OUTPUT_MODEL/train.log

2023-03-19 07:18:21,463	OUTPUT_MODEL	INFO	{'train': {'log_interval': 100, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 16, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'final_annotation_train.txt', 'validation_files': 'final_annotation_val.txt', 'text_cleaners': ['zh_ja_mixture_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 7, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256}, 'speakers': {'5': 0, '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, 'zhongli': 6}, 'symbols': ['_', ',', '.', '!', '?', '-', '~', '…', 'A', 'E', 'I', 'N', 'O', 'Q', 'U', 'a', 'b', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ʃ', 'ʧ', 'ʦ', 'ɯ', 'ɹ', 'ə', 'ɥ', '⁼', 'ʰ', '`', '→', '↓', '↑', ' '], 'model_dir': '././OUTPUT_MODEL', 'max_epochs': 400, 'drop_speaker_embed': True}
2023-03-19 07:18:37,865	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/G_0.pth' (iteration None)
2023-03-19 07:18:40,564	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/D_0.pth' (iteration None)
2023-03-19 07:19:00,886	OUTPUT_MODEL	INFO	Train Epoch: 1 [0%]
2023-03-19 07:19:00,887	OUTPUT_MODEL	INFO	[2.768019676208496, 1.921393871307373, 6.519968509674072, 29.697479248046875, 1.227083444595337, 14.877570152282715, 0, 0.0002]
2023-03-19 07:19:03,403	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_0.pth
2023-03-19 07:19:03,964	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 07:21:32,073	OUTPUT_MODEL	INFO	Train Epoch: 1 [73%]
2023-03-19 07:21:32,076	OUTPUT_MODEL	INFO	[2.2772250175476074, 2.7441136837005615, 7.556460857391357, 23.68376350402832, -3.9489474296569824, 3.644057512283325, 100, 0.0002]
2023-03-19 07:22:21,926	OUTPUT_MODEL	INFO	====> Epoch: 1
2023-03-19 07:23:29,454	OUTPUT_MODEL	INFO	Train Epoch: 2 [46%]
2023-03-19 07:23:29,456	OUTPUT_MODEL	INFO	[2.535923480987549, 2.2842607498168945, 4.655385971069336, 21.444704055786133, 0.9641294479370117, 3.2159464359283447, 200, 0.000199975]
2023-03-19 07:24:39,043	OUTPUT_MODEL	INFO	====> Epoch: 2
2023-03-19 07:25:11,610	OUTPUT_MODEL	INFO	Train Epoch: 3 [19%]
2023-03-19 07:25:11,612	OUTPUT_MODEL	INFO	[2.7335851192474365, 2.437509536743164, 7.0831451416015625, 21.784095764160156, -0.06965750455856323, 2.8098151683807373, 300, 0.000199950003125]
2023-03-19 07:26:46,656	OUTPUT_MODEL	INFO	Train Epoch: 3 [92%]
2023-03-19 07:26:46,657	OUTPUT_MODEL	INFO	[2.2453463077545166, 2.462892532348633, 6.566348552703857, 22.99268913269043, 1.2814807891845703, 3.310159206390381, 400, 0.000199950003125]
2023-03-19 07:26:56,935	OUTPUT_MODEL	INFO	====> Epoch: 3
2023-03-19 07:28:30,123	OUTPUT_MODEL	INFO	Train Epoch: 4 [65%]
2023-03-19 07:28:30,126	OUTPUT_MODEL	INFO	[2.4087836742401123, 2.6702001094818115, 6.33384895324707, 22.592639923095703, 0.059972405433654785, 2.9364330768585205, 500, 0.00019992500937460937]
2023-03-19 07:29:16,412	OUTPUT_MODEL	INFO	====> Epoch: 4
2023-03-19 07:30:12,936	OUTPUT_MODEL	INFO	Train Epoch: 5 [38%]
2023-03-19 07:30:12,938	OUTPUT_MODEL	INFO	[2.308634042739868, 2.9119412899017334, 6.813663482666016, 22.926420211791992, 1.0187842845916748, 2.8449811935424805, 600, 0.00019990001874843754]
2023-03-19 07:31:33,488	OUTPUT_MODEL	INFO	====> Epoch: 5
2023-03-19 07:31:55,108	OUTPUT_MODEL	INFO	Train Epoch: 6 [11%]
2023-03-19 07:31:55,110	OUTPUT_MODEL	INFO	[2.224010944366455, 2.6945202350616455, 6.556964874267578, 21.990264892578125, 1.0710209608078003, 2.62105655670166, 700, 0.00019987503124609398]
2023-03-19 07:33:30,913	OUTPUT_MODEL	INFO	Train Epoch: 6 [84%]
2023-03-19 07:33:30,915	OUTPUT_MODEL	INFO	[2.306527853012085, 2.6618831157684326, 6.107322692871094, 22.25784683227539, -1.74416184425354, 3.106327533721924, 800, 0.00019987503124609398]
2023-03-19 07:33:51,921	OUTPUT_MODEL	INFO	====> Epoch: 6
2023-03-19 07:35:14,456	OUTPUT_MODEL	INFO	Train Epoch: 7 [57%]
2023-03-19 07:35:14,459	OUTPUT_MODEL	INFO	[2.40322208404541, 2.4677236080169678, 5.320391654968262, 21.817230224609375, 0.9494607448577881, 2.811027765274048, 900, 0.0001998500468671882]
2023-03-19 07:36:11,227	OUTPUT_MODEL	INFO	====> Epoch: 7
2023-03-19 07:36:57,909	OUTPUT_MODEL	INFO	Train Epoch: 8 [30%]
2023-03-19 07:36:57,911	OUTPUT_MODEL	INFO	[2.3276591300964355, 2.4463908672332764, 6.027544021606445, 22.01661491394043, 1.0660531520843506, 2.6863389015197754, 1000, 0.00019982506561132978]
2023-03-19 07:36:59,958	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 8 to ././OUTPUT_MODEL/G_1000.pth
2023-03-19 07:37:01,179	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 8 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 07:38:33,542	OUTPUT_MODEL	INFO	====> Epoch: 8
2023-03-19 07:38:46,104	OUTPUT_MODEL	INFO	Train Epoch: 9 [3%]
2023-03-19 07:38:46,106	OUTPUT_MODEL	INFO	[2.1763088703155518, 2.5903215408325195, 7.170840740203857, 22.513301849365234, 1.7762031555175781, 3.154966354370117, 1100, 0.00019980008747812837]
2023-03-19 07:40:20,831	OUTPUT_MODEL	INFO	Train Epoch: 9 [76%]
2023-03-19 07:40:20,834	OUTPUT_MODEL	INFO	[2.5428860187530518, 2.767617702484131, 6.075894832611084, 21.51504135131836, 1.0041288137435913, 3.0273995399475098, 1200, 0.00019980008747812837]
2023-03-19 07:40:52,916	OUTPUT_MODEL	INFO	====> Epoch: 9
2023-03-19 07:42:05,316	OUTPUT_MODEL	INFO	Train Epoch: 10 [49%]
2023-03-19 07:42:05,318	OUTPUT_MODEL	INFO	[2.376089096069336, 2.412353992462158, 5.713926792144775, 22.076250076293945, 1.3192135095596313, 2.743488073348999, 1300, 0.0001997751124671936]
2023-03-19 07:43:11,032	OUTPUT_MODEL	INFO	====> Epoch: 10
2023-03-19 07:43:46,866	OUTPUT_MODEL	INFO	Train Epoch: 11 [22%]
2023-03-19 07:43:46,868	OUTPUT_MODEL	INFO	[2.1788578033447266, 2.7241034507751465, 6.3938889503479, 21.823440551757812, 1.111817479133606, 2.7845702171325684, 1400, 0.00019975014057813518]
2023-03-19 07:45:21,671	OUTPUT_MODEL	INFO	Train Epoch: 11 [95%]
2023-03-19 07:45:21,673	OUTPUT_MODEL	INFO	[2.5284857749938965, 2.76987624168396, 5.337794303894043, 20.92407989501953, 0.9646298885345459, 2.687791585922241, 1500, 0.00019975014057813518]
2023-03-19 07:45:29,348	OUTPUT_MODEL	INFO	====> Epoch: 11
2023-03-19 07:47:05,951	OUTPUT_MODEL	INFO	Train Epoch: 12 [68%]
2023-03-19 07:47:05,954	OUTPUT_MODEL	INFO	[2.095754384994507, 2.895578145980835, 7.3837995529174805, 21.447275161743164, 0.17578619718551636, 2.971895456314087, 1600, 0.00019972517181056292]
2023-03-19 07:47:47,913	OUTPUT_MODEL	INFO	====> Epoch: 12
2023-03-19 07:48:49,488	OUTPUT_MODEL	INFO	Train Epoch: 13 [41%]
2023-03-19 07:48:49,490	OUTPUT_MODEL	INFO	[2.312061309814453, 2.414072036743164, 7.195616245269775, 21.089902877807617, -2.928373336791992, 2.723794460296631, 1700, 0.0001997002061640866]
2023-03-19 07:50:06,367	OUTPUT_MODEL	INFO	====> Epoch: 13
2023-03-19 07:50:31,471	OUTPUT_MODEL	INFO	Train Epoch: 14 [14%]
2023-03-19 07:50:31,473	OUTPUT_MODEL	INFO	[2.198729991912842, 2.882265090942383, 6.568129539489746, 22.430885314941406, -0.019317537546157837, 3.0821292400360107, 1800, 0.00019967524363831608]
2023-03-19 07:52:07,060	OUTPUT_MODEL	INFO	Train Epoch: 14 [87%]
2023-03-19 07:52:07,062	OUTPUT_MODEL	INFO	[2.2970046997070312, 2.645192861557007, 5.778156280517578, 21.69219970703125, 1.136316180229187, 3.0914652347564697, 1900, 0.00019967524363831608]
2023-03-19 07:52:24,478	OUTPUT_MODEL	INFO	====> Epoch: 14
2023-03-19 07:53:50,397	OUTPUT_MODEL	INFO	Train Epoch: 15 [60%]
2023-03-19 07:53:50,400	OUTPUT_MODEL	INFO	[2.177945137023926, 2.738433361053467, 7.274636268615723, 22.73606300354004, 0.7632359862327576, 3.0601048469543457, 2000, 0.0001996502842328613]
2023-03-19 07:53:52,020	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 15 to ././OUTPUT_MODEL/G_2000.pth
2023-03-19 07:53:52,555	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 15 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 07:54:44,932	OUTPUT_MODEL	INFO	====> Epoch: 15
2023-03-19 07:55:34,695	OUTPUT_MODEL	INFO	Train Epoch: 16 [33%]
2023-03-19 07:55:34,698	OUTPUT_MODEL	INFO	[2.431486129760742, 2.6232481002807617, 6.505343437194824, 21.596899032592773, 1.0503499507904053, 2.712838888168335, 2100, 0.00019962532794733217]
2023-03-19 07:57:01,866	OUTPUT_MODEL	INFO	====> Epoch: 16
2023-03-19 07:57:16,787	OUTPUT_MODEL	INFO	Train Epoch: 17 [6%]
2023-03-19 07:57:16,788	OUTPUT_MODEL	INFO	[2.2668962478637695, 2.43839430809021, 6.049099445343018, 20.323530197143555, 1.1809580326080322, 2.728761911392212, 2200, 0.00019960037478133875]
2023-03-19 07:58:51,069	OUTPUT_MODEL	INFO	Train Epoch: 17 [79%]
2023-03-19 07:58:51,071	OUTPUT_MODEL	INFO	[2.3000011444091797, 2.5433456897735596, 5.9731903076171875, 21.139549255371094, 0.04032415151596069, 2.2879700660705566, 2300, 0.00019960037478133875]
2023-03-19 07:59:19,122	OUTPUT_MODEL	INFO	====> Epoch: 17
2023-03-19 08:00:33,854	OUTPUT_MODEL	INFO	Train Epoch: 18 [52%]
2023-03-19 08:00:33,856	OUTPUT_MODEL	INFO	[2.3636155128479004, 2.482144832611084, 6.177975177764893, 21.157926559448242, 0.1991303265094757, 2.5741453170776367, 2400, 0.00019957542473449108]
2023-03-19 08:01:35,528	OUTPUT_MODEL	INFO	====> Epoch: 18
2023-03-19 08:02:14,081	OUTPUT_MODEL	INFO	Train Epoch: 19 [25%]
2023-03-19 08:02:14,083	OUTPUT_MODEL	INFO	[2.1404271125793457, 2.6496834754943848, 6.864394664764404, 22.55076026916504, -0.14120221138000488, 2.7859795093536377, 2500, 0.00019955047780639926]
2023-03-19 08:03:47,386	OUTPUT_MODEL	INFO	Train Epoch: 19 [98%]
2023-03-19 08:03:47,388	OUTPUT_MODEL	INFO	[2.291243553161621, 2.623600959777832, 5.603788375854492, 21.147472381591797, -2.453336477279663, 2.5308847427368164, 2600, 0.00019955047780639926]
2023-03-19 08:03:50,507	OUTPUT_MODEL	INFO	====> Epoch: 19
2023-03-19 08:05:29,064	OUTPUT_MODEL	INFO	Train Epoch: 20 [71%]
2023-03-19 08:05:29,066	OUTPUT_MODEL	INFO	[2.1484482288360596, 2.7674930095672607, 7.452215671539307, 21.479951858520508, -2.826406717300415, 2.9361438751220703, 2700, 0.00019952553399667344]
2023-03-19 08:06:06,625	OUTPUT_MODEL	INFO	====> Epoch: 20
2023-03-19 08:07:09,285	OUTPUT_MODEL	INFO	Train Epoch: 21 [44%]
2023-03-19 08:07:09,287	OUTPUT_MODEL	INFO	[2.225496768951416, 2.734361410140991, 7.5912275314331055, 21.996044158935547, 1.172825574874878, 2.8385438919067383, 2800, 0.00019950059330492385]
2023-03-19 08:08:20,195	OUTPUT_MODEL	INFO	====> Epoch: 21
2023-03-19 08:08:49,010	OUTPUT_MODEL	INFO	Train Epoch: 22 [17%]
2023-03-19 08:08:49,012	OUTPUT_MODEL	INFO	[2.061849594116211, 2.698944091796875, 6.602043151855469, 21.05373191833496, 0.99527907371521, 3.036965847015381, 2900, 0.00019947565573076072]
2023-03-19 08:10:21,226	OUTPUT_MODEL	INFO	Train Epoch: 22 [90%]
2023-03-19 08:10:21,228	OUTPUT_MODEL	INFO	[2.3350932598114014, 2.6007325649261475, 6.743359088897705, 21.142526626586914, -0.08325338363647461, 2.6873672008514404, 3000, 0.00019947565573076072]
2023-03-19 08:10:22,593	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 22 to ././OUTPUT_MODEL/G_3000.pth
2023-03-19 08:10:23,341	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 22 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 08:10:37,003	OUTPUT_MODEL	INFO	====> Epoch: 22
2023-03-19 08:12:03,330	OUTPUT_MODEL	INFO	Train Epoch: 23 [63%]
2023-03-19 08:12:03,332	OUTPUT_MODEL	INFO	[2.3919076919555664, 2.4395039081573486, 6.612514495849609, 22.419504165649414, 1.3721897602081299, 2.956296682357788, 3100, 0.00019945072127379438]
2023-03-19 08:12:51,732	OUTPUT_MODEL	INFO	====> Epoch: 23
2023-03-19 08:13:44,646	OUTPUT_MODEL	INFO	Train Epoch: 24 [36%]
2023-03-19 08:13:44,648	OUTPUT_MODEL	INFO	[2.303422451019287, 2.6689040660858154, 6.611281871795654, 21.111040115356445, 1.0606281757354736, 2.9565141201019287, 3200, 0.00019942578993363514]
2023-03-19 08:15:06,172	OUTPUT_MODEL	INFO	====> Epoch: 24
2023-03-19 08:15:25,680	OUTPUT_MODEL	INFO	Train Epoch: 25 [9%]
2023-03-19 08:15:25,683	OUTPUT_MODEL	INFO	[2.2379932403564453, 2.4821741580963135, 6.5472798347473145, 21.089221954345703, -2.1170806884765625, 2.887577772140503, 3300, 0.00019940086170989343]
2023-03-19 08:16:58,105	OUTPUT_MODEL	INFO	Train Epoch: 25 [82%]
2023-03-19 08:16:58,107	OUTPUT_MODEL	INFO	[2.2082130908966064, 2.5593466758728027, 6.8826398849487305, 22.039506912231445, 1.141390323638916, 2.517772912979126, 3400, 0.00019940086170989343]
2023-03-19 08:17:20,995	OUTPUT_MODEL	INFO	====> Epoch: 25
2023-03-19 08:18:38,110	OUTPUT_MODEL	INFO	Train Epoch: 26 [55%]
2023-03-19 08:18:38,111	OUTPUT_MODEL	INFO	[2.1795859336853027, 2.664618730545044, 6.616400718688965, 20.863616943359375, 0.03481826186180115, 2.606607437133789, 3500, 0.0001993759366021797]
2023-03-19 08:19:35,667	OUTPUT_MODEL	INFO	====> Epoch: 26
2023-03-19 08:20:18,930	OUTPUT_MODEL	INFO	Train Epoch: 27 [28%]
2023-03-19 08:20:18,932	OUTPUT_MODEL	INFO	[1.9792671203613281, 2.701956272125244, 8.546255111694336, 20.897953033447266, -6.18421745300293, 2.609076976776123, 3600, 0.00019935101461010442]
2023-03-19 08:21:50,338	OUTPUT_MODEL	INFO	====> Epoch: 27
2023-03-19 08:21:58,246	OUTPUT_MODEL	INFO	Train Epoch: 28 [1%]
2023-03-19 08:21:58,248	OUTPUT_MODEL	INFO	[2.3124191761016846, 2.529365301132202, 6.26586389541626, 21.809255599975586, 1.1585801839828491, 3.0949342250823975, 3700, 0.00019932609573327815]
2023-03-19 08:23:32,050	OUTPUT_MODEL	INFO	Train Epoch: 28 [74%]
2023-03-19 08:23:32,052	OUTPUT_MODEL	INFO	[2.2365219593048096, 2.695101261138916, 6.9353742599487305, 20.61651039123535, 1.4520642757415771, 2.662355422973633, 3800, 0.00019932609573327815]
2023-03-19 08:24:05,888	OUTPUT_MODEL	INFO	====> Epoch: 28
2023-03-19 08:25:11,729	OUTPUT_MODEL	INFO	Train Epoch: 29 [47%]
2023-03-19 08:25:11,731	OUTPUT_MODEL	INFO	[2.0912790298461914, 2.7131242752075195, 7.0042829513549805, 20.69322395324707, -5.51295280456543, 2.522542715072632, 3900, 0.0001993011799713115]
2023-03-19 08:26:19,241	OUTPUT_MODEL	INFO	====> Epoch: 29
2023-03-19 08:26:52,182	OUTPUT_MODEL	INFO	Train Epoch: 30 [20%]
2023-03-19 08:26:52,184	OUTPUT_MODEL	INFO	[2.2816243171691895, 2.7181928157806396, 7.5636982917785645, 20.23880386352539, 1.4637830257415771, 2.533090591430664, 4000, 0.00019927626732381507]
2023-03-19 08:26:53,551	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 30 to ././OUTPUT_MODEL/G_4000.pth
2023-03-19 08:26:54,176	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 30 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 08:28:27,336	OUTPUT_MODEL	INFO	Train Epoch: 30 [93%]
2023-03-19 08:28:27,338	OUTPUT_MODEL	INFO	[2.0294768810272217, 3.110525608062744, 10.164491653442383, 22.029632568359375, 1.6321773529052734, 2.874009132385254, 4100, 0.00019927626732381507]
2023-03-19 08:28:37,462	OUTPUT_MODEL	INFO	====> Epoch: 30
2023-03-19 08:30:08,463	OUTPUT_MODEL	INFO	Train Epoch: 31 [66%]
2023-03-19 08:30:08,465	OUTPUT_MODEL	INFO	[2.3687591552734375, 2.7882466316223145, 6.383553504943848, 19.41340446472168, 1.3019330501556396, 2.5446853637695312, 4200, 0.00019925135779039958]
2023-03-19 08:30:53,398	OUTPUT_MODEL	INFO	====> Epoch: 31
2023-03-19 08:31:50,554	OUTPUT_MODEL	INFO	Train Epoch: 32 [39%]
2023-03-19 08:31:50,556	OUTPUT_MODEL	INFO	[2.292052984237671, 2.7871711254119873, 5.743388652801514, 21.031726837158203, 1.1751837730407715, 2.6740176677703857, 4300, 0.00019922645137067577]
2023-03-19 08:33:09,284	OUTPUT_MODEL	INFO	====> Epoch: 32
2023-03-19 08:33:32,071	OUTPUT_MODEL	INFO	Train Epoch: 33 [12%]
2023-03-19 08:33:32,073	OUTPUT_MODEL	INFO	[2.121082067489624, 2.599949598312378, 6.863088607788086, 20.474491119384766, 0.15365147590637207, 2.933873414993286, 4400, 0.00019920154806425444]
2023-03-19 08:35:05,129	OUTPUT_MODEL	INFO	Train Epoch: 33 [85%]
2023-03-19 08:35:05,131	OUTPUT_MODEL	INFO	[2.5696191787719727, 2.6605775356292725, 5.66583776473999, 20.507312774658203, -1.2818255424499512, 2.6317567825317383, 4500, 0.00019920154806425444]
2023-03-19 08:35:24,589	OUTPUT_MODEL	INFO	====> Epoch: 33
2023-03-19 08:36:46,208	OUTPUT_MODEL	INFO	Train Epoch: 34 [58%]
2023-03-19 08:36:46,210	OUTPUT_MODEL	INFO	[2.1660523414611816, 2.6705427169799805, 7.658264636993408, 20.35855484008789, 1.387345314025879, 2.9911201000213623, 4600, 0.0001991766478707464]
2023-03-19 08:37:39,194	OUTPUT_MODEL	INFO	====> Epoch: 34
2023-03-19 08:38:25,627	OUTPUT_MODEL	INFO	Train Epoch: 35 [31%]
2023-03-19 08:38:25,628	OUTPUT_MODEL	INFO	[2.009460687637329, 2.9427473545074463, 6.565086841583252, 20.50218963623047, -2.583266019821167, 2.6900246143341064, 4700, 0.00019915175078976256]
2023-03-19 08:39:53,992	OUTPUT_MODEL	INFO	====> Epoch: 35
2023-03-19 08:40:06,911	OUTPUT_MODEL	INFO	Train Epoch: 36 [4%]
2023-03-19 08:40:06,913	OUTPUT_MODEL	INFO	[2.101327419281006, 3.0149827003479004, 7.639118194580078, 20.655118942260742, 1.2312791347503662, 2.6263320446014404, 4800, 0.00019912685682091382]
2023-03-19 08:41:40,246	OUTPUT_MODEL	INFO	Train Epoch: 36 [77%]
2023-03-19 08:41:40,248	OUTPUT_MODEL	INFO	[2.1585516929626465, 2.9256644248962402, 6.887460231781006, 20.941791534423828, 1.1179956197738647, 2.427414894104004, 4900, 0.00019912685682091382]
2023-03-19 08:42:10,351	OUTPUT_MODEL	INFO	====> Epoch: 36
2023-03-19 08:43:22,136	OUTPUT_MODEL	INFO	Train Epoch: 37 [50%]
2023-03-19 08:43:22,137	OUTPUT_MODEL	INFO	[2.1657891273498535, 2.818676233291626, 8.576238632202148, 19.848596572875977, 1.4114537239074707, 1.992946982383728, 5000, 0.0001991019659638112]
2023-03-19 08:43:23,486	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 37 to ././OUTPUT_MODEL/G_5000.pth
2023-03-19 08:43:24,087	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 37 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 08:44:28,517	OUTPUT_MODEL	INFO	====> Epoch: 37
2023-03-19 08:45:05,957	OUTPUT_MODEL	INFO	Train Epoch: 38 [23%]
2023-03-19 08:45:05,960	OUTPUT_MODEL	INFO	[2.2091054916381836, 2.869731903076172, 9.753157615661621, 21.02629852294922, -5.59156608581543, 1.9529590606689453, 5100, 0.0001990770782180657]
2023-03-19 08:46:39,172	OUTPUT_MODEL	INFO	Train Epoch: 38 [96%]
2023-03-19 08:46:39,174	OUTPUT_MODEL	INFO	[2.57089900970459, 2.770625591278076, 10.21678638458252, 17.78519630432129, 1.2550263404846191, 2.005923271179199, 5200, 0.0001990770782180657]
2023-03-19 08:46:44,844	OUTPUT_MODEL	INFO	====> Epoch: 38
2023-03-19 08:48:20,055	OUTPUT_MODEL	INFO	Train Epoch: 39 [69%]
2023-03-19 08:48:20,057	OUTPUT_MODEL	INFO	[2.3553202152252197, 2.9345550537109375, 7.6950273513793945, 21.629680633544922, 1.6046950817108154, 2.6588451862335205, 5300, 0.00019905219358328844]
2023-03-19 08:49:01,003	OUTPUT_MODEL	INFO	====> Epoch: 39
2023-03-19 08:50:02,149	OUTPUT_MODEL	INFO	Train Epoch: 40 [42%]
2023-03-19 08:50:02,151	OUTPUT_MODEL	INFO	[2.31356143951416, 2.6944286823272705, 6.644580364227295, 18.970468521118164, 1.7599613666534424, 2.7115867137908936, 5400, 0.0001990273120590905]
2023-03-19 08:51:16,639	OUTPUT_MODEL	INFO	====> Epoch: 40
2023-03-19 08:51:43,197	OUTPUT_MODEL	INFO	Train Epoch: 41 [15%]
2023-03-19 08:51:43,199	OUTPUT_MODEL	INFO	[2.136007785797119, 2.8355908393859863, 7.208671569824219, 21.42572784423828, 0.005605518817901611, 2.6958107948303223, 5500, 0.00019900243364508313]
2023-03-19 08:53:15,823	OUTPUT_MODEL	INFO	Train Epoch: 41 [88%]
2023-03-19 08:53:15,825	OUTPUT_MODEL	INFO	[2.019536018371582, 3.05614972114563, 7.717067718505859, 20.643564224243164, -1.171074628829956, 3.1746666431427, 5600, 0.00019900243364508313]
2023-03-19 08:53:32,016	OUTPUT_MODEL	INFO	====> Epoch: 41
2023-03-19 08:54:55,683	OUTPUT_MODEL	INFO	Train Epoch: 42 [61%]
2023-03-19 08:54:55,685	OUTPUT_MODEL	INFO	[2.0651745796203613, 2.7332401275634766, 6.6274261474609375, 20.02605438232422, -0.8466860055923462, 2.1027307510375977, 5700, 0.0001989775583408775]
2023-03-19 08:55:46,367	OUTPUT_MODEL	INFO	====> Epoch: 42
2023-03-19 08:56:36,269	OUTPUT_MODEL	INFO	Train Epoch: 43 [34%]
2023-03-19 08:56:36,271	OUTPUT_MODEL	INFO	[2.47926664352417, 2.678997278213501, 7.206518173217773, 17.360368728637695, 1.3104552030563354, 2.3776111602783203, 5800, 0.00019895268614608487]
2023-03-19 08:58:01,480	OUTPUT_MODEL	INFO	====> Epoch: 43
2023-03-19 08:58:17,148	OUTPUT_MODEL	INFO	Train Epoch: 44 [7%]
2023-03-19 08:58:17,150	OUTPUT_MODEL	INFO	[2.224919557571411, 2.6022017002105713, 6.763675212860107, 20.296022415161133, 0.9388125538825989, 2.3183505535125732, 5900, 0.0001989278170603166]
2023-03-19 08:59:49,946	OUTPUT_MODEL	INFO	Train Epoch: 44 [80%]
2023-03-19 08:59:49,947	OUTPUT_MODEL	INFO	[2.0030908584594727, 2.962631940841675, 7.156919479370117, 21.29393196105957, 0.9682693481445312, 2.7539761066436768, 6000, 0.0001989278170603166]
2023-03-19 08:59:51,299	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 44 to ././OUTPUT_MODEL/G_6000.pth
2023-03-19 08:59:51,922	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 44 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 09:00:19,132	OUTPUT_MODEL	INFO	====> Epoch: 44
2023-03-19 09:01:33,265	OUTPUT_MODEL	INFO	Train Epoch: 45 [53%]
2023-03-19 09:01:33,268	OUTPUT_MODEL	INFO	[2.2262377738952637, 2.762049436569214, 6.92403507232666, 20.574960708618164, 1.3221700191497803, 2.5922722816467285, 6100, 0.00019890295108318404]
2023-03-19 09:02:33,699	OUTPUT_MODEL	INFO	====> Epoch: 45
2023-03-19 09:03:13,271	OUTPUT_MODEL	INFO	Train Epoch: 46 [26%]
2023-03-19 09:03:13,273	OUTPUT_MODEL	INFO	[2.1075592041015625, 2.6556544303894043, 7.189697742462158, 20.148548126220703, 0.33404484391212463, 2.746537685394287, 6200, 0.00019887808821429862]
2023-03-19 09:04:46,629	OUTPUT_MODEL	INFO	Train Epoch: 46 [99%]
2023-03-19 09:04:46,633	OUTPUT_MODEL	INFO	[2.191937208175659, 2.7967348098754883, 7.158614635467529, 20.76529312133789, -1.1291816234588623, 2.583735704421997, 6300, 0.00019887808821429862]
2023-03-19 09:04:50,026	OUTPUT_MODEL	INFO	====> Epoch: 46
2023-03-19 09:06:27,468	OUTPUT_MODEL	INFO	Train Epoch: 47 [72%]
2023-03-19 09:06:27,470	OUTPUT_MODEL	INFO	[2.113292694091797, 2.991356134414673, 8.029991149902344, 21.015338897705078, -3.831427574157715, 3.419740915298462, 6400, 0.00019885322845327182]
2023-03-19 09:07:05,027	OUTPUT_MODEL	INFO	====> Epoch: 47
2023-03-19 09:08:09,347	OUTPUT_MODEL	INFO	Train Epoch: 48 [45%]
2023-03-19 09:08:09,350	OUTPUT_MODEL	INFO	[2.200103521347046, 2.8196189403533936, 6.995229721069336, 20.760040283203125, 1.5425654649734497, 2.6970343589782715, 6500, 0.00019882837179971516]
2023-03-19 09:09:20,371	OUTPUT_MODEL	INFO	====> Epoch: 48
2023-03-19 09:09:50,450	OUTPUT_MODEL	INFO	Train Epoch: 49 [18%]
2023-03-19 09:09:50,453	OUTPUT_MODEL	INFO	[2.152475357055664, 2.7897112369537354, 6.8567705154418945, 19.89794921875, -0.14391827583312988, 2.5374205112457275, 6600, 0.00019880351825324018]
2023-03-19 09:11:24,804	OUTPUT_MODEL	INFO	Train Epoch: 49 [91%]
2023-03-19 09:11:24,806	OUTPUT_MODEL	INFO	[2.1705307960510254, 3.079024314880371, 7.659532070159912, 21.308168411254883, 1.3592076301574707, 2.5407614707946777, 6700, 0.00019880351825324018]
2023-03-19 09:11:37,746	OUTPUT_MODEL	INFO	====> Epoch: 49
2023-03-19 09:13:06,200	OUTPUT_MODEL	INFO	Train Epoch: 50 [64%]
2023-03-19 09:13:06,203	OUTPUT_MODEL	INFO	[2.1300406455993652, 2.9006259441375732, 7.431374549865723, 20.722082138061523, 1.256288766860962, 2.703803300857544, 6800, 0.00019877866781345852]
2023-03-19 09:13:54,212	OUTPUT_MODEL	INFO	====> Epoch: 50
2023-03-19 09:14:48,622	OUTPUT_MODEL	INFO	Train Epoch: 51 [36%]
2023-03-19 09:14:48,624	OUTPUT_MODEL	INFO	[2.2859537601470947, 2.900878667831421, 11.74536418914795, 18.181793212890625, 1.1825257539749146, 2.121501922607422, 6900, 0.00019875382047998183]
2023-03-19 09:16:10,998	OUTPUT_MODEL	INFO	====> Epoch: 51
2023-03-19 09:16:31,029	OUTPUT_MODEL	INFO	Train Epoch: 52 [9%]
2023-03-19 09:16:31,031	OUTPUT_MODEL	INFO	[1.929492473602295, 2.715003490447998, 6.699418544769287, 19.033445358276367, 1.0507481098175049, 2.5493123531341553, 7000, 0.00019872897625242182]
2023-03-19 09:16:32,358	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 52 to ././OUTPUT_MODEL/G_7000.pth
2023-03-19 09:16:32,949	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 52 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 09:18:07,634	OUTPUT_MODEL	INFO	Train Epoch: 52 [82%]
2023-03-19 09:18:07,636	OUTPUT_MODEL	INFO	[1.7131946086883545, 3.083000898361206, 8.323352813720703, 20.196983337402344, 0.0835084617137909, 2.494812488555908, 7100, 0.00019872897625242182]
2023-03-19 09:18:30,948	OUTPUT_MODEL	INFO	====> Epoch: 52
2023-03-19 09:19:49,520	OUTPUT_MODEL	INFO	Train Epoch: 53 [55%]
2023-03-19 09:19:49,522	OUTPUT_MODEL	INFO	[1.928857684135437, 3.0019607543945312, 7.096389293670654, 20.79801368713379, 1.480872392654419, 2.6497201919555664, 7200, 0.00019870413513039026]
2023-03-19 09:20:47,180	OUTPUT_MODEL	INFO	====> Epoch: 53
2023-03-19 09:21:30,857	OUTPUT_MODEL	INFO	Train Epoch: 54 [28%]
2023-03-19 09:21:30,860	OUTPUT_MODEL	INFO	[1.9871232509613037, 2.7579193115234375, 6.503532409667969, 19.73297882080078, 1.1434643268585205, 2.203214645385742, 7300, 0.00019867929711349895]
2023-03-19 09:23:05,308	OUTPUT_MODEL	INFO	====> Epoch: 54
2023-03-19 09:23:14,135	OUTPUT_MODEL	INFO	Train Epoch: 55 [1%]
2023-03-19 09:23:14,137	OUTPUT_MODEL	INFO	[2.5149598121643066, 2.585653305053711, 8.015948295593262, 16.948373794555664, 1.2666723728179932, 2.2044410705566406, 7400, 0.00019865446220135974]
2023-03-19 09:24:48,172	OUTPUT_MODEL	INFO	Train Epoch: 55 [74%]
2023-03-19 09:24:48,175	OUTPUT_MODEL	INFO	[2.0288796424865723, 2.970039129257202, 7.647941589355469, 20.510560989379883, -5.467184066772461, 2.750264883041382, 7500, 0.00019865446220135974]
2023-03-19 09:25:22,213	OUTPUT_MODEL	INFO	====> Epoch: 55
2023-03-19 09:26:29,808	OUTPUT_MODEL	INFO	Train Epoch: 56 [47%]
2023-03-19 09:26:29,810	OUTPUT_MODEL	INFO	[1.8546819686889648, 3.0136163234710693, 8.727523803710938, 19.736896514892578, -4.089441299438477, 2.171175718307495, 7600, 0.00019862963039358455]
2023-03-19 09:27:37,782	OUTPUT_MODEL	INFO	====> Epoch: 56
2023-03-19 09:28:11,887	OUTPUT_MODEL	INFO	Train Epoch: 57 [20%]
2023-03-19 09:28:11,889	OUTPUT_MODEL	INFO	[2.292895793914795, 2.717564105987549, 6.499927520751953, 20.115928649902344, 1.0978418588638306, 2.5296881198883057, 7700, 0.00019860480168978534]
2023-03-19 09:29:45,560	OUTPUT_MODEL	INFO	Train Epoch: 57 [93%]
2023-03-19 09:29:45,562	OUTPUT_MODEL	INFO	[2.2394461631774902, 2.802957057952881, 7.689139366149902, 21.173561096191406, -3.7854630947113037, 2.907567024230957, 7800, 0.00019860480168978534]
2023-03-19 09:29:54,949	OUTPUT_MODEL	INFO	====> Epoch: 57
2023-03-19 09:31:27,438	OUTPUT_MODEL	INFO	Train Epoch: 58 [66%]
2023-03-19 09:31:27,441	OUTPUT_MODEL	INFO	[2.0172464847564697, 2.925333261489868, 8.23436164855957, 19.76770782470703, 1.5144882202148438, 2.7766430377960205, 7900, 0.0001985799760895741]
2023-03-19 09:32:10,982	OUTPUT_MODEL	INFO	====> Epoch: 58
2023-03-19 09:33:09,094	OUTPUT_MODEL	INFO	Train Epoch: 59 [39%]
2023-03-19 09:33:09,096	OUTPUT_MODEL	INFO	[1.8546833992004395, 2.8660409450531006, 8.036420822143555, 20.109821319580078, -0.24623006582260132, 2.3286726474761963, 8000, 0.0001985551535925629]
2023-03-19 09:33:11,265	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 59 to ././OUTPUT_MODEL/G_8000.pth
