-
Notifications
You must be signed in to change notification settings - Fork 0
/
xinhuanewstest.txt
110 lines (93 loc) · 7.68 KB
/
xinhuanewstest.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
-----------------trainning----------------------
Iter: 100, Train Loss: 2.4, Train Acc: 20.31%, Val Loss: 2.4, Val Acc: 21.98%, Time: 0:00:06 *
Iter: 200, Train Loss: 2.0, Train Acc: 39.06%, Val Loss: 2.1, Val Acc: 34.62%, Time: 0:00:11 *
Iter: 300, Train Loss: 1.6, Train Acc: 56.25%, Val Loss: 1.9, Val Acc: 39.56%, Time: 0:00:16 *
Iter: 400, Train Loss: 1.7, Train Acc: 46.88%, Val Loss: 1.7, Val Acc: 44.69%, Time: 0:00:21 *
Iter: 500, Train Loss: 1.8, Train Acc: 39.06%, Val Loss: 1.7, Val Acc: 44.32%, Time: 0:00:25
Iter: 600, Train Loss: 1.6, Train Acc: 50.00%, Val Loss: 1.6, Val Acc: 51.83%, Time: 0:00:30 *
Iter: 700, Train Loss: 1.6, Train Acc: 53.12%, Val Loss: 1.5, Val Acc: 54.03%, Time: 0:00:35 *
Iter: 800, Train Loss: 1.5, Train Acc: 56.25%, Val Loss: 1.5, Val Acc: 52.20%, Time: 0:00:40
Iter: 900, Train Loss: 1.8, Train Acc: 43.75%, Val Loss: 1.5, Val Acc: 54.76%, Time: 0:00:45 *
Iter: 1000, Train Loss: 1.4, Train Acc: 51.56%, Val Loss: 1.4, Val Acc: 54.40%, Time: 0:00:49
Iter: 1100, Train Loss: 1.2, Train Acc: 56.25%, Val Loss: 1.4, Val Acc: 56.59%, Time: 0:00:55 *
Iter: 1200, Train Loss: 1.5, Train Acc: 51.56%, Val Loss: 1.4, Val Acc: 56.96%, Time: 0:00:59 *
Iter: 1300, Train Loss: 1.1, Train Acc: 67.19%, Val Loss: 1.3, Val Acc: 56.78%, Time: 0:01:04
Iter: 1400, Train Loss: 1.5, Train Acc: 51.56%, Val Loss: 1.3, Val Acc: 59.16%, Time: 0:01:11 *
Iter: 1500, Train Loss: 1.3, Train Acc: 56.25%, Val Loss: 1.3, Val Acc: 57.33%, Time: 0:01:16
Iter: 1600, Train Loss: 1.7, Train Acc: 50.00%, Val Loss: 1.3, Val Acc: 59.89%, Time: 0:01:20 *
Iter: 1700, Train Loss: 1.2, Train Acc: 64.06%, Val Loss: 1.3, Val Acc: 59.16%, Time: 0:01:25
Iter: 1800, Train Loss: 1.3, Train Acc: 62.50%, Val Loss: 1.3, Val Acc: 59.16%, Time: 0:01:30
Iter: 1900, Train Loss: 1.6, Train Acc: 50.00%, Val Loss: 1.3, Val Acc: 59.34%, Time: 0:01:35
Iter: 2000, Train Loss: 1.5, Train Acc: 53.12%, Val Loss: 1.3, Val Acc: 60.07%, Time: 0:01:40 *
Iter: 2100, Train Loss: 1.4, Train Acc: 51.56%, Val Loss: 1.3, Val Acc: 61.17%, Time: 0:01:45 *
Iter: 2200, Train Loss: 1.2, Train Acc: 57.81%, Val Loss: 1.3, Val Acc: 60.44%, Time: 0:01:49
Iter: 2300, Train Loss: 1.5, Train Acc: 57.81%, Val Loss: 1.3, Val Acc: 59.71%, Time: 0:01:54
Iter: 2400, Train Loss: 1.3, Train Acc: 59.38%, Val Loss: 1.2, Val Acc: 61.90%, Time: 0:01:59 *
Iter: 2500, Train Loss: 1.4, Train Acc: 57.81%, Val Loss: 1.2, Val Acc: 61.72%, Time: 0:02:04
Iter: 2600, Train Loss: 1.3, Train Acc: 64.06%, Val Loss: 1.2, Val Acc: 60.99%, Time: 0:02:08
Iter: 2700, Train Loss: 1.1, Train Acc: 64.06%, Val Loss: 1.2, Val Acc: 60.26%, Time: 0:02:13
Iter: 2800, Train Loss: 1.3, Train Acc: 59.38%, Val Loss: 1.2, Val Acc: 62.27%, Time: 0:02:18 *
Iter: 2900, Train Loss: 1.3, Train Acc: 62.50%, Val Loss: 1.2, Val Acc: 63.19%, Time: 0:02:23 *
Iter: 3000, Train Loss: 1.4, Train Acc: 57.81%, Val Loss: 1.2, Val Acc: 62.64%, Time: 0:02:27
Iter: 3100, Train Loss: 1.5, Train Acc: 53.12%, Val Loss: 1.2, Val Acc: 62.45%, Time: 0:02:32
