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Browse files- v1/8-100kk-limit50kk/laae-0.1/model.pt +3 -0
- v1/8-100kk-limit50kk/laae-0.1/vocab.alphabet +1 -0
- v1/rand-10-12-14-30000k/laae-0.01/model.pt +3 -0
- v1/rand-10-12-14-30000k/laae-0.01/vocab.alphabet +1 -0
- v1/rand-10-12-14-30000k/vae-0.01/model.pt +3 -0
- v1/rand-10-12-14-30000k/vae-0.01/vocab.alphabet +1 -0
- v1/rand-8-10-12-14-16-49955650k/laae-0.01/log.txt +0 -0
- v1/rand-8-10-12-14-16-49955650k/laae-0.01/model.pt +3 -0
- v1/rand-8-10-12-14-16-49955650k/laae-0.01/vocab.alphabet +1 -0
- v1/top100kk-limit45k/laae-0.1/log.txt +0 -0
- v1/top100kk-limit45k/laae-0.1/model.pt +3 -0
- v1/top100kk-limit45k/laae-0.1/vocab.alphabet +1 -0
- v2/8-12_150kk_15_09_24/log.txt +373 -0
- v2/8-12_150kk_15_09_24/model.pt +3 -0
- v2/8-12_150kk_15_09_24/vocab.alphabet +1 -0
- v2/8-12_150kk_17_08_24/log.txt +0 -0
- v2/8-12_150kk_17_08_24/model.pt +3 -0
- v2/8-12_150kk_17_08_24/vocab.alphabet +1 -0
- v2/8-12_150kk_22_09_24/log.txt +425 -0
- v2/8-12_150kk_22_09_24/model.pt +3 -0
- v2/8-12_150kk_22_09_24/vocab.alphabet +1 -0
- v2/8_100kk_09_08_24/log.txt +437 -0
- v2/8_100kk_09_08_24/model.pt +3 -0
- v2/8_100kk_09_08_24/vocab.alphabet +1 -0
- v2/8_100kk_16_08_24/log.txt +437 -0
- v2/8_100kk_16_08_24/model.pt +3 -0
- v2/8_100kk_16_08_24/vocab.alphabet +1 -0
v1/8-100kk-limit50kk/laae-0.1/model.pt
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ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
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v1/rand-10-12-14-30000k/laae-0.01/model.pt
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ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
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ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
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ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
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v1/top100kk-limit45k/laae-0.1/log.txt
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v1/top100kk-limit45k/laae-0.1/model.pt
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ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
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v2/8-12_150kk_15_09_24/log.txt
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| 1 |
+
Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=10000, dim_d=512, dim_emb=80, dim_h=512, dim_z=256, dropout=0.2, epochs=10, lambda_adv=10.0, lambda_kl=0.0, lambda_p=0.0, load_model='', log_interval=100, lr=0.0005, max_len=12, model_type='aae', nlayers=2, no_cuda=True, noise=[0.2, 0.1, 0.0], save_dir='out/8-12_150kk_15_09_24/', train='../data/8-12_150kk/train.txt', valid='../data/8-12_150kk/valid.txt')
|
| 2 |
+
# train on cpu device
|
| 3 |
+
# vocab save out/8-12_150kk_15_09_24/vocab.alphabet
|
| 4 |
+
# train passwords {len(train_dataloader.dataset)}
|
| 5 |
+
# valid passwords 30000000
|
| 6 |
+
# model aae parameters: 12786596
|
| 7 |
+
--------------------------------------------------------------------------------
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| 8 |
+
| epoch 1 | 100/ 12000 batches | rec 35.83, adv 0.78, |lvar| 189.81, loss_d 1.51, loss 43.60,
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| 9 |
+
| epoch 1 | 200/ 12000 batches | rec 33.10, adv 0.71, |lvar| 452.12, loss_d 1.56, loss 40.18,
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| 10 |
+
| epoch 1 | 300/ 12000 batches | rec 32.60, adv 0.64, |lvar| 497.71, loss_d 1.50, loss 38.98,
|
| 11 |
+
| epoch 1 | 400/ 12000 batches | rec 31.92, adv 0.64, |lvar| 407.98, loss_d 1.45, loss 38.29,
|
| 12 |
+
| epoch 1 | 500/ 12000 batches | rec 29.71, adv 0.66, |lvar| 467.08, loss_d 1.43, loss 36.34,
|
| 13 |
+
| epoch 1 | 600/ 12000 batches | rec 28.47, adv 0.69, |lvar| 457.31, loss_d 1.40, loss 35.34,
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| 14 |
+
| epoch 1 | 700/ 12000 batches | rec 28.03, adv 0.70, |lvar| 447.57, loss_d 1.40, loss 35.01,
|
| 15 |
+
| epoch 1 | 800/ 12000 batches | rec 27.89, adv 0.69, |lvar| 417.86, loss_d 1.41, loss 34.79,
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| 16 |
+
| epoch 1 | 900/ 12000 batches | rec 27.64, adv 0.68, |lvar| 503.67, loss_d 1.40, loss 34.43,
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| 17 |
+
| epoch 1 | 1000/ 12000 batches | rec 27.71, adv 0.68, |lvar| 492.68, loss_d 1.41, loss 34.47,
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| 18 |
+
| epoch 1 | 1100/ 12000 batches | rec 26.95, adv 0.68, |lvar| 499.86, loss_d 1.41, loss 33.80,
|
| 19 |
+
| epoch 1 | 1200/ 12000 batches | rec 26.58, adv 0.68, |lvar| 515.47, loss_d 1.41, loss 33.40,
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| 20 |
+
| epoch 1 | 1300/ 12000 batches | rec 26.04, adv 0.68, |lvar| 547.93, loss_d 1.41, loss 32.83,
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| 21 |
+
| epoch 1 | 1400/ 12000 batches | rec 25.55, adv 0.68, |lvar| 594.48, loss_d 1.42, loss 32.39,
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| 22 |
+
| epoch 1 | 1500/ 12000 batches | rec 24.93, adv 0.67, |lvar| 603.72, loss_d 1.44, loss 31.66,
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| 23 |
+
| epoch 1 | 1600/ 12000 batches | rec 24.54, adv 0.67, |lvar| 642.72, loss_d 1.43, loss 31.20,
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| 24 |
+
| epoch 1 | 1700/ 12000 batches | rec 24.10, adv 0.68, |lvar| 761.37, loss_d 1.42, loss 30.89,
|
| 25 |
+
| epoch 1 | 1800/ 12000 batches | rec 23.51, adv 0.68, |lvar| 821.80, loss_d 1.43, loss 30.30,
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| 26 |
+
| epoch 1 | 1900/ 12000 batches | rec 22.89, adv 0.68, |lvar| 843.11, loss_d 1.42, loss 29.71,
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| 27 |
+
| epoch 1 | 2000/ 12000 batches | rec 22.31, adv 0.68, |lvar| 972.62, loss_d 1.43, loss 29.14,
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| 28 |
+
| epoch 1 | 2100/ 12000 batches | rec 22.05, adv 0.69, |lvar| 966.51, loss_d 1.41, loss 28.94,
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| 29 |
+
| epoch 1 | 2200/ 12000 batches | rec 21.56, adv 0.68, |lvar| 1013.03, loss_d 1.42, loss 28.40,
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| 30 |
+
| epoch 1 | 2300/ 12000 batches | rec 20.88, adv 0.68, |lvar| 1113.08, loss_d 1.43, loss 27.65,
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| 31 |
+
| epoch 1 | 2400/ 12000 batches | rec 20.39, adv 0.68, |lvar| 1206.66, loss_d 1.42, loss 27.19,
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| 32 |
+
| epoch 1 | 2500/ 12000 batches | rec 19.90, adv 0.69, |lvar| 1260.29, loss_d 1.42, loss 26.78,
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| 33 |
+
| epoch 1 | 2600/ 12000 batches | rec 19.60, adv 0.68, |lvar| 1266.96, loss_d 1.42, loss 26.42,
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| 34 |
+
| epoch 1 | 2700/ 12000 batches | rec 19.12, adv 0.69, |lvar| 1302.55, loss_d 1.41, loss 25.98,
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| 35 |
+
| epoch 1 | 2800/ 12000 batches | rec 18.80, adv 0.68, |lvar| 1351.78, loss_d 1.42, loss 25.63,
|
| 36 |
+
| epoch 1 | 2900/ 12000 batches | rec 18.70, adv 0.68, |lvar| 1365.41, loss_d 1.42, loss 25.50,
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| 37 |
+
| epoch 1 | 3000/ 12000 batches | rec 18.61, adv 0.69, |lvar| 1378.87, loss_d 1.41, loss 25.47,
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| 38 |
+
| epoch 1 | 3100/ 12000 batches | rec 18.15, adv 0.68, |lvar| 1378.97, loss_d 1.43, loss 24.92,
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| 39 |
+
| epoch 1 | 3200/ 12000 batches | rec 17.92, adv 0.68, |lvar| 1466.06, loss_d 1.42, loss 24.71,
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| 40 |
+
| epoch 1 | 3300/ 12000 batches | rec 17.92, adv 0.68, |lvar| 1440.47, loss_d 1.41, loss 24.73,
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| 41 |
+
| epoch 1 | 3400/ 12000 batches | rec 17.66, adv 0.68, |lvar| 1495.91, loss_d 1.42, loss 24.46,
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| 42 |
+
| epoch 1 | 3500/ 12000 batches | rec 17.43, adv 0.68, |lvar| 1551.80, loss_d 1.43, loss 24.21,
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| 43 |
+
| epoch 1 | 3600/ 12000 batches | rec 17.23, adv 0.68, |lvar| 1555.73, loss_d 1.43, loss 24.01,
|
| 44 |
+
| epoch 1 | 3700/ 12000 batches | rec 17.01, adv 0.67, |lvar| 1600.05, loss_d 1.42, loss 23.68,
|
| 45 |
+
| epoch 1 | 3800/ 12000 batches | rec 16.92, adv 0.68, |lvar| 1677.96, loss_d 1.42, loss 23.74,
|
| 46 |
+
| epoch 1 | 3900/ 12000 batches | rec 16.85, adv 0.68, |lvar| 1667.41, loss_d 1.42, loss 23.61,
|
| 47 |
+
| epoch 1 | 4000/ 12000 batches | rec 16.61, adv 0.68, |lvar| 1666.38, loss_d 1.43, loss 23.39,
|
| 48 |
+
| epoch 1 | 4100/ 12000 batches | rec 16.56, adv 0.67, |lvar| 1721.15, loss_d 1.41, loss 23.31,
|
| 49 |
+
| epoch 1 | 4200/ 12000 batches | rec 16.47, adv 0.69, |lvar| 1725.41, loss_d 1.41, loss 23.33,
|
| 50 |
+
| epoch 1 | 4300/ 12000 batches | rec 16.55, adv 0.68, |lvar| 1665.30, loss_d 1.42, loss 23.32,
|
| 51 |
+
| epoch 1 | 4400/ 12000 batches | rec 16.32, adv 0.68, |lvar| 1660.01, loss_d 1.42, loss 23.14,
|
| 52 |
+
| epoch 1 | 4500/ 12000 batches | rec 16.23, adv 0.68, |lvar| 1765.58, loss_d 1.42, loss 23.02,
|
| 53 |
+
| epoch 1 | 4600/ 12000 batches | rec 16.32, adv 0.67, |lvar| 1730.03, loss_d 1.41, loss 23.05,
|
| 54 |
+
| epoch 1 | 4700/ 12000 batches | rec 16.07, adv 0.68, |lvar| 1779.71, loss_d 1.42, loss 22.88,
|
| 55 |
+
| epoch 1 | 4800/ 12000 batches | rec 15.96, adv 0.68, |lvar| 1769.87, loss_d 1.42, loss 22.76,
|
| 56 |
+
| epoch 1 | 4900/ 12000 batches | rec 15.93, adv 0.67, |lvar| 1731.95, loss_d 1.42, loss 22.68,
|
| 57 |
+
| epoch 1 | 5000/ 12000 batches | rec 15.84, adv 0.68, |lvar| 1891.58, loss_d 1.42, loss 22.62,
|
| 58 |
+
| epoch 1 | 5100/ 12000 batches | rec 15.74, adv 0.68, |lvar| 1890.79, loss_d 1.42, loss 22.50,
|
| 59 |
+
| epoch 1 | 5200/ 12000 batches | rec 15.77, adv 0.68, |lvar| 1824.33, loss_d 1.42, loss 22.54,
|
| 60 |
+
| epoch 1 | 5300/ 12000 batches | rec 15.60, adv 0.68, |lvar| 1906.91, loss_d 1.42, loss 22.42,
|
| 61 |
+
| epoch 1 | 5400/ 12000 batches | rec 15.60, adv 0.68, |lvar| 1896.97, loss_d 1.42, loss 22.38,
|
| 62 |
+
| epoch 1 | 5500/ 12000 batches | rec 15.53, adv 0.68, |lvar| 1882.56, loss_d 1.42, loss 22.32,
|
| 63 |
+
| epoch 1 | 5600/ 12000 batches | rec 15.47, adv 0.68, |lvar| 1911.61, loss_d 1.42, loss 22.24,
|
| 64 |
+
| epoch 1 | 5700/ 12000 batches | rec 15.59, adv 0.68, |lvar| 2010.90, loss_d 1.41, loss 22.37,
|
| 65 |
+
| epoch 1 | 5800/ 12000 batches | rec 15.73, adv 0.69, |lvar| 1771.60, loss_d 1.40, loss 22.61,
|
| 66 |
+
| epoch 1 | 5900/ 12000 batches | rec 15.62, adv 0.69, |lvar| 1758.75, loss_d 1.41, loss 22.50,
|
| 67 |
+
| epoch 1 | 6000/ 12000 batches | rec 15.44, adv 0.68, |lvar| 1763.33, loss_d 1.41, loss 22.24,
|
| 68 |
+
| epoch 1 | 6100/ 12000 batches | rec 15.44, adv 0.68, |lvar| 1745.46, loss_d 1.42, loss 22.26,
|
| 69 |
+
| epoch 1 | 6200/ 12000 batches | rec 15.39, adv 0.68, |lvar| 1870.98, loss_d 1.42, loss 22.16,
|
| 70 |
+
| epoch 1 | 6300/ 12000 batches | rec 15.25, adv 0.68, |lvar| 1850.19, loss_d 1.42, loss 22.05,
|
| 71 |
+
| epoch 1 | 6400/ 12000 batches | rec 15.24, adv 0.68, |lvar| 1833.43, loss_d 1.43, loss 22.00,
|
| 72 |
+
| epoch 1 | 6500/ 12000 batches | rec 15.18, adv 0.67, |lvar| 2010.11, loss_d 1.42, loss 21.93,
|
| 73 |
+
| epoch 1 | 6600/ 12000 batches | rec 15.28, adv 0.68, |lvar| 2027.64, loss_d 1.41, loss 22.09,
|
| 74 |
+
| epoch 1 | 6700/ 12000 batches | rec 15.16, adv 0.68, |lvar| 1980.39, loss_d 1.42, loss 21.96,
|
| 75 |
+
| epoch 1 | 6800/ 12000 batches | rec 15.07, adv 0.68, |lvar| 2061.93, loss_d 1.41, loss 21.89,
|
| 76 |
+
| epoch 1 | 6900/ 12000 batches | rec 15.09, adv 0.68, |lvar| 2146.25, loss_d 1.42, loss 21.88,
|
| 77 |
+
| epoch 1 | 7000/ 12000 batches | rec 14.98, adv 0.68, |lvar| 1997.12, loss_d 1.42, loss 21.76,
|
| 78 |
+
| epoch 1 | 7100/ 12000 batches | rec 15.07, adv 0.68, |lvar| 2078.88, loss_d 1.41, loss 21.87,
|
| 79 |
+
| epoch 1 | 7200/ 12000 batches | rec 15.04, adv 0.68, |lvar| 1971.66, loss_d 1.41, loss 21.87,
|
| 80 |
+
| epoch 1 | 7300/ 12000 batches | rec 15.07, adv 0.68, |lvar| 1989.80, loss_d 1.41, loss 21.89,
|
| 81 |
+
| epoch 1 | 7400/ 12000 batches | rec 15.04, adv 0.68, |lvar| 2039.23, loss_d 1.41, loss 21.84,
|
| 82 |
+
| epoch 1 | 7500/ 12000 batches | rec 14.96, adv 0.68, |lvar| 2091.37, loss_d 1.41, loss 21.77,
|
| 83 |
+
| epoch 1 | 7600/ 12000 batches | rec 14.90, adv 0.68, |lvar| 1949.44, loss_d 1.41, loss 21.74,
|
| 84 |
+
| epoch 1 | 7700/ 12000 batches | rec 15.11, adv 0.68, |lvar| 1953.51, loss_d 1.41, loss 21.91,
|
| 85 |
+
| epoch 1 | 7800/ 12000 batches | rec 15.08, adv 0.68, |lvar| 2094.54, loss_d 1.41, loss 21.91,
|
| 86 |
+
| epoch 1 | 7900/ 12000 batches | rec 14.90, adv 0.68, |lvar| 2088.23, loss_d 1.41, loss 21.72,
|
| 87 |
+
| epoch 1 | 8000/ 12000 batches | rec 14.78, adv 0.68, |lvar| 2096.81, loss_d 1.41, loss 21.60,
|
| 88 |
+
| epoch 1 | 8100/ 12000 batches | rec 14.82, adv 0.68, |lvar| 2119.68, loss_d 1.41, loss 21.60,
|
| 89 |
+
| epoch 1 | 8200/ 12000 batches | rec 14.74, adv 0.68, |lvar| 2023.69, loss_d 1.41, loss 21.54,
|
| 90 |
+
| epoch 1 | 8300/ 12000 batches | rec 14.75, adv 0.68, |lvar| 2115.42, loss_d 1.41, loss 21.56,
|
| 91 |
+
| epoch 1 | 8400/ 12000 batches | rec 14.96, adv 0.68, |lvar| 2104.10, loss_d 1.41, loss 21.81,
|
| 92 |
+
| epoch 1 | 8500/ 12000 batches | rec 14.77, adv 0.68, |lvar| 2019.36, loss_d 1.41, loss 21.58,
|
| 93 |
+
| epoch 1 | 8600/ 12000 batches | rec 14.68, adv 0.68, |lvar| 2079.30, loss_d 1.41, loss 21.50,
|
| 94 |
+
| epoch 1 | 8700/ 12000 batches | rec 14.69, adv 0.68, |lvar| 2113.63, loss_d 1.41, loss 21.50,
|
| 95 |
+
| epoch 1 | 8800/ 12000 batches | rec 14.66, adv 0.68, |lvar| 2201.40, loss_d 1.41, loss 21.48,
|
| 96 |
+
| epoch 1 | 8900/ 12000 batches | rec 14.71, adv 0.68, |lvar| 2111.76, loss_d 1.41, loss 21.51,
|
| 97 |
+
| epoch 1 | 9000/ 12000 batches | rec 14.65, adv 0.69, |lvar| 2077.77, loss_d 1.40, loss 21.50,
|
| 98 |
+
| epoch 1 | 9100/ 12000 batches | rec 14.61, adv 0.69, |lvar| 2121.42, loss_d 1.41, loss 21.48,
|
| 99 |
+
| epoch 1 | 9200/ 12000 batches | rec 14.74, adv 0.68, |lvar| 2227.05, loss_d 1.41, loss 21.54,
|
| 100 |
+
| epoch 1 | 9300/ 12000 batches | rec 14.65, adv 0.68, |lvar| 2048.90, loss_d 1.41, loss 21.49,
|
| 101 |
+
| epoch 1 | 9400/ 12000 batches | rec 14.58, adv 0.68, |lvar| 2076.37, loss_d 1.41, loss 21.42,
|
| 102 |
+
| epoch 1 | 9500/ 12000 batches | rec 14.63, adv 0.68, |lvar| 2221.25, loss_d 1.41, loss 21.44,
|
| 103 |
+
| epoch 1 | 9600/ 12000 batches | rec 14.59, adv 0.68, |lvar| 2321.68, loss_d 1.41, loss 21.41,
|
| 104 |
+
| epoch 1 | 9700/ 12000 batches | rec 14.55, adv 0.69, |lvar| 2182.69, loss_d 1.40, loss 21.42,
|
| 105 |
+
| epoch 1 | 9800/ 12000 batches | rec 14.56, adv 0.69, |lvar| 2240.18, loss_d 1.41, loss 21.41,
|
| 106 |
+
| epoch 1 | 9900/ 12000 batches | rec 14.49, adv 0.68, |lvar| 2228.33, loss_d 1.41, loss 21.31,
|
| 107 |
+
| epoch 1 | 10000/ 12000 batches | rec 14.64, adv 0.68, |lvar| 2344.22, loss_d 1.40, loss 21.47,
|
| 108 |
+
| epoch 1 | 10100/ 12000 batches | rec 14.62, adv 0.69, |lvar| 2176.09, loss_d 1.40, loss 21.47,
|
| 109 |
+
| epoch 1 | 10200/ 12000 batches | rec 14.59, adv 0.69, |lvar| 2227.70, loss_d 1.40, loss 21.45,
|
| 110 |
+
| epoch 1 | 10300/ 12000 batches | rec 14.54, adv 0.68, |lvar| 2158.16, loss_d 1.41, loss 21.38,
|
| 111 |
+
| epoch 1 | 10400/ 12000 batches | rec 14.51, adv 0.69, |lvar| 2252.84, loss_d 1.40, loss 21.37,
|
| 112 |
+
| epoch 1 | 10500/ 12000 batches | rec 14.56, adv 0.69, |lvar| 2294.53, loss_d 1.40, loss 21.42,
|
| 113 |
+
| epoch 1 | 10600/ 12000 batches | rec 14.52, adv 0.69, |lvar| 2159.40, loss_d 1.40, loss 21.40,
|
| 114 |
+
| epoch 1 | 10700/ 12000 batches | rec 14.50, adv 0.68, |lvar| 2167.26, loss_d 1.41, loss 21.34,
|
| 115 |
+
| epoch 1 | 10800/ 12000 batches | rec 14.51, adv 0.68, |lvar| 2184.09, loss_d 1.40, loss 21.35,
|
| 116 |
+
| epoch 1 | 10900/ 12000 batches | rec 14.56, adv 0.68, |lvar| 2132.84, loss_d 1.40, loss 21.39,
|
| 117 |
+
| epoch 1 | 11000/ 12000 batches | rec 14.53, adv 0.69, |lvar| 2184.48, loss_d 1.41, loss 21.41,
|
| 118 |
+
| epoch 1 | 11100/ 12000 batches | rec 14.42, adv 0.69, |lvar| 2236.59, loss_d 1.41, loss 21.27,
|
| 119 |
+
| epoch 1 | 11200/ 12000 batches | rec 14.46, adv 0.68, |lvar| 2223.61, loss_d 1.40, loss 21.31,
|
| 120 |
+
| epoch 1 | 11300/ 12000 batches | rec 14.55, adv 0.69, |lvar| 2119.92, loss_d 1.40, loss 21.40,
|
| 121 |
+
| epoch 1 | 11400/ 12000 batches | rec 14.49, adv 0.69, |lvar| 2094.38, loss_d 1.40, loss 21.36,
|
| 122 |
+
| epoch 1 | 11500/ 12000 batches | rec 14.90, adv 0.69, |lvar| 2157.73, loss_d 1.39, loss 21.78,
|
| 123 |
+
| epoch 1 | 11600/ 12000 batches | rec 14.52, adv 0.69, |lvar| 2136.36, loss_d 1.40, loss 21.43,
|
| 124 |
+
| epoch 1 | 11700/ 12000 batches | rec 14.60, adv 0.69, |lvar| 2225.20, loss_d 1.41, loss 21.46,
|
| 125 |
+
| epoch 1 | 11800/ 12000 batches | rec 14.52, adv 0.68, |lvar| 2271.58, loss_d 1.40, loss 21.34,
|
| 126 |
+
| epoch 1 | 11900/ 12000 batches | rec 14.48, adv 0.68, |lvar| 2152.92, loss_d 1.41, loss 21.32,
|
| 127 |
+
| epoch 1 | 12000/ 12000 batches | rec 14.45, adv 0.69, |lvar| 2239.92, loss_d 1.41, loss 21.31,
|
| 128 |
+
--------------------------------------------------------------------------------
|
| 129 |
+
| end of epoch 1| time 197562s| valid rec 4.16, adv 0.69, |lvar| 2587.93, loss_d 1.41, loss 11.07, | saving model
|
| 130 |
+
--------------------------------------------------------------------------------
|
| 131 |
+
| epoch 2 | 100/ 12000 batches | rec 14.40, adv 0.68, |lvar| 2262.80, loss_d 1.41, loss 21.24,
|
| 132 |
+
| epoch 2 | 200/ 12000 batches | rec 14.41, adv 0.68, |lvar| 2293.89, loss_d 1.41, loss 21.23,
|
| 133 |
+
| epoch 2 | 300/ 12000 batches | rec 14.36, adv 0.69, |lvar| 2344.60, loss_d 1.40, loss 21.21,
|
| 134 |
+
| epoch 2 | 400/ 12000 batches | rec 14.44, adv 0.68, |lvar| 2400.75, loss_d 1.40, loss 21.29,
|
| 135 |
+
| epoch 2 | 500/ 12000 batches | rec 14.39, adv 0.68, |lvar| 2172.87, loss_d 1.40, loss 21.23,
|
| 136 |
+
| epoch 2 | 600/ 12000 batches | rec 14.42, adv 0.69, |lvar| 2318.03, loss_d 1.40, loss 21.29,
|
| 137 |
+
| epoch 2 | 700/ 12000 batches | rec 14.32, adv 0.69, |lvar| 2171.09, loss_d 1.40, loss 21.20,
|
| 138 |
+
| epoch 2 | 800/ 12000 batches | rec 14.50, adv 0.69, |lvar| 2405.14, loss_d 1.39, loss 21.38,
|
| 139 |
+
| epoch 2 | 900/ 12000 batches | rec 14.41, adv 0.69, |lvar| 2170.30, loss_d 1.40, loss 21.28,
|
| 140 |
+
| epoch 2 | 1000/ 12000 batches | rec 14.36, adv 0.69, |lvar| 2279.91, loss_d 1.40, loss 21.26,
|
| 141 |
+
| epoch 2 | 1100/ 12000 batches | rec 14.32, adv 0.68, |lvar| 2267.38, loss_d 1.40, loss 21.15,
|
| 142 |
+
| epoch 2 | 1200/ 12000 batches | rec 14.31, adv 0.69, |lvar| 2267.53, loss_d 1.41, loss 21.17,
|
| 143 |
+
| epoch 2 | 1300/ 12000 batches | rec 14.36, adv 0.68, |lvar| 2180.47, loss_d 1.41, loss 21.20,
|
| 144 |
+
| epoch 2 | 1400/ 12000 batches | rec 14.29, adv 0.68, |lvar| 2534.86, loss_d 1.40, loss 21.12,
|
| 145 |
+
| epoch 2 | 1500/ 12000 batches | rec 14.27, adv 0.68, |lvar| 2374.03, loss_d 1.40, loss 21.12,
|
| 146 |
+
| epoch 2 | 1600/ 12000 batches | rec 14.33, adv 0.69, |lvar| 2462.79, loss_d 1.40, loss 21.20,
|
| 147 |
+
| epoch 2 | 1700/ 12000 batches | rec 14.28, adv 0.69, |lvar| 2374.78, loss_d 1.40, loss 21.13,
|
| 148 |
+
| epoch 2 | 1800/ 12000 batches | rec 14.39, adv 0.68, |lvar| 2328.75, loss_d 1.40, loss 21.24,
|
| 149 |
+
| epoch 2 | 1900/ 12000 batches | rec 14.24, adv 0.69, |lvar| 2410.16, loss_d 1.40, loss 21.13,
|
| 150 |
+
| epoch 2 | 2000/ 12000 batches | rec 14.26, adv 0.69, |lvar| 2451.97, loss_d 1.40, loss 21.11,
|
| 151 |
+
| epoch 2 | 2100/ 12000 batches | rec 14.24, adv 0.69, |lvar| 2307.95, loss_d 1.40, loss 21.09,
|
| 152 |
+
| epoch 2 | 2200/ 12000 batches | rec 14.21, adv 0.69, |lvar| 2318.67, loss_d 1.40, loss 21.07,
|
| 153 |
+
| epoch 2 | 2300/ 12000 batches | rec 14.22, adv 0.69, |lvar| 2436.80, loss_d 1.40, loss 21.08,
|
| 154 |
+
| epoch 2 | 2400/ 12000 batches | rec 14.26, adv 0.69, |lvar| 2439.77, loss_d 1.40, loss 21.12,
|
| 155 |
+
| epoch 2 | 2500/ 12000 batches | rec 14.24, adv 0.69, |lvar| 2340.09, loss_d 1.40, loss 21.13,
|
| 156 |
+
| epoch 2 | 2600/ 12000 batches | rec 14.22, adv 0.69, |lvar| 2451.31, loss_d 1.40, loss 21.09,
|
| 157 |
+
| epoch 2 | 2700/ 12000 batches | rec 14.31, adv 0.69, |lvar| 2350.11, loss_d 1.40, loss 21.18,
|
| 158 |
+
| epoch 2 | 2800/ 12000 batches | rec 14.44, adv 0.69, |lvar| 2377.91, loss_d 1.40, loss 21.31,
|
| 159 |
+
| epoch 2 | 2900/ 12000 batches | rec 14.26, adv 0.69, |lvar| 2381.62, loss_d 1.40, loss 21.14,
|
| 160 |
+
| epoch 2 | 3000/ 12000 batches | rec 14.23, adv 0.69, |lvar| 2418.19, loss_d 1.40, loss 21.12,
|
| 161 |
+
| epoch 2 | 3100/ 12000 batches | rec 14.21, adv 0.69, |lvar| 2413.12, loss_d 1.40, loss 21.08,
|
| 162 |
+
| epoch 2 | 3200/ 12000 batches | rec 14.19, adv 0.69, |lvar| 2273.96, loss_d 1.40, loss 21.06,
|
| 163 |
+
| epoch 2 | 3300/ 12000 batches | rec 14.19, adv 0.69, |lvar| 2447.29, loss_d 1.40, loss 21.04,
|
| 164 |
+
| epoch 2 | 3400/ 12000 batches | rec 14.20, adv 0.69, |lvar| 2311.92, loss_d 1.40, loss 21.07,
|
| 165 |
+
| epoch 2 | 3500/ 12000 batches | rec 14.16, adv 0.69, |lvar| 2465.57, loss_d 1.40, loss 21.02,
|
| 166 |
+
| epoch 2 | 3600/ 12000 batches | rec 14.19, adv 0.69, |lvar| 2706.15, loss_d 1.40, loss 21.05,
|
| 167 |
+
| epoch 2 | 3700/ 12000 batches | rec 14.17, adv 0.69, |lvar| 2452.70, loss_d 1.40, loss 21.04,
|
| 168 |
+
| epoch 2 | 3800/ 12000 batches | rec 14.21, adv 0.69, |lvar| 2366.59, loss_d 1.40, loss 21.08,
|
| 169 |
+
| epoch 2 | 3900/ 12000 batches | rec 14.16, adv 0.69, |lvar| 2331.28, loss_d 1.40, loss 21.04,
|
| 170 |
+
| epoch 2 | 4000/ 12000 batches | rec 14.16, adv 0.69, |lvar| 2501.00, loss_d 1.40, loss 21.03,
|
| 171 |
+
| epoch 2 | 4100/ 12000 batches | rec 14.20, adv 0.69, |lvar| 2457.80, loss_d 1.40, loss 21.07,
|
| 172 |
+