2023-03-19 09:33:12,007	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 59 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 09:34:30,060	OUTPUT_MODEL	INFO	====> Epoch: 59
2023-03-19 09:34:52,790	OUTPUT_MODEL	INFO	Train Epoch: 60 [12%]
2023-03-19 09:34:52,791	OUTPUT_MODEL	INFO	[2.216594934463501, 2.6625912189483643, 6.407079219818115, 20.214000701904297, 1.487845540046692, 2.625788927078247, 8100, 0.00019853033419836382]
2023-03-19 09:36:25,584	OUTPUT_MODEL	INFO	Train Epoch: 60 [85%]
2023-03-19 09:36:25,586	OUTPUT_MODEL	INFO	[1.9613763093948364, 3.0207040309906006, 7.939626693725586, 20.659000396728516, 1.2366454601287842, 2.5766348838806152, 8200, 0.00019853033419836382]
2023-03-19 09:36:45,148	OUTPUT_MODEL	INFO	====> Epoch: 60
2023-03-19 09:38:06,595	OUTPUT_MODEL	INFO	Train Epoch: 61 [58%]
2023-03-19 09:38:06,596	OUTPUT_MODEL	INFO	[1.9568358659744263, 2.804014205932617, 7.8024821281433105, 19.155216217041016, 1.0517914295196533, 2.510216474533081, 8300, 0.000198505517906589]
2023-03-19 09:38:58,993	OUTPUT_MODEL	INFO	====> Epoch: 61
2023-03-19 09:39:47,073	OUTPUT_MODEL	INFO	Train Epoch: 62 [31%]
2023-03-19 09:39:47,075	OUTPUT_MODEL	INFO	[2.1543242931365967, 2.893284559249878, 7.527717113494873, 20.48008155822754, -4.19180154800415, 2.7945973873138428, 8400, 0.00019848070471685067]
2023-03-19 09:41:13,850	OUTPUT_MODEL	INFO	====> Epoch: 62
2023-03-19 09:41:27,517	OUTPUT_MODEL	INFO	Train Epoch: 63 [4%]
2023-03-19 09:41:27,519	OUTPUT_MODEL	INFO	[2.1900534629821777, 2.7220726013183594, 7.063601970672607, 20.688657760620117, 1.293341875076294, 2.7977349758148193, 8500, 0.00019845589462876104]
2023-03-19 09:43:00,671	OUTPUT_MODEL	INFO	Train Epoch: 63 [77%]
2023-03-19 09:43:00,673	OUTPUT_MODEL	INFO	[2.088751792907715, 2.664844036102295, 8.77527141571045, 20.27743911743164, 1.4288314580917358, 2.5708022117614746, 8600, 0.00019845589462876104]
2023-03-19 09:43:29,883	OUTPUT_MODEL	INFO	====> Epoch: 63
2023-03-19 09:44:41,982	OUTPUT_MODEL	INFO	Train Epoch: 64 [50%]
2023-03-19 09:44:41,984	OUTPUT_MODEL	INFO	[1.8227486610412598, 2.9234495162963867, 9.401537895202637, 20.372217178344727, 1.05446195602417, 2.6038408279418945, 8700, 0.00019843108764193245]
2023-03-19 09:45:44,444	OUTPUT_MODEL	INFO	====> Epoch: 64
2023-03-19 09:46:21,916	OUTPUT_MODEL	INFO	Train Epoch: 65 [23%]
2023-03-19 09:46:21,918	OUTPUT_MODEL	INFO	[2.0908474922180176, 3.056321620941162, 7.516476631164551, 20.033161163330078, -2.7519617080688477, 2.3865175247192383, 8800, 0.0001984062837559772]
2023-03-19 09:47:54,470	OUTPUT_MODEL	INFO	Train Epoch: 65 [96%]
2023-03-19 09:47:54,471	OUTPUT_MODEL	INFO	[2.0548622608184814, 2.8700873851776123, 7.9001994132995605, 21.405254364013672, 1.2632529735565186, 2.3811275959014893, 8900, 0.0001984062837559772]
2023-03-19 09:47:59,225	OUTPUT_MODEL	INFO	====> Epoch: 65
2023-03-19 09:49:34,039	OUTPUT_MODEL	INFO	Train Epoch: 66 [69%]
2023-03-19 09:49:34,041	OUTPUT_MODEL	INFO	[2.044814109802246, 2.9541776180267334, 8.14651870727539, 20.451372146606445, 1.0233311653137207, 2.513681411743164, 9000, 0.00019838148297050769]
2023-03-19 09:49:36,018	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 66 to ././OUTPUT_MODEL/G_9000.pth
2023-03-19 09:49:36,778	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 66 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 09:50:16,306	OUTPUT_MODEL	INFO	====> Epoch: 66
2023-03-19 09:51:18,585	OUTPUT_MODEL	INFO	Train Epoch: 67 [42%]
2023-03-19 09:51:18,587	OUTPUT_MODEL	INFO	[2.005793809890747, 3.1091699600219727, 7.4471869468688965, 20.23058319091797, 1.07757568359375, 2.850463390350342, 9100, 0.00019835668528513637]
2023-03-19 09:52:31,510	OUTPUT_MODEL	INFO	====> Epoch: 67
2023-03-19 09:52:59,350	OUTPUT_MODEL	INFO	Train Epoch: 68 [15%]
2023-03-19 09:52:59,352	OUTPUT_MODEL	INFO	[1.7843735218048096, 3.3218066692352295, 9.384087562561035, 20.309123992919922, 1.2028844356536865, 2.215404748916626, 9200, 0.00019833189069947573]
2023-03-19 09:54:31,795	OUTPUT_MODEL	INFO	Train Epoch: 68 [88%]
2023-03-19 09:54:31,797	OUTPUT_MODEL	INFO	[1.935443639755249, 3.168581247329712, 7.473503589630127, 19.351606369018555, 1.0448455810546875, 2.594919204711914, 9300, 0.00019833189069947573]
2023-03-19 09:54:47,064	OUTPUT_MODEL	INFO	====> Epoch: 68
2023-03-19 09:56:12,093	OUTPUT_MODEL	INFO	Train Epoch: 69 [61%]
2023-03-19 09:56:12,096	OUTPUT_MODEL	INFO	[1.931398630142212, 3.0605313777923584, 8.311007499694824, 19.67093276977539, 1.185105800628662, 2.372974395751953, 9400, 0.0001983070992131383]
2023-03-19 09:57:01,445	OUTPUT_MODEL	INFO	====> Epoch: 69
2023-03-19 09:57:51,557	OUTPUT_MODEL	INFO	Train Epoch: 70 [34%]
2023-03-19 09:57:51,559	OUTPUT_MODEL	INFO	[2.113309860229492, 2.8289313316345215, 7.719230651855469, 21.478958129882812, 1.2967971563339233, 2.782158613204956, 9500, 0.00019828231082573666]
2023-03-19 09:59:15,330	OUTPUT_MODEL	INFO	====> Epoch: 70
2023-03-19 09:59:31,756	OUTPUT_MODEL	INFO	Train Epoch: 71 [7%]
2023-03-19 09:59:31,758	OUTPUT_MODEL	INFO	[1.9558463096618652, 3.2950363159179688, 8.227680206298828, 20.050567626953125, 0.2273259460926056, 2.7006218433380127, 9600, 0.00019825752553688343]
2023-03-19 10:01:05,106	OUTPUT_MODEL	INFO	Train Epoch: 71 [80%]
2023-03-19 10:01:05,107	OUTPUT_MODEL	INFO	[2.098029851913452, 3.2080841064453125, 7.9542412757873535, 19.895048141479492, -0.9920666217803955, 2.3262603282928467, 9700, 0.00019825752553688343]
2023-03-19 10:01:30,794	OUTPUT_MODEL	INFO	====> Epoch: 71
2023-03-19 10:02:45,744	OUTPUT_MODEL	INFO	Train Epoch: 72 [53%]
2023-03-19 10:02:45,747	OUTPUT_MODEL	INFO	[1.9557427167892456, 2.5827739238739014, 7.321996212005615, 20.69243812561035, -1.6974865198135376, 2.7394797801971436, 9800, 0.0001982327433461913]
2023-03-19 10:03:45,323	OUTPUT_MODEL	INFO	====> Epoch: 72
2023-03-19 10:04:26,754	OUTPUT_MODEL	INFO	Train Epoch: 73 [26%]
2023-03-19 10:04:26,755	OUTPUT_MODEL	INFO	[2.106454610824585, 3.1124627590179443, 8.293758392333984, 20.483478546142578, -0.05019533634185791, 2.6830058097839355, 9900, 0.00019820796425327303]
2023-03-19 10:05:58,833	OUTPUT_MODEL	INFO	Train Epoch: 73 [99%]
2023-03-19 10:05:58,835	OUTPUT_MODEL	INFO	[2.063965320587158, 3.155410051345825, 8.04088020324707, 20.196794509887695, 1.2512450218200684, 2.226661443710327, 10000, 0.00019820796425327303]
2023-03-19 10:06:00,261	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 73 to ././OUTPUT_MODEL/G_10000.pth
2023-03-19 10:06:00,852	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 73 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 10:06:02,941	OUTPUT_MODEL	INFO	====> Epoch: 73
2023-03-19 10:07:43,498	OUTPUT_MODEL	INFO	Train Epoch: 74 [72%]
2023-03-19 10:07:43,499	OUTPUT_MODEL	INFO	[1.9413807392120361, 3.17144775390625, 8.007587432861328, 20.611587524414062, 1.178780198097229, 2.5375542640686035, 10100, 0.00019818318825774137]
2023-03-19 10:08:19,322	OUTPUT_MODEL	INFO	====> Epoch: 74
2023-03-19 10:09:25,884	OUTPUT_MODEL	INFO	Train Epoch: 75 [45%]
2023-03-19 10:09:25,886	OUTPUT_MODEL	INFO	[1.9138033390045166, 3.004542827606201, 7.8221540451049805, 20.50404167175293, 1.1039570569992065, 2.6632485389709473, 10200, 0.00019815841535920914]
2023-03-19 10:10:35,091	OUTPUT_MODEL	INFO	====> Epoch: 75
2023-03-19 10:11:06,545	OUTPUT_MODEL	INFO	Train Epoch: 76 [18%]
2023-03-19 10:11:06,548	OUTPUT_MODEL	INFO	[2.2219011783599854, 2.7316720485687256, 7.870467662811279, 21.273164749145508, 1.2198076248168945, 2.6751317977905273, 10300, 0.00019813364555728923]
2023-03-19 10:12:39,678	OUTPUT_MODEL	INFO	Train Epoch: 76 [91%]
2023-03-19 10:12:39,680	OUTPUT_MODEL	INFO	[1.920383095741272, 3.1100103855133057, 9.348227500915527, 20.263898849487305, 1.2717044353485107, 2.612281560897827, 10400, 0.00019813364555728923]
2023-03-19 10:12:51,510	OUTPUT_MODEL	INFO	====> Epoch: 76
2023-03-19 10:14:20,115	OUTPUT_MODEL	INFO	Train Epoch: 77 [64%]
2023-03-19 10:14:20,117	OUTPUT_MODEL	INFO	[1.8670815229415894, 2.9954566955566406, 9.401909828186035, 20.469675064086914, -2.8573944568634033, 2.6206116676330566, 10500, 0.00019810887885159456]
2023-03-19 10:15:05,295	OUTPUT_MODEL	INFO	====> Epoch: 77
2023-03-19 10:16:01,013	OUTPUT_MODEL	INFO	Train Epoch: 78 [37%]
2023-03-19 10:16:01,015	OUTPUT_MODEL	INFO	[2.1149606704711914, 2.8291172981262207, 7.6945085525512695, 20.196287155151367, 1.2592827081680298, 2.3761487007141113, 10600, 0.0001980841152417381]
2023-03-19 10:17:19,557	OUTPUT_MODEL	INFO	====> Epoch: 78
2023-03-19 10:17:39,335	OUTPUT_MODEL	INFO	Train Epoch: 79 [10%]
2023-03-19 10:17:39,337	OUTPUT_MODEL	INFO	[1.9538851976394653, 3.1524689197540283, 8.75866413116455, 20.55605697631836, 1.1293696165084839, 2.7732253074645996, 10700, 0.00019805935472733287]
2023-03-19 10:19:12,202	OUTPUT_MODEL	INFO	Train Epoch: 79 [83%]
2023-03-19 10:19:12,204	OUTPUT_MODEL	INFO	[1.7191627025604248, 3.2567644119262695, 8.703531265258789, 20.372875213623047, -2.3174736499786377, 2.4861598014831543, 10800, 0.00019805935472733287]
2023-03-19 10:19:34,632	OUTPUT_MODEL	INFO	====> Epoch: 79
2023-03-19 10:20:52,579	OUTPUT_MODEL	INFO	Train Epoch: 80 [56%]
2023-03-19 10:20:52,581	OUTPUT_MODEL	INFO	[1.7839128971099854, 3.1906139850616455, 10.952308654785156, 19.247398376464844, -2.821104049682617, 2.4264755249023438, 10900, 0.00019803459730799195]
2023-03-19 10:21:48,553	OUTPUT_MODEL	INFO	====> Epoch: 80
2023-03-19 10:22:32,914	OUTPUT_MODEL	INFO	Train Epoch: 81 [29%]
2023-03-19 10:22:32,916	OUTPUT_MODEL	INFO	[1.950652837753296, 3.222370147705078, 8.733671188354492, 19.642005920410156, 1.3321256637573242, 2.482358455657959, 11000, 0.00019800984298332845]
2023-03-19 10:22:34,554	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 81 to ././OUTPUT_MODEL/G_11000.pth
2023-03-19 10:22:35,148	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 81 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 10:24:05,347	OUTPUT_MODEL	INFO	====> Epoch: 81
2023-03-19 10:24:14,902	OUTPUT_MODEL	INFO	Train Epoch: 82 [2%]
2023-03-19 10:24:14,904	OUTPUT_MODEL	INFO	[2.531325101852417, 2.4652316570281982, 7.878106117248535, 17.63850212097168, 1.1991121768951416, 2.1195311546325684, 11100, 0.00019798509175295552]
2023-03-19 10:25:48,147	OUTPUT_MODEL	INFO	Train Epoch: 82 [75%]
2023-03-19 10:25:48,149	OUTPUT_MODEL	INFO	[1.9261192083358765, 3.002774477005005, 8.340904235839844, 19.978273391723633, 1.5847636461257935, 2.630664587020874, 11200, 0.00019798509175295552]
2023-03-19 10:26:21,343	OUTPUT_MODEL	INFO	====> Epoch: 82
2023-03-19 10:27:29,295	OUTPUT_MODEL	INFO	Train Epoch: 83 [48%]
2023-03-19 10:27:29,296	OUTPUT_MODEL	INFO	[2.039679527282715, 3.329221248626709, 7.449399948120117, 20.019346237182617, 1.2028634548187256, 2.623361587524414, 11300, 0.0001979603436164864]
2023-03-19 10:28:35,141	OUTPUT_MODEL	INFO	====> Epoch: 83
2023-03-19 10:29:09,637	OUTPUT_MODEL	INFO	Train Epoch: 84 [21%]
2023-03-19 10:29:09,639	OUTPUT_MODEL	INFO	[2.3721694946289062, 2.7593817710876465, 13.41921329498291, 20.464162826538086, 1.3148869276046753, 2.120932102203369, 11400, 0.00019793559857353432]
2023-03-19 10:30:42,062	OUTPUT_MODEL	INFO	Train Epoch: 84 [94%]
2023-03-19 10:30:42,064	OUTPUT_MODEL	INFO	[1.6776970624923706, 3.4092142581939697, 8.500198364257812, 18.61136245727539, -3.409163475036621, 2.2566635608673096, 11500, 0.00019793559857353432]
2023-03-19 10:30:50,056	OUTPUT_MODEL	INFO	====> Epoch: 84
2023-03-19 10:32:23,431	OUTPUT_MODEL	INFO	Train Epoch: 85 [67%]
2023-03-19 10:32:23,433	OUTPUT_MODEL	INFO	[2.1235551834106445, 2.9781181812286377, 8.580206871032715, 19.75904083251953, 1.280053734779358, 2.465538501739502, 11600, 0.00019791085662371262]
2023-03-19 10:33:05,020	OUTPUT_MODEL	INFO	====> Epoch: 85
2023-03-19 10:34:04,192	OUTPUT_MODEL	INFO	Train Epoch: 86 [40%]
2023-03-19 10:34:04,194	OUTPUT_MODEL	INFO	[1.9850740432739258, 3.1952593326568604, 8.402506828308105, 21.372081756591797, 0.9700257778167725, 2.594923496246338, 11700, 0.00019788611776663464]
2023-03-19 10:35:19,232	OUTPUT_MODEL	INFO	====> Epoch: 86
2023-03-19 10:35:44,228	OUTPUT_MODEL	INFO	Train Epoch: 87 [13%]
2023-03-19 10:35:44,230	OUTPUT_MODEL	INFO	[2.339064836502075, 2.5303635597229004, 9.1635103225708, 17.669677734375, 1.307255744934082, 1.9864698648452759, 11800, 0.0001978613820019138]
2023-03-19 10:37:16,633	OUTPUT_MODEL	INFO	Train Epoch: 87 [86%]
2023-03-19 10:37:16,635	OUTPUT_MODEL	INFO	[1.80305016040802, 3.220841884613037, 8.884725570678711, 20.541173934936523, -0.18684375286102295, 2.513849973678589, 11900, 0.0001978613820019138]
2023-03-19 10:37:34,522	OUTPUT_MODEL	INFO	====> Epoch: 87
2023-03-19 10:38:57,551	OUTPUT_MODEL	INFO	Train Epoch: 88 [59%]
2023-03-19 10:38:57,554	OUTPUT_MODEL	INFO	[1.9692407846450806, 2.983448028564453, 8.3545503616333, 20.055160522460938, 1.3942428827285767, 2.645573139190674, 12000, 0.00019783664932916355]
2023-03-19 10:38:59,960	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 88 to ././OUTPUT_MODEL/G_12000.pth
2023-03-19 10:39:00,479	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 88 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 10:39:52,933	OUTPUT_MODEL	INFO	====> Epoch: 88
2023-03-19 10:40:42,133	OUTPUT_MODEL	INFO	Train Epoch: 89 [32%]
2023-03-19 10:40:42,135	OUTPUT_MODEL	INFO	[1.8052171468734741, 3.2333688735961914, 8.453548431396484, 20.615264892578125, 1.5022447109222412, 2.8251535892486572, 12100, 0.0001978119197479974]
2023-03-19 10:42:09,653	OUTPUT_MODEL	INFO	====> Epoch: 89
2023-03-19 10:42:24,207	OUTPUT_MODEL	INFO	Train Epoch: 90 [5%]
2023-03-19 10:42:24,209	OUTPUT_MODEL	INFO	[2.1813435554504395, 2.771514415740967, 7.208632946014404, 19.605520248413086, 1.0345332622528076, 2.4180734157562256, 12200, 0.0001977871932580289]
2023-03-19 10:43:57,176	OUTPUT_MODEL	INFO	Train Epoch: 90 [78%]
2023-03-19 10:43:57,177	OUTPUT_MODEL	INFO	[1.868781328201294, 2.8193063735961914, 9.268707275390625, 19.446977615356445, -2.712599277496338, 2.611307144165039, 12300, 0.0001977871932580289]
2023-03-19 10:44:25,747	OUTPUT_MODEL	INFO	====> Epoch: 90
2023-03-19 10:45:38,100	OUTPUT_MODEL	INFO	Train Epoch: 91 [51%]
2023-03-19 10:45:38,101	OUTPUT_MODEL	INFO	[2.1249923706054688, 2.751899003982544, 8.17632007598877, 18.699127197265625, -2.995602607727051, 2.65021014213562, 12400, 0.00019776246985887165]
2023-03-19 10:46:39,490	OUTPUT_MODEL	INFO	====> Epoch: 91
2023-03-19 10:47:16,559	OUTPUT_MODEL	INFO	Train Epoch: 92 [24%]
2023-03-19 10:47:16,560	OUTPUT_MODEL	INFO	[2.2016823291778564, 2.8861207962036133, 8.143318176269531, 19.61528778076172, 1.514852523803711, 2.435077428817749, 12500, 0.0001977377495501393]
2023-03-19 10:48:49,569	OUTPUT_MODEL	INFO	Train Epoch: 92 [97%]
2023-03-19 10:48:49,571	OUTPUT_MODEL	INFO	[2.1541342735290527, 2.873887062072754, 9.444825172424316, 20.015335083007812, 1.2779251337051392, 2.6698081493377686, 12600, 0.0001977377495501393]
2023-03-19 10:48:54,335	OUTPUT_MODEL	INFO	====> Epoch: 92
2023-03-19 10:50:29,175	OUTPUT_MODEL	INFO	Train Epoch: 93 [70%]
2023-03-19 10:50:29,177	OUTPUT_MODEL	INFO	[1.9152355194091797, 3.1444640159606934, 9.54922103881836, 19.31732749938965, 1.397902250289917, 2.4550023078918457, 12700, 0.0001977130323314455]
2023-03-19 10:51:07,858	OUTPUT_MODEL	INFO	====> Epoch: 93
2023-03-19 10:52:09,779	OUTPUT_MODEL	INFO	Train Epoch: 94 [43%]
2023-03-19 10:52:09,781	OUTPUT_MODEL	INFO	[2.0162882804870605, 2.978210210800171, 7.36829948425293, 18.906021118164062, 0.9633077383041382, 3.0192313194274902, 12800, 0.00019768831820240408]
2023-03-19 10:53:21,099	OUTPUT_MODEL	INFO	====> Epoch: 94
2023-03-19 10:53:49,122	OUTPUT_MODEL	INFO	Train Epoch: 95 [16%]
2023-03-19 10:53:49,124	OUTPUT_MODEL	INFO	[1.7357542514801025, 3.242328643798828, 9.340928077697754, 19.97647476196289, 1.4245003461837769, 2.7217464447021484, 12900, 0.00019766360716262876]
2023-03-19 10:55:21,053	OUTPUT_MODEL	INFO	Train Epoch: 95 [89%]
2023-03-19 10:55:21,055	OUTPUT_MODEL	INFO	[2.046903371810913, 3.139446496963501, 8.061538696289062, 19.282575607299805, 1.2221990823745728, 2.761680841445923, 13000, 0.00019766360716262876]
2023-03-19 10:55:23,011	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 95 to ././OUTPUT_MODEL/G_13000.pth
2023-03-19 10:55:23,760	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 95 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 10:55:38,507	OUTPUT_MODEL	INFO	====> Epoch: 95
2023-03-19 10:57:04,832	OUTPUT_MODEL	INFO	Train Epoch: 96 [62%]
2023-03-19 10:57:04,834	OUTPUT_MODEL	INFO	[1.8632557392120361, 2.8174943923950195, 8.632399559020996, 19.446523666381836, -0.2805293798446655, 2.6674673557281494, 13100, 0.00019763889921173343]
2023-03-19 10:57:53,631	OUTPUT_MODEL	INFO	====> Epoch: 96
2023-03-19 10:58:45,931	OUTPUT_MODEL	INFO	Train Epoch: 97 [35%]
2023-03-19 10:58:45,933	OUTPUT_MODEL	INFO	[2.013288974761963, 2.9867732524871826, 7.726414680480957, 19.719356536865234, -0.26221615076065063, 2.4274632930755615, 13200, 0.00019761419434933197]
2023-03-19 11:00:07,332	OUTPUT_MODEL	INFO	====> Epoch: 97
2023-03-19 11:00:25,996	OUTPUT_MODEL	INFO	Train Epoch: 98 [8%]
2023-03-19 11:00:25,998	OUTPUT_MODEL	INFO	[1.8908425569534302, 3.420616865158081, 9.836297035217285, 19.83599090576172, 1.4662816524505615, 2.4949135780334473, 13300, 0.0001975894925750383]
2023-03-19 11:01:58,994	OUTPUT_MODEL	INFO	Train Epoch: 98 [81%]
2023-03-19 11:01:58,996	OUTPUT_MODEL	INFO	[1.8195505142211914, 3.1395950317382812, 8.836028099060059, 20.136661529541016, -0.0052713751792907715, 2.917912483215332, 13400, 0.0001975894925750383]
2023-03-19 11:02:22,659	OUTPUT_MODEL	INFO	====> Epoch: 98
2023-03-19 11:03:38,667	OUTPUT_MODEL	INFO	Train Epoch: 99 [54%]
2023-03-19 11:03:38,669	OUTPUT_MODEL	INFO	[1.9940173625946045, 3.0720229148864746, 8.72389030456543, 19.54957389831543, 1.3500230312347412, 2.4325716495513916, 13500, 0.0001975647938884664]
2023-03-19 11:04:36,868	OUTPUT_MODEL	INFO	====> Epoch: 99
2023-03-19 11:05:19,112	OUTPUT_MODEL	INFO	Train Epoch: 100 [27%]
2023-03-19 11:05:19,113	OUTPUT_MODEL	INFO	[1.7818868160247803, 2.9444832801818848, 8.366813659667969, 20.01131820678711, 0.9184349179267883, 2.751687526702881, 13600, 0.00019754009828923033]
2023-03-19 11:06:50,958	OUTPUT_MODEL	INFO	====> Epoch: 100
2023-03-19 11:06:59,234	OUTPUT_MODEL	INFO	Train Epoch: 101 [0%]
2023-03-19 11:06:59,236	OUTPUT_MODEL	INFO	[1.7258493900299072, 3.344059467315674, 9.978780746459961, 20.568906784057617, 1.092111349105835, 2.6189966201782227, 13700, 0.00019751540577694416]
2023-03-19 11:08:32,337	OUTPUT_MODEL	INFO	Train Epoch: 101 [73%]
2023-03-19 11:08:32,339	OUTPUT_MODEL	INFO	[1.6928396224975586, 3.402289390563965, 10.032767295837402, 19.233070373535156, 1.614738941192627, 2.3698511123657227, 13800, 0.00019751540577694416]
2023-03-19 11:09:06,374	OUTPUT_MODEL	INFO	====> Epoch: 101
2023-03-19 11:10:11,404	OUTPUT_MODEL	INFO	Train Epoch: 102 [46%]
2023-03-19 11:10:11,406	OUTPUT_MODEL	INFO	[1.8387556076049805, 3.1862518787384033, 8.792502403259277, 19.2582950592041, 0.9229628443717957, 2.2481021881103516, 13900, 0.00019749071635122203]
2023-03-19 11:11:20,656	OUTPUT_MODEL	INFO	====> Epoch: 102
2023-03-19 11:11:51,581	OUTPUT_MODEL	INFO	Train Epoch: 103 [19%]
2023-03-19 11:11:51,582	OUTPUT_MODEL	INFO	[2.0194931030273438, 2.6866085529327393, 7.825543403625488, 18.29146385192871, 1.2694774866104126, 2.3489131927490234, 14000, 0.00019746603001167813]
2023-03-19 11:11:52,653	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 103 to ././OUTPUT_MODEL/G_14000.pth
2023-03-19 11:11:53,232	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 103 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 11:13:25,968	OUTPUT_MODEL	INFO	Train Epoch: 103 [92%]
2023-03-19 11:13:25,970	OUTPUT_MODEL	INFO	[1.9975510835647583, 2.9516353607177734, 8.216843605041504, 20.777339935302734, 1.2318406105041504, 2.5748484134674072, 14100, 0.00019746603001167813]
2023-03-19 11:13:36,803	OUTPUT_MODEL	INFO	====> Epoch: 103
2023-03-19 11:15:05,720	OUTPUT_MODEL	INFO	Train Epoch: 104 [65%]
2023-03-19 11:15:05,721	OUTPUT_MODEL	INFO	[1.5826694965362549, 3.416844129562378, 8.814953804016113, 20.060958862304688, 1.1653366088867188, 2.3306896686553955, 14200, 0.00019744134675792665]
2023-03-19 11:15:50,029	OUTPUT_MODEL	INFO	====> Epoch: 104
2023-03-19 11:16:44,871	OUTPUT_MODEL	INFO	Train Epoch: 105 [38%]
2023-03-19 11:16:44,873	OUTPUT_MODEL	INFO	[1.9176909923553467, 3.101259231567383, 8.081949234008789, 18.776987075805664, 0.9751270413398743, 2.5510740280151367, 14300, 0.0001974166665895819]
2023-03-19 11:18:03,728	OUTPUT_MODEL	INFO	====> Epoch: 105
2023-03-19 11:18:24,209	OUTPUT_MODEL	INFO	Train Epoch: 106 [11%]
2023-03-19 11:18:24,211	OUTPUT_MODEL	INFO	[1.7117843627929688, 3.2879767417907715, 9.032174110412598, 19.961278915405273, 1.0640764236450195, 2.2767560482025146, 14400, 0.0001973919895062582]
2023-03-19 11:19:57,518	OUTPUT_MODEL	INFO	Train Epoch: 106 [84%]
2023-03-19 11:19:57,520	OUTPUT_MODEL	INFO	[2.0207135677337646, 3.0853776931762695, 7.7209672927856445, 19.43149757385254, -2.181365489959717, 2.562142848968506, 14500, 0.0001973919895062582]
2023-03-19 11:20:18,907	OUTPUT_MODEL	INFO	====> Epoch: 106
2023-03-19 11:21:38,451	OUTPUT_MODEL	INFO	Train Epoch: 107 [57%]
2023-03-19 11:21:38,453	OUTPUT_MODEL	INFO	[1.9763946533203125, 3.1692240238189697, 7.525588512420654, 19.638343811035156, 0.9058389663696289, 3.241367816925049, 14600, 0.0001973673155075699]
2023-03-19 11:22:33,596	OUTPUT_MODEL	INFO	====> Epoch: 107
2023-03-19 11:23:19,528	OUTPUT_MODEL	INFO	Train Epoch: 108 [30%]
2023-03-19 11:23:19,529	OUTPUT_MODEL	INFO	[1.9130282402038574, 3.229607582092285, 8.196810722351074, 20.516510009765625, 1.0323944091796875, 2.261013984680176, 14700, 0.00019734264459313146]
2023-03-19 11:24:48,845	OUTPUT_MODEL	INFO	====> Epoch: 108
2023-03-19 11:25:00,709	OUTPUT_MODEL	INFO	Train Epoch: 109 [3%]
2023-03-19 11:25:00,711	OUTPUT_MODEL	INFO	[1.787132740020752, 3.1954243183135986, 8.574239730834961, 19.655628204345703, 1.668026089668274, 2.5595126152038574, 14800, 0.0001973179767625573]
2023-03-19 11:26:33,648	OUTPUT_MODEL	INFO	Train Epoch: 109 [76%]
2023-03-19 11:26:33,650	OUTPUT_MODEL	INFO	[1.854840636253357, 3.0786094665527344, 8.257341384887695, 19.53325080871582, 0.9978845715522766, 2.800708293914795, 14900, 0.0001973179767625573]
2023-03-19 11:27:04,496	OUTPUT_MODEL	INFO	====> Epoch: 109
2023-03-19 11:28:14,920	OUTPUT_MODEL	INFO	Train Epoch: 110 [49%]
2023-03-19 11:28:14,922	OUTPUT_MODEL	INFO	[1.823750615119934, 3.034860372543335, 8.833657264709473, 20.007619857788086, 1.2687585353851318, 2.6651384830474854, 15000, 0.00019729331201546197]
2023-03-19 11:28:16,434	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 110 to ././OUTPUT_MODEL/G_15000.pth
2023-03-19 11:28:16,970	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 110 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 11:29:21,698	OUTPUT_MODEL	INFO	====> Epoch: 110
2023-03-19 11:29:58,635	OUTPUT_MODEL	INFO	Train Epoch: 111 [22%]
2023-03-19 11:29:58,637	OUTPUT_MODEL	INFO	[1.9007933139801025, 2.9482369422912598, 8.364360809326172, 19.561481475830078, 1.1079390048980713, 2.501002788543701, 15100, 0.00019726865035146003]
2023-03-19 11:31:30,888	OUTPUT_MODEL	INFO	Train Epoch: 111 [95%]
2023-03-19 11:31:30,890	OUTPUT_MODEL	INFO	[1.647691011428833, 3.5528085231781006, 8.95214557647705, 18.802671432495117, 0.9342246651649475, 2.1829464435577393, 15200, 0.00019726865035146003]
2023-03-19 11:31:37,357	OUTPUT_MODEL	INFO	====> Epoch: 111
2023-03-19 11:33:11,538	OUTPUT_MODEL	INFO	Train Epoch: 112 [68%]
2023-03-19 11:33:11,541	OUTPUT_MODEL	INFO	[1.7852821350097656, 3.1207239627838135, 9.240314483642578, 19.03870964050293, -0.04351156949996948, 2.4661104679107666, 15300, 0.0001972439917701661]
2023-03-19 11:33:53,435	OUTPUT_MODEL	INFO	====> Epoch: 112
2023-03-19 11:34:54,293	OUTPUT_MODEL	INFO	Train Epoch: 113 [41%]
2023-03-19 11:34:54,295	OUTPUT_MODEL	INFO	[1.7004003524780273, 3.424109935760498, 10.452657699584961, 17.684146881103516, -3.718299627304077, 2.4723117351531982, 15400, 0.0001972193362711948]
2023-03-19 11:36:10,874	OUTPUT_MODEL	INFO	====> Epoch: 113
2023-03-19 11:36:36,830	OUTPUT_MODEL	INFO	Train Epoch: 114 [14%]
2023-03-19 11:36:36,832	OUTPUT_MODEL	INFO	[1.747533917427063, 3.071709156036377, 9.172768592834473, 20.769935607910156, -0.2696636915206909, 2.4951393604278564, 15500, 0.0001971946838541609]
2023-03-19 11:38:10,692	OUTPUT_MODEL	INFO	Train Epoch: 114 [87%]
2023-03-19 11:38:10,694	OUTPUT_MODEL	INFO	[1.9827301502227783, 3.2058587074279785, 7.783775806427002, 20.09530258178711, 1.107332468032837, 2.4980409145355225, 15600, 0.0001971946838541609]
2023-03-19 11:38:27,902	OUTPUT_MODEL	INFO	====> Epoch: 114
2023-03-19 11:39:52,041	OUTPUT_MODEL	INFO	Train Epoch: 115 [60%]
2023-03-19 11:39:52,044	OUTPUT_MODEL	INFO	[1.7550806999206543, 3.237065553665161, 9.584807395935059, 19.8876953125, 0.7576209306716919, 2.300086736679077, 15700, 0.0001971700345186791]
2023-03-19 11:40:44,091	OUTPUT_MODEL	INFO	====> Epoch: 115
2023-03-19 11:41:33,264	OUTPUT_MODEL	INFO	Train Epoch: 116 [33%]
2023-03-19 11:41:33,266	OUTPUT_MODEL	INFO	[1.8512537479400635, 2.981168746948242, 8.3243989944458, 19.771198272705078, 1.014286756515503, 2.732715368270874, 15800, 0.00019714538826436426]
2023-03-19 11:42:59,808	OUTPUT_MODEL	INFO	====> Epoch: 116
2023-03-19 11:43:14,680	OUTPUT_MODEL	INFO	Train Epoch: 117 [6%]
2023-03-19 11:43:14,682	OUTPUT_MODEL	INFO	[1.9894434213638306, 3.113154649734497, 8.134394645690918, 19.07756996154785, 1.1763384342193604, 2.4868838787078857, 15900, 0.0001971207450908312]
2023-03-19 11:44:48,901	OUTPUT_MODEL	INFO	Train Epoch: 117 [79%]
2023-03-19 11:44:48,903	OUTPUT_MODEL	INFO	[1.6702146530151367, 3.2282352447509766, 8.399438858032227, 18.58998680114746, 0.36347541213035583, 2.17846941947937, 16000, 0.0001971207450908312]
2023-03-19 11:44:50,722	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 117 to ././OUTPUT_MODEL/G_16000.pth
2023-03-19 11:44:51,455	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 117 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 11:45:19,447	OUTPUT_MODEL	INFO	====> Epoch: 117
2023-03-19 11:46:32,765	OUTPUT_MODEL	INFO	Train Epoch: 118 [52%]
2023-03-19 11:46:32,767	OUTPUT_MODEL	INFO	[1.9983092546463013, 3.3638105392456055, 7.464341163635254, 19.08881950378418, 0.16009700298309326, 2.5831589698791504, 16100, 0.00019709610499769482]
2023-03-19 11:47:34,926	OUTPUT_MODEL	INFO	====> Epoch: 118
2023-03-19 11:48:13,914	OUTPUT_MODEL	INFO	Train Epoch: 119 [25%]
2023-03-19 11:48:13,916	OUTPUT_MODEL	INFO	[1.89955472946167, 3.292651653289795, 8.823472023010254, 20.16221809387207, -0.20805341005325317, 2.5508110523223877, 16200, 0.0001970714679845701]
2023-03-19 11:49:47,068	OUTPUT_MODEL	INFO	Train Epoch: 119 [98%]
2023-03-19 11:49:47,070	OUTPUT_MODEL	INFO	[1.839171290397644, 3.287151336669922, 7.9833083152771, 18.94850730895996, -3.337139129638672, 2.5388476848602295, 16300, 0.0001970714679845701]
2023-03-19 11:49:51,118	OUTPUT_MODEL	INFO	====> Epoch: 119
2023-03-19 11:51:29,364	OUTPUT_MODEL	INFO	Train Epoch: 120 [71%]
2023-03-19 11:51:29,366	OUTPUT_MODEL	INFO	[1.6163980960845947, 3.4589250087738037, 9.543699264526367, 18.983352661132812, -3.1642208099365234, 2.2304232120513916, 16400, 0.000197046834051072]
