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[01:32:30] INFO - Epoch 1/50, Iter 0: Loss = 1.0565705299377441, lr = 0.0001
[01:33:06] INFO - Epoch 1/50, Iter 100: Loss = 0.052893124520778656, lr = 0.0001
[01:33:43] INFO - Epoch 1/50, Iter 200: Loss = 0.06410080194473267, lr = 0.0001
[01:34:20] INFO - Epoch 1/50, Iter 300: Loss = 0.036012835800647736, lr = 0.0001
[01:34:57] INFO - Epoch 1/50, Iter 400: Loss = 0.04257039353251457, lr = 0.0001
[01:35:35] INFO - Epoch 1/50, Iter 500: Loss = 0.04468406364321709, lr = 0.0001
[01:36:12] INFO - Epoch 1/50, Iter 600: Loss = 0.031456008553504944, lr = 0.0001
[01:36:49] INFO - Epoch 1/50, Iter 700: Loss = 0.03800628334283829, lr = 0.0001
[01:37:26] INFO - Epoch 1/50, Iter 800: Loss = 0.05088917911052704, lr = 0.0001
[01:38:04] INFO - Epoch 1/50, Iter 900: Loss = 0.04832298308610916, lr = 0.0001
[01:39:47] INFO - Epoch 1/50, Iter 1000: Loss = 0.021001430228352547, lr = 0.0001
[01:40:23] INFO - Epoch 1/50, Iter 1100: Loss = 0.027549631893634796, lr = 0.0001
[01:41:01] INFO - Epoch 1/50, Iter 1200: Loss = 0.026023169979453087, lr = 0.0001
[01:41:39] INFO - Epoch 1/50, Iter 1300: Loss = 0.019382692873477936, lr = 0.0001
[01:42:17] INFO - Epoch 1/50, Iter 1400: Loss = 0.018007593229413033, lr = 0.0001
[01:42:54] INFO - Epoch 1/50, Iter 1500: Loss = 0.029549293220043182, lr = 0.0001
[01:43:31] INFO - Epoch 1/50, Iter 1600: Loss = 0.033689916133880615, lr = 0.0001
[01:44:09] INFO - Epoch 1/50, Iter 1700: Loss = 0.027298524975776672, lr = 0.0001
[01:44:46] INFO - Epoch 1/50, Iter 1800: Loss = 0.035250209271907806, lr = 0.0001
[01:45:24] INFO - Epoch 2/50, Iter 1900: Loss = 0.03265728801488876, lr = 0.0001
[01:47:08] INFO - Epoch 2/50, Iter 2000: Loss = 0.017500419169664383, lr = 0.0001
[01:47:43] INFO - Epoch 2/50, Iter 2100: Loss = 0.022061176598072052, lr = 0.0001
[01:48:20] INFO - Epoch 2/50, Iter 2200: Loss = 0.031774356961250305, lr = 0.0001
[01:48:57] INFO - Epoch 2/50, Iter 2300: Loss = 0.027994144707918167, lr = 0.0001
[01:49:34] INFO - Epoch 2/50, Iter 2400: Loss = 0.021347511559724808, lr = 0.0001
[01:50:12] INFO - Epoch 2/50, Iter 2500: Loss = 0.03212534263730049, lr = 0.0001
[01:50:49] INFO - Epoch 2/50, Iter 2600: Loss = 0.03436075150966644, lr = 0.0001
[01:51:26] INFO - Epoch 2/50, Iter 2700: Loss = 0.039760299026966095, lr = 0.0001
[01:52:03] INFO - Epoch 2/50, Iter 2800: Loss = 0.018602240830659866, lr = 0.0001
[01:52:41] INFO - Epoch 2/50, Iter 2900: Loss = 0.017089033499360085, lr = 0.0001
[01:54:27] INFO - Epoch 2/50, Iter 3000: Loss = 0.01724972389638424, lr = 0.0001
[01:55:02] INFO - Epoch 2/50, Iter 3100: Loss = 0.02486235462129116, lr = 0.0001
[01:55:37] INFO - Epoch 2/50, Iter 3200: Loss = 0.022042328491806984, lr = 0.0001
[01:56:15] INFO - Epoch 2/50, Iter 3300: Loss = 0.026623308658599854, lr = 0.0001
[01:56:52] INFO - Epoch 2/50, Iter 3400: Loss = 0.02405683323740959, lr = 0.0001
[01:57:30] INFO - Epoch 2/50, Iter 3500: Loss = 0.018972214311361313, lr = 0.0001
[01:58:07] INFO - Epoch 2/50, Iter 3600: Loss = 0.021761640906333923, lr = 0.0001
[01:58:44] INFO - Epoch 2/50, Iter 3700: Loss = 0.023115914314985275, lr = 0.0001
[01:59:23] INFO - Epoch 3/50, Iter 3800: Loss = 0.022612307220697403, lr = 0.0001
[02:00:01] INFO - Epoch 3/50, Iter 3900: Loss = 0.017524931579828262, lr = 0.0001
