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  1. v3/8-100kk-11_12_2024/log.txt +294 -0
  2. v3/8-100kk-11_12_2024/model.pt +3 -0
  3. v3/8-100kk-11_12_2024/vocab.alphabet +1 -0
  4. v3/8-100kk-16_12_2024/log.txt +0 -0
  5. v3/8-100kk-16_12_2024/model.pt +3 -0
  6. v3/8-100kk-16_12_2024/vocab.alphabet +1 -0
  7. v3/8-17_12_2024/log.txt +20 -0
  8. v3/8-17_12_2024/vocab.alphabet +1 -0
  9. v3/8-200kk-17_12_2024/log.txt +68 -0
  10. v3/8-200kk-17_12_2024/vocab.alphabet +1 -0
  11. v3/8-500kk-17_12_2024/log.txt +16 -0
  12. v3/8-500kk-17_12_2024/vocab.alphabet +1 -0
  13. v3/8-aae-17_12_2024/log.txt +359 -0
  14. v3/8-aae-17_12_2024/vocab.alphabet +1 -0
  15. v3/8-laae-17_12_2024/log.txt +100 -0
  16. v3/8-laae-17_12_2024/vocab.alphabet +1 -0
  17. v3/8-laae-large_17_12_2024/log.txt +28 -0
  18. v3/8-laae-large_17_12_2024/vocab.alphabet +1 -0
  19. v3/8-vae-17_12_2024/log.txt +44 -0
  20. v3/8-vae-17_12_2024/vocab.alphabet +1 -0
v3/8-100kk-11_12_2024/log.txt ADDED
@@ -0,0 +1,294 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=64, dim_h=256, dim_z=128, dropout=0.3, epochs=50, 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=3, no_cuda=True, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-100kk-11_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-100kk/c98096/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-100kk/c98096/valid.txt')
2
+ # train on cpu device
3
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-100kk-11_12_2024/vocab.alphabet
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+ # train passwords 80000000
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+ # valid passwords 20000000
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+ # model aae parameters: 5433700
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+ --------------------------------------------------------------------------------
8
+ | epoch 1 | 100/ 4000 batches | rec 32.48, adv 0.74, |lvar| 17.77, loss_d 1.58, loss 40.03,
9
+ | epoch 1 | 200/ 4000 batches | rec 29.75, adv 0.74, |lvar| 24.50, loss_d 1.52, loss 37.38,
10
+ | epoch 1 | 300/ 4000 batches | rec 29.08, adv 0.75, |lvar| 19.01, loss_d 1.47, loss 36.80,
11
+ | epoch 1 | 400/ 4000 batches | rec 28.60, adv 0.74, |lvar| 14.87, loss_d 1.44, loss 36.16,
12
+ | epoch 1 | 500/ 4000 batches | rec 28.68, adv 0.74, |lvar| 9.48, loss_d 1.41, loss 36.13,
13
+ | epoch 1 | 600/ 4000 batches | rec 27.68, adv 0.72, |lvar| 6.26, loss_d 1.41, loss 34.92,
14
+ | epoch 1 | 700/ 4000 batches | rec 25.05, adv 0.73, |lvar| 7.26, loss_d 1.38, loss 32.40,
15
+ | epoch 1 | 800/ 4000 batches | rec 23.48, adv 0.73, |lvar| 10.79, loss_d 1.38, loss 30.85,
16
+ | epoch 1 | 900/ 4000 batches | rec 24.06, adv 0.73, |lvar| 16.43, loss_d 1.38, loss 31.52,
17
+ | epoch 1 | 1000/ 4000 batches | rec 22.54, adv 0.72, |lvar| 23.12, loss_d 1.38, loss 30.02,
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+ | epoch 1 | 1100/ 4000 batches | rec 22.05, adv 0.72, |lvar| 25.78, loss_d 1.39, loss 29.47,
19
+ | epoch 1 | 1200/ 4000 batches | rec 22.45, adv 0.71, |lvar| 25.02, loss_d 1.38, loss 29.77,
20
+ | epoch 1 | 1300/ 4000 batches | rec 21.30, adv 0.70, |lvar| 26.92, loss_d 1.39, loss 28.59,
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+ | epoch 1 | 1400/ 4000 batches | rec 20.85, adv 0.70, |lvar| 28.24, loss_d 1.39, loss 28.12,
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+ | epoch 1 | 1500/ 4000 batches | rec 20.19, adv 0.70, |lvar| 30.02, loss_d 1.39, loss 27.46,
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+ | epoch 1 | 1600/ 4000 batches | rec 19.63, adv 0.70, |lvar| 30.70, loss_d 1.39, loss 26.94,
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+ | epoch 1 | 1700/ 4000 batches | rec 19.40, adv 0.70, |lvar| 34.17, loss_d 1.38, loss 26.78,
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+ | epoch 1 | 1800/ 4000 batches | rec 18.30, adv 0.70, |lvar| 41.40, loss_d 1.38, loss 25.69,
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+ | epoch 1 | 1900/ 4000 batches | rec 17.56, adv 0.69, |lvar| 45.39, loss_d 1.39, loss 24.95,
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+ | epoch 1 | 2000/ 4000 batches | rec 16.84, adv 0.70, |lvar| 47.07, loss_d 1.39, loss 24.27,
28
+ | epoch 1 | 2100/ 4000 batches | rec 16.22, adv 0.70, |lvar| 48.51, loss_d 1.39, loss 23.67,
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+ | epoch 1 | 2200/ 4000 batches | rec 15.50, adv 0.70, |lvar| 51.16, loss_d 1.39, loss 22.99,
30
+ | epoch 1 | 2300/ 4000 batches | rec 14.85, adv 0.70, |lvar| 52.14, loss_d 1.39, loss 22.34,
31
+ | epoch 1 | 2400/ 4000 batches | rec 14.05, adv 0.70, |lvar| 53.86, loss_d 1.39, loss 21.57,
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+ | epoch 1 | 2500/ 4000 batches | rec 13.93, adv 0.70, |lvar| 56.30, loss_d 1.38, loss 21.47,
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+ | epoch 1 | 2600/ 4000 batches | rec 12.56, adv 0.70, |lvar| 59.91, loss_d 1.38, loss 20.14,
34
+ | epoch 1 | 2700/ 4000 batches | rec 11.86, adv 0.70, |lvar| 63.06, loss_d 1.38, loss 19.47,
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+ | epoch 1 | 2800/ 4000 batches | rec 11.38, adv 0.70, |lvar| 65.80, loss_d 1.38, loss 19.02,
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+ | epoch 1 | 2900/ 4000 batches | rec 10.84, adv 0.69, |lvar| 68.75, loss_d 1.39, loss 18.46,
37
+ | epoch 1 | 3000/ 4000 batches | rec 10.43, adv 0.70, |lvar| 70.10, loss_d 1.39, loss 18.08,
38
+ | epoch 1 | 3100/ 4000 batches | rec 10.13, adv 0.70, |lvar| 71.31, loss_d 1.38, loss 17.83,
39
+ | epoch 1 | 3200/ 4000 batches | rec 9.46, adv 0.70, |lvar| 72.98, loss_d 1.39, loss 17.16,
40
+ | epoch 1 | 3300/ 4000 batches | rec 9.08, adv 0.69, |lvar| 73.96, loss_d 1.39, loss 16.76,
41
+ | epoch 1 | 3400/ 4000 batches | rec 8.71, adv 0.70, |lvar| 74.02, loss_d 1.39, loss 16.42,
42
+ | epoch 1 | 3500/ 4000 batches | rec 8.50, adv 0.70, |lvar| 73.92, loss_d 1.39, loss 16.22,
43
+ | epoch 1 | 3600/ 4000 batches | rec 8.25, adv 0.70, |lvar| 74.41, loss_d 1.39, loss 15.99,
44
+ | epoch 1 | 3700/ 4000 batches | rec 7.97, adv 0.70, |lvar| 76.38, loss_d 1.39, loss 15.69,
45
+ | epoch 1 | 3800/ 4000 batches | rec 10.10, adv 0.70, |lvar| 77.42, loss_d 1.39, loss 17.83,
46
+ | epoch 1 | 3900/ 4000 batches | rec 7.95, adv 0.69, |lvar| 79.22, loss_d 1.39, loss 15.67,
47
+ | epoch 1 | 4000/ 4000 batches | rec 7.59, adv 0.70, |lvar| 79.55, loss_d 1.39, loss 15.34,
48
+ --------------------------------------------------------------------------------
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+ | end of epoch 1| time 52816s| valid rec 4.80, adv 0.70, |lvar| 79.69, loss_d 1.38, loss 12.62, | saving model
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+ --------------------------------------------------------------------------------
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+ | epoch 2 | 100/ 4000 batches | rec 7.24, adv 0.70, |lvar| 79.76, loss_d 1.39, loss 14.99,
52
+ | epoch 2 | 200/ 4000 batches | rec 7.14, adv 0.70, |lvar| 80.47, loss_d 1.38, loss 14.93,
53
+ | epoch 2 | 300/ 4000 batches | rec 6.98, adv 0.70, |lvar| 81.23, loss_d 1.38, loss 14.77,
54
+ | epoch 2 | 400/ 4000 batches | rec 6.63, adv 0.70, |lvar| 81.79, loss_d 1.38, loss 14.45,
55
+ | epoch 2 | 500/ 4000 batches | rec 6.70, adv 0.70, |lvar| 82.32, loss_d 1.38, loss 14.51,
56
+ | epoch 2 | 600/ 4000 batches | rec 6.27, adv 0.70, |lvar| 82.74, loss_d 1.38, loss 14.08,
57
+ | epoch 2 | 700/ 4000 batches | rec 6.22, adv 0.70, |lvar| 83.32, loss_d 1.38, loss 14.06,
58
+ | epoch 2 | 800/ 4000 batches | rec 6.04, adv 0.70, |lvar| 84.53, loss_d 1.39, loss 13.86,
59
+ | epoch 2 | 900/ 4000 batches | rec 5.70, adv 0.70, |lvar| 84.70, loss_d 1.39, loss 13.52,
60
+ | epoch 2 | 1000/ 4000 batches | rec 5.70, adv 0.70, |lvar| 84.98, loss_d 1.38, loss 13.52,
61
+ | epoch 2 | 1100/ 4000 batches | rec 5.63, adv 0.70, |lvar| 85.23, loss_d 1.38, loss 13.44,
62
+ | epoch 2 | 1200/ 4000 batches | rec 5.33, adv 0.70, |lvar| 85.40, loss_d 1.38, loss 13.15,
63
+ | epoch 2 | 1300/ 4000 batches | rec 5.32, adv 0.70, |lvar| 85.59, loss_d 1.38, loss 13.14,
64
+ | epoch 2 | 1400/ 4000 batches | rec 6.16, adv 0.70, |lvar| 85.73, loss_d 1.38, loss 13.98,
65
+ | epoch 2 | 1500/ 4000 batches | rec 5.04, adv 0.70, |lvar| 86.09, loss_d 1.38, loss 12.86,
66
+ | epoch 2 | 1600/ 4000 batches | rec 5.06, adv 0.70, |lvar| 86.09, loss_d 1.38, loss 12.88,
67
+ | epoch 2 | 1700/ 4000 batches | rec 4.86, adv 0.70, |lvar| 86.17, loss_d 1.38, loss 12.69,
68
+ | epoch 2 | 1800/ 4000 batches | rec 4.91, adv 0.70, |lvar| 86.39, loss_d 1.38, loss 12.75,
69
+ | epoch 2 | 1900/ 4000 batches | rec 4.79, adv 0.70, |lvar| 86.75, loss_d 1.38, loss 12.64,
70
+ | epoch 2 | 2000/ 4000 batches | rec 4.58, adv 0.70, |lvar| 87.46, loss_d 1.38, loss 12.44,
71
+ | epoch 2 | 2100/ 4000 batches | rec 4.72, adv 0.70, |lvar| 88.10, loss_d 1.38, loss 12.59,
72
+ | epoch 2 | 2200/ 4000 batches | rec 4.56, adv 0.70, |lvar| 88.70, loss_d 1.38, loss 12.43,
73
+ | epoch 2 | 2300/ 4000 batches | rec 4.51, adv 0.70, |lvar| 89.03, loss_d 1.38, loss 12.40,
74
+ | epoch 2 | 2400/ 4000 batches | rec 4.47, adv 0.70, |lvar| 89.73, loss_d 1.38, loss 12.37,
75
+ | epoch 2 | 2500/ 4000 batches | rec 4.27, adv 0.70, |lvar| 90.41, loss_d 1.38, loss 12.19,
76
+ | epoch 2 | 2600/ 4000 batches | rec 4.23, adv 0.70, |lvar| 91.41, loss_d 1.38, loss 12.17,
77
+ | epoch 2 | 2700/ 4000 batches | rec 3.99, adv 0.70, |lvar| 93.22, loss_d 1.38, loss 11.96,
78
+ | epoch 2 | 2800/ 4000 batches | rec 4.30, adv 0.70, |lvar| 94.51, loss_d 1.38, loss 12.27,
79
