431,	Top-1 err = 0.424072,	Top-5 err = 0.197998,	data_time = 0.167507,	train_time = 0.453627
[2019-08-23 10:45:22,928] TRAIN Iter 101880: lr = 0.330202,	loss = 2.717636,	Top-1 err = 0.420947,	Top-5 err = 0.197754,	data_time = 0.050924,	train_time = 0.735690
[2019-08-23 10:45:35,653] TRAIN Iter 101900: lr = 0.330168,	loss = 2.747010,	Top-1 err = 0.424121,	Top-5 err = 0.200586,	data_time = 0.050953,	train_time = 0.636226
[2019-08-23 10:45:44,835] TRAIN Iter 101920: lr = 0.330135,	loss = 2.773499,	Top-1 err = 0.423779,	Top-5 err = 0.196143,	data_time = 0.050570,	train_time = 0.459094
[2019-08-23 10:45:59,999] TRAIN Iter 101940: lr = 0.330102,	loss = 2.741032,	Top-1 err = 0.426660,	Top-5 err = 0.200195,	data_time = 0.050747,	train_time = 0.758201
[2019-08-23 10:46:13,627] TRAIN Iter 101960: lr = 0.330068,	loss = 2.673035,	Top-1 err = 0.425977,	Top-5 err = 0.202002,	data_time = 0.139153,	train_time = 0.681402
[2019-08-23 10:46:22,724] TRAIN Iter 101980: lr = 0.330035,	loss = 2.697469,	Top-1 err = 0.425391,	Top-5 err = 0.200830,	data_time = 0.050506,	train_time = 0.454799
[2019-08-23 10:46:38,828] TRAIN Iter 102000: lr = 0.330002,	loss = 2.714982,	Top-1 err = 0.430029,	Top-5 err = 0.199854,	data_time = 0.050552,	train_time = 0.805220
[2019-08-23 10:46:46,916] TRAIN Iter 102020: lr = 0.329968,	loss = 2.726821,	Top-1 err = 0.421387,	Top-5 err = 0.197119,	data_time = 0.050312,	train_time = 0.404343
[2019-08-23 10:46:59,639] TRAIN Iter 102040: lr = 0.329935,	loss = 2.723561,	Top-1 err = 0.427197,	Top-5 err = 0.200146,	data_time = 0.050997,	train_time = 0.636162
[2019-08-23 10:47:15,140] TRAIN Iter 102060: lr = 0.329902,	loss = 2.810429,	Top-1 err = 0.425049,	Top-5 err = 0.199072,	data_time = 0.050507,	train_time = 0.775028
[2019-08-23 10:47:22,535] TRAIN Iter 102080: lr = 0.329868,	loss = 2.727330,	Top-1 err = 0.422559,	Top-5 err = 0.201514,	data_time = 0.050621,	train_time = 0.369734
[2019-08-23 10:47:37,036] TRAIN Iter 102100: lr = 0.329835,	loss = 2.829888,	Top-1 err = 0.430664,	Top-5 err = 0.204102,	data_time = 0.050277,	train_time = 0.725018
[2019-08-23 10:47:48,982] TRAIN Iter 102120: lr = 0.329802,	loss = 2.738870,	Top-1 err = 0.433203,	Top-5 err = 0.204834,	data_time = 0.050701,	train_time = 0.597290
[2019-08-23 10:47:58,353] TRAIN Iter 102140: lr = 0.329768,	loss = 2.704902,	Top-1 err = 0.428076,	Top-5 err = 0.198926,	data_time = 0.050610,	train_time = 0.468557
[2019-08-23 10:48:14,543] TRAIN Iter 102160: lr = 0.329735,	loss = 2.632013,	Top-1 err = 0.428174,	Top-5 err = 0.199658,	data_time = 0.050314,	train_time = 0.809466
[2019-08-23 10:48:22,353] TRAIN Iter 102180: lr = 0.329702,	loss = 2.786911,	Top-1 err = 0.428223,	Top-5 err = 0.203613,	data_time = 0.050468,	train_time = 0.390500
[2019-08-23 10:48:36,363] TRAIN Iter 102200: lr = 0.329668,	loss = 2.776520,	Top-1 err = 0.422705,	Top-5 err = 0.202441,	data_time = 0.050258,	train_time = 0.700480
[2019-08-23 10:48:51,424] TRAIN Iter 102220: lr = 0.329635,	loss = 2.701126,	Top-1 err = 0.424365,	Top-5 err = 0.198193,	data_time = 0.050368,	train_time = 0.753009
[2019-08-23 10:48:58,316] TRAIN Iter 102240: lr = 0.329602,	loss = 2.835787,	Top-1 err = 0.431885,	Top-5 err = 0.205078,	data_time = 0.050529,	train_time = 0.344597
[2019-08-23 10:49:14,259] TRAIN Iter 102260: lr = 0.329568,	loss = 2.777528,	Top-1 err = 0.431396,	Top-5 err = 0.204395,	data_time = 0.050371,	train_time = 0.797125
[2019-08-23 10:49:26,592] TRAIN Iter 102280: lr = 0.329535,	loss = 2.712464,	Top-1 err = 0.426660,	Top-5 err = 0.202197,	data_time = 0.050348,	train_time = 0.616634
[2019-08-23 10:49:37,862] TRAIN Iter 102300: lr = 0.329502,	loss = 2.709472,	Top-1 err = 0.419238,	Top-5 err = 0.196387,	data_time = 0.050672,	train_time = 0.563495
[2019-08-23 10:49:52,562] TRAIN Iter 102320: lr = 0.329468,	loss = 2.782224,	Top-1 err = 0.426270,	Top-5 err = 0.200781,	data_time = 0.050446,	train_time = 0.734973
[2019-08-23 10:49:59,765] TRAIN Iter 102340: lr = 0.329435,	loss = 2.856583,	Top-1 err = 0.430859,	Top-5 err = 0.204248,	data_time = 0.152110,	train_time = 0.360142
[2019-08-23 10:50:13,940] TRAIN Iter 102360: lr = 0.329402,	loss = 2.792457,	Top-1 err = 0.425049,	Top-5 err = 0.200732,	data_time = 0.050695,	train_time = 0.708734
[2019-08-23 10:50:28,710] TRAIN Iter 102380: lr = 0.329368,	loss = 2.672585,	Top-1 err = 0.422852,	Top-5 err = 0.198145,	data_time = 0.050527,	train_time = 0.738483
[2019-08-23 10:50:35,730] TRAIN Iter 102400: lr = 0.329335,	loss = 2.769415,	Top-1 err = 0.426270,	Top-5 err = 0.197510,	data_time = 0.050380,	train_time = 0.351000
[2019-08-23 10:50:53,652] TRAIN Iter 102420: lr = 0.329302,	loss = 2.901980,	Top-1 err = 0.427637,	Top-5 err = 0.202783,	data_time = 0.050633,	train_time = 0.896065
[2019-08-23 10:51:09,350] TRAIN Iter 102440: lr = 0.329268,	loss = 2.709045,	Top-1 err = 0.422070,	Top-5 err = 0.201758,	data_time = 0.050586,	train_time = 0.784893
[2019-08-23 10:51:16,272] TRAIN Iter 102460: lr = 0.329235,	loss = 2.804623,	Top-1 err = 0.428662,	Top-5 err = 0.201563,	data_time = 0.050651,	train_time = 0.346116
[2019-08-23 10:51:31,540] TRAIN Iter 102480: lr = 0.329202,	loss = 2.751260,	Top-1 err = 0.426074,	Top-5 err = 0.204297,	data_time = 0.050620,	train_time = 0.763392
[2019-08-23 10:51:38,249] TRAIN Iter 102500: lr = 0.329168,	loss = 2.821231,	Top-1 err = 0.424072,	Top-5 err = 0.201953,	data_time = 0.050456,	train_time = 0.335422
[2019-08-23 10:51:55,031] TRAIN Iter 102520: lr = 0.329135,	loss = 2.732327,	Top-1 err = 0.428223,	Top-5 err = 0.203809,	data_time = 0.050694,	train_time = 0.839104
[2019-08-23 10:52:11,150] TRAIN Iter 102540: lr = 0.329102,	loss = 2.752619,	Top-1 err = 0.427393,	Top-5 err = 0.200293,	data_time = 0.050009,	train_time = 0.805918
[2019-08-23 10:52:17,951] TRAIN Iter 102560: lr = 0.329068,	loss = 2.652025,	Top-1 err = 0.421924,	Top-5 err = 0.198047,	data_time = 0.050096,	train_time = 0.340014
[2019-08-23 10:52:33,308] TRAIN Iter 102580: lr = 0.329035,	loss = 2.832245,	Top-1 err = 0.436719,	Top-5 err = 0.206250,	data_time = 0.049894,	train_time = 0.767859
[2019-08-23 10:52:45,365] TRAIN Iter 102600: lr = 0.329002,	loss = 3.427718,	Top-1 err = 0.428982,	Top-5 err = 0.211111,	data_time = 0.007101,	train_time = 0.602819
[2019-08-23 10:53:31,285] TRAIN Iter 102620: lr = 0.328968,	loss = 2.740288,	Top-1 err = 0.425879,	Top-5 err = 0.200879,	data_time = 0.050404,	train_time = 2.296013
[2019-08-23 10:53:45,223] TRAIN Iter 102640: lr = 0.328935,	loss = 2.773967,	Top-1 err = 0.420996,	Top-5 err = 0.196045,	data_time = 0.117945,	train_time = 0.696872
[2019-08-23 10:53:52,737] TRAIN Iter 102660: lr = 0.328902,	loss = 2.774844,	Top-1 err = 0.421924,	Top-5 err = 0.198193,	data_time = 0.050545,	train_time = 0.375695
[2019-08-23 10:54:06,321] TRAIN Iter 102680: lr = 0.328868,	loss = 2.627721,	Top-1 err = 0.410596,	Top-5 err = 0.191699,	data_time = 0.050294,	train_time = 0.679159
[2019-08-23 10:54:17,539] TRAIN Iter 102700: lr = 0.328835,	loss = 2.593731,	Top-1 err = 0.414160,	Top-5 err = 0.190771,	data_time = 0.050754,	train_time = 0.560884
[2019-08-23 10:54:25,331] TRAIN Iter 102720: lr = 0.328802,	loss = 2.784441,	Top-1 err = 0.423193,	Top-5 err = 0.196973,	data_time = 0.050396,	train_time = 0.389585
[2019-08-23 10:54:41,368] TRAIN Iter 102740: lr = 0.328768,	loss = 2.725614,	Top-1 err = 0.426221,	Top-5 err = 0.199609,	data_time = 0.050844,	train_time = 0.801825
[2019-08-23 10:54:50,661] TRAIN Iter 102760: lr = 0.328735,	loss = 2.797177,	Top-1 err = 0.420068,	Top-5 err = 0.193604,	data_time = 0.050845,	train_time = 0.464630
[2019-08-23 10:55:03,405] TRAIN Iter 102780: lr = 0.328702,	loss = 2.771468,	Top-1 err = 0.427539,	Top-5 err = 0.203955,	data_time = 0.050386,	train_time = 0.637194
[2019-08-23 10:55:15,312] TRAIN Iter 102800: lr = 0.328668,	loss = 2.760629,	Top-1 err = 0.418457,	Top-5 err = 0.195801,	data_time = 0.050897,	train_time = 0.595366
[2019-08-23 10:55:22,339] TRAIN Iter 102820: lr = 0.328635,	loss = 2.647197,	Top-1 err = 0.420166,	Top-5 err = 0.196826,	data_time = 0.050344,	train_time = 0.351299
[2019-08-23 10:55:38,011] TRAIN Iter 102840: lr = 0.328602,	loss = 2.675466,	Top-1 err = 0.417187,	Top-5 err = 0.193506,	data_time = 0.050648,	train_time = 0.783606
[2019-08-23 10:55:52,024] TRAIN Iter 102860: lr = 0.328568,	loss = 2.844271,	Top-1 err = 0.422803,	Top-5 err = 0.197852,	data_time = 0.050467,	train_time = 0.700623
[2019-08-23 10:55:59,620] TRAIN Iter 102880: lr = 0.328535,	loss = 2.764377,	Top-1 err = 0.425586,	Top-5 err = 0.195801,	data_time = 0.050793,	train_time = 0.379784
[2019-08-23 10:56:13,779] TRAIN Iter 102900: lr = 0.328502,	loss = 2.645813,	Top-1 err = 0.417773,	Top-5 err = 0.196094,	data_time = 0.099012,	train_time = 0.707956
[2019-08-23 10:56:20,894] TRAIN Iter 102920: lr = 0.328468,	loss = 2.758445,	Top-1 err = 0.420801,	Top-5 err = 0.195801,	data_time = 0.050638,	train_time = 0.355741
[2019-08-23 10:56:36,689] TRAIN Iter 102940: lr = 0.328435,	loss = 2.720868,	Top-1 err = 0.422510,	Top-5 err = 0.203467,	data_time = 0.050615,	train_time = 0.789700
[2019-08-23 10:56:51,998] TRAIN Iter 102960: lr = 0.328402,	loss = 2.732133,	Top-1 err = 0.419385,	Top-5 err = 0.196533,	data_time = 0.050809,	train_time = 0.765437
[2019-08-23 10:56:59,773] TRAIN Iter 102980: lr = 0.328368,	loss = 2.661634,	Top-1 err = 0.420020,	Top-5 err = 0.197705,	data_time = 0.050498,	train_time = 0.388782
[2019-08-23 10:57:11,685] TRAIN Iter 103000: lr = 0.328335,	loss = 2.670078,	Top-1 err = 0.419629,	Top-5 err = 0.196338,	data_time = 0.050666,	train_time = 0.595572
[2019-08-23 10:57:25,332] TRAIN Iter 103020: lr = 0.328302,	loss = 2.710596,	Top-1 err = 0.425146,	Top-5 err = 0.197559,	data_time = 0.050982,	train_time = 0.682347
[2019-08-23 10:57:32,996] TRAIN Iter 103040: lr = 0.328268,	loss = 2.803164,	Top-1 err = 0.425488,	Top-5 err = 0.199072,	data_time = 0.050808,	train_time = 0.383174
[2019-08-23 10:57:48,375] TRAIN Iter 103060: lr = 0.328235,	loss = 2.711727,	Top-1 err = 0.428076,	Top-5 err = 0.201367,	data_time = 0.050633,	train_time = 0.768960
[2019-08-23 10:57:56,414] TRAIN Iter 103080: lr = 0.328202,	loss = 2.739006,	Top-1 err = 0.425293,	Top-5 err = 0.198242,	data_time = 0.050443,	train_time = 0.401930
[2019-08-23 10:58:09,406] TRAIN Iter 103100: lr = 0.328168,	loss = 2.694718,	Top-1 err = 0.419238,	Top-5 err = 0.198340,	data_time = 0.050287,	train_time = 0.649570
[2019-08-23 10:58:23,498] TRAIN Iter 103120: lr = 0.328135,	loss = 2.755803,	Top-1 err = 0.422266,	Top-5 err = 0.199121,	data_time = 0.050455,	train_time = 0.704573
[2019-08-23 10:58:30,873] TRAIN Iter 103140: lr = 0.328102,	loss = 2.700453,	Top-1 err = 0.422754,	Top-5 err = 0.195605,	data_time = 0.120683,	train_time = 0.368752
[2019-08-23 10:58:44,672] TRAIN Iter 103160: lr = 0.328068,	loss = 2.718068,	Top-1 err = 0.425684,	Top-5 err = 0.200391,	data_time = 0.050818,	train_time = 0.689928
[2019-08-23 10:58:58,061] TRAIN Iter 103180: lr = 0.328035,	loss = 2.648598,	Top-1 err = 0.423828,	Top-5 err = 0.199316,	data_time = 0.050480,	train_time = 0.669442
[2019-08-23 10:59:06,513] TRAIN Iter 103200: lr = 0.328002,	loss = 2.715912,	Top-1 err = 0.426270,	Top-5 err = 0.202979,	data_time = 0.050353,	train_time = 0.422571
[2019-08-23 10:59:21,620] TRAIN Iter 103220: lr = 0.327968,	loss = 2.717892,	Top-1 err = 0.426563,	Top-5 err = 0.199219,	data_time = 0.050456,	train_time = 0.755360
[2019-08-23 10:59:29,314] TRAIN Iter 103240: lr = 0.327935,	loss = 2.731007,	Top-1 err = 0.428662,	Top-5 err = 0.200977,	data_time = 0.050302,	train_time = 0.384651
[2019-08-23 10:59:44,748] TRAIN Iter 103260: lr = 0.327902,	loss = 2.808058,	Top-1 err = 0.425977,	Top-5 err = 0.196924,	data_time = 0.050618,	train_time = 0.771695
[2019-08-23 10:59:59,513] TRAIN Iter 103280: lr = 0.327868,	loss = 2.750233,	Top-1 err = 0.422754,	Top-5 err = 0.200635,	data_time = 0.050434,	train_time = 0.738245
[2019-08-23 11:00:06,957] TRAIN Iter 103300: lr = 0.327835,	loss = 2.645328,	Top-1 err = 0.420117,	Top-5 err = 0.201611,	data_time = 0.050591,	train_time = 0.372169
[2019-08-23 11:00:21,219] TRAIN Iter 103320: lr = 0.327802,	loss = 2.729161,	Top-1 err = 0.427783,	Top-5 err = 0.204687,	data_time = 0.050695,	train_time = 0.713086
[2019-08-23 11:00:31,938] TRAIN Iter 103340: lr = 0.327768,	loss = 2.760188,	Top-1 err = 0.424121,	Top-5 err = 0.197705,	data_time = 0.050720,	train_time = 0.535965
[2019-08-23 11:00:43,579] TRAIN Iter 103360: lr = 0.327735,	loss = 2.697144,	Top-1 err = 0.421631,	Top-5 err = 0.195410,	data_time = 0.050673,	train_time = 0.582000
[2019-08-23 11:00:56,149] TRAIN Iter 103380: lr = 0.327702,	loss = 2.697248,	Top-1 err = 0.431201,	Top-5 err = 0.209473,	data_time = 0.050497,	train_time = 0.628511
[2019-08-23 11:01:03,308] TRAIN Iter 103400: lr = 0.327668,	loss = 2.711166,	Top-1 err = 0.429883,	Top-5 err = 0.203223,	data_time = 0.096989,	train_time = 0.357934
[2019-08-23 11:01:19,210] TRAIN Iter 103420: lr = 0.327635,	loss = 2.731704,	Top-1 err = 0.424609,	Top-5 err = 0.204492,	data_time = 0.050533,	train_time = 0.795086
[2019-08-23 11:01:33,043] TRAIN Iter 103440: lr = 0.327602,	loss = 2.844930,	Top-1 err = 0.429199,	Top-5 err = 0.201465,	data_time = 0.050976,	train_time = 0.691646
[2019-08-23 11:01:40,465] TRAIN Iter 103460: lr = 0.327568,	loss = 2.788018,	Top-1 err = 0.425488,	Top-5 err = 0.200879,	data_time = 0.050565,	train_time = 0.371052
[2019-08-23 11:01:55,064] TRAIN Iter 103480: lr = 0.327535,	loss = 2.638995,	Top-1 err = 0.423779,	Top-5 err = 0.199902,	data_time = 0.050626,	train_time = 0.729980
[2019-08-23 11:02:08,435] TRAIN Iter 103500: lr = 0.327502,	loss = 2.694253,	Top-1 err = 0.423340,	Top-5 err = 0.200586,	data_time = 0.050282,	train_time = 0.668531
[2019-08-23 11:02:16,385] TRAIN Iter 103520: lr = 0.327468,	loss = 2.795276,	Top-1 err = 0.428613,	Top-5 err = 0.205371,	data_time = 0.050402,	train_time = 0.397463
[2019-08-23 11:02:31,609] TRAIN Iter 103540: lr = 0.327435,	loss = 2.827717,	Top-1 err = 0.430811,	Top-5 err = 0.203711,	data_time = 0.050371,	train_time = 0.761203
[2019-08-23 11:02:38,892] TRAIN Iter 103560: lr = 0.327402,	loss = 2.671665,	Top-1 err = 0.423633,	Top-5 err = 0.200928,	data_time = 0.050284,	train_time = 0.364117
[2019-08-23 11:02:55,021] TRAIN Iter 103580: lr = 0.327368,	loss = 2.759535,	Top-1 err = 0.424609,	Top-5 err = 0.199951,	data_time = 0.050545,	train_time = 0.806429
[2019-08-23 11:03:10,687] TRAIN Iter 103600: lr = 0.327335,	loss = 2.741463,	Top-1 err = 0.427295,	Top-5 err = 0.202295,	data_time = 2.373437,	train_time = 0.783248
[2019-08-23 11:03:18,302] TRAIN Iter 103620: lr = 0.327302,	loss = 2.718498,	Top-1 err = 0.422998,	Top-5 err = 0.198975,	data_time = 0.050433,	train_time = 0.380751
[2019-08-23 11:03:31,915] TRAIN Iter 103640: lr = 0.327268,	loss = 2.783105,	Top-1 err = 0.429541,	Top-5 err = 0.201953,	data_time = 0.050611,	train_time = 0.680659
[2019-08-23 11:03:46,061] TRAIN Iter 103660: lr = 0.327235,	loss = 2.618907,	Top-1 err = 0.417383,	Top-5 err = 0.194629,	data_time = 0.050340,	train_time = 0.707266
[2019-08-23 11:03:56,242] TRAIN Iter 103680: lr = 0.327202,	loss = 2.732977,	Top-1 err = 0.425146,	Top-5 err = 0.200830,	data_time = 0.050245,	train_time = 0.509027
[2019-08-23 11:04:10,582] TRAIN Iter 103700: lr = 0.327168,	loss = 2.787945,	Top-1 err = 0.423193,	Top-5 err = 0.200977,	data_time = 0.130736,	train_time = 0.716993
[2019-08-23 11:04:18,027] TRAIN Iter 103720: lr = 0.327135,	loss = 2.716693,	Top-1 err = 0.421875,	Top-5 err = 0.203418,	data_time = 0.050648,	train_time = 0.372255
[2019-08-23 11:04:33,744] TRAIN Iter 103740: lr = 0.327102,	loss = 2.705569,	Top-1 err = 0.425000,	Top-5 err = 0.197070,	data_time = 0.050536,	train_time = 0.785843
[2019-08-23 11:04:51,135] TRAIN Iter 103760: lr = 0.327068,	loss = 2.861597,	Top-1 err = 0.425049,	Top-5 err = 0.201563,	data_time = 1.654536,	train_time = 0.869507
[2019-08-23 11:04:58,132] TRAIN Iter 103780: lr = 0.327035,	loss = 2.706950,	Top-1 err = 0.432324,	Top-5 err = 0.204590,	data_time = 0.050575,	train_time = 0.349827
[2019-08-23 11:05:16,977] TRAIN Iter 103800: lr = 0.327002,	loss = 2.766372,	Top-1 err = 0.429199,	Top-5 err = 0.205176,	data_time = 0.050019,	train_time = 0.942243
[2019-08-23 11:05:34,521] TRAIN Iter 103820: lr = 0.326968,	loss = 2.747612,	Top-1 err = 0.420361,	Top-5 err = 0.203418,	data_time = 0.049970,	train_time = 0.877209
[2019-08-23 11:05:40,762] TRAIN Iter 103840: lr = 0.326935,	loss = 2.769323,	Top-1 err = 0.427002,	Top-5 err = 0.205859,	data_time = 0.049899,	train_time = 0.312011
[2019-08-23 11:06:32,474] TRAIN Iter 103860: lr = 0.326902,	loss = 2.815741,	Top-1 err = 0.431134,	Top-5 err = 0.203132,	data_time = 0.050489,	train_time = 2.585608
[2019-08-23 11:06:40,072] TRAIN Iter 103880: lr = 0.326868,	loss = 2.740317,	Top-1 err = 0.419531,	Top-5 err = 0.196533,	data_time = 0.050910,	train_time = 0.379853
[2019-08-23 11:06:55,411] TRAIN Iter 103900: lr = 0.326835,	loss = 2.741002,	Top-1 err = 0.420557,	Top-5 err = 0.193652,	data_time = 0.050537,	train_time = 0.766969
[2019-08-23 11:07:03,990] TRAIN Iter 103920: lr = 0.326802,	loss = 2.760660,	Top-1 err = 0.422754,	Top-5 err = 0.195459,	data_time = 0.050482,	train_time = 0.428924
[2019-08-23 11:07:11,667] TRAIN Iter 103940: lr = 0.326768,	loss = 2.664385,	Top-1 err = 0.417285,	Top-5 err = 0.196338,	data_time = 0.050667,	train_time = 0.383814
[2019-08-23 11:07:24,377] TRAIN Iter 103960: lr = 0.326735,	loss = 2.735530,	Top-1 err = 0.413916,	Top-5 err = 0.190967,	data_time = 0.092166,	train_time = 0.635491
[2019-08-23 11:07:31,821] TRAIN Iter 103980: lr = 0.326702,	loss = 2.868612,	Top-1 err = 0.418408,	Top-5 err = 0.194727,	data_time = 0.050539,	train_time = 0.372202
[2019-08-23 11:07:45,507] TRAIN Iter 104000: lr = 0.326668,	loss = 2.580013,	Top-1 err = 0.419922,	Top-5 err = 0.195947,	data_time = 0.050508,	train_time = 0.684284
[2019-08-23 11:08:01,459] TRAIN Iter 104020: lr = 0.326635,	loss = 2.668539,	Top-1 err = 0.420703,	Top-5 err = 0.198535,	data_time = 0.115763,	train_time = 0.797584
[2019-08-23 11:08:09,272] TRAIN Iter 104040: lr = 0.326602,	loss = 2.781185,	Top-1 err = 0.424609,	Top-5 err = 0.193799,	data_time = 0.050545,	train_time = 0.390622
[2019-08-23 11:08:23,551] TRAIN Iter 104060: lr = 0.326568,	loss = 2.710614,	Top-1 err = 0.419922,	Top-5 err = 0.196631,	data_time = 0.050388,	train_time = 0.713931
[2019-08-23 11:08:37,761] TRAIN Iter 104080: lr = 0.326535,	loss = 2.737269,	Top-1 err = 0.415234,	Top-5 err = 0.196680,	data_time = 0.050887,	train_time = 0.710516
[2019-08-23 11:08:45,429] TRAIN Iter 104100: lr = 0.326502,	loss = 2.803674,	Top-1 err = 0.420166,	Top-5 err = 0.201904,	data_time = 0.050390,	train_time = 0.383391
[2019-08-23 11:08:59,339] TRAIN Iter 104120: lr = 0.326468,	loss = 2.815966,	Top-1 err = 0.423047,	Top-5 err = 0.193555,	data_time = 0.050576,	train_time = 0.695448
[2019-08-23 11:09:06,962] TRAIN Iter 104140: lr = 0.326435,	loss = 2.803817,	Top-1 err = 0.421436,	Top-5 err = 0.199365,	data_time = 0.050712,	train_time = 0.381146
[2019-08-23 11:09:21,563] TRAIN Iter 104160: lr = 0.326402,	loss = 2.745435,	Top-1 err = 0.421729,	Top-5 err = 0.201367,	data_time = 0.050755,	train_time = 0.730057
[2019-08-23 11:09:34,236] TRAIN Iter 104180: lr = 0.326368,	loss = 2.711015,	Top-1 err = 0.426123,	Top-5 err = 0.201660,	data_time = 0.050901,	train_time = 0.633617
[2019-08-23 11:09:41,748] TRAIN Iter 104200: lr = 0.326335,	loss = 2.638202,	Top-1 err = 0.418652,	Top-5 err = 0.197607,	data_time = 0.050377,	train_time = 0.375597
[2019-08-23 11:09:55,948] TRAIN Iter 104220: lr = 0.326302,	loss = 2.718601,	Top-1 err = 0.420703,	Top-5 err = 0.193115,	data_time = 0.050501,	train_time = 0.709966
[2019-08-23 11:10:10,214] TRAIN Iter 104240: lr = 0.326268,	loss = 2.746151,	Top-1 err = 0.419043,	Top-5 err = 0.196973,	data_time = 0.050868,	train_time = 0.713308
[2019-08-23 11:10:20,163] TRAIN Iter 104260: lr = 0.326235,	loss = 2.716725,	Top-1 err = 0.420068,	Top-5 err = 0.197070,	data_time = 0.050495,	train_time = 0.497442
[2019-08-23 11:10:34,102] TRAIN Iter 104280: lr = 0.326202,	loss = 2.710297,	Top-1 err = 0.420654,	Top-5 err = 0.199512,	data_time = 0.050448,	train_time = 0.696910
[2019-08-23 11:10:41,334] TRAIN Iter 104300: lr = 0.326168,	loss = 2.724064,	Top-1 err = 0.425049,	Top-5 err = 0.197656,	data_time = 0.050504,	train_time = 0.361607
[2019-08-23 11:10:55,440] TRAIN Iter 104320: lr = 0.326135,	loss = 2.779917,	Top-1 err = 0.424756,	Top-5 err = 0.197803,	data_time = 0.050487,	train_time = 0.705271
[2019-08-23 11:11:10,703] TRAIN Iter 104340: lr = 0.326102,	loss = 2.713856,	Top-1 err = 0.426172,	Top-5 err = 0.199414,	data_time = 0.050362,	train_time = 0.763152
[2019-08-23 11:11:18,080] TRAIN Iter 104360: lr = 0.326068,	loss = 2.855563,	Top-1 err = 0.424658,	Top-5 err = 0.199707,	data_time = 0.050595,	train_time = 0.368829
[2019-08-23 11:11:33,543] TRAIN Iter 104380: lr = 0.326035,	loss = 2.803963,	Top-1 err = 0.422412,	Top-5 err = 0.201367,	data_time = 0.050744,	train_time = 0.773148
[2019-08-23 11:11:45,431] TRAIN Iter 104400: lr = 0.326002,	loss = 2.672350,	Top-1 err = 0.425586,	Top-5 err = 0.196680,	data_time = 0.275266,	train_time = 0.594339
[2019-08-23 11:11:56,718] TRAIN Iter 104420: lr = 0.325968,	loss = 2.708106,	Top-1 err = 0.424561,	Top-5 err = 0.199023,	data_time = 0.050784,	train_time = 0.564367
[2019-08-23 11:12:10,966] TRAIN Iter 104440: lr = 0.325935,	loss = 2.669723,	Top-1 err = 0.428369,	Top-5 err = 0.199805,	data_time = 0.050297,	train_time = 0.712357
[2019-08-23 11:12:18,085] TRAIN Iter 104460: lr = 0.325902,	loss = 2.767364,	Top-1 err = 0.427100,	Top-5 err = 0.206006,	data_time = 0.147467,	train_time = 0.355973
[2019-08-23 11:12:33,537] TRAIN Iter 104480: lr = 0.325868,	loss = 2.656817,	Top-1 err = 0.425928,	Top-5 err = 0.200781,	data_time = 0.050787,	train_time = 0.772556
[2019-08-23 11:12:48,158] TRAIN Iter 104500: lr = 0.325835,	loss = 2.775513,	Top-1 err = 0.426660,	Top-5 err = 0.198486,	data_time = 0.050814,	train_time = 0.731040
[2019-08-23 11:12:55,774] TRAIN Iter 104520: lr = 0.325802,	loss = 2.756192,	Top-1 err = 0.423828,	Top-5 err = 0.197412,	data_time = 0.050207,	train_time = 0.380784
[2019-08-23 11:13:11,698] TRAIN Iter 104540: lr = 0.325768,	loss = 2.799836,	Top-1 err = 0.421973,	Top-5 err = 0.201709,	data_time = 0.050375,	train_time = 0.796197
[2019-08-23 11:13:24,099] TRAIN Iter 104560: lr = 0.325735,	loss = 2.771050,	Top-1 err = 0.422119,	Top-5 err = 0.199023,	data_time = 0.732510,	train_time = 0.620037
[2019-08-23 11:13:36,494] TRAIN Iter 104580: lr = 0.325702,	loss = 2.721035,	Top-1 err = 0.422852,	Top-5 err = 0.198047,	data_time = 0.050481,	train_time = 0.619712
[2019-08-23 11:13:49,764] TRAIN Iter 104600: lr = 0.325668,	loss = 2.710965,	Top-1 err = 0.425195,	Top-5 err = 0.196680,	data_time = 0.050443,	train_time = 0.663500
[2019-08-23 11:13:56,660] TRAIN Iter 104620: lr = 0.325635,	loss = 2.643767,	Top-1 err = 0.429785,	Top-5 err = 0.207129,	data_time = 0.050847,	train_time = 0.344810
[2019-08-23 11:14:13,215] TRAIN Iter 104640: lr = 0.325602,	loss = 2.695247,	Top-1 err = 0.425488,	Top-5 err = 0.199561,	data_time = 0.050575,	train_time = 0.827742
[2019-08-23 11:14:28,377] TRAIN Iter 104660: lr = 0.325568,	loss = 2.768654,	Top-1 err = 0.425879,	Top-5 err = 0.199902,	data_time = 0.050431,	train_time = 0.758058
[2019-08-23 11:14:35,603] TRAIN Iter 104680: lr = 0.325535,	loss = 2.758794,	Top-1 err = 0.432910,	Top-5 err = 0.204687,	data_time = 0.144105,	train_time = 0.361282
[2019-08-23 11:14:50,527] TRAIN Iter 104700: lr = 0.325502,	loss = 2.692416,	Top-1 err = 0.417725,	Top-5 err = 0.201025,	data_time = 0.050509,	train_time = 0.746208
[2019-08-23 11:15:05,343] TRAIN Iter 104720: lr = 0.325468,	loss = 2.751111,	Top-1 err = 0.431689,	Top-5 err = 0.202051,	data_time = 0.050423,	train_time = 0.740751
[2019-08-23 11:15:14,439] TRAIN Iter 104740: lr = 0.325435,	loss = 2.682655,	Top-1 err = 0.426074,	Top-5 err = 0.203516,	data_time = 0.050475,	train_time = 0.454779
[2019-08-23 11:15:29,927] TRAIN Iter 104760: lr = 0.325402,	loss = 2.837769,	Top-1 err = 0.427930,	Top-5 err = 0.203223,	data_time = 0.050357,	train_time = 0.774418
[2019-08-23 11:15:37,015] TRAIN Iter 104780: lr = 0.325368,	loss = 2.654661,	Top-1 err = 0.428125,	Top-5 err = 0.201855,	data_time = 0.050511,	train_time = 0.354353
[2019-08-23 11:15:51,918] TRAIN Iter 104800: lr = 0.325335,	loss = 2.861592,	Top-1 err = 0.427002,	Top-5 err = 0.200586,	data_time = 0.050436,	train_time = 0.745176
[2019-08-23 11:16:07,747] TRAIN Iter 104820: lr = 0.325302,	loss = 2.847001,	Top-1 err = 0.421240,	Top-5 err = 0.200537,	data_time = 0.127764,	train_time = 0.791436
[2019-08-23 11:16:14,866] TRAIN Iter 104840: lr = 0.325268,	loss = 2.652251,	Top-1 err = 0.425732,	Top-5 err = 0.200928,	data_time = 0.050433,	train_time = 0.355895
[2019-08-23 11:16:30,989] TRAIN Iter 104860: lr = 0.325235,	loss = 2.704426,	Top-1 err = 0.425049,	Top-5 err = 0.200879,	data_time = 0.050336,	train_time = 0.806172
[2019-08-23 11:16:46,279] TRAIN Iter 104880: lr = 0.325202,	loss = 2.756364,	Top-1 err = 0.424463,	Top-5 err = 0.199854,	data_time = 0.050472,	train_time = 0.764484
[2019-08-23 11:16:54,136] TRAIN Iter 104900: lr = 0.325168,	loss = 2.795749,	Top-1 err = 0.428662,	Top-5 err = 0.199902,	data_time = 0.050534,	train_time = 0.392823
[2019-08-23 11:17:10,348] TRAIN Iter 104920: lr = 0.325135,	loss = 2.748905,	Top-1 err = 0.423779,	Top-5 err = 0.200195,	data_time = 0.050637,	train_time = 0.810578
[2019-08-23 11:17:17,603] TRAIN Iter 104940: lr = 0.325102,	loss = 2.687859,	Top-1 err = 0.422070,	Top-5 err = 0.196826,	data_time = 0.050507,	train_time = 0.362718
[2019-08-23 11:17:33,646] TRAIN Iter 104960: lr = 0.325068,	loss = 2.856883,	Top-1 err = 0.432910,	Top-5 err = 0.205811,	data_time = 0.050538,	train_time = 0.802132
[2019-08-23 11:17:51,292] TRAIN Iter 104980: lr = 0.325035,	loss = 2.792697,	Top-1 err = 0.424854,	Top-5 err = 0.199121,	data_time = 0.051060,	train_time = 0.882330
[2019-08-23 11:17:58,963] TRAIN Iter 105000: lr = 0.325002,	loss = 2.712556,	Top-1 err = 0.430908,	Top-5 err = 0.206055,	data_time = 0.050867,	train_time = 0.383530
[2019-08-23 11:18:13,662] TRAIN Iter 105020: lr = 0.324968,	loss = 2.776758,	Top-1 err = 0.424316,	Top-5 err = 0.201563,	data_time = 0.050409,	train_time = 0.734932
[2019-08-23 11:18:29,124] TRAIN Iter 105040: lr = 0.324935,	loss = 2.726832,	Top-1 err = 0.428027,	Top-5 err = 0.201074,	data_time = 0.618709,	train_time = 0.773056
[2019-08-23 11:18:37,205] TRAIN Iter 105060: lr = 0.324902,	loss = 2.778456,	Top-1 err = 0.425000,	Top-5 err = 0.198682,	data_time = 0.050045,	train_time = 0.404070
[2019-08-23 11:18:53,894] TRAIN Iter 105080: lr = 0.324868,	loss = 2.599089,	Top-1 err = 0.425342,	Top-5 err = 0.197754,	data_time = 1.123826,	train_time = 0.834405
[2019-08-23 11:19:00,227] TRAIN Iter 105100: lr = 0.324835,	loss = 2.743065,	Top-1 err = 0.427002,	Top-5 err = 0.204297,	data_time = 0.049946,	train_time = 0.316663
[2019-08-23 11:19:50,038] TRAIN Iter 105120: lr = 0.324802,	loss = 2.713597,	Top-1 err = 0.427273,	Top-5 err = 0.205242,	data_time = 0.050743,	train_time = 2.490546
[2019-08-23 11:20:05,338] TRAIN Iter 105140: lr = 0.324768,	loss = 2.755097,	Top-1 err = 0.421729,	Top-5 err = 0.196875,	data_time = 0.050599,	train_time = 0.764933
[2019-08-23 11:20:13,442] TRAIN Iter 105160: lr = 0.324735,	loss = 2.678766,	Top-1 err = 0.418359,	Top-5 err = 0.197363,	data_time = 0.050506,	train_time = 0.405223
[2019-08-23 11:20:28,181] TRAIN Iter 105180: lr = 0.324702,	loss = 2.792115,	Top-1 err = 0.420605,	Top-5 err = 0.198242,	data_time = 0.050392,	train_time = 0.736936
[2019-08-23 11:20:35,790] TRAIN Iter 105200: lr = 0.324668,	loss = 2.704240,	Top-1 err = 0.419336,	Top-5 err = 0.195166,	data_time = 0.050711,	train_time = 0.380404
[2019-08-23 11:20:50,017] TRAIN Iter 105220: lr = 0.324635,	loss = 2.735941,	Top-1 err = 0.420264,	Top-5 err = 0.200244,	data_time = 0.050822,	train_time = 0.711367
[2019-08-23 11:21:03,979] TRAIN Iter 105240: lr = 0.324602,	loss = 2.793945,	Top-1 err = 0.420605,	Top-5 err = 0.193652,	data_time = 0.050604,	train_time = 0.698054
[2019-08-23 11:21:11,289] TRAIN Iter 105260: lr = 0.324568,	loss = 2.683376,	Top-1 err = 0.411572,	Top-5 err = 0.193066,	data_time = 0.050750,	train_time = 0.365498
[2019-08-23 11:21:26,687] TRAIN Iter 105280: lr = 0.324535,	loss = 2.642329,	Top-1 err = 0.422217,	Top-5 err = 0.196191,	data_time = 0.050614,	train_time = 0.769908
[2019-08-23 11:21:41,976] TRAIN Iter 105300: lr = 0.324502,	loss = 2.635761,	Top-1 err = 0.412598,	Top-5 err = 0.194336,	data_time = 0.050400,	train_time = 0.764392
[2019-08-23 11:21:49,244] TRAIN Iter 105320: lr = 0.324468,	loss = 2.697697,	Top-1 err = 0.425488,	Top-5 err = 0.194580,	data_time = 0.125619,	train_time = 0.363400
[2019-08-23 11:22:04,473] TRAIN Iter 105340: lr = 0.324435,	loss = 2.680711,	Top-1 err = 0.423438,	Top-5 err = 0.200391,	data_time = 0.169974,	train_time = 0.761459
[2019-08-23 11:22:12,645] TRAIN Iter 105360: lr = 0.324402,	loss = 2.641765,	Top-1 err = 0.419434,	Top-5 err = 0.195117,	data_time = 0.050340,	train_time = 0.408562
[2019-08-23 11:22:23,204] TRAIN Iter 105380: lr = 0.324368,	loss = 2.632321,	Top-1 err = 0.408252,	Top-5 err = 0.195850,	data_time = 0.050774,	train_time = 0.527922
[2019-08-23 11:22:38,106] TRAIN Iter 105400: lr = 0.324335,	loss = 2.735857,	Top-1 err = 0.422021,	Top-5 err = 0.196826,	data_time = 0.050807,	train_time = 0.745122
[2019-08-23 11:22:45,691] TRAIN Iter 105420: lr = 0.324302,	loss = 2.663731,	Top-1 err = 0.421289,	Top-5 err = 0.197363,	data_time = 0.050289,	train_time = 0.379211
[2019-08-23 11:22:58,704] TRAIN Iter 105440: lr = 0.324268,	loss = 2.718244,	Top-1 err = 0.421289,	Top-5 err = 0.198340,	data_time = 0.050648,	train_time = 0.650656
[2019-08-23 11:23:13,062] TRAIN Iter 105460: lr = 0.324235,	loss = 2.598669,	Top-1 err = 0.417480,	Top-5 err = 0.196777,	data_time = 0.050499,	train_time = 0.717880
[2019-08-23 11:23:20,001] TRAIN Iter 105480: lr = 0.324202,	loss = 2.729972,	Top-1 err = 0.426465,	Top-5 err = 0.199756,	data_time = 0.050853,	train_time = 0.346929
[2019-08-23 11:23:35,491] TRAIN Iter 105500: lr = 0.324168,	loss = 2.669219,	Top-1 err = 0.417920,	Top-5 err = 0.191943,	data_time = 0.050873,	train_time = 0.774503
[2019-08-23 11:23:43,492] TRAIN Iter 105520: lr = 0.324135,	loss = 2.774496,	Top-1 err = 0.422900,	Top-5 err = 0.201465,	data_time = 0.176341,	train_time = 0.400006
[2019-08-23 11:23:53,829] TRAIN Iter 105540: lr = 0.324102,	loss = 2.765472,	Top-1 err = 0.429688,	Top-5 err = 0.202979,	data_time = 0.050345,	train_time = 0.516856
[2019-08-23 11:24:10,054] TRAIN Iter 105560: lr = 0.324068,	loss = 2.716197,	Top-1 err = 0.421777,	Top-5 err = 0.198779,	data_time = 0.050671,	train_time = 0.811205
[2019-08-23 11:24:16,845] TRAIN Iter 105580: lr = 0.324035,	loss = 2.682299,	Top-1 err = 0.420117,	Top-5 err = 0.198535,	data_time = 0.138371,	train_time = 0.339531
[2019-08-23 11:24:33,464] TRAIN Iter 105600: lr = 0.324002,	loss = 2.716573,	Top-1 err = 0.430176,	Top-5 err = 0.202051,	data_time = 0.050756,	train_time = 0.830945
[2019-08-23 11:24:49,751] TRAIN Iter 105620: lr = 0.323968,	loss = 2.712124,	Top-1 err = 0.422217,	Top-5 err = 0.196240,	data_time = 0.050443,	train_time = 0.814352
[2019-08-23 11:24:56,818] TRAIN Iter 105640: lr = 0.323935,	loss = 2.745679,	Top-1 err = 0.418799,	Top-5 err = 0.200244,	data_time = 0.140490,	train_time = 0.353337
[2019-08-23 11:25:11,196] TRAIN Iter 105660: lr = 0.323902,	loss = 2.626792,	Top-1 err = 0.421240,	Top-5 err = 0.197119,	data_time = 0.050170,	train_time = 0.718894
[2019-08-23 11:25:18,794] TRAIN Iter 105680: lr = 0.323868,	loss = 2.679650,	Top-1 err = 0.418262,	Top-5 err = 0.191357,	data_time = 0.139298,	train_time = 0.379860
[2019-08-23 11:25:31,725] TRAIN Iter 105700: lr = 0.323835,	loss = 2.734969,	Top-1 err = 0.425244,	Top-5 err = 0.200342,	data_time = 0.050958,	train_time = 0.646556
[2019-08-23 11:25:47,216] TRAIN Iter 105720: lr = 0.323802,	loss = 2.772046,	Top-1 err = 0.427832,	Top-5 err = 0.200049,	data_time = 0.050403,	train_time = 0.774544
[2019-08-23 11:25:54,145] TRAIN Iter 105740: lr = 0.323768,	loss = 2.726682,	Top-1 err = 0.421582,	Top-5 err = 0.200342,	data_time = 0.050809,	train_time = 0.346422
[2019-08-23 11:26:08,384] TRAIN Iter 105760: lr = 0.323735,	loss = 2.720426,	Top-1 err = 0.424951,	Top-5 err = 0.202588,	data_time = 0.050425,	train_time = 0.711943
[2019-08-23 11:26:24,782] TRAIN Iter 105780: lr = 0.323702,	loss = 2.700221,	Top-1 err = 0.428809,	Top-5 err = 0.202686,	data_time = 0.050603,	train_time = 0.819856
[2019-08-23 11:26:31,891] TRAIN Iter 105800: lr = 0.323668,	loss = 2.786614,	Top-1 err = 0.425781,	Top-5 err = 0.200732,	data_time = 0.050862,	train_time = 0.355466
[2019-08-23 11:26:46,760] TRAIN Iter 105820: lr = 0.323635,	loss = 2.780461,	Top-1 err = 0.426172,	Top-5 err = 0.200586,	data_time = 0.050705,	train_time = 0.743398
[2019-08-23 11:26:53,737] TRAIN Iter 105840: lr = 0.323602,	loss = 2.786394,	Top-1 err = 0.425195,	Top-5 err = 0.199121,	data_time = 0.050393,	train_time = 0.348857
[2019-08-23 11:27:09,962] TRAIN Iter 105860: lr = 0.323568,	loss = 2.741334,	Top-1 err = 0.426611,	Top-5 err = 0.200244,	data_time = 0.050482,	train_time = 0.811256
[2019-08-23 11:27:26,455] TRAIN Iter 105880: lr = 0.323535,	loss = 2.669936,	Top-1 err = 0.420166,	Top-5 err = 0.198730,	data_time = 0.050618,	train_time = 0.824639
[2019-08-23 11:27:33,676] TRAIN Iter 105900: lr = 0.323502,	loss = 2.726619,	Top-1 err = 0.424219,	Top-5 err = 0.196143,	data_time = 0.050459,	train_time = 0.361003
[2019-08-23 11:27:48,661] TRAIN Iter 105920: lr = 0.323468,	loss = 2.712386,	Top-1 err = 0.422607,	Top-5 err = 0.198486,	data_time = 0.050464,	train_time = 0.749251
[2019-08-23 11:28:02,828] TRAIN Iter 105940: lr = 0.323435,	loss = 2.725100,	Top-1 err = 0.427295,	Top-5 err = 0.203271,	data_time = 0.050488,	train_time = 0.708310
[2019-08-23 11:28:09,459] TRAIN Iter 105960: lr = 0.323402,	loss = 2.682739,	Top-1 err = 0.425342,	Top-5 err = 0.200000,	data_time = 0.050710,	train_time = 0.331576
[2019-08-23 11:28:25,433] TRAIN Iter 105980: lr = 0.323368,	loss = 2.766760,	Top-1 err = 0.423389,	Top-5 err = 0.200684,	data_time = 0.050594,	train_time = 0.798685
[2019-08-23 11:28:33,037] TRAIN Iter 106000: lr = 0.323335,	loss = 2.768923,	Top-1 err = 0.426416,	Top-5 err = 0.202441,	data_time = 0.050551,	train_time = 0.380185
[2019-08-23 11:28:48,258] TRAIN Iter 106020: lr = 0.323302,	loss = 2.770021,	Top-1 err = 0.428955,	Top-5 err = 0.200732,	data_time = 0.136479,	train_time = 0.761034
[2019-08-23 11:29:03,847] TRAIN Iter 106040: lr = 0.323268,	loss = 2.754858,	Top-1 err = 0.422217,	Top-5 err = 0.199121,	data_time = 0.050509,	train_time = 0.779431
[2019-08-23 11:29:11,106] TRAIN Iter 106060: lr = 0.323235,	loss = 2.841693,	Top-1 err = 0.428955,	Top-5 err = 0.201025,	data_time = 0.050359,	train_time = 0.362916
[2019-08-23 11:29:25,625] TRAIN Iter 106080: lr = 0.323202,	loss = 2.687892,	Top-1 err = 0.420410,	Top-5 err = 0.199316,	data_time = 0.050533,	train_time = 0.725938
[2019-08-23 11:29:43,343] TRAIN Iter 106100: lr = 0.323168,	loss = 2.803488,	Top-1 err = 0.428857,	Top-5 err = 0.200684,	data_time = 0.050308,	train_time = 0.885900
[2019-08-23 11:29:50,216] TRAIN Iter 106120: lr = 0.323135,	loss = 2.738182,	Top-1 err = 0.428076,	Top-5 err = 0.202246,	data_time = 0.050353,	train_time = 0.343612
[2019-08-23 11:30:05,095] TRAIN Iter 106140: lr = 0.323102,	loss = 2.871939,	Top-1 err = 0.426904,	Top-5 err = 0.200537,	data_time = 0.050507,	train_time = 0.743937
[2019-08-23 11:30:12,673] TRAIN Iter 106160: lr = 0.323068,	loss = 2.658899,	Top-1 err = 0.421436,	Top-5 err = 0.199756,	data_time = 0.050543,	train_time = 0.378885
[2019-08-23 11:30:25,743] TRAIN Iter 106180: lr = 0.323035,	loss = 2.804558,	Top-1 err = 0.419482,	Top-5 err = 0.198389,	data_time = 0.050462,	train_time = 0.653493
[2019-08-23 11:30:42,474] TRAIN Iter 106200: lr = 0.323002,	loss = 2.725987,	Top-1 err = 0.438379,	Top-5 err = 0.206494,	data_time = 0.050609,	train_time = 0.836556
[2019-08-23 11:30:49,578] TRAIN Iter 106220: lr = 0.322968,	loss = 2.723567,	Top-1 err = 0.428760,	Top-5 err = 0.202344,	data_time = 0.050626,	train_time = 0.355184
[2019-08-23 11:31:05,237] TRAIN Iter 106240: lr = 0.322935,	loss = 2.696833,	Top-1 err = 0.423975,	Top-5 err = 0.205859,	data_time = 0.050516,	train_time = 0.782935
[2019-08-23 11:31:17,815] TRAIN Iter 106260: lr = 0.322902,	loss = 2.801046,	Top-1 err = 0.426367,	Top-5 err = 0.200879,	data_time = 0.050529,	train_time = 0.628852
[2019-08-23 11:31:26,596] TRAIN Iter 106280: lr = 0.322868,	loss = 2.766746,	Top-1 err = 0.423389,	Top-5 err = 0.201416,	data_time = 0.050374,	train_time = 0.439045
[2019-08-23 11:31:41,975] TRAIN Iter 106300: lr = 0.322835,	loss = 2.700820,	Top-1 err = 0.428027,	Top-5 err = 0.201172,	data_time = 0.050096,	train_time = 0.768940
[2019-08-23 11:31:49,205] TRAIN Iter 106320: lr = 0.322802,	loss = 2.680921,	Top-1 err = 0.432764,	Top-5 err = 0.204102,	data_time = 0.050266,	train_time = 0.361514
[2019-08-23 11:32:03,222] TRAIN Iter 106340: lr = 0.322768,	loss = 2.694079,	Top-1 err = 0.424170,	Top-5 err = 0.198486,	data_time = 0.049818,	train_time = 0.700830
[2019-08-23 11:32:47,333] TRAIN Iter 106360: lr = 0.322735,	loss = 2.771551,	Top-1 err = 0.439322,	Top-5 err = 0.212834,	data_time = 0.050363,	train_time = 2.205493
[2019-08-23 11:32:54,780] TRAIN Iter 106380: lr = 0.322702,	loss = 2.664951,	Top-1 err = 0.426563,	Top-5 err = 0.197461,	data_time = 0.050642,	train_time = 0.372337
[2019-08-23 11:33:11,530] TRAIN Iter 106400: lr = 0.322668,	loss = 2.664673,	Top-1 err = 0.422021,	Top-5 err = 0.196680,	data_time = 0.050408,	train_time = 0.837481
[2019-08-23 11:33:20,062] TRAIN Iter 106420: lr = 0.322635,	loss = 2.750191,	Top-1 err = 0.422119,	Top-5 err = 0.197803,	data_time = 0.050684,	train_time = 0.426616
[2019-08-23 11:33:32,200] TRAIN Iter 106440: lr = 0.322602,	loss = 2.690717,	Top-1 err = 0.421191,	Top-5 err = 0.195215,	data_time = 0.050552,	train_time = 0.606857
[2019-08-23 11:33:43,654] TRAIN Iter 106460: lr = 0.322568,	loss = 2.581117,	Top-1 err = 0.414551,	Top-5 err = 0.195605,	data_time = 0.050447,	train_time = 0.572681
[2019-08-23 11:33:50,915] TRAIN Iter 106480: lr = 0.322535,	loss = 2.740530,	Top-1 err = 0.412695,	Top-5 err = 0.192334,	data_time = 0.050730,	train_time = 0.363040
[2019-08-23 11:34:05,785] TRAIN Iter 106500: lr = 0.322502,	loss = 2.707214,	Top-1 err = 0.416406,	Top-5 err = 0.196436,	data_time = 0.050560,	train_time = 0.743521
[2019-08-23 11:34:19,351] TRAIN Iter 106520: lr = 0.322468,	loss = 2.614972,	Top-1 err = 0.417920,	Top-5 err = 0.193359,	data_time = 0.050592,	train_time = 0.678247
[2019-08-23 11:34:27,153] TRAIN Iter 106540: lr = 0.322435,	loss = 2.688544,	Top-1 err = 0.415381,	Top-5 err = 0.196436,	data_time = 0.050543,	train_time = 0.390118
[2019-08-23 11:34:38,878] TRAIN Iter 106560: lr = 0.322402,	loss = 2.675622,	Top-1 err = 0.421680,	Top-5 err = 0.194629,	data_time = 0.050255,	train_time = 0.586220
[2019-08-23 11:34:46,217] TRAIN Iter 106580: lr = 0.322368,	loss = 2.630173,	Top-1 err = 0.415430,	Top-5 err = 0.193994,	data_time = 0.050500,	train_time = 0.366922
[2019-08-23 11:35:02,208] TRAIN Iter 106600: lr = 0.322335,	loss = 2.701407,	Top-1 err = 0.414648,	Top-5 err = 0.191504,	data_time = 0.050493,	train_time = 0.799534
[2019-08-23 11:35:14,500] TRAIN Iter 106620: lr = 0.322302,	loss = 2.803635,	Top-1 err = 0.421582,	Top-5 err = 0.196924,	data_time = 0.050253,	train_time = 0.614619
[2019-08-23 11:35:22,015] TRAIN Iter 106640: lr = 0.322268,	loss = 2.647025,	Top-1 err = 0.421631,	Top-5 err = 0.197070,	data_time = 0.050369,	train_time = 0.375733
[2019-08-23 11:35:37,447] TRAIN Iter 106660: lr = 0.322235,	loss = 2.747044,	Top-1 err = 0.421143,	Top-5 err = 0.199463,	data_time = 0.050552,	train_time = 0.771570
[2019-08-23 11:35:46,607] TRAIN Iter 106680: lr = 0.322202,	loss = 2.770144,	Top-1 err = 0.418457,	Top-5 err = 0.196533,	data_time = 1.185267,	train_time = 0.458010
[2019-08-23 11:36:00,212] TRAIN Iter 106700: lr = 0.322168,	loss = 2.663193,	Top-1 err = 0.424902,	Top-5 err = 0.196680,	data_time = 0.050691,	train_time = 0.680200
[2019-08-23 11:36:11,805] TRAIN Iter 106720: lr = 0.322135,	loss = 2.588960,	Top-1 err = 0.425146,	Top-5 err = 0.197607,	data_time = 0.050345,	train_time = 0.579620
[2019-08-23 11:36:19,242] TRAIN Iter 106740: lr = 0.322102,	loss = 2.775907,	Top-1 err = 0.420068,	Top-5 err = 0.196143,	data_time = 0.050933,	train_time = 0.371839
[2019-08-23 11:36:32,777] TRAIN Iter 106760: lr = 0.322068,	loss = 2.711968,	Top-1 err = 0.422559,	Top-5 err = 0.201563,	data_time = 0.050768,	train_time = 0.676760
[2019-08-23 11:36:46,651] TRAIN Iter 106780: lr = 0.322035,	loss = 2.705562,	Top-1 err = 0.422656,	Top-5 err = 0.199316,	data_time = 0.050346,	train_time = 0.693667
[2019-08-23 11:36:56,090] TRAIN Iter 106800: lr = 0.322002,	loss = 2.751095,	Top-1 err = 0.430811,	Top-5 err = 0.201465,	data_time = 0.050598,	train_time = 0.471947
[2019-08-23 11:37:10,872] TRAIN Iter 106820: lr = 0.321968,	loss = 2.722478,	Top-1 err = 0.420117,	Top-5 err = 0.202979,	data_time = 0.050656,	train_time = 0.739084
[2019-08-23 11:37:20,904] TRAIN Iter 106840: lr = 0.321935,	loss = 2.780840,	Top-1 err = 0.421582,	Top-5 err = 0.201855,	data_time = 0.050416,	train_time = 0.501590
[2019-08-23 11:37:32,175] TRAIN Iter 106860: lr = 0.321902,	loss = 2.682382,	Top-1 err = 0.423682,	Top-5 err = 0.201758,	data_time = 0.050895,	train_time = 0.563550
[2019-08-23 11:37:47,015] TRAIN Iter 106880: lr = 0.321868,	loss = 2.711062,	Top-1 err = 0.418701,	Top-5 err = 0.194043,	data_time = 0.050529,	train_time = 0.741958
[2019-08-23 11:37:54,552] TRAIN Iter 106900: lr = 0.321835,	loss = 2.847919,	Top-1 err = 0.424023,	Top-5 err = 0.201416,	data_time = 0.050699,	train_time = 0.376869
[2019-08-23 11:38:08,242] TRAIN Iter 106920: lr = 0.321802,	loss = 2.784074,	Top-1 err = 0.427539,	Top-5 err = 0.202295,	data_time = 0.050710,	train_time = 0.684468
[2019-08-23 11:38:23,817] TRAIN Iter 106940: lr = 0.321768,	loss = 2.669765,	Top-1 err = 0.421143,	Top-5 err = 0.197656,	data_time = 0.050574,	train_time = 0.778709
[2019-08-23 11:38:31,253] TRAIN Iter 106960: lr = 0.321735,	loss = 2.639822,	Top-1 err = 0.421729,	Top-5 err = 0.204687,	data_time = 0.050865,	train_time = 0.371822
[2019-08-23 11:38:46,134] TRAIN Iter 106980: lr = 0.321702,	loss = 2.694157,	Top-1 err = 0.421973,	Top-5 err = 0.200732,	data_time = 0.050261,	train_time = 0.744037
[2019-08-23 11:38:59,296] TRAIN Iter 107000: lr = 0.321668,	loss = 2.662533,	Top-1 err = 0.419580,	Top-5 err = 0.196533,	data_time = 0.050551,	train_time = 0.658062
[2019-08-23 11:39:07,351] TRAIN Iter 107020: lr = 0.321635,	loss = 2.792921,	Top-1 err = 0.420312,	Top-5 err = 0.200391,	data_time = 0.050779,	train_time = 0.402747
[2019-08-23 11:39:22,821] TRAIN Iter 107040: lr = 0.321602,	loss = 2.654317,	Top-1 err = 0.425635,	Top-5 err = 0.198193,	data_time = 0.050873,	train_time = 0.773498
[2019-08-23 11:39:30,036] TRAIN Iter 107060: lr = 0.321568,	loss = 2.724318,	Top-1 err = 0.421045,	Top-5 err = 0.199121,	data_time = 0.050380,	train_time = 0.360722
[2019-08-23 11:39:45,282] TRAIN Iter 107080: lr = 0.321535,	loss = 2.731255,	Top-1 err = 0.419580,	Top-5 err = 0.198584,	data_time = 0.050848,	train_time = 0.762300
[2019-08-23 11:39:57,986] TRAIN Iter 107100: lr = 0.321502,	loss = 2.685241,	Top-1 err = 0.424609,	Top-5 err = 0.201611,	data_time = 0.050602,	train_time = 0.635144
[2019-08-23 11:40:07,552] TRAIN Iter 107120: lr = 0.321468,	loss = 2.725310,	Top-1 err = 0.424902,	Top-5 err = 0.199365,	data_time = 0.050482,	train_time = 0.478306
[2019-08-23 11:40:22,908] TRAIN Iter 107140: lr = 0.321435,	loss = 2.601865,	Top-1 err = 0.424609,	Top-5 err = 0.198047,	data_time = 0.050552,	train_time = 0.767796
[2019-08-23 11:40:35,279] TRAIN Iter 107160: lr = 0.321402,	loss = 2.809126,	Top-1 err = 0.421143,	Top-5 err = 0.195459,	data_time = 0.050958,	train_time = 0.618516
[2019-08-23 11:40:46,817] TRAIN Iter 107180: lr = 0.321368,	loss = 2.708751,	Top-1 err = 0.421436,	Top-5 err = 0.201172,	data_time = 0.050474,	train_time = 0.576912
[2019-08-23 11:41:00,819] TRAIN Iter 107200: lr = 0.321335,	loss = 2.809869,	Top-1 err = 0.427832,	Top-5 err = 0.204004,	data_time = 0.050400,	train_time = 0.700073
[2019-08-23 11:41:07,813] TRAIN Iter 107220: lr = 0.321302,	loss = 2.736903,	Top-1 err = 0.428809,	Top-5 err = 0.202246,	data_time = 0.050183,	train_time = 0.349699
[2019-08-23 11:41:23,371] TRAIN Iter 107240: lr = 0.321268,	loss = 2.773934,	Top-1 err = 0.428857,	Top-5 err = 0.201367,	data_time = 0.050409,	train_time = 0.777853
[2019-08-23 11:41:37,201] TRAIN Iter 107260: lr = 0.321235,	loss = 2.692997,	Top-1 err = 0.422705,	Top-5 err = 0.199561,	data_time = 0.050553,	train_time = 0.691509
[2019-08-23 11:41:45,104] TRAIN Iter 107280: lr = 0.321202,	loss = 2.784158,	Top-1 err = 0.419971,	Top-5 err = 0.195801,	data_time = 0.050874,	train_time = 0.395124
[2019-08-23 11:42:00,992] TRAIN Iter 107300: lr = 0.321168,	loss = 2.661819,	Top-1 err = 0.423145,	Top-5 err = 0.196777,	data_time = 0.050458,	train_time = 0.794400
[2019-08-23 11:42:15,908] TRAIN Iter 107320: lr = 0.321135,	loss = 2.834105,	Top-1 err = 0.422559,	Top-5 err = 0.199268,	data_time = 0.153636,	train_time = 0.745777
[2019-08-23 11:42:25,591] TRAIN Iter 107340: lr = 0.321102,	loss = 2.840353,	Top-1 err = 0.425195,	Top-5 err = 0.198047,	data_time = 0.050443,	train_time = 0.484161
[2019-08-23 11:42:41,557] TRAIN Iter 107360: lr = 0.321068,	loss = 2.830604,	Top-1 err = 0.422217,	Top-5 err = 0.200391,	data_time = 0.050904,	train_time = 0.798256
[2019-08-23 11:42:48,711] TRAIN Iter 107380: lr = 0.321035,	loss = 2.833775,	Top-1 err = 0.420459,	Top-5 err = 0.195166,	data_time = 0.050591,	train_time = 0.357706
[2019-08-23 11:43:04,557] TRAIN Iter 107400: lr = 0.321002,	loss = 2.735740,	Top-1 err = 0.424023,	Top-5 err = 0.203857,	data_time = 0.050469,	train_time = 0.792299
[2019-08-23 11:43:21,386] TRAIN Iter 107420: lr = 0.320968,	loss = 2.776990,	Top-1 err = 0.426514,	Top-5 err = 0.201514,	data_time = 3.377627,	train_time = 0.841422
[2019-08-23 11:43:28,608] TRAIN Iter 107440: lr = 0.320935,	loss = 2.722537,	Top-1 err = 0.423242,	Top-5 err = 0.202100,	data_time = 0.050732,	train_time = 0.361054
[2019-08-23 11:43:44,487] TRAIN Iter 107460: lr = 0.320902,	loss = 2.853373,	Top-1 err = 0.421924,	Top-5 err = 0.201318,	data_time = 0.050417,	train_time = 0.793943
[2019-08-23 11:43:57,778] TRAIN Iter 107480: lr = 0.320868,	loss = 2.714207,	Top-1 err = 0.420068,	Top-5 err = 0.197070,	data_time = 0.050899,	train_time = 0.664553
[2019-08-23 11:44:07,589] TRAIN Iter 107500: lr = 0.320835,	loss = 2.789368,	Top-1 err = 0.414697,	Top-5 err = 0.197607,	data_time = 0.050338,	train_time = 0.490525
[2019-08-23 11:44:23,376] TRAIN Iter 107520: lr = 0.320802,	loss = 2.793472,	Top-1 err = 0.426611,	Top-5 err = 0.202344,	data_time = 0.050268,	train_time = 0.789321
[2019-08-23 11:44:30,954] TRAIN Iter 107540: lr = 0.320768,	loss = 2.849947,	Top-1 err = 0.425781,	Top-5 err = 0.204150,	data_time = 0.050920,	train_time = 0.378911
[2019-08-23 11:44:46,960] TRAIN Iter 107560: lr = 0.320735,	loss = 2.691510,	Top-1 err = 0.427100,	Top-5 err = 0.198486,	data_time = 0.050334,	train_time = 0.800300
[2019-08-23 11:45:02,141] TRAIN Iter 107580: lr = 0.320702,	loss = 2.729619,	Top-1 err = 0.424268,	Top-5 err = 0.200732,	data_time = 2.832387,	train_time = 0.759015
[2019-08-23 11:45:09,023] TRAIN Iter 107600: lr = 0.320668,	loss = 2.700706,	Top-1 err = 0.425732,	Top-5 err = 0.197510,	data_time = 0.049910,	train_time = 0.344075
[2019-08-23 11:45:57,671] TRAIN Iter 107620: lr = 0.320635,	loss = 2.690273,	Top-1 err = 0.435630,	Top-5 err = 0.202154,	data_time = 0.050568,	train_time = 2.432410
[2019-08-23 11:46:05,462] TRAIN Iter 107640: lr = 0.320602,	loss = 2.682337,	Top-1 err = 0.418652,	Top-5 err = 0.193506,	data_time = 0.050418,	train_time = 0.389512
[2019-08-23 11:46:20,507] TRAIN Iter 107660: lr = 0.320568,	loss = 2.793163,	Top-1 err = 0.427051,	Top-5 err = 0.202148,	data_time = 0.050529,	train_time = 0.752231
[2019-08-23 11:46:33,477] TRAIN Iter 107680: lr = 0.320535,	loss = 2.778368,	Top-1 err = 0.414111,	Top-5 err = 0.194971,	data_time = 0.050498,	train_time = 0.648515
[2019-08-23 11:46:41,339] TRAIN Iter 107700: lr = 0.320502,	loss = 2.643885,	Top-1 err = 0.409570,	Top-5 err = 0.188574,	data_time = 0.050164,	train_time = 0.393077
[2019-08-23 11:46:53,132] TRAIN Iter 107720: lr = 0.320468,	loss = 2.751915,	Top-1 err = 0.423828,	Top-5 err = 0.196338,	data_time = 0.050361,	train_time = 0.589614
[2019-08-23 11:47:07,925] TRAIN Iter 107740: lr = 0.320435,	loss = 2.654276,	Top-1 err = 0.421143,	Top-5 err = 0.195117,	data_time = 0.051153,	train_time = 0.739655
[2019-08-23 11:47:15,421] TRAIN Iter 107760: lr = 0.320402,	loss = 2.801444,	Top-1 err = 0.426563,	Top-5 err = 0.201904,	data_time = 0.050512,	train_time = 0.374788
[2019-08-23 11:47:29,909] TRAIN Iter 107780: lr = 0.320368,	loss = 2.723841,	Top-1 err = 0.416260,	Top-5 err = 0.190967,	data_time = 0.138611,	train_time = 0.724352
[2019-08-23 11:47:37,905] TRAIN Iter 107800: lr = 0.320335,	loss = 2.656721,	Top-1 err = 0.418604,	Top-5 err = 0.197900,	data_time = 0.050586,	train_time = 0.399811
[2019-08-23 11:47:50,306] TRAIN Iter 107820: lr = 0.320302,	loss = 2.679799,	Top-1 err = 0.412793,	Top-5 err = 0.192529,	data_time = 0.050301,	train_time = 0.620041
[2019-08-23 11:48:05,084] TRAIN Iter 107840: lr = 0.320268,	loss = 2.731304,	Top-1 err = 0.415137,	Top-5 err = 0.196045,	data_time = 0.050583,	train_time = 0.738876
[2019-08-23 11:48:12,746] TRAIN Iter 107860: lr = 0.320235,	loss = 2.697517,	Top-1 err = 0.418066,	Top-5 err = 0.198242,	data_time = 0.050512,	train_time = 0.383104
[2019-08-23 11:48:26,704] TRAIN Iter 107880: lr = 0.320202,	loss = 2.728146,	Top-1 err = 0.419043,	Top-5 err = 0.194629,	data_time = 0.050327,	train_time = 0.697854
[2019-08-23 11:48:39,522] TRAIN Iter 107900: lr = 0.320168,	loss = 2.764468,	Top-1 err = 0.419287,	Top-5 err = 0.196777,	data_time = 0.141477,	train_time = 0.640882
[2019-08-23 11:48:48,153] TRAIN Iter 107920: lr = 0.320135,	loss = 2.703892,	Top-1 err = 0.424072,	Top-5 err = 0.197119,	data_time = 0.050299,	train_time = 0.431552
[2019-08-23 11:49:03,119] TRAIN Iter 107940: lr = 0.320102,	loss = 2.774687,	Top-1 err = 0.425391,	Top-5 err = 0.197363,	data_time = 0.050691,	train_time = 0.748297
[2019-08-23 11:49:10,868] TRAIN Iter 107960: lr = 0.320068,	loss = 2.843379,	Top-1 err = 0.419141,	Top-5 err = 0.195898,	data_time = 0.050397,	train_time = 0.387432
[2019-08-23 11:49:24,423] TRAIN Iter 107980: lr = 0.320035,	loss = 2.698409,	Top-1 err = 0.419141,	Top-5 err = 0.195312,	data_time = 0.050763,	train_time = 0.677726
[2019-08-23 11:49:38,655] TRAIN Iter 108000: lr = 0.320002,	loss = 2.675705,	Top-1 err = 0.415527,	Top-5 err = 0.191162,	data_time = 0.050586,	train_time = 0.711589
[2019-08-23 11:49:46,904] TRAIN Iter 108020: lr = 0.319968,	loss = 2.799846,	Top-1 err = 0.418604,	Top-5 err = 0.196094,	data_time = 0.050630,	train_time = 0.412417
[2019-08-23 11:50:00,593] TRAIN Iter 108040: lr = 0.319935,	loss = 2.705627,	Top-1 err = 0.427100,	Top-5 err = 0.200146,	data_time = 0.050740,	train_time = 0.684436
[2019-08-23 11:50:13,416] TRAIN Iter 108060: lr = 0.319902,	loss = 2.602578,	Top-1 err = 0.419238,	Top-5 err = 0.193750,	data_time = 0.093612,	train_time = 0.641146
[2019-08-23 11:50:21,779] TRAIN Iter 108080: lr = 0.319868,	loss = 2.577817,	Top-1 err = 0.418994,	Top-5 err = 0.196631,	data_time = 0.050642,	train_time = 0.418145
[2019-08-23 11:50:37,862] TRAIN Iter 108100: lr = 0.319835,	loss = 2.654983,	Top-1 err = 0.425439,	Top-5 err = 0.199512,	data_time = 0.050713,	train_time = 0.804117
[2019-08-23 11:50:45,559] TRAIN Iter 108120: lr = 0.319802,	loss = 2.709306,	Top-1 err = 0.420850,	Top-5 err = 0.196680,	data_time = 0.050951,	train_time = 0.384833
[2019-08-23 11:50:59,643] TRAIN Iter 108140: lr = 0.319768,	loss = 2.725373,	Top-1 err = 0.423389,	Top-5 err = 0.201123,	data_time = 0.050483,	train_time = 0.704226
[2019-08-23 11:51:14,753] TRAIN Iter 108160: lr = 0.319735,	loss = 2.574720,	Top-1 err = 0.418701,	Top-5 err = 0.197266,	data_time = 0.050249,	train_time = 0.755443
[2019-08-23 11:51:21,985] TRAIN Iter 108180: lr = 0.319702,	loss = 2.714781,	Top-1 err = 0.426709,	Top-5 err = 0.203320,	data_time = 0.051007,	train_time = 0.361593
[2019-08-23 11:51:36,550] TRAIN Iter 108200: lr = 0.319668,	loss = 2.794039,	Top-1 err = 0.421484,	Top-5 err = 0.198389,	data_time = 0.050441,	train_time = 0.728257
[2019-08-23 11:51:51,356] TRAIN Iter 108220: lr = 0.319635,	loss = 2.707411,	Top-1 err = 0.421289,	Top-5 err = 0.197998,	data_time = 0.050291,	train_time = 0.740276
[2019-08-23 11:51:58,675] TRAIN Iter 108240: lr = 0.319602,	loss = 2.664255,	Top-1 err = 0.424854,	Top-5 err = 0.200537,	data_time = 0.050604,	train_time = 0.365948
[2019-08-23 11:52:14,383] TRAIN Iter 108260: lr = 0.319568,	loss = 2.731573,	Top-1 err = 0.417627,	Top-5 err = 0.196045,	data_time = 0.050920,	train_time = 0.785385
[2019-08-23 11:52:21,899] TRAIN Iter 108280: lr = 0.319535,	loss = 2.720805,	Top-1 err = 0.416357,	Top-5 err = 0.195996,	data_time = 0.050716,	train_time = 0.375770
[2019-08-23 11:52:36,436] TRAIN Iter 108300: lr = 0.319502,	loss = 2.793550,	Top-1 err = 0.424805,	Top-5 err = 0.199951,	data_time = 0.050391,	train_time = 0.726813
[2019-08-23 11:52:52,094] TRAIN Iter 108320: lr = 0.319468,	loss = 2.670968,	Top-1 err = 0.418604,	Top-5 err = 0.191602,	data_time = 0.050747,	train_time = 0.782903
[2019-08-23 11:52:59,021] TRAIN Iter 108340: lr = 0.319435,	loss = 2.654965,	Top-1 err = 0.418506,	Top-5 err = 0.198242,	data_time = 0.050627,	train_time = 0.346324
[2019-08-23 11:53:14,395] TRAIN Iter 108360: lr = 0.319402,	loss = 2.696036,	Top-1 err = 0.418213,	Top-5 err = 0.195361,	data_time = 0.050817,	train_time = 0.768718
[2019-08-23 11:53:29,254] TRAIN Iter 108380: lr = 0.319368,	loss = 2.762529,	Top-1 err = 0.425195,	Top-5 err = 0.199072,	data_time = 0.050529,	train_time = 0.742945
[2019-08-23 11:53:36,597] TRAIN Iter 108400: lr = 0.319335,	loss = 2.760066,	Top-1 err = 0.417236,	Top-5 err = 0.193701,	data_time = 0.050891,	train_time = 0.367104
[2019-08-23 11:53:50,972] TRAIN Iter 108420: lr = 0.319302,	loss = 2.727882,	Top-1 err = 0.425488,	Top-5 err = 0.199561,	data_time = 0.050237,	train_time = 0.718733
[2019-08-23 11:53:57,921] TRAIN Iter 108440: lr = 0.319268,	loss = 2.769139,	Top-1 err = 0.424023,	Top-5 err = 0.197754,	data_time = 0.050435,	train_time = 0.347432
[2019-08-23 11:54:14,370] TRAIN Iter 108460: lr = 0.319235,	loss = 2.802418,	Top-1 err = 0.426123,	Top-5 err = 0.199268,	data_time = 0.050914,	train_time = 0.822436
[2019-08-23 11:54:28,257] TRAIN Iter 108480: lr = 0.319202,	loss = 2.678927,	Top-1 err = 0.426416,	Top-5 err = 0.197705,	data_time = 0.050419,	train_time = 0.694366
[2019-08-23 11:54:36,862] TRAIN Iter 108500: lr = 0.319168,	loss = 2.778982,	Top-1 err = 0.425391,	Top-5 err = 0.199707,	data_time = 0.050913,	train_time = 0.430232
[2019-08-23 11:54:52,258] TRAIN Iter 108520: lr = 0.319135,	loss = 2.780107,	Top-1 err = 0.423291,	Top-5 err = 0.203125,	data_time = 0.050607,	train_time = 0.769785
[2019-08-23 11:55:08,019] TRAIN Iter 108540: lr = 0.319102,	loss = 2.765528,	Top-1 err = 0.426807,	Top-5 err = 0.201514,	data_time = 0.050628,	train_time = 0.788021
[2019-08-23 11:55:15,643] TRAIN Iter 108560: lr = 0.319068,	loss = 2.758000,	Top-1 err = 0.424512,	Top-5 err = 0.195410,	data_time = 0.050273,	train_time = 0.381167
[2019-08-23 11:55:31,434] TRAIN Iter 108580: lr = 0.319035,	loss = 2.753846,	Top-1 err = 0.425586,	Top-5 err = 0.201416,	data_time = 0.050365,	train_time = 0.789568
[2019-08-23 11:55:38,627] TRAIN Iter 108600: lr = 0.319002,	loss = 2.804873,	Top-1 err = 0.423096,	Top-5 err = 0.196973,	data_time = 0.050604,	train_time = 0.359642
[2019-08-23 11:55:53,704] TRAIN Iter 108620: lr = 0.318968,	loss = 2.721524,	Top-1 err = 0.423389,	Top-5 err = 0.200830,	data_time = 0.050554,	train_time = 0.753817
[2019-08-23 11:56:11,917] TRAIN Iter 108640: lr = 0.318935,	loss = 2.738499,	Top-1 err = 0.420508,	Top-5 err = 0.197852,	data_time = 0.050516,	train_time = 0.910630
[2019-08-23 11:56:18,883] TRAIN Iter 108660: lr = 0.318902,	loss = 2.746627,	Top-1 err = 0.422217,	Top-5 err = 0.199805,	data_time = 0.050403,	train_time = 0.348289
[2019-08-23 11:56:34,384] TRAIN Iter 108680: lr = 0.318868,	loss = 2.722827,	Top-1 err = 0.423633,	Top-5 err = 0.201367,	data_time = 0.050148,	train_time = 0.775053
[2019-08-23 11:56:51,022] TRAIN Iter 108700: lr = 0.318835,	loss = 2.705185,	Top-1 err = 0.428320,	Top-5 err = 0.201416,	data_time = 0.050219,	train_time = 0.831866
[2019-08-23 11:56:58,340] TRAIN Iter 108720: lr = 0.318802,	loss = 2.778786,	Top-1 err = 0.425195,	Top-5 err = 0.198193,	data_time = 0.050450,	train_time = 0.365889
[2019-08-23 11:57:15,016] TRAIN Iter 108740: lr = 0.318768,	loss = 2.822381,	Top-1 err = 0.429639,	Top-5 err = 0.203516,	data_time = 0.050485,	train_time = 0.833789
[2019-08-23 11:57:22,284] TRAIN Iter 108760: lr = 0.318735,	loss = 2.718359,	Top-1 err = 0.427539,	Top-5 err = 0.203613,	data_time = 0.050570,	train_time = 0.363351
[2019-08-23 11:57:38,147] TRAIN Iter 108780: lr = 0.318702,	loss = 2.856519,	Top-1 err = 0.426514,	Top-5 err = 0.200879,	data_time = 0.050719,	train_time = 0.793173
[2019-08-23 11:57:56,167] TRAIN Iter 108800: lr = 0.318668,	loss = 2.694171,	Top-1 err = 0.417187,	Top-5 err = 0.192627,	data_time = 0.108165,	train_time = 0.900991
[2019-08-23 11:58:03,356] TRAIN Iter 108820: lr = 0.318635,	loss = 2.753983,	Top-1 err = 0.427832,	Top-5 err = 0.202246,	data_time = 0.050068,	train_time = 0.359417
[2019-08-23 11:58:18,073] TRAIN Iter 108840: lr = 0.318602,	loss = 2.706244,	Top-1 err = 0.427148,	Top-5 err = 0.200049,	data_time = 0.049923,	train_time = 0.735859
[2019-08-23 11:58:29,499] TRAIN Iter 108860: lr = 0.318568,	loss = 3.055534,	Top-1 err = 0.429732,	Top-5 err = 0.202685,	data_time = 0.007097,	train_time = 0.571272
[2019-08-23 11:59:14,237] TRAIN Iter 108880: lr = 0.318535,	loss = 2.654296,	Top-1 err = 0.434229,	Top-5 err = 0.199902,	data_time = 0.050182,	train_time = 2.236900
[2019-08-23 11:59:29,511] TRAIN Iter 108900: lr = 0.318502,	loss = 2.698117,	Top-1 err = 0.424365,	Top-5 err = 0.193945,	data_time = 0.050416,	train_time = 0.763671
[2019-08-23 11:59:36,850] TRAIN Iter 108920: lr = 0.318468,	loss = 2.681054,	Top-1 err = 0.413330,	Top-5 err = 0.192822,	data_time = 0.112050,	train_time = 0.366942
[2019-08-23 11:59:51,892] TRAIN Iter 108940: lr = 0.318435,	loss = 2.645790,	Top-1 err = 0.414307,	Top-5 err = 0.192383,	data_time = 0.050438,	train_time = 0.752059
[2019-08-23 12:00:03,326] TRAIN Iter 108960: lr = 0.318402,	loss = 2.691092,	Top-1 err = 0.413965,	Top-5 err = 0.193359,	data_time = 0.050653,	train_time = 0.571699
[2019-08-23 12:00:13,994] TRAIN Iter 108980: lr = 0.318368,	loss = 2.709922,	Top-1 err = 0.415234,	Top-5 err = 0.194922,	data_time = 0.050724,	train_time = 0.533378
[2019-08-23 12:00:26,925] TRAIN Iter 109000: lr = 0.318335,	loss = 2.777009,	Top-1 err = 0.414648,	Top-5 err = 0.192236,	data_time = 0.050493,	train_time = 0.646551
[2019-08-23 12:00:34,538] TRAIN Iter 109020: lr = 0.318302,	loss = 2.681062,	Top-1 err = 0.414844,	Top-5 err = 0.193262,	data_time = 0.050336,	train_time = 0.380613
[2019-08-23 12:00:47,849] TRAIN Iter 109040: lr = 0.318268,	loss = 2.778613,	Top-1 err = 0.416992,	Top-5 err = 0.191113,	data_time = 0.050460,	train_time = 0.665539
[2019-08-23 12:01:04,705] TRAIN Iter 109060: lr = 0.318235,	loss = 2.663770,	Top-1 err = 0.416797,	Top-5 err = 0.194922,	data_time = 0.050609,	train_time = 0.842790
[2019-08-23 12:01:12,292] TRAIN Iter 109080: lr = 0.318202,	loss = 2.670668,	Top-1 err = 0.417041,	Top-5 err = 0.195654,	data_time = 0.099982,	train_time = 0.379341
[2019-08-23 12:01:24,600] TRAIN Iter 109100: lr = 0.318168,	loss = 2.665006,	Top-1 err = 0.421729,	Top-5 err = 0.198047,	data_time = 0.050249,	train_time = 0.615360
[2019-08-23 12:01:38,047] TRAIN Iter 109120: lr = 0.318135,	loss = 2.714664,	Top-1 err = 0.420801,	Top-5 err = 0.195605,	data_time = 0.125298,	train_time = 0.672351
[2019-08-23 12:01:45,101] TRAIN Iter 109140: lr = 0.318102,	loss = 2.537506,	Top-1 err = 0.417285,	Top-5 err = 0.193604,	data_time = 0.050441,	train_time = 0.352705
[2019-08-23 12:02:00,310] TRAIN Iter 109160: lr = 0.318068,	loss = 2.675746,	Top-1 err = 0.413574,	Top-5 err = 0.196240,	data_time = 0.050311,	train_time = 0.760422
[2019-08-23 12:02:07,139] TRAIN Iter 109180: lr = 0.318035,	loss = 2.679984,	Top-1 err = 0.413916,	Top-5 err = 0.194727,	data_time = 0.050632,	train_time = 0.341423
[2019-08-23 12:02:23,052] TRAIN Iter 109200: lr = 0.318002,	loss = 2.748675,	Top-1 err = 0.412500,	Top-5 err = 0.193848,	data_time = 0.050339,	train_time = 0.795647
[2019-08-23 12:02:39,404] TRAIN Iter 109220: lr = 0.317968,	loss = 2.809817,	Top-1 err = 0.422363,	Top-5 err = 0.198730,	data_time = 0.050916,	train_time = 0.817604
[2019-08-23 12:02:46,131] TRAIN Iter 109240: lr = 0.317935,	loss = 2.663668,	Top-1 err = 0.409619,	Top-5 err = 0.188965,	data_time = 0.050348,	train_time = 0.336346
[2019-08-23 12:03:02,871] TRAIN Iter 109260: lr = 0.317902,	loss = 2.749819,	Top-1 err = 0.420557,	Top-5 err = 0.197998,	data_time = 0.050474,	train_time = 0.836949
[2019-08-23 12:03:14,790] TRAIN Iter 109280: lr = 0.317868,	loss = 2.645720,	Top-1 err = 0.415576,	Top-5 err = 0.195947,	data_time = 0.120810,	train_time = 0.595950
[2019-08-23 12:03:24,956] TRAIN Iter 109300: lr = 0.317835,	loss = 2.775611,	Top-1 err = 0.421143,	Top-5 err = 0.195264,	data_time = 0.050561,	train_time = 0.508257
[2019-08-23 12:03:40,297] TRAIN Iter 109320: lr = 0.317802,	loss = 2.661637,	Top-1 err = 0.420068,	Top-5 err = 0.197412,	data_time = 0.050883,	train_time = 0.767044
[2019-08-23 12:03:47,893] TRAIN Iter 109340: lr = 0.317768,	loss = 2.691783,	Top-1 err = 0.419287,	Top-5 err = 0.196094,	data_time = 0.050632,	train_time = 0.379779
[2019-08-23 12:04:03,312] TRAIN Iter 109360: lr = 0.317735,	loss = 2.729165,	Top-1 err = 0.429150,	Top-5 err = 0.202197,	data_time = 0.050451,	train_time = 0.770941
[2019-08-23 12:04:17,551] TRAIN Iter 109380: lr = 0.317702,	loss = 2.603802,	Top-1 err = 0.416846,	Top-5 err = 0.191699,	data_time = 0.050719,	train_time = 0.711943
[2019-08-23 12:04:25,082] TRAIN Iter 109400: lr = 0.317668,	loss = 2.712035,	Top-1 err = 0.424023,	Top-5 err = 0.200146,	data_time = 0.050879,	train_time = 0.376530
[2019-08-23 12:04:39,073] TRAIN Iter 109420: lr = 0.317635,	loss = 2.692096,	Top-1 err = 0.425098,	Top-5 err = 0.202100,	data_time = 0.050711,	train_time = 0.699533
[2019-08-23 12:04:49,298] TRAIN Iter 109440: lr = 0.317602,	loss = 2.653768,	Top-1 err = 0.419824,	Top-5 err = 0.202344,	data_time = 0.125370,	train_time = 0.511257
[2019-08-23 12:05:00,510] TRAIN Iter 109460: lr = 0.317568,	loss = 2.723591,	Top-1 err = 0.426367,	Top-5 err = 0.201465,	data_time = 0.050858,	train_time = 0.560591
[2019-08-23 12:05:16,189] TRAIN Iter 109480: lr = 0.317535,	loss = 2.731645,	Top-1 err = 0.421240,	Top-5 err = 0.198779,	data_time = 0.050809,	train_time = 0.783925
[2019-08-23 12:05:23,613] TRAIN Iter 109500: lr = 0.317502,	loss = 2.656873,	Top-1 err = 0.425879,	Top-5 err = 0.196484,	data_time = 0.134064,	train_time = 0.371167
[2019-08-23 12:05:38,307] TRAIN Iter 109520: lr = 0.317468,	loss = 2.678767,	Top-1 err = 0.422559,	Top-5 err = 0.193701,	data_time = 0.050172,	train_time = 0.734707
[2019-08-23 12:05:55,143] TRAIN Iter 109540: lr = 0.317435,	loss = 2.643626,	Top-1 err = 0.416211,	Top-5 err = 0.189844,	data_time = 0.050803,	train_time = 0.841796
[2019-08-23 12:06:02,835] TRAIN Iter 109560: lr = 0.317402,	loss = 2.739326,	Top-1 err = 0.422168,	Top-5 err = 0.198828,	data_time = 0.050617,	train_time = 0.384542
[2019-08-23 12:06:17,665] TRAIN Iter 109580: lr = 0.317368,	loss = 2.601825,	Top-1 err = 0.418604,	Top-5 err = 0.196680,	data_time = 0.120283,	train_time = 0.741490
[2019-08-23 12:06:30,056] TRAIN Iter 109600: lr = 0.317335,	loss = 2.720495,	Top-1 err = 0.426172,	Top-5 err = 0.197021,	data_time = 0.050736,	train_time = 0.619552
[2019-08-23 12:06:38,391] TRAIN Iter 109620: lr = 0.317302,	loss = 2.726077,	Top-1 err = 0.423242,	Top-5 err = 0.200635,	data_time = 0.126312,	train_time = 0.416723
[2019-08-23 12:06:53,493] TRAIN Iter 109640: lr = 0.317268,	loss = 2.798129,	Top-1 err = 0.418213,	Top-5 err = 0.198682,	data_time = 0.050410,	train_time = 0.755086
[2019-08-23 12:07:01,964] TRAIN Iter 109660: lr = 0.317235,	loss = 2.762853,	Top-1 err = 0.422559,	Top-5 err = 0.200684,	data_time = 0.050578,	train_time = 0.423542
[2019-08-23 12:07:16,405] TRAIN Iter 109680: lr = 0.317202,	loss = 2.680913,	Top-1 err = 0.420801,	Top-5 err = 0.198535,	data_time = 0.050747,	train_time = 0.722063
[2019-08-23 12:07:30,905] TRAIN Iter 109700: lr = 0.317168,	loss = 2.765289,	Top-1 err = 0.423389,	Top-5 err = 0.198145,	data_time = 0.050571,	train_time = 0.724937
[2019-08-23 12:07:38,120] TRAIN Iter 109720: lr = 0.317135,	loss = 2.695235,	Top-1 err = 0.421240,	Top-5 err = 0.198389,	data_time = 0.050583,	train_time = 0.360740
[2019-08-23 12:07:54,846] TRAIN Iter 109740: lr = 0.317102,	loss = 2.711761,	Top-1 err = 0.422363,	Top-5 err = 0.199512,	data_time = 0.050843,	train_time = 0.836284
[2019-08-23 12:08:07,243] TRAIN Iter 109760: lr = 0.317068,	loss = 2.564168,	Top-1 err = 0.423486,	Top-5 err = 0.198096,	data_time = 0.050574,	train_time = 0.619867
[2019-08-23 12:08:15,825] TRAIN Iter 109780: lr = 0.317035,	loss = 2.659502,	Top-1 err = 0.429736,	Top-5 err = 0.200537,	data_time = 0.050232,	train_time = 0.429087
[2019-08-23 12:08:31,167] TRAIN Iter 109800: lr = 0.317002,	loss = 2.765877,	Top-1 err = 0.426172,	Top-5 err = 0.199951,	data_time = 0.050583,	train_time = 0.767066
[2019-08-23 12:08:38,217] TRAIN Iter 109820: lr = 0.316968,	loss = 2.771892,	Top-1 err = 0.421094,	Top-5 err = 0.201611,	data_time = 0.050338,	train_time = 0.352490
[2019-08-23 12:08:55,696] TRAIN Iter 109840: lr = 0.316935,	loss = 2.844097,	Top-1 err = 0.423438,	Top-5 err = 0.195703,	data_time = 0.051118,	train_time = 0.873927
[2019-08-23 12:09:09,547] TRAIN Iter 109860: lr = 0.316902,	loss = 2.736545,	Top-1 err = 0.421240,	Top-5 err = 0.201709,	data_time = 0.145370,	train_time = 0.692551
[2019-08-23 12:09:16,213] TRAIN Iter 109880: lr = 0.316868,	loss = 2.745855,	Top-1 err = 0.426758,	Top-5 err = 0.201465,	data_time = 0.050544,	train_time = 0.333264
[2019-08-23 12:09:32,511] TRAIN Iter 109900: lr = 0.316835,	loss = 2.683064,	Top-1 err = 0.425098,	Top-5 err = 0.195898,	data_time = 0.050623,	train_time = 0.814923
[2019-08-23 12:09:46,957] TRAIN Iter 109920: lr = 0.316802,	loss = 2.829005,	Top-1 err = 0.425146,	Top-5 err = 0.198779,	data_time = 0.129999,	train_time = 0.722286
[2019-08-23 12:09:55,605] TRAIN Iter 109940: lr = 0.316768,	loss = 2.749066,	Top-1 err = 0.429199,	Top-5 err = 0.202539,	data_time = 0.050799,	train_time = 0.432372
[2019-08-23 12:10:11,235] TRAIN Iter 109960: lr = 0.316735,	loss = 2.745760,	Top-1 err = 0.427637,	Top-5 err = 0.200928,	data_time = 0.050480,	train_time = 0.781479
[2019-08-23 12:10:18,351] TRAIN Iter 109980: lr = 0.316702,	loss = 2.773136,	Top-1 err = 0.430713,	Top-5 err = 0.201465,	data_time = 0.050177,	train_time = 0.355791
[2019-08-23 12:10:34,277] TRAIN Iter 110000: lr = 0.316668,	loss = 2.647964,	Top-1 err = 0.421484,	Top-5 err = 0.201709,	data_time = 0.050914,	train_time = 0.796276
[2019-08-23 12:11:39,229] TEST Iter 110000: loss = 2.533208,	Top-1 err = 0.388880,	Top-5 err = 0.160360,	val_time = 64.914852
[2019-08-23 12:11:45,493] TRAIN Iter 110020: lr = 0.316635,	loss = 2.679212,	Top-1 err = 0.418066,	Top-5 err = 0.196533,	data_time = 0.050350,	train_time = 0.313176
[2019-08-23 12:11:51,958] TRAIN Iter 110040: lr = 0.316602,	loss = 2.627871,	Top-1 err = 0.419824,	Top-5 err = 0.196777,	data_time = 0.050469,	train_time = 0.323236
[2019-08-23 12:11:58,516] TRAIN Iter 110060: lr = 0.316568,	loss = 2.804602,	Top-1 err = 0.422266,	Top-5 err = 0.201318,	data_time = 0.050193,	train_time = 0.327895
[2019-08-23 12:12:07,824] TRAIN Iter 110080: lr = 0.316535,	loss = 2.693160,	Top-1 err = 0.416260,	Top-5 err = 0.197021,	data_time = 0.049981,	train_time = 0.465381
[2019-08-23 12:12:20,620] TRAIN Iter 110100: lr = 0.316502,	loss = 2.725986,	Top-1 err = 0.421387,	Top-5 err = 0.201904,	data_time = 0.049995,	train_time = 0.639777
[2019-08-23 12:13:06,506] TRAIN Iter 110120: lr = 0.316468,	loss = 2.752271,	Top-1 err = 0.426460,	Top-5 err = 0.206051,	data_time = 0.050459,	train_time = 2.294289
[2019-08-23 12:13:14,074] TRAIN Iter 110140: lr = 0.316435,	loss = 2.746789,	Top-1 err = 0.424902,	Top-5 err = 0.201758,	data_time = 0.050549,	train_time = 0.378374
[2019-08-23 12:13:28,098] TRAIN Iter 110160: lr = 0.316402,	loss = 2.714010,	Top-1 err = 0.411963,	Top-5 err = 0.190869,	data_time = 0.050423,	train_time = 0.701186
[2019-08-23 12:13:39,950] TRAIN Iter 110180: lr = 0.316368,	loss = 2.653299,	Top-1 err = 0.417822,	Top-5 err = 0.192871,	data_time = 3.705855,	train_time = 0.592574
[2019-08-23 12:13:48,282] TRAIN Iter 110200: lr = 0.316335,	loss = 2.792224,	Top-1 err = 0.415234,	Top-5 err = 0.195410,	data_time = 0.050773,	train_time = 0.416573
[2019-08-23 12:14:02,964] TRAIN Iter 110220: lr = 0.316302,	loss = 2.666060,	Top-1 err = 0.416504,	Top-5 err = 0.194092,	data_time = 0.050592,	train_time = 0.734091
[2019-08-23 12:14:10,596] TRAIN Iter 110240: lr = 0.316268,	loss = 2.596891,	Top-1 err = 0.415674,	Top-5 err = 0.190869,	data_time = 0.050707,	train_time = 0.381620
[2019-08-23 12:14:26,279] TRAIN Iter 110260: lr = 0.316235,	loss = 2.742552,	Top-1 err = 0.417773,	Top-5 err = 0.192383,	data_time = 0.050521,	train_time = 0.784098
[2019-08-23 12:14:41,291] TRAIN Iter 110280: lr = 0.316202,	loss = 2.716325,	Top-1 err = 0.419141,	Top-5 err = 0.196436,	data_time = 0.050867,	train_time = 0.750621
[2019-08-23 12:14:48,950] TRAIN Iter 110300: lr = 0.316168,	loss = 2.711860,	Top-1 err = 0.417480,	Top-5 err = 0.195850,	data_time = 0.050993,	train_time = 0.382919
[2019-08-23 12:15:02,477] TRAIN Iter 110320: lr = 0.316135,	loss = 2.780668,	Top-1 err = 0.421338,	Top-5 err = 0.196094,	data_time = 0.050472,	train_time = 0.676320
[2019-08-23 12:15:10,731] TRAIN Iter 110340: lr = 0.316102,	loss = 2.620088,	Top-1 err = 0.418750,	Top-5 err = 0.191553,	data_time = 0.050302,	train_time = 0.412685
[2019-08-23 12:15:23,528] TRAIN Iter 110360: lr = 0.316068,	loss = 2.595481,	Top-1 err = 0.419482,	Top-5 err = 0.193799,	data_time = 0.051048,	train_time = 0.639857
[2019-08-23 12:15:38,966] TRAIN Iter 110380: lr = 0.316035,	loss = 2.722276,	Top-1 err = 0.417773,	Top-5 err = 0.194824,	data_time = 0.050354,	train_time = 0.771863
[2019-08-23 12:15:47,040] TRAIN Iter 110400: lr = 0.316002,	loss = 2.741166,	Top-1 err = 0.417041,	Top-5 err = 0.193994,	data_time = 0.050837,	train_time = 0.403706
[2019-08-23 12:16:00,403] TRAIN Iter 110420: lr = 0.315968,	loss = 2.662012,	Top-1 err = 0.415723,	Top-5 err = 0.190674,	data_time = 0.050547,	train_time = 0.668116
[2019-08-23 12:16:16,287] TRAIN Iter 110440: lr = 0.315935,	loss = 2.786465,	Top-1 err = 0.430762,	Top-5 err = 0.203418,	data_time = 0.050763,	train_time = 0.794217
[2019-08-23 12:16:23,915] TRAIN Iter 110460: lr = 0.315902,	loss = 2.726803,	Top-1 err = 0.419189,	Top-5 err = 0.195557,	data_time = 0.109349,	train_time = 0.381385
[2019-08-23 12:16:38,187] TRAIN Iter 110480: lr = 0.315868,	loss = 2.660892,	Top-1 err = 0.417529,	Top-5 err = 0.191748,	data_time = 0.050487,	train_time = 0.713557
[2019-08-23 12:16:51,612] TRAIN Iter 110500: lr = 0.315835,	loss = 2.619588,	Top-1 err = 0.420801,	Top-5 err = 0.197900,	data_time = 0.109331,	train_time = 0.671268
[2019-08-23 12:16:59,520] TRAIN Iter 110520: lr = 0.315802,	loss = 2.750093,	Top-1 err = 0.419824,	Top-5 err = 0.194824,	data_time = 0.050563,	train_time = 0.395349
[2019-08-23 12:17:15,278] TRAIN Iter 110540: lr = 0.315768,	loss = 2.657751,	Top-1 err = 0.420996,	Top-5 err = 0.199561,	data_time = 0.050709,	train_time = 0.787878
[2019-08-23 12:17:22,777] TRAIN Iter 110560: lr = 0.315735,	loss = 2.641922,	Top-1 err = 0.421533,	Top-5 err = 0.198047,	data_time = 0.050907,	train_time = 0.374941
[2019-08-23 12:17:38,913] TRAIN Iter 110580: lr = 0.315702,	loss = 2.659773,	Top-1 err = 0.422900,	Top-5 err = 0.199463,	data_time = 0.050460,	train_time = 0.806803
[2019-08-23 12:17:54,390] TRAIN Iter 110600: lr = 0.315668,	loss = 2.713682,	Top-1 err = 0.425879,	Top-5 err = 0.198828,	data_time = 0.050863,	train_time = 0.773841
[2019-08-23 12:18:01,908] TRAIN Iter 110620: lr = 0.315635,	loss = 2.811308,	Top-1 err = 0.431543,	Top-5 err = 0.204102,	data_time = 0.050984,	train_time = 0.375861
[2019-08-23 12:18:17,272] TRAIN Iter 110640: lr = 0.315602,	loss = 2.626417,	Top-1 err = 0.418896,	Top-5 err = 0.197266,	data_time = 0.050306,	train_time = 0.768177
[2019-08-23 12:18:31,147] TRAIN Iter 110660: lr = 0.315568,	loss = 2.761121,	Top-1 err = 0.421191,	Top-5 err = 0.201465,	data_time = 0.051397,	train_time = 0.693739
[2019-08-23 12:18:38,569] TRAIN Iter 110680: lr = 0.315535,	loss = 2.708783,	Top-1 err = 0.422607,	Top-5 err = 0.198926,	data_time = 0.051134,	train_time = 0.371089
[2019-08-23 12:18:52,473] TRAIN Iter 110700: lr = 0.315502,	loss = 2.724395,	Top-1 err = 0.418262,	Top-5 err = 0.194678,	data_time = 0.050514,	train_time = 0.695204
[2019-08-23 12:19:00,105] TRAIN Iter 110720: lr = 0.315468,	loss = 2.734183,	Top-1 err = 0.423975,	Top-5 err = 0.196973,	data_time = 0.144100,	train_time = 0.381598
[2019-08-23 12:19:15,263] TRAIN Iter 110740: lr = 0.315435,	loss = 2.790174,	Top-1 err = 0.419385,	Top-5 err = 0.198877,	data_time = 0.157834,	train_time = 0.757883
[2019-08-23 12:19:29,977] TRAIN Iter 110760: lr = 0.315402,	loss = 2.721880,	Top-1 err = 0.420557,	Top-5 err = 0.196240,	data_time = 0.050393,	train_time = 0.735684
[2019-08-23 12:19:37,534] TRAIN Iter 110780: lr = 0.315368,	loss = 2.666841,	Top-1 err = 0.424854,	Top-5 err = 0.201367,	data_time = 0.050624,	train_time = 0.377798
[2019-08-23 12:19:51,079] TRAIN Iter 110800: lr = 0.315335,	loss = 2.667965,	Top-1 err = 0.419629,	Top-5 err = 0.197754,	data_time = 0.050530,	train_time = 0.677268
[2019-08-23 12:20:07,881] TRAIN Iter 110820: lr = 0.315302,	loss = 2.678102,	Top-1 err = 0.422656,	Top-5 err = 0.203320,	data_time = 0.099974,	train_time = 0.840055
[2019-08-23 12:20:15,186] TRAIN Iter 110840: lr = 0.315268,	loss = 2.652022,	Top-1 err = 0.416943,	Top-5 err = 0.195898,	data_time = 0.050409,	train_time = 0.365261
[2019-08-23 12:20:29,444] TRAIN Iter 110860: lr = 0.315235,	loss = 2.776296,	Top-1 err = 0.419141,	Top-5 err = 0.198486,	data_time = 0.050463,	train_time = 0.712878
[2019-08-23 12:20:37,125] TRAIN Iter 110880: lr = 0.315202,	loss = 2.670691,	Top-1 err = 0.416211,	Top-5 err = 0.195557,	data_time = 0.150402,	train_time = 0.384040
[2019-08-23 12:20:51,999] TRAIN Iter 110900: lr = 0.315168,	loss = 2.666101,	Top-1 err = 0.421094,	Top-5 err = 0.194580,	data_time = 0.050421,	train_time = 0.743688
[2019-08-23 12:21:07,659] TRAIN Iter 110920: lr = 0.315135,	loss = 2.690168,	Top-1 err = 0.417529,	Top-5 err = 0.195508,	data_time = 0.050494,	train_time = 0.782967
[2019-08-23 12:21:15,549] TRAIN Iter 110940: lr = 0.315102,	loss = 2.794614,	Top-1 err = 0.420996,	Top-5 err = 0.197119,	data_time = 0.050590,	train_time = 0.394499
[2019-08-23 12:21:30,046] TRAIN Iter 110960: lr = 0.315068,	loss = 2.797865,	Top-1 err = 0.421582,	Top-5 err = 0.198535,	data_time = 0.051098,	train_time = 0.724822
[2019-08-23 12:21:45,201] TRAIN Iter 110980: lr = 0.315035,	loss = 2.724138,	Top-1 err = 0.418604,	Top-5 err = 0.202344,	data_time = 0.050386,	train_time = 0.757742
[2019-08-23 12:21:52,486] TRAIN Iter 111000: lr = 0.315002,	loss = 2.717407,	Top-1 err = 0.418750,	Top-5 err = 0.199805,	data_time = 0.050397,	train_time = 0.364250
[2019-08-23 12:22:06,757] TRAIN Iter 111020: lr = 0.314968,	loss = 2.658448,	Top-1 err = 0.416553,	Top-5 err = 0.194580,	data_time = 0.050508,	train_time = 0.713535
[2019-08-23 12:22:14,573] TRAIN Iter 111040: lr = 0.314935,	loss = 2.773636,	Top-1 err = 0.431055,	Top-5 err = 0.202148,	data_time = 0.050417,	train_time = 0.390769
[2019-08-23 12:22:30,313] TRAIN Iter 111060: lr = 0.314902,	loss = 2.766812,	Top-1 err = 0.429053,	Top-5 err = 0.203809,	data_time = 0.050585,	train_time = 0.787011
[2019-08-23 12:22:47,096] TRAIN Iter 111080: lr = 0.314868,	loss = 2.769690,	Top-1 err = 0.427002,	Top-5 err = 0.202051,	data_time = 0.166098,	train_time = 0.839105
[2019-08-23 12:22:55,195] TRAIN Iter 111100: lr = 0.314835,	loss = 2.783948,	Top-1 err = 0.425781,	Top-5 err = 0.198535,	data_time = 0.050489,	train_time = 0.404928
[2019-08-23 12:23:07,480] TRAIN Iter 111120: lr = 0.314802,	loss = 2.772305,	Top-1 err = 0.422559,	Top-5 err = 0.196484,	data_time = 0.050537,	train_time = 0.614264
[2019-08-23 12:23:24,689] TRAIN Iter 111140: lr = 0.314768,	loss = 2.811874,	Top-1 err = 0.418555,	Top-5 err = 0.203174,	data_time = 0.050597,	train_time = 0.860410
[2019-08-23 12:23:32,157] TRAIN Iter 111160: lr = 0.314735,	loss = 2.709686,	Top-1 err = 0.423438,	Top-5 err = 0.204004,	data_time = 0.050609,	train_time = 0.373403
[2019-08-23 12:23:45,390] TRAIN Iter 111180: lr = 0.314702,	loss = 2.719986,	Top-1 err = 0.423730,	Top-5 err = 0.202246,	data_time = 0.050551,	train_time = 0.661637
[2019-08-23 12:23:52,884] TRAIN Iter 111200: lr = 0.314668,	loss = 2.853082,	Top-1 err = 0.433203,	Top-5 err = 0.204980,	data_time = 0.050775,	train_time = 0.374685
[2019-08-23 12:24:07,946] TRAIN Iter 111220: lr = 0.314635,	loss = 2.662320,	Top-1 err = 0.424072,	Top-5 err = 0.199072,	data_time = 0.050487,	train_time = 0.753088
[2019-08-23 12:24:24,694] TRAIN Iter 111240: lr = 0.314602,	loss = 2.794094,	Top-1 err = 0.422119,	Top-5 err = 0.198633,	data_time = 0.050441,	train_time = 0.837391
[2019-08-23 12:24:32,246] TRAIN Iter 111260: lr = 0.314568,	loss = 2.780368,	Top-1 err = 0.420312,	Top-5 err = 0.199316,	data_time = 0.105304,	train_time = 0.377578
[2019-08-23 12:24:45,365] TRAIN Iter 111280: lr = 0.314535,	loss = 2.704660,	Top-1 err = 0.425537,	Top-5 err = 0.200488,	data_time = 0.050640,	train_time = 0.655927
[2019-08-23 12:25:03,464] TRAIN Iter 111300: lr = 0.314502,	loss = 2.695210,	Top-1 err = 0.422412,	Top-5 err = 0.197998,	data_time = 1.082626,	train_time = 0.904960
[2019-08-23 12:25:10,179] TRAIN Iter 111320: lr = 0.314468,	loss = 2.729598,	Top-1 err = 0.419092,	Top-5 err = 0.195801,	data_time = 0.049952,	train_time = 0.335727
[2019-08-23 12:25:25,247] TRAIN Iter 111340: lr = 0.314435,	loss = 2.800866,	Top-1 err = 0.425879,	Top-5 err = 0.196533,	data_time = 0.049983,	train_time = 0.753373
[2019-08-23 12:25:31,360] TRAIN Iter 111360: lr = 0.314402,	loss = 2.723576,	Top-1 err = 0.420117,	Top-5 err = 0.198193,	data_time = 0.049931,	train_time = 0.305654
[2019-08-23 12:26:15,116] TRAIN Iter 111380: lr = 0.314368,	loss = 2.606226,	Top-1 err = 0.426624,	Top-5 err = 0.202617,	data_time = 0.050532,	train_time = 2.187771
[2019-08-23 12:26:29,571] TRAIN Iter 111400: lr = 0.314335,	loss = 2.729625,	Top-1 err = 0.415283,	Top-5 err = 0.192432,	data_time = 0.115732,	train_time = 0.722704
[2019-08-23 12:26:36,828] TRAIN Iter 111420: lr = 0.314302,	loss = 2.639030,	Top-1 err = 0.412646,	Top-5 err = 0.189062,	data_time = 0.050248,	train_time = 0.362872
[2019-08-23 12:26:52,558] TRAIN Iter 111440: lr = 0.314268,	loss = 2.694514,	Top-1 err = 0.411377,	Top-5 err = 0.191357,	data_time = 0.050437,	train_time = 0.786458
[2019-08-23 12:27:00,691] TRAIN Iter 111460: lr = 0.314235,	loss = 2.681443,	Top-1 err = 0.413232,	Top-5 err = 0.191553,	data_time = 0.050262,	train_time = 0.406632
[2019-08-23 12:27:15,252] TRAIN Iter 111480: lr = 0.314202,	loss = 2.673257,	Top-1 err = 0.409570,	Top-5 err = 0.189941,	data_time = 0.050405,	train_time = 0.728033
[2019-08-23 12:27:29,869] TRAIN Iter 111500: lr = 0.314168,	loss = 2.700042,	Top-1 err = 0.414160,	Top-5 err = 0.193213,	data_time = 0.050477,	train_time = 0.730868
[2019-08-23 12:27:37,896] TRAIN Iter 111520: lr = 0.314135,	loss = 2.719427,	Top-1 err = 0.411475,	Top-5 err = 0.194092,	data_time = 0.050460,	train_time = 0.401344
[2019-08-23 12:27:49,819] TRAIN Iter 111540: lr = 0.314102,	loss = 2.568103,	Top-1 err = 0.414893,	Top-5 err = 0.191455,	data_time = 0.050928,	train_time = 0.596134
[2019-08-23 12:28:01,966] TRAIN Iter 111560: lr = 0.314068,	loss = 2.674074,	Top-1 err = 0.414893,	Top-5 err = 0.194434,	data_time = 0.050521,	train_time = 0.607295
[2019-08-23 12:28:09,439] TRAIN Iter 111580: lr = 0.314035,	loss = 2.775109,	Top-1 err = 0.412598,	Top-5 err = 0.188770,	data_time = 0.050222,	train_time = 0.373673
[2019-08-23 12:28:24,645] TRAIN Iter 111600: lr = 0.314002,	loss = 2.781697,	Top-1 err = 0.422363,	Top-5 err = 0.198730,	data_time = 0.050514,	train_time = 0.760285
[2019-08-23 12:28:31,949] TRAIN Iter 111620: lr = 0.313968,	loss = 2.680126,	Top-1 err = 0.418652,	Top-5 err = 0.198535,	data_time = 0.050400,	train_time = 0.365182
[2019-08-23 12:28:45,195] TRAIN Iter 111640: lr = 0.313935,	loss = 2.738098,	Top-1 err = 0.418164,	Top-5 err = 0.197070,	data_time = 0.050685,	train_time = 0.662296
[2019-08-23 12:28:58,189] TRAIN Iter 111660: lr = 0.313902,	loss = 2.745741,	Top-1 err = 0.419336,	Top-5 err = 0.196533,	data_time = 0.050777,	train_time = 0.649675
[2019-08-23 12:29:04,714] TRAIN Iter 111680: lr = 0.313868,	loss = 2.712126,	Top-1 err = 0.423486,	Top-5 err = 0.198291,	data_time = 0.050365,	train_time = 0.326207
[2019-08-23 12:29:21,769] TRAIN Iter 111700: lr = 0.313835,	loss = 2.575381,	Top-1 err = 0.420068,	Top-5 err = 0.196436,	data_time = 0.050295,	train_time = 0.852756
[2019-08-23 12:29:34,239] TRAIN Iter 111720: lr = 0.313802,	loss = 2.770696,	Top-1 err = 0.421826,	Top-5 err = 0.195068,	data_time = 0.050575,	train_time = 0.623469
[2019-08-23 12:29:41,258] TRAIN Iter 111740: lr = 0.313768,	loss = 2.764462,	Top-1 err = 0.424414,	Top-5 err = 0.196533,	data_time = 0.050465,	train_time = 0.350962
[2019-08-23 12:29:55,479] TRAIN Iter 111760: lr = 0.313735,	loss = 2.607192,	Top-1 err = 0.415869,	Top-5 err = 0.194531,	data_time = 0.050497,	train_time = 0.711020
[2019-08-23 12:30:02,082] TRAIN Iter 111780: lr = 0.313702,	loss = 2.767037,	Top-1 err = 0.417236,	Top-5 err = 0.196826,	data_time = 0.050842,	train_time = 0.330156
[2019-08-23 12:30:16,635] TRAIN Iter 111800: lr = 0.313668,	loss = 2.698516,	Top-1 err = 0.418848,	Top-5 err = 0.196729,	data_time = 0.050446,	train_time = 0.727628
[2019-08-23 12:30:32,784] TRAIN Iter 111820: lr = 0.313635,	loss = 2.769494,	Top-1 err = 0.415332,	Top-5 err = 0.193848,	data_time = 0.050642,	train_time = 0.807415
[2019-08-23 12:30:40,127] TRAIN Iter 111840: lr = 0.313602,	loss = 2.741765,	Top-1 err = 0.415186,	Top-5 err = 0.198437,	data_time = 0.050689,	train_time = 0.367148
[2019-08-23 12:30:55,555] TRAIN Iter 111860: lr = 0.313568,	loss = 2.695940,	Top-1 err = 0.422852,	Top-5 err = 0.194141,	data_time = 0.050536,	train_time = 0.771404
[2019-08-23 12:31:09,355] TRAIN Iter 111880: lr = 0.313535,	loss = 2.816677,	Top-1 err = 0.424463,	Top-5 err = 0.198926,	data_time = 0.050414,	train_time = 0.689963
[2019-08-23 12:31:16,648] TRAIN Iter 111900: lr = 0.313502,	loss = 2.680559,	Top-1 err = 0.415918,	Top-5 err = 0.192236,	data_time = 0.050637,	train_time = 0.364650
[2019-08-23 12:31:31,550] TRAIN Iter 111920: lr = 0.313468,	loss = 2.618714,	Top-1 err = 0.416699,	Top-5 err = 0.195215,	data_time = 0.050885,	train_time = 0.745075
[2019-08-23 12:31:38,636] TRAIN Iter 111940: lr = 0.313435,	loss = 2.719861,	Top-1 err = 0.416650,	Top-5 err = 0.197803,	data_time = 0.050760,	train_time = 0.354287
[2019-08-23 12:31:53,085] TRAIN Iter 111960: lr = 0.313402,	loss = 2.724831,	Top-1 err = 0.416455,	Top-5 err = 0.195459,	data_time = 0.050684,	train_time = 0.722451
[2019-08-23 12:32:08,781] TRAIN Iter 111980: lr = 0.313368,	loss = 2.722520,	Top-1 err = 0.419238,	Top-5 err = 0.198535,	data_time = 0.050581,	train_time = 0.784775
[2019-08-23 12:32:15,438] TRAIN Iter 112000: lr = 0.313335,	loss = 2.750247,	Top-1 err = 0.426611,	Top-5 err = 0.200195,	data_time = 0.050720,	train_time = 0.332840
[2019-08-23 12:32:31,467] TRAIN Iter 112020: lr = 0.313302,	loss = 2.702718,	Top-1 err = 0.421973,	Top-5 err = 0.199756,	data_time = 0.050773,	train_time = 0.801409
[2019-08-23 12:32:44,960] TRAIN Iter 112040: lr = 0.313268,	loss = 2.692601,	Top-1 err = 0.419141,	Top-5 err = 0.197168,	data_time = 0.050542,	train_time = 0.674680
[2019-08-23 12:32:52,013] TRAIN Iter 112060: lr = 0.313235,	loss = 2.750424,	Top-1 err = 0.422607,	Top-5 err = 0.198242,	data_time = 0.050207,	train_time = 0.352630
[2019-08-23 12:33:08,524] TRAIN Iter 112080: lr = 0.313202,	loss = 2.756994,	Top-1 err = 0.418555,	Top-5 err = 0.193848,	data_time = 0.050545,	train_time = 0.825539
[2019-08-23 12:33:15,599] TRAIN Iter 112100: lr = 0.313168,	loss = 2.679837,	Top-1 err = 0.420361,	Top-5 err = 0.197266,	data_time = 0.050304,	train_time = 0.353721
[2019-08-23 12:33:29,875] TRAIN Iter 112120: lr = 0.313135,	loss = 2.828821,	Top-1 err = 0.423779,	Top-5 err = 0.197949,	data_time = 0.050587,	train_time = 0.713767
[2019-08-23 12:33:43,013] TRAIN Iter 112140: lr = 0.313102,	loss = 2.773342,	Top-1 err = 0.418164,	Top-5 err = 0.195215,	data_time = 0.050423,	train_time = 0.656917
[2019-08-23 12:33:50,416] TRAIN Iter 112160: lr = 0.313068,	loss = 2.703496,	Top-1 err = 0.422607,	Top-5 err = 0.198437,	data_time = 0.050536,	train_time = 0.370112
[2019-08-23 12:34:08,850] TRAIN Iter 112180: lr = 0.313035,	loss = 2.695501,	Top-1 err = 0.419092,	Top-5 err = 0.200391,	data_time = 0.050361,	train_time = 0.921712
[2019-08-23 12:34:23,581] TRAIN Iter 112200: lr = 0.313002,	loss = 2.715429,	Top-1 err = 0.419434,	Top-5 err = 0.195459,	data_time = 3.831054,	train_time = 0.736518
[2019-08-23 12:34:30,661] TRAIN Iter 112220: lr = 0.312968,	loss = 2.767943,	Top-1 err = 0.420703,	Top-5 err = 0.198437,	data_time = 0.050726,	train_time = 0.353974
[2019-08-23 12:34:48,331] TRAIN Iter 112240: lr = 0.312935,	loss = 2.718766,	Top-1 err = 0.419482,	Top-5 err = 0.198047,	data_time = 0.050307,	train_time = 0.883511
[2019-08-23 12:34:55,482] TRAIN Iter 112260: lr = 0.312902,	loss = 2.837021,	Top-1 err = 0.426367,	Top-5 err = 0.203418,	data_time = 0.050508,	train_time = 0.357523
[2019-08-23 12:35:10,249] TRAIN Iter 112280: lr = 0.312868,	loss = 2.684344,	Top-1 err = 0.422900,	Top-5 err = 0.201855,	data_time = 0.050298,	train_time = 0.738327
[2019-08-23 12:35:26,875] TRAIN Iter 112300: lr = 0.312835,	loss = 2.748796,	Top-1 err = 0.423975,	Top-5 err = 0.199609,	data_time = 0.050372,	train_time = 0.831313
[2019-08-23 12:35:33,467] TRAIN Iter 112320: lr = 0.312802,	loss = 2.719978,	Top-1 err = 0.427393,	Top-5 err = 0.202979,	data_time = 0.050501,	train_time = 0.329567
[2019-08-23 12:35:51,871] TRAIN Iter 112340: lr = 0.312768,	loss = 2.765915,	Top-1 err = 0.420801,	Top-5 err = 0.196533,	data_time = 0.050374,	train_time = 0.920169
[2019-08-23 12:36:06,759] TRAIN Iter 112360: lr = 0.312735,	loss = 2.698980,	Top-1 err = 0.423242,	Top-5 err = 0.200488,	data_time = 4.800173,	train_time = 0.744391
[2019-08-23 12:36:13,769] TRAIN Iter 112380: lr = 0.312702,	loss = 2.754846,	Top-1 err = 0.424561,	Top-5 err = 0.200977,	data_time = 0.050482,	train_time = 0.350487
[2019-08-23 12:36:29,579] TRAIN Iter 112400: lr = 0.312668,	loss = 2.622378,	Top-1 err = 0.421240,	Top-5 err = 0.203125,	data_time = 0.050488,	train_time = 0.790526
[2019-08-23 12:36:36,727] TRAIN Iter 112420: lr = 0.312635,	loss = 2.688829,	Top-1 err = 0.424854,	Top-5 err = 0.201904,	data_time = 0.050308,	train_time = 0.357383
[2019-08-23 12:36:53,017] TRAIN Iter 112440: lr = 0.312602,	loss = 2.753786,	Top-1 err = 0.423389,	Top-5 err = 0.201953,	data_time = 0.050688,	train_time = 0.814446
[2019-08-23 12:37:09,681] TRAIN Iter 112460: lr = 0.312568,	loss = 2.668789,	Top-1 err = 0.424609,	Top-5 err = 0.198389,	data_time = 0.050453,	train_time = 0.833198
[2019-08-23 12:37:16,497] TRAIN Iter 112480: lr = 0.312535,	loss = 2.749726,	Top-1 err = 0.423242,	Top-5 err = 0.204248,	data_time = 0.050161,	train_time = 0.340807
[2019-08-23 12:37:33,716] TRAIN Iter 112500: lr = 0.312502,	loss = 2.836302,	Top-1 err = 0.426367,	Top-5 err = 0.200488,	data_time = 0.050631,	train_time = 0.860942
[2019-08-23 12:37:49,579] TRAIN Iter 112520: lr = 0.312468,	loss = 2.781291,	Top-1 err = 0.422900,	Top-5 err = 0.197607,	data_time = 4.869908,	train_time = 0.793093
[2019-08-23 12:37:56,058] TRAIN Iter 112540: lr = 0.312435,	loss = 2.573101,	Top-1 err = 0.425195,	Top-5 err = 0.198242,	data_time = 0.050283,	train_time = 0.323958
[2019-08-23 12:38:13,283] TRAIN Iter 112560: lr = 0.312402,	loss = 2.742320,	Top-1 err = 0.424365,	Top-5 err = 0.199219,	data_time = 0.050212,	train_time = 0.861219
[2019-08-23 12:38:20,271] TRAIN Iter 112580: lr = 0.312368,	loss = 2.798979,	Top-1 err = 0.429248,	Top-5 err = 0.209570,	data_time = 0.050035,	train_time = 0.349381
[2019-08-23 12:38:35,686] TRAIN Iter 112600: lr = 0.312335,	loss = 2.636757,	Top-1 err = 0.419043,	Top-5 err = 0.196240,	data_time = 0.049905,	train_time = 0.770722
[2019-08-23 12:39:22,289] TRAIN Iter 112620: lr = 0.312302,	loss = 2.716178,	Top-1 err = 0.430122,	Top-5 err = 0.205309,	data_time = 1.792804,	train_time = 2.330146
[2019-08-23 12:39:29,901] TRAIN Iter 112640: lr = 0.312268,	loss = 2.764239,	Top-1 err = 0.433838,	Top-5 err = 0.205127,	data_time = 0.050612,	train_time = 0.380597
[2019-08-23 12:39:45,018] TRAIN Iter 112660: lr = 0.312235,	loss = 2.711056,	Top-1 err = 0.413086,	Top-5 err = 0.191797,	data_time = 0.050338,	train_time = 0.755841
[2019-08-23 12:39:52,986] TRAIN Iter 112680: lr = 0.312202,	loss = 2.695152,	Top-1 err = 0.419678,	Top-5 err = 0.194141,	data_time = 0.149876,	train_time = 0.398356
[2019-08-23 12:40:04,158] TRAIN Iter 112700: lr = 0.312168,	loss = 2.626779,	Top-1 err = 0.414062,	Top-5 err = 0.192041,	data_time = 0.050215,	train_time = 0.558613
[2019-08-23 12:40:17,416] TRAIN Iter 112720: lr = 0.312135,	loss = 2.709147,	Top-1 err = 0.417187,	Top-5 err = 0.191211,	data_time = 0.050893,	train_time = 0.662875
[2019-08-23 12:40:24,395] TRAIN Iter 112740: lr = 0.312102,	loss = 2.693193,	Top-1 err = 0.418848,	Top-5 err = 0.196533,	data_time = 0.051028,	train_time = 0.348937
[2019-08-23 12:40:40,887] TRAIN Iter 112760: lr = 0.312068,	loss = 2.734228,	Top-1 err = 0.416650,	Top-5 err = 0.193994,	data_time = 0.050452,	train_time = 0.824581
[2019-08-23 12:40:54,235] TRAIN Iter 112780: lr = 0.312035,	loss = 2.721571,	Top-1 err = 0.413965,	Top-5 err = 0.193262,	data_time = 0.050515,	train_time = 0.667382
[2019-08-23 12:41:01,562] TRAIN Iter 112800: lr = 0.312002,	loss = 2.770859,	Top-1 err = 0.419434,	Top-5 err = 0.196191,	data_time = 0.050277,	train_time = 0.366328
[2019-08-23 12:41:17,366] TRAIN Iter 112820: lr = 0.311968,	loss = 2.705163,	Top-1 err = 0.412988,	Top-5 err = 0.190381,	data_time = 0.050412,	train_time = 0.790180
[2019-08-23 12:41:25,286] TRAIN Iter 112840: lr = 0.311935,	loss = 2.748492,	Top-1 err = 0.420068,	Top-5 err = 0.197363,	data_time = 0.050736,	train_time = 0.395998
[2019-08-23 12:41:38,260] TRAIN Iter 112860: lr = 0.311902,	loss = 2.795020,	Top-1 err = 0.415918,	Top-5 err = 0.193262,	data_time = 0.050894,	train_time = 0.648707
[2019-08-23 12:41:52,217] TRAIN Iter 112880: lr = 0.311868,	loss = 2.685357,	Top-1 err = 0.419434,	Top-5 err = 0.196777,	data_time = 0.050347,	train_time = 0.697834
[2019-08-23 12:41:59,381] TRAIN Iter 112900: lr = 0.311835,	loss = 2.682019,	Top-1 err = 0.417432,	Top-5 err = 0.199268,	data_time = 0.050560,	train_time = 0.358169
[2019-08-23 12:42:13,567] TRAIN Iter 112920: lr = 0.311802,	loss = 2.643819,	Top-1 err = 0.413867,	Top-5 err = 0.191602,	data_time = 0.050872,	train_time = 0.709294
[2019-08-23 12:42:28,969] TRAIN Iter 112940: lr = 0.311768,	loss = 2.717626,	Top-1 err = 0.418262,	Top-5 err = 0.191211,	data_time = 0.050333,	train_time = 0.770067
[2019-08-23 12:42:36,284] TRAIN Iter 112960: lr = 0.311735,	loss = 2.731410,	Top-1 err = 0.420264,	Top-5 err = 0.195557,	data_time = 0.050742,	train_time = 0.365726
[2019-08-23 12:42:50,036] TRAIN Iter 112980: lr = 0.311702,	loss = 2.702442,	Top-1 err = 0.416943,	Top-5 err = 0.188818,	data_time = 0.050489,	train_time = 0.687607
[2019-08-23 12:42:57,307] TRAIN Iter 113000: lr = 0.311668,	loss = 2.659184,	Top-1 err = 0.422217,	Top-5 err = 0.204199,	data_time = 0.132658,	train_time = 0.363533
[2019-08-23 12:43:12,345] TRAIN Iter 113020: lr = 0.311635,	loss = 2.679881,	Top-1 err = 0.418115,	Top-5 err = 0.195605,	data_time = 0.050832,	train_time = 0.751914
[2019-08-23 12:43:26,960] TRAIN Iter 113040: lr = 0.311602,	loss = 2.673454,	Top-1 err = 0.410303,	Top-5 err = 0.191504,	data_time = 0.050573,	train_time = 0.730698
[2019-08-23 12:43:34,423] TRAIN Iter 113060: lr = 0.311568,	loss = 2.733637,	Top-1 err = 0.418164,	Top-5 err = 0.193994,	data_time = 0.050628,	train_time = 0.373134
[2019-08-23 12:43:49,027] TRAIN Iter 113080: lr = 0.311535,	loss = 2.652094,	Top-1 err = 0.419189,	Top-5 err = 0.196924,	data_time = 0.050464,	train_time = 0.730203
[2019-08-23 12:44:04,418] TRAIN Iter 113100: lr = 0.311502,	loss = 2.697575,	Top-1 err = 0.415723,	Top-5 err = 0.192041,	data_time = 0.050807,	train_time = 0.769523
[2019-08-23 12:44:11,758] TRAIN Iter 113120: lr = 0.311468,	loss = 2.782132,	Top-1 err = 0.416309,	Top-5 err = 0.196191,	data_time = 0.050591,	train_time = 0.367001
[2019-08-23 12:44:27,300] TRAIN Iter 113140: lr = 0.311435,	loss = 2.722252,	Top-1 err = 0.416846,	Top-5 err = 0.192920,	data_time = 0.050819,	train_time = 0.777105
[2019-08-23 12:44:35,607] TRAIN Iter 113160: lr = 0.311402,	loss = 2.782111,	Top-1 err = 0.424707,	Top-5 err = 0.198486,	data_time = 0.050593,	train_time = 0.415321
[2019-08-23 12:44:48,553] TRAIN Iter 113180: lr = 0.311368,	loss = 2.782107,	Top-1 err = 0.422559,	Top-5 err = 0.197949,	data_time = 0.050733,	train_time = 0.647284
[2019-08-23 12:45:03,543] TRAIN Iter 113200: lr = 0.311335,	loss = 2.609154,	Top-1 err = 0.412354,	Top-5 err = 0.190967,	data_time = 0.050472,	train_time = 0.749459
[2019-08-23 12:45:11,026] TRAIN Iter 113220: lr = 0.311302,	loss = 2.654122,	Top-1 err = 0.421582,	Top-5 err = 0.198096,	data_time = 0.050231,	train_time = 0.374142
[2019-08-23 12:45:24,816] TRAIN Iter 113240: lr = 0.311268,	loss = 2.696265,	Top-1 err = 0.418262,	Top-5 err = 0.199219,	data_time = 0.050592,	train_time = 0.689510
[2019-08-23 12:45:40,598] TRAIN Iter 113260: lr = 0.311235,	loss = 2.798407,	Top-1 err = 0.413477,	Top-5 err = 0.192676,	data_time = 2.132630,	train_time = 0.789052
[2019-08-23 12:45:47,863] TRAIN Iter 113280: lr = 0.311202,	loss = 2.688053,	Top-1 err = 0.422168,	Top-5 err = 0.199951,	data_time = 0.050684,	train_time = 0.363250
[2019-08-23 12:46:03,616] TRAIN Iter 113300: lr = 0.311168,	loss = 2.745189,	Top-1 err = 0.416992,	Top-5 err = 0.193262,	data_time = 0.050472,	train_time = 0.787640
[2019-08-23 12:46:11,901] TRAIN Iter 113320: lr = 0.311135,	loss = 2.730380,	Top-1 err = 0.418848,	Top-5 err = 0.194092,	data_time = 0.050795,	train_time = 0.414238
[2019-08-23 12:46:24,637] TRAIN Iter 113340: lr = 0.311102,	loss = 2.681047,	Top-1 err = 0.419043,	Top-5 err = 0.193262,	data_time = 0.050825,	train_time = 0.636773
[2019-08-23 12:46:40,545] TRAIN Iter 113360: lr = 0.311068,	loss = 2.720612,	Top-1 err = 0.417920,	Top-5 err = 0.199902,	data_time = 0.050773,	train_time = 0.795420
[2019-08-23 12:46:48,179] TRAIN Iter 113380: lr = 0.311035,	loss = 2.835918,	Top-1 err = 0.417383,	Top-5 err = 0.200049,	data_time = 0.050571,	train_time = 0.381647
[2019-08-23 12:47:02,736] TRAIN Iter 113400: lr = 0.311002,	loss = 2.698425,	Top-1 err = 0.422461,	Top-5 err = 0.199951,	data_time = 0.050539,	train_time = 0.727865
[2019-08-23 12:47:17,406] TRAIN Iter 113420: lr = 0.310968,	loss = 2.776267,	Top-1 err = 0.419434,	Top-5 err = 0.197217,	data_time = 4.511906,	train_time = 0.733485
[2019-08-23 12:47:24,611] TRAIN Iter 113440: lr = 0.310935,	loss = 2.631885,	Top-1 err = 0.424268,	Top-5 err = 0.196436,	data_time = 0.146559,	train_time = 0.360204
[2019-08-23 12:47:41,169] TRAIN Iter 113460: lr = 0.310902,	loss = 2.764004,	Top-1 err = 0.418945,	Top-5 err = 0.195605,	data_time = 0.050388,	train_time = 0.827896
[2019-08-23 12:47:48,462] TRAIN Iter 113480: lr = 0.310868,	loss = 2.610811,	Top-1 err = 0.424707,	Top-5 err = 0.201904,	data_time = 0.050894,	train_time = 0.364657
[2019-08-23 12:48:01,947] TRAIN Iter 113500: lr = 0.310835,	loss = 2.704214,	Top-1 err = 0.417480,	Top-5 err = 0.193506,	data_time = 0.050467,	train_time = 0.674233
[2019-08-23 12:48:18,293] TRAIN Iter 113520: lr = 0.310802,	loss = 2.764013,	Top-1 err = 0.420068,	Top-5 err = 0.196631,	data_time = 0.050463,	train_time = 0.817274
[2019-08-23 12:48:25,758] TRAIN Iter 113540: lr = 0.310768,	loss = 2.685118,	Top-1 err = 0.412695,	Top-5 err = 0.193311,	data_time = 0.148216,	train_time = 0.373246
[2019-08-23 12:48:40,497] TRAIN Iter 113560: lr = 0.310735,	loss = 2.639198,	Top-1 err = 0.423535,	Top-5 err = 0.194824,	data_time = 0.050488,	train_time = 0.736930
[2019-08-23 12:48:55,893] TRAIN Iter 113580: lr = 0.310702,	loss = 2.791180,	Top-1 err = 0.418848,	Top-5 err = 0.199414,	data_time = 4.046072,	train_time = 0.769777
[2019-08-23 12:49:03,274] TRAIN Iter 113600: lr = 0.310668,	loss = 2.739842,	Top-1 err = 0.419336,	Top-5 err = 0.197949,	data_time = 0.050572,	train_time = 0.369039
[2019-08-23 12:49:18,385] TRAIN Iter 113620: lr = 0.310635,	loss = 2.667792,	Top-1 err = 0.416309,	Top-5 err = 0.192188,	data_time = 0.050544,	train_time = 0.755515
[2019-08-23 12:49:25,673] TRAIN Iter 113640: lr = 0.310602,	loss = 2.792886,	Top-1 err = 0.421582,	Top-5 err = 0.201172,	data_time = 0.050498,	train_time = 0.364398
[2019-08-23 12:49:42,175] TRAIN Iter 113660: lr = 0.310568,	loss = 2.701264,	Top-1 err = 0.423828,	Top-5 err = 0.199072,	data_time = 0.050531,	train_time = 0.825084
[2019-08-23 12:49:57,849] TRAIN Iter 113680: lr = 0.310535,	loss = 2.773884,	Top-1 err = 0.419336,	Top-5 err = 0.199023,	data_time = 0.115459,	train_time = 0.783712
[2019-08-23 12:50:05,066] TRAIN Iter 113700: lr = 0.310502,	loss = 2.751339,	Top-1 err = 0.428955,	Top-5 err = 0.201074,	data_time = 0.050305,	train_time = 0.360807
[2019-08-23 12:50:21,366] TRAIN Iter 113720: lr = 0.310468,	loss = 2.591778,	Top-1 err = 0.418213,	Top-5 err = 0.193994,	data_time = 0.050510,	train_time = 0.814990
[2019-08-23 12:50:37,504] TRAIN Iter 113740: lr = 0.310435,	loss = 2.673494,	Top-1 err = 0.425146,	Top-5 err = 0.202881,	data_time = 3.771899,	train_time = 0.806915
[2019-08-23 12:50:44,912] TRAIN Iter 113760: lr = 0.310402,	loss = 2.617964,	Top-1 err = 0.419141,	Top-5 err = 0.195898,	data_time = 0.050584,	train_time = 0.370338
[2019-08-23 12:51:00,433] TRAIN Iter 113780: lr = 0.310368,	loss = 2.754118,	Top-1 err = 0.422168,	Top-5 err = 0.199072,	data_time = 0.050945,	train_time = 0.776035
[2019-08-23 12:51:07,263] TRAIN Iter 113800: lr = 0.310335,	loss = 2.704793,	Top-1 err = 0.419971,	Top-5 err = 0.196436,	data_time = 0.050333,	train_time = 0.341506
[2019-08-23 12:51:24,434] TRAIN Iter 113820: lr = 0.310302,	loss = 2.756214,	Top-1 err = 0.423340,	Top-5 err = 0.194287,	data_time = 0.049963,	train_time = 0.858526
[2019-08-23 12:51:40,539] TRAIN Iter 113840: lr = 0.310268,	loss = 2.750540,	Top-1 err = 0.424561,	Top-5 err = 0.202197,	data_time = 0.050026,	train_time = 0.805266
[2019-08-23 12:51:47,596] TRAIN Iter 113860: lr = 0.310235,	loss = 2.665731,	Top-1 err = 0.419287,	Top-5 err = 0.195508,	data_time = 0.049877,	train_time = 0.352836
[2019-08-23 12:52:34,472] TRAIN Iter 113880: lr = 0.310202,	loss = 2.604643,	Top-1 err = 0.420530,	Top-5 err = 0.198208,	data_time = 0.050485,	train_time = 2.343782
[2019-08-23 12:52:42,947] TRAIN Iter 113900: lr = 0.310168,	loss = 2.660504,	Top-1 err = 0.419531,	Top-5 err = 0.196826,	data_time = 0.050319,	train_time = 0.423694
[2019-08-23 12:52:53,962] TRAIN Iter 113920: lr = 0.310135,	loss = 2.781775,	Top-1 err = 0.420508,	Top-5 err = 0.191357,	data_time = 0.050508,	train_time = 0.550734
[2019-08-23 12:53:07,747] TRAIN Iter 113940: lr = 0.310102,	loss = 2.711317,	Top-1 err = 0.417725,	Top-5 err = 0.192334,	data_time = 0.050939,	train_time = 0.689261
[2019-08-23 12:53:15,636] TRAIN Iter 113960: lr = 0.310068,	loss = 2.695544,	Top-1 err = 0.410840,	Top-5 err = 0.192920,	data_time = 0.050586,	train_time = 0.394403
[2019-08-23 12:53:28,895] TRAIN Iter 113980: lr = 0.310035,	loss = 2.714202,	Top-1 err = 0.411230,	Top-5 err = 0.190186,	data_time = 0.050525,	train_time = 0.662964
[2019-08-23 12:53:36,777] TRAIN Iter 114000: lr = 0.310002,	loss = 2.726641,	Top-1 err = 0.419287,	Top-5 err = 0.194531,	data_time = 0.050535,	train_time = 0.394060
[2019-08-23 12:53:48,808] TRAIN Iter 114020: lr = 0.309968,	loss = 2.682729,	Top-1 err = 0.413428,	Top-5 err = 0.193115,	data_time = 0.050646,	train_time = 0.601555
[2019-08-23 12:54:04,524] TRAIN Iter 114040: lr = 0.309935,	loss = 2.628223,	Top-1 err = 0.414648,	Top-5 err = 0.191211,	data_time = 0.050311,	train_time = 0.785778
[2019-08-23 12:54:12,250] TRAIN Iter 114060: lr = 0.309902,	loss = 2.728047,	Top-1 err = 0.419434,	Top-5 err = 0.192676,	data_time = 0.162045,	train_time = 0.386309
[2019-08-23 12:54:25,868] TRAIN Iter 114080: lr = 0.309868,	loss = 2.699230,	Top-1 err = 0.420850,	Top-5 err = 0.199365,	data_time = 0.050487,	train_time = 0.680847
[2019-08-23 12:54:39,289] TRAIN Iter 114100: lr = 0.309835,	loss = 2.710193,	Top-1 err = 0.415723,	Top-5 err = 0.195410,	data_time = 0.833962,	train_time = 0.671074
[2019-08-23 12:54:47,147] TRAIN Iter 114120: lr = 0.309802,	loss = 2.780781,	Top-1 err = 0.424512,	Top-5 err = 0.197168,	data_time = 0.050843,	train_time = 0.392863
[2019-08-23 12:55:02,523] TRAIN Iter 114140: lr = 0.309768,	loss = 2.653695,	Top-1 err = 0.419629,	Top-5 err = 0.192334,	data_time = 0.050385,	train_time = 0.768807
[2019-08-23 12:55:12,576] TRAIN Iter 114160: lr = 0.309735,	loss = 2.616196,	Top-1 err = 0.420312,	Top-5 err = 0.196143,	data_time = 0.050572,	train_time = 0.502597
[2019-08-23 12:55:22,603] TRAIN Iter 114180: lr = 0.309702,	loss = 2.693210,	Top-1 err = 0.416162,	Top-5 err = 0.193652,	data_time = 0.050379,	train_time = 0.501372
[2019-08-23 12:55:36,673] TRAIN Iter 114200: lr = 0.309668,	loss = 2.681791,	Top-1 err = 0.413623,	Top-5 err = 0.191309,	data_time = 0.050510,	train_time = 0.703490
[2019-08-23 12:55:43,807] TRAIN Iter 114220: lr = 0.309635,	loss = 2.680059,	Top-1 err = 0.416406,	Top-5 err = 0.192139,	data_time = 0.050451,	train_time = 0.356670
[2019-08-23 12:56:00,047] TRAIN Iter 114240: lr = 0.309602,	loss = 2.624745,	Top-1 err = 0.413721,	Top-5 err = 0.192871,	data_time = 0.050499,	train_time = 0.811978
[2019-08-23 12:56:15,524] TRAIN Iter 114260: lr = 0.309568,	loss = 2.578487,	Top-1 err = 0.412256,	Top-5 err = 0.193555,	data_time = 2.541562,	train_time = 0.773827
[2019-08-23 12:56:22,681] TRAIN Iter 114280: lr = 0.309535,	loss = 2.707760,	Top-1 err = 0.415283,	Top-5 err = 0.191650,	data_time = 0.050630,	train_time = 0.357832
[2019-08-23 12:56:37,541] TRAIN Iter 114300: lr = 0.309502,	loss = 2.666605,	Top-1 err = 0.421924,	Top-5 err = 0.195654,	data_time = 0.050348,	train_time = 0.742996
[2019-08-23 12:56:50,634] TRAIN Iter 114320: lr = 0.309468,	loss = 2.643529,	Top-1 err = 0.415820,	Top-5 err = 0.195361,	data_time = 3.051123,	train_time = 0.654646
[2019-08-23 12:57:00,169] TRAIN Iter 114340: lr = 0.309435,	loss = 2.691548,	Top-1 err = 0.418994,	Top-5 err = 0.198779,	data_time = 0.050351,	train_time = 0.476751
[2019-08-23 12:57:13,847] TRAIN Iter 114360: lr = 0.309402,	loss = 2.712208,	Top-1 err = 0.422363,	Top-5 err = 0.197998,	data_time = 0.050729,	train_time = 0.683855
[2019-08-23 12:57:21,118] TRAIN Iter 114380: lr = 0.309368,	loss = 2.663047,	Top-1 err = 0.418604,	Top-5 err = 0.193408,	data_time = 0.050513,	train_time = 0.363559
[2019-08-23 12:57:36,317] TRAIN Iter 114400: lr = 0.309335,	loss = 2.714174,	Top-1 err = 0.415918,	Top-5 err = 0.194385,	data_time = 0.050853,	train_time = 0.759914
[2019-08-23 12:57:51,271] TRAIN Iter 114420: lr = 0.309302,	loss = 2.678684,	Top-1 err = 0.418701,	Top-5 err = 0.196875,	data_time = 1.473448,	train_time = 0.747679
[2019-08-23 12:57:59,022] TRAIN Iter 114440: lr = 0.309268,	loss = 2.810830,	Top-1 err = 0.416699,	Top-5 err = 0.197656,	data_time = 0.050966,	train_time = 0.387531
[2019-08-23 12:58:13,514] TRAIN Iter 114460: lr = 0.309235,	loss = 2.703520,	Top-1 err = 0.427979,	Top-5 err = 0.199658,	data_time = 0.050496,	train_time = 0.724612
[2019-08-23 12:58:27,943] TRAIN Iter 114480: lr = 0.309202,	loss = 2.736112,	Top-1 err = 0.422656,	Top-5 err = 0.197314,	data_time = 0.050429,	train_time = 0.721431
[2019-08-23 12:58:36,966] TRAIN Iter 114500: lr = 0.309168,	loss = 2.745332,	Top-1 err = 0.416260,	Top-5 err = 0.193652,	data_time = 0.050564,	train_time = 0.451119
[2019-08-23 12:58:49,960] TRAIN Iter 114520: lr = 0.309135,	loss = 2.789382,	Top-1 err = 0.425635,	Top-5 err = 0.198389,	data_time = 0.050449,	train_time = 0.649695
[2019-08-23 12:58:57,686] TRAIN Iter 114540: lr = 0.309102,	loss = 2.710802,	Top-1 err = 0.415820,	Top-5 err = 0.191504,	data_time = 0.050873,	train_time = 0.386314
[2019-08-23 12:59:11,310] TRAIN Iter 114560: lr = 0.309068,	loss = 2.721536,	Top-1 err = 0.427979,	Top-5 err = 0.200439,	data_time = 0.050448,	train_time = 0.681165
[2019-08-23 12:59:26,823] TRAIN Iter 114580: lr = 0.309035,	loss = 2.740237,	Top-1 err = 0.423584,	Top-5 err = 0.196875,	data_time = 0.050650,	train_time = 0.775620
[2019-08-23 12:59:34,279] TRAIN Iter 114600: lr = 0.309002,	loss = 2.780311,	Top-1 err = 0.416309,	Top-5 err = 0.194629,	data_time = 0.050490,	train_time = 0.372807
[2019-08-23 12:59:50,335] TRAIN Iter 114620: lr = 0.308968,	loss = 2.668540,	Top-1 err = 0.420166,	Top-5 err = 0.193799,	data_time = 0.050529,	train_time = 0.802779
[2019-08-23 13:00:01,663] TRAIN Iter 114640: lr = 0.308935,	loss = 2.675952,	Top-1 err = 0.415967,	Top-5 err = 0.191553,	data_time = 0.050563,	train_time = 0.566388
[2019-08-23 13:00:09,409] TRAIN Iter 114660: lr = 0.308902,	loss = 2.651695,	Top-1 err = 0.421582,	Top-5 err = 0.195508,	data_time = 0.050387,	train_time = 0.387271
[2019-08-23 13:00:25,915] TRAIN Iter 114680: lr = 0.308868,	loss = 2.599834,	Top-1 err = 0.424121,	Top-5 err = 0.199512,	data_time = 0.050294,	train_time = 0.825308
[2019-08-23 13:00:32,979] TRAIN Iter 114700: lr = 0.308835,	loss = 2.825814,	Top-1 err = 0.417529,	Top-5 err = 0.197412,	data_time = 0.050443,	train_time = 0.353182
[2019-08-23 13:00:48,549] TRAIN Iter 114720: lr = 0.308802,	loss = 2.823378,	Top-1 err = 0.427197,	Top-5 err = 0.198730,	data_time = 0.050357,	train_time = 0.778478
[2019-08-23 13:01:05,552] TRAIN Iter 114740: lr = 0.308768,	loss = 2.617297,	Top-1 err = 0.421631,	Top-5 err = 0.201367,	data_time = 0.050511,	train_time = 0.850140
[2019-08-23 13:01:13,089] TRAIN Iter 114760: lr = 0.308735,	loss = 2.716195,	Top-1 err = 0.428223,	Top-5 err = 0.204102,	data_time = 0.050523,	train_time = 0.376842
[2019-08-23 13:01:28,365] TRAIN Iter 114780: lr = 0.308702,	loss = 2.725407,	Top-1 err = 0.422461,	Top-5 err = 0.196729,	data_time = 0.050579,	train_time = 0.763806
[2019-08-23 13:01:43,083] TRAIN Iter 114800: lr = 0.308668,	loss = 2.771043,	Top-1 err = 0.417578,	Top-5 err = 0.198242,	data_time = 0.050712,	train_time = 0.735847
[2019-08-23 13:01:50,189] TRAIN Iter 114820: lr = 0.308635,	loss = 2.720811,	Top-1 err = 0.424854,	Top-5 err = 0.199951,	data_time = 0.051046,	train_time = 0.355324
[2019-08-23 13:02:05,813] TRAIN Iter 114840: lr = 0.308602,	loss = 2.761106,	Top-1 err = 0.420801,	Top-5 err = 0.196240,	data_time = 0.050316,	train_time = 0.781160
[2019-08-23 13:02:13,433] TRAIN Iter 114860: lr = 0.308568,	loss = 2.767761,	Top-1 err = 0.422021,	Top-5 err = 0.194385,	data_time = 0.153944,	train_time = 0.380976
[2019-08-23 13:02:27,506] TRAIN Iter 114880: lr = 0.308535,	loss = 2.685910,	Top-1 err = 0.416748,	Top-5 err = 0.193604,	data_time = 0.050483,	train_time = 0.703673
[2019-08-23 13:02:43,200] TRAIN Iter 114900: lr = 0.308502,	loss = 2.654624,	Top-1 err = 0.417529,	Top-5 err = 0.197314,	data_time = 0.050690,	train_time = 0.784675
[2019-08-23 13:02:50,120] TRAIN Iter 114920: lr = 0.308468,	loss = 2.796620,	Top-1 err = 0.421777,	Top-5 err = 0.200586,	data_time = 0.050366,	train_time = 0.345998
[2019-08-23 13:03:06,196] TRAIN Iter 114940: lr = 0.308435,	loss = 2.749065,	Top-1 err = 0.418408,	Top-5 err = 0.191846,	data_time = 0.050584,	train_time = 0.803786
[2019-08-23 13:03:21,607] TRAIN Iter 114960: lr = 0.308402,	loss = 2.848578,	Top-1 err = 0.426367,	Top-5 err = 0.197656,	data_time = 0.158062,	train_time = 0.770527
[2019-08-23 13:03:28,756] TRAIN Iter 114980: lr = 0.308368,	loss = 2.786588,	Top-1 err = 0.425781,	Top-5 err = 0.202295,	data_time = 0.050487,	train_time = 0.357442
[2019-08-23 13:03:45,127] TRAIN Iter 115000: lr = 0.308335,	loss = 2.741797,	Top-1 err = 0.418750,	Top-5 err = 0.196094,	data_time = 0.050388,	train_time = 0.818526
[2019-08-23 13:03:52,109] TRAIN Iter 115020: lr = 0.308302,	loss = 2.801365,	Top-1 err = 0.422998,	Top-5 err = 0.197559,	data_time = 0.050414,	train_time = 0.349077
[2019-08-23 13:04:08,809] TRAIN Iter 115040: lr = 0.308268,	loss = 2.700494,	Top-1 err = 0.419678,	Top-5 err = 0.199756,	data_time = 0.050289,	train_time = 0.835001
[2019-08-23 13:04:25,335] TRAIN Iter 115060: lr = 0.308235,	loss = 2.693744,	Top-1 err = 0.420459,	Top-5 err = 0.193555,	data_time = 0.050079,	train_time = 0.826293
[2019-08-23 13:04:32,065] TRAIN Iter 115080: lr = 0.308202,	loss = 2.739429,	Top-1 err = 0.418359,	Top-5 err = 0.199707,	data_time = 0.050207,	train_time = 0.336444
[2019-08-23 13:04:48,726] TRAIN Iter 115100: lr = 0.308168,	loss = 2.763491,	Top-1 err = 0.429248,	Top-5 err = 0.196875,	data_time = 0.049892,	train_time = 0.833032
[2019-08-23 13:04:59,475] TRAIN Iter 115120: lr = 0.308135,	loss = 3.031178,	Top-1 err = 0.424053,	Top-5 err = 0.204310,	data_time = 0.007133,	train_time = 0.537458
[2019-08-23 13:05:45,642] TRAIN Iter 115140: lr = 0.308102,	loss = 2.850667,	Top-1 err = 0.419385,	Top-5 err = 0.194971,	data_time = 0.050521,	train_time = 2.308316
[2019-08-23 13:05:57,091] TRAIN Iter 115160: lr = 0.308068,	loss = 2.746020,	Top-1 err = 0.410498,	Top-5 err = 0.188623,	data_time = 0.050540,	train_time = 0.572451
[2019-08-23 13:06:06,255] TRAIN Iter 115180: lr = 0.308035,	loss = 2.701566,	Top-1 err = 0.417773,	Top-5 err = 0.193945,	data_time = 0.050568,	train_time = 0.458171
[2019-08-23 13:06:20,183] TRAIN Iter 115200: lr = 0.308002,	loss = 2.641225,	Top-1 err = 0.411377,	Top-5 err = 0.190430,	data_time = 0.050603,	train_time = 0.696408
[2019-08-23 13:06:31,133] TRAIN Iter 115220: lr = 0.307968,	loss = 2.722649,	Top-1 err = 0.413525,	Top-5 err = 0.193701,	data_time = 0.564303,	train_time = 0.547460
[2019-08-23 13:06:42,023] TRAIN Iter 115240: lr = 0.307935,	loss = 2.559582,	Top-1 err = 0.412891,	Top-5 err = 0.194336,	data_time = 0.050524,	train_time = 0.544473
[2019-08-23 13:06:56,819] TRAIN Iter 115260: lr = 0.307902,	loss = 2.597416,	Top-1 err = 0.411084,	Top-5 err = 0.193359,	data_time = 0.051034,	train_time = 0.739790
[2019-08-23 13:07:04,199] TRAIN Iter 115280: lr = 0.307868,	loss = 2.625326,	Top-1 err = 0.417041,	Top-5 err = 0.197266,	data_time = 0.164158,	train_time = 0.368991
[2019-08-23 13:07:19,318] TRAIN Iter 115300: lr = 0.307835,	loss = 2.631927,	Top-1 err = 0.406055,	Top-5 err = 0.187158,	data_time = 0.050430,	train_time = 0.755955
[2019-08-23 13:07:31,831] TRAIN Iter 115320: lr = 0.307802,	loss = 2.691586,	Top-1 err = 0.414844,	Top-5 err = 0.194580,	data_time = 0.050541,	train_time = 0.625614
[2019-08-23 13:07:38,815] TRAIN Iter 115340: lr = 0.307768,	loss = 2.675699,	Top-1 err = 0.412939,	Top-5 err = 0.188916,	data_time = 0.050351,	train_time = 0.349213
[2019-08-23 13:07:54,021] TRAIN Iter 115360: lr = 0.307735,	loss = 2.635554,	Top-1 err = 0.416309,	Top-5 err = 0.192285,	data_time = 0.050531,	train_time = 0.760267
[2019-08-23 13:08:03,920] TRAIN Iter 115380: lr = 0.307702,	loss = 2.686929,	Top-1 err = 0.414355,	Top-5 err = 0.189453,	data_time = 0.050246,	train_time = 0.494946
[2019-08-23 13:08:15,073] TRAIN Iter 115400: lr = 0.307668,	loss = 2.758898,	Top-1 err = 0.422070,	Top-5 err = 0.199121,	data_time = 0.050390,	train_time = 0.557653
[2019-08-23 13:08:31,578] TRAIN Iter 115420: lr = 0.307635,	loss = 2.575365,	Top-1 err = 0.416553,	Top-5 err = 0.192627,	data_time = 0.050523,	train_time = 0.825229
[2019-08-23 13:08:39,069] TRAIN Iter 115440: lr = 0.307602,	loss = 2.743180,	Top-1 err = 0.416162,	Top-5 err = 0.190918,	data_time = 0.050642,	train_time = 0.374528
[2019-08-23 13:08:53,729] TRAIN Iter 115460: lr = 0.307568,	loss = 2.719815,	Top-1 err = 0.417285,	Top-5 err = 0.195557,	data_time = 0.050552,	train_time = 0.732952
[2019-08-23 13:09:07,299] TRAIN Iter 115480: lr = 0.307535,	loss = 2.684806,	Top-1 err = 0.424414,	Top-5 err = 0.195703,	data_time = 0.050589,	train_time = 0.678534
[2019-08-23 13:09:14,289] TRAIN Iter 115500: lr = 0.307502,	loss = 2.807137,	Top-1 err = 0.420068,	Top-5 err = 0.192920,	data_time = 0.142067,	train_time = 0.349486
[2019-08-23 13:09:29,994] TRAIN Iter 115520: lr = 0.307468,	loss = 2.732419,	Top-1 err = 0.418457,	Top-5 err = 0.194434,	data_time = 0.050559,	train_time = 0.785204
[2019-08-23 13:09:45,984] TRAIN Iter 115540: lr = 0.307435,	loss = 2.680065,	Top-1 err = 0.408545,	Top-5 err = 0.188281,	data_time = 0.050436,	train_time = 0.799475
[2019-08-23 13:09:53,135] TRAIN Iter 115560: lr = 0.307402,	loss = 2.723508,	Top-1 err = 0.415479,	Top-5 err = 0.193359,	data_time = 0.050489,	train_time = 0.357558
[2019-08-23 13:10:05,973] TRAIN Iter 115580: lr = 0.307368,	loss = 2.726737,	Top-1 err = 0.421240,	Top-5 err = 0.196777,	data_time = 0.050399,	train_time = 0.641890
[2019-08-23 13:10:13,593] TRAIN Iter 115600: lr = 0.307335,	loss = 2.716234,	Top-1 err = 0.418066,	Top-5 err = 0.197852,	data_time = 0.050618,	train_time = 0.380978
[2019-08-23 13:10:27,107] TRAIN Iter 115620: lr = 0.307302,	loss = 2.707621,	Top-1 err = 0.419873,	Top-5 err = 0.193799,	data_time = 0.050676,	train_time = 0.675683
[2019-08-23 13:10:41,447] TRAIN Iter 115640: lr = 0.307268,	loss = 2.733833,	Top-1 err = 0.422363,	Top-5 err = 0.198828,	data_time = 0.050203,	train_time = 0.716981
[2019-08-23 13:10:48,481] TRAIN Iter 115660: lr = 0.307235,	loss = 2.594097,	Top-1 err = 0.422949,	Top-5 err = 0.197266,	data_time = 0.050583,	train_time = 0.351704
[2019-08-23 13:11:04,722] TRAIN Iter 115680: lr = 0.307202,	loss = 2.664181,	Top-1 err = 0.417480,	Top-5 err = 0.196436,	data_time = 0.050720,	train_time = 0.812001
[2019-08-23 13:11:19,109] TRAIN Iter 115700: lr = 0.307168,	loss = 2.656333,	Top-1 err = 0.425586,	Top-5 err = 0.199512,	data_time = 0.050632,	train_time = 0.719367
[2019-08-23 13:11:26,690] TRAIN Iter 115720: lr = 0.307135,	loss = 2.711751,	Top-1 err = 0.422412,	Top-5 err = 0.197559,	data_time = 0.050349,	train_time = 0.379024
[2019-08-23 13:11:41,699] TRAIN Iter 115740: lr = 0.307102,	loss = 2.642163,	Top-1 err = 0.413818,	Top-5 err = 0.196191,	data_time = 0.050602,	train_time = 0.750439
[2019-08-23 13:11:49,011] TRAIN Iter 115760: lr = 0.307068,	loss = 2.717199,	Top-1 err = 0.417725,	Top-5 err = 0.192773,	data_time = 0.050772,	train_time = 0.365600
[2019-08-23 13:12:05,003] TRAIN Iter 115780: lr = 0.307035,	loss = 2.654585,	Top-1 err = 0.414795,	Top-5 err = 0.197754,	data_time = 0.050491,	train_time = 0.799562
[2019-08-23 13:12:20,526] TRAIN Iter 115800: lr = 0.307002,	loss = 2.781365,	Top-1 err = 0.417920,	Top-5 err = 0.197314,	data_time = 0.050657,	train_time = 0.776119
[2019-08-23 13:12:27,449] TRAIN Iter 115820: lr = 0.306968,	loss = 2.687524,	Top-1 err = 0.417822,	Top-5 err = 0.197656,	data_time = 0.050607,	train_time = 0.346158
[2019-08-23 13:12:43,628] TRAIN Iter 115840: lr = 0.306935,	loss = 2.761590,	Top-1 err = 0.416895,	Top-5 err = 0.196484,	data_time = 0.050369,	train_time = 0.808958
[2019-08-23 13:12:57,959] TRAIN Iter 115860: lr = 0.306902,	loss = 2.685287,	Top-1 err = 0.420166,	Top-5 err = 0.198730,	data_time = 0.117758,	train_time = 0.716530
[2019-08-23 13:13:06,131] TRAIN Iter 115880: lr = 0.306868,	loss = 2.581823,	Top-1 err = 0.418262,	Top-5 err = 0.193994,	data_time = 0.050541,	train_time = 0.408571
[2019-08-23 13:13:20,632] TRAIN Iter 115900: lr = 0.306835,	loss = 2.740710,	Top-1 err = 0.417285,	Top-5 err = 0.196680,	data_time = 0.050611,	train_time = 0.725018
[2019-08-23 13:13:27,930] TRAIN Iter 115920: lr = 0.306802,	loss = 2.750477,	Top-1 err = 0.420654,	Top-5 err = 0.193018,	data_time = 0.050263,	train_time = 0.364895
[2019-08-23 13:13:44,102] TRAIN Iter 115940: lr = 0.306768,	loss = 2.679120,	Top-1 err = 0.420947,	Top-5 err = 0.198389,	data_time = 0.182310,	train_time = 0.808562
[2019-08-23 13:13:59,581] TRAIN Iter 115960: lr = 0.306735,	loss = 2.792944,	Top-1 err = 0.421729,	Top-5 err = 0.196484,	data_time = 0.050803,	train_time = 0.773955
[2019-08-23 13:14:07,043] TRAIN Iter 115980: lr = 0.306702,	loss = 2.713102,	Top-1 err = 0.417187,	Top-5 err = 0.192529,	data_time = 0.050439,	train_time = 0.373066
[2019-08-23 13:14:20,923] TRAIN Iter 116000: lr = 0.306668,	loss = 2.790267,	Top-1 err = 0.420410,	Top-5 err = 0.198584,	data_time = 0.050498,	train_time = 0.694024
[2019-08-23 13:14:35,447] TRAIN Iter 116020: lr = 0.306635,	loss = 2.774437,	Top-1 err = 0.425684,	Top-5 err = 0.199023,	data_time = 0.050456,	train_time = 0.726170
[2019-08-23 13:14:44,445] TRAIN Iter 116040: lr = 0.306602,	loss = 2.649913,	Top-1 err = 0.422754,	Top-5 err = 0.200342,	data_time = 0.147692,	train_time = 0.449886
[2019-08-23 13:14:59,653] TRAIN Iter 116060: lr = 0.306568,	loss = 2.766181,	Top-1 err = 0.424951,	Top-5 err = 0.201025,	data_time = 0.050417,	train_time = 0.760362
[2019-08-23 13:15:06,967] TRAIN Iter 116080: lr = 0.306535,	loss = 2.772350,	Top-1 err = 0.421826,	Top-5 err = 0.199707,	data_time = 0.127591,	train_time = 0.365686
[2019-08-23 13:15:23,405] TRAIN Iter 116100: lr = 0.306502,	loss = 2.782004,	Top-1 err = 0.417773,	Top-5 err = 0.196289,	data_time = 0.050619,	train_time = 0.821896
[2019-08-23 13:15:38,502] TRAIN Iter 116120: lr = 0.306468,	loss = 2.702793,	Top-1 err = 0.415137,	Top-5 err = 0.194922,	data_time = 0.050818,	train_time = 0.754815
[2019-08-23 13:15:45,964] TRAIN Iter 116140: lr = 0.306435,	loss = 2.802355,	Top-1 err = 0.424902,	Top-5 err = 0.202979,	data_time = 0.050426,	train_time = 0.373125
[2019-08-23 13:16:00,163] TRAIN Iter 116160: lr = 0.306402,	loss = 2.762317,	Top-1 err = 0.428271,	Top-5 err = 0.201025,	data_time = 0.050552,	train_time = 0.709916
[2019-08-23 13:16:15,747] TRAIN Iter 116180: lr = 0.306368,	loss = 2.720640,	Top-1 err = 0.417773,	Top-5 err = 0.196484,	data_time = 0.050345,	train_time = 0.779209
[2019-08-23 13:16:24,972] TRAIN Iter 116200: lr = 0.306335,	loss = 2.711622,	Top-1 err = 0.425244,	Top-5 err = 0.201221,	data_time = 0.050404,	train_time = 0.461202
[2019-08-23 13:16:40,591] TRAIN Iter 116220: lr = 0.306302,	loss = 2.721715,	Top-1 err = 0.425000,	Top-5 err = 0.199072,	data_time = 0.050340,	train_time = 0.780938
[2019-08-23 13:16:48,030] TRAIN Iter 116240: lr = 0.306268,	loss = 2.681517,	Top-1 err = 0.419678,	Top-5 err = 0.199902,	data_time = 0.050427,	train_time = 0.371958
[2019-08-23 13:17:02,778] TRAIN Iter 116260: lr = 0.306235,	loss = 2.733312,	Top-1 err = 0.418945,	Top-5 err = 0.193848,	data_time = 0.050351,	train_time = 0.737394
[2019-08-23 13:17:20,649] TRAIN Iter 116280: lr = 0.306202,	loss = 2.748766,	Top-1 err = 0.421875,	Top-5 err = 0.199512,	data_time = 0.050546,	train_time = 0.893498
[2019-08-23 13:17:27,888] TRAIN Iter 116300: lr = 0.306168,	loss = 2.791881,	Top-1 err = 0.417041,	Top-5 err = 0.197998,	data_time = 0.050458,	train_time = 0.361978
[2019-08-23 13:17:43,426] TRAIN Iter 116320: lr = 0.306135,	loss = 2.680504,	Top-1 err = 0.425293,	Top-5 err = 0.199072,	data_time = 0.049993,	train_time = 0.776852
[2019-08-23 13:17:55,582] TRAIN Iter 116340: lr = 0.306102,	loss = 2.637737,	Top-1 err = 0.419434,	Top-5 err = 0.198975,	data_time = 0.049990,	train_time = 0.607780
[2019-08-23 13:18:05,265] TRAIN Iter 116360: lr = 0.306068,	loss = 2.741151,	Top-1 err = 0.421387,	Top-5 err = 0.195752,	data_time = 0.049916,	train_time = 0.484136
[2019-08-23 13:18:53,878] TRAIN Iter 116380: lr = 0.306035,	loss = 2.710796,	Top-1 err = 0.429056,	Top-5 err = 0.203959,	data_time = 0.050546,	train_time = 2.430645
[2019-08-23 13:19:01,098] TRAIN Iter 116400: lr = 0.306002,	loss = 2.690708,	Top-1 err = 0.416748,	Top-5 err = 0.192432,	data_time = 0.050352,	train_time = 0.361016
[2019-08-23 13:19:17,862] TRAIN Iter 116420: lr = 0.305968,	loss = 2.660678,	Top-1 err = 0.417187,	Top-5 err = 0.188965,	data_time = 0.050632,	train_time = 0.838174
[2019-08-23 13:19:30,509] TRAIN Iter 116440: lr = 0.305935,	loss = 2.573439,	Top-1 err = 0.410059,	Top-5 err = 0.189258,	data_time = 0.050632,	train_time = 0.632316
[2019-08-23 13:19:37,317] TRAIN Iter 116460: lr = 0.305902,	loss = 2.726179,	Top-1 err = 0.414258,	Top-5 err = 0.189551,	data_time = 0.050436,	train_time = 0.340381
[2019-08-23 13:19:51,429] TRAIN Iter 116480: lr = 0.305868,	loss = 2.632830,	Top-1 err = 0.415088,	Top-5 err = 0.187598,	data_time = 0.050462,	train_time = 0.705618
[2019-08-23 13:19:58,453] TRAIN Iter 116500: lr = 0.305835,	loss = 2.788787,	Top-1 err = 0.412939,	Top-5 err = 0.188770,	data_time = 0.050684,	train_time = 0.351170
[2019-08-23 13:20:13,296] TRAIN Iter 116520: lr = 0.305802,	loss = 2.667001,	Top-1 err = 0.413379,	Top-5 err = 0.195752,	data_time = 0.050353,	train_time = 0.742112
[2019-08-23 13:20:27,835] TRAIN Iter 116540: lr = 0.305768,	loss = 2.768990,	Top-1 err = 0.411963,	Top-5 err = 0.188330,	data_time = 0.050509,	train_time = 0.726946
[2019-08-23 13:20:34,834] TRAIN Iter 116560: lr = 0.305735,	loss = 2.586094,	Top-1 err = 0.410107,	Top-5 err = 0.188428,	data_time = 0.050418,	train_time = 0.349930
[2019-08-23 13:20:50,313] TRAIN Iter 116580: lr = 0.305702,	loss = 2.680341,	Top-1 err = 0.414062,	Top-5 err = 0.192529,	data_time = 0.050400,	train_time = 0.773953
[2019-08-23 13:21:04,360] TRAIN Iter 116600: lr = 0.305668,	loss = 2.676074,	Top-1 err = 0.416992,	Top-5 err = 0.191699,	data_time = 0.050333,	train_time = 0.702323
[2019-08-23 13:21:11,591] TRAIN Iter 116620: lr = 0.305635,	loss = 2.688609,	Top-1 err = 0.415576,	Top-5 err = 0.194922,	data_time = 0.050562,	train_time = 0.361550
[2019-08-23 13:21:26,665] TRAIN Iter 116640: lr = 0.305602,	loss = 2.664174,	Top-1 err = 0.406934,	Top-5 err = 0.184570,	data_time = 0.050619,	train_time = 0.753704
[2019-08-23 13:21:33,973] TRAIN Iter 116660: lr = 0.305568,	loss = 2.627089,	Top-1 err = 0.416602,	Top-5 err = 0.195947,	data_time = 0.129778,	train_time = 0.365390
[2019-08-23 13:21:46,916] TRAIN Iter 116680: lr = 0.305535,	loss = 2.677110,	Top-1 err = 0.414355,	Top-5 err = 0.190820,	data_time = 0.050317,	train_time = 0.647113
[2019-08-23 13:22:01,350] TRAIN Iter 116700: lr = 0.305502,	loss = 2.739643,	Top-1 err = 0.413574,	Top-5 err = 0.195605,	data_time = 0.133985,	train_time = 0.721717
[2019-08-23 13:22:08,866] TRAIN Iter 116720: lr = 0.305468,	loss = 2.674742,	Top-1 err = 0.415381,	Top-5 err = 0.195459,	data_time = 0.050817,	train_time = 0.375763
[2019-08-23 13:22:24,768] TRAIN Iter 116740: lr = 0.305435,	loss = 2.607440,	Top-1 err = 0.421924,	Top-5 err = 0.199707,	data_time = 0.050405,	train_time = 0.795083
[2019-08-23 13:22:36,214] TRAIN Iter 116760: lr = 0.305402,	loss = 2.785744,	Top-1 err = 0.417285,	Top-5 err = 0.199023,	data_time = 0.050584,	train_time = 0.572300
[2019-08-23 13:22:46,368] TRAIN Iter 116780: lr = 0.305368,	loss = 2.668880,	Top-1 err = 0.414307,	Top-5 err = 0.197803,	data_time = 0.050357,	train_time = 0.507673
[2019-08-23 13:23:01,252] TRAIN Iter 116800: lr = 0.305335,	loss = 2.625178,	Top-1 err = 0.413574,	Top-5 err = 0.195703,	data_time = 0.050608,	train_time = 0.744188
[2019-08-23 13:23:08,817] TRAIN Iter 116820: lr = 0.305302,	loss = 2.672402,	Top-1 err = 0.424121,	Top-5 err = 0.197314,	data_time = 0.050402,	train_time = 0.378243
[2019-08-23 13:23:23,307] TRAIN Iter 116840: lr = 0.305268,	loss = 2.699145,	Top-1 err = 0.410059,	Top-5 err = 0.193164,	data_time = 0.050408,	train_time = 0.724487
[2019-08-23 13:23:38,913] TRAIN Iter 116860: lr = 0.305235,	loss = 2.736032,	Top-1 err = 0.421436,	Top-5 err = 0.197754,	data_time = 0.050524,	train_time = 0.780261
[2019-08-23 13:23:46,204] TRAIN Iter 116880: lr = 0.305202,	loss = 2.672307,	Top-1 err = 0.417578,	Top-5 err = 0.193945,	data_time = 0.050642,	train_time = 0.364522
[2019-08-23 13:24:01,807] TRAIN Iter 116900: lr = 0.305168,	loss = 2.654784,	Top-1 err = 0.414258,	Top-5 err = 0.194824,	data_time = 0.050789,	train_time = 0.780169
[2019-08-23 13:24:12,851] TRAIN Iter 116920: lr = 0.305135,	loss = 2.686978,	Top-1 err = 0.415918,	Top-5 err = 0.196533,	data_time = 0.050382,	train_time = 0.552186
[2019-08-23 13:24:22,978] TRAIN Iter 116940: lr = 0.305102,	loss = 2.660240,	Top-1 err = 0.409375,	Top-5 err = 0.193018,	data_time = 0.050378,	train_time = 0.506315
[2019-08-23 13:24:36,882] TRAIN Iter 116960: lr = 0.305068,	loss = 2.696681,	Top-1 err = 0.420410,	Top-5 err = 0.195068,	data_time = 0.050529,	train_time = 0.695196
[2019-08-23 13:24:44,055] TRAIN Iter 116980: lr = 0.305035,	loss = 2.723691,	Top-1 err = 0.414014,	Top-5 err = 0.193604,	data_time = 0.050689,	train_time = 0.358632
[2019-08-23 13:25:00,238] TRAIN Iter 117000: lr = 0.305002,	loss = 2.717420,	Top-1 err = 0.421143,	Top-5 err = 0.198242,	data_time = 0.050442,	train_time = 0.809130
[2019-08-23 13:25:16,197] TRAIN Iter 117020: lr = 0.304968,	loss = 2.727153,	Top-1 err = 0.424414,	Top-5 err = 0.199463,	data_time = 0.132627,	train_time = 0.797952
[2019-08-23 13:25:23,495] TRAIN Iter 117040: lr = 0.304935,	loss = 2.781367,	Top-1 err = 0.418115,	Top-5 err = 0.194678,	data_time = 0.050575,	train_time = 0.364900
[2019-08-23 13:25:38,668] TRAIN Iter 117060: lr = 0.304902,	loss = 2.663030,	Top-1 err = 0.418604,	Top-5 err = 0.196338,	data_time = 0.050340,	train_time = 0.758611
[2019-08-23 13:25:51,610] TRAIN Iter 117080: lr = 0.304868,	loss = 2.704784,	Top-1 err = 0.421240,	Top-5 err = 0.196875,	data_time = 0.050528,	train_time = 0.647089
[2019-08-23 13:26:01,552] TRAIN Iter 117100: lr = 0.304835,	loss = 2.591706,	Top-1 err = 0.419092,	Top-5 err = 0.190967,	data_time = 0.050530,	train_time = 0.497083
[2019-08-23 13:26:16,410] TRAIN Iter 117120: lr = 0.304802,	loss = 2.739692,	Top-1 err = 0.422412,	Top-5 err = 0.197314,	data_time = 0.050511,	train_time = 0.742889
[2019-08-23 13:26:23,538] TRAIN Iter 117140: lr = 0.304768,	loss = 2.795092,	Top-1 err = 0.426367,	Top-5 err = 0.199219,	data_time = 0.120244,	train_time = 0.356375
[2019-08-23 13:26:39,468] TRAIN Iter 117160: lr = 0.304735,	loss = 2.667925,	Top-1 err = 0.414014,	Top-5 err = 0.191504,	data_time = 0.050547,	train_time = 0.796506
[2019-08-23 13:26:54,907] TRAIN Iter 117180: lr = 0.304702,	loss = 2.572363,	Top-1 err = 0.424121,	Top-5 err = 0.196436,	data_time = 0.123301,	train_time = 0.771946
[2019-08-23 13:27:02,229] TRAIN Iter 117200: lr = 0.304668,	loss = 2.727419,	Top-1 err = 0.421582,	Top-5 err = 0.197949,	data_time = 0.050313,	train_time = 0.366081
[2019-08-23 13:27:17,269] TRAIN Iter 117220: lr = 0.304635,	loss = 2.759092,	Top-1 err = 0.418652,	Top-5 err = 0.195459,	data_time = 0.050940,	train_time = 0.751972
[2019-08-23 13:27:35,603] TRAIN Iter 117240: lr = 0.304602,	loss = 2.652499,	Top-1 err = 0.421924,	Top-5 err = 0.196924,	data_time = 0.050411,	train_time = 0.916699
[2019-08-23 13:27:43,038] TRAIN Iter 117260: lr = 0.304568,	loss = 2.758497,	Top-1 err = 0.418555,	Top-5 err = 0.195703,	data_time = 0.050498,	train_time = 0.371749
[2019-08-23 13:27:57,428] TRAIN Iter 117280: lr = 0.304535,	loss = 2.753720,	Top-1 err = 0.425635,	Top-5 err = 0.200928,	data_time = 0.050678,	train_time = 0.719470
[2019-08-23 13:28:04,452] TRAIN Iter 117300: lr = 0.304502,	loss = 2.736462,	Top-1 err = 0.423730,	Top-5 err = 0.201221,	data_time = 0.050685,	train_time = 0.351156
[2019-08-23 13:28:20,022] TRAIN Iter 117320: lr = 0.304468,	loss = 2.728084,	Top-1 err = 0.417627,	Top-5 err = 0.196826,	data_time = 0.050401,	train_time = 0.778488
[2019-08-23 13:28:43,452] TRAIN Iter 117340: lr = 0.304435,	loss = 2.647142,	Top-1 err = 0.423340,	Top-5 err = 0.194189,	data_time = 0.050381,	train_time = 1.171484
[2019-08-23 13:28:50,431] TRAIN Iter 117360: lr = 0.304402,	loss = 2.710437,	Top-1 err = 0.420801,	Top-5 err = 0.197314,	data_time = 0.121921,	train_time = 0.348956
[2019-08-23 13:29:05,548] TRAIN Iter 117380: lr = 0.304368,	loss = 2.700472,	Top-1 err = 0.417578,	Top-5 err = 0.198096,	data_time = 0.050344,	train_time = 0.755851
[2019-08-23 13:29:21,449] TRAIN Iter 117400: lr = 0.304335,	loss = 2.741852,	Top-1 err = 0.417383,	Top-5 err = 0.194238,	data_time = 0.112237,	train_time = 0.795028
[2019-08-23 13:29:28,587] TRAIN Iter 117420: lr = 0.304302,	loss = 2.670924,	Top-1 err = 0.423486,	Top-5 err = 0.200586,	data_time = 0.050506,	train_time = 0.356862
[2019-08-23 13:29:44,693] TRAIN Iter 117440: lr = 0.304268,	loss = 2.707361,	Top-1 err = 0.422314,	Top-5 err = 0.194043,	data_time = 0.050570,	train_time = 0.805299
[2019-08-23 13:29:51,878] TRAIN Iter 117460: lr = 0.304235,	loss = 2.676683,	Top-1 err = 0.421582,	Top-5 err = 0.197852,	data_time = 0.050547,	train_time = 0.359256
[2019-08-23 13:30:08,439] TRAIN Iter 117480: lr = 0.304202,	loss = 2.715780,	Top-1 err = 0.421240,	Top-5 err = 0.196973,	data_time = 0.050535,	train_time = 0.828022
[2019-08-23 13:30:24,745] TRAIN Iter 117500: lr = 0.304168,	loss = 2.708883,	Top-1 err = 0.418701,	Top-5 err = 0.196387,	data_time = 0.050712,	train_time = 0.815288
[2019-08-23 13:30:31,761] TRAIN Iter 117520: lr = 0.304135,	loss = 2.666480,	Top-1 err = 0.421826,	Top-5 err = 0.195312,	data_time = 0.050395,	train_time = 0.350774
[2019-08-23 13:30:49,274] TRAIN Iter 117540: lr = 0.304102,	loss = 2.723039,	Top-1 err = 0.423877,	Top-5 err = 0.199316,	data_time = 0.050500,	train_time = 0.875663
[2019-08-23 13:31:05,171] TRAIN Iter 117560: lr = 0.304068,	loss = 2.765888,	Top-1 err = 0.424902,	Top-5 err = 0.197607,	data_time = 0.066670,	train_time = 0.794811
[2019-08-23 13:31:12,313] TRAIN Iter 117580: lr = 0.304035,	loss = 2.859528,	Top-1 err = 0.426904,	Top-5 err = 0.198584,	data_time = 0.050102,	train_time = 0.357105
[2019-08-23 13:31:28,587] TRAIN Iter 117600: lr = 0.304002,	loss = 2.747207,	Top-1 err = 0.420850,	Top-5 err = 0.197217,	data_time = 0.049881,	train_time = 0.813691
[2019-08-23 13:31:34,639] TRAIN Iter 117620: lr = 0.303968,	loss = 2.751415,	Top-1 err = 0.422363,	Top-5 err = 0.195215,	data_time = 0.049898,	train_time = 0.302589
[2019-08-23 13:32:24,917] TRAIN Iter 117640: lr = 0.303935,	loss = 2.633997,	Top-1 err = 0.414930,	Top-5 err = 0.196808,	data_time = 0.050460,	train_time = 2.513850
[2019-08-23 13:32:43,455] TRAIN Iter 117660: lr = 0.303902,	loss = 2.628375,	Top-1 err = 0.415674,	Top-5 err = 0.192090,	data_time = 0.050889,	train_time = 0.926891
[2019-08-23 13:32:51,583] TRAIN Iter 117680: lr = 0.303868,	loss = 2.650460,	Top-1 err = 0.407959,	Top-5 err = 0.186572,	data_time = 0.050448,	train_time = 0.406391
[2019-08-23 13:32:59,585] TRAIN Iter 117700: lr = 0.303835,	loss = 2.700979,	Top-1 err = 0.415137,	Top-5 err = 0.193213,	data_time = 0.050280,	train_time = 0.400081
[2019-08-23 13:33:07,605] TRAIN Iter 117720: lr = 0.303802,	loss = 2.659406,	Top-1 err = 0.406738,	Top-5 err = 0.193799,	data_time = 0.050281,	train_time = 0.401009
[2019-08-23 13:33:15,560] TRAIN Iter 117740: lr = 0.303768,	loss = 2.713529,	Top-1 err = 0.412939,	Top-5 err = 0.188477,	data_time = 0.050387,	train_time = 0.397699
[2019-08-23 13:33:29,263] TRAIN Iter 117760: lr = 0.303735,	loss = 2.672638,	Top-1 err = 0.417432,	Top-5 err = 0.192725,	data_time = 0.050389,	train_time = 0.685172
[2019-08-23 13:33:37,154] TRAIN Iter 117780: lr = 0.303702,	loss = 2.669081,	Top-1 err = 0.414014,	Top-5 err = 0.193457,	data_time = 0.050526,	train_time = 0.394530
[2019-08-23 13:33:50,996] TRAIN Iter 117800: lr = 0.303668,	loss = 2.650692,	Top-1 err = 0.415576,	Top-5 err = 0.191016,	data_time = 0.050935,	train_time = 0.692052
[2019-08-23 13:34:04,507] TRAIN Iter 117820: lr = 0.303635,	loss = 2.664161,	Top-1 err = 0.410254,	Top-5 err = 0.187109,	data_time = 0.050330,	train_time = 0.675574
[2019-08-23 13:34:12,599] TRAIN Iter 117840: lr = 0.303602,	loss = 2.776985,	Top-1 err = 0.413867,	Top-5 err = 0.191406,	data_time = 0.050514,	train_time = 0.404585
[2019-08-23 13:34:26,380] TRAIN Iter 117860: lr = 0.303568,	loss = 2.650903,	Top-1 err = 0.414697,	Top-5 err = 0.188281,	data_time = 0.050495,	train_time = 0.689026
[2019-08-23 13:34:35,054] TRAIN Iter 117880: lr = 0.303535,	loss = 2.710431,	Top-1 err = 0.418701,	Top-5 err = 0.197314,	data_time = 0.050475,	train_time = 0.433684
[2019-08-23 13:34:48,963] TRAIN Iter 117900: lr = 0.303502,	loss = 2.668251,	Top-1 err = 0.410254,	Top-5 err = 0.191162,	data_time = 0.050271,	train_time = 0.695403
[2019-08-23 13:35:02,788] TRAIN Iter 117920: lr = 0.303468,	loss = 2.721368,	Top-1 err = 0.416211,	Top-5 err = 0.194824,	data_time = 0.050315,	train_time = 0.691275
[2019-08-23 13:35:10,784] TRAIN Iter 117940: lr = 0.303435,	loss = 2.648903,	Top-1 err = 0.413232,	Top-5 err = 0.190137,	data_time = 0.050188,	train_time = 0.399770
[2019-08-23 13:35:26,513] TRAIN Iter 117960: lr = 0.303402,	loss = 2.741862,	Top-1 err = 0.416895,	Top-5 err = 0.194385,	data_time = 0.050677,	train_time = 0.786433
[2019-08-23 13:35:41,747] TRAIN Iter 117980: lr = 0.303368,	loss = 2.724086,	Top-1 err = 0.416895,	Top-5 err = 0.199609,	data_time = 0.050449,	train_time = 0.761682
[2019-08-23 13:35:49,403] TRAIN Iter 118000: lr = 0.303335,	loss = 2.774317,	Top-1 err = 0.414062,	Top-5 err = 0.190186,	data_time = 0.050329,	train_time = 0.382774
[2019-08-23 13:36:04,926] TRAIN Iter 118020: lr = 0.303302,	loss = 2.703040,	Top-1 err = 0.412988,	Top-5 err = 0.194385,	data_time = 0.050391,	train_time = 0.776151
[2019-08-23 13:36:11,994] TRAIN Iter 118040: lr = 0.303268,	loss = 2.766138,	Top-1 err = 0.414990,	Top-5 err = 0.195068,	data_time = 0.050813,	train_time = 0.353387
[2019-08-23 13:36:26,921] TRAIN Iter 118060: lr = 0.303235,	loss = 2.741834,	Top-1 err = 0.418262,	Top-5 err = 0.191113,	data_time = 0.050569,	train_time = 0.746348
[2019-08-23 13:36:41,602] TRAIN Iter 118080: lr = 0.303202,	loss = 2.727557,	Top-1 err = 0.412451,	Top-5 err = 0.198730,	data_time = 0.050614,	train_time = 0.734026
[2019-08-23 13:36:49,048] TRAIN Iter 118100: lr = 0.303168,	loss = 2.655370,	Top-1 err = 0.413916,	Top-5 err = 0.192969,	data_time = 0.050241,	train_time = 0.372302
[2019-08-23 13:37:05,661] TRAIN Iter 118120: lr = 0.303135,	loss = 2.740755,	Top-1 err = 0.421094,	Top-5 err = 0.194336,	data_time = 0.050867,	train_time = 0.830614
[2019-08-23 13:37:20,073] TRAIN Iter 118140: lr = 0.303102,	loss = 2.702637,	Top-1 err = 0.418555,	Top-5 err = 0.192676,	data_time = 0.050436,	train_time = 0.720600
[2019-08-23 13:37:27,942] TRAIN Iter 118160: lr = 0.303068,	loss = 2.726329,	Top-1 err = 0.419385,	Top-5 err = 0.196045,	data_time = 0.050471,	train_time = 0.393438
[2019-08-23 13:37:44,688] TRAIN Iter 118180: lr = 0.303035,	loss = 2.658294,	Top-1 err = 0.421338,	Top-5 err = 0.197070,	data_time = 0.051019,	train_time = 0.837273
[2019-08-23 13:37:52,652] TRAIN Iter 118200: lr = 0.303002,	loss = 2.733318,	Top-1 err = 0.416650,	Top-5 err = 0.194482,	data_time = 0.050498,	train_time = 0.398190
[2019-08-23 13:38:06,250] TRAIN Iter 118220: lr = 0.302968,	loss = 2.733210,	Top-1 err = 0.415723,	Top-5 err = 0.192871,	data_time = 0.050348,	train_time = 0.679895
[2019-08-23 13:38:22,173] TRAIN Iter 118240: lr = 0.302935,	loss = 2.831878,	Top-1 err = 0.420068,	Top-5 err = 0.197266,	data_time = 0.050429,	train_time = 0.796145
[2019-08-23 13:38:29,820] TRAIN Iter 118260: lr = 0.302902,	loss = 2.750305,	Top-1 err = 0.416406,	Top-5 err = 0.194385,	data_time = 0.050431,	train_time = 0.382296
[2019-08-23 13:38:43,977] TRAIN Iter 118280: lr = 0.302868,	loss = 2.612699,	Top-1 err = 0.415918,	Top-5 err = 0.197314,	data_time = 0.050756,	train_time = 0.707851
[2019-08-23 13:38:59,351] TRAIN Iter 118300: lr = 0.302835,	loss = 2.744680,	Top-1 err = 0.416357,	Top-5 err = 0.195850,	data_time = 0.050427,	train_time = 0.768698
[2019-08-23 13:39:07,203] TRAIN Iter 118320: lr = 0.302802,	loss = 2.676308,	Top-1 err = 0.419727,	Top-5 err = 0.200732,	data_time = 0.050591,	train_time = 0.392584
[2019-08-23 13:39:22,847] TRAIN Iter 118340: lr = 0.302768,	loss = 2.673195,	Top-1 err = 0.417236,	Top-5 err = 0.195947,	data_time = 0.050523,	train_time = 0.782181
[2019-08-23 13:39:30,449] TRAIN Iter 118360: lr = 0.302735,	loss = 2.849638,	Top-1 err = 0.411426,	Top-5 err = 0.190039,	data_time = 0.050540,	train_time = 0.380093
[2019-08-23 13:39:44,272] TRAIN Iter 118380: lr = 0.302702,	loss = 2.804931,	Top-1 err = 0.415918,	Top-5 err = 0.193262,	data_time = 0.050833,	train_time = 0.691117
[2019-08-23 13:39:59,438] TRAIN Iter 118400: lr = 0.302668,	loss = 2.670455,	Top-1 err = 0.421729,	Top-5 err = 0.198828,	data_time = 0.050605,	train_time = 0.758293
[2019-08-23 13:40:08,600] TRAIN Iter 118420: lr = 0.302635,	loss = 2.714466,	Top-1 err = 0.419971,	Top-5 err = 0.198096,	data_time = 0.050612,	train_time = 0.458070
[2019-08-23 13:40:22,077] TRAIN Iter 118440: lr = 0.302602,	loss = 2.641248,	Top-1 err = 0.414697,	Top-5 err = 0.196533,	data_time = 0.050661,	train_time = 0.673860
[2019-08-23 13:40:36,866] TRAIN Iter 118460: lr = 0.302568,	loss = 2.667896,	Top-1 err = 0.418945,	Top-5 err = 0.196875,	data_time = 0.050361,	train_time = 0.739426
[2019-08-23 13:40:46,271] TRAIN Iter 118480: lr = 0.302535,	loss = 2.806761,	Top-1 err = 0.420557,	Top-5 err = 0.196875,	data_time = 0.050584,	train_time = 0.470221
[2019-08-23 13:41:02,469] TRAIN Iter 118500: lr = 0.302502,	loss = 2.595016,	Top-1 err = 0.414648,	Top-5 err = 0.194336,	data_time = 0.050809,	train_time = 0.809890
[2019-08-23 13:41:10,271] TRAIN Iter 118520: lr = 0.302468,	loss = 2.843585,	Top-1 err = 0.420215,	Top-5 err = 0.194238,	data_time = 0.050674,	train_time = 0.390118
[2019-08-23 13:41:27,010] TRAIN Iter 118540: lr = 0.302435,	loss = 2.808303,	Top-1 err = 0.423877,	Top-5 err = 0.194336,	data_time = 0.092820,	train_time = 0.836915
[2019-08-23 13:41:43,385] TRAIN Iter 118560: lr = 0.302402,	loss = 2.773862,	Top-1 err = 0.417139,	Top-5 err = 0.195215,	data_time = 0.050625,	train_time = 0.818736
[2019-08-23 13:41:51,009] TRAIN Iter 118580: lr = 0.302368,	loss = 2.664686,	Top-1 err = 0.420752,	Top-5 err = 0.195898,	data_time = 0.050497,	train_time = 0.381176
[2019-08-23 13:42:05,127] TRAIN Iter 118600: lr = 0.302335,	loss = 2.801390,	Top-1 err = 0.418555,	Top-5 err = 0.199902,	data_time = 0.050583,	train_time = 0.705882
[2019-08-23 13:42:22,412] TRAIN Iter 118620: lr = 0.302302,	loss = 2.708483,	Top-1 err = 0.424121,	Top-5 err = 0.197656,	data_time = 0.050640,	train_time = 0.864257
[2019-08-23 13:42:30,548] TRAIN Iter 118640: lr = 0.302268,	loss = 2.663288,	Top-1 err = 0.415430,	Top-5 err = 0.199902,	data_time = 0.050951,	train_time = 0.406761
[2019-08-23 13:42:44,912] TRAIN Iter 118660: lr = 0.302235,	loss = 2.706515,	Top-1 err = 0.415918,	Top-5 err = 0.196436,	data_time = 0.050629,	train_time = 0.718215
[2019-08-23 13:42:56,420] TRAIN Iter 118680: lr = 0.302202,	loss = 2.731051,	Top-1 err = 0.422754,	Top-5 err = 0.198730,	data_time = 0.050570,	train_time = 0.575354
[2019-08-23 13:43:11,971] TRAIN Iter 118700: lr = 0.302168,	loss = 2.570247,	Top-1 err = 0.422412,	Top-5 err = 0.198877,	data_time = 0.050567,	train_time = 0.777568
[2019-08-23 13:43:25,809] TRAIN Iter 118720: lr = 0.302135,	loss = 2.641236,	Top-1 err = 0.420068,	Top-5 err = 0.196045,	data_time = 0.050113,	train_time = 0.691843
[2019-08-23 13:43:36,149] TRAIN Iter 118740: lr = 0.302102,	loss = 2.652717,	Top-1 err = 0.421436,	Top-5 err = 0.194092,	data_time = 0.050256,	train_time = 0.517014
[2019-08-23 13:43:50,014] TRAIN Iter 118760: lr = 0.302068,	loss = 2.703294,	Top-1 err = 0.414355,	Top-5 err = 0.195703,	data_time = 0.050465,	train_time = 0.693244
[2019-08-23 13:44:05,458] TRAIN Iter 118780: lr = 0.302035,	loss = 2.740261,	Top-1 err = 0.421191,	Top-5 err = 0.195654,	data_time = 0.177679,	train_time = 0.772157
[2019-08-23 13:44:16,808] TRAIN Iter 118800: lr = 0.302002,	loss = 2.844113,	Top-1 err = 0.421289,	Top-5 err = 0.200830,	data_time = 0.050434,	train_time = 0.567479
[2019-08-23 13:44:32,373] TRAIN Iter 118820: lr = 0.301968,	loss = 2.640085,	Top-1 err = 0.410596,	Top-5 err = 0.190771,	data_time = 0.050291,	train_time = 0.778269
[2019-08-23 13:44:41,407] TRAIN Iter 118840: lr = 0.301935,	loss = 2.725104,	Top-1 err = 0.420508,	Top-5 err = 0.196094,	data_time = 0.050200,	train_time = 0.451664
[2019-08-23 13:44:55,210] TRAIN Iter 118860: lr = 0.301902,	loss = 2.755208,	Top-1 err = 0.419775,	Top-5 err = 0.197021,	data_time = 0.049908,	train_time = 0.690118
[2019-08-23 13:45:45,182] TRAIN Iter 118880: lr = 0.301868,	loss = 2.733795,	Top-1 err = 0.419911,	Top-5 err = 0.200951,	data_time = 0.050632,	train_time = 2.498624
[2019-08-23 13:45:52,426] TRAIN Iter 118900: lr = 0.301835,	loss = 2.611182,	Top-1 err = 0.413135,	Top-5 err = 0.190967,	data_time = 0.050388,	train_time = 0.362170
[2019-08-23 13:46:08,336] TRAIN Iter 118920: lr = 0.301802,	loss = 2.614617,	Top-1 err = 0.410938,	Top-5 err = 0.191064,	data_time = 0.050487,	train_time = 0.795490
[2019-08-23 13:46:16,334] TRAIN Iter 118940: lr = 0.301768,	loss = 2.699383,	Top-1 err = 0.416650,	Top-5 err = 0.194629,	data_time = 0.050585,	train_time = 0.399843
[2019-08-23 13:46:30,307] TRAIN Iter 118960: lr = 0.301735,	loss = 2.650879,	Top-1 err = 0.414844,	Top-5 err = 0.192090,	data_time = 0.050773,	train_time = 0.698663
[2019-08-23 13:46:41,755] TRAIN Iter 118980: lr = 0.301702,	loss = 2.695529,	Top-1 err = 0.414746,	Top-5 err = 0.192285,	data_time = 0.050710,	train_time = 0.572406
[2019-08-23 13:46:48,868] TRAIN Iter 119000: lr = 0.301668,	loss = 2.646867,	Top-1 err = 0.416162,	Top-5 err = 0.191895,	data_time = 0.050799,	train_time = 0.355608
[2019-08-23 13:47:05,362] TRAIN Iter 119020: lr = 0.301635,	loss = 2.755235,	Top-1 err = 0.413379,	Top-5 err = 0.190479,	data_time = 0.050449,	train_time = 0.824703
[2019-08-23 13:47:18,837] TRAIN Iter 119040: lr = 0.301602,	loss = 2.610824,	Top-1 err = 0.411572,	Top-5 err = 0.191211,	data_time = 1.405598,	train_time = 0.673748
[2019-08-23 13:47:26,320] TRAIN Iter 119060: lr = 0.301568,	loss = 2.729707,	Top-1 err = 0.414893,	Top-5 err = 0.193066,	data_time = 0.050127,	train_time = 0.374127
[2019-08-23 13:47:40,345] TRAIN Iter 119080: lr = 0.301535,	loss = 2.681869,	Top-1 err = 0.414258,	Top-5 err = 0.190723,	data_time = 0.050296,	train_time = 0.701240
[2019-08-23 13:47:47,787] TRAIN Iter 119100: lr = 0.301502,	loss = 2.656988,	Top-1 err = 0.411328,	Top-5 err = 0.196094,	data_time = 0.050499,	train_time = 0.372091
[2019-08-23 13:48:02,994] TRAIN Iter 119120: lr = 0.301468,	loss = 2.584536,	Top-1 err = 0.410010,	Top-5 err = 0.189502,	data_time = 0.050332,	train_time = 0.760327
[2019-08-23 13:48:16,541] TRAIN Iter 119140: lr = 0.301435,	loss = 2.710915,	Top-1 err = 0.414648,	Top-5 err = 0.192969,	data_time = 0.050139,	train_time = 0.677327
[2019-08-23 13:48:23,337] TRAIN Iter 119160: lr = 0.301402,	loss = 2.626893,	Top-1 err = 0.410352,	Top-5 err = 0.191016,	data_time = 0.050450,	train_time = 0.339759
[2019-08-23 13:48:39,502] TRAIN Iter 119180: lr = 0.301368,	loss = 2.693027,	Top-1 err = 0.414502,	Top-5 err = 0.188721,	data_time = 0.050876,	train_time = 0.808263
[2019-08-23 13:48:54,278] TRAIN Iter 119200: lr = 0.301335,	loss = 2.673009,	Top-1 err = 0.411523,	Top-5 err = 0.193506,	data_time = 0.874379,	train_time = 0.738800
[2019-08-23 13:49:01,443] TRAIN Iter 119220: lr = 0.301302,	loss = 2.643952,	Top-1 err = 0.414209,	Top-5 err = 0.192236,	data_time = 0.050140,	train_time = 0.358230
[2019-08-23 13:49:16,412] TRAIN Iter 119240: lr = 0.301268,	loss = 2.644973,	Top-1 err = 0.422168,	Top-5 err = 0.196045,	data_time = 0.050605,	train_time = 0.748423
[2019-08-23 13:49:23,931] TRAIN Iter 119260: lr = 0.301235,	loss = 2.679122,	Top-1 err = 0.414844,	Top-5 err = 0.190576,	data_time = 0.050536,	train_time = 0.375945
[2019-08-23 13:49:37,364] TRAIN Iter 119280: lr = 0.301202,	loss = 2.684976,	Top-1 err = 0.419580,	Top-5 err = 0.191113,	data_time = 0.050489,	train_time = 0.671655
[2019-08-23 13:49:53,208] TRAIN Iter 119300: lr = 0.301168,	loss = 2.703165,	Top-1 err = 0.423145,	Top-5 err = 0.195264,	data_time = 0.050336,	train_time = 0.792163
[2019-08-23 13:50:00,460] TRAIN Iter 119320: lr = 0.301135,	loss = 2.712223,	Top-1 err = 0.419043,	Top-5 err = 0.194775,	data_time = 0.050909,	train_time = 0.362589
[2019-08-23 13:50:16,260] TRAIN Iter 119340: lr = 0.301102,	loss = 2.697271,	Top-1 err = 0.416455,	Top-5 err = 0.192920,	data_time = 0.050472,	train_time = 0.789980
[2019-08-23 13:50:28,035] TRAIN Iter 119360: lr = 0.301068,	loss = 2.725018,	Top-1 err = 0.412646,	Top-5 err = 0.192285,	data_time = 0.633064,	train_time = 0.588730
[2019-08-23 13:50:38,622] TRAIN Iter 119380: lr = 0.301035,	loss = 2.669108,	Top-1 err = 0.417285,	Top-5 err = 0.195020,	data_time = 0.050379,	train_time = 0.529328
[2019-08-23 13:50:54,974] TRAIN Iter 119400: lr = 0.301002,	loss = 2.673622,	Top-1 err = 0.411084,	Top-5 err = 0.188086,	data_time = 0.050755,	train_time = 0.817610
[2019-08-23 13:51:02,280] TRAIN Iter 119420: lr = 0.300968,	loss = 2.705462,	Top-1 err = 0.414893,	Top-5 err = 0.198047,	data_time = 0.050940,	train_time = 0.365263
[2019-08-23 13:51:18,242] TRAIN Iter 119440: lr = 0.300935,	loss = 2.673583,	Top-1 err = 0.411035,	Top-5 err = 0.192773,	data_time = 0.050588,	train_time = 0.798124
[2019-08-23 13:51:31,452] TRAIN Iter 119460: lr = 0.300902,	loss = 2.585587,	Top-1 err = 0.415869,	Top-5 err = 0.192432,	data_time = 0.050989,	train_time = 0.660488
[2019-08-23 13:51:38,627] TRAIN Iter 119480: lr = 0.300868,	loss = 2.798181,	Top-1 err = 0.415723,	Top-5 err = 0.194238,	data_time = 0.050599,	train_time = 0.358731
[2019-08-23 13:51:55,490] TRAIN Iter 119500: lr = 0.300835,	loss = 2.765303,	Top-1 err = 0.413623,	Top-5 err = 0.190723,	data_time = 0.050450,	train_time = 0.843115
[2019-08-23 13:52:08,645] TRAIN Iter 119520: lr = 0.300802,	loss = 2.698390,	Top-1 err = 0.414453,	Top-5 err = 0.193994,	data_time = 0.155460,	train_time = 0.657735
[2019-08-23 13:52:18,075] TRAIN Iter 119540: lr = 0.300768,	loss = 2.598449,	Top-1 err = 0.417676,	Top-5 err = 0.193896,	data_time = 0.050607,	train_time = 0.471474
[2019-08-23 13:52:34,879] TRAIN Iter 119560: lr = 0.300735,	loss = 2.714484,	Top-1 err = 0.424170,	Top-5 err = 0.197461,	data_time = 0.050625,	train_time = 0.840197
[2019-08-23 13:52:42,608] TRAIN Iter 119580: lr = 0.300702,	loss = 2.742440,	Top-1 err = 0.416064,	Top-5 err = 0.192188,	data_time = 0.050514,	train_time = 0.386410
[2019-08-23 13:52:56,276] TRAIN Iter 119600: lr = 0.300668,	loss = 2.797471,	Top-1 err = 0.413770,	Top-5 err = 0.193018,	data_time = 0.050168,	train_time = 0.683407
[2019-08-23 13:53:14,734] TRAIN Iter 119620: lr = 0.300635,	loss = 2.659933,	Top-1 err = 0.413428,	Top-5 err = 0.189453,	data_time = 0.050507,	train_time = 0.922871
[2019-08-23 13:53:22,124] TRAIN Iter 119640: lr = 0.300602,	loss = 2.810264,	Top-1 err = 0.420947,	Top-5 err = 0.200586,	data_time = 0.050497,	train_time = 0.369511
[2019-08-23 13:53:35,146] TRAIN Iter 119660: lr = 0.300568,	loss = 2.655256,	Top-1 err = 0.417383,	Top-5 err = 0.196484,	data_time = 0.050468,	train_time = 0.651068
[2019-08-23 13:53:49,302] TRAIN Iter 119680: lr = 0.300535,	loss = 2.747813,	Top-1 err = 0.417627,	Top-5 err = 0.197510,	data_time = 0.050373,	train_time = 0.707779
[2019-08-23 13:53:57,893] TRAIN Iter 119700: lr = 0.300502,	loss = 2.767182,	Top-1 err = 0.418555,	Top-5 err = 0.194238,	data_time = 0.050592,	train_time = 0.429534
[2019-08-23 13:54:13,698] TRAIN Iter 119720: lr = 0.300468,	loss = 2.676620,	Top-1 err = 0.420117,	Top-5 err = 0.199561,	data_time = 0.050432,	train_time = 0.790247
[2019-08-23 13:54:21,242] TRAIN Iter 119740: lr = 0.300435,	loss = 2.716954,	Top-1 err = 0.422266,	Top-5 err = 0.195215,	data_time = 0.050503,	train_time = 0.377189
[2019-08-23 13:54:36,301] TRAIN Iter 119760: lr = 0.300402,	loss = 2.759767,	Top-1 err = 0.417627,	Top-5 err = 0.197217,	data_time = 0.050162,	train_time = 0.752945
[2019-08-23 13:54:52,351] TRAIN Iter 119780: lr = 0.300368,	loss = 2.698703,	Top-1 err = 0.428369,	Top-5 err = 0.205713,	data_time = 0.050478,	train_time = 0.802504
[2019-08-23 13:54:59,945] TRAIN Iter 119800: lr = 0.300335,	loss = 2.615421,	Top-1 err = 0.421387,	Top-5 err = 0.195801,	data_time = 0.050877,	train_time = 0.379688
[2019-08-23 13:55:14,281] TRAIN Iter 119820: lr = 0.300302,	loss = 2.672727,	Top-1 err = 0.419092,	Top-5 err = 0.197363,	data_time = 0.051160,	train_time = 0.716746
[2019-08-23 13:55:24,795] TRAIN Iter 119840: lr = 0.300268,	loss = 2.671842,	Top-1 err = 0.415381,	Top-5 err = 0.195557,	data_time = 0.050404,	train_time = 0.525705
[2019-08-23 13:55:36,257] TRAIN Iter 119860: lr = 0.300235,	loss = 2.680212,	Top-1 err = 0.413330,	Top-5 err = 0.191504,	data_time = 0.147576,	train_time = 0.573097
[2019-08-23 13:55:53,275] TRAIN Iter 119880: lr = 0.300202,	loss = 2.701245,	Top-1 err = 0.419727,	Top-5 err = 0.199414,	data_time = 0.050428,	train_time = 0.850873
[2019-08-23 13:56:00,606] TRAIN Iter 119900: lr = 0.300168,	loss = 2.690381,	Top-1 err = 0.421729,	Top-5 err = 0.196338,	data_time = 0.050552,	train_time = 0.366525
[2019-08-23 13:56:15,729] TRAIN Iter 119920: lr = 0.300135,	loss = 2.754143,	Top-1 err = 0.414697,	Top-5 err = 0.194287,	data_time = 0.050607,	train_time = 0.756135
[2019-08-23 13:56:33,444] TRAIN Iter 119940: lr = 0.300102,	loss = 2.692576,	Top-1 err = 0.416064,	Top-5 err = 0.193799,	data_time = 0.050452,	train_time = 0.885724
[2019-08-23 13:56:40,438] TRAIN Iter 119960: lr = 0.300068,	loss = 2.650986,	Top-1 err = 0.423584,	Top-5 err = 0.197803,	data_time = 0.050287,	train_time = 0.349714
[2019-08-23 13:56:55,985] TRAIN Iter 119980: lr = 0.300035,	loss = 2.763887,	Top-1 err = 0.418994,	Top-5 err = 0.197266,	data_time = 0.050464,	train_time = 0.777331
[2019-08-23 13:57:09,653] TRAIN Iter 120000: lr = 0.300002,	loss = 2.699783,	Top-1 err = 0.418555,	Top-5 err = 0.196680,	data_time = 0.144284,	train_time = 0.683368
[2019-08-23 13:58:11,504] TEST Iter 120000: loss = 2.485142,	Top-1 err = 0.379640,	Top-5 err = 0.152640,	val_time = 61.808094
[2019-08-23 13:58:17,504] TRAIN Iter 120020: lr = 0.299968,	loss = 2.716265,	Top-1 err = 0.415430,	Top-5 err = 0.191797,	data_time = 0.050381,	train_time = 0.299968
[2019-08-23 13:58:23,841] TRAIN Iter 120040: lr = 0.299935,	loss = 2.705712,	Top-1 err = 0.420166,	Top-5 err = 0.200342,	data_time = 0.050471,	train_time = 0.316849
[2019-08-23 13:58:30,125] TRAIN Iter 120060: lr = 0.299902,	loss = 2.661032,	Top-1 err = 0.424609,	Top-5 err = 0.197803,	data_time = 0.050549,	train_time = 0.314172
[2019-08-23 13:58:41,456] TRAIN Iter 120080: lr = 0.299868,	loss = 2.740540,	Top-1 err = 0.421777,	Top-5 err = 0.195557,	data_time = 0.050051,	train_time = 0.566582
[2019-08-23 13:58:56,080] TRAIN Iter 120100: lr = 0.299835,	loss = 2.814959,	Top-1 err = 0.423486,	Top-5 err = 0.196289,	data_time = 0.050062,	train_time = 0.731170
[2019-08-23 13:59:04,444] TRAIN Iter 120120: lr = 0.299802,	loss = 2.691533,	Top-1 err = 0.420215,	Top-5 err = 0.193359,	data_time = 0.049878,	train_time = 0.418163
[2019-08-23 13:59:51,365] TRAIN Iter 120140: lr = 0.299768,	loss = 2.734271,	Top-1 err = 0.428966,	Top-5 err = 0.205643,	data_time = 0.050230,	train_time = 2.346038
[2019-08-23 13:59:58,958] TRAIN Iter 120160: lr = 0.299735,	loss = 2.708081,	Top-1 err = 0.419385,	Top-5 err = 0.195947,	data_time = 0.147856,	train_time = 0.379638
[2019-08-23 14:00:13,272] TRAIN Iter 120180: lr = 0.299702,	loss = 2.658604,	Top-1 err = 0.412744,	Top-5 err = 0.191455,	data_time = 0.050547,	train_time = 0.715674
[2019-08-23 14:00:28,551] TRAIN Iter 120200: lr = 0.299668,	loss = 2.589958,	Top-1 err = 0.409863,	Top-5 err = 0.186133,	data_time = 0.050722,	train_time = 0.763943
[2019-08-23 14:00:35,598] TRAIN Iter 120220: lr = 0.299635,	loss = 2.732307,	Top-1 err = 0.410986,	Top-5 err = 0.186914,	data_time = 0.103138,	train_time = 0.352342
[2019-08-23 14:00:50,462] TRAIN Iter 120240: lr = 0.299602,	loss = 2.652472,	Top-1 err = 0.404834,	Top-5 err = 0.193896,	data_time = 0.050550,	train_time = 0.743172
[2019-08-23 14:01:04,511] TRAIN Iter 120260: lr = 0.299568,	loss = 2.697333,	Top-1 err = 0.409717,	Top-5 err = 0.186768,	data_time = 0.050764,	train_time = 0.702431
[2019-08-23 14:01:12,630] TRAIN Iter 120280: lr = 0.299535,	loss = 2.610021,	Top-1 err = 0.410938,	Top-5 err = 0.193018,	data_time = 0.050450,	train_time = 0.405951
[2019-08-23 14:01:29,710] TRAIN Iter 120300: lr = 0.299502,	loss = 2.763400,	Top-1 err = 0.411230,	Top-5 err = 0.190332,	data_time = 0.050582,	train_time = 0.854019
[2019-08-23 14:01:37,101] TRAIN Iter 120320: lr = 0.299468,	loss = 2.765852,	Top-1 err = 0.408154,	Top-5 err = 0.185742,	data_time = 0.187120,	train_time = 0.369527
[2019-08-23 14:01:52,702] TRAIN Iter 120340: lr = 0.299435,	loss = 2.648684,	Top-1 err = 0.409473,	Top-5 err = 0.189648,	data_time = 0.050322,	train_time = 0.780026
[2019-08-23 14:02:04,692] TRAIN Iter 120360: lr = 0.299402,	loss = 2.672920,	Top-1 err = 0.414746,	Top-5 err = 0.192725,	data_time = 0.050785,	train_time = 0.599494
[2019-08-23 14:02:12,000] TRAIN Iter 120380: lr = 0.299368,	loss = 2.673786,	Top-1 err = 0.410986,	Top-5 err = 0.188379,	data_time = 0.050550,	train_time = 0.365354
[2019-08-23 14:02:27,415] TRAIN Iter 120400: lr = 0.299335,	loss = 2.707006,	Top-1 err = 0.413281,	Top-5 err = 0.192383,	data_time = 0.050276,	train_time = 0.770748
[2019-08-23 14:02:39,742] TRAIN Iter 120420: lr = 0.299302,	loss = 2.649989,	Top-1 err = 0.418359,	Top-5 err = 0.191797,	data_time = 0.050316,	train_time = 0.616362
[2019-08-23 14:02:49,901] TRAIN Iter 120440: lr = 0.299268,	loss = 2.702254,	Top-1 err = 0.409473,	Top-5 err = 0.189893,	data_time = 0.050548,	train_time = 0.507929
[2019-08-23 14:03:05,889] TRAIN Iter 120460: lr = 0.299235,	loss = 2.690187,	Top-1 err = 0.421191,	Top-5 err = 0.197998,	data_time = 0.050232,	train_time = 0.799385
[2019-08-23 14:03:13,554] TRAIN Iter 120480: lr = 0.299202,	loss = 2.606125,	Top-1 err = 0.414160,	Top-5 err = 0.192676,	data_time = 0.050636,	train_time = 0.383217
[2019-08-23 14:03:26,869] TRAIN Iter 120500: lr = 0.299168,	loss = 2.745896,	Top-1 err = 0.415527,	Top-5 err = 0.195068,	data_time = 0.050391,	train_time = 0.665742
[2019-08-23 14:03:42,609] TRAIN Iter 120520: lr = 0.299135,	loss = 2.725366,	Top-1 err = 0.415381,	Top-5 err = 0.195020,	data_time = 0.095086,	train_time = 0.787003
[2019-08-23 14:03:49,714] TRAIN Iter 120540: lr = 0.299102,	loss = 2.706801,	Top-1 err = 0.417773,	Top-5 err = 0.193799,	data_time = 0.050430,	train_time = 0.355221
[2019-08-23 14:04:04,750] TRAIN Iter 120560: lr = 0.299068,	loss = 2.656475,	Top-1 err = 0.410693,	Top-5 err = 0.190039,	data_time = 0.050440,	train_time = 0.751797
[2019-08-23 14:04:20,685] TRAIN Iter 120580: lr = 0.299035,	loss = 2.709443,	Top-1 err = 0.409863,	Top-5 err = 0.188770,	data_time = 0.050671,	train_time = 0.796717
[2019-08-23 14:04:27,723] TRAIN Iter 120600: lr = 0.299002,	loss = 2.659806,	Top-1 err = 0.415088,	Top-5 err = 0.192090,	data_time = 0.050446,	train_time = 0.351873
[2019-08-23 14:04:43,398] TRAIN Iter 120620: lr = 0.298968,	loss = 2.693805,	Top-1 err = 0.413867,	Top-5 err = 0.191943,	data_time = 0.050313,	train_time = 0.783730
[2019-08-23 14:04:50,735] TRAIN Iter 120640: lr = 0.298935,	loss = 2.621973,	Top-1 err = 0.408496,	Top-5 err = 0.189307,	data_time = 0.146147,	train_time = 0.366868
[2019-08-23 14:05:05,562] TRAIN Iter 120660: lr = 0.298902,	loss = 2.686201,	Top-1 err = 0.420947,	Top-5 err = 0.193164,	data_time = 0.050492,	train_time = 0.741309
[2019-08-23 14:05:20,168] TRAIN Iter 120680: lr = 0.298868,	loss = 2.724974,	Top-1 err = 0.413525,	Top-5 err = 0.197461,	data_time = 0.130841,	train_time = 0.730281
[2019-08-23 14:05:27,173] TRAIN Iter 120700: lr = 0.298835,	loss = 2.689857,	Top-1 err = 0.415137,	Top-5 err = 0.192188,	data_time = 0.153955,	train_time = 0.350247
[2019-08-23 14:05:43,277] TRAIN Iter 120720: lr = 0.298802,	loss = 2.689653,	Top-1 err = 0.421729,	Top-5 err = 0.197217,	data_time = 0.050503,	train_time = 0.805219
[2019-08-23 14:05:55,909] TRAIN Iter 120740: lr = 0.298768,	loss = 2.626411,	Top-1 err = 0.425537,	Top-5 err = 0.201416,	data_time = 0.050941,	train_time = 0.631568
[2019-08-23 14:06:06,143] TRAIN Iter 120760: lr = 0.298735,	loss = 2.743591,	Top-1 err = 0.415186,	Top-5 err = 0.191211,	data_time = 0.050482,	train_time = 0.511681
[2019-08-23 14:06:19,591] TRAIN Iter 120780: lr = 0.298702,	loss = 2.629267,	Top-1 err = 0.420752,	Top-5 err = 0.196875,	data_time = 0.050584,	train_time = 0.672373
[2019-08-23 14:06:27,085] TRAIN Iter 120800: lr = 0.298668,	loss = 2.760255,	Top-1 err = 0.415234,	Top-5 err = 0.190918,	data_time = 0.050889,	train_time = 0.374679
[2019-08-23 14:06:43,195] TRAIN Iter 120820: lr = 0.298635,	loss = 2.731781,	Top-1 err = 0.414746,	Top-5 err = 0.192871,	data_time = 0.050368,	train_time = 0.805510
[2019-08-23 14:06:57,169] TRAIN Iter 120840: lr = 0.298602,	loss = 2.614990,	Top-1 err = 0.417041,	Top-5 err = 0.196436,	data_time = 0.050479,	train_time = 0.698655
[2019-08-23 14:07:04,446] TRAIN Iter 120860: lr = 0.298568,	loss = 2.806958,	Top-1 err = 0.421143,	Top-5 err = 0.197021,	data_time = 0.050430,	train_time = 0.363884
[2019-08-23 14:07:19,879] TRAIN Iter 120880: lr = 0.298535,	loss = 2.685082,	Top-1 err = 0.417676,	Top-5 err = 0.192285,	data_time = 0.050595,	train_time = 0.771611
[2019-08-23 14:07:32,089] TRAIN Iter 120900: lr = 0.298502,	loss = 2.758789,	Top-1 err = 0.414014,	Top-5 err = 0.193359,	data_time = 0.050978,	train_time = 0.610478
[2019-08-23 14:07:41,502] TRAIN Iter 120920: lr = 0.298468,	loss = 2.720177,	Top-1 err = 0.416602,	Top-5 err = 0.198633,	data_time = 0.050812,	train_time = 0.470628
[2019-08-23 14:07:58,016] TRAIN Iter 120940: lr = 0.298435,	loss = 2.763965,	Top-1 err = 0.418359,	Top-5 err = 0.197168,	data_time = 0.050539,	train_time = 0.825717
[2019-08-23 14:08:05,503] TRAIN Iter 120960: lr = 0.298402,	loss = 2.697432,	Top-1 err = 0.419580,	Top-5 err = 0.190381,	data_time = 0.050602,	train_time = 0.374306
[2019-08-23 14:08:20,436] TRAIN Iter 120980: lr = 0.298368,	loss = 2.741785,	Top-1 err = 0.417041,	Top-5 err = 0.190771,	data_time = 0.050488,	train_time = 0.746669
[2019-08-23 14:08:36,367] TRAIN Iter 121000: lr = 0.298335,	loss = 2.705709,	Top-1 err = 0.419775,	Top-5 err = 0.195557,	data_time = 0.050482,	train_time = 0.796490
[2019-08-23 14:08:43,545] TRAIN Iter 121020: lr = 0.298302,	loss = 2.707591,	Top-1 err = 0.410645,	Top-5 err = 0.188184,	data_time = 0.050321,	train_time = 0.358892
[2019-08-23 14:08:58,408] TRAIN Iter 121040: lr = 0.298268,	loss = 2.708369,	Top-1 err = 0.419141,	Top-5 err = 0.194434,	data_time = 0.050578,	train_time = 0.743130
[2019-08-23 14:09:07,285] TRAIN Iter 121060: lr = 0.298235,	loss = 2.606949,	Top-1 err = 0.418750,	Top-5 err = 0.194580,	data_time = 0.050658,	train_time = 0.443877
[2019-08-23 14:09:21,119] TRAIN Iter 121080: lr = 0.298202,	loss = 2.642316,	Top-1 err = 0.419336,	Top-5 err = 0.195020,	data_time = 0.154559,	train_time = 0.691663
[2019-08-23 14:09:36,565] TRAIN Iter 121100: lr = 0.298168,	loss = 2.660844,	Top-1 err = 0.417334,	Top-5 err = 0.193018,	data_time = 0.050632,	train_time = 0.772300
[2019-08-23 14:09:43,834] TRAIN Iter 121120: lr = 0.298135,	loss = 2.732526,	Top-1 err = 0.420312,	Top-5 err = 0.196826,	data_time = 0.050768,	train_time = 0.363402
[2019-08-23 14:09:59,298] TRAIN Iter 121140: lr = 0.298102,	loss = 2.626064,	Top-1 err = 0.414697,	Top-5 err = 0.197314,	data_time = 0.050533,	train_time = 0.773225
[2019-08-23 14:10:15,498] TRAIN Iter 121160: lr = 0.298068,	loss = 2.741324,	Top-1 err = 0.419922,	Top-5 err = 0.197070,	data_time = 0.050477,	train_time = 0.809947
[2019-08-23 14:10:22,358] TRAIN Iter 121180: lr = 0.298035,	loss = 2.730855,	Top-1 err = 0.415918,	Top-5 err = 0.193164,	data_time = 0.050397,	train_time = 0.343000
[2019-08-23 14:10:39,031] TRAIN Iter 121200: lr = 0.298002,	loss = 2.641266,	Top-1 err = 0.417480,	Top-5 err = 0.195117,	data_time = 0.050503,	train_time = 0.833636
[2019-08-23 14:10:52,955] TRAIN Iter 121220: lr = 0.297968,	loss = 2.761095,	Top-1 err = 0.424268,	Top-5 err = 0.194678,	data_time = 0.050387,	train_time = 0.696179
[2019-08-23 14:11:01,008] TRAIN Iter 121240: lr = 0.297935,	loss = 2.673202,	Top-1 err = 0.420557,	Top-5 err = 0.199219,	data_time = 0.050332,	train_time = 0.402646
[2019-08-23 14:11:16,041] TRAIN Iter 121260: lr = 0.297902,	loss = 2.783628,	Top-1 err = 0.417627,	Top-5 err = 0.196631,	data_time = 0.050239,	train_time = 0.751631
[2019-08-23 14:11:22,891] TRAIN Iter 121280: lr = 0.297868,	loss = 2.628437,	Top-1 err = 0.416553,	Top-5 err = 0.194824,	data_time = 0.050352,	train_time = 0.342496
[2019-08-23 14:11:40,746] TRAIN Iter 121300: lr = 0.297835,	loss = 2.749224,	Top-1 err = 0.412500,	Top-5 err = 0.193115,	data_time = 0.050322,	train_time = 0.892705
[2019-08-23 14:11:57,579] TRAIN Iter 121320: lr = 0.297802,	loss = 2.682308,	Top-1 err = 0.420117,	Top-5 err = 0.199121,	data_time = 0.050068,	train_time = 0.841668
[2019-08-23 14:12:04,210] TRAIN Iter 121340: lr = 0.297768,	loss = 2.750422,	Top-1 err = 0.419775,	Top-5 err = 0.199365,	data_time = 0.050247,	train_time = 0.331548
[2019-08-23 14:12:19,107] TRAIN Iter 121360: lr = 0.297735,	loss = 2.680084,	Top-1 err = 0.416553,	Top-5 err = 0.194531,	data_time = 0.049968,	train_time = 0.744837
[2019-08-23 14:12:28,929] TRAIN Iter 121380: lr = 0.297702,	loss = 3.089398,	Top-1 err = 0.424458,	Top-5 err = 0.200072,	data_time = 0.007137,	train_time = 0.491073
[2019-08-23 14:13:14,960] TRAIN Iter 121400: lr = 0.297668,	loss = 2.763954,	Top-1 err = 0.422754,	Top-5 err = 0.199170,	data_time = 0.050527,	train_time = 2.301506
[2019-08-23 14:13:30,985] TRAIN Iter 121420: lr = 0.297635,	loss = 2.732747,	Top-1 err = 0.416211,	Top-5 err = 0.190186,	data_time = 0.050440,	train_time = 0.801262
[2019-08-23 14:13:38,785] TRAIN Iter 121440: lr = 0.297602,	loss = 2.739579,	Top-1 err = 0.411621,	Top-5 err = 0.194531,	data_time = 0.050447,	train_time = 0.389973
[2019-08-23 14:13:51,853] TRAIN Iter 121460: lr = 0.297568,	loss = 2.727965,	Top-1 err = 0.410156,	Top-5 err = 0.190527,	data_time = 0.050515,	train_time = 0.653400
[2019-08-23 14:14:00,659] TRAIN Iter 121480: lr = 0.297535,	loss = 2.692634,	Top-1 err = 0.412939,	Top-5 err = 0.192969,	data_time = 0.050283,	train_time = 0.440294
[2019-08-23 14:14:10,198] TRAIN Iter 121500: lr = 0.297502,	loss = 2.720938,	Top-1 err = 0.410840,	Top-5 err = 0.191406,	data_time = 0.050392,	train_time = 0.476904
[2019-08-23 14:14:27,786] TRAIN Iter 121520: lr = 0.297468,	loss = 2.729438,	Top-1 err = 0.410449,	Top-5 err = 0.190918,	data_time = 0.050389,	train_time = 0.879414
[2019-08-23 14:14:35,894] TRAIN Iter 121540: lr = 0.297435,	loss = 2.756493,	Top-1 err = 0.415479,	Top-5 err = 0.192334,	data_time = 0.050544,	train_time = 0.405368
[2019-08-23 14:14:48,500] TRAIN Iter 121560: lr = 0.297402,	loss = 2.693758,	Top-1 err = 0.413037,	Top-5 err = 0.191260,	data_time = 0.050714,	train_time = 0.630272
[2019-08-23 14:15:03,649] TRAIN Iter 121580: lr = 0.297368,	loss = 2.711070,	Top-1 err = 0.414014,	Top-5 err = 0.191406,	data_time = 0.050894,	train_time = 0.757431
[2019-08-23 14:15:11,008] TRAIN Iter 121600: lr = 0.297335,	loss = 2.635643,	Top-1 err = 0.409521,	Top-5 err = 0.192041,	data_time = 0.050382,	train_time = 0.367935
[2019-08-23 14:15:23,366] TRAIN Iter 121620: lr = 0.297302,	loss = 2.729247,	Top-1 err = 0.407422,	Top-5 err = 0.188330,	data_time = 0.050294,	train_time = 0.617899
[2019-08-23 14:15:38,947] TRAIN Iter 121640: lr = 0.297268,	loss = 2.690941,	Top-1 err = 0.404102,	Top-5 err = 0.184375,	data_time = 0.050388,	train_time = 0.779021
[2019-08-23 14:15:46,291] TRAIN Iter 121660: lr = 0.297235,	loss = 2.621366,	Top-1 err = 0.418359,	Top-5 err = 0.192334,	data_time = 0.121561,	train_time = 0.367181
[2019-08-23 14:16:02,034] TRAIN Iter 121680: lr = 0.297202,	loss = 2.675932,	Top-1 err = 0.409375,	Top-5 err = 0.192529,	data_time = 0.050531,	train_time = 0.787135
[2019-08-23 14:16:10,506] TRAIN Iter 121700: lr = 0.297168,	loss = 2.747622,	Top-1 err = 0.414307,	Top-5 err = 0.190869,	data_time = 0.050455,	train_time = 0.423620
[2019-08-23 14:16:22,771] TRAIN Iter 121720: lr = 0.297135,	loss = 2.685160,	Top-1 err = 0.407568,	Top-5 err = 0.191260,	data_time = 0.137753,	train_time = 0.613223
[2019-08-23 14:16:37,692] TRAIN Iter 121740: lr = 0.297102,	loss = 2.711548,	Top-1 err = 0.418750,	Top-5 err = 0.195850,	data_time = 0.050851,	train_time = 0.746025
[2019-08-23 14:16:45,883] TRAIN Iter 121760: lr = 0.297068,	loss = 2.641440,	Top-1 err = 0.415234,	Top-5 err = 0.193262,	data_time = 0.050163,	train_time = 0.409523
[2019-08-23 14:16:58,464] TRAIN Iter 121780: lr = 0.297035,	loss = 2.653070,	Top-1 err = 0.407861,	Top-5 err = 0.187061,	data_time = 0.128847,	train_time = 0.629047
[2019-08-23 14:17:12,559] TRAIN Iter 121800: lr = 0.297002,	loss = 2.690098,	Top-1 err = 0.412695,	Top-5 err = 0.193311,	data_time = 0.050784,	train_time = 0.704753
[2019-08-23 14:17:19,829] TRAIN Iter 121820: lr = 0.296968,	loss = 2.715271,	Top-1 err = 0.414014,	Top-5 err = 0.191162,	data_time = 0.050539,	train_time = 0.363443
[2019-08-23 14:17:35,435] TRAIN Iter 121840: lr = 0.296935,	loss = 2.744535,	Top-1 err = 0.415479,	Top-5 err = 0.191553,	data_time = 0.050478,	train_time = 0.780309
[2019-08-23 14:17:43,588] TRAIN Iter 121860: lr = 0.296902,	loss = 2.678444,	Top-1 err = 0.414795,	Top-5 err = 0.195117,	data_time = 0.050621,	train_time = 0.407607
[2019-08-23 14:17:57,398] TRAIN Iter 121880: lr = 0.296868,	loss = 2.838736,	Top-1 err = 0.410889,	Top-5 err = 0.192188,	data_time = 0.050560,	train_time = 0.690515
[2019-08-23 14:18:12,258] TRAIN Iter 121900: lr = 0.296835,	loss = 2.640033,	Top-1 err = 0.411816,	Top-5 err = 0.191064,	data_time = 0.050356,	train_time = 0.742960
[2019-08-23 14:18:19,994] TRAIN Iter 121920: lr = 0.296802,	loss = 2.750305,	Top-1 err = 0.419092,	Top-5 err = 0.193555,	data_time = 0.050892,	train_time = 0.386806
[2019-08-23 14:18:38,539] TRAIN Iter 121940: lr = 0.296768,	loss = 2.694026,	Top-1 err = 0.411035,	Top-5 err = 0.190625,	data_time = 0.050596,	train_time = 0.927235
[2019-08-23 14:18:49,575] TRAIN Iter 121960: lr = 0.296735,	loss = 2.602270,	Top-1 err = 0.419287,	Top-5 err = 0.192725,	data_time = 0.050383,	train_time = 0.551786
[2019-08-23 14:18:56,489] TRAIN Iter 121980: lr = 0.296702,	loss = 2.599884,	Top-1 err = 0.409229,	Top-5 err = 0.192383,	data_time = 0.050439,	train_time = 0.345693
[2019-08-23 14:19:10,141] TRAIN Iter 122000: lr = 0.296668,	loss = 2.764597,	Top-1 err = 0.414111,	Top-5 err = 0.195654,	data_time = 0.050498,	train_time = 0.682557
[2019-08-23 14:19:17,269] TRAIN Iter 122020: lr = 0.296635,	loss = 2.733691,	Top-1 err = 0.418115,	Top-5 err = 0.193408,	data_time = 0.050709,	train_time = 0.356400
[2019-08-23 14:19:32,719] TRAIN Iter 122040: lr = 0.296602,	loss = 2.791197,	Top-1 err = 0.413037,	Top-5 err = 0.195312,	data_time = 0.050462,	train_time = 0.772480
[2019-08-23 14:19:48,750] TRAIN Iter 122060: lr = 0.296568,	loss = 2.701735,	Top-1 err = 0.415479,	Top-5 err = 0.193994,	data_time = 0.050327,	train_time = 0.801518
[2019-08-23 14:19:55,744] TRAIN Iter 122080: lr = 0.296535,	loss = 2.675011,	Top-1 err = 0.419824,	Top-5 err = 0.191309,	data_time = 0.050632,	train_time = 0.349713
[2019-08-23 14:20:11,122] TRAIN Iter 122100: lr = 0.296502,	loss = 2.661499,	Top-1 err = 0.411084,	Top-5 err = 0.191016,	data_time = 0.050515,	train_time = 0.768901
[2019-08-23 14:20:27,092] TRAIN Iter 122120: lr = 0.296468,	loss = 2.774144,	Top-1 err = 0.416943,	Top-5 err = 0.193164,	data_time = 0.050349,	train_time = 0.798471
[2019-08-23 14:20:34,562] TRAIN Iter 122140: lr = 0.296435,	loss = 2.641411,	Top-1 err = 0.419873,	Top-5 err = 0.197461,	data_time = 0.050476,	train_time = 0.373488
[2019-08-23 14:20:48,414] TRAIN Iter 122160: lr = 0.296402,	loss = 2.632179,	Top-1 err = 0.413721,	Top-5 err = 0.191943,	data_time = 0.050902,	train_time = 0.692589
[2019-08-23 14:20:56,199] TRAIN Iter 122180: lr = 0.296368,	loss = 2.672015,	Top-1 err = 0.418506,	Top-5 err = 0.198193,	data_time = 0.050308,	train_time = 0.389220
[2019-08-23 14:21:09,262] TRAIN Iter 122200: lr = 0.296335,	loss = 2.760440,	Top-1 err = 0.422217,	Top-5 err = 0.198975,	data_time = 0.122155,	train_time = 0.653164
[2019-08-23 14:21:24,297] TRAIN Iter 122220: lr = 0.296302,	loss = 2.714156,	Top-1 err = 0.419336,	Top-5 err = 0.193994,	data_time = 0.050495,	train_time = 0.751701
[2019-08-23 14:21:31,761] TRAIN Iter 122240: lr = 0.296268,	loss = 2.744453,	Top-1 err = 0.418604,	Top-5 err = 0.195410,	data_time = 0.050694,	train_time = 0.373200
[2019-08-23 14:21:47,594] TRAIN Iter 122260: lr = 0.296235,	loss = 2.685605,	Top-1 err = 0.417236,	Top-5 err = 0.192773,	data_time = 0.050431,	train_time = 0.791653
[2019-08-23 14:22:01,480] TRAIN Iter 122280: lr = 0.296202,	loss = 2.716462,	Top-1 err = 0.418408,	Top-5 err = 0.192529,	data_time = 0.050534,	train_time = 0.694278
[2019-08-23 14:22:08,897] TRAIN Iter 122300: lr = 0.296168,	loss = 2.707564,	Top-1 err = 0.416113,	Top-5 err = 0.189355,	data_time = 0.050707,	train_time = 0.370845
[2019-08-23 14:22:22,915] TRAIN Iter 122320: lr = 0.296135,	loss = 2.769370,	Top-1 err = 0.414453,	Top-5 err = 0.194141,	data_time = 0.050654,	train_time = 0.700883
[2019-08-23 14:22:30,101] TRAIN Iter 122340: lr = 0.296102,	loss = 2.678962,	Top-1 err = 0.412158,	Top-5 err = 0.192090,	data_time = 0.050576,	train_time = 0.359272
[2019-08-23 14:22:45,652] TRAIN Iter 122360: lr = 0.296068,	loss = 2.700667,	Top-1 err = 0.416943,	Top-5 err = 0.193799,	data_time = 0.050742,	train_time = 0.777513
[2019-08-23 14:22:59,753] TRAIN Iter 122380: lr = 0.296035,	loss = 2.742745,	Top-1 err = 0.413965,	Top-5 err = 0.197510,	data_time = 0.050549,	train_time = 0.705073
[2019-08-23 14:23:06,864] TRAIN Iter 122400: lr = 0.296002,	loss = 2.756500,	Top-1 err = 0.412354,	Top-5 err = 0.190625,	data_time = 0.050572,	train_time = 0.355536
[2019-08-23 14:23:23,034] TRAIN Iter 122420: lr = 0.295968,	loss = 2.741355,	Top-1 err = 0.420215,	Top-5 err = 0.196094,	data_time = 0.050646,	train_time = 0.808471
[2019-08-23 14:23:36,212] TRAIN Iter 122440: lr = 0.295935,	loss = 2.757213,	Top-1 err = 0.421387,	Top-5 err = 0.198975,	data_time = 0.050310,	train_time = 0.658895
[2019-08-23 14:23:46,676] TRAIN Iter 122460: lr = 0.295902,	loss = 2.630690,	Top-1 err = 0.416699,	Top-5 err = 0.192480,	data_time = 0.050651,	train_time = 0.523184
[2019-08-23 14:24:01,297] TRAIN Iter 122480: lr = 0.295868,	loss = 2.857141,	Top-1 err = 0.418652,	Top-5 err = 0.195557,	data_time = 0.050401,	train_time = 0.731016
[2019-08-23 14:24:08,275] TRAIN Iter 122500: lr = 0.295835,	loss = 2.768404,	Top-1 err = 0.425342,	Top-5 err = 0.198926,	data_time = 0.050619,	train_time = 0.348897
[2019-08-23 14:24:23,887] TRAIN Iter 122520: lr = 0.295802,	loss = 2.744242,	Top-1 err = 0.421387,	Top-5 err = 0.194678,	data_time = 0.050560,	train_time = 0.780581
[2019-08-23 14:24:40,298] TRAIN Iter 122540: lr = 0.295768,	loss = 2.751122,	Top-1 err = 0.419092,	Top-5 err = 0.198486,	data_time = 0.050480,	train_time = 0.820509
[2019-08-23 14:24:47,161] TRAIN Iter 122560: lr = 0.295735,	loss = 2.653836,	Top-1 err = 0.416357,	Top-5 err = 0.196387,	data_time = 0.050441,	train_time = 0.343128
[2019-08-23 14:25:04,092] TRAIN Iter 122580: lr = 0.295702,	loss = 2.720592,	Top-1 err = 0.419238,	Top-5 err = 0.196533,	data_time = 0.050128,	train_time = 0.846553
[2019-08-23 14:25:18,757] TRAIN Iter 122600: lr = 0.295668,	loss = 2.668128,	Top-1 err = 0.420801,	Top-5 err = 0.196777,	data_time = 0.049904,	train_time = 0.733258
[2019-08-23 14:25:25,617] TRAIN Iter 122620: lr = 0.295635,	loss = 2.759431,	Top-1 err = 0.417236,	Top-5 err = 0.193994,	data_time = 0.049896,	train_time = 0.342991
[2019-08-23 14:26:15,271] TRAIN Iter 122640: lr = 0.295602,	loss = 2.769242,	Top-1 err = 0.426208,	Top-5 err = 0.203683,	data_time = 0.050342,	train_time = 2.482645
[2019-08-23 14:26:23,426] TRAIN Iter 122660: lr = 0.295568,	loss = 2.626537,	Top-1 err = 0.414600,	Top-5 err = 0.193018,	data_time = 0.158267,	train_time = 0.407767
[2019-08-23 14:26:35,786] TRAIN Iter 122680: lr = 0.295535,	loss = 2.671984,	Top-1 err = 0.407471,	Top-5 err = 0.190381,	data_time = 0.050432,	train_time = 0.617948
[2019-08-23 14:26:46,569] TRAIN Iter 122700: lr = 0.295502,	loss = 2.662119,	Top-1 err = 0.408447,	Top-5 err = 0.189844,	data_time = 0.050632,	train_time = 0.539168
[2019-08-23 14:26:54,130] TRAIN Iter 122720: lr = 0.295468,	loss = 2.682864,	Top-1 err = 0.409961,	Top-5 err = 0.184375,	data_time = 0.050482,	train_time = 0.378028
[2019-08-23 14:27:05,930] TRAIN Iter 122740: lr = 0.295435,	loss = 2.754513,	Top-1 err = 0.407471,	Top-5 err = 0.189453,	data_time = 0.050438,	train_time = 0.589971
[2019-08-23 14:27:13,406] TRAIN Iter 122760: lr = 0.295402,	loss = 2.752192,	Top-1 err = 0.411426,	Top-5 err = 0.192627,	data_time = 0.050400,	train_time = 0.373783
[2019-08-23 14:27:28,109] TRAIN Iter 122780: lr = 0.295368,	loss = 2.555404,	Top-1 err = 0.408203,	Top-5 err = 0.188135,	data_time = 0.050549,	train_time = 0.735120
[2019-08-23 14:27:44,601] TRAIN Iter 122800: lr = 0.295335,	loss = 2.638298,	Top-1 err = 0.409912,	Top-5 err = 0.191650,	data_time = 0.121388,	train_time = 0.824635
[2019-08-23 14:27:52,268] TRAIN Iter 122820: lr = 0.295302,	loss = 2.710610,	Top-1 err = 0.419482,	Top-5 err = 0.197266,	data_time = 0.050391,	train_time = 0.383314
[2019-08-23 14:28:07,412] TRAIN Iter 122840: lr = 0.295268,	loss = 2.597706,	Top-1 err = 0.416797,	Top-5 err = 0.191797,	data_time = 0.050671,	train_time = 0.757192
[2019-08-23 14:28:20,958] TRAIN Iter 122860: lr = 0.295235,	loss = 2.626423,	Top-1 err = 0.415332,	Top-5 err = 0.190381,	data_time = 0.050733,	train_time = 0.677290
[2019-08-23 14:28:28,724] TRAIN Iter 122880: lr = 0.295202,	loss = 2.705925,	Top-1 err = 0.407373,	Top-5 err = 0.188672,	data_time = 0.050481,	train_time = 0.388288
[2019-08-23 14:28:43,191] TRAIN Iter 122900: lr = 0.295168,	loss = 2.676357,	Top-1 err = 0.417627,	Top-5 err = 0.194092,	data_time = 0.150732,	train_time = 0.723307
[2019-08-23 14:28:50,976] TRAIN Iter 122920: lr = 0.295135,	loss = 2.686413,	Top-1 err = 0.415332,	Top-5 err = 0.193457,	data_time = 0.050479,	train_time = 0.389254
[2019-08-23 14:29:08,022] TRAIN Iter 122940: lr = 0.295102,	loss = 2.751833,	Top-1 err = 0.413965,	Top-5 err = 0.193701,	data_time = 0.050516,	train_time = 0.852315
[2019-08-23 14:29:19,005] TRAIN Iter 122960: lr = 0.295068,	loss = 2.614740,	Top-1 err = 0.413867,	Top-5 err = 0.192041,	data_time = 0.050614,	train_time = 0.549133
[2019-08-23 14:29:26,361] TRAIN Iter 122980: lr = 0.295035,	loss = 2.675145,	Top-1 err = 0.414355,	Top-5 err = 0.190918,	data_time = 0.050712,	train_time = 0.367786
[2019-08-23 14:29:41,211] TRAIN Iter 123000: lr = 0.295002,	loss = 2.733919,	Top-1 err = 0.409424,	Top-5 err = 0.189502,	data_time = 0.118560,	train_time = 0.742475
[2019-08-23 14:29:54,194] TRAIN Iter 123020: lr = 0.294968,	loss = 2.792341,	Top-1 err = 0.420898,	Top-5 err = 0.190137,	data_time = 0.050611,	train_time = 0.649103
[2019-08-23 14:30:01,980] TRAIN Iter 123040: lr = 0.294935,	loss = 2.748067,	Top-1 err = 0.406836,	Top-5 err = 0.188330,	data_time = 0.050317,	train_time = 0.389303
[2019-08-23 14:30:17,920] TRAIN Iter 123060: lr = 0.294902,	loss = 2.639307,	Top-1 err = 0.414355,	Top-5 err = 0.193408,	data_time = 0.050583,	train_time = 0.797007
[2019-08-23 14:30:25,355] TRAIN Iter 123080: lr = 0.294868,	loss = 2.767588,	Top-1 err = 0.411133,	Top-5 err = 0.192578,	data_time = 0.050446,	train_time = 0.371729
[2019-08-23 14:30:39,779] TRAIN Iter 123100: lr = 0.294835,	loss = 2.630519,	Top-1 err = 0.409717,	Top-5 err = 0.196582,	data_time = 0.050466,	train_time = 0.721153
[2019-08-23 14:30:54,373] TRAIN Iter 123120: lr = 0.294802,	loss = 2.797217,	Top-1 err = 0.415918,	Top-5 err = 0.196875,	data_time = 0.050503,	train_time = 0.729692
[2019-08-23 14:31:03,750] TRAIN Iter 123140: lr = 0.294768,	loss = 2.605527,	Top-1 err = 0.415918,	Top-5 err = 0.193848,	data_time = 0.113113,	train_time = 0.468830
[2019-08-23 14:31:17,486] TRAIN Iter 123160: lr = 0.294735,	loss = 2.729756,	Top-1 err = 0.415088,	Top-5 err = 0.191797,	data_time = 0.050393,	train_time = 0.686812
[2019-08-23 14:31:30,650] TRAIN Iter 123180: lr = 0.294702,	loss = 2.658411,	Top-1 err = 0.414404,	Top-5 err = 0.196777,	data_time = 0.050380,	train_time = 0.658194
[2019-08-23 14:31:40,277] TRAIN Iter 123200: lr = 0.294668,	loss = 2.698535,	Top-1 err = 0.414209,	Top-5 err = 0.192139,	data_time = 0.050529,	train_time = 0.481345
[2019-08-23 14:31:55,507] TRAIN Iter 123220: lr = 0.294635,	loss = 2.657334,	Top-1 err = 0.419775,	Top-5 err = 0.196729,	data_time = 0.134073,	train_time = 0.761440
[2019-08-23 14:32:02,746] TRAIN Iter 123240: lr = 0.294602,	loss = 2.674990,	Top-1 err = 0.415771,	Top-5 err = 0.195166,	data_time = 0.050235,	train_time = 0.361939
[2019-08-23 14:32:16,264] TRAIN Iter 123260: lr = 0.294568,	loss = 2.818706,	Top-1 err = 0.418213,	Top-5 err = 0.193652,	data_time = 0.050168,	train_time = 0.675893
[2019-08-23 14:32:31,773] TRAIN Iter 123280: lr = 0.294535,	loss = 2.723231,	Top-1 err = 0.413232,	Top-5 err = 0.193604,	data_time = 0.050409,	train_time = 0.775451
[2019-08-23 14:32:39,230] TRAIN Iter 123300: lr = 0.294502,	loss = 2.669852,	Top-1 err = 0.419531,	Top-5 err = 0.198389,	data_time = 0.130907,	train_time = 0.372836
[2019-08-23 14:32:54,428] TRAIN Iter 123320: lr = 0.294468,	loss = 2.689282,	Top-1 err = 0.423389,	Top-5 err = 0.193213,	data_time = 0.050785,	train_time = 0.759899
[2019-08-23 14:33:09,404] TRAIN Iter 123340: lr = 0.294435,	loss = 2.764050,	Top-1 err = 0.416943,	Top-5 err = 0.193604,	data_time = 0.050727,	train_time = 0.748751
[2019-08-23 14:33:17,097] TRAIN Iter 123360: lr = 0.294402,	loss = 2.695559,	Top-1 err = 0.415332,	Top-5 err = 0.193359,	data_time = 0.050792,	train_time = 0.384637
[2019-08-23 14:33:32,681] TRAIN Iter 123380: lr = 0.294368,	loss = 2.624959,	Top-1 err = 0.412451,	Top-5 err = 0.189404,	data_time = 0.050528,	train_time = 0.779206
[2019-08-23 14:33:39,953] TRAIN Iter 123400: lr = 0.294335,	loss = 2.564680,	Top-1 err = 0.412305,	Top-5 err = 0.189502,	data_time = 0.050614,	train_time = 0.363569
[2019-08-23 14:33:55,901] TRAIN Iter 123420: lr = 0.294302,	loss = 2.632771,	Top-1 err = 0.418799,	Top-5 err = 0.187646,	data_time = 0.050235,	train_time = 0.797373
[2019-08-23 14:34:12,183] TRAIN Iter 123440: lr = 0.294268,	loss = 2.710792,	Top-1 err = 0.415137,	Top-5 err = 0.189648,	data_time = 0.050605,	train_time = 0.814128
[2019-08-23 14:34:19,410] TRAIN Iter 123460: lr = 0.294235,	loss = 2.709740,	Top-1 err = 0.416992,	Top-5 err = 0.199072,	data_time = 0.050554,	train_time = 0.361304
[2019-08-23 14:34:36,376] TRAIN Iter 123480: lr = 0.294202,	loss = 2.674033,	Top-1 err = 0.417139,	Top-5 err = 0.196924,	data_time = 0.050575,	train_time = 0.848295
[2019-08-23 14:34:51,336] TRAIN Iter 123500: lr = 0.294168,	loss = 2.705745,	Top-1 err = 0.423291,	Top-5 err = 0.198047,	data_time = 0.050405,	train_time = 0.748001
[2019-08-23 14:34:58,668] TRAIN Iter 123520: lr = 0.294135,	loss = 2.684042,	Top-1 err = 0.417529,	Top-5 err = 0.191846,	data_time = 0.050543,	train_time = 0.366589
[2019-08-23 14:35:14,512] TRAIN Iter 123540: lr = 0.294102,	loss = 2.735525,	Top-1 err = 0.420020,	Top-5 err = 0.196191,	data_time = 0.050428,	train_time = 0.792164
[2019-08-23 14:35:21,512] TRAIN Iter 123560: lr = 0.294068,	loss = 2.820517,	Top-1 err = 0.421240,	Top-5 err = 0.196924,	data_time = 0.118398,	train_time = 0.349981
[2019-08-23 14:35:38,497] TRAIN Iter 123580: lr = 0.294035,	loss = 2.670997,	Top-1 err = 0.418311,	Top-5 err = 0.194043,	data_time = 0.050493,	train_time = 0.849262
[2019-08-23 14:35:54,163] TRAIN Iter 123600: lr = 0.294002,	loss = 2.746741,	Top-1 err = 0.418457,	Top-5 err = 0.195654,	data_time = 0.050557,	train_time = 0.783275
[2019-08-23 14:36:01,178] TRAIN Iter 123620: lr = 0.293968,	loss = 2.719421,	Top-1 err = 0.418652,	Top-5 err = 0.195898,	data_time = 0.050433,	train_time = 0.350732
[2019-08-23 14:36:18,484] TRAIN Iter 123640: lr = 0.293935,	loss = 2.649425,	Top-1 err = 0.421191,	Top-5 err = 0.194824,	data_time = 0.050533,	train_time = 0.865271
[2019-08-23 14:36:34,976] TRAIN Iter 123660: lr = 0.293902,	loss = 2.726901,	Top-1 err = 0.415674,	Top-5 err = 0.196045,	data_time = 0.050448,	train_time = 0.824614
[2019-08-23 14:36:42,436] TRAIN Iter 123680: lr = 0.293868,	loss = 2.764613,	Top-1 err = 0.414844,	Top-5 err = 0.195654,	data_time = 0.050737,	train_time = 0.372985
[2019-08-23 14:36:59,538] TRAIN Iter 123700: lr = 0.293835,	loss = 2.682168,	Top-1 err = 0.421777,	Top-5 err = 0.194434,	data_time = 0.050701,	train_time = 0.855086
[2019-08-23 14:37:06,357] TRAIN Iter 123720: lr = 0.293802,	loss = 2.675611,	Top-1 err = 0.418701,	Top-5 err = 0.194092,	data_time = 0.050226,	train_time = 0.340914
[2019-08-23 14:37:23,246] TRAIN Iter 123740: lr = 0.293768,	loss = 2.728405,	Top-1 err = 0.424414,	Top-5 err = 0.199707,	data_time = 0.050522,	train_time = 0.844450
[2019-08-23 14:37:39,711] TRAIN Iter 123760: lr = 0.293735,	loss = 2.688114,	Top-1 err = 0.418311,	Top-5 err = 0.196680,	data_time = 0.050538,	train_time = 0.823217
[2019-08-23 14:37:46,631] TRAIN Iter 123780: lr = 0.293702,	loss = 2.736593,	Top-1 err = 0.415918,	Top-5 err = 0.190430,	data_time = 0.050459,	train_time = 0.345986
[2019-08-23 14:38:05,089] TRAIN Iter 123800: lr = 0.293668,	loss = 2.760937,	Top-1 err = 0.418994,	Top-5 err = 0.195898,	data_time = 0.050477,	train_time = 0.922881
[2019-08-23 14:38:22,927] TRAIN Iter 123820: lr = 0.293635,	loss = 2.780775,	Top-1 err = 0.423193,	Top-5 err = 0.200732,	data_time = 0.064063,	train_time = 0.891916
[2019-08-23 14:38:30,038] TRAIN Iter 123840: lr = 0.293602,	loss = 2.662509,	Top-1 err = 0.417578,	Top-5 err = 0.192969,	data_time = 0.050193,	train_time = 0.355518
[2019-08-23 14:38:46,137] TRAIN Iter 123860: lr = 0.293568,	loss = 2.634279,	Top-1 err = 0.419678,	Top-5 err = 0.193018,	data_time = 0.049889,	train_time = 0.804941
[2019-08-23 14:38:52,456] TRAIN Iter 123880: lr = 0.293535,	loss = 2.709333,	Top-1 err = 0.414941,	Top-5 err = 0.191064,	data_time = 0.049949,	train_time = 0.315940
[2019-08-23 14:39:44,995] TRAIN Iter 123900: lr = 0.293502,	loss = 2.597902,	Top-1 err = 0.415571,	Top-5 err = 0.187288,	data_time = 0.050584,	train_time = 2.626947
[2019-08-23 14:39:58,323] TRAIN Iter 123920: lr = 0.293468,	loss = 2.650677,	Top-1 err = 0.412061,	Top-5 err = 0.188379,	data_time = 1.523200,	train_time = 0.666380
[2019-08-23 14:40:05,483] TRAIN Iter 123940: lr = 0.293435,	loss = 2.602395,	Top-1 err = 0.405811,	Top-5 err = 0.186182,	data_time = 0.050494,	train_time = 0.357983
[2019-08-23 14:40:17,924] TRAIN Iter 123960: lr = 0.293402,	loss = 2.764608,	Top-1 err = 0.411279,	Top-5 err = 0.189453,	data_time = 0.050647,	train_time = 0.622028
[2019-08-23 14:40:25,321] TRAIN Iter 123980: lr = 0.293368,	loss = 2.613960,	Top-1 err = 0.407373,	Top-5 err = 0.189258,	data_time = 0.050553,	train_time = 0.369865
[2019-08-23 14:40:41,783] TRAIN Iter 124000: lr = 0.293335,	loss = 2.605910,	Top-1 err = 0.404248,	Top-5 err = 0.183984,	data_time = 0.050321,	train_time = 0.823061
[2019-08-23 14:40:56,094] TRAIN Iter 124020: lr = 0.293302,	loss = 2.692392,	Top-1 err = 0.409424,	Top-5 err = 0.193457,	data_time = 0.050576,	train_time = 0.715518
[2019-08-23 14:41:03,572] TRAIN Iter 124040: lr = 0.293268,	loss = 2.655478,	Top-1 err = 0.411426,	Top-5 err = 0.187744,	data_time = 0.142761,	train_time = 0.373885
[2019-08-23 14:41:20,191] TRAIN Iter 124060: lr = 0.293235,	loss = 2.686296,	Top-1 err = 0.411865,	Top-5 err = 0.192480,	data_time = 0.050455,	train_time = 0.830958
[2019-08-23 14:41:35,516] TRAIN Iter 124080: lr = 0.293202,	loss = 2.605212,	Top-1 err = 0.408984,	Top-5 err = 0.187842,	data_time = 0.050696,	train_time = 0.766231
[2019-08-23 14:41:42,610] TRAIN Iter 124100: lr = 0.293168,	loss = 2.644485,	Top-1 err = 0.408643,	Top-5 err = 0.188379,	data_time = 0.050690,	train_time = 0.354683
[2019-08-23 14:41:54,294] TRAIN Iter 124120: lr = 0.293135,	loss = 2.652177,	Top-1 err = 0.418750,	Top-5 err = 0.192627,	data_time = 0.050582,	train_time = 0.584179
[2019-08-23 14:42:01,254] TRAIN Iter 124140: lr = 0.293102,	loss = 2.732379,	Top-1 err = 0.413037,	Top-5 err = 0.191260,	data_time = 0.123206,	train_time = 0.348002
[2019-08-23 14:42:15,698] TRAIN Iter 124160: lr = 0.293068,	loss = 2.671032,	Top-1 err = 0.408887,	Top-5 err = 0.187842,	data_time = 0.050397,	train_time = 0.722195
[2019-08-23 14:42:30,511] TRAIN Iter 124180: lr = 0.293035,	loss = 2.752000,	Top-1 err = 0.407861,	Top-5 err = 0.187354,	data_time = 1.089918,	train_time = 0.740632
[2019-08-23 14:42:37,677] TRAIN Iter 124200: lr = 0.293002,	loss = 2.650880,	Top-1 err = 0.406885,	Top-5 err = 0.188086,	data_time = 0.050423,	train_time = 0.358266
[2019-08-23 14:42:51,802] TRAIN Iter 124220: lr = 0.292968,	loss = 2.732044,	Top-1 err = 0.407031,	Top-5 err = 0.188232,	data_time = 0.050539,	train_time = 0.706246
[2019-08-23 14:43:06,351] TRAIN Iter 124240: lr = 0.292935,	loss = 2.770284,	Top-1 err = 0.418799,	Top-5 err = 0.194824,	data_time = 0.140647,	train_time = 0.727420
[2019-08-23 14:43:13,576] TRAIN Iter 124260: lr = 0.292902,	loss = 2.633836,	Top-1 err = 0.412109,	Top-5 err = 0.190674,	data_time = 0.050505,	train_time = 0.361237
[2019-08-23 14:43:28,986] TRAIN Iter 124280: lr = 0.292868,	loss = 2.700871,	Top-1 err = 0.415576,	Top-5 err = 0.189209,	data_time = 0.050343,	train_time = 0.770522
[2019-08-23 14:43:36,415] TRAIN Iter 124300: lr = 0.292835,	loss = 2.785092,	Top-1 err = 0.408350,	Top-5 err = 0.194336,	data_time = 0.050477,	train_time = 0.371440
[2019-08-23 14:43:50,373] TRAIN Iter 124320: lr = 0.292802,	loss = 2.620587,	Top-1 err = 0.411914,	Top-5 err = 0.193311,	data_time = 0.050290,	train_time = 0.697865
[2019-08-23 14:44:07,151] TRAIN Iter 124340: lr = 0.292768,	loss = 2.588822,	Top-1 err = 0.411865,	Top-5 err = 0.188721,	data_time = 0.050363,	train_time = 0.838874
[2019-08-23 14:44:14,633] TRAIN Iter 124360: lr = 0.292735,	loss = 2.792888,	Top-1 err = 0.409570,	Top-5 err = 0.187939,	data_time = 0.127824,	train_time = 0.374066
[2019-08-23 14:44:29,314] TRAIN Iter 124380: lr = 0.292702,	loss = 2.673120,	Top-1 err = 0.413818,	Top-5 err = 0.191016,	data_time = 0.050236,	train_time = 0.734040
[2019-08-23 14:44:45,436] TRAIN Iter 124400: lr = 0.292668,	loss = 2.709954,	Top-1 err = 0.416504,	Top-5 err = 0.190283,	data_time = 0.050505,	train_time = 0.806129
[2019-08-23 14:44:52,860] TRAIN Iter 124420: lr = 0.292635,	loss = 2.656525,	Top-1 err = 0.414355,	Top-5 err = 0.190918,	data_time = 0.050644,	train_time = 0.371139
[2019-08-23 14:45:08,398] TRAIN Iter 124440: lr = 0.292602,	loss = 2.761634,	Top-1 err = 0.412012,	Top-5 err = 0.190869,	data_time = 0.050749,	train_time = 0.776913
[2019-08-23 14:45:16,003] TRAIN Iter 124460: lr = 0.292568,	loss = 2.779490,	Top-1 err = 0.414453,	Top-5 err = 0.195605,	data_time = 0.050539,	train_time = 0.380240
[2019-08-23 14:45:31,400] TRAIN Iter 124480: lr = 0.292535,	loss = 2.651799,	Top-1 err = 0.412598,	Top-5 err = 0.192334,	data_time = 0.050300,	train_time = 0.769828
[2019-08-23 14:45:47,037] TRAIN Iter 124500: lr = 0.292502,	loss = 2.705441,	Top-1 err = 0.412451,	Top-5 err = 0.193799,	data_time = 0.050336,	train_time = 0.781809
[2019-08-23 14:45:54,661] TRAIN Iter 124520: lr = 0.292468,	loss = 2.757668,	Top-1 err = 0.410840,	Top-5 err = 0.191406,	data_time = 0.050269,	train_time = 0.381227
[2019-08-23 14:46:09,479] TRAIN Iter 124540: lr = 0.292435,	loss = 2.692564,	Top-1 err = 0.423242,	Top-5 err = 0.196582,	data_time = 0.050525,	train_time = 0.740863
[2019-08-23 14:46:25,155] TRAIN Iter 124560: lr = 0.292402,	loss = 2.709912,	Top-1 err = 0.413037,	Top-5 err = 0.193652,	data_time = 0.050363,	train_time = 0.783782
[2019-08-23 14:46:32,346] TRAIN Iter 124580: lr = 0.292368,	loss = 2.695422,	Top-1 err = 0.415088,	Top-5 err = 0.195801,	data_time = 0.050505,	train_time = 0.359557
[2019-08-23 14:46:47,944] TRAIN Iter 124600: lr = 0.292335,	loss = 2.704311,	Top-1 err = 0.417236,	Top-5 err = 0.193164,	data_time = 0.050566,	train_time = 0.779873
[2019-08-23 14:46:55,557] TRAIN Iter 124620: lr = 0.292302,	loss = 2.679039,	Top-1 err = 0.412061,	Top-5 err = 0.192236,	data_time = 0.050537,	train_time = 0.380623
[2019-08-23 14:47:10,875] TRAIN Iter 124640: lr = 0.292268,	loss = 2.754216,	Top-1 err = 0.418018,	Top-5 err = 0.193262,	data_time = 0.050559,	train_time = 0.765916
[2019-08-23 14:47:25,435] TRAIN Iter 124660: lr = 0.292235,	loss = 2.677865,	Top-1 err = 0.418457,	Top-5 err = 0.195801,	data_time = 0.050471,	train_time = 0.727999
[2019-08-23 14:47:32,878] TRAIN Iter 124680: lr = 0.292202,	loss = 2.653361,	Top-1 err = 0.410596,	Top-5 err = 0.192920,	data_time = 0.050512,	train_time = 0.372097
[2019-08-23 14:47:49,283] TRAIN Iter 124700: lr = 0.292168,	loss = 2.625726,	Top-1 err = 0.410986,	Top-5 err = 0.193506,	data_time = 0.050438,	train_time = 0.820252
[2019-08-23 14:48:03,736] TRAIN Iter 124720: lr = 0.292135,	loss = 2.761239,	Top-1 err = 0.416211,	Top-5 err = 0.190674,	data_time = 0.050951,	train_time = 0.722645
[2019-08-23 14:48:10,643] TRAIN Iter 124740: lr = 0.292102,	loss = 2.640790,	Top-1 err = 0.415527,	Top-5 err = 0.193848,	data_time = 0.050543,	train_time = 0.345343
[2019-08-23 14:48:27,298] TRAIN Iter 124760: lr = 0.292068,	loss = 2.716062,	Top-1 err = 0.420410,	Top-5 err = 0.194629,	data_time = 0.050527,	train_time = 0.832722
[2019-08-23 14:48:34,966] TRAIN Iter 124780: lr = 0.292035,	loss = 2.797315,	Top-1 err = 0.418359,	Top-5 err = 0.196387,	data_time = 0.050418,	train_time = 0.383374
[2019-08-23 14:48:50,304] TRAIN Iter 124800: lr = 0.292002,	loss = 2.795569,	Top-1 err = 0.419336,	Top-5 err = 0.197754,	data_time = 0.050417,	train_time = 0.766895
[2019-08-23 14:49:06,771] TRAIN Iter 124820: lr = 0.291968,	loss = 2.770263,	Top-1 err = 0.416504,	Top-5 err = 0.195703,	data_time = 0.050390,	train_time = 0.823340
[2019-08-23 14:49:14,423] TRAIN Iter 124840: lr = 0.291935,	loss = 2.813439,	Top-1 err = 0.421777,	Top-5 err = 0.201221,	data_time = 0.050550,	train_time = 0.382569
[2019-08-23 14:49:27,694] TRAIN Iter 124860: lr = 0.291902,	loss = 2.692162,	Top-1 err = 0.418750,	Top-5 err = 0.193799,	data_time = 0.050595,	train_time = 0.663568
[2019-08-23 14:49:41,945] TRAIN Iter 124880: lr = 0.291868,	loss = 2.789972,	Top-1 err = 0.423242,	Top-5 err = 0.198145,	data_time = 0.050406,	train_time = 0.712523
[2019-08-23 14:49:48,663] TRAIN Iter 124900: lr = 0.291835,	loss = 2.694313,	Top-1 err = 0.416650,	Top-5 err = 0.193652,	data_time = 0.050509,	train_time = 0.335867
[2019-08-23 14:50:05,187] TRAIN Iter 124920: lr = 0.291802,	loss = 2.703427,	Top-1 err = 0.413574,	Top-5 err = 0.193604,	data_time = 0.050334,	train_time = 0.826214
[2019-08-23 14:50:12,020] TRAIN Iter 124940: lr = 0.291768,	loss = 2.724575,	Top-1 err = 0.413281,	Top-5 err = 0.192822,	data_time = 0.050541,	train_time = 0.341602
[2019-08-23 14:50:28,645] TRAIN Iter 124960: lr = 0.291735,	loss = 2.713199,	Top-1 err = 0.416699,	Top-5 err = 0.196289,	data_time = 0.050498,	train_time = 0.831243
[2019-08-23 14:50:45,824] TRAIN Iter 124980: lr = 0.291702,	loss = 2.737951,	Top-1 err = 0.414648,	Top-5 err = 0.194482,	data_time = 0.050385,	train_time = 0.858944
[2019-08-23 14:50:52,593] TRAIN Iter 125000: lr = 0.291668,	loss = 2.803296,	Top-1 err = 0.423584,	Top-5 err = 0.201514,	data_time = 0.050322,	train_time = 0.338463
[2019-08-23 14:51:09,668] TRAIN Iter 125020: lr = 0.291635,	loss = 2.681572,	Top-1 err = 0.411377,	Top-5 err = 0.189990,	data_time = 0.050806,	train_time = 0.853695
[2019-08-23 14:51:26,797] TRAIN Iter 125040: lr = 0.291602,	loss = 2.654387,	Top-1 err = 0.414404,	Top-5 err = 0.194189,	data_time = 0.050530,	train_time = 0.856478
[2019-08-23 14:51:33,378] TRAIN Iter 125060: lr = 0.291568,	loss = 2.684096,	Top-1 err = 0.417187,	Top-5 err = 0.198242,	data_time = 0.050285,	train_time = 0.329015
[2019-08-23 14:51:51,324] TRAIN Iter 125080: lr = 0.291535,	loss = 2.826463,	Top-1 err = 0.421631,	Top-5 err = 0.197705,	data_time = 0.049896,	train_time = 0.897288
[2019-08-23 14:51:58,338] TRAIN Iter 125100: lr = 0.291502,	loss = 2.736952,	Top-1 err = 0.414062,	Top-5 err = 0.196631,	data_time = 0.050147,	train_time = 0.350681
[2019-08-23 14:52:14,221] TRAIN Iter 125120: lr = 0.291468,	loss = 2.606490,	Top-1 err = 0.418262,	Top-5 err = 0.194482,	data_time = 0.049873,	train_time = 0.794131
[2019-08-23 14:53:04,947] TRAIN Iter 125140: lr = 0.291435,	loss = 2.676893,	Top-1 err = 0.423362,	Top-5 err = 0.195546,	data_time = 0.138228,	train_time = 2.536308
[2019-08-23 14:53:12,289] TRAIN Iter 125160: lr = 0.291402,	loss = 2.697164,	Top-1 err = 0.421484,	Top-5 err = 0.198291,	data_time = 0.050825,	train_time = 0.367093
[2019-08-23 14:53:30,015] TRAIN Iter 125180: lr = 0.291368,	loss = 2.664917,	Top-1 err = 0.405762,	Top-5 err = 0.184375,	data_time = 0.050237,	train_time = 0.886274
[2019-08-23 14:53:38,180] TRAIN Iter 125200: lr = 0.291335,	loss = 2.809775,	Top-1 err = 0.414990,	Top-5 err = 0.191602,	data_time = 0.050364,	train_time = 0.408219
[2019-08-23 14:53:50,831] TRAIN Iter 125220: lr = 0.291302,	loss = 2.668069,	Top-1 err = 0.408936,	Top-5 err = 0.186621,	data_time = 0.050948,	train_time = 0.632552
[2019-08-23 14:54:00,957] TRAIN Iter 125240: lr = 0.291268,	loss = 2.637794,	Top-1 err = 0.410840,	Top-5 err = 0.187939,	data_time = 0.050351,	train_time = 0.506250
[2019-08-23 14:54:08,404] TRAIN Iter 125260: lr = 0.291235,	loss = 2.621038,	Top-1 err = 0.413525,	Top-5 err = 0.189600,	data_time = 0.050408,	train_time = 0.372382
[2019-08-23 14:54:23,713] TRAIN Iter 125280: lr = 0.291202,	loss = 2.640557,	Top-1 err = 0.408643,	Top-5 err = 0.189844,	data_time = 0.050535,	train_time = 0.765395
[2019-08-23 14:54:39,281] TRAIN Iter 125300: lr = 0.291168,	loss = 2.704079,	Top-1 err = 0.408008,	Top-5 err = 0.191260,	data_time = 0.050917,	train_time = 0.778423
[2019-08-23 14:54:46,205] TRAIN Iter 125320: lr = 0.291135,	loss = 2.675567,	Top-1 err = 0.413916,	Top-5 err = 0.186182,	data_time = 0.050395,	train_time = 0.346168
[2019-08-23 14:55:01,112] TRAIN Iter 125340: lr = 0.291102,	loss = 2.601975,	Top-1 err = 0.406396,	Top-5 err = 0.186963,	data_time = 0.050681,	train_time = 0.745326
[2019-08-23 14:55:08,263] TRAIN Iter 125360: lr = 0.291068,	loss = 2.700125,	Top-1 err = 0.414746,	Top-5 err = 0.190918,	data_time = 0.050690,	train_time = 0.357546
[2019-08-23 14:55:22,166] TRAIN Iter 125380: lr = 0.291035,	loss = 2.741959,	Top-1 err = 0.410791,	Top-5 err = 0.190869,	data_time = 0.050527,	train_time = 0.695131
[2019-08-23 14:55:38,480] TRAIN Iter 125400: lr = 0.291002,	loss = 2.647773,	Top-1 err = 0.405420,	Top-5 err = 0.187939,	data_time = 0.050561,	train_time = 0.815709
[2019-08-23 14:55:45,690] TRAIN Iter 125420: lr = 0.290968,	loss = 2.643281,	Top-1 err = 0.412549,	Top-5 err = 0.189355,	data_time = 0.050576,	train_time = 0.360459
[2019-08-23 14:56:01,247] TRAIN Iter 125440: lr = 0.290935,	loss = 2.587540,	Top-1 err = 0.407959,	Top-5 err = 0.189502,	data_time = 0.050110,	train_time = 0.777854
[2019-08-23 14:56:17,320] TRAIN Iter 125460: lr = 0.290902,	loss = 2.572855,	Top-1 err = 0.413037,	Top-5 err = 0.188525,	data_time = 0.050432,	train_time = 0.803640
[2019-08-23 14:56:24,765] TRAIN Iter 125480: lr = 0.290868,	loss = 2.634638,	Top-1 err = 0.412158,	Top-5 err = 0.187842,	data_time = 0.050830,	train_time = 0.372233
[2019-08-23 14:56:38,019] TRAIN Iter 125500: lr = 0.290835,	loss = 2.638198,	Top-1 err = 0.410547,	Top-5 err = 0.189062,	data_time = 0.050616,	train_time = 0.662669
[2019-08-23 14:56:45,709] TRAIN Iter 125520: lr = 0.290802,	loss = 2.590303,	Top-1 err = 0.407715,	Top-5 err = 0.188672,	data_time = 0.050758,	train_time = 0.384488
[2019-08-23 14:56:59,621] TRAIN Iter 125540: lr = 0.290768,	loss = 2.727871,	Top-1 err = 0.413721,	Top-5 err = 0.190625,	data_time = 0.050624,	train_time = 0.695585
[2019-08-23 14:57:14,806] TRAIN Iter 125560: lr = 0.290735,	loss = 2.731100,	Top-1 err = 0.411084,	Top-5 err = 0.192285,	data_time = 0.050753,	train_time = 0.759218
[2019-08-23 14:57:21,829] TRAIN Iter 125580: lr = 0.290702,	loss = 2.676294,	Top-1 err = 0.412354,	Top-5 err = 0.190283,	data_time = 0.131329,	train_time = 0.351169
[2019-08-23 14:57:37,813] TRAIN Iter 125600: lr = 0.290668,	loss = 2.668641,	Top-1 err = 0.411475,	Top-5 err = 0.189893,	data_time = 0.050259,	train_time = 0.799188
[2019-08-23 14:57:53,021] TRAIN Iter 125620: lr = 0.290635,	loss = 2.703418,	Top-1 err = 0.413721,	Top-5 err = 0.193311,	data_time = 0.050652,	train_time = 0.760363
[2019-08-23 14:57:59,981] TRAIN Iter 125640: lr = 0.290602,	loss = 2.691019,	Top-1 err = 0.415381,	Top-5 err = 0.192578,	data_time = 0.050594,	train_time = 0.347989
[2019-08-23 14:58:16,649] TRAIN Iter 125660: lr = 0.290568,	loss = 2.684707,	Top-1 err = 0.416211,	Top-5 err = 0.191895,	data_time = 0.050544,	train_time = 0.833402
[2019-08-23 14:58:24,584] TRAIN Iter 125680: lr = 0.290535,	loss = 2.654927,	Top-1 err = 0.411621,	Top-5 err = 0.190967,	data_time = 0.050892,	train_time = 0.396705
[2019-08-23 14:58:38,725] TRAIN Iter 125700: lr = 0.290502,	loss = 2.685642,	Top-1 err = 0.412305,	Top-5 err = 0.186719,	data_time = 0.050554,	train_time = 0.707059
[2019-08-23 14:58:54,297] TRAIN Iter 125720: lr = 0.290468,	loss = 2.647683,	Top-1 err = 0.416260,	Top-5 err = 0.191992,	data_time = 0.051005,	train_time = 0.778559
[2019-08-23 14:59:01,171] TRAIN Iter 125740: lr = 0.290435,	loss = 2.640905,	Top-1 err = 0.412061,	Top-5 err = 0.191455,	data_time = 0.050776,	train_time = 0.343705
[2019-08-23 14:59:18,271] TRAIN Iter 125760: lr = 0.290402,	loss = 2.704548,	Top-1 err = 0.416699,	Top-5 err = 0.194043,	data_time = 0.050355,	train_time = 0.854960
[2019-08-23 14:59:35,145] TRAIN Iter 125780: lr = 0.290368,	loss = 2.675421,	Top-1 err = 0.413330,	Top-5 err = 0.189111,	data_time = 0.050598,	train_time = 0.843713
[2019-08-23 14:59:42,227] TRAIN Iter 125800: lr = 0.290335,	loss = 2.655317,	Top-1 err = 0.412451,	Top-5 err = 0.191748,	data_time = 0.050352,	train_time = 0.354078
[2019-08-23 14:59:58,943] TRAIN Iter 125820: lr = 0.290302,	loss = 2.674936,	Top-1 err = 0.421094,	Top-5 err = 0.194922,	data_time = 0.050819,	train_time = 0.835776
[2019-08-23 15:00:06,472] TRAIN Iter 125840: lr = 0.290268,	loss = 2.735282,	Top-1 err = 0.421631,	Top-5 err = 0.200537,	data_time = 0.050156,	train_time = 0.376455
[2019-08-23 15:00:21,690] TRAIN Iter 125860: lr = 0.290235,	loss = 2.676292,	Top-1 err = 0.415234,	Top-5 err = 0.197314,	data_time = 0.050476,	train_time = 0.760885
[2019-08-23 15:00:37,679] TRAIN Iter 125880: lr = 0.290202,	loss = 2.713715,	Top-1 err = 0.416650,	Top-5 err = 0.196436,	data_time = 0.050242,	train_time = 0.799450
[2019-08-23 15:00:44,739] TRAIN Iter 125900: lr = 0.290168,	loss = 2.725524,	Top-1 err = 0.415771,	Top-5 err = 0.193652,	data_time = 0.050489,	train_time = 0.352976
[2019-08-23 15:01:01,138] TRAIN Iter 125920: lr = 0.290135,	loss = 2.658087,	Top-1 err = 0.415674,	Top-5 err = 0.195166,	data_time = 0.050263,	train_time = 0.819919
[2019-08-23 15:01:17,317] TRAIN Iter 125940: lr = 0.290102,	loss = 2.720239,	Top-1 err = 0.421045,	Top-5 err = 0.192627,	data_time = 0.050378,	train_time = 0.808917
[2019-08-23 15:01:24,221] TRAIN Iter 125960: lr = 0.290068,	loss = 2.691043,	Top-1 err = 0.414453,	Top-5 err = 0.192188,	data_time = 0.050730,	train_time = 0.345188
[2019-08-23 15:01:41,869] TRAIN Iter 125980: lr = 0.290035,	loss = 2.694704,	Top-1 err = 0.420117,	Top-5 err = 0.197168,	data_time = 0.050551,	train_time = 0.882431
[2019-08-23 15:01:48,736] TRAIN Iter 126000: lr = 0.290002,	loss = 2.712793,	Top-1 err = 0.413867,	Top-5 err = 0.193994,	data_time = 0.050796,	train_time = 0.343336
[2019-08-23 15:02:06,159] TRAIN Iter 126020: lr = 0.289968,	loss = 2.603739,	Top-1 err = 0.416504,	Top-5 err = 0.195459,	data_time = 0.050221,	train_time = 0.871118
[2019-08-23 15:02:24,271] TRAIN Iter 126040: lr = 0.289935,	loss = 2.646925,	Top-1 err = 0.418066,	Top-5 err = 0.198828,	data_time = 0.050656,	train_time = 0.905558
[2019-08-23 15:02:31,043] TRAIN Iter 126060: lr = 0.289902,	loss = 2.719783,	Top-1 err = 0.410498,	Top-5 err = 0.188965,	data_time = 0.050357,	train_time = 0.338593
[2019-08-23 15:02:48,509] TRAIN Iter 126080: lr = 0.289868,	loss = 2.667900,	Top-1 err = 0.417285,	Top-5 err = 0.191895,	data_time = 0.050539,	train_time = 0.873304
[2019-08-23 15:03:05,609] TRAIN Iter 126100: lr = 0.289835,	loss = 2.718006,	Top-1 err = 0.416504,	Top-5 err = 0.194727,	data_time = 0.122419,	train_time = 0.854973
[2019-08-23 15:03:12,739] TRAIN Iter 126120: lr = 0.289802,	loss = 2.660252,	Top-1 err = 0.416309,	Top-5 err = 0.190723,	data_time = 0.050461,	train_time = 0.356501
[2019-08-23 15:03:31,179] TRAIN Iter 126140: lr = 0.289768,	loss = 2.696287,	Top-1 err = 0.412646,	Top-5 err = 0.195508,	data_time = 0.050247,	train_time = 0.921995
[2019-08-23 15:03:37,993] TRAIN Iter 126160: lr = 0.289735,	loss = 2.746854,	Top-1 err = 0.419141,	Top-5 err = 0.196094,	data_time = 0.050485,	train_time = 0.340681
[2019-08-23 15:03:56,375] TRAIN Iter 126180: lr = 0.289702,	loss = 2.772872,	Top-1 err = 0.416064,	Top-5 err = 0.194971,	data_time = 0.050235,	train_time = 0.919097
[2019-08-23 15:04:17,609] TRAIN Iter 126200: lr = 0.289668,	loss = 2.842332,	Top-1 err = 0.415918,	Top-5 err = 0.193115,	data_time = 0.050531,	train_time = 1.061661
[2019-08-23 15:04:24,394] TRAIN Iter 126220: lr = 0.289635,	loss = 2.773649,	Top-1 err = 0.419727,	Top-5 err = 0.196533,	data_time = 0.050556,	train_time = 0.339241
[2019-08-23 15:04:44,819] TRAIN Iter 126240: lr = 0.289602,	loss = 2.644468,	Top-1 err = 0.415820,	Top-5 err = 0.188379,	data_time = 0.050480,	train_time = 1.021231
[2019-08-23 15:05:01,343] TRAIN Iter 126260: lr = 0.289568,	loss = 2.676425,	Top-1 err = 0.416504,	Top-5 err = 0.195850,	data_time = 0.050325,	train_time = 0.826205
[2019-08-23 15:05:08,286] TRAIN Iter 126280: lr = 0.289535,	loss = 2.718744,	Top-1 err = 0.422754,	Top-5 err = 0.199512,	data_time = 0.050426,	train_time = 0.347126
[2019-08-23 15:05:26,640] TRAIN Iter 126300: lr = 0.289502,	loss = 2.699895,	Top-1 err = 0.413818,	Top-5 err = 0.193311,	data_time = 0.050492,	train_time = 0.917673
[2019-08-23 15:05:33,596] TRAIN Iter 126320: lr = 0.289468,	loss = 2.749423,	Top-1 err = 0.423047,	Top-5 err = 0.196826,	data_time = 0.050559,	train_time = 0.347771
[2019-08-23 15:05:52,827] TRAIN Iter 126340: lr = 0.289435,	loss = 2.655539,	Top-1 err = 0.418848,	Top-5 err = 0.196387,	data_time = 0.050015,	train_time = 0.961550
[2019-08-23 15:06:11,743] TRAIN Iter 126360: lr = 0.289402,	loss = 2.693735,	Top-1 err = 0.416553,	Top-5 err = 0.194531,	data_time = 0.049897,	train_time = 0.945803
[2019-08-23 15:06:17,848] TRAIN Iter 126380: lr = 0.289368,	loss = 2.728046,	Top-1 err = 0.420410,	Top-5 err = 0.198437,	data_time = 0.049930,	train_time = 0.305240
[2019-08-23 15:07:10,752] TRAIN Iter 126400: lr = 0.289335,	loss = 2.617663,	Top-1 err = 0.421743,	Top-5 err = 0.205881,	data_time = 0.050774,	train_time = 2.645190
[2019-08-23 15:07:17,974] TRAIN Iter 126420: lr = 0.289302,	loss = 2.722357,	Top-1 err = 0.416943,	Top-5 err = 0.193311,	data_time = 0.149647,	train_time = 0.361071
[2019-08-23 15:07:34,756] TRAIN Iter 126440: lr = 0.289268,	loss = 2.669271,	Top-1 err = 0.402979,	Top-5 err = 0.186914,	data_time = 0.050732,	train_time = 0.839078
[2019-08-23 15:07:51,008] TRAIN Iter 126460: lr = 0.289235,	loss = 2.666697,	Top-1 err = 0.404834,	Top-5 err = 0.189990,	data_time = 0.050265,	train_time = 0.812579
[2019-08-23 15:07:58,452] TRAIN Iter 126480: lr = 0.289202,	loss = 2.635904,	Top-1 err = 0.402490,	Top-5 err = 0.188232,	data_time = 0.050373,	train_time = 0.372170
[2019-08-23 15:08:14,642] TRAIN Iter 126500: lr = 0.289168,	loss = 2.681647,	Top-1 err = 0.407373,	Top-5 err = 0.186035,	data_time = 0.050519,	train_time = 0.809498
[2019-08-23 15:08:31,481] TRAIN Iter 126520: lr = 0.289135,	loss = 2.646799,	Top-1 err = 0.411230,	Top-5 err = 0.188477,	data_time = 0.050546,	train_time = 0.841936
[2019-08-23 15:08:39,021] TRAIN Iter 126540: lr = 0.289102,	loss = 2.677286,	Top-1 err = 0.413037,	Top-5 err = 0.192236,	data_time = 0.050459,	train_time = 0.376992
[2019-08-23 15:08:51,487] TRAIN Iter 126560: lr = 0.289068,	loss = 2.699966,	Top-1 err = 0.407324,	Top-5 err = 0.187305,	data_time = 0.050286,	train_time = 0.623282
[2019-08-23 15:08:58,906] TRAIN Iter 126580: lr = 0.289035,	loss = 2.761627,	Top-1 err = 0.410645,	Top-5 err = 0.191260,	data_time = 0.050686,	train_time = 0.370956
[2019-08-23 15:09:13,175] TRAIN Iter 126600: lr = 0.289002,	loss = 2.553087,	Top-1 err = 0.407764,	Top-5 err = 0.186572,	data_time = 0.050426,	train_time = 0.713415
[2019-08-23 15:09:28,520] TRAIN Iter 126620: lr = 0.288968,	loss = 2.749582,	Top-1 err = 0.414844,	Top-5 err = 0.191846,	data_time = 0.147746,	train_time = 0.767246
[2019-08-23 15:09:35,474] TRAIN Iter 126640: lr = 0.288935,	loss = 2.741117,	Top-1 err = 0.417236,	Top-5 err = 0.189893,	data_time = 0.050282,	train_time = 0.347694
[2019-08-23 15:09:49,186] TRAIN Iter 126660: lr = 0.288902,	loss = 2.703335,	Top-1 err = 0.413770,	Top-5 err = 0.187842,	data_time = 0.050329,	train_time = 0.685573
[2019-08-23 15:10:04,078] TRAIN Iter 126680: lr = 0.288868,	loss = 2.749734,	Top-1 err = 0.415576,	Top-5 err = 0.189453,	data_time = 0.050603,	train_time = 0.744575
[2019-08-23 15:10:11,004] TRAIN Iter 126700: lr = 0.288835,	loss = 2.792600,	Top-1 err = 0.413574,	Top-5 err = 0.193555,	data_time = 0.050735,	train_time = 0.346303
[2019-08-23 15:10:28,496] TRAIN Iter 126720: lr = 0.288802,	loss = 2.649251,	Top-1 err = 0.411426,	Top-5 err = 0.190137,	data_time = 0.050377,	train_time = 0.874586
[2019-08-23 15:10:36,075] TRAIN Iter 126740: lr = 0.288768,	loss = 2.672256,	Top-1 err = 0.409570,	Top-5 err = 0.190479,	data_time = 0.050694,	train_time = 0.378899
[2019-08-23 15:10:49,865] TRAIN Iter 126760: lr = 0.288735,	loss = 2.740839,	Top-1 err = 0.411230,	Top-5 err = 0.188672,	data_time = 0.050434,	train_time = 0.689521
[2019-08-23 15:11:06,593] TRAIN Iter 126780: lr = 0.288702,	loss = 2.705960,	Top-1 err = 0.416504,	Top-5 err = 0.190186,	data_time = 0.050357,	train_time = 0.836385
