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。