+ | epoch 2 | 2900/ 4000 batches | rec 3.59, adv 0.70, |lvar| 95.86, loss_d 1.38, loss 11.59,
80
+ | epoch 2 | 3000/ 4000 batches | rec 3.36, adv 0.70, |lvar| 96.79, loss_d 1.38, loss 11.38,
81
+ | epoch 2 | 3100/ 4000 batches | rec 3.22, adv 0.70, |lvar| 97.64, loss_d 1.38, loss 11.21,
82
+ | epoch 2 | 3200/ 4000 batches | rec 3.09, adv 0.70, |lvar| 98.46, loss_d 1.39, loss 11.07,
83
+ | epoch 2 | 3300/ 4000 batches | rec 2.78, adv 0.70, |lvar| 98.02, loss_d 1.38, loss 10.75,
84
+ | epoch 2 | 3400/ 4000 batches | rec 3.03, adv 0.70, |lvar| 98.98, loss_d 1.38, loss 11.05,
85
+ | epoch 2 | 3500/ 4000 batches | rec 2.75, adv 0.70, |lvar| 99.66, loss_d 1.39, loss 10.74,
86
+ | epoch 2 | 3600/ 4000 batches | rec 3.06, adv 0.70, |lvar| 99.68, loss_d 1.39, loss 11.04,
87
+ | epoch 2 | 3700/ 4000 batches | rec 2.50, adv 0.70, |lvar| 99.36, loss_d 1.39, loss 10.47,
88
+ | epoch 2 | 3800/ 4000 batches | rec 2.17, adv 0.70, |lvar| 98.93, loss_d 1.38, loss 10.14,
89
+ | epoch 2 | 3900/ 4000 batches | rec 2.66, adv 0.70, |lvar| 98.48, loss_d 1.39, loss 10.65,
90
+ | epoch 2 | 4000/ 4000 batches | rec 2.27, adv 0.70, |lvar| 98.55, loss_d 1.39, loss 10.23,
91
+ --------------------------------------------------------------------------------
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+ | end of epoch 2| time 81294s| valid rec 0.56, adv 0.71, |lvar| 96.75, loss_d 1.37, loss 8.68, | saving model
93
+ --------------------------------------------------------------------------------
94
+ | epoch 3 | 100/ 4000 batches | rec 2.47, adv 0.70, |lvar| 99.02, loss_d 1.39, loss 10.44,
95
+ | epoch 3 | 200/ 4000 batches | rec 1.89, adv 0.70, |lvar| 97.64, loss_d 1.38, loss 9.83,
96
+ | epoch 3 | 300/ 4000 batches | rec 2.13, adv 0.70, |lvar| 97.98, loss_d 1.39, loss 10.09,
97
+ | epoch 3 | 400/ 4000 batches | rec 1.83, adv 0.70, |lvar| 98.26, loss_d 1.39, loss 9.76,
98
+ | epoch 3 | 500/ 4000 batches | rec 2.29, adv 0.70, |lvar| 97.21, loss_d 1.38, loss 10.25,
99
+ | epoch 3 | 600/ 4000 batches | rec 2.11, adv 0.70, |lvar| 97.58, loss_d 1.38, loss 10.07,
100
+ | epoch 3 | 700/ 4000 batches | rec 2.10, adv 0.70, |lvar| 97.21, loss_d 1.39, loss 10.06,
101
+ | epoch 3 | 800/ 4000 batches | rec 2.42, adv 0.70, |lvar| 97.32, loss_d 1.38, loss 10.39,
102
+ | epoch 3 | 900/ 4000 batches | rec 1.64, adv 0.70, |lvar| 96.77, loss_d 1.38, loss 9.57,
103
+ | epoch 3 | 1000/ 4000 batches | rec 1.92, adv 0.70, |lvar| 97.48, loss_d 1.38, loss 9.86,
104
+ | epoch 3 | 1100/ 4000 batches | rec 2.58, adv 0.70, |lvar| 98.46, loss_d 1.39, loss 10.57,
105
+ | epoch 3 | 1200/ 4000 batches | rec 1.59, adv 0.70, |lvar| 97.18, loss_d 1.38, loss 9.53,
106
+ | epoch 3 | 1300/ 4000 batches | rec 2.10, adv 0.70, |lvar| 97.13, loss_d 1.38, loss 10.09,
107
+ | epoch 3 | 1400/ 4000 batches | rec 1.48, adv 0.70, |lvar| 97.29, loss_d 1.38, loss 9.42,
108
+ | epoch 3 | 1500/ 4000 batches | rec 2.11, adv 0.70, |lvar| 97.03, loss_d 1.38, loss 10.10,
109
+ | epoch 3 | 1600/ 4000 batches | rec 1.86, adv 0.70, |lvar| 97.61, loss_d 1.39, loss 9.82,
110
+ | epoch 3 | 1700/ 4000 batches | rec 1.61, adv 0.70, |lvar| 97.38, loss_d 1.38, loss 9.54,
111
+ | epoch 3 | 1800/ 4000 batches | rec 1.44, adv 0.70, |lvar| 98.12, loss_d 1.38, loss 9.46,
112
+ | epoch 3 | 1900/ 4000 batches | rec 2.05, adv 0.70, |lvar| 98.16, loss_d 1.38, loss 10.02,
113
+ | epoch 3 | 2000/ 4000 batches | rec 1.29, adv 0.70, |lvar| 98.74, loss_d 1.38, loss 9.25,
114
+ | epoch 3 | 2100/ 4000 batches | rec 2.39, adv 0.70, |lvar| 99.83, loss_d 1.38, loss 10.43,
115
+ | epoch 3 | 2200/ 4000 batches | rec 1.58, adv 0.70, |lvar| 99.32, loss_d 1.38, loss 9.62,
116
+ | epoch 3 | 2300/ 4000 batches | rec 1.46, adv 0.70, |lvar| 99.23, loss_d 1.39, loss 9.41,
117
+ | epoch 3 | 2400/ 4000 batches | rec 1.95, adv 0.71, |lvar| 100.16, loss_d 1.39, loss 10.00,
118
+ | epoch 3 | 2500/ 4000 batches | rec 1.21, adv 0.70, |lvar| 97.14, loss_d 1.38, loss 9.20,
119
+ | epoch 3 | 2600/ 4000 batches | rec 1.15, adv 0.70, |lvar| 98.40, loss_d 1.39, loss 9.15,
120
+ | epoch 3 | 2700/ 4000 batches | rec 1.13, adv 0.70, |lvar| 99.17, loss_d 1.39, loss 9.11,
121
+ | epoch 3 | 2800/ 4000 batches | rec 1.97, adv 0.70, |lvar| 98.14, loss_d 1.39, loss 9.92,
122
+ | epoch 3 | 2900/ 4000 batches | rec 1.09, adv 0.70, |lvar| 98.04, loss_d 1.39, loss 9.04,
123
+ | epoch 3 | 3000/ 4000 batches | rec 1.95, adv 0.70, |lvar| 97.65, loss_d 1.39, loss 9.96,
124
+ | epoch 3 | 3100/ 4000 batches | rec 1.04, adv 0.69, |lvar| 96.44, loss_d 1.39, loss 8.92,
125
+ | epoch 3 | 3200/ 4000 batches | rec 1.00, adv 0.70, |lvar| 96.72, loss_d 1.39, loss 8.93,
126
+ | epoch 3 | 3300/ 4000 batches | rec 2.03, adv 0.70, |lvar| 96.79, loss_d 1.38, loss 9.98,
127
+ | epoch 3 | 3400/ 4000 batches | rec 0.99, adv 0.69, |lvar| 95.61, loss_d 1.38, loss 8.89,
128
+ | epoch 3 | 3500/ 4000 batches | rec 0.96, adv 0.70, |lvar| 95.20, loss_d 1.39, loss 8.90,
129
+ | epoch 3 | 3600/ 4000 batches | rec 0.93, adv 0.70, |lvar| 95.18, loss_d 1.39, loss 8.86,
130
+ | epoch 3 | 3700/ 4000 batches | rec 2.50, adv 0.70, |lvar| 94.97, loss_d 1.38, loss 10.47,
131
+ | epoch 3 | 3800/ 4000 batches | rec 0.93, adv 0.70, |lvar| 93.77, loss_d 1.38, loss 8.83,
132
+ | epoch 3 | 3900/ 4000 batches | rec 1.67, adv 0.70, |lvar| 94.80, loss_d 1.38, loss 9.65,
133
+ | epoch 3 | 4000/ 4000 batches | rec 0.91, adv 0.69, |lvar| 94.66, loss_d 1.39, loss 8.77,
134
+ --------------------------------------------------------------------------------
135
+ | end of epoch 3| time 80655s| valid rec 0.19, adv 0.69, |lvar| 94.95, loss_d 1.39, loss 8.09, | saving model
136
+ --------------------------------------------------------------------------------
137
+ | epoch 4 | 100/ 4000 batches | rec 0.87, adv 0.70, |lvar| 94.47, loss_d 1.39, loss 8.79,
138
+ | epoch 4 | 200/ 4000 batches | rec 2.03, adv 0.70, |lvar| 94.93, loss_d 1.38, loss 10.01,
139
+ | epoch 4 | 300/ 4000 batches | rec 0.89, adv 0.69, |lvar| 93.54, loss_d 1.39, loss 8.74,
140
+ | epoch 4 | 400/ 4000 batches | rec 0.84, adv 0.70, |lvar| 93.05, loss_d 1.38, loss 8.76,
141
+ | epoch 4 | 500/ 4000 batches | rec 1.70, adv 0.70, |lvar| 94.23, loss_d 1.39, loss 9.64,
142
+ | epoch 4 | 600/ 4000 batches | rec 0.91, adv 0.69, |lvar| 93.26, loss_d 1.38, loss 8.79,
143
+ | epoch 4 | 700/ 4000 batches | rec 0.81, adv 0.70, |lvar| 92.61, loss_d 1.38, loss 8.74,
144
+ | epoch 4 | 800/ 4000 batches | rec 1.72, adv 0.70, |lvar| 93.73, loss_d 1.38, loss 9.70,
145
+ | epoch 4 | 900/ 4000 batches | rec 0.81, adv 0.70, |lvar| 92.36, loss_d 1.38, loss 8.72,
146
+ | epoch 4 | 1000/ 4000 batches | rec 0.78, adv 0.70, |lvar| 92.93, loss_d 1.38, loss 8.70,
147
+ | epoch 4 | 1100/ 4000 batches | rec 1.87, adv 0.70, |lvar| 94.05, loss_d 1.38, loss 9.84,
148
+ | epoch 4 | 1200/ 4000 batches | rec 0.80, adv 0.70, |lvar| 92.26, loss_d 1.38, loss 8.71,
149
+ | epoch 4 | 1300/ 4000 batches | rec 0.77, adv 0.70, |lvar| 92.72, loss_d 1.38, loss 8.70,
150
+ | epoch 4 | 1400/ 4000 batches | rec 0.75, adv 0.70, |lvar| 93.41, loss_d 1.38, loss 8.68,
151
+ | epoch 4 | 1500/ 4000 batches | rec 2.44, adv 0.70, |lvar| 94.04, loss_d 1.38, loss 10.43,
152
+ | epoch 4 | 1600/ 4000 batches | rec 0.80, adv 0.70, |lvar| 93.26, loss_d 1.38, loss 8.71,
153
+ | epoch 4 | 1700/ 4000 batches | rec 0.75, adv 0.70, |lvar| 94.10, loss_d 1.39, loss 8.65,
154
+ | epoch 4 | 1800/ 4000 batches | rec 1.60, adv 0.70, |lvar| 94.23, loss_d 1.38, loss 9.58,
155
+ | epoch 4 | 1900/ 4000 batches | rec 0.77, adv 0.69, |lvar| 93.29, loss_d 1.38, loss 8.60,
156
+ | epoch 4 | 2000/ 4000 batches | rec 0.70, adv 0.70, |lvar| 92.95, loss_d 1.39, loss 8.63,
157
+ | epoch 4 | 2100/ 4000 batches | rec 0.67, adv 0.70, |lvar| 92.78, loss_d 1.38, loss 8.59,
158
+ | epoch 4 | 2200/ 4000 batches | rec 1.70, adv 0.70, |lvar| 93.36, loss_d 1.38, loss 9.62,
159
+ | epoch 4 | 2300/ 4000 batches | rec 0.78, adv 0.70, |lvar| 92.91, loss_d 1.38, loss 8.66,
160
+ | epoch 4 | 2400/ 4000 batches | rec 0.66, adv 0.70, |lvar| 92.88, loss_d 1.38, loss 8.57,
161
+ | epoch 4 | 2500/ 4000 batches | rec 0.64, adv 0.70, |lvar| 93.65, loss_d 1.39, loss 8.55,
162
+ | epoch 4 | 2600/ 4000 batches | rec 1.80, adv 0.70, |lvar| 94.26, loss_d 1.38, loss 9.72,
163
+ | epoch 4 | 2700/ 4000 batches | rec 0.63, adv 0.70, |lvar| 92.13, loss_d 1.38, loss 8.56,
164
+ | epoch 4 | 2800/ 4000 batches | rec 0.61, adv 0.70, |lvar| 92.79, loss_d 1.38, loss 8.50,
165
+ | epoch 4 | 2900/ 4000 batches | rec 0.59, adv 0.70, |lvar| 92.79, loss_d 1.39, loss 8.50,
166
+ | epoch 4 | 3000/ 4000 batches | rec 2.05, adv 0.70, |lvar| 94.32, loss_d 1.38, loss 10.02,
167
+ | epoch 4 | 3100/ 4000 batches | rec 0.62, adv 0.70, |lvar| 91.65, loss_d 1.38, loss 8.49,
168
+ | epoch 4 | 3200/ 4000 batches | rec 0.58, adv 0.70, |lvar| 92.52, loss_d 1.38, loss 8.47,
169
+ | epoch 4 | 3300/ 4000 batches | rec 0.56, adv 0.70, |lvar| 92.90, loss_d 1.39, loss 8.48,
170
+ | epoch 4 | 3400/ 4000 batches | rec 1.70, adv 0.70, |lvar| 93.59, loss_d 1.38, loss 9.63,
171
+ | epoch 4 | 3500/ 4000 batches | rec 0.63, adv 0.69, |lvar| 92.00, loss_d 1.38, loss 8.49,
172
+ | epoch 4 | 3600/ 4000 batches | rec 0.55, adv 0.70, |lvar| 91.43, loss_d 1.38, loss 8.48,
173
+ | epoch 4 | 3700/ 4000 batches | rec 0.55, adv 0.70, |lvar| 92.94, loss_d 1.39, loss 8.47,
174
+ | epoch 4 | 3800/ 4000 batches | rec 1.81, adv 0.70, |lvar| 94.83, loss_d 1.38, loss 9.75,
175
+ | epoch 4 | 3900/ 4000 batches | rec 0.56, adv 0.70, |lvar| 91.29, loss_d 1.38, loss 8.47,
176
+ | epoch 4 | 4000/ 4000 batches | rec 0.53, adv 0.70, |lvar| 91.46, loss_d 1.38, loss 8.46,
177
+ --------------------------------------------------------------------------------