[2019-08-23 15:11:13,722] TRAIN Iter 126800: lr = 0.288668,	loss = 2.643138,	Top-1 err = 0.412305,	Top-5 err = 0.188965,	data_time = 0.050542,	train_time = 0.356444
[2019-08-23 15:11:27,763] TRAIN Iter 126820: lr = 0.288635,	loss = 2.716444,	Top-1 err = 0.408984,	Top-5 err = 0.190283,	data_time = 0.050461,	train_time = 0.702019
[2019-08-23 15:11:43,659] TRAIN Iter 126840: lr = 0.288602,	loss = 2.696193,	Top-1 err = 0.409766,	Top-5 err = 0.188916,	data_time = 0.050498,	train_time = 0.794794
[2019-08-23 15:11:50,869] TRAIN Iter 126860: lr = 0.288568,	loss = 2.683506,	Top-1 err = 0.420020,	Top-5 err = 0.191455,	data_time = 0.050429,	train_time = 0.360470
[2019-08-23 15:12:07,002] TRAIN Iter 126880: lr = 0.288535,	loss = 2.627892,	Top-1 err = 0.406982,	Top-5 err = 0.188184,	data_time = 0.050595,	train_time = 0.806630
[2019-08-23 15:12:13,586] TRAIN Iter 126900: lr = 0.288502,	loss = 2.671592,	Top-1 err = 0.410107,	Top-5 err = 0.186523,	data_time = 0.050439,	train_time = 0.329202
[2019-08-23 15:12:32,375] TRAIN Iter 126920: lr = 0.288468,	loss = 2.705571,	Top-1 err = 0.409912,	Top-5 err = 0.192773,	data_time = 0.050465,	train_time = 0.939448
[2019-08-23 15:12:47,580] TRAIN Iter 126940: lr = 0.288435,	loss = 2.700535,	Top-1 err = 0.415918,	Top-5 err = 0.193262,	data_time = 0.117908,	train_time = 0.760211
[2019-08-23 15:12:54,473] TRAIN Iter 126960: lr = 0.288402,	loss = 2.680344,	Top-1 err = 0.416455,	Top-5 err = 0.193555,	data_time = 0.050914,	train_time = 0.344625
[2019-08-23 15:13:09,720] TRAIN Iter 126980: lr = 0.288368,	loss = 2.676821,	Top-1 err = 0.426318,	Top-5 err = 0.202393,	data_time = 0.050547,	train_time = 0.762345
[2019-08-23 15:13:26,517] TRAIN Iter 127000: lr = 0.288335,	loss = 2.775700,	Top-1 err = 0.412207,	Top-5 err = 0.194434,	data_time = 0.050246,	train_time = 0.839872
[2019-08-23 15:13:33,692] TRAIN Iter 127020: lr = 0.288302,	loss = 2.718683,	Top-1 err = 0.417334,	Top-5 err = 0.197168,	data_time = 0.050361,	train_time = 0.358738
[2019-08-23 15:13:50,085] TRAIN Iter 127040: lr = 0.288268,	loss = 2.722877,	Top-1 err = 0.409570,	Top-5 err = 0.191992,	data_time = 0.050553,	train_time = 0.819628
[2019-08-23 15:13:57,122] TRAIN Iter 127060: lr = 0.288235,	loss = 2.734142,	Top-1 err = 0.418213,	Top-5 err = 0.196826,	data_time = 0.050650,	train_time = 0.351807
[2019-08-23 15:14:13,137] TRAIN Iter 127080: lr = 0.288202,	loss = 2.772418,	Top-1 err = 0.415234,	Top-5 err = 0.192090,	data_time = 0.050567,	train_time = 0.800763
[2019-08-23 15:14:30,421] TRAIN Iter 127100: lr = 0.288168,	loss = 2.769424,	Top-1 err = 0.412988,	Top-5 err = 0.195312,	data_time = 0.050752,	train_time = 0.864176
[2019-08-23 15:14:37,464] TRAIN Iter 127120: lr = 0.288135,	loss = 2.640238,	Top-1 err = 0.419043,	Top-5 err = 0.189941,	data_time = 0.050399,	train_time = 0.352141
[2019-08-23 15:14:54,378] TRAIN Iter 127140: lr = 0.288102,	loss = 2.759197,	Top-1 err = 0.412451,	Top-5 err = 0.192725,	data_time = 0.050456,	train_time = 0.845664
[2019-08-23 15:15:07,900] TRAIN Iter 127160: lr = 0.288068,	loss = 2.749801,	Top-1 err = 0.417578,	Top-5 err = 0.195752,	data_time = 0.050573,	train_time = 0.676090
[2019-08-23 15:15:18,337] TRAIN Iter 127180: lr = 0.288035,	loss = 2.721140,	Top-1 err = 0.407275,	Top-5 err = 0.188379,	data_time = 0.050489,	train_time = 0.521838
[2019-08-23 15:15:34,830] TRAIN Iter 127200: lr = 0.288002,	loss = 2.699124,	Top-1 err = 0.413770,	Top-5 err = 0.191699,	data_time = 0.050529,	train_time = 0.824638
[2019-08-23 15:15:42,125] TRAIN Iter 127220: lr = 0.287968,	loss = 2.621971,	Top-1 err = 0.412500,	Top-5 err = 0.191016,	data_time = 0.050578,	train_time = 0.364769
[2019-08-23 15:15:58,269] TRAIN Iter 127240: lr = 0.287935,	loss = 2.623018,	Top-1 err = 0.417139,	Top-5 err = 0.193848,	data_time = 0.050626,	train_time = 0.807174
[2019-08-23 15:16:16,349] TRAIN Iter 127260: lr = 0.287902,	loss = 2.699082,	Top-1 err = 0.417383,	Top-5 err = 0.194092,	data_time = 0.050634,	train_time = 0.903985
[2019-08-23 15:16:23,199] TRAIN Iter 127280: lr = 0.287868,	loss = 2.596770,	Top-1 err = 0.416504,	Top-5 err = 0.197266,	data_time = 0.050399,	train_time = 0.342471
[2019-08-23 15:16:39,989] TRAIN Iter 127300: lr = 0.287835,	loss = 2.702815,	Top-1 err = 0.416553,	Top-5 err = 0.188037,	data_time = 0.050408,	train_time = 0.839472
[2019-08-23 15:16:54,000] TRAIN Iter 127320: lr = 0.287802,	loss = 2.735277,	Top-1 err = 0.411621,	Top-5 err = 0.192383,	data_time = 0.050503,	train_time = 0.700563
[2019-08-23 15:17:03,956] TRAIN Iter 127340: lr = 0.287768,	loss = 2.664462,	Top-1 err = 0.412646,	Top-5 err = 0.191357,	data_time = 0.050519,	train_time = 0.497769
[2019-08-23 15:17:21,356] TRAIN Iter 127360: lr = 0.287735,	loss = 2.685683,	Top-1 err = 0.416699,	Top-5 err = 0.191602,	data_time = 0.050300,	train_time = 0.869968
[2019-08-23 15:17:28,393] TRAIN Iter 127380: lr = 0.287702,	loss = 2.727604,	Top-1 err = 0.414697,	Top-5 err = 0.194385,	data_time = 0.050666,	train_time = 0.351858
[2019-08-23 15:17:46,846] TRAIN Iter 127400: lr = 0.287668,	loss = 2.707455,	Top-1 err = 0.416602,	Top-5 err = 0.196240,	data_time = 0.050462,	train_time = 0.922614
[2019-08-23 15:18:06,323] TRAIN Iter 127420: lr = 0.287635,	loss = 2.741349,	Top-1 err = 0.410107,	Top-5 err = 0.197363,	data_time = 0.050093,	train_time = 0.973858
[2019-08-23 15:18:13,141] TRAIN Iter 127440: lr = 0.287602,	loss = 2.794526,	Top-1 err = 0.422607,	Top-5 err = 0.201563,	data_time = 0.050367,	train_time = 0.340868
[2019-08-23 15:18:30,089] TRAIN Iter 127460: lr = 0.287568,	loss = 2.732258,	Top-1 err = 0.413623,	Top-5 err = 0.195703,	data_time = 0.050366,	train_time = 0.847419
[2019-08-23 15:18:41,732] TRAIN Iter 127480: lr = 0.287535,	loss = 2.748237,	Top-1 err = 0.414014,	Top-5 err = 0.195557,	data_time = 0.693756,	train_time = 0.582105
[2019-08-23 15:18:55,193] TRAIN Iter 127500: lr = 0.287502,	loss = 2.657659,	Top-1 err = 0.413037,	Top-5 err = 0.194189,	data_time = 0.050425,	train_time = 0.673068
[2019-08-23 15:19:13,185] TRAIN Iter 127520: lr = 0.287468,	loss = 2.633682,	Top-1 err = 0.416211,	Top-5 err = 0.195312,	data_time = 0.050383,	train_time = 0.899584
[2019-08-23 15:19:19,793] TRAIN Iter 127540: lr = 0.287435,	loss = 2.586945,	Top-1 err = 0.414355,	Top-5 err = 0.191406,	data_time = 0.050516,	train_time = 0.330355
[2019-08-23 15:19:39,165] TRAIN Iter 127560: lr = 0.287402,	loss = 2.650920,	Top-1 err = 0.416602,	Top-5 err = 0.192676,	data_time = 0.050345,	train_time = 0.968598
[2019-08-23 15:19:58,161] TRAIN Iter 127580: lr = 0.287368,	loss = 2.782192,	Top-1 err = 0.416992,	Top-5 err = 0.196191,	data_time = 0.050094,	train_time = 0.949777
[2019-08-23 15:20:04,916] TRAIN Iter 127600: lr = 0.287335,	loss = 2.645992,	Top-1 err = 0.408887,	Top-5 err = 0.194531,	data_time = 0.050148,	train_time = 0.337775
[2019-08-23 15:20:21,643] TRAIN Iter 127620: lr = 0.287302,	loss = 2.762119,	Top-1 err = 0.420068,	Top-5 err = 0.195898,	data_time = 0.049906,	train_time = 0.836330
[2019-08-23 15:20:27,850] TRAIN Iter 127640: lr = 0.287268,	loss = 3.140789,	Top-1 err = 0.421643,	Top-5 err = 0.197435,	data_time = 0.007135,	train_time = 0.310338
[2019-08-23 15:21:16,750] TRAIN Iter 127660: lr = 0.287235,	loss = 2.712929,	Top-1 err = 0.410010,	Top-5 err = 0.192725,	data_time = 0.050470,	train_time = 2.444987
[2019-08-23 15:21:31,784] TRAIN Iter 127680: lr = 0.287202,	loss = 2.621197,	Top-1 err = 0.408691,	Top-5 err = 0.189209,	data_time = 0.050617,	train_time = 0.751667
[2019-08-23 15:21:39,323] TRAIN Iter 127700: lr = 0.287168,	loss = 2.656619,	Top-1 err = 0.409668,	Top-5 err = 0.186084,	data_time = 0.050384,	train_time = 0.376916
[2019-08-23 15:21:51,888] TRAIN Iter 127720: lr = 0.287135,	loss = 2.605829,	Top-1 err = 0.407861,	Top-5 err = 0.185107,	data_time = 0.050555,	train_time = 0.628218
[2019-08-23 15:22:06,823] TRAIN Iter 127740: lr = 0.287102,	loss = 2.696491,	Top-1 err = 0.409180,	Top-5 err = 0.187012,	data_time = 0.116602,	train_time = 0.746765
[2019-08-23 15:22:13,736] TRAIN Iter 127760: lr = 0.287068,	loss = 2.640794,	Top-1 err = 0.411572,	Top-5 err = 0.192383,	data_time = 0.050772,	train_time = 0.345629
[2019-08-23 15:22:29,370] TRAIN Iter 127780: lr = 0.287035,	loss = 2.664025,	Top-1 err = 0.408838,	Top-5 err = 0.192578,	data_time = 0.050236,	train_time = 0.781689
[2019-08-23 15:22:36,469] TRAIN Iter 127800: lr = 0.287002,	loss = 2.672684,	Top-1 err = 0.407471,	Top-5 err = 0.190674,	data_time = 0.050990,	train_time = 0.354948
[2019-08-23 15:22:51,993] TRAIN Iter 127820: lr = 0.286968,	loss = 2.655780,	Top-1 err = 0.414062,	Top-5 err = 0.191553,	data_time = 0.050626,	train_time = 0.776158
[2019-08-23 15:23:08,862] TRAIN Iter 127840: lr = 0.286935,	loss = 2.591759,	Top-1 err = 0.405762,	Top-5 err = 0.187305,	data_time = 0.090341,	train_time = 0.843450
[2019-08-23 15:23:15,306] TRAIN Iter 127860: lr = 0.286902,	loss = 2.648992,	Top-1 err = 0.412842,	Top-5 err = 0.188281,	data_time = 0.050512,	train_time = 0.322156
[2019-08-23 15:23:32,025] TRAIN Iter 127880: lr = 0.286868,	loss = 2.716261,	Top-1 err = 0.410547,	Top-5 err = 0.189014,	data_time = 0.050506,	train_time = 0.835956
[2019-08-23 15:23:49,385] TRAIN Iter 127900: lr = 0.286835,	loss = 2.638369,	Top-1 err = 0.407813,	Top-5 err = 0.184424,	data_time = 0.050715,	train_time = 0.868006
[2019-08-23 15:23:55,925] TRAIN Iter 127920: lr = 0.286802,	loss = 2.729867,	Top-1 err = 0.415674,	Top-5 err = 0.193359,	data_time = 0.050271,	train_time = 0.326969
[2019-08-23 15:24:11,955] TRAIN Iter 127940: lr = 0.286768,	loss = 2.618198,	Top-1 err = 0.408838,	Top-5 err = 0.190576,	data_time = 0.050352,	train_time = 0.801488
[2019-08-23 15:24:19,492] TRAIN Iter 127960: lr = 0.286735,	loss = 2.605192,	Top-1 err = 0.413086,	Top-5 err = 0.191455,	data_time = 0.050520,	train_time = 0.376814
[2019-08-23 15:24:34,151] TRAIN Iter 127980: lr = 0.286702,	loss = 2.706399,	Top-1 err = 0.411377,	Top-5 err = 0.195264,	data_time = 0.050339,	train_time = 0.732946
[2019-08-23 15:24:51,087] TRAIN Iter 128000: lr = 0.286668,	loss = 2.567118,	Top-1 err = 0.411133,	Top-5 err = 0.187744,	data_time = 0.050567,	train_time = 0.846795
[2019-08-23 15:24:58,304] TRAIN Iter 128020: lr = 0.286635,	loss = 2.713093,	Top-1 err = 0.413867,	Top-5 err = 0.190771,	data_time = 0.102996,	train_time = 0.360859
[2019-08-23 15:25:14,541] TRAIN Iter 128040: lr = 0.286602,	loss = 2.710291,	Top-1 err = 0.409863,	Top-5 err = 0.191162,	data_time = 0.050346,	train_time = 0.811790
[2019-08-23 15:25:31,123] TRAIN Iter 128060: lr = 0.286568,	loss = 2.669759,	Top-1 err = 0.410596,	Top-5 err = 0.184619,	data_time = 0.052423,	train_time = 0.829119
[2019-08-23 15:25:37,777] TRAIN Iter 128080: lr = 0.286535,	loss = 2.601497,	Top-1 err = 0.413721,	Top-5 err = 0.190674,	data_time = 0.050317,	train_time = 0.332692
[2019-08-23 15:25:54,443] TRAIN Iter 128100: lr = 0.286502,	loss = 2.673569,	Top-1 err = 0.407568,	Top-5 err = 0.190234,	data_time = 0.050821,	train_time = 0.833271
[2019-08-23 15:26:01,796] TRAIN Iter 128120: lr = 0.286468,	loss = 2.666413,	Top-1 err = 0.408154,	Top-5 err = 0.189160,	data_time = 0.050322,	train_time = 0.367651
[2019-08-23 15:26:18,243] TRAIN Iter 128140: lr = 0.286435,	loss = 2.659256,	Top-1 err = 0.410059,	Top-5 err = 0.192871,	data_time = 0.050704,	train_time = 0.822333
[2019-08-23 15:26:34,151] TRAIN Iter 128160: lr = 0.286402,	loss = 2.722429,	Top-1 err = 0.417187,	Top-5 err = 0.192627,	data_time = 0.050449,	train_time = 0.795383
[2019-08-23 15:26:41,168] TRAIN Iter 128180: lr = 0.286368,	loss = 2.697806,	Top-1 err = 0.409814,	Top-5 err = 0.191602,	data_time = 0.050261,	train_time = 0.350835
[2019-08-23 15:26:58,012] TRAIN Iter 128200: lr = 0.286335,	loss = 2.657276,	Top-1 err = 0.416211,	Top-5 err = 0.188965,	data_time = 0.050712,	train_time = 0.842167
[2019-08-23 15:27:15,519] TRAIN Iter 128220: lr = 0.286302,	loss = 2.702662,	Top-1 err = 0.415283,	Top-5 err = 0.190918,	data_time = 0.050330,	train_time = 0.875369
[2019-08-23 15:27:22,075] TRAIN Iter 128240: lr = 0.286268,	loss = 2.702828,	Top-1 err = 0.405908,	Top-5 err = 0.190869,	data_time = 0.125111,	train_time = 0.327775
[2019-08-23 15:27:38,677] TRAIN Iter 128260: lr = 0.286235,	loss = 2.632282,	Top-1 err = 0.409473,	Top-5 err = 0.189990,	data_time = 0.050534,	train_time = 0.830071
[2019-08-23 15:27:45,991] TRAIN Iter 128280: lr = 0.286202,	loss = 2.728033,	Top-1 err = 0.411230,	Top-5 err = 0.190137,	data_time = 0.093761,	train_time = 0.365671
[2019-08-23 15:28:00,260] TRAIN Iter 128300: lr = 0.286168,	loss = 2.719715,	Top-1 err = 0.411768,	Top-5 err = 0.190625,	data_time = 0.050353,	train_time = 0.713440
[2019-08-23 15:28:20,341] TRAIN Iter 128320: lr = 0.286135,	loss = 2.682603,	Top-1 err = 0.417725,	Top-5 err = 0.194336,	data_time = 0.050490,	train_time = 1.004028
[2019-08-23 15:28:26,660] TRAIN Iter 128340: lr = 0.286102,	loss = 2.666883,	Top-1 err = 0.411719,	Top-5 err = 0.188818,	data_time = 0.050269,	train_time = 0.315934
[2019-08-23 15:28:47,413] TRAIN Iter 128360: lr = 0.286068,	loss = 2.650146,	Top-1 err = 0.416113,	Top-5 err = 0.192139,	data_time = 0.050354,	train_time = 1.037659
[2019-08-23 15:29:07,526] TRAIN Iter 128380: lr = 0.286035,	loss = 2.601616,	Top-1 err = 0.414355,	Top-5 err = 0.188818,	data_time = 0.050773,	train_time = 1.005632
[2019-08-23 15:29:13,825] TRAIN Iter 128400: lr = 0.286002,	loss = 2.822317,	Top-1 err = 0.417822,	Top-5 err = 0.189746,	data_time = 0.050359,	train_time = 0.314961
[2019-08-23 15:29:34,004] TRAIN Iter 128420: lr = 0.285968,	loss = 2.582520,	Top-1 err = 0.412402,	Top-5 err = 0.191162,	data_time = 0.050598,	train_time = 1.008940
[2019-08-23 15:29:41,095] TRAIN Iter 128440: lr = 0.285935,	loss = 2.721180,	Top-1 err = 0.415479,	Top-5 err = 0.190186,	data_time = 0.050321,	train_time = 0.354509
[2019-08-23 15:29:57,165] TRAIN Iter 128460: lr = 0.285902,	loss = 2.646081,	Top-1 err = 0.414355,	Top-5 err = 0.192676,	data_time = 0.050195,	train_time = 0.803481
[2019-08-23 15:30:15,301] TRAIN Iter 128480: lr = 0.285868,	loss = 2.690656,	Top-1 err = 0.415332,	Top-5 err = 0.192578,	data_time = 0.050260,	train_time = 0.906785
[2019-08-23 15:30:21,674] TRAIN Iter 128500: lr = 0.285835,	loss = 2.675012,	Top-1 err = 0.410010,	Top-5 err = 0.190039,	data_time = 0.050392,	train_time = 0.318634
[2019-08-23 15:30:40,843] TRAIN Iter 128520: lr = 0.285802,	loss = 2.652628,	Top-1 err = 0.417285,	Top-5 err = 0.193311,	data_time = 0.050488,	train_time = 0.958430
[2019-08-23 15:30:58,967] TRAIN Iter 128540: lr = 0.285768,	loss = 2.744423,	Top-1 err = 0.415479,	Top-5 err = 0.194775,	data_time = 0.050293,	train_time = 0.906218
[2019-08-23 15:31:05,516] TRAIN Iter 128560: lr = 0.285735,	loss = 2.712438,	Top-1 err = 0.415186,	Top-5 err = 0.198828,	data_time = 0.050388,	train_time = 0.327433
[2019-08-23 15:31:25,705] TRAIN Iter 128580: lr = 0.285702,	loss = 2.722800,	Top-1 err = 0.413477,	Top-5 err = 0.192480,	data_time = 0.050481,	train_time = 1.009430
[2019-08-23 15:31:32,239] TRAIN Iter 128600: lr = 0.285668,	loss = 2.600124,	Top-1 err = 0.407617,	Top-5 err = 0.188086,	data_time = 0.050289,	train_time = 0.326706
[2019-08-23 15:31:50,802] TRAIN Iter 128620: lr = 0.285635,	loss = 2.631389,	Top-1 err = 0.417773,	Top-5 err = 0.197363,	data_time = 0.050509,	train_time = 0.928126
[2019-08-23 15:32:11,065] TRAIN Iter 128640: lr = 0.285602,	loss = 2.751760,	Top-1 err = 0.417139,	Top-5 err = 0.190771,	data_time = 0.050345,	train_time = 1.013147
[2019-08-23 15:32:17,548] TRAIN Iter 128660: lr = 0.285568,	loss = 2.696254,	Top-1 err = 0.415771,	Top-5 err = 0.194482,	data_time = 0.050128,	train_time = 0.324128
[2019-08-23 15:32:37,334] TRAIN Iter 128680: lr = 0.285535,	loss = 2.799908,	Top-1 err = 0.421484,	Top-5 err = 0.197168,	data_time = 0.050743,	train_time = 0.989301
[2019-08-23 15:32:59,702] TRAIN Iter 128700: lr = 0.285502,	loss = 2.690970,	Top-1 err = 0.417822,	Top-5 err = 0.194775,	data_time = 0.050395,	train_time = 1.118341
[2019-08-23 15:33:06,191] TRAIN Iter 128720: lr = 0.285468,	loss = 2.785969,	Top-1 err = 0.420264,	Top-5 err = 0.195215,	data_time = 0.050180,	train_time = 0.324449
[2019-08-23 15:33:26,389] TRAIN Iter 128740: lr = 0.285435,	loss = 2.736746,	Top-1 err = 0.418408,	Top-5 err = 0.192822,	data_time = 0.050212,	train_time = 1.009898
[2019-08-23 15:33:33,595] TRAIN Iter 128760: lr = 0.285402,	loss = 2.635448,	Top-1 err = 0.412988,	Top-5 err = 0.194580,	data_time = 0.050623,	train_time = 0.360268
[2019-08-23 15:33:50,859] TRAIN Iter 128780: lr = 0.285368,	loss = 2.647585,	Top-1 err = 0.414258,	Top-5 err = 0.190576,	data_time = 0.050818,	train_time = 0.863200
[2019-08-23 15:34:09,651] TRAIN Iter 128800: lr = 0.285335,	loss = 2.706691,	Top-1 err = 0.414258,	Top-5 err = 0.192188,	data_time = 0.050519,	train_time = 0.939574
[2019-08-23 15:34:16,009] TRAIN Iter 128820: lr = 0.285302,	loss = 2.713603,	Top-1 err = 0.415820,	Top-5 err = 0.195850,	data_time = 0.050332,	train_time = 0.317878
[2019-08-23 15:34:34,249] TRAIN Iter 128840: lr = 0.285268,	loss = 2.677788,	Top-1 err = 0.418359,	Top-5 err = 0.196338,	data_time = 0.050064,	train_time = 0.912033
[2019-08-23 15:34:54,236] TRAIN Iter 128860: lr = 0.285235,	loss = 2.767338,	Top-1 err = 0.413281,	Top-5 err = 0.196533,	data_time = 0.050008,	train_time = 0.999307
[2019-08-23 15:35:01,166] TRAIN Iter 128880: lr = 0.285202,	loss = 2.649888,	Top-1 err = 0.417627,	Top-5 err = 0.194482,	data_time = 0.050041,	train_time = 0.346512
[2019-08-23 15:35:57,317] TRAIN Iter 128900: lr = 0.285168,	loss = 2.744386,	Top-1 err = 0.425068,	Top-5 err = 0.196816,	data_time = 0.050430,	train_time = 2.807510
[2019-08-23 15:36:03,885] TRAIN Iter 128920: lr = 0.285135,	loss = 2.673319,	Top-1 err = 0.408691,	Top-5 err = 0.192432,	data_time = 0.050515,	train_time = 0.328388
[2019-08-23 15:36:20,389] TRAIN Iter 128940: lr = 0.285102,	loss = 2.571948,	Top-1 err = 0.408398,	Top-5 err = 0.186768,	data_time = 0.050256,	train_time = 0.825138
[2019-08-23 15:36:35,582] TRAIN Iter 128960: lr = 0.285068,	loss = 2.724976,	Top-1 err = 0.406494,	Top-5 err = 0.186621,	data_time = 2.071549,	train_time = 0.759669
[2019-08-23 15:36:43,589] TRAIN Iter 128980: lr = 0.285035,	loss = 2.629164,	Top-1 err = 0.404736,	Top-5 err = 0.183105,	data_time = 0.050492,	train_time = 0.400340
[2019-08-23 15:37:03,587] TRAIN Iter 129000: lr = 0.285002,	loss = 2.696691,	Top-1 err = 0.408252,	Top-5 err = 0.190039,	data_time = 0.050417,	train_time = 0.999857
[2019-08-23 15:37:11,114] TRAIN Iter 129020: lr = 0.284968,	loss = 2.622550,	Top-1 err = 0.407861,	Top-5 err = 0.189893,	data_time = 0.127451,	train_time = 0.376371
[2019-08-23 15:37:26,448] TRAIN Iter 129040: lr = 0.284935,	loss = 2.689412,	Top-1 err = 0.404395,	Top-5 err = 0.185498,	data_time = 0.050423,	train_time = 0.766673
[2019-08-23 15:37:43,831] TRAIN Iter 129060: lr = 0.284902,	loss = 2.702986,	Top-1 err = 0.403955,	Top-5 err = 0.186279,	data_time = 0.050630,	train_time = 0.869117
[2019-08-23 15:37:50,389] TRAIN Iter 129080: lr = 0.284868,	loss = 2.781801,	Top-1 err = 0.405420,	Top-5 err = 0.185742,	data_time = 0.050581,	train_time = 0.327914
[2019-08-23 15:38:07,162] TRAIN Iter 129100: lr = 0.284835,	loss = 2.683247,	Top-1 err = 0.405322,	Top-5 err = 0.188281,	data_time = 0.050322,	train_time = 0.838625
[2019-08-23 15:38:24,531] TRAIN Iter 129120: lr = 0.284802,	loss = 2.634883,	Top-1 err = 0.407666,	Top-5 err = 0.185010,	data_time = 0.140841,	train_time = 0.868436
[2019-08-23 15:38:31,643] TRAIN Iter 129140: lr = 0.284768,	loss = 2.755099,	Top-1 err = 0.416309,	Top-5 err = 0.192480,	data_time = 0.050284,	train_time = 0.355602
[2019-08-23 15:38:47,630] TRAIN Iter 129160: lr = 0.284735,	loss = 2.659717,	Top-1 err = 0.403174,	Top-5 err = 0.183838,	data_time = 0.050335,	train_time = 0.799313
[2019-08-23 15:38:54,224] TRAIN Iter 129180: lr = 0.284702,	loss = 2.738652,	Top-1 err = 0.414111,	Top-5 err = 0.192578,	data_time = 0.050395,	train_time = 0.329698
[2019-08-23 15:39:11,470] TRAIN Iter 129200: lr = 0.284668,	loss = 2.740445,	Top-1 err = 0.405713,	Top-5 err = 0.186816,	data_time = 0.050381,	train_time = 0.862280
[2019-08-23 15:39:26,399] TRAIN Iter 129220: lr = 0.284635,	loss = 2.707066,	Top-1 err = 0.412207,	Top-5 err = 0.191650,	data_time = 0.143070,	train_time = 0.746446
[2019-08-23 15:39:33,344] TRAIN Iter 129240: lr = 0.284602,	loss = 2.725436,	Top-1 err = 0.416650,	Top-5 err = 0.196973,	data_time = 0.050197,	train_time = 0.347236
[2019-08-23 15:39:50,061] TRAIN Iter 129260: lr = 0.284568,	loss = 2.586019,	Top-1 err = 0.407617,	Top-5 err = 0.188184,	data_time = 0.050162,	train_time = 0.835842
[2019-08-23 15:40:07,929] TRAIN Iter 129280: lr = 0.284535,	loss = 2.624652,	Top-1 err = 0.413770,	Top-5 err = 0.190039,	data_time = 0.050627,	train_time = 0.893348
[2019-08-23 15:40:17,777] TRAIN Iter 129300: lr = 0.284502,	loss = 2.586554,	Top-1 err = 0.405859,	Top-5 err = 0.186768,	data_time = 0.050432,	train_time = 0.492413
[2019-08-23 15:40:36,712] TRAIN Iter 129320: lr = 0.284468,	loss = 2.617719,	Top-1 err = 0.414844,	Top-5 err = 0.191846,	data_time = 0.050510,	train_time = 0.946710
[2019-08-23 15:40:43,555] TRAIN Iter 129340: lr = 0.284435,	loss = 2.650719,	Top-1 err = 0.414209,	Top-5 err = 0.191211,	data_time = 0.050296,	train_time = 0.342140
[2019-08-23 15:41:00,277] TRAIN Iter 129360: lr = 0.284402,	loss = 2.723511,	Top-1 err = 0.415527,	Top-5 err = 0.191064,	data_time = 0.050641,	train_time = 0.836076
[2019-08-23 15:41:14,290] TRAIN Iter 129380: lr = 0.284368,	loss = 2.704933,	Top-1 err = 0.418164,	Top-5 err = 0.190430,	data_time = 0.125846,	train_time = 0.700634
[2019-08-23 15:41:24,999] TRAIN Iter 129400: lr = 0.284335,	loss = 2.555670,	Top-1 err = 0.409131,	Top-5 err = 0.186963,	data_time = 0.050381,	train_time = 0.535455
[2019-08-23 15:41:43,987] TRAIN Iter 129420: lr = 0.284302,	loss = 2.748518,	Top-1 err = 0.414355,	Top-5 err = 0.192529,	data_time = 0.050528,	train_time = 0.949401
[2019-08-23 15:41:55,743] TRAIN Iter 129440: lr = 0.284268,	loss = 2.645935,	Top-1 err = 0.412061,	Top-5 err = 0.195850,	data_time = 0.050673,	train_time = 0.587773
[2019-08-23 15:42:09,423] TRAIN Iter 129460: lr = 0.284235,	loss = 2.763463,	Top-1 err = 0.417480,	Top-5 err = 0.193213,	data_time = 0.050586,	train_time = 0.683994
[2019-08-23 15:42:28,490] TRAIN Iter 129480: lr = 0.284202,	loss = 2.741769,	Top-1 err = 0.407227,	Top-5 err = 0.191797,	data_time = 0.050394,	train_time = 0.953313
[2019-08-23 15:42:35,493] TRAIN Iter 129500: lr = 0.284168,	loss = 2.625484,	Top-1 err = 0.411670,	Top-5 err = 0.193018,	data_time = 0.050536,	train_time = 0.350170
[2019-08-23 15:42:52,102] TRAIN Iter 129520: lr = 0.284135,	loss = 2.632811,	Top-1 err = 0.410352,	Top-5 err = 0.190234,	data_time = 0.050472,	train_time = 0.830427
[2019-08-23 15:43:06,190] TRAIN Iter 129540: lr = 0.284102,	loss = 2.699713,	Top-1 err = 0.415381,	Top-5 err = 0.196094,	data_time = 0.050604,	train_time = 0.704392
[2019-08-23 15:43:14,772] TRAIN Iter 129560: lr = 0.284068,	loss = 2.666584,	Top-1 err = 0.411426,	Top-5 err = 0.194092,	data_time = 0.050474,	train_time = 0.429081
[2019-08-23 15:43:32,836] TRAIN Iter 129580: lr = 0.284035,	loss = 2.687333,	Top-1 err = 0.409082,	Top-5 err = 0.187451,	data_time = 0.050394,	train_time = 0.903161
[2019-08-23 15:43:45,915] TRAIN Iter 129600: lr = 0.284002,	loss = 2.664574,	Top-1 err = 0.418164,	Top-5 err = 0.195264,	data_time = 0.149726,	train_time = 0.653945
[2019-08-23 15:43:56,103] TRAIN Iter 129620: lr = 0.283968,	loss = 2.669081,	Top-1 err = 0.409277,	Top-5 err = 0.189551,	data_time = 0.050501,	train_time = 0.509378
[2019-08-23 15:44:13,796] TRAIN Iter 129640: lr = 0.283935,	loss = 2.698108,	Top-1 err = 0.414746,	Top-5 err = 0.193262,	data_time = 0.050622,	train_time = 0.884658
[2019-08-23 15:44:20,448] TRAIN Iter 129660: lr = 0.283902,	loss = 2.731241,	Top-1 err = 0.414648,	Top-5 err = 0.194678,	data_time = 0.050365,	train_time = 0.332604
[2019-08-23 15:44:38,525] TRAIN Iter 129680: lr = 0.283868,	loss = 2.682921,	Top-1 err = 0.408936,	Top-5 err = 0.188379,	data_time = 0.050607,	train_time = 0.903794
[2019-08-23 15:44:51,181] TRAIN Iter 129700: lr = 0.283835,	loss = 2.660748,	Top-1 err = 0.411670,	Top-5 err = 0.191943,	data_time = 0.150314,	train_time = 0.632806
[2019-08-23 15:45:01,477] TRAIN Iter 129720: lr = 0.283802,	loss = 2.669520,	Top-1 err = 0.414844,	Top-5 err = 0.193213,	data_time = 0.050354,	train_time = 0.514794
[2019-08-23 15:45:19,334] TRAIN Iter 129740: lr = 0.283768,	loss = 2.658875,	Top-1 err = 0.415039,	Top-5 err = 0.193799,	data_time = 0.050507,	train_time = 0.892820
[2019-08-23 15:45:33,467] TRAIN Iter 129760: lr = 0.283735,	loss = 2.693206,	Top-1 err = 0.410986,	Top-5 err = 0.191895,	data_time = 0.163788,	train_time = 0.706624
[2019-08-23 15:45:45,122] TRAIN Iter 129780: lr = 0.283702,	loss = 2.598343,	Top-1 err = 0.417969,	Top-5 err = 0.196289,	data_time = 0.050523,	train_time = 0.582749
[2019-08-23 15:46:00,720] TRAIN Iter 129800: lr = 0.283668,	loss = 2.660968,	Top-1 err = 0.416455,	Top-5 err = 0.193701,	data_time = 0.050847,	train_time = 0.779890
[2019-08-23 15:46:07,459] TRAIN Iter 129820: lr = 0.283635,	loss = 2.686071,	Top-1 err = 0.417920,	Top-5 err = 0.193164,	data_time = 0.050852,	train_time = 0.336954
[2019-08-23 15:46:24,499] TRAIN Iter 129840: lr = 0.283602,	loss = 2.691493,	Top-1 err = 0.414551,	Top-5 err = 0.193652,	data_time = 0.050407,	train_time = 0.851952
[2019-08-23 15:46:41,080] TRAIN Iter 129860: lr = 0.283568,	loss = 2.735069,	Top-1 err = 0.419336,	Top-5 err = 0.192383,	data_time = 0.050177,	train_time = 0.829044
[2019-08-23 15:46:49,524] TRAIN Iter 129880: lr = 0.283535,	loss = 2.617736,	Top-1 err = 0.417529,	Top-5 err = 0.190869,	data_time = 0.050638,	train_time = 0.422197
[2019-08-23 15:47:05,244] TRAIN Iter 129900: lr = 0.283502,	loss = 2.773345,	Top-1 err = 0.415771,	Top-5 err = 0.195361,	data_time = 0.050434,	train_time = 0.785980
[2019-08-23 15:47:21,028] TRAIN Iter 129920: lr = 0.283468,	loss = 2.633789,	Top-1 err = 0.409717,	Top-5 err = 0.190186,	data_time = 0.050218,	train_time = 0.789212
[2019-08-23 15:47:31,187] TRAIN Iter 129940: lr = 0.283435,	loss = 2.675600,	Top-1 err = 0.419824,	Top-5 err = 0.196436,	data_time = 0.050100,	train_time = 0.507904
[2019-08-23 15:47:48,199] TRAIN Iter 129960: lr = 0.283402,	loss = 2.738161,	Top-1 err = 0.414941,	Top-5 err = 0.193701,	data_time = 0.050536,	train_time = 0.850571
[2019-08-23 15:47:54,933] TRAIN Iter 129980: lr = 0.283368,	loss = 2.721499,	Top-1 err = 0.416797,	Top-5 err = 0.193799,	data_time = 0.050248,	train_time = 0.336722
[2019-08-23 15:48:12,598] TRAIN Iter 130000: lr = 0.283335,	loss = 2.666495,	Top-1 err = 0.413574,	Top-5 err = 0.197754,	data_time = 0.050204,	train_time = 0.883213
[2019-08-23 15:49:18,926] TEST Iter 130000: loss = 2.437498,	Top-1 err = 0.369160,	Top-5 err = 0.145280,	val_time = 66.268504
[2019-08-23 15:49:25,112] TRAIN Iter 130020: lr = 0.283302,	loss = 2.717322,	Top-1 err = 0.421924,	Top-5 err = 0.194629,	data_time = 0.050384,	train_time = 0.309289
[2019-08-23 15:49:31,667] TRAIN Iter 130040: lr = 0.283268,	loss = 2.636264,	Top-1 err = 0.414746,	Top-5 err = 0.195166,	data_time = 0.050272,	train_time = 0.327741
[2019-08-23 15:49:38,062] TRAIN Iter 130060: lr = 0.283235,	loss = 2.645469,	Top-1 err = 0.414453,	Top-5 err = 0.192676,	data_time = 0.050634,	train_time = 0.319744
[2019-08-23 15:49:50,428] TRAIN Iter 130080: lr = 0.283202,	loss = 2.638221,	Top-1 err = 0.413281,	Top-5 err = 0.191797,	data_time = 0.162096,	train_time = 0.618275
[2019-08-23 15:50:10,564] TRAIN Iter 130100: lr = 0.283168,	loss = 2.572976,	Top-1 err = 0.408154,	Top-5 err = 0.191309,	data_time = 0.049844,	train_time = 1.006766
[2019-08-23 15:50:19,293] TRAIN Iter 130120: lr = 0.283135,	loss = 2.723375,	Top-1 err = 0.419092,	Top-5 err = 0.195508,	data_time = 0.049966,	train_time = 0.436437
[2019-08-23 15:50:33,973] TRAIN Iter 130140: lr = 0.283102,	loss = 2.729152,	Top-1 err = 0.416943,	Top-5 err = 0.196680,	data_time = 0.049833,	train_time = 0.734008
[2019-08-23 15:51:23,801] TRAIN Iter 130160: lr = 0.283068,	loss = 2.664178,	Top-1 err = 0.418982,	Top-5 err = 0.194716,	data_time = 0.050350,	train_time = 2.491400
[2019-08-23 15:51:38,935] TRAIN Iter 130180: lr = 0.283035,	loss = 2.667721,	Top-1 err = 0.411768,	Top-5 err = 0.189160,	data_time = 0.127986,	train_time = 0.756664
[2019-08-23 15:51:45,820] TRAIN Iter 130200: lr = 0.283002,	loss = 2.719372,	Top-1 err = 0.409473,	Top-5 err = 0.193652,	data_time = 0.050809,	train_time = 0.344226
[2019-08-23 15:52:04,747] TRAIN Iter 130220: lr = 0.282968,	loss = 2.571903,	Top-1 err = 0.402197,	Top-5 err = 0.184814,	data_time = 0.050677,	train_time = 0.946363
[2019-08-23 15:52:12,164] TRAIN Iter 130240: lr = 0.282935,	loss = 2.587249,	Top-1 err = 0.402686,	Top-5 err = 0.185645,	data_time = 0.050629,	train_time = 0.370803
[2019-08-23 15:52:27,915] TRAIN Iter 130260: lr = 0.282902,	loss = 2.643167,	Top-1 err = 0.405322,	Top-5 err = 0.184082,	data_time = 0.050668,	train_time = 0.787535
[2019-08-23 15:52:47,020] TRAIN Iter 130280: lr = 0.282868,	loss = 2.694869,	Top-1 err = 0.402100,	Top-5 err = 0.184717,	data_time = 0.050338,	train_time = 0.955253
[2019-08-23 15:52:53,530] TRAIN Iter 130300: lr = 0.282835,	loss = 2.660450,	Top-1 err = 0.409326,	Top-5 err = 0.187158,	data_time = 0.050191,	train_time = 0.325474
[2019-08-23 15:53:11,715] TRAIN Iter 130320: lr = 0.282802,	loss = 2.677654,	Top-1 err = 0.406250,	Top-5 err = 0.188281,	data_time = 0.050405,	train_time = 0.909243
[2019-08-23 15:53:28,042] TRAIN Iter 130340: lr = 0.282768,	loss = 2.575010,	Top-1 err = 0.411230,	Top-5 err = 0.185791,	data_time = 1.466803,	train_time = 0.816349
[2019-08-23 15:53:34,837] TRAIN Iter 130360: lr = 0.282735,	loss = 2.615811,	Top-1 err = 0.410352,	Top-5 err = 0.187500,	data_time = 0.050342,	train_time = 0.339733
[2019-08-23 15:53:52,578] TRAIN Iter 130380: lr = 0.282702,	loss = 2.639943,	Top-1 err = 0.402832,	Top-5 err = 0.186035,	data_time = 0.050283,	train_time = 0.887017
[2019-08-23 15:53:59,894] TRAIN Iter 130400: lr = 0.282668,	loss = 2.626858,	Top-1 err = 0.407275,	Top-5 err = 0.187207,	data_time = 0.099472,	train_time = 0.365803
[2019-08-23 15:54:15,178] TRAIN Iter 130420: lr = 0.282635,	loss = 2.635492,	Top-1 err = 0.408984,	Top-5 err = 0.189990,	data_time = 0.050288,	train_time = 0.764155
[2019-08-23 15:54:32,919] TRAIN Iter 130440: lr = 0.282602,	loss = 2.691616,	Top-1 err = 0.410693,	Top-5 err = 0.192383,	data_time = 0.050261,	train_time = 0.887042
[2019-08-23 15:54:39,488] TRAIN Iter 130460: lr = 0.282568,	loss = 2.713458,	Top-1 err = 0.406445,	Top-5 err = 0.189990,	data_time = 0.050258,	train_time = 0.328458
[2019-08-23 15:54:58,053] TRAIN Iter 130480: lr = 0.282535,	loss = 2.641234,	Top-1 err = 0.409717,	Top-5 err = 0.188184,	data_time = 0.050422,	train_time = 0.928214
[2019-08-23 15:55:15,569] TRAIN Iter 130500: lr = 0.282502,	loss = 2.648067,	Top-1 err = 0.409717,	Top-5 err = 0.189697,	data_time = 1.038180,	train_time = 0.875814
[2019-08-23 15:55:22,209] TRAIN Iter 130520: lr = 0.282468,	loss = 2.697447,	Top-1 err = 0.412793,	Top-5 err = 0.189941,	data_time = 0.050434,	train_time = 0.331956
[2019-08-23 15:55:40,041] TRAIN Iter 130540: lr = 0.282435,	loss = 2.718660,	Top-1 err = 0.412646,	Top-5 err = 0.195312,	data_time = 0.050552,	train_time = 0.891613
[2019-08-23 15:55:47,409] TRAIN Iter 130560: lr = 0.282402,	loss = 2.694590,	Top-1 err = 0.414453,	Top-5 err = 0.194824,	data_time = 0.050353,	train_time = 0.368390
[2019-08-23 15:56:02,922] TRAIN Iter 130580: lr = 0.282368,	loss = 2.802508,	Top-1 err = 0.411816,	Top-5 err = 0.192090,	data_time = 0.050435,	train_time = 0.775593
[2019-08-23 15:56:20,850] TRAIN Iter 130600: lr = 0.282335,	loss = 2.811600,	Top-1 err = 0.407422,	Top-5 err = 0.188281,	data_time = 0.050610,	train_time = 0.896381
[2019-08-23 15:56:28,017] TRAIN Iter 130620: lr = 0.282302,	loss = 2.670236,	Top-1 err = 0.410693,	Top-5 err = 0.188818,	data_time = 0.050434,	train_time = 0.358381
[2019-08-23 15:56:43,578] TRAIN Iter 130640: lr = 0.282268,	loss = 2.603818,	Top-1 err = 0.415088,	Top-5 err = 0.193066,	data_time = 0.050789,	train_time = 0.778001
[2019-08-23 15:57:01,189] TRAIN Iter 130660: lr = 0.282235,	loss = 2.632348,	Top-1 err = 0.412891,	Top-5 err = 0.189355,	data_time = 2.478447,	train_time = 0.880559
[2019-08-23 15:57:07,903] TRAIN Iter 130680: lr = 0.282202,	loss = 2.705492,	Top-1 err = 0.412500,	Top-5 err = 0.191406,	data_time = 0.050226,	train_time = 0.335675
[2019-08-23 15:57:25,616] TRAIN Iter 130700: lr = 0.282168,	loss = 2.706027,	Top-1 err = 0.412109,	Top-5 err = 0.190137,	data_time = 0.050812,	train_time = 0.885621
[2019-08-23 15:57:32,150] TRAIN Iter 130720: lr = 0.282135,	loss = 2.642968,	Top-1 err = 0.411572,	Top-5 err = 0.189697,	data_time = 0.050530,	train_time = 0.326701
[2019-08-23 15:57:51,185] TRAIN Iter 130740: lr = 0.282102,	loss = 2.745280,	Top-1 err = 0.414014,	Top-5 err = 0.190479,	data_time = 0.050385,	train_time = 0.951747
[2019-08-23 15:58:07,587] TRAIN Iter 130760: lr = 0.282068,	loss = 2.623643,	Top-1 err = 0.416553,	Top-5 err = 0.194189,	data_time = 0.050402,	train_time = 0.820054
[2019-08-23 15:58:14,378] TRAIN Iter 130780: lr = 0.282035,	loss = 2.661572,	Top-1 err = 0.409424,	Top-5 err = 0.182080,	data_time = 0.050672,	train_time = 0.339545
[2019-08-23 15:58:32,384] TRAIN Iter 130800: lr = 0.282002,	loss = 2.704873,	Top-1 err = 0.418701,	Top-5 err = 0.194873,	data_time = 0.050666,	train_time = 0.900276
[2019-08-23 15:58:50,853] TRAIN Iter 130820: lr = 0.281968,	loss = 2.606435,	Top-1 err = 0.410059,	Top-5 err = 0.189990,	data_time = 0.050249,	train_time = 0.923433
[2019-08-23 15:58:57,317] TRAIN Iter 130840: lr = 0.281935,	loss = 2.575802,	Top-1 err = 0.414795,	Top-5 err = 0.193799,	data_time = 0.050258,	train_time = 0.323209
[2019-08-23 15:59:21,828] TRAIN Iter 130860: lr = 0.281902,	loss = 2.654489,	Top-1 err = 0.404932,	Top-5 err = 0.184277,	data_time = 0.050363,	train_time = 1.225551
[2019-08-23 15:59:29,117] TRAIN Iter 130880: lr = 0.281868,	loss = 2.558681,	Top-1 err = 0.419336,	Top-5 err = 0.195166,	data_time = 0.126635,	train_time = 0.364438
[2019-08-23 15:59:45,473] TRAIN Iter 130900: lr = 0.281835,	loss = 2.588063,	Top-1 err = 0.412451,	Top-5 err = 0.194043,	data_time = 0.050319,	train_time = 0.817787
[2019-08-23 16:00:03,386] TRAIN Iter 130920: lr = 0.281802,	loss = 2.646022,	Top-1 err = 0.416260,	Top-5 err = 0.194043,	data_time = 0.050331,	train_time = 0.895616
[2019-08-23 16:00:10,777] TRAIN Iter 130940: lr = 0.281768,	loss = 2.621819,	Top-1 err = 0.408887,	Top-5 err = 0.190479,	data_time = 0.050427,	train_time = 0.369556
[2019-08-23 16:00:26,726] TRAIN Iter 130960: lr = 0.281735,	loss = 2.683094,	Top-1 err = 0.411377,	Top-5 err = 0.189307,	data_time = 0.050253,	train_time = 0.797410
[2019-08-23 16:00:44,541] TRAIN Iter 130980: lr = 0.281702,	loss = 2.671215,	Top-1 err = 0.413428,	Top-5 err = 0.191553,	data_time = 0.050410,	train_time = 0.890730
[2019-08-23 16:00:51,682] TRAIN Iter 131000: lr = 0.281668,	loss = 2.694611,	Top-1 err = 0.409619,	Top-5 err = 0.193750,	data_time = 0.050434,	train_time = 0.357054
[2019-08-23 16:01:06,824] TRAIN Iter 131020: lr = 0.281635,	loss = 2.691952,	Top-1 err = 0.404639,	Top-5 err = 0.189307,	data_time = 0.050165,	train_time = 0.757112
[2019-08-23 16:01:13,864] TRAIN Iter 131040: lr = 0.281602,	loss = 2.654841,	Top-1 err = 0.412256,	Top-5 err = 0.194385,	data_time = 0.050500,	train_time = 0.351958
[2019-08-23 16:01:31,222] TRAIN Iter 131060: lr = 0.281568,	loss = 2.696630,	Top-1 err = 0.417236,	Top-5 err = 0.194727,	data_time = 0.050359,	train_time = 0.867892
[2019-08-23 16:01:49,679] TRAIN Iter 131080: lr = 0.281535,	loss = 2.722784,	Top-1 err = 0.408252,	Top-5 err = 0.193115,	data_time = 0.050438,	train_time = 0.922815
[2019-08-23 16:01:56,498] TRAIN Iter 131100: lr = 0.281502,	loss = 2.667244,	Top-1 err = 0.415283,	Top-5 err = 0.195605,	data_time = 0.050359,	train_time = 0.340957
[2019-08-23 16:02:13,927] TRAIN Iter 131120: lr = 0.281468,	loss = 2.702605,	Top-1 err = 0.417041,	Top-5 err = 0.193018,	data_time = 0.050420,	train_time = 0.871446
[2019-08-23 16:02:29,900] TRAIN Iter 131140: lr = 0.281435,	loss = 2.774484,	Top-1 err = 0.411865,	Top-5 err = 0.192383,	data_time = 0.050667,	train_time = 0.798650
[2019-08-23 16:02:36,673] TRAIN Iter 131160: lr = 0.281402,	loss = 2.620796,	Top-1 err = 0.408691,	Top-5 err = 0.190039,	data_time = 0.050608,	train_time = 0.338616
[2019-08-23 16:02:52,740] TRAIN Iter 131180: lr = 0.281368,	loss = 2.652140,	Top-1 err = 0.423193,	Top-5 err = 0.195020,	data_time = 0.050249,	train_time = 0.803336
[2019-08-23 16:03:00,044] TRAIN Iter 131200: lr = 0.281335,	loss = 2.678397,	Top-1 err = 0.418896,	Top-5 err = 0.193018,	data_time = 0.136736,	train_time = 0.365158
[2019-08-23 16:03:15,664] TRAIN Iter 131220: lr = 0.281302,	loss = 2.638944,	Top-1 err = 0.410449,	Top-5 err = 0.192041,	data_time = 0.050752,	train_time = 0.780993
[2019-08-23 16:03:32,597] TRAIN Iter 131240: lr = 0.281268,	loss = 2.667458,	Top-1 err = 0.408447,	Top-5 err = 0.192285,	data_time = 0.050405,	train_time = 0.846645
[2019-08-23 16:03:39,306] TRAIN Iter 131260: lr = 0.281235,	loss = 2.581078,	Top-1 err = 0.414111,	Top-5 err = 0.194141,	data_time = 0.050525,	train_time = 0.335420
[2019-08-23 16:03:57,000] TRAIN Iter 131280: lr = 0.281202,	loss = 2.702698,	Top-1 err = 0.414160,	Top-5 err = 0.197949,	data_time = 0.050755,	train_time = 0.884683
[2019-08-23 16:04:13,489] TRAIN Iter 131300: lr = 0.281168,	loss = 2.755762,	Top-1 err = 0.415869,	Top-5 err = 0.191064,	data_time = 0.050615,	train_time = 0.824465
[2019-08-23 16:04:20,151] TRAIN Iter 131320: lr = 0.281135,	loss = 2.774434,	Top-1 err = 0.410938,	Top-5 err = 0.194971,	data_time = 0.050817,	train_time = 0.333067
[2019-08-23 16:04:38,476] TRAIN Iter 131340: lr = 0.281102,	loss = 2.694409,	Top-1 err = 0.417822,	Top-5 err = 0.197998,	data_time = 0.049955,	train_time = 0.916265
[2019-08-23 16:04:45,091] TRAIN Iter 131360: lr = 0.281068,	loss = 2.619268,	Top-1 err = 0.412939,	Top-5 err = 0.191846,	data_time = 0.050133,	train_time = 0.330692
[2019-08-23 16:05:03,887] TRAIN Iter 131380: lr = 0.281035,	loss = 2.641348,	Top-1 err = 0.416064,	Top-5 err = 0.197705,	data_time = 0.049913,	train_time = 0.939785
[2019-08-23 16:05:55,797] TRAIN Iter 131400: lr = 0.281002,	loss = 2.662676,	Top-1 err = 0.418233,	Top-5 err = 0.190177,	data_time = 0.050296,	train_time = 2.595505
[2019-08-23 16:06:02,436] TRAIN Iter 131420: lr = 0.280968,	loss = 2.709108,	Top-1 err = 0.410400,	Top-5 err = 0.187500,	data_time = 0.050775,	train_time = 0.331944
[2019-08-23 16:06:19,640] TRAIN Iter 131440: lr = 0.280935,	loss = 2.700227,	Top-1 err = 0.406641,	Top-5 err = 0.184277,	data_time = 0.050637,	train_time = 0.860173
[2019-08-23 16:06:27,034] TRAIN Iter 131460: lr = 0.280902,	loss = 2.626110,	Top-1 err = 0.401904,	Top-5 err = 0.183447,	data_time = 0.050497,	train_time = 0.369690
[2019-08-23 16:06:41,550] TRAIN Iter 131480: lr = 0.280868,	loss = 2.623133,	Top-1 err = 0.408545,	Top-5 err = 0.186279,	data_time = 0.050750,	train_time = 0.725779
[2019-08-23 16:06:58,945] TRAIN Iter 131500: lr = 0.280835,	loss = 2.555369,	Top-1 err = 0.402832,	Top-5 err = 0.184863,	data_time = 0.050387,	train_time = 0.869737
[2019-08-23 16:07:05,603] TRAIN Iter 131520: lr = 0.280802,	loss = 2.565549,	Top-1 err = 0.411670,	Top-5 err = 0.188477,	data_time = 0.050290,	train_time = 0.332906
[2019-08-23 16:07:24,998] TRAIN Iter 131540: lr = 0.280768,	loss = 2.732173,	Top-1 err = 0.404980,	Top-5 err = 0.188281,	data_time = 0.050511,	train_time = 0.969696
[2019-08-23 16:07:41,813] TRAIN Iter 131560: lr = 0.280735,	loss = 2.653407,	Top-1 err = 0.413184,	Top-5 err = 0.189551,	data_time = 1.610411,	train_time = 0.840745
[2019-08-23 16:07:48,631] TRAIN Iter 131580: lr = 0.280702,	loss = 2.710689,	Top-1 err = 0.408789,	Top-5 err = 0.188574,	data_time = 0.050578,	train_time = 0.340884
[2019-08-23 16:08:06,120] TRAIN Iter 131600: lr = 0.280668,	loss = 2.712697,	Top-1 err = 0.412549,	Top-5 err = 0.191992,	data_time = 0.050228,	train_time = 0.874441
[2019-08-23 16:08:13,686] TRAIN Iter 131620: lr = 0.280635,	loss = 2.636659,	Top-1 err = 0.414160,	Top-5 err = 0.191797,	data_time = 0.050632,	train_time = 0.378307
[2019-08-23 16:08:28,892] TRAIN Iter 131640: lr = 0.280602,	loss = 2.679566,	Top-1 err = 0.410010,	Top-5 err = 0.186719,	data_time = 0.050369,	train_time = 0.760259
[2019-08-23 16:08:43,963] TRAIN Iter 131660: lr = 0.280568,	loss = 2.652682,	Top-1 err = 0.407568,	Top-5 err = 0.188867,	data_time = 0.050322,	train_time = 0.753554
[2019-08-23 16:08:50,796] TRAIN Iter 131680: lr = 0.280535,	loss = 2.669134,	Top-1 err = 0.407764,	Top-5 err = 0.187500,	data_time = 0.050606,	train_time = 0.341637
[2019-08-23 16:09:07,581] TRAIN Iter 131700: lr = 0.280502,	loss = 2.628355,	Top-1 err = 0.407520,	Top-5 err = 0.188867,	data_time = 0.050203,	train_time = 0.839222
[2019-08-23 16:09:23,350] TRAIN Iter 131720: lr = 0.280468,	loss = 2.748655,	Top-1 err = 0.410449,	Top-5 err = 0.187305,	data_time = 3.092295,	train_time = 0.788438
[2019-08-23 16:09:29,869] TRAIN Iter 131740: lr = 0.280435,	loss = 2.672981,	Top-1 err = 0.411328,	Top-5 err = 0.188232,	data_time = 0.050541,	train_time = 0.325928
[2019-08-23 16:09:47,387] TRAIN Iter 131760: lr = 0.280402,	loss = 2.713503,	Top-1 err = 0.406250,	Top-5 err = 0.188721,	data_time = 0.050431,	train_time = 0.875888
[2019-08-23 16:09:55,055] TRAIN Iter 131780: lr = 0.280368,	loss = 2.656216,	Top-1 err = 0.403369,	Top-5 err = 0.186279,	data_time = 0.051034,	train_time = 0.383417
[2019-08-23 16:10:09,876] TRAIN Iter 131800: lr = 0.280335,	loss = 2.650729,	Top-1 err = 0.407324,	Top-5 err = 0.187988,	data_time = 0.050278,	train_time = 0.740993
[2019-08-23 16:10:26,663] TRAIN Iter 131820: lr = 0.280302,	loss = 2.684613,	Top-1 err = 0.412061,	Top-5 err = 0.191064,	data_time = 0.050422,	train_time = 0.839376
[2019-08-23 16:10:33,833] TRAIN Iter 131840: lr = 0.280268,	loss = 2.738851,	Top-1 err = 0.406689,	Top-5 err = 0.188428,	data_time = 0.050397,	train_time = 0.358474
[2019-08-23 16:10:49,339] TRAIN Iter 131860: lr = 0.280235,	loss = 2.631479,	Top-1 err = 0.413184,	Top-5 err = 0.189795,	data_time = 0.050526,	train_time = 0.775289
[2019-08-23 16:11:07,143] TRAIN Iter 131880: lr = 0.280202,	loss = 2.659551,	Top-1 err = 0.402393,	Top-5 err = 0.187891,	data_time = 6.391837,	train_time = 0.890195
[2019-08-23 16:11:14,117] TRAIN Iter 131900: lr = 0.280168,	loss = 2.644718,	Top-1 err = 0.408105,	Top-5 err = 0.188721,	data_time = 0.050440,	train_time = 0.348673
[2019-08-23 16:11:29,335] TRAIN Iter 131920: lr = 0.280135,	loss = 2.685840,	Top-1 err = 0.407324,	Top-5 err = 0.186816,	data_time = 0.050375,	train_time = 0.760888
[2019-08-23 16:11:36,430] TRAIN Iter 131940: lr = 0.280102,	loss = 2.702246,	Top-1 err = 0.411914,	Top-5 err = 0.192871,	data_time = 0.050592,	train_time = 0.354743
[2019-08-23 16:11:53,613] TRAIN Iter 131960: lr = 0.280068,	loss = 2.645558,	Top-1 err = 0.406006,	Top-5 err = 0.184619,	data_time = 0.050442,	train_time = 0.859118
[2019-08-23 16:12:09,693] TRAIN Iter 131980: lr = 0.280035,	loss = 2.679710,	Top-1 err = 0.417383,	Top-5 err = 0.192773,	data_time = 0.052433,	train_time = 0.803966
[2019-08-23 16:12:16,554] TRAIN Iter 132000: lr = 0.280002,	loss = 2.634996,	Top-1 err = 0.408447,	Top-5 err = 0.192627,	data_time = 0.050387,	train_time = 0.343061
[2019-08-23 16:12:34,632] TRAIN Iter 132020: lr = 0.279968,	loss = 2.774552,	Top-1 err = 0.412012,	Top-5 err = 0.191357,	data_time = 0.050399,	train_time = 0.903867
[2019-08-23 16:12:49,358] TRAIN Iter 132040: lr = 0.279935,	loss = 2.671239,	Top-1 err = 0.404932,	Top-5 err = 0.186035,	data_time = 3.837508,	train_time = 0.736281
[2019-08-23 16:12:56,492] TRAIN Iter 132060: lr = 0.279902,	loss = 2.673379,	Top-1 err = 0.402930,	Top-5 err = 0.187158,	data_time = 0.050472,	train_time = 0.356722
[2019-08-23 16:13:13,293] TRAIN Iter 132080: lr = 0.279868,	loss = 2.801486,	Top-1 err = 0.412451,	Top-5 err = 0.190967,	data_time = 0.050454,	train_time = 0.840032
[2019-08-23 16:13:20,326] TRAIN Iter 132100: lr = 0.279835,	loss = 2.680542,	Top-1 err = 0.415527,	Top-5 err = 0.190332,	data_time = 0.050708,	train_time = 0.351609
[2019-08-23 16:13:36,569] TRAIN Iter 132120: lr = 0.279802,	loss = 2.742323,	Top-1 err = 0.408203,	Top-5 err = 0.192334,	data_time = 0.050377,	train_time = 0.812157
[2019-08-23 16:13:53,946] TRAIN Iter 132140: lr = 0.279768,	loss = 2.622297,	Top-1 err = 0.414648,	Top-5 err = 0.192090,	data_time = 0.050255,	train_time = 0.868806
[2019-08-23 16:14:00,647] TRAIN Iter 132160: lr = 0.279735,	loss = 2.668604,	Top-1 err = 0.412354,	Top-5 err = 0.193701,	data_time = 0.050242,	train_time = 0.335068
[2019-08-23 16:14:18,386] TRAIN Iter 132180: lr = 0.279702,	loss = 2.724505,	Top-1 err = 0.413330,	Top-5 err = 0.196045,	data_time = 0.050608,	train_time = 0.886939
[2019-08-23 16:14:33,967] TRAIN Iter 132200: lr = 0.279668,	loss = 2.677051,	Top-1 err = 0.412549,	Top-5 err = 0.194678,	data_time = 3.112445,	train_time = 0.779005
[2019-08-23 16:14:41,363] TRAIN Iter 132220: lr = 0.279635,	loss = 2.652228,	Top-1 err = 0.414893,	Top-5 err = 0.190820,	data_time = 0.050416,	train_time = 0.369787
[2019-08-23 16:14:59,994] TRAIN Iter 132240: lr = 0.279602,	loss = 2.724268,	Top-1 err = 0.408252,	Top-5 err = 0.192285,	data_time = 0.050551,	train_time = 0.931549
[2019-08-23 16:15:06,831] TRAIN Iter 132260: lr = 0.279568,	loss = 2.713506,	Top-1 err = 0.406250,	Top-5 err = 0.187109,	data_time = 0.050413,	train_time = 0.341852
[2019-08-23 16:15:23,933] TRAIN Iter 132280: lr = 0.279535,	loss = 2.781577,	Top-1 err = 0.415088,	Top-5 err = 0.190137,	data_time = 0.050418,	train_time = 0.855055
[2019-08-23 16:15:42,317] TRAIN Iter 132300: lr = 0.279502,	loss = 2.713369,	Top-1 err = 0.410352,	Top-5 err = 0.196436,	data_time = 0.125374,	train_time = 0.919190
[2019-08-23 16:15:49,093] TRAIN Iter 132320: lr = 0.279468,	loss = 2.768359,	Top-1 err = 0.414941,	Top-5 err = 0.193164,	data_time = 0.051019,	train_time = 0.338806
[2019-08-23 16:16:08,986] TRAIN Iter 132340: lr = 0.279435,	loss = 2.685711,	Top-1 err = 0.418018,	Top-5 err = 0.194238,	data_time = 0.050551,	train_time = 0.994622
[2019-08-23 16:16:21,600] TRAIN Iter 132360: lr = 0.279402,	loss = 2.706056,	Top-1 err = 0.410107,	Top-5 err = 0.189062,	data_time = 0.989319,	train_time = 0.630703
[2019-08-23 16:16:30,291] TRAIN Iter 132380: lr = 0.279368,	loss = 2.606641,	Top-1 err = 0.409424,	Top-5 err = 0.188135,	data_time = 0.050255,	train_time = 0.434540
[2019-08-23 16:16:48,003] TRAIN Iter 132400: lr = 0.279335,	loss = 2.689920,	Top-1 err = 0.411279,	Top-5 err = 0.192236,	data_time = 0.050492,	train_time = 0.885566
[2019-08-23 16:16:55,038] TRAIN Iter 132420: lr = 0.279302,	loss = 2.757405,	Top-1 err = 0.411670,	Top-5 err = 0.192725,	data_time = 0.050607,	train_time = 0.351754
[2019-08-23 16:17:10,785] TRAIN Iter 132440: lr = 0.279268,	loss = 2.678133,	Top-1 err = 0.410010,	Top-5 err = 0.187109,	data_time = 0.050565,	train_time = 0.787350
[2019-08-23 16:17:27,089] TRAIN Iter 132460: lr = 0.279235,	loss = 2.679679,	Top-1 err = 0.417236,	Top-5 err = 0.192920,	data_time = 0.158144,	train_time = 0.815142
[2019-08-23 16:17:34,494] TRAIN Iter 132480: lr = 0.279202,	loss = 2.750021,	Top-1 err = 0.417822,	Top-5 err = 0.191797,	data_time = 0.050803,	train_time = 0.370237
[2019-08-23 16:17:51,899] TRAIN Iter 132500: lr = 0.279168,	loss = 2.691415,	Top-1 err = 0.416016,	Top-5 err = 0.193018,	data_time = 0.050498,	train_time = 0.870278
[2019-08-23 16:18:05,741] TRAIN Iter 132520: lr = 0.279135,	loss = 2.613948,	Top-1 err = 0.416992,	Top-5 err = 0.193506,	data_time = 0.142034,	train_time = 0.692054
[2019-08-23 16:18:16,590] TRAIN Iter 132540: lr = 0.279102,	loss = 2.612716,	Top-1 err = 0.408594,	Top-5 err = 0.187646,	data_time = 0.050444,	train_time = 0.542423
[2019-08-23 16:18:33,958] TRAIN Iter 132560: lr = 0.279068,	loss = 2.697767,	Top-1 err = 0.412598,	Top-5 err = 0.192529,	data_time = 0.050245,	train_time = 0.868425
[2019-08-23 16:18:40,599] TRAIN Iter 132580: lr = 0.279035,	loss = 2.611895,	Top-1 err = 0.413672,	Top-5 err = 0.191553,	data_time = 0.145849,	train_time = 0.332015
[2019-08-23 16:19:00,849] TRAIN Iter 132600: lr = 0.279002,	loss = 2.638628,	Top-1 err = 0.405957,	Top-5 err = 0.188232,	data_time = 0.050045,	train_time = 1.012494
[2019-08-23 16:19:16,864] TRAIN Iter 132620: lr = 0.278968,	loss = 2.680551,	Top-1 err = 0.407910,	Top-5 err = 0.191260,	data_time = 0.050053,	train_time = 0.800747
[2019-08-23 16:19:23,932] TRAIN Iter 132640: lr = 0.278935,	loss = 2.758294,	Top-1 err = 0.415527,	Top-5 err = 0.193652,	data_time = 0.049922,	train_time = 0.353386
[2019-08-23 16:20:14,153] TRAIN Iter 132660: lr = 0.278902,	loss = 2.684775,	Top-1 err = 0.422319,	Top-5 err = 0.193343,	data_time = 0.050377,	train_time = 2.511052
[2019-08-23 16:20:21,793] TRAIN Iter 132680: lr = 0.278868,	loss = 2.644857,	Top-1 err = 0.407373,	Top-5 err = 0.183545,	data_time = 0.050495,	train_time = 0.381974
[2019-08-23 16:20:36,688] TRAIN Iter 132700: lr = 0.278835,	loss = 2.591839,	Top-1 err = 0.397607,	Top-5 err = 0.179639,	data_time = 0.050493,	train_time = 0.744696
[2019-08-23 16:20:53,374] TRAIN Iter 132720: lr = 0.278802,	loss = 2.617366,	Top-1 err = 0.402100,	Top-5 err = 0.185352,	data_time = 0.050533,	train_time = 0.834298
[2019-08-23 16:21:01,204] TRAIN Iter 132740: lr = 0.278768,	loss = 2.677774,	Top-1 err = 0.401172,	Top-5 err = 0.182861,	data_time = 0.050330,	train_time = 0.391482
[2019-08-23 16:21:15,459] TRAIN Iter 132760: lr = 0.278735,	loss = 2.701612,	Top-1 err = 0.400635,	Top-5 err = 0.182910,	data_time = 0.050281,	train_time = 0.712755
[2019-08-23 16:21:31,052] TRAIN Iter 132780: lr = 0.278702,	loss = 2.716774,	Top-1 err = 0.404346,	Top-5 err = 0.184570,	data_time = 0.050783,	train_time = 0.779656
[2019-08-23 16:21:38,033] TRAIN Iter 132800: lr = 0.278668,	loss = 2.707220,	Top-1 err = 0.401465,	Top-5 err = 0.184424,	data_time = 0.050322,	train_time = 0.349008
[2019-08-23 16:21:53,752] TRAIN Iter 132820: lr = 0.278635,	loss = 2.653965,	Top-1 err = 0.402783,	Top-5 err = 0.184033,	data_time = 0.050391,	train_time = 0.785929
[2019-08-23 16:22:01,626] TRAIN Iter 132840: lr = 0.278602,	loss = 2.650078,	Top-1 err = 0.407666,	Top-5 err = 0.187598,	data_time = 0.050466,	train_time = 0.393708
[2019-08-23 16:22:15,638] TRAIN Iter 132860: lr = 0.278568,	loss = 2.575114,	Top-1 err = 0.407373,	Top-5 err = 0.191211,	data_time = 0.050390,	train_time = 0.700581
[2019-08-23 16:22:32,290] TRAIN Iter 132880: lr = 0.278535,	loss = 2.736713,	Top-1 err = 0.412354,	Top-5 err = 0.191162,	data_time = 0.050421,	train_time = 0.832569
[2019-08-23 16:22:39,376] TRAIN Iter 132900: lr = 0.278502,	loss = 2.662306,	Top-1 err = 0.411865,	Top-5 err = 0.188379,	data_time = 0.050694,	train_time = 0.354285
[2019-08-23 16:22:54,557] TRAIN Iter 132920: lr = 0.278468,	loss = 2.731212,	Top-1 err = 0.407227,	Top-5 err = 0.188379,	data_time = 0.050457,	train_time = 0.759052
[2019-08-23 16:23:10,647] TRAIN Iter 132940: lr = 0.278435,	loss = 2.704956,	Top-1 err = 0.410791,	Top-5 err = 0.188037,	data_time = 0.050848,	train_time = 0.804475
[2019-08-23 16:23:17,352] TRAIN Iter 132960: lr = 0.278402,	loss = 2.634050,	Top-1 err = 0.408350,	Top-5 err = 0.190186,	data_time = 0.050445,	train_time = 0.335238
[2019-08-23 16:23:33,419] TRAIN Iter 132980: lr = 0.278368,	loss = 2.540309,	Top-1 err = 0.407813,	Top-5 err = 0.187646,	data_time = 0.050445,	train_time = 0.803350
[2019-08-23 16:23:41,307] TRAIN Iter 133000: lr = 0.278335,	loss = 2.678798,	Top-1 err = 0.405957,	Top-5 err = 0.187744,	data_time = 0.050341,	train_time = 0.394366
[2019-08-23 16:23:56,356] TRAIN Iter 133020: lr = 0.278302,	loss = 2.676901,	Top-1 err = 0.410205,	Top-5 err = 0.190186,	data_time = 0.050566,	train_time = 0.752447
[2019-08-23 16:24:12,625] TRAIN Iter 133040: lr = 0.278268,	loss = 2.671031,	Top-1 err = 0.409326,	Top-5 err = 0.187451,	data_time = 0.050400,	train_time = 0.813448
[2019-08-23 16:24:19,789] TRAIN Iter 133060: lr = 0.278235,	loss = 2.646502,	Top-1 err = 0.407959,	Top-5 err = 0.189062,	data_time = 0.050399,	train_time = 0.358166
[2019-08-23 16:24:35,719] TRAIN Iter 133080: lr = 0.278202,	loss = 2.730800,	Top-1 err = 0.406738,	Top-5 err = 0.186230,	data_time = 0.050574,	train_time = 0.796483
[2019-08-23 16:24:51,482] TRAIN Iter 133100: lr = 0.278168,	loss = 2.685468,	Top-1 err = 0.409570,	Top-5 err = 0.188574,	data_time = 0.050152,	train_time = 0.788157
[2019-08-23 16:24:58,378] TRAIN Iter 133120: lr = 0.278135,	loss = 2.661376,	Top-1 err = 0.415527,	Top-5 err = 0.190234,	data_time = 0.050636,	train_time = 0.344781
[2019-08-23 16:25:15,829] TRAIN Iter 133140: lr = 0.278102,	loss = 2.606790,	Top-1 err = 0.407178,	Top-5 err = 0.190625,	data_time = 0.050673,	train_time = 0.872518
[2019-08-23 16:25:23,986] TRAIN Iter 133160: lr = 0.278068,	loss = 2.665997,	Top-1 err = 0.416113,	Top-5 err = 0.188135,	data_time = 0.050600,	train_time = 0.407834
[2019-08-23 16:25:37,965] TRAIN Iter 133180: lr = 0.278035,	loss = 2.605426,	Top-1 err = 0.409521,	Top-5 err = 0.189062,	data_time = 0.050545,	train_time = 0.698976
[2019-08-23 16:25:54,804] TRAIN Iter 133200: lr = 0.278002,	loss = 2.691669,	Top-1 err = 0.406836,	Top-5 err = 0.189502,	data_time = 0.050610,	train_time = 0.841908
[2019-08-23 16:26:02,561] TRAIN Iter 133220: lr = 0.277968,	loss = 2.764605,	Top-1 err = 0.410645,	Top-5 err = 0.194971,	data_time = 0.050520,	train_time = 0.387871
[2019-08-23 16:26:19,936] TRAIN Iter 133240: lr = 0.277935,	loss = 2.606811,	Top-1 err = 0.408838,	Top-5 err = 0.190576,	data_time = 0.153135,	train_time = 0.868690
[2019-08-23 16:26:35,672] TRAIN Iter 133260: lr = 0.277902,	loss = 2.677047,	Top-1 err = 0.410693,	Top-5 err = 0.193701,	data_time = 0.050941,	train_time = 0.786816
[2019-08-23 16:26:42,341] TRAIN Iter 133280: lr = 0.277868,	loss = 2.637544,	Top-1 err = 0.405371,	Top-5 err = 0.187744,	data_time = 0.050564,	train_time = 0.333435
[2019-08-23 16:26:59,832] TRAIN Iter 133300: lr = 0.277835,	loss = 2.729329,	Top-1 err = 0.413867,	Top-5 err = 0.198145,	data_time = 0.050400,	train_time = 0.874524
[2019-08-23 16:27:06,968] TRAIN Iter 133320: lr = 0.277802,	loss = 2.647221,	Top-1 err = 0.411133,	Top-5 err = 0.185303,	data_time = 0.050442,	train_time = 0.356781
[2019-08-23 16:27:24,975] TRAIN Iter 133340: lr = 0.277768,	loss = 2.753371,	Top-1 err = 0.412891,	Top-5 err = 0.189795,	data_time = 0.050818,	train_time = 0.900350
[2019-08-23 16:27:42,371] TRAIN Iter 133360: lr = 0.277735,	loss = 2.699852,	Top-1 err = 0.413184,	Top-5 err = 0.190039,	data_time = 0.050436,	train_time = 0.869772
[2019-08-23 16:27:49,177] TRAIN Iter 133380: lr = 0.277702,	loss = 2.753819,	Top-1 err = 0.406934,	Top-5 err = 0.186328,	data_time = 0.050272,	train_time = 0.340283
[2019-08-23 16:28:07,150] TRAIN Iter 133400: lr = 0.277668,	loss = 2.552072,	Top-1 err = 0.407373,	Top-5 err = 0.187598,	data_time = 0.050197,	train_time = 0.898659
[2019-08-23 16:28:23,882] TRAIN Iter 133420: lr = 0.277635,	loss = 2.652619,	Top-1 err = 0.411230,	Top-5 err = 0.188525,	data_time = 0.050403,	train_time = 0.836581
[2019-08-23 16:28:30,695] TRAIN Iter 133440: lr = 0.277602,	loss = 2.662174,	Top-1 err = 0.412500,	Top-5 err = 0.190625,	data_time = 0.050682,	train_time = 0.340642
[2019-08-23 16:28:48,274] TRAIN Iter 133460: lr = 0.277568,	loss = 2.645193,	Top-1 err = 0.409570,	Top-5 err = 0.191846,	data_time = 0.050481,	train_time = 0.878918
[2019-08-23 16:28:55,754] TRAIN Iter 133480: lr = 0.277535,	loss = 2.707772,	Top-1 err = 0.408594,	Top-5 err = 0.192041,	data_time = 0.050917,	train_time = 0.374017
[2019-08-23 16:29:11,827] TRAIN Iter 133500: lr = 0.277502,	loss = 2.664407,	Top-1 err = 0.417529,	Top-5 err = 0.193408,	data_time = 0.050430,	train_time = 0.803621
[2019-08-23 16:29:28,880] TRAIN Iter 133520: lr = 0.277468,	loss = 2.642960,	Top-1 err = 0.409082,	Top-5 err = 0.188867,	data_time = 0.050391,	train_time = 0.852622
[2019-08-23 16:29:35,892] TRAIN Iter 133540: lr = 0.277435,	loss = 2.628650,	Top-1 err = 0.407910,	Top-5 err = 0.191455,	data_time = 0.051006,	train_time = 0.350580
[2019-08-23 16:29:51,287] TRAIN Iter 133560: lr = 0.277402,	loss = 2.761774,	Top-1 err = 0.405811,	Top-5 err = 0.193457,	data_time = 0.050113,	train_time = 0.769762
[2019-08-23 16:30:09,093] TRAIN Iter 133580: lr = 0.277368,	loss = 2.618083,	Top-1 err = 0.409424,	Top-5 err = 0.190918,	data_time = 0.050567,	train_time = 0.890262
[2019-08-23 16:30:15,644] TRAIN Iter 133600: lr = 0.277335,	loss = 2.703111,	Top-1 err = 0.406885,	Top-5 err = 0.189062,	data_time = 0.050794,	train_time = 0.327565
[2019-08-23 16:30:33,604] TRAIN Iter 133620: lr = 0.277302,	loss = 2.636660,	Top-1 err = 0.415967,	Top-5 err = 0.194336,	data_time = 0.050432,	train_time = 0.897945
[2019-08-23 16:30:40,858] TRAIN Iter 133640: lr = 0.277268,	loss = 2.615025,	Top-1 err = 0.411719,	Top-5 err = 0.187598,	data_time = 0.050600,	train_time = 0.362702
[2019-08-23 16:30:57,826] TRAIN Iter 133660: lr = 0.277235,	loss = 2.696786,	Top-1 err = 0.417822,	Top-5 err = 0.193848,	data_time = 0.050448,	train_time = 0.848381
[2019-08-23 16:31:14,734] TRAIN Iter 133680: lr = 0.277202,	loss = 2.640850,	Top-1 err = 0.415137,	Top-5 err = 0.194629,	data_time = 0.050511,	train_time = 0.845386
[2019-08-23 16:31:21,725] TRAIN Iter 133700: lr = 0.277168,	loss = 2.682209,	Top-1 err = 0.415186,	Top-5 err = 0.193604,	data_time = 0.050475,	train_time = 0.349548
[2019-08-23 16:31:39,976] TRAIN Iter 133720: lr = 0.277135,	loss = 2.622102,	Top-1 err = 0.414990,	Top-5 err = 0.193262,	data_time = 0.050348,	train_time = 0.912537
[2019-08-23 16:32:00,343] TRAIN Iter 133740: lr = 0.277102,	loss = 2.761803,	Top-1 err = 0.413232,	Top-5 err = 0.192578,	data_time = 0.050529,	train_time = 1.018334
[2019-08-23 16:32:06,881] TRAIN Iter 133760: lr = 0.277068,	loss = 2.694028,	Top-1 err = 0.417139,	Top-5 err = 0.196875,	data_time = 0.050527,	train_time = 0.326885
[2019-08-23 16:32:29,380] TRAIN Iter 133780: lr = 0.277035,	loss = 2.729819,	Top-1 err = 0.410645,	Top-5 err = 0.190186,	data_time = 0.050347,	train_time = 1.124919
[2019-08-23 16:32:35,931] TRAIN Iter 133800: lr = 0.277002,	loss = 2.669718,	Top-1 err = 0.413818,	Top-5 err = 0.192578,	data_time = 0.050437,	train_time = 0.327551
[2019-08-23 16:32:56,883] TRAIN Iter 133820: lr = 0.276968,	loss = 2.680579,	Top-1 err = 0.418408,	Top-5 err = 0.193213,	data_time = 0.050305,	train_time = 1.047565
[2019-08-23 16:33:17,183] TRAIN Iter 133840: lr = 0.276935,	loss = 2.685611,	Top-1 err = 0.415723,	Top-5 err = 0.194287,	data_time = 0.049948,	train_time = 1.015010
[2019-08-23 16:33:23,708] TRAIN Iter 133860: lr = 0.276902,	loss = 2.707107,	Top-1 err = 0.412061,	Top-5 err = 0.191797,	data_time = 0.049994,	train_time = 0.326214
[2019-08-23 16:33:42,232] TRAIN Iter 133880: lr = 0.276868,	loss = 2.582885,	Top-1 err = 0.415527,	Top-5 err = 0.192334,	data_time = 0.049893,	train_time = 0.926229
[2019-08-23 16:33:54,366] TRAIN Iter 133900: lr = 0.276835,	loss = 3.296902,	Top-1 err = 0.419437,	Top-5 err = 0.200142,	data_time = 0.007143,	train_time = 0.606681
[2019-08-23 16:34:40,177] TRAIN Iter 133920: lr = 0.276802,	loss = 2.713030,	Top-1 err = 0.412158,	Top-5 err = 0.190137,	data_time = 0.050615,	train_time = 2.290531
[2019-08-23 16:35:00,359] TRAIN Iter 133940: lr = 0.276768,	loss = 2.650297,	Top-1 err = 0.409668,	Top-5 err = 0.188086,	data_time = 0.050658,	train_time = 1.009059
[2019-08-23 16:35:08,822] TRAIN Iter 133960: lr = 0.276735,	loss = 2.545603,	Top-1 err = 0.406396,	Top-5 err = 0.188037,	data_time = 0.050429,	train_time = 0.423146
[2019-08-23 16:35:24,679] TRAIN Iter 133980: lr = 0.276702,	loss = 2.757009,	Top-1 err = 0.405029,	Top-5 err = 0.187109,	data_time = 0.050315,	train_time = 0.792821
[2019-08-23 16:35:33,668] TRAIN Iter 134000: lr = 0.276668,	loss = 2.645682,	Top-1 err = 0.404297,	Top-5 err = 0.184424,	data_time = 0.050385,	train_time = 0.449461
[2019-08-23 16:35:41,066] TRAIN Iter 134020: lr = 0.276635,	loss = 2.614699,	Top-1 err = 0.406055,	Top-5 err = 0.181982,	data_time = 0.050658,	train_time = 0.369835
[2019-08-23 16:35:53,962] TRAIN Iter 134040: lr = 0.276602,	loss = 2.727729,	Top-1 err = 0.402881,	Top-5 err = 0.185449,	data_time = 0.050481,	train_time = 0.644831
[2019-08-23 16:36:00,991] TRAIN Iter 134060: lr = 0.276568,	loss = 2.636312,	Top-1 err = 0.412646,	Top-5 err = 0.191309,	data_time = 0.050337,	train_time = 0.351423
[2019-08-23 16:36:18,272] TRAIN Iter 134080: lr = 0.276535,	loss = 2.598984,	Top-1 err = 0.401318,	Top-5 err = 0.184424,	data_time = 0.050307,	train_time = 0.864040
[2019-08-23 16:36:35,791] TRAIN Iter 134100: lr = 0.276502,	loss = 2.619375,	Top-1 err = 0.411328,	Top-5 err = 0.189258,	data_time = 0.154762,	train_time = 0.875910
[2019-08-23 16:36:43,082] TRAIN Iter 134120: lr = 0.276468,	loss = 2.619424,	Top-1 err = 0.406689,	Top-5 err = 0.184033,	data_time = 0.050293,	train_time = 0.364549
[2019-08-23 16:36:56,885] TRAIN Iter 134140: lr = 0.276435,	loss = 2.760807,	Top-1 err = 0.407129,	Top-5 err = 0.188916,	data_time = 0.050355,	train_time = 0.690129
[2019-08-23 16:37:10,865] TRAIN Iter 134160: lr = 0.276402,	loss = 2.739724,	Top-1 err = 0.408984,	Top-5 err = 0.186523,	data_time = 0.050325,	train_time = 0.698998
[2019-08-23 16:37:18,380] TRAIN Iter 134180: lr = 0.276368,	loss = 2.584461,	Top-1 err = 0.406787,	Top-5 err = 0.184668,	data_time = 0.050469,	train_time = 0.375726
[2019-08-23 16:37:34,684] TRAIN Iter 134200: lr = 0.276335,	loss = 2.572548,	Top-1 err = 0.407568,	Top-5 err = 0.188086,	data_time = 0.050328,	train_time = 0.815208
[2019-08-23 16:37:41,592] TRAIN Iter 134220: lr = 0.276302,	loss = 2.645668,	Top-1 err = 0.406885,	Top-5 err = 0.188477,	data_time = 0.050755,	train_time = 0.345382
[2019-08-23 16:37:55,548] TRAIN Iter 134240: lr = 0.276268,	loss = 2.756134,	Top-1 err = 0.413525,	Top-5 err = 0.196094,	data_time = 0.050438,	train_time = 0.697754
[2019-08-23 16:38:11,679] TRAIN Iter 134260: lr = 0.276235,	loss = 2.713397,	Top-1 err = 0.413623,	Top-5 err = 0.190088,	data_time = 0.094198,	train_time = 0.806579
[2019-08-23 16:38:18,767] TRAIN Iter 134280: lr = 0.276202,	loss = 2.655374,	Top-1 err = 0.404248,	Top-5 err = 0.186426,	data_time = 0.050363,	train_time = 0.354344
[2019-08-23 16:38:33,682] TRAIN Iter 134300: lr = 0.276168,	loss = 2.694523,	Top-1 err = 0.404297,	Top-5 err = 0.183447,	data_time = 0.050854,	train_time = 0.745744
[2019-08-23 16:38:50,392] TRAIN Iter 134320: lr = 0.276135,	loss = 2.663478,	Top-1 err = 0.413135,	Top-5 err = 0.192334,	data_time = 0.050588,	train_time = 0.835483
[2019-08-23 16:38:57,793] TRAIN Iter 134340: lr = 0.276102,	loss = 2.673281,	Top-1 err = 0.404639,	Top-5 err = 0.185205,	data_time = 0.050475,	train_time = 0.370045
[2019-08-23 16:39:15,115] TRAIN Iter 134360: lr = 0.276068,	loss = 2.769998,	Top-1 err = 0.412305,	Top-5 err = 0.190088,	data_time = 0.050503,	train_time = 0.866082
[2019-08-23 16:39:21,592] TRAIN Iter 134380: lr = 0.276035,	loss = 2.736453,	Top-1 err = 0.411279,	Top-5 err = 0.191699,	data_time = 0.050535,	train_time = 0.323839
[2019-08-23 16:39:37,104] TRAIN Iter 134400: lr = 0.276002,	loss = 2.651744,	Top-1 err = 0.414111,	Top-5 err = 0.188623,	data_time = 0.050416,	train_time = 0.775589
[2019-08-23 16:39:54,033] TRAIN Iter 134420: lr = 0.275968,	loss = 2.734959,	Top-1 err = 0.416406,	Top-5 err = 0.190430,	data_time = 0.050680,	train_time = 0.846455
[2019-08-23 16:40:01,125] TRAIN Iter 134440: lr = 0.275935,	loss = 2.678912,	Top-1 err = 0.408008,	Top-5 err = 0.183887,	data_time = 0.050385,	train_time = 0.354563
[2019-08-23 16:40:17,480] TRAIN Iter 134460: lr = 0.275902,	loss = 2.689514,	Top-1 err = 0.406250,	Top-5 err = 0.185303,	data_time = 0.109201,	train_time = 0.817725
[2019-08-23 16:40:33,930] TRAIN Iter 134480: lr = 0.275868,	loss = 2.613233,	Top-1 err = 0.414990,	Top-5 err = 0.193799,	data_time = 0.050161,	train_time = 0.822517
[2019-08-23 16:40:41,280] TRAIN Iter 134500: lr = 0.275835,	loss = 2.703030,	Top-1 err = 0.413135,	Top-5 err = 0.193555,	data_time = 0.050377,	train_time = 0.367491
[2019-08-23 16:40:58,711] TRAIN Iter 134520: lr = 0.275802,	loss = 2.720532,	Top-1 err = 0.413770,	Top-5 err = 0.191846,	data_time = 0.050528,	train_time = 0.871529
[2019-08-23 16:41:05,789] TRAIN Iter 134540: lr = 0.275768,	loss = 2.634129,	Top-1 err = 0.414893,	Top-5 err = 0.188574,	data_time = 0.050349,	train_time = 0.353852
[2019-08-23 16:41:22,847] TRAIN Iter 134560: lr = 0.275735,	loss = 2.733272,	Top-1 err = 0.406934,	Top-5 err = 0.190674,	data_time = 0.050282,	train_time = 0.852900
[2019-08-23 16:41:42,203] TRAIN Iter 134580: lr = 0.275702,	loss = 2.667257,	Top-1 err = 0.413428,	Top-5 err = 0.192480,	data_time = 0.146689,	train_time = 0.967793
[2019-08-23 16:41:49,540] TRAIN Iter 134600: lr = 0.275668,	loss = 2.662847,	Top-1 err = 0.411035,	Top-5 err = 0.190137,	data_time = 0.050566,	train_time = 0.366806
[2019-08-23 16:42:04,215] TRAIN Iter 134620: lr = 0.275635,	loss = 2.716696,	Top-1 err = 0.412158,	Top-5 err = 0.193408,	data_time = 0.050454,	train_time = 0.733750
[2019-08-23 16:42:19,604] TRAIN Iter 134640: lr = 0.275602,	loss = 2.669076,	Top-1 err = 0.415527,	Top-5 err = 0.193652,	data_time = 0.050709,	train_time = 0.769448
[2019-08-23 16:42:27,765] TRAIN Iter 134660: lr = 0.275568,	loss = 2.740855,	Top-1 err = 0.403516,	Top-5 err = 0.185596,	data_time = 0.050317,	train_time = 0.408042
[2019-08-23 16:42:45,187] TRAIN Iter 134680: lr = 0.275535,	loss = 2.670978,	Top-1 err = 0.406201,	Top-5 err = 0.190088,	data_time = 0.050966,	train_time = 0.871068
[2019-08-23 16:42:52,124] TRAIN Iter 134700: lr = 0.275502,	loss = 2.620917,	Top-1 err = 0.410156,	Top-5 err = 0.189551,	data_time = 0.050923,	train_time = 0.346868
[2019-08-23 16:43:08,301] TRAIN Iter 134720: lr = 0.275468,	loss = 2.727686,	Top-1 err = 0.410889,	Top-5 err = 0.190918,	data_time = 0.050332,	train_time = 0.808817
[2019-08-23 16:43:24,051] TRAIN Iter 134740: lr = 0.275435,	loss = 2.717834,	Top-1 err = 0.416406,	Top-5 err = 0.191553,	data_time = 0.050434,	train_time = 0.787488
[2019-08-23 16:43:31,366] TRAIN Iter 134760: lr = 0.275402,	loss = 2.713338,	Top-1 err = 0.412939,	Top-5 err = 0.193457,	data_time = 0.050262,	train_time = 0.365721
[2019-08-23 16:43:49,310] TRAIN Iter 134780: lr = 0.275368,	loss = 2.663866,	Top-1 err = 0.411377,	Top-5 err = 0.192188,	data_time = 0.050364,	train_time = 0.897207
[2019-08-23 16:44:06,216] TRAIN Iter 134800: lr = 0.275335,	loss = 2.663608,	Top-1 err = 0.410742,	Top-5 err = 0.188574,	data_time = 0.050246,	train_time = 0.845259
[2019-08-23 16:44:13,749] TRAIN Iter 134820: lr = 0.275302,	loss = 2.669054,	Top-1 err = 0.411426,	Top-5 err = 0.189502,	data_time = 0.050328,	train_time = 0.376664
[2019-08-23 16:44:30,644] TRAIN Iter 134840: lr = 0.275268,	loss = 2.672693,	Top-1 err = 0.409082,	Top-5 err = 0.189795,	data_time = 0.050979,	train_time = 0.844700
[2019-08-23 16:44:37,854] TRAIN Iter 134860: lr = 0.275235,	loss = 2.666299,	Top-1 err = 0.404443,	Top-5 err = 0.189258,	data_time = 0.050600,	train_time = 0.360488
[2019-08-23 16:44:54,745] TRAIN Iter 134880: lr = 0.275202,	loss = 2.646228,	Top-1 err = 0.413379,	Top-5 err = 0.192627,	data_time = 0.050618,	train_time = 0.844566
[2019-08-23 16:45:11,457] TRAIN Iter 134900: lr = 0.275168,	loss = 2.601738,	Top-1 err = 0.416113,	Top-5 err = 0.187744,	data_time = 0.640591,	train_time = 0.835547
[2019-08-23 16:45:19,224] TRAIN Iter 134920: lr = 0.275135,	loss = 2.696574,	Top-1 err = 0.409766,	Top-5 err = 0.190039,	data_time = 0.050446,	train_time = 0.388363
[2019-08-23 16:45:35,552] TRAIN Iter 134940: lr = 0.275102,	loss = 2.712708,	Top-1 err = 0.413818,	Top-5 err = 0.192822,	data_time = 0.050358,	train_time = 0.816350
[2019-08-23 16:45:52,195] TRAIN Iter 134960: lr = 0.275068,	loss = 2.623420,	Top-1 err = 0.412646,	Top-5 err = 0.188916,	data_time = 0.050426,	train_time = 0.832135
[2019-08-23 16:46:00,807] TRAIN Iter 134980: lr = 0.275035,	loss = 2.759310,	Top-1 err = 0.410791,	Top-5 err = 0.189111,	data_time = 0.050624,	train_time = 0.430614
[2019-08-23 16:46:17,182] TRAIN Iter 135000: lr = 0.275002,	loss = 2.704888,	Top-1 err = 0.417480,	Top-5 err = 0.195654,	data_time = 0.050329,	train_time = 0.818748
[2019-08-23 16:46:24,100] TRAIN Iter 135020: lr = 0.274968,	loss = 2.657504,	Top-1 err = 0.410791,	Top-5 err = 0.191650,	data_time = 0.050494,	train_time = 0.345867
[2019-08-23 16:46:42,677] TRAIN Iter 135040: lr = 0.274935,	loss = 2.645189,	Top-1 err = 0.413379,	Top-5 err = 0.194434,	data_time = 0.051200,	train_time = 0.928822
[2019-08-23 16:46:57,561] TRAIN Iter 135060: lr = 0.274902,	loss = 2.700164,	Top-1 err = 0.412354,	Top-5 err = 0.191797,	data_time = 0.050704,	train_time = 0.744168
[2019-08-23 16:47:04,834] TRAIN Iter 135080: lr = 0.274868,	loss = 2.768098,	Top-1 err = 0.411865,	Top-5 err = 0.195410,	data_time = 0.050611,	train_time = 0.363659
[2019-08-23 16:47:19,997] TRAIN Iter 135100: lr = 0.274835,	loss = 2.740864,	Top-1 err = 0.413867,	Top-5 err = 0.190674,	data_time = 0.050090,	train_time = 0.758141
[2019-08-23 16:47:37,270] TRAIN Iter 135120: lr = 0.274802,	loss = 2.658636,	Top-1 err = 0.417383,	Top-5 err = 0.193750,	data_time = 0.049992,	train_time = 0.863602
[2019-08-23 16:47:44,542] TRAIN Iter 135140: lr = 0.274768,	loss = 2.710484,	Top-1 err = 0.410547,	Top-5 err = 0.186670,	data_time = 0.049903,	train_time = 0.363594
[2019-08-23 16:48:35,127] TRAIN Iter 135160: lr = 0.274735,	loss = 2.684738,	Top-1 err = 0.415083,	Top-5 err = 0.190964,	data_time = 0.050288,	train_time = 2.529248
[2019-08-23 16:48:41,949] TRAIN Iter 135180: lr = 0.274702,	loss = 2.614492,	Top-1 err = 0.415869,	Top-5 err = 0.190088,	data_time = 0.050565,	train_time = 0.341103
[2019-08-23 16:48:58,288] TRAIN Iter 135200: lr = 0.274668,	loss = 2.623299,	Top-1 err = 0.405176,	Top-5 err = 0.183545,	data_time = 0.050474,	train_time = 0.816922
[2019-08-23 16:49:14,341] TRAIN Iter 135220: lr = 0.274635,	loss = 2.588507,	Top-1 err = 0.408301,	Top-5 err = 0.185303,	data_time = 0.050867,	train_time = 0.802641
[2019-08-23 16:49:20,737] TRAIN Iter 135240: lr = 0.274602,	loss = 2.574124,	Top-1 err = 0.406641,	Top-5 err = 0.184961,	data_time = 0.050305,	train_time = 0.319784
[2019-08-23 16:49:37,065] TRAIN Iter 135260: lr = 0.274568,	loss = 2.548780,	Top-1 err = 0.397900,	Top-5 err = 0.180566,	data_time = 0.050212,	train_time = 0.816353
[2019-08-23 16:49:44,202] TRAIN Iter 135280: lr = 0.274535,	loss = 2.580787,	Top-1 err = 0.407764,	Top-5 err = 0.185840,	data_time = 0.050864,	train_time = 0.356839
[2019-08-23 16:49:59,369] TRAIN Iter 135300: lr = 0.274502,	loss = 2.668385,	Top-1 err = 0.404736,	Top-5 err = 0.187939,	data_time = 0.050574,	train_time = 0.758369
[2019-08-23 16:50:17,019] TRAIN Iter 135320: lr = 0.274468,	loss = 2.653354,	Top-1 err = 0.414160,	Top-5 err = 0.186865,	data_time = 0.050333,	train_time = 0.882478
[2019-08-23 16:50:24,203] TRAIN Iter 135340: lr = 0.274435,	loss = 2.658284,	Top-1 err = 0.406592,	Top-5 err = 0.188574,	data_time = 0.050620,	train_time = 0.359203
[2019-08-23 16:50:37,784] TRAIN Iter 135360: lr = 0.274402,	loss = 2.627599,	Top-1 err = 0.402490,	Top-5 err = 0.185596,	data_time = 0.050471,	train_time = 0.679010
[2019-08-23 16:50:53,321] TRAIN Iter 135380: lr = 0.274368,	loss = 2.642867,	Top-1 err = 0.413135,	Top-5 err = 0.191699,	data_time = 0.133715,	train_time = 0.776834
[2019-08-23 16:51:00,254] TRAIN Iter 135400: lr = 0.274335,	loss = 2.585601,	Top-1 err = 0.408252,	Top-5 err = 0.185596,	data_time = 0.050428,	train_time = 0.346635
[2019-08-23 16:51:14,868] TRAIN Iter 135420: lr = 0.274302,	loss = 2.716824,	Top-1 err = 0.410498,	Top-5 err = 0.191650,	data_time = 0.050576,	train_time = 0.730691
[2019-08-23 16:51:21,586] TRAIN Iter 135440: lr = 0.274268,	loss = 2.641516,	Top-1 err = 0.405859,	Top-5 err = 0.188672,	data_time = 0.050394,	train_time = 0.335902
[2019-08-23 16:51:36,868] TRAIN Iter 135460: lr = 0.274235,	loss = 2.604877,	Top-1 err = 0.411670,	Top-5 err = 0.187305,	data_time = 0.050479,	train_time = 0.764045
[2019-08-23 16:51:52,857] TRAIN Iter 135480: lr = 0.274202,	loss = 2.673893,	Top-1 err = 0.411914,	Top-5 err = 0.188916,	data_time = 0.050469,	train_time = 0.799448
[2019-08-23 16:51:59,539] TRAIN Iter 135500: lr = 0.274168,	loss = 2.603105,	Top-1 err = 0.408594,	Top-5 err = 0.186377,	data_time = 0.050440,	train_time = 0.334105
[2019-08-23 16:52:16,080] TRAIN Iter 135520: lr = 0.274135,	loss = 2.728186,	Top-1 err = 0.408203,	Top-5 err = 0.191260,	data_time = 0.050189,	train_time = 0.827029
[2019-08-23 16:52:31,463] TRAIN Iter 135540: lr = 0.274102,	loss = 2.518702,	Top-1 err = 0.406494,	Top-5 err = 0.190332,	data_time = 0.050500,	train_time = 0.769139
[2019-08-23 16:52:38,267] TRAIN Iter 135560: lr = 0.274068,	loss = 2.687468,	Top-1 err = 0.415039,	Top-5 err = 0.191699,	data_time = 0.050875,	train_time = 0.340197
[2019-08-23 16:52:54,888] TRAIN Iter 135580: lr = 0.274035,	loss = 2.633231,	Top-1 err = 0.405518,	Top-5 err = 0.187305,	data_time = 0.050399,	train_time = 0.831028
[2019-08-23 16:53:01,796] TRAIN Iter 135600: lr = 0.274002,	loss = 2.653889,	Top-1 err = 0.413379,	Top-5 err = 0.189404,	data_time = 0.050374,	train_time = 0.345384
[2019-08-23 16:53:18,335] TRAIN Iter 135620: lr = 0.273968,	loss = 2.669780,	Top-1 err = 0.402295,	Top-5 err = 0.185938,	data_time = 0.050575,	train_time = 0.826939
[2019-08-23 16:53:34,231] TRAIN Iter 135640: lr = 0.273935,	loss = 2.745363,	Top-1 err = 0.412646,	Top-5 err = 0.188379,	data_time = 0.050428,	train_time = 0.794747
[2019-08-23 16:53:40,682] TRAIN Iter 135660: lr = 0.273902,	loss = 2.675796,	Top-1 err = 0.409961,	Top-5 err = 0.190625,	data_time = 0.050683,	train_time = 0.322542
[2019-08-23 16:53:57,341] TRAIN Iter 135680: lr = 0.273868,	loss = 2.704968,	Top-1 err = 0.404248,	Top-5 err = 0.188184,	data_time = 0.050323,	train_time = 0.832925
[2019-08-23 16:54:14,650] TRAIN Iter 135700: lr = 0.273835,	loss = 2.743538,	Top-1 err = 0.411816,	Top-5 err = 0.188281,	data_time = 0.050506,	train_time = 0.865465
[2019-08-23 16:54:21,759] TRAIN Iter 135720: lr = 0.273802,	loss = 2.713490,	Top-1 err = 0.409180,	Top-5 err = 0.183789,	data_time = 0.050328,	train_time = 0.355455
[2019-08-23 16:54:37,113] TRAIN Iter 135740: lr = 0.273768,	loss = 2.643966,	Top-1 err = 0.409814,	Top-5 err = 0.188818,	data_time = 0.050430,	train_time = 0.767641
[2019-08-23 16:54:43,632] TRAIN Iter 135760: lr = 0.273735,	loss = 2.715143,	Top-1 err = 0.411475,	Top-5 err = 0.191406,	data_time = 0.050531,	train_time = 0.325934
[2019-08-23 16:55:01,470] TRAIN Iter 135780: lr = 0.273702,	loss = 2.854328,	Top-1 err = 0.418506,	Top-5 err = 0.192383,	data_time = 0.050578,	train_time = 0.891911
[2019-08-23 16:55:18,781] TRAIN Iter 135800: lr = 0.273668,	loss = 2.680914,	Top-1 err = 0.412402,	Top-5 err = 0.188086,	data_time = 0.050573,	train_time = 0.865554
[2019-08-23 16:55:25,454] TRAIN Iter 135820: lr = 0.273635,	loss = 2.713399,	Top-1 err = 0.412354,	Top-5 err = 0.191748,	data_time = 0.050427,	train_time = 0.333624
[2019-08-23 16:55:41,205] TRAIN Iter 135840: lr = 0.273602,	loss = 2.648083,	Top-1 err = 0.412061,	Top-5 err = 0.188135,	data_time = 0.050601,	train_time = 0.787507
[2019-08-23 16:55:57,115] TRAIN Iter 135860: lr = 0.273568,	loss = 2.667814,	Top-1 err = 0.408008,	Top-5 err = 0.187109,	data_time = 0.050117,	train_time = 0.795529
[2019-08-23 16:56:04,174] TRAIN Iter 135880: lr = 0.273535,	loss = 2.670801,	Top-1 err = 0.408545,	Top-5 err = 0.187012,	data_time = 0.050431,	train_time = 0.352938
[2019-08-23 16:56:21,789] TRAIN Iter 135900: lr = 0.273502,	loss = 2.701921,	Top-1 err = 0.409277,	Top-5 err = 0.188770,	data_time = 0.050803,	train_time = 0.880715
[2019-08-23 16:56:28,411] TRAIN Iter 135920: lr = 0.273468,	loss = 2.725450,	Top-1 err = 0.411719,	Top-5 err = 0.192334,	data_time = 0.050282,	train_time = 0.331077
[2019-08-23 16:56:45,685] TRAIN Iter 135940: lr = 0.273435,	loss = 2.641243,	Top-1 err = 0.410156,	Top-5 err = 0.187109,	data_time = 0.050512,	train_time = 0.863691
[2019-08-23 16:57:04,881] TRAIN Iter 135960: lr = 0.273402,	loss = 2.745615,	Top-1 err = 0.411279,	Top-5 err = 0.189941,	data_time = 0.050502,	train_time = 0.959790
[2019-08-23 16:57:11,831] TRAIN Iter 135980: lr = 0.273368,	loss = 2.651256,	Top-1 err = 0.412061,	Top-5 err = 0.192236,	data_time = 0.050965,	train_time = 0.347478
[2019-08-23 16:57:26,845] TRAIN Iter 136000: lr = 0.273335,	loss = 2.729702,	Top-1 err = 0.412158,	Top-5 err = 0.191992,	data_time = 0.050552,	train_time = 0.750714
[2019-08-23 16:57:44,551] TRAIN Iter 136020: lr = 0.273302,	loss = 2.750492,	Top-1 err = 0.409863,	Top-5 err = 0.189551,	data_time = 0.050543,	train_time = 0.885284
[2019-08-23 16:57:51,346] TRAIN Iter 136040: lr = 0.273268,	loss = 2.630556,	Top-1 err = 0.405273,	Top-5 err = 0.189160,	data_time = 0.050312,	train_time = 0.339702
[2019-08-23 16:58:09,777] TRAIN Iter 136060: lr = 0.273235,	loss = 2.607126,	Top-1 err = 0.408545,	Top-5 err = 0.189160,	data_time = 0.050497,	train_time = 0.921544
[2019-08-23 16:58:16,665] TRAIN Iter 136080: lr = 0.273202,	loss = 2.824129,	Top-1 err = 0.414404,	Top-5 err = 0.189502,	data_time = 0.050164,	train_time = 0.344389
[2019-08-23 16:58:32,439] TRAIN Iter 136100: lr = 0.273168,	loss = 2.653748,	Top-1 err = 0.411426,	Top-5 err = 0.188037,	data_time = 0.050448,	train_time = 0.788699
[2019-08-23 16:58:50,138] TRAIN Iter 136120: lr = 0.273135,	loss = 2.706604,	Top-1 err = 0.414355,	Top-5 err = 0.190918,	data_time = 0.050239,	train_time = 0.884946
[2019-08-23 16:58:56,698] TRAIN Iter 136140: lr = 0.273102,	loss = 2.665713,	Top-1 err = 0.413037,	Top-5 err = 0.188037,	data_time = 0.050282,	train_time = 0.327978
[2019-08-23 16:59:14,264] TRAIN Iter 136160: lr = 0.273068,	loss = 2.651053,	Top-1 err = 0.411572,	Top-5 err = 0.190381,	data_time = 0.050453,	train_time = 0.878303
[2019-08-23 16:59:34,337] TRAIN Iter 136180: lr = 0.273035,	loss = 2.636829,	Top-1 err = 0.410205,	Top-5 err = 0.189941,	data_time = 0.050351,	train_time = 1.003621
[2019-08-23 16:59:41,407] TRAIN Iter 136200: lr = 0.273002,	loss = 2.672997,	Top-1 err = 0.415234,	Top-5 err = 0.191162,	data_time = 0.050107,	train_time = 0.353502
[2019-08-23 16:59:58,669] TRAIN Iter 136220: lr = 0.272968,	loss = 2.726690,	Top-1 err = 0.409326,	Top-5 err = 0.191553,	data_time = 0.050335,	train_time = 0.863072
[2019-08-23 17:00:05,351] TRAIN Iter 136240: lr = 0.272935,	loss = 2.645430,	Top-1 err = 0.409473,	Top-5 err = 0.191211,	data_time = 0.050396,	train_time = 0.334087
[2019-08-23 17:00:24,388] TRAIN Iter 136260: lr = 0.272902,	loss = 2.651731,	Top-1 err = 0.413721,	Top-5 err = 0.188379,	data_time = 0.050304,	train_time = 0.951810
[2019-08-23 17:00:43,480] TRAIN Iter 136280: lr = 0.272868,	loss = 2.781841,	Top-1 err = 0.412402,	Top-5 err = 0.193066,	data_time = 0.050529,	train_time = 0.954586
[2019-08-23 17:00:50,122] TRAIN Iter 136300: lr = 0.272835,	loss = 2.706198,	Top-1 err = 0.416211,	Top-5 err = 0.194531,	data_time = 0.050458,	train_time = 0.332090
[2019-08-23 17:01:08,572] TRAIN Iter 136320: lr = 0.272802,	loss = 2.703582,	Top-1 err = 0.417383,	Top-5 err = 0.195264,	data_time = 0.050484,	train_time = 0.922496
[2019-08-23 17:01:27,144] TRAIN Iter 136340: lr = 0.272768,	loss = 2.675157,	Top-1 err = 0.409766,	Top-5 err = 0.189502,	data_time = 0.065470,	train_time = 0.928582
[2019-08-23 17:01:33,809] TRAIN Iter 136360: lr = 0.272735,	loss = 2.634285,	Top-1 err = 0.406152,	Top-5 err = 0.190234,	data_time = 0.050108,	train_time = 0.333268
[2019-08-23 17:01:50,521] TRAIN Iter 136380: lr = 0.272702,	loss = 2.646307,	Top-1 err = 0.407373,	Top-5 err = 0.190869,	data_time = 0.049885,	train_time = 0.835588
[2019-08-23 17:01:56,380] TRAIN Iter 136400: lr = 0.272668,	loss = 2.612323,	Top-1 err = 0.409619,	Top-5 err = 0.187891,	data_time = 0.049908,	train_time = 0.292942
[2019-08-23 17:02:50,559] TRAIN Iter 136420: lr = 0.272635,	loss = 2.634854,	Top-1 err = 0.422433,	Top-5 err = 0.194425,	data_time = 0.050424,	train_time = 2.708913
[2019-08-23 17:03:05,500] TRAIN Iter 136440: lr = 0.272602,	loss = 2.656340,	Top-1 err = 0.408545,	Top-5 err = 0.188574,	data_time = 0.050381,	train_time = 0.747023
[2019-08-23 17:03:13,137] TRAIN Iter 136460: lr = 0.272568,	loss = 2.626834,	Top-1 err = 0.404199,	Top-5 err = 0.189062,	data_time = 0.050894,	train_time = 0.381849
[2019-08-23 17:03:25,288] TRAIN Iter 136480: lr = 0.272535,	loss = 2.642995,	Top-1 err = 0.398926,	Top-5 err = 0.183887,	data_time = 0.050447,	train_time = 0.607527
[2019-08-23 17:03:32,713] TRAIN Iter 136500: lr = 0.272502,	loss = 2.706101,	Top-1 err = 0.400293,	Top-5 err = 0.184473,	data_time = 0.050538,	train_time = 0.371223
[2019-08-23 17:03:46,304] TRAIN Iter 136520: lr = 0.272468,	loss = 2.723760,	Top-1 err = 0.406982,	Top-5 err = 0.187451,	data_time = 0.050246,	train_time = 0.679551
[2019-08-23 17:04:02,196] TRAIN Iter 136540: lr = 0.272435,	loss = 2.652640,	Top-1 err = 0.405127,	Top-5 err = 0.185791,	data_time = 0.050398,	train_time = 0.794579
[2019-08-23 17:04:09,054] TRAIN Iter 136560: lr = 0.272402,	loss = 2.646395,	Top-1 err = 0.406055,	Top-5 err = 0.187842,	data_time = 0.050655,	train_time = 0.342897
[2019-08-23 17:04:25,904] TRAIN Iter 136580: lr = 0.272368,	loss = 2.591665,	Top-1 err = 0.402881,	Top-5 err = 0.184424,	data_time = 0.050432,	train_time = 0.842484
[2019-08-23 17:04:39,374] TRAIN Iter 136600: lr = 0.272335,	loss = 2.714624,	Top-1 err = 0.403174,	Top-5 err = 0.186475,	data_time = 0.131746,	train_time = 0.673508
[2019-08-23 17:04:46,738] TRAIN Iter 136620: lr = 0.272302,	loss = 2.657349,	Top-1 err = 0.409863,	Top-5 err = 0.187646,	data_time = 0.050798,	train_time = 0.368148
[2019-08-23 17:05:04,900] TRAIN Iter 136640: lr = 0.272268,	loss = 2.652125,	Top-1 err = 0.409473,	Top-5 err = 0.186621,	data_time = 0.050454,	train_time = 0.908120
[2019-08-23 17:05:11,787] TRAIN Iter 136660: lr = 0.272235,	loss = 2.631058,	Top-1 err = 0.402246,	Top-5 err = 0.187598,	data_time = 0.050458,	train_time = 0.344314
[2019-08-23 17:05:27,855] TRAIN Iter 136680: lr = 0.272202,	loss = 2.653947,	Top-1 err = 0.410400,	Top-5 err = 0.190186,	data_time = 0.050385,	train_time = 0.803375
[2019-08-23 17:05:41,407] TRAIN Iter 136700: lr = 0.272168,	loss = 2.608806,	Top-1 err = 0.405420,	Top-5 err = 0.187500,	data_time = 0.050405,	train_time = 0.677596
[2019-08-23 17:05:48,253] TRAIN Iter 136720: lr = 0.272135,	loss = 2.606688,	Top-1 err = 0.405859,	Top-5 err = 0.186328,	data_time = 0.050522,	train_time = 0.342305
[2019-08-23 17:06:05,265] TRAIN Iter 136740: lr = 0.272102,	loss = 2.526700,	Top-1 err = 0.406201,	Top-5 err = 0.181543,	data_time = 0.050366,	train_time = 0.850558
[2019-08-23 17:06:19,899] TRAIN Iter 136760: lr = 0.272068,	loss = 2.655713,	Top-1 err = 0.412598,	Top-5 err = 0.190527,	data_time = 0.050682,	train_time = 0.731714
[2019-08-23 17:06:28,564] TRAIN Iter 136780: lr = 0.272035,	loss = 2.578538,	Top-1 err = 0.405762,	Top-5 err = 0.189404,	data_time = 0.050413,	train_time = 0.433232
[2019-08-23 17:06:44,236] TRAIN Iter 136800: lr = 0.272002,	loss = 2.660473,	Top-1 err = 0.406299,	Top-5 err = 0.186621,	data_time = 0.050529,	train_time = 0.783576
[2019-08-23 17:06:50,883] TRAIN Iter 136820: lr = 0.271968,	loss = 2.693554,	Top-1 err = 0.415820,	Top-5 err = 0.190674,	data_time = 0.050381,	train_time = 0.332332
[2019-08-23 17:07:07,514] TRAIN Iter 136840: lr = 0.271935,	loss = 2.634833,	Top-1 err = 0.410645,	Top-5 err = 0.187500,	data_time = 0.050546,	train_time = 0.831536
[2019-08-23 17:07:22,951] TRAIN Iter 136860: lr = 0.271902,	loss = 2.676820,	Top-1 err = 0.404932,	Top-5 err = 0.190576,	data_time = 0.050467,	train_time = 0.771858
[2019-08-23 17:07:30,080] TRAIN Iter 136880: lr = 0.271868,	loss = 2.679418,	Top-1 err = 0.409961,	Top-5 err = 0.185840,	data_time = 0.050351,	train_time = 0.356414
[2019-08-23 17:07:45,643] TRAIN Iter 136900: lr = 0.271835,	loss = 2.690032,	Top-1 err = 0.402197,	Top-5 err = 0.187061,	data_time = 0.050751,	train_time = 0.778115
[2019-08-23 17:08:02,938] TRAIN Iter 136920: lr = 0.271802,	loss = 2.636081,	Top-1 err = 0.404541,	Top-5 err = 0.186621,	data_time = 0.117504,	train_time = 0.864763
[2019-08-23 17:08:10,317] TRAIN Iter 136940: lr = 0.271768,	loss = 2.596193,	Top-1 err = 0.406934,	Top-5 err = 0.189502,	data_time = 0.050454,	train_time = 0.368908
[2019-08-23 17:08:25,371] TRAIN Iter 136960: lr = 0.271735,	loss = 2.678678,	Top-1 err = 0.409180,	Top-5 err = 0.189355,	data_time = 0.050252,	train_time = 0.752704
[2019-08-23 17:08:32,359] TRAIN Iter 136980: lr = 0.271702,	loss = 2.709966,	Top-1 err = 0.411133,	Top-5 err = 0.189941,	data_time = 0.050470,	train_time = 0.349379
[2019-08-23 17:08:48,949] TRAIN Iter 137000: lr = 0.271668,	loss = 2.620515,	Top-1 err = 0.416064,	Top-5 err = 0.194434,	data_time = 0.051007,	train_time = 0.829489
[2019-08-23 17:09:04,433] TRAIN Iter 137020: lr = 0.271635,	loss = 2.626714,	Top-1 err = 0.410352,	Top-5 err = 0.194873,	data_time = 0.050611,	train_time = 0.774202
[2019-08-23 17:09:12,029] TRAIN Iter 137040: lr = 0.271602,	loss = 2.751910,	Top-1 err = 0.409961,	Top-5 err = 0.186914,	data_time = 0.050513,	train_time = 0.379777
[2019-08-23 17:09:26,917] TRAIN Iter 137060: lr = 0.271568,	loss = 2.595737,	Top-1 err = 0.405615,	Top-5 err = 0.190430,	data_time = 0.050509,	train_time = 0.744401
[2019-08-23 17:09:42,677] TRAIN Iter 137080: lr = 0.271535,	loss = 2.656022,	Top-1 err = 0.401855,	Top-5 err = 0.187158,	data_time = 0.050522,	train_time = 0.787956
[2019-08-23 17:09:50,343] TRAIN Iter 137100: lr = 0.271502,	loss = 2.674767,	Top-1 err = 0.406299,	Top-5 err = 0.185156,	data_time = 0.050313,	train_time = 0.383307
[2019-08-23 17:10:08,148] TRAIN Iter 137120: lr = 0.271468,	loss = 2.633850,	Top-1 err = 0.412598,	Top-5 err = 0.188379,	data_time = 0.126501,	train_time = 0.890232
[2019-08-23 17:10:15,233] TRAIN Iter 137140: lr = 0.271435,	loss = 2.645818,	Top-1 err = 0.407422,	Top-5 err = 0.189209,	data_time = 0.050311,	train_time = 0.354224
[2019-08-23 17:10:31,162] TRAIN Iter 137160: lr = 0.271402,	loss = 2.746018,	Top-1 err = 0.409570,	Top-5 err = 0.191211,	data_time = 0.050320,	train_time = 0.796441
[2019-08-23 17:10:46,687] TRAIN Iter 137180: lr = 0.271368,	loss = 2.767611,	Top-1 err = 0.416016,	Top-5 err = 0.193555,	data_time = 0.125836,	train_time = 0.776261
[2019-08-23 17:10:54,000] TRAIN Iter 137200: lr = 0.271335,	loss = 2.654667,	Top-1 err = 0.409521,	Top-5 err = 0.187012,	data_time = 0.050518,	train_time = 0.365634
[2019-08-23 17:11:11,075] TRAIN Iter 137220: lr = 0.271302,	loss = 2.750436,	Top-1 err = 0.407861,	Top-5 err = 0.186133,	data_time = 0.050634,	train_time = 0.853700
[2019-08-23 17:11:24,985] TRAIN Iter 137240: lr = 0.271268,	loss = 2.629426,	Top-1 err = 0.410010,	Top-5 err = 0.187305,	data_time = 0.050668,	train_time = 0.695506
[2019-08-23 17:11:34,510] TRAIN Iter 137260: lr = 0.271235,	loss = 2.662418,	Top-1 err = 0.409814,	Top-5 err = 0.187695,	data_time = 0.050307,	train_time = 0.476216
[2019-08-23 17:11:50,459] TRAIN Iter 137280: lr = 0.271202,	loss = 2.709031,	Top-1 err = 0.413184,	Top-5 err = 0.191357,	data_time = 0.050328,	train_time = 0.797441
[2019-08-23 17:11:57,233] TRAIN Iter 137300: lr = 0.271168,	loss = 2.647680,	Top-1 err = 0.413281,	Top-5 err = 0.191943,	data_time = 0.050662,	train_time = 0.338684
[2019-08-23 17:12:13,583] TRAIN Iter 137320: lr = 0.271135,	loss = 2.534281,	Top-1 err = 0.409131,	Top-5 err = 0.187891,	data_time = 0.050542,	train_time = 0.817506
[2019-08-23 17:12:28,774] TRAIN Iter 137340: lr = 0.271102,	loss = 2.662255,	Top-1 err = 0.404248,	Top-5 err = 0.189648,	data_time = 0.050218,	train_time = 0.759559
[2019-08-23 17:12:37,479] TRAIN Iter 137360: lr = 0.271068,	loss = 2.776844,	Top-1 err = 0.414160,	Top-5 err = 0.193359,	data_time = 0.050372,	train_time = 0.435209
[2019-08-23 17:12:55,111] TRAIN Iter 137380: lr = 0.271035,	loss = 2.714500,	Top-1 err = 0.412988,	Top-5 err = 0.193506,	data_time = 0.050180,	train_time = 0.881590
[2019-08-23 17:13:09,946] TRAIN Iter 137400: lr = 0.271002,	loss = 2.627944,	Top-1 err = 0.408984,	Top-5 err = 0.189111,	data_time = 0.050537,	train_time = 0.741759
[2019-08-23 17:13:19,086] TRAIN Iter 137420: lr = 0.270968,	loss = 2.737742,	Top-1 err = 0.409912,	Top-5 err = 0.188232,	data_time = 0.050645,	train_time = 0.456938
[2019-08-23 17:13:37,068] TRAIN Iter 137440: lr = 0.270935,	loss = 2.668905,	Top-1 err = 0.412549,	Top-5 err = 0.191699,	data_time = 0.050777,	train_time = 0.899099
[2019-08-23 17:13:43,697] TRAIN Iter 137460: lr = 0.270902,	loss = 2.672675,	Top-1 err = 0.407813,	Top-5 err = 0.189062,	data_time = 0.050149,	train_time = 0.331440
[2019-08-23 17:14:04,908] TRAIN Iter 137480: lr = 0.270868,	loss = 2.703935,	Top-1 err = 0.416357,	Top-5 err = 0.193213,	data_time = 0.050400,	train_time = 1.060522
[2019-08-23 17:14:21,364] TRAIN Iter 137500: lr = 0.270835,	loss = 2.661210,	Top-1 err = 0.411768,	Top-5 err = 0.188330,	data_time = 0.050443,	train_time = 0.822783
[2019-08-23 17:14:30,697] TRAIN Iter 137520: lr = 0.270802,	loss = 2.636230,	Top-1 err = 0.411523,	Top-5 err = 0.193164,	data_time = 0.050926,	train_time = 0.466677
[2019-08-23 17:14:47,691] TRAIN Iter 137540: lr = 0.270768,	loss = 2.709148,	Top-1 err = 0.413428,	Top-5 err = 0.195850,	data_time = 0.050441,	train_time = 0.849687
[2019-08-23 17:15:06,315] TRAIN Iter 137560: lr = 0.270735,	loss = 2.573997,	Top-1 err = 0.412744,	Top-5 err = 0.186133,	data_time = 0.130283,	train_time = 0.931192
[2019-08-23 17:15:13,307] TRAIN Iter 137580: lr = 0.270702,	loss = 2.664201,	Top-1 err = 0.413477,	Top-5 err = 0.193604,	data_time = 0.050336,	train_time = 0.349587
[2019-08-23 17:15:32,418] TRAIN Iter 137600: lr = 0.270668,	loss = 2.706415,	Top-1 err = 0.412207,	Top-5 err = 0.189746,	data_time = 0.049945,	train_time = 0.955500
[2019-08-23 17:15:39,188] TRAIN Iter 137620: lr = 0.270635,	loss = 2.727266,	Top-1 err = 0.413477,	Top-5 err = 0.192627,	data_time = 0.050140,	train_time = 0.338507
[2019-08-23 17:15:55,658] TRAIN Iter 137640: lr = 0.270602,	loss = 2.850237,	Top-1 err = 0.416504,	Top-5 err = 0.195654,	data_time = 0.049908,	train_time = 0.823471
[2019-08-23 17:16:49,123] TRAIN Iter 137660: lr = 0.270568,	loss = 2.736764,	Top-1 err = 0.418851,	Top-5 err = 0.197929,	data_time = 0.050428,	train_time = 2.673229
[2019-08-23 17:16:55,947] TRAIN Iter 137680: lr = 0.270535,	loss = 2.678216,	Top-1 err = 0.409473,	Top-5 err = 0.191016,	data_time = 0.050473,	train_time = 0.341171
[2019-08-23 17:17:13,431] TRAIN Iter 137700: lr = 0.270502,	loss = 2.602324,	Top-1 err = 0.411182,	Top-5 err = 0.187207,	data_time = 0.050963,	train_time = 0.874205
[2019-08-23 17:17:21,283] TRAIN Iter 137720: lr = 0.270468,	loss = 2.661334,	Top-1 err = 0.400781,	Top-5 err = 0.181592,	data_time = 0.050271,	train_time = 0.392603
[2019-08-23 17:17:35,580] TRAIN Iter 137740: lr = 0.270435,	loss = 2.594873,	Top-1 err = 0.402246,	Top-5 err = 0.184180,	data_time = 0.050446,	train_time = 0.714821
[2019-08-23 17:17:49,487] TRAIN Iter 137760: lr = 0.270402,	loss = 2.618932,	Top-1 err = 0.398779,	Top-5 err = 0.179150,	data_time = 0.050527,	train_time = 0.695329
[2019-08-23 17:17:56,590] TRAIN Iter 137780: lr = 0.270368,	loss = 2.600579,	Top-1 err = 0.409277,	Top-5 err = 0.185742,	data_time = 0.143986,	train_time = 0.355154
[2019-08-23 17:18:12,114] TRAIN Iter 137800: lr = 0.270335,	loss = 2.593650,	Top-1 err = 0.408984,	Top-5 err = 0.183643,	data_time = 0.050731,	train_time = 0.776157
[2019-08-23 17:18:26,782] TRAIN Iter 137820: lr = 0.270302,	loss = 2.661857,	Top-1 err = 0.406055,	Top-5 err = 0.189893,	data_time = 0.050800,	train_time = 0.733428
[2019-08-23 17:18:33,943] TRAIN Iter 137840: lr = 0.270268,	loss = 2.650337,	Top-1 err = 0.402441,	Top-5 err = 0.184961,	data_time = 0.050498,	train_time = 0.358022
[2019-08-23 17:18:49,087] TRAIN Iter 137860: lr = 0.270235,	loss = 2.684456,	Top-1 err = 0.404150,	Top-5 err = 0.186035,	data_time = 0.050788,	train_time = 0.757158
[2019-08-23 17:18:56,074] TRAIN Iter 137880: lr = 0.270202,	loss = 2.638341,	Top-1 err = 0.407227,	Top-5 err = 0.186768,	data_time = 0.050492,	train_time = 0.349369
[2019-08-23 17:19:11,470] TRAIN Iter 137900: lr = 0.270168,	loss = 2.757277,	Top-1 err = 0.410156,	Top-5 err = 0.191064,	data_time = 0.050640,	train_time = 0.769795
[2019-08-23 17:19:27,022] TRAIN Iter 137920: lr = 0.270135,	loss = 2.550598,	Top-1 err = 0.407861,	Top-5 err = 0.190039,	data_time = 0.050752,	train_time = 0.777576
[2019-08-23 17:19:34,266] TRAIN Iter 137940: lr = 0.270102,	loss = 2.651418,	Top-1 err = 0.400439,	Top-5 err = 0.185059,	data_time = 0.050864,	train_time = 0.362168
[2019-08-23 17:19:49,762] TRAIN Iter 137960: lr = 0.270068,	loss = 2.629914,	Top-1 err = 0.402588,	Top-5 err = 0.184766,	data_time = 0.050469,	train_time = 0.774809
[2019-08-23 17:20:03,074] TRAIN Iter 137980: lr = 0.270035,	loss = 2.624886,	Top-1 err = 0.408203,	Top-5 err = 0.189404,	data_time = 0.050788,	train_time = 0.665558
[2019-08-23 17:20:12,990] TRAIN Iter 138000: lr = 0.270002,	loss = 2.615904,	Top-1 err = 0.408252,	Top-5 err = 0.190479,	data_time = 0.050662,	train_time = 0.495821
[2019-08-23 17:20:27,800] TRAIN Iter 138020: lr = 0.269968,	loss = 2.655897,	Top-1 err = 0.409180,	Top-5 err = 0.184766,	data_time = 0.050271,	train_time = 0.740475
[2019-08-23 17:20:35,027] TRAIN Iter 138040: lr = 0.269935,	loss = 2.722064,	Top-1 err = 0.408398,	Top-5 err = 0.190234,	data_time = 0.050690,	train_time = 0.361343
[2019-08-23 17:20:50,810] TRAIN Iter 138060: lr = 0.269902,	loss = 2.658540,	Top-1 err = 0.409082,	Top-5 err = 0.189160,	data_time = 0.050526,	train_time = 0.789125
[2019-08-23 17:21:06,392] TRAIN Iter 138080: lr = 0.269868,	loss = 2.671888,	Top-1 err = 0.406934,	Top-5 err = 0.190039,	data_time = 0.050296,	train_time = 0.779072
[2019-08-23 17:21:13,360] TRAIN Iter 138100: lr = 0.269835,	loss = 2.658528,	Top-1 err = 0.409766,	Top-5 err = 0.186279,	data_time = 0.050473,	train_time = 0.348398
[2019-08-23 17:21:29,746] TRAIN Iter 138120: lr = 0.269802,	loss = 2.694063,	Top-1 err = 0.413232,	Top-5 err = 0.190039,	data_time = 0.050853,	train_time = 0.819257
[2019-08-23 17:21:45,566] TRAIN Iter 138140: lr = 0.269768,	loss = 2.626707,	Top-1 err = 0.402930,	Top-5 err = 0.181982,	data_time = 0.095996,	train_time = 0.790987
[2019-08-23 17:21:52,511] TRAIN Iter 138160: lr = 0.269735,	loss = 2.619816,	Top-1 err = 0.406738,	Top-5 err = 0.185303,	data_time = 0.050588,	train_time = 0.347254
[2019-08-23 17:22:08,726] TRAIN Iter 138180: lr = 0.269702,	loss = 2.611424,	Top-1 err = 0.407617,	Top-5 err = 0.188135,	data_time = 0.050475,	train_time = 0.810753
[2019-08-23 17:22:16,360] TRAIN Iter 138200: lr = 0.269668,	loss = 2.698389,	Top-1 err = 0.417871,	Top-5 err = 0.191895,	data_time = 0.050772,	train_time = 0.381666
[2019-08-23 17:22:32,772] TRAIN Iter 138220: lr = 0.269635,	loss = 2.606612,	Top-1 err = 0.410742,	Top-5 err = 0.191602,	data_time = 0.050553,	train_time = 0.820568
[2019-08-23 17:22:47,980] TRAIN Iter 138240: lr = 0.269602,	loss = 2.673943,	Top-1 err = 0.410449,	Top-5 err = 0.187842,	data_time = 0.050258,	train_time = 0.760375
[2019-08-23 17:22:55,088] TRAIN Iter 138260: lr = 0.269568,	loss = 2.676888,	Top-1 err = 0.404590,	Top-5 err = 0.186035,	data_time = 0.135989,	train_time = 0.355424
[2019-08-23 17:23:10,625] TRAIN Iter 138280: lr = 0.269535,	loss = 2.629885,	Top-1 err = 0.406299,	Top-5 err = 0.189014,	data_time = 0.050844,	train_time = 0.776811
[2019-08-23 17:23:25,960] TRAIN Iter 138300: lr = 0.269502,	loss = 2.690827,	Top-1 err = 0.402588,	Top-5 err = 0.184033,	data_time = 0.142910,	train_time = 0.766737
[2019-08-23 17:23:33,461] TRAIN Iter 138320: lr = 0.269468,	loss = 2.608619,	Top-1 err = 0.401660,	Top-5 err = 0.188379,	data_time = 0.050167,	train_time = 0.375035
[2019-08-23 17:23:50,540] TRAIN Iter 138340: lr = 0.269435,	loss = 2.639943,	Top-1 err = 0.407959,	Top-5 err = 0.186377,	data_time = 0.050529,	train_time = 0.853938
[2019-08-23 17:23:57,817] TRAIN Iter 138360: lr = 0.269402,	loss = 2.630333,	Top-1 err = 0.402930,	Top-5 err = 0.183057,	data_time = 0.050468,	train_time = 0.363825
[2019-08-23 17:24:14,224] TRAIN Iter 138380: lr = 0.269368,	loss = 2.628068,	Top-1 err = 0.413623,	Top-5 err = 0.193164,	data_time = 0.050376,	train_time = 0.820319
[2019-08-23 17:24:29,195] TRAIN Iter 138400: lr = 0.269335,	loss = 2.684080,	Top-1 err = 0.409717,	Top-5 err = 0.188379,	data_time = 0.050331,	train_time = 0.748564
[2019-08-23 17:24:36,240] TRAIN Iter 138420: lr = 0.269302,	loss = 2.748874,	Top-1 err = 0.410596,	Top-5 err = 0.188867,	data_time = 0.050821,	train_time = 0.352213
[2019-08-23 17:24:52,325] TRAIN Iter 138440: lr = 0.269268,	loss = 2.562196,	Top-1 err = 0.407959,	Top-5 err = 0.192871,	data_time = 0.050466,	train_time = 0.804263
[2019-08-23 17:25:08,245] TRAIN Iter 138460: lr = 0.269235,	loss = 2.595316,	Top-1 err = 0.410010,	Top-5 err = 0.192529,	data_time = 0.050469,	train_time = 0.795983
[2019-08-23 17:25:14,969] TRAIN Iter 138480: lr = 0.269202,	loss = 2.598538,	Top-1 err = 0.407715,	Top-5 err = 0.185449,	data_time = 0.050396,	train_time = 0.336177
[2019-08-23 17:25:31,724] TRAIN Iter 138500: lr = 0.269168,	loss = 2.704905,	Top-1 err = 0.409424,	Top-5 err = 0.186768,	data_time = 0.050293,	train_time = 0.837744
[2019-08-23 17:25:38,714] TRAIN Iter 138520: lr = 0.269135,	loss = 2.595759,	Top-1 err = 0.410107,	Top-5 err = 0.185596,	data_time = 0.050398,	train_time = 0.349470
[2019-08-23 17:25:55,468] TRAIN Iter 138540: lr = 0.269102,	loss = 2.663312,	Top-1 err = 0.411572,	Top-5 err = 0.192188,	data_time = 0.050500,	train_time = 0.837697
[2019-08-23 17:26:14,193] TRAIN Iter 138560: lr = 0.269068,	loss = 2.637416,	Top-1 err = 0.412549,	Top-5 err = 0.189600,	data_time = 0.050310,	train_time = 0.936256
[2019-08-23 17:26:21,282] TRAIN Iter 138580: lr = 0.269035,	loss = 2.687420,	Top-1 err = 0.411719,	Top-5 err = 0.188623,	data_time = 0.050455,	train_time = 0.354399
[2019-08-23 17:26:36,254] TRAIN Iter 138600: lr = 0.269002,	loss = 2.683339,	Top-1 err = 0.400732,	Top-5 err = 0.188770,	data_time = 0.050148,	train_time = 0.748619
[2019-08-23 17:26:53,653] TRAIN Iter 138620: lr = 0.268968,	loss = 2.696180,	Top-1 err = 0.407422,	Top-5 err = 0.192676,	data_time = 0.050337,	train_time = 0.869925
[2019-08-23 17:27:00,549] TRAIN Iter 138640: lr = 0.268935,	loss = 2.591810,	Top-1 err = 0.405615,	Top-5 err = 0.185107,	data_time = 0.050371,	train_time = 0.344806
[2019-08-23 17:27:17,559] TRAIN Iter 138660: lr = 0.268902,	loss = 2.738804,	Top-1 err = 0.411426,	Top-5 err = 0.191895,	data_time = 0.050438,	train_time = 0.850447
[2019-08-23 17:27:24,838] TRAIN Iter 138680: lr = 0.268868,	loss = 2.702957,	Top-1 err = 0.411865,	Top-5 err = 0.189209,	data_time = 0.050468,	train_time = 0.363938
[2019-08-23 17:27:42,222] TRAIN Iter 138700: lr = 0.268835,	loss = 2.561926,	Top-1 err = 0.408545,	Top-5 err = 0.188721,	data_time = 0.050673,	train_time = 0.869183
[2019-08-23 17:27:59,994] TRAIN Iter 138720: lr = 0.268802,	loss = 2.645445,	Top-1 err = 0.412354,	Top-5 err = 0.192529,	data_time = 0.050528,	train_time = 0.888589
[2019-08-23 17:28:06,909] TRAIN Iter 138740: lr = 0.268768,	loss = 2.636920,	Top-1 err = 0.407471,	Top-5 err = 0.187354,	data_time = 0.050781,	train_time = 0.345753
[2019-08-23 17:28:23,840] TRAIN Iter 138760: lr = 0.268735,	loss = 2.646585,	Top-1 err = 0.415820,	Top-5 err = 0.191064,	data_time = 0.050630,	train_time = 0.846539
[2019-08-23 17:28:40,141] TRAIN Iter 138780: lr = 0.268702,	loss = 2.640956,	Top-1 err = 0.417529,	Top-5 err = 0.191895,	data_time = 0.050391,	train_time = 0.815023
[2019-08-23 17:28:46,673] TRAIN Iter 138800: lr = 0.268668,	loss = 2.674525,	Top-1 err = 0.407861,	Top-5 err = 0.188330,	data_time = 0.050761,	train_time = 0.326571
[2019-08-23 17:29:04,651] TRAIN Iter 138820: lr = 0.268635,	loss = 2.690737,	Top-1 err = 0.418359,	Top-5 err = 0.194482,	data_time = 0.050504,	train_time = 0.898891
[2019-08-23 17:29:12,092] TRAIN Iter 138840: lr = 0.268602,	loss = 2.821947,	Top-1 err = 0.412842,	Top-5 err = 0.190869,	data_time = 0.050733,	train_time = 0.372034
[2019-08-23 17:29:29,532] TRAIN Iter 138860: lr = 0.268568,	loss = 2.565115,	Top-1 err = 0.411621,	Top-5 err = 0.190918,	data_time = 0.050090,	train_time = 0.872024
[2019-08-23 17:29:46,240] TRAIN Iter 138880: lr = 0.268535,	loss = 2.755586,	Top-1 err = 0.410449,	Top-5 err = 0.189062,	data_time = 0.049935,	train_time = 0.835393
[2019-08-23 17:29:52,377] TRAIN Iter 138900: lr = 0.268502,	loss = 2.672052,	Top-1 err = 0.412158,	Top-5 err = 0.191260,	data_time = 0.049902,	train_time = 0.306842
[2019-08-23 17:30:46,368] TRAIN Iter 138920: lr = 0.268468,	loss = 2.696331,	Top-1 err = 0.419234,	Top-5 err = 0.192859,	data_time = 0.050430,	train_time = 2.699503
[2019-08-23 17:30:54,154] TRAIN Iter 138940: lr = 0.268435,	loss = 2.644044,	Top-1 err = 0.404834,	Top-5 err = 0.183643,	data_time = 0.050717,	train_time = 0.389311
[2019-08-23 17:31:08,243] TRAIN Iter 138960: lr = 0.268402,	loss = 2.653048,	Top-1 err = 0.404346,	Top-5 err = 0.186865,	data_time = 0.050513,	train_time = 0.704421
[2019-08-23 17:31:21,604] TRAIN Iter 138980: lr = 0.268368,	loss = 2.629756,	Top-1 err = 0.406152,	Top-5 err = 0.183398,	data_time = 0.050402,	train_time = 0.668041
[2019-08-23 17:31:28,595] TRAIN Iter 139000: lr = 0.268335,	loss = 2.603934,	Top-1 err = 0.399854,	Top-5 err = 0.181787,	data_time = 0.050392,	train_time = 0.349504
[2019-08-23 17:31:46,479] TRAIN Iter 139020: lr = 0.268302,	loss = 2.722513,	Top-1 err = 0.408203,	Top-5 err = 0.184033,	data_time = 0.050751,	train_time = 0.894206
[2019-08-23 17:32:02,051] TRAIN Iter 139040: lr = 0.268268,	loss = 2.619432,	Top-1 err = 0.406543,	Top-5 err = 0.186475,	data_time = 0.125338,	train_time = 0.778573
[2019-08-23 17:32:08,811] TRAIN Iter 139060: lr = 0.268235,	loss = 2.573329,	Top-1 err = 0.396387,	Top-5 err = 0.182178,	data_time = 0.050536,	train_time = 0.337997
[2019-08-23 17:32:26,072] TRAIN Iter 139080: lr = 0.268202,	loss = 2.676336,	Top-1 err = 0.397559,	Top-5 err = 0.182715,	data_time = 0.050449,	train_time = 0.863037
[2019-08-23 17:32:33,920] TRAIN Iter 139100: lr = 0.268168,	loss = 2.678345,	Top-1 err = 0.402734,	Top-5 err = 0.180664,	data_time = 0.050634,	train_time = 0.392403
[2019-08-23 17:32:48,620] TRAIN Iter 139120: lr = 0.268135,	loss = 2.723500,	Top-1 err = 0.410938,	Top-5 err = 0.186133,	data_time = 0.050490,	train_time = 0.734967
[2019-08-23 17:33:02,848] TRAIN Iter 139140: lr = 0.268102,	loss = 2.696370,	Top-1 err = 0.402246,	Top-5 err = 0.185645,	data_time = 0.113663,	train_time = 0.711373
[2019-08-23 17:33:09,781] TRAIN Iter 139160: lr = 0.268068,	loss = 2.671309,	Top-1 err = 0.405029,	Top-5 err = 0.185938,	data_time = 0.050494,	train_time = 0.346661
[2019-08-23 17:33:25,203] TRAIN Iter 139180: lr = 0.268035,	loss = 2.655514,	Top-1 err = 0.400293,	Top-5 err = 0.181396,	data_time = 0.050369,	train_time = 0.771067
[2019-08-23 17:33:41,587] TRAIN Iter 139200: lr = 0.268002,	loss = 2.679363,	Top-1 err = 0.397168,	Top-5 err = 0.183936,	data_time = 0.050216,	train_time = 0.819213
[2019-08-23 17:33:48,651] TRAIN Iter 139220: lr = 0.267968,	loss = 2.714781,	Top-1 err = 0.405420,	Top-5 err = 0.188281,	data_time = 0.178275,	train_time = 0.353144
[2019-08-23 17:34:03,444] TRAIN Iter 139240: lr = 0.267935,	loss = 2.647817,	Top-1 err = 0.406836,	Top-5 err = 0.188770,	data_time = 0.050728,	train_time = 0.739655
[2019-08-23 17:34:10,923] TRAIN Iter 139260: lr = 0.267902,	loss = 2.620102,	Top-1 err = 0.401953,	Top-5 err = 0.187842,	data_time = 0.050852,	train_time = 0.373943
[2019-08-23 17:34:25,714] TRAIN Iter 139280: lr = 0.267868,	loss = 2.706735,	Top-1 err = 0.406104,	Top-5 err = 0.186182,	data_time = 0.050739,	train_time = 0.739530
[2019-08-23 17:34:42,138] TRAIN Iter 139300: lr = 0.267835,	loss = 2.657042,	Top-1 err = 0.410156,	Top-5 err = 0.188623,	data_time = 0.050379,	train_time = 0.821177
[2019-08-23 17:34:49,108] TRAIN Iter 139320: lr = 0.267802,	loss = 2.624489,	Top-1 err = 0.410107,	Top-5 err = 0.188037,	data_time = 0.050448,	train_time = 0.348485
[2019-08-23 17:35:05,638] TRAIN Iter 139340: lr = 0.267768,	loss = 2.619212,	Top-1 err = 0.411035,	Top-5 err = 0.187793,	data_time = 0.050753,	train_time = 0.826514
[2019-08-23 17:35:20,248] TRAIN Iter 139360: lr = 0.267735,	loss = 2.698695,	Top-1 err = 0.413086,	Top-5 err = 0.194238,	data_time = 0.113703,	train_time = 0.730473
[2019-08-23 17:35:28,089] TRAIN Iter 139380: lr = 0.267702,	loss = 2.626446,	Top-1 err = 0.405322,	Top-5 err = 0.184570,	data_time = 0.050570,	train_time = 0.392001
[2019-08-23 17:35:43,729] TRAIN Iter 139400: lr = 0.267668,	loss = 2.678844,	Top-1 err = 0.407422,	Top-5 err = 0.188477,	data_time = 0.050743,	train_time = 0.781990
[2019-08-23 17:35:50,640] TRAIN Iter 139420: lr = 0.267635,	loss = 2.574015,	Top-1 err = 0.411182,	Top-5 err = 0.186719,	data_time = 0.050759,	train_time = 0.345563
[2019-08-23 17:36:07,205] TRAIN Iter 139440: lr = 0.267602,	loss = 2.752851,	Top-1 err = 0.407422,	Top-5 err = 0.189307,	data_time = 0.050429,	train_time = 0.828234
[2019-08-23 17:36:24,536] TRAIN Iter 139460: lr = 0.267568,	loss = 2.588830,	Top-1 err = 0.403369,	Top-5 err = 0.185547,	data_time = 0.050577,	train_time = 0.866507
[2019-08-23 17:36:31,277] TRAIN Iter 139480: lr = 0.267535,	loss = 2.749943,	Top-1 err = 0.414111,	Top-5 err = 0.190332,	data_time = 0.050518,	train_time = 0.337050
[2019-08-23 17:36:47,952] TRAIN Iter 139500: lr = 0.267502,	loss = 2.677342,	Top-1 err = 0.404687,	Top-5 err = 0.186768,	data_time = 0.050390,	train_time = 0.833750
[2019-08-23 17:37:04,561] TRAIN Iter 139520: lr = 0.267468,	loss = 2.744719,	Top-1 err = 0.403223,	Top-5 err = 0.189648,	data_time = 0.050484,	train_time = 0.830420
[2019-08-23 17:37:11,206] TRAIN Iter 139540: lr = 0.267435,	loss = 2.709548,	Top-1 err = 0.403613,	Top-5 err = 0.184473,	data_time = 0.050488,	train_time = 0.332231
[2019-08-23 17:37:28,007] TRAIN Iter 139560: lr = 0.267402,	loss = 2.665427,	Top-1 err = 0.409277,	Top-5 err = 0.188672,	data_time = 0.050568,	train_time = 0.840064
[2019-08-23 17:37:36,501] TRAIN Iter 139580: lr = 0.267368,	loss = 2.713419,	Top-1 err = 0.413477,	Top-5 err = 0.189111,	data_time = 0.050989,	train_time = 0.424698
[2019-08-23 17:37:54,201] TRAIN Iter 139600: lr = 0.267335,	loss = 2.609412,	Top-1 err = 0.407764,	Top-5 err = 0.189746,	data_time = 0.050706,	train_time = 0.884970
[2019-08-23 17:38:08,676] TRAIN Iter 139620: lr = 0.267302,	loss = 2.612518,	Top-1 err = 0.410352,	Top-5 err = 0.188721,	data_time = 0.050257,	train_time = 0.723744
[2019-08-23 17:38:16,248] TRAIN Iter 139640: lr = 0.267268,	loss = 2.629104,	Top-1 err = 0.411865,	Top-5 err = 0.189600,	data_time = 0.050351,	train_time = 0.378565
[2019-08-23 17:38:31,277] TRAIN Iter 139660: lr = 0.267235,	loss = 2.655225,	Top-1 err = 0.414160,	Top-5 err = 0.185547,	data_time = 0.050218,	train_time = 0.751434
[2019-08-23 17:38:47,512] TRAIN Iter 139680: lr = 0.267202,	loss = 2.742293,	Top-1 err = 0.413965,	Top-5 err = 0.193457,	data_time = 0.050440,	train_time = 0.811762
[2019-08-23 17:38:54,454] TRAIN Iter 139700: lr = 0.267168,	loss = 2.661437,	Top-1 err = 0.414844,	Top-5 err = 0.192041,	data_time = 0.050378,	train_time = 0.347062
[2019-08-23 17:39:11,256] TRAIN Iter 139720: lr = 0.267135,	loss = 2.723865,	Top-1 err = 0.413232,	Top-5 err = 0.194434,	data_time = 0.050120,	train_time = 0.840109
[2019-08-23 17:39:19,089] TRAIN Iter 139740: lr = 0.267102,	loss = 2.719795,	Top-1 err = 0.414111,	Top-5 err = 0.191553,	data_time = 0.113765,	train_time = 0.391615
[2019-08-23 17:39:34,447] TRAIN Iter 139760: lr = 0.267068,	loss = 2.701130,	Top-1 err = 0.411377,	Top-5 err = 0.192676,	data_time = 0.050446,	train_time = 0.767867
[2019-08-23 17:39:51,442] TRAIN Iter 139780: lr = 0.267035,	loss = 2.663908,	Top-1 err = 0.409912,	Top-5 err = 0.190918,	data_time = 0.050463,	train_time = 0.849752
[2019-08-23 17:39:59,300] TRAIN Iter 139800: lr = 0.267002,	loss = 2.666535,	Top-1 err = 0.399512,	Top-5 err = 0.184766,	data_time = 0.129264,	train_time = 0.392872
[2019-08-23 17:40:13,104] TRAIN Iter 139820: lr = 0.266968,	loss = 2.680475,	Top-1 err = 0.414258,	Top-5 err = 0.189941,	data_time = 0.050505,	train_time = 0.690190
[2019-08-23 17:40:29,729] TRAIN Iter 139840: lr = 0.266935,	loss = 2.663516,	Top-1 err = 0.409131,	Top-5 err = 0.190039,	data_time = 0.050802,	train_time = 0.831254
[2019-08-23 17:40:37,117] TRAIN Iter 139860: lr = 0.266902,	loss = 2.658615,	Top-1 err = 0.412207,	Top-5 err = 0.191992,	data_time = 0.050769,	train_time = 0.369362
[2019-08-23 17:40:52,709] TRAIN Iter 139880: lr = 0.266868,	loss = 2.660784,	Top-1 err = 0.410938,	Top-5 err = 0.190283,	data_time = 0.050479,	train_time = 0.779604
[2019-08-23 17:41:00,323] TRAIN Iter 139900: lr = 0.266835,	loss = 2.685457,	Top-1 err = 0.405615,	Top-5 err = 0.190088,	data_time = 0.050270,	train_time = 0.380705
[2019-08-23 17:41:15,880] TRAIN Iter 139920: lr = 0.266802,	loss = 2.816154,	Top-1 err = 0.409912,	Top-5 err = 0.189404,	data_time = 0.050549,	train_time = 0.777825
[2019-08-23 17:41:32,200] TRAIN Iter 139940: lr = 0.266768,	loss = 2.617223,	Top-1 err = 0.402197,	Top-5 err = 0.186914,	data_time = 0.050627,	train_time = 0.815976
[2019-08-23 17:41:39,489] TRAIN Iter 139960: lr = 0.266735,	loss = 2.665245,	Top-1 err = 0.411670,	Top-5 err = 0.189111,	data_time = 0.050520,	train_time = 0.364424
[2019-08-23 17:41:55,611] TRAIN Iter 139980: lr = 0.266702,	loss = 2.639688,	Top-1 err = 0.410986,	Top-5 err = 0.188867,	data_time = 0.050471,	train_time = 0.806089
[2019-08-23 17:42:11,893] TRAIN Iter 140000: lr = 0.266668,	loss = 2.563251,	Top-1 err = 0.408594,	Top-5 err = 0.188525,	data_time = 0.050485,	train_time = 0.814089
[2019-08-23 17:43:12,264] TEST Iter 140000: loss = 2.453335,	Top-1 err = 0.371620,	Top-5 err = 0.146220,	val_time = 60.330134
[2019-08-23 17:43:18,445] TRAIN Iter 140020: lr = 0.266635,	loss = 2.640431,	Top-1 err = 0.404785,	Top-5 err = 0.185400,	data_time = 0.050549,	train_time = 0.309035
[2019-08-23 17:43:25,019] TRAIN Iter 140040: lr = 0.266602,	loss = 2.663826,	Top-1 err = 0.412695,	Top-5 err = 0.188916,	data_time = 0.050443,	train_time = 0.328709
[2019-08-23 17:43:31,561] TRAIN Iter 140060: lr = 0.266568,	loss = 2.634156,	Top-1 err = 0.405957,	Top-5 err = 0.187354,	data_time = 0.050425,	train_time = 0.327085
[2019-08-23 17:43:42,327] TRAIN Iter 140080: lr = 0.266535,	loss = 2.593182,	Top-1 err = 0.410400,	Top-5 err = 0.190430,	data_time = 0.050520,	train_time = 0.538292
[2019-08-23 17:43:59,371] TRAIN Iter 140100: lr = 0.266502,	loss = 2.621263,	Top-1 err = 0.412793,	Top-5 err = 0.189355,	data_time = 0.130086,	train_time = 0.852143
[2019-08-23 17:44:07,740] TRAIN Iter 140120: lr = 0.266468,	loss = 2.639169,	Top-1 err = 0.422656,	Top-5 err = 0.195312,	data_time = 0.050108,	train_time = 0.418447
[2019-08-23 17:44:23,878] TRAIN Iter 140140: lr = 0.266435,	loss = 2.742018,	Top-1 err = 0.410596,	Top-5 err = 0.186572,	data_time = 0.049987,	train_time = 0.806884
[2019-08-23 17:44:30,707] TRAIN Iter 140160: lr = 0.266402,	loss = 3.090023,	Top-1 err = 0.410698,	Top-5 err = 0.188556,	data_time = 0.007041,	train_time = 0.341435
[2019-08-23 17:45:17,251] TRAIN Iter 140180: lr = 0.266368,	loss = 2.734329,	Top-1 err = 0.411475,	Top-5 err = 0.188477,	data_time = 0.050461,	train_time = 2.327177
[2019-08-23 17:45:33,586] TRAIN Iter 140200: lr = 0.266335,	loss = 2.710295,	Top-1 err = 0.410303,	Top-5 err = 0.186523,	data_time = 0.050762,	train_time = 0.816740
[2019-08-23 17:45:40,354] TRAIN Iter 140220: lr = 0.266302,	loss = 2.607387,	Top-1 err = 0.410791,	Top-5 err = 0.187012,	data_time = 0.092069,	train_time = 0.338374
[2019-08-23 17:45:56,234] TRAIN Iter 140240: lr = 0.266268,	loss = 2.604733,	Top-1 err = 0.397852,	Top-5 err = 0.183887,	data_time = 0.050472,	train_time = 0.794023
[2019-08-23 17:46:09,196] TRAIN Iter 140260: lr = 0.266235,	loss = 2.663607,	Top-1 err = 0.401318,	Top-5 err = 0.183350,	data_time = 0.050497,	train_time = 0.648043
[2019-08-23 17:46:18,615] TRAIN Iter 140280: lr = 0.266202,	loss = 2.603291,	Top-1 err = 0.397119,	Top-5 err = 0.183545,	data_time = 0.050581,	train_time = 0.470975
[2019-08-23 17:46:33,970] TRAIN Iter 140300: lr = 0.266168,	loss = 2.660249,	Top-1 err = 0.406348,	Top-5 err = 0.187744,	data_time = 0.091477,	train_time = 0.767713
[2019-08-23 17:46:40,870] TRAIN Iter 140320: lr = 0.266135,	loss = 2.669837,	Top-1 err = 0.400391,	Top-5 err = 0.181201,	data_time = 0.050748,	train_time = 0.345008
[2019-08-23 17:46:59,435] TRAIN Iter 140340: lr = 0.266102,	loss = 2.710931,	Top-1 err = 0.406201,	Top-5 err = 0.185205,	data_time = 0.050785,	train_time = 0.928204
[2019-08-23 17:47:15,364] TRAIN Iter 140360: lr = 0.266068,	loss = 2.561271,	Top-1 err = 0.404687,	Top-5 err = 0.183008,	data_time = 0.050649,	train_time = 0.796443
[2019-08-23 17:47:22,361] TRAIN Iter 140380: lr = 0.266035,	loss = 2.649020,	Top-1 err = 0.404541,	Top-5 err = 0.181934,	data_time = 0.131849,	train_time = 0.349869
[2019-08-23 17:47:37,294] TRAIN Iter 140400: lr = 0.266002,	loss = 2.682518,	Top-1 err = 0.405859,	Top-5 err = 0.188477,	data_time = 0.050550,	train_time = 0.746605
[2019-08-23 17:47:46,951] TRAIN Iter 140420: lr = 0.265968,	loss = 2.713673,	Top-1 err = 0.398535,	Top-5 err = 0.183984,	data_time = 0.050434,	train_time = 0.482860
[2019-08-23 17:48:03,861] TRAIN Iter 140440: lr = 0.265935,	loss = 2.665809,	Top-1 err = 0.405762,	Top-5 err = 0.185059,	data_time = 0.149234,	train_time = 0.845468
[2019-08-23 17:48:16,487] TRAIN Iter 140460: lr = 0.265902,	loss = 2.603407,	Top-1 err = 0.407568,	Top-5 err = 0.187695,	data_time = 0.050329,	train_time = 0.631294
[2019-08-23 17:48:24,355] TRAIN Iter 140480: lr = 0.265868,	loss = 2.697624,	Top-1 err = 0.403027,	Top-5 err = 0.181836,	data_time = 0.051108,	train_time = 0.393389
[2019-08-23 17:48:35,674] TRAIN Iter 140500: lr = 0.265835,	loss = 2.507322,	Top-1 err = 0.406982,	Top-5 err = 0.185400,	data_time = 0.050419,	train_time = 0.565930
[2019-08-23 17:48:50,820] TRAIN Iter 140520: lr = 0.265802,	loss = 2.654181,	Top-1 err = 0.401318,	Top-5 err = 0.185693,	data_time = 0.138019,	train_time = 0.757266
[2019-08-23 17:48:57,783] TRAIN Iter 140540: lr = 0.265768,	loss = 2.731761,	Top-1 err = 0.404785,	Top-5 err = 0.186035,	data_time = 0.050644,	train_time = 0.348155
[2019-08-23 17:49:13,957] TRAIN Iter 140560: lr = 0.265735,	loss = 2.736184,	Top-1 err = 0.404346,	Top-5 err = 0.185352,	data_time = 0.050393,	train_time = 0.808662
[2019-08-23 17:49:26,698] TRAIN Iter 140580: lr = 0.265702,	loss = 2.654250,	Top-1 err = 0.407373,	Top-5 err = 0.186084,	data_time = 0.050357,	train_time = 0.637041
[2019-08-23 17:49:34,828] TRAIN Iter 140600: lr = 0.265668,	loss = 2.659470,	Top-1 err = 0.408936,	Top-5 err = 0.188086,	data_time = 0.050337,	train_time = 0.406511
[2019-08-23 17:49:51,072] TRAIN Iter 140620: lr = 0.265635,	loss = 2.709973,	Top-1 err = 0.408447,	Top-5 err = 0.189844,	data_time = 0.050727,	train_time = 0.812180
[2019-08-23 17:49:58,138] TRAIN Iter 140640: lr = 0.265602,	loss = 2.751827,	Top-1 err = 0.407617,	Top-5 err = 0.187012,	data_time = 0.050452,	train_time = 0.353265
[2019-08-23 17:50:15,552] TRAIN Iter 140660: lr = 0.265568,	loss = 2.733797,	Top-1 err = 0.409863,	Top-5 err = 0.190820,	data_time = 0.050493,	train_time = 0.870709
[2019-08-23 17:50:32,677] TRAIN Iter 140680: lr = 0.265535,	loss = 2.668329,	Top-1 err = 0.408936,	Top-5 err = 0.189844,	data_time = 0.145921,	train_time = 0.856227
[2019-08-23 17:50:39,529] TRAIN Iter 140700: lr = 0.265502,	loss = 2.750232,	Top-1 err = 0.407568,	Top-5 err = 0.191064,	data_time = 0.050369,	train_time = 0.342595
[2019-08-23 17:50:55,530] TRAIN Iter 140720: lr = 0.265468,	loss = 2.662675,	Top-1 err = 0.407861,	Top-5 err = 0.186377,	data_time = 0.050355,	train_time = 0.800027
[2019-08-23 17:51:08,701] TRAIN Iter 140740: lr = 0.265435,	loss = 2.658679,	Top-1 err = 0.410107,	Top-5 err = 0.194189,	data_time = 0.050559,	train_time = 0.658517
[2019-08-23 17:51:19,565] TRAIN Iter 140760: lr = 0.265402,	loss = 2.628309,	Top-1 err = 0.409814,	Top-5 err = 0.187646,	data_time = 0.050412,	train_time = 0.543221
[2019-08-23 17:51:35,821] TRAIN Iter 140780: lr = 0.265368,	loss = 2.636806,	Top-1 err = 0.407861,	Top-5 err = 0.187842,	data_time = 0.050582,	train_time = 0.812741
[2019-08-23 17:51:42,712] TRAIN Iter 140800: lr = 0.265335,	loss = 2.646269,	Top-1 err = 0.405664,	Top-5 err = 0.184473,	data_time = 0.050203,	train_time = 0.344582
[2019-08-23 17:51:59,017] TRAIN Iter 140820: lr = 0.265302,	loss = 2.614969,	Top-1 err = 0.408057,	Top-5 err = 0.187402,	data_time = 0.050674,	train_time = 0.815207
[2019-08-23 17:52:16,747] TRAIN Iter 140840: lr = 0.265268,	loss = 2.665620,	Top-1 err = 0.408887,	Top-5 err = 0.187549,	data_time = 0.050432,	train_time = 0.886479
[2019-08-23 17:52:23,623] TRAIN Iter 140860: lr = 0.265235,	loss = 2.635205,	Top-1 err = 0.408203,	Top-5 err = 0.188379,	data_time = 0.050448,	train_time = 0.343818
[2019-08-23 17:52:40,666] TRAIN Iter 140880: lr = 0.265202,	loss = 2.576998,	Top-1 err = 0.409912,	Top-5 err = 0.192090,	data_time = 0.050574,	train_time = 0.852096
[2019-08-23 17:52:52,148] TRAIN Iter 140900: lr = 0.265168,	loss = 2.735482,	Top-1 err = 0.410400,	Top-5 err = 0.188184,	data_time = 0.050406,	train_time = 0.574106
[2019-08-23 17:53:05,019] TRAIN Iter 140920: lr = 0.265135,	loss = 2.731741,	Top-1 err = 0.413818,	Top-5 err = 0.189453,	data_time = 0.050398,	train_time = 0.643526
[2019-08-23 17:53:21,311] TRAIN Iter 140940: lr = 0.265102,	loss = 2.683721,	Top-1 err = 0.408936,	Top-5 err = 0.191846,	data_time = 0.050468,	train_time = 0.814595
[2019-08-23 17:53:28,632] TRAIN Iter 140960: lr = 0.265068,	loss = 2.507942,	Top-1 err = 0.404004,	Top-5 err = 0.187646,	data_time = 0.050422,	train_time = 0.366056
[2019-08-23 17:53:42,987] TRAIN Iter 140980: lr = 0.265035,	loss = 2.682314,	Top-1 err = 0.409424,	Top-5 err = 0.188379,	data_time = 0.050548,	train_time = 0.717730
[2019-08-23 17:53:59,321] TRAIN Iter 141000: lr = 0.265002,	loss = 2.605350,	Top-1 err = 0.410693,	Top-5 err = 0.191602,	data_time = 0.050328,	train_time = 0.816671
[2019-08-23 17:54:06,459] TRAIN Iter 141020: lr = 0.264968,	loss = 2.606880,	Top-1 err = 0.405127,	Top-5 err = 0.187354,	data_time = 0.050380,	train_time = 0.356903
[2019-08-23 17:54:25,760] TRAIN Iter 141040: lr = 0.264935,	loss = 2.636712,	Top-1 err = 0.400293,	Top-5 err = 0.180713,	data_time = 0.050559,	train_time = 0.965002
[2019-08-23 17:54:38,983] TRAIN Iter 141060: lr = 0.264902,	loss = 2.760114,	Top-1 err = 0.413281,	Top-5 err = 0.191846,	data_time = 0.050444,	train_time = 0.661140
[2019-08-23 17:54:49,923] TRAIN Iter 141080: lr = 0.264868,	loss = 2.551332,	Top-1 err = 0.411865,	Top-5 err = 0.189062,	data_time = 0.050620,	train_time = 0.546985
[2019-08-23 17:55:05,757] TRAIN Iter 141100: lr = 0.264835,	loss = 2.712716,	Top-1 err = 0.400732,	Top-5 err = 0.182959,	data_time = 0.050485,	train_time = 0.791680
[2019-08-23 17:55:12,721] TRAIN Iter 141120: lr = 0.264802,	loss = 2.698994,	Top-1 err = 0.410596,	Top-5 err = 0.184619,	data_time = 0.050349,	train_time = 0.348216
[2019-08-23 17:55:30,526] TRAIN Iter 141140: lr = 0.264768,	loss = 2.537148,	Top-1 err = 0.401660,	Top-5 err = 0.191895,	data_time = 0.050143,	train_time = 0.890227
[2019-08-23 17:55:47,900] TRAIN Iter 141160: lr = 0.264735,	loss = 2.571386,	Top-1 err = 0.415283,	Top-5 err = 0.193213,	data_time = 0.050497,	train_time = 0.868656
[2019-08-23 17:55:55,283] TRAIN Iter 141180: lr = 0.264702,	loss = 2.772368,	Top-1 err = 0.409375,	Top-5 err = 0.191602,	data_time = 0.050522,	train_time = 0.369151
[2019-08-23 17:56:13,759] TRAIN Iter 141200: lr = 0.264668,	loss = 2.676422,	Top-1 err = 0.409375,	Top-5 err = 0.190527,	data_time = 0.050380,	train_time = 0.923801
[2019-08-23 17:56:27,906] TRAIN Iter 141220: lr = 0.264635,	loss = 2.690177,	Top-1 err = 0.415039,	Top-5 err = 0.192773,	data_time = 0.050580,	train_time = 0.707332
[2019-08-23 17:56:38,387] TRAIN Iter 141240: lr = 0.264602,	loss = 2.672345,	Top-1 err = 0.412305,	Top-5 err = 0.189258,	data_time = 0.050479,	train_time = 0.524010
[2019-08-23 17:56:55,670] TRAIN Iter 141260: lr = 0.264568,	loss = 2.766585,	Top-1 err = 0.408252,	Top-5 err = 0.189551,	data_time = 0.050348,	train_time = 0.864174
[2019-08-23 17:57:02,402] TRAIN Iter 141280: lr = 0.264535,	loss = 2.640152,	Top-1 err = 0.409668,	Top-5 err = 0.190332,	data_time = 0.050519,	train_time = 0.336554
[2019-08-23 17:57:21,311] TRAIN Iter 141300: lr = 0.264502,	loss = 2.637648,	Top-1 err = 0.410645,	Top-5 err = 0.191846,	data_time = 0.050140,	train_time = 0.945434
[2019-08-23 17:57:39,479] TRAIN Iter 141320: lr = 0.264468,	loss = 2.623471,	Top-1 err = 0.403955,	Top-5 err = 0.185889,	data_time = 0.150969,	train_time = 0.908431
[2019-08-23 17:57:46,498] TRAIN Iter 141340: lr = 0.264435,	loss = 2.620240,	Top-1 err = 0.402783,	Top-5 err = 0.185303,	data_time = 0.050310,	train_time = 0.350909
[2019-08-23 17:58:03,986] TRAIN Iter 141360: lr = 0.264402,	loss = 2.689962,	Top-1 err = 0.408594,	Top-5 err = 0.186914,	data_time = 0.050054,	train_time = 0.874395
[2019-08-23 17:58:17,897] TRAIN Iter 141380: lr = 0.264368,	loss = 2.735339,	Top-1 err = 0.410303,	Top-5 err = 0.191357,	data_time = 0.049897,	train_time = 0.695538
[2019-08-23 17:58:29,037] TRAIN Iter 141400: lr = 0.264335,	loss = 2.685481,	Top-1 err = 0.410498,	Top-5 err = 0.188330,	data_time = 0.049919,	train_time = 0.556956
[2019-08-23 17:59:16,708] TRAIN Iter 141420: lr = 0.264302,	loss = 2.721351,	Top-1 err = 0.408501,	Top-5 err = 0.190624,	data_time = 0.050278,	train_time = 2.383552
[2019-08-23 17:59:23,978] TRAIN Iter 141440: lr = 0.264268,	loss = 2.702429,	Top-1 err = 0.411133,	Top-5 err = 0.187207,	data_time = 0.050206,	train_time = 0.363485
[2019-08-23 17:59:39,083] TRAIN Iter 141460: lr = 0.264235,	loss = 2.582437,	Top-1 err = 0.402441,	Top-5 err = 0.179150,	data_time = 0.050412,	train_time = 0.755238
[2019-08-23 17:59:47,345] TRAIN Iter 141480: lr = 0.264202,	loss = 2.652407,	Top-1 err = 0.400830,	Top-5 err = 0.177637,	data_time = 0.194475,	train_time = 0.413104
[2019-08-23 17:59:59,597] TRAIN Iter 141500: lr = 0.264168,	loss = 2.717360,	Top-1 err = 0.404102,	Top-5 err = 0.185205,	data_time = 0.050357,	train_time = 0.612576
[2019-08-23 18:00:13,612] TRAIN Iter 141520: lr = 0.264135,	loss = 2.663224,	Top-1 err = 0.406738,	Top-5 err = 0.185156,	data_time = 0.050473,	train_time = 0.700724
[2019-08-23 18:00:20,774] TRAIN Iter 141540: lr = 0.264102,	loss = 2.588533,	Top-1 err = 0.403125,	Top-5 err = 0.185693,	data_time = 0.050902,	train_time = 0.358082
[2019-08-23 18:00:36,675] TRAIN Iter 141560: lr = 0.264068,	loss = 2.615426,	Top-1 err = 0.404590,	Top-5 err = 0.184717,	data_time = 0.050431,	train_time = 0.795031
[2019-08-23 18:00:53,386] TRAIN Iter 141580: lr = 0.264035,	loss = 2.681931,	Top-1 err = 0.401221,	Top-5 err = 0.184961,	data_time = 0.050235,	train_time = 0.835534
[2019-08-23 18:01:00,670] TRAIN Iter 141600: lr = 0.264002,	loss = 2.626151,	Top-1 err = 0.405957,	Top-5 err = 0.186768,	data_time = 0.050456,	train_time = 0.364171
[2019-08-23 18:01:14,326] TRAIN Iter 141620: lr = 0.263968,	loss = 2.664800,	Top-1 err = 0.405029,	Top-5 err = 0.187402,	data_time = 0.050432,	train_time = 0.682809
[2019-08-23 18:01:26,876] TRAIN Iter 141640: lr = 0.263935,	loss = 2.641279,	Top-1 err = 0.403418,	Top-5 err = 0.183008,	data_time = 2.796037,	train_time = 0.627508
[2019-08-23 18:01:36,662] TRAIN Iter 141660: lr = 0.263902,	loss = 2.615512,	Top-1 err = 0.402344,	Top-5 err = 0.184521,	data_time = 0.050498,	train_time = 0.489256
[2019-08-23 18:01:52,173] TRAIN Iter 141680: lr = 0.263868,	loss = 2.597850,	Top-1 err = 0.406982,	Top-5 err = 0.183447,	data_time = 0.050436,	train_time = 0.775531
[2019-08-23 18:01:59,344] TRAIN Iter 141700: lr = 0.263835,	loss = 2.597899,	Top-1 err = 0.397266,	Top-5 err = 0.177734,	data_time = 0.050512,	train_time = 0.358562
[2019-08-23 18:02:14,241] TRAIN Iter 141720: lr = 0.263802,	loss = 2.649894,	Top-1 err = 0.399121,	Top-5 err = 0.183301,	data_time = 0.050277,	train_time = 0.744823
[2019-08-23 18:02:27,585] TRAIN Iter 141740: lr = 0.263768,	loss = 2.596247,	Top-1 err = 0.406494,	Top-5 err = 0.186084,	data_time = 0.050557,	train_time = 0.667166
[2019-08-23 18:02:34,682] TRAIN Iter 141760: lr = 0.263735,	loss = 2.698404,	Top-1 err = 0.408252,	Top-5 err = 0.187451,	data_time = 0.050339,	train_time = 0.354869
[2019-08-23 18:02:50,498] TRAIN Iter 141780: lr = 0.263702,	loss = 2.704963,	Top-1 err = 0.407813,	Top-5 err = 0.190625,	data_time = 0.050613,	train_time = 0.790792
[2019-08-23 18:03:06,153] TRAIN Iter 141800: lr = 0.263668,	loss = 2.752097,	Top-1 err = 0.403271,	Top-5 err = 0.182227,	data_time = 4.649814,	train_time = 0.782697
[2019-08-23 18:03:13,490] TRAIN Iter 141820: lr = 0.263635,	loss = 2.649872,	Top-1 err = 0.403516,	Top-5 err = 0.185742,	data_time = 0.050826,	train_time = 0.366858
[2019-08-23 18:03:29,455] TRAIN Iter 141840: lr = 0.263602,	loss = 2.668954,	Top-1 err = 0.404102,	Top-5 err = 0.186572,	data_time = 0.096720,	train_time = 0.798252
[2019-08-23 18:03:36,306] TRAIN Iter 141860: lr = 0.263568,	loss = 2.678255,	Top-1 err = 0.407275,	Top-5 err = 0.190039,	data_time = 0.050279,	train_time = 0.342491
[2019-08-23 18:03:53,964] TRAIN Iter 141880: lr = 0.263535,	loss = 2.592135,	Top-1 err = 0.409619,	Top-5 err = 0.189941,	data_time = 0.050512,	train_time = 0.882886
[2019-08-23 18:04:09,148] TRAIN Iter 141900: lr = 0.263502,	loss = 2.600516,	Top-1 err = 0.408008,	Top-5 err = 0.189697,	data_time = 0.050479,	train_time = 0.759213
[2019-08-23 18:04:16,228] TRAIN Iter 141920: lr = 0.263468,	loss = 2.704768,	Top-1 err = 0.408691,	Top-5 err = 0.187451,	data_time = 0.050447,	train_time = 0.353994
[2019-08-23 18:04:31,537] TRAIN Iter 141940: lr = 0.263435,	loss = 2.633429,	Top-1 err = 0.410498,	Top-5 err = 0.189453,	data_time = 0.050550,	train_time = 0.765405
[2019-08-23 18:04:45,965] TRAIN Iter 141960: lr = 0.263402,	loss = 2.757952,	Top-1 err = 0.406787,	Top-5 err = 0.183105,	data_time = 6.176438,	train_time = 0.721400
[2019-08-23 18:04:54,209] TRAIN Iter 141980: lr = 0.263368,	loss = 2.662252,	Top-1 err = 0.402734,	Top-5 err = 0.185889,	data_time = 0.050941,	train_time = 0.412184
[2019-08-23 18:05:10,297] TRAIN Iter 142000: lr = 0.263335,	loss = 2.706404,	Top-1 err = 0.407764,	Top-5 err = 0.192578,	data_time = 0.050830,	train_time = 0.804404
[2019-08-23 18:05:17,280] TRAIN Iter 142020: lr = 0.263302,	loss = 2.728476,	Top-1 err = 0.404443,	Top-5 err = 0.188428,	data_time = 0.050483,	train_time = 0.349112
[2019-08-23 18:05:33,127] TRAIN Iter 142040: lr = 0.263268,	loss = 2.720441,	Top-1 err = 0.409668,	Top-5 err = 0.188135,	data_time = 0.050761,	train_time = 0.792326
[2019-08-23 18:05:49,087] TRAIN Iter 142060: lr = 0.263235,	loss = 2.723841,	Top-1 err = 0.408496,	Top-5 err = 0.192578,	data_time = 0.050471,	train_time = 0.798024
[2019-08-23 18:05:56,551] TRAIN Iter 142080: lr = 0.263202,	loss = 2.530529,	Top-1 err = 0.405615,	Top-5 err = 0.182568,	data_time = 0.050234,	train_time = 0.373166
[2019-08-23 18:06:14,612] TRAIN Iter 142100: lr = 0.263168,	loss = 2.700083,	Top-1 err = 0.404980,	Top-5 err = 0.186914,	data_time = 0.051202,	train_time = 0.903040
[2019-08-23 18:06:28,598] TRAIN Iter 142120: lr = 0.263135,	loss = 2.645566,	Top-1 err = 0.408838,	Top-5 err = 0.188525,	data_time = 3.661527,	train_time = 0.699266
[2019-08-23 18:06:38,806] TRAIN Iter 142140: lr = 0.263102,	loss = 2.693141,	Top-1 err = 0.409961,	Top-5 err = 0.189551,	data_time = 0.150444,	train_time = 0.510398
[2019-08-23 18:06:55,403] TRAIN Iter 142160: lr = 0.263068,	loss = 2.660018,	Top-1 err = 0.415088,	Top-5 err = 0.193506,	data_time = 0.050324,	train_time = 0.829816
[2019-08-23 18:07:02,281] TRAIN Iter 142180: lr = 0.263035,	loss = 2.661222,	Top-1 err = 0.407422,	Top-5 err = 0.188770,	data_time = 0.050425,	train_time = 0.343912
[2019-08-23 18:07:18,179] TRAIN Iter 142200: lr = 0.263002,	loss = 2.601055,	Top-1 err = 0.408301,	Top-5 err = 0.185449,	data_time = 0.050515,	train_time = 0.794888
[2019-08-23 18:07:32,656] TRAIN Iter 142220: lr = 0.262968,	loss = 2.683261,	Top-1 err = 0.413623,	Top-5 err = 0.197021,	data_time = 0.050611,	train_time = 0.723813
[2019-08-23 18:07:40,961] TRAIN Iter 142240: lr = 0.262935,	loss = 2.639761,	Top-1 err = 0.410596,	Top-5 err = 0.184131,	data_time = 0.050646,	train_time = 0.415264
[2019-08-23 18:07:57,068] TRAIN Iter 142260: lr = 0.262902,	loss = 2.782698,	Top-1 err = 0.412451,	Top-5 err = 0.185498,	data_time = 0.050390,	train_time = 0.805317
[2019-08-23 18:08:11,933] TRAIN Iter 142280: lr = 0.262868,	loss = 2.637657,	Top-1 err = 0.407275,	Top-5 err = 0.186719,	data_time = 7.197181,	train_time = 0.743249
[2019-08-23 18:08:19,326] TRAIN Iter 142300: lr = 0.262835,	loss = 2.669043,	Top-1 err = 0.403809,	Top-5 err = 0.188477,	data_time = 0.050452,	train_time = 0.369652
[2019-08-23 18:08:35,122] TRAIN Iter 142320: lr = 0.262802,	loss = 2.692201,	Top-1 err = 0.402246,	Top-5 err = 0.186670,	data_time = 0.156960,	train_time = 0.789772
[2019-08-23 18:08:42,182] TRAIN Iter 142340: lr = 0.262768,	loss = 2.689700,	Top-1 err = 0.409961,	Top-5 err = 0.188867,	data_time = 0.050462,	train_time = 0.352998
[2019-08-23 18:08:58,436] TRAIN Iter 142360: lr = 0.262735,	loss = 2.628587,	Top-1 err = 0.403223,	Top-5 err = 0.189648,	data_time = 0.050461,	train_time = 0.812657
[2019-08-23 18:09:13,921] TRAIN Iter 142380: lr = 0.262702,	loss = 2.669647,	Top-1 err = 0.402490,	Top-5 err = 0.184375,	data_time = 0.050565,	train_time = 0.774245
[2019-08-23 18:09:21,843] TRAIN Iter 142400: lr = 0.262668,	loss = 2.690482,	Top-1 err = 0.410254,	Top-5 err = 0.190039,	data_time = 0.050294,	train_time = 0.396071
[2019-08-23 18:09:37,928] TRAIN Iter 142420: lr = 0.262635,	loss = 2.771294,	Top-1 err = 0.408740,	Top-5 err = 0.191016,	data_time = 0.050483,	train_time = 0.804257
[2019-08-23 18:09:50,156] TRAIN Iter 142440: lr = 0.262602,	loss = 2.674387,	Top-1 err = 0.413818,	Top-5 err = 0.187500,	data_time = 2.376359,	train_time = 0.611348
[2019-08-23 18:10:01,311] TRAIN Iter 142460: lr = 0.262568,	loss = 2.654433,	Top-1 err = 0.408350,	Top-5 err = 0.191943,	data_time = 0.050782,	train_time = 0.557778
[2019-08-23 18:10:17,892] TRAIN Iter 142480: lr = 0.262535,	loss = 2.637991,	Top-1 err = 0.407178,	Top-5 err = 0.192480,	data_time = 0.050830,	train_time = 0.828995
[2019-08-23 18:10:25,002] TRAIN Iter 142500: lr = 0.262502,	loss = 2.598315,	Top-1 err = 0.407080,	Top-5 err = 0.188721,	data_time = 0.050759,	train_time = 0.355522
[2019-08-23 18:10:42,326] TRAIN Iter 142520: lr = 0.262468,	loss = 2.656728,	Top-1 err = 0.407910,	Top-5 err = 0.188232,	data_time = 0.050457,	train_time = 0.866154
[2019-08-23 18:10:55,949] TRAIN Iter 142540: lr = 0.262435,	loss = 2.698578,	Top-1 err = 0.410156,	Top-5 err = 0.187891,	data_time = 0.128359,	train_time = 0.681155
[2019-08-23 18:11:06,293] TRAIN Iter 142560: lr = 0.262402,	loss = 2.709755,	Top-1 err = 0.411426,	Top-5 err = 0.190625,	data_time = 0.050209,	train_time = 0.517212
[2019-08-23 18:11:23,362] TRAIN Iter 142580: lr = 0.262368,	loss = 2.712249,	Top-1 err = 0.406934,	Top-5 err = 0.189551,	data_time = 0.050545,	train_time = 0.853401
[2019-08-23 18:11:34,863] TRAIN Iter 142600: lr = 0.262335,	loss = 2.697427,	Top-1 err = 0.404834,	Top-5 err = 0.186719,	data_time = 1.734697,	train_time = 0.575047
[2019-08-23 18:11:48,043] TRAIN Iter 142620: lr = 0.262302,	loss = 2.702656,	Top-1 err = 0.410010,	Top-5 err = 0.188867,	data_time = 0.050200,	train_time = 0.658993
[2019-08-23 18:12:01,756] TRAIN Iter 142640: lr = 0.262268,	loss = 2.611923,	Top-1 err = 0.409814,	Top-5 err = 0.188623,	data_time = 0.049852,	train_time = 0.685627
[2019-08-23 18:12:07,956] TRAIN Iter 142660: lr = 0.262235,	loss = 2.625839,	Top-1 err = 0.411865,	Top-5 err = 0.190869,	data_time = 0.049956,	train_time = 0.310010
[2019-08-23 18:12:55,526] TRAIN Iter 142680: lr = 0.262202,	loss = 2.732138,	Top-1 err = 0.419810,	Top-5 err = 0.193806,	data_time = 0.050197,	train_time = 2.378458
[2019-08-23 18:13:08,145] TRAIN Iter 142700: lr = 0.262168,	loss = 2.666811,	Top-1 err = 0.399365,	Top-5 err = 0.186572,	data_time = 0.050583,	train_time = 0.630919
[2019-08-23 18:13:16,459] TRAIN Iter 142720: lr = 0.262135,	loss = 2.637455,	Top-1 err = 0.404785,	Top-5 err = 0.187793,	data_time = 0.050731,	train_time = 0.415686
[2019-08-23 18:13:31,168] TRAIN Iter 142740: lr = 0.262102,	loss = 2.690637,	Top-1 err = 0.402490,	Top-5 err = 0.186572,	data_time = 0.050969,	train_time = 0.735477
[2019-08-23 18:13:40,335] TRAIN Iter 142760: lr = 0.262068,	loss = 2.678298,	Top-1 err = 0.402197,	Top-5 err = 0.187939,	data_time = 0.051216,	train_time = 0.458296
[2019-08-23 18:13:57,014] TRAIN Iter 142780: lr = 0.262035,	loss = 2.548485,	Top-1 err = 0.402490,	Top-5 err = 0.183789,	data_time = 0.050634,	train_time = 0.833955
[2019-08-23 18:14:06,372] TRAIN Iter 142800: lr = 0.262002,	loss = 2.573047,	Top-1 err = 0.406055,	Top-5 err = 0.186426,	data_time = 0.105803,	train_time = 0.467902
[2019-08-23 18:14:15,594] TRAIN Iter 142820: lr = 0.261968,	loss = 2.544717,	Top-1 err = 0.405713,	Top-5 err = 0.182080,	data_time = 0.050591,	train_time = 0.461048
[2019-08-23 18:14:27,430] TRAIN Iter 142840: lr = 0.261935,	loss = 2.623517,	Top-1 err = 0.401953,	Top-5 err = 0.184570,	data_time = 0.050517,	train_time = 0.591817
[2019-08-23 18:14:40,833] TRAIN Iter 142860: lr = 0.261902,	loss = 2.676882,	Top-1 err = 0.399219,	Top-5 err = 0.183984,	data_time = 0.050534,	train_time = 0.670136
[2019-08-23 18:14:51,630] TRAIN Iter 142880: lr = 0.261868,	loss = 2.608707,	Top-1 err = 0.396875,	Top-5 err = 0.181543,	data_time = 0.050393,	train_time = 0.539804
[2019-08-23 18:15:04,355] TRAIN Iter 142900: lr = 0.261835,	loss = 2.563505,	Top-1 err = 0.401074,	Top-5 err = 0.180664,	data_time = 0.051036,	train_time = 0.636274
[2019-08-23 18:15:12,781] TRAIN Iter 142920: lr = 0.261802,	loss = 2.611586,	Top-1 err = 0.399805,	Top-5 err = 0.187549,	data_time = 0.050286,	train_time = 0.421291
[2019-08-23 18:15:29,008] TRAIN Iter 142940: lr = 0.261768,	loss = 2.660293,	Top-1 err = 0.404150,	Top-5 err = 0.187451,	data_time = 0.050356,	train_time = 0.811304
[2019-08-23 18:15:43,025] TRAIN Iter 142960: lr = 0.261735,	loss = 2.480525,	Top-1 err = 0.399316,	Top-5 err = 0.184668,	data_time = 0.050386,	train_time = 0.700825
[2019-08-23 18:15:50,396] TRAIN Iter 142980: lr = 0.261702,	loss = 2.558316,	Top-1 err = 0.399023,	Top-5 err = 0.183789,	data_time = 0.050335,	train_time = 0.368556
[2019-08-23 18:16:06,382] TRAIN Iter 143000: lr = 0.261668,	loss = 2.586224,	Top-1 err = 0.405127,	Top-5 err = 0.181885,	data_time = 0.050425,	train_time = 0.799302
[2019-08-23 18:16:20,697] TRAIN Iter 143020: lr = 0.261635,	loss = 2.730559,	Top-1 err = 0.397949,	Top-5 err = 0.183398,	data_time = 0.134585,	train_time = 0.715729
[2019-08-23 18:16:28,624] TRAIN Iter 143040: lr = 0.261602,	loss = 2.670957,	Top-1 err = 0.403076,	Top-5 err = 0.182910,	data_time = 0.050632,	train_time = 0.396352
[2019-08-23 18:16:44,075] TRAIN Iter 143060: lr = 0.261568,	loss = 2.621195,	Top-1 err = 0.397363,	Top-5 err = 0.184717,	data_time = 0.050678,	train_time = 0.772535
[2019-08-23 18:16:50,810] TRAIN Iter 143080: lr = 0.261535,	loss = 2.685682,	Top-1 err = 0.401709,	Top-5 err = 0.187109,	data_time = 0.050380,	train_time = 0.336722
[2019-08-23 18:17:07,243] TRAIN Iter 143100: lr = 0.261502,	loss = 2.598590,	Top-1 err = 0.405566,	Top-5 err = 0.183154,	data_time = 0.050783,	train_time = 0.821615
[2019-08-23 18:17:20,915] TRAIN Iter 143120: lr = 0.261468,	loss = 2.682071,	Top-1 err = 0.411670,	Top-5 err = 0.189697,	data_time = 0.134207,	train_time = 0.683617
[2019-08-23 18:17:28,364] TRAIN Iter 143140: lr = 0.261435,	loss = 2.618641,	Top-1 err = 0.403662,	Top-5 err = 0.184912,	data_time = 0.050512,	train_time = 0.372420
[2019-08-23 18:17:44,556] TRAIN Iter 143160: lr = 0.261402,	loss = 2.664850,	Top-1 err = 0.397754,	Top-5 err = 0.183203,	data_time = 0.154774,	train_time = 0.809573
[2019-08-23 18:17:57,799] TRAIN Iter 143180: lr = 0.261368,	loss = 2.562282,	Top-1 err = 0.402734,	Top-5 err = 0.182617,	data_time = 0.219877,	train_time = 0.662133
[2019-08-23 18:18:09,142] TRAIN Iter 143200: lr = 0.261335,	loss = 2.723061,	Top-1 err = 0.405615,	Top-5 err = 0.183643,	data_time = 0.050644,	train_time = 0.567126
[2019-08-23 18:18:25,067] TRAIN Iter 143220: lr = 0.261302,	loss = 2.689492,	Top-1 err = 0.405273,	Top-5 err = 0.183594,	data_time = 0.050564,	train_time = 0.796247
[2019-08-23 18:18:31,470] TRAIN Iter 143240: lr = 0.261268,	loss = 2.652847,	Top-1 err = 0.407715,	Top-5 err = 0.187012,	data_time = 0.050390,	train_time = 0.320145
[2019-08-23 18:18:48,857] TRAIN Iter 143260: lr = 0.261235,	loss = 2.651573,	Top-1 err = 0.406348,	Top-5 err = 0.189893,	data_time = 0.050438,	train_time = 0.869318
[2019-08-23 18:19:05,376] TRAIN Iter 143280: lr = 0.261202,	loss = 2.595992,	Top-1 err = 0.402246,	Top-5 err = 0.186084,	data_time = 0.100873,	train_time = 0.825935
[2019-08-23 18:19:12,778] TRAIN Iter 143300: lr = 0.261168,	loss = 2.646008,	Top-1 err = 0.408789,	Top-5 err = 0.188525,	data_time = 0.050465,	train_time = 0.370095
[2019-08-23 18:19:29,221] TRAIN Iter 143320: lr = 0.261135,	loss = 2.750386,	Top-1 err = 0.409814,	Top-5 err = 0.188281,	data_time = 0.050410,	train_time = 0.822139
[2019-08-23 18:19:44,491] TRAIN Iter 143340: lr = 0.261102,	loss = 2.707083,	Top-1 err = 0.408691,	Top-5 err = 0.186816,	data_time = 2.349760,	train_time = 0.763459
[2019-08-23 18:19:53,248] TRAIN Iter 143360: lr = 0.261068,	loss = 2.662586,	Top-1 err = 0.404395,	Top-5 err = 0.187549,	data_time = 0.050501,	train_time = 0.437856
[2019-08-23 18:20:11,150] TRAIN Iter 143380: lr = 0.261035,	loss = 2.552837,	Top-1 err = 0.406250,	Top-5 err = 0.189648,	data_time = 0.050542,	train_time = 0.895108
[2019-08-23 18:20:17,468] TRAIN Iter 143400: lr = 0.261002,	loss = 2.720128,	Top-1 err = 0.407422,	Top-5 err = 0.189941,	data_time = 0.050503,	train_time = 0.315889
[2019-08-23 18:20:34,494] TRAIN Iter 143420: lr = 0.260968,	loss = 2.714991,	Top-1 err = 0.412988,	Top-5 err = 0.191699,	data_time = 0.050505,	train_time = 0.851241
[2019-08-23 18:20:51,075] TRAIN Iter 143440: lr = 0.260935,	loss = 2.601006,	Top-1 err = 0.406445,	Top-5 err = 0.187988,	data_time = 0.050738,	train_time = 0.829045
[2019-08-23 18:20:57,929] TRAIN Iter 143460: lr = 0.260902,	loss = 2.667149,	Top-1 err = 0.406396,	Top-5 err = 0.192188,	data_time = 0.050529,	train_time = 0.342704
[2019-08-23 18:21:14,981] TRAIN Iter 143480: lr = 0.260868,	loss = 2.565120,	Top-1 err = 0.410107,	Top-5 err = 0.191162,	data_time = 0.050363,	train_time = 0.852603
[2019-08-23 18:21:32,661] TRAIN Iter 143500: lr = 0.260835,	loss = 2.675573,	Top-1 err = 0.405518,	Top-5 err = 0.187305,	data_time = 4.468437,	train_time = 0.883957
[2019-08-23 18:21:39,772] TRAIN Iter 143520: lr = 0.260802,	loss = 2.737637,	Top-1 err = 0.405664,	Top-5 err = 0.184570,	data_time = 0.050202,	train_time = 0.355544
[2019-08-23 18:21:57,400] TRAIN Iter 143540: lr = 0.260768,	loss = 2.624965,	Top-1 err = 0.408447,	Top-5 err = 0.190625,	data_time = 0.050397,	train_time = 0.881385
[2019-08-23 18:22:03,820] TRAIN Iter 143560: lr = 0.260735,	loss = 2.673771,	Top-1 err = 0.413818,	Top-5 err = 0.189697,	data_time = 0.050389,	train_time = 0.320992
[2019-08-23 18:22:22,762] TRAIN Iter 143580: lr = 0.260702,	loss = 2.709614,	Top-1 err = 0.408398,	Top-5 err = 0.193652,	data_time = 0.050631,	train_time = 0.947057
[2019-08-23 18:22:41,176] TRAIN Iter 143600: lr = 0.260668,	loss = 2.649066,	Top-1 err = 0.405371,	Top-5 err = 0.185205,	data_time = 0.050300,	train_time = 0.920700
[2019-08-23 18:22:47,598] TRAIN Iter 143620: lr = 0.260635,	loss = 2.716570,	Top-1 err = 0.404834,	Top-5 err = 0.192236,	data_time = 0.050338,	train_time = 0.321087
[2019-08-23 18:23:05,383] TRAIN Iter 143640: lr = 0.260602,	loss = 2.702596,	Top-1 err = 0.408594,	Top-5 err = 0.187842,	data_time = 0.050272,	train_time = 0.889247
[2019-08-23 18:23:24,738] TRAIN Iter 143660: lr = 0.260568,	loss = 2.690178,	Top-1 err = 0.416992,	Top-5 err = 0.192725,	data_time = 4.684096,	train_time = 0.967714
[2019-08-23 18:23:31,434] TRAIN Iter 143680: lr = 0.260535,	loss = 2.596066,	Top-1 err = 0.412549,	Top-5 err = 0.193262,	data_time = 0.050119,	train_time = 0.334815
[2019-08-23 18:23:48,866] TRAIN Iter 143700: lr = 0.260502,	loss = 2.701556,	Top-1 err = 0.412305,	Top-5 err = 0.188379,	data_time = 0.050612,	train_time = 0.871590
[2019-08-23 18:23:55,335] TRAIN Iter 143720: lr = 0.260468,	loss = 2.701483,	Top-1 err = 0.409375,	Top-5 err = 0.190723,	data_time = 0.050723,	train_time = 0.323444
[2019-08-23 18:24:13,538] TRAIN Iter 143740: lr = 0.260435,	loss = 2.726964,	Top-1 err = 0.410791,	Top-5 err = 0.188818,	data_time = 0.050278,	train_time = 0.910094
[2019-08-23 18:24:30,160] TRAIN Iter 143760: lr = 0.260402,	loss = 2.649567,	Top-1 err = 0.409131,	Top-5 err = 0.190625,	data_time = 0.141783,	train_time = 0.831119
[2019-08-23 18:24:36,867] TRAIN Iter 143780: lr = 0.260368,	loss = 2.678016,	Top-1 err = 0.407373,	Top-5 err = 0.189941,	data_time = 0.050270,	train_time = 0.335304
[2019-08-23 18:24:55,808] TRAIN Iter 143800: lr = 0.260335,	loss = 2.639756,	Top-1 err = 0.403271,	Top-5 err = 0.185938,	data_time = 0.050458,	train_time = 0.947068
[2019-08-23 18:25:14,824] TRAIN Iter 143820: lr = 0.260302,	loss = 2.599064,	Top-1 err = 0.407080,	Top-5 err = 0.186279,	data_time = 3.022943,	train_time = 0.950770
[2019-08-23 18:25:21,814] TRAIN Iter 143840: lr = 0.260268,	loss = 2.622241,	Top-1 err = 0.408008,	Top-5 err = 0.191797,	data_time = 0.050327,	train_time = 0.349497
[2019-08-23 18:25:41,421] TRAIN Iter 143860: lr = 0.260235,	loss = 2.632349,	Top-1 err = 0.408545,	Top-5 err = 0.190234,	data_time = 0.049820,	train_time = 0.980343
[2019-08-23 18:25:47,837] TRAIN Iter 143880: lr = 0.260202,	loss = 2.721611,	Top-1 err = 0.405469,	Top-5 err = 0.186035,	data_time = 0.050014,	train_time = 0.320780
[2019-08-23 18:26:05,717] TRAIN Iter 143900: lr = 0.260168,	loss = 2.664031,	Top-1 err = 0.412646,	Top-5 err = 0.191943,	data_time = 0.049888,	train_time = 0.893991
[2019-08-23 18:26:55,515] TRAIN Iter 143920: lr = 0.260135,	loss = 2.613646,	Top-1 err = 0.403393,	Top-5 err = 0.185204,	data_time = 5.763248,	train_time = 2.489880
[2019-08-23 18:27:02,434] TRAIN Iter 143940: lr = 0.260102,	loss = 2.617539,	Top-1 err = 0.401318,	Top-5 err = 0.183301,	data_time = 0.051751,	train_time = 0.345909
[2019-08-23 18:27:19,079] TRAIN Iter 143960: lr = 0.260068,	loss = 2.636150,	Top-1 err = 0.400635,	Top-5 err = 0.185791,	data_time = 0.050726,	train_time = 0.832225
[2019-08-23 18:27:27,221] TRAIN Iter 143980: lr = 0.260035,	loss = 2.684196,	Top-1 err = 0.397852,	Top-5 err = 0.184180,	data_time = 0.050452,	train_time = 0.407080
[2019-08-23 18:27:39,855] TRAIN Iter 144000: lr = 0.260002,	loss = 2.714150,	Top-1 err = 0.404150,	Top-5 err = 0.184375,	data_time = 0.050622,	train_time = 0.631696
[2019-08-23 18:27:54,313] TRAIN Iter 144020: lr = 0.259968,	loss = 2.666917,	Top-1 err = 0.397559,	Top-5 err = 0.184375,	data_time = 0.050935,	train_time = 0.722923
[2019-08-23 18:28:01,271] TRAIN Iter 144040: lr = 0.259935,	loss = 2.657026,	Top-1 err = 0.397314,	Top-5 err = 0.179053,	data_time = 0.050285,	train_time = 0.347884
[2019-08-23 18:28:16,824] TRAIN Iter 144060: lr = 0.259902,	loss = 2.635213,	Top-1 err = 0.401367,	Top-5 err = 0.181201,	data_time = 0.050469,	train_time = 0.777619
[2019-08-23 18:28:28,829] TRAIN Iter 144080: lr = 0.259868,	loss = 2.702958,	Top-1 err = 0.398340,	Top-5 err = 0.181934,	data_time = 3.876299,	train_time = 0.600247
[2019-08-23 18:28:40,892] TRAIN Iter 144100: lr = 0.259835,	loss = 2.559798,	Top-1 err = 0.397607,	Top-5 err = 0.181934,	data_time = 0.050618,	train_time = 0.603143
[2019-08-23 18:28:54,883] TRAIN Iter 144120: lr = 0.259802,	loss = 2.628298,	Top-1 err = 0.395361,	Top-5 err = 0.177686,	data_time = 0.050497,	train_time = 0.699528
[2019-08-23 18:29:02,525] TRAIN Iter 144140: lr = 0.259768,	loss = 2.590759,	Top-1 err = 0.405225,	Top-5 err = 0.180469,	data_time = 0.050423,	train_time = 0.382074
[2019-08-23 18:29:17,673] TRAIN Iter 144160: lr = 0.259735,	loss = 2.690471,	Top-1 err = 0.403027,	Top-5 err = 0.187012,	data_time = 0.050293,	train_time = 0.757382
[2019-08-23 18:29:33,469] TRAIN Iter 144180: lr = 0.259702,	loss = 2.670918,	Top-1 err = 0.401221,	Top-5 err = 0.183740,	data_time = 0.050560,	train_time = 0.789778
[2019-08-23 18:29:40,752] TRAIN Iter 144200: lr = 0.259668,	loss = 2.730693,	Top-1 err = 0.403662,	Top-5 err = 0.182764,	data_time = 0.050583,	train_time = 0.364132
[2019-08-23 18:29:55,819] TRAIN Iter 144220: lr = 0.259635,	loss = 2.555567,	Top-1 err = 0.398242,	Top-5 err = 0.184424,	data_time = 0.050633,	train_time = 0.753360
[2019-08-23 18:30:06,700] TRAIN Iter 144240: lr = 0.259602,	loss = 2.721220,	Top-1 err = 0.400146,	Top-5 err = 0.182422,	data_time = 1.009575,	train_time = 0.544027
[2019-08-23 18:30:18,695] TRAIN Iter 144260: lr = 0.259568,	loss = 2.618202,	Top-1 err = 0.403076,	Top-5 err = 0.187256,	data_time = 0.050330,	train_time = 0.599713
[2019-08-23 18:30:34,239] TRAIN Iter 144280: lr = 0.259535,	loss = 2.661582,	Top-1 err = 0.403271,	Top-5 err = 0.184814,	data_time = 0.050340,	train_time = 0.777219
[2019-08-23 18:30:41,971] TRAIN Iter 144300: lr = 0.259502,	loss = 2.632467,	Top-1 err = 0.408252,	Top-5 err = 0.188330,	data_time = 0.050452,	train_time = 0.386570
[2019-08-23 18:30:56,594] TRAIN Iter 144320: lr = 0.259468,	loss = 2.604983,	Top-1 err = 0.403174,	Top-5 err = 0.186084,	data_time = 0.050457,	train_time = 0.731123
[2019-08-23 18:31:12,335] TRAIN Iter 144340: lr = 0.259435,	loss = 2.669919,	Top-1 err = 0.399268,	Top-5 err = 0.184912,	data_time = 0.050631,	train_time = 0.787056
[2019-08-23 18:31:19,914] TRAIN Iter 144360: lr = 0.259402,	loss = 2.667949,	Top-1 err = 0.405176,	Top-5 err = 0.183740,	data_time = 0.050771,	train_time = 0.378957
[2019-08-23 18:31:35,391] TRAIN Iter 144380: lr = 0.259368,	loss = 2.543763,	Top-1 err = 0.405273,	Top-5 err = 0.188428,	data_time = 0.050364,	train_time = 0.773815
[2019-08-23 18:31:47,601] TRAIN Iter 144400: lr = 0.259335,	loss = 2.609496,	Top-1 err = 0.403320,	Top-5 err = 0.185596,	data_time = 0.050344,	train_time = 0.610500
[2019-08-23 18:31:56,771] TRAIN Iter 144420: lr = 0.259302,	loss = 2.661338,	Top-1 err = 0.403174,	Top-5 err = 0.182715,	data_time = 0.050419,	train_time = 0.458486
[2019-08-23 18:32:12,738] TRAIN Iter 144440: lr = 0.259268,	loss = 2.705423,	Top-1 err = 0.407031,	Top-5 err = 0.182910,	data_time = 0.050270,	train_time = 0.798315
[2019-08-23 18:32:20,235] TRAIN Iter 144460: lr = 0.259235,	loss = 2.645483,	Top-1 err = 0.404346,	Top-5 err = 0.184521,	data_time = 0.050336,	train_time = 0.374858
[2019-08-23 18:32:35,579] TRAIN Iter 144480: lr = 0.259202,	loss = 2.610166,	Top-1 err = 0.402783,	Top-5 err = 0.182471,	data_time = 0.050695,	train_time = 0.767154
[2019-08-23 18:32:52,232] TRAIN Iter 144500: lr = 0.259168,	loss = 2.645938,	Top-1 err = 0.410156,	Top-5 err = 0.193506,	data_time = 0.050498,	train_time = 0.832643
[2019-08-23 18:32:59,348] TRAIN Iter 144520: lr = 0.259135,	loss = 2.631575,	Top-1 err = 0.406982,	Top-5 err = 0.189502,	data_time = 0.050447,	train_time = 0.355781
[2019-08-23 18:33:14,756] TRAIN Iter 144540: lr = 0.259102,	loss = 2.677603,	Top-1 err = 0.409766,	Top-5 err = 0.185889,	data_time = 0.050437,	train_time = 0.770412
[2019-08-23 18:33:27,920] TRAIN Iter 144560: lr = 0.259068,	loss = 2.596347,	Top-1 err = 0.403662,	Top-5 err = 0.182422,	data_time = 0.050530,	train_time = 0.658144
[2019-08-23 18:33:37,391] TRAIN Iter 144580: lr = 0.259035,	loss = 2.568759,	Top-1 err = 0.407471,	Top-5 err = 0.185742,	data_time = 0.050480,	train_time = 0.473547
[2019-08-23 18:33:54,070] TRAIN Iter 144600: lr = 0.259002,	loss = 2.741622,	Top-1 err = 0.402344,	Top-5 err = 0.183545,	data_time = 0.050645,	train_time = 0.833929
[2019-08-23 18:34:01,933] TRAIN Iter 144620: lr = 0.258968,	loss = 2.601539,	Top-1 err = 0.407373,	Top-5 err = 0.186719,	data_time = 0.050613,	train_time = 0.393135
[2019-08-23 18:34:16,680] TRAIN Iter 144640: lr = 0.258935,	loss = 2.653847,	Top-1 err = 0.409619,	Top-5 err = 0.192480,	data_time = 0.050347,	train_time = 0.737332
[2019-08-23 18:34:34,140] TRAIN Iter 144660: lr = 0.258902,	loss = 2.667913,	Top-1 err = 0.404736,	Top-5 err = 0.188574,	data_time = 0.050404,	train_time = 0.873010
[2019-08-23 18:34:41,157] TRAIN Iter 144680: lr = 0.258868,	loss = 2.626360,	Top-1 err = 0.403223,	Top-5 err = 0.189453,	data_time = 0.129334,	train_time = 0.350842
[2019-08-23 18:34:57,054] TRAIN Iter 144700: lr = 0.258835,	loss = 2.662020,	Top-1 err = 0.411328,	Top-5 err = 0.188965,	data_time = 0.050382,	train_time = 0.794807
[2019-08-23 18:35:12,204] TRAIN Iter 144720: lr = 0.258802,	loss = 2.630621,	Top-1 err = 0.406006,	Top-5 err = 0.183887,	data_time = 0.050461,	train_time = 0.757494
[2019-08-23 18:35:20,233] TRAIN Iter 144740: lr = 0.258768,	loss = 2.633853,	Top-1 err = 0.406250,	Top-5 err = 0.183203,	data_time = 0.050490,	train_time = 0.401460
[2019-08-23 18:35:35,592] TRAIN Iter 144760: lr = 0.258735,	loss = 2.695060,	Top-1 err = 0.413330,	Top-5 err = 0.187988,	data_time = 0.050465,	train_time = 0.767914
[2019-08-23 18:35:43,303] TRAIN Iter 144780: lr = 0.258702,	loss = 2.674153,	Top-1 err = 0.411328,	Top-5 err = 0.188770,	data_time = 0.164723,	train_time = 0.385530
[2019-08-23 18:35:59,175] TRAIN Iter 144800: lr = 0.258668,	loss = 2.717265,	Top-1 err = 0.404150,	Top-5 err = 0.190039,	data_time = 0.050491,	train_time = 0.793582
[2019-08-23 18:36:16,336] TRAIN Iter 144820: lr = 0.258635,	loss = 2.645628,	Top-1 err = 0.406738,	Top-5 err = 0.185498,	data_time = 0.050322,	train_time = 0.858029
[2019-08-23 18:36:23,606] TRAIN Iter 144840: lr = 0.258602,	loss = 2.672781,	Top-1 err = 0.407129,	Top-5 err = 0.190332,	data_time = 0.050265,	train_time = 0.363521
[2019-08-23 18:36:39,376] TRAIN Iter 144860: lr = 0.258568,	loss = 2.705918,	Top-1 err = 0.413867,	Top-5 err = 0.195654,	data_time = 0.050290,	train_time = 0.788491
[2019-08-23 18:36:55,678] TRAIN Iter 144880: lr = 0.258535,	loss = 2.557945,	Top-1 err = 0.405225,	Top-5 err = 0.186865,	data_time = 0.050465,	train_time = 0.815058
[2019-08-23 18:37:02,636] TRAIN Iter 144900: lr = 0.258502,	loss = 2.654300,	Top-1 err = 0.408398,	Top-5 err = 0.190039,	data_time = 0.050278,	train_time = 0.347921
[2019-08-23 18:37:17,897] TRAIN Iter 144920: lr = 0.258468,	loss = 2.676219,	Top-1 err = 0.410059,	Top-5 err = 0.191406,	data_time = 0.050389,	train_time = 0.763012
[2019-08-23 18:37:25,337] TRAIN Iter 144940: lr = 0.258435,	loss = 2.754172,	Top-1 err = 0.401709,	Top-5 err = 0.188379,	data_time = 0.050654,	train_time = 0.371978
[2019-08-23 18:37:40,944] TRAIN Iter 144960: lr = 0.258402,	loss = 2.747712,	Top-1 err = 0.408447,	Top-5 err = 0.191113,	data_time = 0.050567,	train_time = 0.780343
[2019-08-23 18:37:58,232] TRAIN Iter 144980: lr = 0.258368,	loss = 2.682738,	Top-1 err = 0.402881,	Top-5 err = 0.184033,	data_time = 0.050365,	train_time = 0.864366
[2019-08-23 18:38:05,440] TRAIN Iter 145000: lr = 0.258335,	loss = 2.711712,	Top-1 err = 0.408594,	Top-5 err = 0.185938,	data_time = 0.050481,	train_time = 0.360381
[2019-08-23 18:38:22,115] TRAIN Iter 145020: lr = 0.258302,	loss = 2.653839,	Top-1 err = 0.406543,	Top-5 err = 0.188086,	data_time = 0.050273,	train_time = 0.833745
[2019-08-23 18:38:39,068] TRAIN Iter 145040: lr = 0.258268,	loss = 2.685125,	Top-1 err = 0.407471,	Top-5 err = 0.188477,	data_time = 0.050321,	train_time = 0.847643
[2019-08-23 18:38:45,703] TRAIN Iter 145060: lr = 0.258235,	loss = 2.652521,	Top-1 err = 0.410400,	Top-5 err = 0.193018,	data_time = 0.050483,	train_time = 0.331756
[2019-08-23 18:39:04,099] TRAIN Iter 145080: lr = 0.258202,	loss = 2.728663,	Top-1 err = 0.409619,	Top-5 err = 0.188525,	data_time = 0.050368,	train_time = 0.919763
[2019-08-23 18:39:11,398] TRAIN Iter 145100: lr = 0.258168,	loss = 2.657129,	Top-1 err = 0.406006,	Top-5 err = 0.189648,	data_time = 0.050232,	train_time = 0.364935
[2019-08-23 18:39:28,598] TRAIN Iter 145120: lr = 0.258135,	loss = 2.727280,	Top-1 err = 0.409766,	Top-5 err = 0.191846,	data_time = 0.050031,	train_time = 0.859991
[2019-08-23 18:39:44,786] TRAIN Iter 145140: lr = 0.258102,	loss = 2.482356,	Top-1 err = 0.407764,	Top-5 err = 0.186670,	data_time = 0.049957,	train_time = 0.809414
[2019-08-23 18:39:50,861] TRAIN Iter 145160: lr = 0.258068,	loss = 2.577121,	Top-1 err = 0.410742,	Top-5 err = 0.187842,	data_time = 0.049896,	train_time = 0.303718
[2019-08-23 18:40:44,212] TRAIN Iter 145180: lr = 0.258035,	loss = 2.723342,	Top-1 err = 0.414148,	Top-5 err = 0.191802,	data_time = 0.050557,	train_time = 2.667535
[2019-08-23 18:40:51,857] TRAIN Iter 145200: lr = 0.258002,	loss = 2.665417,	Top-1 err = 0.401123,	Top-5 err = 0.185840,	data_time = 0.050686,	train_time = 0.382240
[2019-08-23 18:41:06,731] TRAIN Iter 145220: lr = 0.257968,	loss = 2.601006,	Top-1 err = 0.395264,	Top-5 err = 0.177783,	data_time = 0.050178,	train_time = 0.743677
[2019-08-23 18:41:21,852] TRAIN Iter 145240: lr = 0.257935,	loss = 2.615814,	Top-1 err = 0.401807,	Top-5 err = 0.184229,	data_time = 0.050955,	train_time = 0.756045
[2019-08-23 18:41:29,502] TRAIN Iter 145260: lr = 0.257902,	loss = 2.572693,	Top-1 err = 0.394922,	Top-5 err = 0.184131,	data_time = 0.050809,	train_time = 0.382454
[2019-08-23 18:41:45,066] TRAIN Iter 145280: lr = 0.257868,	loss = 2.716465,	Top-1 err = 0.402246,	Top-5 err = 0.188916,	data_time = 0.050503,	train_time = 0.778182
[2019-08-23 18:41:58,930] TRAIN Iter 145300: lr = 0.257835,	loss = 2.727035,	Top-1 err = 0.397900,	Top-5 err = 0.180566,	data_time = 0.143188,	train_time = 0.693203
[2019-08-23 18:42:06,247] TRAIN Iter 145320: lr = 0.257802,	loss = 2.634227,	Top-1 err = 0.403955,	Top-5 err = 0.185547,	data_time = 0.050264,	train_time = 0.365834
[2019-08-23 18:42:20,635] TRAIN Iter 145340: lr = 0.257768,	loss = 2.730855,	Top-1 err = 0.398096,	Top-5 err = 0.183252,	data_time = 0.050458,	train_time = 0.719413
[2019-08-23 18:42:27,812] TRAIN Iter 145360: lr = 0.257735,	loss = 2.625261,	Top-1 err = 0.401416,	Top-5 err = 0.186426,	data_time = 0.051007,	train_time = 0.358805
[2019-08-23 18:42:44,671] TRAIN Iter 145380: lr = 0.257702,	loss = 2.637669,	Top-1 err = 0.398438,	Top-5 err = 0.181494,	data_time = 0.050371,	train_time = 0.842968
[2019-08-23 18:42:59,404] TRAIN Iter 145400: lr = 0.257668,	loss = 2.576615,	Top-1 err = 0.395459,	Top-5 err = 0.182422,	data_time = 0.050960,	train_time = 0.736621
[2019-08-23 18:43:06,468] TRAIN Iter 145420: lr = 0.257635,	loss = 2.618590,	Top-1 err = 0.405811,	Top-5 err = 0.190186,	data_time = 0.050601,	train_time = 0.353175
[2019-08-23 18:43:21,760] TRAIN Iter 145440: lr = 0.257602,	loss = 2.566734,	Top-1 err = 0.402930,	Top-5 err = 0.183154,	data_time = 0.050929,	train_time = 0.764587
[2019-08-23 18:43:35,678] TRAIN Iter 145460: lr = 0.257568,	loss = 2.598955,	Top-1 err = 0.399365,	Top-5 err = 0.178955,	data_time = 0.050937,	train_time = 0.695891
[2019-08-23 18:43:42,716] TRAIN Iter 145480: lr = 0.257535,	loss = 2.745173,	Top-1 err = 0.402393,	Top-5 err = 0.186523,	data_time = 0.050607,	train_time = 0.351908
[2019-08-23 18:43:58,882] TRAIN Iter 145500: lr = 0.257502,	loss = 2.681028,	Top-1 err = 0.400977,	Top-5 err = 0.181348,	data_time = 0.050498,	train_time = 0.808237
[2019-08-23 18:44:06,369] TRAIN Iter 145520: lr = 0.257468,	loss = 2.705973,	Top-1 err = 0.406006,	Top-5 err = 0.188672,	data_time = 0.050360,	train_time = 0.374346
[2019-08-23 18:44:21,985] TRAIN Iter 145540: lr = 0.257435,	loss = 2.500930,	Top-1 err = 0.404199,	Top-5 err = 0.186328,	data_time = 0.050512,	train_time = 0.780814
[2019-08-23 18:44:37,253] TRAIN Iter 145560: lr = 0.257402,	loss = 2.651459,	Top-1 err = 0.397119,	Top-5 err = 0.181787,	data_time = 0.050381,	train_time = 0.763397
[2019-08-23 18:44:44,144] TRAIN Iter 145580: lr = 0.257368,	loss = 2.548860,	Top-1 err = 0.409082,	Top-5 err = 0.188281,	data_time = 0.050371,	train_time = 0.344538
[2019-08-23 18:45:00,607] TRAIN Iter 145600: lr = 0.257335,	loss = 2.610884,	Top-1 err = 0.401270,	Top-5 err = 0.186621,	data_time = 0.050644,	train_time = 0.823089
[2019-08-23 18:45:14,871] TRAIN Iter 145620: lr = 0.257302,	loss = 2.667101,	Top-1 err = 0.409717,	Top-5 err = 0.187061,	data_time = 0.050613,	train_time = 0.713192
[2019-08-23 18:45:21,746] TRAIN Iter 145640: lr = 0.257268,	loss = 2.652155,	Top-1 err = 0.407422,	Top-5 err = 0.187158,	data_time = 0.050287,	train_time = 0.343750
[2019-08-23 18:45:38,065] TRAIN Iter 145660: lr = 0.257235,	loss = 2.676614,	Top-1 err = 0.406592,	Top-5 err = 0.187793,	data_time = 0.050429,	train_time = 0.815962
[2019-08-23 18:45:45,666] TRAIN Iter 145680: lr = 0.257202,	loss = 2.668590,	Top-1 err = 0.406299,	Top-5 err = 0.186816,	data_time = 0.050692,	train_time = 0.380002
[2019-08-23 18:45:59,543] TRAIN Iter 145700: lr = 0.257168,	loss = 2.745467,	Top-1 err = 0.405273,	Top-5 err = 0.184277,	data_time = 0.050504,	train_time = 0.693868
[2019-08-23 18:46:15,510] TRAIN Iter 145720: lr = 0.257135,	loss = 2.799703,	Top-1 err = 0.396387,	Top-5 err = 0.181836,	data_time = 0.050380,	train_time = 0.798328
[2019-08-23 18:46:22,540] TRAIN Iter 145740: lr = 0.257102,	loss = 2.619573,	Top-1 err = 0.408838,	Top-5 err = 0.189160,	data_time = 0.050641,	train_time = 0.351491
[2019-08-23 18:46:36,891] TRAIN Iter 145760: lr = 0.257068,	loss = 2.656977,	Top-1 err = 0.402783,	Top-5 err = 0.185498,	data_time = 0.050449,	train_time = 0.717534
[2019-08-23 18:46:52,137] TRAIN Iter 145780: lr = 0.257035,	loss = 2.658723,	Top-1 err = 0.401562,	Top-5 err = 0.187793,	data_time = 0.193513,	train_time = 0.762276
[2019-08-23 18:46:59,359] TRAIN Iter 145800: lr = 0.257002,	loss = 2.719408,	Top-1 err = 0.414404,	Top-5 err = 0.193994,	data_time = 0.050834,	train_time = 0.361063
[2019-08-23 18:47:15,763] TRAIN Iter 145820: lr = 0.256968,	loss = 2.685036,	Top-1 err = 0.403613,	Top-5 err = 0.187646,	data_time = 0.050818,	train_time = 0.820224
[2019-08-23 18:47:22,905] TRAIN Iter 145840: lr = 0.256935,	loss = 2.494148,	Top-1 err = 0.408789,	Top-5 err = 0.191113,	data_time = 0.050696,	train_time = 0.357061
[2019-08-23 18:47:38,322] TRAIN Iter 145860: lr = 0.256902,	loss = 2.633736,	Top-1 err = 0.411914,	Top-5 err = 0.189258,	data_time = 0.050328,	train_time = 0.770856
[2019-08-23 18:47:57,422] TRAIN Iter 145880: lr = 0.256868,	loss = 2.675128,	Top-1 err = 0.407568,	Top-5 err = 0.186035,	data_time = 0.050551,	train_time = 0.954985
[2019-08-23 18:48:05,624] TRAIN Iter 145900: lr = 0.256835,	loss = 2.775307,	Top-1 err = 0.401025,	Top-5 err = 0.183350,	data_time = 0.051331,	train_time = 0.410062
[2019-08-23 18:48:20,796] TRAIN Iter 145920: lr = 0.256802,	loss = 2.644910,	Top-1 err = 0.407764,	Top-5 err = 0.188086,	data_time = 0.050498,	train_time = 0.758583
[2019-08-23 18:48:37,693] TRAIN Iter 145940: lr = 0.256768,	loss = 2.659794,	Top-1 err = 0.407178,	Top-5 err = 0.186230,	data_time = 0.050477,	train_time = 0.844838
[2019-08-23 18:48:44,269] TRAIN Iter 145960: lr = 0.256735,	loss = 2.715148,	Top-1 err = 0.403906,	Top-5 err = 0.187939,	data_time = 0.050263,	train_time = 0.328813
[2019-08-23 18:49:01,667] TRAIN Iter 145980: lr = 0.256702,	loss = 2.656498,	Top-1 err = 0.400439,	Top-5 err = 0.186621,	data_time = 0.050368,	train_time = 0.869889
[2019-08-23 18:49:08,564] TRAIN Iter 146000: lr = 0.256668,	loss = 2.640437,	Top-1 err = 0.407275,	Top-5 err = 0.186816,	data_time = 0.050543,	train_time = 0.344792
[2019-08-23 18:49:25,263] TRAIN Iter 146020: lr = 0.256635,	loss = 2.679371,	Top-1 err = 0.407471,	Top-5 err = 0.188428,	data_time = 0.050505,	train_time = 0.834960
[2019-08-23 18:49:43,427] TRAIN Iter 146040: lr = 0.256602,	loss = 2.689395,	Top-1 err = 0.406201,	Top-5 err = 0.184961,	data_time = 0.050123,	train_time = 0.908180
[2019-08-23 18:49:50,183] TRAIN Iter 146060: lr = 0.256568,	loss = 2.685329,	Top-1 err = 0.407422,	Top-5 err = 0.187500,	data_time = 0.050319,	train_time = 0.337810
[2019-08-23 18:50:08,304] TRAIN Iter 146080: lr = 0.256535,	loss = 2.622845,	Top-1 err = 0.404199,	Top-5 err = 0.184521,	data_time = 0.050402,	train_time = 0.906005
[2019-08-23 18:50:25,300] TRAIN Iter 146100: lr = 0.256502,	loss = 2.677154,	Top-1 err = 0.408301,	Top-5 err = 0.186816,	data_time = 0.050418,	train_time = 0.849773
[2019-08-23 18:50:33,293] TRAIN Iter 146120: lr = 0.256468,	loss = 2.684119,	Top-1 err = 0.408301,	Top-5 err = 0.188037,	data_time = 0.050577,	train_time = 0.399637
[2019-08-23 18:50:48,875] TRAIN Iter 146140: lr = 0.256435,	loss = 2.644070,	Top-1 err = 0.408447,	Top-5 err = 0.189014,	data_time = 0.050351,	train_time = 0.779122
[2019-08-23 18:50:56,276] TRAIN Iter 146160: lr = 0.256402,	loss = 2.724005,	Top-1 err = 0.414453,	Top-5 err = 0.188672,	data_time = 0.050722,	train_time = 0.370040
[2019-08-23 18:51:12,982] TRAIN Iter 146180: lr = 0.256368,	loss = 2.639057,	Top-1 err = 0.407373,	Top-5 err = 0.185547,	data_time = 0.050545,	train_time = 0.835262
[2019-08-23 18:51:30,672] TRAIN Iter 146200: lr = 0.256335,	loss = 2.703645,	Top-1 err = 0.408887,	Top-5 err = 0.184033,	data_time = 0.050375,	train_time = 0.884483
[2019-08-23 18:51:38,101] TRAIN Iter 146220: lr = 0.256302,	loss = 2.589003,	Top-1 err = 0.410156,	Top-5 err = 0.187842,	data_time = 0.050361,	train_time = 0.371456
[2019-08-23 18:51:54,698] TRAIN Iter 146240: lr = 0.256268,	loss = 2.672171,	Top-1 err = 0.407178,	Top-5 err = 0.186914,	data_time = 0.050975,	train_time = 0.829802
[2019-08-23 18:52:11,168] TRAIN Iter 146260: lr = 0.256235,	loss = 2.678704,	Top-1 err = 0.412012,	Top-5 err = 0.188330,	data_time = 0.114782,	train_time = 0.823523
[2019-08-23 18:52:18,317] TRAIN Iter 146280: lr = 0.256202,	loss = 2.650822,	Top-1 err = 0.411182,	Top-5 err = 0.190479,	data_time = 0.050235,	train_time = 0.357434
[2019-08-23 18:52:33,752] TRAIN Iter 146300: lr = 0.256168,	loss = 2.639532,	Top-1 err = 0.404883,	Top-5 err = 0.187695,	data_time = 0.050373,	train_time = 0.771696
[2019-08-23 18:52:40,871] TRAIN Iter 146320: lr = 0.256135,	loss = 2.733211,	Top-1 err = 0.415381,	Top-5 err = 0.189209,	data_time = 0.050707,	train_time = 0.355960
[2019-08-23 18:52:57,256] TRAIN Iter 146340: lr = 0.256102,	loss = 2.613474,	Top-1 err = 0.406494,	Top-5 err = 0.190625,	data_time = 0.050293,	train_time = 0.819228
[2019-08-23 18:53:15,047] TRAIN Iter 146360: lr = 0.256068,	loss = 2.744868,	Top-1 err = 0.405078,	Top-5 err = 0.184326,	data_time = 0.050015,	train_time = 0.889547
[2019-08-23 18:53:21,645] TRAIN Iter 146380: lr = 0.256035,	loss = 2.684014,	Top-1 err = 0.405713,	Top-5 err = 0.182861,	data_time = 0.104587,	train_time = 0.329867
[2019-08-23 18:53:38,608] TRAIN Iter 146400: lr = 0.256002,	loss = 2.652196,	Top-1 err = 0.406689,	Top-5 err = 0.182861,	data_time = 0.049947,	train_time = 0.848172
[2019-08-23 18:53:48,827] TRAIN Iter 146420: lr = 0.255968,	loss = 2.849235,	Top-1 err = 0.407893,	Top-5 err = 0.185148,	data_time = 0.007083,	train_time = 0.510929
[2019-08-23 18:54:34,284] TRAIN Iter 146440: lr = 0.255935,	loss = 2.602813,	Top-1 err = 0.403564,	Top-5 err = 0.184326,	data_time = 0.050340,	train_time = 2.272832
[2019-08-23 18:54:51,931] TRAIN Iter 146460: lr = 0.255902,	loss = 2.749590,	Top-1 err = 0.408691,	Top-5 err = 0.188623,	data_time = 0.050366,	train_time = 0.882343
[2019-08-23 18:54:59,384] TRAIN Iter 146480: lr = 0.255868,	loss = 2.626017,	Top-1 err = 0.405859,	Top-5 err = 0.182568,	data_time = 0.050544,	train_time = 0.372602
[2019-08-23 18:55:14,054] TRAIN Iter 146500: lr = 0.255835,	loss = 2.694336,	Top-1 err = 0.402051,	Top-5 err = 0.180176,	data_time = 0.050349,	train_time = 0.733477
[2019-08-23 18:55:27,562] TRAIN Iter 146520: lr = 0.255802,	loss = 2.678618,	Top-1 err = 0.398145,	Top-5 err = 0.182227,	data_time = 0.050653,	train_time = 0.675420
[2019-08-23 18:55:34,609] TRAIN Iter 146540: lr = 0.255768,	loss = 2.641391,	Top-1 err = 0.401367,	Top-5 err = 0.184033,	data_time = 0.050637,	train_time = 0.352316
[2019-08-23 18:55:50,852] TRAIN Iter 146560: lr = 0.255735,	loss = 2.622842,	Top-1 err = 0.395898,	Top-5 err = 0.178027,	data_time = 0.050349,	train_time = 0.812129
[2019-08-23 18:55:58,738] TRAIN Iter 146580: lr = 0.255702,	loss = 2.603439,	Top-1 err = 0.401367,	Top-5 err = 0.181152,	data_time = 0.050763,	train_time = 0.394310
[2019-08-23 18:56:12,832] TRAIN Iter 146600: lr = 0.255668,	loss = 2.511961,	Top-1 err = 0.400293,	Top-5 err = 0.179297,	data_time = 0.050640,	train_time = 0.704654
[2019-08-23 18:56:29,501] TRAIN Iter 146620: lr = 0.255635,	loss = 2.679517,	Top-1 err = 0.401221,	Top-5 err = 0.182666,	data_time = 0.050295,	train_time = 0.833469
[2019-08-23 18:56:36,360] TRAIN Iter 146640: lr = 0.255602,	loss = 2.633825,	Top-1 err = 0.407715,	Top-5 err = 0.186377,	data_time = 0.050213,	train_time = 0.342934
[2019-08-23 18:56:50,604] TRAIN Iter 146660: lr = 0.255568,	loss = 2.617826,	Top-1 err = 0.404053,	Top-5 err = 0.188330,	data_time = 0.050416,	train_time = 0.712162
[2019-08-23 18:57:05,376] TRAIN Iter 146680: lr = 0.255535,	loss = 2.648608,	Top-1 err = 0.398486,	Top-5 err = 0.185645,	data_time = 0.050261,	train_time = 0.738602
[2019-08-23 18:57:12,675] TRAIN Iter 146700: lr = 0.255502,	loss = 2.569105,	Top-1 err = 0.403906,	Top-5 err = 0.184668,	data_time = 0.050371,	train_time = 0.364935
[2019-08-23 18:57:28,618] TRAIN Iter 146720: lr = 0.255468,	loss = 2.623995,	Top-1 err = 0.402588,	Top-5 err = 0.183252,	data_time = 0.050485,	train_time = 0.797125
[2019-08-23 18:57:35,660] TRAIN Iter 146740: lr = 0.255435,	loss = 2.554425,	Top-1 err = 0.401709,	Top-5 err = 0.183887,	data_time = 0.050439,	train_time = 0.352106
[2019-08-23 18:57:52,079] TRAIN Iter 146760: lr = 0.255402,	loss = 2.601534,	Top-1 err = 0.401025,	Top-5 err = 0.186523,	data_time = 0.050303,	train_time = 0.820923
[2019-08-23 18:58:09,110] TRAIN Iter 146780: lr = 0.255368,	loss = 2.729202,	Top-1 err = 0.402979,	Top-5 err = 0.180371,	data_time = 0.050447,	train_time = 0.851558
[2019-08-23 18:58:15,652] TRAIN Iter 146800: lr = 0.255335,	loss = 2.699020,	Top-1 err = 0.402783,	Top-5 err = 0.186670,	data_time = 0.050545,	train_time = 0.327074
[2019-08-23 18:58:33,400] TRAIN Iter 146820: lr = 0.255302,	loss = 2.629799,	Top-1 err = 0.398145,	Top-5 err = 0.180615,	data_time = 0.050337,	train_time = 0.887372
[2019-08-23 18:58:48,706] TRAIN Iter 146840: lr = 0.255268,	loss = 2.691876,	Top-1 err = 0.409863,	Top-5 err = 0.192578,	data_time = 0.050369,	train_time = 0.765319
[2019-08-23 18:58:56,675] TRAIN Iter 146860: lr = 0.255235,	loss = 2.619751,	Top-1 err = 0.405615,	Top-5 err = 0.183740,	data_time = 0.050132,	train_time = 0.398411
[2019-08-23 18:59:15,778] TRAIN Iter 146880: lr = 0.255202,	loss = 2.675497,	Top-1 err = 0.404395,	Top-5 err = 0.185303,	data_time = 0.050501,	train_time = 0.955153
[2019-08-23 18:59:22,629] TRAIN Iter 146900: lr = 0.255168,	loss = 2.659140,	Top-1 err = 0.408203,	Top-5 err = 0.186963,	data_time = 0.050337,	train_time = 0.342506
[2019-08-23 18:59:38,999] TRAIN Iter 146920: lr = 0.255135,	loss = 2.681605,	Top-1 err = 0.402197,	Top-5 err = 0.184131,	data_time = 0.050284,	train_time = 0.818511
[2019-08-23 18:59:56,001] TRAIN Iter 146940: lr = 0.255102,	loss = 2.640574,	Top-1 err = 0.402881,	Top-5 err = 0.183252,	data_time = 0.050455,	train_time = 0.850082
[2019-08-23 19:00:02,608] TRAIN Iter 146960: lr = 0.255068,	loss = 2.687629,	Top-1 err = 0.406445,	Top-5 err = 0.187158,	data_time = 0.050242,	train_time = 0.330325
[2019-08-23 19:00:19,378] TRAIN Iter 146980: lr = 0.255035,	loss = 2.656887,	Top-1 err = 0.411279,	Top-5 err = 0.191064,	data_time = 0.050409,	train_time = 0.838485
[2019-08-23 19:00:37,321] TRAIN Iter 147000: lr = 0.255002,	loss = 2.733976,	Top-1 err = 0.409424,	Top-5 err = 0.190039,	data_time = 0.050345,	train_time = 0.897166
[2019-08-23 19:00:44,127] TRAIN Iter 147020: lr = 0.254968,	loss = 2.642492,	Top-1 err = 0.407617,	Top-5 err = 0.186719,	data_time = 0.050445,	train_time = 0.340284
[2019-08-23 19:00:59,340] TRAIN Iter 147040: lr = 0.254935,	loss = 2.637477,	Top-1 err = 0.405713,	Top-5 err = 0.189600,	data_time = 0.050395,	train_time = 0.760600
[2019-08-23 19:01:05,834] TRAIN Iter 147060: lr = 0.254902,	loss = 2.670817,	Top-1 err = 0.409473,	Top-5 err = 0.186279,	data_time = 0.050200,	train_time = 0.324708
[2019-08-23 19:01:26,127] TRAIN Iter 147080: lr = 0.254868,	loss = 2.637965,	Top-1 err = 0.409717,	Top-5 err = 0.191455,	data_time = 0.050554,	train_time = 1.014612
[2019-08-23 19:01:42,753] TRAIN Iter 147100: lr = 0.254835,	loss = 2.663436,	Top-1 err = 0.403223,	Top-5 err = 0.186572,	data_time = 0.050220,	train_time = 0.831305
[2019-08-23 19:01:49,520] TRAIN Iter 147120: lr = 0.254802,	loss = 2.641071,	Top-1 err = 0.404150,	Top-5 err = 0.185156,	data_time = 0.050429,	train_time = 0.338359
[2019-08-23 19:02:07,772] TRAIN Iter 147140: lr = 0.254768,	loss = 2.607559,	Top-1 err = 0.399951,	Top-5 err = 0.184229,	data_time = 0.050740,	train_time = 0.912573
[2019-08-23 19:02:25,305] TRAIN Iter 147160: lr = 0.254735,	loss = 2.648888,	Top-1 err = 0.409033,	Top-5 err = 0.189307,	data_time = 0.050395,	train_time = 0.876634
[2019-08-23 19:02:31,949] TRAIN Iter 147180: lr = 0.254702,	loss = 2.587577,	Top-1 err = 0.401270,	Top-5 err = 0.185889,	data_time = 0.050579,	train_time = 0.332171
[2019-08-23 19:02:48,835] TRAIN Iter 147200: lr = 0.254668,	loss = 2.674806,	Top-1 err = 0.403662,	Top-5 err = 0.185254,	data_time = 0.050475,	train_time = 0.844273
[2019-08-23 19:02:55,629] TRAIN Iter 147220: lr = 0.254635,	loss = 2.648514,	Top-1 err = 0.408740,	Top-5 err = 0.187842,	data_time = 0.050267,	train_time = 0.339693
[2019-08-23 19:03:13,418] TRAIN Iter 147240: lr = 0.254602,	loss = 2.639782,	Top-1 err = 0.406396,	Top-5 err = 0.184912,	data_time = 0.050712,	train_time = 0.889431
[2019-08-23 19:03:30,069] TRAIN Iter 147260: lr = 0.254568,	loss = 2.620371,	Top-1 err = 0.407080,	Top-5 err = 0.188770,	data_time = 0.050490,	train_time = 0.832551
[2019-08-23 19:03:37,183] TRAIN Iter 147280: lr = 0.254535,	loss = 2.616146,	Top-1 err = 0.404102,	Top-5 err = 0.185791,	data_time = 0.050506,	train_time = 0.355691
[2019-08-23 19:03:52,922] TRAIN Iter 147300: lr = 0.254502,	loss = 2.620433,	Top-1 err = 0.411035,	Top-5 err = 0.194678,	data_time = 0.050499,	train_time = 0.786920
[2019-08-23 19:04:10,310] TRAIN Iter 147320: lr = 0.254468,	loss = 2.687886,	Top-1 err = 0.408887,	Top-5 err = 0.191748,	data_time = 0.050464,	train_time = 0.869409
[2019-08-23 19:04:17,016] TRAIN Iter 147340: lr = 0.254435,	loss = 2.666780,	Top-1 err = 0.408398,	Top-5 err = 0.189648,	data_time = 0.050302,	train_time = 0.335280
[2019-08-23 19:04:36,100] TRAIN Iter 147360: lr = 0.254402,	loss = 2.692465,	Top-1 err = 0.402002,	Top-5 err = 0.185449,	data_time = 0.050644,	train_time = 0.954193
[2019-08-23 19:04:42,628] TRAIN Iter 147380: lr = 0.254368,	loss = 2.671557,	Top-1 err = 0.413330,	Top-5 err = 0.191455,	data_time = 0.050437,	train_time = 0.326365
[2019-08-23 19:05:02,300] TRAIN Iter 147400: lr = 0.254335,	loss = 2.607347,	Top-1 err = 0.402393,	Top-5 err = 0.184717,	data_time = 0.050377,	train_time = 0.983579
[2019-08-23 19:05:21,008] TRAIN Iter 147420: lr = 0.254302,	loss = 2.601267,	Top-1 err = 0.404687,	Top-5 err = 0.184131,	data_time = 0.050422,	train_time = 0.935408
[2019-08-23 19:05:27,566] TRAIN Iter 147440: lr = 0.254268,	loss = 2.599046,	Top-1 err = 0.406250,	Top-5 err = 0.187158,	data_time = 0.050201,	train_time = 0.327900
[2019-08-23 19:05:46,059] TRAIN Iter 147460: lr = 0.254235,	loss = 2.589260,	Top-1 err = 0.410889,	Top-5 err = 0.186084,	data_time = 0.050359,	train_time = 0.924640
[2019-08-23 19:06:04,493] TRAIN Iter 147480: lr = 0.254202,	loss = 2.608857,	Top-1 err = 0.402686,	Top-5 err = 0.181152,	data_time = 0.050406,	train_time = 0.921675
[2019-08-23 19:06:10,915] TRAIN Iter 147500: lr = 0.254168,	loss = 2.652662,	Top-1 err = 0.407373,	Top-5 err = 0.187842,	data_time = 0.050543,	train_time = 0.321095
[2019-08-23 19:06:29,827] TRAIN Iter 147520: lr = 0.254135,	loss = 2.718763,	Top-1 err = 0.411475,	Top-5 err = 0.190186,	data_time = 0.050543,	train_time = 0.945592
[2019-08-23 19:06:36,947] TRAIN Iter 147540: lr = 0.254102,	loss = 2.495226,	Top-1 err = 0.405957,	Top-5 err = 0.185693,	data_time = 0.050570,	train_time = 0.355959
[2019-08-23 19:06:54,077] TRAIN Iter 147560: lr = 0.254068,	loss = 2.665941,	Top-1 err = 0.408643,	Top-5 err = 0.185596,	data_time = 0.050477,	train_time = 0.856515
[2019-08-23 19:07:12,718] TRAIN Iter 147580: lr = 0.254035,	loss = 2.640422,	Top-1 err = 0.401660,	Top-5 err = 0.184570,	data_time = 0.050518,	train_time = 0.932032
[2019-08-23 19:07:19,370] TRAIN Iter 147600: lr = 0.254002,	loss = 2.700852,	Top-1 err = 0.409766,	Top-5 err = 0.190820,	data_time = 0.050357,	train_time = 0.332562
[2019-08-23 19:07:38,813] TRAIN Iter 147620: lr = 0.253968,	loss = 2.629017,	Top-1 err = 0.407227,	Top-5 err = 0.188232,	data_time = 0.050259,	train_time = 0.972173
[2019-08-23 19:08:00,057] TRAIN Iter 147640: lr = 0.253935,	loss = 2.641470,	Top-1 err = 0.406152,	Top-5 err = 0.191406,	data_time = 0.049885,	train_time = 1.062165
[2019-08-23 19:08:06,250] TRAIN Iter 147660: lr = 0.253902,	loss = 2.700260,	Top-1 err = 0.409229,	Top-5 err = 0.185352,	data_time = 0.049875,	train_time = 0.309650
[2019-08-23 19:08:58,365] TRAIN Iter 147680: lr = 0.253868,	loss = 2.739738,	Top-1 err = 0.412335,	Top-5 err = 0.192773,	data_time = 0.050422,	train_time = 2.605747
[2019-08-23 19:09:05,781] TRAIN Iter 147700: lr = 0.253835,	loss = 2.634968,	Top-1 err = 0.408105,	Top-5 err = 0.185596,	data_time = 0.170433,	train_time = 0.370779
[2019-08-23 19:09:23,284] TRAIN Iter 147720: lr = 0.253802,	loss = 2.592060,	Top-1 err = 0.405273,	Top-5 err = 0.187305,	data_time = 0.050338,	train_time = 0.875095
[2019-08-23 19:09:39,613] TRAIN Iter 147740: lr = 0.253768,	loss = 2.620994,	Top-1 err = 0.396094,	Top-5 err = 0.178711,	data_time = 0.050871,	train_time = 0.816471
[2019-08-23 19:09:46,304] TRAIN Iter 147760: lr = 0.253735,	loss = 2.538593,	Top-1 err = 0.397900,	Top-5 err = 0.176221,	data_time = 0.050335,	train_time = 0.334514
[2019-08-23 19:10:03,059] TRAIN Iter 147780: lr = 0.253702,	loss = 2.634167,	Top-1 err = 0.407178,	Top-5 err = 0.185400,	data_time = 0.050302,	train_time = 0.837751
[2019-08-23 19:10:10,307] TRAIN Iter 147800: lr = 0.253668,	loss = 2.614266,	Top-1 err = 0.402002,	Top-5 err = 0.181787,	data_time = 0.051003,	train_time = 0.362375
[2019-08-23 19:10:28,298] TRAIN Iter 147820: lr = 0.253635,	loss = 2.765269,	Top-1 err = 0.400732,	Top-5 err = 0.182959,	data_time = 0.050595,	train_time = 0.899506
[2019-08-23 19:10:43,875] TRAIN Iter 147840: lr = 0.253602,	loss = 2.606059,	Top-1 err = 0.402100,	Top-5 err = 0.180420,	data_time = 0.050453,	train_time = 0.778836
[2019-08-23 19:10:50,975] TRAIN Iter 147860: lr = 0.253568,	loss = 2.602000,	Top-1 err = 0.396973,	Top-5 err = 0.182324,	data_time = 0.050169,	train_time = 0.355013
[2019-08-23 19:11:05,335] TRAIN Iter 147880: lr = 0.253535,	loss = 2.679272,	Top-1 err = 0.400342,	Top-5 err = 0.183301,	data_time = 0.050324,	train_time = 0.717994
[2019-08-23 19:11:20,262] TRAIN Iter 147900: lr = 0.253502,	loss = 2.652765,	Top-1 err = 0.402734,	Top-5 err = 0.182275,	data_time = 0.050822,	train_time = 0.746347
[2019-08-23 19:11:27,075] TRAIN Iter 147920: lr = 0.253468,	loss = 2.561562,	Top-1 err = 0.407910,	Top-5 err = 0.188916,	data_time = 0.050438,	train_time = 0.340619
[2019-08-23 19:11:43,026] TRAIN Iter 147940: lr = 0.253435,	loss = 2.621399,	Top-1 err = 0.402197,	Top-5 err = 0.183496,	data_time = 0.050253,	train_time = 0.797543
[2019-08-23 19:11:49,660] TRAIN Iter 147960: lr = 0.253402,	loss = 2.668130,	Top-1 err = 0.407764,	Top-5 err = 0.182373,	data_time = 0.050558,	train_time = 0.331673
[2019-08-23 19:12:07,394] TRAIN Iter 147980: lr = 0.253368,	loss = 2.563443,	Top-1 err = 0.397119,	Top-5 err = 0.178711,	data_time = 0.050482,	train_time = 0.886681
[2019-08-23 19:12:24,395] TRAIN Iter 148000: lr = 0.253335,	loss = 2.693035,	Top-1 err = 0.400928,	Top-5 err = 0.184863,	data_time = 0.050390,	train_time = 0.850027
[2019-08-23 19:12:31,019] TRAIN Iter 148020: lr = 0.253302,	loss = 2.618284,	Top-1 err = 0.398535,	Top-5 err = 0.185742,	data_time = 0.050711,	train_time = 0.331190
[2019-08-23 19:12:48,039] TRAIN Iter 148040: lr = 0.253268,	loss = 2.592497,	Top-1 err = 0.399707,	Top-5 err = 0.184619,	data_time = 0.050320,	train_time = 0.851016
[2019-08-23 19:13:05,946] TRAIN Iter 148060: lr = 0.253235,	loss = 2.715066,	Top-1 err = 0.406250,	Top-5 err = 0.188281,	data_time = 0.050404,	train_time = 0.895339
[2019-08-23 19:13:12,393] TRAIN Iter 148080: lr = 0.253202,	loss = 2.674918,	Top-1 err = 0.406592,	Top-5 err = 0.190625,	data_time = 0.050261,	train_time = 0.322332
[2019-08-23 19:13:28,543] TRAIN Iter 148100: lr = 0.253168,	loss = 2.685638,	Top-1 err = 0.394824,	Top-5 err = 0.183545,	data_time = 0.050432,	train_time = 0.807485
[2019-08-23 19:13:35,584] TRAIN Iter 148120: lr = 0.253135,	loss = 2.642525,	Top-1 err = 0.398975,	Top-5 err = 0.181250,	data_time = 0.050906,	train_time = 0.352004
[2019-08-23 19:13:51,144] TRAIN Iter 148140: lr = 0.253102,	loss = 2.529367,	Top-1 err = 0.408887,	Top-5 err = 0.186084,	data_time = 0.050523,	train_time = 0.777979
[2019-08-23 19:14:09,371] TRAIN Iter 148160: lr = 0.253068,	loss = 2.666196,	Top-1 err = 0.404395,	Top-5 err = 0.184521,	data_time = 0.050266,	train_time = 0.911385
[2019-08-23 19:14:15,914] TRAIN Iter 148180: lr = 0.253035,	loss = 2.522989,	Top-1 err = 0.402197,	Top-5 err = 0.184082,	data_time = 0.050494,	train_time = 0.327100
[2019-08-23 19:14:33,018] TRAIN Iter 148200: lr = 0.253002,	loss = 2.690718,	Top-1 err = 0.405322,	Top-5 err = 0.187744,	data_time = 0.050591,	train_time = 0.855175
[2019-08-23 19:14:49,691] TRAIN Iter 148220: lr = 0.252968,	loss = 2.638077,	Top-1 err = 0.401562,	Top-5 err = 0.185254,	data_time = 0.050302,	train_time = 0.833647
[2019-08-23 19:14:56,644] TRAIN Iter 148240: lr = 0.252935,	loss = 2.614578,	Top-1 err = 0.401904,	Top-5 err = 0.181836,	data_time = 0.050459,	train_time = 0.347675
[2019-08-23 19:15:14,390] TRAIN Iter 148260: lr = 0.252902,	loss = 2.668094,	Top-1 err = 0.408350,	Top-5 err = 0.188965,	data_time = 0.050208,	train_time = 0.887280
[2019-08-23 19:15:20,829] TRAIN Iter 148280: lr = 0.252868,	loss = 2.699004,	Top-1 err = 0.407568,	Top-5 err = 0.187158,	data_time = 0.050429,	train_time = 0.321935
[2019-08-23 19:15:39,055] TRAIN Iter 148300: lr = 0.252835,	loss = 2.645894,	Top-1 err = 0.402686,	Top-5 err = 0.187939,	data_time = 0.050484,	train_time = 0.911253
[2019-08-23 19:16:02,207] TRAIN Iter 148320: lr = 0.252802,	loss = 2.678935,	Top-1 err = 0.399951,	Top-5 err = 0.184570,	data_time = 0.050435,	train_time = 1.157612
[2019-08-23 19:16:08,671] TRAIN Iter 148340: lr = 0.252768,	loss = 2.602484,	Top-1 err = 0.406396,	Top-5 err = 0.186572,	data_time = 0.050844,	train_time = 0.323180
[2019-08-23 19:16:26,965] TRAIN Iter 148360: lr = 0.252735,	loss = 2.752007,	Top-1 err = 0.407080,	Top-5 err = 0.188574,	data_time = 0.050455,	train_time = 0.914684
[2019-08-23 19:16:45,144] TRAIN Iter 148380: lr = 0.252702,	loss = 2.629662,	Top-1 err = 0.407568,	Top-5 err = 0.185986,	data_time = 0.100913,	train_time = 0.908920
[2019-08-23 19:16:51,591] TRAIN Iter 148400: lr = 0.252668,	loss = 2.693978,	Top-1 err = 0.404590,	Top-5 err = 0.182275,	data_time = 0.050221,	train_time = 0.322373
[2019-08-23 19:17:09,733] TRAIN Iter 148420: lr = 0.252635,	loss = 2.626225,	Top-1 err = 0.399316,	Top-5 err = 0.180566,	data_time = 0.050222,	train_time = 0.907081
[2019-08-23 19:17:16,746] TRAIN Iter 148440: lr = 0.252602,	loss = 2.644629,	Top-1 err = 0.406934,	Top-5 err = 0.184570,	data_time = 0.050603,	train_time = 0.350644
[2019-08-23 19:17:34,349] TRAIN Iter 148460: lr = 0.252568,	loss = 2.599376,	Top-1 err = 0.400488,	Top-5 err = 0.184229,	data_time = 0.050544,	train_time = 0.880106
[2019-08-23 19:17:50,660] TRAIN Iter 148480: lr = 0.252535,	loss = 2.615331,	Top-1 err = 0.407764,	Top-5 err = 0.189062,	data_time = 0.051012,	train_time = 0.815552
[2019-08-23 19:17:57,801] TRAIN Iter 148500: lr = 0.252502,	loss = 2.611537,	Top-1 err = 0.402637,	Top-5 err = 0.183545,	data_time = 0.050911,	train_time = 0.357049
[2019-08-23 19:18:15,577] TRAIN Iter 148520: lr = 0.252468,	loss = 2.695562,	Top-1 err = 0.406299,	Top-5 err = 0.185889,	data_time = 0.050233,	train_time = 0.888773
[2019-08-23 19:18:35,874] TRAIN Iter 148540: lr = 0.252435,	loss = 2.637937,	Top-1 err = 0.406641,	Top-5 err = 0.188086,	data_time = 0.050572,	train_time = 1.014828
[2019-08-23 19:18:42,609] TRAIN Iter 148560: lr = 0.252402,	loss = 2.600204,	Top-1 err = 0.408447,	Top-5 err = 0.187402,	data_time = 0.050169,	train_time = 0.336737
[2019-08-23 19:19:02,502] TRAIN Iter 148580: lr = 0.252368,	loss = 2.638645,	Top-1 err = 0.405908,	Top-5 err = 0.187012,	data_time = 0.050507,	train_time = 0.994650
[2019-08-23 19:19:08,917] TRAIN Iter 148600: lr = 0.252335,	loss = 2.607712,	Top-1 err = 0.401758,	Top-5 err = 0.183057,	data_time = 0.050380,	train_time = 0.320725
[2019-08-23 19:19:28,116] TRAIN Iter 148620: lr = 0.252302,	loss = 2.691715,	Top-1 err = 0.402441,	Top-5 err = 0.188574,	data_time = 0.050353,	train_time = 0.959941
[2019-08-23 19:19:46,138] TRAIN Iter 148640: lr = 0.252268,	loss = 2.614347,	Top-1 err = 0.405078,	Top-5 err = 0.182373,	data_time = 0.050643,	train_time = 0.901081
[2019-08-23 19:19:52,617] TRAIN Iter 148660: lr = 0.252235,	loss = 2.637381,	Top-1 err = 0.406787,	Top-5 err = 0.188428,	data_time = 0.050300,	train_time = 0.323927
[2019-08-23 19:20:12,530] TRAIN Iter 148680: lr = 0.252202,	loss = 2.676229,	Top-1 err = 0.407129,	Top-5 err = 0.187256,	data_time = 0.050291,	train_time = 0.995616
[2019-08-23 19:20:28,908] TRAIN Iter 148700: lr = 0.252168,	loss = 2.614570,	Top-1 err = 0.402441,	Top-5 err = 0.186816,	data_time = 0.050616,	train_time = 0.818921
[2019-08-23 19:20:35,997] TRAIN Iter 148720: lr = 0.252135,	loss = 2.659487,	Top-1 err = 0.414502,	Top-5 err = 0.193408,	data_time = 0.050336,	train_time = 0.354400
[2019-08-23 19:20:52,986] TRAIN Iter 148740: lr = 0.252102,	loss = 2.689716,	Top-1 err = 0.403857,	Top-5 err = 0.183398,	data_time = 0.050471,	train_time = 0.849432
[2019-08-23 19:20:59,509] TRAIN Iter 148760: lr = 0.252068,	loss = 2.633131,	Top-1 err = 0.414160,	Top-5 err = 0.191260,	data_time = 0.050477,	train_time = 0.326167
[2019-08-23 19:21:19,571] TRAIN Iter 148780: lr = 0.252035,	loss = 2.656447,	Top-1 err = 0.411426,	Top-5 err = 0.188525,	data_time = 0.050441,	train_time = 1.003070
[2019-08-23 19:21:39,313] TRAIN Iter 148800: lr = 0.252002,	loss = 2.595603,	Top-1 err = 0.405078,	Top-5 err = 0.185107,	data_time = 0.050436,	train_time = 0.987074
[2019-08-23 19:21:45,826] TRAIN Iter 148820: lr = 0.251968,	loss = 2.659394,	Top-1 err = 0.409131,	Top-5 err = 0.191846,	data_time = 0.050409,	train_time = 0.325662
[2019-08-23 19:22:07,078] TRAIN Iter 148840: lr = 0.251935,	loss = 2.659194,	Top-1 err = 0.411426,	Top-5 err = 0.190137,	data_time = 0.050456,	train_time = 1.062556
[2019-08-23 19:22:28,300] TRAIN Iter 148860: lr = 0.251902,	loss = 2.655410,	Top-1 err = 0.402148,	Top-5 err = 0.184277,	data_time = 0.072064,	train_time = 1.061080
[2019-08-23 19:22:36,135] TRAIN Iter 148880: lr = 0.251868,	loss = 2.740328,	Top-1 err = 0.413086,	Top-5 err = 0.189404,	data_time = 0.050055,	train_time = 0.391762
[2019-08-23 19:22:57,589] TRAIN Iter 148900: lr = 0.251835,	loss = 2.611585,	Top-1 err = 0.400830,	Top-5 err = 0.185352,	data_time = 0.050032,	train_time = 1.072699
[2019-08-23 19:23:03,664] TRAIN Iter 148920: lr = 0.251802,	loss = 2.672282,	Top-1 err = 0.402539,	Top-5 err = 0.185693,	data_time = 0.049919,	train_time = 0.303707
[2019-08-23 19:24:00,002] TRAIN Iter 148940: lr = 0.251768,	loss = 2.667653,	Top-1 err = 0.415173,	Top-5 err = 0.192201,	data_time = 0.050584,	train_time = 2.816923
[2019-08-23 19:24:21,534] TRAIN Iter 148960: lr = 0.251735,	loss = 2.604687,	Top-1 err = 0.396484,	Top-5 err = 0.188037,	data_time = 1.370745,	train_time = 1.076561
[2019-08-23 19:24:28,256] TRAIN Iter 148980: lr = 0.251702,	loss = 2.643427,	Top-1 err = 0.393848,	Top-5 err = 0.182910,	data_time = 0.050484,	train_time = 0.336082
[2019-08-23 19:24:45,967] TRAIN Iter 149000: lr = 0.251668,	loss = 2.657076,	Top-1 err = 0.396338,	Top-5 err = 0.178857,	data_time = 0.050355,	train_time = 0.885565
[2019-08-23 19:24:52,616] TRAIN Iter 149020: lr = 0.251635,	loss = 2.782471,	Top-1 err = 0.398145,	Top-5 err = 0.181445,	data_time = 0.050480,	train_time = 0.332401
[2019-08-23 19:25:10,783] TRAIN Iter 149040: lr = 0.251602,	loss = 2.634422,	Top-1 err = 0.398389,	Top-5 err = 0.180518,	data_time = 0.050349,	train_time = 0.908362
[2019-08-23 19:25:28,760] TRAIN Iter 149060: lr = 0.251568,	loss = 2.715400,	Top-1 err = 0.401123,	Top-5 err = 0.182227,	data_time = 0.050364,	train_time = 0.898802
[2019-08-23 19:25:35,605] TRAIN Iter 149080: lr = 0.251535,	loss = 2.595917,	Top-1 err = 0.394238,	Top-5 err = 0.177100,	data_time = 0.050677,	train_time = 0.342276
[2019-08-23 19:25:52,001] TRAIN Iter 149100: lr = 0.251502,	loss = 2.651499,	Top-1 err = 0.394336,	Top-5 err = 0.180957,	data_time = 0.050281,	train_time = 0.819786
[2019-08-23 19:26:08,692] TRAIN Iter 149120: lr = 0.251468,	loss = 2.557537,	Top-1 err = 0.401709,	Top-5 err = 0.186377,	data_time = 8.900049,	train_time = 0.834521
[2019-08-23 19:26:15,267] TRAIN Iter 149140: lr = 0.251435,	loss = 2.691738,	Top-1 err = 0.401904,	Top-5 err = 0.187207,	data_time = 0.050344,	train_time = 0.328713
[2019-08-23 19:26:32,497] TRAIN Iter 149160: lr = 0.251402,	loss = 2.530857,	Top-1 err = 0.396533,	Top-5 err = 0.177637,	data_time = 0.050292,	train_time = 0.861499
[2019-08-23 19:26:39,176] TRAIN Iter 149180: lr = 0.251368,	loss = 2.584731,	Top-1 err = 0.401562,	Top-5 err = 0.184717,	data_time = 0.050505,	train_time = 0.333946
[2019-08-23 19:26:57,661] TRAIN Iter 149200: lr = 0.251335,	loss = 2.663260,	Top-1 err = 0.400537,	Top-5 err = 0.187451,	data_time = 0.050402,	train_time = 0.924216
[2019-08-23 19:27:15,184] TRAIN Iter 149220: lr = 0.251302,	loss = 2.670141,	Top-1 err = 0.399072,	Top-5 err = 0.184521,	data_time = 0.050552,	train_time = 0.876160
[2019-08-23 19:27:22,667] TRAIN Iter 149240: lr = 0.251268,	loss = 2.673364,	Top-1 err = 0.398193,	Top-5 err = 0.181055,	data_time = 0.051006,	train_time = 0.374121
[2019-08-23 19:27:36,789] TRAIN Iter 149260: lr = 0.251235,	loss = 2.643312,	Top-1 err = 0.400195,	Top-5 err = 0.182324,	data_time = 0.050625,	train_time = 0.706092
[2019-08-23 19:27:51,352] TRAIN Iter 149280: lr = 0.251202,	loss = 2.636318,	Top-1 err = 0.403760,	Top-5 err = 0.183496,	data_time = 4.895198,	train_time = 0.728137
[2019-08-23 19:27:58,654] TRAIN Iter 149300: lr = 0.251168,	loss = 2.759150,	Top-1 err = 0.401416,	Top-5 err = 0.186426,	data_time = 0.050232,	train_time = 0.365090
[2019-08-23 19:28:16,481] TRAIN Iter 149320: lr = 0.251135,	loss = 2.691635,	Top-1 err = 0.404297,	Top-5 err = 0.187207,	data_time = 0.050104,	train_time = 0.891309
[2019-08-23 19:28:23,189] TRAIN Iter 149340: lr = 0.251102,	loss = 2.584475,	Top-1 err = 0.403809,	Top-5 err = 0.182275,	data_time = 0.050424,	train_time = 0.335424
[2019-08-23 19:28:40,105] TRAIN Iter 149360: lr = 0.251068,	loss = 2.734256,	Top-1 err = 0.401611,	Top-5 err = 0.186621,	data_time = 0.050343,	train_time = 0.845744
[2019-08-23 19:28:57,206] TRAIN Iter 149380: lr = 0.251035,	loss = 2.724235,	Top-1 err = 0.403906,	Top-5 err = 0.187012,	data_time = 0.050227,	train_time = 0.855038
[2019-08-23 19:29:03,670] TRAIN Iter 149400: lr = 0.251002,	loss = 2.761161,	Top-1 err = 0.406348,	Top-5 err = 0.186133,	data_time = 0.050383,	train_time = 0.323232
[2019-08-23 19:29:21,574] TRAIN Iter 149420: lr = 0.250968,	loss = 2.656116,	Top-1 err = 0.404346,	Top-5 err = 0.183154,	data_time = 0.050289,	train_time = 0.895160
[2019-08-23 19:29:37,173] TRAIN Iter 149440: lr = 0.250935,	loss = 2.649897,	Top-1 err = 0.401611,	Top-5 err = 0.187451,	data_time = 0.050447,	train_time = 0.779938
[2019-08-23 19:29:45,951] TRAIN Iter 149460: lr = 0.250902,	loss = 2.597941,	Top-1 err = 0.398486,	Top-5 err = 0.179932,	data_time = 0.050941,	train_time = 0.438857
[2019-08-23 19:30:02,630] TRAIN Iter 149480: lr = 0.250868,	loss = 2.500179,	Top-1 err = 0.402637,	Top-5 err = 0.182178,	data_time = 0.050496,	train_time = 0.833980
[2019-08-23 19:30:09,664] TRAIN Iter 149500: lr = 0.250835,	loss = 2.568357,	Top-1 err = 0.399463,	Top-5 err = 0.184912,	data_time = 0.147741,	train_time = 0.351658
[2019-08-23 19:30:26,628] TRAIN Iter 149520: lr = 0.250802,	loss = 2.680529,	Top-1 err = 0.400537,	Top-5 err = 0.184912,	data_time = 0.050602,	train_time = 0.848183
[2019-08-23 19:30:42,239] TRAIN Iter 149540: lr = 0.250768,	loss = 2.673188,	Top-1 err = 0.400195,	Top-5 err = 0.183398,	data_time = 0.050342,	train_time = 0.780522
[2019-08-23 19:30:48,599] TRAIN Iter 149560: lr = 0.250735,	loss = 2.721329,	Top-1 err = 0.402832,	Top-5 err = 0.186719,	data_time = 0.050336,	train_time = 0.318035
[2019-08-23 19:31:08,046] TRAIN Iter 149580: lr = 0.250702,	loss = 2.544340,	Top-1 err = 0.402148,	Top-5 err = 0.184912,	data_time = 0.050654,	train_time = 0.972299
[2019-08-23 19:31:23,430] TRAIN Iter 149600: lr = 0.250668,	loss = 2.639106,	Top-1 err = 0.405615,	Top-5 err = 0.188232,	data_time = 0.050117,	train_time = 0.769217
[2019-08-23 19:31:31,688] TRAIN Iter 149620: lr = 0.250635,	loss = 2.644765,	Top-1 err = 0.405420,	Top-5 err = 0.187305,	data_time = 0.050339,	train_time = 0.412860
[2019-08-23 19:31:50,537] TRAIN Iter 149640: lr = 0.250602,	loss = 2.691075,	Top-1 err = 0.407666,	Top-5 err = 0.185840,	data_time = 0.050471,	train_time = 0.942413
[2019-08-23 19:31:57,702] TRAIN Iter 149660: lr = 0.250568,	loss = 2.656396,	Top-1 err = 0.403809,	Top-5 err = 0.188184,	data_time = 0.050256,	train_time = 0.358240
[2019-08-23 19:32:14,609] TRAIN Iter 149680: lr = 0.250535,	loss = 2.577172,	Top-1 err = 0.407520,	Top-5 err = 0.186279,	data_time = 0.050411,	train_time = 0.845365
[2019-08-23 19:32:32,281] TRAIN Iter 149700: lr = 0.250502,	loss = 2.557379,	Top-1 err = 0.403809,	Top-5 err = 0.184863,	data_time = 0.050391,	train_time = 0.883582
[2019-08-23 19:32:39,079] TRAIN Iter 149720: lr = 0.250468,	loss = 2.590513,	Top-1 err = 0.404150,	Top-5 err = 0.183398,	data_time = 0.050742,	train_time = 0.339902
[2019-08-23 19:32:55,832] TRAIN Iter 149740: lr = 0.250435,	loss = 2.622887,	Top-1 err = 0.407031,	Top-5 err = 0.187793,	data_time = 0.050276,	train_time = 0.837599
[2019-08-23 19:33:11,111] TRAIN Iter 149760: lr = 0.250402,	loss = 2.637174,	Top-1 err = 0.402002,	Top-5 err = 0.181104,	data_time = 0.985467,	train_time = 0.763936
[2019-08-23 19:33:20,334] TRAIN Iter 149780: lr = 0.250368,	loss = 2.563198,	Top-1 err = 0.404346,	Top-5 err = 0.185352,	data_time = 0.050324,	train_time = 0.461171
[2019-08-23 19:33:38,132] TRAIN Iter 149800: lr = 0.250335,	loss = 2.574419,	Top-1 err = 0.403516,	Top-5 err = 0.187598,	data_time = 0.050247,	train_time = 0.889845
[2019-08-23 19:33:44,940] TRAIN Iter 149820: lr = 0.250302,	loss = 2.670958,	Top-1 err = 0.408887,	Top-5 err = 0.186035,	data_time = 0.050510,	train_time = 0.340394
[2019-08-23 19:34:04,106] TRAIN Iter 149840: lr = 0.250268,	loss = 2.515352,	Top-1 err = 0.404541,	Top-5 err = 0.182666,	data_time = 0.050405,	train_time = 0.958317
[2019-08-23 19:34:20,503] TRAIN Iter 149860: lr = 0.250235,	loss = 2.717542,	Top-1 err = 0.409814,	Top-5 err = 0.182959,	data_time = 0.154691,	train_time = 0.819799
[2019-08-23 19:34:27,806] TRAIN Iter 149880: lr = 0.250202,	loss = 2.693131,	Top-1 err = 0.406641,	Top-5 err = 0.186328,	data_time = 0.050689,	train_time = 0.365164
[2019-08-23 19:34:46,737] TRAIN Iter 149900: lr = 0.250168,	loss = 2.661349,	Top-1 err = 0.402686,	Top-5 err = 0.180762,	data_time = 0.050446,	train_time = 0.946531
[2019-08-23 19:35:00,934] TRAIN Iter 149920: lr = 0.250135,	loss = 2.698061,	Top-1 err = 0.402637,	Top-5 err = 0.184473,	data_time = 2.417696,	train_time = 0.709843
[2019-08-23 19:35:10,163] TRAIN Iter 149940: lr = 0.250102,	loss = 2.661989,	Top-1 err = 0.406006,	Top-5 err = 0.186865,	data_time = 0.050830,	train_time = 0.461426
[2019-08-23 19:35:28,776] TRAIN Iter 149960: lr = 0.250068,	loss = 2.639157,	Top-1 err = 0.410645,	Top-5 err = 0.188770,	data_time = 0.050442,	train_time = 0.930642
[2019-08-23 19:35:35,765] TRAIN Iter 149980: lr = 0.250035,	loss = 2.679141,	Top-1 err = 0.401758,	Top-5 err = 0.186230,	data_time = 0.050509,	train_time = 0.349432
[2019-08-23 19:35:52,433] TRAIN Iter 150000: lr = 0.250002,	loss = 2.621225,	Top-1 err = 0.410791,	Top-5 err = 0.186670,	data_time = 0.050385,	train_time = 0.833373
[2019-08-23 19:36:57,261] TEST Iter 150000: loss = 2.473826,	Top-1 err = 0.377580,	Top-5 err = 0.150100,	val_time = 64.775827
[2019-08-23 19:37:03,470] TRAIN Iter 150020: lr = 0.249968,	loss = 2.601387,	Top-1 err = 0.405469,	Top-5 err = 0.188623,	data_time = 0.050492,	train_time = 0.310414
[2019-08-23 19:37:10,006] TRAIN Iter 150040: lr = 0.249935,	loss = 2.624292,	Top-1 err = 0.406152,	Top-5 err = 0.185352,	data_time = 0.050772,	train_time = 0.326797
[2019-08-23 19:37:16,583] TRAIN Iter 150060: lr = 0.249902,	loss = 2.728412,	Top-1 err = 0.403906,	Top-5 err = 0.189404,	data_time = 0.050286,	train_time = 0.328839
[2019-08-23 19:37:27,770] TRAIN Iter 150080: lr = 0.249868,	loss = 2.658526,	Top-1 err = 0.405957,	Top-5 err = 0.188965,	data_time = 0.050460,	train_time = 0.559326
[2019-08-23 19:37:45,929] TRAIN Iter 150100: lr = 0.249835,	loss = 2.625161,	Top-1 err = 0.406348,	Top-5 err = 0.185352,	data_time = 0.050545,	train_time = 0.907957
[2019-08-23 19:37:56,144] TRAIN Iter 150120: lr = 0.249802,	loss = 2.820899,	Top-1 err = 0.406299,	Top-5 err = 0.191064,	data_time = 0.050005,	train_time = 0.510744
[2019-08-23 19:38:15,720] TRAIN Iter 150140: lr = 0.249768,	loss = 2.738039,	Top-1 err = 0.410107,	Top-5 err = 0.191748,	data_time = 0.127520,	train_time = 0.978760
[2019-08-23 19:38:22,789] TRAIN Iter 150160: lr = 0.249735,	loss = 2.617895,	Top-1 err = 0.403809,	Top-5 err = 0.187988,	data_time = 0.049984,	train_time = 0.353469
[2019-08-23 19:39:17,659] TRAIN Iter 150180: lr = 0.249702,	loss = 2.716795,	Top-1 err = 0.411854,	Top-5 err = 0.191900,	data_time = 0.127730,	train_time = 2.743441
[2019-08-23 19:39:24,483] TRAIN Iter 150200: lr = 0.249668,	loss = 2.627881,	Top-1 err = 0.407715,	Top-5 err = 0.185400,	data_time = 0.050206,	train_time = 0.341196
[2019-08-23 19:39:44,268] TRAIN Iter 150220: lr = 0.249635,	loss = 2.632330,	Top-1 err = 0.401562,	Top-5 err = 0.181934,	data_time = 0.050323,	train_time = 0.989213
[2019-08-23 19:39:51,167] TRAIN Iter 150240: lr = 0.249602,	loss = 2.614920,	Top-1 err = 0.400635,	Top-5 err = 0.182227,	data_time = 0.050336,	train_time = 0.344946
[2019-08-23 19:40:07,373] TRAIN Iter 150260: lr = 0.249568,	loss = 2.680873,	Top-1 err = 0.401611,	Top-5 err = 0.185938,	data_time = 0.050432,	train_time = 0.810276
[2019-08-23 19:40:24,966] TRAIN Iter 150280: lr = 0.249535,	loss = 2.631758,	Top-1 err = 0.392725,	Top-5 err = 0.177637,	data_time = 0.050990,	train_time = 0.879653
[2019-08-23 19:40:31,347] TRAIN Iter 150300: lr = 0.249502,	loss = 2.660872,	Top-1 err = 0.400928,	Top-5 err = 0.181689,	data_time = 0.050191,	train_time = 0.319019
[2019-08-23 19:40:49,371] TRAIN Iter 150320: lr = 0.249468,	loss = 2.585104,	Top-1 err = 0.391797,	Top-5 err = 0.178174,	data_time = 0.050936,	train_time = 0.901198
[2019-08-23 19:41:06,230] TRAIN Iter 150340: lr = 0.249435,	loss = 2.568228,	Top-1 err = 0.396826,	Top-5 err = 0.179932,	data_time = 1.555209,	train_time = 0.842941
[2019-08-23 19:41:12,426] TRAIN Iter 150360: lr = 0.249402,	loss = 2.531653,	Top-1 err = 0.395068,	Top-5 err = 0.180078,	data_time = 0.050419,	train_time = 0.309792
[2019-08-23 19:41:29,125] TRAIN Iter 150380: lr = 0.249368,	loss = 2.708285,	Top-1 err = 0.389600,	Top-5 err = 0.180762,	data_time = 0.050340,	train_time = 0.834961
[2019-08-23 19:41:36,074] TRAIN Iter 150400: lr = 0.249335,	loss = 2.614430,	Top-1 err = 0.401074,	Top-5 err = 0.185498,	data_time = 0.050602,	train_time = 0.347421
[2019-08-23 19:41:52,800] TRAIN Iter 150420: lr = 0.249302,	loss = 2.640019,	Top-1 err = 0.395605,	Top-5 err = 0.186768,	data_time = 0.050442,	train_time = 0.836291
[2019-08-23 19:42:08,936] TRAIN Iter 150440: lr = 0.249268,	loss = 2.677582,	Top-1 err = 0.399805,	Top-5 err = 0.183350,	data_time = 0.050510,	train_time = 0.806756
[2019-08-23 19:42:15,511] TRAIN Iter 150460: lr = 0.249235,	loss = 2.692987,	Top-1 err = 0.398828,	Top-5 err = 0.180469,	data_time = 0.050778,	train_time = 0.328731
[2019-08-23 19:42:32,125] TRAIN Iter 150480: lr = 0.249202,	loss = 2.607392,	Top-1 err = 0.398535,	Top-5 err = 0.181104,	data_time = 0.050489,	train_time = 0.830685
[2019-08-23 19:42:49,936] TRAIN Iter 150500: lr = 0.249168,	loss = 2.615191,	Top-1 err = 0.398633,	Top-5 err = 0.177979,	data_time = 3.072188,	train_time = 0.890569
[2019-08-23 19:42:56,202] TRAIN Iter 150520: lr = 0.249135,	loss = 2.556464,	Top-1 err = 0.403223,	Top-5 err = 0.185938,	data_time = 0.050300,	train_time = 0.313289
[2019-08-23 19:43:12,988] TRAIN Iter 150540: lr = 0.249102,	loss = 2.712907,	Top-1 err = 0.405127,	Top-5 err = 0.184521,	data_time = 0.050448,	train_time = 0.839249
[2019-08-23 19:43:20,009] TRAIN Iter 150560: lr = 0.249068,	loss = 2.648867,	Top-1 err = 0.399023,	Top-5 err = 0.180078,	data_time = 0.050466,	train_time = 0.351036
[2019-08-23 19:43:35,820] TRAIN Iter 150580: lr = 0.249035,	loss = 2.597754,	Top-1 err = 0.403271,	Top-5 err = 0.184229,	data_time = 0.050323,	train_time = 0.790560
[2019-08-23 19:43:53,331] TRAIN Iter 150600: lr = 0.249002,	loss = 2.553557,	Top-1 err = 0.402539,	Top-5 err = 0.183301,	data_time = 0.050288,	train_time = 0.875526
[2019-08-23 19:44:00,104] TRAIN Iter 150620: lr = 0.248968,	loss = 2.659288,	Top-1 err = 0.394141,	Top-5 err = 0.179932,	data_time = 0.124058,	train_time = 0.338647
[2019-08-23 19:44:15,649] TRAIN Iter 150640: lr = 0.248935,	loss = 2.608078,	Top-1 err = 0.399707,	Top-5 err = 0.182178,	data_time = 0.050718,	train_time = 0.777232
[2019-08-23 19:44:32,221] TRAIN Iter 150660: lr = 0.248902,	loss = 2.684793,	Top-1 err = 0.403857,	Top-5 err = 0.186035,	data_time = 2.220786,	train_time = 0.828566
[2019-08-23 19:44:38,477] TRAIN Iter 150680: lr = 0.248868,	loss = 2.670834,	Top-1 err = 0.404199,	Top-5 err = 0.187842,	data_time = 0.050320,	train_time = 0.312824
[2019-08-23 19:44:54,865] TRAIN Iter 150700: lr = 0.248835,	loss = 2.530297,	Top-1 err = 0.399463,	Top-5 err = 0.180420,	data_time = 0.050316,	train_time = 0.819393
[2019-08-23 19:45:01,152] TRAIN Iter 150720: lr = 0.248802,	loss = 2.623353,	Top-1 err = 0.401221,	Top-5 err = 0.183398,	data_time = 0.050299,	train_time = 0.314312
[2019-08-23 19:45:22,076] TRAIN Iter 150740: lr = 0.248768,	loss = 2.530252,	Top-1 err = 0.399902,	Top-5 err = 0.183887,	data_time = 0.050843,	train_time = 1.046192
[2019-08-23 19:45:39,098] TRAIN Iter 150760: lr = 0.248735,	loss = 2.536263,	Top-1 err = 0.400732,	Top-5 err = 0.182764,	data_time = 0.050665,	train_time = 0.851087
[2019-08-23 19:45:45,620] TRAIN Iter 150780: lr = 0.248702,	loss = 2.571906,	Top-1 err = 0.397510,	Top-5 err = 0.180225,	data_time = 0.050445,	train_time = 0.326080
[2019-08-23 19:46:04,054] TRAIN Iter 150800: lr = 0.248668,	loss = 2.567450,	Top-1 err = 0.405811,	Top-5 err = 0.184473,	data_time = 0.050404,	train_time = 0.921675
[2019-08-23 19:46:21,154] TRAIN Iter 150820: lr = 0.248635,	loss = 2.570769,	Top-1 err = 0.401514,	Top-5 err = 0.181592,	data_time = 3.569233,	train_time = 0.854989
[2019-08-23 19:46:27,562] TRAIN Iter 150840: lr = 0.248602,	loss = 2.659533,	Top-1 err = 0.404004,	Top-5 err = 0.187256,	data_time = 0.050436,	train_time = 0.320397
[2019-08-23 19:46:45,547] TRAIN Iter 150860: lr = 0.248568,	loss = 2.596474,	Top-1 err = 0.401807,	Top-5 err = 0.184766,	data_time = 0.050500,	train_time = 0.899209
[2019-08-23 19:46:52,226] TRAIN Iter 150880: lr = 0.248535,	loss = 2.604522,	Top-1 err = 0.402197,	Top-5 err = 0.181787,	data_time = 0.050617,	train_time = 0.333970
[2019-08-23 19:47:09,696] TRAIN Iter 150900: lr = 0.248502,	loss = 2.652140,	Top-1 err = 0.404199,	Top-5 err = 0.185449,	data_time = 0.050412,	train_time = 0.873474
[2019-08-23 19:47:27,654] TRAIN Iter 150920: lr = 0.248468,	loss = 2.561986,	Top-1 err = 0.396875,	Top-5 err = 0.181689,	data_time = 0.050339,	train_time = 0.897878
[2019-08-23 19:47:34,039] TRAIN Iter 150940: lr = 0.248435,	loss = 2.636541,	Top-1 err = 0.408496,	Top-5 err = 0.191211,	data_time = 0.050494,	train_time = 0.319195
[2019-08-23 19:47:53,432] TRAIN Iter 150960: lr = 0.248402,	loss = 2.717597,	Top-1 err = 0.403320,	Top-5 err = 0.186084,	data_time = 0.050759,	train_time = 0.969670
[2019-08-23 19:48:11,414] TRAIN Iter 150980: lr = 0.248368,	loss = 2.576771,	Top-1 err = 0.406836,	Top-5 err = 0.189453,	data_time = 3.435706,	train_time = 0.899065
[2019-08-23 19:48:17,792] TRAIN Iter 151000: lr = 0.248335,	loss = 2.595700,	Top-1 err = 0.402637,	Top-5 err = 0.181592,	data_time = 0.050480,	train_time = 0.318869
[2019-08-23 19:48:35,691] TRAIN Iter 151020: lr = 0.248302,	loss = 2.654701,	Top-1 err = 0.403516,	Top-5 err = 0.188574,	data_time = 0.050357,	train_time = 0.894958
[2019-08-23 19:48:42,425] TRAIN Iter 151040: lr = 0.248268,	loss = 2.688575,	Top-1 err = 0.406445,	Top-5 err = 0.186572,	data_time = 0.050841,	train_time = 0.336680
[2019-08-23 19:49:00,504] TRAIN Iter 151060: lr = 0.248235,	loss = 2.727753,	Top-1 err = 0.405615,	Top-5 err = 0.187598,	data_time = 0.050202,	train_time = 0.903951
[2019-08-23 19:49:18,188] TRAIN Iter 151080: lr = 0.248202,	loss = 2.632976,	Top-1 err = 0.406250,	Top-5 err = 0.191748,	data_time = 0.050459,	train_time = 0.884167
[2019-08-23 19:49:24,885] TRAIN Iter 151100: lr = 0.248168,	loss = 2.719727,	Top-1 err = 0.404199,	Top-5 err = 0.183301,	data_time = 0.050511,	train_time = 0.334839
[2019-08-23 19:49:44,441] TRAIN Iter 151120: lr = 0.248135,	loss = 2.692914,	Top-1 err = 0.407861,	Top-5 err = 0.188916,	data_time = 0.050567,	train_time = 0.977817
[2019-08-23 19:50:02,728] TRAIN Iter 151140: lr = 0.248102,	loss = 2.659478,	Top-1 err = 0.405127,	Top-5 err = 0.187305,	data_time = 4.659359,	train_time = 0.914337
[2019-08-23 19:50:09,042] TRAIN Iter 151160: lr = 0.248068,	loss = 2.594409,	Top-1 err = 0.403418,	Top-5 err = 0.190869,	data_time = 0.050419,	train_time = 0.315685
[2019-08-23 19:50:28,023] TRAIN Iter 151180: lr = 0.248035,	loss = 2.633792,	Top-1 err = 0.401465,	Top-5 err = 0.186279,	data_time = 0.050620,	train_time = 0.949002
[2019-08-23 19:50:34,721] TRAIN Iter 151200: lr = 0.248002,	loss = 2.618338,	Top-1 err = 0.409326,	Top-5 err = 0.189209,	data_time = 0.050559,	train_time = 0.334924
[2019-08-23 19:50:53,439] TRAIN Iter 151220: lr = 0.247968,	loss = 2.729929,	Top-1 err = 0.400098,	Top-5 err = 0.183789,	data_time = 0.050422,	train_time = 0.935854
[2019-08-23 19:51:10,989] TRAIN Iter 151240: lr = 0.247935,	loss = 2.771632,	Top-1 err = 0.406592,	Top-5 err = 0.187354,	data_time = 0.050356,	train_time = 0.877507
[2019-08-23 19:51:17,369] TRAIN Iter 151260: lr = 0.247902,	loss = 2.552342,	Top-1 err = 0.403564,	Top-5 err = 0.188428,	data_time = 0.050404,	train_time = 0.318982
[2019-08-23 19:51:34,882] TRAIN Iter 151280: lr = 0.247868,	loss = 2.657571,	Top-1 err = 0.402197,	Top-5 err = 0.187744,	data_time = 0.050525,	train_time = 0.875618
[2019-08-23 19:51:54,721] TRAIN Iter 151300: lr = 0.247835,	loss = 2.540974,	Top-1 err = 0.407031,	Top-5 err = 0.189990,	data_time = 5.779739,	train_time = 0.991962
[2019-08-23 19:52:01,040] TRAIN Iter 151320: lr = 0.247802,	loss = 2.640667,	Top-1 err = 0.409766,	Top-5 err = 0.188574,	data_time = 0.050367,	train_time = 0.315908
[2019-08-23 19:52:20,664] TRAIN Iter 151340: lr = 0.247768,	loss = 2.661194,	Top-1 err = 0.405078,	Top-5 err = 0.188477,	data_time = 0.050488,	train_time = 0.981183
[2019-08-23 19:52:26,905] TRAIN Iter 151360: lr = 0.247735,	loss = 2.645650,	Top-1 err = 0.402539,	Top-5 err = 0.187598,	data_time = 0.050347,	train_time = 0.312060
[2019-08-23 19:52:46,726] TRAIN Iter 151380: lr = 0.247702,	loss = 2.617135,	Top-1 err = 0.402783,	Top-5 err = 0.186035,	data_time = 0.050011,	train_time = 0.991055
[2019-08-23 19:53:05,096] TRAIN Iter 151400: lr = 0.247668,	loss = 2.680868,	Top-1 err = 0.409180,	Top-5 err = 0.183789,	data_time = 0.049999,	train_time = 0.918458
[2019-08-23 19:53:10,952] TRAIN Iter 151420: lr = 0.247635,	loss = 2.627799,	Top-1 err = 0.408154,	Top-5 err = 0.184766,	data_time = 0.050002,	train_time = 0.292785
[2019-08-23 19:53:56,746] TRAIN Iter 151440: lr = 0.247602,	loss = 2.657278,	Top-1 err = 0.414058,	Top-5 err = 0.192183,	data_time = 0.050496,	train_time = 2.289703
[2019-08-23 19:54:04,050] TRAIN Iter 151460: lr = 0.247568,	loss = 2.588648,	Top-1 err = 0.408252,	Top-5 err = 0.181348,	data_time = 0.050555,	train_time = 0.365172
[2019-08-23 19:54:18,616] TRAIN Iter 151480: lr = 0.247535,	loss = 2.741999,	Top-1 err = 0.401904,	Top-5 err = 0.180713,	data_time = 0.050443,	train_time = 0.728292
[2019-08-23 19:54:34,710] TRAIN Iter 151500: lr = 0.247502,	loss = 2.610097,	Top-1 err = 0.395605,	Top-5 err = 0.182080,	data_time = 0.105118,	train_time = 0.804662
[2019-08-23 19:54:42,300] TRAIN Iter 151520: lr = 0.247468,	loss = 2.651749,	Top-1 err = 0.397559,	Top-5 err = 0.180566,	data_time = 0.050397,	train_time = 0.379520
[2019-08-23 19:54:57,749] TRAIN Iter 151540: lr = 0.247435,	loss = 2.583316,	Top-1 err = 0.401221,	Top-5 err = 0.178857,	data_time = 0.050760,	train_time = 0.772395
[2019-08-23 19:55:13,527] TRAIN Iter 151560: lr = 0.247402,	loss = 2.522591,	Top-1 err = 0.398291,	Top-5 err = 0.179980,	data_time = 0.050208,	train_time = 0.788910
[2019-08-23 19:55:20,405] TRAIN Iter 151580: lr = 0.247368,	loss = 2.542216,	Top-1 err = 0.402930,	Top-5 err = 0.181494,	data_time = 0.050837,	train_time = 0.343885
[2019-08-23 19:55:36,539] TRAIN Iter 151600: lr = 0.247335,	loss = 2.501846,	Top-1 err = 0.391504,	Top-5 err = 0.175146,	data_time = 0.050264,	train_time = 0.806689
[2019-08-23 19:55:44,102] TRAIN Iter 151620: lr = 0.247302,	loss = 2.620963,	Top-1 err = 0.396387,	Top-5 err = 0.185498,	data_time = 0.050472,	train_time = 0.378136
[2019-08-23 19:55:59,640] TRAIN Iter 151640: lr = 0.247268,	loss = 2.630269,	Top-1 err = 0.397168,	Top-5 err = 0.184619,	data_time = 0.050306,	train_time = 0.776864
[2019-08-23 19:56:15,570] TRAIN Iter 151660: lr = 0.247235,	loss = 2.671446,	Top-1 err = 0.406592,	Top-5 err = 0.185547,	data_time = 0.050850,	train_time = 0.796518
[2019-08-23 19:56:22,589] TRAIN Iter 151680: lr = 0.247202,	loss = 2.619991,	Top-1 err = 0.395410,	Top-5 err = 0.181250,	data_time = 0.050613,	train_time = 0.350919
[2019-08-23 19:56:37,875] TRAIN Iter 151700: lr = 0.247168,	loss = 2.523777,	Top-1 err = 0.402002,	Top-5 err = 0.180322,	data_time = 0.050389,	train_time = 0.764300