| epoch 2 | 4200/ 12000 batches | rec 14.13, adv 0.69, |lvar| 2325.42, loss_d 1.40, loss 21.01,
|
| 173 |
+
| epoch 2 | 4300/ 12000 batches | rec 14.17, adv 0.69, |lvar| 2520.73, loss_d 1.40, loss 21.04,
|
| 174 |
+
| epoch 2 | 4400/ 12000 batches | rec 14.24, adv 0.69, |lvar| 2545.92, loss_d 1.39, loss 21.13,
|
| 175 |
+
| epoch 2 | 4500/ 12000 batches | rec 14.23, adv 0.69, |lvar| 2285.64, loss_d 1.40, loss 21.14,
|
| 176 |
+
| epoch 2 | 4600/ 12000 batches | rec 14.15, adv 0.69, |lvar| 2323.31, loss_d 1.40, loss 21.02,
|
| 177 |
+
| epoch 2 | 4700/ 12000 batches | rec 14.24, adv 0.69, |lvar| 2367.16, loss_d 1.40, loss 21.12,
|
| 178 |
+
| epoch 2 | 4800/ 12000 batches | rec 14.16, adv 0.69, |lvar| 2293.78, loss_d 1.40, loss 21.03,
|
| 179 |
+
| epoch 2 | 4900/ 12000 batches | rec 14.10, adv 0.69, |lvar| 2455.59, loss_d 1.40, loss 20.96,
|
| 180 |
+
| epoch 2 | 5000/ 12000 batches | rec 14.14, adv 0.69, |lvar| 2401.23, loss_d 1.40, loss 21.01,
|
| 181 |
+
| epoch 2 | 5100/ 12000 batches | rec 14.13, adv 0.69, |lvar| 2353.32, loss_d 1.40, loss 21.00,
|
| 182 |
+
| epoch 2 | 5200/ 12000 batches | rec 14.09, adv 0.69, |lvar| 2508.07, loss_d 1.40, loss 20.95,
|
| 183 |
+
| epoch 2 | 5300/ 12000 batches | rec 14.16, adv 0.69, |lvar| 2352.66, loss_d 1.40, loss 21.04,
|
| 184 |
+
| epoch 2 | 5400/ 12000 batches | rec 14.14, adv 0.69, |lvar| 2444.46, loss_d 1.40, loss 21.00,
|
| 185 |
+
| epoch 2 | 5500/ 12000 batches | rec 14.12, adv 0.69, |lvar| 2314.34, loss_d 1.40, loss 21.00,
|
| 186 |
+
| epoch 2 | 5600/ 12000 batches | rec 14.08, adv 0.69, |lvar| 2530.73, loss_d 1.40, loss 20.96,
|
| 187 |
+
| epoch 2 | 5700/ 12000 batches | rec 14.08, adv 0.69, |lvar| 2600.66, loss_d 1.40, loss 20.96,
|
| 188 |
+
| epoch 2 | 5800/ 12000 batches | rec 14.08, adv 0.69, |lvar| 2484.95, loss_d 1.40, loss 20.96,
|
| 189 |
+
| epoch 2 | 5900/ 12000 batches | rec 14.07, adv 0.69, |lvar| 2560.74, loss_d 1.40, loss 20.95,
|
| 190 |
+
| epoch 2 | 6000/ 12000 batches | rec 14.07, adv 0.69, |lvar| 2545.69, loss_d 1.40, loss 20.94,
|
| 191 |
+
| epoch 2 | 6100/ 12000 batches | rec 14.08, adv 0.69, |lvar| 2712.46, loss_d 1.40, loss 20.97,
|
| 192 |
+
| epoch 2 | 6200/ 12000 batches | rec 14.04, adv 0.69, |lvar| 2629.68, loss_d 1.40, loss 20.92,
|
| 193 |
+
| epoch 2 | 6300/ 12000 batches | rec 14.06, adv 0.69, |lvar| 2496.20, loss_d 1.40, loss 20.94,
|
| 194 |
+
| epoch 2 | 6400/ 12000 batches | rec 14.04, adv 0.69, |lvar| 2624.65, loss_d 1.40, loss 20.93,
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| epoch 2 | 6500/ 12000 batches | rec 14.06, adv 0.69, |lvar| 2444.82, loss_d 1.40, loss 20.96,
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| epoch 2 | 6600/ 12000 batches | rec 14.04, adv 0.69, |lvar| 2391.98, loss_d 1.40, loss 20.92,
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| epoch 2 | 6700/ 12000 batches | rec 14.08, adv 0.69, |lvar| 2544.15, loss_d 1.40, loss 20.97,
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| epoch 2 | 6800/ 12000 batches | rec 14.03, adv 0.69, |lvar| 2505.18, loss_d 1.39, loss 20.91,
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| epoch 2 | 6900/ 12000 batches | rec 14.15, adv 0.69, |lvar| 2611.28, loss_d 1.40, loss 21.05,
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| epoch 2 | 7000/ 12000 batches | rec 14.04, adv 0.69, |lvar| 2524.46, loss_d 1.39, loss 20.94,
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| epoch 2 | 7100/ 12000 batches | rec 14.22, adv 0.69, |lvar| 2393.13, loss_d 1.39, loss 21.12,
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+
| epoch 2 | 7200/ 12000 batches | rec 14.10, adv 0.69, |lvar| 2451.56, loss_d 1.39, loss 21.01,
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+
| epoch 2 | 7300/ 12000 batches | rec 14.04, adv 0.69, |lvar| 2502.05, loss_d 1.40, loss 20.91,
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+
| epoch 2 | 7400/ 12000 batches | rec 14.08, adv 0.69, |lvar| 2519.24, loss_d 1.40, loss 20.97,
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+
| epoch 2 | 7500/ 12000 batches | rec 14.04, adv 0.69, |lvar| 2484.64, loss_d 1.40, loss 20.92,
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+
| epoch 2 | 7600/ 12000 batches | rec 14.03, adv 0.69, |lvar| 2494.95, loss_d 1.40, loss 20.92,
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| epoch 2 | 7700/ 12000 batches | rec 14.38, adv 0.69, |lvar| 2496.24, loss_d 1.39, loss 21.28,
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| epoch 2 | 7800/ 12000 batches | rec 14.51, adv 0.70, |lvar| 2338.26, loss_d 1.38, loss 21.50,
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+
| epoch 2 | 7900/ 12000 batches | rec 14.41, adv 0.70, |lvar| 2256.31, loss_d 1.38, loss 21.38,
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| 210 |
+
| epoch 2 | 8000/ 12000 batches | rec 14.24, adv 0.69, |lvar| 2066.41, loss_d 1.41, loss 21.09,
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| 211 |
+
| epoch 2 | 8100/ 12000 batches | rec 14.13, adv 0.68, |lvar| 2217.84, loss_d 1.41, loss 20.93,
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| 212 |
+
| epoch 2 | 8200/ 12000 batches | rec 14.14, adv 0.68, |lvar| 2256.00, loss_d 1.42, loss 20.94,
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| 213 |
+
| epoch 2 | 8300/ 12000 batches | rec 14.10, adv 0.68, |lvar| 2271.98, loss_d 1.41, loss 20.91,
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+
| epoch 2 | 8400/ 12000 batches | rec 14.10, adv 0.68, |lvar| 2495.56, loss_d 1.42, loss 20.92,
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+
| epoch 2 | 8500/ 12000 batches | rec 14.06, adv 0.68, |lvar| 2549.17, loss_d 1.41, loss 20.88,
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| 216 |
+
| epoch 2 | 8600/ 12000 batches | rec 14.03, adv 0.68, |lvar| 2656.52, loss_d 1.41, loss 20.88,
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| 217 |
+
| epoch 2 | 8700/ 12000 batches | rec 14.04, adv 0.68, |lvar| 2631.69, loss_d 1.41, loss 20.85,
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| 218 |
+
| epoch 2 | 8800/ 12000 batches | rec 14.12, adv 0.69, |lvar| 2699.67, loss_d 1.40, loss 20.98,
|
| 219 |
+
| epoch 2 | 8900/ 12000 batches | rec 14.08, adv 0.69, |lvar| 2350.74, loss_d 1.40, loss 20.96,
|
| 220 |
+
| epoch 2 | 9000/ 12000 batches | rec 14.03, adv 0.69, |lvar| 2469.70, loss_d 1.40, loss 20.89,
|
| 221 |
+
| epoch 2 | 9100/ 12000 batches | rec 14.00, adv 0.69, |lvar| 2451.80, loss_d 1.40, loss 20.87,
|
| 222 |
+
| epoch 2 | 9200/ 12000 batches | rec 14.01, adv 0.69, |lvar| 2357.40, loss_d 1.40, loss 20.88,
|
| 223 |
+
| epoch 2 | 9300/ 12000 batches | rec 14.00, adv 0.69, |lvar| 2546.62, loss_d 1.40, loss 20.87,
|
| 224 |
+
| epoch 2 | 9400/ 12000 batches | rec 14.00, adv 0.69, |lvar| 2666.74, loss_d 1.40, loss 20.87,
|
| 225 |
+
| epoch 2 | 9500/ 12000 batches | rec 13.99, adv 0.69, |lvar| 2752.33, loss_d 1.40, loss 20.87,
|
| 226 |
+
| epoch 2 | 9600/ 12000 batches | rec 13.97, adv 0.69, |lvar| 2664.11, loss_d 1.40, loss 20.85,
|
| 227 |
+
| epoch 2 | 9700/ 12000 batches | rec 13.98, adv 0.69, |lvar| 2500.59, loss_d 1.40, loss 20.86,
|
| 228 |
+
| epoch 2 | 9800/ 12000 batches | rec 13.98, adv 0.69, |lvar| 2866.79, loss_d 1.40, loss 20.86,
|
| 229 |
+
| epoch 2 | 9900/ 12000 batches | rec 13.95, adv 0.69, |lvar| 2875.27, loss_d 1.40, loss 20.83,
|
| 230 |
+
| epoch 2 | 10000/ 12000 batches | rec 13.97, adv 0.69, |lvar| 2603.88, loss_d 1.40, loss 20.84,
|
| 231 |
+
| epoch 2 | 10100/ 12000 batches | rec 14.10, adv 0.69, |lvar| 2661.18, loss_d 1.39, loss 21.01,
|
| 232 |
+
| epoch 2 | 10200/ 12000 batches | rec 13.96, adv 0.69, |lvar| 2674.59, loss_d 1.39, loss 20.85,
|
| 233 |
+
| epoch 2 | 10300/ 12000 batches | rec 13.96, adv 0.69, |lvar| 2526.02, loss_d 1.39, loss 20.85,
|
| 234 |
+
| epoch 2 | 10400/ 12000 batches | rec 13.95, adv 0.69, |lvar| 2535.97, loss_d 1.40, loss 20.84,
|
| 235 |
+
| epoch 2 | 10500/ 12000 batches | rec 13.96, adv 0.69, |lvar| 2730.70, loss_d 1.40, loss 20.84,
|
| 236 |
+
| epoch 2 | 10600/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2791.92, loss_d 1.39, loss 20.83,
|
| 237 |
+
| epoch 2 | 10700/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2893.55, loss_d 1.40, loss 20.81,
|
| 238 |
+
| epoch 2 | 10800/ 12000 batches | rec 13.94, adv 0.69, |lvar| 2697.00, loss_d 1.39, loss 20.82,
|
| 239 |
+
| epoch 2 | 10900/ 12000 batches | rec 13.98, adv 0.69, |lvar| 2750.43, loss_d 1.39, loss 20.88,
|
| 240 |
+
| epoch 2 | 11000/ 12000 batches | rec 13.94, adv 0.69, |lvar| 2837.29, loss_d 1.40, loss 20.82,
|
| 241 |
+
| epoch 2 | 11100/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2747.54, loss_d 1.39, loss 20.82,
|
| 242 |
+
| epoch 2 | 11200/ 12000 batches | rec 13.94, adv 0.69, |lvar| 2766.16, loss_d 1.39, loss 20.83,
|
| 243 |
+
| epoch 2 | 11300/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2918.67, loss_d 1.39, loss 20.83,
|
| 244 |
+
| epoch 2 | 11400/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2619.51, loss_d 1.39, loss 20.81,
|
| 245 |
+
| epoch 2 | 11500/ 12000 batches | rec 13.95, adv 0.69, |lvar| 2488.95, loss_d 1.39, loss 20.85,
|
| 246 |
+
| epoch 2 | 11600/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2680.27, loss_d 1.39, loss 20.83,
|
| 247 |
+
| epoch 2 | 11700/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2794.54, loss_d 1.39, loss 20.82,
|
| 248 |
+
| epoch 2 | 11800/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2772.59, loss_d 1.40, loss 20.83,
|
| 249 |
+
| epoch 2 | 11900/ 12000 batches | rec 13.91, adv 0.69, |lvar| 2713.37, loss_d 1.39, loss 20.80,
|
| 250 |
+
| epoch 2 | 12000/ 12000 batches | rec 14.09, adv 0.69, |lvar| 2553.85, loss_d 1.39, loss 21.00,
|
| 251 |
+
--------------------------------------------------------------------------------
|
| 252 |
+
| end of epoch 2| time 200909s| valid rec 3.55, adv 0.69, |lvar| 2457.22, loss_d 1.38, loss 10.48, | saving model
|
| 253 |
+
--------------------------------------------------------------------------------
|
| 254 |
+
| epoch 3 | 100/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2474.49, loss_d 1.39, loss 20.83,
|
| 255 |
+
| epoch 3 | 200/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2532.58, loss_d 1.39, loss 20.83,
|
| 256 |
+
| epoch 3 | 300/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2661.00, loss_d 1.40, loss 20.81,
|
| 257 |
+
| epoch 3 | 400/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2578.85, loss_d 1.39, loss 20.83,
|
| 258 |
+
| epoch 3 | 500/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2560.80, loss_d 1.39, loss 20.83,
|
| 259 |
+
| epoch 3 | 600/ 12000 batches | rec 13.91, adv 0.69, |lvar| 2609.54, loss_d 1.39, loss 20.81,
|
| 260 |
+
| epoch 3 | 700/ 12000 batches | rec 13.89, adv 0.69, |lvar| 2655.46, loss_d 1.39, loss 20.78,
|
| 261 |
+
| epoch 3 | 800/ 12000 batches | rec 13.90, adv 0.69, |lvar| 2634.49, loss_d 1.39, loss 20.79,
|
| 262 |
+
| epoch 3 | 900/ 12000 batches | rec 13.91, adv 0.69, |lvar| 2709.35, loss_d 1.40, loss 20.80,
|
| 263 |
+
| epoch 3 | 1000/ 12000 batches | rec 13.89, adv 0.69, |lvar| 2800.58, loss_d 1.40, loss 20.78,
|
| 264 |
+
| epoch 3 | 1100/ 12000 batches | rec 13.89, adv 0.69, |lvar| 2806.83, loss_d 1.39, loss 20.77,
|
| 265 |
+
| epoch 3 | 1200/ 12000 batches | rec 13.89, adv 0.69, |lvar| 2888.12, loss_d 1.39, loss 20.79,
|
| 266 |
+
| epoch 3 | 1300/ 12000 batches | rec 13.87, adv 0.69, |lvar| 2815.85, loss_d 1.39, loss 20.76,
|
| 267 |
+
| epoch 3 | 1400/ 12000 batches | rec 13.89, adv 0.69, |lvar| 2773.61, loss_d 1.39, loss 20.78,
|
| 268 |
+
| epoch 3 | 1500/ 12000 batches | rec 13.87, adv 0.69, |lvar| 2691.41, loss_d 1.39, loss 20.76,
|
| 269 |
+
| epoch 3 | 1600/ 12000 batches | rec 13.92, adv 0.69, |lvar| 2713.97, loss_d 1.39, loss 20.82,
|
| 270 |
+
| epoch 3 | 1700/ 12000 batches | rec 13.87, adv 0.69, |lvar| 2670.28, loss_d 1.39, loss 20.77,
|
| 271 |
+
| epoch 3 | 1800/ 12000 batches | rec 13.87, adv 0.69, |lvar| 2782.13, loss_d 1.39, loss 20.77,
|
| 272 |
+
| epoch 3 | 1900/ 12000 batches | rec 13.86, adv 0.69, |lvar| 2712.49, loss_d 1.39, loss 20.77,
|
| 273 |
+
| epoch 3 | 2000/ 12000 batches | rec 13.87, adv 0.69, |lvar| 2799.40, loss_d 1.39, loss 20.77,
|
| 274 |
+
| epoch 3 | 2100/ 12000 batches | rec 13.92, adv 0.69, |lvar| 2726.12, loss_d 1.39, loss 20.83,
|
| 275 |
+
| epoch 3 | 2200/ 12000 batches | rec 13.85, adv 0.69, |lvar| 2707.80, loss_d 1.39, loss 20.76,
|
| 276 |
+
| epoch 3 | 2300/ 12000 batches | rec 13.86, adv 0.69, |lvar| 2692.63, loss_d 1.39, loss 20.76,
|
| 277 |
+
| epoch 3 | 2400/ 12000 batches | rec 13.86, adv 0.69, |lvar| 2831.66, loss_d 1.39, loss 20.76,
|
| 278 |
+
| epoch 3 | 2500/ 12000 batches | rec 13.89, adv 0.69, |lvar| 2757.01, loss_d 1.39, loss 20.78,
|
| 279 |
+
| epoch 3 | 2600/ 12000 batches | rec 13.86, adv 0.69, |lvar| 2758.34, loss_d 1.39, loss 20.76,
|
| 280 |
+
| epoch 3 | 2700/ 12000 batches | rec 13.84, adv 0.69, |lvar| 2736.42, loss_d 1.39, loss 20.74,
|
| 281 |
+
| epoch 3 | 2800/ 12000 batches | rec 13.85, adv 0.69, |lvar| 2797.80, loss_d 1.39, loss 20.75,
|
| 282 |
+
| epoch 3 | 2900/ 12000 batches | rec 13.91, adv 0.69, |lvar| 2762.39, loss_d 1.39, loss 20.81,
|
| 283 |
+
| epoch 3 | 3000/ 12000 batches | rec 13.87, adv 0.69, |lvar| 2565.11, loss_d 1.39, loss 20.79,
|
| 284 |
+
| epoch 3 | 3100/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2678.84, loss_d 1.39, loss 20.84,
|
| 285 |
+
| epoch 3 | 3200/ 12000 batches | rec 13.87, adv 0.69, |lvar| 2532.21, loss_d 1.40, loss 20.75,
|
| 286 |
+
| epoch 3 | 3300/ 12000 batches | rec 13.85, adv 0.69, |lvar| 2591.34, loss_d 1.39, loss 20.74,
|
| 287 |
+
| epoch 3 | 3400/ 12000 batches | rec 13.90, adv 0.69, |lvar| 2654.85, loss_d 1.39, loss 20.79,
|
| 288 |
+
| epoch 3 | 3500/ 12000 batches | rec 13.84, adv 0.69, |lvar| 2697.89, loss_d 1.40, loss 20.73,
|
| 289 |
+
| epoch 3 | 3600/ 12000 batches | rec 13.84, adv 0.69, |lvar| 2741.24, loss_d 1.39, loss 20.73,
|
| 290 |
+
| epoch 3 | 3700/ 12000 batches | rec 13.85, adv 0.69, |lvar| 2926.88, loss_d 1.39, loss 20.74,
|
| 291 |
+
| epoch 3 | 3800/ 12000 batches | rec 13.83, adv 0.69, |lvar| 2814.71, loss_d 1.39, loss 20.73,
|
| 292 |
+
| epoch 3 | 3900/ 12000 batches | rec 13.86, adv 0.69, |lvar| 2631.03, loss_d 1.39, loss 20.76,
|
| 293 |
+
| epoch 3 | 4000/ 12000 batches | rec 13.84, adv 0.69, |lvar| 2786.08, loss_d 1.39, loss 20.74,
|
| 294 |
+
| epoch 3 | 4100/ 12000 batches | rec 13.82, adv 0.69, |lvar| 2675.92, loss_d 1.39, loss 20.72,
|
| 295 |
+
| epoch 3 | 4200/ 12000 batches | rec 13.82, adv 0.69, |lvar| 2775.36, loss_d 1.39, loss 20.71,
|
| 296 |
+
| epoch 3 | 4300/ 12000 batches | rec 13.84, adv 0.69, |lvar| 2989.72, loss_d 1.39, loss 20.74,
|
| 297 |
+
| epoch 3 | 4400/ 12000 batches | rec 13.83, adv 0.69, |lvar| 2721.34, loss_d 1.39, loss 20.73,
|
| 298 |
+
| epoch 3 | 4500/ 12000 batches | rec 13.81, adv 0.69, |lvar| 2847.74, loss_d 1.39, loss 20.71,
|
| 299 |
+
| epoch 3 | 4600/ 12000 batches | rec 13.80, adv 0.69, |lvar| 2849.23, loss_d 1.39, loss 20.71,
|
| 300 |
+
| epoch 3 | 4700/ 12000 batches | rec 13.83, adv 0.69, |lvar| 2910.55, loss_d 1.39, loss 20.73,
|
| 301 |
+
| epoch 3 | 4800/ 12000 batches | rec 13.80, adv 0.69, |lvar| 2878.76, loss_d 1.39, loss 20.71,
|
| 302 |
+
| epoch 3 | 4900/ 12000 batches | rec 13.87, adv 0.69, |lvar| 2824.93, loss_d 1.39, loss 20.78,
|
| 303 |
+
| epoch 3 | 5000/ 12000 batches | rec 13.85, adv 0.69, |lvar| 2661.03, loss_d 1.39, loss 20.76,
|
| 304 |
+
| epoch 3 | 5100/ 12000 batches | rec 13.81, adv 0.69, |lvar| 2736.95, loss_d 1.39, loss 20.69,
|
| 305 |
+
| epoch 3 | 5200/ 12000 batches | rec 13.81, adv 0.69, |lvar| 2913.49, loss_d 1.39, loss 20.72,
|
| 306 |
+
| epoch 3 | 5300/ 12000 batches | rec 13.82, adv 0.69, |lvar| 2769.13, loss_d 1.39, loss 20.72,
|
| 307 |
+
| epoch 3 | 5400/ 12000 batches | rec 13.80, adv 0.69, |lvar| 2833.07, loss_d 1.39, loss 20.70,
|
| 308 |
+
| epoch 3 | 5500/ 12000 batches | rec 13.81, adv 0.69, |lvar| 2865.79, loss_d 1.39, loss 20.71,
|
| 309 |
+
| epoch 3 | 5600/ 12000 batches | rec 13.81, adv 0.69, |lvar| 2823.14, loss_d 1.39, loss 20.71,
|
| 310 |
+
| epoch 3 | 5700/ 12000 batches | rec 13.81, adv 0.69, |lvar| 2930.15, loss_d 1.39, loss 20.71,
|
| 311 |
+
| epoch 3 | 5800/ 12000 batches | rec 13.78, adv 0.69, |lvar| 2882.93, loss_d 1.39, loss 20.68,
|
| 312 |
+
| epoch 3 | 5900/ 12000 batches | rec 13.79, adv 0.69, |lvar| 2822.31, loss_d 1.39, loss 20.69,
|
| 313 |
+
| epoch 3 | 6000/ 12000 batches | rec 13.79, adv 0.69, |lvar| 2731.36, loss_d 1.39, loss 20.69,
|
| 314 |
+
| epoch 3 | 6100/ 12000 batches | rec 13.79, adv 0.69, |lvar| 2834.99, loss_d 1.39, loss 20.69,
|
| 315 |
+
| epoch 3 | 6200/ 12000 batches | rec 13.78, adv 0.69, |lvar| 2743.44, loss_d 1.39, loss 20.68,
|
| 316 |
+
| epoch 3 | 6300/ 12000 batches | rec 13.83, adv 0.69, |lvar| 2903.88, loss_d 1.39, loss 20.74,
|
| 317 |
+
| epoch 3 | 6400/ 12000 batches | rec 13.98, adv 0.69, |lvar| 2686.92, loss_d 1.39, loss 20.92,
|
| 318 |
+
| epoch 3 | 6500/ 12000 batches | rec 13.83, adv 0.69, |lvar| 2514.11, loss_d 1.39, loss 20.74,
|
| 319 |
+
| epoch 3 | 6600/ 12000 batches | rec 13.84, adv 0.69, |lvar| 2539.37, loss_d 1.40, loss 20.72,
|
| 320 |
+
| epoch 3 | 6700/ 12000 batches | rec 13.82, adv 0.69, |lvar| 2461.12, loss_d 1.40, loss 20.71,
|
| 321 |
+
| epoch 3 | 6800/ 12000 batches | rec 13.82, adv 0.69, |lvar| 2462.05, loss_d 1.40, loss 20.71,
|
| 322 |
+
| epoch 3 | 6900/ 12000 batches | rec 13.85, adv 0.69, |lvar| 2586.10, loss_d 1.40, loss 20.73,
|
| 323 |
+
| epoch 3 | 7000/ 12000 batches | rec 13.80, adv 0.69, |lvar| 2648.86, loss_d 1.39, loss 20.69,
|
| 324 |
+
| epoch 3 | 7100/ 12000 batches | rec 13.79, adv 0.69, |lvar| 2680.55, loss_d 1.40, loss 20.67,
|
| 325 |
+
| epoch 3 | 7200/ 12000 batches | rec 13.80, adv 0.69, |lvar| 2814.98, loss_d 1.39, loss 20.69,
|
| 326 |
+
| epoch 3 | 7300/ 12000 batches | rec 13.78, adv 0.69, |lvar| 2741.79, loss_d 1.39, loss 20.67,
|
| 327 |
+
| epoch 3 | 7400/ 12000 batches | rec 13.77, adv 0.69, |lvar| 2934.86, loss_d 1.40, loss 20.66,
|
| 328 |
+
| epoch 3 | 7500/ 12000 batches | rec 13.77, adv 0.69, |lvar| 2870.90, loss_d 1.39, loss 20.67,
|
| 329 |
+
| epoch 3 | 7600/ 12000 batches | rec 13.76, adv 0.69, |lvar| 2896.78, loss_d 1.39, loss 20.66,
|
| 330 |
+
| epoch 3 | 7700/ 12000 batches | rec 13.77, adv 0.69, |lvar| 2836.49, loss_d 1.39, loss 20.67,
|
| 331 |
+
| epoch 3 | 7800/ 12000 batches | rec 13.74, adv 0.69, |lvar| 2868.68, loss_d 1.39, loss 20.64,
|
| 332 |
+
| epoch 3 | 7900/ 12000 batches | rec 13.77, adv 0.69, |lvar| 2910.25, loss_d 1.39, loss 20.67,
|
| 333 |
+
| epoch 3 | 8000/ 12000 batches | rec 13.76, adv 0.69, |lvar| 2879.05, loss_d 1.39, loss 20.67,
|
| 334 |
+
| epoch 3 | 8100/ 12000 batches | rec 13.75, adv 0.69, |lvar| 2910.53, loss_d 1.39, loss 20.65,
|
| 335 |
+
| epoch 3 | 8200/ 12000 batches | rec 13.76, adv 0.69, |lvar| 2870.99, loss_d 1.39, loss 20.66,
|
| 336 |
+
| epoch 3 | 8300/ 12000 batches | rec 13.76, adv 0.69, |lvar| 3098.61, loss_d 1.39, loss 20.67,
|
| 337 |
+
| epoch 3 | 8400/ 12000 batches | rec 13.76, adv 0.69, |lvar| 2945.49, loss_d 1.39, loss 20.66,
|
| 338 |
+
| epoch 3 | 8500/ 12000 batches | rec 13.76, adv 0.69, |lvar| 2987.08, loss_d 1.39, loss 20.67,
|
| 339 |
+
| epoch 3 | 8600/ 12000 batches | rec 13.75, adv 0.69, |lvar| 2971.12, loss_d 1.39, loss 20.66,
|
| 340 |
+
| epoch 3 | 8700/ 12000 batches | rec 13.75, adv 0.69, |lvar| 3107.87, loss_d 1.39, loss 20.65,
|
| 341 |
+
| epoch 3 | 8800/ 12000 batches | rec 13.74, adv 0.69, |lvar| 2829.21, loss_d 1.39, loss 20.65,
|
| 342 |
+
| epoch 3 | 8900/ 12000 batches | rec 13.74, adv 0.69, |lvar| 2911.05, loss_d 1.39, loss 20.65,
|
| 343 |
+
| epoch 3 | 9000/ 12000 batches | rec 13.76, adv 0.69, |lvar| 2811.25, loss_d 1.39, loss 20.66,
|
| 344 |
+
| epoch 3 | 9100/ 12000 batches | rec 13.74, adv 0.69, |lvar| 3059.79, loss_d 1.39, loss 20.65,
|
| 345 |
+
| epoch 3 | 9200/ 12000 batches | rec 13.73, adv 0.69, |lvar| 2880.77, loss_d 1.39, loss 20.64,
|
| 346 |
+
| epoch 3 | 9300/ 12000 batches | rec 13.73, adv 0.69, |lvar| 3031.72, loss_d 1.39, loss 20.64,
|
| 347 |
+
| epoch 3 | 9400/ 12000 batches | rec 13.81, adv 0.69, |lvar| 2967.91, loss_d 1.39, loss 20.72,
|
| 348 |
+
| epoch 3 | 9500/ 12000 batches | rec 13.93, adv 0.69, |lvar| 2508.06, loss_d 1.40, loss 20.81,
|
| 349 |
+
| epoch 3 | 9600/ 12000 batches | rec 13.90, adv 0.69, |lvar| 2572.88, loss_d 1.39, loss 20.82,
|
| 350 |
+
| epoch 3 | 9700/ 12000 batches | rec 13.78, adv 0.69, |lvar| 2607.72, loss_d 1.39, loss 20.70,
|
| 351 |
+
| epoch 3 | 9800/ 12000 batches | rec 13.78, adv 0.69, |lvar| 2637.49, loss_d 1.39, loss 20.68,
|
| 352 |
+
| epoch 3 | 9900/ 12000 batches | rec 13.76, adv 0.69, |lvar| 2553.86, loss_d 1.39, loss 20.66,
|
| 353 |
+
| epoch 3 | 10000/ 12000 batches | rec 13.75, adv 0.69, |lvar| 2533.64, loss_d 1.39, loss 20.65,
|
| 354 |
+
| epoch 3 | 10100/ 12000 batches | rec 13.74, adv 0.69, |lvar| 2668.31, loss_d 1.39, loss 20.65,
|
| 355 |
+
| epoch 3 | 10200/ 12000 batches | rec 13.75, adv 0.69, |lvar| 2790.73, loss_d 1.39, loss 20.65,
|
| 356 |
+
| epoch 3 | 10300/ 12000 batches | rec 13.74, adv 0.69, |lvar| 2833.88, loss_d 1.39, loss 20.64,
|
| 357 |
+
| epoch 3 | 10400/ 12000 batches | rec 13.75, adv 0.69, |lvar| 2911.14, loss_d 1.39, loss 20.65,
|
| 358 |
+
| epoch 3 | 10500/ 12000 batches | rec 13.73, adv 0.69, |lvar| 2812.88, loss_d 1.39, loss 20.63,
|
| 359 |
+
| epoch 3 | 10600/ 12000 batches | rec 13.71, adv 0.69, |lvar| 2736.80, loss_d 1.39, loss 20.61,
|
| 360 |
+
| epoch 3 | 10700/ 12000 batches | rec 13.72, adv 0.69, |lvar| 2870.79, loss_d 1.39, loss 20.62,
|
| 361 |
+
| epoch 3 | 10800/ 12000 batches | rec 13.73, adv 0.69, |lvar| 2900.76, loss_d 1.39, loss 20.63,
|
| 362 |
+
| epoch 3 | 10900/ 12000 batches | rec 13.73, adv 0.69, |lvar| 2896.72, loss_d 1.39, loss 20.63,
|
| 363 |
+
| epoch 3 | 11000/ 12000 batches | rec 13.72, adv 0.69, |lvar| 2893.96, loss_d 1.39, loss 20.62,
|
| 364 |
+
| epoch 3 | 11100/ 12000 batches | rec 13.83, adv 0.69, |lvar| 2857.04, loss_d 1.39, loss 20.74,
|
| 365 |
+
| epoch 3 | 11200/ 12000 batches | rec 13.74, adv 0.69, |lvar| 2614.10, loss_d 1.39, loss 20.64,
|
