作一个5分类的三层网络，分类9*9的图片，收敛标准从0.5到6e-8，共47个收敛标准，每个收敛标准收敛199次，共收敛了47*199次。取平均值统计平均性能pave。观察pave是如何随着收敛标准改变的。
得到的表格
|
f2[0] |
f2[1] |
f2[2] |
f2[3] |
f2[4] |
迭代次数n |
平均准确率p-ave |
δ |
耗时ms/次 |
耗时ms/199次 |
耗时 min/199 |
最大值p-max |
平均值标准差 |
|
0.4854799 |
0.1858553 |
0.272492 |
0.2602853 |
0.2563134 |
1084.7739 |
0.6110079 |
0.5 |
70.015075 |
13949 |
0.2324833 |
0.8237011 |
0.2530133 |
|
0.5326435 |
0.12101 |
0.1954744 |
0.1860563 |
0.1582058 |
1643.7136 |
0.778164 |
0.4 |
78.79397 |
15680 |
0.2613333 |
0.8505546 |
0.0234568 |
|
0.6958971 |
0.0446005 |
0.1856664 |
0.1821425 |
0.1503321 |
1914.8794 |
0.8280271 |
0.3 |
83.59799 |
16654 |
0.2775667 |
0.8669002 |
0.0191745 |
|
0.7952586 |
0.0322357 |
0.1595491 |
0.1555259 |
0.1254544 |
2235.7487 |
0.8500412 |
0.2 |
89.135678 |
17738 |
0.2956333 |
0.8898618 |
0.0184869 |
|
0.3876615 |
0.0156595 |
0.0816177 |
0.0503286 |
0.5872436 |
3160.2261 |
0.8985676 |
0.1 |
104.77387 |
20850 |
0.3475 |
0.9208017 |
0.0088857 |
|
0.0611409 |
0.0047451 |
0.0363387 |
0.0046498 |
0.9119949 |
6747.4322 |
0.9403243 |
0.01 |
166.92462 |
33218 |
0.5536333 |
0.9453201 |
0.0018007 |
|
0.3821803 |
4.17E-04 |
0.0911098 |
0.0609155 |
0.4675613 |
28730.467 |
0.9551943 |
0.001 |
550.80905 |
109626 |
1.8271 |
0.9620549 |
0.004127 |
|
0.3118726 |
3.98E-04 |
0.1211546 |
0.0457933 |
0.5227548 |
31145.402 |
0.9568312 |
9.00E-04 |
587.94975 |
117033 |
1.95055 |
0.9624441 |
0.0036683 |
|
0.2867465 |
3.69E-04 |
0.1261143 |
0.0858857 |
0.5026527 |
34822.437 |
0.9582198 |
8.00E-04 |
644.41709 |
128254 |
2.1375667 |
0.963417 |
0.0029477 |
|
0.3319181 |
3.20E-04 |
0.1361159 |
0.0406462 |
0.4925607 |
39842.181 |
0.9591331 |
7.00E-04 |
738.93467 |
147048 |
2.4508 |
0.96439 |
0.002471 |
|
0.3067792 |
2.99E-04 |
0.1159715 |
0.0656649 |
0.5126319 |
45219.985 |
0.9600444 |
6.00E-04 |
814.98995 |
162229 |
2.7038167 |
0.9657521 |
0.0024014 |
|
0.3268212 |
2.69E-04 |
0.1209193 |
0.0756559 |
0.4774668 |
53172.774 |
0.9615122 |
5.00E-04 |
951.00503 |
189281 |
3.1546833 |
0.9675034 |
0.0029077 |
|
0.251422 |
0.0052492 |
0.1811337 |
0.0605513 |
0.5025688 |
63857.302 |
0.9637104 |
4.00E-04 |
1132.3015 |
225328 |
3.7554667 |
0.9696439 |
0.0029834 |
|
0.2413307 |
0.0102346 |
0.2061994 |
0.0353675 |
0.5075742 |
80150.191 |
0.9662254 |
3.00E-04 |
1408.1156 |
280231 |
4.6705167 |
0.9747033 |
0.00291 |
|
0.1759618 |
1.33E-04 |
0.3669061 |
0.1257354 |
0.3317159 |
113404.32 |
0.9705993 |
2.00E-04 |
1971.5276 |
392334 |
6.5389 |
0.9766492 |
0.0027735 |
|
0.0301947 |
6.71E-05 |
0.5176071 |
0.1508071 |
0.3015366 |
178704.5 |
0.9755657 |
1.00E-04 |
3079.9296 |