[02:01:47] INFO - Epoch 3/50, Iter 4000: Loss = 0.01466086134314537, lr = 0.0001
[02:02:22] INFO - Epoch 3/50, Iter 4100: Loss = 0.031845346093177795, lr = 0.0001
[02:02:57] INFO - Epoch 3/50, Iter 4200: Loss = 0.0237705297768116, lr = 0.0001
[02:03:33] INFO - Epoch 3/50, Iter 4300: Loss = 0.0320965051651001, lr = 0.0001
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[02:04:48] INFO - Epoch 3/50, Iter 4500: Loss = 0.022754667326807976, lr = 0.0001
[02:05:25] INFO - Epoch 3/50, Iter 4600: Loss = 0.030830159783363342, lr = 0.0001
[02:06:03] INFO - Epoch 3/50, Iter 4700: Loss = 0.025824744254350662, lr = 0.0001
[02:06:40] INFO - Epoch 3/50, Iter 4800: Loss = 0.019503239542245865, lr = 0.0001
[02:07:18] INFO - Epoch 3/50, Iter 4900: Loss = 0.021452341228723526, lr = 0.0001
[02:09:05] INFO - Epoch 3/50, Iter 5000: Loss = 0.026259608566761017, lr = 0.0001
[02:09:42] INFO - Epoch 3/50, Iter 5100: Loss = 0.023281030356884003, lr = 0.0001
[02:10:17] INFO - Epoch 3/50, Iter 5200: Loss = 0.020111314952373505, lr = 0.0001
[02:10:52] INFO - Epoch 3/50, Iter 5300: Loss = 0.019614871591329575, lr = 0.0001
[02:11:29] INFO - Epoch 3/50, Iter 5400: Loss = 0.014699136838316917, lr = 0.0001
[02:12:06] INFO - Epoch 3/50, Iter 5500: Loss = 0.021856755018234253, lr = 0.0001
[02:12:44] INFO - Epoch 3/50, Iter 5600: Loss = 0.023845821619033813, lr = 0.0001
[02:13:22] INFO - Epoch 4/50, Iter 5700: Loss = 0.03385388106107712, lr = 0.0001
[02:14:00] INFO - Epoch 4/50, Iter 5800: Loss = 0.03092341311275959, lr = 0.0001
[02:14:38] INFO - Epoch 4/50, Iter 5900: Loss = 0.02334553748369217, lr = 0.0001
[02:16:24] INFO - Epoch 4/50, Iter 6000: Loss = 0.042254190891981125, lr = 0.0001
[02:17:01] INFO - Epoch 4/50, Iter 6100: Loss = 0.026709988713264465, lr = 0.0001
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[02:18:12] INFO - Epoch 4/50, Iter 6300: Loss = 0.02040724828839302, lr = 0.0001
[02:18:47] INFO - Epoch 4/50, Iter 6400: Loss = 0.03485984355211258, lr = 0.0001
[02:19:24] INFO - Epoch 4/50, Iter 6500: Loss = 0.01901041902601719, lr = 0.0001
[02:20:02] INFO - Epoch 4/50, Iter 6600: Loss = 0.0252228956669569, lr = 0.0001
[02:20:39] INFO - Epoch 4/50, Iter 6700: Loss = 0.022261066362261772, lr = 0.0001
[02:21:17] INFO - Epoch 4/50, Iter 6800: Loss = 0.024207420647144318, lr = 0.0001
[02:21:54] INFO - Epoch 4/50, Iter 6900: Loss = 0.02730383910238743, lr = 0.0001
[02:23:42] INFO - Epoch 4/50, Iter 7000: Loss = 0.030614979565143585, lr = 0.0001
[02:24:19] INFO - Epoch 4/50, Iter 7100: Loss = 0.009843399748206139, lr = 0.0001
[02:24:56] INFO - Epoch 4/50, Iter 7200: Loss = 0.030504770576953888, lr = 0.0001
[02:25:31] INFO - Epoch 4/50, Iter 7300: Loss = 0.019922511652112007, lr = 0.0001
[02:26:06] INFO - Epoch 4/50, Iter 7400: Loss = 0.017346005886793137, lr = 0.0001
[02:26:43] INFO - Epoch 5/50, Iter 7500: Loss = 0.04261239618062973, lr = 0.0001
[02:27:21] INFO - Epoch 5/50, Iter 7600: Loss = 0.01848176121711731, lr = 0.0001
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[02:28:38] INFO - Epoch 5/50, Iter 7800: Loss = 0.017152022570371628, lr = 0.0001
[02:29:16] INFO - Epoch 5/50, Iter 7900: Loss = 0.019739195704460144, lr = 0.0001
[02:31:02] INFO - Epoch 5/50, Iter 8000: Loss = 0.035217173397541046, lr = 0.0001