| 366 |
+
| epoch 3 | 11300/ 12000 batches | rec 13.72, adv 0.69, |lvar| 2812.89, loss_d 1.39, loss 20.63,
|
| 367 |
+
| epoch 3 | 11400/ 12000 batches | rec 13.72, adv 0.69, |lvar| 2761.53, loss_d 1.39, loss 20.62,
|
| 368 |
+
| epoch 3 | 11500/ 12000 batches | rec 13.73, adv 0.69, |lvar| 2770.67, loss_d 1.39, loss 20.64,
|
| 369 |
+
| epoch 3 | 11600/ 12000 batches | rec 13.72, adv 0.69, |lvar| 2798.25, loss_d 1.39, loss 20.63,
|
| 370 |
+
| epoch 3 | 11700/ 12000 batches | rec 13.71, adv 0.69, |lvar| 2706.52, loss_d 1.39, loss 20.61,
|
| 371 |
+
| epoch 3 | 11800/ 12000 batches | rec 13.71, adv 0.69, |lvar| 2879.59, loss_d 1.39, loss 20.63,
|
| 372 |
+
| epoch 3 | 11900/ 12000 batches | rec 13.72, adv 0.69, |lvar| 2665.08, loss_d 1.39, loss 20.63,
|
| 373 |
+
| epoch 3 | 12000/ 12000 batches | rec 13.72, adv 0.69, |lvar| 2796.35, loss_d 1.39, loss 20.62,
|
v2/8-12_150kk_15_09_24/model.pt
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
|
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oid sha256:21dbbb6cb249ac0dcb3adc85e76a08602d6ca036b58f1ee9763dd56181eaf65c
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| 3 |
+
size 51158494
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v2/8-12_150kk_15_09_24/vocab.alphabet
ADDED
|
@@ -0,0 +1 @@
|
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| 1 |
+
ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
|
v2/8-12_150kk_17_08_24/log.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
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v2/8-12_150kk_17_08_24/model.pt
ADDED
|
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+
version https://git-lfs.github.com/spec/v1
|
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oid sha256:ba4d8d7e7661f5804427d73e1fcff2fe405dfa74d57f52430ef01458250868fa
|
| 3 |
+
size 84765762
|
v2/8-12_150kk_17_08_24/vocab.alphabet
ADDED
|
@@ -0,0 +1 @@
|
|
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|
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|
| 1 |
+
ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
|
v2/8-12_150kk_22_09_24/log.txt
ADDED
|
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| 1 |
+
Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=10000, dim_d=512, dim_emb=80, dim_h=512, dim_z=256, dropout=0.2, epochs=10, lambda_adv=10.0, lambda_kl=0.0, lambda_p=0.0, load_model='', log_interval=100, lr=0.0005, max_len=12, model_type='aae', nlayers=3, no_cuda=True, noise=[0.0, 0.0, 0.0], save_dir='out/8-12_150kk_22_09_24/', train='../data/8-12_150kk/train.txt', valid='../data/8-12_150kk/valid.txt')
|
| 2 |
+
# train on cpu device
|
| 3 |
+
# vocab save out/8-12_150kk_22_09_24/vocab.alphabet
|
| 4 |
+
# train passwords {len(train_dataloader.dataset)}
|
| 5 |
+
# valid passwords 30000000
|
| 6 |
+
# model aae parameters: 21187492
|
| 7 |
+
--------------------------------------------------------------------------------
|
| 8 |
+
| epoch 1 | 100/ 12000 batches | rec 35.26, adv 0.66, |lvar| 371.79, loss_d 1.72, loss 41.85,
|
| 9 |
+
| epoch 1 | 200/ 12000 batches | rec 32.84, adv 0.62, |lvar| 835.24, loss_d 1.67, loss 39.05,
|
| 10 |
+
| epoch 1 | 300/ 12000 batches | rec 32.50, adv 0.62, |lvar| 936.53, loss_d 1.54, loss 38.69,
|
| 11 |
+
| epoch 1 | 400/ 12000 batches | rec 32.28, adv 0.65, |lvar| 576.33, loss_d 1.43, loss 38.79,
|
| 12 |
+
| epoch 1 | 500/ 12000 batches | rec 29.84, adv 0.67, |lvar| 478.56, loss_d 1.41, loss 36.52,
|
| 13 |
+
| epoch 1 | 600/ 12000 batches | rec 27.98, adv 0.69, |lvar| 461.74, loss_d 1.39, loss 34.86,
|
| 14 |
+
| epoch 1 | 700/ 12000 batches | rec 27.19, adv 0.68, |lvar| 562.34, loss_d 1.40, loss 33.97,
|
| 15 |
+
| epoch 1 | 800/ 12000 batches | rec 26.46, adv 0.68, |lvar| 613.86, loss_d 1.39, loss 33.27,
|
| 16 |
+
| epoch 1 | 900/ 12000 batches | rec 25.88, adv 0.68, |lvar| 658.60, loss_d 1.40, loss 32.70,
|
| 17 |
+
| epoch 1 | 1000/ 12000 batches | rec 25.79, adv 0.70, |lvar| 629.83, loss_d 1.38, loss 32.78,
|
| 18 |
+
| epoch 1 | 1100/ 12000 batches | rec 25.03, adv 0.69, |lvar| 678.34, loss_d 1.38, loss 31.95,
|
| 19 |
+
| epoch 1 | 1200/ 12000 batches | rec 24.74, adv 0.69, |lvar| 722.24, loss_d 1.39, loss 31.68,
|
| 20 |
+
| epoch 1 | 1300/ 12000 batches | rec 24.23, adv 0.69, |lvar| 738.48, loss_d 1.39, loss 31.12,
|
| 21 |
+
| epoch 1 | 1400/ 12000 batches | rec 23.62, adv 0.71, |lvar| 725.41, loss_d 1.37, loss 30.68,
|
| 22 |
+
| epoch 1 | 1500/ 12000 batches | rec 23.34, adv 0.70, |lvar| 776.78, loss_d 1.39, loss 30.33,
|
| 23 |
+
| epoch 1 | 1600/ 12000 batches | rec 22.88, adv 0.70, |lvar| 788.73, loss_d 1.40, loss 29.90,
|
| 24 |
+
| epoch 1 | 1700/ 12000 batches | rec 22.12, adv 0.70, |lvar| 857.03, loss_d 1.40, loss 29.11,
|
| 25 |
+
| epoch 1 | 1800/ 12000 batches | rec 21.00, adv 0.70, |lvar| 870.94, loss_d 1.39, loss 28.00,
|
| 26 |
+
| epoch 1 | 1900/ 12000 batches | rec 20.09, adv 0.70, |lvar| 990.39, loss_d 1.38, loss 27.11,
|
| 27 |
+
| epoch 1 | 2000/ 12000 batches | rec 19.37, adv 0.72, |lvar| 1032.40, loss_d 1.36, loss 26.53,
|
| 28 |
+
| epoch 1 | 2100/ 12000 batches | rec 18.43, adv 0.72, |lvar| 1109.67, loss_d 1.36, loss 25.63,
|
| 29 |
+
| epoch 1 | 2200/ 12000 batches | rec 17.48, adv 0.74, |lvar| 1146.31, loss_d 1.36, loss 24.86,
|
| 30 |
+
| epoch 1 | 2300/ 12000 batches | rec 16.47, adv 0.73, |lvar| 1194.53, loss_d 1.37, loss 23.81,
|
| 31 |
+
| epoch 1 | 2400/ 12000 batches | rec 15.94, adv 0.74, |lvar| 1254.72, loss_d 1.35, loss 23.36,
|
| 32 |
+
| epoch 1 | 2500/ 12000 batches | rec 15.54, adv 0.76, |lvar| 1247.50, loss_d 1.34, loss 23.14,
|
| 33 |
+
| epoch 1 | 2600/ 12000 batches | rec 14.80, adv 0.73, |lvar| 1314.73, loss_d 1.35, loss 22.06,
|
| 34 |
+
| epoch 1 | 2700/ 12000 batches | rec 13.73, adv 0.73, |lvar| 1462.00, loss_d 1.37, loss 21.00,
|
| 35 |
+
| epoch 1 | 2800/ 12000 batches | rec 12.50, adv 0.72, |lvar| 1447.13, loss_d 1.39, loss 19.73,
|
| 36 |
+
| epoch 1 | 2900/ 12000 batches | rec 11.15, adv 0.71, |lvar| 1575.18, loss_d 1.38, loss 18.28,
|
| 37 |
+
| epoch 1 | 3000/ 12000 batches | rec 10.16, adv 0.70, |lvar| 1659.78, loss_d 1.40, loss 17.14,
|
| 38 |
+
| epoch 1 | 3100/ 12000 batches | rec 9.44, adv 0.70, |lvar| 1656.99, loss_d 1.40, loss 16.49,
|
| 39 |
+
| epoch 1 | 3200/ 12000 batches | rec 8.62, adv 0.71, |lvar| 1715.48, loss_d 1.37, loss 15.77,
|
| 40 |
+
| epoch 1 | 3300/ 12000 batches | rec 7.90, adv 0.72, |lvar| 1694.77, loss_d 1.36, loss 15.14,
|
| 41 |
+
| epoch 1 | 3400/ 12000 batches | rec 7.62, adv 0.71, |lvar| 1774.09, loss_d 1.38, loss 14.74,
|
| 42 |
+
| epoch 1 | 3500/ 12000 batches | rec 7.05, adv 0.72, |lvar| 1775.09, loss_d 1.38, loss 14.21,
|
| 43 |
+
| epoch 1 | 3600/ 12000 batches | rec 6.58, adv 0.72, |lvar| 1772.81, loss_d 1.39, loss 13.81,
|
| 44 |
+
| epoch 1 | 3700/ 12000 batches | rec 6.21, adv 0.72, |lvar| 1818.19, loss_d 1.38, loss 13.45,
|
| 45 |
+
| epoch 1 | 3800/ 12000 batches | rec 5.45, adv 0.71, |lvar| 1889.41, loss_d 1.40, loss 12.51,
|
| 46 |
+
| epoch 1 | 3900/ 12000 batches | rec 5.37, adv 0.70, |lvar| 1960.21, loss_d 1.41, loss 12.40,
|
| 47 |
+
| epoch 1 | 4000/ 12000 batches | rec 4.39, adv 0.69, |lvar| 1901.51, loss_d 1.43, loss 11.28,
|
| 48 |
+
| epoch 1 | 4100/ 12000 batches | rec 4.62, adv 0.70, |lvar| 1937.58, loss_d 1.43, loss 11.64,
|
| 49 |
+
| epoch 1 | 4200/ 12000 batches | rec 4.17, adv 0.69, |lvar| 1995.38, loss_d 1.42, loss 11.11,
|
| 50 |
+
| epoch 1 | 4300/ 12000 batches | rec 3.90, adv 0.70, |lvar| 2012.44, loss_d 1.40, loss 10.91,
|
| 51 |
+
| epoch 1 | 4400/ 12000 batches | rec 3.86, adv 0.71, |lvar| 2006.54, loss_d 1.40, loss 10.93,
|
| 52 |
+
| epoch 1 | 4500/ 12000 batches | rec 3.48, adv 0.71, |lvar| 2030.28, loss_d 1.37, loss 10.59,
|
| 53 |
+
| epoch 1 | 4600/ 12000 batches | rec 3.63, adv 0.72, |lvar| 2043.63, loss_d 1.39, loss 10.81,
|
| 54 |
+
| epoch 1 | 4700/ 12000 batches | rec 2.65, adv 0.69, |lvar| 2027.14, loss_d 1.42, loss 9.56,
|
| 55 |
+
| epoch 1 | 4800/ 12000 batches | rec 2.94, adv 0.70, |lvar| 2155.12, loss_d 1.43, loss 9.90,
|
| 56 |
+
| epoch 1 | 4900/ 12000 batches | rec 2.79, adv 0.70, |lvar| 2105.79, loss_d 1.42, loss 9.82,
|
| 57 |
+
| epoch 1 | 5000/ 12000 batches | rec 2.79, adv 0.69, |lvar| 2132.01, loss_d 1.43, loss 9.68,
|
| 58 |
+
| epoch 1 | 5100/ 12000 batches | rec 1.98, adv 0.70, |lvar| 2134.63, loss_d 1.41, loss 8.94,
|
| 59 |
+
| epoch 1 | 5200/ 12000 batches | rec 2.29, adv 0.70, |lvar| 2175.85, loss_d 1.42, loss 9.32,
|
| 60 |
+
| epoch 1 | 5300/ 12000 batches | rec 1.97, adv 0.68, |lvar| 2180.86, loss_d 1.42, loss 8.79,
|
| 61 |
+
| epoch 1 | 5400/ 12000 batches | rec 2.52, adv 0.71, |lvar| 2172.59, loss_d 1.40, loss 9.58,
|
| 62 |
+
| epoch 1 | 5500/ 12000 batches | rec 1.83, adv 0.71, |lvar| 2087.77, loss_d 1.39, loss 8.92,
|
| 63 |
+
| epoch 1 | 5600/ 12000 batches | rec 2.12, adv 0.69, |lvar| 2126.82, loss_d 1.42, loss 9.07,
|
| 64 |
+
| epoch 1 | 5700/ 12000 batches | rec 2.40, adv 0.70, |lvar| 2130.74, loss_d 1.39, loss 9.45,
|
| 65 |
+
| epoch 1 | 5800/ 12000 batches | rec 1.97, adv 0.70, |lvar| 2144.91, loss_d 1.41, loss 8.93,
|
| 66 |
+
| epoch 1 | 5900/ 12000 batches | rec 2.67, adv 0.70, |lvar| 2043.35, loss_d 1.40, loss 9.67,
|
| 67 |
+
| epoch 1 | 6000/ 12000 batches | rec 1.89, adv 0.70, |lvar| 2076.92, loss_d 1.41, loss 8.93,
|
| 68 |
+
| epoch 1 | 6100/ 12000 batches | rec 2.08, adv 0.69, |lvar| 2122.77, loss_d 1.42, loss 8.99,
|
| 69 |
+
| epoch 1 | 6200/ 12000 batches | rec 1.93, adv 0.70, |lvar| 2237.82, loss_d 1.41, loss 8.93,
|
| 70 |
+
| epoch 1 | 6300/ 12000 batches | rec 2.18, adv 0.70, |lvar| 2229.99, loss_d 1.41, loss 9.15,
|
| 71 |
+
| epoch 1 | 6400/ 12000 batches | rec 1.37, adv 0.69, |lvar| 2136.03, loss_d 1.42, loss 8.23,
|
| 72 |
+
| epoch 1 | 6500/ 12000 batches | rec 1.03, adv 0.69, |lvar| 2134.89, loss_d 1.42, loss 7.89,
|
| 73 |
+
| epoch 1 | 6600/ 12000 batches | rec 2.58, adv 0.71, |lvar| 2273.61, loss_d 1.40, loss 9.63,
|
| 74 |
+
| epoch 1 | 6700/ 12000 batches | rec 1.86, adv 0.71, |lvar| 2274.56, loss_d 1.40, loss 8.93,
|
| 75 |
+
| epoch 1 | 6800/ 12000 batches | rec 2.20, adv 0.69, |lvar| 2169.55, loss_d 1.39, loss 9.13,
|
| 76 |
+
| epoch 1 | 6900/ 12000 batches | rec 2.37, adv 0.72, |lvar| 2158.79, loss_d 1.38, loss 9.59,
|
| 77 |
+
| epoch 1 | 7000/ 12000 batches | rec 2.44, adv 0.71, |lvar| 2160.36, loss_d 1.39, loss 9.55,
|
| 78 |
+
| epoch 1 | 7100/ 12000 batches | rec 1.73, adv 0.70, |lvar| 2142.94, loss_d 1.41, loss 8.73,
|
| 79 |
+
| epoch 1 | 7200/ 12000 batches | rec 1.64, adv 0.68, |lvar| 2146.99, loss_d 1.43, loss 8.45,
|
| 80 |
+
| epoch 1 | 7300/ 12000 batches | rec 1.26, adv 0.69, |lvar| 2171.44, loss_d 1.43, loss 8.11,
|
| 81 |
+
| epoch 1 | 7400/ 12000 batches | rec 1.22, adv 0.69, |lvar| 2173.91, loss_d 1.42, loss 8.11,
|
| 82 |
+
| epoch 1 | 7500/ 12000 batches | rec 1.20, adv 0.69, |lvar| 2314.50, loss_d 1.41, loss 8.08,
|
| 83 |
+
| epoch 1 | 7600/ 12000 batches | rec 1.40, adv 0.69, |lvar| 2220.07, loss_d 1.40, loss 8.34,
|
| 84 |
+
| epoch 1 | 7700/ 12000 batches | rec 1.29, adv 0.70, |lvar| 2186.61, loss_d 1.43, loss 8.28,
|
| 85 |
+
| epoch 1 | 7800/ 12000 batches | rec 0.92, adv 0.69, |lvar| 2317.67, loss_d 1.42, loss 7.77,
|
| 86 |
+
| epoch 1 | 7900/ 12000 batches | rec 1.18, adv 0.69, |lvar| 2297.60, loss_d 1.42, loss 8.08,
|
| 87 |
+
| epoch 1 | 8000/ 12000 batches | rec 0.85, adv 0.69, |lvar| 2216.17, loss_d 1.41, loss 7.74,
|
| 88 |
+
| epoch 1 | 8100/ 12000 batches | rec 1.03, adv 0.70, |lvar| 2339.28, loss_d 1.41, loss 7.99,
|
| 89 |
+
| epoch 1 | 8200/ 12000 batches | rec 1.82, adv 0.69, |lvar| 2180.34, loss_d 1.41, loss 8.76,
|
| 90 |
+
| epoch 1 | 8300/ 12000 batches | rec 1.34, adv 0.70, |lvar| 2209.42, loss_d 1.41, loss 8.33,
|
| 91 |
+
| epoch 1 | 8400/ 12000 batches | rec 1.38, adv 0.68, |lvar| 2258.89, loss_d 1.40, loss 8.23,
|
| 92 |
+
| epoch 1 | 8500/ 12000 batches | rec 1.04, adv 0.70, |lvar| 2154.45, loss_d 1.40, loss 8.05,
|
| 93 |
+
| epoch 1 | 8600/ 12000 batches | rec 1.47, adv 0.69, |lvar| 2228.66, loss_d 1.40, loss 8.41,
|
| 94 |
+
| epoch 1 | 8700/ 12000 batches | rec 3.08, adv 0.72, |lvar| 2116.10, loss_d 1.38, loss 10.25,
|
| 95 |
+
| epoch 1 | 8800/ 12000 batches | rec 2.90, adv 0.72, |lvar| 2151.02, loss_d 1.38, loss 10.13,
|
| 96 |
+
| epoch 1 | 8900/ 12000 batches | rec 1.34, adv 0.70, |lvar| 2062.42, loss_d 1.41, loss 8.30,
|
| 97 |
+
| epoch 1 | 9000/ 12000 batches | rec 2.10, adv 0.70, |lvar| 2220.63, loss_d 1.39, loss 9.09,
|
| 98 |
+
| epoch 1 | 9100/ 12000 batches | rec 2.65, adv 0.71, |lvar| 2185.13, loss_d 1.39, loss 9.73,
|
| 99 |
+
| epoch 1 | 9200/ 12000 batches | rec 1.91, adv 0.70, |lvar| 2219.91, loss_d 1.42, loss 8.89,
|
| 100 |
+
| epoch 1 | 9300/ 12000 batches | rec 1.70, adv 0.69, |lvar| 2209.73, loss_d 1.41, loss 8.61,
|
| 101 |
+
| epoch 1 | 9400/ 12000 batches | rec 1.11, adv 0.69, |lvar| 2306.66, loss_d 1.43, loss 7.98,
|
| 102 |
+
| epoch 1 | 9500/ 12000 batches | rec 0.91, adv 0.68, |lvar| 2371.19, loss_d 1.44, loss 7.73,
|
| 103 |
+
| epoch 1 | 9600/ 12000 batches | rec 0.81, adv 0.68, |lvar| 2457.96, loss_d 1.44, loss 7.63,
|
| 104 |
+
| epoch 1 | 9700/ 12000 batches | rec 0.77, adv 0.69, |lvar| 2307.93, loss_d 1.43, loss 7.64,
|
| 105 |
+
| epoch 1 | 9800/ 12000 batches | rec 1.13, adv 0.69, |lvar| 2385.39, loss_d 1.43, loss 8.04,
|
| 106 |
+
| epoch 1 | 9900/ 12000 batches | rec 0.90, adv 0.70, |lvar| 2312.31, loss_d 1.40, loss 7.86,
|
| 107 |
+
| epoch 1 | 10000/ 12000 batches | rec 1.26, adv 0.69, |lvar| 2381.06, loss_d 1.42, loss 8.17,
|
| 108 |
+
| epoch 1 | 10100/ 12000 batches | rec 0.50, adv 0.69, |lvar| 2382.46, loss_d 1.42, loss 7.41,
|
| 109 |
+
| epoch 1 | 10200/ 12000 batches | rec 0.56, adv 0.69, |lvar| 2349.21, loss_d 1.42, loss 7.49,
|
| 110 |
+
| epoch 1 | 10300/ 12000 batches | rec 0.98, adv 0.69, |lvar| 2336.65, loss_d 1.40, loss 7.89,
|
| 111 |
+
| epoch 1 | 10400/ 12000 batches | rec 1.21, adv 0.69, |lvar| 2388.99, loss_d 1.42, loss 8.12,
|
| 112 |
+
| epoch 1 | 10500/ 12000 batches | rec 1.05, adv 0.69, |lvar| 2341.90, loss_d 1.41, loss 8.00,
|
| 113 |
+
| epoch 1 | 10600/ 12000 batches | rec 0.80, adv 0.68, |lvar| 2190.13, loss_d 1.40, loss 7.62,
|
| 114 |
+
| epoch 1 | 10700/ 12000 batches | rec 2.01, adv 0.69, |lvar| 2309.40, loss_d 1.39, loss 8.94,
|
| 115 |
+
| epoch 1 | 10800/ 12000 batches | rec 2.53, adv 0.72, |lvar| 2248.34, loss_d 1.37, loss 9.76,
|
| 116 |
+
| epoch 1 | 10900/ 12000 batches | rec 1.91, adv 0.71, |lvar| 2293.60, loss_d 1.41, loss 8.98,
|
| 117 |
+
| epoch 1 | 11000/ 12000 batches | rec 0.91, adv 0.70, |lvar| 2093.61, loss_d 1.44, loss 7.93,
|
| 118 |
+
| epoch 1 | 11100/ 12000 batches | rec 0.74, adv 0.67, |lvar| 2257.75, loss_d 1.41, loss 7.44,
|
| 119 |
+
| epoch 1 | 11200/ 12000 batches | rec 1.29, adv 0.70, |lvar| 2416.28, loss_d 1.43, loss 8.24,
|
| 120 |
+
| epoch 1 | 11300/ 12000 batches | rec 0.67, adv 0.68, |lvar| 2574.01, loss_d 1.44, loss 7.50,
|
| 121 |
+
| epoch 1 | 11400/ 12000 batches | rec 0.55, adv 0.69, |lvar| 2408.90, loss_d 1.43, loss 7.43,
|
| 122 |
+
| epoch 1 | 11500/ 12000 batches | rec 0.47, adv 0.69, |lvar| 2431.90, loss_d 1.44, loss 7.36,
|
| 123 |
+
| epoch 1 | 11600/ 12000 batches | rec 0.55, adv 0.68, |lvar| 2509.23, loss_d 1.42, loss 7.36,
|
| 124 |
+
| epoch 1 | 11700/ 12000 batches | rec 0.64, adv 0.70, |lvar| 2656.55, loss_d 1.42, loss 7.65,
|
| 125 |
+
| epoch 1 | 11800/ 12000 batches | rec 0.36, adv 0.67, |lvar| 2607.50, loss_d 1.43, loss 7.07,
|
| 126 |
+
| epoch 1 | 11900/ 12000 batches | rec 0.57, adv 0.68, |lvar| 2761.24, loss_d 1.41, loss 7.42,
|
| 127 |
+
| epoch 1 | 12000/ 12000 batches | rec 0.32, adv 0.68, |lvar| 2512.23, loss_d 1.43, loss 7.13,
|
| 128 |
+
--------------------------------------------------------------------------------
|
| 129 |
+
| end of epoch 1| time 289020s| valid rec 0.05, adv 0.74, |lvar| 3074.76, loss_d 1.41, loss 7.40, | saving model
|
| 130 |
+
--------------------------------------------------------------------------------
|
| 131 |
+
| epoch 2 | 100/ 12000 batches | rec 0.90, adv 0.70, |lvar| 2573.20, loss_d 1.42, loss 7.92,
|
| 132 |
+
| epoch 2 | 200/ 12000 batches | rec 0.44, adv 0.67, |lvar| 2487.73, loss_d 1.41, loss 7.17,
|
| 133 |
+
| epoch 2 | 300/ 12000 batches | rec 0.78, adv 0.69, |lvar| 2545.88, loss_d 1.40, loss 7.63,
|
| 134 |
+
| epoch 2 | 400/ 12000 batches | rec 0.39, adv 0.69, |lvar| 2555.51, loss_d 1.42, loss 7.25,
|
| 135 |
+
| epoch 2 | 500/ 12000 batches | rec 0.60, adv 0.68, |lvar| 2378.65, loss_d 1.42, loss 7.40,
|
| 136 |
+
| epoch 2 | 600/ 12000 batches | rec 0.56, adv 0.68, |lvar| 2397.10, loss_d 1.42, loss 7.37,
|
| 137 |
+
| epoch 2 | 700/ 12000 batches | rec 0.86, adv 0.68, |lvar| 2457.96, loss_d 1.43, loss 7.70,
|
| 138 |
+
| epoch 2 | 800/ 12000 batches | rec 0.32, adv 0.68, |lvar| 2247.36, loss_d 1.39, loss 7.13,
|
| 139 |
+
| epoch 2 | 900/ 12000 batches | rec 1.89, adv 0.71, |lvar| 2308.76, loss_d 1.37, loss 8.97,
|
| 140 |
+
| epoch 2 | 1000/ 12000 batches | rec 0.66, adv 0.68, |lvar| 2399.01, loss_d 1.42, loss 7.47,
|
| 141 |
+
| epoch 2 | 1100/ 12000 batches | rec 1.63, adv 0.69, |lvar| 2337.96, loss_d 1.41, loss 8.51,
|
| 142 |
+
| epoch 2 | 1200/ 12000 batches | rec 1.30, adv 0.70, |lvar| 2200.38, loss_d 1.41, loss 8.33,
|
| 143 |
+
| epoch 2 | 1300/ 12000 batches | rec 0.75, adv 0.68, |lvar| 2396.87, loss_d 1.41, loss 7.57,
|
| 144 |
+
| epoch 2 | 1400/ 12000 batches | rec 0.68, adv 0.70, |lvar| 2280.82, loss_d 1.42, loss 7.64,
|
| 145 |
+
| epoch 2 | 1500/ 12000 batches | rec 0.92, adv 0.69, |lvar| 2466.49, loss_d 1.42, loss 7.78,
|
| 146 |
+
| epoch 2 | 1600/ 12000 batches | rec 0.85, adv 0.70, |lvar| 2301.22, loss_d 1.40, loss 7.81,
|
| 147 |
+
| epoch 2 | 1700/ 12000 batches | rec 1.11, adv 0.69, |lvar| 2267.82, loss_d 1.41, loss 8.02,
|
| 148 |
+
| epoch 2 | 1800/ 12000 batches | rec 0.72, adv 0.69, |lvar| 2289.52, loss_d 1.40, loss 7.58,
|
| 149 |
+
| epoch 2 | 1900/ 12000 batches | rec 0.73, adv 0.69, |lvar| 2538.60, loss_d 1.41, loss 7.65,
|
| 150 |
+
| epoch 2 | 2000/ 12000 batches | rec 1.03, adv 0.69, |lvar| 2232.38, loss_d 1.41, loss 7.97,
|
| 151 |
+
| epoch 2 | 2100/ 12000 batches | rec 0.92, adv 0.68, |lvar| 2326.35, loss_d 1.42, loss 7.77,
|
| 152 |
+
| epoch 2 | 2200/ 12000 batches | rec 1.85, adv 0.71, |lvar| 2270.81, loss_d 1.41, loss 8.92,
|
| 153 |
+
| epoch 2 | 2300/ 12000 batches | rec 1.76, adv 0.70, |lvar| 2235.77, loss_d 1.39, loss 8.73,
|
| 154 |
+
| epoch 2 | 2400/ 12000 batches | rec 1.95, adv 0.70, |lvar| 2282.48, loss_d 1.41, loss 8.99,
|
| 155 |
+
| epoch 2 | 2500/ 12000 batches | rec 1.53, adv 0.70, |lvar| 2230.05, loss_d 1.41, loss 8.53,
|
| 156 |
+
| epoch 2 | 2600/ 12000 batches | rec 0.67, adv 0.69, |lvar| 2154.48, loss_d 1.38, loss 7.58,
|
| 157 |
+
| epoch 2 | 2700/ 12000 batches | rec 1.12, adv 0.70, |lvar| 2353.22, loss_d 1.43, loss 8.13,
|
| 158 |
+
| epoch 2 | 2800/ 12000 batches | rec 0.73, adv 0.68, |lvar| 2425.83, loss_d 1.45, loss 7.53,
|
| 159 |
+
| epoch 2 | 2900/ 12000 batches | rec 0.63, adv 0.69, |lvar| 2361.62, loss_d 1.43, loss 7.55,
|
| 160 |
+
| epoch 2 | 3000/ 12000 batches | rec 0.76, adv 0.69, |lvar| 2555.26, loss_d 1.42, loss 7.65,
|
| 161 |
+
| epoch 2 | 3100/ 12000 batches | rec 0.75, adv 0.70, |lvar| 2297.90, loss_d 1.43, loss 7.70,
|
| 162 |
+
| epoch 2 | 3200/ 12000 batches | rec 1.16, adv 0.69, |lvar| 2412.79, loss_d 1.41, loss 8.04,
|
| 163 |
+
| epoch 2 | 3300/ 12000 batches | rec 0.43, adv 0.69, |lvar| 2502.30, loss_d 1.40, loss 7.36,
|
| 164 |
+
| epoch 2 | 3400/ 12000 batches | rec 0.86, adv 0.70, |lvar| 2145.17, loss_d 1.40, loss 7.84,
|
| 165 |
+
| epoch 2 | 3500/ 12000 batches | rec 0.78, adv 0.70, |lvar| 2435.38, loss_d 1.43, loss 7.75,
|
| 166 |
+
| epoch 2 | 3600/ 12000 batches | rec 0.51, adv 0.68, |lvar| 2287.90, loss_d 1.43, loss 7.31,
|
| 167 |
+
| epoch 2 | 3700/ 12000 batches | rec 1.15, adv 0.70, |lvar| 2533.87, loss_d 1.42, loss 8.19,
|
| 168 |
+
| epoch 2 | 3800/ 12000 batches | rec 0.67, adv 0.67, |lvar| 2534.03, loss_d 1.42, loss 7.39,
|
| 169 |
+
| epoch 2 | 3900/ 12000 batches | rec 0.47, adv 0.69, |lvar| 2709.35, loss_d 1.45, loss 7.34,
|
| 170 |
+
| epoch 2 | 4000/ 12000 batches | rec 0.60, adv 0.68, |lvar| 2357.37, loss_d 1.41, loss 7.44,
|
| 171 |
+
| epoch 2 | 4100/ 12000 batches | rec 0.63, adv 0.68, |lvar| 2525.59, loss_d 1.44, loss 7.42,
|
| 172 |
+
| epoch 2 | 4200/ 12000 batches | rec 0.48, adv 0.69, |lvar| 2559.36, loss_d 1.42, loss 7.36,
|
| 173 |
+
| epoch 2 | 4300/ 12000 batches | rec 0.38, adv 0.68, |lvar| 2227.53, loss_d 1.42, loss 7.18,
|
| 174 |
+
| epoch 2 | 4400/ 12000 batches | rec 0.34, adv 0.68, |lvar| 2524.76, loss_d 1.41, loss 7.19,
|
| 175 |
+
| epoch 2 | 4500/ 12000 batches | rec 0.79, adv 0.69, |lvar| 2636.65, loss_d 1.42, loss 7.64,
|
| 176 |
+
| epoch 2 | 4600/ 12000 batches | rec 0.66, adv 0.69, |lvar| 2415.40, loss_d 1.41, loss 7.52,
|
| 177 |
+
| epoch 2 | 4700/ 12000 batches | rec 0.65, adv 0.69, |lvar| 2729.78, loss_d 1.42, loss 7.53,
|
| 178 |
+
| epoch 2 | 4800/ 12000 batches | rec 0.76, adv 0.69, |lvar| 2435.90, loss_d 1.40, loss 7.63,
|
| 179 |
+
| epoch 2 | 4900/ 12000 batches | rec 0.92, adv 0.69, |lvar| 2556.24, loss_d 1.40, loss 7.84,
|
| 180 |
+
| epoch 2 | 5000/ 12000 batches | rec 0.53, adv 0.69, |lvar| 2394.82, loss_d 1.40, loss 7.40,
|
| 181 |
+
| epoch 2 | 5100/ 12000 batches | rec 0.75, adv 0.69, |lvar| 2397.63, loss_d 1.40, loss 7.67,
|
| 182 |
+
| epoch 2 | 5200/ 12000 batches | rec 0.52, adv 0.69, |lvar| 2312.08, loss_d 1.43, loss 7.43,
|
| 183 |
+
| epoch 2 | 5300/ 12000 batches | rec 0.53, adv 0.68, |lvar| 2406.07, loss_d 1.43, loss 7.38,
|
| 184 |
+
| epoch 2 | 5400/ 12000 batches | rec 0.49, adv 0.69, |lvar| 2371.84, loss_d 1.41, loss 7.42,
|
| 185 |
+
| epoch 2 | 5500/ 12000 batches | rec 0.40, adv 0.69, |lvar| 2405.81, loss_d 1.42, loss 7.29,
|
| 186 |
+
| epoch 2 | 5600/ 12000 batches | rec 0.47, adv 0.69, |lvar| 2356.32, loss_d 1.44, loss 7.38,
|
| 187 |
+
| epoch 2 | 5700/ 12000 batches | rec 0.50, adv 0.69, |lvar| 2431.42, loss_d 1.42, loss 7.36,
|
| 188 |
+
| epoch 2 | 5800/ 12000 batches | rec 0.39, adv 0.69, |lvar| 2292.38, loss_d 1.42, loss 7.25,
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| epoch 2 | 5900/ 12000 batches | rec 0.27, adv 0.69, |lvar| 2739.24, loss_d 1.42, loss 7.17,