2023-03-19 11:52:06,947	OUTPUT_MODEL	INFO	====> Epoch: 120
2023-03-19 11:53:10,376	OUTPUT_MODEL	INFO	Train Epoch: 121 [44%]
2023-03-19 11:53:10,378	OUTPUT_MODEL	INFO	[1.9571735858917236, 3.2458813190460205, 9.483908653259277, 19.298961639404297, 1.1432222127914429, 2.635328769683838, 16500, 0.00019702220319681561]
2023-03-19 11:54:21,430	OUTPUT_MODEL	INFO	====> Epoch: 121
2023-03-19 11:54:50,603	OUTPUT_MODEL	INFO	Train Epoch: 122 [17%]
2023-03-19 11:54:50,605	OUTPUT_MODEL	INFO	[1.7852973937988281, 3.3233108520507812, 9.175979614257812, 20.16257667541504, 0.9840466976165771, 2.5478806495666504, 16600, 0.000196997575421416]
2023-03-19 11:56:23,099	OUTPUT_MODEL	INFO	Train Epoch: 122 [90%]
2023-03-19 11:56:23,101	OUTPUT_MODEL	INFO	[1.8772724866867065, 3.420771837234497, 8.859256744384766, 19.40563201904297, -0.37265199422836304, 2.730045795440674, 16700, 0.000196997575421416]
2023-03-19 11:56:36,605	OUTPUT_MODEL	INFO	====> Epoch: 122
2023-03-19 11:58:04,886	OUTPUT_MODEL	INFO	Train Epoch: 123 [63%]
2023-03-19 11:58:04,888	OUTPUT_MODEL	INFO	[1.792160987854004, 3.1927976608276367, 8.821364402770996, 20.49748420715332, 1.33064603805542, 2.7801246643066406, 16800, 0.00019697295072448832]
2023-03-19 11:58:52,324	OUTPUT_MODEL	INFO	====> Epoch: 123
2023-03-19 11:59:45,991	OUTPUT_MODEL	INFO	Train Epoch: 124 [36%]
2023-03-19 11:59:45,993	OUTPUT_MODEL	INFO	[1.8186142444610596, 3.2794551849365234, 8.553092956542969, 18.749799728393555, 1.025472640991211, 2.8595120906829834, 16900, 0.00019694832910564775]
2023-03-19 12:01:08,112	OUTPUT_MODEL	INFO	====> Epoch: 124
2023-03-19 12:01:26,345	OUTPUT_MODEL	INFO	Train Epoch: 125 [9%]
2023-03-19 12:01:26,347	OUTPUT_MODEL	INFO	[2.038801431655884, 3.0297625064849854, 7.352914333343506, 19.50590705871582, -2.6136767864227295, 3.097349166870117, 17000, 0.00019692371056450955]
2023-03-19 12:01:27,711	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 125 to ././OUTPUT_MODEL/G_17000.pth
2023-03-19 12:01:28,254	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 125 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 12:03:02,404	OUTPUT_MODEL	INFO	Train Epoch: 125 [82%]
2023-03-19 12:03:02,407	OUTPUT_MODEL	INFO	[1.8166866302490234, 2.9754557609558105, 9.425463676452637, 19.692598342895508, 1.1183133125305176, 2.7875378131866455, 17100, 0.00019692371056450955]
2023-03-19 12:03:26,572	OUTPUT_MODEL	INFO	====> Epoch: 125
2023-03-19 12:04:43,661	OUTPUT_MODEL	INFO	Train Epoch: 126 [55%]
2023-03-19 12:04:43,663	OUTPUT_MODEL	INFO	[1.7282356023788452, 3.3856618404388428, 8.895662307739258, 19.46796226501465, -0.030000627040863037, 2.7702572345733643, 17200, 0.000196899095100689]
2023-03-19 12:05:41,402	OUTPUT_MODEL	INFO	====> Epoch: 126
2023-03-19 12:06:23,953	OUTPUT_MODEL	INFO	Train Epoch: 127 [28%]
2023-03-19 12:06:23,955	OUTPUT_MODEL	INFO	[1.7694728374481201, 3.385413885116577, 10.388507843017578, 18.229028701782227, -6.534189701080322, 2.447589635848999, 17300, 0.0001968744827138014]
2023-03-19 12:07:55,082	OUTPUT_MODEL	INFO	====> Epoch: 127
2023-03-19 12:08:02,763	OUTPUT_MODEL	INFO	Train Epoch: 128 [1%]
2023-03-19 12:08:02,765	OUTPUT_MODEL	INFO	[2.032182216644287, 2.822026252746582, 8.619634628295898, 20.493911743164062, 1.1319084167480469, 2.9337751865386963, 17400, 0.00019684987340346216]
2023-03-19 12:09:35,988	OUTPUT_MODEL	INFO	Train Epoch: 128 [74%]
2023-03-19 12:09:35,990	OUTPUT_MODEL	INFO	[1.7094378471374512, 3.4267656803131104, 9.407466888427734, 19.465503692626953, 1.4268949031829834, 3.03248929977417, 17500, 0.00019684987340346216]
2023-03-19 12:10:10,235	OUTPUT_MODEL	INFO	====> Epoch: 128
2023-03-19 12:11:17,645	OUTPUT_MODEL	INFO	Train Epoch: 129 [47%]
2023-03-19 12:11:17,647	OUTPUT_MODEL	INFO	[1.6241384744644165, 3.4908864498138428, 8.604843139648438, 18.01312255859375, -5.931260585784912, 2.315715789794922, 17600, 0.00019682526716928672]
2023-03-19 12:12:25,026	OUTPUT_MODEL	INFO	====> Epoch: 129
2023-03-19 12:12:58,251	OUTPUT_MODEL	INFO	Train Epoch: 130 [20%]
2023-03-19 12:12:58,253	OUTPUT_MODEL	INFO	[1.6506867408752441, 3.78420090675354, 12.368849754333496, 19.19969940185547, 1.4342687129974365, 2.356696844100952, 17700, 0.00019680066401089056]
2023-03-19 12:14:32,071	OUTPUT_MODEL	INFO	Train Epoch: 130 [93%]
2023-03-19 12:14:32,073	OUTPUT_MODEL	INFO	[1.7739845514297485, 3.29510498046875, 10.255736351013184, 17.78916358947754, 1.609314203262329, 2.5935494899749756, 17800, 0.00019680066401089056]
2023-03-19 12:14:41,233	OUTPUT_MODEL	INFO	====> Epoch: 130
2023-03-19 12:21:17,929	OUTPUT_MODEL	INFO	{'train': {'log_interval': 100, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 16, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'final_annotation_train.txt', 'validation_files': 'final_annotation_val.txt', 'text_cleaners': ['zh_ja_mixture_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 7, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256}, 'speakers': {'5': 0, '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, 'zhongli': 6}, 'symbols': ['_', ',', '.', '!', '?', '-', '~', '…', 'A', 'E', 'I', 'N', 'O', 'Q', 'U', 'a', 'b', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ʃ', 'ʧ', 'ʦ', 'ɯ', 'ɹ', 'ə', 'ɥ', '⁼', 'ʰ', '`', '→', '↓', '↑', ' '], 'model_dir': '././OUTPUT_MODEL', 'max_epochs': 400, 'drop_speaker_embed': True}
2023-03-19 12:21:26,847	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/G_0.pth' (iteration None)
2023-03-19 12:21:27,845	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/D_0.pth' (iteration None)
2023-03-19 12:21:43,791	OUTPUT_MODEL	INFO	Train Epoch: 1 [0%]
2023-03-19 12:21:43,791	OUTPUT_MODEL	INFO	[2.767787218093872, 1.92179274559021, 6.520725727081299, 29.696561813354492, 1.2269315719604492, 14.877497673034668, 0, 0.0002]
2023-03-19 12:21:46,836	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_0.pth
2023-03-19 12:21:47,502	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 12:24:00,093	OUTPUT_MODEL	INFO	Train Epoch: 1 [73%]
2023-03-19 12:24:00,095	OUTPUT_MODEL	INFO	[2.453425645828247, 2.467136859893799, 7.443902015686035, 23.44963264465332, -3.8365020751953125, 3.744387149810791, 100, 0.0002]
2023-03-19 12:24:39,611	OUTPUT_MODEL	INFO	====> Epoch: 1
2023-03-19 12:26:34,489	OUTPUT_MODEL	INFO	{'train': {'log_interval': 100, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 16, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'final_annotation_train.txt', 'validation_files': 'final_annotation_val.txt', 'text_cleaners': ['zh_ja_mixture_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 7, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256}, 'speakers': {'5': 0, '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, 'zhongli': 6}, 'symbols': ['_', ',', '.', '!', '?', '-', '~', '…', 'A', 'E', 'I', 'N', 'O', 'Q', 'U', 'a', 'b', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ʃ', 'ʧ', 'ʦ', 'ɯ', 'ɹ', 'ə', 'ɥ', '⁼', 'ʰ', '`', '→', '↓', '↑', ' '], 'model_dir': '././OUTPUT_MODEL', 'max_epochs': 400, 'drop_speaker_embed': True}
2023-03-19 12:26:42,356	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/G_0.pth' (iteration None)
2023-03-19 12:26:43,149	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/D_0.pth' (iteration None)
2023-03-19 12:26:59,219	OUTPUT_MODEL	INFO	Train Epoch: 1 [0%]
2023-03-19 12:26:59,220	OUTPUT_MODEL	INFO	[2.7677817344665527, 1.9214215278625488, 6.520136833190918, 29.6988525390625, 1.227083444595337, 14.877415657043457, 0, 0.0002]
2023-03-19 12:27:01,603	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_0.pth
2023-03-19 12:27:02,262	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 12:29:12,671	OUTPUT_MODEL	INFO	Train Epoch: 1 [73%]
2023-03-19 12:29:12,672	OUTPUT_MODEL	INFO	[2.4070534706115723, 2.7815945148468018, 7.737336158752441, 24.218332290649414, -4.317171573638916, 3.675045967102051, 100, 0.0002]
2023-03-19 12:29:52,764	OUTPUT_MODEL	INFO	====> Epoch: 1
2023-03-19 12:30:59,914	OUTPUT_MODEL	INFO	Train Epoch: 2 [46%]
2023-03-19 12:30:59,916	OUTPUT_MODEL	INFO	[2.3954055309295654, 2.4031009674072266, 5.098405361175537, 22.038040161132812, 0.9906678199768066, 3.331892251968384, 200, 0.000199975]
2023-03-19 12:32:09,224	OUTPUT_MODEL	INFO	====> Epoch: 2
2023-03-19 12:32:41,637	OUTPUT_MODEL	INFO	Train Epoch: 3 [19%]
2023-03-19 12:32:41,639	OUTPUT_MODEL	INFO	[2.4407100677490234, 2.584627389907837, 7.0867414474487305, 21.244945526123047, -0.05799323320388794, 2.7842681407928467, 300, 0.000199950003125]
2023-03-19 12:34:16,113	OUTPUT_MODEL	INFO	Train Epoch: 3 [92%]
2023-03-19 12:34:16,115	OUTPUT_MODEL	INFO	[2.386249542236328, 2.809417486190796, 6.614691257476807, 23.013275146484375, 1.2886543273925781, 3.2462124824523926, 400, 0.000199950003125]
2023-03-19 12:34:26,660	OUTPUT_MODEL	INFO	====> Epoch: 3
2023-03-19 12:35:58,748	OUTPUT_MODEL	INFO	Train Epoch: 4 [65%]
2023-03-19 12:35:58,750	OUTPUT_MODEL	INFO	[2.5800366401672363, 2.7873175144195557, 6.05478048324585, 22.482872009277344, 0.15651458501815796, 2.9304158687591553, 500, 0.00019992500937460937]
2023-03-19 12:36:44,230	OUTPUT_MODEL	INFO	====> Epoch: 4
2023-03-19 12:37:40,625	OUTPUT_MODEL	INFO	Train Epoch: 5 [38%]
2023-03-19 12:37:40,627	OUTPUT_MODEL	INFO	[2.64043927192688, 2.278775453567505, 6.130909442901611, 22.535472869873047, 1.0297833681106567, 2.8650543689727783, 600, 0.00019990001874843754]
2023-03-19 12:39:00,313	OUTPUT_MODEL	INFO	====> Epoch: 5
2023-03-19 12:39:21,601	OUTPUT_MODEL	INFO	Train Epoch: 6 [11%]
2023-03-19 12:39:21,603	OUTPUT_MODEL	INFO	[2.3660383224487305, 2.471519947052002, 6.240213394165039, 22.40270233154297, 1.0654938220977783, 2.5494508743286133, 700, 0.00019987503124609398]
2023-03-19 12:40:56,563	OUTPUT_MODEL	INFO	Train Epoch: 6 [84%]
2023-03-19 12:40:56,565	OUTPUT_MODEL	INFO	[2.5481181144714355, 2.5224709510803223, 6.036583423614502, 22.574460983276367, -1.6822093725204468, 3.0775556564331055, 800, 0.00019987503124609398]
2023-03-19 12:41:17,620	OUTPUT_MODEL	INFO	====> Epoch: 6
2023-03-19 12:42:39,629	OUTPUT_MODEL	INFO	Train Epoch: 7 [57%]
2023-03-19 12:42:39,631	OUTPUT_MODEL	INFO	[2.445131301879883, 2.4652178287506104, 5.400321960449219, 21.879268646240234, 0.9934842586517334, 2.8488097190856934, 900, 0.0001998500468671882]
2023-03-19 12:43:36,971	OUTPUT_MODEL	INFO	====> Epoch: 7
2023-03-19 12:44:23,408	OUTPUT_MODEL	INFO	Train Epoch: 8 [30%]
2023-03-19 12:44:23,411	OUTPUT_MODEL	INFO	[2.2248783111572266, 2.617518424987793, 6.354041576385498, 21.75868034362793, 1.0444469451904297, 2.818567991256714, 1000, 0.00019982506561132978]
2023-03-19 12:44:25,434	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 8 to ././OUTPUT_MODEL/G_1000.pth
2023-03-19 12:44:26,323	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 8 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 12:45:58,512	OUTPUT_MODEL	INFO	====> Epoch: 8
2023-03-19 12:46:11,421	OUTPUT_MODEL	INFO	Train Epoch: 9 [3%]
2023-03-19 12:46:11,423	OUTPUT_MODEL	INFO	[2.351282835006714, 2.6842267513275146, 6.8905029296875, 22.640499114990234, 1.7184029817581177, 3.2057912349700928, 1100, 0.00019980008747812837]
2023-03-19 12:47:47,601	OUTPUT_MODEL	INFO	Train Epoch: 9 [76%]
2023-03-19 12:47:47,602	OUTPUT_MODEL	INFO	[2.221555471420288, 2.6440117359161377, 6.428550720214844, 21.28923988342285, 1.0178813934326172, 3.1218628883361816, 1200, 0.00019980008747812837]
2023-03-19 12:48:19,350	OUTPUT_MODEL	INFO	====> Epoch: 9
2023-03-19 12:49:29,818	OUTPUT_MODEL	INFO	Train Epoch: 10 [49%]
2023-03-19 12:49:29,821	OUTPUT_MODEL	INFO	[2.286243438720703, 2.636526346206665, 5.978941440582275, 21.78253746032715, 1.3311371803283691, 2.658245801925659, 1300, 0.0001997751124671936]
2023-03-19 12:50:36,292	OUTPUT_MODEL	INFO	====> Epoch: 10
2023-03-19 12:51:12,454	OUTPUT_MODEL	INFO	Train Epoch: 11 [22%]
2023-03-19 12:51:12,456	OUTPUT_MODEL	INFO	[2.3780837059020996, 2.5189459323883057, 5.845357418060303, 21.63886833190918, 1.1265857219696045, 2.730893611907959, 1400, 0.00019975014057813518]
2023-03-19 12:52:47,742	OUTPUT_MODEL	INFO	Train Epoch: 11 [95%]
2023-03-19 12:52:47,743	OUTPUT_MODEL	INFO	[2.361841917037964, 2.6060292720794678, 5.489840030670166, 20.509418487548828, 0.974402666091919, 2.6455540657043457, 1500, 0.00019975014057813518]
2023-03-19 12:52:54,359	OUTPUT_MODEL	INFO	====> Epoch: 11
2023-03-19 12:54:31,656	OUTPUT_MODEL	INFO	Train Epoch: 12 [68%]
2023-03-19 12:54:31,658	OUTPUT_MODEL	INFO	[2.011817693710327, 3.3316805362701416, 7.822537899017334, 21.024534225463867, 0.18489143252372742, 2.9786009788513184, 1600, 0.00019972517181056292]
2023-03-19 12:55:14,071	OUTPUT_MODEL	INFO	====> Epoch: 12
2023-03-19 12:56:13,220	OUTPUT_MODEL	INFO	Train Epoch: 13 [41%]
2023-03-19 12:56:13,225	OUTPUT_MODEL	INFO	[2.5211570262908936, 2.303274154663086, 6.772347927093506, 20.92189598083496, -3.16015887260437, 2.5897369384765625, 1700, 0.0001997002061640866]
2023-03-19 12:57:29,955	OUTPUT_MODEL	INFO	====> Epoch: 13
2023-03-19 12:57:55,021	OUTPUT_MODEL	INFO	Train Epoch: 14 [14%]
2023-03-19 12:57:55,023	OUTPUT_MODEL	INFO	[2.1538729667663574, 2.5400240421295166, 6.69806432723999, 22.29702377319336, -0.0632944107055664, 3.0114693641662598, 1800, 0.00019967524363831608]
2023-03-19 12:59:29,467	OUTPUT_MODEL	INFO	Train Epoch: 14 [87%]
2023-03-19 12:59:29,469	OUTPUT_MODEL	INFO	[2.335636854171753, 2.579317569732666, 5.700596332550049, 21.48834800720215, 1.1565148830413818, 3.117337465286255, 1900, 0.00019967524363831608]
2023-03-19 12:59:46,713	OUTPUT_MODEL	INFO	====> Epoch: 14
2023-03-19 13:01:11,566	OUTPUT_MODEL	INFO	Train Epoch: 15 [60%]
2023-03-19 13:01:11,568	OUTPUT_MODEL	INFO	[2.2541778087615967, 2.4005074501037598, 6.891348361968994, 21.97374725341797, 0.759367048740387, 2.976179599761963, 2000, 0.0001996502842328613]
2023-03-19 13:01:13,630	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 15 to ././OUTPUT_MODEL/G_2000.pth
2023-03-19 13:01:14,478	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 15 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 13:02:07,593	OUTPUT_MODEL	INFO	====> Epoch: 15
2023-03-19 13:02:57,564	OUTPUT_MODEL	INFO	Train Epoch: 16 [33%]
2023-03-19 13:02:57,566	OUTPUT_MODEL	INFO	[2.23612642288208, 2.8545584678649902, 6.937902450561523, 21.765094757080078, 1.038076639175415, 2.785839557647705, 2100, 0.00019962532794733217]
2023-03-19 13:04:23,761	OUTPUT_MODEL	INFO	====> Epoch: 16
2023-03-19 13:04:38,735	OUTPUT_MODEL	INFO	Train Epoch: 17 [6%]
2023-03-19 13:04:38,738	OUTPUT_MODEL	INFO	[2.2182464599609375, 2.696089744567871, 6.0715765953063965, 20.234621047973633, 1.1873950958251953, 2.738218069076538, 2200, 0.00019960037478133875]
2023-03-19 13:06:12,131	OUTPUT_MODEL	INFO	Train Epoch: 17 [79%]
2023-03-19 13:06:12,134	OUTPUT_MODEL	INFO	[2.206552028656006, 2.643734931945801, 5.972236156463623, 20.466773986816406, -0.1184077262878418, 2.3535733222961426, 2300, 0.00019960037478133875]
2023-03-19 13:06:39,721	OUTPUT_MODEL	INFO	====> Epoch: 17
2023-03-19 13:07:55,197	OUTPUT_MODEL	INFO	Train Epoch: 18 [52%]
2023-03-19 13:07:55,200	OUTPUT_MODEL	INFO	[2.328258752822876, 2.3802335262298584, 6.180731773376465, 20.992557525634766, 0.13319289684295654, 2.5973711013793945, 2400, 0.00019957542473449108]
2023-03-19 13:08:57,312	OUTPUT_MODEL	INFO	====> Epoch: 18
2023-03-19 13:09:37,365	OUTPUT_MODEL	INFO	Train Epoch: 19 [25%]
2023-03-19 13:09:37,367	OUTPUT_MODEL	INFO	[2.0520260334014893, 2.7359557151794434, 7.235138416290283, 22.13399314880371, -0.07294809818267822, 2.8417460918426514, 2500, 0.00019955047780639926]
2023-03-19 13:11:11,912	OUTPUT_MODEL	INFO	Train Epoch: 19 [98%]
2023-03-19 13:11:11,914	OUTPUT_MODEL	INFO	[2.349649429321289, 2.314272165298462, 5.567647933959961, 21.115121841430664, -3.4428136348724365, 2.4760372638702393, 2600, 0.00019955047780639926]
2023-03-19 13:11:15,361	OUTPUT_MODEL	INFO	====> Epoch: 19
2023-03-19 13:12:53,125	OUTPUT_MODEL	INFO	Train Epoch: 20 [71%]
2023-03-19 13:12:53,127	OUTPUT_MODEL	INFO	[2.2518038749694824, 2.5843279361724854, 7.291360378265381, 21.147014617919922, -2.7715225219726562, 3.004561185836792, 2700, 0.00019952553399667344]
2023-03-19 13:13:30,700	OUTPUT_MODEL	INFO	====> Epoch: 20
2023-03-19 13:14:35,756	OUTPUT_MODEL	INFO	Train Epoch: 21 [44%]
2023-03-19 13:14:35,759	OUTPUT_MODEL	INFO	[2.3229660987854004, 2.6707992553710938, 7.560359001159668, 21.709745407104492, 1.1619484424591064, 2.8472986221313477, 2800, 0.00019950059330492385]
2023-03-19 13:15:48,632	OUTPUT_MODEL	INFO	====> Epoch: 21
2023-03-19 13:16:17,825	OUTPUT_MODEL	INFO	Train Epoch: 22 [17%]
2023-03-19 13:16:17,829	OUTPUT_MODEL	INFO	[2.066340446472168, 2.763842821121216, 6.566652774810791, 20.961864471435547, 1.0031161308288574, 3.0934200286865234, 2900, 0.00019947565573076072]
2023-03-19 13:17:51,273	OUTPUT_MODEL	INFO	Train Epoch: 22 [90%]
2023-03-19 13:17:51,275	OUTPUT_MODEL	INFO	[2.523163318634033, 2.4746038913726807, 6.402873992919922, 20.745624542236328, -0.20343202352523804, 2.752408027648926, 3000, 0.00019947565573076072]
2023-03-19 13:17:52,669	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 22 to ././OUTPUT_MODEL/G_3000.pth
2023-03-19 13:17:53,408	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 22 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 13:18:07,378	OUTPUT_MODEL	INFO	====> Epoch: 22
2023-03-19 13:19:36,240	OUTPUT_MODEL	INFO	Train Epoch: 23 [63%]
2023-03-19 13:19:36,242	OUTPUT_MODEL	INFO	[2.230928421020508, 2.694000005722046, 6.515535354614258, 21.90530014038086, 1.347954273223877, 3.114440679550171, 3100, 0.00019945072127379438]
2023-03-19 13:20:23,826	OUTPUT_MODEL	INFO	====> Epoch: 23
2023-03-19 13:21:17,748	OUTPUT_MODEL	INFO	Train Epoch: 24 [36%]
2023-03-19 13:21:17,750	OUTPUT_MODEL	INFO	[2.2452611923217773, 2.6472818851470947, 6.5121636390686035, 21.56891632080078, 1.0643227100372314, 3.016589403152466, 3200, 0.00019942578993363514]
2023-03-19 13:22:39,085	OUTPUT_MODEL	INFO	====> Epoch: 24
2023-03-19 13:22:57,877	OUTPUT_MODEL	INFO	Train Epoch: 25 [9%]
2023-03-19 13:22:57,879	OUTPUT_MODEL	INFO	[2.194096565246582, 2.713341236114502, 6.701142311096191, 20.25050926208496, -1.9157944917678833, 2.8230626583099365, 3300, 0.00019940086170989343]
2023-03-19 13:24:32,133	OUTPUT_MODEL	INFO	Train Epoch: 25 [82%]
2023-03-19 13:24:32,135	OUTPUT_MODEL	INFO	[2.2553348541259766, 2.7851450443267822, 6.922990798950195, 21.866518020629883, 1.1343169212341309, 2.4923484325408936, 3400, 0.00019940086170989343]
2023-03-19 13:24:56,034	OUTPUT_MODEL	INFO	====> Epoch: 25
2023-03-19 13:26:12,559	OUTPUT_MODEL	INFO	Train Epoch: 26 [55%]
2023-03-19 13:26:12,561	OUTPUT_MODEL	INFO	[2.1842145919799805, 2.623324394226074, 6.488988399505615, 21.035337448120117, 0.0491163432598114, 2.5204529762268066, 3500, 0.0001993759366021797]
2023-03-19 13:27:09,938	OUTPUT_MODEL	INFO	====> Epoch: 26
2023-03-19 13:27:52,452	OUTPUT_MODEL	INFO	Train Epoch: 27 [28%]
2023-03-19 13:27:52,454	OUTPUT_MODEL	INFO	[1.9499015808105469, 2.9576849937438965, 8.801591873168945, 20.963382720947266, -4.876099109649658, 2.6008336544036865, 3600, 0.00019935101461010442]
2023-03-19 13:29:24,464	OUTPUT_MODEL	INFO	====> Epoch: 27
2023-03-19 13:29:32,146	OUTPUT_MODEL	INFO	Train Epoch: 28 [1%]
2023-03-19 13:29:32,148	OUTPUT_MODEL	INFO	[2.5095508098602295, 2.5243167877197266, 6.015588760375977, 21.45965576171875, 1.164332389831543, 3.026104211807251, 3700, 0.00019932609573327815]
2023-03-19 13:31:05,440	OUTPUT_MODEL	INFO	Train Epoch: 28 [74%]
2023-03-19 13:31:05,443	OUTPUT_MODEL	INFO	[2.1444172859191895, 2.7436985969543457, 7.100220680236816, 20.628589630126953, 1.4611539840698242, 2.702702522277832, 3800, 0.00019932609573327815]
2023-03-19 13:31:39,842	OUTPUT_MODEL	INFO	====> Epoch: 28
2023-03-19 13:32:46,080	OUTPUT_MODEL	INFO	Train Epoch: 29 [47%]
2023-03-19 13:32:46,082	OUTPUT_MODEL	INFO	[2.0165672302246094, 2.8369438648223877, 7.011700630187988, 20.553321838378906, -5.4350786209106445, 2.5756826400756836, 3900, 0.0001993011799713115]
2023-03-19 13:33:53,686	OUTPUT_MODEL	INFO	====> Epoch: 29
2023-03-19 13:34:26,435	OUTPUT_MODEL	INFO	Train Epoch: 30 [20%]
2023-03-19 13:34:26,437	OUTPUT_MODEL	INFO	[2.2569901943206787, 2.8390042781829834, 7.787677764892578, 20.019317626953125, 1.473963975906372, 2.495082378387451, 4000, 0.00019927626732381507]
2023-03-19 13:34:27,751	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 30 to ././OUTPUT_MODEL/G_4000.pth
2023-03-19 13:34:28,389	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 30 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 13:36:01,954	OUTPUT_MODEL	INFO	Train Epoch: 30 [93%]
2023-03-19 13:36:01,956	OUTPUT_MODEL	INFO	[2.096991777420044, 2.9146804809570312, 9.777377128601074, 21.639089584350586, 1.6289055347442627, 2.882774829864502, 4100, 0.00019927626732381507]
2023-03-19 13:36:12,294	OUTPUT_MODEL	INFO	====> Epoch: 30
2023-03-19 13:37:43,371	OUTPUT_MODEL	INFO	Train Epoch: 31 [66%]
2023-03-19 13:37:43,373	OUTPUT_MODEL	INFO	[2.276550769805908, 2.692239284515381, 6.753173351287842, 19.066652297973633, 1.3248038291931152, 2.5063552856445312, 4200, 0.00019925135779039958]
2023-03-19 13:38:27,241	OUTPUT_MODEL	INFO	====> Epoch: 31
2023-03-19 13:39:23,731	OUTPUT_MODEL	INFO	Train Epoch: 32 [39%]
2023-03-19 13:39:23,733	OUTPUT_MODEL	INFO	[2.3253989219665527, 2.646624803543091, 5.978858470916748, 21.053552627563477, 1.2120862007141113, 2.6606249809265137, 4300, 0.00019922645137067577]
2023-03-19 13:40:41,946	OUTPUT_MODEL	INFO	====> Epoch: 32
2023-03-19 13:41:04,541	OUTPUT_MODEL	INFO	Train Epoch: 33 [12%]
2023-03-19 13:41:04,543	OUTPUT_MODEL	INFO	[1.9918358325958252, 2.773151397705078, 7.33146333694458, 20.627155303955078, 0.1250927448272705, 2.9345860481262207, 4400, 0.00019920154806425444]
2023-03-19 13:42:37,521	OUTPUT_MODEL	INFO	Train Epoch: 33 [85%]
2023-03-19 13:42:37,522	OUTPUT_MODEL	INFO	[2.1235320568084717, 2.831958770751953, 6.170434951782227, 20.476436614990234, -1.306983470916748, 2.669736862182617, 4500, 0.00019920154806425444]
2023-03-19 13:42:57,723	OUTPUT_MODEL	INFO	====> Epoch: 33
2023-03-19 13:44:18,828	OUTPUT_MODEL	INFO	Train Epoch: 34 [58%]
2023-03-19 13:44:18,829	OUTPUT_MODEL	INFO	[2.0676183700561523, 2.714613914489746, 7.752232551574707, 20.59300994873047, 1.3906292915344238, 3.0375077724456787, 4600, 0.0001991766478707464]
2023-03-19 13:45:12,876	OUTPUT_MODEL	INFO	====> Epoch: 34
2023-03-19 13:46:00,894	OUTPUT_MODEL	INFO	Train Epoch: 35 [31%]
2023-03-19 13:46:00,896	OUTPUT_MODEL	INFO	[2.142178535461426, 2.958857297897339, 6.20952033996582, 20.542827606201172, -2.8460447788238525, 2.8164288997650146, 4700, 0.00019915175078976256]
2023-03-19 13:47:29,167	OUTPUT_MODEL	INFO	====> Epoch: 35
2023-03-19 13:47:42,168	OUTPUT_MODEL	INFO	Train Epoch: 36 [4%]
2023-03-19 13:47:42,170	OUTPUT_MODEL	INFO	[2.2238070964813232, 3.0855329036712646, 7.732571125030518, 20.250755310058594, 1.2343031167984009, 2.6520116329193115, 4800, 0.00019912685682091382]
2023-03-19 13:49:15,378	OUTPUT_MODEL	INFO	Train Epoch: 36 [77%]
2023-03-19 13:49:15,381	OUTPUT_MODEL	INFO	[2.1510984897613525, 2.8800716400146484, 6.848825931549072, 20.746074676513672, 1.1209194660186768, 2.4518699645996094, 4900, 0.00019912685682091382]
2023-03-19 13:49:46,077	OUTPUT_MODEL	INFO	====> Epoch: 36
2023-03-19 13:50:57,701	OUTPUT_MODEL	INFO	Train Epoch: 37 [50%]
2023-03-19 13:50:57,703	OUTPUT_MODEL	INFO	[2.3385729789733887, 2.533876657485962, 8.141775131225586, 19.05156898498535, 1.3829758167266846, 1.9425270557403564, 5000, 0.0001991019659638112]
2023-03-19 13:50:59,045	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 37 to ././OUTPUT_MODEL/G_5000.pth
2023-03-19 13:50:59,719	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 37 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 13:52:04,587	OUTPUT_MODEL	INFO	====> Epoch: 37
2023-03-19 13:52:41,491	OUTPUT_MODEL	INFO	Train Epoch: 38 [23%]
2023-03-19 13:52:41,492	OUTPUT_MODEL	INFO	[2.294307231903076, 2.8201985359191895, 9.49155330657959, 20.732419967651367, 1.663513422012329, 1.869168996810913, 5100, 0.0001990770782180657]
2023-03-19 13:54:14,442	OUTPUT_MODEL	INFO	Train Epoch: 38 [96%]
2023-03-19 13:54:14,444	OUTPUT_MODEL	INFO	[2.456505298614502, 2.701749324798584, 10.270216941833496, 18.3362979888916, 1.251596450805664, 1.9917889833450317, 5200, 0.0001990770782180657]
2023-03-19 13:54:21,155	OUTPUT_MODEL	INFO	====> Epoch: 38
2023-03-19 13:55:56,048	OUTPUT_MODEL	INFO	Train Epoch: 39 [69%]
2023-03-19 13:55:56,050	OUTPUT_MODEL	INFO	[2.047771692276001, 2.97009015083313, 8.294321060180664, 21.410564422607422, 1.57881498336792, 2.704367160797119, 5300, 0.00019905219358328844]
2023-03-19 13:56:35,646	OUTPUT_MODEL	INFO	====> Epoch: 39
2023-03-19 13:57:36,606	OUTPUT_MODEL	INFO	Train Epoch: 40 [42%]
2023-03-19 13:57:36,608	OUTPUT_MODEL	INFO	[2.109105110168457, 2.5633015632629395, 7.192248821258545, 19.604408264160156, 1.650091290473938, 2.721791982650757, 5400, 0.0001990273120590905]
2023-03-19 13:58:50,604	OUTPUT_MODEL	INFO	====> Epoch: 40
2023-03-19 13:59:17,454	OUTPUT_MODEL	INFO	Train Epoch: 41 [15%]
2023-03-19 13:59:17,456	OUTPUT_MODEL	INFO	[2.054466962814331, 2.890228509902954, 7.6560750007629395, 21.970003128051758, 1.319176197052002, 2.7206075191497803, 5500, 0.00019900243364508313]
2023-03-19 14:00:50,132	OUTPUT_MODEL	INFO	Train Epoch: 41 [88%]
2023-03-19 14:00:50,133	OUTPUT_MODEL	INFO	[2.0660462379455566, 2.9451451301574707, 7.644406318664551, 20.990642547607422, -0.9969726800918579, 3.1132960319519043, 5600, 0.00019900243364508313]
2023-03-19 14:01:06,333	OUTPUT_MODEL	INFO	====> Epoch: 41
2023-03-19 14:02:31,661	OUTPUT_MODEL	INFO	Train Epoch: 42 [61%]
2023-03-19 14:02:31,662	OUTPUT_MODEL	INFO	[1.9617104530334473, 3.1118407249450684, 6.940706729888916, 20.15882110595703, -0.9898852705955505, 2.12621808052063, 5700, 0.0001989775583408775]
2023-03-19 14:03:21,841	OUTPUT_MODEL	INFO	====> Epoch: 42
2023-03-19 14:04:13,211	OUTPUT_MODEL	INFO	Train Epoch: 43 [34%]
2023-03-19 14:04:13,213	OUTPUT_MODEL	INFO	[2.297740936279297, 2.6714816093444824, 7.381168365478516, 18.034879684448242, 1.3073450326919556, 2.2519028186798096, 5800, 0.00019895268614608487]
2023-03-19 14:05:37,063	OUTPUT_MODEL	INFO	====> Epoch: 43
2023-03-19 14:05:53,608	OUTPUT_MODEL	INFO	Train Epoch: 44 [7%]
2023-03-19 14:05:53,610	OUTPUT_MODEL	INFO	[2.039936065673828, 2.8257620334625244, 6.964200973510742, 20.388933181762695, 0.9470431208610535, 2.3349757194519043, 5900, 0.0001989278170603166]
2023-03-19 14:07:27,111	OUTPUT_MODEL	INFO	Train Epoch: 44 [80%]
2023-03-19 14:07:27,113	OUTPUT_MODEL	INFO	[2.096825361251831, 3.016226291656494, 6.9255290031433105, 21.717355728149414, 0.9618285894393921, 2.712085008621216, 6000, 0.0001989278170603166]
2023-03-19 14:07:29,230	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 44 to ././OUTPUT_MODEL/G_6000.pth
2023-03-19 14:07:30,153	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 44 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 14:07:56,895	OUTPUT_MODEL	INFO	====> Epoch: 44
2023-03-19 14:09:11,947	OUTPUT_MODEL	INFO	Train Epoch: 45 [53%]
2023-03-19 14:09:11,948	OUTPUT_MODEL	INFO	[2.299248218536377, 2.6875391006469727, 6.962509632110596, 20.516433715820312, 1.280914068222046, 2.5289247035980225, 6100, 0.00019890295108318404]