612906 |
10.2151 |
0.9807356 |
0.0020734 |
|
0.0352193 |
6.16E-05 |
0.5728769 |
0.1558284 |
0.2362122 |
189645.84 |
0.9762081 |
9.00E-05 |
3289.3618 |
654583 |
10.909717 |
0.9801518 |
0.0017323 |
|
0.0301881 |
5.37E-05 |
0.5326786 |
0.1708962 |
0.2663572 |
199240.38 |
0.9767577 |
8.00E-05 |
3124.5779 |
621799 |
10.363317 |
0.980541 |
0.0016933 |
|
0.0201314 |
4.96E-05 |
0.5176013 |
0.1909908 |
0.2713782 |
213000.2 |
0.9770862 |
7.00E-05 |
3870.0804 |
770149 |
12.835817 |
0.9820977 |
0.0017903 |
|
0.0201276 |
4.13E-05 |
0.5377 |
0.1608375 |
0.2814242 |
232538.81 |
0.9775898 |
6.00E-05 |
4183.5427 |
832531 |
13.875517 |
0.9819031 |
0.0019222 |
|
0.0251499 |
3.54E-05 |
0.4321747 |
0.2914793 |
0.2512723 |
261837.84 |
0.9784054 |
5.00E-05 |
4671.7035 |
929680 |
15.494667 |
0.9824869 |
0.0017131 |
|
0.020118 |
0.0050529 |
0.4673476 |
0.2864494 |
0.2211226 |
293045.84 |
0.97917 |
4.00E-05 |
4496.6231 |
894833 |
14.913883 |
0.982876 |
0.0017905 |
|
0.0150892 |
2.13E-05 |
0.5125703 |
0.206044 |
0.266342 |
341946.9 |
0.9802202 |
3.00E-05 |
5834.7286 |
1161121 |
19.352017 |
0.9836544 |
0.0016258 |
|
8.67E-06 |
0.0050394 |
0.467342 |
0.2964908 |
0.2311632 |
408835.39 |
0.9810798 |
2.00E-05 |
6995.1508 |
1392042 |
23.2007 |
0.9846274 |
0.0016328 |
|
4.22E-06 |
7.36E-06 |
0.2663364 |
0.4673396 |
0.2663348 |
576604.23 |
0.9824663 |
1.00E-05 |
9819.1457 |
1954028 |
32.567133 |
0.9856003 |
0.0014906 |
|
0.005029 |
6.68E-06 |
0.3567873 |
0.3718623 |
0.266335 |
590518.09 |
0.9824595 |
9.00E-06 |
10070.03 |
2003949 |
33.39915 |
0.9861841 |
0.0016341 |
|
3.66E-06 |
6.13E-06 |
0.361812 |
0.4221129 |
0.2160835 |
609672.87 |
0.9826296 |
8.00E-06 |
10578.678 |
2105158 |
35.085967 |
0.9854057 |
0.0014217 |
|
0.0100532 |
5.28E-06 |
0.3517614 |
0.4321629 |
0.206033 |
649084.43 |
0.9828379 |
7.00E-06 |
11378.653 |
2264389 |
37.739817 |
0.9863787 |
0.0014723 |
|
2.51E-06 |
4.55E-06 |
0.3919619 |
0.402012 |
0.2060328 |
688141.75 |
0.9829699 |
6.00E-06 |
11112.593 |
2211411 |
36.85685 |
0.9863787 |
0.0013737 |
|
0.0050274 |
3.74E-06 |
0.366836 |
0.3919614 |
0.2361828 |
739405.14 |
0.983187 |
5.00E-06 |
13885.628 |
2763247 |
46.054117 |
0.9863787 |
0.0013701 |
|
0.0050268 |
2.85E-06 |
0.3165845 |
0.4221118 |
0.2562828 |
814895.43 |
0.9833523 |
4.00E-06 |
12441.407 |
2475855 |
41.26425 |
0.9875462 |
0.0013463 |
|
0.0100515 |
2.26E-06 |
0.3115589 |
0.4221114 |
0.2562826 |
898370.47 |
0.983451 |
3.00E-06 |
15068.241 |
2998588 |
49.976467 |
0.9867679 |
0.0012352 |
|
0.0100511 |
0.0050265 |
0.3065335 |
0.4422116 |
0.2361817 |
1018724.4 |
0.9834647 |
2.00E-06 |