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| epoch 2 | 6000/ 12000 batches | rec 0.65, adv 0.68, |lvar| 2834.56, loss_d 1.42, loss 7.46,
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| epoch 2 | 6100/ 12000 batches | rec 0.48, adv 0.68, |lvar| 2422.05, loss_d 1.41, loss 7.25,
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| epoch 2 | 6200/ 12000 batches | rec 2.02, adv 0.70, |lvar| 2247.54, loss_d 1.39, loss 9.04,
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| epoch 2 | 6300/ 12000 batches | rec 1.49, adv 0.70, |lvar| 2229.59, loss_d 1.37, loss 8.45,
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| epoch 2 | 6400/ 12000 batches | rec 2.54, adv 0.71, |lvar| 2371.79, loss_d 1.36, loss 9.65,
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| epoch 2 | 6500/ 12000 batches | rec 0.92, adv 0.70, |lvar| 2419.38, loss_d 1.37, loss 7.91,
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| epoch 2 | 6600/ 12000 batches | rec 0.92, adv 0.69, |lvar| 2359.95, loss_d 1.44, loss 7.82,
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| epoch 2 | 6700/ 12000 batches | rec 0.94, adv 0.68, |lvar| 2183.30, loss_d 1.39, loss 7.74,
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| epoch 2 | 6800/ 12000 batches | rec 1.43, adv 0.72, |lvar| 2303.54, loss_d 1.40, loss 8.58,
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| epoch 2 | 6900/ 12000 batches | rec 0.71, adv 0.69, |lvar| 2390.23, loss_d 1.43, loss 7.60,
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| epoch 2 | 7000/ 12000 batches | rec 0.80, adv 0.70, |lvar| 2337.48, loss_d 1.42, loss 7.81,
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| epoch 2 | 7100/ 12000 batches | rec 0.55, adv 0.69, |lvar| 2539.58, loss_d 1.45, loss 7.43,
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| epoch 2 | 7200/ 12000 batches | rec 0.39, adv 0.70, |lvar| 2635.61, loss_d 1.46, loss 7.39,
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| epoch 2 | 7300/ 12000 batches | rec 0.34, adv 0.69, |lvar| 2472.28, loss_d 1.43, loss 7.28,
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| epoch 2 | 7400/ 12000 batches | rec 0.51, adv 0.70, |lvar| 2601.59, loss_d 1.43, loss 7.49,
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| epoch 2 | 7500/ 12000 batches | rec 0.24, adv 0.69, |lvar| 2470.99, loss_d 1.42, loss 7.09,
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| epoch 2 | 7600/ 12000 batches | rec 0.38, adv 0.69, |lvar| 2576.12, loss_d 1.41, loss 7.29,
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| epoch 2 | 7700/ 12000 batches | rec 0.41, adv 0.69, |lvar| 2518.60, loss_d 1.40, loss 7.29,
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| epoch 2 | 7800/ 12000 batches | rec 0.38, adv 0.69, |lvar| 2517.30, loss_d 1.41, loss 7.27,
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| epoch 2 | 7900/ 12000 batches | rec 0.63, adv 0.69, |lvar| 2602.50, loss_d 1.42, loss 7.50,
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| epoch 2 | 8000/ 12000 batches | rec 0.47, adv 0.68, |lvar| 2460.49, loss_d 1.42, loss 7.29,
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| epoch 2 | 8100/ 12000 batches | rec 0.24, adv 0.68, |lvar| 2437.72, loss_d 1.43, loss 7.08,
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| epoch 2 | 8200/ 12000 batches | rec 0.55, adv 0.68, |lvar| 2289.61, loss_d 1.40, loss 7.32,
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| epoch 2 | 8300/ 12000 batches | rec 0.44, adv 0.69, |lvar| 2242.71, loss_d 1.41, loss 7.32,
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| epoch 2 | 8400/ 12000 batches | rec 0.72, adv 0.69, |lvar| 2487.09, loss_d 1.42, loss 7.59,
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| epoch 2 | 8500/ 12000 batches | rec 0.31, adv 0.68, |lvar| 2597.93, loss_d 1.41, loss 7.13,
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| epoch 2 | 8600/ 12000 batches | rec 0.59, adv 0.69, |lvar| 2588.04, loss_d 1.42, loss 7.45,
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| epoch 2 | 8700/ 12000 batches | rec 0.42, adv 0.69, |lvar| 2586.36, loss_d 1.42, loss 7.31,
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| epoch 2 | 8800/ 12000 batches | rec 0.47, adv 0.69, |lvar| 2473.62, loss_d 1.42, loss 7.32,
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| epoch 2 | 8900/ 12000 batches | rec 0.33, adv 0.69, |lvar| 2568.53, loss_d 1.40, loss 7.20,
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| epoch 2 | 9000/ 12000 batches | rec 0.85, adv 0.69, |lvar| 2420.76, loss_d 1.41, loss 7.72,
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| epoch 2 | 9100/ 12000 batches | rec 0.65, adv 0.68, |lvar| 2389.28, loss_d 1.41, loss 7.49,
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| epoch 2 | 9200/ 12000 batches | rec 0.44, adv 0.69, |lvar| 2644.88, loss_d 1.42, loss 7.30,
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| epoch 2 | 9300/ 12000 batches | rec 0.67, adv 0.69, |lvar| 2620.14, loss_d 1.42, loss 7.58,
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| epoch 2 | 9400/ 12000 batches | rec 0.27, adv 0.68, |lvar| 2305.82, loss_d 1.43, loss 7.11,
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| epoch 2 | 9500/ 12000 batches | rec 0.68, adv 0.69, |lvar| 2772.39, loss_d 1.43, loss 7.57,
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| epoch 2 | 9600/ 12000 batches | rec 0.99, adv 0.69, |lvar| 2857.87, loss_d 1.42, loss 7.89,
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| epoch 2 | 9700/ 12000 batches | rec 0.47, adv 0.68, |lvar| 2306.52, loss_d 1.39, loss 7.26,
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| epoch 2 | 9800/ 12000 batches | rec 0.44, adv 0.69, |lvar| 2336.11, loss_d 1.41, loss 7.35,
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| epoch 2 | 9900/ 12000 batches | rec 0.65, adv 0.68, |lvar| 2469.47, loss_d 1.42, loss 7.48,
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| epoch 2 | 10000/ 12000 batches | rec 0.50, adv 0.69, |lvar| 2393.56, loss_d 1.39, loss 7.36,
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| epoch 2 | 10100/ 12000 batches | rec 0.60, adv 0.69, |lvar| 2586.43, loss_d 1.43, loss 7.54,
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| epoch 2 | 10200/ 12000 batches | rec 0.57, adv 0.68, |lvar| 2516.76, loss_d 1.42, loss 7.38,
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| epoch 2 | 10300/ 12000 batches | rec 0.50, adv 0.70, |lvar| 2382.01, loss_d 1.42, loss 7.45,
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| epoch 2 | 10400/ 12000 batches | rec 0.30, adv 0.68, |lvar| 2673.42, loss_d 1.43, loss 7.14,
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| epoch 2 | 10500/ 12000 batches | rec 0.42, adv 0.68, |lvar| 2583.84, loss_d 1.43, loss 7.26,
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| epoch 2 | 10600/ 12000 batches | rec 0.22, adv 0.68, |lvar| 2450.49, loss_d 1.43, loss 7.03,
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| epoch 2 | 10700/ 12000 batches | rec 0.24, adv 0.68, |lvar| 2710.78, loss_d 1.42, loss 7.08,
|
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| epoch 2 | 10800/ 12000 batches | rec 0.28, adv 0.69, |lvar| 2859.79, loss_d 1.43, loss 7.17,
|
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+
| epoch 2 | 10900/ 12000 batches | rec 0.21, adv 0.68, |lvar| 2620.95, loss_d 1.43, loss 7.05,
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+
| epoch 2 | 11000/ 12000 batches | rec 0.25, adv 0.68, |lvar| 2943.19, loss_d 1.42, loss 7.07,
|
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| epoch 2 | 11100/ 12000 batches | rec 0.28, adv 0.68, |lvar| 2529.29, loss_d 1.42, loss 7.09,
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| epoch 2 | 11200/ 12000 batches | rec 0.18, adv 0.68, |lvar| 2677.31, loss_d 1.41, loss 7.01,
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| epoch 2 | 11300/ 12000 batches | rec 0.64, adv 0.68, |lvar| 2496.61, loss_d 1.42, loss 7.48,
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| epoch 2 | 11400/ 12000 batches | rec 0.13, adv 0.68, |lvar| 2372.97, loss_d 1.42, loss 6.89,
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+
| epoch 2 | 11500/ 12000 batches | rec 0.21, adv 0.68, |lvar| 2491.71, loss_d 1.41, loss 7.05,
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| epoch 2 | 11600/ 12000 batches | rec 0.37, adv 0.68, |lvar| 2525.47, loss_d 1.40, loss 7.20,
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| epoch 2 | 11700/ 12000 batches | rec 0.48, adv 0.68, |lvar| 2552.05, loss_d 1.39, loss 7.31,
|
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+
| epoch 2 | 11800/ 12000 batches | rec 0.44, adv 0.69, |lvar| 2476.48, loss_d 1.38, loss 7.34,
|
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| epoch 2 | 11900/ 12000 batches | rec 0.86, adv 0.69, |lvar| 2478.12, loss_d 1.41, loss 7.74,
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+
| epoch 2 | 12000/ 12000 batches | rec 0.61, adv 0.68, |lvar| 2750.29, loss_d 1.43, loss 7.44,
|
| 251 |
+
--------------------------------------------------------------------------------
|
| 252 |
+
| end of epoch 2| time 291454s| valid rec 0.10, adv 0.69, |lvar| 2553.49, loss_d 1.43, loss 7.04, | saving model
|
| 253 |
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--------------------------------------------------------------------------------
|
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+
| epoch 3 | 100/ 12000 batches | rec 0.69, adv 0.68, |lvar| 2579.13, loss_d 1.42, loss 7.51,
|
| 255 |
+
| epoch 3 | 200/ 12000 batches | rec 0.63, adv 0.69, |lvar| 2406.49, loss_d 1.42, loss 7.51,
|
| 256 |
+
| epoch 3 | 300/ 12000 batches | rec 0.26, adv 0.68, |lvar| 2390.90, loss_d 1.39, loss 7.07,
|
| 257 |
+
| epoch 3 | 400/ 12000 batches | rec 0.57, adv 0.68, |lvar| 2327.79, loss_d 1.41, loss 7.41,
|
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| epoch 3 | 500/ 12000 batches | rec 0.36, adv 0.69, |lvar| 2479.21, loss_d 1.41, loss 7.29,
|
| 259 |
+
| epoch 3 | 600/ 12000 batches | rec 0.57, adv 0.68, |lvar| 2363.32, loss_d 1.41, loss 7.41,
|
| 260 |
+
| epoch 3 | 700/ 12000 batches | rec 0.47, adv 0.69, |lvar| 2711.14, loss_d 1.42, loss 7.35,
|
| 261 |
+
| epoch 3 | 800/ 12000 batches | rec 0.29, adv 0.69, |lvar| 2562.13, loss_d 1.41, loss 7.15,
|
| 262 |
+
| epoch 3 | 900/ 12000 batches | rec 0.53, adv 0.69, |lvar| 2647.10, loss_d 1.42, loss 7.48,
|
| 263 |
+
| epoch 3 | 1000/ 12000 batches | rec 0.88, adv 0.69, |lvar| 2437.62, loss_d 1.41, loss 7.74,
|
| 264 |
+
| epoch 3 | 1100/ 12000 batches | rec 0.75, adv 0.68, |lvar| 2419.81, loss_d 1.40, loss 7.57,
|
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+
| epoch 3 | 1200/ 12000 batches | rec 0.43, adv 0.69, |lvar| 2693.82, loss_d 1.42, loss 7.29,
|
| 266 |
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| epoch 3 | 1300/ 12000 batches | rec 0.31, adv 0.68, |lvar| 2544.03, loss_d 1.44, loss 7.15,
|
| 267 |
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| epoch 3 | 1400/ 12000 batches | rec 0.34, adv 0.69, |lvar| 2546.34, loss_d 1.42, loss 7.24,
|
| 268 |
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| epoch 3 | 1500/ 12000 batches | rec 0.21, adv 0.69, |lvar| 2692.64, loss_d 1.42, loss 7.06,
|
| 269 |
+
| epoch 3 | 1600/ 12000 batches | rec 0.28, adv 0.69, |lvar| 2783.51, loss_d 1.42, loss 7.14,
|
| 270 |
+
| epoch 3 | 1700/ 12000 batches | rec 0.14, adv 0.68, |lvar| 2611.30, loss_d 1.43, loss 6.95,
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| epoch 3 | 1800/ 12000 batches | rec 0.35, adv 0.68, |lvar| 2649.59, loss_d 1.41, loss 7.13,
|
| 272 |
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| epoch 3 | 1900/ 12000 batches | rec 0.26, adv 0.68, |lvar| 2570.34, loss_d 1.42, loss 7.08,
|
| 273 |
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| epoch 3 | 2000/ 12000 batches | rec 0.43, adv 0.68, |lvar| 2501.71, loss_d 1.40, loss 7.25,
|
| 274 |
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| epoch 3 | 2100/ 12000 batches | rec 0.14, adv 0.68, |lvar| 2547.55, loss_d 1.42, loss 6.98,
|
| 275 |
+
| epoch 3 | 2200/ 12000 batches | rec 0.22, adv 0.68, |lvar| 2619.70, loss_d 1.42, loss 7.06,
|
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+
| epoch 3 | 2300/ 12000 batches | rec 0.44, adv 0.68, |lvar| 2648.79, loss_d 1.41, loss 7.25,
|
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+
| epoch 3 | 2400/ 12000 batches | rec 0.29, adv 0.68, |lvar| 2595.50, loss_d 1.41, loss 7.10,
|
| 278 |
+
| epoch 3 | 2500/ 12000 batches | rec 0.21, adv 0.68, |lvar| 2661.85, loss_d 1.42, loss 7.06,
|
| 279 |
+
| epoch 3 | 2600/ 12000 batches | rec 0.29, adv 0.68, |lvar| 2425.02, loss_d 1.41, loss 7.13,
|
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+
| epoch 3 | 2700/ 12000 batches | rec 0.30, adv 0.68, |lvar| 2585.42, loss_d 1.41, loss 7.10,
|
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+
| epoch 3 | 2800/ 12000 batches | rec 0.31, adv 0.68, |lvar| 2396.16, loss_d 1.41, loss 7.11,
|
| 282 |
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| epoch 3 | 2900/ 12000 batches | rec 0.41, adv 0.68, |lvar| 2718.09, loss_d 1.41, loss 7.24,
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| epoch 3 | 3000/ 12000 batches | rec 0.44, adv 0.69, |lvar| 2702.43, loss_d 1.42, loss 7.31,
|
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| epoch 3 | 3100/ 12000 batches | rec 0.41, adv 0.68, |lvar| 2650.01, loss_d 1.41, loss 7.21,
|
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| epoch 3 | 3200/ 12000 batches | rec 0.22, adv 0.68, |lvar| 2384.96, loss_d 1.40, loss 7.07,
|
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| epoch 3 | 3300/ 12000 batches | rec 0.34, adv 0.68, |lvar| 2723.98, loss_d 1.42, loss 7.17,
|
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| epoch 3 | 3400/ 12000 batches | rec 0.55, adv 0.69, |lvar| 2574.77, loss_d 1.40, loss 7.44,
|
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| epoch 3 | 3500/ 12000 batches | rec 0.51, adv 0.69, |lvar| 2630.74, loss_d 1.42, loss 7.38,
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| epoch 3 | 3600/ 12000 batches | rec 0.19, adv 0.68, |lvar| 2374.16, loss_d 1.41, loss 7.00,
|
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| epoch 3 | 3700/ 12000 batches | rec 0.30, adv 0.69, |lvar| 2576.89, loss_d 1.42, loss 7.15,
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| epoch 3 | 3800/ 12000 batches | rec 0.19, adv 0.68, |lvar| 2560.24, loss_d 1.42, loss 7.01,
|
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| epoch 3 | 3900/ 12000 batches | rec 0.25, adv 0.68, |lvar| 2838.09, loss_d 1.41, loss 7.07,
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| epoch 3 | 4000/ 12000 batches | rec 0.18, adv 0.69, |lvar| 2667.59, loss_d 1.42, loss 7.03,
|
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| epoch 3 | 4100/ 12000 batches | rec 0.29, adv 0.69, |lvar| 2669.34, loss_d 1.41, loss 7.14,
|
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| epoch 3 | 4200/ 12000 batches | rec 0.26, adv 0.68, |lvar| 2545.47, loss_d 1.41, loss 7.02,
|
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| epoch 3 | 4300/ 12000 batches | rec 0.27, adv 0.68, |lvar| 2652.61, loss_d 1.41, loss 7.10,
|
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| epoch 3 | 4400/ 12000 batches | rec 0.42, adv 0.68, |lvar| 2620.83, loss_d 1.41, loss 7.25,
|
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| epoch 3 | 4500/ 12000 batches | rec 0.31, adv 0.69, |lvar| 2637.93, loss_d 1.41, loss 7.17,
|
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| epoch 3 | 4600/ 12000 batches | rec 0.46, adv 0.68, |lvar| 2350.86, loss_d 1.40, loss 7.28,
|
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+
| epoch 3 | 4700/ 12000 batches | rec 0.29, adv 0.69, |lvar| 2698.94, loss_d 1.42, loss 7.15,
|
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| epoch 3 | 4800/ 12000 batches | rec 0.26, adv 0.69, |lvar| 2492.51, loss_d 1.40, loss 7.11,
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| epoch 3 | 4900/ 12000 batches | rec 0.20, adv 0.68, |lvar| 2518.99, loss_d 1.42, loss 7.05,
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| epoch 3 | 5000/ 12000 batches | rec 0.35, adv 0.69, |lvar| 2565.29, loss_d 1.42, loss 7.21,
|
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+
| epoch 3 | 5100/ 12000 batches | rec 0.14, adv 0.68, |lvar| 2836.78, loss_d 1.40, loss 6.93,
|
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| epoch 3 | 5200/ 12000 batches | rec 0.63, adv 0.69, |lvar| 2846.14, loss_d 1.41, loss 7.53,
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| epoch 3 | 5300/ 12000 batches | rec 1.10, adv 0.68, |lvar| 2476.76, loss_d 1.38, loss 7.94,
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| epoch 3 | 5400/ 12000 batches | rec 0.38, adv 0.68, |lvar| 2390.53, loss_d 1.40, loss 7.18,
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| epoch 3 | 5500/ 12000 batches | rec 0.45, adv 0.68, |lvar| 2517.12, loss_d 1.41, loss 7.28,
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| epoch 3 | 5600/ 12000 batches | rec 0.47, adv 0.70, |lvar| 2452.43, loss_d 1.43, loss 7.43,
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| epoch 3 | 5700/ 12000 batches | rec 0.27, adv 0.69, |lvar| 2434.05, loss_d 1.41, loss 7.14,
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| epoch 3 | 5800/ 12000 batches | rec 0.51, adv 0.69, |lvar| 2594.28, loss_d 1.41, loss 7.38,
|
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| epoch 3 | 5900/ 12000 batches | rec 0.30, adv 0.68, |lvar| 2712.52, loss_d 1.42, loss 7.15,
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| epoch 3 | 6000/ 12000 batches | rec 0.20, adv 0.68, |lvar| 2662.24, loss_d 1.41, loss 7.05,
|
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| epoch 3 | 6100/ 12000 batches | rec 0.31, adv 0.69, |lvar| 2681.59, loss_d 1.42, loss 7.22,
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| epoch 3 | 6200/ 12000 batches | rec 0.13, adv 0.68, |lvar| 2556.95, loss_d 1.41, loss 6.96,
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| epoch 3 | 6300/ 12000 batches | rec 0.27, adv 0.68, |lvar| 2631.78, loss_d 1.41, loss 7.09,
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| epoch 3 | 6400/ 12000 batches | rec 0.21, adv 0.69, |lvar| 2595.93, loss_d 1.41, loss 7.07,
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| epoch 3 | 6500/ 12000 batches | rec 0.22, adv 0.68, |lvar| 2523.92, loss_d 1.41, loss 7.04,
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| epoch 3 | 6600/ 12000 batches | rec 0.17, adv 0.68, |lvar| 2905.88, loss_d 1.41, loss 7.00,
|
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| epoch 3 | 6700/ 12000 batches | rec 0.44, adv 0.68, |lvar| 2419.41, loss_d 1.40, loss 7.28,
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| epoch 3 | 6800/ 12000 batches | rec 0.34, adv 0.68, |lvar| 2695.29, loss_d 1.41, loss 7.18,
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| epoch 3 | 6900/ 12000 batches | rec 0.22, adv 0.68, |lvar| 2774.91, loss_d 1.42, loss 7.04,
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| epoch 3 | 7000/ 12000 batches | rec 0.21, adv 0.68, |lvar| 2479.69, loss_d 1.41, loss 7.04,
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| epoch 3 | 7100/ 12000 batches | rec 0.31, adv 0.69, |lvar| 2487.39, loss_d 1.41, loss 7.17,
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| epoch 3 | 7200/ 12000 batches | rec 0.14, adv 0.68, |lvar| 2493.00, loss_d 1.41, loss 6.94,
|
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| epoch 3 | 7300/ 12000 batches | rec 0.27, adv 0.69, |lvar| 2646.48, loss_d 1.41, loss 7.15,
|
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| epoch 3 | 7400/ 12000 batches | rec 0.52, adv 0.69, |lvar| 2525.70, loss_d 1.41, loss 7.40,
|
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+
| epoch 3 | 7500/ 12000 batches | rec 0.16, adv 0.68, |lvar| 2574.39, loss_d 1.41, loss 6.99,
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| epoch 3 | 7600/ 12000 batches | rec 0.13, adv 0.68, |lvar| 2825.32, loss_d 1.42, loss 6.96,
|
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| epoch 3 | 7700/ 12000 batches | rec 0.29, adv 0.68, |lvar| 3030.94, loss_d 1.41, loss 7.14,
|
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+
| epoch 3 | 7800/ 12000 batches | rec 0.16, adv 0.69, |lvar| 2663.93, loss_d 1.40, loss 7.02,
|
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+
| epoch 3 | 7900/ 12000 batches | rec 0.18, adv 0.68, |lvar| 2887.30, loss_d 1.41, loss 7.02,
|
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+
| epoch 3 | 8000/ 12000 batches | rec 0.08, adv 0.68, |lvar| 2649.34, loss_d 1.41, loss 6.92,
|
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+
| epoch 3 | 8100/ 12000 batches | rec 0.32, adv 0.68, |lvar| 2355.36, loss_d 1.40, loss 7.16,
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| epoch 3 | 8200/ 12000 batches | rec 0.25, adv 0.68, |lvar| 2786.15, loss_d 1.41, loss 7.08,
|
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| epoch 3 | 8300/ 12000 batches | rec 0.05, adv 0.68, |lvar| 2577.91, loss_d 1.41, loss 6.89,
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| epoch 3 | 8400/ 12000 batches | rec 0.31, adv 0.68, |lvar| 2398.97, loss_d 1.40, loss 7.10,
|
| 338 |
+
| epoch 3 | 8500/ 12000 batches | rec 0.40, adv 0.69, |lvar| 2542.82, loss_d 1.40, loss 7.32,
|
| 339 |
+
| epoch 3 | 8600/ 12000 batches | rec 0.28, adv 0.68, |lvar| 2557.28, loss_d 1.40, loss 7.10,
|
| 340 |
+
| epoch 3 | 8700/ 12000 batches | rec 0.25, adv 0.69, |lvar| 2507.98, loss_d 1.40, loss 7.13,
|
| 341 |
+
| epoch 3 | 8800/ 12000 batches | rec 0.41, adv 0.68, |lvar| 2481.80, loss_d 1.40, loss 7.26,
|
| 342 |
+
| epoch 3 | 8900/ 12000 batches | rec 0.26, adv 0.68, |lvar| 2933.07, loss_d 1.40, loss 7.11,
|
| 343 |
+
| epoch 3 | 9000/ 12000 batches | rec 0.23, adv 0.69, |lvar| 2721.23, loss_d 1.41, loss 7.10,
|
| 344 |
+
| epoch 3 | 9100/ 12000 batches | rec 0.26, adv 0.69, |lvar| 2631.52, loss_d 1.41, loss 7.14,
|
| 345 |
+
