blob: e94df128b57d708a9c629bd38c184ceba762aec7 (
plain)
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
|
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
predictions = topk.predict
using no cache
Reading datafile = train-sets/topk.vw
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.000003 0.000003 1 1.0 3.0000 2.9983 4
0.000004 0.000005 2 2.0 0.0000 0.0022 4
0.000003 0.000000 3 3.0 2.0000 2.0000 4
0.000002 0.000000 4 4.0 unknown 0.0000 1
0.000002 0.000003 5 5.0 0.0000 0.0018 4
0.000004 0.000011 6 6.0 3.0000 2.9968 4
0.000003 0.000001 7 7.0 1.0000 1.0007 4
0.000003 0.000000 8 8.0 unknown 0.0000 1
0.000002 0.000000 9 9.0 2.0000 2.0004 4
0.000002 0.000000 10 10.0 1.0000 1.0003 4
0.000002 0.000000 11 11.0 3.0000 2.9995 4
0.000002 0.000000 12 12.0 unknown 0.0000 1
finished run
number of examples per pass = 12
passes used = 1
weighted example sum = 12
weighted label sum = 15
average loss = 1.87357e-06
best constant = 1.25
total feature number = 39
|
