到底应该加几个卷积核?
用81-30-2的网络来二分类mnist的0和1,然后再向这个网络上加卷积核,如果想要性能和效率都实现最优应该加几个最合适?
首先做对照网络
d2(mnist 0,1)81-30-2-(2*k) ,k∈{0,1}
用81-30-2的网络来二分类mnist的0和1,并让0向(1,0)收敛,让1向(0,1)收敛。
用这个没有加卷积核的网络的性能做参照来比较增加卷积核对网络性能的影响。
再制作有卷积核的网络
d2(mnist 0,1)81-con(3*3*n)-(49*n)-30-2-(2*k) ,k∈(0,1)
用带有n个3*3卷积核的网络来分类mnist的0和1,网络结构是(49*n)-30-2. 并让0向(1,0)收敛,让1向(0,1)收敛。让卷积核的数量n分别等于1到9.
也就是共制作了10个网络,让网络的收敛标准统一为
if (Math.abs(网路输出值[0]-目标函数[0])< δ && Math.abs(网络输出值[1]-目标函数[1])< δ ),
其中δ分别等于0.5到1e-6.的34个值。对应每个δ收敛199次,分别记录与之对应的迭代次数,收敛时间,并计算199次的平均准确率和199次的最大准确率来比较这10个网络的性能差异。共收敛了34*199*10次
首先比较这10个网络的最大分辨准确率
81-30-2 |
N=1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
δ |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
0.5 |
0.9881797 |
0.9054374 |
0.9687943 |
0.9579196 |
0.9546099 |
0.9347518 |
0.9867612 |
0.9706856 |
0.9820331 |
0.9910165 |
0.4 |
0.9981087 |
0.9971631 |
0.9943262 |
0.9981087 |
0.9976359 |
0.9962175 |
0.9962175 |
0.9966903 |
0.9981087 |
0.9966903 |
0.3 |
0.9981087 |
0.9981087 |
0.9957447 |
0.9971631 |
0.9981087 |
0.9976359 |
0.9981087 |
0.9981087 |
0.9981087 |
0.9981087 |
0.2 |
0.9990544 |
0.9981087 |
0.9966903 |
0.9976359 |
0.9981087 |
0.9985816 |
0.9976359 |
0.9985816 |
0.9981087 |
0.9976359 |
0.1 |
0.9995272 |
0.9981087 |
0.9957447 |
0.9971631 |
0.9976359 |
0.9976359 |
0.9976359 |
0.9981087 |
0.9981087 |
0.9985816 |
0.01 |
0.9990544 |
0.9971631 |
0.9971631 |
0.9981087 |
0.9981087 |
0.9985816 |
0.9990544 |
0.9990544 |
0.9995272 |
0.9985816 |
0.001 |
0.9990544 |
0.9985816 |
0.9990544 |
0.9990544 |
0.9985816 |
0.9985816 |
0.9990544 |
0.9990544 |
0.9990544 |
0.9985816 |
9.00E-04 |
0.9990544 |
0.9981087 |
0.9981087 |
0.9990544 |
0.9985816 |
0.9990544 |
0.9995272 |
0.9990544 |
0.9990544 |
0.9990544 |
8.00E-04 |
0.9990544 |
0.9985816 |
0.9985816 |
0.9990544 |
0.9990544 |
0.9990544 |
0.9990544 |
0.9990544 |
0.9995272 |
0.9990544 |
7.00E-04 |
0.9990544 |
0.9985816 |
0.9990544 |
0.9990544 |
0.9995272 |
0.9990544 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9990544 |
6.00E-04 |
0.9990544 |
0.9981087 |
0.9985816 |
0.9990544 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
5.00E-04 |
0.9990544 |
0.9981087 |
0.9990544 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
4.00E-04 |
1 |
0.9985816 |
0.9995272 |
1 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
0.9995272 |
3.00E-04 |
1 |
0.9995272 |
0.9995272 |
1 |
1 |
0.9995272 |
1 |
1 |
0.9995272 |
1 |
2.00E-04 |
1 |
0.9985816 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1.00E-04 |
0.9990544 |
0.9985816 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
9.00E-05 |
0.9990544 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
8.00E-05 |
0.9995272 |
0.9985816 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
7.00E-05 |
0.9995272 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
6.00E-05 |
0.9995272 |
0.9990544 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
5.00E-05 |
0.9995272 |
0.9990544 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
4.00E-05 |
0.9995272 |
0.9990544 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
3.00E-05 |
0.9995272 |
0.9990544 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
2.00E-05 |
0.9995272 |
0.9990544 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1.00E-05 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
9.00E-06 |
0.9995272 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
8.00E-06 |
0.9995272 |
0.9990544 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
7.00E-06 |
0.9995272 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
6.00E-06 |
0.9995272 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
5.00E-06 |
1 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
4.00E-06 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
3.00E-06 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
2.00E-06 |
1 |
0.9995272 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1.00E-06 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
效果是很显著的当卷积核的数量>=2的时候网络的最大准确率当δ<=1e-4的时候已经为1,要显著的强于未加卷积核的81-30-2的网络。或许是由于测试集的难度太小了,只观察到了2个卷积核的最大准确率要好于1个卷积核的网络和当δ=0.5是最大准确率随着卷积核的数量的增加而增加,除此并未发现卷积核数量的增加会改善网络最大分辨准确率的现象。
再比较平均分辨准确率
81-30-2 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
δ |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
0.5 |
0.5751737 |
0.503242 |
0.5279756 |
0.5106597 |
0.5228435 |
0.5252337 |
0.5275835 |
0.5127529 |
0.5360657 |
0.5396533 |
0.4 |
0.9954406 |
0.7974934 |
0.8965323 |
0.8834884 |
0.8674745 |
0.830127 |
0.8256673 |
0.7895553 |
0.7497986 |
0.7164261 |
0.3 |
0.9952647 |
0.9536002 |
0.9722537 |
0.9794124 |
0.9801205 |
0.9772218 |
0.9764377 |
0.9727384 |
0.9708923 |
0.9678321 |
0.2 |
0.996429 |
0.9703102 |
0.9787139 |
0.9882129 |
0.989111 |
0.988346 |
0.9892797 |
0.9897692 |
0.9887166 |
0.9880086 |
0.1 |
0.9974815 |
0.9784288 |
0.9860579 |
0.9907267 |
0.9918505 |
0.9929838 |
0.9927676 |
0.9933877 |
0.9934923 |
0.9935612 |
0.01 |
0.9986671 |
0.9780344 |
0.9859035 |
0.9908051 |
0.9919075 |
0.9912541 |
0.9875833 |
0.9876451 |
0.9869275 |
0.986153 |
0.001 |
0.9987479 |
0.9844185 |
0.9938677 |
0.9944712 |
0.9857206 |
0.9875144 |
0.9798805 |
0.9729427 |
0.9615857 |
0.9507158 |
9.00E-04 |
0.9986932 |
0.9881892 |
0.9954358 |
0.9955712 |
0.9923827 |
0.9928199 |
0.9906174 |
0.9851456 |
0.9808736 |
0.9716859 |
8.00E-04 |
0.9985008 |
0.9868729 |
0.9960464 |
0.9959229 |
0.9945591 |
0.9949749 |
0.9936871 |
0.9924754 |
0.9904677 |
0.9890968 |
7.00E-04 |
0.9981776 |
0.9881963 |
0.9964337 |
0.9960678 |
0.9954382 |
0.995652 |
0.9952196 |
0.9944926 |
0.993181 |
0.9939485 |
6.00E-04 |
0.9984913 |
0.9829051 |
0.996581 |
0.9965382 |
0.9965668 |
0.9965501 |
0.9961557 |
0.9956924 |
0.9949012 |
0.9934329 |
5.00E-04 |
0.9987122 |
0.9787567 |
0.9969445 |
0.9970895 |
0.9969968 |
0.9969659 |
0.9968946 |
0.9955831 |
0.9960298 |
0.9952885 |
4.00E-04 |
0.998477 |
0.9836107 |
0.9975053 |
0.9975456 |
0.9975361 |
0.9974886 |
0.996859 |
0.9967568 |
0.9959609 |
0.9944545 |
3.00E-04 |
0.9993371 |
0.9803295 |
0.9977167 |
0.9977714 |
0.9972795 |
0.9968875 |
0.9966333 |
0.9953431 |
0.9648479 |
0.9940578 |
2.00E-04 |
0.9993324 |
0.9759863 |
0.9977476 |
0.9974791 |
0.9966879 |
0.9959823 |
0.9947088 |
0.993775 |
0.9880775 |
0.9921261 |
1.00E-04 |
0.9978474 |
0.9850553 |
0.9984414 |
0.9978403 |
0.9976027 |
0.9964884 |
0.9957043 |
0.9951008 |
0.9960345 |
0.9955356 |
9.00E-05 |
0.9980208 |
0.9857229 |
0.998477 |
0.9984865 |
0.9981824 |
0.9969374 |
0.9971251 |
0.9963102 |
0.9965929 |
0.9952671 |
8.00E-05 |
0.9983939 |
0.9856612 |
0.9986647 |
0.9982085 |
0.9984438 |
0.9974839 |
0.9974934 |
0.9966309 |
0.9974839 |
0.9960916 |
7.00E-05 |
0.9986861 |
0.9855542 |
0.9987479 |
0.9984105 |
0.9985079 |
0.9979709 |
0.9977975 |
0.9975076 |
0.9975932 |
