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由神经网络的迭代次数计算输出值并评价网络性能

發布時間:2025/4/5 编程问答 26 豆豆
生活随笔 收集整理的這篇文章主要介紹了 由神经网络的迭代次数计算输出值并评价网络性能 小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

在《用共振頻率去理解神經網絡-將乙烯模型運行300次的數據》文中將乙烯模型運行了300次,得到了300組輸出與迭代次數的數據。這次計算只用了其中的150組數據,其中的6組明顯不合理被剔除了,由余下的144組數據可以非常直觀的發現迭代次數與輸出值高度相關。


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*輸出迭代次數迭代次數
20.51650986713-11.3704
30.513156124593-11.7328
40.512544164576-12.0111
50.514564169478-12.0405
60.509862169959-12.0433
70.513865184908-12.1276
80.512103188896-12.149
90.511447204082-12.2263
100.511093231931-12.3542
110.509828253718-12.444

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810.5039941349560-14.1153
820.5032361381406-14.1386
830.503331386255-14.1421
840.5027091395038-14.1484
850.5038291447198-14.1851
860.5032461486612-14.212
870.5038021596727-14.2835
880.5023211749888-14.3751
890.5031191776919-14.3904
900.5034251848841-14.4301

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1310.50100334099800-17.3448
1320.50088537053436-17.4279
1330.50053846023712-17.6447
1340.50060558152875-17.8786
1350.50048559579395-17.9028
1360.50040860055128-17.9108
1370.50073770086877-18.0652
1380.50070774569401-18.1272
1390.50051193937143-18.3581
1400.50037394650373-18.3657

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通過這個表格很容易的發現隨著迭代次數的增加輸出越來越小逼近0.5,由于輸出的變化范圍太大,做了數學變換-ln(x),將輸出和變換后的迭代次數畫成圖

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可以相當直觀的看出迭代次數和輸出肯定是有關系的,但不知道是什么關系,這里先假設是簡單的線性關系,用一次函數去擬合這兩組數據

輸出的函數y2=k2*x+b2

迭代次數的函數y1=k1*x+b1

先對輸出圖像做平滑,然后抽出兩組值比如

0.5086

14

0.5007

136

可以算出

k2=

-6.47541E-05

b2=

0.509506557

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然后迭代次數也取兩組值比如

-12.74

18

-17.9

134

算出

k1=

-0.044482759

b1=

-11.93931034

將x代入可以得到

Y(輸出)=0.001455712*(-ln(X))+0.526886758---a

X就是迭代次數

得到圖像


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可以通過公式a簡單由迭代次數去計算輸出值,得到的平均誤差是0.0023也就是0.23%,對于有效位數是在千分位和萬分位的數字來說這個誤差還是太大的,應該是有其他的規律,但對固定結構固定輸入的神經網絡的輸出值是可以被計算的這個總是肯定的。輸入權重可以影響迭代次數,但不會改變網絡的特征輸出。

雖然在網絡運行前不太可能知道會迭代多少次,但運行幾次得到幾組數據之后,可以用這種近似的辦法得到這個網絡特征輸出,并用特征輸出和期望值的差距去判斷網絡的總體性能。

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后經大量實驗證實神經網絡在收斂標準,權重初始化方式,學習率不變的情況下神經網絡的輸出和迭代次數的關系更接近

輸出=系數a*ln(迭代次數)

Y=a.ln(n)

在2018年10月21日《用神經網絡模擬分子:數據重復性檢測》中構造了同系列的34組網絡,實驗表明迭代次數隨著網絡結構的變化曾高度規則變化,網絡越復雜理論收斂迭代次數n越大,特征明顯高度可重復,Y值也呈高度規則變化。在2018年10月21日《用神經網絡模擬分子:數據精確性檢測》中有更為詳細的數據。

