kimsehun725

Kimsehun725's group workspace

Group: 20221020_1510

233

1-6

of 6

Timestamps visible

2022-10-20 10:29:00

Epoch 161: 89%|████████████████████████████████████████████▍ | 16/18 [00:05<00:00, 3.07it/s, loss=0.0232, v_num=xc2k, val/loss=0.017, val/precision=0.868, val/recall=0.906, val/f1=0.887, val/tdr=0.995, val/sa_precision=0.887, val/sa_recall=0.925, val/sa_f1=0.905, train/loss=0.0225]

2022-10-20 10:29:14

Epoch 162: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.96it/s, loss=0.0231, v_num=xc2k, val/loss=0.0168, val/precision=0.870, val/recall=0.906, val/f1=0.888, val/tdr=0.996, val/sa_precision=0.888, val/sa_recall=0.925, val/sa_f1=0.906, train/loss=0.0237]

2022-10-20 10:29:34

Epoch 163: 89%|███████████████████████████████████████████▌ | 16/18 [00:07<00:00, 2.26it/s, loss=0.0234, v_num=xc2k, val/loss=0.0169, val/precision=0.865, val/recall=0.910, val/f1=0.887, val/tdr=0.996, val/sa_precision=0.883, val/sa_recall=0.929, val/sa_f1=0.905, train/loss=0.0233]

2022-10-20 10:29:48

Epoch 164: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 3.11it/s, loss=0.0231, v_num=xc2k, val/loss=0.0167, val/precision=0.866, val/recall=0.909, val/f1=0.887, val/tdr=0.997, val/sa_precision=0.884, val/sa_recall=0.928, val/sa_f1=0.906, train/loss=0.0231]

2022-10-20 10:30:16

Epoch 166: 89%|████████████████████████████████████████████▍ | 16/18 [00:05<00:00, 3.00it/s, loss=0.0222, v_num=xc2k, val/loss=0.017, val/precision=0.866, val/recall=0.909, val/f1=0.887, val/tdr=0.995, val/sa_precision=0.884, val/sa_recall=0.928, val/sa_f1=0.905, train/loss=0.0227]

2022-10-20 10:30:04

Validation: 0it [00:00, ?it/s]

2022-10-20 10:30:30

Epoch 167: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 3.20it/s, loss=0.0237, v_num=xc2k, val/loss=0.0171, val/precision=0.859, val/recall=0.913, val/f1=0.885, val/tdr=0.995, val/sa_precision=0.878, val/sa_recall=0.933, val/sa_f1=0.904, train/loss=0.0227]

2022-10-20 10:30:44

Epoch 168: 89%|███████████████████████████████████████████▌ | 16/18 [00:06<00:00, 2.63it/s, loss=0.0239, v_num=xc2k, val/loss=0.0168, val/precision=0.870, val/recall=0.908, val/f1=0.888, val/tdr=0.996, val/sa_precision=0.888, val/sa_recall=0.927, val/sa_f1=0.907, train/loss=0.0232]

2022-10-20 10:30:58

Epoch 169: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 3.08it/s, loss=0.0231, v_num=xc2k, val/loss=0.0167, val/precision=0.867, val/recall=0.910, val/f1=0.888, val/tdr=0.997, val/sa_precision=0.885, val/sa_recall=0.928, val/sa_f1=0.906, train/loss=0.0233]

2022-10-20 10:31:12

Epoch 170: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.78it/s, loss=0.0233, v_num=xc2k, val/loss=0.0167, val/precision=0.871, val/recall=0.908, val/f1=0.889, val/tdr=0.997, val/sa_precision=0.889, val/sa_recall=0.926, val/sa_f1=0.907, train/loss=0.0232]

2022-10-20 10:31:26

Epoch 171: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.74it/s, loss=0.0234, v_num=xc2k, val/loss=0.0167, val/precision=0.866, val/recall=0.911, val/f1=0.888, val/tdr=0.997, val/sa_precision=0.884, val/sa_recall=0.930, val/sa_f1=0.906, train/loss=0.0235]