0.9964456 |
6.00E-05 |
0.9989475 |
0.9819452 |
0.9987503 |
0.9987431 |
0.9987051 |
0.998439 |
0.9979923 |
0.9976526 |
0.998047 |
0.9972415 |
5.00E-05 |
0.999406 |
0.9837557 |
0.9988667 |
0.9987241 |
0.9986006 |
0.9983891 |
0.998161 |
0.9980517 |
0.9978973 |
0.9973271 |
4.00E-05 |
0.9995248 |
0.9804436 |
0.9987455 |
0.9989617 |
0.9986338 |
0.9955546 |
0.9982774 |
0.9980256 |
0.9980826 |
0.9975789 |
3.00E-05 |
0.9994773 |
0.9804673 |
0.9989071 |
0.9988144 |
0.9986932 |
0.9980969 |
0.9982632 |
0.9982299 |
0.9983345 |
0.9979804 |
2.00E-05 |
0.9993775 |
0.9848581 |
0.9988643 |
0.9989736 |
0.9987146 |
0.9982561 |
0.99842 |
0.9982513 |
0.9983558 |
0.9982988 |
1.00E-05 |
0.9993181 |
0.9863383 |
0.9989498 |
0.9989142 |
0.9986837 |
0.9980612 |
0.9983487 |
0.99866 |
0.9985317 |
0.9982062 |
9.00E-06 |
0.9992991 |
0.9874716 |
0.9989641 |
0.9987954 |
0.9986481 |
0.9984509 |
0.998363 |
0.9984889 |
0.9983036 |
0.9974554 |
8.00E-06 |
0.9992373 |
0.9874503 |
0.9990401 |
0.9963648 |
0.9988001 |
0.9983962 |
0.9962721 |
0.9986315 |
0.996745 |
0.9980232 |
7.00E-06 |
0.9992326 |
0.9876688 |
0.9989498 |
0.9985032 |
0.9987479 |
0.9974744 |
0.9980446 |
0.9985602 |
0.9974435 |
0.9979709 |
6.00E-06 |
0.9992848 |
0.9870345 |
0.9988833 |
0.9985222 |
0.9987289 |
0.9979353 |
0.9980541 |
0.9986837 |
0.998382 |
0.9981872 |
5.00E-06 |
0.9994155 |
0.9861554 |
0.998957 |
0.9985032 |
0.9988144 |
0.9984461 |
0.9983749 |
0.9986124 |
0.9980208 |
0.9982537 |
4.00E-06 |
0.9995153 |
0.9856065 |
0.9989641 |
0.998553 |
0.9987289 |
0.9983273 |
0.9987004 |
0.9957756 |
0.9982727 |
0.9982584 |
3.00E-06 |
0.9996175 |
0.987733 |
0.9989261 |
0.9987669 |
0.9976146 |
0.9986766 |
0.99866 |
0.9975361 |
0.996859 |
0.9983202 |
2.00E-06 |
0.9997933 |
0.9902491 |
0.998938 |
0.9988049 |
0.9980921 |
0.9987027 |
0.99866 |
0.9980493 |
0.9970348 |
0.9965858 |
1.00E-06 |
0.9995985 |
0.9919836 |
0.9989926 |
0.9987312 |
0.9984485 |
0.9969303 |
0.9967545 |
0.9982988 |
0.997586 |
0.9980113 |
这个结果是很意外的,对本次实验的1-9个卷积核,无论加多少个卷积核网络的平均性能都不如未加卷积核的对照网络。也就是如果更在乎平均性能不如不加卷积核。增加卷积核对81*30*2的网络的平均性能没有任何正面影响,而且随着卷积核数量的增加平均性能波动越大,越不稳定。
第三比较迭代次数
81-30-2 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
δ |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
0.5 |
8.9849246 |
17.914573 |
18.356784 |
16.407035 |
17.482412 |
15.547739 |
17.467337 |
12.60804 |
14.236181 |
14.623116 |
0.4 |
151.46734 |
1272.9146 |
155.21106 |
135.27638 |
124.34171 |
117.74372 |
113.80905 |
110.52261 |
106.59799 |
106.54271 |
0.3 |
193.96482 |
1427.2714 |
99 |
84.81407 |
86.361809 |
85.241206 |
81.59799 |
84.592965 |
79.839196 |
80.693467 |
0.2 |
234.47739 |
1559.201 |
97.246231 |
92.437186 |
85.959799 |
87.366834 |
88.311558 |
85.276382 |
80.633166 |
85.889447 |
0.1 |
308.96482 |
1718.005 |
116.82915 |
116.30151 |
113.48241 |
115.65829 |
117.47739 |
124.9196 |
117.65829 |
117.50754 |
0.01 |
648.04523 |
2196.4422 |
298.80402 |
326.79397 |
338.24623 |
338.8392 |
339.51759 |
350.22111 |
347.95477 |
351.40201 |
0.001 |
1987.2211 |
3121.9749 |
1387.6583 |
1500.9146 |
1359.9397 |
1463.8492 |
1509.4573 |
1473.794 |
1451.7889 |
1397.1156 |
9.00E-04 |
2099.9447 |
3145.995 |
1521.3116 |
1674.1809 |
1741.0402 |
1752.8995 |
1745.9196 |
1758.8995 |
1784.8291 |
1689.8291 |
8.00E-04 |
2158.2161 |
3234.5729 |
1683.397 |
1929.9246 |
2128.1256 |
2020.1005 |
1998.7387 |
2082.3819 |
2139.2412 |
2169.9799 |
7.00E-04 |
2339.5528 |
3323.3719 |
1933.0452 |
2141.201 |
2405.0151 |
2250.4523 |
2339.8693 |
2384.5025 |
2390.9799 |
2421.4372 |
6.00E-04 |
2616.3719 |
3390.7889 |
2254.794 |
2411.7437 |
2881.5528 |
2584.4573 |
2677.9648 |
2801.3116 |
2781.1608 |
2799.4774 |
5.00E-04 |
2874.1709 |
3510.9196 |
2749.6181 |
2905.4774 |
3194.9397 |
3009.8894 |
3087.598 |
3136.9698 |
3252.9045 |
3256.0452 |
4.00E-04 |
3103.6784 |
3657.8693 |
3439.6583 |
3576.0704 |
4063.3116 |
3557.0352 |
3701.4874 |
3743.7688 |
3885.201 |
3781.995 |
3.00E-04 |
4253.8643 |
3734.407 |
4653.0854 |
4458.1709 |
5366.3015 |
4798.6784 |
5027.9849 |
5106.8995 |
3931.8844 |
5129.3216 |
2.00E-04 |
5159.8342 |
4094.7638 |
7342.8543 |
6998.9347 |
7857.2864 |
6804.7437 |
7065.5126 |
7001.5779 |
9039.8241 |
7125.9598 |
1.00E-04 |
5313.5477 |
4911.5729 |
15506.523 |
14384.759 |
15661.156 |
12383.598 |
13033.332 |
12325.96 |
18030.437 |
12689.749 |
9.00E-05 |
5457.6281 |
4893.2412 |
13203.487 |
14424.467 |
15587.477 |
14052.729 |
13926.789 |
13759.533 |
19101.362 |
13822.01 |
8.00E-05 |
5814.4121 |
5162.2563 |
13376.839 |
14449.583 |
15865.03 |
15632.03 |
15787.98 |
14914.91 |
19248.337 |
15260.698 |
7.00E-05 |
6418.995 |
5260.407 |
14084.156 |
15548.824 |
17521.437 |
17895.95 |
17362.663 |
17504.095 |
21427.548 |
17085.739 |
6.00E-05 |
7637.1055 |
5498.7236 |
15846.543 |
17738.523 |
18500.673 |
19363.005 |
20086.668 |
19431.678 |
23328.92 |
18479.136 |
5.00E-05 |
9614.9548 |
5636.9146 |
16720.432 |
19301.136 |
19821.92 |
21446.03 |
23328.317 |
21723.905 |
25601.452 |
22090.452 |
4.00E-05 |
12084.593 |
6084.8442 |
19631.955 |
20924.327 |
22956 |
31802.839 |
26592.92 |
25417.492 |
29984.538 |
25991.136 |
3.00E-05 |
14731.327 |
6630.5879 |
22636.035 |
23709.357 |
27418.879 |
37657.161 |
31323.106 |
31166.216 |
33767.291 |
32577.467 |
2.00E-05 |
15502.673 |
7487.6985 |
28829.749 |
31211.613 |
34490.317 |
39645.352 |
41799.608 |
39314.266 |
43519.91 |
39049.779 |
1.00E-05 |
23538.915 |
9588.9899 |
39033.603 |
43837.337 |
48120.171 |
53363.085 |
60223.447 |
73658.322 |
63562.337 |
62636.548 |
9.00E-06 |
24890.412 |
10059.462 |
36848.085 |
42105.221 |
46714.543 |
51339.673 |
60674.99 |
62954.739 |
65050.985 |
86731.849 |
8.00E-06 |
26143.367 |
10449.889 |
37868.141 |
51632.447 |
47212.638 |
51188.322 |
78991.548 |
64876.879 |
77089.065 |
90758.111 |
7.00E-06 |
26976.925 |
11049.116 |
37583.121 |
49078.015 |
49336.06 |
77134.93 |
62342.04 |
69872.558 |
66253.085 |
79820.975 |
6.00E-06 |
30039.93 |
11802.126 |
39620.457 |
47897.337 |
52680.538 |
68720.065 |
63644.176 |
77103.864 |
68074 |
75305.739 |
5.00E-06 |
33905.859 |
12674.02 |
43121.608 |
49984.935 |
52986.186 |
68404.271 |
65834.437 |
75845.513 |
75117.256 |
85185.03 |
4.00E-06 |
38340.221 |
15614.482 |
44738.422 |
53647.573 |
58730.332 |
70417.965 |
75075.065 |
88052.658 |
76259.136 |
99206.302 |
3.00E-06 |
44902.06 |
17613.286 |
49216.608 |
57838.588 |
80131.437 |
79051.045 |
82619.769 |
91937.769 |
97506.925 |
89498.804 |
2.00E-06 |
56018.352 |
24346.503 |
56637.367 |
67256.789 |
83585.859 |
87650.271 |
91820.995 |
97789.352 |
117858.88 |
125675.34 |
1.00E-06 |
72385.156 |
41275.352 |
70958.894 |
85642.864 |
101804.81 |
117332.54 |
131001.35 |
129437.56 |
143227.49 |
145569.59 |
迭代次数随着卷积核的数量的增加而增加,当δ=1e-6的时候81-30-2的迭代次数大于2个核小于3个核。
迭代次数与卷积核的数量之间有怎样的关系?