178
+ | end of epoch 4| time 79828s| valid rec 0.09, adv 0.69, |lvar| 91.49, loss_d 1.37, loss 7.86, | saving model
179
+ --------------------------------------------------------------------------------
180
+ | epoch 5 | 100/ 4000 batches | rec 0.52, adv 0.70, |lvar| 92.17, loss_d 1.38, loss 8.42,
181
+ | epoch 5 | 200/ 4000 batches | rec 0.51, adv 0.70, |lvar| 91.78, loss_d 1.38, loss 8.42,
182
+ | epoch 5 | 300/ 4000 batches | rec 1.82, adv 0.70, |lvar| 93.38, loss_d 1.38, loss 9.77,
183
+ | epoch 5 | 400/ 4000 batches | rec 0.54, adv 0.70, |lvar| 91.53, loss_d 1.38, loss 8.44,
184
+ | epoch 5 | 500/ 4000 batches | rec 0.50, adv 0.70, |lvar| 91.82, loss_d 1.38, loss 8.41,
185
+ | epoch 5 | 600/ 4000 batches | rec 0.50, adv 0.70, |lvar| 91.84, loss_d 1.38, loss 8.39,
186
+ | epoch 5 | 700/ 4000 batches | rec 1.96, adv 0.70, |lvar| 93.21, loss_d 1.38, loss 9.90,
187
+ | epoch 5 | 800/ 4000 batches | rec 0.53, adv 0.70, |lvar| 90.85, loss_d 1.38, loss 8.44,
188
+ | epoch 5 | 900/ 4000 batches | rec 0.50, adv 0.70, |lvar| 91.64, loss_d 1.38, loss 8.42,
189
+ | epoch 5 | 1000/ 4000 batches | rec 0.48, adv 0.70, |lvar| 92.19, loss_d 1.38, loss 8.41,
190
+ | epoch 5 | 1100/ 4000 batches | rec 1.45, adv 0.70, |lvar| 93.26, loss_d 1.38, loss 9.39,
191
+ | epoch 5 | 1200/ 4000 batches | rec 0.50, adv 0.70, |lvar| 90.79, loss_d 1.38, loss 8.40,
192
+ | epoch 5 | 1300/ 4000 batches | rec 0.47, adv 0.70, |lvar| 90.92, loss_d 1.38, loss 8.34,
193
+ | epoch 5 | 1400/ 4000 batches | rec 0.46, adv 0.70, |lvar| 91.40, loss_d 1.38, loss 8.35,
194
+ | epoch 5 | 1500/ 4000 batches | rec 0.45, adv 0.70, |lvar| 91.15, loss_d 1.38, loss 8.36,
195
+ | epoch 5 | 1600/ 4000 batches | rec 1.70, adv 0.70, |lvar| 93.93, loss_d 1.38, loss 9.60,
196
+ | epoch 5 | 1700/ 4000 batches | rec 0.47, adv 0.70, |lvar| 90.28, loss_d 1.38, loss 8.39,
197
+ | epoch 5 | 1800/ 4000 batches | rec 0.46, adv 0.70, |lvar| 91.17, loss_d 1.38, loss 8.36,
198
+ | epoch 5 | 1900/ 4000 batches | rec 0.45, adv 0.70, |lvar| 91.63, loss_d 1.39, loss 8.33,
199
+ | epoch 5 | 2000/ 4000 batches | rec 2.01, adv 0.70, |lvar| 92.78, loss_d 1.38, loss 9.97,
200
+ | epoch 5 | 2100/ 4000 batches | rec 0.48, adv 0.70, |lvar| 90.71, loss_d 1.38, loss 8.39,
201
+ | epoch 5 | 2200/ 4000 batches | rec 0.45, adv 0.70, |lvar| 90.79, loss_d 1.38, loss 8.34,
202
+ | epoch 5 | 2300/ 4000 batches | rec 0.44, adv 0.70, |lvar| 90.94, loss_d 1.38, loss 8.35,
203
+ | epoch 5 | 2400/ 4000 batches | rec 1.28, adv 0.69, |lvar| 92.84, loss_d 1.38, loss 9.15,
204
+ | epoch 5 | 2500/ 4000 batches | rec 0.44, adv 0.70, |lvar| 89.75, loss_d 1.38, loss 8.38,
205
+ | epoch 5 | 2600/ 4000 batches | rec 0.42, adv 0.70, |lvar| 90.31, loss_d 1.38, loss 8.31,
206
+ | epoch 5 | 2700/ 4000 batches | rec 0.41, adv 0.70, |lvar| 90.53, loss_d 1.38, loss 8.29,
207
+ | epoch 5 | 2800/ 4000 batches | rec 1.75, adv 0.70, |lvar| 92.41, loss_d 1.38, loss 9.70,
208
+ | epoch 5 | 2900/ 4000 batches | rec 0.46, adv 0.70, |lvar| 90.43, loss_d 1.38, loss 8.36,
209
+ | epoch 5 | 3000/ 4000 batches | rec 0.42, adv 0.69, |lvar| 90.17, loss_d 1.38, loss 8.26,
210
+ | epoch 5 | 3100/ 4000 batches | rec 0.41, adv 0.70, |lvar| 90.76, loss_d 1.38, loss 8.29,
211
+ | epoch 5 | 3200/ 4000 batches | rec 1.30, adv 0.71, |lvar| 91.97, loss_d 1.38, loss 9.28,
212
+ | epoch 5 | 3300/ 4000 batches | rec 0.45, adv 0.70, |lvar| 90.31, loss_d 1.38, loss 8.30,
213
+ | epoch 5 | 3400/ 4000 batches | rec 0.41, adv 0.70, |lvar| 90.05, loss_d 1.38, loss 8.29,
214
+ | epoch 5 | 3500/ 4000 batches | rec 0.40, adv 0.70, |lvar| 91.15, loss_d 1.39, loss 8.27,
215
+ | epoch 5 | 3600/ 4000 batches | rec 0.40, adv 0.70, |lvar| 90.47, loss_d 1.38, loss 8.28,
216
+ | epoch 5 | 3700/ 4000 batches | rec 1.39, adv 0.70, |lvar| 92.88, loss_d 1.38, loss 9.36,
217
+ | epoch 5 | 3800/ 4000 batches | rec 0.42, adv 0.70, |lvar| 90.38, loss_d 1.39, loss 8.28,
218
+ | epoch 5 | 3900/ 4000 batches | rec 0.39, adv 0.69, |lvar| 89.97, loss_d 1.38, loss 8.22,
219
+ | epoch 5 | 4000/ 4000 batches | rec 0.37, adv 0.70, |lvar| 89.80, loss_d 1.38, loss 8.26,
220
+ --------------------------------------------------------------------------------
221
+ | end of epoch 5| time 81264s| valid rec 0.06, adv 0.70, |lvar| 87.29, loss_d 1.38, loss 7.94,
222
+ --------------------------------------------------------------------------------
223
+ | epoch 6 | 100/ 4000 batches | rec 0.37, adv 0.70, |lvar| 89.71, loss_d 1.38, loss 8.27,
224
+ | epoch 6 | 200/ 4000 batches | rec 1.71, adv 0.70, |lvar| 92.56, loss_d 1.38, loss 9.64,
225
+ | epoch 6 | 300/ 4000 batches | rec 0.41, adv 0.70, |lvar| 89.86, loss_d 1.38, loss 8.29,
226
+ | epoch 6 | 400/ 4000 batches | rec 0.38, adv 0.70, |lvar| 90.14, loss_d 1.38, loss 8.26,
227
+ | epoch 6 | 500/ 4000 batches | rec 0.37, adv 0.70, |lvar| 90.34, loss_d 1.38, loss 8.24,
228
+ | epoch 6 | 600/ 4000 batches | rec 1.38, adv 0.70, |lvar| 91.64, loss_d 1.38, loss 9.28,
229
+ | epoch 6 | 700/ 4000 batches | rec 0.39, adv 0.70, |lvar| 89.50, loss_d 1.38, loss 8.26,
230
+ | epoch 6 | 800/ 4000 batches | rec 0.37, adv 0.70, |lvar| 89.64, loss_d 1.38, loss 8.25,
231
+ | epoch 6 | 900/ 4000 batches | rec 0.36, adv 0.70, |lvar| 90.08, loss_d 1.38, loss 8.22,
232
+ | epoch 6 | 1000/ 4000 batches | rec 0.35, adv 0.70, |lvar| 89.49, loss_d 1.38, loss 8.24,
233
+ | epoch 6 | 1100/ 4000 batches | rec 1.47, adv 0.70, |lvar| 92.55, loss_d 1.38, loss 9.38,
234
+ | epoch 6 | 1200/ 4000 batches | rec 0.37, adv 0.70, |lvar| 89.46, loss_d 1.38, loss 8.23,
235
+ | epoch 6 | 1300/ 4000 batches | rec 0.35, adv 0.70, |lvar| 89.30, loss_d 1.38, loss 8.25,
236
+ | epoch 6 | 1400/ 4000 batches | rec 1.20, adv 0.70, |lvar| 89.93, loss_d 1.38, loss 9.09,
237
+ | epoch 6 | 1500/ 4000 batches | rec 0.49, adv 0.69, |lvar| 91.35, loss_d 1.38, loss 8.34,
238
+ | epoch 6 | 1600/ 4000 batches | rec 0.35, adv 0.70, |lvar| 89.45, loss_d 1.38, loss 8.21,
239
+ | epoch 6 | 1700/ 4000 batches | rec 0.33, adv 0.70, |lvar| 89.26, loss_d 1.38, loss 8.26,
240
+ | epoch 6 | 1800/ 4000 batches | rec 0.33, adv 0.70, |lvar| 89.93, loss_d 1.39, loss 8.21,
241
+ | epoch 6 | 1900/ 4000 batches | rec 0.33, adv 0.70, |lvar| 90.40, loss_d 1.38, loss 8.19,
242
+ | epoch 6 | 2000/ 4000 batches | rec 1.71, adv 0.70, |lvar| 91.95, loss_d 1.38, loss 9.61,
243
+ | epoch 6 | 2100/ 4000 batches | rec 0.36, adv 0.70, |lvar| 89.06, loss_d 1.38, loss 8.24,
244
+ | epoch 6 | 2200/ 4000 batches | rec 0.33, adv 0.70, |lvar| 89.04, loss_d 1.38, loss 8.19,
245
+ | epoch 6 | 2300/ 4000 batches | rec 0.31, adv 0.69, |lvar| 89.46, loss_d 1.38, loss 8.15,
246
+ | epoch 6 | 2400/ 4000 batches | rec 1.23, adv 0.71, |lvar| 91.07, loss_d 1.38, loss 9.22,
247
+ | epoch 6 | 2500/ 4000 batches | rec 0.35, adv 0.69, |lvar| 89.16, loss_d 1.38, loss 8.18,
248
+ | epoch 6 | 2600/ 4000 batches | rec 0.31, adv 0.70, |lvar| 89.12, loss_d 1.38, loss 8.17,
249
+ | epoch 6 | 2700/ 4000 batches | rec 0.30, adv 0.70, |lvar| 89.16, loss_d 1.38, loss 8.15,
250
+ | epoch 6 | 2800/ 4000 batches | rec 0.29, adv 0.70, |lvar| 89.34, loss_d 1.38, loss 8.18,
251
+ | epoch 6 | 2900/ 4000 batches | rec 0.30, adv 0.70, |lvar| 89.54, loss_d 1.39, loss 8.19,
252
+ | epoch 6 | 3000/ 4000 batches | rec 1.75, adv 0.69, |lvar| 91.28, loss_d 1.38, loss 9.59,
253
+ | epoch 6 | 3100/ 4000 batches | rec 0.32, adv 0.70, |lvar| 88.54, loss_d 1.38, loss 8.23,
254
+ | epoch 6 | 3200/ 4000 batches | rec 0.30, adv 0.70, |lvar| 88.96, loss_d 1.38, loss 8.17,
255
+ | epoch 6 | 3300/ 4000 batches | rec 0.29, adv 0.70, |lvar| 89.22, loss_d 1.39, loss 8.15,
256
+ | epoch 6 | 3400/ 4000 batches | rec 1.06, adv 0.71, |lvar| 91.70, loss_d 1.38, loss 9.04,
257
+ | epoch 6 | 3500/ 4000 batches | rec 0.32, adv 0.69, |lvar| 89.49, loss_d 1.38, loss 8.13,
258
+ | epoch 6 | 3600/ 4000 batches | rec 0.29, adv 0.70, |lvar| 88.53, loss_d 1.38, loss 8.13,
259
+ | epoch 6 | 3700/ 4000 batches | rec 0.28, adv 0.70, |lvar| 88.53, loss_d 1.38, loss 8.14,
260
+ | epoch 6 | 3800/ 4000 batches | rec 0.27, adv 0.70, |lvar| 88.95, loss_d 1.39, loss 8.15,
261
+ | epoch 6 | 3900/ 4000 batches | rec 0.27, adv 0.70, |lvar| 89.00, loss_d 1.38, loss 8.12,
262
+ | epoch 6 | 4000/ 4000 batches | rec 1.69, adv 0.70, |lvar| 91.98, loss_d 1.38, loss 9.62,
263
+ --------------------------------------------------------------------------------
264
+ | end of epoch 6| time 82327s| valid rec 0.06, adv 0.69, |lvar| 87.98, loss_d 1.37, loss 7.86,
265
+ --------------------------------------------------------------------------------
266
+ | epoch 7 | 100/ 4000 batches | rec 0.31, adv 0.70, |lvar| 88.24, loss_d 1.38, loss 8.20,
267
+ | epoch 7 | 200/ 4000 batches | rec 0.28, adv 0.70, |lvar| 88.53, loss_d 1.38, loss 8.14,
268
+ | epoch 7 | 300/ 4000 batches | rec 0.27, adv 0.69, |lvar| 88.91, loss_d 1.39, loss 8.11,
269
+ | epoch 7 | 400/ 4000 batches | rec 0.26, adv 0.70, |lvar| 88.58, loss_d 1.38, loss 8.12,
270
+ | epoch 7 | 500/ 4000 batches | rec 1.48, adv 0.70, |lvar| 90.73, loss_d 1.38, loss 9.43,
271
+ | epoch 7 | 600/ 4000 batches | rec 0.32, adv 0.69, |lvar| 88.84, loss_d 1.38, loss 8.15,
272
+ | epoch 7 | 700/ 4000 batches | rec 0.27, adv 0.70, |lvar| 88.03, loss_d 1.38, loss 8.13,
273
+ | epoch 7 | 800/ 4000 batches | rec 0.26, adv 0.70, |lvar| 88.54, loss_d 1.39, loss 8.11,
274
+ | epoch 7 | 900/ 4000 batches | rec 0.25, adv 0.70, |lvar| 88.76, loss_d 1.39, loss 8.09,
275
+ | epoch 7 | 1000/ 4000 batches | rec 0.25, adv 0.70, |lvar| 88.26, loss_d 1.38, loss 8.13,
276
+ | epoch 7 | 1100/ 4000 batches | rec 1.35, adv 0.71, |lvar| 90.62, loss_d 1.38, loss 9.31,
277