2022-10-20 10:31:40

Epoch 172: 89%|████████████████████████████████████████████▍ | 16/18 [00:05<00:00, 2.87it/s, loss=0.0229, v_num=xc2k, val/loss=0.0168, val/precision=0.865, val/recall=0.912, val/f1=0.888, val/tdr=0.997, val/sa_precision=0.883, val/sa_recall=0.931, val/sa_f1=0.906, train/loss=0.023]

2022-10-20 10:31:54

Epoch 173: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 3.06it/s, loss=0.0233, v_num=xc2k, val/loss=0.0169, val/precision=0.870, val/recall=0.906, val/f1=0.888, val/tdr=0.996, val/sa_precision=0.888, val/sa_recall=0.925, val/sa_f1=0.906, train/loss=0.0233]

2022-10-20 10:32:08

Epoch 174: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.92it/s, loss=0.0232, v_num=xc2k, val/loss=0.0168, val/precision=0.870, val/recall=0.908, val/f1=0.889, val/tdr=0.995, val/sa_precision=0.888, val/sa_recall=0.926, val/sa_f1=0.907, train/loss=0.0233]

2022-10-20 10:32:24

Epoch 175: 89%|███████████████████████████████████████████▌ | 16/18 [00:06<00:00, 2.64it/s, loss=0.0216, v_num=xc2k, val/loss=0.0168, val/precision=0.869, val/recall=0.908, val/f1=0.888, val/tdr=0.996, val/sa_precision=0.887, val/sa_recall=0.926, val/sa_f1=0.906, train/loss=0.0228]

2022-10-20 10:32:40

Epoch 176: 89%|███████████████████████████████████████████▌ | 16/18 [00:06<00:00, 2.64it/s, loss=0.0226, v_num=xc2k, val/loss=0.0167, val/precision=0.871, val/recall=0.907, val/f1=0.888, val/tdr=0.997, val/sa_precision=0.889, val/sa_recall=0.926, val/sa_f1=0.907, train/loss=0.0226]

2022-10-20 10:32:54

Epoch 177: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 3.07it/s, loss=0.0233, v_num=xc2k, val/loss=0.0167, val/precision=0.870, val/recall=0.909, val/f1=0.889, val/tdr=0.996, val/sa_precision=0.888, val/sa_recall=0.927, val/sa_f1=0.907, train/loss=0.0228]

2022-10-20 10:33:10

Epoch 178: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.77it/s, loss=0.0225, v_num=xc2k, val/loss=0.0168, val/precision=0.861, val/recall=0.913, val/f1=0.886, val/tdr=0.996, val/sa_precision=0.879, val/sa_recall=0.933, val/sa_f1=0.905, train/loss=0.0226]

2022-10-20 10:33:24

Epoch 179: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 3.01it/s, loss=0.0228, v_num=xc2k, val/loss=0.0168, val/precision=0.863, val/recall=0.914, val/f1=0.888, val/tdr=0.996, val/sa_precision=0.881, val/sa_recall=0.933, val/sa_f1=0.906, train/loss=0.0224]

2022-10-20 10:33:38

Epoch 180: 89%|████████████████████████████████████████████▍ | 16/18 [00:05<00:00, 2.88it/s, loss=0.023, v_num=xc2k, val/loss=0.0167, val/precision=0.871, val/recall=0.907, val/f1=0.889, val/tdr=0.996, val/sa_precision=0.889, val/sa_recall=0.926, val/sa_f1=0.907, train/loss=0.0227]

2022-10-20 10:33:52

Epoch 181: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 3.10it/s, loss=0.0231, v_num=xc2k, val/loss=0.0167, val/precision=0.872, val/recall=0.908, val/f1=0.889, val/tdr=0.996, val/sa_precision=0.890, val/sa_recall=0.926, val/sa_f1=0.907, train/loss=0.0229]

2022-10-20 10:34:06

Epoch 182: 89%|████████████████████████████████████████████▍ | 16/18 [00:05<00:00, 2.93it/s, loss=0.023, v_num=xc2k, val/loss=0.0167, val/precision=0.871, val/recall=0.908, val/f1=0.889, val/tdr=0.996, val/sa_precision=0.888, val/sa_recall=0.926, val/sa_f1=0.907, train/loss=0.0228]