2023-03-19 14:10:12,545	OUTPUT_MODEL	INFO	====> Epoch: 45
2023-03-19 14:10:53,209	OUTPUT_MODEL	INFO	Train Epoch: 46 [26%]
2023-03-19 14:10:53,210	OUTPUT_MODEL	INFO	[1.9957423210144043, 2.863213539123535, 7.808311462402344, 20.77896499633789, 0.22756391763687134, 2.708096504211426, 6200, 0.00019887808821429862]
2023-03-19 14:12:26,079	OUTPUT_MODEL	INFO	Train Epoch: 46 [99%]
2023-03-19 14:12:26,081	OUTPUT_MODEL	INFO	[1.9351831674575806, 3.0195298194885254, 7.738553047180176, 20.869388580322266, -0.9420408606529236, 2.652263879776001, 6300, 0.00019887808821429862]
2023-03-19 14:12:28,502	OUTPUT_MODEL	INFO	====> Epoch: 46
2023-03-19 14:14:08,368	OUTPUT_MODEL	INFO	Train Epoch: 47 [72%]
2023-03-19 14:14:08,369	OUTPUT_MODEL	INFO	[2.073835849761963, 2.855901002883911, 8.007379531860352, 20.74787712097168, -3.882984161376953, 3.4984121322631836, 6400, 0.00019885322845327182]
2023-03-19 14:14:44,423	OUTPUT_MODEL	INFO	====> Epoch: 47
2023-03-19 14:15:49,925	OUTPUT_MODEL	INFO	Train Epoch: 48 [45%]
2023-03-19 14:15:49,927	OUTPUT_MODEL	INFO	[2.1489224433898926, 2.5325357913970947, 6.9408979415893555, 20.77469825744629, 1.5554282665252686, 2.695305824279785, 6500, 0.00019882837179971516]
2023-03-19 14:17:00,368	OUTPUT_MODEL	INFO	====> Epoch: 48
2023-03-19 14:17:30,795	OUTPUT_MODEL	INFO	Train Epoch: 49 [18%]
2023-03-19 14:17:30,796	OUTPUT_MODEL	INFO	[2.1185965538024902, 2.7870707511901855, 6.945887088775635, 19.87975311279297, -0.07208478450775146, 2.5983989238739014, 6600, 0.00019880351825324018]
2023-03-19 14:19:03,702	OUTPUT_MODEL	INFO	Train Epoch: 49 [91%]
2023-03-19 14:19:03,704	OUTPUT_MODEL	INFO	[2.138524293899536, 3.173128604888916, 7.690737247467041, 21.43050193786621, 1.3595328330993652, 2.5055465698242188, 6700, 0.00019880351825324018]
2023-03-19 14:19:16,511	OUTPUT_MODEL	INFO	====> Epoch: 49
2023-03-19 14:20:45,447	OUTPUT_MODEL	INFO	Train Epoch: 50 [64%]
2023-03-19 14:20:45,449	OUTPUT_MODEL	INFO	[2.23408842086792, 2.993419647216797, 7.372537136077881, 20.87449073791504, 1.2371208667755127, 2.7385804653167725, 6800, 0.00019877866781345852]
2023-03-19 14:21:32,361	OUTPUT_MODEL	INFO	====> Epoch: 50
2023-03-19 14:22:27,310	OUTPUT_MODEL	INFO	Train Epoch: 51 [36%]
2023-03-19 14:22:27,312	OUTPUT_MODEL	INFO	[2.4573042392730713, 3.00639009475708, 11.027156829833984, 18.325237274169922, 1.1865880489349365, 2.1762804985046387, 6900, 0.00019875382047998183]
2023-03-19 14:23:47,515	OUTPUT_MODEL	INFO	====> Epoch: 51
2023-03-19 14:24:06,970	OUTPUT_MODEL	INFO	Train Epoch: 52 [9%]
2023-03-19 14:24:06,971	OUTPUT_MODEL	INFO	[1.9852650165557861, 2.6882123947143555, 6.670781135559082, 19.485042572021484, 1.0369094610214233, 2.595994472503662, 7000, 0.00019872897625242182]
2023-03-19 14:24:08,294	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 52 to ././OUTPUT_MODEL/G_7000.pth
2023-03-19 14:24:09,083	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 52 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 14:25:43,676	OUTPUT_MODEL	INFO	Train Epoch: 52 [82%]
2023-03-19 14:25:43,678	OUTPUT_MODEL	INFO	[1.7083518505096436, 2.9993934631347656, 8.13212776184082, 20.167407989501953, 0.10275596380233765, 2.4800865650177, 7100, 0.00019872897625242182]
2023-03-19 14:26:06,829	OUTPUT_MODEL	INFO	====> Epoch: 52
2023-03-19 14:27:26,107	OUTPUT_MODEL	INFO	Train Epoch: 53 [55%]
2023-03-19 14:27:26,109	OUTPUT_MODEL	INFO	[2.0178496837615967, 2.9706969261169434, 7.063908100128174, 20.658626556396484, 1.4972121715545654, 2.663658618927002, 7200, 0.00019870413513039026]
2023-03-19 14:28:22,974	OUTPUT_MODEL	INFO	====> Epoch: 53
2023-03-19 14:29:06,890	OUTPUT_MODEL	INFO	Train Epoch: 54 [28%]
2023-03-19 14:29:06,892	OUTPUT_MODEL	INFO	[2.1665661334991455, 2.8143527507781982, 6.063041687011719, 19.859813690185547, 1.1444849967956543, 2.165475845336914, 7300, 0.00019867929711349895]
2023-03-19 14:30:37,901	OUTPUT_MODEL	INFO	====> Epoch: 54
2023-03-19 14:30:47,024	OUTPUT_MODEL	INFO	Train Epoch: 55 [1%]
2023-03-19 14:30:47,026	OUTPUT_MODEL	INFO	[2.351067543029785, 2.5995354652404785, 8.14001750946045, 17.19231414794922, 1.266895055770874, 2.17266583442688, 7400, 0.00019865446220135974]
2023-03-19 14:32:20,410	OUTPUT_MODEL	INFO	Train Epoch: 55 [74%]
2023-03-19 14:32:20,412	OUTPUT_MODEL	INFO	[2.0660665035247803, 2.9931554794311523, 7.602880954742432, 20.81361198425293, -5.781072616577148, 2.7353947162628174, 7500, 0.00019865446220135974]
2023-03-19 14:32:52,924	OUTPUT_MODEL	INFO	====> Epoch: 55
2023-03-19 14:34:00,136	OUTPUT_MODEL	INFO	Train Epoch: 56 [47%]
2023-03-19 14:34:00,138	OUTPUT_MODEL	INFO	[1.8615485429763794, 3.0904574394226074, 8.597039222717285, 19.537818908691406, -4.237324237823486, 2.2058351039886475, 7600, 0.00019862963039358455]
2023-03-19 14:35:07,927	OUTPUT_MODEL	INFO	====> Epoch: 56
2023-03-19 14:35:41,431	OUTPUT_MODEL	INFO	Train Epoch: 57 [20%]
2023-03-19 14:35:41,433	OUTPUT_MODEL	INFO	[2.0534894466400146, 2.893205404281616, 6.781658172607422, 20.02190589904785, 1.0954604148864746, 2.463099956512451, 7700, 0.00019860480168978534]
2023-03-19 14:37:14,867	OUTPUT_MODEL	INFO	Train Epoch: 57 [93%]
2023-03-19 14:37:14,869	OUTPUT_MODEL	INFO	[2.1747355461120605, 2.5324342250823975, 7.215085506439209, 21.108806610107422, -3.855501651763916, 2.873568534851074, 7800, 0.00019860480168978534]
2023-03-19 14:37:24,068	OUTPUT_MODEL	INFO	====> Epoch: 57
2023-03-19 14:38:56,731	OUTPUT_MODEL	INFO	Train Epoch: 58 [66%]
2023-03-19 14:38:56,733	OUTPUT_MODEL	INFO	[1.9997360706329346, 3.0313522815704346, 8.281033515930176, 19.830974578857422, 1.5063165426254272, 2.7721359729766846, 7900, 0.0001985799760895741]
2023-03-19 14:39:40,001	OUTPUT_MODEL	INFO	====> Epoch: 58
2023-03-19 14:40:38,051	OUTPUT_MODEL	INFO	Train Epoch: 59 [39%]
2023-03-19 14:40:38,054	OUTPUT_MODEL	INFO	[2.065796375274658, 2.7464423179626465, 7.792346000671387, 20.34369659423828, -0.0831902027130127, 2.31380033493042, 8000, 0.0001985551535925629]
2023-03-19 14:40:40,063	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 59 to ././OUTPUT_MODEL/G_8000.pth
2023-03-19 14:40:40,712	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 59 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 14:41:58,423	OUTPUT_MODEL	INFO	====> Epoch: 59
2023-03-19 14:42:21,435	OUTPUT_MODEL	INFO	Train Epoch: 60 [12%]
2023-03-19 14:42:21,437	OUTPUT_MODEL	INFO	[2.212622880935669, 2.613525867462158, 6.349497318267822, 20.4364013671875, 1.478180170059204, 2.632505178451538, 8100, 0.00019853033419836382]
2023-03-19 14:43:56,046	OUTPUT_MODEL	INFO	Train Epoch: 60 [85%]
2023-03-19 14:43:56,049	OUTPUT_MODEL	INFO	[1.9572577476501465, 3.0355167388916016, 7.8750224113464355, 20.653324127197266, 1.2287237644195557, 2.6051228046417236, 8200, 0.00019853033419836382]
2023-03-19 14:44:15,085	OUTPUT_MODEL	INFO	====> Epoch: 60
2023-03-19 14:45:36,615	OUTPUT_MODEL	INFO	Train Epoch: 61 [58%]
2023-03-19 14:45:36,618	OUTPUT_MODEL	INFO	[1.7655950784683228, 3.077423572540283, 8.383987426757812, 19.716188430786133, 1.0620360374450684, 2.454012155532837, 8300, 0.000198505517906589]
2023-03-19 14:46:30,468	OUTPUT_MODEL	INFO	====> Epoch: 61
2023-03-19 14:47:18,018	OUTPUT_MODEL	INFO	Train Epoch: 62 [31%]
2023-03-19 14:47:18,020	OUTPUT_MODEL	INFO	[2.0916965007781982, 3.257516860961914, 7.727144241333008, 20.166099548339844, -3.9113035202026367, 2.8427882194519043, 8400, 0.00019848070471685067]
2023-03-19 14:48:46,575	OUTPUT_MODEL	INFO	====> Epoch: 62
2023-03-19 14:48:59,551	OUTPUT_MODEL	INFO	Train Epoch: 63 [4%]
2023-03-19 14:48:59,553	OUTPUT_MODEL	INFO	[2.236877679824829, 2.5214903354644775, 7.032357692718506, 20.524795532226562, 1.2949362993240356, 2.6885039806365967, 8500, 0.00019845589462876104]
2023-03-19 14:50:33,882	OUTPUT_MODEL	INFO	Train Epoch: 63 [77%]
2023-03-19 14:50:33,884	OUTPUT_MODEL	INFO	[1.8413704633712769, 3.178621530532837, 9.67981243133545, 20.417118072509766, 1.4303702116012573, 2.5743327140808105, 8600, 0.00019845589462876104]
2023-03-19 14:51:03,435	OUTPUT_MODEL	INFO	====> Epoch: 63
2023-03-19 14:52:15,306	OUTPUT_MODEL	INFO	Train Epoch: 64 [50%]
2023-03-19 14:52:15,308	OUTPUT_MODEL	INFO	[1.9657034873962402, 2.8749916553497314, 9.586912155151367, 20.73607063293457, 1.0509257316589355, 2.5968496799468994, 8700, 0.00019843108764193245]
2023-03-19 14:53:19,448	OUTPUT_MODEL	INFO	====> Epoch: 64
2023-03-19 14:53:56,555	OUTPUT_MODEL	INFO	Train Epoch: 65 [23%]
2023-03-19 14:53:56,557	OUTPUT_MODEL	INFO	[2.1502745151519775, 3.040231227874756, 7.469240188598633, 20.177860260009766, 1.2129080295562744, 2.3727877140045166, 8800, 0.0001984062837559772]
2023-03-19 14:55:30,219	OUTPUT_MODEL	INFO	Train Epoch: 65 [96%]
2023-03-19 14:55:30,221	OUTPUT_MODEL	INFO	[1.9708619117736816, 2.8843185901641846, 8.151924133300781, 21.460023880004883, 1.2610739469528198, 2.392225742340088, 8900, 0.0001984062837559772]
2023-03-19 14:55:36,079	OUTPUT_MODEL	INFO	====> Epoch: 65
2023-03-19 14:57:10,623	OUTPUT_MODEL	INFO	Train Epoch: 66 [69%]
2023-03-19 14:57:10,624	OUTPUT_MODEL	INFO	[1.9850441217422485, 3.025097370147705, 8.33395004272461, 20.61599349975586, 1.0346910953521729, 2.5110809803009033, 9000, 0.00019838148297050769]
2023-03-19 14:57:12,048	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 66 to ././OUTPUT_MODEL/G_9000.pth
2023-03-19 14:57:12,686	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 66 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 14:57:52,772	OUTPUT_MODEL	INFO	====> Epoch: 66
2023-03-19 14:58:53,906	OUTPUT_MODEL	INFO	Train Epoch: 67 [42%]
2023-03-19 14:58:53,907	OUTPUT_MODEL	INFO	[1.9770373106002808, 3.247659683227539, 7.998980522155762, 20.975053787231445, 0.5217850208282471, 2.8502416610717773, 9100, 0.00019835668528513637]
2023-03-19 15:00:07,184	OUTPUT_MODEL	INFO	====> Epoch: 67
2023-03-19 15:00:33,558	OUTPUT_MODEL	INFO	Train Epoch: 68 [15%]
2023-03-19 15:00:33,559	OUTPUT_MODEL	INFO	[1.8106662034988403, 3.3546812534332275, 9.322245597839355, 20.75209617614746, 1.2066415548324585, 2.2211053371429443, 9200, 0.00019833189069947573]
2023-03-19 15:02:06,724	OUTPUT_MODEL	INFO	Train Epoch: 68 [88%]
2023-03-19 15:02:06,727	OUTPUT_MODEL	INFO	[1.8114876747131348, 3.252575397491455, 7.858577251434326, 19.149808883666992, -0.30253341794013977, 2.6730921268463135, 9300, 0.00019833189069947573]
2023-03-19 15:02:22,433	OUTPUT_MODEL	INFO	====> Epoch: 68
2023-03-19 15:03:46,693	OUTPUT_MODEL	INFO	Train Epoch: 69 [61%]
2023-03-19 15:03:46,695	OUTPUT_MODEL	INFO	[1.9850659370422363, 3.354565382003784, 8.263161659240723, 19.47747230529785, 1.1715819835662842, 2.369105577468872, 9400, 0.0001983070992131383]
2023-03-19 15:04:36,448	OUTPUT_MODEL	INFO	====> Epoch: 69
2023-03-19 15:05:28,214	OUTPUT_MODEL	INFO	Train Epoch: 70 [34%]
2023-03-19 15:05:28,216	OUTPUT_MODEL	INFO	[2.05625319480896, 3.094625949859619, 7.6816325187683105, 21.038684844970703, 1.2923399209976196, 2.7843849658966064, 9500, 0.00019828231082573666]
2023-03-19 15:06:50,792	OUTPUT_MODEL	INFO	====> Epoch: 70
2023-03-19 15:07:08,108	OUTPUT_MODEL	INFO	Train Epoch: 71 [7%]
2023-03-19 15:07:08,110	OUTPUT_MODEL	INFO	[2.007093906402588, 2.9453113079071045, 7.938732147216797, 19.860410690307617, -0.014033079147338867, 2.6894216537475586, 9600, 0.00019825752553688343]
2023-03-19 15:08:41,356	OUTPUT_MODEL	INFO	Train Epoch: 71 [80%]
2023-03-19 15:08:41,357	OUTPUT_MODEL	INFO	[1.9557877779006958, 3.203474760055542, 7.884796619415283, 20.10051918029785, -0.8332346081733704, 2.318227529525757, 9700, 0.00019825752553688343]
2023-03-19 15:09:05,892	OUTPUT_MODEL	INFO	====> Epoch: 71
2023-03-19 15:10:21,283	OUTPUT_MODEL	INFO	Train Epoch: 72 [53%]
2023-03-19 15:10:21,285	OUTPUT_MODEL	INFO	[2.09415864944458, 2.739206075668335, 7.067381381988525, 21.032047271728516, -1.7067432403564453, 2.701353073120117, 9800, 0.0001982327433461913]
2023-03-19 15:11:20,699	OUTPUT_MODEL	INFO	====> Epoch: 72
2023-03-19 15:12:02,510	OUTPUT_MODEL	INFO	Train Epoch: 73 [26%]
2023-03-19 15:12:02,511	OUTPUT_MODEL	INFO	[2.1608119010925293, 3.002992868423462, 8.532696723937988, 20.1289119720459, -0.10558885335922241, 2.7418525218963623, 9900, 0.00019820796425327303]
2023-03-19 15:13:34,724	OUTPUT_MODEL	INFO	Train Epoch: 73 [99%]
2023-03-19 15:13:34,726	OUTPUT_MODEL	INFO	[2.1478397846221924, 3.0276386737823486, 7.861583232879639, 20.21521759033203, 1.235795259475708, 2.268900156021118, 10000, 0.00019820796425327303]
2023-03-19 15:13:36,465	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 73 to ././OUTPUT_MODEL/G_10000.pth
2023-03-19 15:13:37,120	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 73 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 15:13:38,604	OUTPUT_MODEL	INFO	====> Epoch: 73
2023-03-19 15:15:19,089	OUTPUT_MODEL	INFO	Train Epoch: 74 [72%]
2023-03-19 15:15:19,091	OUTPUT_MODEL	INFO	[1.9325203895568848, 3.066683769226074, 8.101299285888672, 20.819198608398438, 1.1796319484710693, 2.5027682781219482, 10100, 0.00019818318825774137]
2023-03-19 15:15:54,100	OUTPUT_MODEL	INFO	====> Epoch: 74
2023-03-19 15:17:00,127	OUTPUT_MODEL	INFO	Train Epoch: 75 [45%]
2023-03-19 15:17:00,129	OUTPUT_MODEL	INFO	[1.8198609352111816, 3.140775442123413, 8.017081260681152, 20.786861419677734, 1.0974267721176147, 2.7429561614990234, 10200, 0.00019815841535920914]
2023-03-19 15:18:08,491	OUTPUT_MODEL	INFO	====> Epoch: 75
2023-03-19 15:18:39,615	OUTPUT_MODEL	INFO	Train Epoch: 76 [18%]
2023-03-19 15:18:39,617	OUTPUT_MODEL	INFO	[2.1161458492279053, 3.0659995079040527, 8.197466850280762, 21.264610290527344, 1.2149972915649414, 2.7264468669891357, 10300, 0.00019813364555728923]
2023-03-19 15:20:12,231	OUTPUT_MODEL	INFO	Train Epoch: 76 [91%]
2023-03-19 15:20:12,234	OUTPUT_MODEL	INFO	[1.9347426891326904, 3.0423240661621094, 9.386448860168457, 20.416366577148438, 1.2579665184020996, 2.6823065280914307, 10400, 0.00019813364555728923]
2023-03-19 15:20:24,477	OUTPUT_MODEL	INFO	====> Epoch: 76
2023-03-19 15:21:53,787	OUTPUT_MODEL	INFO	Train Epoch: 77 [64%]
2023-03-19 15:21:53,788	OUTPUT_MODEL	INFO	[1.8336825370788574, 3.2425761222839355, 9.46367359161377, 20.48347282409668, -2.5056562423706055, 2.5514402389526367, 10500, 0.00019810887885159456]
2023-03-19 15:22:39,413	OUTPUT_MODEL	INFO	====> Epoch: 77
2023-03-19 15:23:34,569	OUTPUT_MODEL	INFO	Train Epoch: 78 [37%]
2023-03-19 15:23:34,571	OUTPUT_MODEL	INFO	[2.3119542598724365, 3.164257287979126, 7.637701988220215, 20.07072639465332, 1.2661916017532349, 2.3500962257385254, 10600, 0.0001980841152417381]
2023-03-19 15:24:53,285	OUTPUT_MODEL	INFO	====> Epoch: 78
2023-03-19 15:25:13,160	OUTPUT_MODEL	INFO	Train Epoch: 79 [10%]
2023-03-19 15:25:13,162	OUTPUT_MODEL	INFO	[2.0138001441955566, 3.1804986000061035, 8.59029483795166, 20.77556037902832, 1.1260027885437012, 2.7675821781158447, 10700, 0.00019805935472733287]
2023-03-19 15:26:45,614	OUTPUT_MODEL	INFO	Train Epoch: 79 [83%]
2023-03-19 15:26:45,616	OUTPUT_MODEL	INFO	[1.746431589126587, 3.3309202194213867, 8.846961975097656, 19.95379638671875, -2.007610321044922, 2.541994333267212, 10800, 0.00019805935472733287]
2023-03-19 15:27:07,623	OUTPUT_MODEL	INFO	====> Epoch: 79
2023-03-19 16:15:48,637	OUTPUT_MODEL	INFO	{'train': {'log_interval': 100, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 16, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'final_annotation_train.txt', 'validation_files': 'final_annotation_val.txt', 'text_cleaners': ['zh_ja_mixture_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 7, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256}, 'speakers': {'5': 0, '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, 'zhongli': 6}, 'symbols': ['_', ',', '.', '!', '?', '-', '~', '…', 'A', 'E', 'I', 'N', 'O', 'Q', 'U', 'a', 'b', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ʃ', 'ʧ', 'ʦ', 'ɯ', 'ɹ', 'ə', 'ɥ', '⁼', 'ʰ', '`', '→', '↓', '↑', ' '], 'model_dir': '././OUTPUT_MODEL', 'max_epochs': 400, 'drop_speaker_embed': True}
2023-03-19 16:16:19,692	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/G_0.pth' (iteration None)
2023-03-19 16:16:24,600	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/D_0.pth' (iteration None)
2023-03-19 16:17:01,375	OUTPUT_MODEL	INFO	Train Epoch: 1 [0%]
2023-03-19 16:17:01,376	OUTPUT_MODEL	INFO	[2.76706600189209, 1.9225674867630005, 6.522181987762451, 29.698423385620117, 1.2269444465637207, 14.877575874328613, 0, 0.0002]
2023-03-19 16:17:12,989	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_0.pth
2023-03-19 16:17:13,733	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 16:20:37,170	OUTPUT_MODEL	INFO	Train Epoch: 1 [73%]
2023-03-19 16:20:37,172	OUTPUT_MODEL	INFO	[2.323897361755371, 2.647444725036621, 7.564695358276367, 23.52879524230957, -3.831296920776367, 3.741914749145508, 100, 0.0002]
2023-03-19 16:21:33,858	OUTPUT_MODEL	INFO	====> Epoch: 1
2023-03-19 16:22:42,325	OUTPUT_MODEL	INFO	Train Epoch: 2 [46%]
2023-03-19 16:22:42,327	OUTPUT_MODEL	INFO	[2.45338773727417, 2.3658924102783203, 4.88755464553833, 22.062681198120117, 0.9882837533950806, 3.282916307449341, 200, 0.000199975]
2023-03-19 16:23:52,431	OUTPUT_MODEL	INFO	====> Epoch: 2
2023-03-19 16:24:26,293	OUTPUT_MODEL	INFO	Train Epoch: 3 [19%]
2023-03-19 16:24:26,296	OUTPUT_MODEL	INFO	[2.367112636566162, 2.3412723541259766, 7.029816150665283, 21.442169189453125, -0.04448223114013672, 2.8382961750030518, 300, 0.000199950003125]
2023-03-19 16:26:02,103	OUTPUT_MODEL	INFO	Train Epoch: 3 [92%]
2023-03-19 16:26:02,104	OUTPUT_MODEL	INFO	[2.387986183166504, 2.791843891143799, 6.881650924682617, 22.813098907470703, 1.2880083322525024, 3.25240159034729, 400, 0.000199950003125]
2023-03-19 16:26:13,614	OUTPUT_MODEL	INFO	====> Epoch: 3
2023-03-19 16:27:47,072	OUTPUT_MODEL	INFO	Train Epoch: 4 [65%]
2023-03-19 16:27:47,074	OUTPUT_MODEL	INFO	[2.228788375854492, 2.5978047847747803, 6.233620643615723, 22.277149200439453, 0.08219930529594421, 2.9261386394500732, 500, 0.00019992500937460937]
2023-03-19 16:28:33,016	OUTPUT_MODEL	INFO	====> Epoch: 4
2023-03-19 16:29:30,111	OUTPUT_MODEL	INFO	Train Epoch: 5 [38%]
2023-03-19 16:29:30,113	OUTPUT_MODEL	INFO	[2.6866588592529297, 2.7028136253356934, 6.5980658531188965, 23.374053955078125, 1.0055011510849, 2.804399013519287, 600, 0.00019990001874843754]
2023-03-19 16:30:51,422	OUTPUT_MODEL	INFO	====> Epoch: 5
2023-03-19 16:31:14,900	OUTPUT_MODEL	INFO	Train Epoch: 6 [11%]
2023-03-19 16:31:14,902	OUTPUT_MODEL	INFO	[2.3415894508361816, 2.55547833442688, 6.3845014572143555, 22.086090087890625, 1.0606956481933594, 2.6328701972961426, 700, 0.00019987503124609398]
2023-03-19 16:32:50,678	OUTPUT_MODEL	INFO	Train Epoch: 6 [84%]
2023-03-19 16:32:50,680	OUTPUT_MODEL	INFO	[2.5557541847229004, 2.5089170932769775, 5.888435363769531, 22.416431427001953, -1.631118655204773, 3.064133405685425, 800, 0.00019987503124609398]
2023-03-19 16:33:11,765	OUTPUT_MODEL	INFO	====> Epoch: 6
2023-03-19 16:34:34,389	OUTPUT_MODEL	INFO	Train Epoch: 7 [57%]
2023-03-19 16:34:34,391	OUTPUT_MODEL	INFO	[2.4265670776367188, 2.671632766723633, 5.532519817352295, 21.68087387084961, 0.9621152877807617, 2.8601667881011963, 900, 0.0001998500468671882]
2023-03-19 16:35:31,248	OUTPUT_MODEL	INFO	====> Epoch: 7
2023-03-19 16:36:18,397	OUTPUT_MODEL	INFO	Train Epoch: 8 [30%]
2023-03-19 16:36:18,399	OUTPUT_MODEL	INFO	[2.175934076309204, 2.5530190467834473, 6.502542495727539, 22.18924331665039, 1.055881381034851, 2.746509552001953, 1000, 0.00019982506561132978]
2023-03-19 16:36:19,774	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 8 to ././OUTPUT_MODEL/G_1000.pth
2023-03-19 16:36:20,403	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 8 to ././OUTPUT_MODEL/G_latest.pth
2023-03-19 16:37:52,436	OUTPUT_MODEL	INFO	====> Epoch: 8
2023-03-19 16:38:04,228	OUTPUT_MODEL	INFO	Train Epoch: 9 [3%]
2023-03-19 16:38:04,230	OUTPUT_MODEL	INFO	[2.1865968704223633, 2.7663159370422363, 7.073964595794678, 22.50178337097168, 1.730950117111206, 3.1765737533569336, 1100, 0.00019980008747812837]
2023-03-19 16:39:40,050	OUTPUT_MODEL	INFO	Train Epoch: 9 [76%]
2023-03-19 16:39:40,052	OUTPUT_MODEL	INFO	[2.3513598442077637, 2.988492488861084, 6.70481014251709, 21.579673767089844, 1.0284968614578247, 3.0535695552825928, 1200, 0.00019980008747812837]
2023-03-19 16:40:12,015	OUTPUT_MODEL	INFO	====> Epoch: 9
2023-03-19 16:41:25,015	OUTPUT_MODEL	INFO	Train Epoch: 10 [49%]
2023-03-19 16:41:25,017	OUTPUT_MODEL	INFO	[2.371792793273926, 2.5175187587738037, 5.842675685882568, 22.322845458984375, 1.33059561252594, 2.6266956329345703, 1300, 0.0001997751124671936]
2023-03-19 16:42:31,441	OUTPUT_MODEL	INFO	====> Epoch: 10
2023-03-19 16:43:08,114	OUTPUT_MODEL	INFO	Train Epoch: 11 [22%]
2023-03-19 16:43:08,116	OUTPUT_MODEL	INFO	[2.2418293952941895, 2.648120880126953, 6.228461742401123, 21.519784927368164, 1.1223442554473877, 2.7522504329681396, 1400, 0.00019975014057813518]
2023-03-19 16:44:43,350	OUTPUT_MODEL	INFO	Train Epoch: 11 [95%]
2023-03-19 16:44:43,353	OUTPUT_MODEL	INFO	[2.365861654281616, 2.6656136512756348, 5.419013023376465, 20.726938247680664, 0.9708508849143982, 2.6656463146209717, 1500, 0.00019975014057813518]
2023-03-19 16:44:51,502	OUTPUT_MODEL	INFO	====> Epoch: 11
2023-03-19 16:46:27,988	OUTPUT_MODEL	INFO	Train Epoch: 12 [68%]
2023-03-19 16:46:27,990	OUTPUT_MODEL	INFO	[2.3776497840881348, 2.730390787124634, 6.899669647216797, 21.672531127929688, 0.17316722869873047, 2.9545247554779053, 1600, 0.00019972517181056292]
2023-03-19 16:47:09,765	OUTPUT_MODEL	INFO	====> Epoch: 12
2023-03-19 16:48:12,411	OUTPUT_MODEL	INFO	Train Epoch: 13 [41%]
2023-03-19 16:48:12,413	OUTPUT_MODEL	INFO	[2.308154344558716, 2.2740838527679443, 6.90311336517334, 20.64499282836914, -3.651867151260376, 2.5914313793182373, 1700, 0.0001997002061640866]
2023-03-19 16:49:29,757	OUTPUT_MODEL	INFO	====> Epoch: 13
2023-03-19 16:49:56,874	OUTPUT_MODEL	INFO	Train Epoch: 14 [14%]
2023-03-19 16:49:56,876	OUTPUT_MODEL	INFO	[2.1898975372314453, 2.956237316131592, 6.670430660247803, 22.477397918701172, 0.004358440637588501, 3.0439140796661377, 1800, 0.00019967524363831608]
2023-03-19 16:51:30,764	OUTPUT_MODEL	INFO	Train Epoch: 14 [87%]
2023-03-19 16:51:30,766	OUTPUT_MODEL	INFO	[2.328643321990967, 2.5225777626037598, 5.764469146728516, 21.59275245666504, 1.1505229473114014, 3.0506513118743896, 1900, 0.00019967524363831608]
2023-03-19 16:51:46,938	OUTPUT_MODEL	INFO	====> Epoch: 14
2023-03-20 01:19:15,620	OUTPUT_MODEL	INFO	{'train': {'log_interval': 100, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 16, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'final_annotation_train.txt', 'validation_files': 'final_annotation_val.txt', 'text_cleaners': ['zh_ja_mixture_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 7, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256}, 'speakers': {'5': 0, '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, 'zhongli': 6}, 'symbols': ['_', ',', '.', '!', '?', '-', '~', '…', 'A', 'E', 'I', 'N', 'O', 'Q', 'U', 'a', 'b', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ʃ', 'ʧ', 'ʦ', 'ɯ', 'ɹ', 'ə', 'ɥ', '⁼', 'ʰ', '`', '→', '↓', '↑', ' '], 'model_dir': '././OUTPUT_MODEL', 'max_epochs': 400, 'drop_speaker_embed': True}
2023-03-20 01:19:38,810	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/G_0.pth' (iteration None)
2023-03-20 04:51:11,820	OUTPUT_MODEL	INFO	{'train': {'log_interval': 100, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 16, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'final_annotation_train.txt', 'validation_files': 'final_annotation_val.txt', 'text_cleaners': ['zh_ja_mixture_cleaners'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 7, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256}, 'speakers': {'5': 0, '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, 'zhongli': 6}, 'symbols': ['_', ',', '.', '!', '?', '-', '~', '…', 'A', 'E', 'I', 'N', 'O', 'Q', 'U', 'a', 'b', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ʃ', 'ʧ', 'ʦ', 'ɯ', 'ɹ', 'ə', 'ɥ', '⁼', 'ʰ', '`', '→', '↓', '↑', ' '], 'model_dir': '././OUTPUT_MODEL', 'max_epochs': 400, 'drop_speaker_embed': True}
2023-03-20 04:51:29,638	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/G_0.pth' (iteration None)
2023-03-20 04:51:35,460	OUTPUT_MODEL	INFO	Loaded checkpoint './pretrained_models/D_0.pth' (iteration None)
2023-03-20 04:51:51,189	OUTPUT_MODEL	INFO	Train Epoch: 1 [0%]
2023-03-20 04:51:51,190	OUTPUT_MODEL	INFO	[2.7085564136505127, 2.2208938598632812, 6.918517112731934, 28.911531448364258, 1.4329653978347778, 13.872992515563965, 0, 0.0002]
2023-03-20 04:51:53,398	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_0.pth
2023-03-20 04:51:56,948	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 1 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 04:54:25,626	OUTPUT_MODEL	INFO	Train Epoch: 1 [73%]
2023-03-20 04:54:25,626	OUTPUT_MODEL	INFO	[2.5836336612701416, 2.1884448528289795, 5.750481128692627, 24.440425872802734, 1.0401554107666016, 3.4452099800109863, 100, 0.0002]
2023-03-20 04:55:07,486	OUTPUT_MODEL	INFO	====> Epoch: 1
2023-03-20 04:56:07,032	OUTPUT_MODEL	INFO	Train Epoch: 2 [46%]
2023-03-20 04:56:07,034	OUTPUT_MODEL	INFO	[2.5086941719055176, 2.579465866088867, 10.698724746704102, 23.382770538330078, 1.2650682926177979, 2.895745038986206, 200, 0.000199975]
2023-03-20 04:57:09,162	OUTPUT_MODEL	INFO	====> Epoch: 2
2023-03-20 04:57:37,437	OUTPUT_MODEL	INFO	Train Epoch: 3 [19%]
2023-03-20 04:57:37,439	OUTPUT_MODEL	INFO	[2.4934592247009277, 2.2266528606414795, 5.0896430015563965, 22.26545524597168, 1.0432765483856201, 2.3880207538604736, 300, 0.000199950003125]
2023-03-20 04:59:01,566	OUTPUT_MODEL	INFO	Train Epoch: 3 [92%]
2023-03-20 04:59:01,568	OUTPUT_MODEL	INFO	[2.6731979846954346, 2.2243518829345703, 5.382644176483154, 21.796497344970703, 1.0037789344787598, 2.441603422164917, 400, 0.000199950003125]
2023-03-20 04:59:11,048	OUTPUT_MODEL	INFO	====> Epoch: 3
2023-03-20 05:00:33,085	OUTPUT_MODEL	INFO	Train Epoch: 4 [65%]
2023-03-20 05:00:33,087	OUTPUT_MODEL	INFO	[2.414360523223877, 2.706963539123535, 6.298469066619873, 21.67064666748047, 0.9822525978088379, 2.9381680488586426, 500, 0.00019992500937460937]
2023-03-20 05:01:14,614	OUTPUT_MODEL	INFO	====> Epoch: 4
2023-03-20 05:02:05,333	OUTPUT_MODEL	INFO	Train Epoch: 5 [38%]
2023-03-20 05:02:05,335	OUTPUT_MODEL	INFO	[2.3821616172790527, 2.5564615726470947, 6.129635810852051, 21.891035079956055, 0.8665077686309814, 2.6806764602661133, 600, 0.00019990001874843754]
2023-03-20 05:03:17,393	OUTPUT_MODEL	INFO	====> Epoch: 5
2023-03-20 05:03:36,652	OUTPUT_MODEL	INFO	Train Epoch: 6 [11%]
2023-03-20 05:03:36,654	OUTPUT_MODEL	INFO	[2.3278753757476807, 2.4221811294555664, 5.573760032653809, 22.831247329711914, 1.049795389175415, 2.6688055992126465, 700, 0.00019987503124609398]
2023-03-20 05:05:01,875	OUTPUT_MODEL	INFO	Train Epoch: 6 [84%]
2023-03-20 05:05:01,877	OUTPUT_MODEL	INFO	[2.747849225997925, 2.2292628288269043, 5.344616413116455, 21.46335792541504, 1.0770094394683838, 2.444493293762207, 800, 0.00019987503124609398]
2023-03-20 05:05:20,937	OUTPUT_MODEL	INFO	====> Epoch: 6