[02:31:38] INFO - Epoch 5/50, Iter 8100: Loss = 0.024997148662805557, lr = 0.0001
[02:32:15] INFO - Epoch 5/50, Iter 8200: Loss = 0.01869952492415905, lr = 0.0001
[02:32:51] INFO - Epoch 5/50, Iter 8300: Loss = 0.022771766409277916, lr = 0.0001
[02:33:26] INFO - Epoch 5/50, Iter 8400: Loss = 0.021768197417259216, lr = 0.0001
[02:34:01] INFO - Epoch 5/50, Iter 8500: Loss = 0.02135184407234192, lr = 0.0001
[02:34:38] INFO - Epoch 5/50, Iter 8600: Loss = 0.025887122377753258, lr = 0.0001
[02:35:16] INFO - Epoch 5/50, Iter 8700: Loss = 0.020226851105690002, lr = 0.0001
[02:35:53] INFO - Epoch 5/50, Iter 8800: Loss = 0.0163625106215477, lr = 0.0001
[02:36:31] INFO - Epoch 5/50, Iter 8900: Loss = 0.033197011798620224, lr = 0.0001
[02:38:18] INFO - Epoch 5/50, Iter 9000: Loss = 0.018169889226555824, lr = 0.0001
[02:38:56] INFO - Epoch 5/50, Iter 9100: Loss = 0.01835501380264759, lr = 0.0001
[02:39:33] INFO - Epoch 5/50, Iter 9200: Loss = 0.018355708569288254, lr = 0.0001
[02:40:11] INFO - Epoch 5/50, Iter 9300: Loss = 0.02957603707909584, lr = 0.0001
[02:40:47] INFO - Epoch 6/50, Iter 9400: Loss = 0.02988416701555252, lr = 0.0001
[02:41:22] INFO - Epoch 6/50, Iter 9500: Loss = 0.029050955548882484, lr = 0.0001
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[02:42:37] INFO - Epoch 6/50, Iter 9700: Loss = 0.032014522701501846, lr = 0.0001
[02:43:15] INFO - Epoch 6/50, Iter 9800: Loss = 0.02710639499127865, lr = 0.0001
[02:43:53] INFO - Epoch 6/50, Iter 9900: Loss = 0.02623252011835575, lr = 0.0001
[02:45:39] INFO - Epoch 6/50, Iter 10000: Loss = 0.018064428120851517, lr = 0.0001
[02:46:16] INFO - Epoch 6/50, Iter 10100: Loss = 0.017915505915880203, lr = 0.0001
[02:46:52] INFO - Epoch 6/50, Iter 10200: Loss = 0.018055936321616173, lr = 0.0001
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[02:48:06] INFO - Epoch 6/50, Iter 10400: Loss = 0.016980063170194626, lr = 0.0001
[02:48:41] INFO - Epoch 6/50, Iter 10500: Loss = 0.028786756098270416, lr = 0.0001
[02:49:15] INFO - Epoch 6/50, Iter 10600: Loss = 0.0259169340133667, lr = 0.0001
[02:49:52] INFO - Epoch 6/50, Iter 10700: Loss = 0.02411068230867386, lr = 0.0001
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[12:48:29] INFO - Epoch 50/50, Iter 92500: Loss = 0.02273436076939106, lr = 0.0001
[12:49:07] INFO - Epoch 50/50, Iter 92600: Loss = 0.021798046305775642, lr = 0.0001
[12:49:43] INFO - Epoch 50/50, Iter 92700: Loss = 0.02120419591665268, lr = 0.0001
[12:50:20] INFO - Epoch 50/50, Iter 92800: Loss = 0.027433019131422043, lr = 0.0001
[12:50:57] INFO - Epoch 50/50, Iter 92900: Loss = 0.025116585195064545, lr = 0.0001
[12:52:42] INFO - Epoch 50/50, Iter 93000: Loss = 0.02106088027358055, lr = 0.0001
[12:53:19] INFO - Epoch 50/50, Iter 93100: Loss = 0.016250556334853172, lr = 0.0001
[12:53:56] INFO - Epoch 50/50, Iter 93200: Loss = 0.016655270010232925, lr = 0.0001
[12:54:33] INFO - Epoch 50/50, Iter 93300: Loss = 0.020460784435272217, lr = 0.0001
[12:55:10] INFO - Epoch 50/50, Iter 93400: Loss = 0.02524268440902233, lr = 0.0001
[12:55:44] INFO - Epoch 50/50, Iter 93500: Loss = 0.01436917670071125, lr = 0.0001
[12:56:19] INFO - Epoch 50/50, Iter 93600: Loss = 0.019781406968832016, lr = 0.0001
[12:56:56] INFO - Epoch 50/50, Iter 93700: Loss = 0.01460881344974041, lr = 0.0001