| epoch 3 | 9200/ 12000 batches | rec 0.41, adv 0.69, |lvar| 2637.51, loss_d 1.40, loss 7.26,
|
| 346 |
+
| epoch 3 | 9300/ 12000 batches | rec 0.16, adv 0.68, |lvar| 2853.68, loss_d 1.42, loss 7.00,
|
| 347 |
+
| epoch 3 | 9400/ 12000 batches | rec 0.19, adv 0.69, |lvar| 2732.25, loss_d 1.41, loss 7.06,
|
| 348 |
+
| epoch 3 | 9500/ 12000 batches | rec 0.24, adv 0.69, |lvar| 3123.58, loss_d 1.41, loss 7.09,
|
| 349 |
+
| epoch 3 | 9600/ 12000 batches | rec 0.30, adv 0.68, |lvar| 3122.05, loss_d 1.41, loss 7.14,
|
| 350 |
+
| epoch 3 | 9700/ 12000 batches | rec 0.07, adv 0.68, |lvar| 2877.29, loss_d 1.41, loss 6.89,
|
| 351 |
+
| epoch 3 | 9800/ 12000 batches | rec 0.25, adv 0.69, |lvar| 2478.08, loss_d 1.41, loss 7.11,
|
| 352 |
+
| epoch 3 | 9900/ 12000 batches | rec 0.18, adv 0.69, |lvar| 2683.35, loss_d 1.41, loss 7.04,
|
| 353 |
+
| epoch 3 | 10000/ 12000 batches | rec 0.11, adv 0.69, |lvar| 2649.70, loss_d 1.41, loss 6.97,
|
| 354 |
+
| epoch 3 | 10100/ 12000 batches | rec 0.27, adv 0.68, |lvar| 2696.93, loss_d 1.41, loss 7.10,
|
| 355 |
+
| epoch 3 | 10200/ 12000 batches | rec 0.08, adv 0.69, |lvar| 2707.16, loss_d 1.40, loss 6.94,
|
| 356 |
+
| epoch 3 | 10300/ 12000 batches | rec 0.10, adv 0.68, |lvar| 2593.27, loss_d 1.41, loss 6.95,
|
| 357 |
+
| epoch 3 | 10400/ 12000 batches | rec 0.17, adv 0.68, |lvar| 2907.28, loss_d 1.40, loss 7.00,
|
| 358 |
+
| epoch 3 | 10500/ 12000 batches | rec 0.20, adv 0.69, |lvar| 2465.39, loss_d 1.40, loss 7.06,
|
| 359 |
+
| epoch 3 | 10600/ 12000 batches | rec 0.34, adv 0.68, |lvar| 2416.35, loss_d 1.40, loss 7.18,
|
| 360 |
+
| epoch 3 | 10700/ 12000 batches | rec 0.23, adv 0.69, |lvar| 2473.70, loss_d 1.40, loss 7.10,
|
| 361 |
+
| epoch 3 | 10800/ 12000 batches | rec 0.23, adv 0.68, |lvar| 2300.79, loss_d 1.40, loss 7.06,
|
| 362 |
+
| epoch 3 | 10900/ 12000 batches | rec 0.53, adv 0.69, |lvar| 2604.40, loss_d 1.40, loss 7.46,
|
| 363 |
+
| epoch 3 | 11000/ 12000 batches | rec 0.21, adv 0.68, |lvar| 2443.83, loss_d 1.41, loss 7.02,
|
| 364 |
+
| epoch 3 | 11100/ 12000 batches | rec 0.37, adv 0.69, |lvar| 2413.04, loss_d 1.40, loss 7.27,
|
| 365 |
+
| epoch 3 | 11200/ 12000 batches | rec 0.40, adv 0.68, |lvar| 2383.69, loss_d 1.40, loss 7.25,
|
| 366 |
+
| epoch 3 | 11300/ 12000 batches | rec 0.41, adv 0.69, |lvar| 2262.46, loss_d 1.41, loss 7.31,
|
| 367 |
+
| epoch 3 | 11400/ 12000 batches | rec 0.36, adv 0.69, |lvar| 2696.04, loss_d 1.42, loss 7.24,
|
| 368 |
+
| epoch 3 | 11500/ 12000 batches | rec 0.17, adv 0.69, |lvar| 2800.00, loss_d 1.41, loss 7.02,
|
| 369 |
+
| epoch 3 | 11600/ 12000 batches | rec 0.29, adv 0.68, |lvar| 2653.09, loss_d 1.41, loss 7.12,
|
| 370 |
+
| epoch 3 | 11700/ 12000 batches | rec 0.39, adv 0.69, |lvar| 2571.36, loss_d 1.41, loss 7.30,
|
| 371 |
+
| epoch 3 | 11800/ 12000 batches | rec 0.08, adv 0.68, |lvar| 2526.12, loss_d 1.41, loss 6.88,
|
| 372 |
+
| epoch 3 | 11900/ 12000 batches | rec 0.14, adv 0.68, |lvar| 2467.39, loss_d 1.41, loss 6.98,
|
| 373 |
+
| epoch 3 | 12000/ 12000 batches | rec 0.23, adv 0.69, |lvar| 2553.71, loss_d 1.41, loss 7.10,
|
| 374 |
+
--------------------------------------------------------------------------------
|
| 375 |
+
| end of epoch 3| time 293510s| valid rec 0.68, adv 0.71, |lvar| 2724.08, loss_d 1.40, loss 7.79,
|
| 376 |
+
--------------------------------------------------------------------------------
|
| 377 |
+
| epoch 4 | 100/ 12000 batches | rec 0.08, adv 0.68, |lvar| 2414.01, loss_d 1.41, loss 6.90,
|
| 378 |
+
| epoch 4 | 200/ 12000 batches | rec 0.20, adv 0.69, |lvar| 2691.56, loss_d 1.41, loss 7.06,
|
| 379 |
+
| epoch 4 | 300/ 12000 batches | rec 0.14, adv 0.68, |lvar| 2890.11, loss_d 1.40, loss 6.97,
|
| 380 |
+
| epoch 4 | 400/ 12000 batches | rec 0.08, adv 0.69, |lvar| 2739.46, loss_d 1.40, loss 6.95,
|
| 381 |
+
| epoch 4 | 500/ 12000 batches | rec 0.52, adv 0.69, |lvar| 2551.89, loss_d 1.40, loss 7.39,
|
| 382 |
+
| epoch 4 | 600/ 12000 batches | rec 0.09, adv 0.68, |lvar| 2374.84, loss_d 1.41, loss 6.92,
|
| 383 |
+
| epoch 4 | 700/ 12000 batches | rec 0.07, adv 0.68, |lvar| 2662.92, loss_d 1.40, loss 6.92,
|
| 384 |
+
| epoch 4 | 800/ 12000 batches | rec 0.17, adv 0.69, |lvar| 2547.04, loss_d 1.40, loss 7.03,
|
| 385 |
+
| epoch 4 | 900/ 12000 batches | rec 0.27, adv 0.68, |lvar| 2658.23, loss_d 1.40, loss 7.10,
|
| 386 |
+
| epoch 4 | 1000/ 12000 batches | rec 0.04, adv 0.69, |lvar| 2587.16, loss_d 1.40, loss 6.94,
|
| 387 |
+
| epoch 4 | 1100/ 12000 batches | rec 0.15, adv 0.68, |lvar| 2466.85, loss_d 1.40, loss 7.00,
|
| 388 |
+
| epoch 4 | 1200/ 12000 batches | rec 0.23, adv 0.69, |lvar| 2779.58, loss_d 1.40, loss 7.10,
|
| 389 |
+
| epoch 4 | 1300/ 12000 batches | rec 0.18, adv 0.69, |lvar| 2986.69, loss_d 1.40, loss 7.06,
|
| 390 |
+
| epoch 4 | 1400/ 12000 batches | rec 0.11, adv 0.69, |lvar| 2817.01, loss_d 1.40, loss 6.97,
|
| 391 |
+
| epoch 4 | 1500/ 12000 batches | rec 0.20, adv 0.69, |lvar| 2791.19, loss_d 1.40, loss 7.07,
|
| 392 |
+
| epoch 4 | 1600/ 12000 batches | rec 0.13, adv 0.69, |lvar| 2850.83, loss_d 1.40, loss 7.00,
|
| 393 |
+
| epoch 4 | 1700/ 12000 batches | rec 0.11, adv 0.69, |lvar| 2894.92, loss_d 1.40, loss 6.97,
|
| 394 |
+
| epoch 4 | 1800/ 12000 batches | rec 0.14, adv 0.68, |lvar| 2502.93, loss_d 1.40, loss 6.99,
|
| 395 |
+
| epoch 4 | 1900/ 12000 batches | rec 0.48, adv 0.69, |lvar| 2691.18, loss_d 1.39, loss 7.39,
|
| 396 |
+
| epoch 4 | 2000/ 12000 batches | rec 0.80, adv 0.69, |lvar| 2432.83, loss_d 1.40, loss 7.66,
|
| 397 |
+
| epoch 4 | 2100/ 12000 batches | rec 0.29, adv 0.69, |lvar| 2769.18, loss_d 1.41, loss 7.16,
|
| 398 |
+
| epoch 4 | 2200/ 12000 batches | rec 0.16, adv 0.68, |lvar| 2444.99, loss_d 1.40, loss 7.00,
|
| 399 |
+
| epoch 4 | 2300/ 12000 batches | rec 0.25, adv 0.69, |lvar| 2393.11, loss_d 1.40, loss 7.15,
|
| 400 |
+
| epoch 4 | 2400/ 12000 batches | rec 0.20, adv 0.68, |lvar| 2532.12, loss_d 1.40, loss 7.05,
|
| 401 |
+
| epoch 4 | 2500/ 12000 batches | rec 0.09, adv 0.69, |lvar| 2685.83, loss_d 1.41, loss 6.97,
|
| 402 |
+
| epoch 4 | 2600/ 12000 batches | rec 0.22, adv 0.68, |lvar| 2474.30, loss_d 1.40, loss 7.06,
|
| 403 |
+
| epoch 4 | 2700/ 12000 batches | rec 0.13, adv 0.69, |lvar| 2293.35, loss_d 1.40, loss 7.02,
|
| 404 |
+
| epoch 4 | 2800/ 12000 batches | rec 0.23, adv 0.69, |lvar| 2603.34, loss_d 1.40, loss 7.10,
|
| 405 |
+
| epoch 4 | 2900/ 12000 batches | rec 0.08, adv 0.68, |lvar| 2582.61, loss_d 1.41, loss 6.91,
|
| 406 |
+
| epoch 4 | 3000/ 12000 batches | rec 0.11, adv 0.69, |lvar| 2736.74, loss_d 1.40, loss 6.98,
|
| 407 |
+
| epoch 4 | 3100/ 12000 batches | rec 0.19, adv 0.68, |lvar| 2726.25, loss_d 1.40, loss 7.04,
|
| 408 |
+
| epoch 4 | 3200/ 12000 batches | rec 0.17, adv 0.69, |lvar| 2799.46, loss_d 1.41, loss 7.04,
|
| 409 |
+
| epoch 4 | 3300/ 12000 batches | rec 0.11, adv 0.69, |lvar| 2693.50, loss_d 1.41, loss 6.96,
|
| 410 |
+
| epoch 4 | 3400/ 12000 batches | rec 0.17, adv 0.68, |lvar| 2640.59, loss_d 1.40, loss 7.01,
|
| 411 |
+
| epoch 4 | 3500/ 12000 batches | rec 0.08, adv 0.69, |lvar| 2455.40, loss_d 1.40, loss 6.96,
|
| 412 |
+
| epoch 4 | 3600/ 12000 batches | rec 0.16, adv 0.68, |lvar| 2644.20, loss_d 1.40, loss 7.00,
|
| 413 |
+
| epoch 4 | 3700/ 12000 batches | rec 0.18, adv 0.69, |lvar| 2561.81, loss_d 1.40, loss 7.06,
|
| 414 |
+
| epoch 4 | 3800/ 12000 batches | rec 0.20, adv 0.68, |lvar| 2660.50, loss_d 1.40, loss 7.05,
|
| 415 |
+
| epoch 4 | 3900/ 12000 batches | rec 0.24, adv 0.69, |lvar| 2448.84, loss_d 1.39, loss 7.13,
|
| 416 |
+
| epoch 4 | 4000/ 12000 batches | rec 0.23, adv 0.69, |lvar| 2511.47, loss_d 1.39, loss 7.12,
|
| 417 |
+
| epoch 4 | 4100/ 12000 batches | rec 0.32, adv 0.69, |lvar| 2503.82, loss_d 1.40, loss 7.21,
|
| 418 |
+
| epoch 4 | 4200/ 12000 batches | rec 0.25, adv 0.69, |lvar| 2280.98, loss_d 1.40, loss 7.17,
|
| 419 |
+
| epoch 4 | 4300/ 12000 batches | rec 0.52, adv 0.69, |lvar| 2395.45, loss_d 1.40, loss 7.38,
|
| 420 |
+
| epoch 4 | 4400/ 12000 batches | rec 0.26, adv 0.69, |lvar| 2606.03, loss_d 1.42, loss 7.12,
|
| 421 |
+
| epoch 4 | 4500/ 12000 batches | rec 0.30, adv 0.68, |lvar| 2908.62, loss_d 1.41, loss 7.13,
|
| 422 |
+
| epoch 4 | 4600/ 12000 batches | rec 0.19, adv 0.69, |lvar| 3115.40, loss_d 1.41, loss 7.05,
|
| 423 |
+
| epoch 4 | 4700/ 12000 batches | rec 0.14, adv 0.69, |lvar| 2976.34, loss_d 1.41, loss 7.00,
|
| 424 |
+
| epoch 4 | 4800/ 12000 batches | rec 0.09, adv 0.69, |lvar| 2749.06, loss_d 1.41, loss 6.94,
|
| 425 |
+
| epoch 4 | 4900/ 12000 batches | rec 0.13, adv 0.69, |lvar| 2789.19, loss_d 1.40, loss 6.98,
|
v2/8-12_150kk_22_09_24/model.pt
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:59fd4bbf2220e37d0bec6243fa81bd4754a5f7112cf1f638e29e8df37ea78524
|
| 3 |
+
size 84765762
|
v2/8-12_150kk_22_09_24/vocab.alphabet
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
|
v2/8_100kk_09_08_24/log.txt
ADDED
|
@@ -0,0 +1,437 @@
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| 1 |
+
Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=10000, dim_d=512, dim_emb=64, dim_h=256, dim_z=128, dropout=0.3, epochs=10, lambda_adv=10.0, lambda_kl=0.0, lambda_p=0.01, load_model='', log_interval=100, lr=0.0005, max_len=8, model_type='aae', nlayers=2, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='8_100kk_09_08_24/', train='../data/8_100kk/train_40kk.txt', valid='../data/8_100kk/valid_10kk.txt')
|
| 2 |
+
# train on cuda device
|
| 3 |
+
# vocab save 8_100kk_09_08_24/vocab.alphabet
|
| 4 |
+
# train passwords {len(train_dataloader.dataset)}
|
| 5 |
+
# valid passwords 10000000
|
| 6 |
+
# model aae parameters: 3330404
|
| 7 |
+
--------------------------------------------------------------------------------
|
| 8 |
+
| epoch 1 | 100/ 4000 batches | rec 32.58, adv 0.75, |lvar| 15.31, loss_d 1.52, loss 40.19,
|
| 9 |
+
| epoch 1 | 200/ 4000 batches | rec 29.70, adv 0.74, |lvar| 12.86, loss_d 1.47, loss 37.22,
|
| 10 |
+
| epoch 1 | 300/ 4000 batches | rec 29.02, adv 0.74, |lvar| 19.43, loss_d 1.47, loss 36.58,
|
| 11 |
+
| epoch 1 | 400/ 4000 batches | rec 29.15, adv 0.73, |lvar| 11.31, loss_d 1.42, loss 36.56,
|
| 12 |
+
| epoch 1 | 500/ 4000 batches | rec 27.53, adv 0.71, |lvar| 5.31, loss_d 1.39, loss 34.72,
|
| 13 |
+
| epoch 1 | 600/ 4000 batches | rec 25.33, adv 0.72, |lvar| 13.16, loss_d 1.38, loss 32.63,
|
| 14 |
+
| epoch 1 | 700/ 4000 batches | rec 23.96, adv 0.72, |lvar| 24.57, loss_d 1.40, loss 31.41,
|
| 15 |
+
| epoch 1 | 800/ 4000 batches | rec 22.89, adv 0.72, |lvar| 28.85, loss_d 1.39, loss 30.34,
|
| 16 |
+
| epoch 1 | 900/ 4000 batches | rec 22.40, adv 0.71, |lvar| 33.96, loss_d 1.40, loss 29.84,
|
| 17 |
+
| epoch 1 | 1000/ 4000 batches | rec 21.97, adv 0.71, |lvar| 29.73, loss_d 1.40, loss 29.34,
|
| 18 |
+
| epoch 1 | 1100/ 4000 batches | rec 21.60, adv 0.71, |lvar| 26.52, loss_d 1.40, loss 28.96,
|
| 19 |
+
| epoch 1 | 1200/ 4000 batches | rec 21.18, adv 0.70, |lvar| 24.88, loss_d 1.39, loss 28.46,
|
| 20 |
+
| epoch 1 | 1300/ 4000 batches | rec 21.64, adv 0.70, |lvar| 23.63, loss_d 1.38, loss 28.93,
|
| 21 |
+
| epoch 1 | 1400/ 4000 batches | rec 20.14, adv 0.70, |lvar| 27.55, loss_d 1.39, loss 27.43,
|
| 22 |
+
| epoch 1 | 1500/ 4000 batches | rec 19.35, adv 0.70, |lvar| 32.32, loss_d 1.38, loss 26.67,
|
| 23 |
+
| epoch 1 | 1600/ 4000 batches | rec 18.47, adv 0.70, |lvar| 37.76, loss_d 1.38, loss 25.86,
|
| 24 |
+
| epoch 1 | 1700/ 4000 batches | rec 17.83, adv 0.70, |lvar| 45.89, loss_d 1.37, loss 25.31,
|
| 25 |
+
| epoch 1 | 1800/ 4000 batches | rec 16.67, adv 0.70, |lvar| 53.51, loss_d 1.38, loss 24.19,
|
| 26 |
+
| epoch 1 | 1900/ 4000 batches | rec 15.57, adv 0.70, |lvar| 57.45, loss_d 1.38, loss 23.17,
|
| 27 |
+
| epoch 1 | 2000/ 4000 batches | rec 14.63, adv 0.70, |lvar| 60.07, loss_d 1.38, loss 22.26,
|
| 28 |
+
| epoch 1 | 2100/ 4000 batches | rec 13.73, adv 0.70, |lvar| 63.84, loss_d 1.39, loss 21.40,
|
| 29 |
+
| epoch 1 | 2200/ 4000 batches | rec 12.57, adv 0.70, |lvar| 67.84, loss_d 1.39, loss 20.26,
|
| 30 |
+
| epoch 1 | 2300/ 4000 batches | rec 12.56, adv 0.70, |lvar| 69.57, loss_d 1.38, loss 20.27,
|
| 31 |
+
| epoch 1 | 2400/ 4000 batches | rec 11.22, adv 0.70, |lvar| 75.91, loss_d 1.38, loss 19.01,
|
| 32 |
+
| epoch 1 | 2500/ 4000 batches | rec 10.66, adv 0.70, |lvar| 78.74, loss_d 1.39, loss 18.41,
|
| 33 |
+
| epoch 1 | 2600/ 4000 batches | rec 10.00, adv 0.69, |lvar| 79.99, loss_d 1.39, loss 17.74,
|
| 34 |
+
| epoch 1 | 2700/ 4000 batches | rec 9.46, adv 0.70, |lvar| 80.65, loss_d 1.39, loss 17.23,
|
| 35 |
+
| epoch 1 | 2800/ 4000 batches | rec 8.96, adv 0.70, |lvar| 82.28, loss_d 1.38, loss 16.74,
|
| 36 |
+
| epoch 1 | 2900/ 4000 batches | rec 8.51, adv 0.70, |lvar| 83.02, loss_d 1.39, loss 16.30,
|
| 37 |
+
| epoch 1 | 3000/ 4000 batches | rec 8.00, adv 0.70, |lvar| 83.08, loss_d 1.38, loss 15.86,
|
| 38 |
+
| epoch 1 | 3100/ 4000 batches | rec 7.78, adv 0.70, |lvar| 84.54, loss_d 1.39, loss 15.63,
|
| 39 |
+
| epoch 1 | 3200/ 4000 batches | rec 7.75, adv 0.70, |lvar| 86.69, loss_d 1.39, loss 15.60,
|
| 40 |
+
| epoch 1 | 3300/ 4000 batches | rec 6.53, adv 0.70, |lvar| 89.36, loss_d 1.39, loss 14.38,
|
| 41 |
+
| epoch 1 | 3400/ 4000 batches | rec 6.00, adv 0.70, |lvar| 91.25, loss_d 1.38, loss 13.92,
|
| 42 |
+
| epoch 1 | 3500/ 4000 batches | rec 5.56, adv 0.70, |lvar| 93.78, loss_d 1.38, loss 13.49,
|
| 43 |
+
| epoch 1 | 3600/ 4000 batches | rec 5.04, adv 0.70, |lvar| 95.48, loss_d 1.38, loss 13.00,
|
| 44 |
+
| epoch 1 | 3700/ 4000 batches | rec 4.73, adv 0.70, |lvar| 97.16, loss_d 1.39, loss 12.69,
|
| 45 |
+
| epoch 1 | 3800/ 4000 batches | rec 4.14, adv 0.69, |lvar| 99.48, loss_d 1.39, loss 12.09,
|
| 46 |
+
| epoch 1 | 3900/ 4000 batches | rec 3.91, adv 0.70, |lvar| 99.36, loss_d 1.39, loss 11.87,
|
| 47 |
+
| epoch 1 | 4000/ 4000 batches | rec 3.58, adv 0.70, |lvar| 100.07, loss_d 1.39, loss 11.54,
|
| 48 |
+
--------------------------------------------------------------------------------
|
| 49 |
+
| end of epoch 1| time 1426s| valid rec 2.27, adv 0.69, |lvar| 102.98, loss_d 1.40, loss 10.18, | saving model
|
| 50 |
+
--------------------------------------------------------------------------------
|
| 51 |
+
| epoch 2 | 100/ 4000 batches | rec 3.56, adv 0.70, |lvar| 99.68, loss_d 1.38, loss 11.52,
|
| 52 |
+
| epoch 2 | 200/ 4000 batches | rec 3.93, adv 0.70, |lvar| 100.58, loss_d 1.39, loss 11.93,
|
| 53 |
+
| epoch 2 | 300/ 4000 batches | rec 3.01, adv 0.70, |lvar| 100.41, loss_d 1.39, loss 10.96,
|
| 54 |
+
| epoch 2 | 400/ 4000 batches | rec 2.78, adv 0.70, |lvar| 99.88, loss_d 1.39, loss 10.75,
|
| 55 |
+
| epoch 2 | 500/ 4000 batches | rec 2.84, adv 0.70, |lvar| 99.95, loss_d 1.39, loss 10.83,
|
| 56 |
+
| epoch 2 | 600/ 4000 batches | rec 2.48, adv 0.70, |lvar| 100.84, loss_d 1.39, loss 10.46,
|
| 57 |
+
| epoch 2 | 700/ 4000 batches | rec 2.34, adv 0.69, |lvar| 100.91, loss_d 1.39, loss 10.29,
|
| 58 |
+
| epoch 2 | 800/ 4000 batches | rec 2.50, adv 0.70, |lvar| 100.00, loss_d 1.39, loss 10.47,
|
| 59 |
+
| epoch 2 | 900/ 4000 batches | rec 1.92, adv 0.70, |lvar| 99.94, loss_d 1.39, loss 9.88,
|
| 60 |
+
| epoch 2 | 1000/ 4000 batches | rec 2.36, adv 0.69, |lvar| 100.62, loss_d 1.39, loss 10.32,
|
| 61 |
+
| epoch 2 | 1100/ 4000 batches | rec 1.76, adv 0.69, |lvar| 100.11, loss_d 1.39, loss 9.69,
|
| 62 |
+
| epoch 2 | 1200/ 4000 batches | rec 2.21, adv 0.69, |lvar| 100.13, loss_d 1.39, loss 10.14,
|
| 63 |
+
| epoch 2 | 1300/ 4000 batches | rec 1.55, adv 0.69, |lvar| 99.52, loss_d 1.39, loss 9.48,
|
| 64 |
+
| epoch 2 | 1400/ 4000 batches | rec 2.41, adv 0.70, |lvar| 100.06, loss_d 1.39, loss 10.38,
|
| 65 |
+
| epoch 2 | 1500/ 4000 batches | rec 1.42, adv 0.70, |lvar| 98.62, loss_d 1.39, loss 9.37,
|
| 66 |
+
| epoch 2 | 1600/ 4000 batches | rec 1.31, adv 0.69, |lvar| 99.46, loss_d 1.39, loss 9.23,
|
| 67 |
+
| epoch 2 | 1700/ 4000 batches | rec 1.79, adv 0.70, |lvar| 98.50, loss_d 1.39, loss 9.76,
|
| 68 |
+
| epoch 2 | 1800/ 4000 batches | rec 1.17, adv 0.69, |lvar| 98.19, loss_d 1.39, loss 9.04,
|
| 69 |
+
| epoch 2 | 1900/ 4000 batches | rec 1.11, adv 0.69, |lvar| 97.45, loss_d 1.39, loss 9.02,
|
| 70 |
+
| epoch 2 | 2000/ 4000 batches | rec 2.04, adv 0.70, |lvar| 97.42, loss_d 1.38, loss 10.01,
|
| 71 |
+
| epoch 2 | 2100/ 4000 batches | rec 1.03, adv 0.69, |lvar| 97.31, loss_d 1.39, loss 8.93,
|
| 72 |
+
| epoch 2 | 2200/ 4000 batches | rec 0.97, adv 0.69, |lvar| 96.95, loss_d 1.39, loss 8.87,
|
| 73 |
+
| epoch 2 | 2300/ 4000 batches | rec 2.40, adv 0.70, |lvar| 97.12, loss_d 1.38, loss 10.38,
|
| 74 |
+
| epoch 2 | 2400/ 4000 batches | rec 0.96, adv 0.69, |lvar| 96.93, loss_d 1.39, loss 8.87,
|
| 75 |
+
| epoch 2 | 2500/ 4000 batches | rec 0.90, adv 0.69, |lvar| 96.59, loss_d 1.39, loss 8.81,
|
| 76 |
+
| epoch 2 | 2600/ 4000 batches | rec 0.87, adv 0.69, |lvar| 96.22, loss_d 1.39, loss 8.77,
|
| 77 |
+
| epoch 2 | 2700/ 4000 batches | rec 0.84, adv 0.70, |lvar| 96.58, loss_d 1.39, loss 8.77,
|
| 78 |
+
| epoch 2 | 2800/ 4000 batches | rec 1.92, adv 0.70, |lvar| 96.50, loss_d 1.38, loss 9.90,
|
| 79 |
+
| epoch 2 | 2900/ 4000 batches | rec 0.80, adv 0.69, |lvar| 95.83, loss_d 1.38, loss 8.67,
|
| 80 |
+
| epoch 2 | 3000/ 4000 batches | rec 0.77, adv 0.70, |lvar| 95.25, loss_d 1.38, loss 8.71,
|
| 81 |
+
| epoch 2 | 3100/ 4000 batches | rec 2.37, adv 0.70, |lvar| 96.68, loss_d 1.38, loss 10.34,
|
| 82 |
+
| epoch 2 | 3200/ 4000 batches | rec 0.79, adv 0.70, |lvar| 95.82, loss_d 1.38, loss 8.77,
|
| 83 |
+
| epoch 2 | 3300/ 4000 batches | rec 0.76, adv 0.70, |lvar| 95.76, loss_d 1.38, loss 8.70,
|
| 84 |
+
| epoch 2 | 3400/ 4000 batches | rec 0.73, adv 0.70, |lvar| 96.03, loss_d 1.38, loss 8.70,
|
| 85 |
+
| epoch 2 | 3500/ 4000 batches | rec 1.62, adv 0.70, |lvar| 96.14, loss_d 1.38, loss 9.61,
|
| 86 |
+
| epoch 2 | 3600/ 4000 batches | rec 0.72, adv 0.70, |lvar| 95.29, loss_d 1.38, loss 8.64,
|
| 87 |
+
| epoch 2 | 3700/ 4000 batches | rec 0.68, adv 0.70, |lvar| 94.75, loss_d 1.38, loss 8.62,
|
| 88 |
+
| epoch 2 | 3800/ 4000 batches | rec 0.66, adv 0.70, |lvar| 94.86, loss_d 1.38, loss 8.59,
|
| 89 |
+
| epoch 2 | 3900/ 4000 batches | rec 1.29, adv 0.70, |lvar| 93.98, loss_d 1.38, loss 9.22,
|
| 90 |
+
| epoch 2 | 4000/ 4000 batches | rec 1.11, adv 0.70, |lvar| 94.97, loss_d 1.38, loss 9.08,
|
| 91 |
+
--------------------------------------------------------------------------------
|
| 92 |
+
| end of epoch 2| time 1431s| valid rec 0.22, adv 0.72, |lvar| 93.21, loss_d 1.39, loss 8.36, | saving model
|
| 93 |
+
--------------------------------------------------------------------------------
|
| 94 |
+
| epoch 3 | 100/ 4000 batches | rec 0.63, adv 0.70, |lvar| 94.18, loss_d 1.38, loss 8.55,
|
| 95 |
+
| epoch 3 | 200/ 4000 batches | rec 0.61, adv 0.70, |lvar| 94.48, loss_d 1.39, loss 8.52,
|
| 96 |
+
| epoch 3 | 300/ 4000 batches | rec 0.57, adv 0.70, |lvar| 93.87, loss_d 1.38, loss 8.52,
|
| 97 |
+
| epoch 3 | 400/ 4000 batches | rec 2.62, adv 0.70, |lvar| 95.65, loss_d 1.38, loss 10.61,
|
| 98 |
+
| epoch 3 | 500/ 4000 batches | rec 0.67, adv 0.69, |lvar| 96.22, loss_d 1.38, loss 8.56,
|
| 99 |
+
| epoch 3 | 600/ 4000 batches | rec 0.57, adv 0.70, |lvar| 93.50, loss_d 1.39, loss 8.49,
|
| 100 |
+
| epoch 3 | 700/ 4000 batches | rec 0.53, adv 0.70, |lvar| 93.75, loss_d 1.39, loss 8.47,
|
| 101 |
+
| epoch 3 | 800/ 4000 batches | rec 0.51, adv 0.69, |lvar| 93.69, loss_d 1.39, loss 8.39,
|
| 102 |
+
| epoch 3 | 900/ 4000 batches | rec 0.49, adv 0.70, |lvar| 93.34, loss_d 1.39, loss 8.41,
|
| 103 |
+
| epoch 3 | 1000/ 4000 batches | rec 0.48, adv 0.69, |lvar| 93.96, loss_d 1.39, loss 8.36,
|
| 104 |
+
| epoch 3 | 1100/ 4000 batches | rec 2.05, adv 0.70, |lvar| 96.12, loss_d 1.38, loss 10.03,
|
| 105 |
+
| epoch 3 | 1200/ 4000 batches | rec 0.49, adv 0.69, |lvar| 94.10, loss_d 1.38, loss 8.38,
|
| 106 |
+
| epoch 3 | 1300/ 4000 batches | rec 0.46, adv 0.70, |lvar| 92.79, loss_d 1.38, loss 8.36,
|
| 107 |
+
| epoch 3 | 1400/ 4000 batches | rec 0.44, adv 0.70, |lvar| 93.38, loss_d 1.39, loss 8.32,
|
| 108 |
+
| epoch 3 | 1500/ 4000 batches | rec 0.43, adv 0.70, |lvar| 92.85, loss_d 1.39, loss 8.34,
|
| 109 |
+
| epoch 3 | 1600/ 4000 batches | rec 2.05, adv 0.71, |lvar| 94.47, loss_d 1.38, loss 10.05,
|
| 110 |
+
| epoch 3 | 1700/ 4000 batches | rec 0.45, adv 0.70, |lvar| 93.11, loss_d 1.39, loss 8.34,
|
| 111 |
+
| epoch 3 | 1800/ 4000 batches | rec 0.42, adv 0.70, |lvar| 93.32, loss_d 1.39, loss 8.31,
|
| 112 |
+
| epoch 3 | 1900/ 4000 batches | rec 0.40, adv 0.70, |lvar| 91.60, loss_d 1.39, loss 8.30,
|
| 113 |
+
| epoch 3 | 2000/ 4000 batches | rec 0.39, adv 0.70, |lvar| 92.04, loss_d 1.38, loss 8.28,
|
| 114 |
+
| epoch 3 | 2100/ 4000 batches | rec 0.38, adv 0.70, |lvar| 92.42, loss_d 1.39, loss 8.29,
|
| 115 |
+
| epoch 3 | 2200/ 4000 batches | rec 0.36, adv 0.70, |lvar| 91.33, loss_d 1.38, loss 8.24,
|
| 116 |
+
| epoch 3 | 2300/ 4000 batches | rec 2.54, adv 0.70, |lvar| 92.32, loss_d 1.38, loss 10.50,
|
| 117 |
+
| epoch 3 | 2400/ 4000 batches | rec 0.72, adv 0.70, |lvar| 96.91, loss_d 1.38, loss 8.70,
|
| 118 |
+
| epoch 3 | 2500/ 4000 batches | rec 1.07, adv 0.71, |lvar| 94.71, loss_d 1.38, loss 9.10,
|
| 119 |
+
| epoch 3 | 2600/ 4000 batches | rec 0.43, adv 0.69, |lvar| 92.91, loss_d 1.38, loss 8.26,
|
| 120 |
+
| epoch 3 | 2700/ 4000 batches | rec 0.38, adv 0.70, |lvar| 91.02, loss_d 1.38, loss 8.31,
|
| 121 |
+
| epoch 3 | 2800/ 4000 batches | rec 0.35, adv 0.70, |lvar| 90.36, loss_d 1.38, loss 8.25,
|
| 122 |
+
| epoch 3 | 2900/ 4000 batches | rec 0.34, adv 0.70, |lvar| 90.94, loss_d 1.38, loss 8.22,
|
| 123 |
+
| epoch 3 | 3000/ 4000 batches | rec 0.33, adv 0.70, |lvar| 90.86, loss_d 1.38, loss 8.24,
|
| 124 |
+
| epoch 3 | 3100/ 4000 batches | rec 0.33, adv 0.70, |lvar| 90.72, loss_d 1.38, loss 8.21,
|
| 125 |
+
| epoch 3 | 3200/ 4000 batches | rec 0.33, adv 0.70, |lvar| 90.79, loss_d 1.39, loss 8.20,
|
| 126 |
+
| epoch 3 | 3300/ 4000 batches | rec 0.32, adv 0.70, |lvar| 90.94, loss_d 1.39, loss 8.19,
|
| 127 |
+
| epoch 3 | 3400/ 4000 batches | rec 2.49, adv 0.70, |lvar| 91.89, loss_d 1.39, loss 10.43,
|
| 128 |
+
| epoch 3 | 3500/ 4000 batches | rec 0.67, adv 0.70, |lvar| 94.66, loss_d 1.38, loss 8.57,
|
| 129 |
+