[2019-08-23 19:56:54,496] TRAIN Iter 151720: lr = 0.247135,	loss = 2.643191,	Top-1 err = 0.396582,	Top-5 err = 0.177637,	data_time = 2.618757,	train_time = 0.831022
[2019-08-23 19:57:00,952] TRAIN Iter 151740: lr = 0.247102,	loss = 2.493720,	Top-1 err = 0.398242,	Top-5 err = 0.183154,	data_time = 0.050376,	train_time = 0.322794
[2019-08-23 19:57:18,203] TRAIN Iter 151760: lr = 0.247068,	loss = 2.729594,	Top-1 err = 0.404736,	Top-5 err = 0.184766,	data_time = 0.050397,	train_time = 0.862551
[2019-08-23 19:57:25,091] TRAIN Iter 151780: lr = 0.247035,	loss = 2.626832,	Top-1 err = 0.400928,	Top-5 err = 0.186523,	data_time = 0.148407,	train_time = 0.344375
[2019-08-23 19:57:42,247] TRAIN Iter 151800: lr = 0.247002,	loss = 2.654383,	Top-1 err = 0.401416,	Top-5 err = 0.187646,	data_time = 0.050318,	train_time = 0.857786
[2019-08-23 19:57:58,848] TRAIN Iter 151820: lr = 0.246968,	loss = 2.669279,	Top-1 err = 0.402539,	Top-5 err = 0.185742,	data_time = 0.050287,	train_time = 0.830009
[2019-08-23 19:58:05,835] TRAIN Iter 151840: lr = 0.246935,	loss = 2.590117,	Top-1 err = 0.395508,	Top-5 err = 0.180859,	data_time = 0.050426,	train_time = 0.349357
[2019-08-23 19:58:21,730] TRAIN Iter 151860: lr = 0.246902,	loss = 2.576325,	Top-1 err = 0.401904,	Top-5 err = 0.179053,	data_time = 0.050239,	train_time = 0.794731
[2019-08-23 19:58:39,674] TRAIN Iter 151880: lr = 0.246868,	loss = 2.617630,	Top-1 err = 0.403711,	Top-5 err = 0.189551,	data_time = 0.706666,	train_time = 0.897186
[2019-08-23 19:58:46,272] TRAIN Iter 151900: lr = 0.246835,	loss = 2.670055,	Top-1 err = 0.393262,	Top-5 err = 0.177539,	data_time = 0.050370,	train_time = 0.329884
[2019-08-23 19:59:03,197] TRAIN Iter 151920: lr = 0.246802,	loss = 2.604486,	Top-1 err = 0.393652,	Top-5 err = 0.179785,	data_time = 0.050479,	train_time = 0.846268
[2019-08-23 19:59:11,028] TRAIN Iter 151940: lr = 0.246768,	loss = 2.680176,	Top-1 err = 0.392188,	Top-5 err = 0.179492,	data_time = 0.050523,	train_time = 0.391496
[2019-08-23 19:59:26,128] TRAIN Iter 151960: lr = 0.246735,	loss = 2.627973,	Top-1 err = 0.399414,	Top-5 err = 0.181689,	data_time = 0.050497,	train_time = 0.755020
[2019-08-23 19:59:41,513] TRAIN Iter 151980: lr = 0.246702,	loss = 2.461869,	Top-1 err = 0.393311,	Top-5 err = 0.179590,	data_time = 0.050097,	train_time = 0.769222
[2019-08-23 19:59:48,423] TRAIN Iter 152000: lr = 0.246668,	loss = 2.630735,	Top-1 err = 0.401953,	Top-5 err = 0.183984,	data_time = 0.050727,	train_time = 0.345462
[2019-08-23 20:00:05,132] TRAIN Iter 152020: lr = 0.246635,	loss = 2.686590,	Top-1 err = 0.404932,	Top-5 err = 0.181836,	data_time = 0.050415,	train_time = 0.835440
[2019-08-23 20:00:21,125] TRAIN Iter 152040: lr = 0.246602,	loss = 2.575967,	Top-1 err = 0.405029,	Top-5 err = 0.184277,	data_time = 0.608991,	train_time = 0.799652
[2019-08-23 20:00:28,153] TRAIN Iter 152060: lr = 0.246568,	loss = 2.590713,	Top-1 err = 0.406934,	Top-5 err = 0.188135,	data_time = 0.050759,	train_time = 0.351371
[2019-08-23 20:00:50,077] TRAIN Iter 152080: lr = 0.246535,	loss = 2.570245,	Top-1 err = 0.401514,	Top-5 err = 0.182666,	data_time = 0.051042,	train_time = 1.096207
[2019-08-23 20:00:59,425] TRAIN Iter 152100: lr = 0.246502,	loss = 2.528129,	Top-1 err = 0.399170,	Top-5 err = 0.182666,	data_time = 0.050474,	train_time = 0.467391
[2019-08-23 20:01:14,837] TRAIN Iter 152120: lr = 0.246468,	loss = 2.607685,	Top-1 err = 0.408105,	Top-5 err = 0.186865,	data_time = 0.050310,	train_time = 0.770561
[2019-08-23 20:01:23,139] TRAIN Iter 152140: lr = 0.246435,	loss = 2.579485,	Top-1 err = 0.398975,	Top-5 err = 0.183252,	data_time = 0.050475,	train_time = 0.415105
[2019-08-23 20:01:30,164] TRAIN Iter 152160: lr = 0.246402,	loss = 2.586006,	Top-1 err = 0.402344,	Top-5 err = 0.182227,	data_time = 0.050757,	train_time = 0.351221
[2019-08-23 20:01:42,883] TRAIN Iter 152180: lr = 0.246368,	loss = 2.628039,	Top-1 err = 0.403174,	Top-5 err = 0.185693,	data_time = 0.050503,	train_time = 0.635909
[2019-08-23 20:02:01,281] TRAIN Iter 152200: lr = 0.246335,	loss = 2.600262,	Top-1 err = 0.406641,	Top-5 err = 0.185791,	data_time = 0.050582,	train_time = 0.919931
[2019-08-23 20:02:08,672] TRAIN Iter 152220: lr = 0.246302,	loss = 2.691421,	Top-1 err = 0.401123,	Top-5 err = 0.183105,	data_time = 0.050301,	train_time = 0.369491
[2019-08-23 20:02:25,981] TRAIN Iter 152240: lr = 0.246268,	loss = 2.700876,	Top-1 err = 0.408057,	Top-5 err = 0.189014,	data_time = 0.050635,	train_time = 0.865485
[2019-08-23 20:02:33,115] TRAIN Iter 152260: lr = 0.246235,	loss = 2.627306,	Top-1 err = 0.403662,	Top-5 err = 0.183740,	data_time = 0.050260,	train_time = 0.356650
[2019-08-23 20:02:49,579] TRAIN Iter 152280: lr = 0.246202,	loss = 2.703185,	Top-1 err = 0.402002,	Top-5 err = 0.185449,	data_time = 0.050224,	train_time = 0.823219
[2019-08-23 20:03:15,893] TRAIN Iter 152300: lr = 0.246168,	loss = 2.722421,	Top-1 err = 0.404492,	Top-5 err = 0.190430,	data_time = 0.050364,	train_time = 1.315661
[2019-08-23 20:03:22,577] TRAIN Iter 152320: lr = 0.246135,	loss = 2.675390,	Top-1 err = 0.404102,	Top-5 err = 0.188525,	data_time = 0.050500,	train_time = 0.334206
[2019-08-23 20:03:41,409] TRAIN Iter 152340: lr = 0.246102,	loss = 2.672218,	Top-1 err = 0.412354,	Top-5 err = 0.191162,	data_time = 0.050303,	train_time = 0.941579
[2019-08-23 20:03:59,769] TRAIN Iter 152360: lr = 0.246068,	loss = 2.560946,	Top-1 err = 0.403906,	Top-5 err = 0.188574,	data_time = 0.050678,	train_time = 0.917957
[2019-08-23 20:04:07,278] TRAIN Iter 152380: lr = 0.246035,	loss = 2.634835,	Top-1 err = 0.408643,	Top-5 err = 0.190088,	data_time = 0.050513,	train_time = 0.375458
[2019-08-23 20:04:25,271] TRAIN Iter 152400: lr = 0.246002,	loss = 2.715278,	Top-1 err = 0.405078,	Top-5 err = 0.184619,	data_time = 0.050831,	train_time = 0.899622
[2019-08-23 20:04:32,121] TRAIN Iter 152420: lr = 0.245968,	loss = 2.577288,	Top-1 err = 0.406836,	Top-5 err = 0.186719,	data_time = 0.050459,	train_time = 0.342509
[2019-08-23 20:04:49,566] TRAIN Iter 152440: lr = 0.245935,	loss = 2.632046,	Top-1 err = 0.408691,	Top-5 err = 0.183398,	data_time = 0.051033,	train_time = 0.872225
[2019-08-23 20:05:09,617] TRAIN Iter 152460: lr = 0.245902,	loss = 2.618928,	Top-1 err = 0.408154,	Top-5 err = 0.188184,	data_time = 0.050636,	train_time = 1.002553
[2019-08-23 20:05:16,477] TRAIN Iter 152480: lr = 0.245868,	loss = 2.698835,	Top-1 err = 0.407471,	Top-5 err = 0.185645,	data_time = 0.050210,	train_time = 0.342955
[2019-08-23 20:05:35,111] TRAIN Iter 152500: lr = 0.245835,	loss = 2.709133,	Top-1 err = 0.405176,	Top-5 err = 0.187061,	data_time = 0.050693,	train_time = 0.931712
[2019-08-23 20:05:53,758] TRAIN Iter 152520: lr = 0.245802,	loss = 2.574375,	Top-1 err = 0.401855,	Top-5 err = 0.183252,	data_time = 0.521555,	train_time = 0.932296
[2019-08-23 20:06:01,001] TRAIN Iter 152540: lr = 0.245768,	loss = 2.585577,	Top-1 err = 0.408447,	Top-5 err = 0.188965,	data_time = 0.050527,	train_time = 0.362143
[2019-08-23 20:06:20,900] TRAIN Iter 152560: lr = 0.245735,	loss = 2.662760,	Top-1 err = 0.403857,	Top-5 err = 0.185449,	data_time = 0.050482,	train_time = 0.994968
[2019-08-23 20:06:27,470] TRAIN Iter 152580: lr = 0.245702,	loss = 2.702284,	Top-1 err = 0.402783,	Top-5 err = 0.187598,	data_time = 0.050532,	train_time = 0.328456
[2019-08-23 20:06:46,529] TRAIN Iter 152600: lr = 0.245668,	loss = 2.585256,	Top-1 err = 0.409277,	Top-5 err = 0.187402,	data_time = 0.050483,	train_time = 0.952955
[2019-08-23 20:07:09,773] TRAIN Iter 152620: lr = 0.245635,	loss = 2.606307,	Top-1 err = 0.405176,	Top-5 err = 0.186963,	data_time = 0.050211,	train_time = 1.162190
[2019-08-23 20:07:16,378] TRAIN Iter 152640: lr = 0.245602,	loss = 2.638314,	Top-1 err = 0.405322,	Top-5 err = 0.185693,	data_time = 0.049972,	train_time = 0.330244
[2019-08-23 20:07:36,314] TRAIN Iter 152660: lr = 0.245568,	loss = 2.670004,	Top-1 err = 0.401025,	Top-5 err = 0.186719,	data_time = 0.049900,	train_time = 0.996750
[2019-08-23 20:07:44,607] TRAIN Iter 152680: lr = 0.245535,	loss = 2.770642,	Top-1 err = 0.407244,	Top-5 err = 0.188853,	data_time = 0.007078,	train_time = 0.414641
[2019-08-23 20:08:33,400] TRAIN Iter 152700: lr = 0.245502,	loss = 2.662463,	Top-1 err = 0.404590,	Top-5 err = 0.181592,	data_time = 0.050350,	train_time = 2.439657
[2019-08-23 20:08:52,420] TRAIN Iter 152720: lr = 0.245468,	loss = 2.654079,	Top-1 err = 0.393945,	Top-5 err = 0.177246,	data_time = 0.050301,	train_time = 0.950979
[2019-08-23 20:08:59,542] TRAIN Iter 152740: lr = 0.245435,	loss = 2.618565,	Top-1 err = 0.395703,	Top-5 err = 0.181104,	data_time = 0.051040,	train_time = 0.356062
[2019-08-23 20:09:15,295] TRAIN Iter 152760: lr = 0.245402,	loss = 2.559489,	Top-1 err = 0.395801,	Top-5 err = 0.183008,	data_time = 0.050493,	train_time = 0.787664
[2019-08-23 20:09:28,831] TRAIN Iter 152780: lr = 0.245368,	loss = 2.644968,	Top-1 err = 0.394629,	Top-5 err = 0.181396,	data_time = 0.050422,	train_time = 0.676782
[2019-08-23 20:09:37,537] TRAIN Iter 152800: lr = 0.245335,	loss = 2.563336,	Top-1 err = 0.397607,	Top-5 err = 0.182568,	data_time = 0.050583,	train_time = 0.435301
[2019-08-23 20:09:53,646] TRAIN Iter 152820: lr = 0.245302,	loss = 2.688269,	Top-1 err = 0.397607,	Top-5 err = 0.182617,	data_time = 0.050232,	train_time = 0.805396
[2019-08-23 20:10:00,279] TRAIN Iter 152840: lr = 0.245268,	loss = 2.551971,	Top-1 err = 0.394629,	Top-5 err = 0.179053,	data_time = 0.050487,	train_time = 0.331664
[2019-08-23 20:10:15,596] TRAIN Iter 152860: lr = 0.245235,	loss = 2.598952,	Top-1 err = 0.401074,	Top-5 err = 0.180420,	data_time = 0.050354,	train_time = 0.765823
[2019-08-23 20:10:33,115] TRAIN Iter 152880: lr = 0.245202,	loss = 2.667092,	Top-1 err = 0.396582,	Top-5 err = 0.181006,	data_time = 0.050478,	train_time = 0.875926
[2019-08-23 20:10:39,940] TRAIN Iter 152900: lr = 0.245168,	loss = 2.596273,	Top-1 err = 0.398584,	Top-5 err = 0.182666,	data_time = 0.050308,	train_time = 0.341226
[2019-08-23 20:10:56,037] TRAIN Iter 152920: lr = 0.245135,	loss = 2.557708,	Top-1 err = 0.397705,	Top-5 err = 0.181689,	data_time = 0.050356,	train_time = 0.804851
[2019-08-23 20:11:09,511] TRAIN Iter 152940: lr = 0.245102,	loss = 2.578818,	Top-1 err = 0.398389,	Top-5 err = 0.177539,	data_time = 0.119827,	train_time = 0.673668
[2019-08-23 20:11:19,064] TRAIN Iter 152960: lr = 0.245068,	loss = 2.645699,	Top-1 err = 0.395117,	Top-5 err = 0.181348,	data_time = 0.050455,	train_time = 0.477667
[2019-08-23 20:11:35,841] TRAIN Iter 152980: lr = 0.245035,	loss = 2.616638,	Top-1 err = 0.401025,	Top-5 err = 0.180615,	data_time = 0.050839,	train_time = 0.838826
[2019-08-23 20:11:42,320] TRAIN Iter 153000: lr = 0.245002,	loss = 2.600045,	Top-1 err = 0.401660,	Top-5 err = 0.187744,	data_time = 0.050535,	train_time = 0.323908
[2019-08-23 20:11:58,991] TRAIN Iter 153020: lr = 0.244968,	loss = 2.527697,	Top-1 err = 0.403662,	Top-5 err = 0.180811,	data_time = 0.050337,	train_time = 0.833553
[2019-08-23 20:12:14,255] TRAIN Iter 153040: lr = 0.244935,	loss = 2.640264,	Top-1 err = 0.406641,	Top-5 err = 0.186328,	data_time = 0.134536,	train_time = 0.763207
[2019-08-23 20:12:22,005] TRAIN Iter 153060: lr = 0.244902,	loss = 2.679675,	Top-1 err = 0.398584,	Top-5 err = 0.181396,	data_time = 0.050296,	train_time = 0.387469
[2019-08-23 20:12:37,520] TRAIN Iter 153080: lr = 0.244868,	loss = 2.560938,	Top-1 err = 0.400000,	Top-5 err = 0.181201,	data_time = 0.050426,	train_time = 0.775704
[2019-08-23 20:12:51,773] TRAIN Iter 153100: lr = 0.244835,	loss = 2.667516,	Top-1 err = 0.403076,	Top-5 err = 0.187695,	data_time = 0.050099,	train_time = 0.712641
[2019-08-23 20:13:01,099] TRAIN Iter 153120: lr = 0.244802,	loss = 2.611878,	Top-1 err = 0.400049,	Top-5 err = 0.179248,	data_time = 0.050383,	train_time = 0.466316
[2019-08-23 20:13:16,980] TRAIN Iter 153140: lr = 0.244768,	loss = 2.560808,	Top-1 err = 0.397021,	Top-5 err = 0.177246,	data_time = 0.050555,	train_time = 0.794047
[2019-08-23 20:13:23,836] TRAIN Iter 153160: lr = 0.244735,	loss = 2.625557,	Top-1 err = 0.404150,	Top-5 err = 0.182227,	data_time = 0.050411,	train_time = 0.342758
[2019-08-23 20:13:40,147] TRAIN Iter 153180: lr = 0.244702,	loss = 2.693286,	Top-1 err = 0.401807,	Top-5 err = 0.181592,	data_time = 0.050714,	train_time = 0.815556
[2019-08-23 20:13:57,192] TRAIN Iter 153200: lr = 0.244668,	loss = 2.656770,	Top-1 err = 0.399414,	Top-5 err = 0.178906,	data_time = 0.050548,	train_time = 0.852212
[2019-08-23 20:14:05,586] TRAIN Iter 153220: lr = 0.244635,	loss = 2.644857,	Top-1 err = 0.402100,	Top-5 err = 0.183154,	data_time = 0.050309,	train_time = 0.419687
[2019-08-23 20:14:22,533] TRAIN Iter 153240: lr = 0.244602,	loss = 2.640564,	Top-1 err = 0.397656,	Top-5 err = 0.182324,	data_time = 0.050545,	train_time = 0.847358
[2019-08-23 20:14:38,635] TRAIN Iter 153260: lr = 0.244568,	loss = 2.560574,	Top-1 err = 0.401562,	Top-5 err = 0.185596,	data_time = 2.925166,	train_time = 0.805069
[2019-08-23 20:14:47,232] TRAIN Iter 153280: lr = 0.244535,	loss = 2.510217,	Top-1 err = 0.402783,	Top-5 err = 0.181689,	data_time = 0.050216,	train_time = 0.429865
[2019-08-23 20:15:02,262] TRAIN Iter 153300: lr = 0.244502,	loss = 2.634454,	Top-1 err = 0.402979,	Top-5 err = 0.182422,	data_time = 0.050582,	train_time = 0.751476
[2019-08-23 20:15:10,304] TRAIN Iter 153320: lr = 0.244468,	loss = 2.463312,	Top-1 err = 0.403955,	Top-5 err = 0.181250,	data_time = 0.050477,	train_time = 0.402060
[2019-08-23 20:15:29,290] TRAIN Iter 153340: lr = 0.244435,	loss = 2.692305,	Top-1 err = 0.400635,	Top-5 err = 0.189404,	data_time = 0.050527,	train_time = 0.949299
[2019-08-23 20:15:47,177] TRAIN Iter 153360: lr = 0.244402,	loss = 2.612507,	Top-1 err = 0.405322,	Top-5 err = 0.185693,	data_time = 0.050550,	train_time = 0.894354
[2019-08-23 20:15:53,777] TRAIN Iter 153380: lr = 0.244368,	loss = 2.545993,	Top-1 err = 0.392676,	Top-5 err = 0.179248,	data_time = 0.050500,	train_time = 0.329987
[2019-08-23 20:16:10,953] TRAIN Iter 153400: lr = 0.244335,	loss = 2.668686,	Top-1 err = 0.404932,	Top-5 err = 0.183984,	data_time = 0.050642,	train_time = 0.858748
[2019-08-23 20:16:29,122] TRAIN Iter 153420: lr = 0.244302,	loss = 2.605510,	Top-1 err = 0.408008,	Top-5 err = 0.189941,	data_time = 6.807266,	train_time = 0.908479
[2019-08-23 20:16:35,893] TRAIN Iter 153440: lr = 0.244268,	loss = 2.583363,	Top-1 err = 0.405127,	Top-5 err = 0.184961,	data_time = 0.050826,	train_time = 0.338504
[2019-08-23 20:16:53,087] TRAIN Iter 153460: lr = 0.244235,	loss = 2.694147,	Top-1 err = 0.401025,	Top-5 err = 0.186230,	data_time = 0.050420,	train_time = 0.859673
[2019-08-23 20:17:00,135] TRAIN Iter 153480: lr = 0.244202,	loss = 2.623255,	Top-1 err = 0.396729,	Top-5 err = 0.183154,	data_time = 0.050530,	train_time = 0.352416
[2019-08-23 20:17:16,715] TRAIN Iter 153500: lr = 0.244168,	loss = 2.691862,	Top-1 err = 0.403027,	Top-5 err = 0.185498,	data_time = 0.050473,	train_time = 0.828973
[2019-08-23 20:17:32,682] TRAIN Iter 153520: lr = 0.244135,	loss = 2.624953,	Top-1 err = 0.407324,	Top-5 err = 0.185156,	data_time = 0.050686,	train_time = 0.798328
[2019-08-23 20:17:39,554] TRAIN Iter 153540: lr = 0.244102,	loss = 2.556432,	Top-1 err = 0.401367,	Top-5 err = 0.183936,	data_time = 0.050580,	train_time = 0.343576
[2019-08-23 20:17:56,131] TRAIN Iter 153560: lr = 0.244068,	loss = 2.687944,	Top-1 err = 0.403369,	Top-5 err = 0.188574,	data_time = 0.050513,	train_time = 0.828870
[2019-08-23 20:18:14,025] TRAIN Iter 153580: lr = 0.244035,	loss = 2.653544,	Top-1 err = 0.406592,	Top-5 err = 0.186084,	data_time = 6.699463,	train_time = 0.894660
[2019-08-23 20:18:20,839] TRAIN Iter 153600: lr = 0.244002,	loss = 2.639940,	Top-1 err = 0.399219,	Top-5 err = 0.182324,	data_time = 0.050330,	train_time = 0.340712
[2019-08-23 20:18:37,306] TRAIN Iter 153620: lr = 0.243968,	loss = 2.607573,	Top-1 err = 0.403906,	Top-5 err = 0.187012,	data_time = 0.050544,	train_time = 0.823341
[2019-08-23 20:18:44,399] TRAIN Iter 153640: lr = 0.243935,	loss = 2.660408,	Top-1 err = 0.400146,	Top-5 err = 0.182666,	data_time = 0.050164,	train_time = 0.354636
[2019-08-23 20:19:01,952] TRAIN Iter 153660: lr = 0.243902,	loss = 2.691905,	Top-1 err = 0.400830,	Top-5 err = 0.186230,	data_time = 0.050493,	train_time = 0.877625
[2019-08-23 20:19:18,356] TRAIN Iter 153680: lr = 0.243868,	loss = 2.530162,	Top-1 err = 0.408301,	Top-5 err = 0.192334,	data_time = 0.050933,	train_time = 0.820170
[2019-08-23 20:19:25,169] TRAIN Iter 153700: lr = 0.243835,	loss = 2.584233,	Top-1 err = 0.405713,	Top-5 err = 0.181006,	data_time = 0.050506,	train_time = 0.340660
[2019-08-23 20:19:40,626] TRAIN Iter 153720: lr = 0.243802,	loss = 2.565106,	Top-1 err = 0.402295,	Top-5 err = 0.186426,	data_time = 0.050467,	train_time = 0.772830
[2019-08-23 20:19:55,742] TRAIN Iter 153740: lr = 0.243768,	loss = 2.576523,	Top-1 err = 0.404541,	Top-5 err = 0.184912,	data_time = 3.338459,	train_time = 0.755784
[2019-08-23 20:20:04,108] TRAIN Iter 153760: lr = 0.243735,	loss = 2.723793,	Top-1 err = 0.403809,	Top-5 err = 0.185742,	data_time = 0.050662,	train_time = 0.418301
[2019-08-23 20:20:20,347] TRAIN Iter 153780: lr = 0.243702,	loss = 2.738210,	Top-1 err = 0.404541,	Top-5 err = 0.181836,	data_time = 0.050219,	train_time = 0.811924
[2019-08-23 20:20:26,870] TRAIN Iter 153800: lr = 0.243668,	loss = 2.716834,	Top-1 err = 0.411572,	Top-5 err = 0.192139,	data_time = 0.050333,	train_time = 0.326129
[2019-08-23 20:20:47,195] TRAIN Iter 153820: lr = 0.243635,	loss = 2.657236,	Top-1 err = 0.404297,	Top-5 err = 0.184668,	data_time = 0.050443,	train_time = 1.016251
[2019-08-23 20:21:05,473] TRAIN Iter 153840: lr = 0.243602,	loss = 2.644889,	Top-1 err = 0.405566,	Top-5 err = 0.185938,	data_time = 0.050809,	train_time = 0.913875
[2019-08-23 20:21:12,084] TRAIN Iter 153860: lr = 0.243568,	loss = 2.651642,	Top-1 err = 0.407471,	Top-5 err = 0.186182,	data_time = 0.050757,	train_time = 0.330524
[2019-08-23 20:21:32,418] TRAIN Iter 153880: lr = 0.243535,	loss = 2.594309,	Top-1 err = 0.411475,	Top-5 err = 0.188379,	data_time = 0.049909,	train_time = 1.016722
[2019-08-23 20:21:48,606] TRAIN Iter 153900: lr = 0.243502,	loss = 2.725901,	Top-1 err = 0.401758,	Top-5 err = 0.185693,	data_time = 2.456393,	train_time = 0.809361
[2019-08-23 20:21:55,336] TRAIN Iter 153920: lr = 0.243468,	loss = 2.679371,	Top-1 err = 0.402002,	Top-5 err = 0.181299,	data_time = 0.049957,	train_time = 0.336506
[2019-08-23 20:22:48,410] TRAIN Iter 153940: lr = 0.243435,	loss = 2.693597,	Top-1 err = 0.404001,	Top-5 err = 0.187613,	data_time = 0.124601,	train_time = 2.653650
[2019-08-23 20:22:54,927] TRAIN Iter 153960: lr = 0.243402,	loss = 2.617888,	Top-1 err = 0.402002,	Top-5 err = 0.184912,	data_time = 0.050367,	train_time = 0.325863
[2019-08-23 20:23:16,742] TRAIN Iter 153980: lr = 0.243368,	loss = 2.618086,	Top-1 err = 0.400391,	Top-5 err = 0.187549,	data_time = 0.050115,	train_time = 1.090699
[2019-08-23 20:23:34,041] TRAIN Iter 154000: lr = 0.243335,	loss = 2.676811,	Top-1 err = 0.396045,	Top-5 err = 0.180518,	data_time = 0.050591,	train_time = 0.864971
[2019-08-23 20:23:40,554] TRAIN Iter 154020: lr = 0.243302,	loss = 2.604794,	Top-1 err = 0.395459,	Top-5 err = 0.179395,	data_time = 0.050356,	train_time = 0.325643
[2019-08-23 20:23:58,080] TRAIN Iter 154040: lr = 0.243268,	loss = 2.708783,	Top-1 err = 0.387402,	Top-5 err = 0.174609,	data_time = 0.050330,	train_time = 0.876258
[2019-08-23 20:24:05,047] TRAIN Iter 154060: lr = 0.243235,	loss = 2.586314,	Top-1 err = 0.394727,	Top-5 err = 0.175928,	data_time = 0.050437,	train_time = 0.348352
[2019-08-23 20:24:22,705] TRAIN Iter 154080: lr = 0.243202,	loss = 2.675231,	Top-1 err = 0.401025,	Top-5 err = 0.183789,	data_time = 0.050238,	train_time = 0.882900
[2019-08-23 20:24:41,592] TRAIN Iter 154100: lr = 0.243168,	loss = 2.730856,	Top-1 err = 0.400879,	Top-5 err = 0.184912,	data_time = 0.050323,	train_time = 0.944299
[2019-08-23 20:24:49,156] TRAIN Iter 154120: lr = 0.243135,	loss = 2.636579,	Top-1 err = 0.397021,	Top-5 err = 0.183154,	data_time = 0.135325,	train_time = 0.378175
[2019-08-23 20:25:04,392] TRAIN Iter 154140: lr = 0.243102,	loss = 2.526738,	Top-1 err = 0.397949,	Top-5 err = 0.181250,	data_time = 0.050341,	train_time = 0.761829
[2019-08-23 20:25:23,558] TRAIN Iter 154160: lr = 0.243068,	loss = 2.659181,	Top-1 err = 0.391992,	Top-5 err = 0.177441,	data_time = 0.050574,	train_time = 0.958265
[2019-08-23 20:25:30,021] TRAIN Iter 154180: lr = 0.243035,	loss = 2.657239,	Top-1 err = 0.395752,	Top-5 err = 0.180518,	data_time = 0.050405,	train_time = 0.323141
[2019-08-23 20:25:49,906] TRAIN Iter 154200: lr = 0.243002,	loss = 2.647759,	Top-1 err = 0.397461,	Top-5 err = 0.177637,	data_time = 0.050989,	train_time = 0.994256
[2019-08-23 20:25:56,930] TRAIN Iter 154220: lr = 0.242968,	loss = 2.573365,	Top-1 err = 0.403369,	Top-5 err = 0.180957,	data_time = 0.050237,	train_time = 0.351171
[2019-08-23 20:26:14,137] TRAIN Iter 154240: lr = 0.242935,	loss = 2.718528,	Top-1 err = 0.397266,	Top-5 err = 0.183887,	data_time = 0.050310,	train_time = 0.860312
[2019-08-23 20:26:31,877] TRAIN Iter 154260: lr = 0.242902,	loss = 2.641399,	Top-1 err = 0.395508,	Top-5 err = 0.177344,	data_time = 0.050510,	train_time = 0.886993
[2019-08-23 20:26:38,598] TRAIN Iter 154280: lr = 0.242868,	loss = 2.637764,	Top-1 err = 0.395264,	Top-5 err = 0.183447,	data_time = 0.050453,	train_time = 0.336073
[2019-08-23 20:26:55,158] TRAIN Iter 154300: lr = 0.242835,	loss = 2.704944,	Top-1 err = 0.398584,	Top-5 err = 0.186377,	data_time = 0.050310,	train_time = 0.827953
[2019-08-23 20:27:15,532] TRAIN Iter 154320: lr = 0.242802,	loss = 2.678859,	Top-1 err = 0.399756,	Top-5 err = 0.184668,	data_time = 0.050377,	train_time = 1.018692
[2019-08-23 20:27:21,917] TRAIN Iter 154340: lr = 0.242768,	loss = 2.656373,	Top-1 err = 0.400195,	Top-5 err = 0.181982,	data_time = 0.050404,	train_time = 0.319240
[2019-08-23 20:27:41,031] TRAIN Iter 154360: lr = 0.242735,	loss = 2.610101,	Top-1 err = 0.401221,	Top-5 err = 0.186133,	data_time = 0.050433,	train_time = 0.955669
[2019-08-23 20:27:47,505] TRAIN Iter 154380: lr = 0.242702,	loss = 2.524262,	Top-1 err = 0.398975,	Top-5 err = 0.181494,	data_time = 0.050345,	train_time = 0.323723
[2019-08-23 20:28:06,805] TRAIN Iter 154400: lr = 0.242668,	loss = 2.588155,	Top-1 err = 0.402100,	Top-5 err = 0.183105,	data_time = 0.050354,	train_time = 0.964943
[2019-08-23 20:28:24,096] TRAIN Iter 154420: lr = 0.242635,	loss = 2.686703,	Top-1 err = 0.406592,	Top-5 err = 0.184082,	data_time = 0.050280,	train_time = 0.864579
[2019-08-23 20:28:30,538] TRAIN Iter 154440: lr = 0.242602,	loss = 2.589162,	Top-1 err = 0.395703,	Top-5 err = 0.182324,	data_time = 0.050416,	train_time = 0.322079
[2019-08-23 20:28:47,568] TRAIN Iter 154460: lr = 0.242568,	loss = 2.571247,	Top-1 err = 0.399609,	Top-5 err = 0.180615,	data_time = 0.050482,	train_time = 0.851456
[2019-08-23 20:29:05,736] TRAIN Iter 154480: lr = 0.242535,	loss = 2.677134,	Top-1 err = 0.398584,	Top-5 err = 0.183643,	data_time = 0.050376,	train_time = 0.908387
[2019-08-23 20:29:12,216] TRAIN Iter 154500: lr = 0.242502,	loss = 2.653215,	Top-1 err = 0.400146,	Top-5 err = 0.183008,	data_time = 0.050760,	train_time = 0.323991
[2019-08-23 20:29:28,775] TRAIN Iter 154520: lr = 0.242468,	loss = 2.587896,	Top-1 err = 0.398145,	Top-5 err = 0.181543,	data_time = 0.050378,	train_time = 0.827925
[2019-08-23 20:29:35,387] TRAIN Iter 154540: lr = 0.242435,	loss = 2.605272,	Top-1 err = 0.401123,	Top-5 err = 0.182764,	data_time = 0.152658,	train_time = 0.330612
[2019-08-23 20:29:54,514] TRAIN Iter 154560: lr = 0.242402,	loss = 2.596538,	Top-1 err = 0.397217,	Top-5 err = 0.183057,	data_time = 0.050440,	train_time = 0.956340
[2019-08-23 20:30:11,136] TRAIN Iter 154580: lr = 0.242368,	loss = 2.662840,	Top-1 err = 0.401172,	Top-5 err = 0.183789,	data_time = 0.050476,	train_time = 0.831072
[2019-08-23 20:30:17,609] TRAIN Iter 154600: lr = 0.242335,	loss = 2.625496,	Top-1 err = 0.404150,	Top-5 err = 0.187500,	data_time = 0.050212,	train_time = 0.323651
[2019-08-23 20:30:38,707] TRAIN Iter 154620: lr = 0.242302,	loss = 2.718951,	Top-1 err = 0.399121,	Top-5 err = 0.181250,	data_time = 0.050642,	train_time = 1.054890
[2019-08-23 20:30:57,041] TRAIN Iter 154640: lr = 0.242268,	loss = 2.597561,	Top-1 err = 0.404102,	Top-5 err = 0.178906,	data_time = 0.146658,	train_time = 0.916691
[2019-08-23 20:31:03,717] TRAIN Iter 154660: lr = 0.242235,	loss = 2.749900,	Top-1 err = 0.402393,	Top-5 err = 0.183350,	data_time = 0.050961,	train_time = 0.333755
[2019-08-23 20:31:23,154] TRAIN Iter 154680: lr = 0.242202,	loss = 2.670575,	Top-1 err = 0.403320,	Top-5 err = 0.183594,	data_time = 0.050317,	train_time = 0.971873
[2019-08-23 20:31:29,651] TRAIN Iter 154700: lr = 0.242168,	loss = 2.622511,	Top-1 err = 0.398145,	Top-5 err = 0.182422,	data_time = 0.050670,	train_time = 0.324830
[2019-08-23 20:31:50,027] TRAIN Iter 154720: lr = 0.242135,	loss = 2.665922,	Top-1 err = 0.407520,	Top-5 err = 0.187012,	data_time = 0.050316,	train_time = 1.018778
[2019-08-23 20:32:11,652] TRAIN Iter 154740: lr = 0.242102,	loss = 2.626767,	Top-1 err = 0.400977,	Top-5 err = 0.182471,	data_time = 0.050835,	train_time = 1.081228
[2019-08-23 20:32:18,156] TRAIN Iter 154760: lr = 0.242068,	loss = 2.616779,	Top-1 err = 0.399463,	Top-5 err = 0.182568,	data_time = 0.050417,	train_time = 0.325195
[2019-08-23 20:32:38,903] TRAIN Iter 154780: lr = 0.242035,	loss = 2.712170,	Top-1 err = 0.400244,	Top-5 err = 0.184277,	data_time = 0.050755,	train_time = 1.037318
[2019-08-23 20:32:58,915] TRAIN Iter 154800: lr = 0.242002,	loss = 2.685024,	Top-1 err = 0.408789,	Top-5 err = 0.186963,	data_time = 0.050193,	train_time = 1.000569
[2019-08-23 20:33:05,369] TRAIN Iter 154820: lr = 0.241968,	loss = 2.659361,	Top-1 err = 0.399023,	Top-5 err = 0.182910,	data_time = 0.050365,	train_time = 0.322692
[2019-08-23 20:33:26,910] TRAIN Iter 154840: lr = 0.241935,	loss = 2.651643,	Top-1 err = 0.405273,	Top-5 err = 0.183594,	data_time = 0.050409,	train_time = 1.077038
[2019-08-23 20:33:33,268] TRAIN Iter 154860: lr = 0.241902,	loss = 2.726898,	Top-1 err = 0.404639,	Top-5 err = 0.186377,	data_time = 0.050411,	train_time = 0.317901
[2019-08-23 20:33:55,670] TRAIN Iter 154880: lr = 0.241868,	loss = 2.543841,	Top-1 err = 0.402490,	Top-5 err = 0.184277,	data_time = 0.050415,	train_time = 1.120072
[2019-08-23 20:34:17,364] TRAIN Iter 154900: lr = 0.241835,	loss = 2.705098,	Top-1 err = 0.402539,	Top-5 err = 0.185840,	data_time = 0.050272,	train_time = 1.084728
[2019-08-23 20:34:23,820] TRAIN Iter 154920: lr = 0.241802,	loss = 2.721252,	Top-1 err = 0.406104,	Top-5 err = 0.187598,	data_time = 0.050369,	train_time = 0.322784
[2019-08-23 20:34:44,339] TRAIN Iter 154940: lr = 0.241768,	loss = 2.698576,	Top-1 err = 0.399463,	Top-5 err = 0.181982,	data_time = 0.050383,	train_time = 1.025943
[2019-08-23 20:35:05,468] TRAIN Iter 154960: lr = 0.241735,	loss = 2.549273,	Top-1 err = 0.408057,	Top-5 err = 0.184961,	data_time = 0.050802,	train_time = 1.056402
[2019-08-23 20:35:12,241] TRAIN Iter 154980: lr = 0.241702,	loss = 2.651185,	Top-1 err = 0.403906,	Top-5 err = 0.183350,	data_time = 0.050300,	train_time = 0.338620
[2019-08-23 20:35:35,339] TRAIN Iter 155000: lr = 0.241668,	loss = 2.607374,	Top-1 err = 0.405176,	Top-5 err = 0.188184,	data_time = 0.050486,	train_time = 1.154901
[2019-08-23 20:35:42,102] TRAIN Iter 155020: lr = 0.241635,	loss = 2.575424,	Top-1 err = 0.398242,	Top-5 err = 0.184033,	data_time = 0.050294,	train_time = 0.338160
[2019-08-23 20:36:02,708] TRAIN Iter 155040: lr = 0.241602,	loss = 2.618811,	Top-1 err = 0.398145,	Top-5 err = 0.184961,	data_time = 0.050339,	train_time = 1.030289
[2019-08-23 20:36:22,133] TRAIN Iter 155060: lr = 0.241568,	loss = 2.681699,	Top-1 err = 0.398486,	Top-5 err = 0.181787,	data_time = 0.050427,	train_time = 0.971241
[2019-08-23 20:36:28,680] TRAIN Iter 155080: lr = 0.241535,	loss = 2.576545,	Top-1 err = 0.402100,	Top-5 err = 0.186670,	data_time = 0.050122,	train_time = 0.327309
[2019-08-23 20:36:49,707] TRAIN Iter 155100: lr = 0.241502,	loss = 2.689756,	Top-1 err = 0.410645,	Top-5 err = 0.188379,	data_time = 0.050457,	train_time = 1.051346
[2019-08-23 20:37:08,029] TRAIN Iter 155120: lr = 0.241468,	loss = 2.563350,	Top-1 err = 0.404834,	Top-5 err = 0.180127,	data_time = 0.151656,	train_time = 0.916101
[2019-08-23 20:37:14,740] TRAIN Iter 155140: lr = 0.241435,	loss = 2.605790,	Top-1 err = 0.404639,	Top-5 err = 0.185938,	data_time = 0.049917,	train_time = 0.335507
[2019-08-23 20:37:33,258] TRAIN Iter 155160: lr = 0.241402,	loss = 2.581435,	Top-1 err = 0.398584,	Top-5 err = 0.178223,	data_time = 0.049903,	train_time = 0.925926
[2019-08-23 20:37:39,353] TRAIN Iter 155180: lr = 0.241368,	loss = 2.555717,	Top-1 err = 0.413135,	Top-5 err = 0.190186,	data_time = 0.049880,	train_time = 0.304715
[2019-08-23 20:38:29,973] TRAIN Iter 155200: lr = 0.241335,	loss = 2.681728,	Top-1 err = 0.403495,	Top-5 err = 0.188295,	data_time = 0.050511,	train_time = 2.530991
[2019-08-23 20:38:47,908] TRAIN Iter 155220: lr = 0.241302,	loss = 2.640050,	Top-1 err = 0.401855,	Top-5 err = 0.186328,	data_time = 0.150062,	train_time = 0.896704
[2019-08-23 20:38:54,476] TRAIN Iter 155240: lr = 0.241268,	loss = 2.781821,	Top-1 err = 0.401855,	Top-5 err = 0.181689,	data_time = 0.050298,	train_time = 0.328390
[2019-08-23 20:39:11,356] TRAIN Iter 155260: lr = 0.241235,	loss = 2.619029,	Top-1 err = 0.400098,	Top-5 err = 0.182275,	data_time = 0.050231,	train_time = 0.843994
[2019-08-23 20:39:18,380] TRAIN Iter 155280: lr = 0.241202,	loss = 2.530874,	Top-1 err = 0.391406,	Top-5 err = 0.178906,	data_time = 0.050554,	train_time = 0.351171
[2019-08-23 20:39:34,724] TRAIN Iter 155300: lr = 0.241168,	loss = 2.653142,	Top-1 err = 0.396777,	Top-5 err = 0.179541,	data_time = 0.050264,	train_time = 0.817185
[2019-08-23 20:39:48,044] TRAIN Iter 155320: lr = 0.241135,	loss = 2.591511,	Top-1 err = 0.390771,	Top-5 err = 0.172607,	data_time = 0.050512,	train_time = 0.666022
[2019-08-23 20:39:57,597] TRAIN Iter 155340: lr = 0.241102,	loss = 2.609656,	Top-1 err = 0.391846,	Top-5 err = 0.173828,	data_time = 0.050294,	train_time = 0.477624
[2019-08-23 20:40:14,192] TRAIN Iter 155360: lr = 0.241068,	loss = 2.598225,	Top-1 err = 0.392432,	Top-5 err = 0.179492,	data_time = 0.050493,	train_time = 0.829714
[2019-08-23 20:40:33,727] TRAIN Iter 155380: lr = 0.241035,	loss = 2.603239,	Top-1 err = 0.402881,	Top-5 err = 0.184619,	data_time = 0.050698,	train_time = 0.976736
[2019-08-23 20:40:40,320] TRAIN Iter 155400: lr = 0.241002,	loss = 2.695753,	Top-1 err = 0.398486,	Top-5 err = 0.182861,	data_time = 0.050241,	train_time = 0.329648
[2019-08-23 20:40:56,267] TRAIN Iter 155420: lr = 0.240968,	loss = 2.632692,	Top-1 err = 0.398047,	Top-5 err = 0.178662,	data_time = 0.050442,	train_time = 0.797342
[2019-08-23 20:41:03,173] TRAIN Iter 155440: lr = 0.240935,	loss = 2.571093,	Top-1 err = 0.402783,	Top-5 err = 0.182715,	data_time = 0.050855,	train_time = 0.345283
[2019-08-23 20:41:20,312] TRAIN Iter 155460: lr = 0.240902,	loss = 2.657839,	Top-1 err = 0.396045,	Top-5 err = 0.184668,	data_time = 0.050387,	train_time = 0.856945
[2019-08-23 20:41:35,378] TRAIN Iter 155480: lr = 0.240868,	loss = 2.677444,	Top-1 err = 0.399707,	Top-5 err = 0.180615,	data_time = 0.050702,	train_time = 0.753256
[2019-08-23 20:41:42,125] TRAIN Iter 155500: lr = 0.240835,	loss = 2.598014,	Top-1 err = 0.403320,	Top-5 err = 0.179980,	data_time = 0.050443,	train_time = 0.337351
[2019-08-23 20:42:01,035] TRAIN Iter 155520: lr = 0.240802,	loss = 2.628650,	Top-1 err = 0.399414,	Top-5 err = 0.180029,	data_time = 0.050411,	train_time = 0.945470
[2019-08-23 20:42:13,693] TRAIN Iter 155540: lr = 0.240768,	loss = 2.711946,	Top-1 err = 0.399609,	Top-5 err = 0.179834,	data_time = 0.050565,	train_time = 0.632898
[2019-08-23 20:42:20,986] TRAIN Iter 155560: lr = 0.240735,	loss = 2.579951,	Top-1 err = 0.396631,	Top-5 err = 0.177051,	data_time = 0.050459,	train_time = 0.364625
[2019-08-23 20:42:37,477] TRAIN Iter 155580: lr = 0.240702,	loss = 2.677408,	Top-1 err = 0.399951,	Top-5 err = 0.179004,	data_time = 0.050464,	train_time = 0.824581
[2019-08-23 20:42:44,083] TRAIN Iter 155600: lr = 0.240668,	loss = 2.531488,	Top-1 err = 0.398193,	Top-5 err = 0.182227,	data_time = 0.050407,	train_time = 0.330269
[2019-08-23 20:43:01,850] TRAIN Iter 155620: lr = 0.240635,	loss = 2.666980,	Top-1 err = 0.403711,	Top-5 err = 0.189307,	data_time = 0.050376,	train_time = 0.888356
[2019-08-23 20:43:17,753] TRAIN Iter 155640: lr = 0.240602,	loss = 2.747867,	Top-1 err = 0.405664,	Top-5 err = 0.184277,	data_time = 0.050274,	train_time = 0.795117
[2019-08-23 20:43:24,492] TRAIN Iter 155660: lr = 0.240568,	loss = 2.620547,	Top-1 err = 0.401367,	Top-5 err = 0.179639,	data_time = 0.050319,	train_time = 0.336908
[2019-08-23 20:43:41,121] TRAIN Iter 155680: lr = 0.240535,	loss = 2.606610,	Top-1 err = 0.407861,	Top-5 err = 0.187109,	data_time = 0.050344,	train_time = 0.831458
[2019-08-23 20:43:58,582] TRAIN Iter 155700: lr = 0.240502,	loss = 2.621268,	Top-1 err = 0.401123,	Top-5 err = 0.184521,	data_time = 0.050367,	train_time = 0.873044
[2019-08-23 20:44:04,981] TRAIN Iter 155720: lr = 0.240468,	loss = 2.590067,	Top-1 err = 0.396436,	Top-5 err = 0.184326,	data_time = 0.050185,	train_time = 0.319904
[2019-08-23 20:44:22,233] TRAIN Iter 155740: lr = 0.240435,	loss = 2.685424,	Top-1 err = 0.399121,	Top-5 err = 0.187012,	data_time = 0.050536,	train_time = 0.862613
[2019-08-23 20:44:29,221] TRAIN Iter 155760: lr = 0.240402,	loss = 2.545300,	Top-1 err = 0.393018,	Top-5 err = 0.181738,	data_time = 0.152964,	train_time = 0.349400
[2019-08-23 20:44:44,593] TRAIN Iter 155780: lr = 0.240368,	loss = 2.643981,	Top-1 err = 0.400000,	Top-5 err = 0.184863,	data_time = 0.050359,	train_time = 0.768554
[2019-08-23 20:45:01,144] TRAIN Iter 155800: lr = 0.240335,	loss = 2.567219,	Top-1 err = 0.400586,	Top-5 err = 0.187158,	data_time = 0.050364,	train_time = 0.827529
[2019-08-23 20:45:07,595] TRAIN Iter 155820: lr = 0.240302,	loss = 2.671155,	Top-1 err = 0.401660,	Top-5 err = 0.185107,	data_time = 0.050684,	train_time = 0.322541
[2019-08-23 20:45:25,819] TRAIN Iter 155840: lr = 0.240268,	loss = 2.654387,	Top-1 err = 0.403223,	Top-5 err = 0.183936,	data_time = 0.050399,	train_time = 0.911196
[2019-08-23 20:45:43,072] TRAIN Iter 155860: lr = 0.240235,	loss = 2.546203,	Top-1 err = 0.406494,	Top-5 err = 0.189746,	data_time = 0.050346,	train_time = 0.862668
[2019-08-23 20:45:51,115] TRAIN Iter 155880: lr = 0.240202,	loss = 2.719218,	Top-1 err = 0.402051,	Top-5 err = 0.185840,	data_time = 0.050480,	train_time = 0.402135
[2019-08-23 20:46:08,896] TRAIN Iter 155900: lr = 0.240168,	loss = 2.601780,	Top-1 err = 0.400488,	Top-5 err = 0.184814,	data_time = 0.050956,	train_time = 0.888996
[2019-08-23 20:46:15,703] TRAIN Iter 155920: lr = 0.240135,	loss = 2.563112,	Top-1 err = 0.402734,	Top-5 err = 0.183057,	data_time = 0.050811,	train_time = 0.340380
[2019-08-23 20:46:32,737] TRAIN Iter 155940: lr = 0.240102,	loss = 2.617784,	Top-1 err = 0.406641,	Top-5 err = 0.185107,	data_time = 0.050539,	train_time = 0.851681
[2019-08-23 20:46:51,977] TRAIN Iter 155960: lr = 0.240068,	loss = 2.564196,	Top-1 err = 0.399365,	Top-5 err = 0.179834,	data_time = 0.050653,	train_time = 0.961958
[2019-08-23 20:46:58,798] TRAIN Iter 155980: lr = 0.240035,	loss = 2.626412,	Top-1 err = 0.398486,	Top-5 err = 0.182080,	data_time = 0.050273,	train_time = 0.341033
[2019-08-23 20:47:14,841] TRAIN Iter 156000: lr = 0.240002,	loss = 2.619846,	Top-1 err = 0.401025,	Top-5 err = 0.185205,	data_time = 0.050403,	train_time = 0.802178
[2019-08-23 20:47:31,234] TRAIN Iter 156020: lr = 0.239968,	loss = 2.625945,	Top-1 err = 0.401562,	Top-5 err = 0.181104,	data_time = 0.050254,	train_time = 0.819595
[2019-08-23 20:47:38,210] TRAIN Iter 156040: lr = 0.239935,	loss = 2.742785,	Top-1 err = 0.405273,	Top-5 err = 0.185107,	data_time = 0.050347,	train_time = 0.348805
[2019-08-23 20:47:54,836] TRAIN Iter 156060: lr = 0.239902,	loss = 2.596475,	Top-1 err = 0.402441,	Top-5 err = 0.186475,	data_time = 0.050395,	train_time = 0.831309
[2019-08-23 20:48:01,416] TRAIN Iter 156080: lr = 0.239868,	loss = 2.718738,	Top-1 err = 0.404980,	Top-5 err = 0.183301,	data_time = 0.050293,	train_time = 0.328944
[2019-08-23 20:48:19,782] TRAIN Iter 156100: lr = 0.239835,	loss = 2.672634,	Top-1 err = 0.406396,	Top-5 err = 0.183008,	data_time = 0.050568,	train_time = 0.918306
[2019-08-23 20:48:36,298] TRAIN Iter 156120: lr = 0.239802,	loss = 2.621984,	Top-1 err = 0.401758,	Top-5 err = 0.185791,	data_time = 0.273099,	train_time = 0.825809
[2019-08-23 20:48:43,230] TRAIN Iter 156140: lr = 0.239768,	loss = 2.585760,	Top-1 err = 0.406641,	Top-5 err = 0.188770,	data_time = 0.050905,	train_time = 0.346570
[2019-08-23 20:49:00,971] TRAIN Iter 156160: lr = 0.239735,	loss = 2.602700,	Top-1 err = 0.401221,	Top-5 err = 0.184570,	data_time = 0.050399,	train_time = 0.887053
[2019-08-23 20:49:16,418] TRAIN Iter 156180: lr = 0.239702,	loss = 2.626347,	Top-1 err = 0.402637,	Top-5 err = 0.187012,	data_time = 0.050233,	train_time = 0.772292
[2019-08-23 20:49:23,048] TRAIN Iter 156200: lr = 0.239668,	loss = 2.619670,	Top-1 err = 0.400781,	Top-5 err = 0.184180,	data_time = 0.050323,	train_time = 0.331493
[2019-08-23 20:49:38,803] TRAIN Iter 156220: lr = 0.239635,	loss = 2.704514,	Top-1 err = 0.398535,	Top-5 err = 0.179395,	data_time = 0.050476,	train_time = 0.787755
[2019-08-23 20:49:45,640] TRAIN Iter 156240: lr = 0.239602,	loss = 2.656807,	Top-1 err = 0.402100,	Top-5 err = 0.186963,	data_time = 0.050789,	train_time = 0.341817
[2019-08-23 20:50:03,682] TRAIN Iter 156260: lr = 0.239568,	loss = 2.653667,	Top-1 err = 0.406787,	Top-5 err = 0.188672,	data_time = 0.050553,	train_time = 0.902119
[2019-08-23 20:50:19,580] TRAIN Iter 156280: lr = 0.239535,	loss = 2.689297,	Top-1 err = 0.403955,	Top-5 err = 0.184863,	data_time = 0.050554,	train_time = 0.794879
[2019-08-23 20:50:26,384] TRAIN Iter 156300: lr = 0.239502,	loss = 2.645480,	Top-1 err = 0.399561,	Top-5 err = 0.182715,	data_time = 0.050259,	train_time = 0.340157
[2019-08-23 20:50:43,755] TRAIN Iter 156320: lr = 0.239468,	loss = 2.671302,	Top-1 err = 0.402002,	Top-5 err = 0.183154,	data_time = 0.050516,	train_time = 0.868581
[2019-08-23 20:51:01,740] TRAIN Iter 156340: lr = 0.239435,	loss = 2.625401,	Top-1 err = 0.401611,	Top-5 err = 0.178662,	data_time = 0.050385,	train_time = 0.899191
[2019-08-23 20:51:08,443] TRAIN Iter 156360: lr = 0.239402,	loss = 2.595521,	Top-1 err = 0.406006,	Top-5 err = 0.181104,	data_time = 0.050488,	train_time = 0.335145
[2019-08-23 20:51:25,999] TRAIN Iter 156380: lr = 0.239368,	loss = 2.575094,	Top-1 err = 0.402393,	Top-5 err = 0.186133,	data_time = 0.050225,	train_time = 0.877805
[2019-08-23 20:51:32,648] TRAIN Iter 156400: lr = 0.239335,	loss = 2.619403,	Top-1 err = 0.396533,	Top-5 err = 0.183838,	data_time = 0.049894,	train_time = 0.332455
[2019-08-23 20:51:50,562] TRAIN Iter 156420: lr = 0.239302,	loss = 2.723934,	Top-1 err = 0.403955,	Top-5 err = 0.184912,	data_time = 0.049894,	train_time = 0.895655
[2019-08-23 20:52:42,787] TRAIN Iter 156440: lr = 0.239268,	loss = 2.726773,	Top-1 err = 0.407589,	Top-5 err = 0.184513,	data_time = 0.050466,	train_time = 2.611233
[2019-08-23 20:52:49,546] TRAIN Iter 156460: lr = 0.239235,	loss = 2.664725,	Top-1 err = 0.403418,	Top-5 err = 0.183301,	data_time = 0.050314,	train_time = 0.337954
[2019-08-23 20:53:07,118] TRAIN Iter 156480: lr = 0.239202,	loss = 2.603066,	Top-1 err = 0.394922,	Top-5 err = 0.178027,	data_time = 0.050616,	train_time = 0.878546
[2019-08-23 20:53:14,573] TRAIN Iter 156500: lr = 0.239168,	loss = 2.641160,	Top-1 err = 0.393604,	Top-5 err = 0.177441,	data_time = 0.050346,	train_time = 0.372773
[2019-08-23 20:53:30,067] TRAIN Iter 156520: lr = 0.239135,	loss = 2.584668,	Top-1 err = 0.391943,	Top-5 err = 0.179688,	data_time = 0.050543,	train_time = 0.774680
[2019-08-23 20:53:46,773] TRAIN Iter 156540: lr = 0.239102,	loss = 2.670088,	Top-1 err = 0.393311,	Top-5 err = 0.178906,	data_time = 0.050255,	train_time = 0.835275
[2019-08-23 20:53:53,605] TRAIN Iter 156560: lr = 0.239068,	loss = 2.619325,	Top-1 err = 0.394482,	Top-5 err = 0.179346,	data_time = 0.050388,	train_time = 0.341613
[2019-08-23 20:54:11,394] TRAIN Iter 156580: lr = 0.239035,	loss = 2.605974,	Top-1 err = 0.397852,	Top-5 err = 0.180859,	data_time = 0.050183,	train_time = 0.889423
[2019-08-23 20:54:26,064] TRAIN Iter 156600: lr = 0.239002,	loss = 2.591818,	Top-1 err = 0.393359,	Top-5 err = 0.179541,	data_time = 0.126267,	train_time = 0.733468
[2019-08-23 20:54:32,654] TRAIN Iter 156620: lr = 0.238968,	loss = 2.644656,	Top-1 err = 0.394043,	Top-5 err = 0.180078,	data_time = 0.050686,	train_time = 0.329512
[2019-08-23 20:54:50,452] TRAIN Iter 156640: lr = 0.238935,	loss = 2.597351,	Top-1 err = 0.402588,	Top-5 err = 0.181152,	data_time = 0.050516,	train_time = 0.889865
[2019-08-23 20:54:57,246] TRAIN Iter 156660: lr = 0.238902,	loss = 2.520154,	Top-1 err = 0.396680,	Top-5 err = 0.177197,	data_time = 0.050442,	train_time = 0.339694
[2019-08-23 20:55:13,628] TRAIN Iter 156680: lr = 0.238868,	loss = 2.563844,	Top-1 err = 0.396387,	Top-5 err = 0.181592,	data_time = 0.050538,	train_time = 0.819098
[2019-08-23 20:55:31,074] TRAIN Iter 156700: lr = 0.238835,	loss = 2.559120,	Top-1 err = 0.400391,	Top-5 err = 0.182910,	data_time = 4.212188,	train_time = 0.872283
[2019-08-23 20:55:37,786] TRAIN Iter 156720: lr = 0.238802,	loss = 2.668391,	Top-1 err = 0.401855,	Top-5 err = 0.183838,	data_time = 0.147065,	train_time = 0.335602
[2019-08-23 20:55:53,304] TRAIN Iter 156740: lr = 0.238768,	loss = 2.663685,	Top-1 err = 0.398145,	Top-5 err = 0.180859,	data_time = 0.050249,	train_time = 0.775849
[2019-08-23 20:56:08,672] TRAIN Iter 156760: lr = 0.238735,	loss = 2.609948,	Top-1 err = 0.393555,	Top-5 err = 0.180762,	data_time = 0.050495,	train_time = 0.768420
[2019-08-23 20:56:16,472] TRAIN Iter 156780: lr = 0.238702,	loss = 2.728052,	Top-1 err = 0.398779,	Top-5 err = 0.182568,	data_time = 0.050319,	train_time = 0.389977
[2019-08-23 20:56:33,005] TRAIN Iter 156800: lr = 0.238668,	loss = 2.598185,	Top-1 err = 0.394434,	Top-5 err = 0.176904,	data_time = 0.050353,	train_time = 0.826621
[2019-08-23 20:56:39,855] TRAIN Iter 156820: lr = 0.238635,	loss = 2.673244,	Top-1 err = 0.396338,	Top-5 err = 0.180762,	data_time = 0.050614,	train_time = 0.342470
[2019-08-23 20:56:56,504] TRAIN Iter 156840: lr = 0.238602,	loss = 2.543159,	Top-1 err = 0.402979,	Top-5 err = 0.185205,	data_time = 0.050377,	train_time = 0.832440
[2019-08-23 20:57:11,533] TRAIN Iter 156860: lr = 0.238568,	loss = 2.557349,	Top-1 err = 0.397314,	Top-5 err = 0.179053,	data_time = 1.474100,	train_time = 0.751466
[2019-08-23 20:57:18,430] TRAIN Iter 156880: lr = 0.238535,	loss = 2.611415,	Top-1 err = 0.395752,	Top-5 err = 0.178662,	data_time = 0.050923,	train_time = 0.344799
[2019-08-23 20:57:34,469] TRAIN Iter 156900: lr = 0.238502,	loss = 2.586006,	Top-1 err = 0.394824,	Top-5 err = 0.181592,	data_time = 0.050473,	train_time = 0.801972
[2019-08-23 20:57:44,955] TRAIN Iter 156920: lr = 0.238468,	loss = 2.649601,	Top-1 err = 0.399805,	Top-5 err = 0.183350,	data_time = 0.170338,	train_time = 0.524279
[2019-08-23 20:57:57,595] TRAIN Iter 156940: lr = 0.238435,	loss = 2.630883,	Top-1 err = 0.393506,	Top-5 err = 0.177637,	data_time = 0.050326,	train_time = 0.631979
[2019-08-23 20:58:12,155] TRAIN Iter 156960: lr = 0.238402,	loss = 2.661693,	Top-1 err = 0.400879,	Top-5 err = 0.186035,	data_time = 0.050213,	train_time = 0.727982
[2019-08-23 20:58:19,208] TRAIN Iter 156980: lr = 0.238368,	loss = 2.567826,	Top-1 err = 0.404053,	Top-5 err = 0.182324,	data_time = 0.050317,	train_time = 0.352624
[2019-08-23 20:58:34,408] TRAIN Iter 157000: lr = 0.238335,	loss = 2.459496,	Top-1 err = 0.400684,	Top-5 err = 0.180273,	data_time = 0.050433,	train_time = 0.759983
[2019-08-23 20:58:50,377] TRAIN Iter 157020: lr = 0.238302,	loss = 2.623763,	Top-1 err = 0.398193,	Top-5 err = 0.181982,	data_time = 2.910061,	train_time = 0.798484
[2019-08-23 20:58:56,750] TRAIN Iter 157040: lr = 0.238268,	loss = 2.484411,	Top-1 err = 0.402441,	Top-5 err = 0.183447,	data_time = 0.050426,	train_time = 0.318590
[2019-08-23 20:59:13,247] TRAIN Iter 157060: lr = 0.238235,	loss = 2.497092,	Top-1 err = 0.401074,	Top-5 err = 0.183008,	data_time = 0.050289,	train_time = 0.824885
[2019-08-23 20:59:27,634] TRAIN Iter 157080: lr = 0.238202,	loss = 2.589597,	Top-1 err = 0.402441,	Top-5 err = 0.188672,	data_time = 0.050423,	train_time = 0.719297
[2019-08-23 20:59:35,098] TRAIN Iter 157100: lr = 0.238168,	loss = 2.593678,	Top-1 err = 0.401221,	Top-5 err = 0.182715,	data_time = 0.050653,	train_time = 0.373183
[2019-08-23 20:59:51,351] TRAIN Iter 157120: lr = 0.238135,	loss = 2.593468,	Top-1 err = 0.397607,	Top-5 err = 0.180762,	data_time = 0.226504,	train_time = 0.812629
[2019-08-23 20:59:58,286] TRAIN Iter 157140: lr = 0.238102,	loss = 2.619113,	Top-1 err = 0.399609,	Top-5 err = 0.180420,	data_time = 0.050371,	train_time = 0.346761
[2019-08-23 21:00:15,116] TRAIN Iter 157160: lr = 0.238068,	loss = 2.728469,	Top-1 err = 0.403613,	Top-5 err = 0.184229,	data_time = 0.050528,	train_time = 0.841464
[2019-08-23 21:00:31,434] TRAIN Iter 157180: lr = 0.238035,	loss = 2.692834,	Top-1 err = 0.397852,	Top-5 err = 0.186182,	data_time = 0.050258,	train_time = 0.815910
[2019-08-23 21:00:38,584] TRAIN Iter 157200: lr = 0.238002,	loss = 2.589020,	Top-1 err = 0.404883,	Top-5 err = 0.179883,	data_time = 0.126021,	train_time = 0.357501
[2019-08-23 21:00:53,390] TRAIN Iter 157220: lr = 0.237968,	loss = 2.715742,	Top-1 err = 0.404590,	Top-5 err = 0.182666,	data_time = 0.050479,	train_time = 0.740254
[2019-08-23 21:01:08,793] TRAIN Iter 157240: lr = 0.237935,	loss = 2.646320,	Top-1 err = 0.404248,	Top-5 err = 0.186963,	data_time = 0.050467,	train_time = 0.770129
[2019-08-23 21:01:15,808] TRAIN Iter 157260: lr = 0.237902,	loss = 2.663586,	Top-1 err = 0.404297,	Top-5 err = 0.185938,	data_time = 0.050328,	train_time = 0.350768
[2019-08-23 21:01:31,781] TRAIN Iter 157280: lr = 0.237868,	loss = 2.586113,	Top-1 err = 0.402002,	Top-5 err = 0.183740,	data_time = 0.050435,	train_time = 0.798619
[2019-08-23 21:01:38,833] TRAIN Iter 157300: lr = 0.237835,	loss = 2.618240,	Top-1 err = 0.399902,	Top-5 err = 0.182422,	data_time = 0.050329,	train_time = 0.352571
[2019-08-23 21:01:56,129] TRAIN Iter 157320: lr = 0.237802,	loss = 2.654913,	Top-1 err = 0.398291,	Top-5 err = 0.184570,	data_time = 0.051238,	train_time = 0.864784
[2019-08-23 21:02:13,362] TRAIN Iter 157340: lr = 0.237768,	loss = 2.593827,	Top-1 err = 0.402930,	Top-5 err = 0.181982,	data_time = 0.666157,	train_time = 0.861664
[2019-08-23 21:02:20,350] TRAIN Iter 157360: lr = 0.237735,	loss = 2.548042,	Top-1 err = 0.397461,	Top-5 err = 0.184961,	data_time = 0.134630,	train_time = 0.349399
[2019-08-23 21:02:36,922] TRAIN Iter 157380: lr = 0.237702,	loss = 2.695747,	Top-1 err = 0.398340,	Top-5 err = 0.181104,	data_time = 0.050518,	train_time = 0.828544
[2019-08-23 21:02:51,777] TRAIN Iter 157400: lr = 0.237668,	loss = 2.622487,	Top-1 err = 0.406641,	Top-5 err = 0.183105,	data_time = 0.137901,	train_time = 0.742730
[2019-08-23 21:02:59,221] TRAIN Iter 157420: lr = 0.237635,	loss = 2.574896,	Top-1 err = 0.404883,	Top-5 err = 0.187354,	data_time = 0.050546,	train_time = 0.372220
[2019-08-23 21:03:16,898] TRAIN Iter 157440: lr = 0.237602,	loss = 2.598756,	Top-1 err = 0.404248,	Top-5 err = 0.184912,	data_time = 0.050630,	train_time = 0.883831
[2019-08-23 21:03:23,532] TRAIN Iter 157460: lr = 0.237568,	loss = 2.596121,	Top-1 err = 0.399268,	Top-5 err = 0.180127,	data_time = 0.050533,	train_time = 0.331678
[2019-08-23 21:03:41,758] TRAIN Iter 157480: lr = 0.237535,	loss = 2.632049,	Top-1 err = 0.404248,	Top-5 err = 0.187646,	data_time = 0.050409,	train_time = 0.911303
[2019-08-23 21:03:58,500] TRAIN Iter 157500: lr = 0.237502,	loss = 2.645142,	Top-1 err = 0.401904,	Top-5 err = 0.182861,	data_time = 0.050392,	train_time = 0.837068
[2019-08-23 21:04:05,283] TRAIN Iter 157520: lr = 0.237468,	loss = 2.672099,	Top-1 err = 0.404346,	Top-5 err = 0.184473,	data_time = 0.050513,	train_time = 0.339127
[2019-08-23 21:04:22,098] TRAIN Iter 157540: lr = 0.237435,	loss = 2.668669,	Top-1 err = 0.405322,	Top-5 err = 0.187158,	data_time = 0.050602,	train_time = 0.840743
[2019-08-23 21:04:38,103] TRAIN Iter 157560: lr = 0.237402,	loss = 2.751646,	Top-1 err = 0.408984,	Top-5 err = 0.185645,	data_time = 0.138304,	train_time = 0.800255
[2019-08-23 21:04:46,176] TRAIN Iter 157580: lr = 0.237368,	loss = 2.594842,	Top-1 err = 0.401660,	Top-5 err = 0.185693,	data_time = 0.050595,	train_time = 0.403627
[2019-08-23 21:05:04,031] TRAIN Iter 157600: lr = 0.237335,	loss = 2.633774,	Top-1 err = 0.405762,	Top-5 err = 0.183740,	data_time = 0.050681,	train_time = 0.892737
[2019-08-23 21:05:10,860] TRAIN Iter 157620: lr = 0.237302,	loss = 2.559886,	Top-1 err = 0.400928,	Top-5 err = 0.186475,	data_time = 0.132669,	train_time = 0.341418
[2019-08-23 21:05:28,345] TRAIN Iter 157640: lr = 0.237268,	loss = 2.606798,	Top-1 err = 0.407666,	Top-5 err = 0.185254,	data_time = 0.050303,	train_time = 0.874274
[2019-08-23 21:05:45,537] TRAIN Iter 157660: lr = 0.237235,	loss = 2.565646,	Top-1 err = 0.406104,	Top-5 err = 0.186963,	data_time = 1.167575,	train_time = 0.859550
[2019-08-23 21:05:51,553] TRAIN Iter 157680: lr = 0.237202,	loss = 2.681734,	Top-1 err = 0.404541,	Top-5 err = 0.186621,	data_time = 0.049846,	train_time = 0.300787
[2019-08-23 21:06:41,671] TRAIN Iter 157700: lr = 0.237168,	loss = 2.622913,	Top-1 err = 0.412396,	Top-5 err = 0.191134,	data_time = 0.050109,	train_time = 2.505892
[2019-08-23 21:06:49,031] TRAIN Iter 157720: lr = 0.237135,	loss = 2.661623,	Top-1 err = 0.398047,	Top-5 err = 0.178711,	data_time = 0.149378,	train_time = 0.368003
[2019-08-23 21:07:06,115] TRAIN Iter 157740: lr = 0.237102,	loss = 2.556270,	Top-1 err = 0.397461,	Top-5 err = 0.179053,	data_time = 0.050806,	train_time = 0.854187
[2019-08-23 21:07:21,883] TRAIN Iter 157760: lr = 0.237068,	loss = 2.562984,	Top-1 err = 0.393750,	Top-5 err = 0.178613,	data_time = 0.050415,	train_time = 0.788372
[2019-08-23 21:07:29,406] TRAIN Iter 157780: lr = 0.237035,	loss = 2.579308,	Top-1 err = 0.387842,	Top-5 err = 0.173389,	data_time = 0.152351,	train_time = 0.376121
[2019-08-23 21:07:43,352] TRAIN Iter 157800: lr = 0.237002,	loss = 2.593335,	Top-1 err = 0.399561,	Top-5 err = 0.179004,	data_time = 0.050468,	train_time = 0.697313
[2019-08-23 21:07:58,604] TRAIN Iter 157820: lr = 0.236968,	loss = 2.629393,	Top-1 err = 0.401074,	Top-5 err = 0.180127,	data_time = 0.050450,	train_time = 0.762575
[2019-08-23 21:08:05,694] TRAIN Iter 157840: lr = 0.236935,	loss = 2.584723,	Top-1 err = 0.397754,	Top-5 err = 0.180225,	data_time = 0.161940,	train_time = 0.354510
[2019-08-23 21:08:21,067] TRAIN Iter 157860: lr = 0.236902,	loss = 2.712938,	Top-1 err = 0.398242,	Top-5 err = 0.177344,	data_time = 0.050802,	train_time = 0.768620
[2019-08-23 21:08:28,736] TRAIN Iter 157880: lr = 0.236868,	loss = 2.659528,	Top-1 err = 0.393018,	Top-5 err = 0.177686,	data_time = 0.050814,	train_time = 0.383436
[2019-08-23 21:08:43,454] TRAIN Iter 157900: lr = 0.236835,	loss = 2.629532,	Top-1 err = 0.396338,	Top-5 err = 0.179395,	data_time = 0.050972,	train_time = 0.735847
[2019-08-23 21:08:59,844] TRAIN Iter 157920: lr = 0.236802,	loss = 2.647817,	Top-1 err = 0.400928,	Top-5 err = 0.179980,	data_time = 0.050802,	train_time = 0.819484
[2019-08-23 21:09:07,038] TRAIN Iter 157940: lr = 0.236768,	loss = 2.569615,	Top-1 err = 0.400244,	Top-5 err = 0.181250,	data_time = 0.050750,	train_time = 0.359730
[2019-08-23 21:09:23,181] TRAIN Iter 157960: lr = 0.236735,	loss = 2.701890,	Top-1 err = 0.401855,	Top-5 err = 0.181982,	data_time = 0.109196,	train_time = 0.807106
[2019-08-23 21:09:37,056] TRAIN Iter 157980: lr = 0.236702,	loss = 2.594417,	Top-1 err = 0.402051,	Top-5 err = 0.185449,	data_time = 0.050363,	train_time = 0.693764
[2019-08-23 21:09:43,842] TRAIN Iter 158000: lr = 0.236668,	loss = 2.591394,	Top-1 err = 0.394434,	Top-5 err = 0.175537,	data_time = 0.050813,	train_time = 0.339246
[2019-08-23 21:10:00,236] TRAIN Iter 158020: lr = 0.236635,	loss = 2.556863,	Top-1 err = 0.396875,	Top-5 err = 0.179102,	data_time = 0.050513,	train_time = 0.819689
[2019-08-23 21:10:07,810] TRAIN Iter 158040: lr = 0.236602,	loss = 2.588743,	Top-1 err = 0.395605,	Top-5 err = 0.177100,	data_time = 0.050336,	train_time = 0.378686
[2019-08-23 21:10:21,794] TRAIN Iter 158060: lr = 0.236568,	loss = 2.628345,	Top-1 err = 0.394629,	Top-5 err = 0.179980,	data_time = 0.050236,	train_time = 0.699225
[2019-08-23 21:10:39,535] TRAIN Iter 158080: lr = 0.236535,	loss = 2.519313,	Top-1 err = 0.393799,	Top-5 err = 0.179004,	data_time = 0.050734,	train_time = 0.886990
[2019-08-23 21:10:46,817] TRAIN Iter 158100: lr = 0.236502,	loss = 2.623692,	Top-1 err = 0.393604,	Top-5 err = 0.177441,	data_time = 0.050460,	train_time = 0.364104
[2019-08-23 21:11:01,894] TRAIN Iter 158120: lr = 0.236468,	loss = 2.604241,	Top-1 err = 0.398877,	Top-5 err = 0.182178,	data_time = 0.050479,	train_time = 0.753821
[2019-08-23 21:11:18,330] TRAIN Iter 158140: lr = 0.236435,	loss = 2.694103,	Top-1 err = 0.400000,	Top-5 err = 0.185107,	data_time = 0.050451,	train_time = 0.821781
[2019-08-23 21:11:25,005] TRAIN Iter 158160: lr = 0.236402,	loss = 2.773902,	Top-1 err = 0.399023,	Top-5 err = 0.185303,	data_time = 0.050461,	train_time = 0.333774
[2019-08-23 21:11:40,120] TRAIN Iter 158180: lr = 0.236368,	loss = 2.641803,	Top-1 err = 0.400928,	Top-5 err = 0.182471,	data_time = 0.050389,	train_time = 0.755736
[2019-08-23 21:11:47,339] TRAIN Iter 158200: lr = 0.236335,	loss = 2.629277,	Top-1 err = 0.402881,	Top-5 err = 0.184424,	data_time = 0.050365,	train_time = 0.360907
[2019-08-23 21:12:03,744] TRAIN Iter 158220: lr = 0.236302,	loss = 2.650222,	Top-1 err = 0.398682,	Top-5 err = 0.183252,	data_time = 0.050451,	train_time = 0.820234
[2019-08-23 21:12:19,856] TRAIN Iter 158240: lr = 0.236268,	loss = 2.774435,	Top-1 err = 0.400000,	Top-5 err = 0.183398,	data_time = 0.050361,	train_time = 0.805590
[2019-08-23 21:12:27,446] TRAIN Iter 158260: lr = 0.236235,	loss = 2.604975,	Top-1 err = 0.399463,	Top-5 err = 0.184424,	data_time = 0.050416,	train_time = 0.379518
[2019-08-23 21:12:41,870] TRAIN Iter 158280: lr = 0.236202,	loss = 2.676693,	Top-1 err = 0.399121,	Top-5 err = 0.184033,	data_time = 0.050278,	train_time = 0.721187
[2019-08-23 21:12:57,519] TRAIN Iter 158300: lr = 0.236168,	loss = 2.688177,	Top-1 err = 0.400537,	Top-5 err = 0.185938,	data_time = 0.050495,	train_time = 0.782397
[2019-08-23 21:13:04,694] TRAIN Iter 158320: lr = 0.236135,	loss = 2.704809,	Top-1 err = 0.400879,	Top-5 err = 0.182910,	data_time = 0.050388,	train_time = 0.358743
[2019-08-23 21:13:18,968] TRAIN Iter 158340: lr = 0.236102,	loss = 2.666495,	Top-1 err = 0.404883,	Top-5 err = 0.186035,	data_time = 0.050768,	train_time = 0.713690
[2019-08-23 21:13:26,183] TRAIN Iter 158360: lr = 0.236068,	loss = 2.702470,	Top-1 err = 0.404834,	Top-5 err = 0.187988,	data_time = 0.050775,	train_time = 0.360728
[2019-08-23 21:13:42,584] TRAIN Iter 158380: lr = 0.236035,	loss = 2.592466,	Top-1 err = 0.397705,	Top-5 err = 0.180518,	data_time = 0.050249,	train_time = 0.820060
[2019-08-23 21:13:57,360] TRAIN Iter 158400: lr = 0.236002,	loss = 2.593997,	Top-1 err = 0.400098,	Top-5 err = 0.180615,	data_time = 0.051034,	train_time = 0.738770
[2019-08-23 21:14:04,362] TRAIN Iter 158420: lr = 0.235968,	loss = 2.636805,	Top-1 err = 0.403516,	Top-5 err = 0.184180,	data_time = 0.050838,	train_time = 0.350070
[2019-08-23 21:14:23,294] TRAIN Iter 158440: lr = 0.235935,	loss = 2.595928,	Top-1 err = 0.402637,	Top-5 err = 0.187451,	data_time = 0.050670,	train_time = 0.946610
[2019-08-23 21:14:36,623] TRAIN Iter 158460: lr = 0.235902,	loss = 2.641589,	Top-1 err = 0.395996,	Top-5 err = 0.181299,	data_time = 0.053273,	train_time = 0.666429
[2019-08-23 21:14:43,646] TRAIN Iter 158480: lr = 0.235868,	loss = 2.637302,	Top-1 err = 0.404297,	Top-5 err = 0.185107,	data_time = 0.050366,	train_time = 0.351149
[2019-08-23 21:15:00,114] TRAIN Iter 158500: lr = 0.235835,	loss = 2.566767,	Top-1 err = 0.398779,	Top-5 err = 0.183594,	data_time = 0.050400,	train_time = 0.823398
[2019-08-23 21:15:07,513] TRAIN Iter 158520: lr = 0.235802,	loss = 2.712500,	Top-1 err = 0.394580,	Top-5 err = 0.180957,	data_time = 0.050441,	train_time = 0.369928
[2019-08-23 21:15:22,588] TRAIN Iter 158540: lr = 0.235768,	loss = 2.655733,	Top-1 err = 0.404248,	Top-5 err = 0.182959,	data_time = 0.050549,	train_time = 0.753732
[2019-08-23 21:15:39,961] TRAIN Iter 158560: lr = 0.235735,	loss = 2.652050,	Top-1 err = 0.402832,	Top-5 err = 0.178857,	data_time = 0.050548,	train_time = 0.868618
[2019-08-23 21:15:46,790] TRAIN Iter 158580: lr = 0.235702,	loss = 2.608489,	Top-1 err = 0.402979,	Top-5 err = 0.187988,	data_time = 0.050517,	train_time = 0.341430
[2019-08-23 21:16:02,736] TRAIN Iter 158600: lr = 0.235668,	loss = 2.571349,	Top-1 err = 0.402002,	Top-5 err = 0.183984,	data_time = 0.050432,	train_time = 0.797299
[2019-08-23 21:16:19,315] TRAIN Iter 158620: lr = 0.235635,	loss = 2.520271,	Top-1 err = 0.400488,	Top-5 err = 0.183496,	data_time = 0.050359,	train_time = 0.828921
[2019-08-23 21:16:25,821] TRAIN Iter 158640: lr = 0.235602,	loss = 2.616749,	Top-1 err = 0.401123,	Top-5 err = 0.181543,	data_time = 0.050176,	train_time = 0.325315
[2019-08-23 21:16:49,239] TRAIN Iter 158660: lr = 0.235568,	loss = 2.668191,	Top-1 err = 0.402148,	Top-5 err = 0.185791,	data_time = 0.050442,	train_time = 1.170871
[2019-08-23 21:16:56,670] TRAIN Iter 158680: lr = 0.235535,	loss = 2.640959,	Top-1 err = 0.398926,	Top-5 err = 0.180713,	data_time = 0.050391,	train_time = 0.371556
[2019-08-23 21:17:13,202] TRAIN Iter 158700: lr = 0.235502,	loss = 2.503799,	Top-1 err = 0.395947,	Top-5 err = 0.180371,	data_time = 0.050472,	train_time = 0.826552
[2019-08-23 21:17:31,878] TRAIN Iter 158720: lr = 0.235468,	loss = 2.774700,	Top-1 err = 0.404297,	Top-5 err = 0.187305,	data_time = 0.050333,	train_time = 0.933799
[2019-08-23 21:17:39,595] TRAIN Iter 158740: lr = 0.235435,	loss = 2.607788,	Top-1 err = 0.397607,	Top-5 err = 0.184521,	data_time = 0.050311,	train_time = 0.385844
[2019-08-23 21:17:53,926] TRAIN Iter 158760: lr = 0.235402,	loss = 2.545239,	Top-1 err = 0.400195,	Top-5 err = 0.182764,	data_time = 0.050669,	train_time = 0.716517
[2019-08-23 21:18:09,626] TRAIN Iter 158780: lr = 0.235368,	loss = 2.728506,	Top-1 err = 0.400000,	Top-5 err = 0.186279,	data_time = 0.050528,	train_time = 0.784981
[2019-08-23 21:18:16,434] TRAIN Iter 158800: lr = 0.235335,	loss = 2.641223,	Top-1 err = 0.394189,	Top-5 err = 0.181543,	data_time = 0.131242,	train_time = 0.340387
[2019-08-23 21:18:32,821] TRAIN Iter 158820: lr = 0.235302,	loss = 2.665343,	Top-1 err = 0.403906,	Top-5 err = 0.182129,	data_time = 0.050476,	train_time = 0.819347
[2019-08-23 21:18:39,622] TRAIN Iter 158840: lr = 0.235268,	loss = 2.696745,	Top-1 err = 0.410938,	Top-5 err = 0.189062,	data_time = 0.051172,	train_time = 0.340040
[2019-08-23 21:18:56,758] TRAIN Iter 158860: lr = 0.235235,	loss = 2.590878,	Top-1 err = 0.401074,	Top-5 err = 0.181689,	data_time = 0.050718,	train_time = 0.856775
[2019-08-23 21:19:14,571] TRAIN Iter 158880: lr = 0.235202,	loss = 2.664011,	Top-1 err = 0.394775,	Top-5 err = 0.183301,	data_time = 0.050088,	train_time = 0.890643
[2019-08-23 21:19:21,118] TRAIN Iter 158900: lr = 0.235168,	loss = 2.581876,	Top-1 err = 0.405078,	Top-5 err = 0.185791,	data_time = 0.049993,	train_time = 0.327328
[2019-08-23 21:19:36,959] TRAIN Iter 158920: lr = 0.235135,	loss = 2.659207,	Top-1 err = 0.400928,	Top-5 err = 0.182129,	data_time = 0.049905,	train_time = 0.792071
[2019-08-23 21:19:45,352] TRAIN Iter 158940: lr = 0.235102,	loss = 3.036294,	Top-1 err = 0.404913,	Top-5 err = 0.187677,	data_time = 0.007137,	train_time = 0.419622
[2019-08-23 21:20:32,306] TRAIN Iter 158960: lr = 0.235068,	loss = 2.588399,	Top-1 err = 0.399463,	Top-5 err = 0.180176,	data_time = 0.050616,	train_time = 2.347668
[2019-08-23 21:20:49,177] TRAIN Iter 158980: lr = 0.235035,	loss = 2.570498,	Top-1 err = 0.388477,	Top-5 err = 0.175830,	data_time = 0.050928,	train_time = 0.843568
[2019-08-23 21:20:56,446] TRAIN Iter 159000: lr = 0.235002,	loss = 2.678107,	Top-1 err = 0.398779,	Top-5 err = 0.179932,	data_time = 0.050643,	train_time = 0.363431
[2019-08-23 21:21:09,236] TRAIN Iter 159020: lr = 0.234968,	loss = 2.651129,	Top-1 err = 0.398291,	Top-5 err = 0.179004,	data_time = 0.050708,	train_time = 0.639480
[2019-08-23 21:21:24,367] TRAIN Iter 159040: lr = 0.234935,	loss = 2.613257,	Top-1 err = 0.396533,	Top-5 err = 0.182520,	data_time = 0.050440,	train_time = 0.756544
[2019-08-23 21:21:32,254] TRAIN Iter 159060: lr = 0.234902,	loss = 2.595443,	Top-1 err = 0.394629,	Top-5 err = 0.179395,	data_time = 0.050416,	train_time = 0.394339
[2019-08-23 21:21:47,633] TRAIN Iter 159080: lr = 0.234868,	loss = 2.675886,	Top-1 err = 0.392676,	Top-5 err = 0.179297,	data_time = 0.050381,	train_time = 0.768906
[2019-08-23 21:21:54,882] TRAIN Iter 159100: lr = 0.234835,	loss = 2.590631,	Top-1 err = 0.390283,	Top-5 err = 0.171143,	data_time = 0.050712,	train_time = 0.362466
[2019-08-23 21:22:11,025] TRAIN Iter 159120: lr = 0.234802,	loss = 2.698519,	Top-1 err = 0.393945,	Top-5 err = 0.178955,	data_time = 0.050663,	train_time = 0.807129
[2019-08-23 21:22:27,367] TRAIN Iter 159140: lr = 0.234768,	loss = 2.566675,	Top-1 err = 0.396631,	Top-5 err = 0.182031,	data_time = 0.050300,	train_time = 0.817095
[2019-08-23 21:22:34,429] TRAIN Iter 159160: lr = 0.234735,	loss = 2.708408,	Top-1 err = 0.390771,	Top-5 err = 0.179102,	data_time = 0.050309,	train_time = 0.353067
[2019-08-23 21:22:49,202] TRAIN Iter 159180: lr = 0.234702,	loss = 2.575799,	Top-1 err = 0.400098,	Top-5 err = 0.176318,	data_time = 0.050528,	train_time = 0.738654
[2019-08-23 21:23:05,956] TRAIN Iter 159200: lr = 0.234668,	loss = 2.538482,	Top-1 err = 0.392139,	Top-5 err = 0.175000,	data_time = 0.050541,	train_time = 0.837675
[2019-08-23 21:23:13,084] TRAIN Iter 159220: lr = 0.234635,	loss = 2.627897,	Top-1 err = 0.393652,	Top-5 err = 0.178125,	data_time = 0.050422,	train_time = 0.356384
[2019-08-23 21:23:28,701] TRAIN Iter 159240: lr = 0.234602,	loss = 2.531960,	Top-1 err = 0.392236,	Top-5 err = 0.178320,	data_time = 0.050623,	train_time = 0.780822
[2019-08-23 21:23:36,269] TRAIN Iter 159260: lr = 0.234568,	loss = 2.573645,	Top-1 err = 0.394824,	Top-5 err = 0.177344,	data_time = 0.050673,	train_time = 0.378367
[2019-08-23 21:23:51,751] TRAIN Iter 159280: lr = 0.234535,	loss = 2.567385,	Top-1 err = 0.392578,	Top-5 err = 0.179297,	data_time = 0.050421,	train_time = 0.774128
[2019-08-23 21:24:08,860] TRAIN Iter 159300: lr = 0.234502,	loss = 2.631579,	Top-1 err = 0.396338,	Top-5 err = 0.177441,	data_time = 0.050458,	train_time = 0.855413
[2019-08-23 21:24:15,877] TRAIN Iter 159320: lr = 0.234468,	loss = 2.620988,	Top-1 err = 0.392529,	Top-5 err = 0.178809,	data_time = 0.050409,	train_time = 0.350856
[2019-08-23 21:24:32,539] TRAIN Iter 159340: lr = 0.234435,	loss = 2.573902,	Top-1 err = 0.397412,	Top-5 err = 0.181396,	data_time = 0.050381,	train_time = 0.833077
[2019-08-23 21:24:46,288] TRAIN Iter 159360: lr = 0.234402,	loss = 2.623898,	Top-1 err = 0.393799,	Top-5 err = 0.182715,	data_time = 0.117394,	train_time = 0.687407
[2019-08-23 21:24:53,401] TRAIN Iter 159380: lr = 0.234368,	loss = 2.637076,	Top-1 err = 0.399023,	Top-5 err = 0.180029,	data_time = 0.141702,	train_time = 0.355639
[2019-08-23 21:25:06,596] TRAIN Iter 159400: lr = 0.234335,	loss = 2.648813,	Top-1 err = 0.399219,	Top-5 err = 0.178955,	data_time = 0.050255,	train_time = 0.659734
[2019-08-23 21:25:13,248] TRAIN Iter 159420: lr = 0.234302,	loss = 2.660696,	Top-1 err = 0.393896,	Top-5 err = 0.179150,	data_time = 0.050434,	train_time = 0.332585
[2019-08-23 21:25:29,682] TRAIN Iter 159440: lr = 0.234268,	loss = 2.589025,	Top-1 err = 0.399902,	Top-5 err = 0.184180,	data_time = 0.050406,	train_time = 0.821711
[2019-08-23 21:25:47,951] TRAIN Iter 159460: lr = 0.234235,	loss = 2.637115,	Top-1 err = 0.393799,	Top-5 err = 0.177832,	data_time = 0.050704,	train_time = 0.913435
[2019-08-23 21:25:56,114] TRAIN Iter 159480: lr = 0.234202,	loss = 2.681663,	Top-1 err = 0.396924,	Top-5 err = 0.180273,	data_time = 0.050974,	train_time = 0.408131
[2019-08-23 21:26:11,629] TRAIN Iter 159500: lr = 0.234168,	loss = 2.674741,	Top-1 err = 0.396680,	Top-5 err = 0.180420,	data_time = 0.050385,	train_time = 0.775702
[2019-08-23 21:26:26,634] TRAIN Iter 159520: lr = 0.234135,	loss = 2.638404,	Top-1 err = 0.396484,	Top-5 err = 0.182568,	data_time = 0.084545,	train_time = 0.750244
[2019-08-23 21:26:33,445] TRAIN Iter 159540: lr = 0.234102,	loss = 2.620041,	Top-1 err = 0.401465,	Top-5 err = 0.186084,	data_time = 0.050724,	train_time = 0.340526
[2019-08-23 21:26:49,402] TRAIN Iter 159560: lr = 0.234068,	loss = 2.651134,	Top-1 err = 0.400879,	Top-5 err = 0.181104,	data_time = 0.050426,	train_time = 0.797881
[2019-08-23 21:26:57,146] TRAIN Iter 159580: lr = 0.234035,	loss = 2.599988,	Top-1 err = 0.395557,	Top-5 err = 0.181396,	data_time = 0.050326,	train_time = 0.387179
[2019-08-23 21:27:10,898] TRAIN Iter 159600: lr = 0.234002,	loss = 2.698353,	Top-1 err = 0.401904,	Top-5 err = 0.181104,	data_time = 0.050663,	train_time = 0.687573
[2019-08-23 21:27:28,624] TRAIN Iter 159620: lr = 0.233968,	loss = 2.580662,	Top-1 err = 0.398047,	Top-5 err = 0.178125,	data_time = 0.051405,	train_time = 0.886305
[2019-08-23 21:27:38,371] TRAIN Iter 159640: lr = 0.233935,	loss = 2.513171,	Top-1 err = 0.399023,	Top-5 err = 0.182568,	data_time = 0.051682,	train_time = 0.487284
[2019-08-23 21:27:59,027] TRAIN Iter 159660: lr = 0.233902,	loss = 2.641693,	Top-1 err = 0.400732,	Top-5 err = 0.185547,	data_time = 0.050598,	train_time = 1.032828
[2019-08-23 21:28:08,388] TRAIN Iter 159680: lr = 0.233868,	loss = 2.625246,	Top-1 err = 0.399561,	Top-5 err = 0.180859,	data_time = 0.117936,	train_time = 0.467997
[2019-08-23 21:28:15,402] TRAIN Iter 159700: lr = 0.233835,	loss = 2.608277,	Top-1 err = 0.395361,	Top-5 err = 0.180518,	data_time = 0.050991,	train_time = 0.350698
[2019-08-23 21:28:30,750] TRAIN Iter 159720: lr = 0.233802,	loss = 2.650791,	Top-1 err = 0.401514,	Top-5 err = 0.182520,	data_time = 0.050609,	train_time = 0.767403
[2019-08-23 21:28:37,597] TRAIN Iter 159740: lr = 0.233768,	loss = 2.568260,	Top-1 err = 0.396143,	Top-5 err = 0.183057,	data_time = 0.050589,	train_time = 0.342312
[2019-08-23 21:28:54,754] TRAIN Iter 159760: lr = 0.233735,	loss = 2.765402,	Top-1 err = 0.401855,	Top-5 err = 0.186670,	data_time = 0.050687,	train_time = 0.857824
[2019-08-23 21:29:12,325] TRAIN Iter 159780: lr = 0.233702,	loss = 2.693628,	Top-1 err = 0.404541,	Top-5 err = 0.183838,	data_time = 0.050380,	train_time = 0.878537
[2019-08-23 21:29:19,168] TRAIN Iter 159800: lr = 0.233668,	loss = 2.612671,	Top-1 err = 0.406787,	Top-5 err = 0.186523,	data_time = 0.050475,	train_time = 0.342158
[2019-08-23 21:29:34,985] TRAIN Iter 159820: lr = 0.233635,	loss = 2.594270,	Top-1 err = 0.394531,	Top-5 err = 0.186328,	data_time = 0.050472,	train_time = 0.790808
[2019-08-23 21:29:50,290] TRAIN Iter 159840: lr = 0.233602,	loss = 2.624990,	Top-1 err = 0.397217,	Top-5 err = 0.183984,	data_time = 0.134866,	train_time = 0.765256
[2019-08-23 21:29:57,628] TRAIN Iter 159860: lr = 0.233568,	loss = 2.613587,	Top-1 err = 0.403711,	Top-5 err = 0.182471,	data_time = 0.050483,	train_time = 0.366902
[2019-08-23 21:30:14,020] TRAIN Iter 159880: lr = 0.233535,	loss = 2.654357,	Top-1 err = 0.400830,	Top-5 err = 0.180664,	data_time = 0.050731,	train_time = 0.819558
[2019-08-23 21:30:20,870] TRAIN Iter 159900: lr = 0.233502,	loss = 2.637289,	Top-1 err = 0.400977,	Top-5 err = 0.182568,	data_time = 0.050287,	train_time = 0.342479
[2019-08-23 21:30:37,109] TRAIN Iter 159920: lr = 0.233468,	loss = 2.580978,	Top-1 err = 0.399561,	Top-5 err = 0.184766,	data_time = 0.050316,	train_time = 0.811933
[2019-08-23 21:30:51,792] TRAIN Iter 159940: lr = 0.233435,	loss = 2.610268,	Top-1 err = 0.395508,	Top-5 err = 0.177734,	data_time = 0.050759,	train_time = 0.734151
[2019-08-23 21:30:58,831] TRAIN Iter 159960: lr = 0.233402,	loss = 2.592572,	Top-1 err = 0.400635,	Top-5 err = 0.183496,	data_time = 0.050665,	train_time = 0.351938
[2019-08-23 21:31:16,045] TRAIN Iter 159980: lr = 0.233368,	loss = 2.559843,	Top-1 err = 0.398877,	Top-5 err = 0.182764,	data_time = 0.050411,	train_time = 0.860677
[2019-08-23 21:31:33,234] TRAIN Iter 160000: lr = 0.233335,	loss = 2.740513,	Top-1 err = 0.403955,	Top-5 err = 0.185693,	data_time = 0.050621,	train_time = 0.859453
[2019-08-23 21:32:35,228] TEST Iter 160000: loss = 2.427275,	Top-1 err = 0.364060,	Top-5 err = 0.143540,	val_time = 61.950009
[2019-08-23 21:32:41,445] TRAIN Iter 160020: lr = 0.233302,	loss = 2.581079,	Top-1 err = 0.405273,	Top-5 err = 0.188184,	data_time = 0.050323,	train_time = 0.310815
[2019-08-23 21:32:47,900] TRAIN Iter 160040: lr = 0.233268,	loss = 2.688597,	Top-1 err = 0.405957,	Top-5 err = 0.187256,	data_time = 0.050400,	train_time = 0.322746
[2019-08-23 21:32:54,473] TRAIN Iter 160060: lr = 0.233235,	loss = 2.662389,	Top-1 err = 0.399414,	Top-5 err = 0.182031,	data_time = 0.050790,	train_time = 0.328640
[2019-08-23 21:33:04,990] TRAIN Iter 160080: lr = 0.233202,	loss = 2.677475,	Top-1 err = 0.405371,	Top-5 err = 0.184180,	data_time = 0.181796,	train_time = 0.525824
[2019-08-23 21:33:21,623] TRAIN Iter 160100: lr = 0.233168,	loss = 2.668972,	Top-1 err = 0.403223,	Top-5 err = 0.180859,	data_time = 0.112499,	train_time = 0.831603
[2019-08-23 21:33:30,468] TRAIN Iter 160120: lr = 0.233135,	loss = 2.652662,	Top-1 err = 0.405713,	Top-5 err = 0.185547,	data_time = 0.050497,	train_time = 0.442239
[2019-08-23 21:33:49,123] TRAIN Iter 160140: lr = 0.233102,	loss = 2.578910,	Top-1 err = 0.403662,	Top-5 err = 0.185645,	data_time = 0.272678,	train_time = 0.932746
[2019-08-23 21:33:56,863] TRAIN Iter 160160: lr = 0.233068,	loss = 2.659516,	Top-1 err = 0.400000,	Top-5 err = 0.186133,	data_time = 0.049897,	train_time = 0.386987
[2019-08-23 21:34:14,512] TRAIN Iter 160180: lr = 0.233035,	loss = 2.636153,	Top-1 err = 0.400098,	Top-5 err = 0.181348,	data_time = 0.118751,	train_time = 0.882459
[2019-08-23 21:35:02,625] TRAIN Iter 160200: lr = 0.233002,	loss = 2.599150,	Top-1 err = 0.404913,	Top-5 err = 0.181281,	data_time = 0.050590,	train_time = 2.405631
[2019-08-23 21:35:09,602] TRAIN Iter 160220: lr = 0.232968,	loss = 2.520902,	Top-1 err = 0.397510,	Top-5 err = 0.180713,	data_time = 0.050790,	train_time = 0.348817
[2019-08-23 21:35:26,259] TRAIN Iter 160240: lr = 0.232935,	loss = 2.650958,	Top-1 err = 0.392041,	Top-5 err = 0.177100,	data_time = 0.050512,	train_time = 0.832859
[2019-08-23 21:35:42,402] TRAIN Iter 160260: lr = 0.232902,	loss = 2.629841,	Top-1 err = 0.394336,	Top-5 err = 0.179590,	data_time = 0.050758,	train_time = 0.807127
[2019-08-23 21:35:49,484] TRAIN Iter 160280: lr = 0.232868,	loss = 2.501181,	Top-1 err = 0.393066,	Top-5 err = 0.176514,	data_time = 0.050357,	train_time = 0.354065
[2019-08-23 21:36:04,312] TRAIN Iter 160300: lr = 0.232835,	loss = 2.556790,	Top-1 err = 0.393604,	Top-5 err = 0.176465,	data_time = 0.050329,	train_time = 0.741403
[2019-08-23 21:36:11,676] TRAIN Iter 160320: lr = 0.232802,	loss = 2.617563,	Top-1 err = 0.394043,	Top-5 err = 0.180078,	data_time = 0.050580,	train_time = 0.368198
[2019-08-23 21:36:30,495] TRAIN Iter 160340: lr = 0.232768,	loss = 2.685761,	Top-1 err = 0.396045,	Top-5 err = 0.178564,	data_time = 0.050542,	train_time = 0.940918
[2019-08-23 21:36:43,720] TRAIN Iter 160360: lr = 0.232735,	loss = 2.579973,	Top-1 err = 0.397314,	Top-5 err = 0.177930,	data_time = 0.050293,	train_time = 0.661225
[2019-08-23 21:36:51,197] TRAIN Iter 160380: lr = 0.232702,	loss = 2.513894,	Top-1 err = 0.391357,	Top-5 err = 0.175342,	data_time = 0.050558,	train_time = 0.373858
[2019-08-23 21:37:05,979] TRAIN Iter 160400: lr = 0.232668,	loss = 2.609526,	Top-1 err = 0.393213,	Top-5 err = 0.176709,	data_time = 0.050543,	train_time = 0.739072
[2019-08-23 21:37:21,125] TRAIN Iter 160420: lr = 0.232635,	loss = 2.605010,	Top-1 err = 0.393457,	Top-5 err = 0.182666,	data_time = 0.050529,	train_time = 0.757275
[2019-08-23 21:37:28,109] TRAIN Iter 160440: lr = 0.232602,	loss = 2.566466,	Top-1 err = 0.399170,	Top-5 err = 0.180811,	data_time = 0.050367,	train_time = 0.349214
[2019-08-23 21:37:42,052] TRAIN Iter 160460: lr = 0.232568,	loss = 2.708245,	Top-1 err = 0.393848,	Top-5 err = 0.178711,	data_time = 0.050377,	train_time = 0.697121
[2019-08-23 21:37:48,803] TRAIN Iter 160480: lr = 0.232535,	loss = 2.517208,	Top-1 err = 0.395996,	Top-5 err = 0.178418,	data_time = 0.050314,	train_time = 0.337526
[2019-08-23 21:38:07,343] TRAIN Iter 160500: lr = 0.232502,	loss = 2.666435,	Top-1 err = 0.395898,	Top-5 err = 0.180957,	data_time = 0.050793,	train_time = 0.926985
[2019-08-23 21:38:23,998] TRAIN Iter 160520: lr = 0.232468,	loss = 2.652383,	Top-1 err = 0.402441,	Top-5 err = 0.179248,	data_time = 0.050875,	train_time = 0.832736
[2019-08-23 21:38:31,466] TRAIN Iter 160540: lr = 0.232435,	loss = 2.628673,	Top-1 err = 0.398486,	Top-5 err = 0.179443,	data_time = 0.050220,	train_time = 0.373381
[2019-08-23 21:38:44,811] TRAIN Iter 160560: lr = 0.232402,	loss = 2.564240,	Top-1 err = 0.395557,	Top-5 err = 0.176855,	data_time = 0.050622,	train_time = 0.667261
[2019-08-23 21:39:00,805] TRAIN Iter 160580: lr = 0.232368,	loss = 2.635319,	Top-1 err = 0.396533,	Top-5 err = 0.179980,	data_time = 0.050889,	train_time = 0.799669
[2019-08-23 21:39:08,020] TRAIN Iter 160600: lr = 0.232335,	loss = 2.651411,	Top-1 err = 0.397314,	Top-5 err = 0.183105,	data_time = 0.050426,	train_time = 0.360742
[2019-08-23 21:39:23,540] TRAIN Iter 160620: lr = 0.232302,	loss = 2.607634,	Top-1 err = 0.395605,	Top-5 err = 0.182227,	data_time = 0.050513,	train_time = 0.776000
[2019-08-23 21:39:30,508] TRAIN Iter 160640: lr = 0.232268,	loss = 2.608708,	Top-1 err = 0.396826,	Top-5 err = 0.180127,	data_time = 0.050922,	train_time = 0.348391
[2019-08-23 21:39:47,212] TRAIN Iter 160660: lr = 0.232235,	loss = 2.595169,	Top-1 err = 0.400781,	Top-5 err = 0.180029,	data_time = 0.050900,	train_time = 0.835182
[2019-08-23 21:40:04,219] TRAIN Iter 160680: lr = 0.232202,	loss = 2.687501,	Top-1 err = 0.399316,	Top-5 err = 0.182129,	data_time = 0.050441,	train_time = 0.850330
[2019-08-23 21:40:11,120] TRAIN Iter 160700: lr = 0.232168,	loss = 2.586811,	Top-1 err = 0.400000,	Top-5 err = 0.182812,	data_time = 0.050600,	train_time = 0.345054
[2019-08-23 21:40:26,905] TRAIN Iter 160720: lr = 0.232135,	loss = 2.648835,	Top-1 err = 0.394873,	Top-5 err = 0.181055,	data_time = 0.050562,	train_time = 0.789234
[2019-08-23 21:40:45,362] TRAIN Iter 160740: lr = 0.232102,	loss = 2.646162,	Top-1 err = 0.394336,	Top-5 err = 0.178516,	data_time = 0.050455,	train_time = 0.922809
[2019-08-23 21:40:52,084] TRAIN Iter 160760: lr = 0.232068,	loss = 2.655618,	Top-1 err = 0.395508,	Top-5 err = 0.180469,	data_time = 0.050288,	train_time = 0.336117
[2019-08-23 21:41:09,652] TRAIN Iter 160780: lr = 0.232035,	loss = 2.507368,	Top-1 err = 0.398682,	Top-5 err = 0.179541,	data_time = 0.050457,	train_time = 0.878356
[2019-08-23 21:41:16,725] TRAIN Iter 160800: lr = 0.232002,	loss = 2.700677,	Top-1 err = 0.399023,	Top-5 err = 0.182129,	data_time = 0.109626,	train_time = 0.353631
[2019-08-23 21:41:33,112] TRAIN Iter 160820: lr = 0.231968,	loss = 2.655724,	Top-1 err = 0.398633,	Top-5 err = 0.179541,	data_time = 0.050402,	train_time = 0.819369
[2019-08-23 21:41:50,630] TRAIN Iter 160840: lr = 0.231935,	loss = 2.619835,	Top-1 err = 0.396045,	Top-5 err = 0.178027,	data_time = 0.050332,	train_time = 0.875861
[2019-08-23 21:41:57,310] TRAIN Iter 160860: lr = 0.231902,	loss = 2.696826,	Top-1 err = 0.401562,	Top-5 err = 0.183691,	data_time = 0.050776,	train_time = 0.334009
[2019-08-23 21:42:15,343] TRAIN Iter 160880: lr = 0.231868,	loss = 2.651048,	Top-1 err = 0.405762,	Top-5 err = 0.182666,	data_time = 0.050890,	train_time = 0.901608
[2019-08-23 21:42:32,075] TRAIN Iter 160900: lr = 0.231835,	loss = 2.648305,	Top-1 err = 0.399902,	Top-5 err = 0.181836,	data_time = 0.050364,	train_time = 0.836617
[2019-08-23 21:42:39,059] TRAIN Iter 160920: lr = 0.231802,	loss = 2.597654,	Top-1 err = 0.395850,	Top-5 err = 0.181250,	data_time = 0.050723,	train_time = 0.349201
[2019-08-23 21:42:54,879] TRAIN Iter 160940: lr = 0.231768,	loss = 2.638974,	Top-1 err = 0.401562,	Top-5 err = 0.183008,	data_time = 0.050498,	train_time = 0.790975
[2019-08-23 21:43:02,279] TRAIN Iter 160960: lr = 0.231735,	loss = 2.588203,	Top-1 err = 0.404004,	Top-5 err = 0.185254,	data_time = 0.050706,	train_time = 0.369967
[2019-08-23 21:43:19,081] TRAIN Iter 160980: lr = 0.231702,	loss = 2.576548,	Top-1 err = 0.395557,	Top-5 err = 0.181592,	data_time = 0.050470,	train_time = 0.840099
[2019-08-23 21:43:36,168] TRAIN Iter 161000: lr = 0.231668,	loss = 2.636991,	Top-1 err = 0.397363,	Top-5 err = 0.182715,	data_time = 0.050390,	train_time = 0.854342
[2019-08-23 21:43:43,156] TRAIN Iter 161020: lr = 0.231635,	loss = 2.632511,	Top-1 err = 0.398291,	Top-5 err = 0.181934,	data_time = 0.050497,	train_time = 0.349356
[2019-08-23 21:44:00,060] TRAIN Iter 161040: lr = 0.231602,	loss = 2.676284,	Top-1 err = 0.401025,	Top-5 err = 0.186523,	data_time = 0.050581,	train_time = 0.845184
[2019-08-23 21:44:19,068] TRAIN Iter 161060: lr = 0.231568,	loss = 2.579254,	Top-1 err = 0.401270,	Top-5 err = 0.181592,	data_time = 0.050287,	train_time = 0.950428
[2019-08-23 21:44:26,001] TRAIN Iter 161080: lr = 0.231535,	loss = 2.713575,	Top-1 err = 0.397412,	Top-5 err = 0.181836,	data_time = 0.050476,	train_time = 0.346610
[2019-08-23 21:44:43,107] TRAIN Iter 161100: lr = 0.231502,	loss = 2.622029,	Top-1 err = 0.402588,	Top-5 err = 0.185205,	data_time = 0.050525,	train_time = 0.855275
[2019-08-23 21:44:50,260] TRAIN Iter 161120: lr = 0.231468,	loss = 2.660240,	Top-1 err = 0.402100,	Top-5 err = 0.183789,	data_time = 0.149644,	train_time = 0.357663
[2019-08-23 21:45:07,509] TRAIN Iter 161140: lr = 0.231435,	loss = 2.613941,	Top-1 err = 0.401562,	Top-5 err = 0.182324,	data_time = 0.050410,	train_time = 0.862439
[2019-08-23 21:45:27,745] TRAIN Iter 161160: lr = 0.231402,	loss = 2.666548,	Top-1 err = 0.400098,	Top-5 err = 0.181787,	data_time = 0.050507,	train_time = 1.011783
[2019-08-23 21:45:34,460] TRAIN Iter 161180: lr = 0.231368,	loss = 2.621487,	Top-1 err = 0.392969,	Top-5 err = 0.177295,	data_time = 0.050542,	train_time = 0.335711
[2019-08-23 21:45:53,997] TRAIN Iter 161200: lr = 0.231335,	loss = 2.658340,	Top-1 err = 0.402100,	Top-5 err = 0.184375,	data_time = 0.050458,	train_time = 0.976837
[2019-08-23 21:46:13,131] TRAIN Iter 161220: lr = 0.231302,	loss = 2.556597,	Top-1 err = 0.404053,	Top-5 err = 0.184570,	data_time = 0.050472,	train_time = 0.956678
[2019-08-23 21:46:19,727] TRAIN Iter 161240: lr = 0.231268,	loss = 2.627616,	Top-1 err = 0.398096,	Top-5 err = 0.185254,	data_time = 0.050513,	train_time = 0.329821
[2019-08-23 21:46:37,715] TRAIN Iter 161260: lr = 0.231235,	loss = 2.613583,	Top-1 err = 0.397461,	Top-5 err = 0.182959,	data_time = 0.050468,	train_time = 0.899371
[2019-08-23 21:46:44,515] TRAIN Iter 161280: lr = 0.231202,	loss = 2.594196,	Top-1 err = 0.397705,	Top-5 err = 0.183887,	data_time = 0.050438,	train_time = 0.339976
[2019-08-23 21:47:03,769] TRAIN Iter 161300: lr = 0.231168,	loss = 2.542930,	Top-1 err = 0.396826,	Top-5 err = 0.181641,	data_time = 0.050549,	train_time = 0.962695
[2019-08-23 21:47:22,581] TRAIN Iter 161320: lr = 0.231135,	loss = 2.540085,	Top-1 err = 0.398438,	Top-5 err = 0.181592,	data_time = 0.050493,	train_time = 0.940573
[2019-08-23 21:47:29,120] TRAIN Iter 161340: lr = 0.231102,	loss = 2.645213,	Top-1 err = 0.404248,	Top-5 err = 0.183936,	data_time = 0.050370,	train_time = 0.326924
[2019-08-23 21:47:49,593] TRAIN Iter 161360: lr = 0.231068,	loss = 2.654326,	Top-1 err = 0.395752,	Top-5 err = 0.181006,	data_time = 0.050482,	train_time = 1.023664
[2019-08-23 21:48:09,215] TRAIN Iter 161380: lr = 0.231035,	loss = 2.742771,	Top-1 err = 0.406836,	Top-5 err = 0.184131,	data_time = 0.065959,	train_time = 0.981083
[2019-08-23 21:48:15,638] TRAIN Iter 161400: lr = 0.231002,	loss = 2.641169,	Top-1 err = 0.403906,	Top-5 err = 0.187012,	data_time = 0.050082,	train_time = 0.321153
[2019-08-23 21:48:34,878] TRAIN Iter 161420: lr = 0.230968,	loss = 2.593331,	Top-1 err = 0.399219,	Top-5 err = 0.181152,	data_time = 0.049899,	train_time = 0.961945
[2019-08-23 21:48:41,093] TRAIN Iter 161440: lr = 0.230935,	loss = 2.571790,	Top-1 err = 0.401709,	Top-5 err = 0.183154,	data_time = 0.049886,	train_time = 0.310758
[2019-08-23 21:49:35,248] TRAIN Iter 161460: lr = 0.230902,	loss = 2.595922,	Top-1 err = 0.404631,	Top-5 err = 0.184604,	data_time = 0.050426,	train_time = 2.707738
[2019-08-23 21:49:50,395] TRAIN Iter 161480: lr = 0.230868,	loss = 2.563678,	Top-1 err = 0.394971,	Top-5 err = 0.181299,	data_time = 0.102231,	train_time = 0.757310
[2019-08-23 21:49:57,955] TRAIN Iter 161500: lr = 0.230835,	loss = 2.610539,	Top-1 err = 0.394678,	Top-5 err = 0.177441,	data_time = 0.050404,	train_time = 0.377992
[2019-08-23 21:50:14,397] TRAIN Iter 161520: lr = 0.230802,	loss = 2.549678,	Top-1 err = 0.387256,	Top-5 err = 0.177637,	data_time = 0.050422,	train_time = 0.822109
[2019-08-23 21:50:21,343] TRAIN Iter 161540: lr = 0.230768,	loss = 2.666780,	Top-1 err = 0.389893,	Top-5 err = 0.179443,	data_time = 0.050449,	train_time = 0.347253
[2019-08-23 21:50:38,789] TRAIN Iter 161560: lr = 0.230735,	loss = 2.583259,	Top-1 err = 0.388379,	Top-5 err = 0.174365,	data_time = 0.050567,	train_time = 0.872291
[2019-08-23 21:50:55,015] TRAIN Iter 161580: lr = 0.230702,	loss = 2.583593,	Top-1 err = 0.384961,	Top-5 err = 0.172705,	data_time = 0.050485,	train_time = 0.811315
[2019-08-23 21:51:01,843] TRAIN Iter 161600: lr = 0.230668,	loss = 2.542367,	Top-1 err = 0.391699,	Top-5 err = 0.177734,	data_time = 0.050394,	train_time = 0.341396
[2019-08-23 21:51:17,199] TRAIN Iter 161620: lr = 0.230635,	loss = 2.549016,	Top-1 err = 0.389648,	Top-5 err = 0.178174,	data_time = 0.050378,	train_time = 0.767778
[2019-08-23 21:51:33,685] TRAIN Iter 161640: lr = 0.230602,	loss = 2.571753,	Top-1 err = 0.391260,	Top-5 err = 0.173047,	data_time = 3.654757,	train_time = 0.824251
[2019-08-23 21:51:40,313] TRAIN Iter 161660: lr = 0.230568,	loss = 2.584993,	Top-1 err = 0.393115,	Top-5 err = 0.176709,	data_time = 0.050635,	train_time = 0.331391
[2019-08-23 21:51:55,349] TRAIN Iter 161680: lr = 0.230535,	loss = 2.415835,	Top-1 err = 0.391016,	Top-5 err = 0.176025,	data_time = 0.050252,	train_time = 0.751794
[2019-08-23 21:52:02,439] TRAIN Iter 161700: lr = 0.230502,	loss = 2.645511,	Top-1 err = 0.397949,	Top-5 err = 0.180518,	data_time = 0.050651,	train_time = 0.354488
[2019-08-23 21:52:17,238] TRAIN Iter 161720: lr = 0.230468,	loss = 2.636028,	Top-1 err = 0.397949,	Top-5 err = 0.181641,	data_time = 0.050376,	train_time = 0.739939
[2019-08-23 21:52:32,729] TRAIN Iter 161740: lr = 0.230435,	loss = 2.584515,	Top-1 err = 0.394141,	Top-5 err = 0.182764,	data_time = 0.050364,	train_time = 0.774527
[2019-08-23 21:52:39,485] TRAIN Iter 161760: lr = 0.230402,	loss = 2.532812,	Top-1 err = 0.390918,	Top-5 err = 0.175928,	data_time = 0.050629,	train_time = 0.337796
[2019-08-23 21:52:56,203] TRAIN Iter 161780: lr = 0.230368,	loss = 2.607160,	Top-1 err = 0.391602,	Top-5 err = 0.176758,	data_time = 0.050324,	train_time = 0.835890
[2019-08-23 21:53:13,314] TRAIN Iter 161800: lr = 0.230335,	loss = 2.530377,	Top-1 err = 0.391211,	Top-5 err = 0.177295,	data_time = 7.798695,	train_time = 0.855525
[2019-08-23 21:53:19,818] TRAIN Iter 161820: lr = 0.230302,	loss = 2.609895,	Top-1 err = 0.399854,	Top-5 err = 0.180322,	data_time = 0.050431,	train_time = 0.325195
[2019-08-23 21:53:35,332] TRAIN Iter 161840: lr = 0.230268,	loss = 2.609275,	Top-1 err = 0.402197,	Top-5 err = 0.181934,	data_time = 0.050291,	train_time = 0.775709
[2019-08-23 21:53:41,946] TRAIN Iter 161860: lr = 0.230235,	loss = 2.697609,	Top-1 err = 0.395850,	Top-5 err = 0.181299,	data_time = 0.050882,	train_time = 0.330652
[2019-08-23 21:53:58,353] TRAIN Iter 161880: lr = 0.230202,	loss = 2.645440,	Top-1 err = 0.393750,	Top-5 err = 0.175195,	data_time = 0.050424,	train_time = 0.820368
[2019-08-23 21:54:15,911] TRAIN Iter 161900: lr = 0.230168,	loss = 2.562548,	Top-1 err = 0.393994,	Top-5 err = 0.178076,	data_time = 0.050823,	train_time = 0.877892
[2019-08-23 21:54:22,738] TRAIN Iter 161920: lr = 0.230135,	loss = 2.583948,	Top-1 err = 0.395605,	Top-5 err = 0.179004,	data_time = 0.050810,	train_time = 0.341325
[2019-08-23 21:54:39,608] TRAIN Iter 161940: lr = 0.230102,	loss = 2.672943,	Top-1 err = 0.394824,	Top-5 err = 0.179150,	data_time = 0.050789,	train_time = 0.843497
[2019-08-23 21:54:55,550] TRAIN Iter 161960: lr = 0.230068,	loss = 2.642712,	Top-1 err = 0.400146,	Top-5 err = 0.182178,	data_time = 0.050477,	train_time = 0.797083
[2019-08-23 21:55:02,835] TRAIN Iter 161980: lr = 0.230035,	loss = 2.639754,	Top-1 err = 0.404443,	Top-5 err = 0.184766,	data_time = 0.140294,	train_time = 0.364194
[2019-08-23 21:55:19,097] TRAIN Iter 162000: lr = 0.230002,	loss = 2.509004,	Top-1 err = 0.396680,	Top-5 err = 0.179150,	data_time = 0.050328,	train_time = 0.813104
[2019-08-23 21:55:25,738] TRAIN Iter 162020: lr = 0.229968,	loss = 2.647968,	Top-1 err = 0.401758,	Top-5 err = 0.182031,	data_time = 0.050654,	train_time = 0.332050
[2019-08-23 21:55:43,728] TRAIN Iter 162040: lr = 0.229935,	loss = 2.691769,	Top-1 err = 0.401318,	Top-5 err = 0.180225,	data_time = 0.050594,	train_time = 0.899489
[2019-08-23 21:56:00,297] TRAIN Iter 162060: lr = 0.229902,	loss = 2.693819,	Top-1 err = 0.402393,	Top-5 err = 0.183643,	data_time = 0.050512,	train_time = 0.828438
[2019-08-23 21:56:07,056] TRAIN Iter 162080: lr = 0.229868,	loss = 2.651912,	Top-1 err = 0.395850,	Top-5 err = 0.181006,	data_time = 0.050359,	train_time = 0.337927
[2019-08-23 21:56:24,122] TRAIN Iter 162100: lr = 0.229835,	loss = 2.555025,	Top-1 err = 0.391211,	Top-5 err = 0.180908,	data_time = 0.050295,	train_time = 0.853286
[2019-08-23 21:56:41,114] TRAIN Iter 162120: lr = 0.229802,	loss = 2.537394,	Top-1 err = 0.399707,	Top-5 err = 0.180566,	data_time = 0.164931,	train_time = 0.849584
[2019-08-23 21:56:47,859] TRAIN Iter 162140: lr = 0.229768,	loss = 2.582133,	Top-1 err = 0.399707,	Top-5 err = 0.179980,	data_time = 0.050391,	train_time = 0.337233
[2019-08-23 21:57:03,954] TRAIN Iter 162160: lr = 0.229735,	loss = 2.554373,	Top-1 err = 0.405615,	Top-5 err = 0.185938,	data_time = 0.050543,	train_time = 0.804726
[2019-08-23 21:57:11,156] TRAIN Iter 162180: lr = 0.229702,	loss = 2.527178,	Top-1 err = 0.394727,	Top-5 err = 0.179443,	data_time = 0.050313,	train_time = 0.360100
[2019-08-23 21:57:26,242] TRAIN Iter 162200: lr = 0.229668,	loss = 2.693706,	Top-1 err = 0.398145,	Top-5 err = 0.178760,	data_time = 0.050349,	train_time = 0.754298
[2019-08-23 21:57:43,322] TRAIN Iter 162220: lr = 0.229635,	loss = 2.572115,	Top-1 err = 0.396484,	Top-5 err = 0.178516,	data_time = 0.050780,	train_time = 0.853980
[2019-08-23 21:57:50,204] TRAIN Iter 162240: lr = 0.229602,	loss = 2.656811,	Top-1 err = 0.401074,	Top-5 err = 0.183936,	data_time = 0.050446,	train_time = 0.344081
[2019-08-23 21:58:06,020] TRAIN Iter 162260: lr = 0.229568,	loss = 2.655547,	Top-1 err = 0.404346,	Top-5 err = 0.186523,	data_time = 0.050601,	train_time = 0.790772
[2019-08-23 21:58:22,323] TRAIN Iter 162280: lr = 0.229535,	loss = 2.676289,	Top-1 err = 0.404053,	Top-5 err = 0.186816,	data_time = 0.050814,	train_time = 0.815137
[2019-08-23 21:58:29,473] TRAIN Iter 162300: lr = 0.229502,	loss = 2.611662,	Top-1 err = 0.401416,	Top-5 err = 0.181445,	data_time = 0.050944,	train_time = 0.357510
[2019-08-23 21:58:47,255] TRAIN Iter 162320: lr = 0.229468,	loss = 2.586697,	Top-1 err = 0.399805,	Top-5 err = 0.182275,	data_time = 0.050305,	train_time = 0.889097
[2019-08-23 21:58:54,178] TRAIN Iter 162340: lr = 0.229435,	loss = 2.594343,	Top-1 err = 0.405811,	Top-5 err = 0.185010,	data_time = 0.050670,	train_time = 0.346131
[2019-08-23 21:59:11,254] TRAIN Iter 162360: lr = 0.229402,	loss = 2.584905,	Top-1 err = 0.396533,	Top-5 err = 0.181348,	data_time = 0.050515,	train_time = 0.853780
[2019-08-23 21:59:27,747] TRAIN Iter 162380: lr = 0.229368,	loss = 2.571989,	Top-1 err = 0.395801,	Top-5 err = 0.177197,	data_time = 0.050467,	train_time = 0.824648
[2019-08-23 21:59:34,697] TRAIN Iter 162400: lr = 0.229335,	loss = 2.696968,	Top-1 err = 0.403809,	Top-5 err = 0.181787,	data_time = 0.050452,	train_time = 0.347445
[2019-08-23 21:59:51,941] TRAIN Iter 162420: lr = 0.229302,	loss = 2.599052,	Top-1 err = 0.395703,	Top-5 err = 0.182080,	data_time = 0.050324,	train_time = 0.862230
[2019-08-23 22:00:09,229] TRAIN Iter 162440: lr = 0.229268,	loss = 2.543741,	Top-1 err = 0.400146,	Top-5 err = 0.184766,	data_time = 0.050508,	train_time = 0.864349
[2019-08-23 22:00:16,383] TRAIN Iter 162460: lr = 0.229235,	loss = 2.666334,	Top-1 err = 0.401855,	Top-5 err = 0.183545,	data_time = 0.050214,	train_time = 0.357715
[2019-08-23 22:00:34,643] TRAIN Iter 162480: lr = 0.229202,	loss = 2.621116,	Top-1 err = 0.404541,	Top-5 err = 0.179980,	data_time = 0.050445,	train_time = 0.912964
[2019-08-23 22:00:41,599] TRAIN Iter 162500: lr = 0.229168,	loss = 2.615916,	Top-1 err = 0.397217,	Top-5 err = 0.182324,	data_time = 0.050249,	train_time = 0.347793
[2019-08-23 22:00:58,500] TRAIN Iter 162520: lr = 0.229135,	loss = 2.627002,	Top-1 err = 0.402295,	Top-5 err = 0.184082,	data_time = 0.050680,	train_time = 0.845060
[2019-08-23 22:01:16,833] TRAIN Iter 162540: lr = 0.229102,	loss = 2.689898,	Top-1 err = 0.406104,	Top-5 err = 0.185547,	data_time = 0.050119,	train_time = 0.916596
[2019-08-23 22:01:23,371] TRAIN Iter 162560: lr = 0.229068,	loss = 2.601891,	Top-1 err = 0.402930,	Top-5 err = 0.180713,	data_time = 0.050497,	train_time = 0.326910
[2019-08-23 22:01:41,464] TRAIN Iter 162580: lr = 0.229035,	loss = 2.677894,	Top-1 err = 0.401709,	Top-5 err = 0.188721,	data_time = 0.050197,	train_time = 0.904636
[2019-08-23 22:02:00,757] TRAIN Iter 162600: lr = 0.229002,	loss = 2.671918,	Top-1 err = 0.405664,	Top-5 err = 0.188818,	data_time = 0.050601,	train_time = 0.964651
[2019-08-23 22:02:07,312] TRAIN Iter 162620: lr = 0.228968,	loss = 2.607226,	Top-1 err = 0.394629,	Top-5 err = 0.176514,	data_time = 0.050468,	train_time = 0.327740
[2019-08-23 22:02:26,808] TRAIN Iter 162640: lr = 0.228935,	loss = 2.650242,	Top-1 err = 0.399023,	Top-5 err = 0.185645,	data_time = 0.049883,	train_time = 0.974765
[2019-08-23 22:02:33,239] TRAIN Iter 162660: lr = 0.228902,	loss = 2.594076,	Top-1 err = 0.409033,	Top-5 err = 0.185986,	data_time = 0.050047,	train_time = 0.321539
[2019-08-23 22:02:52,253] TRAIN Iter 162680: lr = 0.228868,	loss = 2.573469,	Top-1 err = 0.397168,	Top-5 err = 0.180420,	data_time = 0.049889,	train_time = 0.950681
[2019-08-23 22:03:45,965] TRAIN Iter 162700: lr = 0.228835,	loss = 2.555405,	Top-1 err = 0.402229,	Top-5 err = 0.186513,	data_time = 0.118142,	train_time = 2.685610
[2019-08-23 22:03:52,561] TRAIN Iter 162720: lr = 0.228802,	loss = 2.578351,	Top-1 err = 0.400879,	Top-5 err = 0.184033,	data_time = 0.050339,	train_time = 0.329769
[2019-08-23 22:04:10,455] TRAIN Iter 162740: lr = 0.228768,	loss = 2.520449,	Top-1 err = 0.394629,	Top-5 err = 0.177686,	data_time = 0.050537,	train_time = 0.894662
[2019-08-23 22:04:18,433] TRAIN Iter 162760: lr = 0.228735,	loss = 2.551224,	Top-1 err = 0.391260,	Top-5 err = 0.175928,	data_time = 0.050948,	train_time = 0.398900
[2019-08-23 22:04:33,380] TRAIN Iter 162780: lr = 0.228702,	loss = 2.585277,	Top-1 err = 0.389697,	Top-5 err = 0.175879,	data_time = 0.050770,	train_time = 0.747312
[2019-08-23 22:04:48,442] TRAIN Iter 162800: lr = 0.228668,	loss = 2.557961,	Top-1 err = 0.393115,	Top-5 err = 0.179834,	data_time = 0.050246,	train_time = 0.753094
[2019-08-23 22:04:55,608] TRAIN Iter 162820: lr = 0.228635,	loss = 2.584004,	Top-1 err = 0.386621,	Top-5 err = 0.175146,	data_time = 0.050621,	train_time = 0.358280
[2019-08-23 22:05:11,646] TRAIN Iter 162840: lr = 0.228602,	loss = 2.635754,	Top-1 err = 0.394385,	Top-5 err = 0.175537,	data_time = 0.050408,	train_time = 0.801918
[2019-08-23 22:05:27,959] TRAIN Iter 162860: lr = 0.228568,	loss = 2.565263,	Top-1 err = 0.390381,	Top-5 err = 0.176758,	data_time = 0.127320,	train_time = 0.815647
[2019-08-23 22:05:34,767] TRAIN Iter 162880: lr = 0.228535,	loss = 2.622669,	Top-1 err = 0.387988,	Top-5 err = 0.173828,	data_time = 0.050363,	train_time = 0.340372
[2019-08-23 22:05:51,389] TRAIN Iter 162900: lr = 0.228502,	loss = 2.548411,	Top-1 err = 0.393506,	Top-5 err = 0.174170,	data_time = 0.050207,	train_time = 0.831067
[2019-08-23 22:05:59,543] TRAIN Iter 162920: lr = 0.228468,	loss = 2.577010,	Top-1 err = 0.389307,	Top-5 err = 0.176514,	data_time = 0.050485,	train_time = 0.407721
[2019-08-23 22:06:14,457] TRAIN Iter 162940: lr = 0.228435,	loss = 2.625205,	Top-1 err = 0.404639,	Top-5 err = 0.182666,	data_time = 0.050345,	train_time = 0.745642
[2019-08-23 22:06:30,514] TRAIN Iter 162960: lr = 0.228402,	loss = 2.605094,	Top-1 err = 0.398389,	Top-5 err = 0.179199,	data_time = 0.050347,	train_time = 0.802850
[2019-08-23 22:06:38,238] TRAIN Iter 162980: lr = 0.228368,	loss = 2.666724,	Top-1 err = 0.397363,	Top-5 err = 0.179639,	data_time = 0.050621,	train_time = 0.386172
[2019-08-23 22:06:53,277] TRAIN Iter 163000: lr = 0.228335,	loss = 2.536007,	Top-1 err = 0.395752,	Top-5 err = 0.177588,	data_time = 0.050348,	train_time = 0.751954
[2019-08-23 22:07:10,015] TRAIN Iter 163020: lr = 0.228302,	loss = 2.591900,	Top-1 err = 0.395801,	Top-5 err = 0.177100,	data_time = 0.050308,	train_time = 0.836905
[2019-08-23 22:07:17,093] TRAIN Iter 163040: lr = 0.228268,	loss = 2.644114,	Top-1 err = 0.393799,	Top-5 err = 0.179785,	data_time = 0.050842,	train_time = 0.353862
[2019-08-23 22:07:32,865] TRAIN Iter 163060: lr = 0.228235,	loss = 2.609441,	Top-1 err = 0.392676,	Top-5 err = 0.177051,	data_time = 0.050749,	train_time = 0.788595
[2019-08-23 22:07:40,661] TRAIN Iter 163080: lr = 0.228202,	loss = 2.622617,	Top-1 err = 0.389307,	Top-5 err = 0.176807,	data_time = 0.050481,	train_time = 0.389797
[2019-08-23 22:07:54,045] TRAIN Iter 163100: lr = 0.228168,	loss = 2.693424,	Top-1 err = 0.393750,	Top-5 err = 0.182861,	data_time = 0.050797,	train_time = 0.669154
[2019-08-23 22:08:09,615] TRAIN Iter 163120: lr = 0.228135,	loss = 2.577691,	Top-1 err = 0.400830,	Top-5 err = 0.183105,	data_time = 0.050778,	train_time = 0.778516
[2019-08-23 22:08:16,890] TRAIN Iter 163140: lr = 0.228102,	loss = 2.699416,	Top-1 err = 0.394824,	Top-5 err = 0.177148,	data_time = 0.114257,	train_time = 0.363738
[2019-08-23 22:08:31,784] TRAIN Iter 163160: lr = 0.228068,	loss = 2.658307,	Top-1 err = 0.398828,	Top-5 err = 0.176904,	data_time = 0.050471,	train_time = 0.744687
[2019-08-23 22:08:50,148] TRAIN Iter 163180: lr = 0.228035,	loss = 2.593166,	Top-1 err = 0.397217,	Top-5 err = 0.181982,	data_time = 0.050387,	train_time = 0.918153
[2019-08-23 22:08:56,895] TRAIN Iter 163200: lr = 0.228002,	loss = 2.597334,	Top-1 err = 0.393555,	Top-5 err = 0.182275,	data_time = 0.050545,	train_time = 0.337351
[2019-08-23 22:09:12,311] TRAIN Iter 163220: lr = 0.227968,	loss = 2.624722,	Top-1 err = 0.395508,	Top-5 err = 0.180420,	data_time = 0.050394,	train_time = 0.770813
[2019-08-23 22:09:19,915] TRAIN Iter 163240: lr = 0.227935,	loss = 2.635421,	Top-1 err = 0.397168,	Top-5 err = 0.183008,	data_time = 0.050516,	train_time = 0.380144
[2019-08-23 22:09:35,276] TRAIN Iter 163260: lr = 0.227902,	loss = 2.604954,	Top-1 err = 0.399951,	Top-5 err = 0.181982,	data_time = 0.050643,	train_time = 0.768036
[2019-08-23 22:09:51,700] TRAIN Iter 163280: lr = 0.227868,	loss = 2.618517,	Top-1 err = 0.405615,	Top-5 err = 0.182373,	data_time = 0.050262,	train_time = 0.821212
[2019-08-23 22:09:58,712] TRAIN Iter 163300: lr = 0.227835,	loss = 2.637343,	Top-1 err = 0.395850,	Top-5 err = 0.183203,	data_time = 0.050388,	train_time = 0.350603
[2019-08-23 22:10:15,592] TRAIN Iter 163320: lr = 0.227802,	loss = 2.617109,	Top-1 err = 0.398047,	Top-5 err = 0.180371,	data_time = 0.050693,	train_time = 0.843985
[2019-08-23 22:10:33,270] TRAIN Iter 163340: lr = 0.227768,	loss = 2.593764,	Top-1 err = 0.398145,	Top-5 err = 0.180615,	data_time = 0.050494,	train_time = 0.883858
[2019-08-23 22:10:40,440] TRAIN Iter 163360: lr = 0.227735,	loss = 2.616803,	Top-1 err = 0.403564,	Top-5 err = 0.182324,	data_time = 0.050737,	train_time = 0.358492
[2019-08-23 22:10:55,959] TRAIN Iter 163380: lr = 0.227702,	loss = 2.573818,	Top-1 err = 0.396680,	Top-5 err = 0.179785,	data_time = 0.050893,	train_time = 0.775936
[2019-08-23 22:11:03,618] TRAIN Iter 163400: lr = 0.227668,	loss = 2.646663,	Top-1 err = 0.398438,	Top-5 err = 0.180273,	data_time = 0.050308,	train_time = 0.382920
[2019-08-23 22:11:19,419] TRAIN Iter 163420: lr = 0.227635,	loss = 2.641771,	Top-1 err = 0.399463,	Top-5 err = 0.181738,	data_time = 0.050438,	train_time = 0.790038
[2019-08-23 22:11:36,655] TRAIN Iter 163440: lr = 0.227602,	loss = 2.552959,	Top-1 err = 0.394629,	Top-5 err = 0.181738,	data_time = 0.050260,	train_time = 0.861816
[2019-08-23 22:11:43,507] TRAIN Iter 163460: lr = 0.227568,	loss = 2.646973,	Top-1 err = 0.399902,	Top-5 err = 0.186377,	data_time = 0.050741,	train_time = 0.342563
[2019-08-23 22:12:00,063] TRAIN Iter 163480: lr = 0.227535,	loss = 2.580615,	Top-1 err = 0.397852,	Top-5 err = 0.182959,	data_time = 0.050332,	train_time = 0.827804
[2019-08-23 22:12:16,778] TRAIN Iter 163500: lr = 0.227502,	loss = 2.693894,	Top-1 err = 0.402148,	Top-5 err = 0.182715,	data_time = 0.050437,	train_time = 0.835705
[2019-08-23 22:12:23,352] TRAIN Iter 163520: lr = 0.227468,	loss = 2.548928,	Top-1 err = 0.401172,	Top-5 err = 0.177734,	data_time = 0.050287,	train_time = 0.328695
[2019-08-23 22:12:40,764] TRAIN Iter 163540: lr = 0.227435,	loss = 2.676398,	Top-1 err = 0.396338,	Top-5 err = 0.176904,	data_time = 0.050483,	train_time = 0.870603
[2019-08-23 22:12:47,703] TRAIN Iter 163560: lr = 0.227402,	loss = 2.650012,	Top-1 err = 0.399414,	Top-5 err = 0.180029,	data_time = 0.050709,	train_time = 0.346959
[2019-08-23 22:13:04,857] TRAIN Iter 163580: lr = 0.227368,	loss = 2.651015,	Top-1 err = 0.403125,	Top-5 err = 0.184082,	data_time = 0.050882,	train_time = 0.857685
[2019-08-23 22:13:21,442] TRAIN Iter 163600: lr = 0.227335,	loss = 2.638945,	Top-1 err = 0.403027,	Top-5 err = 0.185010,	data_time = 0.050146,	train_time = 0.829215
[2019-08-23 22:13:28,463] TRAIN Iter 163620: lr = 0.227302,	loss = 2.626666,	Top-1 err = 0.395752,	Top-5 err = 0.180420,	data_time = 0.148082,	train_time = 0.351034
[2019-08-23 22:13:44,757] TRAIN Iter 163640: lr = 0.227268,	loss = 2.681358,	Top-1 err = 0.400684,	Top-5 err = 0.182324,	data_time = 0.050458,	train_time = 0.814683
[2019-08-23 22:14:01,672] TRAIN Iter 163660: lr = 0.227235,	loss = 2.539114,	Top-1 err = 0.398486,	Top-5 err = 0.181250,	data_time = 0.136242,	train_time = 0.845743
[2019-08-23 22:14:08,483] TRAIN Iter 163680: lr = 0.227202,	loss = 2.624420,	Top-1 err = 0.405957,	Top-5 err = 0.184277,	data_time = 0.050909,	train_time = 0.340528
[2019-08-23 22:14:23,911] TRAIN Iter 163700: lr = 0.227168,	loss = 2.627602,	Top-1 err = 0.399219,	Top-5 err = 0.181299,	data_time = 0.050464,	train_time = 0.771373
[2019-08-23 22:14:30,665] TRAIN Iter 163720: lr = 0.227135,	loss = 2.667534,	Top-1 err = 0.407617,	Top-5 err = 0.186963,	data_time = 0.050378,	train_time = 0.337724
[2019-08-23 22:14:49,840] TRAIN Iter 163740: lr = 0.227102,	loss = 2.580535,	Top-1 err = 0.397656,	Top-5 err = 0.185010,	data_time = 0.050594,	train_time = 0.958704
[2019-08-23 22:15:06,917] TRAIN Iter 163760: lr = 0.227068,	loss = 2.585915,	Top-1 err = 0.402100,	Top-5 err = 0.181592,	data_time = 0.050305,	train_time = 0.853856
[2019-08-23 22:15:13,593] TRAIN Iter 163780: lr = 0.227035,	loss = 2.636613,	Top-1 err = 0.391992,	Top-5 err = 0.175781,	data_time = 0.050543,	train_time = 0.333763
[2019-08-23 22:15:33,118] TRAIN Iter 163800: lr = 0.227002,	loss = 2.695012,	Top-1 err = 0.405371,	Top-5 err = 0.184082,	data_time = 0.050467,	train_time = 0.976258
[2019-08-23 22:15:50,775] TRAIN Iter 163820: lr = 0.226968,	loss = 2.645962,	Top-1 err = 0.395020,	Top-5 err = 0.177832,	data_time = 0.050403,	train_time = 0.882828
[2019-08-23 22:15:57,510] TRAIN Iter 163840: lr = 0.226935,	loss = 2.571650,	Top-1 err = 0.398535,	Top-5 err = 0.179004,	data_time = 0.050340,	train_time = 0.336765
[2019-08-23 22:16:17,315] TRAIN Iter 163860: lr = 0.226902,	loss = 2.639133,	Top-1 err = 0.395459,	Top-5 err = 0.183936,	data_time = 0.050545,	train_time = 0.990208
[2019-08-23 22:16:24,353] TRAIN Iter 163880: lr = 0.226868,	loss = 2.611293,	Top-1 err = 0.392676,	Top-5 err = 0.178906,	data_time = 0.050725,	train_time = 0.351900
[2019-08-23 22:16:41,462] TRAIN Iter 163900: lr = 0.226835,	loss = 2.627508,	Top-1 err = 0.402637,	Top-5 err = 0.180322,	data_time = 0.050023,	train_time = 0.855444
[2019-08-23 22:16:58,938] TRAIN Iter 163920: lr = 0.226802,	loss = 2.648685,	Top-1 err = 0.401709,	Top-5 err = 0.182568,	data_time = 0.049911,	train_time = 0.873762
[2019-08-23 22:17:05,127] TRAIN Iter 163940: lr = 0.226768,	loss = 2.666465,	Top-1 err = 0.397510,	Top-5 err = 0.179492,	data_time = 0.049899,	train_time = 0.309430
[2019-08-23 22:17:57,254] TRAIN Iter 163960: lr = 0.226735,	loss = 2.602164,	Top-1 err = 0.407296,	Top-5 err = 0.183795,	data_time = 0.050457,	train_time = 2.606348
[2019-08-23 22:18:04,434] TRAIN Iter 163980: lr = 0.226702,	loss = 2.597306,	Top-1 err = 0.390039,	Top-5 err = 0.181396,	data_time = 0.050504,	train_time = 0.358968
[2019-08-23 22:18:21,099] TRAIN Iter 164000: lr = 0.226668,	loss = 2.515355,	Top-1 err = 0.393701,	Top-5 err = 0.176025,	data_time = 0.050305,	train_time = 0.833265
[2019-08-23 22:18:38,104] TRAIN Iter 164020: lr = 0.226635,	loss = 2.596368,	Top-1 err = 0.391504,	Top-5 err = 0.172461,	data_time = 0.050456,	train_time = 0.850222
[2019-08-23 22:18:45,694] TRAIN Iter 164040: lr = 0.226602,	loss = 2.527296,	Top-1 err = 0.390186,	Top-5 err = 0.174268,	data_time = 0.050499,	train_time = 0.379501
[2019-08-23 22:18:59,790] TRAIN Iter 164060: lr = 0.226568,	loss = 2.627705,	Top-1 err = 0.389697,	Top-5 err = 0.175000,	data_time = 0.050856,	train_time = 0.704781
[2019-08-23 22:19:13,932] TRAIN Iter 164080: lr = 0.226535,	loss = 2.563573,	Top-1 err = 0.387646,	Top-5 err = 0.172705,	data_time = 0.050711,	train_time = 0.707095
[2019-08-23 22:19:21,404] TRAIN Iter 164100: lr = 0.226502,	loss = 2.523967,	Top-1 err = 0.394238,	Top-5 err = 0.179199,	data_time = 0.050324,	train_time = 0.373549
[2019-08-23 22:19:39,039] TRAIN Iter 164120: lr = 0.226468,	loss = 2.688852,	Top-1 err = 0.391846,	Top-5 err = 0.174951,	data_time = 0.050319,	train_time = 0.881773
[2019-08-23 22:19:46,835] TRAIN Iter 164140: lr = 0.226435,	loss = 2.654425,	Top-1 err = 0.396289,	Top-5 err = 0.181250,	data_time = 0.050323,	train_time = 0.389762
[2019-08-23 22:20:01,535] TRAIN Iter 164160: lr = 0.226402,	loss = 2.797740,	Top-1 err = 0.399268,	Top-5 err = 0.175928,	data_time = 0.050138,	train_time = 0.734994
[2019-08-23 22:20:16,929] TRAIN Iter 164180: lr = 0.226368,	loss = 2.687685,	Top-1 err = 0.392090,	Top-5 err = 0.174463,	data_time = 0.050355,	train_time = 0.769692
[2019-08-23 22:20:23,882] TRAIN Iter 164200: lr = 0.226335,	loss = 2.540737,	Top-1 err = 0.396387,	Top-5 err = 0.177051,	data_time = 0.050466,	train_time = 0.347640
[2019-08-23 22:20:39,429] TRAIN Iter 164220: lr = 0.226302,	loss = 2.663115,	Top-1 err = 0.390381,	Top-5 err = 0.177930,	data_time = 0.050340,	train_time = 0.777319
[2019-08-23 22:20:55,178] TRAIN Iter 164240: lr = 0.226268,	loss = 2.675822,	Top-1 err = 0.401709,	Top-5 err = 0.181445,	data_time = 0.050867,	train_time = 0.787428
[2019-08-23 22:21:01,863] TRAIN Iter 164260: lr = 0.226235,	loss = 2.761320,	Top-1 err = 0.396973,	Top-5 err = 0.178662,	data_time = 0.050429,	train_time = 0.334228
[2019-08-23 22:21:17,621] TRAIN Iter 164280: lr = 0.226202,	loss = 2.462104,	Top-1 err = 0.392139,	Top-5 err = 0.175586,	data_time = 0.050608,	train_time = 0.787897
[2019-08-23 22:21:24,416] TRAIN Iter 164300: lr = 0.226168,	loss = 2.610485,	Top-1 err = 0.393750,	Top-5 err = 0.182324,	data_time = 0.050623,	train_time = 0.339745
[2019-08-23 22:21:42,694] TRAIN Iter 164320: lr = 0.226135,	loss = 2.525402,	Top-1 err = 0.392627,	Top-5 err = 0.177930,	data_time = 0.050401,	train_time = 0.913879
[2019-08-23 22:21:59,775] TRAIN Iter 164340: lr = 0.226102,	loss = 2.644113,	Top-1 err = 0.398682,	Top-5 err = 0.180273,	data_time = 0.050524,	train_time = 0.854026
[2019-08-23 22:22:06,981] TRAIN Iter 164360: lr = 0.226068,	loss = 2.621080,	Top-1 err = 0.396582,	Top-5 err = 0.178027,	data_time = 0.050377,	train_time = 0.360281
[2019-08-23 22:22:20,966] TRAIN Iter 164380: lr = 0.226035,	loss = 2.554240,	Top-1 err = 0.394482,	Top-5 err = 0.177783,	data_time = 0.050283,	train_time = 0.699270
[2019-08-23 22:22:38,586] TRAIN Iter 164400: lr = 0.226002,	loss = 2.598002,	Top-1 err = 0.400635,	Top-5 err = 0.182275,	data_time = 0.128528,	train_time = 0.880958
[2019-08-23 22:22:45,408] TRAIN Iter 164420: lr = 0.225968,	loss = 2.570755,	Top-1 err = 0.401416,	Top-5 err = 0.179932,	data_time = 0.050532,	train_time = 0.341124
[2019-08-23 22:23:01,733] TRAIN Iter 164440: lr = 0.225935,	loss = 2.562648,	Top-1 err = 0.397461,	Top-5 err = 0.180664,	data_time = 0.050509,	train_time = 0.816230
[2019-08-23 22:23:09,050] TRAIN Iter 164460: lr = 0.225902,	loss = 2.603161,	Top-1 err = 0.395703,	Top-5 err = 0.177783,	data_time = 0.050530,	train_time = 0.365811
[2019-08-23 22:23:24,761] TRAIN Iter 164480: lr = 0.225868,	loss = 2.627239,	Top-1 err = 0.393994,	Top-5 err = 0.172852,	data_time = 0.050479,	train_time = 0.785545
[2019-08-23 22:23:41,138] TRAIN Iter 164500: lr = 0.225835,	loss = 2.635388,	Top-1 err = 0.393262,	Top-5 err = 0.176367,	data_time = 0.050754,	train_time = 0.818822
[2019-08-23 22:23:48,729] TRAIN Iter 164520: lr = 0.225802,	loss = 2.600148,	Top-1 err = 0.387939,	Top-5 err = 0.173291,	data_time = 0.050760,	train_time = 0.379567
[2019-08-23 22:24:03,267] TRAIN Iter 164540: lr = 0.225768,	loss = 2.616650,	Top-1 err = 0.398584,	Top-5 err = 0.183252,	data_time = 0.050359,	train_time = 0.726871
[2019-08-23 22:24:20,833] TRAIN Iter 164560: lr = 0.225735,	loss = 2.624236,	Top-1 err = 0.396387,	Top-5 err = 0.181885,	data_time = 0.050540,	train_time = 0.878300
[2019-08-23 22:24:28,060] TRAIN Iter 164580: lr = 0.225702,	loss = 2.676041,	Top-1 err = 0.389209,	Top-5 err = 0.176855,	data_time = 0.050425,	train_time = 0.361305
[2019-08-23 22:24:42,366] TRAIN Iter 164600: lr = 0.225668,	loss = 2.595976,	Top-1 err = 0.395410,	Top-5 err = 0.180322,	data_time = 0.050595,	train_time = 0.715330
[2019-08-23 22:24:50,022] TRAIN Iter 164620: lr = 0.225635,	loss = 2.468649,	Top-1 err = 0.393896,	Top-5 err = 0.180762,	data_time = 0.050481,	train_time = 0.382758
[2019-08-23 22:25:06,240] TRAIN Iter 164640: lr = 0.225602,	loss = 2.483634,	Top-1 err = 0.396631,	Top-5 err = 0.180762,	data_time = 0.117245,	train_time = 0.810879
[2019-08-23 22:25:22,503] TRAIN Iter 164660: lr = 0.225568,	loss = 2.674815,	Top-1 err = 0.395801,	Top-5 err = 0.179688,	data_time = 0.050858,	train_time = 0.813134
[2019-08-23 22:25:30,547] TRAIN Iter 164680: lr = 0.225535,	loss = 2.571981,	Top-1 err = 0.398291,	Top-5 err = 0.185352,	data_time = 0.050822,	train_time = 0.402188
[2019-08-23 22:25:45,933] TRAIN Iter 164700: lr = 0.225502,	loss = 2.614801,	Top-1 err = 0.401416,	Top-5 err = 0.180371,	data_time = 0.050778,	train_time = 0.769317
[2019-08-23 22:26:01,245] TRAIN Iter 164720: lr = 0.225468,	loss = 2.672938,	Top-1 err = 0.396777,	Top-5 err = 0.178174,	data_time = 0.050646,	train_time = 0.765542
[2019-08-23 22:26:08,205] TRAIN Iter 164740: lr = 0.225435,	loss = 2.709807,	Top-1 err = 0.396387,	Top-5 err = 0.182959,	data_time = 0.050725,	train_time = 0.348023
[2019-08-23 22:26:24,475] TRAIN Iter 164760: lr = 0.225402,	loss = 2.533270,	Top-1 err = 0.395313,	Top-5 err = 0.180322,	data_time = 0.050498,	train_time = 0.813464
[2019-08-23 22:26:32,257] TRAIN Iter 164780: lr = 0.225368,	loss = 2.556994,	Top-1 err = 0.403125,	Top-5 err = 0.179590,	data_time = 0.050288,	train_time = 0.389117
[2019-08-23 22:26:47,228] TRAIN Iter 164800: lr = 0.225335,	loss = 2.666741,	Top-1 err = 0.399561,	Top-5 err = 0.179492,	data_time = 0.050400,	train_time = 0.748499
[2019-08-23 22:27:04,144] TRAIN Iter 164820: lr = 0.225302,	loss = 2.637895,	Top-1 err = 0.396045,	Top-5 err = 0.181543,	data_time = 0.050553,	train_time = 0.845819
[2019-08-23 22:27:11,992] TRAIN Iter 164840: lr = 0.225268,	loss = 2.574516,	Top-1 err = 0.399023,	Top-5 err = 0.179883,	data_time = 0.050497,	train_time = 0.392373
[2019-08-23 22:27:25,720] TRAIN Iter 164860: lr = 0.225235,	loss = 2.659194,	Top-1 err = 0.399854,	Top-5 err = 0.181348,	data_time = 0.050495,	train_time = 0.686362
[2019-08-23 22:27:42,079] TRAIN Iter 164880: lr = 0.225202,	loss = 2.591970,	Top-1 err = 0.402734,	Top-5 err = 0.180469,	data_time = 0.050551,	train_time = 0.817964
[2019-08-23 22:27:49,365] TRAIN Iter 164900: lr = 0.225168,	loss = 2.640993,	Top-1 err = 0.402295,	Top-5 err = 0.184131,	data_time = 0.050677,	train_time = 0.364283
[2019-08-23 22:28:04,856] TRAIN Iter 164920: lr = 0.225135,	loss = 2.611872,	Top-1 err = 0.400146,	Top-5 err = 0.178613,	data_time = 0.050417,	train_time = 0.774535
[2019-08-23 22:28:12,736] TRAIN Iter 164940: lr = 0.225102,	loss = 2.644902,	Top-1 err = 0.395605,	Top-5 err = 0.179883,	data_time = 0.050509,	train_time = 0.393988
[2019-08-23 22:28:27,755] TRAIN Iter 164960: lr = 0.225068,	loss = 2.606314,	Top-1 err = 0.392920,	Top-5 err = 0.180273,	data_time = 0.132539,	train_time = 0.750939
[2019-08-23 22:28:43,999] TRAIN Iter 164980: lr = 0.225035,	loss = 2.642040,	Top-1 err = 0.400195,	Top-5 err = 0.183789,	data_time = 0.050524,	train_time = 0.812166
[2019-08-23 22:28:51,460] TRAIN Iter 165000: lr = 0.225002,	loss = 2.690022,	Top-1 err = 0.399951,	Top-5 err = 0.185449,	data_time = 0.144055,	train_time = 0.373047
[2019-08-23 22:29:08,675] TRAIN Iter 165020: lr = 0.224968,	loss = 2.688729,	Top-1 err = 0.396582,	Top-5 err = 0.182617,	data_time = 0.050373,	train_time = 0.860744
[2019-08-23 22:29:25,324] TRAIN Iter 165040: lr = 0.224935,	loss = 2.512360,	Top-1 err = 0.396045,	Top-5 err = 0.176709,	data_time = 0.050644,	train_time = 0.832415
[2019-08-23 22:29:32,850] TRAIN Iter 165060: lr = 0.224902,	loss = 2.553847,	Top-1 err = 0.400928,	Top-5 err = 0.181055,	data_time = 0.050634,	train_time = 0.376292
[2019-08-23 22:29:48,082] TRAIN Iter 165080: lr = 0.224868,	loss = 2.601029,	Top-1 err = 0.396924,	Top-5 err = 0.179980,	data_time = 0.050666,	train_time = 0.761587
[2019-08-23 22:29:56,269] TRAIN Iter 165100: lr = 0.224835,	loss = 2.620669,	Top-1 err = 0.405420,	Top-5 err = 0.185205,	data_time = 0.050657,	train_time = 0.409327
[2019-08-23 22:30:11,538] TRAIN Iter 165120: lr = 0.224802,	loss = 2.777108,	Top-1 err = 0.402393,	Top-5 err = 0.184570,	data_time = 0.050169,	train_time = 0.763463
[2019-08-23 22:30:27,525] TRAIN Iter 165140: lr = 0.224768,	loss = 2.659683,	Top-1 err = 0.397363,	Top-5 err = 0.181104,	data_time = 0.050088,	train_time = 0.799335
[2019-08-23 22:30:34,888] TRAIN Iter 165160: lr = 0.224735,	loss = 2.637915,	Top-1 err = 0.398975,	Top-5 err = 0.178564,	data_time = 0.050142,	train_time = 0.368145
[2019-08-23 22:30:50,532] TRAIN Iter 165180: lr = 0.224702,	loss = 2.583519,	Top-1 err = 0.402002,	Top-5 err = 0.183887,	data_time = 0.049981,	train_time = 0.782175
[2019-08-23 22:31:03,780] TRAIN Iter 165200: lr = 0.224668,	loss = 3.099003,	Top-1 err = 0.401569,	Top-5 err = 0.184593,	data_time = 0.007083,	train_time = 0.662394
[2019-08-23 22:31:49,579] TRAIN Iter 165220: lr = 0.224635,	loss = 2.581229,	Top-1 err = 0.394531,	Top-5 err = 0.177686,	data_time = 0.050838,	train_time = 2.289907
[2019-08-23 22:32:03,311] TRAIN Iter 165240: lr = 0.224602,	loss = 2.688456,	Top-1 err = 0.394629,	Top-5 err = 0.177393,	data_time = 0.050408,	train_time = 0.686581
[2019-08-23 22:32:12,279] TRAIN Iter 165260: lr = 0.224568,	loss = 2.483893,	Top-1 err = 0.383301,	Top-5 err = 0.170605,	data_time = 0.050560,	train_time = 0.448374
[2019-08-23 22:32:25,416] TRAIN Iter 165280: lr = 0.224535,	loss = 2.542564,	Top-1 err = 0.387402,	Top-5 err = 0.173340,	data_time = 0.050815,	train_time = 0.656841
[2019-08-23 22:32:37,375] TRAIN Iter 165300: lr = 0.224502,	loss = 2.476942,	Top-1 err = 0.393457,	Top-5 err = 0.176709,	data_time = 2.665987,	train_time = 0.597953
[2019-08-23 22:32:45,657] TRAIN Iter 165320: lr = 0.224468,	loss = 2.671931,	Top-1 err = 0.390967,	Top-5 err = 0.168945,	data_time = 0.050514,	train_time = 0.414096
[2019-08-23 22:33:02,564] TRAIN Iter 165340: lr = 0.224435,	loss = 2.557933,	Top-1 err = 0.386182,	Top-5 err = 0.172070,	data_time = 0.050374,	train_time = 0.845339
[2019-08-23 22:33:09,863] TRAIN Iter 165360: lr = 0.224402,	loss = 2.515814,	Top-1 err = 0.397070,	Top-5 err = 0.179590,	data_time = 0.156512,	train_time = 0.364921
[2019-08-23 22:33:25,452] TRAIN Iter 165380: lr = 0.224368,	loss = 2.583920,	Top-1 err = 0.399023,	Top-5 err = 0.180713,	data_time = 0.050698,	train_time = 0.779450
[2019-08-23 22:33:38,512] TRAIN Iter 165400: lr = 0.224335,	loss = 2.617311,	Top-1 err = 0.388721,	Top-5 err = 0.175342,	data_time = 2.858944,	train_time = 0.652974
[2019-08-23 22:33:45,847] TRAIN Iter 165420: lr = 0.224302,	loss = 2.594507,	Top-1 err = 0.388135,	Top-5 err = 0.177979,	data_time = 0.050480,	train_time = 0.366713
[2019-08-23 22:34:00,834] TRAIN Iter 165440: lr = 0.224268,	loss = 2.542418,	Top-1 err = 0.396680,	Top-5 err = 0.177490,	data_time = 0.050358,	train_time = 0.749339
[2019-08-23 22:34:10,381] TRAIN Iter 165460: lr = 0.224235,	loss = 2.649038,	Top-1 err = 0.393311,	Top-5 err = 0.177344,	data_time = 0.050262,	train_time = 0.477360
[2019-08-23 22:34:23,084] TRAIN Iter 165480: lr = 0.224202,	loss = 2.658350,	Top-1 err = 0.390479,	Top-5 err = 0.172949,	data_time = 0.050490,	train_time = 0.635107
[2019-08-23 22:34:36,413] TRAIN Iter 165500: lr = 0.224168,	loss = 2.630680,	Top-1 err = 0.392920,	Top-5 err = 0.174658,	data_time = 0.050290,	train_time = 0.666472
[2019-08-23 22:34:43,712] TRAIN Iter 165520: lr = 0.224135,	loss = 2.636388,	Top-1 err = 0.389893,	Top-5 err = 0.178125,	data_time = 0.050408,	train_time = 0.364901
[2019-08-23 22:34:57,880] TRAIN Iter 165540: lr = 0.224102,	loss = 2.654979,	Top-1 err = 0.391699,	Top-5 err = 0.181885,	data_time = 0.050488,	train_time = 0.708411
[2019-08-23 22:35:13,159] TRAIN Iter 165560: lr = 0.224068,	loss = 2.576300,	Top-1 err = 0.391699,	Top-5 err = 0.177002,	data_time = 0.648528,	train_time = 0.763934
[2019-08-23 22:35:20,407] TRAIN Iter 165580: lr = 0.224035,	loss = 2.635706,	Top-1 err = 0.393604,	Top-5 err = 0.176465,	data_time = 0.050475,	train_time = 0.362363
[2019-08-23 22:35:34,413] TRAIN Iter 165600: lr = 0.224002,	loss = 2.607796,	Top-1 err = 0.390332,	Top-5 err = 0.177295,	data_time = 0.050303,	train_time = 0.700321
[2019-08-23 22:35:48,549] TRAIN Iter 165620: lr = 0.223968,	loss = 2.636275,	Top-1 err = 0.400000,	Top-5 err = 0.182422,	data_time = 0.050374,	train_time = 0.706770
[2019-08-23 22:35:57,430] TRAIN Iter 165640: lr = 0.223935,	loss = 2.581455,	Top-1 err = 0.390283,	Top-5 err = 0.176270,	data_time = 0.050502,	train_time = 0.444014
[2019-08-23 22:36:11,564] TRAIN Iter 165660: lr = 0.223902,	loss = 2.679022,	Top-1 err = 0.390430,	Top-5 err = 0.176123,	data_time = 0.050421,	train_time = 0.706684
[2019-08-23 22:36:18,691] TRAIN Iter 165680: lr = 0.223868,	loss = 2.594309,	Top-1 err = 0.398340,	Top-5 err = 0.181055,	data_time = 0.159490,	train_time = 0.356333
[2019-08-23 22:36:35,258] TRAIN Iter 165700: lr = 0.223835,	loss = 2.532560,	Top-1 err = 0.397949,	Top-5 err = 0.177002,	data_time = 0.051064,	train_time = 0.828363
[2019-08-23 22:36:48,523] TRAIN Iter 165720: lr = 0.223802,	loss = 2.643673,	Top-1 err = 0.396631,	Top-5 err = 0.178076,	data_time = 0.050855,	train_time = 0.663240
[2019-08-23 22:36:55,498] TRAIN Iter 165740: lr = 0.223768,	loss = 2.627073,	Top-1 err = 0.396289,	Top-5 err = 0.175928,	data_time = 0.143620,	train_time = 0.348722
[2019-08-23 22:37:09,771] TRAIN Iter 165760: lr = 0.223735,	loss = 2.714205,	Top-1 err = 0.396729,	Top-5 err = 0.184814,	data_time = 0.050463,	train_time = 0.713651
[2019-08-23 22:37:23,989] TRAIN Iter 165780: lr = 0.223702,	loss = 2.681030,	Top-1 err = 0.397363,	Top-5 err = 0.180420,	data_time = 0.050481,	train_time = 0.710896
[2019-08-23 22:37:32,610] TRAIN Iter 165800: lr = 0.223668,	loss = 2.606101,	Top-1 err = 0.398291,	Top-5 err = 0.180664,	data_time = 0.050285,	train_time = 0.430999
[2019-08-23 22:37:47,896] TRAIN Iter 165820: lr = 0.223635,	loss = 2.674079,	Top-1 err = 0.398633,	Top-5 err = 0.183252,	data_time = 0.050625,	train_time = 0.764300
[2019-08-23 22:37:54,998] TRAIN Iter 165840: lr = 0.223602,	loss = 2.570911,	Top-1 err = 0.390820,	Top-5 err = 0.174463,	data_time = 0.050466,	train_time = 0.355093
[2019-08-23 22:38:11,248] TRAIN Iter 165860: lr = 0.223568,	loss = 2.580099,	Top-1 err = 0.393311,	Top-5 err = 0.174707,	data_time = 0.050781,	train_time = 0.812490
[2019-08-23 22:38:27,780] TRAIN Iter 165880: lr = 0.223535,	loss = 2.634767,	Top-1 err = 0.401807,	Top-5 err = 0.183154,	data_time = 0.517421,	train_time = 0.826565
[2019-08-23 22:38:34,830] TRAIN Iter 165900: lr = 0.223502,	loss = 2.489127,	Top-1 err = 0.396875,	Top-5 err = 0.179883,	data_time = 0.050691,	train_time = 0.352465
[2019-08-23 22:38:50,155] TRAIN Iter 165920: lr = 0.223468,	loss = 2.583731,	Top-1 err = 0.397070,	Top-5 err = 0.177979,	data_time = 0.050533,	train_time = 0.766235
[2019-08-23 22:39:05,309] TRAIN Iter 165940: lr = 0.223435,	loss = 2.582019,	Top-1 err = 0.399219,	Top-5 err = 0.181055,	data_time = 0.126064,	train_time = 0.757704
[2019-08-23 22:39:15,240] TRAIN Iter 165960: lr = 0.223402,	loss = 2.598180,	Top-1 err = 0.389502,	Top-5 err = 0.178271,	data_time = 0.050585,	train_time = 0.496545
[2019-08-23 22:39:33,238] TRAIN Iter 165980: lr = 0.223368,	loss = 2.582887,	Top-1 err = 0.393945,	Top-5 err = 0.181543,	data_time = 0.050762,	train_time = 0.899854
[2019-08-23 22:39:42,418] TRAIN Iter 166000: lr = 0.223335,	loss = 2.654309,	Top-1 err = 0.402051,	Top-5 err = 0.180811,	data_time = 0.050614,	train_time = 0.458980
[2019-08-23 22:39:53,612] TRAIN Iter 166020: lr = 0.223302,	loss = 2.606180,	Top-1 err = 0.396045,	Top-5 err = 0.180322,	data_time = 0.050894,	train_time = 0.559708
[2019-08-23 22:40:07,901] TRAIN Iter 166040: lr = 0.223268,	loss = 2.662890,	Top-1 err = 0.399805,	Top-5 err = 0.184033,	data_time = 0.050415,	train_time = 0.714419
[2019-08-23 22:40:15,074] TRAIN Iter 166060: lr = 0.223235,	loss = 2.609028,	Top-1 err = 0.396289,	Top-5 err = 0.178320,	data_time = 0.050728,	train_time = 0.358648
[2019-08-23 22:40:30,584] TRAIN Iter 166080: lr = 0.223202,	loss = 2.667051,	Top-1 err = 0.399072,	Top-5 err = 0.183838,	data_time = 0.050549,	train_time = 0.775478
[2019-08-23 22:40:46,767] TRAIN Iter 166100: lr = 0.223168,	loss = 2.680129,	Top-1 err = 0.400537,	Top-5 err = 0.185107,	data_time = 0.050351,	train_time = 0.809167
[2019-08-23 22:40:53,701] TRAIN Iter 166120: lr = 0.223135,	loss = 2.658101,	Top-1 err = 0.399805,	Top-5 err = 0.181152,	data_time = 0.050404,	train_time = 0.346666
[2019-08-23 22:41:09,509] TRAIN Iter 166140: lr = 0.223102,	loss = 2.720911,	Top-1 err = 0.399316,	Top-5 err = 0.183984,	data_time = 0.050506,	train_time = 0.790380
[2019-08-23 22:41:17,016] TRAIN Iter 166160: lr = 0.223068,	loss = 2.502378,	Top-1 err = 0.396533,	Top-5 err = 0.181250,	data_time = 0.050594,	train_time = 0.375358
[2019-08-23 22:41:32,050] TRAIN Iter 166180: lr = 0.223035,	loss = 2.579878,	Top-1 err = 0.396289,	Top-5 err = 0.179150,	data_time = 0.050529,	train_time = 0.751678
[2019-08-23 22:41:49,061] TRAIN Iter 166200: lr = 0.223002,	loss = 2.639671,	Top-1 err = 0.399707,	Top-5 err = 0.180957,	data_time = 0.050671,	train_time = 0.850521
[2019-08-23 22:41:56,705] TRAIN Iter 166220: lr = 0.222968,	loss = 2.646977,	Top-1 err = 0.395068,	Top-5 err = 0.180176,	data_time = 0.050612,	train_time = 0.382168
[2019-08-23 22:42:12,550] TRAIN Iter 166240: lr = 0.222935,	loss = 2.623714,	Top-1 err = 0.397949,	Top-5 err = 0.181738,	data_time = 0.050840,	train_time = 0.792244
[2019-08-23 22:42:29,413] TRAIN Iter 166260: lr = 0.222902,	loss = 2.560346,	Top-1 err = 0.400928,	Top-5 err = 0.185156,	data_time = 0.050393,	train_time = 0.843130
[2019-08-23 22:42:36,005] TRAIN Iter 166280: lr = 0.222868,	loss = 2.574927,	Top-1 err = 0.396582,	Top-5 err = 0.181494,	data_time = 0.050478,	train_time = 0.329562
[2019-08-23 22:42:53,012] TRAIN Iter 166300: lr = 0.222835,	loss = 2.703448,	Top-1 err = 0.398779,	Top-5 err = 0.177930,	data_time = 0.050404,	train_time = 0.850344
[2019-08-23 22:43:00,089] TRAIN Iter 166320: lr = 0.222802,	loss = 2.679990,	Top-1 err = 0.400342,	Top-5 err = 0.183350,	data_time = 0.050528,	train_time = 0.353876
[2019-08-23 22:43:16,141] TRAIN Iter 166340: lr = 0.222768,	loss = 2.599462,	Top-1 err = 0.400586,	Top-5 err = 0.183057,	data_time = 0.050619,	train_time = 0.802544
[2019-08-23 22:43:34,652] TRAIN Iter 166360: lr = 0.222735,	loss = 2.587379,	Top-1 err = 0.398730,	Top-5 err = 0.180566,	data_time = 0.050498,	train_time = 0.925544
[2019-08-23 22:43:41,088] TRAIN Iter 166380: lr = 0.222702,	loss = 2.590224,	Top-1 err = 0.405273,	Top-5 err = 0.182568,	data_time = 0.050363,	train_time = 0.321790
[2019-08-23 22:43:58,982] TRAIN Iter 166400: lr = 0.222668,	loss = 2.623330,	Top-1 err = 0.400195,	Top-5 err = 0.180713,	data_time = 0.050048,	train_time = 0.894714
[2019-08-23 22:44:15,362] TRAIN Iter 166420: lr = 0.222635,	loss = 2.732832,	Top-1 err = 0.394824,	Top-5 err = 0.177686,	data_time = 0.114391,	train_time = 0.818970
[2019-08-23 22:44:21,515] TRAIN Iter 166440: lr = 0.222602,	loss = 2.698718,	Top-1 err = 0.398193,	Top-5 err = 0.182422,	data_time = 0.049886,	train_time = 0.307658
[2019-08-23 22:45:13,045] TRAIN Iter 166460: lr = 0.222568,	loss = 2.682711,	Top-1 err = 0.399013,	Top-5 err = 0.181255,	data_time = 0.050545,	train_time = 2.576453
[2019-08-23 22:45:20,607] TRAIN Iter 166480: lr = 0.222535,	loss = 2.651045,	Top-1 err = 0.397559,	Top-5 err = 0.176074,	data_time = 0.050450,	train_time = 0.378093
[2019-08-23 22:45:33,971] TRAIN Iter 166500: lr = 0.222502,	loss = 2.508940,	Top-1 err = 0.391309,	Top-5 err = 0.171680,	data_time = 0.050301,	train_time = 0.668159
[2019-08-23 22:45:44,972] TRAIN Iter 166520: lr = 0.222468,	loss = 2.547132,	Top-1 err = 0.389941,	Top-5 err = 0.174756,	data_time = 0.050479,	train_time = 0.550049
[2019-08-23 22:45:54,316] TRAIN Iter 166540: lr = 0.222435,	loss = 2.640169,	Top-1 err = 0.395215,	Top-5 err = 0.176855,	data_time = 0.050812,	train_time = 0.467216
[2019-08-23 22:46:09,230] TRAIN Iter 166560: lr = 0.222402,	loss = 2.634280,	Top-1 err = 0.391064,	Top-5 err = 0.177344,	data_time = 0.050950,	train_time = 0.745643
[2019-08-23 22:46:15,825] TRAIN Iter 166580: lr = 0.222368,	loss = 2.519374,	Top-1 err = 0.388428,	Top-5 err = 0.173535,	data_time = 0.050484,	train_time = 0.329755
[2019-08-23 22:46:31,865] TRAIN Iter 166600: lr = 0.222335,	loss = 2.559489,	Top-1 err = 0.389600,	Top-5 err = 0.174756,	data_time = 0.050556,	train_time = 0.801974
[2019-08-23 22:46:47,924] TRAIN Iter 166620: lr = 0.222302,	loss = 2.614440,	Top-1 err = 0.391162,	Top-5 err = 0.178857,	data_time = 0.050516,	train_time = 0.802962
[2019-08-23 22:46:54,386] TRAIN Iter 166640: lr = 0.222268,	loss = 2.490700,	Top-1 err = 0.391943,	Top-5 err = 0.177100,	data_time = 0.122563,	train_time = 0.323092
[2019-08-23 22:47:10,694] TRAIN Iter 166660: lr = 0.222235,	loss = 2.529083,	Top-1 err = 0.390576,	Top-5 err = 0.175684,	data_time = 0.050471,	train_time = 0.815384
[2019-08-23 22:47:27,123] TRAIN Iter 166680: lr = 0.222202,	loss = 2.526308,	Top-1 err = 0.391455,	Top-5 err = 0.174561,	data_time = 0.050400,	train_time = 0.821439
[2019-08-23 22:47:33,942] TRAIN Iter 166700: lr = 0.222168,	loss = 2.589734,	Top-1 err = 0.392285,	Top-5 err = 0.176025,	data_time = 0.050608,	train_time = 0.340911
[2019-08-23 22:47:49,005] TRAIN Iter 166720: lr = 0.222135,	loss = 2.569804,	Top-1 err = 0.391162,	Top-5 err = 0.176221,	data_time = 0.050837,	train_time = 0.753134
[2019-08-23 22:47:56,392] TRAIN Iter 166740: lr = 0.222102,	loss = 2.628503,	Top-1 err = 0.393896,	Top-5 err = 0.180469,	data_time = 0.050467,	train_time = 0.369371
[2019-08-23 22:48:10,437] TRAIN Iter 166760: lr = 0.222068,	loss = 2.612625,	Top-1 err = 0.396240,	Top-5 err = 0.181787,	data_time = 0.050400,	train_time = 0.702210
[2019-08-23 22:48:24,937] TRAIN Iter 166780: lr = 0.222035,	loss = 2.573924,	Top-1 err = 0.387207,	Top-5 err = 0.175146,	data_time = 0.050405,	train_time = 0.724968
[2019-08-23 22:48:31,493] TRAIN Iter 166800: lr = 0.222002,	loss = 2.476157,	Top-1 err = 0.389111,	Top-5 err = 0.174268,	data_time = 0.050726,	train_time = 0.327827
[2019-08-23 22:48:46,988] TRAIN Iter 166820: lr = 0.221968,	loss = 2.624652,	Top-1 err = 0.395068,	Top-5 err = 0.178027,	data_time = 0.050693,	train_time = 0.774723
[2019-08-23 22:49:00,173] TRAIN Iter 166840: lr = 0.221935,	loss = 2.523691,	Top-1 err = 0.395703,	Top-5 err = 0.180322,	data_time = 0.138096,	train_time = 0.659221
[2019-08-23 22:49:07,191] TRAIN Iter 166860: lr = 0.221902,	loss = 2.634105,	Top-1 err = 0.397998,	Top-5 err = 0.181787,	data_time = 0.050505,	train_time = 0.350898
[2019-08-23 22:49:21,805] TRAIN Iter 166880: lr = 0.221868,	loss = 2.536622,	Top-1 err = 0.390479,	Top-5 err = 0.175293,	data_time = 0.050602,	train_time = 0.730673
[2019-08-23 22:49:28,485] TRAIN Iter 166900: lr = 0.221835,	loss = 2.603008,	Top-1 err = 0.395313,	Top-5 err = 0.175928,	data_time = 0.050534,	train_time = 0.334017
[2019-08-23 22:49:46,002] TRAIN Iter 166920: lr = 0.221802,	loss = 2.657208,	Top-1 err = 0.386572,	Top-5 err = 0.174268,	data_time = 0.050566,	train_time = 0.875838
[2019-08-23 22:50:00,495] TRAIN Iter 166940: lr = 0.221768,	loss = 2.664614,	Top-1 err = 0.396143,	Top-5 err = 0.179492,	data_time = 0.050845,	train_time = 0.724615
[2019-08-23 22:50:07,060] TRAIN Iter 166960: lr = 0.221735,	loss = 2.605361,	Top-1 err = 0.394775,	Top-5 err = 0.178564,	data_time = 0.050327,	train_time = 0.328236
[2019-08-23 22:50:23,057] TRAIN Iter 166980: lr = 0.221702,	loss = 2.685175,	Top-1 err = 0.395947,	Top-5 err = 0.177832,	data_time = 0.050409,	train_time = 0.799814
[2019-08-23 22:50:38,163] TRAIN Iter 167000: lr = 0.221668,	loss = 2.620410,	Top-1 err = 0.404736,	Top-5 err = 0.179102,	data_time = 0.050646,	train_time = 0.755300
[2019-08-23 22:50:45,532] TRAIN Iter 167020: lr = 0.221635,	loss = 2.519421,	Top-1 err = 0.391016,	Top-5 err = 0.178271,	data_time = 0.050569,	train_time = 0.368415
[2019-08-23 22:50:59,769] TRAIN Iter 167040: lr = 0.221602,	loss = 2.671917,	Top-1 err = 0.395947,	Top-5 err = 0.181738,	data_time = 0.050609,	train_time = 0.711862
[2019-08-23 22:51:06,799] TRAIN Iter 167060: lr = 0.221568,	loss = 2.632182,	Top-1 err = 0.395605,	Top-5 err = 0.173486,	data_time = 0.050628,	train_time = 0.351499
[2019-08-23 22:51:21,706] TRAIN Iter 167080: lr = 0.221535,	loss = 2.566952,	Top-1 err = 0.395996,	Top-5 err = 0.180225,	data_time = 0.050301,	train_time = 0.745317
[2019-08-23 22:51:36,645] TRAIN Iter 167100: lr = 0.221502,	loss = 2.679445,	Top-1 err = 0.402197,	Top-5 err = 0.186523,	data_time = 0.050196,	train_time = 0.746928
[2019-08-23 22:51:44,787] TRAIN Iter 167120: lr = 0.221468,	loss = 2.617096,	Top-1 err = 0.397021,	Top-5 err = 0.180469,	data_time = 0.050675,	train_time = 0.407075
[2019-08-23 22:52:02,589] TRAIN Iter 167140: lr = 0.221435,	loss = 2.662426,	Top-1 err = 0.400635,	Top-5 err = 0.185889,	data_time = 0.050344,	train_time = 0.890121
[2019-08-23 22:52:12,208] TRAIN Iter 167160: lr = 0.221402,	loss = 2.746576,	Top-1 err = 0.394971,	Top-5 err = 0.182422,	data_time = 0.219271,	train_time = 0.480901
[2019-08-23 22:52:23,116] TRAIN Iter 167180: lr = 0.221368,	loss = 2.611129,	Top-1 err = 0.396533,	Top-5 err = 0.181104,	data_time = 0.050585,	train_time = 0.545393
[2019-08-23 22:52:39,528] TRAIN Iter 167200: lr = 0.221335,	loss = 2.710825,	Top-1 err = 0.402295,	Top-5 err = 0.184570,	data_time = 0.050371,	train_time = 0.820604
[2019-08-23 22:52:46,581] TRAIN Iter 167220: lr = 0.221302,	loss = 2.611218,	Top-1 err = 0.394189,	Top-5 err = 0.180322,	data_time = 0.050194,	train_time = 0.352642
[2019-08-23 22:53:02,790] TRAIN Iter 167240: lr = 0.221268,	loss = 2.562293,	Top-1 err = 0.393701,	Top-5 err = 0.176270,	data_time = 0.050459,	train_time = 0.810437
[2019-08-23 22:53:17,747] TRAIN Iter 167260: lr = 0.221235,	loss = 2.615342,	Top-1 err = 0.398486,	Top-5 err = 0.180859,	data_time = 0.050683,	train_time = 0.747827
[2019-08-23 22:53:25,558] TRAIN Iter 167280: lr = 0.221202,	loss = 2.680740,	Top-1 err = 0.396436,	Top-5 err = 0.183301,	data_time = 0.050545,	train_time = 0.390549
[2019-08-23 22:53:43,113] TRAIN Iter 167300: lr = 0.221168,	loss = 2.710916,	Top-1 err = 0.404883,	Top-5 err = 0.182617,	data_time = 0.050455,	train_time = 0.877711
[2019-08-23 22:53:59,506] TRAIN Iter 167320: lr = 0.221135,	loss = 2.629112,	Top-1 err = 0.399072,	Top-5 err = 0.183398,	data_time = 0.111312,	train_time = 0.819641
[2019-08-23 22:54:06,765] TRAIN Iter 167340: lr = 0.221102,	loss = 2.642168,	Top-1 err = 0.395605,	Top-5 err = 0.175049,	data_time = 0.050378,	train_time = 0.362955
[2019-08-23 22:54:22,798] TRAIN Iter 167360: lr = 0.221068,	loss = 2.584179,	Top-1 err = 0.395703,	Top-5 err = 0.178906,	data_time = 0.050323,	train_time = 0.801628
[2019-08-23 22:54:29,792] TRAIN Iter 167380: lr = 0.221035,	loss = 2.622998,	Top-1 err = 0.397949,	Top-5 err = 0.177490,	data_time = 0.050578,	train_time = 0.349679
[2019-08-23 22:54:46,592] TRAIN Iter 167400: lr = 0.221002,	loss = 2.694493,	Top-1 err = 0.401758,	Top-5 err = 0.185303,	data_time = 0.050680,	train_time = 0.839967
[2019-08-23 22:55:03,524] TRAIN Iter 167420: lr = 0.220968,	loss = 2.592544,	Top-1 err = 0.397266,	Top-5 err = 0.182324,	data_time = 0.050599,	train_time = 0.846610
[2019-08-23 22:55:10,643] TRAIN Iter 167440: lr = 0.220935,	loss = 2.587316,	Top-1 err = 0.400537,	Top-5 err = 0.184326,	data_time = 0.050571,	train_time = 0.355906
[2019-08-23 22:55:27,289] TRAIN Iter 167460: lr = 0.220902,	loss = 2.599226,	Top-1 err = 0.395313,	Top-5 err = 0.176318,	data_time = 0.050364,	train_time = 0.832309
[2019-08-23 22:55:45,548] TRAIN Iter 167480: lr = 0.220868,	loss = 2.660914,	Top-1 err = 0.393799,	Top-5 err = 0.179297,	data_time = 0.136111,	train_time = 0.912951
[2019-08-23 22:55:52,623] TRAIN Iter 167500: lr = 0.220835,	loss = 2.609890,	Top-1 err = 0.399805,	Top-5 err = 0.178760,	data_time = 0.050857,	train_time = 0.353739
[2019-08-23 22:56:09,499] TRAIN Iter 167520: lr = 0.220802,	loss = 2.745624,	Top-1 err = 0.395215,	Top-5 err = 0.181641,	data_time = 0.050521,	train_time = 0.843762
[2019-08-23 22:56:16,528] TRAIN Iter 167540: lr = 0.220768,	loss = 2.551866,	Top-1 err = 0.394043,	Top-5 err = 0.182373,	data_time = 0.050193,	train_time = 0.351458
[2019-08-23 22:56:33,353] TRAIN Iter 167560: lr = 0.220735,	loss = 2.634007,	Top-1 err = 0.397754,	Top-5 err = 0.182520,	data_time = 0.050270,	train_time = 0.841223
[2019-08-23 22:56:52,132] TRAIN Iter 167580: lr = 0.220702,	loss = 2.578676,	Top-1 err = 0.402783,	Top-5 err = 0.186426,	data_time = 0.050520,	train_time = 0.938917
[2019-08-23 22:56:59,071] TRAIN Iter 167600: lr = 0.220668,	loss = 2.569174,	Top-1 err = 0.395166,	Top-5 err = 0.179443,	data_time = 0.050484,	train_time = 0.346952
[2019-08-23 22:57:16,738] TRAIN Iter 167620: lr = 0.220635,	loss = 2.580931,	Top-1 err = 0.396924,	Top-5 err = 0.180078,	data_time = 0.050469,	train_time = 0.883337
[2019-08-23 22:57:34,852] TRAIN Iter 167640: lr = 0.220602,	loss = 2.556367,	Top-1 err = 0.401660,	Top-5 err = 0.182715,	data_time = 0.066643,	train_time = 0.905671
[2019-08-23 22:57:42,043] TRAIN Iter 167660: lr = 0.220568,	loss = 2.650898,	Top-1 err = 0.404834,	Top-5 err = 0.185303,	data_time = 0.050214,	train_time = 0.359548
[2019-08-23 22:57:58,219] TRAIN Iter 167680: lr = 0.220535,	loss = 2.611125,	Top-1 err = 0.395947,	Top-5 err = 0.182910,	data_time = 0.049895,	train_time = 0.808786
[2019-08-23 22:58:04,114] TRAIN Iter 167700: lr = 0.220502,	loss = 2.570657,	Top-1 err = 0.404297,	Top-5 err = 0.183154,	data_time = 0.049889,	train_time = 0.294715
[2019-08-23 22:58:53,942] TRAIN Iter 167720: lr = 0.220468,	loss = 2.639337,	Top-1 err = 0.399159,	Top-5 err = 0.184006,	data_time = 0.050323,	train_time = 2.491386
[2019-08-23 22:59:14,649] TRAIN Iter 167740: lr = 0.220435,	loss = 2.608718,	Top-1 err = 0.393115,	Top-5 err = 0.178613,	data_time = 0.051154,	train_time = 1.035354
[2019-08-23 22:59:22,576] TRAIN Iter 167760: lr = 0.220402,	loss = 2.527621,	Top-1 err = 0.390332,	Top-5 err = 0.175049,	data_time = 0.050763,	train_time = 0.396339
[2019-08-23 22:59:34,502] TRAIN Iter 167780: lr = 0.220368,	loss = 2.546639,	Top-1 err = 0.388330,	Top-5 err = 0.171338,	data_time = 0.050638,	train_time = 0.596267
[2019-08-23 22:59:42,855] TRAIN Iter 167800: lr = 0.220335,	loss = 2.667312,	Top-1 err = 0.393457,	Top-5 err = 0.176465,	data_time = 0.050718,	train_time = 0.417639
[2019-08-23 22:59:50,893] TRAIN Iter 167820: lr = 0.220302,	loss = 2.592647,	Top-1 err = 0.386133,	Top-5 err = 0.177051,	data_time = 0.050512,	train_time = 0.401881
[2019-08-23 23:00:04,367] TRAIN Iter 167840: lr = 0.220268,	loss = 2.549113,	Top-1 err = 0.391211,	Top-5 err = 0.175146,	data_time = 0.050572,	train_time = 0.673681
[2019-08-23 23:00:12,235] TRAIN Iter 167860: lr = 0.220235,	loss = 2.583223,	Top-1 err = 0.392627,	Top-5 err = 0.174902,	data_time = 0.050797,	train_time = 0.393397
[2019-08-23 23:00:27,096] TRAIN Iter 167880: lr = 0.220202,	loss = 2.603074,	Top-1 err = 0.389990,	Top-5 err = 0.180371,	data_time = 0.050470,	train_time = 0.743066
[2019-08-23 23:00:39,410] TRAIN Iter 167900: lr = 0.220168,	loss = 2.526075,	Top-1 err = 0.389014,	Top-5 err = 0.176123,	data_time = 0.556291,	train_time = 0.615672
[2019-08-23 23:00:48,927] TRAIN Iter 167920: lr = 0.220135,	loss = 2.618407,	Top-1 err = 0.389014,	Top-5 err = 0.173242,	data_time = 0.050554,	train_time = 0.475849
[2019-08-23 23:01:03,113] TRAIN Iter 167940: lr = 0.220102,	loss = 2.524200,	Top-1 err = 0.389307,	Top-5 err = 0.176367,	data_time = 0.147673,	train_time = 0.709285
[2019-08-23 23:01:10,745] TRAIN Iter 167960: lr = 0.220068,	loss = 2.614425,	Top-1 err = 0.398535,	Top-5 err = 0.179443,	data_time = 0.050839,	train_time = 0.381572
[2019-08-23 23:01:25,545] TRAIN Iter 167980: lr = 0.220035,	loss = 2.602852,	Top-1 err = 0.387402,	Top-5 err = 0.171143,	data_time = 0.050503,	train_time = 0.739972
[2019-08-23 23:01:39,236] TRAIN Iter 168000: lr = 0.220002,	loss = 2.581253,	Top-1 err = 0.391992,	Top-5 err = 0.172168,	data_time = 0.237762,	train_time = 0.684555
[2019-08-23 23:01:48,192] TRAIN Iter 168020: lr = 0.219968,	loss = 2.590936,	Top-1 err = 0.392578,	Top-5 err = 0.176025,	data_time = 0.050533,	train_time = 0.447774
[2019-08-23 23:02:01,423] TRAIN Iter 168040: lr = 0.219935,	loss = 2.594460,	Top-1 err = 0.391260,	Top-5 err = 0.177051,	data_time = 0.050972,	train_time = 0.661556
[2019-08-23 23:02:15,800] TRAIN Iter 168060: lr = 0.219902,	loss = 2.523717,	Top-1 err = 0.391260,	Top-5 err = 0.180615,	data_time = 0.050363,	train_time = 0.718797
[2019-08-23 23:02:24,406] TRAIN Iter 168080: lr = 0.219868,	loss = 2.610223,	Top-1 err = 0.397998,	Top-5 err = 0.181641,	data_time = 0.050534,	train_time = 0.430318
[2019-08-23 23:02:39,076] TRAIN Iter 168100: lr = 0.219835,	loss = 2.508845,	Top-1 err = 0.396338,	Top-5 err = 0.177246,	data_time = 0.050606,	train_time = 0.733457
[2019-08-23 23:02:46,394] TRAIN Iter 168120: lr = 0.219802,	loss = 2.628870,	Top-1 err = 0.388428,	Top-5 err = 0.174414,	data_time = 0.050708,	train_time = 0.365911
[2019-08-23 23:03:01,766] TRAIN Iter 168140: lr = 0.219768,	loss = 2.614371,	Top-1 err = 0.395850,	Top-5 err = 0.176904,	data_time = 0.050789,	train_time = 0.768586
[2019-08-23 23:03:18,130] TRAIN Iter 168160: lr = 0.219735,	loss = 2.614821,	Top-1 err = 0.389600,	Top-5 err = 0.177539,	data_time = 0.050640,	train_time = 0.818177
[2019-08-23 23:03:25,770] TRAIN Iter 168180: lr = 0.219702,	loss = 2.555080,	Top-1 err = 0.390283,	Top-5 err = 0.178760,	data_time = 0.050478,	train_time = 0.381970
[2019-08-23 23:03:40,883] TRAIN Iter 168200: lr = 0.219668,	loss = 2.580904,	Top-1 err = 0.399902,	Top-5 err = 0.179932,	data_time = 0.050425,	train_time = 0.755646
[2019-08-23 23:03:54,148] TRAIN Iter 168220: lr = 0.219635,	loss = 2.610004,	Top-1 err = 0.397070,	Top-5 err = 0.180420,	data_time = 0.105834,	train_time = 0.663237
[2019-08-23 23:04:02,448] TRAIN Iter 168240: lr = 0.219602,	loss = 2.602502,	Top-1 err = 0.397412,	Top-5 err = 0.179883,	data_time = 0.050392,	train_time = 0.414989
[2019-08-23 23:04:17,870] TRAIN Iter 168260: lr = 0.219568,	loss = 2.560009,	Top-1 err = 0.393896,	Top-5 err = 0.181055,	data_time = 0.050840,	train_time = 0.771093
[2019-08-23 23:04:25,336] TRAIN Iter 168280: lr = 0.219535,	loss = 2.581131,	Top-1 err = 0.395947,	Top-5 err = 0.177490,	data_time = 0.050584,	train_time = 0.373271
[2019-08-23 23:04:41,380] TRAIN Iter 168300: lr = 0.219502,	loss = 2.611282,	Top-1 err = 0.395557,	Top-5 err = 0.176318,	data_time = 0.050288,	train_time = 0.802226
[2019-08-23 23:04:55,975] TRAIN Iter 168320: lr = 0.219468,	loss = 2.688843,	Top-1 err = 0.399219,	Top-5 err = 0.180078,	data_time = 0.050533,	train_time = 0.729734
[2019-08-23 23:05:04,527] TRAIN Iter 168340: lr = 0.219435,	loss = 2.537482,	Top-1 err = 0.395166,	Top-5 err = 0.182422,	data_time = 0.050558,	train_time = 0.427587
[2019-08-23 23:05:21,754] TRAIN Iter 168360: lr = 0.219402,	loss = 2.665596,	Top-1 err = 0.400195,	Top-5 err = 0.183252,	data_time = 0.050241,	train_time = 0.861333
[2019-08-23 23:05:33,979] TRAIN Iter 168380: lr = 0.219368,	loss = 2.597390,	Top-1 err = 0.400195,	Top-5 err = 0.179395,	data_time = 0.050667,	train_time = 0.611220
[2019-08-23 23:05:43,038] TRAIN Iter 168400: lr = 0.219335,	loss = 2.693414,	Top-1 err = 0.393848,	Top-5 err = 0.178760,	data_time = 0.050571,	train_time = 0.452958
[2019-08-23 23:05:58,315] TRAIN Iter 168420: lr = 0.219302,	loss = 2.685764,	Top-1 err = 0.392188,	Top-5 err = 0.174658,	data_time = 0.050487,	train_time = 0.763795
[2019-08-23 23:06:05,848] TRAIN Iter 168440: lr = 0.219268,	loss = 2.561001,	Top-1 err = 0.397119,	Top-5 err = 0.178809,	data_time = 0.050558,	train_time = 0.376642
[2019-08-23 23:06:20,336] TRAIN Iter 168460: lr = 0.219235,	loss = 2.740880,	Top-1 err = 0.400342,	Top-5 err = 0.180029,	data_time = 0.050561,	train_time = 0.724382
[2019-08-23 23:06:34,921] TRAIN Iter 168480: lr = 0.219202,	loss = 2.576997,	Top-1 err = 0.386865,	Top-5 err = 0.175830,	data_time = 1.702769,	train_time = 0.729247
[2019-08-23 23:06:42,684] TRAIN Iter 168500: lr = 0.219168,	loss = 2.555634,	Top-1 err = 0.400146,	Top-5 err = 0.182617,	data_time = 0.050662,	train_time = 0.388141
[2019-08-23 23:06:59,613] TRAIN Iter 168520: lr = 0.219135,	loss = 2.682728,	Top-1 err = 0.392480,	Top-5 err = 0.181348,	data_time = 0.050438,	train_time = 0.846421
[2019-08-23 23:07:12,324] TRAIN Iter 168540: lr = 0.219102,	loss = 2.629017,	Top-1 err = 0.397363,	Top-5 err = 0.179785,	data_time = 0.050741,	train_time = 0.635563
[2019-08-23 23:07:23,622] TRAIN Iter 168560: lr = 0.219068,	loss = 2.610112,	Top-1 err = 0.395752,	Top-5 err = 0.178564,	data_time = 0.050515,	train_time = 0.564858
[2019-08-23 23:07:41,003] TRAIN Iter 168580: lr = 0.219035,	loss = 2.617200,	Top-1 err = 0.399170,	Top-5 err = 0.180957,	data_time = 0.050497,	train_time = 0.869035
[2019-08-23 23:07:47,851] TRAIN Iter 168600: lr = 0.219002,	loss = 2.671805,	Top-1 err = 0.397559,	Top-5 err = 0.175684,	data_time = 0.050364,	train_time = 0.342388
[2019-08-23 23:08:04,553] TRAIN Iter 168620: lr = 0.218968,	loss = 2.711331,	Top-1 err = 0.395264,	Top-5 err = 0.179297,	data_time = 0.050563,	train_time = 0.835067
[2019-08-23 23:08:21,232] TRAIN Iter 168640: lr = 0.218935,	loss = 2.552728,	Top-1 err = 0.392334,	Top-5 err = 0.176563,	data_time = 3.265378,	train_time = 0.833935
[2019-08-23 23:08:28,736] TRAIN Iter 168660: lr = 0.218902,	loss = 2.596770,	Top-1 err = 0.396094,	Top-5 err = 0.184033,	data_time = 0.050311,	train_time = 0.375201
[2019-08-23 23:08:45,441] TRAIN Iter 168680: lr = 0.218868,	loss = 2.666735,	Top-1 err = 0.397607,	Top-5 err = 0.182275,	data_time = 0.050426,	train_time = 0.835258
[2019-08-23 23:08:59,839] TRAIN Iter 168700: lr = 0.218835,	loss = 2.600689,	Top-1 err = 0.399072,	Top-5 err = 0.181445,	data_time = 0.147013,	train_time = 0.719879
[2019-08-23 23:09:10,408] TRAIN Iter 168720: lr = 0.218802,	loss = 2.698455,	Top-1 err = 0.399561,	Top-5 err = 0.181592,	data_time = 0.050626,	train_time = 0.528437
[2019-08-23 23:09:28,517] TRAIN Iter 168740: lr = 0.218768,	loss = 2.596497,	Top-1 err = 0.398340,	Top-5 err = 0.181543,	data_time = 0.050376,	train_time = 0.905430
[2019-08-23 23:09:35,621] TRAIN Iter 168760: lr = 0.218735,	loss = 2.600548,	Top-1 err = 0.398633,	Top-5 err = 0.179248,	data_time = 0.050493,	train_time = 0.355194
[2019-08-23 23:09:52,394] TRAIN Iter 168780: lr = 0.218702,	loss = 2.656916,	Top-1 err = 0.392578,	Top-5 err = 0.176221,	data_time = 0.050572,	train_time = 0.838623
[2019-08-23 23:10:09,345] TRAIN Iter 168800: lr = 0.218668,	loss = 2.623271,	Top-1 err = 0.396045,	Top-5 err = 0.180029,	data_time = 3.733622,	train_time = 0.847554
[2019-08-23 23:10:16,958] TRAIN Iter 168820: lr = 0.218635,	loss = 2.664263,	Top-1 err = 0.397754,	Top-5 err = 0.182129,	data_time = 0.050829,	train_time = 0.380578
[2019-08-23 23:10:33,903] TRAIN Iter 168840: lr = 0.218602,	loss = 2.560819,	Top-1 err = 0.397168,	Top-5 err = 0.178760,	data_time = 0.050591,	train_time = 0.847232
[2019-08-23 23:10:49,304] TRAIN Iter 168860: lr = 0.218568,	loss = 2.623553,	Top-1 err = 0.393555,	Top-5 err = 0.178760,	data_time = 0.050587,	train_time = 0.770074
[2019-08-23 23:10:58,020] TRAIN Iter 168880: lr = 0.218535,	loss = 2.655478,	Top-1 err = 0.400830,	Top-5 err = 0.181934,	data_time = 0.050328,	train_time = 0.435782
[2019-08-23 23:11:17,496] TRAIN Iter 168900: lr = 0.218502,	loss = 2.673599,	Top-1 err = 0.395117,	Top-5 err = 0.176416,	data_time = 0.050049,	train_time = 0.973747
[2019-08-23 23:11:24,515] TRAIN Iter 168920: lr = 0.218468,	loss = 2.619843,	Top-1 err = 0.402100,	Top-5 err = 0.185693,	data_time = 0.049888,	train_time = 0.350964
[2019-08-23 23:11:39,748] TRAIN Iter 168940: lr = 0.218435,	loss = 2.567014,	Top-1 err = 0.401123,	Top-5 err = 0.183057,	data_time = 0.049945,	train_time = 0.761609
[2019-08-23 23:12:29,770] TRAIN Iter 168960: lr = 0.218402,	loss = 2.605639,	Top-1 err = 0.400747,	Top-5 err = 0.185056,	data_time = 0.050493,	train_time = 2.501119
[2019-08-23 23:12:36,828] TRAIN Iter 168980: lr = 0.218368,	loss = 2.576265,	Top-1 err = 0.399316,	Top-5 err = 0.177344,	data_time = 0.050475,	train_time = 0.352883
[2019-08-23 23:12:51,147] TRAIN Iter 169000: lr = 0.218335,	loss = 2.614634,	Top-1 err = 0.393164,	Top-5 err = 0.176563,	data_time = 0.132070,	train_time = 0.715905
[2019-08-23 23:12:58,013] TRAIN Iter 169020: lr = 0.218302,	loss = 2.611829,	Top-1 err = 0.386475,	Top-5 err = 0.172754,	data_time = 0.050710,	train_time = 0.343313
[2019-08-23 23:13:19,156] TRAIN Iter 169040: lr = 0.218268,	loss = 2.584942,	Top-1 err = 0.387598,	Top-5 err = 0.173926,	data_time = 0.050569,	train_time = 1.057126
[2019-08-23 23:13:30,908] TRAIN Iter 169060: lr = 0.218235,	loss = 2.547039,	Top-1 err = 0.386426,	Top-5 err = 0.171729,	data_time = 0.050848,	train_time = 0.587572
[2019-08-23 23:13:38,011] TRAIN Iter 169080: lr = 0.218202,	loss = 2.633731,	Top-1 err = 0.386084,	Top-5 err = 0.172217,	data_time = 0.050686,	train_time = 0.355155
[2019-08-23 23:13:50,777] TRAIN Iter 169100: lr = 0.218168,	loss = 2.499424,	Top-1 err = 0.388037,	Top-5 err = 0.177100,	data_time = 0.050722,	train_time = 0.638291
[2019-08-23 23:14:04,071] TRAIN Iter 169120: lr = 0.218135,	loss = 2.645333,	Top-1 err = 0.392383,	Top-5 err = 0.172070,	data_time = 0.050617,	train_time = 0.664669
[2019-08-23 23:14:11,623] TRAIN Iter 169140: lr = 0.218102,	loss = 2.564609,	Top-1 err = 0.395117,	Top-5 err = 0.177930,	data_time = 0.050719,	train_time = 0.377566
[2019-08-23 23:14:26,585] TRAIN Iter 169160: lr = 0.218068,	loss = 2.754820,	Top-1 err = 0.396289,	Top-5 err = 0.177930,	data_time = 0.118201,	train_time = 0.748122
[2019-08-23 23:14:33,728] TRAIN Iter 169180: lr = 0.218035,	loss = 2.687592,	Top-1 err = 0.388867,	Top-5 err = 0.175195,	data_time = 0.050568,	train_time = 0.357106
[2019-08-23 23:14:48,612] TRAIN Iter 169200: lr = 0.218002,	loss = 2.584774,	Top-1 err = 0.392480,	Top-5 err = 0.179248,	data_time = 0.050536,	train_time = 0.744198
[2019-08-23 23:15:04,923] TRAIN Iter 169220: lr = 0.217968,	loss = 2.658735,	Top-1 err = 0.395313,	Top-5 err = 0.179834,	data_time = 0.087866,	train_time = 0.815523
[2019-08-23 23:15:11,829] TRAIN Iter 169240: lr = 0.217935,	loss = 2.636266,	Top-1 err = 0.393750,	Top-5 err = 0.179150,	data_time = 0.050218,	train_time = 0.345317
[2019-08-23 23:15:25,411] TRAIN Iter 169260: lr = 0.217902,	loss = 2.546258,	Top-1 err = 0.397070,	Top-5 err = 0.178857,	data_time = 0.050400,	train_time = 0.679055
[2019-08-23 23:15:39,726] TRAIN Iter 169280: lr = 0.217868,	loss = 2.545625,	Top-1 err = 0.389453,	Top-5 err = 0.174268,	data_time = 0.050768,	train_time = 0.715736
[2019-08-23 23:15:47,213] TRAIN Iter 169300: lr = 0.217835,	loss = 2.609916,	Top-1 err = 0.393994,	Top-5 err = 0.178076,	data_time = 0.050756,	train_time = 0.374365
[2019-08-23 23:16:02,710] TRAIN Iter 169320: lr = 0.217802,	loss = 2.574865,	Top-1 err = 0.397949,	Top-5 err = 0.180273,	data_time = 0.050377,	train_time = 0.774847
[2019-08-23 23:16:09,844] TRAIN Iter 169340: lr = 0.217768,	loss = 2.535602,	Top-1 err = 0.391406,	Top-5 err = 0.176172,	data_time = 0.050420,	train_time = 0.356677
[2019-08-23 23:16:26,719] TRAIN Iter 169360: lr = 0.217735,	loss = 2.543860,	Top-1 err = 0.388965,	Top-5 err = 0.173242,	data_time = 0.050338,	train_time = 0.843724
[2019-08-23 23:16:41,042] TRAIN Iter 169380: lr = 0.217702,	loss = 2.563486,	Top-1 err = 0.391064,	Top-5 err = 0.179834,	data_time = 0.050428,	train_time = 0.716141
[2019-08-23 23:16:48,524] TRAIN Iter 169400: lr = 0.217668,	loss = 2.619755,	Top-1 err = 0.393945,	Top-5 err = 0.179297,	data_time = 0.050649,	train_time = 0.374061
[2019-08-23 23:17:05,089] TRAIN Iter 169420: lr = 0.217635,	loss = 2.565893,	Top-1 err = 0.395850,	Top-5 err = 0.178857,	data_time = 0.050403,	train_time = 0.828265
[2019-08-23 23:17:20,211] TRAIN Iter 169440: lr = 0.217602,	loss = 2.556253,	Top-1 err = 0.390674,	Top-5 err = 0.176318,	data_time = 0.050766,	train_time = 0.756044
[2019-08-23 23:17:27,886] TRAIN Iter 169460: lr = 0.217568,	loss = 2.600779,	Top-1 err = 0.386621,	Top-5 err = 0.174219,	data_time = 0.050353,	train_time = 0.383735
[2019-08-23 23:17:43,586] TRAIN Iter 169480: lr = 0.217535,	loss = 2.586823,	Top-1 err = 0.388770,	Top-5 err = 0.177246,	data_time = 0.050402,	train_time = 0.785017
[2019-08-23 23:17:50,887] TRAIN Iter 169500: lr = 0.217502,	loss = 2.620041,	Top-1 err = 0.391211,	Top-5 err = 0.177051,	data_time = 0.050859,	train_time = 0.365028
[2019-08-23 23:18:06,827] TRAIN Iter 169520: lr = 0.217468,	loss = 2.587683,	Top-1 err = 0.393213,	Top-5 err = 0.171826,	data_time = 0.050351,	train_time = 0.796962
[2019-08-23 23:18:21,278] TRAIN Iter 169540: lr = 0.217435,	loss = 2.589534,	Top-1 err = 0.389307,	Top-5 err = 0.176074,	data_time = 0.050619,	train_time = 0.722535
[2019-08-23 23:18:28,303] TRAIN Iter 169560: lr = 0.217402,	loss = 2.583434,	Top-1 err = 0.393408,	Top-5 err = 0.179102,	data_time = 0.050768,	train_time = 0.351250
[2019-08-23 23:18:45,355] TRAIN Iter 169580: lr = 0.217368,	loss = 2.538137,	Top-1 err = 0.395752,	Top-5 err = 0.175928,	data_time = 0.050643,	train_time = 0.852607
[2019-08-23 23:19:00,729] TRAIN Iter 169600: lr = 0.217335,	loss = 2.516853,	Top-1 err = 0.393896,	Top-5 err = 0.180664,	data_time = 0.145331,	train_time = 0.768675
[2019-08-23 23:19:07,841] TRAIN Iter 169620: lr = 0.217302,	loss = 2.572453,	Top-1 err = 0.391943,	Top-5 err = 0.178467,	data_time = 0.050353,	train_time = 0.355584
[2019-08-23 23:19:24,501] TRAIN Iter 169640: lr = 0.217268,	loss = 2.571272,	Top-1 err = 0.396240,	Top-5 err = 0.182471,	data_time = 0.050490,	train_time = 0.832990
[2019-08-23 23:19:31,993] TRAIN Iter 169660: lr = 0.217235,	loss = 2.614774,	Top-1 err = 0.395117,	Top-5 err = 0.179102,	data_time = 0.050328,	train_time = 0.374558
[2019-08-23 23:19:47,227] TRAIN Iter 169680: lr = 0.217202,	loss = 2.696797,	Top-1 err = 0.402686,	Top-5 err = 0.186719,	data_time = 0.050359,	train_time = 0.761710
[2019-08-23 23:20:01,294] TRAIN Iter 169700: lr = 0.217168,	loss = 2.697021,	Top-1 err = 0.403271,	Top-5 err = 0.182129,	data_time = 0.050367,	train_time = 0.703319
[2019-08-23 23:20:08,312] TRAIN Iter 169720: lr = 0.217135,	loss = 2.532823,	Top-1 err = 0.399316,	Top-5 err = 0.178516,	data_time = 0.050484,	train_time = 0.350925
[2019-08-23 23:20:26,734] TRAIN Iter 169740: lr = 0.217102,	loss = 2.639481,	Top-1 err = 0.396973,	Top-5 err = 0.179688,	data_time = 0.050519,	train_time = 0.921089
[2019-08-23 23:20:41,680] TRAIN Iter 169760: lr = 0.217068,	loss = 2.670822,	Top-1 err = 0.401953,	Top-5 err = 0.186279,	data_time = 0.050367,	train_time = 0.747246
[2019-08-23 23:20:49,342] TRAIN Iter 169780: lr = 0.217035,	loss = 2.638579,	Top-1 err = 0.392578,	Top-5 err = 0.179834,	data_time = 0.050463,	train_time = 0.383076
[2019-08-23 23:21:07,838] TRAIN Iter 169800: lr = 0.217002,	loss = 2.558767,	Top-1 err = 0.396777,	Top-5 err = 0.180811,	data_time = 0.050306,	train_time = 0.924831
[2019-08-23 23:21:15,064] TRAIN Iter 169820: lr = 0.216968,	loss = 2.591820,	Top-1 err = 0.391455,	Top-5 err = 0.178711,	data_time = 0.050731,	train_time = 0.361247
[2019-08-23 23:21:31,432] TRAIN Iter 169840: lr = 0.216935,	loss = 2.595513,	Top-1 err = 0.391260,	Top-5 err = 0.179590,	data_time = 0.050875,	train_time = 0.818419
[2019-08-23 23:21:46,366] TRAIN Iter 169860: lr = 0.216902,	loss = 2.558009,	Top-1 err = 0.393994,	Top-5 err = 0.179834,	data_time = 0.124493,	train_time = 0.746651
[2019-08-23 23:21:54,289] TRAIN Iter 169880: lr = 0.216868,	loss = 2.556950,	Top-1 err = 0.397168,	Top-5 err = 0.181592,	data_time = 0.050896,	train_time = 0.396141
[2019-08-23 23:22:08,840] TRAIN Iter 169900: lr = 0.216835,	loss = 2.513075,	Top-1 err = 0.390039,	Top-5 err = 0.173535,	data_time = 0.050361,	train_time = 0.727534
[2019-08-23 23:22:24,991] TRAIN Iter 169920: lr = 0.216802,	loss = 2.601472,	Top-1 err = 0.396240,	Top-5 err = 0.179590,	data_time = 0.050869,	train_time = 0.807540
[2019-08-23 23:22:32,257] TRAIN Iter 169940: lr = 0.216768,	loss = 2.517505,	Top-1 err = 0.392578,	Top-5 err = 0.178857,	data_time = 0.050426,	train_time = 0.363272
[2019-08-23 23:22:50,137] TRAIN Iter 169960: lr = 0.216735,	loss = 2.537859,	Top-1 err = 0.388135,	Top-5 err = 0.180469,	data_time = 0.050426,	train_time = 0.894023
[2019-08-23 23:22:57,037] TRAIN Iter 169980: lr = 0.216702,	loss = 2.713859,	Top-1 err = 0.390137,	Top-5 err = 0.178271,	data_time = 0.050478,	train_time = 0.344990
[2019-08-23 23:23:12,847] TRAIN Iter 170000: lr = 0.216668,	loss = 2.567370,	Top-1 err = 0.399658,	Top-5 err = 0.183008,	data_time = 0.050687,	train_time = 0.790442
[2019-08-23 23:24:17,850] TEST Iter 170000: loss = 2.408325,	Top-1 err = 0.360620,	Top-5 err = 0.141080,	val_time = 64.962800
[2019-08-23 23:24:24,022] TRAIN Iter 170020: lr = 0.216635,	loss = 2.657762,	Top-1 err = 0.395557,	Top-5 err = 0.180908,	data_time = 0.050338,	train_time = 0.308553
[2019-08-23 23:24:30,471] TRAIN Iter 170040: lr = 0.216602,	loss = 2.708906,	Top-1 err = 0.399414,	Top-5 err = 0.182080,	data_time = 0.050687,	train_time = 0.322453
[2019-08-23 23:24:37,039] TRAIN Iter 170060: lr = 0.216568,	loss = 2.618125,	Top-1 err = 0.395166,	Top-5 err = 0.181738,	data_time = 0.050655,	train_time = 0.328374
[2019-08-23 23:24:47,028] TRAIN Iter 170080: lr = 0.216535,	loss = 2.569343,	Top-1 err = 0.398340,	Top-5 err = 0.179834,	data_time = 0.109369,	train_time = 0.499440
[2019-08-23 23:25:04,744] TRAIN Iter 170100: lr = 0.216502,	loss = 2.538182,	Top-1 err = 0.392822,	Top-5 err = 0.179004,	data_time = 0.101160,	train_time = 0.885784
[2019-08-23 23:25:14,053] TRAIN Iter 170120: lr = 0.216468,	loss = 2.637293,	Top-1 err = 0.396973,	Top-5 err = 0.179346,	data_time = 0.177022,	train_time = 0.465435
[2019-08-23 23:25:33,217] TRAIN Iter 170140: lr = 0.216435,	loss = 2.612586,	Top-1 err = 0.398633,	Top-5 err = 0.182764,	data_time = 0.050560,	train_time = 0.958183
[2019-08-23 23:25:41,122] TRAIN Iter 170160: lr = 0.216402,	loss = 2.639187,	Top-1 err = 0.393945,	Top-5 err = 0.177637,	data_time = 0.050117,	train_time = 0.395266
[2019-08-23 23:25:58,047] TRAIN Iter 170180: lr = 0.216368,	loss = 2.651183,	Top-1 err = 0.398486,	Top-5 err = 0.179785,	data_time = 0.049832,	train_time = 0.846195
[2019-08-23 23:26:11,872] TRAIN Iter 170200: lr = 0.216335,	loss = 2.668746,	Top-1 err = 0.400684,	Top-5 err = 0.182324,	data_time = 0.049912,	train_time = 0.691285
[2019-08-23 23:26:59,340] TRAIN Iter 170220: lr = 0.216302,	loss = 2.643351,	Top-1 err = 0.390617,	Top-5 err = 0.179214,	data_time = 0.050431,	train_time = 2.373357
[2019-08-23 23:27:06,704] TRAIN Iter 170240: lr = 0.216268,	loss = 2.599161,	Top-1 err = 0.393896,	Top-5 err = 0.177100,	data_time = 0.138566,	train_time = 0.368183
[2019-08-23 23:27:22,308] TRAIN Iter 170260: lr = 0.216235,	loss = 2.508266,	Top-1 err = 0.387842,	Top-5 err = 0.175928,	data_time = 0.050463,	train_time = 0.780210
[2019-08-23 23:27:37,294] TRAIN Iter 170280: lr = 0.216202,	loss = 2.530828,	Top-1 err = 0.386768,	Top-5 err = 0.174365,	data_time = 0.050255,	train_time = 0.749266
[2019-08-23 23:27:44,319] TRAIN Iter 170300: lr = 0.216168,	loss = 2.668908,	Top-1 err = 0.385645,	Top-5 err = 0.172998,	data_time = 0.050601,	train_time = 0.351262
[2019-08-23 23:28:00,673] TRAIN Iter 170320: lr = 0.216135,	loss = 2.541794,	Top-1 err = 0.382715,	Top-5 err = 0.171582,	data_time = 0.050528,	train_time = 0.817673
[2019-08-23 23:28:16,644] TRAIN Iter 170340: lr = 0.216102,	loss = 2.479651,	Top-1 err = 0.389209,	Top-5 err = 0.170850,	data_time = 0.050243,	train_time = 0.798506
[2019-08-23 23:28:23,758] TRAIN Iter 170360: lr = 0.216068,	loss = 2.519969,	Top-1 err = 0.390381,	Top-5 err = 0.176611,	data_time = 0.050701,	train_time = 0.355688
[2019-08-23 23:28:37,822] TRAIN Iter 170380: lr = 0.216035,	loss = 2.634152,	Top-1 err = 0.390332,	Top-5 err = 0.177686,	data_time = 0.050325,	train_time = 0.703186
[2019-08-23 23:28:45,354] TRAIN Iter 170400: lr = 0.216002,	loss = 2.639206,	Top-1 err = 0.389697,	Top-5 err = 0.176660,	data_time = 0.050585,	train_time = 0.376616
[2019-08-23 23:28:59,522] TRAIN Iter 170420: lr = 0.215968,	loss = 2.610667,	Top-1 err = 0.394629,	Top-5 err = 0.181641,	data_time = 0.050408,	train_time = 0.708378
[2019-08-23 23:29:13,151] TRAIN Iter 170440: lr = 0.215935,	loss = 2.638590,	Top-1 err = 0.392285,	Top-5 err = 0.172314,	data_time = 0.050756,	train_time = 0.681434
[2019-08-23 23:29:20,074] TRAIN Iter 170460: lr = 0.215902,	loss = 2.736086,	Top-1 err = 0.391162,	Top-5 err = 0.177197,	data_time = 0.050504,	train_time = 0.346106
[2019-08-23 23:29:35,311] TRAIN Iter 170480: lr = 0.215868,	loss = 2.602952,	Top-1 err = 0.385986,	Top-5 err = 0.176270,	data_time = 0.050458,	train_time = 0.761838
[2019-08-23 23:29:47,334] TRAIN Iter 170500: lr = 0.215835,	loss = 2.539675,	Top-1 err = 0.389160,	Top-5 err = 0.175635,	data_time = 0.112083,	train_time = 0.601169
[2019-08-23 23:29:56,207] TRAIN Iter 170520: lr = 0.215802,	loss = 2.572745,	Top-1 err = 0.389746,	Top-5 err = 0.177539,	data_time = 0.050630,	train_time = 0.443613
[2019-08-23 23:30:11,346] TRAIN Iter 170540: lr = 0.215768,	loss = 2.714366,	Top-1 err = 0.392285,	Top-5 err = 0.173193,	data_time = 0.050582,	train_time = 0.756932
[2019-08-23 23:30:18,480] TRAIN Iter 170560: lr = 0.215735,	loss = 2.595897,	Top-1 err = 0.397607,	Top-5 err = 0.179688,	data_time = 0.050636,	train_time = 0.356687
[2019-08-23 23:30:36,591] TRAIN Iter 170580: lr = 0.215702,	loss = 2.588190,	Top-1 err = 0.393604,	Top-5 err = 0.177930,	data_time = 0.050387,	train_time = 0.905551
[2019-08-23 23:30:48,509] TRAIN Iter 170600: lr = 0.215668,	loss = 2.638032,	Top-1 err = 0.395361,	Top-5 err = 0.180664,	data_time = 0.050882,	train_time = 0.595865
[2019-08-23 23:30:58,615] TRAIN Iter 170620: lr = 0.215635,	loss = 2.579927,	Top-1 err = 0.390918,	Top-5 err = 0.180566,	data_time = 0.050382,	train_time = 0.505324
[2019-08-23 23:31:11,802] TRAIN Iter 170640: lr = 0.215602,	loss = 2.617121,	Top-1 err = 0.393994,	Top-5 err = 0.179297,	data_time = 0.050571,	train_time = 0.659299
[2019-08-23 23:31:25,687] TRAIN Iter 170660: lr = 0.215568,	loss = 2.621630,	Top-1 err = 0.396777,	Top-5 err = 0.181348,	data_time = 0.050606,	train_time = 0.694239
[2019-08-23 23:31:34,009] TRAIN Iter 170680: lr = 0.215535,	loss = 2.460656,	Top-1 err = 0.387793,	Top-5 err = 0.177002,	data_time = 0.050768,	train_time = 0.416071
[2019-08-23 23:31:52,846] TRAIN Iter 170700: lr = 0.215502,	loss = 2.577414,	Top-1 err = 0.396191,	Top-5 err = 0.178418,	data_time = 0.050510,	train_time = 0.941885
[2019-08-23 23:31:59,697] TRAIN Iter 170720: lr = 0.215468,	loss = 2.637156,	Top-1 err = 0.393115,	Top-5 err = 0.182666,	data_time = 0.051032,	train_time = 0.342503
[2019-08-23 23:32:14,943] TRAIN Iter 170740: lr = 0.215435,	loss = 2.702448,	Top-1 err = 0.394824,	Top-5 err = 0.179395,	data_time = 0.050483,	train_time = 0.762311
[2019-08-23 23:32:32,171] TRAIN Iter 170760: lr = 0.215402,	loss = 2.675058,	Top-1 err = 0.390186,	Top-5 err = 0.177100,	data_time = 0.050789,	train_time = 0.861357
[2019-08-23 23:32:39,689] TRAIN Iter 170780: lr = 0.215368,	loss = 2.732357,	Top-1 err = 0.395508,	Top-5 err = 0.180078,	data_time = 0.138762,	train_time = 0.375913
[2019-08-23 23:32:54,184] TRAIN Iter 170800: lr = 0.215335,	loss = 2.658842,	Top-1 err = 0.400732,	Top-5 err = 0.179199,	data_time = 0.050457,	train_time = 0.724727
[2019-08-23 23:33:03,147] TRAIN Iter 170820: lr = 0.215302,	loss = 2.621092,	Top-1 err = 0.390088,	Top-5 err = 0.176123,	data_time = 0.050481,	train_time = 0.448144
[2019-08-23 23:33:16,922] TRAIN Iter 170840: lr = 0.215268,	loss = 2.586458,	Top-1 err = 0.390967,	Top-5 err = 0.176563,	data_time = 0.050227,	train_time = 0.688714
[2019-08-23 23:33:32,351] TRAIN Iter 170860: lr = 0.215235,	loss = 2.667403,	Top-1 err = 0.397754,	Top-5 err = 0.180811,	data_time = 0.157562,	train_time = 0.771454
[2019-08-23 23:33:39,792] TRAIN Iter 170880: lr = 0.215202,	loss = 2.564793,	Top-1 err = 0.392725,	Top-5 err = 0.176074,	data_time = 0.051664,	train_time = 0.372017
[2019-08-23 23:33:54,149] TRAIN Iter 170900: lr = 0.215168,	loss = 2.685393,	Top-1 err = 0.398877,	Top-5 err = 0.183447,	data_time = 0.050544,	train_time = 0.717821
[2019-08-23 23:34:08,529] TRAIN Iter 170920: lr = 0.215135,	loss = 2.595471,	Top-1 err = 0.392969,	Top-5 err = 0.175635,	data_time = 0.102431,	train_time = 0.719020
[2019-08-23 23:34:16,040] TRAIN Iter 170940: lr = 0.215102,	loss = 2.542191,	Top-1 err = 0.384619,	Top-5 err = 0.177051,	data_time = 0.050539,	train_time = 0.375515
[2019-08-23 23:34:32,976] TRAIN Iter 170960: lr = 0.215068,	loss = 2.536239,	Top-1 err = 0.395898,	Top-5 err = 0.184180,	data_time = 0.138047,	train_time = 0.846782
[2019-08-23 23:34:45,791] TRAIN Iter 170980: lr = 0.215035,	loss = 2.622442,	Top-1 err = 0.399707,	Top-5 err = 0.180469,	data_time = 0.050993,	train_time = 0.640728
[2019-08-23 23:34:56,482] TRAIN Iter 171000: lr = 0.215002,	loss = 2.668067,	Top-1 err = 0.397559,	Top-5 err = 0.181152,	data_time = 0.050322,	train_time = 0.534571
[2019-08-23 23:35:12,553] TRAIN Iter 171020: lr = 0.214968,	loss = 2.658989,	Top-1 err = 0.392969,	Top-5 err = 0.177344,	data_time = 0.123376,	train_time = 0.803535
[2019-08-23 23:35:19,543] TRAIN Iter 171040: lr = 0.214935,	loss = 2.526411,	Top-1 err = 0.394678,	Top-5 err = 0.178564,	data_time = 0.050545,	train_time = 0.349483
[2019-08-23 23:35:36,028] TRAIN Iter 171060: lr = 0.214902,	loss = 2.589728,	Top-1 err = 0.388477,	Top-5 err = 0.173389,	data_time = 0.050386,	train_time = 0.824233
[2019-08-23 23:35:50,887] TRAIN Iter 171080: lr = 0.214868,	loss = 2.535215,	Top-1 err = 0.390479,	Top-5 err = 0.179053,	data_time = 0.137969,	train_time = 0.742916
[2019-08-23 23:35:58,186] TRAIN Iter 171100: lr = 0.214835,	loss = 2.649966,	Top-1 err = 0.386182,	Top-5 err = 0.177051,	data_time = 0.050492,	train_time = 0.364928
[2019-08-23 23:36:13,770] TRAIN Iter 171120: lr = 0.214802,	loss = 2.637467,	Top-1 err = 0.397266,	Top-5 err = 0.180859,	data_time = 0.121342,	train_time = 0.779170
[2019-08-23 23:36:29,652] TRAIN Iter 171140: lr = 0.214768,	loss = 2.575369,	Top-1 err = 0.400830,	Top-5 err = 0.183496,	data_time = 0.050589,	train_time = 0.794122
[2019-08-23 23:36:37,826] TRAIN Iter 171160: lr = 0.214735,	loss = 2.656340,	Top-1 err = 0.391357,	Top-5 err = 0.179883,	data_time = 0.050827,	train_time = 0.408650
[2019-08-23 23:36:53,734] TRAIN Iter 171180: lr = 0.214702,	loss = 2.682637,	Top-1 err = 0.397852,	Top-5 err = 0.178662,	data_time = 0.050630,	train_time = 0.795413
[2019-08-23 23:37:01,052] TRAIN Iter 171200: lr = 0.214668,	loss = 2.640217,	Top-1 err = 0.386572,	Top-5 err = 0.176270,	data_time = 0.050465,	train_time = 0.365880
[2019-08-23 23:37:17,649] TRAIN Iter 171220: lr = 0.214635,	loss = 2.629479,	Top-1 err = 0.393848,	Top-5 err = 0.180420,	data_time = 0.050516,	train_time = 0.829841
[2019-08-23 23:37:34,228] TRAIN Iter 171240: lr = 0.214602,	loss = 2.601492,	Top-1 err = 0.396680,	Top-5 err = 0.182959,	data_time = 0.050375,	train_time = 0.828934
[2019-08-23 23:37:41,817] TRAIN Iter 171260: lr = 0.214568,	loss = 2.592405,	Top-1 err = 0.400195,	Top-5 err = 0.180029,	data_time = 0.050374,	train_time = 0.379429
[2019-08-23 23:37:58,699] TRAIN Iter 171280: lr = 0.214535,	loss = 2.629608,	Top-1 err = 0.391260,	Top-5 err = 0.175098,	data_time = 0.050360,	train_time = 0.844108
[2019-08-23 23:38:15,486] TRAIN Iter 171300: lr = 0.214502,	loss = 2.638887,	Top-1 err = 0.394434,	Top-5 err = 0.177393,	data_time = 0.050342,	train_time = 0.839295
[2019-08-23 23:38:23,284] TRAIN Iter 171320: lr = 0.214468,	loss = 2.549225,	Top-1 err = 0.398535,	Top-5 err = 0.181348,	data_time = 0.050322,	train_time = 0.389917
[2019-08-23 23:38:40,516] TRAIN Iter 171340: lr = 0.214435,	loss = 2.601121,	Top-1 err = 0.392822,	Top-5 err = 0.179053,	data_time = 0.050494,	train_time = 0.861578
[2019-08-23 23:38:47,368] TRAIN Iter 171360: lr = 0.214402,	loss = 2.644907,	Top-1 err = 0.397656,	Top-5 err = 0.181836,	data_time = 0.050215,	train_time = 0.342577
[2019-08-23 23:39:06,374] TRAIN Iter 171380: lr = 0.214368,	loss = 2.706218,	Top-1 err = 0.396484,	Top-5 err = 0.179199,	data_time = 0.050759,	train_time = 0.950308
[2019-08-23 23:39:22,562] TRAIN Iter 171400: lr = 0.214335,	loss = 2.538457,	Top-1 err = 0.396875,	Top-5 err = 0.181592,	data_time = 0.050182,	train_time = 0.809399
[2019-08-23 23:39:29,965] TRAIN Iter 171420: lr = 0.214302,	loss = 2.591074,	Top-1 err = 0.395605,	Top-5 err = 0.178125,	data_time = 0.050103,	train_time = 0.370114
[2019-08-23 23:39:46,442] TRAIN Iter 171440: lr = 0.214268,	loss = 2.534340,	Top-1 err = 0.388672,	Top-5 err = 0.179395,	data_time = 0.049911,	train_time = 0.823820
[2019-08-23 23:39:57,314] TRAIN Iter 171460: lr = 0.214235,	loss = 2.876302,	Top-1 err = 0.397874,	Top-5 err = 0.182103,	data_time = 0.007080,	train_time = 0.543601
[2019-08-23 23:40:46,348] TRAIN Iter 171480: lr = 0.214202,	loss = 2.618800,	Top-1 err = 0.387793,	Top-5 err = 0.174268,	data_time = 0.050478,	train_time = 2.451701
[2019-08-23 23:41:02,626] TRAIN Iter 171500: lr = 0.214168,	loss = 2.610708,	Top-1 err = 0.390820,	Top-5 err = 0.176953,	data_time = 0.050549,	train_time = 0.813859
[2019-08-23 23:41:10,396] TRAIN Iter 171520: lr = 0.214135,	loss = 2.540940,	Top-1 err = 0.387939,	Top-5 err = 0.175146,	data_time = 0.050328,	train_time = 0.388499
[2019-08-23 23:41:21,926] TRAIN Iter 171540: lr = 0.214102,	loss = 2.633150,	Top-1 err = 0.386230,	Top-5 err = 0.177148,	data_time = 0.050581,	train_time = 0.576482
[2019-08-23 23:41:35,160] TRAIN Iter 171560: lr = 0.214068,	loss = 2.533174,	Top-1 err = 0.389551,	Top-5 err = 0.172559,	data_time = 3.345090,	train_time = 0.661677
[2019-08-23 23:41:42,650] TRAIN Iter 171580: lr = 0.214035,	loss = 2.589326,	Top-1 err = 0.387256,	Top-5 err = 0.174512,	data_time = 0.050276,	train_time = 0.374504
[2019-08-23 23:41:58,402] TRAIN Iter 171600: lr = 0.214002,	loss = 2.601129,	Top-1 err = 0.387695,	Top-5 err = 0.175342,	data_time = 0.050814,	train_time = 0.787591
[2019-08-23 23:42:05,750] TRAIN Iter 171620: lr = 0.213968,	loss = 2.584815,	Top-1 err = 0.389551,	Top-5 err = 0.173389,	data_time = 0.050407,	train_time = 0.367395
[2019-08-23 23:42:21,527] TRAIN Iter 171640: lr = 0.213935,	loss = 2.698995,	Top-1 err = 0.391455,	Top-5 err = 0.177197,	data_time = 0.050649,	train_time = 0.788817
[2019-08-23 23:42:37,302] TRAIN Iter 171660: lr = 0.213902,	loss = 2.504383,	Top-1 err = 0.385352,	Top-5 err = 0.174072,	data_time = 0.050463,	train_time = 0.788711
[2019-08-23 23:42:44,457] TRAIN Iter 171680: lr = 0.213868,	loss = 2.570953,	Top-1 err = 0.389209,	Top-5 err = 0.171582,	data_time = 0.050522,	train_time = 0.357745
[2019-08-23 23:42:59,388] TRAIN Iter 171700: lr = 0.213835,	loss = 2.587942,	Top-1 err = 0.393359,	Top-5 err = 0.175293,	data_time = 0.050443,	train_time = 0.746552
[2019-08-23 23:43:08,048] TRAIN Iter 171720: lr = 0.213802,	loss = 2.573866,	Top-1 err = 0.389111,	Top-5 err = 0.175830,	data_time = 0.686873,	train_time = 0.432999
[2019-08-23 23:43:19,967] TRAIN Iter 171740: lr = 0.213768,	loss = 2.531546,	Top-1 err = 0.394238,	Top-5 err = 0.175879,	data_time = 0.050705,	train_time = 0.595922
[2019-08-23 23:43:35,292] TRAIN Iter 171760: lr = 0.213735,	loss = 2.533520,	Top-1 err = 0.393311,	Top-5 err = 0.176758,	data_time = 0.050458,	train_time = 0.766234
[2019-08-23 23:43:42,875] TRAIN Iter 171780: lr = 0.213702,	loss = 2.591851,	Top-1 err = 0.388574,	Top-5 err = 0.174658,	data_time = 0.050351,	train_time = 0.379143
[2019-08-23 23:43:56,344] TRAIN Iter 171800: lr = 0.213668,	loss = 2.550809,	Top-1 err = 0.395020,	Top-5 err = 0.177051,	data_time = 0.050344,	train_time = 0.673413
[2019-08-23 23:44:12,959] TRAIN Iter 171820: lr = 0.213635,	loss = 2.621689,	Top-1 err = 0.390674,	Top-5 err = 0.176758,	data_time = 0.050338,	train_time = 0.830744
[2019-08-23 23:44:20,188] TRAIN Iter 171840: lr = 0.213602,	loss = 2.609419,	Top-1 err = 0.394287,	Top-5 err = 0.176172,	data_time = 0.050829,	train_time = 0.361425
[2019-08-23 23:44:35,050] TRAIN Iter 171860: lr = 0.213568,	loss = 2.622493,	Top-1 err = 0.387793,	Top-5 err = 0.171094,	data_time = 0.050601,	train_time = 0.743081
[2019-08-23 23:44:45,954] TRAIN Iter 171880: lr = 0.213535,	loss = 2.658085,	Top-1 err = 0.390527,	Top-5 err = 0.178467,	data_time = 3.002782,	train_time = 0.545185
[2019-08-23 23:44:56,455] TRAIN Iter 171900: lr = 0.213502,	loss = 2.604989,	Top-1 err = 0.399023,	Top-5 err = 0.178320,	data_time = 0.050745,	train_time = 0.525067
[2019-08-23 23:45:11,907] TRAIN Iter 171920: lr = 0.213468,	loss = 2.523667,	Top-1 err = 0.393945,	Top-5 err = 0.177246,	data_time = 0.050733,	train_time = 0.772555
[2019-08-23 23:45:19,493] TRAIN Iter 171940: lr = 0.213435,	loss = 2.608310,	Top-1 err = 0.393555,	Top-5 err = 0.175977,	data_time = 0.050460,	train_time = 0.379323
[2019-08-23 23:45:32,810] TRAIN Iter 171960: lr = 0.213402,	loss = 2.563442,	Top-1 err = 0.393213,	Top-5 err = 0.176709,	data_time = 0.050407,	train_time = 0.665827
[2019-08-23 23:45:50,445] TRAIN Iter 171980: lr = 0.213368,	loss = 2.619314,	Top-1 err = 0.388525,	Top-5 err = 0.178223,	data_time = 0.050810,	train_time = 0.881741
[2019-08-23 23:45:57,278] TRAIN Iter 172000: lr = 0.213335,	loss = 2.579777,	Top-1 err = 0.392285,	Top-5 err = 0.180176,	data_time = 0.050469,	train_time = 0.341635
[2019-08-23 23:46:13,130] TRAIN Iter 172020: lr = 0.213302,	loss = 2.594656,	Top-1 err = 0.383984,	Top-5 err = 0.175977,	data_time = 0.050681,	train_time = 0.792572
[2019-08-23 23:46:29,987] TRAIN Iter 172040: lr = 0.213268,	loss = 2.632226,	Top-1 err = 0.389844,	Top-5 err = 0.175977,	data_time = 7.956156,	train_time = 0.842816
[2019-08-23 23:46:37,421] TRAIN Iter 172060: lr = 0.213235,	loss = 2.578363,	Top-1 err = 0.383691,	Top-5 err = 0.173291,	data_time = 0.050548,	train_time = 0.371684
[2019-08-23 23:46:50,743] TRAIN Iter 172080: lr = 0.213202,	loss = 2.577074,	Top-1 err = 0.391553,	Top-5 err = 0.173926,	data_time = 0.050810,	train_time = 0.666099
[2019-08-23 23:46:57,958] TRAIN Iter 172100: lr = 0.213168,	loss = 2.624446,	Top-1 err = 0.393848,	Top-5 err = 0.177783,	data_time = 0.050678,	train_time = 0.360711
[2019-08-23 23:47:13,217] TRAIN Iter 172120: lr = 0.213135,	loss = 2.618770,	Top-1 err = 0.394531,	Top-5 err = 0.173486,	data_time = 0.051129,	train_time = 0.762943
[2019-08-23 23:47:29,649] TRAIN Iter 172140: lr = 0.213102,	loss = 2.683980,	Top-1 err = 0.395410,	Top-5 err = 0.178320,	data_time = 0.050431,	train_time = 0.821582
[2019-08-23 23:47:36,581] TRAIN Iter 172160: lr = 0.213068,	loss = 2.563822,	Top-1 err = 0.391504,	Top-5 err = 0.174854,	data_time = 0.050498,	train_time = 0.346605
[2019-08-23 23:47:52,436] TRAIN Iter 172180: lr = 0.213035,	loss = 2.633910,	Top-1 err = 0.393848,	Top-5 err = 0.175342,	data_time = 0.050355,	train_time = 0.792723
[2019-08-23 23:48:06,889] TRAIN Iter 172200: lr = 0.213002,	loss = 2.522421,	Top-1 err = 0.393262,	Top-5 err = 0.175342,	data_time = 6.798194,	train_time = 0.722653
[2019-08-23 23:48:14,220] TRAIN Iter 172220: lr = 0.212968,	loss = 2.621061,	Top-1 err = 0.398242,	Top-5 err = 0.180322,	data_time = 0.050431,	train_time = 0.366524
[2019-08-23 23:48:29,771] TRAIN Iter 172240: lr = 0.212935,	loss = 2.576390,	Top-1 err = 0.393408,	Top-5 err = 0.175684,	data_time = 0.050480,	train_time = 0.777523
[2019-08-23 23:48:36,765] TRAIN Iter 172260: lr = 0.212902,	loss = 2.580524,	Top-1 err = 0.390967,	Top-5 err = 0.176611,	data_time = 0.150930,	train_time = 0.349686
[2019-08-23 23:48:53,426] TRAIN Iter 172280: lr = 0.212868,	loss = 2.557519,	Top-1 err = 0.395215,	Top-5 err = 0.177295,	data_time = 0.050549,	train_time = 0.833051
[2019-08-23 23:49:09,356] TRAIN Iter 172300: lr = 0.212835,	loss = 2.506636,	Top-1 err = 0.395166,	Top-5 err = 0.178809,	data_time = 0.050480,	train_time = 0.796497
[2019-08-23 23:49:16,333] TRAIN Iter 172320: lr = 0.212802,	loss = 2.504668,	Top-1 err = 0.395264,	Top-5 err = 0.179541,	data_time = 0.050221,	train_time = 0.348858
[2019-08-23 23:49:32,669] TRAIN Iter 172340: lr = 0.212768,	loss = 2.543571,	Top-1 err = 0.392090,	Top-5 err = 0.179980,	data_time = 0.050463,	train_time = 0.816745
[2019-08-23 23:49:48,267] TRAIN Iter 172360: lr = 0.212735,	loss = 2.529611,	Top-1 err = 0.390039,	Top-5 err = 0.176270,	data_time = 7.226383,	train_time = 0.779883
[2019-08-23 23:49:55,928] TRAIN Iter 172380: lr = 0.212702,	loss = 2.676494,	Top-1 err = 0.394336,	Top-5 err = 0.176172,	data_time = 0.112422,	train_time = 0.383029
[2019-08-23 23:50:12,422] TRAIN Iter 172400: lr = 0.212668,	loss = 2.655347,	Top-1 err = 0.400488,	Top-5 err = 0.178418,	data_time = 0.050473,	train_time = 0.824729
[2019-08-23 23:50:19,521] TRAIN Iter 172420: lr = 0.212635,	loss = 2.573140,	Top-1 err = 0.390625,	Top-5 err = 0.180713,	data_time = 0.050344,	train_time = 0.354895
[2019-08-23 23:50:35,317] TRAIN Iter 172440: lr = 0.212602,	loss = 2.640890,	Top-1 err = 0.389062,	Top-5 err = 0.173828,	data_time = 0.050494,	train_time = 0.789787
[2019-08-23 23:50:50,549] TRAIN Iter 172460: lr = 0.212568,	loss = 2.514754,	Top-1 err = 0.395410,	Top-5 err = 0.179834,	data_time = 0.050624,	train_time = 0.761595
[2019-08-23 23:50:58,283] TRAIN Iter 172480: lr = 0.212535,	loss = 2.565816,	Top-1 err = 0.393848,	Top-5 err = 0.182031,	data_time = 0.137169,	train_time = 0.386711
[2019-08-23 23:51:14,738] TRAIN Iter 172500: lr = 0.212502,	loss = 2.628890,	Top-1 err = 0.400732,	Top-5 err = 0.182861,	data_time = 0.050365,	train_time = 0.822727
[2019-08-23 23:51:31,162] TRAIN Iter 172520: lr = 0.212468,	loss = 2.547421,	Top-1 err = 0.399268,	Top-5 err = 0.180225,	data_time = 5.415941,	train_time = 0.821192
[2019-08-23 23:51:38,258] TRAIN Iter 172540: lr = 0.212435,	loss = 2.631279,	Top-1 err = 0.394434,	Top-5 err = 0.180469,	data_time = 0.050263,	train_time = 0.354775
[2019-08-23 23:51:54,042] TRAIN Iter 172560: lr = 0.212402,	loss = 2.589472,	Top-1 err = 0.396143,	Top-5 err = 0.178662,	data_time = 0.050483,	train_time = 0.789201
[2019-08-23 23:52:00,975] TRAIN Iter 172580: lr = 0.212368,	loss = 2.632044,	Top-1 err = 0.389160,	Top-5 err = 0.177197,	data_time = 0.099484,	train_time = 0.346628
[2019-08-23 23:52:18,458] TRAIN Iter 172600: lr = 0.212335,	loss = 2.605778,	Top-1 err = 0.396631,	Top-5 err = 0.178711,	data_time = 0.050366,	train_time = 0.874129
[2019-08-23 23:52:33,537] TRAIN Iter 172620: lr = 0.212302,	loss = 2.662268,	Top-1 err = 0.402295,	Top-5 err = 0.181348,	data_time = 0.050343,	train_time = 0.753950
[2019-08-23 23:52:40,575] TRAIN Iter 172640: lr = 0.212268,	loss = 2.625236,	Top-1 err = 0.396729,	Top-5 err = 0.180811,	data_time = 0.050576,	train_time = 0.351862
[2019-08-23 23:52:57,949] TRAIN Iter 172660: lr = 0.212235,	loss = 2.598897,	Top-1 err = 0.395166,	Top-5 err = 0.181055,	data_time = 0.050021,	train_time = 0.868672
[2019-08-23 23:53:14,664] TRAIN Iter 172680: lr = 0.212202,	loss = 2.533388,	Top-1 err = 0.389209,	Top-5 err = 0.174023,	data_time = 7.227962,	train_time = 0.835762
[2019-08-23 23:53:21,215] TRAIN Iter 172700: lr = 0.212168,	loss = 2.659179,	Top-1 err = 0.404004,	Top-5 err = 0.182812,	data_time = 0.049862,	train_time = 0.327550
[2019-08-23 23:54:06,323] TRAIN Iter 172720: lr = 0.212135,	loss = 2.602040,	Top-1 err = 0.395001,	Top-5 err = 0.177480,	data_time = 0.150907,	train_time = 2.255363
[2019-08-23 23:54:13,174] TRAIN Iter 172740: lr = 0.212102,	loss = 2.615646,	Top-1 err = 0.393066,	Top-5 err = 0.179297,	data_time = 0.050248,	train_time = 0.342538
[2019-08-23 23:54:29,640] TRAIN Iter 172760: lr = 0.212068,	loss = 2.565351,	Top-1 err = 0.386963,	Top-5 err = 0.171777,	data_time = 0.050733,	train_time = 0.823284
[2019-08-23 23:54:44,730] TRAIN Iter 172780: lr = 0.212035,	loss = 2.487475,	Top-1 err = 0.390576,	Top-5 err = 0.170313,	data_time = 0.090296,	train_time = 0.754509
[2019-08-23 23:54:51,702] TRAIN Iter 172800: lr = 0.212002,	loss = 2.556735,	Top-1 err = 0.383643,	Top-5 err = 0.169385,	data_time = 0.050231,	train_time = 0.348551
[2019-08-23 23:55:07,246] TRAIN Iter 172820: lr = 0.211968,	loss = 2.641680,	Top-1 err = 0.386865,	Top-5 err = 0.174463,	data_time = 0.050561,	train_time = 0.777212
[2019-08-23 23:55:14,884] TRAIN Iter 172840: lr = 0.211935,	loss = 2.626070,	Top-1 err = 0.383643,	Top-5 err = 0.172656,	data_time = 0.127569,	train_time = 0.381864
[2019-08-23 23:55:29,136] TRAIN Iter 172860: lr = 0.211902,	loss = 2.466560,	Top-1 err = 0.383105,	Top-5 err = 0.173926,	data_time = 0.050331,	train_time = 0.712566
[2019-08-23 23:55:44,722] TRAIN Iter 172880: lr = 0.211868,	loss = 2.488173,	Top-1 err = 0.391992,	Top-5 err = 0.175342,	data_time = 0.050457,	train_time = 0.779300
[2019-08-23 23:55:51,933] TRAIN Iter 172900: lr = 0.211835,	loss = 2.599854,	Top-1 err = 0.385156,	Top-5 err = 0.172021,	data_time = 0.050436,	train_time = 0.360542
[2019-08-23 23:56:09,566] TRAIN Iter 172920: lr = 0.211802,	loss = 2.553876,	Top-1 err = 0.392334,	Top-5 err = 0.176465,	data_time = 0.050757,	train_time = 0.881649
[2019-08-23 23:56:21,872] TRAIN Iter 172940: lr = 0.211768,	loss = 2.645480,	Top-1 err = 0.392090,	Top-5 err = 0.177002,	data_time = 0.050452,	train_time = 0.615252
[2019-08-23 23:56:29,179] TRAIN Iter 172960: lr = 0.211735,	loss = 2.610198,	Top-1 err = 0.394775,	Top-5 err = 0.177734,	data_time = 0.050424,	train_time = 0.365376
[2019-08-23 23:56:42,839] TRAIN Iter 172980: lr = 0.211702,	loss = 2.476892,	Top-1 err = 0.384814,	Top-5 err = 0.173193,	data_time = 0.634641,	train_time = 0.682967
[2019-08-23 23:56:49,958] TRAIN Iter 173000: lr = 0.211668,	loss = 2.577347,	Top-1 err = 0.392627,	Top-5 err = 0.179492,	data_time = 0.050471,	train_time = 0.355954
[2019-08-23 23:57:05,468] TRAIN Iter 173020: lr = 0.211635,	loss = 2.642642,	Top-1 err = 0.393262,	Top-5 err = 0.180225,	data_time = 0.050454,	train_time = 0.775449
[2019-08-23 23:57:19,781] TRAIN Iter 173040: lr = 0.211602,	loss = 2.577125,	Top-1 err = 0.385400,	Top-5 err = 0.176807,	data_time = 1.134560,	train_time = 0.715643
[2019-08-23 23:57:26,823] TRAIN Iter 173060: lr = 0.211568,	loss = 2.596312,	Top-1 err = 0.395020,	Top-5 err = 0.175439,	data_time = 0.050319,	train_time = 0.352087
[2019-08-23 23:57:41,812] TRAIN Iter 173080: lr = 0.211535,	loss = 2.680750,	Top-1 err = 0.392236,	Top-5 err = 0.178906,	data_time = 0.050073,	train_time = 0.749439
[2019-08-23 23:57:57,754] TRAIN Iter 173100: lr = 0.211502,	loss = 2.506839,	Top-1 err = 0.386523,	Top-5 err = 0.172559,	data_time = 1.458689,	train_time = 0.797066
[2019-08-23 23:58:05,186] TRAIN Iter 173120: lr = 0.211468,	loss = 2.594320,	Top-1 err = 0.389502,	Top-5 err = 0.174756,	data_time = 0.050492,	train_time = 0.371601
[2019-08-23 23:58:20,394] TRAIN Iter 173140: lr = 0.211435,	loss = 2.570033,	Top-1 err = 0.383643,	Top-5 err = 0.172803,	data_time = 1.027911,	train_time = 0.760381
[2019-08-23 23:58:28,775] TRAIN Iter 173160: lr = 0.211402,	loss = 2.511778,	Top-1 err = 0.385596,	Top-5 err = 0.172314,	data_time = 0.050565,	train_time = 0.419024
[2019-08-23 23:58:41,619] TRAIN Iter 173180: lr = 0.211368,	loss = 2.667046,	Top-1 err = 0.394141,	Top-5 err = 0.181299,	data_time = 0.050553,	train_time = 0.642219
[2019-08-23 23:58:56,205] TRAIN Iter 173200: lr = 0.211335,	loss = 2.634626,	Top-1 err = 0.391992,	Top-5 err = 0.174561,	data_time = 0.050895,	train_time = 0.729249
[2019-08-23 23:59:03,135] TRAIN Iter 173220: lr = 0.211302,	loss = 2.588066,	Top-1 err = 0.389209,	Top-5 err = 0.174170,	data_time = 0.050467,	train_time = 0.346505
[2019-08-23 23:59:19,490] TRAIN Iter 173240: lr = 0.211268,	loss = 2.602264,	Top-1 err = 0.390479,	Top-5 err = 0.179980,	data_time = 0.050489,	train_time = 0.817745
[2019-08-23 23:59:34,653] TRAIN Iter 173260: lr = 0.211235,	loss = 2.632087,	Top-1 err = 0.391455,	Top-5 err = 0.179492,	data_time = 0.050585,	train_time = 0.758147
[2019-08-23 23:59:41,789] TRAIN Iter 173280: lr = 0.211202,	loss = 2.623360,	Top-1 err = 0.393213,	Top-5 err = 0.180518,	data_time = 0.050876,	train_time = 0.356758
[2019-08-23 23:59:58,613] TRAIN Iter 173300: lr = 0.211168,	loss = 2.634680,	Top-1 err = 0.397412,	Top-5 err = 0.180176,	data_time = 0.050561,	train_time = 0.841191
[2019-08-24 00:00:06,501] TRAIN Iter 173320: lr = 0.211135,	loss = 2.557906,	Top-1 err = 0.386523,	Top-5 err = 0.174707,	data_time = 0.050706,	train_time = 0.394412
[2019-08-24 00:00:20,790] TRAIN Iter 173340: lr = 0.211102,	loss = 2.647436,	Top-1 err = 0.390674,	Top-5 err = 0.176611,	data_time = 0.050630,	train_time = 0.714410
[2019-08-24 00:00:36,722] TRAIN Iter 173360: lr = 0.211068,	loss = 2.678008,	Top-1 err = 0.394580,	Top-5 err = 0.180273,	data_time = 0.050729,	train_time = 0.796604
[2019-08-24 00:00:44,426] TRAIN Iter 173380: lr = 0.211035,	loss = 2.651373,	Top-1 err = 0.389697,	Top-5 err = 0.176514,	data_time = 0.051085,	train_time = 0.385187
[2019-08-24 00:00:59,674] TRAIN Iter 173400: lr = 0.211002,	loss = 2.712200,	Top-1 err = 0.396387,	Top-5 err = 0.182812,	data_time = 0.051236,	train_time = 0.762369
[2019-08-24 00:01:14,707] TRAIN Iter 173420: lr = 0.210968,	loss = 2.620717,	Top-1 err = 0.393652,	Top-5 err = 0.181934,	data_time = 0.050681,	train_time = 0.751619
[2019-08-24 00:01:21,659] TRAIN Iter 173440: lr = 0.210935,	loss = 2.621209,	Top-1 err = 0.393164,	Top-5 err = 0.172070,	data_time = 0.111087,	train_time = 0.347601
[2019-08-24 00:01:36,388] TRAIN Iter 173460: lr = 0.210902,	loss = 2.611761,	Top-1 err = 0.393457,	Top-5 err = 0.178320,	data_time = 0.050585,	train_time = 0.736429
[2019-08-24 00:01:43,488] TRAIN Iter 173480: lr = 0.210868,	loss = 2.638496,	Top-1 err = 0.396484,	Top-5 err = 0.177344,	data_time = 0.050497,	train_time = 0.355010
[2019-08-24 00:01:59,280] TRAIN Iter 173500: lr = 0.210835,	loss = 2.647084,	Top-1 err = 0.396631,	Top-5 err = 0.181592,	data_time = 0.050522,	train_time = 0.789552
[2019-08-24 00:02:16,232] TRAIN Iter 173520: lr = 0.210802,	loss = 2.649471,	Top-1 err = 0.389258,	Top-5 err = 0.174170,	data_time = 1.917850,	train_time = 0.847600
[2019-08-24 00:02:23,574] TRAIN Iter 173540: lr = 0.210768,	loss = 2.533233,	Top-1 err = 0.396729,	Top-5 err = 0.182178,	data_time = 0.050816,	train_time = 0.367091
[2019-08-24 00:02:39,513] TRAIN Iter 173560: lr = 0.210735,	loss = 2.570897,	Top-1 err = 0.390283,	Top-5 err = 0.175586,	data_time = 0.050367,	train_time = 0.796911
[2019-08-24 00:02:53,291] TRAIN Iter 173580: lr = 0.210702,	loss = 2.642344,	Top-1 err = 0.396143,	Top-5 err = 0.180713,	data_time = 0.050569,	train_time = 0.688910
[2019-08-24 00:03:02,968] TRAIN Iter 173600: lr = 0.210668,	loss = 2.635500,	Top-1 err = 0.394629,	Top-5 err = 0.180322,	data_time = 0.050567,	train_time = 0.483813
[2019-08-24 00:03:18,560] TRAIN Iter 173620: lr = 0.210635,	loss = 2.581471,	Top-1 err = 0.392969,	Top-5 err = 0.180762,	data_time = 0.050441,	train_time = 0.779624
[2019-08-24 00:03:25,510] TRAIN Iter 173640: lr = 0.210602,	loss = 2.455255,	Top-1 err = 0.386377,	Top-5 err = 0.172168,	data_time = 0.050812,	train_time = 0.347485
[2019-08-24 00:03:42,216] TRAIN Iter 173660: lr = 0.210568,	loss = 2.576608,	Top-1 err = 0.391553,	Top-5 err = 0.178076,	data_time = 0.050292,	train_time = 0.835283
[2019-08-24 00:03:58,508] TRAIN Iter 173680: lr = 0.210535,	loss = 2.571675,	Top-1 err = 0.387793,	Top-5 err = 0.175244,	data_time = 0.050550,	train_time = 0.814558
[2019-08-24 00:04:05,593] TRAIN Iter 173700: lr = 0.210502,	loss = 2.582157,	Top-1 err = 0.387256,	Top-5 err = 0.172852,	data_time = 0.050384,	train_time = 0.354257
[2019-08-24 00:04:21,426] TRAIN Iter 173720: lr = 0.210468,	loss = 2.542936,	Top-1 err = 0.393018,	Top-5 err = 0.175146,	data_time = 0.050429,	train_time = 0.791621
[2019-08-24 00:04:37,332] TRAIN Iter 173740: lr = 0.210435,	loss = 2.570684,	Top-1 err = 0.396289,	Top-5 err = 0.180078,	data_time = 0.110795,	train_time = 0.795285
[2019-08-24 00:04:44,661] TRAIN Iter 173760: lr = 0.210402,	loss = 2.658511,	Top-1 err = 0.391602,	Top-5 err = 0.178271,	data_time = 0.050375,	train_time = 0.366440
[2019-08-24 00:05:01,165] TRAIN Iter 173780: lr = 0.210368,	loss = 2.659789,	Top-1 err = 0.393115,	Top-5 err = 0.178711,	data_time = 0.050474,	train_time = 0.825206
[2019-08-24 00:05:08,287] TRAIN Iter 173800: lr = 0.210335,	loss = 2.643903,	Top-1 err = 0.393652,	Top-5 err = 0.178418,	data_time = 0.050628,	train_time = 0.356073
[2019-08-24 00:05:24,439] TRAIN Iter 173820: lr = 0.210302,	loss = 2.620437,	Top-1 err = 0.389648,	Top-5 err = 0.173535,	data_time = 0.050532,	train_time = 0.807599
[2019-08-24 00:05:40,926] TRAIN Iter 173840: lr = 0.210268,	loss = 2.553147,	Top-1 err = 0.392871,	Top-5 err = 0.181689,	data_time = 0.050338,	train_time = 0.824336
[2019-08-24 00:05:47,725] TRAIN Iter 173860: lr = 0.210235,	loss = 2.618644,	Top-1 err = 0.391064,	Top-5 err = 0.176855,	data_time = 0.050418,	train_time = 0.339906
[2019-08-24 00:06:04,860] TRAIN Iter 173880: lr = 0.210202,	loss = 2.595101,	Top-1 err = 0.389062,	Top-5 err = 0.173437,	data_time = 0.050388,	train_time = 0.856755
[2019-08-24 00:06:21,712] TRAIN Iter 173900: lr = 0.210168,	loss = 2.648162,	Top-1 err = 0.393213,	Top-5 err = 0.176709,	data_time = 0.065605,	train_time = 0.842611
[2019-08-24 00:06:28,641] TRAIN Iter 173920: lr = 0.210135,	loss = 2.511053,	Top-1 err = 0.396143,	Top-5 err = 0.180518,	data_time = 0.050150,	train_time = 0.346391
[2019-08-24 00:06:44,547] TRAIN Iter 173940: lr = 0.210102,	loss = 2.598631,	Top-1 err = 0.391602,	Top-5 err = 0.180225,	data_time = 0.049888,	train_time = 0.795325
[2019-08-24 00:06:50,629] TRAIN Iter 173960: lr = 0.210068,	loss = 2.514971,	Top-1 err = 0.391455,	Top-5 err = 0.176660,	data_time = 0.050053,	train_time = 0.304049
[2019-08-24 00:07:40,039] TRAIN Iter 173980: lr = 0.210035,	loss = 2.657343,	Top-1 err = 0.397705,	Top-5 err = 0.180671,	data_time = 0.050460,	train_time = 2.470485
[2019-08-24 00:07:54,705] TRAIN Iter 174000: lr = 0.210002,	loss = 2.612472,	Top-1 err = 0.393262,	Top-5 err = 0.175732,	data_time = 0.050624,	train_time = 0.733322
[2019-08-24 00:08:01,699] TRAIN Iter 174020: lr = 0.209968,	loss = 2.558079,	Top-1 err = 0.385156,	Top-5 err = 0.174902,	data_time = 0.050168,	train_time = 0.349674
[2019-08-24 00:08:14,902] TRAIN Iter 174040: lr = 0.209935,	loss = 2.622253,	Top-1 err = 0.386084,	Top-5 err = 0.171631,	data_time = 0.050436,	train_time = 0.660126
[2019-08-24 00:08:21,761] TRAIN Iter 174060: lr = 0.209902,	loss = 2.556836,	Top-1 err = 0.387061,	Top-5 err = 0.173730,	data_time = 0.152466,	train_time = 0.342949
[2019-08-24 00:08:36,122] TRAIN Iter 174080: lr = 0.209868,	loss = 2.602113,	Top-1 err = 0.384619,	Top-5 err = 0.172217,	data_time = 0.050467,	train_time = 0.718022
[2019-08-24 00:08:52,831] TRAIN Iter 174100: lr = 0.209835,	loss = 2.613729,	Top-1 err = 0.386377,	Top-5 err = 0.176514,	data_time = 0.137471,	train_time = 0.835420
[2019-08-24 00:09:00,064] TRAIN Iter 174120: lr = 0.209802,	loss = 2.533788,	Top-1 err = 0.387305,	Top-5 err = 0.175977,	data_time = 0.050494,	train_time = 0.361642
[2019-08-24 00:09:15,228] TRAIN Iter 174140: lr = 0.209768,	loss = 2.573403,	Top-1 err = 0.385791,	Top-5 err = 0.172803,	data_time = 0.050335,	train_time = 0.758198
[2019-08-24 00:09:25,033] TRAIN Iter 174160: lr = 0.209735,	loss = 2.569139,	Top-1 err = 0.383984,	Top-5 err = 0.171143,	data_time = 0.050503,	train_time = 0.490233
[2019-08-24 00:09:35,605] TRAIN Iter 174180: lr = 0.209702,	loss = 2.495432,	Top-1 err = 0.382080,	Top-5 err = 0.171680,	data_time = 0.050349,	train_time = 0.528581
[2019-08-24 00:09:50,562] TRAIN Iter 174200: lr = 0.209668,	loss = 2.581928,	Top-1 err = 0.383203,	Top-5 err = 0.172705,	data_time = 0.050656,	train_time = 0.747828
[2019-08-24 00:09:57,335] TRAIN Iter 174220: lr = 0.209635,	loss = 2.660957,	Top-1 err = 0.391846,	Top-5 err = 0.178613,	data_time = 0.050966,	train_time = 0.338659
[2019-08-24 00:10:13,345] TRAIN Iter 174240: lr = 0.209602,	loss = 2.629394,	Top-1 err = 0.387354,	Top-5 err = 0.171094,	data_time = 0.050426,	train_time = 0.800461
[2019-08-24 00:10:29,067] TRAIN Iter 174260: lr = 0.209568,	loss = 2.576280,	Top-1 err = 0.391309,	Top-5 err = 0.177100,	data_time = 0.050375,	train_time = 0.786079
[2019-08-24 00:10:36,060] TRAIN Iter 174280: lr = 0.209535,	loss = 2.575697,	Top-1 err = 0.387061,	Top-5 err = 0.175537,	data_time = 0.050809,	train_time = 0.349644
[2019-08-24 00:10:50,944] TRAIN Iter 174300: lr = 0.209502,	loss = 2.566122,	Top-1 err = 0.387061,	Top-5 err = 0.178076,	data_time = 0.050471,	train_time = 0.744189
[2019-08-24 00:11:04,155] TRAIN Iter 174320: lr = 0.209468,	loss = 2.533043,	Top-1 err = 0.387402,	Top-5 err = 0.170264,	data_time = 0.111556,	train_time = 0.660540
[2019-08-24 00:11:12,404] TRAIN Iter 174340: lr = 0.209435,	loss = 2.608665,	Top-1 err = 0.390967,	Top-5 err = 0.173730,	data_time = 0.050723,	train_time = 0.412428
[2019-08-24 00:11:27,986] TRAIN Iter 174360: lr = 0.209402,	loss = 2.589510,	Top-1 err = 0.394629,	Top-5 err = 0.174658,	data_time = 0.050526,	train_time = 0.779118
[2019-08-24 00:11:35,537] TRAIN Iter 174380: lr = 0.209368,	loss = 2.574024,	Top-1 err = 0.397461,	Top-5 err = 0.177930,	data_time = 0.050312,	train_time = 0.377550
[2019-08-24 00:11:49,176] TRAIN Iter 174400: lr = 0.209335,	loss = 2.574425,	Top-1 err = 0.390430,	Top-5 err = 0.178711,	data_time = 0.050294,	train_time = 0.681920
[2019-08-24 00:12:04,681] TRAIN Iter 174420: lr = 0.209302,	loss = 2.623054,	Top-1 err = 0.387695,	Top-5 err = 0.174365,	data_time = 0.050478,	train_time = 0.775206
[2019-08-24 00:12:11,703] TRAIN Iter 174440: lr = 0.209268,	loss = 2.576954,	Top-1 err = 0.388965,	Top-5 err = 0.173877,	data_time = 0.050379,	train_time = 0.351087
[2019-08-24 00:12:26,803] TRAIN Iter 174460: lr = 0.209235,	loss = 2.571230,	Top-1 err = 0.390186,	Top-5 err = 0.172119,	data_time = 0.050338,	train_time = 0.755001
[2019-08-24 00:12:39,412] TRAIN Iter 174480: lr = 0.209202,	loss = 2.670700,	Top-1 err = 0.390967,	Top-5 err = 0.175146,	data_time = 0.050837,	train_time = 0.630441
[2019-08-24 00:12:49,510] TRAIN Iter 174500: lr = 0.209168,	loss = 2.648157,	Top-1 err = 0.391650,	Top-5 err = 0.176270,	data_time = 0.050170,	train_time = 0.504890
[2019-08-24 00:13:05,251] TRAIN Iter 174520: lr = 0.209135,	loss = 2.590530,	Top-1 err = 0.385498,	Top-5 err = 0.173145,	data_time = 0.050583,	train_time = 0.787020
[2019-08-24 00:13:12,169] TRAIN Iter 174540: lr = 0.209102,	loss = 2.577481,	Top-1 err = 0.387305,	Top-5 err = 0.171240,	data_time = 0.050539,	train_time = 0.345897
[2019-08-24 00:13:28,170] TRAIN Iter 174560: lr = 0.209068,	loss = 2.500013,	Top-1 err = 0.389307,	Top-5 err = 0.174951,	data_time = 0.050835,	train_time = 0.800031
[2019-08-24 00:13:43,504] TRAIN Iter 174580: lr = 0.209035,	loss = 2.580006,	Top-1 err = 0.394434,	Top-5 err = 0.177051,	data_time = 0.050456,	train_time = 0.766674
[2019-08-24 00:13:50,601] TRAIN Iter 174600: lr = 0.209002,	loss = 2.617794,	Top-1 err = 0.395117,	Top-5 err = 0.179199,	data_time = 0.050542,	train_time = 0.354879
[2019-08-24 00:14:05,274] TRAIN Iter 174620: lr = 0.208968,	loss = 2.534199,	Top-1 err = 0.385596,	Top-5 err = 0.175342,	data_time = 0.050331,	train_time = 0.733601
[2019-08-24 00:14:20,560] TRAIN Iter 174640: lr = 0.208935,	loss = 2.646384,	Top-1 err = 0.387939,	Top-5 err = 0.175098,	data_time = 0.050381,	train_time = 0.764314
[2019-08-24 00:14:28,253] TRAIN Iter 174660: lr = 0.208902,	loss = 2.526636,	Top-1 err = 0.387207,	Top-5 err = 0.180225,	data_time = 0.050422,	train_time = 0.384633
[2019-08-24 00:14:43,272] TRAIN Iter 174680: lr = 0.208868,	loss = 2.637940,	Top-1 err = 0.395020,	Top-5 err = 0.179004,	data_time = 0.050221,	train_time = 0.750907
[2019-08-24 00:14:50,290] TRAIN Iter 174700: lr = 0.208835,	loss = 2.619412,	Top-1 err = 0.396240,	Top-5 err = 0.180762,	data_time = 0.051068,	train_time = 0.350900
[2019-08-24 00:15:05,261] TRAIN Iter 174720: lr = 0.208802,	loss = 2.642631,	Top-1 err = 0.393555,	Top-5 err = 0.176367,	data_time = 0.050343,	train_time = 0.748562
[2019-08-24 00:15:22,471] TRAIN Iter 174740: lr = 0.208768,	loss = 2.598641,	Top-1 err = 0.394189,	Top-5 err = 0.178125,	data_time = 0.124296,	train_time = 0.860468
[2019-08-24 00:15:29,436] TRAIN Iter 174760: lr = 0.208735,	loss = 2.570049,	Top-1 err = 0.390430,	Top-5 err = 0.171729,	data_time = 0.050284,	train_time = 0.348241
[2019-08-24 00:15:44,702] TRAIN Iter 174780: lr = 0.208702,	loss = 2.580914,	Top-1 err = 0.391602,	Top-5 err = 0.177979,	data_time = 0.050407,	train_time = 0.763289
[2019-08-24 00:15:57,513] TRAIN Iter 174800: lr = 0.208668,	loss = 2.624433,	Top-1 err = 0.382129,	Top-5 err = 0.176953,	data_time = 0.050670,	train_time = 0.640505
[2019-08-24 00:16:08,197] TRAIN Iter 174820: lr = 0.208635,	loss = 2.655675,	Top-1 err = 0.391699,	Top-5 err = 0.175244,	data_time = 0.050902,	train_time = 0.534200
[2019-08-24 00:16:24,798] TRAIN Iter 174840: lr = 0.208602,	loss = 2.655937,	Top-1 err = 0.395410,	Top-5 err = 0.179590,	data_time = 0.050318,	train_time = 0.830069
[2019-08-24 00:16:31,285] TRAIN Iter 174860: lr = 0.208568,	loss = 2.552124,	Top-1 err = 0.391260,	Top-5 err = 0.173975,	data_time = 0.050452,	train_time = 0.324305
[2019-08-24 00:16:48,223] TRAIN Iter 174880: lr = 0.208535,	loss = 2.627591,	Top-1 err = 0.393164,	Top-5 err = 0.178613,	data_time = 0.050385,	train_time = 0.846885
[2019-08-24 00:17:05,989] TRAIN Iter 174900: lr = 0.208502,	loss = 2.587400,	Top-1 err = 0.389746,	Top-5 err = 0.176270,	data_time = 0.050772,	train_time = 0.888318
[2019-08-24 00:17:12,453] TRAIN Iter 174920: lr = 0.208468,	loss = 2.559602,	Top-1 err = 0.388135,	Top-5 err = 0.171484,	data_time = 0.050307,	train_time = 0.323159
[2019-08-24 00:17:28,954] TRAIN Iter 174940: lr = 0.208435,	loss = 2.556944,	Top-1 err = 0.393945,	Top-5 err = 0.179492,	data_time = 0.050552,	train_time = 0.825024
[2019-08-24 00:17:42,679] TRAIN Iter 174960: lr = 0.208402,	loss = 2.617713,	Top-1 err = 0.392480,	Top-5 err = 0.175000,	data_time = 0.050557,	train_time = 0.686277
[2019-08-24 00:17:53,335] TRAIN Iter 174980: lr = 0.208368,	loss = 2.635803,	Top-1 err = 0.398877,	Top-5 err = 0.177979,	data_time = 0.050203,	train_time = 0.532785
[2019-08-24 00:18:11,282] TRAIN Iter 175000: lr = 0.208335,	loss = 2.707116,	Top-1 err = 0.397119,	Top-5 err = 0.180762,	data_time = 0.050410,	train_time = 0.897305
[2019-08-24 00:18:17,807] TRAIN Iter 175020: lr = 0.208302,	loss = 2.569311,	Top-1 err = 0.398047,	Top-5 err = 0.180762,	data_time = 0.050488,	train_time = 0.326269
[2019-08-24 00:18:34,303] TRAIN Iter 175040: lr = 0.208268,	loss = 2.620312,	Top-1 err = 0.399121,	Top-5 err = 0.181396,	data_time = 0.050514,	train_time = 0.824773
[2019-08-24 00:18:53,096] TRAIN Iter 175060: lr = 0.208235,	loss = 2.548044,	Top-1 err = 0.391748,	Top-5 err = 0.178418,	data_time = 0.050249,	train_time = 0.939642
[2019-08-24 00:18:59,854] TRAIN Iter 175080: lr = 0.208202,	loss = 2.577797,	Top-1 err = 0.391113,	Top-5 err = 0.178320,	data_time = 0.050491,	train_time = 0.337878
[2019-08-24 00:19:19,111] TRAIN Iter 175100: lr = 0.208168,	loss = 2.651495,	Top-1 err = 0.394336,	Top-5 err = 0.181445,	data_time = 0.050368,	train_time = 0.962806
[2019-08-24 00:19:31,363] TRAIN Iter 175120: lr = 0.208135,	loss = 2.654321,	Top-1 err = 0.392334,	Top-5 err = 0.176758,	data_time = 0.050507,	train_time = 0.612597
[2019-08-24 00:19:41,909] TRAIN Iter 175140: lr = 0.208102,	loss = 2.647299,	Top-1 err = 0.398584,	Top-5 err = 0.181348,	data_time = 0.050476,	train_time = 0.527311
[2019-08-24 00:20:00,127] TRAIN Iter 175160: lr = 0.208068,	loss = 2.552290,	Top-1 err = 0.389893,	Top-5 err = 0.176172,	data_time = 0.050188,	train_time = 0.910888
[2019-08-24 00:20:06,595] TRAIN Iter 175180: lr = 0.208035,	loss = 2.640484,	Top-1 err = 0.393262,	Top-5 err = 0.180615,	data_time = 0.049909,	train_time = 0.323385
[2019-08-24 00:20:22,587] TRAIN Iter 175200: lr = 0.208002,	loss = 2.629081,	Top-1 err = 0.391504,	Top-5 err = 0.178271,	data_time = 0.049897,	train_time = 0.799582
[2019-08-24 00:21:11,436] TRAIN Iter 175220: lr = 0.207968,	loss = 2.604986,	Top-1 err = 0.396897,	Top-5 err = 0.178620,	data_time = 2.466330,	train_time = 2.442427
[2019-08-24 00:21:18,709] TRAIN Iter 175240: lr = 0.207935,	loss = 2.530678,	Top-1 err = 0.392822,	Top-5 err = 0.181592,	data_time = 0.050873,	train_time = 0.363629
[2019-08-24 00:21:33,826] TRAIN Iter 175260: lr = 0.207902,	loss = 2.546824,	Top-1 err = 0.384326,	Top-5 err = 0.176807,	data_time = 0.050821,	train_time = 0.755812
[2019-08-24 00:21:41,942] TRAIN Iter 175280: lr = 0.207868,	loss = 2.584210,	Top-1 err = 0.386133,	Top-5 err = 0.172070,	data_time = 0.050522,	train_time = 0.405804
[2019-08-24 00:21:53,723] TRAIN Iter 175300: lr = 0.207835,	loss = 2.639845,	Top-1 err = 0.389893,	Top-5 err = 0.174463,	data_time = 0.050375,	train_time = 0.589018
[2019-08-24 00:22:08,162] TRAIN Iter 175320: lr = 0.207802,	loss = 2.620337,	Top-1 err = 0.392334,	Top-5 err = 0.175586,	data_time = 0.050579,	train_time = 0.721938
[2019-08-24 00:22:15,027] TRAIN Iter 175340: lr = 0.207768,	loss = 2.554805,	Top-1 err = 0.377588,	Top-5 err = 0.169189,	data_time = 0.050495,	train_time = 0.343225
[2019-08-24 00:22:30,659] TRAIN Iter 175360: lr = 0.207735,	loss = 2.604725,	Top-1 err = 0.392188,	Top-5 err = 0.177832,	data_time = 0.050533,	train_time = 0.781610
[2019-08-24 00:22:46,111] TRAIN Iter 175380: lr = 0.207702,	loss = 2.549967,	Top-1 err = 0.388037,	Top-5 err = 0.174170,	data_time = 2.588780,	train_time = 0.772596
[2019-08-24 00:22:53,593] TRAIN Iter 175400: lr = 0.207668,	loss = 2.715875,	Top-1 err = 0.387598,	Top-5 err = 0.178223,	data_time = 0.050386,	train_time = 0.374061
[2019-08-24 00:23:09,589] TRAIN Iter 175420: lr = 0.207635,	loss = 2.529871,	Top-1 err = 0.386475,	Top-5 err = 0.175391,	data_time = 0.050840,	train_time = 0.799783
[2019-08-24 00:23:17,551] TRAIN Iter 175440: lr = 0.207602,	loss = 2.545452,	Top-1 err = 0.385937,	Top-5 err = 0.175684,	data_time = 0.050320,	train_time = 0.398079
[2019-08-24 00:23:32,097] TRAIN Iter 175460: lr = 0.207568,	loss = 2.574633,	Top-1 err = 0.383691,	Top-5 err = 0.175928,	data_time = 0.050414,	train_time = 0.727280
[2019-08-24 00:23:47,410] TRAIN Iter 175480: lr = 0.207535,	loss = 2.598793,	Top-1 err = 0.390234,	Top-5 err = 0.174512,	data_time = 0.050401,	train_time = 0.765671
[2019-08-24 00:23:55,129] TRAIN Iter 175500: lr = 0.207502,	loss = 2.550864,	Top-1 err = 0.386816,	Top-5 err = 0.171533,	data_time = 0.050815,	train_time = 0.385927
[2019-08-24 00:24:08,293] TRAIN Iter 175520: lr = 0.207468,	loss = 2.633647,	Top-1 err = 0.388232,	Top-5 err = 0.177344,	data_time = 0.050375,	train_time = 0.658157
[2019-08-24 00:24:22,133] TRAIN Iter 175540: lr = 0.207435,	loss = 2.561439,	Top-1 err = 0.383838,	Top-5 err = 0.171777,	data_time = 0.608272,	train_time = 0.692010
[2019-08-24 00:24:29,155] TRAIN Iter 175560: lr = 0.207402,	loss = 2.572859,	Top-1 err = 0.391162,	Top-5 err = 0.178271,	data_time = 0.050349,	train_time = 0.351092
[2019-08-24 00:24:44,639] TRAIN Iter 175580: lr = 0.207368,	loss = 2.535144,	Top-1 err = 0.388672,	Top-5 err = 0.171582,	data_time = 0.050716,	train_time = 0.774180
[2019-08-24 00:24:52,434] TRAIN Iter 175600: lr = 0.207335,	loss = 2.598702,	Top-1 err = 0.388867,	Top-5 err = 0.171436,	data_time = 0.050907,	train_time = 0.389712
[2019-08-24 00:25:05,957] TRAIN Iter 175620: lr = 0.207302,	loss = 2.605474,	Top-1 err = 0.388525,	Top-5 err = 0.172461,	data_time = 0.050615,	train_time = 0.676153
[2019-08-24 00:25:21,919] TRAIN Iter 175640: lr = 0.207268,	loss = 2.499212,	Top-1 err = 0.390625,	Top-5 err = 0.173145,	data_time = 0.050546,	train_time = 0.798086
[2019-08-24 00:25:29,399] TRAIN Iter 175660: lr = 0.207235,	loss = 2.590833,	Top-1 err = 0.389062,	Top-5 err = 0.177686,	data_time = 0.050341,	train_time = 0.373959
[2019-08-24 00:25:43,421] TRAIN Iter 175680: lr = 0.207202,	loss = 2.592764,	Top-1 err = 0.394482,	Top-5 err = 0.173145,	data_time = 0.050322,	train_time = 0.701109
[2019-08-24 00:25:59,153] TRAIN Iter 175700: lr = 0.207168,	loss = 2.568100,	Top-1 err = 0.388477,	Top-5 err = 0.175439,	data_time = 0.050232,	train_time = 0.786600
[2019-08-24 00:26:06,166] TRAIN Iter 175720: lr = 0.207135,	loss = 2.660971,	Top-1 err = 0.380371,	Top-5 err = 0.172559,	data_time = 0.050666,	train_time = 0.350598
[2019-08-24 00:26:21,621] TRAIN Iter 175740: lr = 0.207102,	loss = 2.635432,	Top-1 err = 0.396045,	Top-5 err = 0.175488,	data_time = 0.050386,	train_time = 0.772775
[2019-08-24 00:26:29,512] TRAIN Iter 175760: lr = 0.207068,	loss = 2.568796,	Top-1 err = 0.392627,	Top-5 err = 0.179443,	data_time = 0.050457,	train_time = 0.394526
[2019-08-24 00:26:44,341] TRAIN Iter 175780: lr = 0.207035,	loss = 2.529385,	Top-1 err = 0.387842,	Top-5 err = 0.173877,	data_time = 0.050495,	train_time = 0.741447
[2019-08-24 00:26:59,243] TRAIN Iter 175800: lr = 0.207002,	loss = 2.596279,	Top-1 err = 0.389746,	Top-5 err = 0.175635,	data_time = 0.050399,	train_time = 0.745056
[2019-08-24 00:27:06,625] TRAIN Iter 175820: lr = 0.206968,	loss = 2.681626,	Top-1 err = 0.392578,	Top-5 err = 0.175244,	data_time = 0.124172,	train_time = 0.369092
[2019-08-24 00:27:20,823] TRAIN Iter 175840: lr = 0.206935,	loss = 2.530196,	Top-1 err = 0.387256,	Top-5 err = 0.174561,	data_time = 0.050416,	train_time = 0.709883
[2019-08-24 00:27:36,399] TRAIN Iter 175860: lr = 0.206902,	loss = 2.554352,	Top-1 err = 0.392334,	Top-5 err = 0.176904,	data_time = 0.100973,	train_time = 0.778767
[2019-08-24 00:27:43,548] TRAIN Iter 175880: lr = 0.206868,	loss = 2.543620,	Top-1 err = 0.389551,	Top-5 err = 0.175098,	data_time = 0.050553,	train_time = 0.357440
[2019-08-24 00:27:59,374] TRAIN Iter 175900: lr = 0.206835,	loss = 2.562979,	Top-1 err = 0.390576,	Top-5 err = 0.177344,	data_time = 0.050507,	train_time = 0.791294
[2019-08-24 00:28:06,558] TRAIN Iter 175920: lr = 0.206802,	loss = 2.677834,	Top-1 err = 0.390381,	Top-5 err = 0.174756,	data_time = 0.050884,	train_time = 0.359183
[2019-08-24 00:28:22,724] TRAIN Iter 175940: lr = 0.206768,	loss = 2.616052,	Top-1 err = 0.394238,	Top-5 err = 0.177588,	data_time = 0.050729,	train_time = 0.808275
[2019-08-24 00:28:39,566] TRAIN Iter 175960: lr = 0.206735,	loss = 2.590853,	Top-1 err = 0.395313,	Top-5 err = 0.177148,	data_time = 0.050300,	train_time = 0.842094
[2019-08-24 00:28:46,817] TRAIN Iter 175980: lr = 0.206702,	loss = 2.461973,	Top-1 err = 0.394678,	Top-5 err = 0.175684,	data_time = 0.050298,	train_time = 0.362529
[2019-08-24 00:29:02,284] TRAIN Iter 176000: lr = 0.206668,	loss = 2.572508,	Top-1 err = 0.383154,	Top-5 err = 0.175146,	data_time = 0.050743,	train_time = 0.773368
[2019-08-24 00:29:19,394] TRAIN Iter 176020: lr = 0.206635,	loss = 2.628017,	Top-1 err = 0.392139,	Top-5 err = 0.176709,	data_time = 0.082021,	train_time = 0.855463
[2019-08-24 00:29:26,326] TRAIN Iter 176040: lr = 0.206602,	loss = 2.650221,	Top-1 err = 0.394287,	Top-5 err = 0.177100,	data_time = 0.050355,	train_time = 0.346595
[2019-08-24 00:29:42,734] TRAIN Iter 176060: lr = 0.206568,	loss = 2.643156,	Top-1 err = 0.394824,	Top-5 err = 0.180322,	data_time = 0.050564,	train_time = 0.820384
[2019-08-24 00:29:50,283] TRAIN Iter 176080: lr = 0.206535,	loss = 2.468778,	Top-1 err = 0.390234,	Top-5 err = 0.176611,	data_time = 0.050488,	train_time = 0.377438
[2019-08-24 00:30:05,578] TRAIN Iter 176100: lr = 0.206502,	loss = 2.658304,	Top-1 err = 0.392725,	Top-5 err = 0.179688,	data_time = 0.050525,	train_time = 0.764739
[2019-08-24 00:30:22,364] TRAIN Iter 176120: lr = 0.206468,	loss = 2.645606,	Top-1 err = 0.399561,	Top-5 err = 0.178516,	data_time = 0.050503,	train_time = 0.839263
[2019-08-24 00:30:28,988] TRAIN Iter 176140: lr = 0.206435,	loss = 2.677325,	Top-1 err = 0.397314,	Top-5 err = 0.179980,	data_time = 0.050348,	train_time = 0.331194
[2019-08-24 00:30:47,080] TRAIN Iter 176160: lr = 0.206402,	loss = 2.688265,	Top-1 err = 0.393115,	Top-5 err = 0.181152,	data_time = 0.050240,	train_time = 0.904625
[2019-08-24 00:31:03,464] TRAIN Iter 176180: lr = 0.206368,	loss = 2.500860,	Top-1 err = 0.392041,	Top-5 err = 0.174561,	data_time = 0.050214,	train_time = 0.819144
[2019-08-24 00:31:10,438] TRAIN Iter 176200: lr = 0.206335,	loss = 2.664829,	Top-1 err = 0.391211,	Top-5 err = 0.180713,	data_time = 0.134466,	train_time = 0.348702
[2019-08-24 00:31:27,686] TRAIN Iter 176220: lr = 0.206302,	loss = 2.562614,	Top-1 err = 0.397656,	Top-5 err = 0.181494,	data_time = 0.050560,	train_time = 0.862392
[2019-08-24 00:31:35,575] TRAIN Iter 176240: lr = 0.206268,	loss = 2.597857,	Top-1 err = 0.392871,	Top-5 err = 0.174365,	data_time = 0.050182,	train_time = 0.394426
[2019-08-24 00:31:51,444] TRAIN Iter 176260: lr = 0.206235,	loss = 2.588126,	Top-1 err = 0.392773,	Top-5 err = 0.178027,	data_time = 0.050446,	train_time = 0.793433
[2019-08-24 00:32:08,817] TRAIN Iter 176280: lr = 0.206202,	loss = 2.691183,	Top-1 err = 0.390137,	Top-5 err = 0.174316,	data_time = 0.050708,	train_time = 0.868637
[2019-08-24 00:32:15,968] TRAIN Iter 176300: lr = 0.206168,	loss = 2.610312,	Top-1 err = 0.393018,	Top-5 err = 0.176904,	data_time = 0.050606,	train_time = 0.357566
[2019-08-24 00:32:35,080] TRAIN Iter 176320: lr = 0.206135,	loss = 2.700432,	Top-1 err = 0.396289,	Top-5 err = 0.178564,	data_time = 0.050584,	train_time = 0.955555
[2019-08-24 00:32:53,243] TRAIN Iter 176340: lr = 0.206102,	loss = 2.511021,	Top-1 err = 0.388135,	Top-5 err = 0.175537,	data_time = 0.050281,	train_time = 0.908135
[2019-08-24 00:33:00,844] TRAIN Iter 176360: lr = 0.206068,	loss = 2.610583,	Top-1 err = 0.390527,	Top-5 err = 0.174463,	data_time = 0.050457,	train_time = 0.380027
[2019-08-24 00:33:16,625] TRAIN Iter 176380: lr = 0.206035,	loss = 2.653399,	Top-1 err = 0.394873,	Top-5 err = 0.179395,	data_time = 0.050178,	train_time = 0.789053
[2019-08-24 00:33:24,398] TRAIN Iter 176400: lr = 0.206002,	loss = 2.581772,	Top-1 err = 0.396484,	Top-5 err = 0.179492,	data_time = 0.115101,	train_time = 0.388647
[2019-08-24 00:33:40,642] TRAIN Iter 176420: lr = 0.205968,	loss = 2.633665,	Top-1 err = 0.396973,	Top-5 err = 0.181494,	data_time = 0.050145,	train_time = 0.812165
[2019-08-24 00:33:55,120] TRAIN Iter 176440: lr = 0.205935,	loss = 2.471007,	Top-1 err = 0.387842,	Top-5 err = 0.173535,	data_time = 0.049955,	train_time = 0.723921
[2019-08-24 00:34:01,290] TRAIN Iter 176460: lr = 0.205902,	loss = 2.638233,	Top-1 err = 0.399512,	Top-5 err = 0.184521,	data_time = 0.049917,	train_time = 0.308483
[2019-08-24 00:34:56,863] TRAIN Iter 176480: lr = 0.205868,	loss = 2.712562,	Top-1 err = 0.394227,	Top-5 err = 0.182046,	data_time = 0.050533,	train_time = 2.778642
[2019-08-24 00:35:05,044] TRAIN Iter 176500: lr = 0.205835,	loss = 2.680495,	Top-1 err = 0.390820,	Top-5 err = 0.174658,	data_time = 0.050477,	train_time = 0.409037
[2019-08-24 00:35:18,019] TRAIN Iter 176520: lr = 0.205802,	loss = 2.650347,	Top-1 err = 0.387744,	Top-5 err = 0.176074,	data_time = 0.050668,	train_time = 0.648730
[2019-08-24 00:35:30,159] TRAIN Iter 176540: lr = 0.205768,	loss = 2.562896,	Top-1 err = 0.385107,	Top-5 err = 0.174219,	data_time = 0.050483,	train_time = 0.606973
[2019-08-24 00:35:37,328] TRAIN Iter 176560: lr = 0.205735,	loss = 2.598207,	Top-1 err = 0.385645,	Top-5 err = 0.172607,	data_time = 0.050724,	train_time = 0.358450
[2019-08-24 00:35:52,877] TRAIN Iter 176580: lr = 0.205702,	loss = 2.605871,	Top-1 err = 0.389111,	Top-5 err = 0.175146,	data_time = 0.050819,	train_time = 0.777417
[2019-08-24 00:36:08,323] TRAIN Iter 176600: lr = 0.205668,	loss = 2.603496,	Top-1 err = 0.383398,	Top-5 err = 0.171387,	data_time = 0.050310,	train_time = 0.772290
[2019-08-24 00:36:15,802] TRAIN Iter 176620: lr = 0.205635,	loss = 2.692269,	Top-1 err = 0.386133,	Top-5 err = 0.168896,	data_time = 0.050530,	train_time = 0.373951
[2019-08-24 00:36:32,164] TRAIN Iter 176640: lr = 0.205602,	loss = 2.566257,	Top-1 err = 0.393701,	Top-5 err = 0.175879,	data_time = 0.050367,	train_time = 0.818049