当δ=1e-6的时候
i |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
41275.3518 |
70958.8945 |
85642.8643 |
101804.809 |
117332.543 |
131001.352 |
129437.558 |
143227.487 |
145569.588 |
|
n9/ni |
3.52679218 |
2.05146358 |
1.69972816 |
1.42988911 |
1.24065826 |
1.11120676 |
1.12463176 |
1.01635231 |
|
(9/i)^0.5 |
3 |
2.12132034 |
1.73205081 |
1.5 |
1.34164079 |
1.22474487 |
1.13389342 |
1.06066017 |
比较n9/ni he (9/i)^0.5的曲线
如果不用1e-6的数据
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
||
(9/i)^0.5 |
3 |
2.12132034 |
1.73205081 |
1.5 |
1.34164079 |
1.22474487 |
1.13389342 |
1.06066017 |
|
1.00E-06 |
n9/ni |
3.52679218 |
2.05146358 |
1.69972816 |
1.42988911 |
1.24065826 |
1.11120676 |
1.12463176 |
1.01635231 |
2.00E-06 |
n9/ni |
5.16194622 |
2.21894738 |
1.8685896 |
1.50354782 |
1.43382713 |
1.36869936 |
1.28516382 |
1.06632043 |
3.00E-06 |
n9/ni |
5.08132337 |
1.81846754 |
1.54738916 |
1.11690002 |
1.13216471 |
1.08326137 |
0.97347157 |
0.91787126 |
4.00E-06 |
n9/ni |
6.35347999 |
2.21747431 |
1.84922255 |
1.6891834 |
1.40882091 |
1.32142811 |
1.12667015 |
1.30091038 |
可以发现δ越大数据偏离(9/i)^0.5的数据越大,因此可以得出δ越小应该偏离的越小
因此卷积核的数量和迭代次数的关系可以近似为
同一个网络分别用两个卷积核i和j,迭代次数ni与nj的比例与i和j的平方根近似成正比。
再比较收敛时间,
81-30-2 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
δ |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
0.5 |
3.8609 |
2.9683333 |
3.22915 |
3.0758167 |
3.0727 |
2.6932 |
2.8877333 |
3.2802 |
3.8943667 |
3.1882667 |
0.4 |
3.9897833 |
5.1572667 |
3.7429167 |
3.00815 |
3.38625 |
2.8540667 |
3.0715167 |
3.5064667 |
4.2278 |
3.5390833 |
0.3 |
4.0389667 |
5.28525 |
3.5890333 |
2.93415 |
3.44655 |
2.7955833 |
3.0089167 |
3.4277 |
4.0827333 |
3.4944333 |
0.2 |
4.1132833 |
5.4824167 |
3.6008333 |
2.9411833 |
3.2820167 |
2.8023833 |
3.0064833 |
3.4240167 |
4.2887333 |
3.48205 |
0.1 |
4.2630833 |
5.7441667 |
3.6189333 |
2.9800833 |
3.2931667 |
2.8563 |
3.0642167 |
3.4796 |
4.4574 |
3.5777667 |
0.01 |
4.8257167 |
6.294 |
3.9175 |
3.30495 |
3.6747833 |
3.2552833 |
3.5282333 |
3.9865667 |
4.9973 |
4.1705667 |
0.001 |
7.1519167 |
7.4962667 |
5.52965 |
5.0543667 |
5.0211833 |
5.2103833 |
5.8818833 |
4.0525167 |
7.8104667 |
6.89225 |
9.00E-04 |
7.5316 |
7.6164333 |
5.6360167 |
5.3209833 |
5.6673333 |
5.7203833 |
6.35665 |
7.1377 |
8.3780833 |
7.6791833 |
8.00E-04 |
7.5574333 |
5.6474667 |
5.8822833 |
5.69605 |
6.27995 |
6.20195 |
3.4165333 |
7.8454667 |
9.2552167 |
8.5549667 |
7.00E-04 |
7.61015 |
7.8024833 |
6.3749333 |
6.01875 |
6.6235833 |
6.5990333 |
7.4027667 |
9.10465 |
9.9400333 |
9.1247667 |
6.00E-04 |
6.68905 |
7.9165667 |
4.7836 |
6.7381833 |
7.5925167 |
7.1919667 |
8.1009 |
10.543033 |
11.05375 |
6.6901833 |
5.00E-04 |
8.42065 |
7.97625 |
7.5541833 |
7.9375333 |
8.0095833 |
7.9301833 |
8.9317333 |
11.324817 |
9.0229667 |
11.2863 |
4.00E-04 |
8.9801167 |
8.3453 |
8.63575 |
8.6719167 |
9.36495 |
8.8807167 |
10.15705 |
12.484183 |
13.80275 |
12.459117 |
3.00E-04 |
11.11055 |
8.44955 |
10.363067 |
9.9560333 |
8.6762 |
11.110583 |
12.570967 |
13.629883 |
12.0492 |
15.778883 |
2.00E-04 |
12.5902 |
8.8268 |
14.571683 |
13.782333 |
15.49335 |
14.574483 |
13.937267 |
19.829783 |
23.303883 |
19.0359 |
1.00E-04 |
11.362533 |
7.459 |
24.315283 |
20.883117 |
28.114617 |
22.70635 |
27.256617 |
32.459683 |
43.592283 |
34.79835 |
9.00E-05 |
12.6392 |
10.035217 |
23.01875 |
24.37365 |
25.660867 |
27.108233 |
29.82835 |
38.233017 |
44.162883 |
34.761867 |
8.00E-05 |
13.284083 |
10.18275 |
23.097533 |
22.7186 |
28.533333 |
29.837433 |
33.84525 |
36.271417 |
46.440783 |
38.993517 |
7.00E-05 |
14.2286 |
10.175667 |
22.748633 |
26.372533 |
28.32705 |
35.625617 |
34.566933 |
43.42285 |
50.8357 |
45.915383 |
6.00E-05 |
18.913833 |
10.482533 |
27.4144 |
27.024 |
32.6756 |
33.28815 |
39.285333 |
47.61595 |
53.667883 |
46.710967 |
5.00E-05 |
22.811917 |
10.659833 |
26.028067 |
32.1013 |
31.331467 |
39.697517 |
48.636317 |
53.922 |
60.458567 |
56.073933 |
4.00E-05 |
28.900717 |
9.4542833 |
33.178667 |
31.454467 |
37.158517 |
57.390517 |
53.062383 |
61.338983 |
69.37795 |
64.793833 |
3.00E-05 |
34.315117 |
12.241883 |
33.858083 |
37.593033 |
47.695583 |
65.282 |
60.853517 |
74.435183 |
76.400183 |
81.527783 |
2.00E-05 |
35.70935 |
13.391867 |
45.3707 |
47.2158 |
55.3937 |
70.173383 |
80.77465 |
93.8695 |
101.82857 |
96.999667 |
1.00E-05 |
49.970433 |
16.2405 |
59.216133 |
67.597417 |
73.8599 |
96.508467 |
117.40512 |
154.8208 |
143.13805 |
163.38248 |
9.00E-06 |
54.454267 |
14.147383 |
56.49225 |
66.823733 |
75.700417 |
89.18165 |
116.0364 |
132.55487 |
147.19133 |
219.18537 |
8.00E-06 |
58.624667 |
17.571167 |
54.479133 |
74.137283 |
75.429417 |
89.8726 |
152.68102 |
149.9558 |
175.4566 |
226.97932 |
7.00E-06 |
63.180267 |
18.347517 |
50.881683 |
70.2156 |
77.116383 |
135.361 |
119.15367 |
160.45435 |
151.73543 |
199.74935 |
6.00E-06 |
68.21255 |
15.984233 |
55.734583 |
68.86575 |
82.778017 |
118.64055 |
121.53742 |
173.64287 |
157.87352 |
191.23247 |
5.00E-06 |
78.435767 |
20.081167 |
58.66405 |
70.860867 |
86.4666 |
121.92053 |
126.91468 |
171.95883 |
182.70695 |
209.22285 |
4.00E-06 |
85.330317 |
23.638433 |
57.240867 |
74.1 |
90.572867 |
140.49517 |
143.54072 |
184.00032 |
194.00147 |
252.12315 |
3.00E-06 |
99.625517 |
25.88465 |
65.804133 |
81.871683 |
126.52058 |
141.22632 |
157.5286 |
190.83585 |
225.15483 |
240.72125 |
2.00E-06 |
123.47987 |
34.638367 |
76.559983 |
93.11105 |
132.01262 |
159.31823 |
175.38062 |
224.37283 |
288.27608 |
333.47802 |
1.00E-06 |
158.16015 |
56.345083 |
94.149567 |
117.80548 |
155.58073 |
205.7136 |
254.05877 |
296.57368 |
356.3124 |
413.68133 |
随着卷积核数量的增加收敛时间也是增加的。
总结这4个表格
1.增加卷积核可以改善网络的最大性能
2.增加卷积核会降低网络的平均性能,而且卷积核数量越多平均性能越不稳定
3.同一个网络在收敛标准足够小的情况下迭代次数的比与卷积核的数量的平方根的比成正比
4.卷积核的数量越多收敛速度越慢
将这10个网络的性能排序
最大性能 |
9≈ |
8≈ |
7≈ |
6≈ |
5≈ |
4≈ |
3≈ |
2> |
1≈ |
81-30-2 |
平均性能 |
81-30-2> |
1> |
2> |
3> |
4> |
5> |
6> |
7> |
8> |
9 |
迭代次数 |
9> |
8> |
7> |
6> |
5> |
4> |
3> |
81-30-2> |
2> |
1 |
因此81-30-2的网络的最大性能小于卷积核数量大于2的网络,平均性能大于任何加卷积核的网络,迭代次数在2个核与3个核之间,所以针对实验的网络和1-9个核,如果更在乎平均性能就不加卷积核,如果在乎最大性能,最合理的卷积核的数量就是2个,因为2个核的最大性能和收敛效率都是最优的。
实验数据
学习率r=0.1
权重初始化方式 |
Random rand1 =new Random(); |
int ti1=rand1.nextInt(98)+1; |
int xx=1; |
if(ti1%2==0) |
{ xx=-1;} |