Iter: 3200, Train Loss: 1.6, Train Acc: 43.75%, Val Loss: 1.2, Val Acc: 62.64%, Time: 0:02:37
Iter: 3300, Train Loss: 1.3, Train Acc: 60.94%, Val Loss: 1.2, Val Acc: 62.09%, Time: 0:02:42
Iter: 3400, Train Loss: 1.3, Train Acc: 64.06%, Val Loss: 1.2, Val Acc: 63.00%, Time: 0:02:46
Iter: 3500, Train Loss: 1.4, Train Acc: 62.50%, Val Loss: 1.2, Val Acc: 62.82%, Time: 0:02:51
Iter: 3600, Train Loss: 1.3, Train Acc: 57.81%, Val Loss: 1.2, Val Acc: 62.45%, Time: 0:02:56
Iter: 3700, Train Loss: 1.3, Train Acc: 60.94%, Val Loss: 1.2, Val Acc: 63.92%, Time: 0:03:01 *
Iter: 3800, Train Loss: 1.3, Train Acc: 60.94%, Val Loss: 1.2, Val Acc: 63.92%, Time: 0:03:06 *
Iter: 3900, Train Loss: 1.2, Train Acc: 62.50%, Val Loss: 1.2, Val Acc: 62.09%, Time: 0:03:13
Epoch: 2
Iter: 4000, Train Loss: 1.3, Train Acc: 56.25%, Val Loss: 1.2, Val Acc: 63.00%, Time: 0:03:18
Iter: 4100, Train Loss: 1.1, Train Acc: 67.19%, Val Loss: 1.2, Val Acc: 64.47%, Time: 0:03:22 *
Iter: 4200, Train Loss: 1.2, Train Acc: 62.50%, Val Loss: 1.1, Val Acc: 64.29%, Time: 0:03:26
Iter: 4300, Train Loss: 1.4, Train Acc: 51.56%, Val Loss: 1.2, Val Acc: 63.55%, Time: 0:03:31
Iter: 4400, Train Loss: 1.1, Train Acc: 65.62%, Val Loss: 1.2, Val Acc: 63.37%, Time: 0:03:35
Iter: 4500, Train Loss: 1.2, Train Acc: 67.19%, Val Loss: 1.2, Val Acc: 62.45%, Time: 0:03:39
Iter: 4600, Train Loss: 1.4, Train Acc: 53.12%, Val Loss: 1.2, Val Acc: 62.82%, Time: 0:03:44
Iter: 4700, Train Loss: 1.2, Train Acc: 60.94%, Val Loss: 1.2, Val Acc: 63.92%, Time: 0:03:48
Iter: 4800, Train Loss: 1.4, Train Acc: 57.81%, Val Loss: 1.2, Val Acc: 63.19%, Time: 0:03:53
Iter: 4900, Train Loss: 1.1, Train Acc: 70.31%, Val Loss: 1.2, Val Acc: 63.92%, Time: 0:03:57
Iter: 5000, Train Loss: 1.7, Train Acc: 42.19%, Val Loss: 1.1, Val Acc: 63.92%, Time: 0:04:02
Iter: 5100, Train Loss: 1.1, Train Acc: 65.62%, Val Loss: 1.1, Val Acc: 63.55%, Time: 0:04:06
-----------------------test-----------------------------
Precision, Recall and F1-Score...
/root/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
precision recall f1-score support
体育 0.87 0.81 0.84 243
突发事件 1.00 0.06 0.12 16
国际时政 0.69 0.50 0.58 195
社会、法制 0.51 0.35 0.42 170
教育 0.72 0.87 0.79 178
科技 0.66 0.58 0.62 238
文化、艺术 0.62 0.73 0.67 247
国内时政 0.55 0.74 0.63 722
其他 0.69 0.50 0.58 162
环境、能源 0.78 0.57 0.66 201
军事 0.76 0.76 0.76 123
时政 0.00 0.00 0.00 1
卫生、健康 0.67 0.81 0.73 233
娱乐 0.61 0.18 0.28 60
财经 0.78 0.50 0.61 207
accuracy 0.65 2996
macro avg 0.66 0.53 0.55 2996
weighted avg 0.67 0.65 0.65 2996
Confusion Matrix...
[[197 0 0 2 2 0 5 24 0 1 1 0 9 0 2]
[ 0 1 7 1 0 0 1 5 0 0 1 0 0 0 0]
[ 2 0 98 8 2 11 15 33 2 3 12 0 5 0 4]
[ 2 0 1 60 3 7 8 68 3 1 2 0 12 0 3]
[ 4 0 0 2 154 2 3 9 0 0 0 0 4 0 0]
[ 3 0 1 6 5 139 15 45 2 6 2 0 13 1 0]
[ 4 0 2 4 9 7 180 30 3 0 2 0 5 1 0]
[ 4 0 16 15 25 22 24 537 16 12 8 0 29 1 13]
[ 3 0 5 3 4 6 11 39 81 1 1 0 5 3 0]
[ 2 0 2 1 1 5 4 61 0 114 0 0 5 0 6]
[ 1 0 9 3 3 3 1 7 0 1 93 0 2 0 0]
[ 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 1 6 2 1 8 24 1 0 0 0 188 1 1]
[ 4 0 0 3 2 3 16 16 4 0 0 0 0 11 1]
[ 0 0 0 3 2 4 0 76 6 7 0 0 5 0 104]]
Time usage: 0:00:01