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DipakBundheliya
/
flair_ner_model

Text Classification
Flair
English
O
B-ORG
I-ORG
B-GCNUMBER
I-GCNUMBER
B-BGNUMBER
I-BGNUMBER
B-NAME
I-NAME
B-LOCATION
I-LOCATION
B-COUNTRY
I-COUNTRY
Model card Files Community
DipakBundheliya commited on
Commit
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1 Parent(s): 8766e24

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Files changed (4) hide show
  1. final-model.pt +3 -0
  2. loss.tsv +440 -0
  3. test.tsv +23 -0
  4. training.log +851 -0
final-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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loss.tsv ADDED
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384
+ 53 09:08:31 0.0500 0.3261
385
+ 54 09:08:31 0.0500 0.2971
386
+ 55 09:08:31 0.0500 0.2711
387
+ 56 09:08:31 0.0500 0.2311
388
+ 57 09:08:31 0.0500 0.2510
389
+ 58 09:08:32 0.0500 0.2833
390
+ 59 09:08:32 0.0500 0.2467
391
+ 60 09:08:32 0.0500 0.3014
392
+ 61 09:08:32 0.0250 0.2471
393
+ 62 09:08:32 0.0250 0.2270
394
+ 63 09:08:32 0.0250 0.2255
395
+ 64 09:08:33 0.0250 0.2162
396
+ 65 09:08:33 0.0250 0.2357
397
+ 66 09:08:33 0.0250 0.2306
398
+ 67 09:08:33 0.0250 0.2351
399
+ 68 09:08:33 0.0250 0.2446
400
+ 69 09:08:33 0.0125 0.2112
401
+ 70 09:08:34 0.0125 0.2534
402
+ 71 09:08:34 0.0125 0.2213
403
+ 72 09:08:34 0.0125 0.2043
404
+ 73 09:08:34 0.0125 0.2195
405
+ 74 09:08:34 0.0125 0.2241
406
+ 75 09:08:34 0.0125 0.2092
407
+ 76 09:08:35 0.0125 0.2267
408
+ 77 09:08:35 0.0063 0.2296
409
+ 78 09:08:35 0.0063 0.2382
410
+ 79 09:08:35 0.0063 0.2136
411
+ 80 09:08:35 0.0063 0.2083
412
+ 81 09:08:35 0.0031 0.2067
413
+ 82 09:08:36 0.0031 0.2045
414
+ 83 09:08:36 0.0031 0.1940
415
+ 84 09:08:36 0.0031 0.2062
416
+ 85 09:08:36 0.0031 0.2041
417
+ 86 09:08:36 0.0031 0.2278
418
+ 87 09:08:36 0.0031 0.1945
419
+ 88 09:08:37 0.0016 0.2001
420
+ 89 09:08:37 0.0016 0.1861
421
+ 90 09:08:37 0.0016 0.1844
422
+ 91 09:08:37 0.0016 0.2162
423
+ 92 09:08:37 0.0016 0.1962
424
+ 93 09:08:37 0.0016 0.1969
425
+ 94 09:08:38 0.0016 0.1967
426
+ 95 09:08:38 0.0008 0.1958
427
+ 96 09:08:38 0.0008 0.1861
428
+ 97 09:08:38 0.0008 0.1790
429
+ 98 09:08:38 0.0008 0.1840
430
+ 99 09:08:39 0.0008 0.2009
431
+ 100 09:08:39 0.0008 0.1867
432
+ 101 09:08:39 0.0008 0.2111
433
+ 102 09:08:39 0.0004 0.1797
434
+ 103 09:08:39 0.0004 0.2326
435
+ 104 09:08:40 0.0004 0.2138
436
+ 105 09:08:40 0.0004 0.2119
437
+ 106 09:08:40 0.0002 0.1712
438
+ 107 09:08:40 0.0002 0.1791
439
+ 108 09:08:41 0.0002 0.1762
440
+ 109 09:08:41 0.0002 0.2116
test.tsv ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ JAHSMS O O
2
+ ROACXET B-ORG O
3
+ INTHA B-NAME B-NAME
4
+ SMITH I-NAME I-NAME
5
+ 545W75AV O O
6
+ GC3124 B-GCNUMBER B-GCNUMBER
7
+ MIAM B-LOCATION B-LOCATION
8
+ FL33155 I-LOCATION I-LOCATION
9
+ WESIDWNINALS O O
10
+ GC112 B-GCNUMBER B-GCNUMBER
11
+ LINTHASTH B-NAME B-NAME
12
+ GROUND O O
13
+
14
+ Jullen B-NAME B-NAME
15
+ Cohen I-NAME I-NAME
16
+ GC11909 B-GCNUMBER B-GCNUMBER
17
+ 4654SW75THAVE O O
18
+ 33155FL B-LOCATION B-LOCATION
19
+ MiAM I-LOCATION I-LOCATION
20
+ UnedStates B-COUNTRY B-COUNTRY
21
+ 1DMI6 B-ORG B-ORG
22
+ CYCLE O O
23
+
training.log ADDED
@@ -0,0 +1,851 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2024-03-28 09:08:20,895 ----------------------------------------------------------------------------------------------------
2
+ 2024-03-28 09:08:20,897 Model: "SequenceTagger(
3
+ (embeddings): StackedEmbeddings(
4
+ (list_embedding_0): WordEmbeddings(
5
+ 'glove'
6
+ (embedding): Embedding(400001, 100)
7
+ )
8
+ (list_embedding_1): FlairEmbeddings(
9
+ (lm): LanguageModel(
10
+ (drop): Dropout(p=0.05, inplace=False)
11
+ (encoder): Embedding(300, 100)
12
+ (rnn): LSTM(100, 2048)
13
+ )
14
+ )
15
+ (list_embedding_2): FlairEmbeddings(
16
+ (lm): LanguageModel(
17
+ (drop): Dropout(p=0.05, inplace=False)
18
+ (encoder): Embedding(300, 100)
19
+ (rnn): LSTM(100, 2048)
20
+ )
21
+ )
22
+ )
23
+ (word_dropout): WordDropout(p=0.05)
24
+ (locked_dropout): LockedDropout(p=0.5)
25
+ (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)
26
+ (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)
27
+ (linear): Linear(in_features=512, out_features=27, bias=True)
28
+ (loss_function): ViterbiLoss()
29
+ (crf): CRF()
30
+ )"
31
+ 2024-03-28 09:08:20,899 ----------------------------------------------------------------------------------------------------
32
+ 2024-03-28 09:08:20,901 Corpus: 50 train + 16 dev + 2 test sentences
33
+ 2024-03-28 09:08:20,903 ----------------------------------------------------------------------------------------------------
34
+ 2024-03-28 09:08:20,905 Train: 66 sentences
35
+ 2024-03-28 09:08:20,906 (train_with_dev=True, train_with_test=False)
36
+ 2024-03-28 09:08:20,908 ----------------------------------------------------------------------------------------------------
37
+ 2024-03-28 09:08:20,909 Training Params:
38
+ 2024-03-28 09:08:20,910 - learning_rate: "0.1"
39
+ 2024-03-28 09:08:20,912 - mini_batch_size: "32"
40
+ 2024-03-28 09:08:20,913 - max_epochs: "150"
41
+ 2024-03-28 09:08:20,914 - shuffle: "True"
42
+ 2024-03-28 09:08:20,915 ----------------------------------------------------------------------------------------------------
43
+ 2024-03-28 09:08:20,917 Plugins:
44
+ 2024-03-28 09:08:20,918 - AnnealOnPlateau | patience: '3', anneal_factor: '0.5', min_learning_rate: '0.0001'
45
+ 2024-03-28 09:08:20,919 ----------------------------------------------------------------------------------------------------
46
+ 2024-03-28 09:08:20,920 Final evaluation on model from best epoch (best-model.pt)
47
+ 2024-03-28 09:08:20,921 - metric: "('micro avg', 'f1-score')"
48
+ 2024-03-28 09:08:20,923 ----------------------------------------------------------------------------------------------------
49
+ 2024-03-28 09:08:20,924 Computation:
50
+ 2024-03-28 09:08:20,925 - compute on device: cuda:0
51
+ 2024-03-28 09:08:20,927 - embedding storage: cpu
52
+ 2024-03-28 09:08:20,928 ----------------------------------------------------------------------------------------------------
53
+ 2024-03-28 09:08:20,929 Model training base path: "resources/taggers/ner-english"
54
+ 2024-03-28 09:08:20,930 ----------------------------------------------------------------------------------------------------
55
+ 2024-03-28 09:08:20,931 ----------------------------------------------------------------------------------------------------
56
+ 2024-03-28 09:08:21,191 epoch 1 - iter 1/3 - loss 3.36974860 - time (sec): 0.26 - samples/sec: 1392.44 - lr: 0.100000 - momentum: 0.000000
57
+ 2024-03-28 09:08:21,396 epoch 1 - iter 2/3 - loss 3.15954622 - time (sec): 0.46 - samples/sec: 1629.06 - lr: 0.100000 - momentum: 0.000000
58
+ 2024-03-28 09:08:21,493 epoch 1 - iter 3/3 - loss 3.14478873 - time (sec): 0.56 - samples/sec: 1391.02 - lr: 0.100000 - momentum: 0.000000
59
+ 2024-03-28 09:08:21,495 ----------------------------------------------------------------------------------------------------
60
+ 2024-03-28 09:08:21,498 EPOCH 1 done: loss 3.1448 - lr: 0.100000
61
+ 2024-03-28 09:08:21,500 - 0 epochs without improvement
62
+ 2024-03-28 09:08:21,502 ----------------------------------------------------------------------------------------------------
63
+ 2024-03-28 09:08:21,589 epoch 2 - iter 1/3 - loss 2.45131045 - time (sec): 0.08 - samples/sec: 4558.10 - lr: 0.100000 - momentum: 0.000000
64
+ 2024-03-28 09:08:21,668 epoch 2 - iter 2/3 - loss 2.39363852 - time (sec): 0.16 - samples/sec: 4622.19 - lr: 0.100000 - momentum: 0.000000
65
+ 2024-03-28 09:08:21,696 epoch 2 - iter 3/3 - loss 2.39924183 - time (sec): 0.19 - samples/sec: 4072.04 - lr: 0.100000 - momentum: 0.000000
66
+ 2024-03-28 09:08:21,698 ----------------------------------------------------------------------------------------------------
67
+ 2024-03-28 09:08:21,700 EPOCH 2 done: loss 2.3992 - lr: 0.100000
68
+ 2024-03-28 09:08:21,705 - 0 epochs without improvement
69
+ 2024-03-28 09:08:21,709 ----------------------------------------------------------------------------------------------------
70
+ 2024-03-28 09:08:21,802 epoch 3 - iter 1/3 - loss 2.23206190 - time (sec): 0.09 - samples/sec: 4145.47 - lr: 0.100000 - momentum: 0.000000
71
+ 2024-03-28 09:08:21,881 epoch 3 - iter 2/3 - loss 2.25305821 - time (sec): 0.17 - samples/sec: 4484.56 - lr: 0.100000 - momentum: 0.000000
72
+ 2024-03-28 09:08:21,905 epoch 3 - iter 3/3 - loss 2.25758761 - time (sec): 0.19 - samples/sec: 4035.32 - lr: 0.100000 - momentum: 0.000000
73
+ 2024-03-28 09:08:21,907 ----------------------------------------------------------------------------------------------------
74
+ 2024-03-28 09:08:21,908 EPOCH 3 done: loss 2.2576 - lr: 0.100000
75
+ 2024-03-28 09:08:21,910 - 0 epochs without improvement
76
+ 2024-03-28 09:08:21,912 ----------------------------------------------------------------------------------------------------
77
+ 2024-03-28 09:08:21,996 epoch 4 - iter 1/3 - loss 1.98101494 - time (sec): 0.08 - samples/sec: 4904.79 - lr: 0.100000 - momentum: 0.000000
78
+ 2024-03-28 09:08:22,068 epoch 4 - iter 2/3 - loss 2.13153052 - time (sec): 0.15 - samples/sec: 4963.13 - lr: 0.100000 - momentum: 0.000000
79
+ 2024-03-28 09:08:22,095 epoch 4 - iter 3/3 - loss 2.14371007 - time (sec): 0.18 - samples/sec: 4357.04 - lr: 0.100000 - momentum: 0.000000
80
+ 2024-03-28 09:08:22,097 ----------------------------------------------------------------------------------------------------
81
+ 2024-03-28 09:08:22,099 EPOCH 4 done: loss 2.1437 - lr: 0.100000
82
+ 2024-03-28 09:08:22,101 - 0 epochs without improvement
83
+ 2024-03-28 09:08:22,102 ----------------------------------------------------------------------------------------------------
84
+ 2024-03-28 09:08:22,186 epoch 5 - iter 1/3 - loss 1.97350561 - time (sec): 0.08 - samples/sec: 5013.65 - lr: 0.100000 - momentum: 0.000000
85
+ 2024-03-28 09:08:22,263 epoch 5 - iter 2/3 - loss 2.14281019 - time (sec): 0.16 - samples/sec: 4793.23 - lr: 0.100000 - momentum: 0.000000
86
+ 2024-03-28 09:08:22,292 epoch 5 - iter 3/3 - loss 2.13209609 - time (sec): 0.19 - samples/sec: 4168.19 - lr: 0.100000 - momentum: 0.000000
87
+ 2024-03-28 09:08:22,294 ----------------------------------------------------------------------------------------------------
88
+ 2024-03-28 09:08:22,297 EPOCH 5 done: loss 2.1321 - lr: 0.100000
89
+ 2024-03-28 09:08:22,301 - 0 epochs without improvement
90
+ 2024-03-28 09:08:22,303 ----------------------------------------------------------------------------------------------------
91
+ 2024-03-28 09:08:22,380 epoch 6 - iter 1/3 - loss 1.95909884 - time (sec): 0.07 - samples/sec: 4953.78 - lr: 0.100000 - momentum: 0.000000
92
+ 2024-03-28 09:08:22,472 epoch 6 - iter 2/3 - loss 1.98597567 - time (sec): 0.17 - samples/sec: 4577.26 - lr: 0.100000 - momentum: 0.000000
93
+ 2024-03-28 09:08:22,492 epoch 6 - iter 3/3 - loss 1.96322728 - time (sec): 0.19 - samples/sec: 4170.18 - lr: 0.100000 - momentum: 0.000000
94
+ 2024-03-28 09:08:22,494 ----------------------------------------------------------------------------------------------------
95
+ 2024-03-28 09:08:22,496 EPOCH 6 done: loss 1.9632 - lr: 0.100000
96
+ 2024-03-28 09:08:22,499 - 0 epochs without improvement
97
+ 2024-03-28 09:08:22,501 ----------------------------------------------------------------------------------------------------
98
+ 2024-03-28 09:08:22,576 epoch 7 - iter 1/3 - loss 2.11116446 - time (sec): 0.07 - samples/sec: 4956.72 - lr: 0.100000 - momentum: 0.000000
99
+ 2024-03-28 09:08:22,651 epoch 7 - iter 2/3 - loss 2.01348722 - time (sec): 0.15 - samples/sec: 5058.67 - lr: 0.100000 - momentum: 0.000000
100
+ 2024-03-28 09:08:22,678 epoch 7 - iter 3/3 - loss 2.00619598 - time (sec): 0.17 - samples/sec: 4461.42 - lr: 0.100000 - momentum: 0.000000
101
+ 2024-03-28 09:08:22,680 ----------------------------------------------------------------------------------------------------
102
+ 2024-03-28 09:08:22,683 EPOCH 7 done: loss 2.0062 - lr: 0.100000
103
+ 2024-03-28 09:08:22,685 - 1 epochs without improvement
104
+ 2024-03-28 09:08:22,687 ----------------------------------------------------------------------------------------------------
105
+ 2024-03-28 09:08:22,762 epoch 8 - iter 1/3 - loss 1.82821058 - time (sec): 0.07 - samples/sec: 5176.83 - lr: 0.100000 - momentum: 0.000000
106