+ | epoch 7 | 1200/ 4000 batches | rec 0.31, adv 0.69, |lvar| 88.56, loss_d 1.38, loss 8.08,
278
+ | epoch 7 | 1300/ 4000 batches | rec 0.26, adv 0.70, |lvar| 87.54, loss_d 1.38, loss 8.12,
279
+ | epoch 7 | 1400/ 4000 batches | rec 0.25, adv 0.70, |lvar| 87.85, loss_d 1.38, loss 8.11,
280
+ | epoch 7 | 1500/ 4000 batches | rec 0.24, adv 0.70, |lvar| 88.56, loss_d 1.39, loss 8.10,
281
+ | epoch 7 | 1600/ 4000 batches | rec 0.24, adv 0.70, |lvar| 88.31, loss_d 1.38, loss 8.07,
282
+ | epoch 7 | 1700/ 4000 batches | rec 1.32, adv 0.70, |lvar| 90.47, loss_d 1.38, loss 9.25,
283
+ | epoch 7 | 1800/ 4000 batches | rec 0.27, adv 0.69, |lvar| 87.99, loss_d 1.38, loss 8.08,
284
+ | epoch 7 | 1900/ 4000 batches | rec 0.25, adv 0.70, |lvar| 87.51, loss_d 1.38, loss 8.09,
285
+ | epoch 7 | 2000/ 4000 batches | rec 0.23, adv 0.70, |lvar| 87.58, loss_d 1.38, loss 8.10,
286
+ | epoch 7 | 2100/ 4000 batches | rec 0.23, adv 0.70, |lvar| 87.86, loss_d 1.38, loss 8.10,
287
+ | epoch 7 | 2200/ 4000 batches | rec 0.23, adv 0.70, |lvar| 87.61, loss_d 1.38, loss 8.08,
288
+ | epoch 7 | 2300/ 4000 batches | rec 1.53, adv 0.70, |lvar| 91.64, loss_d 1.38, loss 9.43,
289
+ | epoch 7 | 2400/ 4000 batches | rec 0.27, adv 0.70, |lvar| 87.37, loss_d 1.38, loss 8.14,
290
+ | epoch 7 | 2500/ 4000 batches | rec 0.24, adv 0.70, |lvar| 87.51, loss_d 1.38, loss 8.08,
291
+ | epoch 7 | 2600/ 4000 batches | rec 0.23, adv 0.70, |lvar| 87.79, loss_d 1.39, loss 8.06,
292
+ | epoch 7 | 2700/ 4000 batches | rec 0.23, adv 0.70, |lvar| 87.61, loss_d 1.38, loss 8.10,
293
+ | epoch 7 | 2800/ 4000 batches | rec 0.22, adv 0.70, |lvar| 87.85, loss_d 1.38, loss 8.05,
294
+ | epoch 7 | 2900/ 4000 batches | rec 0.22, adv 0.70, |lvar| 87.44, loss_d 1.39, loss 8.11,
v3/8-100kk-11_12_2024/model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:eedb9aa8251ea91def6b03d4463185970d63ca72c03d22f94043c675adfc3c7c
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+ size 21747417
v3/8-100kk-11_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
v3/8-100kk-16_12_2024/log.txt ADDED
The diff for this file is too large to render. See raw diff
 
v3/8-100kk-16_12_2024/model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:8672075e6e1d6ef0b3a0f596b5723f5a46167491e9f3be1633787e9dc99c02fd
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+ size 153621650
v3/8-100kk-16_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
v3/8-17_12_2024/log.txt ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=4096, dim_d=512, dim_emb=64, dim_h=256, dim_z=256, dropout=0.3, epochs=20, 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=1, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
2
+ # train on cuda device
3
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-17_12_2024/vocab.alphabet
4
+ # train passwords 42553894
5
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=4096, dim_d=512, dim_emb=64, dim_h=256, dim_z=256, dropout=0.3, epochs=20, 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=1, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
6
+ # train on cuda device
7
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-17_12_2024/vocab.alphabet
8
+ # train passwords 168000000
9
+ # valid passwords 42553894
10
+ # model vae parameters: 1300067
11
+ --------------------------------------------------------------------------------
12
+ | epoch 1 | 100/ 41016 batches | rec 32.35, kl 4.00, loss 32.75,
13
+ | epoch 1 | 200/ 41016 batches | rec 27.88, kl 5.41, loss 28.42,
14
+ | epoch 1 | 300/ 41016 batches | rec 26.82, kl 5.34, loss 27.35,
15
+ | epoch 1 | 400/ 41016 batches | rec 26.34, kl 5.40, loss 26.88,
16
+ | epoch 1 | 500/ 41016 batches | rec 25.33, kl 5.39, loss 25.87,
17
+ | epoch 1 | 600/ 41016 batches | rec 24.68, kl 4.88, loss 25.17,
18
+ | epoch 1 | 700/ 41016 batches | rec 23.62, kl 5.34, loss 24.15,
19
+ | epoch 1 | 800/ 41016 batches | rec 23.17, kl 5.65, loss 23.74,
20
+ | epoch 1 | 900/ 41016 batches | rec 22.69, kl 6.45, loss 23.33,
v3/8-17_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
v3/8-200kk-17_12_2024/log.txt ADDED
@@ -0,0 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
2
+ # train on cpu device
3
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
4
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
5
+ # train on cpu device
6
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
7
+ # train passwords 160000000
8
+ # valid passwords 40000000
9
+ # model vae parameters: 21367395
10
+ --------------------------------------------------------------------------------
11
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
12
+ # train on cpu device
13
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
14
+ # train passwords 160000000
15
+ # valid passwords 40000000
16
+ # model vae parameters: 21367395
17
+ --------------------------------------------------------------------------------
18
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=2000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
19
+ # train on cpu device
20
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
21
+ # train passwords 160000000
22
+ # valid passwords 40000000
23
+ # model vae parameters: 21367395
24
+ --------------------------------------------------------------------------------
25
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=2000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
26
+ # train on cpu device
27
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
28
+ # train passwords 160000000
29
+ # valid passwords 40000000
30
+ # model vae parameters: 21367395
31
+ --------------------------------------------------------------------------------
32
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=2000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
33
+ # train on cpu device
34
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
35
+ # train passwords 160000000
36
+ # valid passwords 40000000
37
+ # model vae parameters: 21367395
38
+ --------------------------------------------------------------------------------
39
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=2000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
40
+ # train on cpu device
41
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
42
+ # train passwords 160000000
43
+ # valid passwords 40000000
44
+ # model vae parameters: 21367395
45
+ --------------------------------------------------------------------------------
46
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=2000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
47
+ # train on cpu device
48
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
49
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
50
+ # train on cpu device
51
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
52
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
53
+ # train on cpu device
54
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
55
+ # train passwords 160000000
56
+ # valid passwords 160000000
57
+ # model vae parameters: 21367395
58
+ --------------------------------------------------------------------------------
59
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
60
+ # train on cpu device
61
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
62
+ # train passwords 160000000
63
+ # valid passwords 40000000
64
+ # model vae parameters: 21367395
65
+ --------------------------------------------------------------------------------
66
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-200kk/f720d2/valid.txt')
67
+ # train on cpu device
68
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-200kk-17_12_2024/vocab.alphabet
v3/8-200kk-17_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
v3/8-500kk-17_12_2024/log.txt ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-500kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-500kk/a3a16a/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-500kk/a3a16a/valid.txt')
2
+ # train on cpu device
3
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-500kk-17_12_2024/vocab.alphabet
4
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-500kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-500kk/a3a16a/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-500kk/a3a16a/valid.txt')
5
+ # train on cpu device
6
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-500kk-17_12_2024/vocab.alphabet
7
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=20000, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-500kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-500kk/a3a16a/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-500kk/a3a16a/valid.txt')
8
+ # train on cuda device
9
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-500kk-17_12_2024/vocab.alphabet
10
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=4096, dim_d=512, dim_emb=128, dim_h=512, dim_z=256, dropout=0.3, epochs=20, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-500kk-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-500kk/a3a16a/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8-500kk/a3a16a/valid.txt')
11
+ # train on cuda device
12
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-500kk-17_12_2024/vocab.alphabet
13
+ # train passwords 400000000
14
+ # valid passwords 100000000
15
+ # model vae parameters: 21367395
16
+ --------------------------------------------------------------------------------
v3/8-500kk-17_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
v3/8-aae-17_12_2024/log.txt ADDED