2023-03-20 05:06:33,230	OUTPUT_MODEL	INFO	Train Epoch: 7 [57%]
2023-03-20 05:06:33,232	OUTPUT_MODEL	INFO	[2.4970474243164062, 2.6890006065368652, 5.828371524810791, 21.895408630371094, 1.087890625, 2.7133960723876953, 900, 0.0001998500468671882]
2023-03-20 05:07:23,665	OUTPUT_MODEL	INFO	====> Epoch: 7
2023-03-20 05:08:04,885	OUTPUT_MODEL	INFO	Train Epoch: 8 [30%]
2023-03-20 05:08:04,887	OUTPUT_MODEL	INFO	[2.3460614681243896, 2.5388123989105225, 6.182777404785156, 22.26624870300293, 1.0821168422698975, 2.4550745487213135, 1000, 0.00019982506561132978]
2023-03-20 05:08:06,238	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 8 to ././OUTPUT_MODEL/G_1000.pth
2023-03-20 05:08:09,148	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 8 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 05:09:31,181	OUTPUT_MODEL	INFO	====> Epoch: 8
2023-03-20 05:09:41,313	OUTPUT_MODEL	INFO	Train Epoch: 9 [3%]
2023-03-20 05:09:41,314	OUTPUT_MODEL	INFO	[2.4686028957366943, 2.497570276260376, 5.7599992752075195, 22.086750030517578, 0.9498449563980103, 2.510586738586426, 1100, 0.00019980008747812837]
2023-03-20 05:11:06,016	OUTPUT_MODEL	INFO	Train Epoch: 9 [76%]
2023-03-20 05:11:06,018	OUTPUT_MODEL	INFO	[2.5397446155548096, 2.256682872772217, 5.452460765838623, 21.67376136779785, 0.9605969786643982, 2.8113698959350586, 1200, 0.00019980008747812837]
2023-03-20 05:11:34,341	OUTPUT_MODEL	INFO	====> Epoch: 9
2023-03-20 05:12:37,982	OUTPUT_MODEL	INFO	Train Epoch: 10 [49%]
2023-03-20 05:12:37,984	OUTPUT_MODEL	INFO	[2.3714077472686768, 2.962165117263794, 8.00252628326416, 20.8514461517334, 1.168057918548584, 2.5839614868164062, 1300, 0.0001997751124671936]
2023-03-20 05:13:37,554	OUTPUT_MODEL	INFO	====> Epoch: 10
2023-03-20 05:14:09,729	OUTPUT_MODEL	INFO	Train Epoch: 11 [22%]
2023-03-20 05:14:09,730	OUTPUT_MODEL	INFO	[2.325864791870117, 2.5079116821289062, 7.05630350112915, 20.951875686645508, 1.0868475437164307, 2.6657047271728516, 1400, 0.00019975014057813518]
2023-03-20 05:15:34,657	OUTPUT_MODEL	INFO	Train Epoch: 11 [95%]
2023-03-20 05:15:34,659	OUTPUT_MODEL	INFO	[2.4198801517486572, 2.5086779594421387, 6.257854461669922, 21.924415588378906, 1.0245777368545532, 2.573181390762329, 1500, 0.00019975014057813518]
2023-03-20 05:15:40,729	OUTPUT_MODEL	INFO	====> Epoch: 11
2023-03-20 05:17:06,293	OUTPUT_MODEL	INFO	Train Epoch: 12 [68%]
2023-03-20 05:17:06,295	OUTPUT_MODEL	INFO	[2.111722469329834, 3.1188724040985107, 10.066696166992188, 21.249679565429688, 1.1649638414382935, 2.528198003768921, 1600, 0.00019972517181056292]
2023-03-20 05:17:44,427	OUTPUT_MODEL	INFO	====> Epoch: 12
2023-03-20 05:18:38,322	OUTPUT_MODEL	INFO	Train Epoch: 13 [41%]
2023-03-20 05:18:38,324	OUTPUT_MODEL	INFO	[2.1772327423095703, 2.5329771041870117, 6.234670162200928, 21.138046264648438, 1.0930304527282715, 2.406586170196533, 1700, 0.0001997002061640866]
2023-03-20 05:19:46,779	OUTPUT_MODEL	INFO	====> Epoch: 13
2023-03-20 05:20:09,224	OUTPUT_MODEL	INFO	Train Epoch: 14 [14%]
2023-03-20 05:20:09,226	OUTPUT_MODEL	INFO	[2.4297800064086914, 2.423377513885498, 5.343492031097412, 22.5107421875, 1.0312721729278564, 2.1974594593048096, 1800, 0.00019967524363831608]
2023-03-20 05:21:33,709	OUTPUT_MODEL	INFO	Train Epoch: 14 [87%]
2023-03-20 05:21:33,711	OUTPUT_MODEL	INFO	[2.3142142295837402, 2.3111305236816406, 5.861149787902832, 21.61641502380371, 1.0001740455627441, 2.5654854774475098, 1900, 0.00019967524363831608]
2023-03-20 05:21:49,225	OUTPUT_MODEL	INFO	====> Epoch: 14
2023-03-20 05:23:04,524	OUTPUT_MODEL	INFO	Train Epoch: 15 [60%]
2023-03-20 05:23:04,525	OUTPUT_MODEL	INFO	[2.558957576751709, 2.3842828273773193, 5.199512958526611, 22.422033309936523, 0.9508892893791199, 2.363165855407715, 2000, 0.0001996502842328613]
2023-03-20 05:23:05,624	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 15 to ././OUTPUT_MODEL/G_2000.pth
2023-03-20 05:23:06,067	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 15 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 05:23:52,386	OUTPUT_MODEL	INFO	====> Epoch: 15
2023-03-20 05:24:36,720	OUTPUT_MODEL	INFO	Train Epoch: 16 [33%]
2023-03-20 05:24:36,722	OUTPUT_MODEL	INFO	[2.3222079277038574, 2.4199161529541016, 6.0338616371154785, 22.42628288269043, 0.9300016164779663, 2.5799407958984375, 2100, 0.00019962532794733217]
2023-03-20 05:25:54,702	OUTPUT_MODEL	INFO	====> Epoch: 16
2023-03-20 05:26:07,824	OUTPUT_MODEL	INFO	Train Epoch: 17 [6%]
2023-03-20 05:26:07,826	OUTPUT_MODEL	INFO	[2.4395217895507812, 2.3438448905944824, 6.392312526702881, 22.393775939941406, 1.0600595474243164, 2.2696878910064697, 2200, 0.00019960037478133875]
2023-03-20 05:27:32,307	OUTPUT_MODEL	INFO	Train Epoch: 17 [79%]
2023-03-20 05:27:32,309	OUTPUT_MODEL	INFO	[2.411370038986206, 2.385826349258423, 6.200865745544434, 21.293045043945312, 1.0176886320114136, 2.66695237159729, 2300, 0.00019960037478133875]
2023-03-20 05:27:57,237	OUTPUT_MODEL	INFO	====> Epoch: 17
2023-03-20 05:29:03,749	OUTPUT_MODEL	INFO	Train Epoch: 18 [52%]
2023-03-20 05:29:03,751	OUTPUT_MODEL	INFO	[2.338103771209717, 2.5084497928619385, 6.450228214263916, 20.458301544189453, 1.0305733680725098, 2.2200028896331787, 2400, 0.00019957542473449108]
2023-03-20 05:29:59,497	OUTPUT_MODEL	INFO	====> Epoch: 18
2023-03-20 05:30:34,686	OUTPUT_MODEL	INFO	Train Epoch: 19 [25%]
2023-03-20 05:30:34,688	OUTPUT_MODEL	INFO	[2.249535083770752, 2.7612528800964355, 5.765452861785889, 21.434545516967773, 0.8977810144424438, 2.5366902351379395, 2500, 0.00019955047780639926]
2023-03-20 05:31:59,252	OUTPUT_MODEL	INFO	Train Epoch: 19 [98%]
2023-03-20 05:31:59,255	OUTPUT_MODEL	INFO	[2.5016961097717285, 2.359767436981201, 5.6924591064453125, 19.64597511291504, 0.9511029124259949, 2.1031951904296875, 2600, 0.00019955047780639926]
2023-03-20 05:32:02,715	OUTPUT_MODEL	INFO	====> Epoch: 19
2023-03-20 05:33:30,788	OUTPUT_MODEL	INFO	Train Epoch: 20 [71%]
2023-03-20 05:33:30,790	OUTPUT_MODEL	INFO	[2.3634777069091797, 2.4729230403900146, 6.404452323913574, 21.687192916870117, 1.0063040256500244, 2.4803545475006104, 2700, 0.00019952553399667344]
2023-03-20 05:34:04,740	OUTPUT_MODEL	INFO	====> Epoch: 20
2023-03-20 05:35:01,677	OUTPUT_MODEL	INFO	Train Epoch: 21 [44%]
2023-03-20 05:35:01,678	OUTPUT_MODEL	INFO	[2.449660062789917, 2.4449462890625, 5.921932220458984, 21.482526779174805, 1.0885725021362305, 2.444819450378418, 2800, 0.00019950059330492385]
2023-03-20 05:36:06,596	OUTPUT_MODEL	INFO	====> Epoch: 21
2023-03-20 05:36:32,597	OUTPUT_MODEL	INFO	Train Epoch: 22 [17%]
2023-03-20 05:36:32,599	OUTPUT_MODEL	INFO	[2.378692865371704, 2.741948127746582, 6.1366047859191895, 21.873844146728516, 1.0249216556549072, 2.297750473022461, 2900, 0.00019947565573076072]
2023-03-20 05:37:57,459	OUTPUT_MODEL	INFO	Train Epoch: 22 [90%]
2023-03-20 05:37:57,462	OUTPUT_MODEL	INFO	[2.3023388385772705, 2.4632158279418945, 7.0958075523376465, 21.848430633544922, 0.9101219177246094, 2.559555768966675, 3000, 0.00019947565573076072]
2023-03-20 05:37:58,732	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 22 to ././OUTPUT_MODEL/G_3000.pth
2023-03-20 05:37:59,185	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 22 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 05:38:11,288	OUTPUT_MODEL	INFO	====> Epoch: 22
2023-03-20 05:39:30,459	OUTPUT_MODEL	INFO	Train Epoch: 23 [63%]
2023-03-20 05:39:30,461	OUTPUT_MODEL	INFO	[2.618593454360962, 2.2507829666137695, 6.680166244506836, 21.32682228088379, 0.8999291062355042, 2.4003827571868896, 3100, 0.00019945072127379438]
2023-03-20 05:40:14,525	OUTPUT_MODEL	INFO	====> Epoch: 23
2023-03-20 05:41:02,229	OUTPUT_MODEL	INFO	Train Epoch: 24 [36%]
2023-03-20 05:41:02,231	OUTPUT_MODEL	INFO	[2.3183510303497314, 2.349379777908325, 5.5381035804748535, 20.92702865600586, 1.1521114110946655, 2.6439883708953857, 3200, 0.00019942578993363514]
2023-03-20 05:42:16,219	OUTPUT_MODEL	INFO	====> Epoch: 24
2023-03-20 05:42:32,804	OUTPUT_MODEL	INFO	Train Epoch: 25 [9%]
2023-03-20 05:42:32,805	OUTPUT_MODEL	INFO	[2.233380079269409, 2.678537607192993, 6.625271320343018, 21.032094955444336, 1.1368112564086914, 1.9819645881652832, 3300, 0.00019940086170989343]
2023-03-20 05:43:56,899	OUTPUT_MODEL	INFO	Train Epoch: 25 [82%]
2023-03-20 05:43:56,901	OUTPUT_MODEL	INFO	[2.4418411254882812, 2.4007139205932617, 5.6858062744140625, 21.321502685546875, 1.0047379732131958, 2.278641939163208, 3400, 0.00019940086170989343]
2023-03-20 05:44:18,551	OUTPUT_MODEL	INFO	====> Epoch: 25
2023-03-20 05:45:27,590	OUTPUT_MODEL	INFO	Train Epoch: 26 [55%]
2023-03-20 05:45:27,592	OUTPUT_MODEL	INFO	[2.4927730560302734, 2.6393702030181885, 6.181835651397705, 21.02301788330078, 1.1018187999725342, 1.9876865148544312, 3500, 0.0001993759366021797]
2023-03-20 05:46:20,173	OUTPUT_MODEL	INFO	====> Epoch: 26
2023-03-20 05:46:59,685	OUTPUT_MODEL	INFO	Train Epoch: 27 [28%]
2023-03-20 05:46:59,687	OUTPUT_MODEL	INFO	[2.396218776702881, 2.4292943477630615, 6.011164665222168, 21.621994018554688, 1.0323376655578613, 2.481682062149048, 3600, 0.00019935101461010442]
2023-03-20 05:48:22,978	OUTPUT_MODEL	INFO	====> Epoch: 27
2023-03-20 05:48:30,362	OUTPUT_MODEL	INFO	Train Epoch: 28 [1%]
2023-03-20 05:48:30,364	OUTPUT_MODEL	INFO	[2.434113025665283, 2.4552488327026367, 6.0052571296691895, 20.592008590698242, 1.055776596069336, 2.442913293838501, 3700, 0.00019932609573327815]
2023-03-20 05:49:55,454	OUTPUT_MODEL	INFO	Train Epoch: 28 [74%]
2023-03-20 05:49:55,456	OUTPUT_MODEL	INFO	[2.238372802734375, 2.777230739593506, 7.386657238006592, 22.75822639465332, 1.0350539684295654, 2.3606021404266357, 3800, 0.00019932609573327815]
2023-03-20 05:50:25,994	OUTPUT_MODEL	INFO	====> Epoch: 28
2023-03-20 05:51:26,495	OUTPUT_MODEL	INFO	Train Epoch: 29 [47%]
2023-03-20 05:51:26,497	OUTPUT_MODEL	INFO	[2.2708065509796143, 2.809279203414917, 6.843869209289551, 23.192211151123047, 1.0050231218338013, 2.1657826900482178, 3900, 0.0001993011799713115]
2023-03-20 05:52:27,880	OUTPUT_MODEL	INFO	====> Epoch: 29
2023-03-20 05:52:57,346	OUTPUT_MODEL	INFO	Train Epoch: 30 [20%]
2023-03-20 05:52:57,348	OUTPUT_MODEL	INFO	[2.2923078536987305, 2.7639176845550537, 9.149090766906738, 19.117813110351562, 1.2570829391479492, 2.013174533843994, 4000, 0.00019927626732381507]
2023-03-20 05:52:58,668	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 30 to ././OUTPUT_MODEL/G_4000.pth
2023-03-20 05:52:59,117	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 30 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 05:54:24,138	OUTPUT_MODEL	INFO	Train Epoch: 30 [93%]
2023-03-20 05:54:24,140	OUTPUT_MODEL	INFO	[2.3150267601013184, 2.5412557125091553, 7.159514904022217, 21.487512588500977, 0.95357745885849, 2.2474467754364014, 4100, 0.00019927626732381507]
2023-03-20 05:54:32,902	OUTPUT_MODEL	INFO	====> Epoch: 30
2023-03-20 05:55:55,232	OUTPUT_MODEL	INFO	Train Epoch: 31 [66%]
2023-03-20 05:55:55,235	OUTPUT_MODEL	INFO	[2.531769275665283, 2.268995761871338, 4.891177654266357, 20.748960494995117, 1.137803554534912, 1.9323086738586426, 4200, 0.00019925135779039958]
2023-03-20 05:56:35,327	OUTPUT_MODEL	INFO	====> Epoch: 31
2023-03-20 05:57:26,652	OUTPUT_MODEL	INFO	Train Epoch: 32 [39%]
2023-03-20 05:57:26,654	OUTPUT_MODEL	INFO	[2.2591426372528076, 2.717677354812622, 7.704442024230957, 22.112245559692383, 1.1481953859329224, 2.2806475162506104, 4300, 0.00019922645137067577]
2023-03-20 05:58:38,149	OUTPUT_MODEL	INFO	====> Epoch: 32
2023-03-20 05:58:58,487	OUTPUT_MODEL	INFO	Train Epoch: 33 [12%]
2023-03-20 05:58:58,489	OUTPUT_MODEL	INFO	[2.3863348960876465, 2.31925630569458, 5.996677398681641, 21.9753475189209, 0.9978878498077393, 2.6358673572540283, 4400, 0.00019920154806425444]
2023-03-20 06:00:23,794	OUTPUT_MODEL	INFO	Train Epoch: 33 [85%]
2023-03-20 06:00:23,796	OUTPUT_MODEL	INFO	[2.425518274307251, 2.513273000717163, 7.2610673904418945, 20.831571578979492, 1.1499401330947876, 2.25093936920166, 4500, 0.00019920154806425444]
2023-03-20 06:00:42,441	OUTPUT_MODEL	INFO	====> Epoch: 33
2023-03-20 06:01:55,929	OUTPUT_MODEL	INFO	Train Epoch: 34 [58%]
2023-03-20 06:01:55,931	OUTPUT_MODEL	INFO	[2.3910560607910156, 2.545807361602783, 5.594863414764404, 21.33628273010254, 1.0692932605743408, 2.218959331512451, 4600, 0.0001991766478707464]
2023-03-20 06:02:45,448	OUTPUT_MODEL	INFO	====> Epoch: 34
2023-03-20 06:03:27,760	OUTPUT_MODEL	INFO	Train Epoch: 35 [31%]
2023-03-20 06:03:27,762	OUTPUT_MODEL	INFO	[2.2697484493255615, 2.8187873363494873, 7.505356788635254, 22.335460662841797, 0.9759870767593384, 2.2168784141540527, 4700, 0.00019915175078976256]
2023-03-20 06:04:48,987	OUTPUT_MODEL	INFO	====> Epoch: 35
2023-03-20 06:05:00,042	OUTPUT_MODEL	INFO	Train Epoch: 36 [4%]
2023-03-20 06:05:00,044	OUTPUT_MODEL	INFO	[2.218749761581421, 2.6363742351531982, 7.472253322601318, 21.36639976501465, 0.11921560764312744, 2.4373831748962402, 4800, 0.00019912685682091382]
2023-03-20 06:06:25,380	OUTPUT_MODEL	INFO	Train Epoch: 36 [77%]
2023-03-20 06:06:25,382	OUTPUT_MODEL	INFO	[2.2713241577148438, 2.390611171722412, 5.881557464599609, 20.962438583374023, 0.9878108501434326, 2.219108819961548, 4900, 0.00019912685682091382]
2023-03-20 06:06:52,914	OUTPUT_MODEL	INFO	====> Epoch: 36
2023-03-20 06:07:56,848	OUTPUT_MODEL	INFO	Train Epoch: 37 [50%]
2023-03-20 06:07:56,850	OUTPUT_MODEL	INFO	[2.3238558769226074, 2.851607084274292, 6.607328414916992, 21.924644470214844, 1.038264513015747, 2.0841290950775146, 5000, 0.0001991019659638112]
2023-03-20 06:07:58,058	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 37 to ././OUTPUT_MODEL/G_5000.pth
2023-03-20 06:07:58,495	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 37 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 06:08:57,236	OUTPUT_MODEL	INFO	====> Epoch: 37
2023-03-20 06:09:30,214	OUTPUT_MODEL	INFO	Train Epoch: 38 [23%]
2023-03-20 06:09:30,216	OUTPUT_MODEL	INFO	[2.315687417984009, 2.533667802810669, 5.910314559936523, 21.52378273010254, 1.1747164726257324, 2.224759578704834, 5100, 0.0001990770782180657]
2023-03-20 06:10:54,663	OUTPUT_MODEL	INFO	Train Epoch: 38 [96%]
2023-03-20 06:10:54,664	OUTPUT_MODEL	INFO	[2.336763381958008, 2.574896812438965, 6.0556135177612305, 20.756223678588867, 1.0886473655700684, 1.9646687507629395, 5200, 0.0001990770782180657]
2023-03-20 06:11:00,282	OUTPUT_MODEL	INFO	====> Epoch: 38
2023-03-20 06:12:25,686	OUTPUT_MODEL	INFO	Train Epoch: 39 [69%]
2023-03-20 06:12:25,688	OUTPUT_MODEL	INFO	[2.3453662395477295, 2.502741813659668, 6.365427494049072, 21.667299270629883, 1.0664794445037842, 2.400519847869873, 5300, 0.00019905219358328844]
2023-03-20 06:13:02,493	OUTPUT_MODEL	INFO	====> Epoch: 39
2023-03-20 06:13:56,907	OUTPUT_MODEL	INFO	Train Epoch: 40 [42%]
2023-03-20 06:13:56,909	OUTPUT_MODEL	INFO	[2.2867045402526855, 2.6499552726745605, 7.92043399810791, 19.367441177368164, 1.2460732460021973, 1.9455738067626953, 5400, 0.0001990273120590905]
2023-03-20 06:15:04,629	OUTPUT_MODEL	INFO	====> Epoch: 40
2023-03-20 06:15:28,099	OUTPUT_MODEL	INFO	Train Epoch: 41 [15%]
2023-03-20 06:15:28,100	OUTPUT_MODEL	INFO	[2.319932699203491, 2.5248024463653564, 5.856874465942383, 20.759521484375, 1.0042153596878052, 2.6121139526367188, 5500, 0.00019900243364508313]
2023-03-20 06:16:52,121	OUTPUT_MODEL	INFO	Train Epoch: 41 [88%]
2023-03-20 06:16:52,123	OUTPUT_MODEL	INFO	[2.4524905681610107, 2.268375873565674, 5.426504135131836, 19.77986717224121, 1.0994291305541992, 2.6308467388153076, 5600, 0.00019900243364508313]
2023-03-20 06:17:06,786	OUTPUT_MODEL	INFO	====> Epoch: 41
2023-03-20 06:18:22,993	OUTPUT_MODEL	INFO	Train Epoch: 42 [61%]
2023-03-20 06:18:22,995	OUTPUT_MODEL	INFO	[2.296217679977417, 2.4862289428710938, 6.083980083465576, 21.393417358398438, 1.0103909969329834, 2.3783440589904785, 5700, 0.0001989775583408775]
2023-03-20 06:19:09,149	OUTPUT_MODEL	INFO	====> Epoch: 42
2023-03-20 06:19:54,470	OUTPUT_MODEL	INFO	Train Epoch: 43 [34%]
2023-03-20 06:19:54,471	OUTPUT_MODEL	INFO	[2.4225120544433594, 2.3112435340881348, 5.720268726348877, 21.356412887573242, 0.9748706817626953, 2.6254968643188477, 5800, 0.00019895268614608487]
2023-03-20 06:21:10,986	OUTPUT_MODEL	INFO	====> Epoch: 43
2023-03-20 06:21:25,300	OUTPUT_MODEL	INFO	Train Epoch: 44 [7%]
2023-03-20 06:21:25,302	OUTPUT_MODEL	INFO	[2.392667770385742, 2.415858745574951, 5.42399787902832, 20.880842208862305, 1.0011475086212158, 2.3076629638671875, 5900, 0.0001989278170603166]
2023-03-20 06:22:49,640	OUTPUT_MODEL	INFO	Train Epoch: 44 [80%]
2023-03-20 06:22:49,641	OUTPUT_MODEL	INFO	[2.4061245918273926, 2.4468486309051514, 5.794806480407715, 20.808799743652344, 0.9881535768508911, 2.0985186100006104, 6000, 0.0001989278170603166]
2023-03-20 06:22:50,632	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 44 to ././OUTPUT_MODEL/G_6000.pth
2023-03-20 06:22:51,107	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 44 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 06:23:15,269	OUTPUT_MODEL	INFO	====> Epoch: 44
2023-03-20 06:24:22,616	OUTPUT_MODEL	INFO	Train Epoch: 45 [53%]
2023-03-20 06:24:22,618	OUTPUT_MODEL	INFO	[2.344695568084717, 2.51739764213562, 5.785505294799805, 20.910051345825195, 1.0618458986282349, 2.066441059112549, 6100, 0.00019890295108318404]
2023-03-20 06:25:17,538	OUTPUT_MODEL	INFO	====> Epoch: 45
2023-03-20 06:25:53,628	OUTPUT_MODEL	INFO	Train Epoch: 46 [26%]
2023-03-20 06:25:53,630	OUTPUT_MODEL	INFO	[2.1810452938079834, 2.5501790046691895, 6.760313987731934, 21.544937133789062, 1.0653914213180542, 2.5155978202819824, 6200, 0.00019887808821429862]
2023-03-20 06:27:18,271	OUTPUT_MODEL	INFO	Train Epoch: 46 [99%]
2023-03-20 06:27:18,273	OUTPUT_MODEL	INFO	[2.415778160095215, 2.519730567932129, 7.42428731918335, 22.12043571472168, 1.084808349609375, 2.104057550430298, 6300, 0.00019887808821429862]
2023-03-20 06:27:20,291	OUTPUT_MODEL	INFO	====> Epoch: 46
2023-03-20 06:28:49,244	OUTPUT_MODEL	INFO	Train Epoch: 47 [72%]
2023-03-20 06:28:49,246	OUTPUT_MODEL	INFO	[2.275031328201294, 2.567343235015869, 7.659367084503174, 22.6458797454834, 1.208775520324707, 2.4583070278167725, 6400, 0.00019885322845327182]
2023-03-20 06:29:22,398	OUTPUT_MODEL	INFO	====> Epoch: 47
2023-03-20 06:30:20,254	OUTPUT_MODEL	INFO	Train Epoch: 48 [45%]
2023-03-20 06:30:20,256	OUTPUT_MODEL	INFO	[2.5616958141326904, 2.596893310546875, 6.496462821960449, 20.455415725708008, 1.2762694358825684, 1.9882615804672241, 6500, 0.00019882837179971516]
2023-03-20 06:31:24,406	OUTPUT_MODEL	INFO	====> Epoch: 48
2023-03-20 06:31:51,157	OUTPUT_MODEL	INFO	Train Epoch: 49 [18%]
2023-03-20 06:31:51,159	OUTPUT_MODEL	INFO	[2.1804990768432617, 2.5602006912231445, 6.911062717437744, 21.7054443359375, 0.9448407292366028, 2.0211594104766846, 6600, 0.00019880351825324018]
2023-03-20 06:33:15,876	OUTPUT_MODEL	INFO	Train Epoch: 49 [91%]
2023-03-20 06:33:15,878	OUTPUT_MODEL	INFO	[2.3628220558166504, 2.4738526344299316, 5.915909290313721, 21.013687133789062, 1.0099396705627441, 2.492263078689575, 6700, 0.00019880351825324018]
2023-03-20 06:33:27,064	OUTPUT_MODEL	INFO	====> Epoch: 49
2023-03-20 06:34:46,291	OUTPUT_MODEL	INFO	Train Epoch: 50 [64%]
2023-03-20 06:34:46,293	OUTPUT_MODEL	INFO	[2.379373073577881, 2.4842967987060547, 5.927206516265869, 20.563724517822266, 1.078188419342041, 1.9830142259597778, 6800, 0.00019877866781345852]
2023-03-20 06:35:28,731	OUTPUT_MODEL	INFO	====> Epoch: 50
2023-03-20 06:36:16,625	OUTPUT_MODEL	INFO	Train Epoch: 51 [36%]
2023-03-20 06:36:16,627	OUTPUT_MODEL	INFO	[2.182598829269409, 2.574443817138672, 6.8390278816223145, 21.70244026184082, 0.9938815832138062, 2.0827391147613525, 6900, 0.00019875382047998183]
2023-03-20 06:37:29,586	OUTPUT_MODEL	INFO	====> Epoch: 51
2023-03-20 06:37:47,175	OUTPUT_MODEL	INFO	Train Epoch: 52 [9%]
2023-03-20 06:37:47,177	OUTPUT_MODEL	INFO	[2.3460025787353516, 2.49182391166687, 7.098108291625977, 21.946733474731445, 1.0188202857971191, 2.206055164337158, 7000, 0.00019872897625242182]
2023-03-20 06:37:48,494	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 52 to ././OUTPUT_MODEL/G_7000.pth
2023-03-20 06:37:51,883	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 52 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 06:39:16,148	OUTPUT_MODEL	INFO	Train Epoch: 52 [82%]
2023-03-20 06:39:16,150	OUTPUT_MODEL	INFO	[2.3416943550109863, 2.619367837905884, 8.866121292114258, 21.27914047241211, 1.0435935258865356, 2.446051597595215, 7100, 0.00019872897625242182]
2023-03-20 06:39:36,520	OUTPUT_MODEL	INFO	====> Epoch: 52
2023-03-20 06:40:46,575	OUTPUT_MODEL	INFO	Train Epoch: 53 [55%]
2023-03-20 06:40:46,577	OUTPUT_MODEL	INFO	[2.373596668243408, 2.7588155269622803, 6.409639358520508, 21.997127532958984, 0.9294161796569824, 2.488933563232422, 7200, 0.00019870413513039026]
2023-03-20 06:41:37,858	OUTPUT_MODEL	INFO	====> Epoch: 53
2023-03-20 06:42:16,959	OUTPUT_MODEL	INFO	Train Epoch: 54 [28%]
2023-03-20 06:42:16,961	OUTPUT_MODEL	INFO	[2.343045711517334, 2.3762874603271484, 6.479568958282471, 20.9439640045166, 1.084822177886963, 2.297544002532959, 7300, 0.00019867929711349895]
2023-03-20 06:43:39,516	OUTPUT_MODEL	INFO	====> Epoch: 54
2023-03-20 06:43:47,826	OUTPUT_MODEL	INFO	Train Epoch: 55 [1%]
2023-03-20 06:43:47,828	OUTPUT_MODEL	INFO	[2.4111175537109375, 2.4858224391937256, 8.205568313598633, 20.85487174987793, 1.2197136878967285, 1.9240813255310059, 7400, 0.00019865446220135974]
2023-03-20 06:45:11,641	OUTPUT_MODEL	INFO	Train Epoch: 55 [74%]
2023-03-20 06:45:11,642	OUTPUT_MODEL	INFO	[2.075490713119507, 2.733879566192627, 7.0083112716674805, 21.10756492614746, 1.0187435150146484, 2.3595361709594727, 7500, 0.00019865446220135974]
2023-03-20 06:45:41,413	OUTPUT_MODEL	INFO	====> Epoch: 55
2023-03-20 06:46:42,437	OUTPUT_MODEL	INFO	Train Epoch: 56 [47%]
2023-03-20 06:46:42,439	OUTPUT_MODEL	INFO	[2.4146764278411865, 2.8507885932922363, 7.360169887542725, 22.415760040283203, 1.0407931804656982, 2.350841522216797, 7600, 0.00019862963039358455]
2023-03-20 06:47:42,807	OUTPUT_MODEL	INFO	====> Epoch: 56
2023-03-20 06:48:13,092	OUTPUT_MODEL	INFO	Train Epoch: 57 [20%]
2023-03-20 06:48:13,094	OUTPUT_MODEL	INFO	[2.2495527267456055, 2.548194408416748, 6.785152435302734, 21.255382537841797, 1.0955281257629395, 2.1161952018737793, 7700, 0.00019860480168978534]
2023-03-20 06:49:37,013	OUTPUT_MODEL	INFO	Train Epoch: 57 [93%]
2023-03-20 06:49:37,015	OUTPUT_MODEL	INFO	[2.173794984817505, 2.661022186279297, 7.115841388702393, 20.629276275634766, 1.1031360626220703, 2.155308961868286, 7800, 0.00019860480168978534]
2023-03-20 06:49:44,883	OUTPUT_MODEL	INFO	====> Epoch: 57
2023-03-20 06:51:07,290	OUTPUT_MODEL	INFO	Train Epoch: 58 [66%]
2023-03-20 06:51:07,292	OUTPUT_MODEL	INFO	[2.1896283626556396, 2.601907968521118, 6.583775997161865, 20.824275970458984, 0.8944833278656006, 2.1115775108337402, 7900, 0.0001985799760895741]
2023-03-20 06:51:46,596	OUTPUT_MODEL	INFO	====> Epoch: 58
2023-03-20 06:52:38,345	OUTPUT_MODEL	INFO	Train Epoch: 59 [39%]
2023-03-20 06:52:38,346	OUTPUT_MODEL	INFO	[2.1954145431518555, 2.4939887523651123, 6.6407976150512695, 20.928678512573242, 1.0437085628509521, 2.173067569732666, 8000, 0.0001985551535925629]
2023-03-20 06:52:39,600	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 59 to ././OUTPUT_MODEL/G_8000.pth
2023-03-20 06:52:42,404	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 59 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 06:53:52,370	OUTPUT_MODEL	INFO	====> Epoch: 59
2023-03-20 06:54:13,400	OUTPUT_MODEL	INFO	Train Epoch: 60 [12%]
2023-03-20 06:54:13,402	OUTPUT_MODEL	INFO	[2.1614527702331543, 2.6755542755126953, 7.3774800300598145, 21.461620330810547, 0.9664415121078491, 2.302335500717163, 8100, 0.00019853033419836382]
2023-03-20 06:55:37,275	OUTPUT_MODEL	INFO	Train Epoch: 60 [85%]
2023-03-20 06:55:37,278	OUTPUT_MODEL	INFO	[2.2071516513824463, 2.561432361602783, 6.528665065765381, 21.3625431060791, 0.8937692046165466, 1.9133594036102295, 8200, 0.00019853033419836382]
2023-03-20 06:55:54,183	OUTPUT_MODEL	INFO	====> Epoch: 60
2023-03-20 06:57:07,598	OUTPUT_MODEL	INFO	Train Epoch: 61 [58%]
2023-03-20 06:57:07,600	OUTPUT_MODEL	INFO	[2.4026567935943604, 2.6352813243865967, 5.89398193359375, 20.832767486572266, 1.059910774230957, 2.0321571826934814, 8300, 0.000198505517906589]
2023-03-20 06:57:55,691	OUTPUT_MODEL	INFO	====> Epoch: 61
2023-03-20 06:58:38,182	OUTPUT_MODEL	INFO	Train Epoch: 62 [31%]
2023-03-20 06:58:38,184	OUTPUT_MODEL	INFO	[2.2523765563964844, 2.4221158027648926, 5.973262786865234, 21.36760711669922, 1.0674996376037598, 2.035020589828491, 8400, 0.00019848070471685067]
2023-03-20 06:59:57,195	OUTPUT_MODEL	INFO	====> Epoch: 62
2023-03-20 07:00:09,236	OUTPUT_MODEL	INFO	Train Epoch: 63 [4%]
2023-03-20 07:00:09,238	OUTPUT_MODEL	INFO	[2.3156728744506836, 2.642226457595825, 7.153924942016602, 20.615251541137695, 0.9395657777786255, 2.1934311389923096, 8500, 0.00019845589462876104]
2023-03-20 07:01:34,551	OUTPUT_MODEL	INFO	Train Epoch: 63 [77%]
2023-03-20 07:01:34,553	OUTPUT_MODEL	INFO	[2.369943141937256, 2.2795467376708984, 6.403768539428711, 21.014150619506836, 1.0619455575942993, 2.2720086574554443, 8600, 0.00019845589462876104]
2023-03-20 07:02:00,913	OUTPUT_MODEL	INFO	====> Epoch: 63
2023-03-20 07:03:05,555	OUTPUT_MODEL	INFO	Train Epoch: 64 [50%]
2023-03-20 07:03:05,557	OUTPUT_MODEL	INFO	[2.1464638710021973, 2.534351348876953, 6.995076656341553, 21.448284149169922, 1.067678451538086, 2.105104923248291, 8700, 0.00019843108764193245]
2023-03-20 07:04:02,693	OUTPUT_MODEL	INFO	====> Epoch: 64
2023-03-20 07:04:35,880	OUTPUT_MODEL	INFO	Train Epoch: 65 [23%]
2023-03-20 07:04:35,882	OUTPUT_MODEL	INFO	[2.3840267658233643, 2.5473148822784424, 6.865870475769043, 21.11429214477539, 1.1679773330688477, 2.6458561420440674, 8800, 0.0001984062837559772]
2023-03-20 07:05:59,902	OUTPUT_MODEL	INFO	Train Epoch: 65 [96%]
2023-03-20 07:05:59,905	OUTPUT_MODEL	INFO	[2.2166178226470947, 2.591724157333374, 7.665353298187256, 21.44041633605957, 1.0788224935531616, 2.2067275047302246, 8900, 0.0001984062837559772]
2023-03-20 07:06:04,542	OUTPUT_MODEL	INFO	====> Epoch: 65
2023-03-20 07:07:30,421	OUTPUT_MODEL	INFO	Train Epoch: 66 [69%]
2023-03-20 07:07:30,423	OUTPUT_MODEL	INFO	[2.304959297180176, 2.34112286567688, 6.562864303588867, 19.94442367553711, 0.9198429584503174, 2.125624895095825, 9000, 0.00019838148297050769]
2023-03-20 07:07:31,912	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 66 to ././OUTPUT_MODEL/G_9000.pth
2023-03-20 07:07:34,638	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 66 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 07:08:10,707	OUTPUT_MODEL	INFO	====> Epoch: 66
2023-03-20 07:09:05,566	OUTPUT_MODEL	INFO	Train Epoch: 67 [42%]
2023-03-20 07:09:05,567	OUTPUT_MODEL	INFO	[2.2201578617095947, 3.0877323150634766, 6.623358726501465, 20.948078155517578, 0.927273690700531, 2.388641595840454, 9100, 0.00019835668528513637]
2023-03-20 07:10:12,099	OUTPUT_MODEL	INFO	====> Epoch: 67
2023-03-20 07:10:36,136	OUTPUT_MODEL	INFO	Train Epoch: 68 [15%]
2023-03-20 07:10:36,138	OUTPUT_MODEL	INFO	[2.386220932006836, 2.8883137702941895, 11.942041397094727, 19.22420310974121, 1.1705451011657715, 1.9259698390960693, 9200, 0.00019833189069947573]