17163.116 |
3415468 |
56.924467 |
0.9865733 |
0.0014891 |
|
0.0100506 |
6.75E-07 |
0.2462316 |
0.4422113 |
0.3015079 |
1244358.3 |
0.9834852 |
1.00E-06 |
20607.568 |
4100909 |
68.348483 |
0.9865733 |
0.001324 |
|
0.0100506 |
6.44E-07 |
0.3216084 |
0.4522616 |
0.2160808 |
1291161.7 |
0.9834999 |
9.00E-07 |
21746.186 |
4327501 |
72.125017 |
0.987157 |
0.0014341 |
|
0.0050255 |
5.71E-07 |
0.2512566 |
0.432161 |
0.311558 |
1360011.7 |
0.9835537 |
8.00E-07 |
22496.085 |
4476721 |
74.612017 |
0.9867679 |
0.0014877 |
|
0.0050254 |
4.83E-07 |
0.2412064 |
0.432161 |
0.3216083 |
1402576.1 |
0.9834344 |
7.00E-07 |
24104.799 |
4796856 |
79.9476 |
0.9865733 |
0.0014402 |
|
0.0100505 |
4.13E-07 |
0.3015078 |
0.4371861 |
0.2512565 |
1430741.6 |
0.9834246 |
6.00E-07 |
24127.779 |
4801430 |
80.023833 |
0.987157 |
0.0014815 |
|
0.0050253 |
0.0100506 |
0.3366836 |
0.3718594 |
0.2763821 |
1539160.9 |
0.9834148 |
5.00E-07 |
26017.894 |
5177575 |
86.292917 |
0.9867679 |
0.0013904 |
|
0.0100504 |
2.66E-07 |
0.2964825 |
0.4170855 |
0.2763821 |
1618461.8 |
0.983409 |
4.00E-07 |
27559.96 |
5484442 |
91.407367 |
0.9867679 |
0.001473 |
|
0.0100504 |
2.00E-07 |
0.2864323 |
0.4020101 |
0.3015076 |
1702883.1 |
0.9833728 |
3.00E-07 |
29320.739 |
5834845 |
97.247417 |
0.9867679 |
0.0013969 |
|
0.0150755 |
1.47E-07 |
0.2663317 |
0.4221106 |
0.2964825 |
1843820.1 |
0.9831616 |
2.00E-07 |
31024.427 |
6173862 |
102.8977 |
0.9865733 |
0.0013638 |
|
0.0050252 |
0.0050252 |
0.2763819 |
0.3919598 |
0.3216081 |
2188509.1 |
0.9828672 |
1.00E-07 |
37078.025 |
7378528 |
122.97547 |
0.98813 |
0.0015414 |
|
0.0150754 |
0.0100503 |
0.2914573 |
0.3517588 |
0.3316583 |
2266508.3 |
0.9827519 |
9.00E-08 |
38511.402 |
7663774 |
127.72957 |
0.9875462 |
0.0015187 |
|
0.0201005 |
5.58E-08 |
0.2964824 |
0.3567839 |
0.3266332 |
2273906.5 |
0.9828144 |
8.00E-08 |
38618.528 |
7685090 |
128.08483 |
0.9861841 |
0.0014832 |
|
0.0100503 |
4.94E-08 |
0.2713568 |
0.3969849 |
0.3216081 |
2310469.9 |
0.9827157 |
7.00E-08 |
39082.382 |
7777409 |
129.62348 |
0.9867679 |
0.0014673 |
|
0.0150754 |
0.0100503 |
0.2512563 |
0.3668342 |
0.3567839 |
2424313.6 |
0.9827822 |
6.00E-08 |
42822.03 |
8521589 |
142.02648 |
0.9863787 |
0.0015439 |
这张图是δ=5e-5到δ=6e-8的pave曲线，相当直观当δ=8e-7时网络达到峰值，峰值是0.983554。
这张图是δ=1e-5到δ=6e-8的pave曲线，表明对三层网络收敛标准是有最优值的，超过最优值以后网络性能是下降的。
《估算卷积核数量的近似方法》实验表明卷积核数量是有最优值的
《平均分辨准确率对网络隐藏层节点数的非线性变化关系03》实验表明隐藏层节点数是有最优值的。
因为有最优值的存在，如果将网络的收敛标准设定为某一迭代次数，则将迭代次数调大并不必然导致网络的性能改善。如将收敛标准设为6e-8,平均性能只有峰值δ=8e-7的0.999216.但迭代次数却是峰值的1.78倍，耗时是峰值的1.9倍，也就是用了1.9倍的时间却换来性能下降万分之8.或者也可以解释成导致网络性能下降的一个原因恰恰是迭代次数过多了。