[2019-08-24 00:36:40,692] TRAIN Iter 176660: lr = 0.205568,	loss = 2.517608,	Top-1 err = 0.387744,	Top-5 err = 0.174121,	data_time = 0.050667,	train_time = 0.426403
[2019-08-24 00:36:54,400] TRAIN Iter 176680: lr = 0.205535,	loss = 2.618510,	Top-1 err = 0.384863,	Top-5 err = 0.171094,	data_time = 0.050595,	train_time = 0.685394
[2019-08-24 00:37:15,204] TRAIN Iter 176700: lr = 0.205502,	loss = 2.536728,	Top-1 err = 0.389941,	Top-5 err = 0.172656,	data_time = 0.050506,	train_time = 1.040164
[2019-08-24 00:37:22,489] TRAIN Iter 176720: lr = 0.205468,	loss = 2.616495,	Top-1 err = 0.388428,	Top-5 err = 0.175977,	data_time = 0.050540,	train_time = 0.364234
[2019-08-24 00:37:37,429] TRAIN Iter 176740: lr = 0.205435,	loss = 2.691069,	Top-1 err = 0.382080,	Top-5 err = 0.174707,	data_time = 0.050687,	train_time = 0.747021
[2019-08-24 00:37:54,692] TRAIN Iter 176760: lr = 0.205402,	loss = 2.635583,	Top-1 err = 0.386084,	Top-5 err = 0.173047,	data_time = 0.050379,	train_time = 0.863123
[2019-08-24 00:38:01,987] TRAIN Iter 176780: lr = 0.205368,	loss = 2.583043,	Top-1 err = 0.392334,	Top-5 err = 0.170850,	data_time = 0.050661,	train_time = 0.364753
[2019-08-24 00:38:16,381] TRAIN Iter 176800: lr = 0.205335,	loss = 2.564583,	Top-1 err = 0.383154,	Top-5 err = 0.175000,	data_time = 0.050421,	train_time = 0.719690
[2019-08-24 00:38:23,725] TRAIN Iter 176820: lr = 0.205302,	loss = 2.535647,	Top-1 err = 0.383447,	Top-5 err = 0.170068,	data_time = 0.050282,	train_time = 0.367144
[2019-08-24 00:38:39,568] TRAIN Iter 176840: lr = 0.205268,	loss = 2.601210,	Top-1 err = 0.387305,	Top-5 err = 0.171777,	data_time = 0.050515,	train_time = 0.792163
[2019-08-24 00:38:57,733] TRAIN Iter 176860: lr = 0.205235,	loss = 2.578341,	Top-1 err = 0.391699,	Top-5 err = 0.173340,	data_time = 0.050504,	train_time = 0.908233
[2019-08-24 00:39:06,220] TRAIN Iter 176880: lr = 0.205202,	loss = 2.506595,	Top-1 err = 0.391748,	Top-5 err = 0.173389,	data_time = 0.050663,	train_time = 0.424354
[2019-08-24 00:39:17,374] TRAIN Iter 176900: lr = 0.205168,	loss = 2.521688,	Top-1 err = 0.386670,	Top-5 err = 0.171973,	data_time = 0.051230,	train_time = 0.557639
[2019-08-24 00:39:34,615] TRAIN Iter 176920: lr = 0.205135,	loss = 2.561072,	Top-1 err = 0.387256,	Top-5 err = 0.177344,	data_time = 0.050699,	train_time = 0.862066
[2019-08-24 00:39:41,526] TRAIN Iter 176940: lr = 0.205102,	loss = 2.687517,	Top-1 err = 0.381836,	Top-5 err = 0.174316,	data_time = 0.050780,	train_time = 0.345548
[2019-08-24 00:39:56,524] TRAIN Iter 176960: lr = 0.205068,	loss = 2.579485,	Top-1 err = 0.394482,	Top-5 err = 0.177686,	data_time = 0.050776,	train_time = 0.749859
[2019-08-24 00:40:04,497] TRAIN Iter 176980: lr = 0.205035,	loss = 2.615135,	Top-1 err = 0.388379,	Top-5 err = 0.175684,	data_time = 0.050267,	train_time = 0.398632
[2019-08-24 00:40:19,570] TRAIN Iter 177000: lr = 0.205002,	loss = 2.534793,	Top-1 err = 0.392383,	Top-5 err = 0.174365,	data_time = 0.050869,	train_time = 0.753628
[2019-08-24 00:40:34,845] TRAIN Iter 177020: lr = 0.204968,	loss = 2.642403,	Top-1 err = 0.390918,	Top-5 err = 0.176416,	data_time = 0.050480,	train_time = 0.763754
[2019-08-24 00:40:42,297] TRAIN Iter 177040: lr = 0.204935,	loss = 2.538393,	Top-1 err = 0.394043,	Top-5 err = 0.180664,	data_time = 0.050663,	train_time = 0.372568
[2019-08-24 00:40:57,385] TRAIN Iter 177060: lr = 0.204902,	loss = 2.555299,	Top-1 err = 0.387842,	Top-5 err = 0.177734,	data_time = 0.050502,	train_time = 0.754383
[2019-08-24 00:41:13,030] TRAIN Iter 177080: lr = 0.204868,	loss = 2.590625,	Top-1 err = 0.391748,	Top-5 err = 0.176123,	data_time = 0.050312,	train_time = 0.782263
[2019-08-24 00:41:20,135] TRAIN Iter 177100: lr = 0.204835,	loss = 2.609507,	Top-1 err = 0.390430,	Top-5 err = 0.177539,	data_time = 0.144124,	train_time = 0.355218
[2019-08-24 00:41:36,411] TRAIN Iter 177120: lr = 0.204802,	loss = 2.590819,	Top-1 err = 0.394043,	Top-5 err = 0.178711,	data_time = 0.050364,	train_time = 0.813773
[2019-08-24 00:41:44,074] TRAIN Iter 177140: lr = 0.204768,	loss = 2.568371,	Top-1 err = 0.391260,	Top-5 err = 0.176318,	data_time = 0.050390,	train_time = 0.383146
[2019-08-24 00:41:59,576] TRAIN Iter 177160: lr = 0.204735,	loss = 2.625227,	Top-1 err = 0.388623,	Top-5 err = 0.178613,	data_time = 0.050860,	train_time = 0.775095
[2019-08-24 00:42:15,833] TRAIN Iter 177180: lr = 0.204702,	loss = 2.565393,	Top-1 err = 0.394336,	Top-5 err = 0.178857,	data_time = 0.050587,	train_time = 0.812825
[2019-08-24 00:42:23,423] TRAIN Iter 177200: lr = 0.204668,	loss = 2.668866,	Top-1 err = 0.395264,	Top-5 err = 0.177490,	data_time = 0.050385,	train_time = 0.379471
[2019-08-24 00:42:38,372] TRAIN Iter 177220: lr = 0.204635,	loss = 2.589186,	Top-1 err = 0.389551,	Top-5 err = 0.173291,	data_time = 0.050492,	train_time = 0.747457
[2019-08-24 00:42:54,583] TRAIN Iter 177240: lr = 0.204602,	loss = 2.526922,	Top-1 err = 0.391699,	Top-5 err = 0.178223,	data_time = 0.050342,	train_time = 0.810539
[2019-08-24 00:43:02,042] TRAIN Iter 177260: lr = 0.204568,	loss = 2.660527,	Top-1 err = 0.393457,	Top-5 err = 0.175732,	data_time = 0.050374,	train_time = 0.372933
[2019-08-24 00:43:17,732] TRAIN Iter 177280: lr = 0.204535,	loss = 2.570860,	Top-1 err = 0.387842,	Top-5 err = 0.177100,	data_time = 0.050396,	train_time = 0.784462
[2019-08-24 00:43:24,859] TRAIN Iter 177300: lr = 0.204502,	loss = 2.598585,	Top-1 err = 0.390479,	Top-5 err = 0.173584,	data_time = 0.050631,	train_time = 0.356366
[2019-08-24 00:43:42,356] TRAIN Iter 177320: lr = 0.204468,	loss = 2.542198,	Top-1 err = 0.394141,	Top-5 err = 0.182764,	data_time = 0.050871,	train_time = 0.874852
[2019-08-24 00:43:58,378] TRAIN Iter 177340: lr = 0.204435,	loss = 2.623191,	Top-1 err = 0.392334,	Top-5 err = 0.175537,	data_time = 0.050754,	train_time = 0.801064
[2019-08-24 00:44:05,557] TRAIN Iter 177360: lr = 0.204402,	loss = 2.505664,	Top-1 err = 0.387646,	Top-5 err = 0.178223,	data_time = 0.050515,	train_time = 0.358913
[2019-08-24 00:44:22,502] TRAIN Iter 177380: lr = 0.204368,	loss = 2.545296,	Top-1 err = 0.390869,	Top-5 err = 0.178027,	data_time = 0.050337,	train_time = 0.847272
[2019-08-24 00:44:39,121] TRAIN Iter 177400: lr = 0.204335,	loss = 2.674993,	Top-1 err = 0.392969,	Top-5 err = 0.174512,	data_time = 0.050517,	train_time = 0.830901
[2019-08-24 00:44:46,269] TRAIN Iter 177420: lr = 0.204302,	loss = 2.651159,	Top-1 err = 0.397168,	Top-5 err = 0.180713,	data_time = 0.050927,	train_time = 0.357395
[2019-08-24 00:45:02,846] TRAIN Iter 177440: lr = 0.204268,	loss = 2.699848,	Top-1 err = 0.388574,	Top-5 err = 0.178564,	data_time = 0.050541,	train_time = 0.828852
[2019-08-24 00:45:10,291] TRAIN Iter 177460: lr = 0.204235,	loss = 2.685604,	Top-1 err = 0.393457,	Top-5 err = 0.178662,	data_time = 0.050346,	train_time = 0.372241
[2019-08-24 00:45:27,204] TRAIN Iter 177480: lr = 0.204202,	loss = 2.660117,	Top-1 err = 0.398291,	Top-5 err = 0.180029,	data_time = 0.050405,	train_time = 0.845652
[2019-08-24 00:45:45,360] TRAIN Iter 177500: lr = 0.204168,	loss = 2.668247,	Top-1 err = 0.391748,	Top-5 err = 0.178369,	data_time = 0.050490,	train_time = 0.907778
[2019-08-24 00:45:52,706] TRAIN Iter 177520: lr = 0.204135,	loss = 2.536835,	Top-1 err = 0.392090,	Top-5 err = 0.177734,	data_time = 0.130658,	train_time = 0.367285
[2019-08-24 00:46:09,416] TRAIN Iter 177540: lr = 0.204102,	loss = 2.681016,	Top-1 err = 0.393506,	Top-5 err = 0.178516,	data_time = 0.050564,	train_time = 0.835458
[2019-08-24 00:46:26,861] TRAIN Iter 177560: lr = 0.204068,	loss = 2.631155,	Top-1 err = 0.390771,	Top-5 err = 0.173877,	data_time = 0.050413,	train_time = 0.872243
[2019-08-24 00:46:34,344] TRAIN Iter 177580: lr = 0.204035,	loss = 2.592844,	Top-1 err = 0.396631,	Top-5 err = 0.176855,	data_time = 0.050661,	train_time = 0.374164
[2019-08-24 00:46:50,716] TRAIN Iter 177600: lr = 0.204002,	loss = 2.631356,	Top-1 err = 0.388428,	Top-5 err = 0.175586,	data_time = 0.050355,	train_time = 0.818580
[2019-08-24 00:46:58,058] TRAIN Iter 177620: lr = 0.203968,	loss = 2.552904,	Top-1 err = 0.399707,	Top-5 err = 0.183887,	data_time = 0.050362,	train_time = 0.367084
[2019-08-24 00:47:15,572] TRAIN Iter 177640: lr = 0.203935,	loss = 2.520751,	Top-1 err = 0.392920,	Top-5 err = 0.176758,	data_time = 0.050287,	train_time = 0.875698
[2019-08-24 00:47:32,111] TRAIN Iter 177660: lr = 0.203902,	loss = 2.526104,	Top-1 err = 0.396484,	Top-5 err = 0.181006,	data_time = 0.050011,	train_time = 0.826912
[2019-08-24 00:47:39,268] TRAIN Iter 177680: lr = 0.203868,	loss = 2.605868,	Top-1 err = 0.390479,	Top-5 err = 0.178076,	data_time = 0.049882,	train_time = 0.357831
[2019-08-24 00:47:54,580] TRAIN Iter 177700: lr = 0.203835,	loss = 2.700973,	Top-1 err = 0.394678,	Top-5 err = 0.179248,	data_time = 0.050025,	train_time = 0.765592
[2019-08-24 00:48:06,706] TRAIN Iter 177720: lr = 0.203802,	loss = 3.147688,	Top-1 err = 0.401821,	Top-5 err = 0.187189,	data_time = 0.007070,	train_time = 0.606281
[2019-08-24 00:48:51,591] TRAIN Iter 177740: lr = 0.203768,	loss = 2.557937,	Top-1 err = 0.399951,	Top-5 err = 0.181104,	data_time = 0.050350,	train_time = 2.244254
[2019-08-24 00:49:10,586] TRAIN Iter 177760: lr = 0.203735,	loss = 2.580668,	Top-1 err = 0.382959,	Top-5 err = 0.167773,	data_time = 0.050288,	train_time = 0.949726
[2019-08-24 00:49:18,188] TRAIN Iter 177780: lr = 0.203702,	loss = 2.496515,	Top-1 err = 0.383789,	Top-5 err = 0.168994,	data_time = 0.050321,	train_time = 0.380087
[2019-08-24 00:49:32,204] TRAIN Iter 177800: lr = 0.203668,	loss = 2.625427,	Top-1 err = 0.382129,	Top-5 err = 0.170166,	data_time = 0.050352,	train_time = 0.700782
[2019-08-24 00:49:45,010] TRAIN Iter 177820: lr = 0.203635,	loss = 2.584602,	Top-1 err = 0.381250,	Top-5 err = 0.173145,	data_time = 2.309554,	train_time = 0.640281
[2019-08-24 00:49:52,516] TRAIN Iter 177840: lr = 0.203602,	loss = 2.506842,	Top-1 err = 0.381787,	Top-5 err = 0.168750,	data_time = 0.050414,	train_time = 0.375307
[2019-08-24 00:50:06,660] TRAIN Iter 177860: lr = 0.203568,	loss = 2.714226,	Top-1 err = 0.393359,	Top-5 err = 0.176660,	data_time = 0.050366,	train_time = 0.707198
[2019-08-24 00:50:14,527] TRAIN Iter 177880: lr = 0.203535,	loss = 2.582486,	Top-1 err = 0.389648,	Top-5 err = 0.175488,	data_time = 0.050469,	train_time = 0.393293
[2019-08-24 00:50:27,891] TRAIN Iter 177900: lr = 0.203502,	loss = 2.644870,	Top-1 err = 0.386133,	Top-5 err = 0.174756,	data_time = 0.050580,	train_time = 0.668226
[2019-08-24 00:50:42,462] TRAIN Iter 177920: lr = 0.203468,	loss = 2.665359,	Top-1 err = 0.386377,	Top-5 err = 0.174121,	data_time = 0.050577,	train_time = 0.728532
[2019-08-24 00:50:49,751] TRAIN Iter 177940: lr = 0.203435,	loss = 2.616853,	Top-1 err = 0.378516,	Top-5 err = 0.161768,	data_time = 0.050694,	train_time = 0.364429
[2019-08-24 00:51:03,821] TRAIN Iter 177960: lr = 0.203402,	loss = 2.458107,	Top-1 err = 0.391748,	Top-5 err = 0.175244,	data_time = 0.050482,	train_time = 0.703496
[2019-08-24 00:51:18,860] TRAIN Iter 177980: lr = 0.203368,	loss = 2.632698,	Top-1 err = 0.389795,	Top-5 err = 0.173340,	data_time = 2.939076,	train_time = 0.751917
[2019-08-24 00:51:26,085] TRAIN Iter 178000: lr = 0.203335,	loss = 2.673556,	Top-1 err = 0.391748,	Top-5 err = 0.174414,	data_time = 0.050843,	train_time = 0.361217
[2019-08-24 00:51:41,109] TRAIN Iter 178020: lr = 0.203302,	loss = 2.490252,	Top-1 err = 0.389600,	Top-5 err = 0.175586,	data_time = 0.050761,	train_time = 0.751196
[2019-08-24 00:51:48,380] TRAIN Iter 178040: lr = 0.203268,	loss = 2.557406,	Top-1 err = 0.379639,	Top-5 err = 0.171582,	data_time = 0.050803,	train_time = 0.363536
[2019-08-24 00:52:03,095] TRAIN Iter 178060: lr = 0.203235,	loss = 2.538633,	Top-1 err = 0.389307,	Top-5 err = 0.174902,	data_time = 0.050886,	train_time = 0.735743
[2019-08-24 00:52:19,888] TRAIN Iter 178080: lr = 0.203202,	loss = 2.526237,	Top-1 err = 0.388721,	Top-5 err = 0.174805,	data_time = 0.050531,	train_time = 0.839648
[2019-08-24 00:52:27,032] TRAIN Iter 178100: lr = 0.203168,	loss = 2.599231,	Top-1 err = 0.391895,	Top-5 err = 0.172949,	data_time = 0.050266,	train_time = 0.357198
[2019-08-24 00:52:40,281] TRAIN Iter 178120: lr = 0.203135,	loss = 2.535983,	Top-1 err = 0.393164,	Top-5 err = 0.176514,	data_time = 0.050470,	train_time = 0.662398
[2019-08-24 00:52:55,171] TRAIN Iter 178140: lr = 0.203102,	loss = 2.583937,	Top-1 err = 0.384473,	Top-5 err = 0.173389,	data_time = 0.050477,	train_time = 0.744478
[2019-08-24 00:53:03,133] TRAIN Iter 178160: lr = 0.203068,	loss = 2.514623,	Top-1 err = 0.388721,	Top-5 err = 0.173682,	data_time = 0.050813,	train_time = 0.398112
[2019-08-24 00:53:19,357] TRAIN Iter 178180: lr = 0.203035,	loss = 2.509929,	Top-1 err = 0.386182,	Top-5 err = 0.174854,	data_time = 0.050889,	train_time = 0.811179
[2019-08-24 00:53:26,497] TRAIN Iter 178200: lr = 0.203002,	loss = 2.479733,	Top-1 err = 0.384277,	Top-5 err = 0.174268,	data_time = 0.050757,	train_time = 0.356996
[2019-08-24 00:53:41,262] TRAIN Iter 178220: lr = 0.202968,	loss = 2.561718,	Top-1 err = 0.390918,	Top-5 err = 0.174707,	data_time = 0.050672,	train_time = 0.738204
[2019-08-24 00:53:57,116] TRAIN Iter 178240: lr = 0.202935,	loss = 2.569548,	Top-1 err = 0.383936,	Top-5 err = 0.169873,	data_time = 0.050276,	train_time = 0.792716
[2019-08-24 00:54:04,057] TRAIN Iter 178260: lr = 0.202902,	loss = 2.646286,	Top-1 err = 0.387354,	Top-5 err = 0.173584,	data_time = 0.050836,	train_time = 0.347022
[2019-08-24 00:54:21,099] TRAIN Iter 178280: lr = 0.202868,	loss = 2.576012,	Top-1 err = 0.384424,	Top-5 err = 0.174316,	data_time = 0.050389,	train_time = 0.852080
[2019-08-24 00:54:36,769] TRAIN Iter 178300: lr = 0.202835,	loss = 2.670581,	Top-1 err = 0.393408,	Top-5 err = 0.177246,	data_time = 0.050304,	train_time = 0.783480
[2019-08-24 00:54:43,421] TRAIN Iter 178320: lr = 0.202802,	loss = 2.652749,	Top-1 err = 0.391406,	Top-5 err = 0.181641,	data_time = 0.050486,	train_time = 0.332608
[2019-08-24 00:55:00,464] TRAIN Iter 178340: lr = 0.202768,	loss = 2.555989,	Top-1 err = 0.389209,	Top-5 err = 0.176465,	data_time = 0.050561,	train_time = 0.852114
[2019-08-24 00:55:08,269] TRAIN Iter 178360: lr = 0.202735,	loss = 2.519894,	Top-1 err = 0.388232,	Top-5 err = 0.175928,	data_time = 0.050862,	train_time = 0.390242
[2019-08-24 00:55:22,791] TRAIN Iter 178380: lr = 0.202702,	loss = 2.554572,	Top-1 err = 0.388135,	Top-5 err = 0.175195,	data_time = 0.050396,	train_time = 0.726097
[2019-08-24 00:55:38,319] TRAIN Iter 178400: lr = 0.202668,	loss = 2.726427,	Top-1 err = 0.385352,	Top-5 err = 0.175732,	data_time = 0.050527,	train_time = 0.776371
[2019-08-24 00:55:45,521] TRAIN Iter 178420: lr = 0.202635,	loss = 2.596896,	Top-1 err = 0.385840,	Top-5 err = 0.174609,	data_time = 0.050538,	train_time = 0.360104
[2019-08-24 00:56:01,061] TRAIN Iter 178440: lr = 0.202602,	loss = 2.669132,	Top-1 err = 0.393408,	Top-5 err = 0.177100,	data_time = 0.050819,	train_time = 0.776976
[2019-08-24 00:56:16,264] TRAIN Iter 178460: lr = 0.202568,	loss = 2.608107,	Top-1 err = 0.389795,	Top-5 err = 0.176758,	data_time = 0.050465,	train_time = 0.760124
[2019-08-24 00:56:23,470] TRAIN Iter 178480: lr = 0.202535,	loss = 2.620843,	Top-1 err = 0.387598,	Top-5 err = 0.172998,	data_time = 0.050477,	train_time = 0.360295
[2019-08-24 00:56:39,462] TRAIN Iter 178500: lr = 0.202502,	loss = 2.597541,	Top-1 err = 0.396777,	Top-5 err = 0.178711,	data_time = 0.050756,	train_time = 0.799598
[2019-08-24 00:56:46,733] TRAIN Iter 178520: lr = 0.202468,	loss = 2.661978,	Top-1 err = 0.389307,	Top-5 err = 0.179395,	data_time = 0.138099,	train_time = 0.363533
[2019-08-24 00:57:02,210] TRAIN Iter 178540: lr = 0.202435,	loss = 2.628924,	Top-1 err = 0.392236,	Top-5 err = 0.176270,	data_time = 0.050736,	train_time = 0.773855
[2019-08-24 00:57:16,507] TRAIN Iter 178560: lr = 0.202402,	loss = 2.556421,	Top-1 err = 0.386279,	Top-5 err = 0.175342,	data_time = 0.050524,	train_time = 0.714830
[2019-08-24 00:57:23,654] TRAIN Iter 178580: lr = 0.202368,	loss = 2.529160,	Top-1 err = 0.390234,	Top-5 err = 0.174268,	data_time = 0.050423,	train_time = 0.357337
[2019-08-24 00:57:39,707] TRAIN Iter 178600: lr = 0.202335,	loss = 2.627967,	Top-1 err = 0.389746,	Top-5 err = 0.175000,	data_time = 0.050533,	train_time = 0.802621
[2019-08-24 00:57:54,649] TRAIN Iter 178620: lr = 0.202302,	loss = 2.575887,	Top-1 err = 0.393164,	Top-5 err = 0.178320,	data_time = 0.050787,	train_time = 0.747063
[2019-08-24 00:58:01,731] TRAIN Iter 178640: lr = 0.202268,	loss = 2.502362,	Top-1 err = 0.388232,	Top-5 err = 0.175537,	data_time = 0.050378,	train_time = 0.354097
[2019-08-24 00:58:17,858] TRAIN Iter 178660: lr = 0.202235,	loss = 2.669412,	Top-1 err = 0.391357,	Top-5 err = 0.177002,	data_time = 0.050509,	train_time = 0.806365
[2019-08-24 00:58:24,850] TRAIN Iter 178680: lr = 0.202202,	loss = 2.673602,	Top-1 err = 0.395020,	Top-5 err = 0.177637,	data_time = 0.110059,	train_time = 0.349552
[2019-08-24 00:58:40,569] TRAIN Iter 178700: lr = 0.202168,	loss = 2.584916,	Top-1 err = 0.387891,	Top-5 err = 0.173437,	data_time = 0.050479,	train_time = 0.785981
[2019-08-24 00:58:56,213] TRAIN Iter 178720: lr = 0.202135,	loss = 2.547445,	Top-1 err = 0.391650,	Top-5 err = 0.173584,	data_time = 0.050501,	train_time = 0.782155
[2019-08-24 00:59:03,552] TRAIN Iter 178740: lr = 0.202102,	loss = 2.715543,	Top-1 err = 0.391748,	Top-5 err = 0.176758,	data_time = 0.050466,	train_time = 0.366914
[2019-08-24 00:59:19,272] TRAIN Iter 178760: lr = 0.202068,	loss = 2.544143,	Top-1 err = 0.392236,	Top-5 err = 0.178516,	data_time = 0.050770,	train_time = 0.785995
[2019-08-24 00:59:34,967] TRAIN Iter 178780: lr = 0.202035,	loss = 2.594383,	Top-1 err = 0.400586,	Top-5 err = 0.181006,	data_time = 0.050409,	train_time = 0.784769
[2019-08-24 00:59:42,032] TRAIN Iter 178800: lr = 0.202002,	loss = 2.643713,	Top-1 err = 0.390283,	Top-5 err = 0.172998,	data_time = 0.050421,	train_time = 0.353231
[2019-08-24 00:59:59,620] TRAIN Iter 178820: lr = 0.201968,	loss = 2.583874,	Top-1 err = 0.394629,	Top-5 err = 0.177979,	data_time = 0.050465,	train_time = 0.879372
[2019-08-24 01:00:07,144] TRAIN Iter 178840: lr = 0.201935,	loss = 2.650520,	Top-1 err = 0.393555,	Top-5 err = 0.178955,	data_time = 0.050744,	train_time = 0.376189
[2019-08-24 01:00:21,296] TRAIN Iter 178860: lr = 0.201902,	loss = 2.583632,	Top-1 err = 0.386914,	Top-5 err = 0.171191,	data_time = 0.050448,	train_time = 0.707585
[2019-08-24 01:00:37,553] TRAIN Iter 178880: lr = 0.201868,	loss = 2.585066,	Top-1 err = 0.393652,	Top-5 err = 0.177979,	data_time = 0.050751,	train_time = 0.812852
[2019-08-24 01:00:44,698] TRAIN Iter 178900: lr = 0.201835,	loss = 2.647429,	Top-1 err = 0.389453,	Top-5 err = 0.174756,	data_time = 0.050778,	train_time = 0.357241
[2019-08-24 01:01:00,742] TRAIN Iter 178920: lr = 0.201802,	loss = 2.612969,	Top-1 err = 0.394775,	Top-5 err = 0.180225,	data_time = 0.050210,	train_time = 0.802175
[2019-08-24 01:01:13,260] TRAIN Iter 178940: lr = 0.201768,	loss = 2.700660,	Top-1 err = 0.393164,	Top-5 err = 0.175098,	data_time = 0.050055,	train_time = 0.625862
[2019-08-24 01:01:21,704] TRAIN Iter 178960: lr = 0.201735,	loss = 2.637320,	Top-1 err = 0.394727,	Top-5 err = 0.177441,	data_time = 0.049945,	train_time = 0.422193
[2019-08-24 01:02:06,719] TRAIN Iter 178980: lr = 0.201702,	loss = 2.636559,	Top-1 err = 0.395659,	Top-5 err = 0.181615,	data_time = 0.050365,	train_time = 2.250733
[2019-08-24 01:02:14,662] TRAIN Iter 179000: lr = 0.201668,	loss = 2.606204,	Top-1 err = 0.390869,	Top-5 err = 0.174219,	data_time = 0.050390,	train_time = 0.397140
[2019-08-24 01:02:31,926] TRAIN Iter 179020: lr = 0.201635,	loss = 2.432299,	Top-1 err = 0.384619,	Top-5 err = 0.173193,	data_time = 0.050508,	train_time = 0.863194
[2019-08-24 01:02:46,523] TRAIN Iter 179040: lr = 0.201602,	loss = 2.591657,	Top-1 err = 0.381299,	Top-5 err = 0.170215,	data_time = 0.050593,	train_time = 0.729795
[2019-08-24 01:02:53,759] TRAIN Iter 179060: lr = 0.201568,	loss = 2.487873,	Top-1 err = 0.388330,	Top-5 err = 0.169336,	data_time = 0.050659,	train_time = 0.361801
[2019-08-24 01:03:06,706] TRAIN Iter 179080: lr = 0.201535,	loss = 2.573372,	Top-1 err = 0.389111,	Top-5 err = 0.177441,	data_time = 0.050434,	train_time = 0.647323
[2019-08-24 01:03:14,467] TRAIN Iter 179100: lr = 0.201502,	loss = 2.637789,	Top-1 err = 0.385059,	Top-5 err = 0.175098,	data_time = 0.050838,	train_time = 0.388065
[2019-08-24 01:03:27,937] TRAIN Iter 179120: lr = 0.201468,	loss = 2.618941,	Top-1 err = 0.386426,	Top-5 err = 0.173584,	data_time = 0.050694,	train_time = 0.673478
[2019-08-24 01:03:43,759] TRAIN Iter 179140: lr = 0.201435,	loss = 2.461985,	Top-1 err = 0.383350,	Top-5 err = 0.170850,	data_time = 0.050386,	train_time = 0.791075
[2019-08-24 01:03:50,483] TRAIN Iter 179160: lr = 0.201402,	loss = 2.530770,	Top-1 err = 0.387061,	Top-5 err = 0.175879,	data_time = 0.050456,	train_time = 0.336218
[2019-08-24 01:04:06,778] TRAIN Iter 179180: lr = 0.201368,	loss = 2.506490,	Top-1 err = 0.384570,	Top-5 err = 0.173389,	data_time = 0.050298,	train_time = 0.814712
[2019-08-24 01:04:20,541] TRAIN Iter 179200: lr = 0.201335,	loss = 2.691165,	Top-1 err = 0.388770,	Top-5 err = 0.172852,	data_time = 0.050594,	train_time = 0.688133
[2019-08-24 01:04:27,449] TRAIN Iter 179220: lr = 0.201302,	loss = 2.557592,	Top-1 err = 0.388379,	Top-5 err = 0.171094,	data_time = 0.050529,	train_time = 0.345376
[2019-08-24 01:04:43,202] TRAIN Iter 179240: lr = 0.201268,	loss = 2.571111,	Top-1 err = 0.386621,	Top-5 err = 0.173193,	data_time = 0.050447,	train_time = 0.787637
[2019-08-24 01:04:50,429] TRAIN Iter 179260: lr = 0.201235,	loss = 2.546974,	Top-1 err = 0.386719,	Top-5 err = 0.171191,	data_time = 0.050593,	train_time = 0.361348
[2019-08-24 01:05:06,119] TRAIN Iter 179280: lr = 0.201202,	loss = 2.597706,	Top-1 err = 0.385010,	Top-5 err = 0.170850,	data_time = 0.050557,	train_time = 0.784501
[2019-08-24 01:05:22,455] TRAIN Iter 179300: lr = 0.201168,	loss = 2.647256,	Top-1 err = 0.388867,	Top-5 err = 0.175684,	data_time = 0.050445,	train_time = 0.816764
[2019-08-24 01:05:29,997] TRAIN Iter 179320: lr = 0.201135,	loss = 2.523420,	Top-1 err = 0.382422,	Top-5 err = 0.170996,	data_time = 0.050652,	train_time = 0.377091
[2019-08-24 01:05:44,283] TRAIN Iter 179340: lr = 0.201102,	loss = 2.568023,	Top-1 err = 0.389355,	Top-5 err = 0.177588,	data_time = 0.050433,	train_time = 0.714270
[2019-08-24 01:05:58,933] TRAIN Iter 179360: lr = 0.201068,	loss = 2.557246,	Top-1 err = 0.386230,	Top-5 err = 0.175635,	data_time = 0.050337,	train_time = 0.732515
[2019-08-24 01:06:05,899] TRAIN Iter 179380: lr = 0.201035,	loss = 2.589667,	Top-1 err = 0.382373,	Top-5 err = 0.171387,	data_time = 0.050638,	train_time = 0.348253
[2019-08-24 01:06:20,594] TRAIN Iter 179400: lr = 0.201002,	loss = 2.585487,	Top-1 err = 0.388330,	Top-5 err = 0.174463,	data_time = 0.050501,	train_time = 0.734737
[2019-08-24 01:06:28,340] TRAIN Iter 179420: lr = 0.200968,	loss = 2.592190,	Top-1 err = 0.385059,	Top-5 err = 0.172168,	data_time = 0.050805,	train_time = 0.387302
[2019-08-24 01:06:43,273] TRAIN Iter 179440: lr = 0.200935,	loss = 2.495835,	Top-1 err = 0.385986,	Top-5 err = 0.170508,	data_time = 0.050772,	train_time = 0.746631
[2019-08-24 01:06:58,319] TRAIN Iter 179460: lr = 0.200902,	loss = 2.599269,	Top-1 err = 0.387061,	Top-5 err = 0.173584,	data_time = 0.050463,	train_time = 0.752297
[2019-08-24 01:07:05,897] TRAIN Iter 179480: lr = 0.200868,	loss = 2.665989,	Top-1 err = 0.391357,	Top-5 err = 0.175635,	data_time = 0.050455,	train_time = 0.378864
[2019-08-24 01:07:19,947] TRAIN Iter 179500: lr = 0.200835,	loss = 2.586329,	Top-1 err = 0.391895,	Top-5 err = 0.177051,	data_time = 0.050486,	train_time = 0.702485
[2019-08-24 01:07:34,980] TRAIN Iter 179520: lr = 0.200802,	loss = 2.642545,	Top-1 err = 0.391943,	Top-5 err = 0.174072,	data_time = 0.050569,	train_time = 0.751630
[2019-08-24 01:07:42,148] TRAIN Iter 179540: lr = 0.200768,	loss = 2.522840,	Top-1 err = 0.383154,	Top-5 err = 0.171143,	data_time = 0.050752,	train_time = 0.358394
[2019-08-24 01:07:57,451] TRAIN Iter 179560: lr = 0.200735,	loss = 2.576228,	Top-1 err = 0.383594,	Top-5 err = 0.171387,	data_time = 0.050574,	train_time = 0.765129
[2019-08-24 01:08:04,606] TRAIN Iter 179580: lr = 0.200702,	loss = 2.686643,	Top-1 err = 0.394531,	Top-5 err = 0.181934,	data_time = 0.050545,	train_time = 0.357750
[2019-08-24 01:08:19,428] TRAIN Iter 179600: lr = 0.200668,	loss = 2.547513,	Top-1 err = 0.382764,	Top-5 err = 0.173828,	data_time = 0.050770,	train_time = 0.741066
[2019-08-24 01:08:35,049] TRAIN Iter 179620: lr = 0.200635,	loss = 2.577008,	Top-1 err = 0.391553,	Top-5 err = 0.174316,	data_time = 0.050677,	train_time = 0.781065
[2019-08-24 01:08:42,043] TRAIN Iter 179640: lr = 0.200602,	loss = 2.703231,	Top-1 err = 0.386670,	Top-5 err = 0.173975,	data_time = 0.050289,	train_time = 0.349693
[2019-08-24 01:08:57,051] TRAIN Iter 179660: lr = 0.200568,	loss = 2.597313,	Top-1 err = 0.393262,	Top-5 err = 0.171143,	data_time = 0.050869,	train_time = 0.750353
[2019-08-24 01:09:11,616] TRAIN Iter 179680: lr = 0.200535,	loss = 2.605847,	Top-1 err = 0.384619,	Top-5 err = 0.170703,	data_time = 0.050607,	train_time = 0.728264
[2019-08-24 01:09:19,899] TRAIN Iter 179700: lr = 0.200502,	loss = 2.605939,	Top-1 err = 0.391455,	Top-5 err = 0.176074,	data_time = 0.050746,	train_time = 0.414102
[2019-08-24 01:09:35,049] TRAIN Iter 179720: lr = 0.200468,	loss = 2.566867,	Top-1 err = 0.392090,	Top-5 err = 0.174902,	data_time = 0.050293,	train_time = 0.757480
[2019-08-24 01:09:42,083] TRAIN Iter 179740: lr = 0.200435,	loss = 2.707660,	Top-1 err = 0.398193,	Top-5 err = 0.179883,	data_time = 0.050414,	train_time = 0.351689
[2019-08-24 01:09:59,263] TRAIN Iter 179760: lr = 0.200402,	loss = 2.540987,	Top-1 err = 0.386182,	Top-5 err = 0.171338,	data_time = 0.050429,	train_time = 0.859003
[2019-08-24 01:10:13,928] TRAIN Iter 179780: lr = 0.200368,	loss = 2.637133,	Top-1 err = 0.391943,	Top-5 err = 0.179736,	data_time = 0.050503,	train_time = 0.733232
[2019-08-24 01:10:21,171] TRAIN Iter 179800: lr = 0.200335,	loss = 2.597172,	Top-1 err = 0.391357,	Top-5 err = 0.174756,	data_time = 0.050377,	train_time = 0.362134
[2019-08-24 01:10:37,648] TRAIN Iter 179820: lr = 0.200302,	loss = 2.623740,	Top-1 err = 0.389355,	Top-5 err = 0.180420,	data_time = 0.050490,	train_time = 0.823861
[2019-08-24 01:10:53,129] TRAIN Iter 179840: lr = 0.200268,	loss = 2.599369,	Top-1 err = 0.393506,	Top-5 err = 0.182275,	data_time = 0.135108,	train_time = 0.773998
[2019-08-24 01:11:00,777] TRAIN Iter 179860: lr = 0.200235,	loss = 2.570229,	Top-1 err = 0.391016,	Top-5 err = 0.173877,	data_time = 0.050397,	train_time = 0.382426
[2019-08-24 01:11:16,910] TRAIN Iter 179880: lr = 0.200202,	loss = 2.703816,	Top-1 err = 0.392773,	Top-5 err = 0.174805,	data_time = 0.105018,	train_time = 0.806600
[2019-08-24 01:11:24,075] TRAIN Iter 179900: lr = 0.200168,	loss = 2.581069,	Top-1 err = 0.396484,	Top-5 err = 0.176611,	data_time = 0.050485,	train_time = 0.358239
[2019-08-24 01:11:42,376] TRAIN Iter 179920: lr = 0.200135,	loss = 2.662195,	Top-1 err = 0.387891,	Top-5 err = 0.175732,	data_time = 0.050570,	train_time = 0.915036
[2019-08-24 01:11:58,504] TRAIN Iter 179940: lr = 0.200102,	loss = 2.572059,	Top-1 err = 0.388721,	Top-5 err = 0.172949,	data_time = 0.050518,	train_time = 0.806423
[2019-08-24 01:12:05,691] TRAIN Iter 179960: lr = 0.200068,	loss = 2.638852,	Top-1 err = 0.389014,	Top-5 err = 0.175000,	data_time = 0.050500,	train_time = 0.359310
[2019-08-24 01:12:21,001] TRAIN Iter 179980: lr = 0.200035,	loss = 2.561121,	Top-1 err = 0.392236,	Top-5 err = 0.177881,	data_time = 0.050610,	train_time = 0.765491
[2019-08-24 01:12:38,063] TRAIN Iter 180000: lr = 0.200002,	loss = 2.583918,	Top-1 err = 0.389111,	Top-5 err = 0.176611,	data_time = 0.050569,	train_time = 0.853076
[2019-08-24 01:13:41,683] TEST Iter 180000: loss = 2.382557,	Top-1 err = 0.354820,	Top-5 err = 0.138040,	val_time = 63.581879
[2019-08-24 01:13:47,984] TRAIN Iter 180020: lr = 0.199968,	loss = 2.810071,	Top-1 err = 0.396436,	Top-5 err = 0.185840,	data_time = 0.050394,	train_time = 0.315028
[2019-08-24 01:13:54,465] TRAIN Iter 180040: lr = 0.199935,	loss = 2.545900,	Top-1 err = 0.397412,	Top-5 err = 0.180664,	data_time = 0.050222,	train_time = 0.324064
[2019-08-24 01:14:01,010] TRAIN Iter 180060: lr = 0.199902,	loss = 2.578174,	Top-1 err = 0.396631,	Top-5 err = 0.181104,	data_time = 0.050789,	train_time = 0.327216
[2019-08-24 01:14:12,186] TRAIN Iter 180080: lr = 0.199868,	loss = 2.571703,	Top-1 err = 0.387012,	Top-5 err = 0.178760,	data_time = 0.050632,	train_time = 0.558773
[2019-08-24 01:14:27,263] TRAIN Iter 180100: lr = 0.199835,	loss = 2.607967,	Top-1 err = 0.389502,	Top-5 err = 0.177100,	data_time = 0.050511,	train_time = 0.753862
[2019-08-24 01:14:35,694] TRAIN Iter 180120: lr = 0.199802,	loss = 2.604700,	Top-1 err = 0.389941,	Top-5 err = 0.174268,	data_time = 0.193547,	train_time = 0.421541
[2019-08-24 01:14:51,405] TRAIN Iter 180140: lr = 0.199768,	loss = 2.621060,	Top-1 err = 0.387793,	Top-5 err = 0.176074,	data_time = 0.050188,	train_time = 0.785524
[2019-08-24 01:15:00,310] TRAIN Iter 180160: lr = 0.199735,	loss = 2.570290,	Top-1 err = 0.394824,	Top-5 err = 0.177490,	data_time = 0.080824,	train_time = 0.445239
[2019-08-24 01:15:16,302] TRAIN Iter 180180: lr = 0.199702,	loss = 2.583124,	Top-1 err = 0.393750,	Top-5 err = 0.175830,	data_time = 0.049908,	train_time = 0.799558
[2019-08-24 01:15:33,706] TRAIN Iter 180200: lr = 0.199668,	loss = 2.568629,	Top-1 err = 0.386572,	Top-5 err = 0.176465,	data_time = 0.049968,	train_time = 0.870230
[2019-08-24 01:15:41,009] TRAIN Iter 180220: lr = 0.199635,	loss = 2.588960,	Top-1 err = 0.393115,	Top-5 err = 0.178809,	data_time = 0.049891,	train_time = 0.365115
[2019-08-24 01:16:27,650] TRAIN Iter 180240: lr = 0.199602,	loss = 2.619212,	Top-1 err = 0.400436,	Top-5 err = 0.180517,	data_time = 0.050478,	train_time = 2.332061
[2019-08-24 01:16:43,374] TRAIN Iter 180260: lr = 0.199568,	loss = 2.612702,	Top-1 err = 0.389697,	Top-5 err = 0.173828,	data_time = 1.029154,	train_time = 0.786141
[2019-08-24 01:16:50,563] TRAIN Iter 180280: lr = 0.199535,	loss = 2.629767,	Top-1 err = 0.380322,	Top-5 err = 0.169775,	data_time = 0.116150,	train_time = 0.359475
[2019-08-24 01:17:01,952] TRAIN Iter 180300: lr = 0.199502,	loss = 2.381018,	Top-1 err = 0.379834,	Top-5 err = 0.167676,	data_time = 0.050312,	train_time = 0.569393
[2019-08-24 01:17:08,572] TRAIN Iter 180320: lr = 0.199468,	loss = 2.531539,	Top-1 err = 0.381641,	Top-5 err = 0.168604,	data_time = 0.050806,	train_time = 0.330998
[2019-08-24 01:17:24,455] TRAIN Iter 180340: lr = 0.199435,	loss = 2.587255,	Top-1 err = 0.383496,	Top-5 err = 0.172559,	data_time = 0.050320,	train_time = 0.794157
[2019-08-24 01:17:41,325] TRAIN Iter 180360: lr = 0.199402,	loss = 2.581627,	Top-1 err = 0.382617,	Top-5 err = 0.169873,	data_time = 0.050800,	train_time = 0.843498
[2019-08-24 01:17:48,164] TRAIN Iter 180380: lr = 0.199368,	loss = 2.641392,	Top-1 err = 0.383447,	Top-5 err = 0.171973,	data_time = 0.050505,	train_time = 0.341929
[2019-08-24 01:18:04,811] TRAIN Iter 180400: lr = 0.199335,	loss = 2.533186,	Top-1 err = 0.380762,	Top-5 err = 0.169971,	data_time = 0.050343,	train_time = 0.832334
[2019-08-24 01:18:19,633] TRAIN Iter 180420: lr = 0.199302,	loss = 2.545942,	Top-1 err = 0.387012,	Top-5 err = 0.170264,	data_time = 1.058524,	train_time = 0.741066
[2019-08-24 01:18:26,815] TRAIN Iter 180440: lr = 0.199268,	loss = 2.562655,	Top-1 err = 0.384619,	Top-5 err = 0.169678,	data_time = 0.050788,	train_time = 0.359099
[2019-08-24 01:18:41,419] TRAIN Iter 180460: lr = 0.199235,	loss = 2.605112,	Top-1 err = 0.385986,	Top-5 err = 0.170850,	data_time = 0.050336,	train_time = 0.730160
[2019-08-24 01:18:48,498] TRAIN Iter 180480: lr = 0.199202,	loss = 2.558875,	Top-1 err = 0.386572,	Top-5 err = 0.176416,	data_time = 0.050488,	train_time = 0.353968
[2019-08-24 01:19:04,730] TRAIN Iter 180500: lr = 0.199168,	loss = 2.618949,	Top-1 err = 0.386230,	Top-5 err = 0.174072,	data_time = 0.050904,	train_time = 0.811591
[2019-08-24 01:19:21,143] TRAIN Iter 180520: lr = 0.199135,	loss = 2.521498,	Top-1 err = 0.383643,	Top-5 err = 0.169434,	data_time = 0.050426,	train_time = 0.820619
[2019-08-24 01:19:27,965] TRAIN Iter 180540: lr = 0.199102,	loss = 2.580464,	Top-1 err = 0.388379,	Top-5 err = 0.172510,	data_time = 0.050526,	train_time = 0.341080
[2019-08-24 01:19:44,372] TRAIN Iter 180560: lr = 0.199068,	loss = 2.529229,	Top-1 err = 0.386768,	Top-5 err = 0.170996,	data_time = 0.050907,	train_time = 0.820330
[2019-08-24 01:20:00,630] TRAIN Iter 180580: lr = 0.199035,	loss = 2.568635,	Top-1 err = 0.389502,	Top-5 err = 0.169824,	data_time = 0.050419,	train_time = 0.812873
[2019-08-24 01:20:07,343] TRAIN Iter 180600: lr = 0.199002,	loss = 2.636513,	Top-1 err = 0.392676,	Top-5 err = 0.175244,	data_time = 0.050482,	train_time = 0.335670
[2019-08-24 01:20:23,142] TRAIN Iter 180620: lr = 0.198968,	loss = 2.636572,	Top-1 err = 0.391455,	Top-5 err = 0.172461,	data_time = 0.050361,	train_time = 0.789909
[2019-08-24 01:20:30,958] TRAIN Iter 180640: lr = 0.198935,	loss = 2.545708,	Top-1 err = 0.392334,	Top-5 err = 0.176660,	data_time = 0.050576,	train_time = 0.390784
[2019-08-24 01:20:45,279] TRAIN Iter 180660: lr = 0.198902,	loss = 2.580107,	Top-1 err = 0.384863,	Top-5 err = 0.173584,	data_time = 0.050598,	train_time = 0.716068
[2019-08-24 01:21:01,269] TRAIN Iter 180680: lr = 0.198868,	loss = 2.626515,	Top-1 err = 0.391211,	Top-5 err = 0.177881,	data_time = 0.050636,	train_time = 0.799480
[2019-08-24 01:21:08,569] TRAIN Iter 180700: lr = 0.198835,	loss = 2.556732,	Top-1 err = 0.387207,	Top-5 err = 0.173779,	data_time = 0.050614,	train_time = 0.364951
[2019-08-24 01:21:23,603] TRAIN Iter 180720: lr = 0.198802,	loss = 2.670775,	Top-1 err = 0.388330,	Top-5 err = 0.177148,	data_time = 0.050513,	train_time = 0.751707
[2019-08-24 01:21:40,044] TRAIN Iter 180740: lr = 0.198768,	loss = 2.533950,	Top-1 err = 0.388428,	Top-5 err = 0.175537,	data_time = 0.050528,	train_time = 0.822055
[2019-08-24 01:21:47,011] TRAIN Iter 180760: lr = 0.198735,	loss = 2.682107,	Top-1 err = 0.389258,	Top-5 err = 0.178223,	data_time = 0.050485,	train_time = 0.348313
[2019-08-24 01:22:02,271] TRAIN Iter 180780: lr = 0.198702,	loss = 2.629426,	Top-1 err = 0.384814,	Top-5 err = 0.172803,	data_time = 0.050490,	train_time = 0.763002
[2019-08-24 01:22:09,754] TRAIN Iter 180800: lr = 0.198668,	loss = 2.676551,	Top-1 err = 0.398535,	Top-5 err = 0.181152,	data_time = 0.050331,	train_time = 0.374111
[2019-08-24 01:22:24,205] TRAIN Iter 180820: lr = 0.198635,	loss = 2.581094,	Top-1 err = 0.387891,	Top-5 err = 0.177490,	data_time = 0.050467,	train_time = 0.722537
[2019-08-24 01:22:39,862] TRAIN Iter 180840: lr = 0.198602,	loss = 2.562387,	Top-1 err = 0.386133,	Top-5 err = 0.171240,	data_time = 0.050541,	train_time = 0.782843
[2019-08-24 01:22:46,832] TRAIN Iter 180860: lr = 0.198568,	loss = 2.616183,	Top-1 err = 0.393604,	Top-5 err = 0.174072,	data_time = 0.050388,	train_time = 0.348485
[2019-08-24 01:23:03,007] TRAIN Iter 180880: lr = 0.198535,	loss = 2.590456,	Top-1 err = 0.389062,	Top-5 err = 0.175098,	data_time = 0.050473,	train_time = 0.808778
[2019-08-24 01:23:17,817] TRAIN Iter 180900: lr = 0.198502,	loss = 2.542794,	Top-1 err = 0.393311,	Top-5 err = 0.178418,	data_time = 0.050282,	train_time = 0.740441
[2019-08-24 01:23:24,318] TRAIN Iter 180920: lr = 0.198468,	loss = 2.595473,	Top-1 err = 0.389209,	Top-5 err = 0.175635,	data_time = 0.050320,	train_time = 0.325061
[2019-08-24 01:23:41,109] TRAIN Iter 180940: lr = 0.198435,	loss = 2.530659,	Top-1 err = 0.383301,	Top-5 err = 0.172705,	data_time = 0.050464,	train_time = 0.839531
[2019-08-24 01:23:48,685] TRAIN Iter 180960: lr = 0.198402,	loss = 2.596973,	Top-1 err = 0.386230,	Top-5 err = 0.174902,	data_time = 0.050563,	train_time = 0.378795
[2019-08-24 01:24:03,472] TRAIN Iter 180980: lr = 0.198368,	loss = 2.496298,	Top-1 err = 0.392334,	Top-5 err = 0.174414,	data_time = 0.050731,	train_time = 0.739341
[2019-08-24 01:24:19,721] TRAIN Iter 181000: lr = 0.198335,	loss = 2.640178,	Top-1 err = 0.393604,	Top-5 err = 0.173389,	data_time = 0.050457,	train_time = 0.812411
[2019-08-24 01:24:26,594] TRAIN Iter 181020: lr = 0.198302,	loss = 2.520787,	Top-1 err = 0.387305,	Top-5 err = 0.175830,	data_time = 0.050357,	train_time = 0.343621
[2019-08-24 01:24:42,843] TRAIN Iter 181040: lr = 0.198268,	loss = 2.561885,	Top-1 err = 0.387939,	Top-5 err = 0.175830,	data_time = 0.050522,	train_time = 0.812461
[2019-08-24 01:24:58,984] TRAIN Iter 181060: lr = 0.198235,	loss = 2.604503,	Top-1 err = 0.397021,	Top-5 err = 0.177100,	data_time = 0.050402,	train_time = 0.807028
[2019-08-24 01:25:05,353] TRAIN Iter 181080: lr = 0.198202,	loss = 2.620085,	Top-1 err = 0.386768,	Top-5 err = 0.172559,	data_time = 0.050431,	train_time = 0.318451
[2019-08-24 01:25:22,679] TRAIN Iter 181100: lr = 0.198168,	loss = 2.602889,	Top-1 err = 0.383301,	Top-5 err = 0.177930,	data_time = 0.050668,	train_time = 0.866280
[2019-08-24 01:25:29,563] TRAIN Iter 181120: lr = 0.198135,	loss = 2.606746,	Top-1 err = 0.394775,	Top-5 err = 0.179932,	data_time = 0.050378,	train_time = 0.344163
[2019-08-24 01:25:45,994] TRAIN Iter 181140: lr = 0.198102,	loss = 2.569908,	Top-1 err = 0.388525,	Top-5 err = 0.172314,	data_time = 0.050408,	train_time = 0.821573
[2019-08-24 01:26:03,751] TRAIN Iter 181160: lr = 0.198068,	loss = 2.579030,	Top-1 err = 0.394580,	Top-5 err = 0.181348,	data_time = 0.050540,	train_time = 0.887806
[2019-08-24 01:26:10,722] TRAIN Iter 181180: lr = 0.198035,	loss = 2.545075,	Top-1 err = 0.393066,	Top-5 err = 0.180225,	data_time = 0.050429,	train_time = 0.348555
[2019-08-24 01:26:26,743] TRAIN Iter 181200: lr = 0.198002,	loss = 2.601214,	Top-1 err = 0.391895,	Top-5 err = 0.174023,	data_time = 0.050368,	train_time = 0.801019
[2019-08-24 01:26:44,726] TRAIN Iter 181220: lr = 0.197968,	loss = 2.740952,	Top-1 err = 0.390625,	Top-5 err = 0.175586,	data_time = 0.050809,	train_time = 0.899157
[2019-08-24 01:26:51,674] TRAIN Iter 181240: lr = 0.197935,	loss = 2.518860,	Top-1 err = 0.392383,	Top-5 err = 0.177979,	data_time = 0.050407,	train_time = 0.347399
[2019-08-24 01:27:07,446] TRAIN Iter 181260: lr = 0.197902,	loss = 2.584418,	Top-1 err = 0.393652,	Top-5 err = 0.180176,	data_time = 0.050327,	train_time = 0.788586
[2019-08-24 01:27:14,412] TRAIN Iter 181280: lr = 0.197868,	loss = 2.654692,	Top-1 err = 0.396680,	Top-5 err = 0.175732,	data_time = 0.050161,	train_time = 0.348261
[2019-08-24 01:27:34,253] TRAIN Iter 181300: lr = 0.197835,	loss = 2.592027,	Top-1 err = 0.390723,	Top-5 err = 0.176465,	data_time = 0.050373,	train_time = 0.992040
[2019-08-24 01:27:50,661] TRAIN Iter 181320: lr = 0.197802,	loss = 2.490685,	Top-1 err = 0.385352,	Top-5 err = 0.173340,	data_time = 0.050522,	train_time = 0.820375
[2019-08-24 01:27:57,457] TRAIN Iter 181340: lr = 0.197768,	loss = 2.557618,	Top-1 err = 0.390381,	Top-5 err = 0.174854,	data_time = 0.050619,	train_time = 0.339821
[2019-08-24 01:28:14,040] TRAIN Iter 181360: lr = 0.197735,	loss = 2.660242,	Top-1 err = 0.391016,	Top-5 err = 0.179492,	data_time = 0.050665,	train_time = 0.829105
[2019-08-24 01:28:31,638] TRAIN Iter 181380: lr = 0.197702,	loss = 2.581051,	Top-1 err = 0.391357,	Top-5 err = 0.180225,	data_time = 0.050210,	train_time = 0.879920
[2019-08-24 01:28:38,345] TRAIN Iter 181400: lr = 0.197668,	loss = 2.676059,	Top-1 err = 0.389795,	Top-5 err = 0.175391,	data_time = 0.050540,	train_time = 0.335329
[2019-08-24 01:28:56,280] TRAIN Iter 181420: lr = 0.197635,	loss = 2.539253,	Top-1 err = 0.386133,	Top-5 err = 0.175879,	data_time = 0.050054,	train_time = 0.896720
[2019-08-24 01:29:02,940] TRAIN Iter 181440: lr = 0.197602,	loss = 2.663501,	Top-1 err = 0.393164,	Top-5 err = 0.175732,	data_time = 0.050075,	train_time = 0.333008
[2019-08-24 01:29:18,394] TRAIN Iter 181460: lr = 0.197568,	loss = 2.553589,	Top-1 err = 0.394141,	Top-5 err = 0.179639,	data_time = 0.049919,	train_time = 0.772671
[2019-08-24 01:30:08,830] TRAIN Iter 181480: lr = 0.197535,	loss = 2.502819,	Top-1 err = 0.389132,	Top-5 err = 0.173753,	data_time = 0.050333,	train_time = 2.521760
[2019-08-24 01:30:15,483] TRAIN Iter 181500: lr = 0.197502,	loss = 2.519078,	Top-1 err = 0.385205,	Top-5 err = 0.172168,	data_time = 0.050677,	train_time = 0.332623
[2019-08-24 01:30:30,917] TRAIN Iter 181520: lr = 0.197468,	loss = 2.609365,	Top-1 err = 0.384961,	Top-5 err = 0.170947,	data_time = 0.050431,	train_time = 0.771719
[2019-08-24 01:30:38,204] TRAIN Iter 181540: lr = 0.197435,	loss = 2.598439,	Top-1 err = 0.382471,	Top-5 err = 0.169482,	data_time = 0.050509,	train_time = 0.364328
[2019-08-24 01:30:51,656] TRAIN Iter 181560: lr = 0.197402,	loss = 2.604313,	Top-1 err = 0.380615,	Top-5 err = 0.171387,	data_time = 0.050282,	train_time = 0.672587
[2019-08-24 01:31:08,738] TRAIN Iter 181580: lr = 0.197368,	loss = 2.565766,	Top-1 err = 0.379980,	Top-5 err = 0.167676,	data_time = 0.050266,	train_time = 0.854055
[2019-08-24 01:31:15,937] TRAIN Iter 181600: lr = 0.197335,	loss = 2.597057,	Top-1 err = 0.384717,	Top-5 err = 0.167822,	data_time = 0.050560,	train_time = 0.359959
[2019-08-24 01:31:29,951] TRAIN Iter 181620: lr = 0.197302,	loss = 2.543776,	Top-1 err = 0.380762,	Top-5 err = 0.169580,	data_time = 0.050420,	train_time = 0.700701
[2019-08-24 01:31:42,042] TRAIN Iter 181640: lr = 0.197268,	loss = 2.530748,	Top-1 err = 0.382129,	Top-5 err = 0.172998,	data_time = 0.726873,	train_time = 0.604510
[2019-08-24 01:31:53,518] TRAIN Iter 181660: lr = 0.197235,	loss = 2.525525,	Top-1 err = 0.380273,	Top-5 err = 0.169434,	data_time = 0.050853,	train_time = 0.573787
[2019-08-24 01:32:08,311] TRAIN Iter 181680: lr = 0.197202,	loss = 2.550364,	Top-1 err = 0.391064,	Top-5 err = 0.171826,	data_time = 0.050695,	train_time = 0.739657
[2019-08-24 01:32:15,647] TRAIN Iter 181700: lr = 0.197168,	loss = 2.564313,	Top-1 err = 0.391357,	Top-5 err = 0.175537,	data_time = 0.050511,	train_time = 0.366797
[2019-08-24 01:32:28,713] TRAIN Iter 181720: lr = 0.197135,	loss = 2.587724,	Top-1 err = 0.386768,	Top-5 err = 0.168896,	data_time = 0.050748,	train_time = 0.653254
[2019-08-24 01:32:45,171] TRAIN Iter 181740: lr = 0.197102,	loss = 2.596183,	Top-1 err = 0.392529,	Top-5 err = 0.174805,	data_time = 0.050774,	train_time = 0.822887
[2019-08-24 01:32:51,819] TRAIN Iter 181760: lr = 0.197068,	loss = 2.608810,	Top-1 err = 0.387158,	Top-5 err = 0.173242,	data_time = 0.050210,	train_time = 0.332373
[2019-08-24 01:33:07,179] TRAIN Iter 181780: lr = 0.197035,	loss = 2.541635,	Top-1 err = 0.383789,	Top-5 err = 0.168604,	data_time = 0.050569,	train_time = 0.767991
[2019-08-24 01:33:16,891] TRAIN Iter 181800: lr = 0.197002,	loss = 2.605705,	Top-1 err = 0.379346,	Top-5 err = 0.173633,	data_time = 0.110933,	train_time = 0.485587
[2019-08-24 01:33:28,761] TRAIN Iter 181820: lr = 0.196968,	loss = 2.664941,	Top-1 err = 0.385303,	Top-5 err = 0.173535,	data_time = 0.050659,	train_time = 0.593512
[2019-08-24 01:33:43,065] TRAIN Iter 181840: lr = 0.196935,	loss = 2.503800,	Top-1 err = 0.381201,	Top-5 err = 0.169092,	data_time = 0.050526,	train_time = 0.715156
[2019-08-24 01:33:49,737] TRAIN Iter 181860: lr = 0.196902,	loss = 2.654922,	Top-1 err = 0.389453,	Top-5 err = 0.176123,	data_time = 0.050430,	train_time = 0.333617
[2019-08-24 01:34:05,547] TRAIN Iter 181880: lr = 0.196868,	loss = 2.579465,	Top-1 err = 0.386328,	Top-5 err = 0.172314,	data_time = 0.050890,	train_time = 0.790482
[2019-08-24 01:34:21,051] TRAIN Iter 181900: lr = 0.196835,	loss = 2.541643,	Top-1 err = 0.381396,	Top-5 err = 0.170703,	data_time = 0.136380,	train_time = 0.775195
[2019-08-24 01:34:27,958] TRAIN Iter 181920: lr = 0.196802,	loss = 2.615757,	Top-1 err = 0.383740,	Top-5 err = 0.167969,	data_time = 0.050498,	train_time = 0.345315
[2019-08-24 01:34:43,758] TRAIN Iter 181940: lr = 0.196768,	loss = 2.680345,	Top-1 err = 0.384619,	Top-5 err = 0.170947,	data_time = 0.050517,	train_time = 0.790010
[2019-08-24 01:34:56,259] TRAIN Iter 181960: lr = 0.196735,	loss = 2.615286,	Top-1 err = 0.382031,	Top-5 err = 0.173291,	data_time = 0.566999,	train_time = 0.625033
[2019-08-24 01:35:05,480] TRAIN Iter 181980: lr = 0.196702,	loss = 2.520980,	Top-1 err = 0.393848,	Top-5 err = 0.173145,	data_time = 0.050423,	train_time = 0.461034
[2019-08-24 01:35:20,634] TRAIN Iter 182000: lr = 0.196668,	loss = 2.582226,	Top-1 err = 0.381982,	Top-5 err = 0.176855,	data_time = 0.050318,	train_time = 0.757658
[2019-08-24 01:35:27,606] TRAIN Iter 182020: lr = 0.196635,	loss = 2.525548,	Top-1 err = 0.387646,	Top-5 err = 0.172314,	data_time = 0.050375,	train_time = 0.348583
[2019-08-24 01:35:43,644] TRAIN Iter 182040: lr = 0.196602,	loss = 2.542059,	Top-1 err = 0.389502,	Top-5 err = 0.177637,	data_time = 0.050411,	train_time = 0.801876
[2019-08-24 01:35:57,265] TRAIN Iter 182060: lr = 0.196568,	loss = 2.632050,	Top-1 err = 0.387744,	Top-5 err = 0.171240,	data_time = 0.050890,	train_time = 0.681065
[2019-08-24 01:36:04,675] TRAIN Iter 182080: lr = 0.196535,	loss = 2.666893,	Top-1 err = 0.385986,	Top-5 err = 0.174170,	data_time = 0.050578,	train_time = 0.370464
[2019-08-24 01:36:21,490] TRAIN Iter 182100: lr = 0.196502,	loss = 2.562545,	Top-1 err = 0.386865,	Top-5 err = 0.176465,	data_time = 0.050311,	train_time = 0.840722
[2019-08-24 01:36:34,818] TRAIN Iter 182120: lr = 0.196468,	loss = 2.594316,	Top-1 err = 0.389502,	Top-5 err = 0.172412,	data_time = 0.050515,	train_time = 0.666390
[2019-08-24 01:36:43,239] TRAIN Iter 182140: lr = 0.196435,	loss = 2.605462,	Top-1 err = 0.391797,	Top-5 err = 0.176953,	data_time = 0.050560,	train_time = 0.421038
[2019-08-24 01:36:59,509] TRAIN Iter 182160: lr = 0.196402,	loss = 2.516618,	Top-1 err = 0.386816,	Top-5 err = 0.172852,	data_time = 0.143751,	train_time = 0.813487
[2019-08-24 01:37:06,658] TRAIN Iter 182180: lr = 0.196368,	loss = 2.576057,	Top-1 err = 0.387012,	Top-5 err = 0.174268,	data_time = 0.050332,	train_time = 0.357476
[2019-08-24 01:37:22,983] TRAIN Iter 182200: lr = 0.196335,	loss = 2.670407,	Top-1 err = 0.386475,	Top-5 err = 0.176953,	data_time = 0.050634,	train_time = 0.816224
[2019-08-24 01:37:38,646] TRAIN Iter 182220: lr = 0.196302,	loss = 2.570226,	Top-1 err = 0.385400,	Top-5 err = 0.173437,	data_time = 0.050669,	train_time = 0.783120
[2019-08-24 01:37:45,114] TRAIN Iter 182240: lr = 0.196268,	loss = 2.556036,	Top-1 err = 0.387549,	Top-5 err = 0.175195,	data_time = 0.050459,	train_time = 0.323392
[2019-08-24 01:38:02,373] TRAIN Iter 182260: lr = 0.196235,	loss = 2.609909,	Top-1 err = 0.386279,	Top-5 err = 0.176611,	data_time = 0.050385,	train_time = 0.862942
[2019-08-24 01:38:18,081] TRAIN Iter 182280: lr = 0.196202,	loss = 2.615287,	Top-1 err = 0.390381,	Top-5 err = 0.172656,	data_time = 0.050451,	train_time = 0.785393
[2019-08-24 01:38:26,060] TRAIN Iter 182300: lr = 0.196168,	loss = 2.560184,	Top-1 err = 0.389014,	Top-5 err = 0.178516,	data_time = 0.050402,	train_time = 0.398944
[2019-08-24 01:38:39,933] TRAIN Iter 182320: lr = 0.196135,	loss = 2.617362,	Top-1 err = 0.394678,	Top-5 err = 0.176904,	data_time = 0.050314,	train_time = 0.693633
[2019-08-24 01:38:46,665] TRAIN Iter 182340: lr = 0.196102,	loss = 2.513350,	Top-1 err = 0.391504,	Top-5 err = 0.173682,	data_time = 0.123112,	train_time = 0.336584
[2019-08-24 01:39:03,520] TRAIN Iter 182360: lr = 0.196068,	loss = 2.589629,	Top-1 err = 0.388965,	Top-5 err = 0.173437,	data_time = 0.050357,	train_time = 0.842741
[2019-08-24 01:39:18,794] TRAIN Iter 182380: lr = 0.196035,	loss = 2.664398,	Top-1 err = 0.388721,	Top-5 err = 0.176416,	data_time = 0.050294,	train_time = 0.763651
[2019-08-24 01:39:25,573] TRAIN Iter 182400: lr = 0.196002,	loss = 2.629847,	Top-1 err = 0.386963,	Top-5 err = 0.176855,	data_time = 0.153646,	train_time = 0.338975
[2019-08-24 01:39:41,347] TRAIN Iter 182420: lr = 0.195968,	loss = 2.519010,	Top-1 err = 0.385791,	Top-5 err = 0.170410,	data_time = 0.050408,	train_time = 0.788667
[2019-08-24 01:39:53,518] TRAIN Iter 182440: lr = 0.195935,	loss = 2.579362,	Top-1 err = 0.392188,	Top-5 err = 0.178320,	data_time = 0.050960,	train_time = 0.608541
[2019-08-24 01:40:04,332] TRAIN Iter 182460: lr = 0.195902,	loss = 2.538905,	Top-1 err = 0.386963,	Top-5 err = 0.175049,	data_time = 0.050541,	train_time = 0.540682
[2019-08-24 01:40:19,912] TRAIN Iter 182480: lr = 0.195868,	loss = 2.592841,	Top-1 err = 0.384033,	Top-5 err = 0.172168,	data_time = 0.050443,	train_time = 0.778978
[2019-08-24 01:40:26,315] TRAIN Iter 182500: lr = 0.195835,	loss = 2.568271,	Top-1 err = 0.393848,	Top-5 err = 0.178516,	data_time = 0.050441,	train_time = 0.320118
[2019-08-24 01:40:43,688] TRAIN Iter 182520: lr = 0.195802,	loss = 2.611541,	Top-1 err = 0.390625,	Top-5 err = 0.173242,	data_time = 0.050436,	train_time = 0.868678
[2019-08-24 01:41:01,424] TRAIN Iter 182540: lr = 0.195768,	loss = 2.543913,	Top-1 err = 0.389844,	Top-5 err = 0.177686,	data_time = 0.050541,	train_time = 0.886753
[2019-08-24 01:41:07,945] TRAIN Iter 182560: lr = 0.195735,	loss = 2.534829,	Top-1 err = 0.388574,	Top-5 err = 0.177881,	data_time = 0.050393,	train_time = 0.326038
[2019-08-24 01:41:23,098] TRAIN Iter 182580: lr = 0.195702,	loss = 2.640616,	Top-1 err = 0.397266,	Top-5 err = 0.181543,	data_time = 0.050448,	train_time = 0.757681
[2019-08-24 01:41:35,056] TRAIN Iter 182600: lr = 0.195668,	loss = 2.610014,	Top-1 err = 0.394336,	Top-5 err = 0.179785,	data_time = 0.050524,	train_time = 0.597842
[2019-08-24 01:41:47,790] TRAIN Iter 182620: lr = 0.195635,	loss = 2.480616,	Top-1 err = 0.391455,	Top-5 err = 0.177246,	data_time = 0.050642,	train_time = 0.636702
[2019-08-24 01:42:05,394] TRAIN Iter 182640: lr = 0.195602,	loss = 2.534886,	Top-1 err = 0.389062,	Top-5 err = 0.175488,	data_time = 0.050393,	train_time = 0.880169
[2019-08-24 01:42:12,018] TRAIN Iter 182660: lr = 0.195568,	loss = 2.520843,	Top-1 err = 0.395898,	Top-5 err = 0.177002,	data_time = 0.050458,	train_time = 0.331198
[2019-08-24 01:42:29,581] TRAIN Iter 182680: lr = 0.195535,	loss = 2.521543,	Top-1 err = 0.392578,	Top-5 err = 0.176709,	data_time = 0.050117,	train_time = 0.878147
[2019-08-24 01:42:46,859] TRAIN Iter 182700: lr = 0.195502,	loss = 2.641485,	Top-1 err = 0.390576,	Top-5 err = 0.173975,	data_time = 0.049880,	train_time = 0.863878
[2019-08-24 01:42:52,726] TRAIN Iter 182720: lr = 0.195468,	loss = 2.533749,	Top-1 err = 0.389502,	Top-5 err = 0.174707,	data_time = 0.049898,	train_time = 0.293324
[2019-08-24 01:43:42,064] TRAIN Iter 182740: lr = 0.195435,	loss = 2.651720,	Top-1 err = 0.393307,	Top-5 err = 0.178423,	data_time = 0.050252,	train_time = 2.466885
[2019-08-24 01:43:49,904] TRAIN Iter 182760: lr = 0.195402,	loss = 2.541940,	Top-1 err = 0.384229,	Top-5 err = 0.173242,	data_time = 0.050291,	train_time = 0.392014
[2019-08-24 01:44:06,932] TRAIN Iter 182780: lr = 0.195368,	loss = 2.606053,	Top-1 err = 0.385156,	Top-5 err = 0.174316,	data_time = 0.050460,	train_time = 0.851374
[2019-08-24 01:44:18,452] TRAIN Iter 182800: lr = 0.195335,	loss = 2.436475,	Top-1 err = 0.378027,	Top-5 err = 0.168457,	data_time = 0.050450,	train_time = 0.575972
[2019-08-24 01:44:25,926] TRAIN Iter 182820: lr = 0.195302,	loss = 2.582967,	Top-1 err = 0.383838,	Top-5 err = 0.170361,	data_time = 0.050750,	train_time = 0.373681
[2019-08-24 01:44:37,608] TRAIN Iter 182840: lr = 0.195268,	loss = 2.533406,	Top-1 err = 0.380273,	Top-5 err = 0.170898,	data_time = 0.050322,	train_time = 0.584125
[2019-08-24 01:44:51,871] TRAIN Iter 182860: lr = 0.195235,	loss = 2.491317,	Top-1 err = 0.375879,	Top-5 err = 0.165674,	data_time = 0.050414,	train_time = 0.713145
[2019-08-24 01:44:59,701] TRAIN Iter 182880: lr = 0.195202,	loss = 2.569836,	Top-1 err = 0.384033,	Top-5 err = 0.170557,	data_time = 0.050428,	train_time = 0.391473
[2019-08-24 01:45:13,757] TRAIN Iter 182900: lr = 0.195168,	loss = 2.575799,	Top-1 err = 0.377588,	Top-5 err = 0.168311,	data_time = 0.050303,	train_time = 0.702779
[2019-08-24 01:45:21,056] TRAIN Iter 182920: lr = 0.195135,	loss = 2.553828,	Top-1 err = 0.383154,	Top-5 err = 0.172607,	data_time = 0.050477,	train_time = 0.364922
[2019-08-24 01:45:36,217] TRAIN Iter 182940: lr = 0.195102,	loss = 2.602443,	Top-1 err = 0.381787,	Top-5 err = 0.166943,	data_time = 0.050483,	train_time = 0.758046
[2019-08-24 01:45:51,745] TRAIN Iter 182960: lr = 0.195068,	loss = 2.575472,	Top-1 err = 0.381006,	Top-5 err = 0.171289,	data_time = 1.031280,	train_time = 0.776402
[2019-08-24 01:45:59,085] TRAIN Iter 182980: lr = 0.195035,	loss = 2.488911,	Top-1 err = 0.389111,	Top-5 err = 0.173437,	data_time = 0.050390,	train_time = 0.366975
[2019-08-24 01:46:15,111] TRAIN Iter 183000: lr = 0.195002,	loss = 2.613585,	Top-1 err = 0.382178,	Top-5 err = 0.171973,	data_time = 0.050682,	train_time = 0.801265
[2019-08-24 01:46:27,414] TRAIN Iter 183020: lr = 0.194968,	loss = 2.484084,	Top-1 err = 0.383789,	Top-5 err = 0.173779,	data_time = 0.050355,	train_time = 0.615144
[2019-08-24 01:46:36,815] TRAIN Iter 183040: lr = 0.194935,	loss = 2.562935,	Top-1 err = 0.383740,	Top-5 err = 0.171924,	data_time = 0.050357,	train_time = 0.470039
[2019-08-24 01:46:51,853] TRAIN Iter 183060: lr = 0.194902,	loss = 2.518800,	Top-1 err = 0.384570,	Top-5 err = 0.176025,	data_time = 0.050169,	train_time = 0.751906
[2019-08-24 01:46:58,860] TRAIN Iter 183080: lr = 0.194868,	loss = 2.567384,	Top-1 err = 0.379688,	Top-5 err = 0.172510,	data_time = 0.050861,	train_time = 0.350322
[2019-08-24 01:47:13,674] TRAIN Iter 183100: lr = 0.194835,	loss = 2.492790,	Top-1 err = 0.385596,	Top-5 err = 0.172314,	data_time = 0.050630,	train_time = 0.740659
[2019-08-24 01:47:28,862] TRAIN Iter 183120: lr = 0.194802,	loss = 2.580838,	Top-1 err = 0.386133,	Top-5 err = 0.175439,	data_time = 0.708118,	train_time = 0.759395
[2019-08-24 01:47:35,985] TRAIN Iter 183140: lr = 0.194768,	loss = 2.531033,	Top-1 err = 0.389453,	Top-5 err = 0.173340,	data_time = 0.050594,	train_time = 0.356144
[2019-08-24 01:47:54,020] TRAIN Iter 183160: lr = 0.194735,	loss = 2.572738,	Top-1 err = 0.386914,	Top-5 err = 0.174463,	data_time = 0.050563,	train_time = 0.901718
[2019-08-24 01:48:05,603] TRAIN Iter 183180: lr = 0.194702,	loss = 2.576970,	Top-1 err = 0.389746,	Top-5 err = 0.176904,	data_time = 0.143093,	train_time = 0.579125
[2019-08-24 01:48:13,154] TRAIN Iter 183200: lr = 0.194668,	loss = 2.650415,	Top-1 err = 0.390820,	Top-5 err = 0.172705,	data_time = 0.050411,	train_time = 0.377549
[2019-08-24 01:48:30,583] TRAIN Iter 183220: lr = 0.194635,	loss = 2.602089,	Top-1 err = 0.381250,	Top-5 err = 0.170166,	data_time = 0.050797,	train_time = 0.871442
[2019-08-24 01:48:37,923] TRAIN Iter 183240: lr = 0.194602,	loss = 2.595232,	Top-1 err = 0.384326,	Top-5 err = 0.172607,	data_time = 0.050710,	train_time = 0.366999
[2019-08-24 01:48:52,351] TRAIN Iter 183260: lr = 0.194568,	loss = 2.570625,	Top-1 err = 0.385645,	Top-5 err = 0.171240,	data_time = 0.050468,	train_time = 0.721375
[2019-08-24 01:49:07,247] TRAIN Iter 183280: lr = 0.194535,	loss = 2.578803,	Top-1 err = 0.390869,	Top-5 err = 0.175928,	data_time = 0.050379,	train_time = 0.744794
[2019-08-24 01:49:14,440] TRAIN Iter 183300: lr = 0.194502,	loss = 2.458683,	Top-1 err = 0.390332,	Top-5 err = 0.173730,	data_time = 0.050597,	train_time = 0.359612
[2019-08-24 01:49:29,991] TRAIN Iter 183320: lr = 0.194468,	loss = 2.635047,	Top-1 err = 0.386621,	Top-5 err = 0.173682,	data_time = 0.050501,	train_time = 0.777549
[2019-08-24 01:49:45,007] TRAIN Iter 183340: lr = 0.194435,	loss = 2.496653,	Top-1 err = 0.388232,	Top-5 err = 0.171875,	data_time = 0.119505,	train_time = 0.750802
[2019-08-24 01:49:52,668] TRAIN Iter 183360: lr = 0.194402,	loss = 2.573884,	Top-1 err = 0.383643,	Top-5 err = 0.169629,	data_time = 0.050395,	train_time = 0.383016
[2019-08-24 01:50:09,221] TRAIN Iter 183380: lr = 0.194368,	loss = 2.574821,	Top-1 err = 0.384912,	Top-5 err = 0.170508,	data_time = 0.050433,	train_time = 0.827655
[2019-08-24 01:50:16,157] TRAIN Iter 183400: lr = 0.194335,	loss = 2.446105,	Top-1 err = 0.387549,	Top-5 err = 0.176660,	data_time = 0.050510,	train_time = 0.346747
[2019-08-24 01:50:31,259] TRAIN Iter 183420: lr = 0.194302,	loss = 2.645082,	Top-1 err = 0.389014,	Top-5 err = 0.176855,	data_time = 0.050437,	train_time = 0.755098
[2019-08-24 01:50:46,634] TRAIN Iter 183440: lr = 0.194268,	loss = 2.534793,	Top-1 err = 0.391162,	Top-5 err = 0.173779,	data_time = 0.050769,	train_time = 0.768741
[2019-08-24 01:50:53,982] TRAIN Iter 183460: lr = 0.194235,	loss = 2.571780,	Top-1 err = 0.384277,	Top-5 err = 0.173828,	data_time = 0.050613,	train_time = 0.367372
[2019-08-24 01:51:09,825] TRAIN Iter 183480: lr = 0.194202,	loss = 2.616451,	Top-1 err = 0.390723,	Top-5 err = 0.173535,	data_time = 0.050527,	train_time = 0.792132
[2019-08-24 01:51:22,586] TRAIN Iter 183500: lr = 0.194168,	loss = 2.444384,	Top-1 err = 0.387695,	Top-5 err = 0.173877,	data_time = 0.050936,	train_time = 0.638064
[2019-08-24 01:51:30,885] TRAIN Iter 183520: lr = 0.194135,	loss = 2.641704,	Top-1 err = 0.383154,	Top-5 err = 0.171191,	data_time = 0.050434,	train_time = 0.414919
[2019-08-24 01:51:47,042] TRAIN Iter 183540: lr = 0.194102,	loss = 2.503976,	Top-1 err = 0.377734,	Top-5 err = 0.170313,	data_time = 0.050322,	train_time = 0.807830
[2019-08-24 01:51:54,035] TRAIN Iter 183560: lr = 0.194068,	loss = 2.617711,	Top-1 err = 0.387354,	Top-5 err = 0.170215,	data_time = 0.050531,	train_time = 0.349664
[2019-08-24 01:52:09,395] TRAIN Iter 183580: lr = 0.194035,	loss = 2.435444,	Top-1 err = 0.386719,	Top-5 err = 0.173340,	data_time = 0.050521,	train_time = 0.767975
[2019-08-24 01:52:26,573] TRAIN Iter 183600: lr = 0.194002,	loss = 2.575932,	Top-1 err = 0.393408,	Top-5 err = 0.175098,	data_time = 0.050445,	train_time = 0.858861
[2019-08-24 01:52:33,668] TRAIN Iter 183620: lr = 0.193968,	loss = 2.540270,	Top-1 err = 0.387451,	Top-5 err = 0.178369,	data_time = 0.050351,	train_time = 0.354778
[2019-08-24 01:52:48,886] TRAIN Iter 183640: lr = 0.193935,	loss = 2.510353,	Top-1 err = 0.392285,	Top-5 err = 0.177490,	data_time = 0.050829,	train_time = 0.760846
[2019-08-24 01:53:04,177] TRAIN Iter 183660: lr = 0.193902,	loss = 2.564342,	Top-1 err = 0.387158,	Top-5 err = 0.175781,	data_time = 0.050681,	train_time = 0.764531
[2019-08-24 01:53:13,297] TRAIN Iter 183680: lr = 0.193868,	loss = 2.572222,	Top-1 err = 0.393018,	Top-5 err = 0.174561,	data_time = 0.050539,	train_time = 0.455995
[2019-08-24 01:53:29,570] TRAIN Iter 183700: lr = 0.193835,	loss = 2.581123,	Top-1 err = 0.388525,	Top-5 err = 0.177295,	data_time = 0.050480,	train_time = 0.813633
[2019-08-24 01:53:36,378] TRAIN Iter 183720: lr = 0.193802,	loss = 2.573250,	Top-1 err = 0.387988,	Top-5 err = 0.176025,	data_time = 0.151576,	train_time = 0.340373
[2019-08-24 01:53:52,727] TRAIN Iter 183740: lr = 0.193768,	loss = 2.574277,	Top-1 err = 0.385596,	Top-5 err = 0.170898,	data_time = 0.050467,	train_time = 0.817476
[2019-08-24 01:54:10,921] TRAIN Iter 183760: lr = 0.193735,	loss = 2.644974,	Top-1 err = 0.391895,	Top-5 err = 0.176709,	data_time = 0.050421,	train_time = 0.909642
[2019-08-24 01:54:18,178] TRAIN Iter 183780: lr = 0.193702,	loss = 2.652053,	Top-1 err = 0.387158,	Top-5 err = 0.174023,	data_time = 0.050446,	train_time = 0.362872
[2019-08-24 01:54:35,412] TRAIN Iter 183800: lr = 0.193668,	loss = 2.555501,	Top-1 err = 0.389404,	Top-5 err = 0.173926,	data_time = 0.050441,	train_time = 0.861652
[2019-08-24 01:54:52,558] TRAIN Iter 183820: lr = 0.193635,	loss = 2.573948,	Top-1 err = 0.389844,	Top-5 err = 0.180908,	data_time = 0.050206,	train_time = 0.857281
[2019-08-24 01:55:00,326] TRAIN Iter 183840: lr = 0.193602,	loss = 2.541037,	Top-1 err = 0.392236,	Top-5 err = 0.177148,	data_time = 0.050559,	train_time = 0.388386
[2019-08-24 01:55:17,172] TRAIN Iter 183860: lr = 0.193568,	loss = 2.510695,	Top-1 err = 0.395898,	Top-5 err = 0.177588,	data_time = 0.136464,	train_time = 0.842289
[2019-08-24 01:55:24,019] TRAIN Iter 183880: lr = 0.193535,	loss = 2.584077,	Top-1 err = 0.391943,	Top-5 err = 0.177588,	data_time = 0.050237,	train_time = 0.342341
[2019-08-24 01:55:42,209] TRAIN Iter 183900: lr = 0.193502,	loss = 2.647932,	Top-1 err = 0.392432,	Top-5 err = 0.177637,	data_time = 0.050464,	train_time = 0.909483
[2019-08-24 01:55:58,718] TRAIN Iter 183920: lr = 0.193468,	loss = 2.604092,	Top-1 err = 0.392725,	Top-5 err = 0.176221,	data_time = 0.050042,	train_time = 0.825478
[2019-08-24 01:56:05,799] TRAIN Iter 183940: lr = 0.193435,	loss = 2.570124,	Top-1 err = 0.388916,	Top-5 err = 0.172510,	data_time = 0.050004,	train_time = 0.354006
[2019-08-24 01:56:23,383] TRAIN Iter 183960: lr = 0.193402,	loss = 2.696852,	Top-1 err = 0.388379,	Top-5 err = 0.174365,	data_time = 0.049957,	train_time = 0.879190
[2019-08-24 01:56:33,969] TRAIN Iter 183980: lr = 0.193368,	loss = 3.011744,	Top-1 err = 0.393462,	Top-5 err = 0.178986,	data_time = 0.007128,	train_time = 0.529293
[2019-08-24 01:57:21,855] TRAIN Iter 184000: lr = 0.193335,	loss = 2.539408,	Top-1 err = 0.384570,	Top-5 err = 0.171484,	data_time = 0.050631,	train_time = 2.394309
[2019-08-24 01:57:37,134] TRAIN Iter 184020: lr = 0.193302,	loss = 2.565746,	Top-1 err = 0.383008,	Top-5 err = 0.169141,	data_time = 0.050129,	train_time = 0.763897
[2019-08-24 01:57:44,428] TRAIN Iter 184040: lr = 0.193268,	loss = 2.477926,	Top-1 err = 0.379736,	Top-5 err = 0.169580,	data_time = 0.050484,	train_time = 0.364682
[2019-08-24 01:57:57,690] TRAIN Iter 184060: lr = 0.193235,	loss = 2.610466,	Top-1 err = 0.381885,	Top-5 err = 0.174023,	data_time = 0.050483,	train_time = 0.663095
[2019-08-24 01:58:10,481] TRAIN Iter 184080: lr = 0.193202,	loss = 2.547225,	Top-1 err = 0.380029,	Top-5 err = 0.167187,	data_time = 0.050663,	train_time = 0.639557
[2019-08-24 01:58:20,648] TRAIN Iter 184100: lr = 0.193168,	loss = 2.514390,	Top-1 err = 0.383350,	Top-5 err = 0.170654,	data_time = 0.050439,	train_time = 0.508314
[2019-08-24 01:58:34,124] TRAIN Iter 184120: lr = 0.193135,	loss = 2.570542,	Top-1 err = 0.383447,	Top-5 err = 0.170605,	data_time = 0.884796,	train_time = 0.673773
[2019-08-24 01:58:41,347] TRAIN Iter 184140: lr = 0.193102,	loss = 2.594536,	Top-1 err = 0.382520,	Top-5 err = 0.170313,	data_time = 0.050157,	train_time = 0.361134
[2019-08-24 01:58:58,995] TRAIN Iter 184160: lr = 0.193068,	loss = 2.489448,	Top-1 err = 0.382568,	Top-5 err = 0.163525,	data_time = 0.050870,	train_time = 0.882429
[2019-08-24 01:59:11,899] TRAIN Iter 184180: lr = 0.193035,	loss = 2.585209,	Top-1 err = 0.382861,	Top-5 err = 0.167090,	data_time = 0.050319,	train_time = 0.645177
[2019-08-24 01:59:19,774] TRAIN Iter 184200: lr = 0.193002,	loss = 2.524374,	Top-1 err = 0.377100,	Top-5 err = 0.166943,	data_time = 0.050656,	train_time = 0.393711
[2019-08-24 01:59:34,736] TRAIN Iter 184220: lr = 0.192968,	loss = 2.636076,	Top-1 err = 0.382764,	Top-5 err = 0.171777,	data_time = 0.050483,	train_time = 0.748098
[2019-08-24 01:59:50,348] TRAIN Iter 184240: lr = 0.192935,	loss = 2.593283,	Top-1 err = 0.383203,	Top-5 err = 0.172803,	data_time = 0.900109,	train_time = 0.780570
[2019-08-24 01:59:57,631] TRAIN Iter 184260: lr = 0.192902,	loss = 2.525546,	Top-1 err = 0.383154,	Top-5 err = 0.171240,	data_time = 0.050351,	train_time = 0.364160
[2019-08-24 02:00:11,716] TRAIN Iter 184280: lr = 0.192868,	loss = 2.569771,	Top-1 err = 0.381787,	Top-5 err = 0.167920,	data_time = 0.050650,	train_time = 0.704230
[2019-08-24 02:00:18,769] TRAIN Iter 184300: lr = 0.192835,	loss = 2.523805,	Top-1 err = 0.383740,	Top-5 err = 0.170166,	data_time = 0.050630,	train_time = 0.352631
[2019-08-24 02:00:34,819] TRAIN Iter 184320: lr = 0.192802,	loss = 2.501261,	Top-1 err = 0.387549,	Top-5 err = 0.171875,	data_time = 0.050882,	train_time = 0.802495
[2019-08-24 02:00:51,439] TRAIN Iter 184340: lr = 0.192768,	loss = 2.639813,	Top-1 err = 0.384863,	Top-5 err = 0.173779,	data_time = 0.050579,	train_time = 0.830961
[2019-08-24 02:00:58,407] TRAIN Iter 184360: lr = 0.192735,	loss = 2.520918,	Top-1 err = 0.374365,	Top-5 err = 0.168115,	data_time = 0.050447,	train_time = 0.348402
[2019-08-24 02:01:14,222] TRAIN Iter 184380: lr = 0.192702,	loss = 2.529204,	Top-1 err = 0.384863,	Top-5 err = 0.172656,	data_time = 0.050369,	train_time = 0.790731
[2019-08-24 02:01:28,091] TRAIN Iter 184400: lr = 0.192668,	loss = 2.572441,	Top-1 err = 0.382080,	Top-5 err = 0.171045,	data_time = 2.679111,	train_time = 0.693428
[2019-08-24 02:01:36,201] TRAIN Iter 184420: lr = 0.192635,	loss = 2.572322,	Top-1 err = 0.379150,	Top-5 err = 0.167139,	data_time = 0.050727,	train_time = 0.405485
[2019-08-24 02:01:51,968] TRAIN Iter 184440: lr = 0.192602,	loss = 2.594146,	Top-1 err = 0.389014,	Top-5 err = 0.171777,	data_time = 0.147328,	train_time = 0.788325
[2019-08-24 02:01:59,291] TRAIN Iter 184460: lr = 0.192568,	loss = 2.538083,	Top-1 err = 0.385547,	Top-5 err = 0.175098,	data_time = 0.050803,	train_time = 0.366168
[2019-08-24 02:02:14,393] TRAIN Iter 184480: lr = 0.192535,	loss = 2.479614,	Top-1 err = 0.387109,	Top-5 err = 0.170850,	data_time = 0.050473,	train_time = 0.755102
[2019-08-24 02:02:30,206] TRAIN Iter 184500: lr = 0.192502,	loss = 2.549005,	Top-1 err = 0.380762,	Top-5 err = 0.171875,	data_time = 0.050648,	train_time = 0.790610
[2019-08-24 02:02:36,948] TRAIN Iter 184520: lr = 0.192468,	loss = 2.464482,	Top-1 err = 0.382471,	Top-5 err = 0.174561,	data_time = 0.050253,	train_time = 0.337104
[2019-08-24 02:02:52,349] TRAIN Iter 184540: lr = 0.192435,	loss = 2.601098,	Top-1 err = 0.383838,	Top-5 err = 0.172363,	data_time = 0.050256,	train_time = 0.770012
[2019-08-24 02:03:06,693] TRAIN Iter 184560: lr = 0.192402,	loss = 2.569194,	Top-1 err = 0.385693,	Top-5 err = 0.176709,	data_time = 0.050503,	train_time = 0.717191
[2019-08-24 02:03:14,516] TRAIN Iter 184580: lr = 0.192368,	loss = 2.565913,	Top-1 err = 0.393604,	Top-5 err = 0.176514,	data_time = 0.050546,	train_time = 0.391141
[2019-08-24 02:03:31,369] TRAIN Iter 184600: lr = 0.192335,	loss = 2.463430,	Top-1 err = 0.388525,	Top-5 err = 0.170752,	data_time = 0.050565,	train_time = 0.842649
[2019-08-24 02:03:38,614] TRAIN Iter 184620: lr = 0.192302,	loss = 2.542061,	Top-1 err = 0.383691,	Top-5 err = 0.172119,	data_time = 0.050827,	train_time = 0.362236
[2019-08-24 02:03:54,138] TRAIN Iter 184640: lr = 0.192268,	loss = 2.571847,	Top-1 err = 0.381738,	Top-5 err = 0.174219,	data_time = 0.050768,	train_time = 0.776166
[2019-08-24 02:04:09,619] TRAIN Iter 184660: lr = 0.192235,	loss = 2.665474,	Top-1 err = 0.384180,	Top-5 err = 0.173047,	data_time = 0.050669,	train_time = 0.774038
[2019-08-24 02:04:16,578] TRAIN Iter 184680: lr = 0.192202,	loss = 2.565064,	Top-1 err = 0.382227,	Top-5 err = 0.170361,	data_time = 0.050579,	train_time = 0.347956
[2019-08-24 02:04:32,383] TRAIN Iter 184700: lr = 0.192168,	loss = 2.561359,	Top-1 err = 0.385156,	Top-5 err = 0.169727,	data_time = 0.050405,	train_time = 0.790226
[2019-08-24 02:04:46,917] TRAIN Iter 184720: lr = 0.192135,	loss = 2.550075,	Top-1 err = 0.384521,	Top-5 err = 0.171484,	data_time = 0.050341,	train_time = 0.726660
[2019-08-24 02:04:54,245] TRAIN Iter 184740: lr = 0.192102,	loss = 2.490928,	Top-1 err = 0.395898,	Top-5 err = 0.174658,	data_time = 0.050466,	train_time = 0.366400
[2019-08-24 02:05:10,419] TRAIN Iter 184760: lr = 0.192068,	loss = 2.638455,	Top-1 err = 0.384668,	Top-5 err = 0.172803,	data_time = 0.050545,	train_time = 0.808693
[2019-08-24 02:05:17,616] TRAIN Iter 184780: lr = 0.192035,	loss = 2.551476,	Top-1 err = 0.385693,	Top-5 err = 0.172900,	data_time = 0.050734,	train_time = 0.359852
[2019-08-24 02:05:34,337] TRAIN Iter 184800: lr = 0.192002,	loss = 2.579120,	Top-1 err = 0.379688,	Top-5 err = 0.167969,	data_time = 0.181110,	train_time = 0.836022
[2019-08-24 02:05:47,045] TRAIN Iter 184820: lr = 0.191968,	loss = 2.584703,	Top-1 err = 0.391162,	Top-5 err = 0.178809,	data_time = 0.050477,	train_time = 0.635375
[2019-08-24 02:05:54,022] TRAIN Iter 184840: lr = 0.191935,	loss = 2.662102,	Top-1 err = 0.382617,	Top-5 err = 0.168896,	data_time = 0.050397,	train_time = 0.348833
[2019-08-24 02:06:11,148] TRAIN Iter 184860: lr = 0.191902,	loss = 2.615961,	Top-1 err = 0.385156,	Top-5 err = 0.171338,	data_time = 0.050364,	train_time = 0.856264
[2019-08-24 02:06:27,564] TRAIN Iter 184880: lr = 0.191868,	loss = 2.542434,	Top-1 err = 0.385400,	Top-5 err = 0.176611,	data_time = 0.050851,	train_time = 0.820780
[2019-08-24 02:06:34,760] TRAIN Iter 184900: lr = 0.191835,	loss = 2.530562,	Top-1 err = 0.391748,	Top-5 err = 0.177686,	data_time = 0.050553,	train_time = 0.359822
[2019-08-24 02:06:50,175] TRAIN Iter 184920: lr = 0.191802,	loss = 2.703630,	Top-1 err = 0.388867,	Top-5 err = 0.170947,	data_time = 0.050761,	train_time = 0.770702
[2019-08-24 02:06:57,290] TRAIN Iter 184940: lr = 0.191768,	loss = 2.536195,	Top-1 err = 0.389893,	Top-5 err = 0.178906,	data_time = 0.050427,	train_time = 0.355750
[2019-08-24 02:07:12,220] TRAIN Iter 184960: lr = 0.191735,	loss = 2.564574,	Top-1 err = 0.379443,	Top-5 err = 0.169775,	data_time = 0.050434,	train_time = 0.746480
[2019-08-24 02:07:27,790] TRAIN Iter 184980: lr = 0.191702,	loss = 2.641320,	Top-1 err = 0.388232,	Top-5 err = 0.174512,	data_time = 0.050556,	train_time = 0.778470
[2019-08-24 02:07:35,448] TRAIN Iter 185000: lr = 0.191668,	loss = 2.618649,	Top-1 err = 0.391748,	Top-5 err = 0.175342,	data_time = 0.050678,	train_time = 0.382906
[2019-08-24 02:07:51,542] TRAIN Iter 185020: lr = 0.191635,	loss = 2.588820,	Top-1 err = 0.390332,	Top-5 err = 0.172168,	data_time = 0.050447,	train_time = 0.804669
[2019-08-24 02:08:05,126] TRAIN Iter 185040: lr = 0.191602,	loss = 2.629593,	Top-1 err = 0.383105,	Top-5 err = 0.172119,	data_time = 0.050436,	train_time = 0.679197
[2019-08-24 02:08:14,649] TRAIN Iter 185060: lr = 0.191568,	loss = 2.566866,	Top-1 err = 0.396484,	Top-5 err = 0.179199,	data_time = 0.050322,	train_time = 0.476118
[2019-08-24 02:08:31,214] TRAIN Iter 185080: lr = 0.191535,	loss = 2.533187,	Top-1 err = 0.382666,	Top-5 err = 0.170361,	data_time = 0.050480,	train_time = 0.828273
[2019-08-24 02:08:38,184] TRAIN Iter 185100: lr = 0.191502,	loss = 2.626165,	Top-1 err = 0.391260,	Top-5 err = 0.177295,	data_time = 0.050572,	train_time = 0.348462
[2019-08-24 02:08:54,567] TRAIN Iter 185120: lr = 0.191468,	loss = 2.614601,	Top-1 err = 0.387158,	Top-5 err = 0.177246,	data_time = 0.050477,	train_time = 0.819125
[2019-08-24 02:09:09,437] TRAIN Iter 185140: lr = 0.191435,	loss = 2.562270,	Top-1 err = 0.390234,	Top-5 err = 0.174121,	data_time = 0.050560,	train_time = 0.743469
[2019-08-24 02:09:18,319] TRAIN Iter 185160: lr = 0.191402,	loss = 2.591533,	Top-1 err = 0.394629,	Top-5 err = 0.177051,	data_time = 0.050387,	train_time = 0.444131
[2019-08-24 02:09:35,812] TRAIN Iter 185180: lr = 0.191368,	loss = 2.541702,	Top-1 err = 0.388574,	Top-5 err = 0.174805,	data_time = 0.050026,	train_time = 0.874620
[2019-08-24 02:09:51,148] TRAIN Iter 185200: lr = 0.191335,	loss = 2.503199,	Top-1 err = 0.383496,	Top-5 err = 0.171094,	data_time = 0.049959,	train_time = 0.766781
[2019-08-24 02:09:59,026] TRAIN Iter 185220: lr = 0.191302,	loss = 2.671306,	Top-1 err = 0.386963,	Top-5 err = 0.175098,	data_time = 0.049899,	train_time = 0.393895
[2019-08-24 02:10:48,483] TRAIN Iter 185240: lr = 0.191268,	loss = 2.531399,	Top-1 err = 0.390168,	Top-5 err = 0.173560,	data_time = 0.672548,	train_time = 2.472846
[2019-08-24 02:10:55,508] TRAIN Iter 185260: lr = 0.191235,	loss = 2.601192,	Top-1 err = 0.383984,	Top-5 err = 0.168213,	data_time = 0.050655,	train_time = 0.351218
[2019-08-24 02:11:10,928] TRAIN Iter 185280: lr = 0.191202,	loss = 2.570715,	Top-1 err = 0.382861,	Top-5 err = 0.173047,	data_time = 0.050433,	train_time = 0.770974
[2019-08-24 02:11:24,940] TRAIN Iter 185300: lr = 0.191168,	loss = 2.596071,	Top-1 err = 0.378418,	Top-5 err = 0.167236,	data_time = 0.050865,	train_time = 0.700594
[2019-08-24 02:11:34,091] TRAIN Iter 185320: lr = 0.191135,	loss = 2.553326,	Top-1 err = 0.382373,	Top-5 err = 0.170508,	data_time = 0.050541,	train_time = 0.457529
[2019-08-24 02:11:47,925] TRAIN Iter 185340: lr = 0.191102,	loss = 2.515331,	Top-1 err = 0.380273,	Top-5 err = 0.168555,	data_time = 0.050720,	train_time = 0.691674
[2019-08-24 02:11:55,340] TRAIN Iter 185360: lr = 0.191068,	loss = 2.601265,	Top-1 err = 0.385596,	Top-5 err = 0.167236,	data_time = 0.050582,	train_time = 0.370778
[2019-08-24 02:12:10,774] TRAIN Iter 185380: lr = 0.191035,	loss = 2.553619,	Top-1 err = 0.385303,	Top-5 err = 0.170215,	data_time = 0.050552,	train_time = 0.771655
[2019-08-24 02:12:26,606] TRAIN Iter 185400: lr = 0.191002,	loss = 2.561858,	Top-1 err = 0.383252,	Top-5 err = 0.170947,	data_time = 0.050694,	train_time = 0.791571
[2019-08-24 02:12:33,699] TRAIN Iter 185420: lr = 0.190968,	loss = 2.578922,	Top-1 err = 0.388135,	Top-5 err = 0.169531,	data_time = 0.132918,	train_time = 0.354643
[2019-08-24 02:12:47,826] TRAIN Iter 185440: lr = 0.190935,	loss = 2.646091,	Top-1 err = 0.384473,	Top-5 err = 0.172217,	data_time = 0.050458,	train_time = 0.706350
[2019-08-24 02:12:59,803] TRAIN Iter 185460: lr = 0.190902,	loss = 2.608570,	Top-1 err = 0.383887,	Top-5 err = 0.168408,	data_time = 0.050298,	train_time = 0.598818
[2019-08-24 02:13:07,054] TRAIN Iter 185480: lr = 0.190868,	loss = 2.614245,	Top-1 err = 0.387695,	Top-5 err = 0.171045,	data_time = 0.050462,	train_time = 0.362540
[2019-08-24 02:13:22,257] TRAIN Iter 185500: lr = 0.190835,	loss = 2.592122,	Top-1 err = 0.380859,	Top-5 err = 0.170947,	data_time = 0.050629,	train_time = 0.760145
[2019-08-24 02:13:29,518] TRAIN Iter 185520: lr = 0.190802,	loss = 2.664555,	Top-1 err = 0.380273,	Top-5 err = 0.174902,	data_time = 0.050649,	train_time = 0.363047
[2019-08-24 02:13:45,013] TRAIN Iter 185540: lr = 0.190768,	loss = 2.613759,	Top-1 err = 0.380322,	Top-5 err = 0.170801,	data_time = 0.050524,	train_time = 0.774743
[2019-08-24 02:13:59,215] TRAIN Iter 185560: lr = 0.190735,	loss = 2.643618,	Top-1 err = 0.385889,	Top-5 err = 0.171484,	data_time = 0.109558,	train_time = 0.710075
[2019-08-24 02:14:06,383] TRAIN Iter 185580: lr = 0.190702,	loss = 2.562192,	Top-1 err = 0.386572,	Top-5 err = 0.173486,	data_time = 0.050391,	train_time = 0.358394
[2019-08-24 02:14:21,735] TRAIN Iter 185600: lr = 0.190668,	loss = 2.466894,	Top-1 err = 0.386816,	Top-5 err = 0.171973,	data_time = 0.050208,	train_time = 0.767597
[2019-08-24 02:14:36,006] TRAIN Iter 185620: lr = 0.190635,	loss = 2.629647,	Top-1 err = 0.382666,	Top-5 err = 0.165137,	data_time = 0.050321,	train_time = 0.713534
[2019-08-24 02:14:43,299] TRAIN Iter 185640: lr = 0.190602,	loss = 2.565633,	Top-1 err = 0.379492,	Top-5 err = 0.171143,	data_time = 0.050452,	train_time = 0.364601
[2019-08-24 02:14:57,532] TRAIN Iter 185660: lr = 0.190568,	loss = 2.597147,	Top-1 err = 0.385742,	Top-5 err = 0.174512,	data_time = 0.050281,	train_time = 0.711630
[2019-08-24 02:15:04,449] TRAIN Iter 185680: lr = 0.190535,	loss = 2.638312,	Top-1 err = 0.382373,	Top-5 err = 0.171924,	data_time = 0.050351,	train_time = 0.345866
[2019-08-24 02:15:20,247] TRAIN Iter 185700: lr = 0.190502,	loss = 2.641076,	Top-1 err = 0.377930,	Top-5 err = 0.171045,	data_time = 0.050394,	train_time = 0.789871
[2019-08-24 02:15:36,587] TRAIN Iter 185720: lr = 0.190468,	loss = 2.501421,	Top-1 err = 0.381543,	Top-5 err = 0.172266,	data_time = 0.114115,	train_time = 0.817003
[2019-08-24 02:15:43,860] TRAIN Iter 185740: lr = 0.190435,	loss = 2.531028,	Top-1 err = 0.385156,	Top-5 err = 0.172607,	data_time = 0.050302,	train_time = 0.363600
[2019-08-24 02:15:59,313] TRAIN Iter 185760: lr = 0.190402,	loss = 2.591207,	Top-1 err = 0.386133,	Top-5 err = 0.173682,	data_time = 0.050645,	train_time = 0.772650
[2019-08-24 02:16:14,217] TRAIN Iter 185780: lr = 0.190368,	loss = 2.549725,	Top-1 err = 0.387598,	Top-5 err = 0.173730,	data_time = 0.050523,	train_time = 0.745195
[2019-08-24 02:16:21,836] TRAIN Iter 185800: lr = 0.190335,	loss = 2.548732,	Top-1 err = 0.384229,	Top-5 err = 0.172900,	data_time = 0.050285,	train_time = 0.380932
[2019-08-24 02:16:36,772] TRAIN Iter 185820: lr = 0.190302,	loss = 2.490294,	Top-1 err = 0.381152,	Top-5 err = 0.169238,	data_time = 0.050857,	train_time = 0.746787
[2019-08-24 02:16:43,754] TRAIN Iter 185840: lr = 0.190268,	loss = 2.524388,	Top-1 err = 0.386523,	Top-5 err = 0.170898,	data_time = 0.050569,	train_time = 0.349075
[2019-08-24 02:16:59,366] TRAIN Iter 185860: lr = 0.190235,	loss = 2.654150,	Top-1 err = 0.386816,	Top-5 err = 0.173584,	data_time = 0.050513,	train_time = 0.780610
[2019-08-24 02:17:14,289] TRAIN Iter 185880: lr = 0.190202,	loss = 2.534729,	Top-1 err = 0.379785,	Top-5 err = 0.171533,	data_time = 0.050646,	train_time = 0.746119
[2019-08-24 02:17:21,443] TRAIN Iter 185900: lr = 0.190168,	loss = 2.520979,	Top-1 err = 0.379443,	Top-5 err = 0.170605,	data_time = 0.050536,	train_time = 0.357682
[2019-08-24 02:17:37,316] TRAIN Iter 185920: lr = 0.190135,	loss = 2.596135,	Top-1 err = 0.384082,	Top-5 err = 0.172852,	data_time = 0.050442,	train_time = 0.793613
[2019-08-24 02:17:51,182] TRAIN Iter 185940: lr = 0.190102,	loss = 2.599348,	Top-1 err = 0.382324,	Top-5 err = 0.175391,	data_time = 0.050495,	train_time = 0.693291
[2019-08-24 02:17:58,933] TRAIN Iter 185960: lr = 0.190068,	loss = 2.479882,	Top-1 err = 0.386816,	Top-5 err = 0.171143,	data_time = 0.050545,	train_time = 0.387532
[2019-08-24 02:18:14,440] TRAIN Iter 185980: lr = 0.190035,	loss = 2.574543,	Top-1 err = 0.386182,	Top-5 err = 0.170605,	data_time = 0.050722,	train_time = 0.775366
[2019-08-24 02:18:21,638] TRAIN Iter 186000: lr = 0.190002,	loss = 2.561549,	Top-1 err = 0.383545,	Top-5 err = 0.169287,	data_time = 0.050652,	train_time = 0.359852
[2019-08-24 02:18:37,210] TRAIN Iter 186020: lr = 0.189968,	loss = 2.578996,	Top-1 err = 0.387061,	Top-5 err = 0.172559,	data_time = 0.050457,	train_time = 0.778594
[2019-08-24 02:18:51,791] TRAIN Iter 186040: lr = 0.189935,	loss = 2.592893,	Top-1 err = 0.384521,	Top-5 err = 0.173975,	data_time = 0.050144,	train_time = 0.729050
[2019-08-24 02:18:59,413] TRAIN Iter 186060: lr = 0.189902,	loss = 2.509463,	Top-1 err = 0.386426,	Top-5 err = 0.175879,	data_time = 0.050338,	train_time = 0.381108
[2019-08-24 02:19:16,563] TRAIN Iter 186080: lr = 0.189868,	loss = 2.561153,	Top-1 err = 0.386816,	Top-5 err = 0.174170,	data_time = 0.050531,	train_time = 0.857480
[2019-08-24 02:19:31,931] TRAIN Iter 186100: lr = 0.189835,	loss = 2.634040,	Top-1 err = 0.384229,	Top-5 err = 0.174951,	data_time = 0.050155,	train_time = 0.768383
[2019-08-24 02:19:39,426] TRAIN Iter 186120: lr = 0.189802,	loss = 2.534922,	Top-1 err = 0.386621,	Top-5 err = 0.173926,	data_time = 0.050406,	train_time = 0.374739
[2019-08-24 02:19:55,240] TRAIN Iter 186140: lr = 0.189768,	loss = 2.598643,	Top-1 err = 0.388330,	Top-5 err = 0.174170,	data_time = 0.148061,	train_time = 0.790680
[2019-08-24 02:20:02,272] TRAIN Iter 186160: lr = 0.189735,	loss = 2.524961,	Top-1 err = 0.388037,	Top-5 err = 0.173584,	data_time = 0.050449,	train_time = 0.351567
[2019-08-24 02:20:18,314] TRAIN Iter 186180: lr = 0.189702,	loss = 2.495887,	Top-1 err = 0.386621,	Top-5 err = 0.171729,	data_time = 0.050617,	train_time = 0.802072
[2019-08-24 02:20:34,861] TRAIN Iter 186200: lr = 0.189668,	loss = 2.643580,	Top-1 err = 0.390918,	Top-5 err = 0.181152,	data_time = 0.050805,	train_time = 0.827342
[2019-08-24 02:20:42,006] TRAIN Iter 186220: lr = 0.189635,	loss = 2.463730,	Top-1 err = 0.389355,	Top-5 err = 0.176904,	data_time = 0.050177,	train_time = 0.357268
[2019-08-24 02:20:58,675] TRAIN Iter 186240: lr = 0.189602,	loss = 2.539018,	Top-1 err = 0.391211,	Top-5 err = 0.174951,	data_time = 0.050535,	train_time = 0.833410
[2019-08-24 02:21:12,668] TRAIN Iter 186260: lr = 0.189568,	loss = 2.550787,	Top-1 err = 0.398389,	Top-5 err = 0.177734,	data_time = 0.050765,	train_time = 0.699656
[2019-08-24 02:21:21,249] TRAIN Iter 186280: lr = 0.189535,	loss = 2.575657,	Top-1 err = 0.387891,	Top-5 err = 0.173242,	data_time = 0.050704,	train_time = 0.429021
[2019-08-24 02:21:39,096] TRAIN Iter 186300: lr = 0.189502,	loss = 2.585645,	Top-1 err = 0.387012,	Top-5 err = 0.175977,	data_time = 0.050505,	train_time = 0.892360
[2019-08-24 02:21:45,897] TRAIN Iter 186320: lr = 0.189468,	loss = 2.576654,	Top-1 err = 0.382129,	Top-5 err = 0.174219,	data_time = 0.050401,	train_time = 0.340032
[2019-08-24 02:22:03,913] TRAIN Iter 186340: lr = 0.189435,	loss = 2.600204,	Top-1 err = 0.392773,	Top-5 err = 0.176611,	data_time = 0.050827,	train_time = 0.900780
[2019-08-24 02:22:20,619] TRAIN Iter 186360: lr = 0.189402,	loss = 2.587447,	Top-1 err = 0.385889,	Top-5 err = 0.175977,	data_time = 0.050203,	train_time = 0.835288
[2019-08-24 02:22:27,538] TRAIN Iter 186380: lr = 0.189368,	loss = 2.511270,	Top-1 err = 0.387402,	Top-5 err = 0.177930,	data_time = 0.050786,	train_time = 0.345927
[2019-08-24 02:22:46,036] TRAIN Iter 186400: lr = 0.189335,	loss = 2.603920,	Top-1 err = 0.387891,	Top-5 err = 0.175293,	data_time = 0.050547,	train_time = 0.924877
[2019-08-24 02:23:02,566] TRAIN Iter 186420: lr = 0.189302,	loss = 2.540350,	Top-1 err = 0.386523,	Top-5 err = 0.172900,	data_time = 0.066172,	train_time = 0.826473
[2019-08-24 02:23:09,819] TRAIN Iter 186440: lr = 0.189268,	loss = 2.623506,	Top-1 err = 0.387061,	Top-5 err = 0.175049,	data_time = 0.050086,	train_time = 0.362632
[2019-08-24 02:23:26,626] TRAIN Iter 186460: lr = 0.189235,	loss = 2.499162,	Top-1 err = 0.386230,	Top-5 err = 0.173584,	data_time = 0.049940,	train_time = 0.840372
[2019-08-24 02:23:32,749] TRAIN Iter 186480: lr = 0.189202,	loss = 2.562007,	Top-1 err = 0.390869,	Top-5 err = 0.177783,	data_time = 0.049919,	train_time = 0.306101
[2019-08-24 02:24:23,204] TRAIN Iter 186500: lr = 0.189168,	loss = 2.555682,	Top-1 err = 0.383780,	Top-5 err = 0.173323,	data_time = 0.050263,	train_time = 2.522777
[2019-08-24 02:24:38,208] TRAIN Iter 186520: lr = 0.189135,	loss = 2.598021,	Top-1 err = 0.382666,	Top-5 err = 0.173877,	data_time = 2.232370,	train_time = 0.750181
[2019-08-24 02:24:46,001] TRAIN Iter 186540: lr = 0.189102,	loss = 2.572033,	Top-1 err = 0.375488,	Top-5 err = 0.169629,	data_time = 0.050927,	train_time = 0.389601
[2019-08-24 02:24:58,627] TRAIN Iter 186560: lr = 0.189068,	loss = 2.491904,	Top-1 err = 0.378711,	Top-5 err = 0.170264,	data_time = 0.050435,	train_time = 0.631322
[2019-08-24 02:25:06,055] TRAIN Iter 186580: lr = 0.189035,	loss = 2.540655,	Top-1 err = 0.376953,	Top-5 err = 0.167090,	data_time = 0.050905,	train_time = 0.371352
[2019-08-24 02:25:19,218] TRAIN Iter 186600: lr = 0.189002,	loss = 2.572534,	Top-1 err = 0.380811,	Top-5 err = 0.163818,	data_time = 0.050507,	train_time = 0.658134
[2019-08-24 02:25:32,026] TRAIN Iter 186620: lr = 0.188968,	loss = 2.537806,	Top-1 err = 0.386523,	Top-5 err = 0.169971,	data_time = 0.050231,	train_time = 0.640407
[2019-08-24 02:25:39,557] TRAIN Iter 186640: lr = 0.188935,	loss = 2.563201,	Top-1 err = 0.373242,	Top-5 err = 0.165967,	data_time = 0.050536,	train_time = 0.376542
[2019-08-24 02:25:55,514] TRAIN Iter 186660: lr = 0.188902,	loss = 2.625158,	Top-1 err = 0.385400,	Top-5 err = 0.169336,	data_time = 0.050334,	train_time = 0.797825
[2019-08-24 02:26:08,526] TRAIN Iter 186680: lr = 0.188868,	loss = 2.616970,	Top-1 err = 0.382275,	Top-5 err = 0.166943,	data_time = 1.199175,	train_time = 0.650565
[2019-08-24 02:26:17,470] TRAIN Iter 186700: lr = 0.188835,	loss = 2.496662,	Top-1 err = 0.381543,	Top-5 err = 0.168066,	data_time = 0.050521,	train_time = 0.447219
[2019-08-24 02:26:32,626] TRAIN Iter 186720: lr = 0.188802,	loss = 2.477038,	Top-1 err = 0.384912,	Top-5 err = 0.171729,	data_time = 0.050740,	train_time = 0.757760
[2019-08-24 02:26:40,068] TRAIN Iter 186740: lr = 0.188768,	loss = 2.639164,	Top-1 err = 0.382715,	Top-5 err = 0.167773,	data_time = 0.050567,	train_time = 0.372095
[2019-08-24 02:26:54,543] TRAIN Iter 186760: lr = 0.188735,	loss = 2.509523,	Top-1 err = 0.381738,	Top-5 err = 0.167187,	data_time = 0.050477,	train_time = 0.723718
[2019-08-24 02:27:07,707] TRAIN Iter 186780: lr = 0.188702,	loss = 2.555099,	Top-1 err = 0.383838,	Top-5 err = 0.171924,	data_time = 0.050688,	train_time = 0.658215
[2019-08-24 02:27:14,843] TRAIN Iter 186800: lr = 0.188668,	loss = 2.588234,	Top-1 err = 0.381641,	Top-5 err = 0.172559,	data_time = 0.050583,	train_time = 0.356757
[2019-08-24 02:27:31,879] TRAIN Iter 186820: lr = 0.188635,	loss = 2.599850,	Top-1 err = 0.382666,	Top-5 err = 0.169141,	data_time = 0.050343,	train_time = 0.851810
[2019-08-24 02:27:45,596] TRAIN Iter 186840: lr = 0.188602,	loss = 2.581994,	Top-1 err = 0.380664,	Top-5 err = 0.172021,	data_time = 1.766549,	train_time = 0.685845
[2019-08-24 02:27:53,015] TRAIN Iter 186860: lr = 0.188568,	loss = 2.462478,	Top-1 err = 0.384473,	Top-5 err = 0.175049,	data_time = 0.050395,	train_time = 0.370910
[2019-08-24 02:28:07,456] TRAIN Iter 186880: lr = 0.188535,	loss = 2.562168,	Top-1 err = 0.381885,	Top-5 err = 0.169043,	data_time = 0.050474,	train_time = 0.722021
[2019-08-24 02:28:14,782] TRAIN Iter 186900: lr = 0.188502,	loss = 2.514023,	Top-1 err = 0.389307,	Top-5 err = 0.174609,	data_time = 0.050444,	train_time = 0.366320
[2019-08-24 02:28:29,577] TRAIN Iter 186920: lr = 0.188468,	loss = 2.657139,	Top-1 err = 0.384229,	Top-5 err = 0.173828,	data_time = 0.050894,	train_time = 0.739705
[2019-08-24 02:28:46,100] TRAIN Iter 186940: lr = 0.188435,	loss = 2.559728,	Top-1 err = 0.381494,	Top-5 err = 0.171875,	data_time = 0.050477,	train_time = 0.826173
[2019-08-24 02:28:53,333] TRAIN Iter 186960: lr = 0.188402,	loss = 2.681016,	Top-1 err = 0.384521,	Top-5 err = 0.171338,	data_time = 0.050949,	train_time = 0.361604
[2019-08-24 02:29:08,756] TRAIN Iter 186980: lr = 0.188368,	loss = 2.588162,	Top-1 err = 0.389453,	Top-5 err = 0.174902,	data_time = 0.050736,	train_time = 0.771142
[2019-08-24 02:29:25,254] TRAIN Iter 187000: lr = 0.188335,	loss = 2.612066,	Top-1 err = 0.382471,	Top-5 err = 0.172559,	data_time = 0.050892,	train_time = 0.824888
[2019-08-24 02:29:32,515] TRAIN Iter 187020: lr = 0.188302,	loss = 2.597258,	Top-1 err = 0.380664,	Top-5 err = 0.171045,	data_time = 0.050258,	train_time = 0.363031
[2019-08-24 02:29:48,877] TRAIN Iter 187040: lr = 0.188268,	loss = 2.485018,	Top-1 err = 0.380713,	Top-5 err = 0.172998,	data_time = 0.050665,	train_time = 0.818070
[2019-08-24 02:29:56,131] TRAIN Iter 187060: lr = 0.188235,	loss = 2.639741,	Top-1 err = 0.379834,	Top-5 err = 0.173975,	data_time = 0.050621,	train_time = 0.362703
[2019-08-24 02:30:09,874] TRAIN Iter 187080: lr = 0.188202,	loss = 2.502732,	Top-1 err = 0.383838,	Top-5 err = 0.171680,	data_time = 0.050540,	train_time = 0.687139
[2019-08-24 02:30:26,140] TRAIN Iter 187100: lr = 0.188168,	loss = 2.580576,	Top-1 err = 0.382959,	Top-5 err = 0.168799,	data_time = 0.050447,	train_time = 0.813288
[2019-08-24 02:30:32,902] TRAIN Iter 187120: lr = 0.188135,	loss = 2.621480,	Top-1 err = 0.384033,	Top-5 err = 0.168701,	data_time = 0.050402,	train_time = 0.338103
[2019-08-24 02:30:49,300] TRAIN Iter 187140: lr = 0.188102,	loss = 2.577225,	Top-1 err = 0.386816,	Top-5 err = 0.169385,	data_time = 0.050575,	train_time = 0.819862
[2019-08-24 02:31:04,247] TRAIN Iter 187160: lr = 0.188068,	loss = 2.470829,	Top-1 err = 0.385645,	Top-5 err = 0.174072,	data_time = 0.050484,	train_time = 0.747337
[2019-08-24 02:31:11,605] TRAIN Iter 187180: lr = 0.188035,	loss = 2.582254,	Top-1 err = 0.385596,	Top-5 err = 0.173779,	data_time = 0.050572,	train_time = 0.367870
[2019-08-24 02:31:26,292] TRAIN Iter 187200: lr = 0.188002,	loss = 2.514146,	Top-1 err = 0.389941,	Top-5 err = 0.169824,	data_time = 0.050874,	train_time = 0.734354
[2019-08-24 02:31:33,328] TRAIN Iter 187220: lr = 0.187968,	loss = 2.517408,	Top-1 err = 0.389160,	Top-5 err = 0.175049,	data_time = 0.050799,	train_time = 0.351774
[2019-08-24 02:31:48,329] TRAIN Iter 187240: lr = 0.187935,	loss = 2.633024,	Top-1 err = 0.386816,	Top-5 err = 0.176465,	data_time = 0.050437,	train_time = 0.750027
[2019-08-24 02:32:03,645] TRAIN Iter 187260: lr = 0.187902,	loss = 2.623969,	Top-1 err = 0.388770,	Top-5 err = 0.174756,	data_time = 0.050589,	train_time = 0.765819
[2019-08-24 02:32:11,479] TRAIN Iter 187280: lr = 0.187868,	loss = 2.590631,	Top-1 err = 0.385107,	Top-5 err = 0.171338,	data_time = 0.050848,	train_time = 0.391654
[2019-08-24 02:32:27,917] TRAIN Iter 187300: lr = 0.187835,	loss = 2.569331,	Top-1 err = 0.387549,	Top-5 err = 0.173682,	data_time = 0.050388,	train_time = 0.821883
[2019-08-24 02:32:42,893] TRAIN Iter 187320: lr = 0.187802,	loss = 2.511649,	Top-1 err = 0.383936,	Top-5 err = 0.172803,	data_time = 0.134825,	train_time = 0.748814
[2019-08-24 02:32:50,205] TRAIN Iter 187340: lr = 0.187768,	loss = 2.578731,	Top-1 err = 0.390723,	Top-5 err = 0.173877,	data_time = 0.050408,	train_time = 0.365605
[2019-08-24 02:33:06,190] TRAIN Iter 187360: lr = 0.187735,	loss = 2.550954,	Top-1 err = 0.388184,	Top-5 err = 0.179053,	data_time = 0.050666,	train_time = 0.799189
[2019-08-24 02:33:13,201] TRAIN Iter 187380: lr = 0.187702,	loss = 2.582868,	Top-1 err = 0.382764,	Top-5 err = 0.173828,	data_time = 0.050745,	train_time = 0.350545
[2019-08-24 02:33:28,442] TRAIN Iter 187400: lr = 0.187668,	loss = 2.642754,	Top-1 err = 0.383105,	Top-5 err = 0.173096,	data_time = 0.050551,	train_time = 0.762026
[2019-08-24 02:33:45,779] TRAIN Iter 187420: lr = 0.187635,	loss = 2.500285,	Top-1 err = 0.384033,	Top-5 err = 0.168213,	data_time = 0.050932,	train_time = 0.866837
[2019-08-24 02:33:52,705] TRAIN Iter 187440: lr = 0.187602,	loss = 2.617040,	Top-1 err = 0.389258,	Top-5 err = 0.177295,	data_time = 0.050331,	train_time = 0.346294
[2019-08-24 02:34:08,800] TRAIN Iter 187460: lr = 0.187568,	loss = 2.558992,	Top-1 err = 0.389844,	Top-5 err = 0.173682,	data_time = 0.050704,	train_time = 0.804730
[2019-08-24 02:34:25,522] TRAIN Iter 187480: lr = 0.187535,	loss = 2.630105,	Top-1 err = 0.386572,	Top-5 err = 0.174707,	data_time = 0.116969,	train_time = 0.836099
[2019-08-24 02:34:32,566] TRAIN Iter 187500: lr = 0.187502,	loss = 2.612391,	Top-1 err = 0.387988,	Top-5 err = 0.173535,	data_time = 0.050368,	train_time = 0.352204
[2019-08-24 02:34:49,914] TRAIN Iter 187520: lr = 0.187468,	loss = 2.620891,	Top-1 err = 0.387842,	Top-5 err = 0.171826,	data_time = 0.050577,	train_time = 0.867361
[2019-08-24 02:34:56,691] TRAIN Iter 187540: lr = 0.187435,	loss = 2.559633,	Top-1 err = 0.381592,	Top-5 err = 0.175537,	data_time = 0.050416,	train_time = 0.338825
[2019-08-24 02:35:14,278] TRAIN Iter 187560: lr = 0.187402,	loss = 2.566863,	Top-1 err = 0.381641,	Top-5 err = 0.169385,	data_time = 0.050578,	train_time = 0.879353
[2019-08-24 02:35:31,909] TRAIN Iter 187580: lr = 0.187368,	loss = 2.534029,	Top-1 err = 0.387402,	Top-5 err = 0.175586,	data_time = 0.050195,	train_time = 0.881553
[2019-08-24 02:35:38,493] TRAIN Iter 187600: lr = 0.187335,	loss = 2.525191,	Top-1 err = 0.390186,	Top-5 err = 0.174023,	data_time = 0.051895,	train_time = 0.329192
[2019-08-24 02:35:56,675] TRAIN Iter 187620: lr = 0.187302,	loss = 2.532470,	Top-1 err = 0.389697,	Top-5 err = 0.175293,	data_time = 0.050745,	train_time = 0.909059
[2019-08-24 02:36:11,157] TRAIN Iter 187640: lr = 0.187268,	loss = 2.544768,	Top-1 err = 0.387305,	Top-5 err = 0.173486,	data_time = 0.050470,	train_time = 0.724106
[2019-08-24 02:36:20,050] TRAIN Iter 187660: lr = 0.187235,	loss = 2.600714,	Top-1 err = 0.386914,	Top-5 err = 0.174268,	data_time = 0.050522,	train_time = 0.444621
[2019-08-24 02:36:38,176] TRAIN Iter 187680: lr = 0.187202,	loss = 2.596037,	Top-1 err = 0.390674,	Top-5 err = 0.172900,	data_time = 0.050118,	train_time = 0.906313
[2019-08-24 02:36:44,803] TRAIN Iter 187700: lr = 0.187168,	loss = 2.505213,	Top-1 err = 0.394189,	Top-5 err = 0.180371,	data_time = 0.050052,	train_time = 0.331335
[2019-08-24 02:37:02,205] TRAIN Iter 187720: lr = 0.187135,	loss = 2.608911,	Top-1 err = 0.389111,	Top-5 err = 0.176660,	data_time = 0.049943,	train_time = 0.870066
[2019-08-24 02:37:53,236] TRAIN Iter 187740: lr = 0.187102,	loss = 2.665923,	Top-1 err = 0.387407,	Top-5 err = 0.174722,	data_time = 6.117329,	train_time = 2.551527
[2019-08-24 02:38:00,172] TRAIN Iter 187760: lr = 0.187068,	loss = 2.561115,	Top-1 err = 0.391895,	Top-5 err = 0.172559,	data_time = 0.050402,	train_time = 0.346796
[2019-08-24 02:38:16,602] TRAIN Iter 187780: lr = 0.187035,	loss = 2.538085,	Top-1 err = 0.381348,	Top-5 err = 0.170947,	data_time = 0.050508,	train_time = 0.821491
[2019-08-24 02:38:24,525] TRAIN Iter 187800: lr = 0.187002,	loss = 2.458119,	Top-1 err = 0.379395,	Top-5 err = 0.166406,	data_time = 0.050329,	train_time = 0.396103
[2019-08-24 02:38:37,268] TRAIN Iter 187820: lr = 0.186968,	loss = 2.657521,	Top-1 err = 0.384570,	Top-5 err = 0.170410,	data_time = 0.050352,	train_time = 0.637150
[2019-08-24 02:38:54,746] TRAIN Iter 187840: lr = 0.186935,	loss = 2.532566,	Top-1 err = 0.373389,	Top-5 err = 0.164795,	data_time = 0.050899,	train_time = 0.873869
[2019-08-24 02:39:02,686] TRAIN Iter 187860: lr = 0.186902,	loss = 2.525475,	Top-1 err = 0.383496,	Top-5 err = 0.169385,	data_time = 0.050293,	train_time = 0.396982
[2019-08-24 02:39:15,424] TRAIN Iter 187880: lr = 0.186868,	loss = 2.556347,	Top-1 err = 0.384668,	Top-5 err = 0.167578,	data_time = 0.050234,	train_time = 0.636915
[2019-08-24 02:39:28,047] TRAIN Iter 187900: lr = 0.186835,	loss = 2.465090,	Top-1 err = 0.376025,	Top-5 err = 0.167920,	data_time = 4.307171,	train_time = 0.631111
[2019-08-24 02:39:35,931] TRAIN Iter 187920: lr = 0.186802,	loss = 2.475829,	Top-1 err = 0.378467,	Top-5 err = 0.169336,	data_time = 0.050302,	train_time = 0.394192
[2019-08-24 02:39:52,401] TRAIN Iter 187940: lr = 0.186768,	loss = 2.483992,	Top-1 err = 0.384033,	Top-5 err = 0.169287,	data_time = 0.050565,	train_time = 0.823486
[2019-08-24 02:40:00,040] TRAIN Iter 187960: lr = 0.186735,	loss = 2.610264,	Top-1 err = 0.372900,	Top-5 err = 0.167187,	data_time = 0.050537,	train_time = 0.381934
[2019-08-24 02:40:12,961] TRAIN Iter 187980: lr = 0.186702,	loss = 2.545097,	Top-1 err = 0.383057,	Top-5 err = 0.168213,	data_time = 0.050563,	train_time = 0.646069
[2019-08-24 02:40:28,230] TRAIN Iter 188000: lr = 0.186668,	loss = 2.532772,	Top-1 err = 0.378809,	Top-5 err = 0.169531,	data_time = 0.050340,	train_time = 0.763398
[2019-08-24 02:40:35,367] TRAIN Iter 188020: lr = 0.186635,	loss = 2.578188,	Top-1 err = 0.381689,	Top-5 err = 0.168066,	data_time = 0.050362,	train_time = 0.356839
[2019-08-24 02:40:49,489] TRAIN Iter 188040: lr = 0.186602,	loss = 2.573159,	Top-1 err = 0.382178,	Top-5 err = 0.168164,	data_time = 0.050317,	train_time = 0.706107
[2019-08-24 02:41:01,246] TRAIN Iter 188060: lr = 0.186568,	loss = 2.718561,	Top-1 err = 0.385645,	Top-5 err = 0.170898,	data_time = 0.542283,	train_time = 0.587836
[2019-08-24 02:41:09,165] TRAIN Iter 188080: lr = 0.186535,	loss = 2.593670,	Top-1 err = 0.383301,	Top-5 err = 0.167871,	data_time = 0.050380,	train_time = 0.395914
[2019-08-24 02:41:24,320] TRAIN Iter 188100: lr = 0.186502,	loss = 2.596560,	Top-1 err = 0.381787,	Top-5 err = 0.174658,	data_time = 0.050393,	train_time = 0.757730
[2019-08-24 02:41:31,261] TRAIN Iter 188120: lr = 0.186468,	loss = 2.535578,	Top-1 err = 0.384033,	Top-5 err = 0.170361,	data_time = 0.050496,	train_time = 0.347038
[2019-08-24 02:41:45,988] TRAIN Iter 188140: lr = 0.186435,	loss = 2.582117,	Top-1 err = 0.387744,	Top-5 err = 0.168604,	data_time = 0.050585,	train_time = 0.736372
[2019-08-24 02:42:00,733] TRAIN Iter 188160: lr = 0.186402,	loss = 2.559897,	Top-1 err = 0.388818,	Top-5 err = 0.171729,	data_time = 0.050528,	train_time = 0.737227
[2019-08-24 02:42:08,073] TRAIN Iter 188180: lr = 0.186368,	loss = 2.554209,	Top-1 err = 0.387402,	Top-5 err = 0.170313,	data_time = 0.050489,	train_time = 0.366976
[2019-08-24 02:42:23,639] TRAIN Iter 188200: lr = 0.186335,	loss = 2.666865,	Top-1 err = 0.382861,	Top-5 err = 0.175195,	data_time = 0.050362,	train_time = 0.778279
[2019-08-24 02:42:40,148] TRAIN Iter 188220: lr = 0.186302,	loss = 2.587528,	Top-1 err = 0.386182,	Top-5 err = 0.170117,	data_time = 1.070933,	train_time = 0.825465
[2019-08-24 02:42:47,423] TRAIN Iter 188240: lr = 0.186268,	loss = 2.543439,	Top-1 err = 0.376318,	Top-5 err = 0.166943,	data_time = 0.050356,	train_time = 0.363731
[2019-08-24 02:43:03,748] TRAIN Iter 188260: lr = 0.186235,	loss = 2.488827,	Top-1 err = 0.385889,	Top-5 err = 0.173437,	data_time = 0.050887,	train_time = 0.816203
[2019-08-24 02:43:10,862] TRAIN Iter 188280: lr = 0.186202,	loss = 2.556876,	Top-1 err = 0.383789,	Top-5 err = 0.171484,	data_time = 0.050907,	train_time = 0.355712
[2019-08-24 02:43:26,284] TRAIN Iter 188300: lr = 0.186168,	loss = 2.500489,	Top-1 err = 0.387305,	Top-5 err = 0.175586,	data_time = 0.050481,	train_time = 0.771073
[2019-08-24 02:43:40,785] TRAIN Iter 188320: lr = 0.186135,	loss = 2.550253,	Top-1 err = 0.385303,	Top-5 err = 0.171533,	data_time = 0.050628,	train_time = 0.725045
[2019-08-24 02:43:48,009] TRAIN Iter 188340: lr = 0.186102,	loss = 2.647167,	Top-1 err = 0.385254,	Top-5 err = 0.171338,	data_time = 0.050424,	train_time = 0.361192
[2019-08-24 02:44:05,617] TRAIN Iter 188360: lr = 0.186068,	loss = 2.544343,	Top-1 err = 0.389502,	Top-5 err = 0.172363,	data_time = 0.050322,	train_time = 0.880360
[2019-08-24 02:44:21,455] TRAIN Iter 188380: lr = 0.186035,	loss = 2.587878,	Top-1 err = 0.379102,	Top-5 err = 0.167480,	data_time = 0.141468,	train_time = 0.791903
[2019-08-24 02:44:28,334] TRAIN Iter 188400: lr = 0.186002,	loss = 2.572285,	Top-1 err = 0.380664,	Top-5 err = 0.172363,	data_time = 0.050583,	train_time = 0.343933
[2019-08-24 02:44:44,802] TRAIN Iter 188420: lr = 0.185968,	loss = 2.502582,	Top-1 err = 0.385449,	Top-5 err = 0.170752,	data_time = 0.050344,	train_time = 0.823406
[2019-08-24 02:44:51,979] TRAIN Iter 188440: lr = 0.185935,	loss = 2.658580,	Top-1 err = 0.384082,	Top-5 err = 0.166504,	data_time = 0.050163,	train_time = 0.358827
[2019-08-24 02:45:05,985] TRAIN Iter 188460: lr = 0.185902,	loss = 2.626698,	Top-1 err = 0.392090,	Top-5 err = 0.178809,	data_time = 0.050561,	train_time = 0.700293
[2019-08-24 02:45:23,444] TRAIN Iter 188480: lr = 0.185868,	loss = 2.476768,	Top-1 err = 0.387354,	Top-5 err = 0.174951,	data_time = 0.050812,	train_time = 0.872937
[2019-08-24 02:45:30,495] TRAIN Iter 188500: lr = 0.185835,	loss = 2.576147,	Top-1 err = 0.382275,	Top-5 err = 0.173193,	data_time = 0.050612,	train_time = 0.352529
[2019-08-24 02:45:47,177] TRAIN Iter 188520: lr = 0.185802,	loss = 2.591237,	Top-1 err = 0.385156,	Top-5 err = 0.177979,	data_time = 0.050536,	train_time = 0.834102
[2019-08-24 02:46:01,054] TRAIN Iter 188540: lr = 0.185768,	loss = 2.594374,	Top-1 err = 0.381787,	Top-5 err = 0.172217,	data_time = 0.050669,	train_time = 0.693832
[2019-08-24 02:46:08,337] TRAIN Iter 188560: lr = 0.185735,	loss = 2.533003,	Top-1 err = 0.385059,	Top-5 err = 0.169189,	data_time = 0.050409,	train_time = 0.364124
[2019-08-24 02:46:25,955] TRAIN Iter 188580: lr = 0.185702,	loss = 2.505941,	Top-1 err = 0.380273,	Top-5 err = 0.170313,	data_time = 0.050523,	train_time = 0.880885
[2019-08-24 02:46:32,880] TRAIN Iter 188600: lr = 0.185668,	loss = 2.521577,	Top-1 err = 0.386768,	Top-5 err = 0.172656,	data_time = 0.050609,	train_time = 0.346227
[2019-08-24 02:46:49,270] TRAIN Iter 188620: lr = 0.185635,	loss = 2.529237,	Top-1 err = 0.382324,	Top-5 err = 0.171094,	data_time = 0.050502,	train_time = 0.819497
[2019-08-24 02:47:04,306] TRAIN Iter 188640: lr = 0.185602,	loss = 2.642190,	Top-1 err = 0.389355,	Top-5 err = 0.177100,	data_time = 0.050871,	train_time = 0.751788
[2019-08-24 02:47:11,350] TRAIN Iter 188660: lr = 0.185568,	loss = 2.523445,	Top-1 err = 0.379932,	Top-5 err = 0.174072,	data_time = 0.050578,	train_time = 0.352165
[2019-08-24 02:47:28,484] TRAIN Iter 188680: lr = 0.185535,	loss = 2.527540,	Top-1 err = 0.383545,	Top-5 err = 0.166504,	data_time = 0.050373,	train_time = 0.856683
[2019-08-24 02:47:44,166] TRAIN Iter 188700: lr = 0.185502,	loss = 2.499445,	Top-1 err = 0.383154,	Top-5 err = 0.174414,	data_time = 0.050438,	train_time = 0.784088
[2019-08-24 02:47:51,568] TRAIN Iter 188720: lr = 0.185468,	loss = 2.472219,	Top-1 err = 0.382617,	Top-5 err = 0.168945,	data_time = 0.050458,	train_time = 0.370102
[2019-08-24 02:48:10,430] TRAIN Iter 188740: lr = 0.185435,	loss = 2.638262,	Top-1 err = 0.381787,	Top-5 err = 0.175684,	data_time = 0.050461,	train_time = 0.943093
[2019-08-24 02:48:17,273] TRAIN Iter 188760: lr = 0.185402,	loss = 2.561643,	Top-1 err = 0.391064,	Top-5 err = 0.177051,	data_time = 0.050375,	train_time = 0.342107
[2019-08-24 02:48:34,051] TRAIN Iter 188780: lr = 0.185368,	loss = 2.517467,	Top-1 err = 0.386670,	Top-5 err = 0.174707,	data_time = 0.050450,	train_time = 0.838932
[2019-08-24 02:48:52,057] TRAIN Iter 188800: lr = 0.185335,	loss = 2.587165,	Top-1 err = 0.389990,	Top-5 err = 0.172656,	data_time = 0.050404,	train_time = 0.900281
[2019-08-24 02:48:58,921] TRAIN Iter 188820: lr = 0.185302,	loss = 2.556315,	Top-1 err = 0.391016,	Top-5 err = 0.175732,	data_time = 0.050582,	train_time = 0.343185
[2019-08-24 02:49:15,348] TRAIN Iter 188840: lr = 0.185268,	loss = 2.503278,	Top-1 err = 0.380908,	Top-5 err = 0.171924,	data_time = 0.050587,	train_time = 0.821329
[2019-08-24 02:49:34,956] TRAIN Iter 188860: lr = 0.185235,	loss = 2.556764,	Top-1 err = 0.386377,	Top-5 err = 0.172998,	data_time = 0.050563,	train_time = 0.980356
[2019-08-24 02:49:42,052] TRAIN Iter 188880: lr = 0.185202,	loss = 2.622691,	Top-1 err = 0.391846,	Top-5 err = 0.175439,	data_time = 0.050490,	train_time = 0.354807
[2019-08-24 02:49:59,132] TRAIN Iter 188900: lr = 0.185168,	loss = 2.612311,	Top-1 err = 0.391455,	Top-5 err = 0.177539,	data_time = 0.050312,	train_time = 0.853998
[2019-08-24 02:50:05,796] TRAIN Iter 188920: lr = 0.185135,	loss = 2.579960,	Top-1 err = 0.389453,	Top-5 err = 0.173291,	data_time = 0.050432,	train_time = 0.333178
[2019-08-24 02:50:24,279] TRAIN Iter 188940: lr = 0.185102,	loss = 2.643217,	Top-1 err = 0.385156,	Top-5 err = 0.170801,	data_time = 0.050020,	train_time = 0.924132
[2019-08-24 02:50:40,954] TRAIN Iter 188960: lr = 0.185068,	loss = 2.502470,	Top-1 err = 0.392627,	Top-5 err = 0.175586,	data_time = 0.049909,	train_time = 0.833724
[2019-08-24 02:50:47,111] TRAIN Iter 188980: lr = 0.185035,	loss = 2.529256,	Top-1 err = 0.396338,	Top-5 err = 0.179150,	data_time = 0.049899,	train_time = 0.307850
[2019-08-24 02:51:35,264] TRAIN Iter 189000: lr = 0.185002,	loss = 2.542788,	Top-1 err = 0.389694,	Top-5 err = 0.172070,	data_time = 0.050205,	train_time = 2.407643
[2019-08-24 02:51:42,797] TRAIN Iter 189020: lr = 0.184968,	loss = 2.478664,	Top-1 err = 0.379541,	Top-5 err = 0.168896,	data_time = 0.050849,	train_time = 0.376642
[2019-08-24 02:51:56,883] TRAIN Iter 189040: lr = 0.184935,	loss = 2.600640,	Top-1 err = 0.377881,	Top-5 err = 0.167285,	data_time = 0.050366,	train_time = 0.704252
[2019-08-24 02:52:12,408] TRAIN Iter 189060: lr = 0.184902,	loss = 2.627311,	Top-1 err = 0.386816,	Top-5 err = 0.173193,	data_time = 0.050387,	train_time = 0.776245
[2019-08-24 02:52:20,019] TRAIN Iter 189080: lr = 0.184868,	loss = 2.471386,	Top-1 err = 0.378760,	Top-5 err = 0.164990,	data_time = 0.050356,	train_time = 0.380551
[2019-08-24 02:52:32,639] TRAIN Iter 189100: lr = 0.184835,	loss = 2.563822,	Top-1 err = 0.374512,	Top-5 err = 0.169629,	data_time = 0.050553,	train_time = 0.630982
[2019-08-24 02:52:47,263] TRAIN Iter 189120: lr = 0.184802,	loss = 2.506255,	Top-1 err = 0.381250,	Top-5 err = 0.164502,	data_time = 0.050516,	train_time = 0.731169
[2019-08-24 02:52:54,998] TRAIN Iter 189140: lr = 0.184768,	loss = 2.546841,	Top-1 err = 0.377002,	Top-5 err = 0.169531,	data_time = 0.050517,	train_time = 0.386759
[2019-08-24 02:53:11,715] TRAIN Iter 189160: lr = 0.184735,	loss = 2.548068,	Top-1 err = 0.386230,	Top-5 err = 0.170605,	data_time = 0.050401,	train_time = 0.835836
[2019-08-24 02:53:19,632] TRAIN Iter 189180: lr = 0.184702,	loss = 2.572919,	Top-1 err = 0.378125,	Top-5 err = 0.171973,	data_time = 0.050369,	train_time = 0.395822
[2019-08-24 02:53:32,634] TRAIN Iter 189200: lr = 0.184668,	loss = 2.586665,	Top-1 err = 0.376074,	Top-5 err = 0.171582,	data_time = 0.050451,	train_time = 0.650095
[2019-08-24 02:53:47,845] TRAIN Iter 189220: lr = 0.184635,	loss = 2.460446,	Top-1 err = 0.381641,	Top-5 err = 0.167139,	data_time = 0.050829,	train_time = 0.760554
[2019-08-24 02:53:54,733] TRAIN Iter 189240: lr = 0.184602,	loss = 2.510651,	Top-1 err = 0.377441,	Top-5 err = 0.165869,	data_time = 0.050648,	train_time = 0.344368
[2019-08-24 02:54:11,243] TRAIN Iter 189260: lr = 0.184568,	loss = 2.440778,	Top-1 err = 0.380469,	Top-5 err = 0.164014,	data_time = 0.050609,	train_time = 0.825486
[2019-08-24 02:54:26,597] TRAIN Iter 189280: lr = 0.184535,	loss = 2.545317,	Top-1 err = 0.379248,	Top-5 err = 0.165283,	data_time = 0.050482,	train_time = 0.767710
[2019-08-24 02:54:33,857] TRAIN Iter 189300: lr = 0.184502,	loss = 2.569778,	Top-1 err = 0.379346,	Top-5 err = 0.166748,	data_time = 0.050495,	train_time = 0.362983
[2019-08-24 02:54:48,409] TRAIN Iter 189320: lr = 0.184468,	loss = 2.592722,	Top-1 err = 0.373096,	Top-5 err = 0.165234,	data_time = 0.050361,	train_time = 0.727574
[2019-08-24 02:54:56,245] TRAIN Iter 189340: lr = 0.184435,	loss = 2.548066,	Top-1 err = 0.382715,	Top-5 err = 0.170410,	data_time = 0.156312,	train_time = 0.391763
[2019-08-24 02:55:08,687] TRAIN Iter 189360: lr = 0.184402,	loss = 2.650798,	Top-1 err = 0.383643,	Top-5 err = 0.169678,	data_time = 0.050388,	train_time = 0.622084
[2019-08-24 02:55:22,927] TRAIN Iter 189380: lr = 0.184368,	loss = 2.549033,	Top-1 err = 0.379590,	Top-5 err = 0.173145,	data_time = 0.050417,	train_time = 0.711979
[2019-08-24 02:55:29,722] TRAIN Iter 189400: lr = 0.184335,	loss = 2.542978,	Top-1 err = 0.379395,	Top-5 err = 0.167627,	data_time = 0.050398,	train_time = 0.339742
[2019-08-24 02:55:46,675] TRAIN Iter 189420: lr = 0.184302,	loss = 2.538242,	Top-1 err = 0.373291,	Top-5 err = 0.167041,	data_time = 0.050370,	train_time = 0.847610
[2019-08-24 02:56:02,845] TRAIN Iter 189440: lr = 0.184268,	loss = 2.582667,	Top-1 err = 0.379248,	Top-5 err = 0.170166,	data_time = 0.050583,	train_time = 0.808487
[2019-08-24 02:56:10,110] TRAIN Iter 189460: lr = 0.184235,	loss = 2.571549,	Top-1 err = 0.381104,	Top-5 err = 0.169385,	data_time = 0.050399,	train_time = 0.363237
[2019-08-24 02:56:26,615] TRAIN Iter 189480: lr = 0.184202,	loss = 2.565575,	Top-1 err = 0.380664,	Top-5 err = 0.172705,	data_time = 0.050619,	train_time = 0.825266
[2019-08-24 02:56:34,505] TRAIN Iter 189500: lr = 0.184168,	loss = 2.561036,	Top-1 err = 0.389551,	Top-5 err = 0.176025,	data_time = 0.050839,	train_time = 0.394485
[2019-08-24 02:56:48,359] TRAIN Iter 189520: lr = 0.184135,	loss = 2.611552,	Top-1 err = 0.388818,	Top-5 err = 0.173828,	data_time = 0.050742,	train_time = 0.692647
[2019-08-24 02:57:03,706] TRAIN Iter 189540: lr = 0.184102,	loss = 2.542486,	Top-1 err = 0.392627,	Top-5 err = 0.172607,	data_time = 0.050747,	train_time = 0.767343
[2019-08-24 02:57:10,921] TRAIN Iter 189560: lr = 0.184068,	loss = 2.556236,	Top-1 err = 0.383203,	Top-5 err = 0.169971,	data_time = 0.187208,	train_time = 0.360730
[2019-08-24 02:57:25,694] TRAIN Iter 189580: lr = 0.184035,	loss = 2.613341,	Top-1 err = 0.379297,	Top-5 err = 0.167627,	data_time = 0.050316,	train_time = 0.738677
[2019-08-24 02:57:43,374] TRAIN Iter 189600: lr = 0.184002,	loss = 2.564603,	Top-1 err = 0.382959,	Top-5 err = 0.171631,	data_time = 0.050416,	train_time = 0.883974
[2019-08-24 02:57:50,590] TRAIN Iter 189620: lr = 0.183968,	loss = 2.588004,	Top-1 err = 0.387988,	Top-5 err = 0.171777,	data_time = 0.050717,	train_time = 0.360783
[2019-08-24 02:58:06,753] TRAIN Iter 189640: lr = 0.183935,	loss = 2.593092,	Top-1 err = 0.381396,	Top-5 err = 0.169727,	data_time = 0.050729,	train_time = 0.808142
[2019-08-24 02:58:15,032] TRAIN Iter 189660: lr = 0.183902,	loss = 2.610173,	Top-1 err = 0.381396,	Top-5 err = 0.173096,	data_time = 0.050585,	train_time = 0.413919
[2019-08-24 02:58:29,364] TRAIN Iter 189680: lr = 0.183868,	loss = 2.541606,	Top-1 err = 0.380811,	Top-5 err = 0.173975,	data_time = 0.050268,	train_time = 0.716565
[2019-08-24 02:58:44,716] TRAIN Iter 189700: lr = 0.183835,	loss = 2.388880,	Top-1 err = 0.380322,	Top-5 err = 0.166113,	data_time = 0.050860,	train_time = 0.767611
[2019-08-24 02:58:52,366] TRAIN Iter 189720: lr = 0.183802,	loss = 2.584084,	Top-1 err = 0.383740,	Top-5 err = 0.174316,	data_time = 0.051118,	train_time = 0.382498
[2019-08-24 02:59:06,622] TRAIN Iter 189740: lr = 0.183768,	loss = 2.495584,	Top-1 err = 0.390576,	Top-5 err = 0.174609,	data_time = 0.050503,	train_time = 0.712780
[2019-08-24 02:59:22,514] TRAIN Iter 189760: lr = 0.183735,	loss = 2.634171,	Top-1 err = 0.386523,	Top-5 err = 0.176611,	data_time = 0.050509,	train_time = 0.794588
[2019-08-24 02:59:29,782] TRAIN Iter 189780: lr = 0.183702,	loss = 2.412319,	Top-1 err = 0.375488,	Top-5 err = 0.167676,	data_time = 0.050899,	train_time = 0.363377
[2019-08-24 02:59:45,084] TRAIN Iter 189800: lr = 0.183668,	loss = 2.577940,	Top-1 err = 0.386035,	Top-5 err = 0.172217,	data_time = 0.050531,	train_time = 0.765086
[2019-08-24 02:59:52,734] TRAIN Iter 189820: lr = 0.183635,	loss = 2.599179,	Top-1 err = 0.385059,	Top-5 err = 0.170264,	data_time = 0.050263,	train_time = 0.382478
[2019-08-24 03:00:07,370] TRAIN Iter 189840: lr = 0.183602,	loss = 2.497616,	Top-1 err = 0.386768,	Top-5 err = 0.172412,	data_time = 0.050527,	train_time = 0.731766
[2019-08-24 03:00:24,243] TRAIN Iter 189860: lr = 0.183568,	loss = 2.575652,	Top-1 err = 0.390137,	Top-5 err = 0.172949,	data_time = 0.050719,	train_time = 0.843631
[2019-08-24 03:00:31,478] TRAIN Iter 189880: lr = 0.183535,	loss = 2.598853,	Top-1 err = 0.383301,	Top-5 err = 0.167871,	data_time = 0.050827,	train_time = 0.361785
[2019-08-24 03:00:47,314] TRAIN Iter 189900: lr = 0.183502,	loss = 2.619958,	Top-1 err = 0.391602,	Top-5 err = 0.177197,	data_time = 0.050600,	train_time = 0.791776
[2019-08-24 03:01:02,921] TRAIN Iter 189920: lr = 0.183468,	loss = 2.526930,	Top-1 err = 0.389355,	Top-5 err = 0.175977,	data_time = 0.050607,	train_time = 0.780316
[2019-08-24 03:01:09,878] TRAIN Iter 189940: lr = 0.183435,	loss = 2.503170,	Top-1 err = 0.388086,	Top-5 err = 0.173096,	data_time = 0.117652,	train_time = 0.347845
[2019-08-24 03:01:24,320] TRAIN Iter 189960: lr = 0.183402,	loss = 2.495917,	Top-1 err = 0.380762,	Top-5 err = 0.169580,	data_time = 0.050555,	train_time = 0.722060
[2019-08-24 03:01:31,797] TRAIN Iter 189980: lr = 0.183368,	loss = 2.540203,	Top-1 err = 0.385791,	Top-5 err = 0.175195,	data_time = 0.138403,	train_time = 0.373867
[2019-08-24 03:01:45,864] TRAIN Iter 190000: lr = 0.183335,	loss = 2.470299,	Top-1 err = 0.387354,	Top-5 err = 0.171191,	data_time = 0.050387,	train_time = 0.703308
[2019-08-24 03:02:49,658] TEST Iter 190000: loss = 2.334684,	Top-1 err = 0.341100,	Top-5 err = 0.129160,	val_time = 63.742592
[2019-08-24 03:02:55,981] TRAIN Iter 190020: lr = 0.183302,	loss = 2.535524,	Top-1 err = 0.389160,	Top-5 err = 0.170801,	data_time = 0.050308,	train_time = 0.316159
[2019-08-24 03:03:02,532] TRAIN Iter 190040: lr = 0.183268,	loss = 2.537324,	Top-1 err = 0.388379,	Top-5 err = 0.171045,	data_time = 0.050650,	train_time = 0.327539
[2019-08-24 03:03:09,250] TRAIN Iter 190060: lr = 0.183235,	loss = 2.579443,	Top-1 err = 0.387109,	Top-5 err = 0.170361,	data_time = 0.050753,	train_time = 0.335883
[2019-08-24 03:03:18,113] TRAIN Iter 190080: lr = 0.183202,	loss = 2.593909,	Top-1 err = 0.387305,	Top-5 err = 0.175732,	data_time = 0.267494,	train_time = 0.443107
[2019-08-24 03:03:33,968] TRAIN Iter 190100: lr = 0.183168,	loss = 2.613015,	Top-1 err = 0.386768,	Top-5 err = 0.174756,	data_time = 1.183642,	train_time = 0.792751
[2019-08-24 03:03:44,106] TRAIN Iter 190120: lr = 0.183135,	loss = 2.591596,	Top-1 err = 0.387256,	Top-5 err = 0.171582,	data_time = 0.050651,	train_time = 0.506884
[2019-08-24 03:03:58,614] TRAIN Iter 190140: lr = 0.183102,	loss = 2.490047,	Top-1 err = 0.382324,	Top-5 err = 0.173682,	data_time = 0.128711,	train_time = 0.725410
[2019-08-24 03:04:07,646] TRAIN Iter 190160: lr = 0.183068,	loss = 2.620492,	Top-1 err = 0.389697,	Top-5 err = 0.176074,	data_time = 0.050398,	train_time = 0.451564
[2019-08-24 03:04:24,268] TRAIN Iter 190180: lr = 0.183035,	loss = 2.513265,	Top-1 err = 0.383643,	Top-5 err = 0.173096,	data_time = 0.050201,	train_time = 0.831078
[2019-08-24 03:04:37,731] TRAIN Iter 190200: lr = 0.183002,	loss = 2.644522,	Top-1 err = 0.389941,	Top-5 err = 0.173242,	data_time = 0.050149,	train_time = 0.673142
[2019-08-24 03:04:47,561] TRAIN Iter 190220: lr = 0.182968,	loss = 2.582834,	Top-1 err = 0.391650,	Top-5 err = 0.174854,	data_time = 0.049962,	train_time = 0.491474
[2019-08-24 03:04:59,336] TRAIN Iter 190240: lr = 0.182935,	loss = 2.894718,	Top-1 err = 0.392163,	Top-5 err = 0.179841,	data_time = 0.007110,	train_time = 0.588745
[2019-08-24 03:05:47,369] TRAIN Iter 190260: lr = 0.182902,	loss = 2.542757,	Top-1 err = 0.382373,	Top-5 err = 0.173584,	data_time = 0.050371,	train_time = 2.401625
[2019-08-24 03:06:02,746] TRAIN Iter 190280: lr = 0.182868,	loss = 2.515943,	Top-1 err = 0.382666,	Top-5 err = 0.169043,	data_time = 0.050457,	train_time = 0.768837
[2019-08-24 03:06:10,549] TRAIN Iter 190300: lr = 0.182835,	loss = 2.467882,	Top-1 err = 0.371777,	Top-5 err = 0.163135,	data_time = 0.050743,	train_time = 0.390115
[2019-08-24 03:06:19,955] TRAIN Iter 190320: lr = 0.182802,	loss = 2.557030,	Top-1 err = 0.371826,	Top-5 err = 0.163184,	data_time = 0.050534,	train_time = 0.470318
[2019-08-24 03:06:35,787] TRAIN Iter 190340: lr = 0.182768,	loss = 2.483190,	Top-1 err = 0.377539,	Top-5 err = 0.167920,	data_time = 0.050447,	train_time = 0.791565
[2019-08-24 03:06:43,330] TRAIN Iter 190360: lr = 0.182735,	loss = 2.658347,	Top-1 err = 0.375195,	Top-5 err = 0.165820,	data_time = 0.050350,	train_time = 0.377155
[2019-08-24 03:06:59,724] TRAIN Iter 190380: lr = 0.182702,	loss = 2.476906,	Top-1 err = 0.376953,	Top-5 err = 0.166357,	data_time = 0.050455,	train_time = 0.819690
[2019-08-24 03:07:06,633] TRAIN Iter 190400: lr = 0.182668,	loss = 2.436794,	Top-1 err = 0.379736,	Top-5 err = 0.169775,	data_time = 0.050533,	train_time = 0.345405
[2019-08-24 03:07:23,260] TRAIN Iter 190420: lr = 0.182635,	loss = 2.596145,	Top-1 err = 0.380371,	Top-5 err = 0.169385,	data_time = 0.050565,	train_time = 0.831336
[2019-08-24 03:07:38,820] TRAIN Iter 190440: lr = 0.182602,	loss = 2.513887,	Top-1 err = 0.380225,	Top-5 err = 0.164404,	data_time = 0.050916,	train_time = 0.778021
[2019-08-24 03:07:46,047] TRAIN Iter 190460: lr = 0.182568,	loss = 2.497503,	Top-1 err = 0.374414,	Top-5 err = 0.166064,	data_time = 0.050506,	train_time = 0.361348
[2019-08-24 03:07:59,438] TRAIN Iter 190480: lr = 0.182535,	loss = 2.600869,	Top-1 err = 0.374170,	Top-5 err = 0.170166,	data_time = 0.050316,	train_time = 0.669511
[2019-08-24 03:08:13,333] TRAIN Iter 190500: lr = 0.182502,	loss = 2.576416,	Top-1 err = 0.374072,	Top-5 err = 0.165967,	data_time = 0.126976,	train_time = 0.694750
[2019-08-24 03:08:21,817] TRAIN Iter 190520: lr = 0.182468,	loss = 2.584916,	Top-1 err = 0.388721,	Top-5 err = 0.173975,	data_time = 0.050291,	train_time = 0.424152
[2019-08-24 03:08:37,479] TRAIN Iter 190540: lr = 0.182435,	loss = 2.552890,	Top-1 err = 0.377637,	Top-5 err = 0.167627,	data_time = 0.050429,	train_time = 0.783099
[2019-08-24 03:08:44,759] TRAIN Iter 190560: lr = 0.182402,	loss = 2.564939,	Top-1 err = 0.379492,	Top-5 err = 0.170508,	data_time = 0.050983,	train_time = 0.363977
[2019-08-24 03:08:59,733] TRAIN Iter 190580: lr = 0.182368,	loss = 2.535049,	Top-1 err = 0.378467,	Top-5 err = 0.166406,	data_time = 0.050495,	train_time = 0.748676
[2019-08-24 03:09:15,249] TRAIN Iter 190600: lr = 0.182335,	loss = 2.540968,	Top-1 err = 0.383105,	Top-5 err = 0.171289,	data_time = 3.467126,	train_time = 0.775789
[2019-08-24 03:09:22,385] TRAIN Iter 190620: lr = 0.182302,	loss = 2.521038,	Top-1 err = 0.381641,	Top-5 err = 0.168359,	data_time = 0.050635,	train_time = 0.356790
[2019-08-24 03:09:38,018] TRAIN Iter 190640: lr = 0.182268,	loss = 2.458582,	Top-1 err = 0.379590,	Top-5 err = 0.166406,	data_time = 0.050638,	train_time = 0.781668
[2019-08-24 03:09:50,149] TRAIN Iter 190660: lr = 0.182235,	loss = 2.641328,	Top-1 err = 0.382324,	Top-5 err = 0.171533,	data_time = 0.137955,	train_time = 0.606494
[2019-08-24 03:09:59,238] TRAIN Iter 190680: lr = 0.182202,	loss = 2.595987,	Top-1 err = 0.385400,	Top-5 err = 0.172021,	data_time = 0.050226,	train_time = 0.454442
[2019-08-24 03:10:14,606] TRAIN Iter 190700: lr = 0.182168,	loss = 2.510689,	Top-1 err = 0.385596,	Top-5 err = 0.171973,	data_time = 0.050406,	train_time = 0.768381
[2019-08-24 03:10:21,835] TRAIN Iter 190720: lr = 0.182135,	loss = 2.635900,	Top-1 err = 0.386523,	Top-5 err = 0.168311,	data_time = 0.050290,	train_time = 0.361457
[2019-08-24 03:10:37,433] TRAIN Iter 190740: lr = 0.182102,	loss = 2.441233,	Top-1 err = 0.389209,	Top-5 err = 0.171191,	data_time = 0.050811,	train_time = 0.779868
[2019-08-24 03:10:52,235] TRAIN Iter 190760: lr = 0.182068,	loss = 2.587902,	Top-1 err = 0.389111,	Top-5 err = 0.174561,	data_time = 2.092669,	train_time = 0.740121
[2019-08-24 03:10:59,464] TRAIN Iter 190780: lr = 0.182035,	loss = 2.561987,	Top-1 err = 0.383887,	Top-5 err = 0.173535,	data_time = 0.050420,	train_time = 0.361398
[2019-08-24 03:11:16,206] TRAIN Iter 190800: lr = 0.182002,	loss = 2.598766,	Top-1 err = 0.382178,	Top-5 err = 0.172949,	data_time = 0.050530,	train_time = 0.837091
[2019-08-24 03:11:27,445] TRAIN Iter 190820: lr = 0.181968,	loss = 2.640738,	Top-1 err = 0.383447,	Top-5 err = 0.170801,	data_time = 0.050533,	train_time = 0.561966
[2019-08-24 03:11:38,290] TRAIN Iter 190840: lr = 0.181935,	loss = 2.601336,	Top-1 err = 0.385596,	Top-5 err = 0.170996,	data_time = 0.115696,	train_time = 0.542251
[2019-08-24 03:11:53,669] TRAIN Iter 190860: lr = 0.181902,	loss = 2.529325,	Top-1 err = 0.383594,	Top-5 err = 0.174316,	data_time = 0.050388,	train_time = 0.768916
[2019-08-24 03:12:00,762] TRAIN Iter 190880: lr = 0.181868,	loss = 2.582637,	Top-1 err = 0.382178,	Top-5 err = 0.173633,	data_time = 0.050418,	train_time = 0.354656
[2019-08-24 03:12:16,418] TRAIN Iter 190900: lr = 0.181835,	loss = 2.551460,	Top-1 err = 0.388281,	Top-5 err = 0.174219,	data_time = 0.050711,	train_time = 0.782749
[2019-08-24 03:12:32,662] TRAIN Iter 190920: lr = 0.181802,	loss = 2.533670,	Top-1 err = 0.384180,	Top-5 err = 0.170117,	data_time = 3.792009,	train_time = 0.812182
[2019-08-24 03:12:39,896] TRAIN Iter 190940: lr = 0.181768,	loss = 2.575588,	Top-1 err = 0.383057,	Top-5 err = 0.173291,	data_time = 0.050813,	train_time = 0.361696
[2019-08-24 03:12:55,285] TRAIN Iter 190960: lr = 0.181735,	loss = 2.605485,	Top-1 err = 0.382715,	Top-5 err = 0.170020,	data_time = 0.050449,	train_time = 0.769432
[2019-08-24 03:13:05,368] TRAIN Iter 190980: lr = 0.181702,	loss = 2.542735,	Top-1 err = 0.378955,	Top-5 err = 0.170703,	data_time = 0.050496,	train_time = 0.504145
[2019-08-24 03:13:18,156] TRAIN Iter 191000: lr = 0.181668,	loss = 2.537509,	Top-1 err = 0.386084,	Top-5 err = 0.172119,	data_time = 0.050855,	train_time = 0.639412
[2019-08-24 03:13:33,102] TRAIN Iter 191020: lr = 0.181635,	loss = 2.651240,	Top-1 err = 0.386719,	Top-5 err = 0.170166,	data_time = 0.050534,	train_time = 0.747258
[2019-08-24 03:13:40,490] TRAIN Iter 191040: lr = 0.181602,	loss = 2.635972,	Top-1 err = 0.391846,	Top-5 err = 0.171436,	data_time = 0.050931,	train_time = 0.369380
[2019-08-24 03:13:56,063] TRAIN Iter 191060: lr = 0.181568,	loss = 2.601481,	Top-1 err = 0.379443,	Top-5 err = 0.170117,	data_time = 0.050338,	train_time = 0.778665
[2019-08-24 03:14:11,643] TRAIN Iter 191080: lr = 0.181535,	loss = 2.495290,	Top-1 err = 0.380078,	Top-5 err = 0.167578,	data_time = 1.794706,	train_time = 0.778949
[2019-08-24 03:14:18,676] TRAIN Iter 191100: lr = 0.181502,	loss = 2.522003,	Top-1 err = 0.388623,	Top-5 err = 0.172900,	data_time = 0.050258,	train_time = 0.351645
[2019-08-24 03:14:33,805] TRAIN Iter 191120: lr = 0.181468,	loss = 2.557076,	Top-1 err = 0.384766,	Top-5 err = 0.174951,	data_time = 0.050484,	train_time = 0.756459
[2019-08-24 03:14:48,355] TRAIN Iter 191140: lr = 0.181435,	loss = 2.507881,	Top-1 err = 0.383594,	Top-5 err = 0.172119,	data_time = 0.097960,	train_time = 0.727467
[2019-08-24 03:14:56,726] TRAIN Iter 191160: lr = 0.181402,	loss = 2.481678,	Top-1 err = 0.382422,	Top-5 err = 0.169727,	data_time = 0.050806,	train_time = 0.418535
[2019-08-24 03:15:11,904] TRAIN Iter 191180: lr = 0.181368,	loss = 2.659268,	Top-1 err = 0.384180,	Top-5 err = 0.169043,	data_time = 0.050297,	train_time = 0.758885
[2019-08-24 03:15:18,843] TRAIN Iter 191200: lr = 0.181335,	loss = 2.547729,	Top-1 err = 0.383984,	Top-5 err = 0.174707,	data_time = 0.050481,	train_time = 0.346918
[2019-08-24 03:15:35,791] TRAIN Iter 191220: lr = 0.181302,	loss = 2.594908,	Top-1 err = 0.380566,	Top-5 err = 0.167383,	data_time = 0.050580,	train_time = 0.847398
[2019-08-24 03:15:51,051] TRAIN Iter 191240: lr = 0.181268,	loss = 2.559329,	Top-1 err = 0.385547,	Top-5 err = 0.169336,	data_time = 0.050433,	train_time = 0.762997
[2019-08-24 03:15:57,974] TRAIN Iter 191260: lr = 0.181235,	loss = 2.601954,	Top-1 err = 0.387500,	Top-5 err = 0.172656,	data_time = 0.050387,	train_time = 0.346147
[2019-08-24 03:16:13,641] TRAIN Iter 191280: lr = 0.181202,	loss = 2.532990,	Top-1 err = 0.382715,	Top-5 err = 0.174609,	data_time = 0.050486,	train_time = 0.783339
[2019-08-24 03:16:29,290] TRAIN Iter 191300: lr = 0.181168,	loss = 2.636158,	Top-1 err = 0.395361,	Top-5 err = 0.175195,	data_time = 0.050429,	train_time = 0.782435
[2019-08-24 03:16:36,891] TRAIN Iter 191320: lr = 0.181135,	loss = 2.488513,	Top-1 err = 0.383984,	Top-5 err = 0.176270,	data_time = 0.050402,	train_time = 0.380042
[2019-08-24 03:16:54,451] TRAIN Iter 191340: lr = 0.181102,	loss = 2.481062,	Top-1 err = 0.382764,	Top-5 err = 0.174316,	data_time = 0.050305,	train_time = 0.877942
[2019-08-24 03:17:01,400] TRAIN Iter 191360: lr = 0.181068,	loss = 2.555702,	Top-1 err = 0.388672,	Top-5 err = 0.169092,	data_time = 0.050681,	train_time = 0.347435
[2019-08-24 03:17:17,670] TRAIN Iter 191380: lr = 0.181035,	loss = 2.533601,	Top-1 err = 0.387646,	Top-5 err = 0.172754,	data_time = 0.050442,	train_time = 0.813487
[2019-08-24 03:17:36,161] TRAIN Iter 191400: lr = 0.181002,	loss = 2.536649,	Top-1 err = 0.385156,	Top-5 err = 0.173535,	data_time = 0.895278,	train_time = 0.924547
[2019-08-24 03:17:43,235] TRAIN Iter 191420: lr = 0.180968,	loss = 2.603411,	Top-1 err = 0.387061,	Top-5 err = 0.175342,	data_time = 0.050342,	train_time = 0.353712
[2019-08-24 03:18:00,655] TRAIN Iter 191440: lr = 0.180935,	loss = 2.579681,	Top-1 err = 0.387012,	Top-5 err = 0.169678,	data_time = 0.050042,	train_time = 0.870956
[2019-08-24 03:18:16,877] TRAIN Iter 191460: lr = 0.180902,	loss = 2.600180,	Top-1 err = 0.387891,	Top-5 err = 0.173633,	data_time = 0.049952,	train_time = 0.811092
[2019-08-24 03:18:23,411] TRAIN Iter 191480: lr = 0.180868,	loss = 2.549037,	Top-1 err = 0.389355,	Top-5 err = 0.174414,	data_time = 0.049930,	train_time = 0.326680
[2019-08-24 03:19:13,006] TRAIN Iter 191500: lr = 0.180835,	loss = 2.599025,	Top-1 err = 0.383983,	Top-5 err = 0.173673,	data_time = 0.050389,	train_time = 2.479780
[2019-08-24 03:19:20,625] TRAIN Iter 191520: lr = 0.180802,	loss = 2.453998,	Top-1 err = 0.375146,	Top-5 err = 0.166992,	data_time = 0.050627,	train_time = 0.380912
[2019-08-24 03:19:34,992] TRAIN Iter 191540: lr = 0.180768,	loss = 2.452647,	Top-1 err = 0.383105,	Top-5 err = 0.166113,	data_time = 0.050498,	train_time = 0.718350
[2019-08-24 03:19:47,049] TRAIN Iter 191560: lr = 0.180735,	loss = 2.476186,	Top-1 err = 0.370752,	Top-5 err = 0.165479,	data_time = 1.809327,	train_time = 0.602787
[2019-08-24 03:19:54,253] TRAIN Iter 191580: lr = 0.180702,	loss = 2.629225,	Top-1 err = 0.373926,	Top-5 err = 0.167529,	data_time = 0.050355,	train_time = 0.360223
[2019-08-24 03:20:09,890] TRAIN Iter 191600: lr = 0.180668,	loss = 2.498099,	Top-1 err = 0.378271,	Top-5 err = 0.164551,	data_time = 0.050482,	train_time = 0.781814
[2019-08-24 03:20:17,397] TRAIN Iter 191620: lr = 0.180635,	loss = 2.531518,	Top-1 err = 0.378760,	Top-5 err = 0.166357,	data_time = 0.050737,	train_time = 0.375324
[2019-08-24 03:20:31,437] TRAIN Iter 191640: lr = 0.180602,	loss = 2.628159,	Top-1 err = 0.377393,	Top-5 err = 0.170215,	data_time = 0.050479,	train_time = 0.702022
[2019-08-24 03:20:48,431] TRAIN Iter 191660: lr = 0.180568,	loss = 2.455811,	Top-1 err = 0.376953,	Top-5 err = 0.164209,	data_time = 0.050545,	train_time = 0.849660
[2019-08-24 03:20:55,760] TRAIN Iter 191680: lr = 0.180535,	loss = 2.579990,	Top-1 err = 0.379736,	Top-5 err = 0.168994,	data_time = 0.050412,	train_time = 0.366420
[2019-08-24 03:21:09,316] TRAIN Iter 191700: lr = 0.180502,	loss = 2.541817,	Top-1 err = 0.378662,	Top-5 err = 0.164600,	data_time = 0.050433,	train_time = 0.677820
[2019-08-24 03:21:24,616] TRAIN Iter 191720: lr = 0.180468,	loss = 2.544144,	Top-1 err = 0.375049,	Top-5 err = 0.162891,	data_time = 4.190556,	train_time = 0.764952
[2019-08-24 03:21:32,021] TRAIN Iter 191740: lr = 0.180435,	loss = 2.527605,	Top-1 err = 0.380957,	Top-5 err = 0.169043,	data_time = 0.050325,	train_time = 0.370265
[2019-08-24 03:21:46,135] TRAIN Iter 191760: lr = 0.180402,	loss = 2.556755,	Top-1 err = 0.377539,	Top-5 err = 0.168457,	data_time = 0.050587,	train_time = 0.705661
[2019-08-24 03:21:53,668] TRAIN Iter 191780: lr = 0.180368,	loss = 2.482810,	Top-1 err = 0.380127,	Top-5 err = 0.166162,	data_time = 0.050570,	train_time = 0.376665
[2019-08-24 03:22:08,135] TRAIN Iter 191800: lr = 0.180335,	loss = 2.577578,	Top-1 err = 0.381006,	Top-5 err = 0.169678,	data_time = 0.050448,	train_time = 0.723343
[2019-08-24 03:22:24,290] TRAIN Iter 191820: lr = 0.180302,	loss = 2.526547,	Top-1 err = 0.387549,	Top-5 err = 0.170361,	data_time = 0.050637,	train_time = 0.807711
[2019-08-24 03:22:31,918] TRAIN Iter 191840: lr = 0.180268,	loss = 2.524591,	Top-1 err = 0.382666,	Top-5 err = 0.171631,	data_time = 0.050250,	train_time = 0.381379
[2019-08-24 03:22:45,391] TRAIN Iter 191860: lr = 0.180235,	loss = 2.551116,	Top-1 err = 0.383740,	Top-5 err = 0.167725,	data_time = 0.050465,	train_time = 0.673642
[2019-08-24 03:23:01,415] TRAIN Iter 191880: lr = 0.180202,	loss = 2.598086,	Top-1 err = 0.380273,	Top-5 err = 0.167822,	data_time = 4.519185,	train_time = 0.801191
[2019-08-24 03:23:08,500] TRAIN Iter 191900: lr = 0.180168,	loss = 2.632129,	Top-1 err = 0.382080,	Top-5 err = 0.169775,	data_time = 0.123138,	train_time = 0.354219
[2019-08-24 03:23:23,217] TRAIN Iter 191920: lr = 0.180135,	loss = 2.467577,	Top-1 err = 0.381836,	Top-5 err = 0.169385,	data_time = 0.050555,	train_time = 0.735843
[2019-08-24 03:23:30,910] TRAIN Iter 191940: lr = 0.180102,	loss = 2.583972,	Top-1 err = 0.377051,	Top-5 err = 0.166650,	data_time = 0.050808,	train_time = 0.384657
[2019-08-24 03:23:45,192] TRAIN Iter 191960: lr = 0.180068,	loss = 2.558785,	Top-1 err = 0.379639,	Top-5 err = 0.169873,	data_time = 0.050826,	train_time = 0.714046
[2019-08-24 03:24:00,735] TRAIN Iter 191980: lr = 0.180035,	loss = 2.507242,	Top-1 err = 0.377197,	Top-5 err = 0.166748,	data_time = 0.050413,	train_time = 0.777162
[2019-08-24 03:24:08,214] TRAIN Iter 192000: lr = 0.180002,	loss = 2.527927,	Top-1 err = 0.384326,	Top-5 err = 0.171045,	data_time = 0.050856,	train_time = 0.373915
[2019-08-24 03:24:23,416] TRAIN Iter 192020: lr = 0.179968,	loss = 2.451437,	Top-1 err = 0.377734,	Top-5 err = 0.165625,	data_time = 0.051124,	train_time = 0.760104
[2019-08-24 03:24:37,139] TRAIN Iter 192040: lr = 0.179935,	loss = 2.553288,	Top-1 err = 0.376416,	Top-5 err = 0.167773,	data_time = 1.449797,	train_time = 0.686138
[2019-08-24 03:24:44,730] TRAIN Iter 192060: lr = 0.179902,	loss = 2.559457,	Top-1 err = 0.386621,	Top-5 err = 0.172998,	data_time = 0.050588,	train_time = 0.379536
[2019-08-24 03:25:00,031] TRAIN Iter 192080: lr = 0.179868,	loss = 2.563344,	Top-1 err = 0.380225,	Top-5 err = 0.170215,	data_time = 0.050552,	train_time = 0.765039
[2019-08-24 03:25:06,823] TRAIN Iter 192100: lr = 0.179835,	loss = 2.565885,	Top-1 err = 0.386279,	Top-5 err = 0.171484,	data_time = 0.111299,	train_time = 0.339571
[2019-08-24 03:25:23,039] TRAIN Iter 192120: lr = 0.179802,	loss = 2.546120,	Top-1 err = 0.380273,	Top-5 err = 0.169775,	data_time = 0.050496,	train_time = 0.810806
[2019-08-24 03:25:38,139] TRAIN Iter 192140: lr = 0.179768,	loss = 2.588407,	Top-1 err = 0.386084,	Top-5 err = 0.176465,	data_time = 0.050716,	train_time = 0.754988
[2019-08-24 03:25:45,045] TRAIN Iter 192160: lr = 0.179735,	loss = 2.530663,	Top-1 err = 0.384570,	Top-5 err = 0.170557,	data_time = 0.050369,	train_time = 0.345268
[2019-08-24 03:26:02,252] TRAIN Iter 192180: lr = 0.179702,	loss = 2.602744,	Top-1 err = 0.385400,	Top-5 err = 0.170996,	data_time = 0.050520,	train_time = 0.860356
[2019-08-24 03:26:17,097] TRAIN Iter 192200: lr = 0.179668,	loss = 2.550964,	Top-1 err = 0.383984,	Top-5 err = 0.170996,	data_time = 0.149572,	train_time = 0.742237
[2019-08-24 03:26:25,065] TRAIN Iter 192220: lr = 0.179635,	loss = 2.564339,	Top-1 err = 0.386230,	Top-5 err = 0.174219,	data_time = 0.050539,	train_time = 0.398385
[2019-08-24 03:26:42,069] TRAIN Iter 192240: lr = 0.179602,	loss = 2.530227,	Top-1 err = 0.383545,	Top-5 err = 0.168750,	data_time = 0.050453,	train_time = 0.850143
[2019-08-24 03:26:48,925] TRAIN Iter 192260: lr = 0.179568,	loss = 2.507519,	Top-1 err = 0.385498,	Top-5 err = 0.172754,	data_time = 0.050354,	train_time = 0.342830
[2019-08-24 03:27:05,032] TRAIN Iter 192280: lr = 0.179535,	loss = 2.479092,	Top-1 err = 0.382031,	Top-5 err = 0.172510,	data_time = 0.050530,	train_time = 0.805292
[2019-08-24 03:27:21,769] TRAIN Iter 192300: lr = 0.179502,	loss = 2.534744,	Top-1 err = 0.384131,	Top-5 err = 0.170996,	data_time = 0.050388,	train_time = 0.836870
[2019-08-24 03:27:28,574] TRAIN Iter 192320: lr = 0.179468,	loss = 2.576432,	Top-1 err = 0.384668,	Top-5 err = 0.172949,	data_time = 0.051867,	train_time = 0.340200
[2019-08-24 03:27:44,766] TRAIN Iter 192340: lr = 0.179435,	loss = 2.579449,	Top-1 err = 0.381787,	Top-5 err = 0.167676,	data_time = 0.050494,	train_time = 0.809627
[2019-08-24 03:28:01,920] TRAIN Iter 192360: lr = 0.179402,	loss = 2.531926,	Top-1 err = 0.384521,	Top-5 err = 0.174268,	data_time = 3.625631,	train_time = 0.857675
[2019-08-24 03:28:09,111] TRAIN Iter 192380: lr = 0.179368,	loss = 2.578184,	Top-1 err = 0.385449,	Top-5 err = 0.172363,	data_time = 0.050648,	train_time = 0.359545
[2019-08-24 03:28:26,678] TRAIN Iter 192400: lr = 0.179335,	loss = 2.483621,	Top-1 err = 0.379395,	Top-5 err = 0.171338,	data_time = 0.050385,	train_time = 0.878309
[2019-08-24 03:28:34,568] TRAIN Iter 192420: lr = 0.179302,	loss = 2.527750,	Top-1 err = 0.374219,	Top-5 err = 0.168115,	data_time = 0.050511,	train_time = 0.394474
[2019-08-24 03:28:49,437] TRAIN Iter 192440: lr = 0.179268,	loss = 2.562595,	Top-1 err = 0.377783,	Top-5 err = 0.169580,	data_time = 0.050442,	train_time = 0.743444
[2019-08-24 03:29:05,632] TRAIN Iter 192460: lr = 0.179235,	loss = 2.625489,	Top-1 err = 0.383154,	Top-5 err = 0.169971,	data_time = 0.050367,	train_time = 0.809751
[2019-08-24 03:29:12,653] TRAIN Iter 192480: lr = 0.179202,	loss = 2.533685,	Top-1 err = 0.383545,	Top-5 err = 0.171582,	data_time = 0.050401,	train_time = 0.351054
[2019-08-24 03:29:28,715] TRAIN Iter 192500: lr = 0.179168,	loss = 2.595267,	Top-1 err = 0.380469,	Top-5 err = 0.168604,	data_time = 0.050688,	train_time = 0.803048
[2019-08-24 03:29:45,688] TRAIN Iter 192520: lr = 0.179135,	loss = 2.519396,	Top-1 err = 0.381250,	Top-5 err = 0.168652,	data_time = 2.728277,	train_time = 0.848650
[2019-08-24 03:29:52,590] TRAIN Iter 192540: lr = 0.179102,	loss = 2.618027,	Top-1 err = 0.382959,	Top-5 err = 0.169971,	data_time = 0.050348,	train_time = 0.345067
[2019-08-24 03:30:09,060] TRAIN Iter 192560: lr = 0.179068,	loss = 2.564409,	Top-1 err = 0.388184,	Top-5 err = 0.173584,	data_time = 0.050534,	train_time = 0.823520
[2019-08-24 03:30:15,875] TRAIN Iter 192580: lr = 0.179035,	loss = 2.577240,	Top-1 err = 0.393408,	Top-5 err = 0.176318,	data_time = 0.050750,	train_time = 0.340722
[2019-08-24 03:30:32,635] TRAIN Iter 192600: lr = 0.179002,	loss = 2.523692,	Top-1 err = 0.384814,	Top-5 err = 0.172070,	data_time = 0.050640,	train_time = 0.837988
[2019-08-24 03:30:49,661] TRAIN Iter 192620: lr = 0.178968,	loss = 2.535556,	Top-1 err = 0.393115,	Top-5 err = 0.175830,	data_time = 0.050421,	train_time = 0.851305
[2019-08-24 03:30:56,345] TRAIN Iter 192640: lr = 0.178935,	loss = 2.560542,	Top-1 err = 0.381836,	Top-5 err = 0.170850,	data_time = 0.050467,	train_time = 0.334146
[2019-08-24 03:31:15,485] TRAIN Iter 192660: lr = 0.178902,	loss = 2.434064,	Top-1 err = 0.387061,	Top-5 err = 0.172949,	data_time = 0.050762,	train_time = 0.957016
[2019-08-24 03:31:34,012] TRAIN Iter 192680: lr = 0.178868,	loss = 2.512879,	Top-1 err = 0.378076,	Top-5 err = 0.165918,	data_time = 0.063242,	train_time = 0.926341
[2019-08-24 03:31:40,504] TRAIN Iter 192700: lr = 0.178835,	loss = 2.645725,	Top-1 err = 0.394922,	Top-5 err = 0.176465,	data_time = 0.049829,	train_time = 0.324586
[2019-08-24 03:31:57,298] TRAIN Iter 192720: lr = 0.178802,	loss = 2.562344,	Top-1 err = 0.379346,	Top-5 err = 0.169971,	data_time = 0.049929,	train_time = 0.839667
[2019-08-24 03:32:03,349] TRAIN Iter 192740: lr = 0.178768,	loss = 2.663552,	Top-1 err = 0.390479,	Top-5 err = 0.172607,	data_time = 0.049899,	train_time = 0.302563
[2019-08-24 03:32:51,767] TRAIN Iter 192760: lr = 0.178735,	loss = 2.587041,	Top-1 err = 0.383079,	Top-5 err = 0.170995,	data_time = 0.050304,	train_time = 2.420843
[2019-08-24 03:33:06,631] TRAIN Iter 192780: lr = 0.178702,	loss = 2.527323,	Top-1 err = 0.377148,	Top-5 err = 0.168066,	data_time = 0.050279,	train_time = 0.743194
[2019-08-24 03:33:15,102] TRAIN Iter 192800: lr = 0.178668,	loss = 2.467169,	Top-1 err = 0.375049,	Top-5 err = 0.165771,	data_time = 0.050611,	train_time = 0.423568
[2019-08-24 03:33:32,745] TRAIN Iter 192820: lr = 0.178635,	loss = 2.553879,	Top-1 err = 0.375195,	Top-5 err = 0.166357,	data_time = 0.050422,	train_time = 0.882090
[2019-08-24 03:33:40,905] TRAIN Iter 192840: lr = 0.178602,	loss = 2.574240,	Top-1 err = 0.369824,	Top-5 err = 0.162207,	data_time = 0.050596,	train_time = 0.407995
[2019-08-24 03:33:54,118] TRAIN Iter 192860: lr = 0.178568,	loss = 2.607463,	Top-1 err = 0.384961,	Top-5 err = 0.169824,	data_time = 0.050544,	train_time = 0.660654
[2019-08-24 03:34:06,703] TRAIN Iter 192880: lr = 0.178535,	loss = 2.556523,	Top-1 err = 0.377148,	Top-5 err = 0.165820,	data_time = 0.050445,	train_time = 0.629238
[2019-08-24 03:34:14,309] TRAIN Iter 192900: lr = 0.178502,	loss = 2.509864,	Top-1 err = 0.375244,	Top-5 err = 0.167773,	data_time = 0.050533,	train_time = 0.380306
[2019-08-24 03:34:29,759] TRAIN Iter 192920: lr = 0.178468,	loss = 2.569672,	Top-1 err = 0.377734,	Top-5 err = 0.168066,	data_time = 0.050602,	train_time = 0.772472
[2019-08-24 03:34:45,815] TRAIN Iter 192940: lr = 0.178435,	loss = 2.581417,	Top-1 err = 0.377295,	Top-5 err = 0.166504,	data_time = 0.050556,	train_time = 0.802762
[2019-08-24 03:34:53,234] TRAIN Iter 192960: lr = 0.178402,	loss = 2.604965,	Top-1 err = 0.384375,	Top-5 err = 0.171533,	data_time = 0.050277,	train_time = 0.370971
[2019-08-24 03:35:06,864] TRAIN Iter 192980: lr = 0.178368,	loss = 2.519734,	Top-1 err = 0.380762,	Top-5 err = 0.168213,	data_time = 0.050237,	train_time = 0.681458
[2019-08-24 03:35:14,363] TRAIN Iter 193000: lr = 0.178335,	loss = 2.505750,	Top-1 err = 0.380029,	Top-5 err = 0.167090,	data_time = 0.050438,	train_time = 0.374956
[2019-08-24 03:35:29,228] TRAIN Iter 193020: lr = 0.178302,	loss = 2.547374,	Top-1 err = 0.382471,	Top-5 err = 0.170752,	data_time = 0.050429,	train_time = 0.743242
[2019-08-24 03:35:44,502] TRAIN Iter 193040: lr = 0.178268,	loss = 2.512843,	Top-1 err = 0.378125,	Top-5 err = 0.164746,	data_time = 0.050830,	train_time = 0.763648
[2019-08-24 03:35:51,573] TRAIN Iter 193060: lr = 0.178235,	loss = 2.517537,	Top-1 err = 0.387598,	Top-5 err = 0.170947,	data_time = 0.050532,	train_time = 0.353542
[2019-08-24 03:36:05,896] TRAIN Iter 193080: lr = 0.178202,	loss = 2.682859,	Top-1 err = 0.381836,	Top-5 err = 0.168896,	data_time = 0.050962,	train_time = 0.716134
[2019-08-24 03:36:22,144] TRAIN Iter 193100: lr = 0.178168,	loss = 2.595862,	Top-1 err = 0.377588,	Top-5 err = 0.163086,	data_time = 0.051158,	train_time = 0.812392
[2019-08-24 03:36:29,037] TRAIN Iter 193120: lr = 0.178135,	loss = 2.518161,	Top-1 err = 0.375146,	Top-5 err = 0.167676,	data_time = 0.050733,	train_time = 0.344671
[2019-08-24 03:36:44,448] TRAIN Iter 193140: lr = 0.178102,	loss = 2.648803,	Top-1 err = 0.383887,	Top-5 err = 0.171094,	data_time = 0.050810,	train_time = 0.770493
[2019-08-24 03:36:52,144] TRAIN Iter 193160: lr = 0.178068,	loss = 2.572920,	Top-1 err = 0.377344,	Top-5 err = 0.163037,	data_time = 0.050357,	train_time = 0.384825
[2019-08-24 03:37:05,572] TRAIN Iter 193180: lr = 0.178035,	loss = 2.551035,	Top-1 err = 0.382324,	Top-5 err = 0.173730,	data_time = 0.050800,	train_time = 0.671376
[2019-08-24 03:37:21,401] TRAIN Iter 193200: lr = 0.178002,	loss = 2.538616,	Top-1 err = 0.378174,	Top-5 err = 0.167725,	data_time = 0.050287,	train_time = 0.791443
[2019-08-24 03:37:28,689] TRAIN Iter 193220: lr = 0.177968,	loss = 2.477550,	Top-1 err = 0.379004,	Top-5 err = 0.166504,	data_time = 0.050266,	train_time = 0.364353
[2019-08-24 03:37:43,658] TRAIN Iter 193240: lr = 0.177935,	loss = 2.652371,	Top-1 err = 0.381982,	Top-5 err = 0.168945,	data_time = 0.050605,	train_time = 0.748470
[2019-08-24 03:37:58,383] TRAIN Iter 193260: lr = 0.177902,	loss = 2.515210,	Top-1 err = 0.385303,	Top-5 err = 0.174561,	data_time = 0.050302,	train_time = 0.736214
[2019-08-24 03:38:05,298] TRAIN Iter 193280: lr = 0.177868,	loss = 2.569291,	Top-1 err = 0.389600,	Top-5 err = 0.172119,	data_time = 0.050203,	train_time = 0.345720
[2019-08-24 03:38:21,355] TRAIN Iter 193300: lr = 0.177835,	loss = 2.503511,	Top-1 err = 0.384424,	Top-5 err = 0.168115,	data_time = 0.050467,	train_time = 0.802875
[2019-08-24 03:38:28,947] TRAIN Iter 193320: lr = 0.177802,	loss = 2.541066,	Top-1 err = 0.383496,	Top-5 err = 0.166895,	data_time = 0.050662,	train_time = 0.379578
[2019-08-24 03:38:45,432] TRAIN Iter 193340: lr = 0.177768,	loss = 2.555907,	Top-1 err = 0.383789,	Top-5 err = 0.173047,	data_time = 0.050608,	train_time = 0.824248
[2019-08-24 03:39:01,180] TRAIN Iter 193360: lr = 0.177735,	loss = 2.569419,	Top-1 err = 0.387891,	Top-5 err = 0.173242,	data_time = 0.050446,	train_time = 0.787373
[2019-08-24 03:39:08,726] TRAIN Iter 193380: lr = 0.177702,	loss = 2.481099,	Top-1 err = 0.381494,	Top-5 err = 0.164697,	data_time = 0.050582,	train_time = 0.377301
[2019-08-24 03:39:24,114] TRAIN Iter 193400: lr = 0.177668,	loss = 2.490445,	Top-1 err = 0.387598,	Top-5 err = 0.173682,	data_time = 0.050877,	train_time = 0.769383
[2019-08-24 03:39:40,815] TRAIN Iter 193420: lr = 0.177635,	loss = 2.621139,	Top-1 err = 0.382324,	Top-5 err = 0.167920,	data_time = 0.050532,	train_time = 0.835031
[2019-08-24 03:39:48,086] TRAIN Iter 193440: lr = 0.177602,	loss = 2.636540,	Top-1 err = 0.384863,	Top-5 err = 0.173242,	data_time = 0.050917,	train_time = 0.363520
[2019-08-24 03:40:03,183] TRAIN Iter 193460: lr = 0.177568,	loss = 2.523359,	Top-1 err = 0.378125,	Top-5 err = 0.170459,	data_time = 0.050328,	train_time = 0.754855
[2019-08-24 03:40:11,101] TRAIN Iter 193480: lr = 0.177535,	loss = 2.529845,	Top-1 err = 0.382959,	Top-5 err = 0.172119,	data_time = 0.123573,	train_time = 0.395883
[2019-08-24 03:40:26,636] TRAIN Iter 193500: lr = 0.177502,	loss = 2.609032,	Top-1 err = 0.380859,	Top-5 err = 0.168262,	data_time = 0.050461,	train_time = 0.776738
[2019-08-24 03:40:42,486] TRAIN Iter 193520: lr = 0.177468,	loss = 2.480510,	Top-1 err = 0.386133,	Top-5 err = 0.172363,	data_time = 0.050905,	train_time = 0.792436
[2019-08-24 03:40:49,658] TRAIN Iter 193540: lr = 0.177435,	loss = 2.604626,	Top-1 err = 0.381982,	Top-5 err = 0.171680,	data_time = 0.050771,	train_time = 0.358610
[2019-08-24 03:41:05,037] TRAIN Iter 193560: lr = 0.177402,	loss = 2.546788,	Top-1 err = 0.387207,	Top-5 err = 0.171680,	data_time = 0.050468,	train_time = 0.768960
[2019-08-24 03:41:21,588] TRAIN Iter 193580: lr = 0.177368,	loss = 2.566317,	Top-1 err = 0.381299,	Top-5 err = 0.169092,	data_time = 0.050438,	train_time = 0.827502
[2019-08-24 03:41:28,679] TRAIN Iter 193600: lr = 0.177335,	loss = 2.584236,	Top-1 err = 0.381641,	Top-5 err = 0.168311,	data_time = 0.050545,	train_time = 0.354529
[2019-08-24 03:41:44,757] TRAIN Iter 193620: lr = 0.177302,	loss = 2.537162,	Top-1 err = 0.383984,	Top-5 err = 0.170264,	data_time = 0.050405,	train_time = 0.803892
[2019-08-24 03:41:52,091] TRAIN Iter 193640: lr = 0.177268,	loss = 2.506168,	Top-1 err = 0.386084,	Top-5 err = 0.174512,	data_time = 0.139738,	train_time = 0.366682
[2019-08-24 03:42:08,993] TRAIN Iter 193660: lr = 0.177235,	loss = 2.544848,	Top-1 err = 0.376660,	Top-5 err = 0.172314,	data_time = 0.117310,	train_time = 0.845100
[2019-08-24 03:42:26,392] TRAIN Iter 193680: lr = 0.177202,	loss = 2.480856,	Top-1 err = 0.382910,	Top-5 err = 0.170166,	data_time = 0.050403,	train_time = 0.869949
[2019-08-24 03:42:33,755] TRAIN Iter 193700: lr = 0.177168,	loss = 2.681653,	Top-1 err = 0.379443,	Top-5 err = 0.173096,	data_time = 0.050827,	train_time = 0.368110
[2019-08-24 03:42:49,166] TRAIN Iter 193720: lr = 0.177135,	loss = 2.557429,	Top-1 err = 0.384912,	Top-5 err = 0.172314,	data_time = 0.050508,	train_time = 0.770566
[2019-08-24 03:43:06,045] TRAIN Iter 193740: lr = 0.177102,	loss = 2.500619,	Top-1 err = 0.390576,	Top-5 err = 0.172559,	data_time = 0.050488,	train_time = 0.843930
[2019-08-24 03:43:12,939] TRAIN Iter 193760: lr = 0.177068,	loss = 2.526529,	Top-1 err = 0.388037,	Top-5 err = 0.170654,	data_time = 0.050754,	train_time = 0.344654
[2019-08-24 03:43:30,289] TRAIN Iter 193780: lr = 0.177035,	loss = 2.596847,	Top-1 err = 0.386475,	Top-5 err = 0.175684,	data_time = 0.050611,	train_time = 0.867498
[2019-08-24 03:43:37,597] TRAIN Iter 193800: lr = 0.177002,	loss = 2.503831,	Top-1 err = 0.385889,	Top-5 err = 0.174756,	data_time = 0.050471,	train_time = 0.365376
[2019-08-24 03:43:55,238] TRAIN Iter 193820: lr = 0.176968,	loss = 2.533856,	Top-1 err = 0.385449,	Top-5 err = 0.171631,	data_time = 0.050709,	train_time = 0.882060
[2019-08-24 03:44:12,858] TRAIN Iter 193840: lr = 0.176935,	loss = 2.547419,	Top-1 err = 0.385156,	Top-5 err = 0.167773,	data_time = 0.050367,	train_time = 0.880965
[2019-08-24 03:44:20,396] TRAIN Iter 193860: lr = 0.176902,	loss = 2.597028,	Top-1 err = 0.394922,	Top-5 err = 0.174756,	data_time = 0.146770,	train_time = 0.376915
[2019-08-24 03:44:38,169] TRAIN Iter 193880: lr = 0.176868,	loss = 2.536667,	Top-1 err = 0.384912,	Top-5 err = 0.173926,	data_time = 0.050492,	train_time = 0.888652
[2019-08-24 03:44:56,276] TRAIN Iter 193900: lr = 0.176835,	loss = 2.478122,	Top-1 err = 0.382324,	Top-5 err = 0.172949,	data_time = 0.050425,	train_time = 0.905316
[2019-08-24 03:45:02,936] TRAIN Iter 193920: lr = 0.176802,	loss = 2.640934,	Top-1 err = 0.383350,	Top-5 err = 0.172266,	data_time = 0.050568,	train_time = 0.332971
[2019-08-24 03:45:21,602] TRAIN Iter 193940: lr = 0.176768,	loss = 2.552042,	Top-1 err = 0.386377,	Top-5 err = 0.172461,	data_time = 0.050023,	train_time = 0.933291
[2019-08-24 03:45:28,618] TRAIN Iter 193960: lr = 0.176735,	loss = 2.529322,	Top-1 err = 0.382764,	Top-5 err = 0.170947,	data_time = 0.049931,	train_time = 0.350768
[2019-08-24 03:45:45,212] TRAIN Iter 193980: lr = 0.176702,	loss = 2.654464,	Top-1 err = 0.386426,	Top-5 err = 0.172217,	data_time = 0.049929,	train_time = 0.829698
[2019-08-24 03:46:35,580] TRAIN Iter 194000: lr = 0.176668,	loss = 2.615999,	Top-1 err = 0.386594,	Top-5 err = 0.171142,	data_time = 0.050781,	train_time = 2.518379
[2019-08-24 03:46:42,420] TRAIN Iter 194020: lr = 0.176635,	loss = 2.541531,	Top-1 err = 0.382471,	Top-5 err = 0.167822,	data_time = 0.050547,	train_time = 0.341972
[2019-08-24 03:46:58,985] TRAIN Iter 194040: lr = 0.176602,	loss = 2.387538,	Top-1 err = 0.370312,	Top-5 err = 0.165137,	data_time = 0.050612,	train_time = 0.828253
[2019-08-24 03:47:06,356] TRAIN Iter 194060: lr = 0.176568,	loss = 2.477753,	Top-1 err = 0.376953,	Top-5 err = 0.164844,	data_time = 0.050643,	train_time = 0.368563
[2019-08-24 03:47:20,079] TRAIN Iter 194080: lr = 0.176535,	loss = 2.550993,	Top-1 err = 0.375684,	Top-5 err = 0.166553,	data_time = 0.050547,	train_time = 0.686110
[2019-08-24 03:47:32,766] TRAIN Iter 194100: lr = 0.176502,	loss = 2.600668,	Top-1 err = 0.382373,	Top-5 err = 0.171729,	data_time = 0.050454,	train_time = 0.634333
[2019-08-24 03:47:40,223] TRAIN Iter 194120: lr = 0.176468,	loss = 2.474497,	Top-1 err = 0.379004,	Top-5 err = 0.169043,	data_time = 0.050376,	train_time = 0.372863
[2019-08-24 03:47:59,203] TRAIN Iter 194140: lr = 0.176435,	loss = 2.559110,	Top-1 err = 0.372754,	Top-5 err = 0.163184,	data_time = 0.050442,	train_time = 0.948947
[2019-08-24 03:48:12,982] TRAIN Iter 194160: lr = 0.176402,	loss = 2.579753,	Top-1 err = 0.373926,	Top-5 err = 0.164502,	data_time = 0.050966,	train_time = 0.688968
[2019-08-24 03:48:19,896] TRAIN Iter 194180: lr = 0.176368,	loss = 2.600124,	Top-1 err = 0.377637,	Top-5 err = 0.168066,	data_time = 0.050366,	train_time = 0.345674
[2019-08-24 03:48:36,210] TRAIN Iter 194200: lr = 0.176335,	loss = 2.509494,	Top-1 err = 0.372852,	Top-5 err = 0.165039,	data_time = 0.050355,	train_time = 0.815697
[2019-08-24 03:48:43,489] TRAIN Iter 194220: lr = 0.176302,	loss = 2.594048,	Top-1 err = 0.382129,	Top-5 err = 0.170313,	data_time = 0.050662,	train_time = 0.363926
[2019-08-24 03:48:58,188] TRAIN Iter 194240: lr = 0.176268,	loss = 2.530670,	Top-1 err = 0.380615,	Top-5 err = 0.169336,	data_time = 0.050523,	train_time = 0.734923
[2019-08-24 03:49:14,618] TRAIN Iter 194260: lr = 0.176235,	loss = 2.548132,	Top-1 err = 0.377588,	Top-5 err = 0.164307,	data_time = 0.050868,	train_time = 0.821510
[2019-08-24 03:49:21,862] TRAIN Iter 194280: lr = 0.176202,	loss = 2.618057,	Top-1 err = 0.383301,	Top-5 err = 0.168750,	data_time = 0.050270,	train_time = 0.362149
[2019-08-24 03:49:36,312] TRAIN Iter 194300: lr = 0.176168,	loss = 2.605386,	Top-1 err = 0.375000,	Top-5 err = 0.164941,	data_time = 0.050473,	train_time = 0.722485
[2019-08-24 03:49:51,148] TRAIN Iter 194320: lr = 0.176135,	loss = 2.495810,	Top-1 err = 0.376172,	Top-5 err = 0.164258,	data_time = 5.671814,	train_time = 0.741822
[2019-08-24 03:49:58,193] TRAIN Iter 194340: lr = 0.176102,	loss = 2.518083,	Top-1 err = 0.381787,	Top-5 err = 0.171777,	data_time = 0.050311,	train_time = 0.352200
[2019-08-24 03:50:13,827] TRAIN Iter 194360: lr = 0.176068,	loss = 2.454083,	Top-1 err = 0.382666,	Top-5 err = 0.170068,	data_time = 0.050311,	train_time = 0.781718
[2019-08-24 03:50:21,304] TRAIN Iter 194380: lr = 0.176035,	loss = 2.606812,	Top-1 err = 0.379053,	Top-5 err = 0.169678,	data_time = 0.050407,	train_time = 0.373819
[2019-08-24 03:50:36,715] TRAIN Iter 194400: lr = 0.176002,	loss = 2.553864,	Top-1 err = 0.387061,	Top-5 err = 0.172803,	data_time = 0.050268,	train_time = 0.770553
[2019-08-24 03:50:52,261] TRAIN Iter 194420: lr = 0.175968,	loss = 2.555635,	Top-1 err = 0.379541,	Top-5 err = 0.170215,	data_time = 0.050517,	train_time = 0.777274
[2019-08-24 03:50:59,257] TRAIN Iter 194440: lr = 0.175935,	loss = 2.553603,	Top-1 err = 0.379395,	Top-5 err = 0.173437,	data_time = 0.133313,	train_time = 0.349776
[2019-08-24 03:51:14,660] TRAIN Iter 194460: lr = 0.175902,	loss = 2.567522,	Top-1 err = 0.380127,	Top-5 err = 0.168457,	data_time = 0.050665,	train_time = 0.770147
[2019-08-24 03:51:30,929] TRAIN Iter 194480: lr = 0.175868,	loss = 2.605082,	Top-1 err = 0.385937,	Top-5 err = 0.173633,	data_time = 4.721203,	train_time = 0.813450
[2019-08-24 03:51:37,989] TRAIN Iter 194500: lr = 0.175835,	loss = 2.498457,	Top-1 err = 0.380371,	Top-5 err = 0.166211,	data_time = 0.050206,	train_time = 0.352964
[2019-08-24 03:51:53,301] TRAIN Iter 194520: lr = 0.175802,	loss = 2.595812,	Top-1 err = 0.382520,	Top-5 err = 0.167139,	data_time = 0.050462,	train_time = 0.765592
[2019-08-24 03:52:00,441] TRAIN Iter 194540: lr = 0.175768,	loss = 2.530034,	Top-1 err = 0.380664,	Top-5 err = 0.169629,	data_time = 0.050454,	train_time = 0.356964
[2019-08-24 03:52:17,602] TRAIN Iter 194560: lr = 0.175735,	loss = 2.497418,	Top-1 err = 0.376563,	Top-5 err = 0.163525,	data_time = 0.050407,	train_time = 0.858040
[2019-08-24 03:52:33,127] TRAIN Iter 194580: lr = 0.175702,	loss = 2.507255,	Top-1 err = 0.377979,	Top-5 err = 0.168018,	data_time = 0.050436,	train_time = 0.776271
[2019-08-24 03:52:40,360] TRAIN Iter 194600: lr = 0.175668,	loss = 2.584957,	Top-1 err = 0.378174,	Top-5 err = 0.170215,	data_time = 0.050389,	train_time = 0.361630
[2019-08-24 03:52:55,801] TRAIN Iter 194620: lr = 0.175635,	loss = 2.630134,	Top-1 err = 0.379297,	Top-5 err = 0.170605,	data_time = 0.050308,	train_time = 0.772030
[2019-08-24 03:53:09,375] TRAIN Iter 194640: lr = 0.175602,	loss = 2.566899,	Top-1 err = 0.376758,	Top-5 err = 0.167334,	data_time = 1.621506,	train_time = 0.678668
[2019-08-24 03:53:17,954] TRAIN Iter 194660: lr = 0.175568,	loss = 2.613820,	Top-1 err = 0.385791,	Top-5 err = 0.169482,	data_time = 0.050502,	train_time = 0.428955
[2019-08-24 03:53:35,078] TRAIN Iter 194680: lr = 0.175535,	loss = 2.517786,	Top-1 err = 0.388037,	Top-5 err = 0.173633,	data_time = 0.050534,	train_time = 0.856179
[2019-08-24 03:53:42,818] TRAIN Iter 194700: lr = 0.175502,	loss = 2.609268,	Top-1 err = 0.378809,	Top-5 err = 0.165283,	data_time = 0.050222,	train_time = 0.386977
[2019-08-24 03:53:57,368] TRAIN Iter 194720: lr = 0.175468,	loss = 2.591078,	Top-1 err = 0.385693,	Top-5 err = 0.175049,	data_time = 0.050333,	train_time = 0.727480
[2019-08-24 03:54:14,900] TRAIN Iter 194740: lr = 0.175435,	loss = 2.529099,	Top-1 err = 0.388574,	Top-5 err = 0.179150,	data_time = 0.050620,	train_time = 0.876617
[2019-08-24 03:54:22,456] TRAIN Iter 194760: lr = 0.175402,	loss = 2.565010,	Top-1 err = 0.388037,	Top-5 err = 0.171045,	data_time = 0.050804,	train_time = 0.377746
[2019-08-24 03:54:36,077] TRAIN Iter 194780: lr = 0.175368,	loss = 2.547567,	Top-1 err = 0.379834,	Top-5 err = 0.166016,	data_time = 0.050691,	train_time = 0.681067
[2019-08-24 03:54:48,363] TRAIN Iter 194800: lr = 0.175335,	loss = 2.609091,	Top-1 err = 0.380566,	Top-5 err = 0.170459,	data_time = 1.859655,	train_time = 0.614290
[2019-08-24 03:55:00,434] TRAIN Iter 194820: lr = 0.175302,	loss = 2.571712,	Top-1 err = 0.383008,	Top-5 err = 0.165234,	data_time = 0.050551,	train_time = 0.603502
[2019-08-24 03:55:16,394] TRAIN Iter 194840: lr = 0.175268,	loss = 2.578748,	Top-1 err = 0.383398,	Top-5 err = 0.170166,	data_time = 0.050501,	train_time = 0.797984
[2019-08-24 03:55:23,926] TRAIN Iter 194860: lr = 0.175235,	loss = 2.552217,	Top-1 err = 0.385937,	Top-5 err = 0.170215,	data_time = 0.050523,	train_time = 0.376607
[2019-08-24 03:55:38,967] TRAIN Iter 194880: lr = 0.175202,	loss = 2.544615,	Top-1 err = 0.384961,	Top-5 err = 0.168115,	data_time = 0.050603,	train_time = 0.752013
[2019-08-24 03:55:53,876] TRAIN Iter 194900: lr = 0.175168,	loss = 2.556523,	Top-1 err = 0.384619,	Top-5 err = 0.170459,	data_time = 0.050488,	train_time = 0.745451
[2019-08-24 03:56:00,646] TRAIN Iter 194920: lr = 0.175135,	loss = 2.570494,	Top-1 err = 0.383789,	Top-5 err = 0.171289,	data_time = 0.050389,	train_time = 0.338499
[2019-08-24 03:56:17,046] TRAIN Iter 194940: lr = 0.175102,	loss = 2.634521,	Top-1 err = 0.381396,	Top-5 err = 0.176416,	data_time = 0.050812,	train_time = 0.819980
[2019-08-24 03:56:30,897] TRAIN Iter 194960: lr = 0.175068,	loss = 2.635220,	Top-1 err = 0.384473,	Top-5 err = 0.169434,	data_time = 0.050516,	train_time = 0.692531
[2019-08-24 03:56:40,893] TRAIN Iter 194980: lr = 0.175035,	loss = 2.615727,	Top-1 err = 0.381348,	Top-5 err = 0.164795,	data_time = 0.050232,	train_time = 0.499814
[2019-08-24 03:56:56,452] TRAIN Iter 195000: lr = 0.175002,	loss = 2.531628,	Top-1 err = 0.383936,	Top-5 err = 0.169531,	data_time = 0.050413,	train_time = 0.777920
[2019-08-24 03:57:03,502] TRAIN Iter 195020: lr = 0.174968,	loss = 2.591288,	Top-1 err = 0.389941,	Top-5 err = 0.177686,	data_time = 0.050437,	train_time = 0.352494
[2019-08-24 03:57:19,032] TRAIN Iter 195040: lr = 0.174935,	loss = 2.547795,	Top-1 err = 0.376221,	Top-5 err = 0.167480,	data_time = 0.050565,	train_time = 0.776488
[2019-08-24 03:57:35,082] TRAIN Iter 195060: lr = 0.174902,	loss = 2.598850,	Top-1 err = 0.383008,	Top-5 err = 0.171875,	data_time = 0.300174,	train_time = 0.802483
[2019-08-24 03:57:42,378] TRAIN Iter 195080: lr = 0.174868,	loss = 2.502609,	Top-1 err = 0.382861,	Top-5 err = 0.168799,	data_time = 0.050626,	train_time = 0.364794
[2019-08-24 03:57:57,842] TRAIN Iter 195100: lr = 0.174835,	loss = 2.526066,	Top-1 err = 0.383594,	Top-5 err = 0.172852,	data_time = 0.050822,	train_time = 0.773147
[2019-08-24 03:58:11,643] TRAIN Iter 195120: lr = 0.174802,	loss = 2.586504,	Top-1 err = 0.385059,	Top-5 err = 0.173291,	data_time = 2.776335,	train_time = 0.690068
[2019-08-24 03:58:21,759] TRAIN Iter 195140: lr = 0.174768,	loss = 2.550669,	Top-1 err = 0.380273,	Top-5 err = 0.169678,	data_time = 0.132036,	train_time = 0.505775
[2019-08-24 03:58:38,044] TRAIN Iter 195160: lr = 0.174735,	loss = 2.511431,	Top-1 err = 0.381982,	Top-5 err = 0.168848,	data_time = 0.050570,	train_time = 0.814232
[2019-08-24 03:58:45,050] TRAIN Iter 195180: lr = 0.174702,	loss = 2.563807,	Top-1 err = 0.380664,	Top-5 err = 0.168848,	data_time = 0.050458,	train_time = 0.350294
[2019-08-24 03:59:02,030] TRAIN Iter 195200: lr = 0.174668,	loss = 2.493279,	Top-1 err = 0.381299,	Top-5 err = 0.172852,	data_time = 0.050022,	train_time = 0.848995
[2019-08-24 03:59:18,214] TRAIN Iter 195220: lr = 0.174635,	loss = 2.570875,	Top-1 err = 0.381836,	Top-5 err = 0.170068,	data_time = 0.049974,	train_time = 0.809187
[2019-08-24 03:59:24,463] TRAIN Iter 195240: lr = 0.174602,	loss = 2.601346,	Top-1 err = 0.380518,	Top-5 err = 0.172119,	data_time = 0.049906,	train_time = 0.312401
[2019-08-24 04:00:08,159] TRAIN Iter 195260: lr = 0.174568,	loss = 2.637397,	Top-1 err = 0.383830,	Top-5 err = 0.173568,	data_time = 0.050519,	train_time = 2.184803
[2019-08-24 04:00:15,219] TRAIN Iter 195280: lr = 0.174535,	loss = 2.515622,	Top-1 err = 0.380371,	Top-5 err = 0.169873,	data_time = 0.050304,	train_time = 0.352981
[2019-08-24 04:00:32,270] TRAIN Iter 195300: lr = 0.174502,	loss = 2.525831,	Top-1 err = 0.371289,	Top-5 err = 0.159863,	data_time = 0.050474,	train_time = 0.852546
[2019-08-24 04:00:46,896] TRAIN Iter 195320: lr = 0.174468,	loss = 2.530249,	Top-1 err = 0.368701,	Top-5 err = 0.162891,	data_time = 0.050406,	train_time = 0.731278
[2019-08-24 04:00:54,148] TRAIN Iter 195340: lr = 0.174435,	loss = 2.530714,	Top-1 err = 0.383887,	Top-5 err = 0.165967,	data_time = 0.050398,	train_time = 0.362565
[2019-08-24 04:01:09,535] TRAIN Iter 195360: lr = 0.174402,	loss = 2.543341,	Top-1 err = 0.382422,	Top-5 err = 0.168652,	data_time = 0.050521,	train_time = 0.769364
[2019-08-24 04:01:20,528] TRAIN Iter 195380: lr = 0.174368,	loss = 2.481008,	Top-1 err = 0.377539,	Top-5 err = 0.167773,	data_time = 0.142614,	train_time = 0.549625
[2019-08-24 04:01:30,992] TRAIN Iter 195400: lr = 0.174335,	loss = 2.517369,	Top-1 err = 0.364893,	Top-5 err = 0.156396,	data_time = 0.050471,	train_time = 0.523183
[2019-08-24 04:01:46,735] TRAIN Iter 195420: lr = 0.174302,	loss = 2.472269,	Top-1 err = 0.382568,	Top-5 err = 0.176758,	data_time = 0.050829,	train_time = 0.787119
[2019-08-24 04:01:53,901] TRAIN Iter 195440: lr = 0.174268,	loss = 2.497971,	Top-1 err = 0.372559,	Top-5 err = 0.166943,	data_time = 0.157631,	train_time = 0.358322
[2019-08-24 04:02:09,305] TRAIN Iter 195460: lr = 0.174235,	loss = 2.541256,	Top-1 err = 0.372217,	Top-5 err = 0.162354,	data_time = 0.050633,	train_time = 0.770148
[2019-08-24 04:02:24,766] TRAIN Iter 195480: lr = 0.174202,	loss = 2.607928,	Top-1 err = 0.381689,	Top-5 err = 0.165723,	data_time = 2.411703,	train_time = 0.773032
[2019-08-24 04:02:31,867] TRAIN Iter 195500: lr = 0.174168,	loss = 2.466415,	Top-1 err = 0.378955,	Top-5 err = 0.167578,	data_time = 0.050203,	train_time = 0.355026
[2019-08-24 04:02:46,645] TRAIN Iter 195520: lr = 0.174135,	loss = 2.541203,	Top-1 err = 0.380225,	Top-5 err = 0.166553,	data_time = 0.050546,	train_time = 0.738927
[2019-08-24 04:02:59,334] TRAIN Iter 195540: lr = 0.174102,	loss = 2.422654,	Top-1 err = 0.380615,	Top-5 err = 0.169189,	data_time = 0.050534,	train_time = 0.634410
[2019-08-24 04:03:07,473] TRAIN Iter 195560: lr = 0.174068,	loss = 2.509227,	Top-1 err = 0.376221,	Top-5 err = 0.168408,	data_time = 0.050738,	train_time = 0.406968
[2019-08-24 04:03:22,217] TRAIN Iter 195580: lr = 0.174035,	loss = 2.508862,	Top-1 err = 0.368213,	Top-5 err = 0.161865,	data_time = 0.087800,	train_time = 0.737149
[2019-08-24 04:03:29,462] TRAIN Iter 195600: lr = 0.174002,	loss = 2.546471,	Top-1 err = 0.375537,	Top-5 err = 0.169141,	data_time = 0.050834,	train_time = 0.362231
[2019-08-24 04:03:44,089] TRAIN Iter 195620: lr = 0.173968,	loss = 2.531186,	Top-1 err = 0.376563,	Top-5 err = 0.166113,	data_time = 0.050760,	train_time = 0.731352
[2019-08-24 04:03:59,240] TRAIN Iter 195640: lr = 0.173935,	loss = 2.554605,	Top-1 err = 0.383105,	Top-5 err = 0.167822,	data_time = 1.206449,	train_time = 0.757548
[2019-08-24 04:04:06,495] TRAIN Iter 195660: lr = 0.173902,	loss = 2.583994,	Top-1 err = 0.372070,	Top-5 err = 0.160889,	data_time = 0.050614,	train_time = 0.362725
[2019-08-24 04:04:21,117] TRAIN Iter 195680: lr = 0.173868,	loss = 2.475577,	Top-1 err = 0.377246,	Top-5 err = 0.167969,	data_time = 0.050727,	train_time = 0.731107
[2019-08-24 04:04:35,992] TRAIN Iter 195700: lr = 0.173835,	loss = 2.557656,	Top-1 err = 0.381543,	Top-5 err = 0.167285,	data_time = 0.050851,	train_time = 0.743727
[2019-08-24 04:04:43,054] TRAIN Iter 195720: lr = 0.173802,	loss = 2.558663,	Top-1 err = 0.386914,	Top-5 err = 0.174854,	data_time = 0.050808,	train_time = 0.353079
[2019-08-24 04:04:59,848] TRAIN Iter 195740: lr = 0.173768,	loss = 2.590337,	Top-1 err = 0.382715,	Top-5 err = 0.170557,	data_time = 0.050315,	train_time = 0.839672
[2019-08-24 04:05:07,159] TRAIN Iter 195760: lr = 0.173735,	loss = 2.545532,	Top-1 err = 0.382275,	Top-5 err = 0.167285,	data_time = 0.050379,	train_time = 0.365543
[2019-08-24 04:05:23,503] TRAIN Iter 195780: lr = 0.173702,	loss = 2.674396,	Top-1 err = 0.381836,	Top-5 err = 0.169434,	data_time = 0.151201,	train_time = 0.817205
[2019-08-24 04:05:38,857] TRAIN Iter 195800: lr = 0.173668,	loss = 2.620132,	Top-1 err = 0.381543,	Top-5 err = 0.170215,	data_time = 0.050394,	train_time = 0.767643
[2019-08-24 04:05:46,124] TRAIN Iter 195820: lr = 0.173635,	loss = 2.598231,	Top-1 err = 0.380762,	Top-5 err = 0.166602,	data_time = 0.050585,	train_time = 0.363342
[2019-08-24 04:06:00,945] TRAIN Iter 195840: lr = 0.173602,	loss = 2.511180,	Top-1 err = 0.378467,	Top-5 err = 0.167676,	data_time = 0.050661,	train_time = 0.741042
[2019-08-24 04:06:17,122] TRAIN Iter 195860: lr = 0.173568,	loss = 2.527364,	Top-1 err = 0.380225,	Top-5 err = 0.167725,	data_time = 0.050418,	train_time = 0.808828
[2019-08-24 04:06:24,446] TRAIN Iter 195880: lr = 0.173535,	loss = 2.622164,	Top-1 err = 0.382178,	Top-5 err = 0.171729,	data_time = 0.050924,	train_time = 0.366195
[2019-08-24 04:06:38,863] TRAIN Iter 195900: lr = 0.173502,	loss = 2.608679,	Top-1 err = 0.381543,	Top-5 err = 0.170898,	data_time = 0.050480,	train_time = 0.720832
[2019-08-24 04:06:46,268] TRAIN Iter 195920: lr = 0.173468,	loss = 2.589372,	Top-1 err = 0.372754,	Top-5 err = 0.168457,	data_time = 0.050391,	train_time = 0.370238
[2019-08-24 04:07:01,715] TRAIN Iter 195940: lr = 0.173435,	loss = 2.486082,	Top-1 err = 0.376611,	Top-5 err = 0.168311,	data_time = 0.050663,	train_time = 0.772337
[2019-08-24 04:07:17,204] TRAIN Iter 195960: lr = 0.173402,	loss = 2.615561,	Top-1 err = 0.383252,	Top-5 err = 0.172119,	data_time = 0.050337,	train_time = 0.774452
[2019-08-24 04:07:24,040] TRAIN Iter 195980: lr = 0.173368,	loss = 2.540849,	Top-1 err = 0.378467,	Top-5 err = 0.168213,	data_time = 0.145697,	train_time = 0.341767
[2019-08-24 04:07:40,486] TRAIN Iter 196000: lr = 0.173335,	loss = 2.533828,	Top-1 err = 0.378809,	Top-5 err = 0.173242,	data_time = 0.050147,	train_time = 0.822268
[2019-08-24 04:07:57,454] TRAIN Iter 196020: lr = 0.173302,	loss = 2.527234,	Top-1 err = 0.379492,	Top-5 err = 0.171484,	data_time = 0.050574,	train_time = 0.848421
[2019-08-24 04:08:04,251] TRAIN Iter 196040: lr = 0.173268,	loss = 2.519078,	Top-1 err = 0.383740,	Top-5 err = 0.173389,	data_time = 0.050885,	train_time = 0.339827
[2019-08-24 04:08:21,234] TRAIN Iter 196060: lr = 0.173235,	loss = 2.495539,	Top-1 err = 0.384717,	Top-5 err = 0.171924,	data_time = 0.050508,	train_time = 0.849108
[2019-08-24 04:08:28,897] TRAIN Iter 196080: lr = 0.173202,	loss = 2.626052,	Top-1 err = 0.378857,	Top-5 err = 0.166553,	data_time = 0.050576,	train_time = 0.383146
[2019-08-24 04:08:44,983] TRAIN Iter 196100: lr = 0.173168,	loss = 2.587994,	Top-1 err = 0.384180,	Top-5 err = 0.170850,	data_time = 0.050587,	train_time = 0.804311
[2019-08-24 04:09:01,919] TRAIN Iter 196120: lr = 0.173135,	loss = 2.490149,	Top-1 err = 0.385498,	Top-5 err = 0.172559,	data_time = 0.050387,	train_time = 0.846766
[2019-08-24 04:09:09,098] TRAIN Iter 196140: lr = 0.173102,	loss = 2.610093,	Top-1 err = 0.375732,	Top-5 err = 0.167822,	data_time = 0.050531,	train_time = 0.358922
[2019-08-24 04:09:25,810] TRAIN Iter 196160: lr = 0.173068,	loss = 2.569323,	Top-1 err = 0.383057,	Top-5 err = 0.170410,	data_time = 0.050555,	train_time = 0.835617
[2019-08-24 04:09:42,443] TRAIN Iter 196180: lr = 0.173035,	loss = 2.434330,	Top-1 err = 0.384521,	Top-5 err = 0.169922,	data_time = 0.050326,	train_time = 0.831624
[2019-08-24 04:09:49,121] TRAIN Iter 196200: lr = 0.173002,	loss = 2.556711,	Top-1 err = 0.384180,	Top-5 err = 0.167187,	data_time = 0.050344,	train_time = 0.333860
[2019-08-24 04:10:07,753] TRAIN Iter 196220: lr = 0.172968,	loss = 2.591018,	Top-1 err = 0.381592,	Top-5 err = 0.168213,	data_time = 0.050570,	train_time = 0.931582
[2019-08-24 04:10:15,144] TRAIN Iter 196240: lr = 0.172935,	loss = 2.531981,	Top-1 err = 0.381689,	Top-5 err = 0.168359,	data_time = 0.130240,	train_time = 0.369572
[2019-08-24 04:10:30,545] TRAIN Iter 196260: lr = 0.172902,	loss = 2.543954,	Top-1 err = 0.380469,	Top-5 err = 0.170264,	data_time = 0.050582,	train_time = 0.770018
[2019-08-24 04:10:48,050] TRAIN Iter 196280: lr = 0.172868,	loss = 2.512493,	Top-1 err = 0.377734,	Top-5 err = 0.169092,	data_time = 0.050361,	train_time = 0.875232
[2019-08-24 04:10:54,554] TRAIN Iter 196300: lr = 0.172835,	loss = 2.536424,	Top-1 err = 0.383496,	Top-5 err = 0.171826,	data_time = 0.050539,	train_time = 0.325189
[2019-08-24 04:11:13,859] TRAIN Iter 196320: lr = 0.172802,	loss = 2.520679,	Top-1 err = 0.381299,	Top-5 err = 0.170215,	data_time = 0.050322,	train_time = 0.965257
[2019-08-24 04:11:31,013] TRAIN Iter 196340: lr = 0.172768,	loss = 2.606048,	Top-1 err = 0.383496,	Top-5 err = 0.174023,	data_time = 0.050354,	train_time = 0.857660
[2019-08-24 04:11:37,613] TRAIN Iter 196360: lr = 0.172735,	loss = 2.565552,	Top-1 err = 0.381201,	Top-5 err = 0.169678,	data_time = 0.050421,	train_time = 0.329994
[2019-08-24 04:11:55,895] TRAIN Iter 196380: lr = 0.172702,	loss = 2.603160,	Top-1 err = 0.373975,	Top-5 err = 0.169482,	data_time = 0.050514,	train_time = 0.914069
[2019-08-24 04:12:02,745] TRAIN Iter 196400: lr = 0.172668,	loss = 2.514031,	Top-1 err = 0.386426,	Top-5 err = 0.175342,	data_time = 0.050182,	train_time = 0.342527
[2019-08-24 04:12:20,866] TRAIN Iter 196420: lr = 0.172635,	loss = 2.492121,	Top-1 err = 0.382812,	Top-5 err = 0.170459,	data_time = 0.050380,	train_time = 0.906033
[2019-08-24 04:12:39,103] TRAIN Iter 196440: lr = 0.172602,	loss = 2.561629,	Top-1 err = 0.385107,	Top-5 err = 0.171436,	data_time = 0.049907,	train_time = 0.911817
[2019-08-24 04:12:45,832] TRAIN Iter 196460: lr = 0.172568,	loss = 2.546501,	Top-1 err = 0.383252,	Top-5 err = 0.173437,	data_time = 0.141509,	train_time = 0.336443
[2019-08-24 04:13:01,942] TRAIN Iter 196480: lr = 0.172535,	loss = 2.581197,	Top-1 err = 0.382031,	Top-5 err = 0.171338,	data_time = 0.049973,	train_time = 0.805498
[2019-08-24 04:13:14,267] TRAIN Iter 196500: lr = 0.172502,	loss = 2.932156,	Top-1 err = 0.383975,	Top-5 err = 0.172476,	data_time = 0.007061,	train_time = 0.616237
[2019-08-24 04:14:01,115] TRAIN Iter 196520: lr = 0.172468,	loss = 2.511958,	Top-1 err = 0.383838,	Top-5 err = 0.168799,	data_time = 0.050557,	train_time = 2.342380
[2019-08-24 04:14:18,908] TRAIN Iter 196540: lr = 0.172435,	loss = 2.458384,	Top-1 err = 0.377588,	Top-5 err = 0.168311,	data_time = 0.050363,	train_time = 0.889635
[2019-08-24 04:14:26,898] TRAIN Iter 196560: lr = 0.172402,	loss = 2.507916,	Top-1 err = 0.375586,	Top-5 err = 0.166309,	data_time = 0.050441,	train_time = 0.399465
[2019-08-24 04:14:40,369] TRAIN Iter 196580: lr = 0.172368,	loss = 2.459456,	Top-1 err = 0.370020,	Top-5 err = 0.162549,	data_time = 0.050383,	train_time = 0.673555
[2019-08-24 04:14:53,209] TRAIN Iter 196600: lr = 0.172335,	loss = 2.508200,	Top-1 err = 0.373242,	Top-5 err = 0.164844,	data_time = 1.595240,	train_time = 0.641972
[2019-08-24 04:15:00,533] TRAIN Iter 196620: lr = 0.172302,	loss = 2.546283,	Top-1 err = 0.367041,	Top-5 err = 0.163086,	data_time = 0.050830,	train_time = 0.366199
[2019-08-24 04:15:16,660] TRAIN Iter 196640: lr = 0.172268,	loss = 2.633291,	Top-1 err = 0.372266,	Top-5 err = 0.165088,	data_time = 0.050723,	train_time = 0.806303
[2019-08-24 04:15:24,294] TRAIN Iter 196660: lr = 0.172235,	loss = 2.523412,	Top-1 err = 0.377344,	Top-5 err = 0.165820,	data_time = 0.051032,	train_time = 0.381686
[2019-08-24 04:15:41,084] TRAIN Iter 196680: lr = 0.172202,	loss = 2.477347,	Top-1 err = 0.372412,	Top-5 err = 0.162939,	data_time = 0.050845,	train_time = 0.839484
[2019-08-24 04:15:54,403] TRAIN Iter 196700: lr = 0.172168,	loss = 2.394080,	Top-1 err = 0.378467,	Top-5 err = 0.168848,	data_time = 0.050543,	train_time = 0.665961
[2019-08-24 04:16:01,753] TRAIN Iter 196720: lr = 0.172135,	loss = 2.601907,	Top-1 err = 0.375537,	Top-5 err = 0.167090,	data_time = 0.050392,	train_time = 0.367476
[2019-08-24 04:16:17,328] TRAIN Iter 196740: lr = 0.172102,	loss = 2.641644,	Top-1 err = 0.375098,	Top-5 err = 0.161865,	data_time = 0.050282,	train_time = 0.778720
[2019-08-24 04:16:32,578] TRAIN Iter 196760: lr = 0.172068,	loss = 2.474919,	Top-1 err = 0.383350,	Top-5 err = 0.167969,	data_time = 3.095542,	train_time = 0.762486
[2019-08-24 04:16:40,277] TRAIN Iter 196780: lr = 0.172035,	loss = 2.587330,	Top-1 err = 0.377588,	Top-5 err = 0.169482,	data_time = 0.051092,	train_time = 0.384963
[2019-08-24 04:16:53,612] TRAIN Iter 196800: lr = 0.172002,	loss = 2.482500,	Top-1 err = 0.375684,	Top-5 err = 0.168115,	data_time = 0.050530,	train_time = 0.666730
[2019-08-24 04:17:01,863] TRAIN Iter 196820: lr = 0.171968,	loss = 2.487797,	Top-1 err = 0.379102,	Top-5 err = 0.163477,	data_time = 0.050696,	train_time = 0.412526
[2019-08-24 04:17:16,029] TRAIN Iter 196840: lr = 0.171935,	loss = 2.473637,	Top-1 err = 0.376904,	Top-5 err = 0.167041,	data_time = 0.050329,	train_time = 0.708282
[2019-08-24 04:17:30,508] TRAIN Iter 196860: lr = 0.171902,	loss = 2.554649,	Top-1 err = 0.378857,	Top-5 err = 0.171436,	data_time = 0.050456,	train_time = 0.723969
[2019-08-24 04:17:37,507] TRAIN Iter 196880: lr = 0.171868,	loss = 2.492925,	Top-1 err = 0.374463,	Top-5 err = 0.163379,	data_time = 0.050226,	train_time = 0.349895
[2019-08-24 04:17:52,801] TRAIN Iter 196900: lr = 0.171835,	loss = 2.510874,	Top-1 err = 0.382031,	Top-5 err = 0.173535,	data_time = 0.050532,	train_time = 0.764699
[2019-08-24 04:18:08,147] TRAIN Iter 196920: lr = 0.171802,	loss = 2.505572,	Top-1 err = 0.378857,	Top-5 err = 0.168359,	data_time = 7.358463,	train_time = 0.767281
[2019-08-24 04:18:14,975] TRAIN Iter 196940: lr = 0.171768,	loss = 2.610246,	Top-1 err = 0.378662,	Top-5 err = 0.170020,	data_time = 0.050627,	train_time = 0.341425
[2019-08-24 04:18:31,694] TRAIN Iter 196960: lr = 0.171735,	loss = 2.483315,	Top-1 err = 0.379688,	Top-5 err = 0.165967,	data_time = 0.050351,	train_time = 0.835916
[2019-08-24 04:18:39,511] TRAIN Iter 196980: lr = 0.171702,	loss = 2.562451,	Top-1 err = 0.373584,	Top-5 err = 0.169775,	data_time = 0.050435,	train_time = 0.390857
[2019-08-24 04:18:54,409] TRAIN Iter 197000: lr = 0.171668,	loss = 2.524020,	Top-1 err = 0.380957,	Top-5 err = 0.169385,	data_time = 0.050665,	train_time = 0.744873
[2019-08-24 04:19:10,309] TRAIN Iter 197020: lr = 0.171635,	loss = 2.499288,	Top-1 err = 0.375586,	Top-5 err = 0.164600,	data_time = 0.050489,	train_time = 0.794976
[2019-08-24 04:19:17,694] TRAIN Iter 197040: lr = 0.171602,	loss = 2.496647,	Top-1 err = 0.380664,	Top-5 err = 0.171191,	data_time = 0.050462,	train_time = 0.369248
[2019-08-24 04:19:33,432] TRAIN Iter 197060: lr = 0.171568,	loss = 2.574158,	Top-1 err = 0.374707,	Top-5 err = 0.164111,	data_time = 0.050422,	train_time = 0.786880
[2019-08-24 04:19:48,737] TRAIN Iter 197080: lr = 0.171535,	loss = 2.688570,	Top-1 err = 0.374121,	Top-5 err = 0.168848,	data_time = 4.111916,	train_time = 0.765220
[2019-08-24 04:19:56,081] TRAIN Iter 197100: lr = 0.171502,	loss = 2.489161,	Top-1 err = 0.382031,	Top-5 err = 0.168164,	data_time = 0.050876,	train_time = 0.367174
[2019-08-24 04:20:11,283] TRAIN Iter 197120: lr = 0.171468,	loss = 2.479064,	Top-1 err = 0.376855,	Top-5 err = 0.168506,	data_time = 0.050843,	train_time = 0.760125
[2019-08-24 04:20:19,332] TRAIN Iter 197140: lr = 0.171435,	loss = 2.547045,	Top-1 err = 0.384033,	Top-5 err = 0.169482,	data_time = 0.050594,	train_time = 0.402444
[2019-08-24 04:20:34,562] TRAIN Iter 197160: lr = 0.171402,	loss = 2.508543,	Top-1 err = 0.381982,	Top-5 err = 0.167285,	data_time = 0.050360,	train_time = 0.761465
[2019-08-24 04:20:50,209] TRAIN Iter 197180: lr = 0.171368,	loss = 2.573273,	Top-1 err = 0.387158,	Top-5 err = 0.171826,	data_time = 0.050627,	train_time = 0.782335
[2019-08-24 04:20:57,380] TRAIN Iter 197200: lr = 0.171335,	loss = 2.689795,	Top-1 err = 0.373828,	Top-5 err = 0.163770,	data_time = 0.050299,	train_time = 0.358517
[2019-08-24 04:21:13,668] TRAIN Iter 197220: lr = 0.171302,	loss = 2.549586,	Top-1 err = 0.381201,	Top-5 err = 0.172217,	data_time = 0.050462,	train_time = 0.814420
[2019-08-24 04:21:29,812] TRAIN Iter 197240: lr = 0.171268,	loss = 2.547788,	Top-1 err = 0.378076,	Top-5 err = 0.170947,	data_time = 6.186966,	train_time = 0.807166
[2019-08-24 04:21:36,641] TRAIN Iter 197260: lr = 0.171235,	loss = 2.522910,	Top-1 err = 0.383496,	Top-5 err = 0.168018,	data_time = 0.050488,	train_time = 0.341426
[2019-08-24 04:21:53,442] TRAIN Iter 197280: lr = 0.171202,	loss = 2.596725,	Top-1 err = 0.384570,	Top-5 err = 0.173486,	data_time = 0.050811,	train_time = 0.840064
[2019-08-24 04:22:00,835] TRAIN Iter 197300: lr = 0.171168,	loss = 2.509678,	Top-1 err = 0.377002,	Top-5 err = 0.164746,	data_time = 0.050758,	train_time = 0.369622
[2019-08-24 04:22:16,688] TRAIN Iter 197320: lr = 0.171135,	loss = 2.492555,	Top-1 err = 0.385889,	Top-5 err = 0.173096,	data_time = 0.050726,	train_time = 0.792634
[2019-08-24 04:22:33,493] TRAIN Iter 197340: lr = 0.171102,	loss = 2.568621,	Top-1 err = 0.384961,	Top-5 err = 0.171143,	data_time = 0.050541,	train_time = 0.840235
[2019-08-24 04:22:40,729] TRAIN Iter 197360: lr = 0.171068,	loss = 2.532314,	Top-1 err = 0.383936,	Top-5 err = 0.171680,	data_time = 0.050544,	train_time = 0.361788
[2019-08-24 04:22:57,017] TRAIN Iter 197380: lr = 0.171035,	loss = 2.472973,	Top-1 err = 0.375879,	Top-5 err = 0.165430,	data_time = 0.050402,	train_time = 0.814400
[2019-08-24 04:23:14,411] TRAIN Iter 197400: lr = 0.171002,	loss = 2.573758,	Top-1 err = 0.385645,	Top-5 err = 0.172070,	data_time = 7.458956,	train_time = 0.869675
[2019-08-24 04:23:21,011] TRAIN Iter 197420: lr = 0.170968,	loss = 2.576093,	Top-1 err = 0.381201,	Top-5 err = 0.171924,	data_time = 0.050422,	train_time = 0.330007
[2019-08-24 04:23:38,487] TRAIN Iter 197440: lr = 0.170935,	loss = 2.494305,	Top-1 err = 0.386377,	Top-5 err = 0.176660,	data_time = 0.050606,	train_time = 0.873793
[2019-08-24 04:23:45,753] TRAIN Iter 197460: lr = 0.170902,	loss = 2.543077,	Top-1 err = 0.379980,	Top-5 err = 0.169434,	data_time = 0.050380,	train_time = 0.363284
[2019-08-24 04:24:02,160] TRAIN Iter 197480: lr = 0.170868,	loss = 2.462723,	Top-1 err = 0.379248,	Top-5 err = 0.170850,	data_time = 0.050689,	train_time = 0.820334
[2019-08-24 04:24:19,900] TRAIN Iter 197500: lr = 0.170835,	loss = 2.530715,	Top-1 err = 0.380078,	Top-5 err = 0.169531,	data_time = 0.050528,	train_time = 0.886977
[2019-08-24 04:24:26,918] TRAIN Iter 197520: lr = 0.170802,	loss = 2.560385,	Top-1 err = 0.382910,	Top-5 err = 0.174951,	data_time = 0.050221,	train_time = 0.350901
[2019-08-24 04:24:43,412] TRAIN Iter 197540: lr = 0.170768,	loss = 2.540097,	Top-1 err = 0.383594,	Top-5 err = 0.171973,	data_time = 0.050551,	train_time = 0.824673
[2019-08-24 04:25:00,097] TRAIN Iter 197560: lr = 0.170735,	loss = 2.525920,	Top-1 err = 0.388086,	Top-5 err = 0.172852,	data_time = 6.468551,	train_time = 0.834247
[2019-08-24 04:25:07,125] TRAIN Iter 197580: lr = 0.170702,	loss = 2.502955,	Top-1 err = 0.378906,	Top-5 err = 0.165869,	data_time = 0.050243,	train_time = 0.351366
[2019-08-24 04:25:24,688] TRAIN Iter 197600: lr = 0.170668,	loss = 2.538629,	Top-1 err = 0.382275,	Top-5 err = 0.171436,	data_time = 0.050490,	train_time = 0.878139
[2019-08-24 04:25:31,769] TRAIN Iter 197620: lr = 0.170635,	loss = 2.575887,	Top-1 err = 0.385059,	Top-5 err = 0.172705,	data_time = 0.050316,	train_time = 0.354043
[2019-08-24 04:25:47,885] TRAIN Iter 197640: lr = 0.170602,	loss = 2.440622,	Top-1 err = 0.377295,	Top-5 err = 0.168604,	data_time = 0.050603,	train_time = 0.805750
[2019-08-24 04:26:05,437] TRAIN Iter 197660: lr = 0.170568,	loss = 2.486357,	Top-1 err = 0.381055,	Top-5 err = 0.171680,	data_time = 0.050354,	train_time = 0.877584
[2019-08-24 04:26:11,973] TRAIN Iter 197680: lr = 0.170535,	loss = 2.591233,	Top-1 err = 0.385840,	Top-5 err = 0.170703,	data_time = 0.050359,	train_time = 0.326803
[2019-08-24 04:26:29,717] TRAIN Iter 197700: lr = 0.170502,	loss = 2.483597,	Top-1 err = 0.383643,	Top-5 err = 0.166992,	data_time = 0.050233,	train_time = 0.887183
[2019-08-24 04:26:48,393] TRAIN Iter 197720: lr = 0.170468,	loss = 2.543515,	Top-1 err = 0.382568,	Top-5 err = 0.172070,	data_time = 8.509867,	train_time = 0.933781
[2019-08-24 04:26:54,705] TRAIN Iter 197740: lr = 0.170435,	loss = 2.549097,	Top-1 err = 0.379346,	Top-5 err = 0.167383,	data_time = 0.050038,	train_time = 0.315608
[2019-08-24 04:27:42,715] TRAIN Iter 197760: lr = 0.170402,	loss = 2.550259,	Top-1 err = 0.383673,	Top-5 err = 0.166837,	data_time = 0.050443,	train_time = 2.400474
[2019-08-24 04:27:49,796] TRAIN Iter 197780: lr = 0.170368,	loss = 2.529016,	Top-1 err = 0.381445,	Top-5 err = 0.163916,	data_time = 0.050388,	train_time = 0.354031
[2019-08-24 04:28:05,474] TRAIN Iter 197800: lr = 0.170335,	loss = 2.492211,	Top-1 err = 0.375537,	Top-5 err = 0.164209,	data_time = 0.050589,	train_time = 0.783879
[2019-08-24 04:28:22,035] TRAIN Iter 197820: lr = 0.170302,	loss = 2.540154,	Top-1 err = 0.374268,	Top-5 err = 0.167236,	data_time = 0.050912,	train_time = 0.828034
[2019-08-24 04:28:28,968] TRAIN Iter 197840: lr = 0.170268,	loss = 2.449510,	Top-1 err = 0.374121,	Top-5 err = 0.165723,	data_time = 0.050684,	train_time = 0.346646
[2019-08-24 04:28:45,132] TRAIN Iter 197860: lr = 0.170235,	loss = 2.567123,	Top-1 err = 0.376709,	Top-5 err = 0.166504,	data_time = 0.050373,	train_time = 0.808203
[2019-08-24 04:28:52,974] TRAIN Iter 197880: lr = 0.170202,	loss = 2.513475,	Top-1 err = 0.378271,	Top-5 err = 0.166699,	data_time = 0.050818,	train_time = 0.392074
[2019-08-24 04:29:07,059] TRAIN Iter 197900: lr = 0.170168,	loss = 2.538472,	Top-1 err = 0.378564,	Top-5 err = 0.167139,	data_time = 0.050214,	train_time = 0.704213
[2019-08-24 04:29:23,627] TRAIN Iter 197920: lr = 0.170135,	loss = 2.621792,	Top-1 err = 0.377539,	Top-5 err = 0.165771,	data_time = 0.050501,	train_time = 0.828398
[2019-08-24 04:29:31,298] TRAIN Iter 197940: lr = 0.170102,	loss = 2.548416,	Top-1 err = 0.373584,	Top-5 err = 0.166016,	data_time = 0.050464,	train_time = 0.383543
[2019-08-24 04:29:44,535] TRAIN Iter 197960: lr = 0.170068,	loss = 2.535808,	Top-1 err = 0.376953,	Top-5 err = 0.164844,	data_time = 0.050934,	train_time = 0.661826
[2019-08-24 04:29:59,052] TRAIN Iter 197980: lr = 0.170035,	loss = 2.658541,	Top-1 err = 0.376709,	Top-5 err = 0.165527,	data_time = 0.135464,	train_time = 0.725826
[2019-08-24 04:30:05,582] TRAIN Iter 198000: lr = 0.170002,	loss = 2.478233,	Top-1 err = 0.371533,	Top-5 err = 0.163672,	data_time = 0.050375,	train_time = 0.326485
[2019-08-24 04:30:21,740] TRAIN Iter 198020: lr = 0.169968,	loss = 2.473469,	Top-1 err = 0.377148,	Top-5 err = 0.163184,	data_time = 0.050498,	train_time = 0.807894
[2019-08-24 04:30:28,957] TRAIN Iter 198040: lr = 0.169935,	loss = 2.554296,	Top-1 err = 0.374121,	Top-5 err = 0.164307,	data_time = 0.050196,	train_time = 0.360851
[2019-08-24 04:30:43,915] TRAIN Iter 198060: lr = 0.169902,	loss = 2.536407,	Top-1 err = 0.375977,	Top-5 err = 0.165869,	data_time = 0.050307,	train_time = 0.747851
[2019-08-24 04:30:59,992] TRAIN Iter 198080: lr = 0.169868,	loss = 2.542713,	Top-1 err = 0.377832,	Top-5 err = 0.170166,	data_time = 0.050632,	train_time = 0.803871
[2019-08-24 04:31:06,719] TRAIN Iter 198100: lr = 0.169835,	loss = 2.559622,	Top-1 err = 0.374268,	Top-5 err = 0.165820,	data_time = 0.050562,	train_time = 0.336335
[2019-08-24 04:31:22,996] TRAIN Iter 198120: lr = 0.169802,	loss = 2.556309,	Top-1 err = 0.374561,	Top-5 err = 0.168311,	data_time = 0.050587,	train_time = 0.813844
[2019-08-24 04:31:39,678] TRAIN Iter 198140: lr = 0.169768,	loss = 2.562584,	Top-1 err = 0.380811,	Top-5 err = 0.166602,	data_time = 0.050643,	train_time = 0.834078
[2019-08-24 04:31:46,607] TRAIN Iter 198160: lr = 0.169735,	loss = 2.553464,	Top-1 err = 0.377051,	Top-5 err = 0.169043,	data_time = 0.050366,	train_time = 0.346407
[2019-08-24 04:32:03,037] TRAIN Iter 198180: lr = 0.169702,	loss = 2.469402,	Top-1 err = 0.381885,	Top-5 err = 0.168164,	data_time = 0.050445,	train_time = 0.821522
[2019-08-24 04:32:10,860] TRAIN Iter 198200: lr = 0.169668,	loss = 2.668677,	Top-1 err = 0.380322,	Top-5 err = 0.168506,	data_time = 0.050535,	train_time = 0.391091
[2019-08-24 04:32:25,230] TRAIN Iter 198220: lr = 0.169635,	loss = 2.428166,	Top-1 err = 0.379688,	Top-5 err = 0.166846,	data_time = 0.127315,	train_time = 0.718504
[2019-08-24 04:32:40,511] TRAIN Iter 198240: lr = 0.169602,	loss = 2.586711,	Top-1 err = 0.375537,	Top-5 err = 0.166309,	data_time = 0.050430,	train_time = 0.764051
[2019-08-24 04:32:47,519] TRAIN Iter 198260: lr = 0.169568,	loss = 2.455432,	Top-1 err = 0.375586,	Top-5 err = 0.163184,	data_time = 0.050589,	train_time = 0.350394
[2019-08-24 04:33:02,793] TRAIN Iter 198280: lr = 0.169535,	loss = 2.458097,	Top-1 err = 0.380566,	Top-5 err = 0.167627,	data_time = 0.050542,	train_time = 0.763683
[2019-08-24 04:33:18,422] TRAIN Iter 198300: lr = 0.169502,	loss = 2.488965,	Top-1 err = 0.381592,	Top-5 err = 0.169141,	data_time = 0.050890,	train_time = 0.781408
[2019-08-24 04:33:25,104] TRAIN Iter 198320: lr = 0.169468,	loss = 2.531658,	Top-1 err = 0.382080,	Top-5 err = 0.172998,	data_time = 0.050504,	train_time = 0.334087
[2019-08-24 04:33:40,523] TRAIN Iter 198340: lr = 0.169435,	loss = 2.517784,	Top-1 err = 0.372656,	Top-5 err = 0.164209,	data_time = 0.050461,	train_time = 0.770953
[2019-08-24 04:33:47,231] TRAIN Iter 198360: lr = 0.169402,	loss = 2.602886,	Top-1 err = 0.381543,	Top-5 err = 0.169727,	data_time = 0.050722,	train_time = 0.335388
[2019-08-24 04:34:03,963] TRAIN Iter 198380: lr = 0.169368,	loss = 2.454073,	Top-1 err = 0.380029,	Top-5 err = 0.167334,	data_time = 0.050484,	train_time = 0.836580
[2019-08-24 04:34:20,176] TRAIN Iter 198400: lr = 0.169335,	loss = 2.533546,	Top-1 err = 0.376416,	Top-5 err = 0.163965,	data_time = 0.050605,	train_time = 0.810627
[2019-08-24 04:34:26,729] TRAIN Iter 198420: lr = 0.169302,	loss = 2.577415,	Top-1 err = 0.375342,	Top-5 err = 0.167432,	data_time = 0.168805,	train_time = 0.327656
[2019-08-24 04:34:42,855] TRAIN Iter 198440: lr = 0.169268,	loss = 2.452887,	Top-1 err = 0.378760,	Top-5 err = 0.167627,	data_time = 0.050333,	train_time = 0.806246
[2019-08-24 04:34:58,687] TRAIN Iter 198460: lr = 0.169235,	loss = 2.536578,	Top-1 err = 0.379688,	Top-5 err = 0.161572,	data_time = 0.114993,	train_time = 0.791587
[2019-08-24 04:35:05,689] TRAIN Iter 198480: lr = 0.169202,	loss = 2.551504,	Top-1 err = 0.379248,	Top-5 err = 0.170117,	data_time = 0.050885,	train_time = 0.350124
[2019-08-24 04:35:22,401] TRAIN Iter 198500: lr = 0.169168,	loss = 2.515453,	Top-1 err = 0.378955,	Top-5 err = 0.170215,	data_time = 0.050466,	train_time = 0.835569
[2019-08-24 04:35:29,509] TRAIN Iter 198520: lr = 0.169135,	loss = 2.626515,	Top-1 err = 0.379004,	Top-5 err = 0.167578,	data_time = 0.050706,	train_time = 0.355380
[2019-08-24 04:35:46,419] TRAIN Iter 198540: lr = 0.169102,	loss = 2.567085,	Top-1 err = 0.385107,	Top-5 err = 0.171240,	data_time = 0.050505,	train_time = 0.845506
[2019-08-24 04:36:01,005] TRAIN Iter 198560: lr = 0.169068,	loss = 2.467558,	Top-1 err = 0.379053,	Top-5 err = 0.166797,	data_time = 0.050326,	train_time = 0.729258
[2019-08-24 04:36:07,713] TRAIN Iter 198580: lr = 0.169035,	loss = 2.434729,	Top-1 err = 0.381738,	Top-5 err = 0.168799,	data_time = 0.050535,	train_time = 0.335373
[2019-08-24 04:36:23,420] TRAIN Iter 198600: lr = 0.169002,	loss = 2.508826,	Top-1 err = 0.386475,	Top-5 err = 0.170410,	data_time = 0.050140,	train_time = 0.785373
[2019-08-24 04:36:39,346] TRAIN Iter 198620: lr = 0.168968,	loss = 2.593663,	Top-1 err = 0.385889,	Top-5 err = 0.171533,	data_time = 0.122028,	train_time = 0.796257
[2019-08-24 04:36:46,241] TRAIN Iter 198640: lr = 0.168935,	loss = 2.568420,	Top-1 err = 0.378955,	Top-5 err = 0.169482,	data_time = 0.050503,	train_time = 0.344772
[2019-08-24 04:37:03,309] TRAIN Iter 198660: lr = 0.168902,	loss = 2.517227,	Top-1 err = 0.385010,	Top-5 err = 0.172168,	data_time = 0.050819,	train_time = 0.853348
[2019-08-24 04:37:09,936] TRAIN Iter 198680: lr = 0.168868,	loss = 2.583073,	Top-1 err = 0.381543,	Top-5 err = 0.170166,	data_time = 0.050483,	train_time = 0.331359
[2019-08-24 04:37:26,656] TRAIN Iter 198700: lr = 0.168835,	loss = 2.516204,	Top-1 err = 0.379688,	Top-5 err = 0.169922,	data_time = 0.050403,	train_time = 0.835988
[2019-08-24 04:37:44,258] TRAIN Iter 198720: lr = 0.168802,	loss = 2.508358,	Top-1 err = 0.381689,	Top-5 err = 0.168457,	data_time = 0.050563,	train_time = 0.880081
[2019-08-24 04:37:50,672] TRAIN Iter 198740: lr = 0.168768,	loss = 2.529080,	Top-1 err = 0.377832,	Top-5 err = 0.167334,	data_time = 0.100508,	train_time = 0.320690
[2019-08-24 04:38:07,295] TRAIN Iter 198760: lr = 0.168735,	loss = 2.567669,	Top-1 err = 0.375684,	Top-5 err = 0.170020,	data_time = 0.050830,	train_time = 0.831112
[2019-08-24 04:38:24,124] TRAIN Iter 198780: lr = 0.168702,	loss = 2.534443,	Top-1 err = 0.381250,	Top-5 err = 0.171777,	data_time = 0.050341,	train_time = 0.841441
[2019-08-24 04:38:31,186] TRAIN Iter 198800: lr = 0.168668,	loss = 2.491656,	Top-1 err = 0.379639,	Top-5 err = 0.169434,	data_time = 0.050440,	train_time = 0.353104
[2019-08-24 04:38:48,105] TRAIN Iter 198820: lr = 0.168635,	loss = 2.546588,	Top-1 err = 0.383691,	Top-5 err = 0.175098,	data_time = 0.050373,	train_time = 0.845924
[2019-08-24 04:38:54,786] TRAIN Iter 198840: lr = 0.168602,	loss = 2.609148,	Top-1 err = 0.386865,	Top-5 err = 0.173096,	data_time = 0.050271,	train_time = 0.334047
[2019-08-24 04:39:12,281] TRAIN Iter 198860: lr = 0.168568,	loss = 2.546704,	Top-1 err = 0.385645,	Top-5 err = 0.173584,	data_time = 0.050227,	train_time = 0.874750
[2019-08-24 04:39:30,707] TRAIN Iter 198880: lr = 0.168535,	loss = 2.546661,	Top-1 err = 0.376563,	Top-5 err = 0.170215,	data_time = 0.050594,	train_time = 0.921285
[2019-08-24 04:39:37,622] TRAIN Iter 198900: lr = 0.168502,	loss = 2.522387,	Top-1 err = 0.383740,	Top-5 err = 0.174951,	data_time = 0.050577,	train_time = 0.345735
[2019-08-24 04:39:52,752] TRAIN Iter 198920: lr = 0.168468,	loss = 2.551653,	Top-1 err = 0.380127,	Top-5 err = 0.169238,	data_time = 0.050396,	train_time = 0.756452
[2019-08-24 04:40:07,074] TRAIN Iter 198940: lr = 0.168435,	loss = 2.545432,	Top-1 err = 0.382715,	Top-5 err = 0.169824,	data_time = 0.139396,	train_time = 0.716079
[2019-08-24 04:40:14,413] TRAIN Iter 198960: lr = 0.168402,	loss = 2.549004,	Top-1 err = 0.387402,	Top-5 err = 0.173047,	data_time = 0.049996,	train_time = 0.366978
[2019-08-24 04:40:31,098] TRAIN Iter 198980: lr = 0.168368,	loss = 2.521552,	Top-1 err = 0.379492,	Top-5 err = 0.169189,	data_time = 0.441694,	train_time = 0.834228
[2019-08-24 04:40:37,099] TRAIN Iter 199000: lr = 0.168335,	loss = 2.491928,	Top-1 err = 0.383643,	Top-5 err = 0.170215,	data_time = 0.049924,	train_time = 0.300001
[2019-08-24 04:41:27,990] TRAIN Iter 199020: lr = 0.168302,	loss = 2.617553,	Top-1 err = 0.388481,	Top-5 err = 0.172070,	data_time = 0.050814,	train_time = 2.544558
[2019-08-24 04:41:43,396] TRAIN Iter 199040: lr = 0.168268,	loss = 2.590996,	Top-1 err = 0.379053,	Top-5 err = 0.165234,	data_time = 0.050751,	train_time = 0.770254
[2019-08-24 04:41:50,616] TRAIN Iter 199060: lr = 0.168235,	loss = 2.431715,	Top-1 err = 0.377979,	Top-5 err = 0.162158,	data_time = 0.050455,	train_time = 0.361000
[2019-08-24 04:42:04,046] TRAIN Iter 199080: lr = 0.168202,	loss = 2.527588,	Top-1 err = 0.368848,	Top-5 err = 0.164062,	data_time = 0.050452,	train_time = 0.671477
[2019-08-24 04:42:11,149] TRAIN Iter 199100: lr = 0.168168,	loss = 2.487817,	Top-1 err = 0.377783,	Top-5 err = 0.166553,	data_time = 0.050616,	train_time = 0.355160
[2019-08-24 04:42:25,472] TRAIN Iter 199120: lr = 0.168135,	loss = 2.592801,	Top-1 err = 0.374414,	Top-5 err = 0.160986,	data_time = 0.050317,	train_time = 0.716125
[2019-08-24 04:42:41,859] TRAIN Iter 199140: lr = 0.168102,	loss = 2.551039,	Top-1 err = 0.370410,	Top-5 err = 0.163037,	data_time = 0.050855,	train_time = 0.819323
[2019-08-24 04:42:49,200] TRAIN Iter 199160: lr = 0.168068,	loss = 2.456641,	Top-1 err = 0.370410,	Top-5 err = 0.160986,	data_time = 0.140814,	train_time = 0.367061
[2019-08-24 04:43:04,388] TRAIN Iter 199180: lr = 0.168035,	loss = 2.524710,	Top-1 err = 0.375879,	Top-5 err = 0.168945,	data_time = 0.050445,	train_time = 0.759377
[2019-08-24 04:43:19,321] TRAIN Iter 199200: lr = 0.168002,	loss = 2.518894,	Top-1 err = 0.375488,	Top-5 err = 0.163037,	data_time = 2.758426,	train_time = 0.746632
[2019-08-24 04:43:26,435] TRAIN Iter 199220: lr = 0.167968,	loss = 2.593181,	Top-1 err = 0.376172,	Top-5 err = 0.162744,	data_time = 0.093507,	train_time = 0.355702
[2019-08-24 04:43:41,691] TRAIN Iter 199240: lr = 0.167935,	loss = 2.517761,	Top-1 err = 0.374463,	Top-5 err = 0.164600,	data_time = 0.050828,	train_time = 0.762741
[2019-08-24 04:43:49,216] TRAIN Iter 199260: lr = 0.167902,	loss = 2.557547,	Top-1 err = 0.372656,	Top-5 err = 0.162939,	data_time = 0.050802,	train_time = 0.376250
[2019-08-24 04:44:03,750] TRAIN Iter 199280: lr = 0.167868,	loss = 2.587514,	Top-1 err = 0.374854,	Top-5 err = 0.164258,	data_time = 0.050418,	train_time = 0.726650
[2019-08-24 04:44:19,623] TRAIN Iter 199300: lr = 0.167835,	loss = 2.569695,	Top-1 err = 0.381641,	Top-5 err = 0.168359,	data_time = 0.050380,	train_time = 0.793678
[2019-08-24 04:44:26,557] TRAIN Iter 199320: lr = 0.167802,	loss = 2.469720,	Top-1 err = 0.374561,	Top-5 err = 0.166309,	data_time = 0.050260,	train_time = 0.346650
[2019-08-24 04:44:41,985] TRAIN Iter 199340: lr = 0.167768,	loss = 2.541038,	Top-1 err = 0.379834,	Top-5 err = 0.165723,	data_time = 0.050328,	train_time = 0.771403
[2019-08-24 04:44:57,638] TRAIN Iter 199360: lr = 0.167735,	loss = 2.501508,	Top-1 err = 0.376172,	Top-5 err = 0.164209,	data_time = 4.980958,	train_time = 0.782640
[2019-08-24 04:45:04,753] TRAIN Iter 199380: lr = 0.167702,	loss = 2.531061,	Top-1 err = 0.378320,	Top-5 err = 0.165283,	data_time = 0.050343,	train_time = 0.355719
[2019-08-24 04:45:17,729] TRAIN Iter 199400: lr = 0.167668,	loss = 2.612008,	Top-1 err = 0.374219,	Top-5 err = 0.167676,	data_time = 0.050537,	train_time = 0.648822
[2019-08-24 04:45:25,271] TRAIN Iter 199420: lr = 0.167635,	loss = 2.630142,	Top-1 err = 0.376367,	Top-5 err = 0.169385,	data_time = 0.050364,	train_time = 0.377080
[2019-08-24 04:45:40,843] TRAIN Iter 199440: lr = 0.167602,	loss = 2.561198,	Top-1 err = 0.377686,	Top-5 err = 0.167090,	data_time = 0.117716,	train_time = 0.778552
[2019-08-24 04:45:55,369] TRAIN Iter 199460: lr = 0.167568,	loss = 2.584943,	Top-1 err = 0.379785,	Top-5 err = 0.168652,	data_time = 0.050665,	train_time = 0.726299
[2019-08-24 04:46:02,588] TRAIN Iter 199480: lr = 0.167535,	loss = 2.467820,	Top-1 err = 0.372119,	Top-5 err = 0.167676,	data_time = 0.050644,	train_time = 0.360934
[2019-08-24 04:46:18,116] TRAIN Iter 199500: lr = 0.167502,	loss = 2.616292,	Top-1 err = 0.382080,	Top-5 err = 0.166602,	data_time = 0.050471,	train_time = 0.776361
[2019-08-24 04:46:34,400] TRAIN Iter 199520: lr = 0.167468,	loss = 2.504346,	Top-1 err = 0.370801,	Top-5 err = 0.164795,	data_time = 3.512321,	train_time = 0.814213
[2019-08-24 04:46:41,484] TRAIN Iter 199540: lr = 0.167435,	loss = 2.575677,	Top-1 err = 0.375830,	Top-5 err = 0.167969,	data_time = 0.050800,	train_time = 0.354151
[2019-08-24 04:46:56,604] TRAIN Iter 199560: lr = 0.167402,	loss = 2.518831,	Top-1 err = 0.383057,	Top-5 err = 0.171143,	data_time = 0.050390,	train_time = 0.756032
[2019-08-24 04:47:04,166] TRAIN Iter 199580: lr = 0.167368,	loss = 2.479062,	Top-1 err = 0.374512,	Top-5 err = 0.165527,	data_time = 0.050485,	train_time = 0.378062
[2019-08-24 04:47:19,586] TRAIN Iter 199600: lr = 0.167335,	loss = 2.462628,	Top-1 err = 0.380908,	Top-5 err = 0.170020,	data_time = 0.050404,	train_time = 0.770981
[2019-08-24 04:47:35,507] TRAIN Iter 199620: lr = 0.167302,	loss = 2.608442,	Top-1 err = 0.374658,	Top-5 err = 0.167090,	data_time = 0.050382,	train_time = 0.796067
[2019-08-24 04:47:42,867] TRAIN Iter 199640: lr = 0.167268,	loss = 2.552357,	Top-1 err = 0.385937,	Top-5 err = 0.169824,	data_time = 0.050375,	train_time = 0.367949
[2019-08-24 04:47:58,611] TRAIN Iter 199660: lr = 0.167235,	loss = 2.508807,	Top-1 err = 0.380469,	Top-5 err = 0.165576,	data_time = 0.050366,	train_time = 0.787223
[2019-08-24 04:48:15,221] TRAIN Iter 199680: lr = 0.167202,	loss = 2.609572,	Top-1 err = 0.383984,	Top-5 err = 0.171875,	data_time = 3.446092,	train_time = 0.830484
[2019-08-24 04:48:22,366] TRAIN Iter 199700: lr = 0.167168,	loss = 2.533798,	Top-1 err = 0.381738,	Top-5 err = 0.164111,	data_time = 0.050465,	train_time = 0.357211
[2019-08-24 04:48:36,664] TRAIN Iter 199720: lr = 0.167135,	loss = 2.466384,	Top-1 err = 0.372314,	Top-5 err = 0.163379,	data_time = 0.050360,	train_time = 0.714877
[2019-08-24 04:48:44,044] TRAIN Iter 199740: lr = 0.167102,	loss = 2.516916,	Top-1 err = 0.374170,	Top-5 err = 0.163721,	data_time = 0.050543,	train_time = 0.369015
[2019-08-24 04:49:00,441] TRAIN Iter 199760: lr = 0.167068,	loss = 2.549876,	Top-1 err = 0.378906,	Top-5 err = 0.169531,	data_time = 0.050143,	train_time = 0.819827
[2019-08-24 04:49:16,677] TRAIN Iter 199780: lr = 0.167035,	loss = 2.637936,	Top-1 err = 0.378467,	Top-5 err = 0.167822,	data_time = 0.050797,	train_time = 0.811773
[2019-08-24 04:49:23,810] TRAIN Iter 199800: lr = 0.167002,	loss = 2.579400,	Top-1 err = 0.373193,	Top-5 err = 0.169629,	data_time = 0.050719,	train_time = 0.356640
[2019-08-24 04:49:39,055] TRAIN Iter 199820: lr = 0.166968,	loss = 2.579825,	Top-1 err = 0.377051,	Top-5 err = 0.168701,	data_time = 0.050348,	train_time = 0.762232
[2019-08-24 04:49:56,169] TRAIN Iter 199840: lr = 0.166935,	loss = 2.510105,	Top-1 err = 0.375879,	Top-5 err = 0.165332,	data_time = 0.050450,	train_time = 0.855683
[2019-08-24 04:50:03,460] TRAIN Iter 199860: lr = 0.166902,	loss = 2.604929,	Top-1 err = 0.383496,	Top-5 err = 0.170264,	data_time = 0.050721,	train_time = 0.364567
[2019-08-24 04:50:18,701] TRAIN Iter 199880: lr = 0.166868,	loss = 2.496932,	Top-1 err = 0.382617,	Top-5 err = 0.167041,	data_time = 0.050562,	train_time = 0.762041
[2019-08-24 04:50:25,923] TRAIN Iter 199900: lr = 0.166835,	loss = 2.508443,	Top-1 err = 0.380762,	Top-5 err = 0.171875,	data_time = 0.050647,	train_time = 0.361059
[2019-08-24 04:50:41,775] TRAIN Iter 199920: lr = 0.166802,	loss = 2.555130,	Top-1 err = 0.382422,	Top-5 err = 0.165771,	data_time = 0.050555,	train_time = 0.792568
[2019-08-24 04:50:58,005] TRAIN Iter 199940: lr = 0.166768,	loss = 2.478257,	Top-1 err = 0.376855,	Top-5 err = 0.166455,	data_time = 0.050484,	train_time = 0.811494
[2019-08-24 04:51:05,099] TRAIN Iter 199960: lr = 0.166735,	loss = 2.569427,	Top-1 err = 0.378223,	Top-5 err = 0.168604,	data_time = 0.110742,	train_time = 0.354695
[2019-08-24 04:51:21,357] TRAIN Iter 199980: lr = 0.166702,	loss = 2.517094,	Top-1 err = 0.381104,	Top-5 err = 0.171777,	data_time = 0.050374,	train_time = 0.812916
[2019-08-24 04:51:36,179] TRAIN Iter 200000: lr = 0.166668,	loss = 2.513390,	Top-1 err = 0.375391,	Top-5 err = 0.168262,	data_time = 0.050566,	train_time = 0.741069
[2019-08-24 04:52:37,874] TEST Iter 200000: loss = 2.360653,	Top-1 err = 0.349680,	Top-5 err = 0.131880,	val_time = 61.655227
[2019-08-24 04:52:44,160] TRAIN Iter 200020: lr = 0.166635,	loss = 2.601620,	Top-1 err = 0.378613,	Top-5 err = 0.167187,	data_time = 0.050389,	train_time = 0.314300
[2019-08-24 04:52:50,583] TRAIN Iter 200040: lr = 0.166602,	loss = 2.476785,	Top-1 err = 0.375977,	Top-5 err = 0.164600,	data_time = 0.050380,	train_time = 0.321129
[2019-08-24 04:52:57,021] TRAIN Iter 200060: lr = 0.166568,	loss = 2.541811,	Top-1 err = 0.377100,	Top-5 err = 0.166699,	data_time = 0.050115,	train_time = 0.321905
[2019-08-24 04:53:07,408] TRAIN Iter 200080: lr = 0.166535,	loss = 2.456933,	Top-1 err = 0.381641,	Top-5 err = 0.171875,	data_time = 0.050624,	train_time = 0.519329
[2019-08-24 04:53:23,126] TRAIN Iter 200100: lr = 0.166502,	loss = 2.522253,	Top-1 err = 0.376367,	Top-5 err = 0.167920,	data_time = 0.129052,	train_time = 0.785894
[2019-08-24 04:53:32,581] TRAIN Iter 200120: lr = 0.166468,	loss = 2.608898,	Top-1 err = 0.382471,	Top-5 err = 0.174365,	data_time = 0.050418,	train_time = 0.472717
[2019-08-24 04:53:49,648] TRAIN Iter 200140: lr = 0.166435,	loss = 2.523649,	Top-1 err = 0.379639,	Top-5 err = 0.169531,	data_time = 0.050500,	train_time = 0.853338
[2019-08-24 04:53:58,177] TRAIN Iter 200160: lr = 0.166402,	loss = 2.558805,	Top-1 err = 0.387158,	Top-5 err = 0.173926,	data_time = 0.050630,	train_time = 0.426433
[2019-08-24 04:54:14,956] TRAIN Iter 200180: lr = 0.166368,	loss = 2.475677,	Top-1 err = 0.376416,	Top-5 err = 0.169238,	data_time = 0.132823,	train_time = 0.838928
[2019-08-24 04:54:31,398] TRAIN Iter 200200: lr = 0.166335,	loss = 2.586489,	Top-1 err = 0.386328,	Top-5 err = 0.171484,	data_time = 0.049908,	train_time = 0.822107
[2019-08-24 04:54:41,565] TRAIN Iter 200220: lr = 0.166302,	loss = 2.427479,	Top-1 err = 0.379834,	Top-5 err = 0.168018,	data_time = 0.050124,	train_time = 0.508340
[2019-08-24 04:54:57,772] TRAIN Iter 200240: lr = 0.166268,	loss = 2.535544,	Top-1 err = 0.379785,	Top-5 err = 0.169043,	data_time = 0.050025,	train_time = 0.810329
[2019-08-24 04:55:47,213] TRAIN Iter 200260: lr = 0.166235,	loss = 2.543549,	Top-1 err = 0.377309,	Top-5 err = 0.170328,	data_time = 0.050239,	train_time = 2.472045
[2019-08-24 04:55:53,887] TRAIN Iter 200280: lr = 0.166202,	loss = 2.464870,	Top-1 err = 0.376660,	Top-5 err = 0.166162,	data_time = 0.050946,	train_time = 0.333662
[2019-08-24 04:56:10,360] TRAIN Iter 200300: lr = 0.166168,	loss = 2.514734,	Top-1 err = 0.373584,	Top-5 err = 0.166553,	data_time = 0.050586,	train_time = 0.823655
[2019-08-24 04:56:18,495] TRAIN Iter 200320: lr = 0.166135,	loss = 2.495185,	Top-1 err = 0.378418,	Top-5 err = 0.164404,	data_time = 0.050583,	train_time = 0.406703
[2019-08-24 04:56:33,811] TRAIN Iter 200340: lr = 0.166102,	loss = 2.520547,	Top-1 err = 0.370215,	Top-5 err = 0.168359,	data_time = 0.050698,	train_time = 0.765808
[2019-08-24 04:56:46,761] TRAIN Iter 200360: lr = 0.166068,	loss = 2.467602,	Top-1 err = 0.370752,	Top-5 err = 0.167041,	data_time = 0.052587,	train_time = 0.647500
[2019-08-24 04:56:54,068] TRAIN Iter 200380: lr = 0.166035,	loss = 2.487267,	Top-1 err = 0.373389,	Top-5 err = 0.164746,	data_time = 0.050430,	train_time = 0.365330
[2019-08-24 04:57:08,719] TRAIN Iter 200400: lr = 0.166002,	loss = 2.549332,	Top-1 err = 0.373096,	Top-5 err = 0.165039,	data_time = 0.050678,	train_time = 0.732480
[2019-08-24 04:57:25,628] TRAIN Iter 200420: lr = 0.165968,	loss = 2.470268,	Top-1 err = 0.380078,	Top-5 err = 0.167334,	data_time = 0.138557,	train_time = 0.845459
[2019-08-24 04:57:32,793] TRAIN Iter 200440: lr = 0.165935,	loss = 2.450198,	Top-1 err = 0.375000,	Top-5 err = 0.162744,	data_time = 0.050383,	train_time = 0.358227
[2019-08-24 04:57:47,784] TRAIN Iter 200460: lr = 0.165902,	loss = 2.589530,	Top-1 err = 0.370605,	Top-5 err = 0.166846,	data_time = 0.050562,	train_time = 0.749551
[2019-08-24 04:57:55,670] TRAIN Iter 200480: lr = 0.165868,	loss = 2.513020,	Top-1 err = 0.376465,	Top-5 err = 0.165723,	data_time = 0.050804,	train_time = 0.394294
[2019-08-24 04:58:10,713] TRAIN Iter 200500: lr = 0.165835,	loss = 2.467402,	Top-1 err = 0.369287,	Top-5 err = 0.159863,	data_time = 0.050248,	train_time = 0.752139
[2019-08-24 04:58:25,661] TRAIN Iter 200520: lr = 0.165802,	loss = 2.503229,	Top-1 err = 0.372656,	Top-5 err = 0.162842,	data_time = 0.050463,	train_time = 0.747354
[2019-08-24 04:58:33,208] TRAIN Iter 200540: lr = 0.165768,	loss = 2.584269,	Top-1 err = 0.377637,	Top-5 err = 0.164551,	data_time = 0.050357,	train_time = 0.377342
[2019-08-24 04:58:47,668] TRAIN Iter 200560: lr = 0.165735,	loss = 2.604187,	Top-1 err = 0.374219,	Top-5 err = 0.165967,	data_time = 0.050661,	train_time = 0.722972
[2019-08-24 04:59:03,515] TRAIN Iter 200580: lr = 0.165702,	loss = 2.585791,	Top-1 err = 0.376465,	Top-5 err = 0.166748,	data_time = 0.050294,	train_time = 0.792354
[2019-08-24 04:59:10,981] TRAIN Iter 200600: lr = 0.165668,	loss = 2.556607,	Top-1 err = 0.375928,	Top-5 err = 0.166797,	data_time = 0.050704,	train_time = 0.373279
[2019-08-24 04:59:25,837] TRAIN Iter 200620: lr = 0.165635,	loss = 2.487835,	Top-1 err = 0.377148,	Top-5 err = 0.163574,	data_time = 0.050435,	train_time = 0.742806
[2019-08-24 04:59:33,947] TRAIN Iter 200640: lr = 0.165602,	loss = 2.526808,	Top-1 err = 0.375537,	Top-5 err = 0.167383,	data_time = 0.050807,	train_time = 0.405442
[2019-08-24 04:59:47,974] TRAIN Iter 200660: lr = 0.165568,	loss = 2.523047,	Top-1 err = 0.376855,	Top-5 err = 0.167090,	data_time = 0.050697,	train_time = 0.701362
[2019-08-24 05:00:03,020] TRAIN Iter 200680: lr = 0.165535,	loss = 2.477264,	Top-1 err = 0.380078,	Top-5 err = 0.169580,	data_time = 0.050437,	train_time = 0.752299
[2019-08-24 05:00:10,197] TRAIN Iter 200700: lr = 0.165502,	loss = 2.616024,	Top-1 err = 0.377002,	Top-5 err = 0.166064,	data_time = 0.050872,	train_time = 0.358817
[2019-08-24 05:00:25,199] TRAIN Iter 200720: lr = 0.165468,	loss = 2.517138,	Top-1 err = 0.382178,	Top-5 err = 0.167187,	data_time = 0.050507,	train_time = 0.750076
[2019-08-24 05:00:40,557] TRAIN Iter 200740: lr = 0.165435,	loss = 2.509739,	Top-1 err = 0.377783,	Top-5 err = 0.165137,	data_time = 0.146618,	train_time = 0.767914
[2019-08-24 05:00:47,817] TRAIN Iter 200760: lr = 0.165402,	loss = 2.511164,	Top-1 err = 0.382812,	Top-5 err = 0.171826,	data_time = 0.148352,	train_time = 0.362975
[2019-08-24 05:01:03,608] TRAIN Iter 200780: lr = 0.165368,	loss = 2.495115,	Top-1 err = 0.377197,	Top-5 err = 0.165576,	data_time = 0.050448,	train_time = 0.789523
[2019-08-24 05:01:11,324] TRAIN Iter 200800: lr = 0.165335,	loss = 2.574010,	Top-1 err = 0.380273,	Top-5 err = 0.168359,	data_time = 0.050788,	train_time = 0.385806
[2019-08-24 05:01:25,613] TRAIN Iter 200820: lr = 0.165302,	loss = 2.504498,	Top-1 err = 0.374121,	Top-5 err = 0.165918,	data_time = 0.050547,	train_time = 0.714411
[2019-08-24 05:01:42,363] TRAIN Iter 200840: lr = 0.165268,	loss = 2.590742,	Top-1 err = 0.378955,	Top-5 err = 0.169629,	data_time = 0.050364,	train_time = 0.837515
[2019-08-24 05:01:49,459] TRAIN Iter 200860: lr = 0.165235,	loss = 2.585704,	Top-1 err = 0.381396,	Top-5 err = 0.169043,	data_time = 0.050433,	train_time = 0.354754
[2019-08-24 05:02:05,628] TRAIN Iter 200880: lr = 0.165202,	loss = 2.535721,	Top-1 err = 0.373340,	Top-5 err = 0.166992,	data_time = 0.050924,	train_time = 0.808465
[2019-08-24 05:02:22,082] TRAIN Iter 200900: lr = 0.165168,	loss = 2.553540,	Top-1 err = 0.374658,	Top-5 err = 0.164160,	data_time = 0.050459,	train_time = 0.822686
[2019-08-24 05:02:29,130] TRAIN Iter 200920: lr = 0.165135,	loss = 2.532162,	Top-1 err = 0.378955,	Top-5 err = 0.165527,	data_time = 0.050811,	train_time = 0.352391
[2019-08-24 05:02:44,670] TRAIN Iter 200940: lr = 0.165102,	loss = 2.625845,	Top-1 err = 0.377295,	Top-5 err = 0.170264,	data_time = 0.050647,	train_time = 0.776983
[2019-08-24 05:02:51,863] TRAIN Iter 200960: lr = 0.165068,	loss = 2.605093,	Top-1 err = 0.377246,	Top-5 err = 0.169287,	data_time = 0.050600,	train_time = 0.359602
[2019-08-24 05:03:08,556] TRAIN Iter 200980: lr = 0.165035,	loss = 2.532587,	Top-1 err = 0.383057,	Top-5 err = 0.172900,	data_time = 0.050781,	train_time = 0.834660
[2019-08-24 05:03:24,848] TRAIN Iter 201000: lr = 0.165002,	loss = 2.621664,	Top-1 err = 0.377197,	Top-5 err = 0.166748,	data_time = 0.050843,	train_time = 0.814561
[2019-08-24 05:03:31,494] TRAIN Iter 201020: lr = 0.164968,	loss = 2.548030,	Top-1 err = 0.378662,	Top-5 err = 0.167773,	data_time = 0.050367,	train_time = 0.332288
[2019-08-24 05:03:48,554] TRAIN Iter 201040: lr = 0.164935,	loss = 2.564562,	Top-1 err = 0.374463,	Top-5 err = 0.164648,	data_time = 0.050453,	train_time = 0.852997
[2019-08-24 05:04:04,265] TRAIN Iter 201060: lr = 0.164902,	loss = 2.534752,	Top-1 err = 0.381982,	Top-5 err = 0.166260,	data_time = 0.050533,	train_time = 0.785527
[2019-08-24 05:04:12,214] TRAIN Iter 201080: lr = 0.164868,	loss = 2.518602,	Top-1 err = 0.376367,	Top-5 err = 0.169287,	data_time = 0.095219,	train_time = 0.397444
[2019-08-24 05:04:29,630] TRAIN Iter 201100: lr = 0.164835,	loss = 2.616413,	Top-1 err = 0.382471,	Top-5 err = 0.169971,	data_time = 0.050579,	train_time = 0.870779
[2019-08-24 05:04:36,661] TRAIN Iter 201120: lr = 0.164802,	loss = 2.621837,	Top-1 err = 0.378662,	Top-5 err = 0.168213,	data_time = 0.050404,	train_time = 0.351549
[2019-08-24 05:04:53,299] TRAIN Iter 201140: lr = 0.164768,	loss = 2.517223,	Top-1 err = 0.383105,	Top-5 err = 0.169189,	data_time = 0.050521,	train_time = 0.831879
[2019-08-24 05:05:12,125] TRAIN Iter 201160: lr = 0.164735,	loss = 2.559090,	Top-1 err = 0.383594,	Top-5 err = 0.169336,	data_time = 0.050291,	train_time = 0.941276
[2019-08-24 05:05:18,956] TRAIN Iter 201180: lr = 0.164702,	loss = 2.505202,	Top-1 err = 0.377246,	Top-5 err = 0.167920,	data_time = 0.050339,	train_time = 0.341579
[2019-08-24 05:05:36,981] TRAIN Iter 201200: lr = 0.164668,	loss = 2.502621,	Top-1 err = 0.373437,	Top-5 err = 0.164209,	data_time = 0.050468,	train_time = 0.901200
[2019-08-24 05:05:56,653] TRAIN Iter 201220: lr = 0.164635,	loss = 2.498329,	Top-1 err = 0.381396,	Top-5 err = 0.170361,	data_time = 0.050663,	train_time = 0.983589
[2019-08-24 05:06:04,954] TRAIN Iter 201240: lr = 0.164602,	loss = 2.543337,	Top-1 err = 0.379785,	Top-5 err = 0.170508,	data_time = 0.050934,	train_time = 0.415066
[2019-08-24 05:06:24,186] TRAIN Iter 201260: lr = 0.164568,	loss = 2.626675,	Top-1 err = 0.379736,	Top-5 err = 0.168408,	data_time = 0.050515,	train_time = 0.961541
[2019-08-24 05:06:32,009] TRAIN Iter 201280: lr = 0.164535,	loss = 2.568080,	Top-1 err = 0.381250,	Top-5 err = 0.165723,	data_time = 0.050392,	train_time = 0.391182
[2019-08-24 05:06:45,504] TRAIN Iter 201300: lr = 0.164502,	loss = 2.625458,	Top-1 err = 0.382520,	Top-5 err = 0.170703,	data_time = 0.050379,	train_time = 0.674723
[2019-08-24 05:07:03,606] TRAIN Iter 201320: lr = 0.164468,	loss = 2.513583,	Top-1 err = 0.375244,	Top-5 err = 0.166211,	data_time = 0.050487,	train_time = 0.905072
[2019-08-24 05:07:10,401] TRAIN Iter 201340: lr = 0.164435,	loss = 2.505342,	Top-1 err = 0.380420,	Top-5 err = 0.168799,	data_time = 0.050644,	train_time = 0.339741
[2019-08-24 05:07:27,421] TRAIN Iter 201360: lr = 0.164402,	loss = 2.538824,	Top-1 err = 0.374902,	Top-5 err = 0.167383,	data_time = 0.050584,	train_time = 0.850965
[2019-08-24 05:07:45,516] TRAIN Iter 201380: lr = 0.164368,	loss = 2.454174,	Top-1 err = 0.381641,	Top-5 err = 0.169824,	data_time = 0.050400,	train_time = 0.904745
[2019-08-24 05:07:53,031] TRAIN Iter 201400: lr = 0.164335,	loss = 2.480018,	Top-1 err = 0.382617,	Top-5 err = 0.168555,	data_time = 0.050295,	train_time = 0.375744
[2019-08-24 05:08:12,725] TRAIN Iter 201420: lr = 0.164302,	loss = 2.497860,	Top-1 err = 0.380713,	Top-5 err = 0.171484,	data_time = 0.050345,	train_time = 0.984707
[2019-08-24 05:08:19,952] TRAIN Iter 201440: lr = 0.164268,	loss = 2.470785,	Top-1 err = 0.377197,	Top-5 err = 0.166553,	data_time = 0.050478,	train_time = 0.361329
[2019-08-24 05:08:37,876] TRAIN Iter 201460: lr = 0.164235,	loss = 2.548192,	Top-1 err = 0.379883,	Top-5 err = 0.168604,	data_time = 0.050133,	train_time = 0.896169
[2019-08-24 05:08:53,578] TRAIN Iter 201480: lr = 0.164202,	loss = 2.470301,	Top-1 err = 0.367871,	Top-5 err = 0.165088,	data_time = 0.049866,	train_time = 0.785115
[2019-08-24 05:08:59,702] TRAIN Iter 201500: lr = 0.164168,	loss = 2.557485,	Top-1 err = 0.377637,	Top-5 err = 0.165771,	data_time = 0.049886,	train_time = 0.306187
[2019-08-24 05:09:52,201] TRAIN Iter 201520: lr = 0.164135,	loss = 2.563403,	Top-1 err = 0.375348,	Top-5 err = 0.169880,	data_time = 0.050929,	train_time = 2.624892
[2019-08-24 05:09:59,655] TRAIN Iter 201540: lr = 0.164102,	loss = 2.549626,	Top-1 err = 0.375049,	Top-5 err = 0.163623,	data_time = 0.126482,	train_time = 0.372697
[2019-08-24 05:10:14,119] TRAIN Iter 201560: lr = 0.164068,	loss = 2.521082,	Top-1 err = 0.374609,	Top-5 err = 0.163574,	data_time = 0.050297,	train_time = 0.723168