+ 2024-03-28 09:08:22,837 epoch 8 - iter 2/3 - loss 1.92655447 - time (sec): 0.15 - samples/sec: 5095.66 - lr: 0.100000 - momentum: 0.000000
107
+ 2024-03-28 09:08:22,865 epoch 8 - iter 3/3 - loss 1.93318620 - time (sec): 0.18 - samples/sec: 4426.23 - lr: 0.100000 - momentum: 0.000000
108
+ 2024-03-28 09:08:22,867 ----------------------------------------------------------------------------------------------------
109
+ 2024-03-28 09:08:22,869 EPOCH 8 done: loss 1.9332 - lr: 0.100000
110
+ 2024-03-28 09:08:22,873 - 0 epochs without improvement
111
+ 2024-03-28 09:08:22,876 ----------------------------------------------------------------------------------------------------
112
+ 2024-03-28 09:08:22,966 epoch 9 - iter 1/3 - loss 1.64564751 - time (sec): 0.09 - samples/sec: 4254.01 - lr: 0.100000 - momentum: 0.000000
113
+ 2024-03-28 09:08:23,056 epoch 9 - iter 2/3 - loss 1.63239704 - time (sec): 0.18 - samples/sec: 4272.42 - lr: 0.100000 - momentum: 0.000000
114
+ 2024-03-28 09:08:23,086 epoch 9 - iter 3/3 - loss 1.62794558 - time (sec): 0.21 - samples/sec: 3783.46 - lr: 0.100000 - momentum: 0.000000
115
+ 2024-03-28 09:08:23,088 ----------------------------------------------------------------------------------------------------
116
+ 2024-03-28 09:08:23,090 EPOCH 9 done: loss 1.6279 - lr: 0.100000
117
+ 2024-03-28 09:08:23,092 - 0 epochs without improvement
118
+ 2024-03-28 09:08:23,094 ----------------------------------------------------------------------------------------------------
119
+ 2024-03-28 09:08:23,176 epoch 10 - iter 1/3 - loss 1.51423518 - time (sec): 0.08 - samples/sec: 4972.85 - lr: 0.100000 - momentum: 0.000000
120
+ 2024-03-28 09:08:23,263 epoch 10 - iter 2/3 - loss 1.53373787 - time (sec): 0.16 - samples/sec: 4590.31 - lr: 0.100000 - momentum: 0.000000
121
+ 2024-03-28 09:08:23,297 epoch 10 - iter 3/3 - loss 1.52787663 - time (sec): 0.20 - samples/sec: 3938.30 - lr: 0.100000 - momentum: 0.000000
122
+ 2024-03-28 09:08:23,301 ----------------------------------------------------------------------------------------------------
123
+ 2024-03-28 09:08:23,303 EPOCH 10 done: loss 1.5279 - lr: 0.100000
124
+ 2024-03-28 09:08:23,306 - 0 epochs without improvement
125
+ 2024-03-28 09:08:23,308 ----------------------------------------------------------------------------------------------------
126
+ 2024-03-28 09:08:23,394 epoch 11 - iter 1/3 - loss 1.42197424 - time (sec): 0.08 - samples/sec: 4545.88 - lr: 0.100000 - momentum: 0.000000
127
+ 2024-03-28 09:08:23,481 epoch 11 - iter 2/3 - loss 1.35518180 - time (sec): 0.17 - samples/sec: 4371.34 - lr: 0.100000 - momentum: 0.000000
128
+ 2024-03-28 09:08:23,515 epoch 11 - iter 3/3 - loss 1.35447597 - time (sec): 0.21 - samples/sec: 3785.22 - lr: 0.100000 - momentum: 0.000000
129
+ 2024-03-28 09:08:23,517 ----------------------------------------------------------------------------------------------------
130
+ 2024-03-28 09:08:23,520 EPOCH 11 done: loss 1.3545 - lr: 0.100000
131
+ 2024-03-28 09:08:23,523 - 0 epochs without improvement
132
+ 2024-03-28 09:08:23,524 ----------------------------------------------------------------------------------------------------
133
+ 2024-03-28 09:08:23,626 epoch 12 - iter 1/3 - loss 1.40993639 - time (sec): 0.10 - samples/sec: 3734.89 - lr: 0.100000 - momentum: 0.000000
134
+ 2024-03-28 09:08:23,722 epoch 12 - iter 2/3 - loss 1.49738996 - time (sec): 0.20 - samples/sec: 3849.63 - lr: 0.100000 - momentum: 0.000000
135
+ 2024-03-28 09:08:23,759 epoch 12 - iter 3/3 - loss 1.49414163 - time (sec): 0.23 - samples/sec: 3342.20 - lr: 0.100000 - momentum: 0.000000
136
+ 2024-03-28 09:08:23,762 ----------------------------------------------------------------------------------------------------
137
+ 2024-03-28 09:08:23,763 EPOCH 12 done: loss 1.4941 - lr: 0.100000
138
+ 2024-03-28 09:08:23,765 - 1 epochs without improvement
139
+ 2024-03-28 09:08:23,767 ----------------------------------------------------------------------------------------------------
140
+ 2024-03-28 09:08:23,841 epoch 13 - iter 1/3 - loss 1.26951506 - time (sec): 0.07 - samples/sec: 5241.77 - lr: 0.100000 - momentum: 0.000000
141
+ 2024-03-28 09:08:23,902 epoch 13 - iter 2/3 - loss 1.44954909 - time (sec): 0.13 - samples/sec: 5637.53 - lr: 0.100000 - momentum: 0.000000
142
+ 2024-03-28 09:08:23,928 epoch 13 - iter 3/3 - loss 1.42776139 - time (sec): 0.16 - samples/sec: 4888.19 - lr: 0.100000 - momentum: 0.000000
143
+ 2024-03-28 09:08:23,930 ----------------------------------------------------------------------------------------------------
144
+ 2024-03-28 09:08:23,933 EPOCH 13 done: loss 1.4278 - lr: 0.100000
145
+ 2024-03-28 09:08:23,935 - 2 epochs without improvement
146
+ 2024-03-28 09:08:23,938 ----------------------------------------------------------------------------------------------------
147
+ 2024-03-28 09:08:24,001 epoch 14 - iter 1/3 - loss 1.25857317 - time (sec): 0.06 - samples/sec: 6187.07 - lr: 0.100000 - momentum: 0.000000
148
+ 2024-03-28 09:08:24,065 epoch 14 - iter 2/3 - loss 1.24984402 - time (sec): 0.13 - samples/sec: 6026.61 - lr: 0.100000 - momentum: 0.000000
149
+ 2024-03-28 09:08:24,087 epoch 14 - iter 3/3 - loss 1.24510320 - time (sec): 0.15 - samples/sec: 5292.42 - lr: 0.100000 - momentum: 0.000000
150
+ 2024-03-28 09:08:24,089 ----------------------------------------------------------------------------------------------------
151
+ 2024-03-28 09:08:24,091 EPOCH 14 done: loss 1.2451 - lr: 0.100000
152
+ 2024-03-28 09:08:24,093 - 0 epochs without improvement
153
+ 2024-03-28 09:08:24,096 ----------------------------------------------------------------------------------------------------
154
+ 2024-03-28 09:08:24,152 epoch 15 - iter 1/3 - loss 1.13958904 - time (sec): 0.05 - samples/sec: 6868.95 - lr: 0.100000 - momentum: 0.000000
155
+ 2024-03-28 09:08:24,217 epoch 15 - iter 2/3 - loss 1.18272163 - time (sec): 0.12 - samples/sec: 6314.93 - lr: 0.100000 - momentum: 0.000000
156
+ 2024-03-28 09:08:24,241 epoch 15 - iter 3/3 - loss 1.18627405 - time (sec): 0.14 - samples/sec: 5430.97 - lr: 0.100000 - momentum: 0.000000
157
+ 2024-03-28 09:08:24,243 ----------------------------------------------------------------------------------------------------
158
+ 2024-03-28 09:08:24,246 EPOCH 15 done: loss 1.1863 - lr: 0.100000
159
+ 2024-03-28 09:08:24,251 - 0 epochs without improvement
160
+ 2024-03-28 09:08:24,252 ----------------------------------------------------------------------------------------------------
161
+ 2024-03-28 09:08:24,320 epoch 16 - iter 1/3 - loss 1.08566695 - time (sec): 0.06 - samples/sec: 6081.82 - lr: 0.100000 - momentum: 0.000000
162
+ 2024-03-28 09:08:24,380 epoch 16 - iter 2/3 - loss 1.07927891 - time (sec): 0.12 - samples/sec: 6157.72 - lr: 0.100000 - momentum: 0.000000
163
+ 2024-03-28 09:08:24,404 epoch 16 - iter 3/3 - loss 1.08804569 - time (sec): 0.15 - samples/sec: 5318.34 - lr: 0.100000 - momentum: 0.000000
164
+ 2024-03-28 09:08:24,405 ----------------------------------------------------------------------------------------------------
165
+ 2024-03-28 09:08:24,408 EPOCH 16 done: loss 1.0880 - lr: 0.100000
166
+ 2024-03-28 09:08:24,411 - 0 epochs without improvement
167
+ 2024-03-28 09:08:24,414 ----------------------------------------------------------------------------------------------------
168
+ 2024-03-28 09:08:24,478 epoch 17 - iter 1/3 - loss 0.98281485 - time (sec): 0.06 - samples/sec: 6049.56 - lr: 0.100000 - momentum: 0.000000
169
+ 2024-03-28 09:08:24,553 epoch 17 - iter 2/3 - loss 1.19747295 - time (sec): 0.14 - samples/sec: 5529.82 - lr: 0.100000 - momentum: 0.000000
170
+ 2024-03-28 09:08:24,575 epoch 17 - iter 3/3 - loss 1.19897193 - time (sec): 0.16 - samples/sec: 4906.66 - lr: 0.100000 - momentum: 0.000000
171
+ 2024-03-28 09:08:24,577 ----------------------------------------------------------------------------------------------------
172
+ 2024-03-28 09:08:24,580 EPOCH 17 done: loss 1.1990 - lr: 0.100000
173
+ 2024-03-28 09:08:24,583 - 1 epochs without improvement
174
+ 2024-03-28 09:08:24,585 ----------------------------------------------------------------------------------------------------
175
+ 2024-03-28 09:08:24,659 epoch 18 - iter 1/3 - loss 1.06397110 - time (sec): 0.07 - samples/sec: 5470.45 - lr: 0.100000 - momentum: 0.000000
176
+ 2024-03-28 09:08:24,720 epoch 18 - iter 2/3 - loss 1.12673372 - time (sec): 0.13 - samples/sec: 5735.98 - lr: 0.100000 - momentum: 0.000000
177
+ 2024-03-28 09:08:24,742 epoch 18 - iter 3/3 - loss 1.13683503 - time (sec): 0.16 - samples/sec: 5019.50 - lr: 0.100000 - momentum: 0.000000
178
+ 2024-03-28 09:08:24,744 ----------------------------------------------------------------------------------------------------
179
+ 2024-03-28 09:08:24,747 EPOCH 18 done: loss 1.1368 - lr: 0.100000
180
+ 2024-03-28 09:08:24,750 - 2 epochs without improvement
181
+ 2024-03-28 09:08:24,752 ----------------------------------------------------------------------------------------------------
182
+ 2024-03-28 09:08:24,815 epoch 19 - iter 1/3 - loss 1.13094212 - time (sec): 0.06 - samples/sec: 6425.44 - lr: 0.100000 - momentum: 0.000000
183
+ 2024-03-28 09:08:24,880 epoch 19 - iter 2/3 - loss 1.06757936 - time (sec): 0.12 - samples/sec: 6053.73 - lr: 0.100000 - momentum: 0.000000
184
+ 2024-03-28 09:08:24,901 epoch 19 - iter 3/3 - loss 1.07417968 - time (sec): 0.15 - samples/sec: 5333.04 - lr: 0.100000 - momentum: 0.000000
185
+ 2024-03-28 09:08:24,902 ----------------------------------------------------------------------------------------------------
186
+ 2024-03-28 09:08:24,905 EPOCH 19 done: loss 1.0742 - lr: 0.100000
187
+ 2024-03-28 09:08:24,907 - 0 epochs without improvement
188
+ 2024-03-28 09:08:24,910 ----------------------------------------------------------------------------------------------------
189
+ 2024-03-28 09:08:24,974 epoch 20 - iter 1/3 - loss 0.98265959 - time (sec): 0.06 - samples/sec: 6130.25 - lr: 0.100000 - momentum: 0.000000
190
+ 2024-03-28 09:08:25,035 epoch 20 - iter 2/3 - loss 0.95606777 - time (sec): 0.12 - samples/sec: 6115.30 - lr: 0.100000 - momentum: 0.000000
191
+ 2024-03-28 09:08:25,059 epoch 20 - iter 3/3 - loss 0.95184126 - time (sec): 0.15 - samples/sec: 5278.72 - lr: 0.100000 - momentum: 0.000000
192
+ 2024-03-28 09:08:25,061 ----------------------------------------------------------------------------------------------------
193
+ 2024-03-28 09:08:25,066 EPOCH 20 done: loss 0.9518 - lr: 0.100000
194
+ 2024-03-28 09:08:25,068 - 0 epochs without improvement
195
+ 2024-03-28 09:08:25,071 ----------------------------------------------------------------------------------------------------
196
+ 2024-03-28 09:08:25,133 epoch 21 - iter 1/3 - loss 0.86139335 - time (sec): 0.06 - samples/sec: 6546.08 - lr: 0.100000 - momentum: 0.000000
197
+ 2024-03-28 09:08:25,194 epoch 21 - iter 2/3 - loss 0.88997541 - time (sec): 0.12 - samples/sec: 6239.01 - lr: 0.100000 - momentum: 0.000000
198
+ 2024-03-28 09:08:25,216 epoch 21 - iter 3/3 - loss 0.89881944 - time (sec): 0.14 - samples/sec: 5424.95 - lr: 0.100000 - momentum: 0.000000
199
+ 2024-03-28 09:08:25,219 ----------------------------------------------------------------------------------------------------
200
+ 2024-03-28 09:08:25,221 EPOCH 21 done: loss 0.8988 - lr: 0.100000
201
+ 2024-03-28 09:08:25,224 - 0 epochs without improvement
202
+ 2024-03-28 09:08:25,227 ----------------------------------------------------------------------------------------------------
203
+ 2024-03-28 09:08:25,298 epoch 22 - iter 1/3 - loss 0.87613110 - time (sec): 0.07 - samples/sec: 5678.58 - lr: 0.100000 - momentum: 0.000000
204
+ 2024-03-28 09:08:25,360 epoch 22 - iter 2/3 - loss 0.85298542 - time (sec): 0.13 - samples/sec: 5823.45 - lr: 0.100000 - momentum: 0.000000
205
+ 2024-03-28 09:08:25,381 epoch 22 - iter 3/3 - loss 0.85040579 - time (sec): 0.15 - samples/sec: 5127.33 - lr: 0.100000 - momentum: 0.000000
206
+ 2024-03-28 09:08:25,383 ----------------------------------------------------------------------------------------------------
207
+ 2024-03-28 09:08:25,386 EPOCH 22 done: loss 0.8504 - lr: 0.100000
208
+ 2024-03-28 09:08:25,389 - 0 epochs without improvement
209
+ 2024-03-28 09:08:25,391 ----------------------------------------------------------------------------------------------------
210
+ 2024-03-28 09:08:25,454 epoch 23 - iter 1/3 - loss 0.84350519 - time (sec): 0.06 - samples/sec: 6063.21 - lr: 0.100000 - momentum: 0.000000
211