tw[a][b]=xx*((double)ti1/x); |
第一层第二层和卷积核的权重的初始化的x分别为1000,1000,200 |
con(3*3)-(49)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.4991442 |
0.5023601 |
17.914573 |
0.503242 |
0.5 |
894.64322 |
178100 |
2.9683333 |
0.9054374 |
0.5740598 |
0.4261958 |
1272.9146 |
0.7974934 |
0.4 |
1554.8693 |
309436 |
5.1572667 |
0.9971631 |
0.6109585 |
0.3892448 |
1427.2714 |
0.9536002 |
0.3 |
1593.5427 |
317115 |
5.28525 |
0.9981087 |
0.6774132 |
0.3224089 |
1559.201 |
0.9703102 |
0.2 |
1652.1759 |
328945 |
5.4824167 |
0.9981087 |
0.7639885 |
0.2359505 |
1718.005 |
0.9784288 |
0.1 |
1729.1809 |
344650 |
5.7441667 |
0.9981087 |
0.8484488 |
0.1515659 |
2196.4422 |
0.9780344 |
0.01 |
1897.6834 |
377640 |
6.294 |
0.9971631 |
0.8286216 |
0.171378 |
3121.9749 |
0.9844185 |
0.001 |
2260.1809 |
449776 |
7.4962667 |
0.9985816 |
0.798585 |
0.2014137 |
3145.995 |
0.9881892 |
9.00E-04 |
2296.3819 |
456986 |
7.6164333 |
0.9981087 |
0.7635151 |
0.2364872 |
3234.5729 |
0.9868729 |
8.00E-04 |
1702.6734 |
338848 |
5.6474667 |
0.9985816 |
0.8187556 |
0.1812439 |
3323.3719 |
0.9881963 |
7.00E-04 |
2352.5075 |
468149 |
7.8024833 |
0.9985816 |
0.8188199 |
0.1811805 |
3390.7889 |
0.9829051 |
6.00E-04 |
2386.8995 |
474994 |
7.9165667 |
0.9981087 |
0.869077 |
0.1309228 |
3510.9196 |
0.9787567 |
5.00E-04 |
2404.8995 |
478575 |
7.97625 |
0.9981087 |
0.9243685 |
0.0756311 |
3657.8693 |
0.9836107 |
4.00E-04 |
2516.1608 |
500718 |
8.3453 |
0.9985816 |
0.9294495 |
0.0705505 |
3734.407 |
0.9803295 |
3.00E-04 |
2547.5276 |
506973 |
8.44955 |
0.9995272 |
0.864205 |
0.1357951 |
4094.7638 |
0.9759863 |
2.00E-04 |
2661.3367 |
529608 |
8.8268 |
0.9985816 |
0.7386513 |
0.2613488 |
4911.5729 |
0.9850553 |
1.00E-04 |
2248.9347 |
447540 |
7.459 |
0.9985816 |
0.8190485 |
0.1809514 |
4893.2412 |
0.9857229 |
9.00E-05 |
3025.6884 |
602113 |
10.035217 |
0.9995272 |
0.7889079 |
0.2110922 |
5162.2563 |
0.9856612 |
8.00E-05 |
3070.1005 |
610965 |
10.18275 |
0.9985816 |
0.8090114 |
0.1909886 |
5260.407 |
0.9855542 |
7.00E-05 |
3067.9598 |
610540 |
10.175667 |
0.9995272 |
0.8441875 |
0.1558126 |
5498.7236 |
0.9819452 |
6.00E-05 |
3160.402 |
628952 |
10.482533 |
0.9990544 |
0.9145402 |
0.0854598 |
5636.9146 |
0.9837557 |
5.00E-05 |
3214.0201 |
639590 |
10.659833 |
0.9990544 |
0.8441981 |
0.1558019 |
6084.8442 |
0.9804436 |
4.00E-05 |
2850.5276 |
567257 |
9.4542833 |
0.9990544 |
0.8190796 |
0.1809204 |
6630.5879 |
0.9804673 |
3.00E-05 |
3691.0151 |
734513 |
12.241883 |
0.9990544 |
0.7839104 |
0.2160896 |
7487.6985 |
0.9848581 |
2.00E-05 |
4037.6633 |
803512 |
13.391867 |
0.9990544 |
0.7185892 |
0.2814108 |
9588.9899 |
0.9863383 |
1.00E-05 |
4896.5327 |
974430 |
16.2405 |
1 |
0.7286397 |
0.2713602 |
10059.462 |
0.9874716 |
9.00E-06 |
4265.5427 |
848843 |
14.147383 |
0.9995272 |
0.7437155 |
0.2562845 |
10449.889 |
0.9874503 |
8.00E-06 |
5297.8392 |
1054270 |
17.571167 |
0.9990544 |
0.753766 |
0.246234 |
11049.116 |
0.9876688 |
7.00E-06 |
5531.8291 |
1100851 |
18.347517 |
0.9995272 |
0.7688416 |
0.2311584 |
11802.126 |
0.9870345 |
6.00E-06 |
4819.3568 |
959054 |
15.984233 |
0.9995272 |
0.7839173 |
0.2160827 |
12674.02 |
0.9861554 |
5.00E-06 |
6054.3719 |
1204870 |
20.081167 |
0.9995272 |
0.7336668 |
0.2663332 |
15614.482 |
0.9856065 |
4.00E-06 |
7127.1407 |
1418306 |
23.638433 |
1 |
0.7738679 |
0.2261321 |
17613.286 |
0.987733 |
3.00E-06 |
7804.392 |
1553079 |
25.88465 |
1 |
0.7939689 |
0.2060311 |
24346.503 |
0.9902491 |
2.00E-06 |
10443.633 |
2078302 |
34.638367 |
0.9995272 |
0.778894 |
0.221106 |
41275.352 |
0.9919836 |
1.00E-06 |
16988.382 |
3380705 |
56.345083 |
1 |
con(3*3)*2-(49*2)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.4984601 |
0.4988251 |
18.356784 |
0.5279756 |
0.5 |
973.60302 |
193749 |
3.22915 |
0.9687943 |
0.5466968 |
0.4530995 |
155.21106 |
0.8965323 |
0.4 |
1128.4372 |
224575 |
3.7429167 |
0.9943262 |
0.5951047 |
0.4058347 |
99 |
0.9722537 |
0.3 |
1081.8844 |
215342 |
3.5890333 |
0.9957447 |
0.6440861 |
0.3556425 |
97.246231 |
0.9787139 |
0.2 |
1085.6784 |
216050 |
3.6008333 |
0.9966903 |
0.6386903 |
0.3605568 |
116.82915 |
0.9860579 |
0.1 |
1089.2513 |
217136 |
3.6189333 |
0.9957447 |
0.9568039 |
0.0432011 |
298.80402 |
0.9859035 |
0.01 |
1180.7688 |
235050 |
3.9175 |
0.9971631 |
0.9138143 |
0.0861814 |
1387.6583 |
0.9938677 |
0.001 |
1667.1558 |
331779 |
5.52965 |
0.9990544 |
0.8486613 |
0.1513367 |
1521.3116 |
0.9954358 |
9.00E-04 |
1699.2211 |
338161 |
5.6360167 |
0.9981087 |
0.7433548 |
0.2566411 |
1683.397 |
0.9960464 |
8.00E-04 |
1773.5477 |
352937 |
5.8822833 |
0.9985816 |
0.5526884 |
0.4473117 |
1933.0452 |
0.9964337 |
7.00E-04 |
1922.0854 |
382496 |
6.3749333 |
0.9990544 |
0.4422747 |
0.5577228 |
2254.794 |
0.996581 |
6.00E-04 |
1442.2915 |
287016 |
4.7836 |
0.9985816 |
0.5527144 |
0.4472844 |
2749.6181 |
0.9969445 |
5.00E-04 |
2277.4774 |
453251 |
7.5541833 |
0.9990544 |
0.7435355 |
0.256465 |
3439.6583 |
0.9975053 |
4.00E-04 |
2603.7387 |
518145 |
8.63575 |
0.9995272 |
0.7134473 |
0.2865525 |
4653.0854 |
0.9977167 |
3.00E-04 |
3124.4523 |
621784 |
10.363067 |
0.9995272 |
0.5627898 |
0.4372103 |
7342.8543 |
0.9977476 |
2.00E-04 |
4393.4673 |
874301 |
14.571683 |
0.9995272 |
0.6833835 |
0.3166172 |
15506.523 |
0.9984414 |
1.00E-04 |
7331.2261 |
1458917 |
24.315283 |
1 |
0.6431918 |
0.3568082 |
13203.487 |
0.998477 |
9.00E-05 |
6940.2513 |
1381125 |
23.01875 |
1 |
0.738658 |
0.2613421 |
13376.839 |
0.9986647 |
8.00E-05 |
6964.0754 |
1385852 |
23.097533 |
1 |
0.7286127 |
0.271387 |
14084.156 |
0.9987479 |
7.00E-05 |
6858.8844 |
1364918 |
22.748633 |
1 |
0.7235927 |
0.2764073 |
15846.543 |
0.9987503 |
6.00E-05 |
8265.5678 |
1644864 |
27.4144 |
1 |
0.7939424 |
0.2060575 |
16720.432 |
0.9988667 |
5.00E-05 |
7847.6583 |
1561684 |
26.028067 |
1 |
0.7587739 |
0.241226 |
19631.955 |
0.9987455 |
4.00E-05 |
10003.613 |
1990720 |
33.178667 |
1 |
0.7889282 |
0.2110718 |
22636.035 |
0.9989071 |
3.00E-05 |
10208.442 |
2031485 |
33.858083 |
1 |
0.7537589 |
0.246241 |
28829.749 |
0.9988643 |
2.00E-05 |
13679.598 |
2722242 |
45.3707 |
1 |
0.7587891 |
0.241211 |
39033.603 |
0.9989498 |
1.00E-05 |
17854.095 |
3552968 |
59.216133 |
1 |
0.7989898 |
0.2010102 |
36848.085 |
0.9989641 |
9.00E-06 |
17032.834 |
