我正在使用 RcppDL 库做一些实验。训练后，我使用原始数据集重建数据。但是，所有数据都具有相同的值。
我的数据（第一列是 id）：
1 17 13 83 0 0 1 2 0 0 104 0 13
2 1 0 18 0 0 0 0 0 0 4 1 13
3 1 1 58 0 0 0 0 0 0 4 1 21
4 4 15 174 9 0 15 0 0 0 154 0 21
5 0 0 8 0 0 0 1 0 0 4 1 20
6 0 1 4 0 0 0 1 0 0 3 1 20
7 0 0 253 0 0 0 1 0 0 21 1 17
8 0 0 0 0 0 0 1 0 0 7 1 17
9 0 1 49 0 0 0 1 0 0 4 1 11
10 4 3 54 1 0 1 3 0 0 21 0 11
11 0 0 0 0 0 0 1 0 0 5 1 11
12 0 0 0 0 0 0 1 0 0 5 1 11
13 0 0 0 0 0 0 1 0 0 7 1 11
14 0 0 0 0 0 0 0 0 0 4 1 11
15 1 0 0 0 0 0 0 0 0 4 1 11
16 0 2 0 0 0 0 0 0 0 4 1 10
17 0 1 5 0 0 0 1 0 0 33 1 10
18 0 0 4 0 0 9 1 0 0 79 1 14
19 0 0 0 0 0 0 3 0 0 33 1 14
20 0 0 2 0 0 0 0 0 0 4 1 14
21 0 1 9 0 0 0 1 0 1 37 1 14
22 0 0 2 0 0 0 2 0 0 8 1 21
23 0 0 7 0 0 0 2 0 0 7 1 21
24 0 0 1 0 0 0 2 0 0 8 1 21
25 0 0 207 0 0 0 2 0 0 7 1 21
26 0 0 0 0 0 0 0 0 0 8 1 15
27 0 0 0 0 0 0 0 0 0 4 1 18
28 0 0 1 0 0 5 0 0 0 135 1 18
29 0 0 0 0 0 0 0 0 0 4 1 18
30 0 0 19 0 0 0 0 0 0 4 1 22
31 0 0 95 0 0 0 0 0 0 4 1 22
32 2 7 130 6 0 6 0 0 0 148 0 22
33 0 0 0 0 0 0 0 0 0 18 1 12
34 1 0 129 0 0 0 0 0 0 25 1 12
35 0 0 1 0 0 0 0 0 0 8 1 12
36 0 0 0 0 0 0 0 0 0 4 1 12
37 0 0 0 0 0 8 0 0 0 72 1 20
38 0 0 7 0 0 0 0 0 0 4 1 20
39 1 1 57 3 0 5 2 1 0 151 0 20
40 0 1 16 0 0 0 3 1 0 51 1 16
重建后我得到了什么：
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[2,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[3,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[4,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[5,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[6,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[7,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[8,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[9,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[10,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[11,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[12,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[13,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[14,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[15,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[16,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[17,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[18,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[19,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[20,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[21,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[22,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[23,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[24,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[25,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[26,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[27,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[28,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[29,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[30,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[31,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[32,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[33,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[34,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[35,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[36,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[37,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[38,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[39,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
[40,] 0.7905348 0.999999 1 0.4721214 0.01729769 1 0.8770443 0.05453092 0.0353921 1 0.9994972 1
我的代码非常简单：
da_obj <- Rda(x.new)
setCorruptionLevel(da_obj, 0.01)
setHiddenRepresentation(da_obj, 8)
setTrainingEpochs(da_obj, 500)
setLearningRate(da_obj, 0.002)
train(da_obj)
coord <- reconstruct(da_obj, x.new)
谁能帮我弄清楚这里出了什么问题？