2022-10-20 10:34:21

Epoch 183: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.99it/s, loss=0.0239, v_num=xc2k, val/loss=0.0166, val/precision=0.868, val/recall=0.909, val/f1=0.888, val/tdr=0.997, val/sa_precision=0.886, val/sa_recall=0.928, val/sa_f1=0.907, train/loss=0.0233]

2022-10-20 10:34:35

Epoch 184: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.99it/s, loss=0.0236, v_num=xc2k, val/loss=0.0168, val/precision=0.859, val/recall=0.917, val/f1=0.887, val/tdr=0.997, val/sa_precision=0.878, val/sa_recall=0.936, val/sa_f1=0.906, train/loss=0.0233]

2022-10-20 10:34:49

Epoch 185: 89%|████████████████████████████████████████████▍ | 16/18 [00:05<00:00, 2.90it/s, loss=0.023, v_num=xc2k, val/loss=0.0167, val/precision=0.869, val/recall=0.910, val/f1=0.889, val/tdr=0.995, val/sa_precision=0.887, val/sa_recall=0.929, val/sa_f1=0.907, train/loss=0.0236]

2022-10-20 10:35:03

Epoch 186: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.91it/s, loss=0.0226, v_num=xc2k, val/loss=0.0167, val/precision=0.867, val/recall=0.911, val/f1=0.888, val/tdr=0.996, val/sa_precision=0.885, val/sa_recall=0.931, val/sa_f1=0.907, train/loss=0.0223]

2022-10-20 10:35:19

Epoch 187: 89%|████████████████████████████████████████████▍ | 16/18 [00:05<00:00, 2.68it/s, loss=0.0239, v_num=xc2k, val/loss=0.0166, val/precision=0.869, val/recall=0.911, val/f1=0.889, val/tdr=0.997, val/sa_precision=0.887, val/sa_recall=0.930, val/sa_f1=0.908, train/loss=0.023]

2022-10-20 10:35:33

Epoch 188: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.99it/s, loss=0.0227, v_num=xc2k, val/loss=0.0166, val/precision=0.871, val/recall=0.909, val/f1=0.889, val/tdr=0.995, val/sa_precision=0.889, val/sa_recall=0.928, val/sa_f1=0.908, train/loss=0.0233]

2022-10-20 10:35:47

Epoch 189: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.93it/s, loss=0.0227, v_num=xc2k, val/loss=0.0167, val/precision=0.868, val/recall=0.911, val/f1=0.889, val/tdr=0.995, val/sa_precision=0.887, val/sa_recall=0.931, val/sa_f1=0.908, train/loss=0.0227]

2022-10-20 10:36:01

Epoch 190: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 2.92it/s, loss=0.0229, v_num=xc2k, val/loss=0.0167, val/precision=0.869, val/recall=0.911, val/f1=0.889, val/tdr=0.996, val/sa_precision=0.887, val/sa_recall=0.930, val/sa_f1=0.908, train/loss=0.0231]

2022-10-20 10:36:15

Epoch 191: 89%|███████████████████████████████████████████▌ | 16/18 [00:05<00:00, 3.15it/s, loss=0.0227, v_num=xc2k, val/loss=0.0166, val/precision=0.872, val/recall=0.907, val/f1=0.889, val/tdr=0.996, val/sa_precision=0.890, val/sa_recall=0.926, val/sa_f1=0.908, train/loss=0.0229]

2022-10-20 10:36:37

Testing DataLoader 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:09<00:00, 2.44s/it]

2022-10-20 10:36:25

`Trainer.fit` stopped: `max_epochs=192` reached.

2022-10-20 10:36:25

LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]

2022-10-20 10:36:25

Testing: 0it [00:00, ?it/s]

2022-10-20 10:36:37

────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

2022-10-20 10:36:37

Test metric DataLoader 0

2022-10-20 10:36:37

test/f1 0.7631277441978455

2022-10-20 10:36:37

test/loss 0.029839543625712395

2022-10-20 10:36:37

test/precision 0.7464532852172852

2022-10-20 10:36:37

test/recall 0.7807998061180115

2022-10-20 10:36:37

test/sa_f1 0.8336867094039917

2022-10-20 10:36:37

test/sa_precision 0.815367579460144

2022-10-20 10:36:37

test/sa_recall 0.8531008958816528

2022-10-20 10:36:37

test/tdr 0.909693717956543

2022-10-20 10:36:37