3389535 |
56.49225 |
1 |
0.7236146 |
0.2763853 |
37868.141 |
0.9990401 |
8.00E-06 |
16425.859 |
3268748 |
54.479133 |
1 |
0.6884396 |
0.3115604 |
37583.121 |
0.9989498 |
7.00E-06 |
15341.171 |
3052901 |
50.881683 |
1 |
0.6180891 |
0.381911 |
39620.457 |
0.9988833 |
6.00E-06 |
16804.392 |
3344075 |
55.734583 |
1 |
0.6532648 |
0.3467352 |
43121.608 |
0.998957 |
5.00E-06 |
17687.578 |
3519843 |
58.66405 |
1 |
0.6532651 |
0.3467349 |
44738.422 |
0.9989641 |
4.00E-06 |
17258.553 |
3434452 |
57.240867 |
1 |
0.6683407 |
0.3316593 |
49216.608 |
0.9989261 |
3.00E-06 |
19840.442 |
3948248 |
65.804133 |
1 |
0.6130649 |
0.3869351 |
56637.367 |
0.998938 |
2.00E-06 |
23083.397 |
4593599 |
76.559983 |
1 |
0.6633163 |
0.3366837 |
70958.894 |
0.9989926 |
1.00E-06 |
28386.724 |
5648974 |
94.149567 |
1 |
con(3*3)*3-(49*3)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.4992652 |
0.4997364 |
16.407035 |
0.5106597 |
0.5 |
927.36683 |
184549 |
3.0758167 |
0.9579196 |
0.5884402 |
0.4128762 |
135.27638 |
0.8834884 |
0.4 |
906.9799 |
180489 |
3.00815 |
0.9981087 |
0.6537869 |
0.3473519 |
84.81407 |
0.9794124 |
0.3 |
884.57789 |
176049 |
2.93415 |
0.9971631 |
0.7126646 |
0.2878306 |
92.437186 |
0.9882129 |
0.2 |
886.78392 |
176471 |
2.9411833 |
0.9976359 |
0.7324443 |
0.2669509 |
116.30151 |
0.9907267 |
0.1 |
898.50754 |
178805 |
2.9800833 |
0.9971631 |
0.9666153 |
0.0334501 |
326.79397 |
0.9908051 |
0.01 |
996.46231 |
198297 |
3.30495 |
0.9981087 |
0.9639662 |
0.0360329 |
1500.9146 |
0.9944712 |
0.001 |
1523.9246 |
303262 |
5.0543667 |
0.9990544 |
0.8135525 |
0.1864475 |
1674.1809 |
0.9955712 |
9.00E-04 |
1604.3166 |
319259 |
5.3209833 |
0.9990544 |
0.6078763 |
0.3921203 |
1929.9246 |
0.9959229 |
8.00E-04 |
1717.3216 |
341763 |
5.69605 |
0.9990544 |
0.4774135 |
0.5225865 |
2141.201 |
0.9960678 |
7.00E-04 |
1814.6935 |
361125 |
6.01875 |
0.9990544 |
0.4573306 |
0.5426685 |
2411.7437 |
0.9965382 |
6.00E-04 |
2031.603 |
404291 |
6.7381833 |
0.9990544 |
0.6832444 |
0.3167547 |
2905.4774 |
0.9970895 |
5.00E-04 |
2393.2161 |
476252 |
7.9375333 |
0.9995272 |
0.8037951 |
0.1962048 |
3576.0704 |
0.9975456 |
4.00E-04 |
2614.5628 |
520315 |
8.6719167 |
1 |
0.7435807 |
0.2564185 |
4458.1709 |
0.9977714 |
3.00E-04 |
3001.8191 |
597362 |
9.9560333 |
1 |
0.7436279 |
0.2563727 |
6998.9347 |
0.9974791 |
2.00E-04 |
4155.4372 |
826940 |
13.782333 |
1 |
0.668311 |
0.3316889 |
14384.759 |
0.9978403 |
1.00E-04 |
6296.3819 |
1252987 |
20.883117 |
1 |
0.6984594 |
0.3015405 |
14424.467 |
0.9984865 |
9.00E-05 |
7348.8342 |
1462419 |
24.37365 |
1 |
0.6783655 |
0.3216347 |
14449.583 |
0.9982085 |
8.00E-05 |
6849.8291 |
1363116 |
22.7186 |
1 |
0.6030017 |
0.3969984 |
15548.824 |
0.9984105 |
7.00E-05 |
7951.5176 |
1582352 |
26.372533 |
1 |
0.713544 |
0.2864561 |
17738.523 |
0.9987431 |
6.00E-05 |
8147.9397 |
1621440 |
27.024 |
1 |
0.6532518 |
0.3467483 |
19301.136 |
0.9987241 |
5.00E-05 |
9678.6985 |
1926078 |
32.1013 |
1 |
0.768824 |
0.231176 |
20924.327 |
0.9989617 |
4.00E-05 |
9483.7588 |
1887268 |
31.454467 |
1 |
0.6331581 |
0.3668419 |
23709.357 |
0.9988144 |
3.00E-05 |
11334.422 |
2255582 |
37.593033 |
1 |
0.6984847 |
0.301515 |
31211.613 |
0.9989736 |
2.00E-05 |
14235.92 |
2832948 |
47.2158 |
1 |
0.6432133 |
0.3567867 |
43837.337 |
0.9989142 |
1.00E-05 |
20381.035 |
4055845 |
67.597417 |
1 |
0.6582887 |
0.3417113 |
42105.221 |
0.9987954 |
9.00E-06 |
20147.849 |
4009424 |
66.823733 |
1 |
0.457287 |
0.5427129 |
51632.447 |
0.9963648 |
8.00E-06 |
22352.935 |
4448237 |
74.137283 |
1 |
0.6582894 |
0.3417106 |
49078.015 |
0.9985032 |
7.00E-06 |
21170.518 |
4212936 |
70.2156 |
1 |
0.7135654 |
0.2864345 |
47897.337 |
0.9985222 |
6.00E-06 |
20763.447 |
4131945 |
68.86575 |
1 |
0.6532649 |
0.3467351 |
49984.935 |
0.9985032 |
5.00E-06 |
21364.93 |
4251652 |
70.860867 |
1 |
0.6080394 |
0.3919606 |
53647.573 |
0.998553 |
4.00E-06 |
22341.698 |
4446000 |
74.1 |
1 |
0.6381902 |
0.3618098 |
57838.588 |
0.9987669 |
3.00E-06 |
24684.849 |
4912301 |
81.871683 |
1 |
0.5175879 |
0.4824121 |
67256.789 |
0.9988049 |
2.00E-06 |
28073.603 |
5586663 |
93.11105 |
1 |
0.6130651 |
0.3869349 |
85642.864 |
0.9987312 |
1.00E-06 |
35519.231 |
7068329 |
117.80548 |
1 |
con(3*3)*4-(49*4)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.5002834 |
0.4981237 |
17.482412 |
0.5228435 |
0.5 |
926.40704 |
184362 |
3.0727 |
0.9546099 |
0.5892083 |
0.4109681 |
124.34171 |
0.8674745 |
0.4 |
1020.9648 |
203175 |
3.38625 |
0.9976359 |
0.6582225 |
0.3419309 |
86.361809 |
0.9801205 |
0.3 |
1039.1106 |
206793 |
3.44655 |
0.9981087 |
0.7159889 |
0.2840174 |
85.959799 |
0.989111 |
0.2 |
989.50251 |
196921 |
3.2820167 |
0.9981087 |
0.7125465 |
0.2872574 |
113.48241 |
0.9918505 |
0.1 |
992.77889 |
197590 |
3.2931667 |
0.9976359 |
0.9911855 |
0.0088149 |
338.24623 |
0.9919075 |
0.01 |
1107.9749 |
220487 |
3.6747833 |
0.9981087 |
0.8636796 |
0.1363193 |
1359.9397 |
0.9857206 |
0.001 |
1513.8241 |
301271 |
5.0211833 |
0.9985816 |
0.678101 |
0.3219019 |
1741.0402 |
0.9923827 |
9.00E-04 |
1708.7136 |
340040 |
5.6673333 |
0.9985816 |
0.4774182 |
0.5225791 |
2128.1256 |
0.9945591 |
8.00E-04 |
1893.3668 |
376797 |
6.27995 |
0.9990544 |
0.4623561 |
0.5376444 |
2405.0151 |
0.9954382 |
7.00E-04 |
1997.0402 |
397415 |
6.6235833 |
0.9995272 |
0.6279977 |
0.3720034 |
2881.5528 |
0.9965668 |
6.00E-04 |
2289.196 |
455551 |
7.5925167 |
0.9995272 |
0.6782241 |
0.3217751 |
3194.9397 |
0.9969968 |
5.00E-04 |
2414.9497 |
480575 |
8.0095833 |
0.9995272 |
0.8288998 |
0.1710996 |
4063.3116 |
0.9975361 |
4.00E-04 |
2823.5226 |
561897 |
9.36495 |
0.9995272 |
0.7134475 |
0.2865519 |
5366.3015 |
0.9972795 |
3.00E-04 |
2615.9347 |
520572 |
8.6762 |
1 |
0.7134873 |
0.2865119 |
7857.2864 |
0.9966879 |
2.00E-04 |
4671.3568 |
929601 |
15.49335 |
1 |
0.633141 |
0.3668592 |
15661.156 |
0.9976027 |
1.00E-04 |
8476.7538 |
1686877 |
28.114617 |
1 |
0.6532407 |
0.3467603 |
15587.477 |
0.9981824 |
9.00E-05 |
7736.8543 |
1539652 |
25.660867 |
1 |
0.6783646 |
0.321635 |
15865.03 |
0.9984438 |
8.00E-05 |
8603.005 |
1712000 |
28.533333 |
1 |
0.6130498 |
0.3869503 |
17521.437 |
0.9985079 |
7.00E-05 |
8540.804 |
1699623 |
28.32705 |
1 |
0.6532492 |
0.3467511 |
18500.673 |