| epoch 3 | 3600/ 4000 batches | rec 0.36, adv 0.70, |lvar| 91.40, loss_d 1.38, loss 8.31,
|
| 130 |
+
| epoch 3 | 3700/ 4000 batches | rec 0.32, adv 0.70, |lvar| 90.11, loss_d 1.38, loss 8.24,
|
| 131 |
+
| epoch 3 | 3800/ 4000 batches | rec 0.31, adv 0.70, |lvar| 90.14, loss_d 1.38, loss 8.18,
|
| 132 |
+
| epoch 3 | 3900/ 4000 batches | rec 0.30, adv 0.70, |lvar| 89.91, loss_d 1.38, loss 8.20,
|
| 133 |
+
| epoch 3 | 4000/ 4000 batches | rec 0.32, adv 0.70, |lvar| 91.02, loss_d 1.39, loss 8.21,
|
| 134 |
+
--------------------------------------------------------------------------------
|
| 135 |
+
| end of epoch 3| time 1431s| valid rec 13.94, adv 0.68, |lvar| 88.71, loss_d 1.38, loss 21.61,
|
| 136 |
+
--------------------------------------------------------------------------------
|
| 137 |
+
| epoch 4 | 100/ 4000 batches | rec 1.64, adv 0.69, |lvar| 93.41, loss_d 1.38, loss 9.52,
|
| 138 |
+
| epoch 4 | 200/ 4000 batches | rec 0.32, adv 0.70, |lvar| 90.46, loss_d 1.38, loss 8.24,
|
| 139 |
+
| epoch 4 | 300/ 4000 batches | rec 0.29, adv 0.70, |lvar| 89.29, loss_d 1.38, loss 8.19,
|
| 140 |
+
| epoch 4 | 400/ 4000 batches | rec 0.28, adv 0.70, |lvar| 89.62, loss_d 1.38, loss 8.16,
|
| 141 |
+
| epoch 4 | 500/ 4000 batches | rec 0.28, adv 0.70, |lvar| 89.85, loss_d 1.38, loss 8.15,
|
| 142 |
+
| epoch 4 | 600/ 4000 batches | rec 0.28, adv 0.70, |lvar| 89.88, loss_d 1.39, loss 8.15,
|
| 143 |
+
| epoch 4 | 700/ 4000 batches | rec 0.27, adv 0.70, |lvar| 90.10, loss_d 1.38, loss 8.14,
|
| 144 |
+
| epoch 4 | 800/ 4000 batches | rec 2.55, adv 0.71, |lvar| 95.80, loss_d 1.38, loss 10.56,
|
| 145 |
+
| epoch 4 | 900/ 4000 batches | rec 0.34, adv 0.70, |lvar| 92.28, loss_d 1.39, loss 8.26,
|
| 146 |
+
| epoch 4 | 1000/ 4000 batches | rec 0.28, adv 0.70, |lvar| 89.90, loss_d 1.38, loss 8.14,
|
| 147 |
+
| epoch 4 | 1100/ 4000 batches | rec 0.26, adv 0.70, |lvar| 88.69, loss_d 1.38, loss 8.13,
|
| 148 |
+
| epoch 4 | 1200/ 4000 batches | rec 0.26, adv 0.70, |lvar| 88.62, loss_d 1.38, loss 8.13,
|
| 149 |
+
| epoch 4 | 1300/ 4000 batches | rec 0.25, adv 0.70, |lvar| 88.60, loss_d 1.38, loss 8.13,
|
| 150 |
+
| epoch 4 | 1400/ 4000 batches | rec 0.25, adv 0.70, |lvar| 88.97, loss_d 1.38, loss 8.10,
|
| 151 |
+
| epoch 4 | 1500/ 4000 batches | rec 1.85, adv 0.71, |lvar| 90.17, loss_d 1.38, loss 9.82,
|
| 152 |
+
| epoch 4 | 1600/ 4000 batches | rec 0.37, adv 0.69, |lvar| 91.66, loss_d 1.38, loss 8.15,
|
| 153 |
+
| epoch 4 | 1700/ 4000 batches | rec 0.27, adv 0.70, |lvar| 89.44, loss_d 1.38, loss 8.13,
|
| 154 |
+
| epoch 4 | 1800/ 4000 batches | rec 0.26, adv 0.70, |lvar| 89.50, loss_d 1.39, loss 8.12,
|
| 155 |
+
| epoch 4 | 1900/ 4000 batches | rec 0.24, adv 0.70, |lvar| 88.45, loss_d 1.38, loss 8.12,
|
| 156 |
+
| epoch 4 | 2000/ 4000 batches | rec 0.24, adv 0.70, |lvar| 88.49, loss_d 1.38, loss 8.12,
|
| 157 |
+
| epoch 4 | 2100/ 4000 batches | rec 0.23, adv 0.70, |lvar| 88.54, loss_d 1.38, loss 8.11,
|
| 158 |
+
| epoch 4 | 2200/ 4000 batches | rec 0.23, adv 0.69, |lvar| 89.35, loss_d 1.39, loss 8.06,
|
| 159 |
+
| epoch 4 | 2300/ 4000 batches | rec 3.63, adv 0.72, |lvar| 93.92, loss_d 1.38, loss 11.81,
|
| 160 |
+
| epoch 4 | 2400/ 4000 batches | rec 0.47, adv 0.68, |lvar| 96.18, loss_d 1.39, loss 8.22,
|
| 161 |
+
| epoch 4 | 2500/ 4000 batches | rec 0.29, adv 0.70, |lvar| 91.71, loss_d 1.38, loss 8.18,
|
| 162 |
+
| epoch 4 | 2600/ 4000 batches | rec 0.25, adv 0.70, |lvar| 88.38, loss_d 1.38, loss 8.16,
|
| 163 |
+
| epoch 4 | 2700/ 4000 batches | rec 0.24, adv 0.70, |lvar| 88.40, loss_d 1.38, loss 8.12,
|
| 164 |
+
| epoch 4 | 2800/ 4000 batches | rec 0.23, adv 0.70, |lvar| 88.41, loss_d 1.38, loss 8.10,
|
| 165 |
+
| epoch 4 | 2900/ 4000 batches | rec 0.23, adv 0.70, |lvar| 88.45, loss_d 1.38, loss 8.09,
|
| 166 |
+
| epoch 4 | 3000/ 4000 batches | rec 0.22, adv 0.70, |lvar| 88.84, loss_d 1.38, loss 8.06,
|
| 167 |
+
| epoch 4 | 3100/ 4000 batches | rec 0.22, adv 0.70, |lvar| 88.94, loss_d 1.38, loss 8.06,
|
| 168 |
+
| epoch 4 | 3200/ 4000 batches | rec 1.83, adv 0.71, |lvar| 94.20, loss_d 1.39, loss 9.83,
|
| 169 |
+
| epoch 4 | 3300/ 4000 batches | rec 0.29, adv 0.69, |lvar| 91.83, loss_d 1.39, loss 8.07,
|
| 170 |
+
| epoch 4 | 3400/ 4000 batches | rec 0.24, adv 0.70, |lvar| 89.40, loss_d 1.39, loss 8.10,
|
| 171 |
+
| epoch 4 | 3500/ 4000 batches | rec 0.22, adv 0.70, |lvar| 88.25, loss_d 1.38, loss 8.08,
|
| 172 |
+
| epoch 4 | 3600/ 4000 batches | rec 0.21, adv 0.70, |lvar| 88.12, loss_d 1.39, loss 8.07,
|
| 173 |
+
| epoch 4 | 3700/ 4000 batches | rec 0.21, adv 0.70, |lvar| 88.71, loss_d 1.39, loss 8.05,
|
| 174 |
+
| epoch 4 | 3800/ 4000 batches | rec 0.21, adv 0.69, |lvar| 88.56, loss_d 1.39, loss 8.04,
|
| 175 |
+
| epoch 4 | 3900/ 4000 batches | rec 2.45, adv 0.72, |lvar| 91.36, loss_d 1.38, loss 10.60,
|
| 176 |
+
| epoch 4 | 4000/ 4000 batches | rec 0.34, adv 0.67, |lvar| 93.88, loss_d 1.39, loss 7.99,
|
| 177 |
+
--------------------------------------------------------------------------------
|
| 178 |
+
| end of epoch 4| time 1431s| valid rec 0.08, adv 0.72, |lvar| 88.64, loss_d 1.39, loss 8.16, | saving model
|
| 179 |
+
--------------------------------------------------------------------------------
|
| 180 |
+
| epoch 5 | 100/ 4000 batches | rec 0.24, adv 0.70, |lvar| 89.00, loss_d 1.38, loss 8.11,
|
| 181 |
+
| epoch 5 | 200/ 4000 batches | rec 0.21, adv 0.70, |lvar| 88.09, loss_d 1.38, loss 8.09,
|
| 182 |
+
| epoch 5 | 300/ 4000 batches | rec 0.20, adv 0.69, |lvar| 87.36, loss_d 1.38, loss 8.02,
|
| 183 |
+
| epoch 5 | 400/ 4000 batches | rec 0.20, adv 0.70, |lvar| 88.00, loss_d 1.38, loss 8.04,
|
| 184 |
+
| epoch 5 | 500/ 4000 batches | rec 0.20, adv 0.69, |lvar| 88.60, loss_d 1.39, loss 8.00,
|
| 185 |
+
| epoch 5 | 600/ 4000 batches | rec 0.20, adv 0.70, |lvar| 88.10, loss_d 1.39, loss 8.04,
|
| 186 |
+
| epoch 5 | 700/ 4000 batches | rec 0.19, adv 0.69, |lvar| 88.29, loss_d 1.39, loss 7.99,
|
| 187 |
+
| epoch 5 | 800/ 4000 batches | rec 1.84, adv 0.71, |lvar| 91.15, loss_d 1.38, loss 9.87,
|
| 188 |
+
| epoch 5 | 900/ 4000 batches | rec 0.29, adv 0.68, |lvar| 91.08, loss_d 1.39, loss 8.01,
|
| 189 |
+
| epoch 5 | 1000/ 4000 batches | rec 0.21, adv 0.69, |lvar| 87.74, loss_d 1.38, loss 8.02,
|
| 190 |
+
| epoch 5 | 1100/ 4000 batches | rec 0.20, adv 0.70, |lvar| 87.19, loss_d 1.38, loss 8.07,
|
| 191 |
+
| epoch 5 | 1200/ 4000 batches | rec 0.19, adv 0.69, |lvar| 87.75, loss_d 1.38, loss 8.01,
|
| 192 |
+
| epoch 5 | 1300/ 4000 batches | rec 0.18, adv 0.70, |lvar| 87.35, loss_d 1.38, loss 8.02,
|
| 193 |
+
| epoch 5 | 1400/ 4000 batches | rec 0.18, adv 0.70, |lvar| 88.10, loss_d 1.39, loss 8.01,
|
| 194 |
+
| epoch 5 | 1500/ 4000 batches | rec 0.18, adv 0.69, |lvar| 87.07, loss_d 1.38, loss 8.00,
|
| 195 |
+
| epoch 5 | 1600/ 4000 batches | rec 0.18, adv 0.70, |lvar| 87.62, loss_d 1.38, loss 8.01,
|
| 196 |
+
| epoch 5 | 1700/ 4000 batches | rec 1.85, adv 0.71, |lvar| 91.40, loss_d 1.38, loss 9.91,
|
| 197 |
+
| epoch 5 | 1800/ 4000 batches | rec 0.25, adv 0.68, |lvar| 89.80, loss_d 1.39, loss 7.91,
|
| 198 |
+
| epoch 5 | 1900/ 4000 batches | rec 0.19, adv 0.70, |lvar| 86.56, loss_d 1.38, loss 8.03,
|
| 199 |
+
| epoch 5 | 2000/ 4000 batches | rec 0.18, adv 0.70, |lvar| 86.36, loss_d 1.38, loss 8.02,
|
| 200 |
+
| epoch 5 | 2100/ 4000 batches | rec 0.17, adv 0.70, |lvar| 86.70, loss_d 1.38, loss 8.03,
|
| 201 |
+
| epoch 5 | 2200/ 4000 batches | rec 0.17, adv 0.70, |lvar| 86.78, loss_d 1.38, loss 8.00,
|
| 202 |
+
| epoch 5 | 2300/ 4000 batches | rec 0.17, adv 0.70, |lvar| 86.94, loss_d 1.39, loss 8.01,
|
| 203 |
+
| epoch 5 | 2400/ 4000 batches | rec 1.82, adv 0.69, |lvar| 94.11, loss_d 1.38, loss 9.70,
|
| 204 |
+
| epoch 5 | 2500/ 4000 batches | rec 0.21, adv 0.70, |lvar| 86.80, loss_d 1.38, loss 8.07,
|
| 205 |
+
| epoch 5 | 2600/ 4000 batches | rec 0.18, adv 0.70, |lvar| 86.45, loss_d 1.38, loss 8.06,
|
| 206 |
+
| epoch 5 | 2700/ 4000 batches | rec 0.17, adv 0.70, |lvar| 86.06, loss_d 1.38, loss 8.02,
|
| 207 |
+
| epoch 5 | 2800/ 4000 batches | rec 0.17, adv 0.70, |lvar| 86.73, loss_d 1.38, loss 8.01,
|
| 208 |
+
| epoch 5 | 2900/ 4000 batches | rec 0.17, adv 0.69, |lvar| 87.47, loss_d 1.39, loss 7.99,
|
| 209 |
+
| epoch 5 | 3000/ 4000 batches | rec 0.16, adv 0.69, |lvar| 87.76, loss_d 1.39, loss 7.99,
|
| 210 |
+
| epoch 5 | 3100/ 4000 batches | rec 0.16, adv 0.70, |lvar| 86.56, loss_d 1.39, loss 7.98,
|
| 211 |
+
| epoch 5 | 3200/ 4000 batches | rec 0.16, adv 0.70, |lvar| 87.09, loss_d 1.39, loss 8.00,
|
| 212 |
+
| epoch 5 | 3300/ 4000 batches | rec 0.16, adv 0.69, |lvar| 86.99, loss_d 1.39, loss 7.96,
|
| 213 |
+
| epoch 5 | 3400/ 4000 batches | rec 2.79, adv 0.71, |lvar| 95.68, loss_d 1.38, loss 10.88,
|
| 214 |
+
| epoch 5 | 3500/ 4000 batches | rec 0.29, adv 0.69, |lvar| 89.93, loss_d 1.38, loss 8.06,
|
| 215 |
+
| epoch 5 | 3600/ 4000 batches | rec 0.20, adv 0.70, |lvar| 86.96, loss_d 1.38, loss 8.03,
|
| 216 |
+
| epoch 5 | 3700/ 4000 batches | rec 0.17, adv 0.70, |lvar| 85.80, loss_d 1.38, loss 8.02,
|
| 217 |
+
| epoch 5 | 3800/ 4000 batches | rec 0.17, adv 0.70, |lvar| 86.06, loss_d 1.38, loss 8.04,
|
| 218 |
+
| epoch 5 | 3900/ 4000 batches | rec 0.16, adv 0.70, |lvar| 86.32, loss_d 1.38, loss 7.98,
|
| 219 |
+
| epoch 5 | 4000/ 4000 batches | rec 0.16, adv 0.70, |lvar| 86.11, loss_d 1.38, loss 7.99,
|
| 220 |
+
--------------------------------------------------------------------------------
|
| 221 |
+
| end of epoch 5| time 1430s| valid rec 0.04, adv 0.69, |lvar| 84.63, loss_d 1.39, loss 7.84, | saving model
|
| 222 |
+
--------------------------------------------------------------------------------
|
| 223 |
+
| epoch 6 | 100/ 4000 batches | rec 0.16, adv 0.70, |lvar| 86.46, loss_d 1.38, loss 7.98,
|
| 224 |
+
| epoch 6 | 200/ 4000 batches | rec 0.16, adv 0.70, |lvar| 86.58, loss_d 1.38, loss 7.98,
|
| 225 |
+
| epoch 6 | 300/ 4000 batches | rec 0.15, adv 0.69, |lvar| 86.67, loss_d 1.39, loss 7.97,
|
| 226 |
+
| epoch 6 | 400/ 4000 batches | rec 0.15, adv 0.70, |lvar| 86.12, loss_d 1.39, loss 7.98,
|
| 227 |
+
| epoch 6 | 500/ 4000 batches | rec 0.15, adv 0.70, |lvar| 85.79, loss_d 1.39, loss 7.98,
|
| 228 |
+
| epoch 6 | 600/ 4000 batches | rec 2.52, adv 0.72, |lvar| 92.05, loss_d 1.38, loss 10.63,
|
| 229 |
+
| epoch 6 | 700/ 4000 batches | rec 0.26, adv 0.67, |lvar| 89.80, loss_d 1.39, loss 7.86,
|
| 230 |
+
| epoch 6 | 800/ 4000 batches | rec 0.18, adv 0.70, |lvar| 85.19, loss_d 1.38, loss 8.05,
|
| 231 |
+
| epoch 6 | 900/ 4000 batches | rec 0.16, adv 0.70, |lvar| 84.56, loss_d 1.38, loss 8.00,
|
| 232 |
+
| epoch 6 | 1000/ 4000 batches | rec 0.15, adv 0.70, |lvar| 85.01, loss_d 1.38, loss 7.99,
|
| 233 |
+
| epoch 6 | 1100/ 4000 batches | rec 0.15, adv 0.70, |lvar| 85.15, loss_d 1.38, loss 7.98,
|
| 234 |
+
| epoch 6 | 1200/ 4000 batches | rec 0.15, adv 0.70, |lvar| 85.30, loss_d 1.38, loss 7.99,
|
| 235 |
+
| epoch 6 | 1300/ 4000 batches | rec 0.15, adv 0.70, |lvar| 85.50, loss_d 1.39, loss 7.97,
|
| 236 |
+
| epoch 6 | 1400/ 4000 batches | rec 0.14, adv 0.70, |lvar| 85.42, loss_d 1.39, loss 7.97,
|
| 237 |
+
| epoch 6 | 1500/ 4000 batches | rec 0.14, adv 0.69, |lvar| 85.55, loss_d 1.39, loss 7.93,
|
| 238 |
+
| epoch 6 | 1600/ 4000 batches | rec 0.14, adv 0.70, |lvar| 85.32, loss_d 1.39, loss 7.98,
|
| 239 |
+
| epoch 6 | 1700/ 4000 batches | rec 3.12, adv 0.72, |lvar| 91.44, loss_d 1.38, loss 11.23,
|
| 240 |
+
| epoch 6 | 1800/ 4000 batches | rec 0.31, adv 0.67, |lvar| 90.81, loss_d 1.38, loss 7.93,
|
| 241 |
+
| epoch 6 | 1900/ 4000 batches | rec 0.18, adv 0.71, |lvar| 85.04, loss_d 1.38, loss 8.12,
|
| 242 |
+
| epoch 6 | 2000/ 4000 batches | rec 0.16, adv 0.70, |lvar| 84.40, loss_d 1.38, loss 8.02,
|
| 243 |
+
| epoch 6 | 2100/ 4000 batches | rec 0.15, adv 0.70, |lvar| 84.80, loss_d 1.38, loss 7.98,
|
| 244 |
+
| epoch 6 | 2200/ 4000 batches | rec 0.15, adv 0.70, |lvar| 84.52, loss_d 1.38, loss 7.98,
|
| 245 |
+
| epoch 6 | 2300/ 4000 batches | rec 0.14, adv 0.69, |lvar| 85.25, loss_d 1.38, loss 7.94,
|
| 246 |
+
| epoch 6 | 2400/ 4000 batches | rec 0.14, adv 0.70, |lvar| 85.01, loss_d 1.38, loss 7.95,
|
| 247 |
+
| epoch 6 | 2500/ 4000 batches | rec 0.14, adv 0.69, |lvar| 85.05, loss_d 1.39, loss 7.92,
|
| 248 |
+
| epoch 6 | 2600/ 4000 batches | rec 0.14, adv 0.70, |lvar| 84.59, loss_d 1.38, loss 7.98,
|
| 249 |
+
| epoch 6 | 2700/ 4000 batches | rec 0.13, adv 0.70, |lvar| 84.78, loss_d 1.38, loss 7.95,
|
| 250 |
+
| epoch 6 | 2800/ 4000 batches | rec 0.13, adv 0.70, |lvar| 84.42, loss_d 1.38, loss 7.93,
|
| 251 |
+
| epoch 6 | 2900/ 4000 batches | rec 0.14, adv 0.70, |lvar| 84.77, loss_d 1.39, loss 7.96,
|
| 252 |
+
| epoch 6 | 3000/ 4000 batches | rec 0.14, adv 0.70, |lvar| 84.84, loss_d 1.39, loss 7.95,
|
| 253 |
+
| epoch 6 | 3100/ 4000 batches | rec 3.01, adv 0.70, |lvar| 95.09, loss_d 1.38, loss 10.94,
|
| 254 |
+
| epoch 6 | 3200/ 4000 batches | rec 0.20, adv 0.70, |lvar| 85.87, loss_d 1.38, loss 8.09,
|
| 255 |
+
| epoch 6 | 3300/ 4000 batches | rec 0.16, adv 0.70, |lvar| 84.57, loss_d 1.38, loss 7.99,
|
| 256 |
+
| epoch 6 | 3400/ 4000 batches | rec 0.15, adv 0.70, |lvar| 84.39, loss_d 1.38, loss 8.02,
|
| 257 |
+
| epoch 6 | 3500/ 4000 batches | rec 0.14, adv 0.70, |lvar| 83.96, loss_d 1.38, loss 7.93,
|
| 258 |
+
| epoch 6 | 3600/ 4000 batches | rec 0.14, adv 0.70, |lvar| 84.56, loss_d 1.38, loss 7.97,
|
| 259 |
+
| epoch 6 | 3700/ 4000 batches | rec 0.14, adv 0.70, |lvar| 84.40, loss_d 1.38, loss 7.95,
|
| 260 |
+
| epoch 6 | 3800/ 4000 batches | rec 0.13, adv 0.69, |lvar| 84.68, loss_d 1.38, loss 7.92,
|
| 261 |
+
| epoch 6 | 3900/ 4000 batches | rec 0.13, adv 0.69, |lvar| 84.63, loss_d 1.38, loss 7.91,
|
| 262 |
+
| epoch 6 | 4000/ 4000 batches | rec 0.13, adv 0.70, |lvar| 84.23, loss_d 1.39, loss 7.98,
|
| 263 |
+
--------------------------------------------------------------------------------
|
| 264 |
+
| end of epoch 6| time 1430s| valid rec 0.04, adv 0.67, |lvar| 85.13, loss_d 1.38, loss 7.61, | saving model
|
| 265 |
+
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|
| 266 |
+
| epoch 7 | 100/ 4000 batches | rec 2.04, adv 0.71, |lvar| 89.43, loss_d 1.38, loss 10.01,
|
| 267 |
+
| epoch 7 | 200/ 4000 batches | rec 0.21, adv 0.69, |lvar| 87.11, loss_d 1.38, loss 7.94,
|
| 268 |
+
| epoch 7 | 300/ 4000 batches | rec 0.15, adv 0.70, |lvar| 83.77, loss_d 1.38, loss 7.99,
|
| 269 |
+
| epoch 7 | 400/ 4000 batches | rec 0.14, adv 0.70, |lvar| 82.97, loss_d 1.38, loss 8.00,
|
| 270 |
+
| epoch 7 | 500/ 4000 batches | rec 0.13, adv 0.70, |lvar| 83.39, loss_d 1.38, loss 7.97,
|
| 271 |
+
| epoch 7 | 600/ 4000 batches | rec 0.13, adv 0.70, |lvar| 84.11, loss_d 1.38, loss 7.96,
|
| 272 |
+
| epoch 7 | 700/ 4000 batches | rec 0.13, adv 0.69, |lvar| 84.45, loss_d 1.38, loss 7.90,
|
| 273 |
+
| epoch 7 | 800/ 4000 batches | rec 0.13, adv 0.70, |lvar| 84.29, loss_d 1.39, loss 7.93,
|
| 274 |
+
| epoch 7 | 900/ 4000 batches | rec 0.12, adv 0.70, |lvar| 83.90, loss_d 1.38, loss 7.92,
|
| 275 |
+
| epoch 7 | 1000/ 4000 batches | rec 0.13, adv 0.70, |lvar| 84.03, loss_d 1.39, loss 7.94,
|
| 276 |
+
| epoch 7 | 1100/ 4000 batches | rec 0.13, adv 0.69, |lvar| 84.00, loss_d 1.39, loss 7.90,
|
| 277 |
+
| epoch 7 | 1200/ 4000 batches | rec 2.11, adv 0.71, |lvar| 88.84, loss_d 1.38, loss 10.14,
|
| 278 |
+
| epoch 7 | 1300/ 4000 batches | rec 0.23, adv 0.68, |lvar| 87.65, loss_d 1.38, loss 7.90,
|
| 279 |
+
| epoch 7 | 1400/ 4000 batches | rec 0.15, adv 0.70, |lvar| 83.63, loss_d 1.38, loss 8.00,
|
| 280 |
+
| epoch 7 | 1500/ 4000 batches | rec 0.13, adv 0.70, |lvar| 82.92, loss_d 1.38, loss 7.95,
|
| 281 |
+
| epoch 7 | 1600/ 4000 batches | rec 0.13, adv 0.70, |lvar| 83.15, loss_d 1.38, loss 7.96,
|
| 282 |
+
| epoch 7 | 1700/ 4000 batches | rec 0.12, adv 0.69, |lvar| 83.56, loss_d 1.38, loss 7.88,
|
| 283 |
+
| epoch 7 | 1800/ 4000 batches | rec 0.12, adv 0.70, |lvar| 83.60, loss_d 1.38, loss 7.95,
|
| 284 |
+
| epoch 7 | 1900/ 4000 batches | rec 0.12, adv 0.70, |lvar| 83.11, loss_d 1.38, loss 7.92,
|
| 285 |
+
| epoch 7 | 2000/ 4000 batches | rec 0.12, adv 0.69, |lvar| 83.36, loss_d 1.38, loss 7.88,
|
| 286 |
+
| epoch 7 | 2100/ 4000 batches | rec 0.12, adv 0.70, |lvar| 83.96, loss_d 1.39, loss 7.96,
|
| 287 |
+
| epoch 7 | 2200/ 4000 batches | rec 0.12, adv 0.70, |lvar| 82.93, loss_d 1.38, loss 7.90,
|
| 288 |
+
| epoch 7 | 2300/ 4000 batches | rec 0.12, adv 0.70, |lvar| 83.70, loss_d 1.39, loss 7.92,
|
| 289 |
+
| epoch 7 | 2400/ 4000 batches | rec 0.12, adv 0.70, |lvar| 82.90, loss_d 1.38, loss 7.91,
|
| 290 |
+
| epoch 7 | 2500/ 4000 batches | rec 0.12, adv 0.70, |lvar| 83.60, loss_d 1.39, loss 7.94,
|
| 291 |
+
| epoch 7 | 2600/ 4000 batches | rec 2.99, adv 0.71, |lvar| 90.54, loss_d 1.38, loss 11.04,
|
| 292 |
+
| epoch 7 | 2700/ 4000 batches | rec 0.24, adv 0.68, |lvar| 87.57, loss_d 1.38, loss 7.88,
|
| 293 |
+
| epoch 7 | 2800/ 4000 batches | rec 0.15, adv 0.70, |lvar| 83.82, loss_d 1.38, loss 8.00,
|
| 294 |
+
| epoch 7 | 2900/ 4000 batches | rec 0.13, adv 0.70, |lvar| 82.83, loss_d 1.38, loss 7.96,
|
| 295 |
+
| epoch 7 | 3000/ 4000 batches | rec 0.13, adv 0.70, |lvar| 83.54, loss_d 1.38, loss 7.96,
|
| 296 |
+
| epoch 7 | 3100/ 4000 batches | rec 0.12, adv 0.70, |lvar| 83.69, loss_d 1.39, loss 7.93,
|
| 297 |
+
| epoch 7 | 3200/ 4000 batches | rec 0.12, adv 0.70, |lvar| 82.57, loss_d 1.38, loss 7.91,
|
| 298 |
+
| epoch 7 | 3300/ 4000 batches | rec 0.12, adv 0.70, |lvar| 83.17, loss_d 1.39, loss 7.90,
|
| 299 |
+
| epoch 7 | 3400/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.77, loss_d 1.39, loss 7.90,
|
| 300 |
+
| epoch 7 | 3500/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.93, loss_d 1.38, loss 7.91,
|
| 301 |
+
| epoch 7 | 3600/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.55, loss_d 1.38, loss 7.91,
|
| 302 |
+
| epoch 7 | 3700/ 4000 batches | rec 0.11, adv 0.70, |lvar| 83.00, loss_d 1.39, loss 7.90,
|
| 303 |
+
| epoch 7 | 3800/ 4000 batches | rec 0.11, adv 0.69, |lvar| 83.12, loss_d 1.39, loss 7.89,
|
| 304 |
+
| epoch 7 | 3900/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.72, loss_d 1.39, loss 7.90,
|
| 305 |
+
| epoch 7 | 4000/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.82, loss_d 1.39, loss 7.90,
|
| 306 |
+
--------------------------------------------------------------------------------
|
| 307 |
+
| end of epoch 7| time 1430s| valid rec 0.04, adv 0.70, |lvar| 81.80, loss_d 1.38, loss 7.87,
|
| 308 |
+
--------------------------------------------------------------------------------
|
| 309 |
+
| epoch 8 | 100/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.80, loss_d 1.39, loss 7.91,
|
| 310 |
+
| epoch 8 | 200/ 4000 batches | rec 2.95, adv 0.70, |lvar| 94.23, loss_d 1.38, loss 10.84,
|
| 311 |
+
| epoch 8 | 300/ 4000 batches | rec 0.23, adv 0.70, |lvar| 86.78, loss_d 1.39, loss 8.14,
|
| 312 |
+
| epoch 8 | 400/ 4000 batches | rec 0.16, adv 0.70, |lvar| 83.27, loss_d 1.38, loss 8.03,
|
| 313 |
+
| epoch 8 | 500/ 4000 batches | rec 0.15, adv 0.70, |lvar| 83.65, loss_d 1.38, loss 7.97,
|
| 314 |
+
| epoch 8 | 600/ 4000 batches | rec 0.13, adv 0.70, |lvar| 83.25, loss_d 1.38, loss 7.96,
|
| 315 |
+
| epoch 8 | 700/ 4000 batches | rec 0.13, adv 0.70, |lvar| 82.96, loss_d 1.38, loss 7.94,
|
| 316 |
+
| epoch 8 | 800/ 4000 batches | rec 0.12, adv 0.69, |lvar| 82.91, loss_d 1.38, loss 7.88,
|
| 317 |
+
| epoch 8 | 900/ 4000 batches | rec 0.12, adv 0.70, |lvar| 82.87, loss_d 1.38, loss 7.92,
|
| 318 |
+
| epoch 8 | 1000/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.74, loss_d 1.38, loss 7.89,
|
| 319 |
+
| epoch 8 | 1100/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.72, loss_d 1.39, loss 7.90,
|
| 320 |
+
| epoch 8 | 1200/ 4000 batches | rec 0.11, adv 0.69, |lvar| 83.48, loss_d 1.39, loss 7.89,
|
| 321 |
+
| epoch 8 | 1300/ 4000 batches | rec 0.11, adv 0.69, |lvar| 82.78, loss_d 1.39, loss 7.88,
|
| 322 |
+
| epoch 8 | 1400/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.59, loss_d 1.39, loss 7.91,
|
| 323 |
+
| epoch 8 | 1500/ 4000 batches | rec 0.11, adv 0.69, |lvar| 82.27, loss_d 1.39, loss 7.86,
|
| 324 |
+
| epoch 8 | 1600/ 4000 batches | rec 2.89, adv 0.70, |lvar| 92.50, loss_d 1.38, loss 10.83,
|
| 325 |
+
| epoch 8 | 1700/ 4000 batches | rec 0.16, adv 0.69, |lvar| 83.49, loss_d 1.38, loss 7.92,
|
| 326 |
+
| epoch 8 | 1800/ 4000 batches | rec 0.13, adv 0.70, |lvar| 81.76, loss_d 1.38, loss 7.97,
|
| 327 |
+
| epoch 8 | 1900/ 4000 batches | rec 0.11, adv 0.70, |lvar| 81.38, loss_d 1.38, loss 7.93,
|
| 328 |
+
| epoch 8 | 2000/ 4000 batches | rec 0.11, adv 0.70, |lvar| 81.89, loss_d 1.38, loss 7.88,
|
| 329 |
+
| epoch 8 | 2100/ 4000 batches | rec 0.11, adv 0.70, |lvar| 82.12, loss_d 1.39, loss 7.90,
|
| 330 |
+
| epoch 8 | 2200/ 4000 batches | rec 0.11, adv 0.70, |lvar| 81.86, loss_d 1.39, loss 7.89,
|
| 331 |
+
| epoch 8 | 2300/ 4000 batches | rec 0.10, adv 0.69, |lvar| 81.97, loss_d 1.39, loss 7.84,
|
| 332 |
+
| epoch 8 | 2400/ 4000 batches | rec 0.10, adv 0.70, |lvar| 81.74, loss_d 1.39, loss 7.89,
|
| 333 |
+
| epoch 8 | 2500/ 4000 batches | rec 0.10, adv 0.70, |lvar| 82.33, loss_d 1.39, loss 7.91,
|
| 334 |
+
| epoch 8 | 2600/ 4000 batches | rec 0.10, adv 0.69, |lvar| 81.79, loss_d 1.39, loss 7.83,
|
| 335 |
+
| epoch 8 | 2700/ 4000 batches | rec 0.10, adv 0.69, |lvar| 82.16, loss_d 1.39, loss 7.86,
|
| 336 |
+
| epoch 8 | 2800/ 4000 batches | rec 0.10, adv 0.70, |lvar| 81.65, loss_d 1.39, loss 7.92,
|
| 337 |
+
| epoch 8 | 2900/ 4000 batches | rec 0.10, adv 0.70, |lvar| 81.72, loss_d 1.39, loss 7.88,
|
| 338 |
+
| epoch 8 | 3000/ 4000 batches | rec 0.10, adv 0.69, |lvar| 82.61, loss_d 1.39, loss 7.87,
|
| 339 |
+
| epoch 8 | 3100/ 4000 batches | rec 0.10, adv 0.70, |lvar| 82.18, loss_d 1.39, loss 7.87,
|
| 340 |
+
| epoch 8 | 3200/ 4000 batches | rec 0.10, adv 0.70, |lvar| 82.07, loss_d 1.39, loss 7.89,
|
| 341 |
+
| epoch 8 | 3300/ 4000 batches | rec 0.10, adv 0.69, |lvar| 81.33, loss_d 1.39, loss 7.85,
|
| 342 |
+
| epoch 8 | 3400/ 4000 batches | rec 2.42, adv 0.70, |lvar| 93.20, loss_d 1.38, loss 10.31,
|
| 343 |
+
| epoch 8 | 3500/ 4000 batches | rec 0.16, adv 0.70, |lvar| 85.07, loss_d 1.38, loss 8.01,
|
| 344 |
+
| epoch 8 | 3600/ 4000 batches | rec 0.12, adv 0.70, |lvar| 81.42, loss_d 1.38, loss 7.95,