+ 2024-03-28 09:08:25,521 epoch 23 - iter 2/3 - loss 0.80839760 - time (sec): 0.13 - samples/sec: 5952.73 - lr: 0.100000 - momentum: 0.000000
212
+ 2024-03-28 09:08:25,547 epoch 23 - iter 3/3 - loss 0.80830466 - time (sec): 0.15 - samples/sec: 5072.50 - lr: 0.100000 - momentum: 0.000000
213
+ 2024-03-28 09:08:25,549 ----------------------------------------------------------------------------------------------------
214
+ 2024-03-28 09:08:25,551 EPOCH 23 done: loss 0.8083 - lr: 0.100000
215
+ 2024-03-28 09:08:25,553 - 0 epochs without improvement
216
+ 2024-03-28 09:08:25,555 ----------------------------------------------------------------------------------------------------
217
+ 2024-03-28 09:08:25,638 epoch 24 - iter 1/3 - loss 0.81519134 - time (sec): 0.08 - samples/sec: 4674.16 - lr: 0.100000 - momentum: 0.000000
218
+ 2024-03-28 09:08:25,701 epoch 24 - iter 2/3 - loss 0.73801863 - time (sec): 0.14 - samples/sec: 5197.51 - lr: 0.100000 - momentum: 0.000000
219
+ 2024-03-28 09:08:25,725 epoch 24 - iter 3/3 - loss 0.73577389 - time (sec): 0.17 - samples/sec: 4607.71 - lr: 0.100000 - momentum: 0.000000
220
+ 2024-03-28 09:08:25,727 ----------------------------------------------------------------------------------------------------
221
+ 2024-03-28 09:08:25,730 EPOCH 24 done: loss 0.7358 - lr: 0.100000
222
+ 2024-03-28 09:08:25,733 - 0 epochs without improvement
223
+ 2024-03-28 09:08:25,735 ----------------------------------------------------------------------------------------------------
224
+ 2024-03-28 09:08:25,802 epoch 25 - iter 1/3 - loss 0.66769132 - time (sec): 0.06 - samples/sec: 5861.73 - lr: 0.100000 - momentum: 0.000000
225
+ 2024-03-28 09:08:25,871 epoch 25 - iter 2/3 - loss 0.71950535 - time (sec): 0.13 - samples/sec: 5695.03 - lr: 0.100000 - momentum: 0.000000
226
+ 2024-03-28 09:08:25,894 epoch 25 - iter 3/3 - loss 0.72146968 - time (sec): 0.16 - samples/sec: 4988.39 - lr: 0.100000 - momentum: 0.000000
227
+ 2024-03-28 09:08:25,896 ----------------------------------------------------------------------------------------------------
228
+ 2024-03-28 09:08:25,901 EPOCH 25 done: loss 0.7215 - lr: 0.100000
229
+ 2024-03-28 09:08:25,902 - 0 epochs without improvement
230
+ 2024-03-28 09:08:25,906 ----------------------------------------------------------------------------------------------------
231
+ 2024-03-28 09:08:25,965 epoch 26 - iter 1/3 - loss 0.77873421 - time (sec): 0.06 - samples/sec: 6787.99 - lr: 0.100000 - momentum: 0.000000
232
+ 2024-03-28 09:08:26,028 epoch 26 - iter 2/3 - loss 0.79412269 - time (sec): 0.12 - samples/sec: 6309.97 - lr: 0.100000 - momentum: 0.000000
233
+ 2024-03-28 09:08:26,053 epoch 26 - iter 3/3 - loss 0.78410294 - time (sec): 0.14 - samples/sec: 5376.37 - lr: 0.100000 - momentum: 0.000000
234
+ 2024-03-28 09:08:26,055 ----------------------------------------------------------------------------------------------------
235
+ 2024-03-28 09:08:26,056 EPOCH 26 done: loss 0.7841 - lr: 0.100000
236
+ 2024-03-28 09:08:26,057 - 1 epochs without improvement
237
+ 2024-03-28 09:08:26,059 ----------------------------------------------------------------------------------------------------
238
+ 2024-03-28 09:08:26,120 epoch 27 - iter 1/3 - loss 0.67765564 - time (sec): 0.06 - samples/sec: 6209.52 - lr: 0.100000 - momentum: 0.000000
239
+ 2024-03-28 09:08:26,185 epoch 27 - iter 2/3 - loss 0.74440163 - time (sec): 0.12 - samples/sec: 6024.24 - lr: 0.100000 - momentum: 0.000000
240
+ 2024-03-28 09:08:26,211 epoch 27 - iter 3/3 - loss 0.74220062 - time (sec): 0.15 - samples/sec: 5168.46 - lr: 0.100000 - momentum: 0.000000
241
+ 2024-03-28 09:08:26,212 ----------------------------------------------------------------------------------------------------
242
+ 2024-03-28 09:08:26,216 EPOCH 27 done: loss 0.7422 - lr: 0.100000
243
+ 2024-03-28 09:08:26,219 - 2 epochs without improvement
244
+ 2024-03-28 09:08:26,222 ----------------------------------------------------------------------------------------------------
245
+ 2024-03-28 09:08:26,281 epoch 28 - iter 1/3 - loss 0.65576854 - time (sec): 0.06 - samples/sec: 6429.32 - lr: 0.100000 - momentum: 0.000000
246
+ 2024-03-28 09:08:26,346 epoch 28 - iter 2/3 - loss 0.67840381 - time (sec): 0.12 - samples/sec: 6203.36 - lr: 0.100000 - momentum: 0.000000
247
+ 2024-03-28 09:08:26,371 epoch 28 - iter 3/3 - loss 0.69483660 - time (sec): 0.15 - samples/sec: 5328.19 - lr: 0.100000 - momentum: 0.000000
248
+ 2024-03-28 09:08:26,373 ----------------------------------------------------------------------------------------------------
249
+ 2024-03-28 09:08:26,380 EPOCH 28 done: loss 0.6948 - lr: 0.100000
250
+ 2024-03-28 09:08:26,383 - 0 epochs without improvement
251
+ 2024-03-28 09:08:26,385 ----------------------------------------------------------------------------------------------------
252
+ 2024-03-28 09:08:26,453 epoch 29 - iter 1/3 - loss 0.60680922 - time (sec): 0.07 - samples/sec: 5681.74 - lr: 0.100000 - momentum: 0.000000
253
+ 2024-03-28 09:08:26,520 epoch 29 - iter 2/3 - loss 0.71351490 - time (sec): 0.13 - samples/sec: 5698.89 - lr: 0.100000 - momentum: 0.000000
254
+ 2024-03-28 09:08:26,543 epoch 29 - iter 3/3 - loss 0.72190195 - time (sec): 0.16 - samples/sec: 4996.69 - lr: 0.100000 - momentum: 0.000000
255
+ 2024-03-28 09:08:26,545 ----------------------------------------------------------------------------------------------------
256
+ 2024-03-28 09:08:26,547 EPOCH 29 done: loss 0.7219 - lr: 0.100000
257
+ 2024-03-28 09:08:26,551 - 1 epochs without improvement
258
+ 2024-03-28 09:08:26,553 ----------------------------------------------------------------------------------------------------
259
+ 2024-03-28 09:08:26,616 epoch 30 - iter 1/3 - loss 0.54127716 - time (sec): 0.06 - samples/sec: 5980.72 - lr: 0.100000 - momentum: 0.000000
260
+ 2024-03-28 09:08:26,719 epoch 30 - iter 2/3 - loss 0.66156022 - time (sec): 0.16 - samples/sec: 4617.44 - lr: 0.100000 - momentum: 0.000000
261
+ 2024-03-28 09:08:26,754 epoch 30 - iter 3/3 - loss 0.66835733 - time (sec): 0.20 - samples/sec: 3931.30 - lr: 0.100000 - momentum: 0.000000
262
+ 2024-03-28 09:08:26,756 ----------------------------------------------------------------------------------------------------
263
+ 2024-03-28 09:08:26,757 EPOCH 30 done: loss 0.6684 - lr: 0.100000
264
+ 2024-03-28 09:08:26,759 - 0 epochs without improvement
265
+ 2024-03-28 09:08:26,761 ----------------------------------------------------------------------------------------------------
266
+ 2024-03-28 09:08:26,843 epoch 31 - iter 1/3 - loss 0.56766907 - time (sec): 0.08 - samples/sec: 4600.84 - lr: 0.100000 - momentum: 0.000000
267
+ 2024-03-28 09:08:26,931 epoch 31 - iter 2/3 - loss 0.66296679 - time (sec): 0.17 - samples/sec: 4480.14 - lr: 0.100000 - momentum: 0.000000
268
+ 2024-03-28 09:08:26,965 epoch 31 - iter 3/3 - loss 0.66437075 - time (sec): 0.20 - samples/sec: 3851.74 - lr: 0.100000 - momentum: 0.000000
269
+ 2024-03-28 09:08:26,968 ----------------------------------------------------------------------------------------------------
270
+ 2024-03-28 09:08:26,972 EPOCH 31 done: loss 0.6644 - lr: 0.100000
271
+ 2024-03-28 09:08:26,975 - 0 epochs without improvement
272
+ 2024-03-28 09:08:26,979 ----------------------------------------------------------------------------------------------------
273
+ 2024-03-28 09:08:27,072 epoch 32 - iter 1/3 - loss 0.47235793 - time (sec): 0.09 - samples/sec: 4231.03 - lr: 0.100000 - momentum: 0.000000
274
+ 2024-03-28 09:08:27,154 epoch 32 - iter 2/3 - loss 0.66783573 - time (sec): 0.17 - samples/sec: 4445.75 - lr: 0.100000 - momentum: 0.000000
275
+ 2024-03-28 09:08:27,180 epoch 32 - iter 3/3 - loss 0.67434436 - time (sec): 0.20 - samples/sec: 3962.60 - lr: 0.100000 - momentum: 0.000000
276
+ 2024-03-28 09:08:27,185 ----------------------------------------------------------------------------------------------------
277
+ 2024-03-28 09:08:27,187 EPOCH 32 done: loss 0.6743 - lr: 0.100000
278
+ 2024-03-28 09:08:27,189 - 1 epochs without improvement
279
+ 2024-03-28 09:08:27,191 ----------------------------------------------------------------------------------------------------
280
+ 2024-03-28 09:08:27,286 epoch 33 - iter 1/3 - loss 0.51676854 - time (sec): 0.09 - samples/sec: 4329.67 - lr: 0.100000 - momentum: 0.000000
281
+ 2024-03-28 09:08:27,368 epoch 33 - iter 2/3 - loss 0.56572747 - time (sec): 0.17 - samples/sec: 4439.20 - lr: 0.100000 - momentum: 0.000000
282
+ 2024-03-28 09:08:27,393 epoch 33 - iter 3/3 - loss 0.56014238 - time (sec): 0.20 - samples/sec: 3969.22 - lr: 0.100000 - momentum: 0.000000
283
+ 2024-03-28 09:08:27,398 ----------------------------------------------------------------------------------------------------
284
+ 2024-03-28 09:08:27,399 EPOCH 33 done: loss 0.5601 - lr: 0.100000
285
+ 2024-03-28 09:08:27,404 - 0 epochs without improvement
286
+ 2024-03-28 09:08:27,406 ----------------------------------------------------------------------------------------------------
287
+ 2024-03-28 09:08:27,490 epoch 34 - iter 1/3 - loss 0.56149373 - time (sec): 0.08 - samples/sec: 4800.36 - lr: 0.100000 - momentum: 0.000000
288
+ 2024-03-28 09:08:27,571 epoch 34 - iter 2/3 - loss 0.62482820 - time (sec): 0.16 - samples/sec: 4724.24 - lr: 0.100000 - momentum: 0.000000
289
+ 2024-03-28 09:08:27,598 epoch 34 - iter 3/3 - loss 0.62822730 - time (sec): 0.19 - samples/sec: 4169.96 - lr: 0.100000 - momentum: 0.000000
290
+ 2024-03-28 09:08:27,603 ----------------------------------------------------------------------------------------------------
291
+ 2024-03-28 09:08:27,605 EPOCH 34 done: loss 0.6282 - lr: 0.100000
292
+ 2024-03-28 09:08:27,608 - 1 epochs without improvement
293
+ 2024-03-28 09:08:27,611 ----------------------------------------------------------------------------------------------------
294
+ 2024-03-28 09:08:27,687 epoch 35 - iter 1/3 - loss 0.52592365 - time (sec): 0.07 - samples/sec: 5121.35 - lr: 0.100000 - momentum: 0.000000
295
+ 2024-03-28 09:08:27,776 epoch 35 - iter 2/3 - loss 0.55252095 - time (sec): 0.16 - samples/sec: 4680.28 - lr: 0.100000 - momentum: 0.000000
296
+ 2024-03-28 09:08:27,804 epoch 35 - iter 3/3 - loss 0.55461875 - time (sec): 0.19 - samples/sec: 4100.79 - lr: 0.100000 - momentum: 0.000000
297
+ 2024-03-28 09:08:27,807 ----------------------------------------------------------------------------------------------------
298
+ 2024-03-28 09:08:27,810 EPOCH 35 done: loss 0.5546 - lr: 0.100000
299
+ 2024-03-28 09:08:27,812 - 0 epochs without improvement
300
+ 2024-03-28 09:08:27,814 ----------------------------------------------------------------------------------------------------
301
+ 2024-03-28 09:08:27,909 epoch 36 - iter 1/3 - loss 0.55149670 - time (sec): 0.09 - samples/sec: 3986.62 - lr: 0.100000 - momentum: 0.000000
302
+ 2024-03-28 09:08:28,000 epoch 36 - iter 2/3 - loss 0.52226647 - time (sec): 0.18 - samples/sec: 4082.53 - lr: 0.100000 - momentum: 0.000000
303
+ 2024-03-28 09:08:28,040 epoch 36 - iter 3/3 - loss 0.51511517 - time (sec): 0.22 - samples/sec: 3472.83 - lr: 0.100000 - momentum: 0.000000
304
+ 2024-03-28 09:08:28,045 ----------------------------------------------------------------------------------------------------
305
+ 2024-03-28 09:08:28,047 EPOCH 36 done: loss 0.5151 - lr: 0.100000
306
+ 2024-03-28 09:08:28,050 - 0 epochs without improvement
307
+ 2024-03-28 09:08:28,053 ----------------------------------------------------------------------------------------------------
308
+ 2024-03-28 09:08:28,146 epoch 37 - iter 1/3 - loss 0.49687234 - time (sec): 0.09 - samples/sec: 4244.26 - lr: 0.100000 - momentum: 0.000000
309
+ 2024-03-28 09:08:28,228 epoch 37 - iter 2/3 - loss 0.47490515 - time (sec): 0.17 - samples/sec: 4304.77 - lr: 0.100000 - momentum: 0.000000
310
+ 2024-03-28 09:08:28,263 epoch 37 - iter 3/3 - loss 0.48109616 - time (sec): 0.21 - samples/sec: 3743.54 - lr: 0.100000 - momentum: 0.000000
311
+ 2024-03-28 09:08:28,265 ----------------------------------------------------------------------------------------------------
312
+ 2024-03-28 09:08:28,267 EPOCH 37 done: loss 0.4811 - lr: 0.100000
313
+ 2024-03-28 09:08:28,273 - 0 epochs without improvement
314
+ 2024-03-28 09:08:28,276 ----------------------------------------------------------------------------------------------------
315
+ 2024-03-28 09:08:28,371 epoch 38 - iter 1/3 - loss 0.48292834 - time (sec): 0.09 - samples/sec: 4022.76 - lr: 0.100000 - momentum: 0.000000