0.9987051 |
6.00E-05 |
9851.9246 |
1960536 |
32.6756 |
1 |
0.6733504 |
0.3266496 |
19821.92 |
0.9986006 |
5.00E-05 |
9446.6281 |
1879888 |
31.331467 |
1 |
0.6130567 |
0.3869432 |
22956 |
0.9986338 |
4.00E-05 |
11203.487 |
2229511 |
37.158517 |
1 |
0.5879345 |
0.4120654 |
27418.879 |
0.9986932 |
3.00E-05 |
14380.573 |
2861735 |
47.695583 |
1 |
0.5979862 |
0.402014 |
34490.317 |
0.9987146 |
2.00E-05 |
16701.608 |
3323622 |
55.3937 |
1 |
0.603013 |
0.396987 |
48120.171 |
0.9986837 |
1.00E-05 |
22269.231 |
4431594 |
73.8599 |
1 |
0.5527628 |
0.4472371 |
46714.543 |
0.9986481 |
9.00E-06 |
22824.241 |
4542025 |
75.700417 |
1 |
0.6281387 |
0.3718613 |
47212.638 |
0.9988001 |
8.00E-06 |
22742.538 |
4525765 |
75.429417 |
1 |
0.6030137 |
0.3969863 |
49336.06 |
0.9987479 |
7.00E-06 |
23251.171 |
4626983 |
77.116383 |
1 |
0.5477381 |
0.452262 |
52680.538 |
0.9987289 |
6.00E-06 |
24958.196 |
4966681 |
82.778017 |
1 |
0.5879388 |
0.4120611 |
52986.186 |
0.9988144 |
5.00E-06 |
26070.332 |
5187996 |
86.4666 |
1 |
0.6633154 |
0.3366846 |
58730.332 |
0.9987289 |
4.00E-06 |
27308.347 |
5434372 |
90.572867 |
1 |
0.5226129 |
0.4773871 |
80131.437 |
0.9976146 |
3.00E-06 |
38146.819 |
7591235 |
126.52058 |
1 |
0.5326632 |
0.4673368 |
83585.859 |
0.9980921 |
2.00E-06 |
39802.779 |
7920757 |
132.01262 |
1 |
0.5175879 |
0.4824121 |
101804.81 |
0.9984485 |
1.00E-06 |
46908.764 |
9334844 |
155.58073 |
1 |
con(3*3)*5-(49*5)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.502419 |
0.4987094 |
15.547739 |
0.5252337 |
0.5 |
811.94472 |
161592 |
2.6932 |
0.9347518 |
0.593106 |
0.4064401 |
117.74372 |
0.830127 |
0.4 |
860.52261 |
171244 |
2.8540667 |
0.9962175 |
0.6662483 |
0.3337374 |
85.241206 |
0.9772218 |
0.3 |
842.88945 |
167735 |
2.7955833 |
0.9976359 |
0.7607087 |
0.2384696 |
87.366834 |
0.988346 |
0.2 |
844.9397 |
168143 |
2.8023833 |
0.9985816 |
0.758232 |
0.2418952 |
115.65829 |
0.9929838 |
0.1 |
861.19598 |
171378 |
2.8563 |
0.9976359 |
0.9813115 |
0.0186904 |
338.8392 |
0.9912541 |
0.01 |
981.41206 |
195317 |
3.2552833 |
0.9985816 |
0.9740381 |
0.0259685 |
1463.8492 |
0.9875144 |
0.001 |
1570.8894 |
312623 |
5.2103833 |
0.9985816 |
0.7985059 |
0.2014929 |
1752.8995 |
0.9928199 |
9.00E-04 |
1724.7387 |
343223 |
5.7203833 |
0.9990544 |
0.6229303 |
0.3770704 |
2020.1005 |
0.9949749 |
8.00E-04 |
1869.9347 |
372117 |
6.20195 |
0.9990544 |
0.5928377 |
0.4071632 |
2250.4523 |
0.995652 |
7.00E-04 |
1989.5779 |
395942 |
6.5990333 |
0.9990544 |
0.6681521 |
0.3318476 |
2584.4573 |
0.9965501 |
6.00E-04 |
2168.4322 |
431518 |
7.1919667 |
0.9995272 |
0.7836555 |
0.2163438 |
3009.8894 |
0.9969659 |
5.00E-04 |
2390.9296 |
475811 |
7.9301833 |
0.9995272 |
0.8640518 |
0.1359472 |
3557.0352 |
0.9974886 |
4.00E-04 |
2677.603 |
532843 |
8.8807167 |
0.9995272 |
0.6782894 |
0.3217107 |
4798.6784 |
0.9968875 |
3.00E-04 |
3349.8442 |
666635 |
11.110583 |
0.9995272 |
0.7637195 |
0.2362798 |
6804.7437 |
0.9959823 |
2.00E-04 |
4394.2362 |
874469 |
14.574483 |
1 |
0.5929474 |
0.4070529 |
12383.598 |
0.9964884 |
1.00E-04 |
6846.1357 |
1362381 |
22.70635 |
1 |
0.5577792 |
0.4422214 |
14052.729 |
0.9969374 |
9.00E-05 |
8173.1759 |
1626494 |
27.108233 |
1 |
0.5929508 |
0.4070492 |
15632.03 |
0.9974839 |
8.00E-05 |
8996.2111 |
1790246 |
29.837433 |
1 |
0.6230988 |
0.376901 |
17895.95 |
0.9979709 |
7.00E-05 |
10741.392 |
2137537 |
35.625617 |
1 |
0.653249 |
0.3467511 |
19363.005 |
0.998439 |
6.00E-05 |
10036.598 |
1997289 |
33.28815 |
1 |
0.5728566 |
0.4271433 |
21446.03 |
0.9983891 |
5.00E-05 |
11969.06 |
2381851 |
39.697517 |
1 |
0.6683287 |
0.3316711 |
31802.839 |
0.9955546 |
4.00E-05 |
17303.583 |
3443431 |
57.390517 |
1 |
0.537686 |
0.4623137 |
37657.161 |
0.9980969 |
3.00E-05 |
19683.015 |
3916920 |
65.282 |
1 |
0.5075373 |
0.4924627 |
39645.352 |
0.9982561 |
2.00E-05 |
21157.714 |
4210403 |
70.173383 |
1 |
0.452262 |
0.5477379 |
53363.085 |
0.9980612 |
1.00E-05 |
29097.874 |
5790508 |
96.508467 |
1 |
0.5075375 |
0.4924625 |
51339.673 |
0.9984509 |
9.00E-06 |
26888.935 |
5350899 |
89.18165 |
1 |
0.4924624 |
0.5075377 |
51188.322 |
0.9983962 |
8.00E-06 |
27097.266 |
5392356 |
89.8726 |
1 |
0.457287 |
0.542713 |
77134.93 |
0.9974744 |
7.00E-06 |
40812.357 |
8121660 |
135.361 |
1 |
0.5678385 |
0.4321615 |
68720.065 |
0.9979353 |
6.00E-06 |
35771.01 |
7118433 |
118.64055 |
1 |
0.5628135 |
0.4371865 |
68404.271 |
0.9984461 |
5.00E-06 |
36759.96 |
7315232 |
121.92053 |
1 |
0.5778889 |
0.4221111 |
70417.965 |
0.9983273 |
4.00E-06 |
42360.342 |
8429710 |
140.49517 |
1 |
0.4623118 |
0.5376882 |
79051.045 |
0.9986766 |
3.00E-06 |
42580.719 |
8473579 |
141.22632 |
1 |
0.5125628 |
0.4874372 |
87650.271 |
0.9987027 |
2.00E-06 |
48035.603 |
9559094 |
159.31823 |
1 |
0.8442205 |
0.1557795 |
117332.54 |
0.9969303 |
1.00E-06 |
62024.191 |
12342816 |
205.7136 |
1 |
con(3*3)*6-(49*6)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.501056 |
0.4970626 |
17.467337 |
0.5275835 |
0.5 |
870.58291 |
173264 |
2.8877333 |
0.9867612 |
0.5874694 |
0.4137517 |
113.80905 |
0.8256673 |
0.4 |
926.08543 |
184291 |
3.0715167 |
0.9962175 |
0.6751543 |
0.3240751 |
81.59799 |
0.9764377 |
0.3 |
907.21106 |
180535 |
3.0089167 |
0.9981087 |
0.7231123 |
0.2766511 |
88.311558 |
0.9892797 |
0.2 |
906.46734 |
180389 |
3.0064833 |
0.9976359 |
0.776154 |
0.2235614 |
117.47739 |
0.9927676 |
0.1 |
923.88442 |
183853 |
3.0642167 |
0.9976359 |
0.9862619 |
0.0137143 |
339.51759 |
0.9875833 |
0.01 |
1063.7889 |
211694 |
3.5282333 |
0.9990544 |
0.9539836 |
0.0460196 |
1509.4573 |
0.9798805 |
0.001 |
1773.4322 |
352913 |
5.8818833 |
0.9990544 |
0.883782 |
0.1162176 |
1745.9196 |
0.9906174 |
9.00E-04 |
1916.4975 |
381399 |
6.35665 |
0.9995272 |
0.6379837 |
0.362016 |
1998.7387 |
0.9936871 |
8.00E-04 |
1030.1106 |
204992 |
3.4165333 |
0.9990544 |
0.5828051 |
0.4171965 |
2339.8693 |
0.9952196 |
7.00E-04 |
2231.9849 |
444166 |
7.4027667 |
0.9995272 |
0.6380373 |
0.3619622 |
2677.9648 |
0.9961557 |
6.00E-04 |
2442.4774 |
486054 |
8.1009 |
0.9995272 |
0.7937006 |
0.2063007 |
3087.598 |
0.9968946 |
5.00E-04 |
2692.9045 |
535904 |
8.9317333 |
0.9995272 |
0.8439627 |
0.1560347 |
3701.4874 |
0.996859 |
4.00E-04 |
3062.4221 |
609423 |
10.15705 |
0.9995272 |