|
| 345 |
+
| epoch 8 | 3700/ 4000 batches | rec 0.11, adv 0.70, |lvar| 81.21, loss_d 1.38, loss 7.93,
|
| 346 |
+
| epoch 8 | 3800/ 4000 batches | rec 0.10, adv 0.70, |lvar| 81.52, loss_d 1.38, loss 7.88,
|
| 347 |
+
| epoch 8 | 3900/ 4000 batches | rec 0.10, adv 0.70, |lvar| 81.42, loss_d 1.39, loss 7.89,
|
| 348 |
+
| epoch 8 | 4000/ 4000 batches | rec 0.10, adv 0.69, |lvar| 81.48, loss_d 1.39, loss 7.85,
|
| 349 |
+
--------------------------------------------------------------------------------
|
| 350 |
+
| end of epoch 8| time 1438s| valid rec 0.03, adv 0.69, |lvar| 80.13, loss_d 1.38, loss 7.75,
|
| 351 |
+
--------------------------------------------------------------------------------
|
| 352 |
+
| epoch 9 | 100/ 4000 batches | rec 0.10, adv 0.69, |lvar| 81.56, loss_d 1.39, loss 7.86,
|
| 353 |
+
| epoch 9 | 200/ 4000 batches | rec 0.10, adv 0.69, |lvar| 81.43, loss_d 1.39, loss 7.86,
|
| 354 |
+
| epoch 9 | 300/ 4000 batches | rec 0.10, adv 0.70, |lvar| 81.23, loss_d 1.39, loss 7.88,
|
| 355 |
+
| epoch 9 | 400/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.89, loss_d 1.39, loss 7.86,
|
| 356 |
+
| epoch 9 | 500/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.73, loss_d 1.39, loss 7.85,
|
| 357 |
+
| epoch 9 | 600/ 4000 batches | rec 0.09, adv 0.69, |lvar| 81.30, loss_d 1.39, loss 7.86,
|
| 358 |
+
| epoch 9 | 700/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.80, loss_d 1.39, loss 7.85,
|
| 359 |
+
| epoch 9 | 800/ 4000 batches | rec 0.09, adv 0.70, |lvar| 81.23, loss_d 1.39, loss 7.86,
|
| 360 |
+
| epoch 9 | 900/ 4000 batches | rec 0.09, adv 0.69, |lvar| 81.05, loss_d 1.39, loss 7.84,
|
| 361 |
+
| epoch 9 | 1000/ 4000 batches | rec 3.13, adv 0.70, |lvar| 93.16, loss_d 1.38, loss 11.03,
|
| 362 |
+
| epoch 9 | 1100/ 4000 batches | rec 0.19, adv 0.70, |lvar| 85.04, loss_d 1.39, loss 8.02,
|
| 363 |
+
| epoch 9 | 1200/ 4000 batches | rec 0.12, adv 0.70, |lvar| 81.75, loss_d 1.38, loss 7.93,
|
| 364 |
+
| epoch 9 | 1300/ 4000 batches | rec 0.11, adv 0.70, |lvar| 80.56, loss_d 1.38, loss 7.90,
|
| 365 |
+
| epoch 9 | 1400/ 4000 batches | rec 0.10, adv 0.70, |lvar| 80.94, loss_d 1.38, loss 7.92,
|
| 366 |
+
| epoch 9 | 1500/ 4000 batches | rec 0.10, adv 0.70, |lvar| 80.88, loss_d 1.38, loss 7.86,
|
| 367 |
+
| epoch 9 | 1600/ 4000 batches | rec 0.10, adv 0.70, |lvar| 81.11, loss_d 1.39, loss 7.88,
|
| 368 |
+
| epoch 9 | 1700/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.91, loss_d 1.38, loss 7.81,
|
| 369 |
+
| epoch 9 | 1800/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.78, loss_d 1.39, loss 7.84,
|
| 370 |
+
| epoch 9 | 1900/ 4000 batches | rec 0.09, adv 0.69, |lvar| 81.14, loss_d 1.39, loss 7.84,
|
| 371 |
+
| epoch 9 | 2000/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.56, loss_d 1.39, loss 7.89,
|
| 372 |
+
| epoch 9 | 2100/ 4000 batches | rec 0.09, adv 0.69, |lvar| 81.06, loss_d 1.39, loss 7.81,
|
| 373 |
+
| epoch 9 | 2200/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.36, loss_d 1.39, loss 7.88,
|
| 374 |
+
| epoch 9 | 2300/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.80, loss_d 1.39, loss 7.84,
|
| 375 |
+
| epoch 9 | 2400/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.20, loss_d 1.39, loss 7.85,
|
| 376 |
+
| epoch 9 | 2500/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.44, loss_d 1.39, loss 7.83,
|
| 377 |
+
| epoch 9 | 2600/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.71, loss_d 1.39, loss 7.86,
|
| 378 |
+
| epoch 9 | 2700/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.52, loss_d 1.39, loss 7.84,
|
| 379 |
+
| epoch 9 | 2800/ 4000 batches | rec 3.62, adv 0.71, |lvar| 92.64, loss_d 1.38, loss 11.60,
|
| 380 |
+
| epoch 9 | 2900/ 4000 batches | rec 0.19, adv 0.69, |lvar| 85.37, loss_d 1.38, loss 7.90,
|
| 381 |
+
| epoch 9 | 3000/ 4000 batches | rec 0.12, adv 0.70, |lvar| 81.53, loss_d 1.38, loss 7.97,
|
| 382 |
+
| epoch 9 | 3100/ 4000 batches | rec 0.10, adv 0.70, |lvar| 80.21, loss_d 1.38, loss 7.88,
|
| 383 |
+
| epoch 9 | 3200/ 4000 batches | rec 0.10, adv 0.70, |lvar| 80.14, loss_d 1.38, loss 7.86,
|
| 384 |
+
| epoch 9 | 3300/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.31, loss_d 1.38, loss 7.90,
|
| 385 |
+
| epoch 9 | 3400/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.36, loss_d 1.38, loss 7.82,
|
| 386 |
+
| epoch 9 | 3500/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.42, loss_d 1.38, loss 7.88,
|
| 387 |
+
| epoch 9 | 3600/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.00, loss_d 1.38, loss 7.83,
|
| 388 |
+
| epoch 9 | 3700/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.28, loss_d 1.39, loss 7.85,
|
| 389 |
+
| epoch 9 | 3800/ 4000 batches | rec 0.09, adv 0.70, |lvar| 79.82, loss_d 1.38, loss 7.84,
|
| 390 |
+
| epoch 9 | 3900/ 4000 batches | rec 0.09, adv 0.69, |lvar| 79.92, loss_d 1.38, loss 7.84,
|
| 391 |
+
| epoch 9 | 4000/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.16, loss_d 1.39, loss 7.87,
|
| 392 |
+
--------------------------------------------------------------------------------
|
| 393 |
+
| end of epoch 9| time 1435s| valid rec 0.03, adv 0.70, |lvar| 80.53, loss_d 1.39, loss 7.80,
|
| 394 |
+
--------------------------------------------------------------------------------
|
| 395 |
+
| epoch 10 | 100/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.23, loss_d 1.39, loss 7.84,
|
| 396 |
+
| epoch 10 | 200/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.78, loss_d 1.38, loss 7.83,
|
| 397 |
+
| epoch 10 | 300/ 4000 batches | rec 0.09, adv 0.70, |lvar| 80.14, loss_d 1.39, loss 7.85,
|
| 398 |
+
| epoch 10 | 400/ 4000 batches | rec 0.08, adv 0.70, |lvar| 79.64, loss_d 1.39, loss 7.85,
|
| 399 |
+
| epoch 10 | 500/ 4000 batches | rec 0.09, adv 0.69, |lvar| 80.13, loss_d 1.39, loss 7.84,
|
| 400 |
+
| epoch 10 | 600/ 4000 batches | rec 2.93, adv 0.72, |lvar| 85.18, loss_d 1.38, loss 10.94,
|
| 401 |
+
| epoch 10 | 700/ 4000 batches | rec 0.45, adv 0.67, |lvar| 88.83, loss_d 1.39, loss 8.05,
|
| 402 |
+
| epoch 10 | 800/ 4000 batches | rec 0.13, adv 0.70, |lvar| 81.34, loss_d 1.38, loss 7.95,
|
| 403 |
+
| epoch 10 | 900/ 4000 batches | rec 0.10, adv 0.70, |lvar| 80.38, loss_d 1.38, loss 7.92,
|
| 404 |
+
| epoch 10 | 1000/ 4000 batches | rec 0.10, adv 0.70, |lvar| 79.69, loss_d 1.38, loss 7.87,
|
| 405 |
+
| epoch 10 | 1100/ 4000 batches | rec 0.09, adv 0.70, |lvar| 79.65, loss_d 1.38, loss 7.86,
|
| 406 |
+
| epoch 10 | 1200/ 4000 batches | rec 0.09, adv 0.69, |lvar| 79.88, loss_d 1.39, loss 7.80,
|
| 407 |
+
| epoch 10 | 1300/ 4000 batches | rec 0.09, adv 0.70, |lvar| 79.74, loss_d 1.38, loss 7.85,
|
| 408 |
+
| epoch 10 | 1400/ 4000 batches | rec 0.09, adv 0.70, |lvar| 79.51, loss_d 1.38, loss 7.85,
|
| 409 |
+
| epoch 10 | 1500/ 4000 batches | rec 0.09, adv 0.69, |lvar| 79.62, loss_d 1.39, loss 7.83,
|
| 410 |
+
| epoch 10 | 1600/ 4000 batches | rec 0.08, adv 0.70, |lvar| 79.56, loss_d 1.39, loss 7.85,
|
| 411 |
+
| epoch 10 | 1700/ 4000 batches | rec 0.08, adv 0.70, |lvar| 79.58, loss_d 1.39, loss 7.85,
|
| 412 |
+
| epoch 10 | 1800/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.80, loss_d 1.39, loss 7.83,
|
| 413 |
+
| epoch 10 | 1900/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.50, loss_d 1.39, loss 7.82,
|
| 414 |
+
| epoch 10 | 2000/ 4000 batches | rec 0.08, adv 0.70, |lvar| 79.43, loss_d 1.39, loss 7.85,
|
| 415 |
+
| epoch 10 | 2100/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.60, loss_d 1.39, loss 7.83,
|
| 416 |
+
| epoch 10 | 2200/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.60, loss_d 1.39, loss 7.82,
|
| 417 |
+
| epoch 10 | 2300/ 4000 batches | rec 0.08, adv 0.70, |lvar| 79.25, loss_d 1.39, loss 7.84,
|
| 418 |
+
| epoch 10 | 2400/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.50, loss_d 1.39, loss 7.81,
|
| 419 |
+
| epoch 10 | 2500/ 4000 batches | rec 0.08, adv 0.70, |lvar| 79.46, loss_d 1.39, loss 7.85,
|
| 420 |
+
| epoch 10 | 2600/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.49, loss_d 1.39, loss 7.80,
|
| 421 |
+
| epoch 10 | 2700/ 4000 batches | rec 0.08, adv 0.70, |lvar| 79.81, loss_d 1.39, loss 7.83,
|
| 422 |
+
| epoch 10 | 2800/ 4000 batches | rec 2.50, adv 0.70, |lvar| 89.83, loss_d 1.38, loss 10.41,
|
| 423 |
+
| epoch 10 | 2900/ 4000 batches | rec 0.15, adv 0.69, |lvar| 82.37, loss_d 1.38, loss 7.87,
|
| 424 |
+
| epoch 10 | 3000/ 4000 batches | rec 0.10, adv 0.70, |lvar| 79.20, loss_d 1.38, loss 7.89,
|
| 425 |
+
| epoch 10 | 3100/ 4000 batches | rec 0.09, adv 0.70, |lvar| 78.86, loss_d 1.38, loss 7.89,
|
| 426 |
+
| epoch 10 | 3200/ 4000 batches | rec 0.09, adv 0.70, |lvar| 79.12, loss_d 1.38, loss 7.83,
|
| 427 |
+
| epoch 10 | 3300/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.29, loss_d 1.39, loss 7.81,
|
| 428 |
+
| epoch 10 | 3400/ 4000 batches | rec 0.08, adv 0.70, |lvar| 78.83, loss_d 1.39, loss 7.85,
|
| 429 |
+
| epoch 10 | 3500/ 4000 batches | rec 0.08, adv 0.70, |lvar| 78.94, loss_d 1.39, loss 7.83,
|
| 430 |
+
| epoch 10 | 3600/ 4000 batches | rec 0.08, adv 0.70, |lvar| 78.89, loss_d 1.39, loss 7.82,
|
| 431 |
+
| epoch 10 | 3700/ 4000 batches | rec 0.08, adv 0.69, |lvar| 78.83, loss_d 1.39, loss 7.81,
|
| 432 |
+
| epoch 10 | 3800/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.19, loss_d 1.39, loss 7.82,
|
| 433 |
+
| epoch 10 | 3900/ 4000 batches | rec 0.08, adv 0.69, |lvar| 79.17, loss_d 1.39, loss 7.82,
|
| 434 |
+
| epoch 10 | 4000/ 4000 batches | rec 0.08, adv 0.70, |lvar| 78.70, loss_d 1.39, loss 7.82,
|
| 435 |
+
--------------------------------------------------------------------------------
|
| 436 |
+
| end of epoch 10| time 1434s| valid rec 0.03, adv 0.71, |lvar| 78.00, loss_d 1.39, loss 7.91,
|
| 437 |
+
Done training
|
v2/8_100kk_09_08_24/model.pt
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:1bd4647055809348ff8fcf2b7a9eccea36d0fdf24070c1d60302d4aea6fea3b6
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| 3 |
+
size 13330158
|
v2/8_100kk_09_08_24/vocab.alphabet
ADDED
|
@@ -0,0 +1 @@
|
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| 1 |
+
ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
|
v2/8_100kk_16_08_24/log.txt
ADDED
|
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| 1 |
+
Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=10000, dim_d=512, dim_emb=64, dim_h=256, dim_z=128, dropout=0.1, epochs=10, lambda_adv=0.0, lambda_kl=0.1, lambda_p=0.0, load_model='', log_interval=100, lr=0.0005, max_len=8, model_type='vae', nlayers=2, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='8_100kk_16_08_24/', train='../data/8_100kk/train_40kk.txt', valid='../data/8_100kk/valid_10kk.txt')
|
| 2 |
+
# train on cuda device
|
| 3 |
+
# vocab save 8_100kk_16_08_24/vocab.alphabet
|
| 4 |
+
# train passwords {len(train_dataloader.dataset)}
|
| 5 |
+
# valid passwords 10000000
|
| 6 |
+
# model vae parameters: 3263843
|
| 7 |
+
--------------------------------------------------------------------------------
|
| 8 |
+
| epoch 1 | 100/ 4000 batches | rec 31.55, kl 2.77, loss 31.83,
|
| 9 |
+
| epoch 1 | 200/ 4000 batches | rec 27.57, kl 3.27, loss 27.90,
|
| 10 |
+
| epoch 1 | 300/ 4000 batches | rec 26.11, kl 3.96, loss 26.50,
|
| 11 |
+
| epoch 1 | 400/ 4000 batches | rec 25.07, kl 4.49, loss 25.52,
|
| 12 |
+
| epoch 1 | 500/ 4000 batches | rec 23.69, kl 4.34, loss 24.13,
|
| 13 |
+
| epoch 1 | 600/ 4000 batches | rec 22.52, kl 4.52, loss 22.98,
|
| 14 |
+
| epoch 1 | 700/ 4000 batches | rec 21.76, kl 5.13, loss 22.27,
|
| 15 |
+
| epoch 1 | 800/ 4000 batches | rec 21.88, kl 5.93, loss 22.47,
|
| 16 |
+
| epoch 1 | 900/ 4000 batches | rec 21.87, kl 5.38, loss 22.41,
|
| 17 |
+
| epoch 1 | 1000/ 4000 batches | rec 20.84, kl 5.85, loss 21.42,
|
| 18 |
+
| epoch 1 | 1100/ 4000 batches | rec 20.55, kl 6.22, loss 21.17,
|
| 19 |
+
| epoch 1 | 1200/ 4000 batches | rec 20.06, kl 7.17, loss 20.78,
|
| 20 |
+
| epoch 1 | 1300/ 4000 batches | rec 20.83, kl 7.63, loss 21.59,
|
| 21 |
+
| epoch 1 | 1400/ 4000 batches | rec 19.33, kl 8.49, loss 20.18,
|
| 22 |
+
| epoch 1 | 1500/ 4000 batches | rec 18.36, kl 9.95, loss 19.36,
|
| 23 |
+
| epoch 1 | 1600/ 4000 batches | rec 17.51, kl 11.57, loss 18.67,
|
| 24 |
+
| epoch 1 | 1700/ 4000 batches | rec 16.64, kl 13.00, loss 17.94,
|
| 25 |
+
| epoch 1 | 1800/ 4000 batches | rec 15.95, kl 14.42, loss 17.40,
|
| 26 |
+
| epoch 1 | 1900/ 4000 batches | rec 15.11, kl 15.80, loss 16.69,
|
| 27 |
+
| epoch 1 | 2000/ 4000 batches | rec 15.03, kl 16.58, loss 16.69,
|
| 28 |
+
| epoch 1 | 2100/ 4000 batches | rec 13.48, kl 17.79, loss 15.26,
|
| 29 |
+
| epoch 1 | 2200/ 4000 batches | rec 12.78, kl 19.25, loss 14.71,
|
| 30 |
+
| epoch 1 | 2300/ 4000 batches | rec 12.07, kl 20.53, loss 14.12,
|
| 31 |
+
| epoch 1 | 2400/ 4000 batches | rec 11.43, kl 21.43, loss 13.57,
|
| 32 |
+
| epoch 1 | 2500/ 4000 batches | rec 10.96, kl 22.15, loss 13.17,
|
| 33 |
+
| epoch 1 | 2600/ 4000 batches | rec 10.44, kl 22.87, loss 12.73,
|
| 34 |
+
| epoch 1 | 2700/ 4000 batches | rec 10.01, kl 23.51, loss 12.36,
|
| 35 |
+
| epoch 1 | 2800/ 4000 batches | rec 9.70, kl 24.19, loss 12.12,
|
| 36 |
+
| epoch 1 | 2900/ 4000 batches | rec 9.21, kl 24.96, loss 11.70,
|
| 37 |
+
| epoch 1 | 3000/ 4000 batches | rec 8.74, kl 25.91, loss 11.33,
|
| 38 |
+
| epoch 1 | 3100/ 4000 batches | rec 8.27, kl 26.82, loss 10.95,
|
| 39 |
+
| epoch 1 | 3200/ 4000 batches | rec 8.27, kl 27.48, loss 11.02,
|
| 40 |
+
| epoch 1 | 3300/ 4000 batches | rec 6.90, kl 28.21, loss 9.72,
|
| 41 |
+
| epoch 1 | 3400/ 4000 batches | rec 6.33, kl 29.26, loss 9.25,
|
| 42 |
+
| epoch 1 | 3500/ 4000 batches | rec 5.79, kl 30.04, loss 8.80,
|
| 43 |
+
| epoch 1 | 3600/ 4000 batches | rec 5.40, kl 30.60, loss 8.46,
|
| 44 |
+
| epoch 1 | 3700/ 4000 batches | rec 4.85, kl 30.89, loss 7.94,
|
| 45 |
+
| epoch 1 | 3800/ 4000 batches | rec 4.41, kl 31.26, loss 7.53,
|
| 46 |
+
| epoch 1 | 3900/ 4000 batches | rec 4.06, kl 31.63, loss 7.22,
|
| 47 |
+
| epoch 1 | 4000/ 4000 batches | rec 3.77, kl 31.96, loss 6.96,
|
| 48 |
+
--------------------------------------------------------------------------------
|
| 49 |
+
| end of epoch 1| time 1404s| valid rec 2.46, kl 32.02, loss 5.66, | saving model
|
| 50 |
+
--------------------------------------------------------------------------------
|
| 51 |
+
| epoch 2 | 100/ 4000 batches | rec 3.64, kl 32.20, loss 6.86,
|
| 52 |
+
| epoch 2 | 200/ 4000 batches | rec 6.32, kl 32.65, loss 9.59,
|
| 53 |
+
| epoch 2 | 300/ 4000 batches | rec 3.46, kl 32.05, loss 6.67,
|
| 54 |
+
| epoch 2 | 400/ 4000 batches | rec 3.06, kl 32.26, loss 6.29,
|
| 55 |
+
| epoch 2 | 500/ 4000 batches | rec 2.81, kl 32.54, loss 6.06,
|
| 56 |
+
| epoch 2 | 600/ 4000 batches | rec 2.85, kl 32.82, loss 6.14,
|
| 57 |
+
| epoch 2 | 700/ 4000 batches | rec 2.73, kl 32.99, loss 6.03,
|
| 58 |
+
| epoch 2 | 800/ 4000 batches | rec 2.81, kl 33.15, loss 6.13,
|
| 59 |
+
| epoch 2 | 900/ 4000 batches | rec 2.69, kl 33.22, loss 6.02,
|
| 60 |
+
| epoch 2 | 1000/ 4000 batches | rec 2.24, kl 33.09, loss 5.55,
|
| 61 |
+
| epoch 2 | 1100/ 4000 batches | rec 3.63, kl 33.69, loss 7.00,
|
| 62 |
+
| epoch 2 | 1200/ 4000 batches | rec 2.27, kl 32.94, loss 5.56,
|
| 63 |
+
| epoch 2 | 1300/ 4000 batches | rec 2.09, kl 33.06, loss 5.39,
|
| 64 |
+
| epoch 2 | 1400/ 4000 batches | rec 2.03, kl 33.26, loss 5.35,
|
| 65 |
+
| epoch 2 | 1500/ 4000 batches | rec 2.49, kl 33.70, loss 5.86,
|
| 66 |
+
| epoch 2 | 1600/ 4000 batches | rec 1.85, kl 33.31, loss 5.18,
|
| 67 |
+
| epoch 2 | 1700/ 4000 batches | rec 1.78, kl 33.34, loss 5.12,
|
| 68 |
+
| epoch 2 | 1800/ 4000 batches | rec 2.27, kl 33.68, loss 5.64,
|
| 69 |
+
| epoch 2 | 1900/ 4000 batches | rec 1.66, kl 33.33, loss 4.99,
|
| 70 |
+
| epoch 2 | 2000/ 4000 batches | rec 1.92, kl 33.66, loss 5.28,
|
| 71 |
+
| epoch 2 | 2100/ 4000 batches | rec 2.09, kl 33.68, loss 5.45,
|
| 72 |
+
| epoch 2 | 2200/ 4000 batches | rec 1.50, kl 33.30, loss 4.83,
|
| 73 |
+
| epoch 2 | 2300/ 4000 batches | rec 2.12, kl 33.66, loss 5.49,
|
| 74 |
+
| epoch 2 | 2400/ 4000 batches | rec 1.41, kl 33.21, loss 4.73,
|
| 75 |
+
| epoch 2 | 2500/ 4000 batches | rec 1.38, kl 33.24, loss 4.70,
|
| 76 |
+
| epoch 2 | 2600/ 4000 batches | rec 2.52, kl 33.80, loss 5.90,
|
| 77 |
+
| epoch 2 | 2700/ 4000 batches | rec 1.33, kl 33.10, loss 4.64,
|
| 78 |
+
| epoch 2 | 2800/ 4000 batches | rec 1.25, kl 33.11, loss 4.56,
|
| 79 |
+
| epoch 2 | 2900/ 4000 batches | rec 1.22, kl 33.12, loss 4.53,
|
| 80 |
+
| epoch 2 | 3000/ 4000 batches | rec 3.04, kl 33.93, loss 6.43,
|
| 81 |
+
| epoch 2 | 3100/ 4000 batches | rec 1.25, kl 33.10, loss 4.56,
|
| 82 |
+
| epoch 2 | 3200/ 4000 batches | rec 1.15, kl 33.04, loss 4.46,
|
| 83 |
+
| epoch 2 | 3300/ 4000 batches | rec 1.11, kl 33.04, loss 4.41,
|
| 84 |
+
| epoch 2 | 3400/ 4000 batches | rec 1.34, kl 33.31, loss 4.67,
|
| 85 |
+
| epoch 2 | 3500/ 4000 batches | rec 1.05, kl 33.03, loss 4.35,
|
| 86 |
+
| epoch 2 | 3600/ 4000 batches | rec 1.03, kl 33.04, loss 4.33,
|
| 87 |
+
| epoch 2 | 3700/ 4000 batches | rec 2.05, kl 33.62, loss 5.41,
|
| 88 |
+
| epoch 2 | 3800/ 4000 batches | rec 1.05, kl 33.24, loss 4.37,
|
| 89 |
+
| epoch 2 | 3900/ 4000 batches | rec 0.99, kl 33.06, loss 4.29,
|
| 90 |
+
| epoch 2 | 4000/ 4000 batches | rec 0.96, kl 33.04, loss 4.26,
|
| 91 |
+
--------------------------------------------------------------------------------
|
| 92 |
+
| end of epoch 2| time 1460s| valid rec 0.48, kl 33.10, loss 3.79, | saving model
|
| 93 |
+
--------------------------------------------------------------------------------
|
| 94 |
+
| epoch 3 | 100/ 4000 batches | rec 1.93, kl 33.71, loss 5.30,
|
| 95 |
+
| epoch 3 | 200/ 4000 batches | rec 0.94, kl 32.98, loss 4.24,
|
| 96 |
+
| epoch 3 | 300/ 4000 batches | rec 0.90, kl 32.94, loss 4.19,
|
| 97 |
+
| epoch 3 | 400/ 4000 batches | rec 0.89, kl 32.97, loss 4.18,
|
| 98 |
+
| epoch 3 | 500/ 4000 batches | rec 0.87, kl 32.98, loss 4.17,
|
| 99 |
+
| epoch 3 | 600/ 4000 batches | rec 0.84, kl 32.95, loss 4.14,
|
| 100 |
+
| epoch 3 | 700/ 4000 batches | rec 2.01, kl 33.65, loss 5.37,
|
| 101 |
+
| epoch 3 | 800/ 4000 batches | rec 0.83, kl 32.87, loss 4.12,
|
| 102 |
+
| epoch 3 | 900/ 4000 batches | rec 0.80, kl 32.83, loss 4.08,
|
| 103 |
+
| epoch 3 | 1000/ 4000 batches | rec 0.78, kl 32.81, loss 4.06,
|
| 104 |
+
| epoch 3 | 1100/ 4000 batches | rec 1.22, kl 32.91, loss 4.51,
|
| 105 |
+
| epoch 3 | 1200/ 4000 batches | rec 2.87, kl 34.38, loss 6.31,
|
| 106 |
+
| epoch 3 | 1300/ 4000 batches | rec 0.85, kl 32.87, loss 4.14,
|
| 107 |
+
| epoch 3 | 1400/ 4000 batches | rec 0.79, kl 32.75, loss 4.07,
|
| 108 |
+
| epoch 3 | 1500/ 4000 batches | rec 0.76, kl 32.71, loss 4.03,
|
| 109 |
+
| epoch 3 | 1600/ 4000 batches | rec 0.74, kl 32.70, loss 4.01,
|
| 110 |
+
| epoch 3 | 1700/ 4000 batches | rec 1.19, kl 33.18, loss 4.51,
|
| 111 |
+
| epoch 3 | 1800/ 4000 batches | rec 0.74, kl 32.69, loss 4.01,
|
| 112 |
+
| epoch 3 | 1900/ 4000 batches | rec 0.71, kl 32.64, loss 3.97,
|
| 113 |
+
| epoch 3 | 2000/ 4000 batches | rec 0.70, kl 32.63, loss 3.96,
|
| 114 |
+
| epoch 3 | 2100/ 4000 batches | rec 0.69, kl 32.64, loss 3.96,
|
| 115 |
+
| epoch 3 | 2200/ 4000 batches | rec 2.71, kl 33.59, loss 6.07,
|
| 116 |
+
| epoch 3 | 2300/ 4000 batches | rec 0.74, kl 32.67, loss 4.00,
|
| 117 |
+
| epoch 3 | 2400/ 4000 batches | rec 0.68, kl 32.55, loss 3.94,
|
| 118 |
+
| epoch 3 | 2500/ 4000 batches | rec 0.67, kl 32.53, loss 3.92,
|
| 119 |
+
| epoch 3 | 2600/ 4000 batches | rec 0.65, kl 32.54, loss 3.91,
|
| 120 |
+
| epoch 3 | 2700/ 4000 batches | rec 1.67, kl 33.18, loss 4.98,
|
| 121 |
+
| epoch 3 | 2800/ 4000 batches | rec 0.70, kl 32.66, loss 3.97,
|
| 122 |
+
| epoch 3 | 2900/ 4000 batches | rec 0.64, kl 32.48, loss 3.89,
|
| 123 |
+
| epoch 3 | 3000/ 4000 batches | rec 0.63, kl 32.47, loss 3.87,
|
| 124 |
+
| epoch 3 | 3100/ 4000 batches | rec 0.62, kl 32.47, loss 3.86,
|
| 125 |
+
| epoch 3 | 3200/ 4000 batches | rec 0.61, kl 32.47, loss 3.85,
|
| 126 |
+
| epoch 3 | 3300/ 4000 batches | rec 0.60, kl 32.47, loss 3.85,
|
| 127 |
+
| epoch 3 | 3400/ 4000 batches | rec 0.60, kl 32.50, loss 3.85,
|
| 128 |
+
| epoch 3 | 3500/ 4000 batches | rec 2.04, kl 33.48, loss 5.38,
|
| 129 |
+
| epoch 3 | 3600/ 4000 batches | rec 0.60, kl 32.44, loss 3.85,
|
| 130 |
+
| epoch 3 | 3700/ 4000 batches | rec 0.58, kl 32.41, loss 3.82,
|
| 131 |
+
| epoch 3 | 3800/ 4000 batches | rec 0.59, kl 32.46, loss 3.83,
|
| 132 |
+
| epoch 3 | 3900/ 4000 batches | rec 0.56, kl 32.38, loss 3.80,
|
| 133 |
+
| epoch 3 | 4000/ 4000 batches | rec 0.56, kl 32.39, loss 3.80,
|
| 134 |
+
--------------------------------------------------------------------------------
|
| 135 |
+
| end of epoch 3| time 1497s| valid rec 0.31, kl 32.30, loss 3.54, | saving model
|
| 136 |
+
--------------------------------------------------------------------------------
|
| 137 |
+
| epoch 4 | 100/ 4000 batches | rec 1.97, kl 33.27, loss 5.30,
|
| 138 |
+
| epoch 4 | 200/ 4000 batches | rec 0.57, kl 32.32, loss 3.80,
|
| 139 |
+
| epoch 4 | 300/ 4000 batches | rec 0.55, kl 32.30, loss 3.78,
|
| 140 |
+
| epoch 4 | 400/ 4000 batches | rec 0.54, kl 32.29, loss 3.77,
|
| 141 |
+
| epoch 4 | 500/ 4000 batches | rec 0.53, kl 32.30, loss 3.76,
|
| 142 |
+
| epoch 4 | 600/ 4000 batches | rec 1.23, kl 32.54, loss 4.48,
|
| 143 |
+
| epoch 4 | 700/ 4000 batches | rec 1.12, kl 33.06, loss 4.43,
|
| 144 |
+
| epoch 4 | 800/ 4000 batches | rec 0.55, kl 32.26, loss 3.77,
|
| 145 |
+
| epoch 4 | 900/ 4000 batches | rec 0.52, kl 32.26, loss 3.75,
|
| 146 |
+
| epoch 4 | 1000/ 4000 batches | rec 0.51, kl 32.24, loss 3.74,
|
| 147 |
+
| epoch 4 | 1100/ 4000 batches | rec 1.42, kl 33.38, loss 4.76,
|
| 148 |
+
| epoch 4 | 1200/ 4000 batches | rec 0.52, kl 32.27, loss 3.75,
|
| 149 |
+
| epoch 4 | 1300/ 4000 batches | rec 0.52, kl 32.35, loss 3.75,
|
| 150 |
+
| epoch 4 | 1400/ 4000 batches | rec 0.50, kl 32.23, loss 3.73,
|
| 151 |
+
| epoch 4 | 1500/ 4000 batches | rec 0.49, kl 32.21, loss 3.71,
|
| 152 |
+
| epoch 4 | 1600/ 4000 batches | rec 0.49, kl 32.21, loss 3.71,
|
| 153 |
+
| epoch 4 | 1700/ 4000 batches | rec 0.48, kl 32.18, loss 3.70,