2023-03-20 07:12:00,750	OUTPUT_MODEL	INFO	Train Epoch: 68 [88%]
2023-03-20 07:12:00,751	OUTPUT_MODEL	INFO	[2.5888218879699707, 2.4541800022125244, 6.945582866668701, 17.71086311340332, 1.1790974140167236, 2.127612829208374, 9300, 0.00019833189069947573]
2023-03-20 07:12:14,162	OUTPUT_MODEL	INFO	====> Epoch: 68
2023-03-20 07:13:30,544	OUTPUT_MODEL	INFO	Train Epoch: 69 [61%]
2023-03-20 07:13:30,546	OUTPUT_MODEL	INFO	[2.3722825050354004, 2.72517466545105, 7.11226749420166, 21.310815811157227, 0.974115252494812, 2.2551729679107666, 9400, 0.0001983070992131383]
2023-03-20 07:14:15,440	OUTPUT_MODEL	INFO	====> Epoch: 69
2023-03-20 07:15:01,078	OUTPUT_MODEL	INFO	Train Epoch: 70 [34%]
2023-03-20 07:15:01,080	OUTPUT_MODEL	INFO	[2.26186466217041, 2.675645351409912, 8.75265884399414, 21.239503860473633, 0.8775568604469299, 2.1743762493133545, 9500, 0.00019828231082573666]
2023-03-20 07:16:16,880	OUTPUT_MODEL	INFO	====> Epoch: 70
2023-03-20 07:16:31,964	OUTPUT_MODEL	INFO	Train Epoch: 71 [7%]
2023-03-20 07:16:31,966	OUTPUT_MODEL	INFO	[2.3685317039489746, 2.5112521648406982, 5.4444098472595215, 20.206806182861328, 1.115312099456787, 2.2065441608428955, 9600, 0.00019825752553688343]
2023-03-20 07:17:55,928	OUTPUT_MODEL	INFO	Train Epoch: 71 [80%]
2023-03-20 07:17:55,930	OUTPUT_MODEL	INFO	[2.3213939666748047, 2.4777724742889404, 6.090353965759277, 20.595993041992188, 1.1500284671783447, 2.2332072257995605, 9700, 0.00019825752553688343]
2023-03-20 07:18:18,996	OUTPUT_MODEL	INFO	====> Epoch: 71
2023-03-20 07:19:26,706	OUTPUT_MODEL	INFO	Train Epoch: 72 [53%]
2023-03-20 07:19:26,707	OUTPUT_MODEL	INFO	[2.549499034881592, 2.608009099960327, 6.347835063934326, 20.50522232055664, 1.0746936798095703, 2.40590500831604, 9800, 0.0001982327433461913]
2023-03-20 07:20:20,347	OUTPUT_MODEL	INFO	====> Epoch: 72
2023-03-20 07:20:57,019	OUTPUT_MODEL	INFO	Train Epoch: 73 [26%]
2023-03-20 07:20:57,020	OUTPUT_MODEL	INFO	[2.317243814468384, 2.5111398696899414, 6.277637004852295, 21.306026458740234, 0.2541711628437042, 2.4218897819519043, 9900, 0.00019820796425327303]
2023-03-20 07:22:20,735	OUTPUT_MODEL	INFO	Train Epoch: 73 [99%]
2023-03-20 07:22:20,737	OUTPUT_MODEL	INFO	[2.3691835403442383, 2.686812400817871, 6.1563262939453125, 22.640975952148438, 0.8485770225524902, 2.391847610473633, 10000, 0.00019820796425327303]
2023-03-20 07:22:22,062	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 73 to ././OUTPUT_MODEL/G_10000.pth
2023-03-20 07:22:24,678	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 73 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 07:22:26,648	OUTPUT_MODEL	INFO	====> Epoch: 73
2023-03-20 07:23:57,288	OUTPUT_MODEL	INFO	Train Epoch: 74 [72%]
2023-03-20 07:23:57,290	OUTPUT_MODEL	INFO	[2.210081100463867, 2.8229386806488037, 7.153075218200684, 20.959842681884766, 1.0783164501190186, 1.9525015354156494, 10100, 0.00019818318825774137]
2023-03-20 07:24:29,509	OUTPUT_MODEL	INFO	====> Epoch: 74
2023-03-20 07:25:28,146	OUTPUT_MODEL	INFO	Train Epoch: 75 [45%]
2023-03-20 07:25:28,148	OUTPUT_MODEL	INFO	[2.2747745513916016, 2.6988673210144043, 6.451331615447998, 20.98773765563965, 0.9178258776664734, 2.1799211502075195, 10200, 0.00019815841535920914]
2023-03-20 07:26:30,959	OUTPUT_MODEL	INFO	====> Epoch: 75
2023-03-20 07:26:58,578	OUTPUT_MODEL	INFO	Train Epoch: 76 [18%]
2023-03-20 07:26:58,579	OUTPUT_MODEL	INFO	[2.194822311401367, 2.5072548389434814, 7.637608051300049, 20.786239624023438, 1.1043827533721924, 2.1243600845336914, 10300, 0.00019813364555728923]
2023-03-20 07:28:22,993	OUTPUT_MODEL	INFO	Train Epoch: 76 [91%]
2023-03-20 07:28:22,995	OUTPUT_MODEL	INFO	[2.2530040740966797, 2.6126668453216553, 6.6027045249938965, 21.008541107177734, 0.9683436155319214, 2.202589988708496, 10400, 0.00019813364555728923]
2023-03-20 07:28:33,478	OUTPUT_MODEL	INFO	====> Epoch: 76
2023-03-20 07:29:54,290	OUTPUT_MODEL	INFO	Train Epoch: 77 [64%]
2023-03-20 07:29:54,292	OUTPUT_MODEL	INFO	[2.330078601837158, 2.654750108718872, 6.143267631530762, 19.307348251342773, 1.1479781866073608, 2.080238103866577, 10500, 0.00019810887885159456]
2023-03-20 07:30:35,894	OUTPUT_MODEL	INFO	====> Epoch: 77
2023-03-20 07:31:25,708	OUTPUT_MODEL	INFO	Train Epoch: 78 [37%]
2023-03-20 07:31:25,710	OUTPUT_MODEL	INFO	[2.4541852474212646, 2.489478349685669, 5.6941022872924805, 20.56330108642578, 0.9345759749412537, 2.263241767883301, 10600, 0.0001980841152417381]
2023-03-20 07:32:38,382	OUTPUT_MODEL	INFO	====> Epoch: 78
2023-03-20 07:32:56,658	OUTPUT_MODEL	INFO	Train Epoch: 79 [10%]
2023-03-20 07:32:56,660	OUTPUT_MODEL	INFO	[2.47392201423645, 2.770947217941284, 7.123619556427002, 20.136686325073242, 1.1700782775878906, 2.3161003589630127, 10700, 0.00019805935472733287]
2023-03-20 07:34:21,026	OUTPUT_MODEL	INFO	Train Epoch: 79 [83%]
2023-03-20 07:34:21,028	OUTPUT_MODEL	INFO	[2.008061170578003, 3.197000026702881, 7.919595718383789, 20.92676544189453, 1.1292893886566162, 2.366858959197998, 10800, 0.00019805935472733287]
2023-03-20 07:34:40,825	OUTPUT_MODEL	INFO	====> Epoch: 79
2023-03-20 07:35:51,872	OUTPUT_MODEL	INFO	Train Epoch: 80 [56%]
2023-03-20 07:35:51,873	OUTPUT_MODEL	INFO	[2.398155927658081, 2.3024187088012695, 5.694573879241943, 20.731311798095703, 0.9558272957801819, 2.1700685024261475, 10900, 0.00019803459730799195]
2023-03-20 07:36:42,562	OUTPUT_MODEL	INFO	====> Epoch: 80
2023-03-20 07:37:22,731	OUTPUT_MODEL	INFO	Train Epoch: 81 [29%]
2023-03-20 07:37:22,732	OUTPUT_MODEL	INFO	[2.245670795440674, 2.2968814373016357, 6.622286796569824, 20.73750114440918, 0.9882419109344482, 2.5336437225341797, 11000, 0.00019800984298332845]
2023-03-20 07:37:23,799	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 81 to ././OUTPUT_MODEL/G_11000.pth
2023-03-20 07:37:24,246	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 81 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 07:38:46,428	OUTPUT_MODEL	INFO	====> Epoch: 81
2023-03-20 07:38:55,500	OUTPUT_MODEL	INFO	Train Epoch: 82 [2%]
2023-03-20 07:38:55,502	OUTPUT_MODEL	INFO	[2.318716526031494, 2.4844884872436523, 6.464455604553223, 21.069019317626953, 1.009663701057434, 2.2807743549346924, 11100, 0.00019798509175295552]
2023-03-20 07:40:19,781	OUTPUT_MODEL	INFO	Train Epoch: 82 [75%]
2023-03-20 07:40:19,783	OUTPUT_MODEL	INFO	[2.277681827545166, 2.7742202281951904, 8.108365058898926, 20.985414505004883, 1.0835990905761719, 2.076566219329834, 11200, 0.00019798509175295552]
2023-03-20 07:40:48,560	OUTPUT_MODEL	INFO	====> Epoch: 82
2023-03-20 07:41:50,214	OUTPUT_MODEL	INFO	Train Epoch: 83 [48%]
2023-03-20 07:41:50,216	OUTPUT_MODEL	INFO	[2.284376621246338, 2.5702922344207764, 7.398943901062012, 20.58625030517578, 1.0708726644515991, 2.370973587036133, 11300, 0.0001979603436164864]
2023-03-20 07:42:50,143	OUTPUT_MODEL	INFO	====> Epoch: 83
2023-03-20 07:43:21,283	OUTPUT_MODEL	INFO	Train Epoch: 84 [21%]
2023-03-20 07:43:21,285	OUTPUT_MODEL	INFO	[2.4430556297302246, 2.7300751209259033, 6.211954116821289, 20.21200942993164, 1.1850816011428833, 1.9114123582839966, 11400, 0.00019793559857353432]
2023-03-20 07:44:45,584	OUTPUT_MODEL	INFO	Train Epoch: 84 [94%]
2023-03-20 07:44:45,586	OUTPUT_MODEL	INFO	[2.317819595336914, 2.4341840744018555, 6.005996227264404, 20.583707809448242, 1.0727192163467407, 2.3210134506225586, 11500, 0.00019793559857353432]
2023-03-20 07:44:52,562	OUTPUT_MODEL	INFO	====> Epoch: 84
2023-03-20 07:46:16,044	OUTPUT_MODEL	INFO	Train Epoch: 85 [67%]
2023-03-20 07:46:16,046	OUTPUT_MODEL	INFO	[2.3920366764068604, 2.486767530441284, 7.168998718261719, 21.456581115722656, 0.21667331457138062, 2.5581002235412598, 11600, 0.00019791085662371262]
2023-03-20 07:46:54,711	OUTPUT_MODEL	INFO	====> Epoch: 85
2023-03-20 07:47:47,295	OUTPUT_MODEL	INFO	Train Epoch: 86 [40%]
2023-03-20 07:47:47,297	OUTPUT_MODEL	INFO	[2.193307399749756, 2.8182060718536377, 7.144601345062256, 20.722240447998047, 1.1092329025268555, 2.266249895095825, 11700, 0.00019788611776663464]
2023-03-20 07:48:56,275	OUTPUT_MODEL	INFO	====> Epoch: 86
2023-03-20 07:49:18,064	OUTPUT_MODEL	INFO	Train Epoch: 87 [13%]
2023-03-20 07:49:18,066	OUTPUT_MODEL	INFO	[2.2027039527893066, 2.584052085876465, 7.054775714874268, 21.365713119506836, 0.9278396368026733, 1.9516905546188354, 11800, 0.0001978613820019138]
2023-03-20 07:50:42,274	OUTPUT_MODEL	INFO	Train Epoch: 87 [86%]
2023-03-20 07:50:42,276	OUTPUT_MODEL	INFO	[2.411428689956665, 2.763546943664551, 6.951698303222656, 21.04564094543457, 1.1564993858337402, 1.882152795791626, 11900, 0.0001978613820019138]
2023-03-20 07:50:58,498	OUTPUT_MODEL	INFO	====> Epoch: 87
2023-03-20 07:52:12,983	OUTPUT_MODEL	INFO	Train Epoch: 88 [59%]
2023-03-20 07:52:12,985	OUTPUT_MODEL	INFO	[2.3551270961761475, 2.6118764877319336, 6.264754772186279, 20.541332244873047, 1.0630813837051392, 2.0583834648132324, 12000, 0.00019783664932916355]
2023-03-20 07:52:14,290	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 88 to ././OUTPUT_MODEL/G_12000.pth
2023-03-20 07:52:14,726	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 88 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 07:53:02,176	OUTPUT_MODEL	INFO	====> Epoch: 88
2023-03-20 07:53:45,906	OUTPUT_MODEL	INFO	Train Epoch: 89 [32%]
2023-03-20 07:53:45,909	OUTPUT_MODEL	INFO	[2.3703293800354004, 2.5243606567382812, 7.505414962768555, 20.82246971130371, 1.0171974897384644, 1.3189300298690796, 12100, 0.0001978119197479974]
2023-03-20 07:55:04,424	OUTPUT_MODEL	INFO	====> Epoch: 89
2023-03-20 07:55:17,151	OUTPUT_MODEL	INFO	Train Epoch: 90 [5%]
2023-03-20 07:55:17,152	OUTPUT_MODEL	INFO	[2.3221566677093506, 2.540121078491211, 7.17138147354126, 20.579727172851562, -0.28051042556762695, 1.8893665075302124, 12200, 0.0001977871932580289]
2023-03-20 07:56:41,328	OUTPUT_MODEL	INFO	Train Epoch: 90 [78%]
2023-03-20 07:56:41,329	OUTPUT_MODEL	INFO	[2.341301679611206, 3.2032482624053955, 7.412979602813721, 22.322505950927734, 1.038238525390625, 2.426079273223877, 12300, 0.0001977871932580289]
2023-03-20 07:57:06,879	OUTPUT_MODEL	INFO	====> Epoch: 90
2023-03-20 07:58:12,373	OUTPUT_MODEL	INFO	Train Epoch: 91 [51%]
2023-03-20 07:58:12,375	OUTPUT_MODEL	INFO	[2.3715643882751465, 2.564124822616577, 7.418154716491699, 20.297563552856445, 0.9855964183807373, 1.995877742767334, 12400, 0.00019776246985887165]
2023-03-20 07:59:08,719	OUTPUT_MODEL	INFO	====> Epoch: 91
2023-03-20 07:59:43,149	OUTPUT_MODEL	INFO	Train Epoch: 92 [24%]
2023-03-20 07:59:43,151	OUTPUT_MODEL	INFO	[2.3876330852508545, 2.5917437076568604, 6.7727484703063965, 20.931468963623047, 0.9333204627037048, 2.1113474369049072, 12500, 0.0001977377495501393]
2023-03-20 08:01:08,560	OUTPUT_MODEL	INFO	Train Epoch: 92 [97%]
2023-03-20 08:01:08,561	OUTPUT_MODEL	INFO	[2.313535213470459, 2.498619794845581, 6.401463985443115, 21.042917251586914, 1.035508632659912, 2.0261940956115723, 12600, 0.0001977377495501393]
2023-03-20 08:01:12,149	OUTPUT_MODEL	INFO	====> Epoch: 92
2023-03-20 08:02:39,398	OUTPUT_MODEL	INFO	Train Epoch: 93 [70%]
2023-03-20 08:02:39,400	OUTPUT_MODEL	INFO	[2.359423875808716, 2.6220710277557373, 6.109528064727783, 21.273582458496094, 0.9737431406974792, 1.871511697769165, 12700, 0.0001977130323314455]
2023-03-20 08:03:14,796	OUTPUT_MODEL	INFO	====> Epoch: 93
2023-03-20 08:04:11,367	OUTPUT_MODEL	INFO	Train Epoch: 94 [43%]
2023-03-20 08:04:11,369	OUTPUT_MODEL	INFO	[2.385699987411499, 2.7559409141540527, 7.011538505554199, 21.2380313873291, 1.002224087715149, 2.0931501388549805, 12800, 0.00019768831820240408]
2023-03-20 08:05:17,573	OUTPUT_MODEL	INFO	====> Epoch: 94
2023-03-20 08:05:42,947	OUTPUT_MODEL	INFO	Train Epoch: 95 [16%]
2023-03-20 08:05:42,949	OUTPUT_MODEL	INFO	[2.257693290710449, 2.6659696102142334, 6.205726146697998, 20.990724563598633, 1.0827624797821045, 2.049966812133789, 12900, 0.00019766360716262876]
2023-03-20 08:07:07,829	OUTPUT_MODEL	INFO	Train Epoch: 95 [89%]
2023-03-20 08:07:07,831	OUTPUT_MODEL	INFO	[2.339740753173828, 2.794853448867798, 7.400816917419434, 21.204835891723633, 1.0210338830947876, 2.0126538276672363, 13000, 0.00019766360716262876]
2023-03-20 08:07:08,969	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 95 to ././OUTPUT_MODEL/G_13000.pth
2023-03-20 08:07:09,420	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 95 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 08:07:22,337	OUTPUT_MODEL	INFO	====> Epoch: 95
2023-03-20 08:08:41,355	OUTPUT_MODEL	INFO	Train Epoch: 96 [62%]
2023-03-20 08:08:41,357	OUTPUT_MODEL	INFO	[2.194169044494629, 2.571218967437744, 6.699070930480957, 20.792638778686523, 0.9782831072807312, 2.019284963607788, 13100, 0.00019763889921173343]
2023-03-20 08:09:25,744	OUTPUT_MODEL	INFO	====> Epoch: 96
2023-03-20 08:10:12,564	OUTPUT_MODEL	INFO	Train Epoch: 97 [35%]
2023-03-20 08:10:12,566	OUTPUT_MODEL	INFO	[2.2431015968322754, 2.843764305114746, 7.105639934539795, 19.493837356567383, 1.0654139518737793, 2.1833815574645996, 13200, 0.00019761419434933197]
2023-03-20 08:11:28,304	OUTPUT_MODEL	INFO	====> Epoch: 97
2023-03-20 08:11:44,246	OUTPUT_MODEL	INFO	Train Epoch: 98 [8%]
2023-03-20 08:11:44,248	OUTPUT_MODEL	INFO	[2.3178882598876953, 2.5582692623138428, 6.442537307739258, 20.781879425048828, 0.9898683428764343, 2.3219571113586426, 13300, 0.0001975894925750383]
2023-03-20 08:13:08,836	OUTPUT_MODEL	INFO	Train Epoch: 98 [81%]
2023-03-20 08:13:08,838	OUTPUT_MODEL	INFO	[2.4346749782562256, 2.4536800384521484, 7.620916366577148, 19.322486877441406, 1.1694176197052002, 2.1076953411102295, 13400, 0.0001975894925750383]
2023-03-20 08:13:31,286	OUTPUT_MODEL	INFO	====> Epoch: 98
2023-03-20 08:14:39,931	OUTPUT_MODEL	INFO	Train Epoch: 99 [54%]
2023-03-20 08:14:39,932	OUTPUT_MODEL	INFO	[2.3155570030212402, 2.4867911338806152, 6.355431079864502, 20.782703399658203, 0.8662546873092651, 2.1229333877563477, 13500, 0.0001975647938884664]
2023-03-20 08:15:33,427	OUTPUT_MODEL	INFO	====> Epoch: 99
2023-03-20 08:16:11,211	OUTPUT_MODEL	INFO	Train Epoch: 100 [27%]
2023-03-20 08:16:11,213	OUTPUT_MODEL	INFO	[2.352750778198242, 2.674314498901367, 7.154270172119141, 20.9406795501709, 0.9262458086013794, 2.425485372543335, 13600, 0.00019754009828923033]
2023-03-20 08:17:35,298	OUTPUT_MODEL	INFO	====> Epoch: 100
2023-03-20 08:17:42,320	OUTPUT_MODEL	INFO	Train Epoch: 101 [0%]
2023-03-20 08:17:42,322	OUTPUT_MODEL	INFO	[2.092860698699951, 3.0740604400634766, 7.542239665985107, 20.624828338623047, 1.1271179914474487, 1.7282779216766357, 13700, 0.00019751540577694416]
2023-03-20 08:19:06,810	OUTPUT_MODEL	INFO	Train Epoch: 101 [73%]
2023-03-20 08:19:06,812	OUTPUT_MODEL	INFO	[2.581085443496704, 2.260164737701416, 5.955965518951416, 21.687707901000977, 1.0343670845031738, 2.633819341659546, 13800, 0.00019751540577694416]
2023-03-20 08:19:38,063	OUTPUT_MODEL	INFO	====> Epoch: 101
2023-03-20 08:20:37,421	OUTPUT_MODEL	INFO	Train Epoch: 102 [46%]
2023-03-20 08:20:37,423	OUTPUT_MODEL	INFO	[2.323298215866089, 2.7085182666778564, 9.775221824645996, 18.429462432861328, 1.1940045356750488, 2.271658420562744, 13900, 0.00019749071635122203]
2023-03-20 08:21:39,797	OUTPUT_MODEL	INFO	====> Epoch: 102
2023-03-20 08:22:08,124	OUTPUT_MODEL	INFO	Train Epoch: 103 [19%]
2023-03-20 08:22:08,126	OUTPUT_MODEL	INFO	[2.4460675716400146, 2.5594396591186523, 6.022544860839844, 21.726947784423828, 1.0140478610992432, 2.163120746612549, 14000, 0.00019746603001167813]
2023-03-20 08:22:09,391	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 103 to ././OUTPUT_MODEL/G_14000.pth
2023-03-20 08:22:12,785	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 103 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 08:23:37,061	OUTPUT_MODEL	INFO	Train Epoch: 103 [92%]
2023-03-20 08:23:37,063	OUTPUT_MODEL	INFO	[2.306123733520508, 2.6202754974365234, 6.709509372711182, 20.29829216003418, 1.0033624172210693, 2.4091172218322754, 14100, 0.00019746603001167813]
2023-03-20 08:23:46,755	OUTPUT_MODEL	INFO	====> Epoch: 103
2023-03-20 08:25:08,069	OUTPUT_MODEL	INFO	Train Epoch: 104 [65%]
2023-03-20 08:25:08,071	OUTPUT_MODEL	INFO	[2.500486373901367, 2.4629929065704346, 6.237034320831299, 20.53016471862793, 0.9767664670944214, 2.1561503410339355, 14200, 0.00019744134675792665]
2023-03-20 08:25:48,628	OUTPUT_MODEL	INFO	====> Epoch: 104
2023-03-20 08:26:38,895	OUTPUT_MODEL	INFO	Train Epoch: 105 [38%]
2023-03-20 08:26:38,897	OUTPUT_MODEL	INFO	[2.3671772480010986, 2.439262866973877, 6.489326477050781, 20.55289649963379, 0.8788045644760132, 2.3755409717559814, 14300, 0.0001974166665895819]
2023-03-20 08:27:50,848	OUTPUT_MODEL	INFO	====> Epoch: 105
2023-03-20 08:28:10,095	OUTPUT_MODEL	INFO	Train Epoch: 106 [11%]
2023-03-20 08:28:10,097	OUTPUT_MODEL	INFO	[2.457749605178833, 2.4504902362823486, 5.67648983001709, 21.371871948242188, 1.0294866561889648, 2.5223240852355957, 14400, 0.0001973919895062582]
2023-03-20 08:29:34,367	OUTPUT_MODEL	INFO	Train Epoch: 106 [84%]
2023-03-20 08:29:34,368	OUTPUT_MODEL	INFO	[2.336418628692627, 2.655744791030884, 6.78521728515625, 20.21880531311035, 1.0526853799819946, 1.8581053018569946, 14500, 0.0001973919895062582]
2023-03-20 08:29:53,323	OUTPUT_MODEL	INFO	====> Epoch: 106
2023-03-20 08:31:05,133	OUTPUT_MODEL	INFO	Train Epoch: 107 [57%]
2023-03-20 08:31:05,135	OUTPUT_MODEL	INFO	[2.3946611881256104, 2.7000343799591064, 6.14026403427124, 20.938610076904297, 1.0682828426361084, 2.356576919555664, 14600, 0.0001973673155075699]
2023-03-20 08:31:54,879	OUTPUT_MODEL	INFO	====> Epoch: 107
2023-03-20 08:32:35,737	OUTPUT_MODEL	INFO	Train Epoch: 108 [30%]
2023-03-20 08:32:35,739	OUTPUT_MODEL	INFO	[2.1514065265655518, 2.714691400527954, 7.3430867195129395, 21.0421142578125, 1.0434942245483398, 2.6195921897888184, 14700, 0.00019734264459313146]
2023-03-20 08:33:56,467	OUTPUT_MODEL	INFO	====> Epoch: 108
2023-03-20 08:34:06,852	OUTPUT_MODEL	INFO	Train Epoch: 109 [3%]
2023-03-20 08:34:06,854	OUTPUT_MODEL	INFO	[2.308075189590454, 2.4958815574645996, 6.706348419189453, 20.93642234802246, 0.9520865678787231, 1.936238408088684, 14800, 0.0001973179767625573]
2023-03-20 08:35:31,344	OUTPUT_MODEL	INFO	Train Epoch: 109 [76%]
2023-03-20 08:35:31,345	OUTPUT_MODEL	INFO	[2.369896650314331, 2.6174046993255615, 6.521433353424072, 21.68155288696289, 0.9205359220504761, 2.109640598297119, 14900, 0.0001973179767625573]
2023-03-20 08:35:59,908	OUTPUT_MODEL	INFO	====> Epoch: 109
2023-03-20 08:37:03,158	OUTPUT_MODEL	INFO	Train Epoch: 110 [49%]
2023-03-20 08:37:03,160	OUTPUT_MODEL	INFO	[2.3851609230041504, 2.4769108295440674, 8.027097702026367, 21.040071487426758, 1.156479835510254, 2.416295051574707, 15000, 0.00019729331201546197]
2023-03-20 08:37:04,499	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 110 to ././OUTPUT_MODEL/G_15000.pth
2023-03-20 08:37:07,684	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 110 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 08:38:07,222	OUTPUT_MODEL	INFO	====> Epoch: 110
2023-03-20 08:38:39,267	OUTPUT_MODEL	INFO	Train Epoch: 111 [22%]
2023-03-20 08:38:39,269	OUTPUT_MODEL	INFO	[2.2007675170898438, 2.5314464569091797, 8.486234664916992, 21.727537155151367, 1.0774116516113281, 2.0040571689605713, 15100, 0.00019726865035146003]
2023-03-20 08:40:03,842	OUTPUT_MODEL	INFO	Train Epoch: 111 [95%]
2023-03-20 08:40:03,844	OUTPUT_MODEL	INFO	[2.2269532680511475, 2.8185267448425293, 6.992454528808594, 21.02241325378418, 1.010697364807129, 2.6467411518096924, 15200, 0.00019726865035146003]
2023-03-20 08:40:10,111	OUTPUT_MODEL	INFO	====> Epoch: 111
2023-03-20 08:41:34,809	OUTPUT_MODEL	INFO	Train Epoch: 112 [68%]
2023-03-20 08:41:34,811	OUTPUT_MODEL	INFO	[2.0180282592773438, 3.2410340309143066, 12.563876152038574, 20.466524124145508, 1.1309553384780884, 1.9444447755813599, 15300, 0.0001972439917701661]
2023-03-20 08:42:12,166	OUTPUT_MODEL	INFO	====> Epoch: 112
2023-03-20 08:43:05,762	OUTPUT_MODEL	INFO	Train Epoch: 113 [41%]
2023-03-20 08:43:05,764	OUTPUT_MODEL	INFO	[2.255061388015747, 2.7214038372039795, 6.299415588378906, 20.91688346862793, 1.1088670492172241, 1.9430149793624878, 15400, 0.0001972193362711948]
2023-03-20 08:44:14,133	OUTPUT_MODEL	INFO	====> Epoch: 113
2023-03-20 08:44:36,982	OUTPUT_MODEL	INFO	Train Epoch: 114 [14%]
2023-03-20 08:44:36,984	OUTPUT_MODEL	INFO	[2.2320497035980225, 2.6411733627319336, 6.557395935058594, 20.7996768951416, 1.0222570896148682, 2.3571717739105225, 15500, 0.0001971946838541609]
2023-03-20 08:46:01,212	OUTPUT_MODEL	INFO	Train Epoch: 114 [87%]
2023-03-20 08:46:01,214	OUTPUT_MODEL	INFO	[2.2992804050445557, 2.539973497390747, 6.562218189239502, 21.238754272460938, 0.9871796369552612, 2.293264865875244, 15600, 0.0001971946838541609]
2023-03-20 08:46:16,582	OUTPUT_MODEL	INFO	====> Epoch: 114
2023-03-20 08:47:31,512	OUTPUT_MODEL	INFO	Train Epoch: 115 [60%]
2023-03-20 08:47:31,514	OUTPUT_MODEL	INFO	[2.352992296218872, 2.6868741512298584, 5.794214248657227, 20.968521118164062, 0.9467967748641968, 2.0700275897979736, 15700, 0.0001971700345186791]
2023-03-20 08:48:18,290	OUTPUT_MODEL	INFO	====> Epoch: 115
2023-03-20 08:49:02,627	OUTPUT_MODEL	INFO	Train Epoch: 116 [33%]
2023-03-20 08:49:02,629	OUTPUT_MODEL	INFO	[2.2141318321228027, 2.615248203277588, 6.896303653717041, 21.20030403137207, 0.9356857538223267, 2.353039503097534, 15800, 0.00019714538826436426]
2023-03-20 08:50:20,406	OUTPUT_MODEL	INFO	====> Epoch: 116
2023-03-20 08:50:33,954	OUTPUT_MODEL	INFO	Train Epoch: 117 [6%]
2023-03-20 08:50:33,956	OUTPUT_MODEL	INFO	[2.4702796936035156, 2.4463999271392822, 5.541172981262207, 19.460878372192383, 1.0428105592727661, 2.282822847366333, 15900, 0.0001971207450908312]
2023-03-20 08:51:58,481	OUTPUT_MODEL	INFO	Train Epoch: 117 [79%]
2023-03-20 08:51:58,482	OUTPUT_MODEL	INFO	[2.280689239501953, 2.4494011402130127, 6.60907506942749, 20.706523895263672, 1.0273411273956299, 2.4378535747528076, 16000, 0.0001971207450908312]
2023-03-20 08:51:59,698	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 117 to ././OUTPUT_MODEL/G_16000.pth
2023-03-20 08:52:02,848	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 117 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 08:52:27,833	OUTPUT_MODEL	INFO	====> Epoch: 117
2023-03-20 08:53:33,711	OUTPUT_MODEL	INFO	Train Epoch: 118 [52%]
2023-03-20 08:53:33,713	OUTPUT_MODEL	INFO	[2.272207736968994, 2.7271382808685303, 6.2798357009887695, 19.957382202148438, 1.0273211002349854, 1.9169036149978638, 16100, 0.00019709610499769482]
2023-03-20 08:54:29,817	OUTPUT_MODEL	INFO	====> Epoch: 118
2023-03-20 08:55:04,772	OUTPUT_MODEL	INFO	Train Epoch: 119 [25%]
2023-03-20 08:55:04,774	OUTPUT_MODEL	INFO	[2.2250163555145264, 2.47507905960083, 6.486927509307861, 20.76628303527832, 0.896833062171936, 1.760322093963623, 16200, 0.0001970714679845701]
2023-03-20 08:56:28,997	OUTPUT_MODEL	INFO	Train Epoch: 119 [98%]
2023-03-20 08:56:28,999	OUTPUT_MODEL	INFO	[2.1671576499938965, 2.750340461730957, 7.375720977783203, 19.31304168701172, 0.965782880783081, 2.078456401824951, 16300, 0.0001970714679845701]
2023-03-20 08:56:32,133	OUTPUT_MODEL	INFO	====> Epoch: 119
2023-03-20 08:58:00,033	OUTPUT_MODEL	INFO	Train Epoch: 120 [71%]
2023-03-20 08:58:00,035	OUTPUT_MODEL	INFO	[2.154794216156006, 2.8303914070129395, 7.991113185882568, 21.563703536987305, 0.9978339076042175, 2.2748863697052, 16400, 0.000197046834051072]
2023-03-20 08:58:34,547	OUTPUT_MODEL	INFO	====> Epoch: 120
2023-03-20 08:59:31,945	OUTPUT_MODEL	INFO	Train Epoch: 121 [44%]
2023-03-20 08:59:31,947	OUTPUT_MODEL	INFO	[2.368225574493408, 2.539674997329712, 6.732079029083252, 21.45633316040039, 1.0941178798675537, 2.2024412155151367, 16500, 0.00019702220319681561]
2023-03-20 09:00:38,103	OUTPUT_MODEL	INFO	====> Epoch: 121
2023-03-20 09:01:05,264	OUTPUT_MODEL	INFO	Train Epoch: 122 [17%]
2023-03-20 09:01:05,266	OUTPUT_MODEL	INFO	[2.3691442012786865, 2.4568986892700195, 7.128162860870361, 21.328453063964844, 1.009879469871521, 2.0675246715545654, 16600, 0.000196997575421416]
2023-03-20 09:02:30,650	OUTPUT_MODEL	INFO	Train Epoch: 122 [90%]
2023-03-20 09:02:30,652	OUTPUT_MODEL	INFO	[2.1850204467773438, 2.6597163677215576, 7.169649600982666, 21.704504013061523, 0.9177848696708679, 2.0085794925689697, 16700, 0.000196997575421416]
2023-03-20 09:02:42,881	OUTPUT_MODEL	INFO	====> Epoch: 122
2023-03-20 09:04:02,410	OUTPUT_MODEL	INFO	Train Epoch: 123 [63%]
2023-03-20 09:04:02,411	OUTPUT_MODEL	INFO	[2.2776947021484375, 2.6405587196350098, 8.088044166564941, 20.84010124206543, 0.9019166827201843, 2.264185667037964, 16800, 0.00019697295072448832]
2023-03-20 09:04:46,030	OUTPUT_MODEL	INFO	====> Epoch: 123
2023-03-20 09:05:33,953	OUTPUT_MODEL	INFO	Train Epoch: 124 [36%]
2023-03-20 09:05:33,955	OUTPUT_MODEL	INFO	[2.192620038986206, 2.736124038696289, 6.589212894439697, 20.936479568481445, 1.159262776374817, 1.9578560590744019, 16900, 0.00019694832910564775]
2023-03-20 09:06:47,947	OUTPUT_MODEL	INFO	====> Epoch: 124
2023-03-20 09:07:04,628	OUTPUT_MODEL	INFO	Train Epoch: 125 [9%]
2023-03-20 09:07:04,630	OUTPUT_MODEL	INFO	[2.1285290718078613, 2.914964437484741, 7.221214294433594, 19.575559616088867, 1.1411768198013306, 1.922427773475647, 17000, 0.00019692371056450955]
2023-03-20 09:07:06,266	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 125 to ././OUTPUT_MODEL/G_17000.pth
2023-03-20 09:07:09,415	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 125 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 09:08:33,422	OUTPUT_MODEL	INFO	Train Epoch: 125 [82%]
2023-03-20 09:08:33,424	OUTPUT_MODEL	INFO	[2.313883066177368, 2.6480140686035156, 6.604679107666016, 21.223758697509766, 0.9819459319114685, 1.8104972839355469, 17100, 0.00019692371056450955]
2023-03-20 09:08:54,865	OUTPUT_MODEL	INFO	====> Epoch: 125
2023-03-20 09:10:04,022	OUTPUT_MODEL	INFO	Train Epoch: 126 [55%]
2023-03-20 09:10:04,024	OUTPUT_MODEL	INFO	[2.3536245822906494, 2.500152349472046, 6.64579963684082, 19.799110412597656, 1.1131587028503418, 1.645290732383728, 17200, 0.000196899095100689]
2023-03-20 09:10:56,169	OUTPUT_MODEL	INFO	====> Epoch: 126
2023-03-20 09:11:34,369	OUTPUT_MODEL	INFO	Train Epoch: 127 [28%]
2023-03-20 09:11:34,371	OUTPUT_MODEL	INFO	[2.3448710441589355, 2.52601957321167, 6.265294551849365, 21.145179748535156, 1.0154155492782593, 2.1804962158203125, 17300, 0.0001968744827138014]
2023-03-20 09:12:57,212	OUTPUT_MODEL	INFO	====> Epoch: 127
2023-03-20 09:13:04,931	OUTPUT_MODEL	INFO	Train Epoch: 128 [1%]
2023-03-20 09:13:04,933	OUTPUT_MODEL	INFO	[2.236205577850342, 2.6766164302825928, 6.899805068969727, 21.026575088500977, 1.0525791645050049, 2.00675892829895, 17400, 0.00019684987340346216]
2023-03-20 09:14:28,728	OUTPUT_MODEL	INFO	Train Epoch: 128 [74%]
2023-03-20 09:14:28,729	OUTPUT_MODEL	INFO	[2.2275190353393555, 2.5438785552978516, 7.914027214050293, 21.301137924194336, 1.0290029048919678, 1.9070020914077759, 17500, 0.00019684987340346216]
2023-03-20 09:14:59,409	OUTPUT_MODEL	INFO	====> Epoch: 128
2023-03-20 09:15:59,385	OUTPUT_MODEL	INFO	Train Epoch: 129 [47%]
2023-03-20 09:15:59,387	OUTPUT_MODEL	INFO	[2.0739710330963135, 2.5842056274414062, 7.518167495727539, 21.136476516723633, 1.0161404609680176, 2.186821937561035, 17600, 0.00019682526716928672]