0.6782909 |
0.3217102 |
5027.9849 |
0.9966333 |
3.00E-04 |
3790.2362 |
754258 |
12.570967 |
1 |
0.7185103 |
0.2814898 |
7065.5126 |
0.9947088 |
2.00E-04 |
4202.1709 |
836236 |
13.937267 |
1 |
0.5175832 |
0.4824166 |
13033.332 |
0.9957043 |
1.00E-04 |
8218.0653 |
1635397 |
27.256617 |
1 |
0.5376808 |
0.4623192 |
13926.789 |
0.9971251 |
9.00E-05 |
8993.4372 |
1789701 |
29.82835 |
1 |
0.643194 |
0.3568054 |
15787.98 |
0.9974934 |
8.00E-05 |
10204.588 |
2030715 |
33.84525 |
1 |
0.5829033 |
0.4170966 |
17362.663 |
0.9977975 |
7.00E-05 |
10422.186 |
2074016 |
34.566933 |
1 |
0.5577818 |
0.4422181 |
20086.668 |
0.9979923 |
6.00E-05 |
11844.819 |
2357120 |
39.285333 |
1 |
0.5477334 |
0.4522664 |
23328.317 |
0.998161 |
5.00E-05 |
14664.211 |
2918179 |
48.636317 |
1 |
0.4974872 |
0.5025127 |
26592.92 |
0.9982774 |
4.00E-05 |
15998.704 |
3183743 |
53.062383 |
1 |
0.4924626 |
0.5075377 |
31323.106 |
0.9982632 |
3.00E-05 |
18347.794 |
3651211 |
60.853517 |
1 |
0.5125622 |
0.4874379 |
41799.608 |
0.99842 |
2.00E-05 |
24354.166 |
4846479 |
80.77465 |
1 |
0.4824123 |
0.5175876 |
60223.447 |
0.9983487 |
1.00E-05 |
35398.518 |
7044307 |
117.40512 |
1 |
0.4271368 |
0.5728632 |
60674.99 |
0.998363 |
9.00E-06 |
34985.834 |
6962184 |
116.0364 |
1 |
0.7035147 |
0.2964853 |
78991.548 |
0.9962721 |
8.00E-06 |
46034.452 |
9160861 |
152.68102 |
1 |
0.7135651 |
0.2864348 |
62342.04 |
0.9980446 |
7.00E-06 |
35925.714 |
7149220 |
119.15367 |
1 |
0.6582897 |
0.3417102 |
63644.176 |
0.9980541 |
6.00E-06 |
36644.442 |
7292245 |
121.53742 |
1 |
0.6080392 |
0.3919608 |
65834.437 |
0.9983749 |
5.00E-06 |
38265.729 |
7614881 |
126.91468 |
1 |
0.6532652 |
0.3467348 |
75075.065 |
0.9987004 |
4.00E-06 |
43278.563 |
8612443 |
143.54072 |
1 |
0.5628137 |
0.4371863 |
82619.769 |
0.99866 |
3.00E-06 |
47495.97 |
9451716 |
157.5286 |
1 |
0.6231151 |
0.3768849 |
91820.995 |
0.99866 |
2.00E-06 |
52878.578 |
10522837 |
175.38062 |
1 |
0.7487433 |
0.2512567 |
131001.35 |
0.9967545 |
1.00E-06 |
76600.392 |
15243526 |
254.05877 |
1 |
con(3*3)*7-(49*7)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.4971291 |
0.5010397 |
12.60804 |
0.5127529 |
0.5 |
988.92965 |
196812 |
3.2802 |
0.9706856 |
0.5996055 |
0.4001422 |
110.52261 |
0.7895553 |
0.4 |
1057.2261 |
210388 |
3.5064667 |
0.9966903 |
0.6883169 |
0.3111753 |
84.592965 |
0.9727384 |
0.3 |
1033.4724 |
205662 |
3.4277 |
0.9981087 |
0.7744565 |
0.2256916 |
85.276382 |
0.9897692 |
0.2 |
1032.3618 |
205441 |
3.4240167 |
0.9985816 |
0.7646914 |
0.2350993 |
124.9196 |
0.9933877 |
0.1 |
1049.0503 |
208776 |
3.4796 |
0.9981087 |
0.9863947 |
0.0136193 |
350.22111 |
0.9876451 |
0.01 |
1201.9799 |
239194 |
3.9865667 |
0.9990544 |
0.9690574 |
0.0309428 |
1473.794 |
0.9729427 |
0.001 |
1221.8643 |
243151 |
4.0525167 |
0.9990544 |
0.8286096 |
0.171394 |
1758.8995 |
0.9851456 |
9.00E-04 |
2152.0704 |
428262 |
7.1377 |
0.9990544 |
0.6128992 |
0.3871023 |
2082.3819 |
0.9924754 |
8.00E-04 |
2365.4623 |
470728 |
7.8454667 |
0.9990544 |
0.5125463 |
0.487452 |
2384.5025 |
0.9944926 |
7.00E-04 |
2745.1005 |
546279 |
9.10465 |
0.9995272 |
0.6882341 |
0.3117671 |
2801.3116 |
0.9956924 |
6.00E-04 |
3178.7839 |
632582 |
10.543033 |
0.9995272 |
0.7083501 |
0.2916508 |
3136.9698 |
0.9955831 |
5.00E-04 |
3414.5025 |
679489 |
11.324817 |
0.9995272 |
0.8037931 |
0.1962065 |
3743.7688 |
0.9967568 |
4.00E-04 |
3764.0653 |
749051 |
12.484183 |
0.9995272 |
0.5878849 |
0.412115 |
5106.8995 |
0.9953431 |
3.00E-04 |
4109.5075 |
817793 |
13.629883 |
1 |
0.7134868 |
0.286512 |
7001.5779 |
0.993775 |
2.00E-04 |
5978.7889 |
1189787 |
19.829783 |
1 |
0.4723641 |
0.5276354 |
12325.96 |
0.9951008 |
1.00E-04 |
9786.8291 |
1947581 |
32.459683 |
1 |
0.5376815 |
0.4623187 |
13759.533 |
0.9963102 |
9.00E-05 |
11527.457 |
2293981 |
38.233017 |
1 |
0.5577795 |
0.4422207 |
14914.91 |
0.9966309 |
8.00E-05 |
10936.015 |
2176285 |
36.271417 |
1 |
0.4874378 |
0.5125625 |
17504.095 |
0.9975076 |
7.00E-05 |
13092.317 |
2605371 |
43.42285 |
1 |
0.6030036 |
0.3969966 |
19431.678 |
0.9976526 |
6.00E-05 |
14356.563 |
2856957 |
47.61595 |
1 |
0.5376843 |
0.4623156 |
21723.905 |
0.9980517 |
5.00E-05 |
16257.889 |
3235320 |
53.922 |
1 |
0.4974871 |
0.5025127 |
25417.492 |
0.9980256 |
4.00E-05 |
18494.161 |
3680339 |
61.338983 |
1 |
0.4874375 |
0.5125625 |
31166.216 |
0.9982299 |
3.00E-05 |
22442.769 |
4466111 |
74.435183 |
1 |
0.5376871 |
0.4623129 |
39314.266 |
0.9982513 |
2.00E-05 |
28302.357 |
5632170 |
93.8695 |
1 |
0.8542649 |
0.1457352 |
73658.322 |
0.99866 |
1.00E-05 |
46679.623 |
9289248 |
154.8208 |
1 |
0.6984892 |
0.3015108 |
62954.739 |
0.9984889 |
9.00E-06 |
39966.281 |
7953292 |
132.55487 |
1 |
0.5979885 |
0.4020115 |
64876.879 |
0.9986315 |
8.00E-06 |
45212.804 |
8997348 |
149.9558 |
1 |
0.5678383 |
0.4321616 |
69872.558 |
0.9985602 |
7.00E-06 |
48378.196 |
9627261 |
160.45435 |
1 |
0.6281393 |
0.3718607 |
77103.864 |
0.9986837 |
6.00E-06 |
52354.628 |
10418572 |
173.64287 |
1 |
0.5376881 |
0.4623119 |
75845.513 |
0.9986124 |
5.00E-06 |
51846.879 |
10317530 |
171.95883 |
1 |
0.8894444 |
0.1105556 |
88052.658 |
0.9957756 |
4.00E-06 |
55477.472 |
11040019 |
184.00032 |
1 |
0.6934663 |
0.3065337 |
91937.769 |
0.9975361 |
3.00E-06 |
57538.437 |
11450151 |
190.83585 |
1 |
0.6381904 |
0.3618095 |
97789.352 |
0.9980493 |
2.00E-06 |
67650.095 |
13462370 |
224.37283 |
1 |
0.6130651 |
0.3869349 |
129437.56 |
0.9982988 |
1.00E-06 |
89419.121 |
17794421 |
296.57368 |
1 |
con(3*3)*8-(49*8)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.5001463 |
0.4975267 |
14.236181 |
0.5360657 |
0.5 |
1174.1357 |
233662 |
3.8943667 |
0.9820331 |
0.601897 |
0.3990353 |
106.59799 |
0.7497986 |
0.4 |
1274.6834 |
253668 |
4.2278 |
0.9981087 |
0.6882475 |
0.3107847 |
79.839196 |
0.9708923 |
0.3 |
1230.9447 |
244964 |
4.0827333 |
0.9981087 |
0.7821435 |
0.218235 |
80.633166 |
0.9887166 |
0.2 |
1293.0603 |
257324 |
4.2887333 |
0.9981087 |
0.7848443 |
0.2149465 |
117.65829 |
0.9934923 |
0.1 |
1343.8442 |
267444 |
4.4574 |
0.9981087 |
0.9813175 |
0.0186511 |
347.95477 |
0.9869275 |
0.01 |
1506.6935 |
299838 |
4.9973 |
0.9995272 |
0.9740762 |
0.0259265 |
1451.7889 |
0.9615857 |
0.001 |
2354.8945 |
468628 |
7.8104667 |