316
+ 2024-03-28 09:08:28,463 epoch 38 - iter 2/3 - loss 0.60736766 - time (sec): 0.19 - samples/sec: 4059.96 - lr: 0.100000 - momentum: 0.000000
317
+ 2024-03-28 09:08:28,497 epoch 38 - iter 3/3 - loss 0.60272934 - time (sec): 0.22 - samples/sec: 3552.63 - lr: 0.100000 - momentum: 0.000000
318
+ 2024-03-28 09:08:28,499 ----------------------------------------------------------------------------------------------------
319
+ 2024-03-28 09:08:28,502 EPOCH 38 done: loss 0.6027 - lr: 0.100000
320
+ 2024-03-28 09:08:28,505 - 1 epochs without improvement
321
+ 2024-03-28 09:08:28,507 ----------------------------------------------------------------------------------------------------
322
+ 2024-03-28 09:08:28,608 epoch 39 - iter 1/3 - loss 0.46681475 - time (sec): 0.10 - samples/sec: 4013.80 - lr: 0.100000 - momentum: 0.000000
323
+ 2024-03-28 09:08:28,699 epoch 39 - iter 2/3 - loss 0.49050783 - time (sec): 0.19 - samples/sec: 4012.02 - lr: 0.100000 - momentum: 0.000000
324
+ 2024-03-28 09:08:28,731 epoch 39 - iter 3/3 - loss 0.48410297 - time (sec): 0.22 - samples/sec: 3515.46 - lr: 0.100000 - momentum: 0.000000
325
+ 2024-03-28 09:08:28,736 ----------------------------------------------------------------------------------------------------
326
+ 2024-03-28 09:08:28,739 EPOCH 39 done: loss 0.4841 - lr: 0.100000
327
+ 2024-03-28 09:08:28,742 - 2 epochs without improvement
328
+ 2024-03-28 09:08:28,748 ----------------------------------------------------------------------------------------------------
329
+ 2024-03-28 09:08:28,850 epoch 40 - iter 1/3 - loss 0.47415373 - time (sec): 0.10 - samples/sec: 3747.88 - lr: 0.100000 - momentum: 0.000000
330
+ 2024-03-28 09:08:28,916 epoch 40 - iter 2/3 - loss 0.43865486 - time (sec): 0.17 - samples/sec: 4513.33 - lr: 0.100000 - momentum: 0.000000
331
+ 2024-03-28 09:08:28,946 epoch 40 - iter 3/3 - loss 0.44022970 - time (sec): 0.20 - samples/sec: 3985.95 - lr: 0.100000 - momentum: 0.000000
332
+ 2024-03-28 09:08:28,948 ----------------------------------------------------------------------------------------------------
333
+ 2024-03-28 09:08:28,950 EPOCH 40 done: loss 0.4402 - lr: 0.100000
334
+ 2024-03-28 09:08:28,953 - 0 epochs without improvement
335
+ 2024-03-28 09:08:28,955 ----------------------------------------------------------------------------------------------------
336
+ 2024-03-28 09:08:29,022 epoch 41 - iter 1/3 - loss 0.39505556 - time (sec): 0.06 - samples/sec: 6165.00 - lr: 0.100000 - momentum: 0.000000
337
+ 2024-03-28 09:08:29,094 epoch 41 - iter 2/3 - loss 0.45856506 - time (sec): 0.14 - samples/sec: 5563.23 - lr: 0.100000 - momentum: 0.000000
338
+ 2024-03-28 09:08:29,121 epoch 41 - iter 3/3 - loss 0.46752058 - time (sec): 0.16 - samples/sec: 4775.73 - lr: 0.100000 - momentum: 0.000000
339
+ 2024-03-28 09:08:29,123 ----------------------------------------------------------------------------------------------------
340
+ 2024-03-28 09:08:29,126 EPOCH 41 done: loss 0.4675 - lr: 0.100000
341
+ 2024-03-28 09:08:29,129 - 1 epochs without improvement
342
+ 2024-03-28 09:08:29,132 ----------------------------------------------------------------------------------------------------
343
+ 2024-03-28 09:08:29,202 epoch 42 - iter 1/3 - loss 0.40104817 - time (sec): 0.07 - samples/sec: 5656.93 - lr: 0.100000 - momentum: 0.000000
344
+ 2024-03-28 09:08:29,270 epoch 42 - iter 2/3 - loss 0.44249168 - time (sec): 0.14 - samples/sec: 5554.20 - lr: 0.100000 - momentum: 0.000000
345
+ 2024-03-28 09:08:29,295 epoch 42 - iter 3/3 - loss 0.45211151 - time (sec): 0.16 - samples/sec: 4831.38 - lr: 0.100000 - momentum: 0.000000
346
+ 2024-03-28 09:08:29,296 ----------------------------------------------------------------------------------------------------
347
+ 2024-03-28 09:08:29,299 EPOCH 42 done: loss 0.4521 - lr: 0.100000
348
+ 2024-03-28 09:08:29,301 - 2 epochs without improvement
349
+ 2024-03-28 09:08:29,303 ----------------------------------------------------------------------------------------------------
350
+ 2024-03-28 09:08:29,373 epoch 43 - iter 1/3 - loss 0.42974738 - time (sec): 0.07 - samples/sec: 5498.50 - lr: 0.100000 - momentum: 0.000000
351
+ 2024-03-28 09:08:29,445 epoch 43 - iter 2/3 - loss 0.48877276 - time (sec): 0.14 - samples/sec: 5430.97 - lr: 0.100000 - momentum: 0.000000
352
+ 2024-03-28 09:08:29,471 epoch 43 - iter 3/3 - loss 0.50198705 - time (sec): 0.17 - samples/sec: 4719.28 - lr: 0.100000 - momentum: 0.000000
353
+ 2024-03-28 09:08:29,473 ----------------------------------------------------------------------------------------------------
354
+ 2024-03-28 09:08:29,475 EPOCH 43 done: loss 0.5020 - lr: 0.100000
355
+ 2024-03-28 09:08:29,477 - 3 epochs without improvement
356
+ 2024-03-28 09:08:29,480 ----------------------------------------------------------------------------------------------------
357
+ 2024-03-28 09:08:29,553 epoch 44 - iter 1/3 - loss 0.34393486 - time (sec): 0.07 - samples/sec: 5398.09 - lr: 0.100000 - momentum: 0.000000
358
+ 2024-03-28 09:08:29,622 epoch 44 - iter 2/3 - loss 0.42744327 - time (sec): 0.14 - samples/sec: 5421.30 - lr: 0.100000 - momentum: 0.000000
359
+ 2024-03-28 09:08:29,646 epoch 44 - iter 3/3 - loss 0.43221926 - time (sec): 0.16 - samples/sec: 4738.09 - lr: 0.100000 - momentum: 0.000000
360
+ 2024-03-28 09:08:29,648 ----------------------------------------------------------------------------------------------------
361
+ 2024-03-28 09:08:29,651 EPOCH 44 done: loss 0.4322 - lr: 0.100000
362
+ 2024-03-28 09:08:29,654 - 0 epochs without improvement
363
+ 2024-03-28 09:08:29,657 ----------------------------------------------------------------------------------------------------
364
+ 2024-03-28 09:08:29,719 epoch 45 - iter 1/3 - loss 0.35080136 - time (sec): 0.06 - samples/sec: 6469.89 - lr: 0.100000 - momentum: 0.000000
365
+ 2024-03-28 09:08:29,789 epoch 45 - iter 2/3 - loss 0.45650777 - time (sec): 0.13 - samples/sec: 5890.71 - lr: 0.100000 - momentum: 0.000000
366
+ 2024-03-28 09:08:29,822 epoch 45 - iter 3/3 - loss 0.45322049 - time (sec): 0.16 - samples/sec: 4842.83 - lr: 0.100000 - momentum: 0.000000
367
+ 2024-03-28 09:08:29,825 ----------------------------------------------------------------------------------------------------
368
+ 2024-03-28 09:08:29,829 EPOCH 45 done: loss 0.4532 - lr: 0.100000
369
+ 2024-03-28 09:08:29,832 - 1 epochs without improvement
370
+ 2024-03-28 09:08:29,835 ----------------------------------------------------------------------------------------------------
371
+ 2024-03-28 09:08:29,911 epoch 46 - iter 1/3 - loss 0.41917917 - time (sec): 0.07 - samples/sec: 5213.37 - lr: 0.100000 - momentum: 0.000000
372
+ 2024-03-28 09:08:29,981 epoch 46 - iter 2/3 - loss 0.44351342 - time (sec): 0.14 - samples/sec: 5299.67 - lr: 0.100000 - momentum: 0.000000
373
+ 2024-03-28 09:08:30,007 epoch 46 - iter 3/3 - loss 0.43757010 - time (sec): 0.17 - samples/sec: 4606.63 - lr: 0.100000 - momentum: 0.000000
374
+ 2024-03-28 09:08:30,009 ----------------------------------------------------------------------------------------------------
375
+ 2024-03-28 09:08:30,012 EPOCH 46 done: loss 0.4376 - lr: 0.100000
376
+ 2024-03-28 09:08:30,018 - 2 epochs without improvement
377
+ 2024-03-28 09:08:30,021 ----------------------------------------------------------------------------------------------------
378
+ 2024-03-28 09:08:30,087 epoch 47 - iter 1/3 - loss 0.43355327 - time (sec): 0.06 - samples/sec: 6018.18 - lr: 0.100000 - momentum: 0.000000
379
+ 2024-03-28 09:08:30,151 epoch 47 - iter 2/3 - loss 0.47636440 - time (sec): 0.13 - samples/sec: 5894.94 - lr: 0.100000 - momentum: 0.000000
380
+ 2024-03-28 09:08:30,178 epoch 47 - iter 3/3 - loss 0.46188437 - time (sec): 0.15 - samples/sec: 5040.05 - lr: 0.100000 - momentum: 0.000000
381
+ 2024-03-28 09:08:30,180 ----------------------------------------------------------------------------------------------------
382
+ 2024-03-28 09:08:30,182 EPOCH 47 done: loss 0.4619 - lr: 0.100000
383
+ 2024-03-28 09:08:30,184 - 3 epochs without improvement
384
+ 2024-03-28 09:08:30,186 ----------------------------------------------------------------------------------------------------
385
+ 2024-03-28 09:08:30,253 epoch 48 - iter 1/3 - loss 0.41156757 - time (sec): 0.06 - samples/sec: 5842.89 - lr: 0.100000 - momentum: 0.000000
386
+ 2024-03-28 09:08:30,320 epoch 48 - iter 2/3 - loss 0.43935062 - time (sec): 0.13 - samples/sec: 5763.82 - lr: 0.100000 - momentum: 0.000000
387
+ 2024-03-28 09:08:30,344 epoch 48 - iter 3/3 - loss 0.43557776 - time (sec): 0.16 - samples/sec: 5008.81 - lr: 0.100000 - momentum: 0.000000
388
+ 2024-03-28 09:08:30,345 ----------------------------------------------------------------------------------------------------
389
+ 2024-03-28 09:08:30,349 EPOCH 48 done: loss 0.4356 - lr: 0.100000
390
+ 2024-03-28 09:08:30,352 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.05]
391
+ 2024-03-28 09:08:30,355 ----------------------------------------------------------------------------------------------------
392
+ 2024-03-28 09:08:30,421 epoch 49 - iter 1/3 - loss 0.39220638 - time (sec): 0.06 - samples/sec: 6083.85 - lr: 0.050000 - momentum: 0.000000
393
+ 2024-03-28 09:08:30,488 epoch 49 - iter 2/3 - loss 0.36613670 - time (sec): 0.13 - samples/sec: 5923.39 - lr: 0.050000 - momentum: 0.000000
394
+ 2024-03-28 09:08:30,508 epoch 49 - iter 3/3 - loss 0.36897407 - time (sec): 0.15 - samples/sec: 5215.96 - lr: 0.050000 - momentum: 0.000000
395
+ 2024-03-28 09:08:30,510 ----------------------------------------------------------------------------------------------------
396
+ 2024-03-28 09:08:30,518 EPOCH 49 done: loss 0.3690 - lr: 0.050000
397
+ 2024-03-28 09:08:30,521 - 0 epochs without improvement
398
+ 2024-03-28 09:08:30,524 ----------------------------------------------------------------------------------------------------
399
+ 2024-03-28 09:08:30,591 epoch 50 - iter 1/3 - loss 0.33464823 - time (sec): 0.06 - samples/sec: 5912.48 - lr: 0.050000 - momentum: 0.000000
400
+ 2024-03-28 09:08:30,657 epoch 50 - iter 2/3 - loss 0.35002112 - time (sec): 0.13 - samples/sec: 5806.96 - lr: 0.050000 - momentum: 0.000000
401
+ 2024-03-28 09:08:30,681 epoch 50 - iter 3/3 - loss 0.35487791 - time (sec): 0.15 - samples/sec: 5046.70 - lr: 0.050000 - momentum: 0.000000
402
+ 2024-03-28 09:08:30,682 ----------------------------------------------------------------------------------------------------
403
+ 2024-03-28 09:08:30,686 EPOCH 50 done: loss 0.3549 - lr: 0.050000
404
+ 2024-03-28 09:08:30,689 - 0 epochs without improvement
405
+ 2024-03-28 09:08:30,692 ----------------------------------------------------------------------------------------------------
406
+ 2024-03-28 09:08:30,758 epoch 51 - iter 1/3 - loss 0.31067178 - time (sec): 0.06 - samples/sec: 6208.08 - lr: 0.050000 - momentum: 0.000000
407
+ 2024-03-28 09:08:30,829 epoch 51 - iter 2/3 - loss 0.32343769 - time (sec): 0.13 - samples/sec: 5682.84 - lr: 0.050000 - momentum: 0.000000
408
+ 2024-03-28 09:08:30,856 epoch 51 - iter 3/3 - loss 0.31745014 - time (sec): 0.16 - samples/sec: 4862.36 - lr: 0.050000 - momentum: 0.000000
409
+ 2024-03-28 09:08:30,858 ----------------------------------------------------------------------------------------------------
410
+ 2024-03-28 09:08:30,861 EPOCH 51 done: loss 0.3175 - lr: 0.050000
411
+ 2024-03-28 09:08:30,864 - 0 epochs without improvement
412
+ 2024-03-28 09:08:30,868 ----------------------------------------------------------------------------------------------------
413
+ 2024-03-28 09:08:30,930 epoch 52 - iter 1/3 - loss 0.27665802 - time (sec): 0.06 - samples/sec: 6067.43 - lr: 0.050000 - momentum: 0.000000
414
+ 2024-03-28 09:08:31,002 epoch 52 - iter 2/3 - loss 0.30771992 - time (sec): 0.13 - samples/sec: 5702.05 - lr: 0.050000 - momentum: 0.000000
415
+ 2024-03-28 09:08:31,029 epoch 52 - iter 3/3 - loss 0.30199688 - time (sec): 0.16 - samples/sec: 4909.62 - lr: 0.050000 - momentum: 0.000000
416
+ 2024-03-28 09:08:31,031 ----------------------------------------------------------------------------------------------------
417
+ 2024-03-28 09:08:31,035 EPOCH 52 done: loss 0.3020 - lr: 0.050000
418
+ 2024-03-28 09:08:31,038 - 0 epochs without improvement
419
+ 2024-03-28 09:08:31,040 ----------------------------------------------------------------------------------------------------
420