[2019-08-24 05:10:28,646] TRAIN Iter 201580: lr = 0.164035,	loss = 2.388165,	Top-1 err = 0.368652,	Top-5 err = 0.161572,	data_time = 0.101799,	train_time = 0.726361
[2019-08-24 05:10:35,859] TRAIN Iter 201600: lr = 0.164002,	loss = 2.512259,	Top-1 err = 0.374121,	Top-5 err = 0.166260,	data_time = 0.050557,	train_time = 0.360630
[2019-08-24 05:10:50,917] TRAIN Iter 201620: lr = 0.163968,	loss = 2.489609,	Top-1 err = 0.372803,	Top-5 err = 0.162598,	data_time = 0.050398,	train_time = 0.752871
[2019-08-24 05:11:02,132] TRAIN Iter 201640: lr = 0.163935,	loss = 2.624565,	Top-1 err = 0.381982,	Top-5 err = 0.168799,	data_time = 0.133244,	train_time = 0.560722
[2019-08-24 05:11:12,562] TRAIN Iter 201660: lr = 0.163902,	loss = 2.570847,	Top-1 err = 0.376514,	Top-5 err = 0.165039,	data_time = 0.050508,	train_time = 0.521483
[2019-08-24 05:11:28,933] TRAIN Iter 201680: lr = 0.163868,	loss = 2.618893,	Top-1 err = 0.372070,	Top-5 err = 0.163086,	data_time = 0.050340,	train_time = 0.818533
[2019-08-24 05:11:36,569] TRAIN Iter 201700: lr = 0.163835,	loss = 2.582730,	Top-1 err = 0.366846,	Top-5 err = 0.158936,	data_time = 0.120555,	train_time = 0.381785
[2019-08-24 05:11:50,395] TRAIN Iter 201720: lr = 0.163802,	loss = 2.568964,	Top-1 err = 0.372461,	Top-5 err = 0.163672,	data_time = 0.050438,	train_time = 0.691328
[2019-08-24 05:12:05,045] TRAIN Iter 201740: lr = 0.163768,	loss = 2.523367,	Top-1 err = 0.375732,	Top-5 err = 0.168799,	data_time = 0.050817,	train_time = 0.732456
[2019-08-24 05:12:11,880] TRAIN Iter 201760: lr = 0.163735,	loss = 2.490660,	Top-1 err = 0.376611,	Top-5 err = 0.165430,	data_time = 0.050874,	train_time = 0.341725
[2019-08-24 05:12:28,761] TRAIN Iter 201780: lr = 0.163702,	loss = 2.477817,	Top-1 err = 0.370850,	Top-5 err = 0.163428,	data_time = 0.050442,	train_time = 0.844039
[2019-08-24 05:12:40,364] TRAIN Iter 201800: lr = 0.163668,	loss = 2.445318,	Top-1 err = 0.373828,	Top-5 err = 0.166748,	data_time = 0.050316,	train_time = 0.580130
[2019-08-24 05:12:50,668] TRAIN Iter 201820: lr = 0.163635,	loss = 2.493311,	Top-1 err = 0.373926,	Top-5 err = 0.163818,	data_time = 0.050715,	train_time = 0.515185
[2019-08-24 05:13:04,903] TRAIN Iter 201840: lr = 0.163602,	loss = 2.560828,	Top-1 err = 0.379590,	Top-5 err = 0.165967,	data_time = 0.050393,	train_time = 0.711780
[2019-08-24 05:13:12,198] TRAIN Iter 201860: lr = 0.163568,	loss = 2.545437,	Top-1 err = 0.374756,	Top-5 err = 0.164404,	data_time = 0.050445,	train_time = 0.364713
[2019-08-24 05:13:28,429] TRAIN Iter 201880: lr = 0.163535,	loss = 2.462227,	Top-1 err = 0.374219,	Top-5 err = 0.165820,	data_time = 0.050488,	train_time = 0.811552
[2019-08-24 05:13:44,352] TRAIN Iter 201900: lr = 0.163502,	loss = 2.565659,	Top-1 err = 0.379834,	Top-5 err = 0.164404,	data_time = 0.050317,	train_time = 0.796137
[2019-08-24 05:13:51,478] TRAIN Iter 201920: lr = 0.163468,	loss = 2.567083,	Top-1 err = 0.372900,	Top-5 err = 0.168359,	data_time = 0.050627,	train_time = 0.356261
[2019-08-24 05:14:06,835] TRAIN Iter 201940: lr = 0.163435,	loss = 2.465864,	Top-1 err = 0.378662,	Top-5 err = 0.164746,	data_time = 0.050501,	train_time = 0.767807
[2019-08-24 05:14:22,705] TRAIN Iter 201960: lr = 0.163402,	loss = 2.544165,	Top-1 err = 0.376318,	Top-5 err = 0.166748,	data_time = 0.144032,	train_time = 0.793510
[2019-08-24 05:14:29,591] TRAIN Iter 201980: lr = 0.163368,	loss = 2.559192,	Top-1 err = 0.381152,	Top-5 err = 0.166211,	data_time = 0.050699,	train_time = 0.344306
[2019-08-24 05:14:45,705] TRAIN Iter 202000: lr = 0.163335,	loss = 2.500072,	Top-1 err = 0.374805,	Top-5 err = 0.163916,	data_time = 0.050603,	train_time = 0.805660
[2019-08-24 05:14:53,247] TRAIN Iter 202020: lr = 0.163302,	loss = 2.516984,	Top-1 err = 0.375586,	Top-5 err = 0.164062,	data_time = 0.050942,	train_time = 0.377068
[2019-08-24 05:15:08,100] TRAIN Iter 202040: lr = 0.163268,	loss = 2.555594,	Top-1 err = 0.378760,	Top-5 err = 0.170850,	data_time = 0.050475,	train_time = 0.742649
[2019-08-24 05:15:23,293] TRAIN Iter 202060: lr = 0.163235,	loss = 2.594894,	Top-1 err = 0.379346,	Top-5 err = 0.165527,	data_time = 0.050418,	train_time = 0.759619
[2019-08-24 05:15:29,961] TRAIN Iter 202080: lr = 0.163202,	loss = 2.542907,	Top-1 err = 0.377393,	Top-5 err = 0.169971,	data_time = 0.050480,	train_time = 0.333430
[2019-08-24 05:15:47,090] TRAIN Iter 202100: lr = 0.163168,	loss = 2.542007,	Top-1 err = 0.380566,	Top-5 err = 0.167090,	data_time = 0.050324,	train_time = 0.856441
[2019-08-24 05:16:01,909] TRAIN Iter 202120: lr = 0.163135,	loss = 2.505688,	Top-1 err = 0.378809,	Top-5 err = 0.162695,	data_time = 0.050565,	train_time = 0.740923
[2019-08-24 05:16:08,805] TRAIN Iter 202140: lr = 0.163102,	loss = 2.587698,	Top-1 err = 0.376221,	Top-5 err = 0.167969,	data_time = 0.050384,	train_time = 0.344787
[2019-08-24 05:16:26,058] TRAIN Iter 202160: lr = 0.163068,	loss = 2.569530,	Top-1 err = 0.379004,	Top-5 err = 0.165479,	data_time = 0.050695,	train_time = 0.862604
[2019-08-24 05:16:33,098] TRAIN Iter 202180: lr = 0.163035,	loss = 2.432642,	Top-1 err = 0.374805,	Top-5 err = 0.165674,	data_time = 0.050598,	train_time = 0.352012
[2019-08-24 05:16:48,234] TRAIN Iter 202200: lr = 0.163002,	loss = 2.484616,	Top-1 err = 0.375977,	Top-5 err = 0.164648,	data_time = 0.050462,	train_time = 0.756765
[2019-08-24 05:17:04,840] TRAIN Iter 202220: lr = 0.162968,	loss = 2.559562,	Top-1 err = 0.378418,	Top-5 err = 0.169971,	data_time = 0.050464,	train_time = 0.830325
[2019-08-24 05:17:11,610] TRAIN Iter 202240: lr = 0.162935,	loss = 2.579353,	Top-1 err = 0.381885,	Top-5 err = 0.166943,	data_time = 0.050555,	train_time = 0.338457
[2019-08-24 05:17:28,184] TRAIN Iter 202260: lr = 0.162902,	loss = 2.532632,	Top-1 err = 0.380713,	Top-5 err = 0.171436,	data_time = 0.050496,	train_time = 0.828692
[2019-08-24 05:17:42,402] TRAIN Iter 202280: lr = 0.162868,	loss = 2.521564,	Top-1 err = 0.372168,	Top-5 err = 0.166406,	data_time = 0.150960,	train_time = 0.710870
[2019-08-24 05:17:50,974] TRAIN Iter 202300: lr = 0.162835,	loss = 2.538910,	Top-1 err = 0.384082,	Top-5 err = 0.171777,	data_time = 0.050440,	train_time = 0.428599
[2019-08-24 05:18:08,091] TRAIN Iter 202320: lr = 0.162802,	loss = 2.447263,	Top-1 err = 0.380664,	Top-5 err = 0.167627,	data_time = 0.050568,	train_time = 0.855843
[2019-08-24 05:18:15,157] TRAIN Iter 202340: lr = 0.162768,	loss = 2.572357,	Top-1 err = 0.374121,	Top-5 err = 0.166211,	data_time = 0.050367,	train_time = 0.353263
[2019-08-24 05:18:31,400] TRAIN Iter 202360: lr = 0.162735,	loss = 2.572518,	Top-1 err = 0.382861,	Top-5 err = 0.170459,	data_time = 0.050689,	train_time = 0.812155
[2019-08-24 05:18:46,490] TRAIN Iter 202380: lr = 0.162702,	loss = 2.573523,	Top-1 err = 0.378564,	Top-5 err = 0.169580,	data_time = 0.050698,	train_time = 0.754486
[2019-08-24 05:18:53,424] TRAIN Iter 202400: lr = 0.162668,	loss = 2.535599,	Top-1 err = 0.380078,	Top-5 err = 0.165918,	data_time = 0.050289,	train_time = 0.346694
[2019-08-24 05:19:10,844] TRAIN Iter 202420: lr = 0.162635,	loss = 2.484571,	Top-1 err = 0.376123,	Top-5 err = 0.165967,	data_time = 0.050869,	train_time = 0.870980
[2019-08-24 05:19:27,717] TRAIN Iter 202440: lr = 0.162602,	loss = 2.480613,	Top-1 err = 0.381006,	Top-5 err = 0.166504,	data_time = 0.050317,	train_time = 0.843632
[2019-08-24 05:19:34,682] TRAIN Iter 202460: lr = 0.162568,	loss = 2.554391,	Top-1 err = 0.376904,	Top-5 err = 0.164648,	data_time = 0.050435,	train_time = 0.348246
[2019-08-24 05:19:52,350] TRAIN Iter 202480: lr = 0.162535,	loss = 2.584678,	Top-1 err = 0.380957,	Top-5 err = 0.167822,	data_time = 0.112238,	train_time = 0.883381
[2019-08-24 05:19:58,895] TRAIN Iter 202500: lr = 0.162502,	loss = 2.623817,	Top-1 err = 0.376123,	Top-5 err = 0.166357,	data_time = 0.050457,	train_time = 0.327229
[2019-08-24 05:20:18,078] TRAIN Iter 202520: lr = 0.162468,	loss = 2.522483,	Top-1 err = 0.374121,	Top-5 err = 0.159521,	data_time = 0.050372,	train_time = 0.959141
[2019-08-24 05:20:35,006] TRAIN Iter 202540: lr = 0.162435,	loss = 2.523182,	Top-1 err = 0.382959,	Top-5 err = 0.174219,	data_time = 0.050626,	train_time = 0.846381
[2019-08-24 05:20:41,805] TRAIN Iter 202560: lr = 0.162402,	loss = 2.689419,	Top-1 err = 0.381592,	Top-5 err = 0.170020,	data_time = 0.050568,	train_time = 0.339919
[2019-08-24 05:20:59,460] TRAIN Iter 202580: lr = 0.162368,	loss = 2.564152,	Top-1 err = 0.378418,	Top-5 err = 0.164062,	data_time = 0.050249,	train_time = 0.882748
[2019-08-24 05:21:18,092] TRAIN Iter 202600: lr = 0.162335,	loss = 2.481318,	Top-1 err = 0.380908,	Top-5 err = 0.167236,	data_time = 0.050573,	train_time = 0.931600
[2019-08-24 05:21:24,861] TRAIN Iter 202620: lr = 0.162302,	loss = 2.641362,	Top-1 err = 0.382178,	Top-5 err = 0.172607,	data_time = 0.050466,	train_time = 0.338393
[2019-08-24 05:21:44,152] TRAIN Iter 202640: lr = 0.162268,	loss = 2.544882,	Top-1 err = 0.374854,	Top-5 err = 0.166309,	data_time = 0.050490,	train_time = 0.964571
[2019-08-24 05:21:51,000] TRAIN Iter 202660: lr = 0.162235,	loss = 2.614263,	Top-1 err = 0.380371,	Top-5 err = 0.166992,	data_time = 0.050672,	train_time = 0.342392
[2019-08-24 05:22:08,919] TRAIN Iter 202680: lr = 0.162202,	loss = 2.606247,	Top-1 err = 0.383838,	Top-5 err = 0.172266,	data_time = 0.050348,	train_time = 0.895900
[2019-08-24 05:22:27,877] TRAIN Iter 202700: lr = 0.162168,	loss = 2.450037,	Top-1 err = 0.377783,	Top-5 err = 0.168213,	data_time = 0.050139,	train_time = 0.947920
[2019-08-24 05:22:34,519] TRAIN Iter 202720: lr = 0.162135,	loss = 2.523273,	Top-1 err = 0.379053,	Top-5 err = 0.169629,	data_time = 0.050224,	train_time = 0.332089
[2019-08-24 05:22:51,787] TRAIN Iter 202740: lr = 0.162102,	loss = 2.655280,	Top-1 err = 0.388916,	Top-5 err = 0.174658,	data_time = 0.049885,	train_time = 0.863361
[2019-08-24 05:23:02,120] TRAIN Iter 202760: lr = 0.162068,	loss = 3.020134,	Top-1 err = 0.381419,	Top-5 err = 0.169415,	data_time = 0.007044,	train_time = 0.516654
[2019-08-24 05:23:51,212] TRAIN Iter 202780: lr = 0.162035,	loss = 2.491658,	Top-1 err = 0.374365,	Top-5 err = 0.163672,	data_time = 0.050380,	train_time = 2.454558
[2019-08-24 05:24:06,582] TRAIN Iter 202800: lr = 0.162002,	loss = 2.534809,	Top-1 err = 0.370068,	Top-5 err = 0.162549,	data_time = 0.050769,	train_time = 0.768479
[2019-08-24 05:24:13,709] TRAIN Iter 202820: lr = 0.161968,	loss = 2.591957,	Top-1 err = 0.366016,	Top-5 err = 0.165967,	data_time = 0.050424,	train_time = 0.356341
[2019-08-24 05:24:27,804] TRAIN Iter 202840: lr = 0.161935,	loss = 2.429026,	Top-1 err = 0.375391,	Top-5 err = 0.163232,	data_time = 0.050411,	train_time = 0.704740
[2019-08-24 05:24:43,213] TRAIN Iter 202860: lr = 0.161902,	loss = 2.517704,	Top-1 err = 0.370654,	Top-5 err = 0.163330,	data_time = 0.050825,	train_time = 0.770430
[2019-08-24 05:24:50,264] TRAIN Iter 202880: lr = 0.161868,	loss = 2.463135,	Top-1 err = 0.374414,	Top-5 err = 0.160645,	data_time = 0.050287,	train_time = 0.352552
[2019-08-24 05:25:05,055] TRAIN Iter 202900: lr = 0.161835,	loss = 2.452681,	Top-1 err = 0.375439,	Top-5 err = 0.166309,	data_time = 0.050525,	train_time = 0.739535
[2019-08-24 05:25:12,213] TRAIN Iter 202920: lr = 0.161802,	loss = 2.359293,	Top-1 err = 0.365332,	Top-5 err = 0.161084,	data_time = 0.050329,	train_time = 0.357890
[2019-08-24 05:25:26,380] TRAIN Iter 202940: lr = 0.161768,	loss = 2.504313,	Top-1 err = 0.369434,	Top-5 err = 0.161279,	data_time = 0.050482,	train_time = 0.708311
[2019-08-24 05:25:41,656] TRAIN Iter 202960: lr = 0.161735,	loss = 2.462741,	Top-1 err = 0.368994,	Top-5 err = 0.164648,	data_time = 0.050672,	train_time = 0.763796
[2019-08-24 05:25:48,433] TRAIN Iter 202980: lr = 0.161702,	loss = 2.503834,	Top-1 err = 0.370264,	Top-5 err = 0.161523,	data_time = 0.050296,	train_time = 0.338822
[2019-08-24 05:26:04,810] TRAIN Iter 203000: lr = 0.161668,	loss = 2.526443,	Top-1 err = 0.376904,	Top-5 err = 0.167822,	data_time = 0.050320,	train_time = 0.818866
[2019-08-24 05:26:18,894] TRAIN Iter 203020: lr = 0.161635,	loss = 2.532698,	Top-1 err = 0.370752,	Top-5 err = 0.164697,	data_time = 0.134492,	train_time = 0.704183
[2019-08-24 05:26:25,671] TRAIN Iter 203040: lr = 0.161602,	loss = 2.494546,	Top-1 err = 0.378271,	Top-5 err = 0.165234,	data_time = 0.050594,	train_time = 0.338838
[2019-08-24 05:26:40,839] TRAIN Iter 203060: lr = 0.161568,	loss = 2.591530,	Top-1 err = 0.372412,	Top-5 err = 0.166211,	data_time = 0.050304,	train_time = 0.758360
[2019-08-24 05:26:48,183] TRAIN Iter 203080: lr = 0.161535,	loss = 2.560188,	Top-1 err = 0.375635,	Top-5 err = 0.164209,	data_time = 0.116877,	train_time = 0.367197
[2019-08-24 05:27:03,270] TRAIN Iter 203100: lr = 0.161502,	loss = 2.489246,	Top-1 err = 0.373828,	Top-5 err = 0.165430,	data_time = 0.050517,	train_time = 0.754319
[2019-08-24 05:27:20,532] TRAIN Iter 203120: lr = 0.161468,	loss = 2.576465,	Top-1 err = 0.371875,	Top-5 err = 0.163135,	data_time = 0.050559,	train_time = 0.863079
[2019-08-24 05:27:28,080] TRAIN Iter 203140: lr = 0.161435,	loss = 2.431317,	Top-1 err = 0.371387,	Top-5 err = 0.162451,	data_time = 0.050456,	train_time = 0.377386
[2019-08-24 05:27:42,599] TRAIN Iter 203160: lr = 0.161402,	loss = 2.504723,	Top-1 err = 0.374072,	Top-5 err = 0.164746,	data_time = 0.050828,	train_time = 0.725942
[2019-08-24 05:27:56,866] TRAIN Iter 203180: lr = 0.161368,	loss = 2.573356,	Top-1 err = 0.375488,	Top-5 err = 0.166113,	data_time = 0.116021,	train_time = 0.713363
[2019-08-24 05:28:03,594] TRAIN Iter 203200: lr = 0.161335,	loss = 2.511628,	Top-1 err = 0.377148,	Top-5 err = 0.165332,	data_time = 0.050440,	train_time = 0.336376
[2019-08-24 05:28:20,297] TRAIN Iter 203220: lr = 0.161302,	loss = 2.520374,	Top-1 err = 0.369385,	Top-5 err = 0.160498,	data_time = 0.050520,	train_time = 0.835145
[2019-08-24 05:28:27,838] TRAIN Iter 203240: lr = 0.161268,	loss = 2.499794,	Top-1 err = 0.377979,	Top-5 err = 0.165430,	data_time = 0.050798,	train_time = 0.377006
[2019-08-24 05:28:42,416] TRAIN Iter 203260: lr = 0.161235,	loss = 2.527592,	Top-1 err = 0.383447,	Top-5 err = 0.170020,	data_time = 0.050796,	train_time = 0.728905
[2019-08-24 05:28:59,811] TRAIN Iter 203280: lr = 0.161202,	loss = 2.500182,	Top-1 err = 0.372705,	Top-5 err = 0.165039,	data_time = 0.050662,	train_time = 0.869750
[2019-08-24 05:29:06,885] TRAIN Iter 203300: lr = 0.161168,	loss = 2.489490,	Top-1 err = 0.374219,	Top-5 err = 0.165137,	data_time = 0.050743,	train_time = 0.353654
[2019-08-24 05:29:22,010] TRAIN Iter 203320: lr = 0.161135,	loss = 2.540922,	Top-1 err = 0.367969,	Top-5 err = 0.161572,	data_time = 0.050353,	train_time = 0.756230
[2019-08-24 05:29:37,631] TRAIN Iter 203340: lr = 0.161102,	loss = 2.463793,	Top-1 err = 0.374023,	Top-5 err = 0.165967,	data_time = 0.050256,	train_time = 0.781078
[2019-08-24 05:29:44,328] TRAIN Iter 203360: lr = 0.161068,	loss = 2.511148,	Top-1 err = 0.380029,	Top-5 err = 0.168701,	data_time = 0.050490,	train_time = 0.334798
[2019-08-24 05:30:02,673] TRAIN Iter 203380: lr = 0.161035,	loss = 2.454212,	Top-1 err = 0.373633,	Top-5 err = 0.164404,	data_time = 0.050677,	train_time = 0.917265
[2019-08-24 05:30:10,285] TRAIN Iter 203400: lr = 0.161002,	loss = 2.519369,	Top-1 err = 0.375830,	Top-5 err = 0.166748,	data_time = 0.050650,	train_time = 0.380584
[2019-08-24 05:30:23,933] TRAIN Iter 203420: lr = 0.160968,	loss = 2.496341,	Top-1 err = 0.373975,	Top-5 err = 0.165137,	data_time = 0.050836,	train_time = 0.682359
[2019-08-24 05:30:39,700] TRAIN Iter 203440: lr = 0.160935,	loss = 2.579996,	Top-1 err = 0.383398,	Top-5 err = 0.171680,	data_time = 0.050547,	train_time = 0.788364
[2019-08-24 05:30:46,648] TRAIN Iter 203460: lr = 0.160902,	loss = 2.566753,	Top-1 err = 0.373877,	Top-5 err = 0.166162,	data_time = 0.050437,	train_time = 0.347358
[2019-08-24 05:31:01,808] TRAIN Iter 203480: lr = 0.160868,	loss = 2.605992,	Top-1 err = 0.381055,	Top-5 err = 0.167822,	data_time = 0.050714,	train_time = 0.757984
[2019-08-24 05:31:18,969] TRAIN Iter 203500: lr = 0.160835,	loss = 2.513600,	Top-1 err = 0.376172,	Top-5 err = 0.164111,	data_time = 0.050916,	train_time = 0.858063
[2019-08-24 05:31:26,051] TRAIN Iter 203520: lr = 0.160802,	loss = 2.525117,	Top-1 err = 0.382471,	Top-5 err = 0.170068,	data_time = 0.050538,	train_time = 0.354072
[2019-08-24 05:31:42,158] TRAIN Iter 203540: lr = 0.160768,	loss = 2.499117,	Top-1 err = 0.375732,	Top-5 err = 0.166260,	data_time = 0.051738,	train_time = 0.805338
[2019-08-24 05:31:48,928] TRAIN Iter 203560: lr = 0.160735,	loss = 2.592645,	Top-1 err = 0.381689,	Top-5 err = 0.169824,	data_time = 0.050493,	train_time = 0.338458
[2019-08-24 05:32:07,028] TRAIN Iter 203580: lr = 0.160702,	loss = 2.546307,	Top-1 err = 0.373779,	Top-5 err = 0.168018,	data_time = 0.050362,	train_time = 0.905004
[2019-08-24 05:32:25,591] TRAIN Iter 203600: lr = 0.160668,	loss = 2.490099,	Top-1 err = 0.379053,	Top-5 err = 0.168262,	data_time = 0.050592,	train_time = 0.928149
[2019-08-24 05:32:32,885] TRAIN Iter 203620: lr = 0.160635,	loss = 2.553712,	Top-1 err = 0.375049,	Top-5 err = 0.168018,	data_time = 0.050446,	train_time = 0.364669
[2019-08-24 05:32:49,091] TRAIN Iter 203640: lr = 0.160602,	loss = 2.506001,	Top-1 err = 0.374365,	Top-5 err = 0.163721,	data_time = 0.050735,	train_time = 0.810314
[2019-08-24 05:33:05,835] TRAIN Iter 203660: lr = 0.160568,	loss = 2.484723,	Top-1 err = 0.380762,	Top-5 err = 0.168408,	data_time = 0.050628,	train_time = 0.837165
[2019-08-24 05:33:12,820] TRAIN Iter 203680: lr = 0.160535,	loss = 2.497507,	Top-1 err = 0.380859,	Top-5 err = 0.167139,	data_time = 0.050341,	train_time = 0.349239
[2019-08-24 05:33:30,212] TRAIN Iter 203700: lr = 0.160502,	loss = 2.622997,	Top-1 err = 0.379883,	Top-5 err = 0.167627,	data_time = 0.050333,	train_time = 0.869562
[2019-08-24 05:33:37,327] TRAIN Iter 203720: lr = 0.160468,	loss = 2.579191,	Top-1 err = 0.377002,	Top-5 err = 0.167529,	data_time = 0.050392,	train_time = 0.355768
[2019-08-24 05:33:54,529] TRAIN Iter 203740: lr = 0.160435,	loss = 2.554569,	Top-1 err = 0.374951,	Top-5 err = 0.165430,	data_time = 0.050403,	train_time = 0.860058
[2019-08-24 05:34:12,048] TRAIN Iter 203760: lr = 0.160402,	loss = 2.548608,	Top-1 err = 0.375439,	Top-5 err = 0.169287,	data_time = 0.050233,	train_time = 0.875944
[2019-08-24 05:34:19,098] TRAIN Iter 203780: lr = 0.160368,	loss = 2.533049,	Top-1 err = 0.386133,	Top-5 err = 0.170850,	data_time = 0.050650,	train_time = 0.352515
[2019-08-24 05:34:35,251] TRAIN Iter 203800: lr = 0.160335,	loss = 2.589033,	Top-1 err = 0.376318,	Top-5 err = 0.164795,	data_time = 0.050607,	train_time = 0.807640
[2019-08-24 05:34:52,152] TRAIN Iter 203820: lr = 0.160302,	loss = 2.550550,	Top-1 err = 0.373730,	Top-5 err = 0.167627,	data_time = 0.050327,	train_time = 0.845028
[2019-08-24 05:34:58,707] TRAIN Iter 203840: lr = 0.160268,	loss = 2.589886,	Top-1 err = 0.379883,	Top-5 err = 0.169824,	data_time = 0.050511,	train_time = 0.327712
[2019-08-24 05:35:18,813] TRAIN Iter 203860: lr = 0.160235,	loss = 2.514848,	Top-1 err = 0.379443,	Top-5 err = 0.168994,	data_time = 0.050876,	train_time = 1.005309
[2019-08-24 05:35:26,576] TRAIN Iter 203880: lr = 0.160202,	loss = 2.477108,	Top-1 err = 0.369482,	Top-5 err = 0.161572,	data_time = 0.050386,	train_time = 0.388127
[2019-08-24 05:35:41,782] TRAIN Iter 203900: lr = 0.160168,	loss = 2.448716,	Top-1 err = 0.371143,	Top-5 err = 0.163574,	data_time = 0.050622,	train_time = 0.760298
[2019-08-24 05:35:59,374] TRAIN Iter 203920: lr = 0.160135,	loss = 2.483188,	Top-1 err = 0.377490,	Top-5 err = 0.166895,	data_time = 0.050489,	train_time = 0.879583
[2019-08-24 05:36:06,253] TRAIN Iter 203940: lr = 0.160102,	loss = 2.537814,	Top-1 err = 0.382324,	Top-5 err = 0.173877,	data_time = 0.050614,	train_time = 0.343909
[2019-08-24 05:36:23,999] TRAIN Iter 203960: lr = 0.160068,	loss = 2.574001,	Top-1 err = 0.379443,	Top-5 err = 0.167383,	data_time = 0.050045,	train_time = 0.887282
[2019-08-24 05:36:41,357] TRAIN Iter 203980: lr = 0.160035,	loss = 2.482213,	Top-1 err = 0.375781,	Top-5 err = 0.164697,	data_time = 0.049928,	train_time = 0.867921
[2019-08-24 05:36:47,573] TRAIN Iter 204000: lr = 0.160002,	loss = 2.562492,	Top-1 err = 0.377246,	Top-5 err = 0.167676,	data_time = 0.049879,	train_time = 0.310750
[2019-08-24 05:37:38,591] TRAIN Iter 204020: lr = 0.159968,	loss = 2.616489,	Top-1 err = 0.384690,	Top-5 err = 0.165980,	data_time = 0.050663,	train_time = 2.550887
[2019-08-24 05:37:45,903] TRAIN Iter 204040: lr = 0.159935,	loss = 2.562678,	Top-1 err = 0.372803,	Top-5 err = 0.166650,	data_time = 0.050446,	train_time = 0.365578
[2019-08-24 05:38:00,886] TRAIN Iter 204060: lr = 0.159902,	loss = 2.473809,	Top-1 err = 0.370996,	Top-5 err = 0.163086,	data_time = 0.050681,	train_time = 0.749165
[2019-08-24 05:38:14,751] TRAIN Iter 204080: lr = 0.159868,	loss = 2.510305,	Top-1 err = 0.370947,	Top-5 err = 0.159033,	data_time = 1.100349,	train_time = 0.693213
[2019-08-24 05:38:22,975] TRAIN Iter 204100: lr = 0.159835,	loss = 2.522531,	Top-1 err = 0.370068,	Top-5 err = 0.159131,	data_time = 0.050534,	train_time = 0.411226
[2019-08-24 05:38:38,564] TRAIN Iter 204120: lr = 0.159802,	loss = 2.510055,	Top-1 err = 0.369580,	Top-5 err = 0.164111,	data_time = 0.050654,	train_time = 0.779391
[2019-08-24 05:38:46,239] TRAIN Iter 204140: lr = 0.159768,	loss = 2.581217,	Top-1 err = 0.369385,	Top-5 err = 0.163037,	data_time = 0.130949,	train_time = 0.383778
[2019-08-24 05:39:00,687] TRAIN Iter 204160: lr = 0.159735,	loss = 2.475239,	Top-1 err = 0.370166,	Top-5 err = 0.159375,	data_time = 0.050620,	train_time = 0.722363
[2019-08-24 05:39:16,494] TRAIN Iter 204180: lr = 0.159702,	loss = 2.489571,	Top-1 err = 0.371484,	Top-5 err = 0.166260,	data_time = 0.050656,	train_time = 0.790332
[2019-08-24 05:39:23,934] TRAIN Iter 204200: lr = 0.159668,	loss = 2.542672,	Top-1 err = 0.377344,	Top-5 err = 0.163623,	data_time = 0.050327,	train_time = 0.371984
[2019-08-24 05:39:36,916] TRAIN Iter 204220: lr = 0.159635,	loss = 2.420667,	Top-1 err = 0.377637,	Top-5 err = 0.165283,	data_time = 0.050344,	train_time = 0.649083
[2019-08-24 05:39:52,497] TRAIN Iter 204240: lr = 0.159602,	loss = 2.574277,	Top-1 err = 0.374365,	Top-5 err = 0.167725,	data_time = 0.050661,	train_time = 0.779045
[2019-08-24 05:39:59,439] TRAIN Iter 204260: lr = 0.159568,	loss = 2.433805,	Top-1 err = 0.370312,	Top-5 err = 0.159961,	data_time = 0.094313,	train_time = 0.347089
[2019-08-24 05:40:13,897] TRAIN Iter 204280: lr = 0.159535,	loss = 2.529601,	Top-1 err = 0.373291,	Top-5 err = 0.166748,	data_time = 0.050422,	train_time = 0.722859
[2019-08-24 05:40:21,024] TRAIN Iter 204300: lr = 0.159502,	loss = 2.451325,	Top-1 err = 0.374316,	Top-5 err = 0.160156,	data_time = 0.050805,	train_time = 0.356356
[2019-08-24 05:40:37,693] TRAIN Iter 204320: lr = 0.159468,	loss = 2.560067,	Top-1 err = 0.370703,	Top-5 err = 0.160693,	data_time = 0.151795,	train_time = 0.833428
[2019-08-24 05:40:51,600] TRAIN Iter 204340: lr = 0.159435,	loss = 2.551852,	Top-1 err = 0.378076,	Top-5 err = 0.165918,	data_time = 0.050807,	train_time = 0.695347
[2019-08-24 05:40:58,583] TRAIN Iter 204360: lr = 0.159402,	loss = 2.556950,	Top-1 err = 0.378125,	Top-5 err = 0.167578,	data_time = 0.050453,	train_time = 0.349128
[2019-08-24 05:41:14,162] TRAIN Iter 204380: lr = 0.159368,	loss = 2.569788,	Top-1 err = 0.373437,	Top-5 err = 0.165527,	data_time = 0.050359,	train_time = 0.778936
[2019-08-24 05:41:28,788] TRAIN Iter 204400: lr = 0.159335,	loss = 2.444572,	Top-1 err = 0.371094,	Top-5 err = 0.167529,	data_time = 0.050372,	train_time = 0.731272
[2019-08-24 05:41:36,379] TRAIN Iter 204420: lr = 0.159302,	loss = 2.522910,	Top-1 err = 0.369043,	Top-5 err = 0.162744,	data_time = 0.050411,	train_time = 0.379548
[2019-08-24 05:41:51,715] TRAIN Iter 204440: lr = 0.159268,	loss = 2.586172,	Top-1 err = 0.373096,	Top-5 err = 0.164990,	data_time = 0.050603,	train_time = 0.766803
[2019-08-24 05:41:58,812] TRAIN Iter 204460: lr = 0.159235,	loss = 2.403957,	Top-1 err = 0.369922,	Top-5 err = 0.162305,	data_time = 0.050633,	train_time = 0.354831
[2019-08-24 05:42:13,507] TRAIN Iter 204480: lr = 0.159202,	loss = 2.427938,	Top-1 err = 0.373437,	Top-5 err = 0.164258,	data_time = 0.050434,	train_time = 0.734737
[2019-08-24 05:42:29,900] TRAIN Iter 204500: lr = 0.159168,	loss = 2.478924,	Top-1 err = 0.368115,	Top-5 err = 0.164111,	data_time = 0.050442,	train_time = 0.819624
[2019-08-24 05:42:36,768] TRAIN Iter 204520: lr = 0.159135,	loss = 2.529719,	Top-1 err = 0.378613,	Top-5 err = 0.165039,	data_time = 0.050800,	train_time = 0.343362
[2019-08-24 05:42:53,430] TRAIN Iter 204540: lr = 0.159102,	loss = 2.530628,	Top-1 err = 0.368701,	Top-5 err = 0.163281,	data_time = 0.050550,	train_time = 0.833132
[2019-08-24 05:43:07,349] TRAIN Iter 204560: lr = 0.159068,	loss = 2.487726,	Top-1 err = 0.377344,	Top-5 err = 0.164941,	data_time = 0.050326,	train_time = 0.695928
[2019-08-24 05:43:17,657] TRAIN Iter 204580: lr = 0.159035,	loss = 2.479176,	Top-1 err = 0.372754,	Top-5 err = 0.160840,	data_time = 0.050804,	train_time = 0.515382
[2019-08-24 05:43:32,854] TRAIN Iter 204600: lr = 0.159002,	loss = 2.556570,	Top-1 err = 0.380615,	Top-5 err = 0.164697,	data_time = 0.050344,	train_time = 0.759809
[2019-08-24 05:43:39,530] TRAIN Iter 204620: lr = 0.158968,	loss = 2.533310,	Top-1 err = 0.373730,	Top-5 err = 0.161035,	data_time = 0.096883,	train_time = 0.333783
[2019-08-24 05:43:56,236] TRAIN Iter 204640: lr = 0.158935,	loss = 2.583292,	Top-1 err = 0.373535,	Top-5 err = 0.166309,	data_time = 0.050421,	train_time = 0.835326
[2019-08-24 05:44:11,514] TRAIN Iter 204660: lr = 0.158902,	loss = 2.504409,	Top-1 err = 0.373682,	Top-5 err = 0.165332,	data_time = 0.050499,	train_time = 0.763887
[2019-08-24 05:44:18,281] TRAIN Iter 204680: lr = 0.158868,	loss = 2.581029,	Top-1 err = 0.380469,	Top-5 err = 0.169043,	data_time = 0.050569,	train_time = 0.338319
[2019-08-24 05:44:35,275] TRAIN Iter 204700: lr = 0.158835,	loss = 2.483013,	Top-1 err = 0.382861,	Top-5 err = 0.166895,	data_time = 0.050376,	train_time = 0.849674
[2019-08-24 05:44:50,796] TRAIN Iter 204720: lr = 0.158802,	loss = 2.477252,	Top-1 err = 0.375000,	Top-5 err = 0.162109,	data_time = 0.050696,	train_time = 0.776018
[2019-08-24 05:44:57,849] TRAIN Iter 204740: lr = 0.158768,	loss = 2.558827,	Top-1 err = 0.376758,	Top-5 err = 0.164453,	data_time = 0.050553,	train_time = 0.352636
[2019-08-24 05:45:15,457] TRAIN Iter 204760: lr = 0.158735,	loss = 2.582793,	Top-1 err = 0.376953,	Top-5 err = 0.164014,	data_time = 0.050518,	train_time = 0.880418
[2019-08-24 05:45:22,644] TRAIN Iter 204780: lr = 0.158702,	loss = 2.544860,	Top-1 err = 0.376709,	Top-5 err = 0.173047,	data_time = 0.050474,	train_time = 0.359300
[2019-08-24 05:45:37,505] TRAIN Iter 204800: lr = 0.158668,	loss = 2.482288,	Top-1 err = 0.376660,	Top-5 err = 0.166650,	data_time = 0.050728,	train_time = 0.743049
[2019-08-24 05:45:53,736] TRAIN Iter 204820: lr = 0.158635,	loss = 2.481589,	Top-1 err = 0.380127,	Top-5 err = 0.166699,	data_time = 0.050623,	train_time = 0.811532
[2019-08-24 05:46:00,371] TRAIN Iter 204840: lr = 0.158602,	loss = 2.530885,	Top-1 err = 0.380908,	Top-5 err = 0.167432,	data_time = 0.050482,	train_time = 0.331758
[2019-08-24 05:46:17,852] TRAIN Iter 204860: lr = 0.158568,	loss = 2.483180,	Top-1 err = 0.379346,	Top-5 err = 0.167627,	data_time = 0.050392,	train_time = 0.874041
[2019-08-24 05:46:35,625] TRAIN Iter 204880: lr = 0.158535,	loss = 2.427214,	Top-1 err = 0.380420,	Top-5 err = 0.171143,	data_time = 0.050519,	train_time = 0.888601
[2019-08-24 05:46:42,313] TRAIN Iter 204900: lr = 0.158502,	loss = 2.467041,	Top-1 err = 0.376025,	Top-5 err = 0.165869,	data_time = 0.050500,	train_time = 0.334394
[2019-08-24 05:46:59,287] TRAIN Iter 204920: lr = 0.158468,	loss = 2.601415,	Top-1 err = 0.375537,	Top-5 err = 0.167383,	data_time = 0.050486,	train_time = 0.848696
[2019-08-24 05:47:06,765] TRAIN Iter 204940: lr = 0.158435,	loss = 2.542645,	Top-1 err = 0.380371,	Top-5 err = 0.165967,	data_time = 0.050546,	train_time = 0.373882
[2019-08-24 05:47:22,421] TRAIN Iter 204960: lr = 0.158402,	loss = 2.539957,	Top-1 err = 0.377344,	Top-5 err = 0.169336,	data_time = 0.050318,	train_time = 0.782802
[2019-08-24 05:47:39,498] TRAIN Iter 204980: lr = 0.158368,	loss = 2.524458,	Top-1 err = 0.380957,	Top-5 err = 0.172949,	data_time = 0.050894,	train_time = 0.853837
[2019-08-24 05:47:46,288] TRAIN Iter 205000: lr = 0.158335,	loss = 2.583533,	Top-1 err = 0.375439,	Top-5 err = 0.170313,	data_time = 0.050794,	train_time = 0.339502
[2019-08-24 05:48:04,080] TRAIN Iter 205020: lr = 0.158302,	loss = 2.596353,	Top-1 err = 0.381348,	Top-5 err = 0.167773,	data_time = 0.050464,	train_time = 0.889561
[2019-08-24 05:48:20,953] TRAIN Iter 205040: lr = 0.158268,	loss = 2.621432,	Top-1 err = 0.381006,	Top-5 err = 0.171582,	data_time = 0.050379,	train_time = 0.843622
[2019-08-24 05:48:27,637] TRAIN Iter 205060: lr = 0.158235,	loss = 2.471967,	Top-1 err = 0.372461,	Top-5 err = 0.166309,	data_time = 0.050364,	train_time = 0.334194
[2019-08-24 05:48:44,923] TRAIN Iter 205080: lr = 0.158202,	loss = 2.550781,	Top-1 err = 0.378174,	Top-5 err = 0.169092,	data_time = 0.050712,	train_time = 0.864277
[2019-08-24 05:48:52,034] TRAIN Iter 205100: lr = 0.158168,	loss = 2.486802,	Top-1 err = 0.377588,	Top-5 err = 0.170361,	data_time = 0.050262,	train_time = 0.355575
[2019-08-24 05:49:09,151] TRAIN Iter 205120: lr = 0.158135,	loss = 2.506173,	Top-1 err = 0.380322,	Top-5 err = 0.167920,	data_time = 0.050321,	train_time = 0.855807
[2019-08-24 05:49:26,191] TRAIN Iter 205140: lr = 0.158102,	loss = 2.516990,	Top-1 err = 0.374854,	Top-5 err = 0.164453,	data_time = 0.050486,	train_time = 0.852008
[2019-08-24 05:49:32,713] TRAIN Iter 205160: lr = 0.158068,	loss = 2.617517,	Top-1 err = 0.384277,	Top-5 err = 0.174072,	data_time = 0.050384,	train_time = 0.326062
[2019-08-24 05:49:51,039] TRAIN Iter 205180: lr = 0.158035,	loss = 2.471067,	Top-1 err = 0.374951,	Top-5 err = 0.165820,	data_time = 0.050826,	train_time = 0.916298
[2019-08-24 05:50:07,596] TRAIN Iter 205200: lr = 0.158002,	loss = 2.490365,	Top-1 err = 0.375635,	Top-5 err = 0.166943,	data_time = 0.068113,	train_time = 0.827842
[2019-08-24 05:50:14,301] TRAIN Iter 205220: lr = 0.157968,	loss = 2.520481,	Top-1 err = 0.377686,	Top-5 err = 0.170410,	data_time = 0.050279,	train_time = 0.335233
[2019-08-24 05:50:31,336] TRAIN Iter 205240: lr = 0.157935,	loss = 2.526214,	Top-1 err = 0.373193,	Top-5 err = 0.163477,	data_time = 0.049917,	train_time = 0.851747
[2019-08-24 05:50:37,515] TRAIN Iter 205260: lr = 0.157902,	loss = 2.426889,	Top-1 err = 0.372363,	Top-5 err = 0.163232,	data_time = 0.049905,	train_time = 0.308937
[2019-08-24 05:51:28,715] TRAIN Iter 205280: lr = 0.157868,	loss = 2.556965,	Top-1 err = 0.371723,	Top-5 err = 0.166562,	data_time = 0.050463,	train_time = 2.559978
[2019-08-24 05:51:44,038] TRAIN Iter 205300: lr = 0.157835,	loss = 2.569305,	Top-1 err = 0.375293,	Top-5 err = 0.156445,	data_time = 0.050361,	train_time = 0.766132
[2019-08-24 05:51:50,581] TRAIN Iter 205320: lr = 0.157802,	loss = 2.561357,	Top-1 err = 0.363135,	Top-5 err = 0.159570,	data_time = 0.050375,	train_time = 0.327111
[2019-08-24 05:52:05,582] TRAIN Iter 205340: lr = 0.157768,	loss = 2.496311,	Top-1 err = 0.365918,	Top-5 err = 0.159131,	data_time = 0.050487,	train_time = 0.750043
[2019-08-24 05:52:13,270] TRAIN Iter 205360: lr = 0.157735,	loss = 2.447703,	Top-1 err = 0.366455,	Top-5 err = 0.159668,	data_time = 0.050391,	train_time = 0.384417
[2019-08-24 05:52:26,591] TRAIN Iter 205380: lr = 0.157702,	loss = 2.571934,	Top-1 err = 0.368701,	Top-5 err = 0.157520,	data_time = 0.050632,	train_time = 0.666035
[2019-08-24 05:52:42,920] TRAIN Iter 205400: lr = 0.157668,	loss = 2.508145,	Top-1 err = 0.365039,	Top-5 err = 0.160938,	data_time = 0.050743,	train_time = 0.816412
[2019-08-24 05:52:50,017] TRAIN Iter 205420: lr = 0.157635,	loss = 2.589689,	Top-1 err = 0.371582,	Top-5 err = 0.161377,	data_time = 0.156388,	train_time = 0.354861
[2019-08-24 05:53:06,065] TRAIN Iter 205440: lr = 0.157602,	loss = 2.433665,	Top-1 err = 0.364990,	Top-5 err = 0.157031,	data_time = 0.050506,	train_time = 0.802360
[2019-08-24 05:53:21,426] TRAIN Iter 205460: lr = 0.157568,	loss = 2.611155,	Top-1 err = 0.372754,	Top-5 err = 0.164160,	data_time = 1.814236,	train_time = 0.768019
[2019-08-24 05:53:28,373] TRAIN Iter 205480: lr = 0.157535,	loss = 2.533300,	Top-1 err = 0.371191,	Top-5 err = 0.160889,	data_time = 0.050361,	train_time = 0.347375
[2019-08-24 05:53:42,059] TRAIN Iter 205500: lr = 0.157502,	loss = 2.505249,	Top-1 err = 0.374268,	Top-5 err = 0.165527,	data_time = 0.050431,	train_time = 0.684269
[2019-08-24 05:53:48,929] TRAIN Iter 205520: lr = 0.157468,	loss = 2.467471,	Top-1 err = 0.375830,	Top-5 err = 0.163867,	data_time = 0.050838,	train_time = 0.343475
[2019-08-24 05:54:04,035] TRAIN Iter 205540: lr = 0.157435,	loss = 2.446325,	Top-1 err = 0.373291,	Top-5 err = 0.164111,	data_time = 0.050330,	train_time = 0.755309
[2019-08-24 05:54:20,633] TRAIN Iter 205560: lr = 0.157402,	loss = 2.459531,	Top-1 err = 0.373975,	Top-5 err = 0.165283,	data_time = 0.050476,	train_time = 0.829892
[2019-08-24 05:54:27,996] TRAIN Iter 205580: lr = 0.157368,	loss = 2.526273,	Top-1 err = 0.373535,	Top-5 err = 0.159912,	data_time = 0.050617,	train_time = 0.368098
[2019-08-24 05:54:41,265] TRAIN Iter 205600: lr = 0.157335,	loss = 2.630016,	Top-1 err = 0.375098,	Top-5 err = 0.164893,	data_time = 0.050496,	train_time = 0.663431
[2019-08-24 05:54:56,087] TRAIN Iter 205620: lr = 0.157302,	loss = 2.494853,	Top-1 err = 0.374414,	Top-5 err = 0.163965,	data_time = 0.126423,	train_time = 0.741090
[2019-08-24 05:55:03,162] TRAIN Iter 205640: lr = 0.157268,	loss = 2.556801,	Top-1 err = 0.381494,	Top-5 err = 0.166309,	data_time = 0.050466,	train_time = 0.353746
[2019-08-24 05:55:18,228] TRAIN Iter 205660: lr = 0.157235,	loss = 2.463858,	Top-1 err = 0.371777,	Top-5 err = 0.160498,	data_time = 0.050281,	train_time = 0.753279
[2019-08-24 05:55:25,201] TRAIN Iter 205680: lr = 0.157202,	loss = 2.585102,	Top-1 err = 0.374121,	Top-5 err = 0.164014,	data_time = 0.050618,	train_time = 0.348667
[2019-08-24 05:55:42,139] TRAIN Iter 205700: lr = 0.157168,	loss = 2.525599,	Top-1 err = 0.375195,	Top-5 err = 0.162842,	data_time = 0.050413,	train_time = 0.846865
[2019-08-24 05:55:58,425] TRAIN Iter 205720: lr = 0.157135,	loss = 2.458059,	Top-1 err = 0.377441,	Top-5 err = 0.167676,	data_time = 0.050721,	train_time = 0.814270
[2019-08-24 05:56:05,302] TRAIN Iter 205740: lr = 0.157102,	loss = 2.526819,	Top-1 err = 0.370654,	Top-5 err = 0.160938,	data_time = 0.050397,	train_time = 0.343829
[2019-08-24 05:56:21,204] TRAIN Iter 205760: lr = 0.157068,	loss = 2.477631,	Top-1 err = 0.376611,	Top-5 err = 0.168848,	data_time = 0.050318,	train_time = 0.795125
[2019-08-24 05:56:36,292] TRAIN Iter 205780: lr = 0.157035,	loss = 2.499027,	Top-1 err = 0.367725,	Top-5 err = 0.162842,	data_time = 0.162502,	train_time = 0.754382
[2019-08-24 05:56:43,139] TRAIN Iter 205800: lr = 0.157002,	loss = 2.521475,	Top-1 err = 0.367725,	Top-5 err = 0.163135,	data_time = 0.050706,	train_time = 0.342331
[2019-08-24 05:56:59,390] TRAIN Iter 205820: lr = 0.156968,	loss = 2.482068,	Top-1 err = 0.374658,	Top-5 err = 0.163867,	data_time = 0.050228,	train_time = 0.812507
[2019-08-24 05:57:08,451] TRAIN Iter 205840: lr = 0.156935,	loss = 2.526390,	Top-1 err = 0.371240,	Top-5 err = 0.165381,	data_time = 0.207623,	train_time = 0.453044
[2019-08-24 05:57:22,859] TRAIN Iter 205860: lr = 0.156902,	loss = 2.561262,	Top-1 err = 0.373535,	Top-5 err = 0.167139,	data_time = 0.050335,	train_time = 0.720389
[2019-08-24 05:57:35,848] TRAIN Iter 205880: lr = 0.156868,	loss = 2.532457,	Top-1 err = 0.375244,	Top-5 err = 0.161865,	data_time = 0.050649,	train_time = 0.649445
[2019-08-24 05:57:42,521] TRAIN Iter 205900: lr = 0.156835,	loss = 2.421083,	Top-1 err = 0.371875,	Top-5 err = 0.163818,	data_time = 0.050661,	train_time = 0.333633
[2019-08-24 05:57:58,637] TRAIN Iter 205920: lr = 0.156802,	loss = 2.540623,	Top-1 err = 0.376367,	Top-5 err = 0.165527,	data_time = 0.050331,	train_time = 0.805775
[2019-08-24 05:58:13,447] TRAIN Iter 205940: lr = 0.156768,	loss = 2.454835,	Top-1 err = 0.374902,	Top-5 err = 0.166650,	data_time = 0.217861,	train_time = 0.740507
[2019-08-24 05:58:21,521] TRAIN Iter 205960: lr = 0.156735,	loss = 2.568705,	Top-1 err = 0.372363,	Top-5 err = 0.159619,	data_time = 0.050374,	train_time = 0.403693
[2019-08-24 05:58:37,874] TRAIN Iter 205980: lr = 0.156702,	loss = 2.567750,	Top-1 err = 0.378223,	Top-5 err = 0.167285,	data_time = 0.050583,	train_time = 0.817609
[2019-08-24 05:58:44,664] TRAIN Iter 206000: lr = 0.156668,	loss = 2.504048,	Top-1 err = 0.373340,	Top-5 err = 0.162451,	data_time = 0.115389,	train_time = 0.339488
[2019-08-24 05:59:00,529] TRAIN Iter 206020: lr = 0.156635,	loss = 2.545005,	Top-1 err = 0.373291,	Top-5 err = 0.166992,	data_time = 0.050673,	train_time = 0.793227
[2019-08-24 05:59:17,115] TRAIN Iter 206040: lr = 0.156602,	loss = 2.562700,	Top-1 err = 0.379590,	Top-5 err = 0.164404,	data_time = 0.050629,	train_time = 0.829313
[2019-08-24 05:59:23,949] TRAIN Iter 206060: lr = 0.156568,	loss = 2.543814,	Top-1 err = 0.376416,	Top-5 err = 0.164014,	data_time = 0.050476,	train_time = 0.341677
[2019-08-24 05:59:42,613] TRAIN Iter 206080: lr = 0.156535,	loss = 2.606229,	Top-1 err = 0.377930,	Top-5 err = 0.168604,	data_time = 0.050345,	train_time = 0.933165
[2019-08-24 05:59:57,690] TRAIN Iter 206100: lr = 0.156502,	loss = 2.579626,	Top-1 err = 0.375879,	Top-5 err = 0.167773,	data_time = 0.050783,	train_time = 0.753865
[2019-08-24 06:00:04,821] TRAIN Iter 206120: lr = 0.156468,	loss = 2.469181,	Top-1 err = 0.370312,	Top-5 err = 0.160596,	data_time = 0.050518,	train_time = 0.356506
[2019-08-24 06:00:23,472] TRAIN Iter 206140: lr = 0.156435,	loss = 2.526338,	Top-1 err = 0.376514,	Top-5 err = 0.167578,	data_time = 0.050715,	train_time = 0.932563
[2019-08-24 06:00:30,527] TRAIN Iter 206160: lr = 0.156402,	loss = 2.510258,	Top-1 err = 0.380566,	Top-5 err = 0.171875,	data_time = 0.050412,	train_time = 0.352702
[2019-08-24 06:00:45,155] TRAIN Iter 206180: lr = 0.156368,	loss = 2.505033,	Top-1 err = 0.371582,	Top-5 err = 0.163818,	data_time = 0.050614,	train_time = 0.731402
[2019-08-24 06:01:00,435] TRAIN Iter 206200: lr = 0.156335,	loss = 2.529892,	Top-1 err = 0.375781,	Top-5 err = 0.161279,	data_time = 0.050529,	train_time = 0.763961
[2019-08-24 06:01:07,139] TRAIN Iter 206220: lr = 0.156302,	loss = 2.566139,	Top-1 err = 0.385010,	Top-5 err = 0.165967,	data_time = 0.050424,	train_time = 0.335219
[2019-08-24 06:01:23,463] TRAIN Iter 206240: lr = 0.156268,	loss = 2.526444,	Top-1 err = 0.374609,	Top-5 err = 0.166797,	data_time = 0.050458,	train_time = 0.816175
[2019-08-24 06:01:39,940] TRAIN Iter 206260: lr = 0.156235,	loss = 2.618938,	Top-1 err = 0.381055,	Top-5 err = 0.164795,	data_time = 0.088933,	train_time = 0.823853
[2019-08-24 06:01:46,768] TRAIN Iter 206280: lr = 0.156202,	loss = 2.529218,	Top-1 err = 0.380273,	Top-5 err = 0.169824,	data_time = 0.050501,	train_time = 0.341389
[2019-08-24 06:02:03,323] TRAIN Iter 206300: lr = 0.156168,	loss = 2.580635,	Top-1 err = 0.376416,	Top-5 err = 0.165869,	data_time = 0.050580,	train_time = 0.827706
[2019-08-24 06:02:10,057] TRAIN Iter 206320: lr = 0.156135,	loss = 2.575047,	Top-1 err = 0.373975,	Top-5 err = 0.164062,	data_time = 0.050570,	train_time = 0.336668
[2019-08-24 06:02:26,247] TRAIN Iter 206340: lr = 0.156102,	loss = 2.577426,	Top-1 err = 0.376904,	Top-5 err = 0.165625,	data_time = 0.050362,	train_time = 0.809500
[2019-08-24 06:02:42,655] TRAIN Iter 206360: lr = 0.156068,	loss = 2.552972,	Top-1 err = 0.377979,	Top-5 err = 0.166211,	data_time = 0.050418,	train_time = 0.820387
[2019-08-24 06:02:49,682] TRAIN Iter 206380: lr = 0.156035,	loss = 2.512512,	Top-1 err = 0.380029,	Top-5 err = 0.165674,	data_time = 0.050297,	train_time = 0.351330
[2019-08-24 06:03:05,696] TRAIN Iter 206400: lr = 0.156002,	loss = 2.527026,	Top-1 err = 0.375684,	Top-5 err = 0.170703,	data_time = 0.050550,	train_time = 0.800701
[2019-08-24 06:03:21,002] TRAIN Iter 206420: lr = 0.155968,	loss = 2.526533,	Top-1 err = 0.383057,	Top-5 err = 0.172266,	data_time = 0.134147,	train_time = 0.765264
[2019-08-24 06:03:29,564] TRAIN Iter 206440: lr = 0.155935,	loss = 2.572806,	Top-1 err = 0.380713,	Top-5 err = 0.170215,	data_time = 0.050404,	train_time = 0.428097
[2019-08-24 06:03:45,807] TRAIN Iter 206460: lr = 0.155902,	loss = 2.502811,	Top-1 err = 0.388721,	Top-5 err = 0.170264,	data_time = 0.050015,	train_time = 0.812144
[2019-08-24 06:03:52,023] TRAIN Iter 206480: lr = 0.155868,	loss = 2.568713,	Top-1 err = 0.379932,	Top-5 err = 0.166699,	data_time = 0.049889,	train_time = 0.310775
[2019-08-24 06:04:10,058] TRAIN Iter 206500: lr = 0.155835,	loss = 2.565420,	Top-1 err = 0.377197,	Top-5 err = 0.166113,	data_time = 0.049906,	train_time = 0.901773
[2019-08-24 06:04:53,976] TRAIN Iter 206520: lr = 0.155802,	loss = 2.467374,	Top-1 err = 0.386550,	Top-5 err = 0.175867,	data_time = 0.050545,	train_time = 2.195841
[2019-08-24 06:05:04,879] TRAIN Iter 206540: lr = 0.155768,	loss = 2.450599,	Top-1 err = 0.380127,	Top-5 err = 0.163818,	data_time = 0.050411,	train_time = 0.545142
[2019-08-24 06:05:19,910] TRAIN Iter 206560: lr = 0.155735,	loss = 2.515381,	Top-1 err = 0.367334,	Top-5 err = 0.162158,	data_time = 0.384175,	train_time = 0.751528
[2019-08-24 06:05:27,070] TRAIN Iter 206580: lr = 0.155702,	loss = 2.579882,	Top-1 err = 0.369287,	Top-5 err = 0.160986,	data_time = 0.050275,	train_time = 0.358001
[2019-08-24 06:05:43,923] TRAIN Iter 206600: lr = 0.155668,	loss = 2.363151,	Top-1 err = 0.368945,	Top-5 err = 0.161963,	data_time = 0.050885,	train_time = 0.842632
[2019-08-24 06:05:55,153] TRAIN Iter 206620: lr = 0.155635,	loss = 2.509099,	Top-1 err = 0.369385,	Top-5 err = 0.160352,	data_time = 0.050839,	train_time = 0.561488
[2019-08-24 06:06:03,682] TRAIN Iter 206640: lr = 0.155602,	loss = 2.438975,	Top-1 err = 0.369824,	Top-5 err = 0.162646,	data_time = 0.050873,	train_time = 0.426419
[2019-08-24 06:06:16,862] TRAIN Iter 206660: lr = 0.155568,	loss = 2.501594,	Top-1 err = 0.373975,	Top-5 err = 0.165771,	data_time = 0.050425,	train_time = 0.658985
[2019-08-24 06:06:32,833] TRAIN Iter 206680: lr = 0.155535,	loss = 2.543523,	Top-1 err = 0.370605,	Top-5 err = 0.159668,	data_time = 1.638760,	train_time = 0.798566
[2019-08-24 06:06:41,539] TRAIN Iter 206700: lr = 0.155502,	loss = 2.528041,	Top-1 err = 0.368164,	Top-5 err = 0.159717,	data_time = 0.050360,	train_time = 0.435266
[2019-08-24 06:06:58,649] TRAIN Iter 206720: lr = 0.155468,	loss = 2.516851,	Top-1 err = 0.367383,	Top-5 err = 0.161621,	data_time = 0.050435,	train_time = 0.855509
[2019-08-24 06:07:06,365] TRAIN Iter 206740: lr = 0.155435,	loss = 2.565418,	Top-1 err = 0.373437,	Top-5 err = 0.167041,	data_time = 0.183621,	train_time = 0.385781
[2019-08-24 06:07:20,269] TRAIN Iter 206760: lr = 0.155402,	loss = 2.538673,	Top-1 err = 0.372705,	Top-5 err = 0.163672,	data_time = 0.050754,	train_time = 0.695156
[2019-08-24 06:07:36,006] TRAIN Iter 206780: lr = 0.155368,	loss = 2.533361,	Top-1 err = 0.372363,	Top-5 err = 0.165820,	data_time = 0.050596,	train_time = 0.786818
[2019-08-24 06:07:43,455] TRAIN Iter 206800: lr = 0.155335,	loss = 2.464631,	Top-1 err = 0.371045,	Top-5 err = 0.167236,	data_time = 0.050813,	train_time = 0.372450
[2019-08-24 06:07:57,648] TRAIN Iter 206820: lr = 0.155302,	loss = 2.525755,	Top-1 err = 0.368115,	Top-5 err = 0.160791,	data_time = 0.050266,	train_time = 0.709643
[2019-08-24 06:08:12,342] TRAIN Iter 206840: lr = 0.155268,	loss = 2.499985,	Top-1 err = 0.369629,	Top-5 err = 0.162061,	data_time = 6.410559,	train_time = 0.734683
[2019-08-24 06:08:19,683] TRAIN Iter 206860: lr = 0.155235,	loss = 2.546121,	Top-1 err = 0.373975,	Top-5 err = 0.162939,	data_time = 0.050440,	train_time = 0.367060
[2019-08-24 06:08:33,715] TRAIN Iter 206880: lr = 0.155202,	loss = 2.405574,	Top-1 err = 0.371875,	Top-5 err = 0.161426,	data_time = 0.050356,	train_time = 0.701565
[2019-08-24 06:08:40,853] TRAIN Iter 206900: lr = 0.155168,	loss = 2.480728,	Top-1 err = 0.370264,	Top-5 err = 0.163623,	data_time = 0.050551,	train_time = 0.356906
[2019-08-24 06:08:57,709] TRAIN Iter 206920: lr = 0.155135,	loss = 2.451861,	Top-1 err = 0.369922,	Top-5 err = 0.166602,	data_time = 0.050431,	train_time = 0.842782
[2019-08-24 06:09:12,075] TRAIN Iter 206940: lr = 0.155102,	loss = 2.517017,	Top-1 err = 0.366797,	Top-5 err = 0.163037,	data_time = 0.107960,	train_time = 0.718267
[2019-08-24 06:09:19,259] TRAIN Iter 206960: lr = 0.155068,	loss = 2.499019,	Top-1 err = 0.375879,	Top-5 err = 0.166455,	data_time = 0.050629,	train_time = 0.359165
[2019-08-24 06:09:32,642] TRAIN Iter 206980: lr = 0.155035,	loss = 2.575796,	Top-1 err = 0.375293,	Top-5 err = 0.163086,	data_time = 0.050405,	train_time = 0.669133
[2019-08-24 06:09:47,626] TRAIN Iter 207000: lr = 0.155002,	loss = 2.584880,	Top-1 err = 0.375244,	Top-5 err = 0.164258,	data_time = 6.530370,	train_time = 0.749209
[2019-08-24 06:09:54,780] TRAIN Iter 207020: lr = 0.154968,	loss = 2.567366,	Top-1 err = 0.374707,	Top-5 err = 0.165527,	data_time = 0.050410,	train_time = 0.357674
[2019-08-24 06:10:10,808] TRAIN Iter 207040: lr = 0.154935,	loss = 2.504436,	Top-1 err = 0.370703,	Top-5 err = 0.164746,	data_time = 0.050557,	train_time = 0.801376
[2019-08-24 06:10:17,917] TRAIN Iter 207060: lr = 0.154902,	loss = 2.490808,	Top-1 err = 0.369434,	Top-5 err = 0.161670,	data_time = 0.050564,	train_time = 0.355441
[2019-08-24 06:10:33,590] TRAIN Iter 207080: lr = 0.154868,	loss = 2.560239,	Top-1 err = 0.369824,	Top-5 err = 0.163477,	data_time = 0.050439,	train_time = 0.783641
[2019-08-24 06:10:47,188] TRAIN Iter 207100: lr = 0.154835,	loss = 2.565782,	Top-1 err = 0.373486,	Top-5 err = 0.163525,	data_time = 0.050360,	train_time = 0.679881
[2019-08-24 06:10:57,693] TRAIN Iter 207120: lr = 0.154802,	loss = 2.474123,	Top-1 err = 0.373389,	Top-5 err = 0.166309,	data_time = 0.050567,	train_time = 0.525254
[2019-08-24 06:11:11,596] TRAIN Iter 207140: lr = 0.154768,	loss = 2.549469,	Top-1 err = 0.370264,	Top-5 err = 0.165381,	data_time = 0.050547,	train_time = 0.695116
[2019-08-24 06:11:21,824] TRAIN Iter 207160: lr = 0.154735,	loss = 2.484693,	Top-1 err = 0.373096,	Top-5 err = 0.167529,	data_time = 0.178867,	train_time = 0.511403
[2019-08-24 06:11:35,146] TRAIN Iter 207180: lr = 0.154702,	loss = 2.561040,	Top-1 err = 0.366553,	Top-5 err = 0.158545,	data_time = 0.164469,	train_time = 0.666086
[2019-08-24 06:11:50,306] TRAIN Iter 207200: lr = 0.154668,	loss = 2.505641,	Top-1 err = 0.372803,	Top-5 err = 0.167383,	data_time = 0.050355,	train_time = 0.757968
[2019-08-24 06:11:57,634] TRAIN Iter 207220: lr = 0.154635,	loss = 2.622779,	Top-1 err = 0.377295,	Top-5 err = 0.162891,	data_time = 0.108456,	train_time = 0.366408
[2019-08-24 06:12:12,413] TRAIN Iter 207240: lr = 0.154602,	loss = 2.557643,	Top-1 err = 0.374902,	Top-5 err = 0.165820,	data_time = 0.050377,	train_time = 0.738940
[2019-08-24 06:12:26,093] TRAIN Iter 207260: lr = 0.154568,	loss = 2.625591,	Top-1 err = 0.378613,	Top-5 err = 0.167725,	data_time = 0.050426,	train_time = 0.683977
[2019-08-24 06:12:35,442] TRAIN Iter 207280: lr = 0.154535,	loss = 2.444802,	Top-1 err = 0.378174,	Top-5 err = 0.166016,	data_time = 0.050461,	train_time = 0.467408
[2019-08-24 06:12:53,495] TRAIN Iter 207300: lr = 0.154502,	loss = 2.643513,	Top-1 err = 0.375098,	Top-5 err = 0.167627,	data_time = 0.050672,	train_time = 0.902625
[2019-08-24 06:13:05,701] TRAIN Iter 207320: lr = 0.154468,	loss = 2.514550,	Top-1 err = 0.373486,	Top-5 err = 0.166260,	data_time = 0.050515,	train_time = 0.610293
[2019-08-24 06:13:14,399] TRAIN Iter 207340: lr = 0.154435,	loss = 2.403856,	Top-1 err = 0.372217,	Top-5 err = 0.163623,	data_time = 0.050590,	train_time = 0.434919
[2019-08-24 06:13:29,818] TRAIN Iter 207360: lr = 0.154402,	loss = 2.609784,	Top-1 err = 0.371240,	Top-5 err = 0.164746,	data_time = 0.050283,	train_time = 0.770918
[2019-08-24 06:13:36,974] TRAIN Iter 207380: lr = 0.154368,	loss = 2.549603,	Top-1 err = 0.373779,	Top-5 err = 0.165674,	data_time = 0.050552,	train_time = 0.357797
[2019-08-24 06:13:52,252] TRAIN Iter 207400: lr = 0.154335,	loss = 2.641659,	Top-1 err = 0.380713,	Top-5 err = 0.164990,	data_time = 0.050173,	train_time = 0.763861
[2019-08-24 06:14:08,502] TRAIN Iter 207420: lr = 0.154302,	loss = 2.524939,	Top-1 err = 0.378027,	Top-5 err = 0.167822,	data_time = 0.050512,	train_time = 0.812476
[2019-08-24 06:14:16,652] TRAIN Iter 207440: lr = 0.154268,	loss = 2.599659,	Top-1 err = 0.381787,	Top-5 err = 0.172314,	data_time = 0.107577,	train_time = 0.407500
[2019-08-24 06:14:31,574] TRAIN Iter 207460: lr = 0.154235,	loss = 2.505006,	Top-1 err = 0.373828,	Top-5 err = 0.161084,	data_time = 0.050336,	train_time = 0.746111
[2019-08-24 06:14:49,374] TRAIN Iter 207480: lr = 0.154202,	loss = 2.559105,	Top-1 err = 0.383447,	Top-5 err = 0.170898,	data_time = 0.050315,	train_time = 0.889964
[2019-08-24 06:14:56,126] TRAIN Iter 207500: lr = 0.154168,	loss = 2.523086,	Top-1 err = 0.376758,	Top-5 err = 0.170410,	data_time = 0.103892,	train_time = 0.337572
[2019-08-24 06:15:10,966] TRAIN Iter 207520: lr = 0.154135,	loss = 2.532847,	Top-1 err = 0.383301,	Top-5 err = 0.171045,	data_time = 0.050828,	train_time = 0.741997
[2019-08-24 06:15:18,034] TRAIN Iter 207540: lr = 0.154102,	loss = 2.542629,	Top-1 err = 0.373047,	Top-5 err = 0.166211,	data_time = 0.050227,	train_time = 0.353407
[2019-08-24 06:15:34,271] TRAIN Iter 207560: lr = 0.154068,	loss = 2.455273,	Top-1 err = 0.372021,	Top-5 err = 0.165723,	data_time = 0.050309,	train_time = 0.811821
[2019-08-24 06:15:50,868] TRAIN Iter 207580: lr = 0.154035,	loss = 2.465696,	Top-1 err = 0.369727,	Top-5 err = 0.164697,	data_time = 0.050619,	train_time = 0.829857
[2019-08-24 06:15:58,174] TRAIN Iter 207600: lr = 0.154002,	loss = 2.468658,	Top-1 err = 0.375000,	Top-5 err = 0.162305,	data_time = 0.050505,	train_time = 0.365291
[2019-08-24 06:16:11,953] TRAIN Iter 207620: lr = 0.153968,	loss = 2.658691,	Top-1 err = 0.377246,	Top-5 err = 0.170947,	data_time = 0.050493,	train_time = 0.688905
[2019-08-24 06:16:28,341] TRAIN Iter 207640: lr = 0.153935,	loss = 2.440199,	Top-1 err = 0.379395,	Top-5 err = 0.165381,	data_time = 0.050119,	train_time = 0.819375
[2019-08-24 06:16:34,986] TRAIN Iter 207660: lr = 0.153902,	loss = 2.535629,	Top-1 err = 0.376758,	Top-5 err = 0.166650,	data_time = 0.050457,	train_time = 0.332257
[2019-08-24 06:16:50,640] TRAIN Iter 207680: lr = 0.153868,	loss = 2.524765,	Top-1 err = 0.379785,	Top-5 err = 0.167187,	data_time = 0.050273,	train_time = 0.782679
[2019-08-24 06:16:57,514] TRAIN Iter 207700: lr = 0.153835,	loss = 2.561512,	Top-1 err = 0.380957,	Top-5 err = 0.169678,	data_time = 0.050343,	train_time = 0.343699
[2019-08-24 06:17:14,258] TRAIN Iter 207720: lr = 0.153802,	loss = 2.487823,	Top-1 err = 0.373437,	Top-5 err = 0.164746,	data_time = 0.050072,	train_time = 0.837155
[2019-08-24 06:17:31,155] TRAIN Iter 207740: lr = 0.153768,	loss = 2.509623,	Top-1 err = 0.372705,	Top-5 err = 0.164404,	data_time = 0.050093,	train_time = 0.844866
[2019-08-24 06:17:40,263] TRAIN Iter 207760: lr = 0.153735,	loss = 2.383999,	Top-1 err = 0.370557,	Top-5 err = 0.164453,	data_time = 0.050043,	train_time = 0.455371
[2019-08-24 06:18:29,000] TRAIN Iter 207780: lr = 0.153702,	loss = 2.520023,	Top-1 err = 0.381645,	Top-5 err = 0.168511,	data_time = 0.050491,	train_time = 2.436839
[2019-08-24 06:18:36,478] TRAIN Iter 207800: lr = 0.153668,	loss = 2.507886,	Top-1 err = 0.375830,	Top-5 err = 0.167334,	data_time = 0.050228,	train_time = 0.373874
[2019-08-24 06:18:53,589] TRAIN Iter 207820: lr = 0.153635,	loss = 2.476117,	Top-1 err = 0.362842,	Top-5 err = 0.156299,	data_time = 0.138792,	train_time = 0.855534
[2019-08-24 06:19:07,208] TRAIN Iter 207840: lr = 0.153602,	loss = 2.395039,	Top-1 err = 0.365918,	Top-5 err = 0.158789,	data_time = 0.050087,	train_time = 0.680942
[2019-08-24 06:19:15,297] TRAIN Iter 207860: lr = 0.153568,	loss = 2.532228,	Top-1 err = 0.370557,	Top-5 err = 0.163184,	data_time = 0.050587,	train_time = 0.404428
[2019-08-24 06:19:24,135] TRAIN Iter 207880: lr = 0.153535,	loss = 2.563199,	Top-1 err = 0.369092,	Top-5 err = 0.161377,	data_time = 0.050429,	train_time = 0.441890
[2019-08-24 06:19:41,253] TRAIN Iter 207900: lr = 0.153502,	loss = 2.528683,	Top-1 err = 0.366602,	Top-5 err = 0.164111,	data_time = 0.050484,	train_time = 0.855861
[2019-08-24 06:19:48,280] TRAIN Iter 207920: lr = 0.153468,	loss = 2.519035,	Top-1 err = 0.366357,	Top-5 err = 0.157959,	data_time = 0.050466,	train_time = 0.351366
[2019-08-24 06:20:03,486] TRAIN Iter 207940: lr = 0.153435,	loss = 2.493597,	Top-1 err = 0.365869,	Top-5 err = 0.157080,	data_time = 0.050518,	train_time = 0.760295
[2019-08-24 06:20:10,346] TRAIN Iter 207960: lr = 0.153402,	loss = 2.487915,	Top-1 err = 0.368213,	Top-5 err = 0.156201,	data_time = 0.050763,	train_time = 0.342979
[2019-08-24 06:20:26,448] TRAIN Iter 207980: lr = 0.153368,	loss = 2.548609,	Top-1 err = 0.369873,	Top-5 err = 0.160889,	data_time = 0.050315,	train_time = 0.805054
[2019-08-24 06:20:40,295] TRAIN Iter 208000: lr = 0.153335,	loss = 2.440883,	Top-1 err = 0.369727,	Top-5 err = 0.158447,	data_time = 0.050411,	train_time = 0.692329
[2019-08-24 06:20:47,346] TRAIN Iter 208020: lr = 0.153302,	loss = 2.433846,	Top-1 err = 0.374121,	Top-5 err = 0.163770,	data_time = 0.050227,	train_time = 0.352562
[2019-08-24 06:21:04,567] TRAIN Iter 208040: lr = 0.153268,	loss = 2.488699,	Top-1 err = 0.370850,	Top-5 err = 0.164795,	data_time = 0.050482,	train_time = 0.861030
[2019-08-24 06:21:19,232] TRAIN Iter 208060: lr = 0.153235,	loss = 2.478966,	Top-1 err = 0.368945,	Top-5 err = 0.159668,	data_time = 0.149848,	train_time = 0.733233
[2019-08-24 06:21:26,229] TRAIN Iter 208080: lr = 0.153202,	loss = 2.416315,	Top-1 err = 0.375586,	Top-5 err = 0.165430,	data_time = 0.050339,	train_time = 0.349813
[2019-08-24 06:21:41,302] TRAIN Iter 208100: lr = 0.153168,	loss = 2.504351,	Top-1 err = 0.377344,	Top-5 err = 0.165674,	data_time = 0.050428,	train_time = 0.753658
[2019-08-24 06:21:48,694] TRAIN Iter 208120: lr = 0.153135,	loss = 2.510264,	Top-1 err = 0.370264,	Top-5 err = 0.162598,	data_time = 0.050360,	train_time = 0.369595
[2019-08-24 06:22:04,051] TRAIN Iter 208140: lr = 0.153102,	loss = 2.454349,	Top-1 err = 0.373535,	Top-5 err = 0.163574,	data_time = 0.050628,	train_time = 0.767819
[2019-08-24 06:22:20,872] TRAIN Iter 208160: lr = 0.153068,	loss = 2.355931,	Top-1 err = 0.369922,	Top-5 err = 0.163818,	data_time = 0.050650,	train_time = 0.841036
[2019-08-24 06:22:28,714] TRAIN Iter 208180: lr = 0.153035,	loss = 2.560532,	Top-1 err = 0.368945,	Top-5 err = 0.161572,	data_time = 0.107857,	train_time = 0.392095
[2019-08-24 06:22:41,733] TRAIN Iter 208200: lr = 0.153002,	loss = 2.557880,	Top-1 err = 0.373340,	Top-5 err = 0.167285,	data_time = 0.050476,	train_time = 0.650940
[2019-08-24 06:22:58,670] TRAIN Iter 208220: lr = 0.152968,	loss = 2.609637,	Top-1 err = 0.376123,	Top-5 err = 0.165381,	data_time = 0.050817,	train_time = 0.846811
[2019-08-24 06:23:06,183] TRAIN Iter 208240: lr = 0.152935,	loss = 2.539342,	Top-1 err = 0.374023,	Top-5 err = 0.164014,	data_time = 0.050526,	train_time = 0.375666
[2019-08-24 06:23:20,477] TRAIN Iter 208260: lr = 0.152902,	loss = 2.491863,	Top-1 err = 0.369531,	Top-5 err = 0.162744,	data_time = 0.050487,	train_time = 0.714671
[2019-08-24 06:23:28,631] TRAIN Iter 208280: lr = 0.152868,	loss = 2.578701,	Top-1 err = 0.374365,	Top-5 err = 0.162109,	data_time = 0.050546,	train_time = 0.407690
[2019-08-24 06:23:42,955] TRAIN Iter 208300: lr = 0.152835,	loss = 2.501222,	Top-1 err = 0.375635,	Top-5 err = 0.161475,	data_time = 0.050424,	train_time = 0.716196
[2019-08-24 06:23:57,517] TRAIN Iter 208320: lr = 0.152802,	loss = 2.458002,	Top-1 err = 0.372998,	Top-5 err = 0.161035,	data_time = 0.050493,	train_time = 0.728088
[2019-08-24 06:24:04,822] TRAIN Iter 208340: lr = 0.152768,	loss = 2.467544,	Top-1 err = 0.377588,	Top-5 err = 0.166309,	data_time = 0.050395,	train_time = 0.365197
[2019-08-24 06:24:19,963] TRAIN Iter 208360: lr = 0.152735,	loss = 2.542629,	Top-1 err = 0.375537,	Top-5 err = 0.166016,	data_time = 0.050652,	train_time = 0.757035
[2019-08-24 06:24:36,124] TRAIN Iter 208380: lr = 0.152702,	loss = 2.458510,	Top-1 err = 0.372461,	Top-5 err = 0.165234,	data_time = 0.050580,	train_time = 0.808033
[2019-08-24 06:24:43,079] TRAIN Iter 208400: lr = 0.152668,	loss = 2.529649,	Top-1 err = 0.372510,	Top-5 err = 0.163135,	data_time = 0.050588,	train_time = 0.347744
[2019-08-24 06:24:58,197] TRAIN Iter 208420: lr = 0.152635,	loss = 2.467439,	Top-1 err = 0.375146,	Top-5 err = 0.165527,	data_time = 0.050339,	train_time = 0.755886
[2019-08-24 06:25:05,396] TRAIN Iter 208440: lr = 0.152602,	loss = 2.483062,	Top-1 err = 0.385547,	Top-5 err = 0.168652,	data_time = 0.050426,	train_time = 0.359967
[2019-08-24 06:25:22,649] TRAIN Iter 208460: lr = 0.152568,	loss = 2.568431,	Top-1 err = 0.377002,	Top-5 err = 0.167725,	data_time = 0.050778,	train_time = 0.862600
[2019-08-24 06:25:38,626] TRAIN Iter 208480: lr = 0.152535,	loss = 2.560673,	Top-1 err = 0.372852,	Top-5 err = 0.165283,	data_time = 0.050344,	train_time = 0.798853
[2019-08-24 06:25:46,338] TRAIN Iter 208500: lr = 0.152502,	loss = 2.515453,	Top-1 err = 0.368799,	Top-5 err = 0.161914,	data_time = 0.050284,	train_time = 0.385603
[2019-08-24 06:26:01,896] TRAIN Iter 208520: lr = 0.152468,	loss = 2.595111,	Top-1 err = 0.377686,	Top-5 err = 0.167334,	data_time = 0.050647,	train_time = 0.777884
[2019-08-24 06:26:18,653] TRAIN Iter 208540: lr = 0.152435,	loss = 2.541977,	Top-1 err = 0.377246,	Top-5 err = 0.165039,	data_time = 0.050431,	train_time = 0.837801
[2019-08-24 06:26:25,734] TRAIN Iter 208560: lr = 0.152402,	loss = 2.464520,	Top-1 err = 0.375098,	Top-5 err = 0.164014,	data_time = 0.050261,	train_time = 0.354051
[2019-08-24 06:26:41,091] TRAIN Iter 208580: lr = 0.152368,	loss = 2.580089,	Top-1 err = 0.373486,	Top-5 err = 0.164014,	data_time = 0.050641,	train_time = 0.767830
[2019-08-24 06:26:49,232] TRAIN Iter 208600: lr = 0.152335,	loss = 2.502731,	Top-1 err = 0.373682,	Top-5 err = 0.166553,	data_time = 0.050516,	train_time = 0.407055
[2019-08-24 06:27:05,120] TRAIN Iter 208620: lr = 0.152302,	loss = 2.568936,	Top-1 err = 0.372998,	Top-5 err = 0.163086,	data_time = 0.050449,	train_time = 0.794360
[2019-08-24 06:27:21,747] TRAIN Iter 208640: lr = 0.152268,	loss = 2.487761,	Top-1 err = 0.375049,	Top-5 err = 0.167432,	data_time = 0.050667,	train_time = 0.831346
[2019-08-24 06:27:29,243] TRAIN Iter 208660: lr = 0.152235,	loss = 2.493072,	Top-1 err = 0.370557,	Top-5 err = 0.162695,	data_time = 0.147629,	train_time = 0.374801
[2019-08-24 06:27:45,065] TRAIN Iter 208680: lr = 0.152202,	loss = 2.426183,	Top-1 err = 0.378027,	Top-5 err = 0.165234,	data_time = 0.050399,	train_time = 0.791102
[2019-08-24 06:28:01,854] TRAIN Iter 208700: lr = 0.152168,	loss = 2.550426,	Top-1 err = 0.375879,	Top-5 err = 0.162939,	data_time = 0.050598,	train_time = 0.839408
[2019-08-24 06:28:08,583] TRAIN Iter 208720: lr = 0.152135,	loss = 2.499049,	Top-1 err = 0.372900,	Top-5 err = 0.163867,	data_time = 0.050265,	train_time = 0.336446
[2019-08-24 06:28:25,979] TRAIN Iter 208740: lr = 0.152102,	loss = 2.568252,	Top-1 err = 0.375488,	Top-5 err = 0.164502,	data_time = 0.050564,	train_time = 0.869764
[2019-08-24 06:28:33,759] TRAIN Iter 208760: lr = 0.152068,	loss = 2.550396,	Top-1 err = 0.370703,	Top-5 err = 0.162842,	data_time = 0.050754,	train_time = 0.388987
[2019-08-24 06:28:51,250] TRAIN Iter 208780: lr = 0.152035,	loss = 2.403722,	Top-1 err = 0.377051,	Top-5 err = 0.166846,	data_time = 0.050702,	train_time = 0.874559
[2019-08-24 06:29:08,676] TRAIN Iter 208800: lr = 0.152002,	loss = 2.487059,	Top-1 err = 0.366504,	Top-5 err = 0.162256,	data_time = 0.050456,	train_time = 0.871270
[2019-08-24 06:29:16,003] TRAIN Iter 208820: lr = 0.151968,	loss = 2.503305,	Top-1 err = 0.372510,	Top-5 err = 0.166455,	data_time = 0.050495,	train_time = 0.366354
[2019-08-24 06:29:31,526] TRAIN Iter 208840: lr = 0.151935,	loss = 2.591059,	Top-1 err = 0.374268,	Top-5 err = 0.163379,	data_time = 0.050634,	train_time = 0.776145
[2019-08-24 06:29:48,165] TRAIN Iter 208860: lr = 0.151902,	loss = 2.485549,	Top-1 err = 0.374463,	Top-5 err = 0.167871,	data_time = 0.050887,	train_time = 0.831937
[2019-08-24 06:29:54,889] TRAIN Iter 208880: lr = 0.151868,	loss = 2.514114,	Top-1 err = 0.377930,	Top-5 err = 0.168750,	data_time = 0.050651,	train_time = 0.336179
[2019-08-24 06:30:12,882] TRAIN Iter 208900: lr = 0.151835,	loss = 2.630404,	Top-1 err = 0.374658,	Top-5 err = 0.161475,	data_time = 0.150618,	train_time = 0.899625
[2019-08-24 06:30:20,076] TRAIN Iter 208920: lr = 0.151802,	loss = 2.568462,	Top-1 err = 0.379102,	Top-5 err = 0.168311,	data_time = 0.050600,	train_time = 0.359700
[2019-08-24 06:30:36,991] TRAIN Iter 208940: lr = 0.151768,	loss = 2.506239,	Top-1 err = 0.372021,	Top-5 err = 0.167969,	data_time = 0.050305,	train_time = 0.845711
[2019-08-24 06:30:53,754] TRAIN Iter 208960: lr = 0.151735,	loss = 2.591280,	Top-1 err = 0.382129,	Top-5 err = 0.166895,	data_time = 0.050026,	train_time = 0.838159
[2019-08-24 06:31:00,547] TRAIN Iter 208980: lr = 0.151702,	loss = 2.584789,	Top-1 err = 0.378564,	Top-5 err = 0.168799,	data_time = 0.049938,	train_time = 0.339648
[2019-08-24 06:31:16,570] TRAIN Iter 209000: lr = 0.151668,	loss = 2.547258,	Top-1 err = 0.373486,	Top-5 err = 0.166504,	data_time = 0.049915,	train_time = 0.801138
[2019-08-24 06:31:29,129] TRAIN Iter 209020: lr = 0.151635,	loss = 3.143933,	Top-1 err = 0.380702,	Top-5 err = 0.171123,	data_time = 0.007125,	train_time = 0.627922
[2019-08-24 06:32:15,775] TRAIN Iter 209040: lr = 0.151602,	loss = 2.468719,	Top-1 err = 0.378076,	Top-5 err = 0.165918,	data_time = 0.050437,	train_time = 2.332293
[2019-08-24 06:32:33,854] TRAIN Iter 209060: lr = 0.151568,	loss = 2.433723,	Top-1 err = 0.367725,	Top-5 err = 0.161670,	data_time = 0.050452,	train_time = 0.903904
[2019-08-24 06:32:41,731] TRAIN Iter 209080: lr = 0.151535,	loss = 2.521559,	Top-1 err = 0.363086,	Top-5 err = 0.158301,	data_time = 0.050452,	train_time = 0.393864
[2019-08-24 06:32:53,758] TRAIN Iter 209100: lr = 0.151502,	loss = 2.560800,	Top-1 err = 0.367432,	Top-5 err = 0.160596,	data_time = 0.050570,	train_time = 0.601317
[2019-08-24 06:33:05,563] TRAIN Iter 209120: lr = 0.151468,	loss = 2.466609,	Top-1 err = 0.369678,	Top-5 err = 0.163818,	data_time = 3.720583,	train_time = 0.590260
[2019-08-24 06:33:14,093] TRAIN Iter 209140: lr = 0.151435,	loss = 2.476545,	Top-1 err = 0.364404,	Top-5 err = 0.161523,	data_time = 0.050747,	train_time = 0.426487
[2019-08-24 06:33:28,037] TRAIN Iter 209160: lr = 0.151402,	loss = 2.496087,	Top-1 err = 0.366064,	Top-5 err = 0.161523,	data_time = 0.050863,	train_time = 0.697145
[2019-08-24 06:33:35,113] TRAIN Iter 209180: lr = 0.151368,	loss = 2.524119,	Top-1 err = 0.366504,	Top-5 err = 0.157812,	data_time = 0.050733,	train_time = 0.353787
[2019-08-24 06:33:49,138] TRAIN Iter 209200: lr = 0.151335,	loss = 2.490809,	Top-1 err = 0.371729,	Top-5 err = 0.161523,	data_time = 0.050326,	train_time = 0.701230
[2019-08-24 06:34:04,439] TRAIN Iter 209220: lr = 0.151302,	loss = 2.461651,	Top-1 err = 0.372900,	Top-5 err = 0.163184,	data_time = 0.050431,	train_time = 0.765055
[2019-08-24 06:34:11,874] TRAIN Iter 209240: lr = 0.151268,	loss = 2.550635,	Top-1 err = 0.369727,	Top-5 err = 0.159961,	data_time = 0.050609,	train_time = 0.371741
[2019-08-24 06:34:25,392] TRAIN Iter 209260: lr = 0.151235,	loss = 2.465293,	Top-1 err = 0.371045,	Top-5 err = 0.165088,	data_time = 0.050394,	train_time = 0.675888
[2019-08-24 06:34:42,165] TRAIN Iter 209280: lr = 0.151202,	loss = 2.545040,	Top-1 err = 0.370215,	Top-5 err = 0.163965,	data_time = 6.531616,	train_time = 0.838606
[2019-08-24 06:34:49,664] TRAIN Iter 209300: lr = 0.151168,	loss = 2.524635,	Top-1 err = 0.371875,	Top-5 err = 0.163477,	data_time = 0.050519,	train_time = 0.374957
[2019-08-24 06:35:04,546] TRAIN Iter 209320: lr = 0.151135,	loss = 2.676165,	Top-1 err = 0.374121,	Top-5 err = 0.167236,	data_time = 0.050478,	train_time = 0.744100
[2019-08-24 06:35:11,624] TRAIN Iter 209340: lr = 0.151102,	loss = 2.505440,	Top-1 err = 0.369580,	Top-5 err = 0.161328,	data_time = 0.050611,	train_time = 0.353892
[2019-08-24 06:35:26,767] TRAIN Iter 209360: lr = 0.151068,	loss = 2.492062,	Top-1 err = 0.368018,	Top-5 err = 0.159668,	data_time = 0.050527,	train_time = 0.757098
[2019-08-24 06:35:44,474] TRAIN Iter 209380: lr = 0.151035,	loss = 2.518222,	Top-1 err = 0.362939,	Top-5 err = 0.158496,	data_time = 0.050545,	train_time = 0.885358
[2019-08-24 06:35:51,655] TRAIN Iter 209400: lr = 0.151002,	loss = 2.574958,	Top-1 err = 0.370898,	Top-5 err = 0.168213,	data_time = 0.050622,	train_time = 0.359024
[2019-08-24 06:36:06,094] TRAIN Iter 209420: lr = 0.150968,	loss = 2.490059,	Top-1 err = 0.370410,	Top-5 err = 0.163916,	data_time = 0.050282,	train_time = 0.721942
[2019-08-24 06:36:21,909] TRAIN Iter 209440: lr = 0.150935,	loss = 2.454765,	Top-1 err = 0.367920,	Top-5 err = 0.163672,	data_time = 6.121509,	train_time = 0.790721
[2019-08-24 06:36:29,065] TRAIN Iter 209460: lr = 0.150902,	loss = 2.506384,	Top-1 err = 0.371680,	Top-5 err = 0.165918,	data_time = 0.050478,	train_time = 0.357821
[2019-08-24 06:36:43,166] TRAIN Iter 209480: lr = 0.150868,	loss = 2.539983,	Top-1 err = 0.369238,	Top-5 err = 0.161035,	data_time = 0.050531,	train_time = 0.705008
[2019-08-24 06:36:50,493] TRAIN Iter 209500: lr = 0.150835,	loss = 2.529251,	Top-1 err = 0.377979,	Top-5 err = 0.166895,	data_time = 0.125478,	train_time = 0.366344
[2019-08-24 06:37:06,153] TRAIN Iter 209520: lr = 0.150802,	loss = 2.478740,	Top-1 err = 0.366992,	Top-5 err = 0.159961,	data_time = 0.050894,	train_time = 0.782987
[2019-08-24 06:37:21,502] TRAIN Iter 209540: lr = 0.150768,	loss = 2.451432,	Top-1 err = 0.369775,	Top-5 err = 0.162744,	data_time = 0.050786,	train_time = 0.767446
[2019-08-24 06:37:28,505] TRAIN Iter 209560: lr = 0.150735,	loss = 2.502176,	Top-1 err = 0.367627,	Top-5 err = 0.163086,	data_time = 0.050303,	train_time = 0.350134
[2019-08-24 06:37:44,264] TRAIN Iter 209580: lr = 0.150702,	loss = 2.575334,	Top-1 err = 0.370605,	Top-5 err = 0.160059,	data_time = 0.050488,	train_time = 0.787916
[2019-08-24 06:38:00,483] TRAIN Iter 209600: lr = 0.150668,	loss = 2.458301,	Top-1 err = 0.370215,	Top-5 err = 0.167480,	data_time = 5.599684,	train_time = 0.810934
[2019-08-24 06:38:08,281] TRAIN Iter 209620: lr = 0.150635,	loss = 2.569739,	Top-1 err = 0.372314,	Top-5 err = 0.165820,	data_time = 0.050440,	train_time = 0.389908
[2019-08-24 06:38:22,405] TRAIN Iter 209640: lr = 0.150602,	loss = 2.504661,	Top-1 err = 0.373926,	Top-5 err = 0.166455,	data_time = 0.050342,	train_time = 0.706154
[2019-08-24 06:38:29,304] TRAIN Iter 209660: lr = 0.150568,	loss = 2.578611,	Top-1 err = 0.368066,	Top-5 err = 0.163770,	data_time = 0.050422,	train_time = 0.344978
[2019-08-24 06:38:45,774] TRAIN Iter 209680: lr = 0.150535,	loss = 2.487676,	Top-1 err = 0.370068,	Top-5 err = 0.162256,	data_time = 0.050416,	train_time = 0.823442
[2019-08-24 06:39:00,764] TRAIN Iter 209700: lr = 0.150502,	loss = 2.495484,	Top-1 err = 0.367578,	Top-5 err = 0.164062,	data_time = 0.050867,	train_time = 0.749521
[2019-08-24 06:39:08,123] TRAIN Iter 209720: lr = 0.150468,	loss = 2.521492,	Top-1 err = 0.372217,	Top-5 err = 0.162451,	data_time = 0.050370,	train_time = 0.367946
[2019-08-24 06:39:25,271] TRAIN Iter 209740: lr = 0.150435,	loss = 2.532659,	Top-1 err = 0.373145,	Top-5 err = 0.160059,	data_time = 0.050875,	train_time = 0.857338
[2019-08-24 06:39:38,795] TRAIN Iter 209760: lr = 0.150402,	loss = 2.566682,	Top-1 err = 0.371045,	Top-5 err = 0.165576,	data_time = 2.051777,	train_time = 0.676188
[2019-08-24 06:39:46,960] TRAIN Iter 209780: lr = 0.150368,	loss = 2.623365,	Top-1 err = 0.377100,	Top-5 err = 0.166113,	data_time = 0.050571,	train_time = 0.408267
[2019-08-24 06:40:02,825] TRAIN Iter 209800: lr = 0.150335,	loss = 2.541893,	Top-1 err = 0.373047,	Top-5 err = 0.160205,	data_time = 0.050403,	train_time = 0.793229
[2019-08-24 06:40:09,919] TRAIN Iter 209820: lr = 0.150302,	loss = 2.525113,	Top-1 err = 0.370117,	Top-5 err = 0.165039,	data_time = 0.050402,	train_time = 0.354676
[2019-08-24 06:40:29,315] TRAIN Iter 209840: lr = 0.150268,	loss = 2.611596,	Top-1 err = 0.375684,	Top-5 err = 0.164795,	data_time = 0.050456,	train_time = 0.969789
[2019-08-24 06:40:44,576] TRAIN Iter 209860: lr = 0.150235,	loss = 2.528536,	Top-1 err = 0.376660,	Top-5 err = 0.165283,	data_time = 0.050335,	train_time = 0.763059
[2019-08-24 06:40:51,752] TRAIN Iter 209880: lr = 0.150202,	loss = 2.464810,	Top-1 err = 0.371582,	Top-5 err = 0.167139,	data_time = 0.050543,	train_time = 0.358746
[2019-08-24 06:41:06,972] TRAIN Iter 209900: lr = 0.150168,	loss = 2.519040,	Top-1 err = 0.372412,	Top-5 err = 0.166650,	data_time = 0.050437,	train_time = 0.760986
[2019-08-24 06:41:22,721] TRAIN Iter 209920: lr = 0.150135,	loss = 2.549726,	Top-1 err = 0.371680,	Top-5 err = 0.162012,	data_time = 0.093746,	train_time = 0.787474
[2019-08-24 06:41:30,165] TRAIN Iter 209940: lr = 0.150102,	loss = 2.625492,	Top-1 err = 0.372852,	Top-5 err = 0.164746,	data_time = 0.051010,	train_time = 0.372164
[2019-08-24 06:41:46,626] TRAIN Iter 209960: lr = 0.150068,	loss = 2.495633,	Top-1 err = 0.372510,	Top-5 err = 0.160693,	data_time = 0.050812,	train_time = 0.823017
[2019-08-24 06:41:53,388] TRAIN Iter 209980: lr = 0.150035,	loss = 2.453815,	Top-1 err = 0.371143,	Top-5 err = 0.163184,	data_time = 0.050609,	train_time = 0.338127
[2019-08-24 06:42:10,164] TRAIN Iter 210000: lr = 0.150002,	loss = 2.529838,	Top-1 err = 0.373193,	Top-5 err = 0.161621,	data_time = 0.050392,	train_time = 0.838765
[2019-08-24 06:43:15,780] TEST Iter 210000: loss = 2.292864,	Top-1 err = 0.333860,	Top-5 err = 0.122500,	val_time = 65.576532
[2019-08-24 06:43:21,969] TRAIN Iter 210020: lr = 0.149968,	loss = 2.538788,	Top-1 err = 0.375732,	Top-5 err = 0.165137,	data_time = 0.050416,	train_time = 0.309410
[2019-08-24 06:43:28,519] TRAIN Iter 210040: lr = 0.149935,	loss = 2.615757,	Top-1 err = 0.378369,	Top-5 err = 0.166357,	data_time = 0.050404,	train_time = 0.327515
[2019-08-24 06:43:35,241] TRAIN Iter 210060: lr = 0.149902,	loss = 2.511617,	Top-1 err = 0.368896,	Top-5 err = 0.163477,	data_time = 0.121991,	train_time = 0.336086
[2019-08-24 06:43:43,641] TRAIN Iter 210080: lr = 0.149868,	loss = 2.549506,	Top-1 err = 0.382129,	Top-5 err = 0.164893,	data_time = 0.050596,	train_time = 0.419966
[2019-08-24 06:43:59,829] TRAIN Iter 210100: lr = 0.149835,	loss = 2.556858,	Top-1 err = 0.377588,	Top-5 err = 0.167432,	data_time = 0.094457,	train_time = 0.809385
[2019-08-24 06:44:09,474] TRAIN Iter 210120: lr = 0.149802,	loss = 2.503353,	Top-1 err = 0.377832,	Top-5 err = 0.167725,	data_time = 0.050423,	train_time = 0.482253
[2019-08-24 06:44:26,769] TRAIN Iter 210140: lr = 0.149768,	loss = 2.464195,	Top-1 err = 0.377344,	Top-5 err = 0.164404,	data_time = 1.900581,	train_time = 0.864714
[2019-08-24 06:44:34,455] TRAIN Iter 210160: lr = 0.149735,	loss = 2.450248,	Top-1 err = 0.372266,	Top-5 err = 0.163477,	data_time = 0.050202,	train_time = 0.384294
[2019-08-24 06:44:52,065] TRAIN Iter 210180: lr = 0.149702,	loss = 2.597578,	Top-1 err = 0.369629,	Top-5 err = 0.164551,	data_time = 0.050551,	train_time = 0.880491
[2019-08-24 06:45:06,909] TRAIN Iter 210200: lr = 0.149668,	loss = 2.512762,	Top-1 err = 0.372852,	Top-5 err = 0.165186,	data_time = 0.050673,	train_time = 0.742153
[2019-08-24 06:45:15,911] TRAIN Iter 210220: lr = 0.149635,	loss = 2.549747,	Top-1 err = 0.384033,	Top-5 err = 0.168848,	data_time = 0.050188,	train_time = 0.450101
[2019-08-24 06:45:33,148] TRAIN Iter 210240: lr = 0.149602,	loss = 2.491845,	Top-1 err = 0.373877,	Top-5 err = 0.163086,	data_time = 0.050128,	train_time = 0.861825
[2019-08-24 06:45:44,950] TRAIN Iter 210260: lr = 0.149568,	loss = 2.457177,	Top-1 err = 0.377637,	Top-5 err = 0.167285,	data_time = 0.049822,	train_time = 0.590088
[2019-08-24 06:46:32,670] TRAIN Iter 210280: lr = 0.149535,	loss = 2.540409,	Top-1 err = 0.373200,	Top-5 err = 0.160718,	data_time = 0.050590,	train_time = 2.385992
[2019-08-24 06:46:39,721] TRAIN Iter 210300: lr = 0.149502,	loss = 2.411350,	Top-1 err = 0.373877,	Top-5 err = 0.162842,	data_time = 0.050491,	train_time = 0.352541
[2019-08-24 06:46:56,802] TRAIN Iter 210320: lr = 0.149468,	loss = 2.548852,	Top-1 err = 0.376465,	Top-5 err = 0.169189,	data_time = 0.050624,	train_time = 0.854024
[2019-08-24 06:47:11,097] TRAIN Iter 210340: lr = 0.149435,	loss = 2.486282,	Top-1 err = 0.368311,	Top-5 err = 0.160840,	data_time = 0.050536,	train_time = 0.714764
[2019-08-24 06:47:18,215] TRAIN Iter 210360: lr = 0.149402,	loss = 2.419011,	Top-1 err = 0.372363,	Top-5 err = 0.164258,	data_time = 0.050745,	train_time = 0.355863
[2019-08-24 06:47:33,572] TRAIN Iter 210380: lr = 0.149368,	loss = 2.441226,	Top-1 err = 0.365674,	Top-5 err = 0.157422,	data_time = 0.050386,	train_time = 0.767816
[2019-08-24 06:47:41,235] TRAIN Iter 210400: lr = 0.149335,	loss = 2.428358,	Top-1 err = 0.366699,	Top-5 err = 0.156592,	data_time = 0.050593,	train_time = 0.383137
[2019-08-24 06:47:55,999] TRAIN Iter 210420: lr = 0.149302,	loss = 2.593424,	Top-1 err = 0.371875,	Top-5 err = 0.159131,	data_time = 0.050395,	train_time = 0.738227
[2019-08-24 06:48:09,731] TRAIN Iter 210440: lr = 0.149268,	loss = 2.509445,	Top-1 err = 0.374268,	Top-5 err = 0.163867,	data_time = 0.050397,	train_time = 0.686581
[2019-08-24 06:48:17,031] TRAIN Iter 210460: lr = 0.149235,	loss = 2.437333,	Top-1 err = 0.368750,	Top-5 err = 0.162305,	data_time = 0.050433,	train_time = 0.364968
[2019-08-24 06:48:33,093] TRAIN Iter 210480: lr = 0.149202,	loss = 2.377561,	Top-1 err = 0.369678,	Top-5 err = 0.161670,	data_time = 0.050834,	train_time = 0.803059
[2019-08-24 06:48:46,437] TRAIN Iter 210500: lr = 0.149168,	loss = 2.529072,	Top-1 err = 0.369189,	Top-5 err = 0.157812,	data_time = 0.050785,	train_time = 0.667196
[2019-08-24 06:48:54,656] TRAIN Iter 210520: lr = 0.149135,	loss = 2.569592,	Top-1 err = 0.368359,	Top-5 err = 0.160254,	data_time = 0.050801,	train_time = 0.410950
[2019-08-24 06:49:09,874] TRAIN Iter 210540: lr = 0.149102,	loss = 2.496430,	Top-1 err = 0.369092,	Top-5 err = 0.156592,	data_time = 0.097248,	train_time = 0.760904
[2019-08-24 06:49:16,535] TRAIN Iter 210560: lr = 0.149068,	loss = 2.491251,	Top-1 err = 0.366992,	Top-5 err = 0.159863,	data_time = 0.050486,	train_time = 0.333040
[2019-08-24 06:49:33,897] TRAIN Iter 210580: lr = 0.149035,	loss = 2.555977,	Top-1 err = 0.364893,	Top-5 err = 0.159180,	data_time = 0.050873,	train_time = 0.868091
[2019-08-24 06:49:50,785] TRAIN Iter 210600: lr = 0.149002,	loss = 2.537104,	Top-1 err = 0.368945,	Top-5 err = 0.163818,	data_time = 0.050347,	train_time = 0.844349
[2019-08-24 06:49:58,572] TRAIN Iter 210620: lr = 0.148968,	loss = 2.495823,	Top-1 err = 0.370850,	Top-5 err = 0.162549,	data_time = 0.050681,	train_time = 0.389328
[2019-08-24 06:50:12,724] TRAIN Iter 210640: lr = 0.148935,	loss = 2.547066,	Top-1 err = 0.369580,	Top-5 err = 0.161963,	data_time = 0.050496,	train_time = 0.707608
[2019-08-24 06:50:26,191] TRAIN Iter 210660: lr = 0.148902,	loss = 2.476787,	Top-1 err = 0.365820,	Top-5 err = 0.160205,	data_time = 0.138122,	train_time = 0.673336
[2019-08-24 06:50:33,223] TRAIN Iter 210680: lr = 0.148868,	loss = 2.495648,	Top-1 err = 0.369971,	Top-5 err = 0.157812,	data_time = 0.050564,	train_time = 0.351597
[2019-08-24 06:50:48,603] TRAIN Iter 210700: lr = 0.148835,	loss = 2.396719,	Top-1 err = 0.376025,	Top-5 err = 0.166211,	data_time = 0.050515,	train_time = 0.768991
[2019-08-24 06:50:55,561] TRAIN Iter 210720: lr = 0.148802,	loss = 2.629831,	Top-1 err = 0.371094,	Top-5 err = 0.162402,	data_time = 0.050723,	train_time = 0.347866
[2019-08-24 06:51:13,936] TRAIN Iter 210740: lr = 0.148768,	loss = 2.572909,	Top-1 err = 0.371973,	Top-5 err = 0.163574,	data_time = 0.050483,	train_time = 0.918742
[2019-08-24 06:51:28,314] TRAIN Iter 210760: lr = 0.148735,	loss = 2.574969,	Top-1 err = 0.374414,	Top-5 err = 0.160107,	data_time = 0.050403,	train_time = 0.718897
[2019-08-24 06:51:35,833] TRAIN Iter 210780: lr = 0.148702,	loss = 2.493191,	Top-1 err = 0.372510,	Top-5 err = 0.162256,	data_time = 0.145505,	train_time = 0.375907
[2019-08-24 06:51:48,406] TRAIN Iter 210800: lr = 0.148668,	loss = 2.523749,	Top-1 err = 0.377393,	Top-5 err = 0.166943,	data_time = 0.050422,	train_time = 0.628620
[2019-08-24 06:52:03,967] TRAIN Iter 210820: lr = 0.148635,	loss = 2.512782,	Top-1 err = 0.372705,	Top-5 err = 0.167480,	data_time = 0.102365,	train_time = 0.778041
[2019-08-24 06:52:10,973] TRAIN Iter 210840: lr = 0.148602,	loss = 2.465124,	Top-1 err = 0.372070,	Top-5 err = 0.159570,	data_time = 0.050472,	train_time = 0.350291
[2019-08-24 06:52:27,460] TRAIN Iter 210860: lr = 0.148568,	loss = 2.434039,	Top-1 err = 0.374854,	Top-5 err = 0.167334,	data_time = 0.050546,	train_time = 0.824332
[2019-08-24 06:52:34,986] TRAIN Iter 210880: lr = 0.148535,	loss = 2.418997,	Top-1 err = 0.368408,	Top-5 err = 0.160986,	data_time = 0.050465,	train_time = 0.376277
[2019-08-24 06:52:49,114] TRAIN Iter 210900: lr = 0.148502,	loss = 2.524696,	Top-1 err = 0.370850,	Top-5 err = 0.164648,	data_time = 0.050607,	train_time = 0.706401
[2019-08-24 06:53:03,341] TRAIN Iter 210920: lr = 0.148468,	loss = 2.554109,	Top-1 err = 0.374121,	Top-5 err = 0.165479,	data_time = 0.050329,	train_time = 0.711358
[2019-08-24 06:53:10,493] TRAIN Iter 210940: lr = 0.148435,	loss = 2.501819,	Top-1 err = 0.373389,	Top-5 err = 0.164209,	data_time = 0.050500,	train_time = 0.357580
[2019-08-24 06:53:30,257] TRAIN Iter 210960: lr = 0.148402,	loss = 2.550866,	Top-1 err = 0.369434,	Top-5 err = 0.161328,	data_time = 0.050276,	train_time = 0.988161
[2019-08-24 06:53:44,690] TRAIN Iter 210980: lr = 0.148368,	loss = 2.450464,	Top-1 err = 0.363330,	Top-5 err = 0.159912,	data_time = 0.108793,	train_time = 0.721666
[2019-08-24 06:53:52,145] TRAIN Iter 211000: lr = 0.148335,	loss = 2.538191,	Top-1 err = 0.368604,	Top-5 err = 0.160693,	data_time = 0.050379,	train_time = 0.372735
[2019-08-24 06:54:06,387] TRAIN Iter 211020: lr = 0.148302,	loss = 2.617124,	Top-1 err = 0.379443,	Top-5 err = 0.167236,	data_time = 0.050420,	train_time = 0.712084
[2019-08-24 06:54:13,899] TRAIN Iter 211040: lr = 0.148268,	loss = 2.522110,	Top-1 err = 0.369092,	Top-5 err = 0.165625,	data_time = 0.050867,	train_time = 0.375588
[2019-08-24 06:54:27,976] TRAIN Iter 211060: lr = 0.148235,	loss = 2.431370,	Top-1 err = 0.373535,	Top-5 err = 0.161426,	data_time = 0.050478,	train_time = 0.703828
[2019-08-24 06:54:45,701] TRAIN Iter 211080: lr = 0.148202,	loss = 2.482056,	Top-1 err = 0.368066,	Top-5 err = 0.166797,	data_time = 0.050837,	train_time = 0.886205
[2019-08-24 06:54:52,801] TRAIN Iter 211100: lr = 0.148168,	loss = 2.458887,	Top-1 err = 0.371924,	Top-5 err = 0.164502,	data_time = 0.050442,	train_time = 0.355020
[2019-08-24 06:55:06,933] TRAIN Iter 211120: lr = 0.148135,	loss = 2.608899,	Top-1 err = 0.367627,	Top-5 err = 0.162549,	data_time = 0.050478,	train_time = 0.706569
[2019-08-24 06:55:22,783] TRAIN Iter 211140: lr = 0.148102,	loss = 2.507403,	Top-1 err = 0.373633,	Top-5 err = 0.162695,	data_time = 0.170447,	train_time = 0.792470
[2019-08-24 06:55:29,790] TRAIN Iter 211160: lr = 0.148068,	loss = 2.489981,	Top-1 err = 0.372266,	Top-5 err = 0.159717,	data_time = 0.050457,	train_time = 0.350347
[2019-08-24 06:55:46,599] TRAIN Iter 211180: lr = 0.148035,	loss = 2.377405,	Top-1 err = 0.376465,	Top-5 err = 0.166260,	data_time = 0.050418,	train_time = 0.840460
[2019-08-24 06:55:53,462] TRAIN Iter 211200: lr = 0.148002,	loss = 2.500826,	Top-1 err = 0.378223,	Top-5 err = 0.168848,	data_time = 0.050535,	train_time = 0.343141
[2019-08-24 06:56:08,751] TRAIN Iter 211220: lr = 0.147968,	loss = 2.443213,	Top-1 err = 0.372168,	Top-5 err = 0.163770,	data_time = 0.050479,	train_time = 0.764426
[2019-08-24 06:56:24,282] TRAIN Iter 211240: lr = 0.147935,	loss = 2.481164,	Top-1 err = 0.375244,	Top-5 err = 0.164551,	data_time = 0.050401,	train_time = 0.776542
[2019-08-24 06:56:31,183] TRAIN Iter 211260: lr = 0.147902,	loss = 2.590968,	Top-1 err = 0.375293,	Top-5 err = 0.166650,	data_time = 0.050514,	train_time = 0.344997
[2019-08-24 06:56:50,993] TRAIN Iter 211280: lr = 0.147868,	loss = 2.435449,	Top-1 err = 0.371973,	Top-5 err = 0.164014,	data_time = 0.050461,	train_time = 0.990504
[2019-08-24 06:57:05,943] TRAIN Iter 211300: lr = 0.147835,	loss = 2.486981,	Top-1 err = 0.381396,	Top-5 err = 0.167920,	data_time = 0.050333,	train_time = 0.747513
[2019-08-24 06:57:13,149] TRAIN Iter 211320: lr = 0.147802,	loss = 2.539688,	Top-1 err = 0.374219,	Top-5 err = 0.164307,	data_time = 0.050414,	train_time = 0.360267
[2019-08-24 06:57:28,568] TRAIN Iter 211340: lr = 0.147768,	loss = 2.570816,	Top-1 err = 0.369824,	Top-5 err = 0.160693,	data_time = 0.050430,	train_time = 0.770910
[2019-08-24 06:57:35,367] TRAIN Iter 211360: lr = 0.147735,	loss = 2.492849,	Top-1 err = 0.369141,	Top-5 err = 0.162891,	data_time = 0.050606,	train_time = 0.339959
[2019-08-24 06:57:52,463] TRAIN Iter 211380: lr = 0.147702,	loss = 2.582584,	Top-1 err = 0.369922,	Top-5 err = 0.164209,	data_time = 0.050364,	train_time = 0.854783
[2019-08-24 06:58:10,671] TRAIN Iter 211400: lr = 0.147668,	loss = 2.571315,	Top-1 err = 0.375195,	Top-5 err = 0.167578,	data_time = 0.050349,	train_time = 0.910389
[2019-08-24 06:58:17,917] TRAIN Iter 211420: lr = 0.147635,	loss = 2.472023,	Top-1 err = 0.373926,	Top-5 err = 0.164844,	data_time = 0.050506,	train_time = 0.362263
[2019-08-24 06:58:35,335] TRAIN Iter 211440: lr = 0.147602,	loss = 2.552836,	Top-1 err = 0.372412,	Top-5 err = 0.163477,	data_time = 0.050468,	train_time = 0.870880
[2019-08-24 06:58:49,178] TRAIN Iter 211460: lr = 0.147568,	loss = 2.540316,	Top-1 err = 0.372803,	Top-5 err = 0.164258,	data_time = 0.113738,	train_time = 0.692167
[2019-08-24 06:58:58,199] TRAIN Iter 211480: lr = 0.147535,	loss = 2.576145,	Top-1 err = 0.373877,	Top-5 err = 0.165283,	data_time = 0.050077,	train_time = 0.451009
[2019-08-24 06:59:16,553] TRAIN Iter 211500: lr = 0.147502,	loss = 2.519343,	Top-1 err = 0.377051,	Top-5 err = 0.168018,	data_time = 0.049959,	train_time = 0.917694
[2019-08-24 06:59:22,605] TRAIN Iter 211520: lr = 0.147468,	loss = 2.453373,	Top-1 err = 0.374902,	Top-5 err = 0.164160,	data_time = 0.049932,	train_time = 0.302620
[2019-08-24 07:00:13,060] TRAIN Iter 211540: lr = 0.147435,	loss = 2.529708,	Top-1 err = 0.381538,	Top-5 err = 0.172246,	data_time = 0.050476,	train_time = 2.522696
[2019-08-24 07:00:28,649] TRAIN Iter 211560: lr = 0.147402,	loss = 2.543928,	Top-1 err = 0.370215,	Top-5 err = 0.160449,	data_time = 0.050470,	train_time = 0.779463
[2019-08-24 07:00:35,528] TRAIN Iter 211580: lr = 0.147368,	loss = 2.496220,	Top-1 err = 0.365234,	Top-5 err = 0.155273,	data_time = 0.050410,	train_time = 0.343901
[2019-08-24 07:00:50,194] TRAIN Iter 211600: lr = 0.147335,	loss = 2.459239,	Top-1 err = 0.365039,	Top-5 err = 0.156982,	data_time = 0.050452,	train_time = 0.733285
[2019-08-24 07:00:57,551] TRAIN Iter 211620: lr = 0.147302,	loss = 2.368287,	Top-1 err = 0.369336,	Top-5 err = 0.156836,	data_time = 0.050718,	train_time = 0.367852
[2019-08-24 07:01:12,308] TRAIN Iter 211640: lr = 0.147268,	loss = 2.453472,	Top-1 err = 0.367871,	Top-5 err = 0.160498,	data_time = 0.050430,	train_time = 0.737842
[2019-08-24 07:01:25,995] TRAIN Iter 211660: lr = 0.147235,	loss = 2.350006,	Top-1 err = 0.372363,	Top-5 err = 0.162109,	data_time = 0.050881,	train_time = 0.684313
[2019-08-24 07:01:32,750] TRAIN Iter 211680: lr = 0.147202,	loss = 2.541597,	Top-1 err = 0.365918,	Top-5 err = 0.157324,	data_time = 0.050755,	train_time = 0.337740
[2019-08-24 07:01:48,801] TRAIN Iter 211700: lr = 0.147168,	loss = 2.492759,	Top-1 err = 0.367383,	Top-5 err = 0.159521,	data_time = 0.050294,	train_time = 0.802533
[2019-08-24 07:02:06,717] TRAIN Iter 211720: lr = 0.147135,	loss = 2.425819,	Top-1 err = 0.370557,	Top-5 err = 0.162451,	data_time = 0.050477,	train_time = 0.895789
[2019-08-24 07:02:12,938] TRAIN Iter 211740: lr = 0.147102,	loss = 2.569631,	Top-1 err = 0.367725,	Top-5 err = 0.160107,	data_time = 0.050280,	train_time = 0.311048
[2019-08-24 07:02:33,530] TRAIN Iter 211760: lr = 0.147068,	loss = 2.439002,	Top-1 err = 0.363965,	Top-5 err = 0.159473,	data_time = 0.050655,	train_time = 1.029572
[2019-08-24 07:02:40,102] TRAIN Iter 211780: lr = 0.147035,	loss = 2.450480,	Top-1 err = 0.367090,	Top-5 err = 0.165918,	data_time = 0.050369,	train_time = 0.328592
[2019-08-24 07:02:56,508] TRAIN Iter 211800: lr = 0.147002,	loss = 2.499235,	Top-1 err = 0.368555,	Top-5 err = 0.163379,	data_time = 0.050411,	train_time = 0.820276
[2019-08-24 07:03:12,145] TRAIN Iter 211820: lr = 0.146968,	loss = 2.446172,	Top-1 err = 0.366846,	Top-5 err = 0.157178,	data_time = 0.050461,	train_time = 0.781867
[2019-08-24 07:03:18,955] TRAIN Iter 211840: lr = 0.146935,	loss = 2.426078,	Top-1 err = 0.361328,	Top-5 err = 0.156250,	data_time = 0.050544,	train_time = 0.340458
[2019-08-24 07:03:33,079] TRAIN Iter 211860: lr = 0.146902,	loss = 2.606536,	Top-1 err = 0.366504,	Top-5 err = 0.162061,	data_time = 0.050664,	train_time = 0.706190
[2019-08-24 07:03:48,455] TRAIN Iter 211880: lr = 0.146868,	loss = 2.499781,	Top-1 err = 0.365430,	Top-5 err = 0.160547,	data_time = 0.050469,	train_time = 0.768809
[2019-08-24 07:03:55,575] TRAIN Iter 211900: lr = 0.146835,	loss = 2.516536,	Top-1 err = 0.371240,	Top-5 err = 0.161768,	data_time = 0.050532,	train_time = 0.355966
[2019-08-24 07:04:11,479] TRAIN Iter 211920: lr = 0.146802,	loss = 2.415995,	Top-1 err = 0.364795,	Top-5 err = 0.164648,	data_time = 0.113519,	train_time = 0.795212
[2019-08-24 07:04:18,033] TRAIN Iter 211940: lr = 0.146768,	loss = 2.449555,	Top-1 err = 0.370752,	Top-5 err = 0.160938,	data_time = 0.138140,	train_time = 0.327678
[2019-08-24 07:04:33,417] TRAIN Iter 211960: lr = 0.146735,	loss = 2.460878,	Top-1 err = 0.372852,	Top-5 err = 0.166016,	data_time = 0.050315,	train_time = 0.769161
[2019-08-24 07:04:47,796] TRAIN Iter 211980: lr = 0.146702,	loss = 2.488629,	Top-1 err = 0.363525,	Top-5 err = 0.157764,	data_time = 0.050320,	train_time = 0.718938
[2019-08-24 07:04:54,759] TRAIN Iter 212000: lr = 0.146668,	loss = 2.484141,	Top-1 err = 0.367139,	Top-5 err = 0.158643,	data_time = 0.050419,	train_time = 0.348137
[2019-08-24 07:05:12,206] TRAIN Iter 212020: lr = 0.146635,	loss = 2.457630,	Top-1 err = 0.363525,	Top-5 err = 0.160498,	data_time = 0.050256,	train_time = 0.872353
[2019-08-24 07:05:26,937] TRAIN Iter 212040: lr = 0.146602,	loss = 2.573758,	Top-1 err = 0.376318,	Top-5 err = 0.163330,	data_time = 5.516903,	train_time = 0.736524
[2019-08-24 07:05:33,784] TRAIN Iter 212060: lr = 0.146568,	loss = 2.430598,	Top-1 err = 0.372559,	Top-5 err = 0.163086,	data_time = 0.050455,	train_time = 0.342321
[2019-08-24 07:05:49,909] TRAIN Iter 212080: lr = 0.146535,	loss = 2.496210,	Top-1 err = 0.375635,	Top-5 err = 0.166650,	data_time = 0.050499,	train_time = 0.806248
[2019-08-24 07:05:56,597] TRAIN Iter 212100: lr = 0.146502,	loss = 2.525981,	Top-1 err = 0.372314,	Top-5 err = 0.166357,	data_time = 0.165505,	train_time = 0.334374
[2019-08-24 07:06:11,994] TRAIN Iter 212120: lr = 0.146468,	loss = 2.483575,	Top-1 err = 0.370020,	Top-5 err = 0.161670,	data_time = 0.050431,	train_time = 0.769852
[2019-08-24 07:06:28,046] TRAIN Iter 212140: lr = 0.146435,	loss = 2.502514,	Top-1 err = 0.364404,	Top-5 err = 0.161719,	data_time = 0.050655,	train_time = 0.802578
[2019-08-24 07:06:34,779] TRAIN Iter 212160: lr = 0.146402,	loss = 2.560150,	Top-1 err = 0.374316,	Top-5 err = 0.160596,	data_time = 0.050544,	train_time = 0.336642
[2019-08-24 07:06:52,148] TRAIN Iter 212180: lr = 0.146368,	loss = 2.452140,	Top-1 err = 0.378174,	Top-5 err = 0.167090,	data_time = 0.050873,	train_time = 0.868467
[2019-08-24 07:07:07,161] TRAIN Iter 212200: lr = 0.146335,	loss = 2.512228,	Top-1 err = 0.369629,	Top-5 err = 0.162939,	data_time = 6.913038,	train_time = 0.750626
[2019-08-24 07:07:13,733] TRAIN Iter 212220: lr = 0.146302,	loss = 2.394496,	Top-1 err = 0.367432,	Top-5 err = 0.155762,	data_time = 0.050472,	train_time = 0.328564
[2019-08-24 07:07:30,709] TRAIN Iter 212240: lr = 0.146268,	loss = 2.511167,	Top-1 err = 0.371973,	Top-5 err = 0.165723,	data_time = 0.050563,	train_time = 0.848802
[2019-08-24 07:07:38,050] TRAIN Iter 212260: lr = 0.146235,	loss = 2.446293,	Top-1 err = 0.373682,	Top-5 err = 0.160352,	data_time = 0.050574,	train_time = 0.367027
[2019-08-24 07:07:54,157] TRAIN Iter 212280: lr = 0.146202,	loss = 2.516260,	Top-1 err = 0.365283,	Top-5 err = 0.163086,	data_time = 0.050652,	train_time = 0.805313
[2019-08-24 07:08:10,519] TRAIN Iter 212300: lr = 0.146168,	loss = 2.510963,	Top-1 err = 0.371777,	Top-5 err = 0.164453,	data_time = 0.050825,	train_time = 0.818102
[2019-08-24 07:08:17,823] TRAIN Iter 212320: lr = 0.146135,	loss = 2.472153,	Top-1 err = 0.366650,	Top-5 err = 0.157617,	data_time = 0.050488,	train_time = 0.365192
[2019-08-24 07:08:33,144] TRAIN Iter 212340: lr = 0.146102,	loss = 2.612026,	Top-1 err = 0.365625,	Top-5 err = 0.161475,	data_time = 0.050489,	train_time = 0.766014
[2019-08-24 07:08:48,903] TRAIN Iter 212360: lr = 0.146068,	loss = 2.613172,	Top-1 err = 0.373926,	Top-5 err = 0.165332,	data_time = 7.958534,	train_time = 0.787962
[2019-08-24 07:08:55,365] TRAIN Iter 212380: lr = 0.146035,	loss = 2.622695,	Top-1 err = 0.372021,	Top-5 err = 0.159375,	data_time = 0.050234,	train_time = 0.323097
[2019-08-24 07:09:12,071] TRAIN Iter 212400: lr = 0.146002,	loss = 2.432577,	Top-1 err = 0.377344,	Top-5 err = 0.166943,	data_time = 0.050198,	train_time = 0.835260
[2019-08-24 07:09:19,655] TRAIN Iter 212420: lr = 0.145968,	loss = 2.502683,	Top-1 err = 0.373486,	Top-5 err = 0.163184,	data_time = 0.113884,	train_time = 0.379207
[2019-08-24 07:09:33,606] TRAIN Iter 212440: lr = 0.145935,	loss = 2.452611,	Top-1 err = 0.371191,	Top-5 err = 0.163379,	data_time = 0.050356,	train_time = 0.697528
[2019-08-24 07:09:50,163] TRAIN Iter 212460: lr = 0.145902,	loss = 2.584389,	Top-1 err = 0.374170,	Top-5 err = 0.163330,	data_time = 0.050360,	train_time = 0.827846
[2019-08-24 07:09:57,459] TRAIN Iter 212480: lr = 0.145868,	loss = 2.521648,	Top-1 err = 0.375244,	Top-5 err = 0.164551,	data_time = 0.050413,	train_time = 0.364783
[2019-08-24 07:10:12,216] TRAIN Iter 212500: lr = 0.145835,	loss = 2.461082,	Top-1 err = 0.369189,	Top-5 err = 0.166406,	data_time = 0.050481,	train_time = 0.737823
[2019-08-24 07:10:28,321] TRAIN Iter 212520: lr = 0.145802,	loss = 2.457374,	Top-1 err = 0.371973,	Top-5 err = 0.160693,	data_time = 8.522814,	train_time = 0.805226
[2019-08-24 07:10:34,887] TRAIN Iter 212540: lr = 0.145768,	loss = 2.506085,	Top-1 err = 0.372363,	Top-5 err = 0.162012,	data_time = 0.050499,	train_time = 0.328302
[2019-08-24 07:10:50,996] TRAIN Iter 212560: lr = 0.145735,	loss = 2.515084,	Top-1 err = 0.370459,	Top-5 err = 0.160303,	data_time = 0.050317,	train_time = 0.805419
[2019-08-24 07:10:58,041] TRAIN Iter 212580: lr = 0.145702,	loss = 2.558425,	Top-1 err = 0.371729,	Top-5 err = 0.169287,	data_time = 0.050619,	train_time = 0.352244
[2019-08-24 07:11:15,237] TRAIN Iter 212600: lr = 0.145668,	loss = 2.516682,	Top-1 err = 0.367480,	Top-5 err = 0.162109,	data_time = 0.050481,	train_time = 0.859776
[2019-08-24 07:11:31,352] TRAIN Iter 212620: lr = 0.145635,	loss = 2.519934,	Top-1 err = 0.378271,	Top-5 err = 0.169189,	data_time = 0.050483,	train_time = 0.805759
[2019-08-24 07:11:38,091] TRAIN Iter 212640: lr = 0.145602,	loss = 2.472925,	Top-1 err = 0.377002,	Top-5 err = 0.168262,	data_time = 0.050653,	train_time = 0.336949
[2019-08-24 07:11:55,526] TRAIN Iter 212660: lr = 0.145568,	loss = 2.509556,	Top-1 err = 0.371680,	Top-5 err = 0.166797,	data_time = 0.050515,	train_time = 0.871701
[2019-08-24 07:12:12,033] TRAIN Iter 212680: lr = 0.145535,	loss = 2.608335,	Top-1 err = 0.380420,	Top-5 err = 0.163916,	data_time = 8.488385,	train_time = 0.825356
[2019-08-24 07:12:18,692] TRAIN Iter 212700: lr = 0.145502,	loss = 2.526496,	Top-1 err = 0.382373,	Top-5 err = 0.165283,	data_time = 0.050351,	train_time = 0.332928
[2019-08-24 07:12:35,802] TRAIN Iter 212720: lr = 0.145468,	loss = 2.513808,	Top-1 err = 0.379053,	Top-5 err = 0.169482,	data_time = 0.050056,	train_time = 0.855480
[2019-08-24 07:12:42,775] TRAIN Iter 212740: lr = 0.145435,	loss = 2.523885,	Top-1 err = 0.380811,	Top-5 err = 0.165234,	data_time = 0.050005,	train_time = 0.348647
[2019-08-24 07:12:58,074] TRAIN Iter 212760: lr = 0.145402,	loss = 2.489167,	Top-1 err = 0.375537,	Top-5 err = 0.165723,	data_time = 0.049893,	train_time = 0.764957
[2019-08-24 07:13:43,440] TRAIN Iter 212780: lr = 0.145368,	loss = 2.507369,	Top-1 err = 0.367659,	Top-5 err = 0.161839,	data_time = 0.050222,	train_time = 2.268272
[2019-08-24 07:13:50,382] TRAIN Iter 212800: lr = 0.145335,	loss = 2.475860,	Top-1 err = 0.376221,	Top-5 err = 0.164062,	data_time = 0.050707,	train_time = 0.347081
[2019-08-24 07:14:07,747] TRAIN Iter 212820: lr = 0.145302,	loss = 2.396781,	Top-1 err = 0.363428,	Top-5 err = 0.155176,	data_time = 0.050453,	train_time = 0.868206
[2019-08-24 07:14:15,866] TRAIN Iter 212840: lr = 0.145268,	loss = 2.387201,	Top-1 err = 0.362842,	Top-5 err = 0.154980,	data_time = 0.050313,	train_time = 0.405971
[2019-08-24 07:14:28,589] TRAIN Iter 212860: lr = 0.145235,	loss = 2.511410,	Top-1 err = 0.363330,	Top-5 err = 0.154688,	data_time = 0.050491,	train_time = 0.636114
[2019-08-24 07:14:43,079] TRAIN Iter 212880: lr = 0.145202,	loss = 2.516546,	Top-1 err = 0.366992,	Top-5 err = 0.158740,	data_time = 0.050388,	train_time = 0.724507
[2019-08-24 07:14:50,455] TRAIN Iter 212900: lr = 0.145168,	loss = 2.399136,	Top-1 err = 0.363525,	Top-5 err = 0.158398,	data_time = 0.050673,	train_time = 0.368776
[2019-08-24 07:15:04,557] TRAIN Iter 212920: lr = 0.145135,	loss = 2.478707,	Top-1 err = 0.365723,	Top-5 err = 0.164160,	data_time = 0.050351,	train_time = 0.705077
[2019-08-24 07:15:20,211] TRAIN Iter 212940: lr = 0.145102,	loss = 2.519875,	Top-1 err = 0.365039,	Top-5 err = 0.163232,	data_time = 0.126058,	train_time = 0.782673
[2019-08-24 07:15:27,636] TRAIN Iter 212960: lr = 0.145068,	loss = 2.488678,	Top-1 err = 0.368506,	Top-5 err = 0.158105,	data_time = 0.050390,	train_time = 0.371273
[2019-08-24 07:15:41,947] TRAIN Iter 212980: lr = 0.145035,	loss = 2.487199,	Top-1 err = 0.363086,	Top-5 err = 0.156982,	data_time = 0.050342,	train_time = 0.715518
[2019-08-24 07:15:49,331] TRAIN Iter 213000: lr = 0.145002,	loss = 2.477603,	Top-1 err = 0.367578,	Top-5 err = 0.155615,	data_time = 0.050810,	train_time = 0.369205
[2019-08-24 07:16:04,061] TRAIN Iter 213020: lr = 0.144968,	loss = 2.550455,	Top-1 err = 0.368750,	Top-5 err = 0.161475,	data_time = 0.050460,	train_time = 0.736463
[2019-08-24 07:16:18,733] TRAIN Iter 213040: lr = 0.144935,	loss = 2.567006,	Top-1 err = 0.369385,	Top-5 err = 0.161426,	data_time = 0.135411,	train_time = 0.733613
[2019-08-24 07:16:25,716] TRAIN Iter 213060: lr = 0.144902,	loss = 2.412067,	Top-1 err = 0.364551,	Top-5 err = 0.159131,	data_time = 0.050565,	train_time = 0.349121
[2019-08-24 07:16:41,619] TRAIN Iter 213080: lr = 0.144868,	loss = 2.532725,	Top-1 err = 0.371533,	Top-5 err = 0.158740,	data_time = 0.050557,	train_time = 0.795141
[2019-08-24 07:16:56,115] TRAIN Iter 213100: lr = 0.144835,	loss = 2.452021,	Top-1 err = 0.372607,	Top-5 err = 0.167627,	data_time = 3.484452,	train_time = 0.724759
[2019-08-24 07:17:03,176] TRAIN Iter 213120: lr = 0.144802,	loss = 2.410877,	Top-1 err = 0.363281,	Top-5 err = 0.160059,	data_time = 0.050302,	train_time = 0.353042
[2019-08-24 07:17:18,056] TRAIN Iter 213140: lr = 0.144768,	loss = 2.471098,	Top-1 err = 0.370947,	Top-5 err = 0.163525,	data_time = 0.050420,	train_time = 0.743971
[2019-08-24 07:17:25,257] TRAIN Iter 213160: lr = 0.144735,	loss = 2.428432,	Top-1 err = 0.371436,	Top-5 err = 0.159082,	data_time = 0.097943,	train_time = 0.360055
[2019-08-24 07:17:39,144] TRAIN Iter 213180: lr = 0.144702,	loss = 2.536499,	Top-1 err = 0.371631,	Top-5 err = 0.164844,	data_time = 0.050503,	train_time = 0.694350
[2019-08-24 07:17:53,455] TRAIN Iter 213200: lr = 0.144668,	loss = 2.547503,	Top-1 err = 0.367627,	Top-5 err = 0.160010,	data_time = 0.279398,	train_time = 0.715541
[2019-08-24 07:18:00,553] TRAIN Iter 213220: lr = 0.144635,	loss = 2.502583,	Top-1 err = 0.369775,	Top-5 err = 0.163281,	data_time = 0.050519,	train_time = 0.354888
[2019-08-24 07:18:16,626] TRAIN Iter 213240: lr = 0.144602,	loss = 2.472465,	Top-1 err = 0.368115,	Top-5 err = 0.160156,	data_time = 0.050678,	train_time = 0.803589
[2019-08-24 07:18:29,470] TRAIN Iter 213260: lr = 0.144568,	loss = 2.462172,	Top-1 err = 0.369238,	Top-5 err = 0.162891,	data_time = 0.149271,	train_time = 0.642235
[2019-08-24 07:18:38,248] TRAIN Iter 213280: lr = 0.144535,	loss = 2.512886,	Top-1 err = 0.364014,	Top-5 err = 0.161523,	data_time = 0.050308,	train_time = 0.438875
[2019-08-24 07:18:53,777] TRAIN Iter 213300: lr = 0.144502,	loss = 2.556037,	Top-1 err = 0.367139,	Top-5 err = 0.159912,	data_time = 0.051002,	train_time = 0.776422
[2019-08-24 07:19:01,412] TRAIN Iter 213320: lr = 0.144468,	loss = 2.523735,	Top-1 err = 0.375684,	Top-5 err = 0.162012,	data_time = 0.050380,	train_time = 0.381709
[2019-08-24 07:19:18,892] TRAIN Iter 213340: lr = 0.144435,	loss = 2.481487,	Top-1 err = 0.370361,	Top-5 err = 0.160400,	data_time = 0.050279,	train_time = 0.874011
[2019-08-24 07:19:33,682] TRAIN Iter 213360: lr = 0.144402,	loss = 2.481967,	Top-1 err = 0.369189,	Top-5 err = 0.165674,	data_time = 0.265328,	train_time = 0.739481
[2019-08-24 07:19:41,170] TRAIN Iter 213380: lr = 0.144368,	loss = 2.584586,	Top-1 err = 0.371680,	Top-5 err = 0.161035,	data_time = 0.050562,	train_time = 0.374403
[2019-08-24 07:19:57,091] TRAIN Iter 213400: lr = 0.144335,	loss = 2.495627,	Top-1 err = 0.369482,	Top-5 err = 0.161377,	data_time = 0.050377,	train_time = 0.796018
[2019-08-24 07:20:09,483] TRAIN Iter 213420: lr = 0.144302,	loss = 2.510931,	Top-1 err = 0.370215,	Top-5 err = 0.158691,	data_time = 0.050632,	train_time = 0.619579
[2019-08-24 07:20:19,136] TRAIN Iter 213440: lr = 0.144268,	loss = 2.424183,	Top-1 err = 0.369727,	Top-5 err = 0.159082,	data_time = 0.050669,	train_time = 0.482638
[2019-08-24 07:20:36,143] TRAIN Iter 213460: lr = 0.144235,	loss = 2.443957,	Top-1 err = 0.368359,	Top-5 err = 0.163086,	data_time = 0.050724,	train_time = 0.850344
[2019-08-24 07:20:43,223] TRAIN Iter 213480: lr = 0.144202,	loss = 2.505133,	Top-1 err = 0.366309,	Top-5 err = 0.161670,	data_time = 0.050823,	train_time = 0.353984
[2019-08-24 07:20:59,455] TRAIN Iter 213500: lr = 0.144168,	loss = 2.476145,	Top-1 err = 0.372656,	Top-5 err = 0.168018,	data_time = 0.051168,	train_time = 0.811605
[2019-08-24 07:21:14,026] TRAIN Iter 213520: lr = 0.144135,	loss = 2.473730,	Top-1 err = 0.370117,	Top-5 err = 0.161670,	data_time = 0.050836,	train_time = 0.728500
[2019-08-24 07:21:21,113] TRAIN Iter 213540: lr = 0.144102,	loss = 2.556880,	Top-1 err = 0.366357,	Top-5 err = 0.160303,	data_time = 0.050625,	train_time = 0.354356
[2019-08-24 07:21:37,321] TRAIN Iter 213560: lr = 0.144068,	loss = 2.411322,	Top-1 err = 0.377148,	Top-5 err = 0.164307,	data_time = 0.050457,	train_time = 0.810394
[2019-08-24 07:21:49,462] TRAIN Iter 213580: lr = 0.144035,	loss = 2.562450,	Top-1 err = 0.374854,	Top-5 err = 0.163135,	data_time = 0.105801,	train_time = 0.607022
[2019-08-24 07:21:59,036] TRAIN Iter 213600: lr = 0.144002,	loss = 2.551787,	Top-1 err = 0.366455,	Top-5 err = 0.164453,	data_time = 0.050400,	train_time = 0.478700
[2019-08-24 07:22:15,280] TRAIN Iter 213620: lr = 0.143968,	loss = 2.490847,	Top-1 err = 0.379102,	Top-5 err = 0.160645,	data_time = 0.050829,	train_time = 0.812177
[2019-08-24 07:22:22,377] TRAIN Iter 213640: lr = 0.143935,	loss = 2.481241,	Top-1 err = 0.373486,	Top-5 err = 0.165479,	data_time = 0.050496,	train_time = 0.354829
[2019-08-24 07:22:38,405] TRAIN Iter 213660: lr = 0.143902,	loss = 2.546851,	Top-1 err = 0.374072,	Top-5 err = 0.164062,	data_time = 0.050457,	train_time = 0.801373
[2019-08-24 07:22:54,718] TRAIN Iter 213680: lr = 0.143868,	loss = 2.519351,	Top-1 err = 0.376025,	Top-5 err = 0.162988,	data_time = 0.050596,	train_time = 0.815632
[2019-08-24 07:23:01,650] TRAIN Iter 213700: lr = 0.143835,	loss = 2.561874,	Top-1 err = 0.370068,	Top-5 err = 0.163232,	data_time = 0.050434,	train_time = 0.346583
[2019-08-24 07:23:17,666] TRAIN Iter 213720: lr = 0.143802,	loss = 2.610481,	Top-1 err = 0.373633,	Top-5 err = 0.166064,	data_time = 0.050384,	train_time = 0.800809
[2019-08-24 07:23:29,845] TRAIN Iter 213740: lr = 0.143768,	loss = 2.523378,	Top-1 err = 0.369775,	Top-5 err = 0.161182,	data_time = 0.050474,	train_time = 0.608929
[2019-08-24 07:23:40,686] TRAIN Iter 213760: lr = 0.143735,	loss = 2.493379,	Top-1 err = 0.374805,	Top-5 err = 0.163672,	data_time = 0.050508,	train_time = 0.542043
[2019-08-24 07:23:58,973] TRAIN Iter 213780: lr = 0.143702,	loss = 2.497733,	Top-1 err = 0.372607,	Top-5 err = 0.163477,	data_time = 0.050796,	train_time = 0.914353
[2019-08-24 07:24:06,412] TRAIN Iter 213800: lr = 0.143668,	loss = 2.424498,	Top-1 err = 0.375049,	Top-5 err = 0.165820,	data_time = 0.050582,	train_time = 0.371911
[2019-08-24 07:24:23,795] TRAIN Iter 213820: lr = 0.143635,	loss = 2.558982,	Top-1 err = 0.372754,	Top-5 err = 0.163770,	data_time = 0.050423,	train_time = 0.869131
[2019-08-24 07:24:41,745] TRAIN Iter 213840: lr = 0.143602,	loss = 2.498848,	Top-1 err = 0.372461,	Top-5 err = 0.160693,	data_time = 0.050692,	train_time = 0.897505
[2019-08-24 07:24:48,523] TRAIN Iter 213860: lr = 0.143568,	loss = 2.483061,	Top-1 err = 0.367188,	Top-5 err = 0.159473,	data_time = 0.050658,	train_time = 0.338848
[2019-08-24 07:25:06,873] TRAIN Iter 213880: lr = 0.143535,	loss = 2.587910,	Top-1 err = 0.375098,	Top-5 err = 0.167041,	data_time = 0.050344,	train_time = 0.917499
[2019-08-24 07:25:17,881] TRAIN Iter 213900: lr = 0.143502,	loss = 2.647954,	Top-1 err = 0.371533,	Top-5 err = 0.162891,	data_time = 0.050357,	train_time = 0.550379
[2019-08-24 07:25:29,372] TRAIN Iter 213920: lr = 0.143468,	loss = 2.499513,	Top-1 err = 0.372412,	Top-5 err = 0.166797,	data_time = 0.050763,	train_time = 0.574575
[2019-08-24 07:25:48,427] TRAIN Iter 213940: lr = 0.143435,	loss = 2.508193,	Top-1 err = 0.373242,	Top-5 err = 0.164355,	data_time = 0.050121,	train_time = 0.952699
[2019-08-24 07:25:55,819] TRAIN Iter 213960: lr = 0.143402,	loss = 2.454777,	Top-1 err = 0.369336,	Top-5 err = 0.163477,	data_time = 0.141886,	train_time = 0.369603
[2019-08-24 07:26:12,892] TRAIN Iter 213980: lr = 0.143368,	loss = 2.553714,	Top-1 err = 0.374023,	Top-5 err = 0.166699,	data_time = 0.050189,	train_time = 0.853638
[2019-08-24 07:26:28,881] TRAIN Iter 214000: lr = 0.143335,	loss = 2.468419,	Top-1 err = 0.372266,	Top-5 err = 0.162842,	data_time = 0.049934,	train_time = 0.799412
[2019-08-24 07:26:34,946] TRAIN Iter 214020: lr = 0.143302,	loss = 2.489159,	Top-1 err = 0.368750,	Top-5 err = 0.161865,	data_time = 0.049896,	train_time = 0.303261
[2019-08-24 07:27:23,968] TRAIN Iter 214040: lr = 0.143268,	loss = 2.556611,	Top-1 err = 0.372392,	Top-5 err = 0.167128,	data_time = 0.050311,	train_time = 2.451101
[2019-08-24 07:27:31,999] TRAIN Iter 214060: lr = 0.143235,	loss = 2.393214,	Top-1 err = 0.367334,	Top-5 err = 0.162988,	data_time = 0.154747,	train_time = 0.401492
[2019-08-24 07:27:46,171] TRAIN Iter 214080: lr = 0.143202,	loss = 2.453979,	Top-1 err = 0.364795,	Top-5 err = 0.159961,	data_time = 0.050546,	train_time = 0.708599
[2019-08-24 07:27:58,619] TRAIN Iter 214100: lr = 0.143168,	loss = 2.509822,	Top-1 err = 0.363037,	Top-5 err = 0.161621,	data_time = 0.050907,	train_time = 0.622365
[2019-08-24 07:28:06,411] TRAIN Iter 214120: lr = 0.143135,	loss = 2.559523,	Top-1 err = 0.368359,	Top-5 err = 0.164258,	data_time = 0.050409,	train_time = 0.389598
[2019-08-24 07:28:18,642] TRAIN Iter 214140: lr = 0.143102,	loss = 2.427699,	Top-1 err = 0.363086,	Top-5 err = 0.160059,	data_time = 0.050261,	train_time = 0.611526
[2019-08-24 07:28:33,020] TRAIN Iter 214160: lr = 0.143068,	loss = 2.395965,	Top-1 err = 0.360645,	Top-5 err = 0.151807,	data_time = 0.050416,	train_time = 0.718895
[2019-08-24 07:28:40,372] TRAIN Iter 214180: lr = 0.143035,	loss = 2.534119,	Top-1 err = 0.366406,	Top-5 err = 0.159521,	data_time = 0.050445,	train_time = 0.367610
[2019-08-24 07:28:54,654] TRAIN Iter 214200: lr = 0.143002,	loss = 2.427842,	Top-1 err = 0.362891,	Top-5 err = 0.157324,	data_time = 0.050491,	train_time = 0.714093
[2019-08-24 07:29:01,792] TRAIN Iter 214220: lr = 0.142968,	loss = 2.525448,	Top-1 err = 0.362207,	Top-5 err = 0.157178,	data_time = 0.050328,	train_time = 0.356873
[2019-08-24 07:29:21,972] TRAIN Iter 214240: lr = 0.142935,	loss = 2.485200,	Top-1 err = 0.362646,	Top-5 err = 0.156494,	data_time = 0.050280,	train_time = 1.008984
[2019-08-24 07:29:32,689] TRAIN Iter 214260: lr = 0.142902,	loss = 2.456619,	Top-1 err = 0.366650,	Top-5 err = 0.154541,	data_time = 0.050825,	train_time = 0.535822
[2019-08-24 07:29:40,165] TRAIN Iter 214280: lr = 0.142868,	loss = 2.436871,	Top-1 err = 0.366943,	Top-5 err = 0.161572,	data_time = 0.050352,	train_time = 0.373777
[2019-08-24 07:29:54,138] TRAIN Iter 214300: lr = 0.142835,	loss = 2.461767,	Top-1 err = 0.367773,	Top-5 err = 0.160693,	data_time = 0.050389,	train_time = 0.698656
[2019-08-24 07:30:08,789] TRAIN Iter 214320: lr = 0.142802,	loss = 2.546072,	Top-1 err = 0.372559,	Top-5 err = 0.161914,	data_time = 0.167011,	train_time = 0.732530
[2019-08-24 07:30:16,068] TRAIN Iter 214340: lr = 0.142768,	loss = 2.446452,	Top-1 err = 0.368115,	Top-5 err = 0.156348,	data_time = 0.050328,	train_time = 0.363928
[2019-08-24 07:30:31,939] TRAIN Iter 214360: lr = 0.142735,	loss = 2.500798,	Top-1 err = 0.367480,	Top-5 err = 0.159912,	data_time = 0.050506,	train_time = 0.793536
[2019-08-24 07:30:39,224] TRAIN Iter 214380: lr = 0.142702,	loss = 2.414309,	Top-1 err = 0.364404,	Top-5 err = 0.156104,	data_time = 0.178173,	train_time = 0.364249
[2019-08-24 07:30:55,403] TRAIN Iter 214400: lr = 0.142668,	loss = 2.516241,	Top-1 err = 0.370801,	Top-5 err = 0.157568,	data_time = 0.097966,	train_time = 0.808932
[2019-08-24 07:31:09,181] TRAIN Iter 214420: lr = 0.142635,	loss = 2.504803,	Top-1 err = 0.369824,	Top-5 err = 0.158252,	data_time = 0.050817,	train_time = 0.688877
[2019-08-24 07:31:16,307] TRAIN Iter 214440: lr = 0.142602,	loss = 2.545052,	Top-1 err = 0.362207,	Top-5 err = 0.161816,	data_time = 0.050925,	train_time = 0.356300
[2019-08-24 07:31:31,891] TRAIN Iter 214460: lr = 0.142568,	loss = 2.478842,	Top-1 err = 0.371338,	Top-5 err = 0.164990,	data_time = 0.050562,	train_time = 0.779167
[2019-08-24 07:31:47,978] TRAIN Iter 214480: lr = 0.142535,	loss = 2.542668,	Top-1 err = 0.366406,	Top-5 err = 0.163184,	data_time = 0.106120,	train_time = 0.804354
[2019-08-24 07:31:54,775] TRAIN Iter 214500: lr = 0.142502,	loss = 2.451629,	Top-1 err = 0.366602,	Top-5 err = 0.158252,	data_time = 0.050609,	train_time = 0.339816
[2019-08-24 07:32:10,302] TRAIN Iter 214520: lr = 0.142468,	loss = 2.417899,	Top-1 err = 0.368018,	Top-5 err = 0.161084,	data_time = 0.050908,	train_time = 0.776325
[2019-08-24 07:32:17,314] TRAIN Iter 214540: lr = 0.142435,	loss = 2.413174,	Top-1 err = 0.377393,	Top-5 err = 0.164844,	data_time = 0.050347,	train_time = 0.350588
[2019-08-24 07:32:32,948] TRAIN Iter 214560: lr = 0.142402,	loss = 2.470642,	Top-1 err = 0.368506,	Top-5 err = 0.160352,	data_time = 0.050502,	train_time = 0.781730
[2019-08-24 07:32:50,215] TRAIN Iter 214580: lr = 0.142368,	loss = 2.526804,	Top-1 err = 0.373535,	Top-5 err = 0.167285,	data_time = 0.050472,	train_time = 0.863311
[2019-08-24 07:32:57,448] TRAIN Iter 214600: lr = 0.142335,	loss = 2.479277,	Top-1 err = 0.372656,	Top-5 err = 0.160400,	data_time = 0.116425,	train_time = 0.361647
[2019-08-24 07:33:11,273] TRAIN Iter 214620: lr = 0.142302,	loss = 2.475749,	Top-1 err = 0.370459,	Top-5 err = 0.163330,	data_time = 0.050574,	train_time = 0.691222
[2019-08-24 07:33:26,175] TRAIN Iter 214640: lr = 0.142268,	loss = 2.485553,	Top-1 err = 0.377881,	Top-5 err = 0.164697,	data_time = 0.050585,	train_time = 0.745109
[2019-08-24 07:33:33,546] TRAIN Iter 214660: lr = 0.142235,	loss = 2.485306,	Top-1 err = 0.374414,	Top-5 err = 0.164209,	data_time = 0.050503,	train_time = 0.368532
[2019-08-24 07:33:49,160] TRAIN Iter 214680: lr = 0.142202,	loss = 2.490956,	Top-1 err = 0.370215,	Top-5 err = 0.164551,	data_time = 0.050347,	train_time = 0.780685
[2019-08-24 07:33:56,512] TRAIN Iter 214700: lr = 0.142168,	loss = 2.592389,	Top-1 err = 0.371924,	Top-5 err = 0.164502,	data_time = 0.050915,	train_time = 0.367568
[2019-08-24 07:34:13,430] TRAIN Iter 214720: lr = 0.142135,	loss = 2.517464,	Top-1 err = 0.368018,	Top-5 err = 0.156689,	data_time = 0.050875,	train_time = 0.845912
[2019-08-24 07:34:28,450] TRAIN Iter 214740: lr = 0.142102,	loss = 2.479365,	Top-1 err = 0.372168,	Top-5 err = 0.160889,	data_time = 0.050850,	train_time = 0.750947
[2019-08-24 07:34:35,497] TRAIN Iter 214760: lr = 0.142068,	loss = 2.599255,	Top-1 err = 0.370605,	Top-5 err = 0.161963,	data_time = 0.050339,	train_time = 0.352353
[2019-08-24 07:34:50,874] TRAIN Iter 214780: lr = 0.142035,	loss = 2.521301,	Top-1 err = 0.370312,	Top-5 err = 0.158154,	data_time = 0.050529,	train_time = 0.768836
[2019-08-24 07:35:07,058] TRAIN Iter 214800: lr = 0.142002,	loss = 2.456445,	Top-1 err = 0.368945,	Top-5 err = 0.159180,	data_time = 0.050658,	train_time = 0.809211
[2019-08-24 07:35:14,847] TRAIN Iter 214820: lr = 0.141968,	loss = 2.470040,	Top-1 err = 0.371777,	Top-5 err = 0.162012,	data_time = 0.050470,	train_time = 0.389424
[2019-08-24 07:35:28,114] TRAIN Iter 214840: lr = 0.141935,	loss = 2.444561,	Top-1 err = 0.372119,	Top-5 err = 0.160693,	data_time = 0.050385,	train_time = 0.663327
[2019-08-24 07:35:35,431] TRAIN Iter 214860: lr = 0.141902,	loss = 2.454457,	Top-1 err = 0.368604,	Top-5 err = 0.162012,	data_time = 0.050233,	train_time = 0.365828
[2019-08-24 07:35:52,420] TRAIN Iter 214880: lr = 0.141868,	loss = 2.537681,	Top-1 err = 0.370752,	Top-5 err = 0.158447,	data_time = 0.050231,	train_time = 0.849417
[2019-08-24 07:36:08,119] TRAIN Iter 214900: lr = 0.141835,	loss = 2.517926,	Top-1 err = 0.373535,	Top-5 err = 0.165283,	data_time = 0.050671,	train_time = 0.784973
[2019-08-24 07:36:15,696] TRAIN Iter 214920: lr = 0.141802,	loss = 2.396806,	Top-1 err = 0.372705,	Top-5 err = 0.164209,	data_time = 0.050545,	train_time = 0.378798
[2019-08-24 07:36:29,806] TRAIN Iter 214940: lr = 0.141768,	loss = 2.535787,	Top-1 err = 0.371387,	Top-5 err = 0.165381,	data_time = 0.050409,	train_time = 0.705498
[2019-08-24 07:36:45,112] TRAIN Iter 214960: lr = 0.141735,	loss = 2.463712,	Top-1 err = 0.372314,	Top-5 err = 0.160938,	data_time = 0.114559,	train_time = 0.765303
[2019-08-24 07:36:52,216] TRAIN Iter 214980: lr = 0.141702,	loss = 2.513104,	Top-1 err = 0.372607,	Top-5 err = 0.165479,	data_time = 0.050415,	train_time = 0.355150
[2019-08-24 07:37:07,030] TRAIN Iter 215000: lr = 0.141668,	loss = 2.551076,	Top-1 err = 0.374316,	Top-5 err = 0.160205,	data_time = 0.050550,	train_time = 0.740720
[2019-08-24 07:37:14,163] TRAIN Iter 215020: lr = 0.141635,	loss = 2.470016,	Top-1 err = 0.364844,	Top-5 err = 0.157764,	data_time = 0.050497,	train_time = 0.356610
[2019-08-24 07:37:31,817] TRAIN Iter 215040: lr = 0.141602,	loss = 2.449413,	Top-1 err = 0.374658,	Top-5 err = 0.165820,	data_time = 0.050307,	train_time = 0.882714
[2019-08-24 07:37:48,392] TRAIN Iter 215060: lr = 0.141568,	loss = 2.587452,	Top-1 err = 0.371582,	Top-5 err = 0.162256,	data_time = 0.050394,	train_time = 0.828706
[2019-08-24 07:37:55,431] TRAIN Iter 215080: lr = 0.141535,	loss = 2.520821,	Top-1 err = 0.374658,	Top-5 err = 0.162988,	data_time = 0.050540,	train_time = 0.351956
[2019-08-24 07:38:11,543] TRAIN Iter 215100: lr = 0.141502,	loss = 2.549420,	Top-1 err = 0.373682,	Top-5 err = 0.163184,	data_time = 0.050415,	train_time = 0.805563
[2019-08-24 07:38:29,584] TRAIN Iter 215120: lr = 0.141468,	loss = 2.453106,	Top-1 err = 0.368701,	Top-5 err = 0.160547,	data_time = 0.050455,	train_time = 0.902036
[2019-08-24 07:38:36,505] TRAIN Iter 215140: lr = 0.141435,	loss = 2.530324,	Top-1 err = 0.376172,	Top-5 err = 0.162207,	data_time = 0.050226,	train_time = 0.346075
[2019-08-24 07:38:52,720] TRAIN Iter 215160: lr = 0.141402,	loss = 2.405205,	Top-1 err = 0.372803,	Top-5 err = 0.160254,	data_time = 0.050444,	train_time = 0.810731
[2019-08-24 07:38:59,927] TRAIN Iter 215180: lr = 0.141368,	loss = 2.447630,	Top-1 err = 0.372754,	Top-5 err = 0.164746,	data_time = 0.050524,	train_time = 0.360329
[2019-08-24 07:39:17,141] TRAIN Iter 215200: lr = 0.141335,	loss = 2.603869,	Top-1 err = 0.373535,	Top-5 err = 0.163965,	data_time = 0.050152,	train_time = 0.860687
[2019-08-24 07:39:35,215] TRAIN Iter 215220: lr = 0.141302,	loss = 2.482371,	Top-1 err = 0.366650,	Top-5 err = 0.164062,	data_time = 0.049898,	train_time = 0.903685
[2019-08-24 07:39:42,389] TRAIN Iter 215240: lr = 0.141268,	loss = 2.413378,	Top-1 err = 0.370801,	Top-5 err = 0.162451,	data_time = 0.049991,	train_time = 0.358666
[2019-08-24 07:39:57,236] TRAIN Iter 215260: lr = 0.141235,	loss = 2.501923,	Top-1 err = 0.374219,	Top-5 err = 0.168604,	data_time = 0.049905,	train_time = 0.742362
[2019-08-24 07:40:09,981] TRAIN Iter 215280: lr = 0.141202,	loss = 3.066953,	Top-1 err = 0.380710,	Top-5 err = 0.174142,	data_time = 0.007049,	train_time = 0.637214
[2019-08-24 07:40:58,175] TRAIN Iter 215300: lr = 0.141168,	loss = 2.478036,	Top-1 err = 0.368945,	Top-5 err = 0.161182,	data_time = 0.050487,	train_time = 2.409706
[2019-08-24 07:41:15,342] TRAIN Iter 215320: lr = 0.141135,	loss = 2.423132,	Top-1 err = 0.368652,	Top-5 err = 0.162988,	data_time = 0.050479,	train_time = 0.858310
[2019-08-24 07:41:23,211] TRAIN Iter 215340: lr = 0.141102,	loss = 2.520028,	Top-1 err = 0.368115,	Top-5 err = 0.158350,	data_time = 0.050339,	train_time = 0.393461
[2019-08-24 07:41:32,620] TRAIN Iter 215360: lr = 0.141068,	loss = 2.534184,	Top-1 err = 0.360986,	Top-5 err = 0.156250,	data_time = 0.050913,	train_time = 0.470435
[2019-08-24 07:41:46,221] TRAIN Iter 215380: lr = 0.141035,	loss = 2.521006,	Top-1 err = 0.368164,	Top-5 err = 0.160742,	data_time = 0.122129,	train_time = 0.680034
[2019-08-24 07:41:53,815] TRAIN Iter 215400: lr = 0.141002,	loss = 2.501678,	Top-1 err = 0.361914,	Top-5 err = 0.154932,	data_time = 0.050545,	train_time = 0.379698
[2019-08-24 07:42:08,235] TRAIN Iter 215420: lr = 0.140968,	loss = 2.604294,	Top-1 err = 0.363428,	Top-5 err = 0.156787,	data_time = 0.050472,	train_time = 0.720981
[2019-08-24 07:42:15,380] TRAIN Iter 215440: lr = 0.140935,	loss = 2.574685,	Top-1 err = 0.366602,	Top-5 err = 0.159766,	data_time = 0.050408,	train_time = 0.357202
[2019-08-24 07:42:30,709] TRAIN Iter 215460: lr = 0.140902,	loss = 2.524436,	Top-1 err = 0.367090,	Top-5 err = 0.157178,	data_time = 0.050284,	train_time = 0.766473
[2019-08-24 07:42:47,571] TRAIN Iter 215480: lr = 0.140868,	loss = 2.469707,	Top-1 err = 0.361328,	Top-5 err = 0.156494,	data_time = 0.050163,	train_time = 0.843071
[2019-08-24 07:42:54,833] TRAIN Iter 215500: lr = 0.140835,	loss = 2.500868,	Top-1 err = 0.362109,	Top-5 err = 0.157324,	data_time = 0.050972,	train_time = 0.363070
[2019-08-24 07:43:10,623] TRAIN Iter 215520: lr = 0.140802,	loss = 2.553514,	Top-1 err = 0.366797,	Top-5 err = 0.159961,	data_time = 0.050586,	train_time = 0.789481
[2019-08-24 07:43:24,724] TRAIN Iter 215540: lr = 0.140768,	loss = 2.468376,	Top-1 err = 0.363721,	Top-5 err = 0.157324,	data_time = 0.050498,	train_time = 0.705036
[2019-08-24 07:43:32,050] TRAIN Iter 215560: lr = 0.140735,	loss = 2.406757,	Top-1 err = 0.362891,	Top-5 err = 0.163721,	data_time = 0.050202,	train_time = 0.366330
[2019-08-24 07:43:48,272] TRAIN Iter 215580: lr = 0.140702,	loss = 2.551513,	Top-1 err = 0.363232,	Top-5 err = 0.158496,	data_time = 0.050393,	train_time = 0.811077
[2019-08-24 07:43:55,507] TRAIN Iter 215600: lr = 0.140668,	loss = 2.516410,	Top-1 err = 0.363818,	Top-5 err = 0.157324,	data_time = 0.149280,	train_time = 0.361704
[2019-08-24 07:44:11,258] TRAIN Iter 215620: lr = 0.140635,	loss = 2.591395,	Top-1 err = 0.366748,	Top-5 err = 0.159424,	data_time = 0.050667,	train_time = 0.787561
[2019-08-24 07:44:25,219] TRAIN Iter 215640: lr = 0.140602,	loss = 2.515744,	Top-1 err = 0.361279,	Top-5 err = 0.162012,	data_time = 0.050596,	train_time = 0.698022
[2019-08-24 07:44:32,214] TRAIN Iter 215660: lr = 0.140568,	loss = 2.516595,	Top-1 err = 0.363623,	Top-5 err = 0.157959,	data_time = 0.050276,	train_time = 0.349725
[2019-08-24 07:44:51,677] TRAIN Iter 215680: lr = 0.140535,	loss = 2.507658,	Top-1 err = 0.368652,	Top-5 err = 0.163525,	data_time = 0.050665,	train_time = 0.973137
[2019-08-24 07:45:04,150] TRAIN Iter 215700: lr = 0.140502,	loss = 2.440168,	Top-1 err = 0.370459,	Top-5 err = 0.161914,	data_time = 0.052074,	train_time = 0.623625
[2019-08-24 07:45:11,411] TRAIN Iter 215720: lr = 0.140468,	loss = 2.491357,	Top-1 err = 0.368018,	Top-5 err = 0.156104,	data_time = 0.050598,	train_time = 0.363083
[2019-08-24 07:45:26,385] TRAIN Iter 215740: lr = 0.140435,	loss = 2.562958,	Top-1 err = 0.364551,	Top-5 err = 0.159277,	data_time = 0.050433,	train_time = 0.748670
[2019-08-24 07:45:33,641] TRAIN Iter 215760: lr = 0.140402,	loss = 2.604370,	Top-1 err = 0.369629,	Top-5 err = 0.160498,	data_time = 0.050494,	train_time = 0.362785
[2019-08-24 07:45:47,032] TRAIN Iter 215780: lr = 0.140368,	loss = 2.466237,	Top-1 err = 0.366699,	Top-5 err = 0.157910,	data_time = 0.050498,	train_time = 0.669550
[2019-08-24 07:46:02,568] TRAIN Iter 215800: lr = 0.140335,	loss = 2.499486,	Top-1 err = 0.364844,	Top-5 err = 0.155420,	data_time = 0.050433,	train_time = 0.776759
[2019-08-24 07:46:09,738] TRAIN Iter 215820: lr = 0.140302,	loss = 2.508379,	Top-1 err = 0.369141,	Top-5 err = 0.160889,	data_time = 0.050425,	train_time = 0.358508
[2019-08-24 07:46:24,442] TRAIN Iter 215840: lr = 0.140268,	loss = 2.634380,	Top-1 err = 0.368945,	Top-5 err = 0.163135,	data_time = 0.050335,	train_time = 0.735168
[2019-08-24 07:46:38,554] TRAIN Iter 215860: lr = 0.140235,	loss = 2.647254,	Top-1 err = 0.370801,	Top-5 err = 0.161670,	data_time = 0.130388,	train_time = 0.705565
[2019-08-24 07:46:46,985] TRAIN Iter 215880: lr = 0.140202,	loss = 2.537721,	Top-1 err = 0.371436,	Top-5 err = 0.165039,	data_time = 0.050443,	train_time = 0.421583
[2019-08-24 07:47:02,991] TRAIN Iter 215900: lr = 0.140168,	loss = 2.493646,	Top-1 err = 0.363965,	Top-5 err = 0.156982,	data_time = 0.050513,	train_time = 0.800266
[2019-08-24 07:47:10,158] TRAIN Iter 215920: lr = 0.140135,	loss = 2.427586,	Top-1 err = 0.365869,	Top-5 err = 0.158350,	data_time = 0.050827,	train_time = 0.358356
[2019-08-24 07:47:25,015] TRAIN Iter 215940: lr = 0.140102,	loss = 2.505418,	Top-1 err = 0.367188,	Top-5 err = 0.162500,	data_time = 0.050315,	train_time = 0.742808
[2019-08-24 07:47:40,041] TRAIN Iter 215960: lr = 0.140068,	loss = 2.546099,	Top-1 err = 0.367969,	Top-5 err = 0.160791,	data_time = 0.050653,	train_time = 0.751273
[2019-08-24 07:47:47,420] TRAIN Iter 215980: lr = 0.140035,	loss = 2.522248,	Top-1 err = 0.372510,	Top-5 err = 0.163184,	data_time = 0.050505,	train_time = 0.368957
[2019-08-24 07:48:03,778] TRAIN Iter 216000: lr = 0.140002,	loss = 2.525484,	Top-1 err = 0.374170,	Top-5 err = 0.161670,	data_time = 0.050782,	train_time = 0.817903
[2019-08-24 07:48:20,388] TRAIN Iter 216020: lr = 0.139968,	loss = 2.388943,	Top-1 err = 0.366113,	Top-5 err = 0.160693,	data_time = 1.020387,	train_time = 0.830441
[2019-08-24 07:48:27,708] TRAIN Iter 216040: lr = 0.139935,	loss = 2.438766,	Top-1 err = 0.361230,	Top-5 err = 0.158301,	data_time = 0.050349,	train_time = 0.366023
[2019-08-24 07:48:44,739] TRAIN Iter 216060: lr = 0.139902,	loss = 2.444738,	Top-1 err = 0.368457,	Top-5 err = 0.158496,	data_time = 0.050248,	train_time = 0.851503
[2019-08-24 07:48:51,922] TRAIN Iter 216080: lr = 0.139868,	loss = 2.474684,	Top-1 err = 0.370605,	Top-5 err = 0.162549,	data_time = 0.050821,	train_time = 0.359165
[2019-08-24 07:49:07,201] TRAIN Iter 216100: lr = 0.139835,	loss = 2.475159,	Top-1 err = 0.370996,	Top-5 err = 0.163184,	data_time = 0.050206,	train_time = 0.763911
[2019-08-24 07:49:23,983] TRAIN Iter 216120: lr = 0.139802,	loss = 2.495882,	Top-1 err = 0.374658,	Top-5 err = 0.160693,	data_time = 0.050500,	train_time = 0.839098
[2019-08-24 07:49:31,052] TRAIN Iter 216140: lr = 0.139768,	loss = 2.597300,	Top-1 err = 0.371924,	Top-5 err = 0.162012,	data_time = 0.050713,	train_time = 0.353454
[2019-08-24 07:49:47,189] TRAIN Iter 216160: lr = 0.139735,	loss = 2.600466,	Top-1 err = 0.371533,	Top-5 err = 0.167432,	data_time = 0.050503,	train_time = 0.806850
[2019-08-24 07:50:03,927] TRAIN Iter 216180: lr = 0.139702,	loss = 2.449142,	Top-1 err = 0.365479,	Top-5 err = 0.158057,	data_time = 0.050783,	train_time = 0.836842
[2019-08-24 07:50:11,256] TRAIN Iter 216200: lr = 0.139668,	loss = 2.462679,	Top-1 err = 0.369678,	Top-5 err = 0.159619,	data_time = 0.050350,	train_time = 0.366465
[2019-08-24 07:50:27,865] TRAIN Iter 216220: lr = 0.139635,	loss = 2.547739,	Top-1 err = 0.373877,	Top-5 err = 0.164746,	data_time = 0.050335,	train_time = 0.830425
[2019-08-24 07:50:34,614] TRAIN Iter 216240: lr = 0.139602,	loss = 2.554001,	Top-1 err = 0.371191,	Top-5 err = 0.161523,	data_time = 0.050540,	train_time = 0.337440
[2019-08-24 07:50:52,593] TRAIN Iter 216260: lr = 0.139568,	loss = 2.509217,	Top-1 err = 0.367969,	Top-5 err = 0.160791,	data_time = 0.050169,	train_time = 0.898927
[2019-08-24 07:51:09,561] TRAIN Iter 216280: lr = 0.139535,	loss = 2.477705,	Top-1 err = 0.371338,	Top-5 err = 0.162500,	data_time = 0.050433,	train_time = 0.848413
[2019-08-24 07:51:16,639] TRAIN Iter 216300: lr = 0.139502,	loss = 2.372086,	Top-1 err = 0.365576,	Top-5 err = 0.157227,	data_time = 0.050391,	train_time = 0.353873
[2019-08-24 07:51:33,223] TRAIN Iter 216320: lr = 0.139468,	loss = 2.468877,	Top-1 err = 0.370947,	Top-5 err = 0.164258,	data_time = 0.050601,	train_time = 0.829206
[2019-08-24 07:51:49,093] TRAIN Iter 216340: lr = 0.139435,	loss = 2.565971,	Top-1 err = 0.369434,	Top-5 err = 0.160254,	data_time = 0.050378,	train_time = 0.793453
[2019-08-24 07:51:56,324] TRAIN Iter 216360: lr = 0.139402,	loss = 2.495847,	Top-1 err = 0.364600,	Top-5 err = 0.160498,	data_time = 0.050306,	train_time = 0.361555
[2019-08-24 07:52:14,309] TRAIN Iter 216380: lr = 0.139368,	loss = 2.484855,	Top-1 err = 0.370850,	Top-5 err = 0.159570,	data_time = 0.050582,	train_time = 0.899212
[2019-08-24 07:52:21,175] TRAIN Iter 216400: lr = 0.139335,	loss = 2.551214,	Top-1 err = 0.370264,	Top-5 err = 0.165234,	data_time = 0.136789,	train_time = 0.343278
[2019-08-24 07:52:37,746] TRAIN Iter 216420: lr = 0.139302,	loss = 2.436794,	Top-1 err = 0.372070,	Top-5 err = 0.163818,	data_time = 0.050435,	train_time = 0.828552
[2019-08-24 07:52:56,350] TRAIN Iter 216440: lr = 0.139268,	loss = 2.542983,	Top-1 err = 0.364453,	Top-5 err = 0.158643,	data_time = 0.050495,	train_time = 0.930202
[2019-08-24 07:53:03,193] TRAIN Iter 216460: lr = 0.139235,	loss = 2.432424,	Top-1 err = 0.362402,	Top-5 err = 0.161279,	data_time = 0.050391,	train_time = 0.342121
[2019-08-24 07:53:19,654] TRAIN Iter 216480: lr = 0.139202,	loss = 2.586002,	Top-1 err = 0.373535,	Top-5 err = 0.165625,	data_time = 0.050124,	train_time = 0.823052
[2019-08-24 07:53:36,559] TRAIN Iter 216500: lr = 0.139168,	loss = 2.408914,	Top-1 err = 0.371973,	Top-5 err = 0.163770,	data_time = 0.090088,	train_time = 0.845229
[2019-08-24 07:53:42,959] TRAIN Iter 216520: lr = 0.139135,	loss = 2.504504,	Top-1 err = 0.366455,	Top-5 err = 0.163770,	data_time = 0.050034,	train_time = 0.319973
[2019-08-24 07:54:34,621] TRAIN Iter 216540: lr = 0.139102,	loss = 2.511951,	Top-1 err = 0.367670,	Top-5 err = 0.158368,	data_time = 0.050892,	train_time = 2.583107
[2019-08-24 07:54:41,619] TRAIN Iter 216560: lr = 0.139068,	loss = 2.477788,	Top-1 err = 0.364404,	Top-5 err = 0.156201,	data_time = 0.050320,	train_time = 0.349869
[2019-08-24 07:55:00,235] TRAIN Iter 216580: lr = 0.139035,	loss = 2.400173,	Top-1 err = 0.357959,	Top-5 err = 0.158838,	data_time = 0.050559,	train_time = 0.930782
[2019-08-24 07:55:14,974] TRAIN Iter 216600: lr = 0.139002,	loss = 2.504077,	Top-1 err = 0.356641,	Top-5 err = 0.152100,	data_time = 0.050301,	train_time = 0.736924
[2019-08-24 07:55:22,267] TRAIN Iter 216620: lr = 0.138968,	loss = 2.396771,	Top-1 err = 0.358838,	Top-5 err = 0.156250,	data_time = 0.050328,	train_time = 0.364632
[2019-08-24 07:55:34,688] TRAIN Iter 216640: lr = 0.138935,	loss = 2.491406,	Top-1 err = 0.367432,	Top-5 err = 0.156250,	data_time = 0.050543,	train_time = 0.621023
[2019-08-24 07:55:41,963] TRAIN Iter 216660: lr = 0.138902,	loss = 2.473886,	Top-1 err = 0.359912,	Top-5 err = 0.152441,	data_time = 0.050366,	train_time = 0.363776
[2019-08-24 07:55:57,191] TRAIN Iter 216680: lr = 0.138868,	loss = 2.452807,	Top-1 err = 0.367334,	Top-5 err = 0.155664,	data_time = 0.050381,	train_time = 0.761355
[2019-08-24 07:56:12,553] TRAIN Iter 216700: lr = 0.138835,	loss = 2.446409,	Top-1 err = 0.367090,	Top-5 err = 0.155127,	data_time = 0.050235,	train_time = 0.768085
[2019-08-24 07:56:19,294] TRAIN Iter 216720: lr = 0.138802,	loss = 2.503418,	Top-1 err = 0.368311,	Top-5 err = 0.157959,	data_time = 0.050665,	train_time = 0.337036
[2019-08-24 07:56:34,497] TRAIN Iter 216740: lr = 0.138768,	loss = 2.543409,	Top-1 err = 0.360840,	Top-5 err = 0.160742,	data_time = 0.050730,	train_time = 0.760136
[2019-08-24 07:56:48,103] TRAIN Iter 216760: lr = 0.138735,	loss = 2.485333,	Top-1 err = 0.365234,	Top-5 err = 0.159570,	data_time = 0.418538,	train_time = 0.680321
[2019-08-24 07:56:55,232] TRAIN Iter 216780: lr = 0.138702,	loss = 2.540395,	Top-1 err = 0.368896,	Top-5 err = 0.161670,	data_time = 0.050424,	train_time = 0.356397
[2019-08-24 07:57:10,395] TRAIN Iter 216800: lr = 0.138668,	loss = 2.499271,	Top-1 err = 0.366602,	Top-5 err = 0.158496,	data_time = 0.050630,	train_time = 0.758178
[2019-08-24 07:57:17,628] TRAIN Iter 216820: lr = 0.138635,	loss = 2.484772,	Top-1 err = 0.375732,	Top-5 err = 0.163184,	data_time = 0.050805,	train_time = 0.361613
[2019-08-24 07:57:31,859] TRAIN Iter 216840: lr = 0.138602,	loss = 2.570044,	Top-1 err = 0.365479,	Top-5 err = 0.158984,	data_time = 0.050492,	train_time = 0.711557
[2019-08-24 07:57:47,755] TRAIN Iter 216860: lr = 0.138568,	loss = 2.385329,	Top-1 err = 0.353369,	Top-5 err = 0.156250,	data_time = 0.050317,	train_time = 0.794762
[2019-08-24 07:57:54,928] TRAIN Iter 216880: lr = 0.138535,	loss = 2.543356,	Top-1 err = 0.363916,	Top-5 err = 0.162402,	data_time = 0.101336,	train_time = 0.358618
[2019-08-24 07:58:09,034] TRAIN Iter 216900: lr = 0.138502,	loss = 2.394581,	Top-1 err = 0.371826,	Top-5 err = 0.160986,	data_time = 0.050458,	train_time = 0.705319
[2019-08-24 07:58:22,679] TRAIN Iter 216920: lr = 0.138468,	loss = 2.532000,	Top-1 err = 0.371338,	Top-5 err = 0.158887,	data_time = 1.502805,	train_time = 0.682233
[2019-08-24 07:58:29,933] TRAIN Iter 216940: lr = 0.138435,	loss = 2.478766,	Top-1 err = 0.366406,	Top-5 err = 0.158008,	data_time = 0.050392,	train_time = 0.362664
[2019-08-24 07:58:44,412] TRAIN Iter 216960: lr = 0.138402,	loss = 2.526756,	Top-1 err = 0.362988,	Top-5 err = 0.160791,	data_time = 0.050507,	train_time = 0.723946
[2019-08-24 07:58:53,099] TRAIN Iter 216980: lr = 0.138368,	loss = 2.442323,	Top-1 err = 0.357861,	Top-5 err = 0.154199,	data_time = 0.050557,	train_time = 0.434317
[2019-08-24 07:59:09,471] TRAIN Iter 217000: lr = 0.138335,	loss = 2.418234,	Top-1 err = 0.365576,	Top-5 err = 0.160059,	data_time = 0.050486,	train_time = 0.818620
[2019-08-24 07:59:24,164] TRAIN Iter 217020: lr = 0.138302,	loss = 2.445893,	Top-1 err = 0.359814,	Top-5 err = 0.160986,	data_time = 0.050775,	train_time = 0.734617
[2019-08-24 07:59:31,998] TRAIN Iter 217040: lr = 0.138268,	loss = 2.575139,	Top-1 err = 0.364941,	Top-5 err = 0.156982,	data_time = 0.050995,	train_time = 0.391687
[2019-08-24 07:59:47,739] TRAIN Iter 217060: lr = 0.138235,	loss = 2.491575,	Top-1 err = 0.366748,	Top-5 err = 0.158789,	data_time = 0.050570,	train_time = 0.787043
[2019-08-24 08:00:02,101] TRAIN Iter 217080: lr = 0.138202,	loss = 2.548111,	Top-1 err = 0.371777,	Top-5 err = 0.165527,	data_time = 0.050508,	train_time = 0.718094
[2019-08-24 08:00:09,772] TRAIN Iter 217100: lr = 0.138168,	loss = 2.435498,	Top-1 err = 0.370312,	Top-5 err = 0.163477,	data_time = 0.050801,	train_time = 0.383501
[2019-08-24 08:00:25,573] TRAIN Iter 217120: lr = 0.138135,	loss = 2.497076,	Top-1 err = 0.368115,	Top-5 err = 0.157373,	data_time = 0.150909,	train_time = 0.790069
[2019-08-24 08:00:32,927] TRAIN Iter 217140: lr = 0.138102,	loss = 2.523808,	Top-1 err = 0.364111,	Top-5 err = 0.156885,	data_time = 0.050423,	train_time = 0.367683
[2019-08-24 08:00:49,372] TRAIN Iter 217160: lr = 0.138068,	loss = 2.456432,	Top-1 err = 0.361475,	Top-5 err = 0.157373,	data_time = 0.050689,	train_time = 0.822238
[2019-08-24 08:01:05,857] TRAIN Iter 217180: lr = 0.138035,	loss = 2.461285,	Top-1 err = 0.366699,	Top-5 err = 0.161230,	data_time = 0.050510,	train_time = 0.824203
[2019-08-24 08:01:13,025] TRAIN Iter 217200: lr = 0.138002,	loss = 2.588906,	Top-1 err = 0.365381,	Top-5 err = 0.159814,	data_time = 0.050507,	train_time = 0.358405
[2019-08-24 08:01:29,400] TRAIN Iter 217220: lr = 0.137968,	loss = 2.429438,	Top-1 err = 0.374512,	Top-5 err = 0.163379,	data_time = 0.050488,	train_time = 0.818757
[2019-08-24 08:01:46,720] TRAIN Iter 217240: lr = 0.137935,	loss = 2.435454,	Top-1 err = 0.371484,	Top-5 err = 0.163770,	data_time = 0.050608,	train_time = 0.865979
[2019-08-24 08:01:53,734] TRAIN Iter 217260: lr = 0.137902,	loss = 2.515643,	Top-1 err = 0.366260,	Top-5 err = 0.162598,	data_time = 0.050289,	train_time = 0.350680
[2019-08-24 08:02:08,777] TRAIN Iter 217280: lr = 0.137868,	loss = 2.545090,	Top-1 err = 0.369238,	Top-5 err = 0.161816,	data_time = 0.050453,	train_time = 0.752127
[2019-08-24 08:02:16,433] TRAIN Iter 217300: lr = 0.137835,	loss = 2.546610,	Top-1 err = 0.368506,	Top-5 err = 0.163184,	data_time = 0.050447,	train_time = 0.382792
[2019-08-24 08:02:31,755] TRAIN Iter 217320: lr = 0.137802,	loss = 2.437265,	Top-1 err = 0.366943,	Top-5 err = 0.163428,	data_time = 0.050582,	train_time = 0.766061
[2019-08-24 08:02:48,097] TRAIN Iter 217340: lr = 0.137768,	loss = 2.525236,	Top-1 err = 0.368018,	Top-5 err = 0.159375,	data_time = 0.050351,	train_time = 0.817092
[2019-08-24 08:02:55,192] TRAIN Iter 217360: lr = 0.137735,	loss = 2.577741,	Top-1 err = 0.363818,	Top-5 err = 0.160156,	data_time = 0.050495,	train_time = 0.354769
[2019-08-24 08:03:11,367] TRAIN Iter 217380: lr = 0.137702,	loss = 2.426314,	Top-1 err = 0.369580,	Top-5 err = 0.160840,	data_time = 0.050565,	train_time = 0.808695
[2019-08-24 08:03:27,571] TRAIN Iter 217400: lr = 0.137668,	loss = 2.490207,	Top-1 err = 0.367822,	Top-5 err = 0.158496,	data_time = 0.130338,	train_time = 0.810188
[2019-08-24 08:03:34,576] TRAIN Iter 217420: lr = 0.137635,	loss = 2.485806,	Top-1 err = 0.370410,	Top-5 err = 0.159473,	data_time = 0.050614,	train_time = 0.350286
[2019-08-24 08:03:50,272] TRAIN Iter 217440: lr = 0.137602,	loss = 2.542208,	Top-1 err = 0.374463,	Top-5 err = 0.163379,	data_time = 0.050404,	train_time = 0.784740
[2019-08-24 08:03:57,865] TRAIN Iter 217460: lr = 0.137568,	loss = 2.449308,	Top-1 err = 0.369385,	Top-5 err = 0.161377,	data_time = 0.050406,	train_time = 0.379636
[2019-08-24 08:04:12,843] TRAIN Iter 217480: lr = 0.137535,	loss = 2.492622,	Top-1 err = 0.372119,	Top-5 err = 0.165137,	data_time = 0.050431,	train_time = 0.748897
[2019-08-24 08:04:30,890] TRAIN Iter 217500: lr = 0.137502,	loss = 2.574826,	Top-1 err = 0.368311,	Top-5 err = 0.158691,	data_time = 0.050500,	train_time = 0.902355
[2019-08-24 08:04:38,008] TRAIN Iter 217520: lr = 0.137468,	loss = 2.497745,	Top-1 err = 0.368506,	Top-5 err = 0.163525,	data_time = 0.159414,	train_time = 0.355849
[2019-08-24 08:04:55,164] TRAIN Iter 217540: lr = 0.137435,	loss = 2.454281,	Top-1 err = 0.370166,	Top-5 err = 0.164014,	data_time = 0.050581,	train_time = 0.857826
[2019-08-24 08:05:10,083] TRAIN Iter 217560: lr = 0.137402,	loss = 2.474392,	Top-1 err = 0.373096,	Top-5 err = 0.162061,	data_time = 0.050443,	train_time = 0.745895
[2019-08-24 08:05:17,404] TRAIN Iter 217580: lr = 0.137368,	loss = 2.438615,	Top-1 err = 0.366309,	Top-5 err = 0.163428,	data_time = 0.050411,	train_time = 0.366060
[2019-08-24 08:05:34,857] TRAIN Iter 217600: lr = 0.137335,	loss = 2.426278,	Top-1 err = 0.365918,	Top-5 err = 0.158594,	data_time = 0.050377,	train_time = 0.872656
[2019-08-24 08:05:42,392] TRAIN Iter 217620: lr = 0.137302,	loss = 2.562891,	Top-1 err = 0.361914,	Top-5 err = 0.158203,	data_time = 0.165207,	train_time = 0.376700
[2019-08-24 08:05:59,138] TRAIN Iter 217640: lr = 0.137268,	loss = 2.502997,	Top-1 err = 0.371777,	Top-5 err = 0.162891,	data_time = 0.050395,	train_time = 0.837290
[2019-08-24 08:06:15,346] TRAIN Iter 217660: lr = 0.137235,	loss = 2.469961,	Top-1 err = 0.362256,	Top-5 err = 0.157129,	data_time = 0.050519,	train_time = 0.810391
[2019-08-24 08:06:22,296] TRAIN Iter 217680: lr = 0.137202,	loss = 2.538949,	Top-1 err = 0.373047,	Top-5 err = 0.164746,	data_time = 0.050206,	train_time = 0.347470
[2019-08-24 08:06:41,143] TRAIN Iter 217700: lr = 0.137168,	loss = 2.566359,	Top-1 err = 0.368896,	Top-5 err = 0.162744,	data_time = 0.050572,	train_time = 0.942341
[2019-08-24 08:06:57,685] TRAIN Iter 217720: lr = 0.137135,	loss = 2.543190,	Top-1 err = 0.368359,	Top-5 err = 0.164844,	data_time = 0.064327,	train_time = 0.827073
[2019-08-24 08:07:04,506] TRAIN Iter 217740: lr = 0.137102,	loss = 2.465215,	Top-1 err = 0.364502,	Top-5 err = 0.160742,	data_time = 0.050018,	train_time = 0.341066
[2019-08-24 08:07:20,783] TRAIN Iter 217760: lr = 0.137068,	loss = 2.484253,	Top-1 err = 0.366162,	Top-5 err = 0.159766,	data_time = 0.049984,	train_time = 0.813820
[2019-08-24 08:07:26,899] TRAIN Iter 217780: lr = 0.137035,	loss = 2.556572,	Top-1 err = 0.376660,	Top-5 err = 0.167236,	data_time = 0.049906,	train_time = 0.305783
[2019-08-24 08:08:21,269] TRAIN Iter 217800: lr = 0.137002,	loss = 2.463618,	Top-1 err = 0.375071,	Top-5 err = 0.165323,	data_time = 0.050719,	train_time = 2.718513
[2019-08-24 08:08:36,682] TRAIN Iter 217820: lr = 0.136968,	loss = 2.506958,	Top-1 err = 0.367041,	Top-5 err = 0.161230,	data_time = 0.050762,	train_time = 0.770607
[2019-08-24 08:08:44,315] TRAIN Iter 217840: lr = 0.136935,	loss = 2.293452,	Top-1 err = 0.360547,	Top-5 err = 0.155029,	data_time = 0.050529,	train_time = 0.381629
[2019-08-24 08:08:54,224] TRAIN Iter 217860: lr = 0.136902,	loss = 2.458698,	Top-1 err = 0.359766,	Top-5 err = 0.157031,	data_time = 0.050291,	train_time = 0.495441
[2019-08-24 08:09:01,531] TRAIN Iter 217880: lr = 0.136868,	loss = 2.542578,	Top-1 err = 0.362354,	Top-5 err = 0.157666,	data_time = 0.050540,	train_time = 0.365343
[2019-08-24 08:09:16,722] TRAIN Iter 217900: lr = 0.136835,	loss = 2.435044,	Top-1 err = 0.360254,	Top-5 err = 0.157666,	data_time = 0.050628,	train_time = 0.759559
[2019-08-24 08:09:31,274] TRAIN Iter 217920: lr = 0.136802,	loss = 2.453017,	Top-1 err = 0.361279,	Top-5 err = 0.155078,	data_time = 0.050798,	train_time = 0.727573
[2019-08-24 08:09:38,551] TRAIN Iter 217940: lr = 0.136768,	loss = 2.491505,	Top-1 err = 0.367627,	Top-5 err = 0.156299,	data_time = 0.050472,	train_time = 0.363836
[2019-08-24 08:09:53,308] TRAIN Iter 217960: lr = 0.136735,	loss = 2.523273,	Top-1 err = 0.362891,	Top-5 err = 0.157178,	data_time = 0.050821,	train_time = 0.737845
[2019-08-24 08:10:08,807] TRAIN Iter 217980: lr = 0.136702,	loss = 2.478554,	Top-1 err = 0.361914,	Top-5 err = 0.160791,	data_time = 0.050842,	train_time = 0.774933
[2019-08-24 08:10:16,076] TRAIN Iter 218000: lr = 0.136668,	loss = 2.478996,	Top-1 err = 0.362598,	Top-5 err = 0.154834,	data_time = 0.051006,	train_time = 0.363417
[2019-08-24 08:10:31,705] TRAIN Iter 218020: lr = 0.136635,	loss = 2.449374,	Top-1 err = 0.364551,	Top-5 err = 0.157812,	data_time = 0.050743,	train_time = 0.781451
[2019-08-24 08:10:38,670] TRAIN Iter 218040: lr = 0.136602,	loss = 2.461793,	Top-1 err = 0.366895,	Top-5 err = 0.158398,	data_time = 0.159471,	train_time = 0.348208
[2019-08-24 08:10:53,158] TRAIN Iter 218060: lr = 0.136568,	loss = 2.417490,	Top-1 err = 0.364648,	Top-5 err = 0.162695,	data_time = 0.050327,	train_time = 0.724425
[2019-08-24 08:11:09,521] TRAIN Iter 218080: lr = 0.136535,	loss = 2.448425,	Top-1 err = 0.364990,	Top-5 err = 0.160254,	data_time = 0.050319,	train_time = 0.818134
[2019-08-24 08:11:16,676] TRAIN Iter 218100: lr = 0.136502,	loss = 2.494627,	Top-1 err = 0.366260,	Top-5 err = 0.160010,	data_time = 0.050724,	train_time = 0.357701
[2019-08-24 08:11:30,734] TRAIN Iter 218120: lr = 0.136468,	loss = 2.420936,	Top-1 err = 0.364844,	Top-5 err = 0.155615,	data_time = 0.050781,	train_time = 0.702901
[2019-08-24 08:11:47,608] TRAIN Iter 218140: lr = 0.136435,	loss = 2.487444,	Top-1 err = 0.371094,	Top-5 err = 0.161572,	data_time = 0.050776,	train_time = 0.843667
[2019-08-24 08:11:54,634] TRAIN Iter 218160: lr = 0.136402,	loss = 2.531821,	Top-1 err = 0.363477,	Top-5 err = 0.158154,	data_time = 0.050487,	train_time = 0.351312
[2019-08-24 08:12:08,869] TRAIN Iter 218180: lr = 0.136368,	loss = 2.497852,	Top-1 err = 0.367188,	Top-5 err = 0.160107,	data_time = 0.050312,	train_time = 0.711715
[2019-08-24 08:12:16,066] TRAIN Iter 218200: lr = 0.136335,	loss = 2.419854,	Top-1 err = 0.364404,	Top-5 err = 0.160059,	data_time = 0.050406,	train_time = 0.359828
[2019-08-24 08:12:31,022] TRAIN Iter 218220: lr = 0.136302,	loss = 2.501279,	Top-1 err = 0.366162,	Top-5 err = 0.160010,	data_time = 0.050550,	train_time = 0.747802
[2019-08-24 08:12:47,790] TRAIN Iter 218240: lr = 0.136268,	loss = 2.413087,	Top-1 err = 0.361328,	Top-5 err = 0.156836,	data_time = 0.127498,	train_time = 0.838380
[2019-08-24 08:12:54,868] TRAIN Iter 218260: lr = 0.136235,	loss = 2.538817,	Top-1 err = 0.367822,	Top-5 err = 0.159082,	data_time = 0.160315,	train_time = 0.353872
[2019-08-24 08:13:10,022] TRAIN Iter 218280: lr = 0.136202,	loss = 2.584680,	Top-1 err = 0.367236,	Top-5 err = 0.159375,	data_time = 0.050478,	train_time = 0.757681
[2019-08-24 08:13:28,080] TRAIN Iter 218300: lr = 0.136168,	loss = 2.515055,	Top-1 err = 0.362939,	Top-5 err = 0.157129,	data_time = 0.050816,	train_time = 0.902887
[2019-08-24 08:13:35,278] TRAIN Iter 218320: lr = 0.136135,	loss = 2.470156,	Top-1 err = 0.365674,	Top-5 err = 0.155176,	data_time = 0.050605,	train_time = 0.359912
[2019-08-24 08:13:48,861] TRAIN Iter 218340: lr = 0.136102,	loss = 2.576172,	Top-1 err = 0.370459,	Top-5 err = 0.161035,	data_time = 0.050480,	train_time = 0.679123
[2019-08-24 08:13:56,065] TRAIN Iter 218360: lr = 0.136068,	loss = 2.529598,	Top-1 err = 0.371631,	Top-5 err = 0.156641,	data_time = 0.050695,	train_time = 0.360174
[2019-08-24 08:14:11,235] TRAIN Iter 218380: lr = 0.136035,	loss = 2.438775,	Top-1 err = 0.365820,	Top-5 err = 0.157178,	data_time = 0.050312,	train_time = 0.758474
[2019-08-24 08:14:28,262] TRAIN Iter 218400: lr = 0.136002,	loss = 2.484389,	Top-1 err = 0.367871,	Top-5 err = 0.160840,	data_time = 0.050685,	train_time = 0.851367
[2019-08-24 08:14:35,090] TRAIN Iter 218420: lr = 0.135968,	loss = 2.359992,	Top-1 err = 0.368555,	Top-5 err = 0.163477,	data_time = 0.168691,	train_time = 0.341375
[2019-08-24 08:14:52,235] TRAIN Iter 218440: lr = 0.135935,	loss = 2.449570,	Top-1 err = 0.369775,	Top-5 err = 0.157568,	data_time = 0.050249,	train_time = 0.857218
[2019-08-24 08:15:07,803] TRAIN Iter 218460: lr = 0.135902,	loss = 2.519310,	Top-1 err = 0.369385,	Top-5 err = 0.161621,	data_time = 0.050379,	train_time = 0.778367
[2019-08-24 08:15:14,949] TRAIN Iter 218480: lr = 0.135868,	loss = 2.449900,	Top-1 err = 0.366846,	Top-5 err = 0.159814,	data_time = 0.050768,	train_time = 0.357324
[2019-08-24 08:15:31,672] TRAIN Iter 218500: lr = 0.135835,	loss = 2.465945,	Top-1 err = 0.367236,	Top-5 err = 0.158447,	data_time = 0.050498,	train_time = 0.836107
[2019-08-24 08:15:39,573] TRAIN Iter 218520: lr = 0.135802,	loss = 2.484129,	Top-1 err = 0.369629,	Top-5 err = 0.159570,	data_time = 0.050412,	train_time = 0.395036
[2019-08-24 08:15:53,890] TRAIN Iter 218540: lr = 0.135768,	loss = 2.499967,	Top-1 err = 0.368018,	Top-5 err = 0.160693,	data_time = 0.050869,	train_time = 0.715828
[2019-08-24 08:16:11,569] TRAIN Iter 218560: lr = 0.135735,	loss = 2.469185,	Top-1 err = 0.367578,	Top-5 err = 0.159912,	data_time = 0.050323,	train_time = 0.883965
[2019-08-24 08:16:19,009] TRAIN Iter 218580: lr = 0.135702,	loss = 2.565379,	Top-1 err = 0.371680,	Top-5 err = 0.165039,	data_time = 0.115057,	train_time = 0.371960
[2019-08-24 08:16:35,423] TRAIN Iter 218600: lr = 0.135668,	loss = 2.558685,	Top-1 err = 0.374658,	Top-5 err = 0.163281,	data_time = 0.050337,	train_time = 0.820677
[2019-08-24 08:16:49,390] TRAIN Iter 218620: lr = 0.135635,	loss = 2.489492,	Top-1 err = 0.373828,	Top-5 err = 0.166211,	data_time = 0.050649,	train_time = 0.698333
[2019-08-24 08:16:56,177] TRAIN Iter 218640: lr = 0.135602,	loss = 2.497130,	Top-1 err = 0.362891,	Top-5 err = 0.158203,	data_time = 0.050836,	train_time = 0.339339
[2019-08-24 08:17:11,743] TRAIN Iter 218660: lr = 0.135568,	loss = 2.374111,	Top-1 err = 0.365576,	Top-5 err = 0.160303,	data_time = 0.050759,	train_time = 0.778307
[2019-08-24 08:17:18,468] TRAIN Iter 218680: lr = 0.135535,	loss = 2.489183,	Top-1 err = 0.371533,	Top-5 err = 0.159961,	data_time = 0.050401,	train_time = 0.336256
[2019-08-24 08:17:34,873] TRAIN Iter 218700: lr = 0.135502,	loss = 2.534623,	Top-1 err = 0.367236,	Top-5 err = 0.160693,	data_time = 0.050826,	train_time = 0.820207
[2019-08-24 08:17:51,659] TRAIN Iter 218720: lr = 0.135468,	loss = 2.554441,	Top-1 err = 0.368408,	Top-5 err = 0.160449,	data_time = 0.050835,	train_time = 0.839293
[2019-08-24 08:17:58,561] TRAIN Iter 218740: lr = 0.135435,	loss = 2.537242,	Top-1 err = 0.368750,	Top-5 err = 0.160400,	data_time = 0.050494,	train_time = 0.345069
[2019-08-24 08:18:15,683] TRAIN Iter 218760: lr = 0.135402,	loss = 2.657620,	Top-1 err = 0.370312,	Top-5 err = 0.159814,	data_time = 0.050351,	train_time = 0.856097
[2019-08-24 08:18:31,723] TRAIN Iter 218780: lr = 0.135368,	loss = 2.537140,	Top-1 err = 0.373389,	Top-5 err = 0.162891,	data_time = 0.050365,	train_time = 0.802012
[2019-08-24 08:18:38,731] TRAIN Iter 218800: lr = 0.135335,	loss = 2.482242,	Top-1 err = 0.371777,	Top-5 err = 0.161377,	data_time = 0.050621,	train_time = 0.350369
[2019-08-24 08:18:56,704] TRAIN Iter 218820: lr = 0.135302,	loss = 2.504804,	Top-1 err = 0.367383,	Top-5 err = 0.159473,	data_time = 0.050411,	train_time = 0.898613
[2019-08-24 08:19:03,275] TRAIN Iter 218840: lr = 0.135268,	loss = 2.573554,	Top-1 err = 0.375781,	Top-5 err = 0.167432,	data_time = 0.050272,	train_time = 0.328537
[2019-08-24 08:19:21,001] TRAIN Iter 218860: lr = 0.135235,	loss = 2.521764,	Top-1 err = 0.362646,	Top-5 err = 0.159375,	data_time = 0.050313,	train_time = 0.886291
[2019-08-24 08:19:38,758] TRAIN Iter 218880: lr = 0.135202,	loss = 2.504119,	Top-1 err = 0.371533,	Top-5 err = 0.162744,	data_time = 0.050404,	train_time = 0.887850
[2019-08-24 08:19:45,443] TRAIN Iter 218900: lr = 0.135168,	loss = 2.538584,	Top-1 err = 0.367871,	Top-5 err = 0.159277,	data_time = 0.050531,	train_time = 0.334241
[2019-08-24 08:20:01,821] TRAIN Iter 218920: lr = 0.135135,	loss = 2.547373,	Top-1 err = 0.367969,	Top-5 err = 0.160352,	data_time = 0.050595,	train_time = 0.818899
[2019-08-24 08:20:19,883] TRAIN Iter 218940: lr = 0.135102,	loss = 2.647208,	Top-1 err = 0.370605,	Top-5 err = 0.161279,	data_time = 0.050614,	train_time = 0.903091
[2019-08-24 08:20:28,817] TRAIN Iter 218960: lr = 0.135068,	loss = 2.514424,	Top-1 err = 0.373437,	Top-5 err = 0.163428,	data_time = 0.050962,	train_time = 0.446676
[2019-08-24 08:20:44,265] TRAIN Iter 218980: lr = 0.135035,	loss = 2.417974,	Top-1 err = 0.370312,	Top-5 err = 0.164404,	data_time = 0.049993,	train_time = 0.772371
[2019-08-24 08:20:50,928] TRAIN Iter 219000: lr = 0.135002,	loss = 2.574046,	Top-1 err = 0.370947,	Top-5 err = 0.161182,	data_time = 0.050039,	train_time = 0.333117
[2019-08-24 08:21:06,597] TRAIN Iter 219020: lr = 0.134968,	loss = 2.444099,	Top-1 err = 0.369824,	Top-5 err = 0.167480,	data_time = 0.049930,	train_time = 0.783443
[2019-08-24 08:21:53,930] TRAIN Iter 219040: lr = 0.134935,	loss = 2.504638,	Top-1 err = 0.375444,	Top-5 err = 0.166338,	data_time = 0.134922,	train_time = 2.366664
[2019-08-24 08:22:03,060] TRAIN Iter 219060: lr = 0.134902,	loss = 2.479428,	Top-1 err = 0.372705,	Top-5 err = 0.160498,	data_time = 0.051035,	train_time = 0.456444
[2019-08-24 08:22:16,027] TRAIN Iter 219080: lr = 0.134868,	loss = 2.429444,	Top-1 err = 0.351807,	Top-5 err = 0.153418,	data_time = 0.050501,	train_time = 0.648331
[2019-08-24 08:22:23,287] TRAIN Iter 219100: lr = 0.134835,	loss = 2.478645,	Top-1 err = 0.366260,	Top-5 err = 0.155762,	data_time = 0.050649,	train_time = 0.363012
[2019-08-24 08:22:37,052] TRAIN Iter 219120: lr = 0.134802,	loss = 2.472913,	Top-1 err = 0.358936,	Top-5 err = 0.154980,	data_time = 0.050843,	train_time = 0.688229
[2019-08-24 08:22:54,024] TRAIN Iter 219140: lr = 0.134768,	loss = 2.423221,	Top-1 err = 0.361865,	Top-5 err = 0.155811,	data_time = 0.050316,	train_time = 0.848586
[2019-08-24 08:23:01,380] TRAIN Iter 219160: lr = 0.134735,	loss = 2.434881,	Top-1 err = 0.366895,	Top-5 err = 0.161035,	data_time = 0.050541,	train_time = 0.367790
[2019-08-24 08:23:14,684] TRAIN Iter 219180: lr = 0.134702,	loss = 2.518662,	Top-1 err = 0.363477,	Top-5 err = 0.156104,	data_time = 0.050490,	train_time = 0.665171
[2019-08-24 08:23:28,701] TRAIN Iter 219200: lr = 0.134668,	loss = 2.420863,	Top-1 err = 0.368506,	Top-5 err = 0.160889,	data_time = 0.122831,	train_time = 0.700854
[2019-08-24 08:23:36,420] TRAIN Iter 219220: lr = 0.134635,	loss = 2.446659,	Top-1 err = 0.360107,	Top-5 err = 0.153369,	data_time = 0.050739,	train_time = 0.385916
[2019-08-24 08:23:52,321] TRAIN Iter 219240: lr = 0.134602,	loss = 2.488794,	Top-1 err = 0.365430,	Top-5 err = 0.158936,	data_time = 0.050493,	train_time = 0.795027
[2019-08-24 08:23:59,500] TRAIN Iter 219260: lr = 0.134568,	loss = 2.481152,	Top-1 err = 0.359961,	Top-5 err = 0.156445,	data_time = 0.050793,	train_time = 0.358947
[2019-08-24 08:24:15,669] TRAIN Iter 219280: lr = 0.134535,	loss = 2.378433,	Top-1 err = 0.363330,	Top-5 err = 0.154199,	data_time = 0.050276,	train_time = 0.808436
[2019-08-24 08:24:29,250] TRAIN Iter 219300: lr = 0.134502,	loss = 2.447896,	Top-1 err = 0.358350,	Top-5 err = 0.156055,	data_time = 0.050652,	train_time = 0.679037
[2019-08-24 08:24:36,419] TRAIN Iter 219320: lr = 0.134468,	loss = 2.425927,	Top-1 err = 0.362793,	Top-5 err = 0.156543,	data_time = 0.050170,	train_time = 0.358437
[2019-08-24 08:24:52,531] TRAIN Iter 219340: lr = 0.134435,	loss = 2.429026,	Top-1 err = 0.359814,	Top-5 err = 0.155957,	data_time = 0.050403,	train_time = 0.805608
[2019-08-24 08:25:05,619] TRAIN Iter 219360: lr = 0.134402,	loss = 2.435463,	Top-1 err = 0.362402,	Top-5 err = 0.156445,	data_time = 0.087980,	train_time = 0.654351
[2019-08-24 08:25:13,545] TRAIN Iter 219380: lr = 0.134368,	loss = 2.415848,	Top-1 err = 0.363672,	Top-5 err = 0.156006,	data_time = 0.050802,	train_time = 0.396290
[2019-08-24 08:25:28,722] TRAIN Iter 219400: lr = 0.134335,	loss = 2.477539,	Top-1 err = 0.361328,	Top-5 err = 0.156934,	data_time = 0.050467,	train_time = 0.758837
[2019-08-24 08:25:35,867] TRAIN Iter 219420: lr = 0.134302,	loss = 2.498504,	Top-1 err = 0.367041,	Top-5 err = 0.158398,	data_time = 0.050571,	train_time = 0.357247
[2019-08-24 08:25:52,523] TRAIN Iter 219440: lr = 0.134268,	loss = 2.463033,	Top-1 err = 0.366455,	Top-5 err = 0.159961,	data_time = 0.050486,	train_time = 0.832760
[2019-08-24 08:26:06,567] TRAIN Iter 219460: lr = 0.134235,	loss = 2.437515,	Top-1 err = 0.361328,	Top-5 err = 0.157275,	data_time = 0.050253,	train_time = 0.702217
[2019-08-24 08:26:13,469] TRAIN Iter 219480: lr = 0.134202,	loss = 2.532215,	Top-1 err = 0.361035,	Top-5 err = 0.158887,	data_time = 0.050504,	train_time = 0.345071
[2019-08-24 08:26:30,313] TRAIN Iter 219500: lr = 0.134168,	loss = 2.485375,	Top-1 err = 0.366064,	Top-5 err = 0.158203,	data_time = 0.050545,	train_time = 0.842167
[2019-08-24 08:26:39,893] TRAIN Iter 219520: lr = 0.134135,	loss = 2.419879,	Top-1 err = 0.367285,	Top-5 err = 0.156250,	data_time = 0.050688,	train_time = 0.479015
[2019-08-24 08:26:49,865] TRAIN Iter 219540: lr = 0.134102,	loss = 2.435917,	Top-1 err = 0.366260,	Top-5 err = 0.161719,	data_time = 0.050386,	train_time = 0.498569
[2019-08-24 08:27:06,511] TRAIN Iter 219560: lr = 0.134068,	loss = 2.448058,	Top-1 err = 0.364990,	Top-5 err = 0.157764,	data_time = 0.050375,	train_time = 0.832274
[2019-08-24 08:27:13,801] TRAIN Iter 219580: lr = 0.134035,	loss = 2.510962,	Top-1 err = 0.363428,	Top-5 err = 0.159277,	data_time = 0.050560,	train_time = 0.364463
[2019-08-24 08:27:28,420] TRAIN Iter 219600: lr = 0.134002,	loss = 2.473179,	Top-1 err = 0.365576,	Top-5 err = 0.160693,	data_time = 0.050423,	train_time = 0.730978
[2019-08-24 08:27:43,742] TRAIN Iter 219620: lr = 0.133968,	loss = 2.483198,	Top-1 err = 0.359814,	Top-5 err = 0.156104,	data_time = 1.413582,	train_time = 0.766067
[2019-08-24 08:27:50,720] TRAIN Iter 219640: lr = 0.133935,	loss = 2.524076,	Top-1 err = 0.366895,	Top-5 err = 0.161426,	data_time = 0.050311,	train_time = 0.348890
[2019-08-24 08:28:09,703] TRAIN Iter 219660: lr = 0.133902,	loss = 2.427835,	Top-1 err = 0.367090,	Top-5 err = 0.162207,	data_time = 0.050641,	train_time = 0.949122
[2019-08-24 08:28:19,829] TRAIN Iter 219680: lr = 0.133868,	loss = 2.489852,	Top-1 err = 0.363477,	Top-5 err = 0.160547,	data_time = 0.050453,	train_time = 0.506298
[2019-08-24 08:28:31,286] TRAIN Iter 219700: lr = 0.133835,	loss = 2.437646,	Top-1 err = 0.368896,	Top-5 err = 0.158594,	data_time = 0.050584,	train_time = 0.572823
[2019-08-24 08:28:47,315] TRAIN Iter 219720: lr = 0.133802,	loss = 2.438052,	Top-1 err = 0.372412,	Top-5 err = 0.159570,	data_time = 0.050384,	train_time = 0.801438
[2019-08-24 08:28:54,397] TRAIN Iter 219740: lr = 0.133768,	loss = 2.419504,	Top-1 err = 0.362842,	Top-5 err = 0.157178,	data_time = 0.141886,	train_time = 0.354082
[2019-08-24 08:29:11,032] TRAIN Iter 219760: lr = 0.133735,	loss = 2.482512,	Top-1 err = 0.372998,	Top-5 err = 0.160010,	data_time = 0.050292,	train_time = 0.831740
[2019-08-24 08:29:27,762] TRAIN Iter 219780: lr = 0.133702,	loss = 2.485647,	Top-1 err = 0.364404,	Top-5 err = 0.155518,	data_time = 0.050443,	train_time = 0.836487
[2019-08-24 08:29:34,776] TRAIN Iter 219800: lr = 0.133668,	loss = 2.380302,	Top-1 err = 0.362354,	Top-5 err = 0.158301,	data_time = 0.136264,	train_time = 0.350670
[2019-08-24 08:29:51,074] TRAIN Iter 219820: lr = 0.133635,	loss = 2.485014,	Top-1 err = 0.365381,	Top-5 err = 0.162842,	data_time = 0.050621,	train_time = 0.814910
[2019-08-24 08:30:01,170] TRAIN Iter 219840: lr = 0.133602,	loss = 2.415694,	Top-1 err = 0.367090,	Top-5 err = 0.162451,	data_time = 0.050435,	train_time = 0.504797
[2019-08-24 08:30:15,114] TRAIN Iter 219860: lr = 0.133568,	loss = 2.572863,	Top-1 err = 0.370557,	Top-5 err = 0.162256,	data_time = 0.050543,	train_time = 0.697152
[2019-08-24 08:30:33,248] TRAIN Iter 219880: lr = 0.133535,	loss = 2.470365,	Top-1 err = 0.371875,	Top-5 err = 0.163672,	data_time = 0.050563,	train_time = 0.906693
[2019-08-24 08:30:40,207] TRAIN Iter 219900: lr = 0.133502,	loss = 2.511292,	Top-1 err = 0.371631,	Top-5 err = 0.165088,	data_time = 0.050304,	train_time = 0.347963
[2019-08-24 08:30:58,741] TRAIN Iter 219920: lr = 0.133468,	loss = 2.542534,	Top-1 err = 0.370459,	Top-5 err = 0.159814,	data_time = 0.050643,	train_time = 0.926669
[2019-08-24 08:31:13,964] TRAIN Iter 219940: lr = 0.133435,	loss = 2.456145,	Top-1 err = 0.368359,	Top-5 err = 0.160791,	data_time = 0.050525,	train_time = 0.761131
[2019-08-24 08:31:20,716] TRAIN Iter 219960: lr = 0.133402,	loss = 2.519114,	Top-1 err = 0.375195,	Top-5 err = 0.163525,	data_time = 0.050310,	train_time = 0.337584
[2019-08-24 08:31:38,331] TRAIN Iter 219980: lr = 0.133368,	loss = 2.509600,	Top-1 err = 0.373340,	Top-5 err = 0.163477,	data_time = 0.050347,	train_time = 0.880746
[2019-08-24 08:31:48,260] TRAIN Iter 220000: lr = 0.133335,	loss = 2.571983,	Top-1 err = 0.371680,	Top-5 err = 0.162451,	data_time = 0.050609,	train_time = 0.496422
[2019-08-24 08:32:54,424] TEST Iter 220000: loss = 2.369399,	Top-1 err = 0.352260,	Top-5 err = 0.135260,	val_time = 66.121090
[2019-08-24 08:33:00,390] TRAIN Iter 220020: lr = 0.133302,	loss = 2.481215,	Top-1 err = 0.374609,	Top-5 err = 0.161377,	data_time = 0.050354,	train_time = 0.298306
[2019-08-24 08:33:06,628] TRAIN Iter 220040: lr = 0.133268,	loss = 2.523317,	Top-1 err = 0.367529,	Top-5 err = 0.162158,	data_time = 0.050418,	train_time = 0.311893
[2019-08-24 08:33:13,085] TRAIN Iter 220060: lr = 0.133235,	loss = 2.460850,	Top-1 err = 0.366943,	Top-5 err = 0.156982,	data_time = 0.050505,	train_time = 0.322840
[2019-08-24 08:33:24,877] TRAIN Iter 220080: lr = 0.133202,	loss = 2.416827,	Top-1 err = 0.365674,	Top-5 err = 0.159082,	data_time = 0.146795,	train_time = 0.589581
[2019-08-24 08:33:42,178] TRAIN Iter 220100: lr = 0.133168,	loss = 2.538265,	Top-1 err = 0.369482,	Top-5 err = 0.159521,	data_time = 0.170882,	train_time = 0.865036
[2019-08-24 08:33:51,981] TRAIN Iter 220120: lr = 0.133135,	loss = 2.417333,	Top-1 err = 0.364551,	Top-5 err = 0.158740,	data_time = 0.050514,	train_time = 0.490132
[2019-08-24 08:34:06,737] TRAIN Iter 220140: lr = 0.133102,	loss = 2.522105,	Top-1 err = 0.371436,	Top-5 err = 0.162500,	data_time = 0.050609,	train_time = 0.737810
[2019-08-24 08:34:16,315] TRAIN Iter 220160: lr = 0.133068,	loss = 2.516371,	Top-1 err = 0.365332,	Top-5 err = 0.159375,	data_time = 0.050764,	train_time = 0.478884
[2019-08-24 08:34:33,382] TRAIN Iter 220180: lr = 0.133035,	loss = 2.594710,	Top-1 err = 0.373926,	Top-5 err = 0.165918,	data_time = 0.050590,	train_time = 0.853325
[2019-08-24 08:34:49,192] TRAIN Iter 220200: lr = 0.133002,	loss = 2.530435,	Top-1 err = 0.370410,	Top-5 err = 0.161084,	data_time = 0.050768,	train_time = 0.790494
[2019-08-24 08:34:58,424] TRAIN Iter 220220: lr = 0.132968,	loss = 2.444925,	Top-1 err = 0.366504,	Top-5 err = 0.158252,	data_time = 0.050276,	train_time = 0.461577
[2019-08-24 08:35:15,294] TRAIN Iter 220240: lr = 0.132935,	loss = 2.519377,	Top-1 err = 0.368359,	Top-5 err = 0.162891,	data_time = 0.050110,	train_time = 0.843476
[2019-08-24 08:35:31,303] TRAIN Iter 220260: lr = 0.132902,	loss = 2.483939,	Top-1 err = 0.371924,	Top-5 err = 0.159326,	data_time = 0.049995,	train_time = 0.800429
[2019-08-24 08:35:39,340] TRAIN Iter 220280: lr = 0.132868,	loss = 2.560605,	Top-1 err = 0.365918,	Top-5 err = 0.159814,	data_time = 0.049850,	train_time = 0.401864
[2019-08-24 08:36:27,746] TRAIN Iter 220300: lr = 0.132835,	loss = 2.492056,	Top-1 err = 0.370261,	Top-5 err = 0.161841,	data_time = 0.050311,	train_time = 2.420292
[2019-08-24 08:36:35,093] TRAIN Iter 220320: lr = 0.132802,	loss = 2.440402,	Top-1 err = 0.363037,	Top-5 err = 0.156104,	data_time = 0.051285,	train_time = 0.367326
[2019-08-24 08:36:49,239] TRAIN Iter 220340: lr = 0.132768,	loss = 2.490346,	Top-1 err = 0.365430,	Top-5 err = 0.160449,	data_time = 0.050633,	train_time = 0.707243
[2019-08-24 08:37:04,227] TRAIN Iter 220360: lr = 0.132735,	loss = 2.476000,	Top-1 err = 0.360205,	Top-5 err = 0.155469,	data_time = 0.170709,	train_time = 0.749423
[2019-08-24 08:37:11,141] TRAIN Iter 220380: lr = 0.132702,	loss = 2.429660,	Top-1 err = 0.364063,	Top-5 err = 0.155908,	data_time = 0.050483,	train_time = 0.345653
[2019-08-24 08:37:25,727] TRAIN Iter 220400: lr = 0.132668,	loss = 2.526272,	Top-1 err = 0.356738,	Top-5 err = 0.153174,	data_time = 0.050538,	train_time = 0.729320
[2019-08-24 08:37:42,329] TRAIN Iter 220420: lr = 0.132635,	loss = 2.473189,	Top-1 err = 0.358789,	Top-5 err = 0.152197,	data_time = 3.771116,	train_time = 0.830092
[2019-08-24 08:37:49,527] TRAIN Iter 220440: lr = 0.132602,	loss = 2.389913,	Top-1 err = 0.362549,	Top-5 err = 0.156641,	data_time = 0.050655,	train_time = 0.359842
[2019-08-24 08:38:04,684] TRAIN Iter 220460: lr = 0.132568,	loss = 2.483644,	Top-1 err = 0.361523,	Top-5 err = 0.159375,	data_time = 0.050907,	train_time = 0.757834
[2019-08-24 08:38:11,576] TRAIN Iter 220480: lr = 0.132535,	loss = 2.513327,	Top-1 err = 0.363770,	Top-5 err = 0.156592,	data_time = 0.050546,	train_time = 0.344592
[2019-08-24 08:38:27,436] TRAIN Iter 220500: lr = 0.132502,	loss = 2.471027,	Top-1 err = 0.360449,	Top-5 err = 0.156787,	data_time = 0.050731,	train_time = 0.793026
[2019-08-24 08:38:42,881] TRAIN Iter 220520: lr = 0.132468,	loss = 2.464220,	Top-1 err = 0.364209,	Top-5 err = 0.159717,	data_time = 0.050305,	train_time = 0.772240
[2019-08-24 08:38:49,850] TRAIN Iter 220540: lr = 0.132435,	loss = 2.431329,	Top-1 err = 0.358447,	Top-5 err = 0.156299,	data_time = 0.050205,	train_time = 0.348437
[2019-08-24 08:39:04,124] TRAIN Iter 220560: lr = 0.132402,	loss = 2.460193,	Top-1 err = 0.357422,	Top-5 err = 0.155713,	data_time = 0.050516,	train_time = 0.713650
[2019-08-24 08:39:18,197] TRAIN Iter 220580: lr = 0.132368,	loss = 2.458878,	Top-1 err = 0.360303,	Top-5 err = 0.156055,	data_time = 6.627448,	train_time = 0.703665
[2019-08-24 08:39:25,586] TRAIN Iter 220600: lr = 0.132335,	loss = 2.438869,	Top-1 err = 0.364258,	Top-5 err = 0.154883,	data_time = 0.050415,	train_time = 0.369424
[2019-08-24 08:39:41,394] TRAIN Iter 220620: lr = 0.132302,	loss = 2.477375,	Top-1 err = 0.357812,	Top-5 err = 0.155762,	data_time = 0.050443,	train_time = 0.790367
[2019-08-24 08:39:48,364] TRAIN Iter 220640: lr = 0.132268,	loss = 2.460769,	Top-1 err = 0.363281,	Top-5 err = 0.155762,	data_time = 0.129903,	train_time = 0.348501
[2019-08-24 08:40:05,427] TRAIN Iter 220660: lr = 0.132235,	loss = 2.528698,	Top-1 err = 0.363818,	Top-5 err = 0.155615,	data_time = 0.050670,	train_time = 0.853127
[2019-08-24 08:40:18,070] TRAIN Iter 220680: lr = 0.132202,	loss = 2.474158,	Top-1 err = 0.362793,	Top-5 err = 0.159180,	data_time = 1.795102,	train_time = 0.632123
[2019-08-24 08:40:26,934] TRAIN Iter 220700: lr = 0.132168,	loss = 2.530596,	Top-1 err = 0.366162,	Top-5 err = 0.161035,	data_time = 0.165843,	train_time = 0.443231
[2019-08-24 08:40:41,877] TRAIN Iter 220720: lr = 0.132135,	loss = 2.431176,	Top-1 err = 0.364893,	Top-5 err = 0.157324,	data_time = 0.050400,	train_time = 0.747100
[2019-08-24 08:40:54,031] TRAIN Iter 220740: lr = 0.132102,	loss = 2.627249,	Top-1 err = 0.362500,	Top-5 err = 0.158154,	data_time = 3.844710,	train_time = 0.607679
[2019-08-24 08:41:06,205] TRAIN Iter 220760: lr = 0.132068,	loss = 2.452431,	Top-1 err = 0.368896,	Top-5 err = 0.162305,	data_time = 0.050513,	train_time = 0.608728
[2019-08-24 08:41:22,972] TRAIN Iter 220780: lr = 0.132035,	loss = 2.512285,	Top-1 err = 0.358740,	Top-5 err = 0.158984,	data_time = 0.050396,	train_time = 0.838305
[2019-08-24 08:41:30,290] TRAIN Iter 220800: lr = 0.132002,	loss = 2.486727,	Top-1 err = 0.363086,	Top-5 err = 0.157373,	data_time = 0.050733,	train_time = 0.365905
[2019-08-24 08:41:46,322] TRAIN Iter 220820: lr = 0.131968,	loss = 2.427241,	Top-1 err = 0.365820,	Top-5 err = 0.156982,	data_time = 0.050466,	train_time = 0.801562
[2019-08-24 08:42:00,932] TRAIN Iter 220840: lr = 0.131935,	loss = 2.512323,	Top-1 err = 0.361230,	Top-5 err = 0.156738,	data_time = 0.050376,	train_time = 0.730472
[2019-08-24 08:42:08,084] TRAIN Iter 220860: lr = 0.131902,	loss = 2.555592,	Top-1 err = 0.368701,	Top-5 err = 0.159229,	data_time = 0.050621,	train_time = 0.357620
[2019-08-24 08:42:22,212] TRAIN Iter 220880: lr = 0.131868,	loss = 2.499714,	Top-1 err = 0.362500,	Top-5 err = 0.157227,	data_time = 0.050292,	train_time = 0.706373
[2019-08-24 08:42:38,109] TRAIN Iter 220900: lr = 0.131835,	loss = 2.513273,	Top-1 err = 0.370605,	Top-5 err = 0.162109,	data_time = 4.286873,	train_time = 0.794837
[2019-08-24 08:42:45,187] TRAIN Iter 220920: lr = 0.131802,	loss = 2.406476,	Top-1 err = 0.364063,	Top-5 err = 0.158936,	data_time = 0.050572,	train_time = 0.353887
[2019-08-24 08:43:01,918] TRAIN Iter 220940: lr = 0.131768,	loss = 2.418257,	Top-1 err = 0.367773,	Top-5 err = 0.161572,	data_time = 0.050756,	train_time = 0.836517
[2019-08-24 08:43:08,994] TRAIN Iter 220960: lr = 0.131735,	loss = 2.495611,	Top-1 err = 0.369775,	Top-5 err = 0.162793,	data_time = 0.050553,	train_time = 0.353825
[2019-08-24 08:43:22,348] TRAIN Iter 220980: lr = 0.131702,	loss = 2.567778,	Top-1 err = 0.362549,	Top-5 err = 0.159326,	data_time = 0.050393,	train_time = 0.667645
[2019-08-24 08:43:37,557] TRAIN Iter 221000: lr = 0.131668,	loss = 2.577092,	Top-1 err = 0.371680,	Top-5 err = 0.159570,	data_time = 0.106179,	train_time = 0.760459
[2019-08-24 08:43:45,119] TRAIN Iter 221020: lr = 0.131635,	loss = 2.406399,	Top-1 err = 0.363770,	Top-5 err = 0.159912,	data_time = 0.050907,	train_time = 0.378081
[2019-08-24 08:44:00,135] TRAIN Iter 221040: lr = 0.131602,	loss = 2.534560,	Top-1 err = 0.371094,	Top-5 err = 0.159570,	data_time = 0.140788,	train_time = 0.750787
[2019-08-24 08:44:15,965] TRAIN Iter 221060: lr = 0.131568,	loss = 2.582289,	Top-1 err = 0.368994,	Top-5 err = 0.158057,	data_time = 0.050519,	train_time = 0.791478
[2019-08-24 08:44:24,192] TRAIN Iter 221080: lr = 0.131535,	loss = 2.499657,	Top-1 err = 0.367383,	Top-5 err = 0.160742,	data_time = 0.050654,	train_time = 0.411338
[2019-08-24 08:44:39,532] TRAIN Iter 221100: lr = 0.131502,	loss = 2.501905,	Top-1 err = 0.365137,	Top-5 err = 0.163330,	data_time = 0.154113,	train_time = 0.766967
[2019-08-24 08:44:47,053] TRAIN Iter 221120: lr = 0.131468,	loss = 2.561851,	Top-1 err = 0.365332,	Top-5 err = 0.161035,	data_time = 0.050709,	train_time = 0.376037
[2019-08-24 08:45:01,596] TRAIN Iter 221140: lr = 0.131435,	loss = 2.444975,	Top-1 err = 0.364648,	Top-5 err = 0.162988,	data_time = 0.050444,	train_time = 0.727165
[2019-08-24 08:45:15,595] TRAIN Iter 221160: lr = 0.131402,	loss = 2.508202,	Top-1 err = 0.366699,	Top-5 err = 0.160303,	data_time = 0.618568,	train_time = 0.699921
[2019-08-24 08:45:23,128] TRAIN Iter 221180: lr = 0.131368,	loss = 2.552215,	Top-1 err = 0.363477,	Top-5 err = 0.158643,	data_time = 0.050551,	train_time = 0.376658
[2019-08-24 08:45:40,472] TRAIN Iter 221200: lr = 0.131335,	loss = 2.492120,	Top-1 err = 0.367529,	Top-5 err = 0.160449,	data_time = 0.050325,	train_time = 0.867143
[2019-08-24 08:45:57,128] TRAIN Iter 221220: lr = 0.131302,	loss = 2.426046,	Top-1 err = 0.366895,	Top-5 err = 0.160596,	data_time = 0.124386,	train_time = 0.832819
[2019-08-24 08:46:04,538] TRAIN Iter 221240: lr = 0.131268,	loss = 2.493086,	Top-1 err = 0.366650,	Top-5 err = 0.161084,	data_time = 0.050545,	train_time = 0.370495
[2019-08-24 08:46:21,995] TRAIN Iter 221260: lr = 0.131235,	loss = 2.555736,	Top-1 err = 0.373437,	Top-5 err = 0.162012,	data_time = 0.050273,	train_time = 0.872811
[2019-08-24 08:46:29,032] TRAIN Iter 221280: lr = 0.131202,	loss = 2.412915,	Top-1 err = 0.367676,	Top-5 err = 0.160742,	data_time = 0.050581,	train_time = 0.351845
[2019-08-24 08:46:44,527] TRAIN Iter 221300: lr = 0.131168,	loss = 2.512167,	Top-1 err = 0.363623,	Top-5 err = 0.161377,	data_time = 0.050837,	train_time = 0.774743
[2019-08-24 08:47:00,805] TRAIN Iter 221320: lr = 0.131135,	loss = 2.535111,	Top-1 err = 0.363867,	Top-5 err = 0.159912,	data_time = 0.050562,	train_time = 0.813870
[2019-08-24 08:47:07,571] TRAIN Iter 221340: lr = 0.131102,	loss = 2.439682,	Top-1 err = 0.367041,	Top-5 err = 0.163574,	data_time = 0.050521,	train_time = 0.338269
[2019-08-24 08:47:24,419] TRAIN Iter 221360: lr = 0.131068,	loss = 2.379868,	Top-1 err = 0.365332,	Top-5 err = 0.158594,	data_time = 0.050429,	train_time = 0.842403
[2019-08-24 08:47:39,590] TRAIN Iter 221380: lr = 0.131035,	loss = 2.495949,	Top-1 err = 0.373389,	Top-5 err = 0.165137,	data_time = 0.602528,	train_time = 0.758551
[2019-08-24 08:47:46,945] TRAIN Iter 221400: lr = 0.131002,	loss = 2.392129,	Top-1 err = 0.362354,	Top-5 err = 0.158301,	data_time = 0.050401,	train_time = 0.367729
[2019-08-24 08:48:03,509] TRAIN Iter 221420: lr = 0.130968,	loss = 2.525176,	Top-1 err = 0.371045,	Top-5 err = 0.160547,	data_time = 0.050590,	train_time = 0.828181
[2019-08-24 08:48:10,277] TRAIN Iter 221440: lr = 0.130935,	loss = 2.457480,	Top-1 err = 0.367871,	Top-5 err = 0.162158,	data_time = 0.050557,	train_time = 0.338407
[2019-08-24 08:48:26,167] TRAIN Iter 221460: lr = 0.130902,	loss = 2.459255,	Top-1 err = 0.368359,	Top-5 err = 0.160010,	data_time = 0.050508,	train_time = 0.794460
[2019-08-24 08:48:43,791] TRAIN Iter 221480: lr = 0.130868,	loss = 2.419465,	Top-1 err = 0.365576,	Top-5 err = 0.158887,	data_time = 0.050040,	train_time = 0.881201
[2019-08-24 08:48:50,624] TRAIN Iter 221500: lr = 0.130835,	loss = 2.509484,	Top-1 err = 0.373193,	Top-5 err = 0.165576,	data_time = 0.050126,	train_time = 0.341638
[2019-08-24 08:49:06,504] TRAIN Iter 221520: lr = 0.130802,	loss = 2.468242,	Top-1 err = 0.365918,	Top-5 err = 0.158936,	data_time = 0.049899,	train_time = 0.793968
[2019-08-24 08:49:13,736] TRAIN Iter 221540: lr = 0.130768,	loss = 2.990900,	Top-1 err = 0.371149,	Top-5 err = 0.165183,	data_time = 0.007059,	train_time = 0.361610
[2019-08-24 08:50:01,956] TRAIN Iter 221560: lr = 0.130735,	loss = 2.488413,	Top-1 err = 0.365918,	Top-5 err = 0.156250,	data_time = 0.115534,	train_time = 2.410955
[2019-08-24 08:50:16,919] TRAIN Iter 221580: lr = 0.130702,	loss = 2.470082,	Top-1 err = 0.363525,	Top-5 err = 0.160156,	data_time = 0.050528,	train_time = 0.748129
[2019-08-24 08:50:24,944] TRAIN Iter 221600: lr = 0.130668,	loss = 2.579864,	Top-1 err = 0.361621,	Top-5 err = 0.159961,	data_time = 0.050564,	train_time = 0.401256
[2019-08-24 08:50:36,651] TRAIN Iter 221620: lr = 0.130635,	loss = 2.449969,	Top-1 err = 0.356641,	Top-5 err = 0.152393,	data_time = 0.050305,	train_time = 0.585318
[2019-08-24 08:50:48,456] TRAIN Iter 221640: lr = 0.130602,	loss = 2.457925,	Top-1 err = 0.359863,	Top-5 err = 0.158057,	data_time = 0.050381,	train_time = 0.590252
[2019-08-24 08:50:56,215] TRAIN Iter 221660: lr = 0.130568,	loss = 2.499266,	Top-1 err = 0.353076,	Top-5 err = 0.152539,	data_time = 0.050397,	train_time = 0.387952
[2019-08-24 08:51:10,769] TRAIN Iter 221680: lr = 0.130535,	loss = 2.486629,	Top-1 err = 0.362451,	Top-5 err = 0.160449,	data_time = 0.050881,	train_time = 0.727668
[2019-08-24 08:51:18,242] TRAIN Iter 221700: lr = 0.130502,	loss = 2.401216,	Top-1 err = 0.363965,	Top-5 err = 0.156982,	data_time = 0.050204,	train_time = 0.373650
[2019-08-24 08:51:33,202] TRAIN Iter 221720: lr = 0.130468,	loss = 2.563599,	Top-1 err = 0.368066,	Top-5 err = 0.161475,	data_time = 0.050809,	train_time = 0.747982
[2019-08-24 08:51:46,820] TRAIN Iter 221740: lr = 0.130435,	loss = 2.374587,	Top-1 err = 0.366113,	Top-5 err = 0.156445,	data_time = 0.050499,	train_time = 0.680852
[2019-08-24 08:51:53,927] TRAIN Iter 221760: lr = 0.130402,	loss = 2.549011,	Top-1 err = 0.362988,	Top-5 err = 0.162305,	data_time = 0.050278,	train_time = 0.355361
[2019-08-24 08:52:09,401] TRAIN Iter 221780: lr = 0.130368,	loss = 2.405315,	Top-1 err = 0.357959,	Top-5 err = 0.155908,	data_time = 0.050383,	train_time = 0.773697
[2019-08-24 08:52:24,610] TRAIN Iter 221800: lr = 0.130335,	loss = 2.471751,	Top-1 err = 0.356836,	Top-5 err = 0.154590,	data_time = 0.050412,	train_time = 0.760442
[2019-08-24 08:52:31,800] TRAIN Iter 221820: lr = 0.130302,	loss = 2.496541,	Top-1 err = 0.364453,	Top-5 err = 0.156787,	data_time = 0.050394,	train_time = 0.359483
[2019-08-24 08:52:47,794] TRAIN Iter 221840: lr = 0.130268,	loss = 2.426462,	Top-1 err = 0.361279,	Top-5 err = 0.156445,	data_time = 0.050565,	train_time = 0.799689
[2019-08-24 08:52:54,826] TRAIN Iter 221860: lr = 0.130235,	loss = 2.485139,	Top-1 err = 0.362549,	Top-5 err = 0.154395,	data_time = 0.050389,	train_time = 0.351586
[2019-08-24 08:53:10,353] TRAIN Iter 221880: lr = 0.130202,	loss = 2.337084,	Top-1 err = 0.360547,	Top-5 err = 0.158203,	data_time = 0.050516,	train_time = 0.776290
[2019-08-24 08:53:24,885] TRAIN Iter 221900: lr = 0.130168,	loss = 2.472345,	Top-1 err = 0.368506,	Top-5 err = 0.158350,	data_time = 0.050573,	train_time = 0.726588
[2019-08-24 08:53:31,938] TRAIN Iter 221920: lr = 0.130135,	loss = 2.510893,	Top-1 err = 0.364111,	Top-5 err = 0.158691,	data_time = 0.050339,	train_time = 0.352636
[2019-08-24 08:53:47,924] TRAIN Iter 221940: lr = 0.130102,	loss = 2.478562,	Top-1 err = 0.362549,	Top-5 err = 0.156104,	data_time = 0.050279,	train_time = 0.799287
[2019-08-24 08:54:03,646] TRAIN Iter 221960: lr = 0.130068,	loss = 2.460043,	Top-1 err = 0.361475,	Top-5 err = 0.157959,	data_time = 0.050859,	train_time = 0.786130
[2019-08-24 08:54:10,858] TRAIN Iter 221980: lr = 0.130035,	loss = 2.419193,	Top-1 err = 0.358496,	Top-5 err = 0.154443,	data_time = 0.050437,	train_time = 0.360578
[2019-08-24 08:54:25,761] TRAIN Iter 222000: lr = 0.130002,	loss = 2.519096,	Top-1 err = 0.364941,	Top-5 err = 0.157422,	data_time = 0.050511,	train_time = 0.745132
[2019-08-24 08:54:33,474] TRAIN Iter 222020: lr = 0.129968,	loss = 2.618941,	Top-1 err = 0.364258,	Top-5 err = 0.158838,	data_time = 0.050383,	train_time = 0.385598
[2019-08-24 08:54:47,847] TRAIN Iter 222040: lr = 0.129935,	loss = 2.439525,	Top-1 err = 0.358838,	Top-5 err = 0.158496,	data_time = 0.050515,	train_time = 0.718643
[2019-08-24 08:55:04,170] TRAIN Iter 222060: lr = 0.129902,	loss = 2.555757,	Top-1 err = 0.364600,	Top-5 err = 0.160547,	data_time = 0.050324,	train_time = 0.816162
[2019-08-24 08:55:11,340] TRAIN Iter 222080: lr = 0.129868,	loss = 2.510921,	Top-1 err = 0.361719,	Top-5 err = 0.156689,	data_time = 0.050383,	train_time = 0.358455
[2019-08-24 08:55:29,207] TRAIN Iter 222100: lr = 0.129835,	loss = 2.526614,	Top-1 err = 0.370801,	Top-5 err = 0.160596,	data_time = 0.050308,	train_time = 0.893376
[2019-08-24 08:55:42,678] TRAIN Iter 222120: lr = 0.129802,	loss = 2.503456,	Top-1 err = 0.361377,	Top-5 err = 0.157861,	data_time = 0.094855,	train_time = 0.673505
[2019-08-24 08:55:49,810] TRAIN Iter 222140: lr = 0.129768,	loss = 2.586641,	Top-1 err = 0.361475,	Top-5 err = 0.156152,	data_time = 0.050467,	train_time = 0.356622
[2019-08-24 08:56:06,881] TRAIN Iter 222160: lr = 0.129735,	loss = 2.504488,	Top-1 err = 0.363818,	Top-5 err = 0.161719,	data_time = 0.050565,	train_time = 0.853521
[2019-08-24 08:56:14,504] TRAIN Iter 222180: lr = 0.129702,	loss = 2.541141,	Top-1 err = 0.362207,	Top-5 err = 0.158105,	data_time = 0.051066,	train_time = 0.381151
[2019-08-24 08:56:29,740] TRAIN Iter 222200: lr = 0.129668,	loss = 2.540157,	Top-1 err = 0.364844,	Top-5 err = 0.159521,	data_time = 0.050847,	train_time = 0.761741
[2019-08-24 08:56:46,433] TRAIN Iter 222220: lr = 0.129635,	loss = 2.547220,	Top-1 err = 0.362354,	Top-5 err = 0.159912,	data_time = 0.050419,	train_time = 0.834680
[2019-08-24 08:56:53,711] TRAIN Iter 222240: lr = 0.129602,	loss = 2.439140,	Top-1 err = 0.366162,	Top-5 err = 0.161670,	data_time = 0.050522,	train_time = 0.363881
[2019-08-24 08:57:08,366] TRAIN Iter 222260: lr = 0.129568,	loss = 2.425788,	Top-1 err = 0.364502,	Top-5 err = 0.157080,	data_time = 0.050563,	train_time = 0.732709
[2019-08-24 08:57:24,707] TRAIN Iter 222280: lr = 0.129535,	loss = 2.494838,	Top-1 err = 0.366211,	Top-5 err = 0.155566,	data_time = 0.050819,	train_time = 0.817069
[2019-08-24 08:57:31,528] TRAIN Iter 222300: lr = 0.129502,	loss = 2.471949,	Top-1 err = 0.366211,	Top-5 err = 0.160400,	data_time = 0.050338,	train_time = 0.341034
[2019-08-24 08:57:47,849] TRAIN Iter 222320: lr = 0.129468,	loss = 2.409413,	Top-1 err = 0.371680,	Top-5 err = 0.161426,	data_time = 0.050322,	train_time = 0.815993
[2019-08-24 08:57:55,012] TRAIN Iter 222340: lr = 0.129435,	loss = 2.506842,	Top-1 err = 0.370801,	Top-5 err = 0.157178,	data_time = 0.050953,	train_time = 0.358143
[2019-08-24 08:58:11,620] TRAIN Iter 222360: lr = 0.129402,	loss = 2.436064,	Top-1 err = 0.361865,	Top-5 err = 0.153906,	data_time = 0.050604,	train_time = 0.830382
[2019-08-24 08:58:27,339] TRAIN Iter 222380: lr = 0.129368,	loss = 2.531860,	Top-1 err = 0.371533,	Top-5 err = 0.163965,	data_time = 0.050461,	train_time = 0.785972
[2019-08-24 08:58:34,251] TRAIN Iter 222400: lr = 0.129335,	loss = 2.396090,	Top-1 err = 0.372852,	Top-5 err = 0.161572,	data_time = 0.050884,	train_time = 0.345581
[2019-08-24 08:58:49,765] TRAIN Iter 222420: lr = 0.129302,	loss = 2.468954,	Top-1 err = 0.364697,	Top-5 err = 0.160498,	data_time = 0.050715,	train_time = 0.775665
[2019-08-24 08:59:07,018] TRAIN Iter 222440: lr = 0.129268,	loss = 2.456329,	Top-1 err = 0.365576,	Top-5 err = 0.156006,	data_time = 0.050488,	train_time = 0.862628
[2019-08-24 08:59:13,746] TRAIN Iter 222460: lr = 0.129235,	loss = 2.541590,	Top-1 err = 0.368652,	Top-5 err = 0.163184,	data_time = 0.050288,	train_time = 0.336398
[2019-08-24 08:59:30,136] TRAIN Iter 222480: lr = 0.129202,	loss = 2.392423,	Top-1 err = 0.361279,	Top-5 err = 0.155322,	data_time = 0.050397,	train_time = 0.819487
[2019-08-24 08:59:37,365] TRAIN Iter 222500: lr = 0.129168,	loss = 2.509303,	Top-1 err = 0.364355,	Top-5 err = 0.155908,	data_time = 0.050659,	train_time = 0.361434
[2019-08-24 08:59:53,104] TRAIN Iter 222520: lr = 0.129135,	loss = 2.602569,	Top-1 err = 0.370117,	Top-5 err = 0.160840,	data_time = 0.050534,	train_time = 0.786913
[2019-08-24 09:00:09,382] TRAIN Iter 222540: lr = 0.129102,	loss = 2.520160,	Top-1 err = 0.362891,	Top-5 err = 0.163672,	data_time = 0.050276,	train_time = 0.813885
[2019-08-24 09:00:16,440] TRAIN Iter 222560: lr = 0.129068,	loss = 2.549855,	Top-1 err = 0.361377,	Top-5 err = 0.155225,	data_time = 0.050424,	train_time = 0.352885
[2019-08-24 09:00:33,160] TRAIN Iter 222580: lr = 0.129035,	loss = 2.456415,	Top-1 err = 0.361572,	Top-5 err = 0.155176,	data_time = 0.050464,	train_time = 0.836021
[2019-08-24 09:00:51,853] TRAIN Iter 222600: lr = 0.129002,	loss = 2.481495,	Top-1 err = 0.373291,	Top-5 err = 0.162061,	data_time = 0.050412,	train_time = 0.934611
[2019-08-24 09:00:59,163] TRAIN Iter 222620: lr = 0.128968,	loss = 2.511751,	Top-1 err = 0.369678,	Top-5 err = 0.161865,	data_time = 0.050344,	train_time = 0.365468
[2019-08-24 09:01:18,306] TRAIN Iter 222640: lr = 0.128935,	loss = 2.502248,	Top-1 err = 0.363379,	Top-5 err = 0.156787,	data_time = 0.143184,	train_time = 0.957167
[2019-08-24 09:01:25,192] TRAIN Iter 222660: lr = 0.128902,	loss = 2.494563,	Top-1 err = 0.361084,	Top-5 err = 0.157812,	data_time = 0.050573,	train_time = 0.344279
[2019-08-24 09:01:40,791] TRAIN Iter 222680: lr = 0.128868,	loss = 2.407984,	Top-1 err = 0.367090,	Top-5 err = 0.156494,	data_time = 0.050700,	train_time = 0.779923
[2019-08-24 09:01:58,102] TRAIN Iter 222700: lr = 0.128835,	loss = 2.411586,	Top-1 err = 0.366602,	Top-5 err = 0.159863,	data_time = 0.050854,	train_time = 0.865551
[2019-08-24 09:02:05,167] TRAIN Iter 222720: lr = 0.128802,	loss = 2.563139,	Top-1 err = 0.374561,	Top-5 err = 0.163477,	data_time = 0.050469,	train_time = 0.353251
[2019-08-24 09:02:22,901] TRAIN Iter 222740: lr = 0.128768,	loss = 2.470843,	Top-1 err = 0.364990,	Top-5 err = 0.159277,	data_time = 0.050158,	train_time = 0.886657
[2019-08-24 09:02:38,068] TRAIN Iter 222760: lr = 0.128735,	loss = 2.447901,	Top-1 err = 0.361328,	Top-5 err = 0.159375,	data_time = 0.049990,	train_time = 0.758363
[2019-08-24 09:02:45,784] TRAIN Iter 222780: lr = 0.128702,	loss = 2.417976,	Top-1 err = 0.368506,	Top-5 err = 0.162109,	data_time = 0.049932,	train_time = 0.385753
[2019-08-24 09:03:39,463] TRAIN Iter 222800: lr = 0.128668,	loss = 2.548362,	Top-1 err = 0.370741,	Top-5 err = 0.163101,	data_time = 0.050188,	train_time = 2.683955
[2019-08-24 09:03:46,739] TRAIN Iter 222820: lr = 0.128635,	loss = 2.456493,	Top-1 err = 0.358643,	Top-5 err = 0.157422,	data_time = 0.050306,	train_time = 0.363769
[2019-08-24 09:04:01,472] TRAIN Iter 222840: lr = 0.128602,	loss = 2.384449,	Top-1 err = 0.364990,	Top-5 err = 0.155762,	data_time = 0.050622,	train_time = 0.736650
[2019-08-24 09:04:13,915] TRAIN Iter 222860: lr = 0.128568,	loss = 2.444541,	Top-1 err = 0.357666,	Top-5 err = 0.153125,	data_time = 0.050804,	train_time = 0.622114
[2019-08-24 09:04:21,224] TRAIN Iter 222880: lr = 0.128535,	loss = 2.475723,	Top-1 err = 0.360693,	Top-5 err = 0.156055,	data_time = 0.050436,	train_time = 0.365444
[2019-08-24 09:04:36,930] TRAIN Iter 222900: lr = 0.128502,	loss = 2.416856,	Top-1 err = 0.356299,	Top-5 err = 0.155518,	data_time = 0.050482,	train_time = 0.785272
[2019-08-24 09:04:44,438] TRAIN Iter 222920: lr = 0.128468,	loss = 2.474467,	Top-1 err = 0.360693,	Top-5 err = 0.155615,	data_time = 0.051084,	train_time = 0.375400
[2019-08-24 09:04:58,419] TRAIN Iter 222940: lr = 0.128435,	loss = 2.436364,	Top-1 err = 0.358740,	Top-5 err = 0.155322,	data_time = 0.050308,	train_time = 0.699026
[2019-08-24 09:05:13,142] TRAIN Iter 222960: lr = 0.128402,	loss = 2.509389,	Top-1 err = 0.363623,	Top-5 err = 0.161768,	data_time = 0.050456,	train_time = 0.736134
[2019-08-24 09:05:20,298] TRAIN Iter 222980: lr = 0.128368,	loss = 2.545529,	Top-1 err = 0.359814,	Top-5 err = 0.155371,	data_time = 0.050452,	train_time = 0.357825
[2019-08-24 09:05:34,580] TRAIN Iter 223000: lr = 0.128335,	loss = 2.464868,	Top-1 err = 0.360889,	Top-5 err = 0.156982,	data_time = 0.050394,	train_time = 0.714075
[2019-08-24 09:05:48,355] TRAIN Iter 223020: lr = 0.128302,	loss = 2.450982,	Top-1 err = 0.362988,	Top-5 err = 0.154443,	data_time = 0.050430,	train_time = 0.688721
[2019-08-24 09:05:56,099] TRAIN Iter 223040: lr = 0.128268,	loss = 2.535828,	Top-1 err = 0.359766,	Top-5 err = 0.153076,	data_time = 0.050411,	train_time = 0.387210
[2019-08-24 09:06:11,401] TRAIN Iter 223060: lr = 0.128235,	loss = 2.438303,	Top-1 err = 0.357227,	Top-5 err = 0.155225,	data_time = 0.050440,	train_time = 0.765082
[2019-08-24 09:06:18,456] TRAIN Iter 223080: lr = 0.128202,	loss = 2.464325,	Top-1 err = 0.367725,	Top-5 err = 0.158984,	data_time = 0.050281,	train_time = 0.352736
[2019-08-24 09:06:33,046] TRAIN Iter 223100: lr = 0.128168,	loss = 2.452033,	Top-1 err = 0.366553,	Top-5 err = 0.160254,	data_time = 0.050489,	train_time = 0.729472
[2019-08-24 09:06:50,803] TRAIN Iter 223120: lr = 0.128135,	loss = 2.482956,	Top-1 err = 0.361133,	Top-5 err = 0.156445,	data_time = 0.050883,	train_time = 0.887842
[2019-08-24 09:06:57,597] TRAIN Iter 223140: lr = 0.128102,	loss = 2.395996,	Top-1 err = 0.362451,	Top-5 err = 0.158008,	data_time = 0.181385,	train_time = 0.339694
[2019-08-24 09:07:13,226] TRAIN Iter 223160: lr = 0.128068,	loss = 2.485604,	Top-1 err = 0.360254,	Top-5 err = 0.157520,	data_time = 0.050603,	train_time = 0.781410
[2019-08-24 09:07:26,932] TRAIN Iter 223180: lr = 0.128035,	loss = 2.460272,	Top-1 err = 0.358887,	Top-5 err = 0.154395,	data_time = 0.131199,	train_time = 0.685284
[2019-08-24 09:07:34,056] TRAIN Iter 223200: lr = 0.128002,	loss = 2.479257,	Top-1 err = 0.367041,	Top-5 err = 0.158154,	data_time = 0.050810,	train_time = 0.356207
[2019-08-24 09:07:50,818] TRAIN Iter 223220: lr = 0.127968,	loss = 2.427289,	Top-1 err = 0.361719,	Top-5 err = 0.157617,	data_time = 0.050502,	train_time = 0.838094
[2019-08-24 09:07:58,376] TRAIN Iter 223240: lr = 0.127935,	loss = 2.455485,	Top-1 err = 0.353369,	Top-5 err = 0.154688,	data_time = 0.135667,	train_time = 0.377888
[2019-08-24 09:08:11,994] TRAIN Iter 223260: lr = 0.127902,	loss = 2.473209,	Top-1 err = 0.365723,	Top-5 err = 0.155029,	data_time = 0.050837,	train_time = 0.680849
[2019-08-24 09:08:28,657] TRAIN Iter 223280: lr = 0.127868,	loss = 2.371553,	Top-1 err = 0.357129,	Top-5 err = 0.152441,	data_time = 0.050474,	train_time = 0.833179
[2019-08-24 09:08:35,748] TRAIN Iter 223300: lr = 0.127835,	loss = 2.460905,	Top-1 err = 0.364844,	Top-5 err = 0.155029,	data_time = 0.138057,	train_time = 0.354488
[2019-08-24 09:08:49,057] TRAIN Iter 223320: lr = 0.127802,	loss = 2.433872,	Top-1 err = 0.362646,	Top-5 err = 0.155713,	data_time = 0.050517,	train_time = 0.665465
[2019-08-24 09:09:05,293] TRAIN Iter 223340: lr = 0.127768,	loss = 2.497350,	Top-1 err = 0.361523,	Top-5 err = 0.156006,	data_time = 0.050774,	train_time = 0.811756
[2019-08-24 09:09:12,651] TRAIN Iter 223360: lr = 0.127735,	loss = 2.527301,	Top-1 err = 0.361035,	Top-5 err = 0.157471,	data_time = 0.050456,	train_time = 0.367909
[2019-08-24 09:09:28,710] TRAIN Iter 223380: lr = 0.127702,	loss = 2.430439,	Top-1 err = 0.361182,	Top-5 err = 0.156543,	data_time = 0.050296,	train_time = 0.802933
[2019-08-24 09:09:36,003] TRAIN Iter 223400: lr = 0.127668,	loss = 2.473962,	Top-1 err = 0.361426,	Top-5 err = 0.156934,	data_time = 0.050412,	train_time = 0.364649
[2019-08-24 09:09:51,848] TRAIN Iter 223420: lr = 0.127635,	loss = 2.494825,	Top-1 err = 0.365479,	Top-5 err = 0.162451,	data_time = 0.050570,	train_time = 0.792245
[2019-08-24 09:10:07,781] TRAIN Iter 223440: lr = 0.127602,	loss = 2.527544,	Top-1 err = 0.362598,	Top-5 err = 0.158203,	data_time = 0.050583,	train_time = 0.796608
[2019-08-24 09:10:14,688] TRAIN Iter 223460: lr = 0.127568,	loss = 2.540357,	Top-1 err = 0.363135,	Top-5 err = 0.161621,	data_time = 0.050385,	train_time = 0.345357
[2019-08-24 09:10:30,334] TRAIN Iter 223480: lr = 0.127535,	loss = 2.530980,	Top-1 err = 0.370068,	Top-5 err = 0.160938,	data_time = 0.050862,	train_time = 0.782256
[2019-08-24 09:10:46,465] TRAIN Iter 223500: lr = 0.127502,	loss = 2.466704,	Top-1 err = 0.364941,	Top-5 err = 0.157715,	data_time = 0.050673,	train_time = 0.806541
[2019-08-24 09:10:53,675] TRAIN Iter 223520: lr = 0.127468,	loss = 2.550257,	Top-1 err = 0.364014,	Top-5 err = 0.157812,	data_time = 0.050422,	train_time = 0.360480
[2019-08-24 09:11:09,840] TRAIN Iter 223540: lr = 0.127435,	loss = 2.440424,	Top-1 err = 0.359912,	Top-5 err = 0.155469,	data_time = 0.050839,	train_time = 0.808259
[2019-08-24 09:11:16,775] TRAIN Iter 223560: lr = 0.127402,	loss = 2.448956,	Top-1 err = 0.367188,	Top-5 err = 0.161963,	data_time = 0.050787,	train_time = 0.346738
[2019-08-24 09:11:33,759] TRAIN Iter 223580: lr = 0.127368,	loss = 2.538962,	Top-1 err = 0.364355,	Top-5 err = 0.161768,	data_time = 0.050327,	train_time = 0.849162
[2019-08-24 09:11:50,236] TRAIN Iter 223600: lr = 0.127335,	loss = 2.448050,	Top-1 err = 0.363184,	Top-5 err = 0.160205,	data_time = 0.050386,	train_time = 0.823857
[2019-08-24 09:11:57,163] TRAIN Iter 223620: lr = 0.127302,	loss = 2.555988,	Top-1 err = 0.369775,	Top-5 err = 0.160498,	data_time = 0.050719,	train_time = 0.346341
[2019-08-24 09:12:13,574] TRAIN Iter 223640: lr = 0.127268,	loss = 2.528797,	Top-1 err = 0.364941,	Top-5 err = 0.159961,	data_time = 0.050897,	train_time = 0.820530
[2019-08-24 09:12:30,164] TRAIN Iter 223660: lr = 0.127235,	loss = 2.440494,	Top-1 err = 0.367188,	Top-5 err = 0.158984,	data_time = 0.050475,	train_time = 0.829491
[2019-08-24 09:12:37,145] TRAIN Iter 223680: lr = 0.127202,	loss = 2.474371,	Top-1 err = 0.362549,	Top-5 err = 0.156641,	data_time = 0.138965,	train_time = 0.349047
[2019-08-24 09:12:53,710] TRAIN Iter 223700: lr = 0.127168,	loss = 2.557590,	Top-1 err = 0.361816,	Top-5 err = 0.154932,	data_time = 0.050740,	train_time = 0.828215
[2019-08-24 09:13:00,636] TRAIN Iter 223720: lr = 0.127135,	loss = 2.507880,	Top-1 err = 0.365381,	Top-5 err = 0.159082,	data_time = 0.050545,	train_time = 0.346270
[2019-08-24 09:13:17,217] TRAIN Iter 223740: lr = 0.127102,	loss = 2.516910,	Top-1 err = 0.370605,	Top-5 err = 0.158691,	data_time = 0.050161,	train_time = 0.829039
[2019-08-24 09:13:34,461] TRAIN Iter 223760: lr = 0.127068,	loss = 2.478232,	Top-1 err = 0.368848,	Top-5 err = 0.160791,	data_time = 0.050518,	train_time = 0.862191
[2019-08-24 09:13:41,566] TRAIN Iter 223780: lr = 0.127035,	loss = 2.477312,	Top-1 err = 0.361084,	Top-5 err = 0.159131,	data_time = 0.050926,	train_time = 0.355257
[2019-08-24 09:13:58,053] TRAIN Iter 223800: lr = 0.127002,	loss = 2.534889,	Top-1 err = 0.363623,	Top-5 err = 0.161475,	data_time = 0.050391,	train_time = 0.824353
[2019-08-24 09:14:14,952] TRAIN Iter 223820: lr = 0.126968,	loss = 2.500314,	Top-1 err = 0.364551,	Top-5 err = 0.157178,	data_time = 0.050725,	train_time = 0.844915
[2019-08-24 09:14:21,663] TRAIN Iter 223840: lr = 0.126935,	loss = 2.453643,	Top-1 err = 0.371387,	Top-5 err = 0.162158,	data_time = 0.050617,	train_time = 0.335537
[2019-08-24 09:14:38,895] TRAIN Iter 223860: lr = 0.126902,	loss = 2.491253,	Top-1 err = 0.369775,	Top-5 err = 0.162549,	data_time = 0.050598,	train_time = 0.861566
[2019-08-24 09:14:45,916] TRAIN Iter 223880: lr = 0.126868,	loss = 2.483841,	Top-1 err = 0.369971,	Top-5 err = 0.160693,	data_time = 0.050384,	train_time = 0.351034
[2019-08-24 09:15:02,876] TRAIN Iter 223900: lr = 0.126835,	loss = 2.471347,	Top-1 err = 0.369482,	Top-5 err = 0.157471,	data_time = 0.050467,	train_time = 0.848020
[2019-08-24 09:15:20,326] TRAIN Iter 223920: lr = 0.126802,	loss = 2.498472,	Top-1 err = 0.362549,	Top-5 err = 0.155615,	data_time = 0.050421,	train_time = 0.872450
[2019-08-24 09:15:27,306] TRAIN Iter 223940: lr = 0.126768,	loss = 2.414389,	Top-1 err = 0.370117,	Top-5 err = 0.159912,	data_time = 0.050115,	train_time = 0.349015
[2019-08-24 09:15:45,474] TRAIN Iter 223960: lr = 0.126735,	loss = 2.506231,	Top-1 err = 0.362646,	Top-5 err = 0.159131,	data_time = 0.050175,	train_time = 0.908383
[2019-08-24 09:16:04,278] TRAIN Iter 223980: lr = 0.126702,	loss = 2.446650,	Top-1 err = 0.371338,	Top-5 err = 0.160938,	data_time = 0.066109,	train_time = 0.940177
[2019-08-24 09:16:11,256] TRAIN Iter 224000: lr = 0.126668,	loss = 2.561241,	Top-1 err = 0.366846,	Top-5 err = 0.162646,	data_time = 0.050030,	train_time = 0.348872
[2019-08-24 09:16:29,845] TRAIN Iter 224020: lr = 0.126635,	loss = 2.547416,	Top-1 err = 0.364746,	Top-5 err = 0.159082,	data_time = 0.050000,	train_time = 0.929443
[2019-08-24 09:16:36,973] TRAIN Iter 224040: lr = 0.126602,	loss = 2.526422,	Top-1 err = 0.368408,	Top-5 err = 0.160498,	data_time = 0.049967,	train_time = 0.356417
[2019-08-24 09:17:28,352] TRAIN Iter 224060: lr = 0.126568,	loss = 2.560795,	Top-1 err = 0.371288,	Top-5 err = 0.168740,	data_time = 0.050519,	train_time = 2.568891
[2019-08-24 09:17:44,208] TRAIN Iter 224080: lr = 0.126535,	loss = 2.467781,	Top-1 err = 0.355859,	Top-5 err = 0.153174,	data_time = 0.050428,	train_time = 0.792816
[2019-08-24 09:17:51,917] TRAIN Iter 224100: lr = 0.126502,	loss = 2.561546,	Top-1 err = 0.356641,	Top-5 err = 0.152100,	data_time = 0.050546,	train_time = 0.385400
[2019-08-24 09:18:06,811] TRAIN Iter 224120: lr = 0.126468,	loss = 2.530310,	Top-1 err = 0.364209,	Top-5 err = 0.156543,	data_time = 0.050490,	train_time = 0.744724
[2019-08-24 09:18:15,239] TRAIN Iter 224140: lr = 0.126435,	loss = 2.489197,	Top-1 err = 0.352441,	Top-5 err = 0.150439,	data_time = 0.050311,	train_time = 0.421385
[2019-08-24 09:18:27,753] TRAIN Iter 224160: lr = 0.126402,	loss = 2.421588,	Top-1 err = 0.361523,	Top-5 err = 0.158154,	data_time = 0.050421,	train_time = 0.625674
[2019-08-24 09:18:41,925] TRAIN Iter 224180: lr = 0.126368,	loss = 2.500800,	Top-1 err = 0.358203,	Top-5 err = 0.157129,	data_time = 0.050497,	train_time = 0.708566
[2019-08-24 09:18:49,121] TRAIN Iter 224200: lr = 0.126335,	loss = 2.414014,	Top-1 err = 0.360938,	Top-5 err = 0.154150,	data_time = 0.050645,	train_time = 0.359797
[2019-08-24 09:19:02,896] TRAIN Iter 224220: lr = 0.126302,	loss = 2.412838,	Top-1 err = 0.351611,	Top-5 err = 0.149365,	data_time = 0.050596,	train_time = 0.688717
[2019-08-24 09:19:16,765] TRAIN Iter 224240: lr = 0.126268,	loss = 2.543294,	Top-1 err = 0.361914,	Top-5 err = 0.160010,	data_time = 0.050645,	train_time = 0.693449
[2019-08-24 09:19:27,482] TRAIN Iter 224260: lr = 0.126235,	loss = 2.385187,	Top-1 err = 0.359521,	Top-5 err = 0.150146,	data_time = 0.050302,	train_time = 0.535825
[2019-08-24 09:19:39,995] TRAIN Iter 224280: lr = 0.126202,	loss = 2.500217,	Top-1 err = 0.358984,	Top-5 err = 0.154492,	data_time = 0.050696,	train_time = 0.625651
[2019-08-24 09:19:47,346] TRAIN Iter 224300: lr = 0.126168,	loss = 2.409406,	Top-1 err = 0.359131,	Top-5 err = 0.158496,	data_time = 0.050673,	train_time = 0.367556
[2019-08-24 09:20:02,217] TRAIN Iter 224320: lr = 0.126135,	loss = 2.481204,	Top-1 err = 0.361133,	Top-5 err = 0.156787,	data_time = 0.050382,	train_time = 0.743540
[2019-08-24 09:20:17,349] TRAIN Iter 224340: lr = 0.126102,	loss = 2.453839,	Top-1 err = 0.352686,	Top-5 err = 0.155518,	data_time = 0.050720,	train_time = 0.756577
[2019-08-24 09:20:24,894] TRAIN Iter 224360: lr = 0.126068,	loss = 2.387698,	Top-1 err = 0.357471,	Top-5 err = 0.150244,	data_time = 0.050398,	train_time = 0.377191
[2019-08-24 09:20:38,795] TRAIN Iter 224380: lr = 0.126035,	loss = 2.395751,	Top-1 err = 0.361475,	Top-5 err = 0.153613,	data_time = 0.050407,	train_time = 0.695047
[2019-08-24 09:20:53,180] TRAIN Iter 224400: lr = 0.126002,	loss = 2.448996,	Top-1 err = 0.362598,	Top-5 err = 0.158398,	data_time = 0.050448,	train_time = 0.719245
[2019-08-24 09:21:01,355] TRAIN Iter 224420: lr = 0.125968,	loss = 2.490853,	Top-1 err = 0.357812,	Top-5 err = 0.154541,	data_time = 0.050298,	train_time = 0.408761
[2019-08-24 09:21:16,165] TRAIN Iter 224440: lr = 0.125935,	loss = 2.413386,	Top-1 err = 0.360986,	Top-5 err = 0.157617,	data_time = 0.050590,	train_time = 0.740446
[2019-08-24 09:21:23,298] TRAIN Iter 224460: lr = 0.125902,	loss = 2.465714,	Top-1 err = 0.355469,	Top-5 err = 0.152246,	data_time = 0.050523,	train_time = 0.356678
[2019-08-24 09:21:36,809] TRAIN Iter 224480: lr = 0.125868,	loss = 2.516997,	Top-1 err = 0.354199,	Top-5 err = 0.154639,	data_time = 0.050299,	train_time = 0.675529
[2019-08-24 09:21:55,450] TRAIN Iter 224500: lr = 0.125835,	loss = 2.450487,	Top-1 err = 0.362158,	Top-5 err = 0.155518,	data_time = 0.050589,	train_time = 0.932016
[2019-08-24 09:22:03,228] TRAIN Iter 224520: lr = 0.125802,	loss = 2.476449,	Top-1 err = 0.361572,	Top-5 err = 0.159912,	data_time = 0.113003,	train_time = 0.388877
[2019-08-24 09:22:15,824] TRAIN Iter 224540: lr = 0.125768,	loss = 2.480675,	Top-1 err = 0.358496,	Top-5 err = 0.153223,	data_time = 0.050506,	train_time = 0.629790
[2019-08-24 09:22:30,993] TRAIN Iter 224560: lr = 0.125735,	loss = 2.486889,	Top-1 err = 0.361279,	Top-5 err = 0.155957,	data_time = 0.050664,	train_time = 0.758443
[2019-08-24 09:22:39,492] TRAIN Iter 224580: lr = 0.125702,	loss = 2.445569,	Top-1 err = 0.360010,	Top-5 err = 0.159863,	data_time = 0.050515,	train_time = 0.424940
[2019-08-24 09:22:54,734] TRAIN Iter 224600: lr = 0.125668,	loss = 2.505894,	Top-1 err = 0.366992,	Top-5 err = 0.163477,	data_time = 0.050894,	train_time = 0.762063
[2019-08-24 09:23:01,759] TRAIN Iter 224620: lr = 0.125635,	loss = 2.527166,	Top-1 err = 0.364258,	Top-5 err = 0.158936,	data_time = 0.050676,	train_time = 0.351272
[2019-08-24 09:23:17,421] TRAIN Iter 224640: lr = 0.125602,	loss = 2.388844,	Top-1 err = 0.366455,	Top-5 err = 0.158984,	data_time = 0.050192,	train_time = 0.783069
[2019-08-24 09:23:33,491] TRAIN Iter 224660: lr = 0.125568,	loss = 2.455371,	Top-1 err = 0.365137,	Top-5 err = 0.160254,	data_time = 0.050203,	train_time = 0.803454
[2019-08-24 09:23:40,508] TRAIN Iter 224680: lr = 0.125535,	loss = 2.443541,	Top-1 err = 0.362793,	Top-5 err = 0.153955,	data_time = 0.050759,	train_time = 0.350872
[2019-08-24 09:23:56,729] TRAIN Iter 224700: lr = 0.125502,	loss = 2.609696,	Top-1 err = 0.366504,	Top-5 err = 0.156689,	data_time = 0.050278,	train_time = 0.811033
[2019-08-24 09:24:10,749] TRAIN Iter 224720: lr = 0.125468,	loss = 2.424943,	Top-1 err = 0.360107,	Top-5 err = 0.155176,	data_time = 0.050428,	train_time = 0.700999
[2019-08-24 09:24:19,708] TRAIN Iter 224740: lr = 0.125435,	loss = 2.452393,	Top-1 err = 0.362744,	Top-5 err = 0.153467,	data_time = 0.050621,	train_time = 0.447929
[2019-08-24 09:24:35,735] TRAIN Iter 224760: lr = 0.125402,	loss = 2.460505,	Top-1 err = 0.368604,	Top-5 err = 0.159375,	data_time = 0.050347,	train_time = 0.801295
[2019-08-24 09:24:42,593] TRAIN Iter 224780: lr = 0.125368,	loss = 2.490012,	Top-1 err = 0.361768,	Top-5 err = 0.154297,	data_time = 0.050332,	train_time = 0.342893
[2019-08-24 09:24:59,418] TRAIN Iter 224800: lr = 0.125335,	loss = 2.518836,	Top-1 err = 0.365869,	Top-5 err = 0.159473,	data_time = 0.050473,	train_time = 0.841236
[2019-08-24 09:25:15,818] TRAIN Iter 224820: lr = 0.125302,	loss = 2.539577,	Top-1 err = 0.365869,	Top-5 err = 0.161670,	data_time = 0.050372,	train_time = 0.820024
[2019-08-24 09:25:22,817] TRAIN Iter 224840: lr = 0.125268,	loss = 2.556329,	Top-1 err = 0.367236,	Top-5 err = 0.159082,	data_time = 0.050856,	train_time = 0.349903
[2019-08-24 09:25:39,630] TRAIN Iter 224860: lr = 0.125235,	loss = 2.465515,	Top-1 err = 0.366895,	Top-5 err = 0.154883,	data_time = 0.050714,	train_time = 0.840668
[2019-08-24 09:25:55,417] TRAIN Iter 224880: lr = 0.125202,	loss = 2.494158,	Top-1 err = 0.357227,	Top-5 err = 0.157520,	data_time = 0.050491,	train_time = 0.789326
[2019-08-24 09:26:02,841] TRAIN Iter 224900: lr = 0.125168,	loss = 2.475905,	Top-1 err = 0.368652,	Top-5 err = 0.160400,	data_time = 0.050407,	train_time = 0.371180
[2019-08-24 09:26:19,699] TRAIN Iter 224920: lr = 0.125135,	loss = 2.433146,	Top-1 err = 0.361182,	Top-5 err = 0.156689,	data_time = 0.050524,	train_time = 0.842900
[2019-08-24 09:26:27,078] TRAIN Iter 224940: lr = 0.125102,	loss = 2.494756,	Top-1 err = 0.361621,	Top-5 err = 0.157129,	data_time = 0.050369,	train_time = 0.368936
[2019-08-24 09:26:42,498] TRAIN Iter 224960: lr = 0.125068,	loss = 2.533652,	Top-1 err = 0.368213,	Top-5 err = 0.162891,	data_time = 0.050188,	train_time = 0.770959
[2019-08-24 09:26:59,050] TRAIN Iter 224980: lr = 0.125035,	loss = 2.475427,	Top-1 err = 0.366162,	Top-5 err = 0.159570,	data_time = 0.050395,	train_time = 0.827614
[2019-08-24 09:27:06,726] TRAIN Iter 225000: lr = 0.125002,	loss = 2.507752,	Top-1 err = 0.365430,	Top-5 err = 0.160742,	data_time = 0.050578,	train_time = 0.383747
[2019-08-24 09:27:23,719] TRAIN Iter 225020: lr = 0.124968,	loss = 2.478465,	Top-1 err = 0.367334,	Top-5 err = 0.157373,	data_time = 0.050573,	train_time = 0.849644
[2019-08-24 09:27:40,679] TRAIN Iter 225040: lr = 0.124935,	loss = 2.482994,	Top-1 err = 0.361719,	Top-5 err = 0.161377,	data_time = 0.584958,	train_time = 0.848008
[2019-08-24 09:27:47,852] TRAIN Iter 225060: lr = 0.124902,	loss = 2.463978,	Top-1 err = 0.363037,	Top-5 err = 0.158398,	data_time = 0.050529,	train_time = 0.358604
[2019-08-24 09:28:06,291] TRAIN Iter 225080: lr = 0.124868,	loss = 2.452732,	Top-1 err = 0.366260,	Top-5 err = 0.158057,	data_time = 0.050354,	train_time = 0.921963
[2019-08-24 09:28:12,952] TRAIN Iter 225100: lr = 0.124835,	loss = 2.480420,	Top-1 err = 0.370801,	Top-5 err = 0.160205,	data_time = 0.050329,	train_time = 0.333055
[2019-08-24 09:28:30,859] TRAIN Iter 225120: lr = 0.124802,	loss = 2.410448,	Top-1 err = 0.363184,	Top-5 err = 0.157910,	data_time = 0.050491,	train_time = 0.895297
[2019-08-24 09:28:48,364] TRAIN Iter 225140: lr = 0.124768,	loss = 2.453430,	Top-1 err = 0.363818,	Top-5 err = 0.158740,	data_time = 0.050830,	train_time = 0.875238
[2019-08-24 09:28:55,205] TRAIN Iter 225160: lr = 0.124735,	loss = 2.488620,	Top-1 err = 0.360742,	Top-5 err = 0.154883,	data_time = 0.050135,	train_time = 0.342051
[2019-08-24 09:29:12,552] TRAIN Iter 225180: lr = 0.124702,	loss = 2.495093,	Top-1 err = 0.367871,	Top-5 err = 0.159180,	data_time = 0.050524,	train_time = 0.867347
[2019-08-24 09:29:30,944] TRAIN Iter 225200: lr = 0.124668,	loss = 2.449164,	Top-1 err = 0.369971,	Top-5 err = 0.161035,	data_time = 0.193956,	train_time = 0.919579
[2019-08-24 09:29:37,879] TRAIN Iter 225220: lr = 0.124635,	loss = 2.428233,	Top-1 err = 0.368311,	Top-5 err = 0.158496,	data_time = 0.050293,	train_time = 0.346744
[2019-08-24 09:29:57,569] TRAIN Iter 225240: lr = 0.124602,	loss = 2.441166,	Top-1 err = 0.359229,	Top-5 err = 0.158301,	data_time = 0.050079,	train_time = 0.984497
[2019-08-24 09:30:04,521] TRAIN Iter 225260: lr = 0.124568,	loss = 2.431472,	Top-1 err = 0.364893,	Top-5 err = 0.162793,	data_time = 0.049978,	train_time = 0.347573
[2019-08-24 09:30:22,154] TRAIN Iter 225280: lr = 0.124535,	loss = 2.517534,	Top-1 err = 0.364648,	Top-5 err = 0.162012,	data_time = 0.049892,	train_time = 0.881650
[2019-08-24 09:31:16,247] TRAIN Iter 225300: lr = 0.124502,	loss = 2.415769,	Top-1 err = 0.369683,	Top-5 err = 0.158733,	data_time = 0.050878,	train_time = 2.704596
[2019-08-24 09:31:23,729] TRAIN Iter 225320: lr = 0.124468,	loss = 2.480089,	Top-1 err = 0.361279,	Top-5 err = 0.156348,	data_time = 0.050468,	train_time = 0.374099
[2019-08-24 09:31:37,826] TRAIN Iter 225340: lr = 0.124435,	loss = 2.497627,	Top-1 err = 0.356885,	Top-5 err = 0.154004,	data_time = 0.050545,	train_time = 0.704829
[2019-08-24 09:31:45,732] TRAIN Iter 225360: lr = 0.124402,	loss = 2.367538,	Top-1 err = 0.354687,	Top-5 err = 0.150684,	data_time = 0.153542,	train_time = 0.395262
[2019-08-24 09:31:57,999] TRAIN Iter 225380: lr = 0.124368,	loss = 2.420852,	Top-1 err = 0.353857,	Top-5 err = 0.152686,	data_time = 0.050473,	train_time = 0.613361
[2019-08-24 09:32:12,573] TRAIN Iter 225400: lr = 0.124335,	loss = 2.412989,	Top-1 err = 0.358887,	Top-5 err = 0.156396,	data_time = 0.050573,	train_time = 0.728673
[2019-08-24 09:32:19,728] TRAIN Iter 225420: lr = 0.124302,	loss = 2.495498,	Top-1 err = 0.352539,	Top-5 err = 0.150049,	data_time = 0.050521,	train_time = 0.357729
[2019-08-24 09:32:36,437] TRAIN Iter 225440: lr = 0.124268,	loss = 2.425889,	Top-1 err = 0.352100,	Top-5 err = 0.151660,	data_time = 0.050468,	train_time = 0.835461
[2019-08-24 09:32:51,530] TRAIN Iter 225460: lr = 0.124235,	loss = 2.481534,	Top-1 err = 0.357275,	Top-5 err = 0.153467,	data_time = 0.113676,	train_time = 0.754606
[2019-08-24 09:32:58,797] TRAIN Iter 225480: lr = 0.124202,	loss = 2.474033,	Top-1 err = 0.362061,	Top-5 err = 0.151953,	data_time = 0.050522,	train_time = 0.363377
[2019-08-24 09:33:12,703] TRAIN Iter 225500: lr = 0.124168,	loss = 2.468974,	Top-1 err = 0.363672,	Top-5 err = 0.157471,	data_time = 0.050656,	train_time = 0.695265
[2019-08-24 09:33:19,685] TRAIN Iter 225520: lr = 0.124135,	loss = 2.513141,	Top-1 err = 0.355713,	Top-5 err = 0.153076,	data_time = 0.050471,	train_time = 0.349061
[2019-08-24 09:33:37,016] TRAIN Iter 225540: lr = 0.124102,	loss = 2.403022,	Top-1 err = 0.364063,	Top-5 err = 0.158008,	data_time = 0.050368,	train_time = 0.866540
[2019-08-24 09:33:52,524] TRAIN Iter 225560: lr = 0.124068,	loss = 2.507171,	Top-1 err = 0.363574,	Top-5 err = 0.156104,	data_time = 0.050467,	train_time = 0.775424
[2019-08-24 09:33:59,667] TRAIN Iter 225580: lr = 0.124035,	loss = 2.426090,	Top-1 err = 0.361475,	Top-5 err = 0.158691,	data_time = 0.050460,	train_time = 0.357106
[2019-08-24 09:34:13,716] TRAIN Iter 225600: lr = 0.124002,	loss = 2.473799,	Top-1 err = 0.361670,	Top-5 err = 0.155811,	data_time = 0.050716,	train_time = 0.702446
[2019-08-24 09:34:30,821] TRAIN Iter 225620: lr = 0.123968,	loss = 2.438883,	Top-1 err = 0.360400,	Top-5 err = 0.158008,	data_time = 1.474453,	train_time = 0.855252
[2019-08-24 09:34:38,016] TRAIN Iter 225640: lr = 0.123935,	loss = 2.501715,	Top-1 err = 0.358301,	Top-5 err = 0.153906,	data_time = 0.050358,	train_time = 0.359737
[2019-08-24 09:34:52,882] TRAIN Iter 225660: lr = 0.123902,	loss = 2.486325,	Top-1 err = 0.359912,	Top-5 err = 0.154346,	data_time = 0.050419,	train_time = 0.743261
[2019-08-24 09:34:59,945] TRAIN Iter 225680: lr = 0.123868,	loss = 2.432003,	Top-1 err = 0.357324,	Top-5 err = 0.150049,	data_time = 0.050383,	train_time = 0.353147
[2019-08-24 09:35:15,385] TRAIN Iter 225700: lr = 0.123835,	loss = 2.363911,	Top-1 err = 0.357617,	Top-5 err = 0.152832,	data_time = 0.050561,	train_time = 0.771966
[2019-08-24 09:35:30,698] TRAIN Iter 225720: lr = 0.123802,	loss = 2.471228,	Top-1 err = 0.364697,	Top-5 err = 0.155762,	data_time = 0.050668,	train_time = 0.765666
[2019-08-24 09:35:38,221] TRAIN Iter 225740: lr = 0.123768,	loss = 2.431592,	Top-1 err = 0.357959,	Top-5 err = 0.153027,	data_time = 0.050550,	train_time = 0.376112
[2019-08-24 09:35:53,259] TRAIN Iter 225760: lr = 0.123735,	loss = 2.367004,	Top-1 err = 0.358545,	Top-5 err = 0.157324,	data_time = 0.050458,	train_time = 0.751902
[2019-08-24 09:36:08,261] TRAIN Iter 225780: lr = 0.123702,	loss = 2.506539,	Top-1 err = 0.360449,	Top-5 err = 0.153613,	data_time = 0.050506,	train_time = 0.750055
[2019-08-24 09:36:15,390] TRAIN Iter 225800: lr = 0.123668,	loss = 2.428234,	Top-1 err = 0.358887,	Top-5 err = 0.157178,	data_time = 0.050482,	train_time = 0.356453
[2019-08-24 09:36:31,674] TRAIN Iter 225820: lr = 0.123635,	loss = 2.360181,	Top-1 err = 0.356201,	Top-5 err = 0.154541,	data_time = 0.050329,	train_time = 0.814177
[2019-08-24 09:36:39,129] TRAIN Iter 225840: lr = 0.123602,	loss = 2.423656,	Top-1 err = 0.363184,	Top-5 err = 0.156982,	data_time = 0.050386,	train_time = 0.372754
[2019-08-24 09:36:53,384] TRAIN Iter 225860: lr = 0.123568,	loss = 2.435799,	Top-1 err = 0.359131,	Top-5 err = 0.152832,	data_time = 0.050464,	train_time = 0.712713
[2019-08-24 09:37:09,182] TRAIN Iter 225880: lr = 0.123535,	loss = 2.520513,	Top-1 err = 0.361182,	Top-5 err = 0.158301,	data_time = 0.050483,	train_time = 0.789874
[2019-08-24 09:37:16,274] TRAIN Iter 225900: lr = 0.123502,	loss = 2.421745,	Top-1 err = 0.364404,	Top-5 err = 0.156348,	data_time = 0.050648,	train_time = 0.354593
[2019-08-24 09:37:32,218] TRAIN Iter 225920: lr = 0.123468,	loss = 2.496660,	Top-1 err = 0.359766,	Top-5 err = 0.156885,	data_time = 0.050265,	train_time = 0.797223
[2019-08-24 09:37:48,534] TRAIN Iter 225940: lr = 0.123435,	loss = 2.451086,	Top-1 err = 0.363525,	Top-5 err = 0.154297,	data_time = 0.138580,	train_time = 0.815758
[2019-08-24 09:37:55,570] TRAIN Iter 225960: lr = 0.123402,	loss = 2.518987,	Top-1 err = 0.363672,	Top-5 err = 0.159033,	data_time = 0.050520,	train_time = 0.351764
[2019-08-24 09:38:11,423] TRAIN Iter 225980: lr = 0.123368,	loss = 2.477401,	Top-1 err = 0.364404,	Top-5 err = 0.159277,	data_time = 0.050985,	train_time = 0.792657
[2019-08-24 09:38:19,394] TRAIN Iter 226000: lr = 0.123335,	loss = 2.464912,	Top-1 err = 0.367529,	Top-5 err = 0.158643,	data_time = 0.050732,	train_time = 0.398551
[2019-08-24 09:38:34,259] TRAIN Iter 226020: lr = 0.123302,	loss = 2.393849,	Top-1 err = 0.362842,	Top-5 err = 0.157129,	data_time = 0.050810,	train_time = 0.743205
[2019-08-24 09:38:50,169] TRAIN Iter 226040: lr = 0.123268,	loss = 2.420103,	Top-1 err = 0.366650,	Top-5 err = 0.157178,	data_time = 0.050537,	train_time = 0.795491
[2019-08-24 09:38:57,246] TRAIN Iter 226060: lr = 0.123235,	loss = 2.413579,	Top-1 err = 0.359229,	Top-5 err = 0.152344,	data_time = 0.050333,	train_time = 0.353830
[2019-08-24 09:39:13,896] TRAIN Iter 226080: lr = 0.123202,	loss = 2.552587,	Top-1 err = 0.367529,	Top-5 err = 0.157764,	data_time = 0.050533,	train_time = 0.832495
[2019-08-24 09:39:29,738] TRAIN Iter 226100: lr = 0.123168,	loss = 2.421541,	Top-1 err = 0.364941,	Top-5 err = 0.160840,	data_time = 0.050607,	train_time = 0.792105
[2019-08-24 09:39:36,362] TRAIN Iter 226120: lr = 0.123135,	loss = 2.483875,	Top-1 err = 0.369385,	Top-5 err = 0.158789,	data_time = 0.050572,	train_time = 0.331190
[2019-08-24 09:39:53,935] TRAIN Iter 226140: lr = 0.123102,	loss = 2.541152,	Top-1 err = 0.360742,	Top-5 err = 0.157275,	data_time = 0.050382,	train_time = 0.878602
[2019-08-24 09:40:01,363] TRAIN Iter 226160: lr = 0.123068,	loss = 2.378006,	Top-1 err = 0.360254,	Top-5 err = 0.159619,	data_time = 0.050300,	train_time = 0.371405
[2019-08-24 09:40:16,838] TRAIN Iter 226180: lr = 0.123035,	loss = 2.502893,	Top-1 err = 0.365527,	Top-5 err = 0.159521,	data_time = 0.050560,	train_time = 0.773718
[2019-08-24 09:40:33,286] TRAIN Iter 226200: lr = 0.123002,	loss = 2.592432,	Top-1 err = 0.361426,	Top-5 err = 0.160059,	data_time = 0.050471,	train_time = 0.822415
[2019-08-24 09:40:39,931] TRAIN Iter 226220: lr = 0.122968,	loss = 2.521482,	Top-1 err = 0.362695,	Top-5 err = 0.159424,	data_time = 0.050437,	train_time = 0.332244
[2019-08-24 09:40:58,151] TRAIN Iter 226240: lr = 0.122935,	loss = 2.496982,	Top-1 err = 0.368750,	Top-5 err = 0.161768,	data_time = 0.050613,	train_time = 0.910955
[2019-08-24 09:41:13,419] TRAIN Iter 226260: lr = 0.122902,	loss = 2.572119,	Top-1 err = 0.364307,	Top-5 err = 0.160889,	data_time = 0.161614,	train_time = 0.763424
[2019-08-24 09:41:20,415] TRAIN Iter 226280: lr = 0.122868,	loss = 2.446391,	Top-1 err = 0.359863,	Top-5 err = 0.155811,	data_time = 0.050515,	train_time = 0.349776
[2019-08-24 09:41:36,682] TRAIN Iter 226300: lr = 0.122835,	loss = 2.461163,	Top-1 err = 0.360889,	Top-5 err = 0.158447,	data_time = 0.050456,	train_time = 0.813302
[2019-08-24 09:41:44,243] TRAIN Iter 226320: lr = 0.122802,	loss = 2.458750,	Top-1 err = 0.369141,	Top-5 err = 0.162158,	data_time = 0.168176,	train_time = 0.378072
[2019-08-24 09:41:59,824] TRAIN Iter 226340: lr = 0.122768,	loss = 2.497674,	Top-1 err = 0.361182,	Top-5 err = 0.157910,	data_time = 0.050395,	train_time = 0.779008
[2019-08-24 09:42:16,139] TRAIN Iter 226360: lr = 0.122735,	loss = 2.498207,	Top-1 err = 0.363525,	Top-5 err = 0.159375,	data_time = 0.050483,	train_time = 0.815746
[2019-08-24 09:42:22,663] TRAIN Iter 226380: lr = 0.122702,	loss = 2.558955,	Top-1 err = 0.366992,	Top-5 err = 0.157568,	data_time = 0.050442,	train_time = 0.326184
[2019-08-24 09:42:40,219] TRAIN Iter 226400: lr = 0.122668,	loss = 2.454959,	Top-1 err = 0.365039,	Top-5 err = 0.156396,	data_time = 0.050350,	train_time = 0.877799
[2019-08-24 09:42:57,889] TRAIN Iter 226420: lr = 0.122635,	loss = 2.515917,	Top-1 err = 0.363916,	Top-5 err = 0.158740,	data_time = 0.050227,	train_time = 0.883468
[2019-08-24 09:43:04,681] TRAIN Iter 226440: lr = 0.122602,	loss = 2.506691,	Top-1 err = 0.367090,	Top-5 err = 0.161279,	data_time = 0.050373,	train_time = 0.339602
[2019-08-24 09:43:24,350] TRAIN Iter 226460: lr = 0.122568,	loss = 2.483971,	Top-1 err = 0.364307,	Top-5 err = 0.156201,	data_time = 0.050252,	train_time = 0.983427
[2019-08-24 09:43:31,511] TRAIN Iter 226480: lr = 0.122535,	loss = 2.465361,	Top-1 err = 0.361816,	Top-5 err = 0.157324,	data_time = 0.050874,	train_time = 0.358015