@@ -0,0 +1,359 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=4096, dim_d=512, dim_emb=64, dim_h=256, dim_z=64, dropout=0.3, epochs=20, lambda_adv=10.0, lambda_kl=0.0, lambda_p=0.0, load_model='', log_interval=100, lr=0.0005, max_len=8, model_type='aae', nlayers=1, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-aae-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
2
+ # train on cuda device
3
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-aae-17_12_2024/vocab.alphabet
4
+ # train passwords 168000000
5
+ # valid passwords 42553894
6
+ # model aae parameters: 1124580
7
+ --------------------------------------------------------------------------------
8
+ | epoch 1 | 100/ 41016 batches | rec 33.75, adv 0.69, |lvar| 31.84, loss_d 1.47, loss 40.69,
9
+ | epoch 1 | 200/ 41016 batches | rec 30.95, adv 0.66, |lvar| 117.39, loss_d 1.49, loss 37.53,
10
+ | epoch 1 | 300/ 41016 batches | rec 29.54, adv 0.60, |lvar| 171.30, loss_d 1.56, loss 35.54,
11
+ | epoch 1 | 400/ 41016 batches | rec 27.60, adv 0.66, |lvar| 210.51, loss_d 1.47, loss 34.15,
12
+ | epoch 1 | 500/ 41016 batches | rec 26.71, adv 0.64, |lvar| 216.17, loss_d 1.45, loss 33.12,
13
+ | epoch 1 | 600/ 41016 batches | rec 26.05, adv 0.67, |lvar| 199.64, loss_d 1.43, loss 32.73,
14
+ | epoch 1 | 700/ 41016 batches | rec 24.91, adv 0.68, |lvar| 221.50, loss_d 1.43, loss 31.66,
15
+ | epoch 1 | 800/ 41016 batches | rec 24.08, adv 0.69, |lvar| 278.51, loss_d 1.43, loss 31.00,
16
+ | epoch 1 | 900/ 41016 batches | rec 24.17, adv 0.67, |lvar| 261.24, loss_d 1.44, loss 30.91,
17
+ | epoch 1 | 1000/ 41016 batches | rec 23.51, adv 0.68, |lvar| 296.71, loss_d 1.44, loss 30.27,
18
+ | epoch 1 | 1100/ 41016 batches | rec 23.09, adv 0.67, |lvar| 296.83, loss_d 1.43, loss 29.82,
19
+ | epoch 1 | 1200/ 41016 batches | rec 22.63, adv 0.67, |lvar| 324.45, loss_d 1.45, loss 29.29,
20
+ | epoch 1 | 1300/ 41016 batches | rec 22.20, adv 0.69, |lvar| 385.54, loss_d 1.43, loss 29.06,
21
+ | epoch 1 | 1400/ 41016 batches | rec 21.85, adv 0.67, |lvar| 365.93, loss_d 1.42, loss 28.56,
22
+ | epoch 1 | 1500/ 41016 batches | rec 21.85, adv 0.68, |lvar| 381.60, loss_d 1.41, loss 28.62,
23
+ | epoch 1 | 1600/ 41016 batches | rec 21.15, adv 0.69, |lvar| 403.66, loss_d 1.44, loss 28.08,
24
+ | epoch 1 | 1700/ 41016 batches | rec 20.53, adv 0.68, |lvar| 396.62, loss_d 1.43, loss 27.36,
25
+ | epoch 1 | 1800/ 41016 batches | rec 20.45, adv 0.68, |lvar| 409.59, loss_d 1.42, loss 27.26,
26
+ | epoch 1 | 1900/ 41016 batches | rec 19.58, adv 0.68, |lvar| 423.21, loss_d 1.41, loss 26.33,
27
+ | epoch 1 | 2000/ 41016 batches | rec 19.13, adv 0.66, |lvar| 490.96, loss_d 1.43, loss 25.75,
28
+ | epoch 1 | 2100/ 41016 batches | rec 18.67, adv 0.68, |lvar| 461.75, loss_d 1.42, loss 25.51,
29
+ | epoch 1 | 2200/ 41016 batches | rec 18.21, adv 0.70, |lvar| 466.98, loss_d 1.42, loss 25.17,
30
+ | epoch 1 | 2300/ 41016 batches | rec 17.75, adv 0.68, |lvar| 496.99, loss_d 1.41, loss 24.54,
31
+ | epoch 1 | 2400/ 41016 batches | rec 17.87, adv 0.68, |lvar| 492.38, loss_d 1.39, loss 24.69,
32
+ | epoch 1 | 2500/ 41016 batches | rec 16.78, adv 0.70, |lvar| 490.04, loss_d 1.40, loss 23.74,
33
+ | epoch 1 | 2600/ 41016 batches | rec 16.25, adv 0.70, |lvar| 505.92, loss_d 1.40, loss 23.22,
34
+ | epoch 1 | 2700/ 41016 batches | rec 15.62, adv 0.70, |lvar| 585.09, loss_d 1.42, loss 22.61,
35
+ | epoch 1 | 2800/ 41016 batches | rec 15.17, adv 0.69, |lvar| 449.50, loss_d 1.41, loss 22.02,
36
+ | epoch 1 | 2900/ 41016 batches | rec 14.65, adv 0.69, |lvar| 537.59, loss_d 1.40, loss 21.57,
37
+ | epoch 1 | 3000/ 41016 batches | rec 14.12, adv 0.68, |lvar| 529.76, loss_d 1.41, loss 20.97,
38
+ | epoch 1 | 3100/ 41016 batches | rec 13.78, adv 0.69, |lvar| 608.71, loss_d 1.41, loss 20.64,
39
+ | epoch 1 | 3200/ 41016 batches | rec 13.08, adv 0.69, |lvar| 506.58, loss_d 1.40, loss 20.02,
40
+ | epoch 1 | 3300/ 41016 batches | rec 12.85, adv 0.68, |lvar| 538.64, loss_d 1.41, loss 19.63,
41
+ | epoch 1 | 3400/ 41016 batches | rec 12.71, adv 0.69, |lvar| 586.90, loss_d 1.42, loss 19.58,
42
+ | epoch 1 | 3500/ 41016 batches | rec 12.25, adv 0.69, |lvar| 569.55, loss_d 1.41, loss 19.16,
43
+ | epoch 1 | 3600/ 41016 batches | rec 11.85, adv 0.70, |lvar| 582.21, loss_d 1.42, loss 18.83,
44
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+ | epoch 1 | 20700/ 41016 batches | rec 2.52, adv 0.67, |lvar| 759.25, loss_d 1.38, loss 9.26,
215
+ | epoch 1 | 20800/ 41016 batches | rec 0.43, adv 0.70, |lvar| 740.16, loss_d 1.39, loss 7.47,
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+ | epoch 1 | 20900/ 41016 batches | rec 0.32, adv 0.70, |lvar| 749.27, loss_d 1.44, loss 7.34,
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+ | epoch 1 | 21000/ 41016 batches | rec 0.28, adv 0.67, |lvar| 784.46, loss_d 1.42, loss 6.99,
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+ | epoch 1 | 21100/ 41016 batches | rec 0.58, adv 0.67, |lvar| 863.63, loss_d 1.41, loss 7.24,
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+ | epoch 1 | 21200/ 41016 batches | rec 0.30, adv 0.69, |lvar| 836.11, loss_d 1.42, loss 7.19,
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+ | epoch 1 | 21300/ 41016 batches | rec 1.26, adv 0.69, |lvar| 666.26, loss_d 1.39, loss 8.14,
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+ | epoch 1 | 21400/ 41016 batches | rec 0.32, adv 0.70, |lvar| 759.04, loss_d 1.40, loss 7.32,
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+ | epoch 1 | 21500/ 41016 batches | rec 0.28, adv 0.69, |lvar| 590.16, loss_d 1.41, loss 7.21,
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+ | epoch 1 | 21600/ 41016 batches | rec 1.45, adv 0.69, |lvar| 739.68, loss_d 1.38, loss 8.32,
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+ | epoch 1 | 21700/ 41016 batches | rec 0.31, adv 0.69, |lvar| 784.61, loss_d 1.43, loss 7.23,
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+ | epoch 1 | 21800/ 41016 batches | rec 0.26, adv 0.68, |lvar| 869.10, loss_d 1.40, loss 7.06,
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+ | epoch 1 | 21900/ 41016 batches | rec 0.27, adv 0.69, |lvar| 720.79, loss_d 1.43, loss 7.12,
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+ | epoch 1 | 22000/ 41016 batches | rec 0.23, adv 0.68, |lvar| 1037.10, loss_d 1.44, loss 7.00,
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+ | epoch 1 | 22100/ 41016 batches | rec 0.45, adv 0.67, |lvar| 756.16, loss_d 1.42, loss 7.12,
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+ | epoch 1 | 22200/ 41016 batches | rec 1.68, adv 0.69, |lvar| 982.31, loss_d 1.39, loss 8.53,
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+ | epoch 1 | 22300/ 41016 batches | rec 0.27, adv 0.70, |lvar| 809.24, loss_d 1.39, loss 7.24,
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+ | epoch 1 | 22400/ 41016 batches | rec 0.27, adv 0.70, |lvar| 981.85, loss_d 1.39, loss 7.30,
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+ | epoch 1 | 22500/ 41016 batches | rec 0.25, adv 0.69, |lvar| 925.98, loss_d 1.41, loss 7.15,
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+ | epoch 1 | 22600/ 41016 batches | rec 0.24, adv 0.67, |lvar| 621.86, loss_d 1.43, loss 6.97,
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+ | epoch 1 | 22700/ 41016 batches | rec 0.23, adv 0.69, |lvar| 620.95, loss_d 1.42, loss 7.09,
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+ | epoch 1 | 22800/ 41016 batches | rec 0.22, adv 0.68, |lvar| 716.90, loss_d 1.43, loss 6.99,
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+ | epoch 1 | 22900/ 41016 batches | rec 0.21, adv 0.68, |lvar| 827.92, loss_d 1.41, loss 7.05,
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+ | epoch 1 | 23000/ 41016 batches | rec 0.21, adv 0.69, |lvar| 906.99, loss_d 1.43, loss 7.09,
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+ | epoch 1 | 23100/ 41016 batches | rec 2.70, adv 0.68, |lvar| 681.20, loss_d 1.41, loss 9.48,
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+ | epoch 1 | 23200/ 41016 batches | rec 0.34, adv 0.70, |lvar| 544.86, loss_d 1.35, loss 7.37,
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+ | epoch 1 | 23300/ 41016 batches | rec 1.17, adv 0.71, |lvar| 840.29, loss_d 1.39, loss 8.31,
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+ | epoch 1 | 23400/ 41016 batches | rec 0.29, adv 0.70, |lvar| 697.49, loss_d 1.36, loss 7.31,
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+ | epoch 1 | 23500/ 41016 batches | rec 1.19, adv 0.72, |lvar| 822.71, loss_d 1.37, loss 8.35,
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+ | epoch 1 | 23600/ 41016 batches | rec 0.34, adv 0.70, |lvar| 662.30, loss_d 1.42, loss 7.30,
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+ | epoch 1 | 23700/ 41016 batches | rec 0.26, adv 0.67, |lvar| 904.17, loss_d 1.46, loss 6.99,
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+ | epoch 1 | 23800/ 41016 batches | rec 1.26, adv 0.67, |lvar| 672.92, loss_d 1.41, loss 7.99,
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+ | epoch 1 | 23900/ 41016 batches | rec 0.29, adv 0.67, |lvar| 829.98, loss_d 1.46, loss 7.02,
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+ | epoch 1 | 24000/ 41016 batches | rec 0.23, adv 0.67, |lvar| 892.94, loss_d 1.43, loss 6.93,
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+ | epoch 1 | 24100/ 41016 batches | rec 0.20, adv 0.67, |lvar| 824.29, loss_d 1.39, loss 6.92,
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+ | epoch 1 | 24200/ 41016 batches | rec 0.24, adv 0.71, |lvar| 749.18, loss_d 1.40, loss 7.31,
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+ | epoch 1 | 24300/ 41016 batches | rec 0.21, adv 0.69, |lvar| 926.98, loss_d 1.44, loss 7.06,