+ 2024-03-28 09:08:31,108 epoch 53 - iter 1/3 - loss 0.33830257 - time (sec): 0.07 - samples/sec: 5664.12 - lr: 0.050000 - momentum: 0.000000
421
+ 2024-03-28 09:08:31,176 epoch 53 - iter 2/3 - loss 0.33237701 - time (sec): 0.13 - samples/sec: 5624.94 - lr: 0.050000 - momentum: 0.000000
422
+ 2024-03-28 09:08:31,206 epoch 53 - iter 3/3 - loss 0.32609021 - time (sec): 0.16 - samples/sec: 4769.13 - lr: 0.050000 - momentum: 0.000000
423
+ 2024-03-28 09:08:31,209 ----------------------------------------------------------------------------------------------------
424
+ 2024-03-28 09:08:31,211 EPOCH 53 done: loss 0.3261 - lr: 0.050000
425
+ 2024-03-28 09:08:31,212 - 1 epochs without improvement
426
+ 2024-03-28 09:08:31,213 ----------------------------------------------------------------------------------------------------
427
+ 2024-03-28 09:08:31,286 epoch 54 - iter 1/3 - loss 0.32231420 - time (sec): 0.07 - samples/sec: 5390.92 - lr: 0.050000 - momentum: 0.000000
428
+ 2024-03-28 09:08:31,353 epoch 54 - iter 2/3 - loss 0.29146483 - time (sec): 0.14 - samples/sec: 5477.77 - lr: 0.050000 - momentum: 0.000000
429
+ 2024-03-28 09:08:31,377 epoch 54 - iter 3/3 - loss 0.29709430 - time (sec): 0.16 - samples/sec: 4806.07 - lr: 0.050000 - momentum: 0.000000
430
+ 2024-03-28 09:08:31,379 ----------------------------------------------------------------------------------------------------
431
+ 2024-03-28 09:08:31,381 EPOCH 54 done: loss 0.2971 - lr: 0.050000
432
+ 2024-03-28 09:08:31,383 - 0 epochs without improvement
433
+ 2024-03-28 09:08:31,386 ----------------------------------------------------------------------------------------------------
434
+ 2024-03-28 09:08:31,449 epoch 55 - iter 1/3 - loss 0.23736323 - time (sec): 0.06 - samples/sec: 6007.27 - lr: 0.050000 - momentum: 0.000000
435
+ 2024-03-28 09:08:31,518 epoch 55 - iter 2/3 - loss 0.27148632 - time (sec): 0.13 - samples/sec: 5795.54 - lr: 0.050000 - momentum: 0.000000
436
+ 2024-03-28 09:08:31,544 epoch 55 - iter 3/3 - loss 0.27109961 - time (sec): 0.16 - samples/sec: 4991.08 - lr: 0.050000 - momentum: 0.000000
437
+ 2024-03-28 09:08:31,546 ----------------------------------------------------------------------------------------------------
438
+ 2024-03-28 09:08:31,548 EPOCH 55 done: loss 0.2711 - lr: 0.050000
439
+ 2024-03-28 09:08:31,551 - 0 epochs without improvement
440
+ 2024-03-28 09:08:31,553 ----------------------------------------------------------------------------------------------------
441
+ 2024-03-28 09:08:31,621 epoch 56 - iter 1/3 - loss 0.25377297 - time (sec): 0.07 - samples/sec: 5827.21 - lr: 0.050000 - momentum: 0.000000
442
+ 2024-03-28 09:08:31,689 epoch 56 - iter 2/3 - loss 0.22560634 - time (sec): 0.13 - samples/sec: 5653.90 - lr: 0.050000 - momentum: 0.000000
443
+ 2024-03-28 09:08:31,714 epoch 56 - iter 3/3 - loss 0.23113600 - time (sec): 0.16 - samples/sec: 4917.05 - lr: 0.050000 - momentum: 0.000000
444
+ 2024-03-28 09:08:31,716 ----------------------------------------------------------------------------------------------------
445
+ 2024-03-28 09:08:31,719 EPOCH 56 done: loss 0.2311 - lr: 0.050000
446
+ 2024-03-28 09:08:31,721 - 0 epochs without improvement
447
+ 2024-03-28 09:08:31,723 ----------------------------------------------------------------------------------------------------
448
+ 2024-03-28 09:08:31,794 epoch 57 - iter 1/3 - loss 0.26957983 - time (sec): 0.07 - samples/sec: 5797.05 - lr: 0.050000 - momentum: 0.000000
449
+ 2024-03-28 09:08:31,871 epoch 57 - iter 2/3 - loss 0.25384182 - time (sec): 0.15 - samples/sec: 5161.24 - lr: 0.050000 - momentum: 0.000000
450
+ 2024-03-28 09:08:31,895 epoch 57 - iter 3/3 - loss 0.25095547 - time (sec): 0.17 - samples/sec: 4586.07 - lr: 0.050000 - momentum: 0.000000
451
+ 2024-03-28 09:08:31,897 ----------------------------------------------------------------------------------------------------
452
+ 2024-03-28 09:08:31,900 EPOCH 57 done: loss 0.2510 - lr: 0.050000
453
+ 2024-03-28 09:08:31,902 - 1 epochs without improvement
454
+ 2024-03-28 09:08:31,904 ----------------------------------------------------------------------------------------------------
455
+ 2024-03-28 09:08:31,972 epoch 58 - iter 1/3 - loss 0.27891893 - time (sec): 0.06 - samples/sec: 5933.41 - lr: 0.050000 - momentum: 0.000000
456
+ 2024-03-28 09:08:32,036 epoch 58 - iter 2/3 - loss 0.29004808 - time (sec): 0.13 - samples/sec: 5876.73 - lr: 0.050000 - momentum: 0.000000
457
+ 2024-03-28 09:08:32,059 epoch 58 - iter 3/3 - loss 0.28334943 - time (sec): 0.15 - samples/sec: 5115.74 - lr: 0.050000 - momentum: 0.000000
458
+ 2024-03-28 09:08:32,061 ----------------------------------------------------------------------------------------------------
459
+ 2024-03-28 09:08:32,063 EPOCH 58 done: loss 0.2833 - lr: 0.050000
460
+ 2024-03-28 09:08:32,065 - 2 epochs without improvement
461
+ 2024-03-28 09:08:32,067 ----------------------------------------------------------------------------------------------------
462
+ 2024-03-28 09:08:32,134 epoch 59 - iter 1/3 - loss 0.24056747 - time (sec): 0.06 - samples/sec: 5868.06 - lr: 0.050000 - momentum: 0.000000
463
+ 2024-03-28 09:08:32,202 epoch 59 - iter 2/3 - loss 0.24723329 - time (sec): 0.13 - samples/sec: 5678.02 - lr: 0.050000 - momentum: 0.000000
464
+ 2024-03-28 09:08:32,228 epoch 59 - iter 3/3 - loss 0.24669100 - time (sec): 0.16 - samples/sec: 4929.81 - lr: 0.050000 - momentum: 0.000000
465
+ 2024-03-28 09:08:32,229 ----------------------------------------------------------------------------------------------------
466
+ 2024-03-28 09:08:32,231 EPOCH 59 done: loss 0.2467 - lr: 0.050000
467
+ 2024-03-28 09:08:32,233 - 3 epochs without improvement
468
+ 2024-03-28 09:08:32,235 ----------------------------------------------------------------------------------------------------
469
+ 2024-03-28 09:08:32,301 epoch 60 - iter 1/3 - loss 0.36381199 - time (sec): 0.06 - samples/sec: 5833.02 - lr: 0.050000 - momentum: 0.000000
470
+ 2024-03-28 09:08:32,369 epoch 60 - iter 2/3 - loss 0.30104175 - time (sec): 0.13 - samples/sec: 5787.29 - lr: 0.050000 - momentum: 0.000000
471
+ 2024-03-28 09:08:32,392 epoch 60 - iter 3/3 - loss 0.30143368 - time (sec): 0.15 - samples/sec: 5058.64 - lr: 0.050000 - momentum: 0.000000
472
+ 2024-03-28 09:08:32,394 ----------------------------------------------------------------------------------------------------
473
+ 2024-03-28 09:08:32,396 EPOCH 60 done: loss 0.3014 - lr: 0.050000
474
+ 2024-03-28 09:08:32,398 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.025]
475
+ 2024-03-28 09:08:32,401 ----------------------------------------------------------------------------------------------------
476
+ 2024-03-28 09:08:32,468 epoch 61 - iter 1/3 - loss 0.27097580 - time (sec): 0.07 - samples/sec: 5921.59 - lr: 0.025000 - momentum: 0.000000
477
+ 2024-03-28 09:08:32,534 epoch 61 - iter 2/3 - loss 0.25132273 - time (sec): 0.13 - samples/sec: 5734.84 - lr: 0.025000 - momentum: 0.000000
478
+ 2024-03-28 09:08:32,561 epoch 61 - iter 3/3 - loss 0.24710534 - time (sec): 0.16 - samples/sec: 4935.61 - lr: 0.025000 - momentum: 0.000000
479
+ 2024-03-28 09:08:32,563 ----------------------------------------------------------------------------------------------------
480
+ 2024-03-28 09:08:32,565 EPOCH 61 done: loss 0.2471 - lr: 0.025000
481
+ 2024-03-28 09:08:32,568 - 1 epochs without improvement
482
+ 2024-03-28 09:08:32,570 ----------------------------------------------------------------------------------------------------
483
+ 2024-03-28 09:08:32,635 epoch 62 - iter 1/3 - loss 0.22992023 - time (sec): 0.06 - samples/sec: 6211.06 - lr: 0.025000 - momentum: 0.000000
484
+ 2024-03-28 09:08:32,699 epoch 62 - iter 2/3 - loss 0.22807611 - time (sec): 0.13 - samples/sec: 6012.05 - lr: 0.025000 - momentum: 0.000000
485
+ 2024-03-28 09:08:32,721 epoch 62 - iter 3/3 - loss 0.22695615 - time (sec): 0.15 - samples/sec: 5270.95 - lr: 0.025000 - momentum: 0.000000
486
+ 2024-03-28 09:08:32,722 ----------------------------------------------------------------------------------------------------
487
+ 2024-03-28 09:08:32,725 EPOCH 62 done: loss 0.2270 - lr: 0.025000
488
+ 2024-03-28 09:08:32,727 - 0 epochs without improvement
489
+ 2024-03-28 09:08:32,729 ----------------------------------------------------------------------------------------------------
490
+ 2024-03-28 09:08:32,793 epoch 63 - iter 1/3 - loss 0.20431773 - time (sec): 0.06 - samples/sec: 6161.30 - lr: 0.025000 - momentum: 0.000000
491
+ 2024-03-28 09:08:32,861 epoch 63 - iter 2/3 - loss 0.22571899 - time (sec): 0.13 - samples/sec: 5807.57 - lr: 0.025000 - momentum: 0.000000
492
+ 2024-03-28 09:08:32,896 epoch 63 - iter 3/3 - loss 0.22545184 - time (sec): 0.17 - samples/sec: 4699.89 - lr: 0.025000 - momentum: 0.000000
493
+ 2024-03-28 09:08:32,898 ----------------------------------------------------------------------------------------------------
494
+ 2024-03-28 09:08:32,900 EPOCH 63 done: loss 0.2255 - lr: 0.025000
495
+ 2024-03-28 09:08:32,903 - 0 epochs without improvement
496
+ 2024-03-28 09:08:32,905 ----------------------------------------------------------------------------------------------------
497
+ 2024-03-28 09:08:32,975 epoch 64 - iter 1/3 - loss 0.23394853 - time (sec): 0.07 - samples/sec: 5722.79 - lr: 0.025000 - momentum: 0.000000
498
+ 2024-03-28 09:08:33,040 epoch 64 - iter 2/3 - loss 0.21575960 - time (sec): 0.13 - samples/sec: 5699.75 - lr: 0.025000 - momentum: 0.000000
499
+ 2024-03-28 09:08:33,064 epoch 64 - iter 3/3 - loss 0.21618913 - time (sec): 0.16 - samples/sec: 4969.07 - lr: 0.025000 - momentum: 0.000000
500
+ 2024-03-28 09:08:33,065 ----------------------------------------------------------------------------------------------------
501
+ 2024-03-28 09:08:33,068 EPOCH 64 done: loss 0.2162 - lr: 0.025000
502
+ 2024-03-28 09:08:33,070 - 0 epochs without improvement
503
+ 2024-03-28 09:08:33,073 ----------------------------------------------------------------------------------------------------
504
+ 2024-03-28 09:08:33,135 epoch 65 - iter 1/3 - loss 0.20376337 - time (sec): 0.06 - samples/sec: 6044.78 - lr: 0.025000 - momentum: 0.000000
505
+ 2024-03-28 09:08:33,206 epoch 65 - iter 2/3 - loss 0.22490820 - time (sec): 0.13 - samples/sec: 5746.62 - lr: 0.025000 - momentum: 0.000000
506
+ 2024-03-28 09:08:33,230 epoch 65 - iter 3/3 - loss 0.23571646 - time (sec): 0.16 - samples/sec: 4993.89 - lr: 0.025000 - momentum: 0.000000
507
+ 2024-03-28 09:08:33,232 ----------------------------------------------------------------------------------------------------
508
+ 2024-03-28 09:08:33,235 EPOCH 65 done: loss 0.2357 - lr: 0.025000
509
+ 2024-03-28 09:08:33,237 - 1 epochs without improvement
510
+ 2024-03-28 09:08:33,239 ----------------------------------------------------------------------------------------------------
511
+ 2024-03-28 09:08:33,309 epoch 66 - iter 1/3 - loss 0.19798712 - time (sec): 0.07 - samples/sec: 5541.37 - lr: 0.025000 - momentum: 0.000000
512
+ 2024-03-28 09:08:33,375 epoch 66 - iter 2/3 - loss 0.23232096 - time (sec): 0.13 - samples/sec: 5608.84 - lr: 0.025000 - momentum: 0.000000
513
+ 2024-03-28 09:08:33,401 epoch 66 - iter 3/3 - loss 0.23059462 - time (sec): 0.16 - samples/sec: 4867.50 - lr: 0.025000 - momentum: 0.000000
514
+ 2024-03-28 09:08:33,402 ----------------------------------------------------------------------------------------------------
515
+ 2024-03-28 09:08:33,405 EPOCH 66 done: loss 0.2306 - lr: 0.025000
516
+ 2024-03-28 09:08:33,408 - 2 epochs without improvement
517
+ 2024-03-28 09:08:33,409 ----------------------------------------------------------------------------------------------------
518
+ 2024-03-28 09:08:33,483 epoch 67 - iter 1/3 - loss 0.21222671 - time (sec): 0.07 - samples/sec: 5556.93 - lr: 0.025000 - momentum: 0.000000
519
+ 2024-03-28 09:08:33,548 epoch 67 - iter 2/3 - loss 0.23658420 - time (sec): 0.14 - samples/sec: 5581.78 - lr: 0.025000 - momentum: 0.000000
520
+ 2024-03-28 09:08:33,573 epoch 67 - iter 3/3 - loss 0.23513228 - time (sec): 0.16 - samples/sec: 4873.20 - lr: 0.025000 - momentum: 0.000000
521
+ 2024-03-28 09:08:33,574 ----------------------------------------------------------------------------------------------------
522
+ 2024-03-28 09:08:33,577 EPOCH 67 done: loss 0.2351 - lr: 0.025000
523
+ 2024-03-28 09:08:33,579 - 3 epochs without improvement
524
+ 2024-03-28 09:08:33,582 ----------------------------------------------------------------------------------------------------