[2019-08-24 09:43:47,755] TRAIN Iter 226500: lr = 0.122502,	loss = 2.494950,	Top-1 err = 0.360254,	Top-5 err = 0.155127,	data_time = 0.050083,	train_time = 0.812223
[2019-08-24 09:44:03,387] TRAIN Iter 226520: lr = 0.122468,	loss = 2.571857,	Top-1 err = 0.363184,	Top-5 err = 0.157959,	data_time = 0.049933,	train_time = 0.781585
[2019-08-24 09:44:09,451] TRAIN Iter 226540: lr = 0.122435,	loss = 2.523133,	Top-1 err = 0.361230,	Top-5 err = 0.157666,	data_time = 0.049905,	train_time = 0.303188
[2019-08-24 09:45:01,390] TRAIN Iter 226560: lr = 0.122402,	loss = 2.423921,	Top-1 err = 0.373775,	Top-5 err = 0.164904,	data_time = 0.050495,	train_time = 2.596913
[2019-08-24 09:45:08,873] TRAIN Iter 226580: lr = 0.122368,	loss = 2.422527,	Top-1 err = 0.355322,	Top-5 err = 0.157031,	data_time = 0.050297,	train_time = 0.374163
[2019-08-24 09:45:23,952] TRAIN Iter 226600: lr = 0.122335,	loss = 2.478100,	Top-1 err = 0.352881,	Top-5 err = 0.150879,	data_time = 0.050403,	train_time = 0.753933
[2019-08-24 09:45:39,777] TRAIN Iter 226620: lr = 0.122302,	loss = 2.423601,	Top-1 err = 0.353271,	Top-5 err = 0.152881,	data_time = 0.050444,	train_time = 0.791226
[2019-08-24 09:45:47,306] TRAIN Iter 226640: lr = 0.122268,	loss = 2.421965,	Top-1 err = 0.348242,	Top-5 err = 0.148926,	data_time = 0.050374,	train_time = 0.376425
[2019-08-24 09:46:01,976] TRAIN Iter 226660: lr = 0.122235,	loss = 2.397826,	Top-1 err = 0.357080,	Top-5 err = 0.152393,	data_time = 0.050433,	train_time = 0.733478
[2019-08-24 09:46:15,882] TRAIN Iter 226680: lr = 0.122202,	loss = 2.398110,	Top-1 err = 0.352441,	Top-5 err = 0.152686,	data_time = 0.140109,	train_time = 0.695298
[2019-08-24 09:46:23,025] TRAIN Iter 226700: lr = 0.122168,	loss = 2.404737,	Top-1 err = 0.357666,	Top-5 err = 0.154346,	data_time = 0.050344,	train_time = 0.357145
[2019-08-24 09:46:36,474] TRAIN Iter 226720: lr = 0.122135,	loss = 2.477210,	Top-1 err = 0.361084,	Top-5 err = 0.153027,	data_time = 0.050615,	train_time = 0.672435
[2019-08-24 09:46:43,478] TRAIN Iter 226740: lr = 0.122102,	loss = 2.491158,	Top-1 err = 0.360986,	Top-5 err = 0.152441,	data_time = 0.050836,	train_time = 0.350189
[2019-08-24 09:46:58,409] TRAIN Iter 226760: lr = 0.122068,	loss = 2.466962,	Top-1 err = 0.353857,	Top-5 err = 0.151416,	data_time = 0.050438,	train_time = 0.746521
[2019-08-24 09:47:13,827] TRAIN Iter 226780: lr = 0.122035,	loss = 2.462124,	Top-1 err = 0.358740,	Top-5 err = 0.153320,	data_time = 3.150095,	train_time = 0.770790
[2019-08-24 09:47:20,703] TRAIN Iter 226800: lr = 0.122002,	loss = 2.443916,	Top-1 err = 0.358350,	Top-5 err = 0.155225,	data_time = 0.050454,	train_time = 0.343798
[2019-08-24 09:47:35,299] TRAIN Iter 226820: lr = 0.121968,	loss = 2.433459,	Top-1 err = 0.356055,	Top-5 err = 0.152979,	data_time = 0.050651,	train_time = 0.729780
[2019-08-24 09:47:46,421] TRAIN Iter 226840: lr = 0.121935,	loss = 2.528710,	Top-1 err = 0.357373,	Top-5 err = 0.157031,	data_time = 0.050996,	train_time = 0.556109
[2019-08-24 09:47:57,424] TRAIN Iter 226860: lr = 0.121902,	loss = 2.439151,	Top-1 err = 0.356396,	Top-5 err = 0.154834,	data_time = 0.050334,	train_time = 0.550103
[2019-08-24 09:48:11,262] TRAIN Iter 226880: lr = 0.121868,	loss = 2.294228,	Top-1 err = 0.360547,	Top-5 err = 0.156104,	data_time = 0.117691,	train_time = 0.691899
[2019-08-24 09:48:18,355] TRAIN Iter 226900: lr = 0.121835,	loss = 2.466751,	Top-1 err = 0.353320,	Top-5 err = 0.152295,	data_time = 0.050393,	train_time = 0.354644
[2019-08-24 09:48:33,772] TRAIN Iter 226920: lr = 0.121802,	loss = 2.354616,	Top-1 err = 0.355273,	Top-5 err = 0.152051,	data_time = 0.050546,	train_time = 0.770836
[2019-08-24 09:48:50,771] TRAIN Iter 226940: lr = 0.121768,	loss = 2.528482,	Top-1 err = 0.362451,	Top-5 err = 0.156641,	data_time = 5.449712,	train_time = 0.849939
[2019-08-24 09:48:58,083] TRAIN Iter 226960: lr = 0.121735,	loss = 2.563372,	Top-1 err = 0.359473,	Top-5 err = 0.156445,	data_time = 0.050375,	train_time = 0.365561
[2019-08-24 09:49:12,081] TRAIN Iter 226980: lr = 0.121702,	loss = 2.435021,	Top-1 err = 0.358936,	Top-5 err = 0.157275,	data_time = 0.050638,	train_time = 0.699915
[2019-08-24 09:49:21,732] TRAIN Iter 227000: lr = 0.121668,	loss = 2.454257,	Top-1 err = 0.359326,	Top-5 err = 0.155420,	data_time = 0.050734,	train_time = 0.482536
[2019-08-24 09:49:34,412] TRAIN Iter 227020: lr = 0.121635,	loss = 2.495638,	Top-1 err = 0.360742,	Top-5 err = 0.155176,	data_time = 0.050917,	train_time = 0.633993
[2019-08-24 09:49:47,497] TRAIN Iter 227040: lr = 0.121602,	loss = 2.466722,	Top-1 err = 0.365820,	Top-5 err = 0.156787,	data_time = 0.050179,	train_time = 0.654213
[2019-08-24 09:49:55,861] TRAIN Iter 227060: lr = 0.121568,	loss = 2.515553,	Top-1 err = 0.363867,	Top-5 err = 0.157031,	data_time = 0.050440,	train_time = 0.418179
[2019-08-24 09:50:11,603] TRAIN Iter 227080: lr = 0.121535,	loss = 2.489841,	Top-1 err = 0.364453,	Top-5 err = 0.160156,	data_time = 0.050443,	train_time = 0.787084
[2019-08-24 09:50:25,934] TRAIN Iter 227100: lr = 0.121502,	loss = 2.472472,	Top-1 err = 0.362207,	Top-5 err = 0.159326,	data_time = 1.579376,	train_time = 0.716528
[2019-08-24 09:50:34,280] TRAIN Iter 227120: lr = 0.121468,	loss = 2.481412,	Top-1 err = 0.367188,	Top-5 err = 0.160400,	data_time = 0.050574,	train_time = 0.417330
[2019-08-24 09:50:50,268] TRAIN Iter 227140: lr = 0.121435,	loss = 2.493988,	Top-1 err = 0.366309,	Top-5 err = 0.159082,	data_time = 0.050692,	train_time = 0.799378
[2019-08-24 09:50:59,379] TRAIN Iter 227160: lr = 0.121402,	loss = 2.425663,	Top-1 err = 0.356836,	Top-5 err = 0.152295,	data_time = 0.646424,	train_time = 0.455508
[2019-08-24 09:51:13,281] TRAIN Iter 227180: lr = 0.121368,	loss = 2.430323,	Top-1 err = 0.359961,	Top-5 err = 0.156641,	data_time = 0.050620,	train_time = 0.695078
[2019-08-24 09:51:29,615] TRAIN Iter 227200: lr = 0.121335,	loss = 2.470000,	Top-1 err = 0.361572,	Top-5 err = 0.151758,	data_time = 0.050620,	train_time = 0.816724
[2019-08-24 09:51:36,335] TRAIN Iter 227220: lr = 0.121302,	loss = 2.521927,	Top-1 err = 0.361719,	Top-5 err = 0.157373,	data_time = 0.050449,	train_time = 0.335951
[2019-08-24 09:51:53,003] TRAIN Iter 227240: lr = 0.121268,	loss = 2.563312,	Top-1 err = 0.359668,	Top-5 err = 0.154590,	data_time = 0.050500,	train_time = 0.833386
[2019-08-24 09:52:09,427] TRAIN Iter 227260: lr = 0.121235,	loss = 2.428243,	Top-1 err = 0.359082,	Top-5 err = 0.150635,	data_time = 1.220610,	train_time = 0.821228
[2019-08-24 09:52:16,396] TRAIN Iter 227280: lr = 0.121202,	loss = 2.502199,	Top-1 err = 0.356689,	Top-5 err = 0.157324,	data_time = 0.151696,	train_time = 0.348435
[2019-08-24 09:52:32,146] TRAIN Iter 227300: lr = 0.121168,	loss = 2.474176,	Top-1 err = 0.361084,	Top-5 err = 0.158887,	data_time = 0.050243,	train_time = 0.787481
[2019-08-24 09:52:40,429] TRAIN Iter 227320: lr = 0.121135,	loss = 2.470846,	Top-1 err = 0.359375,	Top-5 err = 0.157568,	data_time = 0.199914,	train_time = 0.414108
[2019-08-24 09:52:54,676] TRAIN Iter 227340: lr = 0.121102,	loss = 2.485800,	Top-1 err = 0.356592,	Top-5 err = 0.158057,	data_time = 0.050534,	train_time = 0.712349
[2019-08-24 09:53:11,012] TRAIN Iter 227360: lr = 0.121068,	loss = 2.424722,	Top-1 err = 0.362842,	Top-5 err = 0.155859,	data_time = 0.050620,	train_time = 0.816787
[2019-08-24 09:53:17,776] TRAIN Iter 227380: lr = 0.121035,	loss = 2.471338,	Top-1 err = 0.356348,	Top-5 err = 0.153516,	data_time = 0.107317,	train_time = 0.338164
[2019-08-24 09:53:34,604] TRAIN Iter 227400: lr = 0.121002,	loss = 2.464920,	Top-1 err = 0.362793,	Top-5 err = 0.152979,	data_time = 0.050485,	train_time = 0.841413
[2019-08-24 09:53:51,411] TRAIN Iter 227420: lr = 0.120968,	loss = 2.476366,	Top-1 err = 0.364307,	Top-5 err = 0.155225,	data_time = 1.265869,	train_time = 0.840298
[2019-08-24 09:53:58,155] TRAIN Iter 227440: lr = 0.120935,	loss = 2.454529,	Top-1 err = 0.367334,	Top-5 err = 0.158789,	data_time = 0.050590,	train_time = 0.337224
[2019-08-24 09:54:14,671] TRAIN Iter 227460: lr = 0.120902,	loss = 2.563838,	Top-1 err = 0.364014,	Top-5 err = 0.157861,	data_time = 0.050443,	train_time = 0.825776
[2019-08-24 09:54:24,515] TRAIN Iter 227480: lr = 0.120868,	loss = 2.477437,	Top-1 err = 0.361914,	Top-5 err = 0.158789,	data_time = 1.289006,	train_time = 0.492191
[2019-08-24 09:54:37,902] TRAIN Iter 227500: lr = 0.120835,	loss = 2.429379,	Top-1 err = 0.356787,	Top-5 err = 0.154932,	data_time = 0.050510,	train_time = 0.669323
[2019-08-24 09:54:55,752] TRAIN Iter 227520: lr = 0.120802,	loss = 2.478801,	Top-1 err = 0.362598,	Top-5 err = 0.153516,	data_time = 0.050536,	train_time = 0.892492
[2019-08-24 09:55:02,915] TRAIN Iter 227540: lr = 0.120768,	loss = 2.518153,	Top-1 err = 0.363232,	Top-5 err = 0.156934,	data_time = 0.050585,	train_time = 0.358143
[2019-08-24 09:55:19,400] TRAIN Iter 227560: lr = 0.120735,	loss = 2.369517,	Top-1 err = 0.364893,	Top-5 err = 0.155859,	data_time = 0.050154,	train_time = 0.824226
[2019-08-24 09:55:37,496] TRAIN Iter 227580: lr = 0.120702,	loss = 2.445560,	Top-1 err = 0.367773,	Top-5 err = 0.160010,	data_time = 0.603463,	train_time = 0.904788
[2019-08-24 09:55:44,375] TRAIN Iter 227600: lr = 0.120668,	loss = 2.537495,	Top-1 err = 0.362988,	Top-5 err = 0.157666,	data_time = 0.050597,	train_time = 0.343919
[2019-08-24 09:56:02,080] TRAIN Iter 227620: lr = 0.120635,	loss = 2.507196,	Top-1 err = 0.363574,	Top-5 err = 0.156885,	data_time = 0.050379,	train_time = 0.885247
[2019-08-24 09:56:18,201] TRAIN Iter 227640: lr = 0.120602,	loss = 2.447592,	Top-1 err = 0.358545,	Top-5 err = 0.157666,	data_time = 3.820849,	train_time = 0.806036
[2019-08-24 09:56:27,556] TRAIN Iter 227660: lr = 0.120568,	loss = 2.531208,	Top-1 err = 0.364453,	Top-5 err = 0.156543,	data_time = 0.050640,	train_time = 0.467770
[2019-08-24 09:56:44,724] TRAIN Iter 227680: lr = 0.120535,	loss = 2.559656,	Top-1 err = 0.363965,	Top-5 err = 0.159912,	data_time = 0.050498,	train_time = 0.858355
[2019-08-24 09:56:51,454] TRAIN Iter 227700: lr = 0.120502,	loss = 2.602561,	Top-1 err = 0.366797,	Top-5 err = 0.159521,	data_time = 0.050447,	train_time = 0.336473
[2019-08-24 09:57:09,328] TRAIN Iter 227720: lr = 0.120468,	loss = 2.414185,	Top-1 err = 0.360938,	Top-5 err = 0.155859,	data_time = 0.050510,	train_time = 0.893685
[2019-08-24 09:57:28,207] TRAIN Iter 227740: lr = 0.120435,	loss = 2.523516,	Top-1 err = 0.368555,	Top-5 err = 0.160400,	data_time = 0.049978,	train_time = 0.943937
[2019-08-24 09:57:35,440] TRAIN Iter 227760: lr = 0.120402,	loss = 2.476823,	Top-1 err = 0.366943,	Top-5 err = 0.161670,	data_time = 0.050140,	train_time = 0.361632
[2019-08-24 09:57:50,788] TRAIN Iter 227780: lr = 0.120368,	loss = 2.528895,	Top-1 err = 0.365967,	Top-5 err = 0.159863,	data_time = 0.049880,	train_time = 0.767382
[2019-08-24 09:57:58,435] TRAIN Iter 227800: lr = 0.120335,	loss = 3.020311,	Top-1 err = 0.370376,	Top-5 err = 0.164273,	data_time = 0.007123,	train_time = 0.382350
[2019-08-24 09:58:46,327] TRAIN Iter 227820: lr = 0.120302,	loss = 2.506022,	Top-1 err = 0.359375,	Top-5 err = 0.154297,	data_time = 0.050338,	train_time = 2.394572
[2019-08-24 09:59:01,817] TRAIN Iter 227840: lr = 0.120268,	loss = 2.522228,	Top-1 err = 0.351562,	Top-5 err = 0.149121,	data_time = 0.050388,	train_time = 0.774488
[2019-08-24 09:59:09,432] TRAIN Iter 227860: lr = 0.120235,	loss = 2.440685,	Top-1 err = 0.355615,	Top-5 err = 0.150342,	data_time = 0.050452,	train_time = 0.380746
[2019-08-24 09:59:22,679] TRAIN Iter 227880: lr = 0.120202,	loss = 2.362756,	Top-1 err = 0.350830,	Top-5 err = 0.155078,	data_time = 0.050303,	train_time = 0.662361
[2019-08-24 09:59:37,619] TRAIN Iter 227900: lr = 0.120168,	loss = 2.473917,	Top-1 err = 0.357520,	Top-5 err = 0.154736,	data_time = 0.051037,	train_time = 0.746969
[2019-08-24 09:59:44,615] TRAIN Iter 227920: lr = 0.120135,	loss = 2.489639,	Top-1 err = 0.355518,	Top-5 err = 0.151416,	data_time = 0.050684,	train_time = 0.349788
[2019-08-24 09:59:59,780] TRAIN Iter 227940: lr = 0.120102,	loss = 2.553943,	Top-1 err = 0.361523,	Top-5 err = 0.154834,	data_time = 0.050693,	train_time = 0.758233
[2019-08-24 10:00:07,023] TRAIN Iter 227960: lr = 0.120068,	loss = 2.532110,	Top-1 err = 0.359473,	Top-5 err = 0.153418,	data_time = 0.051093,	train_time = 0.362118
[2019-08-24 10:00:21,972] TRAIN Iter 227980: lr = 0.120035,	loss = 2.468146,	Top-1 err = 0.358398,	Top-5 err = 0.152490,	data_time = 0.050533,	train_time = 0.747436
[2019-08-24 10:00:37,504] TRAIN Iter 228000: lr = 0.120002,	loss = 2.439679,	Top-1 err = 0.357178,	Top-5 err = 0.157373,	data_time = 0.050325,	train_time = 0.776617
[2019-08-24 10:00:44,570] TRAIN Iter 228020: lr = 0.119968,	loss = 2.476895,	Top-1 err = 0.361475,	Top-5 err = 0.156104,	data_time = 0.050524,	train_time = 0.353267
[2019-08-24 10:00:58,810] TRAIN Iter 228040: lr = 0.119935,	loss = 2.432077,	Top-1 err = 0.355664,	Top-5 err = 0.149463,	data_time = 0.050489,	train_time = 0.711998
[2019-08-24 10:01:15,209] TRAIN Iter 228060: lr = 0.119902,	loss = 2.441216,	Top-1 err = 0.350635,	Top-5 err = 0.149658,	data_time = 0.143937,	train_time = 0.819913
[2019-08-24 10:01:21,688] TRAIN Iter 228080: lr = 0.119868,	loss = 2.386460,	Top-1 err = 0.352344,	Top-5 err = 0.151123,	data_time = 0.050311,	train_time = 0.323933
[2019-08-24 10:01:37,586] TRAIN Iter 228100: lr = 0.119835,	loss = 2.411327,	Top-1 err = 0.363477,	Top-5 err = 0.158008,	data_time = 0.050327,	train_time = 0.794909
[2019-08-24 10:01:45,129] TRAIN Iter 228120: lr = 0.119802,	loss = 2.576174,	Top-1 err = 0.357959,	Top-5 err = 0.156201,	data_time = 0.050414,	train_time = 0.377124
[2019-08-24 10:02:00,468] TRAIN Iter 228140: lr = 0.119768,	loss = 2.480323,	Top-1 err = 0.359912,	Top-5 err = 0.158301,	data_time = 0.144114,	train_time = 0.766929
[2019-08-24 10:02:16,246] TRAIN Iter 228160: lr = 0.119735,	loss = 2.463683,	Top-1 err = 0.356885,	Top-5 err = 0.152734,	data_time = 0.050609,	train_time = 0.788895
[2019-08-24 10:02:23,820] TRAIN Iter 228180: lr = 0.119702,	loss = 2.475049,	Top-1 err = 0.360059,	Top-5 err = 0.154785,	data_time = 0.050966,	train_time = 0.378713
[2019-08-24 10:02:35,878] TRAIN Iter 228200: lr = 0.119668,	loss = 2.484245,	Top-1 err = 0.357373,	Top-5 err = 0.153662,	data_time = 0.050475,	train_time = 0.602875
[2019-08-24 10:02:51,532] TRAIN Iter 228220: lr = 0.119635,	loss = 2.508100,	Top-1 err = 0.356787,	Top-5 err = 0.149756,	data_time = 0.050705,	train_time = 0.782687
[2019-08-24 10:02:58,323] TRAIN Iter 228240: lr = 0.119602,	loss = 2.430383,	Top-1 err = 0.361963,	Top-5 err = 0.155225,	data_time = 0.050618,	train_time = 0.339530
[2019-08-24 10:03:13,446] TRAIN Iter 228260: lr = 0.119568,	loss = 2.456311,	Top-1 err = 0.355811,	Top-5 err = 0.153174,	data_time = 0.050459,	train_time = 0.756112
[2019-08-24 10:03:20,496] TRAIN Iter 228280: lr = 0.119535,	loss = 2.424569,	Top-1 err = 0.359814,	Top-5 err = 0.155469,	data_time = 0.050374,	train_time = 0.352510
[2019-08-24 10:03:36,287] TRAIN Iter 228300: lr = 0.119502,	loss = 2.398034,	Top-1 err = 0.359229,	Top-5 err = 0.155176,	data_time = 0.050529,	train_time = 0.789512
[2019-08-24 10:03:53,254] TRAIN Iter 228320: lr = 0.119468,	loss = 2.428184,	Top-1 err = 0.358545,	Top-5 err = 0.155273,	data_time = 0.050391,	train_time = 0.848376
[2019-08-24 10:04:00,284] TRAIN Iter 228340: lr = 0.119435,	loss = 2.490607,	Top-1 err = 0.356689,	Top-5 err = 0.155566,	data_time = 0.050601,	train_time = 0.351485
[2019-08-24 10:04:15,027] TRAIN Iter 228360: lr = 0.119402,	loss = 2.515483,	Top-1 err = 0.366064,	Top-5 err = 0.158740,	data_time = 0.050442,	train_time = 0.737123
[2019-08-24 10:04:30,831] TRAIN Iter 228380: lr = 0.119368,	loss = 2.476670,	Top-1 err = 0.360254,	Top-5 err = 0.152344,	data_time = 0.050868,	train_time = 0.790182
[2019-08-24 10:04:37,939] TRAIN Iter 228400: lr = 0.119335,	loss = 2.409254,	Top-1 err = 0.359424,	Top-5 err = 0.153760,	data_time = 0.050508,	train_time = 0.355385
[2019-08-24 10:04:54,551] TRAIN Iter 228420: lr = 0.119302,	loss = 2.523998,	Top-1 err = 0.362061,	Top-5 err = 0.157227,	data_time = 0.050431,	train_time = 0.830588
[2019-08-24 10:05:01,755] TRAIN Iter 228440: lr = 0.119268,	loss = 2.469679,	Top-1 err = 0.363672,	Top-5 err = 0.152930,	data_time = 0.050445,	train_time = 0.360157
[2019-08-24 10:05:17,272] TRAIN Iter 228460: lr = 0.119235,	loss = 2.484828,	Top-1 err = 0.360938,	Top-5 err = 0.157324,	data_time = 0.050316,	train_time = 0.775834
[2019-08-24 10:05:34,204] TRAIN Iter 228480: lr = 0.119202,	loss = 2.477864,	Top-1 err = 0.358740,	Top-5 err = 0.155762,	data_time = 0.050505,	train_time = 0.846611
[2019-08-24 10:05:41,281] TRAIN Iter 228500: lr = 0.119168,	loss = 2.430868,	Top-1 err = 0.371094,	Top-5 err = 0.158057,	data_time = 0.050569,	train_time = 0.353815
[2019-08-24 10:05:56,831] TRAIN Iter 228520: lr = 0.119135,	loss = 2.427696,	Top-1 err = 0.363037,	Top-5 err = 0.151904,	data_time = 0.050389,	train_time = 0.777516
[2019-08-24 10:06:13,387] TRAIN Iter 228540: lr = 0.119102,	loss = 2.456029,	Top-1 err = 0.357910,	Top-5 err = 0.152588,	data_time = 0.050395,	train_time = 0.827757
[2019-08-24 10:06:20,171] TRAIN Iter 228560: lr = 0.119068,	loss = 2.476790,	Top-1 err = 0.360938,	Top-5 err = 0.155518,	data_time = 0.050394,	train_time = 0.339177
[2019-08-24 10:06:35,836] TRAIN Iter 228580: lr = 0.119035,	loss = 2.547949,	Top-1 err = 0.360303,	Top-5 err = 0.155811,	data_time = 0.050425,	train_time = 0.783258
[2019-08-24 10:06:43,428] TRAIN Iter 228600: lr = 0.119002,	loss = 2.504770,	Top-1 err = 0.361914,	Top-5 err = 0.157812,	data_time = 0.050207,	train_time = 0.379593
[2019-08-24 10:06:57,847] TRAIN Iter 228620: lr = 0.118968,	loss = 2.489914,	Top-1 err = 0.358887,	Top-5 err = 0.158740,	data_time = 0.050499,	train_time = 0.720917
[2019-08-24 10:07:15,808] TRAIN Iter 228640: lr = 0.118935,	loss = 2.513944,	Top-1 err = 0.359326,	Top-5 err = 0.156152,	data_time = 0.050826,	train_time = 0.898025
[2019-08-24 10:07:22,845] TRAIN Iter 228660: lr = 0.118902,	loss = 2.477574,	Top-1 err = 0.360254,	Top-5 err = 0.152979,	data_time = 0.050808,	train_time = 0.351871
[2019-08-24 10:07:37,396] TRAIN Iter 228680: lr = 0.118868,	loss = 2.456472,	Top-1 err = 0.362256,	Top-5 err = 0.159619,	data_time = 0.050273,	train_time = 0.727521
[2019-08-24 10:07:54,908] TRAIN Iter 228700: lr = 0.118835,	loss = 2.443185,	Top-1 err = 0.357715,	Top-5 err = 0.154102,	data_time = 0.050883,	train_time = 0.875591
[2019-08-24 10:08:01,589] TRAIN Iter 228720: lr = 0.118802,	loss = 2.421540,	Top-1 err = 0.359717,	Top-5 err = 0.154590,	data_time = 0.050270,	train_time = 0.334047
[2019-08-24 10:08:18,087] TRAIN Iter 228740: lr = 0.118768,	loss = 2.492810,	Top-1 err = 0.364600,	Top-5 err = 0.153955,	data_time = 0.050547,	train_time = 0.824856
[2019-08-24 10:08:25,420] TRAIN Iter 228760: lr = 0.118735,	loss = 2.516459,	Top-1 err = 0.361475,	Top-5 err = 0.154004,	data_time = 0.050403,	train_time = 0.366633
[2019-08-24 10:08:41,668] TRAIN Iter 228780: lr = 0.118702,	loss = 2.537263,	Top-1 err = 0.365771,	Top-5 err = 0.158887,	data_time = 0.050520,	train_time = 0.812392
[2019-08-24 10:08:58,084] TRAIN Iter 228800: lr = 0.118668,	loss = 2.512996,	Top-1 err = 0.361475,	Top-5 err = 0.159766,	data_time = 0.050625,	train_time = 0.820813
[2019-08-24 10:09:05,155] TRAIN Iter 228820: lr = 0.118635,	loss = 2.414285,	Top-1 err = 0.362598,	Top-5 err = 0.157617,	data_time = 0.050822,	train_time = 0.353541
[2019-08-24 10:09:20,810] TRAIN Iter 228840: lr = 0.118602,	loss = 2.488673,	Top-1 err = 0.358887,	Top-5 err = 0.152686,	data_time = 0.050448,	train_time = 0.782735
[2019-08-24 10:09:37,868] TRAIN Iter 228860: lr = 0.118568,	loss = 2.382966,	Top-1 err = 0.367871,	Top-5 err = 0.161768,	data_time = 0.050704,	train_time = 0.852887
[2019-08-24 10:09:44,604] TRAIN Iter 228880: lr = 0.118535,	loss = 2.422604,	Top-1 err = 0.363086,	Top-5 err = 0.156641,	data_time = 0.050262,	train_time = 0.336789
[2019-08-24 10:10:00,720] TRAIN Iter 228900: lr = 0.118502,	loss = 2.534124,	Top-1 err = 0.359863,	Top-5 err = 0.159131,	data_time = 0.050365,	train_time = 0.805752
[2019-08-24 10:10:08,505] TRAIN Iter 228920: lr = 0.118468,	loss = 2.457831,	Top-1 err = 0.367188,	Top-5 err = 0.160059,	data_time = 0.051046,	train_time = 0.389247
[2019-08-24 10:10:23,215] TRAIN Iter 228940: lr = 0.118435,	loss = 2.524707,	Top-1 err = 0.360645,	Top-5 err = 0.158301,	data_time = 0.050569,	train_time = 0.735476
[2019-08-24 10:10:41,244] TRAIN Iter 228960: lr = 0.118402,	loss = 2.394631,	Top-1 err = 0.360400,	Top-5 err = 0.157227,	data_time = 0.050381,	train_time = 0.901455
[2019-08-24 10:10:48,457] TRAIN Iter 228980: lr = 0.118368,	loss = 2.548472,	Top-1 err = 0.362451,	Top-5 err = 0.158789,	data_time = 0.050747,	train_time = 0.360601
[2019-08-24 10:11:04,549] TRAIN Iter 229000: lr = 0.118335,	loss = 2.479066,	Top-1 err = 0.364111,	Top-5 err = 0.160498,	data_time = 0.050008,	train_time = 0.804591
[2019-08-24 10:11:20,490] TRAIN Iter 229020: lr = 0.118302,	loss = 2.399040,	Top-1 err = 0.361328,	Top-5 err = 0.156934,	data_time = 0.049831,	train_time = 0.797032
[2019-08-24 10:11:26,460] TRAIN Iter 229040: lr = 0.118268,	loss = 2.402303,	Top-1 err = 0.358643,	Top-5 err = 0.160303,	data_time = 0.049907,	train_time = 0.298502
[2019-08-24 10:12:16,216] TRAIN Iter 229060: lr = 0.118235,	loss = 2.516542,	Top-1 err = 0.371991,	Top-5 err = 0.159822,	data_time = 0.050461,	train_time = 2.487796
[2019-08-24 10:12:23,507] TRAIN Iter 229080: lr = 0.118202,	loss = 2.427704,	Top-1 err = 0.362354,	Top-5 err = 0.155469,	data_time = 0.050663,	train_time = 0.364508
[2019-08-24 10:12:38,823] TRAIN Iter 229100: lr = 0.118168,	loss = 2.434984,	Top-1 err = 0.354004,	Top-5 err = 0.148633,	data_time = 0.050462,	train_time = 0.765791
[2019-08-24 10:12:55,267] TRAIN Iter 229120: lr = 0.118135,	loss = 2.526127,	Top-1 err = 0.353467,	Top-5 err = 0.152930,	data_time = 3.346100,	train_time = 0.822200
[2019-08-24 10:13:03,112] TRAIN Iter 229140: lr = 0.118102,	loss = 2.331055,	Top-1 err = 0.355371,	Top-5 err = 0.149805,	data_time = 0.050452,	train_time = 0.392203
[2019-08-24 10:13:16,159] TRAIN Iter 229160: lr = 0.118068,	loss = 2.358356,	Top-1 err = 0.352002,	Top-5 err = 0.150439,	data_time = 0.050518,	train_time = 0.652357
[2019-08-24 10:13:23,508] TRAIN Iter 229180: lr = 0.118035,	loss = 2.460227,	Top-1 err = 0.351025,	Top-5 err = 0.148926,	data_time = 0.133844,	train_time = 0.367437
[2019-08-24 10:13:38,027] TRAIN Iter 229200: lr = 0.118002,	loss = 2.447712,	Top-1 err = 0.352539,	Top-5 err = 0.152490,	data_time = 0.050411,	train_time = 0.725940
[2019-08-24 10:13:54,840] TRAIN Iter 229220: lr = 0.117968,	loss = 2.458295,	Top-1 err = 0.354639,	Top-5 err = 0.154541,	data_time = 0.050343,	train_time = 0.840626
[2019-08-24 10:14:02,365] TRAIN Iter 229240: lr = 0.117935,	loss = 2.510087,	Top-1 err = 0.359473,	Top-5 err = 0.152832,	data_time = 0.050466,	train_time = 0.376254
[2019-08-24 10:14:15,529] TRAIN Iter 229260: lr = 0.117902,	loss = 2.412291,	Top-1 err = 0.353955,	Top-5 err = 0.150830,	data_time = 0.050483,	train_time = 0.658170
[2019-08-24 10:14:30,581] TRAIN Iter 229280: lr = 0.117868,	loss = 2.400738,	Top-1 err = 0.355762,	Top-5 err = 0.151025,	data_time = 0.099431,	train_time = 0.752615
[2019-08-24 10:14:37,844] TRAIN Iter 229300: lr = 0.117835,	loss = 2.439303,	Top-1 err = 0.359863,	Top-5 err = 0.154492,	data_time = 0.050357,	train_time = 0.363109
[2019-08-24 10:14:53,619] TRAIN Iter 229320: lr = 0.117802,	loss = 2.450822,	Top-1 err = 0.354248,	Top-5 err = 0.152100,	data_time = 0.050483,	train_time = 0.783311
[2019-08-24 10:15:01,062] TRAIN Iter 229340: lr = 0.117768,	loss = 2.410785,	Top-1 err = 0.353516,	Top-5 err = 0.152246,	data_time = 0.050853,	train_time = 0.372117
[2019-08-24 10:15:15,898] TRAIN Iter 229360: lr = 0.117735,	loss = 2.496471,	Top-1 err = 0.361963,	Top-5 err = 0.157129,	data_time = 0.050773,	train_time = 0.741820
[2019-08-24 10:15:31,702] TRAIN Iter 229380: lr = 0.117702,	loss = 2.528083,	Top-1 err = 0.356738,	Top-5 err = 0.154248,	data_time = 0.050808,	train_time = 0.790146
[2019-08-24 10:15:38,751] TRAIN Iter 229400: lr = 0.117668,	loss = 2.436242,	Top-1 err = 0.350000,	Top-5 err = 0.150391,	data_time = 0.050521,	train_time = 0.352479
[2019-08-24 10:15:52,940] TRAIN Iter 229420: lr = 0.117635,	loss = 2.420823,	Top-1 err = 0.355127,	Top-5 err = 0.151074,	data_time = 0.050408,	train_time = 0.709428
[2019-08-24 10:16:07,992] TRAIN Iter 229440: lr = 0.117602,	loss = 2.529727,	Top-1 err = 0.356885,	Top-5 err = 0.155078,	data_time = 0.100387,	train_time = 0.752580
[2019-08-24 10:16:15,055] TRAIN Iter 229460: lr = 0.117568,	loss = 2.358756,	Top-1 err = 0.356348,	Top-5 err = 0.150244,	data_time = 0.050351,	train_time = 0.353144
[2019-08-24 10:16:29,098] TRAIN Iter 229480: lr = 0.117535,	loss = 2.380465,	Top-1 err = 0.357471,	Top-5 err = 0.155029,	data_time = 0.050474,	train_time = 0.702103
[2019-08-24 10:16:36,251] TRAIN Iter 229500: lr = 0.117502,	loss = 2.507527,	Top-1 err = 0.360986,	Top-5 err = 0.153516,	data_time = 0.143949,	train_time = 0.357646
[2019-08-24 10:16:51,756] TRAIN Iter 229520: lr = 0.117468,	loss = 2.405031,	Top-1 err = 0.355664,	Top-5 err = 0.152637,	data_time = 0.050620,	train_time = 0.775220
[2019-08-24 10:17:07,520] TRAIN Iter 229540: lr = 0.117435,	loss = 2.528580,	Top-1 err = 0.359814,	Top-5 err = 0.154932,	data_time = 0.050785,	train_time = 0.788181
[2019-08-24 10:17:14,790] TRAIN Iter 229560: lr = 0.117402,	loss = 2.492268,	Top-1 err = 0.361621,	Top-5 err = 0.156201,	data_time = 0.050333,	train_time = 0.363500
[2019-08-24 10:17:28,233] TRAIN Iter 229580: lr = 0.117368,	loss = 2.511841,	Top-1 err = 0.358301,	Top-5 err = 0.157422,	data_time = 0.050394,	train_time = 0.672148
[2019-08-24 10:17:44,383] TRAIN Iter 229600: lr = 0.117335,	loss = 2.466061,	Top-1 err = 0.354687,	Top-5 err = 0.155127,	data_time = 1.955555,	train_time = 0.807496
[2019-08-24 10:17:51,850] TRAIN Iter 229620: lr = 0.117302,	loss = 2.472022,	Top-1 err = 0.350537,	Top-5 err = 0.153809,	data_time = 0.050691,	train_time = 0.373341
[2019-08-24 10:18:08,059] TRAIN Iter 229640: lr = 0.117268,	loss = 2.461092,	Top-1 err = 0.362891,	Top-5 err = 0.157031,	data_time = 0.050807,	train_time = 0.810432
[2019-08-24 10:18:16,138] TRAIN Iter 229660: lr = 0.117235,	loss = 2.525564,	Top-1 err = 0.355420,	Top-5 err = 0.150586,	data_time = 0.050463,	train_time = 0.403936
[2019-08-24 10:18:28,695] TRAIN Iter 229680: lr = 0.117202,	loss = 2.406785,	Top-1 err = 0.359033,	Top-5 err = 0.153906,	data_time = 0.152252,	train_time = 0.627832
[2019-08-24 10:18:43,769] TRAIN Iter 229700: lr = 0.117168,	loss = 2.537050,	Top-1 err = 0.359668,	Top-5 err = 0.155371,	data_time = 0.050471,	train_time = 0.753644
[2019-08-24 10:18:51,223] TRAIN Iter 229720: lr = 0.117135,	loss = 2.425944,	Top-1 err = 0.364551,	Top-5 err = 0.156543,	data_time = 0.050596,	train_time = 0.372698
[2019-08-24 10:19:05,777] TRAIN Iter 229740: lr = 0.117102,	loss = 2.513330,	Top-1 err = 0.361230,	Top-5 err = 0.152783,	data_time = 0.050505,	train_time = 0.727708
[2019-08-24 10:19:22,566] TRAIN Iter 229760: lr = 0.117068,	loss = 2.443013,	Top-1 err = 0.363574,	Top-5 err = 0.161621,	data_time = 0.146186,	train_time = 0.839404
[2019-08-24 10:19:29,826] TRAIN Iter 229780: lr = 0.117035,	loss = 2.413654,	Top-1 err = 0.360693,	Top-5 err = 0.155566,	data_time = 0.050505,	train_time = 0.362981
[2019-08-24 10:19:43,629] TRAIN Iter 229800: lr = 0.117002,	loss = 2.552912,	Top-1 err = 0.365332,	Top-5 err = 0.160791,	data_time = 0.050499,	train_time = 0.690138
[2019-08-24 10:19:51,276] TRAIN Iter 229820: lr = 0.116968,	loss = 2.557941,	Top-1 err = 0.359619,	Top-5 err = 0.158691,	data_time = 0.150198,	train_time = 0.382356
[2019-08-24 10:20:05,059] TRAIN Iter 229840: lr = 0.116935,	loss = 2.437574,	Top-1 err = 0.351514,	Top-5 err = 0.151221,	data_time = 0.050503,	train_time = 0.689138
[2019-08-24 10:20:20,812] TRAIN Iter 229860: lr = 0.116902,	loss = 2.436733,	Top-1 err = 0.358105,	Top-5 err = 0.154150,	data_time = 1.461711,	train_time = 0.787615
[2019-08-24 10:20:27,677] TRAIN Iter 229880: lr = 0.116868,	loss = 2.410351,	Top-1 err = 0.359180,	Top-5 err = 0.154785,	data_time = 0.050736,	train_time = 0.343253
[2019-08-24 10:20:43,198] TRAIN Iter 229900: lr = 0.116835,	loss = 2.537726,	Top-1 err = 0.364893,	Top-5 err = 0.156201,	data_time = 0.050832,	train_time = 0.776015
[2019-08-24 10:20:58,256] TRAIN Iter 229920: lr = 0.116802,	loss = 2.393870,	Top-1 err = 0.362451,	Top-5 err = 0.158301,	data_time = 0.050607,	train_time = 0.752908
[2019-08-24 10:21:07,569] TRAIN Iter 229940: lr = 0.116768,	loss = 2.474045,	Top-1 err = 0.362207,	Top-5 err = 0.157715,	data_time = 0.109588,	train_time = 0.465619
[2019-08-24 10:21:22,677] TRAIN Iter 229960: lr = 0.116735,	loss = 2.468542,	Top-1 err = 0.358496,	Top-5 err = 0.155078,	data_time = 0.050536,	train_time = 0.755408
[2019-08-24 10:21:29,969] TRAIN Iter 229980: lr = 0.116702,	loss = 2.444673,	Top-1 err = 0.363379,	Top-5 err = 0.156543,	data_time = 0.050925,	train_time = 0.364569
[2019-08-24 10:21:45,908] TRAIN Iter 230000: lr = 0.116668,	loss = 2.464454,	Top-1 err = 0.361719,	Top-5 err = 0.153125,	data_time = 0.050507,	train_time = 0.796924
[2019-08-24 10:22:48,609] TEST Iter 230000: loss = 2.260799,	Top-1 err = 0.324380,	Top-5 err = 0.117480,	val_time = 62.661891
[2019-08-24 10:22:54,775] TRAIN Iter 230020: lr = 0.116635,	loss = 2.437147,	Top-1 err = 0.364014,	Top-5 err = 0.158105,	data_time = 0.050352,	train_time = 0.308289
[2019-08-24 10:23:01,238] TRAIN Iter 230040: lr = 0.116602,	loss = 2.419315,	Top-1 err = 0.366748,	Top-5 err = 0.158496,	data_time = 0.050416,	train_time = 0.323131
[2019-08-24 10:23:07,969] TRAIN Iter 230060: lr = 0.116568,	loss = 2.389123,	Top-1 err = 0.361670,	Top-5 err = 0.156836,	data_time = 0.050470,	train_time = 0.336569
[2019-08-24 10:23:15,976] TRAIN Iter 230080: lr = 0.116535,	loss = 2.529361,	Top-1 err = 0.358447,	Top-5 err = 0.156445,	data_time = 0.050521,	train_time = 0.400323
[2019-08-24 10:23:30,133] TRAIN Iter 230100: lr = 0.116502,	loss = 2.483543,	Top-1 err = 0.360156,	Top-5 err = 0.156445,	data_time = 0.050449,	train_time = 0.707811
[2019-08-24 10:23:40,447] TRAIN Iter 230120: lr = 0.116468,	loss = 2.374699,	Top-1 err = 0.365869,	Top-5 err = 0.156006,	data_time = 0.050284,	train_time = 0.515705
[2019-08-24 10:23:55,760] TRAIN Iter 230140: lr = 0.116435,	loss = 2.533079,	Top-1 err = 0.365088,	Top-5 err = 0.159424,	data_time = 0.050890,	train_time = 0.765645
[2019-08-24 10:24:05,496] TRAIN Iter 230160: lr = 0.116402,	loss = 2.424216,	Top-1 err = 0.361816,	Top-5 err = 0.157373,	data_time = 0.050565,	train_time = 0.486771
[2019-08-24 10:24:22,561] TRAIN Iter 230180: lr = 0.116368,	loss = 2.513138,	Top-1 err = 0.364697,	Top-5 err = 0.154492,	data_time = 0.050584,	train_time = 0.853237
[2019-08-24 10:24:35,727] TRAIN Iter 230200: lr = 0.116335,	loss = 2.370698,	Top-1 err = 0.361426,	Top-5 err = 0.152783,	data_time = 0.050659,	train_time = 0.658331
[2019-08-24 10:24:46,973] TRAIN Iter 230220: lr = 0.116302,	loss = 2.415633,	Top-1 err = 0.369824,	Top-5 err = 0.161182,	data_time = 0.050544,	train_time = 0.562274
[2019-08-24 10:25:03,297] TRAIN Iter 230240: lr = 0.116268,	loss = 2.402004,	Top-1 err = 0.360645,	Top-5 err = 0.156396,	data_time = 0.157689,	train_time = 0.816180
[2019-08-24 10:25:16,765] TRAIN Iter 230260: lr = 0.116235,	loss = 2.401304,	Top-1 err = 0.367578,	Top-5 err = 0.157471,	data_time = 0.050136,	train_time = 0.673359
[2019-08-24 10:25:28,511] TRAIN Iter 230280: lr = 0.116202,	loss = 2.446108,	Top-1 err = 0.362549,	Top-5 err = 0.159717,	data_time = 0.049950,	train_time = 0.587291
[2019-08-24 10:25:39,879] TRAIN Iter 230300: lr = 0.116168,	loss = 2.518860,	Top-1 err = 0.364648,	Top-5 err = 0.159424,	data_time = 0.049869,	train_time = 0.568382
[2019-08-24 10:26:27,551] TRAIN Iter 230320: lr = 0.116135,	loss = 2.518838,	Top-1 err = 0.368780,	Top-5 err = 0.162491,	data_time = 0.050483,	train_time = 2.383633
[2019-08-24 10:26:40,186] TRAIN Iter 230340: lr = 0.116102,	loss = 2.431244,	Top-1 err = 0.356152,	Top-5 err = 0.148535,	data_time = 0.050452,	train_time = 0.631700
[2019-08-24 10:26:47,596] TRAIN Iter 230360: lr = 0.116068,	loss = 2.437987,	Top-1 err = 0.355811,	Top-5 err = 0.148535,	data_time = 0.050316,	train_time = 0.370476
[2019-08-24 10:27:01,446] TRAIN Iter 230380: lr = 0.116035,	loss = 2.434752,	Top-1 err = 0.354492,	Top-5 err = 0.153418,	data_time = 0.050396,	train_time = 0.692481
[2019-08-24 10:27:08,918] TRAIN Iter 230400: lr = 0.116002,	loss = 2.445372,	Top-1 err = 0.354834,	Top-5 err = 0.151514,	data_time = 0.051007,	train_time = 0.373618
[2019-08-24 10:27:23,111] TRAIN Iter 230420: lr = 0.115968,	loss = 2.363114,	Top-1 err = 0.349121,	Top-5 err = 0.149658,	data_time = 0.050380,	train_time = 0.709640
[2019-08-24 10:27:37,800] TRAIN Iter 230440: lr = 0.115935,	loss = 2.428328,	Top-1 err = 0.354932,	Top-5 err = 0.157764,	data_time = 0.050631,	train_time = 0.734442
[2019-08-24 10:27:45,076] TRAIN Iter 230460: lr = 0.115902,	loss = 2.381530,	Top-1 err = 0.356006,	Top-5 err = 0.152930,	data_time = 0.050482,	train_time = 0.363786
[2019-08-24 10:28:01,359] TRAIN Iter 230480: lr = 0.115868,	loss = 2.300220,	Top-1 err = 0.350439,	Top-5 err = 0.147559,	data_time = 0.050929,	train_time = 0.814129
[2019-08-24 10:28:22,033] TRAIN Iter 230500: lr = 0.115835,	loss = 2.350749,	Top-1 err = 0.348975,	Top-5 err = 0.147461,	data_time = 0.050479,	train_time = 1.033665
[2019-08-24 10:28:29,974] TRAIN Iter 230520: lr = 0.115802,	loss = 2.444397,	Top-1 err = 0.356348,	Top-5 err = 0.153418,	data_time = 0.050294,	train_time = 0.397052
[2019-08-24 10:28:40,918] TRAIN Iter 230540: lr = 0.115768,	loss = 2.502483,	Top-1 err = 0.353662,	Top-5 err = 0.152832,	data_time = 0.050266,	train_time = 0.547204
[2019-08-24 10:28:48,849] TRAIN Iter 230560: lr = 0.115735,	loss = 2.489276,	Top-1 err = 0.355713,	Top-5 err = 0.152881,	data_time = 0.050786,	train_time = 0.396530
[2019-08-24 10:29:00,605] TRAIN Iter 230580: lr = 0.115702,	loss = 2.548154,	Top-1 err = 0.355322,	Top-5 err = 0.155176,	data_time = 0.050302,	train_time = 0.587777
[2019-08-24 10:29:16,934] TRAIN Iter 230600: lr = 0.115668,	loss = 2.452835,	Top-1 err = 0.360547,	Top-5 err = 0.155029,	data_time = 0.050560,	train_time = 0.816440
[2019-08-24 10:29:23,994] TRAIN Iter 230620: lr = 0.115635,	loss = 2.474228,	Top-1 err = 0.352490,	Top-5 err = 0.156445,	data_time = 0.050609,	train_time = 0.352982
[2019-08-24 10:29:37,989] TRAIN Iter 230640: lr = 0.115602,	loss = 2.372538,	Top-1 err = 0.360645,	Top-5 err = 0.157812,	data_time = 0.050440,	train_time = 0.699746
[2019-08-24 10:29:53,546] TRAIN Iter 230660: lr = 0.115568,	loss = 2.412852,	Top-1 err = 0.358398,	Top-5 err = 0.152588,	data_time = 0.050332,	train_time = 0.777819
[2019-08-24 10:30:00,661] TRAIN Iter 230680: lr = 0.115535,	loss = 2.361980,	Top-1 err = 0.358984,	Top-5 err = 0.152686,	data_time = 0.050990,	train_time = 0.355774
[2019-08-24 10:30:15,121] TRAIN Iter 230700: lr = 0.115502,	loss = 2.407708,	Top-1 err = 0.356543,	Top-5 err = 0.151318,	data_time = 0.050409,	train_time = 0.722952
[2019-08-24 10:30:22,456] TRAIN Iter 230720: lr = 0.115468,	loss = 2.450083,	Top-1 err = 0.357764,	Top-5 err = 0.152881,	data_time = 0.050585,	train_time = 0.366732
[2019-08-24 10:30:37,828] TRAIN Iter 230740: lr = 0.115435,	loss = 2.396848,	Top-1 err = 0.353516,	Top-5 err = 0.149561,	data_time = 0.050496,	train_time = 0.768607
[2019-08-24 10:30:53,399] TRAIN Iter 230760: lr = 0.115402,	loss = 2.525449,	Top-1 err = 0.357373,	Top-5 err = 0.153662,	data_time = 0.050426,	train_time = 0.778524
[2019-08-24 10:31:00,103] TRAIN Iter 230780: lr = 0.115368,	loss = 2.476787,	Top-1 err = 0.353174,	Top-5 err = 0.149951,	data_time = 0.050389,	train_time = 0.335216
[2019-08-24 10:31:15,940] TRAIN Iter 230800: lr = 0.115335,	loss = 2.527092,	Top-1 err = 0.361230,	Top-5 err = 0.152686,	data_time = 0.050533,	train_time = 0.791801
[2019-08-24 10:31:32,476] TRAIN Iter 230820: lr = 0.115302,	loss = 2.450789,	Top-1 err = 0.354492,	Top-5 err = 0.151074,	data_time = 0.050393,	train_time = 0.826781
[2019-08-24 10:31:39,929] TRAIN Iter 230840: lr = 0.115268,	loss = 2.386131,	Top-1 err = 0.353760,	Top-5 err = 0.154688,	data_time = 0.050729,	train_time = 0.372627
[2019-08-24 10:31:55,253] TRAIN Iter 230860: lr = 0.115235,	loss = 2.468079,	Top-1 err = 0.359326,	Top-5 err = 0.159473,	data_time = 0.050505,	train_time = 0.766214
[2019-08-24 10:32:03,207] TRAIN Iter 230880: lr = 0.115202,	loss = 2.509031,	Top-1 err = 0.354346,	Top-5 err = 0.153662,	data_time = 0.050638,	train_time = 0.397692
[2019-08-24 10:32:18,764] TRAIN Iter 230900: lr = 0.115168,	loss = 2.476331,	Top-1 err = 0.358398,	Top-5 err = 0.154883,	data_time = 0.050705,	train_time = 0.777841
[2019-08-24 10:32:34,336] TRAIN Iter 230920: lr = 0.115135,	loss = 2.371410,	Top-1 err = 0.359180,	Top-5 err = 0.154492,	data_time = 0.050548,	train_time = 0.778575
[2019-08-24 10:32:41,814] TRAIN Iter 230940: lr = 0.115102,	loss = 2.466043,	Top-1 err = 0.353711,	Top-5 err = 0.153564,	data_time = 0.050596,	train_time = 0.373884
[2019-08-24 10:32:57,582] TRAIN Iter 230960: lr = 0.115068,	loss = 2.407542,	Top-1 err = 0.356641,	Top-5 err = 0.153564,	data_time = 0.050509,	train_time = 0.788362
[2019-08-24 10:33:13,162] TRAIN Iter 230980: lr = 0.115035,	loss = 2.470025,	Top-1 err = 0.357666,	Top-5 err = 0.151416,	data_time = 0.050627,	train_time = 0.779026
[2019-08-24 10:33:19,855] TRAIN Iter 231000: lr = 0.115002,	loss = 2.398643,	Top-1 err = 0.358350,	Top-5 err = 0.154980,	data_time = 0.050409,	train_time = 0.334590
[2019-08-24 10:33:36,916] TRAIN Iter 231020: lr = 0.114968,	loss = 2.385962,	Top-1 err = 0.361914,	Top-5 err = 0.156543,	data_time = 0.050595,	train_time = 0.853050
[2019-08-24 10:33:44,523] TRAIN Iter 231040: lr = 0.114935,	loss = 2.421922,	Top-1 err = 0.363037,	Top-5 err = 0.156689,	data_time = 0.050465,	train_time = 0.380349
[2019-08-24 10:33:59,460] TRAIN Iter 231060: lr = 0.114902,	loss = 2.403931,	Top-1 err = 0.359961,	Top-5 err = 0.155957,	data_time = 0.050472,	train_time = 0.746828
[2019-08-24 10:34:15,662] TRAIN Iter 231080: lr = 0.114868,	loss = 2.513360,	Top-1 err = 0.360889,	Top-5 err = 0.154248,	data_time = 0.050418,	train_time = 0.810114
[2019-08-24 10:34:23,009] TRAIN Iter 231100: lr = 0.114835,	loss = 2.460377,	Top-1 err = 0.361230,	Top-5 err = 0.157227,	data_time = 0.050480,	train_time = 0.367320
[2019-08-24 10:34:37,960] TRAIN Iter 231120: lr = 0.114802,	loss = 2.510102,	Top-1 err = 0.358008,	Top-5 err = 0.155273,	data_time = 0.050629,	train_time = 0.747550
[2019-08-24 10:34:55,130] TRAIN Iter 231140: lr = 0.114768,	loss = 2.527790,	Top-1 err = 0.363477,	Top-5 err = 0.158008,	data_time = 0.050867,	train_time = 0.858473
[2019-08-24 10:35:02,287] TRAIN Iter 231160: lr = 0.114735,	loss = 2.403625,	Top-1 err = 0.359229,	Top-5 err = 0.153320,	data_time = 0.050349,	train_time = 0.357843
[2019-08-24 10:35:17,253] TRAIN Iter 231180: lr = 0.114702,	loss = 2.546791,	Top-1 err = 0.359326,	Top-5 err = 0.151611,	data_time = 0.050474,	train_time = 0.748273
[2019-08-24 10:35:24,337] TRAIN Iter 231200: lr = 0.114668,	loss = 2.435721,	Top-1 err = 0.360596,	Top-5 err = 0.158740,	data_time = 0.136072,	train_time = 0.354170
[2019-08-24 10:35:40,933] TRAIN Iter 231220: lr = 0.114635,	loss = 2.430345,	Top-1 err = 0.356055,	Top-5 err = 0.153223,	data_time = 0.050481,	train_time = 0.829785
[2019-08-24 10:35:57,270] TRAIN Iter 231240: lr = 0.114602,	loss = 2.568782,	Top-1 err = 0.361670,	Top-5 err = 0.158301,	data_time = 0.050384,	train_time = 0.816870
[2019-08-24 10:36:04,029] TRAIN Iter 231260: lr = 0.114568,	loss = 2.431494,	Top-1 err = 0.357617,	Top-5 err = 0.156836,	data_time = 0.050366,	train_time = 0.337936
[2019-08-24 10:36:22,986] TRAIN Iter 231280: lr = 0.114535,	loss = 2.394663,	Top-1 err = 0.354980,	Top-5 err = 0.152539,	data_time = 0.050404,	train_time = 0.947832
[2019-08-24 10:36:39,793] TRAIN Iter 231300: lr = 0.114502,	loss = 2.436767,	Top-1 err = 0.355859,	Top-5 err = 0.153027,	data_time = 0.050594,	train_time = 0.840310
[2019-08-24 10:36:46,692] TRAIN Iter 231320: lr = 0.114468,	loss = 2.477635,	Top-1 err = 0.362012,	Top-5 err = 0.154590,	data_time = 0.050815,	train_time = 0.344963
[2019-08-24 10:37:03,117] TRAIN Iter 231340: lr = 0.114435,	loss = 2.520569,	Top-1 err = 0.361865,	Top-5 err = 0.154785,	data_time = 0.124097,	train_time = 0.821210
[2019-08-24 10:37:10,439] TRAIN Iter 231360: lr = 0.114402,	loss = 2.458104,	Top-1 err = 0.363086,	Top-5 err = 0.153418,	data_time = 0.050600,	train_time = 0.366110
[2019-08-24 10:37:28,392] TRAIN Iter 231380: lr = 0.114368,	loss = 2.487931,	Top-1 err = 0.359961,	Top-5 err = 0.154932,	data_time = 0.050359,	train_time = 0.897622
[2019-08-24 10:37:48,719] TRAIN Iter 231400: lr = 0.114335,	loss = 2.482702,	Top-1 err = 0.358008,	Top-5 err = 0.153174,	data_time = 0.050648,	train_time = 1.016365
[2019-08-24 10:37:56,240] TRAIN Iter 231420: lr = 0.114302,	loss = 2.355868,	Top-1 err = 0.357520,	Top-5 err = 0.156250,	data_time = 0.050828,	train_time = 0.376028
[2019-08-24 10:38:13,126] TRAIN Iter 231440: lr = 0.114268,	loss = 2.474391,	Top-1 err = 0.360156,	Top-5 err = 0.154785,	data_time = 0.050779,	train_time = 0.844261
[2019-08-24 10:38:30,104] TRAIN Iter 231460: lr = 0.114235,	loss = 2.400928,	Top-1 err = 0.361328,	Top-5 err = 0.152588,	data_time = 0.050788,	train_time = 0.848900
[2019-08-24 10:38:38,732] TRAIN Iter 231480: lr = 0.114202,	loss = 2.413576,	Top-1 err = 0.360498,	Top-5 err = 0.153906,	data_time = 0.050670,	train_time = 0.431371
[2019-08-24 10:38:56,331] TRAIN Iter 231500: lr = 0.114168,	loss = 2.519403,	Top-1 err = 0.370605,	Top-5 err = 0.159766,	data_time = 0.050234,	train_time = 0.879947
[2019-08-24 10:39:04,897] TRAIN Iter 231520: lr = 0.114135,	loss = 2.421069,	Top-1 err = 0.360352,	Top-5 err = 0.156689,	data_time = 0.049940,	train_time = 0.428257
[2019-08-24 10:39:17,062] TRAIN Iter 231540: lr = 0.114102,	loss = 2.368008,	Top-1 err = 0.358496,	Top-5 err = 0.155664,	data_time = 0.049887,	train_time = 0.608232
[2019-08-24 10:40:07,860] TRAIN Iter 231560: lr = 0.114068,	loss = 2.490464,	Top-1 err = 0.361946,	Top-5 err = 0.157015,	data_time = 0.050173,	train_time = 2.539879
[2019-08-24 10:40:15,231] TRAIN Iter 231580: lr = 0.114035,	loss = 2.455295,	Top-1 err = 0.360156,	Top-5 err = 0.153418,	data_time = 0.050444,	train_time = 0.368540
[2019-08-24 10:40:30,360] TRAIN Iter 231600: lr = 0.114002,	loss = 2.562808,	Top-1 err = 0.356641,	Top-5 err = 0.153271,	data_time = 0.050458,	train_time = 0.756469
[2019-08-24 10:40:37,684] TRAIN Iter 231620: lr = 0.113968,	loss = 2.397259,	Top-1 err = 0.352441,	Top-5 err = 0.146191,	data_time = 0.050945,	train_time = 0.366140
[2019-08-24 10:40:54,049] TRAIN Iter 231640: lr = 0.113935,	loss = 2.396613,	Top-1 err = 0.353369,	Top-5 err = 0.151123,	data_time = 0.051065,	train_time = 0.818250
[2019-08-24 10:41:08,432] TRAIN Iter 231660: lr = 0.113902,	loss = 2.409706,	Top-1 err = 0.349854,	Top-5 err = 0.151025,	data_time = 0.050654,	train_time = 0.719139
[2019-08-24 10:41:15,489] TRAIN Iter 231680: lr = 0.113868,	loss = 2.397846,	Top-1 err = 0.354639,	Top-5 err = 0.149756,	data_time = 0.050615,	train_time = 0.352854
[2019-08-24 10:41:31,680] TRAIN Iter 231700: lr = 0.113835,	loss = 2.464101,	Top-1 err = 0.356299,	Top-5 err = 0.153076,	data_time = 0.050498,	train_time = 0.809512
[2019-08-24 10:41:43,202] TRAIN Iter 231720: lr = 0.113802,	loss = 2.441591,	Top-1 err = 0.351709,	Top-5 err = 0.149414,	data_time = 3.323936,	train_time = 0.576120
[2019-08-24 10:41:52,798] TRAIN Iter 231740: lr = 0.113768,	loss = 2.490911,	Top-1 err = 0.357959,	Top-5 err = 0.155029,	data_time = 0.050724,	train_time = 0.479783
[2019-08-24 10:42:07,738] TRAIN Iter 231760: lr = 0.113735,	loss = 2.472033,	Top-1 err = 0.355127,	Top-5 err = 0.153516,	data_time = 0.050527,	train_time = 0.746936
[2019-08-24 10:42:15,697] TRAIN Iter 231780: lr = 0.113702,	loss = 2.431112,	Top-1 err = 0.346582,	Top-5 err = 0.151221,	data_time = 0.166724,	train_time = 0.397958
[2019-08-24 10:42:26,845] TRAIN Iter 231800: lr = 0.113668,	loss = 2.458550,	Top-1 err = 0.352441,	Top-5 err = 0.152930,	data_time = 0.050745,	train_time = 0.557399
[2019-08-24 10:42:41,272] TRAIN Iter 231820: lr = 0.113635,	loss = 2.428472,	Top-1 err = 0.356689,	Top-5 err = 0.151514,	data_time = 0.050491,	train_time = 0.721327
[2019-08-24 10:42:48,681] TRAIN Iter 231840: lr = 0.113602,	loss = 2.452343,	Top-1 err = 0.353662,	Top-5 err = 0.151465,	data_time = 0.050694,	train_time = 0.370446
[2019-08-24 10:43:04,566] TRAIN Iter 231860: lr = 0.113568,	loss = 2.510767,	Top-1 err = 0.357129,	Top-5 err = 0.153320,	data_time = 0.050473,	train_time = 0.794228
[2019-08-24 10:43:13,698] TRAIN Iter 231880: lr = 0.113535,	loss = 2.374101,	Top-1 err = 0.351611,	Top-5 err = 0.151123,	data_time = 0.995644,	train_time = 0.456584
[2019-08-24 10:43:24,604] TRAIN Iter 231900: lr = 0.113502,	loss = 2.408658,	Top-1 err = 0.354492,	Top-5 err = 0.152051,	data_time = 0.050806,	train_time = 0.545305
[2019-08-24 10:43:40,176] TRAIN Iter 231920: lr = 0.113468,	loss = 2.538319,	Top-1 err = 0.356592,	Top-5 err = 0.148730,	data_time = 0.050608,	train_time = 0.778547
[2019-08-24 10:43:46,963] TRAIN Iter 231940: lr = 0.113435,	loss = 2.466922,	Top-1 err = 0.357031,	Top-5 err = 0.152246,	data_time = 0.050497,	train_time = 0.339366
[2019-08-24 10:44:02,793] TRAIN Iter 231960: lr = 0.113402,	loss = 2.471604,	Top-1 err = 0.356348,	Top-5 err = 0.151514,	data_time = 0.050310,	train_time = 0.791481
[2019-08-24 10:44:17,154] TRAIN Iter 231980: lr = 0.113368,	loss = 2.571085,	Top-1 err = 0.357959,	Top-5 err = 0.155518,	data_time = 0.157692,	train_time = 0.718048
[2019-08-24 10:44:26,933] TRAIN Iter 232000: lr = 0.113335,	loss = 2.387627,	Top-1 err = 0.360059,	Top-5 err = 0.156006,	data_time = 0.176754,	train_time = 0.488903
[2019-08-24 10:44:40,050] TRAIN Iter 232020: lr = 0.113302,	loss = 2.487028,	Top-1 err = 0.353613,	Top-5 err = 0.151270,	data_time = 0.050479,	train_time = 0.655863
[2019-08-24 10:44:51,593] TRAIN Iter 232040: lr = 0.113268,	loss = 2.429966,	Top-1 err = 0.358008,	Top-5 err = 0.150977,	data_time = 0.050675,	train_time = 0.577105
[2019-08-24 10:45:01,715] TRAIN Iter 232060: lr = 0.113235,	loss = 2.391569,	Top-1 err = 0.356592,	Top-5 err = 0.151660,	data_time = 0.050335,	train_time = 0.506094
[2019-08-24 10:45:18,276] TRAIN Iter 232080: lr = 0.113202,	loss = 2.497380,	Top-1 err = 0.360693,	Top-5 err = 0.156738,	data_time = 0.050427,	train_time = 0.828044
[2019-08-24 10:45:25,324] TRAIN Iter 232100: lr = 0.113168,	loss = 2.490837,	Top-1 err = 0.357959,	Top-5 err = 0.153516,	data_time = 0.050548,	train_time = 0.352402
[2019-08-24 10:45:40,218] TRAIN Iter 232120: lr = 0.113135,	loss = 2.458989,	Top-1 err = 0.357129,	Top-5 err = 0.151025,	data_time = 0.050761,	train_time = 0.744667
[2019-08-24 10:45:55,189] TRAIN Iter 232140: lr = 0.113102,	loss = 2.376580,	Top-1 err = 0.356738,	Top-5 err = 0.153369,	data_time = 0.050894,	train_time = 0.748563
[2019-08-24 10:46:02,266] TRAIN Iter 232160: lr = 0.113068,	loss = 2.507981,	Top-1 err = 0.357227,	Top-5 err = 0.153613,	data_time = 0.129842,	train_time = 0.353837
[2019-08-24 10:46:16,315] TRAIN Iter 232180: lr = 0.113035,	loss = 2.419236,	Top-1 err = 0.353857,	Top-5 err = 0.153711,	data_time = 0.051015,	train_time = 0.702427
[2019-08-24 10:46:30,135] TRAIN Iter 232200: lr = 0.113002,	loss = 2.509692,	Top-1 err = 0.360010,	Top-5 err = 0.154004,	data_time = 0.050596,	train_time = 0.690964
[2019-08-24 10:46:39,008] TRAIN Iter 232220: lr = 0.112968,	loss = 2.472494,	Top-1 err = 0.355127,	Top-5 err = 0.153809,	data_time = 0.050336,	train_time = 0.443645
[2019-08-24 10:46:55,038] TRAIN Iter 232240: lr = 0.112935,	loss = 2.405216,	Top-1 err = 0.354492,	Top-5 err = 0.152344,	data_time = 0.087381,	train_time = 0.801500
[2019-08-24 10:47:02,133] TRAIN Iter 232260: lr = 0.112902,	loss = 2.517624,	Top-1 err = 0.361426,	Top-5 err = 0.158203,	data_time = 0.162856,	train_time = 0.354737
[2019-08-24 10:47:17,492] TRAIN Iter 232280: lr = 0.112868,	loss = 2.460099,	Top-1 err = 0.359229,	Top-5 err = 0.154053,	data_time = 0.050397,	train_time = 0.767898
[2019-08-24 10:47:34,790] TRAIN Iter 232300: lr = 0.112835,	loss = 2.508242,	Top-1 err = 0.357520,	Top-5 err = 0.156055,	data_time = 0.050606,	train_time = 0.864889
[2019-08-24 10:47:41,984] TRAIN Iter 232320: lr = 0.112802,	loss = 2.430904,	Top-1 err = 0.357764,	Top-5 err = 0.157178,	data_time = 0.130638,	train_time = 0.359686
[2019-08-24 10:47:57,984] TRAIN Iter 232340: lr = 0.112768,	loss = 2.501752,	Top-1 err = 0.358838,	Top-5 err = 0.155225,	data_time = 0.050495,	train_time = 0.799990
[2019-08-24 10:48:13,244] TRAIN Iter 232360: lr = 0.112735,	loss = 2.435077,	Top-1 err = 0.357227,	Top-5 err = 0.156348,	data_time = 0.088786,	train_time = 0.763021
[2019-08-24 10:48:20,652] TRAIN Iter 232380: lr = 0.112702,	loss = 2.513188,	Top-1 err = 0.363428,	Top-5 err = 0.156543,	data_time = 0.050663,	train_time = 0.370388
[2019-08-24 10:48:36,812] TRAIN Iter 232400: lr = 0.112668,	loss = 2.500709,	Top-1 err = 0.360547,	Top-5 err = 0.150977,	data_time = 0.050736,	train_time = 0.807956
[2019-08-24 10:48:43,797] TRAIN Iter 232420: lr = 0.112635,	loss = 2.496743,	Top-1 err = 0.357080,	Top-5 err = 0.154932,	data_time = 0.050519,	train_time = 0.349272
[2019-08-24 10:48:59,221] TRAIN Iter 232440: lr = 0.112602,	loss = 2.412665,	Top-1 err = 0.360693,	Top-5 err = 0.154297,	data_time = 0.050788,	train_time = 0.771172
[2019-08-24 10:49:15,548] TRAIN Iter 232460: lr = 0.112568,	loss = 2.433615,	Top-1 err = 0.360254,	Top-5 err = 0.158740,	data_time = 0.050465,	train_time = 0.816326
[2019-08-24 10:49:22,768] TRAIN Iter 232480: lr = 0.112535,	loss = 2.548536,	Top-1 err = 0.359717,	Top-5 err = 0.156641,	data_time = 0.123600,	train_time = 0.360958
[2019-08-24 10:49:37,621] TRAIN Iter 232500: lr = 0.112502,	loss = 2.558068,	Top-1 err = 0.362891,	Top-5 err = 0.156104,	data_time = 0.050948,	train_time = 0.742657
[2019-08-24 10:49:54,074] TRAIN Iter 232520: lr = 0.112468,	loss = 2.534157,	Top-1 err = 0.359521,	Top-5 err = 0.156738,	data_time = 0.938357,	train_time = 0.822621
[2019-08-24 10:50:01,453] TRAIN Iter 232540: lr = 0.112435,	loss = 2.387683,	Top-1 err = 0.355859,	Top-5 err = 0.154785,	data_time = 0.050435,	train_time = 0.368928
[2019-08-24 10:50:17,386] TRAIN Iter 232560: lr = 0.112402,	loss = 2.528608,	Top-1 err = 0.356348,	Top-5 err = 0.156006,	data_time = 0.050272,	train_time = 0.796664
[2019-08-24 10:50:24,423] TRAIN Iter 232580: lr = 0.112368,	loss = 2.488998,	Top-1 err = 0.358643,	Top-5 err = 0.157520,	data_time = 0.050544,	train_time = 0.351811
[2019-08-24 10:50:41,671] TRAIN Iter 232600: lr = 0.112335,	loss = 2.560063,	Top-1 err = 0.357031,	Top-5 err = 0.152979,	data_time = 0.050485,	train_time = 0.862400
[2019-08-24 10:50:58,276] TRAIN Iter 232620: lr = 0.112302,	loss = 2.451231,	Top-1 err = 0.358740,	Top-5 err = 0.153418,	data_time = 0.050911,	train_time = 0.830219
[2019-08-24 10:51:05,240] TRAIN Iter 232640: lr = 0.112268,	loss = 2.495449,	Top-1 err = 0.365234,	Top-5 err = 0.160498,	data_time = 0.050588,	train_time = 0.348197
[2019-08-24 10:51:20,926] TRAIN Iter 232660: lr = 0.112235,	loss = 2.502742,	Top-1 err = 0.359570,	Top-5 err = 0.151953,	data_time = 0.050459,	train_time = 0.784303
[2019-08-24 10:51:37,037] TRAIN Iter 232680: lr = 0.112202,	loss = 2.603884,	Top-1 err = 0.356250,	Top-5 err = 0.156055,	data_time = 2.732239,	train_time = 0.805508
[2019-08-24 10:51:44,102] TRAIN Iter 232700: lr = 0.112168,	loss = 2.497320,	Top-1 err = 0.361182,	Top-5 err = 0.153271,	data_time = 0.050807,	train_time = 0.353236
[2019-08-24 10:51:59,215] TRAIN Iter 232720: lr = 0.112135,	loss = 2.462571,	Top-1 err = 0.361377,	Top-5 err = 0.157373,	data_time = 0.050578,	train_time = 0.755672
[2019-08-24 10:52:06,400] TRAIN Iter 232740: lr = 0.112102,	loss = 2.455624,	Top-1 err = 0.361133,	Top-5 err = 0.155029,	data_time = 0.050489,	train_time = 0.359219
[2019-08-24 10:52:21,110] TRAIN Iter 232760: lr = 0.112068,	loss = 2.466157,	Top-1 err = 0.356641,	Top-5 err = 0.151709,	data_time = 0.050222,	train_time = 0.735458
[2019-08-24 10:52:37,475] TRAIN Iter 232780: lr = 0.112035,	loss = 2.431340,	Top-1 err = 0.357812,	Top-5 err = 0.152686,	data_time = 0.049991,	train_time = 0.818281
[2019-08-24 10:52:44,021] TRAIN Iter 232800: lr = 0.112002,	loss = 2.475251,	Top-1 err = 0.363232,	Top-5 err = 0.159082,	data_time = 0.049892,	train_time = 0.327264
[2019-08-24 10:53:32,453] TRAIN Iter 232820: lr = 0.111968,	loss = 2.482009,	Top-1 err = 0.366110,	Top-5 err = 0.157357,	data_time = 0.050160,	train_time = 2.421565
[2019-08-24 10:53:40,293] TRAIN Iter 232840: lr = 0.111935,	loss = 2.443658,	Top-1 err = 0.358154,	Top-5 err = 0.152490,	data_time = 0.051066,	train_time = 0.392014
[2019-08-24 10:53:55,042] TRAIN Iter 232860: lr = 0.111902,	loss = 2.424801,	Top-1 err = 0.353613,	Top-5 err = 0.147754,	data_time = 0.050441,	train_time = 0.737420
[2019-08-24 10:54:07,924] TRAIN Iter 232880: lr = 0.111868,	loss = 2.490600,	Top-1 err = 0.355859,	Top-5 err = 0.153418,	data_time = 0.050740,	train_time = 0.644101
[2019-08-24 10:54:14,965] TRAIN Iter 232900: lr = 0.111835,	loss = 2.497859,	Top-1 err = 0.356201,	Top-5 err = 0.152686,	data_time = 0.123185,	train_time = 0.352019
[2019-08-24 10:54:31,067] TRAIN Iter 232920: lr = 0.111802,	loss = 2.465314,	Top-1 err = 0.349756,	Top-5 err = 0.150830,	data_time = 0.050340,	train_time = 0.805095
[2019-08-24 10:54:45,709] TRAIN Iter 232940: lr = 0.111768,	loss = 2.410315,	Top-1 err = 0.352881,	Top-5 err = 0.150146,	data_time = 0.050543,	train_time = 0.732055
[2019-08-24 10:54:52,739] TRAIN Iter 232960: lr = 0.111735,	loss = 2.422601,	Top-1 err = 0.350977,	Top-5 err = 0.149805,	data_time = 0.050722,	train_time = 0.351501
[2019-08-24 10:55:08,911] TRAIN Iter 232980: lr = 0.111702,	loss = 2.559562,	Top-1 err = 0.347314,	Top-5 err = 0.146973,	data_time = 0.050555,	train_time = 0.808590
[2019-08-24 10:55:16,631] TRAIN Iter 233000: lr = 0.111668,	loss = 2.485118,	Top-1 err = 0.354980,	Top-5 err = 0.150781,	data_time = 0.050382,	train_time = 0.385976
[2019-08-24 10:55:30,265] TRAIN Iter 233020: lr = 0.111635,	loss = 2.447862,	Top-1 err = 0.352930,	Top-5 err = 0.151270,	data_time = 0.050473,	train_time = 0.681704
[2019-08-24 10:55:45,633] TRAIN Iter 233040: lr = 0.111602,	loss = 2.459819,	Top-1 err = 0.356348,	Top-5 err = 0.153125,	data_time = 0.050364,	train_time = 0.768389
[2019-08-24 10:55:52,958] TRAIN Iter 233060: lr = 0.111568,	loss = 2.379789,	Top-1 err = 0.354395,	Top-5 err = 0.150244,	data_time = 0.098865,	train_time = 0.366249
[2019-08-24 10:56:06,271] TRAIN Iter 233080: lr = 0.111535,	loss = 2.477531,	Top-1 err = 0.353809,	Top-5 err = 0.153320,	data_time = 0.050412,	train_time = 0.665631
[2019-08-24 10:56:21,624] TRAIN Iter 233100: lr = 0.111502,	loss = 2.454087,	Top-1 err = 0.352930,	Top-5 err = 0.153369,	data_time = 0.050199,	train_time = 0.767627
[2019-08-24 10:56:28,954] TRAIN Iter 233120: lr = 0.111468,	loss = 2.427677,	Top-1 err = 0.359424,	Top-5 err = 0.152148,	data_time = 0.050371,	train_time = 0.366493
[2019-08-24 10:56:44,291] TRAIN Iter 233140: lr = 0.111435,	loss = 2.444340,	Top-1 err = 0.353467,	Top-5 err = 0.150195,	data_time = 0.050470,	train_time = 0.766805
[2019-08-24 10:56:52,043] TRAIN Iter 233160: lr = 0.111402,	loss = 2.487587,	Top-1 err = 0.351318,	Top-5 err = 0.148486,	data_time = 0.050492,	train_time = 0.387582
[2019-08-24 10:57:05,489] TRAIN Iter 233180: lr = 0.111368,	loss = 2.484180,	Top-1 err = 0.353076,	Top-5 err = 0.152441,	data_time = 0.050566,	train_time = 0.672332
[2019-08-24 10:57:20,867] TRAIN Iter 233200: lr = 0.111335,	loss = 2.432699,	Top-1 err = 0.353223,	Top-5 err = 0.150439,	data_time = 0.050465,	train_time = 0.768866
[2019-08-24 10:57:28,195] TRAIN Iter 233220: lr = 0.111302,	loss = 2.478186,	Top-1 err = 0.351318,	Top-5 err = 0.149121,	data_time = 0.050691,	train_time = 0.366366
[2019-08-24 10:57:41,454] TRAIN Iter 233240: lr = 0.111268,	loss = 2.476704,	Top-1 err = 0.356836,	Top-5 err = 0.152979,	data_time = 0.050510,	train_time = 0.662941
[2019-08-24 10:57:56,740] TRAIN Iter 233260: lr = 0.111235,	loss = 2.488502,	Top-1 err = 0.352979,	Top-5 err = 0.155078,	data_time = 0.050346,	train_time = 0.764274
[2019-08-24 10:58:04,040] TRAIN Iter 233280: lr = 0.111202,	loss = 2.473698,	Top-1 err = 0.356787,	Top-5 err = 0.150391,	data_time = 0.050458,	train_time = 0.364982
[2019-08-24 10:58:19,025] TRAIN Iter 233300: lr = 0.111168,	loss = 2.422658,	Top-1 err = 0.350391,	Top-5 err = 0.150879,	data_time = 0.050476,	train_time = 0.749227
[2019-08-24 10:58:26,290] TRAIN Iter 233320: lr = 0.111135,	loss = 2.534658,	Top-1 err = 0.357910,	Top-5 err = 0.157129,	data_time = 0.050733,	train_time = 0.363285
[2019-08-24 10:58:40,708] TRAIN Iter 233340: lr = 0.111102,	loss = 2.475037,	Top-1 err = 0.358984,	Top-5 err = 0.153809,	data_time = 0.050709,	train_time = 0.720856
[2019-08-24 10:58:55,293] TRAIN Iter 233360: lr = 0.111068,	loss = 2.368349,	Top-1 err = 0.354980,	Top-5 err = 0.150244,	data_time = 0.050910,	train_time = 0.729258
[2019-08-24 10:59:02,784] TRAIN Iter 233380: lr = 0.111035,	loss = 2.421474,	Top-1 err = 0.358936,	Top-5 err = 0.151270,	data_time = 0.050668,	train_time = 0.374536
[2019-08-24 10:59:19,047] TRAIN Iter 233400: lr = 0.111002,	loss = 2.368838,	Top-1 err = 0.362354,	Top-5 err = 0.156348,	data_time = 0.050482,	train_time = 0.813128
[2019-08-24 10:59:34,047] TRAIN Iter 233420: lr = 0.110968,	loss = 2.457323,	Top-1 err = 0.352930,	Top-5 err = 0.149365,	data_time = 0.128362,	train_time = 0.749987
[2019-08-24 10:59:41,581] TRAIN Iter 233440: lr = 0.110935,	loss = 2.447938,	Top-1 err = 0.360498,	Top-5 err = 0.157520,	data_time = 0.050489,	train_time = 0.376666
[2019-08-24 10:59:58,766] TRAIN Iter 233460: lr = 0.110902,	loss = 2.509934,	Top-1 err = 0.357861,	Top-5 err = 0.156738,	data_time = 0.050758,	train_time = 0.859239
[2019-08-24 11:00:06,177] TRAIN Iter 233480: lr = 0.110868,	loss = 2.448338,	Top-1 err = 0.359375,	Top-5 err = 0.155371,	data_time = 0.134026,	train_time = 0.370533
[2019-08-24 11:00:19,877] TRAIN Iter 233500: lr = 0.110835,	loss = 2.335198,	Top-1 err = 0.356250,	Top-5 err = 0.156445,	data_time = 0.050957,	train_time = 0.685004
[2019-08-24 11:00:35,969] TRAIN Iter 233520: lr = 0.110802,	loss = 2.486491,	Top-1 err = 0.358545,	Top-5 err = 0.153613,	data_time = 0.050895,	train_time = 0.804562
[2019-08-24 11:00:44,652] TRAIN Iter 233540: lr = 0.110768,	loss = 2.511078,	Top-1 err = 0.356641,	Top-5 err = 0.153711,	data_time = 0.050324,	train_time = 0.434139
[2019-08-24 11:01:00,600] TRAIN Iter 233560: lr = 0.110735,	loss = 2.561534,	Top-1 err = 0.360742,	Top-5 err = 0.155322,	data_time = 0.050886,	train_time = 0.797406
[2019-08-24 11:01:14,087] TRAIN Iter 233580: lr = 0.110702,	loss = 2.401430,	Top-1 err = 0.354248,	Top-5 err = 0.152051,	data_time = 0.050359,	train_time = 0.674359
[2019-08-24 11:01:23,360] TRAIN Iter 233600: lr = 0.110668,	loss = 2.428119,	Top-1 err = 0.359912,	Top-5 err = 0.158643,	data_time = 0.050860,	train_time = 0.463623
[2019-08-24 11:01:38,543] TRAIN Iter 233620: lr = 0.110635,	loss = 2.448992,	Top-1 err = 0.358789,	Top-5 err = 0.150391,	data_time = 0.050229,	train_time = 0.759099
[2019-08-24 11:01:45,478] TRAIN Iter 233640: lr = 0.110602,	loss = 2.455101,	Top-1 err = 0.356445,	Top-5 err = 0.152881,	data_time = 0.050245,	train_time = 0.346771
[2019-08-24 11:02:01,795] TRAIN Iter 233660: lr = 0.110568,	loss = 2.462566,	Top-1 err = 0.359863,	Top-5 err = 0.155322,	data_time = 0.050574,	train_time = 0.815850
[2019-08-24 11:02:17,044] TRAIN Iter 233680: lr = 0.110535,	loss = 2.497295,	Top-1 err = 0.357373,	Top-5 err = 0.153418,	data_time = 0.050512,	train_time = 0.762409
[2019-08-24 11:02:25,673] TRAIN Iter 233700: lr = 0.110502,	loss = 2.464671,	Top-1 err = 0.357715,	Top-5 err = 0.151172,	data_time = 0.050238,	train_time = 0.431461
[2019-08-24 11:02:42,184] TRAIN Iter 233720: lr = 0.110468,	loss = 2.510170,	Top-1 err = 0.359375,	Top-5 err = 0.152539,	data_time = 0.050449,	train_time = 0.825530
[2019-08-24 11:02:57,241] TRAIN Iter 233740: lr = 0.110435,	loss = 2.432402,	Top-1 err = 0.355225,	Top-5 err = 0.155908,	data_time = 0.050601,	train_time = 0.752836
[2019-08-24 11:03:04,873] TRAIN Iter 233760: lr = 0.110402,	loss = 2.428155,	Top-1 err = 0.354443,	Top-5 err = 0.150537,	data_time = 0.050485,	train_time = 0.381571
[2019-08-24 11:03:22,107] TRAIN Iter 233780: lr = 0.110368,	loss = 2.454102,	Top-1 err = 0.366211,	Top-5 err = 0.159521,	data_time = 0.050529,	train_time = 0.861665
[2019-08-24 11:03:29,170] TRAIN Iter 233800: lr = 0.110335,	loss = 2.507731,	Top-1 err = 0.359082,	Top-5 err = 0.154590,	data_time = 0.050972,	train_time = 0.353132
[2019-08-24 11:03:45,008] TRAIN Iter 233820: lr = 0.110302,	loss = 2.435633,	Top-1 err = 0.357959,	Top-5 err = 0.151562,	data_time = 0.050722,	train_time = 0.791908
[2019-08-24 11:04:02,195] TRAIN Iter 233840: lr = 0.110268,	loss = 2.482807,	Top-1 err = 0.360352,	Top-5 err = 0.155713,	data_time = 0.050417,	train_time = 0.859346
[2019-08-24 11:04:08,911] TRAIN Iter 233860: lr = 0.110235,	loss = 2.491183,	Top-1 err = 0.361963,	Top-5 err = 0.155322,	data_time = 0.050566,	train_time = 0.335762
[2019-08-24 11:04:25,438] TRAIN Iter 233880: lr = 0.110202,	loss = 2.528651,	Top-1 err = 0.364697,	Top-5 err = 0.156885,	data_time = 0.050660,	train_time = 0.826358
[2019-08-24 11:04:41,964] TRAIN Iter 233900: lr = 0.110168,	loss = 2.492326,	Top-1 err = 0.356250,	Top-5 err = 0.153076,	data_time = 0.125918,	train_time = 0.826253
[2019-08-24 11:04:49,032] TRAIN Iter 233920: lr = 0.110135,	loss = 2.522893,	Top-1 err = 0.357373,	Top-5 err = 0.155566,	data_time = 0.050564,	train_time = 0.353419
[2019-08-24 11:05:05,408] TRAIN Iter 233940: lr = 0.110102,	loss = 2.408592,	Top-1 err = 0.361475,	Top-5 err = 0.154248,	data_time = 0.050702,	train_time = 0.818756
[2019-08-24 11:05:12,050] TRAIN Iter 233960: lr = 0.110068,	loss = 2.435256,	Top-1 err = 0.360986,	Top-5 err = 0.153125,	data_time = 0.050364,	train_time = 0.332109
[2019-08-24 11:05:30,090] TRAIN Iter 233980: lr = 0.110035,	loss = 2.484501,	Top-1 err = 0.360498,	Top-5 err = 0.158057,	data_time = 0.050671,	train_time = 0.901984
[2019-08-24 11:05:47,132] TRAIN Iter 234000: lr = 0.110002,	loss = 2.532055,	Top-1 err = 0.359326,	Top-5 err = 0.153564,	data_time = 0.050101,	train_time = 0.852063
[2019-08-24 11:05:53,679] TRAIN Iter 234020: lr = 0.109968,	loss = 2.404440,	Top-1 err = 0.361328,	Top-5 err = 0.154883,	data_time = 0.050157,	train_time = 0.327347
[2019-08-24 11:06:11,589] TRAIN Iter 234040: lr = 0.109935,	loss = 2.480813,	Top-1 err = 0.360938,	Top-5 err = 0.158740,	data_time = 0.050067,	train_time = 0.895492
[2019-08-24 11:06:20,217] TRAIN Iter 234060: lr = 0.109902,	loss = 2.960249,	Top-1 err = 0.368178,	Top-5 err = 0.160107,	data_time = 0.007127,	train_time = 0.431387
[2019-08-24 11:07:07,238] TRAIN Iter 234080: lr = 0.109868,	loss = 2.531270,	Top-1 err = 0.357275,	Top-5 err = 0.153125,	data_time = 0.050434,	train_time = 2.351040
[2019-08-24 11:07:23,477] TRAIN Iter 234100: lr = 0.109835,	loss = 2.337911,	Top-1 err = 0.353467,	Top-5 err = 0.149609,	data_time = 0.050505,	train_time = 0.811929
[2019-08-24 11:07:30,861] TRAIN Iter 234120: lr = 0.109802,	loss = 2.566451,	Top-1 err = 0.356055,	Top-5 err = 0.153223,	data_time = 0.050611,	train_time = 0.369188
[2019-08-24 11:07:45,414] TRAIN Iter 234140: lr = 0.109768,	loss = 2.380576,	Top-1 err = 0.354541,	Top-5 err = 0.148730,	data_time = 0.050566,	train_time = 0.727639
[2019-08-24 11:07:57,398] TRAIN Iter 234160: lr = 0.109735,	loss = 2.469416,	Top-1 err = 0.348096,	Top-5 err = 0.144580,	data_time = 0.050394,	train_time = 0.599179
[2019-08-24 11:08:06,473] TRAIN Iter 234180: lr = 0.109702,	loss = 2.384761,	Top-1 err = 0.350244,	Top-5 err = 0.151807,	data_time = 0.143883,	train_time = 0.453740
[2019-08-24 11:08:20,746] TRAIN Iter 234200: lr = 0.109668,	loss = 2.447312,	Top-1 err = 0.353711,	Top-5 err = 0.154346,	data_time = 0.050503,	train_time = 0.713643
[2019-08-24 11:08:27,916] TRAIN Iter 234220: lr = 0.109635,	loss = 2.517569,	Top-1 err = 0.357666,	Top-5 err = 0.154053,	data_time = 0.050454,	train_time = 0.358492
[2019-08-24 11:08:43,107] TRAIN Iter 234240: lr = 0.109602,	loss = 2.542355,	Top-1 err = 0.349902,	Top-5 err = 0.147949,	data_time = 0.050336,	train_time = 0.759513
[2019-08-24 11:09:00,043] TRAIN Iter 234260: lr = 0.109568,	loss = 2.452979,	Top-1 err = 0.352930,	Top-5 err = 0.150293,	data_time = 0.050265,	train_time = 0.846773
[2019-08-24 11:09:06,765] TRAIN Iter 234280: lr = 0.109535,	loss = 2.347419,	Top-1 err = 0.352197,	Top-5 err = 0.153613,	data_time = 0.050557,	train_time = 0.336079
[2019-08-24 11:09:22,145] TRAIN Iter 234300: lr = 0.109502,	loss = 2.429612,	Top-1 err = 0.348291,	Top-5 err = 0.150391,	data_time = 0.050627,	train_time = 0.769000
[2019-08-24 11:09:34,404] TRAIN Iter 234320: lr = 0.109468,	loss = 2.400362,	Top-1 err = 0.351123,	Top-5 err = 0.146729,	data_time = 0.050591,	train_time = 0.612941
[2019-08-24 11:09:51,369] TRAIN Iter 234340: lr = 0.109435,	loss = 2.498703,	Top-1 err = 0.358398,	Top-5 err = 0.151514,	data_time = 0.050286,	train_time = 0.848249
[2019-08-24 11:10:01,503] TRAIN Iter 234360: lr = 0.109402,	loss = 2.402480,	Top-1 err = 0.354004,	Top-5 err = 0.151514,	data_time = 0.050580,	train_time = 0.506655
[2019-08-24 11:10:10,312] TRAIN Iter 234380: lr = 0.109368,	loss = 2.406831,	Top-1 err = 0.353467,	Top-5 err = 0.151660,	data_time = 0.051251,	train_time = 0.440464
[2019-08-24 11:10:23,389] TRAIN Iter 234400: lr = 0.109335,	loss = 2.394324,	Top-1 err = 0.353271,	Top-5 err = 0.150977,	data_time = 0.050235,	train_time = 0.653815
[2019-08-24 11:10:36,059] TRAIN Iter 234420: lr = 0.109302,	loss = 2.454744,	Top-1 err = 0.354248,	Top-5 err = 0.153223,	data_time = 0.050508,	train_time = 0.633495
[2019-08-24 11:10:42,741] TRAIN Iter 234440: lr = 0.109268,	loss = 2.503778,	Top-1 err = 0.357959,	Top-5 err = 0.156836,	data_time = 0.050196,	train_time = 0.334100
[2019-08-24 11:10:57,930] TRAIN Iter 234460: lr = 0.109235,	loss = 2.385803,	Top-1 err = 0.356201,	Top-5 err = 0.151367,	data_time = 0.050816,	train_time = 0.759435
[2019-08-24 11:11:11,555] TRAIN Iter 234480: lr = 0.109202,	loss = 2.429638,	Top-1 err = 0.356787,	Top-5 err = 0.153174,	data_time = 0.050735,	train_time = 0.681199
[2019-08-24 11:11:19,618] TRAIN Iter 234500: lr = 0.109168,	loss = 2.370656,	Top-1 err = 0.358105,	Top-5 err = 0.155078,	data_time = 0.050588,	train_time = 0.403155
[2019-08-24 11:11:34,870] TRAIN Iter 234520: lr = 0.109135,	loss = 2.461143,	Top-1 err = 0.356641,	Top-5 err = 0.155029,	data_time = 0.050543,	train_time = 0.762574
[2019-08-24 11:11:41,755] TRAIN Iter 234540: lr = 0.109102,	loss = 2.497939,	Top-1 err = 0.359766,	Top-5 err = 0.154541,	data_time = 0.050187,	train_time = 0.344268
[2019-08-24 11:11:59,354] TRAIN Iter 234560: lr = 0.109068,	loss = 2.397940,	Top-1 err = 0.352979,	Top-5 err = 0.150977,	data_time = 0.050455,	train_time = 0.879916
[2019-08-24 11:12:17,776] TRAIN Iter 234580: lr = 0.109035,	loss = 2.463285,	Top-1 err = 0.354590,	Top-5 err = 0.147656,	data_time = 0.050490,	train_time = 0.921083
[2019-08-24 11:12:24,770] TRAIN Iter 234600: lr = 0.109002,	loss = 2.492855,	Top-1 err = 0.355566,	Top-5 err = 0.153564,	data_time = 0.050265,	train_time = 0.349701
[2019-08-24 11:12:39,320] TRAIN Iter 234620: lr = 0.108968,	loss = 2.538570,	Top-1 err = 0.348242,	Top-5 err = 0.148242,	data_time = 0.050514,	train_time = 0.727472
[2019-08-24 11:12:57,525] TRAIN Iter 234640: lr = 0.108935,	loss = 2.516533,	Top-1 err = 0.354834,	Top-5 err = 0.151904,	data_time = 0.050351,	train_time = 0.910225
[2019-08-24 11:13:04,297] TRAIN Iter 234660: lr = 0.108902,	loss = 2.429667,	Top-1 err = 0.361084,	Top-5 err = 0.153125,	data_time = 0.050264,	train_time = 0.338611
[2019-08-24 11:13:21,498] TRAIN Iter 234680: lr = 0.108868,	loss = 2.432708,	Top-1 err = 0.363965,	Top-5 err = 0.158691,	data_time = 0.050291,	train_time = 0.860004
[2019-08-24 11:13:29,035] TRAIN Iter 234700: lr = 0.108835,	loss = 2.349814,	Top-1 err = 0.359131,	Top-5 err = 0.151416,	data_time = 0.050848,	train_time = 0.376864
[2019-08-24 11:13:44,665] TRAIN Iter 234720: lr = 0.108802,	loss = 2.367346,	Top-1 err = 0.354541,	Top-5 err = 0.150195,	data_time = 0.050756,	train_time = 0.781461
[2019-08-24 11:13:58,938] TRAIN Iter 234740: lr = 0.108768,	loss = 2.429620,	Top-1 err = 0.358643,	Top-5 err = 0.153418,	data_time = 0.050841,	train_time = 0.713634
[2019-08-24 11:14:05,899] TRAIN Iter 234760: lr = 0.108735,	loss = 2.398975,	Top-1 err = 0.357031,	Top-5 err = 0.154150,	data_time = 0.050530,	train_time = 0.348053
[2019-08-24 11:14:21,322] TRAIN Iter 234780: lr = 0.108702,	loss = 2.507419,	Top-1 err = 0.356055,	Top-5 err = 0.153418,	data_time = 0.050747,	train_time = 0.771116
[2019-08-24 11:14:39,082] TRAIN Iter 234800: lr = 0.108668,	loss = 2.429614,	Top-1 err = 0.352539,	Top-5 err = 0.149902,	data_time = 0.050402,	train_time = 0.887984
[2019-08-24 11:14:45,960] TRAIN Iter 234820: lr = 0.108635,	loss = 2.362410,	Top-1 err = 0.354199,	Top-5 err = 0.152783,	data_time = 0.115528,	train_time = 0.343916
[2019-08-24 11:15:03,619] TRAIN Iter 234840: lr = 0.108602,	loss = 2.431049,	Top-1 err = 0.358398,	Top-5 err = 0.154053,	data_time = 0.050517,	train_time = 0.882944
[2019-08-24 11:15:10,929] TRAIN Iter 234860: lr = 0.108568,	loss = 2.410404,	Top-1 err = 0.353223,	Top-5 err = 0.155908,	data_time = 0.050807,	train_time = 0.365467
[2019-08-24 11:15:25,154] TRAIN Iter 234880: lr = 0.108535,	loss = 2.405851,	Top-1 err = 0.353955,	Top-5 err = 0.153711,	data_time = 0.050536,	train_time = 0.711240
[2019-08-24 11:15:42,053] TRAIN Iter 234900: lr = 0.108502,	loss = 2.507128,	Top-1 err = 0.358887,	Top-5 err = 0.157227,	data_time = 0.050477,	train_time = 0.844928
[2019-08-24 11:15:48,972] TRAIN Iter 234920: lr = 0.108468,	loss = 2.358064,	Top-1 err = 0.358350,	Top-5 err = 0.152246,	data_time = 0.050494,	train_time = 0.345928
[2019-08-24 11:16:04,759] TRAIN Iter 234940: lr = 0.108435,	loss = 2.464915,	Top-1 err = 0.358838,	Top-5 err = 0.155566,	data_time = 0.050649,	train_time = 0.789326
[2019-08-24 11:16:21,194] TRAIN Iter 234960: lr = 0.108402,	loss = 2.535268,	Top-1 err = 0.355908,	Top-5 err = 0.156055,	data_time = 0.050282,	train_time = 0.821770
[2019-08-24 11:16:27,984] TRAIN Iter 234980: lr = 0.108368,	loss = 2.540331,	Top-1 err = 0.362402,	Top-5 err = 0.154248,	data_time = 0.050313,	train_time = 0.339480
[2019-08-24 11:16:45,682] TRAIN Iter 235000: lr = 0.108335,	loss = 2.547971,	Top-1 err = 0.357568,	Top-5 err = 0.152295,	data_time = 0.050755,	train_time = 0.884873
[2019-08-24 11:16:52,470] TRAIN Iter 235020: lr = 0.108302,	loss = 2.432428,	Top-1 err = 0.355566,	Top-5 err = 0.152686,	data_time = 0.050336,	train_time = 0.339363
[2019-08-24 11:17:08,893] TRAIN Iter 235040: lr = 0.108268,	loss = 2.452982,	Top-1 err = 0.352832,	Top-5 err = 0.151660,	data_time = 0.050421,	train_time = 0.821176
[2019-08-24 11:17:26,630] TRAIN Iter 235060: lr = 0.108235,	loss = 2.443019,	Top-1 err = 0.359668,	Top-5 err = 0.158057,	data_time = 0.050395,	train_time = 0.886817
[2019-08-24 11:17:33,423] TRAIN Iter 235080: lr = 0.108202,	loss = 2.439403,	Top-1 err = 0.354248,	Top-5 err = 0.150195,	data_time = 0.050605,	train_time = 0.339637
[2019-08-24 11:17:51,417] TRAIN Iter 235100: lr = 0.108168,	loss = 2.429772,	Top-1 err = 0.355518,	Top-5 err = 0.149121,	data_time = 0.050535,	train_time = 0.899677
[2019-08-24 11:18:09,080] TRAIN Iter 235120: lr = 0.108135,	loss = 2.515813,	Top-1 err = 0.359619,	Top-5 err = 0.152637,	data_time = 0.050673,	train_time = 0.883127
[2019-08-24 11:18:15,902] TRAIN Iter 235140: lr = 0.108102,	loss = 2.457125,	Top-1 err = 0.360254,	Top-5 err = 0.155225,	data_time = 0.050268,	train_time = 0.341113
[2019-08-24 11:18:33,817] TRAIN Iter 235160: lr = 0.108068,	loss = 2.466547,	Top-1 err = 0.360791,	Top-5 err = 0.153223,	data_time = 0.050503,	train_time = 0.895706
[2019-08-24 11:18:40,528] TRAIN Iter 235180: lr = 0.108035,	loss = 2.462342,	Top-1 err = 0.360938,	Top-5 err = 0.153223,	data_time = 0.050478,	train_time = 0.335540
[2019-08-24 11:18:58,872] TRAIN Iter 235200: lr = 0.108002,	loss = 2.530231,	Top-1 err = 0.359375,	Top-5 err = 0.153760,	data_time = 0.050216,	train_time = 0.917217
[2019-08-24 11:19:17,833] TRAIN Iter 235220: lr = 0.107968,	loss = 2.528981,	Top-1 err = 0.360938,	Top-5 err = 0.157764,	data_time = 0.050802,	train_time = 0.947993
[2019-08-24 11:19:24,781] TRAIN Iter 235240: lr = 0.107935,	loss = 2.487264,	Top-1 err = 0.360254,	Top-5 err = 0.153662,	data_time = 0.050560,	train_time = 0.347405
[2019-08-24 11:19:42,462] TRAIN Iter 235260: lr = 0.107902,	loss = 2.450332,	Top-1 err = 0.366064,	Top-5 err = 0.154443,	data_time = 0.050056,	train_time = 0.884058
[2019-08-24 11:19:59,982] TRAIN Iter 235280: lr = 0.107868,	loss = 2.468071,	Top-1 err = 0.365430,	Top-5 err = 0.160840,	data_time = 0.049997,	train_time = 0.875977
[2019-08-24 11:20:06,076] TRAIN Iter 235300: lr = 0.107835,	loss = 2.443886,	Top-1 err = 0.356738,	Top-5 err = 0.152930,	data_time = 0.049904,	train_time = 0.304664
[2019-08-24 11:20:56,834] TRAIN Iter 235320: lr = 0.107802,	loss = 2.477178,	Top-1 err = 0.346668,	Top-5 err = 0.149749,	data_time = 0.050505,	train_time = 2.537918
[2019-08-24 11:21:03,996] TRAIN Iter 235340: lr = 0.107768,	loss = 2.463274,	Top-1 err = 0.353369,	Top-5 err = 0.152393,	data_time = 0.050540,	train_time = 0.358048
[2019-08-24 11:21:20,530] TRAIN Iter 235360: lr = 0.107735,	loss = 2.467617,	Top-1 err = 0.352539,	Top-5 err = 0.150488,	data_time = 0.050444,	train_time = 0.826690
[2019-08-24 11:21:33,371] TRAIN Iter 235380: lr = 0.107702,	loss = 2.472449,	Top-1 err = 0.348730,	Top-5 err = 0.147119,	data_time = 0.130368,	train_time = 0.642028
[2019-08-24 11:21:41,710] TRAIN Iter 235400: lr = 0.107668,	loss = 2.375213,	Top-1 err = 0.351025,	Top-5 err = 0.146973,	data_time = 0.050693,	train_time = 0.416979
[2019-08-24 11:21:57,442] TRAIN Iter 235420: lr = 0.107635,	loss = 2.449142,	Top-1 err = 0.351562,	Top-5 err = 0.150391,	data_time = 0.050495,	train_time = 0.786560
[2019-08-24 11:22:05,850] TRAIN Iter 235440: lr = 0.107602,	loss = 2.521223,	Top-1 err = 0.355420,	Top-5 err = 0.149512,	data_time = 0.156446,	train_time = 0.420366
[2019-08-24 11:22:18,052] TRAIN Iter 235460: lr = 0.107568,	loss = 2.509539,	Top-1 err = 0.353613,	Top-5 err = 0.147607,	data_time = 0.050498,	train_time = 0.610085
[2019-08-24 11:22:32,564] TRAIN Iter 235480: lr = 0.107535,	loss = 2.425333,	Top-1 err = 0.354297,	Top-5 err = 0.149316,	data_time = 0.050554,	train_time = 0.725600
[2019-08-24 11:22:39,521] TRAIN Iter 235500: lr = 0.107502,	loss = 2.416564,	Top-1 err = 0.352832,	Top-5 err = 0.150879,	data_time = 0.050499,	train_time = 0.347833
[2019-08-24 11:22:56,288] TRAIN Iter 235520: lr = 0.107468,	loss = 2.463057,	Top-1 err = 0.352441,	Top-5 err = 0.148633,	data_time = 0.050404,	train_time = 0.838320
[2019-08-24 11:23:09,582] TRAIN Iter 235540: lr = 0.107435,	loss = 2.397852,	Top-1 err = 0.345947,	Top-5 err = 0.150439,	data_time = 0.149948,	train_time = 0.664718
[2019-08-24 11:23:18,061] TRAIN Iter 235560: lr = 0.107402,	loss = 2.495055,	Top-1 err = 0.352148,	Top-5 err = 0.149561,	data_time = 0.050297,	train_time = 0.423928
[2019-08-24 11:23:32,697] TRAIN Iter 235580: lr = 0.107368,	loss = 2.505991,	Top-1 err = 0.351367,	Top-5 err = 0.152930,	data_time = 0.050784,	train_time = 0.731785
[2019-08-24 11:23:39,927] TRAIN Iter 235600: lr = 0.107335,	loss = 2.411962,	Top-1 err = 0.351172,	Top-5 err = 0.149658,	data_time = 0.050338,	train_time = 0.361486
[2019-08-24 11:23:54,909] TRAIN Iter 235620: lr = 0.107302,	loss = 2.436959,	Top-1 err = 0.350488,	Top-5 err = 0.151270,	data_time = 0.050490,	train_time = 0.749071
[2019-08-24 11:24:11,949] TRAIN Iter 235640: lr = 0.107268,	loss = 2.451690,	Top-1 err = 0.358740,	Top-5 err = 0.148779,	data_time = 0.050375,	train_time = 0.852011
[2019-08-24 11:24:18,846] TRAIN Iter 235660: lr = 0.107235,	loss = 2.480045,	Top-1 err = 0.356982,	Top-5 err = 0.154590,	data_time = 0.109236,	train_time = 0.344822
[2019-08-24 11:24:33,271] TRAIN Iter 235680: lr = 0.107202,	loss = 2.403367,	Top-1 err = 0.355762,	Top-5 err = 0.147754,	data_time = 0.050337,	train_time = 0.721222
[2019-08-24 11:24:48,932] TRAIN Iter 235700: lr = 0.107168,	loss = 2.488407,	Top-1 err = 0.352783,	Top-5 err = 0.152979,	data_time = 0.050285,	train_time = 0.783044
[2019-08-24 11:24:55,960] TRAIN Iter 235720: lr = 0.107135,	loss = 2.424616,	Top-1 err = 0.351758,	Top-5 err = 0.146729,	data_time = 0.050514,	train_time = 0.351370
[2019-08-24 11:25:15,117] TRAIN Iter 235740: lr = 0.107102,	loss = 2.439175,	Top-1 err = 0.357861,	Top-5 err = 0.154590,	data_time = 0.051126,	train_time = 0.957838
[2019-08-24 11:25:24,554] TRAIN Iter 235760: lr = 0.107068,	loss = 2.405630,	Top-1 err = 0.348535,	Top-5 err = 0.150879,	data_time = 0.050504,	train_time = 0.471849
[2019-08-24 11:25:34,885] TRAIN Iter 235780: lr = 0.107035,	loss = 2.404352,	Top-1 err = 0.350586,	Top-5 err = 0.149170,	data_time = 0.050388,	train_time = 0.516544
[2019-08-24 11:25:50,134] TRAIN Iter 235800: lr = 0.107002,	loss = 2.415374,	Top-1 err = 0.357129,	Top-5 err = 0.152832,	data_time = 0.050791,	train_time = 0.762446
[2019-08-24 11:25:57,098] TRAIN Iter 235820: lr = 0.106968,	loss = 2.399770,	Top-1 err = 0.348535,	Top-5 err = 0.151416,	data_time = 0.050687,	train_time = 0.348154
[2019-08-24 11:26:12,779] TRAIN Iter 235840: lr = 0.106935,	loss = 2.528537,	Top-1 err = 0.349219,	Top-5 err = 0.148730,	data_time = 0.050782,	train_time = 0.784038
[2019-08-24 11:26:28,619] TRAIN Iter 235860: lr = 0.106902,	loss = 2.399442,	Top-1 err = 0.349268,	Top-5 err = 0.149072,	data_time = 0.102096,	train_time = 0.791986
[2019-08-24 11:26:35,585] TRAIN Iter 235880: lr = 0.106868,	loss = 2.374691,	Top-1 err = 0.356738,	Top-5 err = 0.153613,	data_time = 0.050394,	train_time = 0.348260
[2019-08-24 11:26:52,298] TRAIN Iter 235900: lr = 0.106835,	loss = 2.545611,	Top-1 err = 0.355371,	Top-5 err = 0.154395,	data_time = 0.050234,	train_time = 0.835675
[2019-08-24 11:27:00,463] TRAIN Iter 235920: lr = 0.106802,	loss = 2.533115,	Top-1 err = 0.360938,	Top-5 err = 0.151709,	data_time = 0.050490,	train_time = 0.408233
[2019-08-24 11:27:14,686] TRAIN Iter 235940: lr = 0.106768,	loss = 2.403406,	Top-1 err = 0.356934,	Top-5 err = 0.151904,	data_time = 0.050525,	train_time = 0.711120
[2019-08-24 11:27:29,595] TRAIN Iter 235960: lr = 0.106735,	loss = 2.500160,	Top-1 err = 0.358154,	Top-5 err = 0.156348,	data_time = 0.050380,	train_time = 0.745436
[2019-08-24 11:27:36,772] TRAIN Iter 235980: lr = 0.106702,	loss = 2.311149,	Top-1 err = 0.355225,	Top-5 err = 0.151514,	data_time = 0.050318,	train_time = 0.358835
[2019-08-24 11:27:53,005] TRAIN Iter 236000: lr = 0.106668,	loss = 2.514261,	Top-1 err = 0.354541,	Top-5 err = 0.151221,	data_time = 0.050707,	train_time = 0.811616
[2019-08-24 11:28:09,294] TRAIN Iter 236020: lr = 0.106635,	loss = 2.284663,	Top-1 err = 0.348730,	Top-5 err = 0.151465,	data_time = 0.050458,	train_time = 0.814476
[2019-08-24 11:28:16,598] TRAIN Iter 236040: lr = 0.106602,	loss = 2.441844,	Top-1 err = 0.349414,	Top-5 err = 0.147754,	data_time = 0.050429,	train_time = 0.365138
[2019-08-24 11:28:32,642] TRAIN Iter 236060: lr = 0.106568,	loss = 2.366093,	Top-1 err = 0.354883,	Top-5 err = 0.151074,	data_time = 0.050463,	train_time = 0.802233
[2019-08-24 11:28:39,969] TRAIN Iter 236080: lr = 0.106535,	loss = 2.352959,	Top-1 err = 0.356299,	Top-5 err = 0.154883,	data_time = 0.050357,	train_time = 0.366315
[2019-08-24 11:28:54,133] TRAIN Iter 236100: lr = 0.106502,	loss = 2.372072,	Top-1 err = 0.357520,	Top-5 err = 0.153223,	data_time = 0.050357,	train_time = 0.708191
[2019-08-24 11:29:10,576] TRAIN Iter 236120: lr = 0.106468,	loss = 2.422282,	Top-1 err = 0.358789,	Top-5 err = 0.151904,	data_time = 0.050450,	train_time = 0.822129
[2019-08-24 11:29:17,676] TRAIN Iter 236140: lr = 0.106435,	loss = 2.510837,	Top-1 err = 0.353564,	Top-5 err = 0.151514,	data_time = 0.050598,	train_time = 0.354980
[2019-08-24 11:29:33,153] TRAIN Iter 236160: lr = 0.106402,	loss = 2.440695,	Top-1 err = 0.355762,	Top-5 err = 0.150781,	data_time = 0.050553,	train_time = 0.773815
[2019-08-24 11:29:48,070] TRAIN Iter 236180: lr = 0.106368,	loss = 2.379701,	Top-1 err = 0.358740,	Top-5 err = 0.152246,	data_time = 0.050385,	train_time = 0.745865
[2019-08-24 11:29:55,653] TRAIN Iter 236200: lr = 0.106335,	loss = 2.428755,	Top-1 err = 0.353857,	Top-5 err = 0.149219,	data_time = 0.154030,	train_time = 0.379139
[2019-08-24 11:30:09,747] TRAIN Iter 236220: lr = 0.106302,	loss = 2.385970,	Top-1 err = 0.352979,	Top-5 err = 0.149121,	data_time = 0.050469,	train_time = 0.704679
[2019-08-24 11:30:16,974] TRAIN Iter 236240: lr = 0.106268,	loss = 2.464203,	Top-1 err = 0.352002,	Top-5 err = 0.153662,	data_time = 0.050469,	train_time = 0.361324
[2019-08-24 11:30:33,375] TRAIN Iter 236260: lr = 0.106235,	loss = 2.472469,	Top-1 err = 0.357666,	Top-5 err = 0.150928,	data_time = 0.050334,	train_time = 0.820055
[2019-08-24 11:30:48,754] TRAIN Iter 236280: lr = 0.106202,	loss = 2.380950,	Top-1 err = 0.353564,	Top-5 err = 0.150977,	data_time = 0.050516,	train_time = 0.768932
[2019-08-24 11:30:57,084] TRAIN Iter 236300: lr = 0.106168,	loss = 2.402991,	Top-1 err = 0.362744,	Top-5 err = 0.155322,	data_time = 0.050583,	train_time = 0.416480
[2019-08-24 11:31:12,314] TRAIN Iter 236320: lr = 0.106135,	loss = 2.445043,	Top-1 err = 0.361523,	Top-5 err = 0.155029,	data_time = 0.130873,	train_time = 0.761466
[2019-08-24 11:31:28,928] TRAIN Iter 236340: lr = 0.106102,	loss = 2.472955,	Top-1 err = 0.361182,	Top-5 err = 0.152051,	data_time = 0.105276,	train_time = 0.830667
[2019-08-24 11:31:36,625] TRAIN Iter 236360: lr = 0.106068,	loss = 2.414208,	Top-1 err = 0.357520,	Top-5 err = 0.148291,	data_time = 0.050753,	train_time = 0.384874
[2019-08-24 11:31:54,028] TRAIN Iter 236380: lr = 0.106035,	loss = 2.439003,	Top-1 err = 0.354687,	Top-5 err = 0.151318,	data_time = 0.051027,	train_time = 0.870110
[2019-08-24 11:32:02,607] TRAIN Iter 236400: lr = 0.106002,	loss = 2.377792,	Top-1 err = 0.357324,	Top-5 err = 0.154736,	data_time = 0.050353,	train_time = 0.428934
[2019-08-24 11:32:17,581] TRAIN Iter 236420: lr = 0.105968,	loss = 2.399211,	Top-1 err = 0.362451,	Top-5 err = 0.155713,	data_time = 0.050214,	train_time = 0.748683
[2019-08-24 11:32:35,727] TRAIN Iter 236440: lr = 0.105935,	loss = 2.466293,	Top-1 err = 0.360498,	Top-5 err = 0.153174,	data_time = 0.050352,	train_time = 0.907280
[2019-08-24 11:32:43,013] TRAIN Iter 236460: lr = 0.105902,	loss = 2.556764,	Top-1 err = 0.356494,	Top-5 err = 0.152686,	data_time = 0.050518,	train_time = 0.364294
[2019-08-24 11:32:57,997] TRAIN Iter 236480: lr = 0.105868,	loss = 2.475235,	Top-1 err = 0.359814,	Top-5 err = 0.155127,	data_time = 0.050404,	train_time = 0.749198
[2019-08-24 11:33:15,466] TRAIN Iter 236500: lr = 0.105835,	loss = 2.441734,	Top-1 err = 0.359033,	Top-5 err = 0.153564,	data_time = 0.066903,	train_time = 0.873432
[2019-08-24 11:33:23,170] TRAIN Iter 236520: lr = 0.105802,	loss = 2.384345,	Top-1 err = 0.359814,	Top-5 err = 0.153320,	data_time = 0.050029,	train_time = 0.385192
[2019-08-24 11:33:40,037] TRAIN Iter 236540: lr = 0.105768,	loss = 2.376721,	Top-1 err = 0.355859,	Top-5 err = 0.151025,	data_time = 0.049901,	train_time = 0.843339
[2019-08-24 11:33:46,117] TRAIN Iter 236560: lr = 0.105735,	loss = 2.481045,	Top-1 err = 0.363916,	Top-5 err = 0.156006,	data_time = 0.049886,	train_time = 0.303977
[2019-08-24 11:34:38,356] TRAIN Iter 236580: lr = 0.105702,	loss = 2.448300,	Top-1 err = 0.355403,	Top-5 err = 0.153872,	data_time = 0.050311,	train_time = 2.611926
[2019-08-24 11:34:53,625] TRAIN Iter 236600: lr = 0.105668,	loss = 2.490815,	Top-1 err = 0.349268,	Top-5 err = 0.147314,	data_time = 0.050856,	train_time = 0.763439
[2019-08-24 11:35:00,842] TRAIN Iter 236620: lr = 0.105635,	loss = 2.404968,	Top-1 err = 0.346533,	Top-5 err = 0.147168,	data_time = 0.107396,	train_time = 0.360833
[2019-08-24 11:35:12,532] TRAIN Iter 236640: lr = 0.105602,	loss = 2.457785,	Top-1 err = 0.348535,	Top-5 err = 0.148486,	data_time = 0.050418,	train_time = 0.584472
[2019-08-24 11:35:19,780] TRAIN Iter 236660: lr = 0.105568,	loss = 2.409829,	Top-1 err = 0.346143,	Top-5 err = 0.148877,	data_time = 0.050962,	train_time = 0.362375
[2019-08-24 11:35:34,372] TRAIN Iter 236680: lr = 0.105535,	loss = 2.396827,	Top-1 err = 0.351904,	Top-5 err = 0.147900,	data_time = 0.050516,	train_time = 0.729629
[2019-08-24 11:35:50,166] TRAIN Iter 236700: lr = 0.105502,	loss = 2.469183,	Top-1 err = 0.347021,	Top-5 err = 0.144678,	data_time = 0.050323,	train_time = 0.789653
[2019-08-24 11:35:56,921] TRAIN Iter 236720: lr = 0.105468,	loss = 2.388336,	Top-1 err = 0.346533,	Top-5 err = 0.143701,	data_time = 0.050599,	train_time = 0.337741
[2019-08-24 11:36:12,271] TRAIN Iter 236740: lr = 0.105435,	loss = 2.501618,	Top-1 err = 0.351855,	Top-5 err = 0.151123,	data_time = 0.050346,	train_time = 0.767466
[2019-08-24 11:36:26,693] TRAIN Iter 236760: lr = 0.105402,	loss = 2.490065,	Top-1 err = 0.351416,	Top-5 err = 0.151172,	data_time = 0.108078,	train_time = 0.721119
[2019-08-24 11:36:33,782] TRAIN Iter 236780: lr = 0.105368,	loss = 2.371632,	Top-1 err = 0.349854,	Top-5 err = 0.145996,	data_time = 0.050445,	train_time = 0.354438
[2019-08-24 11:36:50,715] TRAIN Iter 236800: lr = 0.105335,	loss = 2.369838,	Top-1 err = 0.355908,	Top-5 err = 0.153369,	data_time = 0.050628,	train_time = 0.846604
[2019-08-24 11:36:58,336] TRAIN Iter 236820: lr = 0.105302,	loss = 2.497748,	Top-1 err = 0.351807,	Top-5 err = 0.152686,	data_time = 0.050561,	train_time = 0.381062
[2019-08-24 11:37:12,239] TRAIN Iter 236840: lr = 0.105268,	loss = 2.433507,	Top-1 err = 0.347266,	Top-5 err = 0.148633,	data_time = 0.050296,	train_time = 0.695131
[2019-08-24 11:37:28,040] TRAIN Iter 236860: lr = 0.105235,	loss = 2.512813,	Top-1 err = 0.354834,	Top-5 err = 0.150928,	data_time = 0.050889,	train_time = 0.790027
[2019-08-24 11:37:35,468] TRAIN Iter 236880: lr = 0.105202,	loss = 2.561346,	Top-1 err = 0.351465,	Top-5 err = 0.151465,	data_time = 0.050284,	train_time = 0.371402
[2019-08-24 11:37:48,802] TRAIN Iter 236900: lr = 0.105168,	loss = 2.400164,	Top-1 err = 0.353760,	Top-5 err = 0.148828,	data_time = 0.050603,	train_time = 0.666673
[2019-08-24 11:38:03,462] TRAIN Iter 236920: lr = 0.105135,	loss = 2.382342,	Top-1 err = 0.353027,	Top-5 err = 0.149756,	data_time = 0.104532,	train_time = 0.732984
[2019-08-24 11:38:10,474] TRAIN Iter 236940: lr = 0.105102,	loss = 2.361403,	Top-1 err = 0.357812,	Top-5 err = 0.150781,	data_time = 0.050787,	train_time = 0.350612
[2019-08-24 11:38:25,695] TRAIN Iter 236960: lr = 0.105068,	loss = 2.436329,	Top-1 err = 0.351904,	Top-5 err = 0.148779,	data_time = 0.050789,	train_time = 0.761001
[2019-08-24 11:38:33,088] TRAIN Iter 236980: lr = 0.105035,	loss = 2.510749,	Top-1 err = 0.354492,	Top-5 err = 0.152686,	data_time = 0.118413,	train_time = 0.369628
[2019-08-24 11:38:47,292] TRAIN Iter 237000: lr = 0.105002,	loss = 2.464947,	Top-1 err = 0.355225,	Top-5 err = 0.151367,	data_time = 0.050233,	train_time = 0.710230
[2019-08-24 11:39:03,204] TRAIN Iter 237020: lr = 0.104968,	loss = 2.435762,	Top-1 err = 0.360107,	Top-5 err = 0.151660,	data_time = 0.050732,	train_time = 0.795587
[2019-08-24 11:39:10,266] TRAIN Iter 237040: lr = 0.104935,	loss = 2.439899,	Top-1 err = 0.357715,	Top-5 err = 0.156885,	data_time = 0.050934,	train_time = 0.353057
[2019-08-24 11:39:27,221] TRAIN Iter 237060: lr = 0.104902,	loss = 2.393412,	Top-1 err = 0.350928,	Top-5 err = 0.147266,	data_time = 0.050914,	train_time = 0.847744
[2019-08-24 11:39:43,031] TRAIN Iter 237080: lr = 0.104868,	loss = 2.402302,	Top-1 err = 0.354150,	Top-5 err = 0.152197,	data_time = 0.050935,	train_time = 0.790467
[2019-08-24 11:39:50,000] TRAIN Iter 237100: lr = 0.104835,	loss = 2.472832,	Top-1 err = 0.358691,	Top-5 err = 0.150537,	data_time = 0.050570,	train_time = 0.348460
[2019-08-24 11:40:05,880] TRAIN Iter 237120: lr = 0.104802,	loss = 2.341423,	Top-1 err = 0.354687,	Top-5 err = 0.151709,	data_time = 0.050756,	train_time = 0.793993
[2019-08-24 11:40:13,387] TRAIN Iter 237140: lr = 0.104768,	loss = 2.511861,	Top-1 err = 0.352051,	Top-5 err = 0.152979,	data_time = 0.050586,	train_time = 0.375346
[2019-08-24 11:40:28,430] TRAIN Iter 237160: lr = 0.104735,	loss = 2.409603,	Top-1 err = 0.350977,	Top-5 err = 0.150293,	data_time = 0.050382,	train_time = 0.752112
[2019-08-24 11:40:45,623] TRAIN Iter 237180: lr = 0.104702,	loss = 2.318302,	Top-1 err = 0.354395,	Top-5 err = 0.149219,	data_time = 0.050955,	train_time = 0.859652
[2019-08-24 11:40:52,664] TRAIN Iter 237200: lr = 0.104668,	loss = 2.366151,	Top-1 err = 0.357227,	Top-5 err = 0.150293,	data_time = 0.149405,	train_time = 0.352031
[2019-08-24 11:41:08,092] TRAIN Iter 237220: lr = 0.104635,	loss = 2.396820,	Top-1 err = 0.350439,	Top-5 err = 0.149805,	data_time = 0.050475,	train_time = 0.771392
[2019-08-24 11:41:23,890] TRAIN Iter 237240: lr = 0.104602,	loss = 2.398238,	Top-1 err = 0.349463,	Top-5 err = 0.146484,	data_time = 0.127800,	train_time = 0.789880
[2019-08-24 11:41:30,608] TRAIN Iter 237260: lr = 0.104568,	loss = 2.290652,	Top-1 err = 0.351172,	Top-5 err = 0.146924,	data_time = 0.050498,	train_time = 0.335854
[2019-08-24 11:41:47,617] TRAIN Iter 237280: lr = 0.104535,	loss = 2.362495,	Top-1 err = 0.352832,	Top-5 err = 0.154102,	data_time = 0.050780,	train_time = 0.850434
[2019-08-24 11:41:55,716] TRAIN Iter 237300: lr = 0.104502,	loss = 2.385988,	Top-1 err = 0.356641,	Top-5 err = 0.153760,	data_time = 0.050754,	train_time = 0.404952
[2019-08-24 11:42:11,221] TRAIN Iter 237320: lr = 0.104468,	loss = 2.440137,	Top-1 err = 0.355322,	Top-5 err = 0.152441,	data_time = 0.050871,	train_time = 0.775209
[2019-08-24 11:42:26,536] TRAIN Iter 237340: lr = 0.104435,	loss = 2.393619,	Top-1 err = 0.356201,	Top-5 err = 0.153760,	data_time = 0.050344,	train_time = 0.765770
[2019-08-24 11:42:33,921] TRAIN Iter 237360: lr = 0.104402,	loss = 2.487741,	Top-1 err = 0.356055,	Top-5 err = 0.152295,	data_time = 0.140998,	train_time = 0.369213
[2019-08-24 11:42:49,563] TRAIN Iter 237380: lr = 0.104368,	loss = 2.437483,	Top-1 err = 0.353516,	Top-5 err = 0.152148,	data_time = 0.050430,	train_time = 0.782106
[2019-08-24 11:43:06,910] TRAIN Iter 237400: lr = 0.104335,	loss = 2.512875,	Top-1 err = 0.352686,	Top-5 err = 0.155518,	data_time = 0.050866,	train_time = 0.867339
[2019-08-24 11:43:15,557] TRAIN Iter 237420: lr = 0.104302,	loss = 2.470218,	Top-1 err = 0.354883,	Top-5 err = 0.152979,	data_time = 0.051178,	train_time = 0.432317
[2019-08-24 11:43:29,538] TRAIN Iter 237440: lr = 0.104268,	loss = 2.440352,	Top-1 err = 0.357568,	Top-5 err = 0.152832,	data_time = 0.050547,	train_time = 0.699016
[2019-08-24 11:43:37,380] TRAIN Iter 237460: lr = 0.104235,	loss = 2.408020,	Top-1 err = 0.355664,	Top-5 err = 0.150586,	data_time = 0.109154,	train_time = 0.392124
[2019-08-24 11:43:51,626] TRAIN Iter 237480: lr = 0.104202,	loss = 2.458731,	Top-1 err = 0.354346,	Top-5 err = 0.154199,	data_time = 0.050629,	train_time = 0.712247
[2019-08-24 11:44:08,533] TRAIN Iter 237500: lr = 0.104168,	loss = 2.494912,	Top-1 err = 0.357666,	Top-5 err = 0.157227,	data_time = 0.050823,	train_time = 0.845378
[2019-08-24 11:44:15,869] TRAIN Iter 237520: lr = 0.104135,	loss = 2.392271,	Top-1 err = 0.359912,	Top-5 err = 0.150586,	data_time = 0.050519,	train_time = 0.366746
[2019-08-24 11:44:31,301] TRAIN Iter 237540: lr = 0.104102,	loss = 2.430711,	Top-1 err = 0.354736,	Top-5 err = 0.152637,	data_time = 0.050329,	train_time = 0.771574
[2019-08-24 11:44:47,207] TRAIN Iter 237560: lr = 0.104068,	loss = 2.411949,	Top-1 err = 0.354102,	Top-5 err = 0.152344,	data_time = 0.050437,	train_time = 0.795282
[2019-08-24 11:44:53,993] TRAIN Iter 237580: lr = 0.104035,	loss = 2.507310,	Top-1 err = 0.359326,	Top-5 err = 0.152881,	data_time = 0.050374,	train_time = 0.339326
[2019-08-24 11:45:10,755] TRAIN Iter 237600: lr = 0.104002,	loss = 2.409211,	Top-1 err = 0.356543,	Top-5 err = 0.152979,	data_time = 0.050399,	train_time = 0.838077
[2019-08-24 11:45:18,453] TRAIN Iter 237620: lr = 0.103968,	loss = 2.492233,	Top-1 err = 0.356250,	Top-5 err = 0.149658,	data_time = 0.050356,	train_time = 0.384890
[2019-08-24 11:45:32,466] TRAIN Iter 237640: lr = 0.103935,	loss = 2.468160,	Top-1 err = 0.357520,	Top-5 err = 0.152734,	data_time = 0.050172,	train_time = 0.700636
[2019-08-24 11:45:49,669] TRAIN Iter 237660: lr = 0.103902,	loss = 2.446467,	Top-1 err = 0.353125,	Top-5 err = 0.152881,	data_time = 0.050666,	train_time = 0.860130
[2019-08-24 11:45:56,732] TRAIN Iter 237680: lr = 0.103868,	loss = 2.441804,	Top-1 err = 0.355615,	Top-5 err = 0.151758,	data_time = 0.051002,	train_time = 0.353115
[2019-08-24 11:46:13,377] TRAIN Iter 237700: lr = 0.103835,	loss = 2.498927,	Top-1 err = 0.359082,	Top-5 err = 0.153662,	data_time = 0.050694,	train_time = 0.832261
[2019-08-24 11:46:32,683] TRAIN Iter 237720: lr = 0.103802,	loss = 2.387102,	Top-1 err = 0.353809,	Top-5 err = 0.152197,	data_time = 0.050473,	train_time = 0.965258
[2019-08-24 11:46:39,459] TRAIN Iter 237740: lr = 0.103768,	loss = 2.489894,	Top-1 err = 0.361670,	Top-5 err = 0.155127,	data_time = 0.144137,	train_time = 0.338811
[2019-08-24 11:46:56,745] TRAIN Iter 237760: lr = 0.103735,	loss = 2.386641,	Top-1 err = 0.354932,	Top-5 err = 0.155176,	data_time = 0.050125,	train_time = 0.864253
[2019-08-24 11:47:03,660] TRAIN Iter 237780: lr = 0.103702,	loss = 2.382457,	Top-1 err = 0.355029,	Top-5 err = 0.150781,	data_time = 0.131308,	train_time = 0.345746
[2019-08-24 11:47:19,541] TRAIN Iter 237800: lr = 0.103668,	loss = 2.447408,	Top-1 err = 0.357617,	Top-5 err = 0.153320,	data_time = 0.049897,	train_time = 0.794037
[2019-08-24 11:48:14,207] TRAIN Iter 237820: lr = 0.103635,	loss = 2.499309,	Top-1 err = 0.358153,	Top-5 err = 0.158640,	data_time = 0.554156,	train_time = 2.733291
[2019-08-24 11:48:21,289] TRAIN Iter 237840: lr = 0.103602,	loss = 2.500662,	Top-1 err = 0.358594,	Top-5 err = 0.151904,	data_time = 0.050400,	train_time = 0.354064
[2019-08-24 11:48:37,287] TRAIN Iter 237860: lr = 0.103568,	loss = 2.497067,	Top-1 err = 0.347412,	Top-5 err = 0.148096,	data_time = 0.050467,	train_time = 0.799884
[2019-08-24 11:48:45,157] TRAIN Iter 237880: lr = 0.103535,	loss = 2.427595,	Top-1 err = 0.344727,	Top-5 err = 0.150439,	data_time = 0.050498,	train_time = 0.393494
[2019-08-24 11:48:59,400] TRAIN Iter 237900: lr = 0.103502,	loss = 2.421179,	Top-1 err = 0.347314,	Top-5 err = 0.146436,	data_time = 0.050327,	train_time = 0.712138
[2019-08-24 11:49:13,956] TRAIN Iter 237920: lr = 0.103468,	loss = 2.461724,	Top-1 err = 0.350781,	Top-5 err = 0.147559,	data_time = 0.050499,	train_time = 0.727787
[2019-08-24 11:49:21,158] TRAIN Iter 237940: lr = 0.103435,	loss = 2.437400,	Top-1 err = 0.343652,	Top-5 err = 0.149512,	data_time = 0.050674,	train_time = 0.360061
[2019-08-24 11:49:36,790] TRAIN Iter 237960: lr = 0.103402,	loss = 2.413070,	Top-1 err = 0.349365,	Top-5 err = 0.150488,	data_time = 0.050525,	train_time = 0.781601
[2019-08-24 11:49:52,475] TRAIN Iter 237980: lr = 0.103368,	loss = 2.432879,	Top-1 err = 0.355469,	Top-5 err = 0.151221,	data_time = 2.126754,	train_time = 0.784255
[2019-08-24 11:49:59,461] TRAIN Iter 238000: lr = 0.103335,	loss = 2.390644,	Top-1 err = 0.355518,	Top-5 err = 0.149951,	data_time = 0.138643,	train_time = 0.349247
[2019-08-24 11:50:14,292] TRAIN Iter 238020: lr = 0.103302,	loss = 2.430737,	Top-1 err = 0.351123,	Top-5 err = 0.149072,	data_time = 0.050408,	train_time = 0.741541
[2019-08-24 11:50:21,692] TRAIN Iter 238040: lr = 0.103268,	loss = 2.446974,	Top-1 err = 0.347314,	Top-5 err = 0.144434,	data_time = 0.050560,	train_time = 0.370018
[2019-08-24 11:50:34,958] TRAIN Iter 238060: lr = 0.103235,	loss = 2.407143,	Top-1 err = 0.351172,	Top-5 err = 0.151807,	data_time = 0.050388,	train_time = 0.663267
[2019-08-24 11:50:51,054] TRAIN Iter 238080: lr = 0.103202,	loss = 2.380480,	Top-1 err = 0.349414,	Top-5 err = 0.147998,	data_time = 0.050343,	train_time = 0.804785
[2019-08-24 11:50:57,834] TRAIN Iter 238100: lr = 0.103168,	loss = 2.516162,	Top-1 err = 0.352295,	Top-5 err = 0.148389,	data_time = 0.135525,	train_time = 0.338982
[2019-08-24 11:51:12,102] TRAIN Iter 238120: lr = 0.103135,	loss = 2.384193,	Top-1 err = 0.349219,	Top-5 err = 0.147314,	data_time = 0.050426,	train_time = 0.713380
[2019-08-24 11:51:26,666] TRAIN Iter 238140: lr = 0.103102,	loss = 2.413572,	Top-1 err = 0.351465,	Top-5 err = 0.152100,	data_time = 0.050702,	train_time = 0.728183
[2019-08-24 11:51:33,471] TRAIN Iter 238160: lr = 0.103068,	loss = 2.419275,	Top-1 err = 0.351318,	Top-5 err = 0.150293,	data_time = 0.050316,	train_time = 0.340259
[2019-08-24 11:51:50,735] TRAIN Iter 238180: lr = 0.103035,	loss = 2.436156,	Top-1 err = 0.352393,	Top-5 err = 0.149414,	data_time = 0.050450,	train_time = 0.863203
[2019-08-24 11:51:58,835] TRAIN Iter 238200: lr = 0.103002,	loss = 2.365277,	Top-1 err = 0.354346,	Top-5 err = 0.148096,	data_time = 0.142668,	train_time = 0.404982
[2019-08-24 11:52:11,172] TRAIN Iter 238220: lr = 0.102968,	loss = 2.403738,	Top-1 err = 0.353027,	Top-5 err = 0.151855,	data_time = 0.050454,	train_time = 0.616838
[2019-08-24 11:52:26,761] TRAIN Iter 238240: lr = 0.102935,	loss = 2.340030,	Top-1 err = 0.355176,	Top-5 err = 0.149609,	data_time = 0.050529,	train_time = 0.779399
[2019-08-24 11:52:33,682] TRAIN Iter 238260: lr = 0.102902,	loss = 2.450860,	Top-1 err = 0.349316,	Top-5 err = 0.147217,	data_time = 0.050502,	train_time = 0.346065
[2019-08-24 11:52:50,324] TRAIN Iter 238280: lr = 0.102868,	loss = 2.439629,	Top-1 err = 0.352930,	Top-5 err = 0.151318,	data_time = 0.052323,	train_time = 0.832091
[2019-08-24 11:53:03,267] TRAIN Iter 238300: lr = 0.102835,	loss = 2.372802,	Top-1 err = 0.349023,	Top-5 err = 0.148438,	data_time = 0.050560,	train_time = 0.647136
[2019-08-24 11:53:10,556] TRAIN Iter 238320: lr = 0.102802,	loss = 2.364297,	Top-1 err = 0.349854,	Top-5 err = 0.145557,	data_time = 0.050431,	train_time = 0.364397
[2019-08-24 11:53:27,108] TRAIN Iter 238340: lr = 0.102768,	loss = 2.436494,	Top-1 err = 0.354492,	Top-5 err = 0.149561,	data_time = 0.050627,	train_time = 0.827595
[2019-08-24 11:53:34,779] TRAIN Iter 238360: lr = 0.102735,	loss = 2.450490,	Top-1 err = 0.358203,	Top-5 err = 0.150879,	data_time = 0.050627,	train_time = 0.383560
[2019-08-24 11:53:49,867] TRAIN Iter 238380: lr = 0.102702,	loss = 2.327555,	Top-1 err = 0.352832,	Top-5 err = 0.151953,	data_time = 0.050438,	train_time = 0.754395
[2019-08-24 11:54:05,168] TRAIN Iter 238400: lr = 0.102668,	loss = 2.416865,	Top-1 err = 0.352881,	Top-5 err = 0.151904,	data_time = 0.050973,	train_time = 0.765029
[2019-08-24 11:54:12,252] TRAIN Iter 238420: lr = 0.102635,	loss = 2.419645,	Top-1 err = 0.351904,	Top-5 err = 0.149512,	data_time = 0.050399,	train_time = 0.354174
[2019-08-24 11:54:26,924] TRAIN Iter 238440: lr = 0.102602,	loss = 2.457335,	Top-1 err = 0.352783,	Top-5 err = 0.152637,	data_time = 0.050693,	train_time = 0.733556
[2019-08-24 11:54:43,228] TRAIN Iter 238460: lr = 0.102568,	loss = 2.401229,	Top-1 err = 0.348340,	Top-5 err = 0.148340,	data_time = 0.050431,	train_time = 0.815186
[2019-08-24 11:54:50,515] TRAIN Iter 238480: lr = 0.102535,	loss = 2.408322,	Top-1 err = 0.345996,	Top-5 err = 0.149805,	data_time = 0.050514,	train_time = 0.364380
[2019-08-24 11:55:06,467] TRAIN Iter 238500: lr = 0.102502,	loss = 2.379628,	Top-1 err = 0.355176,	Top-5 err = 0.152148,	data_time = 0.050430,	train_time = 0.797556
[2019-08-24 11:55:13,479] TRAIN Iter 238520: lr = 0.102468,	loss = 2.338947,	Top-1 err = 0.353174,	Top-5 err = 0.150635,	data_time = 0.165636,	train_time = 0.350611
[2019-08-24 11:55:29,498] TRAIN Iter 238540: lr = 0.102435,	loss = 2.497674,	Top-1 err = 0.355859,	Top-5 err = 0.155469,	data_time = 0.050458,	train_time = 0.800908
[2019-08-24 11:55:44,421] TRAIN Iter 238560: lr = 0.102402,	loss = 2.410061,	Top-1 err = 0.349658,	Top-5 err = 0.148389,	data_time = 0.050241,	train_time = 0.746165
[2019-08-24 11:55:51,908] TRAIN Iter 238580: lr = 0.102368,	loss = 2.460034,	Top-1 err = 0.352490,	Top-5 err = 0.152002,	data_time = 0.050697,	train_time = 0.374347
[2019-08-24 11:56:08,425] TRAIN Iter 238600: lr = 0.102335,	loss = 2.419293,	Top-1 err = 0.352344,	Top-5 err = 0.151904,	data_time = 0.050460,	train_time = 0.825795
[2019-08-24 11:56:26,583] TRAIN Iter 238620: lr = 0.102302,	loss = 2.454576,	Top-1 err = 0.351660,	Top-5 err = 0.152783,	data_time = 0.050401,	train_time = 0.907913
[2019-08-24 11:56:33,391] TRAIN Iter 238640: lr = 0.102268,	loss = 2.374812,	Top-1 err = 0.351807,	Top-5 err = 0.152393,	data_time = 0.050531,	train_time = 0.340387
[2019-08-24 11:56:49,591] TRAIN Iter 238660: lr = 0.102235,	loss = 2.409066,	Top-1 err = 0.352539,	Top-5 err = 0.146436,	data_time = 0.050436,	train_time = 0.809968
[2019-08-24 11:56:56,961] TRAIN Iter 238680: lr = 0.102202,	loss = 2.450858,	Top-1 err = 0.360352,	Top-5 err = 0.152246,	data_time = 0.167682,	train_time = 0.368516
[2019-08-24 11:57:13,313] TRAIN Iter 238700: lr = 0.102168,	loss = 2.465594,	Top-1 err = 0.357080,	Top-5 err = 0.157275,	data_time = 0.050871,	train_time = 0.817578
[2019-08-24 11:57:27,820] TRAIN Iter 238720: lr = 0.102135,	loss = 2.337655,	Top-1 err = 0.350098,	Top-5 err = 0.153613,	data_time = 0.131227,	train_time = 0.725295
[2019-08-24 11:57:34,625] TRAIN Iter 238740: lr = 0.102102,	loss = 2.448821,	Top-1 err = 0.363379,	Top-5 err = 0.153906,	data_time = 0.050460,	train_time = 0.340285
[2019-08-24 11:57:51,499] TRAIN Iter 238760: lr = 0.102068,	loss = 2.408175,	Top-1 err = 0.355225,	Top-5 err = 0.152734,	data_time = 0.050492,	train_time = 0.843646
[2019-08-24 11:58:09,109] TRAIN Iter 238780: lr = 0.102035,	loss = 2.401950,	Top-1 err = 0.349365,	Top-5 err = 0.148584,	data_time = 0.050774,	train_time = 0.880482
[2019-08-24 11:58:16,258] TRAIN Iter 238800: lr = 0.102002,	loss = 2.503047,	Top-1 err = 0.359033,	Top-5 err = 0.153516,	data_time = 0.050303,	train_time = 0.357455
[2019-08-24 11:58:33,406] TRAIN Iter 238820: lr = 0.101968,	loss = 2.556615,	Top-1 err = 0.351270,	Top-5 err = 0.153174,	data_time = 0.050484,	train_time = 0.857406
[2019-08-24 11:58:40,514] TRAIN Iter 238840: lr = 0.101935,	loss = 2.440346,	Top-1 err = 0.358887,	Top-5 err = 0.151953,	data_time = 0.050584,	train_time = 0.355373
[2019-08-24 11:58:58,300] TRAIN Iter 238860: lr = 0.101902,	loss = 2.395858,	Top-1 err = 0.353516,	Top-5 err = 0.151953,	data_time = 0.050370,	train_time = 0.889272
[2019-08-24 11:59:15,851] TRAIN Iter 238880: lr = 0.101868,	loss = 2.358296,	Top-1 err = 0.347412,	Top-5 err = 0.150244,	data_time = 0.050902,	train_time = 0.877536
[2019-08-24 11:59:22,598] TRAIN Iter 238900: lr = 0.101835,	loss = 2.525497,	Top-1 err = 0.356738,	Top-5 err = 0.157959,	data_time = 0.050472,	train_time = 0.337364
[2019-08-24 11:59:39,650] TRAIN Iter 238920: lr = 0.101802,	loss = 2.366590,	Top-1 err = 0.357031,	Top-5 err = 0.153906,	data_time = 0.050560,	train_time = 0.852577
[2019-08-24 11:59:57,671] TRAIN Iter 238940: lr = 0.101768,	loss = 2.352882,	Top-1 err = 0.351758,	Top-5 err = 0.151709,	data_time = 0.050708,	train_time = 0.900999
[2019-08-24 12:00:05,490] TRAIN Iter 238960: lr = 0.101735,	loss = 2.482857,	Top-1 err = 0.357227,	Top-5 err = 0.150049,	data_time = 0.050947,	train_time = 0.390979
[2019-08-24 12:00:23,181] TRAIN Iter 238980: lr = 0.101702,	loss = 2.545668,	Top-1 err = 0.353467,	Top-5 err = 0.155371,	data_time = 0.050201,	train_time = 0.884520
[2019-08-24 12:00:29,987] TRAIN Iter 239000: lr = 0.101668,	loss = 2.430002,	Top-1 err = 0.354004,	Top-5 err = 0.151562,	data_time = 0.050438,	train_time = 0.340263
[2019-08-24 12:00:48,797] TRAIN Iter 239020: lr = 0.101635,	loss = 2.397764,	Top-1 err = 0.356348,	Top-5 err = 0.153564,	data_time = 0.050052,	train_time = 0.940507
[2019-08-24 12:01:09,513] TRAIN Iter 239040: lr = 0.101602,	loss = 2.374946,	Top-1 err = 0.355469,	Top-5 err = 0.156152,	data_time = 0.200114,	train_time = 1.035779
[2019-08-24 12:01:15,598] TRAIN Iter 239060: lr = 0.101568,	loss = 2.449622,	Top-1 err = 0.358496,	Top-5 err = 0.154492,	data_time = 0.049966,	train_time = 0.304235
[2019-08-24 12:02:04,328] TRAIN Iter 239080: lr = 0.101535,	loss = 2.415483,	Top-1 err = 0.362725,	Top-5 err = 0.150587,	data_time = 0.050282,	train_time = 2.436478
[2019-08-24 12:02:11,644] TRAIN Iter 239100: lr = 0.101502,	loss = 2.426257,	Top-1 err = 0.350781,	Top-5 err = 0.148877,	data_time = 0.050886,	train_time = 0.365806
[2019-08-24 12:02:27,717] TRAIN Iter 239120: lr = 0.101468,	loss = 2.426865,	Top-1 err = 0.352832,	Top-5 err = 0.151562,	data_time = 0.050500,	train_time = 0.803637
[2019-08-24 12:02:42,693] TRAIN Iter 239140: lr = 0.101435,	loss = 2.445902,	Top-1 err = 0.350781,	Top-5 err = 0.150098,	data_time = 0.050461,	train_time = 0.748770
[2019-08-24 12:02:49,616] TRAIN Iter 239160: lr = 0.101402,	loss = 2.415023,	Top-1 err = 0.351465,	Top-5 err = 0.148926,	data_time = 0.050717,	train_time = 0.346113
[2019-08-24 12:03:05,177] TRAIN Iter 239180: lr = 0.101368,	loss = 2.316715,	Top-1 err = 0.353760,	Top-5 err = 0.148975,	data_time = 0.050343,	train_time = 0.778039
[2019-08-24 12:03:19,598] TRAIN Iter 239200: lr = 0.101335,	loss = 2.491104,	Top-1 err = 0.352979,	Top-5 err = 0.149902,	data_time = 0.550628,	train_time = 0.721042
[2019-08-24 12:03:26,417] TRAIN Iter 239220: lr = 0.101302,	loss = 2.467739,	Top-1 err = 0.345313,	Top-5 err = 0.149121,	data_time = 0.050565,	train_time = 0.340955
[2019-08-24 12:03:43,771] TRAIN Iter 239240: lr = 0.101268,	loss = 2.402023,	Top-1 err = 0.349219,	Top-5 err = 0.146826,	data_time = 0.050396,	train_time = 0.867664
[2019-08-24 12:03:50,848] TRAIN Iter 239260: lr = 0.101235,	loss = 2.365900,	Top-1 err = 0.342773,	Top-5 err = 0.146338,	data_time = 0.050570,	train_time = 0.353853
[2019-08-24 12:04:05,221] TRAIN Iter 239280: lr = 0.101202,	loss = 2.381342,	Top-1 err = 0.351221,	Top-5 err = 0.148193,	data_time = 0.050442,	train_time = 0.718642
[2019-08-24 12:04:19,141] TRAIN Iter 239300: lr = 0.101168,	loss = 2.446793,	Top-1 err = 0.350439,	Top-5 err = 0.154590,	data_time = 0.050262,	train_time = 0.695989
[2019-08-24 12:04:25,847] TRAIN Iter 239320: lr = 0.101135,	loss = 2.465708,	Top-1 err = 0.348730,	Top-5 err = 0.147070,	data_time = 0.050313,	train_time = 0.335277
[2019-08-24 12:04:42,155] TRAIN Iter 239340: lr = 0.101102,	loss = 2.298805,	Top-1 err = 0.349121,	Top-5 err = 0.149463,	data_time = 0.050450,	train_time = 0.815382
[2019-08-24 12:04:59,146] TRAIN Iter 239360: lr = 0.101068,	loss = 2.406086,	Top-1 err = 0.345752,	Top-5 err = 0.148535,	data_time = 4.077403,	train_time = 0.849520
[2019-08-24 12:05:06,011] TRAIN Iter 239380: lr = 0.101035,	loss = 2.310263,	Top-1 err = 0.343164,	Top-5 err = 0.149658,	data_time = 0.050428,	train_time = 0.343249
[2019-08-24 12:05:21,800] TRAIN Iter 239400: lr = 0.101002,	loss = 2.403428,	Top-1 err = 0.353564,	Top-5 err = 0.146631,	data_time = 0.050610,	train_time = 0.789451
[2019-08-24 12:05:29,553] TRAIN Iter 239420: lr = 0.100968,	loss = 2.414677,	Top-1 err = 0.356006,	Top-5 err = 0.148486,	data_time = 0.050515,	train_time = 0.387648
[2019-08-24 12:05:43,070] TRAIN Iter 239440: lr = 0.100935,	loss = 2.403554,	Top-1 err = 0.352441,	Top-5 err = 0.149805,	data_time = 0.050255,	train_time = 0.675819
[2019-08-24 12:05:58,993] TRAIN Iter 239460: lr = 0.100902,	loss = 2.420648,	Top-1 err = 0.357178,	Top-5 err = 0.151758,	data_time = 0.050458,	train_time = 0.796148
[2019-08-24 12:06:06,085] TRAIN Iter 239480: lr = 0.100868,	loss = 2.352906,	Top-1 err = 0.350391,	Top-5 err = 0.149268,	data_time = 0.050578,	train_time = 0.354577
[2019-08-24 12:06:25,306] TRAIN Iter 239500: lr = 0.100835,	loss = 2.507354,	Top-1 err = 0.353320,	Top-5 err = 0.147900,	data_time = 0.050851,	train_time = 0.961024
[2019-08-24 12:06:38,364] TRAIN Iter 239520: lr = 0.100802,	loss = 2.430941,	Top-1 err = 0.349170,	Top-5 err = 0.145605,	data_time = 0.050327,	train_time = 0.652867
[2019-08-24 12:06:45,669] TRAIN Iter 239540: lr = 0.100768,	loss = 2.430471,	Top-1 err = 0.351758,	Top-5 err = 0.148877,	data_time = 0.177828,	train_time = 0.365232
[2019-08-24 12:06:59,024] TRAIN Iter 239560: lr = 0.100735,	loss = 2.450773,	Top-1 err = 0.353320,	Top-5 err = 0.151123,	data_time = 0.050340,	train_time = 0.667775
[2019-08-24 12:07:06,132] TRAIN Iter 239580: lr = 0.100702,	loss = 2.361731,	Top-1 err = 0.351318,	Top-5 err = 0.148877,	data_time = 0.050402,	train_time = 0.355374
[2019-08-24 12:07:20,185] TRAIN Iter 239600: lr = 0.100668,	loss = 2.450231,	Top-1 err = 0.349170,	Top-5 err = 0.152393,	data_time = 0.050475,	train_time = 0.702653
[2019-08-24 12:07:35,454] TRAIN Iter 239620: lr = 0.100635,	loss = 2.357520,	Top-1 err = 0.353076,	Top-5 err = 0.147949,	data_time = 0.050394,	train_time = 0.763435
[2019-08-24 12:07:42,525] TRAIN Iter 239640: lr = 0.100602,	loss = 2.349475,	Top-1 err = 0.351416,	Top-5 err = 0.148535,	data_time = 0.050420,	train_time = 0.353510
[2019-08-24 12:07:59,001] TRAIN Iter 239660: lr = 0.100568,	loss = 2.417301,	Top-1 err = 0.355176,	Top-5 err = 0.152197,	data_time = 0.050696,	train_time = 0.823787
[2019-08-24 12:08:15,094] TRAIN Iter 239680: lr = 0.100535,	loss = 2.417005,	Top-1 err = 0.353174,	Top-5 err = 0.151025,	data_time = 0.050829,	train_time = 0.804629
[2019-08-24 12:08:22,647] TRAIN Iter 239700: lr = 0.100502,	loss = 2.416391,	Top-1 err = 0.352441,	Top-5 err = 0.150342,	data_time = 0.050144,	train_time = 0.377654
[2019-08-24 12:08:39,150] TRAIN Iter 239720: lr = 0.100468,	loss = 2.383740,	Top-1 err = 0.351904,	Top-5 err = 0.147559,	data_time = 0.050492,	train_time = 0.825120
[2019-08-24 12:08:46,046] TRAIN Iter 239740: lr = 0.100435,	loss = 2.373567,	Top-1 err = 0.349316,	Top-5 err = 0.155469,	data_time = 0.050468,	train_time = 0.344776
[2019-08-24 12:09:01,839] TRAIN Iter 239760: lr = 0.100402,	loss = 2.426961,	Top-1 err = 0.350195,	Top-5 err = 0.147119,	data_time = 0.050394,	train_time = 0.789662
[2019-08-24 12:09:18,520] TRAIN Iter 239780: lr = 0.100368,	loss = 2.378954,	Top-1 err = 0.353809,	Top-5 err = 0.151416,	data_time = 0.050525,	train_time = 0.834037
[2019-08-24 12:09:25,628] TRAIN Iter 239800: lr = 0.100335,	loss = 2.391607,	Top-1 err = 0.349170,	Top-5 err = 0.152100,	data_time = 0.050653,	train_time = 0.355363
[2019-08-24 12:09:41,447] TRAIN Iter 239820: lr = 0.100302,	loss = 2.500398,	Top-1 err = 0.354346,	Top-5 err = 0.154785,	data_time = 0.050498,	train_time = 0.790966
[2019-08-24 12:09:56,851] TRAIN Iter 239840: lr = 0.100268,	loss = 2.505981,	Top-1 err = 0.350781,	Top-5 err = 0.151318,	data_time = 0.050469,	train_time = 0.770169
[2019-08-24 12:10:03,624] TRAIN Iter 239860: lr = 0.100235,	loss = 2.367541,	Top-1 err = 0.350391,	Top-5 err = 0.150830,	data_time = 0.050353,	train_time = 0.338653
[2019-08-24 12:10:20,303] TRAIN Iter 239880: lr = 0.100202,	loss = 2.414899,	Top-1 err = 0.355029,	Top-5 err = 0.149023,	data_time = 0.050606,	train_time = 0.833880
[2019-08-24 12:10:27,599] TRAIN Iter 239900: lr = 0.100168,	loss = 2.443086,	Top-1 err = 0.353955,	Top-5 err = 0.149756,	data_time = 0.050187,	train_time = 0.364782
[2019-08-24 12:10:41,364] TRAIN Iter 239920: lr = 0.100135,	loss = 2.496893,	Top-1 err = 0.350342,	Top-5 err = 0.151855,	data_time = 0.050357,	train_time = 0.688245
[2019-08-24 12:10:56,671] TRAIN Iter 239940: lr = 0.100102,	loss = 2.467470,	Top-1 err = 0.352100,	Top-5 err = 0.148438,	data_time = 0.051100,	train_time = 0.765361
[2019-08-24 12:11:03,522] TRAIN Iter 239960: lr = 0.100068,	loss = 2.390399,	Top-1 err = 0.353516,	Top-5 err = 0.150928,	data_time = 0.050522,	train_time = 0.342541
[2019-08-24 12:11:19,669] TRAIN Iter 239980: lr = 0.100035,	loss = 2.534383,	Top-1 err = 0.349072,	Top-5 err = 0.151465,	data_time = 0.050504,	train_time = 0.807302
[2019-08-24 12:11:34,675] TRAIN Iter 240000: lr = 0.100002,	loss = 2.343413,	Top-1 err = 0.351367,	Top-5 err = 0.148682,	data_time = 0.050457,	train_time = 0.750286
[2019-08-24 12:12:35,915] TEST Iter 240000: loss = 2.294419,	Top-1 err = 0.331920,	Top-5 err = 0.124400,	val_time = 61.195230
[2019-08-24 12:12:42,137] TRAIN Iter 240020: lr = 0.099968,	loss = 2.359454,	Top-1 err = 0.347461,	Top-5 err = 0.151367,	data_time = 0.050400,	train_time = 0.311107
[2019-08-24 12:12:48,770] TRAIN Iter 240040: lr = 0.099935,	loss = 2.459370,	Top-1 err = 0.355664,	Top-5 err = 0.151318,	data_time = 0.050895,	train_time = 0.331633
[2019-08-24 12:12:55,351] TRAIN Iter 240060: lr = 0.099902,	loss = 2.392841,	Top-1 err = 0.356738,	Top-5 err = 0.151221,	data_time = 0.050418,	train_time = 0.329026
[2019-08-24 12:13:03,664] TRAIN Iter 240080: lr = 0.099868,	loss = 2.419449,	Top-1 err = 0.349365,	Top-5 err = 0.149609,	data_time = 0.050593,	train_time = 0.415598
[2019-08-24 12:13:18,496] TRAIN Iter 240100: lr = 0.099835,	loss = 2.491306,	Top-1 err = 0.355859,	Top-5 err = 0.152637,	data_time = 0.226511,	train_time = 0.741593
[2019-08-24 12:13:29,595] TRAIN Iter 240120: lr = 0.099802,	loss = 2.382297,	Top-1 err = 0.351172,	Top-5 err = 0.152832,	data_time = 0.703634,	train_time = 0.554938
[2019-08-24 12:13:44,175] TRAIN Iter 240140: lr = 0.099768,	loss = 2.527616,	Top-1 err = 0.355322,	Top-5 err = 0.148047,	data_time = 0.121313,	train_time = 0.728970
[2019-08-24 12:13:53,629] TRAIN Iter 240160: lr = 0.099735,	loss = 2.426666,	Top-1 err = 0.357373,	Top-5 err = 0.152490,	data_time = 0.050704,	train_time = 0.472698
[2019-08-24 12:14:09,741] TRAIN Iter 240180: lr = 0.099702,	loss = 2.475509,	Top-1 err = 0.358252,	Top-5 err = 0.151855,	data_time = 0.050428,	train_time = 0.805587
[2019-08-24 12:14:23,271] TRAIN Iter 240200: lr = 0.099668,	loss = 2.453365,	Top-1 err = 0.352441,	Top-5 err = 0.150146,	data_time = 0.050496,	train_time = 0.676487
[2019-08-24 12:14:34,447] TRAIN Iter 240220: lr = 0.099635,	loss = 2.532207,	Top-1 err = 0.352588,	Top-5 err = 0.149756,	data_time = 0.050537,	train_time = 0.558768
[2019-08-24 12:14:52,252] TRAIN Iter 240240: lr = 0.099602,	loss = 2.483088,	Top-1 err = 0.356543,	Top-5 err = 0.155469,	data_time = 0.050350,	train_time = 0.890241
[2019-08-24 12:15:05,310] TRAIN Iter 240260: lr = 0.099568,	loss = 2.412515,	Top-1 err = 0.358057,	Top-5 err = 0.151270,	data_time = 0.050146,	train_time = 0.652876
[2019-08-24 12:15:17,137] TRAIN Iter 240280: lr = 0.099535,	loss = 2.407829,	Top-1 err = 0.359229,	Top-5 err = 0.154688,	data_time = 1.010563,	train_time = 0.591371
[2019-08-24 12:15:32,428] TRAIN Iter 240300: lr = 0.099502,	loss = 2.437570,	Top-1 err = 0.348682,	Top-5 err = 0.148242,	data_time = 0.049890,	train_time = 0.764547
[2019-08-24 12:15:39,651] TRAIN Iter 240320: lr = 0.099468,	loss = 2.729919,	Top-1 err = 0.357194,	Top-5 err = 0.155802,	data_time = 0.007163,	train_time = 0.361101
[2019-08-24 12:16:27,663] TRAIN Iter 240340: lr = 0.099435,	loss = 2.458742,	Top-1 err = 0.353955,	Top-5 err = 0.146094,	data_time = 0.050700,	train_time = 2.400597
[2019-08-24 12:16:49,413] TRAIN Iter 240360: lr = 0.099402,	loss = 2.533458,	Top-1 err = 0.350781,	Top-5 err = 0.155078,	data_time = 0.050905,	train_time = 1.087459
[2019-08-24 12:16:58,778] TRAIN Iter 240380: lr = 0.099368,	loss = 2.439692,	Top-1 err = 0.352148,	Top-5 err = 0.145947,	data_time = 0.050307,	train_time = 0.468268
[2019-08-24 12:17:08,512] TRAIN Iter 240400: lr = 0.099335,	loss = 2.346164,	Top-1 err = 0.350293,	Top-5 err = 0.147803,	data_time = 0.120363,	train_time = 0.486676
[2019-08-24 12:17:16,884] TRAIN Iter 240420: lr = 0.099302,	loss = 2.446393,	Top-1 err = 0.349463,	Top-5 err = 0.146680,	data_time = 0.050591,	train_time = 0.418572
[2019-08-24 12:17:24,492] TRAIN Iter 240440: lr = 0.099268,	loss = 2.387133,	Top-1 err = 0.346875,	Top-5 err = 0.147217,	data_time = 0.050322,	train_time = 0.380387
[2019-08-24 12:17:35,529] TRAIN Iter 240460: lr = 0.099235,	loss = 2.371725,	Top-1 err = 0.346729,	Top-5 err = 0.141943,	data_time = 0.050863,	train_time = 0.551840
[2019-08-24 12:17:43,266] TRAIN Iter 240480: lr = 0.099202,	loss = 2.336417,	Top-1 err = 0.349414,	Top-5 err = 0.149561,	data_time = 0.050437,	train_time = 0.386857
[2019-08-24 12:17:58,814] TRAIN Iter 240500: lr = 0.099168,	loss = 2.395706,	Top-1 err = 0.348682,	Top-5 err = 0.147070,	data_time = 0.050577,	train_time = 0.777374
[2019-08-24 12:18:13,265] TRAIN Iter 240520: lr = 0.099135,	loss = 2.452891,	Top-1 err = 0.348486,	Top-5 err = 0.145410,	data_time = 0.050379,	train_time = 0.722531
[2019-08-24 12:18:21,306] TRAIN Iter 240540: lr = 0.099102,	loss = 2.415858,	Top-1 err = 0.351123,	Top-5 err = 0.149707,	data_time = 0.050882,	train_time = 0.402009
[2019-08-24 12:18:35,970] TRAIN Iter 240560: lr = 0.099068,	loss = 2.343101,	Top-1 err = 0.345557,	Top-5 err = 0.149414,	data_time = 0.050377,	train_time = 0.733190
[2019-08-24 12:18:50,250] TRAIN Iter 240580: lr = 0.099035,	loss = 2.338892,	Top-1 err = 0.346045,	Top-5 err = 0.147510,	data_time = 1.522640,	train_time = 0.714015
[2019-08-24 12:18:58,381] TRAIN Iter 240600: lr = 0.099002,	loss = 2.376976,	Top-1 err = 0.344043,	Top-5 err = 0.146045,	data_time = 0.050420,	train_time = 0.406549
[2019-08-24 12:19:12,990] TRAIN Iter 240620: lr = 0.098968,	loss = 2.444321,	Top-1 err = 0.349902,	Top-5 err = 0.151318,	data_time = 0.050504,	train_time = 0.730395
[2019-08-24 12:19:20,822] TRAIN Iter 240640: lr = 0.098935,	loss = 2.294801,	Top-1 err = 0.347168,	Top-5 err = 0.148096,	data_time = 0.050583,	train_time = 0.391623
[2019-08-24 12:19:34,672] TRAIN Iter 240660: lr = 0.098902,	loss = 2.450639,	Top-1 err = 0.348730,	Top-5 err = 0.145898,	data_time = 0.050454,	train_time = 0.692480
[2019-08-24 12:19:50,476] TRAIN Iter 240680: lr = 0.098868,	loss = 2.372871,	Top-1 err = 0.348682,	Top-5 err = 0.152100,	data_time = 0.050201,	train_time = 0.790165
[2019-08-24 12:19:58,223] TRAIN Iter 240700: lr = 0.098835,	loss = 2.378238,	Top-1 err = 0.353516,	Top-5 err = 0.145264,	data_time = 0.050409,	train_time = 0.387327
[2019-08-24 12:20:13,673] TRAIN Iter 240720: lr = 0.098802,	loss = 2.433907,	Top-1 err = 0.346436,	Top-5 err = 0.149072,	data_time = 0.050452,	train_time = 0.772511
[2019-08-24 12:20:28,970] TRAIN Iter 240740: lr = 0.098768,	loss = 2.407900,	Top-1 err = 0.342139,	Top-5 err = 0.145752,	data_time = 2.718913,	train_time = 0.764816
[2019-08-24 12:20:36,874] TRAIN Iter 240760: lr = 0.098735,	loss = 2.356053,	Top-1 err = 0.346631,	Top-5 err = 0.146240,	data_time = 0.134795,	train_time = 0.395176
[2019-08-24 12:20:52,332] TRAIN Iter 240780: lr = 0.098702,	loss = 2.418690,	Top-1 err = 0.353076,	Top-5 err = 0.148730,	data_time = 0.050595,	train_time = 0.772895
[2019-08-24 12:20:59,761] TRAIN Iter 240800: lr = 0.098668,	loss = 2.406399,	Top-1 err = 0.354785,	Top-5 err = 0.154395,	data_time = 0.164842,	train_time = 0.371436
[2019-08-24 12:21:15,557] TRAIN Iter 240820: lr = 0.098635,	loss = 2.357823,	Top-1 err = 0.344043,	Top-5 err = 0.146094,	data_time = 0.181919,	train_time = 0.789770
[2019-08-24 12:21:31,144] TRAIN Iter 240840: lr = 0.098602,	loss = 2.507456,	Top-1 err = 0.347656,	Top-5 err = 0.149561,	data_time = 0.050307,	train_time = 0.779376
[2019-08-24 12:21:38,596] TRAIN Iter 240860: lr = 0.098568,	loss = 2.424912,	Top-1 err = 0.350342,	Top-5 err = 0.151416,	data_time = 0.050547,	train_time = 0.372590
[2019-08-24 12:21:54,241] TRAIN Iter 240880: lr = 0.098535,	loss = 2.443071,	Top-1 err = 0.346631,	Top-5 err = 0.147314,	data_time = 0.050361,	train_time = 0.782197
[2019-08-24 12:22:09,600] TRAIN Iter 240900: lr = 0.098502,	loss = 2.459347,	Top-1 err = 0.354199,	Top-5 err = 0.148047,	data_time = 0.419623,	train_time = 0.767953
[2019-08-24 12:22:17,475] TRAIN Iter 240920: lr = 0.098468,	loss = 2.262294,	Top-1 err = 0.344434,	Top-5 err = 0.147510,	data_time = 0.050499,	train_time = 0.393754
[2019-08-24 12:22:32,964] TRAIN Iter 240940: lr = 0.098435,	loss = 2.386326,	Top-1 err = 0.352881,	Top-5 err = 0.150537,	data_time = 0.050526,	train_time = 0.774429
[2019-08-24 12:22:40,284] TRAIN Iter 240960: lr = 0.098402,	loss = 2.414506,	Top-1 err = 0.349805,	Top-5 err = 0.148047,	data_time = 0.050354,	train_time = 0.365955
[2019-08-24 12:22:56,931] TRAIN Iter 240980: lr = 0.098368,	loss = 2.471886,	Top-1 err = 0.348291,	Top-5 err = 0.148340,	data_time = 0.052272,	train_time = 0.832347
[2019-08-24 12:23:12,604] TRAIN Iter 241000: lr = 0.098335,	loss = 2.485126,	Top-1 err = 0.352295,	Top-5 err = 0.150098,	data_time = 0.050529,	train_time = 0.783660
[2019-08-24 12:23:21,395] TRAIN Iter 241020: lr = 0.098302,	loss = 2.479685,	Top-1 err = 0.352832,	Top-5 err = 0.150977,	data_time = 0.050433,	train_time = 0.439517
[2019-08-24 12:23:35,935] TRAIN Iter 241040: lr = 0.098268,	loss = 2.449523,	Top-1 err = 0.352686,	Top-5 err = 0.152246,	data_time = 0.050304,	train_time = 0.726999
[2019-08-24 12:23:52,271] TRAIN Iter 241060: lr = 0.098235,	loss = 2.479915,	Top-1 err = 0.356055,	Top-5 err = 0.152295,	data_time = 0.050506,	train_time = 0.816755
[2019-08-24 12:23:59,991] TRAIN Iter 241080: lr = 0.098202,	loss = 2.322104,	Top-1 err = 0.352002,	Top-5 err = 0.145166,	data_time = 0.050874,	train_time = 0.386006
[2019-08-24 12:24:14,332] TRAIN Iter 241100: lr = 0.098168,	loss = 2.469118,	Top-1 err = 0.354443,	Top-5 err = 0.150391,	data_time = 0.050395,	train_time = 0.717019
[2019-08-24 12:24:24,611] TRAIN Iter 241120: lr = 0.098135,	loss = 2.384872,	Top-1 err = 0.356836,	Top-5 err = 0.151611,	data_time = 0.050315,	train_time = 0.513939
[2019-08-24 12:24:39,897] TRAIN Iter 241140: lr = 0.098102,	loss = 2.360735,	Top-1 err = 0.349463,	Top-5 err = 0.149902,	data_time = 1.885937,	train_time = 0.764280
[2019-08-24 12:24:54,086] TRAIN Iter 241160: lr = 0.098068,	loss = 2.528657,	Top-1 err = 0.353809,	Top-5 err = 0.151904,	data_time = 0.126933,	train_time = 0.709462
[2019-08-24 12:25:04,922] TRAIN Iter 241180: lr = 0.098035,	loss = 2.461545,	Top-1 err = 0.355469,	Top-5 err = 0.154248,	data_time = 0.050456,	train_time = 0.541761
[2019-08-24 12:25:18,939] TRAIN Iter 241200: lr = 0.098002,	loss = 2.388308,	Top-1 err = 0.350830,	Top-5 err = 0.152539,	data_time = 0.050435,	train_time = 0.700861
[2019-08-24 12:25:34,811] TRAIN Iter 241220: lr = 0.097968,	loss = 2.387617,	Top-1 err = 0.350195,	Top-5 err = 0.150537,	data_time = 0.050576,	train_time = 0.793556
[2019-08-24 12:25:45,170] TRAIN Iter 241240: lr = 0.097935,	loss = 2.472687,	Top-1 err = 0.349463,	Top-5 err = 0.150098,	data_time = 0.050586,	train_time = 0.517967
[2019-08-24 12:25:58,364] TRAIN Iter 241260: lr = 0.097902,	loss = 2.395748,	Top-1 err = 0.354443,	Top-5 err = 0.149756,	data_time = 0.050256,	train_time = 0.659694
[2019-08-24 12:26:09,444] TRAIN Iter 241280: lr = 0.097868,	loss = 2.502531,	Top-1 err = 0.354932,	Top-5 err = 0.152246,	data_time = 0.050336,	train_time = 0.553959
[2019-08-24 12:26:26,128] TRAIN Iter 241300: lr = 0.097835,	loss = 2.531497,	Top-1 err = 0.352588,	Top-5 err = 0.152002,	data_time = 3.280268,	train_time = 0.834192
[2019-08-24 12:26:41,971] TRAIN Iter 241320: lr = 0.097802,	loss = 2.432599,	Top-1 err = 0.355273,	Top-5 err = 0.151611,	data_time = 0.050384,	train_time = 0.792165
[2019-08-24 12:26:50,929] TRAIN Iter 241340: lr = 0.097768,	loss = 2.331558,	Top-1 err = 0.350537,	Top-5 err = 0.149414,	data_time = 0.050633,	train_time = 0.447856
[2019-08-24 12:27:11,098] TRAIN Iter 241360: lr = 0.097735,	loss = 2.454003,	Top-1 err = 0.351025,	Top-5 err = 0.148291,	data_time = 0.051491,	train_time = 1.008458
[2019-08-24 12:27:25,650] TRAIN Iter 241380: lr = 0.097702,	loss = 2.538241,	Top-1 err = 0.357666,	Top-5 err = 0.152051,	data_time = 2.728050,	train_time = 0.727571
[2019-08-24 12:27:32,976] TRAIN Iter 241400: lr = 0.097668,	loss = 2.483834,	Top-1 err = 0.355273,	Top-5 err = 0.151953,	data_time = 0.050502,	train_time = 0.366300
[2019-08-24 12:27:51,049] TRAIN Iter 241420: lr = 0.097635,	loss = 2.406693,	Top-1 err = 0.348291,	Top-5 err = 0.154004,	data_time = 0.050364,	train_time = 0.903597
[2019-08-24 12:27:58,187] TRAIN Iter 241440: lr = 0.097602,	loss = 2.457885,	Top-1 err = 0.346973,	Top-5 err = 0.147949,	data_time = 0.050376,	train_time = 0.356908
[2019-08-24 12:28:14,780] TRAIN Iter 241460: lr = 0.097568,	loss = 2.448834,	Top-1 err = 0.350781,	Top-5 err = 0.150781,	data_time = 0.050481,	train_time = 0.829625
[2019-08-24 12:28:32,513] TRAIN Iter 241480: lr = 0.097535,	loss = 2.448571,	Top-1 err = 0.354004,	Top-5 err = 0.155420,	data_time = 0.050194,	train_time = 0.886654
[2019-08-24 12:28:39,443] TRAIN Iter 241500: lr = 0.097502,	loss = 2.369242,	Top-1 err = 0.351660,	Top-5 err = 0.150586,	data_time = 0.136163,	train_time = 0.346450
[2019-08-24 12:28:57,472] TRAIN Iter 241520: lr = 0.097468,	loss = 2.425232,	Top-1 err = 0.355078,	Top-5 err = 0.149951,	data_time = 0.050020,	train_time = 0.901451
[2019-08-24 12:29:14,061] TRAIN Iter 241540: lr = 0.097435,	loss = 2.396306,	Top-1 err = 0.352148,	Top-5 err = 0.150781,	data_time = 0.820122,	train_time = 0.829422
[2019-08-24 12:29:20,759] TRAIN Iter 241560: lr = 0.097402,	loss = 2.395144,	Top-1 err = 0.355420,	Top-5 err = 0.150439,	data_time = 0.049913,	train_time = 0.334892
[2019-08-24 12:30:11,633] TRAIN Iter 241580: lr = 0.097368,	loss = 2.584762,	Top-1 err = 0.360366,	Top-5 err = 0.152044,	data_time = 0.050381,	train_time = 2.543702
[2019-08-24 12:30:18,217] TRAIN Iter 241600: lr = 0.097335,	loss = 2.438383,	Top-1 err = 0.352002,	Top-5 err = 0.149707,	data_time = 0.050456,	train_time = 0.329189
[2019-08-24 12:30:34,884] TRAIN Iter 241620: lr = 0.097302,	loss = 2.383126,	Top-1 err = 0.344824,	Top-5 err = 0.145508,	data_time = 0.050683,	train_time = 0.833299
[2019-08-24 12:30:49,182] TRAIN Iter 241640: lr = 0.097268,	loss = 2.380558,	Top-1 err = 0.343115,	Top-5 err = 0.145557,	data_time = 2.985381,	train_time = 0.714933
[2019-08-24 12:30:56,681] TRAIN Iter 241660: lr = 0.097235,	loss = 2.458094,	Top-1 err = 0.341650,	Top-5 err = 0.142187,	data_time = 0.050562,	train_time = 0.374912
[2019-08-24 12:31:12,697] TRAIN Iter 241680: lr = 0.097202,	loss = 2.380285,	Top-1 err = 0.344336,	Top-5 err = 0.145508,	data_time = 0.050235,	train_time = 0.800777
[2019-08-24 12:31:20,169] TRAIN Iter 241700: lr = 0.097168,	loss = 2.357103,	Top-1 err = 0.348633,	Top-5 err = 0.149414,	data_time = 0.050657,	train_time = 0.373609
[2019-08-24 12:31:35,489] TRAIN Iter 241720: lr = 0.097135,	loss = 2.329808,	Top-1 err = 0.343652,	Top-5 err = 0.144922,	data_time = 0.050196,	train_time = 0.765993
[2019-08-24 12:31:50,605] TRAIN Iter 241740: lr = 0.097102,	loss = 2.386579,	Top-1 err = 0.346973,	Top-5 err = 0.145313,	data_time = 0.050384,	train_time = 0.755767
[2019-08-24 12:31:57,509] TRAIN Iter 241760: lr = 0.097068,	loss = 2.420841,	Top-1 err = 0.353711,	Top-5 err = 0.148584,	data_time = 0.050372,	train_time = 0.345190
[2019-08-24 12:32:12,837] TRAIN Iter 241780: lr = 0.097035,	loss = 2.453189,	Top-1 err = 0.348340,	Top-5 err = 0.148486,	data_time = 0.050276,	train_time = 0.766360
[2019-08-24 12:32:23,623] TRAIN Iter 241800: lr = 0.097002,	loss = 2.395030,	Top-1 err = 0.349951,	Top-5 err = 0.149707,	data_time = 0.194637,	train_time = 0.539290
[2019-08-24 12:32:34,888] TRAIN Iter 241820: lr = 0.096968,	loss = 2.393867,	Top-1 err = 0.345020,	Top-5 err = 0.144287,	data_time = 0.050325,	train_time = 0.563267
[2019-08-24 12:32:50,810] TRAIN Iter 241840: lr = 0.096935,	loss = 2.389647,	Top-1 err = 0.351514,	Top-5 err = 0.147656,	data_time = 0.050366,	train_time = 0.796057
[2019-08-24 12:32:58,422] TRAIN Iter 241860: lr = 0.096902,	loss = 2.400769,	Top-1 err = 0.348389,	Top-5 err = 0.148242,	data_time = 0.050745,	train_time = 0.380584
[2019-08-24 12:33:13,767] TRAIN Iter 241880: lr = 0.096868,	loss = 2.451416,	Top-1 err = 0.347998,	Top-5 err = 0.145508,	data_time = 0.050949,	train_time = 0.767277
[2019-08-24 12:33:27,503] TRAIN Iter 241900: lr = 0.096835,	loss = 2.400190,	Top-1 err = 0.348047,	Top-5 err = 0.144043,	data_time = 0.050259,	train_time = 0.686781
[2019-08-24 12:33:34,776] TRAIN Iter 241920: lr = 0.096802,	loss = 2.397655,	Top-1 err = 0.343408,	Top-5 err = 0.145947,	data_time = 0.050591,	train_time = 0.363609
[2019-08-24 12:33:48,969] TRAIN Iter 241940: lr = 0.096768,	loss = 2.432667,	Top-1 err = 0.347998,	Top-5 err = 0.147070,	data_time = 0.050445,	train_time = 0.709635
[2019-08-24 12:34:01,714] TRAIN Iter 241960: lr = 0.096735,	loss = 2.439312,	Top-1 err = 0.349854,	Top-5 err = 0.146387,	data_time = 5.054731,	train_time = 0.637229
[2019-08-24 12:34:10,414] TRAIN Iter 241980: lr = 0.096702,	loss = 2.438502,	Top-1 err = 0.352686,	Top-5 err = 0.146387,	data_time = 0.150160,	train_time = 0.435000
[2019-08-24 12:34:26,434] TRAIN Iter 242000: lr = 0.096668,	loss = 2.443355,	Top-1 err = 0.352197,	Top-5 err = 0.148291,	data_time = 0.050334,	train_time = 0.800978
[2019-08-24 12:34:34,198] TRAIN Iter 242020: lr = 0.096635,	loss = 2.471776,	Top-1 err = 0.349609,	Top-5 err = 0.151318,	data_time = 0.168877,	train_time = 0.388182
[2019-08-24 12:34:48,263] TRAIN Iter 242040: lr = 0.096602,	loss = 2.360195,	Top-1 err = 0.354150,	Top-5 err = 0.151318,	data_time = 0.050869,	train_time = 0.703256
[2019-08-24 12:35:04,287] TRAIN Iter 242060: lr = 0.096568,	loss = 2.447461,	Top-1 err = 0.349658,	Top-5 err = 0.148096,	data_time = 0.050595,	train_time = 0.801181
[2019-08-24 12:35:11,533] TRAIN Iter 242080: lr = 0.096535,	loss = 2.420606,	Top-1 err = 0.348193,	Top-5 err = 0.145410,	data_time = 0.050735,	train_time = 0.362256
[2019-08-24 12:35:26,425] TRAIN Iter 242100: lr = 0.096502,	loss = 2.366715,	Top-1 err = 0.353418,	Top-5 err = 0.149219,	data_time = 0.050639,	train_time = 0.744615
[2019-08-24 12:35:39,226] TRAIN Iter 242120: lr = 0.096468,	loss = 2.352926,	Top-1 err = 0.353857,	Top-5 err = 0.152686,	data_time = 0.050800,	train_time = 0.640028
[2019-08-24 12:35:49,406] TRAIN Iter 242140: lr = 0.096435,	loss = 2.384732,	Top-1 err = 0.350293,	Top-5 err = 0.150391,	data_time = 0.050452,	train_time = 0.508979
[2019-08-24 12:36:05,564] TRAIN Iter 242160: lr = 0.096402,	loss = 2.389461,	Top-1 err = 0.349316,	Top-5 err = 0.145947,	data_time = 0.050496,	train_time = 0.807896
[2019-08-24 12:36:12,986] TRAIN Iter 242180: lr = 0.096368,	loss = 2.518057,	Top-1 err = 0.344873,	Top-5 err = 0.146533,	data_time = 0.050700,	train_time = 0.371093
[2019-08-24 12:36:27,238] TRAIN Iter 242200: lr = 0.096335,	loss = 2.405892,	Top-1 err = 0.349463,	Top-5 err = 0.149609,	data_time = 0.050540,	train_time = 0.712582
[2019-08-24 12:36:44,005] TRAIN Iter 242220: lr = 0.096302,	loss = 2.363091,	Top-1 err = 0.350049,	Top-5 err = 0.149658,	data_time = 0.050625,	train_time = 0.838328
[2019-08-24 12:36:51,151] TRAIN Iter 242240: lr = 0.096268,	loss = 2.438614,	Top-1 err = 0.345068,	Top-5 err = 0.149707,	data_time = 0.050486,	train_time = 0.357278
[2019-08-24 12:37:06,143] TRAIN Iter 242260: lr = 0.096235,	loss = 2.415130,	Top-1 err = 0.355469,	Top-5 err = 0.150781,	data_time = 0.050390,	train_time = 0.749581
[2019-08-24 12:37:18,733] TRAIN Iter 242280: lr = 0.096202,	loss = 2.416356,	Top-1 err = 0.353809,	Top-5 err = 0.153369,	data_time = 0.603510,	train_time = 0.629491
[2019-08-24 12:37:29,729] TRAIN Iter 242300: lr = 0.096168,	loss = 2.480089,	Top-1 err = 0.345703,	Top-5 err = 0.148340,	data_time = 0.050617,	train_time = 0.549782
[2019-08-24 12:37:46,384] TRAIN Iter 242320: lr = 0.096135,	loss = 2.398029,	Top-1 err = 0.348340,	Top-5 err = 0.149805,	data_time = 0.050944,	train_time = 0.832747
[2019-08-24 12:37:53,728] TRAIN Iter 242340: lr = 0.096102,	loss = 2.353994,	Top-1 err = 0.351514,	Top-5 err = 0.151611,	data_time = 0.050532,	train_time = 0.367168
[2019-08-24 12:38:08,059] TRAIN Iter 242360: lr = 0.096068,	loss = 2.400770,	Top-1 err = 0.350684,	Top-5 err = 0.151416,	data_time = 0.050503,	train_time = 0.716583
[2019-08-24 12:38:24,491] TRAIN Iter 242380: lr = 0.096035,	loss = 2.447821,	Top-1 err = 0.346484,	Top-5 err = 0.150684,	data_time = 0.050927,	train_time = 0.821548
[2019-08-24 12:38:31,310] TRAIN Iter 242400: lr = 0.096002,	loss = 2.420344,	Top-1 err = 0.349365,	Top-5 err = 0.147266,	data_time = 0.050573,	train_time = 0.340956
[2019-08-24 12:38:47,208] TRAIN Iter 242420: lr = 0.095968,	loss = 2.432793,	Top-1 err = 0.348682,	Top-5 err = 0.147021,	data_time = 0.050863,	train_time = 0.794884
[2019-08-24 12:39:02,811] TRAIN Iter 242440: lr = 0.095935,	loss = 2.310632,	Top-1 err = 0.347607,	Top-5 err = 0.149365,	data_time = 1.767744,	train_time = 0.780147
[2019-08-24 12:39:11,125] TRAIN Iter 242460: lr = 0.095902,	loss = 2.410955,	Top-1 err = 0.349365,	Top-5 err = 0.152197,	data_time = 0.050682,	train_time = 0.415663
[2019-08-24 12:39:26,625] TRAIN Iter 242480: lr = 0.095868,	loss = 2.430331,	Top-1 err = 0.353076,	Top-5 err = 0.148584,	data_time = 0.050543,	train_time = 0.775001
[2019-08-24 12:39:33,829] TRAIN Iter 242500: lr = 0.095835,	loss = 2.431084,	Top-1 err = 0.347070,	Top-5 err = 0.149707,	data_time = 0.050663,	train_time = 0.360193
[2019-08-24 12:39:49,133] TRAIN Iter 242520: lr = 0.095802,	loss = 2.439308,	Top-1 err = 0.352881,	Top-5 err = 0.147363,	data_time = 0.050462,	train_time = 0.765182
[2019-08-24 12:40:06,361] TRAIN Iter 242540: lr = 0.095768,	loss = 2.389142,	Top-1 err = 0.340869,	Top-5 err = 0.144189,	data_time = 0.050355,	train_time = 0.861352
[2019-08-24 12:40:13,522] TRAIN Iter 242560: lr = 0.095735,	loss = 2.559331,	Top-1 err = 0.350977,	Top-5 err = 0.150293,	data_time = 0.050846,	train_time = 0.358030
[2019-08-24 12:40:27,689] TRAIN Iter 242580: lr = 0.095702,	loss = 2.354284,	Top-1 err = 0.352490,	Top-5 err = 0.148047,	data_time = 0.050172,	train_time = 0.708371
[2019-08-24 12:40:41,640] TRAIN Iter 242600: lr = 0.095668,	loss = 2.419769,	Top-1 err = 0.353076,	Top-5 err = 0.153027,	data_time = 1.698611,	train_time = 0.697533
[2019-08-24 12:40:50,149] TRAIN Iter 242620: lr = 0.095635,	loss = 2.398484,	Top-1 err = 0.354346,	Top-5 err = 0.151709,	data_time = 0.050493,	train_time = 0.425405
[2019-08-24 12:41:05,152] TRAIN Iter 242640: lr = 0.095602,	loss = 2.451015,	Top-1 err = 0.356006,	Top-5 err = 0.154004,	data_time = 0.050811,	train_time = 0.750148
[2019-08-24 12:41:12,227] TRAIN Iter 242660: lr = 0.095568,	loss = 2.505597,	Top-1 err = 0.352393,	Top-5 err = 0.149512,	data_time = 0.050540,	train_time = 0.353733
[2019-08-24 12:41:27,967] TRAIN Iter 242680: lr = 0.095535,	loss = 2.393722,	Top-1 err = 0.354102,	Top-5 err = 0.153809,	data_time = 0.050508,	train_time = 0.787014
[2019-08-24 12:41:45,789] TRAIN Iter 242700: lr = 0.095502,	loss = 2.435656,	Top-1 err = 0.352588,	Top-5 err = 0.146533,	data_time = 0.050468,	train_time = 0.891085
[2019-08-24 12:41:52,578] TRAIN Iter 242720: lr = 0.095468,	loss = 2.473760,	Top-1 err = 0.349756,	Top-5 err = 0.151270,	data_time = 0.050605,	train_time = 0.339450
[2019-08-24 12:42:08,229] TRAIN Iter 242740: lr = 0.095435,	loss = 2.517244,	Top-1 err = 0.353125,	Top-5 err = 0.149170,	data_time = 0.050512,	train_time = 0.782492
[2019-08-24 12:42:23,773] TRAIN Iter 242760: lr = 0.095402,	loss = 2.378081,	Top-1 err = 0.353467,	Top-5 err = 0.150830,	data_time = 0.066663,	train_time = 0.777219
[2019-08-24 12:42:30,409] TRAIN Iter 242780: lr = 0.095368,	loss = 2.346323,	Top-1 err = 0.356592,	Top-5 err = 0.154150,	data_time = 0.050108,	train_time = 0.331755
[2019-08-24 12:42:45,956] TRAIN Iter 242800: lr = 0.095335,	loss = 2.408681,	Top-1 err = 0.350000,	Top-5 err = 0.149707,	data_time = 0.050025,	train_time = 0.777375
[2019-08-24 12:42:51,907] TRAIN Iter 242820: lr = 0.095302,	loss = 2.355050,	Top-1 err = 0.348828,	Top-5 err = 0.148242,	data_time = 0.050033,	train_time = 0.297530
[2019-08-24 12:43:42,368] TRAIN Iter 242840: lr = 0.095268,	loss = 2.369214,	Top-1 err = 0.348811,	Top-5 err = 0.155435,	data_time = 0.050220,	train_time = 2.523014
[2019-08-24 12:43:56,031] TRAIN Iter 242860: lr = 0.095235,	loss = 2.488133,	Top-1 err = 0.346045,	Top-5 err = 0.146582,	data_time = 0.267698,	train_time = 0.683156
[2019-08-24 12:44:04,877] TRAIN Iter 242880: lr = 0.095202,	loss = 2.411007,	Top-1 err = 0.350293,	Top-5 err = 0.150244,	data_time = 0.050319,	train_time = 0.442251
[2019-08-24 12:44:19,883] TRAIN Iter 242900: lr = 0.095168,	loss = 2.442363,	Top-1 err = 0.342822,	Top-5 err = 0.148438,	data_time = 0.050648,	train_time = 0.750263
[2019-08-24 12:44:27,749] TRAIN Iter 242920: lr = 0.095135,	loss = 2.538406,	Top-1 err = 0.344531,	Top-5 err = 0.146191,	data_time = 0.050393,	train_time = 0.393302
[2019-08-24 12:44:41,166] TRAIN Iter 242940: lr = 0.095102,	loss = 2.348609,	Top-1 err = 0.341455,	Top-5 err = 0.144580,	data_time = 0.050908,	train_time = 0.670852
[2019-08-24 12:44:57,412] TRAIN Iter 242960: lr = 0.095068,	loss = 2.447104,	Top-1 err = 0.345801,	Top-5 err = 0.143408,	data_time = 0.050243,	train_time = 0.812278
[2019-08-24 12:45:05,216] TRAIN Iter 242980: lr = 0.095035,	loss = 2.433711,	Top-1 err = 0.347559,	Top-5 err = 0.145410,	data_time = 0.050610,	train_time = 0.390173
[2019-08-24 12:45:19,200] TRAIN Iter 243000: lr = 0.095002,	loss = 2.370986,	Top-1 err = 0.339502,	Top-5 err = 0.144189,	data_time = 0.050404,	train_time = 0.699180
[2019-08-24 12:45:33,300] TRAIN Iter 243020: lr = 0.094968,	loss = 2.487814,	Top-1 err = 0.345117,	Top-5 err = 0.145508,	data_time = 2.411095,	train_time = 0.704991
[2019-08-24 12:45:40,745] TRAIN Iter 243040: lr = 0.094935,	loss = 2.422325,	Top-1 err = 0.345264,	Top-5 err = 0.145215,	data_time = 0.050461,	train_time = 0.372268
[2019-08-24 12:45:55,368] TRAIN Iter 243060: lr = 0.094902,	loss = 2.430900,	Top-1 err = 0.346143,	Top-5 err = 0.147607,	data_time = 0.050327,	train_time = 0.731122
[2019-08-24 12:46:03,079] TRAIN Iter 243080: lr = 0.094868,	loss = 2.503774,	Top-1 err = 0.348682,	Top-5 err = 0.147656,	data_time = 0.050410,	train_time = 0.385522
[2019-08-24 12:46:17,017] TRAIN Iter 243100: lr = 0.094835,	loss = 2.476537,	Top-1 err = 0.349463,	Top-5 err = 0.151611,	data_time = 0.125767,	train_time = 0.696921
[2019-08-24 12:46:30,659] TRAIN Iter 243120: lr = 0.094802,	loss = 2.420199,	Top-1 err = 0.345410,	Top-5 err = 0.144678,	data_time = 0.050363,	train_time = 0.682076
[2019-08-24 12:46:37,580] TRAIN Iter 243140: lr = 0.094768,	loss = 2.348732,	Top-1 err = 0.349854,	Top-5 err = 0.147900,	data_time = 0.050451,	train_time = 0.346042
[2019-08-24 12:46:51,712] TRAIN Iter 243160: lr = 0.094735,	loss = 2.396779,	Top-1 err = 0.343799,	Top-5 err = 0.144531,	data_time = 0.050543,	train_time = 0.706550
[2019-08-24 12:47:04,302] TRAIN Iter 243180: lr = 0.094702,	loss = 2.364175,	Top-1 err = 0.343945,	Top-5 err = 0.146582,	data_time = 0.050402,	train_time = 0.629508
[2019-08-24 12:47:13,863] TRAIN Iter 243200: lr = 0.094668,	loss = 2.412815,	Top-1 err = 0.348291,	Top-5 err = 0.148779,	data_time = 0.050512,	train_time = 0.478028
[2019-08-24 12:47:30,356] TRAIN Iter 243220: lr = 0.094635,	loss = 2.458038,	Top-1 err = 0.345947,	Top-5 err = 0.149707,	data_time = 0.050704,	train_time = 0.824616
[2019-08-24 12:47:37,719] TRAIN Iter 243240: lr = 0.094602,	loss = 2.409320,	Top-1 err = 0.346484,	Top-5 err = 0.149951,	data_time = 0.050376,	train_time = 0.368151
[2019-08-24 12:47:51,860] TRAIN Iter 243260: lr = 0.094568,	loss = 2.426440,	Top-1 err = 0.342529,	Top-5 err = 0.149463,	data_time = 0.050479,	train_time = 0.707022
[2019-08-24 12:48:07,296] TRAIN Iter 243280: lr = 0.094535,	loss = 2.449916,	Top-1 err = 0.350195,	Top-5 err = 0.148730,	data_time = 0.143868,	train_time = 0.771802
[2019-08-24 12:48:14,173] TRAIN Iter 243300: lr = 0.094502,	loss = 2.434281,	Top-1 err = 0.344336,	Top-5 err = 0.149512,	data_time = 0.050503,	train_time = 0.343868
[2019-08-24 12:48:31,171] TRAIN Iter 243320: lr = 0.094468,	loss = 2.364313,	Top-1 err = 0.348486,	Top-5 err = 0.145410,	data_time = 0.050363,	train_time = 0.849858
[2019-08-24 12:48:47,323] TRAIN Iter 243340: lr = 0.094435,	loss = 2.349207,	Top-1 err = 0.350293,	Top-5 err = 0.144141,	data_time = 0.050912,	train_time = 0.807599
[2019-08-24 12:48:54,581] TRAIN Iter 243360: lr = 0.094402,	loss = 2.411172,	Top-1 err = 0.345264,	Top-5 err = 0.146533,	data_time = 0.050497,	train_time = 0.362868
[2019-08-24 12:49:10,651] TRAIN Iter 243380: lr = 0.094368,	loss = 2.365494,	Top-1 err = 0.350537,	Top-5 err = 0.149219,	data_time = 0.050647,	train_time = 0.803492
[2019-08-24 12:49:18,512] TRAIN Iter 243400: lr = 0.094335,	loss = 2.364193,	Top-1 err = 0.352295,	Top-5 err = 0.151074,	data_time = 0.144196,	train_time = 0.393043
[2019-08-24 12:49:32,611] TRAIN Iter 243420: lr = 0.094302,	loss = 2.427822,	Top-1 err = 0.353418,	Top-5 err = 0.151807,	data_time = 0.050351,	train_time = 0.704935
[2019-08-24 12:49:49,916] TRAIN Iter 243440: lr = 0.094268,	loss = 2.404514,	Top-1 err = 0.348096,	Top-5 err = 0.148633,	data_time = 0.050322,	train_time = 0.865225
[2019-08-24 12:49:57,761] TRAIN Iter 243460: lr = 0.094235,	loss = 2.395193,	Top-1 err = 0.350488,	Top-5 err = 0.149316,	data_time = 0.143797,	train_time = 0.392251
[2019-08-24 12:50:11,835] TRAIN Iter 243480: lr = 0.094202,	loss = 2.369198,	Top-1 err = 0.347900,	Top-5 err = 0.151855,	data_time = 0.050442,	train_time = 0.703706
[2019-08-24 12:50:26,727] TRAIN Iter 243500: lr = 0.094168,	loss = 2.453141,	Top-1 err = 0.351709,	Top-5 err = 0.148779,	data_time = 0.050683,	train_time = 0.744553
[2019-08-24 12:50:33,609] TRAIN Iter 243520: lr = 0.094135,	loss = 2.429095,	Top-1 err = 0.353662,	Top-5 err = 0.153027,	data_time = 0.050333,	train_time = 0.344083
[2019-08-24 12:50:50,618] TRAIN Iter 243540: lr = 0.094102,	loss = 2.322720,	Top-1 err = 0.343799,	Top-5 err = 0.144922,	data_time = 0.050560,	train_time = 0.850451
[2019-08-24 12:50:57,935] TRAIN Iter 243560: lr = 0.094068,	loss = 2.437844,	Top-1 err = 0.346143,	Top-5 err = 0.146777,	data_time = 0.050460,	train_time = 0.365857
[2019-08-24 12:51:13,662] TRAIN Iter 243580: lr = 0.094035,	loss = 2.430617,	Top-1 err = 0.348877,	Top-5 err = 0.149365,	data_time = 0.050601,	train_time = 0.786301
[2019-08-24 12:51:29,243] TRAIN Iter 243600: lr = 0.094002,	loss = 2.427748,	Top-1 err = 0.352686,	Top-5 err = 0.150732,	data_time = 0.050641,	train_time = 0.779027
[2019-08-24 12:51:36,230] TRAIN Iter 243620: lr = 0.093968,	loss = 2.357281,	Top-1 err = 0.345459,	Top-5 err = 0.148145,	data_time = 0.161783,	train_time = 0.349369
[2019-08-24 12:51:54,198] TRAIN Iter 243640: lr = 0.093935,	loss = 2.494023,	Top-1 err = 0.354639,	Top-5 err = 0.152148,	data_time = 0.050359,	train_time = 0.898372
[2019-08-24 12:52:08,941] TRAIN Iter 243660: lr = 0.093902,	loss = 2.385687,	Top-1 err = 0.356494,	Top-5 err = 0.151758,	data_time = 0.050679,	train_time = 0.737146
[2019-08-24 12:52:15,721] TRAIN Iter 243680: lr = 0.093868,	loss = 2.422764,	Top-1 err = 0.351416,	Top-5 err = 0.150342,	data_time = 0.050472,	train_time = 0.338970
[2019-08-24 12:52:32,189] TRAIN Iter 243700: lr = 0.093835,	loss = 2.375309,	Top-1 err = 0.350439,	Top-5 err = 0.151758,	data_time = 0.051495,	train_time = 0.823397
[2019-08-24 12:52:39,749] TRAIN Iter 243720: lr = 0.093802,	loss = 2.444417,	Top-1 err = 0.350488,	Top-5 err = 0.148340,	data_time = 0.050815,	train_time = 0.377987
[2019-08-24 12:52:55,201] TRAIN Iter 243740: lr = 0.093768,	loss = 2.310239,	Top-1 err = 0.347949,	Top-5 err = 0.148145,	data_time = 0.050624,	train_time = 0.772591
[2019-08-24 12:53:09,290] TRAIN Iter 243760: lr = 0.093735,	loss = 2.421887,	Top-1 err = 0.346338,	Top-5 err = 0.147119,	data_time = 0.050586,	train_time = 0.704409
[2019-08-24 12:53:16,154] TRAIN Iter 243780: lr = 0.093702,	loss = 2.406568,	Top-1 err = 0.348096,	Top-5 err = 0.147217,	data_time = 0.050836,	train_time = 0.343210
[2019-08-24 12:53:33,011] TRAIN Iter 243800: lr = 0.093668,	loss = 2.332044,	Top-1 err = 0.345313,	Top-5 err = 0.149023,	data_time = 0.050853,	train_time = 0.842811
[2019-08-24 12:53:47,618] TRAIN Iter 243820: lr = 0.093635,	loss = 2.471323,	Top-1 err = 0.350195,	Top-5 err = 0.149219,	data_time = 0.050276,	train_time = 0.730351
[2019-08-24 12:53:55,389] TRAIN Iter 243840: lr = 0.093602,	loss = 2.375637,	Top-1 err = 0.351709,	Top-5 err = 0.150879,	data_time = 0.050543,	train_time = 0.388546
[2019-08-24 12:54:12,268] TRAIN Iter 243860: lr = 0.093568,	loss = 2.287438,	Top-1 err = 0.340869,	Top-5 err = 0.146924,	data_time = 0.050493,	train_time = 0.843938
[2019-08-24 12:54:19,221] TRAIN Iter 243880: lr = 0.093535,	loss = 2.458268,	Top-1 err = 0.353516,	Top-5 err = 0.151367,	data_time = 0.050721,	train_time = 0.347630
[2019-08-24 12:54:35,446] TRAIN Iter 243900: lr = 0.093502,	loss = 2.500073,	Top-1 err = 0.350049,	Top-5 err = 0.152344,	data_time = 0.050312,	train_time = 0.811240
[2019-08-24 12:54:53,630] TRAIN Iter 243920: lr = 0.093468,	loss = 2.446217,	Top-1 err = 0.345410,	Top-5 err = 0.150098,	data_time = 0.050743,	train_time = 0.909171
[2019-08-24 12:55:00,737] TRAIN Iter 243940: lr = 0.093435,	loss = 2.338865,	Top-1 err = 0.346436,	Top-5 err = 0.150781,	data_time = 0.050812,	train_time = 0.355318
[2019-08-24 12:55:15,996] TRAIN Iter 243960: lr = 0.093402,	loss = 2.391262,	Top-1 err = 0.353711,	Top-5 err = 0.151807,	data_time = 0.050527,	train_time = 0.762936
[2019-08-24 12:55:33,263] TRAIN Iter 243980: lr = 0.093368,	loss = 2.439772,	Top-1 err = 0.357715,	Top-5 err = 0.153418,	data_time = 0.125147,	train_time = 0.863371
[2019-08-24 12:55:40,076] TRAIN Iter 244000: lr = 0.093335,	loss = 2.392132,	Top-1 err = 0.348389,	Top-5 err = 0.149707,	data_time = 0.050707,	train_time = 0.340640
[2019-08-24 12:55:59,044] TRAIN Iter 244020: lr = 0.093302,	loss = 2.374392,	Top-1 err = 0.346143,	Top-5 err = 0.147510,	data_time = 0.050063,	train_time = 0.948359
[2019-08-24 12:56:06,196] TRAIN Iter 244040: lr = 0.093268,	loss = 2.532931,	Top-1 err = 0.355225,	Top-5 err = 0.151709,	data_time = 0.050051,	train_time = 0.357577
[2019-08-24 12:56:20,962] TRAIN Iter 244060: lr = 0.093235,	loss = 2.399348,	Top-1 err = 0.352295,	Top-5 err = 0.147070,	data_time = 0.049908,	train_time = 0.738300
[2019-08-24 12:57:10,932] TRAIN Iter 244080: lr = 0.093202,	loss = 2.346559,	Top-1 err = 0.357036,	Top-5 err = 0.147868,	data_time = 3.896357,	train_time = 2.498479
[2019-08-24 12:57:18,032] TRAIN Iter 244100: lr = 0.093168,	loss = 2.359252,	Top-1 err = 0.346484,	Top-5 err = 0.144824,	data_time = 0.050792,	train_time = 0.355000
[2019-08-24 12:57:33,351] TRAIN Iter 244120: lr = 0.093135,	loss = 2.387017,	Top-1 err = 0.340771,	Top-5 err = 0.143457,	data_time = 0.050478,	train_time = 0.765911
[2019-08-24 12:57:41,121] TRAIN Iter 244140: lr = 0.093102,	loss = 2.393401,	Top-1 err = 0.340527,	Top-5 err = 0.139795,	data_time = 0.147502,	train_time = 0.388510
[2019-08-24 12:57:54,986] TRAIN Iter 244160: lr = 0.093068,	loss = 2.405845,	Top-1 err = 0.343457,	Top-5 err = 0.144678,	data_time = 0.050802,	train_time = 0.693227
[2019-08-24 12:58:10,631] TRAIN Iter 244180: lr = 0.093035,	loss = 2.416158,	Top-1 err = 0.345801,	Top-5 err = 0.144092,	data_time = 0.050633,	train_time = 0.782245
[2019-08-24 12:58:18,033] TRAIN Iter 244200: lr = 0.093002,	loss = 2.448451,	Top-1 err = 0.349268,	Top-5 err = 0.146875,	data_time = 0.050811,	train_time = 0.370045
[2019-08-24 12:58:34,363] TRAIN Iter 244220: lr = 0.092968,	loss = 2.422963,	Top-1 err = 0.346631,	Top-5 err = 0.141553,	data_time = 0.050570,	train_time = 0.816513
[2019-08-24 12:58:51,500] TRAIN Iter 244240: lr = 0.092935,	loss = 2.334892,	Top-1 err = 0.343213,	Top-5 err = 0.144092,	data_time = 0.050398,	train_time = 0.856843
[2019-08-24 12:58:58,690] TRAIN Iter 244260: lr = 0.092902,	loss = 2.379314,	Top-1 err = 0.338330,	Top-5 err = 0.141748,	data_time = 0.050767,	train_time = 0.359462
[2019-08-24 12:59:12,295] TRAIN Iter 244280: lr = 0.092868,	loss = 2.347276,	Top-1 err = 0.343213,	Top-5 err = 0.145215,	data_time = 0.050399,	train_time = 0.680233
[2019-08-24 12:59:20,381] TRAIN Iter 244300: lr = 0.092835,	loss = 2.404033,	Top-1 err = 0.345850,	Top-5 err = 0.143311,	data_time = 0.050718,	train_time = 0.404311
[2019-08-24 12:59:33,477] TRAIN Iter 244320: lr = 0.092802,	loss = 2.382829,	Top-1 err = 0.345947,	Top-5 err = 0.146875,	data_time = 0.050385,	train_time = 0.654779
[2019-08-24 12:59:48,699] TRAIN Iter 244340: lr = 0.092768,	loss = 2.371293,	Top-1 err = 0.344336,	Top-5 err = 0.146240,	data_time = 0.050367,	train_time = 0.761088
[2019-08-24 12:59:56,258] TRAIN Iter 244360: lr = 0.092735,	loss = 2.359358,	Top-1 err = 0.343994,	Top-5 err = 0.145361,	data_time = 0.050553,	train_time = 0.377901
[2019-08-24 13:00:09,920] TRAIN Iter 244380: lr = 0.092702,	loss = 2.379346,	Top-1 err = 0.346582,	Top-5 err = 0.147021,	data_time = 0.050800,	train_time = 0.683089
[2019-08-24 13:00:24,471] TRAIN Iter 244400: lr = 0.092668,	loss = 2.433791,	Top-1 err = 0.342871,	Top-5 err = 0.146094,	data_time = 0.141914,	train_time = 0.727551
[2019-08-24 13:00:31,327] TRAIN Iter 244420: lr = 0.092635,	loss = 2.387981,	Top-1 err = 0.345264,	Top-5 err = 0.150830,	data_time = 0.050724,	train_time = 0.342815
[2019-08-24 13:00:46,467] TRAIN Iter 244440: lr = 0.092602,	loss = 2.404132,	Top-1 err = 0.342187,	Top-5 err = 0.141895,	data_time = 0.050336,	train_time = 0.756961
[2019-08-24 13:00:53,849] TRAIN Iter 244460: lr = 0.092568,	loss = 2.377545,	Top-1 err = 0.344482,	Top-5 err = 0.145850,	data_time = 0.050681,	train_time = 0.369090
[2019-08-24 13:01:09,011] TRAIN Iter 244480: lr = 0.092535,	loss = 2.441767,	Top-1 err = 0.348877,	Top-5 err = 0.147021,	data_time = 0.050433,	train_time = 0.758080
[2019-08-24 13:01:24,704] TRAIN Iter 244500: lr = 0.092502,	loss = 2.347217,	Top-1 err = 0.348389,	Top-5 err = 0.144238,	data_time = 0.050489,	train_time = 0.784621
[2019-08-24 13:01:31,989] TRAIN Iter 244520: lr = 0.092468,	loss = 2.378087,	Top-1 err = 0.345313,	Top-5 err = 0.147119,	data_time = 0.097753,	train_time = 0.364253
[2019-08-24 13:01:45,161] TRAIN Iter 244540: lr = 0.092435,	loss = 2.389301,	Top-1 err = 0.350000,	Top-5 err = 0.148926,	data_time = 0.050562,	train_time = 0.658608
[2019-08-24 13:02:00,183] TRAIN Iter 244560: lr = 0.092402,	loss = 2.354517,	Top-1 err = 0.344482,	Top-5 err = 0.146387,	data_time = 0.099834,	train_time = 0.751050
[2019-08-24 13:02:07,811] TRAIN Iter 244580: lr = 0.092368,	loss = 2.539515,	Top-1 err = 0.345410,	Top-5 err = 0.146045,	data_time = 0.050376,	train_time = 0.381391
[2019-08-24 13:02:23,449] TRAIN Iter 244600: lr = 0.092335,	loss = 2.380847,	Top-1 err = 0.354980,	Top-5 err = 0.152930,	data_time = 0.050351,	train_time = 0.781906
[2019-08-24 13:02:31,014] TRAIN Iter 244620: lr = 0.092302,	loss = 2.531949,	Top-1 err = 0.348340,	Top-5 err = 0.146680,	data_time = 0.050387,	train_time = 0.378227
[2019-08-24 13:02:46,243] TRAIN Iter 244640: lr = 0.092268,	loss = 2.425475,	Top-1 err = 0.347705,	Top-5 err = 0.147900,	data_time = 0.050710,	train_time = 0.761446
[2019-08-24 13:03:02,307] TRAIN Iter 244660: lr = 0.092235,	loss = 2.444705,	Top-1 err = 0.345752,	Top-5 err = 0.143848,	data_time = 0.050284,	train_time = 0.803160
[2019-08-24 13:03:09,317] TRAIN Iter 244680: lr = 0.092202,	loss = 2.406649,	Top-1 err = 0.351123,	Top-5 err = 0.149023,	data_time = 0.050278,	train_time = 0.350489
[2019-08-24 13:03:24,588] TRAIN Iter 244700: lr = 0.092168,	loss = 2.412829,	Top-1 err = 0.348828,	Top-5 err = 0.151465,	data_time = 0.050357,	train_time = 0.763561
[2019-08-24 13:03:40,005] TRAIN Iter 244720: lr = 0.092135,	loss = 2.317353,	Top-1 err = 0.342236,	Top-5 err = 0.147607,	data_time = 0.050671,	train_time = 0.770850
[2019-08-24 13:03:46,937] TRAIN Iter 244740: lr = 0.092102,	loss = 2.436928,	Top-1 err = 0.350537,	Top-5 err = 0.148828,	data_time = 0.050519,	train_time = 0.346582
[2019-08-24 13:04:04,213] TRAIN Iter 244760: lr = 0.092068,	loss = 2.441863,	Top-1 err = 0.347852,	Top-5 err = 0.145068,	data_time = 0.050489,	train_time = 0.863764
[2019-08-24 13:04:11,734] TRAIN Iter 244780: lr = 0.092035,	loss = 2.437084,	Top-1 err = 0.350146,	Top-5 err = 0.150244,	data_time = 0.050564,	train_time = 0.376038
[2019-08-24 13:04:26,347] TRAIN Iter 244800: lr = 0.092002,	loss = 2.448552,	Top-1 err = 0.348291,	Top-5 err = 0.151025,	data_time = 0.050377,	train_time = 0.730611
[2019-08-24 13:04:43,098] TRAIN Iter 244820: lr = 0.091968,	loss = 2.464174,	Top-1 err = 0.352246,	Top-5 err = 0.148779,	data_time = 0.050326,	train_time = 0.837580
[2019-08-24 13:04:50,387] TRAIN Iter 244840: lr = 0.091935,	loss = 2.396213,	Top-1 err = 0.348877,	Top-5 err = 0.143066,	data_time = 0.050508,	train_time = 0.364397
[2019-08-24 13:05:04,994] TRAIN Iter 244860: lr = 0.091902,	loss = 2.441436,	Top-1 err = 0.341699,	Top-5 err = 0.146240,	data_time = 0.050566,	train_time = 0.730354
[2019-08-24 13:05:21,496] TRAIN Iter 244880: lr = 0.091868,	loss = 2.498120,	Top-1 err = 0.353760,	Top-5 err = 0.155518,	data_time = 0.050903,	train_time = 0.825060
[2019-08-24 13:05:28,268] TRAIN Iter 244900: lr = 0.091835,	loss = 2.447834,	Top-1 err = 0.351562,	Top-5 err = 0.151270,	data_time = 0.050842,	train_time = 0.338636
[2019-08-24 13:05:45,255] TRAIN Iter 244920: lr = 0.091802,	loss = 2.476793,	Top-1 err = 0.348975,	Top-5 err = 0.153516,	data_time = 0.050744,	train_time = 0.849299
[2019-08-24 13:05:52,461] TRAIN Iter 244940: lr = 0.091768,	loss = 2.361135,	Top-1 err = 0.350146,	Top-5 err = 0.146826,	data_time = 0.050171,	train_time = 0.360308
[2019-08-24 13:06:08,266] TRAIN Iter 244960: lr = 0.091735,	loss = 2.442942,	Top-1 err = 0.350049,	Top-5 err = 0.152197,	data_time = 0.050312,	train_time = 0.790223
[2019-08-24 13:06:25,015] TRAIN Iter 244980: lr = 0.091702,	loss = 2.367906,	Top-1 err = 0.350830,	Top-5 err = 0.145459,	data_time = 0.050660,	train_time = 0.837443
[2019-08-24 13:06:32,137] TRAIN Iter 245000: lr = 0.091668,	loss = 2.428899,	Top-1 err = 0.352295,	Top-5 err = 0.148828,	data_time = 0.050884,	train_time = 0.356087
[2019-08-24 13:06:49,345] TRAIN Iter 245020: lr = 0.091635,	loss = 2.330564,	Top-1 err = 0.346631,	Top-5 err = 0.145117,	data_time = 0.050441,	train_time = 0.860394
[2019-08-24 13:07:07,149] TRAIN Iter 245040: lr = 0.091602,	loss = 2.380658,	Top-1 err = 0.346045,	Top-5 err = 0.146973,	data_time = 0.050573,	train_time = 0.890180
[2019-08-24 13:07:14,324] TRAIN Iter 245060: lr = 0.091568,	loss = 2.474114,	Top-1 err = 0.347559,	Top-5 err = 0.148389,	data_time = 0.050668,	train_time = 0.358720
[2019-08-24 13:07:29,263] TRAIN Iter 245080: lr = 0.091535,	loss = 2.414786,	Top-1 err = 0.349902,	Top-5 err = 0.150879,	data_time = 0.050637,	train_time = 0.746934
[2019-08-24 13:07:36,598] TRAIN Iter 245100: lr = 0.091502,	loss = 2.381207,	Top-1 err = 0.353223,	Top-5 err = 0.149707,	data_time = 0.050626,	train_time = 0.366717
[2019-08-24 13:07:52,274] TRAIN Iter 245120: lr = 0.091468,	loss = 2.404815,	Top-1 err = 0.352441,	Top-5 err = 0.147021,	data_time = 0.050387,	train_time = 0.783786
[2019-08-24 13:08:08,824] TRAIN Iter 245140: lr = 0.091435,	loss = 2.443028,	Top-1 err = 0.349707,	Top-5 err = 0.142285,	data_time = 0.050248,	train_time = 0.827506
[2019-08-24 13:08:15,695] TRAIN Iter 245160: lr = 0.091402,	loss = 2.360425,	Top-1 err = 0.352930,	Top-5 err = 0.151953,	data_time = 0.135396,	train_time = 0.343545
[2019-08-24 13:08:32,711] TRAIN Iter 245180: lr = 0.091368,	loss = 2.411888,	Top-1 err = 0.353467,	Top-5 err = 0.153906,	data_time = 0.050668,	train_time = 0.850773
[2019-08-24 13:08:49,814] TRAIN Iter 245200: lr = 0.091335,	loss = 2.423440,	Top-1 err = 0.355420,	Top-5 err = 0.153418,	data_time = 0.050138,	train_time = 0.855146
[2019-08-24 13:08:56,614] TRAIN Iter 245220: lr = 0.091302,	loss = 2.378180,	Top-1 err = 0.354199,	Top-5 err = 0.151562,	data_time = 0.050317,	train_time = 0.339972
[2019-08-24 13:09:14,566] TRAIN Iter 245240: lr = 0.091268,	loss = 2.425570,	Top-1 err = 0.354687,	Top-5 err = 0.152246,	data_time = 0.050577,	train_time = 0.897582
[2019-08-24 13:09:21,576] TRAIN Iter 245260: lr = 0.091235,	loss = 2.418794,	Top-1 err = 0.347461,	Top-5 err = 0.150488,	data_time = 0.050225,	train_time = 0.350464
[2019-08-24 13:09:38,647] TRAIN Iter 245280: lr = 0.091202,	loss = 2.414752,	Top-1 err = 0.347021,	Top-5 err = 0.150293,	data_time = 0.050026,	train_time = 0.853557
[2019-08-24 13:09:55,965] TRAIN Iter 245300: lr = 0.091168,	loss = 2.485360,	Top-1 err = 0.352881,	Top-5 err = 0.151123,	data_time = 0.049887,	train_time = 0.865887
[2019-08-24 13:10:02,268] TRAIN Iter 245320: lr = 0.091135,	loss = 2.371764,	Top-1 err = 0.359863,	Top-5 err = 0.151953,	data_time = 0.049854,	train_time = 0.315156
[2019-08-24 13:10:56,206] TRAIN Iter 245340: lr = 0.091102,	loss = 2.361560,	Top-1 err = 0.356427,	Top-5 err = 0.150162,	data_time = 0.050816,	train_time = 2.696864
[2019-08-24 13:11:04,103] TRAIN Iter 245360: lr = 0.091068,	loss = 2.478779,	Top-1 err = 0.346826,	Top-5 err = 0.147607,	data_time = 0.050260,	train_time = 0.394812
[2019-08-24 13:11:16,780] TRAIN Iter 245380: lr = 0.091035,	loss = 2.497641,	Top-1 err = 0.342432,	Top-5 err = 0.143066,	data_time = 0.050906,	train_time = 0.633872
[2019-08-24 13:11:29,645] TRAIN Iter 245400: lr = 0.091002,	loss = 2.361068,	Top-1 err = 0.341992,	Top-5 err = 0.141455,	data_time = 0.050381,	train_time = 0.643238
[2019-08-24 13:11:36,804] TRAIN Iter 245420: lr = 0.090968,	loss = 2.400347,	Top-1 err = 0.340381,	Top-5 err = 0.141846,	data_time = 0.050606,	train_time = 0.357909
[2019-08-24 13:11:51,287] TRAIN Iter 245440: lr = 0.090935,	loss = 2.377829,	Top-1 err = 0.345947,	Top-5 err = 0.145801,	data_time = 0.050419,	train_time = 0.724125
[2019-08-24 13:12:06,050] TRAIN Iter 245460: lr = 0.090902,	loss = 2.370635,	Top-1 err = 0.347363,	Top-5 err = 0.148389,	data_time = 0.116238,	train_time = 0.738159
[2019-08-24 13:12:13,297] TRAIN Iter 245480: lr = 0.090868,	loss = 2.317799,	Top-1 err = 0.342041,	Top-5 err = 0.142236,	data_time = 0.050434,	train_time = 0.362343
[2019-08-24 13:12:29,610] TRAIN Iter 245500: lr = 0.090835,	loss = 2.410669,	Top-1 err = 0.342041,	Top-5 err = 0.143311,	data_time = 0.050438,	train_time = 0.815611
[2019-08-24 13:12:37,220] TRAIN Iter 245520: lr = 0.090802,	loss = 2.466845,	Top-1 err = 0.343164,	Top-5 err = 0.144092,	data_time = 0.050962,	train_time = 0.380466
[2019-08-24 13:12:50,567] TRAIN Iter 245540: lr = 0.090768,	loss = 2.373791,	Top-1 err = 0.340234,	Top-5 err = 0.144873,	data_time = 0.050377,	train_time = 0.667360
[2019-08-24 13:13:06,597] TRAIN Iter 245560: lr = 0.090735,	loss = 2.294171,	Top-1 err = 0.344092,	Top-5 err = 0.145264,	data_time = 0.050511,	train_time = 0.801480
[2019-08-24 13:13:13,929] TRAIN Iter 245580: lr = 0.090702,	loss = 2.284844,	Top-1 err = 0.338623,	Top-5 err = 0.141943,	data_time = 0.050934,	train_time = 0.366566
[2019-08-24 13:13:26,750] TRAIN Iter 245600: lr = 0.090668,	loss = 2.435102,	Top-1 err = 0.343555,	Top-5 err = 0.143555,	data_time = 0.050515,	train_time = 0.641048
[2019-08-24 13:13:42,385] TRAIN Iter 245620: lr = 0.090635,	loss = 2.507803,	Top-1 err = 0.346875,	Top-5 err = 0.145801,	data_time = 0.050414,	train_time = 0.781755
[2019-08-24 13:13:49,555] TRAIN Iter 245640: lr = 0.090602,	loss = 2.448221,	Top-1 err = 0.348633,	Top-5 err = 0.147656,	data_time = 0.050304,	train_time = 0.358458
[2019-08-24 13:14:02,963] TRAIN Iter 245660: lr = 0.090568,	loss = 2.461640,	Top-1 err = 0.344775,	Top-5 err = 0.142871,	data_time = 0.050443,	train_time = 0.670428
[2019-08-24 13:14:10,182] TRAIN Iter 245680: lr = 0.090535,	loss = 2.374421,	Top-1 err = 0.342822,	Top-5 err = 0.144092,	data_time = 0.050323,	train_time = 0.360902
[2019-08-24 13:14:26,321] TRAIN Iter 245700: lr = 0.090502,	loss = 2.419876,	Top-1 err = 0.340869,	Top-5 err = 0.143750,	data_time = 0.050370,	train_time = 0.806962
[2019-08-24 13:14:40,018] TRAIN Iter 245720: lr = 0.090468,	loss = 2.404821,	Top-1 err = 0.342041,	Top-5 err = 0.147412,	data_time = 0.050587,	train_time = 0.684804
[2019-08-24 13:14:47,715] TRAIN Iter 245740: lr = 0.090435,	loss = 2.423700,	Top-1 err = 0.347314,	Top-5 err = 0.144141,	data_time = 0.050795,	train_time = 0.384849
[2019-08-24 13:15:02,531] TRAIN Iter 245760: lr = 0.090402,	loss = 2.446665,	Top-1 err = 0.346436,	Top-5 err = 0.147461,	data_time = 0.108319,	train_time = 0.740790
[2019-08-24 13:15:16,259] TRAIN Iter 245780: lr = 0.090368,	loss = 2.402671,	Top-1 err = 0.346289,	Top-5 err = 0.142334,	data_time = 0.051080,	train_time = 0.686367
[2019-08-24 13:15:25,548] TRAIN Iter 245800: lr = 0.090335,	loss = 2.478524,	Top-1 err = 0.348340,	Top-5 err = 0.146680,	data_time = 0.050475,	train_time = 0.464468
[2019-08-24 13:15:41,555] TRAIN Iter 245820: lr = 0.090302,	loss = 2.452748,	Top-1 err = 0.354297,	Top-5 err = 0.149951,	data_time = 0.050518,	train_time = 0.800301
[2019-08-24 13:15:48,685] TRAIN Iter 245840: lr = 0.090268,	loss = 2.349653,	Top-1 err = 0.341309,	Top-5 err = 0.142383,	data_time = 0.050622,	train_time = 0.356509
[2019-08-24 13:16:03,865] TRAIN Iter 245860: lr = 0.090235,	loss = 2.344547,	Top-1 err = 0.341309,	Top-5 err = 0.143555,	data_time = 0.050598,	train_time = 0.758955
[2019-08-24 13:16:18,913] TRAIN Iter 245880: lr = 0.090202,	loss = 2.417329,	Top-1 err = 0.350195,	Top-5 err = 0.149902,	data_time = 0.112665,	train_time = 0.752412
[2019-08-24 13:16:25,821] TRAIN Iter 245900: lr = 0.090168,	loss = 2.354892,	Top-1 err = 0.349609,	Top-5 err = 0.150342,	data_time = 0.050689,	train_time = 0.345378
[2019-08-24 13:16:43,252] TRAIN Iter 245920: lr = 0.090135,	loss = 2.528346,	Top-1 err = 0.343945,	Top-5 err = 0.147168,	data_time = 0.050331,	train_time = 0.871533
[2019-08-24 13:16:54,103] TRAIN Iter 245940: lr = 0.090102,	loss = 2.415887,	Top-1 err = 0.349072,	Top-5 err = 0.147314,	data_time = 0.050293,	train_time = 0.542521
[2019-08-24 13:17:05,302] TRAIN Iter 245960: lr = 0.090068,	loss = 2.419265,	Top-1 err = 0.348096,	Top-5 err = 0.151270,	data_time = 0.050515,	train_time = 0.559968
[2019-08-24 13:17:20,683] TRAIN Iter 245980: lr = 0.090035,	loss = 2.407296,	Top-1 err = 0.347168,	Top-5 err = 0.147314,	data_time = 0.050448,	train_time = 0.769006
[2019-08-24 13:17:27,323] TRAIN Iter 246000: lr = 0.090002,	loss = 2.411317,	Top-1 err = 0.353906,	Top-5 err = 0.151416,	data_time = 0.050411,	train_time = 0.331980
[2019-08-24 13:17:44,963] TRAIN Iter 246020: lr = 0.089968,	loss = 2.421734,	Top-1 err = 0.348242,	Top-5 err = 0.146094,	data_time = 0.050651,	train_time = 0.882024
[2019-08-24 13:18:01,821] TRAIN Iter 246040: lr = 0.089935,	loss = 2.409846,	Top-1 err = 0.352441,	Top-5 err = 0.151416,	data_time = 0.050736,	train_time = 0.842836
[2019-08-24 13:18:08,835] TRAIN Iter 246060: lr = 0.089902,	loss = 2.478696,	Top-1 err = 0.348145,	Top-5 err = 0.151611,	data_time = 0.136723,	train_time = 0.350710
[2019-08-24 13:18:24,972] TRAIN Iter 246080: lr = 0.089868,	loss = 2.464935,	Top-1 err = 0.351367,	Top-5 err = 0.150488,	data_time = 0.050548,	train_time = 0.806822
[2019-08-24 13:18:40,632] TRAIN Iter 246100: lr = 0.089835,	loss = 2.468595,	Top-1 err = 0.350830,	Top-5 err = 0.153711,	data_time = 0.050437,	train_time = 0.783014
[2019-08-24 13:18:47,705] TRAIN Iter 246120: lr = 0.089802,	loss = 2.520016,	Top-1 err = 0.347949,	Top-5 err = 0.147754,	data_time = 0.050633,	train_time = 0.353635
[2019-08-24 13:19:03,787] TRAIN Iter 246140: lr = 0.089768,	loss = 2.433987,	Top-1 err = 0.346924,	Top-5 err = 0.149951,	data_time = 0.050444,	train_time = 0.804075
[2019-08-24 13:19:10,723] TRAIN Iter 246160: lr = 0.089735,	loss = 2.428013,	Top-1 err = 0.351123,	Top-5 err = 0.147998,	data_time = 0.050596,	train_time = 0.346793
[2019-08-24 13:19:26,502] TRAIN Iter 246180: lr = 0.089702,	loss = 2.322993,	Top-1 err = 0.343066,	Top-5 err = 0.144434,	data_time = 0.050769,	train_time = 0.788940
[2019-08-24 13:19:42,549] TRAIN Iter 246200: lr = 0.089668,	loss = 2.398628,	Top-1 err = 0.345264,	Top-5 err = 0.147949,	data_time = 0.050499,	train_time = 0.802305
[2019-08-24 13:19:49,706] TRAIN Iter 246220: lr = 0.089635,	loss = 2.429917,	Top-1 err = 0.347949,	Top-5 err = 0.148389,	data_time = 0.050580,	train_time = 0.357835
[2019-08-24 13:20:06,630] TRAIN Iter 246240: lr = 0.089602,	loss = 2.405365,	Top-1 err = 0.349414,	Top-5 err = 0.146094,	data_time = 0.050517,	train_time = 0.846210
[2019-08-24 13:20:22,782] TRAIN Iter 246260: lr = 0.089568,	loss = 2.479972,	Top-1 err = 0.354053,	Top-5 err = 0.149707,	data_time = 0.050442,	train_time = 0.807583
[2019-08-24 13:20:30,445] TRAIN Iter 246280: lr = 0.089535,	loss = 2.326481,	Top-1 err = 0.349756,	Top-5 err = 0.146436,	data_time = 0.050262,	train_time = 0.383144
[2019-08-24 13:20:46,601] TRAIN Iter 246300: lr = 0.089502,	loss = 2.422681,	Top-1 err = 0.349023,	Top-5 err = 0.151074,	data_time = 0.050481,	train_time = 0.807746
[2019-08-24 13:20:53,212] TRAIN Iter 246320: lr = 0.089468,	loss = 2.391262,	Top-1 err = 0.348682,	Top-5 err = 0.147754,	data_time = 0.050751,	train_time = 0.330573
[2019-08-24 13:21:09,541] TRAIN Iter 246340: lr = 0.089435,	loss = 2.364744,	Top-1 err = 0.343359,	Top-5 err = 0.147705,	data_time = 0.050723,	train_time = 0.816425
[2019-08-24 13:21:25,623] TRAIN Iter 246360: lr = 0.089402,	loss = 2.335082,	Top-1 err = 0.340039,	Top-5 err = 0.142236,	data_time = 0.050409,	train_time = 0.804073
[2019-08-24 13:21:32,452] TRAIN Iter 246380: lr = 0.089368,	loss = 2.466175,	Top-1 err = 0.351416,	Top-5 err = 0.153906,	data_time = 0.050189,	train_time = 0.341462
[2019-08-24 13:21:49,427] TRAIN Iter 246400: lr = 0.089335,	loss = 2.394276,	Top-1 err = 0.345557,	Top-5 err = 0.146338,	data_time = 0.050966,	train_time = 0.848720
[2019-08-24 13:22:07,706] TRAIN Iter 246420: lr = 0.089302,	loss = 2.418138,	Top-1 err = 0.352344,	Top-5 err = 0.149023,	data_time = 0.100125,	train_time = 0.913916
[2019-08-24 13:22:14,943] TRAIN Iter 246440: lr = 0.089268,	loss = 2.444922,	Top-1 err = 0.351074,	Top-5 err = 0.149463,	data_time = 0.050810,	train_time = 0.361840
[2019-08-24 13:22:32,668] TRAIN Iter 246460: lr = 0.089235,	loss = 2.390111,	Top-1 err = 0.352490,	Top-5 err = 0.148828,	data_time = 0.050413,	train_time = 0.886254
[2019-08-24 13:22:39,427] TRAIN Iter 246480: lr = 0.089202,	loss = 2.498413,	Top-1 err = 0.349512,	Top-5 err = 0.151709,	data_time = 0.050820,	train_time = 0.337906
[2019-08-24 13:22:57,193] TRAIN Iter 246500: lr = 0.089168,	loss = 2.386761,	Top-1 err = 0.348535,	Top-5 err = 0.145996,	data_time = 0.050317,	train_time = 0.888295
[2019-08-24 13:23:14,699] TRAIN Iter 246520: lr = 0.089135,	loss = 2.475998,	Top-1 err = 0.348096,	Top-5 err = 0.150684,	data_time = 0.050070,	train_time = 0.875310
[2019-08-24 13:23:21,326] TRAIN Iter 246540: lr = 0.089102,	loss = 2.432716,	Top-1 err = 0.353467,	Top-5 err = 0.150293,	data_time = 0.050075,	train_time = 0.331295
[2019-08-24 13:23:37,648] TRAIN Iter 246560: lr = 0.089068,	loss = 2.350717,	Top-1 err = 0.348877,	Top-5 err = 0.149951,	data_time = 0.049900,	train_time = 0.816125
[2019-08-24 13:23:47,637] TRAIN Iter 246580: lr = 0.089035,	loss = 2.890699,	Top-1 err = 0.356088,	Top-5 err = 0.152792,	data_time = 0.007129,	train_time = 0.499423
[2019-08-24 13:24:33,031] TRAIN Iter 246600: lr = 0.089002,	loss = 2.449408,	Top-1 err = 0.346191,	Top-5 err = 0.146973,	data_time = 0.050408,	train_time = 2.269684
[2019-08-24 13:24:48,160] TRAIN Iter 246620: lr = 0.088968,	loss = 2.383445,	Top-1 err = 0.346582,	Top-5 err = 0.148535,	data_time = 0.050499,	train_time = 0.756445
[2019-08-24 13:24:55,068] TRAIN Iter 246640: lr = 0.088935,	loss = 2.434866,	Top-1 err = 0.337549,	Top-5 err = 0.141162,	data_time = 0.050221,	train_time = 0.345380
[2019-08-24 13:25:11,804] TRAIN Iter 246660: lr = 0.088902,	loss = 2.358777,	Top-1 err = 0.341016,	Top-5 err = 0.140381,	data_time = 0.050540,	train_time = 0.836782
[2019-08-24 13:25:23,634] TRAIN Iter 246680: lr = 0.088868,	loss = 2.391953,	Top-1 err = 0.339941,	Top-5 err = 0.143408,	data_time = 1.467400,	train_time = 0.591466
[2019-08-24 13:25:33,194] TRAIN Iter 246700: lr = 0.088835,	loss = 2.368232,	Top-1 err = 0.340088,	Top-5 err = 0.144092,	data_time = 0.050632,	train_time = 0.478022
[2019-08-24 13:25:50,357] TRAIN Iter 246720: lr = 0.088802,	loss = 2.441709,	Top-1 err = 0.341504,	Top-5 err = 0.144092,	data_time = 0.050529,	train_time = 0.858138
[2019-08-24 13:25:58,142] TRAIN Iter 246740: lr = 0.088768,	loss = 2.422534,	Top-1 err = 0.343213,	Top-5 err = 0.143066,	data_time = 0.050940,	train_time = 0.389187
[2019-08-24 13:26:12,005] TRAIN Iter 246760: lr = 0.088735,	loss = 2.408142,	Top-1 err = 0.340381,	Top-5 err = 0.142822,	data_time = 0.050379,	train_time = 0.693162
[2019-08-24 13:26:23,157] TRAIN Iter 246780: lr = 0.088702,	loss = 2.428090,	Top-1 err = 0.341113,	Top-5 err = 0.146924,	data_time = 0.050493,	train_time = 0.557568
[2019-08-24 13:26:32,061] TRAIN Iter 246800: lr = 0.088668,	loss = 2.419172,	Top-1 err = 0.343506,	Top-5 err = 0.145752,	data_time = 0.050496,	train_time = 0.445230
[2019-08-24 13:26:47,955] TRAIN Iter 246820: lr = 0.088635,	loss = 2.362466,	Top-1 err = 0.342139,	Top-5 err = 0.140674,	data_time = 0.050270,	train_time = 0.794651
[2019-08-24 13:27:05,137] TRAIN Iter 246840: lr = 0.088602,	loss = 2.395942,	Top-1 err = 0.343457,	Top-5 err = 0.146436,	data_time = 0.050587,	train_time = 0.859087
[2019-08-24 13:27:11,839] TRAIN Iter 246860: lr = 0.088568,	loss = 2.332155,	Top-1 err = 0.343604,	Top-5 err = 0.145313,	data_time = 0.050445,	train_time = 0.335100
[2019-08-24 13:27:27,322] TRAIN Iter 246880: lr = 0.088535,	loss = 2.372319,	Top-1 err = 0.343555,	Top-5 err = 0.145166,	data_time = 0.050322,	train_time = 0.774112
[2019-08-24 13:27:34,929] TRAIN Iter 246900: lr = 0.088502,	loss = 2.331475,	Top-1 err = 0.344092,	Top-5 err = 0.144922,	data_time = 0.051003,	train_time = 0.380353
[2019-08-24 13:27:50,880] TRAIN Iter 246920: lr = 0.088468,	loss = 2.401149,	Top-1 err = 0.340137,	Top-5 err = 0.144873,	data_time = 0.050247,	train_time = 0.797537
[2019-08-24 13:28:05,874] TRAIN Iter 246940: lr = 0.088435,	loss = 2.385576,	Top-1 err = 0.347461,	Top-5 err = 0.146191,	data_time = 0.050433,	train_time = 0.749691
[2019-08-24 13:28:13,212] TRAIN Iter 246960: lr = 0.088402,	loss = 2.491531,	Top-1 err = 0.346484,	Top-5 err = 0.143164,	data_time = 0.103011,	train_time = 0.366845
[2019-08-24 13:28:26,409] TRAIN Iter 246980: lr = 0.088368,	loss = 2.371835,	Top-1 err = 0.346680,	Top-5 err = 0.144238,	data_time = 0.050394,	train_time = 0.659859
[2019-08-24 13:28:41,680] TRAIN Iter 247000: lr = 0.088335,	loss = 2.402229,	Top-1 err = 0.346045,	Top-5 err = 0.147803,	data_time = 0.138960,	train_time = 0.763525
[2019-08-24 13:28:49,423] TRAIN Iter 247020: lr = 0.088302,	loss = 2.404185,	Top-1 err = 0.347705,	Top-5 err = 0.145703,	data_time = 0.050881,	train_time = 0.387171
[2019-08-24 13:29:06,042] TRAIN Iter 247040: lr = 0.088268,	loss = 2.361412,	Top-1 err = 0.345508,	Top-5 err = 0.145361,	data_time = 0.050439,	train_time = 0.830895
[2019-08-24 13:29:13,609] TRAIN Iter 247060: lr = 0.088235,	loss = 2.319410,	Top-1 err = 0.345068,	Top-5 err = 0.143994,	data_time = 0.050589,	train_time = 0.378362
[2019-08-24 13:29:28,749] TRAIN Iter 247080: lr = 0.088202,	loss = 2.441051,	Top-1 err = 0.346924,	Top-5 err = 0.142480,	data_time = 0.050337,	train_time = 0.756990
[2019-08-24 13:29:43,912] TRAIN Iter 247100: lr = 0.088168,	loss = 2.407362,	Top-1 err = 0.347510,	Top-5 err = 0.145459,	data_time = 0.050769,	train_time = 0.758136
[2019-08-24 13:29:51,141] TRAIN Iter 247120: lr = 0.088135,	loss = 2.270694,	Top-1 err = 0.345361,	Top-5 err = 0.142383,	data_time = 0.136423,	train_time = 0.361399
[2019-08-24 13:30:06,313] TRAIN Iter 247140: lr = 0.088102,	loss = 2.488738,	Top-1 err = 0.343604,	Top-5 err = 0.148193,	data_time = 0.050341,	train_time = 0.758593
[2019-08-24 13:30:21,935] TRAIN Iter 247160: lr = 0.088068,	loss = 2.456221,	Top-1 err = 0.343018,	Top-5 err = 0.147607,	data_time = 0.050972,	train_time = 0.781092
[2019-08-24 13:30:28,552] TRAIN Iter 247180: lr = 0.088035,	loss = 2.356582,	Top-1 err = 0.347656,	Top-5 err = 0.147559,	data_time = 0.050325,	train_time = 0.330821
[2019-08-24 13:30:44,720] TRAIN Iter 247200: lr = 0.088002,	loss = 2.445222,	Top-1 err = 0.347412,	Top-5 err = 0.143994,	data_time = 0.050576,	train_time = 0.808386
[2019-08-24 13:30:52,172] TRAIN Iter 247220: lr = 0.087968,	loss = 2.459314,	Top-1 err = 0.348145,	Top-5 err = 0.147998,	data_time = 0.050513,	train_time = 0.372616
[2019-08-24 13:31:06,906] TRAIN Iter 247240: lr = 0.087935,	loss = 2.450775,	Top-1 err = 0.346582,	Top-5 err = 0.143750,	data_time = 0.050523,	train_time = 0.736668
[2019-08-24 13:31:22,703] TRAIN Iter 247260: lr = 0.087902,	loss = 2.454899,	Top-1 err = 0.346338,	Top-5 err = 0.148486,	data_time = 0.050435,	train_time = 0.789865
[2019-08-24 13:31:29,635] TRAIN Iter 247280: lr = 0.087868,	loss = 2.418813,	Top-1 err = 0.347021,	Top-5 err = 0.146191,	data_time = 0.050574,	train_time = 0.346566
[2019-08-24 13:31:46,219] TRAIN Iter 247300: lr = 0.087835,	loss = 2.377809,	Top-1 err = 0.347852,	Top-5 err = 0.147559,	data_time = 0.050653,	train_time = 0.829182
[2019-08-24 13:32:01,002] TRAIN Iter 247320: lr = 0.087802,	loss = 2.356143,	Top-1 err = 0.348340,	Top-5 err = 0.149365,	data_time = 0.050411,	train_time = 0.739161
[2019-08-24 13:32:07,756] TRAIN Iter 247340: lr = 0.087768,	loss = 2.440006,	Top-1 err = 0.350586,	Top-5 err = 0.148096,	data_time = 0.050335,	train_time = 0.337640
[2019-08-24 13:32:23,596] TRAIN Iter 247360: lr = 0.087735,	loss = 2.414023,	Top-1 err = 0.345703,	Top-5 err = 0.146387,	data_time = 0.050250,	train_time = 0.792027
[2019-08-24 13:32:30,639] TRAIN Iter 247380: lr = 0.087702,	loss = 2.401804,	Top-1 err = 0.352686,	Top-5 err = 0.147510,	data_time = 0.050534,	train_time = 0.352141
[2019-08-24 13:32:46,394] TRAIN Iter 247400: lr = 0.087668,	loss = 2.311709,	Top-1 err = 0.348242,	Top-5 err = 0.145605,	data_time = 0.050538,	train_time = 0.787693
[2019-08-24 13:33:03,137] TRAIN Iter 247420: lr = 0.087635,	loss = 2.481880,	Top-1 err = 0.349707,	Top-5 err = 0.143213,	data_time = 0.050553,	train_time = 0.837143
[2019-08-24 13:33:10,158] TRAIN Iter 247440: lr = 0.087602,	loss = 2.391551,	Top-1 err = 0.345410,	Top-5 err = 0.147314,	data_time = 0.050848,	train_time = 0.351078
[2019-08-24 13:33:28,531] TRAIN Iter 247460: lr = 0.087568,	loss = 2.375980,	Top-1 err = 0.349902,	Top-5 err = 0.148535,	data_time = 0.050356,	train_time = 0.918623
[2019-08-24 13:33:44,809] TRAIN Iter 247480: lr = 0.087535,	loss = 2.368974,	Top-1 err = 0.348926,	Top-5 err = 0.148438,	data_time = 0.050411,	train_time = 0.813878
[2019-08-24 13:33:51,917] TRAIN Iter 247500: lr = 0.087502,	loss = 2.339880,	Top-1 err = 0.345264,	Top-5 err = 0.144531,	data_time = 0.050485,	train_time = 0.355393
[2019-08-24 13:34:07,515] TRAIN Iter 247520: lr = 0.087468,	loss = 2.384831,	Top-1 err = 0.350488,	Top-5 err = 0.150049,	data_time = 0.050316,	train_time = 0.779887
[2019-08-24 13:34:15,152] TRAIN Iter 247540: lr = 0.087435,	loss = 2.385628,	Top-1 err = 0.345996,	Top-5 err = 0.147363,	data_time = 0.138386,	train_time = 0.381793
[2019-08-24 13:34:29,772] TRAIN Iter 247560: lr = 0.087402,	loss = 2.355991,	Top-1 err = 0.345947,	Top-5 err = 0.145459,	data_time = 0.050638,	train_time = 0.730986
[2019-08-24 13:34:46,227] TRAIN Iter 247580: lr = 0.087368,	loss = 2.439319,	Top-1 err = 0.352539,	Top-5 err = 0.149707,	data_time = 0.050493,	train_time = 0.822772
[2019-08-24 13:34:53,709] TRAIN Iter 247600: lr = 0.087335,	loss = 2.377700,	Top-1 err = 0.351367,	Top-5 err = 0.151025,	data_time = 0.050786,	train_time = 0.374090
[2019-08-24 13:35:09,872] TRAIN Iter 247620: lr = 0.087302,	loss = 2.351324,	Top-1 err = 0.346289,	Top-5 err = 0.146533,	data_time = 0.050421,	train_time = 0.808137
[2019-08-24 13:35:26,014] TRAIN Iter 247640: lr = 0.087268,	loss = 2.431699,	Top-1 err = 0.346533,	Top-5 err = 0.146777,	data_time = 0.050381,	train_time = 0.807052
[2019-08-24 13:35:33,041] TRAIN Iter 247660: lr = 0.087235,	loss = 2.445529,	Top-1 err = 0.342383,	Top-5 err = 0.143115,	data_time = 0.050545,	train_time = 0.351360
[2019-08-24 13:35:48,104] TRAIN Iter 247680: lr = 0.087202,	loss = 2.501592,	Top-1 err = 0.351465,	Top-5 err = 0.150830,	data_time = 0.050731,	train_time = 0.753138
[2019-08-24 13:35:55,197] TRAIN Iter 247700: lr = 0.087168,	loss = 2.498894,	Top-1 err = 0.351074,	Top-5 err = 0.149316,	data_time = 0.050784,	train_time = 0.354642
[2019-08-24 13:36:12,694] TRAIN Iter 247720: lr = 0.087135,	loss = 2.453891,	Top-1 err = 0.348438,	Top-5 err = 0.149951,	data_time = 0.050311,	train_time = 0.874804
[2019-08-24 13:36:27,569] TRAIN Iter 247740: lr = 0.087102,	loss = 2.383203,	Top-1 err = 0.352197,	Top-5 err = 0.150684,	data_time = 0.050660,	train_time = 0.743716
[2019-08-24 13:36:34,359] TRAIN Iter 247760: lr = 0.087068,	loss = 2.447807,	Top-1 err = 0.342676,	Top-5 err = 0.148389,	data_time = 0.050596,	train_time = 0.339526
[2019-08-24 13:36:52,668] TRAIN Iter 247780: lr = 0.087035,	loss = 2.435261,	Top-1 err = 0.350049,	Top-5 err = 0.152344,	data_time = 0.049860,	train_time = 0.915424
[2019-08-24 13:37:09,169] TRAIN Iter 247800: lr = 0.087002,	loss = 2.422301,	Top-1 err = 0.350146,	Top-5 err = 0.152588,	data_time = 0.049910,	train_time = 0.825035
[2019-08-24 13:37:15,302] TRAIN Iter 247820: lr = 0.086968,	loss = 2.340952,	Top-1 err = 0.355469,	Top-5 err = 0.147559,	data_time = 0.049912,	train_time = 0.306632
[2019-08-24 13:38:03,473] TRAIN Iter 247840: lr = 0.086935,	loss = 2.403770,	Top-1 err = 0.350559,	Top-5 err = 0.147347,	data_time = 0.050312,	train_time = 2.408510
[2019-08-24 13:38:11,009] TRAIN Iter 247860: lr = 0.086902,	loss = 2.365468,	Top-1 err = 0.351123,	Top-5 err = 0.149414,	data_time = 0.050953,	train_time = 0.376791
[2019-08-24 13:38:25,853] TRAIN Iter 247880: lr = 0.086868,	loss = 2.418053,	Top-1 err = 0.339795,	Top-5 err = 0.140332,	data_time = 0.050402,	train_time = 0.742193
[2019-08-24 13:38:37,276] TRAIN Iter 247900: lr = 0.086835,	loss = 2.452106,	Top-1 err = 0.344727,	Top-5 err = 0.143701,	data_time = 0.170887,	train_time = 0.571140
[2019-08-24 13:38:45,203] TRAIN Iter 247920: lr = 0.086802,	loss = 2.366542,	Top-1 err = 0.346045,	Top-5 err = 0.146143,	data_time = 0.050546,	train_time = 0.396341
[2019-08-24 13:38:59,397] TRAIN Iter 247940: lr = 0.086768,	loss = 2.371142,	Top-1 err = 0.340771,	Top-5 err = 0.145020,	data_time = 0.050655,	train_time = 0.709683
[2019-08-24 13:39:06,863] TRAIN Iter 247960: lr = 0.086735,	loss = 2.361796,	Top-1 err = 0.340381,	Top-5 err = 0.140576,	data_time = 0.050581,	train_time = 0.373265
[2019-08-24 13:39:22,963] TRAIN Iter 247980: lr = 0.086702,	loss = 2.331120,	Top-1 err = 0.343311,	Top-5 err = 0.143115,	data_time = 0.050371,	train_time = 0.804992
[2019-08-24 13:39:37,819] TRAIN Iter 248000: lr = 0.086668,	loss = 2.393990,	Top-1 err = 0.339893,	Top-5 err = 0.140674,	data_time = 0.050439,	train_time = 0.742787
[2019-08-24 13:39:44,780] TRAIN Iter 248020: lr = 0.086635,	loss = 2.455357,	Top-1 err = 0.341455,	Top-5 err = 0.140039,	data_time = 0.050560,	train_time = 0.348069
[2019-08-24 13:40:00,085] TRAIN Iter 248040: lr = 0.086602,	loss = 2.483719,	Top-1 err = 0.346289,	Top-5 err = 0.147705,	data_time = 0.050502,	train_time = 0.765197
[2019-08-24 13:40:17,522] TRAIN Iter 248060: lr = 0.086568,	loss = 2.352964,	Top-1 err = 0.339648,	Top-5 err = 0.138086,	data_time = 0.050406,	train_time = 0.871868
[2019-08-24 13:40:24,424] TRAIN Iter 248080: lr = 0.086535,	loss = 2.343567,	Top-1 err = 0.342383,	Top-5 err = 0.141113,	data_time = 0.050433,	train_time = 0.345054
[2019-08-24 13:40:38,740] TRAIN Iter 248100: lr = 0.086502,	loss = 2.284279,	Top-1 err = 0.334912,	Top-5 err = 0.140576,	data_time = 0.050335,	train_time = 0.715812
[2019-08-24 13:40:46,220] TRAIN Iter 248120: lr = 0.086468,	loss = 2.340990,	Top-1 err = 0.342773,	Top-5 err = 0.140723,	data_time = 0.050393,	train_time = 0.373948
[2019-08-24 13:41:01,637] TRAIN Iter 248140: lr = 0.086435,	loss = 2.384220,	Top-1 err = 0.341406,	Top-5 err = 0.148633,	data_time = 0.050533,	train_time = 0.770833
[2019-08-24 13:41:16,388] TRAIN Iter 248160: lr = 0.086402,	loss = 2.312030,	Top-1 err = 0.337012,	Top-5 err = 0.145850,	data_time = 0.050276,	train_time = 0.737560
[2019-08-24 13:41:23,726] TRAIN Iter 248180: lr = 0.086368,	loss = 2.415511,	Top-1 err = 0.337793,	Top-5 err = 0.142969,	data_time = 0.050488,	train_time = 0.366903
[2019-08-24 13:41:37,360] TRAIN Iter 248200: lr = 0.086335,	loss = 2.424755,	Top-1 err = 0.341162,	Top-5 err = 0.144189,	data_time = 0.050416,	train_time = 0.681655
[2019-08-24 13:41:51,572] TRAIN Iter 248220: lr = 0.086302,	loss = 2.422104,	Top-1 err = 0.345752,	Top-5 err = 0.146338,	data_time = 0.102346,	train_time = 0.710598
[2019-08-24 13:41:58,548] TRAIN Iter 248240: lr = 0.086268,	loss = 2.401123,	Top-1 err = 0.343750,	Top-5 err = 0.144922,	data_time = 0.050689,	train_time = 0.348793
[2019-08-24 13:42:14,650] TRAIN Iter 248260: lr = 0.086235,	loss = 2.406439,	Top-1 err = 0.342432,	Top-5 err = 0.146436,	data_time = 0.050434,	train_time = 0.805107
[2019-08-24 13:42:22,213] TRAIN Iter 248280: lr = 0.086202,	loss = 2.465125,	Top-1 err = 0.344141,	Top-5 err = 0.145068,	data_time = 0.050619,	train_time = 0.378120
[2019-08-24 13:42:36,957] TRAIN Iter 248300: lr = 0.086168,	loss = 2.491865,	Top-1 err = 0.345215,	Top-5 err = 0.145703,	data_time = 0.050461,	train_time = 0.737183
[2019-08-24 13:42:52,167] TRAIN Iter 248320: lr = 0.086135,	loss = 2.409089,	Top-1 err = 0.347266,	Top-5 err = 0.147510,	data_time = 0.050584,	train_time = 0.760459
[2019-08-24 13:42:59,210] TRAIN Iter 248340: lr = 0.086102,	loss = 2.340108,	Top-1 err = 0.342529,	Top-5 err = 0.142822,	data_time = 0.051006,	train_time = 0.352175
[2019-08-24 13:43:14,544] TRAIN Iter 248360: lr = 0.086068,	loss = 2.373518,	Top-1 err = 0.347754,	Top-5 err = 0.147363,	data_time = 0.050756,	train_time = 0.766684
[2019-08-24 13:43:29,804] TRAIN Iter 248380: lr = 0.086035,	loss = 2.393275,	Top-1 err = 0.348047,	Top-5 err = 0.146191,	data_time = 0.050385,	train_time = 0.762959
[2019-08-24 13:43:37,503] TRAIN Iter 248400: lr = 0.086002,	loss = 2.404074,	Top-1 err = 0.341113,	Top-5 err = 0.146436,	data_time = 0.050373,	train_time = 0.384967
[2019-08-24 13:43:52,565] TRAIN Iter 248420: lr = 0.085968,	loss = 2.489320,	Top-1 err = 0.347119,	Top-5 err = 0.145801,	data_time = 0.050141,	train_time = 0.753054
[2019-08-24 13:43:59,855] TRAIN Iter 248440: lr = 0.085935,	loss = 2.360398,	Top-1 err = 0.343311,	Top-5 err = 0.142383,	data_time = 0.050465,	train_time = 0.364496
[2019-08-24 13:44:15,184] TRAIN Iter 248460: lr = 0.085902,	loss = 2.360451,	Top-1 err = 0.346289,	Top-5 err = 0.146826,	data_time = 0.050480,	train_time = 0.766413
[2019-08-24 13:44:30,112] TRAIN Iter 248480: lr = 0.085868,	loss = 2.421262,	Top-1 err = 0.345850,	Top-5 err = 0.147119,	data_time = 0.050890,	train_time = 0.746393
[2019-08-24 13:44:37,532] TRAIN Iter 248500: lr = 0.085835,	loss = 2.456110,	Top-1 err = 0.342676,	Top-5 err = 0.145313,	data_time = 0.050848,	train_time = 0.370996
[2019-08-24 13:44:52,325] TRAIN Iter 248520: lr = 0.085802,	loss = 2.405572,	Top-1 err = 0.343457,	Top-5 err = 0.144971,	data_time = 0.050525,	train_time = 0.739657
[2019-08-24 13:45:07,766] TRAIN Iter 248540: lr = 0.085768,	loss = 2.380461,	Top-1 err = 0.349951,	Top-5 err = 0.145801,	data_time = 0.050456,	train_time = 0.772008
[2019-08-24 13:45:15,401] TRAIN Iter 248560: lr = 0.085735,	loss = 2.293779,	Top-1 err = 0.342236,	Top-5 err = 0.144434,	data_time = 0.050323,	train_time = 0.381770
[2019-08-24 13:45:31,350] TRAIN Iter 248580: lr = 0.085702,	loss = 2.377875,	Top-1 err = 0.347461,	Top-5 err = 0.143994,	data_time = 0.050413,	train_time = 0.797434
[2019-08-24 13:45:38,649] TRAIN Iter 248600: lr = 0.085668,	loss = 2.399440,	Top-1 err = 0.349951,	Top-5 err = 0.149561,	data_time = 0.050515,	train_time = 0.364934
[2019-08-24 13:45:54,783] TRAIN Iter 248620: lr = 0.085635,	loss = 2.382137,	Top-1 err = 0.347363,	Top-5 err = 0.148535,	data_time = 0.050480,	train_time = 0.806686
[2019-08-24 13:46:10,902] TRAIN Iter 248640: lr = 0.085602,	loss = 2.363882,	Top-1 err = 0.342432,	Top-5 err = 0.144434,	data_time = 0.050499,	train_time = 0.805922
[2019-08-24 13:46:17,822] TRAIN Iter 248660: lr = 0.085568,	loss = 2.439980,	Top-1 err = 0.349072,	Top-5 err = 0.145215,	data_time = 0.050334,	train_time = 0.345997
[2019-08-24 13:46:33,819] TRAIN Iter 248680: lr = 0.085535,	loss = 2.414140,	Top-1 err = 0.348389,	Top-5 err = 0.146289,	data_time = 0.050392,	train_time = 0.799809
[2019-08-24 13:46:49,109] TRAIN Iter 248700: lr = 0.085502,	loss = 2.347780,	Top-1 err = 0.345996,	Top-5 err = 0.144873,	data_time = 0.111793,	train_time = 0.764471
[2019-08-24 13:46:57,262] TRAIN Iter 248720: lr = 0.085468,	loss = 2.344771,	Top-1 err = 0.346973,	Top-5 err = 0.148975,	data_time = 0.050447,	train_time = 0.407647
[2019-08-24 13:47:13,534] TRAIN Iter 248740: lr = 0.085435,	loss = 2.396045,	Top-1 err = 0.349414,	Top-5 err = 0.145752,	data_time = 0.050341,	train_time = 0.813607
[2019-08-24 13:47:20,481] TRAIN Iter 248760: lr = 0.085402,	loss = 2.418819,	Top-1 err = 0.343066,	Top-5 err = 0.146533,	data_time = 0.050524,	train_time = 0.347345
[2019-08-24 13:47:37,359] TRAIN Iter 248780: lr = 0.085368,	loss = 2.436422,	Top-1 err = 0.347314,	Top-5 err = 0.148584,	data_time = 0.050492,	train_time = 0.843850
[2019-08-24 13:47:53,374] TRAIN Iter 248800: lr = 0.085335,	loss = 2.437799,	Top-1 err = 0.340576,	Top-5 err = 0.144336,	data_time = 0.128736,	train_time = 0.800749
[2019-08-24 13:48:00,316] TRAIN Iter 248820: lr = 0.085302,	loss = 2.413853,	Top-1 err = 0.346045,	Top-5 err = 0.148828,	data_time = 0.050550,	train_time = 0.347086
[2019-08-24 13:48:17,033] TRAIN Iter 248840: lr = 0.085268,	loss = 2.357543,	Top-1 err = 0.352344,	Top-5 err = 0.149561,	data_time = 0.050951,	train_time = 0.835830
[2019-08-24 13:48:35,567] TRAIN Iter 248860: lr = 0.085235,	loss = 2.396951,	Top-1 err = 0.347168,	Top-5 err = 0.146533,	data_time = 0.050466,	train_time = 0.926684
[2019-08-24 13:48:42,448] TRAIN Iter 248880: lr = 0.085202,	loss = 2.413779,	Top-1 err = 0.346631,	Top-5 err = 0.146680,	data_time = 0.050382,	train_time = 0.344047
[2019-08-24 13:48:59,021] TRAIN Iter 248900: lr = 0.085168,	loss = 2.457076,	Top-1 err = 0.348730,	Top-5 err = 0.145361,	data_time = 0.050371,	train_time = 0.828615
[2019-08-24 13:49:05,987] TRAIN Iter 248920: lr = 0.085135,	loss = 2.449790,	Top-1 err = 0.348486,	Top-5 err = 0.147217,	data_time = 0.050543,	train_time = 0.348303
[2019-08-24 13:49:22,026] TRAIN Iter 248940: lr = 0.085102,	loss = 2.343149,	Top-1 err = 0.344629,	Top-5 err = 0.147217,	data_time = 0.050523,	train_time = 0.801938
[2019-08-24 13:49:40,499] TRAIN Iter 248960: lr = 0.085068,	loss = 2.511808,	Top-1 err = 0.349414,	Top-5 err = 0.148193,	data_time = 0.050546,	train_time = 0.923656
[2019-08-24 13:49:47,292] TRAIN Iter 248980: lr = 0.085035,	loss = 2.413486,	Top-1 err = 0.344043,	Top-5 err = 0.144971,	data_time = 0.050527,	train_time = 0.339616
[2019-08-24 13:50:05,413] TRAIN Iter 249000: lr = 0.085002,	loss = 2.357652,	Top-1 err = 0.350244,	Top-5 err = 0.147119,	data_time = 0.050517,	train_time = 0.906029
[2019-08-24 13:50:22,533] TRAIN Iter 249020: lr = 0.084968,	loss = 2.396988,	Top-1 err = 0.348730,	Top-5 err = 0.149609,	data_time = 0.066345,	train_time = 0.856006
[2019-08-24 13:50:30,506] TRAIN Iter 249040: lr = 0.084935,	loss = 2.332091,	Top-1 err = 0.350928,	Top-5 err = 0.147168,	data_time = 0.050169,	train_time = 0.398608
[2019-08-24 13:50:46,332] TRAIN Iter 249060: lr = 0.084902,	loss = 2.464509,	Top-1 err = 0.350537,	Top-5 err = 0.149072,	data_time = 0.049935,	train_time = 0.791303
[2019-08-24 13:50:52,449] TRAIN Iter 249080: lr = 0.084868,	loss = 2.407302,	Top-1 err = 0.339746,	Top-5 err = 0.146875,	data_time = 0.049929,	train_time = 0.305835
[2019-08-24 13:51:42,524] TRAIN Iter 249100: lr = 0.084835,	loss = 2.331172,	Top-1 err = 0.344590,	Top-5 err = 0.147982,	data_time = 0.050191,	train_time = 2.503713
[2019-08-24 13:51:54,520] TRAIN Iter 249120: lr = 0.084802,	loss = 2.330750,	Top-1 err = 0.337939,	Top-5 err = 0.140332,	data_time = 0.050389,	train_time = 0.599788
[2019-08-24 13:52:06,071] TRAIN Iter 249140: lr = 0.084768,	loss = 2.347435,	Top-1 err = 0.339062,	Top-5 err = 0.140723,	data_time = 0.050508,	train_time = 0.577553
[2019-08-24 13:52:21,063] TRAIN Iter 249160: lr = 0.084735,	loss = 2.391637,	Top-1 err = 0.341357,	Top-5 err = 0.140527,	data_time = 0.122498,	train_time = 0.749596
[2019-08-24 13:52:28,554] TRAIN Iter 249180: lr = 0.084702,	loss = 2.392075,	Top-1 err = 0.342529,	Top-5 err = 0.143750,	data_time = 0.050450,	train_time = 0.374511
[2019-08-24 13:52:45,314] TRAIN Iter 249200: lr = 0.084668,	loss = 2.373106,	Top-1 err = 0.332813,	Top-5 err = 0.140674,	data_time = 0.050498,	train_time = 0.837982
[2019-08-24 13:53:01,444] TRAIN Iter 249220: lr = 0.084635,	loss = 2.430304,	Top-1 err = 0.342090,	Top-5 err = 0.140771,	data_time = 0.050667,	train_time = 0.806482
[2019-08-24 13:53:09,064] TRAIN Iter 249240: lr = 0.084602,	loss = 2.423414,	Top-1 err = 0.340674,	Top-5 err = 0.137891,	data_time = 0.050328,	train_time = 0.381002
[2019-08-24 13:53:23,210] TRAIN Iter 249260: lr = 0.084568,	loss = 2.388348,	Top-1 err = 0.341260,	Top-5 err = 0.143311,	data_time = 0.050762,	train_time = 0.707279
[2019-08-24 13:53:39,834] TRAIN Iter 249280: lr = 0.084535,	loss = 2.396994,	Top-1 err = 0.346289,	Top-5 err = 0.145117,	data_time = 0.050380,	train_time = 0.831184
[2019-08-24 13:53:47,347] TRAIN Iter 249300: lr = 0.084502,	loss = 2.326471,	Top-1 err = 0.336768,	Top-5 err = 0.140527,	data_time = 0.142160,	train_time = 0.375665
[2019-08-24 13:53:59,753] TRAIN Iter 249320: lr = 0.084468,	loss = 2.418205,	Top-1 err = 0.340771,	Top-5 err = 0.148340,	data_time = 0.050616,	train_time = 0.620252
[2019-08-24 13:54:07,707] TRAIN Iter 249340: lr = 0.084435,	loss = 2.369009,	Top-1 err = 0.340039,	Top-5 err = 0.143115,	data_time = 0.050646,	train_time = 0.397688
[2019-08-24 13:54:19,163] TRAIN Iter 249360: lr = 0.084402,	loss = 2.335059,	Top-1 err = 0.341797,	Top-5 err = 0.145215,	data_time = 0.050800,	train_time = 0.572776
[2019-08-24 13:54:33,664] TRAIN Iter 249380: lr = 0.084368,	loss = 2.372183,	Top-1 err = 0.339551,	Top-5 err = 0.140918,	data_time = 0.050308,	train_time = 0.725070
[2019-08-24 13:54:40,542] TRAIN Iter 249400: lr = 0.084335,	loss = 2.413290,	Top-1 err = 0.347559,	Top-5 err = 0.146094,	data_time = 0.050618,	train_time = 0.343855
[2019-08-24 13:54:58,417] TRAIN Iter 249420: lr = 0.084302,	loss = 2.404157,	Top-1 err = 0.350879,	Top-5 err = 0.149316,	data_time = 0.050268,	train_time = 0.893779
[2019-08-24 13:55:13,294] TRAIN Iter 249440: lr = 0.084268,	loss = 2.406435,	Top-1 err = 0.337695,	Top-5 err = 0.141357,	data_time = 0.050406,	train_time = 0.743832
[2019-08-24 13:55:20,534] TRAIN Iter 249460: lr = 0.084235,	loss = 2.344289,	Top-1 err = 0.335840,	Top-5 err = 0.142236,	data_time = 0.050696,	train_time = 0.361952
[2019-08-24 13:55:35,054] TRAIN Iter 249480: lr = 0.084202,	loss = 2.359946,	Top-1 err = 0.338477,	Top-5 err = 0.143115,	data_time = 0.050424,	train_time = 0.726024
[2019-08-24 13:55:42,288] TRAIN Iter 249500: lr = 0.084168,	loss = 2.399617,	Top-1 err = 0.345898,	Top-5 err = 0.145557,	data_time = 0.050388,	train_time = 0.361676
[2019-08-24 13:55:58,651] TRAIN Iter 249520: lr = 0.084135,	loss = 2.367618,	Top-1 err = 0.344922,	Top-5 err = 0.147314,	data_time = 0.050624,	train_time = 0.818110
[2019-08-24 13:56:13,749] TRAIN Iter 249540: lr = 0.084102,	loss = 2.295979,	Top-1 err = 0.345703,	Top-5 err = 0.145410,	data_time = 0.050422,	train_time = 0.754894
[2019-08-24 13:56:20,846] TRAIN Iter 249560: lr = 0.084068,	loss = 2.361610,	Top-1 err = 0.334717,	Top-5 err = 0.140723,	data_time = 0.050567,	train_time = 0.354836
[2019-08-24 13:56:36,583] TRAIN Iter 249580: lr = 0.084035,	loss = 2.368288,	Top-1 err = 0.347705,	Top-5 err = 0.144727,	data_time = 0.164607,	train_time = 0.786847
[2019-08-24 13:56:50,926] TRAIN Iter 249600: lr = 0.084002,	loss = 2.370311,	Top-1 err = 0.338184,	Top-5 err = 0.139551,	data_time = 0.050537,	train_time = 0.717149
[2019-08-24 13:56:58,165] TRAIN Iter 249620: lr = 0.083968,	loss = 2.414454,	Top-1 err = 0.340234,	Top-5 err = 0.144287,	data_time = 0.050349,	train_time = 0.361893
[2019-08-24 13:57:15,489] TRAIN Iter 249640: lr = 0.083935,	loss = 2.503134,	Top-1 err = 0.341602,	Top-5 err = 0.143555,	data_time = 0.050543,	train_time = 0.866202
[2019-08-24 13:57:22,811] TRAIN Iter 249660: lr = 0.083902,	loss = 2.528278,	Top-1 err = 0.349512,	Top-5 err = 0.147119,	data_time = 0.050671,	train_time = 0.366065
[2019-08-24 13:57:38,172] TRAIN Iter 249680: lr = 0.083868,	loss = 2.442383,	Top-1 err = 0.347656,	Top-5 err = 0.145508,	data_time = 0.050756,	train_time = 0.768081
[2019-08-24 13:57:54,628] TRAIN Iter 249700: lr = 0.083835,	loss = 2.333626,	Top-1 err = 0.352637,	Top-5 err = 0.144922,	data_time = 0.050401,	train_time = 0.822784
[2019-08-24 13:58:01,555] TRAIN Iter 249720: lr = 0.083802,	loss = 2.364762,	Top-1 err = 0.339453,	Top-5 err = 0.141260,	data_time = 0.051015,	train_time = 0.346308
[2019-08-24 13:58:16,578] TRAIN Iter 249740: lr = 0.083768,	loss = 2.290217,	Top-1 err = 0.345166,	Top-5 err = 0.143896,	data_time = 0.050542,	train_time = 0.751161
[2019-08-24 13:58:29,825] TRAIN Iter 249760: lr = 0.083735,	loss = 2.311439,	Top-1 err = 0.347559,	Top-5 err = 0.146631,	data_time = 0.050936,	train_time = 0.662337
[2019-08-24 13:58:40,244] TRAIN Iter 249780: lr = 0.083702,	loss = 2.524748,	Top-1 err = 0.345215,	Top-5 err = 0.145410,	data_time = 0.050483,	train_time = 0.520928
[2019-08-24 13:58:55,468] TRAIN Iter 249800: lr = 0.083668,	loss = 2.405720,	Top-1 err = 0.345410,	Top-5 err = 0.145117,	data_time = 0.050441,	train_time = 0.761168
[2019-08-24 13:59:02,740] TRAIN Iter 249820: lr = 0.083635,	loss = 2.397110,	Top-1 err = 0.348047,	Top-5 err = 0.146924,	data_time = 0.050271,	train_time = 0.363602
[2019-08-24 13:59:19,689] TRAIN Iter 249840: lr = 0.083602,	loss = 2.333611,	Top-1 err = 0.342139,	Top-5 err = 0.144189,	data_time = 0.050512,	train_time = 0.847444
[2019-08-24 13:59:35,185] TRAIN Iter 249860: lr = 0.083568,	loss = 2.513523,	Top-1 err = 0.349609,	Top-5 err = 0.147070,	data_time = 0.050565,	train_time = 0.774748
[2019-08-24 13:59:42,187] TRAIN Iter 249880: lr = 0.083535,	loss = 2.336900,	Top-1 err = 0.341699,	Top-5 err = 0.143115,	data_time = 0.050856,	train_time = 0.350091
[2019-08-24 13:59:58,818] TRAIN Iter 249900: lr = 0.083502,	loss = 2.359165,	Top-1 err = 0.341357,	Top-5 err = 0.145654,	data_time = 0.050793,	train_time = 0.831571
[2019-08-24 14:00:12,485] TRAIN Iter 249920: lr = 0.083468,	loss = 2.519082,	Top-1 err = 0.340039,	Top-5 err = 0.141846,	data_time = 0.050265,	train_time = 0.683329
[2019-08-24 14:00:23,110] TRAIN Iter 249940: lr = 0.083435,	loss = 2.476481,	Top-1 err = 0.345947,	Top-5 err = 0.149316,	data_time = 0.050441,	train_time = 0.531211
[2019-08-24 14:00:39,376] TRAIN Iter 249960: lr = 0.083402,	loss = 2.378939,	Top-1 err = 0.348438,	Top-5 err = 0.146045,	data_time = 0.050436,	train_time = 0.813327
[2019-08-24 14:00:46,305] TRAIN Iter 249980: lr = 0.083368,	loss = 2.409904,	Top-1 err = 0.348926,	Top-5 err = 0.149170,	data_time = 0.050520,	train_time = 0.346411
[2019-08-24 14:01:03,741] TRAIN Iter 250000: lr = 0.083335,	loss = 2.374063,	Top-1 err = 0.343018,	Top-5 err = 0.144336,	data_time = 0.050704,	train_time = 0.871795
[2019-08-24 14:02:09,314] TEST Iter 250000: loss = 2.189832,	Top-1 err = 0.306140,	Top-5 err = 0.108100,	val_time = 65.535430
[2019-08-24 14:02:15,573] TRAIN Iter 250020: lr = 0.083302,	loss = 2.477628,	Top-1 err = 0.344873,	Top-5 err = 0.143652,	data_time = 0.050707,	train_time = 0.312904
[2019-08-24 14:02:22,080] TRAIN Iter 250040: lr = 0.083268,	loss = 2.344484,	Top-1 err = 0.348584,	Top-5 err = 0.147852,	data_time = 0.050451,	train_time = 0.325317
[2019-08-24 14:02:28,799] TRAIN Iter 250060: lr = 0.083235,	loss = 2.309170,	Top-1 err = 0.341846,	Top-5 err = 0.142236,	data_time = 0.050642,	train_time = 0.335959
[2019-08-24 14:02:37,164] TRAIN Iter 250080: lr = 0.083202,	loss = 2.329431,	Top-1 err = 0.343311,	Top-5 err = 0.142969,	data_time = 0.225634,	train_time = 0.418257
[2019-08-24 14:02:52,528] TRAIN Iter 250100: lr = 0.083168,	loss = 2.445155,	Top-1 err = 0.347754,	Top-5 err = 0.148633,	data_time = 0.755078,	train_time = 0.768157
[2019-08-24 14:03:03,891] TRAIN Iter 250120: lr = 0.083135,	loss = 2.399893,	Top-1 err = 0.349072,	Top-5 err = 0.145166,	data_time = 0.050473,	train_time = 0.568153
[2019-08-24 14:03:18,557] TRAIN Iter 250140: lr = 0.083102,	loss = 2.336573,	Top-1 err = 0.345703,	Top-5 err = 0.144775,	data_time = 0.050537,	train_time = 0.733284
[2019-08-24 14:03:27,648] TRAIN Iter 250160: lr = 0.083068,	loss = 2.362440,	Top-1 err = 0.345020,	Top-5 err = 0.148242,	data_time = 0.050848,	train_time = 0.454529
[2019-08-24 14:03:44,189] TRAIN Iter 250180: lr = 0.083035,	loss = 2.494783,	Top-1 err = 0.346191,	Top-5 err = 0.143652,	data_time = 0.050503,	train_time = 0.827009
[2019-08-24 14:04:00,051] TRAIN Iter 250200: lr = 0.083002,	loss = 2.367578,	Top-1 err = 0.339795,	Top-5 err = 0.144922,	data_time = 0.050715,	train_time = 0.793095
[2019-08-24 14:04:09,167] TRAIN Iter 250220: lr = 0.082968,	loss = 2.409126,	Top-1 err = 0.344727,	Top-5 err = 0.147510,	data_time = 0.050584,	train_time = 0.455804
[2019-08-24 14:04:24,925] TRAIN Iter 250240: lr = 0.082935,	loss = 2.453250,	Top-1 err = 0.345947,	Top-5 err = 0.145410,	data_time = 0.050920,	train_time = 0.787864
[2019-08-24 14:04:43,276] TRAIN Iter 250260: lr = 0.082902,	loss = 2.372665,	Top-1 err = 0.347998,	Top-5 err = 0.142676,	data_time = 0.050445,	train_time = 0.917537
[2019-08-24 14:04:51,987] TRAIN Iter 250280: lr = 0.082868,	loss = 2.441960,	Top-1 err = 0.343018,	Top-5 err = 0.144141,	data_time = 0.050144,	train_time = 0.435557
[2019-08-24 14:05:08,472] TRAIN Iter 250300: lr = 0.082835,	loss = 2.481353,	Top-1 err = 0.349854,	Top-5 err = 0.149365,	data_time = 0.049937,	train_time = 0.824248
[2019-08-24 14:05:16,385] TRAIN Iter 250320: lr = 0.082802,	loss = 2.402487,	Top-1 err = 0.345801,	Top-5 err = 0.148779,	data_time = 0.049849,	train_time = 0.395628
[2019-08-24 14:06:06,826] TRAIN Iter 250340: lr = 0.082768,	loss = 2.403501,	Top-1 err = 0.350470,	Top-5 err = 0.152334,	data_time = 0.142706,	train_time = 2.522012
[2019-08-24 14:06:13,783] TRAIN Iter 250360: lr = 0.082735,	loss = 2.365879,	Top-1 err = 0.343115,	Top-5 err = 0.144336,	data_time = 0.050362,	train_time = 0.347848
[2019-08-24 14:06:31,326] TRAIN Iter 250380: lr = 0.082702,	loss = 2.345670,	Top-1 err = 0.339160,	Top-5 err = 0.142139,	data_time = 0.050374,	train_time = 0.877125
[2019-08-24 14:06:39,321] TRAIN Iter 250400: lr = 0.082668,	loss = 2.364664,	Top-1 err = 0.337549,	Top-5 err = 0.144775,	data_time = 0.050348,	train_time = 0.399718
[2019-08-24 14:06:51,882] TRAIN Iter 250420: lr = 0.082635,	loss = 2.266772,	Top-1 err = 0.333447,	Top-5 err = 0.140576,	data_time = 0.050440,	train_time = 0.628023
[2019-08-24 14:07:07,903] TRAIN Iter 250440: lr = 0.082602,	loss = 2.346234,	Top-1 err = 0.337695,	Top-5 err = 0.144043,	data_time = 0.050798,	train_time = 0.801045
[2019-08-24 14:07:15,142] TRAIN Iter 250460: lr = 0.082568,	loss = 2.368896,	Top-1 err = 0.338428,	Top-5 err = 0.139160,	data_time = 0.178241,	train_time = 0.361948
[2019-08-24 14:07:29,568] TRAIN Iter 250480: lr = 0.082535,	loss = 2.415345,	Top-1 err = 0.340039,	Top-5 err = 0.143311,	data_time = 0.050321,	train_time = 0.721268
[2019-08-24 14:07:43,195] TRAIN Iter 250500: lr = 0.082502,	loss = 2.404860,	Top-1 err = 0.341162,	Top-5 err = 0.143506,	data_time = 0.050534,	train_time = 0.681370
[2019-08-24 14:07:51,210] TRAIN Iter 250520: lr = 0.082468,	loss = 2.271540,	Top-1 err = 0.341846,	Top-5 err = 0.139697,	data_time = 0.050737,	train_time = 0.400700
[2019-08-24 14:08:06,485] TRAIN Iter 250540: lr = 0.082435,	loss = 2.435699,	Top-1 err = 0.339258,	Top-5 err = 0.141309,	data_time = 0.050478,	train_time = 0.763758
[2019-08-24 14:08:13,509] TRAIN Iter 250560: lr = 0.082402,	loss = 2.384420,	Top-1 err = 0.334375,	Top-5 err = 0.138281,	data_time = 0.163697,	train_time = 0.351198
[2019-08-24 14:08:28,506] TRAIN Iter 250580: lr = 0.082368,	loss = 2.415380,	Top-1 err = 0.344531,	Top-5 err = 0.142969,	data_time = 0.050690,	train_time = 0.749839
[2019-08-24 14:08:46,048] TRAIN Iter 250600: lr = 0.082335,	loss = 2.450104,	Top-1 err = 0.338574,	Top-5 err = 0.141797,	data_time = 0.050363,	train_time = 0.877077
[2019-08-24 14:08:53,679] TRAIN Iter 250620: lr = 0.082302,	loss = 2.328814,	Top-1 err = 0.339990,	Top-5 err = 0.140869,	data_time = 0.050498,	train_time = 0.381509
[2019-08-24 14:09:05,669] TRAIN Iter 250640: lr = 0.082268,	loss = 2.339600,	Top-1 err = 0.334863,	Top-5 err = 0.139502,	data_time = 0.050242,	train_time = 0.599494
[2019-08-24 14:09:17,054] TRAIN Iter 250660: lr = 0.082235,	loss = 2.374418,	Top-1 err = 0.341553,	Top-5 err = 0.143994,	data_time = 0.050745,	train_time = 0.569255
[2019-08-24 14:09:27,950] TRAIN Iter 250680: lr = 0.082202,	loss = 2.428396,	Top-1 err = 0.348584,	Top-5 err = 0.148096,	data_time = 0.050498,	train_time = 0.544766
[2019-08-24 14:09:43,307] TRAIN Iter 250700: lr = 0.082168,	loss = 2.444175,	Top-1 err = 0.342432,	Top-5 err = 0.142236,	data_time = 0.050417,	train_time = 0.767855
[2019-08-24 14:09:50,570] TRAIN Iter 250720: lr = 0.082135,	loss = 2.360487,	Top-1 err = 0.342773,	Top-5 err = 0.144678,	data_time = 0.050339,	train_time = 0.363107
[2019-08-24 14:10:06,622] TRAIN Iter 250740: lr = 0.082102,	loss = 2.401966,	Top-1 err = 0.343945,	Top-5 err = 0.143896,	data_time = 0.050930,	train_time = 0.802615
[2019-08-24 14:10:22,692] TRAIN Iter 250760: lr = 0.082068,	loss = 2.421445,	Top-1 err = 0.344629,	Top-5 err = 0.144434,	data_time = 0.050675,	train_time = 0.803486
[2019-08-24 14:10:30,294] TRAIN Iter 250780: lr = 0.082035,	loss = 2.325222,	Top-1 err = 0.343457,	Top-5 err = 0.139551,	data_time = 0.050631,	train_time = 0.380072
[2019-08-24 14:10:43,571] TRAIN Iter 250800: lr = 0.082002,	loss = 2.286352,	Top-1 err = 0.344629,	Top-5 err = 0.139844,	data_time = 0.050240,	train_time = 0.663823
[2019-08-24 14:10:57,587] TRAIN Iter 250820: lr = 0.081968,	loss = 2.388816,	Top-1 err = 0.341016,	Top-5 err = 0.144434,	data_time = 0.050429,	train_time = 0.700830
[2019-08-24 14:11:06,448] TRAIN Iter 250840: lr = 0.081935,	loss = 2.406107,	Top-1 err = 0.345117,	Top-5 err = 0.143652,	data_time = 0.050841,	train_time = 0.443027
[2019-08-24 14:11:21,940] TRAIN Iter 250860: lr = 0.081902,	loss = 2.374606,	Top-1 err = 0.342480,	Top-5 err = 0.144141,	data_time = 0.050525,	train_time = 0.774569
[2019-08-24 14:11:28,916] TRAIN Iter 250880: lr = 0.081868,	loss = 2.392261,	Top-1 err = 0.340967,	Top-5 err = 0.142920,	data_time = 0.050421,	train_time = 0.348808
[2019-08-24 14:11:45,516] TRAIN Iter 250900: lr = 0.081835,	loss = 2.360558,	Top-1 err = 0.340088,	Top-5 err = 0.140771,	data_time = 0.050482,	train_time = 0.829947
[2019-08-24 14:12:01,703] TRAIN Iter 250920: lr = 0.081802,	loss = 2.532996,	Top-1 err = 0.343115,	Top-5 err = 0.141797,	data_time = 0.050579,	train_time = 0.809355
[2019-08-24 14:12:08,848] TRAIN Iter 250940: lr = 0.081768,	loss = 2.386949,	Top-1 err = 0.347510,	Top-5 err = 0.147900,	data_time = 0.142329,	train_time = 0.357226
[2019-08-24 14:12:25,327] TRAIN Iter 250960: lr = 0.081735,	loss = 2.344915,	Top-1 err = 0.343066,	Top-5 err = 0.148291,	data_time = 0.050392,	train_time = 0.823924
[2019-08-24 14:12:39,502] TRAIN Iter 250980: lr = 0.081702,	loss = 2.306730,	Top-1 err = 0.345654,	Top-5 err = 0.142773,	data_time = 0.050514,	train_time = 0.708780
[2019-08-24 14:12:48,124] TRAIN Iter 251000: lr = 0.081668,	loss = 2.402761,	Top-1 err = 0.342773,	Top-5 err = 0.148828,	data_time = 0.050358,	train_time = 0.431064
[2019-08-24 14:13:03,879] TRAIN Iter 251020: lr = 0.081635,	loss = 2.361005,	Top-1 err = 0.348145,	Top-5 err = 0.143945,	data_time = 0.050544,	train_time = 0.787757
[2019-08-24 14:13:11,067] TRAIN Iter 251040: lr = 0.081602,	loss = 2.405611,	Top-1 err = 0.346094,	Top-5 err = 0.145117,	data_time = 0.050406,	train_time = 0.359346
[2019-08-24 14:13:27,825] TRAIN Iter 251060: lr = 0.081568,	loss = 2.430741,	Top-1 err = 0.340820,	Top-5 err = 0.144336,	data_time = 0.050306,	train_time = 0.837910
[2019-08-24 14:13:43,000] TRAIN Iter 251080: lr = 0.081535,	loss = 2.431649,	Top-1 err = 0.341162,	Top-5 err = 0.143066,	data_time = 0.050304,	train_time = 0.758744
[2019-08-24 14:13:50,028] TRAIN Iter 251100: lr = 0.081502,	loss = 2.286159,	Top-1 err = 0.345459,	Top-5 err = 0.144141,	data_time = 0.050571,	train_time = 0.351366
[2019-08-24 14:14:05,923] TRAIN Iter 251120: lr = 0.081468,	loss = 2.401095,	Top-1 err = 0.339160,	Top-5 err = 0.143555,	data_time = 0.050349,	train_time = 0.794732
[2019-08-24 14:14:20,605] TRAIN Iter 251140: lr = 0.081435,	loss = 2.461414,	Top-1 err = 0.348682,	Top-5 err = 0.143799,	data_time = 0.050447,	train_time = 0.734088
[2019-08-24 14:14:28,585] TRAIN Iter 251160: lr = 0.081402,	loss = 2.437222,	Top-1 err = 0.339893,	Top-5 err = 0.148730,	data_time = 0.050684,	train_time = 0.398990
[2019-08-24 14:14:46,263] TRAIN Iter 251180: lr = 0.081368,	loss = 2.400968,	Top-1 err = 0.345264,	Top-5 err = 0.146777,	data_time = 0.050636,	train_time = 0.883877
[2019-08-24 14:14:53,604] TRAIN Iter 251200: lr = 0.081335,	loss = 2.324821,	Top-1 err = 0.342871,	Top-5 err = 0.144775,	data_time = 0.050450,	train_time = 0.367059
[2019-08-24 14:15:09,296] TRAIN Iter 251220: lr = 0.081302,	loss = 2.425638,	Top-1 err = 0.341699,	Top-5 err = 0.145801,	data_time = 0.050528,	train_time = 0.784589
[2019-08-24 14:15:27,574] TRAIN Iter 251240: lr = 0.081268,	loss = 2.381823,	Top-1 err = 0.341943,	Top-5 err = 0.146143,	data_time = 0.050172,	train_time = 0.913900
[2019-08-24 14:15:34,566] TRAIN Iter 251260: lr = 0.081235,	loss = 2.431524,	Top-1 err = 0.350049,	Top-5 err = 0.146240,	data_time = 0.050588,	train_time = 0.349547
[2019-08-24 14:15:51,168] TRAIN Iter 251280: lr = 0.081202,	loss = 2.440252,	Top-1 err = 0.348438,	Top-5 err = 0.145605,	data_time = 0.050357,	train_time = 0.830101
[2019-08-24 14:16:05,335] TRAIN Iter 251300: lr = 0.081168,	loss = 2.403621,	Top-1 err = 0.346777,	Top-5 err = 0.148926,	data_time = 0.050721,	train_time = 0.708336
[2019-08-24 14:16:15,095] TRAIN Iter 251320: lr = 0.081135,	loss = 2.402610,	Top-1 err = 0.344482,	Top-5 err = 0.143066,	data_time = 0.050494,	train_time = 0.487998
[2019-08-24 14:16:31,998] TRAIN Iter 251340: lr = 0.081102,	loss = 2.435798,	Top-1 err = 0.338477,	Top-5 err = 0.142383,	data_time = 0.050565,	train_time = 0.845108
[2019-08-24 14:16:39,015] TRAIN Iter 251360: lr = 0.081068,	loss = 2.356684,	Top-1 err = 0.346924,	Top-5 err = 0.145898,	data_time = 0.158216,	train_time = 0.350870
[2019-08-24 14:16:56,035] TRAIN Iter 251380: lr = 0.081035,	loss = 2.455851,	Top-1 err = 0.342871,	Top-5 err = 0.143262,	data_time = 0.050488,	train_time = 0.850961
[2019-08-24 14:17:13,080] TRAIN Iter 251400: lr = 0.081002,	loss = 2.303876,	Top-1 err = 0.341260,	Top-5 err = 0.142725,	data_time = 0.050373,	train_time = 0.852266