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+ | epoch 1 | 24400/ 41016 batches | rec 0.18, adv 0.68, |lvar| 742.12, loss_d 1.45, loss 7.00,
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+ | epoch 1 | 24500/ 41016 batches | rec 0.16, adv 0.68, |lvar| 680.86, loss_d 1.41, loss 6.99,
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+ | epoch 1 | 24600/ 41016 batches | rec 1.68, adv 0.70, |lvar| 1040.93, loss_d 1.40, loss 8.64,
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+ | epoch 1 | 24700/ 41016 batches | rec 0.22, adv 0.70, |lvar| 1032.01, loss_d 1.34, loss 7.24,
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+ | epoch 1 | 24800/ 41016 batches | rec 0.26, adv 0.72, |lvar| 1065.80, loss_d 1.36, loss 7.48,
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+ | epoch 1 | 24900/ 41016 batches | rec 0.25, adv 0.70, |lvar| 782.97, loss_d 1.37, loss 7.23,
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+ | epoch 1 | 25000/ 41016 batches | rec 0.26, adv 0.70, |lvar| 742.67, loss_d 1.40, loss 7.22,
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+ | epoch 1 | 25100/ 41016 batches | rec 1.51, adv 0.69, |lvar| 713.02, loss_d 1.38, loss 8.36,
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+ | epoch 1 | 25200/ 41016 batches | rec 0.95, adv 0.69, |lvar| 895.13, loss_d 1.39, loss 7.90,
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+ | epoch 1 | 25300/ 41016 batches | rec 1.41, adv 0.73, |lvar| 841.84, loss_d 1.39, loss 8.71,
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+ | epoch 1 | 25400/ 41016 batches | rec 0.61, adv 0.70, |lvar| 1028.42, loss_d 1.43, loss 7.58,
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+ | epoch 1 | 25500/ 41016 batches | rec 0.28, adv 0.70, |lvar| 661.73, loss_d 1.46, loss 7.24,
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+ | epoch 1 | 25600/ 41016 batches | rec 1.06, adv 0.67, |lvar| 722.13, loss_d 1.44, loss 7.76,
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+ | epoch 1 | 25700/ 41016 batches | rec 0.26, adv 0.67, |lvar| 800.79, loss_d 1.41, loss 6.97,
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+ | epoch 1 | 25800/ 41016 batches | rec 0.23, adv 0.67, |lvar| 860.86, loss_d 1.44, loss 6.96,
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+ | epoch 1 | 25900/ 41016 batches | rec 0.23, adv 0.70, |lvar| 791.04, loss_d 1.44, loss 7.24,
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+ | epoch 1 | 26000/ 41016 batches | rec 0.18, adv 0.67, |lvar| 750.28, loss_d 1.44, loss 6.87,
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+ | epoch 1 | 26100/ 41016 batches | rec 0.40, adv 0.66, |lvar| 713.50, loss_d 1.40, loss 6.98,
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+ | epoch 1 | 26200/ 41016 batches | rec 0.21, adv 0.68, |lvar| 1078.57, loss_d 1.45, loss 7.03,
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+ | epoch 1 | 26300/ 41016 batches | rec 0.16, adv 0.68, |lvar| 851.35, loss_d 1.41, loss 6.92,
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+ | epoch 1 | 26400/ 41016 batches | rec 0.20, adv 0.69, |lvar| 975.98, loss_d 1.44, loss 7.14,
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+ | epoch 1 | 26500/ 41016 batches | rec 0.16, adv 0.68, |lvar| 1022.80, loss_d 1.44, loss 6.95,
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+ | epoch 1 | 26600/ 41016 batches | rec 0.87, adv 0.68, |lvar| 687.81, loss_d 1.38, loss 7.71,
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+ | epoch 1 | 26700/ 41016 batches | rec 0.18, adv 0.70, |lvar| 654.28, loss_d 1.39, loss 7.21,
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+ | epoch 1 | 26800/ 41016 batches | rec 0.16, adv 0.69, |lvar| 811.80, loss_d 1.41, loss 7.08,
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+ | epoch 1 | 26900/ 41016 batches | rec 0.14, adv 0.67, |lvar| 863.91, loss_d 1.43, loss 6.86,
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+ | epoch 1 | 27000/ 41016 batches | rec 0.14, adv 0.68, |lvar| 941.35, loss_d 1.43, loss 6.98,
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+ | epoch 1 | 27100/ 41016 batches | rec 0.13, adv 0.68, |lvar| 867.42, loss_d 1.43, loss 6.89,
279
+ | epoch 1 | 27200/ 41016 batches | rec 0.12, adv 0.68, |lvar| 827.07, loss_d 1.41, loss 6.92,
280
+ | epoch 1 | 27300/ 41016 batches | rec 0.12, adv 0.67, |lvar| 930.54, loss_d 1.42, loss 6.87,
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+ | epoch 1 | 27400/ 41016 batches | rec 1.28, adv 0.69, |lvar| 721.26, loss_d 1.37, loss 8.19,
282
+ | epoch 1 | 27500/ 41016 batches | rec 0.16, adv 0.71, |lvar| 862.79, loss_d 1.37, loss 7.26,
283
+ | epoch 1 | 27600/ 41016 batches | rec 0.19, adv 0.71, |lvar| 976.56, loss_d 1.43, loss 7.27,
284
+ | epoch 1 | 27700/ 41016 batches | rec 0.13, adv 0.68, |lvar| 784.88, loss_d 1.42, loss 6.95,
285
+ | epoch 1 | 27800/ 41016 batches | rec 1.85, adv 0.67, |lvar| 711.72, loss_d 1.43, loss 8.58,
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+ | epoch 1 | 27900/ 41016 batches | rec 0.20, adv 0.69, |lvar| 674.01, loss_d 1.38, loss 7.08,
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+ | epoch 1 | 28000/ 41016 batches | rec 0.17, adv 0.70, |lvar| 716.67, loss_d 1.39, loss 7.18,
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+ | epoch 1 | 28100/ 41016 batches | rec 0.17, adv 0.70, |lvar| 635.97, loss_d 1.41, loss 7.15,
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+ | epoch 1 | 28200/ 41016 batches | rec 0.15, adv 0.69, |lvar| 876.56, loss_d 1.43, loss 7.02,
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+ | epoch 1 | 28300/ 41016 batches | rec 0.14, adv 0.67, |lvar| 727.15, loss_d 1.44, loss 6.84,
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+ | epoch 1 | 28400/ 41016 batches | rec 0.11, adv 0.65, |lvar| 558.28, loss_d 1.40, loss 6.63,
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+ | epoch 1 | 28500/ 41016 batches | rec 0.14, adv 0.70, |lvar| 762.39, loss_d 1.42, loss 7.11,
293
+ | epoch 1 | 28600/ 41016 batches | rec 0.12, adv 0.67, |lvar| 871.92, loss_d 1.41, loss 6.86,
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+ | epoch 1 | 28700/ 41016 batches | rec 0.12, adv 0.69, |lvar| 762.30, loss_d 1.41, loss 6.97,
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+ | epoch 1 | 28800/ 41016 batches | rec 0.12, adv 0.68, |lvar| 777.03, loss_d 1.42, loss 6.94,
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+ | epoch 1 | 28900/ 41016 batches | rec 0.10, adv 0.70, |lvar| 827.49, loss_d 1.42, loss 7.06,
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+ | epoch 1 | 29000/ 41016 batches | rec 0.10, adv 0.67, |lvar| 744.94, loss_d 1.40, loss 6.85,
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+ | epoch 1 | 29100/ 41016 batches | rec 0.11, adv 0.69, |lvar| 676.99, loss_d 1.39, loss 6.97,
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+ | epoch 1 | 29200/ 41016 batches | rec 0.10, adv 0.69, |lvar| 935.28, loss_d 1.42, loss 6.99,
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+ | epoch 1 | 29300/ 41016 batches | rec 1.55, adv 0.68, |lvar| 659.51, loss_d 1.39, loss 8.38,
301
+ | epoch 1 | 29400/ 41016 batches | rec 0.18, adv 0.70, |lvar| 806.15, loss_d 1.38, loss 7.18,
302
+ | epoch 1 | 29500/ 41016 batches | rec 1.26, adv 0.73, |lvar| 613.30, loss_d 1.35, loss 8.52,
303
+ | epoch 1 | 29600/ 41016 batches | rec 0.18, adv 0.70, |lvar| 661.85, loss_d 1.41, loss 7.20,
304
+ | epoch 1 | 29700/ 41016 batches | rec 0.15, adv 0.67, |lvar| 494.12, loss_d 1.40, loss 6.80,
305
+ | epoch 1 | 29800/ 41016 batches | rec 0.16, adv 0.69, |lvar| 675.37, loss_d 1.41, loss 7.01,
306
+ | epoch 1 | 29900/ 41016 batches | rec 0.51, adv 0.67, |lvar| 760.35, loss_d 1.43, loss 7.20,
307
+ | epoch 1 | 30000/ 41016 batches | rec 1.04, adv 0.70, |lvar| 525.04, loss_d 1.38, loss 8.06,
308
+ | epoch 1 | 30100/ 41016 batches | rec 0.21, adv 0.71, |lvar| 609.03, loss_d 1.37, loss 7.36,
309
+ | epoch 1 | 30200/ 41016 batches | rec 0.19, adv 0.69, |lvar| 969.21, loss_d 1.43, loss 7.05,
310
+ | epoch 1 | 30300/ 41016 batches | rec 1.11, adv 0.68, |lvar| 1004.70, loss_d 1.41, loss 7.90,
311
+ | epoch 1 | 30400/ 41016 batches | rec 0.36, adv 0.70, |lvar| 596.58, loss_d 1.39, loss 7.37,
312
+ | epoch 1 | 30500/ 41016 batches | rec 0.93, adv 0.69, |lvar| 958.90, loss_d 1.43, loss 7.81,
313
+ | epoch 1 | 30600/ 41016 batches | rec 0.19, adv 0.69, |lvar| 746.22, loss_d 1.39, loss 7.04,
314
+ | epoch 1 | 30700/ 41016 batches | rec 0.18, adv 0.70, |lvar| 609.38, loss_d 1.40, loss 7.15,
315
+ | epoch 1 | 30800/ 41016 batches | rec 0.18, adv 0.68, |lvar| 867.91, loss_d 1.41, loss 6.98,
316
+ | epoch 1 | 30900/ 41016 batches | rec 0.16, adv 0.68, |lvar| 791.74, loss_d 1.43, loss 6.95,
317
+ | epoch 1 | 31000/ 41016 batches | rec 0.15, adv 0.69, |lvar| 810.29, loss_d 1.45, loss 7.07,
318
+ | epoch 1 | 31100/ 41016 batches | rec 0.13, adv 0.67, |lvar| 843.48, loss_d 1.42, loss 6.81,
319
+ | epoch 1 | 31200/ 41016 batches | rec 0.13, adv 0.68, |lvar| 1047.44, loss_d 1.43, loss 6.95,
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+ | epoch 1 | 31300/ 41016 batches | rec 0.11, adv 0.67, |lvar| 765.80, loss_d 1.41, loss 6.77,
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+ | epoch 1 | 31400/ 41016 batches | rec 0.13, adv 0.70, |lvar| 863.07, loss_d 1.42, loss 7.11,
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+ | epoch 1 | 31500/ 41016 batches | rec 0.10, adv 0.68, |lvar| 713.81, loss_d 1.42, loss 6.92,