525
+ 2024-03-28 09:08:33,650 epoch 68 - iter 1/3 - loss 0.20024892 - time (sec): 0.07 - samples/sec: 5705.76 - lr: 0.025000 - momentum: 0.000000
526
+ 2024-03-28 09:08:33,723 epoch 68 - iter 2/3 - loss 0.24506107 - time (sec): 0.14 - samples/sec: 5452.59 - lr: 0.025000 - momentum: 0.000000
527
+ 2024-03-28 09:08:33,746 epoch 68 - iter 3/3 - loss 0.24463388 - time (sec): 0.16 - samples/sec: 4800.57 - lr: 0.025000 - momentum: 0.000000
528
+ 2024-03-28 09:08:33,748 ----------------------------------------------------------------------------------------------------
529
+ 2024-03-28 09:08:33,751 EPOCH 68 done: loss 0.2446 - lr: 0.025000
530
+ 2024-03-28 09:08:33,753 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0125]
531
+ 2024-03-28 09:08:33,755 ----------------------------------------------------------------------------------------------------
532
+ 2024-03-28 09:08:33,820 epoch 69 - iter 1/3 - loss 0.21504179 - time (sec): 0.06 - samples/sec: 6058.29 - lr: 0.012500 - momentum: 0.000000
533
+ 2024-03-28 09:08:33,886 epoch 69 - iter 2/3 - loss 0.21413674 - time (sec): 0.13 - samples/sec: 5940.03 - lr: 0.012500 - momentum: 0.000000
534
+ 2024-03-28 09:08:33,919 epoch 69 - iter 3/3 - loss 0.21118687 - time (sec): 0.16 - samples/sec: 4841.80 - lr: 0.012500 - momentum: 0.000000
535
+ 2024-03-28 09:08:33,921 ----------------------------------------------------------------------------------------------------
536
+ 2024-03-28 09:08:33,923 EPOCH 69 done: loss 0.2112 - lr: 0.012500
537
+ 2024-03-28 09:08:33,925 - 0 epochs without improvement
538
+ 2024-03-28 09:08:33,927 ----------------------------------------------------------------------------------------------------
539
+ 2024-03-28 09:08:33,995 epoch 70 - iter 1/3 - loss 0.22739715 - time (sec): 0.07 - samples/sec: 5485.75 - lr: 0.012500 - momentum: 0.000000
540
+ 2024-03-28 09:08:34,061 epoch 70 - iter 2/3 - loss 0.25014319 - time (sec): 0.13 - samples/sec: 5710.43 - lr: 0.012500 - momentum: 0.000000
541
+ 2024-03-28 09:08:34,085 epoch 70 - iter 3/3 - loss 0.25337325 - time (sec): 0.16 - samples/sec: 4971.79 - lr: 0.012500 - momentum: 0.000000
542
+ 2024-03-28 09:08:34,087 ----------------------------------------------------------------------------------------------------
543
+ 2024-03-28 09:08:34,090 EPOCH 70 done: loss 0.2534 - lr: 0.012500
544
+ 2024-03-28 09:08:34,092 - 1 epochs without improvement
545
+ 2024-03-28 09:08:34,094 ----------------------------------------------------------------------------------------------------
546
+ 2024-03-28 09:08:34,160 epoch 71 - iter 1/3 - loss 0.24353060 - time (sec): 0.06 - samples/sec: 5866.57 - lr: 0.012500 - momentum: 0.000000
547
+ 2024-03-28 09:08:34,228 epoch 71 - iter 2/3 - loss 0.21777601 - time (sec): 0.13 - samples/sec: 5735.40 - lr: 0.012500 - momentum: 0.000000
548
+ 2024-03-28 09:08:34,251 epoch 71 - iter 3/3 - loss 0.22126061 - time (sec): 0.16 - samples/sec: 5025.26 - lr: 0.012500 - momentum: 0.000000
549
+ 2024-03-28 09:08:34,252 ----------------------------------------------------------------------------------------------------
550
+ 2024-03-28 09:08:34,254 EPOCH 71 done: loss 0.2213 - lr: 0.012500
551
+ 2024-03-28 09:08:34,257 - 2 epochs without improvement
552
+ 2024-03-28 09:08:34,259 ----------------------------------------------------------------------------------------------------
553
+ 2024-03-28 09:08:34,328 epoch 72 - iter 1/3 - loss 0.19077828 - time (sec): 0.07 - samples/sec: 5835.39 - lr: 0.012500 - momentum: 0.000000
554
+ 2024-03-28 09:08:34,390 epoch 72 - iter 2/3 - loss 0.20655965 - time (sec): 0.13 - samples/sec: 5885.98 - lr: 0.012500 - momentum: 0.000000
555
+ 2024-03-28 09:08:34,412 epoch 72 - iter 3/3 - loss 0.20427307 - time (sec): 0.15 - samples/sec: 5150.74 - lr: 0.012500 - momentum: 0.000000
556
+ 2024-03-28 09:08:34,416 ----------------------------------------------------------------------------------------------------
557
+ 2024-03-28 09:08:34,419 EPOCH 72 done: loss 0.2043 - lr: 0.012500
558
+ 2024-03-28 09:08:34,421 - 0 epochs without improvement
559
+ 2024-03-28 09:08:34,424 ----------------------------------------------------------------------------------------------------
560
+ 2024-03-28 09:08:34,493 epoch 73 - iter 1/3 - loss 0.20394309 - time (sec): 0.07 - samples/sec: 5939.40 - lr: 0.012500 - momentum: 0.000000
561
+ 2024-03-28 09:08:34,555 epoch 73 - iter 2/3 - loss 0.20850289 - time (sec): 0.13 - samples/sec: 5859.13 - lr: 0.012500 - momentum: 0.000000
562
+ 2024-03-28 09:08:34,579 epoch 73 - iter 3/3 - loss 0.21953239 - time (sec): 0.15 - samples/sec: 5098.95 - lr: 0.012500 - momentum: 0.000000
563
+ 2024-03-28 09:08:34,580 ----------------------------------------------------------------------------------------------------
564
+ 2024-03-28 09:08:34,583 EPOCH 73 done: loss 0.2195 - lr: 0.012500
565
+ 2024-03-28 09:08:34,586 - 1 epochs without improvement
566
+ 2024-03-28 09:08:34,588 ----------------------------------------------------------------------------------------------------
567
+ 2024-03-28 09:08:34,658 epoch 74 - iter 1/3 - loss 0.24642578 - time (sec): 0.07 - samples/sec: 5595.57 - lr: 0.012500 - momentum: 0.000000
568
+ 2024-03-28 09:08:34,722 epoch 74 - iter 2/3 - loss 0.21810751 - time (sec): 0.13 - samples/sec: 5737.34 - lr: 0.012500 - momentum: 0.000000
569
+ 2024-03-28 09:08:34,747 epoch 74 - iter 3/3 - loss 0.22412623 - time (sec): 0.16 - samples/sec: 4994.73 - lr: 0.012500 - momentum: 0.000000
570
+ 2024-03-28 09:08:34,749 ----------------------------------------------------------------------------------------------------
571
+ 2024-03-28 09:08:34,751 EPOCH 74 done: loss 0.2241 - lr: 0.012500
572
+ 2024-03-28 09:08:34,754 - 2 epochs without improvement
573
+ 2024-03-28 09:08:34,755 ----------------------------------------------------------------------------------------------------
574
+ 2024-03-28 09:08:34,826 epoch 75 - iter 1/3 - loss 0.20324577 - time (sec): 0.07 - samples/sec: 5632.71 - lr: 0.012500 - momentum: 0.000000
575
+ 2024-03-28 09:08:34,891 epoch 75 - iter 2/3 - loss 0.21292139 - time (sec): 0.13 - samples/sec: 5723.80 - lr: 0.012500 - momentum: 0.000000
576
+ 2024-03-28 09:08:34,915 epoch 75 - iter 3/3 - loss 0.20921929 - time (sec): 0.16 - samples/sec: 4991.02 - lr: 0.012500 - momentum: 0.000000
577
+ 2024-03-28 09:08:34,916 ----------------------------------------------------------------------------------------------------
578
+ 2024-03-28 09:08:34,919 EPOCH 75 done: loss 0.2092 - lr: 0.012500
579
+ 2024-03-28 09:08:34,921 - 3 epochs without improvement
580
+ 2024-03-28 09:08:34,923 ----------------------------------------------------------------------------------------------------
581
+ 2024-03-28 09:08:34,996 epoch 76 - iter 1/3 - loss 0.23860086 - time (sec): 0.07 - samples/sec: 5597.94 - lr: 0.012500 - momentum: 0.000000
582
+ 2024-03-28 09:08:35,061 epoch 76 - iter 2/3 - loss 0.22212134 - time (sec): 0.13 - samples/sec: 5727.10 - lr: 0.012500 - momentum: 0.000000
583
+ 2024-03-28 09:08:35,085 epoch 76 - iter 3/3 - loss 0.22672753 - time (sec): 0.16 - samples/sec: 5015.31 - lr: 0.012500 - momentum: 0.000000
584
+ 2024-03-28 09:08:35,086 ----------------------------------------------------------------------------------------------------
585
+ 2024-03-28 09:08:35,088 EPOCH 76 done: loss 0.2267 - lr: 0.012500
586
+ 2024-03-28 09:08:35,090 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.00625]
587
+ 2024-03-28 09:08:35,093 ----------------------------------------------------------------------------------------------------
588
+ 2024-03-28 09:08:35,157 epoch 77 - iter 1/3 - loss 0.20140748 - time (sec): 0.06 - samples/sec: 5931.86 - lr: 0.006250 - momentum: 0.000000
589
+ 2024-03-28 09:08:35,223 epoch 77 - iter 2/3 - loss 0.23543165 - time (sec): 0.13 - samples/sec: 5886.34 - lr: 0.006250 - momentum: 0.000000
590
+ 2024-03-28 09:08:35,245 epoch 77 - iter 3/3 - loss 0.22959387 - time (sec): 0.15 - samples/sec: 5180.90 - lr: 0.006250 - momentum: 0.000000
591
+ 2024-03-28 09:08:35,246 ----------------------------------------------------------------------------------------------------
592
+ 2024-03-28 09:08:35,249 EPOCH 77 done: loss 0.2296 - lr: 0.006250
593
+ 2024-03-28 09:08:35,251 - 1 epochs without improvement
594
+ 2024-03-28 09:08:35,253 ----------------------------------------------------------------------------------------------------
595
+ 2024-03-28 09:08:35,319 epoch 78 - iter 1/3 - loss 0.26190517 - time (sec): 0.06 - samples/sec: 5839.55 - lr: 0.006250 - momentum: 0.000000
596
+ 2024-03-28 09:08:35,385 epoch 78 - iter 2/3 - loss 0.23953494 - time (sec): 0.13 - samples/sec: 5857.85 - lr: 0.006250 - momentum: 0.000000
597
+ 2024-03-28 09:08:35,409 epoch 78 - iter 3/3 - loss 0.23820210 - time (sec): 0.15 - samples/sec: 5083.93 - lr: 0.006250 - momentum: 0.000000
598
+ 2024-03-28 09:08:35,411 ----------------------------------------------------------------------------------------------------
599
+ 2024-03-28 09:08:35,414 EPOCH 78 done: loss 0.2382 - lr: 0.006250
600
+ 2024-03-28 09:08:35,416 - 2 epochs without improvement
601
+ 2024-03-28 09:08:35,418 ----------------------------------------------------------------------------------------------------
602
+ 2024-03-28 09:08:35,488 epoch 79 - iter 1/3 - loss 0.19539345 - time (sec): 0.07 - samples/sec: 5625.49 - lr: 0.006250 - momentum: 0.000000
603
+ 2024-03-28 09:08:35,555 epoch 79 - iter 2/3 - loss 0.20920196 - time (sec): 0.13 - samples/sec: 5661.67 - lr: 0.006250 - momentum: 0.000000
604
+ 2024-03-28 09:08:35,580 epoch 79 - iter 3/3 - loss 0.21356759 - time (sec): 0.16 - samples/sec: 4902.78 - lr: 0.006250 - momentum: 0.000000
605
+ 2024-03-28 09:08:35,581 ----------------------------------------------------------------------------------------------------
606
+ 2024-03-28 09:08:35,584 EPOCH 79 done: loss 0.2136 - lr: 0.006250
607
+ 2024-03-28 09:08:35,587 - 3 epochs without improvement
608
+ 2024-03-28 09:08:35,589 ----------------------------------------------------------------------------------------------------
609
+ 2024-03-28 09:08:35,652 epoch 80 - iter 1/3 - loss 0.22265025 - time (sec): 0.06 - samples/sec: 6036.04 - lr: 0.006250 - momentum: 0.000000
610
+ 2024-03-28 09:08:35,719 epoch 80 - iter 2/3 - loss 0.20324885 - time (sec): 0.13 - samples/sec: 5911.13 - lr: 0.006250 - momentum: 0.000000
611
+ 2024-03-28 09:08:35,744 epoch 80 - iter 3/3 - loss 0.20831160 - time (sec): 0.15 - samples/sec: 5091.36 - lr: 0.006250 - momentum: 0.000000
612
+ 2024-03-28 09:08:35,746 ----------------------------------------------------------------------------------------------------
613
+ 2024-03-28 09:08:35,748 EPOCH 80 done: loss 0.2083 - lr: 0.006250
614
+ 2024-03-28 09:08:35,751 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.003125]
615
+ 2024-03-28 09:08:35,753 ----------------------------------------------------------------------------------------------------
616
+ 2024-03-28 09:08:35,825 epoch 81 - iter 1/3 - loss 0.18628309 - time (sec): 0.07 - samples/sec: 5587.97 - lr: 0.003125 - momentum: 0.000000
617
+ 2024-03-28 09:08:35,889 epoch 81 - iter 2/3 - loss 0.20686248 - time (sec): 0.13 - samples/sec: 5664.59 - lr: 0.003125 - momentum: 0.000000
618
+ 2024-03-28 09:08:35,911 epoch 81 - iter 3/3 - loss 0.20672222 - time (sec): 0.16 - samples/sec: 4994.39 - lr: 0.003125 - momentum: 0.000000
619
+ 2024-03-28 09:08:35,913 ----------------------------------------------------------------------------------------------------
620
+ 2024-03-28 09:08:35,916 EPOCH 81 done: loss 0.2067 - lr: 0.003125
621
+ 2024-03-28 09:08:35,918 - 1 epochs without improvement
622
+ 2024-03-28 09:08:35,920 ----------------------------------------------------------------------------------------------------
623
+ 2024-03-28 09:08:35,992 epoch 82 - iter 1/3 - loss 0.19897849 - time (sec): 0.07 - samples/sec: 5515.18 - lr: 0.003125 - momentum: 0.000000
624
+ 2024-03-28 09:08:36,072 epoch 82 - iter 2/3 - loss 0.20879538 - time (sec): 0.15 - samples/sec: 5088.16 - lr: 0.003125 - momentum: 0.000000
625
+ 2024-03-28 09:08:36,095 epoch 82 - iter 3/3 - loss 0.20445322 - time (sec): 0.17 - samples/sec: 4533.89 - lr: 0.003125 - momentum: 0.000000
626
+ 2024-03-28 09:08:36,097 ----------------------------------------------------------------------------------------------------
627
+ 2024-03-28 09:08:36,099 EPOCH 82 done: loss 0.2045 - lr: 0.003125