[2019-08-24 14:17:20,055] TRAIN Iter 251420: lr = 0.080968,	loss = 2.374189,	Top-1 err = 0.350146,	Top-5 err = 0.150244,	data_time = 0.050573,	train_time = 0.348709
[2019-08-24 14:17:38,328] TRAIN Iter 251440: lr = 0.080935,	loss = 2.466067,	Top-1 err = 0.348730,	Top-5 err = 0.149121,	data_time = 0.050374,	train_time = 0.913629
[2019-08-24 14:17:53,714] TRAIN Iter 251460: lr = 0.080902,	loss = 2.348113,	Top-1 err = 0.344385,	Top-5 err = 0.142676,	data_time = 0.050384,	train_time = 0.769292
[2019-08-24 14:18:01,410] TRAIN Iter 251480: lr = 0.080868,	loss = 2.389350,	Top-1 err = 0.337451,	Top-5 err = 0.138818,	data_time = 0.050491,	train_time = 0.384785
[2019-08-24 14:18:19,246] TRAIN Iter 251500: lr = 0.080835,	loss = 2.390671,	Top-1 err = 0.345508,	Top-5 err = 0.147559,	data_time = 0.050511,	train_time = 0.891803
[2019-08-24 14:18:26,221] TRAIN Iter 251520: lr = 0.080802,	loss = 2.364866,	Top-1 err = 0.341797,	Top-5 err = 0.142822,	data_time = 0.050394,	train_time = 0.348724
[2019-08-24 14:18:43,807] TRAIN Iter 251540: lr = 0.080768,	loss = 2.312284,	Top-1 err = 0.344092,	Top-5 err = 0.145508,	data_time = 0.050060,	train_time = 0.879293
[2019-08-24 14:19:01,338] TRAIN Iter 251560: lr = 0.080735,	loss = 2.483144,	Top-1 err = 0.348975,	Top-5 err = 0.151758,	data_time = 0.049997,	train_time = 0.876519
[2019-08-24 14:19:07,628] TRAIN Iter 251580: lr = 0.080702,	loss = 2.498936,	Top-1 err = 0.345752,	Top-5 err = 0.147705,	data_time = 0.049884,	train_time = 0.314501
[2019-08-24 14:19:59,907] TRAIN Iter 251600: lr = 0.080668,	loss = 2.364582,	Top-1 err = 0.347352,	Top-5 err = 0.145825,	data_time = 0.050878,	train_time = 2.613921
[2019-08-24 14:20:07,840] TRAIN Iter 251620: lr = 0.080635,	loss = 2.303239,	Top-1 err = 0.338135,	Top-5 err = 0.139600,	data_time = 0.050601,	train_time = 0.396636
[2019-08-24 14:20:19,822] TRAIN Iter 251640: lr = 0.080602,	loss = 2.294486,	Top-1 err = 0.337695,	Top-5 err = 0.138867,	data_time = 0.050540,	train_time = 0.599082
[2019-08-24 14:20:34,793] TRAIN Iter 251660: lr = 0.080568,	loss = 2.417829,	Top-1 err = 0.339795,	Top-5 err = 0.140088,	data_time = 0.050608,	train_time = 0.748558
[2019-08-24 14:20:41,632] TRAIN Iter 251680: lr = 0.080535,	loss = 2.339008,	Top-1 err = 0.338867,	Top-5 err = 0.139795,	data_time = 0.050575,	train_time = 0.341944
[2019-08-24 14:20:55,811] TRAIN Iter 251700: lr = 0.080502,	loss = 2.432869,	Top-1 err = 0.335254,	Top-5 err = 0.141699,	data_time = 0.050313,	train_time = 0.708925
[2019-08-24 14:21:08,235] TRAIN Iter 251720: lr = 0.080468,	loss = 2.401217,	Top-1 err = 0.344043,	Top-5 err = 0.140869,	data_time = 0.050429,	train_time = 0.621167
[2019-08-24 14:21:16,519] TRAIN Iter 251740: lr = 0.080435,	loss = 2.384066,	Top-1 err = 0.334229,	Top-5 err = 0.139795,	data_time = 0.050443,	train_time = 0.414203
[2019-08-24 14:21:32,055] TRAIN Iter 251760: lr = 0.080402,	loss = 2.341582,	Top-1 err = 0.338770,	Top-5 err = 0.139355,	data_time = 0.050872,	train_time = 0.776799
[2019-08-24 14:21:39,159] TRAIN Iter 251780: lr = 0.080368,	loss = 2.380686,	Top-1 err = 0.335791,	Top-5 err = 0.139453,	data_time = 0.142903,	train_time = 0.355172
[2019-08-24 14:21:55,330] TRAIN Iter 251800: lr = 0.080335,	loss = 2.463960,	Top-1 err = 0.337939,	Top-5 err = 0.139551,	data_time = 0.050462,	train_time = 0.808547
[2019-08-24 14:22:11,669] TRAIN Iter 251820: lr = 0.080302,	loss = 2.369109,	Top-1 err = 0.340527,	Top-5 err = 0.139941,	data_time = 0.131342,	train_time = 0.816947
[2019-08-24 14:22:18,578] TRAIN Iter 251840: lr = 0.080268,	loss = 2.364760,	Top-1 err = 0.339111,	Top-5 err = 0.139209,	data_time = 0.127687,	train_time = 0.345430
[2019-08-24 14:22:32,257] TRAIN Iter 251860: lr = 0.080235,	loss = 2.386045,	Top-1 err = 0.343066,	Top-5 err = 0.144141,	data_time = 0.050285,	train_time = 0.683899
[2019-08-24 14:22:47,079] TRAIN Iter 251880: lr = 0.080202,	loss = 2.373504,	Top-1 err = 0.338818,	Top-5 err = 0.140674,	data_time = 0.050552,	train_time = 0.741109
[2019-08-24 14:22:54,338] TRAIN Iter 251900: lr = 0.080168,	loss = 2.306395,	Top-1 err = 0.342578,	Top-5 err = 0.142187,	data_time = 0.050455,	train_time = 0.362919
[2019-08-24 14:23:10,433] TRAIN Iter 251920: lr = 0.080135,	loss = 2.317864,	Top-1 err = 0.340283,	Top-5 err = 0.143652,	data_time = 0.050483,	train_time = 0.804745
[2019-08-24 14:23:17,453] TRAIN Iter 251940: lr = 0.080102,	loss = 2.308406,	Top-1 err = 0.340527,	Top-5 err = 0.141016,	data_time = 0.050712,	train_time = 0.351019
[2019-08-24 14:23:34,500] TRAIN Iter 251960: lr = 0.080068,	loss = 2.363487,	Top-1 err = 0.340332,	Top-5 err = 0.139063,	data_time = 0.050417,	train_time = 0.852297
[2019-08-24 14:23:49,305] TRAIN Iter 251980: lr = 0.080035,	loss = 2.375630,	Top-1 err = 0.341113,	Top-5 err = 0.145117,	data_time = 0.050506,	train_time = 0.740271
[2019-08-24 14:23:56,578] TRAIN Iter 252000: lr = 0.080002,	loss = 2.421824,	Top-1 err = 0.342090,	Top-5 err = 0.142920,	data_time = 0.050353,	train_time = 0.363595
[2019-08-24 14:24:09,368] TRAIN Iter 252020: lr = 0.079968,	loss = 2.378027,	Top-1 err = 0.338818,	Top-5 err = 0.139258,	data_time = 0.050755,	train_time = 0.639502
[2019-08-24 14:24:23,729] TRAIN Iter 252040: lr = 0.079935,	loss = 2.231891,	Top-1 err = 0.334326,	Top-5 err = 0.138184,	data_time = 0.050117,	train_time = 0.718052
[2019-08-24 14:24:31,297] TRAIN Iter 252060: lr = 0.079902,	loss = 2.370281,	Top-1 err = 0.338770,	Top-5 err = 0.140430,	data_time = 0.050380,	train_time = 0.378385
[2019-08-24 14:24:47,092] TRAIN Iter 252080: lr = 0.079868,	loss = 2.411071,	Top-1 err = 0.337402,	Top-5 err = 0.143750,	data_time = 0.050740,	train_time = 0.789744
[2019-08-24 14:24:54,249] TRAIN Iter 252100: lr = 0.079835,	loss = 2.457016,	Top-1 err = 0.344434,	Top-5 err = 0.143945,	data_time = 0.050372,	train_time = 0.357830
[2019-08-24 14:25:09,585] TRAIN Iter 252120: lr = 0.079802,	loss = 2.457905,	Top-1 err = 0.344531,	Top-5 err = 0.145459,	data_time = 0.050614,	train_time = 0.766778
[2019-08-24 14:25:24,879] TRAIN Iter 252140: lr = 0.079768,	loss = 2.453083,	Top-1 err = 0.346777,	Top-5 err = 0.146484,	data_time = 0.050676,	train_time = 0.764695
[2019-08-24 14:25:32,482] TRAIN Iter 252160: lr = 0.079735,	loss = 2.334306,	Top-1 err = 0.338281,	Top-5 err = 0.140576,	data_time = 0.050259,	train_time = 0.380138
[2019-08-24 14:25:47,007] TRAIN Iter 252180: lr = 0.079702,	loss = 2.466252,	Top-1 err = 0.335498,	Top-5 err = 0.137500,	data_time = 0.050614,	train_time = 0.726236
[2019-08-24 14:26:02,449] TRAIN Iter 252200: lr = 0.079668,	loss = 2.339275,	Top-1 err = 0.335010,	Top-5 err = 0.140234,	data_time = 0.050541,	train_time = 0.772085
[2019-08-24 14:26:09,938] TRAIN Iter 252220: lr = 0.079635,	loss = 2.345190,	Top-1 err = 0.336572,	Top-5 err = 0.140576,	data_time = 0.050748,	train_time = 0.374404
[2019-08-24 14:26:25,383] TRAIN Iter 252240: lr = 0.079602,	loss = 2.433171,	Top-1 err = 0.343652,	Top-5 err = 0.144238,	data_time = 0.051035,	train_time = 0.772247
[2019-08-24 14:26:32,468] TRAIN Iter 252260: lr = 0.079568,	loss = 2.394050,	Top-1 err = 0.345264,	Top-5 err = 0.148389,	data_time = 0.050540,	train_time = 0.354228
[2019-08-24 14:26:47,891] TRAIN Iter 252280: lr = 0.079535,	loss = 2.391032,	Top-1 err = 0.349072,	Top-5 err = 0.147266,	data_time = 0.050553,	train_time = 0.771169
[2019-08-24 14:27:03,485] TRAIN Iter 252300: lr = 0.079502,	loss = 2.410200,	Top-1 err = 0.341992,	Top-5 err = 0.140527,	data_time = 0.050568,	train_time = 0.779655
[2019-08-24 14:27:10,897] TRAIN Iter 252320: lr = 0.079468,	loss = 2.350223,	Top-1 err = 0.346045,	Top-5 err = 0.145508,	data_time = 0.050395,	train_time = 0.370578
[2019-08-24 14:27:26,761] TRAIN Iter 252340: lr = 0.079435,	loss = 2.372753,	Top-1 err = 0.341455,	Top-5 err = 0.140088,	data_time = 0.050567,	train_time = 0.793205
[2019-08-24 14:27:41,272] TRAIN Iter 252360: lr = 0.079402,	loss = 2.314964,	Top-1 err = 0.341846,	Top-5 err = 0.144141,	data_time = 0.050666,	train_time = 0.725538
[2019-08-24 14:27:49,990] TRAIN Iter 252380: lr = 0.079368,	loss = 2.425514,	Top-1 err = 0.339600,	Top-5 err = 0.143164,	data_time = 0.050785,	train_time = 0.435907
[2019-08-24 14:28:06,394] TRAIN Iter 252400: lr = 0.079335,	loss = 2.343467,	Top-1 err = 0.341943,	Top-5 err = 0.142432,	data_time = 0.050444,	train_time = 0.820155
[2019-08-24 14:28:13,427] TRAIN Iter 252420: lr = 0.079302,	loss = 2.321403,	Top-1 err = 0.343408,	Top-5 err = 0.146484,	data_time = 0.050414,	train_time = 0.351625
[2019-08-24 14:28:28,913] TRAIN Iter 252440: lr = 0.079268,	loss = 2.411935,	Top-1 err = 0.346143,	Top-5 err = 0.144971,	data_time = 0.050535,	train_time = 0.774317
[2019-08-24 14:28:44,822] TRAIN Iter 252460: lr = 0.079235,	loss = 2.359970,	Top-1 err = 0.336279,	Top-5 err = 0.141553,	data_time = 0.050143,	train_time = 0.795419
[2019-08-24 14:28:51,927] TRAIN Iter 252480: lr = 0.079202,	loss = 2.362124,	Top-1 err = 0.341260,	Top-5 err = 0.142334,	data_time = 0.050522,	train_time = 0.355259
[2019-08-24 14:29:09,786] TRAIN Iter 252500: lr = 0.079168,	loss = 2.354262,	Top-1 err = 0.344092,	Top-5 err = 0.142187,	data_time = 0.050517,	train_time = 0.892931
[2019-08-24 14:29:24,409] TRAIN Iter 252520: lr = 0.079135,	loss = 2.295075,	Top-1 err = 0.346045,	Top-5 err = 0.142432,	data_time = 0.118962,	train_time = 0.731102
[2019-08-24 14:29:32,718] TRAIN Iter 252540: lr = 0.079102,	loss = 2.420389,	Top-1 err = 0.347363,	Top-5 err = 0.144287,	data_time = 0.108499,	train_time = 0.415435
[2019-08-24 14:29:50,281] TRAIN Iter 252560: lr = 0.079068,	loss = 2.328350,	Top-1 err = 0.341992,	Top-5 err = 0.143359,	data_time = 0.050519,	train_time = 0.878144
[2019-08-24 14:29:57,551] TRAIN Iter 252580: lr = 0.079035,	loss = 2.459515,	Top-1 err = 0.340918,	Top-5 err = 0.143945,	data_time = 0.050294,	train_time = 0.363489
[2019-08-24 14:30:13,677] TRAIN Iter 252600: lr = 0.079002,	loss = 2.430809,	Top-1 err = 0.341016,	Top-5 err = 0.145947,	data_time = 0.050436,	train_time = 0.806308
[2019-08-24 14:30:30,968] TRAIN Iter 252620: lr = 0.078968,	loss = 2.342288,	Top-1 err = 0.343359,	Top-5 err = 0.147266,	data_time = 0.050865,	train_time = 0.864533
[2019-08-24 14:30:38,290] TRAIN Iter 252640: lr = 0.078935,	loss = 2.340158,	Top-1 err = 0.343359,	Top-5 err = 0.145410,	data_time = 0.050589,	train_time = 0.366078
[2019-08-24 14:30:53,101] TRAIN Iter 252660: lr = 0.078902,	loss = 2.528154,	Top-1 err = 0.344043,	Top-5 err = 0.145801,	data_time = 0.050325,	train_time = 0.740564
[2019-08-24 14:31:09,013] TRAIN Iter 252680: lr = 0.078868,	loss = 2.416207,	Top-1 err = 0.342822,	Top-5 err = 0.143799,	data_time = 0.050529,	train_time = 0.795585
[2019-08-24 14:31:18,529] TRAIN Iter 252700: lr = 0.078835,	loss = 2.436358,	Top-1 err = 0.342285,	Top-5 err = 0.143164,	data_time = 0.050486,	train_time = 0.475785
[2019-08-24 14:31:43,776] TRAIN Iter 252720: lr = 0.078802,	loss = 2.365075,	Top-1 err = 0.336230,	Top-5 err = 0.143555,	data_time = 0.050433,	train_time = 1.262338
[2019-08-24 14:31:50,775] TRAIN Iter 252740: lr = 0.078768,	loss = 2.351372,	Top-1 err = 0.345850,	Top-5 err = 0.145605,	data_time = 0.096129,	train_time = 0.349902
[2019-08-24 14:32:09,333] TRAIN Iter 252760: lr = 0.078735,	loss = 2.339851,	Top-1 err = 0.345361,	Top-5 err = 0.146533,	data_time = 0.050605,	train_time = 0.927889
[2019-08-24 14:32:27,372] TRAIN Iter 252780: lr = 0.078702,	loss = 2.411680,	Top-1 err = 0.338477,	Top-5 err = 0.143506,	data_time = 0.050003,	train_time = 0.901927
[2019-08-24 14:32:34,191] TRAIN Iter 252800: lr = 0.078668,	loss = 2.332408,	Top-1 err = 0.340820,	Top-5 err = 0.147021,	data_time = 0.050067,	train_time = 0.340932
[2019-08-24 14:32:50,927] TRAIN Iter 252820: lr = 0.078635,	loss = 2.362856,	Top-1 err = 0.344336,	Top-5 err = 0.147266,	data_time = 0.049913,	train_time = 0.836829
[2019-08-24 14:32:58,932] TRAIN Iter 252840: lr = 0.078602,	loss = 2.579582,	Top-1 err = 0.342531,	Top-5 err = 0.146462,	data_time = 0.007089,	train_time = 0.400221
[2019-08-24 14:33:46,350] TRAIN Iter 252860: lr = 0.078568,	loss = 2.360046,	Top-1 err = 0.333838,	Top-5 err = 0.140186,	data_time = 0.050490,	train_time = 2.370888
[2019-08-24 14:34:03,900] TRAIN Iter 252880: lr = 0.078535,	loss = 2.394904,	Top-1 err = 0.336963,	Top-5 err = 0.141113,	data_time = 0.050570,	train_time = 0.877465
[2019-08-24 14:34:13,033] TRAIN Iter 252900: lr = 0.078502,	loss = 2.431025,	Top-1 err = 0.333594,	Top-5 err = 0.141064,	data_time = 0.050849,	train_time = 0.456625
[2019-08-24 14:34:26,224] TRAIN Iter 252920: lr = 0.078468,	loss = 2.418252,	Top-1 err = 0.339355,	Top-5 err = 0.140576,	data_time = 0.050480,	train_time = 0.659566
[2019-08-24 14:34:34,632] TRAIN Iter 252940: lr = 0.078435,	loss = 2.303465,	Top-1 err = 0.335645,	Top-5 err = 0.141650,	data_time = 0.050540,	train_time = 0.420354
[2019-08-24 14:34:42,745] TRAIN Iter 252960: lr = 0.078402,	loss = 2.312083,	Top-1 err = 0.336475,	Top-5 err = 0.137939,	data_time = 0.050481,	train_time = 0.405658
[2019-08-24 14:34:58,489] TRAIN Iter 252980: lr = 0.078368,	loss = 2.377537,	Top-1 err = 0.335693,	Top-5 err = 0.140332,	data_time = 0.050345,	train_time = 0.787191
[2019-08-24 14:35:06,464] TRAIN Iter 253000: lr = 0.078335,	loss = 2.367566,	Top-1 err = 0.334570,	Top-5 err = 0.137988,	data_time = 0.050472,	train_time = 0.398709
[2019-08-24 14:35:22,286] TRAIN Iter 253020: lr = 0.078302,	loss = 2.310822,	Top-1 err = 0.331494,	Top-5 err = 0.140039,	data_time = 0.050574,	train_time = 0.791102
[2019-08-24 14:35:36,238] TRAIN Iter 253040: lr = 0.078268,	loss = 2.252823,	Top-1 err = 0.335498,	Top-5 err = 0.138525,	data_time = 0.050368,	train_time = 0.697583
[2019-08-24 14:35:43,456] TRAIN Iter 253060: lr = 0.078235,	loss = 2.297299,	Top-1 err = 0.334082,	Top-5 err = 0.139014,	data_time = 0.050325,	train_time = 0.360888
[2019-08-24 14:35:58,770] TRAIN Iter 253080: lr = 0.078202,	loss = 2.328022,	Top-1 err = 0.335742,	Top-5 err = 0.142285,	data_time = 0.050416,	train_time = 0.765709
[2019-08-24 14:36:12,316] TRAIN Iter 253100: lr = 0.078168,	loss = 2.467600,	Top-1 err = 0.339990,	Top-5 err = 0.141064,	data_time = 0.050408,	train_time = 0.677274
[2019-08-24 14:36:20,905] TRAIN Iter 253120: lr = 0.078135,	loss = 2.465791,	Top-1 err = 0.341455,	Top-5 err = 0.140625,	data_time = 0.050884,	train_time = 0.429427
[2019-08-24 14:36:35,755] TRAIN Iter 253140: lr = 0.078102,	loss = 2.295326,	Top-1 err = 0.336035,	Top-5 err = 0.140576,	data_time = 0.050419,	train_time = 0.742465
[2019-08-24 14:36:42,674] TRAIN Iter 253160: lr = 0.078068,	loss = 2.317777,	Top-1 err = 0.332910,	Top-5 err = 0.139063,	data_time = 0.050319,	train_time = 0.345970
[2019-08-24 14:36:57,446] TRAIN Iter 253180: lr = 0.078035,	loss = 2.308658,	Top-1 err = 0.338721,	Top-5 err = 0.142139,	data_time = 0.050489,	train_time = 0.738573
[2019-08-24 14:37:13,677] TRAIN Iter 253200: lr = 0.078002,	loss = 2.311509,	Top-1 err = 0.341943,	Top-5 err = 0.142871,	data_time = 0.050541,	train_time = 0.811557
[2019-08-24 14:37:21,251] TRAIN Iter 253220: lr = 0.077968,	loss = 2.371649,	Top-1 err = 0.337988,	Top-5 err = 0.142139,	data_time = 0.050345,	train_time = 0.378675
[2019-08-24 14:37:36,745] TRAIN Iter 253240: lr = 0.077935,	loss = 2.446532,	Top-1 err = 0.338672,	Top-5 err = 0.139893,	data_time = 0.050499,	train_time = 0.774666
[2019-08-24 14:37:48,984] TRAIN Iter 253260: lr = 0.077902,	loss = 2.388024,	Top-1 err = 0.337939,	Top-5 err = 0.143359,	data_time = 0.050753,	train_time = 0.611964
[2019-08-24 14:37:58,847] TRAIN Iter 253280: lr = 0.077868,	loss = 2.406595,	Top-1 err = 0.345117,	Top-5 err = 0.146826,	data_time = 0.050727,	train_time = 0.493152
[2019-08-24 14:38:13,808] TRAIN Iter 253300: lr = 0.077835,	loss = 2.380394,	Top-1 err = 0.342383,	Top-5 err = 0.139258,	data_time = 0.050397,	train_time = 0.747988
[2019-08-24 14:38:20,936] TRAIN Iter 253320: lr = 0.077802,	loss = 2.371774,	Top-1 err = 0.334570,	Top-5 err = 0.138623,	data_time = 0.050489,	train_time = 0.356407
[2019-08-24 14:38:36,101] TRAIN Iter 253340: lr = 0.077768,	loss = 2.353921,	Top-1 err = 0.340039,	Top-5 err = 0.142090,	data_time = 0.050346,	train_time = 0.758213
[2019-08-24 14:38:51,470] TRAIN Iter 253360: lr = 0.077735,	loss = 2.359361,	Top-1 err = 0.342773,	Top-5 err = 0.139258,	data_time = 0.050644,	train_time = 0.768443
[2019-08-24 14:38:58,810] TRAIN Iter 253380: lr = 0.077702,	loss = 2.386411,	Top-1 err = 0.339355,	Top-5 err = 0.141406,	data_time = 0.050658,	train_time = 0.367013
[2019-08-24 14:39:13,790] TRAIN Iter 253400: lr = 0.077668,	loss = 2.371334,	Top-1 err = 0.341504,	Top-5 err = 0.144678,	data_time = 0.157571,	train_time = 0.748976
[2019-08-24 14:39:29,207] TRAIN Iter 253420: lr = 0.077635,	loss = 2.410990,	Top-1 err = 0.342725,	Top-5 err = 0.143652,	data_time = 0.050430,	train_time = 0.770821
[2019-08-24 14:39:37,117] TRAIN Iter 253440: lr = 0.077602,	loss = 2.380410,	Top-1 err = 0.342529,	Top-5 err = 0.141797,	data_time = 0.050484,	train_time = 0.395496
[2019-08-24 14:39:53,929] TRAIN Iter 253460: lr = 0.077568,	loss = 2.430648,	Top-1 err = 0.340186,	Top-5 err = 0.141699,	data_time = 0.050271,	train_time = 0.840602
[2019-08-24 14:40:00,766] TRAIN Iter 253480: lr = 0.077535,	loss = 2.387569,	Top-1 err = 0.340869,	Top-5 err = 0.140479,	data_time = 0.050604,	train_time = 0.341845
[2019-08-24 14:40:17,254] TRAIN Iter 253500: lr = 0.077502,	loss = 2.422568,	Top-1 err = 0.341016,	Top-5 err = 0.144824,	data_time = 0.050816,	train_time = 0.824370
[2019-08-24 14:40:32,740] TRAIN Iter 253520: lr = 0.077468,	loss = 2.341835,	Top-1 err = 0.340527,	Top-5 err = 0.143262,	data_time = 0.050342,	train_time = 0.774271
[2019-08-24 14:40:39,868] TRAIN Iter 253540: lr = 0.077435,	loss = 2.323972,	Top-1 err = 0.343018,	Top-5 err = 0.142920,	data_time = 0.050580,	train_time = 0.356395
[2019-08-24 14:40:56,856] TRAIN Iter 253560: lr = 0.077402,	loss = 2.454349,	Top-1 err = 0.344971,	Top-5 err = 0.144434,	data_time = 0.050583,	train_time = 0.849374
[2019-08-24 14:41:10,830] TRAIN Iter 253580: lr = 0.077368,	loss = 2.393192,	Top-1 err = 0.340527,	Top-5 err = 0.140918,	data_time = 0.119700,	train_time = 0.698714
[2019-08-24 14:41:19,168] TRAIN Iter 253600: lr = 0.077335,	loss = 2.203228,	Top-1 err = 0.334229,	Top-5 err = 0.139648,	data_time = 0.050319,	train_time = 0.416885
[2019-08-24 14:41:34,647] TRAIN Iter 253620: lr = 0.077302,	loss = 2.419704,	Top-1 err = 0.340283,	Top-5 err = 0.142090,	data_time = 0.117848,	train_time = 0.773896
[2019-08-24 14:41:41,651] TRAIN Iter 253640: lr = 0.077268,	loss = 2.410602,	Top-1 err = 0.339746,	Top-5 err = 0.142090,	data_time = 0.050611,	train_time = 0.350226
[2019-08-24 14:41:58,108] TRAIN Iter 253660: lr = 0.077235,	loss = 2.394151,	Top-1 err = 0.343799,	Top-5 err = 0.141699,	data_time = 0.050421,	train_time = 0.822801
[2019-08-24 14:42:13,291] TRAIN Iter 253680: lr = 0.077202,	loss = 2.290646,	Top-1 err = 0.342578,	Top-5 err = 0.139844,	data_time = 0.050635,	train_time = 0.759136
[2019-08-24 14:42:20,455] TRAIN Iter 253700: lr = 0.077168,	loss = 2.451381,	Top-1 err = 0.342383,	Top-5 err = 0.148926,	data_time = 0.050236,	train_time = 0.358178
[2019-08-24 14:42:37,176] TRAIN Iter 253720: lr = 0.077135,	loss = 2.372602,	Top-1 err = 0.338916,	Top-5 err = 0.141602,	data_time = 0.050718,	train_time = 0.836062
[2019-08-24 14:42:52,720] TRAIN Iter 253740: lr = 0.077102,	loss = 2.337374,	Top-1 err = 0.344385,	Top-5 err = 0.142383,	data_time = 0.050283,	train_time = 0.777208
[2019-08-24 14:43:01,866] TRAIN Iter 253760: lr = 0.077068,	loss = 2.429418,	Top-1 err = 0.345557,	Top-5 err = 0.144629,	data_time = 0.050623,	train_time = 0.457248
[2019-08-24 14:43:18,093] TRAIN Iter 253780: lr = 0.077035,	loss = 2.430175,	Top-1 err = 0.340088,	Top-5 err = 0.147119,	data_time = 0.050586,	train_time = 0.811376
[2019-08-24 14:43:24,895] TRAIN Iter 253800: lr = 0.077002,	loss = 2.479223,	Top-1 err = 0.347803,	Top-5 err = 0.146436,	data_time = 0.050753,	train_time = 0.340042
[2019-08-24 14:43:42,708] TRAIN Iter 253820: lr = 0.076968,	loss = 2.415623,	Top-1 err = 0.343164,	Top-5 err = 0.141895,	data_time = 0.050431,	train_time = 0.890652
[2019-08-24 14:43:57,021] TRAIN Iter 253840: lr = 0.076935,	loss = 2.374909,	Top-1 err = 0.344629,	Top-5 err = 0.142480,	data_time = 0.050375,	train_time = 0.715620
[2019-08-24 14:44:05,173] TRAIN Iter 253860: lr = 0.076902,	loss = 2.369611,	Top-1 err = 0.338965,	Top-5 err = 0.140967,	data_time = 0.050603,	train_time = 0.407603
[2019-08-24 14:44:22,954] TRAIN Iter 253880: lr = 0.076868,	loss = 2.379325,	Top-1 err = 0.341943,	Top-5 err = 0.144434,	data_time = 0.050554,	train_time = 0.889012
[2019-08-24 14:44:39,741] TRAIN Iter 253900: lr = 0.076835,	loss = 2.382889,	Top-1 err = 0.344775,	Top-5 err = 0.144580,	data_time = 0.050644,	train_time = 0.839331
[2019-08-24 14:44:48,333] TRAIN Iter 253920: lr = 0.076802,	loss = 2.311624,	Top-1 err = 0.345898,	Top-5 err = 0.145361,	data_time = 0.050380,	train_time = 0.429597
[2019-08-24 14:45:01,389] TRAIN Iter 253940: lr = 0.076768,	loss = 2.420740,	Top-1 err = 0.336377,	Top-5 err = 0.147754,	data_time = 0.108730,	train_time = 0.652770
[2019-08-24 14:45:08,433] TRAIN Iter 253960: lr = 0.076735,	loss = 2.407413,	Top-1 err = 0.343018,	Top-5 err = 0.143945,	data_time = 0.050501,	train_time = 0.352186
[2019-08-24 14:45:26,126] TRAIN Iter 253980: lr = 0.076702,	loss = 2.383423,	Top-1 err = 0.347559,	Top-5 err = 0.146875,	data_time = 0.050430,	train_time = 0.884675
[2019-08-24 14:45:40,170] TRAIN Iter 254000: lr = 0.076668,	loss = 2.374541,	Top-1 err = 0.341455,	Top-5 err = 0.142578,	data_time = 0.050524,	train_time = 0.702140
[2019-08-24 14:45:48,864] TRAIN Iter 254020: lr = 0.076635,	loss = 2.343645,	Top-1 err = 0.344043,	Top-5 err = 0.144629,	data_time = 0.050341,	train_time = 0.434727
[2019-08-24 14:46:05,805] TRAIN Iter 254040: lr = 0.076602,	loss = 2.472101,	Top-1 err = 0.341016,	Top-5 err = 0.142871,	data_time = 0.050428,	train_time = 0.847033
[2019-08-24 14:46:21,816] TRAIN Iter 254060: lr = 0.076568,	loss = 2.334544,	Top-1 err = 0.343994,	Top-5 err = 0.145068,	data_time = 0.049908,	train_time = 0.800516
[2019-08-24 14:46:28,674] TRAIN Iter 254080: lr = 0.076535,	loss = 2.371973,	Top-1 err = 0.335547,	Top-5 err = 0.138232,	data_time = 0.050028,	train_time = 0.342886
[2019-08-24 14:47:21,387] TRAIN Iter 254100: lr = 0.076502,	loss = 2.531473,	Top-1 err = 0.347428,	Top-5 err = 0.147339,	data_time = 0.050441,	train_time = 2.635632
[2019-08-24 14:47:29,077] TRAIN Iter 254120: lr = 0.076468,	loss = 2.379132,	Top-1 err = 0.335059,	Top-5 err = 0.141357,	data_time = 0.050388,	train_time = 0.384509
[2019-08-24 14:47:43,406] TRAIN Iter 254140: lr = 0.076435,	loss = 2.286565,	Top-1 err = 0.332666,	Top-5 err = 0.138330,	data_time = 0.050704,	train_time = 0.716403
[2019-08-24 14:47:54,330] TRAIN Iter 254160: lr = 0.076402,	loss = 2.443625,	Top-1 err = 0.340771,	Top-5 err = 0.145361,	data_time = 0.050320,	train_time = 0.546219
[2019-08-24 14:48:02,194] TRAIN Iter 254180: lr = 0.076368,	loss = 2.386543,	Top-1 err = 0.332715,	Top-5 err = 0.135596,	data_time = 0.050388,	train_time = 0.393170
[2019-08-24 14:48:20,198] TRAIN Iter 254200: lr = 0.076335,	loss = 2.315015,	Top-1 err = 0.336621,	Top-5 err = 0.139307,	data_time = 0.050575,	train_time = 0.900204
[2019-08-24 14:48:28,198] TRAIN Iter 254220: lr = 0.076302,	loss = 2.379287,	Top-1 err = 0.333154,	Top-5 err = 0.136621,	data_time = 0.133760,	train_time = 0.399973
[2019-08-24 14:48:42,636] TRAIN Iter 254240: lr = 0.076268,	loss = 2.433786,	Top-1 err = 0.335742,	Top-5 err = 0.140381,	data_time = 0.050292,	train_time = 0.721887
[2019-08-24 14:48:58,895] TRAIN Iter 254260: lr = 0.076235,	loss = 2.405718,	Top-1 err = 0.331689,	Top-5 err = 0.138379,	data_time = 0.050477,	train_time = 0.812903
[2019-08-24 14:49:06,444] TRAIN Iter 254280: lr = 0.076202,	loss = 2.448076,	Top-1 err = 0.335449,	Top-5 err = 0.143701,	data_time = 0.050316,	train_time = 0.377474
[2019-08-24 14:49:18,785] TRAIN Iter 254300: lr = 0.076168,	loss = 2.401325,	Top-1 err = 0.335449,	Top-5 err = 0.139648,	data_time = 0.050255,	train_time = 0.617041
[2019-08-24 14:49:35,325] TRAIN Iter 254320: lr = 0.076135,	loss = 2.393524,	Top-1 err = 0.335107,	Top-5 err = 0.137549,	data_time = 0.050440,	train_time = 0.826972
[2019-08-24 14:49:43,010] TRAIN Iter 254340: lr = 0.076102,	loss = 2.322699,	Top-1 err = 0.334766,	Top-5 err = 0.141309,	data_time = 0.051304,	train_time = 0.384242
[2019-08-24 14:49:58,067] TRAIN Iter 254360: lr = 0.076068,	loss = 2.255514,	Top-1 err = 0.336523,	Top-5 err = 0.142285,	data_time = 0.050436,	train_time = 0.752819
[2019-08-24 14:50:06,798] TRAIN Iter 254380: lr = 0.076035,	loss = 2.487647,	Top-1 err = 0.335645,	Top-5 err = 0.140283,	data_time = 0.050436,	train_time = 0.436524
[2019-08-24 14:50:21,471] TRAIN Iter 254400: lr = 0.076002,	loss = 2.425660,	Top-1 err = 0.330908,	Top-5 err = 0.141113,	data_time = 0.050268,	train_time = 0.733661
[2019-08-24 14:50:34,773] TRAIN Iter 254420: lr = 0.075968,	loss = 2.336930,	Top-1 err = 0.337939,	Top-5 err = 0.141064,	data_time = 0.050488,	train_time = 0.665052
[2019-08-24 14:50:42,287] TRAIN Iter 254440: lr = 0.075935,	loss = 2.359371,	Top-1 err = 0.340039,	Top-5 err = 0.141602,	data_time = 0.050432,	train_time = 0.375679
[2019-08-24 14:50:57,840] TRAIN Iter 254460: lr = 0.075902,	loss = 2.391664,	Top-1 err = 0.344141,	Top-5 err = 0.143652,	data_time = 0.050309,	train_time = 0.777681
[2019-08-24 14:51:13,493] TRAIN Iter 254480: lr = 0.075868,	loss = 2.415295,	Top-1 err = 0.341455,	Top-5 err = 0.144824,	data_time = 0.050297,	train_time = 0.782633
[2019-08-24 14:51:20,863] TRAIN Iter 254500: lr = 0.075835,	loss = 2.402041,	Top-1 err = 0.338672,	Top-5 err = 0.143555,	data_time = 0.050938,	train_time = 0.368449
[2019-08-24 14:51:36,160] TRAIN Iter 254520: lr = 0.075802,	loss = 2.360616,	Top-1 err = 0.335107,	Top-5 err = 0.141211,	data_time = 0.050344,	train_time = 0.764845
[2019-08-24 14:51:44,079] TRAIN Iter 254540: lr = 0.075768,	loss = 2.324049,	Top-1 err = 0.338330,	Top-5 err = 0.142822,	data_time = 0.050343,	train_time = 0.395955
[2019-08-24 14:51:59,263] TRAIN Iter 254560: lr = 0.075735,	loss = 2.404327,	Top-1 err = 0.340283,	Top-5 err = 0.144922,	data_time = 0.050554,	train_time = 0.759185
[2019-08-24 14:52:13,569] TRAIN Iter 254580: lr = 0.075702,	loss = 2.399672,	Top-1 err = 0.342773,	Top-5 err = 0.144727,	data_time = 0.050232,	train_time = 0.715283
[2019-08-24 14:52:21,027] TRAIN Iter 254600: lr = 0.075668,	loss = 2.443415,	Top-1 err = 0.342822,	Top-5 err = 0.145850,	data_time = 0.050457,	train_time = 0.372885
[2019-08-24 14:52:36,372] TRAIN Iter 254620: lr = 0.075635,	loss = 2.381230,	Top-1 err = 0.337109,	Top-5 err = 0.141748,	data_time = 0.050296,	train_time = 0.767223
[2019-08-24 14:52:51,863] TRAIN Iter 254640: lr = 0.075602,	loss = 2.381381,	Top-1 err = 0.337354,	Top-5 err = 0.138770,	data_time = 0.050456,	train_time = 0.774528
[2019-08-24 14:52:58,984] TRAIN Iter 254660: lr = 0.075568,	loss = 2.418316,	Top-1 err = 0.338330,	Top-5 err = 0.140674,	data_time = 0.050420,	train_time = 0.356053
[2019-08-24 14:53:14,352] TRAIN Iter 254680: lr = 0.075535,	loss = 2.391112,	Top-1 err = 0.336914,	Top-5 err = 0.140234,	data_time = 0.050386,	train_time = 0.768392
[2019-08-24 14:53:22,368] TRAIN Iter 254700: lr = 0.075502,	loss = 2.337911,	Top-1 err = 0.344189,	Top-5 err = 0.143262,	data_time = 0.050774,	train_time = 0.400782
[2019-08-24 14:53:37,197] TRAIN Iter 254720: lr = 0.075468,	loss = 2.460925,	Top-1 err = 0.343164,	Top-5 err = 0.143604,	data_time = 0.050563,	train_time = 0.741442
[2019-08-24 14:53:53,479] TRAIN Iter 254740: lr = 0.075435,	loss = 2.382699,	Top-1 err = 0.341895,	Top-5 err = 0.140332,	data_time = 0.050390,	train_time = 0.814068
[2019-08-24 14:54:00,747] TRAIN Iter 254760: lr = 0.075402,	loss = 2.334831,	Top-1 err = 0.340918,	Top-5 err = 0.143652,	data_time = 0.050651,	train_time = 0.363385
[2019-08-24 14:54:16,798] TRAIN Iter 254780: lr = 0.075368,	loss = 2.398644,	Top-1 err = 0.342676,	Top-5 err = 0.144287,	data_time = 0.050363,	train_time = 0.802550
[2019-08-24 14:54:31,980] TRAIN Iter 254800: lr = 0.075335,	loss = 2.265591,	Top-1 err = 0.343945,	Top-5 err = 0.142529,	data_time = 0.050504,	train_time = 0.759107
[2019-08-24 14:54:38,990] TRAIN Iter 254820: lr = 0.075302,	loss = 2.425998,	Top-1 err = 0.345410,	Top-5 err = 0.139893,	data_time = 0.050963,	train_time = 0.350450
[2019-08-24 14:54:55,061] TRAIN Iter 254840: lr = 0.075268,	loss = 2.370320,	Top-1 err = 0.339209,	Top-5 err = 0.141895,	data_time = 0.050502,	train_time = 0.803546
[2019-08-24 14:55:02,824] TRAIN Iter 254860: lr = 0.075235,	loss = 2.436765,	Top-1 err = 0.340381,	Top-5 err = 0.142676,	data_time = 0.050111,	train_time = 0.388124
[2019-08-24 14:55:18,765] TRAIN Iter 254880: lr = 0.075202,	loss = 2.308150,	Top-1 err = 0.336523,	Top-5 err = 0.140625,	data_time = 0.050838,	train_time = 0.797053
[2019-08-24 14:55:34,477] TRAIN Iter 254900: lr = 0.075168,	loss = 2.362022,	Top-1 err = 0.335742,	Top-5 err = 0.140088,	data_time = 0.050502,	train_time = 0.785591
[2019-08-24 14:55:41,623] TRAIN Iter 254920: lr = 0.075135,	loss = 2.360128,	Top-1 err = 0.341650,	Top-5 err = 0.142090,	data_time = 0.050390,	train_time = 0.357248
[2019-08-24 14:55:59,251] TRAIN Iter 254940: lr = 0.075102,	loss = 2.377071,	Top-1 err = 0.338330,	Top-5 err = 0.143848,	data_time = 0.050480,	train_time = 0.881425
[2019-08-24 14:56:14,174] TRAIN Iter 254960: lr = 0.075068,	loss = 2.358086,	Top-1 err = 0.340918,	Top-5 err = 0.143701,	data_time = 0.050361,	train_time = 0.746135
[2019-08-24 14:56:21,390] TRAIN Iter 254980: lr = 0.075035,	loss = 2.356101,	Top-1 err = 0.333838,	Top-5 err = 0.138965,	data_time = 0.050318,	train_time = 0.360757
[2019-08-24 14:56:37,545] TRAIN Iter 255000: lr = 0.075002,	loss = 2.381337,	Top-1 err = 0.339209,	Top-5 err = 0.143555,	data_time = 0.050216,	train_time = 0.807761
[2019-08-24 14:56:45,364] TRAIN Iter 255020: lr = 0.074968,	loss = 2.359245,	Top-1 err = 0.341260,	Top-5 err = 0.145410,	data_time = 0.050802,	train_time = 0.390926
[2019-08-24 14:57:00,874] TRAIN Iter 255040: lr = 0.074935,	loss = 2.362805,	Top-1 err = 0.341260,	Top-5 err = 0.143457,	data_time = 0.050616,	train_time = 0.775476
[2019-08-24 14:57:16,312] TRAIN Iter 255060: lr = 0.074902,	loss = 2.326924,	Top-1 err = 0.342285,	Top-5 err = 0.144434,	data_time = 0.050892,	train_time = 0.771880
[2019-08-24 14:57:23,808] TRAIN Iter 255080: lr = 0.074868,	loss = 2.465075,	Top-1 err = 0.340771,	Top-5 err = 0.141455,	data_time = 0.050642,	train_time = 0.374811
[2019-08-24 14:57:39,549] TRAIN Iter 255100: lr = 0.074835,	loss = 2.350636,	Top-1 err = 0.341748,	Top-5 err = 0.145801,	data_time = 0.050472,	train_time = 0.787031
[2019-08-24 14:57:55,272] TRAIN Iter 255120: lr = 0.074802,	loss = 2.437304,	Top-1 err = 0.344971,	Top-5 err = 0.141016,	data_time = 0.050444,	train_time = 0.786157
[2019-08-24 14:58:02,263] TRAIN Iter 255140: lr = 0.074768,	loss = 2.461657,	Top-1 err = 0.340918,	Top-5 err = 0.147510,	data_time = 0.050476,	train_time = 0.349515
[2019-08-24 14:58:18,455] TRAIN Iter 255160: lr = 0.074735,	loss = 2.335024,	Top-1 err = 0.344141,	Top-5 err = 0.145459,	data_time = 0.050529,	train_time = 0.809570
[2019-08-24 14:58:25,854] TRAIN Iter 255180: lr = 0.074702,	loss = 2.399849,	Top-1 err = 0.341504,	Top-5 err = 0.139941,	data_time = 0.103088,	train_time = 0.369960
[2019-08-24 14:58:41,361] TRAIN Iter 255200: lr = 0.074668,	loss = 2.407576,	Top-1 err = 0.338574,	Top-5 err = 0.141602,	data_time = 0.050482,	train_time = 0.775327
[2019-08-24 14:58:57,684] TRAIN Iter 255220: lr = 0.074635,	loss = 2.329093,	Top-1 err = 0.340723,	Top-5 err = 0.140332,	data_time = 0.050590,	train_time = 0.816117
[2019-08-24 14:59:05,023] TRAIN Iter 255240: lr = 0.074602,	loss = 2.419277,	Top-1 err = 0.346387,	Top-5 err = 0.146484,	data_time = 0.137998,	train_time = 0.366937
[2019-08-24 14:59:20,426] TRAIN Iter 255260: lr = 0.074568,	loss = 2.347443,	Top-1 err = 0.342139,	Top-5 err = 0.147168,	data_time = 0.050506,	train_time = 0.770149
[2019-08-24 14:59:37,999] TRAIN Iter 255280: lr = 0.074535,	loss = 2.388179,	Top-1 err = 0.338721,	Top-5 err = 0.143994,	data_time = 0.072815,	train_time = 0.878661
[2019-08-24 14:59:45,198] TRAIN Iter 255300: lr = 0.074502,	loss = 2.432787,	Top-1 err = 0.344092,	Top-5 err = 0.146240,	data_time = 0.050045,	train_time = 0.359896
[2019-08-24 14:59:58,829] TRAIN Iter 255320: lr = 0.074468,	loss = 2.350550,	Top-1 err = 0.340723,	Top-5 err = 0.141895,	data_time = 0.049892,	train_time = 0.681542
[2019-08-24 15:00:05,008] TRAIN Iter 255340: lr = 0.074435,	loss = 2.263472,	Top-1 err = 0.341016,	Top-5 err = 0.141650,	data_time = 0.049888,	train_time = 0.308969
[2019-08-24 15:00:58,166] TRAIN Iter 255360: lr = 0.074402,	loss = 2.368381,	Top-1 err = 0.338598,	Top-5 err = 0.143921,	data_time = 0.050330,	train_time = 2.657881
[2019-08-24 15:01:11,748] TRAIN Iter 255380: lr = 0.074368,	loss = 2.363855,	Top-1 err = 0.330420,	Top-5 err = 0.138477,	data_time = 0.050501,	train_time = 0.679075
[2019-08-24 15:01:21,252] TRAIN Iter 255400: lr = 0.074335,	loss = 2.321370,	Top-1 err = 0.334180,	Top-5 err = 0.136621,	data_time = 0.050348,	train_time = 0.475157
[2019-08-24 15:01:35,780] TRAIN Iter 255420: lr = 0.074302,	loss = 2.365959,	Top-1 err = 0.339160,	Top-5 err = 0.137988,	data_time = 0.050322,	train_time = 0.726381
[2019-08-24 15:01:43,546] TRAIN Iter 255440: lr = 0.074268,	loss = 2.270391,	Top-1 err = 0.326172,	Top-5 err = 0.134277,	data_time = 0.050715,	train_time = 0.388296
[2019-08-24 15:01:57,562] TRAIN Iter 255460: lr = 0.074235,	loss = 2.316910,	Top-1 err = 0.337744,	Top-5 err = 0.136230,	data_time = 0.050423,	train_time = 0.700790
[2019-08-24 15:02:12,866] TRAIN Iter 255480: lr = 0.074202,	loss = 2.353222,	Top-1 err = 0.334326,	Top-5 err = 0.136328,	data_time = 0.050239,	train_time = 0.765184
[2019-08-24 15:02:20,263] TRAIN Iter 255500: lr = 0.074168,	loss = 2.366008,	Top-1 err = 0.331445,	Top-5 err = 0.137988,	data_time = 0.050789,	train_time = 0.369832
[2019-08-24 15:02:33,652] TRAIN Iter 255520: lr = 0.074135,	loss = 2.338015,	Top-1 err = 0.336523,	Top-5 err = 0.138623,	data_time = 0.050342,	train_time = 0.669453
[2019-08-24 15:02:44,890] TRAIN Iter 255540: lr = 0.074102,	loss = 2.409688,	Top-1 err = 0.333447,	Top-5 err = 0.136963,	data_time = 0.050554,	train_time = 0.561874
[2019-08-24 15:02:52,561] TRAIN Iter 255560: lr = 0.074068,	loss = 2.357254,	Top-1 err = 0.335547,	Top-5 err = 0.137061,	data_time = 0.050199,	train_time = 0.383538
[2019-08-24 15:03:08,146] TRAIN Iter 255580: lr = 0.074035,	loss = 2.338914,	Top-1 err = 0.337207,	Top-5 err = 0.139600,	data_time = 0.050357,	train_time = 0.779264
[2019-08-24 15:03:15,406] TRAIN Iter 255600: lr = 0.074002,	loss = 2.308380,	Top-1 err = 0.338525,	Top-5 err = 0.139355,	data_time = 0.050833,	train_time = 0.362983
[2019-08-24 15:03:29,914] TRAIN Iter 255620: lr = 0.073968,	loss = 2.299665,	Top-1 err = 0.334326,	Top-5 err = 0.136230,	data_time = 0.050278,	train_time = 0.725391
[2019-08-24 15:03:45,557] TRAIN Iter 255640: lr = 0.073935,	loss = 2.314985,	Top-1 err = 0.338965,	Top-5 err = 0.140576,	data_time = 0.050812,	train_time = 0.782099
[2019-08-24 15:03:52,876] TRAIN Iter 255660: lr = 0.073902,	loss = 2.412054,	Top-1 err = 0.336914,	Top-5 err = 0.140527,	data_time = 0.109490,	train_time = 0.365929
[2019-08-24 15:04:06,076] TRAIN Iter 255680: lr = 0.073868,	loss = 2.336212,	Top-1 err = 0.335059,	Top-5 err = 0.140674,	data_time = 0.050331,	train_time = 0.660020
[2019-08-24 15:04:19,612] TRAIN Iter 255700: lr = 0.073835,	loss = 2.389856,	Top-1 err = 0.341504,	Top-5 err = 0.141992,	data_time = 0.050627,	train_time = 0.676753
[2019-08-24 15:04:29,191] TRAIN Iter 255720: lr = 0.073802,	loss = 2.375705,	Top-1 err = 0.331152,	Top-5 err = 0.137695,	data_time = 0.050423,	train_time = 0.478933
[2019-08-24 15:04:44,396] TRAIN Iter 255740: lr = 0.073768,	loss = 2.363045,	Top-1 err = 0.337402,	Top-5 err = 0.140625,	data_time = 0.050852,	train_time = 0.760263
[2019-08-24 15:04:51,405] TRAIN Iter 255760: lr = 0.073735,	loss = 2.455282,	Top-1 err = 0.338574,	Top-5 err = 0.138965,	data_time = 0.050159,	train_time = 0.350423
[2019-08-24 15:05:06,949] TRAIN Iter 255780: lr = 0.073702,	loss = 2.322229,	Top-1 err = 0.339648,	Top-5 err = 0.144238,	data_time = 0.050463,	train_time = 0.777184
[2019-08-24 15:05:23,501] TRAIN Iter 255800: lr = 0.073668,	loss = 2.425343,	Top-1 err = 0.335840,	Top-5 err = 0.140479,	data_time = 0.050297,	train_time = 0.827616
[2019-08-24 15:05:30,635] TRAIN Iter 255820: lr = 0.073635,	loss = 2.376241,	Top-1 err = 0.342041,	Top-5 err = 0.144043,	data_time = 0.050429,	train_time = 0.356673
[2019-08-24 15:05:45,101] TRAIN Iter 255840: lr = 0.073602,	loss = 2.426605,	Top-1 err = 0.337988,	Top-5 err = 0.140430,	data_time = 0.050694,	train_time = 0.723287
[2019-08-24 15:05:56,218] TRAIN Iter 255860: lr = 0.073568,	loss = 2.415526,	Top-1 err = 0.343213,	Top-5 err = 0.140234,	data_time = 2.826109,	train_time = 0.555818
[2019-08-24 15:06:06,427] TRAIN Iter 255880: lr = 0.073535,	loss = 2.357794,	Top-1 err = 0.341992,	Top-5 err = 0.141162,	data_time = 0.050309,	train_time = 0.510453
[2019-08-24 15:06:21,510] TRAIN Iter 255900: lr = 0.073502,	loss = 2.324665,	Top-1 err = 0.339502,	Top-5 err = 0.142090,	data_time = 0.050530,	train_time = 0.754143
[2019-08-24 15:06:28,658] TRAIN Iter 255920: lr = 0.073468,	loss = 2.358665,	Top-1 err = 0.343799,	Top-5 err = 0.141748,	data_time = 0.127971,	train_time = 0.357346
[2019-08-24 15:06:44,172] TRAIN Iter 255940: lr = 0.073435,	loss = 2.374317,	Top-1 err = 0.337061,	Top-5 err = 0.140137,	data_time = 0.050824,	train_time = 0.775693
[2019-08-24 15:06:57,436] TRAIN Iter 255960: lr = 0.073402,	loss = 2.486235,	Top-1 err = 0.337842,	Top-5 err = 0.141162,	data_time = 0.050522,	train_time = 0.663219
[2019-08-24 15:07:04,682] TRAIN Iter 255980: lr = 0.073368,	loss = 2.320285,	Top-1 err = 0.341260,	Top-5 err = 0.139600,	data_time = 0.050818,	train_time = 0.362292
[2019-08-24 15:07:20,966] TRAIN Iter 256000: lr = 0.073335,	loss = 2.429870,	Top-1 err = 0.340088,	Top-5 err = 0.144189,	data_time = 0.050436,	train_time = 0.814174
[2019-08-24 15:07:34,129] TRAIN Iter 256020: lr = 0.073302,	loss = 2.389127,	Top-1 err = 0.340820,	Top-5 err = 0.141699,	data_time = 2.975641,	train_time = 0.658112
[2019-08-24 15:07:46,064] TRAIN Iter 256040: lr = 0.073268,	loss = 2.313427,	Top-1 err = 0.334619,	Top-5 err = 0.138525,	data_time = 0.050904,	train_time = 0.596770
[2019-08-24 15:08:01,529] TRAIN Iter 256060: lr = 0.073235,	loss = 2.278920,	Top-1 err = 0.338428,	Top-5 err = 0.145068,	data_time = 0.050570,	train_time = 0.773240
[2019-08-24 15:08:08,606] TRAIN Iter 256080: lr = 0.073202,	loss = 2.388481,	Top-1 err = 0.342090,	Top-5 err = 0.142480,	data_time = 0.050792,	train_time = 0.353803
[2019-08-24 15:08:24,751] TRAIN Iter 256100: lr = 0.073168,	loss = 2.403073,	Top-1 err = 0.337256,	Top-5 err = 0.139648,	data_time = 0.050335,	train_time = 0.807272
[2019-08-24 15:08:40,838] TRAIN Iter 256120: lr = 0.073135,	loss = 2.342360,	Top-1 err = 0.345996,	Top-5 err = 0.141943,	data_time = 0.050499,	train_time = 0.804329
[2019-08-24 15:08:47,781] TRAIN Iter 256140: lr = 0.073102,	loss = 2.315969,	Top-1 err = 0.343311,	Top-5 err = 0.144043,	data_time = 0.050437,	train_time = 0.347106
[2019-08-24 15:09:03,998] TRAIN Iter 256160: lr = 0.073068,	loss = 2.412344,	Top-1 err = 0.343896,	Top-5 err = 0.146338,	data_time = 0.050511,	train_time = 0.810836
[2019-08-24 15:09:18,215] TRAIN Iter 256180: lr = 0.073035,	loss = 2.376373,	Top-1 err = 0.338770,	Top-5 err = 0.139990,	data_time = 5.098546,	train_time = 0.710857
[2019-08-24 15:09:26,473] TRAIN Iter 256200: lr = 0.073002,	loss = 2.337364,	Top-1 err = 0.341162,	Top-5 err = 0.140967,	data_time = 0.050728,	train_time = 0.412878
[2019-08-24 15:09:42,666] TRAIN Iter 256220: lr = 0.072968,	loss = 2.341610,	Top-1 err = 0.337109,	Top-5 err = 0.141162,	data_time = 0.050285,	train_time = 0.809626
[2019-08-24 15:09:49,805] TRAIN Iter 256240: lr = 0.072935,	loss = 2.467868,	Top-1 err = 0.339355,	Top-5 err = 0.142285,	data_time = 0.050524,	train_time = 0.356959
[2019-08-24 15:10:07,950] TRAIN Iter 256260: lr = 0.072902,	loss = 2.343217,	Top-1 err = 0.340625,	Top-5 err = 0.143457,	data_time = 0.050593,	train_time = 0.907228
[2019-08-24 15:10:23,417] TRAIN Iter 256280: lr = 0.072868,	loss = 2.443170,	Top-1 err = 0.330225,	Top-5 err = 0.136523,	data_time = 0.050646,	train_time = 0.773335
[2019-08-24 15:10:30,451] TRAIN Iter 256300: lr = 0.072835,	loss = 2.459971,	Top-1 err = 0.342187,	Top-5 err = 0.144580,	data_time = 0.050543,	train_time = 0.351664
[2019-08-24 15:10:47,013] TRAIN Iter 256320: lr = 0.072802,	loss = 2.415184,	Top-1 err = 0.341699,	Top-5 err = 0.144385,	data_time = 0.050426,	train_time = 0.828107
[2019-08-24 15:11:03,324] TRAIN Iter 256340: lr = 0.072768,	loss = 2.327800,	Top-1 err = 0.335352,	Top-5 err = 0.139209,	data_time = 2.450831,	train_time = 0.815493
[2019-08-24 15:11:10,707] TRAIN Iter 256360: lr = 0.072735,	loss = 2.346999,	Top-1 err = 0.334619,	Top-5 err = 0.138770,	data_time = 0.050392,	train_time = 0.369153
[2019-08-24 15:11:27,657] TRAIN Iter 256380: lr = 0.072702,	loss = 2.392351,	Top-1 err = 0.340186,	Top-5 err = 0.145850,	data_time = 0.050194,	train_time = 0.847468
[2019-08-24 15:11:34,773] TRAIN Iter 256400: lr = 0.072668,	loss = 2.374161,	Top-1 err = 0.342822,	Top-5 err = 0.146729,	data_time = 0.050505,	train_time = 0.355809
[2019-08-24 15:11:51,954] TRAIN Iter 256420: lr = 0.072635,	loss = 2.379315,	Top-1 err = 0.343262,	Top-5 err = 0.143506,	data_time = 0.050493,	train_time = 0.859010
[2019-08-24 15:12:09,591] TRAIN Iter 256440: lr = 0.072602,	loss = 2.366197,	Top-1 err = 0.341650,	Top-5 err = 0.145752,	data_time = 0.050806,	train_time = 0.881847
[2019-08-24 15:12:16,371] TRAIN Iter 256460: lr = 0.072568,	loss = 2.417870,	Top-1 err = 0.337549,	Top-5 err = 0.138672,	data_time = 0.050933,	train_time = 0.338982
[2019-08-24 15:12:32,841] TRAIN Iter 256480: lr = 0.072535,	loss = 2.399831,	Top-1 err = 0.341846,	Top-5 err = 0.150000,	data_time = 0.050453,	train_time = 0.823507
[2019-08-24 15:12:50,651] TRAIN Iter 256500: lr = 0.072502,	loss = 2.314690,	Top-1 err = 0.337500,	Top-5 err = 0.140527,	data_time = 2.370193,	train_time = 0.890455
[2019-08-24 15:12:57,596] TRAIN Iter 256520: lr = 0.072468,	loss = 2.392117,	Top-1 err = 0.346240,	Top-5 err = 0.143701,	data_time = 0.050184,	train_time = 0.347267
[2019-08-24 15:13:15,911] TRAIN Iter 256540: lr = 0.072435,	loss = 2.365045,	Top-1 err = 0.337842,	Top-5 err = 0.139746,	data_time = 0.050123,	train_time = 0.915743
[2019-08-24 15:13:22,962] TRAIN Iter 256560: lr = 0.072402,	loss = 2.385114,	Top-1 err = 0.337891,	Top-5 err = 0.142822,	data_time = 0.050019,	train_time = 0.352517
[2019-08-24 15:13:38,763] TRAIN Iter 256580: lr = 0.072368,	loss = 2.344559,	Top-1 err = 0.345996,	Top-5 err = 0.144971,	data_time = 0.050025,	train_time = 0.790027
[2019-08-24 15:14:28,672] TRAIN Iter 256600: lr = 0.072335,	loss = 2.360275,	Top-1 err = 0.341040,	Top-5 err = 0.145542,	data_time = 5.808455,	train_time = 2.495440
[2019-08-24 15:14:35,860] TRAIN Iter 256620: lr = 0.072302,	loss = 2.347595,	Top-1 err = 0.331494,	Top-5 err = 0.138037,	data_time = 0.050472,	train_time = 0.359406
[2019-08-24 15:14:49,593] TRAIN Iter 256640: lr = 0.072268,	loss = 2.322365,	Top-1 err = 0.335059,	Top-5 err = 0.135205,	data_time = 0.050603,	train_time = 0.686629
[2019-08-24 15:14:57,143] TRAIN Iter 256660: lr = 0.072235,	loss = 2.452700,	Top-1 err = 0.336182,	Top-5 err = 0.136182,	data_time = 0.050458,	train_time = 0.377500
[2019-08-24 15:15:12,580] TRAIN Iter 256680: lr = 0.072202,	loss = 2.353746,	Top-1 err = 0.331104,	Top-5 err = 0.137109,	data_time = 0.050438,	train_time = 0.771826
[2019-08-24 15:15:33,033] TRAIN Iter 256700: lr = 0.072168,	loss = 2.328085,	Top-1 err = 0.328174,	Top-5 err = 0.136865,	data_time = 0.050392,	train_time = 1.022629
[2019-08-24 15:15:42,358] TRAIN Iter 256720: lr = 0.072135,	loss = 2.317269,	Top-1 err = 0.334668,	Top-5 err = 0.141064,	data_time = 0.051200,	train_time = 0.466227
[2019-08-24 15:15:53,548] TRAIN Iter 256740: lr = 0.072102,	loss = 2.353178,	Top-1 err = 0.331641,	Top-5 err = 0.141797,	data_time = 0.158065,	train_time = 0.559508
[2019-08-24 15:16:01,634] TRAIN Iter 256760: lr = 0.072068,	loss = 2.247394,	Top-1 err = 0.325293,	Top-5 err = 0.134277,	data_time = 0.050436,	train_time = 0.404285
[2019-08-24 15:16:09,271] TRAIN Iter 256780: lr = 0.072035,	loss = 2.381918,	Top-1 err = 0.335059,	Top-5 err = 0.142676,	data_time = 0.050471,	train_time = 0.381829
[2019-08-24 15:16:19,883] TRAIN Iter 256800: lr = 0.072002,	loss = 2.331401,	Top-1 err = 0.331006,	Top-5 err = 0.138574,	data_time = 0.050298,	train_time = 0.530598
[2019-08-24 15:16:27,457] TRAIN Iter 256820: lr = 0.071968,	loss = 2.301780,	Top-1 err = 0.338818,	Top-5 err = 0.139648,	data_time = 0.050594,	train_time = 0.378652
[2019-08-24 15:16:41,624] TRAIN Iter 256840: lr = 0.071935,	loss = 2.338764,	Top-1 err = 0.339453,	Top-5 err = 0.139209,	data_time = 0.050332,	train_time = 0.708355
[2019-08-24 15:16:56,335] TRAIN Iter 256860: lr = 0.071902,	loss = 2.412643,	Top-1 err = 0.338330,	Top-5 err = 0.142822,	data_time = 0.050781,	train_time = 0.735510
[2019-08-24 15:17:04,049] TRAIN Iter 256880: lr = 0.071868,	loss = 2.379236,	Top-1 err = 0.334326,	Top-5 err = 0.138818,	data_time = 0.050537,	train_time = 0.385728
[2019-08-24 15:17:18,536] TRAIN Iter 256900: lr = 0.071835,	loss = 2.440048,	Top-1 err = 0.339062,	Top-5 err = 0.141309,	data_time = 0.050587,	train_time = 0.724308
[2019-08-24 15:17:32,055] TRAIN Iter 256920: lr = 0.071802,	loss = 2.415343,	Top-1 err = 0.339014,	Top-5 err = 0.139893,	data_time = 0.758546,	train_time = 0.675941
[2019-08-24 15:17:40,225] TRAIN Iter 256940: lr = 0.071768,	loss = 2.261201,	Top-1 err = 0.332324,	Top-5 err = 0.133691,	data_time = 0.126761,	train_time = 0.408511
[2019-08-24 15:17:54,241] TRAIN Iter 256960: lr = 0.071735,	loss = 2.411320,	Top-1 err = 0.338623,	Top-5 err = 0.143604,	data_time = 0.050235,	train_time = 0.700744
[2019-08-24 15:18:03,192] TRAIN Iter 256980: lr = 0.071702,	loss = 2.372307,	Top-1 err = 0.337939,	Top-5 err = 0.141406,	data_time = 0.050450,	train_time = 0.447521
[2019-08-24 15:18:17,536] TRAIN Iter 257000: lr = 0.071668,	loss = 2.283887,	Top-1 err = 0.329004,	Top-5 err = 0.136279,	data_time = 0.142703,	train_time = 0.717201
[2019-08-24 15:18:31,859] TRAIN Iter 257020: lr = 0.071635,	loss = 2.375796,	Top-1 err = 0.336768,	Top-5 err = 0.139111,	data_time = 0.050385,	train_time = 0.716116
[2019-08-24 15:18:39,712] TRAIN Iter 257040: lr = 0.071602,	loss = 2.368461,	Top-1 err = 0.333447,	Top-5 err = 0.140723,	data_time = 0.050455,	train_time = 0.392640
[2019-08-24 15:18:54,131] TRAIN Iter 257060: lr = 0.071568,	loss = 2.351558,	Top-1 err = 0.336035,	Top-5 err = 0.139453,	data_time = 0.050313,	train_time = 0.720943
[2019-08-24 15:19:09,472] TRAIN Iter 257080: lr = 0.071535,	loss = 2.369519,	Top-1 err = 0.331055,	Top-5 err = 0.139990,	data_time = 0.050504,	train_time = 0.767049
[2019-08-24 15:19:17,648] TRAIN Iter 257100: lr = 0.071502,	loss = 2.404219,	Top-1 err = 0.337549,	Top-5 err = 0.141943,	data_time = 0.050263,	train_time = 0.408774
[2019-08-24 15:19:33,761] TRAIN Iter 257120: lr = 0.071468,	loss = 2.279602,	Top-1 err = 0.339893,	Top-5 err = 0.140771,	data_time = 0.050628,	train_time = 0.805622
[2019-08-24 15:19:42,216] TRAIN Iter 257140: lr = 0.071435,	loss = 2.297227,	Top-1 err = 0.336523,	Top-5 err = 0.139160,	data_time = 0.050396,	train_time = 0.422757
[2019-08-24 15:19:57,882] TRAIN Iter 257160: lr = 0.071402,	loss = 2.347089,	Top-1 err = 0.336328,	Top-5 err = 0.140869,	data_time = 0.050526,	train_time = 0.783266
[2019-08-24 15:20:12,842] TRAIN Iter 257180: lr = 0.071368,	loss = 2.335551,	Top-1 err = 0.337402,	Top-5 err = 0.142334,	data_time = 0.050637,	train_time = 0.747995
[2019-08-24 15:20:20,996] TRAIN Iter 257200: lr = 0.071335,	loss = 2.363352,	Top-1 err = 0.334180,	Top-5 err = 0.140479,	data_time = 0.050473,	train_time = 0.407664
[2019-08-24 15:20:36,863] TRAIN Iter 257220: lr = 0.071302,	loss = 2.352026,	Top-1 err = 0.335254,	Top-5 err = 0.137500,	data_time = 0.050874,	train_time = 0.793339
[2019-08-24 15:20:52,021] TRAIN Iter 257240: lr = 0.071268,	loss = 2.349907,	Top-1 err = 0.336230,	Top-5 err = 0.139648,	data_time = 0.050598,	train_time = 0.757888
[2019-08-24 15:21:00,306] TRAIN Iter 257260: lr = 0.071235,	loss = 2.359677,	Top-1 err = 0.335449,	Top-5 err = 0.137402,	data_time = 0.050524,	train_time = 0.414237
[2019-08-24 15:21:16,001] TRAIN Iter 257280: lr = 0.071202,	loss = 2.389799,	Top-1 err = 0.337256,	Top-5 err = 0.143311,	data_time = 0.050577,	train_time = 0.784760
[2019-08-24 15:21:24,223] TRAIN Iter 257300: lr = 0.071168,	loss = 2.330218,	Top-1 err = 0.337695,	Top-5 err = 0.136865,	data_time = 0.050655,	train_time = 0.411054
[2019-08-24 15:21:38,765] TRAIN Iter 257320: lr = 0.071135,	loss = 2.305749,	Top-1 err = 0.334180,	Top-5 err = 0.136719,	data_time = 0.050556,	train_time = 0.727094
[2019-08-24 15:21:54,738] TRAIN Iter 257340: lr = 0.071102,	loss = 2.375311,	Top-1 err = 0.341943,	Top-5 err = 0.140039,	data_time = 0.050318,	train_time = 0.798618
[2019-08-24 15:22:02,678] TRAIN Iter 257360: lr = 0.071068,	loss = 2.344946,	Top-1 err = 0.335352,	Top-5 err = 0.137500,	data_time = 0.050637,	train_time = 0.397004
[2019-08-24 15:22:18,324] TRAIN Iter 257380: lr = 0.071035,	loss = 2.361563,	Top-1 err = 0.340234,	Top-5 err = 0.145654,	data_time = 0.050513,	train_time = 0.782268
[2019-08-24 15:22:33,782] TRAIN Iter 257400: lr = 0.071002,	loss = 2.350309,	Top-1 err = 0.333105,	Top-5 err = 0.139893,	data_time = 0.137773,	train_time = 0.772913
[2019-08-24 15:22:41,968] TRAIN Iter 257420: lr = 0.070968,	loss = 2.349349,	Top-1 err = 0.336816,	Top-5 err = 0.139063,	data_time = 0.050491,	train_time = 0.409259
[2019-08-24 15:22:58,619] TRAIN Iter 257440: lr = 0.070935,	loss = 2.407912,	Top-1 err = 0.337451,	Top-5 err = 0.140820,	data_time = 0.050320,	train_time = 0.832568
[2019-08-24 15:23:06,267] TRAIN Iter 257460: lr = 0.070902,	loss = 2.410494,	Top-1 err = 0.337305,	Top-5 err = 0.139941,	data_time = 0.050694,	train_time = 0.382371
[2019-08-24 15:23:21,255] TRAIN Iter 257480: lr = 0.070868,	loss = 2.319833,	Top-1 err = 0.341016,	Top-5 err = 0.143262,	data_time = 0.050583,	train_time = 0.749403
[2019-08-24 15:23:36,955] TRAIN Iter 257500: lr = 0.070835,	loss = 2.308787,	Top-1 err = 0.337305,	Top-5 err = 0.139258,	data_time = 0.050494,	train_time = 0.784990
[2019-08-24 15:23:45,859] TRAIN Iter 257520: lr = 0.070802,	loss = 2.284035,	Top-1 err = 0.333594,	Top-5 err = 0.138867,	data_time = 0.050498,	train_time = 0.445161
[2019-08-24 15:24:03,260] TRAIN Iter 257540: lr = 0.070768,	loss = 2.450204,	Top-1 err = 0.338574,	Top-5 err = 0.140234,	data_time = 0.050358,	train_time = 0.870026
[2019-08-24 15:24:18,652] TRAIN Iter 257560: lr = 0.070735,	loss = 2.319954,	Top-1 err = 0.337500,	Top-5 err = 0.143750,	data_time = 0.050456,	train_time = 0.769616
[2019-08-24 15:24:27,288] TRAIN Iter 257580: lr = 0.070702,	loss = 2.492547,	Top-1 err = 0.340869,	Top-5 err = 0.142920,	data_time = 0.439740,	train_time = 0.431784
[2019-08-24 15:24:44,544] TRAIN Iter 257600: lr = 0.070668,	loss = 2.398024,	Top-1 err = 0.339111,	Top-5 err = 0.139990,	data_time = 0.050469,	train_time = 0.862802
[2019-08-24 15:24:53,095] TRAIN Iter 257620: lr = 0.070635,	loss = 2.463643,	Top-1 err = 0.336475,	Top-5 err = 0.141260,	data_time = 0.050254,	train_time = 0.427532
[2019-08-24 15:25:08,625] TRAIN Iter 257640: lr = 0.070602,	loss = 2.345642,	Top-1 err = 0.334424,	Top-5 err = 0.139355,	data_time = 0.050419,	train_time = 0.776458
[2019-08-24 15:25:24,586] TRAIN Iter 257660: lr = 0.070568,	loss = 2.405643,	Top-1 err = 0.344336,	Top-5 err = 0.148242,	data_time = 0.430564,	train_time = 0.798053
[2019-08-24 15:25:36,066] TRAIN Iter 257680: lr = 0.070535,	loss = 2.337215,	Top-1 err = 0.339209,	Top-5 err = 0.145557,	data_time = 0.050519,	train_time = 0.573988
[2019-08-24 15:25:50,768] TRAIN Iter 257700: lr = 0.070502,	loss = 2.350482,	Top-1 err = 0.335059,	Top-5 err = 0.137354,	data_time = 0.050896,	train_time = 0.735097
[2019-08-24 15:26:07,157] TRAIN Iter 257720: lr = 0.070468,	loss = 2.402724,	Top-1 err = 0.337842,	Top-5 err = 0.142773,	data_time = 0.050593,	train_time = 0.819421
[2019-08-24 15:26:18,253] TRAIN Iter 257740: lr = 0.070435,	loss = 2.384643,	Top-1 err = 0.344482,	Top-5 err = 0.144678,	data_time = 1.475039,	train_time = 0.554770
[2019-08-24 15:26:33,070] TRAIN Iter 257760: lr = 0.070402,	loss = 2.352169,	Top-1 err = 0.338770,	Top-5 err = 0.139307,	data_time = 0.050524,	train_time = 0.740837
[2019-08-24 15:26:46,024] TRAIN Iter 257780: lr = 0.070368,	loss = 2.433416,	Top-1 err = 0.339941,	Top-5 err = 0.139502,	data_time = 0.050667,	train_time = 0.647683
[2019-08-24 15:26:59,781] TRAIN Iter 257800: lr = 0.070335,	loss = 2.300729,	Top-1 err = 0.339014,	Top-5 err = 0.141357,	data_time = 0.050028,	train_time = 0.687846
[2019-08-24 15:27:16,197] TRAIN Iter 257820: lr = 0.070302,	loss = 2.420249,	Top-1 err = 0.337012,	Top-5 err = 0.141602,	data_time = 0.049989,	train_time = 0.820779
[2019-08-24 15:27:23,686] TRAIN Iter 257840: lr = 0.070268,	loss = 2.358789,	Top-1 err = 0.342187,	Top-5 err = 0.144189,	data_time = 0.049910,	train_time = 0.374454
[2019-08-24 15:28:17,173] TRAIN Iter 257860: lr = 0.070235,	loss = 2.338019,	Top-1 err = 0.337757,	Top-5 err = 0.146238,	data_time = 0.050316,	train_time = 2.674326
[2019-08-24 15:28:24,685] TRAIN Iter 257880: lr = 0.070202,	loss = 2.363268,	Top-1 err = 0.336426,	Top-5 err = 0.139795,	data_time = 0.142430,	train_time = 0.375570
[2019-08-24 15:28:37,578] TRAIN Iter 257900: lr = 0.070168,	loss = 2.304596,	Top-1 err = 0.335303,	Top-5 err = 0.138623,	data_time = 0.050386,	train_time = 0.644654
[2019-08-24 15:28:51,612] TRAIN Iter 257920: lr = 0.070135,	loss = 2.374906,	Top-1 err = 0.335303,	Top-5 err = 0.139990,	data_time = 0.050660,	train_time = 0.701683
[2019-08-24 15:28:58,282] TRAIN Iter 257940: lr = 0.070102,	loss = 2.313291,	Top-1 err = 0.338037,	Top-5 err = 0.137500,	data_time = 0.050694,	train_time = 0.333488
[2019-08-24 15:29:14,669] TRAIN Iter 257960: lr = 0.070068,	loss = 2.223105,	Top-1 err = 0.331592,	Top-5 err = 0.137549,	data_time = 0.050519,	train_time = 0.819316
[2019-08-24 15:29:28,045] TRAIN Iter 257980: lr = 0.070035,	loss = 2.390825,	Top-1 err = 0.329980,	Top-5 err = 0.137354,	data_time = 3.979913,	train_time = 0.668809
[2019-08-24 15:29:36,970] TRAIN Iter 258000: lr = 0.070002,	loss = 2.345417,	Top-1 err = 0.329053,	Top-5 err = 0.136621,	data_time = 0.050804,	train_time = 0.446211
[2019-08-24 15:29:51,651] TRAIN Iter 258020: lr = 0.069968,	loss = 2.404294,	Top-1 err = 0.329785,	Top-5 err = 0.134912,	data_time = 0.050789,	train_time = 0.734062
[2019-08-24 15:29:58,516] TRAIN Iter 258040: lr = 0.069935,	loss = 2.321860,	Top-1 err = 0.329199,	Top-5 err = 0.136670,	data_time = 0.050431,	train_time = 0.343190
[2019-08-24 15:30:12,610] TRAIN Iter 258060: lr = 0.069902,	loss = 2.348759,	Top-1 err = 0.333447,	Top-5 err = 0.138379,	data_time = 0.050259,	train_time = 0.704694
[2019-08-24 15:30:27,540] TRAIN Iter 258080: lr = 0.069868,	loss = 2.342392,	Top-1 err = 0.337256,	Top-5 err = 0.137793,	data_time = 0.050358,	train_time = 0.746507
[2019-08-24 15:30:34,649] TRAIN Iter 258100: lr = 0.069835,	loss = 2.445826,	Top-1 err = 0.333447,	Top-5 err = 0.137061,	data_time = 0.050517,	train_time = 0.355440
[2019-08-24 15:30:51,156] TRAIN Iter 258120: lr = 0.069802,	loss = 2.324771,	Top-1 err = 0.328418,	Top-5 err = 0.136377,	data_time = 0.050448,	train_time = 0.825346
[2019-08-24 15:31:00,851] TRAIN Iter 258140: lr = 0.069768,	loss = 2.297828,	Top-1 err = 0.334424,	Top-5 err = 0.138086,	data_time = 2.011221,	train_time = 0.484700
[2019-08-24 15:31:13,139] TRAIN Iter 258160: lr = 0.069735,	loss = 2.399871,	Top-1 err = 0.335400,	Top-5 err = 0.140332,	data_time = 0.050736,	train_time = 0.614387
[2019-08-24 15:31:27,735] TRAIN Iter 258180: lr = 0.069702,	loss = 2.469970,	Top-1 err = 0.332715,	Top-5 err = 0.138818,	data_time = 0.111634,	train_time = 0.729781
[2019-08-24 15:31:34,430] TRAIN Iter 258200: lr = 0.069668,	loss = 2.310658,	Top-1 err = 0.325244,	Top-5 err = 0.134082,	data_time = 0.050535,	train_time = 0.334743
[2019-08-24 15:31:51,335] TRAIN Iter 258220: lr = 0.069635,	loss = 2.373833,	Top-1 err = 0.331299,	Top-5 err = 0.139844,	data_time = 0.050429,	train_time = 0.845255
[2019-08-24 15:32:04,475] TRAIN Iter 258240: lr = 0.069602,	loss = 2.297295,	Top-1 err = 0.336230,	Top-5 err = 0.137793,	data_time = 0.050805,	train_time = 0.656955
[2019-08-24 15:32:12,981] TRAIN Iter 258260: lr = 0.069568,	loss = 2.382144,	Top-1 err = 0.335449,	Top-5 err = 0.137451,	data_time = 0.050708,	train_time = 0.425281
[2019-08-24 15:32:29,748] TRAIN Iter 258280: lr = 0.069535,	loss = 2.399675,	Top-1 err = 0.334082,	Top-5 err = 0.139941,	data_time = 0.050458,	train_time = 0.838379
[2019-08-24 15:32:42,329] TRAIN Iter 258300: lr = 0.069502,	loss = 2.372634,	Top-1 err = 0.332617,	Top-5 err = 0.139111,	data_time = 1.195888,	train_time = 0.629028
[2019-08-24 15:32:51,890] TRAIN Iter 258320: lr = 0.069468,	loss = 2.389686,	Top-1 err = 0.337158,	Top-5 err = 0.142432,	data_time = 0.050356,	train_time = 0.477997
[2019-08-24 15:33:09,221] TRAIN Iter 258340: lr = 0.069435,	loss = 2.460066,	Top-1 err = 0.333594,	Top-5 err = 0.138086,	data_time = 0.050388,	train_time = 0.866571
[2019-08-24 15:33:16,180] TRAIN Iter 258360: lr = 0.069402,	loss = 2.341707,	Top-1 err = 0.338428,	Top-5 err = 0.136670,	data_time = 0.050469,	train_time = 0.347913
[2019-08-24 15:33:32,115] TRAIN Iter 258380: lr = 0.069368,	loss = 2.363492,	Top-1 err = 0.337158,	Top-5 err = 0.140479,	data_time = 0.050562,	train_time = 0.796751
[2019-08-24 15:33:48,160] TRAIN Iter 258400: lr = 0.069335,	loss = 2.367696,	Top-1 err = 0.336084,	Top-5 err = 0.140234,	data_time = 0.050361,	train_time = 0.802260
[2019-08-24 15:33:55,165] TRAIN Iter 258420: lr = 0.069302,	loss = 2.444901,	Top-1 err = 0.333691,	Top-5 err = 0.140820,	data_time = 0.050513,	train_time = 0.350196
[2019-08-24 15:34:11,565] TRAIN Iter 258440: lr = 0.069268,	loss = 2.304585,	Top-1 err = 0.334570,	Top-5 err = 0.139355,	data_time = 0.050511,	train_time = 0.819990
[2019-08-24 15:34:26,319] TRAIN Iter 258460: lr = 0.069235,	loss = 2.347847,	Top-1 err = 0.338037,	Top-5 err = 0.139844,	data_time = 0.050320,	train_time = 0.737688
[2019-08-24 15:34:35,279] TRAIN Iter 258480: lr = 0.069202,	loss = 2.352025,	Top-1 err = 0.335986,	Top-5 err = 0.142969,	data_time = 0.050895,	train_time = 0.447991
[2019-08-24 15:34:51,167] TRAIN Iter 258500: lr = 0.069168,	loss = 2.342773,	Top-1 err = 0.334766,	Top-5 err = 0.140186,	data_time = 0.050369,	train_time = 0.794364
[2019-08-24 15:34:58,468] TRAIN Iter 258520: lr = 0.069135,	loss = 2.398260,	Top-1 err = 0.339404,	Top-5 err = 0.140576,	data_time = 0.050457,	train_time = 0.365047
[2019-08-24 15:35:12,836] TRAIN Iter 258540: lr = 0.069102,	loss = 2.373161,	Top-1 err = 0.339062,	Top-5 err = 0.140625,	data_time = 0.050713,	train_time = 0.718418
[2019-08-24 15:35:29,482] TRAIN Iter 258560: lr = 0.069068,	loss = 2.307815,	Top-1 err = 0.334570,	Top-5 err = 0.139307,	data_time = 0.050508,	train_time = 0.832277
[2019-08-24 15:35:37,194] TRAIN Iter 258580: lr = 0.069035,	loss = 2.435802,	Top-1 err = 0.340869,	Top-5 err = 0.141211,	data_time = 0.050433,	train_time = 0.385558
[2019-08-24 15:35:50,868] TRAIN Iter 258600: lr = 0.069002,	loss = 2.304441,	Top-1 err = 0.334717,	Top-5 err = 0.142871,	data_time = 0.050713,	train_time = 0.683702
[2019-08-24 15:36:06,358] TRAIN Iter 258620: lr = 0.068968,	loss = 2.346641,	Top-1 err = 0.336230,	Top-5 err = 0.138770,	data_time = 0.050833,	train_time = 0.774460
[2019-08-24 15:36:13,691] TRAIN Iter 258640: lr = 0.068935,	loss = 2.344481,	Top-1 err = 0.331982,	Top-5 err = 0.137012,	data_time = 0.050530,	train_time = 0.366650
[2019-08-24 15:36:30,332] TRAIN Iter 258660: lr = 0.068902,	loss = 2.334995,	Top-1 err = 0.338232,	Top-5 err = 0.141016,	data_time = 0.050882,	train_time = 0.832057
[2019-08-24 15:36:37,149] TRAIN Iter 258680: lr = 0.068868,	loss = 2.390316,	Top-1 err = 0.335693,	Top-5 err = 0.144971,	data_time = 0.050398,	train_time = 0.340829
[2019-08-24 15:36:52,225] TRAIN Iter 258700: lr = 0.068835,	loss = 2.399623,	Top-1 err = 0.336182,	Top-5 err = 0.142627,	data_time = 0.050292,	train_time = 0.753774
[2019-08-24 15:37:08,780] TRAIN Iter 258720: lr = 0.068802,	loss = 2.374847,	Top-1 err = 0.336035,	Top-5 err = 0.137646,	data_time = 0.050533,	train_time = 0.827744
[2019-08-24 15:37:15,308] TRAIN Iter 258740: lr = 0.068768,	loss = 2.315022,	Top-1 err = 0.334668,	Top-5 err = 0.138232,	data_time = 0.050524,	train_time = 0.326365
[2019-08-24 15:37:31,757] TRAIN Iter 258760: lr = 0.068735,	loss = 2.392344,	Top-1 err = 0.337500,	Top-5 err = 0.136523,	data_time = 0.050634,	train_time = 0.822469
[2019-08-24 15:37:48,185] TRAIN Iter 258780: lr = 0.068702,	loss = 2.421895,	Top-1 err = 0.336084,	Top-5 err = 0.139600,	data_time = 0.148266,	train_time = 0.821365
[2019-08-24 15:37:55,425] TRAIN Iter 258800: lr = 0.068668,	loss = 2.308126,	Top-1 err = 0.339746,	Top-5 err = 0.140430,	data_time = 0.050376,	train_time = 0.362019
[2019-08-24 15:38:10,910] TRAIN Iter 258820: lr = 0.068635,	loss = 2.340683,	Top-1 err = 0.340283,	Top-5 err = 0.141162,	data_time = 0.050602,	train_time = 0.774216
[2019-08-24 15:38:17,730] TRAIN Iter 258840: lr = 0.068602,	loss = 2.477496,	Top-1 err = 0.339600,	Top-5 err = 0.139697,	data_time = 0.050513,	train_time = 0.340980
[2019-08-24 15:38:34,362] TRAIN Iter 258860: lr = 0.068568,	loss = 2.329416,	Top-1 err = 0.343311,	Top-5 err = 0.143994,	data_time = 0.050452,	train_time = 0.831602
[2019-08-24 15:38:51,668] TRAIN Iter 258880: lr = 0.068535,	loss = 2.301182,	Top-1 err = 0.332861,	Top-5 err = 0.136523,	data_time = 0.050373,	train_time = 0.865269
[2019-08-24 15:38:58,534] TRAIN Iter 258900: lr = 0.068502,	loss = 2.314444,	Top-1 err = 0.340576,	Top-5 err = 0.143213,	data_time = 0.050367,	train_time = 0.343308
[2019-08-24 15:39:15,570] TRAIN Iter 258920: lr = 0.068468,	loss = 2.401335,	Top-1 err = 0.335400,	Top-5 err = 0.141992,	data_time = 0.050377,	train_time = 0.851775
[2019-08-24 15:39:31,049] TRAIN Iter 258940: lr = 0.068435,	loss = 2.370794,	Top-1 err = 0.341504,	Top-5 err = 0.142529,	data_time = 2.564373,	train_time = 0.773928
[2019-08-24 15:39:38,098] TRAIN Iter 258960: lr = 0.068402,	loss = 2.367861,	Top-1 err = 0.341162,	Top-5 err = 0.142822,	data_time = 0.050407,	train_time = 0.352464
[2019-08-24 15:39:55,486] TRAIN Iter 258980: lr = 0.068368,	loss = 2.377841,	Top-1 err = 0.335596,	Top-5 err = 0.139453,	data_time = 0.050459,	train_time = 0.869373
[2019-08-24 15:40:02,382] TRAIN Iter 259000: lr = 0.068335,	loss = 2.345696,	Top-1 err = 0.339648,	Top-5 err = 0.142480,	data_time = 0.050326,	train_time = 0.344777
[2019-08-24 15:40:19,483] TRAIN Iter 259020: lr = 0.068302,	loss = 2.303160,	Top-1 err = 0.338770,	Top-5 err = 0.138525,	data_time = 0.050347,	train_time = 0.855036
[2019-08-24 15:40:37,682] TRAIN Iter 259040: lr = 0.068268,	loss = 2.259920,	Top-1 err = 0.335449,	Top-5 err = 0.138770,	data_time = 0.049867,	train_time = 0.909967
[2019-08-24 15:40:44,005] TRAIN Iter 259060: lr = 0.068235,	loss = 2.360361,	Top-1 err = 0.343555,	Top-5 err = 0.141016,	data_time = 0.050022,	train_time = 0.316107
[2019-08-24 15:41:01,962] TRAIN Iter 259080: lr = 0.068202,	loss = 2.432988,	Top-1 err = 0.344775,	Top-5 err = 0.142871,	data_time = 0.049878,	train_time = 0.897830
[2019-08-24 15:41:11,158] TRAIN Iter 259100: lr = 0.068168,	loss = 2.587485,	Top-1 err = 0.343191,	Top-5 err = 0.146722,	data_time = 0.007150,	train_time = 0.459828
[2019-08-24 15:41:59,567] TRAIN Iter 259120: lr = 0.068135,	loss = 2.472865,	Top-1 err = 0.331689,	Top-5 err = 0.138623,	data_time = 0.050353,	train_time = 2.420407
[2019-08-24 15:42:17,793] TRAIN Iter 259140: lr = 0.068102,	loss = 2.353780,	Top-1 err = 0.328955,	Top-5 err = 0.135254,	data_time = 0.050174,	train_time = 0.911285
[2019-08-24 15:42:25,607] TRAIN Iter 259160: lr = 0.068068,	loss = 2.327511,	Top-1 err = 0.333789,	Top-5 err = 0.140332,	data_time = 0.050526,	train_time = 0.390637
[2019-08-24 15:42:38,460] TRAIN Iter 259180: lr = 0.068035,	loss = 2.367324,	Top-1 err = 0.335449,	Top-5 err = 0.139746,	data_time = 0.050508,	train_time = 0.642628
[2019-08-24 15:42:52,311] TRAIN Iter 259200: lr = 0.068002,	loss = 2.392206,	Top-1 err = 0.328076,	Top-5 err = 0.137939,	data_time = 0.050709,	train_time = 0.692534
[2019-08-24 15:42:59,747] TRAIN Iter 259220: lr = 0.067968,	loss = 2.401140,	Top-1 err = 0.327979,	Top-5 err = 0.136768,	data_time = 0.050537,	train_time = 0.371768
[2019-08-24 15:43:15,290] TRAIN Iter 259240: lr = 0.067935,	loss = 2.300080,	Top-1 err = 0.325732,	Top-5 err = 0.133789,	data_time = 0.095311,	train_time = 0.777125
[2019-08-24 15:43:22,281] TRAIN Iter 259260: lr = 0.067902,	loss = 2.263789,	Top-1 err = 0.333887,	Top-5 err = 0.138818,	data_time = 0.050416,	train_time = 0.349575
[2019-08-24 15:43:37,523] TRAIN Iter 259280: lr = 0.067868,	loss = 2.298190,	Top-1 err = 0.326953,	Top-5 err = 0.136963,	data_time = 0.050356,	train_time = 0.762068
[2019-08-24 15:43:53,068] TRAIN Iter 259300: lr = 0.067835,	loss = 2.401519,	Top-1 err = 0.338672,	Top-5 err = 0.143018,	data_time = 0.050651,	train_time = 0.777237
[2019-08-24 15:44:01,070] TRAIN Iter 259320: lr = 0.067802,	loss = 2.287846,	Top-1 err = 0.330518,	Top-5 err = 0.139600,	data_time = 0.050717,	train_time = 0.400092
[2019-08-24 15:44:16,907] TRAIN Iter 259340: lr = 0.067768,	loss = 2.422791,	Top-1 err = 0.338379,	Top-5 err = 0.139404,	data_time = 0.050418,	train_time = 0.791801
[2019-08-24 15:44:28,203] TRAIN Iter 259360: lr = 0.067735,	loss = 2.329113,	Top-1 err = 0.331885,	Top-5 err = 0.137402,	data_time = 0.050584,	train_time = 0.564790
[2019-08-24 15:44:38,668] TRAIN Iter 259380: lr = 0.067702,	loss = 2.371893,	Top-1 err = 0.330713,	Top-5 err = 0.137500,	data_time = 0.050475,	train_time = 0.523273
[2019-08-24 15:44:54,481] TRAIN Iter 259400: lr = 0.067668,	loss = 2.358630,	Top-1 err = 0.328418,	Top-5 err = 0.136865,	data_time = 1.870601,	train_time = 0.790598
[2019-08-24 15:45:01,246] TRAIN Iter 259420: lr = 0.067635,	loss = 2.395101,	Top-1 err = 0.335889,	Top-5 err = 0.136816,	data_time = 0.050647,	train_time = 0.338241
[2019-08-24 15:45:17,135] TRAIN Iter 259440: lr = 0.067602,	loss = 2.378469,	Top-1 err = 0.324365,	Top-5 err = 0.135303,	data_time = 0.050749,	train_time = 0.794422
[2019-08-24 15:45:29,758] TRAIN Iter 259460: lr = 0.067568,	loss = 2.296581,	Top-1 err = 0.329102,	Top-5 err = 0.138525,	data_time = 0.050430,	train_time = 0.631163
[2019-08-24 15:45:40,593] TRAIN Iter 259480: lr = 0.067535,	loss = 2.297348,	Top-1 err = 0.329346,	Top-5 err = 0.136230,	data_time = 0.050351,	train_time = 0.541757
[2019-08-24 15:45:56,244] TRAIN Iter 259500: lr = 0.067502,	loss = 2.346092,	Top-1 err = 0.331787,	Top-5 err = 0.143018,	data_time = 0.050622,	train_time = 0.782513
[2019-08-24 15:46:07,844] TRAIN Iter 259520: lr = 0.067468,	loss = 2.359547,	Top-1 err = 0.334082,	Top-5 err = 0.140234,	data_time = 1.131190,	train_time = 0.580002
[2019-08-24 15:46:19,487] TRAIN Iter 259540: lr = 0.067435,	loss = 2.346752,	Top-1 err = 0.329395,	Top-5 err = 0.135254,	data_time = 0.050713,	train_time = 0.582110
[2019-08-24 15:46:36,326] TRAIN Iter 259560: lr = 0.067402,	loss = 2.416268,	Top-1 err = 0.330811,	Top-5 err = 0.136670,	data_time = 2.262159,	train_time = 0.841945
[2019-08-24 15:46:43,136] TRAIN Iter 259580: lr = 0.067368,	loss = 2.349326,	Top-1 err = 0.334912,	Top-5 err = 0.138135,	data_time = 0.050490,	train_time = 0.340494
[2019-08-24 15:46:59,460] TRAIN Iter 259600: lr = 0.067335,	loss = 2.371339,	Top-1 err = 0.339941,	Top-5 err = 0.143604,	data_time = 0.050784,	train_time = 0.816163
[2019-08-24 15:47:16,293] TRAIN Iter 259620: lr = 0.067302,	loss = 2.276799,	Top-1 err = 0.333643,	Top-5 err = 0.137646,	data_time = 0.050842,	train_time = 0.841639
[2019-08-24 15:47:23,313] TRAIN Iter 259640: lr = 0.067268,	loss = 2.260267,	Top-1 err = 0.334521,	Top-5 err = 0.133740,	data_time = 0.050330,	train_time = 0.350972
[2019-08-24 15:47:39,165] TRAIN Iter 259660: lr = 0.067235,	loss = 2.349229,	Top-1 err = 0.336768,	Top-5 err = 0.141162,	data_time = 0.050472,	train_time = 0.792603
[2019-08-24 15:47:49,919] TRAIN Iter 259680: lr = 0.067202,	loss = 2.410026,	Top-1 err = 0.333789,	Top-5 err = 0.138721,	data_time = 0.050618,	train_time = 0.537712
[2019-08-24 15:48:03,103] TRAIN Iter 259700: lr = 0.067168,	loss = 2.376513,	Top-1 err = 0.331152,	Top-5 err = 0.138330,	data_time = 0.050852,	train_time = 0.659153
[2019-08-24 15:48:19,736] TRAIN Iter 259720: lr = 0.067135,	loss = 2.348524,	Top-1 err = 0.335791,	Top-5 err = 0.137305,	data_time = 0.050540,	train_time = 0.831649
[2019-08-24 15:48:27,056] TRAIN Iter 259740: lr = 0.067102,	loss = 2.254162,	Top-1 err = 0.336084,	Top-5 err = 0.139014,	data_time = 0.141579,	train_time = 0.365974
[2019-08-24 15:48:42,497] TRAIN Iter 259760: lr = 0.067068,	loss = 2.293012,	Top-1 err = 0.335449,	Top-5 err = 0.135400,	data_time = 0.050547,	train_time = 0.772060
[2019-08-24 15:48:57,750] TRAIN Iter 259780: lr = 0.067035,	loss = 2.357273,	Top-1 err = 0.332178,	Top-5 err = 0.137598,	data_time = 0.365519,	train_time = 0.762622
[2019-08-24 15:49:04,547] TRAIN Iter 259800: lr = 0.067002,	loss = 2.371271,	Top-1 err = 0.338721,	Top-5 err = 0.139551,	data_time = 0.050474,	train_time = 0.339826
[2019-08-24 15:49:21,769] TRAIN Iter 259820: lr = 0.066968,	loss = 2.337080,	Top-1 err = 0.338037,	Top-5 err = 0.137891,	data_time = 0.050523,	train_time = 0.861090
[2019-08-24 15:49:33,933] TRAIN Iter 259840: lr = 0.066935,	loss = 2.415191,	Top-1 err = 0.332080,	Top-5 err = 0.141504,	data_time = 1.051121,	train_time = 0.608168
[2019-08-24 15:49:45,780] TRAIN Iter 259860: lr = 0.066902,	loss = 2.392194,	Top-1 err = 0.336475,	Top-5 err = 0.142773,	data_time = 0.050430,	train_time = 0.592376
[2019-08-24 15:50:01,095] TRAIN Iter 259880: lr = 0.066868,	loss = 2.469802,	Top-1 err = 0.337402,	Top-5 err = 0.140869,	data_time = 0.050852,	train_time = 0.765701
[2019-08-24 15:50:08,202] TRAIN Iter 259900: lr = 0.066835,	loss = 2.269560,	Top-1 err = 0.333740,	Top-5 err = 0.137646,	data_time = 0.050421,	train_time = 0.355352
[2019-08-24 15:50:24,069] TRAIN Iter 259920: lr = 0.066802,	loss = 2.341151,	Top-1 err = 0.338623,	Top-5 err = 0.142773,	data_time = 0.050348,	train_time = 0.793323
[2019-08-24 15:50:40,929] TRAIN Iter 259940: lr = 0.066768,	loss = 2.294306,	Top-1 err = 0.330273,	Top-5 err = 0.139063,	data_time = 0.215392,	train_time = 0.842987
[2019-08-24 15:50:47,528] TRAIN Iter 259960: lr = 0.066735,	loss = 2.373260,	Top-1 err = 0.339551,	Top-5 err = 0.139551,	data_time = 0.050275,	train_time = 0.329962
[2019-08-24 15:51:04,883] TRAIN Iter 259980: lr = 0.066702,	loss = 2.383801,	Top-1 err = 0.340283,	Top-5 err = 0.139941,	data_time = 0.050430,	train_time = 0.867720
[2019-08-24 15:51:17,582] TRAIN Iter 260000: lr = 0.066668,	loss = 2.316551,	Top-1 err = 0.336182,	Top-5 err = 0.142725,	data_time = 2.163625,	train_time = 0.634924
[2019-08-24 15:52:22,389] TEST Iter 260000: loss = 2.171507,	Top-1 err = 0.301700,	Top-5 err = 0.105880,	val_time = 64.749023
[2019-08-24 15:52:28,630] TRAIN Iter 260020: lr = 0.066635,	loss = 2.351423,	Top-1 err = 0.336182,	Top-5 err = 0.139355,	data_time = 0.050536,	train_time = 0.312028
[2019-08-24 15:52:35,055] TRAIN Iter 260040: lr = 0.066602,	loss = 2.411318,	Top-1 err = 0.336719,	Top-5 err = 0.141992,	data_time = 0.050492,	train_time = 0.321205
[2019-08-24 15:52:41,736] TRAIN Iter 260060: lr = 0.066568,	loss = 2.398693,	Top-1 err = 0.332715,	Top-5 err = 0.138965,	data_time = 0.050496,	train_time = 0.334076
[2019-08-24 15:52:50,554] TRAIN Iter 260080: lr = 0.066535,	loss = 2.375423,	Top-1 err = 0.333154,	Top-5 err = 0.138086,	data_time = 0.050431,	train_time = 0.440864
[2019-08-24 15:53:06,465] TRAIN Iter 260100: lr = 0.066502,	loss = 2.350915,	Top-1 err = 0.337012,	Top-5 err = 0.141064,	data_time = 0.050495,	train_time = 0.795532
[2019-08-24 15:53:14,972] TRAIN Iter 260120: lr = 0.066468,	loss = 2.299567,	Top-1 err = 0.335010,	Top-5 err = 0.137695,	data_time = 0.050625,	train_time = 0.425325
[2019-08-24 15:53:32,843] TRAIN Iter 260140: lr = 0.066435,	loss = 2.442959,	Top-1 err = 0.338037,	Top-5 err = 0.143115,	data_time = 0.050222,	train_time = 0.893564
[2019-08-24 15:53:41,311] TRAIN Iter 260160: lr = 0.066402,	loss = 2.345480,	Top-1 err = 0.337646,	Top-5 err = 0.137207,	data_time = 0.050514,	train_time = 0.423383
[2019-08-24 15:53:58,580] TRAIN Iter 260180: lr = 0.066368,	loss = 2.346896,	Top-1 err = 0.339160,	Top-5 err = 0.140137,	data_time = 0.366940,	train_time = 0.863418
[2019-08-24 15:54:14,977] TRAIN Iter 260200: lr = 0.066335,	loss = 2.256855,	Top-1 err = 0.342383,	Top-5 err = 0.146924,	data_time = 0.050541,	train_time = 0.819867
[2019-08-24 15:54:23,827] TRAIN Iter 260220: lr = 0.066302,	loss = 2.288122,	Top-1 err = 0.339404,	Top-5 err = 0.144434,	data_time = 0.050194,	train_time = 0.442487
[2019-08-24 15:54:41,207] TRAIN Iter 260240: lr = 0.066268,	loss = 2.388646,	Top-1 err = 0.336523,	Top-5 err = 0.140625,	data_time = 0.050321,	train_time = 0.868940
[2019-08-24 15:54:58,512] TRAIN Iter 260260: lr = 0.066235,	loss = 2.303370,	Top-1 err = 0.338623,	Top-5 err = 0.139941,	data_time = 0.591042,	train_time = 0.865276
[2019-08-24 15:55:08,264] TRAIN Iter 260280: lr = 0.066202,	loss = 2.335817,	Top-1 err = 0.335938,	Top-5 err = 0.138574,	data_time = 0.050366,	train_time = 0.487576
[2019-08-24 15:55:26,465] TRAIN Iter 260300: lr = 0.066168,	loss = 2.415455,	Top-1 err = 0.341406,	Top-5 err = 0.139111,	data_time = 0.252862,	train_time = 0.910053
[2019-08-24 15:55:36,158] TRAIN Iter 260320: lr = 0.066135,	loss = 2.390347,	Top-1 err = 0.337012,	Top-5 err = 0.140674,	data_time = 0.050026,	train_time = 0.484595
[2019-08-24 15:55:52,437] TRAIN Iter 260340: lr = 0.066102,	loss = 2.413862,	Top-1 err = 0.340869,	Top-5 err = 0.140918,	data_time = 0.177065,	train_time = 0.813984
[2019-08-24 15:56:40,718] TRAIN Iter 260360: lr = 0.066068,	loss = 2.403672,	Top-1 err = 0.337061,	Top-5 err = 0.142554,	data_time = 0.050269,	train_time = 2.414034
[2019-08-24 15:56:48,388] TRAIN Iter 260380: lr = 0.066035,	loss = 2.285599,	Top-1 err = 0.333838,	Top-5 err = 0.134668,	data_time = 0.050686,	train_time = 0.383460
[2019-08-24 15:57:07,073] TRAIN Iter 260400: lr = 0.066002,	loss = 2.257251,	Top-1 err = 0.333838,	Top-5 err = 0.137451,	data_time = 0.050837,	train_time = 0.934239
[2019-08-24 15:57:18,541] TRAIN Iter 260420: lr = 0.065968,	loss = 2.375955,	Top-1 err = 0.332568,	Top-5 err = 0.137109,	data_time = 0.050646,	train_time = 0.573359
[2019-08-24 15:57:27,040] TRAIN Iter 260440: lr = 0.065935,	loss = 2.345723,	Top-1 err = 0.324609,	Top-5 err = 0.136377,	data_time = 0.050568,	train_time = 0.424946
[2019-08-24 15:57:41,412] TRAIN Iter 260460: lr = 0.065902,	loss = 2.368408,	Top-1 err = 0.328467,	Top-5 err = 0.138037,	data_time = 0.050201,	train_time = 0.718576
[2019-08-24 15:57:48,351] TRAIN Iter 260480: lr = 0.065868,	loss = 2.365495,	Top-1 err = 0.334229,	Top-5 err = 0.135254,	data_time = 0.050244,	train_time = 0.346967
[2019-08-24 15:58:04,617] TRAIN Iter 260500: lr = 0.065835,	loss = 2.345917,	Top-1 err = 0.329199,	Top-5 err = 0.136328,	data_time = 0.050577,	train_time = 0.813275
[2019-08-24 15:58:19,947] TRAIN Iter 260520: lr = 0.065802,	loss = 2.306206,	Top-1 err = 0.327393,	Top-5 err = 0.137598,	data_time = 0.050630,	train_time = 0.766489
[2019-08-24 15:58:27,496] TRAIN Iter 260540: lr = 0.065768,	loss = 2.301723,	Top-1 err = 0.327686,	Top-5 err = 0.133447,	data_time = 0.050489,	train_time = 0.377439
[2019-08-24 15:58:43,917] TRAIN Iter 260560: lr = 0.065735,	loss = 2.382965,	Top-1 err = 0.319531,	Top-5 err = 0.129932,	data_time = 0.050415,	train_time = 0.821048
[2019-08-24 15:58:57,565] TRAIN Iter 260580: lr = 0.065702,	loss = 2.325976,	Top-1 err = 0.331299,	Top-5 err = 0.137451,	data_time = 0.050398,	train_time = 0.682339
[2019-08-24 15:59:05,374] TRAIN Iter 260600: lr = 0.065668,	loss = 2.287992,	Top-1 err = 0.331787,	Top-5 err = 0.137939,	data_time = 0.050551,	train_time = 0.390477
[2019-08-24 15:59:24,226] TRAIN Iter 260620: lr = 0.065635,	loss = 2.335909,	Top-1 err = 0.328271,	Top-5 err = 0.137500,	data_time = 0.050333,	train_time = 0.942547
[2019-08-24 15:59:31,269] TRAIN Iter 260640: lr = 0.065602,	loss = 2.429842,	Top-1 err = 0.331885,	Top-5 err = 0.134766,	data_time = 0.050365,	train_time = 0.352148
[2019-08-24 15:59:46,621] TRAIN Iter 260660: lr = 0.065568,	loss = 2.376667,	Top-1 err = 0.336377,	Top-5 err = 0.139111,	data_time = 0.050447,	train_time = 0.767577
[2019-08-24 16:00:00,711] TRAIN Iter 260680: lr = 0.065535,	loss = 2.356798,	Top-1 err = 0.328906,	Top-5 err = 0.136963,	data_time = 0.050498,	train_time = 0.704513
[2019-08-24 16:00:07,517] TRAIN Iter 260700: lr = 0.065502,	loss = 2.426514,	Top-1 err = 0.329834,	Top-5 err = 0.131641,	data_time = 0.050354,	train_time = 0.340301
[2019-08-24 16:00:24,091] TRAIN Iter 260720: lr = 0.065468,	loss = 2.288300,	Top-1 err = 0.336475,	Top-5 err = 0.139795,	data_time = 0.050610,	train_time = 0.828674
[2019-08-24 16:00:40,412] TRAIN Iter 260740: lr = 0.065435,	loss = 2.398717,	Top-1 err = 0.330566,	Top-5 err = 0.140625,	data_time = 0.050276,	train_time = 0.816019
[2019-08-24 16:00:47,523] TRAIN Iter 260760: lr = 0.065402,	loss = 2.381284,	Top-1 err = 0.331396,	Top-5 err = 0.141064,	data_time = 0.050422,	train_time = 0.355531
[2019-08-24 16:01:05,417] TRAIN Iter 260780: lr = 0.065368,	loss = 2.244919,	Top-1 err = 0.332861,	Top-5 err = 0.135937,	data_time = 0.050345,	train_time = 0.894680
[2019-08-24 16:01:12,669] TRAIN Iter 260800: lr = 0.065335,	loss = 2.457529,	Top-1 err = 0.336670,	Top-5 err = 0.141016,	data_time = 0.050710,	train_time = 0.362624
[2019-08-24 16:01:27,843] TRAIN Iter 260820: lr = 0.065302,	loss = 2.380522,	Top-1 err = 0.330078,	Top-5 err = 0.136865,	data_time = 0.050285,	train_time = 0.758651
[2019-08-24 16:01:42,911] TRAIN Iter 260840: lr = 0.065268,	loss = 2.404058,	Top-1 err = 0.327539,	Top-5 err = 0.135596,	data_time = 0.050254,	train_time = 0.753415
[2019-08-24 16:01:49,716] TRAIN Iter 260860: lr = 0.065235,	loss = 2.330240,	Top-1 err = 0.332275,	Top-5 err = 0.134912,	data_time = 0.050805,	train_time = 0.340207
[2019-08-24 16:02:07,178] TRAIN Iter 260880: lr = 0.065202,	loss = 2.340632,	Top-1 err = 0.334277,	Top-5 err = 0.139307,	data_time = 0.050472,	train_time = 0.873090
[2019-08-24 16:02:24,905] TRAIN Iter 260900: lr = 0.065168,	loss = 2.425818,	Top-1 err = 0.334277,	Top-5 err = 0.139111,	data_time = 0.113891,	train_time = 0.886373
[2019-08-24 16:02:31,985] TRAIN Iter 260920: lr = 0.065135,	loss = 2.461188,	Top-1 err = 0.336133,	Top-5 err = 0.139502,	data_time = 0.050416,	train_time = 0.353985
[2019-08-24 16:02:47,682] TRAIN Iter 260940: lr = 0.065102,	loss = 2.379560,	Top-1 err = 0.332715,	Top-5 err = 0.142139,	data_time = 0.051016,	train_time = 0.784822
[2019-08-24 16:02:54,571] TRAIN Iter 260960: lr = 0.065068,	loss = 2.285158,	Top-1 err = 0.330811,	Top-5 err = 0.137695,	data_time = 0.051005,	train_time = 0.344427
[2019-08-24 16:03:11,424] TRAIN Iter 260980: lr = 0.065035,	loss = 2.412043,	Top-1 err = 0.337109,	Top-5 err = 0.139307,	data_time = 0.050502,	train_time = 0.842648
[2019-08-24 16:03:26,038] TRAIN Iter 261000: lr = 0.065002,	loss = 2.399222,	Top-1 err = 0.331152,	Top-5 err = 0.132910,	data_time = 0.050523,	train_time = 0.730661
[2019-08-24 16:03:33,056] TRAIN Iter 261020: lr = 0.064968,	loss = 2.385624,	Top-1 err = 0.331982,	Top-5 err = 0.140967,	data_time = 0.050431,	train_time = 0.350909
[2019-08-24 16:03:48,804] TRAIN Iter 261040: lr = 0.064935,	loss = 2.483635,	Top-1 err = 0.339697,	Top-5 err = 0.142236,	data_time = 0.050627,	train_time = 0.787362
[2019-08-24 16:04:02,160] TRAIN Iter 261060: lr = 0.064902,	loss = 2.415266,	Top-1 err = 0.335107,	Top-5 err = 0.140283,	data_time = 0.050534,	train_time = 0.667790
[2019-08-24 16:04:13,710] TRAIN Iter 261080: lr = 0.064868,	loss = 2.371404,	Top-1 err = 0.333643,	Top-5 err = 0.140576,	data_time = 0.050308,	train_time = 0.577490
[2019-08-24 16:04:29,820] TRAIN Iter 261100: lr = 0.064835,	loss = 2.316959,	Top-1 err = 0.331689,	Top-5 err = 0.135596,	data_time = 0.050426,	train_time = 0.805461
[2019-08-24 16:04:36,551] TRAIN Iter 261120: lr = 0.064802,	loss = 2.451241,	Top-1 err = 0.336670,	Top-5 err = 0.137207,	data_time = 0.050618,	train_time = 0.336554
[2019-08-24 16:04:52,459] TRAIN Iter 261140: lr = 0.064768,	loss = 2.342068,	Top-1 err = 0.332275,	Top-5 err = 0.138672,	data_time = 0.050474,	train_time = 0.795398
[2019-08-24 16:05:07,724] TRAIN Iter 261160: lr = 0.064735,	loss = 2.355738,	Top-1 err = 0.336377,	Top-5 err = 0.141504,	data_time = 0.050226,	train_time = 0.763248
[2019-08-24 16:05:15,030] TRAIN Iter 261180: lr = 0.064702,	loss = 2.408140,	Top-1 err = 0.329590,	Top-5 err = 0.135303,	data_time = 0.050573,	train_time = 0.365250
[2019-08-24 16:05:32,609] TRAIN Iter 261200: lr = 0.064668,	loss = 2.311929,	Top-1 err = 0.333594,	Top-5 err = 0.137695,	data_time = 0.050730,	train_time = 0.878955
[2019-08-24 16:05:45,669] TRAIN Iter 261220: lr = 0.064635,	loss = 2.316319,	Top-1 err = 0.335400,	Top-5 err = 0.140527,	data_time = 0.050564,	train_time = 0.652956
[2019-08-24 16:05:56,716] TRAIN Iter 261240: lr = 0.064602,	loss = 2.310292,	Top-1 err = 0.332031,	Top-5 err = 0.138672,	data_time = 0.050861,	train_time = 0.552370
[2019-08-24 16:06:14,933] TRAIN Iter 261260: lr = 0.064568,	loss = 2.325427,	Top-1 err = 0.331592,	Top-5 err = 0.141797,	data_time = 0.050359,	train_time = 0.910831
[2019-08-24 16:06:21,666] TRAIN Iter 261280: lr = 0.064535,	loss = 2.425103,	Top-1 err = 0.334570,	Top-5 err = 0.140771,	data_time = 0.050308,	train_time = 0.336614
[2019-08-24 16:06:38,077] TRAIN Iter 261300: lr = 0.064502,	loss = 2.273223,	Top-1 err = 0.330811,	Top-5 err = 0.136035,	data_time = 0.050259,	train_time = 0.820535
[2019-08-24 16:06:54,346] TRAIN Iter 261320: lr = 0.064468,	loss = 2.297995,	Top-1 err = 0.333545,	Top-5 err = 0.139746,	data_time = 0.050555,	train_time = 0.813460
[2019-08-24 16:07:01,398] TRAIN Iter 261340: lr = 0.064435,	loss = 2.388023,	Top-1 err = 0.341650,	Top-5 err = 0.142383,	data_time = 0.050551,	train_time = 0.352566
[2019-08-24 16:07:18,511] TRAIN Iter 261360: lr = 0.064402,	loss = 2.345973,	Top-1 err = 0.332861,	Top-5 err = 0.137549,	data_time = 0.050456,	train_time = 0.855649
[2019-08-24 16:07:34,519] TRAIN Iter 261380: lr = 0.064368,	loss = 2.360614,	Top-1 err = 0.335596,	Top-5 err = 0.140283,	data_time = 0.271655,	train_time = 0.800387
[2019-08-24 16:07:47,655] TRAIN Iter 261400: lr = 0.064335,	loss = 2.383399,	Top-1 err = 0.333301,	Top-5 err = 0.138574,	data_time = 0.050474,	train_time = 0.656779
[2019-08-24 16:08:02,849] TRAIN Iter 261420: lr = 0.064302,	loss = 2.379251,	Top-1 err = 0.331689,	Top-5 err = 0.136621,	data_time = 0.133728,	train_time = 0.759694
[2019-08-24 16:08:09,765] TRAIN Iter 261440: lr = 0.064268,	loss = 2.339106,	Top-1 err = 0.329150,	Top-5 err = 0.137793,	data_time = 0.050512,	train_time = 0.345800
[2019-08-24 16:08:27,887] TRAIN Iter 261460: lr = 0.064235,	loss = 2.386522,	Top-1 err = 0.332471,	Top-5 err = 0.139990,	data_time = 0.050506,	train_time = 0.906072
[2019-08-24 16:08:45,860] TRAIN Iter 261480: lr = 0.064202,	loss = 2.407377,	Top-1 err = 0.339404,	Top-5 err = 0.141748,	data_time = 0.050477,	train_time = 0.898658
[2019-08-24 16:08:52,852] TRAIN Iter 261500: lr = 0.064168,	loss = 2.406681,	Top-1 err = 0.340771,	Top-5 err = 0.142236,	data_time = 0.050341,	train_time = 0.349579
[2019-08-24 16:09:10,707] TRAIN Iter 261520: lr = 0.064135,	loss = 2.355034,	Top-1 err = 0.337549,	Top-5 err = 0.141309,	data_time = 0.050572,	train_time = 0.892701
[2019-08-24 16:09:24,659] TRAIN Iter 261540: lr = 0.064102,	loss = 2.276762,	Top-1 err = 0.332813,	Top-5 err = 0.139063,	data_time = 0.148936,	train_time = 0.697590
[2019-08-24 16:09:34,985] TRAIN Iter 261560: lr = 0.064068,	loss = 2.343077,	Top-1 err = 0.339746,	Top-5 err = 0.144482,	data_time = 0.050050,	train_time = 0.516293
[2019-08-24 16:09:51,161] TRAIN Iter 261580: lr = 0.064035,	loss = 2.341040,	Top-1 err = 0.333643,	Top-5 err = 0.140576,	data_time = 0.049922,	train_time = 0.808789
[2019-08-24 16:09:57,187] TRAIN Iter 261600: lr = 0.064002,	loss = 2.264099,	Top-1 err = 0.331299,	Top-5 err = 0.139893,	data_time = 0.049978,	train_time = 0.301305
[2019-08-24 16:10:48,188] TRAIN Iter 261620: lr = 0.063968,	loss = 2.308525,	Top-1 err = 0.337393,	Top-5 err = 0.143732,	data_time = 0.050311,	train_time = 2.550024
[2019-08-24 16:11:03,921] TRAIN Iter 261640: lr = 0.063935,	loss = 2.324954,	Top-1 err = 0.328955,	Top-5 err = 0.136914,	data_time = 0.164432,	train_time = 0.786646
[2019-08-24 16:11:11,106] TRAIN Iter 261660: lr = 0.063902,	loss = 2.335857,	Top-1 err = 0.326758,	Top-5 err = 0.135107,	data_time = 0.050422,	train_time = 0.359204
[2019-08-24 16:11:25,333] TRAIN Iter 261680: lr = 0.063868,	loss = 2.308122,	Top-1 err = 0.325732,	Top-5 err = 0.135205,	data_time = 0.050241,	train_time = 0.711365
[2019-08-24 16:11:32,655] TRAIN Iter 261700: lr = 0.063835,	loss = 2.291344,	Top-1 err = 0.327588,	Top-5 err = 0.134082,	data_time = 0.050204,	train_time = 0.366075
[2019-08-24 16:11:46,533] TRAIN Iter 261720: lr = 0.063802,	loss = 2.323055,	Top-1 err = 0.320850,	Top-5 err = 0.131104,	data_time = 0.050423,	train_time = 0.693873
[2019-08-24 16:12:00,716] TRAIN Iter 261740: lr = 0.063768,	loss = 2.376050,	Top-1 err = 0.329541,	Top-5 err = 0.136621,	data_time = 0.050382,	train_time = 0.709129
[2019-08-24 16:12:07,629] TRAIN Iter 261760: lr = 0.063735,	loss = 2.348166,	Top-1 err = 0.329785,	Top-5 err = 0.135352,	data_time = 0.050170,	train_time = 0.345631
[2019-08-24 16:12:25,297] TRAIN Iter 261780: lr = 0.063702,	loss = 2.346208,	Top-1 err = 0.330273,	Top-5 err = 0.133594,	data_time = 0.050268,	train_time = 0.883428
[2019-08-24 16:12:39,773] TRAIN Iter 261800: lr = 0.063668,	loss = 2.309730,	Top-1 err = 0.326562,	Top-5 err = 0.133350,	data_time = 0.050627,	train_time = 0.723783
[2019-08-24 16:12:47,754] TRAIN Iter 261820: lr = 0.063635,	loss = 2.316359,	Top-1 err = 0.326025,	Top-5 err = 0.134082,	data_time = 0.050264,	train_time = 0.399015
[2019-08-24 16:13:03,997] TRAIN Iter 261840: lr = 0.063602,	loss = 2.357824,	Top-1 err = 0.330176,	Top-5 err = 0.136279,	data_time = 0.050506,	train_time = 0.812126
[2019-08-24 16:13:11,047] TRAIN Iter 261860: lr = 0.063568,	loss = 2.281873,	Top-1 err = 0.325098,	Top-5 err = 0.134961,	data_time = 0.050356,	train_time = 0.352487
[2019-08-24 16:13:25,643] TRAIN Iter 261880: lr = 0.063535,	loss = 2.400146,	Top-1 err = 0.334473,	Top-5 err = 0.142187,	data_time = 0.050486,	train_time = 0.729805
[2019-08-24 16:13:42,201] TRAIN Iter 261900: lr = 0.063502,	loss = 2.334216,	Top-1 err = 0.334326,	Top-5 err = 0.136377,	data_time = 0.050708,	train_time = 0.827894
[2019-08-24 16:13:48,854] TRAIN Iter 261920: lr = 0.063468,	loss = 2.347291,	Top-1 err = 0.326367,	Top-5 err = 0.134473,	data_time = 0.050301,	train_time = 0.332609
[2019-08-24 16:14:05,155] TRAIN Iter 261940: lr = 0.063435,	loss = 2.443278,	Top-1 err = 0.332568,	Top-5 err = 0.138623,	data_time = 0.050433,	train_time = 0.815022
[2019-08-24 16:14:18,155] TRAIN Iter 261960: lr = 0.063402,	loss = 2.329485,	Top-1 err = 0.331982,	Top-5 err = 0.133984,	data_time = 0.204081,	train_time = 0.649984
[2019-08-24 16:14:27,979] TRAIN Iter 261980: lr = 0.063368,	loss = 2.348497,	Top-1 err = 0.329834,	Top-5 err = 0.134424,	data_time = 0.050742,	train_time = 0.491200
[2019-08-24 16:14:44,110] TRAIN Iter 262000: lr = 0.063335,	loss = 2.348290,	Top-1 err = 0.333398,	Top-5 err = 0.137305,	data_time = 0.050329,	train_time = 0.806535
[2019-08-24 16:14:51,592] TRAIN Iter 262020: lr = 0.063302,	loss = 2.300522,	Top-1 err = 0.336816,	Top-5 err = 0.136426,	data_time = 0.050528,	train_time = 0.374099
[2019-08-24 16:15:06,251] TRAIN Iter 262040: lr = 0.063268,	loss = 2.346035,	Top-1 err = 0.331982,	Top-5 err = 0.136426,	data_time = 0.050392,	train_time = 0.732939
[2019-08-24 16:15:22,612] TRAIN Iter 262060: lr = 0.063235,	loss = 2.394732,	Top-1 err = 0.333936,	Top-5 err = 0.135352,	data_time = 0.050195,	train_time = 0.818028
[2019-08-24 16:15:29,583] TRAIN Iter 262080: lr = 0.063202,	loss = 2.341498,	Top-1 err = 0.325195,	Top-5 err = 0.137061,	data_time = 0.050341,	train_time = 0.348551
[2019-08-24 16:15:44,402] TRAIN Iter 262100: lr = 0.063168,	loss = 2.325782,	Top-1 err = 0.329199,	Top-5 err = 0.136133,	data_time = 0.050409,	train_time = 0.740907
[2019-08-24 16:16:01,046] TRAIN Iter 262120: lr = 0.063135,	loss = 2.304255,	Top-1 err = 0.331494,	Top-5 err = 0.137207,	data_time = 0.050536,	train_time = 0.832175
[2019-08-24 16:16:07,882] TRAIN Iter 262140: lr = 0.063102,	loss = 2.412152,	Top-1 err = 0.334229,	Top-5 err = 0.139453,	data_time = 0.050411,	train_time = 0.341829
[2019-08-24 16:16:23,760] TRAIN Iter 262160: lr = 0.063068,	loss = 2.396886,	Top-1 err = 0.335547,	Top-5 err = 0.140137,	data_time = 0.050689,	train_time = 0.793872
[2019-08-24 16:16:31,140] TRAIN Iter 262180: lr = 0.063035,	loss = 2.354751,	Top-1 err = 0.330908,	Top-5 err = 0.140186,	data_time = 0.050573,	train_time = 0.368958
[2019-08-24 16:16:47,300] TRAIN Iter 262200: lr = 0.063002,	loss = 2.356939,	Top-1 err = 0.333691,	Top-5 err = 0.137061,	data_time = 0.050471,	train_time = 0.807990
[2019-08-24 16:17:03,311] TRAIN Iter 262220: lr = 0.062968,	loss = 2.287480,	Top-1 err = 0.323828,	Top-5 err = 0.132422,	data_time = 0.050411,	train_time = 0.800536
[2019-08-24 16:17:10,071] TRAIN Iter 262240: lr = 0.062935,	loss = 2.362693,	Top-1 err = 0.329004,	Top-5 err = 0.135156,	data_time = 0.050350,	train_time = 0.338011
[2019-08-24 16:17:27,559] TRAIN Iter 262260: lr = 0.062902,	loss = 2.294666,	Top-1 err = 0.335156,	Top-5 err = 0.138184,	data_time = 0.050453,	train_time = 0.874381
[2019-08-24 16:17:42,331] TRAIN Iter 262280: lr = 0.062868,	loss = 2.330739,	Top-1 err = 0.328125,	Top-5 err = 0.136035,	data_time = 0.050462,	train_time = 0.738567
[2019-08-24 16:17:50,047] TRAIN Iter 262300: lr = 0.062835,	loss = 2.320418,	Top-1 err = 0.330859,	Top-5 err = 0.139160,	data_time = 0.132397,	train_time = 0.385800
[2019-08-24 16:18:05,288] TRAIN Iter 262320: lr = 0.062802,	loss = 2.371004,	Top-1 err = 0.332324,	Top-5 err = 0.138818,	data_time = 0.050540,	train_time = 0.762056
[2019-08-24 16:18:12,314] TRAIN Iter 262340: lr = 0.062768,	loss = 2.374828,	Top-1 err = 0.331494,	Top-5 err = 0.137207,	data_time = 0.050520,	train_time = 0.351258
[2019-08-24 16:18:29,251] TRAIN Iter 262360: lr = 0.062735,	loss = 2.254390,	Top-1 err = 0.332715,	Top-5 err = 0.136084,	data_time = 0.050399,	train_time = 0.846839
[2019-08-24 16:18:46,475] TRAIN Iter 262380: lr = 0.062702,	loss = 2.351874,	Top-1 err = 0.330322,	Top-5 err = 0.137402,	data_time = 0.050556,	train_time = 0.861171
[2019-08-24 16:18:53,434] TRAIN Iter 262400: lr = 0.062668,	loss = 2.444391,	Top-1 err = 0.332764,	Top-5 err = 0.135889,	data_time = 0.050509,	train_time = 0.347944
[2019-08-24 16:19:10,426] TRAIN Iter 262420: lr = 0.062635,	loss = 2.290075,	Top-1 err = 0.334619,	Top-5 err = 0.139990,	data_time = 0.050347,	train_time = 0.849601
[2019-08-24 16:19:27,776] TRAIN Iter 262440: lr = 0.062602,	loss = 2.336674,	Top-1 err = 0.327295,	Top-5 err = 0.134668,	data_time = 0.050372,	train_time = 0.867465
[2019-08-24 16:19:34,866] TRAIN Iter 262460: lr = 0.062568,	loss = 2.315199,	Top-1 err = 0.338330,	Top-5 err = 0.139990,	data_time = 0.050593,	train_time = 0.354502
[2019-08-24 16:19:51,375] TRAIN Iter 262480: lr = 0.062535,	loss = 2.349160,	Top-1 err = 0.334180,	Top-5 err = 0.133936,	data_time = 0.050356,	train_time = 0.825430
[2019-08-24 16:19:58,371] TRAIN Iter 262500: lr = 0.062502,	loss = 2.305502,	Top-1 err = 0.328418,	Top-5 err = 0.138232,	data_time = 0.050493,	train_time = 0.349788
[2019-08-24 16:20:15,915] TRAIN Iter 262520: lr = 0.062468,	loss = 2.384734,	Top-1 err = 0.328516,	Top-5 err = 0.138281,	data_time = 0.050575,	train_time = 0.877200
[2019-08-24 16:20:34,404] TRAIN Iter 262540: lr = 0.062435,	loss = 2.336446,	Top-1 err = 0.329883,	Top-5 err = 0.136768,	data_time = 0.050428,	train_time = 0.924412
[2019-08-24 16:20:41,079] TRAIN Iter 262560: lr = 0.062402,	loss = 2.325864,	Top-1 err = 0.332959,	Top-5 err = 0.135449,	data_time = 0.050564,	train_time = 0.333756
[2019-08-24 16:20:57,980] TRAIN Iter 262580: lr = 0.062368,	loss = 2.356834,	Top-1 err = 0.333154,	Top-5 err = 0.138818,	data_time = 0.050339,	train_time = 0.845047
[2019-08-24 16:21:13,324] TRAIN Iter 262600: lr = 0.062335,	loss = 2.392039,	Top-1 err = 0.333691,	Top-5 err = 0.139551,	data_time = 0.050761,	train_time = 0.767155
[2019-08-24 16:21:21,848] TRAIN Iter 262620: lr = 0.062302,	loss = 2.391465,	Top-1 err = 0.331299,	Top-5 err = 0.138428,	data_time = 0.050345,	train_time = 0.426193
[2019-08-24 16:21:40,795] TRAIN Iter 262640: lr = 0.062268,	loss = 2.347944,	Top-1 err = 0.334229,	Top-5 err = 0.138770,	data_time = 0.050550,	train_time = 0.947362
[2019-08-24 16:21:47,939] TRAIN Iter 262660: lr = 0.062235,	loss = 2.296192,	Top-1 err = 0.334033,	Top-5 err = 0.139551,	data_time = 0.050179,	train_time = 0.357164
[2019-08-24 16:22:05,765] TRAIN Iter 262680: lr = 0.062202,	loss = 2.383402,	Top-1 err = 0.332568,	Top-5 err = 0.138965,	data_time = 0.050562,	train_time = 0.891272
[2019-08-24 16:22:23,571] TRAIN Iter 262700: lr = 0.062168,	loss = 2.359897,	Top-1 err = 0.336572,	Top-5 err = 0.138965,	data_time = 0.050393,	train_time = 0.890316
[2019-08-24 16:22:30,199] TRAIN Iter 262720: lr = 0.062135,	loss = 2.329250,	Top-1 err = 0.332227,	Top-5 err = 0.140137,	data_time = 0.050477,	train_time = 0.331398
[2019-08-24 16:22:48,226] TRAIN Iter 262740: lr = 0.062102,	loss = 2.305277,	Top-1 err = 0.334570,	Top-5 err = 0.140332,	data_time = 0.050482,	train_time = 0.901290
[2019-08-24 16:23:04,446] TRAIN Iter 262760: lr = 0.062068,	loss = 2.363998,	Top-1 err = 0.334668,	Top-5 err = 0.141797,	data_time = 0.188961,	train_time = 0.811034
[2019-08-24 16:23:15,234] TRAIN Iter 262780: lr = 0.062035,	loss = 2.262128,	Top-1 err = 0.335400,	Top-5 err = 0.137793,	data_time = 0.050422,	train_time = 0.539371
[2019-08-24 16:23:33,210] TRAIN Iter 262800: lr = 0.062002,	loss = 2.288823,	Top-1 err = 0.330078,	Top-5 err = 0.136133,	data_time = 0.050015,	train_time = 0.898798
[2019-08-24 16:23:40,054] TRAIN Iter 262820: lr = 0.061968,	loss = 2.435683,	Top-1 err = 0.331250,	Top-5 err = 0.139160,	data_time = 0.050054,	train_time = 0.342158
[2019-08-24 16:23:57,426] TRAIN Iter 262840: lr = 0.061935,	loss = 2.347511,	Top-1 err = 0.337500,	Top-5 err = 0.139844,	data_time = 0.049994,	train_time = 0.868588
[2019-08-24 16:24:46,110] TRAIN Iter 262860: lr = 0.061902,	loss = 2.351499,	Top-1 err = 0.333377,	Top-5 err = 0.138601,	data_time = 0.051017,	train_time = 2.434188
[2019-08-24 16:24:53,545] TRAIN Iter 262880: lr = 0.061868,	loss = 2.329210,	Top-1 err = 0.333350,	Top-5 err = 0.136670,	data_time = 0.050346,	train_time = 0.371701
[2019-08-24 16:25:10,825] TRAIN Iter 262900: lr = 0.061835,	loss = 2.331686,	Top-1 err = 0.324561,	Top-5 err = 0.134375,	data_time = 0.050570,	train_time = 0.863981
[2019-08-24 16:25:17,543] TRAIN Iter 262920: lr = 0.061802,	loss = 2.298540,	Top-1 err = 0.323291,	Top-5 err = 0.132861,	data_time = 0.132673,	train_time = 0.335891
[2019-08-24 16:25:35,148] TRAIN Iter 262940: lr = 0.061768,	loss = 2.354876,	Top-1 err = 0.332373,	Top-5 err = 0.134375,	data_time = 0.050657,	train_time = 0.880232
[2019-08-24 16:25:52,726] TRAIN Iter 262960: lr = 0.061735,	loss = 2.270174,	Top-1 err = 0.333301,	Top-5 err = 0.136279,	data_time = 0.050303,	train_time = 0.878909
[2019-08-24 16:26:00,016] TRAIN Iter 262980: lr = 0.061702,	loss = 2.241174,	Top-1 err = 0.321973,	Top-5 err = 0.134131,	data_time = 0.050755,	train_time = 0.364469
[2019-08-24 16:26:16,410] TRAIN Iter 263000: lr = 0.061668,	loss = 2.302486,	Top-1 err = 0.326953,	Top-5 err = 0.137988,	data_time = 0.050522,	train_time = 0.819705
[2019-08-24 16:26:30,801] TRAIN Iter 263020: lr = 0.061635,	loss = 2.326785,	Top-1 err = 0.328955,	Top-5 err = 0.134180,	data_time = 0.138299,	train_time = 0.719535
[2019-08-24 16:26:38,078] TRAIN Iter 263040: lr = 0.061602,	loss = 2.325796,	Top-1 err = 0.326074,	Top-5 err = 0.134375,	data_time = 0.050452,	train_time = 0.363840
[2019-08-24 16:26:54,304] TRAIN Iter 263060: lr = 0.061568,	loss = 2.328395,	Top-1 err = 0.327490,	Top-5 err = 0.132764,	data_time = 0.050455,	train_time = 0.811276
[2019-08-24 16:27:01,701] TRAIN Iter 263080: lr = 0.061535,	loss = 2.293865,	Top-1 err = 0.325684,	Top-5 err = 0.133545,	data_time = 0.050441,	train_time = 0.369822
[2019-08-24 16:27:16,818] TRAIN Iter 263100: lr = 0.061502,	loss = 2.360302,	Top-1 err = 0.327100,	Top-5 err = 0.135693,	data_time = 0.050409,	train_time = 0.755849
[2019-08-24 16:27:33,449] TRAIN Iter 263120: lr = 0.061468,	loss = 2.359043,	Top-1 err = 0.323975,	Top-5 err = 0.131982,	data_time = 0.050474,	train_time = 0.831521
[2019-08-24 16:27:40,466] TRAIN Iter 263140: lr = 0.061435,	loss = 2.370790,	Top-1 err = 0.326025,	Top-5 err = 0.132861,	data_time = 0.050553,	train_time = 0.350837
[2019-08-24 16:27:55,930] TRAIN Iter 263160: lr = 0.061402,	loss = 2.459822,	Top-1 err = 0.330176,	Top-5 err = 0.138135,	data_time = 0.050316,	train_time = 0.773210
[2019-08-24 16:28:11,962] TRAIN Iter 263180: lr = 0.061368,	loss = 2.360050,	Top-1 err = 0.327100,	Top-5 err = 0.133447,	data_time = 0.050363,	train_time = 0.801571
[2019-08-24 16:28:18,796] TRAIN Iter 263200: lr = 0.061335,	loss = 2.335993,	Top-1 err = 0.331396,	Top-5 err = 0.134375,	data_time = 0.050448,	train_time = 0.341666
[2019-08-24 16:28:36,501] TRAIN Iter 263220: lr = 0.061302,	loss = 2.335010,	Top-1 err = 0.332227,	Top-5 err = 0.131396,	data_time = 0.050842,	train_time = 0.885269
[2019-08-24 16:28:43,804] TRAIN Iter 263240: lr = 0.061268,	loss = 2.316619,	Top-1 err = 0.323535,	Top-5 err = 0.132031,	data_time = 0.050490,	train_time = 0.365094
[2019-08-24 16:29:00,922] TRAIN Iter 263260: lr = 0.061235,	loss = 2.335152,	Top-1 err = 0.328564,	Top-5 err = 0.137354,	data_time = 0.050468,	train_time = 0.855905
[2019-08-24 16:29:18,397] TRAIN Iter 263280: lr = 0.061202,	loss = 2.388705,	Top-1 err = 0.324072,	Top-5 err = 0.133789,	data_time = 0.050864,	train_time = 0.873731
[2019-08-24 16:29:25,769] TRAIN Iter 263300: lr = 0.061168,	loss = 2.410542,	Top-1 err = 0.325488,	Top-5 err = 0.135791,	data_time = 0.050497,	train_time = 0.368576
[2019-08-24 16:29:41,069] TRAIN Iter 263320: lr = 0.061135,	loss = 2.255844,	Top-1 err = 0.329541,	Top-5 err = 0.134619,	data_time = 0.050542,	train_time = 0.765013
[2019-08-24 16:29:57,043] TRAIN Iter 263340: lr = 0.061102,	loss = 2.398788,	Top-1 err = 0.329492,	Top-5 err = 0.137012,	data_time = 0.050674,	train_time = 0.798673
[2019-08-24 16:30:03,909] TRAIN Iter 263360: lr = 0.061068,	loss = 2.277669,	Top-1 err = 0.334717,	Top-5 err = 0.137451,	data_time = 0.050971,	train_time = 0.343306
[2019-08-24 16:30:20,015] TRAIN Iter 263380: lr = 0.061035,	loss = 2.343813,	Top-1 err = 0.332373,	Top-5 err = 0.136572,	data_time = 0.050526,	train_time = 0.805274
[2019-08-24 16:30:27,688] TRAIN Iter 263400: lr = 0.061002,	loss = 2.331674,	Top-1 err = 0.328320,	Top-5 err = 0.136914,	data_time = 0.050144,	train_time = 0.383617
[2019-08-24 16:30:42,975] TRAIN Iter 263420: lr = 0.060968,	loss = 2.330427,	Top-1 err = 0.327148,	Top-5 err = 0.136475,	data_time = 0.050369,	train_time = 0.764340
[2019-08-24 16:31:00,343] TRAIN Iter 263440: lr = 0.060935,	loss = 2.340767,	Top-1 err = 0.328125,	Top-5 err = 0.133740,	data_time = 0.050544,	train_time = 0.868399
[2019-08-24 16:31:07,222] TRAIN Iter 263460: lr = 0.060902,	loss = 2.339081,	Top-1 err = 0.333594,	Top-5 err = 0.138965,	data_time = 0.050414,	train_time = 0.343935
[2019-08-24 16:31:26,026] TRAIN Iter 263480: lr = 0.060868,	loss = 2.316077,	Top-1 err = 0.332959,	Top-5 err = 0.139990,	data_time = 0.050461,	train_time = 0.940185
[2019-08-24 16:31:41,941] TRAIN Iter 263500: lr = 0.060835,	loss = 2.385102,	Top-1 err = 0.332422,	Top-5 err = 0.140625,	data_time = 0.050320,	train_time = 0.795737
[2019-08-24 16:31:48,698] TRAIN Iter 263520: lr = 0.060802,	loss = 2.253439,	Top-1 err = 0.327881,	Top-5 err = 0.136426,	data_time = 0.050767,	train_time = 0.337844
[2019-08-24 16:32:04,566] TRAIN Iter 263540: lr = 0.060768,	loss = 2.401635,	Top-1 err = 0.326514,	Top-5 err = 0.135693,	data_time = 0.050478,	train_time = 0.793346
[2019-08-24 16:32:11,892] TRAIN Iter 263560: lr = 0.060735,	loss = 2.335527,	Top-1 err = 0.333691,	Top-5 err = 0.139941,	data_time = 0.150702,	train_time = 0.366291
[2019-08-24 16:32:27,985] TRAIN Iter 263580: lr = 0.060702,	loss = 2.339154,	Top-1 err = 0.328662,	Top-5 err = 0.134766,	data_time = 0.050735,	train_time = 0.804654
[2019-08-24 16:32:45,634] TRAIN Iter 263600: lr = 0.060668,	loss = 2.300399,	Top-1 err = 0.333643,	Top-5 err = 0.141162,	data_time = 0.050557,	train_time = 0.882409
[2019-08-24 16:32:52,830] TRAIN Iter 263620: lr = 0.060635,	loss = 2.354078,	Top-1 err = 0.333105,	Top-5 err = 0.139258,	data_time = 0.050746,	train_time = 0.359780
[2019-08-24 16:33:08,045] TRAIN Iter 263640: lr = 0.060602,	loss = 2.275885,	Top-1 err = 0.329395,	Top-5 err = 0.138135,	data_time = 0.050310,	train_time = 0.760738
[2019-08-24 16:33:25,531] TRAIN Iter 263660: lr = 0.060568,	loss = 2.423525,	Top-1 err = 0.336182,	Top-5 err = 0.140820,	data_time = 0.050592,	train_time = 0.874295
[2019-08-24 16:33:32,411] TRAIN Iter 263680: lr = 0.060535,	loss = 2.291253,	Top-1 err = 0.323828,	Top-5 err = 0.130908,	data_time = 0.050313,	train_time = 0.343996
[2019-08-24 16:33:48,816] TRAIN Iter 263700: lr = 0.060502,	loss = 2.282728,	Top-1 err = 0.330811,	Top-5 err = 0.136035,	data_time = 0.050708,	train_time = 0.820238
[2019-08-24 16:33:56,401] TRAIN Iter 263720: lr = 0.060468,	loss = 2.356291,	Top-1 err = 0.340283,	Top-5 err = 0.140234,	data_time = 0.050667,	train_time = 0.379228
[2019-08-24 16:34:11,644] TRAIN Iter 263740: lr = 0.060435,	loss = 2.383543,	Top-1 err = 0.330811,	Top-5 err = 0.136914,	data_time = 0.050524,	train_time = 0.762148
[2019-08-24 16:34:28,106] TRAIN Iter 263760: lr = 0.060402,	loss = 2.358127,	Top-1 err = 0.327002,	Top-5 err = 0.136328,	data_time = 0.050366,	train_time = 0.823057
[2019-08-24 16:34:35,152] TRAIN Iter 263780: lr = 0.060368,	loss = 2.322433,	Top-1 err = 0.332520,	Top-5 err = 0.140332,	data_time = 0.050503,	train_time = 0.352292
[2019-08-24 16:34:52,239] TRAIN Iter 263800: lr = 0.060335,	loss = 2.303757,	Top-1 err = 0.329688,	Top-5 err = 0.138770,	data_time = 0.050610,	train_time = 0.854350
[2019-08-24 16:35:07,493] TRAIN Iter 263820: lr = 0.060302,	loss = 2.381199,	Top-1 err = 0.329639,	Top-5 err = 0.137305,	data_time = 0.104173,	train_time = 0.762651
[2019-08-24 16:35:14,578] TRAIN Iter 263840: lr = 0.060268,	loss = 2.403396,	Top-1 err = 0.332373,	Top-5 err = 0.136621,	data_time = 0.050370,	train_time = 0.354242
[2019-08-24 16:35:30,379] TRAIN Iter 263860: lr = 0.060235,	loss = 2.382000,	Top-1 err = 0.332617,	Top-5 err = 0.137305,	data_time = 0.050170,	train_time = 0.790034
[2019-08-24 16:35:37,327] TRAIN Iter 263880: lr = 0.060202,	loss = 2.423246,	Top-1 err = 0.333203,	Top-5 err = 0.138281,	data_time = 0.050973,	train_time = 0.347397
[2019-08-24 16:35:53,800] TRAIN Iter 263900: lr = 0.060168,	loss = 2.375717,	Top-1 err = 0.327734,	Top-5 err = 0.138525,	data_time = 0.050365,	train_time = 0.823667
[2019-08-24 16:36:09,887] TRAIN Iter 263920: lr = 0.060135,	loss = 2.335096,	Top-1 err = 0.331738,	Top-5 err = 0.139404,	data_time = 0.158998,	train_time = 0.804330
[2019-08-24 16:36:16,920] TRAIN Iter 263940: lr = 0.060102,	loss = 2.341221,	Top-1 err = 0.336182,	Top-5 err = 0.137598,	data_time = 0.050354,	train_time = 0.351619
[2019-08-24 16:36:33,744] TRAIN Iter 263960: lr = 0.060068,	loss = 2.357581,	Top-1 err = 0.332227,	Top-5 err = 0.136816,	data_time = 0.050573,	train_time = 0.841173
[2019-08-24 16:36:49,691] TRAIN Iter 263980: lr = 0.060035,	loss = 2.346387,	Top-1 err = 0.327930,	Top-5 err = 0.135303,	data_time = 0.091295,	train_time = 0.797356
[2019-08-24 16:36:57,699] TRAIN Iter 264000: lr = 0.060002,	loss = 2.352770,	Top-1 err = 0.334131,	Top-5 err = 0.140234,	data_time = 0.050168,	train_time = 0.400369
[2019-08-24 16:37:16,662] TRAIN Iter 264020: lr = 0.059968,	loss = 2.389335,	Top-1 err = 0.333789,	Top-5 err = 0.138428,	data_time = 0.050450,	train_time = 0.948127
[2019-08-24 16:37:23,287] TRAIN Iter 264040: lr = 0.059935,	loss = 2.286185,	Top-1 err = 0.325732,	Top-5 err = 0.135303,	data_time = 0.050693,	train_time = 0.331276
[2019-08-24 16:37:42,232] TRAIN Iter 264060: lr = 0.059902,	loss = 2.350616,	Top-1 err = 0.335791,	Top-5 err = 0.140283,	data_time = 0.050022,	train_time = 0.947190
[2019-08-24 16:37:57,743] TRAIN Iter 264080: lr = 0.059868,	loss = 2.379651,	Top-1 err = 0.333643,	Top-5 err = 0.137402,	data_time = 0.049890,	train_time = 0.775570
[2019-08-24 16:38:04,337] TRAIN Iter 264100: lr = 0.059835,	loss = 2.325875,	Top-1 err = 0.328516,	Top-5 err = 0.135400,	data_time = 0.050033,	train_time = 0.329675
[2019-08-24 16:38:55,948] TRAIN Iter 264120: lr = 0.059802,	loss = 2.298293,	Top-1 err = 0.335938,	Top-5 err = 0.138309,	data_time = 0.050542,	train_time = 2.580550
[2019-08-24 16:39:02,940] TRAIN Iter 264140: lr = 0.059768,	loss = 2.334252,	Top-1 err = 0.330859,	Top-5 err = 0.137500,	data_time = 0.051089,	train_time = 0.349546
[2019-08-24 16:39:22,095] TRAIN Iter 264160: lr = 0.059735,	loss = 2.293527,	Top-1 err = 0.326562,	Top-5 err = 0.134473,	data_time = 0.050370,	train_time = 0.957743
[2019-08-24 16:39:34,715] TRAIN Iter 264180: lr = 0.059702,	loss = 2.297644,	Top-1 err = 0.327930,	Top-5 err = 0.135205,	data_time = 0.050450,	train_time = 0.630984
[2019-08-24 16:39:41,467] TRAIN Iter 264200: lr = 0.059668,	loss = 2.244903,	Top-1 err = 0.320898,	Top-5 err = 0.127490,	data_time = 0.130030,	train_time = 0.337619
[2019-08-24 16:39:55,349] TRAIN Iter 264220: lr = 0.059635,	loss = 2.320556,	Top-1 err = 0.326514,	Top-5 err = 0.134863,	data_time = 0.050789,	train_time = 0.694059
[2019-08-24 16:40:09,746] TRAIN Iter 264240: lr = 0.059602,	loss = 2.306400,	Top-1 err = 0.324219,	Top-5 err = 0.133350,	data_time = 0.050463,	train_time = 0.719854
[2019-08-24 16:40:17,245] TRAIN Iter 264260: lr = 0.059568,	loss = 2.278900,	Top-1 err = 0.327100,	Top-5 err = 0.137402,	data_time = 0.050310,	train_time = 0.374921
[2019-08-24 16:40:32,649] TRAIN Iter 264280: lr = 0.059535,	loss = 2.317060,	Top-1 err = 0.324121,	Top-5 err = 0.135352,	data_time = 0.050765,	train_time = 0.770213
[2019-08-24 16:40:39,295] TRAIN Iter 264300: lr = 0.059502,	loss = 2.280643,	Top-1 err = 0.327441,	Top-5 err = 0.135937,	data_time = 0.050428,	train_time = 0.332257
[2019-08-24 16:40:54,644] TRAIN Iter 264320: lr = 0.059468,	loss = 2.290143,	Top-1 err = 0.335107,	Top-5 err = 0.138818,	data_time = 0.050514,	train_time = 0.767429
[2019-08-24 16:41:11,049] TRAIN Iter 264340: lr = 0.059435,	loss = 2.342007,	Top-1 err = 0.329590,	Top-5 err = 0.136768,	data_time = 0.139407,	train_time = 0.820238
[2019-08-24 16:41:17,990] TRAIN Iter 264360: lr = 0.059402,	loss = 2.319713,	Top-1 err = 0.328027,	Top-5 err = 0.134326,	data_time = 0.050327,	train_time = 0.347039
[2019-08-24 16:41:34,948] TRAIN Iter 264380: lr = 0.059368,	loss = 2.380185,	Top-1 err = 0.329834,	Top-5 err = 0.135400,	data_time = 0.050239,	train_time = 0.847913
[2019-08-24 16:41:53,388] TRAIN Iter 264400: lr = 0.059335,	loss = 2.358163,	Top-1 err = 0.326025,	Top-5 err = 0.128711,	data_time = 0.050324,	train_time = 0.921960
[2019-08-24 16:42:00,000] TRAIN Iter 264420: lr = 0.059302,	loss = 2.355938,	Top-1 err = 0.328223,	Top-5 err = 0.133984,	data_time = 0.050258,	train_time = 0.330618
[2019-08-24 16:42:16,006] TRAIN Iter 264440: lr = 0.059268,	loss = 2.368374,	Top-1 err = 0.328467,	Top-5 err = 0.130615,	data_time = 0.050490,	train_time = 0.800256
[2019-08-24 16:42:23,347] TRAIN Iter 264460: lr = 0.059235,	loss = 2.285820,	Top-1 err = 0.325293,	Top-5 err = 0.130469,	data_time = 0.121223,	train_time = 0.367040
[2019-08-24 16:42:37,293] TRAIN Iter 264480: lr = 0.059202,	loss = 2.391577,	Top-1 err = 0.327832,	Top-5 err = 0.137207,	data_time = 0.050625,	train_time = 0.697270
[2019-08-24 16:42:53,188] TRAIN Iter 264500: lr = 0.059168,	loss = 2.303173,	Top-1 err = 0.321826,	Top-5 err = 0.132275,	data_time = 0.050317,	train_time = 0.794742
[2019-08-24 16:42:59,668] TRAIN Iter 264520: lr = 0.059135,	loss = 2.281547,	Top-1 err = 0.324902,	Top-5 err = 0.129883,	data_time = 0.050347,	train_time = 0.323991
[2019-08-24 16:43:17,585] TRAIN Iter 264540: lr = 0.059102,	loss = 2.308957,	Top-1 err = 0.330518,	Top-5 err = 0.138721,	data_time = 0.050771,	train_time = 0.895846
[2019-08-24 16:43:34,314] TRAIN Iter 264560: lr = 0.059068,	loss = 2.313025,	Top-1 err = 0.326270,	Top-5 err = 0.134131,	data_time = 0.050526,	train_time = 0.836414
[2019-08-24 16:43:41,558] TRAIN Iter 264580: lr = 0.059035,	loss = 2.309160,	Top-1 err = 0.324072,	Top-5 err = 0.134082,	data_time = 0.050326,	train_time = 0.362203
[2019-08-24 16:43:57,683] TRAIN Iter 264600: lr = 0.059002,	loss = 2.347367,	Top-1 err = 0.330078,	Top-5 err = 0.137695,	data_time = 0.050224,	train_time = 0.806230
[2019-08-24 16:44:05,303] TRAIN Iter 264620: lr = 0.058968,	loss = 2.275221,	Top-1 err = 0.322217,	Top-5 err = 0.131494,	data_time = 0.050399,	train_time = 0.380961
[2019-08-24 16:44:19,753] TRAIN Iter 264640: lr = 0.058935,	loss = 2.313386,	Top-1 err = 0.331738,	Top-5 err = 0.136279,	data_time = 0.050545,	train_time = 0.722491
[2019-08-24 16:44:36,135] TRAIN Iter 264660: lr = 0.058902,	loss = 2.380820,	Top-1 err = 0.334521,	Top-5 err = 0.135303,	data_time = 0.050317,	train_time = 0.819129
[2019-08-24 16:44:42,859] TRAIN Iter 264680: lr = 0.058868,	loss = 2.373411,	Top-1 err = 0.328564,	Top-5 err = 0.136963,	data_time = 0.050313,	train_time = 0.336152
[2019-08-24 16:44:59,715] TRAIN Iter 264700: lr = 0.058835,	loss = 2.316646,	Top-1 err = 0.331592,	Top-5 err = 0.140186,	data_time = 0.050934,	train_time = 0.842783
[2019-08-24 16:45:14,964] TRAIN Iter 264720: lr = 0.058802,	loss = 2.344409,	Top-1 err = 0.328320,	Top-5 err = 0.136230,	data_time = 0.050410,	train_time = 0.762463
[2019-08-24 16:45:21,699] TRAIN Iter 264740: lr = 0.058768,	loss = 2.374639,	Top-1 err = 0.330957,	Top-5 err = 0.136621,	data_time = 0.050241,	train_time = 0.336738
[2019-08-24 16:45:37,378] TRAIN Iter 264760: lr = 0.058735,	loss = 2.324102,	Top-1 err = 0.329492,	Top-5 err = 0.136816,	data_time = 0.050840,	train_time = 0.783908
[2019-08-24 16:45:44,356] TRAIN Iter 264780: lr = 0.058702,	loss = 2.327920,	Top-1 err = 0.323047,	Top-5 err = 0.132373,	data_time = 0.050364,	train_time = 0.348882
[2019-08-24 16:46:00,535] TRAIN Iter 264800: lr = 0.058668,	loss = 2.267248,	Top-1 err = 0.331396,	Top-5 err = 0.138330,	data_time = 0.050602,	train_time = 0.808938
[2019-08-24 16:46:17,542] TRAIN Iter 264820: lr = 0.058635,	loss = 2.369370,	Top-1 err = 0.327197,	Top-5 err = 0.135693,	data_time = 0.050767,	train_time = 0.850341
[2019-08-24 16:46:24,133] TRAIN Iter 264840: lr = 0.058602,	loss = 2.361736,	Top-1 err = 0.333789,	Top-5 err = 0.137109,	data_time = 0.050355,	train_time = 0.329545
[2019-08-24 16:46:40,168] TRAIN Iter 264860: lr = 0.058568,	loss = 2.345739,	Top-1 err = 0.332031,	Top-5 err = 0.135498,	data_time = 0.050756,	train_time = 0.801716
[2019-08-24 16:46:57,096] TRAIN Iter 264880: lr = 0.058535,	loss = 2.376143,	Top-1 err = 0.328369,	Top-5 err = 0.135645,	data_time = 0.169421,	train_time = 0.846428
[2019-08-24 16:47:04,030] TRAIN Iter 264900: lr = 0.058502,	loss = 2.332174,	Top-1 err = 0.325146,	Top-5 err = 0.133691,	data_time = 0.050407,	train_time = 0.346666
[2019-08-24 16:47:21,022] TRAIN Iter 264920: lr = 0.058468,	loss = 2.319892,	Top-1 err = 0.324707,	Top-5 err = 0.131689,	data_time = 0.050318,	train_time = 0.849590
[2019-08-24 16:47:28,022] TRAIN Iter 264940: lr = 0.058435,	loss = 2.398205,	Top-1 err = 0.333691,	Top-5 err = 0.137207,	data_time = 0.050573,	train_time = 0.349984
[2019-08-24 16:47:44,612] TRAIN Iter 264960: lr = 0.058402,	loss = 2.335307,	Top-1 err = 0.330811,	Top-5 err = 0.136084,	data_time = 0.050487,	train_time = 0.829463
[2019-08-24 16:48:01,594] TRAIN Iter 264980: lr = 0.058368,	loss = 2.278642,	Top-1 err = 0.330371,	Top-5 err = 0.132764,	data_time = 0.050619,	train_time = 0.849121
[2019-08-24 16:48:08,401] TRAIN Iter 265000: lr = 0.058335,	loss = 2.309597,	Top-1 err = 0.328613,	Top-5 err = 0.139160,	data_time = 0.050817,	train_time = 0.340313
[2019-08-24 16:48:26,755] TRAIN Iter 265020: lr = 0.058302,	loss = 2.388360,	Top-1 err = 0.331641,	Top-5 err = 0.138477,	data_time = 0.050487,	train_time = 0.917702
[2019-08-24 16:48:43,745] TRAIN Iter 265040: lr = 0.058268,	loss = 2.353277,	Top-1 err = 0.327832,	Top-5 err = 0.134570,	data_time = 0.050653,	train_time = 0.849454
[2019-08-24 16:48:50,693] TRAIN Iter 265060: lr = 0.058235,	loss = 2.418099,	Top-1 err = 0.328271,	Top-5 err = 0.134570,	data_time = 0.050524,	train_time = 0.347430
[2019-08-24 16:49:06,909] TRAIN Iter 265080: lr = 0.058202,	loss = 2.222756,	Top-1 err = 0.327490,	Top-5 err = 0.137012,	data_time = 0.050361,	train_time = 0.810760
[2019-08-24 16:49:13,994] TRAIN Iter 265100: lr = 0.058168,	loss = 2.368159,	Top-1 err = 0.329004,	Top-5 err = 0.136279,	data_time = 0.050352,	train_time = 0.354235
[2019-08-24 16:49:30,652] TRAIN Iter 265120: lr = 0.058135,	loss = 2.396545,	Top-1 err = 0.329346,	Top-5 err = 0.136719,	data_time = 0.050581,	train_time = 0.832868
[2019-08-24 16:49:49,046] TRAIN Iter 265140: lr = 0.058102,	loss = 2.387416,	Top-1 err = 0.330469,	Top-5 err = 0.140186,	data_time = 0.050831,	train_time = 0.919700
[2019-08-24 16:49:55,976] TRAIN Iter 265160: lr = 0.058068,	loss = 2.367315,	Top-1 err = 0.329248,	Top-5 err = 0.137158,	data_time = 0.050373,	train_time = 0.346473
[2019-08-24 16:50:13,737] TRAIN Iter 265180: lr = 0.058035,	loss = 2.414574,	Top-1 err = 0.327881,	Top-5 err = 0.133252,	data_time = 0.050627,	train_time = 0.888043
[2019-08-24 16:50:30,371] TRAIN Iter 265200: lr = 0.058002,	loss = 2.385210,	Top-1 err = 0.329980,	Top-5 err = 0.135840,	data_time = 0.050576,	train_time = 0.831698
[2019-08-24 16:50:36,973] TRAIN Iter 265220: lr = 0.057968,	loss = 2.385293,	Top-1 err = 0.326416,	Top-5 err = 0.137646,	data_time = 0.050671,	train_time = 0.330082
[2019-08-24 16:50:53,727] TRAIN Iter 265240: lr = 0.057935,	loss = 2.367080,	Top-1 err = 0.330957,	Top-5 err = 0.135693,	data_time = 0.050322,	train_time = 0.837680
[2019-08-24 16:51:00,700] TRAIN Iter 265260: lr = 0.057902,	loss = 2.319321,	Top-1 err = 0.332275,	Top-5 err = 0.133398,	data_time = 0.050461,	train_time = 0.348642
[2019-08-24 16:51:17,825] TRAIN Iter 265280: lr = 0.057868,	loss = 2.312297,	Top-1 err = 0.335303,	Top-5 err = 0.141455,	data_time = 0.050254,	train_time = 0.856254
[2019-08-24 16:51:36,309] TRAIN Iter 265300: lr = 0.057835,	loss = 2.331644,	Top-1 err = 0.337061,	Top-5 err = 0.137354,	data_time = 0.050385,	train_time = 0.924193
[2019-08-24 16:51:42,949] TRAIN Iter 265320: lr = 0.057802,	loss = 2.397705,	Top-1 err = 0.335693,	Top-5 err = 0.140674,	data_time = 0.149114,	train_time = 0.331977
[2019-08-24 16:51:59,465] TRAIN Iter 265340: lr = 0.057768,	loss = 2.290353,	Top-1 err = 0.334229,	Top-5 err = 0.137500,	data_time = 0.049965,	train_time = 0.825760
[2019-08-24 16:52:08,494] TRAIN Iter 265360: lr = 0.057735,	loss = 2.778295,	Top-1 err = 0.333235,	Top-5 err = 0.134767,	data_time = 0.007100,	train_time = 0.451448
[2019-08-24 16:52:55,542] TRAIN Iter 265380: lr = 0.057702,	loss = 2.443563,	Top-1 err = 0.328027,	Top-5 err = 0.135742,	data_time = 0.050392,	train_time = 2.352379
[2019-08-24 16:53:12,939] TRAIN Iter 265400: lr = 0.057668,	loss = 2.254688,	Top-1 err = 0.321582,	Top-5 err = 0.131641,	data_time = 0.050493,	train_time = 0.869868
[2019-08-24 16:53:20,334] TRAIN Iter 265420: lr = 0.057635,	loss = 2.273260,	Top-1 err = 0.316504,	Top-5 err = 0.129102,	data_time = 0.050527,	train_time = 0.369705
[2019-08-24 16:53:33,628] TRAIN Iter 265440: lr = 0.057602,	loss = 2.314351,	Top-1 err = 0.323633,	Top-5 err = 0.131641,	data_time = 0.050671,	train_time = 0.664694
[2019-08-24 16:53:48,381] TRAIN Iter 265460: lr = 0.057568,	loss = 2.248128,	Top-1 err = 0.319873,	Top-5 err = 0.129639,	data_time = 5.300581,	train_time = 0.737662
[2019-08-24 16:53:55,803] TRAIN Iter 265480: lr = 0.057535,	loss = 2.265273,	Top-1 err = 0.327979,	Top-5 err = 0.136182,	data_time = 0.111289,	train_time = 0.371064
[2019-08-24 16:54:11,267] TRAIN Iter 265500: lr = 0.057502,	loss = 2.369457,	Top-1 err = 0.326465,	Top-5 err = 0.135693,	data_time = 0.050648,	train_time = 0.773204
[2019-08-24 16:54:18,746] TRAIN Iter 265520: lr = 0.057468,	loss = 2.324922,	Top-1 err = 0.320801,	Top-5 err = 0.133984,	data_time = 0.050301,	train_time = 0.373916
[2019-08-24 16:54:32,810] TRAIN Iter 265540: lr = 0.057435,	loss = 2.313426,	Top-1 err = 0.327197,	Top-5 err = 0.136230,	data_time = 0.050246,	train_time = 0.703165
[2019-08-24 16:54:48,878] TRAIN Iter 265560: lr = 0.057402,	loss = 2.268525,	Top-1 err = 0.324268,	Top-5 err = 0.132178,	data_time = 0.050287,	train_time = 0.803402
[2019-08-24 16:54:55,967] TRAIN Iter 265580: lr = 0.057368,	loss = 2.334816,	Top-1 err = 0.327588,	Top-5 err = 0.134131,	data_time = 0.050615,	train_time = 0.354459
[2019-08-24 16:55:12,304] TRAIN Iter 265600: lr = 0.057335,	loss = 2.307074,	Top-1 err = 0.331543,	Top-5 err = 0.137012,	data_time = 0.050447,	train_time = 0.816830
[2019-08-24 16:55:27,655] TRAIN Iter 265620: lr = 0.057302,	loss = 2.322099,	Top-1 err = 0.331592,	Top-5 err = 0.135449,	data_time = 6.989873,	train_time = 0.767517
[2019-08-24 16:55:34,361] TRAIN Iter 265640: lr = 0.057268,	loss = 2.333478,	Top-1 err = 0.327783,	Top-5 err = 0.135742,	data_time = 0.050337,	train_time = 0.335313
[2019-08-24 16:55:49,785] TRAIN Iter 265660: lr = 0.057235,	loss = 2.319447,	Top-1 err = 0.321680,	Top-5 err = 0.132275,	data_time = 0.050369,	train_time = 0.771162
[2019-08-24 16:55:57,473] TRAIN Iter 265680: lr = 0.057202,	loss = 2.349933,	Top-1 err = 0.324463,	Top-5 err = 0.134521,	data_time = 0.050676,	train_time = 0.384409
[2019-08-24 16:56:11,525] TRAIN Iter 265700: lr = 0.057168,	loss = 2.332531,	Top-1 err = 0.331543,	Top-5 err = 0.137158,	data_time = 0.050429,	train_time = 0.702560
[2019-08-24 16:56:28,211] TRAIN Iter 265720: lr = 0.057135,	loss = 2.320096,	Top-1 err = 0.329102,	Top-5 err = 0.135840,	data_time = 0.050326,	train_time = 0.834298
[2019-08-24 16:56:35,059] TRAIN Iter 265740: lr = 0.057102,	loss = 2.324411,	Top-1 err = 0.325293,	Top-5 err = 0.133398,	data_time = 0.050343,	train_time = 0.342381
[2019-08-24 16:56:52,136] TRAIN Iter 265760: lr = 0.057068,	loss = 2.414475,	Top-1 err = 0.323535,	Top-5 err = 0.131494,	data_time = 0.050423,	train_time = 0.853856
[2019-08-24 16:57:06,610] TRAIN Iter 265780: lr = 0.057035,	loss = 2.344410,	Top-1 err = 0.330029,	Top-5 err = 0.135791,	data_time = 5.892037,	train_time = 0.723667
[2019-08-24 16:57:13,438] TRAIN Iter 265800: lr = 0.057002,	loss = 2.290069,	Top-1 err = 0.327246,	Top-5 err = 0.133594,	data_time = 0.125770,	train_time = 0.341397
[2019-08-24 16:57:28,040] TRAIN Iter 265820: lr = 0.056968,	loss = 2.340515,	Top-1 err = 0.326709,	Top-5 err = 0.131201,	data_time = 0.050291,	train_time = 0.730098
[2019-08-24 16:57:35,412] TRAIN Iter 265840: lr = 0.056935,	loss = 2.364825,	Top-1 err = 0.325195,	Top-5 err = 0.132812,	data_time = 0.050524,	train_time = 0.368546
[2019-08-24 16:57:49,964] TRAIN Iter 265860: lr = 0.056902,	loss = 2.344646,	Top-1 err = 0.327686,	Top-5 err = 0.133496,	data_time = 0.050491,	train_time = 0.727612
[2019-08-24 16:58:06,012] TRAIN Iter 265880: lr = 0.056868,	loss = 2.420603,	Top-1 err = 0.329297,	Top-5 err = 0.134668,	data_time = 0.050662,	train_time = 0.802365
[2019-08-24 16:58:13,137] TRAIN Iter 265900: lr = 0.056835,	loss = 2.310193,	Top-1 err = 0.326562,	Top-5 err = 0.135498,	data_time = 0.050735,	train_time = 0.356223
[2019-08-24 16:58:29,084] TRAIN Iter 265920: lr = 0.056802,	loss = 2.328880,	Top-1 err = 0.326660,	Top-5 err = 0.132520,	data_time = 0.050282,	train_time = 0.797381
[2019-08-24 16:58:44,894] TRAIN Iter 265940: lr = 0.056768,	loss = 2.306442,	Top-1 err = 0.329932,	Top-5 err = 0.135156,	data_time = 3.016453,	train_time = 0.790469
[2019-08-24 16:58:52,033] TRAIN Iter 265960: lr = 0.056735,	loss = 2.364893,	Top-1 err = 0.326758,	Top-5 err = 0.136865,	data_time = 0.050863,	train_time = 0.356941
[2019-08-24 16:59:07,655] TRAIN Iter 265980: lr = 0.056702,	loss = 2.253061,	Top-1 err = 0.325146,	Top-5 err = 0.134717,	data_time = 0.050266,	train_time = 0.781090
[2019-08-24 16:59:15,429] TRAIN Iter 266000: lr = 0.056668,	loss = 2.382041,	Top-1 err = 0.325635,	Top-5 err = 0.131885,	data_time = 0.050856,	train_time = 0.388660
[2019-08-24 16:59:29,887] TRAIN Iter 266020: lr = 0.056635,	loss = 2.441925,	Top-1 err = 0.326807,	Top-5 err = 0.136768,	data_time = 0.050424,	train_time = 0.722877
[2019-08-24 16:59:46,495] TRAIN Iter 266040: lr = 0.056602,	loss = 2.321689,	Top-1 err = 0.333301,	Top-5 err = 0.139111,	data_time = 0.050451,	train_time = 0.830395
[2019-08-24 16:59:53,872] TRAIN Iter 266060: lr = 0.056568,	loss = 2.363012,	Top-1 err = 0.327686,	Top-5 err = 0.133398,	data_time = 0.050465,	train_time = 0.368828
[2019-08-24 17:00:09,694] TRAIN Iter 266080: lr = 0.056535,	loss = 2.346821,	Top-1 err = 0.326562,	Top-5 err = 0.133838,	data_time = 0.050358,	train_time = 0.791091
[2019-08-24 17:00:26,626] TRAIN Iter 266100: lr = 0.056502,	loss = 2.337468,	Top-1 err = 0.333545,	Top-5 err = 0.141309,	data_time = 1.869659,	train_time = 0.846602
[2019-08-24 17:00:33,726] TRAIN Iter 266120: lr = 0.056468,	loss = 2.362126,	Top-1 err = 0.330127,	Top-5 err = 0.134814,	data_time = 0.050783,	train_time = 0.354974
[2019-08-24 17:00:49,729] TRAIN Iter 266140: lr = 0.056435,	loss = 2.270836,	Top-1 err = 0.329688,	Top-5 err = 0.135400,	data_time = 0.050434,	train_time = 0.800123
[2019-08-24 17:00:57,603] TRAIN Iter 266160: lr = 0.056402,	loss = 2.359955,	Top-1 err = 0.328760,	Top-5 err = 0.133936,	data_time = 0.050817,	train_time = 0.393698
[2019-08-24 17:01:12,335] TRAIN Iter 266180: lr = 0.056368,	loss = 2.376059,	Top-1 err = 0.326758,	Top-5 err = 0.136914,	data_time = 0.050354,	train_time = 0.736570
[2019-08-24 17:01:28,897] TRAIN Iter 266200: lr = 0.056335,	loss = 2.353734,	Top-1 err = 0.327783,	Top-5 err = 0.132715,	data_time = 0.050553,	train_time = 0.828134
[2019-08-24 17:01:35,830] TRAIN Iter 266220: lr = 0.056302,	loss = 2.343484,	Top-1 err = 0.324219,	Top-5 err = 0.131104,	data_time = 0.050643,	train_time = 0.346613
[2019-08-24 17:01:52,728] TRAIN Iter 266240: lr = 0.056268,	loss = 2.301166,	Top-1 err = 0.326318,	Top-5 err = 0.136035,	data_time = 0.050353,	train_time = 0.844899
[2019-08-24 17:02:09,913] TRAIN Iter 266260: lr = 0.056235,	loss = 2.377259,	Top-1 err = 0.335449,	Top-5 err = 0.139209,	data_time = 0.690103,	train_time = 0.859247
[2019-08-24 17:02:16,403] TRAIN Iter 266280: lr = 0.056202,	loss = 2.362573,	Top-1 err = 0.331250,	Top-5 err = 0.138428,	data_time = 0.050605,	train_time = 0.324463
[2019-08-24 17:02:33,863] TRAIN Iter 266300: lr = 0.056168,	loss = 2.374834,	Top-1 err = 0.321777,	Top-5 err = 0.133008,	data_time = 0.050383,	train_time = 0.872970
[2019-08-24 17:02:40,802] TRAIN Iter 266320: lr = 0.056135,	loss = 2.281076,	Top-1 err = 0.331201,	Top-5 err = 0.136475,	data_time = 0.050159,	train_time = 0.346974
[2019-08-24 17:02:57,538] TRAIN Iter 266340: lr = 0.056102,	loss = 2.368445,	Top-1 err = 0.330469,	Top-5 err = 0.136035,	data_time = 0.050354,	train_time = 0.836781
[2019-08-24 17:03:14,651] TRAIN Iter 266360: lr = 0.056068,	loss = 2.286482,	Top-1 err = 0.322705,	Top-5 err = 0.137256,	data_time = 0.050183,	train_time = 0.855627
[2019-08-24 17:03:21,353] TRAIN Iter 266380: lr = 0.056035,	loss = 2.385792,	Top-1 err = 0.326172,	Top-5 err = 0.137012,	data_time = 0.050295,	train_time = 0.335102
[2019-08-24 17:03:40,937] TRAIN Iter 266400: lr = 0.056002,	loss = 2.357270,	Top-1 err = 0.329639,	Top-5 err = 0.132568,	data_time = 0.050295,	train_time = 0.979158
[2019-08-24 17:03:59,738] TRAIN Iter 266420: lr = 0.055968,	loss = 2.339004,	Top-1 err = 0.333105,	Top-5 err = 0.132715,	data_time = 4.447699,	train_time = 0.940044
[2019-08-24 17:04:06,485] TRAIN Iter 266440: lr = 0.055935,	loss = 2.422361,	Top-1 err = 0.335938,	Top-5 err = 0.137158,	data_time = 0.050552,	train_time = 0.337332
[2019-08-24 17:04:24,431] TRAIN Iter 266460: lr = 0.055902,	loss = 2.398210,	Top-1 err = 0.329004,	Top-5 err = 0.135693,	data_time = 0.050813,	train_time = 0.897309
[2019-08-24 17:04:31,332] TRAIN Iter 266480: lr = 0.055868,	loss = 2.389668,	Top-1 err = 0.330859,	Top-5 err = 0.137988,	data_time = 0.050367,	train_time = 0.344990
[2019-08-24 17:04:52,147] TRAIN Iter 266500: lr = 0.055835,	loss = 2.335413,	Top-1 err = 0.330908,	Top-5 err = 0.136523,	data_time = 0.050374,	train_time = 1.040740
[2019-08-24 17:05:11,922] TRAIN Iter 266520: lr = 0.055802,	loss = 2.347165,	Top-1 err = 0.327051,	Top-5 err = 0.135596,	data_time = 0.050158,	train_time = 0.988760
[2019-08-24 17:05:18,761] TRAIN Iter 266540: lr = 0.055768,	loss = 2.398864,	Top-1 err = 0.331396,	Top-5 err = 0.132910,	data_time = 0.050433,	train_time = 0.341943
[2019-08-24 17:05:37,318] TRAIN Iter 266560: lr = 0.055735,	loss = 2.381408,	Top-1 err = 0.332324,	Top-5 err = 0.139502,	data_time = 0.049971,	train_time = 0.927834
[2019-08-24 17:05:56,403] TRAIN Iter 266580: lr = 0.055702,	loss = 2.284521,	Top-1 err = 0.326025,	Top-5 err = 0.134912,	data_time = 2.907596,	train_time = 0.954232
[2019-08-24 17:06:02,610] TRAIN Iter 266600: lr = 0.055668,	loss = 2.370261,	Top-1 err = 0.332959,	Top-5 err = 0.138867,	data_time = 0.049946,	train_time = 0.310329
[2019-08-24 17:06:54,777] TRAIN Iter 266620: lr = 0.055635,	loss = 2.250405,	Top-1 err = 0.330690,	Top-5 err = 0.139486,	data_time = 0.050472,	train_time = 2.608355
[2019-08-24 17:07:02,320] TRAIN Iter 266640: lr = 0.055602,	loss = 2.326838,	Top-1 err = 0.328564,	Top-5 err = 0.137598,	data_time = 0.050349,	train_time = 0.377095
[2019-08-24 17:07:16,738] TRAIN Iter 266660: lr = 0.055568,	loss = 2.269181,	Top-1 err = 0.323047,	Top-5 err = 0.131445,	data_time = 0.050515,	train_time = 0.720899
[2019-08-24 17:07:29,298] TRAIN Iter 266680: lr = 0.055535,	loss = 2.259769,	Top-1 err = 0.321729,	Top-5 err = 0.134033,	data_time = 0.139014,	train_time = 0.628023
[2019-08-24 17:07:36,417] TRAIN Iter 266700: lr = 0.055502,	loss = 2.322746,	Top-1 err = 0.321631,	Top-5 err = 0.130518,	data_time = 0.050486,	train_time = 0.355897
[2019-08-24 17:07:54,238] TRAIN Iter 266720: lr = 0.055468,	loss = 2.295048,	Top-1 err = 0.315479,	Top-5 err = 0.126465,	data_time = 0.050530,	train_time = 0.891064
[2019-08-24 17:08:01,193] TRAIN Iter 266740: lr = 0.055435,	loss = 2.210079,	Top-1 err = 0.320068,	Top-5 err = 0.129004,	data_time = 0.050544,	train_time = 0.347733
[2019-08-24 17:08:15,761] TRAIN Iter 266760: lr = 0.055402,	loss = 2.370279,	Top-1 err = 0.322705,	Top-5 err = 0.132861,	data_time = 0.050791,	train_time = 0.728384
[2019-08-24 17:08:31,260] TRAIN Iter 266780: lr = 0.055368,	loss = 2.397232,	Top-1 err = 0.325293,	Top-5 err = 0.129590,	data_time = 0.050619,	train_time = 0.774949
[2019-08-24 17:08:38,232] TRAIN Iter 266800: lr = 0.055335,	loss = 2.308374,	Top-1 err = 0.321826,	Top-5 err = 0.133496,	data_time = 0.050360,	train_time = 0.348587
[2019-08-24 17:08:54,675] TRAIN Iter 266820: lr = 0.055302,	loss = 2.341021,	Top-1 err = 0.319434,	Top-5 err = 0.130859,	data_time = 0.050381,	train_time = 0.822123
[2019-08-24 17:09:10,815] TRAIN Iter 266840: lr = 0.055268,	loss = 2.241928,	Top-1 err = 0.320264,	Top-5 err = 0.132520,	data_time = 0.050333,	train_time = 0.806961
[2019-08-24 17:09:17,899] TRAIN Iter 266860: lr = 0.055235,	loss = 2.352587,	Top-1 err = 0.326123,	Top-5 err = 0.133154,	data_time = 0.050398,	train_time = 0.354181
[2019-08-24 17:09:34,043] TRAIN Iter 266880: lr = 0.055202,	loss = 2.342043,	Top-1 err = 0.328418,	Top-5 err = 0.135693,	data_time = 0.050474,	train_time = 0.807210
[2019-08-24 17:09:40,832] TRAIN Iter 266900: lr = 0.055168,	loss = 2.303613,	Top-1 err = 0.318750,	Top-5 err = 0.132275,	data_time = 0.050379,	train_time = 0.339407
[2019-08-24 17:09:58,450] TRAIN Iter 266920: lr = 0.055135,	loss = 2.422573,	Top-1 err = 0.322021,	Top-5 err = 0.134131,	data_time = 0.050759,	train_time = 0.880904
[2019-08-24 17:10:14,813] TRAIN Iter 266940: lr = 0.055102,	loss = 2.324578,	Top-1 err = 0.322070,	Top-5 err = 0.128857,	data_time = 0.051026,	train_time = 0.818147
[2019-08-24 17:10:21,475] TRAIN Iter 266960: lr = 0.055068,	loss = 2.245539,	Top-1 err = 0.317725,	Top-5 err = 0.127539,	data_time = 0.050716,	train_time = 0.333057
[2019-08-24 17:10:37,801] TRAIN Iter 266980: lr = 0.055035,	loss = 2.409147,	Top-1 err = 0.320703,	Top-5 err = 0.130762,	data_time = 0.050139,	train_time = 0.816327
[2019-08-24 17:10:52,548] TRAIN Iter 267000: lr = 0.055002,	loss = 2.301082,	Top-1 err = 0.325684,	Top-5 err = 0.135400,	data_time = 0.050472,	train_time = 0.737341
[2019-08-24 17:10:59,897] TRAIN Iter 267020: lr = 0.054968,	loss = 2.259425,	Top-1 err = 0.322510,	Top-5 err = 0.132959,	data_time = 0.050631,	train_time = 0.367426
[2019-08-24 17:11:13,336] TRAIN Iter 267040: lr = 0.054935,	loss = 2.316672,	Top-1 err = 0.321924,	Top-5 err = 0.131836,	data_time = 0.050511,	train_time = 0.671938
[2019-08-24 17:11:20,287] TRAIN Iter 267060: lr = 0.054902,	loss = 2.294212,	Top-1 err = 0.322559,	Top-5 err = 0.128906,	data_time = 0.050460,	train_time = 0.347536
[2019-08-24 17:11:37,600] TRAIN Iter 267080: lr = 0.054868,	loss = 2.387859,	Top-1 err = 0.321729,	Top-5 err = 0.132178,	data_time = 0.050416,	train_time = 0.865637
[2019-08-24 17:11:53,688] TRAIN Iter 267100: lr = 0.054835,	loss = 2.403172,	Top-1 err = 0.321875,	Top-5 err = 0.134180,	data_time = 0.050543,	train_time = 0.804360
[2019-08-24 17:12:00,506] TRAIN Iter 267120: lr = 0.054802,	loss = 2.370980,	Top-1 err = 0.327588,	Top-5 err = 0.133496,	data_time = 0.109537,	train_time = 0.340904
[2019-08-24 17:12:16,712] TRAIN Iter 267140: lr = 0.054768,	loss = 2.396499,	Top-1 err = 0.320557,	Top-5 err = 0.135205,	data_time = 0.050910,	train_time = 0.810275
[2019-08-24 17:12:33,379] TRAIN Iter 267160: lr = 0.054735,	loss = 2.398919,	Top-1 err = 0.323193,	Top-5 err = 0.132910,	data_time = 0.050340,	train_time = 0.833345
[2019-08-24 17:12:40,292] TRAIN Iter 267180: lr = 0.054702,	loss = 2.394765,	Top-1 err = 0.320850,	Top-5 err = 0.132275,	data_time = 0.050520,	train_time = 0.345618
[2019-08-24 17:12:56,057] TRAIN Iter 267200: lr = 0.054668,	loss = 2.302287,	Top-1 err = 0.324512,	Top-5 err = 0.135059,	data_time = 0.050821,	train_time = 0.788241
[2019-08-24 17:13:02,797] TRAIN Iter 267220: lr = 0.054635,	loss = 2.274901,	Top-1 err = 0.321240,	Top-5 err = 0.130713,	data_time = 0.050345,	train_time = 0.336996
[2019-08-24 17:13:19,281] TRAIN Iter 267240: lr = 0.054602,	loss = 2.283954,	Top-1 err = 0.327490,	Top-5 err = 0.134229,	data_time = 0.050307,	train_time = 0.824169
[2019-08-24 17:13:35,829] TRAIN Iter 267260: lr = 0.054568,	loss = 2.356772,	Top-1 err = 0.332178,	Top-5 err = 0.135547,	data_time = 0.050686,	train_time = 0.827395
[2019-08-24 17:13:42,314] TRAIN Iter 267280: lr = 0.054535,	loss = 2.366993,	Top-1 err = 0.328271,	Top-5 err = 0.133350,	data_time = 0.050542,	train_time = 0.324235
[2019-08-24 17:14:01,252] TRAIN Iter 267300: lr = 0.054502,	loss = 2.272079,	Top-1 err = 0.324951,	Top-5 err = 0.130908,	data_time = 0.050470,	train_time = 0.946888
[2019-08-24 17:14:17,907] TRAIN Iter 267320: lr = 0.054468,	loss = 2.267245,	Top-1 err = 0.320312,	Top-5 err = 0.132129,	data_time = 0.050436,	train_time = 0.832736
[2019-08-24 17:14:24,538] TRAIN Iter 267340: lr = 0.054435,	loss = 2.269305,	Top-1 err = 0.326270,	Top-5 err = 0.133691,	data_time = 0.050490,	train_time = 0.331524
[2019-08-24 17:14:41,904] TRAIN Iter 267360: lr = 0.054402,	loss = 2.449058,	Top-1 err = 0.330176,	Top-5 err = 0.136279,	data_time = 0.050391,	train_time = 0.868302
[2019-08-24 17:14:49,502] TRAIN Iter 267380: lr = 0.054368,	loss = 2.326807,	Top-1 err = 0.329199,	Top-5 err = 0.134229,	data_time = 0.050455,	train_time = 0.379850
[2019-08-24 17:15:04,804] TRAIN Iter 267400: lr = 0.054335,	loss = 2.407042,	Top-1 err = 0.330127,	Top-5 err = 0.137402,	data_time = 0.050467,	train_time = 0.765132
[2019-08-24 17:15:21,800] TRAIN Iter 267420: lr = 0.054302,	loss = 2.340718,	Top-1 err = 0.321729,	Top-5 err = 0.133838,	data_time = 0.050344,	train_time = 0.849749
[2019-08-24 17:15:28,726] TRAIN Iter 267440: lr = 0.054268,	loss = 2.393746,	Top-1 err = 0.325439,	Top-5 err = 0.135889,	data_time = 0.050679,	train_time = 0.346289
[2019-08-24 17:15:45,026] TRAIN Iter 267460: lr = 0.054235,	loss = 2.291407,	Top-1 err = 0.329346,	Top-5 err = 0.137598,	data_time = 0.050321,	train_time = 0.815000
[2019-08-24 17:16:02,873] TRAIN Iter 267480: lr = 0.054202,	loss = 2.306929,	Top-1 err = 0.327881,	Top-5 err = 0.135498,	data_time = 0.050176,	train_time = 0.892329
[2019-08-24 17:16:09,734] TRAIN Iter 267500: lr = 0.054168,	loss = 2.372470,	Top-1 err = 0.331641,	Top-5 err = 0.137549,	data_time = 0.050399,	train_time = 0.343040
[2019-08-24 17:16:25,387] TRAIN Iter 267520: lr = 0.054135,	loss = 2.342096,	Top-1 err = 0.320654,	Top-5 err = 0.129980,	data_time = 0.050487,	train_time = 0.782621
[2019-08-24 17:16:32,047] TRAIN Iter 267540: lr = 0.054102,	loss = 2.318151,	Top-1 err = 0.331396,	Top-5 err = 0.134863,	data_time = 0.050699,	train_time = 0.333017
[2019-08-24 17:16:51,417] TRAIN Iter 267560: lr = 0.054068,	loss = 2.329716,	Top-1 err = 0.331006,	Top-5 err = 0.136523,	data_time = 0.050375,	train_time = 0.968486
[2019-08-24 17:17:08,302] TRAIN Iter 267580: lr = 0.054035,	loss = 2.278457,	Top-1 err = 0.332910,	Top-5 err = 0.137451,	data_time = 0.050429,	train_time = 0.844238
[2019-08-24 17:17:15,404] TRAIN Iter 267600: lr = 0.054002,	loss = 2.402977,	Top-1 err = 0.327295,	Top-5 err = 0.137598,	data_time = 0.050425,	train_time = 0.355050
[2019-08-24 17:17:31,446] TRAIN Iter 267620: lr = 0.053968,	loss = 2.266228,	Top-1 err = 0.327441,	Top-5 err = 0.137061,	data_time = 0.050382,	train_time = 0.802108
[2019-08-24 17:17:50,047] TRAIN Iter 267640: lr = 0.053935,	loss = 2.308497,	Top-1 err = 0.329639,	Top-5 err = 0.136426,	data_time = 0.050376,	train_time = 0.930005
[2019-08-24 17:17:56,728] TRAIN Iter 267660: lr = 0.053902,	loss = 2.349170,	Top-1 err = 0.327637,	Top-5 err = 0.131641,	data_time = 0.050898,	train_time = 0.334033
[2019-08-24 17:18:14,751] TRAIN Iter 267680: lr = 0.053868,	loss = 2.279543,	Top-1 err = 0.330420,	Top-5 err = 0.133203,	data_time = 0.050582,	train_time = 0.901161
[2019-08-24 17:18:22,833] TRAIN Iter 267700: lr = 0.053835,	loss = 2.313494,	Top-1 err = 0.326562,	Top-5 err = 0.132666,	data_time = 0.050347,	train_time = 0.404065
[2019-08-24 17:18:40,907] TRAIN Iter 267720: lr = 0.053802,	loss = 2.325699,	Top-1 err = 0.326318,	Top-5 err = 0.135352,	data_time = 0.050805,	train_time = 0.903727
[2019-08-24 17:18:58,641] TRAIN Iter 267740: lr = 0.053768,	loss = 2.321976,	Top-1 err = 0.330225,	Top-5 err = 0.138379,	data_time = 0.050294,	train_time = 0.886675
[2019-08-24 17:19:10,139] TRAIN Iter 267760: lr = 0.053735,	loss = 2.290872,	Top-1 err = 0.321875,	Top-5 err = 0.129541,	data_time = 0.153343,	train_time = 0.574898
[2019-08-24 17:19:24,721] TRAIN Iter 267780: lr = 0.053702,	loss = 2.259384,	Top-1 err = 0.325928,	Top-5 err = 0.131738,	data_time = 0.050298,	train_time = 0.729056
[2019-08-24 17:19:45,231] TRAIN Iter 267800: lr = 0.053668,	loss = 2.345625,	Top-1 err = 0.330566,	Top-5 err = 0.137646,	data_time = 0.124001,	train_time = 1.025484
[2019-08-24 17:19:53,551] TRAIN Iter 267820: lr = 0.053635,	loss = 2.331989,	Top-1 err = 0.329785,	Top-5 err = 0.137500,	data_time = 0.049989,	train_time = 0.415973
[2019-08-24 17:20:08,522] TRAIN Iter 267840: lr = 0.053602,	loss = 2.307679,	Top-1 err = 0.327979,	Top-5 err = 0.135986,	data_time = 0.049926,	train_time = 0.748541
[2019-08-24 17:20:15,135] TRAIN Iter 267860: lr = 0.053568,	loss = 2.231032,	Top-1 err = 0.325977,	Top-5 err = 0.133594,	data_time = 0.049890,	train_time = 0.330676
[2019-08-24 17:21:08,460] TRAIN Iter 267880: lr = 0.053535,	loss = 2.336081,	Top-1 err = 0.324977,	Top-5 err = 0.132196,	data_time = 0.050664,	train_time = 2.666201
[2019-08-24 17:21:21,674] TRAIN Iter 267900: lr = 0.053502,	loss = 2.270502,	Top-1 err = 0.318945,	Top-5 err = 0.126416,	data_time = 0.050493,	train_time = 0.660667
[2019-08-24 17:21:29,705] TRAIN Iter 267920: lr = 0.053468,	loss = 2.197706,	Top-1 err = 0.319824,	Top-5 err = 0.130371,	data_time = 0.050458,	train_time = 0.401549
[2019-08-24 17:21:43,465] TRAIN Iter 267940: lr = 0.053435,	loss = 2.315335,	Top-1 err = 0.321680,	Top-5 err = 0.133887,	data_time = 0.050299,	train_time = 0.687969
[2019-08-24 17:21:50,251] TRAIN Iter 267960: lr = 0.053402,	loss = 2.322149,	Top-1 err = 0.322510,	Top-5 err = 0.132129,	data_time = 0.127641,	train_time = 0.339319
[2019-08-24 17:22:06,053] TRAIN Iter 267980: lr = 0.053368,	loss = 2.280117,	Top-1 err = 0.323437,	Top-5 err = 0.129248,	data_time = 0.050358,	train_time = 0.790089
[2019-08-24 17:22:23,186] TRAIN Iter 268000: lr = 0.053335,	loss = 2.312518,	Top-1 err = 0.321533,	Top-5 err = 0.130957,	data_time = 0.050538,	train_time = 0.856643
[2019-08-24 17:22:29,937] TRAIN Iter 268020: lr = 0.053302,	loss = 2.326319,	Top-1 err = 0.322168,	Top-5 err = 0.134863,	data_time = 0.050507,	train_time = 0.337494
[2019-08-24 17:22:47,007] TRAIN Iter 268040: lr = 0.053268,	loss = 2.349931,	Top-1 err = 0.325195,	Top-5 err = 0.132666,	data_time = 0.050423,	train_time = 0.853510
[2019-08-24 17:23:01,914] TRAIN Iter 268060: lr = 0.053235,	loss = 2.311094,	Top-1 err = 0.319775,	Top-5 err = 0.127490,	data_time = 3.370265,	train_time = 0.745356
[2019-08-24 17:23:08,842] TRAIN Iter 268080: lr = 0.053202,	loss = 2.311137,	Top-1 err = 0.316064,	Top-5 err = 0.127979,	data_time = 0.050460,	train_time = 0.346354
[2019-08-24 17:23:25,770] TRAIN Iter 268100: lr = 0.053168,	loss = 2.358182,	Top-1 err = 0.327832,	Top-5 err = 0.139502,	data_time = 0.050490,	train_time = 0.846389
[2019-08-24 17:23:33,364] TRAIN Iter 268120: lr = 0.053135,	loss = 2.381005,	Top-1 err = 0.324756,	Top-5 err = 0.134473,	data_time = 0.050393,	train_time = 0.379688
[2019-08-24 17:23:47,829] TRAIN Iter 268140: lr = 0.053102,	loss = 2.336752,	Top-1 err = 0.317090,	Top-5 err = 0.130127,	data_time = 0.050269,	train_time = 0.723263
[2019-08-24 17:24:04,752] TRAIN Iter 268160: lr = 0.053068,	loss = 2.345236,	Top-1 err = 0.325537,	Top-5 err = 0.133057,	data_time = 0.050401,	train_time = 0.846137
[2019-08-24 17:24:12,108] TRAIN Iter 268180: lr = 0.053035,	loss = 2.306732,	Top-1 err = 0.319873,	Top-5 err = 0.130225,	data_time = 0.050638,	train_time = 0.367794
[2019-08-24 17:24:27,456] TRAIN Iter 268200: lr = 0.053002,	loss = 2.328640,	Top-1 err = 0.320508,	Top-5 err = 0.133838,	data_time = 0.050396,	train_time = 0.767362
[2019-08-24 17:24:44,435] TRAIN Iter 268220: lr = 0.052968,	loss = 2.278871,	Top-1 err = 0.329395,	Top-5 err = 0.137061,	data_time = 6.607822,	train_time = 0.848949
[2019-08-24 17:24:51,316] TRAIN Iter 268240: lr = 0.052935,	loss = 2.324935,	Top-1 err = 0.325928,	Top-5 err = 0.137500,	data_time = 0.050414,	train_time = 0.344038
[2019-08-24 17:25:06,328] TRAIN Iter 268260: lr = 0.052902,	loss = 2.361310,	Top-1 err = 0.318799,	Top-5 err = 0.132520,	data_time = 0.050194,	train_time = 0.750556
[2019-08-24 17:25:13,848] TRAIN Iter 268280: lr = 0.052868,	loss = 2.355573,	Top-1 err = 0.324561,	Top-5 err = 0.135596,	data_time = 0.050703,	train_time = 0.376009
[2019-08-24 17:25:28,517] TRAIN Iter 268300: lr = 0.052835,	loss = 2.339149,	Top-1 err = 0.329150,	Top-5 err = 0.135010,	data_time = 0.050764,	train_time = 0.733414
[2019-08-24 17:25:45,391] TRAIN Iter 268320: lr = 0.052802,	loss = 2.314604,	Top-1 err = 0.320508,	Top-5 err = 0.131250,	data_time = 0.050952,	train_time = 0.843728
[2019-08-24 17:25:52,251] TRAIN Iter 268340: lr = 0.052768,	loss = 2.358714,	Top-1 err = 0.323926,	Top-5 err = 0.133887,	data_time = 0.050953,	train_time = 0.342974
[2019-08-24 17:26:09,735] TRAIN Iter 268360: lr = 0.052735,	loss = 2.248413,	Top-1 err = 0.316846,	Top-5 err = 0.128418,	data_time = 0.050479,	train_time = 0.874157
[2019-08-24 17:26:25,959] TRAIN Iter 268380: lr = 0.052702,	loss = 2.301147,	Top-1 err = 0.319678,	Top-5 err = 0.131445,	data_time = 0.912438,	train_time = 0.811225
[2019-08-24 17:26:32,574] TRAIN Iter 268400: lr = 0.052668,	loss = 2.271993,	Top-1 err = 0.316162,	Top-5 err = 0.124512,	data_time = 0.050589,	train_time = 0.330725
[2019-08-24 17:26:48,154] TRAIN Iter 268420: lr = 0.052635,	loss = 2.281964,	Top-1 err = 0.326904,	Top-5 err = 0.132764,	data_time = 0.050195,	train_time = 0.778968
[2019-08-24 17:26:55,440] TRAIN Iter 268440: lr = 0.052602,	loss = 2.240949,	Top-1 err = 0.321777,	Top-5 err = 0.131250,	data_time = 0.050868,	train_time = 0.364316
[2019-08-24 17:27:11,734] TRAIN Iter 268460: lr = 0.052568,	loss = 2.278327,	Top-1 err = 0.326123,	Top-5 err = 0.134131,	data_time = 0.157283,	train_time = 0.814662
[2019-08-24 17:27:27,630] TRAIN Iter 268480: lr = 0.052535,	loss = 2.336411,	Top-1 err = 0.321826,	Top-5 err = 0.134033,	data_time = 0.050451,	train_time = 0.794798
[2019-08-24 17:27:34,542] TRAIN Iter 268500: lr = 0.052502,	loss = 2.311053,	Top-1 err = 0.329395,	Top-5 err = 0.134131,	data_time = 0.050555,	train_time = 0.345560
[2019-08-24 17:27:51,478] TRAIN Iter 268520: lr = 0.052468,	loss = 2.301059,	Top-1 err = 0.323975,	Top-5 err = 0.134570,	data_time = 0.050524,	train_time = 0.846810
[2019-08-24 17:28:08,325] TRAIN Iter 268540: lr = 0.052435,	loss = 2.378676,	Top-1 err = 0.327246,	Top-5 err = 0.135791,	data_time = 2.521387,	train_time = 0.842308
[2019-08-24 17:28:15,008] TRAIN Iter 268560: lr = 0.052402,	loss = 2.264547,	Top-1 err = 0.323486,	Top-5 err = 0.131299,	data_time = 0.163266,	train_time = 0.334177
[2019-08-24 17:28:31,622] TRAIN Iter 268580: lr = 0.052368,	loss = 2.310529,	Top-1 err = 0.322168,	Top-5 err = 0.134326,	data_time = 0.050531,	train_time = 0.830677
[2019-08-24 17:28:39,195] TRAIN Iter 268600: lr = 0.052335,	loss = 2.307862,	Top-1 err = 0.325098,	Top-5 err = 0.130859,	data_time = 0.050644,	train_time = 0.378598
[2019-08-24 17:28:55,240] TRAIN Iter 268620: lr = 0.052302,	loss = 2.296319,	Top-1 err = 0.325537,	Top-5 err = 0.131250,	data_time = 0.050382,	train_time = 0.802273
[2019-08-24 17:29:11,036] TRAIN Iter 268640: lr = 0.052268,	loss = 2.251434,	Top-1 err = 0.329590,	Top-5 err = 0.137061,	data_time = 0.050540,	train_time = 0.789763
[2019-08-24 17:29:18,311] TRAIN Iter 268660: lr = 0.052235,	loss = 2.293799,	Top-1 err = 0.321338,	Top-5 err = 0.133008,	data_time = 0.050601,	train_time = 0.363741
[2019-08-24 17:29:33,306] TRAIN Iter 268680: lr = 0.052202,	loss = 2.262763,	Top-1 err = 0.325879,	Top-5 err = 0.133643,	data_time = 0.050913,	train_time = 0.749735
[2019-08-24 17:29:50,392] TRAIN Iter 268700: lr = 0.052168,	loss = 2.310880,	Top-1 err = 0.325928,	Top-5 err = 0.129150,	data_time = 4.017950,	train_time = 0.854294
[2019-08-24 17:29:57,202] TRAIN Iter 268720: lr = 0.052135,	loss = 2.313741,	Top-1 err = 0.317334,	Top-5 err = 0.128418,	data_time = 0.050670,	train_time = 0.340474
[2019-08-24 17:30:13,698] TRAIN Iter 268740: lr = 0.052102,	loss = 2.381830,	Top-1 err = 0.331982,	Top-5 err = 0.136426,	data_time = 0.050355,	train_time = 0.824788
[2019-08-24 17:30:21,203] TRAIN Iter 268760: lr = 0.052068,	loss = 2.304226,	Top-1 err = 0.321533,	Top-5 err = 0.135205,	data_time = 0.050650,	train_time = 0.375246
[2019-08-24 17:30:37,492] TRAIN Iter 268780: lr = 0.052035,	loss = 2.321111,	Top-1 err = 0.329443,	Top-5 err = 0.133008,	data_time = 0.050535,	train_time = 0.814460
[2019-08-24 17:30:53,020] TRAIN Iter 268800: lr = 0.052002,	loss = 2.306945,	Top-1 err = 0.325439,	Top-5 err = 0.137109,	data_time = 0.050567,	train_time = 0.776332
[2019-08-24 17:30:59,892] TRAIN Iter 268820: lr = 0.051968,	loss = 2.330007,	Top-1 err = 0.321631,	Top-5 err = 0.130273,	data_time = 0.050521,	train_time = 0.343599
[2019-08-24 17:31:16,409] TRAIN Iter 268840: lr = 0.051935,	loss = 2.339924,	Top-1 err = 0.323828,	Top-5 err = 0.130469,	data_time = 0.050306,	train_time = 0.825863
[2019-08-24 17:31:32,561] TRAIN Iter 268860: lr = 0.051902,	loss = 2.398887,	Top-1 err = 0.327881,	Top-5 err = 0.132910,	data_time = 3.841255,	train_time = 0.807563
[2019-08-24 17:31:39,510] TRAIN Iter 268880: lr = 0.051868,	loss = 2.420563,	Top-1 err = 0.328320,	Top-5 err = 0.135840,	data_time = 0.050431,	train_time = 0.347463
[2019-08-24 17:31:54,990] TRAIN Iter 268900: lr = 0.051835,	loss = 2.346689,	Top-1 err = 0.328711,	Top-5 err = 0.134131,	data_time = 0.050476,	train_time = 0.773963
[2019-08-24 17:32:01,965] TRAIN Iter 268920: lr = 0.051802,	loss = 2.356700,	Top-1 err = 0.322705,	Top-5 err = 0.130664,	data_time = 0.050408,	train_time = 0.348735
[2019-08-24 17:32:19,008] TRAIN Iter 268940: lr = 0.051768,	loss = 2.282115,	Top-1 err = 0.328467,	Top-5 err = 0.135010,	data_time = 0.050345,	train_time = 0.852115
[2019-08-24 17:32:35,711] TRAIN Iter 268960: lr = 0.051735,	loss = 2.278964,	Top-1 err = 0.325049,	Top-5 err = 0.132227,	data_time = 0.050825,	train_time = 0.835156
[2019-08-24 17:32:42,718] TRAIN Iter 268980: lr = 0.051702,	loss = 2.308223,	Top-1 err = 0.319629,	Top-5 err = 0.127881,	data_time = 0.050529,	train_time = 0.350317
[2019-08-24 17:32:57,832] TRAIN Iter 269000: lr = 0.051668,	loss = 2.314031,	Top-1 err = 0.329248,	Top-5 err = 0.136230,	data_time = 0.050713,	train_time = 0.755705
[2019-08-24 17:33:14,863] TRAIN Iter 269020: lr = 0.051635,	loss = 2.384971,	Top-1 err = 0.331006,	Top-5 err = 0.135400,	data_time = 4.535463,	train_time = 0.851516
[2019-08-24 17:33:21,592] TRAIN Iter 269040: lr = 0.051602,	loss = 2.300806,	Top-1 err = 0.328467,	Top-5 err = 0.131006,	data_time = 0.050303,	train_time = 0.336451
[2019-08-24 17:33:38,303] TRAIN Iter 269060: lr = 0.051568,	loss = 2.347831,	Top-1 err = 0.327539,	Top-5 err = 0.136084,	data_time = 0.050050,	train_time = 0.835522
[2019-08-24 17:33:44,830] TRAIN Iter 269080: lr = 0.051535,	loss = 2.364692,	Top-1 err = 0.325049,	Top-5 err = 0.130615,	data_time = 0.050106,	train_time = 0.326343
[2019-08-24 17:34:02,054] TRAIN Iter 269100: lr = 0.051502,	loss = 2.376073,	Top-1 err = 0.324365,	Top-5 err = 0.133545,	data_time = 0.049952,	train_time = 0.861200
[2019-08-24 17:34:45,007] TRAIN Iter 269120: lr = 0.051468,	loss = 2.347839,	Top-1 err = 0.328215,	Top-5 err = 0.139411,	data_time = 0.050701,	train_time = 2.147653
[2019-08-24 17:34:55,876] TRAIN Iter 269140: lr = 0.051435,	loss = 2.320610,	Top-1 err = 0.320801,	Top-5 err = 0.126514,	data_time = 0.050575,	train_time = 0.543422
[2019-08-24 17:35:12,112] TRAIN Iter 269160: lr = 0.051402,	loss = 2.322547,	Top-1 err = 0.320703,	Top-5 err = 0.130127,	data_time = 0.050436,	train_time = 0.811778
[2019-08-24 17:35:20,060] TRAIN Iter 269180: lr = 0.051368,	loss = 2.344931,	Top-1 err = 0.320068,	Top-5 err = 0.129541,	data_time = 0.050488,	train_time = 0.397408
[2019-08-24 17:35:33,335] TRAIN Iter 269200: lr = 0.051335,	loss = 2.290574,	Top-1 err = 0.317480,	Top-5 err = 0.130371,	data_time = 0.050682,	train_time = 0.663710
[2019-08-24 17:35:49,216] TRAIN Iter 269220: lr = 0.051302,	loss = 2.267619,	Top-1 err = 0.314307,	Top-5 err = 0.125732,	data_time = 0.050570,	train_time = 0.794036
[2019-08-24 17:35:56,495] TRAIN Iter 269240: lr = 0.051268,	loss = 2.323546,	Top-1 err = 0.321045,	Top-5 err = 0.129932,	data_time = 0.103519,	train_time = 0.363909
[2019-08-24 17:36:11,704] TRAIN Iter 269260: lr = 0.051235,	loss = 2.266630,	Top-1 err = 0.321826,	Top-5 err = 0.129053,	data_time = 0.050629,	train_time = 0.760444
[2019-08-24 17:36:19,982] TRAIN Iter 269280: lr = 0.051202,	loss = 2.309038,	Top-1 err = 0.317236,	Top-5 err = 0.129395,	data_time = 0.050556,	train_time = 0.413898
[2019-08-24 17:36:35,363] TRAIN Iter 269300: lr = 0.051168,	loss = 2.276832,	Top-1 err = 0.318750,	Top-5 err = 0.130371,	data_time = 0.126450,	train_time = 0.769036
[2019-08-24 17:36:50,228] TRAIN Iter 269320: lr = 0.051135,	loss = 2.271981,	Top-1 err = 0.320654,	Top-5 err = 0.130225,	data_time = 0.050372,	train_time = 0.743257
[2019-08-24 17:36:57,760] TRAIN Iter 269340: lr = 0.051102,	loss = 2.355862,	Top-1 err = 0.321484,	Top-5 err = 0.129590,	data_time = 0.145742,	train_time = 0.376545
[2019-08-24 17:37:12,587] TRAIN Iter 269360: lr = 0.051068,	loss = 2.313015,	Top-1 err = 0.321094,	Top-5 err = 0.131445,	data_time = 0.050787,	train_time = 0.741349
[2019-08-24 17:37:28,320] TRAIN Iter 269380: lr = 0.051035,	loss = 2.279363,	Top-1 err = 0.320703,	Top-5 err = 0.132227,	data_time = 0.050653,	train_time = 0.786667
[2019-08-24 17:37:35,642] TRAIN Iter 269400: lr = 0.051002,	loss = 2.305048,	Top-1 err = 0.317676,	Top-5 err = 0.130273,	data_time = 0.050405,	train_time = 0.366085
[2019-08-24 17:37:49,822] TRAIN Iter 269420: lr = 0.050968,	loss = 2.338871,	Top-1 err = 0.319775,	Top-5 err = 0.129443,	data_time = 0.050416,	train_time = 0.708981
[2019-08-24 17:38:01,455] TRAIN Iter 269440: lr = 0.050935,	loss = 2.219934,	Top-1 err = 0.319531,	Top-5 err = 0.126221,	data_time = 0.050512,	train_time = 0.581598
[2019-08-24 17:38:11,898] TRAIN Iter 269460: lr = 0.050902,	loss = 2.223421,	Top-1 err = 0.320508,	Top-5 err = 0.131201,	data_time = 0.050397,	train_time = 0.522166
[2019-08-24 17:38:26,842] TRAIN Iter 269480: lr = 0.050868,	loss = 2.262274,	Top-1 err = 0.322607,	Top-5 err = 0.133496,	data_time = 0.050377,	train_time = 0.747171
[2019-08-24 17:38:34,183] TRAIN Iter 269500: lr = 0.050835,	loss = 2.458310,	Top-1 err = 0.325488,	Top-5 err = 0.136426,	data_time = 0.102978,	train_time = 0.367060
[2019-08-24 17:38:48,718] TRAIN Iter 269520: lr = 0.050802,	loss = 2.262083,	Top-1 err = 0.317627,	Top-5 err = 0.127832,	data_time = 0.050246,	train_time = 0.726698
[2019-08-24 17:39:04,282] TRAIN Iter 269540: lr = 0.050768,	loss = 2.276326,	Top-1 err = 0.322803,	Top-5 err = 0.132373,	data_time = 0.050519,	train_time = 0.778189
[2019-08-24 17:39:11,095] TRAIN Iter 269560: lr = 0.050735,	loss = 2.358930,	Top-1 err = 0.323633,	Top-5 err = 0.133252,	data_time = 0.098979,	train_time = 0.340671
[2019-08-24 17:39:28,412] TRAIN Iter 269580: lr = 0.050702,	loss = 2.344499,	Top-1 err = 0.323291,	Top-5 err = 0.131592,	data_time = 0.050299,	train_time = 0.865823
[2019-08-24 17:39:37,475] TRAIN Iter 269600: lr = 0.050668,	loss = 2.294971,	Top-1 err = 0.320801,	Top-5 err = 0.127686,	data_time = 0.050264,	train_time = 0.453131
[2019-08-24 17:39:50,398] TRAIN Iter 269620: lr = 0.050635,	loss = 2.329839,	Top-1 err = 0.321533,	Top-5 err = 0.133643,	data_time = 0.050676,	train_time = 0.646109
[2019-08-24 17:40:05,502] TRAIN Iter 269640: lr = 0.050602,	loss = 2.324720,	Top-1 err = 0.324707,	Top-5 err = 0.134033,	data_time = 0.050467,	train_time = 0.755211
[2019-08-24 17:40:13,143] TRAIN Iter 269660: lr = 0.050568,	loss = 2.225649,	Top-1 err = 0.322314,	Top-5 err = 0.131250,	data_time = 0.108152,	train_time = 0.382018
[2019-08-24 17:40:27,332] TRAIN Iter 269680: lr = 0.050535,	loss = 2.348191,	Top-1 err = 0.323291,	Top-5 err = 0.133789,	data_time = 0.050411,	train_time = 0.709440
[2019-08-24 17:40:42,601] TRAIN Iter 269700: lr = 0.050502,	loss = 2.369221,	Top-1 err = 0.325146,	Top-5 err = 0.134961,	data_time = 0.050272,	train_time = 0.763447
[2019-08-24 17:40:49,936] TRAIN Iter 269720: lr = 0.050468,	loss = 2.351287,	Top-1 err = 0.322998,	Top-5 err = 0.134717,	data_time = 0.050605,	train_time = 0.366742
[2019-08-24 17:41:04,527] TRAIN Iter 269740: lr = 0.050435,	loss = 2.376127,	Top-1 err = 0.326074,	Top-5 err = 0.136621,	data_time = 0.050394,	train_time = 0.729537
[2019-08-24 17:41:16,677] TRAIN Iter 269760: lr = 0.050402,	loss = 2.276703,	Top-1 err = 0.323584,	Top-5 err = 0.130566,	data_time = 0.050474,	train_time = 0.607461
[2019-08-24 17:41:28,555] TRAIN Iter 269780: lr = 0.050368,	loss = 2.336425,	Top-1 err = 0.323389,	Top-5 err = 0.133252,	data_time = 0.050359,	train_time = 0.593919
[2019-08-24 17:41:45,485] TRAIN Iter 269800: lr = 0.050335,	loss = 2.379845,	Top-1 err = 0.317236,	Top-5 err = 0.131152,	data_time = 0.050322,	train_time = 0.846465
[2019-08-24 17:41:52,739] TRAIN Iter 269820: lr = 0.050302,	loss = 2.297444,	Top-1 err = 0.327344,	Top-5 err = 0.134229,	data_time = 0.050713,	train_time = 0.362713
[2019-08-24 17:42:08,993] TRAIN Iter 269840: lr = 0.050268,	loss = 2.247182,	Top-1 err = 0.326562,	Top-5 err = 0.133447,	data_time = 0.050348,	train_time = 0.812640
[2019-08-24 17:42:23,983] TRAIN Iter 269860: lr = 0.050235,	loss = 2.281044,	Top-1 err = 0.330469,	Top-5 err = 0.132666,	data_time = 0.050457,	train_time = 0.749487
[2019-08-24 17:42:30,676] TRAIN Iter 269880: lr = 0.050202,	loss = 2.271443,	Top-1 err = 0.317529,	Top-5 err = 0.132910,	data_time = 0.050605,	train_time = 0.334637
[2019-08-24 17:42:48,309] TRAIN Iter 269900: lr = 0.050168,	loss = 2.328190,	Top-1 err = 0.327051,	Top-5 err = 0.130225,	data_time = 0.050838,	train_time = 0.881683
[2019-08-24 17:43:03,962] TRAIN Iter 269920: lr = 0.050135,	loss = 2.323971,	Top-1 err = 0.323975,	Top-5 err = 0.134619,	data_time = 0.122165,	train_time = 0.782616
[2019-08-24 17:43:11,668] TRAIN Iter 269940: lr = 0.050102,	loss = 2.262547,	Top-1 err = 0.329785,	Top-5 err = 0.136182,	data_time = 0.050632,	train_time = 0.385285
[2019-08-24 17:43:28,619] TRAIN Iter 269960: lr = 0.050068,	loss = 2.307925,	Top-1 err = 0.317334,	Top-5 err = 0.131006,	data_time = 0.050364,	train_time = 0.847541
[2019-08-24 17:43:36,347] TRAIN Iter 269980: lr = 0.050035,	loss = 2.290645,	Top-1 err = 0.326318,	Top-5 err = 0.135254,	data_time = 0.050315,	train_time = 0.386372
[2019-08-24 17:43:50,965] TRAIN Iter 270000: lr = 0.050002,	loss = 2.328200,	Top-1 err = 0.321045,	Top-5 err = 0.129248,	data_time = 0.050244,	train_time = 0.730889
[2019-08-24 17:44:53,909] TEST Iter 270000: loss = 2.151345,	Top-1 err = 0.298240,	Top-5 err = 0.102660,	val_time = 62.904486
[2019-08-24 17:45:00,188] TRAIN Iter 270020: lr = 0.049968,	loss = 2.234487,	Top-1 err = 0.322998,	Top-5 err = 0.128271,	data_time = 0.050322,	train_time = 0.313892
[2019-08-24 17:45:06,690] TRAIN Iter 270040: lr = 0.049935,	loss = 2.300869,	Top-1 err = 0.324707,	Top-5 err = 0.134033,	data_time = 0.050511,	train_time = 0.325103
[2019-08-24 17:45:13,224] TRAIN Iter 270060: lr = 0.049902,	loss = 2.346216,	Top-1 err = 0.322314,	Top-5 err = 0.132910,	data_time = 0.050354,	train_time = 0.326696
[2019-08-24 17:45:22,760] TRAIN Iter 270080: lr = 0.049868,	loss = 2.313895,	Top-1 err = 0.324609,	Top-5 err = 0.129932,	data_time = 0.051080,	train_time = 0.476787
[2019-08-24 17:45:38,753] TRAIN Iter 270100: lr = 0.049835,	loss = 2.366688,	Top-1 err = 0.330859,	Top-5 err = 0.135937,	data_time = 0.050363,	train_time = 0.799615
[2019-08-24 17:45:47,188] TRAIN Iter 270120: lr = 0.049802,	loss = 2.295797,	Top-1 err = 0.320996,	Top-5 err = 0.133545,	data_time = 0.050609,	train_time = 0.421736
[2019-08-24 17:46:03,234] TRAIN Iter 270140: lr = 0.049768,	loss = 2.375536,	Top-1 err = 0.322900,	Top-5 err = 0.132129,	data_time = 0.050618,	train_time = 0.802289
[2019-08-24 17:46:11,878] TRAIN Iter 270160: lr = 0.049735,	loss = 2.284177,	Top-1 err = 0.330957,	Top-5 err = 0.134131,	data_time = 0.050399,	train_time = 0.432224
[2019-08-24 17:46:27,132] TRAIN Iter 270180: lr = 0.049702,	loss = 2.326250,	Top-1 err = 0.325928,	Top-5 err = 0.136035,	data_time = 0.286953,	train_time = 0.762639
[2019-08-24 17:46:42,632] TRAIN Iter 270200: lr = 0.049668,	loss = 2.293052,	Top-1 err = 0.327490,	Top-5 err = 0.137012,	data_time = 0.050560,	train_time = 0.774986
[2019-08-24 17:46:52,162] TRAIN Iter 270220: lr = 0.049635,	loss = 2.300905,	Top-1 err = 0.324170,	Top-5 err = 0.130811,	data_time = 0.050810,	train_time = 0.476516
[2019-08-24 17:47:10,381] TRAIN Iter 270240: lr = 0.049602,	loss = 2.150516,	Top-1 err = 0.322656,	Top-5 err = 0.133594,	data_time = 0.470247,	train_time = 0.910953
[2019-08-24 17:47:26,652] TRAIN Iter 270260: lr = 0.049568,	loss = 2.294853,	Top-1 err = 0.324316,	Top-5 err = 0.132227,	data_time = 0.050428,	train_time = 0.813503
[2019-08-24 17:47:36,784] TRAIN Iter 270280: lr = 0.049535,	loss = 2.319302,	Top-1 err = 0.323779,	Top-5 err = 0.132812,	data_time = 0.050684,	train_time = 0.506615
[2019-08-24 17:47:53,360] TRAIN Iter 270300: lr = 0.049502,	loss = 2.324022,	Top-1 err = 0.326611,	Top-5 err = 0.134473,	data_time = 0.050791,	train_time = 0.828769
[2019-08-24 17:48:03,077] TRAIN Iter 270320: lr = 0.049468,	loss = 2.329360,	Top-1 err = 0.323779,	Top-5 err = 0.132715,	data_time = 0.050001,	train_time = 0.485848
[2019-08-24 17:48:21,102] TRAIN Iter 270340: lr = 0.049435,	loss = 2.364759,	Top-1 err = 0.324658,	Top-5 err = 0.133105,	data_time = 0.050059,	train_time = 0.901215
[2019-08-24 17:48:34,375] TRAIN Iter 270360: lr = 0.049402,	loss = 2.175494,	Top-1 err = 0.322656,	Top-5 err = 0.129785,	data_time = 0.734891,	train_time = 0.663643
[2019-08-24 17:49:23,021] TRAIN Iter 270380: lr = 0.049368,	loss = 2.316056,	Top-1 err = 0.324657,	Top-5 err = 0.135839,	data_time = 0.050654,	train_time = 2.432280
[2019-08-24 17:49:30,716] TRAIN Iter 270400: lr = 0.049335,	loss = 2.286895,	Top-1 err = 0.317773,	Top-5 err = 0.129102,	data_time = 0.051070,	train_time = 0.384756
[2019-08-24 17:49:46,413] TRAIN Iter 270420: lr = 0.049302,	loss = 2.252671,	Top-1 err = 0.323730,	Top-5 err = 0.132324,	data_time = 0.050302,	train_time = 0.784790
[2019-08-24 17:49:57,259] TRAIN Iter 270440: lr = 0.049268,	loss = 2.249103,	Top-1 err = 0.317627,	Top-5 err = 0.124121,	data_time = 0.050494,	train_time = 0.542280
[2019-08-24 17:50:04,464] TRAIN Iter 270460: lr = 0.049235,	loss = 2.267867,	Top-1 err = 0.315771,	Top-5 err = 0.126562,	data_time = 0.050479,	train_time = 0.360270
[2019-08-24 17:50:18,983] TRAIN Iter 270480: lr = 0.049202,	loss = 2.302241,	Top-1 err = 0.319238,	Top-5 err = 0.127686,	data_time = 0.050252,	train_time = 0.725906
[2019-08-24 17:50:32,858] TRAIN Iter 270500: lr = 0.049168,	loss = 2.287772,	Top-1 err = 0.313916,	Top-5 err = 0.129102,	data_time = 0.050441,	train_time = 0.693758
[2019-08-24 17:50:41,604] TRAIN Iter 270520: lr = 0.049135,	loss = 2.184545,	Top-1 err = 0.316455,	Top-5 err = 0.125732,	data_time = 0.050503,	train_time = 0.437275
[2019-08-24 17:50:57,532] TRAIN Iter 270540: lr = 0.049102,	loss = 2.275287,	Top-1 err = 0.315381,	Top-5 err = 0.129834,	data_time = 2.573507,	train_time = 0.796381
[2019-08-24 17:51:04,824] TRAIN Iter 270560: lr = 0.049068,	loss = 2.269103,	Top-1 err = 0.319287,	Top-5 err = 0.130225,	data_time = 0.050344,	train_time = 0.364582
[2019-08-24 17:51:20,538] TRAIN Iter 270580: lr = 0.049035,	loss = 2.396149,	Top-1 err = 0.320068,	Top-5 err = 0.128955,	data_time = 0.050332,	train_time = 0.785711
[2019-08-24 17:51:36,249] TRAIN Iter 270600: lr = 0.049002,	loss = 2.224570,	Top-1 err = 0.317773,	Top-5 err = 0.125098,	data_time = 0.050330,	train_time = 0.785518
[2019-08-24 17:51:43,401] TRAIN Iter 270620: lr = 0.048968,	loss = 2.361746,	Top-1 err = 0.319385,	Top-5 err = 0.131836,	data_time = 0.050666,	train_time = 0.357606
[2019-08-24 17:51:59,846] TRAIN Iter 270640: lr = 0.048935,	loss = 2.304777,	Top-1 err = 0.317676,	Top-5 err = 0.132275,	data_time = 0.050653,	train_time = 0.822208
[2019-08-24 17:52:14,558] TRAIN Iter 270660: lr = 0.048902,	loss = 2.317360,	Top-1 err = 0.319971,	Top-5 err = 0.127637,	data_time = 0.132379,	train_time = 0.735610
[2019-08-24 17:52:22,385] TRAIN Iter 270680: lr = 0.048868,	loss = 2.293769,	Top-1 err = 0.320508,	Top-5 err = 0.129590,	data_time = 0.051000,	train_time = 0.391333
[2019-08-24 17:52:37,722] TRAIN Iter 270700: lr = 0.048835,	loss = 2.351155,	Top-1 err = 0.320508,	Top-5 err = 0.131250,	data_time = 0.050541,	train_time = 0.766810
[2019-08-24 17:52:44,634] TRAIN Iter 270720: lr = 0.048802,	loss = 2.304112,	Top-1 err = 0.319531,	Top-5 err = 0.130469,	data_time = 0.050587,	train_time = 0.345609
[2019-08-24 17:53:00,881] TRAIN Iter 270740: lr = 0.048768,	loss = 2.282020,	Top-1 err = 0.320264,	Top-5 err = 0.128809,	data_time = 0.050952,	train_time = 0.812348
[2019-08-24 17:53:16,347] TRAIN Iter 270760: lr = 0.048735,	loss = 2.313806,	Top-1 err = 0.316797,	Top-5 err = 0.128125,	data_time = 0.050792,	train_time = 0.773252
[2019-08-24 17:53:23,619] TRAIN Iter 270780: lr = 0.048702,	loss = 2.306694,	Top-1 err = 0.324463,	Top-5 err = 0.130176,	data_time = 0.050526,	train_time = 0.363602
[2019-08-24 17:53:40,489] TRAIN Iter 270800: lr = 0.048668,	loss = 2.337831,	Top-1 err = 0.315771,	Top-5 err = 0.126514,	data_time = 0.050569,	train_time = 0.843516
[2019-08-24 17:53:56,335] TRAIN Iter 270820: lr = 0.048635,	loss = 2.282483,	Top-1 err = 0.326221,	Top-5 err = 0.131982,	data_time = 0.050637,	train_time = 0.792260
[2019-08-24 17:54:03,737] TRAIN Iter 270840: lr = 0.048602,	loss = 2.271882,	Top-1 err = 0.315869,	Top-5 err = 0.125635,	data_time = 0.050375,	train_time = 0.370117
[2019-08-24 17:54:19,234] TRAIN Iter 270860: lr = 0.048568,	loss = 2.330388,	Top-1 err = 0.325195,	Top-5 err = 0.131641,	data_time = 0.092533,	train_time = 0.774799
[2019-08-24 17:54:25,935] TRAIN Iter 270880: lr = 0.048535,	loss = 2.264473,	Top-1 err = 0.318164,	Top-5 err = 0.131738,	data_time = 0.050860,	train_time = 0.335069
[2019-08-24 17:54:42,476] TRAIN Iter 270900: lr = 0.048502,	loss = 2.197038,	Top-1 err = 0.316357,	Top-5 err = 0.129785,	data_time = 0.050786,	train_time = 0.827028
[2019-08-24 17:54:58,547] TRAIN Iter 270920: lr = 0.048468,	loss = 2.246808,	Top-1 err = 0.320117,	Top-5 err = 0.128809,	data_time = 0.050150,	train_time = 0.803522
[2019-08-24 17:55:05,637] TRAIN Iter 270940: lr = 0.048435,	loss = 2.317447,	Top-1 err = 0.321045,	Top-5 err = 0.130518,	data_time = 0.050465,	train_time = 0.354495
[2019-08-24 17:55:23,717] TRAIN Iter 270960: lr = 0.048402,	loss = 2.426128,	Top-1 err = 0.324756,	Top-5 err = 0.133105,	data_time = 0.050303,	train_time = 0.903977
[2019-08-24 17:55:40,114] TRAIN Iter 270980: lr = 0.048368,	loss = 2.321406,	Top-1 err = 0.314600,	Top-5 err = 0.129102,	data_time = 0.050227,	train_time = 0.819847
[2019-08-24 17:55:47,570] TRAIN Iter 271000: lr = 0.048335,	loss = 2.368159,	Top-1 err = 0.321289,	Top-5 err = 0.129932,	data_time = 0.050389,	train_time = 0.372764
[2019-08-24 17:56:04,624] TRAIN Iter 271020: lr = 0.048302,	loss = 2.304184,	Top-1 err = 0.325098,	Top-5 err = 0.132520,	data_time = 0.583667,	train_time = 0.852705
[2019-08-24 17:56:11,500] TRAIN Iter 271040: lr = 0.048268,	loss = 2.390823,	Top-1 err = 0.323584,	Top-5 err = 0.132861,	data_time = 0.050242,	train_time = 0.343760
[2019-08-24 17:56:28,469] TRAIN Iter 271060: lr = 0.048235,	loss = 2.228097,	Top-1 err = 0.323975,	Top-5 err = 0.129443,	data_time = 0.050342,	train_time = 0.848456
[2019-08-24 17:56:44,247] TRAIN Iter 271080: lr = 0.048202,	loss = 2.272753,	Top-1 err = 0.319043,	Top-5 err = 0.133691,	data_time = 0.050469,	train_time = 0.788906
[2019-08-24 17:56:51,759] TRAIN Iter 271100: lr = 0.048168,	loss = 2.276606,	Top-1 err = 0.323389,	Top-5 err = 0.133398,	data_time = 0.050316,	train_time = 0.375575
[2019-08-24 17:57:10,180] TRAIN Iter 271120: lr = 0.048135,	loss = 2.265864,	Top-1 err = 0.317676,	Top-5 err = 0.128125,	data_time = 0.050374,	train_time = 0.921042
[2019-08-24 17:57:27,075] TRAIN Iter 271140: lr = 0.048102,	loss = 2.389548,	Top-1 err = 0.329150,	Top-5 err = 0.138770,	data_time = 2.391750,	train_time = 0.844723
[2019-08-24 17:57:34,337] TRAIN Iter 271160: lr = 0.048068,	loss = 2.192795,	Top-1 err = 0.327051,	Top-5 err = 0.132129,	data_time = 0.050488,	train_time = 0.363098
[2019-08-24 17:57:53,221] TRAIN Iter 271180: lr = 0.048035,	loss = 2.283334,	Top-1 err = 0.319629,	Top-5 err = 0.133252,	data_time = 0.117999,	train_time = 0.944169
[2019-08-24 17:57:59,918] TRAIN Iter 271200: lr = 0.048002,	loss = 2.355375,	Top-1 err = 0.327539,	Top-5 err = 0.133154,	data_time = 0.050592,	train_time = 0.334865
[2019-08-24 17:58:17,295] TRAIN Iter 271220: lr = 0.047968,	loss = 2.337522,	Top-1 err = 0.324316,	Top-5 err = 0.133887,	data_time = 0.050754,	train_time = 0.868824
[2019-08-24 17:58:33,926] TRAIN Iter 271240: lr = 0.047935,	loss = 2.311768,	Top-1 err = 0.323535,	Top-5 err = 0.133691,	data_time = 0.050486,	train_time = 0.831553
[2019-08-24 17:58:40,618] TRAIN Iter 271260: lr = 0.047902,	loss = 2.308787,	Top-1 err = 0.324121,	Top-5 err = 0.135059,	data_time = 0.050660,	train_time = 0.334542
[2019-08-24 17:58:59,219] TRAIN Iter 271280: lr = 0.047868,	loss = 2.275780,	Top-1 err = 0.322754,	Top-5 err = 0.130908,	data_time = 0.050405,	train_time = 0.930065
[2019-08-24 17:59:15,662] TRAIN Iter 271300: lr = 0.047835,	loss = 2.185875,	Top-1 err = 0.321387,	Top-5 err = 0.134619,	data_time = 2.837904,	train_time = 0.822149
[2019-08-24 17:59:23,212] TRAIN Iter 271320: lr = 0.047802,	loss = 2.436693,	Top-1 err = 0.322119,	Top-5 err = 0.133691,	data_time = 0.050248,	train_time = 0.377477
[2019-08-24 17:59:40,591] TRAIN Iter 271340: lr = 0.047768,	loss = 2.356475,	Top-1 err = 0.323486,	Top-5 err = 0.131836,	data_time = 0.137612,	train_time = 0.868923
[2019-08-24 17:59:47,940] TRAIN Iter 271360: lr = 0.047735,	loss = 2.326959,	Top-1 err = 0.320801,	Top-5 err = 0.135937,	data_time = 0.050367,	train_time = 0.367432
[2019-08-24 18:00:05,486] TRAIN Iter 271380: lr = 0.047702,	loss = 2.256397,	Top-1 err = 0.323291,	Top-5 err = 0.131836,	data_time = 0.050421,	train_time = 0.877284
[2019-08-24 18:00:23,835] TRAIN Iter 271400: lr = 0.047668,	loss = 2.288149,	Top-1 err = 0.319287,	Top-5 err = 0.131592,	data_time = 0.050370,	train_time = 0.917427
[2019-08-24 18:00:30,750] TRAIN Iter 271420: lr = 0.047635,	loss = 2.283887,	Top-1 err = 0.321533,	Top-5 err = 0.131689,	data_time = 0.050527,	train_time = 0.345733
[2019-08-24 18:00:48,747] TRAIN Iter 271440: lr = 0.047602,	loss = 2.255759,	Top-1 err = 0.319922,	Top-5 err = 0.132910,	data_time = 0.050304,	train_time = 0.899870
[2019-08-24 18:01:06,174] TRAIN Iter 271460: lr = 0.047568,	loss = 2.286466,	Top-1 err = 0.318359,	Top-5 err = 0.127051,	data_time = 3.098988,	train_time = 0.871324
[2019-08-24 18:01:14,220] TRAIN Iter 271480: lr = 0.047535,	loss = 2.314780,	Top-1 err = 0.324902,	Top-5 err = 0.132715,	data_time = 0.050319,	train_time = 0.402274
[2019-08-24 18:01:33,415] TRAIN Iter 271500: lr = 0.047502,	loss = 2.264105,	Top-1 err = 0.325293,	Top-5 err = 0.134814,	data_time = 1.125186,	train_time = 0.959752
[2019-08-24 18:01:40,031] TRAIN Iter 271520: lr = 0.047468,	loss = 2.383609,	Top-1 err = 0.321191,	Top-5 err = 0.130957,	data_time = 0.050791,	train_time = 0.330772
[2019-08-24 18:01:58,823] TRAIN Iter 271540: lr = 0.047435,	loss = 2.352993,	Top-1 err = 0.323242,	Top-5 err = 0.130469,	data_time = 0.050570,	train_time = 0.939573
[2019-08-24 18:02:18,835] TRAIN Iter 271560: lr = 0.047402,	loss = 2.307207,	Top-1 err = 0.328174,	Top-5 err = 0.129688,	data_time = 0.050112,	train_time = 1.000595
[2019-08-24 18:02:25,759] TRAIN Iter 271580: lr = 0.047368,	loss = 2.332817,	Top-1 err = 0.327441,	Top-5 err = 0.140332,	data_time = 0.050011,	train_time = 0.346175
[2019-08-24 18:02:43,215] TRAIN Iter 271600: lr = 0.047335,	loss = 2.431211,	Top-1 err = 0.320996,	Top-5 err = 0.133496,	data_time = 0.049901,	train_time = 0.872782
[2019-08-24 18:02:52,157] TRAIN Iter 271620: lr = 0.047302,	loss = 2.813587,	Top-1 err = 0.324967,	Top-5 err = 0.135148,	data_time = 0.007063,	train_time = 0.447119
[2019-08-24 18:03:38,805] TRAIN Iter 271640: lr = 0.047268,	loss = 2.245559,	Top-1 err = 0.321143,	Top-5 err = 0.130078,	data_time = 0.050568,	train_time = 2.332377
[2019-08-24 18:03:55,700] TRAIN Iter 271660: lr = 0.047235,	loss = 2.314098,	Top-1 err = 0.318115,	Top-5 err = 0.129248,	data_time = 0.050235,	train_time = 0.844726
[2019-08-24 18:04:02,642] TRAIN Iter 271680: lr = 0.047202,	loss = 2.382842,	Top-1 err = 0.319482,	Top-5 err = 0.130469,	data_time = 0.050536,	train_time = 0.347075
[2019-08-24 18:04:16,481] TRAIN Iter 271700: lr = 0.047168,	loss = 2.230268,	Top-1 err = 0.312305,	Top-5 err = 0.127881,	data_time = 0.050763,	train_time = 0.691943
[2019-08-24 18:04:32,109] TRAIN Iter 271720: lr = 0.047135,	loss = 2.293908,	Top-1 err = 0.314746,	Top-5 err = 0.127393,	data_time = 0.138991,	train_time = 0.781386
[2019-08-24 18:04:39,142] TRAIN Iter 271740: lr = 0.047102,	loss = 2.274014,	Top-1 err = 0.315088,	Top-5 err = 0.128027,	data_time = 0.050265,	train_time = 0.351628
[2019-08-24 18:04:56,316] TRAIN Iter 271760: lr = 0.047068,	loss = 2.249871,	Top-1 err = 0.324951,	Top-5 err = 0.131836,	data_time = 0.050497,	train_time = 0.858695
[2019-08-24 18:05:03,163] TRAIN Iter 271780: lr = 0.047035,	loss = 2.239102,	Top-1 err = 0.316748,	Top-5 err = 0.127783,	data_time = 0.050241,	train_time = 0.342322
[2019-08-24 18:05:18,884] TRAIN Iter 271800: lr = 0.047002,	loss = 2.290566,	Top-1 err = 0.313428,	Top-5 err = 0.126904,	data_time = 0.050295,	train_time = 0.786041
[2019-08-24 18:05:35,279] TRAIN Iter 271820: lr = 0.046968,	loss = 2.313268,	Top-1 err = 0.310840,	Top-5 err = 0.125049,	data_time = 0.105163,	train_time = 0.819738
[2019-08-24 18:05:41,859] TRAIN Iter 271840: lr = 0.046935,	loss = 2.305233,	Top-1 err = 0.311426,	Top-5 err = 0.127246,	data_time = 0.050514,	train_time = 0.329027
[2019-08-24 18:05:58,869] TRAIN Iter 271860: lr = 0.046902,	loss = 2.211907,	Top-1 err = 0.313623,	Top-5 err = 0.125342,	data_time = 0.050617,	train_time = 0.850474
[2019-08-24 18:06:13,403] TRAIN Iter 271880: lr = 0.046868,	loss = 2.248104,	Top-1 err = 0.314355,	Top-5 err = 0.127490,	data_time = 0.050460,	train_time = 0.726689
[2019-08-24 18:06:20,370] TRAIN Iter 271900: lr = 0.046835,	loss = 2.309199,	Top-1 err = 0.315186,	Top-5 err = 0.123584,	data_time = 0.050560,	train_time = 0.348292
[2019-08-24 18:06:36,698] TRAIN Iter 271920: lr = 0.046802,	loss = 2.245715,	Top-1 err = 0.317383,	Top-5 err = 0.126562,	data_time = 0.050495,	train_time = 0.816410
[2019-08-24 18:06:43,467] TRAIN Iter 271940: lr = 0.046768,	loss = 2.283266,	Top-1 err = 0.318457,	Top-5 err = 0.130859,	data_time = 0.050543,	train_time = 0.338444
[2019-08-24 18:06:58,300] TRAIN Iter 271960: lr = 0.046735,	loss = 2.204573,	Top-1 err = 0.316846,	Top-5 err = 0.127637,	data_time = 0.050580,	train_time = 0.741608
[2019-08-24 18:07:14,813] TRAIN Iter 271980: lr = 0.046702,	loss = 2.253916,	Top-1 err = 0.320264,	Top-5 err = 0.127197,	data_time = 0.109809,	train_time = 0.825642
[2019-08-24 18:07:21,309] TRAIN Iter 272000: lr = 0.046668,	loss = 2.245792,	Top-1 err = 0.313330,	Top-5 err = 0.124414,	data_time = 0.050325,	train_time = 0.324801
[2019-08-24 18:07:36,640] TRAIN Iter 272020: lr = 0.046635,	loss = 2.293594,	Top-1 err = 0.312646,	Top-5 err = 0.127881,	data_time = 0.050523,	train_time = 0.766525
[2019-08-24 18:07:52,342] TRAIN Iter 272040: lr = 0.046602,	loss = 2.236859,	Top-1 err = 0.316943,	Top-5 err = 0.130908,	data_time = 0.050323,	train_time = 0.785103
[2019-08-24 18:07:59,089] TRAIN Iter 272060: lr = 0.046568,	loss = 2.294147,	Top-1 err = 0.316260,	Top-5 err = 0.132275,	data_time = 0.050472,	train_time = 0.337302
[2019-08-24 18:08:16,665] TRAIN Iter 272080: lr = 0.046535,	loss = 2.311573,	Top-1 err = 0.322217,	Top-5 err = 0.129443,	data_time = 0.050507,	train_time = 0.878789
[2019-08-24 18:08:23,387] TRAIN Iter 272100: lr = 0.046502,	loss = 2.377082,	Top-1 err = 0.322021,	Top-5 err = 0.130518,	data_time = 0.050257,	train_time = 0.336077
[2019-08-24 18:08:40,363] TRAIN Iter 272120: lr = 0.046468,	loss = 2.376470,	Top-1 err = 0.318701,	Top-5 err = 0.130225,	data_time = 0.050387,	train_time = 0.848803
[2019-08-24 18:08:56,779] TRAIN Iter 272140: lr = 0.046435,	loss = 2.310140,	Top-1 err = 0.311865,	Top-5 err = 0.126953,	data_time = 0.050708,	train_time = 0.820803
[2019-08-24 18:09:03,267] TRAIN Iter 272160: lr = 0.046402,	loss = 2.315331,	Top-1 err = 0.318457,	Top-5 err = 0.126025,	data_time = 0.050302,	train_time = 0.324381
[2019-08-24 18:09:20,165] TRAIN Iter 272180: lr = 0.046368,	loss = 2.283023,	Top-1 err = 0.320166,	Top-5 err = 0.131885,	data_time = 0.050707,	train_time = 0.844869
[2019-08-24 18:09:35,948] TRAIN Iter 272200: lr = 0.046335,	loss = 2.349466,	Top-1 err = 0.318896,	Top-5 err = 0.130713,	data_time = 0.557972,	train_time = 0.789144
[2019-08-24 18:09:42,520] TRAIN Iter 272220: lr = 0.046302,	loss = 2.285088,	Top-1 err = 0.322852,	Top-5 err = 0.130322,	data_time = 0.050400,	train_time = 0.328599
[2019-08-24 18:09:58,666] TRAIN Iter 272240: lr = 0.046268,	loss = 2.375952,	Top-1 err = 0.319531,	Top-5 err = 0.129688,	data_time = 0.050513,	train_time = 0.807244
[2019-08-24 18:10:05,763] TRAIN Iter 272260: lr = 0.046235,	loss = 2.261272,	Top-1 err = 0.318604,	Top-5 err = 0.133545,	data_time = 0.050572,	train_time = 0.354855
[2019-08-24 18:10:22,226] TRAIN Iter 272280: lr = 0.046202,	loss = 2.294775,	Top-1 err = 0.328271,	Top-5 err = 0.134131,	data_time = 0.050519,	train_time = 0.823128
[2019-08-24 18:10:38,582] TRAIN Iter 272300: lr = 0.046168,	loss = 2.306341,	Top-1 err = 0.327197,	Top-5 err = 0.138525,	data_time = 0.050841,	train_time = 0.817790
[2019-08-24 18:10:45,930] TRAIN Iter 272320: lr = 0.046135,	loss = 2.400932,	Top-1 err = 0.319873,	Top-5 err = 0.125684,	data_time = 0.050440,	train_time = 0.367391
[2019-08-24 18:11:02,330] TRAIN Iter 272340: lr = 0.046102,	loss = 2.379156,	Top-1 err = 0.321436,	Top-5 err = 0.133643,	data_time = 0.050424,	train_time = 0.819987
[2019-08-24 18:11:18,931] TRAIN Iter 272360: lr = 0.046068,	loss = 2.231875,	Top-1 err = 0.322070,	Top-5 err = 0.131738,	data_time = 0.050255,	train_time = 0.830008
[2019-08-24 18:11:25,429] TRAIN Iter 272380: lr = 0.046035,	loss = 2.329930,	Top-1 err = 0.319824,	Top-5 err = 0.134277,	data_time = 0.050510,	train_time = 0.324914
[2019-08-24 18:11:41,513] TRAIN Iter 272400: lr = 0.046002,	loss = 2.213556,	Top-1 err = 0.318555,	Top-5 err = 0.130078,	data_time = 0.050443,	train_time = 0.804171
[2019-08-24 18:11:48,285] TRAIN Iter 272420: lr = 0.045968,	loss = 2.291555,	Top-1 err = 0.324561,	Top-5 err = 0.137061,	data_time = 0.050733,	train_time = 0.338579
[2019-08-24 18:12:06,480] TRAIN Iter 272440: lr = 0.045935,	loss = 2.339290,	Top-1 err = 0.323730,	Top-5 err = 0.131689,	data_time = 0.050688,	train_time = 0.909738
[2019-08-24 18:12:21,989] TRAIN Iter 272460: lr = 0.045902,	loss = 2.346782,	Top-1 err = 0.323242,	Top-5 err = 0.133594,	data_time = 0.050464,	train_time = 0.775441
[2019-08-24 18:12:28,689] TRAIN Iter 272480: lr = 0.045868,	loss = 2.253493,	Top-1 err = 0.320605,	Top-5 err = 0.127881,	data_time = 0.050383,	train_time = 0.334998
[2019-08-24 18:12:44,131] TRAIN Iter 272500: lr = 0.045835,	loss = 2.395800,	Top-1 err = 0.325293,	Top-5 err = 0.132031,	data_time = 0.050493,	train_time = 0.772094
[2019-08-24 18:13:01,189] TRAIN Iter 272520: lr = 0.045802,	loss = 2.303402,	Top-1 err = 0.323730,	Top-5 err = 0.133740,	data_time = 0.122953,	train_time = 0.852840
[2019-08-24 18:13:07,662] TRAIN Iter 272540: lr = 0.045768,	loss = 2.300628,	Top-1 err = 0.317725,	Top-5 err = 0.126562,	data_time = 0.050328,	train_time = 0.323678
[2019-08-24 18:13:25,566] TRAIN Iter 272560: lr = 0.045735,	loss = 2.332885,	Top-1 err = 0.321387,	Top-5 err = 0.130859,	data_time = 0.050809,	train_time = 0.895138
[2019-08-24 18:13:32,625] TRAIN Iter 272580: lr = 0.045702,	loss = 2.303333,	Top-1 err = 0.323926,	Top-5 err = 0.128076,	data_time = 0.050614,	train_time = 0.352982
[2019-08-24 18:13:49,204] TRAIN Iter 272600: lr = 0.045668,	loss = 2.236059,	Top-1 err = 0.314795,	Top-5 err = 0.128320,	data_time = 0.050504,	train_time = 0.828924
[2019-08-24 18:14:06,778] TRAIN Iter 272620: lr = 0.045635,	loss = 2.379706,	Top-1 err = 0.321338,	Top-5 err = 0.126904,	data_time = 0.050662,	train_time = 0.878661
[2019-08-24 18:14:13,541] TRAIN Iter 272640: lr = 0.045602,	loss = 2.397572,	Top-1 err = 0.324365,	Top-5 err = 0.132031,	data_time = 0.050360,	train_time = 0.338174
[2019-08-24 18:14:30,458] TRAIN Iter 272660: lr = 0.045568,	loss = 2.340965,	Top-1 err = 0.319678,	Top-5 err = 0.128027,	data_time = 0.050537,	train_time = 0.845793
[2019-08-24 18:14:50,747] TRAIN Iter 272680: lr = 0.045535,	loss = 2.286475,	Top-1 err = 0.320215,	Top-5 err = 0.130615,	data_time = 0.050487,	train_time = 1.014462
[2019-08-24 18:14:57,497] TRAIN Iter 272700: lr = 0.045502,	loss = 2.318556,	Top-1 err = 0.319189,	Top-5 err = 0.130225,	data_time = 0.050548,	train_time = 0.337462
[2019-08-24 18:15:17,295] TRAIN Iter 272720: lr = 0.045468,	loss = 2.331234,	Top-1 err = 0.326172,	Top-5 err = 0.131592,	data_time = 0.050761,	train_time = 0.989883
[2019-08-24 18:15:24,231] TRAIN Iter 272740: lr = 0.045435,	loss = 2.266474,	Top-1 err = 0.321729,	Top-5 err = 0.129980,	data_time = 0.050205,	train_time = 0.346816
[2019-08-24 18:15:40,896] TRAIN Iter 272760: lr = 0.045402,	loss = 2.319445,	Top-1 err = 0.314355,	Top-5 err = 0.129053,	data_time = 0.050554,	train_time = 0.833221
[2019-08-24 18:16:00,654] TRAIN Iter 272780: lr = 0.045368,	loss = 2.328695,	Top-1 err = 0.317578,	Top-5 err = 0.133057,	data_time = 0.050389,	train_time = 0.987902
[2019-08-24 18:16:07,184] TRAIN Iter 272800: lr = 0.045335,	loss = 2.248506,	Top-1 err = 0.325195,	Top-5 err = 0.131787,	data_time = 0.050525,	train_time = 0.326469
[2019-08-24 18:16:27,714] TRAIN Iter 272820: lr = 0.045302,	loss = 2.201155,	Top-1 err = 0.321484,	Top-5 err = 0.131445,	data_time = 0.050156,	train_time = 1.026510
[2019-08-24 18:16:46,901] TRAIN Iter 272840: lr = 0.045268,	loss = 2.315428,	Top-1 err = 0.323682,	Top-5 err = 0.131396,	data_time = 0.049959,	train_time = 0.959302
[2019-08-24 18:16:52,779] TRAIN Iter 272860: lr = 0.045235,	loss = 2.324914,	Top-1 err = 0.322412,	Top-5 err = 0.130273,	data_time = 0.049867,	train_time = 0.293889
[2019-08-24 18:17:49,224] TRAIN Iter 272880: lr = 0.045202,	loss = 2.318621,	Top-1 err = 0.324135,	Top-5 err = 0.132876,	data_time = 0.050677,	train_time = 2.822268
[2019-08-24 18:17:56,578] TRAIN Iter 272900: lr = 0.045168,	loss = 2.344083,	Top-1 err = 0.319092,	Top-5 err = 0.129443,	data_time = 0.050483,	train_time = 0.367642
[2019-08-24 18:18:11,970] TRAIN Iter 272920: lr = 0.045135,	loss = 2.296570,	Top-1 err = 0.318457,	Top-5 err = 0.124902,	data_time = 0.050827,	train_time = 0.769621
[2019-08-24 18:18:23,059] TRAIN Iter 272940: lr = 0.045102,	loss = 2.310495,	Top-1 err = 0.317529,	Top-5 err = 0.127197,	data_time = 0.050370,	train_time = 0.554408
[2019-08-24 18:18:30,625] TRAIN Iter 272960: lr = 0.045068,	loss = 2.311455,	Top-1 err = 0.318115,	Top-5 err = 0.128027,	data_time = 0.050475,	train_time = 0.378307
[2019-08-24 18:18:46,469] TRAIN Iter 272980: lr = 0.045035,	loss = 2.315811,	Top-1 err = 0.316895,	Top-5 err = 0.124463,	data_time = 0.050452,	train_time = 0.792193
[2019-08-24 18:18:53,369] TRAIN Iter 273000: lr = 0.045002,	loss = 2.317120,	Top-1 err = 0.321582,	Top-5 err = 0.127393,	data_time = 0.050172,	train_time = 0.345000
[2019-08-24 18:19:07,599] TRAIN Iter 273020: lr = 0.044968,	loss = 2.293344,	Top-1 err = 0.310986,	Top-5 err = 0.122412,	data_time = 0.050344,	train_time = 0.711461
[2019-08-24 18:19:24,579] TRAIN Iter 273040: lr = 0.044935,	loss = 2.301563,	Top-1 err = 0.320068,	Top-5 err = 0.128711,	data_time = 0.050802,	train_time = 0.848996
[2019-08-24 18:19:31,675] TRAIN Iter 273060: lr = 0.044902,	loss = 2.400540,	Top-1 err = 0.320068,	Top-5 err = 0.132275,	data_time = 0.050453,	train_time = 0.354762
[2019-08-24 18:19:46,936] TRAIN Iter 273080: lr = 0.044868,	loss = 2.211434,	Top-1 err = 0.319531,	Top-5 err = 0.129883,	data_time = 0.050602,	train_time = 0.763048
[2019-08-24 18:19:59,993] TRAIN Iter 273100: lr = 0.044835,	loss = 2.197673,	Top-1 err = 0.317139,	Top-5 err = 0.127539,	data_time = 0.106370,	train_time = 0.652831
[2019-08-24 18:20:08,718] TRAIN Iter 273120: lr = 0.044802,	loss = 2.299684,	Top-1 err = 0.314990,	Top-5 err = 0.127002,	data_time = 0.050330,	train_time = 0.436239
[2019-08-24 18:20:24,324] TRAIN Iter 273140: lr = 0.044768,	loss = 2.306414,	Top-1 err = 0.318945,	Top-5 err = 0.127979,	data_time = 0.095931,	train_time = 0.780281
[2019-08-24 18:20:31,596] TRAIN Iter 273160: lr = 0.044735,	loss = 2.276797,	Top-1 err = 0.317480,	Top-5 err = 0.126807,	data_time = 0.097148,	train_time = 0.363590
[2019-08-24 18:20:46,741] TRAIN Iter 273180: lr = 0.044702,	loss = 2.236574,	Top-1 err = 0.321191,	Top-5 err = 0.129883,	data_time = 0.051009,	train_time = 0.757266
[2019-08-24 18:21:03,030] TRAIN Iter 273200: lr = 0.044668,	loss = 2.297557,	Top-1 err = 0.318506,	Top-5 err = 0.131201,	data_time = 0.050301,	train_time = 0.814421
[2019-08-24 18:21:09,971] TRAIN Iter 273220: lr = 0.044635,	loss = 2.320981,	Top-1 err = 0.319727,	Top-5 err = 0.130664,	data_time = 0.050394,	train_time = 0.347017
[2019-08-24 18:21:26,123] TRAIN Iter 273240: lr = 0.044602,	loss = 2.293722,	Top-1 err = 0.318994,	Top-5 err = 0.129199,	data_time = 0.050374,	train_time = 0.807620
[2019-08-24 18:21:41,360] TRAIN Iter 273260: lr = 0.044568,	loss = 2.308488,	Top-1 err = 0.322217,	Top-5 err = 0.135645,	data_time = 0.050282,	train_time = 0.761802
[2019-08-24 18:21:48,291] TRAIN Iter 273280: lr = 0.044535,	loss = 2.309886,	Top-1 err = 0.314648,	Top-5 err = 0.124512,	data_time = 0.050251,	train_time = 0.346562
[2019-08-24 18:22:03,208] TRAIN Iter 273300: lr = 0.044502,	loss = 2.333944,	Top-1 err = 0.313184,	Top-5 err = 0.124658,	data_time = 0.050482,	train_time = 0.745810
[2019-08-24 18:22:10,096] TRAIN Iter 273320: lr = 0.044468,	loss = 2.348142,	Top-1 err = 0.318164,	Top-5 err = 0.131934,	data_time = 0.050700,	train_time = 0.344380
[2019-08-24 18:22:23,999] TRAIN Iter 273340: lr = 0.044435,	loss = 2.228603,	Top-1 err = 0.315576,	Top-5 err = 0.128271,	data_time = 0.050601,	train_time = 0.695136
[2019-08-24 18:22:40,586] TRAIN Iter 273360: lr = 0.044402,	loss = 2.330506,	Top-1 err = 0.316797,	Top-5 err = 0.130127,	data_time = 0.606260,	train_time = 0.829354
[2019-08-24 18:22:47,598] TRAIN Iter 273380: lr = 0.044368,	loss = 2.365036,	Top-1 err = 0.317676,	Top-5 err = 0.130518,	data_time = 0.050521,	train_time = 0.350616
[2019-08-24 18:23:03,366] TRAIN Iter 273400: lr = 0.044335,	loss = 2.346193,	Top-1 err = 0.314697,	Top-5 err = 0.128955,	data_time = 0.050155,	train_time = 0.788342
[2019-08-24 18:23:20,903] TRAIN Iter 273420: lr = 0.044302,	loss = 2.342003,	Top-1 err = 0.319824,	Top-5 err = 0.129688,	data_time = 0.050381,	train_time = 0.876842
[2019-08-24 18:23:27,951] TRAIN Iter 273440: lr = 0.044268,	loss = 2.354056,	Top-1 err = 0.322266,	Top-5 err = 0.133203,	data_time = 0.050079,	train_time = 0.352424
[2019-08-24 18:23:44,941] TRAIN Iter 273460: lr = 0.044235,	loss = 2.250718,	Top-1 err = 0.316260,	Top-5 err = 0.127002,	data_time = 0.050680,	train_time = 0.849459
[2019-08-24 18:23:52,209] TRAIN Iter 273480: lr = 0.044202,	loss = 2.227742,	Top-1 err = 0.317090,	Top-5 err = 0.126465,	data_time = 0.050367,	train_time = 0.363377
[2019-08-24 18:24:07,331] TRAIN Iter 273500: lr = 0.044168,	loss = 2.274909,	Top-1 err = 0.318164,	Top-5 err = 0.127393,	data_time = 0.050472,	train_time = 0.756102
[2019-08-24 18:24:21,675] TRAIN Iter 273520: lr = 0.044135,	loss = 2.311244,	Top-1 err = 0.316406,	Top-5 err = 0.127637,	data_time = 0.139446,	train_time = 0.717199
[2019-08-24 18:24:28,767] TRAIN Iter 273540: lr = 0.044102,	loss = 2.311173,	Top-1 err = 0.317529,	Top-5 err = 0.127441,	data_time = 0.050601,	train_time = 0.354586
[2019-08-24 18:24:46,196] TRAIN Iter 273560: lr = 0.044068,	loss = 2.354431,	Top-1 err = 0.320557,	Top-5 err = 0.129492,	data_time = 0.050390,	train_time = 0.871399
[2019-08-24 18:25:01,856] TRAIN Iter 273580: lr = 0.044035,	loss = 2.353957,	Top-1 err = 0.324121,	Top-5 err = 0.133545,	data_time = 0.932731,	train_time = 0.783004
[2019-08-24 18:25:08,820] TRAIN Iter 273600: lr = 0.044002,	loss = 2.349439,	Top-1 err = 0.320801,	Top-5 err = 0.127344,	data_time = 0.050319,	train_time = 0.348178
[2019-08-24 18:25:25,728] TRAIN Iter 273620: lr = 0.043968,	loss = 2.300781,	Top-1 err = 0.315771,	Top-5 err = 0.128271,	data_time = 0.050489,	train_time = 0.845402
[2019-08-24 18:25:32,511] TRAIN Iter 273640: lr = 0.043935,	loss = 2.327103,	Top-1 err = 0.318604,	Top-5 err = 0.130957,	data_time = 0.050448,	train_time = 0.339119
[2019-08-24 18:25:48,950] TRAIN Iter 273660: lr = 0.043902,	loss = 2.390141,	Top-1 err = 0.316211,	Top-5 err = 0.128516,	data_time = 0.142165,	train_time = 0.821938
[2019-08-24 18:26:03,787] TRAIN Iter 273680: lr = 0.043868,	loss = 2.283458,	Top-1 err = 0.315820,	Top-5 err = 0.125098,	data_time = 0.092350,	train_time = 0.741840
[2019-08-24 18:26:11,132] TRAIN Iter 273700: lr = 0.043835,	loss = 2.275409,	Top-1 err = 0.317139,	Top-5 err = 0.131201,	data_time = 0.050489,	train_time = 0.367231
[2019-08-24 18:26:27,887] TRAIN Iter 273720: lr = 0.043802,	loss = 2.293363,	Top-1 err = 0.325000,	Top-5 err = 0.132617,	data_time = 0.050224,	train_time = 0.837756
[2019-08-24 18:26:42,955] TRAIN Iter 273740: lr = 0.043768,	loss = 2.300088,	Top-1 err = 0.323340,	Top-5 err = 0.131348,	data_time = 0.050269,	train_time = 0.753376
[2019-08-24 18:26:50,630] TRAIN Iter 273760: lr = 0.043735,	loss = 2.269651,	Top-1 err = 0.321338,	Top-5 err = 0.129688,	data_time = 0.050486,	train_time = 0.383708
[2019-08-24 18:27:06,569] TRAIN Iter 273780: lr = 0.043702,	loss = 2.290426,	Top-1 err = 0.319482,	Top-5 err = 0.129834,	data_time = 0.050554,	train_time = 0.796971
[2019-08-24 18:27:13,761] TRAIN Iter 273800: lr = 0.043668,	loss = 2.391423,	Top-1 err = 0.322070,	Top-5 err = 0.131250,	data_time = 0.050384,	train_time = 0.359578
[2019-08-24 18:27:28,814] TRAIN Iter 273820: lr = 0.043635,	loss = 2.323654,	Top-1 err = 0.314160,	Top-5 err = 0.124121,	data_time = 0.050796,	train_time = 0.752623
[2019-08-24 18:27:46,338] TRAIN Iter 273840: lr = 0.043602,	loss = 2.328193,	Top-1 err = 0.318848,	Top-5 err = 0.133350,	data_time = 0.050266,	train_time = 0.876176
[2019-08-24 18:27:53,396] TRAIN Iter 273860: lr = 0.043568,	loss = 2.257721,	Top-1 err = 0.320410,	Top-5 err = 0.131396,	data_time = 0.050304,	train_time = 0.352879
[2019-08-24 18:28:09,369] TRAIN Iter 273880: lr = 0.043535,	loss = 2.245100,	Top-1 err = 0.312695,	Top-5 err = 0.128906,	data_time = 0.050506,	train_time = 0.798674
[2019-08-24 18:28:26,360] TRAIN Iter 273900: lr = 0.043502,	loss = 2.251975,	Top-1 err = 0.319385,	Top-5 err = 0.129004,	data_time = 0.050519,	train_time = 0.849527
[2019-08-24 18:28:33,438] TRAIN Iter 273920: lr = 0.043468,	loss = 2.246397,	Top-1 err = 0.322607,	Top-5 err = 0.133984,	data_time = 0.050499,	train_time = 0.353852
[2019-08-24 18:28:48,756] TRAIN Iter 273940: lr = 0.043435,	loss = 2.304018,	Top-1 err = 0.319238,	Top-5 err = 0.128125,	data_time = 0.050531,	train_time = 0.765925
[2019-08-24 18:28:56,047] TRAIN Iter 273960: lr = 0.043402,	loss = 2.288670,	Top-1 err = 0.321582,	Top-5 err = 0.128125,	data_time = 0.050790,	train_time = 0.364509
[2019-08-24 18:29:11,256] TRAIN Iter 273980: lr = 0.043368,	loss = 2.315714,	Top-1 err = 0.318799,	Top-5 err = 0.128906,	data_time = 0.050305,	train_time = 0.760465
[2019-08-24 18:29:27,188] TRAIN Iter 274000: lr = 0.043335,	loss = 2.311285,	Top-1 err = 0.320117,	Top-5 err = 0.131006,	data_time = 0.050579,	train_time = 0.796551
[2019-08-24 18:29:34,035] TRAIN Iter 274020: lr = 0.043302,	loss = 2.268142,	Top-1 err = 0.318115,	Top-5 err = 0.131299,	data_time = 0.050436,	train_time = 0.342353
[2019-08-24 18:29:50,496] TRAIN Iter 274040: lr = 0.043268,	loss = 2.312202,	Top-1 err = 0.315625,	Top-5 err = 0.129980,	data_time = 0.050210,	train_time = 0.823030
[2019-08-24 18:30:07,809] TRAIN Iter 274060: lr = 0.043235,	loss = 2.316778,	Top-1 err = 0.315576,	Top-5 err = 0.124365,	data_time = 0.182177,	train_time = 0.865630
[2019-08-24 18:30:15,468] TRAIN Iter 274080: lr = 0.043202,	loss = 2.279779,	Top-1 err = 0.315234,	Top-5 err = 0.127539,	data_time = 0.049991,	train_time = 0.382954
[2019-08-24 18:30:32,002] TRAIN Iter 274100: lr = 0.043168,	loss = 2.312623,	Top-1 err = 0.320410,	Top-5 err = 0.130225,	data_time = 0.049930,	train_time = 0.826665
[2019-08-24 18:30:38,208] TRAIN Iter 274120: lr = 0.043135,	loss = 2.337666,	Top-1 err = 0.320752,	Top-5 err = 0.130078,	data_time = 0.049902,	train_time = 0.310325
[2019-08-24 18:31:29,386] TRAIN Iter 274140: lr = 0.043102,	loss = 2.321273,	Top-1 err = 0.318930,	Top-5 err = 0.133212,	data_time = 0.050327,	train_time = 2.558847
[2019-08-24 18:31:44,814] TRAIN Iter 274160: lr = 0.043068,	loss = 2.380646,	Top-1 err = 0.314844,	Top-5 err = 0.123877,	data_time = 0.050656,	train_time = 0.771428
[2019-08-24 18:31:52,076] TRAIN Iter 274180: lr = 0.043035,	loss = 2.318442,	Top-1 err = 0.306006,	Top-5 err = 0.121582,	data_time = 0.161860,	train_time = 0.363038
[2019-08-24 18:32:06,658] TRAIN Iter 274200: lr = 0.043002,	loss = 2.243124,	Top-1 err = 0.309668,	Top-5 err = 0.126123,	data_time = 0.050510,	train_time = 0.729125
[2019-08-24 18:32:14,577] TRAIN Iter 274220: lr = 0.042968,	loss = 2.204157,	Top-1 err = 0.306592,	Top-5 err = 0.124902,	data_time = 0.050851,	train_time = 0.395938
[2019-08-24 18:32:28,842] TRAIN Iter 274240: lr = 0.042935,	loss = 2.279511,	Top-1 err = 0.317334,	Top-5 err = 0.127295,	data_time = 0.050951,	train_time = 0.713210
[2019-08-24 18:32:44,295] TRAIN Iter 274260: lr = 0.042902,	loss = 2.291896,	Top-1 err = 0.311572,	Top-5 err = 0.124268,	data_time = 0.050465,	train_time = 0.772639
[2019-08-24 18:32:51,693] TRAIN Iter 274280: lr = 0.042868,	loss = 2.237344,	Top-1 err = 0.308008,	Top-5 err = 0.122705,	data_time = 0.117115,	train_time = 0.369892
[2019-08-24 18:33:06,529] TRAIN Iter 274300: lr = 0.042835,	loss = 2.227160,	Top-1 err = 0.314941,	Top-5 err = 0.125879,	data_time = 0.050398,	train_time = 0.741764
[2019-08-24 18:33:22,533] TRAIN Iter 274320: lr = 0.042802,	loss = 2.321782,	Top-1 err = 0.311670,	Top-5 err = 0.124805,	data_time = 0.050593,	train_time = 0.800221
[2019-08-24 18:33:29,804] TRAIN Iter 274340: lr = 0.042768,	loss = 2.343362,	Top-1 err = 0.318262,	Top-5 err = 0.130566,	data_time = 0.050494,	train_time = 0.363531
[2019-08-24 18:33:42,654] TRAIN Iter 274360: lr = 0.042735,	loss = 2.330413,	Top-1 err = 0.319434,	Top-5 err = 0.129248,	data_time = 0.050791,	train_time = 0.642471
[2019-08-24 18:33:50,252] TRAIN Iter 274380: lr = 0.042702,	loss = 2.251882,	Top-1 err = 0.316113,	Top-5 err = 0.123291,	data_time = 0.050719,	train_time = 0.379888
[2019-08-24 18:34:05,713] TRAIN Iter 274400: lr = 0.042668,	loss = 2.290264,	Top-1 err = 0.312207,	Top-5 err = 0.127539,	data_time = 0.051039,	train_time = 0.773018
[2019-08-24 18:34:19,919] TRAIN Iter 274420: lr = 0.042635,	loss = 2.261338,	Top-1 err = 0.315771,	Top-5 err = 0.123633,	data_time = 0.050509,	train_time = 0.710311
[2019-08-24 18:34:27,028] TRAIN Iter 274440: lr = 0.042602,	loss = 2.241981,	Top-1 err = 0.315088,	Top-5 err = 0.130029,	data_time = 0.050868,	train_time = 0.355437
[2019-08-24 18:34:41,429] TRAIN Iter 274460: lr = 0.042568,	loss = 2.254876,	Top-1 err = 0.310156,	Top-5 err = 0.125781,	data_time = 0.050358,	train_time = 0.720011
[2019-08-24 18:34:58,127] TRAIN Iter 274480: lr = 0.042535,	loss = 2.227924,	Top-1 err = 0.317041,	Top-5 err = 0.123975,	data_time = 0.050800,	train_time = 0.834890
[2019-08-24 18:35:05,409] TRAIN Iter 274500: lr = 0.042502,	loss = 2.313637,	Top-1 err = 0.312598,	Top-5 err = 0.126270,	data_time = 0.050362,	train_time = 0.364088
[2019-08-24 18:35:20,954] TRAIN Iter 274520: lr = 0.042468,	loss = 2.247947,	Top-1 err = 0.312646,	Top-5 err = 0.124170,	data_time = 0.050679,	train_time = 0.777255
[2019-08-24 18:35:28,721] TRAIN Iter 274540: lr = 0.042435,	loss = 2.289117,	Top-1 err = 0.314941,	Top-5 err = 0.129443,	data_time = 0.131873,	train_time = 0.388327
[2019-08-24 18:35:41,915] TRAIN Iter 274560: lr = 0.042402,	loss = 2.389523,	Top-1 err = 0.313135,	Top-5 err = 0.131152,	data_time = 0.050650,	train_time = 0.659672
[2019-08-24 18:35:59,181] TRAIN Iter 274580: lr = 0.042368,	loss = 2.275909,	Top-1 err = 0.315967,	Top-5 err = 0.125098,	data_time = 0.050497,	train_time = 0.863282
[2019-08-24 18:36:06,778] TRAIN Iter 274600: lr = 0.042335,	loss = 2.281445,	Top-1 err = 0.319238,	Top-5 err = 0.131787,	data_time = 0.050493,	train_time = 0.379825
[2019-08-24 18:36:21,089] TRAIN Iter 274620: lr = 0.042302,	loss = 2.241170,	Top-1 err = 0.319336,	Top-5 err = 0.130615,	data_time = 0.050753,	train_time = 0.715557
[2019-08-24 18:36:37,312] TRAIN Iter 274640: lr = 0.042268,	loss = 2.353894,	Top-1 err = 0.320654,	Top-5 err = 0.130322,	data_time = 0.050891,	train_time = 0.811120
[2019-08-24 18:36:44,543] TRAIN Iter 274660: lr = 0.042235,	loss = 2.325575,	Top-1 err = 0.321973,	Top-5 err = 0.132617,	data_time = 0.050489,	train_time = 0.361561
[2019-08-24 18:36:58,797] TRAIN Iter 274680: lr = 0.042202,	loss = 2.306997,	Top-1 err = 0.317383,	Top-5 err = 0.128125,	data_time = 0.051147,	train_time = 0.712693
[2019-08-24 18:37:06,250] TRAIN Iter 274700: lr = 0.042168,	loss = 2.245052,	Top-1 err = 0.314355,	Top-5 err = 0.128174,	data_time = 0.050311,	train_time = 0.372623
[2019-08-24 18:37:22,061] TRAIN Iter 274720: lr = 0.042135,	loss = 2.267744,	Top-1 err = 0.310059,	Top-5 err = 0.124902,	data_time = 0.050460,	train_time = 0.790530
[2019-08-24 18:37:38,607] TRAIN Iter 274740: lr = 0.042102,	loss = 2.305985,	Top-1 err = 0.321045,	Top-5 err = 0.131055,	data_time = 0.050426,	train_time = 0.827278
[2019-08-24 18:37:45,707] TRAIN Iter 274760: lr = 0.042068,	loss = 2.288307,	Top-1 err = 0.314746,	Top-5 err = 0.128809,	data_time = 0.050353,	train_time = 0.354970
[2019-08-24 18:38:01,468] TRAIN Iter 274780: lr = 0.042035,	loss = 2.326674,	Top-1 err = 0.316016,	Top-5 err = 0.129980,	data_time = 0.050780,	train_time = 0.788065
[2019-08-24 18:38:15,869] TRAIN Iter 274800: lr = 0.042002,	loss = 2.292966,	Top-1 err = 0.317139,	Top-5 err = 0.129883,	data_time = 0.050682,	train_time = 0.720030
[2019-08-24 18:38:22,867] TRAIN Iter 274820: lr = 0.041968,	loss = 2.274458,	Top-1 err = 0.315381,	Top-5 err = 0.124072,	data_time = 0.050317,	train_time = 0.349889
[2019-08-24 18:38:39,377] TRAIN Iter 274840: lr = 0.041935,	loss = 2.257760,	Top-1 err = 0.318994,	Top-5 err = 0.128125,	data_time = 0.050424,	train_time = 0.825472
[2019-08-24 18:38:46,613] TRAIN Iter 274860: lr = 0.041902,	loss = 2.262870,	Top-1 err = 0.315088,	Top-5 err = 0.126758,	data_time = 0.050514,	train_time = 0.361806
[2019-08-24 18:39:03,137] TRAIN Iter 274880: lr = 0.041868,	loss = 2.200631,	Top-1 err = 0.319434,	Top-5 err = 0.125586,	data_time = 0.050440,	train_time = 0.826177
[2019-08-24 18:39:20,276] TRAIN Iter 274900: lr = 0.041835,	loss = 2.238374,	Top-1 err = 0.322900,	Top-5 err = 0.133740,	data_time = 0.050523,	train_time = 0.856928
[2019-08-24 18:39:27,378] TRAIN Iter 274920: lr = 0.041802,	loss = 2.331606,	Top-1 err = 0.316504,	Top-5 err = 0.126367,	data_time = 0.050421,	train_time = 0.355104
[2019-08-24 18:39:43,956] TRAIN Iter 274940: lr = 0.041768,	loss = 2.271770,	Top-1 err = 0.319727,	Top-5 err = 0.130859,	data_time = 0.050333,	train_time = 0.828862
[2019-08-24 18:39:56,426] TRAIN Iter 274960: lr = 0.041735,	loss = 2.327514,	Top-1 err = 0.319141,	Top-5 err = 0.128955,	data_time = 1.545670,	train_time = 0.623485
[2019-08-24 18:40:07,393] TRAIN Iter 274980: lr = 0.041702,	loss = 2.299338,	Top-1 err = 0.315039,	Top-5 err = 0.128223,	data_time = 0.050439,	train_time = 0.548344
[2019-08-24 18:40:22,265] TRAIN Iter 275000: lr = 0.041668,	loss = 2.309992,	Top-1 err = 0.317383,	Top-5 err = 0.130273,	data_time = 0.050860,	train_time = 0.743600
[2019-08-24 18:40:29,562] TRAIN Iter 275020: lr = 0.041635,	loss = 2.379804,	Top-1 err = 0.313818,	Top-5 err = 0.125928,	data_time = 0.050439,	train_time = 0.364808
[2019-08-24 18:40:46,343] TRAIN Iter 275040: lr = 0.041602,	loss = 2.316263,	Top-1 err = 0.318604,	Top-5 err = 0.127197,	data_time = 0.050605,	train_time = 0.839065
[2019-08-24 18:41:01,856] TRAIN Iter 275060: lr = 0.041568,	loss = 2.348677,	Top-1 err = 0.319629,	Top-5 err = 0.128955,	data_time = 0.050660,	train_time = 0.775617
[2019-08-24 18:41:08,701] TRAIN Iter 275080: lr = 0.041535,	loss = 2.409767,	Top-1 err = 0.318457,	Top-5 err = 0.131641,	data_time = 0.050788,	train_time = 0.342254
[2019-08-24 18:41:25,786] TRAIN Iter 275100: lr = 0.041502,	loss = 2.253607,	Top-1 err = 0.316211,	Top-5 err = 0.130908,	data_time = 0.050526,	train_time = 0.854224
[2019-08-24 18:41:38,932] TRAIN Iter 275120: lr = 0.041468,	loss = 2.280045,	Top-1 err = 0.317725,	Top-5 err = 0.128027,	data_time = 0.089428,	train_time = 0.657262
[2019-08-24 18:41:49,275] TRAIN Iter 275140: lr = 0.041435,	loss = 2.196388,	Top-1 err = 0.321729,	Top-5 err = 0.132520,	data_time = 0.050350,	train_time = 0.517164
[2019-08-24 18:42:07,413] TRAIN Iter 275160: lr = 0.041402,	loss = 2.375662,	Top-1 err = 0.315088,	Top-5 err = 0.128369,	data_time = 0.050379,	train_time = 0.906884
[2019-08-24 18:42:14,492] TRAIN Iter 275180: lr = 0.041368,	loss = 2.359278,	Top-1 err = 0.321045,	Top-5 err = 0.129346,	data_time = 0.050640,	train_time = 0.353930
[2019-08-24 18:42:30,781] TRAIN Iter 275200: lr = 0.041335,	loss = 2.279994,	Top-1 err = 0.318555,	Top-5 err = 0.129199,	data_time = 0.050370,	train_time = 0.814439
[2019-08-24 18:42:47,763] TRAIN Iter 275220: lr = 0.041302,	loss = 2.205925,	Top-1 err = 0.321582,	Top-5 err = 0.129688,	data_time = 0.050326,	train_time = 0.849060
[2019-08-24 18:42:54,515] TRAIN Iter 275240: lr = 0.041268,	loss = 2.340416,	Top-1 err = 0.319043,	Top-5 err = 0.128418,	data_time = 0.127683,	train_time = 0.337586
[2019-08-24 18:43:11,829] TRAIN Iter 275260: lr = 0.041235,	loss = 2.242781,	Top-1 err = 0.319434,	Top-5 err = 0.128613,	data_time = 0.050545,	train_time = 0.865689
[2019-08-24 18:43:27,812] TRAIN Iter 275280: lr = 0.041202,	loss = 2.246956,	Top-1 err = 0.319775,	Top-5 err = 0.128467,	data_time = 0.050388,	train_time = 0.799149
[2019-08-24 18:43:37,696] TRAIN Iter 275300: lr = 0.041168,	loss = 2.221998,	Top-1 err = 0.318164,	Top-5 err = 0.128955,	data_time = 0.050587,	train_time = 0.494197
[2019-08-24 18:43:53,851] TRAIN Iter 275320: lr = 0.041135,	loss = 2.277025,	Top-1 err = 0.315137,	Top-5 err = 0.130713,	data_time = 0.050030,	train_time = 0.807730
[2019-08-24 18:44:00,964] TRAIN Iter 275340: lr = 0.041102,	loss = 2.333917,	Top-1 err = 0.318213,	Top-5 err = 0.128418,	data_time = 0.050128,	train_time = 0.355636
[2019-08-24 18:44:16,811] TRAIN Iter 275360: lr = 0.041068,	loss = 2.291080,	Top-1 err = 0.318018,	Top-5 err = 0.130371,	data_time = 0.049939,	train_time = 0.792334
[2019-08-24 18:45:07,138] TRAIN Iter 275380: lr = 0.041035,	loss = 2.210582,	Top-1 err = 0.318127,	Top-5 err = 0.128762,	data_time = 0.050621,	train_time = 2.516319
[2019-08-24 18:45:14,132] TRAIN Iter 275400: lr = 0.041002,	loss = 2.350500,	Top-1 err = 0.324072,	Top-5 err = 0.129639,	data_time = 0.050766,	train_time = 0.349688
[2019-08-24 18:45:31,643] TRAIN Iter 275420: lr = 0.040968,	loss = 2.238369,	Top-1 err = 0.305859,	Top-5 err = 0.125293,	data_time = 0.050507,	train_time = 0.875523
[2019-08-24 18:45:39,679] TRAIN Iter 275440: lr = 0.040935,	loss = 2.266443,	Top-1 err = 0.313867,	Top-5 err = 0.123535,	data_time = 0.050563,	train_time = 0.401780
[2019-08-24 18:45:51,887] TRAIN Iter 275460: lr = 0.040902,	loss = 2.258374,	Top-1 err = 0.308398,	Top-5 err = 0.122852,	data_time = 0.050379,	train_time = 0.610425
[2019-08-24 18:46:04,915] TRAIN Iter 275480: lr = 0.040868,	loss = 2.192827,	Top-1 err = 0.310596,	Top-5 err = 0.126074,	data_time = 0.050775,	train_time = 0.651380
[2019-08-24 18:46:11,881] TRAIN Iter 275500: lr = 0.040835,	loss = 2.293667,	Top-1 err = 0.309521,	Top-5 err = 0.121387,	data_time = 0.050331,	train_time = 0.348254
[2019-08-24 18:46:27,139] TRAIN Iter 275520: lr = 0.040802,	loss = 2.345502,	Top-1 err = 0.314893,	Top-5 err = 0.128467,	data_time = 0.050798,	train_time = 0.762887
[2019-08-24 18:46:42,206] TRAIN Iter 275540: lr = 0.040768,	loss = 2.287286,	Top-1 err = 0.311719,	Top-5 err = 0.123828,	data_time = 2.809742,	train_time = 0.753363
[2019-08-24 18:46:49,754] TRAIN Iter 275560: lr = 0.040735,	loss = 2.295160,	Top-1 err = 0.310010,	Top-5 err = 0.123828,	data_time = 0.050367,	train_time = 0.377358
[2019-08-24 18:47:03,603] TRAIN Iter 275580: lr = 0.040702,	loss = 2.231724,	Top-1 err = 0.317676,	Top-5 err = 0.129639,	data_time = 0.050282,	train_time = 0.692435
[2019-08-24 18:47:10,402] TRAIN Iter 275600: lr = 0.040668,	loss = 2.266704,	Top-1 err = 0.314014,	Top-5 err = 0.122998,	data_time = 0.050459,	train_time = 0.339946
[2019-08-24 18:47:25,286] TRAIN Iter 275620: lr = 0.040635,	loss = 2.265203,	Top-1 err = 0.315137,	Top-5 err = 0.129248,	data_time = 0.050359,	train_time = 0.744188
[2019-08-24 18:47:39,446] TRAIN Iter 275640: lr = 0.040602,	loss = 2.238686,	Top-1 err = 0.310645,	Top-5 err = 0.125195,	data_time = 0.050452,	train_time = 0.708003
[2019-08-24 18:47:46,564] TRAIN Iter 275660: lr = 0.040568,	loss = 2.263023,	Top-1 err = 0.315479,	Top-5 err = 0.129980,	data_time = 0.050223,	train_time = 0.355874
[2019-08-24 18:48:01,025] TRAIN Iter 275680: lr = 0.040535,	loss = 2.246861,	Top-1 err = 0.315576,	Top-5 err = 0.128369,	data_time = 0.050505,	train_time = 0.723010
[2019-08-24 18:48:14,643] TRAIN Iter 275700: lr = 0.040502,	loss = 2.249712,	Top-1 err = 0.313965,	Top-5 err = 0.128564,	data_time = 0.050486,	train_time = 0.680933
[2019-08-24 18:48:22,336] TRAIN Iter 275720: lr = 0.040468,	loss = 2.230922,	Top-1 err = 0.315332,	Top-5 err = 0.124609,	data_time = 0.050489,	train_time = 0.384610
[2019-08-24 18:48:38,638] TRAIN Iter 275740: lr = 0.040435,	loss = 2.149253,	Top-1 err = 0.313477,	Top-5 err = 0.126953,	data_time = 0.050343,	train_time = 0.815099
[2019-08-24 18:48:45,318] TRAIN Iter 275760: lr = 0.040402,	loss = 2.285079,	Top-1 err = 0.311670,	Top-5 err = 0.124072,	data_time = 0.050475,	train_time = 0.333954
[2019-08-24 18:49:01,336] TRAIN Iter 275780: lr = 0.040368,	loss = 2.305143,	Top-1 err = 0.308398,	Top-5 err = 0.122705,	data_time = 0.050470,	train_time = 0.800897
[2019-08-24 18:49:16,916] TRAIN Iter 275800: lr = 0.040335,	loss = 2.275454,	Top-1 err = 0.315527,	Top-5 err = 0.128076,	data_time = 0.050996,	train_time = 0.778975
[2019-08-24 18:49:24,414] TRAIN Iter 275820: lr = 0.040302,	loss = 2.381840,	Top-1 err = 0.311328,	Top-5 err = 0.124854,	data_time = 0.051085,	train_time = 0.374876
[2019-08-24 18:49:39,350] TRAIN Iter 275840: lr = 0.040268,	loss = 2.285774,	Top-1 err = 0.321143,	Top-5 err = 0.129639,	data_time = 0.050511,	train_time = 0.746808
[2019-08-24 18:49:54,329] TRAIN Iter 275860: lr = 0.040235,	loss = 2.250709,	Top-1 err = 0.315039,	Top-5 err = 0.126855,	data_time = 0.050579,	train_time = 0.748914
[2019-08-24 18:50:01,742] TRAIN Iter 275880: lr = 0.040202,	loss = 2.235485,	Top-1 err = 0.309180,	Top-5 err = 0.124365,	data_time = 0.050645,	train_time = 0.370655
[2019-08-24 18:50:18,610] TRAIN Iter 275900: lr = 0.040168,	loss = 2.209755,	Top-1 err = 0.314648,	Top-5 err = 0.124219,	data_time = 0.050414,	train_time = 0.843373
[2019-08-24 18:50:25,946] TRAIN Iter 275920: lr = 0.040135,	loss = 2.271266,	Top-1 err = 0.313818,	Top-5 err = 0.125244,	data_time = 0.050485,	train_time = 0.366775
[2019-08-24 18:50:40,302] TRAIN Iter 275940: lr = 0.040102,	loss = 2.234863,	Top-1 err = 0.316602,	Top-5 err = 0.129932,	data_time = 0.050636,	train_time = 0.717786
[2019-08-24 18:50:55,902] TRAIN Iter 275960: lr = 0.040068,	loss = 2.267310,	Top-1 err = 0.318164,	Top-5 err = 0.126709,	data_time = 0.132565,	train_time = 0.780014
[2019-08-24 18:51:02,785] TRAIN Iter 275980: lr = 0.040035,	loss = 2.274872,	Top-1 err = 0.320312,	Top-5 err = 0.128662,	data_time = 0.050950,	train_time = 0.344138
[2019-08-24 18:51:18,004] TRAIN Iter 276000: lr = 0.040002,	loss = 2.244829,	Top-1 err = 0.315527,	Top-5 err = 0.129297,	data_time = 0.050399,	train_time = 0.760934
[2019-08-24 18:51:33,071] TRAIN Iter 276020: lr = 0.039968,	loss = 2.266538,	Top-1 err = 0.307910,	Top-5 err = 0.127148,	data_time = 0.050432,	train_time = 0.753354
[2019-08-24 18:51:40,228] TRAIN Iter 276040: lr = 0.039935,	loss = 2.259712,	Top-1 err = 0.311768,	Top-5 err = 0.128027,	data_time = 0.050242,	train_time = 0.357829
[2019-08-24 18:51:57,235] TRAIN Iter 276060: lr = 0.039902,	loss = 2.386910,	Top-1 err = 0.317432,	Top-5 err = 0.127930,	data_time = 0.050618,	train_time = 0.850309
[2019-08-24 18:52:04,220] TRAIN Iter 276080: lr = 0.039868,	loss = 2.413916,	Top-1 err = 0.315674,	Top-5 err = 0.129102,	data_time = 0.050440,	train_time = 0.349244
[2019-08-24 18:52:20,826] TRAIN Iter 276100: lr = 0.039835,	loss = 2.258253,	Top-1 err = 0.316064,	Top-5 err = 0.125830,	data_time = 0.050321,	train_time = 0.830307
[2019-08-24 18:52:37,531] TRAIN Iter 276120: lr = 0.039802,	loss = 2.248383,	Top-1 err = 0.313428,	Top-5 err = 0.125977,	data_time = 0.050189,	train_time = 0.835235
[2019-08-24 18:52:44,524] TRAIN Iter 276140: lr = 0.039768,	loss = 2.269818,	Top-1 err = 0.315283,	Top-5 err = 0.129541,	data_time = 0.050594,	train_time = 0.349641
[2019-08-24 18:52:59,508] TRAIN Iter 276160: lr = 0.039735,	loss = 2.312978,	Top-1 err = 0.314307,	Top-5 err = 0.126562,	data_time = 0.050794,	train_time = 0.749167
[2019-08-24 18:53:16,743] TRAIN Iter 276180: lr = 0.039702,	loss = 2.247252,	Top-1 err = 0.315869,	Top-5 err = 0.126318,	data_time = 0.110953,	train_time = 0.861722
[2019-08-24 18:53:23,775] TRAIN Iter 276200: lr = 0.039668,	loss = 2.257356,	Top-1 err = 0.311963,	Top-5 err = 0.126904,	data_time = 0.050579,	train_time = 0.351612
[2019-08-24 18:53:40,090] TRAIN Iter 276220: lr = 0.039635,	loss = 2.316148,	Top-1 err = 0.320361,	Top-5 err = 0.130469,	data_time = 0.050780,	train_time = 0.815705
[2019-08-24 18:53:47,676] TRAIN Iter 276240: lr = 0.039602,	loss = 2.393790,	Top-1 err = 0.319141,	Top-5 err = 0.131787,	data_time = 0.050659,	train_time = 0.379311
[2019-08-24 18:54:03,437] TRAIN Iter 276260: lr = 0.039568,	loss = 2.247770,	Top-1 err = 0.317676,	Top-5 err = 0.130273,	data_time = 0.050435,	train_time = 0.788055
[2019-08-24 18:54:19,417] TRAIN Iter 276280: lr = 0.039535,	loss = 2.300106,	Top-1 err = 0.316455,	Top-5 err = 0.129395,	data_time = 0.050519,	train_time = 0.798963
[2019-08-24 18:54:26,379] TRAIN Iter 276300: lr = 0.039502,	loss = 2.306626,	Top-1 err = 0.309033,	Top-5 err = 0.124756,	data_time = 0.050415,	train_time = 0.348072
[2019-08-24 18:54:43,119] TRAIN Iter 276320: lr = 0.039468,	loss = 2.228417,	Top-1 err = 0.321045,	Top-5 err = 0.132275,	data_time = 0.050292,	train_time = 0.837007
[2019-08-24 18:54:59,188] TRAIN Iter 276340: lr = 0.039435,	loss = 2.275620,	Top-1 err = 0.318994,	Top-5 err = 0.127588,	data_time = 0.050423,	train_time = 0.803427