2023-03-20 09:17:00,781	OUTPUT_MODEL	INFO	====> Epoch: 129
2023-03-20 09:17:30,112	OUTPUT_MODEL	INFO	Train Epoch: 130 [20%]
2023-03-20 09:17:30,114	OUTPUT_MODEL	INFO	[2.3141698837280273, 2.686121940612793, 11.473016738891602, 19.295970916748047, 1.2395532131195068, 2.105855703353882, 17700, 0.00019680066401089056]
2023-03-20 09:18:54,078	OUTPUT_MODEL	INFO	Train Epoch: 130 [93%]
2023-03-20 09:18:54,079	OUTPUT_MODEL	INFO	[2.167222023010254, 2.890559434890747, 7.7699127197265625, 21.15714454650879, 0.9327160716056824, 2.3781745433807373, 17800, 0.00019680066401089056]
2023-03-20 09:19:02,695	OUTPUT_MODEL	INFO	====> Epoch: 130
2023-03-20 09:20:24,141	OUTPUT_MODEL	INFO	Train Epoch: 131 [66%]
2023-03-20 09:20:24,143	OUTPUT_MODEL	INFO	[2.1653411388397217, 2.7909579277038574, 6.789185523986816, 20.81007194519043, 1.1178703308105469, 1.884048581123352, 17900, 0.00019677606392788917]
2023-03-20 09:21:04,290	OUTPUT_MODEL	INFO	====> Epoch: 131
2023-03-20 09:21:55,063	OUTPUT_MODEL	INFO	Train Epoch: 132 [39%]
2023-03-20 09:21:55,065	OUTPUT_MODEL	INFO	[2.2986092567443848, 2.6188340187072754, 8.174047470092773, 20.48558807373047, 1.1584718227386475, 2.3779773712158203, 18000, 0.00019675146691989817]
2023-03-20 09:21:56,396	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 132 to ././OUTPUT_MODEL/G_18000.pth
2023-03-20 09:21:56,854	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 132 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 09:23:07,857	OUTPUT_MODEL	INFO	====> Epoch: 132
2023-03-20 09:23:27,893	OUTPUT_MODEL	INFO	Train Epoch: 133 [12%]
2023-03-20 09:23:27,894	OUTPUT_MODEL	INFO	[2.346721649169922, 2.4095168113708496, 6.616203308105469, 20.629770278930664, 0.9981781244277954, 2.2905256748199463, 18100, 0.00019672687298653317]
2023-03-20 09:24:51,602	OUTPUT_MODEL	INFO	Train Epoch: 133 [85%]
2023-03-20 09:24:51,604	OUTPUT_MODEL	INFO	[2.2084860801696777, 2.745917320251465, 10.246076583862305, 20.814716339111328, 1.154919147491455, 2.0152649879455566, 18200, 0.00019672687298653317]
2023-03-20 09:25:09,603	OUTPUT_MODEL	INFO	====> Epoch: 133
2023-03-20 09:26:22,013	OUTPUT_MODEL	INFO	Train Epoch: 134 [58%]
2023-03-20 09:26:22,015	OUTPUT_MODEL	INFO	[2.331979513168335, 2.7191319465637207, 6.776646137237549, 21.164155960083008, 1.0741961002349854, 2.24226713180542, 18300, 0.00019670228212740986]
2023-03-20 09:27:10,731	OUTPUT_MODEL	INFO	====> Epoch: 134
2023-03-20 09:27:52,424	OUTPUT_MODEL	INFO	Train Epoch: 135 [31%]
2023-03-20 09:27:52,426	OUTPUT_MODEL	INFO	[2.297797441482544, 2.828986167907715, 7.3642578125, 21.167116165161133, 0.9668316841125488, 2.01474666595459, 18400, 0.00019667769434214392]
2023-03-20 09:29:11,771	OUTPUT_MODEL	INFO	====> Epoch: 135
2023-03-20 09:29:22,678	OUTPUT_MODEL	INFO	Train Epoch: 136 [4%]
2023-03-20 09:29:22,680	OUTPUT_MODEL	INFO	[2.363123893737793, 2.7673046588897705, 8.529550552368164, 20.491167068481445, 0.042582154273986816, 1.991466760635376, 18500, 0.00019665310963035113]
2023-03-20 09:30:46,467	OUTPUT_MODEL	INFO	Train Epoch: 136 [77%]
2023-03-20 09:30:46,468	OUTPUT_MODEL	INFO	[2.17141056060791, 2.8172013759613037, 6.361166954040527, 20.080486297607422, 0.990118145942688, 2.0279083251953125, 18600, 0.00019665310963035113]
2023-03-20 09:31:14,149	OUTPUT_MODEL	INFO	====> Epoch: 136
2023-03-20 09:32:17,737	OUTPUT_MODEL	INFO	Train Epoch: 137 [50%]
2023-03-20 09:32:17,739	OUTPUT_MODEL	INFO	[2.4509949684143066, 2.5297579765319824, 6.510143280029297, 21.661577224731445, 1.0155518054962158, 1.8260382413864136, 18700, 0.00019662852799164733]
2023-03-20 09:33:15,667	OUTPUT_MODEL	INFO	====> Epoch: 137
2023-03-20 09:33:48,193	OUTPUT_MODEL	INFO	Train Epoch: 138 [23%]
2023-03-20 09:33:48,195	OUTPUT_MODEL	INFO	[2.2642464637756348, 2.5315358638763428, 6.386641502380371, 21.66007423400879, 1.1681804656982422, 2.295581817626953, 18800, 0.00019660394942564837]
2023-03-20 09:35:12,263	OUTPUT_MODEL	INFO	Train Epoch: 138 [96%]
2023-03-20 09:35:12,265	OUTPUT_MODEL	INFO	[2.166489839553833, 2.7201480865478516, 7.146355152130127, 20.62055778503418, 1.0905542373657227, 2.2655293941497803, 18900, 0.00019660394942564837]
2023-03-20 09:35:17,735	OUTPUT_MODEL	INFO	====> Epoch: 138
2023-03-20 09:36:42,872	OUTPUT_MODEL	INFO	Train Epoch: 139 [69%]
2023-03-20 09:36:42,874	OUTPUT_MODEL	INFO	[2.406036853790283, 2.7855634689331055, 6.692385196685791, 21.12078285217285, 1.0599098205566406, 2.240670680999756, 19000, 0.00019657937393197016]
2023-03-20 09:36:44,107	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 139 to ././OUTPUT_MODEL/G_19000.pth
2023-03-20 09:36:44,550	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 139 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 09:37:21,034	OUTPUT_MODEL	INFO	====> Epoch: 139
2023-03-20 09:38:15,292	OUTPUT_MODEL	INFO	Train Epoch: 140 [42%]
2023-03-20 09:38:15,294	OUTPUT_MODEL	INFO	[2.048689126968384, 2.918403148651123, 9.06787109375, 20.059192657470703, 1.2296338081359863, 1.8923338651657104, 19100, 0.00019655480151022865]
2023-03-20 09:39:22,910	OUTPUT_MODEL	INFO	====> Epoch: 140
2023-03-20 09:39:46,123	OUTPUT_MODEL	INFO	Train Epoch: 141 [15%]
2023-03-20 09:39:46,124	OUTPUT_MODEL	INFO	[2.185502052307129, 2.7467076778411865, 6.832791328430176, 21.55177116394043, 0.9789677858352661, 2.0616536140441895, 19200, 0.00019653023216003985]
2023-03-20 09:41:10,314	OUTPUT_MODEL	INFO	Train Epoch: 141 [88%]
2023-03-20 09:41:10,316	OUTPUT_MODEL	INFO	[2.4510509967803955, 2.373511791229248, 7.904383659362793, 21.173912048339844, 1.1028590202331543, 2.3851287364959717, 19300, 0.00019653023216003985]
2023-03-20 09:41:25,166	OUTPUT_MODEL	INFO	====> Epoch: 141
2023-03-20 09:42:41,069	OUTPUT_MODEL	INFO	Train Epoch: 142 [61%]
2023-03-20 09:42:41,071	OUTPUT_MODEL	INFO	[2.222172260284424, 2.8863532543182373, 7.1654887199401855, 21.046199798583984, 1.0000325441360474, 2.2201201915740967, 19400, 0.00019650566588101984]
2023-03-20 09:43:26,620	OUTPUT_MODEL	INFO	====> Epoch: 142
2023-03-20 09:44:11,964	OUTPUT_MODEL	INFO	Train Epoch: 143 [34%]
2023-03-20 09:44:11,965	OUTPUT_MODEL	INFO	[2.2356066703796387, 2.6044416427612305, 6.104043483734131, 21.158172607421875, 0.9521328806877136, 2.117732286453247, 19500, 0.0001964811026727847]
2023-03-20 09:45:28,395	OUTPUT_MODEL	INFO	====> Epoch: 143
2023-03-20 09:45:42,563	OUTPUT_MODEL	INFO	Train Epoch: 144 [7%]
2023-03-20 09:45:42,565	OUTPUT_MODEL	INFO	[2.245537042617798, 2.6586883068084717, 7.804027080535889, 21.117185592651367, 0.9906278252601624, 2.1668543815612793, 19600, 0.00019645654253495058]
2023-03-20 09:47:07,049	OUTPUT_MODEL	INFO	Train Epoch: 144 [80%]
2023-03-20 09:47:07,051	OUTPUT_MODEL	INFO	[2.3713793754577637, 2.6340041160583496, 6.369852542877197, 20.914628982543945, 0.9706466197967529, 2.450453758239746, 19700, 0.00019645654253495058]
2023-03-20 09:47:31,948	OUTPUT_MODEL	INFO	====> Epoch: 144
2023-03-20 09:48:38,805	OUTPUT_MODEL	INFO	Train Epoch: 145 [53%]
2023-03-20 09:48:38,807	OUTPUT_MODEL	INFO	[2.1739981174468994, 2.8538644313812256, 6.951966762542725, 20.879650115966797, 1.0608255863189697, 2.1561622619628906, 19800, 0.0001964319854671337]
2023-03-20 09:49:33,536	OUTPUT_MODEL	INFO	====> Epoch: 145
2023-03-20 09:50:09,799	OUTPUT_MODEL	INFO	Train Epoch: 146 [26%]
2023-03-20 09:50:09,801	OUTPUT_MODEL	INFO	[2.2468457221984863, 2.667266845703125, 6.948541164398193, 21.01181983947754, 1.0636100769042969, 1.8702213764190674, 19900, 0.0001964074314689503]
2023-03-20 09:51:33,894	OUTPUT_MODEL	INFO	Train Epoch: 146 [99%]
2023-03-20 09:51:33,895	OUTPUT_MODEL	INFO	[2.4496679306030273, 2.4083058834075928, 6.897172927856445, 20.908218383789062, 1.0876970291137695, 1.8607343435287476, 20000, 0.0001964074314689503]
2023-03-20 09:51:35,184	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 146 to ././OUTPUT_MODEL/G_20000.pth
2023-03-20 09:51:35,676	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 146 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 09:51:38,458	OUTPUT_MODEL	INFO	====> Epoch: 146
2023-03-20 09:53:07,302	OUTPUT_MODEL	INFO	Train Epoch: 147 [72%]
2023-03-20 09:53:07,304	OUTPUT_MODEL	INFO	[2.2548165321350098, 2.7127628326416016, 7.130604267120361, 20.856903076171875, 1.2361938953399658, 2.4349231719970703, 20100, 0.00019638288054001668]
2023-03-20 09:53:40,485	OUTPUT_MODEL	INFO	====> Epoch: 147
2023-03-20 09:54:38,293	OUTPUT_MODEL	INFO	Train Epoch: 148 [45%]
2023-03-20 09:54:38,295	OUTPUT_MODEL	INFO	[2.2806320190429688, 2.626404047012329, 7.575266361236572, 20.199283599853516, 1.261871099472046, 2.2735507488250732, 20200, 0.00019635833267994917]
2023-03-20 09:55:42,846	OUTPUT_MODEL	INFO	====> Epoch: 148
2023-03-20 09:56:09,694	OUTPUT_MODEL	INFO	Train Epoch: 149 [18%]
2023-03-20 09:56:09,696	OUTPUT_MODEL	INFO	[2.189758062362671, 2.7204182147979736, 7.162673473358154, 20.975887298583984, 0.9318118691444397, 2.0012004375457764, 20300, 0.00019633378788836418]
2023-03-20 09:57:33,862	OUTPUT_MODEL	INFO	Train Epoch: 149 [91%]
2023-03-20 09:57:33,864	OUTPUT_MODEL	INFO	[2.3857719898223877, 2.545811176300049, 6.725675106048584, 20.9865779876709, 1.0381011962890625, 2.3047831058502197, 20400, 0.00019633378788836418]
2023-03-20 09:57:45,072	OUTPUT_MODEL	INFO	====> Epoch: 149
2023-03-20 09:59:04,391	OUTPUT_MODEL	INFO	Train Epoch: 150 [64%]
2023-03-20 09:59:04,393	OUTPUT_MODEL	INFO	[2.332963466644287, 2.658878803253174, 6.930632591247559, 21.047666549682617, 1.0627295970916748, 1.8769971132278442, 20500, 0.00019630924616487812]
2023-03-20 09:59:46,652	OUTPUT_MODEL	INFO	====> Epoch: 150
2023-03-20 10:00:35,968	OUTPUT_MODEL	INFO	Train Epoch: 151 [36%]
2023-03-20 10:00:35,970	OUTPUT_MODEL	INFO	[2.2610089778900146, 2.823590040206909, 7.033543109893799, 20.870264053344727, 0.961732804775238, 2.1553916931152344, 20600, 0.0001962847075091075]
2023-03-20 10:01:50,011	OUTPUT_MODEL	INFO	====> Epoch: 151
2023-03-20 10:02:07,494	OUTPUT_MODEL	INFO	Train Epoch: 152 [9%]
2023-03-20 10:02:07,496	OUTPUT_MODEL	INFO	[2.4248294830322266, 2.4738857746124268, 7.286777496337891, 20.524459838867188, 0.9120668768882751, 1.9256350994110107, 20700, 0.00019626017192066886]
2023-03-20 10:03:32,073	OUTPUT_MODEL	INFO	Train Epoch: 152 [82%]
2023-03-20 10:03:32,075	OUTPUT_MODEL	INFO	[2.3138041496276855, 2.265110969543457, 8.615909576416016, 20.489538192749023, 1.0569630861282349, 2.1951918601989746, 20800, 0.00019626017192066886]
2023-03-20 10:03:52,922	OUTPUT_MODEL	INFO	====> Epoch: 152
2023-03-20 10:05:03,493	OUTPUT_MODEL	INFO	Train Epoch: 153 [55%]
2023-03-20 10:05:03,494	OUTPUT_MODEL	INFO	[2.354685068130493, 2.629967212677002, 6.578222751617432, 21.197265625, 0.918994665145874, 1.97328519821167, 20900, 0.00019623563939917877]
2023-03-20 10:05:55,273	OUTPUT_MODEL	INFO	====> Epoch: 153
2023-03-20 10:06:34,604	OUTPUT_MODEL	INFO	Train Epoch: 154 [28%]
2023-03-20 10:06:34,606	OUTPUT_MODEL	INFO	[2.3196163177490234, 2.516697406768799, 6.234090328216553, 20.0762882232666, 1.112680196762085, 1.9773197174072266, 21000, 0.00019621110994425385]
2023-03-20 10:06:35,753	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 154 to ././OUTPUT_MODEL/G_21000.pth
2023-03-20 10:06:36,292	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 154 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 10:07:59,508	OUTPUT_MODEL	INFO	====> Epoch: 154
2023-03-20 10:08:07,809	OUTPUT_MODEL	INFO	Train Epoch: 155 [1%]
2023-03-20 10:08:07,811	OUTPUT_MODEL	INFO	[2.328713893890381, 2.6710915565490723, 8.34668254852295, 18.518415451049805, 1.1961438655853271, 1.9331507682800293, 21100, 0.00019618658355551082]
2023-03-20 10:09:32,752	OUTPUT_MODEL	INFO	Train Epoch: 155 [74%]
2023-03-20 10:09:32,754	OUTPUT_MODEL	INFO	[2.302839756011963, 2.777268171310425, 6.270135402679443, 20.639869689941406, 1.0190954208374023, 1.751977562904358, 21200, 0.00019618658355551082]
2023-03-20 10:10:03,120	OUTPUT_MODEL	INFO	====> Epoch: 155
2023-03-20 10:11:04,868	OUTPUT_MODEL	INFO	Train Epoch: 156 [47%]
2023-03-20 10:11:04,870	OUTPUT_MODEL	INFO	[2.055332660675049, 2.811868906021118, 7.820174217224121, 20.99708366394043, 1.0209599733352661, 2.2040016651153564, 21300, 0.00019616206023256638]
2023-03-20 10:12:06,335	OUTPUT_MODEL	INFO	====> Epoch: 156
2023-03-20 10:12:36,981	OUTPUT_MODEL	INFO	Train Epoch: 157 [20%]
2023-03-20 10:12:36,983	OUTPUT_MODEL	INFO	[2.4537737369537354, 2.3545339107513428, 6.889698028564453, 20.220300674438477, 1.0814297199249268, 2.2123661041259766, 21400, 0.0001961375399750373]
2023-03-20 10:14:02,109	OUTPUT_MODEL	INFO	Train Epoch: 157 [93%]
2023-03-20 10:14:02,111	OUTPUT_MODEL	INFO	[2.398317813873291, 2.5380256175994873, 6.455047130584717, 19.90985107421875, 1.100104808807373, 1.8051801919937134, 21500, 0.0001961375399750373]
2023-03-20 10:14:09,992	OUTPUT_MODEL	INFO	====> Epoch: 157
2023-03-20 10:15:33,047	OUTPUT_MODEL	INFO	Train Epoch: 158 [66%]
2023-03-20 10:15:33,049	OUTPUT_MODEL	INFO	[2.246039390563965, 2.6203954219818115, 7.433890342712402, 20.531497955322266, 0.895749568939209, 1.9291623830795288, 21600, 0.0001961130227825404]
2023-03-20 10:16:12,182	OUTPUT_MODEL	INFO	====> Epoch: 158
2023-03-20 10:17:04,144	OUTPUT_MODEL	INFO	Train Epoch: 159 [39%]
2023-03-20 10:17:04,146	OUTPUT_MODEL	INFO	[2.2385306358337402, 2.590235710144043, 6.947330474853516, 20.64833641052246, 1.0414984226226807, 2.0050742626190186, 21700, 0.00019608850865469258]
2023-03-20 10:18:14,315	OUTPUT_MODEL	INFO	====> Epoch: 159
2023-03-20 10:18:35,511	OUTPUT_MODEL	INFO	Train Epoch: 160 [12%]
2023-03-20 10:18:35,512	OUTPUT_MODEL	INFO	[2.2152395248413086, 2.6400978565216064, 7.099494934082031, 20.84355354309082, 0.9584530591964722, 2.093154191970825, 21800, 0.00019606399759111075]
2023-03-20 10:20:00,140	OUTPUT_MODEL	INFO	Train Epoch: 160 [85%]
2023-03-20 10:20:00,141	OUTPUT_MODEL	INFO	[2.0822794437408447, 2.6601741313934326, 7.726863384246826, 21.194196701049805, 0.8916058540344238, 2.21152925491333, 21900, 0.00019606399759111075]
2023-03-20 10:20:17,429	OUTPUT_MODEL	INFO	====> Epoch: 160
2023-03-20 10:21:31,009	OUTPUT_MODEL	INFO	Train Epoch: 161 [58%]
2023-03-20 10:21:31,011	OUTPUT_MODEL	INFO	[2.3681795597076416, 2.540426254272461, 6.053557395935059, 21.098302841186523, 1.0478644371032715, 2.0474374294281006, 22000, 0.00019603948959141186]
2023-03-20 10:21:32,103	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 161 to ././OUTPUT_MODEL/G_22000.pth
2023-03-20 10:21:32,637	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 161 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 10:22:21,110	OUTPUT_MODEL	INFO	====> Epoch: 161
2023-03-20 10:23:03,798	OUTPUT_MODEL	INFO	Train Epoch: 162 [31%]
2023-03-20 10:23:03,799	OUTPUT_MODEL	INFO	[2.292825698852539, 2.5393755435943604, 6.780707836151123, 20.91854476928711, 1.0478198528289795, 1.7606037855148315, 22100, 0.0001960149846552129]
2023-03-20 10:24:22,901	OUTPUT_MODEL	INFO	====> Epoch: 162
2023-03-20 10:24:34,745	OUTPUT_MODEL	INFO	Train Epoch: 163 [4%]
2023-03-20 10:24:34,747	OUTPUT_MODEL	INFO	[2.037843942642212, 3.1054022312164307, 8.844539642333984, 21.17815399169922, 0.9129490852355957, 2.3199410438537598, 22200, 0.000195990482782131]
2023-03-20 10:25:59,050	OUTPUT_MODEL	INFO	Train Epoch: 163 [77%]
2023-03-20 10:25:59,052	OUTPUT_MODEL	INFO	[2.1756017208099365, 3.016584873199463, 7.655688285827637, 20.880014419555664, 1.0316364765167236, 2.4046545028686523, 22300, 0.000195990482782131]
2023-03-20 10:26:25,613	OUTPUT_MODEL	INFO	====> Epoch: 163
2023-03-20 10:27:30,376	OUTPUT_MODEL	INFO	Train Epoch: 164 [50%]
2023-03-20 10:27:30,378	OUTPUT_MODEL	INFO	[2.191734790802002, 2.5035674571990967, 6.758562088012695, 21.008201599121094, 1.0586862564086914, 1.8586580753326416, 22400, 0.00019596598397178324]
2023-03-20 10:28:27,993	OUTPUT_MODEL	INFO	====> Epoch: 164
2023-03-20 10:29:01,891	OUTPUT_MODEL	INFO	Train Epoch: 165 [23%]
2023-03-20 10:29:01,893	OUTPUT_MODEL	INFO	[2.1251940727233887, 2.7015540599823, 8.51766586303711, 21.095577239990234, 1.1286307573318481, 2.0214762687683105, 22500, 0.00019594148822378677]
2023-03-20 10:30:26,428	OUTPUT_MODEL	INFO	Train Epoch: 165 [96%]
2023-03-20 10:30:26,430	OUTPUT_MODEL	INFO	[2.3564352989196777, 2.671255588531494, 6.347494602203369, 20.426618576049805, 1.0641498565673828, 2.1871893405914307, 22600, 0.00019594148822378677]
2023-03-20 10:30:30,911	OUTPUT_MODEL	INFO	====> Epoch: 165
2023-03-20 10:31:57,645	OUTPUT_MODEL	INFO	Train Epoch: 166 [69%]
2023-03-20 10:31:57,647	OUTPUT_MODEL	INFO	[2.1877291202545166, 2.5464460849761963, 7.895575523376465, 20.526762008666992, 0.9171377420425415, 2.0555684566497803, 22700, 0.00019591699553775878]
2023-03-20 10:32:33,054	OUTPUT_MODEL	INFO	====> Epoch: 166
2023-03-20 10:33:28,002	OUTPUT_MODEL	INFO	Train Epoch: 167 [42%]
2023-03-20 10:33:28,004	OUTPUT_MODEL	INFO	[2.1582212448120117, 2.474581480026245, 7.0791707038879395, 20.91151237487793, 0.9357600212097168, 2.380732297897339, 22800, 0.00019589250591331656]
2023-03-20 10:34:34,284	OUTPUT_MODEL	INFO	====> Epoch: 167
2023-03-20 10:34:58,539	OUTPUT_MODEL	INFO	Train Epoch: 168 [15%]
2023-03-20 10:34:58,541	OUTPUT_MODEL	INFO	[2.137352228164673, 2.785722255706787, 13.705077171325684, 19.32684326171875, 1.1548681259155273, 1.8379937410354614, 22900, 0.0001958680193500774]
2023-03-20 10:36:23,191	OUTPUT_MODEL	INFO	Train Epoch: 168 [88%]
2023-03-20 10:36:23,193	OUTPUT_MODEL	INFO	[2.482903480529785, 2.9324941635131836, 11.695865631103516, 20.21991729736328, 1.1683063507080078, 1.8924118280410767, 23000, 0.0001958680193500774]
2023-03-20 10:36:24,296	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 168 to ././OUTPUT_MODEL/G_23000.pth
2023-03-20 10:36:24,745	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 168 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 10:36:38,499	OUTPUT_MODEL	INFO	====> Epoch: 168
2023-03-20 10:37:56,189	OUTPUT_MODEL	INFO	Train Epoch: 169 [61%]
2023-03-20 10:37:56,191	OUTPUT_MODEL	INFO	[2.2988884449005127, 2.7463021278381348, 8.048616409301758, 20.68337059020996, 0.9667842388153076, 2.3011550903320312, 23100, 0.00019584353584765863]
2023-03-20 10:38:41,244	OUTPUT_MODEL	INFO	====> Epoch: 169
2023-03-20 10:39:26,976	OUTPUT_MODEL	INFO	Train Epoch: 170 [34%]
2023-03-20 10:39:26,978	OUTPUT_MODEL	INFO	[2.1269309520721436, 2.846863031387329, 8.848323822021484, 20.044830322265625, 0.8790212869644165, 2.1915042400360107, 23200, 0.00019581905540567768]
2023-03-20 10:40:43,707	OUTPUT_MODEL	INFO	====> Epoch: 170
2023-03-20 10:40:58,772	OUTPUT_MODEL	INFO	Train Epoch: 171 [7%]
2023-03-20 10:40:58,774	OUTPUT_MODEL	INFO	[2.1796796321868896, 2.6500260829925537, 7.499296188354492, 20.116479873657227, 1.0930167436599731, 2.143648862838745, 23300, 0.00019579457802375197]
2023-03-20 10:42:23,862	OUTPUT_MODEL	INFO	Train Epoch: 171 [80%]
2023-03-20 10:42:23,863	OUTPUT_MODEL	INFO	[2.188450574874878, 2.558199644088745, 7.242681980133057, 20.66600799560547, 1.1605833768844604, 2.1052350997924805, 23400, 0.00019579457802375197]
2023-03-20 10:42:47,143	OUTPUT_MODEL	INFO	====> Epoch: 171
2023-03-20 10:43:55,878	OUTPUT_MODEL	INFO	Train Epoch: 172 [53%]
2023-03-20 10:43:55,880	OUTPUT_MODEL	INFO	[2.240506410598755, 2.6086647510528564, 7.234076499938965, 19.882417678833008, 1.0587759017944336, 2.051619529724121, 23500, 0.000195770103701499]
2023-03-20 10:44:50,030	OUTPUT_MODEL	INFO	====> Epoch: 172
2023-03-20 10:45:27,098	OUTPUT_MODEL	INFO	Train Epoch: 173 [26%]
2023-03-20 10:45:27,101	OUTPUT_MODEL	INFO	[2.1498820781707764, 2.8350794315338135, 7.983364105224609, 20.600261688232422, 0.12930116057395935, 1.7824519872665405, 23600, 0.0001957456324385363]
2023-03-20 10:46:51,781	OUTPUT_MODEL	INFO	Train Epoch: 173 [99%]
2023-03-20 10:46:51,783	OUTPUT_MODEL	INFO	[2.4730589389801025, 2.584176778793335, 6.334465503692627, 20.796085357666016, 0.8412808179855347, 2.0179638862609863, 23700, 0.0001957456324385363]
2023-03-20 10:46:53,347	OUTPUT_MODEL	INFO	====> Epoch: 173
2023-03-20 10:48:22,981	OUTPUT_MODEL	INFO	Train Epoch: 174 [72%]
2023-03-20 10:48:22,983	OUTPUT_MODEL	INFO	[2.1529650688171387, 2.8519959449768066, 7.617620944976807, 20.687166213989258, 1.0512588024139404, 2.003096342086792, 23800, 0.00019572116423448148]
2023-03-20 10:48:55,340	OUTPUT_MODEL	INFO	====> Epoch: 174
2023-03-20 10:49:53,739	OUTPUT_MODEL	INFO	Train Epoch: 175 [45%]
2023-03-20 10:49:53,741	OUTPUT_MODEL	INFO	[2.1123528480529785, 2.5870285034179688, 6.616996765136719, 20.852540969848633, 0.9252743721008301, 2.032073736190796, 23900, 0.00019569669908895215]
2023-03-20 10:50:56,961	OUTPUT_MODEL	INFO	====> Epoch: 175
2023-03-20 10:51:24,693	OUTPUT_MODEL	INFO	Train Epoch: 176 [18%]
2023-03-20 10:51:24,695	OUTPUT_MODEL	INFO	[2.1056997776031494, 2.8605740070343018, 8.199437141418457, 20.685699462890625, 1.0782819986343384, 1.6545218229293823, 24000, 0.00019567223700156603]
2023-03-20 10:51:26,365	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 176 to ././OUTPUT_MODEL/G_24000.pth
2023-03-20 10:51:26,915	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 176 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 10:52:51,695	OUTPUT_MODEL	INFO	Train Epoch: 176 [91%]
2023-03-20 10:52:51,696	OUTPUT_MODEL	INFO	[2.277381181716919, 2.7685446739196777, 6.969987392425537, 20.5206241607666, 0.9634971618652344, 1.829461932182312, 24100, 0.00019567223700156603]
2023-03-20 10:53:02,108	OUTPUT_MODEL	INFO	====> Epoch: 176
2023-03-20 10:54:22,612	OUTPUT_MODEL	INFO	Train Epoch: 177 [64%]
2023-03-20 10:54:22,614	OUTPUT_MODEL	INFO	[2.4085922241210938, 2.4456610679626465, 6.2808518409729, 20.371305465698242, 1.1267465353012085, 1.9850443601608276, 24200, 0.00019564777797194082]
2023-03-20 10:55:04,628	OUTPUT_MODEL	INFO	====> Epoch: 177
2023-03-20 10:55:54,482	OUTPUT_MODEL	INFO	Train Epoch: 178 [37%]
2023-03-20 10:55:54,483	OUTPUT_MODEL	INFO	[2.248671054840088, 2.502758026123047, 6.825430870056152, 21.452312469482422, 0.9304234981536865, 1.983133316040039, 24300, 0.00019562332199969432]
2023-03-20 10:57:07,370	OUTPUT_MODEL	INFO	====> Epoch: 178
2023-03-20 10:57:26,157	OUTPUT_MODEL	INFO	Train Epoch: 179 [10%]
2023-03-20 10:57:26,159	OUTPUT_MODEL	INFO	[2.2315165996551514, 2.7164580821990967, 7.932145595550537, 20.026710510253906, 1.1805018186569214, 1.9871855974197388, 24400, 0.00019559886908444434]
2023-03-20 10:58:50,795	OUTPUT_MODEL	INFO	Train Epoch: 179 [83%]
2023-03-20 10:58:50,797	OUTPUT_MODEL	INFO	[2.2280020713806152, 2.94952392578125, 7.330118656158447, 19.25713348388672, 1.1238970756530762, 2.040158987045288, 24500, 0.00019559886908444434]
2023-03-20 10:59:10,583	OUTPUT_MODEL	INFO	====> Epoch: 179
2023-03-20 11:00:23,120	OUTPUT_MODEL	INFO	Train Epoch: 180 [56%]
2023-03-20 11:00:23,123	OUTPUT_MODEL	INFO	[2.2582626342773438, 2.751279592514038, 6.756885051727295, 20.77353286743164, 0.9361482262611389, 2.1428966522216797, 24600, 0.00019557441922580878]
2023-03-20 11:01:14,840	OUTPUT_MODEL	INFO	====> Epoch: 180
2023-03-20 11:01:55,294	OUTPUT_MODEL	INFO	Train Epoch: 181 [29%]
2023-03-20 11:01:55,295	OUTPUT_MODEL	INFO	[2.2461910247802734, 2.4028267860412598, 7.030677795410156, 20.789480209350586, 0.9675514698028564, 2.168562173843384, 24700, 0.00019554997242340555]
2023-03-20 11:03:16,948	OUTPUT_MODEL	INFO	====> Epoch: 181
2023-03-20 11:03:26,250	OUTPUT_MODEL	INFO	Train Epoch: 182 [2%]
2023-03-20 11:03:26,252	OUTPUT_MODEL	INFO	[2.2026848793029785, 2.7832396030426025, 6.858623504638672, 20.495481491088867, 0.9780598878860474, 2.1222002506256104, 24800, 0.00019552552867685262]
2023-03-20 11:04:50,888	OUTPUT_MODEL	INFO	Train Epoch: 182 [75%]
2023-03-20 11:04:50,890	OUTPUT_MODEL	INFO	[2.0939669609069824, 2.9474081993103027, 8.92290210723877, 20.540477752685547, 1.0787602663040161, 1.774308681488037, 24900, 0.00019552552867685262]
2023-03-20 11:05:19,930	OUTPUT_MODEL	INFO	====> Epoch: 182
2023-03-20 11:06:22,433	OUTPUT_MODEL	INFO	Train Epoch: 183 [48%]
2023-03-20 11:06:22,435	OUTPUT_MODEL	INFO	[2.1907362937927246, 2.9749979972839355, 7.39637565612793, 20.889434814453125, 1.0745242834091187, 2.077282190322876, 25000, 0.000195501087985768]
2023-03-20 11:06:23,837	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 183 to ././OUTPUT_MODEL/G_25000.pth
2023-03-20 11:06:24,337	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 183 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 11:07:24,498	OUTPUT_MODEL	INFO	====> Epoch: 183
2023-03-20 11:07:56,030	OUTPUT_MODEL	INFO	Train Epoch: 184 [21%]
2023-03-20 11:07:56,032	OUTPUT_MODEL	INFO	[2.3268520832061768, 2.706749677658081, 6.816662788391113, 20.299476623535156, 1.1500511169433594, 1.8022257089614868, 25100, 0.00019547665034976976]
2023-03-20 11:09:20,731	OUTPUT_MODEL	INFO	Train Epoch: 184 [94%]
2023-03-20 11:09:20,733	OUTPUT_MODEL	INFO	[2.435062885284424, 2.4325003623962402, 6.683997631072998, 20.74047088623047, 1.065792202949524, 2.1580941677093506, 25200, 0.00019547665034976976]
2023-03-20 11:09:27,814	OUTPUT_MODEL	INFO	====> Epoch: 184
2023-03-20 11:10:52,054	OUTPUT_MODEL	INFO	Train Epoch: 185 [67%]
2023-03-20 11:10:52,056	OUTPUT_MODEL	INFO	[2.035989284515381, 2.9694273471832275, 8.119749069213867, 20.235349655151367, 0.06503257155418396, 2.1074440479278564, 25300, 0.00019545221576847604]
2023-03-20 11:11:30,405	OUTPUT_MODEL	INFO	====> Epoch: 185
2023-03-20 11:12:22,998	OUTPUT_MODEL	INFO	Train Epoch: 186 [40%]
2023-03-20 11:12:23,000	OUTPUT_MODEL	INFO	[2.2870402336120605, 2.442133903503418, 6.648044109344482, 20.392555236816406, 1.1004811525344849, 2.2874345779418945, 25400, 0.00019542778424150499]
2023-03-20 11:13:32,352	OUTPUT_MODEL	INFO	====> Epoch: 186
2023-03-20 11:13:53,957	OUTPUT_MODEL	INFO	Train Epoch: 187 [13%]
2023-03-20 11:13:53,959	OUTPUT_MODEL	INFO	[2.3688554763793945, 2.6251182556152344, 7.337269306182861, 21.38823127746582, 0.9138239622116089, 1.9944090843200684, 25500, 0.0001954033557684748]
2023-03-20 11:15:19,007	OUTPUT_MODEL	INFO	Train Epoch: 187 [86%]
2023-03-20 11:15:19,009	OUTPUT_MODEL	INFO	[2.258650302886963, 2.681300401687622, 7.154935359954834, 21.4086971282959, 1.1422598361968994, 2.1599953174591064, 25600, 0.0001954033557684748]
2023-03-20 11:15:35,708	OUTPUT_MODEL	INFO	====> Epoch: 187
2023-03-20 11:16:50,486	OUTPUT_MODEL	INFO	Train Epoch: 188 [59%]
2023-03-20 11:16:50,487	OUTPUT_MODEL	INFO	[2.285973072052002, 2.484903335571289, 6.885167121887207, 20.586519241333008, 1.0502686500549316, 2.0718467235565186, 25700, 0.0001953789303490037]
2023-03-20 11:17:38,073	OUTPUT_MODEL	INFO	====> Epoch: 188
2023-03-20 11:18:21,106	OUTPUT_MODEL	INFO	Train Epoch: 189 [32%]
2023-03-20 11:18:21,108	OUTPUT_MODEL	INFO	[2.1028363704681396, 2.882397174835205, 9.037912368774414, 20.962785720825195, 1.018007755279541, 1.7433383464813232, 25800, 0.00019535450798271008]
2023-03-20 11:19:39,258	OUTPUT_MODEL	INFO	====> Epoch: 189
2023-03-20 11:19:51,887	OUTPUT_MODEL	INFO	Train Epoch: 190 [5%]
2023-03-20 11:19:51,888	OUTPUT_MODEL	INFO	[2.029541492462158, 3.0682992935180664, 8.371903419494629, 19.9461727142334, 1.0342148542404175, 1.7925965785980225, 25900, 0.00019533008866921223]
2023-03-20 11:21:15,912	OUTPUT_MODEL	INFO	Train Epoch: 190 [78%]