628
+ 2024-03-28 09:08:36,102 - 2 epochs without improvement
629
+ 2024-03-28 09:08:36,104 ----------------------------------------------------------------------------------------------------
630
+ 2024-03-28 09:08:36,169 epoch 83 - iter 1/3 - loss 0.17155356 - time (sec): 0.06 - samples/sec: 6148.63 - lr: 0.003125 - momentum: 0.000000
631
+ 2024-03-28 09:08:36,234 epoch 83 - iter 2/3 - loss 0.19129354 - time (sec): 0.13 - samples/sec: 5926.47 - lr: 0.003125 - momentum: 0.000000
632
+ 2024-03-28 09:08:36,258 epoch 83 - iter 3/3 - loss 0.19397805 - time (sec): 0.15 - samples/sec: 5158.06 - lr: 0.003125 - momentum: 0.000000
633
+ 2024-03-28 09:08:36,259 ----------------------------------------------------------------------------------------------------
634
+ 2024-03-28 09:08:36,262 EPOCH 83 done: loss 0.1940 - lr: 0.003125
635
+ 2024-03-28 09:08:36,264 - 0 epochs without improvement
636
+ 2024-03-28 09:08:36,267 ----------------------------------------------------------------------------------------------------
637
+ 2024-03-28 09:08:36,335 epoch 84 - iter 1/3 - loss 0.19546600 - time (sec): 0.07 - samples/sec: 5642.23 - lr: 0.003125 - momentum: 0.000000
638
+ 2024-03-28 09:08:36,403 epoch 84 - iter 2/3 - loss 0.20660319 - time (sec): 0.13 - samples/sec: 5619.65 - lr: 0.003125 - momentum: 0.000000
639
+ 2024-03-28 09:08:36,430 epoch 84 - iter 3/3 - loss 0.20619125 - time (sec): 0.16 - samples/sec: 4857.78 - lr: 0.003125 - momentum: 0.000000
640
+ 2024-03-28 09:08:36,432 ----------------------------------------------------------------------------------------------------
641
+ 2024-03-28 09:08:36,434 EPOCH 84 done: loss 0.2062 - lr: 0.003125
642
+ 2024-03-28 09:08:36,436 - 1 epochs without improvement
643
+ 2024-03-28 09:08:36,438 ----------------------------------------------------------------------------------------------------
644
+ 2024-03-28 09:08:36,505 epoch 85 - iter 1/3 - loss 0.21508853 - time (sec): 0.06 - samples/sec: 5964.38 - lr: 0.003125 - momentum: 0.000000
645
+ 2024-03-28 09:08:36,575 epoch 85 - iter 2/3 - loss 0.20740067 - time (sec): 0.13 - samples/sec: 5656.74 - lr: 0.003125 - momentum: 0.000000
646
+ 2024-03-28 09:08:36,598 epoch 85 - iter 3/3 - loss 0.20411952 - time (sec): 0.16 - samples/sec: 4966.24 - lr: 0.003125 - momentum: 0.000000
647
+ 2024-03-28 09:08:36,599 ----------------------------------------------------------------------------------------------------
648
+ 2024-03-28 09:08:36,602 EPOCH 85 done: loss 0.2041 - lr: 0.003125
649
+ 2024-03-28 09:08:36,604 - 2 epochs without improvement
650
+ 2024-03-28 09:08:36,606 ----------------------------------------------------------------------------------------------------
651
+ 2024-03-28 09:08:36,673 epoch 86 - iter 1/3 - loss 0.23413151 - time (sec): 0.07 - samples/sec: 5765.88 - lr: 0.003125 - momentum: 0.000000
652
+ 2024-03-28 09:08:36,738 epoch 86 - iter 2/3 - loss 0.21575944 - time (sec): 0.13 - samples/sec: 5775.19 - lr: 0.003125 - momentum: 0.000000
653
+ 2024-03-28 09:08:36,765 epoch 86 - iter 3/3 - loss 0.22781102 - time (sec): 0.16 - samples/sec: 4949.48 - lr: 0.003125 - momentum: 0.000000
654
+ 2024-03-28 09:08:36,769 ----------------------------------------------------------------------------------------------------
655
+ 2024-03-28 09:08:36,771 EPOCH 86 done: loss 0.2278 - lr: 0.003125
656
+ 2024-03-28 09:08:36,773 - 3 epochs without improvement
657
+ 2024-03-28 09:08:36,775 ----------------------------------------------------------------------------------------------------
658
+ 2024-03-28 09:08:36,843 epoch 87 - iter 1/3 - loss 0.19966387 - time (sec): 0.07 - samples/sec: 5803.18 - lr: 0.003125 - momentum: 0.000000
659
+ 2024-03-28 09:08:36,907 epoch 87 - iter 2/3 - loss 0.19670426 - time (sec): 0.13 - samples/sec: 5831.49 - lr: 0.003125 - momentum: 0.000000
660
+ 2024-03-28 09:08:36,934 epoch 87 - iter 3/3 - loss 0.19452721 - time (sec): 0.16 - samples/sec: 4979.61 - lr: 0.003125 - momentum: 0.000000
661
+ 2024-03-28 09:08:36,936 ----------------------------------------------------------------------------------------------------
662
+ 2024-03-28 09:08:36,938 EPOCH 87 done: loss 0.1945 - lr: 0.003125
663
+ 2024-03-28 09:08:36,940 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0015625]
664
+ 2024-03-28 09:08:36,942 ----------------------------------------------------------------------------------------------------
665
+ 2024-03-28 09:08:37,016 epoch 88 - iter 1/3 - loss 0.22008498 - time (sec): 0.07 - samples/sec: 5298.35 - lr: 0.001563 - momentum: 0.000000
666
+ 2024-03-28 09:08:37,083 epoch 88 - iter 2/3 - loss 0.19665170 - time (sec): 0.14 - samples/sec: 5437.93 - lr: 0.001563 - momentum: 0.000000
667
+ 2024-03-28 09:08:37,108 epoch 88 - iter 3/3 - loss 0.20012108 - time (sec): 0.16 - samples/sec: 4768.16 - lr: 0.001563 - momentum: 0.000000
668
+ 2024-03-28 09:08:37,109 ----------------------------------------------------------------------------------------------------
669
+ 2024-03-28 09:08:37,112 EPOCH 88 done: loss 0.2001 - lr: 0.001563
670
+ 2024-03-28 09:08:37,114 - 1 epochs without improvement
671
+ 2024-03-28 09:08:37,117 ----------------------------------------------------------------------------------------------------
672
+ 2024-03-28 09:08:37,189 epoch 89 - iter 1/3 - loss 0.18575605 - time (sec): 0.07 - samples/sec: 5497.97 - lr: 0.001563 - momentum: 0.000000
673
+ 2024-03-28 09:08:37,256 epoch 89 - iter 2/3 - loss 0.18895481 - time (sec): 0.14 - samples/sec: 5476.09 - lr: 0.001563 - momentum: 0.000000
674
+ 2024-03-28 09:08:37,281 epoch 89 - iter 3/3 - loss 0.18614399 - time (sec): 0.16 - samples/sec: 4788.19 - lr: 0.001563 - momentum: 0.000000
675
+ 2024-03-28 09:08:37,283 ----------------------------------------------------------------------------------------------------
676
+ 2024-03-28 09:08:37,285 EPOCH 89 done: loss 0.1861 - lr: 0.001563
677
+ 2024-03-28 09:08:37,287 - 0 epochs without improvement
678
+ 2024-03-28 09:08:37,290 ----------------------------------------------------------------------------------------------------
679
+ 2024-03-28 09:08:37,360 epoch 90 - iter 1/3 - loss 0.20966875 - time (sec): 0.07 - samples/sec: 5526.63 - lr: 0.001563 - momentum: 0.000000
680
+ 2024-03-28 09:08:37,429 epoch 90 - iter 2/3 - loss 0.18502192 - time (sec): 0.14 - samples/sec: 5561.81 - lr: 0.001563 - momentum: 0.000000
681
+ 2024-03-28 09:08:37,454 epoch 90 - iter 3/3 - loss 0.18437769 - time (sec): 0.16 - samples/sec: 4814.59 - lr: 0.001563 - momentum: 0.000000
682
+ 2024-03-28 09:08:37,456 ----------------------------------------------------------------------------------------------------
683
+ 2024-03-28 09:08:37,459 EPOCH 90 done: loss 0.1844 - lr: 0.001563
684
+ 2024-03-28 09:08:37,461 - 0 epochs without improvement
685
+ 2024-03-28 09:08:37,463 ----------------------------------------------------------------------------------------------------
686
+ 2024-03-28 09:08:37,542 epoch 91 - iter 1/3 - loss 0.21700736 - time (sec): 0.08 - samples/sec: 5196.77 - lr: 0.001563 - momentum: 0.000000
687
+ 2024-03-28 09:08:37,608 epoch 91 - iter 2/3 - loss 0.21150135 - time (sec): 0.14 - samples/sec: 5288.80 - lr: 0.001563 - momentum: 0.000000
688
+ 2024-03-28 09:08:37,633 epoch 91 - iter 3/3 - loss 0.21616639 - time (sec): 0.17 - samples/sec: 4642.85 - lr: 0.001563 - momentum: 0.000000
689
+ 2024-03-28 09:08:37,634 ----------------------------------------------------------------------------------------------------
690
+ 2024-03-28 09:08:37,637 EPOCH 91 done: loss 0.2162 - lr: 0.001563
691
+ 2024-03-28 09:08:37,640 - 1 epochs without improvement
692
+ 2024-03-28 09:08:37,642 ----------------------------------------------------------------------------------------------------
693
+ 2024-03-28 09:08:37,712 epoch 92 - iter 1/3 - loss 0.17263027 - time (sec): 0.07 - samples/sec: 5502.15 - lr: 0.001563 - momentum: 0.000000
694
+ 2024-03-28 09:08:37,779 epoch 92 - iter 2/3 - loss 0.20051331 - time (sec): 0.13 - samples/sec: 5610.57 - lr: 0.001563 - momentum: 0.000000
695
+ 2024-03-28 09:08:37,804 epoch 92 - iter 3/3 - loss 0.19621649 - time (sec): 0.16 - samples/sec: 4883.81 - lr: 0.001563 - momentum: 0.000000
696
+ 2024-03-28 09:08:37,806 ----------------------------------------------------------------------------------------------------
697
+ 2024-03-28 09:08:37,808 EPOCH 92 done: loss 0.1962 - lr: 0.001563
698
+ 2024-03-28 09:08:37,810 - 2 epochs without improvement
699
+ 2024-03-28 09:08:37,812 ----------------------------------------------------------------------------------------------------
700
+ 2024-03-28 09:08:37,876 epoch 93 - iter 1/3 - loss 0.16124828 - time (sec): 0.06 - samples/sec: 5986.96 - lr: 0.001563 - momentum: 0.000000
701
+ 2024-03-28 09:08:37,940 epoch 93 - iter 2/3 - loss 0.20056266 - time (sec): 0.13 - samples/sec: 5938.83 - lr: 0.001563 - momentum: 0.000000
702
+ 2024-03-28 09:08:37,967 epoch 93 - iter 3/3 - loss 0.19693890 - time (sec): 0.15 - samples/sec: 5078.24 - lr: 0.001563 - momentum: 0.000000
703
+ 2024-03-28 09:08:37,969 ----------------------------------------------------------------------------------------------------
704
+ 2024-03-28 09:08:37,971 EPOCH 93 done: loss 0.1969 - lr: 0.001563
705
+ 2024-03-28 09:08:37,974 - 3 epochs without improvement
706
+ 2024-03-28 09:08:37,975 ----------------------------------------------------------------------------------------------------
707
+ 2024-03-28 09:08:38,049 epoch 94 - iter 1/3 - loss 0.19580606 - time (sec): 0.07 - samples/sec: 5265.94 - lr: 0.001563 - momentum: 0.000000
708
+ 2024-03-28 09:08:38,114 epoch 94 - iter 2/3 - loss 0.20014706 - time (sec): 0.14 - samples/sec: 5594.41 - lr: 0.001563 - momentum: 0.000000
709
+ 2024-03-28 09:08:38,144 epoch 94 - iter 3/3 - loss 0.19673995 - time (sec): 0.17 - samples/sec: 4713.15 - lr: 0.001563 - momentum: 0.000000
710
+ 2024-03-28 09:08:38,145 ----------------------------------------------------------------------------------------------------
711
+ 2024-03-28 09:08:38,148 EPOCH 94 done: loss 0.1967 - lr: 0.001563
712
+ 2024-03-28 09:08:38,150 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.00078125]
713
+ 2024-03-28 09:08:38,152 ----------------------------------------------------------------------------------------------------
714
+ 2024-03-28 09:08:38,220 epoch 95 - iter 1/3 - loss 0.21017605 - time (sec): 0.07 - samples/sec: 5624.92 - lr: 0.000781 - momentum: 0.000000
715
+ 2024-03-28 09:08:38,289 epoch 95 - iter 2/3 - loss 0.19381217 - time (sec): 0.14 - samples/sec: 5552.42 - lr: 0.000781 - momentum: 0.000000
716
+ 2024-03-28 09:08:38,317 epoch 95 - iter 3/3 - loss 0.19578398 - time (sec): 0.16 - samples/sec: 4797.42 - lr: 0.000781 - momentum: 0.000000
717
+ 2024-03-28 09:08:38,318 ----------------------------------------------------------------------------------------------------
718
+ 2024-03-28 09:08:38,321 EPOCH 95 done: loss 0.1958 - lr: 0.000781
719
+ 2024-03-28 09:08:38,323 - 1 epochs without improvement
720
+ 2024-03-28 09:08:38,325 ----------------------------------------------------------------------------------------------------
721
+ 2024-03-28 09:08:38,391 epoch 96 - iter 1/3 - loss 0.15655139 - time (sec): 0.06 - samples/sec: 5790.11 - lr: 0.000781 - momentum: 0.000000
722
+ 2024-03-28 09:08:38,461 epoch 96 - iter 2/3 - loss 0.19074659 - time (sec): 0.13 - samples/sec: 5643.29 - lr: 0.000781 - momentum: 0.000000
723
+ 2024-03-28 09:08:38,486 epoch 96 - iter 3/3 - loss 0.18613870 - time (sec): 0.16 - samples/sec: 4899.18 - lr: 0.000781 - momentum: 0.000000
724
+ 2024-03-28 09:08:38,487 ----------------------------------------------------------------------------------------------------
725
+ 2024-03-28 09:08:38,490 EPOCH 96 done: loss 0.1861 - lr: 0.000781
726
+ 2024-03-28 09:08:38,493 - 2 epochs without improvement
727
+ 2024-03-28 09:08:38,495 ----------------------------------------------------------------------------------------------------
728
+ 2024-03-28 09:08:38,560 epoch 97 - iter 1/3 - loss 0.18081108 - time (sec): 0.06 - samples/sec: 5971.45 - lr: 0.000781 - momentum: 0.000000
729
+ 2024-03-28 09:08:38,628 epoch 97 - iter 2/3 - loss 0.18019803 - time (sec): 0.13 - samples/sec: 5857.20 - lr: 0.000781 - momentum: 0.000000
730
+ 2024-03-28 09:08:38,654 epoch 97 - iter 3/3 - loss 0.17897194 - time (sec): 0.16 - samples/sec: 5010.46 - lr: 0.000781 - momentum: 0.000000
731
+ 2024-03-28 09:08:38,655 ----------------------------------------------------------------------------------------------------