[2019-08-24 18:55:06,962] TRAIN Iter 276360: lr = 0.039402,	loss = 2.395682,	Top-1 err = 0.319434,	Top-5 err = 0.128906,	data_time = 0.050436,	train_time = 0.388673
[2019-08-24 18:55:23,323] TRAIN Iter 276380: lr = 0.039368,	loss = 2.288859,	Top-1 err = 0.320850,	Top-5 err = 0.130176,	data_time = 0.050493,	train_time = 0.818052
[2019-08-24 18:55:30,838] TRAIN Iter 276400: lr = 0.039335,	loss = 2.232099,	Top-1 err = 0.317236,	Top-5 err = 0.130078,	data_time = 0.050540,	train_time = 0.375727
[2019-08-24 18:55:46,289] TRAIN Iter 276420: lr = 0.039302,	loss = 2.249115,	Top-1 err = 0.307764,	Top-5 err = 0.128174,	data_time = 0.050257,	train_time = 0.772559
[2019-08-24 18:56:04,336] TRAIN Iter 276440: lr = 0.039268,	loss = 2.206690,	Top-1 err = 0.317090,	Top-5 err = 0.128320,	data_time = 0.050514,	train_time = 0.902300
[2019-08-24 18:56:11,082] TRAIN Iter 276460: lr = 0.039235,	loss = 2.300901,	Top-1 err = 0.317529,	Top-5 err = 0.126465,	data_time = 0.050399,	train_time = 0.337311
[2019-08-24 18:56:28,948] TRAIN Iter 276480: lr = 0.039202,	loss = 2.192769,	Top-1 err = 0.316309,	Top-5 err = 0.125000,	data_time = 0.050654,	train_time = 0.893294
[2019-08-24 18:56:48,553] TRAIN Iter 276500: lr = 0.039168,	loss = 2.332759,	Top-1 err = 0.312109,	Top-5 err = 0.126123,	data_time = 0.050480,	train_time = 0.980223
[2019-08-24 18:56:55,252] TRAIN Iter 276520: lr = 0.039135,	loss = 2.240457,	Top-1 err = 0.315381,	Top-5 err = 0.126318,	data_time = 0.050187,	train_time = 0.334913
[2019-08-24 18:57:13,515] TRAIN Iter 276540: lr = 0.039102,	loss = 2.339162,	Top-1 err = 0.319922,	Top-5 err = 0.130908,	data_time = 0.050346,	train_time = 0.913153
[2019-08-24 18:57:21,078] TRAIN Iter 276560: lr = 0.039068,	loss = 2.256739,	Top-1 err = 0.321240,	Top-5 err = 0.129395,	data_time = 0.050370,	train_time = 0.378160
[2019-08-24 18:57:36,477] TRAIN Iter 276580: lr = 0.039035,	loss = 2.263192,	Top-1 err = 0.313916,	Top-5 err = 0.131592,	data_time = 0.050086,	train_time = 0.769916
[2019-08-24 18:57:52,712] TRAIN Iter 276600: lr = 0.039002,	loss = 2.295598,	Top-1 err = 0.312012,	Top-5 err = 0.127881,	data_time = 0.049908,	train_time = 0.811728
[2019-08-24 18:57:58,898] TRAIN Iter 276620: lr = 0.038968,	loss = 2.225866,	Top-1 err = 0.311572,	Top-5 err = 0.125732,	data_time = 0.049861,	train_time = 0.309297
[2019-08-24 18:58:51,847] TRAIN Iter 276640: lr = 0.038935,	loss = 2.285060,	Top-1 err = 0.312240,	Top-5 err = 0.130493,	data_time = 0.050443,	train_time = 2.647450
[2019-08-24 18:58:58,974] TRAIN Iter 276660: lr = 0.038902,	loss = 2.134405,	Top-1 err = 0.309814,	Top-5 err = 0.120557,	data_time = 0.050385,	train_time = 0.356340
[2019-08-24 18:59:15,911] TRAIN Iter 276680: lr = 0.038868,	loss = 2.314106,	Top-1 err = 0.308057,	Top-5 err = 0.124023,	data_time = 0.050639,	train_time = 0.846802
[2019-08-24 18:59:31,213] TRAIN Iter 276700: lr = 0.038835,	loss = 2.320578,	Top-1 err = 0.313818,	Top-5 err = 0.124609,	data_time = 0.050569,	train_time = 0.765106
[2019-08-24 18:59:39,054] TRAIN Iter 276720: lr = 0.038802,	loss = 2.183923,	Top-1 err = 0.302295,	Top-5 err = 0.118896,	data_time = 0.050532,	train_time = 0.392026
[2019-08-24 18:59:51,851] TRAIN Iter 276740: lr = 0.038768,	loss = 2.211541,	Top-1 err = 0.306836,	Top-5 err = 0.116992,	data_time = 0.050550,	train_time = 0.639833
[2019-08-24 19:00:06,067] TRAIN Iter 276760: lr = 0.038735,	loss = 2.296684,	Top-1 err = 0.311230,	Top-5 err = 0.122314,	data_time = 6.545796,	train_time = 0.710778
[2019-08-24 19:00:15,418] TRAIN Iter 276780: lr = 0.038702,	loss = 2.254641,	Top-1 err = 0.313379,	Top-5 err = 0.123828,	data_time = 0.050425,	train_time = 0.467556
[2019-08-24 19:00:31,255] TRAIN Iter 276800: lr = 0.038668,	loss = 2.227696,	Top-1 err = 0.312744,	Top-5 err = 0.124023,	data_time = 0.050598,	train_time = 0.791851
[2019-08-24 19:00:38,801] TRAIN Iter 276820: lr = 0.038635,	loss = 2.284414,	Top-1 err = 0.312988,	Top-5 err = 0.127881,	data_time = 0.106379,	train_time = 0.377258
[2019-08-24 19:00:53,923] TRAIN Iter 276840: lr = 0.038602,	loss = 2.294795,	Top-1 err = 0.309277,	Top-5 err = 0.122949,	data_time = 0.050329,	train_time = 0.756077
[2019-08-24 19:01:07,538] TRAIN Iter 276860: lr = 0.038568,	loss = 2.279346,	Top-1 err = 0.312061,	Top-5 err = 0.122852,	data_time = 0.097735,	train_time = 0.680763
[2019-08-24 19:01:14,539] TRAIN Iter 276880: lr = 0.038535,	loss = 2.190570,	Top-1 err = 0.311963,	Top-5 err = 0.120947,	data_time = 0.050742,	train_time = 0.350016
[2019-08-24 19:01:29,152] TRAIN Iter 276900: lr = 0.038502,	loss = 2.244359,	Top-1 err = 0.309033,	Top-5 err = 0.124658,	data_time = 0.050359,	train_time = 0.730617
[2019-08-24 19:01:42,434] TRAIN Iter 276920: lr = 0.038468,	loss = 2.120358,	Top-1 err = 0.302979,	Top-5 err = 0.123242,	data_time = 0.101309,	train_time = 0.664126
[2019-08-24 19:01:50,172] TRAIN Iter 276940: lr = 0.038435,	loss = 2.236618,	Top-1 err = 0.311279,	Top-5 err = 0.125488,	data_time = 0.050496,	train_time = 0.386841
[2019-08-24 19:02:04,480] TRAIN Iter 276960: lr = 0.038402,	loss = 2.266364,	Top-1 err = 0.310840,	Top-5 err = 0.125244,	data_time = 0.130594,	train_time = 0.715430
[2019-08-24 19:02:11,699] TRAIN Iter 276980: lr = 0.038368,	loss = 2.238690,	Top-1 err = 0.311475,	Top-5 err = 0.123926,	data_time = 0.050812,	train_time = 0.360930
[2019-08-24 19:02:26,746] TRAIN Iter 277000: lr = 0.038335,	loss = 2.319883,	Top-1 err = 0.308301,	Top-5 err = 0.124219,	data_time = 0.050637,	train_time = 0.752290
[2019-08-24 19:02:43,452] TRAIN Iter 277020: lr = 0.038302,	loss = 2.275523,	Top-1 err = 0.308008,	Top-5 err = 0.124854,	data_time = 0.050509,	train_time = 0.835288
[2019-08-24 19:02:50,822] TRAIN Iter 277040: lr = 0.038268,	loss = 2.253467,	Top-1 err = 0.308008,	Top-5 err = 0.123047,	data_time = 0.136270,	train_time = 0.368526
[2019-08-24 19:03:05,914] TRAIN Iter 277060: lr = 0.038235,	loss = 2.250237,	Top-1 err = 0.318896,	Top-5 err = 0.128564,	data_time = 0.050354,	train_time = 0.754577
[2019-08-24 19:03:21,196] TRAIN Iter 277080: lr = 0.038202,	loss = 2.366546,	Top-1 err = 0.311816,	Top-5 err = 0.124414,	data_time = 1.027919,	train_time = 0.764043
[2019-08-24 19:03:30,103] TRAIN Iter 277100: lr = 0.038168,	loss = 2.328234,	Top-1 err = 0.308154,	Top-5 err = 0.121045,	data_time = 0.050447,	train_time = 0.445360
[2019-08-24 19:03:45,691] TRAIN Iter 277120: lr = 0.038135,	loss = 2.345502,	Top-1 err = 0.312891,	Top-5 err = 0.124805,	data_time = 0.050550,	train_time = 0.779373
[2019-08-24 19:03:52,986] TRAIN Iter 277140: lr = 0.038102,	loss = 2.181650,	Top-1 err = 0.309619,	Top-5 err = 0.122803,	data_time = 0.157581,	train_time = 0.364764
[2019-08-24 19:04:08,973] TRAIN Iter 277160: lr = 0.038068,	loss = 2.241752,	Top-1 err = 0.307568,	Top-5 err = 0.124512,	data_time = 0.126175,	train_time = 0.799327
[2019-08-24 19:04:24,080] TRAIN Iter 277180: lr = 0.038035,	loss = 2.243358,	Top-1 err = 0.312891,	Top-5 err = 0.128174,	data_time = 0.135844,	train_time = 0.755352
[2019-08-24 19:04:31,314] TRAIN Iter 277200: lr = 0.038002,	loss = 2.210460,	Top-1 err = 0.317236,	Top-5 err = 0.127441,	data_time = 0.050584,	train_time = 0.361655
[2019-08-24 19:04:47,600] TRAIN Iter 277220: lr = 0.037968,	loss = 2.296981,	Top-1 err = 0.312451,	Top-5 err = 0.126904,	data_time = 0.050498,	train_time = 0.814286
[2019-08-24 19:05:01,272] TRAIN Iter 277240: lr = 0.037935,	loss = 2.272057,	Top-1 err = 0.314600,	Top-5 err = 0.122852,	data_time = 5.296226,	train_time = 0.683614
[2019-08-24 19:05:08,584] TRAIN Iter 277260: lr = 0.037902,	loss = 2.390899,	Top-1 err = 0.314160,	Top-5 err = 0.127588,	data_time = 0.050488,	train_time = 0.365565
[2019-08-24 19:05:25,207] TRAIN Iter 277280: lr = 0.037868,	loss = 2.326563,	Top-1 err = 0.315283,	Top-5 err = 0.126660,	data_time = 0.050572,	train_time = 0.831128
[2019-08-24 19:05:32,268] TRAIN Iter 277300: lr = 0.037835,	loss = 2.283390,	Top-1 err = 0.308154,	Top-5 err = 0.122705,	data_time = 0.050542,	train_time = 0.353052
[2019-08-24 19:05:49,528] TRAIN Iter 277320: lr = 0.037802,	loss = 2.331520,	Top-1 err = 0.312744,	Top-5 err = 0.128809,	data_time = 0.050437,	train_time = 0.862966
[2019-08-24 19:06:09,416] TRAIN Iter 277340: lr = 0.037768,	loss = 2.349120,	Top-1 err = 0.309961,	Top-5 err = 0.126270,	data_time = 0.050205,	train_time = 0.994392
[2019-08-24 19:06:17,135] TRAIN Iter 277360: lr = 0.037735,	loss = 2.295449,	Top-1 err = 0.310254,	Top-5 err = 0.124951,	data_time = 0.050392,	train_time = 0.385944
[2019-08-24 19:06:27,794] TRAIN Iter 277380: lr = 0.037702,	loss = 2.360658,	Top-1 err = 0.315625,	Top-5 err = 0.126855,	data_time = 0.050480,	train_time = 0.532956
[2019-08-24 19:06:41,875] TRAIN Iter 277400: lr = 0.037668,	loss = 2.299856,	Top-1 err = 0.315674,	Top-5 err = 0.123486,	data_time = 2.669389,	train_time = 0.704008
[2019-08-24 19:06:51,715] TRAIN Iter 277420: lr = 0.037635,	loss = 2.285475,	Top-1 err = 0.312598,	Top-5 err = 0.126904,	data_time = 0.050596,	train_time = 0.492004
[2019-08-24 19:07:06,703] TRAIN Iter 277440: lr = 0.037602,	loss = 2.307624,	Top-1 err = 0.308594,	Top-5 err = 0.123047,	data_time = 0.050500,	train_time = 0.749366
[2019-08-24 19:07:13,962] TRAIN Iter 277460: lr = 0.037568,	loss = 2.260287,	Top-1 err = 0.313965,	Top-5 err = 0.128076,	data_time = 0.050485,	train_time = 0.362960
[2019-08-24 19:07:31,332] TRAIN Iter 277480: lr = 0.037535,	loss = 2.246810,	Top-1 err = 0.315723,	Top-5 err = 0.129199,	data_time = 0.050810,	train_time = 0.868476
[2019-08-24 19:07:46,342] TRAIN Iter 277500: lr = 0.037502,	loss = 2.249539,	Top-1 err = 0.317090,	Top-5 err = 0.130273,	data_time = 0.050345,	train_time = 0.750500
[2019-08-24 19:07:53,413] TRAIN Iter 277520: lr = 0.037468,	loss = 2.282231,	Top-1 err = 0.309619,	Top-5 err = 0.127148,	data_time = 0.050869,	train_time = 0.353540
[2019-08-24 19:08:10,098] TRAIN Iter 277540: lr = 0.037435,	loss = 2.307518,	Top-1 err = 0.314795,	Top-5 err = 0.127588,	data_time = 0.050383,	train_time = 0.834228
[2019-08-24 19:08:23,876] TRAIN Iter 277560: lr = 0.037402,	loss = 2.262046,	Top-1 err = 0.318066,	Top-5 err = 0.129248,	data_time = 2.314888,	train_time = 0.688864
[2019-08-24 19:08:33,398] TRAIN Iter 277580: lr = 0.037368,	loss = 2.259724,	Top-1 err = 0.308984,	Top-5 err = 0.126172,	data_time = 0.050399,	train_time = 0.476080
[2019-08-24 19:08:48,527] TRAIN Iter 277600: lr = 0.037335,	loss = 2.176855,	Top-1 err = 0.312793,	Top-5 err = 0.126270,	data_time = 0.050750,	train_time = 0.756470
[2019-08-24 19:08:55,421] TRAIN Iter 277620: lr = 0.037302,	loss = 2.217804,	Top-1 err = 0.310547,	Top-5 err = 0.125342,	data_time = 0.052147,	train_time = 0.344660
[2019-08-24 19:09:12,196] TRAIN Iter 277640: lr = 0.037268,	loss = 2.306057,	Top-1 err = 0.316504,	Top-5 err = 0.125879,	data_time = 0.050392,	train_time = 0.838724
[2019-08-24 19:09:29,303] TRAIN Iter 277660: lr = 0.037235,	loss = 2.303856,	Top-1 err = 0.316260,	Top-5 err = 0.127539,	data_time = 0.050516,	train_time = 0.855360
[2019-08-24 19:09:36,318] TRAIN Iter 277680: lr = 0.037202,	loss = 2.255013,	Top-1 err = 0.316211,	Top-5 err = 0.126953,	data_time = 0.050362,	train_time = 0.350710
[2019-08-24 19:09:54,439] TRAIN Iter 277700: lr = 0.037168,	loss = 2.223571,	Top-1 err = 0.314551,	Top-5 err = 0.127539,	data_time = 0.050656,	train_time = 0.906040
[2019-08-24 19:10:10,838] TRAIN Iter 277720: lr = 0.037135,	loss = 2.357078,	Top-1 err = 0.314502,	Top-5 err = 0.129150,	data_time = 0.050551,	train_time = 0.819957
[2019-08-24 19:10:18,199] TRAIN Iter 277740: lr = 0.037102,	loss = 2.271032,	Top-1 err = 0.312646,	Top-5 err = 0.129736,	data_time = 0.050373,	train_time = 0.367998
[2019-08-24 19:10:35,974] TRAIN Iter 277760: lr = 0.037068,	loss = 2.279926,	Top-1 err = 0.314111,	Top-5 err = 0.127246,	data_time = 0.050524,	train_time = 0.888777
[2019-08-24 19:10:42,859] TRAIN Iter 277780: lr = 0.037035,	loss = 2.256807,	Top-1 err = 0.317529,	Top-5 err = 0.128271,	data_time = 0.147545,	train_time = 0.344198
[2019-08-24 19:11:01,723] TRAIN Iter 277800: lr = 0.037002,	loss = 2.185443,	Top-1 err = 0.309961,	Top-5 err = 0.125244,	data_time = 0.050588,	train_time = 0.943207
[2019-08-24 19:11:20,498] TRAIN Iter 277820: lr = 0.036968,	loss = 2.170254,	Top-1 err = 0.308545,	Top-5 err = 0.120996,	data_time = 0.050059,	train_time = 0.938734
[2019-08-24 19:11:27,395] TRAIN Iter 277840: lr = 0.036935,	loss = 2.237123,	Top-1 err = 0.314795,	Top-5 err = 0.128369,	data_time = 0.050160,	train_time = 0.344842
[2019-08-24 19:11:44,453] TRAIN Iter 277860: lr = 0.036902,	loss = 2.345187,	Top-1 err = 0.314844,	Top-5 err = 0.128564,	data_time = 0.049930,	train_time = 0.852859
[2019-08-24 19:11:56,423] TRAIN Iter 277880: lr = 0.036868,	loss = 2.473050,	Top-1 err = 0.318215,	Top-5 err = 0.124596,	data_time = 0.007095,	train_time = 0.598507
[2019-08-24 19:12:46,047] TRAIN Iter 277900: lr = 0.036835,	loss = 2.320504,	Top-1 err = 0.306689,	Top-5 err = 0.120947,	data_time = 0.050457,	train_time = 2.481179
[2019-08-24 19:13:01,233] TRAIN Iter 277920: lr = 0.036802,	loss = 2.212800,	Top-1 err = 0.307129,	Top-5 err = 0.120898,	data_time = 0.050319,	train_time = 0.759313
[2019-08-24 19:13:08,458] TRAIN Iter 277940: lr = 0.036768,	loss = 2.223603,	Top-1 err = 0.308350,	Top-5 err = 0.123438,	data_time = 0.050668,	train_time = 0.361198
[2019-08-24 19:13:22,371] TRAIN Iter 277960: lr = 0.036735,	loss = 2.165908,	Top-1 err = 0.304980,	Top-5 err = 0.117871,	data_time = 0.050777,	train_time = 0.695647
[2019-08-24 19:13:36,553] TRAIN Iter 277980: lr = 0.036702,	loss = 2.215422,	Top-1 err = 0.308447,	Top-5 err = 0.122998,	data_time = 0.119304,	train_time = 0.709084
[2019-08-24 19:13:43,700] TRAIN Iter 278000: lr = 0.036668,	loss = 2.238185,	Top-1 err = 0.308789,	Top-5 err = 0.122949,	data_time = 0.050570,	train_time = 0.357325
[2019-08-24 19:14:00,197] TRAIN Iter 278020: lr = 0.036635,	loss = 2.318489,	Top-1 err = 0.308203,	Top-5 err = 0.122803,	data_time = 0.050123,	train_time = 0.824865
[2019-08-24 19:14:07,549] TRAIN Iter 278040: lr = 0.036602,	loss = 2.252818,	Top-1 err = 0.309131,	Top-5 err = 0.121436,	data_time = 0.050394,	train_time = 0.367600
[2019-08-24 19:14:21,523] TRAIN Iter 278060: lr = 0.036568,	loss = 2.242871,	Top-1 err = 0.308887,	Top-5 err = 0.122461,	data_time = 0.050768,	train_time = 0.698637
[2019-08-24 19:14:34,819] TRAIN Iter 278080: lr = 0.036535,	loss = 2.217901,	Top-1 err = 0.310596,	Top-5 err = 0.124707,	data_time = 0.050313,	train_time = 0.664829
[2019-08-24 19:14:42,900] TRAIN Iter 278100: lr = 0.036502,	loss = 2.136085,	Top-1 err = 0.314160,	Top-5 err = 0.124512,	data_time = 0.050684,	train_time = 0.404008
[2019-08-24 19:14:57,858] TRAIN Iter 278120: lr = 0.036468,	loss = 2.358862,	Top-1 err = 0.316943,	Top-5 err = 0.126416,	data_time = 0.050197,	train_time = 0.747904
[2019-08-24 19:15:12,044] TRAIN Iter 278140: lr = 0.036435,	loss = 2.292071,	Top-1 err = 0.311230,	Top-5 err = 0.121387,	data_time = 0.127525,	train_time = 0.709284
[2019-08-24 19:15:19,748] TRAIN Iter 278160: lr = 0.036402,	loss = 2.207991,	Top-1 err = 0.306006,	Top-5 err = 0.126904,	data_time = 0.050503,	train_time = 0.385149
[2019-08-24 19:15:34,412] TRAIN Iter 278180: lr = 0.036368,	loss = 2.189862,	Top-1 err = 0.315479,	Top-5 err = 0.123682,	data_time = 0.050916,	train_time = 0.733234
[2019-08-24 19:15:41,319] TRAIN Iter 278200: lr = 0.036335,	loss = 2.277884,	Top-1 err = 0.309961,	Top-5 err = 0.125049,	data_time = 0.104548,	train_time = 0.345287
[2019-08-24 19:15:56,122] TRAIN Iter 278220: lr = 0.036302,	loss = 2.251116,	Top-1 err = 0.305811,	Top-5 err = 0.123486,	data_time = 0.050413,	train_time = 0.740164
[2019-08-24 19:16:09,949] TRAIN Iter 278240: lr = 0.036268,	loss = 2.316544,	Top-1 err = 0.311084,	Top-5 err = 0.123828,	data_time = 0.050508,	train_time = 0.691312
[2019-08-24 19:16:18,381] TRAIN Iter 278260: lr = 0.036235,	loss = 2.220594,	Top-1 err = 0.304980,	Top-5 err = 0.121875,	data_time = 0.050382,	train_time = 0.421582
[2019-08-24 19:16:33,801] TRAIN Iter 278280: lr = 0.036202,	loss = 2.269789,	Top-1 err = 0.310596,	Top-5 err = 0.122998,	data_time = 0.050455,	train_time = 0.771020
[2019-08-24 19:16:48,423] TRAIN Iter 278300: lr = 0.036168,	loss = 2.247386,	Top-1 err = 0.309863,	Top-5 err = 0.119971,	data_time = 0.098559,	train_time = 0.731062
[2019-08-24 19:16:56,772] TRAIN Iter 278320: lr = 0.036135,	loss = 2.292797,	Top-1 err = 0.309814,	Top-5 err = 0.126318,	data_time = 0.050395,	train_time = 0.417422
[2019-08-24 19:17:11,761] TRAIN Iter 278340: lr = 0.036102,	loss = 2.267063,	Top-1 err = 0.311621,	Top-5 err = 0.125391,	data_time = 0.050580,	train_time = 0.749465
[2019-08-24 19:17:19,208] TRAIN Iter 278360: lr = 0.036068,	loss = 2.315078,	Top-1 err = 0.312793,	Top-5 err = 0.120801,	data_time = 0.050574,	train_time = 0.372308
[2019-08-24 19:17:34,155] TRAIN Iter 278380: lr = 0.036035,	loss = 2.289495,	Top-1 err = 0.311621,	Top-5 err = 0.126318,	data_time = 0.050206,	train_time = 0.747368
[2019-08-24 19:17:49,487] TRAIN Iter 278400: lr = 0.036002,	loss = 2.247399,	Top-1 err = 0.311133,	Top-5 err = 0.124170,	data_time = 0.050278,	train_time = 0.766561
[2019-08-24 19:17:56,617] TRAIN Iter 278420: lr = 0.035968,	loss = 2.238106,	Top-1 err = 0.310498,	Top-5 err = 0.123584,	data_time = 0.050679,	train_time = 0.356483
[2019-08-24 19:18:13,022] TRAIN Iter 278440: lr = 0.035935,	loss = 2.200270,	Top-1 err = 0.306055,	Top-5 err = 0.125098,	data_time = 0.050433,	train_time = 0.820248
[2019-08-24 19:18:28,231] TRAIN Iter 278460: lr = 0.035902,	loss = 2.224871,	Top-1 err = 0.312695,	Top-5 err = 0.124219,	data_time = 0.050421,	train_time = 0.760435
[2019-08-24 19:18:35,775] TRAIN Iter 278480: lr = 0.035868,	loss = 2.294853,	Top-1 err = 0.311523,	Top-5 err = 0.124951,	data_time = 0.050415,	train_time = 0.377183
[2019-08-24 19:18:51,814] TRAIN Iter 278500: lr = 0.035835,	loss = 2.298938,	Top-1 err = 0.309521,	Top-5 err = 0.126855,	data_time = 0.050484,	train_time = 0.801953
[2019-08-24 19:18:58,951] TRAIN Iter 278520: lr = 0.035802,	loss = 2.266983,	Top-1 err = 0.313330,	Top-5 err = 0.124658,	data_time = 0.143180,	train_time = 0.356817
[2019-08-24 19:19:14,236] TRAIN Iter 278540: lr = 0.035768,	loss = 2.278142,	Top-1 err = 0.310254,	Top-5 err = 0.125928,	data_time = 0.050555,	train_time = 0.764242
[2019-08-24 19:19:29,672] TRAIN Iter 278560: lr = 0.035735,	loss = 2.234451,	Top-1 err = 0.313525,	Top-5 err = 0.128516,	data_time = 0.050933,	train_time = 0.771781
[2019-08-24 19:19:37,041] TRAIN Iter 278580: lr = 0.035702,	loss = 2.248041,	Top-1 err = 0.314551,	Top-5 err = 0.127051,	data_time = 0.142979,	train_time = 0.368423
[2019-08-24 19:19:53,920] TRAIN Iter 278600: lr = 0.035668,	loss = 2.302042,	Top-1 err = 0.313086,	Top-5 err = 0.125244,	data_time = 0.050465,	train_time = 0.843959
[2019-08-24 19:20:09,135] TRAIN Iter 278620: lr = 0.035635,	loss = 2.177613,	Top-1 err = 0.310498,	Top-5 err = 0.125684,	data_time = 0.135447,	train_time = 0.760755
[2019-08-24 19:20:16,604] TRAIN Iter 278640: lr = 0.035602,	loss = 2.288283,	Top-1 err = 0.314941,	Top-5 err = 0.128076,	data_time = 0.050877,	train_time = 0.373429
[2019-08-24 19:20:32,301] TRAIN Iter 278660: lr = 0.035568,	loss = 2.206924,	Top-1 err = 0.306055,	Top-5 err = 0.124121,	data_time = 0.050810,	train_time = 0.784808
[2019-08-24 19:20:39,243] TRAIN Iter 278680: lr = 0.035535,	loss = 2.246980,	Top-1 err = 0.314063,	Top-5 err = 0.128564,	data_time = 0.050268,	train_time = 0.347127
[2019-08-24 19:20:56,026] TRAIN Iter 278700: lr = 0.035502,	loss = 2.236435,	Top-1 err = 0.310449,	Top-5 err = 0.127539,	data_time = 0.050397,	train_time = 0.839098
[2019-08-24 19:21:10,623] TRAIN Iter 278720: lr = 0.035468,	loss = 2.255709,	Top-1 err = 0.309570,	Top-5 err = 0.124219,	data_time = 0.050436,	train_time = 0.729835
[2019-08-24 19:21:18,791] TRAIN Iter 278740: lr = 0.035435,	loss = 2.271058,	Top-1 err = 0.311377,	Top-5 err = 0.122412,	data_time = 0.050488,	train_time = 0.408394
[2019-08-24 19:21:35,411] TRAIN Iter 278760: lr = 0.035402,	loss = 2.193393,	Top-1 err = 0.308154,	Top-5 err = 0.127393,	data_time = 0.050805,	train_time = 0.830971
[2019-08-24 19:21:49,884] TRAIN Iter 278780: lr = 0.035368,	loss = 2.275166,	Top-1 err = 0.311279,	Top-5 err = 0.127051,	data_time = 0.050624,	train_time = 0.723636
[2019-08-24 19:21:58,994] TRAIN Iter 278800: lr = 0.035335,	loss = 2.241775,	Top-1 err = 0.312988,	Top-5 err = 0.128711,	data_time = 0.050509,	train_time = 0.455511
[2019-08-24 19:22:16,171] TRAIN Iter 278820: lr = 0.035302,	loss = 2.241252,	Top-1 err = 0.309131,	Top-5 err = 0.126758,	data_time = 0.050362,	train_time = 0.858817
[2019-08-24 19:22:23,129] TRAIN Iter 278840: lr = 0.035268,	loss = 2.282834,	Top-1 err = 0.308008,	Top-5 err = 0.123535,	data_time = 0.050236,	train_time = 0.347923
[2019-08-24 19:22:40,724] TRAIN Iter 278860: lr = 0.035235,	loss = 2.275212,	Top-1 err = 0.321143,	Top-5 err = 0.130176,	data_time = 0.050524,	train_time = 0.879734
[2019-08-24 19:22:53,537] TRAIN Iter 278880: lr = 0.035202,	loss = 2.225044,	Top-1 err = 0.310303,	Top-5 err = 0.123291,	data_time = 0.050480,	train_time = 0.640635
[2019-08-24 19:23:05,968] TRAIN Iter 278900: lr = 0.035168,	loss = 2.216143,	Top-1 err = 0.307275,	Top-5 err = 0.126855,	data_time = 0.050303,	train_time = 0.621495
[2019-08-24 19:23:23,919] TRAIN Iter 278920: lr = 0.035135,	loss = 2.179255,	Top-1 err = 0.311719,	Top-5 err = 0.125244,	data_time = 0.050328,	train_time = 0.897530
[2019-08-24 19:23:39,552] TRAIN Iter 278940: lr = 0.035102,	loss = 2.177640,	Top-1 err = 0.316602,	Top-5 err = 0.130811,	data_time = 0.050477,	train_time = 0.781630
[2019-08-24 19:23:47,868] TRAIN Iter 278960: lr = 0.035068,	loss = 2.255534,	Top-1 err = 0.316504,	Top-5 err = 0.128125,	data_time = 0.050147,	train_time = 0.415797
[2019-08-24 19:24:05,118] TRAIN Iter 278980: lr = 0.035035,	loss = 2.346415,	Top-1 err = 0.316992,	Top-5 err = 0.128467,	data_time = 0.050679,	train_time = 0.862511
[2019-08-24 19:24:12,432] TRAIN Iter 279000: lr = 0.035002,	loss = 2.267960,	Top-1 err = 0.310107,	Top-5 err = 0.124805,	data_time = 0.050214,	train_time = 0.365691
[2019-08-24 19:24:29,412] TRAIN Iter 279020: lr = 0.034968,	loss = 2.172059,	Top-1 err = 0.307715,	Top-5 err = 0.122070,	data_time = 0.050472,	train_time = 0.848965
[2019-08-24 19:24:44,283] TRAIN Iter 279040: lr = 0.034935,	loss = 2.230743,	Top-1 err = 0.311182,	Top-5 err = 0.127686,	data_time = 0.050205,	train_time = 0.743562
[2019-08-24 19:24:53,823] TRAIN Iter 279060: lr = 0.034902,	loss = 2.241107,	Top-1 err = 0.314209,	Top-5 err = 0.124609,	data_time = 0.050614,	train_time = 0.476953
[2019-08-24 19:25:13,085] TRAIN Iter 279080: lr = 0.034868,	loss = 2.213701,	Top-1 err = 0.316406,	Top-5 err = 0.127295,	data_time = 0.144156,	train_time = 0.963096
[2019-08-24 19:25:26,989] TRAIN Iter 279100: lr = 0.034835,	loss = 2.234333,	Top-1 err = 0.312158,	Top-5 err = 0.123828,	data_time = 0.049913,	train_time = 0.695197
[2019-08-24 19:25:35,433] TRAIN Iter 279120: lr = 0.034802,	loss = 2.270315,	Top-1 err = 0.313232,	Top-5 err = 0.124023,	data_time = 0.049911,	train_time = 0.422201
[2019-08-24 19:26:25,777] TRAIN Iter 279140: lr = 0.034768,	loss = 2.197931,	Top-1 err = 0.312934,	Top-5 err = 0.124815,	data_time = 0.050304,	train_time = 2.517176
[2019-08-24 19:26:32,724] TRAIN Iter 279160: lr = 0.034735,	loss = 2.189148,	Top-1 err = 0.299219,	Top-5 err = 0.118164,	data_time = 0.051104,	train_time = 0.347300
[2019-08-24 19:26:49,459] TRAIN Iter 279180: lr = 0.034702,	loss = 2.254045,	Top-1 err = 0.306445,	Top-5 err = 0.123242,	data_time = 0.050435,	train_time = 0.836741
[2019-08-24 19:27:04,702] TRAIN Iter 279200: lr = 0.034668,	loss = 2.242397,	Top-1 err = 0.302734,	Top-5 err = 0.119824,	data_time = 3.164650,	train_time = 0.762131
[2019-08-24 19:27:11,850] TRAIN Iter 279220: lr = 0.034635,	loss = 2.212165,	Top-1 err = 0.308398,	Top-5 err = 0.122998,	data_time = 0.050589,	train_time = 0.357407
[2019-08-24 19:27:25,300] TRAIN Iter 279240: lr = 0.034602,	loss = 2.212563,	Top-1 err = 0.307275,	Top-5 err = 0.122852,	data_time = 0.050519,	train_time = 0.672476
[2019-08-24 19:27:32,502] TRAIN Iter 279260: lr = 0.034568,	loss = 2.217679,	Top-1 err = 0.301367,	Top-5 err = 0.118506,	data_time = 0.125157,	train_time = 0.360077
[2019-08-24 19:27:49,976] TRAIN Iter 279280: lr = 0.034535,	loss = 2.322791,	Top-1 err = 0.304785,	Top-5 err = 0.118896,	data_time = 0.050477,	train_time = 0.873711
[2019-08-24 19:28:06,158] TRAIN Iter 279300: lr = 0.034502,	loss = 2.243155,	Top-1 err = 0.302051,	Top-5 err = 0.118555,	data_time = 0.050484,	train_time = 0.809082
[2019-08-24 19:28:13,412] TRAIN Iter 279320: lr = 0.034468,	loss = 2.227270,	Top-1 err = 0.301318,	Top-5 err = 0.121973,	data_time = 0.050353,	train_time = 0.362658
[2019-08-24 19:28:28,401] TRAIN Iter 279340: lr = 0.034435,	loss = 2.268689,	Top-1 err = 0.307324,	Top-5 err = 0.122559,	data_time = 0.050379,	train_time = 0.749425
[2019-08-24 19:28:44,217] TRAIN Iter 279360: lr = 0.034402,	loss = 2.307577,	Top-1 err = 0.306934,	Top-5 err = 0.125537,	data_time = 4.680557,	train_time = 0.790807
[2019-08-24 19:28:51,696] TRAIN Iter 279380: lr = 0.034368,	loss = 2.272326,	Top-1 err = 0.304248,	Top-5 err = 0.121094,	data_time = 0.050376,	train_time = 0.373924
[2019-08-24 19:29:04,948] TRAIN Iter 279400: lr = 0.034335,	loss = 2.259268,	Top-1 err = 0.304541,	Top-5 err = 0.121387,	data_time = 0.050538,	train_time = 0.662597
[2019-08-24 19:29:12,648] TRAIN Iter 279420: lr = 0.034302,	loss = 2.242106,	Top-1 err = 0.304053,	Top-5 err = 0.118945,	data_time = 0.050293,	train_time = 0.384953
[2019-08-24 19:29:26,840] TRAIN Iter 279440: lr = 0.034268,	loss = 2.275490,	Top-1 err = 0.310254,	Top-5 err = 0.124756,	data_time = 0.050604,	train_time = 0.709581
[2019-08-24 19:29:42,898] TRAIN Iter 279460: lr = 0.034235,	loss = 2.312067,	Top-1 err = 0.306250,	Top-5 err = 0.122949,	data_time = 0.050327,	train_time = 0.802903
[2019-08-24 19:29:50,147] TRAIN Iter 279480: lr = 0.034202,	loss = 2.264702,	Top-1 err = 0.309766,	Top-5 err = 0.127197,	data_time = 0.050309,	train_time = 0.362423
[2019-08-24 19:30:06,183] TRAIN Iter 279500: lr = 0.034168,	loss = 2.259143,	Top-1 err = 0.305273,	Top-5 err = 0.119531,	data_time = 0.050736,	train_time = 0.801785
[2019-08-24 19:30:20,425] TRAIN Iter 279520: lr = 0.034135,	loss = 2.201397,	Top-1 err = 0.306494,	Top-5 err = 0.122119,	data_time = 4.997695,	train_time = 0.712092
[2019-08-24 19:30:27,359] TRAIN Iter 279540: lr = 0.034102,	loss = 2.286491,	Top-1 err = 0.310840,	Top-5 err = 0.124805,	data_time = 0.050306,	train_time = 0.346695
[2019-08-24 19:30:43,477] TRAIN Iter 279560: lr = 0.034068,	loss = 2.228162,	Top-1 err = 0.311377,	Top-5 err = 0.125732,	data_time = 0.050490,	train_time = 0.805899
[2019-08-24 19:30:51,212] TRAIN Iter 279580: lr = 0.034035,	loss = 2.269072,	Top-1 err = 0.308936,	Top-5 err = 0.122070,	data_time = 0.139538,	train_time = 0.386706
[2019-08-24 19:31:06,185] TRAIN Iter 279600: lr = 0.034002,	loss = 2.248527,	Top-1 err = 0.308545,	Top-5 err = 0.124072,	data_time = 0.050650,	train_time = 0.748634
[2019-08-24 19:31:22,232] TRAIN Iter 279620: lr = 0.033968,	loss = 2.209770,	Top-1 err = 0.308545,	Top-5 err = 0.119287,	data_time = 0.050418,	train_time = 0.802341
[2019-08-24 19:31:29,401] TRAIN Iter 279640: lr = 0.033935,	loss = 2.255555,	Top-1 err = 0.314502,	Top-5 err = 0.124072,	data_time = 0.050330,	train_time = 0.358433
[2019-08-24 19:31:45,635] TRAIN Iter 279660: lr = 0.033902,	loss = 2.193146,	Top-1 err = 0.307568,	Top-5 err = 0.120313,	data_time = 0.050491,	train_time = 0.811685
[2019-08-24 19:32:01,059] TRAIN Iter 279680: lr = 0.033868,	loss = 2.243287,	Top-1 err = 0.304883,	Top-5 err = 0.122852,	data_time = 0.482701,	train_time = 0.771196
[2019-08-24 19:32:07,807] TRAIN Iter 279700: lr = 0.033835,	loss = 2.242234,	Top-1 err = 0.308789,	Top-5 err = 0.122412,	data_time = 0.050827,	train_time = 0.337395
[2019-08-24 19:32:23,082] TRAIN Iter 279720: lr = 0.033802,	loss = 2.211994,	Top-1 err = 0.312402,	Top-5 err = 0.125635,	data_time = 0.050336,	train_time = 0.763723
[2019-08-24 19:32:30,557] TRAIN Iter 279740: lr = 0.033768,	loss = 2.160329,	Top-1 err = 0.310400,	Top-5 err = 0.124951,	data_time = 0.051110,	train_time = 0.373723
[2019-08-24 19:32:47,250] TRAIN Iter 279760: lr = 0.033735,	loss = 2.298059,	Top-1 err = 0.314990,	Top-5 err = 0.125342,	data_time = 0.050240,	train_time = 0.834667
[2019-08-24 19:33:03,941] TRAIN Iter 279780: lr = 0.033702,	loss = 2.235107,	Top-1 err = 0.310693,	Top-5 err = 0.126660,	data_time = 0.050366,	train_time = 0.834526
[2019-08-24 19:33:11,323] TRAIN Iter 279800: lr = 0.033668,	loss = 2.233800,	Top-1 err = 0.305127,	Top-5 err = 0.120947,	data_time = 0.051079,	train_time = 0.369076
[2019-08-24 19:33:26,243] TRAIN Iter 279820: lr = 0.033635,	loss = 2.183058,	Top-1 err = 0.307910,	Top-5 err = 0.121582,	data_time = 0.050435,	train_time = 0.746005
[2019-08-24 19:33:42,808] TRAIN Iter 279840: lr = 0.033602,	loss = 2.208223,	Top-1 err = 0.307520,	Top-5 err = 0.121094,	data_time = 0.050406,	train_time = 0.828225
[2019-08-24 19:33:49,980] TRAIN Iter 279860: lr = 0.033568,	loss = 2.308035,	Top-1 err = 0.307715,	Top-5 err = 0.125244,	data_time = 0.050294,	train_time = 0.358572
[2019-08-24 19:34:05,070] TRAIN Iter 279880: lr = 0.033535,	loss = 2.275029,	Top-1 err = 0.307910,	Top-5 err = 0.121484,	data_time = 0.050500,	train_time = 0.754507
[2019-08-24 19:34:12,997] TRAIN Iter 279900: lr = 0.033502,	loss = 2.238209,	Top-1 err = 0.310498,	Top-5 err = 0.126367,	data_time = 0.050452,	train_time = 0.396292
[2019-08-24 19:34:27,827] TRAIN Iter 279920: lr = 0.033468,	loss = 2.208196,	Top-1 err = 0.309619,	Top-5 err = 0.125293,	data_time = 0.050537,	train_time = 0.741494
[2019-08-24 19:34:44,479] TRAIN Iter 279940: lr = 0.033435,	loss = 2.312468,	Top-1 err = 0.309863,	Top-5 err = 0.122998,	data_time = 0.050542,	train_time = 0.832599
[2019-08-24 19:34:51,620] TRAIN Iter 279960: lr = 0.033402,	loss = 2.313319,	Top-1 err = 0.315625,	Top-5 err = 0.127881,	data_time = 0.050575,	train_time = 0.357029
[2019-08-24 19:35:08,055] TRAIN Iter 279980: lr = 0.033368,	loss = 2.284200,	Top-1 err = 0.309375,	Top-5 err = 0.123047,	data_time = 0.050121,	train_time = 0.821759
[2019-08-24 19:35:24,484] TRAIN Iter 280000: lr = 0.033335,	loss = 2.298329,	Top-1 err = 0.314502,	Top-5 err = 0.127979,	data_time = 3.847007,	train_time = 0.821433
[2019-08-24 19:36:23,133] TEST Iter 280000: loss = 2.107369,	Top-1 err = 0.286600,	Top-5 err = 0.095620,	val_time = 58.610862
[2019-08-24 19:36:29,447] TRAIN Iter 280020: lr = 0.033302,	loss = 2.260820,	Top-1 err = 0.310693,	Top-5 err = 0.122021,	data_time = 0.050539,	train_time = 0.315659
[2019-08-24 19:36:35,859] TRAIN Iter 280040: lr = 0.033268,	loss = 2.331718,	Top-1 err = 0.309521,	Top-5 err = 0.123096,	data_time = 0.050537,	train_time = 0.320609
[2019-08-24 19:36:42,522] TRAIN Iter 280060: lr = 0.033235,	loss = 2.193146,	Top-1 err = 0.309033,	Top-5 err = 0.125391,	data_time = 0.050718,	train_time = 0.333143
[2019-08-24 19:36:51,144] TRAIN Iter 280080: lr = 0.033202,	loss = 2.272897,	Top-1 err = 0.307324,	Top-5 err = 0.123193,	data_time = 0.050419,	train_time = 0.431089
[2019-08-24 19:37:06,704] TRAIN Iter 280100: lr = 0.033168,	loss = 2.254935,	Top-1 err = 0.310889,	Top-5 err = 0.123242,	data_time = 0.050415,	train_time = 0.777988
[2019-08-24 19:37:15,404] TRAIN Iter 280120: lr = 0.033135,	loss = 2.241387,	Top-1 err = 0.301807,	Top-5 err = 0.121094,	data_time = 0.050950,	train_time = 0.434985
[2019-08-24 19:37:31,701] TRAIN Iter 280140: lr = 0.033102,	loss = 2.266655,	Top-1 err = 0.313867,	Top-5 err = 0.127490,	data_time = 0.050479,	train_time = 0.814825
[2019-08-24 19:37:40,526] TRAIN Iter 280160: lr = 0.033068,	loss = 2.195619,	Top-1 err = 0.309766,	Top-5 err = 0.126562,	data_time = 0.050442,	train_time = 0.441234
[2019-08-24 19:37:57,120] TRAIN Iter 280180: lr = 0.033035,	loss = 2.274997,	Top-1 err = 0.309277,	Top-5 err = 0.123779,	data_time = 0.050916,	train_time = 0.829660
[2019-08-24 19:38:11,222] TRAIN Iter 280200: lr = 0.033002,	loss = 2.300138,	Top-1 err = 0.307861,	Top-5 err = 0.124072,	data_time = 0.050507,	train_time = 0.705094
[2019-08-24 19:38:20,106] TRAIN Iter 280220: lr = 0.032968,	loss = 2.238200,	Top-1 err = 0.309961,	Top-5 err = 0.125830,	data_time = 0.050719,	train_time = 0.444182
[2019-08-24 19:38:37,897] TRAIN Iter 280240: lr = 0.032935,	loss = 2.254796,	Top-1 err = 0.307471,	Top-5 err = 0.127100,	data_time = 4.352657,	train_time = 0.889548
[2019-08-24 19:38:51,868] TRAIN Iter 280260: lr = 0.032902,	loss = 2.237659,	Top-1 err = 0.314990,	Top-5 err = 0.125537,	data_time = 0.050400,	train_time = 0.698530
[2019-08-24 19:39:03,496] TRAIN Iter 280280: lr = 0.032868,	loss = 2.275668,	Top-1 err = 0.306641,	Top-5 err = 0.122852,	data_time = 0.050496,	train_time = 0.581383
[2019-08-24 19:39:18,371] TRAIN Iter 280300: lr = 0.032835,	loss = 2.251188,	Top-1 err = 0.311133,	Top-5 err = 0.126123,	data_time = 0.050352,	train_time = 0.743779
[2019-08-24 19:39:27,623] TRAIN Iter 280320: lr = 0.032802,	loss = 2.209784,	Top-1 err = 0.313916,	Top-5 err = 0.124121,	data_time = 0.075660,	train_time = 0.462544
[2019-08-24 19:39:45,565] TRAIN Iter 280340: lr = 0.032768,	loss = 2.256495,	Top-1 err = 0.314697,	Top-5 err = 0.125732,	data_time = 0.050006,	train_time = 0.897130
[2019-08-24 19:39:58,438] TRAIN Iter 280360: lr = 0.032735,	loss = 2.273140,	Top-1 err = 0.309521,	Top-5 err = 0.123096,	data_time = 0.049958,	train_time = 0.643606
[2019-08-24 19:40:07,396] TRAIN Iter 280380: lr = 0.032702,	loss = 2.300960,	Top-1 err = 0.311816,	Top-5 err = 0.124902,	data_time = 0.049956,	train_time = 0.447881
[2019-08-24 19:40:54,774] TRAIN Iter 280400: lr = 0.032668,	loss = 2.250507,	Top-1 err = 0.309580,	Top-5 err = 0.124596,	data_time = 0.050302,	train_time = 2.368901
[2019-08-24 19:41:10,215] TRAIN Iter 280420: lr = 0.032635,	loss = 2.203548,	Top-1 err = 0.309082,	Top-5 err = 0.122705,	data_time = 0.964575,	train_time = 0.772022
[2019-08-24 19:41:17,236] TRAIN Iter 280440: lr = 0.032602,	loss = 2.228250,	Top-1 err = 0.302686,	Top-5 err = 0.118896,	data_time = 0.050542,	train_time = 0.351056
[2019-08-24 19:41:33,214] TRAIN Iter 280460: lr = 0.032568,	loss = 2.263563,	Top-1 err = 0.303711,	Top-5 err = 0.120361,	data_time = 0.050448,	train_time = 0.798895
[2019-08-24 19:41:40,855] TRAIN Iter 280480: lr = 0.032535,	loss = 2.248145,	Top-1 err = 0.299756,	Top-5 err = 0.117871,	data_time = 0.096499,	train_time = 0.382015
[2019-08-24 19:41:53,759] TRAIN Iter 280500: lr = 0.032502,	loss = 2.250497,	Top-1 err = 0.303613,	Top-5 err = 0.121533,	data_time = 0.050521,	train_time = 0.645193
[2019-08-24 19:42:10,775] TRAIN Iter 280520: lr = 0.032468,	loss = 2.314435,	Top-1 err = 0.303906,	Top-5 err = 0.116992,	data_time = 0.050468,	train_time = 0.850780
[2019-08-24 19:42:17,907] TRAIN Iter 280540: lr = 0.032435,	loss = 2.233971,	Top-1 err = 0.304150,	Top-5 err = 0.117969,	data_time = 0.050605,	train_time = 0.356590
[2019-08-24 19:42:33,115] TRAIN Iter 280560: lr = 0.032402,	loss = 2.206096,	Top-1 err = 0.302393,	Top-5 err = 0.119580,	data_time = 0.050488,	train_time = 0.760368
[2019-08-24 19:42:44,609] TRAIN Iter 280580: lr = 0.032368,	loss = 2.274230,	Top-1 err = 0.310352,	Top-5 err = 0.123633,	data_time = 0.050725,	train_time = 0.574695
[2019-08-24 19:42:55,501] TRAIN Iter 280600: lr = 0.032335,	loss = 2.231544,	Top-1 err = 0.306982,	Top-5 err = 0.119385,	data_time = 0.050319,	train_time = 0.544572
[2019-08-24 19:43:11,462] TRAIN Iter 280620: lr = 0.032302,	loss = 2.172574,	Top-1 err = 0.301318,	Top-5 err = 0.120020,	data_time = 0.050517,	train_time = 0.798064
[2019-08-24 19:43:19,213] TRAIN Iter 280640: lr = 0.032268,	loss = 2.253432,	Top-1 err = 0.306152,	Top-5 err = 0.121484,	data_time = 0.050315,	train_time = 0.387531
[2019-08-24 19:43:33,857] TRAIN Iter 280660: lr = 0.032235,	loss = 2.232346,	Top-1 err = 0.309619,	Top-5 err = 0.122559,	data_time = 0.050334,	train_time = 0.732195
[2019-08-24 19:43:48,745] TRAIN Iter 280680: lr = 0.032202,	loss = 2.315548,	Top-1 err = 0.307227,	Top-5 err = 0.121289,	data_time = 0.050392,	train_time = 0.744377
[2019-08-24 19:43:55,830] TRAIN Iter 280700: lr = 0.032168,	loss = 2.302168,	Top-1 err = 0.304834,	Top-5 err = 0.125439,	data_time = 0.050632,	train_time = 0.354250
[2019-08-24 19:44:10,592] TRAIN Iter 280720: lr = 0.032135,	loss = 2.203128,	Top-1 err = 0.306689,	Top-5 err = 0.122656,	data_time = 0.050802,	train_time = 0.738065
[2019-08-24 19:44:23,666] TRAIN Iter 280740: lr = 0.032102,	loss = 2.199739,	Top-1 err = 0.302197,	Top-5 err = 0.121143,	data_time = 0.050496,	train_time = 0.653675
[2019-08-24 19:44:31,550] TRAIN Iter 280760: lr = 0.032068,	loss = 2.255961,	Top-1 err = 0.307324,	Top-5 err = 0.121240,	data_time = 0.050561,	train_time = 0.394195
[2019-08-24 19:44:48,055] TRAIN Iter 280780: lr = 0.032035,	loss = 2.160340,	Top-1 err = 0.309961,	Top-5 err = 0.122803,	data_time = 0.050790,	train_time = 0.825222
[2019-08-24 19:44:55,666] TRAIN Iter 280800: lr = 0.032002,	loss = 2.284567,	Top-1 err = 0.306592,	Top-5 err = 0.124609,	data_time = 0.051003,	train_time = 0.380554
[2019-08-24 19:45:09,292] TRAIN Iter 280820: lr = 0.031968,	loss = 2.363108,	Top-1 err = 0.304102,	Top-5 err = 0.124707,	data_time = 0.050384,	train_time = 0.681308
[2019-08-24 19:45:23,143] TRAIN Iter 280840: lr = 0.031935,	loss = 2.314890,	Top-1 err = 0.298145,	Top-5 err = 0.119922,	data_time = 0.050467,	train_time = 0.692522
[2019-08-24 19:45:30,326] TRAIN Iter 280860: lr = 0.031902,	loss = 2.289009,	Top-1 err = 0.312549,	Top-5 err = 0.124707,	data_time = 0.050406,	train_time = 0.359125
[2019-08-24 19:45:44,373] TRAIN Iter 280880: lr = 0.031868,	loss = 2.235792,	Top-1 err = 0.303516,	Top-5 err = 0.117725,	data_time = 0.050353,	train_time = 0.702360
[2019-08-24 19:45:55,559] TRAIN Iter 280900: lr = 0.031835,	loss = 2.217833,	Top-1 err = 0.303711,	Top-5 err = 0.121094,	data_time = 0.050548,	train_time = 0.559258
[2019-08-24 19:46:06,037] TRAIN Iter 280920: lr = 0.031802,	loss = 2.281438,	Top-1 err = 0.308057,	Top-5 err = 0.122412,	data_time = 0.050472,	train_time = 0.523877
[2019-08-24 19:46:21,136] TRAIN Iter 280940: lr = 0.031768,	loss = 2.244833,	Top-1 err = 0.302734,	Top-5 err = 0.118213,	data_time = 0.050430,	train_time = 0.754953
[2019-08-24 19:46:28,260] TRAIN Iter 280960: lr = 0.031735,	loss = 2.243950,	Top-1 err = 0.304395,	Top-5 err = 0.120264,	data_time = 0.050386,	train_time = 0.356188
[2019-08-24 19:46:42,677] TRAIN Iter 280980: lr = 0.031702,	loss = 2.284873,	Top-1 err = 0.309473,	Top-5 err = 0.126074,	data_time = 0.050397,	train_time = 0.720848
[2019-08-24 19:46:59,294] TRAIN Iter 281000: lr = 0.031668,	loss = 2.270334,	Top-1 err = 0.303418,	Top-5 err = 0.119336,	data_time = 0.050562,	train_time = 0.830810
[2019-08-24 19:47:06,540] TRAIN Iter 281020: lr = 0.031635,	loss = 2.267852,	Top-1 err = 0.311182,	Top-5 err = 0.125635,	data_time = 0.050731,	train_time = 0.362266
[2019-08-24 19:47:21,058] TRAIN Iter 281040: lr = 0.031602,	loss = 2.292747,	Top-1 err = 0.308398,	Top-5 err = 0.123291,	data_time = 0.050347,	train_time = 0.725887
[2019-08-24 19:47:36,301] TRAIN Iter 281060: lr = 0.031568,	loss = 2.271093,	Top-1 err = 0.310547,	Top-5 err = 0.124463,	data_time = 0.159527,	train_time = 0.762148
[2019-08-24 19:47:43,582] TRAIN Iter 281080: lr = 0.031535,	loss = 2.186991,	Top-1 err = 0.310449,	Top-5 err = 0.122852,	data_time = 0.050304,	train_time = 0.364046
[2019-08-24 19:47:59,328] TRAIN Iter 281100: lr = 0.031502,	loss = 2.198236,	Top-1 err = 0.305176,	Top-5 err = 0.121436,	data_time = 0.050431,	train_time = 0.787307
[2019-08-24 19:48:06,880] TRAIN Iter 281120: lr = 0.031468,	loss = 2.201355,	Top-1 err = 0.313428,	Top-5 err = 0.123242,	data_time = 0.050639,	train_time = 0.377582
[2019-08-24 19:48:20,658] TRAIN Iter 281140: lr = 0.031435,	loss = 2.281856,	Top-1 err = 0.304883,	Top-5 err = 0.122217,	data_time = 0.050449,	train_time = 0.688888
[2019-08-24 19:48:36,668] TRAIN Iter 281160: lr = 0.031402,	loss = 2.263119,	Top-1 err = 0.305029,	Top-5 err = 0.121191,	data_time = 0.050671,	train_time = 0.800461
[2019-08-24 19:48:44,121] TRAIN Iter 281180: lr = 0.031368,	loss = 2.217994,	Top-1 err = 0.309375,	Top-5 err = 0.128418,	data_time = 0.050799,	train_time = 0.372647
[2019-08-24 19:49:00,891] TRAIN Iter 281200: lr = 0.031335,	loss = 2.167147,	Top-1 err = 0.301416,	Top-5 err = 0.116650,	data_time = 0.050468,	train_time = 0.838481
[2019-08-24 19:49:14,524] TRAIN Iter 281220: lr = 0.031302,	loss = 2.204428,	Top-1 err = 0.308496,	Top-5 err = 0.125879,	data_time = 0.050694,	train_time = 0.681633
[2019-08-24 19:49:21,854] TRAIN Iter 281240: lr = 0.031268,	loss = 2.243788,	Top-1 err = 0.310156,	Top-5 err = 0.122412,	data_time = 0.050597,	train_time = 0.366459
[2019-08-24 19:49:38,413] TRAIN Iter 281260: lr = 0.031235,	loss = 2.239783,	Top-1 err = 0.305762,	Top-5 err = 0.119727,	data_time = 0.050423,	train_time = 0.827949
[2019-08-24 19:49:45,586] TRAIN Iter 281280: lr = 0.031202,	loss = 2.247145,	Top-1 err = 0.303760,	Top-5 err = 0.123291,	data_time = 0.050239,	train_time = 0.358633
[2019-08-24 19:50:01,246] TRAIN Iter 281300: lr = 0.031168,	loss = 2.277152,	Top-1 err = 0.308203,	Top-5 err = 0.126807,	data_time = 0.192551,	train_time = 0.782979
[2019-08-24 19:50:16,415] TRAIN Iter 281320: lr = 0.031135,	loss = 2.232471,	Top-1 err = 0.310010,	Top-5 err = 0.123145,	data_time = 0.050723,	train_time = 0.758453
[2019-08-24 19:50:23,637] TRAIN Iter 281340: lr = 0.031102,	loss = 2.326787,	Top-1 err = 0.314111,	Top-5 err = 0.124854,	data_time = 0.050505,	train_time = 0.361057
[2019-08-24 19:50:39,235] TRAIN Iter 281360: lr = 0.031068,	loss = 2.299083,	Top-1 err = 0.306201,	Top-5 err = 0.122314,	data_time = 0.050453,	train_time = 0.779901
[2019-08-24 19:50:50,826] TRAIN Iter 281380: lr = 0.031035,	loss = 2.256097,	Top-1 err = 0.312402,	Top-5 err = 0.127246,	data_time = 0.128000,	train_time = 0.579557
[2019-08-24 19:51:00,245] TRAIN Iter 281400: lr = 0.031002,	loss = 2.281970,	Top-1 err = 0.304199,	Top-5 err = 0.121045,	data_time = 0.050617,	train_time = 0.470918
[2019-08-24 19:51:16,021] TRAIN Iter 281420: lr = 0.030968,	loss = 2.268081,	Top-1 err = 0.304541,	Top-5 err = 0.121094,	data_time = 0.533526,	train_time = 0.788809
[2019-08-24 19:51:23,055] TRAIN Iter 281440: lr = 0.030935,	loss = 2.274550,	Top-1 err = 0.306836,	Top-5 err = 0.123779,	data_time = 0.050202,	train_time = 0.351671
[2019-08-24 19:51:38,246] TRAIN Iter 281460: lr = 0.030902,	loss = 2.242785,	Top-1 err = 0.304443,	Top-5 err = 0.123779,	data_time = 0.050498,	train_time = 0.759501
[2019-08-24 19:51:53,839] TRAIN Iter 281480: lr = 0.030868,	loss = 2.266232,	Top-1 err = 0.307178,	Top-5 err = 0.122266,	data_time = 0.050366,	train_time = 0.779639
[2019-08-24 19:52:01,528] TRAIN Iter 281500: lr = 0.030835,	loss = 2.173618,	Top-1 err = 0.308594,	Top-5 err = 0.122754,	data_time = 0.050557,	train_time = 0.384471
[2019-08-24 19:52:17,267] TRAIN Iter 281520: lr = 0.030802,	loss = 2.169621,	Top-1 err = 0.303320,	Top-5 err = 0.122510,	data_time = 0.050436,	train_time = 0.786943
[2019-08-24 19:52:28,829] TRAIN Iter 281540: lr = 0.030768,	loss = 2.250481,	Top-1 err = 0.314648,	Top-5 err = 0.127393,	data_time = 0.050512,	train_time = 0.578084
[2019-08-24 19:52:39,687] TRAIN Iter 281560: lr = 0.030735,	loss = 2.301565,	Top-1 err = 0.312500,	Top-5 err = 0.125830,	data_time = 0.050494,	train_time = 0.542874
[2019-08-24 19:52:55,292] TRAIN Iter 281580: lr = 0.030702,	loss = 2.153450,	Top-1 err = 0.301172,	Top-5 err = 0.123145,	data_time = 1.052481,	train_time = 0.780240
[2019-08-24 19:53:03,033] TRAIN Iter 281600: lr = 0.030668,	loss = 2.295115,	Top-1 err = 0.307715,	Top-5 err = 0.123828,	data_time = 0.050027,	train_time = 0.387014
[2019-08-24 19:53:18,364] TRAIN Iter 281620: lr = 0.030635,	loss = 2.187078,	Top-1 err = 0.308838,	Top-5 err = 0.122217,	data_time = 0.049896,	train_time = 0.766523
[2019-08-24 19:54:03,428] TRAIN Iter 281640: lr = 0.030602,	loss = 2.254375,	Top-1 err = 0.307100,	Top-5 err = 0.128419,	data_time = 0.050795,	train_time = 2.253178
[2019-08-24 19:54:10,318] TRAIN Iter 281660: lr = 0.030568,	loss = 2.229434,	Top-1 err = 0.303564,	Top-5 err = 0.118994,	data_time = 0.050738,	train_time = 0.344512
[2019-08-24 19:54:26,018] TRAIN Iter 281680: lr = 0.030535,	loss = 2.252654,	Top-1 err = 0.297021,	Top-5 err = 0.115479,	data_time = 0.050411,	train_time = 0.784962
[2019-08-24 19:54:33,634] TRAIN Iter 281700: lr = 0.030502,	loss = 2.288780,	Top-1 err = 0.304590,	Top-5 err = 0.122900,	data_time = 0.050472,	train_time = 0.380826
[2019-08-24 19:54:47,906] TRAIN Iter 281720: lr = 0.030468,	loss = 2.220153,	Top-1 err = 0.302441,	Top-5 err = 0.120605,	data_time = 0.050509,	train_time = 0.713565
[2019-08-24 19:55:01,603] TRAIN Iter 281740: lr = 0.030435,	loss = 2.174316,	Top-1 err = 0.299121,	Top-5 err = 0.119482,	data_time = 0.050636,	train_time = 0.684852
[2019-08-24 19:55:09,234] TRAIN Iter 281760: lr = 0.030402,	loss = 2.210201,	Top-1 err = 0.300879,	Top-5 err = 0.122900,	data_time = 0.050348,	train_time = 0.381501
[2019-08-24 19:55:24,846] TRAIN Iter 281780: lr = 0.030368,	loss = 2.182835,	Top-1 err = 0.306494,	Top-5 err = 0.121240,	data_time = 0.050652,	train_time = 0.780591
[2019-08-24 19:55:36,984] TRAIN Iter 281800: lr = 0.030335,	loss = 2.259017,	Top-1 err = 0.301367,	Top-5 err = 0.119092,	data_time = 0.050350,	train_time = 0.606876
[2019-08-24 19:55:46,559] TRAIN Iter 281820: lr = 0.030302,	loss = 2.259301,	Top-1 err = 0.298633,	Top-5 err = 0.121387,	data_time = 0.050656,	train_time = 0.478729
[2019-08-24 19:56:02,394] TRAIN Iter 281840: lr = 0.030268,	loss = 2.186709,	Top-1 err = 0.303320,	Top-5 err = 0.121191,	data_time = 0.050368,	train_time = 0.791763
[2019-08-24 19:56:10,450] TRAIN Iter 281860: lr = 0.030235,	loss = 2.185492,	Top-1 err = 0.306104,	Top-5 err = 0.121582,	data_time = 0.050576,	train_time = 0.402786
[2019-08-24 19:56:23,177] TRAIN Iter 281880: lr = 0.030202,	loss = 2.230745,	Top-1 err = 0.301660,	Top-5 err = 0.120898,	data_time = 0.050455,	train_time = 0.636340
[2019-08-24 19:56:37,689] TRAIN Iter 281900: lr = 0.030168,	loss = 2.211537,	Top-1 err = 0.305762,	Top-5 err = 0.123145,	data_time = 0.050892,	train_time = 0.725579
[2019-08-24 19:56:45,191] TRAIN Iter 281920: lr = 0.030135,	loss = 2.299958,	Top-1 err = 0.305615,	Top-5 err = 0.120801,	data_time = 0.050409,	train_time = 0.375086
[2019-08-24 19:56:58,359] TRAIN Iter 281940: lr = 0.030102,	loss = 2.199976,	Top-1 err = 0.300244,	Top-5 err = 0.121533,	data_time = 0.050338,	train_time = 0.658391
[2019-08-24 19:57:12,760] TRAIN Iter 281960: lr = 0.030068,	loss = 2.228630,	Top-1 err = 0.302002,	Top-5 err = 0.119531,	data_time = 4.095310,	train_time = 0.720045
[2019-08-24 19:57:21,028] TRAIN Iter 281980: lr = 0.030035,	loss = 2.242172,	Top-1 err = 0.309619,	Top-5 err = 0.120703,	data_time = 0.050476,	train_time = 0.413376
[2019-08-24 19:57:34,816] TRAIN Iter 282000: lr = 0.030002,	loss = 2.246749,	Top-1 err = 0.306982,	Top-5 err = 0.119580,	data_time = 0.050377,	train_time = 0.689368
[2019-08-24 19:57:42,576] TRAIN Iter 282020: lr = 0.029968,	loss = 2.278432,	Top-1 err = 0.301416,	Top-5 err = 0.117676,	data_time = 0.050975,	train_time = 0.388009
[2019-08-24 19:57:57,685] TRAIN Iter 282040: lr = 0.029935,	loss = 2.309203,	Top-1 err = 0.304492,	Top-5 err = 0.121094,	data_time = 0.050252,	train_time = 0.755417
[2019-08-24 19:58:13,313] TRAIN Iter 282060: lr = 0.029902,	loss = 2.177613,	Top-1 err = 0.303027,	Top-5 err = 0.121338,	data_time = 0.050453,	train_time = 0.781382
[2019-08-24 19:58:20,999] TRAIN Iter 282080: lr = 0.029868,	loss = 2.314492,	Top-1 err = 0.306201,	Top-5 err = 0.121338,	data_time = 0.120394,	train_time = 0.384293
[2019-08-24 19:58:34,544] TRAIN Iter 282100: lr = 0.029835,	loss = 2.204978,	Top-1 err = 0.306104,	Top-5 err = 0.121436,	data_time = 0.050575,	train_time = 0.677233
[2019-08-24 19:58:49,791] TRAIN Iter 282120: lr = 0.029802,	loss = 2.212884,	Top-1 err = 0.303711,	Top-5 err = 0.122412,	data_time = 4.037340,	train_time = 0.762319
[2019-08-24 19:58:57,182] TRAIN Iter 282140: lr = 0.029768,	loss = 2.245758,	Top-1 err = 0.299219,	Top-5 err = 0.121875,	data_time = 0.050328,	train_time = 0.369564
[2019-08-24 19:59:11,800] TRAIN Iter 282160: lr = 0.029735,	loss = 2.236676,	Top-1 err = 0.307129,	Top-5 err = 0.122461,	data_time = 0.050735,	train_time = 0.730854
[2019-08-24 19:59:19,669] TRAIN Iter 282180: lr = 0.029702,	loss = 2.227347,	Top-1 err = 0.304980,	Top-5 err = 0.120850,	data_time = 0.050781,	train_time = 0.393470
[2019-08-24 19:59:34,157] TRAIN Iter 282200: lr = 0.029668,	loss = 2.177784,	Top-1 err = 0.301660,	Top-5 err = 0.117822,	data_time = 0.050604,	train_time = 0.724342
[2019-08-24 19:59:49,743] TRAIN Iter 282220: lr = 0.029635,	loss = 2.205235,	Top-1 err = 0.305518,	Top-5 err = 0.123047,	data_time = 0.050376,	train_time = 0.779309
[2019-08-24 19:59:57,244] TRAIN Iter 282240: lr = 0.029602,	loss = 2.240540,	Top-1 err = 0.310254,	Top-5 err = 0.121240,	data_time = 0.050464,	train_time = 0.375016
[2019-08-24 20:00:12,632] TRAIN Iter 282260: lr = 0.029568,	loss = 2.167331,	Top-1 err = 0.304492,	Top-5 err = 0.121582,	data_time = 0.050497,	train_time = 0.769398
[2019-08-24 20:00:27,489] TRAIN Iter 282280: lr = 0.029535,	loss = 2.218135,	Top-1 err = 0.304297,	Top-5 err = 0.124658,	data_time = 5.227811,	train_time = 0.742861
[2019-08-24 20:00:34,767] TRAIN Iter 282300: lr = 0.029502,	loss = 2.224853,	Top-1 err = 0.303906,	Top-5 err = 0.120215,	data_time = 0.050425,	train_time = 0.363865
[2019-08-24 20:00:50,074] TRAIN Iter 282320: lr = 0.029468,	loss = 2.256503,	Top-1 err = 0.306982,	Top-5 err = 0.119678,	data_time = 0.050586,	train_time = 0.765364
[2019-08-24 20:00:57,688] TRAIN Iter 282340: lr = 0.029435,	loss = 2.328892,	Top-1 err = 0.304785,	Top-5 err = 0.122900,	data_time = 0.091857,	train_time = 0.380673
[2019-08-24 20:01:11,071] TRAIN Iter 282360: lr = 0.029402,	loss = 2.238830,	Top-1 err = 0.307520,	Top-5 err = 0.118018,	data_time = 0.050720,	train_time = 0.669143
[2019-08-24 20:01:25,747] TRAIN Iter 282380: lr = 0.029368,	loss = 2.252968,	Top-1 err = 0.307617,	Top-5 err = 0.123730,	data_time = 0.288011,	train_time = 0.733758
[2019-08-24 20:01:33,105] TRAIN Iter 282400: lr = 0.029335,	loss = 2.216305,	Top-1 err = 0.308643,	Top-5 err = 0.118555,	data_time = 0.050311,	train_time = 0.367913
[2019-08-24 20:01:50,628] TRAIN Iter 282420: lr = 0.029302,	loss = 2.232131,	Top-1 err = 0.308838,	Top-5 err = 0.120605,	data_time = 0.050490,	train_time = 0.876140
[2019-08-24 20:02:05,126] TRAIN Iter 282440: lr = 0.029268,	loss = 2.204262,	Top-1 err = 0.304297,	Top-5 err = 0.121533,	data_time = 2.424788,	train_time = 0.724861
[2019-08-24 20:02:12,396] TRAIN Iter 282460: lr = 0.029235,	loss = 2.181421,	Top-1 err = 0.302734,	Top-5 err = 0.121631,	data_time = 0.050998,	train_time = 0.363480
[2019-08-24 20:02:28,935] TRAIN Iter 282480: lr = 0.029202,	loss = 2.313283,	Top-1 err = 0.306250,	Top-5 err = 0.120215,	data_time = 0.050455,	train_time = 0.826961
[2019-08-24 20:02:36,164] TRAIN Iter 282500: lr = 0.029168,	loss = 2.197467,	Top-1 err = 0.302734,	Top-5 err = 0.120654,	data_time = 0.050488,	train_time = 0.361429
[2019-08-24 20:02:52,093] TRAIN Iter 282520: lr = 0.029135,	loss = 2.209538,	Top-1 err = 0.309619,	Top-5 err = 0.124121,	data_time = 0.050550,	train_time = 0.796397
[2019-08-24 20:03:08,659] TRAIN Iter 282540: lr = 0.029102,	loss = 2.167356,	Top-1 err = 0.306104,	Top-5 err = 0.123145,	data_time = 0.050602,	train_time = 0.828307
[2019-08-24 20:03:15,516] TRAIN Iter 282560: lr = 0.029068,	loss = 2.311140,	Top-1 err = 0.306738,	Top-5 err = 0.122852,	data_time = 0.050603,	train_time = 0.342838
[2019-08-24 20:03:31,017] TRAIN Iter 282580: lr = 0.029035,	loss = 2.110652,	Top-1 err = 0.306250,	Top-5 err = 0.117773,	data_time = 0.050527,	train_time = 0.775028
[2019-08-24 20:03:45,724] TRAIN Iter 282600: lr = 0.029002,	loss = 2.216860,	Top-1 err = 0.302295,	Top-5 err = 0.120605,	data_time = 0.407565,	train_time = 0.735352
[2019-08-24 20:03:53,682] TRAIN Iter 282620: lr = 0.028968,	loss = 2.305710,	Top-1 err = 0.301953,	Top-5 err = 0.119580,	data_time = 0.050465,	train_time = 0.397852
[2019-08-24 20:04:09,914] TRAIN Iter 282640: lr = 0.028935,	loss = 2.225582,	Top-1 err = 0.308398,	Top-5 err = 0.123633,	data_time = 0.050335,	train_time = 0.811607
[2019-08-24 20:04:17,125] TRAIN Iter 282660: lr = 0.028902,	loss = 2.280165,	Top-1 err = 0.302051,	Top-5 err = 0.122363,	data_time = 0.050735,	train_time = 0.360523
[2019-08-24 20:04:32,724] TRAIN Iter 282680: lr = 0.028868,	loss = 2.278665,	Top-1 err = 0.304492,	Top-5 err = 0.118213,	data_time = 0.050442,	train_time = 0.779954
[2019-08-24 20:04:49,857] TRAIN Iter 282700: lr = 0.028835,	loss = 2.227707,	Top-1 err = 0.312939,	Top-5 err = 0.124316,	data_time = 0.050497,	train_time = 0.856628
[2019-08-24 20:04:56,687] TRAIN Iter 282720: lr = 0.028802,	loss = 2.197803,	Top-1 err = 0.301611,	Top-5 err = 0.119336,	data_time = 0.050249,	train_time = 0.341496
[2019-08-24 20:05:13,062] TRAIN Iter 282740: lr = 0.028768,	loss = 2.220123,	Top-1 err = 0.301465,	Top-5 err = 0.119775,	data_time = 0.050296,	train_time = 0.818727
[2019-08-24 20:05:29,436] TRAIN Iter 282760: lr = 0.028735,	loss = 2.244747,	Top-1 err = 0.306494,	Top-5 err = 0.124072,	data_time = 3.363657,	train_time = 0.818675
[2019-08-24 20:05:37,238] TRAIN Iter 282780: lr = 0.028702,	loss = 2.251649,	Top-1 err = 0.303760,	Top-5 err = 0.119971,	data_time = 0.050421,	train_time = 0.390111
[2019-08-24 20:05:53,534] TRAIN Iter 282800: lr = 0.028668,	loss = 2.269255,	Top-1 err = 0.310010,	Top-5 err = 0.125439,	data_time = 0.050337,	train_time = 0.814759
[2019-08-24 20:06:00,641] TRAIN Iter 282820: lr = 0.028635,	loss = 2.245413,	Top-1 err = 0.302637,	Top-5 err = 0.124658,	data_time = 0.050492,	train_time = 0.355373
[2019-08-24 20:06:16,582] TRAIN Iter 282840: lr = 0.028602,	loss = 2.250188,	Top-1 err = 0.307031,	Top-5 err = 0.120117,	data_time = 0.050102,	train_time = 0.797036
[2019-08-24 20:06:31,570] TRAIN Iter 282860: lr = 0.028568,	loss = 2.274723,	Top-1 err = 0.307666,	Top-5 err = 0.123877,	data_time = 0.164209,	train_time = 0.749372
[2019-08-24 20:06:37,826] TRAIN Iter 282880: lr = 0.028535,	loss = 2.218158,	Top-1 err = 0.308154,	Top-5 err = 0.122461,	data_time = 0.049981,	train_time = 0.312785
[2019-08-24 20:07:28,205] TRAIN Iter 282900: lr = 0.028502,	loss = 2.191683,	Top-1 err = 0.302606,	Top-5 err = 0.121413,	data_time = 0.050352,	train_time = 2.518919
[2019-08-24 20:07:36,754] TRAIN Iter 282920: lr = 0.028468,	loss = 2.263528,	Top-1 err = 0.300391,	Top-5 err = 0.118555,	data_time = 0.050561,	train_time = 0.427455
[2019-08-24 20:07:47,241] TRAIN Iter 282940: lr = 0.028435,	loss = 2.208554,	Top-1 err = 0.297266,	Top-5 err = 0.118213,	data_time = 0.050671,	train_time = 0.524306
[2019-08-24 20:07:56,243] TRAIN Iter 282960: lr = 0.028402,	loss = 2.250890,	Top-1 err = 0.300049,	Top-5 err = 0.117725,	data_time = 0.050726,	train_time = 0.450091
[2019-08-24 20:08:04,198] TRAIN Iter 282980: lr = 0.028368,	loss = 2.314242,	Top-1 err = 0.303516,	Top-5 err = 0.117871,	data_time = 0.050336,	train_time = 0.397730
[2019-08-24 20:08:18,959] TRAIN Iter 283000: lr = 0.028335,	loss = 2.216581,	Top-1 err = 0.304883,	Top-5 err = 0.117334,	data_time = 0.050570,	train_time = 0.738054
[2019-08-24 20:08:32,841] TRAIN Iter 283020: lr = 0.028302,	loss = 2.241427,	Top-1 err = 0.295410,	Top-5 err = 0.115625,	data_time = 0.050673,	train_time = 0.694076
[2019-08-24 20:08:41,250] TRAIN Iter 283040: lr = 0.028268,	loss = 2.251791,	Top-1 err = 0.300977,	Top-5 err = 0.118652,	data_time = 0.050889,	train_time = 0.420445
[2019-08-24 20:08:55,969] TRAIN Iter 283060: lr = 0.028235,	loss = 2.213214,	Top-1 err = 0.300586,	Top-5 err = 0.116846,	data_time = 0.050363,	train_time = 0.735952
[2019-08-24 20:09:03,633] TRAIN Iter 283080: lr = 0.028202,	loss = 2.315296,	Top-1 err = 0.299609,	Top-5 err = 0.119629,	data_time = 0.050375,	train_time = 0.383152
[2019-08-24 20:09:18,864] TRAIN Iter 283100: lr = 0.028168,	loss = 2.236201,	Top-1 err = 0.301416,	Top-5 err = 0.119580,	data_time = 0.050529,	train_time = 0.761560
[2019-08-24 20:09:31,015] TRAIN Iter 283120: lr = 0.028135,	loss = 2.206629,	Top-1 err = 0.304004,	Top-5 err = 0.122021,	data_time = 0.050814,	train_time = 0.607534
[2019-08-24 20:09:40,100] TRAIN Iter 283140: lr = 0.028102,	loss = 2.279210,	Top-1 err = 0.297510,	Top-5 err = 0.119971,	data_time = 0.050282,	train_time = 0.454256
[2019-08-24 20:09:55,373] TRAIN Iter 283160: lr = 0.028068,	loss = 2.199545,	Top-1 err = 0.300049,	Top-5 err = 0.118750,	data_time = 0.050327,	train_time = 0.763612
[2019-08-24 20:10:04,511] TRAIN Iter 283180: lr = 0.028035,	loss = 2.186437,	Top-1 err = 0.305078,	Top-5 err = 0.120020,	data_time = 0.202474,	train_time = 0.456862
[2019-08-24 20:10:16,248] TRAIN Iter 283200: lr = 0.028002,	loss = 2.204172,	Top-1 err = 0.300830,	Top-5 err = 0.123340,	data_time = 0.050327,	train_time = 0.586843
[2019-08-24 20:10:29,996] TRAIN Iter 283220: lr = 0.027968,	loss = 2.236029,	Top-1 err = 0.302637,	Top-5 err = 0.119531,	data_time = 0.164826,	train_time = 0.687383
[2019-08-24 20:10:37,659] TRAIN Iter 283240: lr = 0.027935,	loss = 2.198015,	Top-1 err = 0.303906,	Top-5 err = 0.121924,	data_time = 0.050565,	train_time = 0.383150
[2019-08-24 20:10:53,039] TRAIN Iter 283260: lr = 0.027902,	loss = 2.184823,	Top-1 err = 0.296289,	Top-5 err = 0.117676,	data_time = 0.050288,	train_time = 0.768987
[2019-08-24 20:11:06,311] TRAIN Iter 283280: lr = 0.027868,	loss = 2.197687,	Top-1 err = 0.297998,	Top-5 err = 0.116699,	data_time = 0.050316,	train_time = 0.663569
[2019-08-24 20:11:15,119] TRAIN Iter 283300: lr = 0.027835,	loss = 2.256724,	Top-1 err = 0.297754,	Top-5 err = 0.117236,	data_time = 0.050798,	train_time = 0.440417
[2019-08-24 20:11:31,579] TRAIN Iter 283320: lr = 0.027802,	loss = 2.232091,	Top-1 err = 0.304150,	Top-5 err = 0.118213,	data_time = 0.050452,	train_time = 0.822974
[2019-08-24 20:11:44,192] TRAIN Iter 283340: lr = 0.027768,	loss = 2.136052,	Top-1 err = 0.299951,	Top-5 err = 0.115674,	data_time = 3.694131,	train_time = 0.630640
[2019-08-24 20:11:54,825] TRAIN Iter 283360: lr = 0.027735,	loss = 2.264177,	Top-1 err = 0.301270,	Top-5 err = 0.121094,	data_time = 0.050615,	train_time = 0.531641
[2019-08-24 20:12:10,332] TRAIN Iter 283380: lr = 0.027702,	loss = 2.201530,	Top-1 err = 0.297705,	Top-5 err = 0.118799,	data_time = 0.050639,	train_time = 0.775313
[2019-08-24 20:12:17,604] TRAIN Iter 283400: lr = 0.027668,	loss = 2.183109,	Top-1 err = 0.304980,	Top-5 err = 0.120361,	data_time = 0.140122,	train_time = 0.363611
[2019-08-24 20:12:32,741] TRAIN Iter 283420: lr = 0.027635,	loss = 2.196999,	Top-1 err = 0.301758,	Top-5 err = 0.119922,	data_time = 0.051126,	train_time = 0.756814
[2019-08-24 20:12:48,206] TRAIN Iter 283440: lr = 0.027602,	loss = 2.177014,	Top-1 err = 0.303076,	Top-5 err = 0.122021,	data_time = 0.050242,	train_time = 0.773258
[2019-08-24 20:12:55,279] TRAIN Iter 283460: lr = 0.027568,	loss = 2.199444,	Top-1 err = 0.300635,	Top-5 err = 0.117334,	data_time = 0.050230,	train_time = 0.353642
[2019-08-24 20:13:10,691] TRAIN Iter 283480: lr = 0.027535,	loss = 2.242288,	Top-1 err = 0.300732,	Top-5 err = 0.120215,	data_time = 0.050913,	train_time = 0.770539
[2019-08-24 20:13:21,252] TRAIN Iter 283500: lr = 0.027502,	loss = 2.173142,	Top-1 err = 0.303711,	Top-5 err = 0.117139,	data_time = 0.050252,	train_time = 0.528079
[2019-08-24 20:13:33,241] TRAIN Iter 283520: lr = 0.027468,	loss = 2.212133,	Top-1 err = 0.304443,	Top-5 err = 0.120850,	data_time = 0.050454,	train_time = 0.599398
[2019-08-24 20:13:49,220] TRAIN Iter 283540: lr = 0.027435,	loss = 2.231167,	Top-1 err = 0.302686,	Top-5 err = 0.120947,	data_time = 0.050436,	train_time = 0.798955
[2019-08-24 20:13:56,441] TRAIN Iter 283560: lr = 0.027402,	loss = 2.189504,	Top-1 err = 0.307178,	Top-5 err = 0.121680,	data_time = 0.050349,	train_time = 0.361058
[2019-08-24 20:14:12,129] TRAIN Iter 283580: lr = 0.027368,	loss = 2.214413,	Top-1 err = 0.301709,	Top-5 err = 0.119824,	data_time = 0.050519,	train_time = 0.784341
[2019-08-24 20:14:26,902] TRAIN Iter 283600: lr = 0.027335,	loss = 2.236246,	Top-1 err = 0.305615,	Top-5 err = 0.123730,	data_time = 0.152880,	train_time = 0.738667
[2019-08-24 20:14:33,811] TRAIN Iter 283620: lr = 0.027302,	loss = 2.291806,	Top-1 err = 0.307031,	Top-5 err = 0.120703,	data_time = 0.050526,	train_time = 0.345407
[2019-08-24 20:14:49,281] TRAIN Iter 283640: lr = 0.027268,	loss = 2.164789,	Top-1 err = 0.300732,	Top-5 err = 0.117773,	data_time = 0.050498,	train_time = 0.773519
[2019-08-24 20:15:01,906] TRAIN Iter 283660: lr = 0.027235,	loss = 2.284551,	Top-1 err = 0.305469,	Top-5 err = 0.122314,	data_time = 0.050588,	train_time = 0.631204
[2019-08-24 20:15:11,315] TRAIN Iter 283680: lr = 0.027202,	loss = 2.399165,	Top-1 err = 0.305371,	Top-5 err = 0.121875,	data_time = 0.050847,	train_time = 0.470445
[2019-08-24 20:15:27,311] TRAIN Iter 283700: lr = 0.027168,	loss = 2.299363,	Top-1 err = 0.305225,	Top-5 err = 0.119336,	data_time = 0.050547,	train_time = 0.799810
[2019-08-24 20:15:34,464] TRAIN Iter 283720: lr = 0.027135,	loss = 2.194687,	Top-1 err = 0.304346,	Top-5 err = 0.122461,	data_time = 0.164011,	train_time = 0.357614
[2019-08-24 20:15:49,963] TRAIN Iter 283740: lr = 0.027102,	loss = 2.240573,	Top-1 err = 0.300732,	Top-5 err = 0.115186,	data_time = 0.050733,	train_time = 0.774930
[2019-08-24 20:16:07,205] TRAIN Iter 283760: lr = 0.027068,	loss = 2.307723,	Top-1 err = 0.307666,	Top-5 err = 0.121729,	data_time = 0.050510,	train_time = 0.862086
[2019-08-24 20:16:14,101] TRAIN Iter 283780: lr = 0.027035,	loss = 2.164026,	Top-1 err = 0.302539,	Top-5 err = 0.117773,	data_time = 0.050802,	train_time = 0.344814
[2019-08-24 20:16:28,431] TRAIN Iter 283800: lr = 0.027002,	loss = 2.290441,	Top-1 err = 0.300293,	Top-5 err = 0.119482,	data_time = 0.050279,	train_time = 0.716502
[2019-08-24 20:16:44,096] TRAIN Iter 283820: lr = 0.026968,	loss = 2.211908,	Top-1 err = 0.302393,	Top-5 err = 0.123096,	data_time = 0.050582,	train_time = 0.783190
[2019-08-24 20:16:52,718] TRAIN Iter 283840: lr = 0.026935,	loss = 2.273485,	Top-1 err = 0.301367,	Top-5 err = 0.120801,	data_time = 0.050512,	train_time = 0.431102
[2019-08-24 20:17:08,948] TRAIN Iter 283860: lr = 0.026902,	loss = 2.198169,	Top-1 err = 0.303564,	Top-5 err = 0.122314,	data_time = 0.050441,	train_time = 0.811500
[2019-08-24 20:17:15,850] TRAIN Iter 283880: lr = 0.026868,	loss = 2.299178,	Top-1 err = 0.308301,	Top-5 err = 0.120605,	data_time = 0.116343,	train_time = 0.345061
[2019-08-24 20:17:31,098] TRAIN Iter 283900: lr = 0.026835,	loss = 2.190388,	Top-1 err = 0.302734,	Top-5 err = 0.118750,	data_time = 0.050615,	train_time = 0.762422
[2019-08-24 20:17:48,246] TRAIN Iter 283920: lr = 0.026802,	loss = 2.259340,	Top-1 err = 0.306250,	Top-5 err = 0.121387,	data_time = 0.050488,	train_time = 0.857397
[2019-08-24 20:17:56,784] TRAIN Iter 283940: lr = 0.026768,	loss = 2.133483,	Top-1 err = 0.301318,	Top-5 err = 0.119336,	data_time = 1.871829,	train_time = 0.426842
[2019-08-24 20:18:13,382] TRAIN Iter 283960: lr = 0.026735,	loss = 2.217550,	Top-1 err = 0.304150,	Top-5 err = 0.121729,	data_time = 0.050416,	train_time = 0.829907
[2019-08-24 20:18:29,184] TRAIN Iter 283980: lr = 0.026702,	loss = 2.207932,	Top-1 err = 0.308398,	Top-5 err = 0.122168,	data_time = 0.050285,	train_time = 0.790077
[2019-08-24 20:18:37,067] TRAIN Iter 284000: lr = 0.026668,	loss = 2.281873,	Top-1 err = 0.312695,	Top-5 err = 0.126074,	data_time = 0.050929,	train_time = 0.394145
[2019-08-24 20:18:53,827] TRAIN Iter 284020: lr = 0.026635,	loss = 2.154191,	Top-1 err = 0.310352,	Top-5 err = 0.123438,	data_time = 0.050450,	train_time = 0.837973
[2019-08-24 20:19:01,278] TRAIN Iter 284040: lr = 0.026602,	loss = 2.190727,	Top-1 err = 0.295361,	Top-5 err = 0.118018,	data_time = 0.050344,	train_time = 0.372534
[2019-08-24 20:19:18,086] TRAIN Iter 284060: lr = 0.026568,	loss = 2.258947,	Top-1 err = 0.303613,	Top-5 err = 0.117383,	data_time = 0.050204,	train_time = 0.840389
[2019-08-24 20:19:35,846] TRAIN Iter 284080: lr = 0.026535,	loss = 2.236878,	Top-1 err = 0.306396,	Top-5 err = 0.120752,	data_time = 0.050024,	train_time = 0.887945
[2019-08-24 20:19:43,013] TRAIN Iter 284100: lr = 0.026502,	loss = 2.139291,	Top-1 err = 0.306689,	Top-5 err = 0.123535,	data_time = 0.050019,	train_time = 0.358337
[2019-08-24 20:19:58,128] TRAIN Iter 284120: lr = 0.026468,	loss = 2.264253,	Top-1 err = 0.308301,	Top-5 err = 0.124268,	data_time = 0.049834,	train_time = 0.755751
[2019-08-24 20:20:09,506] TRAIN Iter 284140: lr = 0.026435,	loss = 2.595309,	Top-1 err = 0.307684,	Top-5 err = 0.124235,	data_time = 0.007043,	train_time = 0.568874
[2019-08-24 20:20:55,812] TRAIN Iter 284160: lr = 0.026402,	loss = 2.214524,	Top-1 err = 0.298193,	Top-5 err = 0.116016,	data_time = 0.050319,	train_time = 2.315313
[2019-08-24 20:21:11,123] TRAIN Iter 284180: lr = 0.026368,	loss = 2.242856,	Top-1 err = 0.298242,	Top-5 err = 0.121484,	data_time = 0.050492,	train_time = 0.765539
[2019-08-24 20:21:18,130] TRAIN Iter 284200: lr = 0.026335,	loss = 2.236296,	Top-1 err = 0.292822,	Top-5 err = 0.115088,	data_time = 0.050779,	train_time = 0.350313
[2019-08-24 20:21:34,210] TRAIN Iter 284220: lr = 0.026302,	loss = 2.246429,	Top-1 err = 0.299658,	Top-5 err = 0.119629,	data_time = 0.050812,	train_time = 0.803964
[2019-08-24 20:21:47,685] TRAIN Iter 284240: lr = 0.026268,	loss = 2.241671,	Top-1 err = 0.298633,	Top-5 err = 0.117529,	data_time = 0.050411,	train_time = 0.673741
[2019-08-24 20:21:54,797] TRAIN Iter 284260: lr = 0.026235,	loss = 2.168270,	Top-1 err = 0.299170,	Top-5 err = 0.120410,	data_time = 0.050601,	train_time = 0.355580
[2019-08-24 20:22:10,939] TRAIN Iter 284280: lr = 0.026202,	loss = 2.197729,	Top-1 err = 0.299805,	Top-5 err = 0.117236,	data_time = 0.050942,	train_time = 0.807107
[2019-08-24 20:22:18,771] TRAIN Iter 284300: lr = 0.026168,	loss = 2.238626,	Top-1 err = 0.305518,	Top-5 err = 0.121729,	data_time = 0.050514,	train_time = 0.391596
[2019-08-24 20:22:33,695] TRAIN Iter 284320: lr = 0.026135,	loss = 2.197532,	Top-1 err = 0.300098,	Top-5 err = 0.120801,	data_time = 0.050553,	train_time = 0.746169
[2019-08-24 20:22:48,457] TRAIN Iter 284340: lr = 0.026102,	loss = 2.215606,	Top-1 err = 0.303662,	Top-5 err = 0.117871,	data_time = 0.050338,	train_time = 0.738087
[2019-08-24 20:22:55,787] TRAIN Iter 284360: lr = 0.026068,	loss = 2.207026,	Top-1 err = 0.299463,	Top-5 err = 0.117578,	data_time = 0.050344,	train_time = 0.366462
[2019-08-24 20:23:08,873] TRAIN Iter 284380: lr = 0.026035,	loss = 2.230748,	Top-1 err = 0.302783,	Top-5 err = 0.119092,	data_time = 0.050658,	train_time = 0.654324
[2019-08-24 20:23:21,295] TRAIN Iter 284400: lr = 0.026002,	loss = 2.217804,	Top-1 err = 0.300049,	Top-5 err = 0.116602,	data_time = 0.050481,	train_time = 0.621066
[2019-08-24 20:23:28,736] TRAIN Iter 284420: lr = 0.025968,	loss = 2.207990,	Top-1 err = 0.295898,	Top-5 err = 0.115332,	data_time = 0.050401,	train_time = 0.372044
[2019-08-24 20:23:44,163] TRAIN Iter 284440: lr = 0.025935,	loss = 2.191246,	Top-1 err = 0.300000,	Top-5 err = 0.119971,	data_time = 0.206035,	train_time = 0.771307
[2019-08-24 20:23:51,478] TRAIN Iter 284460: lr = 0.025902,	loss = 2.191797,	Top-1 err = 0.294922,	Top-5 err = 0.117529,	data_time = 0.050578,	train_time = 0.365748
[2019-08-24 20:24:07,629] TRAIN Iter 284480: lr = 0.025868,	loss = 2.222196,	Top-1 err = 0.296631,	Top-5 err = 0.115039,	data_time = 0.050637,	train_time = 0.807556
[2019-08-24 20:24:19,331] TRAIN Iter 284500: lr = 0.025835,	loss = 2.207596,	Top-1 err = 0.296533,	Top-5 err = 0.115283,	data_time = 0.050498,	train_time = 0.585071
[2019-08-24 20:24:27,969] TRAIN Iter 284520: lr = 0.025802,	loss = 2.183760,	Top-1 err = 0.300977,	Top-5 err = 0.121045,	data_time = 0.050510,	train_time = 0.431891
[2019-08-24 20:24:43,354] TRAIN Iter 284540: lr = 0.025768,	loss = 2.191930,	Top-1 err = 0.298926,	Top-5 err = 0.119775,	data_time = 0.050247,	train_time = 0.769235
[2019-08-24 20:24:54,260] TRAIN Iter 284560: lr = 0.025735,	loss = 2.282615,	Top-1 err = 0.302881,	Top-5 err = 0.120361,	data_time = 0.050332,	train_time = 0.545295
[2019-08-24 20:25:04,223] TRAIN Iter 284580: lr = 0.025702,	loss = 2.232789,	Top-1 err = 0.305469,	Top-5 err = 0.122021,	data_time = 0.050353,	train_time = 0.498102
[2019-08-24 20:25:19,098] TRAIN Iter 284600: lr = 0.025668,	loss = 2.138285,	Top-1 err = 0.300586,	Top-5 err = 0.117139,	data_time = 0.133523,	train_time = 0.743748
[2019-08-24 20:25:26,191] TRAIN Iter 284620: lr = 0.025635,	loss = 2.168237,	Top-1 err = 0.297998,	Top-5 err = 0.118262,	data_time = 0.050377,	train_time = 0.354635
[2019-08-24 20:25:40,699] TRAIN Iter 284640: lr = 0.025602,	loss = 2.204680,	Top-1 err = 0.302490,	Top-5 err = 0.121680,	data_time = 0.050302,	train_time = 0.725401
[2019-08-24 20:25:53,866] TRAIN Iter 284660: lr = 0.025568,	loss = 2.178484,	Top-1 err = 0.299219,	Top-5 err = 0.120020,	data_time = 0.050589,	train_time = 0.658342
[2019-08-24 20:26:03,314] TRAIN Iter 284680: lr = 0.025535,	loss = 2.221804,	Top-1 err = 0.295898,	Top-5 err = 0.118848,	data_time = 0.050516,	train_time = 0.472345
[2019-08-24 20:26:18,752] TRAIN Iter 284700: lr = 0.025502,	loss = 2.218578,	Top-1 err = 0.298730,	Top-5 err = 0.117529,	data_time = 0.050515,	train_time = 0.771903
[2019-08-24 20:26:34,168] TRAIN Iter 284720: lr = 0.025468,	loss = 2.274067,	Top-1 err = 0.303027,	Top-5 err = 0.117871,	data_time = 0.050882,	train_time = 0.770805
[2019-08-24 20:26:41,654] TRAIN Iter 284740: lr = 0.025435,	loss = 2.287523,	Top-1 err = 0.304736,	Top-5 err = 0.120459,	data_time = 0.050414,	train_time = 0.374252
[2019-08-24 20:26:58,064] TRAIN Iter 284760: lr = 0.025402,	loss = 2.246356,	Top-1 err = 0.300244,	Top-5 err = 0.113867,	data_time = 0.050591,	train_time = 0.820502
[2019-08-24 20:27:05,249] TRAIN Iter 284780: lr = 0.025368,	loss = 2.206978,	Top-1 err = 0.299170,	Top-5 err = 0.116748,	data_time = 0.050385,	train_time = 0.359251
[2019-08-24 20:27:20,091] TRAIN Iter 284800: lr = 0.025335,	loss = 2.314182,	Top-1 err = 0.297168,	Top-5 err = 0.117090,	data_time = 0.050540,	train_time = 0.742054
[2019-08-24 20:27:35,355] TRAIN Iter 284820: lr = 0.025302,	loss = 2.215621,	Top-1 err = 0.296289,	Top-5 err = 0.116943,	data_time = 0.050494,	train_time = 0.763206
[2019-08-24 20:27:42,595] TRAIN Iter 284840: lr = 0.025268,	loss = 2.173344,	Top-1 err = 0.300391,	Top-5 err = 0.116943,	data_time = 0.050805,	train_time = 0.361970
[2019-08-24 20:27:56,568] TRAIN Iter 284860: lr = 0.025235,	loss = 2.289152,	Top-1 err = 0.302588,	Top-5 err = 0.121094,	data_time = 0.050627,	train_time = 0.698639
[2019-08-24 20:28:11,421] TRAIN Iter 284880: lr = 0.025202,	loss = 2.217831,	Top-1 err = 0.305127,	Top-5 err = 0.120313,	data_time = 0.050142,	train_time = 0.742637
[2019-08-24 20:28:18,834] TRAIN Iter 284900: lr = 0.025168,	loss = 2.186771,	Top-1 err = 0.292285,	Top-5 err = 0.116943,	data_time = 0.050468,	train_time = 0.370654
[2019-08-24 20:28:33,381] TRAIN Iter 284920: lr = 0.025135,	loss = 2.260444,	Top-1 err = 0.299805,	Top-5 err = 0.118604,	data_time = 0.050661,	train_time = 0.727348
[2019-08-24 20:28:40,507] TRAIN Iter 284940: lr = 0.025102,	loss = 2.297882,	Top-1 err = 0.299170,	Top-5 err = 0.118408,	data_time = 0.123411,	train_time = 0.356243
[2019-08-24 20:28:56,796] TRAIN Iter 284960: lr = 0.025068,	loss = 2.148119,	Top-1 err = 0.300537,	Top-5 err = 0.119824,	data_time = 0.050630,	train_time = 0.814461
[2019-08-24 20:29:09,561] TRAIN Iter 284980: lr = 0.025035,	loss = 2.256504,	Top-1 err = 0.297412,	Top-5 err = 0.117920,	data_time = 0.050519,	train_time = 0.638226
[2019-08-24 20:29:18,566] TRAIN Iter 285000: lr = 0.025002,	loss = 2.156698,	Top-1 err = 0.299609,	Top-5 err = 0.117432,	data_time = 0.050353,	train_time = 0.450230
[2019-08-24 20:29:35,385] TRAIN Iter 285020: lr = 0.024968,	loss = 2.167624,	Top-1 err = 0.299512,	Top-5 err = 0.117676,	data_time = 0.050732,	train_time = 0.840959
[2019-08-24 20:29:48,407] TRAIN Iter 285040: lr = 0.024935,	loss = 2.191013,	Top-1 err = 0.297607,	Top-5 err = 0.122754,	data_time = 0.050402,	train_time = 0.651056
[2019-08-24 20:29:58,441] TRAIN Iter 285060: lr = 0.024902,	loss = 2.262993,	Top-1 err = 0.300000,	Top-5 err = 0.120752,	data_time = 0.051090,	train_time = 0.501718
[2019-08-24 20:30:12,881] TRAIN Iter 285080: lr = 0.024868,	loss = 2.275313,	Top-1 err = 0.300830,	Top-5 err = 0.117773,	data_time = 0.050408,	train_time = 0.721956
[2019-08-24 20:30:19,927] TRAIN Iter 285100: lr = 0.024835,	loss = 2.250756,	Top-1 err = 0.297266,	Top-5 err = 0.118066,	data_time = 0.050739,	train_time = 0.352309
[2019-08-24 20:30:36,480] TRAIN Iter 285120: lr = 0.024802,	loss = 2.215350,	Top-1 err = 0.302686,	Top-5 err = 0.122119,	data_time = 0.050494,	train_time = 0.827629
[2019-08-24 20:30:50,699] TRAIN Iter 285140: lr = 0.024768,	loss = 2.117366,	Top-1 err = 0.298535,	Top-5 err = 0.119238,	data_time = 0.050301,	train_time = 0.710925
[2019-08-24 20:31:00,047] TRAIN Iter 285160: lr = 0.024735,	loss = 2.218132,	Top-1 err = 0.298389,	Top-5 err = 0.118213,	data_time = 0.050816,	train_time = 0.467390
[2019-08-24 20:31:15,507] TRAIN Iter 285180: lr = 0.024702,	loss = 2.196628,	Top-1 err = 0.304736,	Top-5 err = 0.118115,	data_time = 0.050439,	train_time = 0.773014
[2019-08-24 20:31:33,229] TRAIN Iter 285200: lr = 0.024668,	loss = 2.236807,	Top-1 err = 0.308984,	Top-5 err = 0.121777,	data_time = 0.050684,	train_time = 0.886049
[2019-08-24 20:31:39,974] TRAIN Iter 285220: lr = 0.024635,	loss = 2.261479,	Top-1 err = 0.297363,	Top-5 err = 0.114062,	data_time = 0.050435,	train_time = 0.337232
[2019-08-24 20:31:55,312] TRAIN Iter 285240: lr = 0.024602,	loss = 2.252108,	Top-1 err = 0.304102,	Top-5 err = 0.119727,	data_time = 0.050380,	train_time = 0.766914
[2019-08-24 20:32:02,557] TRAIN Iter 285260: lr = 0.024568,	loss = 2.255658,	Top-1 err = 0.297070,	Top-5 err = 0.119482,	data_time = 0.050610,	train_time = 0.362244
[2019-08-24 20:32:16,738] TRAIN Iter 285280: lr = 0.024535,	loss = 2.278306,	Top-1 err = 0.304248,	Top-5 err = 0.121143,	data_time = 0.050595,	train_time = 0.709005
[2019-08-24 20:32:31,112] TRAIN Iter 285300: lr = 0.024502,	loss = 2.159662,	Top-1 err = 0.301904,	Top-5 err = 0.116455,	data_time = 0.050896,	train_time = 0.718715
[2019-08-24 20:32:39,767] TRAIN Iter 285320: lr = 0.024468,	loss = 2.213877,	Top-1 err = 0.301855,	Top-5 err = 0.117676,	data_time = 0.050272,	train_time = 0.432735
[2019-08-24 20:32:56,824] TRAIN Iter 285340: lr = 0.024435,	loss = 2.301854,	Top-1 err = 0.296338,	Top-5 err = 0.117139,	data_time = 0.050072,	train_time = 0.852823
[2019-08-24 20:33:10,016] TRAIN Iter 285360: lr = 0.024402,	loss = 2.258996,	Top-1 err = 0.309082,	Top-5 err = 0.124072,	data_time = 0.049939,	train_time = 0.659573
[2019-08-24 20:33:18,386] TRAIN Iter 285380: lr = 0.024368,	loss = 2.246453,	Top-1 err = 0.305518,	Top-5 err = 0.120459,	data_time = 0.049913,	train_time = 0.418488
[2019-08-24 20:34:08,422] TRAIN Iter 285400: lr = 0.024335,	loss = 2.125730,	Top-1 err = 0.298367,	Top-5 err = 0.119907,	data_time = 0.050429,	train_time = 2.501804
[2019-08-24 20:34:15,924] TRAIN Iter 285420: lr = 0.024302,	loss = 2.232559,	Top-1 err = 0.302295,	Top-5 err = 0.121484,	data_time = 0.050391,	train_time = 0.375068
[2019-08-24 20:34:29,371] TRAIN Iter 285440: lr = 0.024268,	loss = 2.088338,	Top-1 err = 0.295508,	Top-5 err = 0.118750,	data_time = 0.050538,	train_time = 0.672366
[2019-08-24 20:34:44,239] TRAIN Iter 285460: lr = 0.024235,	loss = 2.165354,	Top-1 err = 0.291992,	Top-5 err = 0.116357,	data_time = 0.050478,	train_time = 0.743388
[2019-08-24 20:34:51,829] TRAIN Iter 285480: lr = 0.024202,	loss = 2.122024,	Top-1 err = 0.296533,	Top-5 err = 0.116846,	data_time = 0.050504,	train_time = 0.379481
[2019-08-24 20:35:04,637] TRAIN Iter 285500: lr = 0.024168,	loss = 2.309874,	Top-1 err = 0.299170,	Top-5 err = 0.118018,	data_time = 0.050496,	train_time = 0.640353
[2019-08-24 20:35:12,115] TRAIN Iter 285520: lr = 0.024135,	loss = 2.186666,	Top-1 err = 0.292969,	Top-5 err = 0.111377,	data_time = 0.050777,	train_time = 0.373869
[2019-08-24 20:35:27,081] TRAIN Iter 285540: lr = 0.024102,	loss = 2.155355,	Top-1 err = 0.294287,	Top-5 err = 0.118213,	data_time = 0.050615,	train_time = 0.748324
[2019-08-24 20:35:40,709] TRAIN Iter 285560: lr = 0.024068,	loss = 2.226380,	Top-1 err = 0.294678,	Top-5 err = 0.113867,	data_time = 0.050583,	train_time = 0.681376
[2019-08-24 20:35:47,898] TRAIN Iter 285580: lr = 0.024035,	loss = 2.181859,	Top-1 err = 0.294238,	Top-5 err = 0.114014,	data_time = 0.050503,	train_time = 0.359435
[2019-08-24 20:36:02,451] TRAIN Iter 285600: lr = 0.024002,	loss = 2.192455,	Top-1 err = 0.296387,	Top-5 err = 0.113770,	data_time = 0.050418,	train_time = 0.727646
[2019-08-24 20:36:15,550] TRAIN Iter 285620: lr = 0.023968,	loss = 2.192157,	Top-1 err = 0.295801,	Top-5 err = 0.113379,	data_time = 0.050449,	train_time = 0.654927
[2019-08-24 20:36:22,854] TRAIN Iter 285640: lr = 0.023935,	loss = 2.169035,	Top-1 err = 0.300293,	Top-5 err = 0.113428,	data_time = 0.050449,	train_time = 0.365191
[2019-08-24 20:36:37,525] TRAIN Iter 285660: lr = 0.023902,	loss = 2.299883,	Top-1 err = 0.295898,	Top-5 err = 0.118506,	data_time = 0.050303,	train_time = 0.733520
[2019-08-24 20:36:44,858] TRAIN Iter 285680: lr = 0.023868,	loss = 2.247131,	Top-1 err = 0.298535,	Top-5 err = 0.117725,	data_time = 0.050481,	train_time = 0.366627
[2019-08-24 20:36:59,430] TRAIN Iter 285700: lr = 0.023835,	loss = 2.287740,	Top-1 err = 0.298682,	Top-5 err = 0.114941,	data_time = 0.050456,	train_time = 0.728614
[2019-08-24 20:37:13,635] TRAIN Iter 285720: lr = 0.023802,	loss = 2.178415,	Top-1 err = 0.300879,	Top-5 err = 0.118066,	data_time = 0.050536,	train_time = 0.710190
[2019-08-24 20:37:21,026] TRAIN Iter 285740: lr = 0.023768,	loss = 2.242907,	Top-1 err = 0.296729,	Top-5 err = 0.115625,	data_time = 0.050704,	train_time = 0.369543
[2019-08-24 20:37:35,694] TRAIN Iter 285760: lr = 0.023735,	loss = 2.195907,	Top-1 err = 0.296240,	Top-5 err = 0.115234,	data_time = 0.050397,	train_time = 0.733402
[2019-08-24 20:37:49,090] TRAIN Iter 285780: lr = 0.023702,	loss = 2.186475,	Top-1 err = 0.296387,	Top-5 err = 0.117822,	data_time = 0.050366,	train_time = 0.669809
[2019-08-24 20:37:57,949] TRAIN Iter 285800: lr = 0.023668,	loss = 2.159542,	Top-1 err = 0.293408,	Top-5 err = 0.115381,	data_time = 0.050270,	train_time = 0.442914
[2019-08-24 20:38:13,391] TRAIN Iter 285820: lr = 0.023635,	loss = 2.195071,	Top-1 err = 0.294434,	Top-5 err = 0.112695,	data_time = 0.050771,	train_time = 0.772098
[2019-08-24 20:38:22,082] TRAIN Iter 285840: lr = 0.023602,	loss = 2.235645,	Top-1 err = 0.295264,	Top-5 err = 0.115967,	data_time = 0.050618,	train_time = 0.434534
[2019-08-24 20:38:36,844] TRAIN Iter 285860: lr = 0.023568,	loss = 2.162574,	Top-1 err = 0.294678,	Top-5 err = 0.115967,	data_time = 0.050719,	train_time = 0.738072
[2019-08-24 20:38:50,512] TRAIN Iter 285880: lr = 0.023535,	loss = 2.215562,	Top-1 err = 0.292676,	Top-5 err = 0.113379,	data_time = 0.602426,	train_time = 0.683384
[2019-08-24 20:38:57,773] TRAIN Iter 285900: lr = 0.023502,	loss = 2.159823,	Top-1 err = 0.295117,	Top-5 err = 0.119580,	data_time = 0.050384,	train_time = 0.363057
[2019-08-24 20:39:12,473] TRAIN Iter 285920: lr = 0.023468,	loss = 2.147110,	Top-1 err = 0.299951,	Top-5 err = 0.112402,	data_time = 0.050441,	train_time = 0.734971
[2019-08-24 20:39:26,544] TRAIN Iter 285940: lr = 0.023435,	loss = 2.206875,	Top-1 err = 0.296338,	Top-5 err = 0.117529,	data_time = 2.455531,	train_time = 0.703548
[2019-08-24 20:39:34,988] TRAIN Iter 285960: lr = 0.023402,	loss = 2.225311,	Top-1 err = 0.293555,	Top-5 err = 0.120557,	data_time = 0.050521,	train_time = 0.422156
[2019-08-24 20:39:49,418] TRAIN Iter 285980: lr = 0.023368,	loss = 2.212792,	Top-1 err = 0.295264,	Top-5 err = 0.119824,	data_time = 0.050416,	train_time = 0.721521
[2019-08-24 20:39:56,859] TRAIN Iter 286000: lr = 0.023335,	loss = 2.289554,	Top-1 err = 0.293066,	Top-5 err = 0.115674,	data_time = 0.050715,	train_time = 0.372014
[2019-08-24 20:40:11,815] TRAIN Iter 286020: lr = 0.023302,	loss = 2.228709,	Top-1 err = 0.304004,	Top-5 err = 0.120508,	data_time = 0.144675,	train_time = 0.747806
[2019-08-24 20:40:26,055] TRAIN Iter 286040: lr = 0.023268,	loss = 2.209199,	Top-1 err = 0.301855,	Top-5 err = 0.117578,	data_time = 0.050468,	train_time = 0.711948
[2019-08-24 20:40:33,340] TRAIN Iter 286060: lr = 0.023235,	loss = 2.271441,	Top-1 err = 0.299707,	Top-5 err = 0.118408,	data_time = 0.050426,	train_time = 0.364234
[2019-08-24 20:40:48,747] TRAIN Iter 286080: lr = 0.023202,	loss = 2.256994,	Top-1 err = 0.300879,	Top-5 err = 0.117578,	data_time = 0.050802,	train_time = 0.770361
[2019-08-24 20:41:01,784] TRAIN Iter 286100: lr = 0.023168,	loss = 2.188972,	Top-1 err = 0.292920,	Top-5 err = 0.118359,	data_time = 0.628993,	train_time = 0.651829
[2019-08-24 20:41:10,478] TRAIN Iter 286120: lr = 0.023135,	loss = 2.230699,	Top-1 err = 0.297998,	Top-5 err = 0.118066,	data_time = 0.116706,	train_time = 0.434677
[2019-08-24 20:41:26,469] TRAIN Iter 286140: lr = 0.023102,	loss = 2.150299,	Top-1 err = 0.305811,	Top-5 err = 0.120654,	data_time = 0.050394,	train_time = 0.799528
[2019-08-24 20:41:34,401] TRAIN Iter 286160: lr = 0.023068,	loss = 2.273460,	Top-1 err = 0.301904,	Top-5 err = 0.120410,	data_time = 0.050489,	train_time = 0.396610
[2019-08-24 20:41:47,854] TRAIN Iter 286180: lr = 0.023035,	loss = 2.316191,	Top-1 err = 0.299268,	Top-5 err = 0.122363,	data_time = 0.050455,	train_time = 0.672631
[2019-08-24 20:42:02,684] TRAIN Iter 286200: lr = 0.023002,	loss = 2.246818,	Top-1 err = 0.296143,	Top-5 err = 0.118359,	data_time = 0.050410,	train_time = 0.741494
[2019-08-24 20:42:09,562] TRAIN Iter 286220: lr = 0.022968,	loss = 2.234917,	Top-1 err = 0.300928,	Top-5 err = 0.120898,	data_time = 0.050767,	train_time = 0.343850
[2019-08-24 20:42:24,998] TRAIN Iter 286240: lr = 0.022935,	loss = 2.158824,	Top-1 err = 0.299414,	Top-5 err = 0.119141,	data_time = 0.050667,	train_time = 0.771787
[2019-08-24 20:42:37,194] TRAIN Iter 286260: lr = 0.022902,	loss = 2.204951,	Top-1 err = 0.298145,	Top-5 err = 0.116748,	data_time = 0.050524,	train_time = 0.609798
[2019-08-24 20:42:47,874] TRAIN Iter 286280: lr = 0.022868,	loss = 2.200231,	Top-1 err = 0.296045,	Top-5 err = 0.117529,	data_time = 0.050307,	train_time = 0.533981
[2019-08-24 20:43:03,461] TRAIN Iter 286300: lr = 0.022835,	loss = 2.166883,	Top-1 err = 0.299365,	Top-5 err = 0.116211,	data_time = 0.050649,	train_time = 0.779344
[2019-08-24 20:43:10,996] TRAIN Iter 286320: lr = 0.022802,	loss = 2.229078,	Top-1 err = 0.299365,	Top-5 err = 0.119092,	data_time = 0.050551,	train_time = 0.376736
[2019-08-24 20:43:27,033] TRAIN Iter 286340: lr = 0.022768,	loss = 2.279899,	Top-1 err = 0.295312,	Top-5 err = 0.116016,	data_time = 0.050482,	train_time = 0.801795
[2019-08-24 20:43:42,001] TRAIN Iter 286360: lr = 0.022735,	loss = 2.236659,	Top-1 err = 0.299268,	Top-5 err = 0.118457,	data_time = 0.050566,	train_time = 0.748415
[2019-08-24 20:43:48,935] TRAIN Iter 286380: lr = 0.022702,	loss = 2.174168,	Top-1 err = 0.301660,	Top-5 err = 0.119629,	data_time = 0.107215,	train_time = 0.346685
[2019-08-24 20:44:04,768] TRAIN Iter 286400: lr = 0.022668,	loss = 2.180554,	Top-1 err = 0.303467,	Top-5 err = 0.117676,	data_time = 0.050682,	train_time = 0.791627
[2019-08-24 20:44:19,606] TRAIN Iter 286420: lr = 0.022635,	loss = 2.276311,	Top-1 err = 0.296387,	Top-5 err = 0.118408,	data_time = 0.050656,	train_time = 0.741905
[2019-08-24 20:44:28,989] TRAIN Iter 286440: lr = 0.022602,	loss = 2.235116,	Top-1 err = 0.301855,	Top-5 err = 0.120508,	data_time = 0.050472,	train_time = 0.469114
[2019-08-24 20:44:44,629] TRAIN Iter 286460: lr = 0.022568,	loss = 2.221890,	Top-1 err = 0.295117,	Top-5 err = 0.114893,	data_time = 0.050405,	train_time = 0.781975
[2019-08-24 20:44:52,201] TRAIN Iter 286480: lr = 0.022535,	loss = 2.223536,	Top-1 err = 0.297168,	Top-5 err = 0.120215,	data_time = 0.050432,	train_time = 0.378612
[2019-08-24 20:45:06,779] TRAIN Iter 286500: lr = 0.022502,	loss = 2.229643,	Top-1 err = 0.299512,	Top-5 err = 0.121191,	data_time = 0.050303,	train_time = 0.728841
[2019-08-24 20:45:23,459] TRAIN Iter 286520: lr = 0.022468,	loss = 2.182255,	Top-1 err = 0.300342,	Top-5 err = 0.118018,	data_time = 0.050687,	train_time = 0.834024
[2019-08-24 20:45:30,066] TRAIN Iter 286540: lr = 0.022435,	loss = 2.266642,	Top-1 err = 0.302881,	Top-5 err = 0.120215,	data_time = 0.050544,	train_time = 0.330298
[2019-08-24 20:45:47,857] TRAIN Iter 286560: lr = 0.022402,	loss = 2.173341,	Top-1 err = 0.299121,	Top-5 err = 0.119971,	data_time = 0.050407,	train_time = 0.889546
[2019-08-24 20:46:02,575] TRAIN Iter 286580: lr = 0.022368,	loss = 2.220081,	Top-1 err = 0.297314,	Top-5 err = 0.116064,	data_time = 0.180622,	train_time = 0.735914
[2019-08-24 20:46:11,009] TRAIN Iter 286600: lr = 0.022335,	loss = 2.229290,	Top-1 err = 0.300244,	Top-5 err = 0.119922,	data_time = 0.050143,	train_time = 0.421656
[2019-08-24 20:46:27,448] TRAIN Iter 286620: lr = 0.022302,	loss = 2.287970,	Top-1 err = 0.297070,	Top-5 err = 0.119922,	data_time = 0.049910,	train_time = 0.821968
[2019-08-24 20:46:33,655] TRAIN Iter 286640: lr = 0.022268,	loss = 2.114627,	Top-1 err = 0.298096,	Top-5 err = 0.118457,	data_time = 0.049884,	train_time = 0.310321
[2019-08-24 20:47:23,792] TRAIN Iter 286660: lr = 0.022235,	loss = 2.230786,	Top-1 err = 0.300367,	Top-5 err = 0.117036,	data_time = 0.050939,	train_time = 2.506843
[2019-08-24 20:47:36,770] TRAIN Iter 286680: lr = 0.022202,	loss = 2.183833,	Top-1 err = 0.287402,	Top-5 err = 0.113965,	data_time = 0.050414,	train_time = 0.648878
[2019-08-24 20:47:46,700] TRAIN Iter 286700: lr = 0.022168,	loss = 2.167430,	Top-1 err = 0.294678,	Top-5 err = 0.115186,	data_time = 0.050695,	train_time = 0.496494
[2019-08-24 20:47:58,309] TRAIN Iter 286720: lr = 0.022135,	loss = 2.202830,	Top-1 err = 0.292627,	Top-5 err = 0.112646,	data_time = 0.050152,	train_time = 0.580395
[2019-08-24 20:48:05,639] TRAIN Iter 286740: lr = 0.022102,	loss = 2.187029,	Top-1 err = 0.298779,	Top-5 err = 0.115088,	data_time = 0.050406,	train_time = 0.366498
[2019-08-24 20:48:20,121] TRAIN Iter 286760: lr = 0.022068,	loss = 2.228429,	Top-1 err = 0.297949,	Top-5 err = 0.119043,	data_time = 0.050363,	train_time = 0.724118
[2019-08-24 20:48:34,422] TRAIN Iter 286780: lr = 0.022035,	loss = 2.267465,	Top-1 err = 0.289941,	Top-5 err = 0.115186,	data_time = 0.050426,	train_time = 0.715006
[2019-08-24 20:48:42,121] TRAIN Iter 286800: lr = 0.022002,	loss = 2.201663,	Top-1 err = 0.297559,	Top-5 err = 0.113477,	data_time = 0.050669,	train_time = 0.384961
[2019-08-24 20:48:56,317] TRAIN Iter 286820: lr = 0.021968,	loss = 2.145814,	Top-1 err = 0.290234,	Top-5 err = 0.112256,	data_time = 0.050935,	train_time = 0.709763
[2019-08-24 20:49:07,733] TRAIN Iter 286840: lr = 0.021935,	loss = 2.219799,	Top-1 err = 0.297803,	Top-5 err = 0.115918,	data_time = 0.050919,	train_time = 0.570808
[2019-08-24 20:49:17,276] TRAIN Iter 286860: lr = 0.021902,	loss = 2.248269,	Top-1 err = 0.292334,	Top-5 err = 0.114990,	data_time = 0.050514,	train_time = 0.477109
[2019-08-24 20:49:32,114] TRAIN Iter 286880: lr = 0.021868,	loss = 2.216551,	Top-1 err = 0.296631,	Top-5 err = 0.118750,	data_time = 0.050408,	train_time = 0.741875
[2019-08-24 20:49:39,293] TRAIN Iter 286900: lr = 0.021835,	loss = 2.174772,	Top-1 err = 0.293848,	Top-5 err = 0.114600,	data_time = 0.142575,	train_time = 0.358952
[2019-08-24 20:49:55,275] TRAIN Iter 286920: lr = 0.021802,	loss = 2.216472,	Top-1 err = 0.294873,	Top-5 err = 0.110156,	data_time = 0.050863,	train_time = 0.799078
[2019-08-24 20:50:11,116] TRAIN Iter 286940: lr = 0.021768,	loss = 2.134670,	Top-1 err = 0.297314,	Top-5 err = 0.115430,	data_time = 0.050620,	train_time = 0.792048
[2019-08-24 20:50:18,458] TRAIN Iter 286960: lr = 0.021735,	loss = 2.113927,	Top-1 err = 0.291211,	Top-5 err = 0.113477,	data_time = 0.050367,	train_time = 0.367073
[2019-08-24 20:50:30,885] TRAIN Iter 286980: lr = 0.021702,	loss = 2.274357,	Top-1 err = 0.296875,	Top-5 err = 0.118213,	data_time = 0.050786,	train_time = 0.621333
[2019-08-24 20:50:44,232] TRAIN Iter 287000: lr = 0.021668,	loss = 2.121196,	Top-1 err = 0.296875,	Top-5 err = 0.119922,	data_time = 0.137218,	train_time = 0.667363
[2019-08-24 20:50:51,654] TRAIN Iter 287020: lr = 0.021635,	loss = 2.162507,	Top-1 err = 0.296826,	Top-5 err = 0.113428,	data_time = 0.050345,	train_time = 0.371093
[2019-08-24 20:51:05,641] TRAIN Iter 287040: lr = 0.021602,	loss = 2.260583,	Top-1 err = 0.296143,	Top-5 err = 0.113818,	data_time = 0.291801,	train_time = 0.699320
[2019-08-24 20:51:13,118] TRAIN Iter 287060: lr = 0.021568,	loss = 2.243277,	Top-1 err = 0.289941,	Top-5 err = 0.112988,	data_time = 0.050210,	train_time = 0.373833
[2019-08-24 20:51:28,356] TRAIN Iter 287080: lr = 0.021535,	loss = 2.148137,	Top-1 err = 0.294727,	Top-5 err = 0.113867,	data_time = 0.050264,	train_time = 0.761900
[2019-08-24 20:51:41,785] TRAIN Iter 287100: lr = 0.021502,	loss = 2.231895,	Top-1 err = 0.293604,	Top-5 err = 0.115137,	data_time = 0.050209,	train_time = 0.671414
[2019-08-24 20:51:50,340] TRAIN Iter 287120: lr = 0.021468,	loss = 2.165001,	Top-1 err = 0.298438,	Top-5 err = 0.117236,	data_time = 0.050500,	train_time = 0.427732
[2019-08-24 20:52:04,862] TRAIN Iter 287140: lr = 0.021435,	loss = 2.134521,	Top-1 err = 0.302002,	Top-5 err = 0.117285,	data_time = 0.050324,	train_time = 0.726096
[2019-08-24 20:52:16,954] TRAIN Iter 287160: lr = 0.021402,	loss = 2.246693,	Top-1 err = 0.297949,	Top-5 err = 0.119922,	data_time = 0.050429,	train_time = 0.604583
[2019-08-24 20:52:26,802] TRAIN Iter 287180: lr = 0.021368,	loss = 2.086512,	Top-1 err = 0.298047,	Top-5 err = 0.116895,	data_time = 0.050826,	train_time = 0.492382
[2019-08-24 20:52:41,465] TRAIN Iter 287200: lr = 0.021335,	loss = 2.177315,	Top-1 err = 0.298926,	Top-5 err = 0.118750,	data_time = 0.050386,	train_time = 0.733110
[2019-08-24 20:52:49,157] TRAIN Iter 287220: lr = 0.021302,	loss = 2.165859,	Top-1 err = 0.287500,	Top-5 err = 0.113672,	data_time = 0.050580,	train_time = 0.384601
[2019-08-24 20:53:03,334] TRAIN Iter 287240: lr = 0.021268,	loss = 2.146864,	Top-1 err = 0.296924,	Top-5 err = 0.118457,	data_time = 0.050453,	train_time = 0.708860
[2019-08-24 20:53:16,675] TRAIN Iter 287260: lr = 0.021235,	loss = 2.264940,	Top-1 err = 0.293457,	Top-5 err = 0.116211,	data_time = 0.252150,	train_time = 0.667005
[2019-08-24 20:53:25,631] TRAIN Iter 287280: lr = 0.021202,	loss = 2.201203,	Top-1 err = 0.296240,	Top-5 err = 0.116357,	data_time = 0.050817,	train_time = 0.447783
[2019-08-24 20:53:40,229] TRAIN Iter 287300: lr = 0.021168,	loss = 2.208081,	Top-1 err = 0.296143,	Top-5 err = 0.114795,	data_time = 0.050663,	train_time = 0.729897
[2019-08-24 20:53:56,443] TRAIN Iter 287320: lr = 0.021135,	loss = 2.269904,	Top-1 err = 0.295898,	Top-5 err = 0.116797,	data_time = 0.123886,	train_time = 0.810678
[2019-08-24 20:54:03,715] TRAIN Iter 287340: lr = 0.021102,	loss = 2.203032,	Top-1 err = 0.300293,	Top-5 err = 0.121631,	data_time = 0.050552,	train_time = 0.363594
[2019-08-24 20:54:18,826] TRAIN Iter 287360: lr = 0.021068,	loss = 2.177179,	Top-1 err = 0.298779,	Top-5 err = 0.116602,	data_time = 0.050403,	train_time = 0.755520
[2019-08-24 20:54:26,575] TRAIN Iter 287380: lr = 0.021035,	loss = 2.247874,	Top-1 err = 0.294727,	Top-5 err = 0.113086,	data_time = 0.050481,	train_time = 0.387463
[2019-08-24 20:54:40,131] TRAIN Iter 287400: lr = 0.021002,	loss = 2.244741,	Top-1 err = 0.294189,	Top-5 err = 0.117871,	data_time = 0.050403,	train_time = 0.677783
[2019-08-24 20:54:55,256] TRAIN Iter 287420: lr = 0.020968,	loss = 2.233924,	Top-1 err = 0.300049,	Top-5 err = 0.116064,	data_time = 0.050608,	train_time = 0.756208
[2019-08-24 20:55:02,454] TRAIN Iter 287440: lr = 0.020935,	loss = 2.219454,	Top-1 err = 0.297070,	Top-5 err = 0.116113,	data_time = 0.050884,	train_time = 0.359885
[2019-08-24 20:55:18,376] TRAIN Iter 287460: lr = 0.020902,	loss = 2.304435,	Top-1 err = 0.300732,	Top-5 err = 0.116943,	data_time = 0.050458,	train_time = 0.796108
[2019-08-24 20:55:33,596] TRAIN Iter 287480: lr = 0.020868,	loss = 2.193818,	Top-1 err = 0.291406,	Top-5 err = 0.117969,	data_time = 0.144077,	train_time = 0.760955
[2019-08-24 20:55:40,746] TRAIN Iter 287500: lr = 0.020835,	loss = 2.197372,	Top-1 err = 0.289355,	Top-5 err = 0.111328,	data_time = 0.050292,	train_time = 0.357489
[2019-08-24 20:55:56,231] TRAIN Iter 287520: lr = 0.020802,	loss = 2.197569,	Top-1 err = 0.293018,	Top-5 err = 0.116113,	data_time = 0.050684,	train_time = 0.774273
[2019-08-24 20:56:03,752] TRAIN Iter 287540: lr = 0.020768,	loss = 2.237168,	Top-1 err = 0.300342,	Top-5 err = 0.116357,	data_time = 0.050609,	train_time = 0.375989
[2019-08-24 20:56:18,275] TRAIN Iter 287560: lr = 0.020735,	loss = 2.178511,	Top-1 err = 0.295361,	Top-5 err = 0.114453,	data_time = 0.050208,	train_time = 0.726151
[2019-08-24 20:56:34,294] TRAIN Iter 287580: lr = 0.020702,	loss = 2.241777,	Top-1 err = 0.301563,	Top-5 err = 0.119775,	data_time = 0.135103,	train_time = 0.800927
[2019-08-24 20:56:41,648] TRAIN Iter 287600: lr = 0.020668,	loss = 2.176744,	Top-1 err = 0.298242,	Top-5 err = 0.116162,	data_time = 0.050490,	train_time = 0.367682
[2019-08-24 20:56:55,618] TRAIN Iter 287620: lr = 0.020635,	loss = 2.147613,	Top-1 err = 0.295850,	Top-5 err = 0.116162,	data_time = 0.050586,	train_time = 0.698507
[2019-08-24 20:57:09,994] TRAIN Iter 287640: lr = 0.020602,	loss = 2.115944,	Top-1 err = 0.292725,	Top-5 err = 0.117627,	data_time = 0.050764,	train_time = 0.718770
[2019-08-24 20:57:18,661] TRAIN Iter 287660: lr = 0.020568,	loss = 2.206820,	Top-1 err = 0.295654,	Top-5 err = 0.114160,	data_time = 0.050410,	train_time = 0.433344
[2019-08-24 20:57:33,771] TRAIN Iter 287680: lr = 0.020535,	loss = 2.263388,	Top-1 err = 0.293750,	Top-5 err = 0.115186,	data_time = 0.050699,	train_time = 0.755488
[2019-08-24 20:57:41,002] TRAIN Iter 287700: lr = 0.020502,	loss = 2.306274,	Top-1 err = 0.295020,	Top-5 err = 0.116504,	data_time = 0.050271,	train_time = 0.361528
[2019-08-24 20:57:58,016] TRAIN Iter 287720: lr = 0.020468,	loss = 2.215152,	Top-1 err = 0.297461,	Top-5 err = 0.115918,	data_time = 0.050199,	train_time = 0.850686
[2019-08-24 20:58:13,975] TRAIN Iter 287740: lr = 0.020435,	loss = 2.156626,	Top-1 err = 0.299219,	Top-5 err = 0.121338,	data_time = 0.050783,	train_time = 0.797963
[2019-08-24 20:58:20,971] TRAIN Iter 287760: lr = 0.020402,	loss = 2.242424,	Top-1 err = 0.296875,	Top-5 err = 0.117041,	data_time = 0.050725,	train_time = 0.349746
[2019-08-24 20:58:36,760] TRAIN Iter 287780: lr = 0.020368,	loss = 2.244869,	Top-1 err = 0.296094,	Top-5 err = 0.116797,	data_time = 0.050487,	train_time = 0.789438
[2019-08-24 20:58:51,370] TRAIN Iter 287800: lr = 0.020335,	loss = 2.244862,	Top-1 err = 0.295752,	Top-5 err = 0.113721,	data_time = 0.050555,	train_time = 0.730495
[2019-08-24 20:58:58,579] TRAIN Iter 287820: lr = 0.020302,	loss = 2.235975,	Top-1 err = 0.306641,	Top-5 err = 0.117773,	data_time = 0.050527,	train_time = 0.360443
[2019-08-24 20:59:14,845] TRAIN Iter 287840: lr = 0.020268,	loss = 2.207446,	Top-1 err = 0.301904,	Top-5 err = 0.116748,	data_time = 0.050070,	train_time = 0.813272
[2019-08-24 20:59:21,596] TRAIN Iter 287860: lr = 0.020235,	loss = 2.270511,	Top-1 err = 0.296338,	Top-5 err = 0.116162,	data_time = 0.050069,	train_time = 0.337565
[2019-08-24 20:59:38,309] TRAIN Iter 287880: lr = 0.020202,	loss = 2.247643,	Top-1 err = 0.296533,	Top-5 err = 0.117578,	data_time = 0.049906,	train_time = 0.835637
[2019-08-24 21:00:27,665] TRAIN Iter 287900: lr = 0.020168,	loss = 2.275445,	Top-1 err = 0.297276,	Top-5 err = 0.118589,	data_time = 0.123593,	train_time = 2.467781
[2019-08-24 21:00:34,766] TRAIN Iter 287920: lr = 0.020135,	loss = 2.141414,	Top-1 err = 0.294336,	Top-5 err = 0.114209,	data_time = 0.050475,	train_time = 0.354986
[2019-08-24 21:00:49,574] TRAIN Iter 287940: lr = 0.020102,	loss = 2.285800,	Top-1 err = 0.290479,	Top-5 err = 0.114502,	data_time = 0.050888,	train_time = 0.740402
[2019-08-24 21:00:57,153] TRAIN Iter 287960: lr = 0.020068,	loss = 2.189303,	Top-1 err = 0.292285,	Top-5 err = 0.111572,	data_time = 0.050978,	train_time = 0.378958
[2019-08-24 21:01:11,553] TRAIN Iter 287980: lr = 0.020035,	loss = 2.238241,	Top-1 err = 0.293555,	Top-5 err = 0.113818,	data_time = 0.181494,	train_time = 0.719975
[2019-08-24 21:01:23,751] TRAIN Iter 288000: lr = 0.020002,	loss = 2.147620,	Top-1 err = 0.294141,	Top-5 err = 0.112500,	data_time = 0.050441,	train_time = 0.609891
[2019-08-24 21:01:31,122] TRAIN Iter 288020: lr = 0.019968,	loss = 2.226523,	Top-1 err = 0.292334,	Top-5 err = 0.112451,	data_time = 0.050673,	train_time = 0.368518
[2019-08-24 21:01:44,926] TRAIN Iter 288040: lr = 0.019935,	loss = 2.326661,	Top-1 err = 0.286621,	Top-5 err = 0.112451,	data_time = 0.050620,	train_time = 0.690189
[2019-08-24 21:01:58,932] TRAIN Iter 288060: lr = 0.019902,	loss = 2.136183,	Top-1 err = 0.289844,	Top-5 err = 0.114648,	data_time = 0.050456,	train_time = 0.700297
[2019-08-24 21:02:07,242] TRAIN Iter 288080: lr = 0.019868,	loss = 2.231750,	Top-1 err = 0.288281,	Top-5 err = 0.110156,	data_time = 0.050458,	train_time = 0.415459
[2019-08-24 21:02:22,139] TRAIN Iter 288100: lr = 0.019835,	loss = 2.254414,	Top-1 err = 0.299414,	Top-5 err = 0.116602,	data_time = 0.050487,	train_time = 0.744869
[2019-08-24 21:02:29,199] TRAIN Iter 288120: lr = 0.019802,	loss = 2.181424,	Top-1 err = 0.289844,	Top-5 err = 0.115186,	data_time = 0.050596,	train_time = 0.352990
[2019-08-24 21:02:43,184] TRAIN Iter 288140: lr = 0.019768,	loss = 2.237898,	Top-1 err = 0.298633,	Top-5 err = 0.114307,	data_time = 0.050236,	train_time = 0.699234
[2019-08-24 21:02:58,541] TRAIN Iter 288160: lr = 0.019735,	loss = 2.173796,	Top-1 err = 0.289160,	Top-5 err = 0.112939,	data_time = 0.050452,	train_time = 0.767831
[2019-08-24 21:03:05,902] TRAIN Iter 288180: lr = 0.019702,	loss = 2.202526,	Top-1 err = 0.292578,	Top-5 err = 0.114062,	data_time = 0.050768,	train_time = 0.368035
[2019-08-24 21:03:18,620] TRAIN Iter 288200: lr = 0.019668,	loss = 2.132470,	Top-1 err = 0.296533,	Top-5 err = 0.113721,	data_time = 0.050596,	train_time = 0.635886
[2019-08-24 21:03:33,941] TRAIN Iter 288220: lr = 0.019635,	loss = 2.202710,	Top-1 err = 0.290283,	Top-5 err = 0.113525,	data_time = 0.933932,	train_time = 0.766035
[2019-08-24 21:03:41,150] TRAIN Iter 288240: lr = 0.019602,	loss = 2.132934,	Top-1 err = 0.290674,	Top-5 err = 0.113379,	data_time = 0.050590,	train_time = 0.360428
[2019-08-24 21:03:56,324] TRAIN Iter 288260: lr = 0.019568,	loss = 2.178919,	Top-1 err = 0.296924,	Top-5 err = 0.113477,	data_time = 0.050309,	train_time = 0.758662
[2019-08-24 21:04:03,353] TRAIN Iter 288280: lr = 0.019535,	loss = 2.129796,	Top-1 err = 0.288721,	Top-5 err = 0.113232,	data_time = 0.050376,	train_time = 0.351460
[2019-08-24 21:04:20,050] TRAIN Iter 288300: lr = 0.019502,	loss = 2.222079,	Top-1 err = 0.296582,	Top-5 err = 0.115039,	data_time = 0.050527,	train_time = 0.834809
[2019-08-24 21:04:34,905] TRAIN Iter 288320: lr = 0.019468,	loss = 2.232326,	Top-1 err = 0.298047,	Top-5 err = 0.118359,	data_time = 0.050659,	train_time = 0.742753
[2019-08-24 21:04:42,390] TRAIN Iter 288340: lr = 0.019435,	loss = 2.138017,	Top-1 err = 0.288428,	Top-5 err = 0.111768,	data_time = 0.050551,	train_time = 0.374216
[2019-08-24 21:04:56,547] TRAIN Iter 288360: lr = 0.019402,	loss = 2.180495,	Top-1 err = 0.299414,	Top-5 err = 0.117041,	data_time = 0.050445,	train_time = 0.707881
[2019-08-24 21:05:11,285] TRAIN Iter 288380: lr = 0.019368,	loss = 2.201028,	Top-1 err = 0.292773,	Top-5 err = 0.117773,	data_time = 5.387643,	train_time = 0.736879
[2019-08-24 21:05:18,428] TRAIN Iter 288400: lr = 0.019335,	loss = 2.208944,	Top-1 err = 0.292871,	Top-5 err = 0.113330,	data_time = 0.050149,	train_time = 0.357107
[2019-08-24 21:05:32,719] TRAIN Iter 288420: lr = 0.019302,	loss = 2.197939,	Top-1 err = 0.293359,	Top-5 err = 0.115088,	data_time = 0.050377,	train_time = 0.714545
[2019-08-24 21:05:40,097] TRAIN Iter 288440: lr = 0.019268,	loss = 2.162207,	Top-1 err = 0.291650,	Top-5 err = 0.112305,	data_time = 0.050476,	train_time = 0.368873
[2019-08-24 21:05:54,704] TRAIN Iter 288460: lr = 0.019235,	loss = 2.109505,	Top-1 err = 0.289502,	Top-5 err = 0.113281,	data_time = 0.050517,	train_time = 0.730359
[2019-08-24 21:06:10,719] TRAIN Iter 288480: lr = 0.019202,	loss = 2.198192,	Top-1 err = 0.293701,	Top-5 err = 0.116357,	data_time = 0.050334,	train_time = 0.800709
[2019-08-24 21:06:18,200] TRAIN Iter 288500: lr = 0.019168,	loss = 2.197962,	Top-1 err = 0.297363,	Top-5 err = 0.114697,	data_time = 0.050290,	train_time = 0.374042
[2019-08-24 21:06:33,271] TRAIN Iter 288520: lr = 0.019135,	loss = 2.192099,	Top-1 err = 0.295557,	Top-5 err = 0.115234,	data_time = 0.050418,	train_time = 0.753563
[2019-08-24 21:06:49,496] TRAIN Iter 288540: lr = 0.019102,	loss = 2.148958,	Top-1 err = 0.292676,	Top-5 err = 0.112695,	data_time = 5.771683,	train_time = 0.811250
[2019-08-24 21:06:56,203] TRAIN Iter 288560: lr = 0.019068,	loss = 2.295460,	Top-1 err = 0.294629,	Top-5 err = 0.117480,	data_time = 0.050527,	train_time = 0.335311
[2019-08-24 21:07:11,878] TRAIN Iter 288580: lr = 0.019035,	loss = 2.210693,	Top-1 err = 0.295996,	Top-5 err = 0.114844,	data_time = 0.050537,	train_time = 0.783726
[2019-08-24 21:07:19,443] TRAIN Iter 288600: lr = 0.019002,	loss = 2.185647,	Top-1 err = 0.291602,	Top-5 err = 0.114551,	data_time = 0.050275,	train_time = 0.378238
[2019-08-24 21:07:34,187] TRAIN Iter 288620: lr = 0.018968,	loss = 2.239197,	Top-1 err = 0.291357,	Top-5 err = 0.115527,	data_time = 0.050685,	train_time = 0.737191
[2019-08-24 21:07:49,610] TRAIN Iter 288640: lr = 0.018935,	loss = 2.188243,	Top-1 err = 0.295361,	Top-5 err = 0.114404,	data_time = 0.050448,	train_time = 0.771127
[2019-08-24 21:07:56,701] TRAIN Iter 288660: lr = 0.018902,	loss = 2.260824,	Top-1 err = 0.294531,	Top-5 err = 0.114697,	data_time = 0.050217,	train_time = 0.354540
[2019-08-24 21:08:11,420] TRAIN Iter 288680: lr = 0.018868,	loss = 2.185652,	Top-1 err = 0.292285,	Top-5 err = 0.114209,	data_time = 0.050365,	train_time = 0.735946
[2019-08-24 21:08:27,527] TRAIN Iter 288700: lr = 0.018835,	loss = 2.156921,	Top-1 err = 0.299707,	Top-5 err = 0.114990,	data_time = 7.941518,	train_time = 0.805325
[2019-08-24 21:08:34,297] TRAIN Iter 288720: lr = 0.018802,	loss = 2.256170,	Top-1 err = 0.296143,	Top-5 err = 0.112744,	data_time = 0.050766,	train_time = 0.338508
[2019-08-24 21:08:49,700] TRAIN Iter 288740: lr = 0.018768,	loss = 2.251810,	Top-1 err = 0.286865,	Top-5 err = 0.114746,	data_time = 0.050802,	train_time = 0.770111
[2019-08-24 21:08:57,395] TRAIN Iter 288760: lr = 0.018735,	loss = 2.148976,	Top-1 err = 0.291797,	Top-5 err = 0.115332,	data_time = 0.050380,	train_time = 0.384762
[2019-08-24 21:09:13,233] TRAIN Iter 288780: lr = 0.018702,	loss = 2.194359,	Top-1 err = 0.293115,	Top-5 err = 0.118604,	data_time = 0.050315,	train_time = 0.791898
[2019-08-24 21:09:28,251] TRAIN Iter 288800: lr = 0.018668,	loss = 2.267296,	Top-1 err = 0.289404,	Top-5 err = 0.113281,	data_time = 0.050448,	train_time = 0.750879
[2019-08-24 21:09:36,013] TRAIN Iter 288820: lr = 0.018635,	loss = 2.215512,	Top-1 err = 0.297266,	Top-5 err = 0.114209,	data_time = 0.050419,	train_time = 0.388068
[2019-08-24 21:09:51,022] TRAIN Iter 288840: lr = 0.018602,	loss = 2.185468,	Top-1 err = 0.296826,	Top-5 err = 0.114600,	data_time = 0.050428,	train_time = 0.750432
[2019-08-24 21:10:06,957] TRAIN Iter 288860: lr = 0.018568,	loss = 2.215992,	Top-1 err = 0.300879,	Top-5 err = 0.116357,	data_time = 7.837773,	train_time = 0.796726
[2019-08-24 21:10:13,857] TRAIN Iter 288880: lr = 0.018535,	loss = 2.163282,	Top-1 err = 0.293359,	Top-5 err = 0.113770,	data_time = 0.050559,	train_time = 0.345007
[2019-08-24 21:10:30,026] TRAIN Iter 288900: lr = 0.018502,	loss = 2.212233,	Top-1 err = 0.289795,	Top-5 err = 0.115430,	data_time = 0.050278,	train_time = 0.808417
[2019-08-24 21:10:37,994] TRAIN Iter 288920: lr = 0.018468,	loss = 2.159191,	Top-1 err = 0.288574,	Top-5 err = 0.112793,	data_time = 0.050429,	train_time = 0.398388
[2019-08-24 21:10:51,219] TRAIN Iter 288940: lr = 0.018435,	loss = 2.235460,	Top-1 err = 0.291553,	Top-5 err = 0.116748,	data_time = 0.050517,	train_time = 0.661272
[2019-08-24 21:11:08,096] TRAIN Iter 288960: lr = 0.018402,	loss = 2.173681,	Top-1 err = 0.293750,	Top-5 err = 0.114551,	data_time = 0.050272,	train_time = 0.843831
[2019-08-24 21:11:15,477] TRAIN Iter 288980: lr = 0.018368,	loss = 2.200738,	Top-1 err = 0.295312,	Top-5 err = 0.114355,	data_time = 0.050506,	train_time = 0.369040
[2019-08-24 21:11:30,871] TRAIN Iter 289000: lr = 0.018335,	loss = 2.246080,	Top-1 err = 0.292578,	Top-5 err = 0.117578,	data_time = 0.050457,	train_time = 0.769691
[2019-08-24 21:11:45,951] TRAIN Iter 289020: lr = 0.018302,	loss = 2.136685,	Top-1 err = 0.294824,	Top-5 err = 0.117285,	data_time = 2.820166,	train_time = 0.753944
[2019-08-24 21:11:52,807] TRAIN Iter 289040: lr = 0.018268,	loss = 2.124113,	Top-1 err = 0.290088,	Top-5 err = 0.111914,	data_time = 0.050652,	train_time = 0.342820
[2019-08-24 21:12:07,955] TRAIN Iter 289060: lr = 0.018235,	loss = 2.193181,	Top-1 err = 0.293555,	Top-5 err = 0.116699,	data_time = 0.050185,	train_time = 0.757360
[2019-08-24 21:12:15,569] TRAIN Iter 289080: lr = 0.018202,	loss = 2.219052,	Top-1 err = 0.293018,	Top-5 err = 0.114160,	data_time = 0.050703,	train_time = 0.380715
[2019-08-24 21:12:31,084] TRAIN Iter 289100: lr = 0.018168,	loss = 2.182844,	Top-1 err = 0.295752,	Top-5 err = 0.116504,	data_time = 0.050100,	train_time = 0.775712
[2019-08-24 21:12:46,847] TRAIN Iter 289120: lr = 0.018135,	loss = 2.216836,	Top-1 err = 0.293799,	Top-5 err = 0.115820,	data_time = 0.049954,	train_time = 0.788128
[2019-08-24 21:12:52,736] TRAIN Iter 289140: lr = 0.018102,	loss = 2.139048,	Top-1 err = 0.294141,	Top-5 err = 0.115479,	data_time = 0.049904,	train_time = 0.294448
[2019-08-24 21:13:40,290] TRAIN Iter 289160: lr = 0.018068,	loss = 2.229294,	Top-1 err = 0.302684,	Top-5 err = 0.114050,	data_time = 0.050533,	train_time = 2.377677
[2019-08-24 21:13:47,563] TRAIN Iter 289180: lr = 0.018035,	loss = 2.222540,	Top-1 err = 0.292529,	Top-5 err = 0.114746,	data_time = 0.050381,	train_time = 0.363653
[2019-08-24 21:14:05,709] TRAIN Iter 289200: lr = 0.018002,	loss = 2.114037,	Top-1 err = 0.291357,	Top-5 err = 0.113086,	data_time = 0.050854,	train_time = 0.907288
[2019-08-24 21:14:20,498] TRAIN Iter 289220: lr = 0.017968,	loss = 2.197630,	Top-1 err = 0.286914,	Top-5 err = 0.111963,	data_time = 0.050486,	train_time = 0.739423
[2019-08-24 21:14:28,690] TRAIN Iter 289240: lr = 0.017935,	loss = 2.263684,	Top-1 err = 0.290918,	Top-5 err = 0.116260,	data_time = 0.050458,	train_time = 0.409593
[2019-08-24 21:14:39,303] TRAIN Iter 289260: lr = 0.017902,	loss = 2.181874,	Top-1 err = 0.287695,	Top-5 err = 0.112354,	data_time = 0.050876,	train_time = 0.530641
[2019-08-24 21:14:52,579] TRAIN Iter 289280: lr = 0.017868,	loss = 2.205765,	Top-1 err = 0.291748,	Top-5 err = 0.113281,	data_time = 0.050689,	train_time = 0.663777
[2019-08-24 21:14:59,838] TRAIN Iter 289300: lr = 0.017835,	loss = 2.164754,	Top-1 err = 0.287451,	Top-5 err = 0.109766,	data_time = 0.050458,	train_time = 0.362947
[2019-08-24 21:15:15,889] TRAIN Iter 289320: lr = 0.017802,	loss = 2.158217,	Top-1 err = 0.284619,	Top-5 err = 0.108691,	data_time = 0.050495,	train_time = 0.802511
[2019-08-24 21:15:23,311] TRAIN Iter 289340: lr = 0.017768,	loss = 2.204472,	Top-1 err = 0.292725,	Top-5 err = 0.110937,	data_time = 0.050202,	train_time = 0.371086
[2019-08-24 21:15:36,966] TRAIN Iter 289360: lr = 0.017735,	loss = 2.143763,	Top-1 err = 0.287939,	Top-5 err = 0.111475,	data_time = 0.050348,	train_time = 0.682730
[2019-08-24 21:15:50,222] TRAIN Iter 289380: lr = 0.017702,	loss = 2.197201,	Top-1 err = 0.289355,	Top-5 err = 0.111914,	data_time = 0.091618,	train_time = 0.662807
[2019-08-24 21:15:57,336] TRAIN Iter 289400: lr = 0.017668,	loss = 2.134811,	Top-1 err = 0.292578,	Top-5 err = 0.109863,	data_time = 0.050711,	train_time = 0.355639
[2019-08-24 21:16:12,744] TRAIN Iter 289420: lr = 0.017635,	loss = 2.146452,	Top-1 err = 0.286865,	Top-5 err = 0.110254,	data_time = 0.050559,	train_time = 0.770432
[2019-08-24 21:16:28,889] TRAIN Iter 289440: lr = 0.017602,	loss = 2.121161,	Top-1 err = 0.282617,	Top-5 err = 0.109033,	data_time = 0.105997,	train_time = 0.807206
[2019-08-24 21:16:36,202] TRAIN Iter 289460: lr = 0.017568,	loss = 2.199309,	Top-1 err = 0.295508,	Top-5 err = 0.116455,	data_time = 0.050672,	train_time = 0.365640
[2019-08-24 21:16:50,641] TRAIN Iter 289480: lr = 0.017535,	loss = 2.145093,	Top-1 err = 0.288721,	Top-5 err = 0.112109,	data_time = 0.050752,	train_time = 0.721934
[2019-08-24 21:16:57,909] TRAIN Iter 289500: lr = 0.017502,	loss = 2.217475,	Top-1 err = 0.294971,	Top-5 err = 0.113721,	data_time = 0.050429,	train_time = 0.363385
[2019-08-24 21:17:14,596] TRAIN Iter 289520: lr = 0.017468,	loss = 2.175929,	Top-1 err = 0.290137,	Top-5 err = 0.116553,	data_time = 0.050683,	train_time = 0.834321
[2019-08-24 21:17:28,675] TRAIN Iter 289540: lr = 0.017435,	loss = 2.190607,	Top-1 err = 0.294824,	Top-5 err = 0.113330,	data_time = 0.050459,	train_time = 0.703972
[2019-08-24 21:17:36,085] TRAIN Iter 289560: lr = 0.017402,	loss = 2.115870,	Top-1 err = 0.292627,	Top-5 err = 0.115674,	data_time = 0.050319,	train_time = 0.370458
[2019-08-24 21:17:49,176] TRAIN Iter 289580: lr = 0.017368,	loss = 2.249592,	Top-1 err = 0.285303,	Top-5 err = 0.114746,	data_time = 0.050393,	train_time = 0.654530
[2019-08-24 21:18:05,393] TRAIN Iter 289600: lr = 0.017335,	loss = 2.197534,	Top-1 err = 0.297168,	Top-5 err = 0.112256,	data_time = 0.145833,	train_time = 0.810829
[2019-08-24 21:18:12,488] TRAIN Iter 289620: lr = 0.017302,	loss = 2.175659,	Top-1 err = 0.290625,	Top-5 err = 0.112256,	data_time = 0.050748,	train_time = 0.354743
[2019-08-24 21:18:27,785] TRAIN Iter 289640: lr = 0.017268,	loss = 2.204175,	Top-1 err = 0.296533,	Top-5 err = 0.113330,	data_time = 0.050354,	train_time = 0.764842
[2019-08-24 21:18:35,414] TRAIN Iter 289660: lr = 0.017235,	loss = 2.186011,	Top-1 err = 0.289795,	Top-5 err = 0.114600,	data_time = 0.146503,	train_time = 0.381451
[2019-08-24 21:18:50,707] TRAIN Iter 289680: lr = 0.017202,	loss = 2.272928,	Top-1 err = 0.292139,	Top-5 err = 0.113867,	data_time = 0.050495,	train_time = 0.764650
[2019-08-24 21:19:07,089] TRAIN Iter 289700: lr = 0.017168,	loss = 2.170906,	Top-1 err = 0.292383,	Top-5 err = 0.114795,	data_time = 0.050670,	train_time = 0.819042
[2019-08-24 21:19:15,020] TRAIN Iter 289720: lr = 0.017135,	loss = 2.143977,	Top-1 err = 0.292334,	Top-5 err = 0.117529,	data_time = 0.050407,	train_time = 0.396557
[2019-08-24 21:19:28,516] TRAIN Iter 289740: lr = 0.017102,	loss = 2.165218,	Top-1 err = 0.288135,	Top-5 err = 0.112109,	data_time = 0.050471,	train_time = 0.674801
[2019-08-24 21:19:43,491] TRAIN Iter 289760: lr = 0.017068,	loss = 2.258697,	Top-1 err = 0.291650,	Top-5 err = 0.115625,	data_time = 0.050785,	train_time = 0.748700
[2019-08-24 21:19:50,351] TRAIN Iter 289780: lr = 0.017035,	loss = 2.206884,	Top-1 err = 0.292334,	Top-5 err = 0.116064,	data_time = 0.050513,	train_time = 0.342992
[2019-08-24 21:20:05,076] TRAIN Iter 289800: lr = 0.017002,	loss = 2.303155,	Top-1 err = 0.289014,	Top-5 err = 0.112891,	data_time = 0.050826,	train_time = 0.736241
[2019-08-24 21:20:12,381] TRAIN Iter 289820: lr = 0.016968,	loss = 2.136317,	Top-1 err = 0.291357,	Top-5 err = 0.115283,	data_time = 0.050124,	train_time = 0.365261
[2019-08-24 21:20:28,520] TRAIN Iter 289840: lr = 0.016935,	loss = 2.171184,	Top-1 err = 0.291797,	Top-5 err = 0.111328,	data_time = 0.050275,	train_time = 0.806914
[2019-08-24 21:20:44,848] TRAIN Iter 289860: lr = 0.016902,	loss = 2.161646,	Top-1 err = 0.290771,	Top-5 err = 0.114697,	data_time = 0.050354,	train_time = 0.816408
[2019-08-24 21:20:52,347] TRAIN Iter 289880: lr = 0.016868,	loss = 2.135751,	Top-1 err = 0.290869,	Top-5 err = 0.112012,	data_time = 0.050515,	train_time = 0.374931
[2019-08-24 21:21:05,588] TRAIN Iter 289900: lr = 0.016835,	loss = 2.081895,	Top-1 err = 0.292480,	Top-5 err = 0.111475,	data_time = 0.050719,	train_time = 0.662017
[2019-08-24 21:21:22,144] TRAIN Iter 289920: lr = 0.016802,	loss = 2.193033,	Top-1 err = 0.289307,	Top-5 err = 0.115625,	data_time = 0.050661,	train_time = 0.827795
[2019-08-24 21:21:29,176] TRAIN Iter 289940: lr = 0.016768,	loss = 2.135363,	Top-1 err = 0.299121,	Top-5 err = 0.110791,	data_time = 0.127830,	train_time = 0.351579
[2019-08-24 21:21:43,636] TRAIN Iter 289960: lr = 0.016735,	loss = 2.198598,	Top-1 err = 0.285693,	Top-5 err = 0.109570,	data_time = 0.050347,	train_time = 0.722979
[2019-08-24 21:21:50,641] TRAIN Iter 289980: lr = 0.016702,	loss = 2.133233,	Top-1 err = 0.291699,	Top-5 err = 0.111523,	data_time = 0.050551,	train_time = 0.350251
[2019-08-24 21:22:07,639] TRAIN Iter 290000: lr = 0.016668,	loss = 2.110613,	Top-1 err = 0.287891,	Top-5 err = 0.113721,	data_time = 0.050366,	train_time = 0.849889
[2019-08-24 21:23:11,600] TEST Iter 290000: loss = 2.064854,	Top-1 err = 0.275940,	Top-5 err = 0.090820,	val_time = 62.883298
[2019-08-24 21:23:18,568] TRAIN Iter 290020: lr = 0.016635,	loss = 2.212609,	Top-1 err = 0.291162,	Top-5 err = 0.113672,	data_time = 0.050274,	train_time = 0.348397
[2019-08-24 21:23:25,056] TRAIN Iter 290040: lr = 0.016602,	loss = 2.173551,	Top-1 err = 0.290576,	Top-5 err = 0.113184,	data_time = 0.050501,	train_time = 0.324379
[2019-08-24 21:23:32,029] TRAIN Iter 290060: lr = 0.016568,	loss = 2.193835,	Top-1 err = 0.289697,	Top-5 err = 0.113916,	data_time = 0.050608,	train_time = 0.348635
[2019-08-24 21:23:40,411] TRAIN Iter 290080: lr = 0.016535,	loss = 2.146163,	Top-1 err = 0.284668,	Top-5 err = 0.109326,	data_time = 0.050642,	train_time = 0.419074
[2019-08-24 21:23:52,809] TRAIN Iter 290100: lr = 0.016502,	loss = 2.144812,	Top-1 err = 0.293555,	Top-5 err = 0.115039,	data_time = 0.162254,	train_time = 0.619889
[2019-08-24 21:24:04,184] TRAIN Iter 290120: lr = 0.016468,	loss = 2.207120,	Top-1 err = 0.286279,	Top-5 err = 0.114941,	data_time = 0.050611,	train_time = 0.568759
[2019-08-24 21:24:18,433] TRAIN Iter 290140: lr = 0.016435,	loss = 2.202432,	Top-1 err = 0.291064,	Top-5 err = 0.112158,	data_time = 0.726401,	train_time = 0.712405
[2019-08-24 21:24:27,898] TRAIN Iter 290160: lr = 0.016402,	loss = 2.116899,	Top-1 err = 0.289355,	Top-5 err = 0.110645,	data_time = 0.050504,	train_time = 0.473259
[2019-08-24 21:24:43,442] TRAIN Iter 290180: lr = 0.016368,	loss = 2.179052,	Top-1 err = 0.290771,	Top-5 err = 0.114062,	data_time = 0.171951,	train_time = 0.777159
[2019-08-24 21:24:56,560] TRAIN Iter 290200: lr = 0.016335,	loss = 2.199303,	Top-1 err = 0.290625,	Top-5 err = 0.112256,	data_time = 0.050390,	train_time = 0.655912
[2019-08-24 21:25:07,344] TRAIN Iter 290220: lr = 0.016302,	loss = 2.224081,	Top-1 err = 0.291699,	Top-5 err = 0.114795,	data_time = 0.524810,	train_time = 0.539159
[2019-08-24 21:25:23,354] TRAIN Iter 290240: lr = 0.016268,	loss = 2.180580,	Top-1 err = 0.285400,	Top-5 err = 0.110107,	data_time = 0.050383,	train_time = 0.800512
[2019-08-24 21:25:37,070] TRAIN Iter 290260: lr = 0.016235,	loss = 2.236231,	Top-1 err = 0.293652,	Top-5 err = 0.116748,	data_time = 0.050529,	train_time = 0.685790
[2019-08-24 21:25:47,808] TRAIN Iter 290280: lr = 0.016202,	loss = 2.195637,	Top-1 err = 0.292920,	Top-5 err = 0.114111,	data_time = 0.143151,	train_time = 0.536852
[2019-08-24 21:26:02,662] TRAIN Iter 290300: lr = 0.016168,	loss = 2.145016,	Top-1 err = 0.294238,	Top-5 err = 0.113770,	data_time = 0.965247,	train_time = 0.742728
[2019-08-24 21:26:11,829] TRAIN Iter 290320: lr = 0.016135,	loss = 2.215407,	Top-1 err = 0.295215,	Top-5 err = 0.115234,	data_time = 0.050458,	train_time = 0.458328
[2019-08-24 21:26:27,743] TRAIN Iter 290340: lr = 0.016102,	loss = 2.156199,	Top-1 err = 0.295410,	Top-5 err = 0.114160,	data_time = 0.050050,	train_time = 0.795653
[2019-08-24 21:26:43,250] TRAIN Iter 290360: lr = 0.016068,	loss = 2.192214,	Top-1 err = 0.292383,	Top-5 err = 0.111475,	data_time = 0.050054,	train_time = 0.775360
[2019-08-24 21:26:52,031] TRAIN Iter 290380: lr = 0.016035,	loss = 2.234219,	Top-1 err = 0.292480,	Top-5 err = 0.113965,	data_time = 0.050104,	train_time = 0.439039
[2019-08-24 21:27:03,921] TRAIN Iter 290400: lr = 0.016002,	loss = 2.570628,	Top-1 err = 0.295078,	Top-5 err = 0.115530,	data_time = 0.007136,	train_time = 0.594487
[2019-08-24 21:27:50,427] TRAIN Iter 290420: lr = 0.015968,	loss = 2.201780,	Top-1 err = 0.288525,	Top-5 err = 0.109961,	data_time = 0.050794,	train_time = 2.325267
[2019-08-24 21:28:10,611] TRAIN Iter 290440: lr = 0.015935,	loss = 2.179944,	Top-1 err = 0.288574,	Top-5 err = 0.109766,	data_time = 0.050863,	train_time = 1.009180
[2019-08-24 21:28:19,434] TRAIN Iter 290460: lr = 0.015902,	loss = 2.180439,	Top-1 err = 0.285449,	Top-5 err = 0.111865,	data_time = 0.050542,	train_time = 0.441153
[2019-08-24 21:28:30,982] TRAIN Iter 290480: lr = 0.015868,	loss = 2.136408,	Top-1 err = 0.288672,	Top-5 err = 0.111328,	data_time = 0.050382,	train_time = 0.577403
[2019-08-24 21:28:39,437] TRAIN Iter 290500: lr = 0.015835,	loss = 2.153007,	Top-1 err = 0.291309,	Top-5 err = 0.114453,	data_time = 0.050956,	train_time = 0.422731
[2019-08-24 21:28:47,224] TRAIN Iter 290520: lr = 0.015802,	loss = 2.149163,	Top-1 err = 0.291260,	Top-5 err = 0.112256,	data_time = 0.050597,	train_time = 0.389333
[2019-08-24 21:29:02,042] TRAIN Iter 290540: lr = 0.015768,	loss = 2.229243,	Top-1 err = 0.289551,	Top-5 err = 0.115088,	data_time = 0.050812,	train_time = 0.740866
[2019-08-24 21:29:09,184] TRAIN Iter 290560: lr = 0.015735,	loss = 2.146441,	Top-1 err = 0.283691,	Top-5 err = 0.108936,	data_time = 0.050175,	train_time = 0.357120
[2019-08-24 21:29:24,471] TRAIN Iter 290580: lr = 0.015702,	loss = 2.131431,	Top-1 err = 0.292676,	Top-5 err = 0.112842,	data_time = 0.050650,	train_time = 0.764296
[2019-08-24 21:29:38,303] TRAIN Iter 290600: lr = 0.015668,	loss = 2.198957,	Top-1 err = 0.280908,	Top-5 err = 0.108594,	data_time = 0.050151,	train_time = 0.691609
[2019-08-24 21:29:45,670] TRAIN Iter 290620: lr = 0.015635,	loss = 2.139585,	Top-1 err = 0.293018,	Top-5 err = 0.111914,	data_time = 0.136899,	train_time = 0.368352
[2019-08-24 21:30:01,283] TRAIN Iter 290640: lr = 0.015602,	loss = 2.163080,	Top-1 err = 0.290088,	Top-5 err = 0.114111,	data_time = 0.050927,	train_time = 0.780623
[2019-08-24 21:30:16,004] TRAIN Iter 290660: lr = 0.015568,	loss = 2.155540,	Top-1 err = 0.284326,	Top-5 err = 0.108594,	data_time = 0.050836,	train_time = 0.736037
[2019-08-24 21:30:23,608] TRAIN Iter 290680: lr = 0.015535,	loss = 2.164397,	Top-1 err = 0.289209,	Top-5 err = 0.112500,	data_time = 0.050606,	train_time = 0.380190
[2019-08-24 21:30:37,349] TRAIN Iter 290700: lr = 0.015502,	loss = 2.136200,	Top-1 err = 0.286035,	Top-5 err = 0.113037,	data_time = 0.050592,	train_time = 0.687039
[2019-08-24 21:30:44,449] TRAIN Iter 290720: lr = 0.015468,	loss = 2.118535,	Top-1 err = 0.292578,	Top-5 err = 0.109521,	data_time = 0.051062,	train_time = 0.354949
[2019-08-24 21:30:59,027] TRAIN Iter 290740: lr = 0.015435,	loss = 2.180050,	Top-1 err = 0.289062,	Top-5 err = 0.111572,	data_time = 0.050836,	train_time = 0.728892
[2019-08-24 21:31:13,962] TRAIN Iter 290760: lr = 0.015402,	loss = 2.131938,	Top-1 err = 0.280713,	Top-5 err = 0.107520,	data_time = 0.050648,	train_time = 0.746760
[2019-08-24 21:31:20,947] TRAIN Iter 290780: lr = 0.015368,	loss = 2.236118,	Top-1 err = 0.290381,	Top-5 err = 0.113086,	data_time = 0.051146,	train_time = 0.349224
[2019-08-24 21:31:36,107] TRAIN Iter 290800: lr = 0.015335,	loss = 2.190416,	Top-1 err = 0.285889,	Top-5 err = 0.114258,	data_time = 0.050473,	train_time = 0.757996
[2019-08-24 21:31:50,691] TRAIN Iter 290820: lr = 0.015302,	loss = 2.153885,	Top-1 err = 0.285889,	Top-5 err = 0.109521,	data_time = 0.050442,	train_time = 0.729171
[2019-08-24 21:31:58,441] TRAIN Iter 290840: lr = 0.015268,	loss = 2.123385,	Top-1 err = 0.288428,	Top-5 err = 0.110205,	data_time = 0.050413,	train_time = 0.387474
[2019-08-24 21:32:13,026] TRAIN Iter 290860: lr = 0.015235,	loss = 2.151513,	Top-1 err = 0.280225,	Top-5 err = 0.111523,	data_time = 0.050425,	train_time = 0.729269
[2019-08-24 21:32:20,463] TRAIN Iter 290880: lr = 0.015202,	loss = 2.177270,	Top-1 err = 0.292090,	Top-5 err = 0.111865,	data_time = 0.050591,	train_time = 0.371810
[2019-08-24 21:32:34,774] TRAIN Iter 290900: lr = 0.015168,	loss = 2.172034,	Top-1 err = 0.288623,	Top-5 err = 0.110400,	data_time = 0.050468,	train_time = 0.715528
[2019-08-24 21:32:49,244] TRAIN Iter 290920: lr = 0.015135,	loss = 2.127590,	Top-1 err = 0.290918,	Top-5 err = 0.112939,	data_time = 0.050822,	train_time = 0.723510
[2019-08-24 21:32:56,627] TRAIN Iter 290940: lr = 0.015102,	loss = 2.138571,	Top-1 err = 0.286279,	Top-5 err = 0.113379,	data_time = 0.050356,	train_time = 0.369151
[2019-08-24 21:33:12,308] TRAIN Iter 290960: lr = 0.015068,	loss = 2.208370,	Top-1 err = 0.288184,	Top-5 err = 0.112695,	data_time = 0.050208,	train_time = 0.783994
[2019-08-24 21:33:31,168] TRAIN Iter 290980: lr = 0.015035,	loss = 2.176105,	Top-1 err = 0.287939,	Top-5 err = 0.112549,	data_time = 0.050694,	train_time = 0.942986
[2019-08-24 21:33:40,727] TRAIN Iter 291000: lr = 0.015002,	loss = 2.071354,	Top-1 err = 0.287549,	Top-5 err = 0.109619,	data_time = 0.050566,	train_time = 0.477968
[2019-08-24 21:33:51,854] TRAIN Iter 291020: lr = 0.014968,	loss = 2.080223,	Top-1 err = 0.288721,	Top-5 err = 0.111572,	data_time = 0.050370,	train_time = 0.556334
[2019-08-24 21:33:59,117] TRAIN Iter 291040: lr = 0.014935,	loss = 2.182225,	Top-1 err = 0.294434,	Top-5 err = 0.114453,	data_time = 0.050338,	train_time = 0.363111
[2019-08-24 21:34:14,223] TRAIN Iter 291060: lr = 0.014902,	loss = 2.157780,	Top-1 err = 0.281738,	Top-5 err = 0.106885,	data_time = 0.050530,	train_time = 0.755297
[2019-08-24 21:34:28,326] TRAIN Iter 291080: lr = 0.014868,	loss = 2.107947,	Top-1 err = 0.281445,	Top-5 err = 0.109473,	data_time = 0.050572,	train_time = 0.705133
[2019-08-24 21:34:36,883] TRAIN Iter 291100: lr = 0.014835,	loss = 2.150223,	Top-1 err = 0.288330,	Top-5 err = 0.111719,	data_time = 0.050333,	train_time = 0.427854
[2019-08-24 21:34:52,024] TRAIN Iter 291120: lr = 0.014802,	loss = 2.144772,	Top-1 err = 0.291650,	Top-5 err = 0.112451,	data_time = 0.050719,	train_time = 0.756994
[2019-08-24 21:35:06,660] TRAIN Iter 291140: lr = 0.014768,	loss = 2.092093,	Top-1 err = 0.289404,	Top-5 err = 0.111182,	data_time = 0.131432,	train_time = 0.731789
[2019-08-24 21:35:14,892] TRAIN Iter 291160: lr = 0.014735,	loss = 2.281397,	Top-1 err = 0.289502,	Top-5 err = 0.111719,	data_time = 0.050574,	train_time = 0.411628
[2019-08-24 21:35:30,333] TRAIN Iter 291180: lr = 0.014702,	loss = 2.222715,	Top-1 err = 0.289697,	Top-5 err = 0.111230,	data_time = 0.050546,	train_time = 0.772008
[2019-08-24 21:35:37,618] TRAIN Iter 291200: lr = 0.014668,	loss = 2.186577,	Top-1 err = 0.285693,	Top-5 err = 0.114111,	data_time = 0.050551,	train_time = 0.364257
[2019-08-24 21:35:55,306] TRAIN Iter 291220: lr = 0.014635,	loss = 2.123424,	Top-1 err = 0.288672,	Top-5 err = 0.111426,	data_time = 0.050841,	train_time = 0.884389
[2019-08-24 21:36:09,853] TRAIN Iter 291240: lr = 0.014602,	loss = 2.182285,	Top-1 err = 0.289697,	Top-5 err = 0.109229,	data_time = 0.050652,	train_time = 0.727345
[2019-08-24 21:36:17,198] TRAIN Iter 291260: lr = 0.014568,	loss = 2.034987,	Top-1 err = 0.288037,	Top-5 err = 0.110449,	data_time = 0.050401,	train_time = 0.367195
[2019-08-24 21:36:33,897] TRAIN Iter 291280: lr = 0.014535,	loss = 2.165378,	Top-1 err = 0.290771,	Top-5 err = 0.112402,	data_time = 0.105978,	train_time = 0.834945
[2019-08-24 21:36:49,492] TRAIN Iter 291300: lr = 0.014502,	loss = 2.245598,	Top-1 err = 0.289697,	Top-5 err = 0.114111,	data_time = 0.050811,	train_time = 0.779761
[2019-08-24 21:36:56,963] TRAIN Iter 291320: lr = 0.014468,	loss = 2.164693,	Top-1 err = 0.289746,	Top-5 err = 0.112451,	data_time = 0.050685,	train_time = 0.373528
[2019-08-24 21:37:14,839] TRAIN Iter 291340: lr = 0.014435,	loss = 2.191060,	Top-1 err = 0.288428,	Top-5 err = 0.114600,	data_time = 0.050451,	train_time = 0.893788
[2019-08-24 21:37:21,686] TRAIN Iter 291360: lr = 0.014402,	loss = 2.165798,	Top-1 err = 0.289600,	Top-5 err = 0.111523,	data_time = 0.050386,	train_time = 0.342304
[2019-08-24 21:37:38,584] TRAIN Iter 291380: lr = 0.014368,	loss = 2.212084,	Top-1 err = 0.287109,	Top-5 err = 0.113770,	data_time = 0.050634,	train_time = 0.844921
[2019-08-24 21:37:56,878] TRAIN Iter 291400: lr = 0.014335,	loss = 2.256388,	Top-1 err = 0.288574,	Top-5 err = 0.111035,	data_time = 0.050723,	train_time = 0.914644
[2019-08-24 21:38:04,105] TRAIN Iter 291420: lr = 0.014302,	loss = 2.095356,	Top-1 err = 0.287061,	Top-5 err = 0.109619,	data_time = 0.050277,	train_time = 0.361358
[2019-08-24 21:38:20,167] TRAIN Iter 291440: lr = 0.014268,	loss = 2.179766,	Top-1 err = 0.290820,	Top-5 err = 0.113525,	data_time = 0.050683,	train_time = 0.803090
[2019-08-24 21:38:35,397] TRAIN Iter 291460: lr = 0.014235,	loss = 2.207293,	Top-1 err = 0.291064,	Top-5 err = 0.115479,	data_time = 0.050411,	train_time = 0.761471
[2019-08-24 21:38:42,330] TRAIN Iter 291480: lr = 0.014202,	loss = 2.213056,	Top-1 err = 0.289258,	Top-5 err = 0.110742,	data_time = 0.050487,	train_time = 0.346636
[2019-08-24 21:39:00,172] TRAIN Iter 291500: lr = 0.014168,	loss = 2.138447,	Top-1 err = 0.287500,	Top-5 err = 0.115527,	data_time = 0.050468,	train_time = 0.892106
[2019-08-24 21:39:07,228] TRAIN Iter 291520: lr = 0.014135,	loss = 2.207395,	Top-1 err = 0.291016,	Top-5 err = 0.116162,	data_time = 0.050412,	train_time = 0.352793
[2019-08-24 21:39:23,200] TRAIN Iter 291540: lr = 0.014102,	loss = 2.144011,	Top-1 err = 0.293359,	Top-5 err = 0.112451,	data_time = 0.050312,	train_time = 0.798551
[2019-08-24 21:39:38,804] TRAIN Iter 291560: lr = 0.014068,	loss = 2.232944,	Top-1 err = 0.290820,	Top-5 err = 0.111426,	data_time = 0.050785,	train_time = 0.780205
[2019-08-24 21:39:45,884] TRAIN Iter 291580: lr = 0.014035,	loss = 2.303005,	Top-1 err = 0.292334,	Top-5 err = 0.112402,	data_time = 0.050830,	train_time = 0.353961
[2019-08-24 21:40:02,659] TRAIN Iter 291600: lr = 0.014002,	loss = 2.221835,	Top-1 err = 0.295459,	Top-5 err = 0.116992,	data_time = 0.050026,	train_time = 0.838738
[2019-08-24 21:40:18,921] TRAIN Iter 291620: lr = 0.013968,	loss = 2.215436,	Top-1 err = 0.296045,	Top-5 err = 0.117529,	data_time = 0.049931,	train_time = 0.813102
[2019-08-24 21:40:25,527] TRAIN Iter 291640: lr = 0.013935,	loss = 2.205083,	Top-1 err = 0.290381,	Top-5 err = 0.112939,	data_time = 0.049924,	train_time = 0.330299
[2019-08-24 21:41:16,156] TRAIN Iter 291660: lr = 0.013902,	loss = 2.134174,	Top-1 err = 0.293238,	Top-5 err = 0.118751,	data_time = 0.050446,	train_time = 2.531411
[2019-08-24 21:41:23,124] TRAIN Iter 291680: lr = 0.013868,	loss = 2.218887,	Top-1 err = 0.282813,	Top-5 err = 0.108789,	data_time = 0.050355,	train_time = 0.348388
[2019-08-24 21:41:38,554] TRAIN Iter 291700: lr = 0.013835,	loss = 2.184417,	Top-1 err = 0.284619,	Top-5 err = 0.111475,	data_time = 0.050450,	train_time = 0.771483
[2019-08-24 21:41:53,442] TRAIN Iter 291720: lr = 0.013802,	loss = 2.080388,	Top-1 err = 0.280127,	Top-5 err = 0.106201,	data_time = 0.157070,	train_time = 0.744402
[2019-08-24 21:42:01,021] TRAIN Iter 291740: lr = 0.013768,	loss = 2.218760,	Top-1 err = 0.287988,	Top-5 err = 0.113574,	data_time = 0.050665,	train_time = 0.378937
[2019-08-24 21:42:14,160] TRAIN Iter 291760: lr = 0.013735,	loss = 2.180520,	Top-1 err = 0.284424,	Top-5 err = 0.110498,	data_time = 0.050349,	train_time = 0.656931
[2019-08-24 21:42:21,383] TRAIN Iter 291780: lr = 0.013702,	loss = 2.191055,	Top-1 err = 0.284082,	Top-5 err = 0.110498,	data_time = 0.050400,	train_time = 0.361111
[2019-08-24 21:42:36,063] TRAIN Iter 291800: lr = 0.013668,	loss = 2.207163,	Top-1 err = 0.287939,	Top-5 err = 0.111914,	data_time = 0.050542,	train_time = 0.734015
[2019-08-24 21:42:51,888] TRAIN Iter 291820: lr = 0.013635,	loss = 2.233038,	Top-1 err = 0.287500,	Top-5 err = 0.111768,	data_time = 0.137259,	train_time = 0.791237
[2019-08-24 21:42:58,950] TRAIN Iter 291840: lr = 0.013602,	loss = 2.182010,	Top-1 err = 0.284131,	Top-5 err = 0.110791,	data_time = 0.050762,	train_time = 0.353063
[2019-08-24 21:43:13,167] TRAIN Iter 291860: lr = 0.013568,	loss = 2.185431,	Top-1 err = 0.282764,	Top-5 err = 0.109619,	data_time = 0.050274,	train_time = 0.710827
[2019-08-24 21:43:26,245] TRAIN Iter 291880: lr = 0.013535,	loss = 2.182294,	Top-1 err = 0.283594,	Top-5 err = 0.109570,	data_time = 0.136010,	train_time = 0.653912
[2019-08-24 21:43:33,574] TRAIN Iter 291900: lr = 0.013502,	loss = 2.170413,	Top-1 err = 0.284863,	Top-5 err = 0.111035,	data_time = 0.050410,	train_time = 0.366420
[2019-08-24 21:43:47,987] TRAIN Iter 291920: lr = 0.013468,	loss = 2.100587,	Top-1 err = 0.278369,	Top-5 err = 0.106885,	data_time = 0.050938,	train_time = 0.720650
[2019-08-24 21:43:55,274] TRAIN Iter 291940: lr = 0.013435,	loss = 2.045970,	Top-1 err = 0.283789,	Top-5 err = 0.110254,	data_time = 0.050345,	train_time = 0.364345
[2019-08-24 21:44:11,274] TRAIN Iter 291960: lr = 0.013402,	loss = 2.245845,	Top-1 err = 0.288037,	Top-5 err = 0.113330,	data_time = 0.050601,	train_time = 0.799958
[2019-08-24 21:44:24,509] TRAIN Iter 291980: lr = 0.013368,	loss = 2.106163,	Top-1 err = 0.287256,	Top-5 err = 0.109082,	data_time = 0.050362,	train_time = 0.661719
[2019-08-24 21:44:31,626] TRAIN Iter 292000: lr = 0.013335,	loss = 2.229069,	Top-1 err = 0.285107,	Top-5 err = 0.110547,	data_time = 0.050471,	train_time = 0.355840
[2019-08-24 21:44:49,269] TRAIN Iter 292020: lr = 0.013302,	loss = 2.128077,	Top-1 err = 0.285352,	Top-5 err = 0.109766,	data_time = 0.050646,	train_time = 0.882144
[2019-08-24 21:45:04,375] TRAIN Iter 292040: lr = 0.013268,	loss = 2.142250,	Top-1 err = 0.286084,	Top-5 err = 0.111328,	data_time = 0.050450,	train_time = 0.755275
[2019-08-24 21:45:11,584] TRAIN Iter 292060: lr = 0.013235,	loss = 2.241512,	Top-1 err = 0.289941,	Top-5 err = 0.107715,	data_time = 0.155772,	train_time = 0.360457
[2019-08-24 21:45:24,913] TRAIN Iter 292080: lr = 0.013202,	loss = 2.075370,	Top-1 err = 0.285742,	Top-5 err = 0.111670,	data_time = 0.050454,	train_time = 0.666418
[2019-08-24 21:45:32,454] TRAIN Iter 292100: lr = 0.013168,	loss = 2.196119,	Top-1 err = 0.280322,	Top-5 err = 0.104932,	data_time = 0.050493,	train_time = 0.377061
[2019-08-24 21:45:47,551] TRAIN Iter 292120: lr = 0.013135,	loss = 2.139239,	Top-1 err = 0.285156,	Top-5 err = 0.111279,	data_time = 0.050464,	train_time = 0.754839
[2019-08-24 21:46:00,510] TRAIN Iter 292140: lr = 0.013102,	loss = 2.164240,	Top-1 err = 0.285938,	Top-5 err = 0.109180,	data_time = 0.050297,	train_time = 0.647931
[2019-08-24 21:46:07,526] TRAIN Iter 292160: lr = 0.013068,	loss = 2.197960,	Top-1 err = 0.291504,	Top-5 err = 0.110547,	data_time = 0.050823,	train_time = 0.350786
[2019-08-24 21:46:24,152] TRAIN Iter 292180: lr = 0.013035,	loss = 2.142570,	Top-1 err = 0.281299,	Top-5 err = 0.111963,	data_time = 0.050482,	train_time = 0.831274
[2019-08-24 21:46:36,959] TRAIN Iter 292200: lr = 0.013002,	loss = 2.188634,	Top-1 err = 0.288770,	Top-5 err = 0.110254,	data_time = 0.133419,	train_time = 0.640341
[2019-08-24 21:46:45,555] TRAIN Iter 292220: lr = 0.012968,	loss = 2.173889,	Top-1 err = 0.286719,	Top-5 err = 0.113037,	data_time = 0.050356,	train_time = 0.429774
[2019-08-24 21:47:00,287] TRAIN Iter 292240: lr = 0.012935,	loss = 2.139961,	Top-1 err = 0.285596,	Top-5 err = 0.111475,	data_time = 0.050792,	train_time = 0.736581
[2019-08-24 21:47:07,488] TRAIN Iter 292260: lr = 0.012902,	loss = 2.173958,	Top-1 err = 0.291650,	Top-5 err = 0.114307,	data_time = 0.050865,	train_time = 0.360071
[2019-08-24 21:47:22,723] TRAIN Iter 292280: lr = 0.012868,	loss = 2.174161,	Top-1 err = 0.291162,	Top-5 err = 0.116357,	data_time = 0.050450,	train_time = 0.761731
[2019-08-24 21:47:38,746] TRAIN Iter 292300: lr = 0.012835,	loss = 2.147262,	Top-1 err = 0.285107,	Top-5 err = 0.110303,	data_time = 0.050376,	train_time = 0.801131
[2019-08-24 21:47:46,228] TRAIN Iter 292320: lr = 0.012802,	loss = 2.173726,	Top-1 err = 0.284473,	Top-5 err = 0.108057,	data_time = 0.050356,	train_time = 0.374083
[2019-08-24 21:47:59,870] TRAIN Iter 292340: lr = 0.012768,	loss = 2.153008,	Top-1 err = 0.288477,	Top-5 err = 0.109131,	data_time = 0.050734,	train_time = 0.682071
[2019-08-24 21:48:13,225] TRAIN Iter 292360: lr = 0.012735,	loss = 2.208109,	Top-1 err = 0.286328,	Top-5 err = 0.112891,	data_time = 0.050383,	train_time = 0.667719
[2019-08-24 21:48:22,458] TRAIN Iter 292380: lr = 0.012702,	loss = 2.221978,	Top-1 err = 0.284277,	Top-5 err = 0.111523,	data_time = 0.050579,	train_time = 0.461669
[2019-08-24 21:48:38,332] TRAIN Iter 292400: lr = 0.012668,	loss = 2.074801,	Top-1 err = 0.283887,	Top-5 err = 0.109717,	data_time = 0.050497,	train_time = 0.793698
[2019-08-24 21:48:45,946] TRAIN Iter 292420: lr = 0.012635,	loss = 2.202244,	Top-1 err = 0.291895,	Top-5 err = 0.117529,	data_time = 0.050893,	train_time = 0.380681
[2019-08-24 21:49:01,166] TRAIN Iter 292440: lr = 0.012602,	loss = 2.205731,	Top-1 err = 0.285352,	Top-5 err = 0.109961,	data_time = 0.050573,	train_time = 0.760986
[2019-08-24 21:49:16,840] TRAIN Iter 292460: lr = 0.012568,	loss = 2.182895,	Top-1 err = 0.289551,	Top-5 err = 0.112109,	data_time = 0.050426,	train_time = 0.783688
[2019-08-24 21:49:23,826] TRAIN Iter 292480: lr = 0.012535,	loss = 2.126659,	Top-1 err = 0.286230,	Top-5 err = 0.110840,	data_time = 0.050306,	train_time = 0.349261
[2019-08-24 21:49:39,532] TRAIN Iter 292500: lr = 0.012502,	loss = 2.103208,	Top-1 err = 0.287891,	Top-5 err = 0.111719,	data_time = 0.050575,	train_time = 0.785316
[2019-08-24 21:49:52,550] TRAIN Iter 292520: lr = 0.012468,	loss = 2.193887,	Top-1 err = 0.285107,	Top-5 err = 0.112109,	data_time = 0.128501,	train_time = 0.650851
[2019-08-24 21:50:00,769] TRAIN Iter 292540: lr = 0.012435,	loss = 2.207291,	Top-1 err = 0.289795,	Top-5 err = 0.110254,	data_time = 0.050573,	train_time = 0.410938
[2019-08-24 21:50:17,087] TRAIN Iter 292560: lr = 0.012402,	loss = 2.117685,	Top-1 err = 0.286377,	Top-5 err = 0.111914,	data_time = 0.050486,	train_time = 0.815883
[2019-08-24 21:50:24,191] TRAIN Iter 292580: lr = 0.012368,	loss = 2.104367,	Top-1 err = 0.282666,	Top-5 err = 0.109131,	data_time = 0.050232,	train_time = 0.355210
[2019-08-24 21:50:40,537] TRAIN Iter 292600: lr = 0.012335,	loss = 2.093497,	Top-1 err = 0.288574,	Top-5 err = 0.110937,	data_time = 0.050594,	train_time = 0.817253
[2019-08-24 21:50:57,721] TRAIN Iter 292620: lr = 0.012302,	loss = 2.090385,	Top-1 err = 0.290674,	Top-5 err = 0.111670,	data_time = 0.050368,	train_time = 0.859179
[2019-08-24 21:51:05,044] TRAIN Iter 292640: lr = 0.012268,	loss = 2.245659,	Top-1 err = 0.286572,	Top-5 err = 0.113965,	data_time = 0.050619,	train_time = 0.366166
[2019-08-24 21:51:19,960] TRAIN Iter 292660: lr = 0.012235,	loss = 2.164351,	Top-1 err = 0.286230,	Top-5 err = 0.109424,	data_time = 0.050423,	train_time = 0.745789
[2019-08-24 21:51:35,661] TRAIN Iter 292680: lr = 0.012202,	loss = 2.185287,	Top-1 err = 0.285693,	Top-5 err = 0.110693,	data_time = 0.050341,	train_time = 0.785036
[2019-08-24 21:51:42,891] TRAIN Iter 292700: lr = 0.012168,	loss = 2.136368,	Top-1 err = 0.289844,	Top-5 err = 0.110303,	data_time = 0.050498,	train_time = 0.361494
[2019-08-24 21:51:57,916] TRAIN Iter 292720: lr = 0.012135,	loss = 2.176977,	Top-1 err = 0.284033,	Top-5 err = 0.111719,	data_time = 0.050447,	train_time = 0.751223
[2019-08-24 21:52:05,500] TRAIN Iter 292740: lr = 0.012102,	loss = 2.212586,	Top-1 err = 0.289502,	Top-5 err = 0.111475,	data_time = 0.050560,	train_time = 0.379169
[2019-08-24 21:52:21,181] TRAIN Iter 292760: lr = 0.012068,	loss = 2.146228,	Top-1 err = 0.289307,	Top-5 err = 0.110645,	data_time = 0.050515,	train_time = 0.784016
[2019-08-24 21:52:37,237] TRAIN Iter 292780: lr = 0.012035,	loss = 2.189838,	Top-1 err = 0.280762,	Top-5 err = 0.111279,	data_time = 0.050559,	train_time = 0.802811
[2019-08-24 21:52:44,122] TRAIN Iter 292800: lr = 0.012002,	loss = 2.141738,	Top-1 err = 0.282275,	Top-5 err = 0.111523,	data_time = 0.050368,	train_time = 0.344211
[2019-08-24 21:52:59,681] TRAIN Iter 292820: lr = 0.011968,	loss = 2.141632,	Top-1 err = 0.285547,	Top-5 err = 0.112354,	data_time = 0.050646,	train_time = 0.777959
[2019-08-24 21:53:16,054] TRAIN Iter 292840: lr = 0.011935,	loss = 2.224658,	Top-1 err = 0.286230,	Top-5 err = 0.110937,	data_time = 0.132444,	train_time = 0.818629
[2019-08-24 21:53:22,909] TRAIN Iter 292860: lr = 0.011902,	loss = 2.214279,	Top-1 err = 0.286816,	Top-5 err = 0.111279,	data_time = 0.050135,	train_time = 0.342728
[2019-08-24 21:53:38,535] TRAIN Iter 292880: lr = 0.011868,	loss = 2.247598,	Top-1 err = 0.293262,	Top-5 err = 0.115137,	data_time = 0.049917,	train_time = 0.781316
[2019-08-24 21:53:44,743] TRAIN Iter 292900: lr = 0.011835,	loss = 2.219104,	Top-1 err = 0.287402,	Top-5 err = 0.111865,	data_time = 0.049917,	train_time = 0.310372
[2019-08-24 21:54:37,004] TRAIN Iter 292920: lr = 0.011802,	loss = 2.146164,	Top-1 err = 0.280644,	Top-5 err = 0.107573,	data_time = 0.050750,	train_time = 2.613056
[2019-08-24 21:54:52,360] TRAIN Iter 292940: lr = 0.011768,	loss = 2.166434,	Top-1 err = 0.280420,	Top-5 err = 0.109082,	data_time = 0.050718,	train_time = 0.767777
[2019-08-24 21:55:00,168] TRAIN Iter 292960: lr = 0.011735,	loss = 2.143080,	Top-1 err = 0.283740,	Top-5 err = 0.108545,	data_time = 0.050368,	train_time = 0.390391
[2019-08-24 21:55:11,007] TRAIN Iter 292980: lr = 0.011702,	loss = 2.152804,	Top-1 err = 0.281055,	Top-5 err = 0.105762,	data_time = 0.050448,	train_time = 0.541924
[2019-08-24 21:55:18,577] TRAIN Iter 293000: lr = 0.011668,	loss = 2.180838,	Top-1 err = 0.281885,	Top-5 err = 0.112402,	data_time = 0.050262,	train_time = 0.378491
[2019-08-24 21:55:30,656] TRAIN Iter 293020: lr = 0.011635,	loss = 2.156548,	Top-1 err = 0.283740,	Top-5 err = 0.110059,	data_time = 0.050472,	train_time = 0.603949
[2019-08-24 21:55:45,121] TRAIN Iter 293040: lr = 0.011602,	loss = 2.166281,	Top-1 err = 0.279687,	Top-5 err = 0.105566,	data_time = 0.050868,	train_time = 0.723238
[2019-08-24 21:55:52,508] TRAIN Iter 293060: lr = 0.011568,	loss = 2.153632,	Top-1 err = 0.280273,	Top-5 err = 0.107471,	data_time = 0.050542,	train_time = 0.369299
[2019-08-24 21:56:08,948] TRAIN Iter 293080: lr = 0.011535,	loss = 2.165605,	Top-1 err = 0.283936,	Top-5 err = 0.109033,	data_time = 0.050908,	train_time = 0.822029
[2019-08-24 21:56:21,538] TRAIN Iter 293100: lr = 0.011502,	loss = 2.112953,	Top-1 err = 0.282715,	Top-5 err = 0.109033,	data_time = 0.050740,	train_time = 0.629447
[2019-08-24 21:56:29,181] TRAIN Iter 293120: lr = 0.011468,	loss = 2.146492,	Top-1 err = 0.281445,	Top-5 err = 0.109424,	data_time = 0.050490,	train_time = 0.382166
[2019-08-24 21:56:44,094] TRAIN Iter 293140: lr = 0.011435,	loss = 2.173985,	Top-1 err = 0.279443,	Top-5 err = 0.105811,	data_time = 0.050422,	train_time = 0.745594
[2019-08-24 21:56:51,349] TRAIN Iter 293160: lr = 0.011402,	loss = 2.108797,	Top-1 err = 0.283154,	Top-5 err = 0.109277,	data_time = 0.135120,	train_time = 0.362726
[2019-08-24 21:57:06,920] TRAIN Iter 293180: lr = 0.011368,	loss = 2.179730,	Top-1 err = 0.283057,	Top-5 err = 0.106689,	data_time = 0.050368,	train_time = 0.778535
[2019-08-24 21:57:20,121] TRAIN Iter 293200: lr = 0.011335,	loss = 2.178301,	Top-1 err = 0.287939,	Top-5 err = 0.112256,	data_time = 0.050439,	train_time = 0.660058
[2019-08-24 21:57:27,276] TRAIN Iter 293220: lr = 0.011302,	loss = 2.157385,	Top-1 err = 0.281494,	Top-5 err = 0.106787,	data_time = 0.112050,	train_time = 0.357743
[2019-08-24 21:57:43,741] TRAIN Iter 293240: lr = 0.011268,	loss = 2.163872,	Top-1 err = 0.291211,	Top-5 err = 0.111816,	data_time = 0.050322,	train_time = 0.823224
[2019-08-24 21:57:58,708] TRAIN Iter 293260: lr = 0.011235,	loss = 2.083805,	Top-1 err = 0.279639,	Top-5 err = 0.108887,	data_time = 0.138716,	train_time = 0.748354
[2019-08-24 21:58:05,954] TRAIN Iter 293280: lr = 0.011202,	loss = 2.058576,	Top-1 err = 0.282373,	Top-5 err = 0.107422,	data_time = 0.050341,	train_time = 0.362284
[2019-08-24 21:58:20,888] TRAIN Iter 293300: lr = 0.011168,	loss = 2.267388,	Top-1 err = 0.282910,	Top-5 err = 0.108496,	data_time = 0.050452,	train_time = 0.746673
[2019-08-24 21:58:28,445] TRAIN Iter 293320: lr = 0.011135,	loss = 2.171679,	Top-1 err = 0.285791,	Top-5 err = 0.110791,	data_time = 0.050580,	train_time = 0.377812
[2019-08-24 21:58:43,440] TRAIN Iter 293340: lr = 0.011102,	loss = 2.119290,	Top-1 err = 0.284717,	Top-5 err = 0.109570,	data_time = 0.050512,	train_time = 0.749769
[2019-08-24 21:58:59,394] TRAIN Iter 293360: lr = 0.011068,	loss = 2.152381,	Top-1 err = 0.279932,	Top-5 err = 0.108838,	data_time = 0.050619,	train_time = 0.797680
[2019-08-24 21:59:06,860] TRAIN Iter 293380: lr = 0.011035,	loss = 2.130256,	Top-1 err = 0.284033,	Top-5 err = 0.111670,	data_time = 0.154207,	train_time = 0.373271
[2019-08-24 21:59:21,487] TRAIN Iter 293400: lr = 0.011002,	loss = 2.180838,	Top-1 err = 0.286426,	Top-5 err = 0.111035,	data_time = 0.050575,	train_time = 0.731353
[2019-08-24 21:59:36,657] TRAIN Iter 293420: lr = 0.010968,	loss = 2.109362,	Top-1 err = 0.286523,	Top-5 err = 0.110498,	data_time = 0.050776,	train_time = 0.758474
[2019-08-24 21:59:43,883] TRAIN Iter 293440: lr = 0.010935,	loss = 2.191508,	Top-1 err = 0.285400,	Top-5 err = 0.112891,	data_time = 0.050678,	train_time = 0.361313
[2019-08-24 21:59:59,811] TRAIN Iter 293460: lr = 0.010902,	loss = 2.075847,	Top-1 err = 0.277002,	Top-5 err = 0.107617,	data_time = 0.050496,	train_time = 0.796369
[2019-08-24 22:00:07,704] TRAIN Iter 293480: lr = 0.010868,	loss = 2.123154,	Top-1 err = 0.282764,	Top-5 err = 0.110645,	data_time = 0.050295,	train_time = 0.394617
[2019-08-24 22:00:21,516] TRAIN Iter 293500: lr = 0.010835,	loss = 2.161481,	Top-1 err = 0.281885,	Top-5 err = 0.108203,	data_time = 0.050494,	train_time = 0.690581
[2019-08-24 22:00:36,929] TRAIN Iter 293520: lr = 0.010802,	loss = 2.168941,	Top-1 err = 0.278369,	Top-5 err = 0.108008,	data_time = 0.050550,	train_time = 0.770642
[2019-08-24 22:00:43,912] TRAIN Iter 293540: lr = 0.010768,	loss = 2.171286,	Top-1 err = 0.286230,	Top-5 err = 0.110596,	data_time = 0.050579,	train_time = 0.349126
[2019-08-24 22:00:59,415] TRAIN Iter 293560: lr = 0.010735,	loss = 2.159455,	Top-1 err = 0.281250,	Top-5 err = 0.107617,	data_time = 0.050370,	train_time = 0.775156
[2019-08-24 22:01:13,799] TRAIN Iter 293580: lr = 0.010702,	loss = 2.098643,	Top-1 err = 0.281006,	Top-5 err = 0.110937,	data_time = 0.137191,	train_time = 0.719182
[2019-08-24 22:01:21,071] TRAIN Iter 293600: lr = 0.010668,	loss = 2.065845,	Top-1 err = 0.285547,	Top-5 err = 0.104346,	data_time = 0.050609,	train_time = 0.363590
[2019-08-24 22:01:36,163] TRAIN Iter 293620: lr = 0.010635,	loss = 2.128743,	Top-1 err = 0.280078,	Top-5 err = 0.107031,	data_time = 0.050462,	train_time = 0.754602
[2019-08-24 22:01:43,533] TRAIN Iter 293640: lr = 0.010602,	loss = 2.214236,	Top-1 err = 0.279443,	Top-5 err = 0.112451,	data_time = 0.108708,	train_time = 0.368448
[2019-08-24 22:01:58,575] TRAIN Iter 293660: lr = 0.010568,	loss = 2.148756,	Top-1 err = 0.287842,	Top-5 err = 0.108594,	data_time = 0.050762,	train_time = 0.752089
[2019-08-24 22:02:14,731] TRAIN Iter 293680: lr = 0.010535,	loss = 2.156597,	Top-1 err = 0.283154,	Top-5 err = 0.108789,	data_time = 0.050399,	train_time = 0.807807
[2019-08-24 22:02:21,944] TRAIN Iter 293700: lr = 0.010502,	loss = 2.126626,	Top-1 err = 0.282422,	Top-5 err = 0.109619,	data_time = 0.161304,	train_time = 0.360646
[2019-08-24 22:02:36,443] TRAIN Iter 293720: lr = 0.010468,	loss = 2.220897,	Top-1 err = 0.284619,	Top-5 err = 0.109619,	data_time = 0.050791,	train_time = 0.724889
[2019-08-24 22:02:52,872] TRAIN Iter 293740: lr = 0.010435,	loss = 2.137835,	Top-1 err = 0.280127,	Top-5 err = 0.106348,	data_time = 0.050489,	train_time = 0.821443
[2019-08-24 22:03:00,074] TRAIN Iter 293760: lr = 0.010402,	loss = 2.169222,	Top-1 err = 0.278223,	Top-5 err = 0.109717,	data_time = 0.050506,	train_time = 0.360078
[2019-08-24 22:03:14,868] TRAIN Iter 293780: lr = 0.010368,	loss = 2.180925,	Top-1 err = 0.285986,	Top-5 err = 0.110693,	data_time = 0.050511,	train_time = 0.739707
[2019-08-24 22:03:22,624] TRAIN Iter 293800: lr = 0.010335,	loss = 2.091436,	Top-1 err = 0.277539,	Top-5 err = 0.111377,	data_time = 0.126841,	train_time = 0.387795
[2019-08-24 22:03:36,800] TRAIN Iter 293820: lr = 0.010302,	loss = 2.149370,	Top-1 err = 0.281641,	Top-5 err = 0.108301,	data_time = 0.050513,	train_time = 0.708765
[2019-08-24 22:03:53,137] TRAIN Iter 293840: lr = 0.010268,	loss = 2.113605,	Top-1 err = 0.286621,	Top-5 err = 0.106396,	data_time = 0.050376,	train_time = 0.816840
[2019-08-24 22:04:01,753] TRAIN Iter 293860: lr = 0.010235,	loss = 2.198289,	Top-1 err = 0.284131,	Top-5 err = 0.109619,	data_time = 0.051412,	train_time = 0.430815
[2019-08-24 22:04:15,844] TRAIN Iter 293880: lr = 0.010202,	loss = 2.198956,	Top-1 err = 0.288086,	Top-5 err = 0.108984,	data_time = 0.050605,	train_time = 0.704521
[2019-08-24 22:04:31,516] TRAIN Iter 293900: lr = 0.010168,	loss = 2.137129,	Top-1 err = 0.284961,	Top-5 err = 0.111572,	data_time = 0.128649,	train_time = 0.783556
[2019-08-24 22:04:38,684] TRAIN Iter 293920: lr = 0.010135,	loss = 2.180673,	Top-1 err = 0.285791,	Top-5 err = 0.110596,	data_time = 0.050698,	train_time = 0.358406
[2019-08-24 22:04:54,821] TRAIN Iter 293940: lr = 0.010102,	loss = 2.145178,	Top-1 err = 0.281006,	Top-5 err = 0.107178,	data_time = 0.050464,	train_time = 0.806857
[2019-08-24 22:05:02,186] TRAIN Iter 293960: lr = 0.010068,	loss = 2.126753,	Top-1 err = 0.284180,	Top-5 err = 0.107471,	data_time = 0.050554,	train_time = 0.368197
[2019-08-24 22:05:17,779] TRAIN Iter 293980: lr = 0.010035,	loss = 2.103012,	Top-1 err = 0.285645,	Top-5 err = 0.111328,	data_time = 0.050567,	train_time = 0.779637
[2019-08-24 22:05:33,672] TRAIN Iter 294000: lr = 0.010002,	loss = 2.138379,	Top-1 err = 0.279785,	Top-5 err = 0.105859,	data_time = 0.050607,	train_time = 0.794645
[2019-08-24 22:05:40,559] TRAIN Iter 294020: lr = 0.009968,	loss = 2.132849,	Top-1 err = 0.282324,	Top-5 err = 0.110205,	data_time = 0.050664,	train_time = 0.344345
[2019-08-24 22:05:56,885] TRAIN Iter 294040: lr = 0.009935,	loss = 2.160229,	Top-1 err = 0.283740,	Top-5 err = 0.107959,	data_time = 0.050738,	train_time = 0.816288
[2019-08-24 22:06:14,194] TRAIN Iter 294060: lr = 0.009902,	loss = 2.187963,	Top-1 err = 0.288525,	Top-5 err = 0.114844,	data_time = 0.050382,	train_time = 0.865416
[2019-08-24 22:06:21,162] TRAIN Iter 294080: lr = 0.009868,	loss = 2.152872,	Top-1 err = 0.284814,	Top-5 err = 0.111914,	data_time = 0.050831,	train_time = 0.348401
[2019-08-24 22:06:37,625] TRAIN Iter 294100: lr = 0.009835,	loss = 2.054075,	Top-1 err = 0.280713,	Top-5 err = 0.108691,	data_time = 0.050166,	train_time = 0.823128
[2019-08-24 22:06:44,613] TRAIN Iter 294120: lr = 0.009802,	loss = 2.151916,	Top-1 err = 0.283057,	Top-5 err = 0.111377,	data_time = 0.049961,	train_time = 0.349410
[2019-08-24 22:06:59,478] TRAIN Iter 294140: lr = 0.009768,	loss = 2.166253,	Top-1 err = 0.287305,	Top-5 err = 0.113037,	data_time = 0.049937,	train_time = 0.743207
[2019-08-24 22:07:51,747] TRAIN Iter 294160: lr = 0.009735,	loss = 2.083193,	Top-1 err = 0.288983,	Top-5 err = 0.115837,	data_time = 0.050286,	train_time = 2.613465
[2019-08-24 22:07:58,603] TRAIN Iter 294180: lr = 0.009702,	loss = 2.149160,	Top-1 err = 0.279883,	Top-5 err = 0.107471,	data_time = 0.132997,	train_time = 0.342758
[2019-08-24 22:08:14,778] TRAIN Iter 294200: lr = 0.009668,	loss = 2.151712,	Top-1 err = 0.280469,	Top-5 err = 0.107422,	data_time = 0.050805,	train_time = 0.808732
[2019-08-24 22:08:22,949] TRAIN Iter 294220: lr = 0.009635,	loss = 2.101061,	Top-1 err = 0.280371,	Top-5 err = 0.111133,	data_time = 0.050684,	train_time = 0.408571
[2019-08-24 22:08:35,374] TRAIN Iter 294240: lr = 0.009602,	loss = 2.104385,	Top-1 err = 0.283984,	Top-5 err = 0.112988,	data_time = 0.050877,	train_time = 0.621216
[2019-08-24 22:08:49,326] TRAIN Iter 294260: lr = 0.009568,	loss = 2.114676,	Top-1 err = 0.277832,	Top-5 err = 0.106592,	data_time = 0.050555,	train_time = 0.697602
[2019-08-24 22:08:56,600] TRAIN Iter 294280: lr = 0.009535,	loss = 2.105967,	Top-1 err = 0.277734,	Top-5 err = 0.108350,	data_time = 0.050516,	train_time = 0.363666
[2019-08-24 22:09:11,717] TRAIN Iter 294300: lr = 0.009502,	loss = 2.191285,	Top-1 err = 0.287158,	Top-5 err = 0.110937,	data_time = 0.050859,	train_time = 0.755850
[2019-08-24 22:09:26,904] TRAIN Iter 294320: lr = 0.009468,	loss = 2.137465,	Top-1 err = 0.273975,	Top-5 err = 0.106104,	data_time = 0.050524,	train_time = 0.759338
[2019-08-24 22:09:34,099] TRAIN Iter 294340: lr = 0.009435,	loss = 2.149954,	Top-1 err = 0.287158,	Top-5 err = 0.110010,	data_time = 0.050482,	train_time = 0.359741
[2019-08-24 22:09:48,995] TRAIN Iter 294360: lr = 0.009402,	loss = 2.239606,	Top-1 err = 0.279687,	Top-5 err = 0.114844,	data_time = 0.050280,	train_time = 0.744740
[2019-08-24 22:09:56,498] TRAIN Iter 294380: lr = 0.009368,	loss = 2.161994,	Top-1 err = 0.282422,	Top-5 err = 0.108398,	data_time = 0.050345,	train_time = 0.375174
[2019-08-24 22:10:11,876] TRAIN Iter 294400: lr = 0.009335,	loss = 2.155371,	Top-1 err = 0.275049,	Top-5 err = 0.102490,	data_time = 0.050647,	train_time = 0.768847
[2019-08-24 22:10:26,809] TRAIN Iter 294420: lr = 0.009302,	loss = 2.155890,	Top-1 err = 0.275732,	Top-5 err = 0.105908,	data_time = 0.050556,	train_time = 0.746647
[2019-08-24 22:10:34,267] TRAIN Iter 294440: lr = 0.009268,	loss = 2.155840,	Top-1 err = 0.278711,	Top-5 err = 0.104053,	data_time = 0.050377,	train_time = 0.372911
[2019-08-24 22:10:48,157] TRAIN Iter 294460: lr = 0.009235,	loss = 2.190475,	Top-1 err = 0.281299,	Top-5 err = 0.108887,	data_time = 0.050347,	train_time = 0.694456
[2019-08-24 22:11:01,874] TRAIN Iter 294480: lr = 0.009202,	loss = 2.166421,	Top-1 err = 0.284375,	Top-5 err = 0.110449,	data_time = 0.050358,	train_time = 0.685836
[2019-08-24 22:11:08,662] TRAIN Iter 294500: lr = 0.009168,	loss = 2.148727,	Top-1 err = 0.282373,	Top-5 err = 0.108350,	data_time = 0.136742,	train_time = 0.339396
[2019-08-24 22:11:24,465] TRAIN Iter 294520: lr = 0.009135,	loss = 2.168454,	Top-1 err = 0.285449,	Top-5 err = 0.109473,	data_time = 0.050262,	train_time = 0.790133
[2019-08-24 22:11:32,125] TRAIN Iter 294540: lr = 0.009102,	loss = 2.195471,	Top-1 err = 0.285010,	Top-5 err = 0.111719,	data_time = 0.050481,	train_time = 0.382969
[2019-08-24 22:11:47,032] TRAIN Iter 294560: lr = 0.009068,	loss = 2.139361,	Top-1 err = 0.285254,	Top-5 err = 0.106836,	data_time = 0.050828,	train_time = 0.745365
[2019-08-24 22:12:02,134] TRAIN Iter 294580: lr = 0.009035,	loss = 2.036121,	Top-1 err = 0.279150,	Top-5 err = 0.109521,	data_time = 0.050879,	train_time = 0.755060
[2019-08-24 22:12:09,668] TRAIN Iter 294600: lr = 0.009002,	loss = 2.153560,	Top-1 err = 0.282813,	Top-5 err = 0.106494,	data_time = 0.050539,	train_time = 0.376687
[2019-08-24 22:12:23,429] TRAIN Iter 294620: lr = 0.008968,	loss = 2.219803,	Top-1 err = 0.283154,	Top-5 err = 0.104980,	data_time = 0.050275,	train_time = 0.688029
[2019-08-24 22:12:39,325] TRAIN Iter 294640: lr = 0.008935,	loss = 2.167833,	Top-1 err = 0.279102,	Top-5 err = 0.105762,	data_time = 0.050585,	train_time = 0.794783
[2019-08-24 22:12:46,759] TRAIN Iter 294660: lr = 0.008902,	loss = 2.131132,	Top-1 err = 0.279248,	Top-5 err = 0.104834,	data_time = 0.050535,	train_time = 0.371718
[2019-08-24 22:13:01,167] TRAIN Iter 294680: lr = 0.008868,	loss = 2.169235,	Top-1 err = 0.276172,	Top-5 err = 0.106299,	data_time = 0.050456,	train_time = 0.720393
[2019-08-24 22:13:09,018] TRAIN Iter 294700: lr = 0.008835,	loss = 2.180271,	Top-1 err = 0.282959,	Top-5 err = 0.108057,	data_time = 0.050197,	train_time = 0.392497
[2019-08-24 22:13:22,438] TRAIN Iter 294720: lr = 0.008802,	loss = 2.141202,	Top-1 err = 0.279834,	Top-5 err = 0.104395,	data_time = 0.050402,	train_time = 0.670997
[2019-08-24 22:13:38,200] TRAIN Iter 294740: lr = 0.008768,	loss = 2.109266,	Top-1 err = 0.278467,	Top-5 err = 0.105566,	data_time = 0.050423,	train_time = 0.788074
[2019-08-24 22:13:45,436] TRAIN Iter 294760: lr = 0.008735,	loss = 2.163037,	Top-1 err = 0.283545,	Top-5 err = 0.105713,	data_time = 0.050327,	train_time = 0.361784
[2019-08-24 22:14:01,441] TRAIN Iter 294780: lr = 0.008702,	loss = 2.186081,	Top-1 err = 0.277686,	Top-5 err = 0.105225,	data_time = 0.050640,	train_time = 0.800282
[2019-08-24 22:14:16,927] TRAIN Iter 294800: lr = 0.008668,	loss = 2.073837,	Top-1 err = 0.277197,	Top-5 err = 0.106055,	data_time = 0.050549,	train_time = 0.774284
[2019-08-24 22:14:23,920] TRAIN Iter 294820: lr = 0.008635,	loss = 2.105950,	Top-1 err = 0.281934,	Top-5 err = 0.108643,	data_time = 0.050557,	train_time = 0.349613
[2019-08-24 22:14:38,834] TRAIN Iter 294840: lr = 0.008602,	loss = 2.207204,	Top-1 err = 0.285791,	Top-5 err = 0.110791,	data_time = 0.050780,	train_time = 0.745676
[2019-08-24 22:14:46,868] TRAIN Iter 294860: lr = 0.008568,	loss = 2.116044,	Top-1 err = 0.280518,	Top-5 err = 0.106006,	data_time = 0.050519,	train_time = 0.401684
[2019-08-24 22:15:01,372] TRAIN Iter 294880: lr = 0.008535,	loss = 2.154377,	Top-1 err = 0.284619,	Top-5 err = 0.111816,	data_time = 0.050404,	train_time = 0.725202
[2019-08-24 22:15:16,490] TRAIN Iter 294900: lr = 0.008502,	loss = 2.174744,	Top-1 err = 0.281934,	Top-5 err = 0.107227,	data_time = 0.050423,	train_time = 0.755881
[2019-08-24 22:15:23,301] TRAIN Iter 294920: lr = 0.008468,	loss = 2.128155,	Top-1 err = 0.282715,	Top-5 err = 0.108008,	data_time = 0.050407,	train_time = 0.340551
[2019-08-24 22:15:40,221] TRAIN Iter 294940: lr = 0.008435,	loss = 2.172337,	Top-1 err = 0.283545,	Top-5 err = 0.107275,	data_time = 0.050826,	train_time = 0.846001
[2019-08-24 22:15:57,123] TRAIN Iter 294960: lr = 0.008402,	loss = 2.186780,	Top-1 err = 0.277686,	Top-5 err = 0.109229,	data_time = 0.050389,	train_time = 0.845084
[2019-08-24 22:16:04,060] TRAIN Iter 294980: lr = 0.008368,	loss = 2.196455,	Top-1 err = 0.280615,	Top-5 err = 0.107275,	data_time = 0.050623,	train_time = 0.346806
[2019-08-24 22:16:19,495] TRAIN Iter 295000: lr = 0.008335,	loss = 2.135207,	Top-1 err = 0.282764,	Top-5 err = 0.107666,	data_time = 0.050394,	train_time = 0.771766
[2019-08-24 22:16:27,241] TRAIN Iter 295020: lr = 0.008302,	loss = 2.196458,	Top-1 err = 0.282080,	Top-5 err = 0.108496,	data_time = 0.050497,	train_time = 0.387283
[2019-08-24 22:16:41,642] TRAIN Iter 295040: lr = 0.008268,	loss = 2.118932,	Top-1 err = 0.282031,	Top-5 err = 0.107471,	data_time = 0.050889,	train_time = 0.720039
[2019-08-24 22:16:57,065] TRAIN Iter 295060: lr = 0.008235,	loss = 2.133156,	Top-1 err = 0.285840,	Top-5 err = 0.108936,	data_time = 0.050397,	train_time = 0.771116
[2019-08-24 22:17:04,319] TRAIN Iter 295080: lr = 0.008202,	loss = 2.153340,	Top-1 err = 0.283545,	Top-5 err = 0.110400,	data_time = 0.050647,	train_time = 0.362706
[2019-08-24 22:17:19,610] TRAIN Iter 295100: lr = 0.008168,	loss = 2.160462,	Top-1 err = 0.284814,	Top-5 err = 0.105762,	data_time = 0.050518,	train_time = 0.764502
[2019-08-24 22:17:36,776] TRAIN Iter 295120: lr = 0.008135,	loss = 2.174924,	Top-1 err = 0.281934,	Top-5 err = 0.106006,	data_time = 0.050716,	train_time = 0.858282
[2019-08-24 22:17:43,759] TRAIN Iter 295140: lr = 0.008102,	loss = 2.200306,	Top-1 err = 0.278369,	Top-5 err = 0.112158,	data_time = 0.162198,	train_time = 0.349154
[2019-08-24 22:17:59,069] TRAIN Iter 295160: lr = 0.008068,	loss = 2.251801,	Top-1 err = 0.287744,	Top-5 err = 0.110693,	data_time = 0.050554,	train_time = 0.765464
[2019-08-24 22:18:06,423] TRAIN Iter 295180: lr = 0.008035,	loss = 2.145599,	Top-1 err = 0.281348,	Top-5 err = 0.109912,	data_time = 0.050497,	train_time = 0.367715
[2019-08-24 22:18:22,970] TRAIN Iter 295200: lr = 0.008002,	loss = 2.122546,	Top-1 err = 0.285303,	Top-5 err = 0.107178,	data_time = 0.050297,	train_time = 0.827314
[2019-08-24 22:18:38,667] TRAIN Iter 295220: lr = 0.007968,	loss = 2.120819,	Top-1 err = 0.279492,	Top-5 err = 0.105713,	data_time = 0.050418,	train_time = 0.784863
[2019-08-24 22:18:45,663] TRAIN Iter 295240: lr = 0.007935,	loss = 2.162281,	Top-1 err = 0.283057,	Top-5 err = 0.106299,	data_time = 0.050241,	train_time = 0.349800
[2019-08-24 22:19:01,787] TRAIN Iter 295260: lr = 0.007902,	loss = 2.165072,	Top-1 err = 0.279639,	Top-5 err = 0.111230,	data_time = 0.050256,	train_time = 0.806150
[2019-08-24 22:19:18,660] TRAIN Iter 295280: lr = 0.007868,	loss = 2.208565,	Top-1 err = 0.278076,	Top-5 err = 0.107813,	data_time = 0.050505,	train_time = 0.843654
[2019-08-24 22:19:25,255] TRAIN Iter 295300: lr = 0.007835,	loss = 2.108092,	Top-1 err = 0.285547,	Top-5 err = 0.110010,	data_time = 0.050483,	train_time = 0.329756
[2019-08-24 22:19:42,864] TRAIN Iter 295320: lr = 0.007802,	loss = 2.190815,	Top-1 err = 0.277539,	Top-5 err = 0.107764,	data_time = 0.050557,	train_time = 0.880400
[2019-08-24 22:19:50,283] TRAIN Iter 295340: lr = 0.007768,	loss = 2.143545,	Top-1 err = 0.287061,	Top-5 err = 0.109229,	data_time = 0.050336,	train_time = 0.370949
[2019-08-24 22:20:06,943] TRAIN Iter 295360: lr = 0.007735,	loss = 2.167272,	Top-1 err = 0.279395,	Top-5 err = 0.108398,	data_time = 0.116920,	train_time = 0.832980
[2019-08-24 22:20:22,170] TRAIN Iter 295380: lr = 0.007702,	loss = 2.116298,	Top-1 err = 0.285498,	Top-5 err = 0.108789,	data_time = 0.049904,	train_time = 0.761366
[2019-08-24 22:20:28,093] TRAIN Iter 295400: lr = 0.007668,	loss = 2.092175,	Top-1 err = 0.279053,	Top-5 err = 0.109082,	data_time = 0.049862,	train_time = 0.296111
[2019-08-24 22:21:20,710] TRAIN Iter 295420: lr = 0.007635,	loss = 2.153778,	Top-1 err = 0.285760,	Top-5 err = 0.111191,	data_time = 0.050645,	train_time = 2.630832
[2019-08-24 22:21:28,322] TRAIN Iter 295440: lr = 0.007602,	loss = 2.054055,	Top-1 err = 0.279736,	Top-5 err = 0.106494,	data_time = 0.050812,	train_time = 0.380576
[2019-08-24 22:21:42,265] TRAIN Iter 295460: lr = 0.007568,	loss = 2.179376,	Top-1 err = 0.280518,	Top-5 err = 0.108691,	data_time = 0.050453,	train_time = 0.697170
[2019-08-24 22:21:55,208] TRAIN Iter 295480: lr = 0.007535,	loss = 2.209816,	Top-1 err = 0.276172,	Top-5 err = 0.104395,	data_time = 0.050475,	train_time = 0.647100
[2019-08-24 22:22:02,562] TRAIN Iter 295500: lr = 0.007502,	loss = 2.204628,	Top-1 err = 0.281201,	Top-5 err = 0.113867,	data_time = 0.050437,	train_time = 0.367704
[2019-08-24 22:22:17,117] TRAIN Iter 295520: lr = 0.007468,	loss = 2.163440,	Top-1 err = 0.276318,	Top-5 err = 0.107617,	data_time = 0.050506,	train_time = 0.727754
[2019-08-24 22:22:30,342] TRAIN Iter 295540: lr = 0.007435,	loss = 2.058165,	Top-1 err = 0.274121,	Top-5 err = 0.101025,	data_time = 0.050571,	train_time = 0.661227
[2019-08-24 22:22:37,564] TRAIN Iter 295560: lr = 0.007402,	loss = 2.125851,	Top-1 err = 0.273242,	Top-5 err = 0.103857,	data_time = 0.050322,	train_time = 0.361058
[2019-08-24 22:22:53,394] TRAIN Iter 295580: lr = 0.007368,	loss = 2.142180,	Top-1 err = 0.279297,	Top-5 err = 0.108936,	data_time = 0.050513,	train_time = 0.791504
[2019-08-24 22:23:00,825] TRAIN Iter 295600: lr = 0.007335,	loss = 2.128420,	Top-1 err = 0.278271,	Top-5 err = 0.105957,	data_time = 0.050626,	train_time = 0.371554
[2019-08-24 22:23:14,861] TRAIN Iter 295620: lr = 0.007302,	loss = 2.043887,	Top-1 err = 0.276318,	Top-5 err = 0.104004,	data_time = 0.050643,	train_time = 0.701748
[2019-08-24 22:23:28,230] TRAIN Iter 295640: lr = 0.007268,	loss = 2.190645,	Top-1 err = 0.276660,	Top-5 err = 0.107129,	data_time = 0.661398,	train_time = 0.668441
[2019-08-24 22:23:35,580] TRAIN Iter 295660: lr = 0.007235,	loss = 2.050564,	Top-1 err = 0.276416,	Top-5 err = 0.106689,	data_time = 0.050374,	train_time = 0.367521
[2019-08-24 22:23:50,595] TRAIN Iter 295680: lr = 0.007202,	loss = 2.213346,	Top-1 err = 0.280713,	Top-5 err = 0.107715,	data_time = 0.050569,	train_time = 0.750733
[2019-08-24 22:24:05,647] TRAIN Iter 295700: lr = 0.007168,	loss = 2.139345,	Top-1 err = 0.274121,	Top-5 err = 0.106689,	data_time = 0.050544,	train_time = 0.752553
[2019-08-24 22:24:12,976] TRAIN Iter 295720: lr = 0.007135,	loss = 2.109791,	Top-1 err = 0.277197,	Top-5 err = 0.104150,	data_time = 0.050625,	train_time = 0.366444
[2019-08-24 22:24:25,545] TRAIN Iter 295740: lr = 0.007102,	loss = 2.125972,	Top-1 err = 0.279150,	Top-5 err = 0.107666,	data_time = 0.050484,	train_time = 0.628432
[2019-08-24 22:24:33,241] TRAIN Iter 295760: lr = 0.007068,	loss = 2.132254,	Top-1 err = 0.279199,	Top-5 err = 0.106836,	data_time = 0.050303,	train_time = 0.384818
[2019-08-24 22:24:48,669] TRAIN Iter 295780: lr = 0.007035,	loss = 2.080657,	Top-1 err = 0.277393,	Top-5 err = 0.104980,	data_time = 0.050313,	train_time = 0.771347
[2019-08-24 22:25:03,155] TRAIN Iter 295800: lr = 0.007002,	loss = 2.154764,	Top-1 err = 0.272119,	Top-5 err = 0.105371,	data_time = 0.050183,	train_time = 0.724291
[2019-08-24 22:25:10,289] TRAIN Iter 295820: lr = 0.006968,	loss = 2.083107,	Top-1 err = 0.277051,	Top-5 err = 0.102148,	data_time = 0.050515,	train_time = 0.356724
[2019-08-24 22:25:25,423] TRAIN Iter 295840: lr = 0.006935,	loss = 2.149966,	Top-1 err = 0.275293,	Top-5 err = 0.104785,	data_time = 0.050431,	train_time = 0.756641
[2019-08-24 22:25:38,832] TRAIN Iter 295860: lr = 0.006902,	loss = 2.168721,	Top-1 err = 0.281104,	Top-5 err = 0.103320,	data_time = 0.143614,	train_time = 0.670476
[2019-08-24 22:25:46,499] TRAIN Iter 295880: lr = 0.006868,	loss = 2.132637,	Top-1 err = 0.274121,	Top-5 err = 0.106445,	data_time = 0.050581,	train_time = 0.383304
[2019-08-24 22:26:02,679] TRAIN Iter 295900: lr = 0.006835,	loss = 2.145540,	Top-1 err = 0.282520,	Top-5 err = 0.105957,	data_time = 0.050525,	train_time = 0.809018
[2019-08-24 22:26:10,543] TRAIN Iter 295920: lr = 0.006802,	loss = 2.160104,	Top-1 err = 0.278516,	Top-5 err = 0.107861,	data_time = 0.050436,	train_time = 0.393167
[2019-08-24 22:26:23,499] TRAIN Iter 295940: lr = 0.006768,	loss = 2.109203,	Top-1 err = 0.278662,	Top-5 err = 0.108301,	data_time = 0.050350,	train_time = 0.647766
[2019-08-24 22:26:38,003] TRAIN Iter 295960: lr = 0.006735,	loss = 2.107349,	Top-1 err = 0.284326,	Top-5 err = 0.112109,	data_time = 0.050463,	train_time = 0.725215
[2019-08-24 22:26:45,136] TRAIN Iter 295980: lr = 0.006702,	loss = 2.189985,	Top-1 err = 0.275098,	Top-5 err = 0.108301,	data_time = 0.050695,	train_time = 0.356630
[2019-08-24 22:27:00,258] TRAIN Iter 296000: lr = 0.006668,	loss = 2.127511,	Top-1 err = 0.286621,	Top-5 err = 0.111328,	data_time = 0.050323,	train_time = 0.756090
[2019-08-24 22:27:13,302] TRAIN Iter 296020: lr = 0.006635,	loss = 2.126170,	Top-1 err = 0.275879,	Top-5 err = 0.106494,	data_time = 0.114491,	train_time = 0.652151
[2019-08-24 22:27:22,471] TRAIN Iter 296040: lr = 0.006602,	loss = 2.136489,	Top-1 err = 0.275439,	Top-5 err = 0.103711,	data_time = 0.050724,	train_time = 0.458468
[2019-08-24 22:27:38,493] TRAIN Iter 296060: lr = 0.006568,	loss = 2.160379,	Top-1 err = 0.283789,	Top-5 err = 0.108887,	data_time = 0.050352,	train_time = 0.801074
[2019-08-24 22:27:45,920] TRAIN Iter 296080: lr = 0.006535,	loss = 2.103934,	Top-1 err = 0.281104,	Top-5 err = 0.107813,	data_time = 0.050668,	train_time = 0.371347
[2019-08-24 22:28:00,559] TRAIN Iter 296100: lr = 0.006502,	loss = 2.158727,	Top-1 err = 0.280371,	Top-5 err = 0.107422,	data_time = 0.050410,	train_time = 0.731908
[2019-08-24 22:28:17,026] TRAIN Iter 296120: lr = 0.006468,	loss = 2.203728,	Top-1 err = 0.281104,	Top-5 err = 0.110400,	data_time = 0.050848,	train_time = 0.823356
[2019-08-24 22:28:24,110] TRAIN Iter 296140: lr = 0.006435,	loss = 2.189455,	Top-1 err = 0.281348,	Top-5 err = 0.106592,	data_time = 0.134829,	train_time = 0.354158
[2019-08-24 22:28:37,988] TRAIN Iter 296160: lr = 0.006402,	loss = 2.158056,	Top-1 err = 0.281494,	Top-5 err = 0.106641,	data_time = 0.050754,	train_time = 0.693901
[2019-08-24 22:28:54,373] TRAIN Iter 296180: lr = 0.006368,	loss = 2.136840,	Top-1 err = 0.274219,	Top-5 err = 0.109180,	data_time = 2.314184,	train_time = 0.819259
[2019-08-24 22:29:01,660] TRAIN Iter 296200: lr = 0.006335,	loss = 2.139903,	Top-1 err = 0.272461,	Top-5 err = 0.102881,	data_time = 0.050456,	train_time = 0.364329
[2019-08-24 22:29:18,501] TRAIN Iter 296220: lr = 0.006302,	loss = 2.073026,	Top-1 err = 0.274658,	Top-5 err = 0.102686,	data_time = 0.050282,	train_time = 0.842038
[2019-08-24 22:29:26,001] TRAIN Iter 296240: lr = 0.006268,	loss = 2.116958,	Top-1 err = 0.284619,	Top-5 err = 0.110596,	data_time = 0.050841,	train_time = 0.375001
[2019-08-24 22:29:41,498] TRAIN Iter 296260: lr = 0.006235,	loss = 2.111742,	Top-1 err = 0.286230,	Top-5 err = 0.108398,	data_time = 0.050434,	train_time = 0.774798
[2019-08-24 22:29:57,356] TRAIN Iter 296280: lr = 0.006202,	loss = 2.107456,	Top-1 err = 0.279150,	Top-5 err = 0.105029,	data_time = 0.050462,	train_time = 0.792892
[2019-08-24 22:30:04,692] TRAIN Iter 296300: lr = 0.006168,	loss = 2.198591,	Top-1 err = 0.278271,	Top-5 err = 0.105225,	data_time = 0.050409,	train_time = 0.366816
[2019-08-24 22:30:19,325] TRAIN Iter 296320: lr = 0.006135,	loss = 2.150974,	Top-1 err = 0.279736,	Top-5 err = 0.105322,	data_time = 0.050365,	train_time = 0.731598
[2019-08-24 22:30:35,372] TRAIN Iter 296340: lr = 0.006102,	loss = 2.041783,	Top-1 err = 0.273291,	Top-5 err = 0.105566,	data_time = 0.638796,	train_time = 0.802335
[2019-08-24 22:30:43,405] TRAIN Iter 296360: lr = 0.006068,	loss = 2.063653,	Top-1 err = 0.271973,	Top-5 err = 0.102490,	data_time = 0.050306,	train_time = 0.401646
[2019-08-24 22:31:00,208] TRAIN Iter 296380: lr = 0.006035,	loss = 2.062244,	Top-1 err = 0.276611,	Top-5 err = 0.104541,	data_time = 0.050430,	train_time = 0.840126
[2019-08-24 22:31:07,339] TRAIN Iter 296400: lr = 0.006002,	loss = 2.173303,	Top-1 err = 0.272656,	Top-5 err = 0.101367,	data_time = 0.050478,	train_time = 0.356537
[2019-08-24 22:31:23,239] TRAIN Iter 296420: lr = 0.005968,	loss = 2.112392,	Top-1 err = 0.276953,	Top-5 err = 0.106104,	data_time = 0.050274,	train_time = 0.795003
[2019-08-24 22:31:40,271] TRAIN Iter 296440: lr = 0.005935,	loss = 2.091863,	Top-1 err = 0.281104,	Top-5 err = 0.107080,	data_time = 0.050533,	train_time = 0.851602
[2019-08-24 22:31:47,926] TRAIN Iter 296460: lr = 0.005902,	loss = 2.167090,	Top-1 err = 0.279150,	Top-5 err = 0.111279,	data_time = 0.051113,	train_time = 0.382730
[2019-08-24 22:32:04,505] TRAIN Iter 296480: lr = 0.005868,	loss = 2.097874,	Top-1 err = 0.282959,	Top-5 err = 0.107031,	data_time = 0.050713,	train_time = 0.828905
[2019-08-24 22:32:21,819] TRAIN Iter 296500: lr = 0.005835,	loss = 2.146424,	Top-1 err = 0.281641,	Top-5 err = 0.108057,	data_time = 1.174474,	train_time = 0.865689
[2019-08-24 22:32:31,553] TRAIN Iter 296520: lr = 0.005802,	loss = 2.110335,	Top-1 err = 0.275098,	Top-5 err = 0.104492,	data_time = 0.050811,	train_time = 0.486714
[2019-08-24 22:32:43,069] TRAIN Iter 296540: lr = 0.005768,	loss = 2.089135,	Top-1 err = 0.280762,	Top-5 err = 0.107959,	data_time = 0.050207,	train_time = 0.575770
[2019-08-24 22:32:50,235] TRAIN Iter 296560: lr = 0.005735,	loss = 2.147172,	Top-1 err = 0.284570,	Top-5 err = 0.107422,	data_time = 0.050818,	train_time = 0.358269
[2019-08-24 22:33:06,611] TRAIN Iter 296580: lr = 0.005702,	loss = 2.084591,	Top-1 err = 0.273291,	Top-5 err = 0.103760,	data_time = 0.050523,	train_time = 0.818795
[2019-08-24 22:33:23,963] TRAIN Iter 296600: lr = 0.005668,	loss = 2.096339,	Top-1 err = 0.275195,	Top-5 err = 0.108057,	data_time = 0.050090,	train_time = 0.867581
[2019-08-24 22:33:30,892] TRAIN Iter 296620: lr = 0.005635,	loss = 2.124402,	Top-1 err = 0.282324,	Top-5 err = 0.109326,	data_time = 0.050142,	train_time = 0.346467
[2019-08-24 22:33:46,175] TRAIN Iter 296640: lr = 0.005602,	loss = 2.189500,	Top-1 err = 0.283008,	Top-5 err = 0.109668,	data_time = 0.049831,	train_time = 0.764088
[2019-08-24 22:33:53,623] TRAIN Iter 296660: lr = 0.005568,	loss = 2.459203,	Top-1 err = 0.275572,	Top-5 err = 0.104219,	data_time = 0.007071,	train_time = 0.372408
[2019-08-24 22:34:40,371] TRAIN Iter 296680: lr = 0.005535,	loss = 2.133693,	Top-1 err = 0.277930,	Top-5 err = 0.104980,	data_time = 0.050435,	train_time = 2.337396
[2019-08-24 22:34:55,167] TRAIN Iter 296700: lr = 0.005502,	loss = 2.149793,	Top-1 err = 0.274121,	Top-5 err = 0.105371,	data_time = 0.050441,	train_time = 0.739797
[2019-08-24 22:35:02,665] TRAIN Iter 296720: lr = 0.005468,	loss = 2.097738,	Top-1 err = 0.274707,	Top-5 err = 0.105322,	data_time = 0.050345,	train_time = 0.374866
[2019-08-24 22:35:15,389] TRAIN Iter 296740: lr = 0.005435,	loss = 2.160198,	Top-1 err = 0.277539,	Top-5 err = 0.103516,	data_time = 0.050337,	train_time = 0.636174
[2019-08-24 22:35:25,229] TRAIN Iter 296760: lr = 0.005402,	loss = 2.017961,	Top-1 err = 0.279395,	Top-5 err = 0.105029,	data_time = 0.050996,	train_time = 0.492002
[2019-08-24 22:35:35,605] TRAIN Iter 296780: lr = 0.005368,	loss = 2.036232,	Top-1 err = 0.275537,	Top-5 err = 0.105176,	data_time = 0.050427,	train_time = 0.518768
[2019-08-24 22:35:50,930] TRAIN Iter 296800: lr = 0.005335,	loss = 2.136824,	Top-1 err = 0.276855,	Top-5 err = 0.105371,	data_time = 0.108884,	train_time = 0.766269
[2019-08-24 22:35:57,896] TRAIN Iter 296820: lr = 0.005302,	loss = 2.156520,	Top-1 err = 0.278027,	Top-5 err = 0.106006,	data_time = 0.050604,	train_time = 0.348281
[2019-08-24 22:36:13,021] TRAIN Iter 296840: lr = 0.005268,	loss = 2.110687,	Top-1 err = 0.282031,	Top-5 err = 0.110107,	data_time = 0.050966,	train_time = 0.756204
[2019-08-24 22:36:28,222] TRAIN Iter 296860: lr = 0.005235,	loss = 2.152209,	Top-1 err = 0.276562,	Top-5 err = 0.108350,	data_time = 0.113142,	train_time = 0.760052
[2019-08-24 22:36:35,628] TRAIN Iter 296880: lr = 0.005202,	loss = 2.124037,	Top-1 err = 0.279980,	Top-5 err = 0.107764,	data_time = 0.050252,	train_time = 0.370287
[2019-08-24 22:36:47,888] TRAIN Iter 296900: lr = 0.005168,	loss = 2.150832,	Top-1 err = 0.278125,	Top-5 err = 0.106055,	data_time = 0.050373,	train_time = 0.612967
[2019-08-24 22:37:00,663] TRAIN Iter 296920: lr = 0.005135,	loss = 2.053427,	Top-1 err = 0.274219,	Top-5 err = 0.103906,	data_time = 0.125880,	train_time = 0.638736
[2019-08-24 22:37:10,481] TRAIN Iter 296940: lr = 0.005102,	loss = 2.071326,	Top-1 err = 0.279541,	Top-5 err = 0.107764,	data_time = 0.050555,	train_time = 0.490898
[2019-08-24 22:37:26,201] TRAIN Iter 296960: lr = 0.005068,	loss = 2.122605,	Top-1 err = 0.271533,	Top-5 err = 0.103613,	data_time = 0.050944,	train_time = 0.785971
[2019-08-24 22:37:33,919] TRAIN Iter 296980: lr = 0.005035,	loss = 2.151711,	Top-1 err = 0.280371,	Top-5 err = 0.109375,	data_time = 0.050331,	train_time = 0.385896
[2019-08-24 22:37:47,516] TRAIN Iter 297000: lr = 0.005002,	loss = 2.135751,	Top-1 err = 0.277637,	Top-5 err = 0.104053,	data_time = 0.050382,	train_time = 0.679840
[2019-08-24 22:38:01,355] TRAIN Iter 297020: lr = 0.004968,	loss = 2.112955,	Top-1 err = 0.275781,	Top-5 err = 0.103271,	data_time = 0.050783,	train_time = 0.691929
[2019-08-24 22:38:08,722] TRAIN Iter 297040: lr = 0.004935,	loss = 2.131881,	Top-1 err = 0.270557,	Top-5 err = 0.100146,	data_time = 0.050789,	train_time = 0.368363
[2019-08-24 22:38:22,838] TRAIN Iter 297060: lr = 0.004902,	loss = 2.116389,	Top-1 err = 0.278418,	Top-5 err = 0.103418,	data_time = 0.050543,	train_time = 0.705747
[2019-08-24 22:38:36,157] TRAIN Iter 297080: lr = 0.004868,	loss = 2.177724,	Top-1 err = 0.268848,	Top-5 err = 0.100977,	data_time = 0.050422,	train_time = 0.665953
[2019-08-24 22:38:44,042] TRAIN Iter 297100: lr = 0.004835,	loss = 2.109302,	Top-1 err = 0.275391,	Top-5 err = 0.103076,	data_time = 0.050648,	train_time = 0.394254
[2019-08-24 22:38:59,207] TRAIN Iter 297120: lr = 0.004802,	loss = 2.188764,	Top-1 err = 0.275000,	Top-5 err = 0.102051,	data_time = 0.050470,	train_time = 0.758213
[2019-08-24 22:39:06,238] TRAIN Iter 297140: lr = 0.004768,	loss = 2.136895,	Top-1 err = 0.275391,	Top-5 err = 0.104102,	data_time = 0.050812,	train_time = 0.351548
[2019-08-24 22:39:21,842] TRAIN Iter 297160: lr = 0.004735,	loss = 2.154057,	Top-1 err = 0.270996,	Top-5 err = 0.107178,	data_time = 0.050741,	train_time = 0.780158
[2019-08-24 22:39:39,112] TRAIN Iter 297180: lr = 0.004702,	loss = 2.120972,	Top-1 err = 0.279541,	Top-5 err = 0.106494,	data_time = 0.050959,	train_time = 0.863530
[2019-08-24 22:39:46,851] TRAIN Iter 297200: lr = 0.004668,	loss = 2.056296,	Top-1 err = 0.275391,	Top-5 err = 0.105859,	data_time = 0.050257,	train_time = 0.386937
[2019-08-24 22:39:59,510] TRAIN Iter 297220: lr = 0.004635,	loss = 2.136834,	Top-1 err = 0.275391,	Top-5 err = 0.108252,	data_time = 0.050381,	train_time = 0.632934
[2019-08-24 22:40:14,013] TRAIN Iter 297240: lr = 0.004602,	loss = 2.160069,	Top-1 err = 0.281299,	Top-5 err = 0.107422,	data_time = 0.141060,	train_time = 0.725136
[2019-08-24 22:40:21,666] TRAIN Iter 297260: lr = 0.004568,	loss = 2.133106,	Top-1 err = 0.278174,	Top-5 err = 0.108057,	data_time = 0.050563,	train_time = 0.382630
[2019-08-24 22:40:37,207] TRAIN Iter 297280: lr = 0.004535,	loss = 2.179234,	Top-1 err = 0.277295,	Top-5 err = 0.105908,	data_time = 0.050511,	train_time = 0.777018
[2019-08-24 22:40:44,002] TRAIN Iter 297300: lr = 0.004502,	loss = 2.186006,	Top-1 err = 0.271289,	Top-5 err = 0.104346,	data_time = 0.050902,	train_time = 0.339761
[2019-08-24 22:40:59,730] TRAIN Iter 297320: lr = 0.004468,	loss = 2.055503,	Top-1 err = 0.276123,	Top-5 err = 0.104248,	data_time = 0.050421,	train_time = 0.786392
[2019-08-24 22:41:16,633] TRAIN Iter 297340: lr = 0.004435,	loss = 2.150537,	Top-1 err = 0.278125,	Top-5 err = 0.104785,	data_time = 0.050362,	train_time = 0.845096
[2019-08-24 22:41:23,442] TRAIN Iter 297360: lr = 0.004402,	loss = 2.177166,	Top-1 err = 0.278711,	Top-5 err = 0.105957,	data_time = 0.050300,	train_time = 0.340474
[2019-08-24 22:41:39,330] TRAIN Iter 297380: lr = 0.004368,	loss = 2.096502,	Top-1 err = 0.276514,	Top-5 err = 0.105713,	data_time = 0.050403,	train_time = 0.794384
[2019-08-24 22:41:56,464] TRAIN Iter 297400: lr = 0.004335,	loss = 2.151992,	Top-1 err = 0.277441,	Top-5 err = 0.107275,	data_time = 0.050532,	train_time = 0.856660
[2019-08-24 22:42:03,826] TRAIN Iter 297420: lr = 0.004302,	loss = 2.039635,	Top-1 err = 0.270898,	Top-5 err = 0.100586,	data_time = 0.050480,	train_time = 0.368082
[2019-08-24 22:42:18,673] TRAIN Iter 297440: lr = 0.004268,	loss = 2.150317,	Top-1 err = 0.276025,	Top-5 err = 0.105859,	data_time = 0.050309,	train_time = 0.742345
[2019-08-24 22:42:25,987] TRAIN Iter 297460: lr = 0.004235,	loss = 2.145676,	Top-1 err = 0.271973,	Top-5 err = 0.105371,	data_time = 0.050609,	train_time = 0.365680
[2019-08-24 22:42:41,744] TRAIN Iter 297480: lr = 0.004202,	loss = 2.010742,	Top-1 err = 0.272705,	Top-5 err = 0.101709,	data_time = 0.050493,	train_time = 0.787863
[2019-08-24 22:42:56,479] TRAIN Iter 297500: lr = 0.004168,	loss = 2.076729,	Top-1 err = 0.274072,	Top-5 err = 0.105566,	data_time = 0.050418,	train_time = 0.736737
[2019-08-24 22:43:03,693] TRAIN Iter 297520: lr = 0.004135,	loss = 2.136373,	Top-1 err = 0.270410,	Top-5 err = 0.103076,	data_time = 0.050327,	train_time = 0.360693
[2019-08-24 22:43:18,455] TRAIN Iter 297540: lr = 0.004102,	loss = 2.201916,	Top-1 err = 0.273242,	Top-5 err = 0.102930,	data_time = 0.050401,	train_time = 0.738045
[2019-08-24 22:43:34,824] TRAIN Iter 297560: lr = 0.004068,	loss = 2.173388,	Top-1 err = 0.278662,	Top-5 err = 0.108545,	data_time = 0.050605,	train_time = 0.818458
[2019-08-24 22:43:41,995] TRAIN Iter 297580: lr = 0.004035,	loss = 2.106856,	Top-1 err = 0.276514,	Top-5 err = 0.103760,	data_time = 0.050494,	train_time = 0.358550
[2019-08-24 22:43:57,297] TRAIN Iter 297600: lr = 0.004002,	loss = 2.145704,	Top-1 err = 0.279443,	Top-5 err = 0.105615,	data_time = 0.050490,	train_time = 0.765055
[2019-08-24 22:44:04,373] TRAIN Iter 297620: lr = 0.003968,	loss = 2.138030,	Top-1 err = 0.279590,	Top-5 err = 0.104590,	data_time = 0.050841,	train_time = 0.353805
[2019-08-24 22:44:20,288] TRAIN Iter 297640: lr = 0.003935,	loss = 2.138327,	Top-1 err = 0.277686,	Top-5 err = 0.105420,	data_time = 0.050408,	train_time = 0.795732
[2019-08-24 22:44:37,614] TRAIN Iter 297660: lr = 0.003902,	loss = 2.101229,	Top-1 err = 0.271777,	Top-5 err = 0.103906,	data_time = 0.050499,	train_time = 0.866274
[2019-08-24 22:44:45,157] TRAIN Iter 297680: lr = 0.003868,	loss = 2.160360,	Top-1 err = 0.280566,	Top-5 err = 0.105127,	data_time = 0.050770,	train_time = 0.377169
[2019-08-24 22:45:02,562] TRAIN Iter 297700: lr = 0.003835,	loss = 2.188715,	Top-1 err = 0.278760,	Top-5 err = 0.106934,	data_time = 0.050368,	train_time = 0.870223
[2019-08-24 22:45:17,075] TRAIN Iter 297720: lr = 0.003802,	loss = 2.173389,	Top-1 err = 0.281885,	Top-5 err = 0.106006,	data_time = 0.050325,	train_time = 0.725619
[2019-08-24 22:45:23,840] TRAIN Iter 297740: lr = 0.003768,	loss = 2.139752,	Top-1 err = 0.276953,	Top-5 err = 0.104053,	data_time = 0.050431,	train_time = 0.338252
[2019-08-24 22:45:41,914] TRAIN Iter 297760: lr = 0.003735,	loss = 2.118264,	Top-1 err = 0.277734,	Top-5 err = 0.105518,	data_time = 0.050308,	train_time = 0.903692
[2019-08-24 22:45:48,631] TRAIN Iter 297780: lr = 0.003702,	loss = 2.128910,	Top-1 err = 0.281787,	Top-5 err = 0.108105,	data_time = 0.050108,	train_time = 0.335827
[2019-08-24 22:46:07,313] TRAIN Iter 297800: lr = 0.003668,	loss = 2.088065,	Top-1 err = 0.267725,	Top-5 err = 0.102002,	data_time = 0.050736,	train_time = 0.934090
[2019-08-24 22:46:24,275] TRAIN Iter 297820: lr = 0.003635,	loss = 2.080703,	Top-1 err = 0.282520,	Top-5 err = 0.106787,	data_time = 0.050680,	train_time = 0.848076
[2019-08-24 22:46:30,863] TRAIN Iter 297840: lr = 0.003602,	loss = 2.157750,	Top-1 err = 0.280371,	Top-5 err = 0.110449,	data_time = 0.050561,	train_time = 0.329367
[2019-08-24 22:46:48,567] TRAIN Iter 297860: lr = 0.003568,	loss = 2.170696,	Top-1 err = 0.276172,	Top-5 err = 0.109570,	data_time = 0.050128,	train_time = 0.885183
[2019-08-24 22:47:05,175] TRAIN Iter 297880: lr = 0.003535,	loss = 2.040458,	Top-1 err = 0.274316,	Top-5 err = 0.101660,	data_time = 0.110271,	train_time = 0.830394
[2019-08-24 22:47:11,228] TRAIN Iter 297900: lr = 0.003502,	loss = 2.043600,	Top-1 err = 0.270166,	Top-5 err = 0.103711,	data_time = 0.049949,	train_time = 0.302649
[2019-08-24 22:48:00,383] TRAIN Iter 297920: lr = 0.003468,	loss = 2.099827,	Top-1 err = 0.278486,	Top-5 err = 0.104512,	data_time = 0.050418,	train_time = 2.457721
[2019-08-24 22:48:07,866] TRAIN Iter 297940: lr = 0.003435,	loss = 2.120210,	Top-1 err = 0.274658,	Top-5 err = 0.101611,	data_time = 0.050677,	train_time = 0.374120
[2019-08-24 22:48:25,267] TRAIN Iter 297960: lr = 0.003402,	loss = 2.067041,	Top-1 err = 0.274023,	Top-5 err = 0.103564,	data_time = 0.050643,	train_time = 0.870066
[2019-08-24 22:48:39,103] TRAIN Iter 297980: lr = 0.003368,	loss = 2.028288,	Top-1 err = 0.271533,	Top-5 err = 0.099902,	data_time = 3.417681,	train_time = 0.691755
[2019-08-24 22:48:46,884] TRAIN Iter 298000: lr = 0.003335,	loss = 2.096788,	Top-1 err = 0.271436,	Top-5 err = 0.102197,	data_time = 0.195760,	train_time = 0.389050
[2019-08-24 22:48:56,583] TRAIN Iter 298020: lr = 0.003302,	loss = 2.127561,	Top-1 err = 0.275342,	Top-5 err = 0.105908,	data_time = 0.050520,	train_time = 0.484926
[2019-08-24 22:49:04,216] TRAIN Iter 298040: lr = 0.003268,	loss = 2.141632,	Top-1 err = 0.269922,	Top-5 err = 0.101611,	data_time = 0.050736,	train_time = 0.381655
[2019-08-24 22:49:19,534] TRAIN Iter 298060: lr = 0.003235,	loss = 2.134690,	Top-1 err = 0.271973,	Top-5 err = 0.100293,	data_time = 0.050553,	train_time = 0.765891
[2019-08-24 22:49:34,438] TRAIN Iter 298080: lr = 0.003202,	loss = 2.128086,	Top-1 err = 0.272510,	Top-5 err = 0.100439,	data_time = 0.050705,	train_time = 0.745169
[2019-08-24 22:49:41,678] TRAIN Iter 298100: lr = 0.003168,	loss = 2.157145,	Top-1 err = 0.276514,	Top-5 err = 0.104883,	data_time = 0.050373,	train_time = 0.361988
[2019-08-24 22:49:56,111] TRAIN Iter 298120: lr = 0.003135,	loss = 2.068400,	Top-1 err = 0.276514,	Top-5 err = 0.106152,	data_time = 0.050523,	train_time = 0.721655
[2019-08-24 22:50:10,148] TRAIN Iter 298140: lr = 0.003102,	loss = 2.221454,	Top-1 err = 0.277051,	Top-5 err = 0.105176,	data_time = 0.122243,	train_time = 0.701821
[2019-08-24 22:50:17,415] TRAIN Iter 298160: lr = 0.003068,	loss = 2.034497,	Top-1 err = 0.273584,	Top-5 err = 0.105957,	data_time = 0.050290,	train_time = 0.363344
[2019-08-24 22:50:32,343] TRAIN Iter 298180: lr = 0.003035,	loss = 2.225481,	Top-1 err = 0.275977,	Top-5 err = 0.104150,	data_time = 0.050390,	train_time = 0.746381
[2019-08-24 22:50:39,809] TRAIN Iter 298200: lr = 0.003002,	loss = 2.167599,	Top-1 err = 0.271973,	Top-5 err = 0.107080,	data_time = 0.050652,	train_time = 0.373303
[2019-08-24 22:50:53,594] TRAIN Iter 298220: lr = 0.002968,	loss = 2.120025,	Top-1 err = 0.271387,	Top-5 err = 0.104102,	data_time = 0.050487,	train_time = 0.689242
[2019-08-24 22:51:09,174] TRAIN Iter 298240: lr = 0.002935,	loss = 2.118049,	Top-1 err = 0.273975,	Top-5 err = 0.105518,	data_time = 0.050625,	train_time = 0.778960
[2019-08-24 22:51:16,097] TRAIN Iter 298260: lr = 0.002902,	loss = 2.100120,	Top-1 err = 0.273242,	Top-5 err = 0.103320,	data_time = 0.122214,	train_time = 0.346127
[2019-08-24 22:51:30,155] TRAIN Iter 298280: lr = 0.002868,	loss = 2.108981,	Top-1 err = 0.269922,	Top-5 err = 0.104980,	data_time = 0.050822,	train_time = 0.702905
[2019-08-24 22:51:44,314] TRAIN Iter 298300: lr = 0.002835,	loss = 2.068174,	Top-1 err = 0.272852,	Top-5 err = 0.106250,	data_time = 0.117902,	train_time = 0.707938
[2019-08-24 22:51:51,895] TRAIN Iter 298320: lr = 0.002802,	loss = 2.094946,	Top-1 err = 0.270703,	Top-5 err = 0.099951,	data_time = 0.050786,	train_time = 0.379045
[2019-08-24 22:52:07,257] TRAIN Iter 298340: lr = 0.002768,	loss = 2.028636,	Top-1 err = 0.277295,	Top-5 err = 0.102832,	data_time = 0.050349,	train_time = 0.768071
[2019-08-24 22:52:14,459] TRAIN Iter 298360: lr = 0.002735,	loss = 2.123092,	Top-1 err = 0.269385,	Top-5 err = 0.102295,	data_time = 0.050710,	train_time = 0.360095
[2019-08-24 22:52:30,002] TRAIN Iter 298380: lr = 0.002702,	loss = 2.189026,	Top-1 err = 0.277393,	Top-5 err = 0.105908,	data_time = 0.050830,	train_time = 0.777102
[2019-08-24 22:52:44,867] TRAIN Iter 298400: lr = 0.002668,	loss = 2.125430,	Top-1 err = 0.268896,	Top-5 err = 0.103418,	data_time = 0.050600,	train_time = 0.743236
[2019-08-24 22:52:52,239] TRAIN Iter 298420: lr = 0.002635,	loss = 2.110729,	Top-1 err = 0.270703,	Top-5 err = 0.102588,	data_time = 0.050557,	train_time = 0.368600
[2019-08-24 22:53:05,258] TRAIN Iter 298440: lr = 0.002602,	loss = 2.055654,	Top-1 err = 0.273438,	Top-5 err = 0.102490,	data_time = 0.050401,	train_time = 0.650957
[2019-08-24 22:53:19,866] TRAIN Iter 298460: lr = 0.002568,	loss = 2.160976,	Top-1 err = 0.278906,	Top-5 err = 0.105225,	data_time = 0.155579,	train_time = 0.730384
[2019-08-24 22:53:27,641] TRAIN Iter 298480: lr = 0.002535,	loss = 2.098530,	Top-1 err = 0.271973,	Top-5 err = 0.102100,	data_time = 0.050492,	train_time = 0.388745
[2019-08-24 22:53:43,062] TRAIN Iter 298500: lr = 0.002502,	loss = 2.186275,	Top-1 err = 0.275146,	Top-5 err = 0.103906,	data_time = 0.050204,	train_time = 0.771008
[2019-08-24 22:53:50,319] TRAIN Iter 298520: lr = 0.002468,	loss = 2.075309,	Top-1 err = 0.274854,	Top-5 err = 0.101855,	data_time = 0.050496,	train_time = 0.362836
[2019-08-24 22:54:04,938] TRAIN Iter 298540: lr = 0.002435,	loss = 2.210995,	Top-1 err = 0.274561,	Top-5 err = 0.104834,	data_time = 0.050483,	train_time = 0.730960
[2019-08-24 22:54:22,556] TRAIN Iter 298560: lr = 0.002402,	loss = 2.107421,	Top-1 err = 0.276416,	Top-5 err = 0.102490,	data_time = 0.111113,	train_time = 0.880851
[2019-08-24 22:54:29,416] TRAIN Iter 298580: lr = 0.002368,	loss = 2.146201,	Top-1 err = 0.273779,	Top-5 err = 0.102734,	data_time = 0.050652,	train_time = 0.342983
[2019-08-24 22:54:44,818] TRAIN Iter 298600: lr = 0.002335,	loss = 2.044246,	Top-1 err = 0.275098,	Top-5 err = 0.102588,	data_time = 0.050475,	train_time = 0.770127
[2019-08-24 22:54:57,922] TRAIN Iter 298620: lr = 0.002302,	loss = 2.154012,	Top-1 err = 0.276074,	Top-5 err = 0.103320,	data_time = 0.050481,	train_time = 0.655179
[2019-08-24 22:55:05,390] TRAIN Iter 298640: lr = 0.002268,	loss = 2.155404,	Top-1 err = 0.278076,	Top-5 err = 0.106787,	data_time = 0.050293,	train_time = 0.373399
[2019-08-24 22:55:20,958] TRAIN Iter 298660: lr = 0.002235,	loss = 2.135200,	Top-1 err = 0.272510,	Top-5 err = 0.104346,	data_time = 0.050393,	train_time = 0.778386
[2019-08-24 22:55:28,326] TRAIN Iter 298680: lr = 0.002202,	loss = 2.095956,	Top-1 err = 0.270654,	Top-5 err = 0.102490,	data_time = 0.141870,	train_time = 0.368376
[2019-08-24 22:55:43,519] TRAIN Iter 298700: lr = 0.002168,	loss = 2.107970,	Top-1 err = 0.277588,	Top-5 err = 0.105371,	data_time = 0.050613,	train_time = 0.759630
[2019-08-24 22:55:59,692] TRAIN Iter 298720: lr = 0.002135,	loss = 2.119357,	Top-1 err = 0.273682,	Top-5 err = 0.105127,	data_time = 0.050577,	train_time = 0.808626
[2019-08-24 22:56:07,607] TRAIN Iter 298740: lr = 0.002102,	loss = 2.146054,	Top-1 err = 0.274219,	Top-5 err = 0.104395,	data_time = 0.050500,	train_time = 0.395723
[2019-08-24 22:56:22,432] TRAIN Iter 298760: lr = 0.002068,	loss = 2.127301,	Top-1 err = 0.268555,	Top-5 err = 0.104834,	data_time = 0.050335,	train_time = 0.741249
[2019-08-24 22:56:37,679] TRAIN Iter 298780: lr = 0.002035,	loss = 2.105162,	Top-1 err = 0.277734,	Top-5 err = 0.104834,	data_time = 0.050304,	train_time = 0.762329
[2019-08-24 22:56:47,283] TRAIN Iter 298800: lr = 0.002002,	loss = 2.193848,	Top-1 err = 0.274512,	Top-5 err = 0.103320,	data_time = 0.050405,	train_time = 0.480195
[2019-08-24 22:57:02,955] TRAIN Iter 298820: lr = 0.001968,	loss = 2.134724,	Top-1 err = 0.272461,	Top-5 err = 0.103662,	data_time = 0.050435,	train_time = 0.783613
[2019-08-24 22:57:12,011] TRAIN Iter 298840: lr = 0.001935,	loss = 2.121360,	Top-1 err = 0.275000,	Top-5 err = 0.104492,	data_time = 0.050255,	train_time = 0.452791
[2019-08-24 22:57:27,289] TRAIN Iter 298860: lr = 0.001902,	loss = 2.138636,	Top-1 err = 0.276953,	Top-5 err = 0.105469,	data_time = 1.471413,	train_time = 0.763886
[2019-08-24 22:57:42,885] TRAIN Iter 298880: lr = 0.001868,	loss = 2.049436,	Top-1 err = 0.277246,	Top-5 err = 0.106787,	data_time = 0.050512,	train_time = 0.779743
[2019-08-24 22:57:50,614] TRAIN Iter 298900: lr = 0.001835,	loss = 2.068687,	Top-1 err = 0.274268,	Top-5 err = 0.104248,	data_time = 0.050548,	train_time = 0.386455
[2019-08-24 22:58:04,785] TRAIN Iter 298920: lr = 0.001802,	loss = 2.123507,	Top-1 err = 0.276123,	Top-5 err = 0.104883,	data_time = 0.050457,	train_time = 0.708517
[2019-08-24 22:58:17,271] TRAIN Iter 298940: lr = 0.001768,	loss = 2.168502,	Top-1 err = 0.276123,	Top-5 err = 0.105273,	data_time = 0.131261,	train_time = 0.624333
[2019-08-24 22:58:29,988] TRAIN Iter 298960: lr = 0.001735,	loss = 2.070345,	Top-1 err = 0.274268,	Top-5 err = 0.104102,	data_time = 0.050402,	train_time = 0.635833
[2019-08-24 22:58:44,254] TRAIN Iter 298980: lr = 0.001702,	loss = 2.008199,	Top-1 err = 0.273828,	Top-5 err = 0.101709,	data_time = 0.050502,	train_time = 0.713285
[2019-08-24 22:58:53,962] TRAIN Iter 299000: lr = 0.001668,	loss = 2.135293,	Top-1 err = 0.277832,	Top-5 err = 0.103027,	data_time = 0.050696,	train_time = 0.485390
[2019-08-24 22:59:11,434] TRAIN Iter 299020: lr = 0.001635,	loss = 2.026692,	Top-1 err = 0.275391,	Top-5 err = 0.102637,	data_time = 3.743930,	train_time = 0.873547
[2019-08-24 22:59:24,194] TRAIN Iter 299040: lr = 0.001602,	loss = 2.112883,	Top-1 err = 0.267822,	Top-5 err = 0.100537,	data_time = 0.050362,	train_time = 0.637998
[2019-08-24 22:59:35,672] TRAIN Iter 299060: lr = 0.001568,	loss = 2.107471,	Top-1 err = 0.273730,	Top-5 err = 0.105127,	data_time = 0.050820,	train_time = 0.573895
[2019-08-24 22:59:49,468] TRAIN Iter 299080: lr = 0.001535,	loss = 2.154808,	Top-1 err = 0.278369,	Top-5 err = 0.104297,	data_time = 0.050524,	train_time = 0.689791
[2019-08-24 23:00:02,411] TRAIN Iter 299100: lr = 0.001502,	loss = 2.099948,	Top-1 err = 0.269629,	Top-5 err = 0.104492,	data_time = 0.135309,	train_time = 0.647105
[2019-08-24 23:00:16,697] TRAIN Iter 299120: lr = 0.001468,	loss = 2.149820,	Top-1 err = 0.275586,	Top-5 err = 0.102979,	data_time = 0.050102,	train_time = 0.714314
[2019-08-24 23:00:28,743] TRAIN Iter 299140: lr = 0.001435,	loss = 2.154042,	Top-1 err = 0.272852,	Top-5 err = 0.103076,	data_time = 0.049955,	train_time = 0.602286
[2019-08-24 23:00:37,458] TRAIN Iter 299160: lr = 0.001402,	loss = 2.158062,	Top-1 err = 0.275537,	Top-5 err = 0.103369,	data_time = 0.049882,	train_time = 0.435741
[2019-08-24 23:01:24,896] TRAIN Iter 299180: lr = 0.001368,	loss = 2.102266,	Top-1 err = 0.278352,	Top-5 err = 0.107984,	data_time = 0.050599,	train_time = 2.371866
[2019-08-24 23:01:38,887] TRAIN Iter 299200: lr = 0.001335,	loss = 2.129705,	Top-1 err = 0.270117,	Top-5 err = 0.104004,	data_time = 0.050334,	train_time = 0.699529
[2019-08-24 23:01:46,126] TRAIN Iter 299220: lr = 0.001302,	loss = 2.092546,	Top-1 err = 0.278027,	Top-5 err = 0.106836,	data_time = 0.050577,	train_time = 0.361934
[2019-08-24 23:02:01,884] TRAIN Iter 299240: lr = 0.001268,	loss = 2.100123,	Top-1 err = 0.276855,	Top-5 err = 0.106836,	data_time = 0.050537,	train_time = 0.787909
[2019-08-24 23:02:09,699] TRAIN Iter 299260: lr = 0.001235,	loss = 2.099874,	Top-1 err = 0.277441,	Top-5 err = 0.103564,	data_time = 0.050470,	train_time = 0.390698
[2019-08-24 23:02:25,722] TRAIN Iter 299280: lr = 0.001202,	loss = 2.109752,	Top-1 err = 0.272412,	Top-5 err = 0.102148,	data_time = 0.050898,	train_time = 0.801137
[2019-08-24 23:02:39,849] TRAIN Iter 299300: lr = 0.001168,	loss = 2.127013,	Top-1 err = 0.271143,	Top-5 err = 0.103320,	data_time = 0.050390,	train_time = 0.706347
[2019-08-24 23:02:47,609] TRAIN Iter 299320: lr = 0.001135,	loss = 2.120006,	Top-1 err = 0.271289,	Top-5 err = 0.103662,	data_time = 0.050839,	train_time = 0.388019
[2019-08-24 23:03:02,467] TRAIN Iter 299340: lr = 0.001102,	loss = 2.165617,	Top-1 err = 0.277979,	Top-5 err = 0.105518,	data_time = 0.050601,	train_time = 0.742855
[2019-08-24 23:03:14,875] TRAIN Iter 299360: lr = 0.001068,	loss = 2.163898,	Top-1 err = 0.274805,	Top-5 err = 0.104932,	data_time = 0.050528,	train_time = 0.620387
[2019-08-24 23:03:22,461] TRAIN Iter 299380: lr = 0.001035,	loss = 2.148860,	Top-1 err = 0.273096,	Top-5 err = 0.105469,	data_time = 0.050709,	train_time = 0.379276
[2019-08-24 23:03:36,535] TRAIN Iter 299400: lr = 0.001002,	loss = 2.066236,	Top-1 err = 0.273291,	Top-5 err = 0.099805,	data_time = 0.095245,	train_time = 0.703678
[2019-08-24 23:03:43,879] TRAIN Iter 299420: lr = 0.000968,	loss = 2.143861,	Top-1 err = 0.274268,	Top-5 err = 0.107617,	data_time = 0.144773,	train_time = 0.367207
[2019-08-24 23:03:59,122] TRAIN Iter 299440: lr = 0.000935,	loss = 2.171402,	Top-1 err = 0.270361,	Top-5 err = 0.099170,	data_time = 0.050257,	train_time = 0.762143
[2019-08-24 23:04:15,551] TRAIN Iter 299460: lr = 0.000902,	loss = 2.042707,	Top-1 err = 0.264600,	Top-5 err = 0.104004,	data_time = 0.050538,	train_time = 0.821414
[2019-08-24 23:04:22,449] TRAIN Iter 299480: lr = 0.000868,	loss = 2.154334,	Top-1 err = 0.274463,	Top-5 err = 0.103418,	data_time = 0.050454,	train_time = 0.344892
[2019-08-24 23:04:35,811] TRAIN Iter 299500: lr = 0.000835,	loss = 2.066854,	Top-1 err = 0.267432,	Top-5 err = 0.097559,	data_time = 0.050445,	train_time = 0.668091
[2019-08-24 23:04:52,729] TRAIN Iter 299520: lr = 0.000802,	loss = 2.058427,	Top-1 err = 0.274707,	Top-5 err = 0.104639,	data_time = 0.050755,	train_time = 0.845887
[2019-08-24 23:04:59,823] TRAIN Iter 299540: lr = 0.000768,	loss = 2.141253,	Top-1 err = 0.274414,	Top-5 err = 0.105176,	data_time = 0.050802,	train_time = 0.354689
[2019-08-24 23:05:14,621] TRAIN Iter 299560: lr = 0.000735,	loss = 2.082102,	Top-1 err = 0.267285,	Top-5 err = 0.104004,	data_time = 0.050405,	train_time = 0.739892
[2019-08-24 23:05:22,216] TRAIN Iter 299580: lr = 0.000702,	loss = 2.040108,	Top-1 err = 0.269971,	Top-5 err = 0.104102,	data_time = 0.050545,	train_time = 0.379702
[2019-08-24 23:05:37,637] TRAIN Iter 299600: lr = 0.000668,	loss = 2.071590,	Top-1 err = 0.272559,	Top-5 err = 0.101855,	data_time = 0.050409,	train_time = 0.771043
[2019-08-24 23:05:51,724] TRAIN Iter 299620: lr = 0.000635,	loss = 2.144411,	Top-1 err = 0.272119,	Top-5 err = 0.103027,	data_time = 0.050361,	train_time = 0.704334
[2019-08-24 23:05:58,953] TRAIN Iter 299640: lr = 0.000602,	loss = 2.146612,	Top-1 err = 0.270117,	Top-5 err = 0.102783,	data_time = 0.174581,	train_time = 0.361430
[2019-08-24 23:06:14,601] TRAIN Iter 299660: lr = 0.000568,	loss = 2.111404,	Top-1 err = 0.272705,	Top-5 err = 0.099805,	data_time = 0.050533,	train_time = 0.782371
[2019-08-24 23:06:26,509] TRAIN Iter 299680: lr = 0.000535,	loss = 2.136738,	Top-1 err = 0.276318,	Top-5 err = 0.107129,	data_time = 0.050571,	train_time = 0.595397
[2019-08-24 23:06:37,002] TRAIN Iter 299700: lr = 0.000502,	loss = 2.125097,	Top-1 err = 0.270264,	Top-5 err = 0.105664,	data_time = 0.050629,	train_time = 0.524648
[2019-08-24 23:06:51,031] TRAIN Iter 299720: lr = 0.000468,	loss = 2.082906,	Top-1 err = 0.275635,	Top-5 err = 0.107031,	data_time = 0.050726,	train_time = 0.701451
[2019-08-24 23:06:58,589] TRAIN Iter 299740: lr = 0.000435,	loss = 2.170384,	Top-1 err = 0.277734,	Top-5 err = 0.105518,	data_time = 0.050617,	train_time = 0.377867
[2019-08-24 23:07:12,870] TRAIN Iter 299760: lr = 0.000402,	loss = 2.066898,	Top-1 err = 0.267920,	Top-5 err = 0.101855,	data_time = 0.050636,	train_time = 0.714016
[2019-08-24 23:07:27,891] TRAIN Iter 299780: lr = 0.000368,	loss = 2.086826,	Top-1 err = 0.274951,	Top-5 err = 0.102197,	data_time = 0.050673,	train_time = 0.751064
[2019-08-24 23:07:35,109] TRAIN Iter 299800: lr = 0.000335,	loss = 2.179305,	Top-1 err = 0.271680,	Top-5 err = 0.104004,	data_time = 0.050556,	train_time = 0.360898
[2019-08-24 23:07:49,450] TRAIN Iter 299820: lr = 0.000302,	loss = 2.141145,	Top-1 err = 0.279590,	Top-5 err = 0.107178,	data_time = 0.050750,	train_time = 0.717019
[2019-08-24 23:08:01,951] TRAIN Iter 299840: lr = 0.000268,	loss = 2.102434,	Top-1 err = 0.272803,	Top-5 err = 0.102734,	data_time = 0.050779,	train_time = 0.625059
[2019-08-24 23:08:12,795] TRAIN Iter 299860: lr = 0.000235,	loss = 2.074723,	Top-1 err = 0.273535,	Top-5 err = 0.105811,	data_time = 0.050870,	train_time = 0.542144
[2019-08-24 23:08:27,035] TRAIN Iter 299880: lr = 0.000202,	loss = 2.116715,	Top-1 err = 0.272949,	Top-5 err = 0.104248,	data_time = 0.050465,	train_time = 0.711985
[2019-08-24 23:08:34,573] TRAIN Iter 299900: lr = 0.000168,	loss = 2.145214,	Top-1 err = 0.272168,	Top-5 err = 0.102393,	data_time = 0.142872,	train_time = 0.376907
[2019-08-24 23:08:49,534] TRAIN Iter 299920: lr = 0.000135,	loss = 2.159396,	Top-1 err = 0.269775,	Top-5 err = 0.104834,	data_time = 0.050304,	train_time = 0.748051
[2019-08-24 23:09:05,695] TRAIN Iter 299940: lr = 0.000102,	loss = 2.018271,	Top-1 err = 0.270459,	Top-5 err = 0.103906,	data_time = 0.050383,	train_time = 0.808023
[2019-08-24 23:09:12,938] TRAIN Iter 299960: lr = 0.000068,	loss = 2.116511,	Top-1 err = 0.274219,	Top-5 err = 0.104785,	data_time = 0.050348,	train_time = 0.362137
[2019-08-24 23:09:28,703] TRAIN Iter 299980: lr = 0.000035,	loss = 2.091904,	Top-1 err = 0.273145,	Top-5 err = 0.104199,	data_time = 0.050476,	train_time = 0.788234
[2019-08-24 23:09:39,317] TRAIN Iter 300000: lr = 0.000002,	loss = 2.036230,	Top-1 err = 0.272754,	Top-5 err = 0.103906,	data_time = 0.050539,	train_time = 0.530690
[2019-08-24 23:10:42,189] TEST Iter 300000: loss = 2.020179,	Top-1 err = 0.264480,	Top-5 err = 0.084260,	val_time = 62.832821
[2019-08-24 23:10:48,442] TRAIN Iter 300020: lr = 0.000000,	loss = 2.114983,	Top-1 err = 0.268848,	Top-5 err = 0.101562,	data_time = 0.050609,	train_time = 0.312661
[2019-08-24 23:10:55,060] TRAIN Iter 300040: lr = 0.000000,	loss = 2.114973,	Top-1 err = 0.275928,	Top-5 err = 0.103906,	data_time = 0.050694,	train_time = 0.330877
[2019-08-24 23:11:02,131] TRAIN Iter 300060: lr = 0.000000,	loss = 2.145009,	Top-1 err = 0.275879,	Top-5 err = 0.104346,	data_time = 0.050454,	train_time = 0.353510
[2019-08-24 23:11:10,528] TRAIN Iter 300080: lr = 0.000000,	loss = 2.079818,	Top-1 err = 0.273633,	Top-5 err = 0.104688,	data_time = 0.050539,	train_time = 0.419851
[2019-08-24 23:11:24,174] TRAIN Iter 300100: lr = 0.000000,	loss = 2.104878,	Top-1 err = 0.274902,	Top-5 err = 0.105859,	data_time = 0.050627,	train_time = 0.682319
[2019-08-24 23:11:33,133] TRAIN Iter 300120: lr = 0.000000,	loss = 2.025311,	Top-1 err = 0.267578,	Top-5 err = 0.100684,	data_time = 0.050757,	train_time = 0.447921
[2019-08-24 23:11:48,573] TRAIN Iter 300140: lr = 0.000000,	loss = 2.050138,	Top-1 err = 0.272705,	Top-5 err = 0.102734,	data_time = 0.050549,	train_time = 0.771960
[2019-08-24 23:11:57,935] TRAIN Iter 300160: lr = 0.000000,	loss = 2.052523,	Top-1 err = 0.275098,	Top-5 err = 0.106885,	data_time = 0.050571,	train_time = 0.468100
[2019-08-24 23:12:12,115] TRAIN Iter 300180: lr = 0.000000,	loss = 2.171817,	Top-1 err = 0.276416,	Top-5 err = 0.107568,	data_time = 0.050594,	train_time = 0.708990
[2019-08-24 23:12:27,695] TRAIN Iter 300200: lr = 0.000000,	loss = 2.168839,	Top-1 err = 0.272461,	Top-5 err = 0.105322,	data_time = 0.050442,	train_time = 0.779004
[2019-08-24 23:12:36,611] TRAIN Iter 300220: lr = 0.000000,	loss = 2.135940,	Top-1 err = 0.270557,	Top-5 err = 0.100732,	data_time = 0.050646,	train_time = 0.445763
[2019-08-24 23:12:52,749] TRAIN Iter 300240: lr = 0.000000,	loss = 2.158212,	Top-1 err = 0.269385,	Top-5 err = 0.105273,	data_time = 0.050634,	train_time = 0.806915
[2019-08-24 23:13:08,155] TRAIN Iter 300260: lr = 0.000000,	loss = 2.113767,	Top-1 err = 0.268604,	Top-5 err = 0.100391,	data_time = 0.050387,	train_time = 0.770267
[2019-08-24 23:13:18,993] TRAIN Iter 300280: lr = 0.000000,	loss = 2.154423,	Top-1 err = 0.271143,	Top-5 err = 0.100684,	data_time = 2.346207,	train_time = 0.541863
[2019-08-24 23:13:33,416] TRAIN Iter 300300: lr = 0.000000,	loss = 2.104230,	Top-1 err = 0.273193,	Top-5 err = 0.103662,	data_time = 0.050491,	train_time = 0.721148
[2019-08-24 23:13:43,834] TRAIN Iter 300320: lr = 0.000000,	loss = 2.043985,	Top-1 err = 0.272510,	Top-5 err = 0.103516,	data_time = 0.050827,	train_time = 0.520898
[2019-08-24 23:13:59,333] TRAIN Iter 300340: lr = 0.000000,	loss = 2.171772,	Top-1 err = 0.269629,	Top-5 err = 0.104346,	data_time = 0.125817,	train_time = 0.774942
[2019-08-24 23:14:13,860] TRAIN Iter 300360: lr = 0.000000,	loss = 2.156113,	Top-1 err = 0.272461,	Top-5 err = 0.100586,	data_time = 0.049958,	train_time = 0.726353
[2019-08-24 23:14:25,331] TRAIN Iter 300380: lr = 0.000000,	loss = 2.072084,	Top-1 err = 0.273438,	Top-5 err = 0.104541,	data_time = 0.049979,	train_time = 0.573522
[2019-08-24 23:14:40,022] TRAIN Iter 300400: lr = 0.000000,	loss = 2.130244,	Top-1 err = 0.272803,	Top-5 err = 0.108154,	data_time = 0.050057,	train_time = 0.734504
[2019-08-24 23:15:27,240] TRAIN Iter 300420: lr = 0.000000,	loss = 2.148952,	Top-1 err = 0.274563,	Top-5 err = 0.102706,	data_time = 0.050584,	train_time = 2.360918
[2019-08-24 23:15:34,382] TRAIN Iter 300440: lr = 0.000000,	loss = 2.089450,	Top-1 err = 0.275391,	Top-5 err = 0.103174,	data_time = 0.050841,	train_time = 0.357090
[2019-08-24 23:15:49,723] TRAIN Iter 300460: lr = 0.000000,	loss = 2.023973,	Top-1 err = 0.260254,	Top-5 err = 0.097021,	data_time = 0.050515,	train_time = 0.766993
[2019-08-24 23:15:57,932] TRAIN Iter 300480: lr = 0.000000,	loss = 2.076497,	Top-1 err = 0.268457,	Top-5 err = 0.101611,	data_time = 0.050583,	train_time = 0.410459
[2019-08-24 23:16:11,295] TRAIN Iter 300500: lr = 0.000000,	loss = 2.134119,	Top-1 err = 0.272510,	Top-5 err = 0.101709,	data_time = 0.050893,	train_time = 0.668132
[2019-08-24 23:16:25,349] TRAIN Iter 300520: lr = 0.000000,	loss = 2.096189,	Top-1 err = 0.269580,	Top-5 err = 0.102295,	data_time = 0.050835,	train_time = 0.702708
[2019-08-24 23:16:32,803] TRAIN Iter 300540: lr = 0.000000,	loss = 2.094400,	Top-1 err = 0.274854,	Top-5 err = 0.102393,	data_time = 0.050609,	train_time = 0.372674
[2019-08-24 23:16:47,418] TRAIN Iter 300560: lr = 0.000000,	loss = 2.133333,	Top-1 err = 0.267578,	Top-5 err = 0.100049,	data_time = 0.050403,	train_time = 0.730728
[2019-08-24 23:17:03,055] TRAIN Iter 300580: lr = 0.000000,	loss = 2.059472,	Top-1 err = 0.268750,	Top-5 err = 0.103955,	data_time = 0.050753,	train_time = 0.781865
[2019-08-24 23:17:09,941] TRAIN Iter 300600: lr = 0.000000,	loss = 2.090071,	Top-1 err = 0.270654,	Top-5 err = 0.101514,	data_time = 0.050436,	train_time = 0.344279
[2019-08-24 23:17:25,787] TRAIN Iter 300620: lr = 0.000000,	loss = 2.119318,	Top-1 err = 0.272656,	Top-5 err = 0.104443,	data_time = 0.050681,	train_time = 0.792245
[2019-08-24 23:17:33,593] TRAIN Iter 300640: lr = 0.000000,	loss = 2.054474,	Top-1 err = 0.271436,	Top-5 err = 0.107178,	data_time = 0.050572,	train_time = 0.390312
[2019-08-24 23:17:47,468] TRAIN Iter 300660: lr = 0.000000,	loss = 2.036821,	Top-1 err = 0.271289,	Top-5 err = 0.106689,	data_time = 0.050572,	train_time = 0.693744
[2019-08-24 23:18:00,385] TRAIN Iter 300680: lr = 0.000000,	loss = 2.126055,	Top-1 err = 0.275879,	Top-5 err = 0.108740,	data_time = 0.050531,	train_time = 0.645821
[2019-08-24 23:18:07,635] TRAIN Iter 300700: lr = 0.000000,	loss = 2.077751,	Top-1 err = 0.275879,	Top-5 err = 0.104199,	data_time = 0.050912,	train_time = 0.362485
[2019-08-24 23:18:22,044] TRAIN Iter 300720: lr = 0.000000,	loss = 2.139327,	Top-1 err = 0.273291,	Top-5 err = 0.101416,	data_time = 0.050538,	train_time = 0.720452
[2019-08-24 23:18:37,517] TRAIN Iter 300740: lr = 0.000000,	loss = 2.131507,	Top-1 err = 0.275439,	Top-5 err = 0.105615,	data_time = 0.050622,	train_time = 0.773647
[2019-08-24 23:18:44,773] TRAIN Iter 300760: lr = 0.000000,	loss = 2.107857,	Top-1 err = 0.268750,	Top-5 err = 0.102002,	data_time = 0.050637,	train_time = 0.362771
[2019-08-24 23:19:01,015] TRAIN Iter 300780: lr = 0.000000,	loss = 2.158899,	Top-1 err = 0.272510,	Top-5 err = 0.102881,	data_time = 0.050457,	train_time = 0.812096
[2019-08-24 23:19:08,317] TRAIN Iter 300800: lr = 0.000000,	loss = 2.167763,	Top-1 err = 0.274854,	Top-5 err = 0.105127,	data_time = 0.050247,	train_time = 0.365074
[2019-08-24 23:19:21,195] TRAIN Iter 300820: lr = 0.000000,	loss = 2.117852,	Top-1 err = 0.269482,	Top-5 err = 0.102344,	data_time = 0.050642,	train_time = 0.643900
[2019-08-24 23:19:36,130] TRAIN Iter 300840: lr = 0.000000,	loss = 2.030093,	Top-1 err = 0.267285,	Top-5 err = 0.102832,	data_time = 0.050511,	train_time = 0.746711
[2019-08-24 23:19:43,425] TRAIN Iter 300860: lr = 0.000000,	loss = 2.085793,	Top-1 err = 0.277734,	Top-5 err = 0.103711,	data_time = 0.050520,	train_time = 0.364751
[2019-08-24 23:19:59,235] TRAIN Iter 300880: lr = 0.000000,	loss = 2.103662,	Top-1 err = 0.273779,	Top-5 err = 0.102930,	data_time = 0.050623,	train_time = 0.790489
[2019-08-24 23:20:14,257] TRAIN Iter 300900: lr = 0.000000,	loss = 2.088490,	Top-1 err = 0.271436,	Top-5 err = 0.103369,	data_time = 0.050532,	train_time = 0.751092
[2019-08-24 23:20:21,537] TRAIN Iter 300920: lr = 0.000000,	loss = 2.140311,	Top-1 err = 0.272559,	Top-5 err = 0.104102,	data_time = 0.050506,	train_time = 0.363970
[2019-08-24 23:20:37,047] TRAIN Iter 300940: lr = 0.000000,	loss = 2.107535,	Top-1 err = 0.272070,	Top-5 err = 0.102588,	data_time = 0.050406,	train_time = 0.775487
[2019-08-24 23:20:44,189] TRAIN Iter 300960: lr = 0.000000,	loss = 2.118403,	Top-1 err = 0.270801,	Top-5 err = 0.100391,	data_time = 0.050792,	train_time = 0.357085
[2019-08-24 23:20:58,634] TRAIN Iter 300980: lr = 0.000000,	loss = 2.151598,	Top-1 err = 0.276855,	Top-5 err = 0.105371,	data_time = 0.050778,	train_time = 0.722220
[2019-08-24 23:21:14,511] TRAIN Iter 301000: lr = 0.000000,	loss = 2.150064,	Top-1 err = 0.271533,	Top-5 err = 0.104736,	data_time = 2.866905,	train_time = 0.793837
[2019-08-24 23:21:21,915] TRAIN Iter 301020: lr = 0.000000,	loss = 2.074792,	Top-1 err = 0.273047,	Top-5 err = 0.104883,	data_time = 0.050829,	train_time = 0.370167
[2019-08-24 23:21:36,504] TRAIN Iter 301040: lr = 0.000000,	loss = 2.148529,	Top-1 err = 0.276318,	Top-5 err = 0.106738,	data_time = 0.050747,	train_time = 0.729438
[2019-08-24 23:21:50,473] TRAIN Iter 301060: lr = 0.000000,	loss = 2.159469,	Top-1 err = 0.275439,	Top-5 err = 0.106104,	data_time = 0.052074,	train_time = 0.698477
[2019-08-24 23:21:58,338] TRAIN Iter 301080: lr = 0.000000,	loss = 2.047672,	Top-1 err = 0.266309,	Top-5 err = 0.100781,	data_time = 0.050476,	train_time = 0.393229
[2019-08-24 23:22:14,253] TRAIN Iter 301100: lr = 0.000000,	loss = 2.094563,	Top-1 err = 0.271143,	Top-5 err = 0.107373,	data_time = 0.050555,	train_time = 0.795701
[2019-08-24 23:22:21,549] TRAIN Iter 301120: lr = 0.000000,	loss = 2.050719,	Top-1 err = 0.271777,	Top-5 err = 0.102393,	data_time = 0.050517,	train_time = 0.364789
[2019-08-24 23:22:36,370] TRAIN Iter 301140: lr = 0.000000,	loss = 2.106414,	Top-1 err = 0.272607,	Top-5 err = 0.100732,	data_time = 0.050610,	train_time = 0.741054
[2019-08-24 23:22:51,929] TRAIN Iter 301160: lr = 0.000000,	loss = 2.147447,	Top-1 err = 0.271826,	Top-5 err = 0.102002,	data_time = 0.323906,	train_time = 0.777928
[2019-08-24 23:22:59,256] TRAIN Iter 301180: lr = 0.000000,	loss = 2.094594,	Top-1 err = 0.272461,	Top-5 err = 0.103613,	data_time = 0.050774,	train_time = 0.366327
[2019-08-24 23:23:14,794] TRAIN Iter 301200: lr = 0.000000,	loss = 2.174880,	Top-1 err = 0.277148,	Top-5 err = 0.106787,	data_time = 0.050783,	train_time = 0.776888
[2019-08-24 23:23:30,990] TRAIN Iter 301220: lr = 0.000000,	loss = 2.098396,	Top-1 err = 0.277197,	Top-5 err = 0.104102,	data_time = 0.051091,	train_time = 0.809792
[2019-08-24 23:23:39,525] TRAIN Iter 301240: lr = 0.000000,	loss = 2.155086,	Top-1 err = 0.272705,	Top-5 err = 0.104834,	data_time = 0.151335,	train_time = 0.426762
[2019-08-24 23:24:52,156] TEST Iter 301250: loss = 2.018473,	Top-1 err = 0.263420,	Top-5 err = 0.084240,	val_time = 62.240580