2023-03-20 11:21:15,914	OUTPUT_MODEL	INFO	[2.349364757537842, 2.498849868774414, 6.059263229370117, 20.755447387695312, 1.0414196252822876, 2.3278067111968994, 26000, 0.00019533008866921223]
2023-03-20 11:21:17,258	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 190 to ././OUTPUT_MODEL/G_26000.pth
2023-03-20 11:21:17,785	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 190 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 11:21:43,569	OUTPUT_MODEL	INFO	====> Epoch: 190
2023-03-20 11:22:48,444	OUTPUT_MODEL	INFO	Train Epoch: 191 [51%]
2023-03-20 11:22:48,446	OUTPUT_MODEL	INFO	[2.237301826477051, 2.6277832984924316, 7.427600383758545, 19.887344360351562, 0.9752035140991211, 2.356083869934082, 26100, 0.00019530567240812858]
2023-03-20 11:23:45,033	OUTPUT_MODEL	INFO	====> Epoch: 191
2023-03-20 11:24:19,224	OUTPUT_MODEL	INFO	Train Epoch: 192 [24%]
2023-03-20 11:24:19,226	OUTPUT_MODEL	INFO	[2.2307116985321045, 2.7331197261810303, 8.382960319519043, 20.82465362548828, 0.9184662103652954, 1.9046621322631836, 26200, 0.00019528125919907757]
2023-03-20 11:25:42,963	OUTPUT_MODEL	INFO	Train Epoch: 192 [97%]
2023-03-20 11:25:42,964	OUTPUT_MODEL	INFO	[2.3268237113952637, 2.2988414764404297, 6.254750728607178, 20.1995792388916, 1.0320661067962646, 2.0636332035064697, 26300, 0.00019528125919907757]
2023-03-20 11:25:46,510	OUTPUT_MODEL	INFO	====> Epoch: 192
2023-03-20 11:27:13,015	OUTPUT_MODEL	INFO	Train Epoch: 193 [70%]
2023-03-20 11:27:13,017	OUTPUT_MODEL	INFO	[2.2700533866882324, 2.635828733444214, 6.534947395324707, 20.469148635864258, 0.961995005607605, 1.8181284666061401, 26400, 0.00019525684904167766]
2023-03-20 11:27:47,834	OUTPUT_MODEL	INFO	====> Epoch: 193
2023-03-20 11:28:43,295	OUTPUT_MODEL	INFO	Train Epoch: 194 [43%]
2023-03-20 11:28:43,297	OUTPUT_MODEL	INFO	[2.2723031044006348, 2.47902774810791, 6.1885762214660645, 19.82864761352539, 0.9884249567985535, 2.1927297115325928, 26500, 0.00019523244193554745]
2023-03-20 11:29:48,671	OUTPUT_MODEL	INFO	====> Epoch: 194
2023-03-20 11:30:13,905	OUTPUT_MODEL	INFO	Train Epoch: 195 [16%]
2023-03-20 11:30:13,907	OUTPUT_MODEL	INFO	[2.4620394706726074, 2.456700563430786, 5.7645769119262695, 20.225862503051758, 1.0872533321380615, 2.2842838764190674, 26600, 0.0001952080378803055]
2023-03-20 11:31:38,229	OUTPUT_MODEL	INFO	Train Epoch: 195 [89%]
2023-03-20 11:31:38,231	OUTPUT_MODEL	INFO	[2.0604560375213623, 2.9491052627563477, 7.920343399047852, 20.81699562072754, 1.0347931385040283, 1.8710837364196777, 26700, 0.0001952080378803055]
2023-03-20 11:31:51,117	OUTPUT_MODEL	INFO	====> Epoch: 195
2023-03-20 11:33:08,408	OUTPUT_MODEL	INFO	Train Epoch: 196 [62%]
2023-03-20 11:33:08,411	OUTPUT_MODEL	INFO	[2.0562617778778076, 2.8279030323028564, 7.001455307006836, 20.566909790039062, 0.9749987125396729, 2.1805121898651123, 26800, 0.00019518363687557043]
2023-03-20 11:33:52,482	OUTPUT_MODEL	INFO	====> Epoch: 196
2023-03-20 11:34:39,241	OUTPUT_MODEL	INFO	Train Epoch: 197 [35%]
2023-03-20 11:34:39,244	OUTPUT_MODEL	INFO	[1.9868052005767822, 3.0092194080352783, 7.630010604858398, 19.301490783691406, 1.0537792444229126, 1.8935201168060303, 26900, 0.00019515923892096098]
2023-03-20 11:35:53,939	OUTPUT_MODEL	INFO	====> Epoch: 197
2023-03-20 11:36:10,087	OUTPUT_MODEL	INFO	Train Epoch: 198 [8%]
2023-03-20 11:36:10,089	OUTPUT_MODEL	INFO	[2.2091054916381836, 2.5192301273345947, 7.305696487426758, 20.15118980407715, 0.979110598564148, 2.207300901412964, 27000, 0.00019513484401609586]
2023-03-20 11:36:11,476	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 198 to ././OUTPUT_MODEL/G_27000.pth
2023-03-20 11:36:12,000	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 198 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 11:37:36,029	OUTPUT_MODEL	INFO	Train Epoch: 198 [81%]
2023-03-20 11:37:36,030	OUTPUT_MODEL	INFO	[2.440140962600708, 2.7459120750427246, 8.89706802368164, 18.62025260925293, 1.1518537998199463, 2.06067156791687, 27100, 0.00019513484401609586]
2023-03-20 11:37:57,931	OUTPUT_MODEL	INFO	====> Epoch: 198
2023-03-20 11:39:06,260	OUTPUT_MODEL	INFO	Train Epoch: 199 [54%]
2023-03-20 11:39:06,262	OUTPUT_MODEL	INFO	[2.165672540664673, 2.5236828327178955, 6.643775463104248, 20.027647018432617, 0.8894328474998474, 1.7553157806396484, 27200, 0.00019511045216059385]
2023-03-20 11:39:59,247	OUTPUT_MODEL	INFO	====> Epoch: 199
2023-03-20 11:40:36,657	OUTPUT_MODEL	INFO	Train Epoch: 200 [27%]
2023-03-20 11:40:36,659	OUTPUT_MODEL	INFO	[2.0734944343566895, 2.8179187774658203, 8.076297760009766, 20.656391143798828, 0.8956226706504822, 2.1407690048217773, 27300, 0.00019508606335407377]
2023-03-20 11:42:00,434	OUTPUT_MODEL	INFO	====> Epoch: 200
2023-03-20 11:42:07,019	OUTPUT_MODEL	INFO	Train Epoch: 201 [0%]
2023-03-20 11:42:07,021	OUTPUT_MODEL	INFO	[2.004234790802002, 2.9168834686279297, 8.89865493774414, 20.92208480834961, 1.1095597743988037, 1.809404969215393, 27400, 0.00019506167759615451]
2023-03-20 11:43:31,263	OUTPUT_MODEL	INFO	Train Epoch: 201 [73%]
2023-03-20 11:43:31,265	OUTPUT_MODEL	INFO	[2.2410693168640137, 2.585336208343506, 7.115887641906738, 20.943439483642578, 1.029303789138794, 2.3608126640319824, 27500, 0.00019506167759615451]
2023-03-20 11:44:02,799	OUTPUT_MODEL	INFO	====> Epoch: 201
2023-03-20 11:45:01,974	OUTPUT_MODEL	INFO	Train Epoch: 202 [46%]
2023-03-20 11:45:01,976	OUTPUT_MODEL	INFO	[2.2939717769622803, 2.761626720428467, 11.03116226196289, 18.551122665405273, 1.1761293411254883, 2.2686121463775635, 27600, 0.000195037294886455]
2023-03-20 11:46:04,300	OUTPUT_MODEL	INFO	====> Epoch: 202
2023-03-20 11:46:32,559	OUTPUT_MODEL	INFO	Train Epoch: 203 [19%]
2023-03-20 11:46:32,560	OUTPUT_MODEL	INFO	[2.264193058013916, 2.8920955657958984, 7.240257263183594, 21.16084861755371, 1.0080666542053223, 1.9319627285003662, 27700, 0.00019501291522459419]
2023-03-20 11:47:57,179	OUTPUT_MODEL	INFO	Train Epoch: 203 [92%]
2023-03-20 11:47:57,181	OUTPUT_MODEL	INFO	[2.223723888397217, 2.7799274921417236, 6.90838623046875, 20.29168701171875, 0.9870033860206604, 2.012070417404175, 27800, 0.00019501291522459419]
2023-03-20 11:48:06,653	OUTPUT_MODEL	INFO	====> Epoch: 203
2023-03-20 11:49:26,988	OUTPUT_MODEL	INFO	Train Epoch: 204 [65%]
2023-03-20 11:49:26,990	OUTPUT_MODEL	INFO	[2.2776296138763428, 2.758803367614746, 7.3639235496521, 21.082788467407227, 0.9638789892196655, 1.7611849308013916, 27900, 0.0001949885386101911]
2023-03-20 11:50:07,896	OUTPUT_MODEL	INFO	====> Epoch: 204
2023-03-20 11:50:57,600	OUTPUT_MODEL	INFO	Train Epoch: 205 [38%]
2023-03-20 11:50:57,602	OUTPUT_MODEL	INFO	[2.0934510231018066, 2.703035593032837, 8.251220703125, 20.573822021484375, 0.8559115529060364, 2.178816318511963, 28000, 0.00019496416504286482]
2023-03-20 11:50:58,801	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 205 to ././OUTPUT_MODEL/G_28000.pth
2023-03-20 11:50:59,278	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 205 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 11:52:10,814	OUTPUT_MODEL	INFO	====> Epoch: 205
2023-03-20 11:52:29,979	OUTPUT_MODEL	INFO	Train Epoch: 206 [11%]
2023-03-20 11:52:29,981	OUTPUT_MODEL	INFO	[2.1722965240478516, 2.7546896934509277, 6.5834808349609375, 21.316429138183594, 1.0137029886245728, 2.0526561737060547, 28100, 0.00019493979452223445]
2023-03-20 11:53:53,875	OUTPUT_MODEL	INFO	Train Epoch: 206 [84%]
2023-03-20 11:53:53,877	OUTPUT_MODEL	INFO	[2.272019147872925, 2.764718770980835, 6.669269561767578, 19.693710327148438, 1.02699875831604, 2.004396677017212, 28200, 0.00019493979452223445]
2023-03-20 11:54:13,120	OUTPUT_MODEL	INFO	====> Epoch: 206
2023-03-20 11:55:24,667	OUTPUT_MODEL	INFO	Train Epoch: 207 [57%]
2023-03-20 11:55:24,668	OUTPUT_MODEL	INFO	[2.1709070205688477, 2.61242413520813, 6.767266273498535, 21.15502166748047, 1.0423681735992432, 1.7976446151733398, 28300, 0.00019491542704791915]
2023-03-20 11:56:14,528	OUTPUT_MODEL	INFO	====> Epoch: 207
2023-03-20 11:56:55,586	OUTPUT_MODEL	INFO	Train Epoch: 208 [30%]
2023-03-20 11:56:55,587	OUTPUT_MODEL	INFO	[2.2818942070007324, 2.427534818649292, 7.536084175109863, 20.60780143737793, 1.0337437391281128, 2.4298081398010254, 28400, 0.00019489106261953815]
2023-03-20 11:58:16,075	OUTPUT_MODEL	INFO	====> Epoch: 208
2023-03-20 11:58:26,147	OUTPUT_MODEL	INFO	Train Epoch: 209 [3%]
2023-03-20 11:58:26,149	OUTPUT_MODEL	INFO	[2.3668265342712402, 2.566288471221924, 7.311249256134033, 20.405126571655273, 0.9622765183448792, 2.0640358924865723, 28500, 0.0001948667012367107]
2023-03-20 11:59:49,906	OUTPUT_MODEL	INFO	Train Epoch: 209 [76%]
2023-03-20 11:59:49,907	OUTPUT_MODEL	INFO	[2.304072618484497, 2.5693767070770264, 6.793314456939697, 20.636314392089844, 0.9339377880096436, 1.883727788925171, 28600, 0.0001948667012367107]
2023-03-20 12:00:18,436	OUTPUT_MODEL	INFO	====> Epoch: 209
2023-03-20 12:01:22,483	OUTPUT_MODEL	INFO	Train Epoch: 210 [49%]
2023-03-20 12:01:22,485	OUTPUT_MODEL	INFO	[2.1456456184387207, 2.9877350330352783, 9.499155044555664, 21.226350784301758, 1.1504476070404053, 2.0918211936950684, 28700, 0.0001948423428990561]
2023-03-20 12:02:21,281	OUTPUT_MODEL	INFO	====> Epoch: 210
2023-03-20 12:02:53,348	OUTPUT_MODEL	INFO	Train Epoch: 211 [22%]
2023-03-20 12:02:53,350	OUTPUT_MODEL	INFO	[2.3151469230651855, 2.886549234390259, 8.527459144592285, 21.02116584777832, 1.0638890266418457, 2.2183587551116943, 28800, 0.0001948179876061937]
2023-03-20 12:04:17,905	OUTPUT_MODEL	INFO	Train Epoch: 211 [95%]
2023-03-20 12:04:17,907	OUTPUT_MODEL	INFO	[2.2657175064086914, 2.8225882053375244, 7.48565149307251, 20.862768173217773, 0.9937882423400879, 2.482916831970215, 28900, 0.0001948179876061937]
2023-03-20 12:04:24,111	OUTPUT_MODEL	INFO	====> Epoch: 211
2023-03-20 12:05:48,906	OUTPUT_MODEL	INFO	Train Epoch: 212 [68%]
2023-03-20 12:05:48,908	OUTPUT_MODEL	INFO	[2.182781934738159, 3.431051254272461, 13.137081146240234, 18.888612747192383, 1.1286795139312744, 2.050941228866577, 29000, 0.00019479363535774292]
2023-03-20 12:05:49,994	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 212 to ././OUTPUT_MODEL/G_29000.pth
2023-03-20 12:05:50,479	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 212 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 12:06:28,271	OUTPUT_MODEL	INFO	====> Epoch: 212
2023-03-20 12:07:21,973	OUTPUT_MODEL	INFO	Train Epoch: 213 [41%]
2023-03-20 12:07:21,975	OUTPUT_MODEL	INFO	[2.2314460277557373, 2.6069369316101074, 6.438302516937256, 19.920007705688477, 1.0866857767105103, 1.7733066082000732, 29100, 0.0001947692861533232]
2023-03-20 12:08:30,451	OUTPUT_MODEL	INFO	====> Epoch: 213
2023-03-20 12:08:52,820	OUTPUT_MODEL	INFO	Train Epoch: 214 [14%]
2023-03-20 12:08:52,822	OUTPUT_MODEL	INFO	[2.198514938354492, 2.926867961883545, 6.591020584106445, 20.86577606201172, 1.0050023794174194, 1.4621126651763916, 29200, 0.000194744939992554]
2023-03-20 12:10:17,434	OUTPUT_MODEL	INFO	Train Epoch: 214 [87%]
2023-03-20 12:10:17,436	OUTPUT_MODEL	INFO	[2.280895709991455, 2.522156238555908, 6.226226329803467, 20.919294357299805, 0.9871393442153931, 1.6118378639221191, 29300, 0.000194744939992554]
2023-03-20 12:10:33,315	OUTPUT_MODEL	INFO	====> Epoch: 214
2023-03-20 12:11:48,658	OUTPUT_MODEL	INFO	Train Epoch: 215 [60%]
2023-03-20 12:11:48,659	OUTPUT_MODEL	INFO	[2.259922742843628, 2.5272216796875, 6.428264141082764, 21.361412048339844, 0.9319770336151123, 1.8670905828475952, 29400, 0.00019472059687505495]
2023-03-20 12:12:35,429	OUTPUT_MODEL	INFO	====> Epoch: 215
2023-03-20 12:13:19,603	OUTPUT_MODEL	INFO	Train Epoch: 216 [33%]
2023-03-20 12:13:19,605	OUTPUT_MODEL	INFO	[2.118598461151123, 2.7218334674835205, 6.708568096160889, 20.96635627746582, 0.8956384658813477, 2.150635004043579, 29500, 0.00019469625680044555]
2023-03-20 12:14:36,985	OUTPUT_MODEL	INFO	====> Epoch: 216
2023-03-20 12:14:50,372	OUTPUT_MODEL	INFO	Train Epoch: 217 [6%]
2023-03-20 12:14:50,374	OUTPUT_MODEL	INFO	[2.15924334526062, 3.0601961612701416, 8.386127471923828, 20.813962936401367, 1.0402190685272217, 2.1770946979522705, 29600, 0.0001946719197683455]
2023-03-20 12:16:14,418	OUTPUT_MODEL	INFO	Train Epoch: 217 [79%]
2023-03-20 12:16:14,420	OUTPUT_MODEL	INFO	[2.2699134349823, 2.7199478149414062, 7.278733253479004, 20.89040184020996, 1.0174510478973389, 2.1472232341766357, 29700, 0.0001946719197683455]
2023-03-20 12:16:39,244	OUTPUT_MODEL	INFO	====> Epoch: 217
2023-03-20 12:17:45,254	OUTPUT_MODEL	INFO	Train Epoch: 218 [52%]
2023-03-20 12:17:45,255	OUTPUT_MODEL	INFO	[2.150876045227051, 2.8134429454803467, 6.3703107833862305, 20.198896408081055, 1.0136507749557495, 1.7834395170211792, 29800, 0.00019464758577837445]
2023-03-20 12:18:40,831	OUTPUT_MODEL	INFO	====> Epoch: 218
2023-03-20 12:19:16,043	OUTPUT_MODEL	INFO	Train Epoch: 219 [25%]
2023-03-20 12:19:16,044	OUTPUT_MODEL	INFO	[2.3615636825561523, 2.6550121307373047, 6.981574535369873, 20.907135009765625, 0.8836679458618164, 2.0210368633270264, 29900, 0.00019462325483015215]
2023-03-20 12:20:40,520	OUTPUT_MODEL	INFO	Train Epoch: 219 [98%]
2023-03-20 12:20:40,521	OUTPUT_MODEL	INFO	[2.252777099609375, 2.538205862045288, 6.86692476272583, 19.672901153564453, 0.9643998742103577, 1.6524735689163208, 30000, 0.00019462325483015215]
2023-03-20 12:20:41,618	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 219 to ././OUTPUT_MODEL/G_30000.pth
2023-03-20 12:20:42,050	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 219 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 12:20:44,782	OUTPUT_MODEL	INFO	====> Epoch: 219
2023-03-20 12:22:12,962	OUTPUT_MODEL	INFO	Train Epoch: 220 [71%]
2023-03-20 12:22:12,964	OUTPUT_MODEL	INFO	[2.2005808353424072, 2.763200521469116, 7.6295857429504395, 20.700864791870117, 0.9762744903564453, 1.810441493988037, 30100, 0.00019459892692329838]
2023-03-20 12:22:46,878	OUTPUT_MODEL	INFO	====> Epoch: 220
2023-03-20 12:23:43,635	OUTPUT_MODEL	INFO	Train Epoch: 221 [44%]
2023-03-20 12:23:43,637	OUTPUT_MODEL	INFO	[2.347283363342285, 2.4438235759735107, 8.119300842285156, 21.252779006958008, 1.0746283531188965, 2.033754348754883, 30200, 0.00019457460205743297]
2023-03-20 12:24:48,170	OUTPUT_MODEL	INFO	====> Epoch: 221
2023-03-20 12:25:13,998	OUTPUT_MODEL	INFO	Train Epoch: 222 [17%]
2023-03-20 12:25:13,999	OUTPUT_MODEL	INFO	[2.296860456466675, 2.441878080368042, 7.048616886138916, 20.66661834716797, 0.9854447841644287, 1.8570780754089355, 30300, 0.00019455028023217577]
2023-03-20 12:26:38,051	OUTPUT_MODEL	INFO	Train Epoch: 222 [90%]
2023-03-20 12:26:38,053	OUTPUT_MODEL	INFO	[1.9217073917388916, 2.993685722351074, 8.592245101928711, 20.69857406616211, 0.9045910835266113, 1.8679918050765991, 30400, 0.00019455028023217577]
2023-03-20 12:26:50,498	OUTPUT_MODEL	INFO	====> Epoch: 222
2023-03-20 12:28:09,149	OUTPUT_MODEL	INFO	Train Epoch: 223 [63%]
2023-03-20 12:28:09,151	OUTPUT_MODEL	INFO	[1.925619125366211, 2.8415274620056152, 8.773937225341797, 20.39116096496582, 0.8838157653808594, 2.420335531234741, 30500, 0.00019452596144714675]
2023-03-20 12:28:51,982	OUTPUT_MODEL	INFO	====> Epoch: 223
2023-03-20 12:29:39,317	OUTPUT_MODEL	INFO	Train Epoch: 224 [36%]
2023-03-20 12:29:39,319	OUTPUT_MODEL	INFO	[2.22790789604187, 2.7678303718566895, 6.481828689575195, 20.501371383666992, 1.1086300611495972, 2.1198835372924805, 30600, 0.00019450164570196585]
2023-03-20 12:30:53,313	OUTPUT_MODEL	INFO	====> Epoch: 224
2023-03-20 12:31:10,068	OUTPUT_MODEL	INFO	Train Epoch: 225 [9%]
2023-03-20 12:31:10,070	OUTPUT_MODEL	INFO	[2.109374523162842, 2.7549631595611572, 7.466064453125, 19.87153434753418, 1.128915548324585, 1.7296732664108276, 30700, 0.0001944773329962531]
2023-03-20 12:32:34,297	OUTPUT_MODEL	INFO	Train Epoch: 225 [82%]
2023-03-20 12:32:34,298	OUTPUT_MODEL	INFO	[2.177018404006958, 2.7806758880615234, 8.001260757446289, 21.250732421875, 0.9763782024383545, 1.8947794437408447, 30800, 0.0001944773329962531]
2023-03-20 12:32:55,851	OUTPUT_MODEL	INFO	====> Epoch: 225
2023-03-20 12:34:05,407	OUTPUT_MODEL	INFO	Train Epoch: 226 [55%]
2023-03-20 12:34:05,409	OUTPUT_MODEL	INFO	[2.086327075958252, 2.693244457244873, 7.681199073791504, 20.206628799438477, 1.0999724864959717, 1.686098337173462, 30900, 0.00019445302332962857]
2023-03-20 12:34:58,023	OUTPUT_MODEL	INFO	====> Epoch: 226
2023-03-20 12:35:36,524	OUTPUT_MODEL	INFO	Train Epoch: 227 [28%]
2023-03-20 12:35:36,526	OUTPUT_MODEL	INFO	[2.3866279125213623, 2.827719211578369, 7.240045547485352, 20.66889190673828, 0.9979809522628784, 2.1797165870666504, 31000, 0.00019442871670171237]
2023-03-20 12:35:37,824	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 227 to ././OUTPUT_MODEL/G_31000.pth
2023-03-20 12:35:38,360	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 227 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 12:37:01,572	OUTPUT_MODEL	INFO	====> Epoch: 227
2023-03-20 12:37:09,064	OUTPUT_MODEL	INFO	Train Epoch: 228 [1%]
2023-03-20 12:37:09,065	OUTPUT_MODEL	INFO	[2.2098307609558105, 2.701385498046875, 7.348618507385254, 20.137428283691406, 1.0425012111663818, 1.486329197883606, 31100, 0.00019440441311212466]
2023-03-20 12:38:33,413	OUTPUT_MODEL	INFO	Train Epoch: 228 [74%]
2023-03-20 12:38:33,414	OUTPUT_MODEL	INFO	[2.264812469482422, 2.6625094413757324, 8.245150566101074, 20.59290313720703, 1.0066006183624268, 1.8974846601486206, 31200, 0.00019440441311212466]
2023-03-20 12:39:04,034	OUTPUT_MODEL	INFO	====> Epoch: 228
2023-03-20 12:40:04,293	OUTPUT_MODEL	INFO	Train Epoch: 229 [47%]
2023-03-20 12:40:04,294	OUTPUT_MODEL	INFO	[2.146735668182373, 2.474417209625244, 7.476199626922607, 20.443307876586914, 0.9812145829200745, 1.958962082862854, 31300, 0.00019438011256048564]
2023-03-20 12:41:05,436	OUTPUT_MODEL	INFO	====> Epoch: 229
2023-03-20 12:41:34,740	OUTPUT_MODEL	INFO	Train Epoch: 230 [20%]
2023-03-20 12:41:34,742	OUTPUT_MODEL	INFO	[2.260993719100952, 2.747004270553589, 11.765440940856934, 18.505685806274414, 1.230820894241333, 1.9649279117584229, 31400, 0.00019435581504641556]
2023-03-20 12:42:59,922	OUTPUT_MODEL	INFO	Train Epoch: 230 [93%]
2023-03-20 12:42:59,924	OUTPUT_MODEL	INFO	[2.2024221420288086, 2.8175270557403564, 8.435487747192383, 20.536632537841797, 0.9361230731010437, 2.311070680618286, 31500, 0.00019435581504641556]
2023-03-20 12:43:08,379	OUTPUT_MODEL	INFO	====> Epoch: 230
2023-03-20 12:44:30,062	OUTPUT_MODEL	INFO	Train Epoch: 231 [66%]
2023-03-20 12:44:30,064	OUTPUT_MODEL	INFO	[2.0508158206939697, 2.8934929370880127, 7.9501543045043945, 20.557668685913086, 1.1197437047958374, 1.6290470361709595, 31600, 0.00019433152056953475]
2023-03-20 12:45:09,990	OUTPUT_MODEL	INFO	====> Epoch: 231
2023-03-20 12:46:00,727	OUTPUT_MODEL	INFO	Train Epoch: 232 [39%]
2023-03-20 12:46:00,729	OUTPUT_MODEL	INFO	[2.2164852619171143, 2.8313472270965576, 8.416749954223633, 20.741291046142578, 1.146538257598877, 2.092190980911255, 31700, 0.00019430722912946354]
2023-03-20 12:47:11,381	OUTPUT_MODEL	INFO	====> Epoch: 232
2023-03-20 12:47:31,375	OUTPUT_MODEL	INFO	Train Epoch: 233 [12%]
2023-03-20 12:47:31,377	OUTPUT_MODEL	INFO	[2.256300210952759, 2.705226421356201, 7.692996025085449, 20.98098373413086, 0.9718567132949829, 2.3696177005767822, 31800, 0.00019428294072582235]
2023-03-20 12:48:55,716	OUTPUT_MODEL	INFO	Train Epoch: 233 [85%]
2023-03-20 12:48:55,718	OUTPUT_MODEL	INFO	[2.3423595428466797, 2.6811883449554443, 7.3469038009643555, 19.242938995361328, 1.1331672668457031, 2.294752836227417, 31900, 0.00019428294072582235]
2023-03-20 12:49:13,739	OUTPUT_MODEL	INFO	====> Epoch: 233
2023-03-20 12:50:26,379	OUTPUT_MODEL	INFO	Train Epoch: 234 [58%]
2023-03-20 12:50:26,381	OUTPUT_MODEL	INFO	[2.1826813220977783, 2.8805651664733887, 7.390274524688721, 21.439090728759766, 1.0522582530975342, 2.3720974922180176, 32000, 0.0001942586553582316]
2023-03-20 12:50:27,690	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 234 to ././OUTPUT_MODEL/G_32000.pth
2023-03-20 12:50:28,203	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 234 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 12:51:17,543	OUTPUT_MODEL	INFO	====> Epoch: 234
2023-03-20 12:51:59,338	OUTPUT_MODEL	INFO	Train Epoch: 235 [31%]
2023-03-20 12:51:59,339	OUTPUT_MODEL	INFO	[2.1974031925201416, 2.545377016067505, 7.6437482833862305, 20.482017517089844, 0.942965030670166, 2.0135269165039062, 32100, 0.00019423437302631182]
2023-03-20 12:53:19,328	OUTPUT_MODEL	INFO	====> Epoch: 235
2023-03-20 12:53:30,036	OUTPUT_MODEL	INFO	Train Epoch: 236 [4%]
2023-03-20 12:53:30,038	OUTPUT_MODEL	INFO	[2.0025508403778076, 2.854823350906372, 9.084258079528809, 20.407976150512695, 0.03931725025177002, 1.9139665365219116, 32200, 0.00019421009372968352]
2023-03-20 12:54:54,126	OUTPUT_MODEL	INFO	Train Epoch: 236 [77%]
2023-03-20 12:54:54,127	OUTPUT_MODEL	INFO	[2.045295238494873, 2.918140172958374, 7.523714542388916, 20.698009490966797, 0.9769881367683411, 2.0245227813720703, 32300, 0.00019421009372968352]
2023-03-20 12:55:21,542	OUTPUT_MODEL	INFO	====> Epoch: 236
2023-03-20 12:56:25,177	OUTPUT_MODEL	INFO	Train Epoch: 237 [50%]
2023-03-20 12:56:25,179	OUTPUT_MODEL	INFO	[2.2304847240448, 2.7285773754119873, 6.306233882904053, 20.747087478637695, 1.0364124774932861, 1.8198518753051758, 32400, 0.0001941858174679673]
2023-03-20 12:57:23,614	OUTPUT_MODEL	INFO	====> Epoch: 237
2023-03-20 12:57:56,169	OUTPUT_MODEL	INFO	Train Epoch: 238 [23%]
2023-03-20 12:57:56,171	OUTPUT_MODEL	INFO	[2.218294620513916, 2.732158899307251, 6.65161657333374, 20.953927993774414, 1.15159010887146, 1.9266279935836792, 32500, 0.0001941615442407838]
2023-03-20 12:59:20,490	OUTPUT_MODEL	INFO	Train Epoch: 238 [96%]
2023-03-20 12:59:20,492	OUTPUT_MODEL	INFO	[2.192352533340454, 2.738469123840332, 7.640737533569336, 19.77387809753418, 1.0695301294326782, 1.9842498302459717, 32600, 0.0001941615442407838]
2023-03-20 12:59:25,889	OUTPUT_MODEL	INFO	====> Epoch: 238
2023-03-20 13:00:51,636	OUTPUT_MODEL	INFO	Train Epoch: 239 [69%]
2023-03-20 13:00:51,638	OUTPUT_MODEL	INFO	[2.2370927333831787, 2.7310616970062256, 7.350245475769043, 20.635038375854492, 1.0577303171157837, 2.3561010360717773, 32700, 0.0001941372740477537]
2023-03-20 13:01:28,463	OUTPUT_MODEL	INFO	====> Epoch: 239
2023-03-20 13:02:22,953	OUTPUT_MODEL	INFO	Train Epoch: 240 [42%]
2023-03-20 13:02:22,955	OUTPUT_MODEL	INFO	[2.057305097579956, 3.1294443607330322, 10.775593757629395, 19.778831481933594, 1.201339840888977, 1.830135703086853, 32800, 0.0001941130068884977]
2023-03-20 13:03:30,232	OUTPUT_MODEL	INFO	====> Epoch: 240
2023-03-20 13:03:53,634	OUTPUT_MODEL	INFO	Train Epoch: 241 [15%]
2023-03-20 13:03:53,636	OUTPUT_MODEL	INFO	[2.092315673828125, 2.977921724319458, 6.884316921234131, 21.222457885742188, 0.9537841081619263, 1.929465651512146, 32900, 0.00019408874276263664]
2023-03-20 13:05:17,805	OUTPUT_MODEL	INFO	Train Epoch: 241 [88%]
2023-03-20 13:05:17,807	OUTPUT_MODEL	INFO	[2.1406612396240234, 2.9761714935302734, 8.275925636291504, 21.386754989624023, 1.0796229839324951, 2.483407497406006, 33000, 0.00019408874276263664]
2023-03-20 13:05:19,126	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 241 to ././OUTPUT_MODEL/G_33000.pth
2023-03-20 13:05:19,573	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 241 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 13:05:34,454	OUTPUT_MODEL	INFO	====> Epoch: 241
2023-03-20 13:06:51,096	OUTPUT_MODEL	INFO	Train Epoch: 242 [61%]
2023-03-20 13:06:51,098	OUTPUT_MODEL	INFO	[2.4122374057769775, 2.7649409770965576, 6.087351322174072, 20.287628173828125, 0.9873552322387695, 1.9493181705474854, 33100, 0.0001940644816697913]
2023-03-20 13:07:36,588	OUTPUT_MODEL	INFO	====> Epoch: 242
2023-03-20 13:08:21,823	OUTPUT_MODEL	INFO	Train Epoch: 243 [34%]
2023-03-20 13:08:21,825	OUTPUT_MODEL	INFO	[2.3053250312805176, 2.752185583114624, 6.894016265869141, 20.82826805114746, 0.9481443762779236, 2.093461751937866, 33200, 0.00019404022360958257]
2023-03-20 13:09:37,886	OUTPUT_MODEL	INFO	====> Epoch: 243
2023-03-20 13:09:52,185	OUTPUT_MODEL	INFO	Train Epoch: 244 [7%]
2023-03-20 13:09:52,187	OUTPUT_MODEL	INFO	[2.1775269508361816, 2.512152910232544, 7.258467197418213, 19.32333755493164, 0.9917401075363159, 1.7101858854293823, 33300, 0.00019401596858163137]
2023-03-20 13:11:16,347	OUTPUT_MODEL	INFO	Train Epoch: 244 [80%]
2023-03-20 13:11:16,349	OUTPUT_MODEL	INFO	[2.252617120742798, 2.624211311340332, 7.068161487579346, 21.10245704650879, 0.9545121192932129, 1.8735849857330322, 33400, 0.00019401596858163137]
2023-03-20 13:11:40,351	OUTPUT_MODEL	INFO	====> Epoch: 244
2023-03-20 13:12:47,661	OUTPUT_MODEL	INFO	Train Epoch: 245 [53%]
2023-03-20 13:12:47,663	OUTPUT_MODEL	INFO	[2.4550909996032715, 2.430968761444092, 6.055636882781982, 19.064254760742188, 1.060678482055664, 2.1733591556549072, 33500, 0.00019399171658555865]
2023-03-20 13:13:42,347	OUTPUT_MODEL	INFO	====> Epoch: 245
2023-03-20 13:14:18,238	OUTPUT_MODEL	INFO	Train Epoch: 246 [26%]
2023-03-20 13:14:18,240	OUTPUT_MODEL	INFO	[2.2468955516815186, 2.5925018787384033, 7.032855987548828, 20.545429229736328, 1.0344223976135254, 1.5621097087860107, 33600, 0.00019396746762098544]
2023-03-20 13:15:42,524	OUTPUT_MODEL	INFO	Train Epoch: 246 [99%]
2023-03-20 13:15:42,526	OUTPUT_MODEL	INFO	[2.182661294937134, 2.736867904663086, 8.217529296875, 20.907873153686523, 1.0769116878509521, 2.05627179145813, 33700, 0.00019396746762098544]
2023-03-20 13:15:44,567	OUTPUT_MODEL	INFO	====> Epoch: 246
2023-03-20 13:17:12,892	OUTPUT_MODEL	INFO	Train Epoch: 247 [72%]
2023-03-20 13:17:12,894	OUTPUT_MODEL	INFO	[2.2445948123931885, 2.904226303100586, 6.902346134185791, 19.464757919311523, 1.1934210062026978, 2.1130900382995605, 33800, 0.0001939432216875328]
2023-03-20 13:17:46,039	OUTPUT_MODEL	INFO	====> Epoch: 247
2023-03-20 13:18:43,493	OUTPUT_MODEL	INFO	Train Epoch: 248 [45%]
2023-03-20 13:18:43,495	OUTPUT_MODEL	INFO	[2.2886157035827637, 2.672990322113037, 8.556007385253906, 18.793716430664062, 1.2510035037994385, 1.8285882472991943, 33900, 0.00019391897878482186]
2023-03-20 13:19:47,519	OUTPUT_MODEL	INFO	====> Epoch: 248
2023-03-20 13:20:14,233	OUTPUT_MODEL	INFO	Train Epoch: 249 [18%]
2023-03-20 13:20:14,235	OUTPUT_MODEL	INFO	[2.152531147003174, 2.6600613594055176, 7.606066703796387, 21.092744827270508, 0.9213927388191223, 1.6107100248336792, 34000, 0.00019389473891247375]
2023-03-20 13:20:15,389	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 249 to ././OUTPUT_MODEL/G_34000.pth
2023-03-20 13:20:15,832	OUTPUT_MODEL	INFO	Saving model and optimizer state at iteration 249 to ././OUTPUT_MODEL/G_latest.pth
2023-03-20 13:21:40,360	OUTPUT_MODEL	INFO	Train Epoch: 249 [91%]
2023-03-20 13:21:40,362	OUTPUT_MODEL	INFO	[2.351404905319214, 2.6596992015838623, 6.8907790184021, 20.363155364990234, 1.0284374952316284, 2.252542495727539, 34100, 0.00019389473891247375]
2023-03-20 13:21:51,915	OUTPUT_MODEL	INFO	====> Epoch: 249
2023-03-20 13:23:11,044	OUTPUT_MODEL	INFO	Train Epoch: 250 [64%]
2023-03-20 13:23:11,046	OUTPUT_MODEL	INFO	[2.21124267578125, 2.8291497230529785, 6.627447605133057, 19.623552322387695, 1.073622465133667, 1.6207945346832275, 34200, 0.00019387050207010967]
2023-03-20 13:23:53,993	OUTPUT_MODEL	INFO	====> Epoch: 250