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+ | epoch 1 | 31600/ 41016 batches | rec 0.11, adv 0.68, |lvar| 863.33, loss_d 1.41, loss 6.93,
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+ | epoch 1 | 31700/ 41016 batches | rec 0.11, adv 0.69, |lvar| 1224.40, loss_d 1.43, loss 6.97,
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+ | epoch 1 | 31800/ 41016 batches | rec 1.77, adv 0.67, |lvar| 805.79, loss_d 1.40, loss 8.49,
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+ | epoch 1 | 31900/ 41016 batches | rec 0.26, adv 0.71, |lvar| 572.64, loss_d 1.36, loss 7.31,
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+ | epoch 1 | 32000/ 41016 batches | rec 0.18, adv 0.72, |lvar| 620.65, loss_d 1.34, loss 7.36,
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+ | epoch 1 | 32100/ 41016 batches | rec 0.18, adv 0.70, |lvar| 671.41, loss_d 1.37, loss 7.21,
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+ | epoch 1 | 32200/ 41016 batches | rec 0.19, adv 0.70, |lvar| 626.82, loss_d 1.40, loss 7.22,
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+ | epoch 1 | 32300/ 41016 batches | rec 0.17, adv 0.68, |lvar| 633.97, loss_d 1.39, loss 6.93,
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+ | epoch 1 | 32400/ 41016 batches | rec 0.21, adv 0.70, |lvar| 640.36, loss_d 1.42, loss 7.17,
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+ | epoch 1 | 32500/ 41016 batches | rec 0.17, adv 0.68, |lvar| 653.70, loss_d 1.40, loss 7.01,
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+ | epoch 1 | 32600/ 41016 batches | rec 0.20, adv 0.69, |lvar| 689.36, loss_d 1.41, loss 7.09,
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+ | epoch 1 | 32700/ 41016 batches | rec 0.19, adv 0.68, |lvar| 712.07, loss_d 1.41, loss 7.03,
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+ | epoch 1 | 32800/ 41016 batches | rec 1.08, adv 0.69, |lvar| 650.99, loss_d 1.40, loss 7.97,
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+ | epoch 1 | 32900/ 41016 batches | rec 0.21, adv 0.70, |lvar| 588.52, loss_d 1.35, loss 7.21,
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+ | epoch 1 | 33000/ 41016 batches | rec 0.22, adv 0.72, |lvar| 652.01, loss_d 1.38, loss 7.46,
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+ | epoch 1 | 33100/ 41016 batches | rec 0.24, adv 0.69, |lvar| 716.45, loss_d 1.39, loss 7.15,
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+ | epoch 1 | 33200/ 41016 batches | rec 1.36, adv 0.69, |lvar| 631.57, loss_d 1.42, loss 8.22,
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+ | epoch 1 | 33300/ 41016 batches | rec 0.25, adv 0.72, |lvar| 580.07, loss_d 1.36, loss 7.41,
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+ | epoch 1 | 33400/ 41016 batches | rec 1.51, adv 0.70, |lvar| 638.28, loss_d 1.39, loss 8.53,
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+ | epoch 1 | 33500/ 41016 batches | rec 0.30, adv 0.70, |lvar| 612.71, loss_d 1.36, loss 7.35,
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+ | epoch 1 | 33600/ 41016 batches | rec 0.31, adv 0.71, |lvar| 653.66, loss_d 1.41, loss 7.40,
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+ | epoch 1 | 33700/ 41016 batches | rec 0.35, adv 0.70, |lvar| 643.81, loss_d 1.44, loss 7.31,
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+ | epoch 1 | 33800/ 41016 batches | rec 0.30, adv 0.66, |lvar| 660.84, loss_d 1.43, loss 6.89,
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+ | epoch 1 | 33900/ 41016 batches | rec 0.26, adv 0.69, |lvar| 660.20, loss_d 1.41, loss 7.16,
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+ | epoch 1 | 34000/ 41016 batches | rec 0.28, adv 0.71, |lvar| 671.07, loss_d 1.40, loss 7.33,
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+ | epoch 1 | 34100/ 41016 batches | rec 0.30, adv 0.69, |lvar| 678.06, loss_d 1.43, loss 7.23,
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+ | epoch 1 | 34200/ 41016 batches | rec 0.26, adv 0.69, |lvar| 749.28, loss_d 1.43, loss 7.11,
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+ | epoch 1 | 34300/ 41016 batches | rec 0.24, adv 0.69, |lvar| 672.58, loss_d 1.44, loss 7.10,
351
+ | epoch 1 | 34400/ 41016 batches | rec 0.25, adv 0.67, |lvar| 718.01, loss_d 1.45, loss 6.98,
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+ | epoch 1 | 34500/ 41016 batches | rec 0.24, adv 0.69, |lvar| 733.24, loss_d 1.44, loss 7.11,
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+ | epoch 1 | 34600/ 41016 batches | rec 0.23, adv 0.68, |lvar| 702.75, loss_d 1.43, loss 7.03,
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+ | epoch 1 | 34700/ 41016 batches | rec 0.20, adv 0.68, |lvar| 761.64, loss_d 1.42, loss 7.02,
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+ | epoch 1 | 34800/ 41016 batches | rec 1.91, adv 0.70, |lvar| 683.89, loss_d 1.36, loss 8.90,
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+ | epoch 1 | 34900/ 41016 batches | rec 0.27, adv 0.72, |lvar| 681.17, loss_d 1.36, loss 7.45,
357
+ | epoch 1 | 35000/ 41016 batches | rec 0.27, adv 0.70, |lvar| 675.36, loss_d 1.39, loss 7.30,
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+ | epoch 1 | 35100/ 41016 batches | rec 1.60, adv 0.72, |lvar| 657.78, loss_d 1.36, loss 8.76,
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+ | epoch 1 | 35200/ 41016 batches | rec 0.36, adv 0.70, |lvar| 624.03, loss_d 1.42, loss 7.32,
v3/8-aae-17_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
v3/8-laae-17_12_2024/log.txt ADDED
@@ -0,0 +1,100 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=4096, dim_d=512, dim_emb=64, dim_h=256, dim_z=64, dropout=0.3, epochs=20, lambda_adv=10.0, lambda_kl=0.0, lambda_p=0.1, load_model='', log_interval=100, lr=0.0005, max_len=8, model_type='vae', nlayers=1, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
2
+ # train on cuda device
3
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-17_12_2024/vocab.alphabet
4
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=8192, dim_d=512, dim_emb=64, dim_h=256, dim_z=64, dropout=0.3, epochs=20, lambda_adv=10.0, lambda_kl=0.0, lambda_p=0.1, load_model='', log_interval=100, lr=0.0005, max_len=8, model_type='vae', nlayers=1, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
5
+ # train on cuda device
6
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-17_12_2024/vocab.alphabet
7
+ # train passwords 168000000
8
+ # valid passwords 42553894
9
+ # model vae parameters: 1090787
10
+ --------------------------------------------------------------------------------
11
+ | epoch 1 | 100/ 20508 batches | rec 32.37, kl 63.64, loss 32.37,
12
+ | epoch 1 | 200/ 20508 batches | rec 28.03, kl 165.43, loss 28.03,
13
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=16384, dim_d=512, dim_emb=64, dim_h=256, dim_z=64, dropout=0.3, epochs=20, lambda_adv=10.0, lambda_kl=0.0, lambda_p=0.1, load_model='', log_interval=100, lr=0.0005, max_len=8, model_type='vae', nlayers=1, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
14
+ # train on cuda device
15
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-17_12_2024/vocab.alphabet
16
+ # train passwords 168000000
17
+ # valid passwords 42553894
18
+ # model vae parameters: 1090787
19
+ --------------------------------------------------------------------------------
20
+ | epoch 1 | 100/ 10254 batches | rec 32.19, kl 90.64, loss 32.19,
21
+ | epoch 1 | 200/ 10254 batches | rec 28.26, kl 185.01, loss 28.26,
22
+ | epoch 1 | 300/ 10254 batches | rec 26.91, kl 198.70, loss 26.91,
23
+ | epoch 1 | 400/ 10254 batches | rec 26.21, kl 208.23, loss 26.21,
24
+ | epoch 1 | 500/ 10254 batches | rec 25.50, kl 232.10, loss 25.50,
25
+ | epoch 1 | 600/ 10254 batches | rec 24.45, kl 292.45, loss 24.45,
26
+ | epoch 1 | 700/ 10254 batches | rec 23.94, kl 273.08, loss 23.94,
27
+ | epoch 1 | 800/ 10254 batches | rec 23.02, kl 294.63, loss 23.02,
28
+ | epoch 1 | 900/ 10254 batches | rec 23.14, kl 253.67, loss 23.14,
29
+ | epoch 1 | 1000/ 10254 batches | rec 21.98, kl 313.45, loss 21.98,
30
+ | epoch 1 | 1100/ 10254 batches | rec 21.47, kl 333.10, loss 21.47,
31
+ | epoch 1 | 1200/ 10254 batches | rec 21.20, kl 319.14, loss 21.20,
32
+ | epoch 1 | 1300/ 10254 batches | rec 20.50, kl 287.97, loss 20.50,
33
+ | epoch 1 | 1400/ 10254 batches | rec 19.86, kl 308.07, loss 19.86,
34
+ | epoch 1 | 1500/ 10254 batches | rec 19.55, kl 286.05, loss 19.55,
35
+ | epoch 1 | 1600/ 10254 batches | rec 18.37, kl 287.59, loss 18.37,
36
+ | epoch 1 | 1700/ 10254 batches | rec 17.53, kl 299.41, loss 17.53,
37
+ | epoch 1 | 1800/ 10254 batches | rec 16.67, kl 307.29, loss 16.67,
38
+ | epoch 1 | 1900/ 10254 batches | rec 15.66, kl 318.82, loss 15.66,
39
+ | epoch 1 | 2000/ 10254 batches | rec 15.16, kl 311.42, loss 15.16,
40
+ | epoch 1 | 2100/ 10254 batches | rec 13.75, kl 331.22, loss 13.75,
41
+ | epoch 1 | 2200/ 10254 batches | rec 12.93, kl 338.65, loss 12.93,
42
+ | epoch 1 | 2300/ 10254 batches | rec 12.08, kl 341.99, loss 12.08,
43
+ | epoch 1 | 2400/ 10254 batches | rec 11.20, kl 346.17, loss 11.20,
44
+ | epoch 1 | 2500/ 10254 batches | rec 10.38, kl 349.88, loss 10.38,
45
+ | epoch 1 | 2600/ 10254 batches | rec 9.48, kl 358.58, loss 9.48,
46
+ | epoch 1 | 2700/ 10254 batches | rec 9.66, kl 337.86, loss 9.66,
47
+ | epoch 1 | 2800/ 10254 batches | rec 7.92, kl 374.54, loss 7.92,
48
+ | epoch 1 | 2900/ 10254 batches | rec 7.18, kl 389.70, loss 7.18,
49
+ | epoch 1 | 3000/ 10254 batches | rec 6.44, kl 396.89, loss 6.44,
50
+ | epoch 1 | 3100/ 10254 batches | rec 5.80, kl 403.73, loss 5.80,
51
+ | epoch 1 | 3200/ 10254 batches | rec 5.18, kl 410.81, loss 5.18,
52