|
| 154 |
+
| epoch 4 | 1800/ 4000 batches | rec 0.49, kl 32.23, loss 3.71,
|
| 155 |
+
| epoch 4 | 1900/ 4000 batches | rec 0.49, kl 32.30, loss 3.72,
|
| 156 |
+
| epoch 4 | 2000/ 4000 batches | rec 2.92, kl 34.00, loss 6.31,
|
| 157 |
+
| epoch 4 | 2100/ 4000 batches | rec 0.54, kl 32.36, loss 3.77,
|
| 158 |
+
| epoch 4 | 2200/ 4000 batches | rec 0.50, kl 32.25, loss 3.73,
|
| 159 |
+
| epoch 4 | 2300/ 4000 batches | rec 0.48, kl 32.20, loss 3.70,
|
| 160 |
+
| epoch 4 | 2400/ 4000 batches | rec 0.47, kl 32.17, loss 3.69,
|
| 161 |
+
| epoch 4 | 2500/ 4000 batches | rec 0.46, kl 32.15, loss 3.68,
|
| 162 |
+
| epoch 4 | 2600/ 4000 batches | rec 0.46, kl 32.15, loss 3.67,
|
| 163 |
+
| epoch 4 | 2700/ 4000 batches | rec 0.46, kl 32.15, loss 3.67,
|
| 164 |
+
| epoch 4 | 2800/ 4000 batches | rec 1.99, kl 32.89, loss 5.28,
|
| 165 |
+
| epoch 4 | 2900/ 4000 batches | rec 0.53, kl 32.17, loss 3.74,
|
| 166 |
+
| epoch 4 | 3000/ 4000 batches | rec 1.36, kl 33.16, loss 4.67,
|
| 167 |
+
| epoch 4 | 3100/ 4000 batches | rec 0.50, kl 32.20, loss 3.72,
|
| 168 |
+
| epoch 4 | 3200/ 4000 batches | rec 0.47, kl 32.11, loss 3.68,
|
| 169 |
+
| epoch 4 | 3300/ 4000 batches | rec 0.46, kl 32.07, loss 3.66,
|
| 170 |
+
| epoch 4 | 3400/ 4000 batches | rec 0.47, kl 32.23, loss 3.70,
|
| 171 |
+
| epoch 4 | 3500/ 4000 batches | rec 0.48, kl 32.56, loss 3.74,
|
| 172 |
+
| epoch 4 | 3600/ 4000 batches | rec 0.47, kl 32.20, loss 3.69,
|
| 173 |
+
| epoch 4 | 3700/ 4000 batches | rec 0.45, kl 32.12, loss 3.66,
|
| 174 |
+
| epoch 4 | 3800/ 4000 batches | rec 0.44, kl 32.08, loss 3.65,
|
| 175 |
+
| epoch 4 | 3900/ 4000 batches | rec 0.44, kl 32.06, loss 3.64,
|
| 176 |
+
| epoch 4 | 4000/ 4000 batches | rec 2.05, kl 33.39, loss 5.39,
|
| 177 |
+
--------------------------------------------------------------------------------
|
| 178 |
+
| end of epoch 4| time 1454s| valid rec 0.29, kl 32.20, loss 3.51, | saving model
|
| 179 |
+
--------------------------------------------------------------------------------
|
| 180 |
+
| epoch 5 | 100/ 4000 batches | rec 0.47, kl 32.12, loss 3.68,
|
| 181 |
+
| epoch 5 | 200/ 4000 batches | rec 0.45, kl 32.00, loss 3.65,
|
| 182 |
+
| epoch 5 | 300/ 4000 batches | rec 0.43, kl 31.98, loss 3.63,
|
| 183 |
+
| epoch 5 | 400/ 4000 batches | rec 0.43, kl 31.95, loss 3.62,
|
| 184 |
+
| epoch 5 | 500/ 4000 batches | rec 0.42, kl 31.95, loss 3.62,
|
| 185 |
+
| epoch 5 | 600/ 4000 batches | rec 0.42, kl 31.96, loss 3.61,
|
| 186 |
+
| epoch 5 | 700/ 4000 batches | rec 0.41, kl 31.93, loss 3.61,
|
| 187 |
+
| epoch 5 | 800/ 4000 batches | rec 1.85, kl 32.80, loss 5.13,
|
| 188 |
+
| epoch 5 | 900/ 4000 batches | rec 0.50, kl 32.13, loss 3.71,
|
| 189 |
+
| epoch 5 | 1000/ 4000 batches | rec 0.43, kl 31.90, loss 3.62,
|
| 190 |
+
| epoch 5 | 1100/ 4000 batches | rec 0.98, kl 32.80, loss 4.26,
|
| 191 |
+
| epoch 5 | 1200/ 4000 batches | rec 0.43, kl 31.91, loss 3.62,
|
| 192 |
+
| epoch 5 | 1300/ 4000 batches | rec 0.42, kl 31.87, loss 3.61,
|
| 193 |
+
| epoch 5 | 1400/ 4000 batches | rec 0.41, kl 31.90, loss 3.60,
|
| 194 |
+
| epoch 5 | 1500/ 4000 batches | rec 0.85, kl 32.60, loss 4.11,
|
| 195 |
+
| epoch 5 | 1600/ 4000 batches | rec 0.42, kl 31.88, loss 3.60,
|
| 196 |
+
| epoch 5 | 1700/ 4000 batches | rec 0.41, kl 31.85, loss 3.59,
|
| 197 |
+
| epoch 5 | 1800/ 4000 batches | rec 0.40, kl 31.84, loss 3.59,
|
| 198 |
+
| epoch 5 | 1900/ 4000 batches | rec 0.40, kl 31.83, loss 3.58,
|
| 199 |
+
| epoch 5 | 2000/ 4000 batches | rec 0.39, kl 31.84, loss 3.58,
|
| 200 |
+
| epoch 5 | 2100/ 4000 batches | rec 0.39, kl 31.82, loss 3.57,
|
| 201 |
+
| epoch 5 | 2200/ 4000 batches | rec 0.39, kl 31.81, loss 3.57,
|
| 202 |
+
| epoch 5 | 2300/ 4000 batches | rec 0.39, kl 31.81, loss 3.57,
|
| 203 |
+
| epoch 5 | 2400/ 4000 batches | rec 0.38, kl 31.82, loss 3.57,
|
| 204 |
+
| epoch 5 | 2500/ 4000 batches | rec 0.39, kl 31.84, loss 3.57,
|
| 205 |
+
| epoch 5 | 2600/ 4000 batches | rec 1.75, kl 32.41, loss 4.99,
|
| 206 |
+
| epoch 5 | 2700/ 4000 batches | rec 0.61, kl 32.49, loss 3.86,
|
| 207 |
+
| epoch 5 | 2800/ 4000 batches | rec 0.41, kl 31.84, loss 3.60,
|
| 208 |
+
| epoch 5 | 2900/ 4000 batches | rec 0.40, kl 31.76, loss 3.57,
|
| 209 |
+
| epoch 5 | 3000/ 4000 batches | rec 0.38, kl 31.73, loss 3.56,
|
| 210 |
+
| epoch 5 | 3100/ 4000 batches | rec 0.38, kl 31.73, loss 3.55,
|
| 211 |
+
| epoch 5 | 3200/ 4000 batches | rec 0.37, kl 31.73, loss 3.55,
|
| 212 |
+
| epoch 5 | 3300/ 4000 batches | rec 0.37, kl 31.70, loss 3.54,
|
| 213 |
+
| epoch 5 | 3400/ 4000 batches | rec 1.62, kl 33.03, loss 4.92,
|
| 214 |
+
| epoch 5 | 3500/ 4000 batches | rec 0.41, kl 31.77, loss 3.58,
|
| 215 |
+
| epoch 5 | 3600/ 4000 batches | rec 1.70, kl 33.42, loss 5.04,
|
| 216 |
+
| epoch 5 | 3700/ 4000 batches | rec 0.46, kl 32.28, loss 3.69,
|
| 217 |
+
| epoch 5 | 3800/ 4000 batches | rec 0.41, kl 31.96, loss 3.61,
|
| 218 |
+
| epoch 5 | 3900/ 4000 batches | rec 0.40, kl 31.85, loss 3.58,
|
| 219 |
+
| epoch 5 | 4000/ 4000 batches | rec 0.39, kl 31.77, loss 3.56,
|
| 220 |
+
--------------------------------------------------------------------------------
|
| 221 |
+
| end of epoch 5| time 1419s| valid rec 0.23, kl 31.69, loss 3.39, | saving model
|
| 222 |
+
--------------------------------------------------------------------------------
|
| 223 |
+
| epoch 6 | 100/ 4000 batches | rec 0.38, kl 31.73, loss 3.55,
|
| 224 |
+
| epoch 6 | 200/ 4000 batches | rec 0.38, kl 31.71, loss 3.55,
|
| 225 |
+
| epoch 6 | 300/ 4000 batches | rec 0.37, kl 31.69, loss 3.54,
|
| 226 |
+
| epoch 6 | 400/ 4000 batches | rec 0.36, kl 31.67, loss 3.53,
|
| 227 |
+
| epoch 6 | 500/ 4000 batches | rec 0.36, kl 31.67, loss 3.53,
|
| 228 |
+
| epoch 6 | 600/ 4000 batches | rec 0.37, kl 31.73, loss 3.54,
|
| 229 |
+
| epoch 6 | 700/ 4000 batches | rec 0.36, kl 31.67, loss 3.53,
|
| 230 |
+
| epoch 6 | 800/ 4000 batches | rec 0.36, kl 31.66, loss 3.53,
|
| 231 |
+
| epoch 6 | 900/ 4000 batches | rec 0.36, kl 31.68, loss 3.53,
|
| 232 |
+
| epoch 6 | 1000/ 4000 batches | rec 1.71, kl 32.73, loss 4.98,
|
| 233 |
+
| epoch 6 | 1100/ 4000 batches | rec 0.41, kl 31.74, loss 3.58,
|
| 234 |
+
| epoch 6 | 1200/ 4000 batches | rec 0.37, kl 31.58, loss 3.53,
|
| 235 |
+
| epoch 6 | 1300/ 4000 batches | rec 0.36, kl 31.55, loss 3.52,
|
| 236 |
+
| epoch 6 | 1400/ 4000 batches | rec 0.36, kl 31.57, loss 3.52,
|
| 237 |
+
| epoch 6 | 1500/ 4000 batches | rec 0.35, kl 31.57, loss 3.51,
|
| 238 |
+
| epoch 6 | 1600/ 4000 batches | rec 1.60, kl 32.71, loss 4.88,
|
| 239 |
+
| epoch 6 | 1700/ 4000 batches | rec 0.44, kl 32.00, loss 3.64,
|
| 240 |
+
| epoch 6 | 1800/ 4000 batches | rec 1.14, kl 32.94, loss 4.43,
|
| 241 |
+
| epoch 6 | 1900/ 4000 batches | rec 0.44, kl 32.32, loss 3.67,
|
| 242 |
+
| epoch 6 | 2000/ 4000 batches | rec 0.38, kl 31.74, loss 3.56,
|
| 243 |
+
| epoch 6 | 2100/ 4000 batches | rec 0.37, kl 31.77, loss 3.55,
|
| 244 |
+
| epoch 6 | 2200/ 4000 batches | rec 0.37, kl 31.79, loss 3.54,
|
| 245 |
+
| epoch 6 | 2300/ 4000 batches | rec 2.08, kl 34.52, loss 5.54,
|
| 246 |
+
| epoch 6 | 2400/ 4000 batches | rec 0.43, kl 32.53, loss 3.68,
|
| 247 |
+
| epoch 6 | 2500/ 4000 batches | rec 0.39, kl 32.01, loss 3.59,
|
| 248 |
+
| epoch 6 | 2600/ 4000 batches | rec 0.37, kl 31.81, loss 3.55,
|
| 249 |
+
| epoch 6 | 2700/ 4000 batches | rec 0.36, kl 31.71, loss 3.53,
|
| 250 |
+
| epoch 6 | 2800/ 4000 batches | rec 0.35, kl 31.64, loss 3.52,
|
| 251 |
+
| epoch 6 | 2900/ 4000 batches | rec 0.35, kl 31.60, loss 3.51,
|
| 252 |
+
| epoch 6 | 3000/ 4000 batches | rec 0.34, kl 31.57, loss 3.50,
|
| 253 |
+
| epoch 6 | 3100/ 4000 batches | rec 0.34, kl 31.57, loss 3.50,
|
| 254 |
+
| epoch 6 | 3200/ 4000 batches | rec 0.34, kl 31.53, loss 3.49,
|
| 255 |
+
| epoch 6 | 3300/ 4000 batches | rec 0.34, kl 31.52, loss 3.49,
|
| 256 |
+
| epoch 6 | 3400/ 4000 batches | rec 0.33, kl 31.51, loss 3.48,
|
| 257 |
+
| epoch 6 | 3500/ 4000 batches | rec 0.33, kl 31.50, loss 3.48,
|
| 258 |
+
| epoch 6 | 3600/ 4000 batches | rec 0.33, kl 31.48, loss 3.48,
|
| 259 |
+
| epoch 6 | 3700/ 4000 batches | rec 0.33, kl 31.50, loss 3.48,
|
| 260 |
+
| epoch 6 | 3800/ 4000 batches | rec 0.33, kl 31.50, loss 3.48,
|
| 261 |
+
| epoch 6 | 3900/ 4000 batches | rec 0.33, kl 31.48, loss 3.48,
|
| 262 |
+
| epoch 6 | 4000/ 4000 batches | rec 0.33, kl 31.50, loss 3.48,
|
| 263 |
+
--------------------------------------------------------------------------------
|
| 264 |
+
| end of epoch 6| time 1413s| valid rec 0.20, kl 31.45, loss 3.34, | saving model
|
| 265 |
+
--------------------------------------------------------------------------------
|
| 266 |
+
| epoch 7 | 100/ 4000 batches | rec 0.33, kl 31.50, loss 3.48,
|
| 267 |
+
| epoch 7 | 200/ 4000 batches | rec 1.85, kl 32.56, loss 5.10,
|
| 268 |
+
| epoch 7 | 300/ 4000 batches | rec 0.35, kl 31.42, loss 3.49,
|
| 269 |
+
| epoch 7 | 400/ 4000 batches | rec 0.33, kl 31.38, loss 3.47,
|
| 270 |
+
| epoch 7 | 500/ 4000 batches | rec 0.34, kl 31.65, loss 3.51,
|
| 271 |
+
| epoch 7 | 600/ 4000 batches | rec 0.34, kl 31.59, loss 3.50,
|
| 272 |
+
| epoch 7 | 700/ 4000 batches | rec 0.33, kl 31.42, loss 3.47,
|
| 273 |
+
| epoch 7 | 800/ 4000 batches | rec 0.33, kl 31.50, loss 3.48,
|
| 274 |
+
| epoch 7 | 900/ 4000 batches | rec 0.33, kl 31.40, loss 3.47,
|
| 275 |
+
| epoch 7 | 1000/ 4000 batches | rec 0.32, kl 31.38, loss 3.46,
|
| 276 |
+
| epoch 7 | 1100/ 4000 batches | rec 0.32, kl 31.37, loss 3.46,
|
| 277 |
+
| epoch 7 | 1200/ 4000 batches | rec 0.32, kl 31.38, loss 3.46,
|
| 278 |
+
| epoch 7 | 1300/ 4000 batches | rec 1.53, kl 31.91, loss 4.72,
|
| 279 |
+
| epoch 7 | 1400/ 4000 batches | rec 0.65, kl 32.27, loss 3.88,
|
| 280 |
+
| epoch 7 | 1500/ 4000 batches | rec 0.34, kl 31.36, loss 3.48,
|
| 281 |
+
| epoch 7 | 1600/ 4000 batches | rec 0.32, kl 31.31, loss 3.46,
|
| 282 |
+
| epoch 7 | 1700/ 4000 batches | rec 0.32, kl 31.29, loss 3.45,
|
| 283 |
+
| epoch 7 | 1800/ 4000 batches | rec 0.31, kl 31.28, loss 3.44,
|
| 284 |
+
| epoch 7 | 1900/ 4000 batches | rec 0.31, kl 31.28, loss 3.44,
|
| 285 |
+
| epoch 7 | 2000/ 4000 batches | rec 0.31, kl 31.28, loss 3.44,
|
| 286 |
+
| epoch 7 | 2100/ 4000 batches | rec 0.31, kl 31.27, loss 3.44,
|
| 287 |
+
| epoch 7 | 2200/ 4000 batches | rec 0.31, kl 31.27, loss 3.43,
|
| 288 |
+
| epoch 7 | 2300/ 4000 batches | rec 0.31, kl 31.28, loss 3.43,
|
| 289 |
+
| epoch 7 | 2400/ 4000 batches | rec 0.31, kl 31.27, loss 3.43,
|
| 290 |
+
| epoch 7 | 2500/ 4000 batches | rec 0.31, kl 31.28, loss 3.44,
|
| 291 |
+
| epoch 7 | 2600/ 4000 batches | rec 1.14, kl 31.59, loss 4.30,
|
| 292 |
+
| epoch 7 | 2700/ 4000 batches | rec 0.81, kl 32.24, loss 4.03,
|
| 293 |
+
| epoch 7 | 2800/ 4000 batches | rec 0.33, kl 31.22, loss 3.45,
|
| 294 |
+
| epoch 7 | 2900/ 4000 batches | rec 0.31, kl 31.20, loss 3.43,
|
| 295 |
+
| epoch 7 | 3000/ 4000 batches | rec 0.31, kl 31.18, loss 3.43,
|
| 296 |
+
| epoch 7 | 3100/ 4000 batches | rec 0.31, kl 31.21, loss 3.43,
|
| 297 |
+
| epoch 7 | 3200/ 4000 batches | rec 0.30, kl 31.19, loss 3.42,
|
| 298 |
+
| epoch 7 | 3300/ 4000 batches | rec 0.30, kl 31.25, loss 3.43,
|
| 299 |
+
| epoch 7 | 3400/ 4000 batches | rec 0.30, kl 31.20, loss 3.42,
|
| 300 |
+
| epoch 7 | 3500/ 4000 batches | rec 0.30, kl 31.21, loss 3.42,
|
| 301 |
+
| epoch 7 | 3600/ 4000 batches | rec 0.30, kl 31.20, loss 3.42,
|
| 302 |
+
| epoch 7 | 3700/ 4000 batches | rec 1.44, kl 32.32, loss 4.67,
|
| 303 |
+
| epoch 7 | 3800/ 4000 batches | rec 0.35, kl 31.37, loss 3.49,
|
| 304 |
+
| epoch 7 | 3900/ 4000 batches | rec 0.32, kl 31.19, loss 3.43,
|
| 305 |
+
| epoch 7 | 4000/ 4000 batches | rec 0.31, kl 31.15, loss 3.42,
|
| 306 |
+
--------------------------------------------------------------------------------
|
| 307 |
+
| end of epoch 7| time 1413s| valid rec 0.20, kl 31.19, loss 3.32, | saving model
|
| 308 |
+
--------------------------------------------------------------------------------
|
| 309 |
+
| epoch 8 | 100/ 4000 batches | rec 0.30, kl 31.13, loss 3.41,
|
| 310 |
+
| epoch 8 | 200/ 4000 batches | rec 0.30, kl 31.12, loss 3.41,
|
| 311 |
+
| epoch 8 | 300/ 4000 batches | rec 0.30, kl 31.13, loss 3.41,
|
| 312 |
+
| epoch 8 | 400/ 4000 batches | rec 0.30, kl 31.14, loss 3.41,
|
| 313 |
+
| epoch 8 | 500/ 4000 batches | rec 0.30, kl 31.15, loss 3.41,
|
| 314 |
+
| epoch 8 | 600/ 4000 batches | rec 0.30, kl 31.13, loss 3.41,
|
| 315 |
+
| epoch 8 | 700/ 4000 batches | rec 0.29, kl 31.12, loss 3.41,
|
| 316 |
+
| epoch 8 | 800/ 4000 batches | rec 0.30, kl 31.16, loss 3.41,
|
| 317 |
+
| epoch 8 | 900/ 4000 batches | rec 0.30, kl 31.18, loss 3.42,
|
| 318 |
+
| epoch 8 | 1000/ 4000 batches | rec 0.30, kl 31.15, loss 3.41,
|
| 319 |
+
| epoch 8 | 1100/ 4000 batches | rec 0.29, kl 31.14, loss 3.41,
|
| 320 |
+
| epoch 8 | 1200/ 4000 batches | rec 1.67, kl 32.33, loss 4.90,
|
| 321 |
+
| epoch 8 | 1300/ 4000 batches | rec 0.34, kl 31.15, loss 3.45,
|
| 322 |
+
| epoch 8 | 1400/ 4000 batches | rec 0.31, kl 31.04, loss 3.41,
|
| 323 |
+
| epoch 8 | 1500/ 4000 batches | rec 0.30, kl 31.03, loss 3.40,
|
| 324 |
+
| epoch 8 | 1600/ 4000 batches | rec 0.29, kl 31.03, loss 3.40,
|
| 325 |
+
| epoch 8 | 1700/ 4000 batches | rec 0.29, kl 31.02, loss 3.39,
|
| 326 |
+
| epoch 8 | 1800/ 4000 batches | rec 0.29, kl 31.03, loss 3.39,
|
| 327 |
+
| epoch 8 | 1900/ 4000 batches | rec 0.29, kl 31.05, loss 3.39,
|
| 328 |
+
| epoch 8 | 2000/ 4000 batches | rec 0.29, kl 31.04, loss 3.40,
|
| 329 |
+
| epoch 8 | 2100/ 4000 batches | rec 0.29, kl 31.05, loss 3.39,
|
| 330 |
+
| epoch 8 | 2200/ 4000 batches | rec 0.29, kl 31.04, loss 3.39,
|
| 331 |
+
| epoch 8 | 2300/ 4000 batches | rec 0.29, kl 31.04, loss 3.39,
|
| 332 |
+
| epoch 8 | 2400/ 4000 batches | rec 0.29, kl 31.04, loss 3.39,
|
| 333 |
+
| epoch 8 | 2500/ 4000 batches | rec 0.29, kl 31.03, loss 3.39,
|
| 334 |
+
| epoch 8 | 2600/ 4000 batches | rec 0.29, kl 31.06, loss 3.39,
|
| 335 |
+
| epoch 8 | 2700/ 4000 batches | rec 1.80, kl 32.68, loss 5.06,
|
| 336 |
+
| epoch 8 | 2800/ 4000 batches | rec 0.31, kl 31.11, loss 3.42,
|
| 337 |
+
| epoch 8 | 2900/ 4000 batches | rec 0.30, kl 31.05, loss 3.41,
|
| 338 |
+
| epoch 8 | 3000/ 4000 batches | rec 0.29, kl 31.04, loss 3.40,
|
| 339 |
+
| epoch 8 | 3100/ 4000 batches | rec 0.29, kl 31.01, loss 3.39,
|
| 340 |
+
| epoch 8 | 3200/ 4000 batches | rec 0.28, kl 31.00, loss 3.38,
|
| 341 |
+
| epoch 8 | 3300/ 4000 batches | rec 0.28, kl 30.97, loss 3.38,
|
| 342 |
+
| epoch 8 | 3400/ 4000 batches | rec 0.28, kl 30.97, loss 3.38,
|
| 343 |
+
| epoch 8 | 3500/ 4000 batches | rec 0.28, kl 30.97, loss 3.38,
|
| 344 |
+
| epoch 8 | 3600/ 4000 batches | rec 0.28, kl 30.96, loss 3.38,
|
| 345 |
+
| epoch 8 | 3700/ 4000 batches | rec 0.28, kl 30.97, loss 3.38,
|
| 346 |
+
| epoch 8 | 3800/ 4000 batches | rec 0.28, kl 30.98, loss 3.38,
|
| 347 |
+
| epoch 8 | 3900/ 4000 batches | rec 0.28, kl 30.98, loss 3.38,
|
| 348 |
+
| epoch 8 | 4000/ 4000 batches | rec 0.28, kl 30.98, loss 3.38,
|
| 349 |
+
--------------------------------------------------------------------------------
|
| 350 |
+
| end of epoch 8| time 1415s| valid rec 0.20, kl 30.86, loss 3.29, | saving model
|
| 351 |
+
--------------------------------------------------------------------------------
|
| 352 |
+
| epoch 9 | 100/ 4000 batches | rec 1.01, kl 31.11, loss 4.12,
|
| 353 |
+
| epoch 9 | 200/ 4000 batches | rec 0.86, kl 32.05, loss 4.06,
|
| 354 |
+
| epoch 9 | 300/ 4000 batches | rec 0.30, kl 30.91, loss 3.39,
|
| 355 |
+
| epoch 9 | 400/ 4000 batches | rec 0.29, kl 30.88, loss 3.37,
|
| 356 |
+
| epoch 9 | 500/ 4000 batches | rec 0.28, kl 30.88, loss 3.37,
|
| 357 |
+
| epoch 9 | 600/ 4000 batches | rec 0.28, kl 30.87, loss 3.36,
|
| 358 |
+
| epoch 9 | 700/ 4000 batches | rec 2.50, kl 33.43, loss 5.84,
|
| 359 |
+
| epoch 9 | 800/ 4000 batches | rec 0.35, kl 31.48, loss 3.50,
|
| 360 |
+
| epoch 9 | 900/ 4000 batches | rec 0.31, kl 31.19, loss 3.43,
|
| 361 |
+
| epoch 9 | 1000/ 4000 batches | rec 0.30, kl 31.09, loss 3.41,
|
| 362 |
+
| epoch 9 | 1100/ 4000 batches | rec 0.29, kl 31.02, loss 3.39,
|
| 363 |
+
| epoch 9 | 1200/ 4000 batches | rec 0.28, kl 30.97, loss 3.38,
|
| 364 |
+
| epoch 9 | 1300/ 4000 batches | rec 0.28, kl 30.95, loss 3.38,
|
| 365 |
+
| epoch 9 | 1400/ 4000 batches | rec 0.28, kl 30.93, loss 3.37,
|
| 366 |
+
| epoch 9 | 1500/ 4000 batches | rec 0.28, kl 30.93, loss 3.37,
|
| 367 |
+
| epoch 9 | 1600/ 4000 batches | rec 0.27, kl 30.92, loss 3.37,
|
| 368 |
+
| epoch 9 | 1700/ 4000 batches | rec 0.27, kl 30.89, loss 3.36,
|
| 369 |
+
| epoch 9 | 1800/ 4000 batches | rec 0.27, kl 30.90, loss 3.36,
|
| 370 |
+
| epoch 9 | 1900/ 4000 batches | rec 0.27, kl 30.90, loss 3.36,
|
| 371 |
+
| epoch 9 | 2000/ 4000 batches | rec 0.27, kl 30.88, loss 3.36,
|
| 372 |
+
| epoch 9 | 2100/ 4000 batches | rec 0.27, kl 30.88, loss 3.36,
|
| 373 |
+
| epoch 9 | 2200/ 4000 batches | rec 0.27, kl 30.87, loss 3.36,
|
| 374 |
+
| epoch 9 | 2300/ 4000 batches | rec 0.27, kl 30.88, loss 3.36,
|
| 375 |
+
| epoch 9 | 2400/ 4000 batches | rec 0.27, kl 30.87, loss 3.36,
|
| 376 |
+
| epoch 9 | 2500/ 4000 batches | rec 0.27, kl 30.88, loss 3.36,
|
| 377 |
+
| epoch 9 | 2600/ 4000 batches | rec 2.22, kl 32.59, loss 5.48,
|
| 378 |
+
| epoch 9 | 2700/ 4000 batches | rec 0.34, kl 31.20, loss 3.46,
|
| 379 |
+
| epoch 9 | 2800/ 4000 batches | rec 0.30, kl 30.96, loss 3.39,
|
| 380 |
+
| epoch 9 | 2900/ 4000 batches | rec 0.28, kl 30.90, loss 3.37,
|
| 381 |
+
| epoch 9 | 3000/ 4000 batches | rec 0.28, kl 30.84, loss 3.36,
|
| 382 |
+
| epoch 9 | 3100/ 4000 batches | rec 0.27, kl 30.85, loss 3.36,
|
| 383 |
+
| epoch 9 | 3200/ 4000 batches | rec 0.27, kl 30.84, loss 3.36,
|
| 384 |
+
| epoch 9 | 3300/ 4000 batches | rec 0.27, kl 30.81, loss 3.35,
|
| 385 |
+
| epoch 9 | 3400/ 4000 batches | rec 0.27, kl 30.83, loss 3.35,
|
| 386 |
+
| epoch 9 | 3500/ 4000 batches | rec 0.27, kl 30.81, loss 3.35,
|
| 387 |
+
| epoch 9 | 3600/ 4000 batches | rec 0.27, kl 30.80, loss 3.35,
|
| 388 |
+
| epoch 9 | 3700/ 4000 batches | rec 0.27, kl 30.83, loss 3.35,
|
| 389 |
+
| epoch 9 | 3800/ 4000 batches | rec 0.27, kl 30.82, loss 3.35,
|
| 390 |
+
| epoch 9 | 3900/ 4000 batches | rec 0.27, kl 30.81, loss 3.35,
|
| 391 |
+
| epoch 9 | 4000/ 4000 batches | rec 0.27, kl 30.83, loss 3.35,
|
| 392 |
+
--------------------------------------------------------------------------------
|
| 393 |
+
| end of epoch 9| time 1412s| valid rec 0.19, kl 30.80, loss 3.27, | saving model
|
| 394 |
+
--------------------------------------------------------------------------------
|
| 395 |
+
| epoch 10 | 100/ 4000 batches | rec 0.27, kl 30.81, loss 3.35,
|
| 396 |
+
| epoch 10 | 200/ 4000 batches | rec 1.62, kl 32.08, loss 4.83,
|
| 397 |
+
| epoch 10 | 300/ 4000 batches | rec 0.30, kl 30.79, loss 3.38,
|
| 398 |
+
| epoch 10 | 400/ 4000 batches | rec 0.28, kl 30.71, loss 3.35,
|
| 399 |
+
| epoch 10 | 500/ 4000 batches | rec 0.27, kl 30.71, loss 3.34,
|
| 400 |
+
| epoch 10 | 600/ 4000 batches | rec 0.27, kl 30.70, loss 3.34,
|
| 401 |
+
| epoch 10 | 700/ 4000 batches | rec 0.26, kl 30.70, loss 3.33,
|
| 402 |
+
| epoch 10 | 800/ 4000 batches | rec 0.26, kl 30.70, loss 3.33,
|
| 403 |
+
| epoch 10 | 900/ 4000 batches | rec 0.26, kl 30.71, loss 3.33,
|
| 404 |
+
| epoch 10 | 1000/ 4000 batches | rec 0.26, kl 30.71, loss 3.33,
|
| 405 |
+
| epoch 10 | 1100/ 4000 batches | rec 0.26, kl 30.71, loss 3.33,
|
| 406 |
+
| epoch 10 | 1200/ 4000 batches | rec 0.26, kl 30.70, loss 3.33,
|
| 407 |
+
| epoch 10 | 1300/ 4000 batches | rec 0.26, kl 30.71, loss 3.33,
|
| 408 |
+
| epoch 10 | 1400/ 4000 batches | rec 0.26, kl 30.73, loss 3.33,
|
| 409 |
+
| epoch 10 | 1500/ 4000 batches | rec 0.26, kl 30.72, loss 3.33,
|
| 410 |
+
| epoch 10 | 1600/ 4000 batches | rec 0.26, kl 30.74, loss 3.34,
|
| 411 |
+
| epoch 10 | 1700/ 4000 batches | rec 0.26, kl 30.71, loss 3.33,
|
| 412 |
+
| epoch 10 | 1800/ 4000 batches | rec 0.26, kl 30.76, loss 3.34,
|
| 413 |
+
| epoch 10 | 1900/ 4000 batches | rec 1.51, kl 31.84, loss 4.70,
|
| 414 |
+
| epoch 10 | 2000/ 4000 batches | rec 0.32, kl 30.88, loss 3.40,
|
| 415 |
+
| epoch 10 | 2100/ 4000 batches | rec 0.28, kl 30.66, loss 3.34,
|
| 416 |
+
| epoch 10 | 2200/ 4000 batches | rec 0.27, kl 30.64, loss 3.33,
|
| 417 |
+
| epoch 10 | 2300/ 4000 batches | rec 0.26, kl 30.63, loss 3.33,
|
| 418 |
+
| epoch 10 | 2400/ 4000 batches | rec 0.26, kl 30.62, loss 3.32,
|
| 419 |
+
| epoch 10 | 2500/ 4000 batches | rec 0.26, kl 30.61, loss 3.32,
|
| 420 |
+
| epoch 10 | 2600/ 4000 batches | rec 0.26, kl 30.62, loss 3.32,
|
| 421 |
+
| epoch 10 | 2700/ 4000 batches | rec 0.26, kl 30.63, loss 3.32,
|
| 422 |
+
| epoch 10 | 2800/ 4000 batches | rec 0.26, kl 30.65, loss 3.32,
|
| 423 |
+
| epoch 10 | 2900/ 4000 batches | rec 0.26, kl 30.63, loss 3.32,
|
| 424 |
+
| epoch 10 | 3000/ 4000 batches | rec 0.26, kl 30.62, loss 3.32,
|
| 425 |
+
| epoch 10 | 3100/ 4000 batches | rec 0.26, kl 30.66, loss 3.33,
|
| 426 |
+
| epoch 10 | 3200/ 4000 batches | rec 0.26, kl 30.68, loss 3.33,
|
| 427 |
+
| epoch 10 | 3300/ 4000 batches | rec 0.26, kl 30.67, loss 3.33,
|
| 428 |
+
| epoch 10 | 3400/ 4000 batches | rec 0.26, kl 30.67, loss 3.33,
|
| 429 |
+
| epoch 10 | 3500/ 4000 batches | rec 0.26, kl 30.65, loss 3.33,
|
| 430 |
+
| epoch 10 | 3600/ 4000 batches | rec 1.64, kl 31.97, loss 4.83,
|
| 431 |
+
| epoch 10 | 3700/ 4000 batches | rec 0.29, kl 30.66, loss 3.36,
|
| 432 |
+
| epoch 10 | 3800/ 4000 batches | rec 0.27, kl 30.57, loss 3.33,
|
| 433 |
+
| epoch 10 | 3900/ 4000 batches | rec 0.26, kl 30.55, loss 3.32,
|
| 434 |
+
| epoch 10 | 4000/ 4000 batches | rec 0.26, kl 30.56, loss 3.32,
|
| 435 |
+
--------------------------------------------------------------------------------
|
| 436 |
+
| end of epoch 10| time 1414s| valid rec 0.17, kl 30.79, loss 3.25, | saving model
|
| 437 |
+
Done training
|
v2/8_100kk_16_08_24/model.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:66287cf6a1dd950db3c28f550629eb86cc7e613b29677d9ed7bf9ca25a51493e
|
| 3 |
+
size 13062430
|
v2/8_100kk_16_08_24/vocab.alphabet
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
|