## 樣本的輸入層的數目: ##
8
## 樣本的隱含層的數目: ##
16
## 樣本的輸出層的數目: ##
1
## 訓練網絡所采用的神經網絡算法: ##
0
## 網絡中使用的函數的索引值: ##
2
## 輸入層到隱含層的權值矩陣 ##
-0.5215422233972622  -1.0405939289268549  -0.2806018151022972  0.5426643291889387  0.5078727633418680  0.6807115374346709  -0.0555001355372568  0.6395059434984600  
0.9334929430214390  -0.6467866389045992  -0.6500849326513749  0.0387765958970571  -0.1466992636509180  0.1742936531282840  -0.3766926743859030  -2.1263419617002057  
0.9779765958797670  -0.7416393986330502  -0.4702883663943964  -0.0200576056555077  -0.0925641274727188  -0.0202044891792851  -0.3212562685892291  -1.6534196295196133  
0.0308971986340014  -0.1746234178230572  0.0555845925076605  0.2085495311070889  0.0228543028739844  0.1979870488024491  0.0370914137458179  0.2049483205555183  
0.9147356098746311  0.6374530626026369  -0.2131588542041736  0.4288405123250410  0.1125625053429897  0.5571094517823023  -0.3546386526104706  0.3764033973814512  
1.8790777286937321  -0.2667498650650697  0.1373214235478724  -0.1750537600700010  0.0929872353425144  -0.2744845076747345  0.1121744970105214  -0.2633376780090545  
0.3641812272254685  0.0156065872884350  0.0616863021191434  0.6538426649702711  0.9853565767537488  1.3683891475047540  0.4483755733151397  0.0043995795004997  
-0.0126512682501700  -0.6091747424677450  -0.0255056183344098  -0.1288287433236206  -0.0170389780698992  -0.1262809305658444  -0.0233084131861256  -0.1252076662234888  
-1.1641975942057410  0.7874099134487534  -0.0527900438350203  0.2383288316034443  0.5549863557846571  0.4942676815708375  -0.0555092938677055  0.4769079464121561  
0.3924720379537772  -0.4067690639229706  1.1351450999825667  -0.3861062233654913  -1.5187645769833533  0.3841154203600182  0.8624130187374414  1.1930782042787622  
0.0076087087376530  0.0623385971505753  -0.0143739155013608  -0.0930028735678798  -0.0153040795917815  -0.0938097206430281  -0.0147293040353408  -0.0925401500322408  
-0.0052768508830857  0.7004281305263189  0.0328332326636389  0.1638660583456754  0.0276619136140507  0.1623207190124439  0.0292024626854133  0.1648355457063817  
0.0054785333294525  -0.9238373762920544  -0.0016438175832775  0.0214019160511506  0.0083444561810835  0.0231839673949871  0.0025832549949055  0.0214263794533518  
1.3970610441901534  0.5906367509768723  -0.2379886475455792  0.4623568261923024  0.3082857504259428  0.6923839246612838  -0.4196446198926910  0.5064955740248780  
1.8545890841406132  0.6258253280963055  -0.2290603438082973  0.4311461151680554  0.2520886299809797  0.6081191199106377  -0.4205937581496633  0.4285333597920515  
-1.0741095890253578  -0.6493411722296878  0.2144476416800629  -0.4245008937765050  -0.1140166248613109  -0.5490885510740753  0.3631958574855230  -0.3683668754976004  
## 隱含層的閥值矩陣 ##
-0.1002831598747657  
0.2518863886799819  
0.1835591431104390  
-2.0060990986758371  
0.0161947819641849  
0.3286831818002249  
0.0361204931640625  
-0.5801512406299189  
0.9508638142473360  
0.9927483043636042  
-0.8853701987492699  
-2.7305993088536802  
-0.1706290269507230  
-0.1178590244974150  
-0.1620251141751556  
0.0096720840376792  
## 隱含層到輸出層的權值矩陣 ##
0.0009323994954074  -0.0022291801590837  0.0036395834743509  -0.3856061862174862  -0.3370695228374089  0.0077825956571264  -0.0002170638111569  1.1009111234056390  -0.0081119980576590  0.0002183492923227  -2.5772469556838171  2.3680639609983318  -0.5760725305472232  0.0496181282691147  -0.0707315728489010  -0.3509086166104439  
## 輸出層的閥值矩陣 ##
0.6351304012458499