+ | epoch 1 | 3300/ 10254 batches | rec 4.66, kl 410.57, loss 4.66,
53
+ | epoch 1 | 3400/ 10254 batches | rec 7.07, kl 336.64, loss 7.07,
54
+ | epoch 1 | 3500/ 10254 batches | rec 4.39, kl 384.61, loss 4.39,
55
+ | epoch 1 | 3600/ 10254 batches | rec 3.90, kl 412.69, loss 3.90,
56
+ | epoch 1 | 3700/ 10254 batches | rec 3.55, kl 428.19, loss 3.55,
57
+ | epoch 1 | 3800/ 10254 batches | rec 3.30, kl 436.64, loss 3.30,
58
+ | epoch 1 | 3900/ 10254 batches | rec 3.35, kl 427.80, loss 3.35,
59
+ | epoch 1 | 4000/ 10254 batches | rec 2.73, kl 435.15, loss 2.73,
60
+ | epoch 1 | 4100/ 10254 batches | rec 5.08, kl 403.18, loss 5.08,
61
+ | epoch 1 | 4200/ 10254 batches | rec 2.98, kl 364.25, loss 2.98,
62
+ | epoch 1 | 4300/ 10254 batches | rec 2.44, kl 409.86, loss 2.44,
63
+ | epoch 1 | 4400/ 10254 batches | rec 2.20, kl 433.79, loss 2.20,
64
+ | epoch 1 | 4500/ 10254 batches | rec 2.03, kl 450.49, loss 2.03,
65
+ | epoch 1 | 4600/ 10254 batches | rec 1.89, kl 462.63, loss 1.89,
66
+ | epoch 1 | 4700/ 10254 batches | rec 2.38, kl 435.23, loss 2.38,
67
+ | epoch 1 | 4800/ 10254 batches | rec 1.68, kl 439.16, loss 1.68,
68
+ | epoch 1 | 4900/ 10254 batches | rec 1.56, kl 457.61, loss 1.56,
69
+ | epoch 1 | 5000/ 10254 batches | rec 1.46, kl 469.79, loss 1.46,
70
+ | epoch 1 | 5100/ 10254 batches | rec 1.38, kl 478.64, loss 1.38,
71
+ | epoch 1 | 5200/ 10254 batches | rec 3.73, kl 364.19, loss 3.73,
72
+ | epoch 1 | 5300/ 10254 batches | rec 1.38, kl 413.63, loss 1.38,
73
+ | epoch 1 | 5400/ 10254 batches | rec 1.24, kl 438.56, loss 1.24,
74
+ | epoch 1 | 5500/ 10254 batches | rec 1.15, kl 455.55, loss 1.15,
75
+ | epoch 1 | 5600/ 10254 batches | rec 1.08, kl 469.21, loss 1.08,
76
+ | epoch 1 | 5700/ 10254 batches | rec 1.00, kl 479.54, loss 1.00,
77
+ | epoch 1 | 5800/ 10254 batches | rec 2.24, kl 422.64, loss 2.24,
78
+ | epoch 1 | 5900/ 10254 batches | rec 1.03, kl 417.42, loss 1.03,
79
+ | epoch 1 | 6000/ 10254 batches | rec 0.91, kl 440.43, loss 0.91,
80
+ | epoch 1 | 6100/ 10254 batches | rec 0.84, kl 455.47, loss 0.84,
81
+ | epoch 1 | 6200/ 10254 batches | rec 0.78, kl 467.96, loss 0.78,
82
+ | epoch 1 | 6300/ 10254 batches | rec 0.73, kl 478.03, loss 0.73,
83
+ | epoch 1 | 6400/ 10254 batches | rec 0.69, kl 486.53, loss 0.69,
84
+ | epoch 1 | 6500/ 10254 batches | rec 0.64, kl 494.12, loss 0.64,
85
+ | epoch 1 | 6600/ 10254 batches | rec 0.60, kl 501.03, loss 0.60,
86
+ | epoch 1 | 6700/ 10254 batches | rec 0.56, kl 506.97, loss 0.56,
87
+ | epoch 1 | 6800/ 10254 batches | rec 4.76, kl 336.11, loss 4.76,
88
+ | epoch 1 | 6900/ 10254 batches | rec 0.89, kl 401.95, loss 0.89,
89
+ | epoch 1 | 7000/ 10254 batches | rec 0.70, kl 434.12, loss 0.70,
90
+ | epoch 1 | 7100/ 10254 batches | rec 0.62, kl 454.71, loss 0.62,
91
+ | epoch 1 | 7200/ 10254 batches | rec 0.55, kl 470.73, loss 0.55,
92
+ | epoch 1 | 7300/ 10254 batches | rec 0.50, kl 483.74, loss 0.50,
93
+ | epoch 1 | 7400/ 10254 batches | rec 0.47, kl 494.45, loss 0.47,
94
+ | epoch 1 | 7500/ 10254 batches | rec 0.43, kl 503.82, loss 0.43,
95
+ | epoch 1 | 7600/ 10254 batches | rec 0.41, kl 511.15, loss 0.41,
96
+ | epoch 1 | 7700/ 10254 batches | rec 2.51, kl 440.65, loss 2.51,
97
+ | epoch 1 | 7800/ 10254 batches | rec 0.76, kl 351.91, loss 0.76,
98
+ | epoch 1 | 7900/ 10254 batches | rec 0.49, kl 392.31, loss 0.49,
99
+ | epoch 1 | 8000/ 10254 batches | rec 0.41, kl 412.87, loss 0.41,
100
+ | epoch 1 | 8100/ 10254 batches | rec 0.37, kl 426.91, loss 0.37,
v3/8-laae-17_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
v3/8-laae-large_17_12_2024/log.txt ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=32768, dim_d=512, dim_emb=256, dim_h=1024, dim_z=128, dropout=0.3, epochs=30, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-large_17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
2
+ # train on cuda device
3
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-large_17_12_2024/vocab.alphabet
4
+ # train passwords 168000000
5
+ # valid passwords 42553894
6
+ # model aae parameters: 83662180
7
+ --------------------------------------------------------------------------------
8
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=8192, dim_d=512, dim_emb=256, dim_h=1024, dim_z=128, dropout=0.3, epochs=30, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-large_17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
9
+ # train on cuda device
10
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-large_17_12_2024/vocab.alphabet
11
+ # 168000000 train passwords was loaded
12
+ # 42553894 valid passwords was loaded
13
+ # model aae with parameters was init: 83662180
14
+ --------------------------------------------------------------------------------
15
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=4096, dim_d=512, dim_emb=256, dim_h=1024, dim_z=128, dropout=0.3, epochs=30, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-large_17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
16
+ # train on cuda device
17
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-large_17_12_2024/vocab.alphabet
18
+ # 168000000 train passwords was loaded
19
+ # 42553894 valid passwords was loaded
20
+ # model aae with parameters was init: 83662180
21
+ --------------------------------------------------------------------------------
22
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=2048, dim_d=512, dim_emb=256, dim_h=1024, dim_z=128, dropout=0.3, epochs=30, 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=3, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-large_17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
23
+ # train on cuda device
24
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-laae-large_17_12_2024/vocab.alphabet
25
+ # 168000000 train passwords was loaded
26
+ # 42553894 valid passwords was loaded
27
+ # model aae with parameters was init: 83662180
28
+ --------------------------------------------------------------------------------
v3/8-laae-large_17_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>
v3/8-vae-17_12_2024/log.txt ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Namespace(alphabet=None, b1=0.5, b2=0.999, batch_size=4096, dim_d=512, dim_emb=64, dim_h=256, dim_z=64, dropout=0.3, epochs=20, 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=1, no_cuda=False, noise=[0.0, 0.0, 0.0], save_dir='/mnt/hdd4/julia_dir/leo/pae/out/v3/8-vae-17_12_2024', train='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/train.txt', valid='/mnt/hdd4/julia_dir/leo/data/hashes.org-list/8/valid.txt')
2
+ # train on cuda device
3
+ # vocab save /mnt/hdd4/julia_dir/leo/pae/out/v3/8-vae-17_12_2024/vocab.alphabet
4
+ # train passwords 168000000
5
+ # valid passwords 42553894
6
+ # model vae parameters: 1090787
7
+ --------------------------------------------------------------------------------
8
+ | epoch 1 | 100/ 41016 batches | rec 32.53, kl 3.13, loss 32.84,
9
+ | epoch 1 | 200/ 41016 batches | rec 28.31, kl 4.31, loss 28.74,
10
+ | epoch 1 | 300/ 41016 batches | rec 26.78, kl 4.87, loss 27.27,
11
+ | epoch 1 | 400/ 41016 batches | rec 26.07, kl 5.26, loss 26.60,
12
+ | epoch 1 | 500/ 41016 batches | rec 25.15, kl 5.44, loss 25.70,
13
+ | epoch 1 | 600/ 41016 batches | rec 24.67, kl 5.26, loss 25.19,
14
+ | epoch 1 | 700/ 41016 batches | rec 23.48, kl 5.45, loss 24.03,
15
+ | epoch 1 | 800/ 41016 batches | rec 23.03, kl 5.76, loss 23.61,
16
+ | epoch 1 | 900/ 41016 batches | rec 22.44, kl 6.75, loss 23.11,
17
+ | epoch 1 | 1000/ 41016 batches | rec 22.53, kl 7.51, loss 23.28,
18
+ | epoch 1 | 1100/ 41016 batches | rec 21.56, kl 8.15, loss 22.37,
19
+ | epoch 1 | 1200/ 41016 batches | rec 21.07, kl 9.11, loss 21.98,
20
+ | epoch 1 | 1300/ 41016 batches | rec 20.61, kl 10.12, loss 21.63,
21
+ | epoch 1 | 1400/ 41016 batches | rec 20.03, kl 11.36, loss 21.16,
22
+ | epoch 1 | 1500/ 41016 batches | rec 19.31, kl 12.48, loss 20.56,
23
+ | epoch 1 | 1600/ 41016 batches | rec 18.54, kl 13.69, loss 19.91,
24
+ | epoch 1 | 1700/ 41016 batches | rec 19.01, kl 14.91, loss 20.50,
25
+ | epoch 1 | 1800/ 41016 batches | rec 17.50, kl 15.64, loss 19.06,
26
+ | epoch 1 | 1900/ 41016 batches | rec 16.89, kl 16.75, loss 18.56,
27
+ | epoch 1 | 2000/ 41016 batches | rec 16.39, kl 17.61, loss 18.15,
28
+ | epoch 1 | 2100/ 41016 batches | rec 15.83, kl 18.55, loss 17.68,
29
+ | epoch 1 | 2200/ 41016 batches | rec 15.22, kl 19.64, loss 17.18,
30
+ | epoch 1 | 2300/ 41016 batches | rec 14.68, kl 20.74, loss 16.75,
31
+ | epoch 1 | 2400/ 41016 batches | rec 14.05, kl 21.99, loss 16.25,
32
+ | epoch 1 | 2500/ 41016 batches | rec 13.47, kl 23.13, loss 15.79,
33
+ | epoch 1 | 2600/ 41016 batches | rec 12.93, kl 24.01, loss 15.33,
34
+ | epoch 1 | 2700/ 41016 batches | rec 12.40, kl 24.78, loss 14.88,
35
+ | epoch 1 | 2800/ 41016 batches | rec 11.81, kl 25.53, loss 14.36,
36
+ | epoch 1 | 2900/ 41016 batches | rec 11.26, kl 26.47, loss 13.91,
37
+ | epoch 1 | 3000/ 41016 batches | rec 10.75, kl 27.36, loss 13.48,
38
+ | epoch 1 | 3100/ 41016 batches | rec 10.22, kl 28.28, loss 13.05,
39
+ | epoch 1 | 3200/ 41016 batches | rec 9.72, kl 29.19, loss 12.64,
40
+ | epoch 1 | 3300/ 41016 batches | rec 9.32, kl 29.97, loss 12.31,
41
+ | epoch 1 | 3400/ 41016 batches | rec 8.97, kl 30.69, loss 12.04,
42
+ | epoch 1 | 3500/ 41016 batches | rec 8.59, kl 31.29, loss 11.71,
43
+ | epoch 1 | 3600/ 41016 batches | rec 8.26, kl 31.85, loss 11.44,
44
+ | epoch 1 | 3700/ 41016 batches | rec 9.51, kl 32.30, loss 12.74,
v3/8-vae-17_12_2024/vocab.alphabet ADDED
@@ -0,0 +1 @@
 
 
1
+ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"'`!^@#$%&.,?:;~-+*=_/\|[]{}()<>