在2018年11月17日《神經網絡結構與輸出值之間的關系》有輸出值變化規律的詳細數據。

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實驗數據

*輸出迭代次數迭代次數計算值誤差
20.51650986713-11.37040.510335-0.01195
30.513156124593-11.73280.509807-0.00653
40.512544164576-12.01110.509402-0.00613
50.514564169478-12.04050.509359-0.01012
60.509862169959-12.04330.509355-0.00099
70.513865184908-12.12760.509232-0.00901
80.512103188896-12.1490.509201-0.00567
90.511447204082-12.22630.509089-0.00461
100.511093231931-12.35420.508903-0.00429
110.509828253718-12.4440.508772-0.00207
120.509942263930-12.48340.508714-0.00241
130.508077275618-12.52680.5086510.00113
140.507283293796-12.59060.5085580.002513
150.509628313571-12.65580.508464-0.00229
160.507789320830-12.67870.508430.001262
170.508679322715-12.68450.508422-0.00051
180.510065330262-12.70760.508388-0.00329
190.507998342228-12.74320.5083360.000666
200.508769346631-12.7560.508318-0.00089
210.508902347396-12.75820.508314-0.00116
220.508954348106-12.76030.508311-0.00126
230.507023357612-12.78720.5082720.002464
240.507826369138-12.81890.5082260.000788
250.507427373511-12.83070.5082090.00154
260.508347385912-12.86340.508161-0.00036
270.50897414579-12.9350.508057-0.00179
280.507568435026-12.98320.5079870.000825
290.507557435726-12.98480.5079850.000842
300.506943445736-13.00750.5079520.001989
310.508557460928-13.0410.507903-0.00129
320.50686465972-13.05190.5078870.002026
330.506789467585-13.05530.5078820.002156
340.507385496763-13.11590.5077940.000806
350.508386510314-13.14280.507755-0.00124
360.508223510541-13.14320.507754-0.00092
370.505149512141-13.14640.5077490.005148
380.505831516517-13.15490.5077370.003767
390.508849520660-13.16290.507725-0.00221
400.504402532222-13.18480.5076930.006525
410.506183551801-13.22090.5076410.002881
420.506764553739-13.22440.5076360.00172
430.504039572627-13.2580.5075870.007038
440.506264575598-13.26320.5075790.002598
450.504858585025-13.27940.5075560.005343
460.505587586087-13.28120.5075530.003889
470.50819596912-13.29950.507526-0.0013
480.50504615982-13.3310.5074810.004833
490.506155618640-13.33530.5074740.002607
500.505588645834-13.37830.5074120.003607
510.505614671661-13.41750.5073550.003443
520.506141693969-13.45020.5073070.002304
530.507431699873-13.45870.507295-0.00027
540.504775717198-13.48310.5072590.004922
550.506531750614-13.52860.5071930.001306
560.505581755332-13.53490.5071840.003171
570.504047785305-13.57380.5071270.006111
580.504673802453-13.59540.5070960.004801
590.504772838870-13.63980.5070310.004476
600.503381840281-13.64150.5070290.007246
610.504552868670-13.67470.506980.004814
620.504552887241-13.69590.506950.004752
630.503548934152-13.74740.5068750.006607
640.504572944298-13.75820.5068590.004532
650.506335970624-13.78570.5068190.000956
660.5036989617-13.80510.5067910.006335
670.5047581001063-13.81660.5067740.003994
680.5055661004941-13.82040.5067680.002377
690.5042161049234-13.86360.5067050.004937
700.504071069259-13.88250.5066780.005175
710.5031951092914-13.90440.5066460.006857
720.5047371098825-13.90980.5066380.003767
730.5045221100358-13.91110.5066360.00419
740.5058331194748-13.99340.5065160.001351
750.504311200766-13.99850.5065090.004361
760.5043041242820-14.03290.5064590.004272
770.5061971264917-14.05050.5064330.000467
780.502791305698-14.08220.5063870.007154
790.5036781309950-14.08550.5063820.00537
800.5073891335669-14.10490.506354-0.00204
810.5039941349560-14.11530.5063390.004653
820.5032361381406-14.13860.5063050.006098
830.503331386255-14.14210.50630.005901
840.5027091395038-14.14840.5062910.007125
850.5038291447198-14.18510.5062370.00478
860.5032461486612-14.2120.5061980.005866
870.5038021596727-14.28350.5060940.00455
880.5023211749888-14.37510.5059610.007247
890.5031191776919-14.39040.5059380.005605
900.5034251848841-14.43010.5058810.004879
910.503041895990-14.45530.5058440.005573
920.4991321901928-14.45840.505840.013439
930.5001811993833-14.50560.5057710.011175
940.5038932053206-14.53490.5057280.003641
950.5019262314784-14.65480.5055540.007226
960.5021722671842-14.79830.5053450.006318
970.5027662679391-14.80110.5053410.00512
980.5059062745179-14.82540.505305-0.00119
990.5027582888629-14.87630.5052310.004919
1000.5015763096264-14.94570.505130.007086
1010.5016324090919-15.22430.5047250.006164
1020.5022744503546-15.32040.5045850.004599
1030.5018194662151-15.3550.5045340.00541
1040.5020674884791-15.40160.5044660.00478
1050.5017294907008-15.40620.504460.005443
1060.5023754931587-15.41120.5044530.004136
1070.5013885158308-15.45610.5043870.005982
1080.5017645170149-15.45840.5043840.005221
1090.5018436139307-15.63020.5041340.004564
1100.5018996192504-15.63890.5041210.004428
1110.5015317285906-15.80150.5038840.004692
1120.5021567856737-15.87690.5037750.003223
1130.501469053680-16.01870.5035680.004204
1140.5018439606597-16.0780.5034820.003266
1150.501079920757-16.11010.5034350.004719
1160.50118416322171-16.6080.502710.003044
1170.50104816810859-16.63750.5026670.003231
1180.5010717221260-16.66170.5026320.003118
1190.50119317654126-16.68650.5025960.002799
1200.50080318346351-16.72490.502540.003468
1210.50097418675513-16.74270.5025140.003074
1220.50147423812301-16.98570.502160.00137
1230.50119524330009-17.00720.5021290.001864
1240.50094324577379-17.01730.5021140.002337
1250.500725078250-17.03750.5020850.002766
1260.50120526386757-17.08840.5020110.001609
1270.5007728370298-17.16090.5019050.002268
1280.50044528796741-17.17580.5018840.002875
1290.50058529173786-17.18880.5018650.002557
1300.50087133265639-17.320.5016740.001603
1310.50100334099800-17.34480.5016380.001267
1320.50088537053436-17.42790.5015170.001262
1330.50053846023712-17.64470.5012010.001325
1340.50060558152875-17.87860.5008610.000511
1350.50048559579395-17.90280.5008250.000679
1360.50040860055128-17.91080.5008140.000811
1370.50073770086877-18.06520.500589-0.0003
1380.50070774569401-18.12720.500499-0.00042
1390.50051193937143-18.35810.500163-0.0007
1400.50037394650373-18.36570.500152-0.00044
1410.500299131563386-18.6950.499672-0.00125
1420.500419148811826-18.81820.499493-0.00185
1430.50031198305891-19.10530.499075-0.00247
1440.500405221802724-19.21730.498912-0.00298
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平均值0.50463513009477.13-14.49470.5057870.002299

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