732
+ 2024-03-28 09:08:38,658 EPOCH 97 done: loss 0.1790 - lr: 0.000781
733
+ 2024-03-28 09:08:38,660 - 0 epochs without improvement
734
+ 2024-03-28 09:08:38,663 ----------------------------------------------------------------------------------------------------
735
+ 2024-03-28 09:08:38,729 epoch 98 - iter 1/3 - loss 0.19285375 - time (sec): 0.06 - samples/sec: 5864.54 - lr: 0.000781 - momentum: 0.000000
736
+ 2024-03-28 09:08:38,796 epoch 98 - iter 2/3 - loss 0.18586519 - time (sec): 0.13 - samples/sec: 5727.76 - lr: 0.000781 - momentum: 0.000000
737
+ 2024-03-28 09:08:38,821 epoch 98 - iter 3/3 - loss 0.18403817 - time (sec): 0.16 - samples/sec: 4977.22 - lr: 0.000781 - momentum: 0.000000
738
+ 2024-03-28 09:08:38,823 ----------------------------------------------------------------------------------------------------
739
+ 2024-03-28 09:08:38,825 EPOCH 98 done: loss 0.1840 - lr: 0.000781
740
+ 2024-03-28 09:08:38,828 - 1 epochs without improvement
741
+ 2024-03-28 09:08:38,830 ----------------------------------------------------------------------------------------------------
742
+ 2024-03-28 09:08:38,920 epoch 99 - iter 1/3 - loss 0.22237257 - time (sec): 0.09 - samples/sec: 4446.36 - lr: 0.000781 - momentum: 0.000000
743
+ 2024-03-28 09:08:39,009 epoch 99 - iter 2/3 - loss 0.20208282 - time (sec): 0.18 - samples/sec: 4306.20 - lr: 0.000781 - momentum: 0.000000
744
+ 2024-03-28 09:08:39,044 epoch 99 - iter 3/3 - loss 0.20092824 - time (sec): 0.21 - samples/sec: 3689.11 - lr: 0.000781 - momentum: 0.000000
745
+ 2024-03-28 09:08:39,046 ----------------------------------------------------------------------------------------------------
746
+ 2024-03-28 09:08:39,048 EPOCH 99 done: loss 0.2009 - lr: 0.000781
747
+ 2024-03-28 09:08:39,050 - 2 epochs without improvement
748
+ 2024-03-28 09:08:39,052 ----------------------------------------------------------------------------------------------------
749
+ 2024-03-28 09:08:39,138 epoch 100 - iter 1/3 - loss 0.19105241 - time (sec): 0.08 - samples/sec: 4345.36 - lr: 0.000781 - momentum: 0.000000
750
+ 2024-03-28 09:08:39,232 epoch 100 - iter 2/3 - loss 0.19192969 - time (sec): 0.18 - samples/sec: 4216.99 - lr: 0.000781 - momentum: 0.000000
751
+ 2024-03-28 09:08:39,262 epoch 100 - iter 3/3 - loss 0.18671563 - time (sec): 0.21 - samples/sec: 3743.15 - lr: 0.000781 - momentum: 0.000000
752
+ 2024-03-28 09:08:39,264 ----------------------------------------------------------------------------------------------------
753
+ 2024-03-28 09:08:39,266 EPOCH 100 done: loss 0.1867 - lr: 0.000781
754
+ 2024-03-28 09:08:39,268 - 3 epochs without improvement
755
+ 2024-03-28 09:08:39,270 ----------------------------------------------------------------------------------------------------
756
+ 2024-03-28 09:08:39,363 epoch 101 - iter 1/3 - loss 0.23572141 - time (sec): 0.09 - samples/sec: 4301.97 - lr: 0.000781 - momentum: 0.000000
757
+ 2024-03-28 09:08:39,448 epoch 101 - iter 2/3 - loss 0.21389694 - time (sec): 0.17 - samples/sec: 4428.51 - lr: 0.000781 - momentum: 0.000000
758
+ 2024-03-28 09:08:39,479 epoch 101 - iter 3/3 - loss 0.21106680 - time (sec): 0.20 - samples/sec: 3867.97 - lr: 0.000781 - momentum: 0.000000
759
+ 2024-03-28 09:08:39,483 ----------------------------------------------------------------------------------------------------
760
+ 2024-03-28 09:08:39,486 EPOCH 101 done: loss 0.2111 - lr: 0.000781
761
+ 2024-03-28 09:08:39,490 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.000390625]
762
+ 2024-03-28 09:08:39,495 ----------------------------------------------------------------------------------------------------
763
+ 2024-03-28 09:08:39,578 epoch 102 - iter 1/3 - loss 0.20411731 - time (sec): 0.08 - samples/sec: 4631.87 - lr: 0.000391 - momentum: 0.000000
764
+ 2024-03-28 09:08:39,671 epoch 102 - iter 2/3 - loss 0.17361511 - time (sec): 0.17 - samples/sec: 4339.71 - lr: 0.000391 - momentum: 0.000000
765
+ 2024-03-28 09:08:39,700 epoch 102 - iter 3/3 - loss 0.17970168 - time (sec): 0.20 - samples/sec: 3836.73 - lr: 0.000391 - momentum: 0.000000
766
+ 2024-03-28 09:08:39,702 ----------------------------------------------------------------------------------------------------
767
+ 2024-03-28 09:08:39,705 EPOCH 102 done: loss 0.1797 - lr: 0.000391
768
+ 2024-03-28 09:08:39,708 - 1 epochs without improvement
769
+ 2024-03-28 09:08:39,711 ----------------------------------------------------------------------------------------------------
770
+ 2024-03-28 09:08:39,799 epoch 103 - iter 1/3 - loss 0.17368401 - time (sec): 0.09 - samples/sec: 4284.76 - lr: 0.000391 - momentum: 0.000000
771
+ 2024-03-28 09:08:39,884 epoch 103 - iter 2/3 - loss 0.22948218 - time (sec): 0.17 - samples/sec: 4427.71 - lr: 0.000391 - momentum: 0.000000
772
+ 2024-03-28 09:08:39,911 epoch 103 - iter 3/3 - loss 0.23261170 - time (sec): 0.20 - samples/sec: 3924.92 - lr: 0.000391 - momentum: 0.000000
773
+ 2024-03-28 09:08:39,913 ----------------------------------------------------------------------------------------------------
774
+ 2024-03-28 09:08:39,915 EPOCH 103 done: loss 0.2326 - lr: 0.000391
775
+ 2024-03-28 09:08:39,918 - 2 epochs without improvement
776
+ 2024-03-28 09:08:39,920 ----------------------------------------------------------------------------------------------------
777
+ 2024-03-28 09:08:40,002 epoch 104 - iter 1/3 - loss 0.18409026 - time (sec): 0.08 - samples/sec: 4455.73 - lr: 0.000391 - momentum: 0.000000
778
+ 2024-03-28 09:08:40,092 epoch 104 - iter 2/3 - loss 0.21870441 - time (sec): 0.17 - samples/sec: 4451.58 - lr: 0.000391 - momentum: 0.000000
779
+ 2024-03-28 09:08:40,127 epoch 104 - iter 3/3 - loss 0.21383352 - time (sec): 0.20 - samples/sec: 3802.98 - lr: 0.000391 - momentum: 0.000000
780
+ 2024-03-28 09:08:40,130 ----------------------------------------------------------------------------------------------------
781
+ 2024-03-28 09:08:40,131 EPOCH 104 done: loss 0.2138 - lr: 0.000391
782
+ 2024-03-28 09:08:40,133 - 3 epochs without improvement
783
+ 2024-03-28 09:08:40,134 ----------------------------------------------------------------------------------------------------
784
+ 2024-03-28 09:08:40,227 epoch 105 - iter 1/3 - loss 0.20639474 - time (sec): 0.09 - samples/sec: 4308.96 - lr: 0.000391 - momentum: 0.000000
785
+ 2024-03-28 09:08:40,329 epoch 105 - iter 2/3 - loss 0.21218268 - time (sec): 0.19 - samples/sec: 3934.47 - lr: 0.000391 - momentum: 0.000000
786
+ 2024-03-28 09:08:40,357 epoch 105 - iter 3/3 - loss 0.21193148 - time (sec): 0.22 - samples/sec: 3521.54 - lr: 0.000391 - momentum: 0.000000
787
+ 2024-03-28 09:08:40,360 ----------------------------------------------------------------------------------------------------
788
+ 2024-03-28 09:08:40,362 EPOCH 105 done: loss 0.2119 - lr: 0.000391
789
+ 2024-03-28 09:08:40,366 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0001953125]
790
+ 2024-03-28 09:08:40,368 ----------------------------------------------------------------------------------------------------
791
+ 2024-03-28 09:08:40,468 epoch 106 - iter 1/3 - loss 0.16320036 - time (sec): 0.10 - samples/sec: 3747.98 - lr: 0.000195 - momentum: 0.000000
792
+ 2024-03-28 09:08:40,565 epoch 106 - iter 2/3 - loss 0.17305550 - time (sec): 0.19 - samples/sec: 3911.81 - lr: 0.000195 - momentum: 0.000000
793
+ 2024-03-28 09:08:40,593 epoch 106 - iter 3/3 - loss 0.17119106 - time (sec): 0.22 - samples/sec: 3498.36 - lr: 0.000195 - momentum: 0.000000
794
+ 2024-03-28 09:08:40,595 ----------------------------------------------------------------------------------------------------
795
+ 2024-03-28 09:08:40,597 EPOCH 106 done: loss 0.1712 - lr: 0.000195
796
+ 2024-03-28 09:08:40,602 - 0 epochs without improvement
797
+ 2024-03-28 09:08:40,606 ----------------------------------------------------------------------------------------------------
798
+ 2024-03-28 09:08:40,713 epoch 107 - iter 1/3 - loss 0.20166751 - time (sec): 0.10 - samples/sec: 3547.84 - lr: 0.000195 - momentum: 0.000000
799
+ 2024-03-28 09:08:40,807 epoch 107 - iter 2/3 - loss 0.17208012 - time (sec): 0.20 - samples/sec: 3844.10 - lr: 0.000195 - momentum: 0.000000
800
+ 2024-03-28 09:08:40,844 epoch 107 - iter 3/3 - loss 0.17909875 - time (sec): 0.23 - samples/sec: 3328.13 - lr: 0.000195 - momentum: 0.000000
801
+ 2024-03-28 09:08:40,848 ----------------------------------------------------------------------------------------------------
802
+ 2024-03-28 09:08:40,851 EPOCH 107 done: loss 0.1791 - lr: 0.000195
803
+ 2024-03-28 09:08:40,855 - 1 epochs without improvement
804
+ 2024-03-28 09:08:40,857 ----------------------------------------------------------------------------------------------------
805
+ 2024-03-28 09:08:40,961 epoch 108 - iter 1/3 - loss 0.19488302 - time (sec): 0.10 - samples/sec: 3733.82 - lr: 0.000195 - momentum: 0.000000
806
+ 2024-03-28 09:08:41,054 epoch 108 - iter 2/3 - loss 0.17380854 - time (sec): 0.20 - samples/sec: 3831.73 - lr: 0.000195 - momentum: 0.000000
807
+ 2024-03-28 09:08:41,096 epoch 108 - iter 3/3 - loss 0.17624595 - time (sec): 0.24 - samples/sec: 3278.64 - lr: 0.000195 - momentum: 0.000000
808
+ 2024-03-28 09:08:41,101 ----------------------------------------------------------------------------------------------------
809
+ 2024-03-28 09:08:41,102 EPOCH 108 done: loss 0.1762 - lr: 0.000195
810
+ 2024-03-28 09:08:41,104 - 2 epochs without improvement
811
+ 2024-03-28 09:08:41,106 ----------------------------------------------------------------------------------------------------
812
+ 2024-03-28 09:08:41,217 epoch 109 - iter 1/3 - loss 0.21638976 - time (sec): 0.11 - samples/sec: 3421.91 - lr: 0.000195 - momentum: 0.000000
813
+ 2024-03-28 09:08:41,289 epoch 109 - iter 2/3 - loss 0.20551143 - time (sec): 0.18 - samples/sec: 4150.51 - lr: 0.000195 - momentum: 0.000000
814
+ 2024-03-28 09:08:41,318 epoch 109 - iter 3/3 - loss 0.21160093 - time (sec): 0.21 - samples/sec: 3709.13 - lr: 0.000195 - momentum: 0.000000
815
+ 2024-03-28 09:08:41,319 ----------------------------------------------------------------------------------------------------
816
+ 2024-03-28 09:08:41,322 EPOCH 109 done: loss 0.2116 - lr: 0.000195
817
+ 2024-03-28 09:08:41,325 - 3 epochs without improvement
818
+ 2024-03-28 09:08:41,327 ----------------------------------------------------------------------------------------------------
819
+ 2024-03-28 09:08:41,398 epoch 110 - iter 1/3 - loss 0.19369786 - time (sec): 0.07 - samples/sec: 5551.41 - lr: 0.000195 - momentum: 0.000000
820
+ 2024-03-28 09:08:41,471 epoch 110 - iter 2/3 - loss 0.19350566 - time (sec): 0.14 - samples/sec: 5283.12 - lr: 0.000195 - momentum: 0.000000
821
+ 2024-03-28 09:08:41,501 epoch 110 - iter 3/3 - loss 0.19654441 - time (sec): 0.17 - samples/sec: 4532.03 - lr: 0.000195 - momentum: 0.000000
822
+ 2024-03-28 09:08:41,503 ----------------------------------------------------------------------------------------------------
823
+ 2024-03-28 09:08:41,506 EPOCH 110 done: loss 0.1965 - lr: 0.000195
824
+ 2024-03-28 09:08:41,509 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [9.765625e-05]
825
+ 2024-03-28 09:08:41,512 ----------------------------------------------------------------------------------------------------
826
+ 2024-03-28 09:08:41,517 learning rate too small - quitting training!
827
+ 2024-03-28 09:08:41,519 ----------------------------------------------------------------------------------------------------
828
+ 2024-03-28 09:08:41,520 Saving model ...
829
+ 2024-03-28 09:08:43,132 Done.
830
+ 2024-03-28 09:08:43,136 ----------------------------------------------------------------------------------------------------
831
+ 2024-03-28 09:08:43,140 Testing using last state of model ...
832
+ 2024-03-28 09:08:43,249
833
+ Results:
834
+ - F-score (micro) 0.9524
835
+ - F-score (macro) 0.9333
836
+ - Accuracy 0.9091
837
+
838
+ By class:
839
+ precision recall f1-score support
840
+
841
+ NAME 1.0000 1.0000 1.0000 3
842
+ GCNUMBER 1.0000 1.0000 1.0000 3
843
+ LOCATION 1.0000 1.0000 1.0000 2
844
+ ORG 1.0000 0.5000 0.6667 2
845
+ COUNTRY 1.0000 1.0000 1.0000 1
846
+
847
+ micro avg 1.0000 0.9091 0.9524 11
848
+ macro avg 1.0000 0.9000 0.9333 11
849
+ weighted avg 1.0000 0.9091 0.9394 11
850
+
851
+ 2024-03-28 09:08:43,252 ----------------------------------------------------------------------------------------------------