0.9990544 |
0.7884817 |
0.2115207 |
1784.8291 |
0.9808736 |
9.00E-04 |
2526.0402 |
502685 |
8.3780833 |
0.9990544 |
0.6179132 |
0.3820865 |
2139.2412 |
0.9904677 |
8.00E-04 |
2790.5126 |
555313 |
9.2552167 |
0.9995272 |
0.537636 |
0.462363 |
2390.9799 |
0.993181 |
7.00E-04 |
2996.9849 |
596402 |
9.9400333 |
0.9995272 |
0.597879 |
0.402118 |
2781.1608 |
0.9949012 |
6.00E-04 |
3332.7739 |
663225 |
11.05375 |
0.9995272 |
0.7334545 |
0.2665463 |
3252.9045 |
0.9960298 |
5.00E-04 |
2720.4472 |
541378 |
9.0229667 |
0.9995272 |
0.7987748 |
0.2012279 |
3885.201 |
0.9959609 |
4.00E-04 |
4161.5729 |
828165 |
13.80275 |
0.9995272 |
0.7486054 |
0.2513941 |
3931.8844 |
0.9648479 |
3.00E-04 |
3632.8392 |
722952 |
12.0492 |
0.9995272 |
0.4974828 |
0.5025161 |
9039.8241 |
0.9880775 |
2.00E-04 |
7026.2111 |
1398233 |
23.303883 |
1 |
0.7085037 |
0.2914961 |
18030.437 |
0.9960345 |
1.00E-04 |
13143.327 |
2615537 |
43.592283 |
1 |
0.6180692 |
0.3819299 |
19101.362 |
0.9965929 |
9.00E-05 |
13315.437 |
2649773 |
44.162883 |
1 |
0.5979739 |
0.4020261 |
19248.337 |
0.9974839 |
8.00E-05 |
14002.246 |
2786447 |
46.440783 |
1 |
0.6130506 |
0.3869502 |
21427.548 |
0.9975932 |
7.00E-05 |
15327.266 |
3050142 |
50.8357 |
1 |
0.5778799 |
0.4221196 |
23328.92 |
0.998047 |
6.00E-05 |
16181.271 |
3220073 |
53.667883 |
1 |
0.5276353 |
0.4723644 |
25601.452 |
0.9978973 |
5.00E-05 |
18228.638 |
3627514 |
60.458567 |
1 |
0.4974876 |
0.5025126 |
29984.538 |
0.9980826 |
4.00E-05 |
20917.975 |
4162677 |
69.37795 |
1 |
0.4974873 |
0.5025126 |
33767.291 |
0.9983345 |
3.00E-05 |
23035.141 |
4584011 |
76.400183 |
1 |
0.4723627 |
0.5276373 |
43519.91 |
0.9983558 |
2.00E-05 |
30702.08 |
6109714 |
101.82857 |
1 |
0.4472371 |
0.5527629 |
63562.337 |
0.9985317 |
1.00E-05 |
43157.121 |
8588283 |
143.13805 |
1 |
0.4020116 |
0.5979884 |
65050.985 |
0.9983036 |
9.00E-06 |
44379.211 |
8831480 |
147.19133 |
1 |
0.8643163 |
0.1356837 |
77089.065 |
0.996745 |
8.00E-06 |
52901.477 |
10527396 |
175.4566 |
1 |
0.7537657 |
0.2462343 |
66253.085 |
0.9974435 |
7.00E-06 |
45749.377 |
9104126 |
151.73543 |
1 |
0.7035154 |
0.2964845 |
68074 |
0.998382 |
6.00E-06 |
47599.975 |
9472411 |
157.87352 |
1 |
0.6633151 |
0.3366849 |
75117.256 |
0.9980208 |
5.00E-06 |
55087.513 |
10962417 |
182.70695 |
1 |
0.6633154 |
0.3366846 |
76259.136 |
0.9982727 |
4.00E-06 |
58492.899 |
11640088 |
194.00147 |
1 |
0.8643196 |
0.1356804 |
97506.925 |
0.996859 |
3.00E-06 |
67885.874 |
13509290 |
225.15483 |
1 |
0.7236173 |
0.2763827 |
117858.88 |
0.9970348 |
2.00E-06 |
86917.412 |
17296565 |
288.27608 |
1 |
0.6633163 |
0.3366837 |
143227.49 |
0.997586 |
1.00E-06 |
107430.87 |
21378744 |
356.3124 |
1 |
con(3*3)*9-(49*9)*30*2-2 |
cpa |
|||||||
f2[0] |
f2[1] |
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 |
0.5004345 |
0.4969997 |
14.623116 |
0.5396533 |
0.5 |
961.28141 |
191296 |
3.1882667 |
0.9910165 |
0.5935062 |
0.4070302 |
106.54271 |
0.7164261 |
0.4 |
1067.0402 |
212345 |
3.5390833 |
0.9966903 |
0.7005314 |
0.3000457 |
80.693467 |
0.9678321 |
0.3 |
1053.598 |
209666 |
3.4944333 |
0.9981087 |
0.7944988 |
0.2049604 |
85.889447 |
0.9880086 |
0.2 |
1049.7789 |
208923 |
3.48205 |
0.9976359 |
0.7428472 |
0.2569587 |
117.50754 |
0.9935612 |
0.1 |
1078.7085 |
214666 |
3.5777667 |
0.9985816 |
0.9912877 |
0.0086961 |
351.40201 |
0.986153 |
0.01 |
1257.4573 |
250234 |
4.1705667 |
0.9985816 |
0.9841139 |
0.0158891 |
1397.1156 |
0.9507158 |
0.001 |
2077.8945 |
413535 |
6.89225 |
0.9985816 |
0.9038809 |
0.0961187 |
1689.8291 |
0.9716859 |
9.00E-04 |
2315.3266 |
460751 |
7.6791833 |
0.9990544 |
0.6530397 |
0.3469581 |
2169.9799 |
0.9890968 |
8.00E-04 |
2579.3015 |
513298 |
8.5549667 |
0.9990544 |
0.5326165 |
0.4673838 |
2421.4372 |
0.9939485 |
7.00E-04 |
2751.1859 |
547486 |
9.1247667 |
0.9990544 |
0.5928609 |
0.4071387 |
2799.4774 |
0.9934329 |
6.00E-04 |
2017.1407 |
401411 |
6.6901833 |
0.9995272 |
0.78868 |
0.2113211 |
3256.0452 |
0.9952885 |
5.00E-04 |
3402.8995 |
677178 |
11.2863 |
0.9995272 |
0.7686425 |
0.2313571 |
3781.995 |
0.9944545 |
4.00E-04 |
3756.5176 |
747547 |
12.459117 |
0.9995272 |
0.6381136 |
0.3618876 |
5129.3216 |
0.9940578 |
3.00E-04 |
4757.3668 |
946733 |
15.778883 |
1 |
0.5929282 |
0.4070717 |
7125.9598 |
0.9921261 |
2.00E-04 |
5739.3869 |
1142154 |
19.0359 |
1 |
0.4723621 |
0.5276377 |
12689.749 |
0.9955356 |
1.00E-04 |
10491.95 |
2087901 |
34.79835 |
1 |
0.5025093 |
0.4974911 |
13822.01 |
0.9952671 |
9.00E-05 |
10480.879 |
2085712 |
34.761867 |
1 |
0.4874366 |
0.5125632 |
15260.698 |
0.9960916 |
8.00E-05 |
11756.834 |
2339611 |
38.993517 |
1 |
0.5125593 |
0.4874406 |
17085.739 |
0.9964456 |
7.00E-05 |
13843.829 |
2754923 |
45.915383 |
1 |
0.5728555 |
0.4271448 |
18479.136 |
0.9972415 |
6.00E-05 |
14083.709 |
2802658 |
46.710967 |
1 |
0.5326597 |
0.4673405 |
22090.452 |
0.9973271 |
5.00E-05 |
16906.704 |
3364436 |
56.073933 |
1 |
0.5628089 |
0.4371908 |
25991.136 |
0.9975789 |
4.00E-05 |
19535.799 |
3887630 |
64.793833 |
1 |
0.5427109 |
0.4572892 |
32577.467 |
0.9979804 |
3.00E-05 |
24581.161 |
4891667 |
81.527783 |
1 |
0.4824124 |
0.5175875 |
39049.779 |
0.9982988 |
2.00E-05 |
29246.05 |
5819980 |
96.999667 |
1 |
0.4874373 |
0.5125627 |
62636.548 |
0.9982062 |
1.00E-05 |
49261.045 |
9802949 |
163.38248 |
1 |
0.8040151 |
0.1959848 |
86731.849 |
0.9974554 |
9.00E-06 |
66086.025 |
13151122 |
219.18537 |
1 |
0.5527631 |
0.4472369 |
90758.111 |
0.9980232 |
8.00E-06 |
68435.97 |
13618759 |
226.97932 |
1 |
0.532663 |
0.467337 |
79820.975 |
0.9979709 |
7.00E-06 |
60225.889 |
11984961 |
199.74935 |
1 |
0.5427131 |
0.4572869 |
75305.739 |
0.9981872 |
6.00E-06 |
57658.015 |
11473948 |
191.23247 |
1 |
0.8693433 |
0.1306567 |
85185.03 |
0.9982537 |
5.00E-06 |
63082.186 |
12553371 |
209.22285 |
1 |
0.6733656 |
0.3266344 |
99206.302 |
0.9982584 |
4.00E-06 |
76016.95 |
15127389 |
252.12315 |
1 |
0.6633157 |
0.3366843 |
89498.804 |
0.9983202 |
3.00E-06 |
72579.111 |
14443275 |
240.72125 |
1 |
0.8341696 |
0.1658304 |
125675.34 |
0.9965858 |
2.00E-06 |
100546.08 |
20008681 |
333.47802 |
1 |
0.7236177 |
0.2763823 |
145569.59 |
0.9980113 |
1.00E-06 |
124728.04 |
24820880 |
413.68133 |
1 |
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